From b99505d3faa823d1a595f8c865a17f2ea75f7233 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Fri, 22 Dec 2023 11:34:49 -0300
Subject: [PATCH 01/24] Reference Date Fix

First point in #7
---
 src/crnpy/crnpy.py | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index a7a73de..b6df9f4 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -423,7 +423,31 @@ def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expan
 
     return df_flux
 
+def get_reference_neutron_flux(station, date = pd.to_datetime("2011-05-01")):
+    """Function to retrieve reference neutron flux from the Neutron Monitor Database. Default date is 2011-05-01, following previous studies (Zreda et a., 2012, Hawdon et al., 2014, Bogena et al., 2022).
 
+    Args:
+        station (str): Neutron Monitor station to retrieve data from.
+        date (datetime): Date of the reference neutron flux. Default is 2011-05-01.
+
+    Returns:
+        (float): Reference neutron flux in counts per hour.
+
+    References:
+        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., & Rosolem, R. (2012). COSMOS: The cosmic-ray soil moisture observing system. Hydrology and Earth System Sciences, 16(11), 4079-4099.
+
+        Hawdon, A., McJannet, D., & Wallace, J. (2014). Calibration and correction procedures for cosmic‐ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.
+
+        Bogena, H. R., Schrön, M., Jakobi, J., Ney, P., Zacharias, S., Andreasen, M., ... & Vereecken, H. (2022). COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors. Earth System Science Data, 14(3), 1125-1151.
+
+"""
+
+    # Get flux for 2011-05-01
+    df_flux = get_incoming_flux(station, date, date + pd.Timedelta(hours=24))
+    if df_flux is None:
+        warnings.warn(f"Reference neutron flux for {station} not available. Returning NaN.")
+    else:
+        return df_flux['counts'].median()
 
 def smooth_1d(values, window=5, order=3, method='moving_median'):
     """Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).

From a19b0410ce1cacc4337fe18f462c03828079c013 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Fri, 22 Dec 2023 18:54:07 -0300
Subject: [PATCH 02/24] New reference flux corrections

Addressing second part of #7
---
 src/crnpy/crnpy.py | 113 ++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 111 insertions(+), 2 deletions(-)

diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index b6df9f4..7369e20 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -291,7 +291,9 @@ def correction_humidity(abs_humidity, Aref):
     fw = 1 + 0.0054*(A - Aref) # Zreda et al. 2017 Eq 6.
     return fw
 
-def correction_incoming_flux(incoming_neutrons, incoming_Ref=None):
+
+
+def correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None, Rc_method=None, Rc_site=None, site_atmdepth=None, Rc_ref=None, ref_atmdepth=None):
     r"""Correction factor for incoming neutron flux.
 
     This function corrects neutron counts for incoming neutron flux using the method described in Anderson et al. (2017). The correction is performed using the following equation:
@@ -331,7 +333,28 @@ def correction_incoming_flux(incoming_neutrons, incoming_Ref=None):
         incoming_Ref = incoming_neutrons[0]
         warnings.warn('Reference incoming neutron flux not provided. Using first value of incoming neutron flux.')
     fi = incoming_neutrons / incoming_Ref
-    fi.fillna(1.0, inplace=True)  # Use a value of 1 for days without data
+
+    if Rc_method is not None:
+        if Rc_ref is None:
+            raise ValueError('Reference cutoff rigidity not provided.')
+        if Rc_site is None:
+            raise ValueError('Site cutoff rigidity not provided.')
+
+        if Rc_method == 'McJannetandDesilets2023':
+            if latitude is None or elevation is None:
+                raise ValueError('Latitude and elevation are required inputs for McJannet and Desilets (2023) method.')
+            tau = location_factor(site_atmdepth, Rc_site, ref_atmdepth, Rc_ref)
+            fi = 1 / (tau * fi + 1 - tau)
+
+        elif Rc_method == 'Hawdonetal2014':
+            Rc_corr = -0.075 * (Rc_site - Rc_ref)+ 1.0
+            fi = (fi - 1.0) * Rc_corr + 1.0
+
+        else:
+            raise ValueError('Cutoff rigidity method not found. Valid options are: McJannetandDesilets2023, Hawdonetal2014.')
+
+    if fill_na is not None:
+        fi.fillna(fill_na, inplace=True)  # Use a value of 1 for days without data
 
     return fi
 
@@ -812,6 +835,10 @@ def cutoff_rigidity(lat,lon):
         2.52 GV (Value from NMD is 2.40 GV)
 
     References:
+        Hawdon, A., McJannet, D., & Wallace, J. (2014). Calibration and correction procedures
+        for cosmic‐ray neutron soil moisture probes located across Australia. Water Resources Research,
+        50(6), 5029-5043.
+
         Smart, D. & Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program:
         Theory, Software Description and Example. NASA STI/Recon Technical Report N.
 
@@ -834,6 +861,88 @@ def cutoff_rigidity(lat,lon):
     return np.round(zq,2)
 
 
+def atmospheric_depth(elevation, latitude):
+    """Function to estimate the atmospheric depth for any point on Earth according to McJannet and Desilets, 2023
+
+    This function is required in the calculation of the location-dependent reference correction proposed by McJannet and Desilets, 2023.
+
+    Args:
+        elevation (float): Elevation in meters above sea level.
+        latitude (float): Geographic latitude in decimal degrees. Value in range -90 to 90
+
+    Returns:
+        (float): Atmospheric depth in g/cm2
+
+    References:
+        Atmosphere, U. S. (1976). US standard atmosphere. National Oceanic and Atmospheric Administration.
+
+        McJannet, D. L., & Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.
+    """
+
+    density_of_rock = 2670  # Density of rock in kg/m3
+    air_pressure_sea_level = 1013.25  # Air pressure at sea level in hPa
+    air_molar_mass = 0.0289644  # Air molar mass in kg/mol
+    universal_gas_constant = 8.3144598  # Universal gas constant in J/(mol*K)
+    reference_temperature = 288.15  # Reference temperature Kelvin
+    temperature_lapse_rate = -0.0065  # Temperature lapse rate in K/m
+
+    # Gravity at sea-level calculation
+    gravity_sea_level = 9.780327 * (
+                1 + 0.0053024 * np.sin(np.radians(latitude)) ** 2 - 0.0000058 * np.sin(2 * np.radians(latitude)) ** 2)
+    # Free air correction
+    free_air = -3.086 * 10 ** -6 * elevation
+    # Bouguer correction
+    bouguer_corr = 4.194 * 10 ** -10 * density_of_rock * elevation
+    # Total gravity
+    gravity = gravity_sea_level + free_air + bouguer_corr
+
+    # Air pressure calculation
+    reference_air_pressure = air_pressure_sea_level * (1 + temperature_lapse_rate / reference_temperature * elevation) ** ((-gravity * air_molar_mass) / (universal_gas_constant * temperature_lapse_rate))
+
+    # Atmospheric depth calculation
+    atmospheric_depth = (10 * reference_air_pressure) / gravity
+    return atmospheric_depth
+
+
+
+def location_factor(site_atmospheric_depth, site_Rc, reference_atmospheric_depth, reference_Rc):
+    """
+    Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.
+
+
+    Args:
+        site_atmospheric_depth (float): Atmospheric depth at the site in g/cm2. Can be estimated using the function `atmospheric_depth()`
+        site_Rc (float): Cutoff rigidity at the site in GV. Can be estimated using the function `cutoff_rigidity()`
+        reference_atmospheric_depth (float): Atmospheric depth at the reference location in g/cm2.
+        reference_Rc (float): Cutoff rigidity at the reference location in GV.
+
+    Returns:
+        (float): Location-dependent correction factor.
+
+    References:
+        McJannet, D. L., & Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.
+
+    """
+
+    # Renamed variables based on the provided table
+    c0 = -0.0009  # from C39
+    c1 = 1.7699  # from C40
+    c2 = 0.0064  # from C41
+    c3 = 1.8855  # from C42
+    c4 = 0.000013  # from C43
+    c5 = -1.2237  # from C44
+    epsilon = 1  # from C45
+
+    # Translated formula with renamed variables from McJannet and Desilets, 2023
+    tau_new = epsilon * (c0 * reference_atmospheric_depth + c1) * (
+                1 - np.exp(-(c2 * reference_atmospheric_depth + c3) * reference_Rc ** (c4 * reference_atmospheric_depth + c5)))
+
+    norm_factor = 1/tau_new
+
+    # Calculate the result using the provided parameters
+    tau = epsilon * norm_factor * (c0 * site_atmospheric_depth + c1) * (1 - np.exp(-(c2 * site_atmospheric_depth + c3) * site_Rc**(c4 * site_atmospheric_depth + c5)))
+    return tau
+
 def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):
     """Search for potential reference neutron monitoring stations based on cutoff rigidity.
 

From 62c878d0f4b325d358c8e5116171167fcdab0bc9 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Thu, 4 Jan 2024 18:08:57 -0300
Subject: [PATCH 03/24] =?UTF-8?q?Added=20the=20revised=20sample=20weightin?=
 =?UTF-8?q?g=20procedure=20(Schr=C3=B6n=20et=20al.,=202017).?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Proposed in issue #9
---
 src/crnpy/crnpy.py | 200 +++++++++++++++++++++++++++++++++------------
 1 file changed, 147 insertions(+), 53 deletions(-)

diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index 7369e20..9720bc7 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -689,15 +689,18 @@ def abs_humidity(relative_humidity, temp):
     return abs_h
 
 
-def nrad_weight(h,theta,distances,depth,rhob=1.4):
+def nrad_weight(h,theta,distances,depth,rhob=1.4, method=None, p=None, Hveg=None)
     """Function to compute distance weights corresponding to each soil sample.
 
     Args:
-        h (float): Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.
-        theta (array or pd.Series or pd.DataFrame): Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)
-        distances (array or pd.Series or pd.DataFrame): Distances from the location of each sample to the origin (0.5 - 600 m)
-        depth (array or pd.Series or pd.DataFrame): Depths for each sample (m)
-        rhob (float): Bulk density in g/cm^3
+        h (np.array or pd.Series): Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.
+        theta (np.array or pd.Series): Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)
+        distances (np.array or pd.Series): Distances from the location of each sample to the origin (0.5 - 600 m)
+        depth (np.array or pd.Series): Depths for each sample (m)
+        rhob (np.array or pd.Series): Bulk density in g/cm^3
+        p (np.array or pd.Series): Atmospheric pressure in hPa. Required for the 'Schron_2017' method.
+        Hveg (np.array or pd.Series): Vegetation height in m. Required for the 'Schron_2017' method.
+        method (str): Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.
 
     Returns:
         (array or pd.Series or pd.DataFrame): Distance weights for each sample.
@@ -706,61 +709,152 @@ def nrad_weight(h,theta,distances,depth,rhob=1.4):
         Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015).
         Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray
         neutrons. Water Resour. Res. 51, 5772–5790. doi:10.1002/2015WR017169
+
+        Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L.,
+        Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M., Rebmann, C.,
+        Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.: Improving calibration and
+        validation of cosmic-ray neutron sensors in the light of spatial sensitivity,
+        Hydrol. Earth Syst. Sci., 21, 5009–5030, https://doi.org/10.5194/hess-21-5009-2017, 2017.
     """
 
-    # Table A1. Parameters for Fi and D86
-    p10 = 8735;       p11 = 17.1758; p12 = 11720;      p13 = 0.00978;   p14 = 7045;      p15 = 0.003632;
-    p20 = 2.7925e-2;  p21 = 5.0399;  p22 = 2.8544e-2;  p23 = 0.002455;  p24 = 6.851e-5;  p25 = 9.2926;
-    p30 = 247970;     p31 = 17.63;   p32 = 374655;     p33 = 0.00191;   p34 = 195725;
-    p40 = 5.4818e-2;  p41 = 15.921;  p42 = 0.6373;     p43 = 5.99e-2;   p44 = 5.425e-4;
-    p50 = 1383702;    p51 = 4.156;   p52 = 5325;       p53 = 0.00238;   p54 = 0.0156;    p55 = 0.130;     p56 = 1521;
-    p60 = 6.031e-5;   p61 = 98.5;    p62 = 1.0466e-3;
-    p70 = 11747;      p71 = 41.66;   p72 = 4521;       p73 = 0.01998;   p74 = 0.00604;   p75 = 2534;      p76 = 0.00475;
-    p80 = 1.543e-2;   p81 = 10.06;   p82 = 1.807e-2;   p83 = 0.0011;    p84 = 8.81e-5;   p85 = 0.0405;    p86 = 20.24;
-    p90 = 8.321;      p91 = 0.14249; p92 = 0.96655;    p93 = 26.42;     p94 = 0.0567;
-
-
-    # Numerical determination of the penetration depth (86%) (Eq. 8)
-    D86 = 1/rhob*(p90+p91*(p92+np.exp(-1*distances/100))*(p93+theta)/(p94+theta))
-
-    # Depth weights (Eq. 7)
-    Wd = np.exp(-2*depth/D86)
-
-    if h == 0:
-        W = 1 # skip distance weighting
-
-    elif (h >= 0.1) and (h<= 50):
-        # Functions for Fi (Appendix A in Köhli et al., 2015)
-        F1 = p10*(1+p13*h)*np.exp(-p11*theta)+p12*(1+p15*h)-p14*theta
-        F2 = ((-p20+p24*h)*np.exp(-p21*theta/(1+p25*theta))+p22)*(1+h*p23)
-        F3 = (p30*(1+p33*h)*np.exp(-p31*theta)+p32-p34*theta)
-        F4 = p40*np.exp(-p41*theta)+p42-p43*theta+p44*h
-        F5 = p50*(0.02-1/p55/(h-p55+p56*theta))*(p54-theta)*np.exp(-p51*(theta-p54))+p52*(0.7-h*theta*p53)
-        F6 = p60*(h+p61)+p62*theta
-        F7 = (p70*(1-p76*h)*np.exp(-p71*theta*(1-h*p74))+p72-p75*theta)*(2+h*p73)
-        F8 = ((-p80+p84*h)*np.exp(-p81*theta/(1+p85*h+p86*theta))+p82)*(2+h*p83)
-
-        # Distance weights (Eq. 3)
-        W = np.ones_like(distances)*np.nan
-        for i in range(len(distances)):
-            if (distances[i]<=50) and (distances[i]>0.5):
-                W[i]=F1[i]*(np.exp(-F2[i]*distances[i]))+F3[i]*np.exp(-F4[i]*distances[i])
-
-            elif (distances[i]>50) and (distances[i]<600):
-                W[i]=F5[i]*(np.exp(-F6[i]*distances[i]))+F7[i]*np.exp(-F8[i]*distances[i])
+    if method=='Kohli_2015':
 
-            else:
-                raise ValueError('Input distances are not valid.')
+        # Table A1. Parameters for Fi and D86
+        p10 = 8735;       p11 = 17.1758; p12 = 11720;      p13 = 0.00978;   p14 = 7045;      p15 = 0.003632;
+        p20 = 2.7925e-2;  p21 = 5.0399;  p22 = 2.8544e-2;  p23 = 0.002455;  p24 = 6.851e-5;  p25 = 9.2926;
+        p30 = 247970;     p31 = 17.63;   p32 = 374655;     p33 = 0.00191;   p34 = 195725;
+        p40 = 5.4818e-2;  p41 = 15.921;  p42 = 0.6373;     p43 = 5.99e-2;   p44 = 5.425e-4;
+        p50 = 1383702;    p51 = 4.156;   p52 = 5325;       p53 = 0.00238;   p54 = 0.0156;    p55 = 0.130;     p56 = 1521;
+        p60 = 6.031e-5;   p61 = 98.5;    p62 = 1.0466e-3;
+        p70 = 11747;      p71 = 41.66;   p72 = 4521;       p73 = 0.01998;   p74 = 0.00604;   p75 = 2534;      p76 = 0.00475;
+        p80 = 1.543e-2;   p81 = 10.06;   p82 = 1.807e-2;   p83 = 0.0011;    p84 = 8.81e-5;   p85 = 0.0405;    p86 = 20.24;
+        p90 = 8.321;      p91 = 0.14249; p92 = 0.96655;    p93 = 26.42;     p94 = 0.0567;
 
-    else:
-        raise ValueError('Air humidity values are out of range.')
 
+        # Numerical determination of the penetration depth (86%) (Eq. 8)
+        D86 = 1/rhob*(p90+p91*(p92+np.exp(-1*distances/100))*(p93+theta)/(p94+theta))
+
+        # Depth weights (Eq. 7)
+        Wd = np.exp(-2*depth/D86)
 
-    # Combined and normalized weights
-    weights = Wd*W/np.nansum(Wd*W)
+        if h == 0:
+            W = 1 # skip distance weighting
 
-    return weights
+        elif (h >= 0.1) and (h<= 50):
+            # Functions for Fi (Appendix A in Köhli et al., 2015)
+            F1 = p10*(1+p13*h)*np.exp(-p11*theta)+p12*(1+p15*h)-p14*theta
+            F2 = ((-p20+p24*h)*np.exp(-p21*theta/(1+p25*theta))+p22)*(1+h*p23)
+            F3 = (p30*(1+p33*h)*np.exp(-p31*theta)+p32-p34*theta)
+            F4 = p40*np.exp(-p41*theta)+p42-p43*theta+p44*h
+            F5 = p50*(0.02-1/p55/(h-p55+p56*theta))*(p54-theta)*np.exp(-p51*(theta-p54))+p52*(0.7-h*theta*p53)
+            F6 = p60*(h+p61)+p62*theta
+            F7 = (p70*(1-p76*h)*np.exp(-p71*theta*(1-h*p74))+p72-p75*theta)*(2+h*p73)
+            F8 = ((-p80+p84*h)*np.exp(-p81*theta/(1+p85*h+p86*theta))+p82)*(2+h*p83)
 
+            # Distance weights (Eq. 3)
+            W = np.ones_like(distances)*np.nan
+            for i in range(len(distances)):
+                if (distances[i]<=50) and (distances[i]>0.5):
+                    W[i]=F1[i]*(np.exp(-F2[i]*distances[i]))+F3[i]*np.exp(-F4[i]*distances[i])
+
+                elif (distances[i]>50) and (distances[i]<600):
+                    W[i]=F5[i]*(np.exp(-F6[i]*distances[i]))+F7[i]*np.exp(-F8[i]*distances[i])
+
+                else:
+                    raise ValueError('Input distances are not valid.')
+
+        else:
+            raise ValueError('Air humidity values are out of range.')
+
+
+        # Combined and normalized weights
+        weights = Wd*W/np.nansum(Wd*W)
+        return weights
+    elif method=='Schron_2017':
+        # Horizontal distance weights According to Eq. 6 and Table A1 in Schrön et al. (2017)
+        # Method for calculating the horizontal distance weights from 0 to 1m
+        def WrX(r, x, y):
+            x00 = 3.7
+            a00 = 8735; a01 = 22.689; a02 = 11720; a03 = 0.00978; a04 = 9306; a05 = 0.003632
+            a10 = 2.7925e-2; a11 = 6.6577; a12 = 0.028544; a13 = 0.002455; a14 = 6.851e-5; a15 = 12.2755
+            a20 = 247970; a21 = 23.289; a22 = 374655; a23 = 0.00191; a24 = 258552
+            a30 = 5.4818e-2; a31 = 21.032; a32 = 0.6373; a33 = 0.0791; a34 = 5.425e-4
+
+            x0 = x00
+            A0 = (a00 * (1 + a03 * x) * np.exp(-a01 * y) + a02 * (1 + a05 * x) - a04 * y)
+            A1 = ((-a10 + a14 * x) * np.exp(-a11 * y / (1 + a15 * y)) + a12) * (1 + x * a13)
+            A2 = (a20 * (1 + a23 * x) * np.exp(-a21 * y) + a22 - a24 * y)
+            A3 = a30 * np.exp(-a31 * y) + a32 - a33 * y + a34 * x
+
+            return ((A0 * (np.exp(-A1 * r)) + A2 * np.exp(-A3 * r)) * (1 - np.exp(-x0 * r)))
+
+        # Method for calculating the horizontal distance weights from 1 to 50m
+        def WrA(r, x, y):
+            a00 = 8735; a01 = 22.689; a02 = 11720; a03 = 0.00978; a04 = 9306; a05 = 0.003632
+            a10 = 2.7925e-2; a11 = 6.6577; a12 = 0.028544; a13 = 0.002455; a14 = 6.851e-5; a15 = 12.2755
+            a20 = 247970; a21 = 23.289; a22 = 374655; a23 = 0.00191; a24 = 258552
+            a30 = 5.4818e-2; a31 = 21.032; a32 = 0.6373; a33 = 0.0791; a34 = 5.425e-4
+
+            A0 = (a00 * (1 + a03 * x) * np.exp(-a01 * y) + a02 * (1 + a05 * x) - a04 * y)
+            A1 = ((-a10 + a14 * x) * np.exp(-a11 * y / (1 + a15 * y)) + a12) * (1 + x * a13)
+            A2 = (a20 * (1 + a23 * x) * np.exp(-a21 * y) + a22 - a24 * y)
+            A3 = a30 * np.exp(-a31 * y) + a32 - a33 * y + a34 * x
+
+            return A0 * np.exp(-A1 * r) + A2 * np.exp(-A3 * r)
+
+        # Method for calculating the horizontal distance weights from 50 to 600m
+        def WrB(r, x, y):
+            b00 = 39006; b01 = 15002337; b02 = 2009.24; b03 = 0.01181; b04 = 3.146; b05 = 16.7417; b06 = 3727
+            b10 = 6.031e-5; b11 = 98.5; b12 = 0.0013826
+            b20 = 11747; b21 = 55.033; b22 = 4521; b23 = 0.01998; b24 = 0.00604; b25 = 3347.4; b26 = 0.00475
+            b30 = 1.543e-2; b31 = 13.29; b32 = 1.807e-2; b33 = 0.0011; b34 = 8.81e-5; b35 = 0.0405; b36 = 26.74
+
+            B0 = (b00 - b01 / (b02 * y + x - 0.13)) * (b03 - y) * np.exp(-b04 * y) - b05 * x * y + b06
+            B1 = b10 * (x + b11) + b12 * y
+            B2 = (b20 * (1 - b26 * x) * np.exp(-b21 * y * (1 - x * b24)) + b22 - b25 * y) * (2 + x * b23)
+            B3 = ((-b30 + b34 * x) * np.exp(-b31 * y / (1 + b35 * x + b36 * y)) + b32) * (2 + x * b33)
+
+            return B0 * np.exp(-B1 * r) + B2 * np.exp(-B3 * r)
+
+        def rscaled(r, p, Hveg, y):
+            Fp = 0.4922 / (0.86 - np.exp(-p / 1013.25))
+            Fveg = 1 - 0.17 * (1 - np.exp(-0.41 * Hveg)) * (1 + np.exp(-9.25 * y))
+            return r / Fp / Fveg
+
+        # Rename variables to be consistent with the revised paper
+        r = distances
+        x = h
+        y = theta
+        bd = rhob
+
+        if Hveg is not None and p is not None:
+            r = rscaled(r, p, Hveg, y)
+
+        Wr = np.zeros(len(r))
+
+        # See Eq. 6 in Schron et al. (2017)
+        r0_idx = (r <= 1)
+        r1_idx = (r > 1) & (r <= 50)
+        r2_idx = (r > 50) & (r < 600)
+        Wr[r0_idx] = WrX(r[r0_idx], x[r0_idx], y[r0_idx])
+        Wr[r1_idx] = WrA(r[r1_idx], x[r1_idx], y[r1_idx])
+        Wr[r2_idx] = WrB(r[r2_idx], x[r2_idx], y[r2_idx])
+
+        # Vertical distance weights
+        def D86(r, bd, y):
+            return 1 / bd * (8.321 + 0.14249 * (0.96655 + np.exp(-0.01 * r)) * (20 + y) / (0.0429 + y))
+
+        def Wd(d, r, bd, y):
+            return np.exp(-2 * d / D86(r, bd, y))
+
+        # Calculate the vertical distance weights
+        Wd = Wd(d, r, bd, y)
+
+        # Combined and normalized weights
+        # Combined and normalized weights
+        weights = Wd * Wr / np.nansum(Wd * Wr)
+
+        return weights
 
 
 def exp_filter(sm,T=1):

From 5f28836c51afc781bd119ce18aa0ba713bfb3867 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Thu, 4 Jan 2024 18:10:32 -0300
Subject: [PATCH 04/24] Unversion the crnpy.egg-info

#11
---
 .gitignore                          |  2 +-
 crnpy.egg-info/PKG-INFO             |  4 ----
 crnpy.egg-info/SOURCES.txt          | 10 ----------
 crnpy.egg-info/dependency_links.txt |  1 -
 crnpy.egg-info/top_level.txt        |  1 -
 5 files changed, 1 insertion(+), 17 deletions(-)
 delete mode 100644 crnpy.egg-info/PKG-INFO
 delete mode 100644 crnpy.egg-info/SOURCES.txt
 delete mode 100644 crnpy.egg-info/dependency_links.txt
 delete mode 100644 crnpy.egg-info/top_level.txt

diff --git a/.gitignore b/.gitignore
index 26b422a..25281f3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,4 +7,4 @@
 */.cache/*
 site/*
 .cache/*
-.pytest_cache/*
\ No newline at end of file
+.pytest_cache/*
diff --git a/crnpy.egg-info/PKG-INFO b/crnpy.egg-info/PKG-INFO
deleted file mode 100644
index 2a8541b..0000000
--- a/crnpy.egg-info/PKG-INFO
+++ /dev/null
@@ -1,4 +0,0 @@
-Metadata-Version: 2.1
-Name: crnpy
-Version: 0.2.post28
-License-File: LICENSE
diff --git a/crnpy.egg-info/SOURCES.txt b/crnpy.egg-info/SOURCES.txt
deleted file mode 100644
index 62a3e73..0000000
--- a/crnpy.egg-info/SOURCES.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-LICENSE
-README.md
-setup.py
-crnpy.egg-info/PKG-INFO
-crnpy.egg-info/SOURCES.txt
-crnpy.egg-info/dependency_links.txt
-crnpy.egg-info/top_level.txt
-src/crnpy/__init__.py
-src/crnpy/crnpy.py
-src/crnpy/readers.py
\ No newline at end of file
diff --git a/crnpy.egg-info/dependency_links.txt b/crnpy.egg-info/dependency_links.txt
deleted file mode 100644
index 8b13789..0000000
--- a/crnpy.egg-info/dependency_links.txt
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/crnpy.egg-info/top_level.txt b/crnpy.egg-info/top_level.txt
deleted file mode 100644
index 8b13789..0000000
--- a/crnpy.egg-info/top_level.txt
+++ /dev/null
@@ -1 +0,0 @@
-

From 89fd570aa378319dbd2468ca693e3c2d739cc820 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Thu, 4 Jan 2024 18:12:01 -0300
Subject: [PATCH 05/24] Update .gitignore

#11
---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 25281f3..6d970eb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,3 +8,4 @@
 site/*
 .cache/*
 .pytest_cache/*
+crnpy.egg-info/*

From 1a67744cc6c0f90826a3c388ddeacfc2c70819c7 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Thu, 4 Jan 2024 18:16:48 -0300
Subject: [PATCH 06/24] Update README.md

#11
---
 README.md | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 83237c4..5ad79b8 100644
--- a/README.md
+++ b/README.md
@@ -22,7 +22,6 @@ CRNPs are a valuable tool for non-invasive soil moisture estimation at the hecto
 - Modular: The library is designed to be modular, allowing users to easily customize the processing workflow to their needs.
 
 
-
 ## Installation
 To install the CRNPy library, you can use Python's package manager. Open a terminal and type:
 
@@ -39,6 +38,12 @@ Ideally dependencies should be installed automatically. If not, you can install
 The CRNPy library is compatible with Python 3.7 and above.
 See [requirements.txt](https://github.com/soilwater/crnpy/blob/main/requirements.txt) for a list of dependencies.
 
+## Examples
+
+- [https://soilwater.github.io/crnpy/examples/stationary/example_RDT_station/](Processing and analyzing data from a stationary detector)
+- [https://soilwater.github.io/crnpy/examples/rover/Hydroinnova_rover_example/](Processing and analyzing data from a roving detector)
+- [https://soilwater.github.io/crnpy/examples/calibration/calibration/](Device-specific field calibration)
+
 ## Authors
 The CRNPy library was developed at the Kansas State University Soil Water Processes Lab by:
 

From 662336cba634d4fd4c7395c5e1ad57997692940e Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Fri, 5 Jan 2024 01:23:41 -0300
Subject: [PATCH 07/24] Improvements listed in #11

---
 src/crnpy/crnpy.py | 92 +++++++++-------------------------------------
 1 file changed, 18 insertions(+), 74 deletions(-)

diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index 9720bc7..35fed20 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -13,7 +13,7 @@
 import pandas as pd
 import requests
 import io
-
+import utm
 
 from scipy.signal import savgol_filter
 from scipy.interpolate import griddata
@@ -108,12 +108,11 @@ def fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_times
     return df
 
 
-def total_raw_counts(counts, nan_strategy=None, timestamp_col=None):
+def total_raw_counts(counts):
     """Compute the sum of uncorrected neutron counts for all detectors.
 
     Args:
         counts (pandas.DataFrame): Dataframe containing only the columns with neutron counts.
-        nan_strategy (str): Strategy to use for NaN values. Options are 'interpolate', 'average', or None. Default is None.
 
     Returns:
         (pandas.DataFrame): Dataframe with the sum of uncorrected neutron counts for all detectors.
@@ -235,7 +234,7 @@ def correction_pressure(pressure, Pref, L):
 
 
     Args:
-        atm_pressure (list or array): Atmospheric pressure readings. Long-term average pressure is recommended.
+        pressure (list or array): Atmospheric pressure readings. Long-term average pressure is recommended.
         Pref (float): Reference atmospheric pressure.
         L (float): Atmospheric attenuation coefficient.
 
@@ -278,7 +277,6 @@ def correction_humidity(abs_humidity, Aref):
 
     Args:
         abs_humidity (list or array): Relative humidity readings.
-        temp (list or array): Temperature readings (Celsius).
         Aref (float): Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.
 
     Returns:
@@ -377,7 +375,7 @@ def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expan
         Documentation available:https://www.nmdb.eu/nest/help.php#howto
     """
 
-    # Example: get_incoming_flux(station='IRKT',start_date='2020-04-10 11:00:00',end_date='2020-06-18 17:00:00')
+    # Example: get_incoming_neutron_flux(station='IRKT',start_date='2020-04-10 11:00:00',end_date='2020-06-18 17:00:00')
     # Template url = 'http://nest.nmdb.eu/draw_graph.php?formchk=1&stations[]=KERG&output=ascii&tabchoice=revori&dtype=corr_for_efficiency&date_choice=bydate&start_year=2009&start_month=09&start_day=01&start_hour=00&start_min=00&end_year=2009&end_month=09&end_day=05&end_hour=23&end_min=59&yunits=0'
 
 
@@ -388,7 +386,6 @@ def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expan
     # Convert local time to UTC
     start_date = start_date - pd.Timedelta(hours=utc_offset)
     end_date = end_date - pd.Timedelta(hours=utc_offset)
-    date_format = '%Y-%m-%d %H:%M:%S'
     root = 'http://www.nmdb.eu/nest/draw_graph.php?'
     url_par = [ 'formchk=1',
                 'stations[]=' + station,
@@ -466,7 +463,7 @@ def get_reference_neutron_flux(station, date = pd.to_datetime("2011-05-01")):
 """
 
     # Get flux for 2011-05-01
-    df_flux = get_incoming_flux(station, date, date + pd.Timedelta(hours=24))
+    df_flux = get_incoming_neutron_flux(station, date, date + pd.Timedelta(hours=24))
     if df_flux is None:
         warnings.warn(f"Reference neutron flux for {station} not available. Returning NaN.")
     else:
@@ -489,6 +486,9 @@ def smooth_1d(values, window=5, order=3, method='moving_median'):
         Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.
         Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.
         doi.org/10.3389/frwa.2020.00009
+
+        Savitzky, A., & Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures.
+        Analytical chemistry, 36(8), 1627-1639.
     """
 
     if method == 'moving_average':
@@ -615,7 +615,6 @@ def counts_to_vwc(counts, N0, Wlat, Wsoc ,bulk_density, a0=0.0808,a1=0.372,a2=0.
 
 
 def sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017'):
-    # Convert docstring to google format
     """Function that computes the estimated sensing depth of the cosmic-ray neutron probe.
     The function offers several methods to compute the depth at which 86 % of the neutrons
     probe the soil profile.
@@ -645,7 +644,7 @@ def sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='S
     # Determine sensing depth (D86)
     if method == 'Schron_2017':
         # See Appendix A of Schrön et al. (2017)
-        Fp = 0.4922 / (0.86 - np.exp(-1 * pressure / p_ref));
+        Fp = 0.4922 / (0.86 - np.exp(-1 * pressure / p_ref))
         Fveg = 0
         results = []
         for d in dist:
@@ -660,7 +659,6 @@ def sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='S
         results = 5.8/(bulk_density*Wlat+vwc+0.0829)
     else:
         raise ValueError('Method not recognized. Please select either "Schron_2017" or "Franz_2012".')
-
     return results
 
 def abs_humidity(relative_humidity, temp):
@@ -1044,6 +1042,7 @@ def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):
         Rc (float): Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.
         start_date (datetime): Start date for the period of interest.
         end_date (datetime): End date for the period of interest.
+        verbose (bool): If True, print a expanded output of the incoming neutron flux data.
 
     Returns:
         (list): List of top five stations with closes cutoff rigidity.
@@ -1154,7 +1153,7 @@ def lattice_water(clay_content, total_carbon=None):
     return lattice_water
 
 
-def latlon_to_utm(lat, lon, utm_zone_number, missing_values=None):
+def latlon_to_utm(lat, lon, utm_zone_number=None, utm_zone_letter=None):
     """Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.
 
     Function only applies to non-polar coordinates.
@@ -1167,7 +1166,8 @@ def latlon_to_utm(lat, lon, utm_zone_number, missing_values=None):
     Args:
         lat (float, array): Latitude in decimal degrees.
         lon (float, array): Longitude in decimal degrees.
-        utm_zone_number (int): Universal Transverse Mercator (UTM) zone.
+        utm_zone_number (int): UTM zone number. If None, the zone number is automatically calculated.
+        utm_zone_letter (str): UTM zone letter. If None, the zone letter is automatically calculated.
 
     Returns:
         (float, float): Tuple of easting and northing coordinates in meters. First element is easting, second is northing.
@@ -1178,68 +1178,12 @@ def latlon_to_utm(lat, lon, utm_zone_number, missing_values=None):
 
          [https://www.maptools.com/tutorials/grid_zone_details#](https://www.maptools.com/tutorials/grid_zone_details#)
     """
+    if utm_zone_number is None or utm_zone_letter is None:
+        easting, northing, zone_number, zone_letter = utm.from_latlon(lat, lon)
+    else:
+        easting, northing, zone_number, zone_letter = utm.from_latlon(lat, lon, utm_zone_number, utm_zone_letter)
 
-
-    # Define constants
-    R = 6_378_137  # Earth's radius at the Equator in meters
-
-    # Convert input data to Numpy arrays
-    if (type(lat) is not np.ndarray) or (type(lon) is not np.ndarray):
-        try:
-            lat = np.array(lat)
-            lon = np.array(lon)
-        except:
-            raise "Input values cannot be converted to Numpy arrays."
-
-    # Check latitude range
-    if np.any(lat < -80) | np.any(lat > 84):
-        raise "One or more latitude values exceed the range -80 to 84"
-
-    # Check longitude range
-    if np.any(lon < -180) | np.any(lon > 180):
-        raise "One or more longitude values exceed the range -180 to 180"
-
-    # Constants
-    K0 = 0.9996
-    E = 0.00669438
-    E_P2 = E / (1 - E)
-
-    M1 = (1 - E / 4 - 3 * E ** 2 / 64 - 5 * E ** 3 / 256)
-    M2 = (3 * E / 8 + 3 * E ** 2 / 32 + 45 * E ** 3 / 1024)
-    M3 = (15 * E ** 2 / 256 + 45 * E ** 3 / 1024)
-    M4 = (35 * E ** 3 / 3072)
-
-    # Trigonometric operations
-    lat_rad = np.radians(lat)
-    lon_rad = np.radians(lon)
-
-    lat_sin = np.sin(lat_rad)
-    lat_cos = np.cos(lat_rad)
-    lat_tan = lat_sin / lat_cos
-    lat_tan2 = lat_tan * lat_tan
-    lat_tan4 = lat_tan2 * lat_tan2
-
-    # Find central meridian.
-    central_lon = (utm_zone_number * 6 - 180) - 3  # Zones are every 6 degrees.
-    central_lon_rad = np.radians(central_lon)
-
-    n = R / np.sqrt(1 - E * lat_sin ** 2)
-    c = E_P2 * lat_cos ** 2
-
-    with np.errstate(divide='ignore', invalid='ignore'):
-        a = lat_cos * (np.remainder(((lon_rad - central_lon_rad) + np.pi), (2 * np.pi)) - np.pi)
-    m = R * (M1 * lat_rad - M2 * np.sin(2 * lat_rad) + M3 * np.sin(4 * lat_rad) - M4 * np.sin(6 * lat_rad))
-
-    easting = K0 * n * (a + a ** 3 / 6 * (1 - lat_tan2 + c) + a ** 5 / 120 * (
-                5 - 18 * lat_tan2 + lat_tan4 + 72 * c - 58 * E_P2)) + 500_000
-    northing = K0 * (m + n * lat_tan * (
-                a ** 2 / 2 + a ** 4 / 24 * (5 - lat_tan2 + 9 * c + 4 * c ** 2) + a ** 6 / 720 * (
-                    61 - 58 * lat_tan2 + lat_tan4 + 600 * c - 330 * E_P2)))
-
-    if np.any(lat < 0):
-        northing += 10_000_000
-
-    return easting, northing
+    return easting, northing, zone_number, zone_letter
 
 def euclidean_distance(px, py, x, y):
     """Function that computes the Euclidean distance between one point

From 54dfcd571adef6c9613f6a5128bcb61946755a70 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Fri, 5 Jan 2024 17:46:32 -0300
Subject: [PATCH 08/24] Fixes

---
 requirements.txt   | 3 ++-
 setup.py           | 4 ++--
 src/crnpy/crnpy.py | 2 +-
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 3f061eb..67f2cd1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,5 @@
 numpy>=1.24.3
 pandas>=2.0.1
 requests>=2.31.0
-scipy>=1.10.1
\ No newline at end of file
+scipy>=1.10.1
+utm>=0.7.0
diff --git a/setup.py b/setup.py
index 43acb44..5f2cc7d 100644
--- a/setup.py
+++ b/setup.py
@@ -3,14 +3,14 @@
 
 setuptools.setup(
     name="crnpy",
-    version="0.5.1",
+    version="0.6.0",
     packages=['crnpy'],
     package_dir = {"": "src"},
     description="A Python package for the estimation and processing of soil moisture data from cosmic-ray neutron counts.",
     include_package_data=True,
     long_description=open('README.md').read(),
     long_description_content_type='text/markdown',
-    url="https://github.com/soilwater/crnpy",
+    url="https://soilwater.github.io/crnpy/",
     author="Joaquin Peraza, Andres Patrignani",
     author_email="jperaza@ksu.edu, andrespatrignani@ksu.edu",
     license="MIT",
diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index 35fed20..e3080d6 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -687,7 +687,7 @@ def abs_humidity(relative_humidity, temp):
     return abs_h
 
 
-def nrad_weight(h,theta,distances,depth,rhob=1.4, method=None, p=None, Hveg=None)
+def nrad_weight(h,theta,distances,depth,rhob=1.4, method=None, p=None, Hveg=None):
     """Function to compute distance weights corresponding to each soil sample.
 
     Args:

From 15df273d5d7969eefc04097d19e946c766b184ce Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Sat, 6 Jan 2024 15:25:30 -0300
Subject: [PATCH 09/24] check/convert types

#11
---
 src/crnpy/crnpy.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index e3080d6..65aeb38 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -134,7 +134,7 @@ def is_outlier(x, method, window=11, min_val=None, max_val=None):
     """Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.
 
     Args:
-        x (pandas.DataFrame): Variable containing only the columns with neutron counts.
+        x (pd.DataFrame or pd.Series): Variable containing only the columns with neutron counts.
         method (str): Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad
         window (int, optional): Window size for the moving central tendency. Default is 11.
         min_val (int or float): Minimum value for a reading to be considered valid. Default is None.
@@ -491,6 +491,10 @@ def smooth_1d(values, window=5, order=3, method='moving_median'):
         Analytical chemistry, 36(8), 1627-1639.
     """
 
+    if not isinstance(x, pd.Series) and not isinstance(x, pd.DataFrame):
+        raise ValueError('Input must be a pandas Series or DataFrame')
+
+
     if method == 'moving_average':
         corrected_counts = values.rolling(window=window, center=True, min_periods=1).mean()
     elif method == 'moving_median':

From e1706ac1016a5f959c3bc2bb0833cb3b998c7d59 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Mon, 8 Jan 2024 19:48:30 -0300
Subject: [PATCH 10/24] format and doc update

---
 .DS_Store                                     |   Bin 0 -> 6148 bytes
 .gitignore                                    |     1 +
 .idea/crnpy.iml                               |     5 +-
 .idea/misc.xml                                |     2 +-
 build/lib/crnpy/crnpy.py                      |   741 +-
 site/404.html                                 |   176 +-
 site/assets/_mkdocstrings.css                 |    58 +-
 .../images/social/correction_routines.png     |   Bin 64142 -> 64214 bytes
 .../examples/calibration/calibration.png      |   Bin 64782 -> 71743 bytes
 .../rover/Hydroinnova_rover_example.png       |   Bin 61445 -> 63061 bytes
 .../stationary/example_RDT_station.png        |   Bin 60181 -> 66595 bytes
 site/assets/images/social/index.png           |   Bin 57336 -> 57410 bytes
 site/assets/images/social/reference.png       |   Bin 59113 -> 59194 bytes
 site/correction_routines/index.html           |   262 +-
 .../calibration/calibration/index.html        |  2688 ++--
 .../Hydroinnova_rover_example/index.html      |  2345 +--
 .../stationary/example_RDT_station/index.html |  2670 ++-
 site/index.html                               |   260 +-
 site/objects.inv                              |   Bin 467 -> 787 bytes
 site/reference/index.html                     | 13433 +++++++++++++---
 site/search/search_index.json                 |     2 +-
 site/sitemap.xml                              |    14 +-
 site/sitemap.xml.gz                           |   Bin 310 -> 310 bytes
 src/crnpy.egg-info/PKG-INFO                   |    16 +-
 src/crnpy/crnpy.py                            |   424 +-
 25 files changed, 15624 insertions(+), 7473 deletions(-)
 create mode 100644 .DS_Store

diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..8ceb77b03969e4447ffb693919f548c0470f50d9
GIT binary patch
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zRBWpk4<cm;X5a4Y%!YYocRBzd+Jk5Zpbh{oDxtHA!#6_vq;s;wmK8)nXN1H2DNI#}
z>$f>?Yp@Jh2L2lZbax$yK*25KU+-`DlqPx6YQ2l4inF{@b*t{0dmY@XVNmpnLEi55
zFKKnAR2oj^z3@DVhu!+dvC4{GlEr-;kVG-MTwNqtq=s!Z$f8`w24=&pxwUS+F&cT^
zK~sAByW^%D9qzW8a?jfzk8AGc*7nh9_aS}E)Qcfd;CrKGopA!MIQh)yIp}Ap%I?rc
z{to1jKmqZ><jMKW<XOQy`*6D;S#mZrIZl3Wi@#@p?~uFp`y*_mWxz79pbXIaAW;c@
z#?qjEI&jb}0HT9tEjXuMf?_12&sZA79TZ_g5lyJz7DJeDv`hB$8B2pE90WHXg1;=d
zLlOGxc)rx`Abf*%v<z4VrWvT1Wu5B(3Etr!`v2)5yRr;e1{R6|;dFvd8&l%iI+q;P
uT8VmtN<x06K^=kvmt#GlQrtk*f_suCh(2R!5G^S7kASAZ4wivGW#9vKoWX?v

literal 0
HcmV?d00001

diff --git a/.gitignore b/.gitignore
index 6d970eb..e88baf2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,3 +9,4 @@ site/*
 .cache/*
 .pytest_cache/*
 crnpy.egg-info/*
+.idea/*
diff --git a/.idea/crnpy.iml b/.idea/crnpy.iml
index 7fc4fdf..68d8a3d 100644
--- a/.idea/crnpy.iml
+++ b/.idea/crnpy.iml
@@ -4,11 +4,14 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.10 (crnpy)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.11 (crnpy)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyDocumentationSettings">
     <option name="format" value="NUMPY" />
     <option name="myDocStringFormat" value="NumPy" />
   </component>
+  <component name="SonarLintModuleSettings">
+    <option name="uniqueId" value="7a23bc73-9054-4e8d-9f5a-f0d1afe24c71" />
+  </component>
 </module>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
index efc4ace..aedf446 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (crnpy)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (crnpy)" project-jdk-type="Python SDK" />
 </project>
\ No newline at end of file
diff --git a/build/lib/crnpy/crnpy.py b/build/lib/crnpy/crnpy.py
index a7a73de..bc923a1 100644
--- a/build/lib/crnpy/crnpy.py
+++ b/build/lib/crnpy/crnpy.py
@@ -5,20 +5,19 @@
  Created by Joaquin Peraza and Andres Patrignani.
 """
 
-# Import modules
-import sys
-import warnings
+import crnpy.data as data
+import io
 import numbers
 import numpy as np
 import pandas as pd
 import requests
-import io
-
+import sys
+import utm
+import warnings
 
-from scipy.signal import savgol_filter
 from scipy.interpolate import griddata
+from scipy.signal import savgol_filter
 from scipy.special import erfcinv
-import crnpy.data as data
 
 # Define python version
 python_version = (3, 7)  # tuple of (major, minor) version requirement
@@ -42,24 +41,24 @@ def remove_incomplete_intervals(df, timestamp_col, integration_time, remove_firs
     Returns:
         (pandas.DataFrame): 
     """
-    
+
     # Check format of timestamp column
     if df[timestamp_col].dtype != 'datetime64[ns]':
         raise TypeError('timestamp_col must be datetime64. Use `pd.to_datetime()` to fix this issue.')
 
     # Check if differences in timestamps are below or above the provided integration time
     idx_delta = df[timestamp_col].diff().dt.total_seconds() != integration_time
-    
+
     if remove_first:
         idx_delta[0] = True
-        
+
     # Select rows that meet the specified integration time
     df = df[~idx_delta]
     df.reset_index(drop=True, inplace=True)
 
     # Notify user about the number of rows that have been removed
     print(f"Removed a total of {sum(idx_delta)} rows.")
-    
+
     return df
 
 
@@ -94,40 +93,39 @@ def fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_times
     for date in date_range:
         if date not in df[timestamp_col].values:
             if verbose:
-                print('Adding missing date:',date)
-            new_line = pd.DataFrame({timestamp_col:date}, index=[-1]) # By default fills columns with np.nan
-            df = pd.concat([df,new_line])
+                print('Adding missing date:', date)
+            new_line = pd.DataFrame({timestamp_col: date}, index=[-1])  # By default fills columns with np.nan
+            df = pd.concat([df, new_line])
             counter += 1
 
     df.sort_values(by=timestamp_col, inplace=True)
     df.reset_index(drop=True, inplace=True)
-    
+
     # Notify user about the number of rows that have been removed
     print(f"Added a total of {counter} missing timestamps.")
-    
+
     return df
 
 
-def total_raw_counts(counts, nan_strategy=None, timestamp_col=None):
+def total_raw_counts(counts):
     """Compute the sum of uncorrected neutron counts for all detectors.
 
     Args:
         counts (pandas.DataFrame): Dataframe containing only the columns with neutron counts.
-        nan_strategy (str): Strategy to use for NaN values. Options are 'interpolate', 'average', or None. Default is None.
 
     Returns:
         (pandas.DataFrame): Dataframe with the sum of uncorrected neutron counts for all detectors.
     """
 
     if counts.shape[0] > 1:
-        counts = counts.apply(lambda x: x.fillna(counts.mean(axis=1)),axis=0)
+        counts = counts.apply(lambda x: x.fillna(counts.mean(axis=1)), axis=0)
 
     # Compute sum of counts
     total_raw_counts = counts.sum(axis=1)
-    
+
     # Replace zeros with NaN
     total_raw_counts = total_raw_counts.replace(0, np.nan)
-    
+
     return total_raw_counts
 
 
@@ -135,7 +133,7 @@ def is_outlier(x, method, window=11, min_val=None, max_val=None):
     """Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.
 
     Args:
-        x (pandas.DataFrame): Variable containing only the columns with neutron counts.
+        x (pd.DataFrame or pd.Series): Variable containing only the columns with neutron counts.
         method (str): Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad
         window (int, optional): Window size for the moving central tendency. Default is 11.
         min_val (int or float): Minimum value for a reading to be considered valid. Default is None.
@@ -147,7 +145,7 @@ def is_outlier(x, method, window=11, min_val=None, max_val=None):
     References:
         Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.
     """
-        
+
     if not isinstance(x, pd.Series):
         raise TypeError('x must of type pandas.Series')
 
@@ -164,24 +162,24 @@ def is_outlier(x, method, window=11, min_val=None, max_val=None):
         iqr = q3 - q1
         high_fence = q3 + (1.5 * iqr)
         low_fence = q1 - (1.5 * iqr)
-        idx_outliers = (x<low_fence ) | (x>high_fence )
+        idx_outliers = (x < low_fence) | (x > high_fence)
 
     elif method == 'moviqr':
         q1 = x.rolling(window, center=True).quantile(0.25)
         q3 = x.rolling(window, center=True).quantile(0.75)
         iqr = q3 - q1
-        ub = q3 + (1.5 * iqr) # Upper boundary
-        lb = q1 - (1.5 * iqr) # Lower boundary
+        ub = q3 + (1.5 * iqr)  # Upper boundary
+        lb = q1 - (1.5 * iqr)  # Lower boundary
         idx_outliers = (x < lb) | (x > ub)
 
     elif method == 'zscore':
-        zscore = (x - x.mean())/x.std()
+        zscore = (x - x.mean()) / x.std()
         idx_outliers = (zscore < -3) | (zscore > 3)
 
     elif method == 'movzscore':
         movmean = x.rolling(window=window, center=True).mean()
         movstd = x.rolling(window=window, center=True).std()
-        movzscore = (x - movmean)/movstd
+        movzscore = (x - movmean) / movstd
         idx_outliers = (movzscore < -3) | (movzscore > 3)
 
     elif method == 'modified_zscore':
@@ -196,10 +194,10 @@ def is_outlier(x, method, window=11, min_val=None, max_val=None):
 
     elif method == 'scaled_mad':
         # Returns true for elements more than three scaled MAD from the median. 
-        c = -1 / (np.sqrt(2)*erfcinv(3/2))
+        c = -1 / (np.sqrt(2) * erfcinv(3 / 2))
         median = np.nanmedian(x)
-        mad = c*np.nanmedian(np.abs(x - median))
-        idx_outliers = x > (median + 3*mad)
+        mad = c * np.nanmedian(np.abs(x - median))
+        idx_outliers = x > (median + 3 * mad)
 
     else:
         raise TypeError('Outlier detection method not found.')
@@ -235,7 +233,7 @@ def correction_pressure(pressure, Pref, L):
 
 
     Args:
-        atm_pressure (list or array): Atmospheric pressure readings. Long-term average pressure is recommended.
+        pressure (list or array): Atmospheric pressure readings. Long-term average pressure is recommended.
         Pref (float): Reference atmospheric pressure.
         L (float): Atmospheric attenuation coefficient.
 
@@ -247,7 +245,7 @@ def correction_pressure(pressure, Pref, L):
     """
 
     # Compute pressure correction factor
-    fp = np.exp((Pref - pressure) / L) # Zreda et al. 2017 Eq 5.
+    fp = np.exp((Pref - pressure) / L)  # Zreda et al. 2017 Eq 5.
 
     return fp
 
@@ -278,7 +276,6 @@ def correction_humidity(abs_humidity, Aref):
 
     Args:
         abs_humidity (list or array): Relative humidity readings.
-        temp (list or array): Temperature readings (Celsius).
         Aref (float): Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.
 
     Returns:
@@ -288,10 +285,12 @@ def correction_humidity(abs_humidity, Aref):
         M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086
     """
     A = abs_humidity
-    fw = 1 + 0.0054*(A - Aref) # Zreda et al. 2017 Eq 6.
+    fw = 1 + 0.0054 * (A - Aref)  # Zreda et al. 2017 Eq 6.
     return fw
 
-def correction_incoming_flux(incoming_neutrons, incoming_Ref=None):
+
+def correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None, Rc_method=None, Rc_site=None,
+                             site_atmdepth=None, Rc_ref=None, ref_atmdepth=None):
     r"""Correction factor for incoming neutron flux.
 
     This function corrects neutron counts for incoming neutron flux using the method described in Anderson et al. (2017). The correction is performed using the following equation:
@@ -331,7 +330,27 @@ def correction_incoming_flux(incoming_neutrons, incoming_Ref=None):
         incoming_Ref = incoming_neutrons[0]
         warnings.warn('Reference incoming neutron flux not provided. Using first value of incoming neutron flux.')
     fi = incoming_neutrons / incoming_Ref
-    fi.fillna(1.0, inplace=True)  # Use a value of 1 for days without data
+
+    if Rc_method is not None:
+        if Rc_ref is None:
+            raise ValueError('Reference cutoff rigidity not provided.')
+        if Rc_site is None:
+            raise ValueError('Site cutoff rigidity not provided.')
+
+        if Rc_method == 'McJannetandDesilets2023':
+            tau = location_factor(site_atmdepth, Rc_site, ref_atmdepth, Rc_ref)
+            fi = 1 / (tau * fi + 1 - tau)
+
+        elif Rc_method == 'Hawdonetal2014':
+            Rc_corr = -0.075 * (Rc_site - Rc_ref) + 1.0
+            fi = (fi - 1.0) * Rc_corr + 1.0
+
+        else:
+            raise ValueError(
+                'Cutoff rigidity method not found. Valid options are: McJannetandDesilets2023, Hawdonetal2014.')
+
+    if fill_na is not None:
+        fi.fillna(fill_na, inplace=True)  # Use a value of 1 for days without data
 
     return fi
 
@@ -354,10 +373,9 @@ def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expan
         Documentation available:https://www.nmdb.eu/nest/help.php#howto
     """
 
-    # Example: get_incoming_flux(station='IRKT',start_date='2020-04-10 11:00:00',end_date='2020-06-18 17:00:00')
+    # Example: get_incoming_neutron_flux(station='IRKT',start_date='2020-04-10 11:00:00',end_date='2020-06-18 17:00:00')
     # Template url = 'http://nest.nmdb.eu/draw_graph.php?formchk=1&stations[]=KERG&output=ascii&tabchoice=revori&dtype=corr_for_efficiency&date_choice=bydate&start_year=2009&start_month=09&start_day=01&start_hour=00&start_min=00&end_year=2009&end_month=09&end_day=05&end_hour=23&end_min=59&yunits=0'
 
-
     # Expand the time window by 1 hour to ensure an extra observation is included in the request.
     start_date -= pd.Timedelta(hours=expand_window)
     end_date += pd.Timedelta(hours=expand_window)
@@ -365,26 +383,25 @@ def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expan
     # Convert local time to UTC
     start_date = start_date - pd.Timedelta(hours=utc_offset)
     end_date = end_date - pd.Timedelta(hours=utc_offset)
-    date_format = '%Y-%m-%d %H:%M:%S'
     root = 'http://www.nmdb.eu/nest/draw_graph.php?'
-    url_par = [ 'formchk=1',
-                'stations[]=' + station,
-                'output=ascii',
-                'tabchoice=revori',
-                'dtype=corr_for_efficiency',
-                'tresolution=' + str(60),
-                'date_choice=bydate',
-                'start_year=' + str(start_date.year),
-                'start_month=' + str(start_date.month),
-                'start_day=' + str(start_date.day),
-                'start_hour=' + str(start_date.hour),
-                'start_min=' + str(start_date.minute),
-                'end_year=' + str(end_date.year),
-                'end_month=' + str(end_date.month),
-                'end_day=' + str(end_date.day),
-                'end_hour=' + str(end_date.hour),
-                'end_min=' + str(end_date.minute),
-                'yunits=0']
+    url_par = ['formchk=1',
+               'stations[]=' + station,
+               'output=ascii',
+               'tabchoice=revori',
+               'dtype=corr_for_efficiency',
+               'tresolution=' + str(60),
+               'date_choice=bydate',
+               'start_year=' + str(start_date.year),
+               'start_month=' + str(start_date.month),
+               'start_day=' + str(start_date.day),
+               'start_hour=' + str(start_date.hour),
+               'start_min=' + str(start_date.minute),
+               'end_year=' + str(end_date.year),
+               'end_month=' + str(end_date.month),
+               'end_day=' + str(end_date.day),
+               'end_hour=' + str(end_date.hour),
+               'end_min=' + str(end_date.minute),
+               'yunits=0']
 
     url = root + '&'.join(url_par)
 
@@ -398,9 +415,9 @@ def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expan
     start = r.find("RCORR_E\n") + 8
     end = r.find('\n</code></pre><br>Total') - 1
     s = r[start:end]
-    s2 = ''.join([row.replace(';',',') for row in s])
+    s2 = ''.join([row.replace(';', ',') for row in s])
     try:
-        df_flux = pd.read_csv(io.StringIO(s2), names=['timestamp','counts'])
+        df_flux = pd.read_csv(io.StringIO(s2), names=['timestamp', 'counts'])
     except:
         if verbose:
             print(f"Error retrieving data from {url}")
@@ -424,6 +441,32 @@ def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expan
     return df_flux
 
 
+def get_reference_neutron_flux(station, date=pd.to_datetime("2011-05-01")):
+    """Function to retrieve reference neutron flux from the Neutron Monitor Database. Default date is 2011-05-01, following previous studies (Zreda et a., 2012, Hawdon et al., 2014, Bogena et al., 2022).
+
+    Args:
+        station (str): Neutron Monitor station to retrieve data from.
+        date (datetime): Date of the reference neutron flux. Default is 2011-05-01.
+
+    Returns:
+        (float): Reference neutron flux in counts per hour.
+
+    References:
+        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., & Rosolem, R. (2012). COSMOS: The cosmic-ray soil moisture observing system. Hydrology and Earth System Sciences, 16(11), 4079-4099.
+
+        Hawdon, A., McJannet, D., & Wallace, J. (2014). Calibration and correction procedures for cosmic‐ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.
+
+        Bogena, H. R., Schrön, M., Jakobi, J., Ney, P., Zacharias, S., Andreasen, M., ... & Vereecken, H. (2022). COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors. Earth System Science Data, 14(3), 1125-1151.
+
+"""
+
+    # Get flux for 2011-05-01
+    df_flux = get_incoming_neutron_flux(station, date, date + pd.Timedelta(hours=24))
+    if df_flux is None:
+        warnings.warn(f"Reference neutron flux for {station} not available. Returning NaN.")
+    else:
+        return df_flux['counts'].median()
+
 
 def smooth_1d(values, window=5, order=3, method='moving_median'):
     """Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).
@@ -442,8 +485,14 @@ def smooth_1d(values, window=5, order=3, method='moving_median'):
         Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.
         Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.
         doi.org/10.3389/frwa.2020.00009
+
+        Savitzky, A., & Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures.
+        Analytical chemistry, 36(8), 1627-1639.
     """
 
+    if not isinstance(x, pd.Series) and not isinstance(x, pd.DataFrame):
+        raise ValueError('Input must be a pandas Series or DataFrame')
+
     if method == 'moving_average':
         corrected_counts = values.rolling(window=window, center=True, min_periods=1).mean()
     elif method == 'moving_median':
@@ -454,13 +503,14 @@ def smooth_1d(values, window=5, order=3, method='moving_median'):
             print('Dataframe contains NaN values. Please remove NaN values before smoothing the data.')
 
         if type(values) == pd.core.series.Series:
-            filtered = savgol_filter(values,window,order)
-            corrected_counts = pd.DataFrame(filtered,columns=['smoothed'], index=values.index)
+            filtered = savgol_filter(values, window, order)
+            corrected_counts = pd.DataFrame(filtered, columns=['smoothed'], index=values.index)
         elif type(values) == pd.core.frame.DataFrame:
             for col in values.columns:
-                values[col] = savgol_filter(values[col],window,order)
+                values[col] = savgol_filter(values[col], window, order)
     else:
-        raise ValueError('Invalid method. Please select a valid filtering method., options are: moving_average, moving_median, savitzky_golay')
+        raise ValueError(
+            'Invalid method. Please select a valid filtering method., options are: moving_average, moving_median, savitzky_golay')
     corrected_counts = corrected_counts.ffill(limit=window).bfill(limit=window).copy()
     return corrected_counts
 
@@ -483,7 +533,8 @@ def correction_bwe(counts, bwe, r2_N0=0.05):
         Water Resour. Res., 51, 2030–2046, doi:10.1002/ 2014WR016443.
     """
 
-    return counts/(1 - bwe*r2_N0)
+    return counts / (1 - bwe * r2_N0)
+
 
 def biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494):
     """Function to convert biomass to biomass water equivalent.
@@ -505,7 +556,8 @@ def biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494):
     return (biomass_fresh - biomass_dry) + fWE * biomass_dry
 
 
-def correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0.12, p0=0.42, p1=0.5, p2=1.06, p3=4, p4=0.16, p6=0.94, p7=1.10, p8=2.70, p9=0.01):
+def correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0.12, p0=0.42, p1=0.5, p2=1.06, p3=4,
+                    p4=0.16, p6=0.94, p7=1.10, p8=2.70, p9=0.01):
     """Function to correct for road effects in neutron counts.
     following the approach described in Schrön et al., 2018.
 
@@ -525,7 +577,7 @@ def correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0
         of field soil moisture and the influence of roads.WaterResources Research,54,6441–6459.
         https://doi. org/10.1029/2017WR021719
     """
-    F1 = p0 * (1-np.exp(-p1*road_width))
+    F1 = p0 * (1 - np.exp(-p1 * road_width))
     F2 = -p2 - p3 * theta_road - ((p4 + theta_road) / (theta_N))
     F3 = p6 * np.exp(-p7 * (road_width ** -p8) * road_distance ** 4) + (1 - p6) * np.exp(-p9 * road_distance)
 
@@ -535,7 +587,8 @@ def correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0
 
     return corrected_counts
 
-def counts_to_vwc(counts, N0, Wlat, Wsoc ,bulk_density, a0=0.0808,a1=0.372,a2=0.115):
+
+def counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=0.115):
     r"""Function to convert corrected and filtered neutron counts into volumetric water content.
 
     This method implements soil moisture estimation using the non-linear relationship between neutron count and soil volumetric water content following the approach described in Desilets et al., 2010.
@@ -562,13 +615,11 @@ def counts_to_vwc(counts, N0, Wlat, Wsoc ,bulk_density, a0=0.0808,a1=0.372,a2=0.
     """
 
     # Convert neutron counts into vwc
-    vwc = (a0 / (counts/N0-a1) - a2 - Wlat - Wsoc) * bulk_density
+    vwc = (a0 / (counts / N0 - a1) - a2 - Wlat - Wsoc) * bulk_density
     return vwc
 
 
-
 def sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017'):
-    # Convert docstring to google format
     """Function that computes the estimated sensing depth of the cosmic-ray neutron probe.
     The function offers several methods to compute the depth at which 86 % of the neutrons
     probe the soil profile.
@@ -598,24 +649,25 @@ def sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='S
     # Determine sensing depth (D86)
     if method == 'Schron_2017':
         # See Appendix A of Schrön et al. (2017)
-        Fp = 0.4922 / (0.86 - np.exp(-1 * pressure / p_ref));
+        Fp = 0.4922 / (0.86 - np.exp(-1 * pressure / p_ref))
         Fveg = 0
         results = []
         for d in dist:
             # Compute r_star
-            r_start = d/Fp
+            r_start = d / Fp
 
             # Compute soil depth that accounts for 86% of the neutron flux
-            D86 = 1/ bulk_density * (8.321+0.14249*(0.96655 + np.exp(-0.01*r_start))*(20+(Wlat+vwc)) / (0.0429+(Wlat+vwc)))
+            D86 = 1 / bulk_density * (8.321 + 0.14249 * (0.96655 + np.exp(-0.01 * r_start)) * (20 + (Wlat + vwc)) / (
+                        0.0429 + (Wlat + vwc)))
             results.append(D86)
 
     elif method == 'Franz_2012':
-        results = 5.8/(bulk_density*Wlat+vwc+0.0829)
+        results = 5.8 / (bulk_density * Wlat + vwc + 0.0829)
     else:
         raise ValueError('Method not recognized. Please select either "Schron_2017" or "Franz_2012".')
-
     return results
 
+
 def abs_humidity(relative_humidity, temp):
     """
     Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.
@@ -631,7 +683,7 @@ def abs_humidity(relative_humidity, temp):
     ### Atmospheric water vapor factor
     # Saturation vapor pressure
     e_sat = 0.611 * np.exp(17.502 * temp / (
-                temp + 240.97)) * 1000  # in Pascals Eq. 3.8 p.41 Environmental Biophysics (Campbell and Norman)
+            temp + 240.97)) * 1000  # in Pascals Eq. 3.8 p.41 Environmental Biophysics (Campbell and Norman)
 
     # Vapor pressure Pascals
     Pw = e_sat * relative_humidity / 100
@@ -642,15 +694,18 @@ def abs_humidity(relative_humidity, temp):
     return abs_h
 
 
-def nrad_weight(h,theta,distances,depth,rhob=1.4):
+def nrad_weight(h, theta, distances, depth, rhob=1.4, method=None, p=None, Hveg=None):
     """Function to compute distance weights corresponding to each soil sample.
 
     Args:
-        h (float): Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.
-        theta (array or pd.Series or pd.DataFrame): Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)
-        distances (array or pd.Series or pd.DataFrame): Distances from the location of each sample to the origin (0.5 - 600 m)
-        depth (array or pd.Series or pd.DataFrame): Depths for each sample (m)
-        rhob (float): Bulk density in g/cm^3
+        h (np.array or pd.Series): Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.
+        theta (np.array or pd.Series): Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)
+        distances (np.array or pd.Series): Distances from the location of each sample to the origin (0.5 - 600 m)
+        depth (np.array or pd.Series): Depths for each sample (m)
+        rhob (np.array or pd.Series): Bulk density in g/cm^3
+        p (np.array or pd.Series): Atmospheric pressure in hPa. Required for the 'Schron_2017' method.
+        Hveg (np.array or pd.Series): Vegetation height in m. Required for the 'Schron_2017' method.
+        method (str): Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.
 
     Returns:
         (array or pd.Series or pd.DataFrame): Distance weights for each sample.
@@ -659,64 +714,252 @@ def nrad_weight(h,theta,distances,depth,rhob=1.4):
         Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015).
         Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray
         neutrons. Water Resour. Res. 51, 5772–5790. doi:10.1002/2015WR017169
-    """
-
-    # Table A1. Parameters for Fi and D86
-    p10 = 8735;       p11 = 17.1758; p12 = 11720;      p13 = 0.00978;   p14 = 7045;      p15 = 0.003632;
-    p20 = 2.7925e-2;  p21 = 5.0399;  p22 = 2.8544e-2;  p23 = 0.002455;  p24 = 6.851e-5;  p25 = 9.2926;
-    p30 = 247970;     p31 = 17.63;   p32 = 374655;     p33 = 0.00191;   p34 = 195725;
-    p40 = 5.4818e-2;  p41 = 15.921;  p42 = 0.6373;     p43 = 5.99e-2;   p44 = 5.425e-4;
-    p50 = 1383702;    p51 = 4.156;   p52 = 5325;       p53 = 0.00238;   p54 = 0.0156;    p55 = 0.130;     p56 = 1521;
-    p60 = 6.031e-5;   p61 = 98.5;    p62 = 1.0466e-3;
-    p70 = 11747;      p71 = 41.66;   p72 = 4521;       p73 = 0.01998;   p74 = 0.00604;   p75 = 2534;      p76 = 0.00475;
-    p80 = 1.543e-2;   p81 = 10.06;   p82 = 1.807e-2;   p83 = 0.0011;    p84 = 8.81e-5;   p85 = 0.0405;    p86 = 20.24;
-    p90 = 8.321;      p91 = 0.14249; p92 = 0.96655;    p93 = 26.42;     p94 = 0.0567;
-
-
-    # Numerical determination of the penetration depth (86%) (Eq. 8)
-    D86 = 1/rhob*(p90+p91*(p92+np.exp(-1*distances/100))*(p93+theta)/(p94+theta))
-
-    # Depth weights (Eq. 7)
-    Wd = np.exp(-2*depth/D86)
-
-    if h == 0:
-        W = 1 # skip distance weighting
-
-    elif (h >= 0.1) and (h<= 50):
-        # Functions for Fi (Appendix A in Köhli et al., 2015)
-        F1 = p10*(1+p13*h)*np.exp(-p11*theta)+p12*(1+p15*h)-p14*theta
-        F2 = ((-p20+p24*h)*np.exp(-p21*theta/(1+p25*theta))+p22)*(1+h*p23)
-        F3 = (p30*(1+p33*h)*np.exp(-p31*theta)+p32-p34*theta)
-        F4 = p40*np.exp(-p41*theta)+p42-p43*theta+p44*h
-        F5 = p50*(0.02-1/p55/(h-p55+p56*theta))*(p54-theta)*np.exp(-p51*(theta-p54))+p52*(0.7-h*theta*p53)
-        F6 = p60*(h+p61)+p62*theta
-        F7 = (p70*(1-p76*h)*np.exp(-p71*theta*(1-h*p74))+p72-p75*theta)*(2+h*p73)
-        F8 = ((-p80+p84*h)*np.exp(-p81*theta/(1+p85*h+p86*theta))+p82)*(2+h*p83)
-
-        # Distance weights (Eq. 3)
-        W = np.ones_like(distances)*np.nan
-        for i in range(len(distances)):
-            if (distances[i]<=50) and (distances[i]>0.5):
-                W[i]=F1[i]*(np.exp(-F2[i]*distances[i]))+F3[i]*np.exp(-F4[i]*distances[i])
-
-            elif (distances[i]>50) and (distances[i]<600):
-                W[i]=F5[i]*(np.exp(-F6[i]*distances[i]))+F7[i]*np.exp(-F8[i]*distances[i])
-
-            else:
-                raise ValueError('Input distances are not valid.')
-
-    else:
-        raise ValueError('Air humidity values are out of range.')
-
-
-    # Combined and normalized weights
-    weights = Wd*W/np.nansum(Wd*W)
-
-    return weights
 
+        Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L.,
+        Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M., Rebmann, C.,
+        Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.: Improving calibration and
+        validation of cosmic-ray neutron sensors in the light of spatial sensitivity,
+        Hydrol. Earth Syst. Sci., 21, 5009–5030, https://doi.org/10.5194/hess-21-5009-2017, 2017.
+    """
 
+    if method == 'Kohli_2015':
+
+        # Table A1. Parameters for Fi and D86
+        p10 = 8735;
+        p11 = 17.1758;
+        p12 = 11720;
+        p13 = 0.00978;
+        p14 = 7045;
+        p15 = 0.003632;
+        p20 = 2.7925e-2;
+        p21 = 5.0399;
+        p22 = 2.8544e-2;
+        p23 = 0.002455;
+        p24 = 6.851e-5;
+        p25 = 9.2926;
+        p30 = 247970;
+        p31 = 17.63;
+        p32 = 374655;
+        p33 = 0.00191;
+        p34 = 195725;
+        p40 = 5.4818e-2;
+        p41 = 15.921;
+        p42 = 0.6373;
+        p43 = 5.99e-2;
+        p44 = 5.425e-4;
+        p50 = 1383702;
+        p51 = 4.156;
+        p52 = 5325;
+        p53 = 0.00238;
+        p54 = 0.0156;
+        p55 = 0.130;
+        p56 = 1521;
+        p60 = 6.031e-5;
+        p61 = 98.5;
+        p62 = 1.0466e-3;
+        p70 = 11747;
+        p71 = 41.66;
+        p72 = 4521;
+        p73 = 0.01998;
+        p74 = 0.00604;
+        p75 = 2534;
+        p76 = 0.00475;
+        p80 = 1.543e-2;
+        p81 = 10.06;
+        p82 = 1.807e-2;
+        p83 = 0.0011;
+        p84 = 8.81e-5;
+        p85 = 0.0405;
+        p86 = 20.24;
+        p90 = 8.321;
+        p91 = 0.14249;
+        p92 = 0.96655;
+        p93 = 26.42;
+        p94 = 0.0567;
+
+        # Numerical determination of the penetration depth (86%) (Eq. 8)
+        D86 = 1 / rhob * (p90 + p91 * (p92 + np.exp(-1 * distances / 100)) * (p93 + theta) / (p94 + theta))
+
+        # Depth weights (Eq. 7)
+        Wd = np.exp(-2 * depth / D86)
+
+        if h == 0:
+            W = 1  # skip distance weighting
+
+        elif (h >= 0.1) and (h <= 50):
+            # Functions for Fi (Appendix A in Köhli et al., 2015)
+            F1 = p10 * (1 + p13 * h) * np.exp(-p11 * theta) + p12 * (1 + p15 * h) - p14 * theta
+            F2 = ((-p20 + p24 * h) * np.exp(-p21 * theta / (1 + p25 * theta)) + p22) * (1 + h * p23)
+            F3 = (p30 * (1 + p33 * h) * np.exp(-p31 * theta) + p32 - p34 * theta)
+            F4 = p40 * np.exp(-p41 * theta) + p42 - p43 * theta + p44 * h
+            F5 = p50 * (0.02 - 1 / p55 / (h - p55 + p56 * theta)) * (p54 - theta) * np.exp(
+                -p51 * (theta - p54)) + p52 * (0.7 - h * theta * p53)
+            F6 = p60 * (h + p61) + p62 * theta
+            F7 = (p70 * (1 - p76 * h) * np.exp(-p71 * theta * (1 - h * p74)) + p72 - p75 * theta) * (2 + h * p73)
+            F8 = ((-p80 + p84 * h) * np.exp(-p81 * theta / (1 + p85 * h + p86 * theta)) + p82) * (2 + h * p83)
+
+            # Distance weights (Eq. 3)
+            W = np.ones_like(distances) * np.nan
+            for i in range(len(distances)):
+                if (distances[i] <= 50) and (distances[i] > 0.5):
+                    W[i] = F1[i] * (np.exp(-F2[i] * distances[i])) + F3[i] * np.exp(-F4[i] * distances[i])
+
+                elif (distances[i] > 50) and (distances[i] < 600):
+                    W[i] = F5[i] * (np.exp(-F6[i] * distances[i])) + F7[i] * np.exp(-F8[i] * distances[i])
+
+                else:
+                    raise ValueError('Input distances are not valid.')
 
-def exp_filter(sm,T=1):
+        else:
+            raise ValueError('Air humidity values are out of range.')
+
+        # Combined and normalized weights
+        weights = Wd * W / np.nansum(Wd * W)
+        return weights
+    elif method == 'Schron_2017':
+        # Horizontal distance weights According to Eq. 6 and Table A1 in Schrön et al. (2017)
+        # Method for calculating the horizontal distance weights from 0 to 1m
+        def WrX(r, x, y):
+            x00 = 3.7
+            a00 = 8735;
+            a01 = 22.689;
+            a02 = 11720;
+            a03 = 0.00978;
+            a04 = 9306;
+            a05 = 0.003632
+            a10 = 2.7925e-2;
+            a11 = 6.6577;
+            a12 = 0.028544;
+            a13 = 0.002455;
+            a14 = 6.851e-5;
+            a15 = 12.2755
+            a20 = 247970;
+            a21 = 23.289;
+            a22 = 374655;
+            a23 = 0.00191;
+            a24 = 258552
+            a30 = 5.4818e-2;
+            a31 = 21.032;
+            a32 = 0.6373;
+            a33 = 0.0791;
+            a34 = 5.425e-4
+
+            x0 = x00
+            A0 = (a00 * (1 + a03 * x) * np.exp(-a01 * y) + a02 * (1 + a05 * x) - a04 * y)
+            A1 = ((-a10 + a14 * x) * np.exp(-a11 * y / (1 + a15 * y)) + a12) * (1 + x * a13)
+            A2 = (a20 * (1 + a23 * x) * np.exp(-a21 * y) + a22 - a24 * y)
+            A3 = a30 * np.exp(-a31 * y) + a32 - a33 * y + a34 * x
+
+            return ((A0 * (np.exp(-A1 * r)) + A2 * np.exp(-A3 * r)) * (1 - np.exp(-x0 * r)))
+
+        # Method for calculating the horizontal distance weights from 1 to 50m
+        def WrA(r, x, y):
+            a00 = 8735;
+            a01 = 22.689;
+            a02 = 11720;
+            a03 = 0.00978;
+            a04 = 9306;
+            a05 = 0.003632
+            a10 = 2.7925e-2;
+            a11 = 6.6577;
+            a12 = 0.028544;
+            a13 = 0.002455;
+            a14 = 6.851e-5;
+            a15 = 12.2755
+            a20 = 247970;
+            a21 = 23.289;
+            a22 = 374655;
+            a23 = 0.00191;
+            a24 = 258552
+            a30 = 5.4818e-2;
+            a31 = 21.032;
+            a32 = 0.6373;
+            a33 = 0.0791;
+            a34 = 5.425e-4
+
+            A0 = (a00 * (1 + a03 * x) * np.exp(-a01 * y) + a02 * (1 + a05 * x) - a04 * y)
+            A1 = ((-a10 + a14 * x) * np.exp(-a11 * y / (1 + a15 * y)) + a12) * (1 + x * a13)
+            A2 = (a20 * (1 + a23 * x) * np.exp(-a21 * y) + a22 - a24 * y)
+            A3 = a30 * np.exp(-a31 * y) + a32 - a33 * y + a34 * x
+
+            return A0 * np.exp(-A1 * r) + A2 * np.exp(-A3 * r)
+
+        # Method for calculating the horizontal distance weights from 50 to 600m
+        def WrB(r, x, y):
+            b00 = 39006;
+            b01 = 15002337;
+            b02 = 2009.24;
+            b03 = 0.01181;
+            b04 = 3.146;
+            b05 = 16.7417;
+            b06 = 3727
+            b10 = 6.031e-5;
+            b11 = 98.5;
+            b12 = 0.0013826
+            b20 = 11747;
+            b21 = 55.033;
+            b22 = 4521;
+            b23 = 0.01998;
+            b24 = 0.00604;
+            b25 = 3347.4;
+            b26 = 0.00475
+            b30 = 1.543e-2;
+            b31 = 13.29;
+            b32 = 1.807e-2;
+            b33 = 0.0011;
+            b34 = 8.81e-5;
+            b35 = 0.0405;
+            b36 = 26.74
+
+            B0 = (b00 - b01 / (b02 * y + x - 0.13)) * (b03 - y) * np.exp(-b04 * y) - b05 * x * y + b06
+            B1 = b10 * (x + b11) + b12 * y
+            B2 = (b20 * (1 - b26 * x) * np.exp(-b21 * y * (1 - x * b24)) + b22 - b25 * y) * (2 + x * b23)
+            B3 = ((-b30 + b34 * x) * np.exp(-b31 * y / (1 + b35 * x + b36 * y)) + b32) * (2 + x * b33)
+
+            return B0 * np.exp(-B1 * r) + B2 * np.exp(-B3 * r)
+
+        def rscaled(r, p, Hveg, y):
+            Fp = 0.4922 / (0.86 - np.exp(-p / 1013.25))
+            Fveg = 1 - 0.17 * (1 - np.exp(-0.41 * Hveg)) * (1 + np.exp(-9.25 * y))
+            return r / Fp / Fveg
+
+        # Rename variables to be consistent with the revised paper
+        r = distances
+        x = h
+        y = theta
+        bd = rhob
+
+        if Hveg is not None and p is not None:
+            r = rscaled(r, p, Hveg, y)
+
+        Wr = np.zeros(len(r))
+
+        # See Eq. 6 in Schron et al. (2017)
+        r0_idx = (r <= 1)
+        r1_idx = (r > 1) & (r <= 50)
+        r2_idx = (r > 50) & (r < 600)
+        Wr[r0_idx] = WrX(r[r0_idx], x[r0_idx], y[r0_idx])
+        Wr[r1_idx] = WrA(r[r1_idx], x[r1_idx], y[r1_idx])
+        Wr[r2_idx] = WrB(r[r2_idx], x[r2_idx], y[r2_idx])
+
+        # Vertical distance weights
+        def D86(r, bd, y):
+            return 1 / bd * (8.321 + 0.14249 * (0.96655 + np.exp(-0.01 * r)) * (20 + y) / (0.0429 + y))
+
+        def Wd(d, r, bd, y):
+            return np.exp(-2 * d / D86(r, bd, y))
+
+        # Calculate the vertical distance weights
+        Wd = Wd(d, r, bd, y)
+
+        # Combined and normalized weights
+        # Combined and normalized weights
+        weights = Wd * Wr / np.nansum(Wd * Wr)
+
+        return weights
+
+
+def exp_filter(sm, T=1):
     """Exponential filter to estimate soil moisture in the rootzone from surface observtions.
 
     Args:
@@ -738,7 +981,6 @@ def exp_filter(sm,T=1):
         Soil Science Society of America Journal.
     """
 
-
     # Parameters
     t_delta = 1
     sm_min = np.min(sm)
@@ -746,18 +988,18 @@ def exp_filter(sm,T=1):
     ms = (sm - sm_min) / (sm_max - sm_min)
 
     # Pre-allocate soil water index array and recursive constant K
-    SWI = np.ones_like(ms)*np.nan
-    K = np.ones_like(ms)*np.nan
+    SWI = np.ones_like(ms) * np.nan
+    K = np.ones_like(ms) * np.nan
 
     # Initial conditions
     SWI[0] = ms[0]
     K[0] = 1
 
     # Values from 2 to N
-    for n in range(1,len(SWI)):
-        if ~np.isnan(ms[n]) & ~np.isnan(ms[n-1]):
-            K[n] = K[n-1] / (K[n-1] + np.exp(-t_delta/T))
-            SWI[n] = SWI[n-1] + K[n]*(ms[n] - SWI[n-1])
+    for n in range(1, len(SWI)):
+        if ~np.isnan(ms[n]) & ~np.isnan(ms[n - 1]):
+            K[n] = K[n - 1] / (K[n - 1] + np.exp(-t_delta / T))
+            SWI[n] = SWI[n - 1] + K[n] * (ms[n] - SWI[n - 1])
         else:
             continue
 
@@ -767,7 +1009,7 @@ def exp_filter(sm,T=1):
     return sm_subsurface
 
 
-def cutoff_rigidity(lat,lon):
+def cutoff_rigidity(lat, lon):
     """Function to estimate the approximate cutoff rigidity for any point on Earth according to the
     tabulated data of Smart and Shea, 2019. The returned value can be used to select the appropriate
     neutron monitor station to estimate the cosmic-ray neutron intensity at the location of interest.
@@ -788,6 +1030,10 @@ def cutoff_rigidity(lat,lon):
         2.52 GV (Value from NMD is 2.40 GV)
 
     References:
+        Hawdon, A., McJannet, D., & Wallace, J. (2014). Calibration and correction procedures
+        for cosmic‐ray neutron soil moisture probes located across Australia. Water Resources Research,
+        50(6), 5029-5043.
+
         Smart, D. & Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program:
         Theory, Software Description and Example. NASA STI/Recon Technical Report N.
 
@@ -798,16 +1044,102 @@ def cutoff_rigidity(lat,lon):
     yq = lat
 
     if xq < 0:
-        xq = xq*-1 + 180
+        xq = xq * -1 + 180
     Z = np.array(data.cutoff_rigidity)
     x = np.linspace(0, 360, Z.shape[1])
     y = np.linspace(90, -90, Z.shape[0])
     X, Y = np.meshgrid(x, y)
-    points = np.array( (X.flatten(), Y.flatten()) ).T
+    points = np.array((X.flatten(), Y.flatten())).T
     values = Z.flatten()
-    zq = griddata(points, values, (xq,yq))
+    zq = griddata(points, values, (xq, yq))
+
+    return np.round(zq, 2)
+
+
+def atmospheric_depth(elevation, latitude):
+    """Function to estimate the atmospheric depth for any point on Earth according to McJannet and Desilets, 2023
+
+    This function is required in the calculation of the location-dependent reference correction proposed by McJannet and Desilets, 2023.
+
+    Args:
+        elevation (float): Elevation in meters above sea level.
+        latitude (float): Geographic latitude in decimal degrees. Value in range -90 to 90
+
+    Returns:
+        (float): Atmospheric depth in g/cm2
+
+    References:
+        Atmosphere, U. S. (1976). US standard atmosphere. National Oceanic and Atmospheric Administration.
+
+        McJannet, D. L., & Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.
+    """
+
+    density_of_rock = 2670  # Density of rock in kg/m3
+    air_pressure_sea_level = 1013.25  # Air pressure at sea level in hPa
+    air_molar_mass = 0.0289644  # Air molar mass in kg/mol
+    universal_gas_constant = 8.3144598  # Universal gas constant in J/(mol*K)
+    reference_temperature = 288.15  # Reference temperature Kelvin
+    temperature_lapse_rate = -0.0065  # Temperature lapse rate in K/m
+
+    # Gravity at sea-level calculation
+    gravity_sea_level = 9.780327 * (
+            1 + 0.0053024 * np.sin(np.radians(latitude)) ** 2 - 0.0000058 * np.sin(2 * np.radians(latitude)) ** 2)
+    # Free air correction
+    free_air = -3.086 * 10 ** -6 * elevation
+    # Bouguer correction
+    bouguer_corr = 4.194 * 10 ** -10 * density_of_rock * elevation
+    # Total gravity
+    gravity = gravity_sea_level + free_air + bouguer_corr
+
+    # Air pressure calculation
+    reference_air_pressure = air_pressure_sea_level * (
+                1 + temperature_lapse_rate / reference_temperature * elevation) ** ((-gravity * air_molar_mass) / (
+                universal_gas_constant * temperature_lapse_rate))
+
+    # Atmospheric depth calculation
+    atmospheric_depth = (10 * reference_air_pressure) / gravity
+    return atmospheric_depth
+
+
+def location_factor(site_atmospheric_depth, site_Rc, reference_atmospheric_depth, reference_Rc):
+    """
+    Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.
+
+
+    Args:
+        site_atmospheric_depth (float): Atmospheric depth at the site in g/cm2. Can be estimated using the function `atmospheric_depth()`
+        site_Rc (float): Cutoff rigidity at the site in GV. Can be estimated using the function `cutoff_rigidity()`
+        reference_atmospheric_depth (float): Atmospheric depth at the reference location in g/cm2.
+        reference_Rc (float): Cutoff rigidity at the reference location in GV.
+
+    Returns:
+        (float): Location-dependent correction factor.
+
+    References:
+        McJannet, D. L., & Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.
+
+    """
+
+    # Renamed variables based on the provided table
+    c0 = -0.0009  # from C39
+    c1 = 1.7699  # from C40
+    c2 = 0.0064  # from C41
+    c3 = 1.8855  # from C42
+    c4 = 0.000013  # from C43
+    c5 = -1.2237  # from C44
+    epsilon = 1  # from C45
+
+    # Translated formula with renamed variables from McJannet and Desilets, 2023
+    tau_new = epsilon * (c0 * reference_atmospheric_depth + c1) * (
+            1 - np.exp(
+        -(c2 * reference_atmospheric_depth + c3) * reference_Rc ** (c4 * reference_atmospheric_depth + c5)))
 
-    return np.round(zq,2)
+    norm_factor = 1 / tau_new
+
+    # Calculate the result using the provided parameters
+    tau = epsilon * norm_factor * (c0 * site_atmospheric_depth + c1) * (
+                1 - np.exp(-(c2 * site_atmospheric_depth + c3) * site_Rc ** (c4 * site_atmospheric_depth + c5)))
+    return tau
 
 
 def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):
@@ -817,6 +1149,7 @@ def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):
         Rc (float): Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.
         start_date (datetime): Start date for the period of interest.
         end_date (datetime): End date for the period of interest.
+        verbose (bool): If True, print a expanded output of the incoming neutron flux data.
 
     Returns:
         (list): List of top five stations with closes cutoff rigidity.
@@ -846,7 +1179,7 @@ def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):
     """
 
     # Load file with list of neutron monitoring stations
-    stations = pd.DataFrame(data.neutron_detectors, columns=["STID","NAME","R","Altitude_m"])
+    stations = pd.DataFrame(data.neutron_detectors, columns=["STID", "NAME", "R", "Altitude_m"])
 
     # Sort stations by closest cutoff rigidity
     idx_R = (stations['R'] - Rc).abs().argsort()
@@ -857,9 +1190,10 @@ def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):
             station = stations.iloc[idx_R[i]]["STID"]
             try:
                 if get_incoming_neutron_flux(start_date, end_date, station, verbose=verbose) is not None:
-                    stations.iloc[idx_R[i],-1] = True
+                    stations.iloc[idx_R[i], -1] = True
             except:
                 pass
+
         if sum(stations["Period available"] == True) == 0:
             print("No stations available for the selected period!")
         else:
@@ -871,7 +1205,8 @@ def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):
 
     # Print results
     print('')
-    print("""Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations""")
+    print(
+        """Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations""")
     print('')
     print(f"Your cutoff rigidity is {Rc} GV")
     print(result)
@@ -890,7 +1225,7 @@ def interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps):
         (pd.Series): Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps
     """
     incoming_flux = np.array([])
-    for k,timestamp in enumerate(crnp_timestamps):
+    for k, timestamp in enumerate(crnp_timestamps):
         if timestamp in nmdb_timestamps.values:
             idx = timestamp == nmdb_timestamps
             incoming_flux = np.append(incoming_flux, nmdb_counts.loc[idx])
@@ -927,7 +1262,7 @@ def lattice_water(clay_content, total_carbon=None):
     return lattice_water
 
 
-def latlon_to_utm(lat, lon, utm_zone_number, missing_values=None):
+def latlon_to_utm(lat, lon, utm_zone_number=None, utm_zone_letter=None):
     """Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.
 
     Function only applies to non-polar coordinates.
@@ -940,7 +1275,8 @@ def latlon_to_utm(lat, lon, utm_zone_number, missing_values=None):
     Args:
         lat (float, array): Latitude in decimal degrees.
         lon (float, array): Longitude in decimal degrees.
-        utm_zone_number (int): Universal Transverse Mercator (UTM) zone.
+        utm_zone_number (int): UTM zone number. If None, the zone number is automatically calculated.
+        utm_zone_letter (str): UTM zone letter. If None, the zone letter is automatically calculated.
 
     Returns:
         (float, float): Tuple of easting and northing coordinates in meters. First element is easting, second is northing.
@@ -951,68 +1287,13 @@ def latlon_to_utm(lat, lon, utm_zone_number, missing_values=None):
 
          [https://www.maptools.com/tutorials/grid_zone_details#](https://www.maptools.com/tutorials/grid_zone_details#)
     """
+    if utm_zone_number is None or utm_zone_letter is None:
+        easting, northing, zone_number, zone_letter = utm.from_latlon(lat, lon)
+    else:
+        easting, northing, zone_number, zone_letter = utm.from_latlon(lat, lon, utm_zone_number, utm_zone_letter)
 
+    return easting, northing, zone_number, zone_letter
 
-    # Define constants
-    R = 6_378_137  # Earth's radius at the Equator in meters
-
-    # Convert input data to Numpy arrays
-    if (type(lat) is not np.ndarray) or (type(lon) is not np.ndarray):
-        try:
-            lat = np.array(lat)
-            lon = np.array(lon)
-        except:
-            raise "Input values cannot be converted to Numpy arrays."
-
-    # Check latitude range
-    if np.any(lat < -80) | np.any(lat > 84):
-        raise "One or more latitude values exceed the range -80 to 84"
-
-    # Check longitude range
-    if np.any(lon < -180) | np.any(lon > 180):
-        raise "One or more longitude values exceed the range -180 to 180"
-
-    # Constants
-    K0 = 0.9996
-    E = 0.00669438
-    E_P2 = E / (1 - E)
-
-    M1 = (1 - E / 4 - 3 * E ** 2 / 64 - 5 * E ** 3 / 256)
-    M2 = (3 * E / 8 + 3 * E ** 2 / 32 + 45 * E ** 3 / 1024)
-    M3 = (15 * E ** 2 / 256 + 45 * E ** 3 / 1024)
-    M4 = (35 * E ** 3 / 3072)
-
-    # Trigonometric operations
-    lat_rad = np.radians(lat)
-    lon_rad = np.radians(lon)
-
-    lat_sin = np.sin(lat_rad)
-    lat_cos = np.cos(lat_rad)
-    lat_tan = lat_sin / lat_cos
-    lat_tan2 = lat_tan * lat_tan
-    lat_tan4 = lat_tan2 * lat_tan2
-
-    # Find central meridian.
-    central_lon = (utm_zone_number * 6 - 180) - 3  # Zones are every 6 degrees.
-    central_lon_rad = np.radians(central_lon)
-
-    n = R / np.sqrt(1 - E * lat_sin ** 2)
-    c = E_P2 * lat_cos ** 2
-
-    with np.errstate(divide='ignore', invalid='ignore'):
-        a = lat_cos * (np.remainder(((lon_rad - central_lon_rad) + np.pi), (2 * np.pi)) - np.pi)
-    m = R * (M1 * lat_rad - M2 * np.sin(2 * lat_rad) + M3 * np.sin(4 * lat_rad) - M4 * np.sin(6 * lat_rad))
-
-    easting = K0 * n * (a + a ** 3 / 6 * (1 - lat_tan2 + c) + a ** 5 / 120 * (
-                5 - 18 * lat_tan2 + lat_tan4 + 72 * c - 58 * E_P2)) + 500_000
-    northing = K0 * (m + n * lat_tan * (
-                a ** 2 / 2 + a ** 4 / 24 * (5 - lat_tan2 + 9 * c + 4 * c ** 2) + a ** 6 / 720 * (
-                    61 - 58 * lat_tan2 + lat_tan4 + 600 * c - 330 * E_P2)))
-
-    if np.any(lat < 0):
-        northing += 10_000_000
-
-    return easting, northing
 
 def euclidean_distance(px, py, x, y):
     """Function that computes the Euclidean distance between one point
@@ -1068,7 +1349,6 @@ def spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=Fa
         distances = euclidean_distance(px, py, x, y)
         idx_within_buffer = distances <= buffer
 
-
         if np.isnan(z[k]):
             z_new_val = np.nan
         elif len(distances[idx_within_buffer]) > min_neighbours:
@@ -1079,7 +1359,7 @@ def spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=Fa
             else:
                 raise f"Method {method} does not exist. Provide either 'mean' or 'median'."
         else:
-            z_new_val = z[k] # If there are not enough neighbours, keep the original value
+            z_new_val = z[k]  # If there are not enough neighbours, keep the original value
 
         # Append smoothed value to array
         z_smooth = np.append(z_smooth, z_new_val)
@@ -1160,7 +1440,8 @@ def interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000):
     z = z[~idx_nan]
 
     if idx_nan.any():
-        print(f"WARNING: {np.isnan(x).sum()}, {np.isnan(y).sum()}, and {np.isnan(z).sum()} NaN values were dropped from x, y, and z.")
+        print(
+            f"WARNING: {np.isnan(x).sum()}, {np.isnan(y).sum()}, and {np.isnan(z).sum()} NaN values were dropped from x, y, and z.")
 
     # Create 2D grid for interpolation
     Nx = round((np.max(x) - np.min(x)) / dx) + 1
@@ -1195,9 +1476,9 @@ def rover_centered_coordinates(x, y):
     """
 
     # Make it datatype agnostic
-    if(isinstance(x, pd.Series)):
+    if (isinstance(x, pd.Series)):
         x = x.values
-    if(isinstance(y, pd.Series)):
+    if (isinstance(y, pd.Series)):
         y = y.values
 
     # Do the average of the two points
@@ -1208,7 +1489,6 @@ def rover_centered_coordinates(x, y):
     x_est = np.insert(x_est, 0, x[0])
     y_est = np.insert(y_est, 0, y[0])
 
-
     return x_est, y_est
 
 
@@ -1242,13 +1522,13 @@ def uncertainty_counts(raw_counts, metric="std", fp=1, fw=1, fi=1):
     if metric == "std":
         uncertainty = np.sqrt(raw_counts) * s
     elif metric == "cv":
-        uncertainty = 1 /  np.sqrt(raw_counts) * s
+        uncertainty = 1 / np.sqrt(raw_counts) * s
     else:
         raise f"Metric {metric} does not exist. Provide either 'std' or 'cv' for standard deviation or coefficient of variation."
     return uncertainty
 
 
-def uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808,a1=0.372,a2=0.115):
+def uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808, a1=0.372, a2=0.115):
     r"""Function to estimate the uncertainty propagated to volumetric water content.
 
     The uncertainty of the volumetric water content is estimated by propagating the uncertainty of the raw counts.
@@ -1275,17 +1555,8 @@ def uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808,a1
 
     Ncorr = raw_counts * fw / (fp * fi)
     sigma_N = uncertainty_counts(raw_counts, metric="std", fp=fp, fw=fw, fi=fi)
-    sigma_GWC = sigma_N * ((a0*N0) / ((Ncorr - a1*N0)**4)) * np.sqrt((Ncorr - a1 * N0)**4 + 8 * sigma_N**2 * (Ncorr - a1 * N0)**2 + 15 * sigma_N**4)
+    sigma_GWC = sigma_N * ((a0 * N0) / ((Ncorr - a1 * N0) ** 4)) * np.sqrt(
+        (Ncorr - a1 * N0) ** 4 + 8 * sigma_N ** 2 * (Ncorr - a1 * N0) ** 2 + 15 * sigma_N ** 4)
     sigma_VWC = sigma_GWC * bulk_density
 
     return sigma_VWC
-
-
-
-
-
-
-
-
-
-
diff --git a/site/404.html b/site/404.html
index 7cb91ac..66aa860 100644
--- a/site/404.html
+++ b/site/404.html
@@ -12,8 +12,9 @@
       
       
       
+      
       <link rel="icon" href="/crnpy/img/logo/favicon.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.18">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.5.3">
     
     
       
@@ -21,12 +22,15 @@
       
     
     
-      <link rel="stylesheet" href="/crnpy/assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="/crnpy/assets/stylesheets/main.50c56a3b.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -54,9 +58,6 @@
     <body dir="ltr">
   
     
-    
-      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
-    
     <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
     <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
     <label class="md-overlay" for="__drawer"></label>
@@ -80,6 +81,7 @@
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
+      
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
@@ -100,8 +102,12 @@
     </div>
     
     
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+    
+    
     
       <label class="md-header__button md-icon" for="__search">
+        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -110,12 +116,15 @@
     <form class="md-search__form" name="search">
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
+        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
+        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
+          
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -139,7 +148,7 @@
         <a href="https://github.com/soilwater/crnpy" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -169,6 +178,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="/crnpy/." title="CRNPy Documentation" class="md-nav__button md-logo" aria-label="CRNPy Documentation" data-md-component="logo">
@@ -183,7 +193,7 @@
       <a href="https://github.com/soilwater/crnpy" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -195,14 +205,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="/crnpy/." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -210,14 +225,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="/crnpy/sensor_description/" class="md-nav__link">
-        Sensor description
+        
+  
+  <span class="md-ellipsis">
+    Sensor description
+  </span>
+  
+
       </a>
     </li>
   
@@ -225,14 +245,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="/crnpy/correction_routines/" class="md-nav__link">
-        Correction routines
+        
+  
+  <span class="md-ellipsis">
+    Correction routines
+  </span>
+  
+
       </a>
     </li>
   
@@ -240,91 +265,126 @@
     
       
       
-      
-
+  
   
   
   
     
+    
+    
+    
+    
     <li class="md-nav__item md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="/crnpy/examples/stationary/example_RDT_station/" class="md-nav__link">
-        Processing and analyzing data from a stationary detector
+        
+  
+  <span class="md-ellipsis">
+    Processing and analyzing data from a stationary detector
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="/crnpy/examples/rover/Hydroinnova_rover_example/" class="md-nav__link">
-        Processing and analyzing data from a roving detector
+        
+  
+  <span class="md-ellipsis">
+    Processing and analyzing data from a roving detector
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="/crnpy/examples/calibration/calibration/" class="md-nav__link">
-        Device-specific field calibration
+        
+  
+  <span class="md-ellipsis">
+    Device-specific field calibration
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="/crnpy/reference/" class="md-nav__link">
-        Reference
+        
+  
+  <span class="md-ellipsis">
+    Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -364,6 +424,7 @@ <h1>404 - Not found</h1>
             </div>
           
           
+<script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script>
         </div>
         
       </main>
@@ -391,22 +452,17 @@ <h1>404 - Not found</h1>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "/crnpy/", "features": [], "search": "/crnpy/assets/javascripts/workers/search.74e28a9f.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    
+    <script id="__config" type="application/json">{"base": "/crnpy/", "features": [], "search": "/crnpy/assets/javascripts/workers/search.f886a092.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="/crnpy/assets/javascripts/bundle.220ee61c.min.js"></script>
+      <script src="/crnpy/assets/javascripts/bundle.d7c377c4.min.js"></script>
       
-        
-          <script src="/crnpy/javascripts/katex.js"></script>
-        
+        <script src="/crnpy/javascripts/katex.js"></script>
       
-        
-          <script src="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js"></script>
-        
+        <script src="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js"></script>
       
-        
-          <script src="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js"></script>
-        
+        <script src="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js"></script>
       
     
   </body>
diff --git a/site/assets/_mkdocstrings.css b/site/assets/_mkdocstrings.css
index a65078d..049a254 100644
--- a/site/assets/_mkdocstrings.css
+++ b/site/assets/_mkdocstrings.css
@@ -1,18 +1,13 @@
 
-/* Don't capitalize names. */
-h5.doc-heading {
-  text-transform: none !important;
-}
-
-/* Avoid breaking parameters name, etc. in table cells. */
+/* Avoid breaking parameter names, etc. in table cells. */
 .doc-contents td code {
   word-break: normal !important;
 }
 
-/* For pieces of Markdown rendered in table cells. */
-.doc-contents td p {
-  margin-top: 0 !important;
-  margin-bottom: 0 !important;
+/* No line break before first paragraph of descriptions. */
+.doc-md-description,
+.doc-md-description>p:first-child {
+  display: inline;
 }
 
 /* Max width for docstring sections tables. */
@@ -21,16 +16,49 @@ h5.doc-heading {
   display: table !important;
   width: 100%;
 }
+
 .doc .md-typeset__table tr {
   display: table-row;
 }
 
-/* Avoid line breaks in rendered fields. */
-.field-body p {
-  display: inline;
-}
-
 /* Defaults in Spacy table style. */
 .doc-param-default {
   float: right;
 }
+
+/* Keep headings consistent. */
+h1.doc-heading,
+h2.doc-heading,
+h3.doc-heading,
+h4.doc-heading,
+h5.doc-heading,
+h6.doc-heading {
+  font-weight: 400;
+  line-height: 1.5;
+  color: inherit;
+  text-transform: none;
+}
+
+h1.doc-heading {
+  font-size: 1.6rem;
+}
+
+h2.doc-heading {
+  font-size: 1.2rem;
+}
+
+h3.doc-heading {
+  font-size: 1.15rem;
+}
+
+h4.doc-heading {
+  font-size: 1.10rem;
+}
+
+h5.doc-heading {
+  font-size: 1.05rem;
+}
+
+h6.doc-heading {
+  font-size: 1rem;
+}
\ No newline at end of file
diff --git a/site/assets/images/social/correction_routines.png b/site/assets/images/social/correction_routines.png
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diff --git a/site/assets/images/social/examples/calibration/calibration.png b/site/assets/images/social/examples/calibration/calibration.png
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diff --git a/site/assets/images/social/examples/rover/Hydroinnova_rover_example.png b/site/assets/images/social/examples/rover/Hydroinnova_rover_example.png
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-          Correction routines
+          
+  
+  <span class="md-ellipsis">
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+
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       <a href="./" class="md-nav__link md-nav__link--active">
-        Correction routines
+        
+  
+  <span class="md-ellipsis">
+    Correction routines
+  </span>
+  
+
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@@ -318,14 +351,18 @@
         
           <li class="md-nav__item">
   <a href="#stationary-crnp-processing" class="md-nav__link">
-    Stationary CRNP processing
+    <span class="md-ellipsis">
+      Stationary CRNP processing
+    </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
   <a href="#roving-crnp-processing" class="md-nav__link">
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+    <span class="md-ellipsis">
+      Roving CRNP processing
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         <li class="md-nav__item">
   <a href="#incoming-neutron-flux" class="md-nav__link">
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+    <span class="md-ellipsis">
+      Incoming neutron flux
+    </span>
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   <a href="#atmospheric-corrections" class="md-nav__link">
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+    <span class="md-ellipsis">
+      Atmospheric corrections
+    </span>
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 </li>
       
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   <a href="#biomass-correction" class="md-nav__link">
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+    <span class="md-ellipsis">
+      Biomass correction
+    </span>
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+    <span class="md-ellipsis">
+      Road correction
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 </li>
       
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-    Additional corrections
+    <span class="md-ellipsis">
+      Additional corrections
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       <a href="../examples/stationary/example_RDT_station/" class="md-nav__link">
-        Processing and analyzing data from a stationary detector
+        
+  
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+  </span>
+  
+
       </a>
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+  
   
   
   
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       <a href="../examples/rover/Hydroinnova_rover_example/" class="md-nav__link">
-        Processing and analyzing data from a roving detector
+        
+  
+  <span class="md-ellipsis">
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+
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-            
-          
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+                
+  
   
   
   
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       <a href="../examples/calibration/calibration/" class="md-nav__link">
-        Device-specific field calibration
+        
+  
+  <span class="md-ellipsis">
+    Device-specific field calibration
+  </span>
+  
+
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+      
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-      
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-        Reference
+        
+  
+  <span class="md-ellipsis">
+    Reference
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@@ -496,7 +578,9 @@
       
         <li class="md-nav__item">
   <a href="#overview" class="md-nav__link">
-    Overview
+    <span class="md-ellipsis">
+      Overview
+    </span>
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@@ -504,14 +588,18 @@
         
           <li class="md-nav__item">
   <a href="#stationary-crnp-processing" class="md-nav__link">
-    Stationary CRNP processing
+    <span class="md-ellipsis">
+      Stationary CRNP processing
+    </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
   <a href="#roving-crnp-processing" class="md-nav__link">
-    Roving CRNP processing
+    <span class="md-ellipsis">
+      Roving CRNP processing
+    </span>
   </a>
   
 </li>
@@ -523,35 +611,45 @@
       
         <li class="md-nav__item">
   <a href="#incoming-neutron-flux" class="md-nav__link">
-    Incoming neutron flux
+    <span class="md-ellipsis">
+      Incoming neutron flux
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#atmospheric-corrections" class="md-nav__link">
-    Atmospheric corrections
+    <span class="md-ellipsis">
+      Atmospheric corrections
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#biomass-correction" class="md-nav__link">
-    Biomass correction
+    <span class="md-ellipsis">
+      Biomass correction
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#road-correction" class="md-nav__link">
-    Road correction
+    <span class="md-ellipsis">
+      Road correction
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#additional-corrections" class="md-nav__link">
-    Additional corrections
+    <span class="md-ellipsis">
+      Additional corrections
+    </span>
   </a>
   
 </li>
@@ -582,10 +680,10 @@ <h1>Correction routines</h1>
 <h3 id="overview">Overview</h3>
 <p>The CRNPy library provides the tools to correct and process neutron counts recorded with both stationary and roving cosmic-ray neutron probes. Below are two flowcharts describing the typical steps from the correction of raw neutron counts to the conversion into volumetric soil water content.</p>
 <h4 id="stationary-crnp-processing">Stationary CRNP processing</h4>
-<p><a class="glightbox" href="../img/workflow_rdt.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="CRNPy Processing Workflow" src="../img/workflow_rdt.png" /></a>
+<p><a class="glightbox" href="../img/workflow_rdt.png" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="CRNPy Processing Workflow" src="../img/workflow_rdt.png" /></a>
 Dashed lines indicate optional steps. See the complete <a href="../examples/stationary/example_RDT_station/">example notebook</a>.</p>
 <h4 id="roving-crnp-processing">Roving CRNP processing</h4>
-<p><a class="glightbox" href="../img/workflow_hydroinnova.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="CRNPy Processing Workflow" src="../img/workflow_hydroinnova.png" /></a>
+<p><a class="glightbox" href="../img/workflow_hydroinnova.png" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="CRNPy Processing Workflow" src="../img/workflow_hydroinnova.png" /></a>
 Dashed lines indicate optional steps. See the complete <a href="../examples/rover/Hydroinnova_rover_example/">example notebook</a>.</p>
 <h3 id="incoming-neutron-flux">Incoming neutron flux</h3>
 <p>The CRNPy library includes a complete set of methods for correcting the raw observed neutron counts for natural variation in the incoming neutron flux, including a set of tools for searching and downloading data from reference neutron monitors from the NMDB database (www.nmdb.eu) with similar the cut-off rigidity as the study location (Klein et al., 2009; Shea &amp; Smart., 2019).</p>
@@ -732,12 +830,23 @@ <h3 id="additional-corrections">Additional corrections</h3>
 
 
 
+
+
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+  
+
+
+
+
+
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+<script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script>
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diff --git a/site/examples/calibration/calibration/index.html b/site/examples/calibration/calibration/index.html
index 15f07e0..1adb942 100644
--- a/site/examples/calibration/calibration/index.html
+++ b/site/examples/calibration/calibration/index.html
@@ -18,8 +18,9 @@
       
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+      <link rel="stylesheet" href="../../../assets/stylesheets/main.50c56a3b.min.css">
       
       
 
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+        <meta  property="og:title"  content="Example device calibration - CRNPy Documentation" >
       
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+        <meta  name="twitter:title"  content="Example device calibration - CRNPy Documentation" >
       
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   <div class="md-source__repository">
     GitHub
@@ -243,14 +253,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -258,14 +273,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../../sensor_description/" class="md-nav__link">
-        Sensor description
+        
+  
+  <span class="md-ellipsis">
+    Sensor description
+  </span>
+  
+
       </a>
     </li>
   
@@ -273,14 +293,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../../correction_routines/" class="md-nav__link">
-        Correction routines
+        
+  
+  <span class="md-ellipsis">
+    Correction routines
+  </span>
+  
+
       </a>
     </li>
   
@@ -288,70 +313,93 @@
     
       
       
-      
-
   
   
     
   
   
+  
+    
+    
+    
+    
     
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../stationary/example_RDT_station/" class="md-nav__link">
-        Processing and analyzing data from a stationary detector
+        
+  
+  <span class="md-ellipsis">
+    Processing and analyzing data from a stationary detector
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../rover/Hydroinnova_rover_example/" class="md-nav__link">
-        Processing and analyzing data from a roving detector
+        
+  
+  <span class="md-ellipsis">
+    Processing and analyzing data from a roving detector
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
   
   
+  
     <li class="md-nav__item md-nav__item--active">
       
       <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
@@ -361,12 +409,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Device-specific field calibration
+          
+  
+  <span class="md-ellipsis">
+    Device-specific field calibration
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Device-specific field calibration
+        
+  
+  <span class="md-ellipsis">
+    Device-specific field calibration
+  </span>
+  
+
       </a>
       
         
@@ -386,21 +446,27 @@
       
         <li class="md-nav__item">
   <a href="#read-calibration-field-survey-data" class="md-nav__link">
-    Read calibration field survey data
+    <span class="md-ellipsis">
+      Read calibration field survey data
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#read-data-from-crnp" class="md-nav__link">
-    Read data from CRNP
+    <span class="md-ellipsis">
+      Read data from CRNP
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#correct-neutron-counts" class="md-nav__link">
-    Correct neutron counts
+    <span class="md-ellipsis">
+      Correct neutron counts
+    </span>
   </a>
   
     <nav class="md-nav" aria-label="Correct neutron counts">
@@ -408,7 +474,9 @@
         
           <li class="md-nav__item">
   <a href="#determine-field-average-soil-moisture-and-bulk-density" class="md-nav__link">
-    Determine field-average soil moisture and bulk density
+    <span class="md-ellipsis">
+      Determine field-average soil moisture and bulk density
+    </span>
   </a>
   
 </li>
@@ -420,14 +488,18 @@
       
         <li class="md-nav__item">
   <a href="#solving-for-n_0" class="md-nav__link">
-    Solving for $N_0$
+    <span class="md-ellipsis">
+      Solving for $N_0$
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#references" class="md-nav__link">
-    References:
+    <span class="md-ellipsis">
+      References:
+    </span>
   </a>
   
 </li>
@@ -439,24 +511,30 @@
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../../reference/" class="md-nav__link">
-        Reference
+        
+  
+  <span class="md-ellipsis">
+    Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -490,21 +568,27 @@
       
         <li class="md-nav__item">
   <a href="#read-calibration-field-survey-data" class="md-nav__link">
-    Read calibration field survey data
+    <span class="md-ellipsis">
+      Read calibration field survey data
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#read-data-from-crnp" class="md-nav__link">
-    Read data from CRNP
+    <span class="md-ellipsis">
+      Read data from CRNP
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#correct-neutron-counts" class="md-nav__link">
-    Correct neutron counts
+    <span class="md-ellipsis">
+      Correct neutron counts
+    </span>
   </a>
   
     <nav class="md-nav" aria-label="Correct neutron counts">
@@ -512,7 +596,9 @@
         
           <li class="md-nav__item">
   <a href="#determine-field-average-soil-moisture-and-bulk-density" class="md-nav__link">
-    Determine field-average soil moisture and bulk density
+    <span class="md-ellipsis">
+      Determine field-average soil moisture and bulk density
+    </span>
   </a>
   
 </li>
@@ -524,14 +610,18 @@
       
         <li class="md-nav__item">
   <a href="#solving-for-n_0" class="md-nav__link">
-    Solving for $N_0$
+    <span class="md-ellipsis">
+      Solving for $N_0$
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#references" class="md-nav__link">
-    References:
+    <span class="md-ellipsis">
+      References:
+    </span>
   </a>
   
 </li>
@@ -722,12 +812,7 @@
         }, 1000)
       })
 </script>
-
-
-
-
 <style type="text/css">
-    
     pre { line-height: 125%; }
 td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
 span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
@@ -777,791 +862,16 @@
 .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
 .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
   </style>
-
-
-
-
 <style type="text/css">
-/*
-This file is taken from the built JupyterLab theme.css
-Found on share/nbconvert/templates/lab/static
-Some changes have been made and marked with CHANGE
-*/
-
-.jupyter-wrapper {
-    /* Elevation
-   *
-   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
-   *
-   * https://github.com/material-components/material-components-web
-   * https://material-components-web.appspot.com/elevation.html
-   */
-
-    --jp-shadow-base-lightness: 0;
-    --jp-shadow-umbra-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        0.2
-    );
-    --jp-shadow-penumbra-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        0.14
-    );
-    --jp-shadow-ambient-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        0.12
-    );
-    --jp-elevation-z0: none;
-    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
-        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 3px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
-        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 5px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
-        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 10px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
-        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 18px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
-        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
-        0px 3px 14px 2px var(--jp-shadow-ambient-color);
-    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
-        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
-        0px 5px 22px 4px var(--jp-shadow-ambient-color);
-    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
-        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
-        0px 6px 30px 5px var(--jp-shadow-ambient-color);
-    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
-        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
-        0px 8px 38px 7px var(--jp-shadow-ambient-color);
-    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
-        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
-        0px 9px 46px 8px var(--jp-shadow-ambient-color);
-
-    /* Borders
-   *
-   * The following variables, specify the visual styling of borders in JupyterLab.
-   */
-
-    --jp-border-width: 1px;
-    --jp-border-color0: var(--md-grey-400);
-    --jp-border-color1: var(--md-grey-400);
-    --jp-border-color2: var(--md-grey-300);
-    --jp-border-color3: var(--md-grey-200);
-    --jp-border-radius: 2px;
-
-    /* UI Fonts
-   *
-   * The UI font CSS variables are used for the typography all of the JupyterLab
-   * user interface elements that are not directly user generated content.
-   *
-   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
-   * is applied to a parent element. When children elements, such as headings, are sized
-   * in em all things will be computed relative to that body size.
-   */
-
-    --jp-ui-font-scale-factor: 1.2;
-    --jp-ui-font-size0: 0.83333em;
-    --jp-ui-font-size1: 13px; /* Base font size */
-    --jp-ui-font-size2: 1.2em;
-    --jp-ui-font-size3: 1.44em;
-
-    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
-        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
-        "Segoe UI Symbol";
-
-    /*
-   * Use these font colors against the corresponding main layout colors.
-   * In a light theme, these go from dark to light.
-   */
-
-    /* Defaults use Material Design specification */
-    --jp-ui-font-color0: rgba(0, 0, 0, 1);
-    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
-    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
-    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
-
-    /*
-   * Use these against the brand/accent/warn/error colors.
-   * These will typically go from light to darker, in both a dark and light theme.
-   */
-
-    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
-    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
-    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
-    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
-
-    /* Content Fonts
-   *
-   * Content font variables are used for typography of user generated content.
-   *
-   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
-   * is applied to a parent element. When children elements, such as headings, are sized
-   * in em all things will be computed relative to that body size.
-   */
-
-    --jp-content-line-height: 1.6;
-    --jp-content-font-scale-factor: 1.2;
-    --jp-content-font-size0: 0.83333em;
-    --jp-content-font-size1: 14px; /* Base font size */
-    --jp-content-font-size2: 1.2em;
-    --jp-content-font-size3: 1.44em;
-    --jp-content-font-size4: 1.728em;
-    --jp-content-font-size5: 2.0736em;
-
-    /* This gives a magnification of about 125% in presentation mode over normal. */
-    --jp-content-presentation-font-size1: 17px;
-
-    --jp-content-heading-line-height: 1;
-    --jp-content-heading-margin-top: 1.2em;
-    --jp-content-heading-margin-bottom: 0.8em;
-    --jp-content-heading-font-weight: 500;
-
-    /* Defaults use Material Design specification */
-    --jp-content-font-color0: rgba(0, 0, 0, 1);
-    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
-    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
-    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
-
-    --jp-content-link-color: var(--md-blue-700);
-
-    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
-        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
-        "Segoe UI Symbol";
-
-    /*
-   * Code Fonts
-   *
-   * Code font variables are used for typography of code and other monospaces content.
-   */
-
-    --jp-code-font-size: 13px;
-    --jp-code-line-height: 1.3077; /* 17px for 13px base */
-    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
-    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
-        monospace;
-    --jp-code-font-family: var(--jp-code-font-family-default);
-
-    /* This gives a magnification of about 125% in presentation mode over normal. */
-    --jp-code-presentation-font-size: 16px;
-
-    /* may need to tweak cursor width if you change font size */
-    --jp-code-cursor-width0: 1.4px;
-    --jp-code-cursor-width1: 2px;
-    --jp-code-cursor-width2: 4px;
-
-    /* Layout
-   *
-   * The following are the main layout colors use in JupyterLab. In a light
-   * theme these would go from light to dark.
-   */
-
-    --jp-layout-color0: white;
-    --jp-layout-color1: white;
-    --jp-layout-color2: var(--md-grey-200);
-    --jp-layout-color3: var(--md-grey-400);
-    --jp-layout-color4: var(--md-grey-600);
-
-    /* Inverse Layout
-   *
-   * The following are the inverse layout colors use in JupyterLab. In a light
-   * theme these would go from dark to light.
-   */
-
-    --jp-inverse-layout-color0: #111111;
-    --jp-inverse-layout-color1: var(--md-grey-900);
-    --jp-inverse-layout-color2: var(--md-grey-800);
-    --jp-inverse-layout-color3: var(--md-grey-700);
-    --jp-inverse-layout-color4: var(--md-grey-600);
-
-    /* Brand/accent */
-
-    --jp-brand-color0: var(--md-blue-900);
-    --jp-brand-color1: var(--md-blue-700);
-    --jp-brand-color2: var(--md-blue-300);
-    --jp-brand-color3: var(--md-blue-100);
-    --jp-brand-color4: var(--md-blue-50);
-
-    --jp-accent-color0: var(--md-green-900);
-    --jp-accent-color1: var(--md-green-700);
-    --jp-accent-color2: var(--md-green-300);
-    --jp-accent-color3: var(--md-green-100);
-
-    /* State colors (warn, error, success, info) */
-
-    --jp-warn-color0: var(--md-orange-900);
-    --jp-warn-color1: var(--md-orange-700);
-    --jp-warn-color2: var(--md-orange-300);
-    --jp-warn-color3: var(--md-orange-100);
-
-    --jp-error-color0: var(--md-red-900);
-    --jp-error-color1: var(--md-red-700);
-    --jp-error-color2: var(--md-red-300);
-    --jp-error-color3: var(--md-red-100);
-
-    --jp-success-color0: var(--md-green-900);
-    --jp-success-color1: var(--md-green-700);
-    --jp-success-color2: var(--md-green-300);
-    --jp-success-color3: var(--md-green-100);
-
-    --jp-info-color0: var(--md-cyan-900);
-    --jp-info-color1: var(--md-cyan-700);
-    --jp-info-color2: var(--md-cyan-300);
-    --jp-info-color3: var(--md-cyan-100);
-
-    /* Cell specific styles */
-
-    --jp-cell-padding: 5px;
-
-    --jp-cell-collapser-width: 8px;
-    --jp-cell-collapser-min-height: 20px;
-    --jp-cell-collapser-not-active-hover-opacity: 0.6;
-
-    --jp-cell-editor-background: var(--md-grey-100);
-    --jp-cell-editor-border-color: var(--md-grey-300);
-    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
-    --jp-cell-editor-active-background: var(--jp-layout-color0);
-    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
-
-    --jp-cell-prompt-width: 64px;
-    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
-    --jp-cell-prompt-letter-spacing: 0px;
-    --jp-cell-prompt-opacity: 1;
-    --jp-cell-prompt-not-active-opacity: 0.5;
-    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
-    /* A custom blend of MD grey and blue 600
-   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
-    --jp-cell-inprompt-font-color: #307fc1;
-    /* A custom blend of MD grey and orange 600
-   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
-    --jp-cell-outprompt-font-color: #bf5b3d;
-
-    /* Notebook specific styles */
-
-    --jp-notebook-padding: 10px;
-    --jp-notebook-select-background: var(--jp-layout-color1);
-    --jp-notebook-multiselected-color: var(--md-blue-50);
-
-    /* The scroll padding is calculated to fill enough space at the bottom of the
-  notebook to show one single-line cell (with appropriate padding) at the top
-  when the notebook is scrolled all the way to the bottom. We also subtract one
-  pixel so that no scrollbar appears if we have just one single-line cell in the
-  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
-  */
-    --jp-notebook-scroll-padding: calc(
-        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
-            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
-    );
-
-    /* Rendermime styles */
-
-    --jp-rendermime-error-background: #fdd;
-    --jp-rendermime-table-row-background: var(--md-grey-100);
-    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
-
-    /* Dialog specific styles */
-
-    --jp-dialog-background: rgba(0, 0, 0, 0.25);
-
-    /* Console specific styles */
-
-    --jp-console-padding: 10px;
-
-    /* Toolbar specific styles */
-
-    --jp-toolbar-border-color: var(--jp-border-color1);
-    --jp-toolbar-micro-height: 8px;
-    --jp-toolbar-background: var(--jp-layout-color1);
-    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
-    --jp-toolbar-header-margin: 4px 4px 0px 4px;
-    --jp-toolbar-active-background: var(--md-grey-300);
-
-    /* Statusbar specific styles */
-
-    --jp-statusbar-height: 24px;
-
-    /* Input field styles */
-
-    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
-    --jp-input-active-background: var(--jp-layout-color1);
-    --jp-input-hover-background: var(--jp-layout-color1);
-    --jp-input-background: var(--md-grey-100);
-    --jp-input-border-color: var(--jp-border-color1);
-    --jp-input-active-border-color: var(--jp-brand-color1);
-    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
-
-    /* General editor styles */
-
-    --jp-editor-selected-background: #d9d9d9;
-    --jp-editor-selected-focused-background: #d7d4f0;
-    --jp-editor-cursor-color: var(--jp-ui-font-color0);
-
-    /* Code mirror specific styles */
-
-    --jp-mirror-editor-keyword-color: #008000;
-    --jp-mirror-editor-atom-color: #88f;
-    --jp-mirror-editor-number-color: #080;
-    --jp-mirror-editor-def-color: #00f;
-    --jp-mirror-editor-variable-color: var(--md-grey-900);
-    --jp-mirror-editor-variable-2-color: #05a;
-    --jp-mirror-editor-variable-3-color: #085;
-    --jp-mirror-editor-punctuation-color: #05a;
-    --jp-mirror-editor-property-color: #05a;
-    --jp-mirror-editor-operator-color: #aa22ff;
-    --jp-mirror-editor-comment-color: #408080;
-    --jp-mirror-editor-string-color: #ba2121;
-    --jp-mirror-editor-string-2-color: #708;
-    --jp-mirror-editor-meta-color: #aa22ff;
-    --jp-mirror-editor-qualifier-color: #555;
-    --jp-mirror-editor-builtin-color: #008000;
-    --jp-mirror-editor-bracket-color: #997;
-    --jp-mirror-editor-tag-color: #170;
-    --jp-mirror-editor-attribute-color: #00c;
-    --jp-mirror-editor-header-color: blue;
-    --jp-mirror-editor-quote-color: #090;
-    --jp-mirror-editor-link-color: #00c;
-    --jp-mirror-editor-error-color: #f00;
-    --jp-mirror-editor-hr-color: #999;
-
-    /* Vega extension styles */
-
-    --jp-vega-background: white;
-
-    /* Sidebar-related styles */
-
-    --jp-sidebar-min-width: 250px;
-
-    /* Search-related styles */
-
-    --jp-search-toggle-off-opacity: 0.5;
-    --jp-search-toggle-hover-opacity: 0.8;
-    --jp-search-toggle-on-opacity: 1;
-    --jp-search-selected-match-background-color: rgb(245, 200, 0);
-    --jp-search-selected-match-color: black;
-    --jp-search-unselected-match-background-color: var(
-        --jp-inverse-layout-color0
-    );
-    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
-
-    /* Icon colors that work well with light or dark backgrounds */
-    --jp-icon-contrast-color0: var(--md-purple-600);
-    --jp-icon-contrast-color1: var(--md-green-600);
-    --jp-icon-contrast-color2: var(--md-pink-600);
-    --jp-icon-contrast-color3: var(--md-blue-600);
-}
-
-[data-md-color-scheme="slate"] .jupyter-wrapper {
-    /* Elevation
-   *
-   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
-   *
-   * https://github.com/material-components/material-components-web
-   * https://material-components-web.appspot.com/elevation.html
-   */
-
-    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
-   * colors used in the theme as well. */
-    --jp-shadow-base-lightness: 32;
-    --jp-shadow-umbra-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        0.2
-    );
-    --jp-shadow-penumbra-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        0.14
-    );
-    --jp-shadow-ambient-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        0.12
-    );
-    --jp-elevation-z0: none;
-    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
-        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 3px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
-        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 5px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
-        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 10px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
-        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 18px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
-        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
-        0px 3px 14px 2px var(--jp-shadow-ambient-color);
-    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
-        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
-        0px 5px 22px 4px var(--jp-shadow-ambient-color);
-    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
-        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
-        0px 6px 30px 5px var(--jp-shadow-ambient-color);
-    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
-        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
-        0px 8px 38px 7px var(--jp-shadow-ambient-color);
-    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
-        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
-        0px 9px 46px 8px var(--jp-shadow-ambient-color);
-
-    /* Borders
-   *
-   * The following variables, specify the visual styling of borders in JupyterLab.
-   */
-
-    --jp-border-width: 1px;
-    --jp-border-color0: var(--md-grey-700);
-    --jp-border-color1: var(--md-grey-700);
-    --jp-border-color2: var(--md-grey-800);
-    --jp-border-color3: var(--md-grey-900);
-    --jp-border-radius: 2px;
-
-    /* UI Fonts
-   *
-   * The UI font CSS variables are used for the typography all of the JupyterLab
-   * user interface elements that are not directly user generated content.
-   *
-   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
-   * is applied to a parent element. When children elements, such as headings, are sized
-   * in em all things will be computed relative to that body size.
-   */
-
-    --jp-ui-font-scale-factor: 1.2;
-    --jp-ui-font-size0: 0.83333em;
-    --jp-ui-font-size1: 13px; /* Base font size */
-    --jp-ui-font-size2: 1.2em;
-    --jp-ui-font-size3: 1.44em;
-
-    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
-        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
-        "Segoe UI Symbol";
-
-    /*
-   * Use these font colors against the corresponding main layout colors.
-   * In a light theme, these go from dark to light.
-   */
-
-    /* Defaults use Material Design specification */
-    --jp-ui-font-color0: rgba(255, 255, 255, 1);
-    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
-    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
-    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
-
-    /*
-   * Use these against the brand/accent/warn/error colors.
-   * These will typically go from light to darker, in both a dark and light theme.
-   */
-
-    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
-    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
-    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
-    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
-
-    /* Content Fonts
-   *
-   * Content font variables are used for typography of user generated content.
-   *
-   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
-   * is applied to a parent element. When children elements, such as headings, are sized
-   * in em all things will be computed relative to that body size.
-   */
-
-    --jp-content-line-height: 1.6;
-    --jp-content-font-scale-factor: 1.2;
-    --jp-content-font-size0: 0.83333em;
-    --jp-content-font-size1: 14px; /* Base font size */
-    --jp-content-font-size2: 1.2em;
-    --jp-content-font-size3: 1.44em;
-    --jp-content-font-size4: 1.728em;
-    --jp-content-font-size5: 2.0736em;
-
-    /* This gives a magnification of about 125% in presentation mode over normal. */
-    --jp-content-presentation-font-size1: 17px;
-
-    --jp-content-heading-line-height: 1;
-    --jp-content-heading-margin-top: 1.2em;
-    --jp-content-heading-margin-bottom: 0.8em;
-    --jp-content-heading-font-weight: 500;
-
-    /* Defaults use Material Design specification */
-    --jp-content-font-color0: rgba(255, 255, 255, 1);
-    --jp-content-font-color1: rgba(255, 255, 255, 1);
-    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
-    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
-
-    --jp-content-link-color: var(--md-blue-300);
-
-    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
-        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
-        "Segoe UI Symbol";
-
-    /*
-   * Code Fonts
-   *
-   * Code font variables are used for typography of code and other monospaces content.
-   */
-
-    --jp-code-font-size: 13px;
-    --jp-code-line-height: 1.3077; /* 17px for 13px base */
-    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
-    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
-        monospace;
-    --jp-code-font-family: var(--jp-code-font-family-default);
-
-    /* This gives a magnification of about 125% in presentation mode over normal. */
-    --jp-code-presentation-font-size: 16px;
-
-    /* may need to tweak cursor width if you change font size */
-    --jp-code-cursor-width0: 1.4px;
-    --jp-code-cursor-width1: 2px;
-    --jp-code-cursor-width2: 4px;
-
-    /* Layout
-   *
-   * The following are the main layout colors use in JupyterLab. In a light
-   * theme these would go from light to dark.
-   */
-
-    --jp-layout-color0: #111111;
-    --jp-layout-color1: var(--md-grey-900);
-    --jp-layout-color2: var(--md-grey-800);
-    --jp-layout-color3: var(--md-grey-700);
-    --jp-layout-color4: var(--md-grey-600);
-
-    /* Inverse Layout
-   *
-   * The following are the inverse layout colors use in JupyterLab. In a light
-   * theme these would go from dark to light.
-   */
-
-    --jp-inverse-layout-color0: white;
-    --jp-inverse-layout-color1: white;
-    --jp-inverse-layout-color2: var(--md-grey-200);
-    --jp-inverse-layout-color3: var(--md-grey-400);
-    --jp-inverse-layout-color4: var(--md-grey-600);
-
-    /* Brand/accent */
-
-    --jp-brand-color0: var(--md-blue-700);
-    --jp-brand-color1: var(--md-blue-500);
-    --jp-brand-color2: var(--md-blue-300);
-    --jp-brand-color3: var(--md-blue-100);
-    --jp-brand-color4: var(--md-blue-50);
-
-    --jp-accent-color0: var(--md-green-700);
-    --jp-accent-color1: var(--md-green-500);
-    --jp-accent-color2: var(--md-green-300);
-    --jp-accent-color3: var(--md-green-100);
-
-    /* State colors (warn, error, success, info) */
-
-    --jp-warn-color0: var(--md-orange-700);
-    --jp-warn-color1: var(--md-orange-500);
-    --jp-warn-color2: var(--md-orange-300);
-    --jp-warn-color3: var(--md-orange-100);
-
-    --jp-error-color0: var(--md-red-700);
-    --jp-error-color1: var(--md-red-500);
-    --jp-error-color2: var(--md-red-300);
-    --jp-error-color3: var(--md-red-100);
-
-    --jp-success-color0: var(--md-green-700);
-    --jp-success-color1: var(--md-green-500);
-    --jp-success-color2: var(--md-green-300);
-    --jp-success-color3: var(--md-green-100);
-
-    --jp-info-color0: var(--md-cyan-700);
-    --jp-info-color1: var(--md-cyan-500);
-    --jp-info-color2: var(--md-cyan-300);
-    --jp-info-color3: var(--md-cyan-100);
-
-    /* Cell specific styles */
-
-    --jp-cell-padding: 5px;
-
-    --jp-cell-collapser-width: 8px;
-    --jp-cell-collapser-min-height: 20px;
-    --jp-cell-collapser-not-active-hover-opacity: 0.6;
-
-    --jp-cell-editor-background: var(--jp-layout-color1);
-    --jp-cell-editor-border-color: var(--md-grey-700);
-    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
-    --jp-cell-editor-active-background: var(--jp-layout-color0);
-    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
-
-    --jp-cell-prompt-width: 64px;
-    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
-    --jp-cell-prompt-letter-spacing: 0px;
-    --jp-cell-prompt-opacity: 1;
-    --jp-cell-prompt-not-active-opacity: 1;
-    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
-
-    /* A custom blend of MD grey and blue 600
-   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
-    --jp-cell-inprompt-font-color: #307fc1;
-    /* A custom blend of MD grey and orange 600
-   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
-    --jp-cell-outprompt-font-color: #bf5b3d;
-
-    /* Notebook specific styles */
-
-    --jp-notebook-padding: 10px;
-    --jp-notebook-select-background: var(--jp-layout-color1);
-    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
-
-    /* The scroll padding is calculated to fill enough space at the bottom of the
-  notebook to show one single-line cell (with appropriate padding) at the top
-  when the notebook is scrolled all the way to the bottom. We also subtract one
-  pixel so that no scrollbar appears if we have just one single-line cell in the
-  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
-  */
-    --jp-notebook-scroll-padding: calc(
-        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
-            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
-    );
-
-    /* Rendermime styles */
-
-    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
-    --jp-rendermime-table-row-background: var(--md-grey-900);
-    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
-
-    /* Dialog specific styles */
-
-    --jp-dialog-background: rgba(0, 0, 0, 0.6);
-
-    /* Console specific styles */
-
-    --jp-console-padding: 10px;
-
-    /* Toolbar specific styles */
-
-    --jp-toolbar-border-color: var(--jp-border-color2);
-    --jp-toolbar-micro-height: 8px;
-    --jp-toolbar-background: var(--jp-layout-color1);
-    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
-    --jp-toolbar-header-margin: 4px 4px 0px 4px;
-    --jp-toolbar-active-background: var(--jp-layout-color0);
-
-    /* Statusbar specific styles */
-
-    --jp-statusbar-height: 24px;
-
-    /* Input field styles */
-
-    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
-    --jp-input-active-background: var(--jp-layout-color0);
-    --jp-input-hover-background: var(--jp-layout-color2);
-    --jp-input-background: var(--md-grey-800);
-    --jp-input-border-color: var(--jp-border-color1);
-    --jp-input-active-border-color: var(--jp-brand-color1);
-    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
-
-    /* General editor styles */
-
-    --jp-editor-selected-background: var(--jp-layout-color2);
-    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
-    --jp-editor-cursor-color: var(--jp-ui-font-color0);
-
-    /* Code mirror specific styles */
-
-    --jp-mirror-editor-keyword-color: var(--md-green-500);
-    --jp-mirror-editor-atom-color: var(--md-blue-300);
-    --jp-mirror-editor-number-color: var(--md-green-400);
-    --jp-mirror-editor-def-color: var(--md-blue-600);
-    --jp-mirror-editor-variable-color: var(--md-grey-300);
-    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
-    --jp-mirror-editor-variable-3-color: var(--md-green-600);
-    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
-    --jp-mirror-editor-property-color: var(--md-blue-400);
-    --jp-mirror-editor-operator-color: #aa22ff;
-    --jp-mirror-editor-comment-color: #408080;
-    --jp-mirror-editor-string-color: #ff7070;
-    --jp-mirror-editor-string-2-color: var(--md-purple-300);
-    --jp-mirror-editor-meta-color: #aa22ff;
-    --jp-mirror-editor-qualifier-color: #555;
-    --jp-mirror-editor-builtin-color: var(--md-green-600);
-    --jp-mirror-editor-bracket-color: #997;
-    --jp-mirror-editor-tag-color: var(--md-green-700);
-    --jp-mirror-editor-attribute-color: var(--md-blue-700);
-    --jp-mirror-editor-header-color: var(--md-blue-500);
-    --jp-mirror-editor-quote-color: var(--md-green-300);
-    --jp-mirror-editor-link-color: var(--md-blue-700);
-    --jp-mirror-editor-error-color: #f00;
-    --jp-mirror-editor-hr-color: #999;
-
-    /* Vega extension styles */
-
-    --jp-vega-background: var(--md-grey-400);
-
-    /* Sidebar-related styles */
-
-    --jp-sidebar-min-width: 250px;
-
-    /* Search-related styles */
-
-    --jp-search-toggle-off-opacity: 0.6;
-    --jp-search-toggle-hover-opacity: 0.8;
-    --jp-search-toggle-on-opacity: 1;
-    --jp-search-selected-match-background-color: rgb(255, 225, 0);
-    --jp-search-selected-match-color: black;
-    --jp-search-unselected-match-background-color: var(
-        --jp-inverse-layout-color0
-    );
-    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
-
-    /* scrollbar related styles. Supports every browser except Edge. */
-
-    /* colors based on JetBrain's Darcula theme */
-
-    --jp-scrollbar-background-color: #3f4244;
-    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
-
-    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
-
-    /* hacks for setting the thumb shape. These do nothing in Firefox */
-
-    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
-    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
-
-    /* Icon colors that work well with light or dark backgrounds */
-    --jp-icon-contrast-color0: var(--md-purple-600);
-    --jp-icon-contrast-color1: var(--md-green-600);
-    --jp-icon-contrast-color2: var(--md-pink-600);
-    --jp-icon-contrast-color3: var(--md-blue-600);
-}
-
-:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: 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#f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: 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-<h1 id="device-specific-field-calibration">Device-specific field calibration<a class="anchor-link" href="#device-specific-field-calibration">&#182;</a></h1><p>The calibration of a cosmic-ray neutron probe (CRNP) is an essential step to ensure accurate soil moisture measurements. The CRNP operates by counting fast neutrons produced from cosmic rays, which are predominantly moderated by water molecules in the soil. The parameter $N_0$ is typically considered a device-specific constant that represents the neutron count rate in the absence of soil moisture conditions.</p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h1 id="device-specific-field-calibration">Device-specific field calibration<a class="anchor-link" href="#device-specific-field-calibration">¶</a></h1><p>The calibration of a cosmic-ray neutron probe (CRNP) is an essential step to ensure accurate soil moisture measurements. The CRNP operates by counting fast neutrons produced from cosmic rays, which are predominantly moderated by water molecules in the soil. The parameter $N_0$ is typically considered a device-specific constant that represents the neutron count rate in the absence of soil moisture conditions.</p>
 <p>$\theta(N) =\frac{a_0}{(\frac{N}{N_0}) - a_1} - a_2 $ (Desilets et al., 2010).</p>
 <p>For the calibration of the stationary detector, a total of 14 undisturbed soil cores were collected at radial distances of 5, 50, and 100 m from the detector. In this example each soil sample was split into four depth segments: 0-5 cm, 5-10 cm, 10-25 cm, and 25-40 cm. Soil samples were processed and soil moisture was determined using the thermo-gravimetric method.</p>
-<a class="glightbox" href="../../../img/layout.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img src="../../../img/layout.png" style="max-width:500px"></a>
+<a class="glightbox" href="../../../img/layout.png" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="No description has been provided for this image" src="../../../img/layout.png" style="max-width:500px"/></a>
 <p>Figure 1. Horizontal layout and vertical layout used in this particular example calibration, it can be customized by the user depending on their needs.</p>
 <p><a href="https://github.com/soilwater/crnpy/blob/main/docs/examples/calibration/template.xlsx">Download the following template</a>  for collecting your own calibration soil samples:</p>
-<a class="glightbox" href="../../../img/template.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img src="../../../img/template.png" style="max-width:500px"></a>
-
+<a class="glightbox" href="../../../img/template.png" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="No description has been provided for this image" src="../../../img/template.png" style="max-width:500px"/></a>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Importing required libraries</span>
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+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Importing required libraries</span>
 <span class="kn">import</span> <span class="nn">crnpy</span>
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">root</span>
 </pre></div>
-<div id="cell-1" class="clipboard-copy-txt"># Importing required libraries
+<div class="clipboard-copy-txt" id="cell-1"># Importing required libraries
 import crnpy
 
 import numpy as np
 import pandas as pd
 from scipy.optimize import root
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-<h2 id="read-calibration-field-survey-data">Read calibration field survey data<a class="anchor-link" href="#read-calibration-field-survey-data">&#182;</a></h2><p>For each sample it is required to know the bulk density ($\rho_\beta$) and the volumetric water content ($\theta_v$). See the details of the calculation used in the <a href="https://github.com/soilwater/crnpy/blob/main/docs/examples/calibration/soil_data.csv">filled example</a>.</p>
-
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="read-calibration-field-survey-data">Read calibration field survey data<a class="anchor-link" href="#read-calibration-field-survey-data">¶</a></h2><p>For each sample it is required to know the bulk density ($\rho_\beta$) and the volumetric water content ($\theta_v$). See the details of the calculation used in the <a href="https://github.com/soilwater/crnpy/blob/main/docs/examples/calibration/soil_data.csv">filled example</a>.</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Load the soil samples data and the CRNP dataset using pandas</span>
-<span class="n">url_soil_samples</span> <span class="o">=</span> <span class="s2">&quot;https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/soil_data.csv&quot;</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Load the soil samples data and the CRNP dataset using pandas</span>
+<span class="n">url_soil_samples</span> <span class="o">=</span> <span class="s2">"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/soil_data.csv"</span>
 <span class="n">df_soil</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">url_soil_samples</span><span class="p">)</span>
 <span class="n">df_soil</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
-<div id="cell-2" class="clipboard-copy-txt"># Load the soil samples data and the CRNP dataset using pandas
+<div class="clipboard-copy-txt" id="cell-2"># Load the soil samples data and the CRNP dataset using pandas
 url_soil_samples = "https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/soil_data.csv"
 df_soil = pd.read_csv(url_soil_samples)
 df_soil.head(3)
 </div>
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+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
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-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -1730,197 +1172,186 @@ <h2 id="read-calibration-field-survey-data">Read calibration field survey data<a
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>field</th>
-      <th>date</th>
-      <th>core_number</th>
-      <th>distance_from_station</th>
-      <th>latitude</th>
-      <th>longitude</th>
-      <th>top_depth</th>
-      <th>bottom_depth</th>
-      <th>core_diameter</th>
-      <th>wet_mass_with_bag</th>
-      <th>...</th>
-      <th>can_number</th>
-      <th>mass_empty_can</th>
-      <th>wet_mass_with_can</th>
-      <th>dry_mass_with_can</th>
-      <th>mass_water</th>
-      <th>theta_g</th>
-      <th>volume</th>
-      <th>bulk_density</th>
-      <th>theta_v</th>
-      <th>Observation</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>Flickner</td>
-      <td>22-Oct</td>
-      <td>1</td>
-      <td>5</td>
-      <td>N38.23459</td>
-      <td>W97.57101</td>
-      <td>0</td>
-      <td>5</td>
-      <td>30.49</td>
-      <td>45.31</td>
-      <td>...</td>
-      <td>1</td>
-      <td>52.10</td>
-      <td>92.03</td>
-      <td>85.31</td>
-      <td>6.72</td>
-      <td>0.202</td>
-      <td>36.514864</td>
-      <td>0.909</td>
-      <td>0.184403</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>Flickner</td>
-      <td>22-Oct</td>
-      <td>1</td>
-      <td>5</td>
-      <td>N38.23459</td>
-      <td>W97.57101</td>
-      <td>5</td>
-      <td>10</td>
-      <td>30.49</td>
-      <td>69.53</td>
-      <td>...</td>
-      <td>2</td>
-      <td>51.85</td>
-      <td>115.97</td>
-      <td>103.85</td>
-      <td>12.12</td>
-      <td>0.233</td>
-      <td>36.514864</td>
-      <td>1.424</td>
-      <td>0.332585</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>Flickner</td>
-      <td>22-Oct</td>
-      <td>1</td>
-      <td>5</td>
-      <td>N38.23459</td>
-      <td>W97.57101</td>
-      <td>10</td>
-      <td>25</td>
-      <td>30.49</td>
-      <td>214.90</td>
-      <td>...</td>
-      <td>3</td>
-      <td>51.56</td>
-      <td>260.97</td>
-      <td>219.77</td>
-      <td>41.20</td>
-      <td>0.245</td>
-      <td>109.544592</td>
-      <td>1.536</td>
-      <td>0.376856</td>
-      <td>NaN</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>field</th>
+<th>date</th>
+<th>core_number</th>
+<th>distance_from_station</th>
+<th>latitude</th>
+<th>longitude</th>
+<th>top_depth</th>
+<th>bottom_depth</th>
+<th>core_diameter</th>
+<th>wet_mass_with_bag</th>
+<th>...</th>
+<th>can_number</th>
+<th>mass_empty_can</th>
+<th>wet_mass_with_can</th>
+<th>dry_mass_with_can</th>
+<th>mass_water</th>
+<th>theta_g</th>
+<th>volume</th>
+<th>bulk_density</th>
+<th>theta_v</th>
+<th>Observation</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>Flickner</td>
+<td>22-Oct</td>
+<td>1</td>
+<td>5</td>
+<td>N38.23459</td>
+<td>W97.57101</td>
+<td>0</td>
+<td>5</td>
+<td>30.49</td>
+<td>45.31</td>
+<td>...</td>
+<td>1</td>
+<td>52.10</td>
+<td>92.03</td>
+<td>85.31</td>
+<td>6.72</td>
+<td>0.202</td>
+<td>36.514864</td>
+<td>0.909</td>
+<td>0.184403</td>
+<td>NaN</td>
+</tr>
+<tr>
+<th>1</th>
+<td>Flickner</td>
+<td>22-Oct</td>
+<td>1</td>
+<td>5</td>
+<td>N38.23459</td>
+<td>W97.57101</td>
+<td>5</td>
+<td>10</td>
+<td>30.49</td>
+<td>69.53</td>
+<td>...</td>
+<td>2</td>
+<td>51.85</td>
+<td>115.97</td>
+<td>103.85</td>
+<td>12.12</td>
+<td>0.233</td>
+<td>36.514864</td>
+<td>1.424</td>
+<td>0.332585</td>
+<td>NaN</td>
+</tr>
+<tr>
+<th>2</th>
+<td>Flickner</td>
+<td>22-Oct</td>
+<td>1</td>
+<td>5</td>
+<td>N38.23459</td>
+<td>W97.57101</td>
+<td>10</td>
+<td>25</td>
+<td>30.49</td>
+<td>214.90</td>
+<td>...</td>
+<td>3</td>
+<td>51.56</td>
+<td>260.97</td>
+<td>219.77</td>
+<td>41.20</td>
+<td>0.245</td>
+<td>109.544592</td>
+<td>1.536</td>
+<td>0.376856</td>
+<td>NaN</td>
+</tr>
+</tbody>
 </table>
 <p>3 rows × 21 columns</p>
 </div>
 </div>
-
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
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-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Define start and end of field survey calibration</span>
-<span class="n">calibration_start</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="s2">&quot;2021-10-22 08:00&quot;</span><span class="p">)</span>
-<span class="n">calibration_end</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="s2">&quot;2021-10-22 16:00&quot;</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-3">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Define start and end of field survey calibration</span>
+<span class="n">calibration_start</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="s2">"2021-10-22 08:00"</span><span class="p">)</span>
+<span class="n">calibration_end</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="s2">"2021-10-22 16:00"</span><span class="p">)</span>
 </pre></div>
-<div id="cell-3" class="clipboard-copy-txt"># Define start and end of field survey calibration
+<div class="clipboard-copy-txt" id="cell-3"># Define start and end of field survey calibration
 calibration_start = pd.to_datetime("2021-10-22 08:00")
 calibration_end = pd.to_datetime("2021-10-22 16:00")
 </div>
-        
-        </div>
-    </div>
-
 </div>
 </div>
-
 </div>
 </div>
-<div id="cell-id=a7ea1da2-4acb-47a0-a22a-3165424952ed" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=a7ea1da2-4acb-47a0-a22a-3165424952ed">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h2 id="read-data-from-crnp">Read data from CRNP<a class="anchor-link" href="#read-data-from-crnp">&#182;</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="read-data-from-crnp">Read data from CRNP<a class="anchor-link" href="#read-data-from-crnp">¶</a></h2>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div id="cell-id=4317bdea-2810-418d-8a17-5f1a99a0ea34" class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=4317bdea-2810-418d-8a17-5f1a99a0ea34">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
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-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Load the station data</span>
-<span class="n">url_station_data</span> <span class="o">=</span> <span class="s2">&quot;https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/station_data.csv&quot;</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-4">
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+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Load the station data</span>
+<span class="n">url_station_data</span> <span class="o">=</span> <span class="s2">"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/station_data.csv"</span>
 <span class="n">df_station</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">url_station_data</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span>
 
-<span class="c1">#  Parse dates (you can also use the option `parse_dates=[&#39;TIMESTAMP]` in pd.read_csv()</span>
-<span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;TIMESTAMP&#39;</span><span class="p">],</span> <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;%Y-%m-</span><span class="si">%d</span><span class="s1"> %H:%M:%S&#39;</span><span class="p">)</span>
+<span class="c1">#  Parse dates (you can also use the option `parse_dates=['TIMESTAMP]` in pd.read_csv()</span>
+<span class="n">df_station</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">'TIMESTAMP'</span><span class="p">],</span> <span class="nb">format</span><span class="o">=</span><span class="s1">'%Y-%m-</span><span class="si">%d</span><span class="s1"> %H:%M:%S'</span><span class="p">)</span>
 
 <span class="n">df_station</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
-<div id="cell-4" class="clipboard-copy-txt"># Load the station data
+<div class="clipboard-copy-txt" id="cell-4"># Load the station data
 url_station_data = "https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/station_data.csv"
 df_station = pd.read_csv(url_station_data, skiprows=[0,2,3])
 
@@ -1929,28 +1360,19 @@ <h2 id="read-data-from-crnp">Read data from CRNP<a class="anchor-link" href="#re
 
 df_station.head(3)
 </div>
-        
-        </div>
-    </div>
-
 </div>
 </div>
-
+</div>
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+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
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-<style scoped>
+<style scoped="">
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         vertical-align: middle;
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@@ -1964,179 +1386,164 @@ <h2 id="read-data-from-crnp">Read data from CRNP<a class="anchor-link" href="#re
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>TIMESTAMP</th>
-      <th>RECORD</th>
-      <th>station</th>
-      <th>farm</th>
-      <th>field</th>
-      <th>latitude</th>
-      <th>longitude</th>
-      <th>altitude</th>
-      <th>battery_voltage_Min</th>
-      <th>PTemp_Avg</th>
-      <th>...</th>
-      <th>wind_speed_gust_Max</th>
-      <th>air_temperature_Avg</th>
-      <th>vapor_pressure_Avg</th>
-      <th>barometric_pressure_Avg</th>
-      <th>relative_humidity_Avg</th>
-      <th>humidity_sensor_temperature_Avg</th>
-      <th>tilt_north_south_Avg</th>
-      <th>tilt_west_east_Avg</th>
-      <th>NDVI_Avg</th>
-      <th>timestamp</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>2021-09-22 12:00:00</td>
-      <td>0</td>
-      <td>KS003</td>
-      <td>Flickner</td>
-      <td>Rainfed South</td>
-      <td>38.23461</td>
-      <td>-97.57095</td>
-      <td>455</td>
-      <td>13.52</td>
-      <td>29.04</td>
-      <td>...</td>
-      <td>4.20</td>
-      <td>22.30</td>
-      <td>9.20</td>
-      <td>973</td>
-      <td>41.30</td>
-      <td>26.4</td>
-      <td>-1.000</td>
-      <td>1.000</td>
-      <td>0.311</td>
-      <td>2021-09-22 12:00:00</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2021-09-22 13:00:00</td>
-      <td>1</td>
-      <td>KS003</td>
-      <td>Flickner</td>
-      <td>Rainfed South</td>
-      <td>38.23461</td>
-      <td>-97.57095</td>
-      <td>455</td>
-      <td>13.53</td>
-      <td>29.98</td>
-      <td>...</td>
-      <td>9.02</td>
-      <td>22.90</td>
-      <td>9.08</td>
-      <td>972</td>
-      <td>32.63</td>
-      <td>26.8</td>
-      <td>-0.975</td>
-      <td>0.950</td>
-      <td>0.308</td>
-      <td>2021-09-22 13:00:00</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>2021-09-22 14:00:00</td>
-      <td>2</td>
-      <td>KS003</td>
-      <td>Flickner</td>
-      <td>Rainfed South</td>
-      <td>38.23461</td>
-      <td>-97.57095</td>
-      <td>455</td>
-      <td>13.53</td>
-      <td>30.43</td>
-      <td>...</td>
-      <td>5.54</td>
-      <td>23.38</td>
-      <td>8.68</td>
-      <td>971</td>
-      <td>30.25</td>
-      <td>27.2</td>
-      <td>-0.775</td>
-      <td>0.625</td>
-      <td>0.31</td>
-      <td>2021-09-22 14:00:00</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>TIMESTAMP</th>
+<th>RECORD</th>
+<th>station</th>
+<th>farm</th>
+<th>field</th>
+<th>latitude</th>
+<th>longitude</th>
+<th>altitude</th>
+<th>battery_voltage_Min</th>
+<th>PTemp_Avg</th>
+<th>...</th>
+<th>wind_speed_gust_Max</th>
+<th>air_temperature_Avg</th>
+<th>vapor_pressure_Avg</th>
+<th>barometric_pressure_Avg</th>
+<th>relative_humidity_Avg</th>
+<th>humidity_sensor_temperature_Avg</th>
+<th>tilt_north_south_Avg</th>
+<th>tilt_west_east_Avg</th>
+<th>NDVI_Avg</th>
+<th>timestamp</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>2021-09-22 12:00:00</td>
+<td>0</td>
+<td>KS003</td>
+<td>Flickner</td>
+<td>Rainfed South</td>
+<td>38.23461</td>
+<td>-97.57095</td>
+<td>455</td>
+<td>13.52</td>
+<td>29.04</td>
+<td>...</td>
+<td>4.20</td>
+<td>22.30</td>
+<td>9.20</td>
+<td>973</td>
+<td>41.30</td>
+<td>26.4</td>
+<td>-1.000</td>
+<td>1.000</td>
+<td>0.311</td>
+<td>2021-09-22 12:00:00</td>
+</tr>
+<tr>
+<th>1</th>
+<td>2021-09-22 13:00:00</td>
+<td>1</td>
+<td>KS003</td>
+<td>Flickner</td>
+<td>Rainfed South</td>
+<td>38.23461</td>
+<td>-97.57095</td>
+<td>455</td>
+<td>13.53</td>
+<td>29.98</td>
+<td>...</td>
+<td>9.02</td>
+<td>22.90</td>
+<td>9.08</td>
+<td>972</td>
+<td>32.63</td>
+<td>26.8</td>
+<td>-0.975</td>
+<td>0.950</td>
+<td>0.308</td>
+<td>2021-09-22 13:00:00</td>
+</tr>
+<tr>
+<th>2</th>
+<td>2021-09-22 14:00:00</td>
+<td>2</td>
+<td>KS003</td>
+<td>Flickner</td>
+<td>Rainfed South</td>
+<td>38.23461</td>
+<td>-97.57095</td>
+<td>455</td>
+<td>13.53</td>
+<td>30.43</td>
+<td>...</td>
+<td>5.54</td>
+<td>23.38</td>
+<td>8.68</td>
+<td>971</td>
+<td>30.25</td>
+<td>27.2</td>
+<td>-0.775</td>
+<td>0.625</td>
+<td>0.31</td>
+<td>2021-09-22 14:00:00</td>
+</tr>
+</tbody>
 </table>
 <p>3 rows × 28 columns</p>
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-
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 </div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
-        <div class="CodeMirror cm-s-jupyter">
-            <div class="zeroclipboard-container">
-                <clipboard-copy for="cell-5">
-                <div>
-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Define date in which the probe was deployed in the field (i.e., first record)</span>
-<span class="n">deployment_date</span> <span class="o">=</span> <span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-5">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Define date in which the probe was deployed in the field (i.e., first record)</span>
+<span class="n">deployment_date</span> <span class="o">=</span> <span class="n">df_station</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
 
 <span class="c1"># Filter station data from the first record to the end of the field survey calibration</span>
 <span class="c1"># This is important since we are considering the incoming flux on the first day as the reference value</span>
-<span class="n">idx_period</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">deployment_date</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">calibration_end</span><span class="p">)</span>
+<span class="n">idx_period</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">deployment_date</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">calibration_end</span><span class="p">)</span>
 <span class="n">df_station</span> <span class="o">=</span> <span class="n">df_station</span><span class="p">[</span><span class="n">idx_period</span><span class="p">]</span>
 <span class="n">df_station</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
-<div id="cell-5" class="clipboard-copy-txt"># Define date in which the probe was deployed in the field (i.e., first record)
+<div class="clipboard-copy-txt" id="cell-5"># Define date in which the probe was deployed in the field (i.e., first record)
 deployment_date = df_station['timestamp'].iloc[0]
 
 # Filter station data from the first record to the end of the field survey calibration
 # This is important since we are considering the incoming flux on the first day as the reference value
-idx_period = (df_station['timestamp'] >= deployment_date) & (df_station['timestamp'] <= calibration_end)
+idx_period = (df_station['timestamp'] &gt;= deployment_date) &amp; (df_station['timestamp'] &lt;= calibration_end)
 df_station = df_station[idx_period]
 df_station.head(3)
 </div>
-        
-        </div>
-    </div>
-
 </div>
 </div>
-
+</div>
+</div>
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+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -2150,220 +1557,208 @@ <h2 id="read-data-from-crnp">Read data from CRNP<a class="anchor-link" href="#re
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>TIMESTAMP</th>
-      <th>RECORD</th>
-      <th>station</th>
-      <th>farm</th>
-      <th>field</th>
-      <th>latitude</th>
-      <th>longitude</th>
-      <th>altitude</th>
-      <th>battery_voltage_Min</th>
-      <th>PTemp_Avg</th>
-      <th>...</th>
-      <th>wind_speed_gust_Max</th>
-      <th>air_temperature_Avg</th>
-      <th>vapor_pressure_Avg</th>
-      <th>barometric_pressure_Avg</th>
-      <th>relative_humidity_Avg</th>
-      <th>humidity_sensor_temperature_Avg</th>
-      <th>tilt_north_south_Avg</th>
-      <th>tilt_west_east_Avg</th>
-      <th>NDVI_Avg</th>
-      <th>timestamp</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>2021-09-22 12:00:00</td>
-      <td>0</td>
-      <td>KS003</td>
-      <td>Flickner</td>
-      <td>Rainfed South</td>
-      <td>38.23461</td>
-      <td>-97.57095</td>
-      <td>455</td>
-      <td>13.52</td>
-      <td>29.04</td>
-      <td>...</td>
-      <td>4.20</td>
-      <td>22.30</td>
-      <td>9.20</td>
-      <td>973</td>
-      <td>41.30</td>
-      <td>26.4</td>
-      <td>-1.000</td>
-      <td>1.000</td>
-      <td>0.311</td>
-      <td>2021-09-22 12:00:00</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2021-09-22 13:00:00</td>
-      <td>1</td>
-      <td>KS003</td>
-      <td>Flickner</td>
-      <td>Rainfed South</td>
-      <td>38.23461</td>
-      <td>-97.57095</td>
-      <td>455</td>
-      <td>13.53</td>
-      <td>29.98</td>
-      <td>...</td>
-      <td>9.02</td>
-      <td>22.90</td>
-      <td>9.08</td>
-      <td>972</td>
-      <td>32.63</td>
-      <td>26.8</td>
-      <td>-0.975</td>
-      <td>0.950</td>
-      <td>0.308</td>
-      <td>2021-09-22 13:00:00</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>2021-09-22 14:00:00</td>
-      <td>2</td>
-      <td>KS003</td>
-      <td>Flickner</td>
-      <td>Rainfed South</td>
-      <td>38.23461</td>
-      <td>-97.57095</td>
-      <td>455</td>
-      <td>13.53</td>
-      <td>30.43</td>
-      <td>...</td>
-      <td>5.54</td>
-      <td>23.38</td>
-      <td>8.68</td>
-      <td>971</td>
-      <td>30.25</td>
-      <td>27.2</td>
-      <td>-0.775</td>
-      <td>0.625</td>
-      <td>0.31</td>
-      <td>2021-09-22 14:00:00</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>TIMESTAMP</th>
+<th>RECORD</th>
+<th>station</th>
+<th>farm</th>
+<th>field</th>
+<th>latitude</th>
+<th>longitude</th>
+<th>altitude</th>
+<th>battery_voltage_Min</th>
+<th>PTemp_Avg</th>
+<th>...</th>
+<th>wind_speed_gust_Max</th>
+<th>air_temperature_Avg</th>
+<th>vapor_pressure_Avg</th>
+<th>barometric_pressure_Avg</th>
+<th>relative_humidity_Avg</th>
+<th>humidity_sensor_temperature_Avg</th>
+<th>tilt_north_south_Avg</th>
+<th>tilt_west_east_Avg</th>
+<th>NDVI_Avg</th>
+<th>timestamp</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>2021-09-22 12:00:00</td>
+<td>0</td>
+<td>KS003</td>
+<td>Flickner</td>
+<td>Rainfed South</td>
+<td>38.23461</td>
+<td>-97.57095</td>
+<td>455</td>
+<td>13.52</td>
+<td>29.04</td>
+<td>...</td>
+<td>4.20</td>
+<td>22.30</td>
+<td>9.20</td>
+<td>973</td>
+<td>41.30</td>
+<td>26.4</td>
+<td>-1.000</td>
+<td>1.000</td>
+<td>0.311</td>
+<td>2021-09-22 12:00:00</td>
+</tr>
+<tr>
+<th>1</th>
+<td>2021-09-22 13:00:00</td>
+<td>1</td>
+<td>KS003</td>
+<td>Flickner</td>
+<td>Rainfed South</td>
+<td>38.23461</td>
+<td>-97.57095</td>
+<td>455</td>
+<td>13.53</td>
+<td>29.98</td>
+<td>...</td>
+<td>9.02</td>
+<td>22.90</td>
+<td>9.08</td>
+<td>972</td>
+<td>32.63</td>
+<td>26.8</td>
+<td>-0.975</td>
+<td>0.950</td>
+<td>0.308</td>
+<td>2021-09-22 13:00:00</td>
+</tr>
+<tr>
+<th>2</th>
+<td>2021-09-22 14:00:00</td>
+<td>2</td>
+<td>KS003</td>
+<td>Flickner</td>
+<td>Rainfed South</td>
+<td>38.23461</td>
+<td>-97.57095</td>
+<td>455</td>
+<td>13.53</td>
+<td>30.43</td>
+<td>...</td>
+<td>5.54</td>
+<td>23.38</td>
+<td>8.68</td>
+<td>971</td>
+<td>30.25</td>
+<td>27.2</td>
+<td>-0.775</td>
+<td>0.625</td>
+<td>0.31</td>
+<td>2021-09-22 14:00:00</td>
+</tr>
+</tbody>
 </table>
 <p>3 rows × 28 columns</p>
 </div>
 </div>
-
 </div>
-
 </div>
-
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-
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-<div id="cell-id=71fa30f6-f912-4f2e-936e-c6461bd613f0" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=71fa30f6-f912-4f2e-936e-c6461bd613f0">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <blockquote>
 <p>This step is useful to trim large timeseries. For instance, the station data in our case extends until <code>2022-07-11 09:45:00</code>, but the field calibration was conducted in <code>2021-10-22 16:00</code>. Since all the station observation after the date of the field calibration are not relevent, we decided to only work with the data that we need from <code>2021-09-22 12:00:00</code> until <code>2021-10-22 16:00</code>. This could help getting data of incoming neutron flux from a single reference neutron monitor. So, if you are running this code shortly after the calibration field survey, then there is no need to filter station data.</p>
 </blockquote>
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-<div id="cell-id=23d9e906-bc51-4c57-8a69-721379d42d53" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=23d9e906-bc51-4c57-8a69-721379d42d53">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h2 id="correct-neutron-counts">Correct neutron counts<a class="anchor-link" href="#correct-neutron-counts">&#182;</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="correct-neutron-counts">Correct neutron counts<a class="anchor-link" href="#correct-neutron-counts">¶</a></h2><pre><code>                                             </code></pre>
 </div>
 </div>
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=5d025dd3-849e-4bf5-abfb-95d00cf88907">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Compute total neutron counts by adding the counts from both probe detectors</span>
-<span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;total_raw_counts&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">total_raw_counts</span><span class="p">(</span><span class="n">df_station</span><span class="p">[[</span><span class="s1">&#39;counts_1_Tot&#39;</span><span class="p">,</span><span class="s1">&#39;counts_2_Tot&#39;</span><span class="p">]],</span>
-                                                    <span class="n">nan_strategy</span><span class="o">=</span><span class="s1">&#39;average&#39;</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
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+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Compute total neutron counts by adding the counts from both probe detectors</span>
+<span class="n">df_station</span><span class="p">[</span><span class="s1">'total_raw_counts'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">total_raw_counts</span><span class="p">(</span><span class="n">df_station</span><span class="p">[[</span><span class="s1">'counts_1_Tot'</span><span class="p">,</span><span class="s1">'counts_2_Tot'</span><span class="p">]],</span>
+                                                    <span class="n">nan_strategy</span><span class="o">=</span><span class="s1">'average'</span><span class="p">)</span>
 </pre></div>
-<div id="cell-6" class="clipboard-copy-txt"># Compute total neutron counts by adding the counts from both probe detectors
+<div class="clipboard-copy-txt" id="cell-6"># Compute total neutron counts by adding the counts from both probe detectors
 df_station['total_raw_counts'] = crnpy.total_raw_counts(df_station[['counts_1_Tot','counts_2_Tot']],
                                                     nan_strategy='average')
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+</div>
+</div>
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+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=036787fd-fe1b-41fb-8cb4-d708168d4709">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Atmospheric corrections</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Atmospheric corrections</span>
 
 <span class="c1"># Fill NaN values in atmospheric data</span>
-<span class="n">df_station</span><span class="p">[[</span><span class="s1">&#39;barometric_pressure_Avg&#39;</span><span class="p">,</span><span class="s1">&#39;relative_humidity_Avg&#39;</span><span class="p">,</span> <span class="s1">&#39;air_temperature_Avg&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df_station</span><span class="p">[[</span><span class="s1">&#39;barometric_pressure_Avg&#39;</span><span class="p">,</span><span class="s1">&#39;relative_humidity_Avg&#39;</span><span class="p">,</span> <span class="s1">&#39;air_temperature_Avg&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">&#39;pchip&#39;</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">limit_direction</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">)</span>
+<span class="n">df_station</span><span class="p">[[</span><span class="s1">'barometric_pressure_Avg'</span><span class="p">,</span><span class="s1">'relative_humidity_Avg'</span><span class="p">,</span> <span class="s1">'air_temperature_Avg'</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df_station</span><span class="p">[[</span><span class="s1">'barometric_pressure_Avg'</span><span class="p">,</span><span class="s1">'relative_humidity_Avg'</span><span class="p">,</span> <span class="s1">'air_temperature_Avg'</span><span class="p">]]</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">'pchip'</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">limit_direction</span><span class="o">=</span><span class="s1">'both'</span><span class="p">)</span>
 
 <span class="c1"># Calculate absolute humidity</span>
-<span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;abs_humidity&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">abs_humidity</span><span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;relative_humidity_Avg&#39;</span><span class="p">],</span> <span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;air_temperature_Avg&#39;</span><span class="p">])</span>
+<span class="n">df_station</span><span class="p">[</span><span class="s1">'abs_humidity'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">abs_humidity</span><span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">'relative_humidity_Avg'</span><span class="p">],</span> <span class="n">df_station</span><span class="p">[</span><span class="s1">'air_temperature_Avg'</span><span class="p">])</span>
 
 <span class="c1"># Compute correction factor for atmospheric pressure</span>
 <span class="c1"># Reference atmospheric pressure for the location is 976 Pa</span>
 <span class="c1"># Using an atmospheric attentuation coefficient of 130 g/cm2</span>
-<span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;fp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_pressure</span><span class="p">(</span><span class="n">pressure</span><span class="o">=</span><span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;barometric_pressure_Avg&#39;</span><span class="p">],</span>
+<span class="n">df_station</span><span class="p">[</span><span class="s1">'fp'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_pressure</span><span class="p">(</span><span class="n">pressure</span><span class="o">=</span><span class="n">df_station</span><span class="p">[</span><span class="s1">'barometric_pressure_Avg'</span><span class="p">],</span>
                                              <span class="n">Pref</span><span class="o">=</span><span class="mi">976</span><span class="p">,</span> <span class="n">L</span><span class="o">=</span><span class="mi">130</span><span class="p">)</span>
 
 <span class="c1"># Compute correction factor for air humidity</span>
-<span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;fw&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_humidity</span><span class="p">(</span><span class="n">abs_humidity</span><span class="o">=</span><span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;abs_humidity&#39;</span><span class="p">],</span>
+<span class="n">df_station</span><span class="p">[</span><span class="s1">'fw'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_humidity</span><span class="p">(</span><span class="n">abs_humidity</span><span class="o">=</span><span class="n">df_station</span><span class="p">[</span><span class="s1">'abs_humidity'</span><span class="p">],</span>
                                              <span class="n">Aref</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 </pre></div>
-<div id="cell-7" class="clipboard-copy-txt"># Atmospheric corrections
+<div class="clipboard-copy-txt" id="cell-7"># Atmospheric corrections
 
 # Fill NaN values in atmospheric data
 df_station[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']] = df_station[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')
@@ -2381,44 +1776,39 @@ <h2 id="correct-neutron-counts">Correct neutron counts<a class="anchor-link" hre
 df_station['fw'] = crnpy.correction_humidity(abs_humidity=df_station['abs_humidity'],
                                              Aref=0)
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Find the cutoff rigidity for the location</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Find the cutoff rigidity for the location</span>
 <span class="n">cutoff_rigidity</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">cutoff_rigidity</span><span class="p">(</span><span class="mf">39.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">96.6</span><span class="p">)</span>
 
 <span class="c1"># Filtering the time window from experiment setup to the end of the calibration</span>
 <span class="n">crnpy</span><span class="o">.</span><span class="n">find_neutron_monitor</span><span class="p">(</span><span class="n">cutoff_rigidity</span><span class="p">,</span>
-                           <span class="n">start_date</span><span class="o">=</span><span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-                           <span class="n">end_date</span><span class="o">=</span><span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                           <span class="n">start_date</span><span class="o">=</span><span class="n">df_station</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+                           <span class="n">end_date</span><span class="o">=</span><span class="n">df_station</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
 </pre></div>
-<div id="cell-8" class="clipboard-copy-txt"># Find the cutoff rigidity for the location
+<div class="clipboard-copy-txt" id="cell-8"># Find the cutoff rigidity for the location
 cutoff_rigidity = crnpy.cutoff_rigidity(39.1, -96.6)
 
 # Filtering the time window from experiment setup to the end of the calibration
@@ -2426,27 +1816,19 @@ <h2 id="correct-neutron-counts">Correct neutron counts<a class="anchor-link" hre
                            start_date=df_station['timestamp'].iloc[0],
                            end_date=df_station['timestamp'].iloc[-1])
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 <pre>
-Select a station with an altitude similar to that of your location. For more information go to: &#39;https://www.nmdb.eu/nest/help.php#helpstations
+Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations
 
 Your cutoff rigidity is 2.87 GV
      STID     NAME     R  Altitude_m  Period available
@@ -2457,14 +1839,10 @@ <h2 id="correct-neutron-counts">Correct neutron counts<a class="anchor-link" hre
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+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[8]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
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-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -2478,90 +1856,84 @@ <h2 id="correct-neutron-counts">Correct neutron counts<a class="anchor-link" hre
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>STID</th>
-      <th>NAME</th>
-      <th>R</th>
-      <th>Altitude_m</th>
-      <th>Period available</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>13</th>
-      <td>DRBS</td>
-      <td>Dourbes</td>
-      <td>3.18</td>
-      <td>225</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>40</th>
-      <td>NEWK</td>
-      <td>Newark</td>
-      <td>2.40</td>
-      <td>50</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>28</th>
-      <td>KIEL2</td>
-      <td>KielRT</td>
-      <td>2.36</td>
-      <td>54</td>
-      <td>True</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>STID</th>
+<th>NAME</th>
+<th>R</th>
+<th>Altitude_m</th>
+<th>Period available</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>13</th>
+<td>DRBS</td>
+<td>Dourbes</td>
+<td>3.18</td>
+<td>225</td>
+<td>True</td>
+</tr>
+<tr>
+<th>40</th>
+<td>NEWK</td>
+<td>Newark</td>
+<td>2.40</td>
+<td>50</td>
+<td>True</td>
+</tr>
+<tr>
+<th>28</th>
+<td>KIEL2</td>
+<td>KielRT</td>
+<td>2.36</td>
+<td>54</td>
+<td>True</td>
+</tr>
+</tbody>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Incoming neutron flux correction</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Incoming neutron flux correction</span>
 
 <span class="c1"># Download data for the reference neutron monitor and add it to the DataFrame</span>
 <span class="n">nmdb</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">deployment_date</span><span class="p">,</span>
                                         <span class="n">calibration_end</span><span class="p">,</span>
-                                        <span class="n">station</span><span class="o">=</span><span class="s2">&quot;DRBS&quot;</span><span class="p">,</span>
+                                        <span class="n">station</span><span class="o">=</span><span class="s2">"DRBS"</span><span class="p">,</span>
                                         <span class="n">utc_offset</span><span class="o">=-</span><span class="mi">6</span><span class="p">)</span>
 
 <span class="c1"># Interpolate incoming neutron flux to match the timestamps in our station data</span>
-<span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;incoming_flux&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">interpolate_incoming_flux</span><span class="p">(</span><span class="n">nmdb</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">],</span> <span class="n">nmdb</span><span class="p">[</span><span class="s1">&#39;counts&#39;</span><span class="p">],</span> <span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">])</span>
+<span class="n">df_station</span><span class="p">[</span><span class="s1">'incoming_flux'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">interpolate_incoming_flux</span><span class="p">(</span><span class="n">nmdb</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">],</span> <span class="n">nmdb</span><span class="p">[</span><span class="s1">'counts'</span><span class="p">],</span> <span class="n">df_station</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">])</span>
 
 <span class="c1"># Compute correction factor for incoming neutron flux</span>
-<span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;fi&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_incoming_flux</span><span class="p">(</span><span class="n">incoming_neutrons</span><span class="o">=</span><span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;incoming_flux&#39;</span><span class="p">],</span>
-                                                  <span class="n">incoming_Ref</span><span class="o">=</span><span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;incoming_flux&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">df_station</span><span class="p">[</span><span class="s1">'fi'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_incoming_flux</span><span class="p">(</span><span class="n">incoming_neutrons</span><span class="o">=</span><span class="n">df_station</span><span class="p">[</span><span class="s1">'incoming_flux'</span><span class="p">],</span>
+                                                  <span class="n">incoming_Ref</span><span class="o">=</span><span class="n">df_station</span><span class="p">[</span><span class="s1">'incoming_flux'</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 </pre></div>
-<div id="cell-9" class="clipboard-copy-txt"># Incoming neutron flux correction
+<div class="clipboard-copy-txt" id="cell-9"># Incoming neutron flux correction
 
 # Download data for the reference neutron monitor and add it to the DataFrame
 nmdb = crnpy.get_incoming_neutron_flux(deployment_date,
@@ -2577,203 +1949,173 @@ <h2 id="correct-neutron-counts">Correct neutron counts<a class="anchor-link" hre
                                                   incoming_Ref=df_station['incoming_flux'].iloc[0])
 
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Apply correction factors</span>
-<span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;total_corrected_neutrons&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;total_raw_counts&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;fw&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;fp&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;fi&#39;</span><span class="p">])</span>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Apply correction factors</span>
+<span class="n">df_station</span><span class="p">[</span><span class="s1">'total_corrected_neutrons'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_station</span><span class="p">[</span><span class="s1">'total_raw_counts'</span><span class="p">]</span> <span class="o">*</span> <span class="n">df_station</span><span class="p">[</span><span class="s1">'fw'</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">'fp'</span><span class="p">]</span> <span class="o">*</span> <span class="n">df_station</span><span class="p">[</span><span class="s1">'fi'</span><span class="p">])</span>
 </pre></div>
-<div id="cell-10" class="clipboard-copy-txt"># Apply correction factors
+<div class="clipboard-copy-txt" id="cell-10"># Apply correction factors
 df_station['total_corrected_neutrons'] = df_station['total_raw_counts'] * df_station['fw'] / (df_station['fp'] * df_station['fi'])
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-<h3 id="determine-field-average-soil-moisture-and-bulk-density">Determine field-average soil moisture and bulk density<a class="anchor-link" href="#determine-field-average-soil-moisture-and-bulk-density">&#182;</a></h3><p>Using the function <a href="../../../reference/#crnpy.crnpy.nrad_weight"><code>nrad_weight()</code></a> the weights corresponding to each soil sample will be computed considering air-humidity, sample depth, and distance from station and bulk density.</p>
-
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="determine-field-average-soil-moisture-and-bulk-density">Determine field-average soil moisture and bulk density<a class="anchor-link" href="#determine-field-average-soil-moisture-and-bulk-density">¶</a></h3><p>Using the function <a href="../../../reference/#crnpy.crnpy.nrad_weight"><code>nrad_weight()</code></a> the weights corresponding to each soil sample will be computed considering air-humidity, sample depth, and distance from station and bulk density.</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Compute the weights of each sample for the field average</span>
-<span class="n">nrad_weights</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">nrad_weight</span><span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;abs_humidity&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">df_soil</span><span class="p">[</span><span class="s1">&#39;theta_v&#39;</span><span class="p">],</span> <span class="n">df_soil</span><span class="p">[</span><span class="s1">&#39;distance_from_station&#39;</span><span class="p">],</span> <span class="p">(</span><span class="n">df_soil</span><span class="p">[</span><span class="s1">&#39;bottom_depth&#39;</span><span class="p">]</span><span class="o">+</span><span class="n">df_soil</span><span class="p">[</span><span class="s1">&#39;top_depth&#39;</span><span class="p">])</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="n">rhob</span><span class="o">=</span><span class="n">df_soil</span><span class="p">[</span><span class="s1">&#39;bulk_density&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Compute the weights of each sample for the field average</span>
+<span class="n">nrad_weights</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">nrad_weight</span><span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">'abs_humidity'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">df_soil</span><span class="p">[</span><span class="s1">'theta_v'</span><span class="p">],</span> <span class="n">df_soil</span><span class="p">[</span><span class="s1">'distance_from_station'</span><span class="p">],</span> <span class="p">(</span><span class="n">df_soil</span><span class="p">[</span><span class="s1">'bottom_depth'</span><span class="p">]</span><span class="o">+</span><span class="n">df_soil</span><span class="p">[</span><span class="s1">'top_depth'</span><span class="p">])</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="n">rhob</span><span class="o">=</span><span class="n">df_soil</span><span class="p">[</span><span class="s1">'bulk_density'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
 
 <span class="c1"># Apply distance weights to volumetric water content and bulk density</span>
-<span class="n">field_theta_v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">df_soil</span><span class="p">[</span><span class="s1">&#39;theta_v&#39;</span><span class="p">]</span><span class="o">*</span><span class="n">nrad_weights</span><span class="p">)</span>
-<span class="n">field_bulk_density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">df_soil</span><span class="p">[</span><span class="s1">&#39;bulk_density&#39;</span><span class="p">]</span><span class="o">*</span><span class="n">nrad_weights</span><span class="p">)</span>
+<span class="n">field_theta_v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">df_soil</span><span class="p">[</span><span class="s1">'theta_v'</span><span class="p">]</span><span class="o">*</span><span class="n">nrad_weights</span><span class="p">)</span>
+<span class="n">field_bulk_density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">df_soil</span><span class="p">[</span><span class="s1">'bulk_density'</span><span class="p">]</span><span class="o">*</span><span class="n">nrad_weights</span><span class="p">)</span>
 </pre></div>
-<div id="cell-11" class="clipboard-copy-txt"># Compute the weights of each sample for the field average
+<div class="clipboard-copy-txt" id="cell-11"># Compute the weights of each sample for the field average
 nrad_weights = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'], df_soil['distance_from_station'], (df_soil['bottom_depth']+df_soil['top_depth'])/2, rhob=df_soil['bulk_density'].mean())
 
 # Apply distance weights to volumetric water content and bulk density
 field_theta_v = np.sum(df_soil['theta_v']*nrad_weights)
 field_bulk_density = np.sum(df_soil['bulk_density']*nrad_weights)
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Determine the mean corrected counts during the calibration survey</span>
-<span class="n">idx_cal_period</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">calibration_start</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">calibration_end</span><span class="p">)</span>
-<span class="n">mean_cal_counts</span> <span class="o">=</span> <span class="n">df_station</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">idx_cal_period</span><span class="p">,</span> <span class="s1">&#39;total_corrected_neutrons&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Determine the mean corrected counts during the calibration survey</span>
+<span class="n">idx_cal_period</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">calibration_start</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df_station</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">calibration_end</span><span class="p">)</span>
+<span class="n">mean_cal_counts</span> <span class="o">=</span> <span class="n">df_station</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">idx_cal_period</span><span class="p">,</span> <span class="s1">'total_corrected_neutrons'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
 
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Mean volumetric Water content during calibration survey: </span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">field_theta_v</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Mean corrected counts during calibration: </span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">mean_cal_counts</span><span class="p">)</span><span class="si">}</span><span class="s2"> counts&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Mean volumetric Water content during calibration survey: </span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">field_theta_v</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Mean corrected counts during calibration: </span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">mean_cal_counts</span><span class="p">)</span><span class="si">}</span><span class="s2"> counts"</span><span class="p">)</span>
 </pre></div>
-<div id="cell-12" class="clipboard-copy-txt"># Determine the mean corrected counts during the calibration survey
-idx_cal_period = (df_station['timestamp'] >= calibration_start) & (df_station['timestamp'] <= calibration_end)
+<div class="clipboard-copy-txt" id="cell-12"># Determine the mean corrected counts during the calibration survey
+idx_cal_period = (df_station['timestamp'] &gt;= calibration_start) &amp; (df_station['timestamp'] &lt;= calibration_end)
 mean_cal_counts = df_station.loc[idx_cal_period, 'total_corrected_neutrons'].mean()
 
 print(f"Mean volumetric Water content during calibration survey: {round(field_theta_v,3)}")
 print(f"Mean corrected counts during calibration: {round(mean_cal_counts)} counts")
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 <pre>Mean volumetric Water content during calibration survey: 0.263
 Mean corrected counts during calibration: 1542 counts
 </pre>
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-<h2 id="solving-for-n_0">Solving for $N_0$<a class="anchor-link" href="#solving-for-n_0">&#182;</a></h2><p>Previous steps estimated the field volumetric water content of <code>0.263</code> and an average neutron count of <code>1542</code>. Using <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html"><code>scipy.optimize.root()</code></a> $N_0$ is estimated given the observed value of $\theta_v$ and neutron counts.</p>
-
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="solving-for-n_0">Solving for $N_0$<a class="anchor-link" href="#solving-for-n_0">¶</a></h2><p>Previous steps estimated the field volumetric water content of <code>0.263</code> and an average neutron count of <code>1542</code>. Using <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html"><code>scipy.optimize.root()</code></a> $N_0$ is estimated given the observed value of $\theta_v$ and neutron counts.</p>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Define the function for which we want to find the roots</span>
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+</svg>
+</div>
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+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Define the function for which we want to find the roots</span>
 <span class="n">VWC_func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">N0</span> <span class="p">:</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">counts_to_vwc</span><span class="p">(</span><span class="n">mean_cal_counts</span><span class="p">,</span> <span class="n">N0</span><span class="p">,</span> <span class="n">bulk_density</span><span class="o">=</span><span class="n">field_bulk_density</span><span class="p">,</span> <span class="n">Wlat</span><span class="o">=</span><span class="mf">0.03</span><span class="p">,</span> <span class="n">Wsoc</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span> <span class="o">-</span> <span class="n">field_theta_v</span>
 
 <span class="c1"># Make an initial guess for N0</span>
@@ -2783,9 +2125,9 @@ <h2 id="solving-for-n_0">Solving for $N_0$<a class="anchor-link" href="#solving-
 <span class="n">sol</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">root</span><span class="p">(</span><span class="n">VWC_func</span><span class="p">,</span> <span class="n">N0_initial_guess</span><span class="p">)</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
 
 <span class="c1"># Print the solution</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The solved value for N0 is: </span><span class="si">{</span><span class="n">sol</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"The solved value for N0 is: </span><span class="si">{</span><span class="n">sol</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
 </pre></div>
-<div id="cell-13" class="clipboard-copy-txt"># Define the function for which we want to find the roots
+<div class="clipboard-copy-txt" id="cell-13"># Define the function for which we want to find the roots
 VWC_func = lambda N0 : crnpy.counts_to_vwc(mean_cal_counts, N0, bulk_density=field_bulk_density, Wlat=0.03, Wsoc=0.01) - field_theta_v
 
 # Make an initial guess for N0
@@ -2797,51 +2139,45 @@ <h2 id="solving-for-n_0">Solving for $N_0$<a class="anchor-link" href="#solving-
 # Print the solution
 print(f"The solved value for N0 is: {sol}")
 </div>
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+</div>
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+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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 <pre>The solved value for N0 is: 2644
 </pre>
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-<h2 id="references">References:<a class="anchor-link" href="#references">&#182;</a></h2><p>Desilets, D., Zreda, M., &amp; Ferré, T. P. (2010). Nature's neutron probe: Land surface hydrology at an elusive scale with cosmic rays. Water Resources Research, 46(11).</p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="references">References:<a class="anchor-link" href="#references">¶</a></h2><p>Desilets, D., Zreda, M., &amp; Ferré, T. P. (2010). Nature's neutron probe: Land surface hydrology at an elusive scale with cosmic rays. Water Resources Research, 46(11).</p>
 <p>Dong, J., &amp; Ochsner, T. E. (2018). Soil texture often exerts a stronger influence than precipitation on mesoscale soil moisture patterns. Water Resources Research, 54(3), 2199-2211.</p>
 <p>Patrignani, A., Ochsner, T. E., Montag, B., &amp; Bellinger, S. (2021). A novel lithium foil cosmic-ray neutron detector for measuring field-scale soil moisture. Frontiers in Water, 3, 673185.</p>
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diff --git a/site/examples/rover/Hydroinnova_rover_example/index.html b/site/examples/rover/Hydroinnova_rover_example/index.html
index 786eec0..f351280 100644
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+++ b/site/examples/rover/Hydroinnova_rover_example/index.html
@@ -18,8 +18,9 @@
       
         <link rel="next" href="../../calibration/calibration/">
       
+      
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+      <link rel="stylesheet" href="../../../assets/stylesheets/main.50c56a3b.min.css">
       
       
 
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         <meta  name="twitter:card"  content="summary_large_image" >
       
-        <meta  name="twitter:title"  content="Example 2 - CRNPy Documentation" >
+        <meta  name="twitter:title"  content="Example rover - CRNPy Documentation" >
       
         <meta  name="twitter:description"  content="CRNPy is a Python package for the estimation and processing of soil moisture data from cosmic-ray neutron counts." >
       
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+        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -158,12 +164,15 @@
     <form class="md-search__form" name="search">
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
+        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
+        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
+          
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -187,7 +196,7 @@
         <a href="https://github.com/soilwater/crnpy" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -217,6 +226,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../../.." title="CRNPy Documentation" class="md-nav__button md-logo" aria-label="CRNPy Documentation" data-md-component="logo">
@@ -231,7 +241,7 @@
       <a href="https://github.com/soilwater/crnpy" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -243,14 +253,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -258,14 +273,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../../sensor_description/" class="md-nav__link">
-        Sensor description
+        
+  
+  <span class="md-ellipsis">
+    Sensor description
+  </span>
+  
+
       </a>
     </li>
   
@@ -273,14 +293,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../../correction_routines/" class="md-nav__link">
-        Correction routines
+        
+  
+  <span class="md-ellipsis">
+    Correction routines
+  </span>
+  
+
       </a>
     </li>
   
@@ -288,56 +313,72 @@
     
       
       
-      
-
   
   
     
   
   
+  
+    
+    
+    
+    
     
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-      
-      
-      
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../stationary/example_RDT_station/" class="md-nav__link">
-        Processing and analyzing data from a stationary detector
+        
+  
+  <span class="md-ellipsis">
+    Processing and analyzing data from a stationary detector
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
   
   
     
   
   
+  
     <li class="md-nav__item md-nav__item--active">
       
       <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
@@ -347,12 +388,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Processing and analyzing data from a roving detector
+          
+  
+  <span class="md-ellipsis">
+    Processing and analyzing data from a roving detector
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Processing and analyzing data from a roving detector
+        
+  
+  <span class="md-ellipsis">
+    Processing and analyzing data from a roving detector
+  </span>
+  
+
       </a>
       
         
@@ -372,7 +425,9 @@
       
         <li class="md-nav__item">
   <a href="#neutron-count-correction" class="md-nav__link">
-    Neutron count correction
+    <span class="md-ellipsis">
+      Neutron count correction
+    </span>
   </a>
   
     <nav class="md-nav" aria-label="Neutron count correction">
@@ -380,14 +435,18 @@
         
           <li class="md-nav__item">
   <a href="#incommng-neutron-flux" class="md-nav__link">
-    Incommng neutron flux
+    <span class="md-ellipsis">
+      Incommng neutron flux
+    </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
   <a href="#atmospheric-correction" class="md-nav__link">
-    Atmospheric correction
+    <span class="md-ellipsis">
+      Atmospheric correction
+    </span>
   </a>
   
 </li>
@@ -404,38 +463,51 @@
     </li>
   
 
-            
-          
+              
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../calibration/calibration/" class="md-nav__link">
-        Device-specific field calibration
+        
+  
+  <span class="md-ellipsis">
+    Device-specific field calibration
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../../reference/" class="md-nav__link">
-        Reference
+        
+  
+  <span class="md-ellipsis">
+    Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -469,7 +541,9 @@
       
         <li class="md-nav__item">
   <a href="#neutron-count-correction" class="md-nav__link">
-    Neutron count correction
+    <span class="md-ellipsis">
+      Neutron count correction
+    </span>
   </a>
   
     <nav class="md-nav" aria-label="Neutron count correction">
@@ -477,14 +551,18 @@
         
           <li class="md-nav__item">
   <a href="#incommng-neutron-flux" class="md-nav__link">
-    Incommng neutron flux
+    <span class="md-ellipsis">
+      Incommng neutron flux
+    </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
   <a href="#atmospheric-correction" class="md-nav__link">
-    Atmospheric correction
+    <span class="md-ellipsis">
+      Atmospheric correction
+    </span>
   </a>
   
 </li>
@@ -680,12 +758,7 @@
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       })
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-
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-
-
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-    
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 span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
@@ -735,791 +808,16 @@
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 .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
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-
-
-
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-/*
-This file is taken from the built JupyterLab theme.css
-Found on share/nbconvert/templates/lab/static
-Some changes have been made and marked with CHANGE
-*/
-
-.jupyter-wrapper {
-    /* Elevation
-   *
-   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
-   *
-   * https://github.com/material-components/material-components-web
-   * https://material-components-web.appspot.com/elevation.html
-   */
-
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-    --jp-shadow-umbra-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
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-    );
-    --jp-shadow-penumbra-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        0.14
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-    --jp-shadow-ambient-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
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-    --jp-elevation-z0: none;
-    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
-        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 3px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
-        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 5px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
-        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 10px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
-        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 18px 0px var(--jp-shadow-ambient-color);
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-        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
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-    --jp-layout-color1: white;
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-    --jp-layout-color3: var(--md-grey-400);
-    --jp-layout-color4: var(--md-grey-600);
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-    /* Inverse Layout
-   *
-   * The following are the inverse layout colors use in JupyterLab. In a light
-   * theme these would go from dark to light.
-   */
-
-    --jp-inverse-layout-color0: #111111;
-    --jp-inverse-layout-color1: var(--md-grey-900);
-    --jp-inverse-layout-color2: var(--md-grey-800);
-    --jp-inverse-layout-color3: var(--md-grey-700);
-    --jp-inverse-layout-color4: var(--md-grey-600);
-
-    /* Brand/accent */
-
-    --jp-brand-color0: var(--md-blue-900);
-    --jp-brand-color1: var(--md-blue-700);
-    --jp-brand-color2: var(--md-blue-300);
-    --jp-brand-color3: var(--md-blue-100);
-    --jp-brand-color4: var(--md-blue-50);
-
-    --jp-accent-color0: var(--md-green-900);
-    --jp-accent-color1: var(--md-green-700);
-    --jp-accent-color2: var(--md-green-300);
-    --jp-accent-color3: var(--md-green-100);
-
-    /* State colors (warn, error, success, info) */
-
-    --jp-warn-color0: var(--md-orange-900);
-    --jp-warn-color1: var(--md-orange-700);
-    --jp-warn-color2: var(--md-orange-300);
-    --jp-warn-color3: var(--md-orange-100);
-
-    --jp-error-color0: var(--md-red-900);
-    --jp-error-color1: var(--md-red-700);
-    --jp-error-color2: var(--md-red-300);
-    --jp-error-color3: var(--md-red-100);
-
-    --jp-success-color0: var(--md-green-900);
-    --jp-success-color1: var(--md-green-700);
-    --jp-success-color2: var(--md-green-300);
-    --jp-success-color3: var(--md-green-100);
-
-    --jp-info-color0: var(--md-cyan-900);
-    --jp-info-color1: var(--md-cyan-700);
-    --jp-info-color2: var(--md-cyan-300);
-    --jp-info-color3: var(--md-cyan-100);
-
-    /* Cell specific styles */
-
-    --jp-cell-padding: 5px;
-
-    --jp-cell-collapser-width: 8px;
-    --jp-cell-collapser-min-height: 20px;
-    --jp-cell-collapser-not-active-hover-opacity: 0.6;
-
-    --jp-cell-editor-background: var(--md-grey-100);
-    --jp-cell-editor-border-color: var(--md-grey-300);
-    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
-    --jp-cell-editor-active-background: var(--jp-layout-color0);
-    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
-
-    --jp-cell-prompt-width: 64px;
-    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
-    --jp-cell-prompt-letter-spacing: 0px;
-    --jp-cell-prompt-opacity: 1;
-    --jp-cell-prompt-not-active-opacity: 0.5;
-    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
-    /* A custom blend of MD grey and blue 600
-   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
-    --jp-cell-inprompt-font-color: #307fc1;
-    /* A custom blend of MD grey and orange 600
-   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
-    --jp-cell-outprompt-font-color: #bf5b3d;
-
-    /* Notebook specific styles */
-
-    --jp-notebook-padding: 10px;
-    --jp-notebook-select-background: var(--jp-layout-color1);
-    --jp-notebook-multiselected-color: var(--md-blue-50);
-
-    /* The scroll padding is calculated to fill enough space at the bottom of the
-  notebook to show one single-line cell (with appropriate padding) at the top
-  when the notebook is scrolled all the way to the bottom. We also subtract one
-  pixel so that no scrollbar appears if we have just one single-line cell in the
-  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
-  */
-    --jp-notebook-scroll-padding: calc(
-        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
-            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
-    );
-
-    /* Rendermime styles */
-
-    --jp-rendermime-error-background: #fdd;
-    --jp-rendermime-table-row-background: var(--md-grey-100);
-    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
-
-    /* Dialog specific styles */
-
-    --jp-dialog-background: rgba(0, 0, 0, 0.25);
-
-    /* Console specific styles */
-
-    --jp-console-padding: 10px;
-
-    /* Toolbar specific styles */
-
-    --jp-toolbar-border-color: var(--jp-border-color1);
-    --jp-toolbar-micro-height: 8px;
-    --jp-toolbar-background: var(--jp-layout-color1);
-    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
-    --jp-toolbar-header-margin: 4px 4px 0px 4px;
-    --jp-toolbar-active-background: var(--md-grey-300);
-
-    /* Statusbar specific styles */
-
-    --jp-statusbar-height: 24px;
-
-    /* Input field styles */
-
-    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
-    --jp-input-active-background: var(--jp-layout-color1);
-    --jp-input-hover-background: var(--jp-layout-color1);
-    --jp-input-background: var(--md-grey-100);
-    --jp-input-border-color: var(--jp-border-color1);
-    --jp-input-active-border-color: var(--jp-brand-color1);
-    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
-
-    /* General editor styles */
-
-    --jp-editor-selected-background: #d9d9d9;
-    --jp-editor-selected-focused-background: #d7d4f0;
-    --jp-editor-cursor-color: var(--jp-ui-font-color0);
-
-    /* Code mirror specific styles */
-
-    --jp-mirror-editor-keyword-color: #008000;
-    --jp-mirror-editor-atom-color: #88f;
-    --jp-mirror-editor-number-color: #080;
-    --jp-mirror-editor-def-color: #00f;
-    --jp-mirror-editor-variable-color: var(--md-grey-900);
-    --jp-mirror-editor-variable-2-color: #05a;
-    --jp-mirror-editor-variable-3-color: #085;
-    --jp-mirror-editor-punctuation-color: #05a;
-    --jp-mirror-editor-property-color: #05a;
-    --jp-mirror-editor-operator-color: #aa22ff;
-    --jp-mirror-editor-comment-color: #408080;
-    --jp-mirror-editor-string-color: #ba2121;
-    --jp-mirror-editor-string-2-color: #708;
-    --jp-mirror-editor-meta-color: #aa22ff;
-    --jp-mirror-editor-qualifier-color: #555;
-    --jp-mirror-editor-builtin-color: #008000;
-    --jp-mirror-editor-bracket-color: #997;
-    --jp-mirror-editor-tag-color: #170;
-    --jp-mirror-editor-attribute-color: #00c;
-    --jp-mirror-editor-header-color: blue;
-    --jp-mirror-editor-quote-color: #090;
-    --jp-mirror-editor-link-color: #00c;
-    --jp-mirror-editor-error-color: #f00;
-    --jp-mirror-editor-hr-color: #999;
-
-    /* Vega extension styles */
-
-    --jp-vega-background: white;
-
-    /* Sidebar-related styles */
-
-    --jp-sidebar-min-width: 250px;
-
-    /* Search-related styles */
-
-    --jp-search-toggle-off-opacity: 0.5;
-    --jp-search-toggle-hover-opacity: 0.8;
-    --jp-search-toggle-on-opacity: 1;
-    --jp-search-selected-match-background-color: rgb(245, 200, 0);
-    --jp-search-selected-match-color: black;
-    --jp-search-unselected-match-background-color: var(
-        --jp-inverse-layout-color0
-    );
-    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
-
-    /* Icon colors that work well with light or dark backgrounds */
-    --jp-icon-contrast-color0: var(--md-purple-600);
-    --jp-icon-contrast-color1: var(--md-green-600);
-    --jp-icon-contrast-color2: var(--md-pink-600);
-    --jp-icon-contrast-color3: var(--md-blue-600);
-}
-
-[data-md-color-scheme="slate"] .jupyter-wrapper {
-    /* Elevation
-   *
-   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
-   *
-   * https://github.com/material-components/material-components-web
-   * https://material-components-web.appspot.com/elevation.html
-   */
-
-    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
-   * colors used in the theme as well. */
-    --jp-shadow-base-lightness: 32;
-    --jp-shadow-umbra-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        0.2
-    );
-    --jp-shadow-penumbra-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        0.14
-    );
-    --jp-shadow-ambient-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        0.12
-    );
-    --jp-elevation-z0: none;
-    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
-        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 3px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
-        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 5px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
-        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 10px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
-        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 18px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
-        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
-        0px 3px 14px 2px var(--jp-shadow-ambient-color);
-    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
-        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
-        0px 5px 22px 4px var(--jp-shadow-ambient-color);
-    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
-        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
-        0px 6px 30px 5px var(--jp-shadow-ambient-color);
-    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
-        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
-        0px 8px 38px 7px var(--jp-shadow-ambient-color);
-    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
-        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
-        0px 9px 46px 8px var(--jp-shadow-ambient-color);
-
-    /* Borders
-   *
-   * The following variables, specify the visual styling of borders in JupyterLab.
-   */
-
-    --jp-border-width: 1px;
-    --jp-border-color0: var(--md-grey-700);
-    --jp-border-color1: var(--md-grey-700);
-    --jp-border-color2: var(--md-grey-800);
-    --jp-border-color3: var(--md-grey-900);
-    --jp-border-radius: 2px;
-
-    /* UI Fonts
-   *
-   * The UI font CSS variables are used for the typography all of the JupyterLab
-   * user interface elements that are not directly user generated content.
-   *
-   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
-   * is applied to a parent element. When children elements, such as headings, are sized
-   * in em all things will be computed relative to that body size.
-   */
-
-    --jp-ui-font-scale-factor: 1.2;
-    --jp-ui-font-size0: 0.83333em;
-    --jp-ui-font-size1: 13px; /* Base font size */
-    --jp-ui-font-size2: 1.2em;
-    --jp-ui-font-size3: 1.44em;
-
-    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
-        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
-        "Segoe UI Symbol";
-
-    /*
-   * Use these font colors against the corresponding main layout colors.
-   * In a light theme, these go from dark to light.
-   */
-
-    /* Defaults use Material Design specification */
-    --jp-ui-font-color0: rgba(255, 255, 255, 1);
-    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
-    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
-    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
-
-    /*
-   * Use these against the brand/accent/warn/error colors.
-   * These will typically go from light to darker, in both a dark and light theme.
-   */
-
-    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
-    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
-    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
-    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
-
-    /* Content Fonts
-   *
-   * Content font variables are used for typography of user generated content.
-   *
-   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
-   * is applied to a parent element. When children elements, such as headings, are sized
-   * in em all things will be computed relative to that body size.
-   */
-
-    --jp-content-line-height: 1.6;
-    --jp-content-font-scale-factor: 1.2;
-    --jp-content-font-size0: 0.83333em;
-    --jp-content-font-size1: 14px; /* Base font size */
-    --jp-content-font-size2: 1.2em;
-    --jp-content-font-size3: 1.44em;
-    --jp-content-font-size4: 1.728em;
-    --jp-content-font-size5: 2.0736em;
-
-    /* This gives a magnification of about 125% in presentation mode over normal. */
-    --jp-content-presentation-font-size1: 17px;
-
-    --jp-content-heading-line-height: 1;
-    --jp-content-heading-margin-top: 1.2em;
-    --jp-content-heading-margin-bottom: 0.8em;
-    --jp-content-heading-font-weight: 500;
-
-    /* Defaults use Material Design specification */
-    --jp-content-font-color0: rgba(255, 255, 255, 1);
-    --jp-content-font-color1: rgba(255, 255, 255, 1);
-    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
-    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
-
-    --jp-content-link-color: var(--md-blue-300);
-
-    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
-        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
-        "Segoe UI Symbol";
-
-    /*
-   * Code Fonts
-   *
-   * Code font variables are used for typography of code and other monospaces content.
-   */
-
-    --jp-code-font-size: 13px;
-    --jp-code-line-height: 1.3077; /* 17px for 13px base */
-    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
-    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
-        monospace;
-    --jp-code-font-family: var(--jp-code-font-family-default);
-
-    /* This gives a magnification of about 125% in presentation mode over normal. */
-    --jp-code-presentation-font-size: 16px;
-
-    /* may need to tweak cursor width if you change font size */
-    --jp-code-cursor-width0: 1.4px;
-    --jp-code-cursor-width1: 2px;
-    --jp-code-cursor-width2: 4px;
-
-    /* Layout
-   *
-   * The following are the main layout colors use in JupyterLab. In a light
-   * theme these would go from light to dark.
-   */
-
-    --jp-layout-color0: #111111;
-    --jp-layout-color1: var(--md-grey-900);
-    --jp-layout-color2: var(--md-grey-800);
-    --jp-layout-color3: var(--md-grey-700);
-    --jp-layout-color4: var(--md-grey-600);
-
-    /* Inverse Layout
-   *
-   * The following are the inverse layout colors use in JupyterLab. In a light
-   * theme these would go from dark to light.
-   */
-
-    --jp-inverse-layout-color0: white;
-    --jp-inverse-layout-color1: white;
-    --jp-inverse-layout-color2: var(--md-grey-200);
-    --jp-inverse-layout-color3: var(--md-grey-400);
-    --jp-inverse-layout-color4: var(--md-grey-600);
-
-    /* Brand/accent */
-
-    --jp-brand-color0: var(--md-blue-700);
-    --jp-brand-color1: var(--md-blue-500);
-    --jp-brand-color2: var(--md-blue-300);
-    --jp-brand-color3: var(--md-blue-100);
-    --jp-brand-color4: var(--md-blue-50);
-
-    --jp-accent-color0: var(--md-green-700);
-    --jp-accent-color1: var(--md-green-500);
-    --jp-accent-color2: var(--md-green-300);
-    --jp-accent-color3: var(--md-green-100);
-
-    /* State colors (warn, error, success, info) */
-
-    --jp-warn-color0: var(--md-orange-700);
-    --jp-warn-color1: var(--md-orange-500);
-    --jp-warn-color2: var(--md-orange-300);
-    --jp-warn-color3: var(--md-orange-100);
-
-    --jp-error-color0: var(--md-red-700);
-    --jp-error-color1: var(--md-red-500);
-    --jp-error-color2: var(--md-red-300);
-    --jp-error-color3: var(--md-red-100);
-
-    --jp-success-color0: var(--md-green-700);
-    --jp-success-color1: var(--md-green-500);
-    --jp-success-color2: var(--md-green-300);
-    --jp-success-color3: var(--md-green-100);
-
-    --jp-info-color0: var(--md-cyan-700);
-    --jp-info-color1: var(--md-cyan-500);
-    --jp-info-color2: var(--md-cyan-300);
-    --jp-info-color3: var(--md-cyan-100);
-
-    /* Cell specific styles */
-
-    --jp-cell-padding: 5px;
-
-    --jp-cell-collapser-width: 8px;
-    --jp-cell-collapser-min-height: 20px;
-    --jp-cell-collapser-not-active-hover-opacity: 0.6;
-
-    --jp-cell-editor-background: var(--jp-layout-color1);
-    --jp-cell-editor-border-color: var(--md-grey-700);
-    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
-    --jp-cell-editor-active-background: var(--jp-layout-color0);
-    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
-
-    --jp-cell-prompt-width: 64px;
-    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
-    --jp-cell-prompt-letter-spacing: 0px;
-    --jp-cell-prompt-opacity: 1;
-    --jp-cell-prompt-not-active-opacity: 1;
-    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
-
-    /* A custom blend of MD grey and blue 600
-   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
-    --jp-cell-inprompt-font-color: #307fc1;
-    /* A custom blend of MD grey and orange 600
-   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
-    --jp-cell-outprompt-font-color: #bf5b3d;
-
-    /* Notebook specific styles */
-
-    --jp-notebook-padding: 10px;
-    --jp-notebook-select-background: var(--jp-layout-color1);
-    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
-
-    /* The scroll padding is calculated to fill enough space at the bottom of the
-  notebook to show one single-line cell (with appropriate padding) at the top
-  when the notebook is scrolled all the way to the bottom. We also subtract one
-  pixel so that no scrollbar appears if we have just one single-line cell in the
-  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
-  */
-    --jp-notebook-scroll-padding: calc(
-        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
-            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
-    );
-
-    /* Rendermime styles */
-
-    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
-    --jp-rendermime-table-row-background: var(--md-grey-900);
-    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
-
-    /* Dialog specific styles */
-
-    --jp-dialog-background: rgba(0, 0, 0, 0.6);
-
-    /* Console specific styles */
-
-    --jp-console-padding: 10px;
-
-    /* Toolbar specific styles */
-
-    --jp-toolbar-border-color: var(--jp-border-color2);
-    --jp-toolbar-micro-height: 8px;
-    --jp-toolbar-background: var(--jp-layout-color1);
-    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
-    --jp-toolbar-header-margin: 4px 4px 0px 4px;
-    --jp-toolbar-active-background: var(--jp-layout-color0);
-
-    /* Statusbar specific styles */
-
-    --jp-statusbar-height: 24px;
-
-    /* Input field styles */
-
-    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
-    --jp-input-active-background: var(--jp-layout-color0);
-    --jp-input-hover-background: var(--jp-layout-color2);
-    --jp-input-background: var(--md-grey-800);
-    --jp-input-border-color: var(--jp-border-color1);
-    --jp-input-active-border-color: var(--jp-brand-color1);
-    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
-
-    /* General editor styles */
-
-    --jp-editor-selected-background: var(--jp-layout-color2);
-    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
-    --jp-editor-cursor-color: var(--jp-ui-font-color0);
-
-    /* Code mirror specific styles */
-
-    --jp-mirror-editor-keyword-color: var(--md-green-500);
-    --jp-mirror-editor-atom-color: var(--md-blue-300);
-    --jp-mirror-editor-number-color: var(--md-green-400);
-    --jp-mirror-editor-def-color: var(--md-blue-600);
-    --jp-mirror-editor-variable-color: var(--md-grey-300);
-    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
-    --jp-mirror-editor-variable-3-color: var(--md-green-600);
-    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
-    --jp-mirror-editor-property-color: var(--md-blue-400);
-    --jp-mirror-editor-operator-color: #aa22ff;
-    --jp-mirror-editor-comment-color: #408080;
-    --jp-mirror-editor-string-color: #ff7070;
-    --jp-mirror-editor-string-2-color: var(--md-purple-300);
-    --jp-mirror-editor-meta-color: #aa22ff;
-    --jp-mirror-editor-qualifier-color: #555;
-    --jp-mirror-editor-builtin-color: var(--md-green-600);
-    --jp-mirror-editor-bracket-color: #997;
-    --jp-mirror-editor-tag-color: var(--md-green-700);
-    --jp-mirror-editor-attribute-color: var(--md-blue-700);
-    --jp-mirror-editor-header-color: var(--md-blue-500);
-    --jp-mirror-editor-quote-color: var(--md-green-300);
-    --jp-mirror-editor-link-color: var(--md-blue-700);
-    --jp-mirror-editor-error-color: #f00;
-    --jp-mirror-editor-hr-color: #999;
-
-    /* Vega extension styles */
-
-    --jp-vega-background: var(--md-grey-400);
-
-    /* Sidebar-related styles */
-
-    --jp-sidebar-min-width: 250px;
-
-    /* Search-related styles */
-
-    --jp-search-toggle-off-opacity: 0.6;
-    --jp-search-toggle-hover-opacity: 0.8;
-    --jp-search-toggle-on-opacity: 1;
-    --jp-search-selected-match-background-color: rgb(255, 225, 0);
-    --jp-search-selected-match-color: black;
-    --jp-search-unselected-match-background-color: var(
-        --jp-inverse-layout-color0
-    );
-    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
-
-    /* scrollbar related styles. Supports every browser except Edge. */
-
-    /* colors based on JetBrain's Darcula theme */
-
-    --jp-scrollbar-background-color: #3f4244;
-    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
-
-    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
-
-    /* hacks for setting the thumb shape. These do nothing in Firefox */
-
-    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
-    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
-
-    /* Icon colors that work well with light or dark backgrounds */
-    --jp-icon-contrast-color0: var(--md-purple-600);
-    --jp-icon-contrast-color1: var(--md-green-600);
-    --jp-icon-contrast-color2: var(--md-pink-600);
-    --jp-icon-contrast-color3: var(--md-blue-600);
-}
-
-:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: 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#f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: 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-<h1 id="processing-and-analyzing-data-from-a-roving-detector">Processing and analyzing data from a roving detector<a class="anchor-link" href="#processing-and-analyzing-data-from-a-roving-detector">&#182;</a></h1><p>This tutorial demonstrates how to process and analyze data from a rover using the Cosmic Ray Neutron Python (CRNPy) library. The steps include loading the data, converting geographical coordinates to Cartesian coordinates, normalizing neutron counts, correcting for atmospheric effects, and visualizing the results.</p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h1 id="processing-and-analyzing-data-from-a-roving-detector">Processing and analyzing data from a roving detector<a class="anchor-link" href="#processing-and-analyzing-data-from-a-roving-detector">¶</a></h1><p>This tutorial demonstrates how to process and analyze data from a rover using the Cosmic Ray Neutron Python (CRNPy) library. The steps include loading the data, converting geographical coordinates to Cartesian coordinates, normalizing neutron counts, correcting for atmospheric effects, and visualizing the results.</p>
 <p>First import the required packages.</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Import modules</span>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Import modules</span>
 <span class="kn">import</span> <span class="nn">crnpy</span>
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 </pre></div>
-<div id="cell-1" class="clipboard-copy-txt"># Import modules
+<div class="clipboard-copy-txt" id="cell-1"># Import modules
 import crnpy
 
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 </div>
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-        </div>
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 <p>Load the data stored in the <a href="https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/rover/gypsum_transect_01_may_2018.KSU">.csv file</a>.</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Load sample dataset</span>
-<span class="n">filepath</span> <span class="o">=</span> <span class="s2">&quot;https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/rover/gypsum_transect_01_may_2018.KSU&quot;</span>
-<span class="n">col_names</span> <span class="o">=</span> <span class="s1">&#39;RecordNum,Date Time(UTC),barometric_pressure_Avg,P4_mb,P1_mb,T1_C,RH1,air_temperature_Avg,relative_humidity_Avg,Vbat,N1Cts,N2Cts,N3Cts,N4Cts,N5Cts,N6Cts,N7Cts,N8Cts,N1ETsec,N3ETsec,N5ETsec,N7ETsec,N1T(C),N1RH,N5T(C),N5RH,GpsUTC,LatDec,LongDec,Alt,Qual,NumSats,HDOP,Speed_kmh,COG,SpeedQuality,strDate&#39;</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-2">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Load sample dataset</span>
+<span class="n">filepath</span> <span class="o">=</span> <span class="s2">"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/rover/gypsum_transect_01_may_2018.KSU"</span>
+<span class="n">col_names</span> <span class="o">=</span> <span class="s1">'RecordNum,Date Time(UTC),barometric_pressure_Avg,P4_mb,P1_mb,T1_C,RH1,air_temperature_Avg,relative_humidity_Avg,Vbat,N1Cts,N2Cts,N3Cts,N4Cts,N5Cts,N6Cts,N7Cts,N8Cts,N1ETsec,N3ETsec,N5ETsec,N7ETsec,N1T(C),N1RH,N5T(C),N5RH,GpsUTC,LatDec,LongDec,Alt,Qual,NumSats,HDOP,Speed_kmh,COG,SpeedQuality,strDate'</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">','</span><span class="p">)</span>
 
 <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="n">col_names</span><span class="p">)</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;LongDec&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;LongDec&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="o">-</span><span class="mi">1</span> <span class="c1"># Raw data is in absolute values</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'LongDec'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'LongDec'</span><span class="p">]</span> <span class="o">*</span> <span class="o">-</span><span class="mi">1</span> <span class="c1"># Raw data is in absolute values</span>
 
 <span class="c1"># Parse timestamps and set as index</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;Date Time(UTC)&#39;</span><span class="p">])</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'Date Time(UTC)'</span><span class="p">])</span>
 
 <span class="c1"># Remove rows with missing coordinates</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;LatDec&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;LongDec&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;LatDec&#39;</span><span class="p">,</span><span class="s1">&#39;LongDec&#39;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'LatDec'</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'LongDec'</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s1">'LatDec'</span><span class="p">,</span><span class="s1">'LongDec'</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">df</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
 <span class="c1"># Display a few rows to visualize dataset</span>
 <span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
-<div id="cell-2" class="clipboard-copy-txt"># Load sample dataset
+<div class="clipboard-copy-txt" id="cell-2"># Load sample dataset
 filepath = "https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/rover/gypsum_transect_01_may_2018.KSU"
 col_names = 'RecordNum,Date Time(UTC),barometric_pressure_Avg,P4_mb,P1_mb,T1_C,RH1,air_temperature_Avg,relative_humidity_Avg,Vbat,N1Cts,N2Cts,N3Cts,N4Cts,N5Cts,N6Cts,N7Cts,N8Cts,N1ETsec,N3ETsec,N5ETsec,N7ETsec,N1T(C),N1RH,N5T(C),N5RH,GpsUTC,LatDec,LongDec,Alt,Qual,NumSats,HDOP,Speed_kmh,COG,SpeedQuality,strDate'.split(',')
 
@@ -1676,28 +1115,19 @@ <h1 id="processing-and-analyzing-data-from-a-roving-detector">Processing and ana
 # Display a few rows to visualize dataset
 df.head(3)
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@@ -1711,169 +1141,162 @@ <h1 id="processing-and-analyzing-data-from-a-roving-detector">Processing and ana
     }
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 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>RecordNum</th>
-      <th>Date Time(UTC)</th>
-      <th>barometric_pressure_Avg</th>
-      <th>P4_mb</th>
-      <th>P1_mb</th>
-      <th>T1_C</th>
-      <th>RH1</th>
-      <th>air_temperature_Avg</th>
-      <th>relative_humidity_Avg</th>
-      <th>Vbat</th>
-      <th>...</th>
-      <th>LongDec</th>
-      <th>Alt</th>
-      <th>Qual</th>
-      <th>NumSats</th>
-      <th>HDOP</th>
-      <th>Speed_kmh</th>
-      <th>COG</th>
-      <th>SpeedQuality</th>
-      <th>strDate</th>
-      <th>timestamp</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>2</td>
-      <td>2018/05/01 14:15:00</td>
-      <td>962.95</td>
-      <td>962.8</td>
-      <td>961.4</td>
-      <td>23.2</td>
-      <td>35.4</td>
-      <td>20.9</td>
-      <td>72.7</td>
-      <td>13.574</td>
-      <td>...</td>
-      <td>-97.37195</td>
-      <td>393.2</td>
-      <td>2.0</td>
-      <td>10</td>
-      <td>0.8</td>
-      <td>0.00</td>
-      <td>270.7</td>
-      <td>A</td>
-      <td>10518.0</td>
-      <td>2018-05-01 14:15:00</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>3</td>
-      <td>2018/05/01 14:16:00</td>
-      <td>962.88</td>
-      <td>962.7</td>
-      <td>961.3</td>
-      <td>23.3</td>
-      <td>35.5</td>
-      <td>21.0</td>
-      <td>72.7</td>
-      <td>13.417</td>
-      <td>...</td>
-      <td>-97.37197</td>
-      <td>387.2</td>
-      <td>2.0</td>
-      <td>11</td>
-      <td>0.8</td>
-      <td>0.00</td>
-      <td>270.7</td>
-      <td>A</td>
-      <td>10518.0</td>
-      <td>2018-05-01 14:16:00</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>4</td>
-      <td>2018/05/01 14:17:00</td>
-      <td>962.64</td>
-      <td>962.5</td>
-      <td>961.1</td>
-      <td>23.4</td>
-      <td>35.4</td>
-      <td>21.2</td>
-      <td>72.2</td>
-      <td>13.282</td>
-      <td>...</td>
-      <td>-97.37199</td>
-      <td>388.2</td>
-      <td>1.0</td>
-      <td>11</td>
-      <td>0.8</td>
-      <td>3.89</td>
-      <td>356.3</td>
-      <td>A</td>
-      <td>10518.0</td>
-      <td>2018-05-01 14:17:00</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>RecordNum</th>
+<th>Date Time(UTC)</th>
+<th>barometric_pressure_Avg</th>
+<th>P4_mb</th>
+<th>P1_mb</th>
+<th>T1_C</th>
+<th>RH1</th>
+<th>air_temperature_Avg</th>
+<th>relative_humidity_Avg</th>
+<th>Vbat</th>
+<th>...</th>
+<th>LongDec</th>
+<th>Alt</th>
+<th>Qual</th>
+<th>NumSats</th>
+<th>HDOP</th>
+<th>Speed_kmh</th>
+<th>COG</th>
+<th>SpeedQuality</th>
+<th>strDate</th>
+<th>timestamp</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>2</td>
+<td>2018/05/01 14:15:00</td>
+<td>962.95</td>
+<td>962.8</td>
+<td>961.4</td>
+<td>23.2</td>
+<td>35.4</td>
+<td>20.9</td>
+<td>72.7</td>
+<td>13.574</td>
+<td>...</td>
+<td>-97.37195</td>
+<td>393.2</td>
+<td>2.0</td>
+<td>10</td>
+<td>0.8</td>
+<td>0.00</td>
+<td>270.7</td>
+<td>A</td>
+<td>10518.0</td>
+<td>2018-05-01 14:15:00</td>
+</tr>
+<tr>
+<th>1</th>
+<td>3</td>
+<td>2018/05/01 14:16:00</td>
+<td>962.88</td>
+<td>962.7</td>
+<td>961.3</td>
+<td>23.3</td>
+<td>35.5</td>
+<td>21.0</td>
+<td>72.7</td>
+<td>13.417</td>
+<td>...</td>
+<td>-97.37197</td>
+<td>387.2</td>
+<td>2.0</td>
+<td>11</td>
+<td>0.8</td>
+<td>0.00</td>
+<td>270.7</td>
+<td>A</td>
+<td>10518.0</td>
+<td>2018-05-01 14:16:00</td>
+</tr>
+<tr>
+<th>2</th>
+<td>4</td>
+<td>2018/05/01 14:17:00</td>
+<td>962.64</td>
+<td>962.5</td>
+<td>961.1</td>
+<td>23.4</td>
+<td>35.4</td>
+<td>21.2</td>
+<td>72.2</td>
+<td>13.282</td>
+<td>...</td>
+<td>-97.37199</td>
+<td>388.2</td>
+<td>1.0</td>
+<td>11</td>
+<td>0.8</td>
+<td>3.89</td>
+<td>356.3</td>
+<td>A</td>
+<td>10518.0</td>
+<td>2018-05-01 14:17:00</td>
+</tr>
+</tbody>
 </table>
 <p>3 rows × 38 columns</p>
 </div>
 </div>
-
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-
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-
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-
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-<div id="cell-id=eac04f4a-b856-41df-9428-f6712b31c7b1" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=eac04f4a-b856-41df-9428-f6712b31c7b1">
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 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>Next, convert the latitude and longitude to UTM coordinates (x and y). A scatter plot is created to visualize the survey points.</p>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
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-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=7daa1ec2-2886-4078-b6f2-d7ed007dd398">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
-        <div class="CodeMirror cm-s-jupyter">
-            <div class="zeroclipboard-container">
-                <clipboard-copy for="cell-3">
-                <div>
-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
-                    </div>
-                </clipboard-copy>
-            </div>
-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Convert Lat and Lon to X and Y</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;x&#39;</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">latlon_to_utm</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;LatDec&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;LongDec&#39;</span><span class="p">],</span> <span class="mi">14</span><span class="p">,</span> <span class="n">missing_values</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Convert Lat and Lon to X and Y</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'x'</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">latlon_to_utm</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'LatDec'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'LongDec'</span><span class="p">],</span> <span class="mi">14</span><span class="p">,</span> <span class="n">missing_values</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
 
 <span class="c1"># Create figure of survey points</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;x&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">&quot;Observation end&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'x'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">'w'</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"Observation end"</span><span class="p">)</span>
 
 <span class="c1"># Estimate the center of the observation, assuming each timestamp is observation end time.</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;x&#39;</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">rover_centered_coordinates</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;x&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">])</span> 
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;x&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Observation center&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'x'</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">rover_centered_coordinates</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'x'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">])</span> 
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'x'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'+'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Observation center"</span><span class="p">)</span>
 
 <span class="n">plt</span><span class="o">.</span><span class="n">ticklabel_format</span><span class="p">(</span><span class="n">scilimits</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Easting&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Northing&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Easting'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Northing'</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="p">[</span><span class="mf">1.1</span><span class="p">,</span><span class="mf">.8</span><span class="p">])</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
-<div id="cell-3" class="clipboard-copy-txt"># Convert Lat and Lon to X and Y
+<div class="clipboard-copy-txt" id="cell-3"># Convert Lat and Lon to X and Y
 df['x'],df['y'] = crnpy.latlon_to_utm(df['LatDec'], df['LongDec'], 14, missing_values=0.0)
 
 # Create figure of survey points
@@ -1890,137 +1313,111 @@ <h1 id="processing-and-analyzing-data-from-a-roving-detector">Processing and ana
 plt.legend(loc=[1.1,.8])
 plt.show()
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15bW1uLhoYGdO/eHQBQV1cHAOjYsSO/TUBAAIKDg/H555/jH//4h+h+uEdOgYG2L0ddXR2/f6DpcZUnbI2oXy4yot6ZovWk/aDpTh1hNv6fEHoiIoYxBpPJBLPZ7HhjQtoApVKJwMBAKBQKh9tKGj1t3LgRX331Fb788ku3Xj9z5kz07dsXCQlNhftvvPFGDBgwABkZGVi9ejU6d+6MlStX4ueff7b5ofbbb7/hlVdewTPPPGO3rezsbMyfP9+t42ypsLCQzx/Ly8sT5I+lpqYiPz8fYzQPeKUt0vZIOfuUPxObFS92aSn//5SjSwDPZwNsb+rr63HmzBnU1tZKfSiEeFVISAj69OmDoKAgu9tJNhjt1KlTGD58OHbu3Mnn5sbFxeFvf/ubzcFolhYvXoylS5eitLRUkNt78OBBPPXUU/j222+hVCqRkJCAgIAAMMawbds2wT5qampw3333oXv37ti6dSs6dOhgsz2xHt3+/fu7PADBbDYjMjIS0dHRor11Wq0WFRUVqKqqkl0gY2twllzJcYCePTQYjTijtT9j/XkwWmNjI3+ePXv2RFBQkFM9YIT4M8YY6uvrcf78eZjNZlx//fVWM6u2fIEkNm/ezAAwpVLJ/wBgCoWCKZVKu6WTli1bxrp27cq+/PJLm9v8+eef7Ny5c4wxxkaMGMH++c9/CtbX1NSwO+64g40cOZJdvnzZ5eN3Ny+L8i9tc3f2rfasvdS+9YZLdQ1NNYhviOL/TgJCurLwgYNY7oe+nwmMtB3eqDnuLH/O0b18+TKrrKxkly5dkvpQCPG6S5cuscrKSocxnGRdZiNHjkR5eblg2RNPPIEbb7wRM2fOtPlNe+nSpVi0aBF27NiB4cOH29w/VyqsqqoKBw4cwCuvvMKvq6mpwejRoxEcHIytW7cKcnp9jfLHrHFz0tfWmy2WNf+/nHt2afapZtuLt2LiI6m4P0mLir+WfbpjG1YszcbER1IRrPReySjStnljNsD2xG5vFyFtlLN/15JFEF26dLEK9jp37owePXrwyydNmoR+/fohOzsbALBkyRLMnTsXubm5UKlU+PXXXwEAoaGhCA0NBQBs2rQJPXv2xIABA1BeXo4pU6ZAq9Vi1KhRAJqC3FGjRqG2thbr169HTU0NP7CsZ8+ePk8XoPwxa2K5l8MX7uL/nx5JE8uSUbkfbETUyzsBACNG3EYlo4goOZYsJIRY8+uuspMnTwoi9rfeegv19fVITU0VbDdv3jy8/PLLAJp6QqdNm4azZ8+iT58+mDRpEv7973/z23711VfYt28fACAyMlKwn2PHjkGlUvnmZP5CI+qJKyhvuYler8fx48eRl5eH0I5BVl9+MjIyoFarodfrqQec8OiJCCHEr+6apaWldn+3HJRjy4svvogXX3zR5vq4uDgwCSeDoxH11ioXjAbQlK7A9eQemJOAkCD5XANiH6X8EEJsUalUmDp1KqZOnSr1oXgFNzvmH3/8gW7dukl9OC57+eWXUVRUhG+++UbqQwEAUOKOBLj8sfLycqjVaoSFhUGtVqOiokKW+WMhQYF//Sgtlin55XJUW2/660eYt8wtlxvLlB8xckz5IY7V1pugmlUC1awSWb5v2rpTp07hySefRN++fREUFITw8HBMmTIFFy5ckPrQvCYuLs4qQFer1Thz5gw/1oh4Rp5RhB+g/DFiD+UtC7VM+WGM8e+da665BitXrqSUH0J8yGw2t+r96qeffsIdd9yBQYMGIS8vDxEREfj+++8xffp0bNu2DV988QU/WVRrM5vNUCgUPhvkFxQUhN69e/tk33JEPboS4vLHxo8fj7i4ONkHudx89ccXJ8q2J5eI41J+iouLcfvtt2PAgAGIj4/HhAkTMHLkSGzfvh2pqamyfw+RJvRExLsKCwsRGRnJv+fi4+MRGRmJwsJCn7U5efJkBAUF4ZNPPsE999yDAQMGYOzYsdi1axdOnz6N2bNnC7Y3Go0YP348OnfujH79+mHVqlX8OsYYXn75ZQwYMADBwcHo27evIMWxrq4O6enp6NevHzp37ozbb79dkDq5du1adOvWDVu3bsWQIUMQHByM//3vf+jYsSP+/PNPwXFMmTIF9957LwDgwoULGD9+PPr164eQkBBER0cjLy+P3/bxxx/HZ599hv/85z9QKBRQKBQ4fvw4SktLoVAoBPsuKCjATTfdhODgYH4WVUtcR8CTTz6JLl26YMCAAfjvf/9r9xpbjgvq1KkTbr75ZuTn5/PrueP49NNPMXz4cISEhECtVuPw4cOC/SxevBjXXHMNunTpgqeeegpXrlyx226ra41aZ+2RP9dOJG3fpboGdqmugZ03XuFrxp43XuGXy9X06dMZAKbRaJjBYGBGo5EZDAam0Wi8Xh+VtF3ce8bWjzf5872Aq6PrTq14jmVNYsv3nC9qEnMuXLjAFAoFy8rKEl3/9NNPs6uuuoo1NjYyxhgLDw9nXbp0YdnZ2ezw4cPstddeY0qlkn3yySeMMcY2bdrEwsLC2Mcff8xOnDjB9u3bx/773//y+/vHP/7B1Go127NnDzt69ChbtmwZCw4OZkeOHGGMMbZmzRrWoUMHplar2d69e9kPP/zALl68yK655hr2v//9j9+PyWQSLPv555/ZsmXL2Ndff81+/PFH/rj27dvHGGuq93/HHXewp59+mp05c4adOXOGr50OgP3xxx+MMcYOHDjAAgIC2IIFC9jhw4fZmjVrWKdOndiaNWv4tsPDw1n37t3ZqlWrWFVVFcvOzmYBAQHshx9+sHmdFy5cyG688Ua2fft29uOPP7I1a9aw4OBgVlpayhhrrvt/++23s9LSUvb999+z2NhYplar+X188MEHLDg4mP3vf/9jP/zwA5s9ezbr0qULu/nmm535p/aIs3/fFOi6yRsfbjRBAnGE/kaamUwmplKpWFJSEjObzYJ1ZrOZJSUlsYiICFlPqkGaUKDbxNNAV6r33BdffMEAsM2bN4uuX7FiBQPAzp49yxhrCvLGjBkj2OaRRx5hY8eOZYwxtnz5cjZo0CBWX19vta8TJ04wpVLJTp8+LVg+cuRIlpGRwRhrCnQBsG+++UawzZQpU9i9997L/75jxw4WHBzMB6hiEhMTWVpaGv/7Pffcw6ZMmSLYpmWgO2HCBHbfffcJtpk+fTobMmQI/3t4eDh79NFH+d8bGxtZr1692FtvvSV6HFeuXGEhISGsrKxMsPypp55i48ePFxzHrl27+PUlJSUMAP83dccdd1hNyHX77bf7VaBLqQvEr5jNZpSWliIvLw+lpaUwm82OX0RkgSsxlpmZaZUbFxAQgIyMDBw7dgx6vV6iIyT+onLBaFQuGI0DcxL4ZQfmJPDLiXOkfs8xFyok3XHHHVa/Hzp0CADw0EMP4fLly7juuuvw9NNPY/PmzTCZmlJYysvLYTabMWjQIL4mf2hoKD777DP8+OOP/P6CgoIwdOhQQRsTJ05EaWkpfvnlFwDAhg0bkJiYyFdKMJvNeOWVVxAdHY3u3bsjNDQUO3bswMmTJ126DocOHcKdd94pWHbnnXeiqqpKcI+0PD6FQoHevXvj3Llzovs8evQoamtrcd999wnO+7333hOcd8v9cgN+uf0eOnQIt99+u2D7lv8WUqNESAnQTGDiCgsLkZaWJigjx+Uiya0SBYfLWyZUYow4T+wzlKvkQpwn1XsuMjISCoUChw4dwoMPPmi1/tChQ7jqqqvQs2dPp/bXv39/HD58GLt27cLOnTvxz3/+E8uWLcNnn32GixcvQqlU4uDBg1Y5/txEVADQqVMnKBQKwfrbbrsNAwcOxMaNG/H8889j8+bNWLt2Lb9+2bJl+M9//oNXX30V0dHR6Ny5M6ZOnYr6+noXrobzOnToIPhdoVCgsbFRdNuLFy8CAEpKStCvXz/BuuDgYJv75a6Brf36I+rRlcCQuTswZO4OwSj64Qt38cvlqLCwEKmpqYiOjobBYIDRaITBYEB0dDRSU1N9OuiBtA2WJcbEykZRiTFCvEuqsn49evTAfffdhzfffBOXL18WrPv111+xYcMGPPLII4LA84svvhBs98UXX2Dw4MH87506dUJSUhJee+01lJaWwmAwoLy8HLfccgvMZjPOnTuHyMhIwY8zlQ8mTpyIDRs24KOPPkJAQAASE5s7Jvbu3Yvk5GQ8+uijuPnmm3HdddfhyJEjgtcHBQU5fHI5ePBg7N27V7Bs7969GDRokNsDcLlBdSdPnrQ67/79+zu9n8GDB/OTcHFa/ltIjQJdIjnL6V2LiooQExOD0NBQxMTEoKioCBqNBunp6ZTGIHOWJcZa9ibQrIJEDFVy8YyU77k33ngDdXV1GD16NPbs2YNTp05h+/btuO+++9CvXz8sWrRIsP3evXuxdOlSHDlyBKtWrcKmTZswZcoUAE1VE9555x1UVFTgp59+wvr169GpUyeEh4dj0KBBmDhxIiZNmoTCwkIcO3YM+/fvR3Z2NkpKShwe58SJE/HVV19h0aJFSE1NFfSGXn/99di5cyfKyspw6NAhPPvsszh79qzg9SqVCvv27cPx48fx22+/ifaUpqWl4dNPP8Urr7yCI0eOYN26dXjjjTeQnp7uzqUFAHTp0gXp6el46aWXsG7dOvz444/46quv8Prrr2PdunVO72fKlCl49913sWbNGhw5cgTz5s3D999/7/Zx+YTPs4XbKU8GINCIeiEu4d1gMIiuLysrYwCYTqdr3QPzE9wo3NzcXKbT6WQ92Cr3wwIWENSRjdU+xL93tuk+Z4kPPMgCgjpS1QXS6trzYDTGhFUXysrKWE1NDSsrK/Np1QXO8ePH2WOPPcauueYa1qFDB9a/f3/2r3/9i/3222+C7cLDw9n8+fPZQw89xEJCQljv3r3Zf/7zH3795s2b2e23387CwsJY586dWUxMjGCAVX19PZs7dy5TqVSsQ4cOrE+fPuzBBx9k3333HWOsaTBa165dbR7niBEjGAC2e/duwfILFy6w5ORkFhoaynr16sXmzJnDJk2axJKTk/ltDh8+zGJiYlinTp0YAHbs2DGrwWiMMZafn8+GDBnCOnTowAYMGMCWLVtmdQ1WrlwpWHbzzTezefPm2TzuxsZG9uqrr7IbbriBdejQgfXs2ZONHj2affbZZ4wx60FxjDH29ddf88fJWbRoEbv66qtZaGgoe+yxx9iMGTP8ajCagjEJ58Ntw2pqatC1a1dUV1cjLCzMrX3U1pv4VIXKBaNl2+OQl5eHCRMmwGg0CnKiOEajEWFhYcjNzcX48eMlOELpUN6ykGqW/R4Wymcmrc0b9wJfuXLlCo4dO4aIiAh07NjR7f2IfQ5FREQgJydHlp9DxD84+/dNqQtEcjS9qzjKWybEM1TFxTtSUlJw9OhR6HQ65ObmQqfToaqqioJc0iZQj66b/PlbfFtjNpsRGRmJ6OhoFBUVCcrYNDY2QqvVoqKiAlVVVbKZ+YquiTjLiiXcYM4DcxIQEtR0DeT6VIRYa62nIf58L/BWjy4h/oh6dEmbYTm9q1arFfRearVaFBcXIycnR1YBndT1K/1VSFDgXz9Ki2VKfjkhAD0NIYQ0o0CX+IWUlBTk5+ejvLwcarUaYWFhUKvVqKioQH5+vuwekVHNWPu4nt2W/08IVXEhhFiiLhAJmc1m6PV6nDlzBn369EFsbKysei1bSklJQXJyMl0TCPOWY2JirNbLNW+ZY9l7Sz25xBL3NCQvL8/m0xC1Wg29Xo+4uDhpDpIQ0mroDiERGk0vTqlU0s0HwvqVYjm6cq0ZS7MKEkfoaQghxBKlLkiA8seII5S3LI5mFSSOUBUXQoglqrrgJndH2tJoevuotrAQ1a8Uojq6xJHW/oylqguESIOqLvgpGk1PXEH1K4UqF4xG9rA6oGgWv+zU6xOB/GlNy4ns0dMQQoglCnRbGeWPiautN/31I8y95JbLGZe3PH78eMTFxcn6Br29eCsmPpKKqBuu55d9umMbhg65ARMfobQf0oSquMiLSqXCq6++KvVheE1paSkUCgX+/PNPqQ+lXaBAt5VR/pg4yr0kjliWjdr4wUZ++YgRt1HZKGKFnoa0fadOncKTTz6Jvn37IigoCOHh4ZgyZQouXLgg9aF5TVxcHKZOnSpYplarcebMGXTt2lWag/Iif/gSIu8ESAnQaHriKspbbmJZNiq0Y5BVPi6VjSItURWXtuunn37CHXfcgUGDBiEvLw8RERH4/vvvMX36dGzbtg1ffPEFunfvLsmxmc1mKBQKq/RDbwkKCkLv3r19su+2qr6+HkFBQW69lnp0Wxnlj4mrXDAalQtG48CcBH7ZgTkJ/HJCKO2HEOnU1pugmlUC1aySVkknmzx5MoKCgvDJJ5/gnnvuwYABAzB27Fjs2rULp0+fxuzZswXbG41GjB8/Hp07d0a/fv2watUqfh1jDC+//DIGDBiA4OBg9O3bFy+++CK/vq6uDunp6ejXrx86d+6M22+/HaWlpfz6tWvXolu3bti6dSuGDBmC4OBg/O9//0PHjh2t0gumTJmCe++9FwBw4cIFjB8/Hv369UNISAiio6ORl5fHb/v444/js88+w3/+8x8oFAooFAocP35cNHWhoKAAN910E4KDg/lSpJa4DrQnn3wSXbp0wYABA/Df//7X7jVubGzE0qVLERkZieDgYAwYMACLFi3i1586dQoPP/wwunXrhu7duyM5OVkwMPrxxx+HVqtFTk4O+vTpgx49emDy5MloaGgA0NRbfeLECbz00kv8+XE+//xzxMbGolOnTujfvz9efPFFXLp0SXA+r7zyCiZNmoSwsDA888wzds/FLkbcUl1dzQCw6upqt15fUFDAVCoVA8D/REREsIKCAi8fadtyqa6Bhc8sZuEzi9mlugapD0dSl+oa2KW6BnbeeIW/JueNV/jlcqPT6RgAZjAYRNeXlZUxAEyn07XugRFZ8/Re4EuXL19mlZWV7PLlyx7vqzU/my9cuMAUCgXLysoSXf/000+zq666ijU2NjLGGAsPD2ddunRh2dnZ7PDhw+y1115jSqWSffLJJ4wxxjZt2sTCwsLYxx9/zE6cOMH27dvH/vvf//L7+8c//sHUajXbs2cPO3r0KFu2bBkLDg5mR44cYYwxtmbNGtahQwemVqvZ3r172Q8//MAuXrzIrrnmGva///2P34/JZBIs+/nnn9myZcvY119/zX788Uf+uPbt28cYY+zPP/9kd9xxB3v66afZmTNn2JkzZ5jJZOI/6/744w/GGGMHDhxgAQEBbMGCBezw4cNszZo1rFOnTmzNmjV82+Hh4ax79+5s1apVrKqqimVnZ7OAgAD2ww8/2LzOM2bMYFdddRVbu3YtO3r0KNPr9eztt99mjDFWX1/PBg8ezJ588kn23XffscrKSjZhwgR2ww03sLq6OsYYY4899hgLCwtjzz33HDt06BD76KOPWEhICH9tL1y4wK699lq2YMEC/vwYY+zo0aOsc+fObOXKlezIkSNs79697JZbbmGPP/644HzCwsJYTk4OO3r0KDt69KjV8Tv7902Brpu88eHG/UHn5uYynU7HTCaTF4+wbaJAtxl3HWz9yI3JZGIqlYolJSWx+vp6wXunvr6eJSUlsYiICHofEQFff6a090BXii/cX3zxBQPANm/eLLp+xYoVDAA7e/YsY6wpKBozZoxgm0ceeYSNHTuWMcbY8uXL2aBBg1h9fb3Vvk6cOMGUSiU7ffq0YPnIkSNZRkYGY6wp0AXAvvnmG8E2U6ZMYffeey//+44dO1hwcDAfoIpJTExkaWlp/O/33HMPmzJlimCbloHuhAkT2H333SfYZvr06WzIkCH87+Hh4ezRRx/lf29sbGS9evVib731luhx1NTUsODgYD6wben9999nN9xwA/9lgjHG6urqWKdOndiOHTsYY02Bbnh4uOAz96GHHmKPPPKI4LhWrlwp2PdTTz3FnnnmGcEyvV7PAgIC+L/V8PBwptVqRY+N4+zftzyT/fwE5Y9ZCwkKpFqoRBSX9jNu3Dh0794dFy9e5NeFhobi4sWLKCgokF3aDyG+JDYY2HLQsC8/r5kLZf7vuOMOq9+5QVAPPfQQXn31VVx33XUYM2YM7r//fiQlJSEwMBDl5eUwm80YNGiQ4PV1dXXo0aMH/3tQUBCGDh0q2GbixImIiYnBL7/8gr59+2LDhg1ITExEt27dADTl8mZlZeHDDz/E6dOnUV9fj7q6OoSEhLhwFYBDhw4hOTlZsOzOO+/Eq6++CrPZzH/mWR6fQqFA7969ce7cOZv7rKurw8iRI0XXf/vttzh69Ci6dOkiWH7lyhX8+OOP/O833XST4DO3T58+KC8vt3s+3377Lb777jts2LCBX8YYQ2NjI44dO4bBgwcDAIYPH253P86iQJcQP8XlJtfWm/kby4E5CQgJkncgp1AoEBcXh9mzZyMqKgoVFRVYsGgxKm96GtP2A2M0JtkO2CPNaLrotisyMhIKhQKHDh3Cgw8+aLX+0KFDuOqqq9CzZ0+n9te/f38cPnwYu3btws6dO/HPf/4Ty5Ytw2effYaLFy9CqVTi4MGDVl+SQ0ND+f/v1KmTIMcUAG677TYMHDgQGzduxPPPP4/Nmzdj7dq1/Pply5bhP//5D1599VVER0ejc+fOmDp1Kurr6124Gs7r0KGD4HeFQoHGxkbRbTt16mR3XxcvXsSwYcMEwSjH8rq70qblvp999llBnjRnwIAB/P937tzZ7n6cRe90CdFoemKP2N9DSJBStn8nluXFLCuWxMTE4MNNHyLq5Z38dvTRRqTsiWxPpPjC3aNHD9x3331488038dJLLwmCsl9//RUbNmzApEmTBIHnF198IdjHF198wfcMAk2BXVJSEpKSkjB58mTceOONKC8vxy233AKz2Yxz5865Ve1o4sSJ2LBhA6699loEBAQgMbH572rv3r1ITk7Go48+CqBp8NeRI0cwZMgQfpugoCCHJREHDx6MvXv3Cpbt3bsXgwYNcvsJ1vXXX49OnTrh008/xT/+8Q+r9bfeeis++OAD9OrVy6MZ/8TO79Zbb0VlZSUiIyPd3q8rqOoC8StmsxmlpaXIy8tDaWkp1UQlPLFZBbkJRa6Ymh9x6vaU0UQjhHhJSFDgXz9Ki2VKfrmvvPHGG6irq8Po0aOxZ88enDp1Ctu3b8d9992Hfv36CaoDAE2B39KlS3HkyBGsWrUKmzZtwpQpUwA0VU145513UFFRgZ9++gnr169Hp06dEB4ejkGDBmHixImYNGkSCgsLcezYMezfvx/Z2dkoKbE/5TjQFOh+9dVXWLRoEVJTUxEcHMyvu/7667Fz506UlZXh0KFDePbZZ3H27FnB61UqFfbt24fjx4/jt99+E+0NTUtLw6effopXXnkFR44cwbp16/DGG28gPT3dnUsLAOjYsSNmzpyJGTNm4L333sOPP/6IL774Au+88w5/XldffTWSk5Oh1+tx7NgxlJaW4sUXX8TPP//sdDsqlQp79uzB6dOn8dtvvwEAZs6cibKyMrzwwgv45ptvUFVVhS1btuCFF15w+3zsoW4PCdAjNXGFhYVIS0sTlC/hyqjIucg75S03ESsvJtZr96KuFtA1LafrJl+U+tO2XX/99Thw4ADmzZuHhx9+GL///jt69+4NrVaLefPmWdXQTUtLw4EDBzB//nyEhYVhxYoVGD266W+gW7duWLx4MaZNmwaz2Yzo6Gh89NFHfA7umjVrsHDhQqSlpeH06dO4+uqrERMTA41G4/A4IyMjMWLECOzfv99qYoQ5c+bgp59+wujRoxESEoJnnnkGWq0W1dXV/Dbp6el47LHHMGTIEFy+fBnHjh2zauPWW2/Fhx9+iLlz5+KVV15Bnz59sGDBAjz++OMuXlWhf//73wgMDMTcuXPxyy+/oE+fPnjuuecAACEhIdizZw9mzpyJlJQUGI1G9OvXDyNHjnSph3fBggV49tlnMXDgQNTV1YExhqFDh+Kzzz7D7NmzERsbC8YYBg4ciEceecSj87FFwVzJ9ia8mpoadO3aFdXV1S5366tm2f+WKMebc2FhIVJTU6HRaJCZmcnnXmZlZaG4uJim7SQoLS1FfHw8DAYDYmJiANB7iTjm6xQxT+4FvnblyhUcO3YMERER6Nixo9SHQ4hXOfv3Lc+uQ+JX7OVeFhUVQavVIj09HcnJyTSiXsbEZhXkeu0uXmnAiKzdAIB9GfHo0sm9GXSI/zKbzdDr9Thz5gz69OmD2NhY+jwghDhEga4E6JGakOXUri2nVAwICKCpXQmA5vJiqamp0Gq1yMjI4Hv+Fy1eBgx+CgDQpVOQbNJ/uODv9OnTOH/+PHr27Il+/fr5PAh0N+h0t3fVk7QmSv0hRN7kcTfwMzSaXoimdrXPFz1ZUvSOeaPNlJQU5OfnIy0tDWq1ml+uirwBGNzczqeffsZP4RkXF4e4uLhWCfw8CThdDQLFgr/AwECYTCangkApgk53WKY15eXlCdKaUlNTKa2JEGKf3ekkiE3emA2HZgFr0t6mdvXmjHdiU0WHh4ez+fPnu71/sX2qVCqnp5925+/W3TZttWXrGhcUFLBrrrlG0E5gYCDr2bOnS225cn5i5xYYGOjWOTrbbkFBAVMoFEyj0TCDwcCMRiMzGAwsKSmJKRQKNnz4cKZQKOy27e6/o0KhYElJSaLt2mrP3dm1LGfDM5vNgnVms9kvZsNr7zOjEeKvaApgH/PnDzdf8OV0xVJO7erKeTkTGLgT0Nnar1hQkZ2dzUJDQ+3u395xuhuouHodvNWmK23ZCv40Gg1TKBQMgNNttWbAed542apde0Ggs8GfRqMRfd9IEXS6O521N74E+3qqdX++F3CBQG1trdSHQojX1dbWUqDrS1J+uPn6g7slb/fG2WoDgFUQx/3uzbbcPS9HbXjS29Vyv2JBhbP7t9cL6knvmDsBkrttutoW145Go7HZTpcuXZhKpbLZ1skLF60CsJMXLvk84HQ1CHQ2+HvjjTdEg0Apgk5328zNzWUAmNFoFF1fU1PDALDc3FzR9Z4+vXCGPwe6JpOJVVZWst9++03qQyHE63777TdWWVnpMAaSZ1JoG+Zpfpw7eYCtlR8nNrXrwoUL8fHHH3tl/5ZcOS9n6h67UznC3n71e4QD9JzZf9rMTNw3NhF1FnNsWO57f9nnHg36c2emKXcHGrraljPtfPTRRzAajU61xYldqvOoTbVajbS0NBQXF7vUrj3O5rRzs0l5K7fdk1x6dwfg9unTBwBQUVHBl5SzVFFRAQDo3rM3X2qO+1yj3N6mAZzdunXDuXPnADTVRm05jS0hbQ1jDLW1tTh37hy6devmcBwEBboScnVwTmt/cLtb9svVCTHstbN161a75cXcmXzD1fNyJuhyJ6BztF+gOXhwZv/jt/yO6PnC11vuL/vmGsE+W/LFoD9fDTRs+YXN2XbcacsWXwecXHDYkrPB3+XLlwXbt9yvr4LOlu0B7g/AFSspx2lsbER2djYiIiKgvlMN7Gr+W6eShc169+4NAHywS0h70a1bN/7v2x4KdCXias+spx/c7gSE/tQbZ6unsTV7Ge3xVUDHBRVO7X/LHrv78iRQAdwLkNxt09W2nG3HXlsXLtYhdmmpYJ1+Rjx6hIrX5PVFwMk5MGekzSDQ2eBv+/btiIiIQGxsrOD1vg46W7bnCXsl5bKzs1GyYyfWr99g9RRD/0UZTpw+QyUL0fSkrE+fPujVqxcaGhqkPhxCvKJDhw5Of0mlQFcC7vTMehqcuRMQtlbZr9YuL+Zqe84EXe4EdPb2azabMfSD5qDCmf2fXDEO2z7ehmExatHjDFYqPApU3AmQ3A2ObLXFafmFbdjtdyB84CAsXLgQW7duFW2nS5cu6NGjh+22Qq2Pv0eo7Zq8vgg4nVlnGfwlJycLZhLMzs5GcXExhg0bhpKSEuTn53utx9JR0MnNYGivPXdq2toqKRcREYH+L+Uj4yCQcbD5s4z72x8wrYBKFlpQKpXtvveaEDEU6LYyd3tmpag162+9cWI9ja3Ry+hMgOdOQGd/v4GCoGLGjBkIDw/HokWLsGXLFtH9q67ti4T4u1FnZiL7a+JpoOIqbwRHlux+YUtdgY+XJuGBBx4Q5HlnZWWhpKQEjDGsXbvWJ4GftwJOZ+vZ2gr+AgMDwRjDhQsXHKYyeTvo9GXOa0pKCpKTk61SvQbO3m73dRUVFbjtttusXufo6QUhpB1pjZFx7ZG7I23dHbnsaZmd1hwxb9mmM5UQLNsxXq4TvMbZWpmuVF3wpBKAs1UXysrKWE1NDSsrK3Or6oLlPluOGk9MTLS7f2eOs+U+IyIivDoS3VdtOhq9b6uObq9evXx2fvbq6Pr6unIVWNavX89WrlzJ1q9f3yqVWFq78osttj7XamqvsPCBg9jw4cNFqy4MHz7cayUL/bnqAiGEyou5zd0PN2fL5ax9P1cQrHircLonNVBdCd5cbYtrJ/GBB/nX7N6z1+n6rq15Xo726+0g0jKomD9/vlf2L0Wg4o02nfnCZjKZ2K5du9icOXPYnDlz2K5du1ot8GvtgNOSnCegETv36dOnMwCidZUBsOnTp3ulbQp0CfFvFOi6ydc9utt37rb64PZGcOaterPe7qm6VNfAcj8sYOE3RPHHFxDSlakib2C5H7ZeT5w3zsvXQaS/9KZJSc5BnS1yviYtz91RXWVbtY3dQYEuIf5NwRhrTugjTqupqUHXrl1RXV2NsLAwp19nNpsRGRmJ6Oho0VzOBx5MxfeVlfjiwFe4Pbuphqdlzun24q1W1RoiIiKQk5Pj05qQrpZCcxVXA9MWV3MJneXr8yK+4Wo96PbMsqKKWJ66HK9NaWkp4uPjYTAYRPPwDQYD1Go1dDqdx1UX3L0XEEJaBwW6bvLkw82y6kLLwTnfDXnW7muPL05sl8GZVIGuP6NgjjiD3jvW8vLyMGHCBBiNRoSGWpfTMBqNCAsLQ25uLsaPH+9RWxToEuLf6M4pAXsjlzHE8euVSmW7q/3obhF7QghpydOa0YSQ9oMCXYlw5XJKS0tRWloKAIiLi8MI9V1QKpWyC/jcLWLfHrkzuQeRL/qSaM2y3F/uh/mIenkngKZr1TEwwCeTWxBC/BPdMSW0ZcsWQb7twoUL+dnRxmge4LeTa8AnV+5M7iE37TF9x130JdGaZY3jvz/yd2DwUwCAffv2Y+WyxT6pGU0I8U8BjjchvsDl6UZHR8NgMMBoNMJgMCA6OhqpqanYsmWr1IcoCa6I/fHFibK+URPbCgsLERkZifj4eEyYMAHx8fGIjIxEYWGh1IdG/MgYzQPY8EE+Kg5X8csSxtyP8kNHsOED301uQQjxLzQYzU2eDEBwVHlBq9WioqICVVVV1OMgQzSK3jbLgZyWM5FlZWXxvXQUwBCg9Qbp0WA0Qvwb9ehKQK/X4/jx45g2IwPXZW6DalYJH9wEBAQgIyMDx44dg16vl/hIiRRCggL/+lFaLFPyy+Wq5fTZMTExCA0N5afP1mg0SE9Ph9lsdrwzQgghsiDfu6aEzpw5AwAYMmQIUPy51fqoqCjBdnJD+ZdEDPcFMS8vT/AUBGj+gqhWq6HX69tdVRJn0XunGQ3SI4QAFOhKonvP3lB0CMbX5d/zyyxH1cu59E1hYaHVhBjcAD25PZLm8pVJE+6LH/dFsCW5f0Gk944Q9/SDe1rWhMn6qQghckSpCxJ4dlctBkwrwLPb/uCXDV+4C0Pm7sCQuTtkW/rG0QA9Gmwkb5a1UcXI/QsivXcIIcQaDUZzkycDEBwNkji5NEl2g2pogB5xhP5GxNF1EddagzppMBoh/o2e4UiAyx3btHkr5n0dDAA49fpEsIYrCA9XyS7IBSj/0hLlWYqzrI2q1Wqtps+Wa21Ueu+Io3rUhBCAAl1JcD0JDz34AOZ93fRh/O7qtxB+rXyDGsq/bEJ5lvbZmz5bjl8QAXrvEEKIPX6To7t48WIoFApMnTrV5jZvv/02YmNjcdVVV+Gqq65CQkIC9u/fL9jm7NmzePzxx9G3b1+EhIRgzJgxqKqqEmxz5coVTJ48GT169EBoaCjGjRuHs2fP+uK0nPbQww8hLi5OlkEuQPmXAOVZOislJQVHjx6FTqdDbm4udDodqqqqZBnkAvTesaVywWhULhiNA3MS+GUH5iTwywkh8uAXObpffvklHn74YYSFhSE+Ph6vvvqq6HYTJ07EnXfeCbVajY4dO2LJkiXYvHkzvv/+e/Tr1w+MMajVanTo0AHLly9HWFgYVqxYge3bt6OyshKdO3cGADz//PMoKSnB2rVr0bVrV7zwwgsICAjA3r17nT5mysvyLrnnGcr9/In76G/HPuPlOkTPb0pZWJ0QgoT4u716HeheQIifYxIzGo3s+uuvZzt37mT33HMPmzJlitOvNZlMrEuXLmzdunWMMcYOHz7MALCKigp+G7PZzHr27Mnefvttxhhjf/75J+vQoQPbtGkTv82hQ4cYAGYwGJxuu7q6mgFg1dXVTr+G2FdQUMAUCgVLSkpiZWVlrKamhpWVlbGkpCSmUChYQUGB1IfoMzqdzu7fYFlZGQPAdDpd6x4YaRPk/N6xp6CggIUPHMTCZxaz8JnFTNEhmKlUKq9eD7oXEOLfJE9dmDx5MhITE5GQkOB44xZqa2vR0NCA7t27AwDq6uoAAB07duS3CQgIQHBwMD7/vGlihoMHD6KhoUHQ3o033ogBAwbAYDDYbKuurg41NTWCH2+orTdBNatEMDuaXHH5l+Xl5VCr1QgLC4NarUZFRUW7z7+kPEvX0XunmZzfO7ZwqUBDh9yAjdoeqJhzD8r2lFIqECEyI+lgtI0bN+Krr77Cl19+6dbrZ86cib59+/JBKxewZmRkYPXq1ejcuTNWrlyJn3/+mQ8Qfv31VwQFBaFbt26CfV1zzTX49ddfbbaVnZ2N+fPnu3Wc9lhOV6rfo/f6Y7W2JiUlBcnJybKrOmCZZxkTE2O1Xq55lsR5cn3viGk5XTSXzsFNF63VapGeno7k5GRZXh9C5ESyHt1Tp05hypQp2LBhg6AH1lmLFy/Gxo0bsXnzZv71HTp0QGFhIY4cOYLu3bsjJCQEOp0OY8eOtSq746qMjAxUV1fzP6dOnfJof7X1JuRtKkT0LcP5ZfcnP4iBNwxB3iZ59zQolUrExcVh/PjxshmgFxsbC5VKhaysLDQ2NgrWNTY2ynYSETG19aa/fswWy8z8cjmT43tHDFdyLTMz02bJtWPHjkGv10t0hISQ1iJZj+7Bgwdx7tw53Hrrrfwys9mMPXv24I033kBdXZ3ND+mcnBwsXrwYu3btwtChQwXrhg0bhm+++QbV1dWor69Hz549cfvtt2P48KaAsnfv3qivr8eff/4p6NU9e/YsevfubfN4g4ODERwc7MEZCzXVeAwGtIv5Zf3/tQEAkHEQCFYWyvJxo1xRjVjnUX1U4ohlKpBYXWpKBSJEPiQLdEeOHIny8nLBsieeeAI33ngjZs6cafOGvnTpUixatAg7duzgg1cxXbt2BQBUVVXhwIEDeOWVVwA0BcIdOnTAp59+inHjxgEADh8+jJMnT+KOO+7wxqk5ZJmuYIucH6vV1pv4YKZywWjZzE1PNWKJp+T63mmJS/F54403sHr1aqu61M8884xgO0JI+yXZp2CXLl2sBt507twZPXr04JdPmjQJ/fr1Q3Z2NgBgyZIlmDt3LnJzc6FSqfic2tDQUISGhgIANm3ahJ49e2LAgAEoLy/HlClToNVqMWrUKABNAfBTTz2FadOmoXv37ggLC8O//vUv3HHHHaK5kb6g1+txcsU47N6tQ9TfbrWannLfvv24d8kx2c1kRCjP0hlcDVRbU7sSEhsbi549eyIzMxMajQZ5eXn8E5KsrCzMnj0bvXr1olQgQmTAr7/unzx5UpBf9dZbb6G+vh6pqamC7ebNm4eXX34ZQNOjqGnTpuHs2bPo06cPJk2ahH//+9+C7VeuXImAgACMGzcOdXV1GD16NN58802fnw/nzJkzYA11GP63aARY3JxDgpQICQrE8L9F89vJieXc9M3Lmv9fLr1TXJ4lESf2d8C9d+SK3jvWAgICkJiYKDoY7YEHHsDBgwclPkJCSGvwiwkj2iJPioSXlpYiPj4eBoMBQ28dbvWo0WAwQK1WQ6fTySrgUc0qsbueci+JJXpM34zeO0KWn7FiT+q8+RlLE0YQ4t/ke2eQkOUI+6KiIsFNiEbYEwrgnBMSFCi7AI44h+pSE0I4dAeVAI2wF0e5l4S4h947QlSXmhDCkXxmNLmimYyshQQF/vVjnbcsh15Nqg9L3CX3905LVJeaEMKR3yegH6ER9sQS1Yd1jVh9VHrvEMD6qdlL02fhiZI/AABRh97BxzJ9akaIHFGgS/wO5V4SRwoLC5GWlmZVH3X58uWyfBrCofdOM8u61CNH3osB0woAAN9XVsr2qRkhckRVF9zkjZG2dLMmlixLRInlWcrxEbSYwsJCpKamQqPRIDMzU1Aflctvp/cPAZreU2azGbo9ZXhRVwsA2JcRjy6dggB45z1FVRcI8W8U6LrJ0w837mZ9f5IWFYOfAgCs1XTHiqXZdLOWOaq6YJvZbEZkZCSio6MF9VGBptxLrVaLiooKVFVV0WNp0ipl1yjQJcS/0WA0CZjNZqSlpUGj0WDjBxv55SNG3IaioiJoNBqkp6c7NVVwe2Q2m1FaWoq8vDyUlpbK9joQa3q9HsePH0dmZqYgyAWaJgjIyMjAsWNNswrKEb13CCFEiLqKJKDX63Hi9BmseT8XV0zNHercaPuXps/CvXffKcspgCmdg/Is7aH6qLbRe8caV3btwsV6xC7VAQD0M+LRIzRIysMihLQi6tGVwJkzZzBgWgEeL/5dMKp++MJdGDJ3Bz86WG43ay6dIzo6GgaDAUajEQaDAdHR0UhNTUVhYaHUh0gkZlkfVYxc66PSe0ccV16tU1Dzra5TUIBsy64RIkeUo+smT6cAfnz7JbvbnFiikdUUwJR7SZxBfyfW6JrYxg3wvHCxDrFLSwEA+hlx6BEaDIAGoxEiBxTousmTDzez2YyBNwxB1E034d33N2BE1m4ATSPsOwYq8PdH/o7K8m9kdWNqzbnp/R3Vh7XPsuqCrVkF5fSont47ttFgNEIIpS5IQKlUYsXSbHz8URGe/L+J/PLyrw9iwsOp+PijIuTk5MgquKHcyyaFhYWIjIxEfHw8JkyYgPj4eERGRsr20bMYmlVQiN47hBBiGwW6EuFu1hXff88vGznyXtnerCn3kvIsXZGSkoKjR49Cp9MhNzcXOp0OVVVVsnvfAPTesadywWhULhiNA3MS+GUH5iTwywkh7R+lLrjJW4+r6DF1E7nnGcr9/In76G/HMV/WpqbUBUL8G/XoSqzOzPD49kvI+DYMI9R3yfZGxM1NX1xcDK1WK+jR1Gq1KC4ubtfpHFQflrhL7u8dZ1jWE9bv0VN9YUJkhAJd4jfknHtJeZauq603QTWrBKpZJfzoermS83vHkcLCQgwdciNOLNHgxBINxtx3L+W9EyIjFOhKpLbehNp6E4yX6/llu3R6GC/XyfqmLdfcS8qzJJ6S63vHHsp7J4RQjq6bPM3Lao2yN6TtoDxL53FfBGvrzfyEKwfmJCAkqOm60EQABBC+pwoKCrB3715+LMSdd96JcePGeeU9RTm6hPg3uiP4qcLCQln3xMgNl2eZmpoKrVZrsz6s3INcAPygIkuWMwzSl0TvkGqgbMt21Wo1ysrKbB6HrYFmXN77s88+i0GDBllNjfzMM8/go48+kuVU64TICaUuSMBsNgP50xB16B3sz7yXX35gTgIqXr4PUYfeQXp6umwHTMg195LyLNsGs9mM0tJS5OXlobS0tFXep8626a33jqf1nN09Dst2Jz72BB7ffgmD5u7EvaPGuHwcXD57ZmamaOrC7NmzBdsRQtonCnQloNfrceLHI5iTMQOhHTvwy0OClAjtGITZs6bTCHuZ8laepT8HY2JcCYy4Gqjzb6njl516fSJOrhgH5E/zaTAmRQDY2m1Kldfast1ffz3Lr1MoFFi/fr3gOPI2Ff411qH576y23syPf+jVqxcCAwOh0WhQVFSEmJgYhIaGIiYmBkVFRUhMTERgYCB69erlk/MhhPgHCnQlQCPsxbX2AD1/DQaVSiXi4uIwfvx4xMXFufy4uC0GY64ICQrE9uKteHJS86yCZ06dQNmeUgwdcoPPgjEpAkBn2+TeO7aCPmeZzWakpaXZDA41Go3dp03uHodlu7kf5mPorcMRENSRXz/6/iTMefkV3HTzLfxxZBwMxpC5OwRpK8MX7sKQuTv4VAaTyWSzZF9mZiZMJvk8MSJErmgwmps8GYDgydz0UuTNtVabngzQc7UgfGFhIdLS0qzy9pYvX+5U76k7BejdbdOVtrjASKPRIDMzk8/zzcrK4vN8HZ2fO9fS3TbdGVjGDTK6aegtqBj8lOA4nRm450mb7g4W9HWbA2dvt3q9GEdtevLZBLj/HrZs9+9FFxzuw2AwYPyW3+1ul31zDSZMmACj0YjQ0FCr9UajEWFhYcjNzcX48ePt7sseGoxGiH+jwWgSiI2NhUqlQlZWlugNLDs7GxEREYiNjRW8ri0FZ+625+hYvJGnahmY5eXlCQKz1NRUn+TDutum5TV0pGVvHPd3xfXGabVapKenIzk52WEw1rys+f9tBWOetOnOwDJukFFeXp5VMMZNrqFWq20OMvK0TVsTekjZprMctSnV0yZBu0WfOdw+KioKJ+/piTXvrkGiNkX0y8P+ss8BNJXmEwvaqWQfIfJAga4ELEfYJycnY8yYMejUqRMuX76M7du3o6SkxGqEfVsKztzBD9C76Sa8+/4GjMjaDaDpxtUxUIG/P/J3pH/4jVXA5Gpw1paCQbHHvfba8vdgzFsj26UIxvy9zcoFDwEQ9hi7w7KeszvBYeWC0VbHYRl8OtOu2D7+35huuH/USGz7eBu/HWuoQ/i1fQT7DglS8u8LdzsUCCHtCwW6EklJSUF6ejpee+01FBcX88uDg4ORnp4uCCDbUnDmbnvcAL2N69dZDdALCQrE7FnTRQMmV4OzthIM2urJtdeWvwdjYtwJjKQIxvy9TbH31IE5IxESFOhSm54Gh2LHYRl8utMuALyxcjlU1/ZFQvzdVsdRZxbPvqOSfYQQgAajSaawsBA5OTkYNWqUYJDJqFGjkJOTIxjYwgVKtgZVZGRk2K3SwA3OsDdooyVP2nSnvdYK0tpKMOhsuoIlT2dX46oZHJiTwC87MCeBX+6LNkOCAv/6se6VsxUcWQZFjY2NgnXOBmPUpnibXHBYXFwMrVYr+GzSarUoLi5GTk6O14ND8XYv8uu3bd+GV155Bfv377c6jpCgQBxfnIjjixOtzosr2fdd5WGM3/I7ohftwZ33xFPJPkJkhHp0JeBqb2lbCc480bL3qmVvpa2AydWeMn/vmXOWWOApRW+cFI+HpeipayttckGfJ7jgMC0tDWq1ml8eERHhdHDoznHYajc4OBiN9XV49NFHXT4Obr/3jU1E9Pym9+u2j7chIf5u6sklRC4YcUt1dTUDwKqrq11+rU6nYwCYwWAQXV9WVsYAMJ1O59b2LV2qa2CX6hrYeeMVFj6zmIXPLGbnjVf45d44Rk/bM5lMTKVSsaSkJGY2mwXrzGYzS0pKYhEREcxkMtk8R64tW214ox1X23O3TbFr2HQdL9ttr6CggCkUCpaUlMTKyspYTU0NKysrY0lJSUyhULCCggK7x+rquXmrTXdMnz6dBQcHMwD8T3BwMJs+fbpP2mOs6VxVKpWgzYiICJ+do1RtMtb0d6vT6Vhubi7T6XQO3xO+areurs7t43Dns8hVntwLCCG+R4Gumzz5cMvNzWUAmNFoFA0qampqGACWm5vLGGs7wZkn7THmWcDkSlttKRi0bMeVttp7MMZdT41Gw1atWsXeffddtmrVKqbRaHwaXDMmTQDoSptSBaj+yPK9I/bjDRToEuLfKNB1k7d6dMUCprKyMqboECxY3paCM3fb49psjYCprQSD7lxDxvw/GPO0HW988WuPxP7GVCqVz3t//RUFuoQQCnTd5MmHm8lkYuEDB7HEBx5kZ6trBY/UjJfrWOIDDzJV5A1WAU5bCc48ZTKZ2K5du9icOXPYnDlz2K5du3wStLTnYLA98zSVp72y/GJqMBiY0WhkBoPB5ykk/oxSFwghNDOamzydDcfRDELzb6nDvK+DAQgHOwUrFe12ZjSOpxNjkPYtLy+vVWa8aks8nbmtvfP25DWWaGY0QvwbVV3wU1yQC1jXTvVW4X1nKZXKVmtTiokx2goppn/2R76oYtHWSTFxByGEtAXUo+smT7/Fc5MqGC/X4/ZsHQDgtfgQvKirtfs6T0sH+TPqlbKNermb0d+JNerllg716BLi32jCCIlwRdu7dArilyXEx7pVtL+98HRijPaK6+WOjo4WFPCPjo5GamqqYHIROZBqUgN/5unEHe2d2WxGaWkp8vLyUFpa2jTlOCFEFijQlZjlB65+jx7BSoXLsxm1F1JMjOHvWk4uEhMTg9DQUH5yEY1Gg/T0dNnduLnJBcrLy6FWqxEWFga1Wi3bGa88nUWtPSssLERkZCTi4+MxYcIExMfHIzIyUnZfEAmRKwp0JVRYWIjo6KH872PvHyvrD2DqlbJGvdy2paSk4OjRo9DpdMjNzYVOp0NVVZXsglyAerltoachhBDK0XWTp3lZ3Afw/UlaVAx+CgCwVtMdK5Y2T+0ptxs25V5ao9xL4gqxXO6IiAjk5OTQ54mPPk8oR5cQ/0Y9uhIwm82YNiMD9ydp8e77G/jlUX+7Fbkf5uP+JK0sH0dTr5Q16uUmrqBe7mb0NIQQAlCPrts8+RZfWlqKx7dfsrvNiSUa6HQ6WZYCol6pZtTLbZ8v66OStq21noZQjy4h/o16dCXg7GAqOQ26skS9Us2ol5sQ99DTEEIIQBNGSKJPnz44+dgY7N6tQ9TfbuUnhOBmQNu3bz/uXSLvD+DWnKTC33EVBtLS0qBWq/nlERERsszlBprrUNfWmy2WNf8/9ewSy0oUYk9D5FyJghA5odQFN3nyuKq+vh5hYWG47777kLepAFEv7wTQ9Oi1Y2AAkpOTsWvXLlRXVyMoKMjB3ohc0MxozRxNod2eJ1ZxhNI5mlnOtJiRkcHPtJid7b1Bv5S6QIh/k+8noITKyspQV1eHkpIS/P2RvwN/VV3Yt28/Vi5bjJKSEjDGUFZWJtteTbpZW6NebkJcQ09DCCEUPUiAy719//33MWfOHJzYqgEA3Luk6QP4/fffx6OPPirbHF1CHOFmCaytN1ul/sgVpXOIS0lJQXJyMj0NIUSm5PnJJzEu93bgwIE4evSo1Qfw/v37BdvJCd2sbaNe7mZi587NIChX3N+GJe5LACDvdA56GkKIfMn3riChloMkLD+A5T5Igm7WhBBCCPEWCnQlwJWMSk1NhVartTlIgh6tEYB6ue0JCQqkLz9/oXQOQgixJt87pMS4QRLTps/C+C2/A1v24OSKcQjv10fWgyToZm2NermJMyidwz5K/SFEnuidLjWFQuoj8Ct0syaEEEKIt1D0IJG8TYV49NGJGH1/Eir/WrZt5268viIHD42fiE15G2Tbq0uEqJfbNqotbI3SOYQo9YcQeaMJI9zkSZFws9mMgbO3290m4MN/oaqqSvY3bdKMHr0KFRYWIi0tDcePH+eXqVQqLF++nL4kEp6vJxehCSMI8W8Bjjch3qbX6x1uc+zYMae2I0SOuBmvoqOjYTAYYDQaYTAYEB0djdTUVBQWFkp9iIQQQvwA9ei6yZNv8Xl5eZj42BP49dezqEMgYpeWAgD0M+LQIzQYRuNF9L76KuTm5mL8+PE+OHpC2i6z2YzIyEhER0ejqKgIAQHN39cbGxuh1WpRUVEh2ycilM4hZJm6IJb64+mTEerRJcS/UY+uBLiJICorKwFYDkZTWCyX54QRHLPZjNLSUuTl5aG0tBRms9nxi4gs6PV6HD9+HJmZmYIgFwACAgKQkZEh2ycihYWFiIyMRHx8PCZMmID4+HhERkbKuoc7JCjwrx+lxTIlv5wQ0r659S6vqakRXa5QKBAcHIygoCCPDqq9i42NxYBpBXi8+HegWNe8fGnz/8t1wgiAci+JfdzU2FFRUaLrueVym0KbS+fQaDTIy8vja3NnZWUhNTVV1mULCSHy5VaPbrdu3XDVVVdZ/XTr1g2dOnVCeHg45s2bh8bGRm8fb7vgzGPEnJwcWT5upNxL4gj3pKOiokJ0PbdcTk9EzGYz0tLSoNFoUFRUhJiYGISGhiImJgZFRUXQaDRIT0+X9ZMRrhrF8cWJ1JNLiIy4laP73nvvYfbs2Xj88ccxYsQIAMD+/fuxbt06zJkzB+fPn0dOTg6mT5+OzMxMrx+0P/A0L6u23oQtW7Yi49/zAO1iAMCp1ydiQN/eyMrKwviH5NfzQrmX9lHuZRP6O7FWWlqK+Ph4GAwGxMTEWK03GAxQq9XQ6XSCKceJ5yhHlxA/x9xw7733sg8++MBq+QcffMDuvfdexhhj7733Hrvhhhvc2X2bUF1dzQCw6upqj/ZTU3uFhc8sZuEzi9n2nbuZyWTy0hG2PTqdjgFgBoNBdH1ZWRkDwHQ6XesemB8oKChgKpWKAeB/VCoVKygokPrQJFFQUMAUCgVLSkpiZWVlrKamhpWVlbGkpCSmUChkd11yc3MZAGY0GkXX19TUMAAsNze3lY+s/fPWvYAQ4htupS6UlZXhlltusVp+yy23wGAwAADuuusunDx50ul9Ll68GAqFAlOnTrW5zdtvv43Y2Fg+VSIhIQH79+8XbHPx4kW88MILuPbaa9GpUycMGTIE/+///T/BNr/++iv+7//+D71790bnzp1x6623oqCgwOljJb5BuZfiKJ3DGjeFdnl5OdRqNcLCwqBWq1FRUSHLXFRK53CMBrgSIlPuRMfXX389mzlzptXymTNnskGDBjHGGPvyyy9Z3759ndrf/v37mUqlYkOHDmVTpkyxud2ECRPYqlWr2Ndff80OHTrEHn/8cda1a1f2888/89s8/fTTbODAgUyn07Fjx46x1atXM6VSybZs2cJvc99997HbbruN7du3j/3444/slVdeYQEBAeyrr75y8gp451s89dIJUY+uNZPJxFQqFUtKSmJms1mwzmw2s6SkJBYRESHbJwEmk4npdDqWm5vLdDqdrK8D/Z3Y5svPWurRJcS/uRXobtmyhQUFBbGhQ4eyp556ij311FPs5ptvZsHBweyjjz5ijDH25ptvspdeesnhvoxGI7v++uvZzp072T333GM30G3JZDKxLl26sHXr1vHLbrrpJrZgwQLBdrfeeiubPXs2/3vnzp3Ze++9J9ime/fu7O2333a6bU8/3Cwfver0ZXz6QuIDD8ry0StjdLMWQ8E/cRalc4izvC4Gg4EZjUZmMBi8dl0o0CXEv7kV6DLG2E8//cRmzpzJHnzwQfbggw+yWbNmsWPHjrm8n0mTJrGpU6cyxpjLgW5NTQ3r2LEjH1wz1tSjO3z4cPbzzz+zxsZGtnv3bhYaGso+++wzfpv77ruPJSYmsgsXLjCz2czy8vJYSEgIq6qqstnWlStXWHV1Nf9z6tQptz/cWgZ0l+oa+EDXeLlOlgEdh27WQpR7SVwh1nMZEREhu/cNpzW+PFOgS4h/czvQ9Ya8vDwWFRXFLl++zBhzPdB9/vnn2XXXXce/nrGmgHTSpEkMAAsMDGRBQUGCHl/GGPvjjz/YqFGj+G3CwsLYjh077LY1b948wc2D+3Hnw43rpdPpy9ilugZ23tg8IO288QrbvWcvU3QIlm0vHd2sm1GPrmOWXxQv1TVIfTiSo3SOZq3x/qFAlxD/5nYxwT///BP79+/HuXPnrOrlTpo0yeHrT506hSlTpmDnzp3o2LGjy+0vXrwYGzduRGlpqeD1r7/+Or744gts3boV4eHh2LNnDyZPnoy+ffsiISEBAPDvf/8bf/75J3bt2oWrr74aRUVFePjhh6HX6xEdHS3aXkZGBqZNm8b/XlNTg/79+7t83EDzYKqmCSN2CNZxU1QOmFYgu0FXnJSUFCQnJ1MpLTRNLqJSqZCVlSVaSis7O1vWk4sQa0qlkkqI/YUGuBJC3Ap0P/roI0ycOBEXL15EWFgYFIrmaWwVCoVTge7Bgwdx7tw53Hrrrfwys9mMPXv24I033kBdXZ3NwCYnJweLFy/Grl27MHToUH755cuXkZmZic2bNyMxMREAMHToUHzzzTfIyclBQkICfvzxR7zxxhuoqKjATTfdBAC4+eabodfrsWrVKqsKDZzg4GAEBwc7vjhOcHbks5xHSNPNuolSqcTy5cuRmpoKrVaLjIwMfsar7OxsFBcXIz8/X5ZfAmrrTX/912yxrPn/aVIAYlmNQqy+MFWjIKT9c+tOkJaWhieffBJZWVkICQlxq+GRI0eivLxcsOyJJ57AjTfeiJkzZ9q8cS9duhSLFi3Cjh07MHz4cMG6hoYGNDQ0CHq9gKZgget1rq2tBQC72/habGwsrrnmGgz5/m18uOlDXDExvid3f+a9eHzieHz9zdeIXfRTqxwP8W9cKa20tDSo1Wp+eUREhCxLaXGGzN1htYx7HwHA8cWJrXk4fqW23sRfn8oFo2Ub9NMTEUKIW59+p0+fxosvvuh2kAsAXbp0sXqc1LlzZ/To0YNfPmnSJPTr1w/Z2dkAgCVLlmDu3LnIzc2FSqXCr7/+CgAIDQ1FaGgowsLCcM8992D69On8VMSfffYZ3nvvPaxYsQIAcOONNyIyMhLPPvsscnJy0KNHDxQVFWHnzp0oLi52+3xc1djYiO0lWzHh4VS8NH0Wv/zJ/5uI7SVb0bNnz1Y7Fn9EN2ohSucgxHX0RIQQ4lb0MHr0aBw4cADXXXedt49H4OTJk4Jv4G+99Rbq6+uRmpoq2G7evHl4+eWXAQAbN25ERkYGJk6ciN9//x3h4eFYtGgRnnvuOQBAhw4d8PHHH2PWrFlISkrCxYsXERkZiXXr1uH+++/36flw9Ho9zp8/j+zsbKxevRojR96LAdOaJqz4vrISixYtQmZmJvR6PT2+JzxK5xCqXDAaQFO6AteTe2BOAkKC5Bu0UDqHNXoiQoi8KRhjzNUXvfPOO1iwYAGeeOIJREdHo0OHDoL1DzzwgNcO0F95Mr95Xl4eJkyYAKPRiE6dOln10tXW1iIsLAy5ubkYP368j87AP1neqMWCFzneqAHq4baHrk0z1awSu+vlnM5hNpt98kTEk3sBIcT33LojPP300wCABQsWWK1TKBQ0taIDLQdIxMXFNd+st2/HmsSrBNvJCeVdEkJ8gZ6IECJPbgW6rTVoq72yN0ACAJbn5NAACQKAHkU7IyQokL4A/YXSOQghRIjukhKwHCDxwIOpmPrSS7jwZw2/ftuu3Vi/9l3UmRlCZHZ/ohu1EPVwE1eIffEJCVLSFyJQigshcuX0O/21117DM888g44dO+K1116zu+2LL77o8YG1d9wAiWn7g/GP7TWCdf3/tQEZB4GMgztkF8jQjZoQQggh3uJ09LBy5UpMnDgRHTt2xMqVK21up1AoKNAlxEuoh9sxXw0yassonaMZpf8QIm9uVV0gno+0NZvNiIyMxE1Db8HGDzZaTRrx5P9NxPeVlTj6w/eyv2kTeuxqS2FhIdLS0nD8+HF+mUqlwvLly6lsFAHg+0oUVHWBEP8W4HgT4gt6vR7Hjx/HnIwZCO0YJOihC+3YAbNnTcfxo4eh1+slPEpC/FdhYSFSU1MRHR0Ng8EAo9EIg8GA6OhopKamorCwUOpDJIQQIjG3enTNZjPWrl2LTz/9FOfOnbOqwrB7926vHaC/8vRbvGUt3YCgjoJBR5ULRsNcd1m2tXTbInp83rq4JyLR0dGiU7tqtVpUVFSgqqqK/h1kzte1ualHlxD/5tY7fMqUKVi7di0SExMRFRUFhULh7eNq9yxr6Q69dTi/nHssbThYIdjO16QI1Oy16cnxePJad1IE6PF56+OeiOTl5VmV5wsICEBGRgbUarVsZxekL17NaIArITLH3NCjRw9WUlLizkvbjerqagaAVVdXu/V6k8nEwgcOYokPPMjOVtey8JnFLHxmMTtvvML+vFjLbr/zbtazZ0+2a9cuZjKZvHz0QgUFBUylUjEA/I9KpWIFBQUOX3uproE/9kt1DV5pU2xd+MBBTrXjybm4cz4FBQVMoVCwpKQkZjAYmNFoZAaDgSUlJTGFQuF0u46YTCam0+lYbm4u0+l0Pv+b8He5ubkMADMajaLra2pqGACWm5vbykcmPU/fA+2Vu59Vjnh6LyCE+JZbObpBQUGIjIz0KMCWO6VSCaSuQMXgpzAiqznVY/jCXbj5ld349a4ZOH/+PBISEhAZGelUvmFtvQmqWSVQzSrhH9c5IkWeo6M2x40bZ7Uu6qabfHoutfWmv36EI7O55WLMZjPS0tKg0WhQVFSEmJgYhIaGIiYmBkVFRdBoNEhPT/d4psDCwkJERkYiPj4eEyZMQHx8vNN/E+2V5RMRMdxyuc0uSHnLtnGVKI4vTqTeXEJkxK0c3eXLl+Onn37CG2+8Idu0BW/kZTkaDVwx5x5UVFQgKysLxcXFyM/Pt/so3NXH7p7kObqb9+aozQceeAB79uzBhQsX0KFDB76di1ca+C8E+zLi0aVTkKAdT3M23RmZXVpaivj4eBgMBsTExFitNxgMUKvV0Ol0bj8+5wIXjUaDzMxMREVFufQ30V5Rjq41uibSoBxdQvyb04Fuy5vp7t270b17d9x0003o0KGDYJ0ceg288eHGBXHGy/W4PVsHABhyeB0+zFuPgIAAPojzVdDpSaDmbskeV9t0th1Pg053zsdyQGFoaKjVeqPR6NGAQgpc7LP8EpCRkcF/CcjOzpbll4DW+OJFrFGgS4h/c/r5TdeuXQW/P/jgg14/GLkRC0BnpL2E0I5BgmWOBte4O03smTNnAABRUVGi67nl3Hbe4Ks2Pd2vOxMzWD4+FwssPH18TgOu7ONmF0xLS4NareaXR0REyC7IBaR5P7clNECPEHlyOtBds2aNL4+D/GXIkCGiy31xk/IkUHN3xi5X2xRr57X4ECTEx3rtXAD3RmbHxsZCpVIhKytLtMc1OzsbERERiI2NtbkPeyhwcSwlJQXJyckUwMD3X7zaMqqMQoiMuTOCLT4+nv3xxx9Wy6urq1l8fLwng+PaDG+OtLUcDazTl4luU1ZWxgAwnU4n+vpLdQ3svPGKoHoDt9wWk8nEVCoVS0pKYmazWbDObDazpKQkFhERYXeEv6sjmR21mZiYyLp06cLq6+sF64yX6/h2amqv+ORc3Dkfy6oLZWVlrKamhpWVlXml6oJOp2MAmMFgEF1v72+CyI+33gPtja8ro1DVBUL8m1uBrkKhYGfPnrVafvbsWRYYGOjxQbUFvgp0Ex94sNWCTsY8D9R80SYAq3WJDzzosB1fBp2OzqdlOaeIiAiP26PAhbhKqveAv2qN9xAFuoT4N5cC3W+//ZZ9++23TKFQMJ1Ox//+7bffsq+++oplZWWx8PBwHx2qf/HGh5tYT6yyczeW+MCDbPeeva0SdDLmu0DN3TY9OR4pzoUx39W5pcCFuEqq94A/ao2nIhToEuLfXCovFhAQwJcTE3tZp06d8Prrr+PJJ590JXuiTWqN8mInlmgANA2uycnJ8WkuGc2M5r/E8gtb42+irXBnNrv2rr29B9zl68ooAFVdIMTfuXRHOHbsGBhjuO6667B//3707NmTXxcUFIRevXrJ8sPUV3Jzc1vtJqVUKlt95L69Nj05HinOxZdowBVxVXt7D7iLBugRQlwKdMPDw9HQ0IDHHnsMPXr0QHh4uK+OSxayh9Xh0UcnYvT9Sai84TEAwOqxV+H1FTnYtn0bgvM20M2KAKDARYxl/ejmZc3/Tz27xNeVUQgh/s+tmdG6deuGr7/+GhEREb44pjbB08dVlpMB5H6Yj6iXdwJoevTaMTBA9pMB0ONo4oi7k5bIAb1/mvl6YhFKXSDEvwU43sRacnIyioqKvHwo8sJNBpCZmWlzMoBjx45Br9dLdITE39TWm6CaVQLVrBK+N5MQYh83sUh5eTnUajXCwsKgVqtRUVEhy4lFCJEbt77mX3/99ViwYAH27t2LYcOGoXPnzoL1L774olcOrj2znAwgJCjQqvdJrpMB0ONo4ix3Jy1pz+j9I47y3AmRL7c+9d555x1069YNBw8exMGDBwXrFAoFBbpOoEES4tydzrg9o+BFnDuz2bV39P6xjfLcCZEnt+4Ix44d8/ZxyA4NkiDOouCFEEIIcY/HXR/cWDauvi5xjlKpxPLly5Gamork5GSMGTMGnTp1wuXLl7F9+3aUlJQgPz9fdo/W6HE0cZVY6o9c0fvHNhqgR4g8uf1Of++997Bs2TJUVVUBAAYNGoTp06fj//7v/7x2cO1dSkoK0tPT8dprr6G4uJhfHhwcjPT0dFkOkqDH0dYoeCHOovcPIYQIuVV1YcWKFXj++edx//3348MPP8SHH36IMWPG4LnnnsPKlSu9fYztVmFhIXJycjBq1CgYDAYYjUYYDAaMGjUKOTk5KCwslPoQiR8ICQr860dpsUzJLyeE2FZbb/rrR5jjzi0nhLRvbtXRjYiIwPz58zFp0iTB8nXr1uHll1+WRQ6vN+voiuXoyr2OLrFGj17F0XS3xB5f11umOrqE+De37pRnzpyBWq22Wq5Wq2VXDstdXB3dvLw8XDE1YsjcbQCaA5iMjAyo1Wro9XoaKUwAUC6qmMLCQqSlpeH48eP8MpVKheXLl8sy9YcQQoiQW6kLkZGR+PDDD62Wf/DBB7j++us9Pig5sKyjK0audXQJcRY341V0dLQg9Sc6OhqpqamU+kMANHUeVC4YjQNzEvhlB+Yk8MsJIe2bWz268+fPxyOPPII9e/bgzjvvBADs3bsXn376qWgATKz16dMHig7BOPBNOaL+diu/nMsjO/BNOb8dIUTIbDYjLS0NGo1GkPoTExODoqIiaLVapKenIzk5WbZpDJTS0YQG6BEib27l6ALAwYMHsWLFCvzwww8AgMGDByMtLQ233HKLVw/QX3kjR3fg7O12twn48F+yzdGlm7Q4ui5NSktLER8fD4PBIDrhisFggFqthk6nk2XqD6V0WPNVjjvl6BLi39x+pw8bNgwbNmzw5rHIijPBSU5OjiyDGLpJi6Pr0oxSf2zjUjo0Gg3y8vIQFRWFiooKZGVlITU1Ffn5+bL7ewEox50QuXIpRzcgIABKpdLuT2AgPQ5yVuWC0cgeVgcUzeKXnXp9IhQFacgeVifLmxHlXYqj6yJkOYW2GLlOod0ypSMmJgahoaF8SodGo0F6ejrMZrPjnRFCSDvgUurCli1bbK4zGAx47bXX0NjYiCtXrnjl4PyZNx9XGS/XIXp+00QAqxNCkBB/tyx7cqnkmji6LtbomoijlA77fJH6Q6kLhPg55qEffviBabVaplQq2aRJk9jx48c93WWbUF1dzQCw6upqj/d1qa6Bhc8sZuEzi9mlugYvHF3bpNPpGABmMBhE15eVlTEATKfTte6BSYyui7iCggKmUChYUlISKysrYzU1NaysrIwlJSUxhULBCgoKpD7EVpebm8sAMKPRKLq+pqaGAWC5ubmtfGTSKygoYCqVigHgf1Qqlcd/J968FxBCvM+t8mIA8Msvv+Dpp59GdHQ0TCYTvvnmG6xbtw7h4eEeB99yw+WOHV+cKOuRwJR3KY6ui7iUlBTk5+ejvLwcarUaYWFhUKvVqKiokG0eKqV0iKPUH0Lky+VAt7q6GjNnzkRkZCS+//57fPrpp/joo49s3oSJY7X1JqhmlUA1q0TWU1LSTVocXRfbUlJScPToUeh0OuTm5kKn06GqqkqWQS4AxMbGQqVSISsrC42NjYJ1jY2NyM7ORkREBGJjYyU6wtZHecuEyJwr3b9Llixh3bt3Z0OGDGFFRUW+6mVuEyh1wftMJhNTqVQsKSmJmc1mwTqz2cySkpJYREQEM5lMEh2hNOi6EFdQSoeQr1N/KHWBEP/m0nPyWbNmoVOnToiMjMS6deuwbt060e3oMZBzuN5b4+V6ftkunR7xd6uhVMqvoLlSqcTy5cuRmpoKrVaLjIwMvjRSdnY2iouLkZ+fL6vBRQBdF+IaLqUjLS1NMFV7RESELFM6KPWHEHlzKZKaNGkSFAqFr45Fdrji5ZZe1NUCuqYKDHKs+Ug3aXF0Xezz1WQAbVVKSgqSk5NpchEIU3/EKlHIOfWHEDlwe2Y0ufNGSRnVrBK761eMkGctXYBmALOFros4CnSJLb4uRUflxQjxb3Q3kIjZbAbypyHqppvw7vsbMCJrNwDgwJwEdAxU4O+P/B3pH36D5ORkWQYySqVSlnU+HaHrIsSl/9TWmy2WNf8/BbyEUn8IkTe6C0hEr9fjxI9HsHH9OoR27MAvDwlqys2dPWs61Go19Ho9BTaE2CCW/jN84S7+/+WY/sOhXu5mlPpDiHzJ95NPYjRAwjG6UVuja0KIeyhvmRB5orukRFoOkGjZ80QDJAhxrHLBaABN6QpcT+6BOQkICZJv8ELpHLZR6g8h8iPfTzyJWRZ2FxsgIcfC7hy6UVujayJO7Ly59B+5onQOQghpJt+7gcRogIRtdKO2RteEEM9R6g8h8kPvcgnRAAlCvCMkKJCC/b9QOgchhDSjQFdiKSkp0Gg0ePPNN/Hjjz9i4MCB+Oc//4mgoCCpD00ydKO2RteEOIvSOaxR6g8h8kXvbokVFhYiLS0Nx48f55f95z//wfLly2Xbo0s3amt0TQhxH6X+ECJfAY43Ib5SWFiI1NRUREdHw2AwwGg0wmAwIDo6GqmpqSgsLJT6EAlpE8xmM0pLS5GXl4fS0tKmCVlkjkvnOL44kb4QEUJki6YAdpOn0z5aTkuZ+2E+ol7eCaDpEXXHwACPp6UkRC7EnoqoVCpZPxUhQpapC2KpP558EaApgAnxb9SjKxG9Xo/jx48jMzNTUFoMAAICApCRkYFjx45Br9dLdISE+D96KkKcERIU+NeP0mKZkl9OCGm/6B0ukTNnzkDRIRjXDRosOkDiukGD+e0IIdbMZjPS0tKg0WgEtahjYmJQVFQErVaL9PR0JCcny/KpiNlsplnACCGyR4GuRPr06YMB0wowYunnguWWAyS47eSKbtTW6Jo0456K5OXl2XwqolarodfrZTcbFqVziKMydITID6UuSMSZGc/kOjMa0HSjjoyMRHx8PCZMmID4+HhERkbK+lE0XRMh7mlHVFSU6HpuudyeilA6ByGENKNAVyJKpRLZw+pwamUqhhxexy//f2O6YcgPTb83Pvw66szyGytIN2prdE2scU87KioqRNdzy+X0VKRlOkdMTAxCQ0P5dA6NRoP09HSqSkEIkQ/mJ7KzsxkANmXKFJvb/Pe//2V33XUX69atG+vWrRsbOXIk27dvn2Abo9HIJk+ezPr168c6duzIBg8ezN566y2rfZWVlbH4+HgWEhLCunTpwmJjY1ltba3Tx1tdXc0AsOrqaqdfI6agoICFDxzEwmcWs/CZxUzRIZipIm/gf79U1+DR/tsak8nEVCoVS0pKYmazWbDObDazpKQkFhERwUwmk0RH2Promoij62JNp9MxAMxgMIiuLysrYwCYTqdr3QNrx7x1LyCE+IZf9Oh++eWXWL16NYYOHWp3u9LSUowfPx46nQ4GgwH9+/fHqFGjcPr0aX6badOmYfv27Vi/fj0OHTqEqVOn4oUXXsDWrVv5bQwGA8aMGYNRo0Zh//79+PLLL/HCCy9Y5fm1hpSUFJSXf8f/XvTRx/jiwFf877X1ZtTWm/jyOO0dVaOwRtdEnFKpxPLly1FcXAytVivo6dZqtSguLkZOTo6scpgpncM+qrdMiPxIPhjt4sWLmDhxIt5++20sXLjQ7rYbNmwQ/P6///0PBQUF+PTTTzFp0iQAQFlZGR577DF+8MkzzzyD1atXY//+/XjggQcAAC+99BJefPFFzJo1i9/XDTfc4MWzck2XTsE4vjgRqlkleFF3GdDp+HVym72HbtTW6JrYlpKSgvz8fKSlpUGtVvPLIyIikJ+fL7uBV5bpHDExMVbrXUnnkGLgoy/bpAF6hMiT5D26kydPRmJiIhISElx+bW1tLRoaGtC9e3d+mVqtxtatW3H69GkwxqDT6XDkyBGMGjUKAHDu3Dns27cPvXr1glqtxjXXXIN77rkHn3/+ua1mAAB1dXWoqakR/BDvo7xLa3RN7EtJScHRo0eh0+mQm5sLnU6HqqoqnwQvUvQIutJmbGwsVCoVsrKy0NjYKFjX2NiI7Oxspwa5ejLwsbbeBNWsEqhmlbj0JMqdNp1ti8txj4qKwqpVq/Duu+9i1apViIqKkm2OOyGyIWXeRF5eHouKimKXL19mjDF2zz332M3Rben5559n1113Hf96xhi7cuUKmzRpEgPAAgMDWVBQEFu3bh2/3mAwMACse/fu7N1332VfffUVmzp1KgsKCmJHjhyx2da8efMYAKsfb+ZlXaprYJfqGth54xU+R/e88Qq/XA7kkndpMpmYTqdjubm5TKfT2T0ff74mrpyHL9utqb3idF77pboGt3LgCwoKmEqlErz/VSoVKygocOr17rTrTpsFBQVMoVCwpKQktnvPXr7NxAceZAqFwuHxWr7eYDAwo9HIDAYDS0pKcur17p6nO2060xb3/hk+fLjotRw+fLhH7x/K0SXEv0kW6J48eZL16tWLffvtt/wyVwLd7OxsdtVVVwlezxhjy5YtY4MGDWJbt25l3377LXv99ddZaGgo27lzJ2OMsb179zIALCMjQ/C66OhoNmvWLJvtXblyhVVXV/M/p06d8tqHmyc37faEuw5Tp05lAJhGo2FlZWWspqaGlZWVOX2jdbfd1grWPA1eWuOaOEOKwM9Wu5YDOn0R6Hoa/LnTridtctdI0SGYb1MVeYPD4/TkS5W7X9TdadOVtrgBevauJTwYoEeBLiH+TbJAd/PmzQwAUyqV/A/3YaRUKu0GG8uWLWNdu3ZlX375pWB5bW0t69ChAysuLhYsf+qpp9jo0aMZY4z99NNPDAB7//33Bds8/PDDbMKECU4fvzerLji6aUvRa+ZJm97quQoODhb8HhER4VHPjrPt+jJY80bw4uw18SUpAj+xds9e+JPp9GVsrPYhhwFPawZinrbrjTZraq+wLdt3823++uclh+fqSdUGrh1bP95s05W21q9fzwIDA+1ey8DAQLZ+/Xqbx2gPBbqE+DfJBqONHDkS5eXlgmVPPPEEbrzxRsycOdPmAISlS5di0aJF2LFjB4YPHy5Y19DQgIaGBquR6Uqlks9XU6lU6Nu3Lw4fPizY5siRIxg7dqynp+USLm9Mo9EgLy8PUVFRqKioQFZWFoqXJiE/Px/bi7e6PYCitt6EIXN3AAAqF4x2ek731h60Yfc6FBdjypQpSE5O9vpgGHvtpqamen0wk6dT1qakpCA5OVnymdE8PQ8ul1Js6msANv9OxdpVzSppWnnDY/x2tgZwcu8FS84M9vR0BjZ32vVFm7dnNw9ytXWuUgx89HWb58+fh8lkslu15KOPPsL58+fd2j8hxL9JFuh26dLF6oOtc+fO6NGjB7980qRJ6NevH7KzswEAS5Yswdy5c5GbmwuVSoVff/0VABAaGorQ0FCEhYXhnnvuwfTp09GpUyeEh4fjs88+w3vvvYcVK1YAABQKBaZPn4558+bh5ptvxt/+9jesW7cOP/zwA/Lz81vt/J0JFp5//nmcP3++1QIxwLPgz50Axt51yP0wH1Ev78RmAAtuv8PnQRPXrq+CNW9MWatUKiWfzlaKwM9Ru77UHoM/Wzyp2lC5YDSApvcB9+95YE4CQoLsfxFzp01X2urZsycAx9eS244Q0r5IXl7MnpMnTwpuaG+99Rbq6+uRmpoq2G7evHl4+eWXAQAbN25ERkYGJk6ciN9//x3h4eFYtGgRnnvuOX77qVOn4sqVK3jppZfw+++/4+abb8bOnTsxcODAVjkvwHGwMGPGDMTHx7sViEkV/Pmi54pTtrcMoxPivdKmM+36IlhzJXhxtzfe28SOQ6ogTKxdsYBn9hAjJv491er1rRmIedquN9qsrTdh+MJP+WXOnKtl1QbLzwDAcdUGsb/RkCClw79dd9p0pa1+/foBcHwtue0IIe2LXwW6paWldn+3fJRuS+/evbFmzRqH282aNUtQR7e1OQoWampqHD5usxWISRX8uUPsOogF6id/Ocsv90bQJ0Ww5s0ap1KSIvCz1a7Y38KAvtfYCIRaLxDztF0p2gSaJ+FITU2FVqtFRkYG/1QnOzsbxcXFyM/P92q6jK/b5K7lokWLsGXLFqtrmZWV5VTJNUJI2+RXga6cOAoWvvzySwBt63Gpt3quxAL1RZVdsOiv5ZaBelvqpXMmeFFF3oBht99hszceQKv09Np7KjDs9jskCcIcXT+O+k612Mvd1taCP7F/O8vljq6zp5NwhAQFujy5jbttOtOWo2tZUlLi9X8/Qoj/UDDGmNQH0RbV1NSga9euqK6uRlhYmMuvN5vNiIyMRHR0tOhN+6677oLBYIDBYBANxAwGA9RqNXQ6nVXvquWNTiwIs3WjKy0tRXx8vFtttmzf2WBM7DrwA4xsELuxufqo39H112q1qKioQFVVld0boKvtWuZAiwUvA2Z8ZPf1lQtG8+2tTgjB7+d/9WhQmq2ZqBz9G6wYUWf3PJwJiNxJz3B0/Xw5G5rYIM2IiAjk5OT4VZvuvH/EyGFmNG/8+3l6LyCE+BYFum7yxoebvZv2Rx99hGuuuQYjRoxwOxBrq8HfdYMGo7KyEstWvIrKv0bTOwrUpQqa3G3X1g132v5gu689MCeB//Jy6vWJYA1XAADh/fpYVcVwdGz2qms4Oo7jixMlCfwA29fv8ccfx/XXX+/ToKwtBH/eCnTbI1/8+1GgS4ifk7a6Wdvlyzq6XG1UTycJ8LROaWtOTCBazzbyBp9PnCFVbVpbdYpt1Vx1VDdU7N/H3r+/ozq4uR8WOFX71R9mRps/fz4LDw93uxZye0MzLNrmbr1te6iOLiH+jXp03eTNb/H2ehnayuNSb2h5HYbdfgei5zf1XvoyH1WKXjpHWvbGiuUtW/opayzf4/7t94egVCptpq4EKxVO99zXmZlfVH+wxbJXPjMz06oGsy9TGfydv1Tu8Ce+uCbUo0uIf6NA102t+eHWFh6XEu9qeUMGAP0ePe5PfhD9/7UBgHU6B5dDHT6z2O6+147p7HQu9gj1XX4bLHkr1aa9okC3GTdu4cLFesQubZo4Qz8jHj1CgwB4VsmFAl1C/Jt8P/n8iKMbkhSTBPjDxARyJjaa/Pfzv/I5uU3bCCsV2KqW0ZIr1TXcGUHfWqQoh9eW+PO/XWsTeyLCBbyAvPOWCWnvKNAlfot6lYUc1dflSqKtTghB7N2xNlMX9pd9zm/fluv5SjVxBSGEkLaj9ebRJFZq601//QhrlXLL5aywsBCRkZGIj4/HhAkTEB8fj8jISBQWFkp9aJKJjY1FeL8+GFq5Gj9ljRX05lrWr02IvxshQYGCmr5c729IUKCgHq1l/dmW+/H3AvqWtZDFtJWAnRBCiO9QoCuhIXN3YMjcHYJZy4Yv3MUvlytugFF0dDQMBgOMRiMMBgOio6ORmpoq22CXK3xfXFwMrVYruDZarRbFxcXIyclx2Ovtrf1Irb0E7L5iNptRWlqKvLw8lJaWwmw2O34RIYS0MzQYzU3eGIBA9S6t0QAjx7xVFUOq6hreJOUEEv7MXo1kOV6P5sFodYhdWgoA0M+IQ4/QpnrRNBiNkPaLAl03eePDzd0ZzNozb83O1l5xecunT5/G+fPn0bNnT/Tr18/rM6O1Je0hYPcmKrlmG5UXI0R+5BdJ+RGxD9mWI+nlhgYY2Wavl87d4LQ9VNdISUlBcnJymw/YvcFsNiMtLQ0ajUbwRCQmJgZFRUXQarVIT09HcnKyLK8PIUR+KEeX+BUaYCSO8pbt4wL28ePHIy4uTrZBHFdyLTMz02bJtWPHjkGv10t0hNLiSq4dX5wo6w4FQuSEUhfcRI+rfINydK3RNSHOysvLw4QJE2A0GhEaGmq13mg0IiwsDLm5uRg/frwERyg9b6fr0L2AEP9GPbp+gkZIN2kvFQG8Sa/X48TpM/huyLO4LnOboPQc9dIRS/RExD4qW0iI/FCg6wfow1coJSUF+fn5KC8vh1qtRlhYGNRqNSoqKmQ5kMZRPrKc85Y5tfUmqGaVQDWrRNY1qKnkmm1c+k9UVBRWrVqFd999F6tWrUJUVBSl/xDSjlGgKzHKvRSXkpKCo0ePQqfTITc3FzqdDlVVVbILcmvrTejeszcUHTpaLBNOKiL3XjrSjJ6IiOMG6Q0bNgwVFRWYPHkynnzySUyePBkVFRUYNmwY0tPTZfskjZD2jHJ03eSNvCzKvST2mM1mDJy93e42P2WNlfXfCZXoE0cl14S4soUKhcJm2TXGmFtlCylHlxD/Js+7gJ/gRkjn5eXZHCGtVquh1+vbfAko4houUMEjb9jdjuuly8/Pl12QC0B0BkHLmQblOOkKQCXXWjp9+jQCAwMxduxYm2XXtm3bhtOnT0t8pIQQb6NAV0JUM9Y+XxR3bwssC/5P03THkCFD8HX593h22x8AgFOvTwRruAIACLi2ryzzlolj7aFGsrecP38eJpPJbtm1jz76COfPn5foCAkhviKPyMFPWY6QFpsFjHIv5cey4H/uh/mIenknUPw5DswZCWz7FADQq3tX5Cxe5dGMaO1F5YLRAGynLhACAD179gTguFOB244Q0n7QYDQJ0QhpcdxAq9p6s8Uy4QCs9spewX/O2bNn0a9fP1lPjMAJCQr860dpsUzJL5czqkTRrF+/fgAcl13jtiOEtB/yvhNIjBshnZqaCq1Wi4yMDH6ARHZ2tmxzL+Wcd3nmzBkoOgTjukGDBYE+oEDlgtEwGi+i95I62aazEOIOrlNh0aJF2LJli9XA36ysLFl2KhAiBxToSoyrGZuWlga1Ws0vj4iIoNxLGerTpw8GTCvAiKWfC5ZbBvrcdqQZN7UrEVaiaF7W/P9y7Ol21KlQUlIiy04FQuSAyou5ydslZbw9LWVbJueSUfX19Rg0d6fdbc7+JxXV1dUICgpqpaMibYlqVond9XL+QuCLsmtUXowQ/9Z+I4Y2hkZINxMLZLm8y/aurKwMJ1eMg0KhwOj7k1B5w2MAgP83phveWLkc27ZvQ2P9FZSVldHfCyEuorJrhMhP+48cCGlDzpw5A9ZQh/fXr8fseQuAG5qWJ95/P/q/mIv+g5/CyRXjKEe3BXoi0owqUdhXZ2Z4fPslAGGoHHeXbP9OCJELqrrgJ2iEtDUu7/L44kRZ9OYCzbm3AwcORHn5d/zyzZs3i25Hmh5HR0ZGIj4+HhMmTEB8fDwiIyNlO302VaIghJBmFOgS4kcsS851Du6AygWjUblgNNR33slvEz5wEIbdfgd9IULz5BrR0dEwGAwwGo0wGAyIjo5GamqqbINdYo0rT3jhYj2/7MLFelmULSREzmgwmpu8NQBBzgOviDjLmdG+G/Ks3W3Xjuks28f0ZrMZkZGRiI6OFkzrCjSVjNJqtaioqEBVVZUsrw8R8tUgPRqMRoh/oyhKYnKuGeuIXPMuLUvOYYj9bePj46FSqbB8+XLZlaLjJtfIy8uzOa2rWq2GXq+ngXuEECJTlLpA/JLc8y5TUlJw9OhRrE4IwfyoGrwWH8KvG3J4HdZqumP/jLtk/ZieG5DnaFpXGrhHCCHyRT26EqMR0tYsH93n5eXxhd2zsrKQmpoqm4k0lEolRifEAwCMl+sAXdPfx4d56xHasamGbq+YGBQVFUGr1SI9PR3Jycmy6PUGmgfkVVRUICYmxmo9N62rnAfuyfWpCCGEcChH103ezsuqrTfxaQyVC0bLNjeX8i7F7dilw7O7agGI/30YDAao1WrodDrZPKanvxX7xCZHkGuaC9A8HuLCxTrELi0FAOhnxKFHaDAA98dDUI4uIf6NUheIX+HyLjMzM23mXR47dgx6vV6iI5TG7+d/xYklGlTMuUf0hizHx/TctK7FxcXQarWCqgtarRbFxcXIycmRbZCbmpqKm4begvCZxQifWQydvky2aS5Ac9k1LrAFgB6hwVR2jZB2jgJdPyHHmrFiKO9SnOVjejFyfUzPDdwrLy+HWq1GWFgY1Go1KioqZJPi0pLZbEZaWhoSExOx8YON/PIRI25DUVEREhMTkZ6eDrPZLOFREkJI65BvREX8EuVdirOsryv2mD47OxsRERGIjY2V8CilQdO6Cun1epw4fQZr3s/FFVNzZlptfVNgO21GBu69+07ZVqPgOhUIIfJAObpuorws36C8S9ssB+llZGTwg/Sys7NRXFws2x5MIrRhwwbMLu9md5sTSzRYv349Jk6c2DoH5Ue8PUCP7gWE+DdKXSB+hfIubaPH9MQZ58+f9+p27YncyxYSIkeUuuAnqAxQM8sJE9RqNb88IiJC9gEdPaYXR1VLmvXs2RO/ZDyCUaNG4d33N2BE1m4ATWULOwYq8PdH/o7TgYHo2bOnxEfaurgnIomJiZg+fTo6deqEy5cvY9u2bbIqW0iI3FDqgpu8+biKygCJo+CfOIsC3WalpaWIj4+HQqHA/UlaVAx+CgCwJvEqrFy2GMXFxWCMybIU3dVXX43ffvvN6rP26quvxoULF9xKiaLUBUL8G6UuSIzrZYiOjhY8ppdzGSCOUqlEXFwcxo8fj7i4OApy/1Jbb4JqVglUs0r42qByVVtv+uvHbLHMzC+XI27g4rBhw1DxQxW/PGH0WFRUVGDYsGGyG7jIlS08ePCg6GftwYMHZVm2kBA5kG+3hx/gygBpNBrBwKsYGc92RYgruF5cS9wMgwBkObqey3NPTU3FWE0yvv9rec6SbOz+ZBtKSkqQn58vq8+U06dPIzAwEGPHjrX5Wbtt2zacPn1a4iMlhHgb9ehKiCZHsI96LoWo95I4a4zmAWz4IB/fHznKL0uflYmKH6qw4QP55aKeP38eJpPJ7metyWSS5QA9Qto76tGVEE2OQFxBvZfWKheMBtAU8HPX4sCcBIQEyae3UkzT30owoF3ML+v/rw1gADIOAuMfkuzQfMpWrjY38M7RZ63cBugRIgfUoyshmu1KHPVcEmdx07daBrYhQUqa1pUI9OvXD4Djz1puO0JI+0FVF9zkjZG2NDmCONWsErvr5dhzCYAP8m31Xso5sKOqC0Lc34rxcj1uz9YBAF6LD0H83Woolcp2d30cvTeClQpERkYiKioKW7ZssfqsTU5Oxvfff09VFwhph9rXp10bYzloRKvV2pztSk5BLrFNLDjhei/ljqZ1FQoJCkRhYSGmzcgAUlcAALRJYxHer4/HZQvFyv4B8HkpwJbtqtVqlJWV4cyZM8j41jrAbJnWY++zVo4D9AiRC7pDSowmR7BGeZekrZGq5rOtdrmyhfcnacE9rN+9W4cVS7MdTo5gr3dcrOb3Nddcg8bGRsFALlt1wMX27UxvvFi7wcHBqKurAwCEzyx2eK3os5YQmWLELdXV1QwAq66u9sr+TCYT0+l0LDc3l+l0OmYymbyy37bsUl0DC59ZzMJnFrNLdQ1SHw5xkVR/063dbkFBAVOpVAwA/6NSqVhBQYFTr3f379xWu5s2bWIqlYolJSUxs9kseI3ZbGZJSUksIiKC1dReEW3X1vEUFBQwhULBkpKSmMFgYEajkRkMBpaYmMgUCgXLzs7mlyUlJTGFQmF1DcT27ej8bbWr0WiYQqFg69evZzp9GUt84EGm7NyN39d54xV2qa7Bap/e/vvw9r2AEOJdFOi6iT7cfE+qQLc9Bmie7Nudf4fWDv6485s6dSrr06dPq7VrKwizFeh5o01n2gXADAaD6GvLysoYALZ9525Bu9zPeeMVq2CxpvaKU8Ez93fVcpnYvk9euMjOGy+zkxcu2QxOTSaT0+2azWaW+MCDDq+ltz9X6F5AiH+j1AXit0KCAvHjojHQ6/XYUrCpVR4Jezods7uDolxt13i5DtHzm9I6VieEICH+bpvXpbWnmOYem2s0GuTl5fG5kFlZWQ4fm7vbnuX5aTQazJ492+ftejrhi+UAquZlzf9v62/HUbvJycnYvn07Bg8eLPr66wYNhqJDME7+chZAF75dy5xWjuWyE8ePIy8vz2YdWrVaDb1ej7i4OKtlj2+/ZLXv2KWldts7vjiRrzXubLtp6el4ouQP0fMmhMgTlRfzIzRBglBhYSEiIyMRHx+PCRMmID4+HpGRkU5Pi+zq9ZRqOmZX2y0sLER09FD+97H3j7V5XTw5J3fKvLUMwmJiYhAaGsoHYRqNBunp6TCbzaKvd7VN7vyioqLQt29fJCUlYcuWLT5vF/B8wpchc3dgyNwdguBu+MJd/HJbHLWbmZkJk8mEDRs2iL7+9mV7MWBaARZVdhG06wxXan57ow64q7XGh/8tGieWaJB9c43VFwUqW0iIPFGPLvFLrd0r6K+9cy3bzdtUiEcfnYjR9yeh8q99bNu5G6+vyMFD4ydiU94G/rp4ek7uTFDhag+cJ21ant/UqVMxcuRIFBQU+LxdjlQTvjjb7jvvvIPnnnvOqpSWIwfmJFgNAtXv0WPMkqZ6szExMVavEav5bbmsctxdAIQ9x/oZcegUpMTl+kbELtUJ2uNY1hp3pl3L31s+XaEJVwiRJwp0/YC7QVJ7JUXQ2ZoBmrvtxsbGIuNgMPq/lM8HuQDw7LY/gMFPof/gp5Ce/i/+unh6Tu5ozeDP8vyOHTvWau1yXA3CWnK3uoiz7X711VeipbRO7diJ9es34L6xiQ7b5crXJcTfDZVKhaysLNGa39nZ2YiIiOBLjbVcJvY+7REazFddaNkeJzY21ul2W/5eZ6YS8YQQCnT9AvU0CEkRdPp779yZM2dsPgK3xD0qj4uL8/ic3AnEWjP4szy/K1eutFq7HFeCMDHu1kV2tt0lS5ZgxowZVqW0uF5/WwGm2GN8ezW/Fy1ahI8//hiLFi1CbW2tV+uA22s3KysLJSUleP/997F//36+zQ0f5KPOzKy+6B6YM/Kv3xRUtpAQGaFAl/gdKYJOf++d69OnD86cOYOTK57Ar7+eRUBQR6t2jMaL6L2k+bp4ek7uBGKtGfxZnp8UQadUE744225KSgpSUlJcru9ra/INW3Voe/fujauvvhqZmZnIzMwEYLs2rdi+HU32Yavd4OBgMMbw6KOPCtqctj8YGQeFX3Ytv+hy79WmtmnCFULaPanLPrRV3iwpY6+sjxzrx+7atcup8ki7du0SXe/O9XSljJE97pTFcrZdnU7HXxexdrjrotPpJD0ny9JXZWVlrKamhpWVlXm95FbL82utdsXOt2UptYiICKdLmrlLqnbFStW1Rkm+lm3U1dWJtsn9+9n6ofJihMgLBbpu8sWHG02Q0GTXrl0sMDCQaTQa0QBNo9GwwMBAm4Eux18DNHfbtQzsjJfrBO3YCly9cU7uaK0grOX5rV+/nvXt29cvgr/WQBPNWHPmi643rxsFuoT4N78JdLOzsxkANmXKFJvb/Pe//2V33XUX69atG+vWrRsbOXIk27dvn2Abo9HIJk+ezPr168c6duzIBg8ezN566y3R/TU2NrIxY8YwAGzz5s0uHS8Fur6Tm5vLANgN0ACw3Nxcu/tpj71z7gSu/tTz5wti59e7d282depUCv5kzN4Mb55MZtISBbqE+De/CHT379/PVCoVGzp0qN1Ad8KECWzVqlXs66+/ZocOHWKPP/4469q1K/v555/5bZ5++mk2cOBAptPp2LFjx9jq1auZUqlkW7ZssdrfihUr2NixY/0m0CVNuEf02dnZogFaVlaW4BG9t/l775w7gWt77/lr7+dHXCcW6HpjJruW6F5AiH9TMMYkrcFy8eJF3HrrrXjzzTexcOFC/O1vf8Orr77q1GvNZjOuuuoqvPHGG5g0aRKApoFKjzzyCP7973/z2w0bNgxjx47FwoUL+WXffPMNNBoNDhw4gD59+mDz5s3QarVOH3dNTQ26du2K6upqhIWFOf064pjZbEZkZCSio6NRUFCAvXv38oNp7rzzTowbNw4VFRWoqqry6Sxp/sxsNrs8yIgQObP8XBEbsKjVat36XKF7ASH+TfKZ0SZPnozExEQkJCS4/Nra2lo0NDSge/fu/DK1Wo2tW7fi9OnTYIxBp9PhyJEjGDVqlOB1EyZMwKpVq9C7d2+n2qqrq0NNTY3gh/gGN6q8uLgY48aNQ3BwMDQaDYKDgzFu3DgUFxcjJydH1oGdUqlEXFwcxo8fj7i4OFlfCw7NLEjs8XQmO0JI2yRpXZWNGzfiq6++wpdffunW62fOnIm+ffsKguTXX38dzzzzDK699loEBgYiICAAb7/9Nu6++25+m5deeglqtRrJyclOt5WdnY358+e7dZzOol66ZrZKCtkqW0QIIfZIVSubECItyQLdU6dOYcqUKdi5cyc6duzo8usXL16MjRs3orS0VPD6119/HV988QW2bt2K8PBw7NmzB5MnT+YD4q1bt2L37t34+uuvXWovIyMD06ZN43+vqalB//79XT5uWwoLC5GWlobjx4/zy1QqFZYvXy7boC4lJQX3jU1E9PymGpirE0KQEH+3bIN/Sy2nN5VzLVCaWZA4o1evXgAc15XmtiOEtA+S3QEOHjyIc+fO4dZbb+WXmc1m7NmzB2+88Qbq6upsBjQ5OTlYvHgxdu3ahaFDh/LLL1++jMzMTGzevBmJiU0FyIcOHYpvvvkGOTk5SEhIwO7du/Hjjz+iW7dugn2OGzcOsbGxKC0tFW0zODgYwcHBnp20DYWFhUhNTYVGo0FeXp5g5p/U1FRZ92Ba/g3E3i3fHm5iG80sSJwVGBiIRYsWYcuWLVY5ullZWQgMpC9FhLQ3kr2rR44cifLycsGyJ554AjfeeCNmzpxpM6BZunQpFi1ahB07dmD48OGCdQ0NDWhoaLDKv1IqlWhsbAQAzJo1C//4xz8E66Ojo7Fy5UokJSV5elouM5vNSEtLg0ajEQyQiImJQVFREbRaLdLT05GcnCyrII966cTRdSHEPefOnYPJZEJJSYnojHIlJSVgjOHcuXNSHyohxIskuyt26dLFKleqc+fO6NGjB7980qRJ6NevH7KzswEAS5Yswdy5c5GbmwuVSoVff/0VABAaGorQ0FCEhYXhnnvuwfTp09GpUyeEh4fjs88+w3vvvYcVK1YAaJquUmwA2oABAxAREeHLUxbFDZDIy8uzOUBCrVZDr9cjLi6u1Y9PKtRLJ46uizV3p18m8sJNGZ2VlYXVq1db5f4vWrQImZmZNqfEJoS0TX7d/XPy5ElB8PfWW2+hvr4eqampgu3mzZuHl19+GUDTALeMjAxMnDgRv//+O8LDw7Fo0SI899xzrXnoTqMBEoR4RqwXOyRISb3bRCA2NhYqlQplZWU4cuSIaNnCiIgIxMbGSn2ohBAv8qs7Qcv82Ja/Ww7UsqV3795Ys2aNS+1KWUqY6z1wNEBCbr0MXC/dhYt1iF1aCgDQz4hDj1Df5Em3FdR7SYh7uLKFqampGDduHDIyMqDRaFBRUcGXLczPz5dVihghcuBXga4ccb0MWVlZokXMs7OzZd7LoLDx//JEvZe2hQQFyjJ1gziPyhYSIj90d5SYZS+D2AAJufYyiOWixi7V8f9PAQ0hxB0pKSlITk6mmuWEyAQFun6AehmIq6j30hpNuEKcVWdmeHz7JQBhqBx3F/2dENKOUaDrJ6iXQYhyUYkraMIVQgghYgIcb0Jai1KpRFxcHMaPH4+4uDjZBrlAU49l04/SYpmSX04Ih5twJTo6GgaDAUajEQaDAdHR0UhNTUVhYaHUh0j8RG296a8fYR1qbjkhpP1RMClLDrRhNTU16Nq1K6qrqxEWFib14bRbxst1NAWwCHpM38RsNiMyMhLR0dGigzm1Wi0qKipQVVUly+tDhFSzSuyudycdiO4FhPg36tH1I7X1JqhmlUA1q4R6F9DUUzd0yI04sUSDE0s0GHPfvYiMjJR9D11hYSEiIyMRHx+PCRMmID4+XrbXhZtwJTMz0+aEK8eOHYNer5foCAkhhEiJAl3il+hxtDi6LkI04QpxxYE5I3Fgzkh88tLd/LJPXrqbX04IaX8odcFN3nxcxfXe2hp4JbecVHocLY6ui7XS0lLEx8fDYDCITrhiMBigVquh0+lkNYU2EUepC4TIDwW6bvLmh5svPnzbMgpexNF1sUbBP3EFBbqEyI+8ugpJm0CPo8XRdbFGE64QV1DZQkLkhwJdP0AfvkJ9+vQBAFRUVIj2XFZUVAi2kwu6LuJowhXiLJpCmxD5ocFofoBqxgqp1WoEBwdj0aJFaGxsFKxrbGxEVlYWOnbsKAhq5CA2NhYqlQpZWVmi1yU7OxsRERGIjY2V6Ailk5KSgqNHj0Kn0yE3Nxc6nQ5VVVUU5BJCiMxRoEv8TllZGerq6lBSUgKtViuoLqDValFSUoIrV66grKxM6kNtVdxj+uLiYtHrUlxcjJycHNk+pqcJV4izuCm0jy9OlGVnAiFyQu9wP8J9+Modl2P6/vvvY86cOVaPo99//308+uijsspF5dBjekI8RxOuECIfFOgSv8PlmA4cOBBHjx61uiHt379fsJ3cpKSkIDk5mW7ULdTWmzBk7g4ATXnv1FNHxBQWFiItLQ3Hjx/nl6lUKixfvpy+KBLSDlHqAvE7lrmoCoVC8DhaoVDIOheVQ4/pCXEdTbhCiPxQoEv8DuWiElfU1pv++jFbLDPzywkBmtIV0tLSkJiYiKKiIsTExCA0NBQxMTEoKipCYmIi0tPTYTabHe+MENJm0IQRbvJFkXDKGxPiHjGeOH0GA6YVAAAUBWlYviRL9o8Y6TF9M5pwhTjDVxOu0IQRhPg36tH1E4WFhYiMjER8fDwmTJiA+Ph4REZGyvpRGlcyatvH2/hl3333reyDXEKI606fPg3A8YQr3HaEkPaBAl0/QHlj4mrrTagzMwyLaa4uUGeGrB9J02N6a5ULRqNywWgcmJPALzswJ4FfTggAnD9/HkDzxCotccu57Qgh7YN8n3f6CS5vTKPRoKioCAEBTd89uLwxrVaL9PR0JCcnyy6NgXs0b4mbOQ6Q5yNpuibWaLYr4oyePXsiMDAQWVlZgs9aoHnClcDAQPTs2VPCoySEeBv16EpMr9fj+PHjyMzMFHzwAkBAQAAyMjJw7Ngx6PV6iY6QEELavn79+sFkMtkd5GoymdCvXz+pD5UQ4kXU5SExbtIDR3ljcpwcgXvsfOFiHWKXlgIA9DPi0CM0WMKjkhZ3TWrrzXxP7oE5CYLpo+WKJlwh9nBlC6+++mqUl5dbTbgybNgwXLhwQdZlCwlpjyjQlRg36UFFRYXoSGAub0yukyM0Udj4f/mhx/SEuIcrW5iamsqXEuvUqRMuX76M7du3o6SkBPn5+bJLESOkvaPyYm7yVkkZs9mMyMhIREdHi+aNabVaVFRUoKqqSnYfwFQ2yjYqL0aIe/I2FSLjoPCpkCdlC6m8GCH+je6OErPsZdBqtcjIyEBUVBQqKiqQnZ2N4uJi6mUgVugxvTWqQ02ckZz8ADIOCgd1fvfdt+jSSb4pUYS0Z9Sj6yZvf4sXm389IiICOTk5sq0by5XLspWPSr2YhCP2/lGpVFi+fLls3z9EiPs8scz55zxx9TFMee4pBAV1cPlzhXp0CfFvFOi6iWZGaz30mJ7Yw9Wh1mg0yMzM5J+IZGVl8U9EKNgljlKhOK4+KaFAlxD/RuXF/IhSqcQI9V3I+DYMj2+/hDozfQch4sxmM0pLS5GXl4fS0lKYzWbHL2qHWtahjomJQWhoKF+HWqPRID09XbbXh7huxowZUh8CIcSLKNAlfi9YqcDaMZ2RfXMN9pd9LvughaaLbkZ1qImzKheMxjdz7sW59elW69Y9eRs+S78HN/ywHq+//jrq6+slOEJCiC9QoOsnaGpXcRTUCdF00UJUh5o4KyQoEGv/txq9Hs2xWvfYu1/inpzPcPjGR3HlyhW8+eabEhwhIcQXKNnRT9DUrta4oO7+JC3YI28AANZqumPF0mykpqbKLveSpotuYpmzvTqhNwCqQ02c8+OPPwKdr3duO0JIu0A9usQvWQZ1Gz/YyC8fMeI22eZe0mN6a+o71VCpVMjKykJjY6NgXWNjI7KzsxEREUGzXREAwMCBA/Hzm0/gtYRuyPvHbfzyjNs6ojTtbvxnZFd+O0JI+0A9un6CpnYV0uv1OHH6DNa8n4srpuZBeVxqx0vTZ+Heu++EXq9HXFycREfZulx9TN/eqlVYlpvj1JmBrKXL8eijE6kOdRsgZWUZs9mMm266Cdf+cw1e3PUngC/5ddlfXkH2l3sAAB07dsQ///nPVjkmQojvte07XztCU7sKnTlzBgOmFeDx4t+B4uYUDst0Dm47uZD7dNG203uC0f+lfJR/8ALUajW/LiIiok2kt0gR/EnRpqe1jj354mbZdvjMYrvbjh49GkFBQU7vmxDi3yh1gfilXr16eXW79iA2Ntapx/TDbr/D5wMbfV3ezJ39L1y4ECtXrsT69euh0+lQVVXl90GuJ4Mta+tNUM0qgWpWiUv/rp4O8HSnXSkHUbZse/+Mu7BW0x1DDq/jtzkwJwEVL9+Hmyr/h++++05WKVGEtHuMuKW6upoBYNXV1VIfSru0a9cu1qFjZ3Z/kpadra5l4TOLWfjMYnbeeIUZL9ex+5O0LDAwkO3atUvqQxVlMpmYTqdjubm5TKfTMZPJ5JX9FhQUMIVCwZKSklhZWRmrqalhZWVlTKPRMIVCwQoKCvhrZevHmWO8VNfAb19Te0Ww3aZNm5hKpWIA+B+VSsUKCgpsXoPtO3fz+7tU1+DwHMX2n/thAbtU18DOG6/w+1q7sYCFDxzEFB2C7R6LP7L8tzQYDMxoNDKDwcCSkpL4f0t7LP+NHF1Tb7XpTrsmk4mpVCqWlJTEzGazYJ3ZbGZJSUksIiLC5nvkUl2D1b/7eeMVfrm7bRsv1wn+xhljrKysjAFgOp3O4Xlx6F5AiH+T53Nx4hfsPT49d+4cGq5cwrbiLXjy/yYCg58CAJR/fRArly3GtuJiMMZw7tw5t9vwFXce0Tr7WDYlJQX5+fn45z//KXhMHxgYiKuvvtqp4+NniMqfhhM/HnF4jNHRQwXbAYBGo0FeXh6uGzQYI5Z+DgbgofGp2JS3ASkpKVbXQNEhGAOmFTg8trxNhcg4GAw88gZ0mu4Y/rdofpaziY80VdoYo3mA3/7JSROROPo+bFy/TjAjWmtX5XD1sbonFTTEcpUt/99W255W7XC3XW4QZV5ens1BlGq12ma+vScVaRy1zSnbW4bRCfFUjo6QdohSF/yQFLNeedKmu48y7T0+5fJMs7KyUPH99/zrEkaPxXdDnsWAGR9B0SHYbj5qe35Ee/bsWdxxxx2YP38+Pv74Y5SWliImJgapqanIHlaHygWjcWBOAr/9gTkJfy0byS+Luukmq2N8aPxE5G0qFAQwQ6Jvhk5fhl/OXUDfvn2h0WiwZcuWv2Yh68xvN3bMWKSnp2PTpk38NdDpy3D2wp/YtnM3v92mzVtF0yjMZjMyMjL430eMuM3hLGdjx4x1akY0Z/8tW+u950kFjSFzd2DI3B2CYG/4wl38cl+06Um7UtY6ttd2SFAgymffjRNLNPj9/K8A2n+eOyFyRIGun2mLOXuuciYY5PJRy8rKUHWogp8Z7eOtRfx+wsNVNstGSZET6M50tK5OFMK1kZSUhM8//xxz587F2LFjceedd/JtzJ6ZjmClokXFDvbX65tze999fwOG3jocAUEd+WPs/1I+Mg4GC4KZyhsew+PFv+OO5Qb88ssvmD17Nq6YGq2O+4WX0nD851/wr5ear8Hjxb9jxNLP8ey2P/jt5n0dbBUc1dabsEu3B6fOnBW9DpaB2MF9Bqwd0xknlmgwJ2OG10qtufM+cHeiFymCP6kCTstBlGIcBZeVC0bb/OLGVavxRttUjo6Q9olSF/wIF5xxj4Vb41GsJ2268yjTlceny5cvR2pqKsaNG9dUTmzUWHxd/j2AWgDAywuzUGdmgNkkaKstPaJ19bGsK22MUN9lsc9PrdoZkdXcy3p8caLV/myJiooSPe7ntv/Jpydkans4vT+g+Tr0/9cGi2MWXgexQMxR0Hbi5zOigSiH+7d0933g7mN1TypouFuK0NOqHe62azmI0vL9CDhX69iTijSO2s7KyoJKpUJgYCC0Wi2VoyOkHaJA10+0xZw9d27yrgRqXD5qWloanij5AyjZK9h+3tfBmPf1Dqu2pMoJbI0eM1faCAkK5I+Vz8t1YN/0O9GnT2/849nJ+KTzvQCagxn9Hj3GLLHdOyZ2HGLB0anXJ+Ld1W/hoYcfcuqYOGKBmKOgbV5FGOZVCP89W/5bSjHjnCfBn7uBn1QBp1Kp5L+0tnatY3ttL1q0CCUlTe+L2NjYNlOOjhDiGgp0/YQnwZm7gZmnAaE7XA0GU1JSkJycjIGzt/usDW9xp8fM1V4yd3vluHZ26fR4UVdrs52fjhwCa6jD9RED8Mlf4/y4YCYh/m4+UKr4MB8BAQGC4x5yeB2+/epLnDlzBhXappxhsSCINVxB+LV9/n979x4UVfn/Afx9dnEXXAVFZRAEFE0oBSLxlhY6kTfEsYs6kylpZSreRktMK0tSs7yU99QJNcO0vIyZ5gU1R7RJDS/lCBreBiWrQRek1lg+vz/6sV9WQGF3z+6yvV8z+8ee85zneR/g7H72cM6zVuvOzeoNs9mMqNg4YOAHlfJVVYjVpGiznoStavYcB7ae5XRF8efKgrPih1Zb5zqu+MHNUWO/9957eOihh5z+5RVE5ESunvahrnL0lDIZGRkCQIqKiqpcbzQaBYBkZGRUWlfT6aQcOaaIbdP+HDx4UADIsWPHqlxf3fQ+tRnL1jHs2S8R+6ZRqumUTfZO1WQs+d8+Ff1lqnb7wqKSKtuVT0/Vv39/OXr0qBT8UWhpp9F5W6Yeuzdfxf1r2Sai2nwZm/83PdqBw1mW6dOqmv6quqnWKratye/S3uPg3v2r6VRf5ftw71RqrVq1UnV6NFeMWU6tafdcOTanFyNybyx0beToFzd7ijNbCzN7C8KK49f0Td4Rc2o+aCx7x7Blv8rVpPiydyxbx7h3nMQBz9x3++rGiYuLEy8vLwEgSj29pb+Mzfffrrb5Ks6NW10hVtOi7X4/X0ccB7YWuiKuKf5cWXB6Gha6RO6Nha6NHP3i5ojizNkTuds6rqMKofuNZc8Ytu5XxbHVPmNm7xg13b66dps3b75voWRvvtoUYjVpe7/fpaOOA/pvYqFL5N5Y6NpIjRc3e4szZ56BtFddKAbt4YwzZvaOUdPtbR2nLp01dNVxQHUfC10i96aIiDjsgt//EKPRCD8/P9y+fRu+vr4O67eqb9Vq1aoV5s+fr9rdwK4YE3DOt5a54pvRqG5y1XFAdZta7wVE5BgsdG2k5oubK4ozFoREPA6o9ljoErk3Fro24osbERHxvYDIvfErgImIiIjII7HQJSIiIiKPxEKXiIiIiDwSC10iIiIi8kgsdImIiIjII7HQJSIiIiKPxEKXiIiIiDwSC10iIiIi8kgsdImIiIjII7HQJSIiIiKP5OXqAHVV+TcnG41GFychIiJXKX8PKH9PICL3wkLXRkVFRQCAkJAQFychIiJX+/PPP+Hn5+fqGER0D0X4MdQmZWVluH79Oho2bAhFUVwdp1pGoxEhISG4du0afH19XR3nvphVHcyqnrqUl1nVcfv2bYSGhqKwsBCNGjVydRwiugfP6NpIo9GgRYsWro5RY76+vm7/hlGOWdXBrOqpS3mZVR0aDW95IXJHPDKJiIiIyCOx0CUiIiIij8RC18Pp9XrMnDkTer3e1VEeiFnVwazqqUt5mVUddSkr0X8Rb0YjIiIiIo/EM7pERERE5JFY6BIRERGRR2KhS0REREQeiYUuEREREXkkFrp1TH5+Pl588UU0adIEPj4+iIqKwokTJ6ptf+TIEXTr1s3SPjIyEosWLaq2/QcffABFUTBp0iS3zPruu+9CURSrR2RkpFtmtaVfV2Vt2bJlpZ+roihISUlxu6xmsxlvv/02WrVqBR8fH7Ru3RppaWmw975aNbIWFRVh0qRJCAsLg4+PDx5//HEcP37crpy2ZK0oKysLXl5eePTRRyutW7ZsGVq2bAlvb2907twZP/74o1tmPXz4MJKSkhAUFARFUbB9+3a7c6qVde7cuejYsSMaNmyIgIAADBw4EDk5OQ7JS0QPxm9Gq0MKCwvRrVs39OzZE7t370azZs1w4cIFNG7cuNptDAYDxo0bh+joaBgMBhw5cgSvvfYaDAYDRo0aZdX2+PHj+PTTTxEdHe3WWdu1a4f9+/dbnnt52fdnrFZWW/p1Vdbjx4/DbDZbtvn555/x9NNPY9CgQW6Xdd68eVixYgXWrVuHdu3a4cSJExgxYgT8/PwwYcIEt8r6yiuv4Oeff8bnn3+OoKAgbNiwAQkJCTh37hyCg4OdlrXcrVu3MHz4cDz11FP47bffrNZt2rQJkydPxsqVK9G5c2d8/PHH6N27N3JychAQEOBWWe/cuYOYmBiMHDkSzz77rE3ZnJX1+++/R0pKCjp27IjS0lJMnz4dvXr1wrlz52AwGBySnYjuQ6jOSE1Nle7du9vdzzPPPCMvvvii1bKioiJ56KGHZN++fRIfHy8TJ060awy1ss6cOVNiYmLs7rcitbI6qt+K1PwbqGjixInSunVrKSsrs3kMtbImJibKyJEjrdo8++yzMnToUJvHUCNrSUmJaLVa2blzp1Wbxx57TGbMmGHzGPZkHTJkiLz11ltVHkedOnWSlJQUy3Oz2SxBQUEyd+5ct8taEQDZtm2bbQErcEZWEZGbN28KAPn+++9tGouIaoeXLtQhO3bsQFxcHAYNGoSAgADExsZi9erVteojOzsbR48eRXx8vNXylJQUJCYmIiEhwe2zXrhwAUFBQQgPD8fQoUNx9epVt8zqiH6dlbWiu3fvYsOGDRg5ciQURXG7rI8//jgyMzORm5sLADh9+jSOHDmCvn37ulXW0tJSmM1meHt7W7Xz8fHBkSNHnJ41PT0deXl5mDlzZqV1d+/excmTJ62Of41Gg4SEBBw7dsytsqrFWVlv374NAPD397crLxHVkKsrbao5vV4ver1e3nzzTfnpp5/k008/FW9vb1m7du0Dtw0ODhadTicajUZmzZpltW7jxo3Svn17+euvv0REHHJGV62su3btks2bN8vp06flu+++k65du0poaKgYjUa3y2pPv87OWtGmTZtEq9VKfn6+zTnVzGo2myU1NVUURREvLy9RFEXmzJnjllm7du0q8fHxkp+fL6WlpfL555+LRqORtm3bOjVrbm6uBAQESE5OjohU/s9Ifn6+AJCjR49abffGG29Ip06d3CrrveCgM7rOyGo2myUxMVG6detmd14iqhkWunVIvXr1pGvXrlbLxo8fL126dHngtnl5eXLmzBlZtWqV+Pv7S0ZGhoiIXL16VQICAuT06dOWto4odNXIWpXCwkLx9fWVNWvWuF1We/p1dtaKevXqJf3797c5o9pZN27cKC1atJCNGzfKmTNnZP369eLv72/XBwi1sl68eFGefPJJASBarVY6duwoQ4cOlcjISKdlLS0tlbi4OFmxYoVlmbMKXTWy3stRha4zso4ePVrCwsLk2rVrduclopphoVuHhIaGyssvv2y1bPny5RIUFFSrftLS0ixnlLZt22Z5Ey5/ABBFUUSr1UppaanbZK1OXFycTJs2rdYZy6mV1VH9OiNrucuXL4tGo5Ht27fbnLGcWllbtGghS5curdQmIiLC7bKWKy4uluvXr4uIyODBg6Vfv35Oy1pYWFjpGFcUxbIsMzNTTCaTaLXaSgXj8OHDZcCAAW6V9V6OKnTVzpqSkiItWrSQvLw8u7MSUc1x1oU6pFu3bpWmpcnNzUVYWFit+ikrK4PJZAIAPPXUUzh79qzV+hEjRiAyMhKpqanQarVuk7UqxcXF+PXXXzFs2DCbcgLqZXVUv87IWi49PR0BAQFITEy0OWM5tbKWlJRAo7G+vUCr1aKsrMztspYzGAwwGAwoLCzEnj178OGHHzotq6+vb6VjfPny5Thw4AC+/vprtGrVCjqdDh06dEBmZiYGDhxo2ZfMzEyMGzfOrbKqRa2sIoLx48dj27ZtOHTokKr7QERVcHWlTTX3448/ipeXl8yePVsuXLggX3zxhdSvX182bNhgaTNt2jQZNmyY5fnSpUtlx44dkpubK7m5ubJmzRpp2LDhfe/6dsSlC2plnTJlihw6dEguXbokWVlZkpCQIE2bNpWbN2+6Xdaa9OsuWUX+vX4wNDRUUlNTbc7njKzJyckSHBwsO3fulEuXLsnWrVuladOmMnXqVLfL+t1338nu3bslLy9P9u7dKzExMdK5c2e5e/euU7Peq6p/sX/55Zei1+tl7dq1cu7cORk1apQ0atRICgoK3C5rUVGRZGdnS3Z2tgCQhQsXSnZ2tly5csXtso4ZM0b8/Pzk0KFDcuPGDcujpKTE5qxEVHMsdOuYb775Rtq3by96vV4iIyNl1apVVuuTk5MlPj7e8nzx4sXSrl07qV+/vvj6+kpsbKwsX75czGZztWM4otBVK+uQIUOkefPmotPpJDg4WIYMGSIXL150y6w16dedsu7Zs0cAWG6scQQ1shqNRpk4caKEhoaKt7e3hIeHy4wZM8RkMrld1k2bNkl4eLjodDoJDAyUlJQUuXXrll05bcl6r+quJV2yZImEhoaKTqeTTp06yQ8//OCWWQ8ePCgAKj2Sk5PdLmtVOQFIenq6XVmJqGYUETu/ToiIiIiIyA1xHl0iIiIi8kgsdImIiIjII7HQJSIiIiKPxEKXiIiIiDwSC10iIiIi8kgsdImIiIjII7HQJSIiIiKPxEKXiByqR48emDRpkqtjEFkcPnwYSUlJCAoKgqIo2L59e622v3z5MhRFqfT44Ycf1AlMRA7DQpfIg7300ktVvkH36dPH7r4PHToERVFw69Ytq+Vbt25FWlqa3f0TOcqdO3cQExODZcuW2dXP/v37cePGDcujQ4cODkpIRGrxcnUAIlJXnz59kJ6ebrVMr9erNp6/v79qfRPZom/fvujbt2+1600mE2bMmIGNGzfi1q1baN++PebNm4cePXpYtWvSpAkCAwNVTktEjsQzukQeTq/XIzAw0OrRuHFjAMDChQsRFRUFg8GAkJAQjB07FsXFxZZtr1y5gqSkJDRu3BgGgwHt2rXDrl27cPnyZfTs2RMA0LhxYyiKgpdeeglA5UsXWrZsiTlz5mDkyJFo2LAhQkNDsWrVKquMR48exaOPPgpvb2/ExcVh+/btUBQFp06dUvVnQwQA48aNw7Fjx/Dll1/izJkzGDRoEPr06YMLFy5YtRswYAACAgLQvXt37Nixw0Vpiag2WOgS/YdpNBosXrwYv/zyC9atW4cDBw5g6tSplvUpKSkwmUw4fPgwzp49i3nz5qFBgwYICQnBli1bAAA5OTm4ceMGPvnkk2rHWbBgAeLi4pCdnY2xY8dizJgxyMnJAQAYjUYkJSUhKioKP/30E9LS0pCamqrujhP9v6tXryI9PR1fffUVnnjiCbRu3Rqvv/46unfvbvlPSIMGDbBgwQJ89dVX+Pbbb9G9e3cMHDiQxS5RHcBLF4g83M6dO9GgQQOrZdOnT8f06dMrnXl9//33MXr0aCxfvhzAv0XAc889h6ioKABAeHi4pX35JQoBAQFo1KjRfTP069cPY8eOBQCkpqZi0aJFOHjwICIiIpCRkQFFUbB69Wp4e3vjkUceQX5+Pl599VV7d53ogc6ePQuz2Yy2bdtaLTeZTGjSpAkAoGnTppg8ebJlXceOHXH9+nV89NFHGDBggFPzElHtsNAl8nA9e/bEihUrrJaVF6n79+/H3Llzcf78eRiNRpSWluLvv/9GSUkJ6tevjwkTJmDMmDHYu3cvEhIS8NxzzyE6OrrWGSpuoygKAgMDcfPmTQD/nhGOjo6Gt7e3pU2nTp1s2VWiWisuLoZWq8XJkyeh1Wqt1t37AbGizp07Y9++fWrHIyI78dIFIg9nMBjQpk0bq4e/vz8uX76M/v37Izo6Glu2bMHJkyctd6XfvXsXAPDKK68gLy8Pw4YNw9mzZxEXF4clS5bUOkO9evWsniuKgrKyMvt3jshOsbGxMJvNuHnzZqXj5H43np06dQrNmzd3YlIisgXP6BL9R508eRJlZWVYsGABNJp/P/Nu3ry5UruQkBCMHj0ao0ePxptvvonVq1dj/Pjx0Ol0AACz2WxXjoiICGzYsAEmk8kyG8Tx48ft6pOoouLiYly8eNHy/NKlSzh16hT8/f3Rtm1bDB06FMOHD8eCBQsQGxuL33//HZmZmYiOjkZiYiLWrVsHnU6H2NhYAP9OoffZZ59hzZo1rtolIqohFrpEHs5kMqGgoMBqmZeXF9q0aYN//vkHS5YsQVJSErKysrBy5UqrdpMmTULfvn3Rtm1bFBYW4uDBg3j44YcBAGFhYVAUBTt37kS/fv3g4+Nz33/1VueFF17AjBkzMGrUKEybNg1Xr17F/PnzAfx75pfIXidOnLDMEgLAcr1tcnIy1q5di/T0dLz//vuYMmUK8vPz0bRpU3Tp0gX9+/e3bJOWloYrV67Ay8sLkZGR2LRpE55//nmn7wsR1ZIQkcdKTk4WAJUeERERIiKycOFCad68ufj4+Ejv3r1l/fr1AkAKCwtFRGTcuHHSunVr0ev10qxZMxk2bJj88ccflv5nzZolgYGBoiiKJCcni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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Define columns names</span>
-<span class="n">counts_colums</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;N1Cts&#39;</span><span class="p">,</span> <span class="s1">&#39;N2Cts&#39;</span><span class="p">,</span> <span class="s1">&#39;N3Cts&#39;</span><span class="p">,</span><span class="s1">&#39;N4Cts&#39;</span><span class="p">,</span> <span class="s1">&#39;N5Cts&#39;</span><span class="p">,</span> <span class="s1">&#39;N6Cts&#39;</span><span class="p">,</span> <span class="s1">&#39;N7Cts&#39;</span><span class="p">,</span> <span class="s1">&#39;N8Cts&#39;</span><span class="p">]</span>
-<span class="n">cont_times_col</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;N1ETsec&#39;</span><span class="p">,</span> <span class="s1">&#39;N1ETsec&#39;</span><span class="p">,</span> <span class="s1">&#39;N3ETsec&#39;</span><span class="p">,</span><span class="s1">&#39;N3ETsec&#39;</span><span class="p">,</span> <span class="s1">&#39;N5ETsec&#39;</span><span class="p">,</span> <span class="s1">&#39;N5ETsec&#39;</span><span class="p">,</span> <span class="s1">&#39;N7ETsec&#39;</span><span class="p">,</span> <span class="s1">&#39;N7ETsec&#39;</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Define columns names</span>
+<span class="n">counts_colums</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'N1Cts'</span><span class="p">,</span> <span class="s1">'N2Cts'</span><span class="p">,</span> <span class="s1">'N3Cts'</span><span class="p">,</span><span class="s1">'N4Cts'</span><span class="p">,</span> <span class="s1">'N5Cts'</span><span class="p">,</span> <span class="s1">'N6Cts'</span><span class="p">,</span> <span class="s1">'N7Cts'</span><span class="p">,</span> <span class="s1">'N8Cts'</span><span class="p">]</span>
+<span class="n">cont_times_col</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'N1ETsec'</span><span class="p">,</span> <span class="s1">'N1ETsec'</span><span class="p">,</span> <span class="s1">'N3ETsec'</span><span class="p">,</span><span class="s1">'N3ETsec'</span><span class="p">,</span> <span class="s1">'N5ETsec'</span><span class="p">,</span> <span class="s1">'N5ETsec'</span><span class="p">,</span> <span class="s1">'N7ETsec'</span><span class="p">,</span> <span class="s1">'N7ETsec'</span><span class="p">]</span>
 
 <span class="c1"># Compute total neutron counts</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;total_raw_counts&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">total_raw_counts</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">counts_colums</span><span class="p">])</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'total_raw_counts'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">total_raw_counts</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">counts_colums</span><span class="p">])</span>
 </pre></div>
-<div id="cell-4" class="clipboard-copy-txt"># Define columns names
+<div class="clipboard-copy-txt" id="cell-4"># Define columns names
 counts_colums = ['N1Cts', 'N2Cts', 'N3Cts','N4Cts', 'N5Cts', 'N6Cts', 'N7Cts', 'N8Cts']
 cont_times_col = ['N1ETsec', 'N1ETsec', 'N3ETsec','N3ETsec', 'N5ETsec', 'N5ETsec', 'N7ETsec', 'N7ETsec']
 
 # Compute total neutron counts
 df['total_raw_counts'] = crnpy.total_raw_counts(df[counts_colums])
 </div>
-        
-        </div>
-    </div>
-
 </div>
 </div>
-
 </div>
 </div>
-<div id="cell-id=03ac5454-ea0a-495c-8364-1a92438ec270" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=03ac5454-ea0a-495c-8364-1a92438ec270">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h3 id="neutron-count-correction">Neutron count correction<a class="anchor-link" href="#neutron-count-correction">&#182;</a></h3><h4 id="incommng-neutron-flux">Incommng neutron flux<a class="anchor-link" href="#incommng-neutron-flux">&#182;</a></h4><p>Find stations with a cutoff rigidity similar to the estimated value based on the latitude and longitude, ensuring that the reference station is under a similar earth electromagnetic field. Note that the station is hardcoded in the second line as <code>station=&quot;NEWK&quot;</code>. The user is required to manually define this after considering the potential options suggested. See <a href="../../../reference/#crnpy.crnpy.get_incoming_neutron_flux"><code>get_incoming_neutron_flux()</code></a>, <a href="../../../reference/#crnpy.crnpy.find_neutron_monitor"><code>find_neutron_monitor()</code></a> and <a href="../../../reference/#crnpy.crnpy.correction_incoming_flux"><code>correction_incoming_flux()</code></a>.</p>
-
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="neutron-count-correction">Neutron count correction<a class="anchor-link" href="#neutron-count-correction">¶</a></h3><h4 id="incommng-neutron-flux">Incommng neutron flux<a class="anchor-link" href="#incommng-neutron-flux">¶</a></h4><p>Find stations with a cutoff rigidity similar to the estimated value based on the latitude and longitude, ensuring that the reference station is under a similar earth electromagnetic field. Note that the station is hardcoded in the second line as <code>station="NEWK"</code>. The user is required to manually define this after considering the potential options suggested. See <a href="../../../reference/#crnpy.crnpy.get_incoming_neutron_flux"><code>get_incoming_neutron_flux()</code></a>, <a href="../../../reference/#crnpy.crnpy.find_neutron_monitor"><code>find_neutron_monitor()</code></a> and <a href="../../../reference/#crnpy.crnpy.correction_incoming_flux"><code>correction_incoming_flux()</code></a>.</p>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div id="cell-id=07527c85-e47a-4f62-9477-a8f470b87aff" class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=07527c85-e47a-4f62-9477-a8f470b87aff">
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 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-                    </svg>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Define transect start and end dates</span>
-<span class="n">start_date</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span>
-<span class="n">end_date</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-5">
+<div>
+<span class="notice" hidden="">Copied!</span>
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Define transect start and end dates</span>
+<span class="n">start_date</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">'timestamp'</span><span class="p">]</span>
+<span class="n">end_date</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="s1">'timestamp'</span><span class="p">]</span>
 
 <span class="c1">#Find stations with cutoff rigidity similar to estimated by lat,lon</span>
-<span class="n">crnpy</span><span class="o">.</span><span class="n">find_neutron_monitor</span><span class="p">(</span><span class="n">crnpy</span><span class="o">.</span><span class="n">cutoff_rigidity</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;LatDec&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;LongDec&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]),</span> 
+<span class="n">crnpy</span><span class="o">.</span><span class="n">find_neutron_monitor</span><span class="p">(</span><span class="n">crnpy</span><span class="o">.</span><span class="n">cutoff_rigidity</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'LatDec'</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'LongDec'</span><span class="p">][</span><span class="mi">0</span><span class="p">]),</span> 
                              <span class="n">start_date</span><span class="o">=</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="o">=</span><span class="n">end_date</span><span class="p">)</span>
 </pre></div>
-<div id="cell-5" class="clipboard-copy-txt"># Define transect start and end dates
+<div class="clipboard-copy-txt" id="cell-5"># Define transect start and end dates
 start_date = df.iloc[0]['timestamp']
 end_date = df.iloc[-1]['timestamp']
 
@@ -2028,27 +1425,19 @@ <h3 id="neutron-count-correction">Neutron count correction<a class="anchor-link"
 crnpy.find_neutron_monitor(crnpy.cutoff_rigidity(df['LatDec'][0], df['LongDec'][0]), 
                              start_date=start_date, end_date=end_date)
 </div>
-        
-        </div>
-    </div>
-
 </div>
 </div>
-
+</div>
+</div>
 <div class="jp-Cell-outputWrapper">
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-
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-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
 <pre>
-Select a station with an altitude similar to that of your location. For more information go to: &#39;https://www.nmdb.eu/nest/help.php#helpstations
+Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations
 
 Your cutoff rigidity is 2.99 GV
     STID                          NAME     R  Altitude_m  Period available
@@ -2062,14 +1451,10 @@ <h3 id="neutron-count-correction">Neutron count correction<a class="anchor-link"
 </div>
 </div>
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -2083,230 +1468,208 @@ <h3 id="neutron-count-correction">Neutron count correction<a class="anchor-link"
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>STID</th>
-      <th>NAME</th>
-      <th>R</th>
-      <th>Altitude_m</th>
-      <th>Period available</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>13</th>
-      <td>DRBS</td>
-      <td>Dourbes</td>
-      <td>3.18</td>
-      <td>225</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>31</th>
-      <td>MCRL</td>
-      <td>Mobile Cosmic Ray Laboratory</td>
-      <td>2.46</td>
-      <td>200</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>33</th>
-      <td>MOSC</td>
-      <td>Moscow</td>
-      <td>2.43</td>
-      <td>200</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>40</th>
-      <td>NEWK</td>
-      <td>Newark</td>
-      <td>2.40</td>
-      <td>50</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>20</th>
-      <td>IRK3</td>
-      <td>Irkustk 3</td>
-      <td>3.64</td>
-      <td>3000</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>21</th>
-      <td>IRKT</td>
-      <td>Irkustk</td>
-      <td>3.64</td>
-      <td>435</td>
-      <td>True</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>STID</th>
+<th>NAME</th>
+<th>R</th>
+<th>Altitude_m</th>
+<th>Period available</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>13</th>
+<td>DRBS</td>
+<td>Dourbes</td>
+<td>3.18</td>
+<td>225</td>
+<td>True</td>
+</tr>
+<tr>
+<th>31</th>
+<td>MCRL</td>
+<td>Mobile Cosmic Ray Laboratory</td>
+<td>2.46</td>
+<td>200</td>
+<td>True</td>
+</tr>
+<tr>
+<th>33</th>
+<td>MOSC</td>
+<td>Moscow</td>
+<td>2.43</td>
+<td>200</td>
+<td>True</td>
+</tr>
+<tr>
+<th>40</th>
+<td>NEWK</td>
+<td>Newark</td>
+<td>2.40</td>
+<td>50</td>
+<td>True</td>
+</tr>
+<tr>
+<th>20</th>
+<td>IRK3</td>
+<td>Irkustk 3</td>
+<td>3.64</td>
+<td>3000</td>
+<td>True</td>
+</tr>
+<tr>
+<th>21</th>
+<td>IRKT</td>
+<td>Irkustk</td>
+<td>3.64</td>
+<td>435</td>
+<td>True</td>
+</tr>
+</tbody>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Download incoming neutron flux data from the Neutron Monitor Database (NMDB).</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Download incoming neutron flux data from the Neutron Monitor Database (NMDB).</span>
 <span class="c1"># Use utc_offset for Central Standard Time.</span>
-<span class="n">nmdb</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">station</span><span class="o">=</span><span class="s2">&quot;NEWK&quot;</span><span class="p">,</span> <span class="n">utc_offset</span><span class="o">=-</span><span class="mi">6</span><span class="p">)</span>
+<span class="n">nmdb</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">station</span><span class="o">=</span><span class="s2">"NEWK"</span><span class="p">,</span> <span class="n">utc_offset</span><span class="o">=-</span><span class="mi">6</span><span class="p">)</span>
 </pre></div>
-<div id="cell-6" class="clipboard-copy-txt"># Download incoming neutron flux data from the Neutron Monitor Database (NMDB).
+<div class="clipboard-copy-txt" id="cell-6"># Download incoming neutron flux data from the Neutron Monitor Database (NMDB).
 # Use utc_offset for Central Standard Time.
 nmdb = crnpy.get_incoming_neutron_flux(start_date, end_date, station="NEWK", utc_offset=-6)
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Interpolate incoming neutron flux to match the timestamps in our rover data</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;incoming_flux&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">interpolate_incoming_flux</span><span class="p">(</span><span class="n">nmdb</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">],</span> <span class="n">nmdb</span><span class="p">[</span><span class="s1">&#39;counts&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">])</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Interpolate incoming neutron flux to match the timestamps in our rover data</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'incoming_flux'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">interpolate_incoming_flux</span><span class="p">(</span><span class="n">nmdb</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">],</span> <span class="n">nmdb</span><span class="p">[</span><span class="s1">'counts'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">])</span>
 </pre></div>
-<div id="cell-7" class="clipboard-copy-txt"># Interpolate incoming neutron flux to match the timestamps in our rover data
+<div class="clipboard-copy-txt" id="cell-7"># Interpolate incoming neutron flux to match the timestamps in our rover data
 df['incoming_flux'] = crnpy.interpolate_incoming_flux(nmdb['timestamp'], nmdb['counts'], df['timestamp'])
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Compute correction factor for incoming neutron flux</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;fi&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_incoming_flux</span><span class="p">(</span><span class="n">incoming_neutrons</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;incoming_flux&#39;</span><span class="p">],</span>
-                                          <span class="n">incoming_Ref</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;incoming_flux&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Compute correction factor for incoming neutron flux</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'fi'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_incoming_flux</span><span class="p">(</span><span class="n">incoming_neutrons</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'incoming_flux'</span><span class="p">],</span>
+                                          <span class="n">incoming_Ref</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'incoming_flux'</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 </pre></div>
-<div id="cell-8" class="clipboard-copy-txt"># Compute correction factor for incoming neutron flux
+<div class="clipboard-copy-txt" id="cell-8"># Compute correction factor for incoming neutron flux
 df['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df['incoming_flux'],
                                           incoming_Ref=df['incoming_flux'].iloc[0])
 </div>
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+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=e8dd9c03-e131-411e-aae4-439cf801b27e">
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-<h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" href="#atmospheric-correction">&#182;</a></h4><p>NaN values in the atmospheric data are filled. The neutron counts are then corrected for atmospheric variables. See <a href="../../../reference/#crnpy.crnpy.correction_humidity"><code>correction_humidity()</code></a> and <a href="../../../reference/#crnpy.crnpy.correction_pressure"><code>correction_pressure()</code></a></p>
-
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" href="#atmospheric-correction">¶</a></h4><p>NaN values in the atmospheric data are filled. The neutron counts are then corrected for atmospheric variables. See <a href="../../../reference/#crnpy.crnpy.correction_humidity"><code>correction_humidity()</code></a> and <a href="../../../reference/#crnpy.crnpy.correction_pressure"><code>correction_pressure()</code></a></p>
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-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Fill NaN values in atmospheric data</span>
-<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;barometric_pressure_Avg&#39;</span><span class="p">,</span> <span class="s1">&#39;relative_humidity_Avg&#39;</span><span class="p">,</span> <span class="s1">&#39;air_temperature_Avg&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">&#39;barometric_pressure_Avg&#39;</span><span class="p">,</span><span class="s1">&#39;relative_humidity_Avg&#39;</span><span class="p">,</span> <span class="s1">&#39;air_temperature_Avg&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">&#39;pchip&#39;</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">limit_direction</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">)</span>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Fill NaN values in atmospheric data</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">'barometric_pressure_Avg'</span><span class="p">,</span> <span class="s1">'relative_humidity_Avg'</span><span class="p">,</span> <span class="s1">'air_temperature_Avg'</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">'barometric_pressure_Avg'</span><span class="p">,</span><span class="s1">'relative_humidity_Avg'</span><span class="p">,</span> <span class="s1">'air_temperature_Avg'</span><span class="p">]]</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">'pchip'</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">limit_direction</span><span class="o">=</span><span class="s1">'both'</span><span class="p">)</span>
 
 <span class="c1"># Compute the pressure correction factor</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;fp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_pressure</span><span class="p">(</span><span class="n">pressure</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;barometric_pressure_Avg&#39;</span><span class="p">],</span> <span class="n">Pref</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;barometric_pressure_Avg&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">L</span><span class="o">=</span><span class="mi">130</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'fp'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_pressure</span><span class="p">(</span><span class="n">pressure</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'barometric_pressure_Avg'</span><span class="p">],</span> <span class="n">Pref</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'barometric_pressure_Avg'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">L</span><span class="o">=</span><span class="mi">130</span><span class="p">)</span>
 
 <span class="c1"># Estimate the absolute humidity and compute the vapor pressure correction factor</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;abs_humidity&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">abs_humidity</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;relative_humidity_Avg&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;air_temperature_Avg&#39;</span><span class="p">])</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;fw&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_humidity</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;abs_humidity&#39;</span><span class="p">],</span> <span class="n">Aref</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'abs_humidity'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">abs_humidity</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'relative_humidity_Avg'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'air_temperature_Avg'</span><span class="p">])</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'fw'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_humidity</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'abs_humidity'</span><span class="p">],</span> <span class="n">Aref</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 </pre></div>
-<div id="cell-9" class="clipboard-copy-txt"># Fill NaN values in atmospheric data
+<div class="clipboard-copy-txt" id="cell-9"># Fill NaN values in atmospheric data
 df[['barometric_pressure_Avg', 'relative_humidity_Avg', 'air_temperature_Avg']] = df[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')
 
 # Compute the pressure correction factor
@@ -2316,104 +1679,93 @@ <h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" hre
 df['abs_humidity'] = crnpy.abs_humidity(df['relative_humidity_Avg'], df['air_temperature_Avg'])
 df['fw'] = crnpy.correction_humidity(df['abs_humidity'], Aref=0)
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Apply correction factors</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;total_corrected_neutrons&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;total_raw_counts&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;fw&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;fp&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;fi&#39;</span><span class="p">])</span>
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Apply correction factors</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'total_corrected_neutrons'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'total_raw_counts'</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s1">'fw'</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'fp'</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s1">'fi'</span><span class="p">])</span>
 </pre></div>
-<div id="cell-10" class="clipboard-copy-txt"># Apply correction factors
+<div class="clipboard-copy-txt" id="cell-10"># Apply correction factors
 df['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])
 </div>
-        
-        </div>
-    </div>
-
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-
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 </div>
-<div id="cell-id=8fbd8cfc-6b69-4a1c-a662-77531c3df4f1" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+</div>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>The corrected counts are smoothed using a 2D smoothing function. The smoothed counts are then converted to volumetric water content (VWC) using the counts_to_vwc function.</p>
-
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Smooth variable</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;corrected_neutrons_smoothed&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">spatial_average</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;x&#39;</span><span class="p">],</span>
-                                        <span class="n">df</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">],</span>
-                                        <span class="n">df</span><span class="p">[</span><span class="s1">&#39;total_corrected_neutrons&#39;</span><span class="p">],</span>
-                                        <span class="n">buffer</span><span class="o">=</span> <span class="mi">800</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="n">rnd</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Smooth variable</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'corrected_neutrons_smoothed'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">spatial_average</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'x'</span><span class="p">],</span>
+                                        <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span>
+                                        <span class="n">df</span><span class="p">[</span><span class="s1">'total_corrected_neutrons'</span><span class="p">],</span>
+                                        <span class="n">buffer</span><span class="o">=</span> <span class="mi">800</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'median'</span><span class="p">,</span> <span class="n">rnd</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
 <span class="c1"># Estimate lattice water based on texture</span>
 <span class="n">lattice_water</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">lattice_water</span><span class="p">(</span><span class="n">clay_content</span><span class="o">=</span><span class="mf">0.35</span><span class="p">)</span>
 
 <span class="c1"># Estimate Soil Volumetric Water Content</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">counts_to_vwc</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;corrected_neutrons_smoothed&#39;</span><span class="p">],</span> <span class="n">N0</span><span class="o">=</span><span class="mi">550</span><span class="p">,</span> <span class="n">bulk_density</span><span class="o">=</span><span class="mf">1.3</span><span class="p">,</span> <span class="n">Wlat</span><span class="o">=</span><span class="n">lattice_water</span><span class="p">,</span> <span class="n">Wsoc</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'VWC'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">counts_to_vwc</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'corrected_neutrons_smoothed'</span><span class="p">],</span> <span class="n">N0</span><span class="o">=</span><span class="mi">550</span><span class="p">,</span> <span class="n">bulk_density</span><span class="o">=</span><span class="mf">1.3</span><span class="p">,</span> <span class="n">Wlat</span><span class="o">=</span><span class="n">lattice_water</span><span class="p">,</span> <span class="n">Wsoc</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
 
 <span class="c1"># Drop VWC NaN values before interpolating values</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">])</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'VWC'</span><span class="p">])</span>
 
 <span class="c1"># Interpolate variable using IDW (https://en.wikipedia.org/wiki/Inverse_distance_weighting)</span>
-<span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span><span class="p">,</span> <span class="n">Z_pred</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">interpolate_2d</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;x&#39;</span><span class="p">],</span>
-                                        <span class="n">df</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">],</span>
-                                        <span class="n">df</span><span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">],</span>
-                                        <span class="n">dx</span><span class="o">=</span><span class="mi">250</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mi">250</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;idw&#39;</span><span class="p">)</span>
+<span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span><span class="p">,</span> <span class="n">Z_pred</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">interpolate_2d</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'x'</span><span class="p">],</span>
+                                        <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span>
+                                        <span class="n">df</span><span class="p">[</span><span class="s1">'VWC'</span><span class="p">],</span>
+                                        <span class="n">dx</span><span class="o">=</span><span class="mi">250</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mi">250</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'idw'</span><span class="p">)</span>
 </pre></div>
-<div id="cell-11" class="clipboard-copy-txt"># Smooth variable
+<div class="clipboard-copy-txt" id="cell-11"># Smooth variable
 df['corrected_neutrons_smoothed'] = crnpy.spatial_average(df['x'],
                                         df['y'],
                                         df['total_corrected_neutrons'],
@@ -2434,61 +1786,55 @@ <h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" hre
                                         df['VWC'],
                                         dx=250, dy=250, method='idw')
 </div>
-        
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+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=d159b9a4-0682-467c-98de-9cb78fee4e78">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>A gridded map of the Volumetric Water Content (VWC) is created using the interpolated x, y, and VWC values.</p>
-
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Show interpolated grid</span>
-<span class="n">cmap</span> <span class="o">=</span> <span class="s1">&#39;RdYlBu&#39;</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Show interpolated grid</span>
+<span class="n">cmap</span> <span class="o">=</span> <span class="s1">'RdYlBu'</span>
 
 <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Gridded map&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Gridded map'</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span><span class="p">,</span> <span class="n">Z_pred</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s2">&quot;Volumetric Water Content $(cm^3 \cdot cm^{-3})$&quot;</span><span class="p">,</span> <span class="n">location</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;x&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s2">"Volumetric Water Content $(cm^3 \cdot cm^{-3})$"</span><span class="p">,</span> <span class="n">location</span><span class="o">=</span><span class="s1">'right'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'x'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">'w'</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">ticklabel_format</span><span class="p">(</span><span class="n">scilimits</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Easting&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Northing&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Easting'</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Northing'</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
-<div id="cell-12" class="clipboard-copy-txt"># Show interpolated grid
+<div class="clipboard-copy-txt" id="cell-12"># Show interpolated grid
 cmap = 'RdYlBu'
 
 plt.figure(figsize=(6,6))
@@ -2501,85 +1847,65 @@ <h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" hre
 plt.ylabel('Northing', size=14)
 plt.show()
 </div>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Show contour map</span>
-<span class="n">cmap</span> <span class="o">=</span> <span class="s1">&#39;RdYlBu&#39;</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Show contour map</span>
+<span class="n">cmap</span> <span class="o">=</span> <span class="s1">'RdYlBu'</span>
 
 <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Contours&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Contours'</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span><span class="p">,</span> <span class="n">Z_pred</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">ticklabel_format</span><span class="p">(</span><span class="n">scilimits</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s2">&quot;Volumetric Water Content $(cm^3 \cdot cm^{-3})$&quot;</span><span class="p">,</span> <span class="n">location</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;x&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s2">"Volumetric Water Content $(cm^3 \cdot cm^{-3})$"</span><span class="p">,</span> <span class="n">location</span><span class="o">=</span><span class="s1">'right'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'x'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">'w'</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
-<div id="cell-13" class="clipboard-copy-txt"># Show contour map
+<div class="clipboard-copy-txt" id="cell-13"># Show contour map
 cmap = 'RdYlBu'
 
 plt.figure(figsize=(7,6))
@@ -2590,43 +1916,40 @@ <h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" hre
 plt.scatter(df['x'], df['y'], marker='o', edgecolor='k', facecolor='w')
 plt.show()
 </div>
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" 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"/></a>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div> <!-- jp-Notebook -->
+</div> <!-- jupyter-wrapper -->
 
+        <style>
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+        </style>
+        
 
 
 
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+    
+    <script id="__config" type="application/json">{"base": "../../..", "features": [], "search": "../../../assets/javascripts/workers/search.f886a092.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../../assets/javascripts/bundle.220ee61c.min.js"></script>
+      <script src="../../../assets/javascripts/bundle.d7c377c4.min.js"></script>
       
-        
-          <script src="../../../javascripts/katex.js"></script>
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+        <script src="../../../javascripts/katex.js"></script>
       
-        
-          <script src="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js"></script>
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+        <script src="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js"></script>
       
-        
-          <script src="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js"></script>
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+        <script src="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js"></script>
       
     
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diff --git a/site/examples/stationary/example_RDT_station/index.html b/site/examples/stationary/example_RDT_station/index.html
index b24fa2b..42bc83b 100644
--- a/site/examples/stationary/example_RDT_station/index.html
+++ b/site/examples/stationary/example_RDT_station/index.html
@@ -18,8 +18,9 @@
       
         <link rel="next" href="../../rover/Hydroinnova_rover_example/">
       
+      
       <link rel="icon" href="../../../img/logo/favicon.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.18">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.5.3">
     
     
       
@@ -27,12 +28,15 @@
       
     
     
-      <link rel="stylesheet" href="../../../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../../../assets/stylesheets/main.50c56a3b.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -56,7 +60,7 @@
       
         <meta  property="og:type"  content="website" >
       
-        <meta  property="og:title"  content="Example 1 - CRNPy Documentation" >
+        <meta  property="og:title"  content="Example stationary - CRNPy Documentation" >
       
         <meta  property="og:description"  content="CRNPy is a Python package for the estimation and processing of soil moisture data from cosmic-ray neutron counts." >
       
@@ -72,7 +76,7 @@
       
         <meta  name="twitter:card"  content="summary_large_image" >
       
-        <meta  name="twitter:title"  content="Example 1 - CRNPy Documentation" >
+        <meta  name="twitter:title"  content="Example stationary - CRNPy Documentation" >
       
         <meta  name="twitter:description"  content="CRNPy is a Python package for the estimation and processing of soil moisture data from cosmic-ray neutron counts." >
       
@@ -97,9 +101,6 @@
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     <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
     <label class="md-overlay" for="__drawer"></label>
@@ -128,6 +129,7 @@
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
+      
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
@@ -148,8 +150,12 @@
     </div>
     
     
+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+    
+    
     
       <label class="md-header__button md-icon" for="__search">
+        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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       <label class="md-search__icon md-icon" for="__search">
+        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
+        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
+          
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
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@@ -187,7 +196,7 @@
         <a href="https://github.com/soilwater/crnpy" title="Go to repository" class="md-source" data-md-component="source">
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -217,6 +226,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href="../../.." title="CRNPy Documentation" class="md-nav__button md-logo" aria-label="CRNPy Documentation" data-md-component="logo">
@@ -231,7 +241,7 @@
       <a href="https://github.com/soilwater/crnpy" title="Go to repository" class="md-source" data-md-component="source">
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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     GitHub
@@ -243,14 +253,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../.." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -258,14 +273,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../../sensor_description/" class="md-nav__link">
-        Sensor description
+        
+  
+  <span class="md-ellipsis">
+    Sensor description
+  </span>
+  
+
       </a>
     </li>
   
@@ -273,14 +293,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../../correction_routines/" class="md-nav__link">
-        Correction routines
+        
+  
+  <span class="md-ellipsis">
+    Correction routines
+  </span>
+  
+
       </a>
     </li>
   
@@ -288,42 +313,51 @@
     
       
       
-      
-
   
   
     
   
   
+  
+    
+    
+    
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-      
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-      
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-          Examples
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-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
   
   
     
   
   
+  
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@@ -333,12 +367,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Processing and analyzing data from a stationary detector
+          
+  
+  <span class="md-ellipsis">
+    Processing and analyzing data from a stationary detector
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Processing and analyzing data from a stationary detector
+        
+  
+  <span class="md-ellipsis">
+    Processing and analyzing data from a stationary detector
+  </span>
+  
+
       </a>
       
         
@@ -358,7 +404,9 @@
       
         <li class="md-nav__item">
   <a href="#neutron-count-correction" class="md-nav__link">
-    Neutron count correction
+    <span class="md-ellipsis">
+      Neutron count correction
+    </span>
   </a>
   
     <nav class="md-nav" aria-label="Neutron count correction">
@@ -366,14 +414,18 @@
         
           <li class="md-nav__item">
   <a href="#incomming-neutron-flux" class="md-nav__link">
-    Incomming neutron flux
+    <span class="md-ellipsis">
+      Incomming neutron flux
+    </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
   <a href="#atmospheric-correction" class="md-nav__link">
-    Atmospheric correction
+    <span class="md-ellipsis">
+      Atmospheric correction
+    </span>
   </a>
   
 </li>
@@ -385,14 +437,18 @@
       
         <li class="md-nav__item">
   <a href="#sensing-depth-and-soil-water-storage" class="md-nav__link">
-    Sensing depth and soil water storage
+    <span class="md-ellipsis">
+      Sensing depth and soil water storage
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#uncertainty-estimation" class="md-nav__link">
-    Uncertainty estimation
+    <span class="md-ellipsis">
+      Uncertainty estimation
+    </span>
   </a>
   
 </li>
@@ -404,52 +460,72 @@
     </li>
   
 
-            
-          
+              
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../rover/Hydroinnova_rover_example/" class="md-nav__link">
-        Processing and analyzing data from a roving detector
+        
+  
+  <span class="md-ellipsis">
+    Processing and analyzing data from a roving detector
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../calibration/calibration/" class="md-nav__link">
-        Device-specific field calibration
+        
+  
+  <span class="md-ellipsis">
+    Device-specific field calibration
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="../../../reference/" class="md-nav__link">
-        Reference
+        
+  
+  <span class="md-ellipsis">
+    Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -483,7 +559,9 @@
       
         <li class="md-nav__item">
   <a href="#neutron-count-correction" class="md-nav__link">
-    Neutron count correction
+    <span class="md-ellipsis">
+      Neutron count correction
+    </span>
   </a>
   
     <nav class="md-nav" aria-label="Neutron count correction">
@@ -491,14 +569,18 @@
         
           <li class="md-nav__item">
   <a href="#incomming-neutron-flux" class="md-nav__link">
-    Incomming neutron flux
+    <span class="md-ellipsis">
+      Incomming neutron flux
+    </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
   <a href="#atmospheric-correction" class="md-nav__link">
-    Atmospheric correction
+    <span class="md-ellipsis">
+      Atmospheric correction
+    </span>
   </a>
   
 </li>
@@ -510,14 +592,18 @@
       
         <li class="md-nav__item">
   <a href="#sensing-depth-and-soil-water-storage" class="md-nav__link">
-    Sensing depth and soil water storage
+    <span class="md-ellipsis">
+      Sensing depth and soil water storage
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#uncertainty-estimation" class="md-nav__link">
-    Uncertainty estimation
+    <span class="md-ellipsis">
+      Uncertainty estimation
+    </span>
   </a>
   
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-    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
-        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 10px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
-        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 18px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
-        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
-        0px 3px 14px 2px var(--jp-shadow-ambient-color);
-    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
-        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
-        0px 5px 22px 4px var(--jp-shadow-ambient-color);
-    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
-        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
-        0px 6px 30px 5px var(--jp-shadow-ambient-color);
-    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
-        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
-        0px 8px 38px 7px var(--jp-shadow-ambient-color);
-    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
-        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
-        0px 9px 46px 8px var(--jp-shadow-ambient-color);
-
-    /* Borders
-   *
-   * The following variables, specify the visual styling of borders in JupyterLab.
-   */
-
-    --jp-border-width: 1px;
-    --jp-border-color0: var(--md-grey-400);
-    --jp-border-color1: var(--md-grey-400);
-    --jp-border-color2: var(--md-grey-300);
-    --jp-border-color3: var(--md-grey-200);
-    --jp-border-radius: 2px;
-
-    /* UI Fonts
-   *
-   * The UI font CSS variables are used for the typography all of the JupyterLab
-   * user interface elements that are not directly user generated content.
-   *
-   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
-   * is applied to a parent element. When children elements, such as headings, are sized
-   * in em all things will be computed relative to that body size.
-   */
-
-    --jp-ui-font-scale-factor: 1.2;
-    --jp-ui-font-size0: 0.83333em;
-    --jp-ui-font-size1: 13px; /* Base font size */
-    --jp-ui-font-size2: 1.2em;
-    --jp-ui-font-size3: 1.44em;
-
-    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
-        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
-        "Segoe UI Symbol";
-
-    /*
-   * Use these font colors against the corresponding main layout colors.
-   * In a light theme, these go from dark to light.
-   */
-
-    /* Defaults use Material Design specification */
-    --jp-ui-font-color0: rgba(0, 0, 0, 1);
-    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
-    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
-    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
-
-    /*
-   * Use these against the brand/accent/warn/error colors.
-   * These will typically go from light to darker, in both a dark and light theme.
-   */
-
-    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
-    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
-    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
-    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
-
-    /* Content Fonts
-   *
-   * Content font variables are used for typography of user generated content.
-   *
-   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
-   * is applied to a parent element. When children elements, such as headings, are sized
-   * in em all things will be computed relative to that body size.
-   */
-
-    --jp-content-line-height: 1.6;
-    --jp-content-font-scale-factor: 1.2;
-    --jp-content-font-size0: 0.83333em;
-    --jp-content-font-size1: 14px; /* Base font size */
-    --jp-content-font-size2: 1.2em;
-    --jp-content-font-size3: 1.44em;
-    --jp-content-font-size4: 1.728em;
-    --jp-content-font-size5: 2.0736em;
-
-    /* This gives a magnification of about 125% in presentation mode over normal. */
-    --jp-content-presentation-font-size1: 17px;
-
-    --jp-content-heading-line-height: 1;
-    --jp-content-heading-margin-top: 1.2em;
-    --jp-content-heading-margin-bottom: 0.8em;
-    --jp-content-heading-font-weight: 500;
-
-    /* Defaults use Material Design specification */
-    --jp-content-font-color0: rgba(0, 0, 0, 1);
-    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
-    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
-    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
-
-    --jp-content-link-color: var(--md-blue-700);
-
-    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
-        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
-        "Segoe UI Symbol";
-
-    /*
-   * Code Fonts
-   *
-   * Code font variables are used for typography of code and other monospaces content.
-   */
-
-    --jp-code-font-size: 13px;
-    --jp-code-line-height: 1.3077; /* 17px for 13px base */
-    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
-    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
-        monospace;
-    --jp-code-font-family: var(--jp-code-font-family-default);
-
-    /* This gives a magnification of about 125% in presentation mode over normal. */
-    --jp-code-presentation-font-size: 16px;
-
-    /* may need to tweak cursor width if you change font size */
-    --jp-code-cursor-width0: 1.4px;
-    --jp-code-cursor-width1: 2px;
-    --jp-code-cursor-width2: 4px;
-
-    /* Layout
-   *
-   * The following are the main layout colors use in JupyterLab. In a light
-   * theme these would go from light to dark.
-   */
-
-    --jp-layout-color0: white;
-    --jp-layout-color1: white;
-    --jp-layout-color2: var(--md-grey-200);
-    --jp-layout-color3: var(--md-grey-400);
-    --jp-layout-color4: var(--md-grey-600);
-
-    /* Inverse Layout
-   *
-   * The following are the inverse layout colors use in JupyterLab. In a light
-   * theme these would go from dark to light.
-   */
-
-    --jp-inverse-layout-color0: #111111;
-    --jp-inverse-layout-color1: var(--md-grey-900);
-    --jp-inverse-layout-color2: var(--md-grey-800);
-    --jp-inverse-layout-color3: var(--md-grey-700);
-    --jp-inverse-layout-color4: var(--md-grey-600);
-
-    /* Brand/accent */
-
-    --jp-brand-color0: var(--md-blue-900);
-    --jp-brand-color1: var(--md-blue-700);
-    --jp-brand-color2: var(--md-blue-300);
-    --jp-brand-color3: var(--md-blue-100);
-    --jp-brand-color4: var(--md-blue-50);
-
-    --jp-accent-color0: var(--md-green-900);
-    --jp-accent-color1: var(--md-green-700);
-    --jp-accent-color2: var(--md-green-300);
-    --jp-accent-color3: var(--md-green-100);
-
-    /* State colors (warn, error, success, info) */
-
-    --jp-warn-color0: var(--md-orange-900);
-    --jp-warn-color1: var(--md-orange-700);
-    --jp-warn-color2: var(--md-orange-300);
-    --jp-warn-color3: var(--md-orange-100);
-
-    --jp-error-color0: var(--md-red-900);
-    --jp-error-color1: var(--md-red-700);
-    --jp-error-color2: var(--md-red-300);
-    --jp-error-color3: var(--md-red-100);
-
-    --jp-success-color0: var(--md-green-900);
-    --jp-success-color1: var(--md-green-700);
-    --jp-success-color2: var(--md-green-300);
-    --jp-success-color3: var(--md-green-100);
-
-    --jp-info-color0: var(--md-cyan-900);
-    --jp-info-color1: var(--md-cyan-700);
-    --jp-info-color2: var(--md-cyan-300);
-    --jp-info-color3: var(--md-cyan-100);
-
-    /* Cell specific styles */
-
-    --jp-cell-padding: 5px;
-
-    --jp-cell-collapser-width: 8px;
-    --jp-cell-collapser-min-height: 20px;
-    --jp-cell-collapser-not-active-hover-opacity: 0.6;
-
-    --jp-cell-editor-background: var(--md-grey-100);
-    --jp-cell-editor-border-color: var(--md-grey-300);
-    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
-    --jp-cell-editor-active-background: var(--jp-layout-color0);
-    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
-
-    --jp-cell-prompt-width: 64px;
-    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
-    --jp-cell-prompt-letter-spacing: 0px;
-    --jp-cell-prompt-opacity: 1;
-    --jp-cell-prompt-not-active-opacity: 0.5;
-    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
-    /* A custom blend of MD grey and blue 600
-   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
-    --jp-cell-inprompt-font-color: #307fc1;
-    /* A custom blend of MD grey and orange 600
-   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
-    --jp-cell-outprompt-font-color: #bf5b3d;
-
-    /* Notebook specific styles */
-
-    --jp-notebook-padding: 10px;
-    --jp-notebook-select-background: var(--jp-layout-color1);
-    --jp-notebook-multiselected-color: var(--md-blue-50);
-
-    /* The scroll padding is calculated to fill enough space at the bottom of the
-  notebook to show one single-line cell (with appropriate padding) at the top
-  when the notebook is scrolled all the way to the bottom. We also subtract one
-  pixel so that no scrollbar appears if we have just one single-line cell in the
-  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
-  */
-    --jp-notebook-scroll-padding: calc(
-        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
-            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
-    );
-
-    /* Rendermime styles */
-
-    --jp-rendermime-error-background: #fdd;
-    --jp-rendermime-table-row-background: var(--md-grey-100);
-    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
-
-    /* Dialog specific styles */
-
-    --jp-dialog-background: rgba(0, 0, 0, 0.25);
-
-    /* Console specific styles */
-
-    --jp-console-padding: 10px;
-
-    /* Toolbar specific styles */
-
-    --jp-toolbar-border-color: var(--jp-border-color1);
-    --jp-toolbar-micro-height: 8px;
-    --jp-toolbar-background: var(--jp-layout-color1);
-    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
-    --jp-toolbar-header-margin: 4px 4px 0px 4px;
-    --jp-toolbar-active-background: var(--md-grey-300);
-
-    /* Statusbar specific styles */
-
-    --jp-statusbar-height: 24px;
-
-    /* Input field styles */
-
-    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
-    --jp-input-active-background: var(--jp-layout-color1);
-    --jp-input-hover-background: var(--jp-layout-color1);
-    --jp-input-background: var(--md-grey-100);
-    --jp-input-border-color: var(--jp-border-color1);
-    --jp-input-active-border-color: var(--jp-brand-color1);
-    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
-
-    /* General editor styles */
-
-    --jp-editor-selected-background: #d9d9d9;
-    --jp-editor-selected-focused-background: #d7d4f0;
-    --jp-editor-cursor-color: var(--jp-ui-font-color0);
-
-    /* Code mirror specific styles */
-
-    --jp-mirror-editor-keyword-color: #008000;
-    --jp-mirror-editor-atom-color: #88f;
-    --jp-mirror-editor-number-color: #080;
-    --jp-mirror-editor-def-color: #00f;
-    --jp-mirror-editor-variable-color: var(--md-grey-900);
-    --jp-mirror-editor-variable-2-color: #05a;
-    --jp-mirror-editor-variable-3-color: #085;
-    --jp-mirror-editor-punctuation-color: #05a;
-    --jp-mirror-editor-property-color: #05a;
-    --jp-mirror-editor-operator-color: #aa22ff;
-    --jp-mirror-editor-comment-color: #408080;
-    --jp-mirror-editor-string-color: #ba2121;
-    --jp-mirror-editor-string-2-color: #708;
-    --jp-mirror-editor-meta-color: #aa22ff;
-    --jp-mirror-editor-qualifier-color: #555;
-    --jp-mirror-editor-builtin-color: #008000;
-    --jp-mirror-editor-bracket-color: #997;
-    --jp-mirror-editor-tag-color: #170;
-    --jp-mirror-editor-attribute-color: #00c;
-    --jp-mirror-editor-header-color: blue;
-    --jp-mirror-editor-quote-color: #090;
-    --jp-mirror-editor-link-color: #00c;
-    --jp-mirror-editor-error-color: #f00;
-    --jp-mirror-editor-hr-color: #999;
-
-    /* Vega extension styles */
-
-    --jp-vega-background: white;
-
-    /* Sidebar-related styles */
-
-    --jp-sidebar-min-width: 250px;
-
-    /* Search-related styles */
-
-    --jp-search-toggle-off-opacity: 0.5;
-    --jp-search-toggle-hover-opacity: 0.8;
-    --jp-search-toggle-on-opacity: 1;
-    --jp-search-selected-match-background-color: rgb(245, 200, 0);
-    --jp-search-selected-match-color: black;
-    --jp-search-unselected-match-background-color: var(
-        --jp-inverse-layout-color0
-    );
-    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
-
-    /* Icon colors that work well with light or dark backgrounds */
-    --jp-icon-contrast-color0: var(--md-purple-600);
-    --jp-icon-contrast-color1: var(--md-green-600);
-    --jp-icon-contrast-color2: var(--md-pink-600);
-    --jp-icon-contrast-color3: var(--md-blue-600);
-}
-
-[data-md-color-scheme="slate"] .jupyter-wrapper {
-    /* Elevation
-   *
-   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
-   *
-   * https://github.com/material-components/material-components-web
-   * https://material-components-web.appspot.com/elevation.html
-   */
-
-    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
-   * colors used in the theme as well. */
-    --jp-shadow-base-lightness: 32;
-    --jp-shadow-umbra-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        0.2
-    );
-    --jp-shadow-penumbra-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        0.14
-    );
-    --jp-shadow-ambient-color: rgba(
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        var(--jp-shadow-base-lightness),
-        0.12
-    );
-    --jp-elevation-z0: none;
-    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
-        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 3px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
-        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 5px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
-        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 10px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
-        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
-        0px 1px 18px 0px var(--jp-shadow-ambient-color);
-    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
-        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
-        0px 3px 14px 2px var(--jp-shadow-ambient-color);
-    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
-        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
-        0px 5px 22px 4px var(--jp-shadow-ambient-color);
-    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
-        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
-        0px 6px 30px 5px var(--jp-shadow-ambient-color);
-    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
-        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
-        0px 8px 38px 7px var(--jp-shadow-ambient-color);
-    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
-        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
-        0px 9px 46px 8px var(--jp-shadow-ambient-color);
-
-    /* Borders
-   *
-   * The following variables, specify the visual styling of borders in JupyterLab.
-   */
-
-    --jp-border-width: 1px;
-    --jp-border-color0: var(--md-grey-700);
-    --jp-border-color1: var(--md-grey-700);
-    --jp-border-color2: var(--md-grey-800);
-    --jp-border-color3: var(--md-grey-900);
-    --jp-border-radius: 2px;
-
-    /* UI Fonts
-   *
-   * The UI font CSS variables are used for the typography all of the JupyterLab
-   * user interface elements that are not directly user generated content.
-   *
-   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
-   * is applied to a parent element. When children elements, such as headings, are sized
-   * in em all things will be computed relative to that body size.
-   */
-
-    --jp-ui-font-scale-factor: 1.2;
-    --jp-ui-font-size0: 0.83333em;
-    --jp-ui-font-size1: 13px; /* Base font size */
-    --jp-ui-font-size2: 1.2em;
-    --jp-ui-font-size3: 1.44em;
-
-    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
-        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
-        "Segoe UI Symbol";
-
-    /*
-   * Use these font colors against the corresponding main layout colors.
-   * In a light theme, these go from dark to light.
-   */
-
-    /* Defaults use Material Design specification */
-    --jp-ui-font-color0: rgba(255, 255, 255, 1);
-    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
-    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
-    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
-
-    /*
-   * Use these against the brand/accent/warn/error colors.
-   * These will typically go from light to darker, in both a dark and light theme.
-   */
-
-    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
-    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
-    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
-    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
-
-    /* Content Fonts
-   *
-   * Content font variables are used for typography of user generated content.
-   *
-   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
-   * is applied to a parent element. When children elements, such as headings, are sized
-   * in em all things will be computed relative to that body size.
-   */
-
-    --jp-content-line-height: 1.6;
-    --jp-content-font-scale-factor: 1.2;
-    --jp-content-font-size0: 0.83333em;
-    --jp-content-font-size1: 14px; /* Base font size */
-    --jp-content-font-size2: 1.2em;
-    --jp-content-font-size3: 1.44em;
-    --jp-content-font-size4: 1.728em;
-    --jp-content-font-size5: 2.0736em;
-
-    /* This gives a magnification of about 125% in presentation mode over normal. */
-    --jp-content-presentation-font-size1: 17px;
-
-    --jp-content-heading-line-height: 1;
-    --jp-content-heading-margin-top: 1.2em;
-    --jp-content-heading-margin-bottom: 0.8em;
-    --jp-content-heading-font-weight: 500;
-
-    /* Defaults use Material Design specification */
-    --jp-content-font-color0: rgba(255, 255, 255, 1);
-    --jp-content-font-color1: rgba(255, 255, 255, 1);
-    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
-    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
-
-    --jp-content-link-color: var(--md-blue-300);
-
-    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
-        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
-        "Segoe UI Symbol";
-
-    /*
-   * Code Fonts
-   *
-   * Code font variables are used for typography of code and other monospaces content.
-   */
-
-    --jp-code-font-size: 13px;
-    --jp-code-line-height: 1.3077; /* 17px for 13px base */
-    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
-    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
-        monospace;
-    --jp-code-font-family: var(--jp-code-font-family-default);
-
-    /* This gives a magnification of about 125% in presentation mode over normal. */
-    --jp-code-presentation-font-size: 16px;
-
-    /* may need to tweak cursor width if you change font size */
-    --jp-code-cursor-width0: 1.4px;
-    --jp-code-cursor-width1: 2px;
-    --jp-code-cursor-width2: 4px;
-
-    /* Layout
-   *
-   * The following are the main layout colors use in JupyterLab. In a light
-   * theme these would go from light to dark.
-   */
-
-    --jp-layout-color0: #111111;
-    --jp-layout-color1: var(--md-grey-900);
-    --jp-layout-color2: var(--md-grey-800);
-    --jp-layout-color3: var(--md-grey-700);
-    --jp-layout-color4: var(--md-grey-600);
-
-    /* Inverse Layout
-   *
-   * The following are the inverse layout colors use in JupyterLab. In a light
-   * theme these would go from dark to light.
-   */
-
-    --jp-inverse-layout-color0: white;
-    --jp-inverse-layout-color1: white;
-    --jp-inverse-layout-color2: var(--md-grey-200);
-    --jp-inverse-layout-color3: var(--md-grey-400);
-    --jp-inverse-layout-color4: var(--md-grey-600);
-
-    /* Brand/accent */
-
-    --jp-brand-color0: var(--md-blue-700);
-    --jp-brand-color1: var(--md-blue-500);
-    --jp-brand-color2: var(--md-blue-300);
-    --jp-brand-color3: var(--md-blue-100);
-    --jp-brand-color4: var(--md-blue-50);
-
-    --jp-accent-color0: var(--md-green-700);
-    --jp-accent-color1: var(--md-green-500);
-    --jp-accent-color2: var(--md-green-300);
-    --jp-accent-color3: var(--md-green-100);
-
-    /* State colors (warn, error, success, info) */
-
-    --jp-warn-color0: var(--md-orange-700);
-    --jp-warn-color1: var(--md-orange-500);
-    --jp-warn-color2: var(--md-orange-300);
-    --jp-warn-color3: var(--md-orange-100);
-
-    --jp-error-color0: var(--md-red-700);
-    --jp-error-color1: var(--md-red-500);
-    --jp-error-color2: var(--md-red-300);
-    --jp-error-color3: var(--md-red-100);
-
-    --jp-success-color0: var(--md-green-700);
-    --jp-success-color1: var(--md-green-500);
-    --jp-success-color2: var(--md-green-300);
-    --jp-success-color3: var(--md-green-100);
-
-    --jp-info-color0: var(--md-cyan-700);
-    --jp-info-color1: var(--md-cyan-500);
-    --jp-info-color2: var(--md-cyan-300);
-    --jp-info-color3: var(--md-cyan-100);
-
-    /* Cell specific styles */
-
-    --jp-cell-padding: 5px;
-
-    --jp-cell-collapser-width: 8px;
-    --jp-cell-collapser-min-height: 20px;
-    --jp-cell-collapser-not-active-hover-opacity: 0.6;
-
-    --jp-cell-editor-background: var(--jp-layout-color1);
-    --jp-cell-editor-border-color: var(--md-grey-700);
-    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
-    --jp-cell-editor-active-background: var(--jp-layout-color0);
-    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
-
-    --jp-cell-prompt-width: 64px;
-    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
-    --jp-cell-prompt-letter-spacing: 0px;
-    --jp-cell-prompt-opacity: 1;
-    --jp-cell-prompt-not-active-opacity: 1;
-    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
-
-    /* A custom blend of MD grey and blue 600
-   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
-    --jp-cell-inprompt-font-color: #307fc1;
-    /* A custom blend of MD grey and orange 600
-   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
-    --jp-cell-outprompt-font-color: #bf5b3d;
-
-    /* Notebook specific styles */
-
-    --jp-notebook-padding: 10px;
-    --jp-notebook-select-background: var(--jp-layout-color1);
-    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
-
-    /* The scroll padding is calculated to fill enough space at the bottom of the
-  notebook to show one single-line cell (with appropriate padding) at the top
-  when the notebook is scrolled all the way to the bottom. We also subtract one
-  pixel so that no scrollbar appears if we have just one single-line cell in the
-  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
-  */
-    --jp-notebook-scroll-padding: calc(
-        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
-            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
-    );
-
-    /* Rendermime styles */
-
-    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
-    --jp-rendermime-table-row-background: var(--md-grey-900);
-    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
-
-    /* Dialog specific styles */
-
-    --jp-dialog-background: rgba(0, 0, 0, 0.6);
-
-    /* Console specific styles */
-
-    --jp-console-padding: 10px;
-
-    /* Toolbar specific styles */
-
-    --jp-toolbar-border-color: var(--jp-border-color2);
-    --jp-toolbar-micro-height: 8px;
-    --jp-toolbar-background: var(--jp-layout-color1);
-    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
-    --jp-toolbar-header-margin: 4px 4px 0px 4px;
-    --jp-toolbar-active-background: var(--jp-layout-color0);
-
-    /* Statusbar specific styles */
-
-    --jp-statusbar-height: 24px;
-
-    /* Input field styles */
-
-    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
-    --jp-input-active-background: var(--jp-layout-color0);
-    --jp-input-hover-background: var(--jp-layout-color2);
-    --jp-input-background: var(--md-grey-800);
-    --jp-input-border-color: var(--jp-border-color1);
-    --jp-input-active-border-color: var(--jp-brand-color1);
-    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
-
-    /* General editor styles */
-
-    --jp-editor-selected-background: var(--jp-layout-color2);
-    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
-    --jp-editor-cursor-color: var(--jp-ui-font-color0);
-
-    /* Code mirror specific styles */
-
-    --jp-mirror-editor-keyword-color: var(--md-green-500);
-    --jp-mirror-editor-atom-color: var(--md-blue-300);
-    --jp-mirror-editor-number-color: var(--md-green-400);
-    --jp-mirror-editor-def-color: var(--md-blue-600);
-    --jp-mirror-editor-variable-color: var(--md-grey-300);
-    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
-    --jp-mirror-editor-variable-3-color: var(--md-green-600);
-    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
-    --jp-mirror-editor-property-color: var(--md-blue-400);
-    --jp-mirror-editor-operator-color: #aa22ff;
-    --jp-mirror-editor-comment-color: #408080;
-    --jp-mirror-editor-string-color: #ff7070;
-    --jp-mirror-editor-string-2-color: var(--md-purple-300);
-    --jp-mirror-editor-meta-color: #aa22ff;
-    --jp-mirror-editor-qualifier-color: #555;
-    --jp-mirror-editor-builtin-color: var(--md-green-600);
-    --jp-mirror-editor-bracket-color: #997;
-    --jp-mirror-editor-tag-color: var(--md-green-700);
-    --jp-mirror-editor-attribute-color: var(--md-blue-700);
-    --jp-mirror-editor-header-color: var(--md-blue-500);
-    --jp-mirror-editor-quote-color: var(--md-green-300);
-    --jp-mirror-editor-link-color: var(--md-blue-700);
-    --jp-mirror-editor-error-color: #f00;
-    --jp-mirror-editor-hr-color: #999;
-
-    /* Vega extension styles */
-
-    --jp-vega-background: var(--md-grey-400);
-
-    /* Sidebar-related styles */
-
-    --jp-sidebar-min-width: 250px;
-
-    /* Search-related styles */
-
-    --jp-search-toggle-off-opacity: 0.6;
-    --jp-search-toggle-hover-opacity: 0.8;
-    --jp-search-toggle-on-opacity: 1;
-    --jp-search-selected-match-background-color: rgb(255, 225, 0);
-    --jp-search-selected-match-color: black;
-    --jp-search-unselected-match-background-color: var(
-        --jp-inverse-layout-color0
-    );
-    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
-
-    /* scrollbar related styles. Supports every browser except Edge. */
-
-    /* colors based on JetBrain's Darcula theme */
-
-    --jp-scrollbar-background-color: #3f4244;
-    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
-
-    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
-
-    /* hacks for setting the thumb shape. These do nothing in Firefox */
-
-    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
-    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
-
-    /* Icon colors that work well with light or dark backgrounds */
-    --jp-icon-contrast-color0: var(--md-purple-600);
-    --jp-icon-contrast-color1: var(--md-green-600);
-    --jp-icon-contrast-color2: var(--md-pink-600);
-    --jp-icon-contrast-color3: var(--md-blue-600);
-}
-
-:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{/*!
-
-Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
-Licensed under the Apache License, Version 2.0.
-
-*//*!
-
-Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
-Licensed under the Apache License, Version 2.0.
-
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#f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: 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-<h1 id="processing-and-analyzing-data-from-a-stationary-detector">Processing and analyzing data from a stationary detector<a class="anchor-link" href="#processing-and-analyzing-data-from-a-stationary-detector">&#182;</a></h1><p>This tutorial demonstrates how to process and analyze neutron counts of epithermal neutrons recorded with a stationary cosmic-ray neutron probe consisting of three lithium-foil detectors manufactured by Radiation Detection Technologies, Inc (RDT). The tutorial covers all the steps from reading the file with raw data obtained from the probe in the field, outlier removal, atmospheric corrections, and conversion of corrected neutron counts into volumetric soil water content.</p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h1 id="processing-and-analyzing-data-from-a-stationary-detector">Processing and analyzing data from a stationary detector<a class="anchor-link" href="#processing-and-analyzing-data-from-a-stationary-detector">¶</a></h1><p>This tutorial demonstrates how to process and analyze neutron counts of epithermal neutrons recorded with a stationary cosmic-ray neutron probe consisting of three lithium-foil detectors manufactured by Radiation Detection Technologies, Inc (RDT). The tutorial covers all the steps from reading the file with raw data obtained from the probe in the field, outlier removal, atmospheric corrections, and conversion of corrected neutron counts into volumetric soil water content.</p>
 <p>The tutorial assumes that you are working within the <code>Anaconda</code> environment, which has all the necessary modules. Also, make sure that the <code>CRNPy</code> library is installed in your machine.</p>
 <p>The raw dataset to follow this example can be obtained at the <a href="https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/stationary/rdt.csv">.csv file</a>.</p>
 <p>This device was installed in the vicinity of the KONA site of the National Ecological Observatory Network within the Konza Prairie Biological station (<code>39.110596, -96.613050, 320 meters a.s.l.</code>)</p>
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+<div class="jp-Cell-inputWrapper" tabindex="0">
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Import required libraries</span>
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+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Import required libraries</span>
 <span class="kn">import</span> <span class="nn">crnpy</span>
 
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 </pre></div>
-<div id="cell-1" class="clipboard-copy-txt"># Import required libraries
+<div class="clipboard-copy-txt" id="cell-1"># Import required libraries
 import crnpy
 
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 </div>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Load observations from a stationary detector</span>
-<span class="n">filepath</span> <span class="o">=</span> <span class="s2">&quot;https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/stationary/rdt.csv&quot;</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Load observations from a stationary detector</span>
+<span class="n">filepath</span> <span class="o">=</span> <span class="s2">"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/stationary/rdt.csv"</span>
 
 <span class="c1"># Read the DataFrame</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">,</span><span class="s1">&#39;barometric_pressure_Avg&#39;</span><span class="p">,</span><span class="s1">&#39;relative_humidity_Avg&#39;</span><span class="p">,</span> <span class="s1">&#39;air_temperature_Avg&#39;</span><span class="p">,</span><span class="s1">&#39;DP&#39;</span><span class="p">,</span><span class="s1">&#39;BattVolt&#39;</span><span class="p">,</span><span class="s1">&#39;counts_1_Tot&#39;</span><span class="p">,</span><span class="s1">&#39;counts_2_Tot&#39;</span><span class="p">,</span><span class="s1">&#39;counts_3_Tot&#39;</span><span class="p">])</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">,</span><span class="s1">'barometric_pressure_Avg'</span><span class="p">,</span><span class="s1">'relative_humidity_Avg'</span><span class="p">,</span> <span class="s1">'air_temperature_Avg'</span><span class="p">,</span><span class="s1">'DP'</span><span class="p">,</span><span class="s1">'BattVolt'</span><span class="p">,</span><span class="s1">'counts_1_Tot'</span><span class="p">,</span><span class="s1">'counts_2_Tot'</span><span class="p">,</span><span class="s1">'counts_3_Tot'</span><span class="p">])</span>
 
 <span class="c1"># Parse timestamps</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">])</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">])</span>
 
 <span class="c1"># Display a few rows of the dataset</span>
 <span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
-<div id="cell-2" class="clipboard-copy-txt"># Load observations from a stationary detector
+<div class="clipboard-copy-txt" id="cell-2"># Load observations from a stationary detector
 filepath = "https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/stationary/rdt.csv"
 
 # Read the DataFrame
@@ -1680,28 +1128,19 @@ <h1 id="processing-and-analyzing-data-from-a-stationary-detector">Processing and
 # Display a few rows of the dataset
 df.head(3)
 </div>
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@@ -1715,177 +1154,157 @@ <h1 id="processing-and-analyzing-data-from-a-stationary-detector">Processing and
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>timestamp</th>
-      <th>barometric_pressure_Avg</th>
-      <th>relative_humidity_Avg</th>
-      <th>air_temperature_Avg</th>
-      <th>DP</th>
-      <th>BattVolt</th>
-      <th>counts_1_Tot</th>
-      <th>counts_2_Tot</th>
-      <th>counts_3_Tot</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>2020-04-10 09:47:00</td>
-      <td>983.8</td>
-      <td>29.0</td>
-      <td>9.6</td>
-      <td>-7.4</td>
-      <td>14.4</td>
-      <td>848</td>
-      <td>716.0</td>
-      <td>742</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2020-04-10 10:47:00</td>
-      <td>982.3</td>
-      <td>25.0</td>
-      <td>10.9</td>
-      <td>-7.9</td>
-      <td>14.3</td>
-      <td>436</td>
-      <td>7200.0</td>
-      <td>796</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>2020-04-10 11:17:00</td>
-      <td>980.8</td>
-      <td>25.0</td>
-      <td>11.5</td>
-      <td>-7.4</td>
-      <td>13.7</td>
-      <td>389</td>
-      <td>396.0</td>
-      <td>354</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>timestamp</th>
+<th>barometric_pressure_Avg</th>
+<th>relative_humidity_Avg</th>
+<th>air_temperature_Avg</th>
+<th>DP</th>
+<th>BattVolt</th>
+<th>counts_1_Tot</th>
+<th>counts_2_Tot</th>
+<th>counts_3_Tot</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>2020-04-10 09:47:00</td>
+<td>983.8</td>
+<td>29.0</td>
+<td>9.6</td>
+<td>-7.4</td>
+<td>14.4</td>
+<td>848</td>
+<td>716.0</td>
+<td>742</td>
+</tr>
+<tr>
+<th>1</th>
+<td>2020-04-10 10:47:00</td>
+<td>982.3</td>
+<td>25.0</td>
+<td>10.9</td>
+<td>-7.9</td>
+<td>14.3</td>
+<td>436</td>
+<td>7200.0</td>
+<td>796</td>
+</tr>
+<tr>
+<th>2</th>
+<td>2020-04-10 11:17:00</td>
+<td>980.8</td>
+<td>25.0</td>
+<td>11.5</td>
+<td>-7.4</td>
+<td>13.7</td>
+<td>389</td>
+<td>396.0</td>
+<td>354</td>
+</tr>
+</tbody>
 </table>
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=cc9e9b6b-93fa-4eb5-b7dc-5b4522764dca">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
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-        <div class="CodeMirror cm-s-jupyter">
-            <div class="zeroclipboard-container">
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-                <div>
-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Remove rows with incomplete intervals</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">remove_incomplete_intervals</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="o">=</span><span class="s1">&#39;timestamp&#39;</span><span class="p">,</span> <span class="n">integration_time</span><span class="o">=</span><span class="mi">3600</span><span class="p">,</span> <span class="n">remove_first</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-3">
+<div>
+<span class="notice" hidden="">Copied!</span>
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+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Remove rows with incomplete intervals</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">remove_incomplete_intervals</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="o">=</span><span class="s1">'timestamp'</span><span class="p">,</span> <span class="n">integration_time</span><span class="o">=</span><span class="mi">3600</span><span class="p">,</span> <span class="n">remove_first</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
 <span class="c1"># Fill missing timestamps to create a conplete record</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">fill_missing_timestamps</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="o">=</span><span class="s1">&#39;timestamp&#39;</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="n">round_timestamp</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">fill_missing_timestamps</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="o">=</span><span class="s1">'timestamp'</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">'H'</span><span class="p">,</span> <span class="n">round_timestamp</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
-<div id="cell-3" class="clipboard-copy-txt"># Remove rows with incomplete intervals
+<div class="clipboard-copy-txt" id="cell-3"># Remove rows with incomplete intervals
 df = crnpy.remove_incomplete_intervals(df, timestamp_col='timestamp', integration_time=3600, remove_first=True)
 
 # Fill missing timestamps to create a conplete record
 df = crnpy.fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True)
 </div>
-        
-        </div>
-    </div>
-
 </div>
 </div>
-
+</div>
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
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-    
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-
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+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
 <pre>Removed a total of 48 rows.
 Added a total of 60 missing timestamps.
 </pre>
 </div>
 </div>
-
 </div>
-
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-
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 </div>
-<div id="cell-id=06ba6259-4ef4-432b-a4d3-1314a864e768" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=06ba6259-4ef4-432b-a4d3-1314a864e768">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>Utilize the <a href="../../../reference/#crnpy.crnpy.total_raw_counts"><code>total_raw_counts()</code></a> function to calculate the total counts across all detectors. After calculating the total counts, identify outliers using the <a href="../../../reference/#crnpy.crnpy.is_outlier"><code>is_outlier()</code></a> function.</p>
-
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=4f88a547">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
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-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Flag and fill outliers</span>
-<span class="n">cols_counts</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;counts_1_Tot&#39;</span><span class="p">,</span><span class="s1">&#39;counts_2_Tot&#39;</span><span class="p">,</span><span class="s1">&#39;counts_3_Tot&#39;</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<span class="notice" hidden="">Copied!</span>
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+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Flag and fill outliers</span>
+<span class="n">cols_counts</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'counts_1_Tot'</span><span class="p">,</span><span class="s1">'counts_2_Tot'</span><span class="p">,</span><span class="s1">'counts_3_Tot'</span><span class="p">]</span>
 <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">cols_counts</span><span class="p">:</span>
     
     <span class="c1"># Find outliers</span>
-    <span class="n">idx_outliers</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">is_outlier</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">],</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;scaled_mad&#39;</span><span class="p">,</span> <span class="n">min_val</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">max_val</span><span class="o">=</span><span class="mi">2000</span><span class="p">)</span>
+    <span class="n">idx_outliers</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">is_outlier</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">],</span> <span class="n">method</span><span class="o">=</span><span class="s1">'scaled_mad'</span><span class="p">,</span> <span class="n">min_val</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">max_val</span><span class="o">=</span><span class="mi">2000</span><span class="p">)</span>
     <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">idx_outliers</span><span class="p">,</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
     
     <span class="c1"># Fill missing values with linear interpolation and round to nearest integer</span>
-    <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">limit_direction</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">()</span>
+    <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">'linear'</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">limit_direction</span><span class="o">=</span><span class="s1">'both'</span><span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">()</span>
 </pre></div>
-<div id="cell-4" class="clipboard-copy-txt"># Flag and fill outliers
+<div class="clipboard-copy-txt" id="cell-4"># Flag and fill outliers
 cols_counts = ['counts_1_Tot','counts_2_Tot','counts_3_Tot']
 for col in cols_counts:
     
@@ -1896,88 +1315,78 @@ <h1 id="processing-and-analyzing-data-from-a-stationary-detector">Processing and
     # Fill missing values with linear interpolation and round to nearest integer
     df[col] = df[col].interpolate(method='linear', limit=3, limit_direction='both').round()
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Compute total cunts</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;total_raw_counts&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">total_raw_counts</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">cols_counts</span><span class="p">],</span> <span class="n">nan_strategy</span><span class="o">=</span><span class="s1">&#39;average&#39;</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Compute total cunts</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'total_raw_counts'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">total_raw_counts</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">cols_counts</span><span class="p">],</span> <span class="n">nan_strategy</span><span class="o">=</span><span class="s1">'average'</span><span class="p">)</span>
 </pre></div>
-<div id="cell-5" class="clipboard-copy-txt"># Compute total cunts
+<div class="clipboard-copy-txt" id="cell-5"># Compute total cunts
 df['total_raw_counts'] = crnpy.total_raw_counts(df[cols_counts], nan_strategy='average')
 </div>
-        
-        </div>
-    </div>
-
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-
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Plot total counts</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
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+</svg>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Plot total counts</span>
 <span class="c1"># Create a new figure and plot the data</span>
 <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>  <span class="c1"># Set the figure size</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;total_raw_counts&#39;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Raw Counts&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">.8</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'total_raw_counts'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Raw Counts'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">.8</span><span class="p">)</span>
 <span class="c1"># Set the labels for the x-axis and y-axis</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Date&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Total Raw Counts&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Date"</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Total Raw Counts"</span><span class="p">)</span>
 
 <span class="c1"># Set the title of the plot</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Total Raw Counts Over Time&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Total Raw Counts Over Time'</span><span class="p">)</span>
 
 <span class="c1"># Adjust size of axes labels</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;major&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'both'</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">'major'</span><span class="p">)</span>
 
 <span class="c1"># Show the plot</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
-<div id="cell-6" class="clipboard-copy-txt"># Plot total counts
+<div class="clipboard-copy-txt" id="cell-6"># Plot total counts
 # Create a new figure and plot the data
 fig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size
 ax.plot(df['timestamp'], df['total_raw_counts'], label='Raw Counts', color='black', linewidth=.8)
@@ -1994,77 +1403,57 @@ <h1 id="processing-and-analyzing-data-from-a-stationary-detector">Processing and
 # Show the plot
 plt.show()
 </div>
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" 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"/></a>
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-<div id="cell-id=118e790d-dbd8-4c0a-b607-776ffd3260a3" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=118e790d-dbd8-4c0a-b607-776ffd3260a3">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h3 id="neutron-count-correction">Neutron count correction<a class="anchor-link" href="#neutron-count-correction">&#182;</a></h3><h4 id="incomming-neutron-flux">Incomming neutron flux<a class="anchor-link" href="#incomming-neutron-flux">&#182;</a></h4><p>Find similar neutron monitors based on altitude and cutoff rigidity, which is estimated based on the geographic location using latitude and longitude values. See <a href="../../../reference/#crnpy.crnpy.get_incoming_neutron_flux"><code>get_incoming_neutron_flux()</code></a>, <a href="../../../reference/#crnpy.crnpy.find_neutron_monitor"><code>find_neutron_monitor()</code></a> and <a href="../../../reference/#crnpy.crnpy.correction_incoming_flux"><code>correction_incoming_flux()</code></a>.</p>
-<p>In this particular example we selected the <code>&quot;IRKT&quot;</code> monitor since it has a similar cuoff rigidity and altitude as our site. The reference neutron monitor will likely be different for your location. Note that the function only reports neutron monitors based on cutoff rigidity and leaves to the user the selection of a reference neutron monitor based on the altitude of the location.</p>
-
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="neutron-count-correction">Neutron count correction<a class="anchor-link" href="#neutron-count-correction">¶</a></h3><h4 id="incomming-neutron-flux">Incomming neutron flux<a class="anchor-link" href="#incomming-neutron-flux">¶</a></h4><p>Find similar neutron monitors based on altitude and cutoff rigidity, which is estimated based on the geographic location using latitude and longitude values. See <a href="../../../reference/#crnpy.crnpy.get_incoming_neutron_flux"><code>get_incoming_neutron_flux()</code></a>, <a href="../../../reference/#crnpy.crnpy.find_neutron_monitor"><code>find_neutron_monitor()</code></a> and <a href="../../../reference/#crnpy.crnpy.correction_incoming_flux"><code>correction_incoming_flux()</code></a>.</p>
+<p>In this particular example we selected the <code>"IRKT"</code> monitor since it has a similar cuoff rigidity and altitude as our site. The reference neutron monitor will likely be different for your location. Note that the function only reports neutron monitors based on cutoff rigidity and leaves to the user the selection of a reference neutron monitor based on the altitude of the location.</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Define study start and end dates</span>
-<span class="n">start_date</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span>
-<span class="n">end_date</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-7">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Define study start and end dates</span>
+<span class="n">start_date</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">'timestamp'</span><span class="p">]</span>
+<span class="n">end_date</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="s1">'timestamp'</span><span class="p">]</span>
 
 <span class="c1"># Define geographic coordiantes</span>
 <span class="n">lat</span> <span class="o">=</span> <span class="mf">39.110596</span>
@@ -2073,7 +1462,7 @@ <h3 id="neutron-count-correction">Neutron count correction<a class="anchor-link"
 <span class="c1"># Find stations with cutoff rigidity similar to estimated by lat,lon</span>
 <span class="n">crnpy</span><span class="o">.</span><span class="n">find_neutron_monitor</span><span class="p">(</span><span class="n">crnpy</span><span class="o">.</span><span class="n">cutoff_rigidity</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">),</span> <span class="n">start_date</span><span class="o">=</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="o">=</span><span class="n">end_date</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 </pre></div>
-<div id="cell-7" class="clipboard-copy-txt"># Define study start and end dates
+<div class="clipboard-copy-txt" id="cell-7"># Define study start and end dates
 start_date = df.iloc[0]['timestamp']
 end_date = df.iloc[-1]['timestamp']
 
@@ -2084,27 +1473,19 @@ <h3 id="neutron-count-correction">Neutron count correction<a class="anchor-link"
 # Find stations with cutoff rigidity similar to estimated by lat,lon
 crnpy.find_neutron_monitor(crnpy.cutoff_rigidity(lat, lon), start_date=start_date, end_date=end_date, verbose=False)
 </div>
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 <pre>
-Select a station with an altitude similar to that of your location. For more information go to: &#39;https://www.nmdb.eu/nest/help.php#helpstations
+Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations
 
 Your cutoff rigidity is 2.87 GV
      STID     NAME     R  Altitude_m  Period available
@@ -2116,14 +1497,10 @@ <h3 id="neutron-count-correction">Neutron count correction<a class="anchor-link"
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+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
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-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
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@@ -2137,214 +1514,192 @@ <h3 id="neutron-count-correction">Neutron count correction<a class="anchor-link"
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>STID</th>
-      <th>NAME</th>
-      <th>R</th>
-      <th>Altitude_m</th>
-      <th>Period available</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>13</th>
-      <td>DRBS</td>
-      <td>Dourbes</td>
-      <td>3.18</td>
-      <td>225</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>40</th>
-      <td>NEWK</td>
-      <td>Newark</td>
-      <td>2.40</td>
-      <td>50</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>28</th>
-      <td>KIEL2</td>
-      <td>KielRT</td>
-      <td>2.36</td>
-      <td>54</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>21</th>
-      <td>IRKT</td>
-      <td>Irkustk</td>
-      <td>3.64</td>
-      <td>435</td>
-      <td>True</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>STID</th>
+<th>NAME</th>
+<th>R</th>
+<th>Altitude_m</th>
+<th>Period available</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>13</th>
+<td>DRBS</td>
+<td>Dourbes</td>
+<td>3.18</td>
+<td>225</td>
+<td>True</td>
+</tr>
+<tr>
+<th>40</th>
+<td>NEWK</td>
+<td>Newark</td>
+<td>2.40</td>
+<td>50</td>
+<td>True</td>
+</tr>
+<tr>
+<th>28</th>
+<td>KIEL2</td>
+<td>KielRT</td>
+<td>2.36</td>
+<td>54</td>
+<td>True</td>
+</tr>
+<tr>
+<th>21</th>
+<td>IRKT</td>
+<td>Irkustk</td>
+<td>3.64</td>
+<td>435</td>
+<td>True</td>
+</tr>
+</tbody>
 </table>
 </div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Download incoming neutron flux data from the Neutron Monitor Database (NMDB).</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Download incoming neutron flux data from the Neutron Monitor Database (NMDB).</span>
 <span class="c1"># Use utc_offset for Central Standard Time.</span>
-<span class="n">nmdb</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">station</span><span class="o">=</span><span class="s2">&quot;IRKT&quot;</span><span class="p">,</span> <span class="n">utc_offset</span><span class="o">=-</span><span class="mi">6</span><span class="p">)</span>
+<span class="n">nmdb</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">station</span><span class="o">=</span><span class="s2">"IRKT"</span><span class="p">,</span> <span class="n">utc_offset</span><span class="o">=-</span><span class="mi">6</span><span class="p">)</span>
 </pre></div>
-<div id="cell-8" class="clipboard-copy-txt"># Download incoming neutron flux data from the Neutron Monitor Database (NMDB).
+<div class="clipboard-copy-txt" id="cell-8"># Download incoming neutron flux data from the Neutron Monitor Database (NMDB).
 # Use utc_offset for Central Standard Time.
 nmdb = crnpy.get_incoming_neutron_flux(start_date, end_date, station="IRKT", utc_offset=-6)
 </div>
-        
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+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=64f2ce8d-6f2f-45ae-894c-9691e9285f72">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-                    </svg>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Interpolate incoming neutron flux to match the timestamps in our station data</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;incoming_flux&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">interpolate_incoming_flux</span><span class="p">(</span><span class="n">nmdb</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">],</span> <span class="n">nmdb</span><span class="p">[</span><span class="s1">&#39;counts&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">])</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Interpolate incoming neutron flux to match the timestamps in our station data</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'incoming_flux'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">interpolate_incoming_flux</span><span class="p">(</span><span class="n">nmdb</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">],</span> <span class="n">nmdb</span><span class="p">[</span><span class="s1">'counts'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">])</span>
 </pre></div>
-<div id="cell-9" class="clipboard-copy-txt"># Interpolate incoming neutron flux to match the timestamps in our station data
+<div class="clipboard-copy-txt" id="cell-9"># Interpolate incoming neutron flux to match the timestamps in our station data
 df['incoming_flux'] = crnpy.interpolate_incoming_flux(nmdb['timestamp'], nmdb['counts'], df['timestamp'])
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Compute correction factor for incoming neutron flux</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;fi&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_incoming_flux</span><span class="p">(</span><span class="n">incoming_neutrons</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;incoming_flux&#39;</span><span class="p">],</span>
-                                          <span class="n">incoming_Ref</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;incoming_flux&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Compute correction factor for incoming neutron flux</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'fi'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_incoming_flux</span><span class="p">(</span><span class="n">incoming_neutrons</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'incoming_flux'</span><span class="p">],</span>
+                                          <span class="n">incoming_Ref</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'incoming_flux'</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 </pre></div>
-<div id="cell-10" class="clipboard-copy-txt"># Compute correction factor for incoming neutron flux
+<div class="clipboard-copy-txt" id="cell-10"># Compute correction factor for incoming neutron flux
 df['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df['incoming_flux'],
                                           incoming_Ref=df['incoming_flux'].iloc[0])
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-<h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" href="#atmospheric-correction">&#182;</a></h4><p>This section is about correcting the atmospheric variables. First, fill the missing values in the atmospheric data, then correct the count for atmospheric variables using <a href="../../../reference/#crnpy.crnpy.correction_humidity"><code>correction_humidity()</code></a> and <a href="../../../reference/#crnpy.crnpy.correction_pressure"><code>correction_pressure()</code></a>.</p>
-
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+<h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" href="#atmospheric-correction">¶</a></h4><p>This section is about correcting the atmospheric variables. First, fill the missing values in the atmospheric data, then correct the count for atmospheric variables using <a href="../../../reference/#crnpy.crnpy.correction_humidity"><code>correction_humidity()</code></a> and <a href="../../../reference/#crnpy.crnpy.correction_pressure"><code>correction_pressure()</code></a>.</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Fill NaN values in atmospheric data and neutron counts</span>
-<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;barometric_pressure_Avg&#39;</span><span class="p">,</span><span class="s1">&#39;relative_humidity_Avg&#39;</span><span class="p">,</span> <span class="s1">&#39;air_temperature_Avg&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">&#39;barometric_pressure_Avg&#39;</span><span class="p">,</span><span class="s1">&#39;relative_humidity_Avg&#39;</span><span class="p">,</span> <span class="s1">&#39;air_temperature_Avg&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">&#39;pchip&#39;</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">limit_direction</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">)</span>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Fill NaN values in atmospheric data and neutron counts</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">'barometric_pressure_Avg'</span><span class="p">,</span><span class="s1">'relative_humidity_Avg'</span><span class="p">,</span> <span class="s1">'air_temperature_Avg'</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">'barometric_pressure_Avg'</span><span class="p">,</span><span class="s1">'relative_humidity_Avg'</span><span class="p">,</span> <span class="s1">'air_temperature_Avg'</span><span class="p">]]</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">'pchip'</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">limit_direction</span><span class="o">=</span><span class="s1">'both'</span><span class="p">)</span>
 
 <span class="c1"># Compute the pressure correction factor </span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;fp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_pressure</span><span class="p">(</span><span class="n">pressure</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;barometric_pressure_Avg&#39;</span><span class="p">],</span> <span class="n">Pref</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;barometric_pressure_Avg&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">L</span><span class="o">=</span><span class="mi">130</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'fp'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_pressure</span><span class="p">(</span><span class="n">pressure</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'barometric_pressure_Avg'</span><span class="p">],</span> <span class="n">Pref</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'barometric_pressure_Avg'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">L</span><span class="o">=</span><span class="mi">130</span><span class="p">)</span>
 
 <span class="c1"># Calculate the absolute humidity (g cm^-3) and the vapor pressure correction factor</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;abs_humidity&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">abs_humidity</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;relative_humidity_Avg&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;air_temperature_Avg&#39;</span><span class="p">])</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;fw&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_humidity</span><span class="p">(</span><span class="n">abs_humidity</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;abs_humidity&#39;</span><span class="p">],</span> <span class="n">Aref</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'abs_humidity'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">abs_humidity</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'relative_humidity_Avg'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'air_temperature_Avg'</span><span class="p">])</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'fw'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">correction_humidity</span><span class="p">(</span><span class="n">abs_humidity</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'abs_humidity'</span><span class="p">],</span> <span class="n">Aref</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 </pre></div>
-<div id="cell-11" class="clipboard-copy-txt"># Fill NaN values in atmospheric data and neutron counts
+<div class="clipboard-copy-txt" id="cell-11"># Fill NaN values in atmospheric data and neutron counts
 df[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']] = df[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')
 
 # Compute the pressure correction factor 
@@ -2354,46 +1709,41 @@ <h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" hre
 df['abs_humidity'] = crnpy.abs_humidity(df['relative_humidity_Avg'], df['air_temperature_Avg'])
 df['fw'] = crnpy.correction_humidity(abs_humidity=df['abs_humidity'], Aref=0)
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Plot all the correction factors</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Plot all the correction factors</span>
 <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>  <span class="c1"># Set the figure size</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;fp&#39;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Pressure&#39;</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;tomato&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;fw&#39;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Humidity&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;navy&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;fi&#39;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Incoming Flux&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;olive&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Date&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Correction Factor&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'fp'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Pressure'</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">'tomato'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'fw'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Humidity'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'navy'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'fi'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Incoming Flux'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'olive'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Date"</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Correction Factor'</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Correction Factors for Pressure, Humidity, and Incoming Flux&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Correction Factors for Pressure, Humidity, and Incoming Flux'</span><span class="p">)</span>
 </pre></div>
-<div id="cell-12" class="clipboard-copy-txt"># Plot all the correction factors
+<div class="clipboard-copy-txt" id="cell-12"># Plot all the correction factors
 fig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size
 ax.plot(df['timestamp'], df['fp'], label='Pressure',color='tomato', linewidth=1)
 ax.plot(df['timestamp'], df['fw'], label='Humidity', color='navy', linewidth=1)
@@ -2403,119 +1753,89 @@ <h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" hre
 ax.legend()
 ax.set_title('Correction Factors for Pressure, Humidity, and Incoming Flux')
 </div>
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"
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" 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"/></a>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Apply correction factors</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;total_corrected_neutrons&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;total_raw_counts&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;fw&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;fp&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;fi&#39;</span><span class="p">])</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Apply correction factors</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'total_corrected_neutrons'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'total_raw_counts'</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s1">'fw'</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'fp'</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s1">'fi'</span><span class="p">])</span>
 </pre></div>
-<div id="cell-13" class="clipboard-copy-txt"># Apply correction factors
+<div class="clipboard-copy-txt" id="cell-13"># Apply correction factors
 df['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>  <span class="c1"># Set the figure size</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>  <span class="c1"># Set the figure size</span>
 
 <span class="c1"># Subplot 1: Raw and Corrected Counts</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;total_raw_counts&#39;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Raw Counts&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">.8</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;total_corrected_neutrons&#39;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Corrected Counts&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;teal&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">.8</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Date&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Counts&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'total_raw_counts'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Raw Counts'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'dashed'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">.8</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'total_corrected_neutrons'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Corrected Counts'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'teal'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">.8</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Date"</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Counts'</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Raw and Corrected Counts&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Raw and Corrected Counts'</span><span class="p">)</span>
 </pre></div>
-<div id="cell-14" class="clipboard-copy-txt">fig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size
+<div class="clipboard-copy-txt" id="cell-14">fig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size
 
 # Subplot 1: Raw and Corrected Counts
 ax.plot(df['timestamp'], df['total_raw_counts'], label='Raw Counts', color='black', linestyle='dashed', linewidth=.8)
@@ -2525,101 +1845,75 @@ <h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" hre
 ax.legend()
 ax.set_title('Raw and Corrected Counts')
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-<pre>Text(0.5, 1.0, &#39;Raw and Corrected Counts&#39;)</pre>
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+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+<pre>Text(0.5, 1.0, 'Raw and Corrected Counts')</pre>
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iBGYOXMmLl++jA4dOkCj0SAsLAxbtmzBwoUL0atXL3h4eGD+/PkYOnQoGjRogH79+sHb2xtXrlxBamqq4N5Wv359bNq0CRMmTECDBg3g5uaGbt26YcCAAdi8eTM++eQTHDt2DM2aNYPZbMatW7ewefNmHDhwAG+99Rbq1KmDvn374tdff0VCQgKaNm2KI0eO4O7duw6dj0ajwfbt29G+fXu0bNkSffr0QbNmzaDRaHDjxg2sX78e3t7e+OGHH6DRaDBr1iwMHjwYrVq1Qt++fYXU7+XKlcP48eOF/Q4ZMgTz5s1Dx44d8fHHH+PZs2dYtmwZatSoISQ8yS7169cHkJEMpGPHjlCpVPjggw8wdOhQxMXFoW3btihTpgzu37+PRYsWoU6dOnZLHDAYDEaeko+ZEBkMBuONhqR+P3funNV3ZrOZq1ChAlehQgXOZDJxFouF+/HHHzl/f39Op9NxdevW5YKDg61SaBN69+7NAeA2bdokLDMYDJyLiwun1Wqt0pDL4egxSdrun3/+2WofkEk7vm3bNq5atWqcTqfjqlevzm3fvt3medhi69atXIcOHbgiRYpwarWaK1myJPf+++9zx48fF6137949rlevXpyXlxfn5OTENWzYkAsODhatQ1K/b9myxeo4H330Eefq6mqzHb///jtXv359ztnZmXN3d+cCAwO5SZMmcU+ePBGtt3v3bq5p06acs7Mz5+HhwTVs2JDbsGGD8H1ycjLXr18/zsvLyyoNvsFg4GbNmsXVqFGD0+l0nLe3N1e/fn1uxowZXEJCgrBeWloaN3bsWK5o0aKcq6sr161bN+7hw4cOpX4nxMfHc1OnTuUCAwM5FxcXzsnJiatZsyY3ZcoULioqSrTupk2buLp163I6nY4rUqQI9+GHH3KPHj2y2ufatWu58uXLc1qtlqtTpw534MCBl7qHTCYTN2bMGM7Hx4dTKBRCGnhyT/j6+nJarZYrW7YsN2LECKt2MxgMxqtEwXG5FLnMYDAYDAaDwWAwGAwBFrPFYDAYDAaDwWAwGHkAE1sMBoPBYDAYDAaDkQcwscVgMBgMBoPBYDAYeQATWwwGg8FgMBgMBoORBzCxxWAwGAwGg8FgMBh5ABNbDAaDwWAwGAwGg5EHsKLGDmCxWPDkyRO4u7tDoVDkd3MYDAaDwWAwGAxGPsFxHJKSklCqVCkolfZtV0xsOcCTJ0/g5+eX381gMBgMBoPBYDAYBYSHDx+iTJkydtdhYssB3N3dAWRcUA8Pj3xuDYPBYDAYDAaDwcgvEhMT4efnJ2gEezCx5QDEddDDw4OJLQaDwWAwGAwGg+FQeBFLkMFgMBgMBoPBYDAYeQATWwwGg8FgMBgMBoORBzCxxWAwGAwGg8FgMBh5AIvZYjAYjNcYjuNgMplgNpvzuykMhiwajQYqlSq/m8FgMBh5AhNbDAaD8ZpiMBgQFRWF1NTU/G4Kg2EThUKBMmXKwM3NLb+bwmAwGLkOE1sMBoPxGmKxWBAREQGVSoVSpUpBq9WyouyMAgfHcYiJicGjR49QqVIlZuFiMBivHUxsMRgMxmuIwWCAxWKBn58fXFxc8rs5DIZNfHx8EBkZCaPRyMQWg/GGEZeWhmSDAWU9PfO7KXkGS5DBYDAYrzFKJevmGQUbZnFlMN5c2qxaBf8FC/K7GXkKewszGAwGg8FgMBiMV879Fy/yuwl5DhNbDAaDwWAwGAwG45WjfAMs20xsMRgMBoPBYDAYjFcOE1sMBoPBYLxiBg0aBIVCAYVCAY1Gg4CAAEyaNAnp6emvvC2XLl1C7969Ubx4cTg5OaFSpUoYNmwY7ty580rbcfz4cSgUCrx4A1xuGAzGm8ObELPJxBaDwWAwChydOnVCVFQUwsPDMX/+fPz222+YNm3aK21DcHAwGjduDL1ej3Xr1iE0NBRr166Fp6cnvv3221faFgaDwXgdef2lFhNbDAaDwSiA6HQ6lChRAn5+fggKCkL79u1x6NAh4fvY2Fj07dsXpUuXhouLCwIDA7Fhwwbh++DgYHh5ecFsNgMALl++DIVCgcmTJwvrDB06FP3795c9fmpqKgYPHowuXbpg9+7daN++PQICAtCoUSPMmTMHv/32m7DuiRMn0LBhQ+h0OpQsWRKTJ0+GyWQSvi9XrhwWSLJt1alTB9OnTxc+KxQKLF++HN27d4eLiwsqVaqE3bt3AwAiIyPRpk0bAIC3tzcUCgUGDRoEANi6dSsCAwPh7OyMokWLon379khJScnGlWYwGIz8g7kRMhgMBuO1Q6/XIzExUfiXlpYGAEhLSxMt1+v1AICUlBTRcoPBAABITk4WLScCg16WmJgoCJ6ccv36dfz777/QarXCsvT0dNSvXx979uzB9evXMXz4cAwYMABnz54FALRo0QJJSUm4dOkSgAxBVKxYMRw/flzYx4kTJ9C6dWvZYx44cADPnz/HpEmTZL/38vICADx+/BhdunRBgwYNcOXKFSxduhR//vkn/ve//2X7PGfMmIE+ffrg6tWr6NKlCz788EPExcXBz88P27ZtAwDcvn0bUVFRWLhwIaKiotC3b18MGTIEoaGhOH78OHr06AGO47J9bAaDwcgPmNhiMBgMxmvHzJkz4enpKfwbM2YMAGDMmDGi5TNnzgQA9OjRQ7R89erVAIBGjRqJlh85cgQAUKZMGdHyW7duZbuNwcHBcHNzg5OTEwIDA/Hs2TNMnDhR+L506dL44osvUKdOHZQvXx5jxoxBp06dsHnzZgCAp6cn6tSpI4ir48ePY/z48bh06RKSk5Px+PFj3L17F61atZI9flhYGACgatWqdtv566+/ws/PD4sXL0bVqlURFBSEGTNmYO7cubBYLNk650GDBqFv376oWLEifvzxRyQnJ+Ps2bNQqVQoUqQIAMDX1xclSpSAp6cnoqKiYDKZ0KNHD5QrVw6BgYEYOXIk3NzcsnVcBoPByC/ehJgtdX43gMFgMBivlilTpmDChAnCZ41GAwBYtGgR5s2bJyzX6XQAgO3bt4usU05OTgCAM2fOiASFi4sLAODRo0ei47m6uma7jW3atMHSpUuRkpKC+fPnQ61Wo2fPnsL3ZrMZP/74IzZv3ozHjx/DYDBAr9cLbQCAVq1a4fjx4/j8889x8uRJzJw5E5s3b8apU6cQFxeHUqVKoVKlSrLHd9Q6FBoaiiZNmogGDM2aNUNycjIePXqEsmXLOnzOtWrVEv52dXWFh4cHnj17ZnP92rVro127dggMDETHjh3RoUMH9OrVC97e3g4fk8FgMPKTN8GyxcQWg8FgvGHodDpBSNE4OzvD2dnZarktsWTLguLh4fFyDeSPWbFiRQDAihUrULt2bfz555/4+OOPAQA///wzFi5ciAULFiAwMBCurq4YN26c4OIIAK1bt8aKFStw5coVaDQaVK1aFa1bt8bx48cRHx9v06oFAJUrVwYA3Lp1C02aNHmpc1EqlVbizWg0Wq1HRC9BoVDYtY6pVCocOnQI//77Lw4ePIhFixbh66+/xpkzZxAQEPBSbWYwGIxXwesvtZgbIYPBYDAKOEqlEl999RW++eYbIb4sJCQE7733Hvr374/atWujfPnyVunYSdzW/PnzBWFFxNbx48dtxmsBQIcOHVCsWDHMnj1b9nuSgr1atWo4ffq0SEyFhITA3d0dZcqUAQD4+PggKipK+D4xMRERERHZugYkXk0a/6ZQKNCsWTPMmDEDly5dglarxY4dO7K1bwaDwcgv3gQ3Qia2GAwGg1Hg6d27N1QqFZYsWQIAqFSpkmDVCQ0NxYgRIxAdHS3axtvbG7Vq1cK6desEYdWyZUtcvHgRd+7csWvZcnV1xfLly7Fnzx68++67OHz4MCIjI3H+/HlMmjQJn3zyCQBg5MiRePjwIcaMGYNbt25h165dmDZtGiZMmAClMuMV27ZtW6xZswYnT57EtWvX8NFHH0GlUmXr/P39/aFQKBAcHIyYmBgkJyfjzJkz+PHHH3H+/Hk8ePAA27dvR0xMDKpVq5atfTMYDEZ+8Sa4ETKxxWAwGIwCj1qtxujRozF79mykpKTgm2++Qb169dCxY0e0bt0aJUqUQFBQkNV2rVq1gtlsFsRWkSJFUL16dZQoUQJVqlSxe8z33nsP//77LzQaDfr164eqVauib9++SEhIELINli5dGnv37sXZs2dRu3ZtfPLJJ/j444/xzTffCPuZMmUKWrVqhXfeeQddu3ZFUFAQKlSokK3zL126NGbMmIHJkyejePHiGD16NDw8PPDPP/+gS5cuqFy5Mr755hvMnTsXnTt3zta+GQwGIzfZcuMGtt686dC6b4LYUnAsR2yWJCYmwtPTEwkJCbkSi8BgMBh5TXp6OiIiIhAQECAktGAwCiLsXmUwXi/G7tsHjuOwqEuXLNet+MsvuBcfD+4VF61/WbKjDZhli8FgMBgMBoPBYOQKJosF6VRhd3sQy5Y5m6UyChNMbDFyhbi0NFyTxEswGAwGg8FgMN4sTBYL0h0sZk8SZBgcXL8wwsQWI1d4f+tW1Fq2LL+bwWAwGAwGg8HIR8w5sGxlJbb2hoVBMWPGS7ctP2Bii5ErPEtJye8mMBgMBoPBYDDyGRPHZVts6bMQW5eo8hmFDVbUmJErGF9j8y+DwWAwGAwGI2tarVyJp8nJ8Pf0dGh9koswK8uWpRDn88tXy9bMmTPRoEEDuLu7w9fXF0FBQbh9+7bVeqdPn0bbtm3h6uoKDw8PtGzZUihsCQBxcXH48MMP4eHhAS8vL3z88cdITk4W7ePq1ato0aIFnJyc4OfnZ7NQJSNnmF7jwEYGg8FgMBgMRtb8c/8+7sTGOmzZIuNHJrbyiBMnTmDUqFH477//cOjQIRiNRnTo0AEplEva6dOn0alTJ3To0AFnz57FuXPnMHr0aKFYJAB8+OGHuHHjBg4dOoTg4GD8888/GD58uPB9YmIiOnToAH9/f1y4cAE///wzpk+fjt9///2Vnu/rDBNbDAaDwWAwGAwADosto4Niy1yIxVa+uhHu379f9HnlypXw9fXFhQsX0LJlSwDA+PHjMXbsWEyePFlYjy5EGRoaiv379+PcuXN46623AACLFi1Cly5dMGfOHJQqVQrr1q2DwWDAihUroNVqUaNGDVy+fBnz5s0TiTJGzjEyscVgMBgMBoPBgONii0zW67NYn1m2comEhAQAQJEiRQAAz549w5kzZ+Dr64umTZuiePHiaNWqFU6dOiVsc/r0aXh5eQlCCwDat28PpVKJM2fOCOu0bNkSWq1WWKdjx464ffs24uPjrdqh1+uRmJgo+sewD4vZYjAYDAaDwWAAWSe8ENbjRRZzI3wFWCwWjBs3Ds2aNUPNmjUBAOHh4QCA6dOnY9iwYdi/fz/q1auHdu3aISwsDADw9OlT+Pr6ivalVqtRpEgRPH36VFinePHionXIZ7IOzcyZM+Hp6Sn88/Pzy92TfQ1hboQMBoORO6xcuRJeXl753QwGg8EQcS8uDhuuXXNoXUcsWxzHIY7PwdBw+XKb68WnpTGxlRuMGjUK169fx8aNG4VlFn4AP2LECAwePBh169bF/PnzUaVKFaxYsSLP2jJlyhQkJCQI/x4+fJhnx3pdYGKLwWDkJk+fPsWYMWNQvnx56HQ6+Pn5oVu3bjhy5Eh+N02W/BBIx44dQ5cuXVC0aFG4uLigevXq+Pzzz/H48eNX2g4mDhmMN4PhwcHot327Q+s6IrYS9XqHwlCKzJ6N45GRDh23IFIgxNbo0aMRHByMY8eOoUyZMsLykiVLAgCqV68uWr9atWp48OABAKBEiRJ49uyZ6HuTyYS4uDiUKFFCWCc6Olq0DvlM1qHR6XTw8PAQ/WPYh4ktBoORW0RGRqJ+/fo4evQofv75Z1y7dg379+9HmzZtMGrUqBzv12AwyC43Go053md+8dtvv6F9+/YoUaIEtm3bhps3b2LZsmVISEjA3Llz87t5DAbjNUSR9SoCjoit56mpUFMJ7+yNJR/woUZcIbRw5avY4jgOo0ePxo4dO3D06FEEBASIvi9XrhxKlSpllQ7+zp078Pf3BwA0adIEL168wIULF4Tvjx49CovFgkaNGgnr/PPPP6IX6qFDh1ClShV4e3vn1em9UbAEGQwGI7cYOXIkFAoFzp49i549e6Jy5cqoUaMGJkyYgP/++09Y78GDB3jvvffg5uYGDw8P9OnTRzSxNn36dNSpUwfLly9HQEAAnJycAAAKhQJLly7Fu+++C1dXV/zwww8AgF27dqFevXpwcnJC+fLlMWPGDJioAcOLFy8wYsQIFC9eHE5OTqhZsyaCg4Nx/PhxDB48GAkJCVAoFFAoFJg+fTqAjBjgL774AqVLl4arqysaNWqE48ePi8535cqVKFu2LFxcXNC9e3fExsbavT6PHj3C2LFjMXbsWKxYsQKtW7dGuXLl0LJlSyxfvhxTp04V1t22bRtq1KgBnU6HcuXKWQkxhUKBnTt3ipZ5eXlh5cqVADKEr0KhwPbt29GmTRu4uLigdu3aOH36NADYPfdff/0VlSpVgpOTE4oXL45evXrZPS8Gg1GwUSgcl1v2xJbJYoFixgzcjo1FMRcXYXmSXm9zGzLOLIzuhPmajXDUqFFYv349du3aBXd3dyF+ytPTE87OzlAoFJg4cSKmTZuG2rVro06dOli1ahVu3bqFrVu3AsiwcnXq1AnDhg3DsmXLYDQaMXr0aHzwwQcoVaoUAKBfv36YMWMGPv74Y3z55Ze4fv06Fi5ciPnz5+fbub8ucByHY5GRLEEGg1FI4DgOSTYsPHmJu1br0Is6Li4O+/fvxw8//ABXV1er74m7msViEYTWiRMnYDKZMGrUKLz//vsiMXP37l1s27YN27dvh0qlEpZPnz4dP/30ExYsWAC1Wo2TJ09i4MCB+OWXX9CiRQvcu3dPyFY7bdo0WCwWdO7cGUlJSVi7di0qVKiAmzdvQqVSoWnTpliwYAGmTp0qTA66ubkByPDcuHnzJjZu3IhSpUphx44d6NSpE65du4ZKlSrhzJkz+PjjjzFz5kwEBQVh//79mDZtmt1rtGXLFhgMBkyaNEn2e3KNLly4gD59+mD69Ol4//338e+//2LkyJEoWrQoBg0alOVvQfP1119jzpw5qFSpEr7++mv07dsXd+/etXnu58+fx9ixY7FmzRo0bdoUcXFxOHnyZLaOyWAwChbZsWwZzGZwHCfb75OkGEfCw1HMxQVP+dq4iXo9vJ2dResSSxaxepksFqiUBcIxz2HyVWwtXboUANC6dWvR8r/++kt4EYwbNw7p6ekYP3484uLiULt2bRw6dAgVKlQQ1l+3bh1Gjx6Ndu3aQalUomfPnvjll1+E7z09PXHw4EGMGjUK9evXR7FixTB16lSW9j0XuBodjXarV+d3MxgMhoMkGQzw/OmnV37chMmT4aHTZbne3bt3wXEcqlatane9I0eO4Nq1a4iIiBCSGK1evRo1atTAuXPn0KBBAwAZroOrV6+Gj4+PaPt+/fph8ODBwuchQ4Zg8uTJ+OijjwAA5cuXx/fff49JkyZh2rRpOHz4MM6ePYvQ0FBUrlxZWIfg6ekJhUIhck1/8OAB/vrrLzx48ECY/Pviiy+wf/9+/PXXX/jxxx+xcOFCdOrUSRBOlStXxr///mtVGoUmLCwMHh4egqu9LebNm4d27drh22+/FfZ98+ZN/Pzzz9kWW1988QW6du0KAJgxYwZq1KiBu3fvomrVqjbP3dXVFe+88w7c3d3h7++PunXrZuuYDEZh4+rVq+jTpw9u3bqV303JE7Jj2QIyMhI6qa2lBhFOh8LDUbloUVznw4HkJgJpkQUUznpb+Sq2HPW7nDx5sqjOlpQiRYpg/fr1dvdRq1atN3pWbffu3Th69CgWLFiQq/vNrjnXwnE4Eh6OtymxzGAwXh3uWi0S7PSneXlcR3D0vRAaGgo/Pz9Rttjq1avDy8sLoaGhgtjy9/e3EloAROVCAODKlSsICQkRXAoBwGw2Iz09Hampqbh8+TLKlCkjCC1HuHbtGsxms9U2er0eRYsWFc6je/fuou+bNGliV2zZmi2WEhoaivfee0+0rFmzZliwYAHMZrPI0pcVtWrVEv4mIu/Zs2c2RfHbb78Nf39/lC9fHp06dUKnTp3QvXt3uFAuQwzG68ajR4/w4sWL/G5GnqHMptjaeesWnNVqvCfpJ4hL4LVnz/BBzZrYHhoKIMOyJYWsSzyozIUwbCVfxRbj1TFy5Ej4+/sjNDQU1atXh8ViyfYMhRzZNeXuDQtDtw0bwGXhJsNgMPIGhULhkIUpv6hUqRIUCkWuzQzLuSLKLU9OTsaMGTPQo0cPq3WdnJzgLHFtcYTk5GSoVCpcuHDBStgQN8OcULlyZSQkJCAqKipL61ZWKBQKK4ErlzBEo9GItgEyMwbL4e7ujosXL+L48eM4ePAgpk6diunTp+PcuXMscyHjtUWv1yMhIQEWiwXKQubq5gjZHTX23bYNRZydESsRW3QiDH9PT+FvWbHFiyyphasw8frdCQwrUlNTER0djbVr1+L+/fsAMgYBuYEqm4KNxXYxGAx7FClSBB07dsSSJUuQkpJi9T2ZNa5WrRoePnwoKs1x8+ZNvHjxwiqDrSPUq1cPt2/fRsWKFa3+KZVK1KpVC48ePcKdO3dkt9dqtTBL+re6devCbDbj2bNnVvskLnfVqlXDmTNnRNvRSUDk6NWrF7RaLWbPni37PX2NQkJCRN+FhISgcuXKgvjz8fFBVFSU8H1YWBhSU1PtHl+K3LkDGTUv27dvj9mzZ+Pq1auIjIzE0aNHs7VvBqMw8fz5c6SnpyMxMTG/m1JgcKUmagh/UEntNNRElFyCDKPEfbAwuhEysfUGcP/+fZhMJnz00UdCjIG9GUkA6LNlC/bfvZvlvrNr2cquCZrBYLx5LFmyBGazGQ0bNsS2bdsQFhaG0NBQ/PLLL2jSpAkAoH379ggMDMSHH36Iixcv4uzZsxg4cCBatWpl5SLoCFOnTsXq1asxY8YM3LhxA6Ghodi4cSO++eYbAECrVq3QsmVL9OzZE4cOHUJERAT27dsnuPuVK1cOycnJOHLkCJ4/f47U1FRUrlwZH374IQYOHIjt27cjIiICZ8+excyZM7Fnzx4AwNixY7F//37MmTMHYWFhWLx4sV0XQgDw8/PD/PnzsXDhQnz88cc4ceIE7t+/j5CQEIwYMQLff/89AODzzz/HkSNH8P333+POnTtYtWoVFi9ejC+++ELYV9u2bbF48WJcunQJ58+fxyeffCKyYjmC3LkHBwfjl19+weXLl3H//n2sXr0aFosFVapUyda+GYzCRLNmzQAA8fHx+dyS3GXx2bNINhgEq/a/2ag/6y7jSTGVSmJEp3438JM2e8PC8A4fHiSdpGeWLUaBpFq1arh+/TouXbqEypUrg+M4eFJmWzm23LyJmadO5XpbiNgqjKk7GQzGq6F8+fK4ePEi2rRpg88//xw1a9bE22+/jSNHjgiJlRQKBXbt2gVvb2+0bNkS7du3R/ny5bFp06YcHbNjx44IDg7GwYMH0aBBAzRu3Bjz588XyowAGWnUGzRogL59+6J69eqYNGmSYNFp2rQpPvnkE7z//vvw8fERrE5//fUXBg4ciM8//xxVqlRBUFAQzp07h7JlywIAGjdujD/++AMLFy5E7dq1cfDgQUHg2WPkyJE4ePAgHj9+jO7du6Nq1aoYOnQoPDw8BDFVr149bN68GRs3bkTNmjUxdepUfPfdd6LkGHPnzoWfnx9atGiBfv364Ysvvsh2XJXcuXt5eWH79u1o27YtqlWrhmXLlmHDhg2oUaNGtvbNYBQmqlevjqJFi752YmvMvn1Yd/WqMIZrtmKFw9u6ZRGvq6HEFrFiLT57FnvCwkTLCCxmi/FK2Hj9OjpUqIAiDsYQREVFISwsDOnp6di7dy+CgoLwzz//oHHjxna3i3XAlYQWTY64FJJZEZPFAm02grMZDMabRcmSJbF48WIsXrzY5jply5bFrl27bH4/ffp0oeYTja0kHB07dkTHjh1t7q9IkSJYYWeQsXTpUkEMEjQaDWbMmIEZM2bY3G7IkCEYMmSIaNnnn39uc31C+/bt0b59e7vr9OzZEz179rT5falSpXDgwAHRMjrAv1y5clbXy8vLy2qZ3LlL64kxGK8769atQ2xsrKwLdGEnUa/PMmZLbiI9q200KhV6VquGbaGhgtWK3o/UksUsW4xXwqi9e3HfwWw369evR5UqVYTinGfOnIHRaHQo+DwuLS3LdegZBjlTsRQyK5IqE4DNYDAYDAaDUVgJDw/HwIED0aJFi/xuSq6TqNdnmVhNzuoktUxJUSuV2NqnD7pUqiQIKVqySd0IWcwW45WgUigcvtkiIyORlJSEKlWq4O+//8Z3330HAA4FQEclJ+P28+d218muOyARW96zZmVrOwaDwWAwGIyCTFpaGp4+fYoLVAKI1wVHLFtyY1NDFonRiBuhRqmUtWxJxdrWmzcdaG3BgomtQohKqXTYZzU2NhZAhitI165d8b///Q/lypXDt99+i6FDh2a5fdUlS+x+Tz9YjrSJJchgMBgMBoPxOpKWloYjR47YdRsuiHAcl2WNwwQHLFtyE/BZZaEm2QjVtsSWZPul58/b3V9BhImtQkh2LFscx6FDhw74/vvvUbRoUQwePBhLliyBn58f/vzzTzx58uSl2kI/EI60iUktBoPBYDAYryMDBgzAN998g0ePHuV3U7JF1SVL0JXP/meLBEcsWzl0IyT/C26EdixbhTHen4mtQog6G5atwYMHY+7cudBoNIiPj0fp0qXRoEEDNGrUCEBG2uGXgW6HIy6FtGWrMGaUYTAKG1nNVjIY+Q27RxmvC/Xq1UOXLl1eeiL7VXMnNhb7bJT7Ic+nQzFbObFsyYktme17VqsGgIktxitCRd2QcigUCmzbtg0AEBgYiJo1a2Zsx9+gs2fPxu+//44RI0Zg586d2Lt3r93j2TuWJZtuhPQDJFcpnMFg5A6kVlJ2C9QyGK8ag8EAIPMdxWAUVoYPH46DBw8iICAgy3qmhQULLbayWFc6Dizi7JxlzJacZUsas1Xe2xtb+/QBAOgKYT/BUr8XQhxxI7xz5w4AYODAgWjZsiWGDh0KjUYDs9mMqKgoDB48GD/99BNKly6dZQHLJL0e3jbSzJuz6UZIP0Dx6ek298tgMF4OlUoFLy8vPHv2DADg4uKS5awkg/GqsVgsiImJgYuLC9RqNiRh5C6PExNxOzYWbQMCXs3xHj9GvXr1cPr06VdyPDkOh4cj0NcXxd3ccmV/lmxYtugx3pru3VHW0xPds6h9KBezRaxpBrMZ6SaTqBZXYbRssZ6tEJJVgoxOnTqhaNGiAICnT58Ky48cOYJmzZrB2dkZSUlJcHJywrfffpvl8ez529IPloUPsLT3MNLtfpGenuWxGQxGzilRogQACIKLwSiIKJVKlC1blk0GMHKdD7dvx4n798FNm/ZKjpeWlgZnZ2esWLEC7du3F4qXv0reXrMGpdzd8XjChFzZH5lIvxMbizt80jWCYsYMXP3kEwQWLy5aFwD8PDxQ1NnZyo1Q6jZsz43Q86efoFQoEODlJazPxBbjlZCVZWv//v0IDAwEAKSkpMDV1RUAUKtWLVy/fh2rV6/G8uXL0a9fPyQnJ6N48eJo166daB8apVIQWfb8baWij4P9JBi0OEvmXUcYDEbeoFAoULJkSfj6+sLIatsxCiharRZKJYtqYOQ+r7IA7sOHDwWxNXPmTBQtWjRfxBYARCcnO7xutw0b7H6fVYhIeHx8ptji110dFITmZcviXny81YS9dPxqz40w3WQCkGn9ApjYYrwiskqQUaZMGbz33nsAxGKrfv36+O677zBw4EDMnj0bHh4e2LFjBwICAqzElpNaDSMvhhy1bAEZD5rSzoNgtpPOk8Fg5A0qlYrFwzAYjDeOV2ktLVu2LJYtW4a2bdti1apVL5Uk40V6Oq5FR6OFv3+Ots9OmZ2QBw/sfp+dIsJk3QG1awPImLiXxmxJx69ZpX4n+yHoCqG7MZtKKoRklSDj0aNHOHHiBABg48aNaNWqFQAgLi4OH3zwASpVqoRixYrBw8MDOp0OeplEFU7UzWwvuJE8EOSxzuqhpB8yuf0O2LEDVRcvtrsPBoPBYDAYjKx4lY6pZcqUQY0aNVCsWDGULFnypcTWiOBgtFy5MsfbKxQKpBgMOBwebnc9juOyTFZmK9M0vbzzunU49/gxLBwnEnpalQoWjsPUY8dwIjISgPU4UdaNULpOIbdsMbFVCHEkQcZ6vl5C2bJl4eHhASBzhufPP//E8+fP7YoteubArhsh3w4izoiYuhMbCwVV1I/jOKQajTj7+LGwTE5sbb5xA7clPsEMBoPBYDAY2eVVWbY4jsOjR4/QokULnD59Gm3btkWFChVyvL8rVLx9TlAqFDgSEYG316xBeHy8zfXSTKZsTZLLLecA7L97F2uuXoXZYoGKuuZEJB2NiMA1PnZYuj/ajZCMN+1Ztgqj2Cp8tjiGkCAjIiICrq6u8PX1FX3funVrYVagSJEiuHbtGipVqiR8bzKZ8M4776BChQpo06ZNljVO7LkRdl63DgAwvnFj/HjqlPCAPEhIEK3367lzGL1vn2iZnNhi4dEMBoPBYDByg1c1pjDxsUUA4OzsjH79+r3U/p6/ZMkOpUIhpEi/ExuL8t7esuvZsmp5zJyJJn5+2P3BB1Zi7P0aNfD3nTtWY0MlbwhQUcKIiKQUoxGpfNywlWVLLhuhpD10ZsXCmPqdWbYKIcSy1WrePMzmxQ7NgAEDoNFooNfrodfrBcvW0aNHAWTM9Pzzzz8AgKCgIPTo0UP4TKBnFbKqkQAAk5s3B2D9EJH9XImOttpGVmyxbFQMBoPBYDBygVc1pjBQCb+cnZ1x+PBhrHwJN0AXviSPI/VL5VAqFML4y94+kmyIrSSDAQfv3cNa3lpFo1IqoTeZBGFErrBKobBp2Uo1GpFGxJYdy5YtN8Ky/Di2c8WKGFavns3zKagwsVUIIQkyHhYrhr9CQ62+//jjj+Hr64ukpCQAEMSWq6srHj9+jGfPniExMREAsGvXLgDA/fv3RfuwcBx+7dIFpd3dHUpkQWYyrvNmYvKgkAdH7mHXM8sWg8FgMBiMPCKvxxTp6elISUmxEltXrlzBnj17crxfN60WAJAgI4YG79qFhn/8YXd7BTInu+3F+GcVrwXIx2yZOQ56ypoHZAo8acwWAKQYDLYtW/z4UaNUwsR/Jz2mn6cnAGDvhx+iWT5leHwZmNgqhNAJMtJkHhStVovvvvsOiYmJUKvVcHJyAgD06tULS5cuRYsWLQBkzPjcvXsXAISMhQSzxYKGpUtDp1bbdSMU2sQ/XC3++stqPwAgtwdi2TJZLBiwYwcMZjOzbDEYDAaDwcgV8npM0bZtW5QoUULI9jp06FCUKlUKOp1OJMCyCxEpcvVIN1y7hnNZJN9QUrH99mKyshJbSjs5AqTlexx1I5QKKZWMZUt6zFLu7nbbWdBhYqsQIkqQwd+kFotFSHRhMBgwb948hIeH4/Hjx0Jn8/jxY/zvf/+DRqOBn58fgIwCfBm7Ed8KZHYiKikJbVetkm0HbeZV2aiRYiuNJ5AptmJSUrD26lU8Skxkli0Gg8FgMBiFgtOnTyM5ORkeHh4YNmwYUlNToVKpbCYfcxQyxovnx2g0zryLoT0S9HrM4LNS23MjzEpsKWRcA4nbH7G6kfGdUmZdhUIBFZ8ZMdWGGyG9XzJmjJOct6tGg+fPn+PQoUN221tQYWKrEKKi6my91agRTCYTunbtCicnJ1j45b/++ivefvttLFu2TNiOiK7Y2Fg8fPgQAJDKB2Hevn1bFOBJxFaayQQOYmE1dt8+9Ny8WRTAKK3pQL6z60ZoMuFeXJywjlKhYJYtBoPBYDAYuUJ26k3lhHnz5uHrr7/Gs2fP8McffwiZoOvWrYuePXvmeL8kfCOFKkZ/6N49XHn6FM526kzRE9sXo6IA2HYjnPPvv/j22DHRMmmsFLFW0dfRQ6uFUqEQrG6xZNJexrIFZFjpjBYLUvkxpi1LGRFbHMfhWUqK6DudWo1r167hk08+kT/xAg4TW4UQlUKBT3hf4JS0NPz+++/Yv38/gAxBNWfOHGHdhg0bCn/rdDoAQM+ePYUHqmrVqgCAyZMni2pCWCQPTBolxBadPYvtoaEiAWUltiTma7mHa9ft26i4aJEw22E0m5lli8FgMBgMRq6Q12OKkSNHYvLkyYiLiwMA3LlzBwqFAm+99RaGDRuW4/0SgZRKia0Oa9ei49q1QvIMOeRi7G2Jm4mHDuHas2ei7H6hz58L9bCAzNgvejz4Y7t2cNVokMCLrWF//w0gwxAgjdkCxEkygIzJdwWAQwMGiNYjYis+Pd0qgZpOpUJYWBjCs6gbVlBhYqsQoqZu+lt37iCWr0vVr18/KBQKtGvXDgDg5uaGTp06Cevu378fZSWBhaVLlxb+pr+TPjBSky5g+wHmOM7KsiXnRkhqP5AHUM9ithgMBoPBYOQSeT2m6NatG4oVKwaDwQBvb2+hzM7Zs2cxYcKEHO+XxMqnUWILAIq5uNgVW3JWLFuWrUb8+K8Mn0QNAD7bvx+tqdARqWtgcVdXuOt0cNVqrZJ3rLx8GfvCwkRuhEBm3BadIEOjUqF9+fKI//JLYT21Uondt29jyK5dcJWco5NajacvWXssP2FiqxBCzzCkGgzo3r07Fi9ejFmzZiE5ORl169bFsGHDEC8pZJeeno6QkBDRshIlSgh/R1KzGY6ILVtVxT/evRu3nz8HQCXIkFmXmKBJkKU0sw2DwWAwGAxGQeXEiRPQ6/UwGAzQ8hkEAbx0fJHJYoFWpRJZtgDA19XVvmVLRljZipHy5ROjFXF2FpYVpf4G+JgtyrJFxnK0ZYvwMDER00+csBK4cpYtIsi8+ARuQKYhYdft26LlQIYboSefkbAwwsRWIUQ0a6BUonr16nj27Bn8/PywfPlyAMB3332H69evi7YbPXo0xo8fL1pWo0YNfPrppwCAGzduAMiwTEnFVqxMgT3pAxw2ZgwAYE9YGO7w1jZbMVsj6tcXXBPj+QdWz9wICy3h8fE4zccBMjJRzJiBMP5ZYDAYDMarJa/HFERYFC9eHOPGjROWa7Xal0qQYTSb4a7VCuMkYuHycXUVEmRYOA5/XLiAWadOCdvJWbFseSGR9PJOVAxYvERAEcsWGQ8KYkvGskWQugBqpWJLxtUQEHtt6SRxaTqVCu3btxdKGRU2mNgqhNCWLRc3N/zxxx/47rvvAEAQU4GBgejQoYNou4iICGzdutVqf61btwYAJCQkINlggPK775BmMslatn6mLGPSB9jHxQVARvacdL6DkEvj6aHTwZ2aASL7TjeZmBthIaXNqlVoumJFfjejQEHiFm/ExORzSxgMBuPNJK8TZJAxi5+fHyZPniwsf9lshCaLBR46nSBQiOjafOMGbvD1TNOMRgwPDsbkI0eE7eRitmy5EdLCqSxvNXoumVgXYrbIefLruWo0smnpAWuxZeVGaLHIZrCmxZaTRGw5qdWIi4tDQECA7DELOkxsFUJoy1aFSpVw584d4bMzbwJ+/vy5KLugPfr06YMuXbogISFB9JDIia1Jhw8Ly6SugcRUbLRYhI5BSJBBPewWjhNmOoBMq5neZGKWrUIKcwG1hkw4SH3PGQwG401g0/Xr+e7xQMSQNMtebrF8+XJs2LABFy5cwMiRI4XlFStWxPTp03O8XyMvtkJjYtBnyxbRGCqGHzPJWaxkLVs2xBYdU088k54mJ4vWsXCc4EYY+dlnOMwntUgyGDD39Gn5tkvFloxlSxrXBQDelAsjnbQDyLB03b17F8WKFZM9ZkGHia1CCD0jEBMbi4SEBACAl5cXAgMDhULFZskNTyfDkNKpUycEBASIOqRUKvWmbIIMyQNMC6g0iWUrXZJWnl6X7FsvMyPDYBRWSP0SV8qKy2AUFp4mJ2dZg4fx+hKXlvbSk2jbb93C0YiIXGpRziBDelsx5i9L165d0aBBAzx58gT//vuvsLxkyZIYPHhwjvdLLFubbtzA33fuyAqrdJnfRzZmy8a5k+UdK1QQrErSlOsmi0WIsfL38kJR3oPpOm9dk0NqtSLCKivLVueKFfFrly4ArC1bOpUKjx49whHKileYYGKrEKKmZgSeRkfjxYsXAIDmzZujUaNGcOIDC728vETb/fHHH2jfvr3sPseMGYNOnTqJZkWmfvut8HdcWprVzJD0AaZnKoh/MdkfnTo+LS1NJLaEmC3mRsh4jSADVbWNgt8MRkGm5Ny56LR2bX43g5FPFJ09G0N2736pfZgtlgIziSonQnKDXr16oWLFilYJMiIjI9G0adMc79doNsPTyQnx6elIN5msXPMAYBxf8ofGkWyEYbGxSNTrYbJY8GuXLpjQpAmUCgWUCoXVuiaLRbZ21jxJmAqNnNUKyBwX2rJsKRQKIVZLGrPlpFYjmbe65ZWVMi9ho4BCiOimVygwbNgw3Lx5E3///TfGjh2L8uXLY/78+bh8+bJouxcvXmDu3Lmy+9yyZQs2b94setDKlC4tpASNS0sTFdcDrGeKFAqF4JsruBHK1IrgAJuWLSa1CieFr+vLe4jYsuXCwWAUdML42kGMN5Mzjx691PZmjpO1vrxKyASurbill+XcuXMAYCW2LBaL8F124XjXPQ++NioAWSszKVpMI1tnS3LulRcvxuBdu2CSWJjkJgZNFots7awOFSqIPhfnMxsC1pYtMj5IM5ky3BIlx71w4QKO8cWVSRuklq0hgwYJhgRLIXynMrFVCJHOCDRq1AjVqlXD06dPMWXKFKjVavTq1QtXrlwRrTd9+nT0799fdp/nzp3DP//8I+qQihUtivvjxmFuhw6IS09HvMSVUG4QSXxzrSxbEqFGiy0i4phli/E6QV6O0kmJK0+f5vsA5FWQYjAgkre6MxiMwgcpy5JTzBZLvvd1yhyIreysS8Ys9evXxxdffCEsd3Jygslkcjh2Xu74HlotKnh7A4BsMgo5a5ejdbZiUlJgtlhEAktObJmJOJKMzaSWJ1o82UtKsuTsWRgl+1uzZg1+/PFHAJnJNDjJmHH9unVCcgxpiExhgImtQgh9U7u6u+ODDz7A7t27kZycjK1bt0KtVsPPzw9du3YVbXf37l1cu3ZNdp9OTk7Q82Zlgl/p0lAqFCjm4oKYlBSruC05P2AioohlKzw+HhaOE7kRQqEQPdQpfIeeTiXIkDMTl5w7F8P5SuUMRkEnib+vpc9Jnd9+w8yTJ/OjSa+Uwbt2IWDhwvxuBuMlYFNfbzYvLbYKgmWL/99RAWU0m6H5/ns84GPhs4JYsypXroygoCBhOUlRnpSU5HBbCaStb1eogG9btrSZ+U/ONdLRmC2NSpVh2aJEjy3LlpwboTSBhb390P3I2P370WrlStH+Vq9ejcN88jUhdkzGanfgwAG0adMGykLoml/4WswQ3dSexYohLi4ORqMR7u7uADJVf6pMbSxb6HQ6pKenix5K0klUKlIEt2NjrWoqyAWcCm6E/KzEB9u24Y8LF8SF+Si/XCDTxVBvNguzRKdl3BeeJidje2iow+fEYOQ2UUlJDvuL27JsAS8/iCkM3HdwsMJgMAom0tCB7FKQYrYcFVtkPWkKdDmePXuGL7/8EqdOncKyZcswbdo04TtXV1fs2LFDyBBNYzSbEWqnJAgRTO0CAvBRnTpw1+nkxZaDli05L6SopCScefw4S8sWnSCDRurmR4sn6bpyb0x6HVo8Ee8orUxbzpw5g/Lly0MtOXZhgImtQgj9QMQnJuL+/ftwcnISxFbwuXOAZNYBAKpXr25zny1btkS3bt1ED+riX34BANTw9cWzlBSEx8eLtrHnRkjPZkW+eCF2I1QqRbMipEO/9fy5kAmn87p1WHf1qtX+8yrIlfFyFMaA1ZxQat48bJAUC7eFXhK3SCN1wXgdYVaRwg9z62bkhL9v30bndesKhmWLv4flhIkc2XmT7dq1Cxs2bIDRaER4eDjiqBhHhUKBoKAgIc6IZnZICKr/+qvN/ZJxGBnruWu1SMhlN8LQ58+RajSKxpNySSvMNmK2pO+wJXwWQcA6ZksO+jrXrFlT+JtM2D+KjBSWBfDJ3gwGA/78808kJiZmuf+CBhNbhRD6Rk4zGvHs2TPodDo4Oztj0KBBaLN5M9C6NUqVKiXa7scff8Qnn3yCGElqTwBo0aIF+vTpI3ooSUE+D50O3k5OVjMxjrgRAsC9+HixGyEAjvqcajTCRaPBn5cuCcsS9Xr037HDav+OdpiMV8ubIbUyiHLQLYTM6Fo4zuq+lbpgMBgMxutAmXnz8O7Gjdh/926BiNkik12OWraIJ4IjE4ibN2/GhQsX0KZNGyQmJgqug4QGDRrg1KlTVttJ06tLIW0lk9c2LVs2YrGcJULIVup3QDyelFvLUTfCdlSxYaloS5WxkNJCccyYMWjbti2ATIFJn0H4Z59h9erVePr0aca2hdAzhImtQojoRlapsGTJEtSvXx8KhQJDhw4FALTp3h3//fefsNrft2/jreBgVO/QAb5z5lh1JIcOHcLXX38t6pBqUpYwjUqVIzdCIMP9T7ouufG0KhXC4+NRv2RJ4buqdorWMcsWI7/ROCiUDJTY0v3vf5h06JDw3Ztg2WIwGG8ej6nJqIJg2SJCw1GxlZ3ssQmUq7Sc2DIajSJrFyErGUcm54jwcNNqHXYj/O/RIzT18xMts3dOtGVLTmAei4yUdSPUSt6D9Oc25cqJvsvKKqdSqTBlyhQAme/XBnXrCt+/ePEC48ePF0JkcpJ0JL9hYqsQQmYYiqvVgEqFmoGB8OYz1owaNQoAYFKpRMkwDty7BwCY+L//AbCeaXj69ClCQkJEHZIblcpTrVTiRXo6ilL+x3azEVIPQzSZxaHWJ0PN7lWrirYD7Fuv8ip9KwBs375dKAjNYNhC42BwruBGyHHgAPz877/C/fsmWLaYCxqD8WZjtlheujDyy2LKoWXLEWgXwYkTJ6J3796i7z09PdG/f3+rvjArq5nJYhHqXgG8G6FM6nc5N8ILUVFoVLq01f7i09Kw5cYNq+ugzsKydeDePdyNi7OybNHnNLlZM9Hn37p1E62bVdtXrlyJWbNmidqTxtfUAjJqs8bGxgqfWTZCxiuB3IzT2rQBALRq2xY3btwAACHd+8kzZ0TZCMkDpuf/l2YW1Ol0VtkIBw4cKPytUijwIj0dvpQAk6sgLjcQFUzmfLYZACBDTXc+kw89KyJXT4LmCm9Kzm169uyJ77//Pk/2zXh9yIlli0AsvtJZQQajIMLkMuNlKBCWLX5M46hXDN1fpxqNUMyYIRt6AUCwxgBA3bp1Ub58edH3Xl5estkIsxJ0RklKdnedTlawyO3lZkwMahUvLlq25Nw5fHP0KPps3Wo10U5brKQisG6JEgCAiBcvbBYqDvT1xcz27UXLpPFdcudLjzXPnz8vZCMkY8hEyiJopNo8ePBgFClSRLYtBZl8FVszZ85EgwYN4O7uDl9fXwQFBeH27duy63Ich86dO0OhUGDnzp2i7x48eICuXbvCxcUFvr6+mDhxopWZ8fjx46hXrx50Oh0qVqyIlStX5tFZ5T3kIfRycwMAaJydkSy9maniegB1Y/NiKV5i1jWp1RnZCKkHIJQXcOSYCXo9SvDHBIB+27dbtU1uEPkiPR1KAD0pV0Ef/mEhGW0O7NkjfNfbTiIPAFglqR+Wm2g0mjzbd15i5jM5Pnjw4JUfW/3ddw5lbirskBeRXMYmOUjMFv1MkRfdm+BGyAbqDMabDYnZSjUaMXLPnnxJpJRtN0ISs4VM9zdp9thVq1YhNTUVWq0Wo0aNwq1bt9C0aVMcOHBAtN78+fPh4+NjdYysroLJYhFNXLvzboTFXV3x57vv2twuIT0dUcnJqOnrK1putFjwDz82kIote5YtN34cGRYXZ7N2lptkrGkPeh+0dwdtFSOTmRwlLmmxZTabZTM8FnTyVWydOHECo0aNwn///YdDhw7BaDSiQ4cOSJGZRViwYIGsW4rZbEbXrl1hMBjw77//YtWqVVi5ciWmTp0qrBMREYGuXbuiTZs2uHz5MsaNG4ehQ4daPRiFBfIQXj1/HgBgrFEDjbdtAwC0a9cuYyWq8jhAdTS8TzFt2Uo1GvHhtWsYPnGiqEOaSBXoI26ExSmxJds2GzP2ao7DNr6NAKDmf0tnXtyQhBldK1WCaxYPb9E8fNC02eg4ChKkM0rIh3Tb9oJvXyfIzKijboRyli3iUmtrlvB1JDtuOQwG4/Xh+rNnSDeZcPDePSw9fz5f0sBnlSAjNCYG8fx46FlKiihBBgmHkE4iDxo0CK6urmjfvj2eODlhweHDePr0qVXmQX9/f9kxRVaiU28yiS1bvNhSKZXwksluSHiemgq1UomiLi5W35F3TopEONqL2SK/V0R8vM0Mg/R47Y9u3fArlZWQMKV5c3zbsqUoXTz9d+fOna3aE0VNHBenLHWrV6/GPT4spjCRr2Jr//79GDRoEGrUqIHatWtj5cqVePDgAS5cuCBa7/Lly5g7dy5WrFhhtY+DBw/i5s2bWLt2LerUqYPOnTvj+++/x5IlS4SMJcuWLUNAQADmzp2LatWqYfTo0ejVqxfmz5//Ss4zt7GqQ8DPYlyNjsbIkSMBAAonJyGOC8gcKFZq3BgAhM4FyBRedVu2FHdI1IOnImKLciMk9KhWLbNtNh5IN0kHEcu7IApZc0gnbLHASM1oyHVKxWQ6ktwigMqokxXHIyPxYwEpTkssuWXKlMnnlry+GCRBy1lBx2yRF0sEXz7hTRGoAPLdjehN505sLL45ejS/m8F4A0kzmaA3m3GJL1CbneQTuUVWMVvVf/0VfbZuxb8PH6L4nDmiNhK3b3sTRjvc3bHs+XPZBBnz58/H48eP8e2334qWZ9X7X3v2DFWoRGEkG6FKobBKfkGTYjTCVaORncwjViVpog25bIT1S5ZE35o1hb47OiXF5nvPlfIGGlqvHj5t0MBqnR/btcN3bdqIrFm02Pr888/RsGFDAJljyCLUWNPNzQ2TJ08WPrNshC8JmZWn/TFTU1PRr18/LFmyBCV4/1Ga06dPIzAwUKR8O3bsiMTERCGO6fTp02gv8Snt2LEjTp8+LdsOvV6PxMRE0b+CxLkzZwBkPCRKiwU16tQBANRetgw9evQAALh7e+Py5cvCNiTpRJxKBbVSifj0dFx48gQ3qVmdYePHizqkihUqCH+rlUok6fXwkRE62/r0Ef6WWrZciOWKPByhocCFC1DynZeTRGz9999/WLhgQWa7ZTrIrCxfjvLixQtMmjRJ+JySkoLPP//c4e1H792LrwvIIEar1aJq1ap4/vx5fjfltYWIrezGbNFB4ltu3gTwZlh7iCcCE1v5y9x//8UPOZwUYklOGC9LuskkxG3nxySTI26E0cnJwkSYhXIjHLt/PwBg/n//iWLUGzZsKBo7ABnjV09PT9EyX19fNG/eXBTbBWRt2Tr3+DEaU0kuSJ0tlVKJEm5umNuhA0rxdVVpUgwGuGm1slYo8j6SuvzLWbbODx+O9T17Cn33i/R0Ib5eSnbcCHU2LFu7d+/G7NmzRe35qG9f4ftr167hp59+QrNmzeDh4cGyEb4MFosF48aNQ7NmzUQFzsaPH4+mTZvivffek93u6dOnIqEFZJocSU5+W+skJiYiTZIoAsiIJfP09BT++dmZScgPgnfvBgCoVSooLRYYZF6IFgBly5YVPpOOJtZkQtVixaA3mfDWH3+g6/r1QvzWvZgYUYdUkoqxUikUSDEabZqwY2JicOnSJWhVKpF1i1ihUl68yFiwaRPw99/CjUfcCEmmwqJFi6IuLx4BcQp5Qm7Njj158gTLli0DkOGGt3DhQlG6/KwoSANmrVYLo9GIR48e5XdTXluybdni19ebzeCQEcO0js8QarZYYDCbce7x47xoaoFC7hlmvDpehWA6cPfuG1PYnJE90k0mwWKSH5YtIUGGHRdGC8chhe+nyBjIbLHgaEQEgAyx9cXBg8L6np6eSExMFHkPXblyxcqzxMfHB6dOnYKLZJI6q7FDksGAIlS4BLFsEevUhCZN4CEJFQF4y5ZWK2vZIi6RsZIxr72YLXqiTG7s17VSJXz61lt2z4Vmx/vvC3/Twmvbtm34+uuvwXGcMJkZeeeO8H0qLxBDQkKgVqtZNsKXYdSoUbh+/To2btwoLNu9ezeOHj2KBZSl41UwZcoUJCQkCP8ePnz4So+fFSTeqXnTptCp1QiLjrZaR9qx0CLKVaMRZpoCvLwEN8I0pRKr1qwR1ps3d27mMfkH0slGYP/UqVNRr149NC5dGmUoU7qrRgMXjQYqSSerkli2Or79NoAM69Jz6nzoh72Uuzuc1OpcS/+elpaGpKQkcByHp0+f4quvvsIhqhZSVhSkoUVMTAzu3btX4KywrxNEbNkKFLa1PglI9nRyElwuYlJT8XNICBouX54HLS0YkPOXqw/DKBw4cqdbOA6d1q3Dg3yIF2UUfNJNJkFcyFm2LkVFoe2qVXl2fEcsWxaOQxIfviB4JEjaSsdyHTp0CMuWLRONTcuUKWMVnyV1KyRIr8KpBw9EKfLNHGcVs6U3m0UiSm7SL9lggKtGI/uOIpNesRLLFr1PaRgIPf7ylBF3wf36oRk1qZ8VjSkxSrsfxsXFISQkBImJiUIbLvIeXEBmTHrVqlVx4sQJVK5c2eFjFhQKhNgaPXo0goODcezYMdHMwNGjR3Hv3j14eXlBrVZDzQ/Me/bsidatWwMASpQogWiJ2CCfiduhrXU8PDxks5rodDp4eHiI/hUkiInYZDTCzckJSonpGrDuWOjPzhoNgsPCAGTMmHTftCnjC40G23bsENZr2LChkP6dFlt7+/WzOl6VKlXQs2dPfNuqFeqXKgUAKO3ujvdr1IC7VgslnyBj+fLlaNiwIWrVqJFxSJLs4+JFAEB8fDweREYK+6XrdRnNZrhrtbnmikBXIydJWbIzY1KQLFt6/kWhzyJtPiPnkJcwPYO/LywM/9mwJhLLFglI9tDphH1MO34cu6mZu9eRNKMRGqUSW3nXSUb+kNd2LXJPl1u4MI+PxJCy9eZNeP70U343wy56k0mwLslZtjZcv45j1Ds/L44PyIutH/75B0DGu5xkHNTLJDYCMgUSPUZo166d8Hz5SjIAAkCjRo1kx5hSK3CLv/7C71SuApPFInIFJFYgepnUemW2WJBiMGRYtmSEGBlL2XMjlLoE0gLQ005ijpywjg95AQAlybDt5ZVZZ4tKmf8Tf4936NAB7u7ueFwIPULyVWxxHIfRo0djx44dOHr0qFVygsmTJ+Pq1au4fPmy8A/ICDr866+/AABNmjTBtWvX8Izypz106BA8PDxQnU8h3qRJExw5ckS070OHDqFJkyZ5eHZ5R21eqFy/ehUJcXGwODmhBT+7YGtWho590qlUuMMXiKOzErpVqwZlUJDwmeM4rOEtXSpKbJWVEXdHjx4Vsg2Sfab+73+Y1ro1PHQ6FHFzQ9++fXHq1Ck0atQI6SQrG8k8ww9YK1WqhCrUrEVUUhL+5IWY0WKBs0aTa5YtkjkoPT1dMFNnJ/CyILnNEB/mcpLK7YzcQxBb1LIu69ej6Z9/yq5PXlTEsuWh04mew3A+RuB1JdlgQMPSpTPr7DFynQ+3b8eKS5fsrmPPjbDSokUIjYl5qTbQxUnzw03sTWbX7dtZ1qXMbzhQZTDy4Z1Jji03bvjm2DEAGe2aw8fwE2uO9F4m73uj0QjUrAmo1ShSpIggtuTiiCwWi2yoCn0VyH5pcWeW1Nkik9L2LFsTDhxAitGYEbNlx7IlrbFqT2xVoPInZCc2yxH8qHHkqFGjhL/JdYynxvSHDx+Gl5cX/v77b1SqVKlQZhLPV7E1atQorF27FuvXr4e7uzuePn2Kp0+fCjdniRIlULNmTdE/ICMWiQizDh06oHr16hgwYACuXLmCAwcO4JtvvsGoUaOg482en3zyCcLDwzFp0iTcunULv/76KzZv3ozx48fnz4m/JMOHDgWQIVQMqamAQoHh9esDsH6QCPSD7KRWI1GvR2l3d5zi02u28vdHsrc3LJIHasKECQAyH0idWi07a5JKzZaQwsfx8fEwm83w0OlQNzAQBoMBK1euxKJFixDg7w+AcsniO8TY2Fjc5/2kgYzB7NC//waQYdlyzkU3QhcXF5QuXRqenp6w8PsMDAx0ePuCZNkymUzQarVo2LAh9Ho9kqnq628aHMdh282buS6G5VK5A7CZEpesn2I0QqdSWbngSmu3vE6YLRYk6vXwcnLKteeVYc36a9cwKyQkx9vfjYvDHt7LIafQs98pRiMmHz6M//EWA0bekt8Tfjtv3UIQFfpx20aCJhIPlR9i3JZli+7HjWaz0B/bmrAmn4xGI9CrF7QNGuD+/fvC2GHu3LlQzJiBq5QX1ZMnTwAAf/NjGOm+gIz4LCAzmRhpq0qm9pTIsiV57yQbDBmWLY1G9p1ERGeS5L2jsiO2jg4ciCg+aVhe1tL89NNPUatWLQBAIj8JWZeqtzpnzhxERETg3LlzKF26tLBuYSJfxdbSpUuRkJCA1q1bo2TJksK/TcStzQFUKhWCg4OhUqnQpEkT9O/fHwMHDsR3330nrBMQEIA9e/bg0KFDqF27NubOnYvly5ejY8eOeXFaec5BPkOORqVCMS8vAJmJKKbxMzVSpG6EAEQ1s6pSaUZpbvIuQOTBd1KrZX2FhwwZgmbNmgEAlnfrhs2NGgHI6JjcdTpESl/oRiO8nZzgT2Y3+I7gaXQ00vmHWsOnmxc2sVjgpFbnWoft5uaG2NhYJCYm4q233gLHcfjwww8d3r7gSC2gVKlS6NatG4KDg7Fs2TLB/fNNJDolBb22bEEkScqSS8i5EQK2Y7j0VMyWk1otSnsLvN5Z+rxmzUJ8ejo8nZzeqDT3+UFWMYRy3ybq9Zh+/DgA67o7om0diE+kayelGAyYFRKCb228hxi5S34+WacePED3TZuw6/ZtYVlz3uNICrnH8qMvMJjN0KlUVpmN6TER3Rfrs7BskWfix5kzoaL69M8++wwARJZi4kIojTGi3yEkhorOciuN2SKWLaUdy1Y5Ly8hQYa9PiHJYMCQOnXQpVIlq7ZIxZankxNK8ONEX5myP7nFsGHD0JfPPmhOTsbTzz/H0jlzAF643rx5E+3bt0fRokURGRkpSgNfWJDPdvCKyMmsjNw2/v7+2Lt3r93tWrdujUtZuFsUFk4ePw40aACtRgOLwQCo1XDXavFls2Y2g5Tp2Ucyw16vRAlc5OtfeNvwx43kfanpmC05E/XRo0cRws+wumq18OU7GYPBAA+dDv9J4zYsFtwbPhwPeBeIigEBuAuguK8vGlStip1GI2r6+uISH1cFZHSOuelGGBgYCGdnZ0RERKBkyZKYMG8e2gYGYoiDgiu/ZxVpXFxc4ObmhsuXL+Po0aM49gYPdoioSc3lLHg2LVs2XmwGKmbLSa0WZV963SGzxJ46nSjukpH7ZFUgW04w7Q0Lw4wTJwBkWh1yCv1ueZ2ttQWR/PSukCZaAGzHB5L7Qu7dndfvUb3ZDFet1m4cO/2uoN0OVQqFIBBJK935lOvzFixAj3nzhHqkUjEGZIqtKlWqiM6TPmNiMUozGuH500/4rnVrq5gtwbJF7ZvsL2zMGMw6dQpGiwVGsxkuarVIbAX6+kKhUAgWt9MPH+K7Nm3Qs1o1FJk9WySAbbkKxk2aJJv90B5du3aFQqFAcHBwluueOnUKK1euhK+vr2C1mjVrFpw3bEDXbt2w7+RJUeknXTbbUhAoEAkyGNlDzT94jRo0QFm+FoO7TgdfV1eR/zb9cNMzN0789v5eXuhetSoAiNKMEpo0aYJbt25lHJMSW+RBpmfqw8LCRAGiJNWpq6sr3LVaWPR61KFSugPA8+fPhQ6lcsWKADKyEd64fh0A4EPNpFg4DhaOg7NajUmHD+dKHMjlO3cQ37QpUlNTcf78eaxzdsZPVG2yrCg4Uisj7eyqVasQHBxcKKur5ybknnoZy9HDhAT8K8lCSsTTk6QktKRmcOVcNp4mJ+Mxnxky1WSCVqWysmwRCpI7am7jqdM5NDnyiGXRzDG23FjtQQ+qXlYgiSxb+ZTm/05sLBQzZuTLsd9U5Opd2hqsE9e1/HIjdJWZpBUVL6beFYm8+LkbHi4SIqSfJvVgnzx5kmHZ4sdDBpnkWnLJMQCJZYvEuBuNSNTrcTQy0ipmSyvjRkgsdcVcXKBRqWA0mzOseDKTevS+opKT4abVCgKOvg5tJXkTCN7OztnuZ44ePepwbBXJNvjs2TMh/8K8efNw4uBBbF2zBpGRkdDwHllNmjRB9+7ds9WWggATW4UQYlJOSkqCb9GiADJSg7pqNEigxBZtNqdfiMSyRacI9ZbpFOiiz+RB06lUwr7omY7y5cuLghy9vLzQs2dPzJ49G0eCg2FJS8OGDRvw22+/oX79+ggODoafn58wU7OXn/1QKBQI4wUeXUCZpLInbT+dC+n4N128CDRogJSUFCExhikbA9+EApTqmLT/0qVLIteGNxmpRSUhPR2KGTMcqvvUZ+tWNFuxQrSMvEz/efAAJ/lYR0Dejavk3Lm4n5AAf09P6E0m6OxYtuRe0q8LHjpdlgOskAcP4Dd//itq0evH1ehoPMxmX6Sl+ojctGzZc0nMS142yUdB49dz5/Db+fNZrvcqvStSDAZEU7HAzlR/RtrhTMUd0ZC06q/ajZDjOIcsWyaLBUqFAjqVSuiPpeVzyDnG8snF6tSrhzWRkQA/NqLHWIJroEYjZNEW7Yv6m1jVyP/efJyrXEp2ehl57lw0GmiUygzLlsUierYJUpdDN61WmPyjBfJnjRrBPHWq1fY5YcKECRgyZIjN7y0WC2rUqIHly5cjnQoX2bdvH8xmM54/fy6KdSMT+NHR0XCjQmAKCzkSWxcvXsQ1vjgnAOzatQtBQUH46quvspXNjZEzWrVoAQCIDA8XOgB3nQ6uWq3IsvUlVTMqXcaN0IUWW1mk9aQtWyXc3KBTqUTWsOPHj2PatGnC54oVK2Lbtm346quv8HTTJnxUsSJGjBiBESNGoHbt2ggICICaTrbBn4dKpRLMxSKxxXeMpDPPjUKdpEvau3dvZjbLbAx8kwpQljWTyYSiRYvi2LFj6NmzJwYPHpzfTcp3pKKKiK/7DgxMpfVGgExRpJLMZNrzjy/r6Yk0kwkapVJ4uUnXf53FlkalynKAJS2yycg+c/791+Z3cnenowLJkV6WHmTmlxvh62YdHrV3Lz7ZsyfL9V7lWXfftAklqNqbNGR8IddvApn3xau2bJFxw+PwcLu1R4GMCQi92YyP+AG+L1/ChvTb5Fqn8mOsgQMH4gSVsp48U9tCQ1Hs558zjyPjYUGLZLIduUZefJyrWsaNkH53kPeGRqkUWbbIb3B79Gih3VKx5c5btp5+/rko46BCoXC4jmRWmM1m2XT4hLS0NNy8eRMnT57EvHnz4ObmBldXVxiNRsTFxcFiseD7778HANSrVw9r164FAPz888/o1q1brrTxVZIjsTVixAjc4WvEhIeH44MPPoCLiwu2bNmCSZMm5WoDGda836sXgIwHkHRyHjpdhmWLmiFYcOaMMFsiF7PlqtUKokVq/nc7exYAsJCvnUInyPDQ6ZD+zTei7GqV+GBLQgSfUXDw4MHQmc1YvmgRzp07BwBYsWIFatSogaVLl6J6tWoAAE++ltnz58/xlK+hQLsRko4ylp/BzI3ugIit+fPn4+jRowCAAD+/XNjzq8dkMsHd3R1NmzZF+/bt0bhx4/xuUr4jtWyRe9gRK4CcpZfcg2SCgAxS7cXM+Lq6Ip24EfLPS0ERW/FpaaJ+IS9QKRRZDrDI9WBpw3NOduNY6cm3l41tlGYjzC0eJyZiPJ8MKiteL6mVgZcDdY1epWUrLC5O9JmeRCGTvO68t0sP/r1OSLaTICMvz4Dcm0lxcaJn5EV6OtZTBgPAuv8hQo3024/i4xEeH480/lwsHId0mVgvkhGangDYvHmzaN/0OZPtHvF1pUgGV5VMggx6GXlvKBQKaJRK3I6NzZjY44VZZd7rCbAWW8QFtHgeWoiioqLsxlYlJyfD2dkZI0eORL9+/dCmTRukpKTAaDSC4zh8+umnwvazZs0SLIQ9evSAj49PnrU7r8iR2Lpz544Qf7Nlyxa0bNkS69evx8qVK4VaS4y8Yyv/4KpVKqGT06pUVpYtIMPFJOTBgywtWy4S8//sd94BkJHONDU1VegkaVco+sEfP348avD1vwDgAe9mNWnSJLx48QIDBgywMimHhITAzHdW5SlfYbJf2rJFCsee4BM/OGLZMpjNdmc8fciMjlotCMEK5crhoaMuirk0A5Qb1K9fH2vXroVSqYTRaBRVtn9TsRWz9dCB+CC5gQ55WZNnhgwg5HzZ6/DWWZVSmSm2ZIKcASAmF9P0P0hIwI7QUIfWLTJ7Ngbv2pXtY0w9dgx/ZZFsiLiyqJXKLIUAuRqvs4Uvr7F3jUlfeSQ8XHADoyci7PWlOclGmFusunIFC86ccWhduVpFhR0/fgLSHnl1vnIiju63jGaz4BoIZMZk+bi4YHi9etjau7doW6HO1iuYUKHjuYV702DA9lu3hLi+n06dwjhJPJE0W2EkPw4gfVlkUhIq/PILXPkEGWY+jlw4Fv9MufNCJp4XXbr27TFUkiBMbrv7fPZcjVJpXWdL5t1B95calQoH7t3D6itXrNwIOYmVDMj7QucAsHbtWkyfPt3m9/Xr18eRI0eQkpKCDh06CNnBnZ2d4evri19//RVqtRrDhw/H6NGj8TNlLSyM5EhscRwn1BY4fPgwunTpAgDw8/PDcxt1Fhi5x7UrVwAALs7OohgtN61W9BkAgjZuRPO//hK9EHUyMVvCw7hnD/Dnn1i3bh369OmDBw8eICUlRXiZ09Ys+sFftGgRbty4IXwmpvNq1aphxowZWLt2LWbNmiVqW5kyZQB+vz2pauLjxo4FIE7a0WX9eigtFsHd0BFTt+5//8PndgI0K5Yvz5+ICuG8Je6PZctQli8QnRVOL1FRPSI+PldnJdVqNUqWLAmO4zBv3jyEOjjgfp2xFZv1WEZs9dy8Gb/yghsAjPw64fHxwgCSPANStw85y5avqyv+6NYNKoUC6SYTOJMJT/kJA+na1ZcuzcZZ2edYRITDA1QAuJGDWJfv//kHn2bh4tS4TBms6d4dKqUySzdCMqDXM7GVY+yKLf7/9mvWoOKiRbj1/DkOUEl0XnbglVeWrey0iwxeXyfrqK34J5qs3iDZecfQ/SItBsg+6AH7B9u24Z0NG4TPIfzkarrJhHolS4pEOj2RK2vZysX34OPERBSfM0cQgsK9aTQKE7aOIli2JOLFp3hxAHzSLmq5tP+K5kWfvkIFSN845JxJTBkAoVSJ0WKxitmSS5ARRU3SaWSsYDTS/iHA29tqndzGYrHALNOn+7q6ok6JEnj8+DGaNm2Kdu3awcvLC99++y3Kly+PQYMG4fr165gyZQo6duwIpVKJ27dvw/sVtDkvyZHYeuutt/C///0Pa9aswYkTJ9C1a1cAGa5jxfkbkZF3EHNq9apVRW6DrhqN1UwXcRGhB57kwaXrMVTz8cHwevWAc+eAhw8REhIiWEcMBoMwi0IHxZLO9969e7h58yZ68e6NQIa/bsmSJQEAP/30EwDrdJ01a9bEY74DfMqnoAeAPbzPtDTVKEedq6Mv4n+oRAZSnhALllqd8S+j4ajPF4jOCpdsmOAjX7zAlMOHhU6v/C+/YHsuCqIjR46gdevWAID79+/n2n4LM7ZSjtO12wjbQ0MxlUqX/4CvC1fhl18wcOdOAJkv33SJ2JIT/kbedz4lORnpJhPSU1Nx+tSpjO3zMEbJZLFka7Y7p/75WR3DxM/MZseNkFm2co6jiX2SDQY0+fNPkQuVPetVQno6bpB4VhvQv1tu/obZuTfJ2b9Oli1HXEM5OyIz1WiE8rvv8OfFiw4dr8z8+bjw5EmGJYSPlQEyryktti5R7+vmZcvih5MnMW7/fqSbTFbF20Vi6yXFcGhMjN2sk8TCRvpqvdkMJcfB081NdD0dubNm8c/IY8n79Bb/brBwnEg8Sifhoni3QF+ZMTFH/U+2e8yvbzCbbdbZooUpfa/TNbrkEmTQ48SPateWzT6d21SrVk02OUjEZ5/h9Mcfi5a1adMG8fHxCA8Px40bNxAZGYm9e/di/fr1WLZsGYCMpGuFmRyJrfnz5+PixYsYPXo0vv76a1Tk03Zv3boVTZs2zdUGMqzR8DdwYkKCyJJF/HDplxSZCaFfQeRhLObiInQ6Xk5O+K1bN6GGBJCRSUehUMBoNGYGwFIP8oBatVDe3R0VK1aEh4eHqCCwn58f3nrrLVG71Wo1Zs+ejW7duiE6OholS5ZExw4dAADX+HTvAHCLN7lXLVYMO99/X1jOpaUJmX8cfanaW+8ZX3eiW/fuAHkhmM2oUKGCQ/tOzob7178PH+KnkBBc4CvKA0BMLlZkN5lMQppZMnhq0qQJ1q9fn2vHKGzYsmxJrb8EOlFDLDXADOOzT5GXNRFxRLTJii2LBRqVCrt37sSLpCSY0tORys8c51YAshxGi0V2QOMxcyaC+Thbmpy2Jaunz8zPzDpk2eL/JwP16kuWYN3Vqzlq15uKI26EBKl7rb07IMlgQE2J5bX6kiWiWKq8ciPMltgiouM1ElvShA5ykLOVusABmb/FF1SiLFuQ+ydBr7eapCLXlIwlRu/dK8R41/T1Ra9q1RAWF4eFZ84gnc+8SuOahWUrO1ygRJ49yJ2jN5ngrNUi6N13Rc+I9N5S2GuX5Hdo2aYNgIzfh95KOglHaoTKiQTa7TXdZEIxSeZlW3W26PFMMz8/fMqPsTQy6wrHgtjinJfvH5oJEyagbdu2VstdNBorQV6EStJRs2ZNdOvWDc7Ozvjhhx+E5W+k2KpduzauXbuGhIQEUQa6n3/+GatXr861xjHk6cT7tsZER2NYvXrozItd0gHSD6TcLD7pxIu5uFiZyBMTE4WYJaVSiYkTJ8LNzU02/uXTBg2wjX+Yrl27Jqp9UL16dVHazpEjRwIAfHx8MHbsWJjNZpw/fx7X+UGVlk7QQbkstieufgDK+vgIcVJyLxc57M2kEVfYe/fvY+LXX2cs5DiU5muXEU6ePImZM2dabZ9uY9BOk6TX42lysjCQpAcmuek+QRJk3L9/Hy1atMC0adPg6ur6RmcHtRWzRT8TEY8eYbdMAWgd9fKji1wCmSKuA58dSS5my2SxZNzHTk54qtfj1u3bSOVnLi15mJTClmUryWDAPn5Gliargri2yOreNXMcVEqlQzFbtHvm7efPEfr8OQ6Gh+eoXa87g3ftEmJBaMg1PB4ZiUWUG+mm69fxt0RkS5OiZNcaFPr8ORacOYMHfKIZen9f8YmGcoPsDApfJzdC8mw5Ytki5y0nzMg7J81ohN5kQrcNG2zuk/RpaqVSZAWhj0EsLUvOnRMSYagUCpG4Ov3okdVAmq7H9bK/T1b9juCex3/Wm82AyYRVVF1EwHoCgrPn/sq3uU3RokB0tDDhm24yia4neU8QsfXHxYsZVmGZ+5i2xOrNZlSgXOTIhJmcZYt+Vk8NGYJfea8yWmBJ3Qg5XtARctrnZweFQoEff/wRLVu2zHLdxo0bo2PHjujAT7wTnJychDitESNGiDynCiM5Elvly5cXag3QpKeno3Llyi/dKIZ9WvM3sFqtxq9du2Ivb1FyNH07SaLh7eSEWW+/jT39+gnrbN26FQsWLEBAQAA0Gg1mzZqFYsWK2Ry4Fi9eHC1atMC7774rWn6Wz2a4ePFi1KhRAxMmTAAA/PnnnwgPD8eECRMyMh3ynVUqbeWhsgDRHU70/fuZYsvGrN++sDCRm4G9mTTS8Yc/eIATJHWyUokmTZqI1psyZQq++uorm/uREhcXJ3T6ndetQ8m5c4XrR7c7N+dgTSYTNBoNVCoVBg0aBA8PDxw5cgRX+Pi+NxFH3AhbzpmD9/75x2qdMmXKCH/rJb+drSyHNEazGfq0NIDPqKlUqwGyXR7OvhvtJIWRcxfLKzdCwbLlgBshmTgxmM2o/uuvAIAALy9hMM/IZOXlyzgh4yZMrnGPTZswlrI6fbBtG8Lj40XrSn856QD8jwsXcNOBWD7/BQtw/smTPIu1y055D3JOr4Nly5INsUXeM3KTj/QE3/2EBATfuYP4tDSkGY3otmGD6BkmfZpaqbSy/Mu5EZKC7yqqpAWBiK2TfPkR2rIl997Oi1+MtFlvMsGQnm5lnSJ3lqtGA+zaZbfkS4C/P2A04v1KlTI8YPjz1fPufgTynkgxGtHK3x8eOh1+OXMGWpmMfBbKsqU3mVCRsuwYiGWLrrNFChDbuL9pgSXnRkh7eeRG2RxHiIiIEEJJ5CCx8b///jvc3d2FDINarRaVKlXCsGHDhPt7wIABSCzkhe9zJLYiIyNlA9/0ej0eZTMIkZF91vGuYUrJDIatoqnC9/xDSMSWSqlEEWdndKHStvfu3Rtz585Fly5d4O7ujkGDBiEiIsKu2Bo3bhzGjx+PUnxdCiDjHgEy3AnXrVuHb775BgBw6tQpjBgxAk5OThmCnX+YilBpSovxf+tUKlEHr3/xQphVsjUYeMJbD8jsnL2BXkm+velGIy6SGAalEh999JFoPfLADx8+XLwDG51W0aJFBQvvbX5SgnR29As0Ny1b3bt3x5EjR+Dv7w+O4zBx4kQAwNXX2B1rR2io3bTVtr6jxZaOzxooJbBWLeFvqWVL+izYciN01moz68dpNBg3ZkzGl3k4+/6qYrboI8SnpeE85R4LZFq2pG6ER8LDESEZ/BupQaEvL07dtFr4L1iArZIsXm8y5DrJDabIvRkv48mQFfTvk6TXY3hwMGrwopegmDFDOD6dqTNJr8+z8gFvaoIMix0BJUVwI7Rj2aL/NlosuBMbi+A7d0T9GPlbqVBYWbbINZWbVLJwnNW4gwz8Scw1HbOlf0lPiywTglDtAjLOW0m8DCiI4FArlaiWnm7XjTAiPBxapRJ+JUpkiC1SxNiGZStJr4erVovuVasi1WSCSU5g0mLLbEYJNzds7d0bnzdpIrgRqmUElK37Wy5zIQ39W9vq87PK+Dd+/HhZI4scJBzlY0lsFk10dDRu3bqFoKAgHDt2DEWKFIFarYbBYEBycjI+/PBDlCpVCoMHD0bz5s0RJuOZUZjIltjavXs3du/eDQA4cOCA8Hn37t3YsWMHvv/+ewRQKbwZeUM0X4fKlapD5QjkgZSmAZVjyZIlMBqN2Lt3L2JjY23Gv8TFxaFnz55o06aNaOaBiPGgoCD8/vvv2C+pl6LT6dCyZUvU4OtxtOH9oGsGBmLLpk0AMjoYumNQm0yCwJl+4oTsS74o7/51hY/HsjUTlGo0YtjJk/yO1ZkB5kol0iRuOqRj/OOPP0TL5WasCOG8G5RZMkA3WixWrg6PEhNlXYOyQ2JiIp4+fQqNRoNPP/1UcJFs2rQp9A64O74qHiYkoF8ulYfosXkzfjt/3ub3xJ3jQUIC4tLShBcwXR4h3cbgNEKmWKXUjZBA/46tV67E1ehoXI2OhreHBzT8wFSl1eIecefKw9l3k8Vi857nOA53YmORbDAItcZyw39/4M6daCB5NohlS+pG2H7NGvTZulW0Lm3Zasi78JLf6LSjZRjeAMjkgdSSAGS/zpatbUm/KQf5nYpSwfUqpTJPLFtfHTmCCQcPOry+ILaYZUvAaLEIViXSZxnNZkFoJKSnC9vTk4GOWLYIyQaDldsgmcwSrEeUG6EjcWgvg7QEgMliyRBSkusj1PbjOKxfv95mMeaMnVng7uoK/5Il4VakiGDZSjWZROKHTpChUSrhotEg1WiEUWbsJHIjNJmgU6nQs3p1eDk5wcD34bR7OhG6tibS5GLy6e/SHBBbK1aswCZ+7CXHggULcMiB+D8ADlmh9Ho9qlativDwcGi1WixatAh+fn7o2LEjKleujJkzZ6Jfv34oyk++u1Cu/YWRbImtoKAgBAUFQaFQ4KOPPhI+BwUF4YMPPsChQ4cw10aFcUbu4aNW4524ONSg4pkcgXSyk5s3x7lhw7Jc32w2Q6PRiBJkAMCzZ88EC+YzKpHATj5rG5CZ+r1Xr1749ddf8YJPawoAJUuWFArwnufTbZMMelFPnuDnWbOgU6mgUChEJm9TcrLImiStKQZkvqBI6ldbM0Gi2Tu1WuhAW7Vta+VGaGsCwZbYqly5MoKCggBkdo60GyFZRl4MfvPno8js2YI4yAnbtm3DiBEjoFarEc9bDqpUqYJq1arByckJ0dHRMJjN6Lh2bb5mfdseGooNVDKUl0UuXopAMlP5L1iATmvXCr8B/eKxFdP2iLLU6KlZYen2QObveOHJE5y4fx+1+exJN69fF7apWasWjpEXFf3C3L4dAZKC4i+D0Y5l60ZMDKosXoz+27ejLJ9p1N71c5TbMuU+BMuWjBuhVCyQAZjBbBYEBXkWFAoFph47hnfesEQvcn0buTZyM9cvI7bo30dq1ZCu5/Pzz6K2qZXKPLFszeQzdwKOeQDYy8pX2LAXhyVFsFjZsGw5azTQqVSCgNKbzYIIKjVvHn7h4/vo95OVZcuO2LJwnNXzXMPXF0DmoJ52I1Rk4X2TFfbuhSPh4ei/Y0dGm/n7wGSxwM3ZGbUCA0X7INfAZLFg+fLlMNib7OQ4JMTGwpCSkvEM8tdh9+3bIu8JwYuFLyzsyoste+dBLFvEOqhRKmUtW4osxBZ938sJR3o7W2IrLS0NFy5ckP0OAGrVqgU3Nzds3rxZNvEFTbly5ex+L0Wr1aJZs2ZwcnLCgQMHcOLECdy9exd9+/bFnDlzAGTfuFDQyNab1mKxwGKxoGzZsnj27Jnw2WKxQK/X4/bt23iHL4bLyDu0Gg2Cf/lFJGAI0lkmGvKIOWs0eIty+aNp3ry58LdSqYRGo4HJZBKJrVq1asHPzw8ARO6k7dq1E/6uW7cuACCFKjAIAF999RXeffdd/Pzzz9Bqtfiaj4UKu3s349x0OuwNDoZWpsPo2LKlTbFVdv58nIiMtIqrkZvpjEpKErkh1q5fXxBbLu7uVgPwtWvXCkkz9u7di2cpKXianCxYwKQujRs3bhRmYazEFl9DA7B2iXiZWb/hw4fj8OHD0FAvttu3b+P48eMAMkRxdHIyDt67hxjJb/IqyYm/eP/t23HLRv0+OZcqAl10MzYtTXjx0YNDWxXu5XzxbbkRRvFZp6Qv1tiYGOG+io2Ph4W/r3SUuGrRqBEUds4hu9hzIyTPRCh1LXPDsvVUkpUz1WhEQnq6zWyErhJxKaRpNpmQajRCrVQKYstkseCXM2ewJyxMKMj7JuD50084xtf+I9irYfVSYov6fWxl6gQAj59+wvPUVFEWVZVCkef10ey5xZ5/8gStV67Efb7Ex+tk2XLEjdAgmQiSfqdVqWAwm7H79m0AGX0X3QeTOntpVB+XHcuW1I2wQ4UKqFqsGIDMvp4ek8j9OtkpSE1bhILv3MG/Dx8K2y84cwaX+b6YtmyV8PVFO74sCpBxrUjbzBYLNm/eDGzcCGzebOOgHEx6PXRKZUZdrfffhzIxEUazGQ8pC87mGzcEoUMsWykGg/w5U+ehN5sFwUp+L+IZIMWm2LKRBr6clxfer1FDtK5cj09q5drj6tWrCA0NxeXLl3FMJqkUDZk8r0SFqNBIRbNWq8WpU6dESTCcnZ0xg4q/f6PEFiEiIgLF+AeK8eohN2QSH59E42nHtc0RTp48iSf8rL5SqcS0adMQEBAgGmDSAov+m87iV7NmTQDAhQsXUKZMGQwYMABAhtWnR48eiIiIwPbt23H2v/8AZM6wq1QquOt0cJURjT27doUvFWNDvxQeJiYi+M4dK1cvuZnOwKVL0X7NGuEzp1Lh7U6dAAAlS5fGli1bROsPXrECj/k4rq5duyJw6VKUnDtX8MWWxje88847WL58OQBrsWWixZakw3Hk5RoRESEbL0mIiYlBgwYN8Ouvv6Jfv34oVaoUnJycUKxYMaTx18uRLIp5RU6G9uuuXcMaSaIPIkzlXKoISZRo9tTphFTI6SYT4tLSEJOSgmSZZwgQv9TM1IsbsM7wmZqWhvsvXlil8rcYjdDyojvi/n2kkyKX1PY9e/SAm4eHzXPILkb+RS0HeXnTls2XEVvneHfmJMnkRMM//kBUcrJNy5aLpFgrbdlKMRjgqdMJYstgNguDtVYrV+a4rYUJcu9J76dUyhUMEPcfueVGaM+yZYu8itki2Du33bdv48T9+0jmr9XrZNly5DfNyrKlVanAAVjEJ6xKN5lk+2DBzdBiEU1S0e2Rs4JLLVv0vslYRLBsGY2Il5kglh7HEcwWC7pt2IBmK1Zg3unTAMR9Gd1nJ7x4gfm8dYQsoy1fJpMpI8ugjfhQVxcXwGyGN6mr6e4OD1dXQVQSYlJThezJGpVKcCOUm2Ak57r/7t0MN0Ji2VKphAlZOXFrazKBvu/pCciIzz7DdEpoAtZ9Psdx2LdvHwBkaSxp3LgxateubeX9Y4svvvhCdrlCoRCKFNetWxe1+BhperLY2dkZu3btAgCMGTNGcCcsrOTYh+TIkSP46quvMHToUAwZMkT0j5G3ECEjTZABZBQnprk0YgRODR6Mk4MHO2RVWLhwIfr27YtKlSpBqVRi8ODBKFOmDKa3bo2PeWvVggULhELFnp6e6Nq1K7755huRny55SKZNm4YPPvgA7/P1slatWoVDhw5hw4YNuHr1aqYfLt82FxcXFFcqMSA9HZMnTxa1bdu6dUihBiByAwNpLSQzxyFRrxe56MVKXAYmTpkCFf+QcwqFqOYDAGy7ehXQajFv3jwAwDNiGbJxPZ88eYJbt24ByOxU0yg3DVuWLUfc+8qXL2/TVbdixYq4du0a6gwZgr9iYrB+/XrcuXMHaWlpKFmypBDc+kKSoKAwQq6no5YtLycnRPLiQG8yIXDpUpSaNw9aycCfUEImi5Ity6OrpyfKLVyIpZL4MYvJBAt5RpVKVCWzfNQL05CejmcOZH5zFKll60h4uChYHBAPjl9GbE0/cUJ2OZktl4vZAjJLVBDoAqSpRiM8nZyE55UeiCTq9XiRng7FjBl5PsDPT0i/Rl+ndJNJWE6upzmXxJbIjTCbEzEmiyXP3ZLtWat8+PeHip/1fh0sW+QM7P2mZx8/xrqrV+1atp4mJ1u5lEnTlSuo5UBGH5dqNMoWIpaztHASyxbdn5T28EDMxInwJAlVUlNlk0UQHLJsycTmXeStWbQ4oa2DL2JjRdkGjWazcN04AEajEa1atcK8efOw8r33rI7p7+cHmM3woiwraqUSdSTJlZ6npqI0nxhCS8VsqWQmjsl59N22DacfPRIEq0apFIoay4nb5rxHkRT6esj16HVLlEBtvriytM8n9ULbtWuHvn37yu4fyBBCJUuWxM6dO3GaF7hZQTIOWrXXbIanpycCAwOxePFiIcZcrVYL3iYffPCBsH6jRo0cOl5BJkdia8aMGejQoQOOHDmC58+fIz4+XvSPkbds4xMMqGQGml0lZts6JUqgWdmyaF62rENWha+//honTpxAQEAAlEolBgwYgJMnT+KrFi2wnE/vXrJkSaF+QkBAAHr16oU2bdqILC6P+YFtcnIyypYtK7izHTt2DHPmzBEeKBc+2LoE33H5+/tj0sSJKObkZJVU49Lp03CigiTlBgbkRUKsGGaLBdOOHcPPISE2z1lvMmEgP0nw5OlT1KtXT/Q9x3dOJH09cXG092og7pNyboTkxWhl2XJw0CIXfDp27FgcOHAA7733HpaEhuIcv+/Lly9DoVDg7NmzQi0zufsmp2S3Rk9uISQLkHmRPeKvj8iy5eQETqUCDAYYLBY8SUqCyWKxmZrWT+YlQQ9SlnfrJgz0Uvj7Q+rq2KRxY1Hile+mTs34k1rnWXQ0EnIxpa1UbL23cSNCefFD7kHa7etlaq5I3SbD4+PxnHYx47MRGvg4RXK/u9qxbKUajWLLlsUiDEQ8dDpc52NE7WWhLOzE8ZNB9EDZ66ef0JWPWzNRs/KE/LJsmSyWPHcjtHdu5Pm/yU8kvU6WLXvvg16bN6P/jh12LVvvb92KsLg4keVJKrZIT0G7EaYYjXi7fHlBOGTlRuhkQ2wBGbU8hWc1JcXuOdH91s2YGFEJFyn07+zCH5/uy6wSZFDH9Zo1SyROu3XrhrVr16JPnz5IlRERN2/cQDFvbzhRXkPunp4Y37ixaD2TxYLSvJeCRqWCq1aLVKNRGBfR73v6rZlqNArXUKtSycZsAUDKV1/hl86dZa+HnCcGzdlhw3Cez6Ys/Y1IOMqRI0fsxsQZjUaMHz8e9erVy9LtcNKkSQCAzjbam56ejsjISOzbtw/NmjUTvKmqVasGvV6P/v37o379+mjSpAnatGmD/v372z1eYSBHYmvZsmVYuXIlzpw5g507d2LHjh2if4y8hcwCyGVn+bxJEzwYN052O0csW615k/NBPhNUaGgonksGkePGjRMsmBERERg8eDDatWsnElskQYaHhwdWrFiB4OBg0T6cnJxQtGhRIZ16nTp1AACPHj/GsGHDcOrUKasaUVxammgQLA0iVygUQidKhFiSwYBUo9Fu8om4tDTBZceiUMBgMODUqVNIsFHnh7g4qmUG+uQaJNSogd8vXJB1I/Th06tyEHeSjrgRvvPOO3hPZvZt4cKFKF++PDQajWjCo1u3bgCAqKgowQxfnA9gzg2yK7ZyWuNDuh0R02Tp9WfPcPDePQCZbp3JEjdCjYsLfD09RfuRxj2aLBZ8eegQlvCJW6TfEXpWr55lGuKK5cpBwQtcrZMTzvEvcgv1nKSlpCA3h4dGSTZCo8UiDKTI/7nlRphqNOIKP6sMABV++UXk6kfqbIXFxeHT4GBh0EXPmitmzBAKGBOx5UGJrZsxMcI95qHTCUI6OwN8aVHPl+VObGymdTsPIGKL/p30ZrMweUD6CXrg+ipituQ49+QJfrMTVJ+j9kjOxd65Efe33XyMyOtg2aKtMrbeW+S5lSbvkSOdL7sCWIstArmOcXxsawVvbzwcP17UHrnELFI3Qrn+RDiHEiUwSibWh/xi9O9+gsoGK7fuferdTPoTkRshNSEhFVuA+NmqUKEC/vzzT5QpUwYjR460PqjFglIlSojeQZxSiWo+PrhBra9RKlGMH5MJMVtGo9AWkdii/k42GKzcCOVitlw0GpvvT/rayU04qPkC84D1b0RP3s6aNUt2/wSFQiFkPrZHVvsh40NSz5JMvrdq1Uo4zqpVq9CvX79XVhcsr8mR2DIYDGjatGlut4XhIF5eXqhWrRo8ZGI9FAoF/Dw94efhIcy8Z4cdO3ZgKj8DD0DIRkhz7do1wU2OWLAAiMSRyWRCr169MGLECFy+fFlY383NDVWqVIGvry+6d+8uzHzcvXsX2LgRJc+fx+zZs/H333+LjqlVqYCnT1GScrmy50ZIviMvF7pzlT66kw4fxme8FY1TKGA0GtGiRQuh5oQzdR3v3LkDEz/QUsq8fMxmMwIDA3GxWDGMCA4WXv6yRY05TtQxOuKOM3LkSHh5eVktHzduHB4/fpzh80x15MTlNDU1VehU03IQl2GL/JpJJgN38nv33LwZHdeuFbkOJun1wjVddeUKLkZFwUUq2iSD5t23b2M2KXAtgR7Q0C9CNR+cL2Xj+vXg+BdcseLFsY9MOFC/j6e7e66KLally2SxCAMp8n9OxJZccopUoxF1fvsNQOaAh04Wo6Je8EcjIwWxQNYl9w7ZRm8yIUXiRngxKgr3+MkDD51OSFmfHde11VeuwPmHHxxePyumHT+O1XlYLPwwJT7lsGXZym7dPvLbmCwWRPGxi1IX66zYdONGttZ3hHESjwZ7Yktq4czPeNTcgn5+fyTlSSSQ55bcI3diY/HYAQu5LbFF3k/Dg4ORYjDAVauFQqGAUqEQ3mFmiwUL+dhmuq223AgJgtiiLDVl5s3DFwcPYuetW0I/QJ/3IxvnQu7xA3xCLQD45exZuP74o2h72rJVxMsLH/Ex49LzBYD//e9/9rNocxyuXbokWkSuIR0j7+vqmukOSMVsEejJaPpJpQUrSZBhK2bLFu/XrIn+fNxTVhMO9G+Unp6OihUrCuVhrpF6ozIsWLAAarUaJ06cwMWLF+0eg9TZsoU07px43ezduxcAsGbNGqSlpcHPzw/Xrl3D559/bnd/hYEcia2hQ4di/RuWircgoVarERoaajVQpLk+ciTCSBFVHkeGVRqNRpT8RKlU4qSNDh/IfGi0Wi2K8z7BQEZ2m88++wxARla8u3znOGzYMPTu3Ru9e/fGs2fPhDioBw8fYliLFqjp7487pB4RRbfKlfH9jBnQUEV65QYjgtiiXroxqamideVm6AgWZEwmVKxYUaj9VY6kfv/wQ8yfPx8KEkwsM1uu1WpxvWfPzP3xHd/f/DkZJQMk+sWX7oBrVJcuXQQ3Uprly5cjISHByi1u1apVAIB/nz9HGD8bFZeLrr7ZtmzlcDvpQJLMbJIXSzlegNLZoQxmsyiZxfcnTyIyNFS0n+eSeKkbVCkDKfRvRfvT23quEl+8APj7sFh6eqY7HPUiKh8QANcsXkzZQVpawEJZddJzaNmKSUlBiblzEStJ2JBCzbpLXQMBCNkISVvI+uSYZEBVhHclTjOZYDCbRZYtGheNRijaS2K2UgwGbM5iwH8jF2PiAPtJSHKDr44eBWBbbBllrBkmyj3ZUZZ27YopzZvj1vPnKMX3w+HZ7BvsxUzmlJ185jyCvWstFVv618C9lO4bSU2znbduiUoskOeK3CODd+1CLb7khD1uPX+OLVQiiJsxMYhPS8MhXuADQDIVs6VUKETCRWptsXCccA9806IF5nboYHXMFJln+XFSEnbeuoXumzbhGG/Fos+b9OPSfp9MBjxMTBSsSEDGfUD39XSbK5Qvj3KSWKdIiUcDOY4bSYIhOkkLOMm7nlwfWhBVLVZMEJ5aSmyRtlio+1gacypN/W4rZssW5by8sKZ794xzyKKUCN3nt2rVCqVKlcIYyVhRislkwrhx4/Do0SP07t07y/aQODA57x85iNj6j0+YBmQkyJg6dSpiYmKE9O+FmRyJrfT0dMybNw+tWrXCmDFjMGHCBNE/Rt5SpUoVAJmmWDk8dLrMwFQeR82xHTp0EFwU69ati8WLF8seH8gUWzqdDkWKFBE6lKpVqwpp5CtXrowKFSoAAObNm4fvv/8e9+/fx+7du4U6XQqFAuHh4UJtLwAik75SocCIESNEYkJuhk6aMc5Nq8WTpCTRQMTejJGLmxsePHiAuxUrYv7Vq1hz5Qri4uIyvqxUCUuLF0cif204meuZlpZm072sgrc3PqcKdRolYivehtuiFLlK6mazGWq1GkePHhWu37hx4xAQEIABAwZgcVwcJvNxa9mdAbdHdt126JS7L0O3DRsAZP7eFfnMRvfIb8UjFQiQWvX8/UUfI+xky6J/K6VCIVxHW3eT2WDAh0lJ2Nm5M0K//z4z0QP5nffvR4WAALjKveBziImylpLfJk0itmgceaETy5L0t6YHunLimWQjBDJmcsnAkKxLrjURD2RQ5qrRyIqtoi4uwjpkX8vOn8f7kiLJUnIjvT2NvfT6uUENPslRVpYt2kpOhGp2KOnmhg58v0y4FxcH32ykWM4LsSW1alx/9sxmnyUVW3ldNPdVQO6tXtWrC38vO39esHgC1pYtINP91B7Tjh/HQr62FgCEPHyIIrNniyyUSXq9MHmipLKJyllbOGRONg2uWxcVJMmlFAoFjs2ciZoyz7NRYtGinyny/qb7nGSDAV8ePpyxrdmMrpUqidKa022jsxE+ffwYMyhvHQDCpA3BiR8rOUnGTADQsEEDq9hgObFV3cdHlOiCWL2S+HuUHn8lSt5LL2vZItwaNQqtqHfamTNnhLEXQalQwGw2g+M4TJw4EXFxcfj9998BAG+99ZbsfslY86+//kKtWrVs1h6VQixVUooUKSKENVSuXFmwhBFPHCBDbF2SWBQLMzkSW1evXkWdOnWgVCpx/fp1XLp0Sfh3+fLlXG4iQwqpNyCXjdAejg459Hq9UEfr7bffFuKpCL///rvgk/v333+jf//+2MoPeIj4WrRokc20n0DGPQRkxp0plEocOXIEZcqUgVarRY8ePbB//37hIVcqFGjRooXIbVE6k6tA5sv2RXo6NEolfF1d8TQ5WWzZsnPdnqWk4PytW0DjxtiTmIiBO3ciKjra5voE4sIhV/sMAKoVKwY/SbyQ0WzOPAeJ9a3j2rUIkXNPc3KSzehkNpuhVKlw7Ngxwc1wwYIFePDgAVavXg0AiMmDIPLsDDqT9HpRMUlHuJrFtSf7Kcl31mESsfVcKraMRijttJkuZkwTGhODP6mOn57hrUC9eNypWUWLyYT7V6/CMzUVOrUaTRo0EO1zx5dfIuHFC8TmoqWRtmxJ64JJizEDjgmRB8SKKPnNDGYzWpQtixZly8paVaSWLXJ/mywWKGbMENwHyYBZSHqiUlmJrWIuLlApFAjnny8Sq+KI2M9tsWXmLYZ5RarRKAy65CC/K23FfZqcjMF8BlhH0apUogFdRHw8XqSnowI/ceEI9kov5AQ5V7gOa9fiDNXv01iJrdcgSyW5t7R8/A4gjr0E5Ms40LxMH5+o1wtiQsVbtjZdv459d+9aCQALx8HH1RUf1a6NMjZKWKTduIE2MiU2iHVHmICTiV+m3xNp1G9tMJuhUijgRYkj+jl/Z/16nHrwIMNF9vFjq5gtaf8ya9YszJo1S2S1GcoLuaTERBgl4oyIUXqyqn358sLkg4YvYaNApnDUUu+GGMk4gY7ZIgmFcpK8qEqxYiJRd/DgQYRTIh0ASrm7Q61WY8qUKaI6WCqVCmPHjhWte+PGjYxwB/76KRQKzJ07FxGSGoA0tAUvLS1N9JmQnJwMLy8v9OjRA4sWLRKW05aw1q1bZ3uMW5DJ0ZkcO3bM5r+jvAsEI++wlbghKxy1bL148ULw4Q0KCsLly5eFQr/h4eG4fv26kEhjwYIFiIuLE9YnD1ZsbKzdzJRCNkJebJUuVQre3t7o1KkT+vfvjz59+iA8PFwoHKxQKHDmzJmM+g5r18JTq8VPp04JA0EC6Zxj09LgotHATatFIhW7A9i3bJ198gRt9uwRPktrAtmiF1+bSxrfRnDX6awGaLRly9vdXUg/DwAH793DOjn/6cmTESOTKW/S5MmotHYtJv3wAwyUC+XZs2czi0vyLim2UtbnhOy81D1++gnHsxHIfvv2bdTmXWOk925NPskHOT4R0LTlUG8246ujR1Gcnqk3maC2c+wUGzEf1yXuhSqlUrBgVqZeWO6UD//QwYNx9+5d7N+/H1qtFp+PGQOFxYIAPvOmRqOB2Wh8qTiTsNhY+C9YIHymrS7SmnM0JPuVI0LkocRlk6Di07q3CwiAyWKxmsSgY7ZI8U4gM54yjBf/pFgvGUw6qdVW4s1Vo8Gfly4hmHfHzY4VJ7fDq/PaspVqNMLbycnKDZZArg2d/TLVaMS/Dx+KPl/OIoj9q8mTRQO6tVevooW/P77j3afl8JDUcZTLBvoy3LPxzrD1e6dKxJUmCxeqwgC5t3R8Zjog456jLdNCggyTSXby8GUSpiTq9ULhceJGuJt/7uTEllqpxMqgILtWTjkvHKml3cJPYrT86y8hgyr9fqH7BIPFApVSKUqUQz+RDxMT8eelSzCazbIJMqSujQMHDsSkSZNEseIdlUoEhYcj9OZNVKlYUbS+1LLVrXJlvFO5ssgdUKlQZGTB5bdJpSb+LJLrSFvEyHXJiWVLCm0pAoDIzz7Dp/yk38WLF0VFg81ms0h89e3bFzVr1sSBAwcEsdW8eXNUqFAB42wkYQMy3tV7+DHUe++9JzsOjI2NRXh4OKZNm4aOHTsKyytWrIimTZviq6++gq+vL/r164fvvvsu+ydeAHl9ZOMbRLly5XDjxo0sgxBzSlRUFCIl2YBIcoUKFSpg1KhRGDhwoPDd3r17hc/koTSbzXZTjBNzfVBQEICM+lHx8fFYt24dGjRogJ07dwLI7KCUyBAy/fv3B+7eRWN3d6QYjWi+YgW28j7oqUajkKHweWoq3LRauGm1SDIYRB21rZgtuZdWMQeTjAhxFDbElodOZ/UCpGtumdPToZAMXJxsDGSKSzpQAPj6228BAE8SEpBAzZrRM1XRfCfuk4vZCB11IzzJiyySaCGrwUBSUhKqVq0qfJa6EZHfKqv9nHrwQJQ57tOPP4anHbc9LxvF2qVijxYplymLF+0v7+/vj0qVKsHZ2RndunXDseBgcL/8ggoXLwK//46ePXvCxckJ4N06csK5J0/wICFBuD4mKhuhtOYcTSm+73BEbBHrgVRYKxUK6M1muGm1MJrNeKtUKdH3JBshIHYjjJVk2yMDH3vp/F0lg2gyK55Vva3ic+ZYFQd+Wcx5LLZSjEZ4OTlh79278J41Cysl3iLkd70rseI+oawH3x49irp88hJbnP/vP9Hvfz0mBjV8fNC+fHl48/3zh5LrK+0Ppc/lSBtuSI5iyw3QVkp6qWVLmcviLz+wcBwUyOjjaJdRetKEdiOUe09kN36PJoG3bBUtWhQmgwFmKt5S6nbsqEv6ACpBBelHpDGkFo5DyIMHOPnggVVMLiB+1o1mc0b/Qy2Te0eYLBZwZjMguR50WZAyZcoIFpVHjx4Jyy0mE3auXg2NSoUykppaJO07EUT9a9WCUqEQRBP5TWjLm56aVEuQuhFSsV7kns4NsdW9e3fRdfH38oJSoUDPnj3RqVMnaLVa+Pj44KOPPgIA9OnTR1iXZBwsU6YMdDodhg4dCrPZjNjYWKGUD5BRtufevXt4+PAhTCYTFAoFunTpIlzTNBn31nv37oHjONSuXVu0vFWrVggJCcGmTZtw+vRpdO7c2WatrsJGjn7NNm3aoG3btjb/MfKe6tWrZ3sbR2d46UQX5OFLkrgB3JYEMVeqVAlJSUlw5q0nJpPJbnCkj48PevfujQoVKuAthQKleIFw584dLFy4EBs3bgSQkS1OrVSiI+93TJJDuPLHeZiYiN68VWnxuXOYxw8gYlJS4KbVCuZ+Ryxb3sTyQ2G2WODsQAwD2aeczzeQ4V4mnZ2lLVuJcXF4wrvMTec7Mp1KJTuoi5emKzeZ8G6PHgCA8hL/7HLlygEScZEok1kupzg66GzJpwTXOiiSLly4IKSFBWAl/g1mM7QqlUNij14jPioKFjtB9NE2Ztbl4p3IM0GXRqDdCL8YMwYnT55EQEAAVq5cicOHD+Oj995DEZ0OePIEer0+o86cUon0HGaIJHEBJCGM0WLB89RUtF650qrmHA3JVKpUKHD7+XPByiQHGbhJr7VSoUC6yZQhtiwWOEuswKTOFiB2IyRii8RNEDFIBhlyg0ephdlgNiMqKQlTqZe+HM9SUmy6os4/fRrnbbiN2iMv3Qg5jkMqL7bI7yZ1D6RdpW1hL/ZQwGxG44YNhY9PkpKE+5f8Vs4S6xgRW0v4OjvS58KRAeKXX35p5dpEsCUS4tPTMTskBGX4RB5hsbHYc+eO1b2dmwXC8wsLx0HJFwSn3enkLFscMp8N+v3+MrFrcWlpcNdqERcXB6PBAAvHCW535Pclx8zqObh27Rru3r2b4ZFC2safE7FKkfOycJzVPS1KIEWdP3EjpCe3pG0hYqtMqVJYyN83BPo4tMCiIX2y0WDAIT5DprtWizblyqEzbwGiLfdAZgwjEVm02BKVxpFMctFZDMnkkJeNsUR2uHbtGipXrmxVm7N169Zo3749nj17BicnJ8F69JCyjpNQj6dPn0Kn02HSpEnQ6/UICwsThQtFR0cL9VQXLFiAlJQU6HQ6rOTf+Xfu3MG///6LUqVK4aeffsKJEyfQrl07Yfsikji/p0+f4t69e9BqtYiPj8emTZte+joUBHIkturUqYPatWsL/6pXrw6DwYCLFy8iMDAwt9vIeMW0bt1a6GhIgCJ5WJdRGY/oGZM9e/YgIiJC6FBGjBhhN1lKtWrV8M8//yA4OBjnp01DUlQUBgwYgIYNGwpukp07d4avry+M336L/rVqYdu2bXj48CE+/fRT+JUubXPf7vzgz02rFWbEHcnAJudzbrJYUEoyYy8HGVSWtLGuh05nJbbi09MzXmJmM2A2I5W/5jNOnAAA/BQSgpp8zSiaO1SCjOTkZCQkJOAw7777408/ZVoUe/XCuD/+ACSxczGSumkvg9TakdVMZzw/GMpKbN26dUv0EiTntC44GP+Eh8NgNsNFo8m2u8zGv/7CC5kBWQs+8coDG65X8TKzc0ZS64u6n8jLf8W778LMi4fHjx/j559/hk6ng5ubm+il6+HmBp2zs8NZmwgGsxlXnj4VXswxvPWOXI8T9+8Lv41csokSvADfeP06qi5ZgsqSJDjSYwE2LFu82KLXI9C1XWjLFgnkl8bTEdFFBh6dKNcdabZDg9mcZa0rci/aGhBOOHgQ4w8csLsPOXLLjfBweDiarVghWkbiNbydnW0mLzFRv6stcWNPiAlYLKIZ/8eJiVa/JT1IBjJFelM/P7xdvrxVvTN7mV4Jq1atEsXe0tgSCfFpaQi+cweP+QmO3lu24J0NG17bbIRKhSKj5pINN0L63iCTHHQ/9LJuhCXd3VGvXj3odDqYLZn1voilugH/nsvqOShevDi0Wi0mTpyI0vw9Ke0naMu5tN27bt0SvqfP38i7Ec6hsh9KW6JRqWCyWFA7MBDpdqzbX375pfB3vXr1QHoaocSKxSK4IT794gvs+/BDYX1pXJVOYtHypgQTHbtk5UZIWbYAvi7kS8RDms1m6PV63L17F2FhYSIRBQCTJ09Gs2bNcOzYMTx8+BAzZ8602gdJDBYVFYXY2Fi0aNECer0eEydOFNYhfew9vsalu7s7TCYTDAYDunXrBg8PD4wePRrNmjVDVFQUzpw5I3Ir7N69O2IlE31k7Ons7IzvvvsO+/bty/F1KEjkSGzNnz9f9G/x4sU4deoUxo0bJ2RCYxQ8shMoTmKqgoOD8f777wsuiydOnBDcu+jOw8XFBbVq1RKEkr+/PypK/JxpYmNjER0dLbwgVCoVjhw5goSEBOEe6tOnj5CKValUokePHlCpVFi6dCmS7dQUIXEzxI0QEHfwtuKMSsi4l5k5DrESd53ML62tZV9QHbeoTTKWrdVXrqDj2rVCZy4XWxMqI4xSqYFUjx49MlL1k4KFSiU4cn41a0LZrJlMs3MvY5f0ZZvVy9ebF7RZxXpJC3aTwVn/CxfQas2aDEuKWu3QoGJmu3ZInjJF+CxN4wsAL/himyJZQq6TyWQ1eLUlKsm9p1GpYDAYMHv2bNSpUwerVq2CVqvFkiVLRKn7nXQ6KFUq4XlzlCmHD6POb78JVg8iPOQK3aZQ91Vp/jmuzGeCcgRBbEljtnjLFjlnqUsfCa4HxJYtEktEu/e5a7XCoIps061yZeF7qWVLbzaLnifp73E3Lk4Qc7ld6Da33AhXX7kiirMCMn8r2rIlxUj9rrbSPEuLEzeRJOcBkFHvjXp+niQlCb8lOcYeKn4VyEhvDWT8tmql0ipmyF7yIUJ0dDRuUunHaexZtujsukRgSMWWXPKgwkC6ySSaHFAqFGI3QkmCDPpd7swP1EWWLX67GGpgbI825cqJPpd0c8OFCxfg5uICC8cJxyb3vT8vROSegrS0NBgMBnz++efw9fVF2bJl8ejRIzzm03dLXULp517anw/ZvRtL+QLzUsuWUqGAh04nCBRbboRXLl3ClzJ1mpa1bQvMn48fqBp83t7eMPJ9aceOHTFz5ky0bNECxfn73kWjEbk5k/ELObZOYtkqTo0piNcPx3EwSNpKx2wBgE82MoLKsWTJErzzzjvCGE0aN5WSkiKkZwcyn/OiMu+FEiVKwGAwIDo6GkeOHMHQoUOF9zMdi+fm5oYWLVoI4wuVSoX4+Hh8/vnnePvttzF8+HDUqFEDjRs3xnvvvQeLxYLt27dbHY+M/3Q6XZbFkwsTuRqz1b9/f6yQzNQxCg45qcRduXJlbNy4EeXLl4fRaMSGDRswcuRILFy4UHio3nvvPcHyQB7uTz/9FNOmTbO5X+KC5Uy57j158gQlSpSARqNBnz59cOzYMSxduhQAhBpSZNCdZMdNhrjC0G6E9CDU1uDLVqAxXZdKNOSj9kPE1vxffpFvk04nG1/yKDFRmGHWU9Xm7UEP9EjiEvDX32SxwJdPHQ3Yr0WWG0ivZVaD0CQHY7YGDhwoSi9L+7trkXFezhpNltermZ8fJjdvnhnzo1KBkxG113h3NAP1jKjIegaDldgym83CvUsHFZN7T61Uwmg0IiQkBNevX4dWqxXqtpH1ly9fniFYDAY8sFEY2RY3JSKcCBf6un5x6BAAsRshGbBWyYbYMtqwbAEQYrbI33QPo1IqRfEY0vuftmx5OjkJkw1kUOVMDWqkMVsGs1lkVZHeh5UWLUI//kWe2y5/uWXZktsDGXh66nRWQoI+PpBh2ZKrbwaj0crqd1qmVuL1a9dEfZjebBbu3xODBgEbNuCff/4Rvvd1dUVzPn6CWC31ZjOaUPEsWbkRchwH+PjYLI9h5N2DpbxITxcVkCX3Bn2NFLGxhTYbofMPP2AJLypoN8K1164hPi3NphshkOl2S7/fS/Nuc0VlXOPl2CypneSh0yEoKAgv4uNFIkhvNuP+uHFY2rWr0FYp3t7eePfdd/HHH38Iy0wmkzB5VZZK6AOI3Qjl3gvEiieybPFuhDRWboS8ZSsyPNwqQQaADHfuhARRxjsfHx8gOBj45x/cvn0bkydPxoP79zNqJtqBHFlq2fqASk1PJqwNZrPV/U9nIwQyLcg55bPPPsPhw4fRq1cvABDVZKUnWwcMGIAJEyYI4yy6zE9p3nsoKSlJ2Gbjxo2Ij49HamoqfvvtN2Hc5+npiQEDBiA6OloQYCqVCufPn8fQoUMRExODkydPwmAwoHjx4li6dClmz54t23YitooXLw7vbGRGLejkqtg6ffq0zZgVRuHl559/xn///ScIpLFjx6Jt27bCA9inTx/hoSPLEhMTrawTNGQmnzxYgYGBqFixImrXro0+ffr8n72rDo/iar9nPRsnSgIkAYKG4O7u7lqsSKFQpKWFluJWXFrcKe7uFggQHBIsQQJEiRBPVn9/7NybO7Ozm4TSft/vK+d5eEg2s7OzM1deOe950b59exiNRtrgWKPRIDY2Fo0aNQJgvf6MzWyJ1WxZMtCFyoaAOa3FjTUmmPMQI6NilSqi57aRy80oN+x5nB0cUMjNjdaziIFEz9w4gQuj0YiFixejxZAhmDF7NgDTZsg6sJkidKLPqdhF7+WOHQDyNmyJwZ2XszXv+HG8rlqV/s5bdLmshqXMljUDw93REY6Cv/f39we4CJqBMfS8CI0kJwcfs7NR3NERiIkBYJKo1XCGni/T14QYqzIAq1evRlJSEk6dOgWlUomOHTsiICAAQ4YMAWDaTIiqobUG5WIQZkHJ7+z9OPLsGQBTk1ICMk6F/XCswVJmS8sZgMTZYn8GTNkP8l4p8zMB62w529ggS6eDTCKhBnR9pjBa6FTk6HS8eyA2DsjfP2dfOeDz1WyJXVemVgu1XA4budziWhDJZcMyNBozJxQA5BqNWa8qMWNz2NdfmwkHkLWzoa8viqSno2LFivRvEuSOH6I0maPT8dZioUHx56NHCGEogxqNBhg9GrEizn6v/fsRlZbGc7IJsnU66my5/vabaGZLbWMDVybQ9P8N97i1haURJmVlYW5QELR6fd7Olsg58xtgZWtNJdz7jhw5Ai0nkMG2kfBxcrJas5WTk4MrV65gxIgR9DWdTsdz7MVgydkaf+YMYtLSzGu2RHp+sZBLpbj27h0MjKMH5N47e5UK1atX592j3bt3A0+fAhcv4ty5c2jYsCEyXr1C2zyEX8h9IAFb4myVY8Yj6SkqJljE9tkCTLWRp06dstj3qqBgM1YkQDt48GC8f/8egYGBtIaqRYsW9DgS2HZ2doZer4dEIuE50H/88QckEgnq1asHGxsbXLx4EW/evIGLiwvCw8OhUqlQq1YtAEC/fv2g4u53jRo14O3tjV8Fvc8IHBwcsGjRIiiVSnzzzTe8cfT/GZ/kbHXt2pX3r0uXLqhduzYGDx78P3Nj/hfxqRLI586dQ2hoKE8ko3379tRhmjBhAmQyGRwdHakBkZKSAicx6goH4pSXKVMG27ZtQ4UKFRAREYFdu3bBx8cHc+fORWRkJOLj4/H48WNkZGRAKpXSRslCI4EFm9kS0ggNRqNoZivA3R2NfH0xpmZNgMkEZGq1vObFJdnvJOJsPWKaQ7Kw1jcHBgNsFArIFApRKqEQpTh6VUBAANY8fIhzPj7o0asXAJMRym4e7gIVJQC8xtB/FQYiq8sJDeRlgkq5TTovZ2vZnTsAU//J0pOlXMaE1Gz9dP487nCGCgAUtxINu7BpE2oyThwAFPH0NAm+GAzQM4aelGyKWi0+5uSgpKsrynJZKbY+8e6dO/Q9ZLwNHTwYvXv3ho2NDXx9fdGrVy8cOnQIYWFhuM1FsCdMmGCKzkqlvMydGHQGA04xtXpm9CmGbkRAqChsZqs2F61s5Otr1tDWEqhAhuCZ6QwGXs2WmbMllaJK4cLoVq4c9AyNEAAqeXryjCcnlQrX3r6FnhOIAIBSrq6YwbWYcOEc5M5ly6JesWLQ6PU4HRGRe43MucnnEFrwX6URGoxGLLt5kyepT34OT0xEu507P/m8QmRqtVArFIhOSzN3mDgkpKTgXkwM0jUa6DiHtQKjMCoXG0vcPWHpmDfOn4ejQNGWNboHDBjAa2Av4ahtQG5mK1ung4o5J5vZytBo0P/QIay7e5e+Rsa5m0ht696wMJwMDxdtt6HV66ljmZSVRfcyXmbLaBRtNP//BWScsjRCIFdISazPFpB7z/9KPzmVXA4fbm/jqYFyY53UbAmz02JjeOzYsViwYAFPRW769Ok8BoAY9BacLcDUa+3g06f0d63BYPZ9hYHRW1FRuPzmDfQw1cYCwMWvvqJBHB8vL7oWiyE9PR1BQUEo+eEDOjABNWsgaw9pkeDF0AiJsjI7ZklNFysZD5iCHjt37sRdZu58KnJycnhOm1qtxpQpU7B582ZcunQJCoUCNjY2ePXqFU9zgShF16pVC+7u7lQhmuDRo0dITk7G9evXUahQITx//hwajQYSiQROTk48O+T777/HgwcPMGfOHPqdNBZo0mq1GhMnToRSqcTAgQPNen/9f8UnOVtOTk68fy4uLmjcuDFOnjxplTr2Bf9ZfAqNEDBFGtLS0uDg4IBJkyYBACI5KW/AlMWSSqWoVKmSSZqde83RQpNDwFSTM2TIENjb2+Pq1au4du0aANOCtGnTJjx79gxubm7YuXMnrQVzdHSkHF83Kwa1I5vZ4jZoYjCWXbWKFugDgFyng3HaNISOGoWlrVujS9myAGNwGAEomQxdA4YWwBaw0s3PQlGrSibDt4KmthQGA2Kjo/E0PNyqs0We32vu3j99+hSbLlwAALTmaB3C7JnY1vXBivJcQaHnZIrBGArWkM5l2iwZwA9iY3EnOtos6m/HPAMVV/9mq1DgRVISFly/TuX/AcCPZKQEiOjfH6kREbyGjG08PfFLw4YmyiJTIwEAMVxmqFL58viYnQ0buRxKbmzdvHmTHsfW9BFnIy0lBZcuXcK5c+dQuXJljB8/HhEREWjXrh1KlCgBAAgPD6eGUl6F/QeePEFbzqh/mZRkFh3VG414n5rKu36iHMcKZAyqXBn6X3+FRCIxq9OwBEuZrRydjgrRkN+FmS2FTIapDRsiW6fjOVslBPOXVd5ivxtZsYhRMrl+fbja2iJHr8cS5hmw3zuSo/yQdYA4iWKZpPysiG9TUjD+zBna4yszKwspXODp2IsXOMk4wQWB2AzQ6PVQyWS0ia+TSoUjvXujChM0OZucjGrr1iFDq8UrbowGDR5sov59+IBsMbEV7t4TZ+qbuDggKwupgloO9vlt27bNrH0HzWyRmi293qSoycGGcZQItZUVCVBxx0YLaLPk2ZB5bXZfDAbe+CHrHCvhnZGWhtcC1dLPjTcfP9Lx9blBxilLIwRMY5tkkQlYR4NkeD51fyd4/M03pvOwLxqN0BsMSOOcZLP9RWROLV++nBrIzZs3h0ajQaVKlShLxRIMRqPFmr2nHz4gghmrYjRCoTImURotU6ECjnO1re52drlObGYmT/ALMJUykOzO9OnTIZPJcPPmTQwaNMjqtZM9x9/FBRPr1KF0bbbv4naO/ZGl1Zp6fyE3iESuScnUbhUrVszqZ1pDkSJFaL27i4sLr0YyPj6eZ5vJGRpqHKPcSpyrR48ewdbWFh07dqTnJCCUwX1Mn9F3797xFK0Bk7NFzlUQlCpV6pOUt/8b8UnO1ubNm3n/Nm7ciPnz56MlowzzBf99+NSl2NHREWlpaXBxceE1wdu+fTsAkzolAAQFBaFevXro2LEjpk6dio4dO1o8p0qlwsaNGyGVSrFhwwa8efMGbdq0QcOGDemmMWzYMEyfPh2AadJduHABtra26NGjh3U1Qm6Bc1KpaHqebNThQrELwYItJjttwywwkXFxACdwwG54x168QHhiIipXq8Z7r5yjpyllMkzjovQEz0aPNv1gMABGI96kpSEyKgrIygIePjS7DhIJestK1XKL9ltuY+mxbx/vOSeKSJmLySOn5uRgE9MvKr/QGwyQSSRw5qKieTlbJLOVrtGg5759ZtmSKmvXosb69WbncWAyilnZ2dByNVtBjNNPUMyCk3/8+HEsXrwYLxnZaRdOgU1MDVD78iWwYgWkRiMuvn6N9JQUuln07duXHmcUySa1bN4cgGnchoWFYeHChVAqlXj16hXP6CCGkh9TnyYGEiBIzsqC/8qVZlLtb1NSUGzpUqpKCOQ6MKxAhpONDR23+Y2EC9UInW1ssL1LF3peW4UCEpiMHdbg+23BAtja2kIllyNHp+MZatUE2VXWyK/NSP6T9xRijBKxLDHbU4r0nxIKbnxqhosYvZ24dhSxcXG09YWTwKEDgLV37uAPKxFzAjHnT6vXQy6V0j4+HnZ26FimDG4MHUqPIY180zUaqBnjrKGvL7wPHQJExn8ZLqtAFB5pjaAwqME8B09PTzTnxjFg2j/IeGVphJlMoT1b20LGIvsJKdw4ThWsw8RQ1hoMFjNbPGeLaRdQhBi0RuNfkjzPD4ovX47iy5f/Lef+8/FjrLlzh0cjBEzOlrBmi8yzzZ060Xn8V3N6JDhBzvf111/DxcUFOoOBOrXCWk+xMVyoUCFIJBKMHTsW58+fx6VLl9C6dWtUsUCxJ7BEIwRMc5cV1hCjEQodQbJm+vr74x63t7nZ2tJ7l5qUxFPWA0ysj8WLF9PfZTIZXF1drZbHZP/8M82WKWQyLGrZUnRtrcBRctM1Gthwfy/q6AgPOzt6TeSZy6VSzJ07V7QZdH5w+/ZtSuHLyMjAUyYrGBERwUuKkFoyhUIBg8HAEz4DTEqDERER8PX1RfXq1ak6c5kyZWhfUWKn1apVC3q9nu6nXl5eKF++PA4cOIC2XLsIgnZcgPjfgr9Us3X37l3s2LEDO3bsoBLhX/C/h65du6J+/fq4ceMGRo4cia+//hpAbtO70NBQemzx4sVx7NgxbN68OV8Ka2SR0ev1OHXqFN69ewclt+EXK1YMtra2aNu2LZRKJSpVqoSoqCjs27fPYp8WIDd6625nRw0lSxQ+oTKfWENVUlvyVdmy6F20KPD4senaBRvN88RE5Ag2gFIc/UBY9O1ma0sdO+/ChQEvLxyJiUFGTg6g04nSJImzJaq4xRgorMGrENkkxGrW1t65g6FHj/Je2/rgQZ7GS2pODuQGA61DysvZknNj4umHD9j35AnPEWAhrCsLYyJzer0eeqMRtgoF3qWm0ga9jbh77WJBRp1kW6XMs1CKFJdTSCRAUhLu37sHAHiRmspzzu1OnAAOH+Z9Z9I+4KcffkD16tUxb948VKhQAcHBwVCpVHj69CnOnj1LjyfRWUcrlFsg12EiRo8wAkwMW7YOihVSIGDrYfJytoTURELj0RsMcFSp6PdWyWRQyGTI1ul4n3Xm1ClkZWXRekWWglRZQG9l7+GQKlVg5IwBYmCSOa2UyWCrUPCy0wAQmZKCY5wD9JILMJBroc6WBUNu3OnTmH/tGnQGAxpu3myWXWavLUurRWp6OqI52iqJYLMCKiNPnMDokyfRYvt23GforZvu38dObu0AxDNbOoMBCpkM33GGEjlGTAb6Y3Y2PEntHXeNCQkJ6Glri7mCfpelOcroqBo1UP/8eZw4ccKUYRXcEzbYdP/+fawVNEa+xKl2sgIZGua7F2bGFG1My3zGU27dNlqQ/9aKZLaUXL8pdvyw2c+oBQuARYsAg+EfUSP8HBWAYu0YAOCbEyfMaIQ6g8GsqbFcKkU1Ly/0r1iRriGfmtkqJNgjyHnWr1+PQk5OVNkycdIkdGBopYD4vfgoyPy1atUK79+/5/VmEoM1Zwsw7TUERI0Q4GdFxXDt1i3MnDoVgCmTRD5DCZhlbgFg3bp1NFC8fv16TJs2Da1atbJ4XWI2A4tgbm9syZ0jXaOBjLvW4oUKIXLcOHoseeYKqRSPHz9G8eLFC1zPCwA7duzAhQsXMGbMGAB8OychIYFHWydOEHGQhA4ekXJPSkpCzZo1KdVw4sSJvGMnTZqEGjVqQK/X0/saHR2NzZs3Izs7G5MnT6av9+/fnwbr/y34JGcrPj4eTZs2RY0aNTB27FiMHTsW1apVQ7NmzfDhf6Cp4P8qPnUx7tChA5o2bYr379/j2bNnWLBgAQBTrwYA6NatGwBg5MiR2LZtGwBToSkbTbEEMsFJNMXV1RUKhQK1a9dGREQEJk2ahJMnT2L37t1o0qQJPScpNhUDiZIXsrGh0S+tXi/uOOQjs0VQRq1GABN1txVsUhkaDZ4KpNafchkqobP1ZNAgWuSdlpEBv1u3AHA0O0FBLwFZMFnjj9YylSuX+5WY94htXWLS70Jjzmg0YtCRI7gikjli8TE7G9nJyVjCyfpaEiMo4uAAqUSCj6QflEBKWAihCpGGWdT13PMljoMnl00iGU0buRzYsMHsnISKym6wYspnXtz5K5FILDdGNHo9/blWrVp4d/UqIo8cQYUKFeh7SZ1Qh/btcefOHVy4cAGXL1+GUqlEPW5s1OayFnPmzKHj8xLXW83SOCV1V+R/leC64zknizVCiXHG1myxhoGQhsMiS6uFYtYsxKanUyPmt+BgGLmaRzJPSGNRBWd4s0Zk+3btUKNGDdqcm61V8BE4l5aMLOJskfGpkMlQ0cPDTDIdADru3g2NXk+Fbk5w9D6S0WI/4xJDJV1+6xamXLiAtJwcBL19S52ExcHB2BsWhlgmcxObng5IpYjmAk3EOBL2DANMfbS2MRnqoUePoh9Hg/7q0CHsFanv1BoMkEulZpLSYo5xplYLcPeUNBDVaDTYO2kSJtatyzv2GEcJkkkkCOM+98qVK2bOlnA+CGtaZnIRbFb6nc2OFWfG13NOMZPNKCZz90moGkjGRo7A2VLk5GBe8+Zm6pPs/baXSuGqUv0jma3PgfepqXCYN88iZZw4WwSklilbp0OfAwcw4tgx6AwGjKtdG3KmcfinZrbejh/P+518dvv27fHy+XOqbOmUj+CpJQgdMDGI9dliwTpbWo5RwUJM7RcAElJTIUtPx85y5aBkepfJIe5sbd68GUe5wGOXLl2wYcOGv1Q7RdpsBHBZtDSNBm5cUE4hlfJsDiWzzv3+++949+4d7XdVEJByj5UrVwLg7/lsX6u5c+fSoLmrqyvu3bsHuVzOb8Cs01EH6syZMzjD9SY8dOgQihUrRp2vy5cvY+vWrTxnCzCxmHJycjBp0iQMHz4cFy5cQK9evbCIsxn+LfgkZ2vMmDFIS0tDWFgYkpKSkJSUhNDQUKSmpv7PFLP9L+JTF+P9+/dj9uzZtG5L2PGbNL5s2bIl0tLSaAbGWs0WAZmUFStWRK1atVCsWDG0atWK9tSSyWRQKpVUcIMIZFiLYLI1WzLGUH7I8JEJhNk3a85WSkoKT9UnS2BgiUYruQ1VaMR4eXnhCUdJS0tPx3ZuUUzOygJ0Ol6NEgFZAItwlIUqVaqgBil85SLhAD/aKEadshMUxQPmsvfEaBdTBmORnJ1toj3mUbOlNxqhlMnohkiOsrS5smIsAHBTKjWTXyfXRp4ZFUORSNC4QQOzc3p6eqJs2bLwYPjkpbj6KYqXL3GUo0MTp4j0MMtkxlyhQoXQpk0bLFiwAG84w/1E374ox/VjyeAMg5SUFJw9exZKpRKBgYEoVKgQWrduDcBEvyXjM4MIHaxebdboFsgdW7S5qOB5xTEOAWB6nsSAZbOHrJNmLbNFMkfCWqsMrj0BuefONjaQMPUl5Prq+/jg119+QUhICHXw2PoaB8G8s1SrQZwtcp+UMhlqFy2KSxZqczR6PdJycnjjlowxdvw05YJCBCyNkWRibkVFISw+HrUYxz2Gc7ZI9p2cW8zZAsSVxwBgO1O7wFIgdQYDby6SedKmTRvR87zj7kMJrv7u3r17cHZ2zj0HmY/ceJRJpdi+fTsUCgXGjx+PUoJeiOw6Va9ePV4ggYUmJwdJWVmm58Y8O01mJjB9OpCVhUdcQIzNbCVzhvvGLVt4BfJkrKbm5PCcLWl8PLZv2IBsjYY3Dlm6bKumTZGYmAg7W9s86bj/DSDBAEuZGOJssUEC4mztDg3Funv3oOOcciB3bmRotVgUHFzg6xH2aiPnO3HiBKDTITY9HQ5Kpdmas75DBxzs2TNfn5EfZytDq7XqbH20QCMkgQlhzRaFXA47OzsawCRrlIuzsyiVjc0kNWjQAG5ubvj222/zvH5LINcZ6eqKLK0W6RoNbAV9teilMgI05Do+xdliUaZMGZRkxJCKFSuGsmXLolmzZlixYgWlxstkMpQrVw4SiQSpjDhPYGAg9Ho9pFIpHjLBo1OnTkGr1dIa5Pfv3+Ply5coW7Ysz6ErUqQIpk6diidPnqB27doYMmQIOnTogLlz5/6l7/X/DZ/kbJ0+fRp//PEHyjHR9PLly+P333//n+n2/L+IT81sxcXF4datW0hNTYWDgwPeczVDZJIRBUpHR0ekpKRgKFdfkB9nSyKR4OLFi6hUqRJu3bqFEydOwGAwYNKkSZBKpdDr9dBoNEhJSYGzszP8OKPCWmNeUqtir1TyaISrQkLoMb6cgSzMxAgzBiyqVK4MR0dHWuypFThXopQ4C85W0aJFc/s9yWQow1HgVG5ucHdxQXOGBjRv3jy8fv2aijMULlwYiYmJKFq0qFn3dYDvwKhEnDZvESUwkjkgUT9SEL3oxg2cYVTfhEjOygKys/N2tgwGqGQy0KfGZBwBoHfv3li4cCE9PlXgbAHANK4XFgExykiGUMoaHSJRsx9++AGjRo3iNduuwKxhAACdDlW4GsT379/jp59+gpT09DEYUIGL4p06dQoRERFU/hYA2pYqRR19cGNQp9OhYcOG6NOnDy5fvozk5GRaszV79uzcHjLcWHqRmIjbXP0dCxLVDeOYA0LDIlbgbKnkcnpMuoXMlnA9YGsiii5dSn9mswXbHj5EDifiAOQGNsj4+aNtW2zp1AlBgwdj4W+/QSaTUceMjUwLjYy+Fox68h0kjLNV1YqapkavR4ZWC1dm3BOjtujSpXjy4QO2MwYDOa+DSkWNW/J/ukaDKME4jM/IgJ29Pfw5RVAy15JFWiwA5qqRgPma84Bp3ElqtgjIfHr58qXo+fXc577jarAeP36M9PR0+r1IbQhxtqQSCQICAlCuXDns378f4YLmwuw61bFjR1OvG24sSyUS7Nq1CwDw+8qVVA2yECNIU5UofWZl4UxEBBRSKS/gQ9YVg4MDz6gl9yklO5vnbOVoNHhw9y6SU1N5mS12rT2wfz8Ak5OQY4Ge99+EZC6QYSnAQJ0t7u9EIIN13NlxwgZNzrx8iad/kV3EWxe0WrxNSRFtsvt11aroIlw/YerVVFeQWSXjxhrqbdqEUCuMFfZ+aRkaIYHF1ioKBZo0aUJFG8hcLVGiBGXisCCBlBo1aiAuLg4ajYbXALigYDNwWx8+RFpODhI4erGQUUJrt6RS6vAIGxIXFJcvX0bNmjXp782bN6cZptjYWMouysrKglqtxs6dO+Hi4oKmnA1Sq1YtlC5dGpcvX8bjx495YiEvXrzA5cuXMWjQIPj4+GDWrFl48eIFrWkFADc3N4wfPx4pKSkYOHAgT1zt34RPcrYMBgNPipmAFNh9wX8nPjWz5eLiguPHj6NixYqYOHEifcbkfzcumu/k5ISUlBTKxc2PswUAM2fOxN27dxEUFIQlS5bg5MmTAPi9IZKSkuDk5ESdLF8rUqyuajVc1WpUKlyYGrMZWi12PHpkUhsEoOaMDGGGjBiPnRiHhCyHMo7eQ5wCO8EGlGEls5WdmUmdVMCk5viSCCVIpejRrRtUEgneJyfD28MDRmZjnTJlCg4dOgQlZ/Scv3gRhQsXxrFjx3iLGgHrbIlFCt8xfW8IiPGbqdXiamQkzQIefvYM4zjaAMGVN29wjTPuaLPRPEQI9EYjVHI53TDTOEOLGMJ7rlzBWkZCWysSGFjBUS0J1nONGIXZt8T0dNH+JPv27UNYWBivgfAzgbEJvZ5SVV+9eYOLFy8isFIl09927qQZKJYiy9YdhoSEYFytWgBXm3Lz5k2cOnUKrVq1QlpaGipXrkyDVDdu3KAb8XsLmRECIoowmFPVEj5XM2dLJqPGGTsuWWNaaPQPOHQIzvPnU2OQfA4bgR/NzU3iQFFnixs/5dzdMZBzVv/8808YOCcb4Ge2hEZGn8BA0fVpVI0a6F+xIq+PjVqhsBgU0XLOFtuXi73+2PR0fCWQMAZMgRli3JL/0zUaPBEYrjk6HQwA5KRZOtN/SAximS0xByyRaUrN3hvyvSdOnCh6fhKtv3fvHoxGIwYOHMjrs+dMfubuvQSmegmqCiZY/9jxMWPGDPj7+yNs1CgAgJNCQZ2+1b//Th0eNlPu4+Njkrh2cUFKTg6alSjBy2wVJRH2Fi0QxRiR5D6lCDJbkEgAgwF3k5JwPY/G36kfP+KFlcDQfwtIhoYEMYTzkDhbZI5r9HozgQw2A8pmnO5GR6P8H3/8peuTijhbHiLOliWMHj0aS7lgTaVKlWA0GtGgQQNoNBp8LRDJEGbVhMENSxCjEVrKFJYuUgR//PEHdR7I+hYZGYk5c+aYHU9q0m/fvo3Y2Fhcu3YNs7k+lp8C9vmEREVZzWwRyKVS2NnZYeDAgSjL2SwFgZubG90Dvby8eM3Jd+zYgWwmOERseeJ0XeaCmhcvXgQAHDlyBDY2NihTpgwqVKiATQzzQqvVIiUlBSNHjqSBliNHjvCEdZKSktCwYcMCf4f/NXySs9W0aVN89913iGYisFFRURg/fjxtjvYF/zvo1q0bpFIpChUqhHr16lH6B+EDN2nSBICJ1kaiFkOGDBFVeBMDiZjUr1/fpH7EbSpVq1aFWq3G5s2b8d1332HOnDn46quvYDQaeT08hHBQqZAwaRLcbG15UWIvBwdU55yoZ5yxLDTwHFQq6KZOxeFhw3CF+16kj4wwEyCXyQDO4Aes0wj79+5tJuVKIldKGxsoFArYSqV4/OYNnoeF4RVjNJQqVQqVKlVCLIn6SSSmezR8OCCSpWIzEWLOj1hdJTHwMrVaNNqyBa04mVqA3zsHABpv3YoGmzcDMEUJCzs7w4uLGorVTHTbuxdJWVlQymQwcp+TJRR6GDkSUdz9BoAskWCOEFncxiyMuloqPl++fDkePXqED1wtCQB8FFA0nO3tKeUiPT0dISEhSCeO0IsXVDZ+xYoV1Egi4yI+Ph61a9fGr7VqYTNn8BBFqO+++w5KpRIPHjygm9iKFSvoRjyFqSES3m9A3EDfxKh9pgh6K6nkcvoedgwoRLImBC+Tk5GSk4MXTLZUqAJHQJwtIhBBxg9rAJH7Q77j7tBQOt8UUinCx4zBmnbt0J9T6RKjNdYtVgzbu3Sh10qcAScL6mCnIyJw8OlT3rlYMQ2hSAY5r71SSY1bNrN1g1X+RC6ly4ZzYliDWAxiz01MKj6RyXawz5/cww0iNYhAbla3Vs2aVBksnDl/LLcet2KUUK0JFbDOlru7O1q2bAkvBweEjRqFn3198cuUKQBM8+bSwIEAgEjGCdq0aRPu3LmDtoULY27TpqhVpAhv/MUyY4utseE1J2b3DYkELTgjOTErC/0CAylV1wyckMTfDWu1jvkBGY9kzAgDJ0IaIckIszVe7Dhhr8dShpXFkd690d2KnDY525QpU1C3Zk1EpqTAXYQhYQlSqZSuew8fPsShQ4dQtGhRVK5cGRs6deId6yyYx5ZEbIQQVSMUCWz81rw5bo0bh/379+MWF6wjQZ93796ZCcAAoKISBB4eHjwaXkFBnk8VDw88iI3F2NOnqaCVWFNywLSe7ty5E1u2bEFhkV6ZRqPRarP28PBwxDDiPKwC7vnz57Fnzx76O+mHSuy1pKQk9OnTh/79+fPnCAkJoUwmst81b96c2mqdO3emIiI//vgjr2ZLIpEgKCiI2osE9QT17f/r+CRna9WqVUhNTYWfnx9KliyJkiVLonjx4khNTTW7oV/w34Pf27bFTIH8eH6gVCqh1+uxe/duLF68mMp5SrnF7sSJEwBMmS6yoBVUnZLNiJI0vl6vR1ZWFt68eYOqVavSSb58+XLcZCiBQrCNOdmNyN3WNvd3QtEQ2ZxlUinGjx+PRo0amY5hosIsFAoFEBcHcMZzhlYLVUICGrAbE9kgRT6HpPZlCgVkMhlytFocjY6GVFC3Ea7R4Ldly5BOaDfkO3h7i8o88+gpIoa72IZGFm4x41DM+CdIzcmBg1KJNq1bQy6RIEung8FoxIJr1+jnEBU7lkZIaprEhC/yDe78HgJDIE3E2Tp48CBevHgBJycnlH76FOB6NAmVpLp07Ejr+EhPILFMoIODAw4fPoy6deuiJmdYeHl5ISAgAFeuXEFjbp4RSu2jR4/ouCZZhTFjxogabmJ1cmLPRVj3xMJGLhctwGcDBkJHnBh/rONGKExi5wdyM1tykSg7GVNa5jpYaWN/FxeMqF4d27t0Mbs2IYTOlpg8OABM5dTyLNWjWfrOb54/N3OcxJx2ncEArU5Hg0rkPZaK88WeQU+O9saCUsv0elGHWGlhbvhwAZzY2FgaBNu9e3euShj3LCszdC/yXLp27Wp2PjZjGBkZiYEDB+LIkSMI8PBAP9ZQNhqpRH86k5UlUeyTI0dicoMGkDFOAwA85vqCkXMQsONbJXC22Oxzu1Kl0NsC5RQWAgOfG9bWQ0uQzJiBEG4dIfV9WoMBv1y8iKVMvzjA9MwlDI2QZsIEjAUylwrazNhJpbLqPJHzzZgxA56urhYzW0ajEc/Y58m8zmLQoEHIyMigvZ7Y8S10tvJqdk+gYfpsEeEOsXXKU6FA7cqVsWfPHoRwNoOvkxNsFQozIQeCsmXLUvn39evXY/DgwVbVCPMCWRNLOTnRQNYbmQzYvh2TLDgccqkUZ8+eRdeuXfGHSKayZ8+ePDo8C6PRiCVLliAqKopmxNmyi+zsbGq72dnZoSIJdkmlkEql+Pbbbym7SKFQ4Mcff0RCQgJ9DwGrzxAbG4uyZcvS8SAUyADAc+CmTJmCA1wLnX8LPsnZKlasGO7du4cTJ05g3LhxGDduHE6ePIl79+6hKKPW9gX/XWjl74+pnANRUFy7dg2PHz+GRqOBnZ0dbt26RYtLe/ToAcAkakCyXH/F2fLz80ORIkWgVCqxePFizJgxAwkJCZT3/ebNGyQw2QkhWCOU3RglEgndSHr36mX1elhesV5QN0I/h/Tf4jaXDI0GHoUL8xsuEyNCJApFsnMajQZnzpxBJueUyIxG/oY+eDDOarW5FBmpVJTGSyDMWHja2qIhkwETc7bIRiVWd2bNuMjW6XDnxg1s2rQJuqwsZGm1+JCRgZ8uXKCqbgRKmQzZ3PmjuSxdfGIiva9iwh3WQDYS1hCY3aQJJhFhCwa//PILPnz4AEdHR/i7uKAKd/9sBPcx9v17uqm0bNkST58+hYT7vUGDBtTRtbe3R9GiRbF//366kf36668ICwtDenq6qUkycps9ZmRkUPpgA0a8g/a8Yq5BLfJsxYx2ByvOqUomQ45eb5U6LBwnxGFgC9G1AhohPb8FGiHrPHbhnKgOHTrQ18hYEpMyz8+1KvJwtogTWErQD4iAvY/f1qhBKZa2cjk19LJ1OuQIZOwJdAYDIJUigcuIkkyKpXoRMSdZDCQjoRNmtrj/o0Xq+ABTc3jAJECg0+lga2uLcePGIYtk87h5tmDWLPoeInBEjCV7pnZKONffvHlDa3IBAImJgFaL7Vu3UseXzSblCDKsMqmUt96wlFa23QF7n3jUKomEioAApnEnlCqfM2cOunfvDuj10H5iL7WCQGzs5gfPuD2LlbmfExSE2QzFCzCnESYL2hyQ94oFOPIDuVRqtTaZrMd169bFob17ka3TwUvQzBYAzpw5w6vbtwRhI1x27goVDi2xEgjmNWuG0q6uvJqt28OG0fYfQsj0erx48QI2NjY06HN/xAiEDR2Kffv2iTpbAKjicqVKlbB27VqcO3fO6nVZA1kTj23ZQvfXUYGBwMuXFtexQjY2mDJlCq5fv44oJtj3888/Y/fu3Th37pzF9jc6nQ6zZs2CTCajTiPrbGVlZWHIkCHQ6/WYPn06j+kSGxuL+vXrI4Xbu8k9i42N5d2rb7/9Fj179kTRokXRr18/AKaMFqE8siUgxNny8/PDunXrcPr0aXTv3t2s8fH/Ogo0Sy9evIjy5csjNTUVEokELVq0wJgxYzBmzBjUqFEDAQEBCAoK+ruu9Qv+gxgwYABu375Nm/ux9UqEHkf+tmbNGtSoUaNA52e7hNesWRMRERGQSqUYTRr/IjfdrVKpkC3Y1FnwMluss8X8vpurD7IUMRZrJig0BiUSCR4zfXMytFoUcnUVd7ZEHBzSb8mQkYGRI0cCnIOqkslQXFiTplLB3cMDAOBZpIjFgnmFVGpWP5KSlAR7ZlF3487DghhMYsahTCKBZMYMRIvw6bN1OpNUPQBotTSzBZhHXFVyOZWiV3HjJyUtDeAcdGk+qIMEv7dti+2c3DVxtvQGA35u2BC1/fzwC9dXhYAU4xctWhTNmzenzVdJs1eCdGYDUyiVKFu2LF0kg4KCqLNVpEgRDB06FL1798YD7rkRuWDW4CSbXEZGBpw5IYEqVapQTjsxbDyYjexzZLaIISysiWAhjECTcRObng5XtRpyqZT2+BGCGHqO3PnFouyzOAOf3ROosyViIOYns0UMF0sqmcRRHV2jhuhndN27l/68afJkRHOGRiFnZ5qBmX31KmzmzLHqbBEDIq/MVr6dLYZGKFaz9caC+iLpAdelSxc4OzvTIJRGo0Hkt9/SNYVkuLKys6mI1enTp1GuXDnUvn8fvQICAPCfAemdF8eouBZ2coLvjh0oWbIkfdasM2XmbAkyW5nMPSVUzJi0NN794zkzEgmPFRCVkGCWDZHL5Th79iwqBgSghGA+L7x+HVvz6O9UUFiqs7EEcn/InCRjggQxhFSymVeu8GiEQhVWmUSCHL2ens8SrXEfFwQ1u36ZzGpvKOrE3L5N96+KIoZxJpehs0ZnA8ydLRvm+QrpwGKsBBYednZIzcmB3mike7mvszOqiFDtAEBuNEKpVEKhUFDHoZBajQM7duD333/HQI4KKwRpLTNo0CB07NjR4nH5AZlT2sREU09CqRQ9uICH2L27OXQoRlSvDp1OB3d3d55Axty5c3Hq1ClMnDgR7du3F/08YruQvad9+/Y8e6x79+6oWbMm9Ho9fvjhByr9DphKG8Tqq/z9/XnOFmGwsUFpth3PhQsX6M8ymQyrVq1Ceno6+vbti6tXr2KqYH/+N6BAq8ayZcswbNgwUeEDJycnjBgxAkuWLMn3+ebNm4caNWrAwcEBHh4e6Ny5M6/gPykpCWPGjEGZMmWgVqvh4+ODsWPHUq+b4O3bt2jXrh1sbW3h4eGBH374wcxYvnz5MqpWrQqVSgV/f3/al+QL8ge5XI6srCzqnEgkEqpqR4pHibM1cuRIs/4s1vDq1SueepFMJqPnYjM4xMBp164dz8kRgjVChRsRNQbzUM/jjR+jEavbtUNbZiPvULo0JtSuDS8vL2qAp2s0eBMZCS0bieQW+Pr16vGUj9q3b4/Tp0+brikrC3Z2dhjAyei2bdUK1QWFxJDJ8IGjIHh4edGNTgjSXJb3nQHIGdqlGAecZLbEjEOy2X8Qaa6Yo9fnOls6HbK0WosqW2w9SA53THhkJMBlW9MKoPgkBRDPRfyIs0U2aqlUitYC2sfbt29RunRp+Pn5oXr16vjAbQzF2dq/BQswgClGfsv1cmoWF4fm3M/FOd760KFD8eDBA1y9epVupg8fPsSPP/5IMzqASSQmICAAu3fvphSL4sWL8yLCfklJ8GEMOI3IsxV7Li6MEALANwKFcvhiIGN/HldnS8bNpTdvUNrVFYXt7S3WbJH3WqMRTud6MrFRYTEFNQJrdCjyeeReW4oIEydMKZOJKqgRFLKxgburK3RElvn1a+o4EfEXMWdLazBAplDQwAd1tixktt4xEsrWYDGzxX3v+fPnm16w8Dk3btzAlClT8N133wEwOVue9vYYQxqmct+lVq1aSEpKgp2dHa5fv46nT59Cp9OJNqcVKsoBJsbCzJkzadNXAPBg1hNCmyXrtkwq5a+xjJH/6s0bxKWnw3vJEuwMDaX9iAxGIyTk+zLOVuXERIxt1gyFBON+5syZSE1NhY+3NxIFtsGThAQzkZO/ioJmtsjcJfOTiIGQnmvCcXbh9WtIJRKUd3cHYF6HZadUIi0nh849YWaLrLO1LbCM2MyWWO8s3izk7n0lkT2jSpUq6NevH098ATDN95kzZ9Lfa9WqhW3btlGni3UuS7u4YApzP9OsBFEBwE6hoGJA7N5uiX0h45ytFi1aULocAJQuXRoVK1a0aPQPGDAALi4ukMlkCA8P59U8fSp0mZko4uAAR5UKy5YtAyCuqlyraFFToEung4+PDw20aDQaKBQKTJs2DVOnTsWxY8dEP0cr2CsWLFjAc7YGDBiAmjVriioZV61aldItCfPm3r17KFWqFH2dwM7ODleuXEFwcDAGDx7Mq9EXqpJ3794dgGldmDt3Li09+TehQM7Ww4cPaY8YMbRs2bJAzd+uXLmC0aNH4+bNmzh37hy0Wi1atmxJo9DR0dGIjo7GokWLEBoaii1btuD06dN0QQdMg7Vdu3bQaDQIDg7G1q1bsWXLFvz666/0mNevX6Ndu3Zo0qQJHjx4gHHjxuHrr7+mzdm+IG/I5XLMmTOHx/MnGyrhDrOcXksF3WIoXrw4da6EYM9JnITatWvT1LUY2MyWJRohyTRZisr99ttvWM2JX9jY2GBk9eq8aODRPn0wonp1U9SJkRzPyMqCQqDmBJgi5wMGDKAvHz9+nHKWpVlZGDp0KLTcuE9LSjJXyZPJkMRlYx6HhVlUKBKLuhoNBrgzG/pzkQJ9kr0QaxZLMlrCTT1Lq0W2TgdnQuHgMlvEaBdmRNheRIncOUMZAQItOX8+ItFGvR7gNl03jkpFNuq0tDTaj43FixcvEB0djdWrVyOVixY+Z1QFMxMTqRIVkOv8Hdu2DYOJkhX3vsjISGr4s9kAuVxODV7ApMgXFhaGypUrw9bWFjY2NujRowdWrFhBj3EQNDKOEVFdy9LpcmXlOVj7nYxVaxRQYgQT0Rjy3A4/e4ahVaogXaPB4CNHzKilY2vWhCtn8FqjEW7duhVr167lUUrI9YhlsQpCeRxbqxaalyiBjYzRD+QamllarfU+Yj/+CL1GAw13Xnd3d+o4kWCBcGXwsbWF3mCAAbmUV20ema2P2dkWm9ey2Pn4MRps3gytXo+0jx9pXyJyDV27doVs/XoaERfi9u3bmDdvHtavX49mzZpBymXfyhMDk7mGfv36ISMjg/bMadKkieg6+PXXX5vRrG7fvo2BAwciISGBvseWcWp9fHxgNBpp/ZiMqT0CgOKcZD4AZGZnU0fk7MuXKMONk+DwcBp4UqnVOMBRcZv4+UEtl5tltoi9EHLjBiIYoRnAlFWy1OvsU1HQmi0yf4SZLdLGQUzFViqRoF9gIO4OH2429lUyGVJzcqijJBznwjkphEIqRbfy5VGlcGF8/Okn0c+m4NbWEoIm84CJFnb79m1an0rQvHlznnrmtm3bULVqVcTHx8PIqdISyKRSeDPnziuzxTpqUhFnawwjcQ4AJYsVw5EjRzB48GBer7r379/j0aNH+Enk+wOg/WMfP36MFy9eIOIzqFxK4uLgaWsLR5UK9vb2KF26NLVvLl++bJYVrlixIkaPHk33ioyMDGi1Wpw6dQpz5syBRCIRnbdGoxHlypWj/U8DAgKwl8nojx07Fjdv3qT2G3sOuVwOg8GAFi1aoGHDhujYsSOqVq2KPXv28Fo1ACYKc0pKCl6/fo02bdrw1nSWkQSY6JgArJY//K+jQKtGXFyc1Zsll8tFlc4s4fTp0xg0aBACAgJQqVIlbNmyBW/fvqUOW4UKFXDgwAF06NABJUuWRNOmTTFnzhwcO3aMZh7Onj2LJ0+eYMeOHahcuTLatGmDWbNm4ffff6cL/po1a1C8eHEsXrwY5cqVw7fffovu3btTedIvyBv+/v6oXLmyKZPDgSjaNGV6Qt2+fRuenp48h/ivIjg4GNeuXaOLh0KhgC/XbwsAPv74I/pXrEiNbhvBYk4gAWMMcguMJQJEhQoVKHVAZSU7ULJkSco9ztBoYJRIeLLtxMhxdnQUdQBw6BDGlCiBsmXLYjeXbb137RpiBKIMEoUCD0h/IBsbXoSYYH6zZqJRV71WS7nbAJAk0reDGI0XBMYKkLsB5uh0qLF+PX3ddu5cnI6IQJXAQBQrVgxlSpakDhhgrtDGmgRkS7VheyZJJKbnIsygMZ+Z+6X09DiS5Ui1EhUlHPzExESEhYXR569jNne1Ws3bMDTc+YKDgykVjhjBoaGhcOAMbmKQDho0CDdv3sSuXbswdOhQJCcn5zZGhikwlJ2dbRbN7Na5M/zLlKGbXjERhclMrRa/t23Le00pk/EMDjaLRQw7a5kVYsQRCg6bkXSztcXH7GyEJyXx+m8BwMKWLem8EvbZYuebRqPBkSNHMG/ePNr8tKAULOG1EnQvXx7nBgzAEC4DPKBiRZQoVIiOvUZ+fnm2ungfGUkzrJkZGbkCNBYQfe8eEj9+hBGAPffs88psAbmy/Na++/V373Dt7VvoDAYEXblCmwmT792kSRPYpqaiYpkyvPeResKSJUsigKMCnj9/Hg8ePIBEIsE333xjOjAnB0XCwoAPHyhtaO7cuZgxYwZ+/fVX0XXQ0dGRUp2TkpLQvn17XiCBZNefh4cDBw8CBw7QAvv13JyVCfpsvWMoiVqdjjffiBEen5hIWQBqtRrunINZzt8fRYoUMavZIpDB/DnoDIZ8ObsFQYGdLYEDIXT+jDAfG1KJBBKJhAY1CIo4OOBDZiaMyJ17QvaGMNssdv0VPT1xj63FE3w2YFJLrc85KGLnevnyJc34JCQkICsrC9u2bYNEIuG1OTly5AgqVKiA8PBwSCQShDN/k0ulvACuxcbEHNiMNrvWkOvzFwQjfNzc0KRJE2zdupXncBBWDmGXCEHEHFxcXPDu3Ttc4oR3PhWGX3+FPjERxV1d4ahSQaFQoH///tTZatKkCdatW8d7z86dO9GuXTsULlwYqampcHZ2xs8//4x79+5RGzlLpJ7PyckJN27cwI0bN+hrLA35woULSE5OprY8G+gmr1WrVg0vXrygLU7279+PtoL95/vvv6cZ9549e/JsDJUgEKjVajFr1iw634V//zegQKtGkSJFEBoaavHvjx494hnjBQWhB7pYiN6RYxwdHWnK8saNGwgMDOQV27Vq1Qqpqakmo4o7htX9J8ewg5FFTk4OUlNTef/+7Th27BimTJmC3bt309eIhPCdO3foa4cPHzaLgPxV2Nra5jbL5LB58mT6s5ONDeoXK4ZaRYogbfJkOqHHjBmDB1xNA4Ews2WwYiSRhcdaQ0OJRIKq1aoB4CKYEgnP2SM0DGdHR54UK2CSlMXDhxjTv7/pmrlN2cZoNGuCbOfgQGmEcHICRDbKsbVq0fex26+HmxutdwP4hqtkxgxsefCAqqBZU/PS6PW4I1Ko716oECpXrgxtVhY/syWgE75h+N0JXGYrVihVnJVlTpWKigKYRrSmL2GgmS1yv8NFsnKAKQj0448/5l6vuzvNRoqJUQAAduxAPe4+LV++HGvWrDG9zghkrF+/HiNHjqT9TGrUqAFvb2/o9Xps3LgRarUaDRs2RO/evQFYWdeMRmgY+qVBYBzGpqcjNj3djPKjlMmQNnkyxnLRXNbZYsdgdRHnDcgNNLja2uK0IFPMUnGFCn7EMKzk6Yl2XKZCTIZao9HA1tYWer2eUj2tUbCsZaI6lSmDQVz/LiFa+/tjWNWqpvuh0WBinTqwkcvzbOLeolmz3Kx0ZiYy86Aw6TQakzCGRIIIrmaSOFtzgoLQaMsW0UizzmBAaHw8dAZDng6g1mAA9HpaFE/Ol5qailGjRmHqzz/zjidCLNHR0TR6/Pvvv+calq9eAX/+CQCI2rcPRby86Dn79OmDzp0747fffhOlU6empqJZs2ZYuHAh7bXIChORfTFHo4HjmzcAR+3+/fffMXz4cADmma0XTE2kMPOtlMnw+rvvMJH7TgAAiQS7uDYUJw4fRkREhBmNkEAG8zVHbzR+9sxWQQMGJLM19dIlGIxGUedPIZPhwldf0d/JXBDSgOv5+NA55lAAZ6sRUwOcFw2SzJsRI0bwA4cCsPuZu7s7+vbtS/dKNii/k6uPJka9jnWYJBLcFKgxWkNhpv6L/d5irScA4Mn9+yhXrhxu377Ns1Mqc2uJJYGMfv36ISAgADNnzoRarbZY251fSCQShISEwF2phINKBYlEgl9//RWZmZl0PgrV/tauXYuoqCj07dsXGzZsgFarxdOnT5GZmUmfkVhteXp6Oi3tIEwNEuBLSUnBkydPYGNjA6VSiUKFCvH2JVtbWxw4cADz589HSEgI7ccVHR1tdq/UgnlYrVo1HOSosUJ7zdnZmQabu3Xrhrlz5+b31v3PoECrRtu2bTF16lReQzSCrKwsTJs2zWLRXl4wGAwYN24c6tWrR6N6QiQkJGDWrFl0IQdMKilCVRPyO4ngWTomNTVVNDIwb948ODk50X/C/kj/Rhw/fhzPnz/nLTrjuHoANuozZ84ci5TAT0XlypXN0tIeOh0CGPpK38BA/N62LW9zCg0NRTLTR0kikdBoGJUhtWKQkcXFmIccbXWOD52u0aCQqysKswIUnAE3ddw4s2hO9erVMWDAgNxoMXesUiKBVPCZJUqXRiW2jovj87Ng67U8GGM7hcvGEAjl7s+9egWtwQB7pdIiHQqwHL3fu3Mnjh07hlfPn/MyW1UEPUxYo51koR4LFZWyslBHpFYEhw7xfnVzcUEMR+0gG48lMQJhJql58+Zw4sQqbCxsop5paejCqW2KGdAODg7w8vLCN998gzTOcVyzZg0OMdcZHR2N27dv06CPp6cnjEaj2aZ19swZPHv+nN57o+B6W3BNwoX0KaVMBluFgjqM9iIUmzvDhuHiV1+hVpEiZt+BNYKFNVDWlA7J/X4wciSlF4k1WB0zZgyKFi0KnU5HsxbWehRZc4487e2xWdCjh+BUv35o4OsLpUyGdI2G1qPkJYnNXktAhQq4zxSKi8JgQCqXzUnlnjnrMFyNjISUqVUh0BoMOBUejk5lyyJARJyGhc5g4InpGIxGrFq1CmlpaabsrGAsunPrgKenJ6Ulr1q1CikpKaa12mgEGNrwmDFjaO2Ki4sLIiMjsWvXLt4Yr1evHq/OjgYakGtE37x5kwZW9QaDxTVfmNnKZu6Xg7s7z8lTSKXwc3bG8nnz6GtGiQRK7jNdHBzw/fffm82DoKAgGI1GuDg4QCLI+Ov+BhqhQiZD6ZUrcVukJYQYSE3Ww7g4/HT+vOg6pZBKeQE2S86Wt709KnOZaEtqhGQus07VuQED8AeXmcgrM0c+u0SJErg+cSKef/ut6HHCdfXw4cM048LaCSQwTgSCwAgtyaVSkxMk6HdoCWXd3FCM0wxg53dpzmEQ3gu9VgutVguFQoHk5GRKnSUCDmI1UwR2dnaUvfA50LdvX8hSU+FhZ0e1DfR6PYxGI/bt28crMwBMtYjv3r1Dw4YNcfnyZXz48AEHDx6kaoSAuLOVkJCARYsWAcgt5yBqz0SBVK1Ww8bGBklJSXBj+ta9evWKtqQBQG19g8Fgtm/9zAV+nj59SgPv58+fBwAze/vVq1dUqXry5Mn0538TCuRs/fLLL0hKSkLp0qXx22+/4ciRIzhy5AgWLFiAMmXKICkpiT6AgmL06NEIDQ3lZU5YpKamol27dihfvjwtvP67MHnyZKSkpNB/7yxEzP9NmDNnDhITE3mLaCHO0GIFU8qUKYM/uUjq54RQ6t3NzQ05DD3DQaWCL1nMOVy+fBn3mGiWBLkL9C4u2mYpsgXkGn95RciJkEKGRgOVWg1XJos0pF8/YPp0ZCYkmEXHSpUqhf79+9MmpD9NmgQAKOLqivZ+fsDRo5Bwi52zqytK5NFYUSaR0IaZzozRYS8QCigmaAhtMBqh1ethp1RazWwJ6WQUZMF3csLXx45ZdtjYwn/OINAKNke5RoMmghoAMRh0OpTx9cV0b2/4cc9dwkXaHBwccICLsAnhxtFKBg0eDADwZ6PoDIjgDsDfOOTcM3Rzc8P333+PNm3a4AUn6hMREQFHR0dqACsUCmzcuBGXL1+2+l2UMhl0BgN1ZrMFAaDn3NgXKncRB4cYT6xhRurXKnh4wEGlEjWwWENXmOET1oPlBTE1wl9//RXlypWDq6srNSStGXp5ZX3yglImQ1pODnXshM6WVCJB4qRJOMBRGk8fP07/plKrMUlQ6G8GgwHp3BwQqhFag85gQGpODtxtbUX7Fdkx916r16MY4xjnaDT8JquCz7vFZQX69++PnJwc9OzZEw4ODggPD6c0ehY//fQTDQg8evQIcrncTCAjODgYhw8fxm+//QYAPOXTsmXLonjx4khLS6NGdGx8PE+JjIVUkNnSMD+32rsXUQxrhDwvD8Yh9fb2piqlUZGRWLRoEfTZ2VDKZGjj749a8fEICQnBlClT0KNrV3gJAqP6v4FGqJBKEZ6UhNMW6niEda8sjXBhcLCosyWVSHgZM3IvWAdsU8eO+KVhQ7gKemQJx7nYXFPIZDTbnVdmjpwtKioKyMpCKZGM/KZNm/gtATgQJ0ChUPAUhoFcNWGsW4dpnCiSXCqFWq1G97Q0UcVDgo5lymB+s2aQSiQ4xLVtYR2rGtycEd4LrVYLuVxO12KS0SIsKmEQl8WcOXPQjBMP+hxwdHREXVtbbOnUCcHBwQBynaBGjRqZZbZ0Oh3kcjnq1q2LkJAQnmPo4uKCkSNH0j2KhVAg45tvvkH79u1x69YtSs+11KT51q1bNLlQr1499OvXD4GBgVi5cqVZQIWUDL1584Zm1UlPMKF64+LFi6mdOGvWLMxjAir/FhTI2fL09ERwcDAqVKiAyZMno0uXLujSpQumTJmCChUq4Nq1a5+knf/tt9/i+PHjuHTpkmifrrS0NLRu3RoODg44dOgQL0VduHBhnjQtkCtVSwQVLB3j6OholgoFTBupo6Mj79+/HWRjZR2GunXr8ni6gKnb+CFBFuJzQLgQ2dvb58vQucBEaHkCGRzykq0FrFMNAeAVV+eUmpWFuA8fkMEYEE6ccXX8+HEz0ZDVq1ejVatWlBc9c+ZMDFapsGfaNJTw9QXevYORu96rkZE4xcipSkSuiXUKizF03mKCrIa7ILqu5xrX2iuVVmtP4izRQ4lzxUXIWN49j55EmjumpyOL9JAROHAlChemhls1FxeUsxAxP33yJFJTU+HLrAVG7vwGg0E04geYMgHly5enm4GbBWrfzZs3aZCFtDno378/3Yh8fHwQEhKC6OhoaqhmZWVhzZo1OHLkCADTXHF0dLRKiwZyo//ESU0X0JY97Ozwe9u21Klk3wfkZmhYZ4tkDonjIdaHhh35Qil1a7LyYhDSCI1GIzp16oQuXbpg1apVNNtkjcKUV1AjLyikUqRpNKJUWvJ3F7UaXTklSHdGuOOJSHNWs+szGuHAPQNbjs6U1xqkkEpNohcaDRyUSnja2cFGLsdyRmiKfW6bHjxAowYN6JjLTE+nvdoA5Dpbz56hXpEiNDO/g6Pa7d27F/b29mjRogUAE+tAaPiQWlupVJrrbAkaUH///fe89h4Ev/zyC/bt24fvvvuOGn8SmQze3t6Uqmtvb09Fk2QSCe7GxKD73r14nZwMuWC/bcioApPxyBqCEpkMUm5snuWc46SkJBSyscGYmjVx8/ffsWjRIsybNw9vX79GvEBhTWcw5FkHVFBY6xWXnJWFeps2IZJhEwgFZuJF1lEjIJrZYufE4CpV4GprC0/BcxFmi1UWAhvk/uYns8X2vTQIxnhwcDCGDh0qSq8na52Liwt1KNq2bYs1a9ZApVKZ6rMyM+l6JJNKkZGRgf2bN6O+SPadoEWJEviRo6GJUQZ9OUdOeC+Iw9KmTRsetU0ul6Nz584YNmyYxc9s3rw5ili5poLCyckJGampcFCpqF6AwWBAcHAwPDw86LwkIBm5QoUKmZpc6/XUDurUqRNWr14tmlEW7n29e/dGpUqVKJ0TYLKMApDsWuPGjdGhQwcsXboUly5dgkQioc9TCFaBnGTNSa0nQbly5ejeeeTIEf6a9i9BgauVfX19cfLkSSQkJODWrVu4efMmEhIScPLkScofzy+MRiO+/fZbHDp0CBcvXhR9f2pqKlq2bAmlUomjR4+aDa46derg8ePHvMjauXPn4OjoSCMrderU4en+k2OI4MIX5A25XI758+fzGrIaDAZR+dG/Q9ZT+Nx///13M6dBCA8PD55zxRPIKADEokc8cOfMMRqhNxp5aoSO3MaoUqlobQ+hAgkjz3K5HJsnT8aBfftMEUKDgSeEEcHQL4RUMyFY1SahKMjDR494x9LMlkJhNbMVZ6F2TZgVYSO3PClfbqMIv3+fvi6skqlbpQpCOdrd+vr1salRI1StWtWM471l40YA4jSQjIwM9OKyFy5qNS8zQJptK7h7HyWi/AcAT548oZQTMublcjkyOBqZJen9mJgY2uQ7vzx/pVwOI9c7B+CruwEm+mV1b2+zOj4CscxWl7JlMZCLNgLApk6d8JoRNwD4jnBBaITWroE4gHq9HkePHsXTp09x5coVeu3Woup50f7yglImg8FopMam0HkTGpkfGNqgVERwRohvRoyAA5fNd+UCC8IaIaFTa8M1S07NyYGDSgUPOzs4qVQ84QP2ub1NScGObdtQunRp9ExLQ6GDB3mF899zKm+OL19iDtOKgm210a5dO1rzRaiCQG7QgDhFCoUCxYsXx5AhQ+hz0+l0GDlyJNRqNUaPHs2TjC5cuDCqVauG6tWr84KXRgBeXl40W3Dp0iWarZdJpUjIzMSBp09RYsUKuHt7o24etPxbt27Rn6OiojBt5Ei4vnuHmdOmATDN7/Lu7ihsb4/p06cjiaOghQQH472ACvqpNVs99u2jLAF6Lu5Zk3slNh+JY8WOZaGceRSzjpNnbzQaec6btZmwuGVL7OGktNnrISAZcOF8EjYGtwSpRILXjFCScI19xO0fjRs3RhWO2k5qhFQqFVavXg25XA4nJycYjUa0atWKOt+7du2CwWBAJudwyqVSBAYGchdoOXDxLUNtU4pk0Um2j52P+3r0QIMGDXD06FE0adIErZh2IBqNBocPH0YvLkv2T4DtVaVQKNCtWzeMGzeOzlVheU6nTp1QqFAhlCtXDjExMZAwTvCKFSsgkUhEm52r1Wp07tyZluM0atQIS5cuRWlGCVTIFCIg558+fTq9N2/fvkWnTp1wT1D/Hh4eDqlUykt+kM8UMtTatm1L6fTff/895syZY/E+/a/i06ShYKKQ1ahRAzVr1qR0soJi9OjR2LFjB3bu3AkHBwfExsYiNjaW1lERRysjIwMbN25EamoqPYYsAC1btkT58uUxYMAAPHz4EGfOnMEvv/yC0aNHU6rHyJEj8erVK0yaNAnPnj3DH3/8gb1792L8+PGf+vX/dahevTo8PDx4tLuvv/7aTH2SdBb/nHj48CGltBAolUoePUUMf/75J69+is1skR4SeeW1FO/eoZ+gUaYQvKybUskTJ+jKSVOrVCq0adMGUwoXBjhJeSKZWo0T2CDG4TfffIMff/wRZ06fBpj7/eD589yMlhXDVC6V8hwgshmTdwj7WRkBJKWk4EN0tFVnK9aCs9W7Rw8ULVoUMzlnMoUxLtj3kGyA1GBANikKFjgW4Q8e0PupVCpRu3ZtTJ06FVOmTOF/KPfsrXHuAZPxu3DhQrPorCElBZg+3WqUl0Q1iWLVsWPHckUBcnJo5rwEZzB17twZkZGRiIqKwoIFCyxGD4Xo0bUrSpUpQzNb/symCJiMPJmAZsRCJuJs+Tg5YUvnzvR3e6XSLDNmjUYo6qxbAZlX5FgSSLh69SoWLVqUW7P1N9MIAVikEVYRijcxY8e6NIYJd0JC8JJzXIRqhATFBfdYLpViwKFD2PzgARyUSnhwmS123AnrcpRyOdRqNeyTk5EcEYFz585hwYIFqFu3Lpw4lkVqcjKvGam7uztiYmLw+++/Y+LEiTQr27ZtWxqIpHWqHCQSCfz9/fHdd9+hg1SKk337Qq1WIzo6mjYtPXz4MEqWLInLly/j/fv3tFccr9ZZKsXdu3dpbdfQoUPxww8/ADAfO1Hx8ejMZBRZGI1GxMTE4BVTx2mUSEx9Cw8fxrNnz2Bvb4/s7GxcHDgQVby8MHfuXCqZrZRKzZzfT1UjPPLsGWIETdxJtpiMK5WI05Il4mwJs/csCF1XIpHwnDdr+5K7nR16csqTgPk9draQlSbZy/xktvz9/fHtt9/yemsCJrExonC5c+dOuLi4YMCAAbh79y7OnDmDjh07mtEHV65cSQXKVCoVAgICMI1rzSOTSKgaodk6M306iggaIgOgbTvYtYT0HCQ9Ie0UCnQvXx4KhQLu7u7Yu3cvL2BHZODZhut/NzZs2EBb1uh0OpQtWxbbtm2je5jQ2dq8eTN8fHyQk5ODs2fPwsfHxyyQnZOTg1u3btE2RkajEffv38eOHTsQGhpK7Zz4+HiMGDECV69excWLFy0K2ZF9smrVqvDjFJ8JrXfUqFG8Y4sVK2a2r167do33HoJ69erREqOFCxea7+f/Anyys/U5sHr1aqSkpKBx48bw8vKi/0g69d69e7h16xYeP34Mf39/3jFkM5HJZDh+/DhkMhnq1KmD/v3746uvvuI11StevDhOnDiBc+fOoVKlSli8eDE2bNjAi3R8gXUsXLgQP/30E6+ZcEhICI1WEGzdupVGaj4XEhMTzcQlxo8fj5Lx8ZQSJIY///wT/lwTWgKyoXXo0AFA3jRC7caNuJNHo24X1niQyaBiDFdvT0/0798fbm5ukEgk8AEAzvjx8/ND06ZN6aIGmDKuhw4dgqOjI/yY2qrCMhnSjUZI8xGldbO15dHCyCZGnDmhk6o3GBAVG4u34eFWBTISGQOL3RaLennBx8cHcVxxLOtgvWboNJncZuLm5ERrN3LYa8nJQVJUFHW2iDyvWI1mby7qJtZvLZmRtveyt4eNjQ3q16+PzMxMGqlnI4xiyMrKoucmEfsHDx5QI0gqlWLBggVYunQpLsyahV+43kLkfAXpJ5KUlITUtDTq6KYKjDy90QiZVGrRURHLbOUnS8RztgSZHfb9YnVGQlBni7sW4myp1Wro9XpqmE6uX58XlWfxV2mE1NmyQCM8yqlCEqzhgh4AkJxX9hrAh7g4JHHP5jlXp2fmbAkCj8mcfD5gMqzFnK3GzPwHTNm/mjVr8upFJk2ahMaNG+eqERoM6NmzJ6VBxcbG4uXLl/j666+xa9cuXL9+nTvMgCdcz74NGzagHSf6ApgMoufPnyMgIACDe/RAeaUSmzdvxtGjR6lhWqRIEYwdOxaNGzfGvn376LguxQagJBIqOw+YMh+kQF84ZuNTUvCau3dCGGGi77JqtkYAJ0+eRGJiInbu3Im6deuiWLFiCAoKwm+//QatVkubLytlMnFnq4CZLaPRCK3BYBZ4+sg0nhb7bkBuVp8VBUkWEeEisNR0XEwd0hKEc10oICI8p7Xs8szGjbG8dWu0bNkSJUqUwLlz53i0NKEadWJiIvr164dDhw7h48eP6Nu3Lxo2bMg7Rqg2+PTpUxTn1BHlUikOHz5s+h6Ca9m9e7dZ+cCFCxdQiwvqsd+azCfyjEj27siRI2jZsiWio6NRpkwZSrclDqFYXePfBWLPAiaqLMnuEJtK6GxNmDABiYmJSE1NRatWrfDhwwfaLJhkkHQ6HQYOHEj73z59+hRdu3al/cMIW0yv1+PUqVNYsWKFVXGKypUr4/Tp07QBNZArwiNcn4kTzvb8JC1/unXrxjvW1taWd85/I/6jzpbRaBT9N2jQIACmNLWlY1gDlVAbMzMz8eHDByxatIjXzZqc6/79+8jJycHLly/pZ3xB/rBnzx7Ex8fzolyk2JLFggULrBadfgqaNm1KI6UEcrkc5fR6WuwuhhMnTmAko1wpAdC1XDnYHjsmWtwrhgYNGojWEbIgNQZOnCHiyThft2/eRFJSEtq0aQOlUgmNRkOpF4ApO7ie6SPVvHlzGnX7yNQfFHd3R8lKlSAnC56FgnTA5GyxtDCyEZHaM6GzZTAakZaRAWi1VjNbbMNJNhMya84cBAcH4/flywEA0xhBiFdsTy/uOuItOOMOSiVcbGxoITXJmFN1Uua637x6hatXr9KooJSpOWHXBkLrCg4O5tVn0iivBfqYjY0N3Vw2bdqEjIwM3jiQyWTo0KEDxo0bh+vXryPm/XvcuHGDNr8siFTwjWvXEPnuHaURJgr6oJHMliWI1WzlpxeQNRohiwI5WyTqr1Jh1apVptpKnY46Qs42NryoPIu/mtkiBpZShEaojonhSYYbjUaE5qN5NgsZ0xohm8tyaPV6lGeUQT04p03ovBKUcnGBt4ND7vNZuxatBMXqMokES5cuRd++fXmvz507l86BEn5+GDJkCBUjatiwIerXr4/k5GTY2dkhICAAw4cPN8uudurUiRpmRqMRqampiIiIgI+PD6pXr44hQ4aYXTMpev/hhx/oPZ09e3Zu9DolxSzoxn4XFjqp1KKDYTQaabaYQiKhGW4AaNasGdzd3fHdd99ROuShQ4dMNkGxYpAJgnKfIpBBHCVLzhYRChJbKwmNkF1jP1rJbBEWhAS5zdmF788L+XW28lOzNbVRIzQoUgQPHz7EhAkT0LFjR14WU9h412g0Uop/r169UKJECRqIIJg/fz4WLlwIAFRJ+iE392RcVhQA1IJnR+oOWej1epqRFrakAEz3ek/37jjJzR1Ss6VUKuHt7U1LAkgwQCgm8XfiwIED2LRpEwBTGQRBmzZtEBMTY5btWbZsGTIyMmigOSIiAqtWrQKQKzai0+nM7jcAetyVK1cAmO7b8ePHsX//fqviFDt37oSjoyNv7SR7mbeghYhEIoGdnR2VlwdAM2xCnYNz585hMtOu59+I/6iz9QX/f7Br1y4AfCNSTBL/m2++EW/e+xchNIrlcjlmzpxJpUbFkJaWhnkMN1gikUAhk6GxpyeVdJXkYZQ2atSIRnYs4T3nPOg5ipEDY9TZyOU4efIkoqKiYDAYMHbsWNy/f5/yp52dnS3ScJ2ZzdfPzQ16lQoKuRwLW7SgPbmE6FauHH5t2JCf2eK+I4kSlhY0RjUYjTBIJIBGY1Ugg609INHR8u7uAMleiWx+PGfrzRtIsrNRjSuiFSInJQXOCgV1rghFddOmTabsEnP+mzduoF+/fnTzaiCXA69eYcaMGQCAbZxcupuFjEWhQoVQsmRJuFqgNLGQy+V0k1ZyGx87D9atW4eNGzciOTkZ3t7e6Nq1K75ieubkeX6ZDAYAaZxRozcaeYX9JLNl8f3c34iq3aFevaxmfAnubtmC8pyhZK2OY1K9enmei2zN5DqHDh0Kg8GAYsWKISAggFL7rGWS/2pmqysXYRVTRhS2+Dh+/DhWrViRvxMTx1wioZkTOXevdUYjegcEYB3X8oTQmUTFE7Kz0cjPDxcHDsw1eBlZfIKRw4fj4MGDlHbDNsYmzpa7mxuio6PpPevOZQvVajU6duyIe/fuYd26dZRGRDB8+HDMmzcPnTp1glqtpuvq27dvzeo4SK0Ya8zZ2dnB19cXjx49Qnx8PCarVKaGxpZunTDTJJXCwYozUKdOHR4zws3NDQqFAjdv3sTSpUtx9uxZXLhwgQZURo4cSSPmXTt2hJNgPn9KZotkroTOFKFHk//FhDeI8qCeCwrvePQISVacLba+0EWtRhpnkIo5EpbAzps7w4aZ0YUJSHAlr0BMXFwcryaPpWoLsy9paWlm9dRCx7tKlSr4/vvvAYA2wSXjWC6VwmAw4LvvvoOdQDzF1dXVTOyhUKFCtMm13mBA1apVaW8nwNTUuGdAAOpwtolOp4NCoYBKpcKlS5fQtWtXGI1GurcQuuw/AScnJ8qS2MjVHAOmGsSYmBhe42GDwQCj0Qi5XE6drczMTBQqVAh9+/ZFZmYmNm/ejGLFivGDSmo1vLy8eDS+GTNmYMSIEZTyZ6leCzBRF+uKtF4ZN26caFsnW1tbXrazZMmSaNWqFc8BA0wlQ2zz6n8jvjhbX5AvEPoIa2Q2adJENBL6uXHhwgUz1UNCAxM2CmZhMBh4TY0lMNXfTJs2DX5+fnDIykKZPCJbs2fPxtU8JKFjuI3JwC1iWQyNjjh1z58/z91oAF42yxLcGQl5XWoqolNT4eLkhKTEREgFRhTB/p490SMggF+zJRDIcBY4dwaj0VQbptHkO7NVgVvMw0aNov3BxBDJbS4AgD//hOP69ShswQHSbN+O3tWrU+ECsokQVT8J+6yMRjg6OlJDYMegQcC2bZSaQTYuOwtOhLe3N54/f24xs2UJDlzEzlLmatiwYVizZo3VlgJCyDk1wiSuHswAYNDhw1Bz38VSZouMIblUCluFghpRncuWNTP2161bZ1ZMfXnXLtQX9jkTQV9SwA5gogVRIeLY0NYKu3Zh7NixaNu2LZYuXZqvRrB/NbPVh7vO99x95J1PcP/mz59v3jzbAsq+eoUir1+jetWqVCCDOlsGAxQyGb33JHsmRgPrxAQ5qMGr15u1VMhMT8eePXsoZYiIDwCgc/jWjRt49uwZNcRIHQ3J3pJxPWPGDB51EDAZPvv374eNjQ1v/I8cORKNGjWiNCOxNaps2bJYu3YtPnIBlsTXr1GmeHE0a9YMK1euRGxsLO8cewSF8nqZDOU5sQQhjEYjIiMjcf/+fUrz1up0VNijcuXKSE9PR3JyMrZs2YL4+HisXr2aBkLCHj1CoqCnoN5CE2FrIL3TLGW2yP+izhaT2UrIzMSAQ4dwl5l3CqkUSVyLDyC3vlDC/B0oGI2QDWA42digd4UKeDRypMXjLAU1FixYgOzsbB6NE8it4zEajTxDnDTEFir2iRnrBGq1GsWKFUM5Lhgk41T2li9fTkUzCMSuU61W0/1Bz9UnERsgc8oUTBCsTySz1ahRI8oo0ev10Gg0GDduXIGCYn8VrLNFaLoKhQJr1qxB1apVabaNXDcAmpUrW7YstFotZJzyZ/369TFo0CDY29vzhOWKFy8OGxsbHmXPz88PpUuXps/R2p5nKeu1dOlSDObapbCoWbMmz3nbtm0bTp8+bbY/GgyGvIXG/sfxxdn6gnxBLpdjwYIFPIpWenq6mcrj34GmTZuaZX9WrFiBChUqiEr3E3zzzTe8bEgZFxe0adMGtWrVQnJyMt5MmoQQjopiCa6urrlNhy2gslwOxMSYCrmRSw3Bu3fUUJDL5byi1EZcnxFrcGAWTEeVChqpFAq5HB/i4mBgjOkyItkZNrNFjAay2N5g1L4A08Yut7dHu8aNrV4Pm9la0Lw5upUrx9voqdoiA5ZCM3f2bBTz8sKJzZtFz+9iNKJCuXIo8uYNsH272YJdhmm+CJgcWeJsCamev06dCgBwd3BAjRo1zDjkr169MqkLWpKztwByPFsYzN6DmJgYeHh4FKj9gbOjI+QKBVK5c9vZ2+MCqwbGZLZYtTtS6yDjnC0xeXeCESNGmCk6AnwVO4KiVs6zqGVL0deFMvQET58+xZ9//gmJRIIqhQvThqxi+KtqhHKpFMf69MEQzqiylinTarVW1c8AUIntZ9u3I2rrVpT294eCW2+8OQNTq9dDLpVS57YQF+UXGss1vL1545d81/CnT9HQ1xdDuP4/AHBiwwaoVCqkpKSgSZMmtHbLxcUFv/7yCwBApVAgLi4OblzW/dq1a/D19aWGFMlix8fHU8GJpk2bAjBRakkwwNvbG2vWrEHr1q3x7t07TJkyBb252rbbt2+jYcOGKMHVvbZt2xYxMTFo3bo1zXDk5ORQJcInT57g6NGjmDRpEi5evIhLly4h4sUL0+c4ONAsTlMLmdI///wTK1euxLBhwyi9KzklBS1btoS3tzdsbGxgY2ODrKwsJCYmYsKECfjqq6/oXHz08CFSBG0TSGYrPy0+CEj2UpjlF9ZsidW3kgbGBqMRa7gejzFM8E0ulaKQWo27w4fjdL9+GMw9dzJWyTgqCI2QDYLJJBJIJBIEirTgycuB++mnn/D8+XOe0mqFChXotQ0fPhwtWrSgwUdXV1d06NABRYsWpfe3Xr165lRQBjY2Nnj79i303L27dvUqHZcGgVNsa2tr9twcHBwg4V6L+/ABnp6edN9RKxRma8hXX32FI0eOwN/fn4p0aLlGxwWhen8OlChRAmW4gEtWVhY6deqECRMm0Mx1ZmYmduzYgczMTEgkEkycOBF2dnaQSCSmOrfixVGtWjWoVCqsXLkSEokEd+/exbp166gs+5MnTxAYGIgzZ85QRs7AgQMxbtw4tCVNra04W9bsKTGcOHGCqp1aw/Pnz6mj+W/FF2frC/KFhg0bIjU1lUcpqFy5Mny5Qtd/GlFRUQgNDbWadVq2bBl8OCN8TsmS8H74kPba+PjxI1xcXPLsEO/t7c3rsC6Gii4ucN2/HzrOGbHhot6O165R59TGxqbAMrPsomivVAJqNeQwpygtatkSvwqKktnMFtmMbYlEvSATZTAaoVcoEM3JNVsCu6nX8/HBfq5ebvny5XBzcxN1MFKZz+rQoQOuXbuGuowkOYukpCQcPnwYCgB4+dJsMyzLRMbatm0LLy8vGAwGnDt3jhoEV65cMTm43O92Mhl27dqF/fv3885FFv6CZKAAwDUsDHj0iGfIE3EAW1tbqhBXEIGM1i1bonCRItTZcvf0pIZchkbDy2xFTZhA33fy5En8+OOPNLPVMyAAb8eNE/2MKlWqiNZAmH3/K1cQJKgVIrAkPQ/kOvTCsfno0SNan3BvxAizhqwslrZqhe//YjuO9qVLUyofa3jZC+a5mJMpxOgaNVCCM2IqVqyIy5cu4S2XpciQShEUGYlsnQ4qJrOlVigwqW5d1MtD3pwYvv169YKrrS02duoEAGjl4gK3pCQYDAa8evUKaYxYSpUqVWDLfbecrCxs2bIF33O1x0WKFMGbN2/MHMwFCxZQaXSiegrkGveurq7o2LEjHj16hBMnTqBPnz64ceMGPe7s2bOoXbs2du7ciT/++INmR0mj45ycHMhkMty5cwerV69GUFAQ2rVrh7Zt26JTp07UqHaztaXOyxsr68yLFy9gNBpx+vRpFHn7FjVTU5GYmIjo6GioVCqo1WpkZ2dj7ty52LFjB7Zv356rXqpQ8ByKh7Gx0Oj1MBiNVjP2QljKbKULqNtima0B3BqoNxrxK1e7KpSQB4CqXl5o5e+fGxjgXidjtiA0QpY9YI1ubO2MRqMR9vb2kMlkvH3n0aNHNNDp5uaGzMxMKoCxceNGtGrVCklJSVAoFAgMDDQLaonhwIEDeMGpWm7buhVKpRI2NjZmxqhEIjG75tu3b8PI3fcJEyciLi7OTGacRXJyMuLj43H+/HlaN2YwGDBu3Lh8121/LrRo0QLLli0DYHK24uLiEBwcjESuNvvhw4cYMGAANm7cCIVCgUWLFtFg7Z9//gkPDw+cPn2aJxam1Wrh6elJ5+zTp08RExODEiVK4MOHD9S5y87ORrdu3RAWFmZVryC/CrosOVTO8gAAj+ZJREFUSuWh1gyY2ElHjx4t8Ln/l/DF2fqCfKFfv36YM2cOL+p17969PCl2fxeIASdmQAKmKFHv3r3xFdek7+cJE7Brxw4aBcuvkf348WPKN7cEPz8/eHt74weulYCacxJSU1OhVqvRo0cPKs38qShepAigVsPFzs6Mc1/G1RUzBApDbGaLODwTHR2Bjx/NIpwGoxFRSUm4n8ezFBobBJ6ennB3d8c4EUOfzYbNmTMH58+fh9pCzcbOnTsxatQo2hhT6Gxt7NMHv3EUpCuXL+PgwYP46aefaDEwYMoYZmRkANx7U+LjcwU2GJQoUQJOTk4Fjm4WfvcOOHiQZ9T+9NNP+PDhAzIzM2lksCDnjY2ORmpaGmTcexISE6lhaj9vntWarQsXLkAmkcBWoYBEIkExhnrK4v79+9ToJqhduzZPkfVCjx7AlSvIFBwHmGpBxKhJBE+5GgjikHXmZOdlMlme8vwEfQIDsdBC5uxTUIppJi10QoTqpmbQaBB75QpS791D1apV0aFDB2SkpuId911eJCej4ZYtSNdo4KBSUfqXUibDghYtqKS3JZDsSAjTKPTa4MHo7+oKlVJJ5wBLn3ZycsJMosxpMMDFxQXltFpgzhxERUWJ1q+WK1cOcXFxUKvVWLhwITp16sT77tHR0fD29qbjNikpCVuYRsNKpRLNmjVD3759MWvWLJQuXRrOzs7YvXs3tm3bhokTJ2LatGlYuHAhevbsSYMMp06dQlpaGopw2Xwn5jMTmHogHiQS1KlThxqYUZs2wSUqitaMFStWDPPnz0fbtm1Flc2Ucjlvbau8di1COSEhmzlzUH3dOmRqtfiQkYG3VqLsWgs1W0LlyWydDhdevUIFTkCEhaXMVH7rEgtCI2QbKFsT0rF2zuTkZErRrFy5MoKDg+Hl5YXVq1fT5sVicyY8PBw5OTnQ6XRYsmSJqDqsEEOHDs1lnBgMOHnyJLKzs82M0QcPHphlYXjqgYwYD0FmZiaqVKlCM6N//vknJkyYQN8XHBwMOzs7+Pv704ztP4X4+HjawqZz5864efMmgoKCcPToUchkMrx//x6AKSudlpaGrl270sDo+PHjERwcjE2bNmH8+PGUJaPT6dC/f39M5PrvZWZmwtbWlo6zt1wfybVr12L06NEYOXIkr2m4EAMHDqSCOPmB0WikCoRfYB1fnK0vyBcIf59d2AqaFficIIuwUCGHICMjA3v27MEwshBIJAgNDaWZrPxeO6sOaAk+Pj549uwZsrjMhBNjCKSnp+PJkydwd3en1Jt6+RAcoOA2fCfOGHKysYFLPvrasZmt5xynetOyZUB8PDUEyOablZODjzk5gIU+WgSZIrUP2dnZ6N27N54+fYoDBw6Y/T01J4fShw4fPozu3btbpJ726dMHHh4eKF++PGbOnGkm++vi7IwWXFYsIz0d4eHhCA4OxtGjRylFhII09tRqRbnizs7O+PjxY4HHsBgd6fDhw2jbti3u3LlDC5MLktl69PAhPiQmwokzsKN1OrN7bcmIatCgAc1sWUPhwoWpoE1OTg6SkpLQpUsXKoWflJSE+v7+KOTkBK1Wi5MnTwLINZKrCWhwQjgJHOhFixZh165dkMlkZkXu/xS2delCM4FpAjn9I0eOWH3v8IQEbJo3D61atcK7d+8wZ84cyEXGSrpGAwelkgZAaPPmPMYVzVzodJBIJPj48SPq+fiYaGaFClGaMSvOc/DgQWRyc3TihAno0KEDVq9eDWi1KFWqlFkvwsqVK1OBDFLLWLduXUxiaoaII+wpQjsjxfcXL14EYMpkuLi4IDk5GQ4ODhgwYACqV6+OBg0a4MaNGwgPDzcb9025IBA7Piw5uu3at0fx4sV5giAKhQIKhQKenp5wc3NDmTJlcO/ePbO1AQBKlyoFGys0qLsxMYhNT0ftjRvhy2UYxEAyW73278dPjAMr5mztDQtDmKDXJGCemRLrycVC6IQVhEb4FcMUsJrZsuJsEQEZZ2dnPH/+HDt27EBMTAxGjx5NW2mQcQCAUgWNRiNdXytXrmzWX0kMNjY2lMLr6+ODuLg4+Pr6opAgUOTs7IzagnowlolCglOsaEdWVhYePHhAnStSs6VSqVCyZEksWLCA1mqRTNc/hfT0dEyZMgVGoxFbt26lr0+ZMgVarZbKtet0OmRlZeHQoUN0XKhUKoSFhWHWrFkwGAy0ZlKn0/FofEJnixUGOnPmDIKCgrBgwQKL19i1a1deDdgXfD58cba+IF8gxhe7US5YsMCMkvZPgThb69evR2ZmJi5fvszLupHr2rRhA33t6dOndGOwy4ecNQB4eXlRmVVLuHLlCrRaLY5wqki2CgVc1Gqc5ugNYWFhSEhIoAvf9evX8+1wSThDlSjNOalUvP5b0RMmoJSFmi2pRILq3t7w5Jy/wYMHw9XFBRW5OgFiVCQlJyPLaASY+yeEJWOB1EtYitimaTS0CJxsiuXyUMrz8fGxGF1TMJHOhQsX0vvIRvX9/PzwO/fcc7KyPmthbvfu3c0KwPft24fbt2+jWrVq8PT0RJs2bSg/Pj9QyGQwAnjx6lVu02oBLBlRy5Ytg7eDAy+LI4bY2Fh6/4cPHw5XV1fs2LEDCoUCb968QaNGjfDLL78gOTkZWq0W7dq1w1hwAij5wIm+fZHIGPGDBg2CwWCAj48PWn7GbFVBYKtQ4BHXb4oVyNDpdGbNQXmYPh3+/v6oVKkSnjx5gpo1a6J27dq8OkmCdI0G9kqlmex8XoIg1HDn/ifqaL1798bRo0exgRu/3YU9yTiDOSYqChqNhhqfnTt3NnOYHjx4QClgJCj1448/ojJTH0bW0WAmw0bw8OFDAKDy8kI0a9YM3bp1w1dffYWDBw/yhC0AIDAwELU4Z95VrUaP8uXhuWGDmXodwYmffoKtrS1PmGPu3LmQyWSIi4vDhw8f0KBBA3Ts2JFmW4iBCgB1ateGvUByWojEzEy+QqoI2F5d+7keZUDuekmQrdfTdc9oNOJ+TAwNTgidpfac+mx9C8JGwtWzIDTC7+vWpS1QrGW2LEnuAyYjXalUIjAwEDdv3sSTJ08QHBwMiURC1/hfuUbEAChdWiKRwMnJCVlZWXnS7QnUajUdxxKjESEhIRgyZAi99pHly0N2/jwCAgIwwdcXGYwkulwup0wFUq/I68vGnZcEEVjp95cvX+LIkSNYvny5KZOWD9Gezwl3d3fo9XrExsZizJgx9PUnT57g7du3dC7qdDqamSOvqVQqZGZmQiaT4c8//4SDgwPu37+Pe/fuUccLMPUPY6mczs7OVPSCZMut9RbbtGlTnsHlL/g0fHG2viBfEKMCSaXSf7zIlIBwi9euXYvAwEA0adIE8+fPh1arhZ2dHW7evAmFQoG3JOojkWDgwIFQq9Xo379/vrnJ27dvp40QLeE1J2bgx2UOZBIJEidNQiumjiozM5MnUZtfYRHj/fsAcjdKZxsbJDLqP14Was7UCgVkEgmuDR6MF99+CwB49uwZsrOyKGWIGHxxKSkmbryF5pvvxo+3KP9NxgVR/1tE6IycYZ+l1cKXc1aJzLol6ifBrVu36GYuRLnSpVHMaMSPQ4dCLpejVatWmDlzJo83XrlyZSQGBWFCiRLQaDRmPT/+CiZMmEAbxhKwEWM/Pz/Mnj2bGoP5gVImg0EiwbvYWBgtbISWjChPT080K1ECuy00CmZx9uxZ3u+PHz/GmTNnULx4cYSGhtLmmsQ5tcnMRJF83jtHlYrWSgGmcSGTyVCtWjWrfV3+blDDgRMOiIqKQmJiIlauXGkmB08QEBCASZMm4datW7h//z4kEgl8fX1RRqTWMDEtDT27dOFltpYvX44wpvm7GNgsiUQiodmD169f4+7du5TuHMgoQQKgztlOrrUBodMtXLjQrI1BamoqlVtmRQvYnoUsTWs8R4Mmma8+ffpY/Q5ZWVmIj49HSkoK+vfvj7lz56Jdu3YYNWoUxo0bh3PnzuEk59SqZDLs7dEDY7/5RrxvoUYDxMXhypUrOHXqFO3H9OjRI7rHSKVS3OFEJ+bMmQO9Xs8bW48fPsTHPIrwE600GCYQPhv2dXYeZut01Ek6HRGBquvWUUdN6NDt69ED6ZMn45iFeyoMVuWXRpiWlsZTGbWW2RpcpQpCBJLcBFlZWdBoNHjy5AkyMjJgZ2eHOnXq8GjArxnRHgAYO3YsZTNYcqDFUKtWLZTi6OBvuLrEadOmYTxHQ7+wbh06e3pCJpNBYjTysvaBgYFUanzD4sV4+PAhpdABubLmOp0OMTExmDx5Muzs7FCxYkU6p+rWrctrQP9Pwd7eHkqlEq9fv8aqVaugUqkQGBiIhQsXYu3atRg1ahTi4uIwcuRI6HQ6SKVS6hA2btwYdnZ2kMlksLGxQWBgIIoXL459+/YBAM0oNmjQgNL60tLSYGtri7S0NKSmpsLfggooi3uMevMXfF58cba+IF+oXLkyJjDF+f9pjBs3jgoTVK9eHRKO73/v3j1kZmYiJSUFv/32G1w4p6pChQqwtbVF5cqVcejQoXyr0DVs2DBPNUJizDtw2TKWQmRvb4+TJ0+iXLlyqFmzJn09307q6dNQLVhAnS0nlQqp+VD1UcvlkEmlUMnlcOCirT4+PshIT8fVoCAAuUZFlkQCmUSCExb65RR1dKQR2+FVqyKOqWEjUU87OzssWbIEjQkFISQEjYsUgdZgQPfy5fFu/HgMHDgQQN71Ms+44mkxaLVa7G3dGvNnzoRMJkPFihVRkevbNXToUEgkEnz48AG/Tp6M34cNQ+PGjS02XP0U/PLLL1brLgwGA6pVqyaaKbAED3d3yJVKZGRnQ2pBplrMiOrWrVuB6g7Ife/Rowc14kn2AjC1RQgMDET58uWxePFic0O/ANDr9ZDL5Xj27BlfuvwfhBhtKiIiAgkJCbC3t7dIqyLjhRhnJ0+exL59+yAXyYan5uQg9cMHXs3W7NmzeS0nAHNxAvmrVxju54c3b97g2rVrdAwfPnwY8+bNw6xZszB8+HDeWAsMDMSsmTMBAAFcdrh27drw9PTEyZMnzehBDg4O8PDwwMuXL1GjRg06r8qXL0+PcXJywqFDhzBkyBAsXboUDx8+pFLxpEFply5dAMCsdlUulyM7OxsymQxhYWG0pqRs2bLo1q0bwsPDEcfVqZI1ccqUKRap34ApILB161ZcvnwZX331Fa5cuUKlxVUqFe3f8/jxY/j5+fEYAlHv3yOTc6YsPdtEJnv/lUDQJy49He9TU3kZLHam6wwGnkiMhslskd5bZE3tzVCqJ9Wta2oAq1RaFJkRrij51eX8/vvvUaRIEfp9rWW25FIpaghoeQSEbRAQEIC0tDS6p3Xo0IE6UkRkiGRhIyMjqQBDQbB7927kEOqfwUDVL0kgIfz5cwwdOlS03pNkZ041aoRiWVmoWLEiT6SLOCcKhYLWky9atAhOTk50PgcHByPrMzMe8gOJRIKRI0cilVPMHDVqFEaNGoXBgwdDpVJBLpdDKpVCo9HA2dkZS5cupe/dsGEDWrVqRZU5L1y4AGdnZ2rHEIGMHTt20N6T9vb2iI6Oxty5c+Ho6EiDJ9ao87/++uu/vvnw34UvztYX5Atly5b9j9ZoCREeHk6NoqZNm8Le3h5qtRq1atVCnTp1YG9vj3HjxlFny8PTEzY2NqhXrx4yMjLy3Tleq9Xy6IliCAgIQGBgIIpw0WNhvUabNm3opkxqZPJbKD1p0iQMHTwYdsTZsrFBfp4CyWyxWLx4sUnBj9uYaSG4XI5CajX27duHwhYcITaz5sEYnY6OjoiKikKhQoUwfvx42JIIJyeJDQAvX7xA5RIl0KBBAwwbNgxqtRreViLM1moL4uLiUKdOHTx+/Bhubm5YuHAhDh8+jPDwcGRlZcFgMKAvo6YXExODBw8eWDxfQUEMUBaNGjXiRd8B640jhahXpw7SpVJ8yMmxSPVhn+X1IUNQ9NgxHDhwgKccZw2NGzemzlNWVhbtmSKk0jx+/BinT5/G+fPn89WewBJ0Oh0t+t7OZWH+EyCRXwdHR7i4uMDJyQmxsbFwc3MzrQFc5hgAwK0JxPEhxtm1a9eQnJyMW4xjSqABAI2Gl9lKSEgwk7EWYuHcuVg3aBD8/PywaNEialRqNBo6ltatW4ebN2/S98yYMQOBROzFaMT48ePRvXt3xMbGok2bNqJBDLlcjhIlSkClUuHGjRt4+PAhTzVUqVTC19eXfk7hwoXN+mtVqlQJkyZNosESAoVCQdUIHzx4gBMnTuDVq1cYO3YsGjRogClTptA6N7lUCqPRiHbt2lFjnQduzms0GtjY2ODFixfYu3cvMjMzadZPpVJh/fr1qFWrFmbOnIl3797xghpKhQIkJ2WJhpfArOXbHz3if881a1Bs6VIejZBdp7UCZ0ur11OniIgHiakeLsgjky/8HIDfjD1o8GBct9DPkhju5Nt+avsEtq7Sz8+PUqUPHjxInWPiWBFVyyNHjtDeVQXB7t27qXADcnJQq1YtU5aJodZ27NhR1Nki7S7279iBNWvWYMWKFbC1taVZeZlMZmK0vH2L0NBQlC1bFm/fvsWzZ8+wZs0aep7NmzejJ0e9/CexaNEimm0/duwYLl68iNDQUDp3f/rpJyxfvhxOTk4YO3Ysfd/WrVthNBqxcuVKtGzZEocPH0alSpUwa9YsxMTE0Abo9+/fxyNmXLONhD09PfH27VuMskINr1ixomiLkC/46/jibH1BvqBUKv+rUsxERlShUGD9+vUwGAx4+/YtIiMjUbFiRWRnZ2PgwIFoyy1sF8+cgbOzM6VS5ddxjIuLM1NxE6J8+fL4/vvvUYajslmjcly9ehUhISH5+mzAxOd2dnamNVtFHR3zFfVkm9yykCKXokKisAaJBJLsbGzZsgVT3d0xS6BsCOQ6WyqBOpTRaIRWq4VGo0GtWrWoSAgMBuoUbtuyBYmJiTh79izq1asHtVqN6N9+AyzQM6tUqWLx+RDn4NWrV5g2bRqaNWuGRo0aYf78+fjw4QMkEgmPonn69Ok81SQLAjFHcMSIEbzaxaZNmxbIUXnLUTCDOLEFMbBjqm6xYrDhspv5lS++fPkyFYkICQlBUFAQBgwYgFq1atFjyMbcpk0bnDp1Ch06dKCGLotLly7xMmKWPq9t27aQy+V5qhFmZ2cXKBOYX0gkEhrc0Gg0aNu2LbKzs9G8eXNoNBqTgXPkCLBrl+kNW7cCe/dSw5cYZ76+vnB0dISnyLM3mE5O5xqtbbQicKDVanHz5k06lg8dOoS+ffuiefPmvP4/ZcuW5WUFu3TpQu9T9Pv3SE1NxfHjxyGRSKzWYQAmJ3rw4MFITEw0U3irWrUqnW8qlYpXAwKY1srffvvNjNmwYcMG7N27F/PmzcOUKVMwbNgw3vzQ6/U8Z0un0+HkyZPi40EiQfny5dGnTx+accjOzsbz58/h5+eHXbt2UdrXzZs3zRrpAiZni3y6Jal3azTCOG7t+pTM1rBjxyye91PgyYgs1ffxQV0LrQTIvaSZLQt7T3p6OpycnCyOk0uXLtGfu3btim856vmMGTMQx6lHkvdSRwm5zl5BsG7dOhNt1HQyWvPMOls6nQ5Pnz7Ft99+y2MmkPtNnPzGXG9IkuEh+1GFChUQFRWF9PR0LFu2jI4dsv8nJSUViPr4uUCyRvXr10dERASt9yUZO7lcDq1Wi4iICNp/DDDZDTNmzMDSpUshkUjQqVMnKJVKrF69Gl5eXti9ezeeP3+eu65xILX2ADBgwIA8Kfxf8Pfhi7P1BfnCqlWr/pEGxvkFoa+NGjUKPj4+8Pf3h1KpxK1bt/Dw4UOo1Wo8f/4clQMDUe7cOSAxEWvXrqW88/w6Wy9fvsTy5cutHlO8eHF89dVXNLNlLbr4xx9/mNVWWMO6deswefJkFHV0RNPixdEzIADOeQh2AECghwf29ehh9rpEIqFRX7Y2QcZtflkfP9LGrCyIsyWkwSQkJMDPzw86nQ4hISEwcBHS7ydMoOpSdTj65J49ezBq1ChTvZzRCPeUFIBIWTMICAig6mlCkOcmk8nw8OFDXL16FRKJBCdPnqRjgogGBAQEUHWmzwUxZ2vdunU8KeoLFy6I16VYQCRTC+Fu4dkKs5Q/cs24S5YsyWuwbAmOjo6oX78+Nm3ahGfPnuHQoUMoX7486tatizt37kAul2PMmDGoxNQlPXz4ULT2rGnTpjxKrBji4+Oh0WjypUZ479491KtXr0CNZ/ODjIwM2ucuJycHO3bsoBS5+fPnU6fGgct+2yQkAE+e4OHDh3TOh4SE0Mh+MwuOsBLgZbbq16+PaiJUuZCQEDx48IBmytnndvToUVy4cAEuLi6UFqXVas2cXRnnKLVr0wbXrl2jNUx5ZcrZeSMGIq+uUqmwdu1a9OvXD1M4YQIiPnPu3DneewoXLgxfX1+UKFECZ8+exc2bN2l7i1OnTmHXrl1owgUdFFIpDUhYohEvWLAAarWaF6hwc3ODVCrNpZrBJNghrB8C+GqEFp2tPFgKAMwyW6137ECL7duRlpND1z87hQIavR4PLNSWFhTCNdcznwJOBGTmWKIRGo1GpKam0rH3gms2TcDWL27ZsgWXuR5hS5YsoZlIVgCDZLc+Zc7a2NgA796h3vnz6NO7N6Kjo9G5c+fcfY3QCcPDERoaygtOks9zdnamNPLly5dT55vImhNhnvfv3+PNmzfUsSJiPYGBgWa1t/8EiAoqGwCcMGECrVGUy+VUwId1jGvUqIEjR47wWAJyuZzHtFizZg0vMw6Al42OjY3F8+fPsWLFis//xb4gT3xxtr4gX/D19aWGy38Tli9fjjdv3uDhw4cIDQ2lUeMlS5bA0dER+/fvx1NuUb1y5Qrlov8dwh7Wmr4STJw40WqfCyG8vLxgb28PO6USF776CjZyObzyIa8rk0rRQuRzataoQbMZbARXyW1wSUlJotFRmtkSfEcSWVWr1ZBKpdByBlVqSgok3Dlv37pFz52ZmYmRI0di1apVFg2/MmXKUGdCCBKVl8lk2LNnD7RaLXJycnD16lW6oVauXBkpKSm4e/fuZ3e2xo0bRzdGggsXLuS7BlAMSibT8MGC8SZ8JqR2ZdKkSVi4cKGZ+IUQqampOHLkCK1rA4DZs2fDx8cHLi4uKFOmDJo0aWKWsbLkKOVlZLVv3x7BwcEoVqyY2f0SgjSYzi+1Nz8ICgpCRERE7gvcdyZZ6v79+5vqaOzskM2J6Kxbt44e/u7dO1SoUAHLly+nxqalNSM7LY0GWJQyGaZPn47VI0cCs2bxjqtVqxaqVKkiml0gWa5Ro0ZhxowZAEyBHmHQgcyZ50+f4vnz57kZjTzWnrycLXJNSqUS3t7e2LFjB/3sJ5wi33RBYKR3794oUaIEBg8ejNu3b+PFixe0Hqx169bw8fFBOY56lpiQQNUwRZ0toxEdOnRATk4Or+kqybCwmDNnDt69e4fixYvz+pCVKlkSSu7cORbGrVhmKyo1Fd8cP05/F2a2zrx8ifOvXmHN3bu5zpZSievv3uH6u3ein1MQPBw5Eme5fpCAib2gvXMHe/bsyfO9GzZswM2bNy1mtoKCgkwOY+vWAEzZwoiICJQpU4YnmZ6ZmUmVK3fv3s2j5ZE1ftasWbS/JQlo5VfVlwURaCItTE6dOgUfHx985Jr77tqxA97e3pTGS9p6ZGVloV+/fqhevTqKFy9O2xqMHTuWtrUg2fu3b9/SGqWcnBzROkGxXm1/N8g8Jz0pS5cujRMnTlCBKaVSCYPBgEePHvEyW0RplM1KX7hwgVLDZ8yYgXv37sHX15enJrhmzRq8ffsWlStXpvvj5w5qfUH+8MXZ+oJ84eDBg3j58uV/+jIoWGUdYuhKpVJqsD158gQODg5IS0ujEbnly5fDxcUFtWvXNqPSfA6UY3ri/J3I4qKTt4cNK/ibjUZoOUOEzWwpdDq0b98effv2Fc3MWaIRkui8VCqFg4MDsrlrW7d2Le0JJJdKeWILcXFxOHv2rEXFQWtwd3fHrFmzUKpUKchkMtStW5dmK0hhcFZWFs1kuLm5UYrG58CAAQOwdu1a3mt/dfNix6Ilmo8wYt25c2d06tQJgClamp8aMZIFIdHdjIwMbN26FSVKlLAoIiLmAH38+BGJnGFkCUQgw8/Pj2ZILEEuaA3wOdCwYUM6LoSoUKECdTifPXuGSUOHAtOnIy4uztS3CqbaitDQUPz55580IzOAMYgJVDIZpFIpkjnqnVImQ3R0NObPn0975BF0794dU6dOhbu7O3799Vf06tULAQEBGDp0KL7++mskJSXh+vXrtH5q2LBh1KkmkHLGfhRn5JOxl9/MViErPfoGDRokKocdGhqK9+/fmzUvlcvlyMnJgVQqxahRo/Ddd9+hffv22Lx5Mz3mCFdjs37NGtja2mLNmjXi9K27dwGYsioHDx6kyohi9ZZ2dnZYtmwZXr16xaM2PnnyBOncXsBmttoxSqV7BOPcaDRix6NHWMN9PmBZjRAAL7P1uVDR0xPejKrsu/HjsX/+/HxRhBMSEuDq6moxs0UCDoR+mpWVRec0+91ycnJ4/bRIUFIqldI1/uzZs5SWW7t2bcTFxeHatWsF/bpo2LAhypcvj5SUFOziKLwrVqygGa1JP/yAunXromTJkli5ciV1pEig5M6dO+jQoQNVIRw6dChucQE9sYxnoUKFIJFI6PceMGAA7Ozs/iPOFtmLAgICUK1aNdSvXx/Pnz+nc37JkiVYuHAhdDodL7hTunRpHt0XMNUjk3tSuXJlXL16FXfv3jVrTVKsWDHcv3//S/+s/zC+OFtfkC9IJJJ/vC+FNbRt25Zm2sjmPnDgQCqB2rBhQ0ycOJH25ShXrhyKFi2KokWL8iKBnxNl3dxgnDbts59XiEQu0i7/hGLo2yEh2LptGwC+UVG1XDnMmzcPAQEBolQUItBRWLBBGQwGk0SvRILt27ejGFNLQbYFdxcXzJ07Fz169ICnpyfCwsJw+/ZtJCQkFKh+DTA5I2XKlIGfnx/kcjlKlixJo5aPHz9G79690aFDB7x9+xYDBgxAUlISlllpYFpQDBw40MwA+6tjqRDjpBMqjf7XX3k1d6wDnJiYiOvXr6MHRxO9ceNGvhoHlytXDpMnT+ZlDoR0om+++QbVq1fHlClTsHz5clHK66NHj/Dq1Sv6e1xcHO4yxiqQK5Dx6tUrM2EFIYiz9TkzW2bnY+4fEc0ATPRoUvh/6NAhavzJ5XJaw0auTzSzxRn12ZxDppTJMGHCBBw8eJDWugBA7SJF8PHjR3z8+BGpqam4f/8+NBoNVqxYgZ9++gk1atTA999/j127dlEBi7CwMLOABOljdTskBE+ePEHjxo3RqFGjPJ0tf39/pKSk8JQICcLDw9GrVy9s2bJF9L0eHh6iNVLE2ZLJZLh8+TIuXLiAsmXL8sZXFqGhGgx48eIFRowYwd9HyBrEZWZfv36NO3fuYMmSJfj6669FnXpbW1ucPn2aZgMI0lJSaCCJdbaWtGol+r0Ak5CGVkDDZX8X9taK4BQd7f4GZoQQ+QkSjRkzBqVKlRJ1ujMzMzFEIKxB5McVCgUvw/j111/zGADE2Ro6dChdA7Zt24YLFy5Ap9Nh9uzZuHbt2if12Rw7diw+fPhAHQyaweHu+7s3b9C0aVPodDqMGTMGQZx6LsseuHjxIn3/nTt3EB8fD4Cfia9atSq9R0DuPN6+fTsyMjL+I87WlClTUK5cOeh0OqhUKnTq1AmVKlWiGcI3b97gxYsXmDJlCr766iv6voCAAKxfvx69evWir40cORIpKSmoWrUqpRMePHhQNCO6ZMkSyhZxsNAu5gv+Xvz3WM9f8AUFQFBQEN1sSZYrPT0dbdu2xeTJk1GkSBHUrVsXcrkcly9fhkQigbu7O7y9vSGVSvOtBvi5kVetS77AbUrKT8jOvXv5EpFcXQVrVJTx8cHjx4/RrVs3s8yWTqdDUUdHlChUCJ0EUr8+Pj50g+vQoQPcGQOBLC7v3rxBhw4d0L59eyxYsAA+Pj6IiYmBRqOhkdL8IisrCz179kRYWBh8fX2xfft2Sn2bMmUKdu3aRZteAiYD+nNSRu+z6nUcunfvTikvn4I6TJ1UrcKF4avT4dLFi1Azz5eM19TUVLi5uSEhIYHXPywvEYpOnTohOzsbOTk5WLVqFR2Ht2/f5h23du1avHr1CnPnzsXXX38t6mw1bNgQlStXpsb55cuXecpZgKkuQ6lUUhEHayhevDjmz59foFrGvPDkyROEhYXRyL5zoUJo06YN5s2bx8uKL1iwgBo1wcHBuHr1Kq5cuQKdTkcNIFJcb01ohXW2SJZx4cKFKO3khAoeHljWujWaNWuGp0+fIiIiAsHBwUhJSUGzZs2wbNkyLFq0CJs2beIJZLi4uJg5OUpuriXEx6NcuXJo2rQpra+xBoPBgO+++040m+zv70+pXQUB62zNnDmTKqKxsGcyQFWqVEGdOnVy/2g0AlFRZmIi5PufO3eO1oCxaNSoEdavX2/2vWVSKXWqWWdLTCiIQKvX84JO5DWCcIE4kp7LPH/OzJYl5McZIGuvWLhHTEXX3d0dxYoVQ5cuXXjiFq9fv+YdTwJYZL0GTMELhUIBmUwGmUyGbt26UYemINizZw8+fPiAjh07AjBlbUxfgvsWBgNGjRqF8PBwALk0Y/b6duzYgWOcKIlCoaCBFb1eT3vKhYaGolWrVrx1hQjOnD17tkB1tZ8L/v7+ePr0KSIjIxEcHEwVQslas2nTJixatAhlypSh9x0wUYrXrVtH6YeAaf4tXrwYISEhcHV1Rbt27VC3bl1RlsPOnTuRkZGB9PT0PFvZfMHfgy/O1hf8vwShDTRv3hzNmjVD7dq1ERQUhJCQEDg4OODt27dYsmQJmnDKek+ePEFaWppJxCEfggJ/F65evcqLeH8KKnPS1NaMCEv4plcvVGnWDAAQyaiOnTt2DKtXr0ZiYqIZ73/nzp1wtrHBy7FjzWiEWVlZlKLSsWNHnCbqR0YjlOQ83P1u1qwZBg4cSGkh/fr1K/D1ExpFSkoKBg0aBLVaDVtbW8yfPx/+/v6QSCSfVX1QCLEsVq9evcycloIgJz4e4Jq/Fra3R+Ts2YiLi4NaxKBjszWlSpWiWbvKlSsDMGWdWBoXueYjR44gLi4OS5Yswfbt2yldkUSEAVPE2WAwUGpKp06d8PTpU4vXTTbtpUuXmqkJPnnyxKwpqiX4+fmhZMmSn6RsZglyuRzu7u7YuXMnABP10cnJCTVq1MiTQtyQa0ZOjH5iCInRHElYwpkbl2zbh0WLFkGzciXujxgBGdc/5+PHj8jOzkZiYiIuXrwIwFR3QwQqwsLCqHN37NgxszqppzdvArNnI4FbQ7Zv356vwFFsbCy2bNlice0ZPny41f5XYli3bh1iY2OxYMECdOvWDZ07dzY7xoHca6USLi4u/GyU0WhSgOT6g/Xq1QsDBgyg3z8yMlI08z1z5kw0aNDArCmtXCqFUcTZYjP1bQSNXTV6vVn2ylK9FwCaybTUokGIU5+wxgFAu3btTEJCeUCoRshCLNt948YN/PTTT9i7dy+CgoKoMMYff/xBj/Hy8qJ1vePGjUN4eDiMRiPS0tLM5s6n7KVXrlwBYMo8NWrUCAcPHjRR/RlnCwDtG0e+I5t5I04+YJqnZD2zsbGhAYVevXrh1q1blBoMAD///DP8/f3/I+IYgEnaHchtHkyyUISRQwQyunfvjpUrV9L3paenY+3atZjF1IHK5XIsXLiQBpRPnDiBt2/figYXFQoFBgwYgJYtW1In9gv+WXxxtr7g/zW6d++Op0+f4vXr19DpdLh27Rru378POzs7SCQSFC5cGGXLlgVgqkn4nAbdp2D69OnUUfxUEDllxSc4Wy2qVQO47FPH3bvp62nx8VCpVKbO9QWgyb179w7NOOctMzMTHzne/7z58ymlsIeAjkXoK3n1LxMDoSDJZDKEhIQgKysLarUa+/fvp9S2yMhIXtbnc+LvoJ/GxsZSKWRXjuIRFBTEy2wREMNj69at8Pb2RkxMDJo1a0b73YSGhpo5W+Q9pHA6KioKPXr0QPfu3VGjRg3Ur18fADB16lR4eXnR94WGhlqdL+RvoaGhZn+7fPkyUlNT8+Vs3b17Fz169OBJSucXer3eLNtoNBpptPzXX3+lr+/evZv2FxPiyZMnvJ5BRqMR8+fPR1hYGK0xWrJkidn7pBIJevfujQYVK8I4bRqkXHP1H374AQCQkZZGgyLTpk3DnTt3eMpvAJ/Wc/36dVGqH4FcLgd0OjqX8ms4EcPUkmMWHh6O6OjofJ2LIDU11dRTzIrB3YNriAyVCklJSbTQH4DJuNbpAC4rOHr0aJQtW5bn9LENawnu3buHoKAgM2erVKlSkHOvWcpsnezXD4+/+Yb+rjUYzDNb1hwI7rz5pRG2LIAYEovt27djh4XWGCzcOQqyQWRdsrW1xfTp0+Hr64uTJ0+iRIkSOH/+PG2W2759ezo/WIoe2S8BEzOA1DKePn0apZj6N+DT1kOWltimTRvEx8fj1KlTqN+gAQDAw80NdnZ2OHXqFIDc9at06dJYtmwZqlevjjJlytAx/fPPP1M12ooVK8LJyQmBgYHw8/PDx48fzcSDPnz4gBkzZvxH2C1kjqnVapw5cwbTp09HzZo1KRuDSL8TJVcCsgYRRxUwNSQnmXuyHrx//17U2SJ/Dw4OplTpL/hn8cXZ+oL/lyALysiRI6HX6xEXF4f09HRotVocP34cBw4cgIuLC06dOoVnHM/+6tWrorUH/yTmz59P6ROfCuIMCZsn5+u96el4yBURe9jZoQrn8OSkpMDd3R1ardasZuvPP/+0eD6tVks3BUdHR2RyalD37t1DDnfufVz0jt08pk+fbtWotASyadjY2MDFxQU9e/aEh4cH7ty5g/DwcAwfPhzfffcdSpUqhTt37tC6ps+FxYsXf9YaMIC7L1zGKoczesLCwkQzW2TTrV+/PpRKJRYsWAAHBwfa7HP9+vW0xoGARLiJQEZKSgo8PT2xf/9+qNVqXLt2DV27doW7uzuPtqVUKvNVRyWmxNijRw9ERETAy8sLv/32m9X3k8/8FIGMM2fOmFGZxIrkCb3snUA9rnbt2gBM2du+ffvyGp0ajUYMGzaMGnvuIgI4NjY2WLVqFe+1RYsWUacuIyOD12QUAF8lETBbk7p3725+/RzI+C+owE9eaoVkjSwIpk+fjnLlymHmzJkWj6HfjVuvec6WIPMSHByMKVOm0HvXqFEj0flL6riEz8O7cGH6nHMYZys5ORkHevbEMq52i1VU1er1Zs6VpYbIABBQrhyA/Ge2PqXJsNFohL+/v5lTLobt27fj1atXojRCR0dHTJs2DZGRkdi0aRNevXrF66cFgFLV0tPTKdOAVeWUSqXQ6/VUQKdt27a897PBmfyCOGyVKlWi9OsKFSrAjXPCli1ZAn9/f0oT7datGz5+/IiHDx9i3LhxSElJQaVKlWitZY0aNbB9+3YkJiYiKioKKSkpePz4MWbNmgVPT0+zIIlQ6OWfxMGDB+n63LJlS7i6uiIkJISqKLq6usLFxQU6JqAC5Co4svWO27dvp3V9bPNi0nuMBWtzEJrlF/yz+OJsfcH/S4g1jbW1tYVWq6WbMFmICB3j/Pnz8PT05PUL+f8IEk38FBqhUiaDAcDH7GzEZ2Tg/u+/Q2IwICspCd27d8fWrVvNDASxxrYEr1+/pkIlDg4OSOOyHfv27kUCoajp9ViyZAmcmB5SCQkJlMI2sU4dDOZocHlev1KJRYsWoVSpUvD398eePXugUqnQqlUrzJ8/H2vXrkX9+vURHR2NCxcufHZ+eqtWrT77OeVyOc1s1eCi+o6OjqKZLZKxK1myJDXGbt++jUhOvlzM8SHOAjlmyJAhlE5GsgMHDx7kvcfDw4NSWoQwGo1YsWIFdZZLlixJezEREGOhUKFC6NChAzUmxEA+41OcLbVabdZKwVKPNgCUVkxw+PBhzJ8/H7Nnz8bEiRMp7RAA9u/fj+DgYKvCQDqdDm5ubjxaklarRdu2bVGzZk1kZmbS3mWBgYE4fvw4Ro4cidZcDVfPnj0xcuRItGrVClevXkWPHj0sKkMCuVSqgtabEMPNUnF8qVKleFLT+QEZO9YcuW2cGA+USgwdOhRz584FANwdPhwVrl/n1ToKx9qLFy9E70X58uUhlUqpo0zwJCyMUgI1ej08OPrnujVr0LVcOXzHHc9SobUGgxmN8KOVbC7Juv2dNVuEytuAy/RYw+XLl/H+/XuUEhHTeP/+Pdq3b4/IyEjs378fAHgNq0uXLg1PT0/ExcVRpw0APRbIlX6vyFHXScAGEA8k5AeNGzeGk5MTzp49i8ePH2Pz5s2wsbGhPeSm/vwzihYtikaNGlGp+Tdv3tCxEB4ejrp169Lea0SVUCqVmgUN5s+fj27duvFe69q1K6Wy/9MoVqwYZRIAuVk+IjQyYsQILF++3MzZcnFxQcmSJXlr0YMHDyjlm6zxXl5eotlgIlL09OlTfMNkdr/gn8MXZ+sL/l+iRIkS9GdSdD1u3DhUqFCBRkNZXn/RokXRtGlTFClSJF+b2H8zCGXkU2iEaqUSkMlw9uVLlHdzAzIyYJudjQ2LF6NZs2YoXry4mbNlTTL248eP9P7//PPPGDlyJP2blOHg9+vXj0f7mTFjBjZs2AAAWNSyJTZxMub5wcSJE81ogjExMbym2zExMfjxxx95NLLPgSZNmnx2+kmRIkWAiAhg3jzUrlQJJUuWRLFixUQzW+/fv6c/k804KiqKbrZidTdkg65YsSL27NmDqVOnUqqQkMo5e/Zs/Pjjj2jZsiVWrFiBclwkn8W2bdsgl8tp3cHs2bN52ZrQ0FB8/PgRMpkMMTExKFasGE+iWwiSPcuvSAObuXr27JlZS4px48bR2ggWAwcOpHVSBJ6entQZkkgkoo6DteedyTnJrKPYuXNnvHnzBosWLaJR7FevXuHx48c8ZbWYmBi0bNkS7dq1Q9GiRbFq1SocO3ZMPDPHoUWLFjAajTQj0aVLFzNxEjG4urrCaDRanMvXr183uzd5gWQJ89UgXqfDxo0baTPcql5eeMI0ZQbMM1WNGzcWVeTz9vaGwWAwu16tRsMTyHCztYX0t99wYu9e3nHs+iYmkJFjJZtLGjvnN7P1KSC0zPwo/U2dOhUNGzZEOQcHMyXc1NRUXLlyhSe0QLJKTk5OUKlUGDx4MB0/N27cAACcPn2aHv/999+jePHi1GFhazg3bdrEo7XlF76+vvjpp5+QnJyMkJAQql5JlBNfhoejQoUKePHiBUaPHg3A5GywgaQdO3bg6tWrAHLrn96/f0+phwBQr149UUrdDz/8YJbh/k+BiPWQutAXL17g8uXL2LZtGy+ra29vj/3792MY0+5l48aNAEz0aDIHY2JizAJfgOlZ7dq1C2XKlPmvUpX+N+HLXf+C/5dgF1UiThAREYHOnTtj6tSpAExGklwup5LLnp6e8PDwMIvi/5OoUaMGTw3tU0AoI59CI7RRKACZDMFv36IBFx2f7OiIZtWq4datW6hSpYqZ8WGNdtC/f3+sX78egClSyjpUYSTqqdfD09OTZ9i7uLh8VjpDXFwcLyJLUFADMi+IqaP9VdAsRU4OHBwc4Onpifr16/MyWxqNBklJSbwsI2tIkKxAYGAgb0MGTE7MV199hQsXLuDBgwfo3bs3pWLduHEDI0aMoMpnv/zyCxYsWIAdO3agXbt2fNoXh4EDB2LUqFE4c+YMFZxhnVrSU02tVlMjoF69egBMFDBhBqtKlSrYv38/NcStISIiAiVKlKCZMrGassDAQMybNw8GgwGLFi0CALi6udE+bEK0atVKVGXSmpMl4STzdYS2xhjG6ZzceePGjXH79m04OTlRGg9pwhsUFIT4+Hh8/fXX2LdvHzZu3Ii9e/ciOzvbqjRzQkIC3NzcqENWpUoVLF++3OLxBHq9Hj4+PhYFMqZOnZqvGiEWhO5ojdIok8mAP/6gAjAkUwGYxqzBYKAZV+EzOHLkiGifLYlEgg0bNlARB/pZnBFpMBqh0esRGxUFQ2Ymr00BwHeUNHo9sgWZrWgrWViCvzuzBVjuuceCBFnE6L46nc6sro2wF1q0aIHHjx8jMzPTLDvFClGMHDkSxYsXN6sNAkyS6p0KECRj0b9/fwwYMIB3PupMGQxYsGABVaqdO3cuihYtynO29u/fT8VTyNybOHEir8n19evXRZ0tDw8PU1+v/wIQwRhCD79w4QIWLVoEuVzOe3Z6vR6jR4/mMXpKlSqFjh07olevXvDz80NAQABKliwpWkNJpPL/UyrMX/DF2fqC/6cgG+jSpUuh0+lQpkwZ7Ny5EwcOHMDp06cxc+ZMqNVqumBnZ2d/Urf7z42QkJC/rAb0V2iEHm5ugEyGjYcPowRHrxw8eDBUKhXS0tKg1WppDcMf5cvDfft22ttHDEFBQdQgunfvHi8SfZdzggMDAgDkMwL+iRg/fjyv3ubvwt8hkEGEIfbt24cHDx4gODgYbdu2BWvC3r59G6VKleIZYAqFAqNHj0bNmjXRunVrAKZaLuE1pqWlYdu2bdDr9Zg3bx5u3brFc2x69uxJjyGQy+Vo3bo1jeSLoV+/fqhVqxb69esnahiWKFECHh4eyM7OptHrkiVL4pdffuEd5+/vj+zsbEoB+uWXXyz2XyMOOzG8OnToYGZQfvPNN1i5ciV2795NqauJSUlUtEIIiURC1RxZEHqsGIwM3RDgZ7ZI5NhgMGDChAmQy+U0E89S5YhEM4nME1hztsLCwpCYmEjvw/Lly/NlQGVnZ+Pdu3cWnS1fX98Cq4P27t0bCxYsoMEtMchkMiA+nopgsBlpiUQCiUQCHx8fPHr0iGcoA6ZnbWm+DR061IwOToJPxNnKsEAHdLaxgXHaNDjb2CBTq8UFgTO20QqNk2TOWIEMDzs77P+MtaEKhQJr167NV6bXmrOl1WqpM0OCJnq9HleuXOEFpsi8I0p3rODN+PHjcfbsWVqXKHwen7oeFi1aFCVLluQ5W0eOHjX9wO0/R44cQePGjTFlyhRERkZSKiMAnoAEcS5Is3aCwYMHi9ZHR0dHi2Z//hMoX748pk2bRucwoW536NABR8n9gGkeBQcH8yjsEomEHkMoty9fvjRbDwGTSm1+gjJf8Pfhi7P1Bf8vMWzYMPTp04duuM+fP4der8e1a9dw8+ZNTJ06FUqlEiqVCnZ2dggKCsKAAQP+w1dtUkQk0fZPxV+mEQJI9/aGk16PkydPYunSpZBIJHB1dTU5W9wGPqpPH3x4+ZK3yQHAgAEDaPZkw4YNtI8SW5M1bdo0qtzVhKNt/p3O1qVLl3jNHN3c3P6WQuC/o20AcRw6duxIjWgXFxecYfpThYWFISkpCRqNBra2trh8+TKkUiliY2NRt25d+oxSUlJ49UMXLlygWaAuRBkOpuwPKZYn0d8BAwZQvr9EIkFCQgLN0ohBJpNRCilr7EVGRmL69On0c8+ePYsDBw4AMNVaCmsKDh8+jP79+1OnY86cOZg/f77oZ1asWBGOjo7UuSGiOCw+fPgAnU6HSZMm8bJ8QpXGvNCkSRPRvlRCbNq0iddsVPj9EhMTaU8gALzmpL169cLChQt5mXprogNkDhEjVaynjhjI8ewc/auQSqX48ccfaZ87MbRr1w6LFi3C1q1bAeRKXAsRGBjIy6gAprYS27dvz/f1lOIYA3qDARq9norNsBLaLFJzcrD/yRO8Y5wyfxcXk9ork+1yF1wXwM9slXF1Rbc8xH4uXryYr8bjgGnu9e/fP1/qnERBU8zZ8vHxoWI+sbGx6NWrF2bPni2a4d2/fz/tk8ZmDMPCwvDu3Tu67gnFXD51PVy+fDnWrFnDo8oRld26detSmjPJ5KekpKBp06b45ZdfMGTIEDRu3JjOBVKzVaJECfTr1w/FixdH/fr18e7dO9H7cvv2bZ4j858EUYwkUCgU0Ol0ZjVbBGwWslevXvS9ffv2xYgRIwCIP5P8iK18wd+LL87WF/y/hLe3N6KiojBgwACqxDN79mwsW7aMF9l58OABMjIy/mt4ylu3brUYYc8vSCzxUzJbOiYDsWrSJOzZswfbt2+Ho6MjlX6n6lycwSEUhNixYwett9JoNPR+s5nDixcvAtxm+CfXw+XvdLbGjRvHoyj5+vri0qVLlPP/ubBv3z6eYfw5QMamQqFAgwYNaDEzGEOBOC7e3t7IzMykkdCZM2ciOTmZGpQTJkzg1VU0b94chw4dAmCqxejfvz+Sk5OhUCjQuHFjeHt78+bL06dP0adPH9rAlBiILAWUZF5kMhmCgoIQGRlJaYLk9enTp1ND6datW1R++ejRo2ZqfB8+fADAzw5ZyiodPXoUe/fupX8Xo5llZ2fDxsaGn/FJTCywgp9KpRKlURI0KloUVQoXRqNGjeh3AIC9ghohFp6enrh79y4N/KxYsQJeXl40MwlAtE6J4FPVCFUqFZKTk0WL5z8VZD6zPZrEPnfIkCG05kdM0dESbGxsCvQ9nTlHkmS2oNejVKlSFvtVGYxGzL9+HTbcZ7QsWRL1fXyQodVCyjhGpZimuMS5YamIrfIh796sWTPs27cvX98jPT0ddnZ2uH//vsXMUXBwMLZs2YINGzZAo9Hw6rII3N3d0bdvXwCmfk579uwRFRwZOnQo6tWrB4lEgqJFi2L8+PH0b2q1GllZWTSDRijC5G+f0tQYMDmUderUoe0oAKCUXI56MTEYMGAAFY5gM2pbtmzB7NmzcfbsWdSpU4fWWhI664sXL/D27Vu8fv0a165dw/nz50XbrHTt2hUBHNvivw1eXl4ICAiw6Gyx67CHh4cpqAkT1Z9I+gsFgwBTHel/iw30b8WXu/8F/+8hjJayxqNcLqfNTP9X4MsZFZ/Cv7bljAT75GSE3r2LrVu3Ii4uDs7OzqhWrRoePnyI39eu5b1HGBXr3bs3zTwQoxzgc/2DTp8GuDqgzq1bIzQ09G/li7du3ZpG9gATF37NmjXo06fPZ/2c8uXLf3aBFZaCJ5VKaR1ECSaKvJvriUYMccLdL1++PFQqFaWHiUVyidNjZ2eH4cOHU+Pzjz/+QPPmzVGzZk16rFqtRqNGjeDi4kJ7vqSlpcHOzo4qX6WmpuL27dsoWrQowsPDYW9vjzNnzqBXr15YsmQJrUEjxoKtrS2v3iIqKop3feSac3JycO7cORw4cAAzZ87EjBkzzL7P4cOHsXjxYvqdHj16ZCZ1bOZsLVwIHDwoapD+FWxs3BgT7e1Rv359LF68mBbdlylTxqK89PHjx7FmzRqqosiuXSEhIUhKSqJ96MRAaHjEaSnInMpPk9xPgTUhh9OnT8PFxQU3btxARETE32rkPufocBlaLbK0WkCvR7Vq1aw6vwBgy61fDkollFIpdAYDDIzjzzpWjtzaS2iEfSpUwM9cE2wnK88NyL8ADMlMNGzY0CwwQTBx4kQMHjwYK1euxJkzZ0QzF7du3UIrTu6eKPexIAGSjh07UhbA+/fvea0+iLMl+b/27jsqiuuLA/h36SDNQhMpKooFFRXsXQS7KLHXRBMLFuwxMT9LYu8t9kJi1xhNjCUoGhVsoKCgiA1FpUYp0sv7/bHMyw67VFkQuJ9zOAd2Z2ffLrOzc1+5VyKBjo6OKLFEcnIy/P39C/WaclNRUcHvv/8uGnXU09WF765dOHLkCD938Dpi2dl8avGbN29Qp04dfg4Uso8K9ddkKVqzZWdnJ5fJ8nPh4uKiMBshIB3xy13nTJaw7ktRUOXs7CwKbEnpo2CLlFtHjhzB9evXeU97+/btMXjwYFF6ZzU1NcTHx1eoYfS61arJZZ4qLO2cCwILmdTRzZs350lGEhIS8FamuKmhoSHS0tJEhWkdHBx4j6udnR3PcKanpycd8dq0CXj4kAdbLDs73/TxyvDvv/9i06ZNouyIJcHOzo4nkygpTZo0QXh4OL9wFr5QT8+YgQE5tWSEWlVCxi5ZsoWDLSwsRKNCR44c4QFnkyZNRIFieno6Ll++LJewwtDQEJaWlhg3bpwoi6QwyrVw4UJs3LgRgYGBmDBhAk6dOoWffvoJJ0+e5BnNhHYB0mBL6JFVV1eXW+Mj7Ld27dpwdnaGm5sb3r9/j8WLF8v1xMfGxsLLywsBAQF49uwZtm3bhqtXr4qCsp9//hn9+vX7b7QtKQnIzOS9wCXFxsYGv/76KyIiIrBmzRpYW1vj4cOH0NHRwaBBg5Camsqn2ALSmmDC56xatWqiLJKANHlO1apV833ORo0agTHGg66vvvqKjzKXlfwKiAsXjDExMahbt26+geSnErIMGq1Zg+f//gtkZeHo0aOiKZyKCJfjaioq/yUdkhnZkp0yKGTf+zcnUY5sZkPrvEbQsrMxc+ZMubIDeRGCrWbNmsHX11fhNhMnTkTXrl3x008/oV+/fgrXASckJPAEDAJh1KNVq1Z8uvHIkSP5yOyMGTNEU4enTp2KXr164ejRo0hOTsbDhw8L9RoKIpwbZNs3ffp0ODk5iYrSC9MMa9euLfoOHzJkCJ8GLYzcyyZCMTU1hZmZmcL1SxEREXJZWD8XoaGh8PT0REBAgGi0G5DWP8vvHCZ8fygq//H27dtPru9JPg0FW6TcqlmzJjp06IDBgwejSpUq8PHxgYGBAZycnPg2wpd9Xj2Epa1Tp05FXohekoQaM3q6uvy9cXNzQ1ZWFkJDQ9GsWTPUyJk65erqik2bNuHixYuiXrYaNWrg4sWLAKSFTYW1QBKJRHrxKARW//yDGrdu4dChQ2WWbl92sXdJKEyGsOKQrZtkZ2eHDRs24NWTJzDI6a1ctGgRli5dqjDjomw9rFq1avE064wxODs7w9LSEtOmTcPq1atFBXhVVVXx9u1bubUhqampCAgI4EHlo0ePAPy3GH/ZsmU4dOgQ/vzzTz6q8cMPP6B79+7o2LEj/9IXelodHR3Rp08fZGVlISMjQ27tSt++fXHnzh307NmTX2Bs374dgLjGW0pKCk8VnpycLLoolN2ntbU1qlevjsOHD4MxhiNHjmDNmjX5JnopKiHAiY6O5q+zbt26fA3bs2fP0KxZM/yVk4Xv3LlzcHZ25muQjIyMkJqaWuTR3qioKEgkEp5N0szMrERfV1FNmzZNVCsrN+GiujSSE6nJTFP+kJaG6oUcyWM5n+mszExk5HQ8qMv8XxSleZ8yYQIAoLHMtEjZ6YayVFRU4OjomG+tOVlCsGVqaiqqiyUrKCgIV65c4QFIYbMR/u9//8Pdu3dFa68+fvzIP+uxsbGiDoAOHTrAzs6OTwOWLT3xKUaNGoUhQ4aIvldevXoFX19fvHr1igfI9erVw4YNG+Dr64snT56I9iEEV8L7Kls7KzIyEhEREQqD+127donq6X1OHj9+jE2bNiEoKEguaGrcuDE/v+SFMaawXMfff/+Nc+fOlWhbSdFQsEXKPUdHR0ybNg2A9CJIWIwPSE/WKioqn8185X/++afIKZZLkpAe+W14OI4dOwYdHR3o6upi8uTJfI3O05zRhNOnT2PSpEl8Mf+7d+8gkUgwbtw4XL16FYC0ir3suhnZqXyIi8OXTZpgzJgxpfLaSoMyshEqUr9+fSxYsID3nF+/fl2UXl12Xr67uztPBDF//nzeG56ZmYnq1atj1qxZfLqO7JQv2bVissaOHQvGGJ96JHyesnKlyB4xYgTS09P5yLKXlxd27tzJp6sJyRjat2+P8ePH8wvC3KUX6tSpgxcvXuDs2bOYPn06mjVrBgMDAyQnJ4tGA9avX4+IiAhUqVIFSUlJMDc35/W0hGArJSUFpqamuHDhAnbs2IEZM2ZAT0+Pnx9KirAWJjo6GtbW1lBXV0eLFi34BdLWrVvx5MkTnvVMGLESRh2FkYiinpdyT8Fcu3ZtvuvKlM3S0lIuBbusUg22ZN7LfQEB/9X5K4BKznF96cgR7NqxAwB40AVIpxdyOcFPAxsb7GvYEN/KFKjd0acP/lAwXS86OhojRozgnRYFqVq1KiIjI2Ftbc0D+dwyMzMxYMAAPiqdV7AlBDPC/+j169dYtWoVHw0SCM9z6NAh0ej0ypUrMX/+fLi4uGDdunWYMmVKoV5DQVRVVZGdnS0Kts6dO4ekpCQsXbqUZ0FNS0vDzJkz0a9fP4TmlFuQ3Qfw33lJtm3q6uro27evwiyjnzPhO3jAgAEK16MW1+XLl3HmzJkS2x8pus/jCpSQTyR7ISjUbQGkvYTZ2dlKTc5QHmVnZ2PgwIHw8/ODh4cHDA0N+RqdloyhS876h5SUFOzbtw86OjoKe2b37t0rV6fF2dkZjDF0794dDRs2lMtmWBrMzMyKvXg7P6UVbGlpaSEtLU3hCdrExEQ0NcrW1hb169cHIJ2GIlzgCwFIVFQU9PT00KNHD9G6HSGIVrSuQdbbt2/Rr18/XjDZwsICM2bMQGZmJvT19fk6snHjxqFz584ICgrC3Llz+Sign58fZs+eDS0tLYwbN06ut3ndunUYNmwYbty4gV69esHe3h7x8fE4ePCgKLjQ1taGm5sbBg0aBA0NDRgZGfFeXOG1zpgxAx8+fICWlhbWr1+PzZs3o2/fvnnW2Coq4f+hpaUFdXV1uLi44NSpUxg1ahRcXV35dsJrFEbmhLTanTt3Rs+ePT85+BDOZ7mngJa2uXPn5nsR17VrV+zbt09hb3tJq5urYHNMzlS/guoqqjEGrF6N5L//5kmBDGWmRgrrs/a2bQvkjKyGBAcjMTJSNDJZXUcHbRQkNxEyRgoBkTA6mddat+zsbHz8+BHbt28Xd17JkEgkSEhIQJ8+fWBtba3w+61NmzZ8hPjWrVuYNm0afvjhB4Wj47Kvo1POGjRAOurl4+ODKlWqYObMmflOGS2Ko0ePwsvLS5yNMCfw6tOnD5+dItST09bWxo8//oiJEyfyhFjCaxaSIEkkEgwZMgQtWrRAs2bN+GhceSJ8B+eVIKO4fHx8cOPGjRLbHyk6CrZIhSBb3FS2pz46OhpA0bN3VXRpKSkYOnQoTxxStWpVaGhoQEVFBbb16qGrzMWRsbExQkJCYGtry6ebCNmiZBNkCP7++28MGDAAly9fxtdff82/8EtTzZo1cerUKSxZsqRE93v37l25IqnKoKmpidTUVLTKme4jm4I8KipKVCdm8eLF8PDwACANeISLOyEA+fjxIxITE+Hl5SUKtqpUqYLq1avnuY5GGI2pXr06UlJS+P4GDhyIXr16ITMzE9evX4eTkxPGjBmD169fIywsDC9evMCaNWt4B8j79+/x119/4e3btzhw4IDcCJkQxKelpcHV1RV+fn54+fIlvvnmGx4kBQYGwt/fH9WrV8cvv/wCV1dX+Pr68syNQvAiHJ9aWlqiUaOS6tX10NDAQiMjSCQSpKenY+/evZBIJNi/fz8voD1s2DA+tU74XwhTNc3NzREbGwtHR8diTUmVrccDSNfUfGopiU91+vTpPO9LTU2Fo6MjD9SVSTN3pwFjsLe3V5i+nzGGpe3aAQBMDA2B5GQsmD8ffXv3BgCeNh4A/so5dpIjI3FW6OTIylKYyl3RuUGY7ipsL2QLzR1sHTx4ELt370ZMTAxsbGxw584dtG/fHk+ePMHixYv5GiVAWgD6ypUreP78OV6+fClKciMwMjLi085Wr16dZwp82fqELVu2FBUq1tbWRlBQEOrUqVOiCY5UVFTQsmVL0ciTmpoanJycRJ1kO3JGGlNSUjBlyhQcP34clpaWOHjwIN9OGLXeuXMn/P39ce/ePfj5+Sltyrcy1axZE71791b4vfop2rVrJ8oYS0ofBVukQpD9MpcNrITfSzLlcUWhq6vLpz1VrVoVFhYWSEtLw+HDh0ULcZOSkjB8+HDEx8cjMTERZmZmfC1KXl8KQh0TV1dXjBs3Tu4CW9lCQ0Mxd+5cdO/evUT3m56eXiqLqw0NDWFjY4NvWraE9po1fITEzs4OgHQxuSAzM1NhAhjZYAuQTreV7bU+fPgwWrRokWc67h49emDRokWwsrLCpUuX+GL2TZs2oWnTprC3t8e5c+cQHx8PT09PeHt74/z583x9ntDzrKWlhdTUVJ4xMffUKKGdqampuH//PrZv386nBwojN/b29jh8+DAaNGjAC2kLo17NmjXjo3NTp04FIB2tk704LKkRyZAbN/Awp8f85s2bcHNzw+bNmwFIywLs2LEDP/74o9xIjhD49enThydZKM7FlImJCVRUVPh7aGJiwusMlYVdu3Zh9+7ded4fGBiIJk2ayE1/VIY3MpnyAAASCaZPn441a9bIbRsQEIBdOcW1a5maYs+ePRg5ciQMcoL1FJlR/H9zjttpkyfz0hmqAO/gkKUoOYHw+RM+Z4mJiWjcuLHcKNHo0aPxzTff8DVb+/btg6+vL7Zt24YlS5bgyJEjfFthWuDDhw/x119/QSKRyGXcPX78OM9GOH/+fLl2CWv9ZGce+Pv788yngDS5TXx8PDrITJcsCaqqqvD29hZ1xFWvXh2XLl0SzZQQMqAKdu7ciWnTpsHX15dntjQ3N4eNjQ2ioqLw/Plzvu3nmgQjP3Z2dli3bh1q1Kih1GQypPRRsEUqhD59+sDIyAhnz57lKWGB/4Kt+Pj4smraZ8nK0lKUnvrbb79FRkYGrl69Ch0dHdjmZMET1lv5+Pjg9u3bkEgk6N+/P5+K0qdPH56NEIAoW1WtWrUQGRkJNTW1Ul8zl5iYiBMnTvApJyWldevWPOBRpsaNG8Pb2xsSiQTZmZk8cFGU8ENNTY0Hs2ZmZjz7oKGhIcLDw/HLL7/waZWygVVmZia8vLzyLLbav39/LFmyBLa2ttDR0eHPMXfuXGRmZuLu3bsYPXo0wsLC+IXn119/jbp166JGjRo8mNDU1ERaWhpSUlJgYWGBBQsWiJ4nIyMDNjY26N69OxITExEbG4uQnGmswtQrJycn7NmzBzNnzsShQ4dw/PhxfhEbGBjIpw6/ffsWu3btQtOmTdGnTx9ejqCkeonPnTvHR8lcXV1x6tQpPnrVrl07jBkzBvXq1YOTkxMYY3w0QUi7LKTQLq6aNWsiKyvrsxmp//rrr/NNkCEEDqUxjVs11zlGQ1sbkydP5lPRZAOh2NhYqOYE4G9fvkRaWhpWrlyJQ7/8It1AZlSkiVCwOCvrv/Wn2dkKA8ikpCQg12e0Tp06OHDgANrljKR16tQJs2bNkht5cXFxgbq6Oi8uLHQgVKtWDR06dBCtX5QN9ITfheQxgkuXLslNM9XQ0IC9vT0GDx7MpyIL628BYMKECXxKMiCdanzr1q18a6kVh/B9IJvkpn///qhXr56oZp1swd8GDRrAwMAAKSkp+Pnnn3mnBSA9B8q+1o4dOxY61f7nJCwsDN9//z2Cg4NFNc1I+UfBFqkQAgICEBMTgwsXLqCR8OWI/4ItRVNJKjNNDQ20b98eqampGD9+PGJiYpCUlISuXbsiIyMDOjo6cHd3x9KlS/ljhAxo1atXx9q1a5GVlYVFixahZcuWfBth6tmqVavQqVMn+Pj4lOh0iKISppGWlLwWrJe0hIQE7Nq1C4B0PaKqqqpc1kCBqqoqD5gsLCwwbtw4ANJg6tWrV9DS0sL48eOxc+dOUVZO4UInr1FHYY7/r7/+iuTkZL7dnj178O7dO2zevBlJSUnQ1NTEL7/8Al1dXXTt2hUqKio4evQoH1mytrbG4sWLkZKSgvDwcLlEAdOnT4e3tzcGDx6MDx8+wNPTk/d4C+/3pUuXsHDhQjx79gxGRkaIjY0VrSEUau1cvXqVH3Nr165FUlISTp06pbDO0KcS3g8h6cW5c+fwS87F+rFjx6CqqgovLy+oqqpiQk72uspGuAAujQQZuQM6CysrPlUvOTkZurq62Lt3L6KionDmzBm8ypnyl/ThA9zd3aVTVnP+p7XV1YGcBAUP7t2T7jAzE1o5AZCpkRFWrVol14YaNWoAJ09iVevWPLGGtbU1MjMzcS9nP+rq6vycK2v9+vUYP348PD09oaGhAQ0NDUyePBk//PADbty4IVqnKZtZLq8RkN27d+PatWui2/bv34+zZ8+KghzZ3+/cucM7DwBpcJ9fApTiGj58OAYNGiTqNHj58iWePn0q+r5wcHBA75ypnSEhIdDW1uZBqOyo17Zt20TfQ7lrb5YXMTEx2LdvH06fPi36v5Dyj4ItUiEkJCRAVVUVW7duFdUmETKiKaugZ3kVlzMtTVNTE35+fvD39+dffBkZGbh//z62bduG+vXr89TuQrC1fPlyANIpdQsXLuSjEIC0F7ZevXqws7ODoaEhBg4ciC+++KI0X5pISQd63t7euH37donuU5H4+HhMnDgRVatWxYABA/iFomD06NH890mTJmHZsmVgjGHfvn08QI6MjESHDh3QqVMnrFu3DoA4WBSChbzeI2G9hFDkVJiKJ6RwnzFjBtLS0qCpqQk1NTV8/PgR48ePR3Z2tugC0MTEBBMnToS1tTU0NDTkklUYGRnx+lPDhg2DlZUV7OzspOtqli7lF3+RkZEICAhAjRo1EBsbCxcXF17EVJjKlp6ezp979erV6NixI96/f48+ffoU8p3P36BBg3iPs/D+OTg48BHD5ORkdOnSBdHR0Tw5z+bNm2FjY1Miz1/eCJ0AnzKaV1i5R8+TZQI9YU2voaEhWrVqhW3btvHAKlF2qlrOSFzYixdATseEmTAaLHNcjxkxAomJiWCMiaaotm3bFnp6euhjYYGEnBHcY8eOYcKECTy7nFBnTnZE+cCBA1izZg0YY3w6d506dXDmzBmcPXsWLVu25IloAGmngjC1Twgy81tTJXQ2XL58Gebm5qJpx7du3eK/P3jwgK89VKa4uDg8fPhQFGwJ74/s+Sg2NpYHlo0bN+ZF3AFxcN2tWzdRx2DtXMlSygshQcbQoUP5tGtSMVCwRSoEPz8/fvEj+yUm3KYoNW5lJjuaERgYiMTERP4l16VLF75oOj09HStWrECPHj3kFnQL67tkvxS0tbURGhqK3r17Q1NTE+bm5mWSmtrKygoWFhaiKTElwdraWuFi9JImXFDExcXh4MGDMDY25sk+mjRpIppeo6+vj02bNkFNTQ1eXl48dbNwzIeHh/POBtmpNcJC+LymeApBNSCteyUEGZmZmTxos7GxQZUqVaCuro45c+YgJSUF33//vWiNR0REBNq2bQsbGxu4u7vLjaR5eHjAzc0NgHTakp6eHp4/f44VK1bA399ftIBfQ0MDDRs2hK2tLSwtLUVToG7duoX09HTe833o0CHcuHEDEyZMKLEkEp07d+Y97c2aNUPXrl0xZMgQREdHo1evXtDR0cGVK1fQt29fANK1g7LTnSqbTp064a+//iqVaYRmZmaivyNyZjNcvnwZycnJUFNTg5ubG8KFtV05x2G4TFILSU6w1UGmntHLy5chOXsWw4YOhUHOOqu9u3fzqcqynx8bGxu0b99edN4R1hEJ30vCdEbZ76Qvv/wSBw4cwM6dOxEeHo7r16/D3d0d7969w4YNG9CqVStRMfWMjAz069cPL168QJMmTTBu3DjRmi6BcM44cuQIli5din379iEoKEjUZn9/f9FjZNd1Kou3tzfevXsnmuataH21MBJvbGwMZ2dnVK9enQedssGls7MzfHx8MGPGDDRt2lQ0u6U8EVK/l3Q2QlL2KNgiFYJskUbZKSsSiQTm5uY8ex6RSsi1hq1evXpQU1NDnTp1kJaWxqcDAtK58nPmzMHkyZP5uoERI0ZARUVFLkFGcnIyJBIJrl+/DgMDA2zdupUXci1NNWrUwM2bN+XWB5UXsiNDX331lWjKXEhICNavX8//3rp1Kx48eIDs7GzMnDmTr5ORTTwRkZMGWzbYys7OhqWlZZ5tWLBgAT5+/IhOnTrB39+fr3ucPXs2TE1NAUiTRJiYmGDGjBm8Ds7Ro0dF62MYY7h16xb++usvbNiwQS7Ykl1rMXDgQGhoaOD58+f47rvvsHXrVtGokIaGBpydnbFixQpUq1aNjxIA0lEFW1tbvjbGxcWFX1TK1g76FNOnT8fKlSsBSEcXhFGAV69e4cKFC/z/5ubmBsYYQkNDcfz48RJ57vJIR0eHB6fKJhz3gxo0kN6gooKmTZti9+7duH37No4ePYpvv/0WPj4+2LRpEw+23L/5hu9jSU5ioNdhYWiYszYzISYGzM8P9evXx4mc/2UVTU3o6OjA0NCQr28FpIFVVlYWbwvwX/r/iRMnIiEhQWESDdl1xoA0eBBG0JOSkrB9+3ZR0J6RkQENDQ3Url0bBw8exIYNG9C/f3+5/QrrS6dMmcLr9KWnp/PPfZcuXUTZ/3r16iWajqcsKioqaNiwoeh7W01NDXZ2dqJkV6tXrwYgnQ6+YcMGhIeHQ1dXF3/++aeo3QkJCZgzZw48PT3x4MGDEqsHVtqMjY0xbdo0uRpkpPyjYItUCKtXr8a7d+8AiKesqKio4M2bN3w6IZGS/eAzxuDo6AhVVVU8e/YMN2/exM6dO0Xbz58/H2/evMHWrVuhqamJQ4cOQV9fXy7YEnqwAwMDsWjRIrRv375M1mwFBwejV69efD1NeaOtrY3evXsjKioKR44cQUpKCp49e4Z///0XGRkZ0mlQOdTU1HgwJSt3Br42bdqIgq3g4GAeNOUlPT0d1tbW2LNnD09Z/eOPP8LU1BQ9e/bEzz//jIyMDMybN49noPz7779FyQOEAOTJkye8XcLFaJMmTXD+/HnRqMScOXN4Xar09HRs376dH0empqaIjo7Gjz/+qLDGVO/evXmCkNWrVys1C+aaNWvQpk0bAICvry8YY3IXzXp6euV2/Uh5E5dTcFgYQ9PS0cGZM2ewd+9ejBgxAhkZGThx4gR0dHTw9u1bdM35X9WzsuKfi3s501lfvXiBxzmdB845HUxLly5FSM5n4Ltvv8XBgwcRHh7Oj2tAOo3ay8tLlMhGaBcgLWadlJSEjRs3ijoRZAOpunXrQkVFhdeJEtbuCHXxAKBnz55o3rw5AGkWzKpVq4qyEQqffWH6sGzWPzU1NSxatAhubm78+BU8f/5ctGZLWVRUVODn5wdPT09+m4mJCYKCgkRrU3Ov71RXV0d6ejrWr18v6lRVU1NDeno6f6/zSvrzuTM2NsaiRYvg6OhYauuDSemgYItUGGZmZjh//ny+vfVEql3dugpvF0ahunXrBhUVFXh6esLW1haPHz/GnDlz0LhxY8ybNw8eHh6IiorCzJkzRb2TQmDFGENYWBhu375dJsHW+/fv8fDhQ4VBSHmgoaGBtWvXwtnZGYD0YqJu3bp8hFZ2fYKampooXbIw8mJrawvGGF6+fImBAwdCV1dXNPUmNjYWd+7cybcd58+fx7Nnz2BhYYGsrCwwxtCvXz/ExcVhy5Yt+Omnn6CmpobJkycDAJ9qKLu+RHZKpJubG7Zu3cqz9AUFBSE6Ohqurq48hfrz5895wJSRkYGLFy9i9erVSE9Ph729PVJSUngvfW47duzgQZ+skirGKuvbb7/low9qampo3bq13FqRiRMnii6SifIIo5gpOSNHOnp6qCtznhs3bhzCwsJgb2+P1atXwy4nPf+yxYt5oHI9Z1qqmqoqkNNxNDRniisArF2zBsjKggTA9evX+VrDWbNmYdCgQdi8eTM0NTVFQf7kyZN5cgsHBwf8+uuvMDU1lUtrLoyQPX/+HCoqKvyz2rt3b1SpUoWncQekWSCFwH7Tpk0AwEeWgf+mKMquXxaoq6sjNTUVv/32Gy/hIXj06BG+++47uceUNKFDVHY6Y4sWLXgwJRBGhYX/o7a2NlRVVXHlyhVRrUE1NTXRNPcvv/xSqe1XlujoaHzxxRe4c+cOzcapYCjYIhXGkydPsGjRolLJfFWeOXh5YUgeIxpCJrvo6GgsXLgQAwYMQEhICLp168YXmScmJmLfvn2IjY3F/PnzRdM+cheSLeu55wWN3HzOvv76awQGBgIQLwb/6aefRNPnhPs2btyItm3b8vo58fHx+OOPPxAeHg49PT3RxQkAvHnzpsA2NGnSBL6+vggPD0dWVhaysrJw9uxZZGRkYNy4cVBXV4dEIuGZC+fMmQNAnJBGW1sbJ0+ehEQiQXBwMADpBaJwUbVs2TKsXbuWJ7nw8vLij7W2tsbhw4excOFC1K5dG+Hh4dKMb3n466+/RIEnIA0Y3d3dC3ytRSU7VUxNTQ23b9/G5cuXRdskJibSetFSInwOzuak5m9ibi76H9WrV0/UEbdl40YAQBV1dYwePRqPHj1Cg5y1Vq0dHHiwJVsiQy8zE/D0REZGBubPn887FTZs2IDff/8dI0eORI0aNUTBVosWLUT1n6KiojBhwgRRZlADAwPs2rWL1zesXbs2DA0N0b9/f6xbtw5JSUn46aef+PbLli3jmQaF1y177s1rZMfT0xMWFhZ8X7KlOoR9lWTx4rw4Ozujffv2ou+GV69e8emRgi5dugD4b92bEGwB4iyKu3btQs+ePTF8+HA4ODiUyhpBZUhPT8eZM2ewZ8+esm4KKWFlGmytWLECjo6OfKqFq6uraEgekM7nd3d3R/Xq1aGrqws3Nze5LC2vX7/mNVWMjY15HRhZV69eRYsWLaCpqQkbGxscOHBA2S+PlLKoqCjcuXNHLpEDEft5wwb0zBkxyU1IZhEUFISlS5fyLGuampo82Nq4cSOvndS/f3+5ejM9e/ZE27ZtoaWlBQcHB1GPbGnp2LEj4uPjS+XCQVl8fHwASEe5ZNdw9ejRQ7TdV199hY4dOyIgIAD/+9//0L59e6SlpeHJkycYMGAAOnXqxKcAyirMeyMUDgUg6rFXVVWFj48Pb5eamhq0tLSwf/9+/rdAVVUVbm5uGDRoEGrVqgVAmuZZmDqVlpaGrKwsVKlSBXPnzuXPMXXqVKxevRrJycl4+vQpwsLCEBMTAx0dHWhqauLGjRuii+kFCxZAT09PdLEGSC8oZdfVKIMQAOb+3tm0aRM25lzUE+XiF9hZWTjVpg065lobpaOjI/5f5Bw7VTQ0MGHCBDRs2BDqOQFLxNu3PNgS7UNbG1rR0TAxMcG9e/ewYsUKAED79u0BAI0aNUJUVJQoWVP9+vX5tGxdXV188cUX+Pjxo+hYiY+PR+fOnWFlZcVHqi0sLPD48WN88803mD59uqhI799//82n2ynKRqijo4O0tDT+GGHt6smTJ6Grqyv3GSltUVFR8PHxkQu2AHE2QtnROjMzM9F9su9fnTp18PPPP2PPnj3Q0tISjayXJ8LrE0b5ScVRpsHWP//8A3d3d9y6dQteXl7IyMiAs7OzaAHpzJkz8eeff+LEiRP4559/5DLYZGVloU+fPkhPT4evry88PT1x4MAB0TSTly9fok+fPujatSsCAgLg4eGBCRMm4OLFi6X6eolyCVOFyvqL5HPn6OiY57QqBwcHGBgYiKZxAdLMa7JJMzQ0NJCWloZz587JFec8d+4cWrZsCS0tLSQkJJRZwKOMqWNlYe3atXz+PmNMLhtiUlIS4uLi4OnpiSFDhiAuLg5jx44VXYyMGDECderUET2ue/fucrflpqKigpiYGGRnZ6N169a8I0O4SBKK9aqrq+PXX39F9+7dRWnpBULWwk6dOmHQoEEwMDDgwfvp06fx559/QktLC5aWlnwt1tatW3Hq1CmkpKTwNZcaGhqQSCSYNGkS0tLSRMdW3759UbVqVbnP/7x580TZG0vKlClT+OekY8eOqFu3rtxza2pq5lkHiZQsnq0vKwt/HD2KPTkBTseOHQFIjx0hA6dAlTHoyayNaZVToLlz+/aATCBgYmKCL7/8Era2tkhNTeXnP4HQMfL48WOEhYXxbJy7d+9GdHQ0P6aHDh3KR2lkR6oEX331FTIzM3HixAk0adIET58+xe7duyGRSESfZ9m1sp06dcKyZctw/vx5fn9aWhrOnz/Pj8fly5fj559/xp9//ol3796VSir+/AQGBkJfX5+fP4D/Ag3Z7xlh9L127dqwt7cXfd5lZ1QMGDAAa9asQVJSEqpUqVLmr6+4hPNqeZ6RQfLAPiPR0dEMAPvnn38YY4zFxcUxdXV1duLECb7N48ePGQB28+ZNxhhj586dYyoqKiwyMpJvs337dqavr8/S0tIYY4zNmzePNW7cWPRcQ4cOZS4uLoVqV3x8PAPA4uPjP+n1EeV79+5dWTehXPv222+ZgYEBc3Z2ZgCY7CkiOTmZ9enThwFgq1at4p/F8PBw0T4AsGPHjrE//viDAWDe3t6l/TIqBOH9B8AyMjLy3M7T05N16dKFjRkzhm/v6urKrl27xiwtLdnRo0fZqFGjWO7T/YMHD9j06dML1ZZDhw6xuXPnst69ezN7e3uWmprKDAwM2IMHDxhjjJ06dYrNnj2bDRkyhGlpack93tDQkH3xxRfM0NCQt3H79u3M3NycNW7cmP3222/MysqKAWC//fYbA8AaNWrEunTpwn766Sc2dOhQBoCFhIQwxhgLDAxkhoaGjDHGWrduzQAwBwcHNmvWLP7dIPs+tm/fvlCvs7hevXrFADAfHx+lPg/JW1ZWFsPixQw9erC+ffuyVq1aiT5DwnfDmzdv2NGjRxkApvbDD+zvR4/4Ps6EhDAsXsx6f/EFQ82azOB//2MAmK6uLhs7dix7//49Y4yxmzdvMmNjY9H+hZ+LFy+yDx8+sH///ZcBYBoaGvzYzv0jWLx4sej26tWrs0OHDolu69ixI9/ewcGB/fbbb/zvkJAQ3jbGpN+Duc8b27ZtYwBYZGQk27NnDwPAunbtWvL/iEK4dOkSq1atGktNTeW3+fv7sxo1aoi2279/v8L3y9fXl8XFxfG/+/fvzwCwhQsXMgDM2NhY+S9CCVJTU5mxsTEbMWJEWTeFFEJRYoPPas2WkFpYGAL29/dHRkYGnJyc+DYNGjSApaUlT+V78+ZNNGnSRFTLx8XFBQkJCXx9wM2bN0X7ELbJKx1wWloaEhISRD+kfMhda4UUzbfffovJkyfzTFiC8+fP4/z582jbti1cXFwwb948Xv9F0XseERGBfv36wdLSskwSZFQECxcu5HWk8hsdVFdXx9WrV0UzAk6fPg1NTU00atQIQ4cORZs2beSmHzZp0oQvri/IyJEjsWbNGgQHB2Pt2rXQ1NSEmZkZ9PT0AEhTth86dAjZ2dlyWRAB6QjPixcvRJn5unfvDgcHBwQHB0NTU5NPnenfvz+v15WRkYHvv/8es2bNgr6+Pv9uWL58Oc88FhYWhn379iEjIwNdunSRy7AGiNdXKcOLnFpNQqptUvqEkZ+vxo5Fs2bNUL9+fSQkJOCbnNTuwlo+c3NzXrA7MzQUNWVGQaJ8fWFw+jS8z54F3r1DB39/TJ8+HR8/foSnpyevSdWmTRte7FvQrVs3AMCYMWPg5+fHk2KoqqryRDeC1atX81pQqampopHXFi1aQFVVlV+/CGRHwqZOncqT0Rw4cAANGjRA69at+f3CcgxFa5fU1NTQr18/zJgxAz179szj3VQuTU1NvH//nq9JFdoVGxsryjKauwaYoG3btqIMw8KIkKqqKm7evMlHGssbTU1NbNmyha9VIxXHZxNsZWdnw8PDA+3bt+dfWJGRkdDQ0BAttgakQ/qROQULIyMj5YqmCn8XtE1CQoKokrpgxYoVMDAw4D8WFhYl8hoJ+dwZGBhgxYoV6N69O1xcXHD27FkAwJ9//gk3NzcEBgbiwIED8PDwgKurK7y8vOS+0B0cHNCrVy8wxvD69WvRQmZSeG3atOGZ7PIqPAwgzy/mVq1a8alFcXFxCAsLK3ZbhKQzr1694uuTHj9+zFPrf/PNN4iMjMSCBQsUpmQX1vyNGDGC36atrc2zpWlra8PExAT6+vrw9PSEtrY2NDQ04OPjgw4dOkBPTw/x8fF8DaHsxdSOHTvQt29faGtrY9q0aXJJKgBxHT5lUFdXh4WFRYWZuloeCZ+R0JAQLFu2DNbW1tDT0+PrpYTgKDQ09L9i2MeOYWdOenQAePTgAeIDAnhh38jISAwcOJDfL3RCRUVF4enTp6Ln9/b2xvHjx6Gvr4/Q0FCebOjw4cOiQsHbtm3DzZs38ejRI9y/f5+vqxLWkd+7dw8pKSl8KpyQWU92H2PHjkW9evUAAL///jsAaVr5c+fOAQCvhyjbSTNy5EgA/2Uv3bRpE+bNm1fQ26oU7dq1w6NHj0RTooXPtuw6LiH1u42NTb5lPGSDrTZt2ojS6pcnmZmZWLVqleiYIxXDZxNsubu7IygoCEePHi3rpmDBggWIj4/nP7ziPCGVwLNnz3Dnzh1069aN954Ka4Zq1KgBU1NT+Pr6IigoSFS3SXD37l3Ur18fEokE9evXR/2cDF+kaMaPH8+TAeU3smVmZobAwEAcOXIE3bt3R7NmzQAAV65cwW+//QZAmhUv98VhUdja2vJEJ4rWIAmL9fPKPLl//36oq6vDz88PgPQCcuHChYiJicFPP/2Ebt26YfDgwUhISOABmNDJ5ePjg8OHD0MikfDizrLrOlxdXWFkZAQtLS28evUKMTExoue+fv260otbq6mpydVcIqVLCLZu5BzzsuURQkNDsXfvXgDyo5yy9YyENU4OOWu3/P39+QiVrDdv3ogClaZNm0JNTQ0tWrTA06dPRdkvu3XrJqq1FRQUhOvXr/N2CWu/Ro4cyZ/L0dER9vb2aNKkCWbNmgUAcHJyQkBAAIKCgjB8+HDcu3cPgPgzJ5S6UJSxMzU1Fb/++it0dXV59tCyIhQ1liVcZ8l23gkjgs+ePcu3s2jXrl0YMWJEua9NpaKignv37uH06dNl3RRSwj6LYGvq1Kk4e/Ysrly5wrNVAdJFgrKF6gRRUVF8AaGpqalcdkLh74K20dfXV3ixqKmpCX19fdEPIZXFqVOnkJiYiPnz5/P6JsLIhnChrampiXnz5hWYPvzJkycFJmEgignnrMJMw2zatCnu3LmD0aNH86k53bp141OPVqxY8UmFPnV0dBAfH4/Xr1+LahcJFC1ul/Xq1St8+PAB9evXR5cuXfDhwwfcv38fgDTVe2JiIhwdHbFw4UIMGTIELVq0EE2Lio6OBvDfhfKoUaPk6lcJF6W5k1RcuHAh35HBkiD0ylNynrKjoqIC/PgjkJN5U7ZQcL169fgxmvuCXPZvYZsPHz7w22Q/N8I1hYaGhmjEvmvXrsjMzISNjY1ccoMhQ4aIpgQeOXKEf4bu3LkDPz8/SCQSqKqqonv37gCk9aXq1q2LUaNGYdWqVVi5ciUiIiLQq1cvjBs3Djdv3uTPLxucCOUdYmNjsX79elE7rl69ilWrVkFFRQX9+vX77GbsWFhYQE1NTdSxJASl165dy/f8lZmZiY0bN2Lu3LlKb6cyCecpoVOKVBxlGmwxxjB16lT8/vvv8Pb2lisI2bJlS6irq4umhTx58gSvX79G27ZtAUjn7j58+JB/GQPSL299fX0+J7pt27ZyU0u8vLz4Pggh/1FVVcXgwYMB/Heh4eDgAC0tLT61RViXwzOAEaWZNm1aobb766+/+NQlQFoIVOj1Fi7miuvbb7/FrVu38Pvvv+e5DmTTpk15XsAJJTesra3x5s0b6OrqIiYmBi4uLvD390dYWBiysrIQFRXFb5OtQyR0igkXw05OThgyZIjoOfr06QMAfB2ZYNmyZfj++++L/doLQ5jiRMFW2RqTM1UOAB/VzU3oMBI6UWU7XHv27Inz58+L1jL9+++/aNq0KSZPnsxHjXP/n2XXPgYFBYnu8/b2xr59+wBIg/K4uDheDHv9+vXo378/Vq5cCYlEwj+vmzdvRoMGDTBv3jxcvHgRN27cQFpaGrp06YIhQ4bwrHuAtOixUJdJdg2U0PkgiI6ORlBQEBhjaNmyJR+N/lyYmZnJ1aRbuHAhfvnlF3Ts2DHf89eyZcswdOhQnj6+vKPzSAWk3Fwd+Zs8eTIzMDBgV69eZREREfwnOTmZbzNp0iRmaWnJvL29mZ+fH2vbti1r27Ytvz8zM5PZ2dkxZ2dnFhAQwC5cuMCMjIzYggUL+DYvXrxgOjo6bO7cuezx48ds27ZtTFVVlV24cKFQ7aRshKQy2bBhAzM3N2e7d+9mx44dU7jN3r17GQDRZ5WULEtLS7ksXPmZN28e337Xrl1s1KhRrE2bNiXSFldXV75v2axnAj8/P+bq6spiYmIUPv7kyZMMAGvQoAEDwEaOHMlq1arFMyg+efKE1apVS/Ravby8GADWqlUrtnz58gKzMjImzZiZnZ0tug0Ac3R0LMarLrywsDAGgEVHRyv1eUj+TE1NWfv27ZmxsTFbsmSJwm3S0tLY+fPnGQC2bds2ufs9PT3Zt99+ywAwNzc3nglz2LBhLCgoiDHGWEREhMLsggDYggULGACmpaXFALDvvvuOXbx4UbSNtra2wix72dnZbMWKFaxmzZr8NmGbWrVqsapVqzIrKyumra3NHj9+zLcRPisA2OHDh1nTpk3lzhubNm0q9LmkvFm0aBEDwNavX1/WTflkANiGDRvKuhmkEIoSG5TpJy+vk9X+/fv5NikpKWzKlCmsatWqTEdHhw0cOJBFRESI9hMWFsZ69erFtLW1WY0aNdjs2bPlvpSvXLnC7O3tmYaGBqtTp47oOQpCwRapTA4fPswAsLNnz/Lbbt++zcaOHctev37NGJOmutbW1pa7sCUl5/jx42z//v3MwMCgUNsLF3kA2O7du9mVK1fYrFmzSqQtsufnpKQkhduoqKiwly9fKrxPuNgUgraLFy+ya9eu8YvasLAwdvLkSWZlZcUfc/nyZR7QBwUFMXt7+2Idb8hJC69MQhmEzMxMpT4PyZ+GhgYLDQ1lANikSZPy3TYgIIABYG/fvuW3CSnhe/fuzQAwFxcXnjIdANu3bx/fdufOnaLPxbJly5iBgQH/e+PGjczDw4Nvv23bNnb69Gn24cMHtmjRIvbVV1+xly9fsj179rCBAwfy7by9vVnt2rX538L+AgICWMeOHRkAduTIEZaYmMgYk5a6kW3H119/rbCTZseOHRU22Fq3bh0DwDZt2lTWTflkO3fuZH5+fmXdDFII5Sb1O5MGe3I/slNhtLS0sG3bNrx//x5JSUk4deqU3JxoKysrnDt3DsnJyYiJicHatWvlFmp36dIF9+/fR1paGp4/fy56DkLIf4YPH4769euLEiGEh4fD09OTT805f/48Zs6cWWYFiyuDly9fYvr06bwkRkGERCRdu3bFgQMH8OrVK6yTybT2KWTPuYoSZLi7uyM7OzvPBBnOzs7o3Lkzz6jWqVMnMMZ49jRjY2MkJCSIMhkK0wF1dHRgbW2N+/fvF/t4a9y4cbEeV1jCFCdlrw0j+UtPT+cp3gta6yikDheOQeC/DMbC8RIfH4+RI0fy0jFCkpnk5GTRNFdAuhZLSCkPAB4eHnyqHyAtgj1gwAAYGhpiy5Yt2LdvH9zc3NCgQQPRGp1u3brh5cuX/O/Ro0fD2NgYjRo1wo8//ogaNWpg2LBhfAr33bt3+bb5FcPt1KmTqD0VifC//JSp0p+LDRs2ULmUCoi+GQghIsHBwQgNDRVdVMte+ALS5A3COgGiHPPnz+fZ9wpjwIABGDt2LF6+fImgoCBMmTIF3t7eJdIWOzs7viZK0QWNUOMrr4ud9+/f459//uG1DUNDQ9G5c2c8ePAAI0eOhLa2NpYuXSpKZCTU1AKkpQeKG2g9ePAAixYtKtZjC0vIKKfsel6kYFevXkWTJk3kalvlJpzfZDsIhLUyQtBy69Yt/Pnnn/Dy8sIff/zBs1pmZGRgzZo12LBhA8LCwnDixAmkp6cjPT1d9BzLli1T+Nw1a9YEIE3z3qFDB7k1Or169eK/Dxw4ECYmJtDT08OSJUsQGxsLiUTC19PKfuY+fPjA69XlrjdnaGgoCgYrkvHjx2PixImic0Z5FRISgjt37pR1M0gJo2CLECIi1DCSTTQg9KIKiQDCw8NFSWmI8hQ2nfGLFy9Qv359nmwiOTkZb9++LZE2pKSk4N9//0VWVpbC+4We2LwSpgijAJ07d0bnzp1FWQtjY2MBSIsZz5kzh99et25djBkzBgAKPbqnyL59+/D8+fNiP74w7Ozs8PTp0wrRs17eGRgY4OHDhwUWtlU0Qiscx7JJM4TR1n79+vFzohAceXl5wcrKCl988QVCQ0MxduxYjB49usA21q9fnwdcgDhgMjAwwKpVq/jfFy9exOjRo5GWliZKviFbW0qQlpaGw4cPA5B+nmQFBgYWqm3lUVxcHEaPHo3hw4eXdVNKBFNQGJ6UbxRsEUJEVFVV0aNHD9jb2/PbrKysAIAX0ly+fDn/UifKERkZie+++46PKBXE19eXZ90TsqnJXtB9iiFDhuDWrVt5FlDW0tLC3Llz5TIBCjQ1NSGRSCCRSPDPP//AzMyM73fXrl0AgJiYGD7yJRBGirp161bsQqUbN25UejZCAOW2kGpF0rVrV9jY2ODMmTOYNGlSvtvq6+tDS0tLNPWua9eu8Pb25kWBAcWZ4YTbFF0U7969u8DP3alTp2BmZob3798DABYvXszvi4+Px5IlS/jf165dw4ULFwBIR4iF2oeCQYMGieoyTZkyBQDw3XffibYTCgRXRI8fP0aHDh0+qZbg5+LRo0cYP358WTeDlDAKtgghIioqKvDy8hKNJpibm4MxxsszGBkZVZhexM+ViYkJqlatmmcK69yEqXwA+IhQSaXmF1JbC3Vvcps+fTquXbuG5ORkhfdramqCMYaDBw8C+K9XvlatWrC0tAQgDRZzj0Z4eHjAw8MD9erV+6QLKWGaH6nYrl69Cg0NDfTv3593EOVFTU0NKSkpojTvdevWxaVLl3iNrjZt2igcARNGk2RHwCZOnAgA+OKLL/Du3TsA0qm9irx8+RJ///03H7Xu0aMHv2/nzp2iEazHjx/z6cBZWVkwNjYW7atHjx6iEWw9PT2Ym5vLPaeTk1OJdb58boQ6dwWNZpYHDRs2pLWfFZDi1cyEkEpLuPiNiYnhC49J2XB0dJSrP5gX2elEUVFRWLx4cYkVLn3x4kW+9+vp6eH27dt5Tn8RRryaNWvGR698fX1ha2vLt1myZAl++eUX0eNatmyJli1bfkrTSSVSvXp1NGjQoNDbSyQS3LlzB46OjgCkBbCXL1+OnTt3ApCuc5Id5ZKVex1gnz59cOvWLZw9exaANKCKi4tT+FhhOjZjDD/88AOmTJmC48ePA5B2dilan/j8+XMcPnwYgwcPhr+/P79927ZtmDp1KgBpp5iqqipev37NC5oLmjZtWmLTij83wujkp0w3JkSZKHwmhIh069YNABVW/Bx4e3uLMpPlR+jJd3BwgJ+fH+Li4kqsJ7ugC1hhLYiiUQBAehFYq1YtNG7cGDVq1AAgLTYvu6A9NDQUoaGhosdNnjz5kzNeamtro3Pnzp+0D1I+xMbGIjg4uEiPCQkJ4b8LI6BWVlY4fPgwEhIS5EaSBIsXLxZN/+vduzd8fX3537Vr1+ZFkPMikUhgamrKpxMCwNdffy1q04ABA9CjRw9IJBLMnz8ftra2GDFiBL9ftpBv//790bp1awDAhAkT8n3uikQYwf/48WMZt4QQxSjYIoSI3Lt3DwAFW5+DwgZaANChQwesXr0afn5+ePXqFTZu3FikbIb5adq0KVq0aIG6desqvH/BggXYvXt3nqnf09LS8ObNG9y9e5cnxMjt9OnTePPmjeg22YvQ4nry5InSsxGSz0dRp5s2b96c/y4cv1ZWVlBVVYWvr2+eU2dzU1VV5dlaBYXpKHB3d5f7nLu6uvLfe/ToARcXF9SpUwf9+vVT+LyC7du3Y//+/QAKTn1f0cyYMQPt2rUr62YQohAFW4QQEWEhtex6BFI22rVrl+doUW41a9bE3LlzAfxXL6ikLrhCQkJQvXp1udpCgm7duuXbky6kqe7atSuaNm2qcJuRI0di1KhRotvmzp2LadOmFbPVUhYWFhUiJTQpHCH5SmEwxmBnZ8f/FoIjIdgCir7ucfny5UXaXmiHoEGDBpg8eTL/293dHbNnzwagOJDMnQHz0KFDACpfsLVx48Y8p3wSUtYo2CKEiOjo6MDR0ZHWa30GjI2N5bKPFYbQW1/YQK0gnTt3FmWnLCqhHc+fP+dFZ3MLCQmBl5eX6DYHBwdeN4iQgtjY2IjWARZVu3bt4OfnB21tbX68GxoaFmkfX331FV/3WliymQNDQkLg4eGhcDtFRYuHDBmCGzduAAA2b96MY8eOwcrKqtAlIwghykfBFiFEJDk5GXfv3s2zphIpPTVr1kStWrWK9BgNDQ2eVOJT1zsJtmzZgpMnTxb78cL0LKFmkSLOzs4YNmxYsZ+DEMZYkYMjWYaGhvyzIwQ2Re10at26NV6/fs0LIBfExsZGND338OHDeb4GRVkGGzduzEezevbsCSMjI7i7u1e6kS1CPmeUjZAQIiJ80VP62bLXpk0b/P7770V6THp6OjIzM7F3794Sa8fs2bOLHPTlFhwcjEaNGmHNmjUK7x81apTcNEJCiiKvaa7Fcf/+fTg4OBQ5eBMSVuSuc5UXZ2dnHDt2jE+BCw0NRVRUlNx2Pj4+CpN1rF27Ftu3bwfwXzr7efPm8SnFhJCyR8EWIUSkYcOG0NDQKLFREVK6TE1N0bZtW7Rq1arE9rl27dpP3kejRo1KoCWElI4PHz7g2bNnxe500tPTy7MUgqzY2FhUqVKF/y2b4VBWXskfAgMDAUhHtM3NzeHo6Ig//vij6A0mhCgNdV0TQkT8/PyQnp5e1s0gxRQZGUkpkAn5RIGBgXnWySpJx48fx6VLl/jfrVq1QosWLQr9+I0bN2Lt2rUwNzeHmpoaBg4cyOvaEUI+DzSyRQgRSU5OLusmkE9UUvW1CKmsvvrqK153qyiOHDmCESNGFGpUSyC7bcuWLYu0TszY2Bi2trZ4+fIlYmJi8ObNmxIr+UAIKRk0skUIEbGyskK9evXKuhmkmBhjBRYhJoTkr2bNmvjxxx+L/Lhhw4ahZ8+ehd7e0dERU6ZM4X/7+flh5cqVRXrO7t27A5Cmge/evbtcVk9CSNmikS1CiMjr16+LXBiUEEKI1I4dO3D79u1CbWtmZiYqhjxp0qQiJ+UQEmqoqalBTU0NTk5ORXo8IUS5KNgihIgMHToUNWrUKOtmEEJIuaSrq1vodVcLFiwQ1c8KDQ2Fj49PkZ7P398fgHyBY0LI54GmERJCRHR0dNCvX7+ybgYhhJRLM2bMgI2NTaG2bdOmDaytrfnfqqqqRc4EW61aNdSoUQO6urpFehwhpHRQsEUIIYQQUkI+JZvrsmXLipxNNDs7G7GxscV+TkKIctE0QkIIIYSQErJu3Tr079+/1J5PyCCbnZ1NUwkJ+QzRyBYhhBBCSAmpVasWRo4cWWrP16xZMwAodgFmQohy0SeTEEIIIaScioiIAIAir/UihJQOCrYIIYQQQsqpsLCwsm4CISQfFGwRQgghhJRT9erVg5GRUVk3gxCSBwq2CCGEEELKqaSkJMTExJR1MwgheaBgixBCCCGknKpduzZatWpV1s0ghOSBgi1CCCGEkHKqVq1auH37dlk3gxCSBwq2CCGEEEIIIUQJKNgihBBCCCGEECWgYIsQQgghhBBClICCLUIIIYQQQghRAgq2CCGEEEIIIUQJKNgihBBCCCGEECWgYIsQQgghhBBClICCLUIIIYQQQghRArWybkB5wBgDACQkJJRxSwghhBBCCCFlSYgJhBghPxRsFUJiYiIAwMLCooxbQgghhBBCCPkcJCYmwsDAIN9tJKwwIVkll52djXfv3kFPTw8SiaSsm1NuJSQkwMLCAuHh4dDX1y/r5pBKjo5HUlx07JDyiI5bUlx07MhjjCExMRE1a9aEikr+q7JoZKsQVFRUUKtWrbJuRoWhr69PH1by2aDjkRQXHTukPKLjlhQXHTtiBY1oCShBBiGEEEIIIYQoAQVbhBBCCCGEEKIEFGyRUqOpqYlFixZBU1OzrJtCCB2PpNjo2CHlER23pLjo2Pk0lCCDEEIIIYQQQpSARrYIIYQQQgghRAko2CKEEEIIIYQQJaBgixBCCCGEEEKUgIItQgghhBBCCFECCrYqoBUrVsDR0RF6enowNjaGq6srnjx5ItomNTUV7u7uqF69OnR1deHm5oaoqCh+f2BgIIYPHw4LCwtoa2ujYcOG2LRpk9xzXb16FS1atICmpiZsbGxw4MCBAtvHGMP//vc/mJmZQVtbG05OTnj69KnCbdPS0mBvbw+JRIKAgIB89xsREYERI0agfv36UFFRgYeHh9w2Bw4cgEQiEf1oaWkV2GZSfBXheLS2tpY7blauXFngvgtqz7Vr19CvXz/UrFkTEokEp0+fLnCflUllPXboXFa+VYTjFgD++usvtG7dGtra2qhatSpcXV0L3PeDBw/QsWNHaGlpwcLCAqtXrxbdHxwcDDc3N/652LhxY4H7rEwq67GTmpqKcePGoUmTJlBTU1O4/dWrV+XOeRKJBJGRkQW2u8wxUuG4uLiw/fv3s6CgIBYQEMB69+7NLC0t2cePH/k2kyZNYhYWFuzy5cvMz8+PtWnThrVr147fv3fvXjZ9+nR29epV9vz5c/brr78ybW1ttmXLFr7NixcvmI6ODps1axZ79OgR27JlC1NVVWUXLlzIt30rV65kBgYG7PTp0ywwMJD179+f1a5dm6WkpMhtO336dNarVy8GgN2/fz/f/b58+ZJNnz6deXp6Mnt7ezZjxgy5bfbv38/09fVZREQE/4mMjMx3v+TTVITj0crKii1dulR03Mi2X5HCtOfcuXPs+++/Z6dOnWIA2O+//17Yt7VSqKzHDp3LyreKcNyePHmSVa1alW3fvp09efKEBQcHs2PHjuW73/j4eGZiYsJGjhzJgoKC2JEjR5i2tjbbuXMn3+bOnTtszpw57MiRI8zU1JRt2LChsG9rpVBZj52PHz+ySZMmsV27djEXFxc2YMAAuW2uXLnCALAnT56IzntZWVkFva1ljoKtSiA6OpoBYP/88w9jjLG4uDimrq7OTpw4wbd5/PgxA8Bu3ryZ536mTJnCunbtyv+eN28ea9y4sWiboUOHMhcXlzz3kZ2dzUxNTdmaNWv4bXFxcUxTU5MdOXJEtO25c+dYgwYNWHBwcKGCLVmdO3fO8wLFwMCg0PshJa88Ho9WVlZFvigoanso2CpYZTl2ZNG5rPwrb8dtRkYGMzc3Z3v27CnS6/z5559Z1apVWVpaGr9t/vz5zNbWVuH2n/rZqAwqy7Eja+zYsfkGWx8+fCj2vssKTSOsBOLj4wEA1apVAwD4+/sjIyMDTk5OfJsGDRrA0tISN2/ezHc/wj4A4ObNm6J9AICLi0u++3j58iUiIyNFjzMwMEDr1q1Fj4uKisLXX3+NX3/9FTo6OoV8pYXz8eNHWFlZwcLCAgMGDEBwcHCJ7p/krzwejwCwcuVKVK9eHc2bN8eaNWuQmZmZ7+ssTntI/irLsVNYdC4rH8rbcXvv3j28ffsWKioqaN68OczMzNCrVy8EBQXl+zpv3ryJTp06QUNDQ9SeJ0+e4MOHD/k+lihWWY6dorC3t4eZmRl69OgBHx+fEtuvMlGwVcFlZ2fDw8MD7du3h52dHQAgMjISGhoaMDQ0FG1rYmKS59xXX19fHDt2DN988w2/LTIyEiYmJnL7SEhIQEpKisL9CPtX9DjhPsYYxo0bh0mTJsHBwaHwL7YQbG1tsW/fPpw5cwYHDx5EdnY22rVrhzdv3pTo8xDFyuPxCADTp0/H0aNHceXKFUycOBHLly/HvHnz8n2txWkPyVtlOnYKg85l5UN5PG5fvHgBAFi8eDEWLlyIs2fPomrVqujSpQvev3+f52vNqz2yz0sKrzIdO4VhZmaGHTt24LfffsNvv/0GCwsLdOnSBffu3fuk/ZYGCrYqOHd3dwQFBeHo0aPF3kdQUBAGDBiARYsWwdnZudCPO3ToEHR1dfnP9evXC/W4LVu2IDExEQsWLMhzG9n9Tpo0qdBtatu2LcaMGQN7e3t07twZp06dgpGREXbu3FnofZDiK4/HIwDMmjULXbp0QdOmTTFp0iSsW7cOW7ZsQVpaGoDiH4+k8OjYEaNzWflQHo/b7OxsAMD3338PNzc3tGzZEvv374dEIsGJEycAAI0bN+b77dWrV9FfFCkQHTtitra2mDhxIlq2bIl27dph3759aNeuHTZs2FDofZQVtbJuAFGeqVOn4uzZs7h27Rpq1arFbzc1NUV6ejri4uJEvSNRUVEwNTUV7ePRo0fo3r07vvnmGyxcuFB0n6mpqSgDjrAPfX19aGtro3///mjdujW/z9zcHBEREXw7MzMz0ePs7e0BAN7e3rh58yY0NTVF+3ZwcMDIkSPh6ekpykyor69f+DclF3V1dTRv3hzPnj0r9j5I4ZTX41GR1q1bIzMzE2FhYbC1tVV4PBbUHlJ4le3YKQ46l31+yutxK9zeqFEjfr+mpibq1KmD169fAwDOnTuHjIwMAODns7zaI9xHCq+yHTvF1apVK9y4ceOT9lEqynrRGCl52dnZzN3dndWsWZOFhobK3S8ssDx58iS/LSQkRG6BZVBQEDM2NmZz585V+Dzz5s1jdnZ2otuGDx9eqAWWa9eu5bfFx8eLFli+evWKPXz4kP9cvHiRAWAnT55k4eHhhXoP8lpUnltmZiaztbVlM2fOLNR+SdGV9+NRkYMHDzIVFRX2/v37PLcpantACTLkVNZjRxady8qf8n7cCn/LJjlIT09nxsbGosyCuQkJMtLT0/ltCxYsoAQZRVBZjx1ZeSXIUMTJyYkNHDiwUNuWJQq2KqDJkyczAwMDdvXqVVF6zOTkZL7NpEmTmKWlJfP29mZ+fn6sbdu2rG3btvz+hw8fMiMjIzZq1CjRPqKjo/k2QurQuXPnssePH7Nt27YVOnWooaEhO3PmDHvw4AEbMGBAnqnfGZOmQUYhsxHev3+f3b9/n7Vs2ZKNGDGC3b9/nwUHB/P7lyxZwi5evMieP3/O/P392bBhw5iWlpZoG1Kyyvvx6OvryzZs2MACAgLY8+fP2cGDB5mRkREbM2ZMvvstTHsSExP5MQuArV+/nt2/f5+9evWqSO9xRVVZjx3G6FxWnpX345YxxmbMmMHMzc3ZxYsXWUhICBs/fjwzNjbOt5MgLi6OmZiYsNGjR7OgoCB29OhRpqOjI7rITktL48e2mZkZmzNnDrt//z57+vRpkd7jiqqyHjuMMRYcHMzu37/P+vXrx7p06cKPE8GGDRvY6dOn2dOnT9nDhw/ZjBkzmIqKCrt06VJh394yQ8FWBQRA4c/+/fv5NikpKWzKlCmsatWqTEdHhw0cOJBFRETw+xctWqRwH1ZWVqLnunLlCrO3t2caGhqsTp06oufIS3Z2Nvvhhx+YiYkJ09TUZN27d2dPnjzJc/uiBFsFtdnDw4NZWloyDQ0NZmJiwnr37s3u3btX4H5J8ZX349Hf35+1bt2aGRgYMC0tLdawYUO2fPlylpqaWuC+C2qPkMo298/YsWML3HdlUJmPHTqXlV/l/bhlTDoaMXv2bGZsbMz09PSYk5MTCwoKKnDfgYGBrEOHDkxTU5OZm5uzlStXiu4Xvs9z/3Tu3LnAfVcGlfnYsbKyUthuwapVq1jdunWZlpYWq1atGuvSpQvz9vYucL+fAwljjBV2yiEhhBBCCCGEkMKhbISEEEIIIYQQogQUbBFCCCGEEEKIElCwRQghhBBCCCFKQMEWIYQQQgghhCgBBVuEEEIIIYQQogQUbBFCCCGEEEKIElCwRQghhBBCCCFKQMEWIYQQQgghhCgBBVuEEEIIIYQQogQUbBFCCKl0xo0bB4lEAolEAnV1dZiYmKBHjx7Yt28fsrOzC72fAwcOwNDQUHkNJYQQUq5RsEUIIaRS6tmzJyIiIhAWFobz58+ja9eumDFjBvr27YvMzMyybh4hhJAKgIItQgghlZKmpiZMTU1hbm6OFi1a4LvvvsOZM2dw/vx5HDhwAACwfv16NGnSBFWqVIGFhQWmTJmCjx8/AgCuXr2KL7/8EvHx8XyUbPHixQCAtLQ0zJkzB+bm5qhSpQpat26Nq1evls0LJYQQUmYo2CKEEEJydOvWDc2aNcOpU6cAACoqKti8eTOCg4Ph6ekJb29vzJs3DwDQrl07bNy4Efr6+oiIiEBERATmzJkDAJg6dSpu3ryJo0eP4sGDBxg8eDB69uyJp0+fltlrI4QQUvokjDFW1o0ghBBCStO4ceMQFxeH06dPy903bNgwPHjwAI8ePZK77+TJk5g0aRJiY2MBSNdseXh4IC4ujm/z+vVr1KlTB69fv0bNmjX57U5OTmjVqhWWL19e4q+HEELI50mtrBtACCGEfE4YY5BIJACAS5cuYcWKFQgJCUFCQgIyMzORmpqK5ORk6OjoKHz8w4cPkZWVhfr164tuT0tLQ/Xq1ZXefkIIIZ8PCrYIIYQQGY8fP0bt2rURFhaGvn37YvLkyVi2bBmqVauGGzduYPz48UhPT88z2Pr48SNUVVXh7+8PVVVV0X26urql8RIIIYR8JijYIoQQQnJ4e3vj4cOHmDlzJvz9/ZGdnY1169ZBRUW6xPn48eOi7TU0NJCVlSW6rXnz5sjKykJ0dDQ6duxYam0nhBDy+aFgixBCSKWUlpaGyMhIZGVlISoqChcuXMCKFSvQt29fjBkzBkFBQcjIyMCWLVvQr18/+Pj4YMeOHaJ9WFtb4+PHj7h8+TKaNWsGHR0d1K9fHyNHjsSYMWOwbt06NG/eHDExMbh8+TKaNm2KPn36lNErJoQQUtooGyEhhJBK6cKFCzAzM4O1tTV69uyJK1euYPPmzThz5gxUVVXRrFkzrF+/HqtWrYKdnR0OHTqEFStWiPbRrl07TJo0CUOHDoWRkRFWr14NANi/fz/GjBmD2bNnw9bWFq6urrh79y4sLS3L4qUSQggpI5SNkBBCCCGEEEKUgEa2CCGEEEIIIUQJKNgihBBCCCGEECWgYIsQQgghhBBClICCLUIIIYQQQghRAgq2CCGEEEIIIUQJKNgihBBCCCGEECWgYIsQQgghhBBClICCLUIIIYQQQghRAgq2CCGEEEIIIUQJKNgihBBCCCGEECWgYIsQQgghhBBClOD/A7gWIzNrc5QAAAAASUVORK5CYII=" 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 <p>Convert the smoothed neutron counts to Volumetric Water Content (VWC) using the <a href="../../../reference/#crnpy.crnpy.counts_to_vwc"><code>counts_to_vwc()</code></a>. The function considers the smoothed neutron counts, $N_0$ specific parameter, soil bulk density, weight percent of latent water (Wlat), and weight percent of soil organic carbon (Wsoc). After conversion, plot the VWC against the timestamp for visual analysis. <a href="../../../reference/#crnpy.crnpy.smooth_1d"><code>smooth_1d()</code></a> is applied for smoothing the data using a Savitzky-Golay filter.</p>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Device N0 parameter</span>
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Device N0 parameter</span>
 <span class="n">N0_rdt</span> <span class="o">=</span> <span class="mi">3767</span> <span class="c1"># Patrignani, A., Ochsner, T. E., Montag, B., &amp; Bellinger, S. (2021). A novel lithium foil cosmic-ray neutron detector for measuring field-scale soil moisture. Frontiers in Water, 3, 673185.</span>
 
 <span class="c1"># Estimate lattice water (%) based on texture</span>
 <span class="n">lattice_water</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">lattice_water</span><span class="p">(</span><span class="n">clay_content</span><span class="o">=</span><span class="mf">0.35</span><span class="p">)</span>
 
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">counts_to_vwc</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;total_corrected_neutrons&#39;</span><span class="p">],</span> <span class="n">N0</span><span class="o">=</span><span class="n">N0_rdt</span><span class="p">,</span> <span class="n">bulk_density</span><span class="o">=</span><span class="mf">1.33</span><span class="p">,</span> <span class="n">Wlat</span><span class="o">=</span><span class="n">lattice_water</span><span class="p">,</span> <span class="n">Wsoc</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'VWC'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">counts_to_vwc</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'total_corrected_neutrons'</span><span class="p">],</span> <span class="n">N0</span><span class="o">=</span><span class="n">N0_rdt</span><span class="p">,</span> <span class="n">bulk_density</span><span class="o">=</span><span class="mf">1.33</span><span class="p">,</span> <span class="n">Wlat</span><span class="o">=</span><span class="n">lattice_water</span><span class="p">,</span> <span class="n">Wsoc</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
 
 <span class="c1"># Drop any NaN beofre smoothing</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s1">'VWC'</span><span class="p">])</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
 
 <span class="c1"># Filter using the Savitzky-Golay filter, drop NaN values and timestamps</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">smooth_1d</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">],</span> <span class="n">window</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;savitzky_golay&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'VWC'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">smooth_1d</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'VWC'</span><span class="p">],</span> <span class="n">window</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"savitzky_golay"</span><span class="p">)</span>
 </pre></div>
-<div id="cell-15" class="clipboard-copy-txt"># Device N0 parameter
-N0_rdt = 3767 # Patrignani, A., Ochsner, T. E., Montag, B., & Bellinger, S. (2021). A novel lithium foil cosmic-ray neutron detector for measuring field-scale soil moisture. Frontiers in Water, 3, 673185.
+<div class="clipboard-copy-txt" id="cell-15"># Device N0 parameter
+N0_rdt = 3767 # Patrignani, A., Ochsner, T. E., Montag, B., &amp; Bellinger, S. (2021). A novel lithium foil cosmic-ray neutron detector for measuring field-scale soil moisture. Frontiers in Water, 3, 673185.
 
 # Estimate lattice water (%) based on texture
 lattice_water = crnpy.lattice_water(clay_content=0.35)
@@ -2632,51 +1926,46 @@ <h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" hre
 # Filter using the Savitzky-Golay filter, drop NaN values and timestamps
 df['VWC'] = crnpy.smooth_1d(df['VWC'], window=11, order=3, method="savitzky_golay")
 </div>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Plot the obtained time series of volumetric water content</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Plot the obtained time series of volumetric water content</span>
 <span class="c1"># Create a new figure and plot the data</span>
 <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>  <span class="c1"># Set the figure size</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;teal&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'VWC'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'teal'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
 
 <span class="c1"># Set the labels for the x-axis and y-axis</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Date&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;Volumetric Water Content ($m^3 \cdot m^{-3}$)&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Date"</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">"Volumetric Water Content ($m^3 \cdot m^{-3}$)"</span><span class="p">)</span>
 
 <span class="c1"># Set the title of the plot</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Soil Moisture&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Soil Moisture'</span><span class="p">)</span>
 
 <span class="c1"># Show the plot</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
-<div id="cell-16" class="clipboard-copy-txt"># Plot the obtained time series of volumetric water content
+<div class="clipboard-copy-txt" id="cell-16"># Plot the obtained time series of volumetric water content
 # Create a new figure and plot the data
 fig, ax = plt.subplots(figsize=(10, 4))  # Set the figure size
 ax.plot(df['timestamp'], df['VWC'], color='teal', linewidth=1.0)
@@ -2691,140 +1980,107 @@ <h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" hre
 # Show the plot
 plt.show()
 </div>
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-
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" 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-<h3 id="sensing-depth-and-soil-water-storage">Sensing depth and soil water storage<a class="anchor-link" href="#sensing-depth-and-soil-water-storage">&#182;</a></h3><p>Estimate the <a href="https://soilwater.github.io/crnpy/reference/#crnpy.crnpy.sensing_depth"><code>sensing_depth()</code></a>  at which 86 % of the neutrons probes the soil profile, and estimate the soil water <a href="https://soilwater.github.io/crnpy/reference/#crnpy.crnpy.storage"><code>storage()</code></a> of the top 50 cm using and exponential filter.</p>
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+<h3 id="sensing-depth-and-soil-water-storage">Sensing depth and soil water storage<a class="anchor-link" href="#sensing-depth-and-soil-water-storage">¶</a></h3><p>Estimate the <a href="https://soilwater.github.io/crnpy/reference/#crnpy.crnpy.sensing_depth"><code>sensing_depth()</code></a>  at which 86 % of the neutrons probes the soil profile, and estimate the soil water <a href="https://soilwater.github.io/crnpy/reference/#crnpy.crnpy.storage"><code>storage()</code></a> of the top 50 cm using and exponential filter.</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Estimate sensing depth</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;sensing_depth&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">sensing_depth</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;barometric_pressure_Avg&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;barometric_pressure_Avg&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">bulk_density</span><span class="o">=</span><span class="mf">1.33</span><span class="p">,</span> <span class="n">Wlat</span><span class="o">=</span><span class="n">lattice_water</span><span class="p">,</span> <span class="n">method</span> <span class="o">=</span> <span class="s2">&quot;Franz_2012&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Average sensing depth: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;sensing_depth&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span><span class="mi">2</span><span class="p">)</span><span class="si">}</span><span class="s2"> cm&quot;</span><span class="p">)</span>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Estimate sensing depth</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'sensing_depth'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">sensing_depth</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'VWC'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'barometric_pressure_Avg'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'barometric_pressure_Avg'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">bulk_density</span><span class="o">=</span><span class="mf">1.33</span><span class="p">,</span> <span class="n">Wlat</span><span class="o">=</span><span class="n">lattice_water</span><span class="p">,</span> <span class="n">method</span> <span class="o">=</span> <span class="s2">"Franz_2012"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Average sensing depth: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'sensing_depth'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span><span class="mi">2</span><span class="p">)</span><span class="si">}</span><span class="s2"> cm"</span><span class="p">)</span>
 </pre></div>
-<div id="cell-17" class="clipboard-copy-txt"># Estimate sensing depth
+<div class="clipboard-copy-txt" id="cell-17"># Estimate sensing depth
 df['sensing_depth'] = crnpy.sensing_depth(df['VWC'], df['barometric_pressure_Avg'], df['barometric_pressure_Avg'].mean(), bulk_density=1.33, Wlat=lattice_water, method = "Franz_2012")
 print(f"Average sensing depth: {np.round(df['sensing_depth'].mean(),2)} cm")
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 <pre>Average sensing depth: 15.36 cm
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Compute the storage using the exponential filter</span>
-<span class="n">surface</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">]</span>
-<span class="n">subsurface</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">exp_filter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">])</span>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Compute the storage using the exponential filter</span>
+<span class="n">surface</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'VWC'</span><span class="p">]</span>
+<span class="n">subsurface</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">exp_filter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'VWC'</span><span class="p">])</span>
 
 <span class="n">z_surface</span> <span class="o">=</span> <span class="mi">150</span> <span class="c1"># Average depth in mm obtained from previous cell using crnpy.sensing_depth()</span>
 <span class="n">z_subsurface</span> <span class="o">=</span> <span class="mi">350</span> <span class="c1"># Arbitrary subsurface depth in mm</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;storage&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="n">surface</span><span class="o">*</span><span class="n">z_surface</span><span class="p">,</span> <span class="n">subsurface</span><span class="o">*</span><span class="n">z_subsurface</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'storage'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="n">surface</span><span class="o">*</span><span class="n">z_surface</span><span class="p">,</span> <span class="n">subsurface</span><span class="o">*</span><span class="n">z_subsurface</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 </pre></div>
-<div id="cell-18" class="clipboard-copy-txt"># Compute the storage using the exponential filter
+<div class="clipboard-copy-txt" id="cell-18"># Compute the storage using the exponential filter
 surface = df['VWC']
 subsurface = crnpy.exp_filter(df['VWC'])
 
@@ -2832,51 +2088,46 @@ <h3 id="sensing-depth-and-soil-water-storage">Sensing depth and soil water stora
 z_subsurface = 350 # Arbitrary subsurface depth in mm
 df['storage'] = np.sum([surface*z_surface, subsurface*z_subsurface], axis=0)
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Plot the obtained time series of soil water storage</span>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Plot the obtained time series of soil water storage</span>
 <span class="c1"># Create a new figure and plot the data</span>
 <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>  <span class="c1"># Set the figure size</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;storage&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;teal&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'storage'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'teal'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
 
 <span class="c1"># Set the labels for the x-axis and y-axis</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Date&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Storage (mm)&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Date"</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Storage (mm)"</span><span class="p">)</span>
 
 <span class="c1"># Set the title of the plot</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Soil Water Storage&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Soil Water Storage'</span><span class="p">)</span>
 
 <span class="c1"># Show the plot</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
-<div id="cell-19" class="clipboard-copy-txt"># Plot the obtained time series of soil water storage
+<div class="clipboard-copy-txt" id="cell-19"># Plot the obtained time series of soil water storage
 # Create a new figure and plot the data
 fig, ax = plt.subplots(figsize=(10, 4))  # Set the figure size
 ax.plot(df['timestamp'], df['storage'], color='teal', linewidth=1.0)
@@ -2891,87 +2142,67 @@ <h3 id="sensing-depth-and-soil-water-storage">Sensing depth and soil water stora
 # Show the plot
 plt.show()
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GkgtX74cc+fOxaRJkxAMBnH77bfj1FNPxZYtW2CxWFBZWYnKyko88MADGD16NCoqKvDrX/8alZWV+O9//wsACIVCOPPMM1FWVoZvvvkGBw8exJVXXgmdToe///3vSR1HZWUlysvLM/lWCYIgCIIgCILoQezbtw/9+/eP+3xWmU3U1NSgpKQEy5cvx7Rp0xS3efPNNzF79my4XC5otVp8/PHHOOuss1BZWYnS0lIAwFNPPYXbbrsNNTU1iu5DPp8PPp+P/+50OjFgwADs27cPdrs9M2+OIAiCIAiCIIisp6mpCeXl5WhsbIzIgosmq+zPnU4nAKCgoCDhNna7naftrVq1CuPGjeNBFACcdtppuOGGG7B582ZMmDAhZh/33nsv7r777pjH7XY7BVIEQRAEQRAEQbRb8pM1ZhPhcBg333wzjjvuOIwdO1Zxm9raWtxzzz247rrr+GOHDh2KCKIA8N8PHTqkuJ8FCxbA6XTyn3379qXpXRAEQRAEQRAE0RvIGkVq7ty52LRpE1asWKH4fFNTE84880yMHj0ad911V6dey2AwwGAwdGofBEEQBEEQBEH0XrJCkZo3bx4+/PBDLF26VLGgq7m5GaeffjpsNhveeecd6HQ6/lxZWRmqqqoitme/l5WVZfbACYIgCIIgCILolXRrICVJEubNm4d33nkHS5YsweDBg2O2aWpqwqmnngq9Xo/3338fRqMx4vnJkydj48aNqK6u5o99/vnnsNvtGD16dMbfA0EQBEEQBEEQvY9uTe2bO3cuXnnlFbz33nuw2Wy8psnhcMBkMvEgyu1246WXXkJTUxOampoAAMXFxdBoNDj11FMxevRoXHHFFbj//vtx6NAh/PGPf8TcuXMpfY8gCIIgCIIgiIzQrfbn8ZwwnnvuOfzyl7/EsmXLcOKJJypus3v3bgwaNAgAUFFRgRtuuAHLli2DxWLBnDlz8I9//CPphrxNTU1wOBzcEZAgCIIgCIIgiN5JsrFBVvWR6i4okCIIgiAIgiAIAkg+NsgKswmCIAiCIAiCIIieBAVSBEEQBEEQBEEQKUKBFEEQBEEQBEEQRIpQIEUQBEEQRNbz/vbtUN19NwKhUHcfCkEQBAAKpAiCIAiC6AE8tGoVAKDZ7+/mIyEIgmiFAimCIAiCILIedyAAAGihQIogiCyBAimCIAiCILIeFwVSBEFkGRRIEQRBEASR9TBFqtnn6+YjIQiCaIUCKYIgCIIgsh5vMAiAFCmCILIHCqQIgiAIgsh69BoNAAqkCILIHiiQIgiCIAgi6zFqtQDItY8giOyBAimCIAiCILIenbp1ykKKFEEQ2QIFUgRBEARBZD0hSQJAZhMEQWQPFEgRBEEQBJH1BMNhAG2mEwRBEN0NBVIEQRAEQWQ9ITmQ8oVC3XwkBEEQrVAgRRAEQRBE1kOKFEEQ2QYFUgRBEARBZD0USBEEkW1QIEUQBEEQRNbDzCYokCIIIlugQIogCIIgiKyHFCmCILINCqQIgiAIgsh6KJAiCCLboECKIAiCIIisJ0SBFEEQWQYFUgRBEARBZD2kSBEEkW1QIEUQBEEQRNaTSiAVDIfx4Y4dmT4kgiB6ORRIEQRBEASR1UiShJAkQa1SJRVI/XvtWpz96qtYvX9/FxwdQRC9FQqkCIIgCILIasKy9blFp0sqkGr0egEAPzc0ZPS4CILo3XRrIHXvvfdi0qRJsNlsKCkpwbnnnovt27dHbOP1ejF37lwUFhbCarXiggsuQFVVVcQ2e/fuxZlnngmz2YySkhLceuutCFIONUEQBEHkBCytz6rXJxVIsW2219Zm9LgIgujddGsgtXz5csydOxerV6/G559/jkAggFNPPRUul4tvc8stt+CDDz7Am2++ieXLl6OyshLnn38+fz4UCuHMM8+E3+/HN998g+effx6LFy/Gn//85+54SwRBEARBpBnWjNeSZCC1v7kZAFAtzCcIgiDSjbY7X/yTTz6J+H3x4sUoKSnBunXrMG3aNDidTjzzzDN45ZVXcNJJJwEAnnvuOYwaNQqrV6/Gsccei88++wxbtmzBF198gdLSUhxxxBG45557cNttt+Guu+6CXq/vjrdGEN3G7oYGSACG5Od396EQBEGkBVGRavB42t3++4MHAQCNPl9Gj4sgiN5NVtVIOZ1OAEBBQQEAYN26dQgEAjj55JP5NiNHjsSAAQOwatUqAMCqVaswbtw4lJaW8m1OO+00NDU1YfPmzYqv4/P50NTUFPFDELnCkIULMXThwu4+DIIgiLSRSmrfoZYW/CCXALBaKYIgiEyQNYFUOBzGzTffjOOOOw5jx44FABw6dAh6vR55eXkR25aWluLQoUN8GzGIYs+z55S499574XA4+E95eXma3w1BEARBEOmCNeNNxmyCBU/DCgrgpECKIIgMkjWB1Ny5c7Fp0ya89tprGX+tBQsWwOl08p99+/Zl/DUJgiAIgugYoiIVkiT+uxIs0OpjtfKgyh0I4Kinn8bwRx/N/MESBNFr6NYaKca8efPw4Ycf4quvvkL//v3542VlZfD7/WhsbIxQpaqqqlBWVsa3WbNmTcT+mKsf2yYag8EAg8GQ5ndBEARBEEQmYGYTVrnu2RcMQhunBtoTCAAAyqxW7KqvBwDM/d//sE6umyIIgkgX3apISZKEefPm4Z133sGSJUswePDgiOcnTpwInU6HL7/8kj+2fft27N27F5MnTwYATJ48GRs3bkR1dTXf5vPPP4fdbsfo0aO75o0QBEEQBJExgkJqH4CE6X3suTJBkVpz4ECGj5AgiN5ItypSc+fOxSuvvIL33nsPNpuN1zQ5HA6YTCY4HA5cc801mD9/PgoKCmC323HjjTdi8uTJOPbYYwEAp556KkaPHo0rrrgC999/Pw4dOoQ//vGPmDt3LqlOBEEQBJEDiKl9QOJAyiM/V2w2wxMMIhQOwydsL0kSfKEQvMEg8ozGDB41QRC5TrcqUk8++SScTiemT5+OPn368J/XX3+db/Pwww/jrLPOwgUXXIBp06ahrKwMb7/9Nn9eo9Hgww8/hEajweTJkzF79mxceeWV+Mtf/tIdb4kgCIIgiDQTSiGQYs8Vmc0AAFcgAE8wiKFySwhfKIQ5776L/PvuQyAUyuRhEwSR43SrIiXJOc+JMBqNePzxx/H444/H3WbgwIH43//+l85DIwiCIAgiS+CpfSkEUsUWCwCgxe+HLxjEQIcDPzU0wOn1Ym1lJQDgu8pKTCHnXoIgOkjWuPYRBJFeaKWVIIhcIaXUPtlsgilSLX4/fKEQSq1WAIDT5+MpfdRniiCIzkCBFEHkKG55MkEQBNHTiXbta0+R0ms0sMt10i1+P7zBIErkwKrJ54NK3rbJ58vcQRMEkfNQIEUQOYTYW4UCKYIgcoVUXPs8wSCMWi0PupxeL4LhMErkVD+n1wtWWECBFEEQnYECKYLIIURnKgqkCILIFVKtkTIJgVSdxwMAbYGUz4cWvx8AsElonUIQBJEqFEgRRA4hTi58VCNFEESOkKprn6hI1bndANoCqSYhkHp0zRr+PEEQRKpQIEUQOYRHmFwkmmgQBEH0JFJK7QsEYNRq+ba1cqBk0eth0mrh9HrRLKT0OSm9jyCIDkKBFEHkEGI6HwVSBEHkCimn9ul00KjVMGm1qJdT+4xaLRxGY0RqH4CIoIogCCIVKJAiiBzCJUwOKJAiCCJXYK59eo0GOrU6KbMJoDUVkNVIGWQnv31OJySAu/qR4QRBEB2FAimCyCFcpEgRBJGDMEVKq1bDqNUmZTYBtCpYLLXPqNXCYTBgV0MDAOCJM84AADQLC1AEQRCpQIEUQeQQlNpHEEQukkogFVeR0mqRbzJhS00NAGBIfj4ASu0jCKLjUCBFEDmEmNrno0CKIIgcgbn2aVSq5BQp2WjCGqVIHVFaimqXCwAwKC8PAClSBEF0HAqkCCKHoNQ+giBykWhFKlF7B2+0IiUHUgaNBpP69ePbFVssMGg0ONjcnMEjJwgil6FAiiByCJffDxVaJxsUSBEEkSuklNoXCMCo0QBoDaQavF4ArYpUX5uNb6dVq+ELhfDnZcsyd+AEQeQ0FEgRRA7hCgRg0evbnWgQBEH0JJhrnyZZswkhtY9h0etRYDJFbDtn/PgMHC1BEL0FCqQIIodw+f2w6HQUSBEEkVN02GxCDqgAwKTVxgRSk/v3h0alysAREwTRG6BAiiByCDcpUgRB5CDBcBgqAOpkzSaEGimgtT5Ko1Yj32iM2Nag1SIkSTxQIwiCSAUKpAgih/CFQjBqtRRIEQSRU4TCYWjUrVOWZAIpY1QgZZaVKZ1cO8Vg2yVyOa33eCjQIghCEQqkCCKH8IdC0CXhakUQBNGTCIbD0CYZSHkCgZgaKbOQ4gcA540cCaBVqQISu5wW3n8/5n70UccPniCInEXb3QdAEET6CIRC0Gk00IHszwmCyB1CkpR0IKWkSJmEQKrxttt4YMUVqaiFp2afDwdbWjDQ4QAAfPbzz2l6JwRB5BKkSBFEDuEPh6HXaGDQaCiQIggiZwiGw9wUIlEgFQqHEQiHYwIptWAo4TAaeYqfQd4uen9XvfceRjz2GA7IPaZsgvsfQRAEgxQpgsghAnJqH/WRIggilxBT+xItFLHHo80mwrJ9ejQstS+6RmrdwYMAgLe3bgUA2AyGzhw+QRA5CgVSBJFD+EMh6DUa6EmRIggih0jWbMIjPx6tSMULpIxxFClmLrFeDqhIkSIIQglK7SOIHCIQDkOn0ZBrH0EQOUWyZhNckYoym5DiKVJxaqTqPR4AwJaaGgBtilSTz4dZr76KnxsaOvxeCILIHSiQIogcIiArUhRIEQSRS6QaSDGliQVKg/LyFLdXUqSC4TDcgQAA4IeqKv4YADz7/ff4YMcOvCOn/BEE0buhQIogcgiyPycIIhcJSVJSZhMeOQBiNVJD8/MxJD8fD556quL2SjVSzT5fzHYssNrT2AigVZkiCILo1kDqq6++wtlnn42+fftCpVLh3XffjXi+paUF8+bNQ//+/WEymTB69Gg89dRTEdt4vV7MnTsXhYWFsFqtuOCCC1AlryARRG+DpfaRax9BELlERxUpm8GAn266CRP69FHcXkmRciYIpNhze5zOjrwNgiByjG4NpFwuF8aPH4/HH39c8fn58+fjk08+wUsvvYStW7fi5ptvxrx58/D+++/zbW655RZ88MEHePPNN7F8+XJUVlbi/PPP76q3QBBZhZ9S+wiCyEGiA6lAOIyQnG4n4omqkWoPpRoppjaphO2Y0uX0egEAlbItOkEQvZtude2bOXMmZs6cGff5b775BnPmzMH06dMBANdddx3+/e9/Y82aNZg1axacTieeeeYZvPLKKzjppJMAAM899xxGjRqF1atX49hjj+2Kt0EQWUNASO2jQIogiFwh2rUPaA1+zOrI9eBoRao9WAogC5QA4EBTEwCgn92O/fL/oxUpt7A9QRC9l6yukZoyZQref/99HDhwAJIkYenSpdixYwdOlXOd161bh0AggJNPPpn/zciRIzFgwACsWrUq7n59Ph+ampoifggiFyBFiiCIXCRakQJiez8BqQdSOo0GWrUaLjkwWnPgAM545RUAQF+bDQBQYrGgxe8H0KZIeSiQIggCWR5IPfrooxg9ejT69+8PvV6P008/HY8//jimTZsGADh06BD0ej3yotx4SktLcejQobj7vffee+FwOPhPeXl5Jt8GQXQZgXCYFCmCIHIOpUBKaYyLNptIBotOB5ccKLG+UQBQaDIBAE4cNAj7mpqwqbqaFCmCICLI+kBq9erVeP/997Fu3To8+OCDmDt3Lr744otO7XfBggVwOp38Z9++fWk6YoLoXkT7c6XVWoIgiJ5ItGsfoBxIpapIAYBFr+eBkVh3NdDhAABcNHo0AOCHQ4fg9HqhVql4LRZBEL2bbq2RSoTH48Htt9+Od955B2eeeSYA4PDDD8eGDRvwwAMP4OSTT0ZZWRn8fj8aGxsjVKmqqiqUlZXF3bfBYIBBbq5HELmEPxSCTqNBntEITzAIl98Pi9yQkiAIoqeSrCLlDgSgUamgk23Nk8Gi0/HUvpDQuLfMagUA6GUn1Fq3Gw1eL/rabKRIEQQBIIsVqUAggEAgAHVUIalGo0FYXjGaOHEidDodvvzyS/789u3bsXfvXkyePLlLj5cgsgGW2jdQXljYSxa9BJEy/1q9Gn//+uvuPgxCINlAyunzIc9oTGnfZiG1zy+49/1i3DgAwJF9+qDQbMaexkYEw2GU2+0USBEEAaCbFamWlhbs2rWL/757925s2LABBQUFGDBgAE444QTceuutMJlMGDhwIJYvX44XXngBDz30EADA4XDgmmuuwfz581FQUAC73Y4bb7wRkydPJsc+olfCzCZYSkqF04lRxcXdfFRENJIkocHrRYFcg0FkF7d8+ikA4A9Tp0KtUrWzNdEVhCQpxrVPKZBq9HrhSDGQsuj1XJFqlM0kAGB4YSGkO+8E0FovtaO+HgBQ7nBg9f79kCQJKro+CKJX062B1Nq1a3HiiSfy3+fPnw8AmDNnDhYvXozXXnsNCxYswOWXX476+noMHDgQf/vb3/DrX/+a/83DDz8MtVqNCy64AD6fD6eddhqeeOKJLn8vBJENBOTUvn52OzQqFSoaG7v7kAgFPtm1C2e88goWnn46bjzmmO4+HCIOlc3N6G+3d/dhEGhfkZIkCW9s3owKpzNlRUpM7RMDKZFCsxlfV1QAAMrtdkhotV9PpRaLIIjco1tHgOnTp0MS8pGjKSsrw3PPPZdwH0ajEY8//njcpr4E0ZtgqX1atRr97XbsoUAqK/mpoQEAcN/KlRRIZTFk2JI9tBdI/VhVhUvfegsAcESCGmklzDodT9WLF0gVmc1oltP/yuXg2hMIUCBFEL2crK2RIggidVhqHwAMzMtDBdVIZSX1Hg8AoMrlSriYRHQvPqFehuheQuFwXNe+FXv34oh//5tv+7O8UJEsFr2e10g1xFOkhDTccjl1muqkCIKgQIogcgRJkhAMh7lb1UCHA69u2oRat7ubj4yIpk7+TILhMJrkvjRE9kGKVPYgKlKGqEDqfzt3AgAuGTMGAFL+ThWbzdjX1ASgVZHKMxqx4qqrIrZhgdSsESP4/1mTXoIgei8USBFEjhCU3Sx18mTjpMGDAQAr9+7ttmMilKkXVr0p0M1eSJHKHoLhcFyziY3V1Th92DC8duGFAIBfHnFESvueNnAgfm5oQEVjIxo8HswZPx7HDRgQsY1dbply3siR6Cen9u2Xgy+CIHovFEgRRI4QYIGUrEhdcfjhUKtUqHK5MvJ6T373HR5atSoj+8516txuDJYt6uvkND8i+yBFKnsISRJXpLRqNTQqFbzBIELhMFbv34+JffoAAMJ//jOeO+eclPbNaqp21tej0etFvoJZBXNvtOr1GOBwQAVgN9WgEkSvhwIpgsgRAvLqOVOkNGo1is1mVLW0ZOT13tm2De9u25aRfecan/30EzxCPUWN242RRUUASJHKNpiyC5AilU2IqX1AqyrlDQbxQ1UVat1unDZ0KAB0yI681GIBAFS1tPDUvmjE/eo1GvS327E7xVosgiByDwqkCCJHiFakAKDUasWhDAVSNW53XIcroo1GrxenvfQSbvr4Y/7YPqcTo+X+XlRnkV2IAa+fAqmsQTSbANoCqWpZcR8kK7wdwaLXw6rXY6/TCU8wqBhIHdu/PwDw722JxUJqMkEQ3Wt/ThBE+ohWpIDWldZMpfbVuFy8ZoGID5vorTt4EEBrXUeVy4XhhYX8dyJ7EJ3YKLUve4inSLGFCKte36n9l1osvC2BTa6HEpk6YAA8d9zB67OMWi0plgRBUCBFELmCkiJVYDLxiXw6kSQJtW439VBJghr5/LPV672yJf2wggKoQIFUthERSNFEOWtoL5CydDKQ6me3Y21lJQDAFGdcE8c79voEQfRuaDmZIHIEJUUq32iM2xelM7T4/fCFQmjy+RCmPkgJYYEs+3w+2bULOrUa40tLYdBqSfXIMpyCdTZ9NtlDSJJiUvt8oRBcfj/0Gg3vn9dRLhkzBj9UVQFobdDbHgYKpAiCAAVSBJEzKClS+SZTRuqYamSDBAlAM/VBSggLpEJywLnmwAEc3a8fCs1mWtXOQhqEuhdSpLKHRIpUZ9P6AGCUbP4CAKYkAin67hIEAVAgRRA5Q1xFKgMF0aLTnJMCqYQwRbDe44EkSXAHAnDIxew0Gcs+RAWXFKnsIdOBVIHcZBeIn9onQt9dgiAACqQIImdQUqTyjEY4fT6EBEvndFAj1F2Rc19imPNbMBxGk88HVyDAU4eoYD37qJcXHmx6Pbn2ZREhoSEvkP5AqtBs5v9PJrXPqNFQIEUQBAVSBJErKCpS8iprU5pVoxpRkaJAKiEBYTJe63bDLQRSBpqMZR0NHg/sBgPMOh0FuVlElypSSab2kWJJEAQFUgSRI7DVc1GRcsg2vulOvxMVKUrtS4yoatR5PK2BlGChTIFUdtHg9aLAZCIjkCwjXiDVnKZASkznI7MJgiCShQIpgsgReGqfMNlglsAu2SJ4T2NjWswhqlwu9LfbAVBqX3sEwmEY5OC2xe+PUKRoVTv7aPB4kG80wqjVwkOfTdag5NrnDQax1+nkY1FnUAn7phopgiCShQIpgsgRAgqKlEWesLvk3jiDH3kEhz/1FKROWpbvbmzEyKIi6NRqSu1rB38oxNOGogMpg1YLL6WPZRUNXi/yTSZY9Xq+AEF0P0qKVGVzM1bt34/hBQVpfa1krNQpkCIIAqBAiiCyDkmSsL+pKeVgJxlFCmhVpR5bs6ZTx7i7oQFD8vK4mQURn0SBFE3Gso8Grxf5RiOsej1ahOa8RPcSVDCb2FlfDwAYXVyc1tcS1al40HeXIAiAAimCyDr+vW4dyh9+GP/buTOlv0tGkWK8vnlzyscVDIdxyX//i1319djT2IhBeXlwGI2U2tcOgXAYdoMBKrQFUizApclY9sFS+6x6PVpIkcoaQgqKFABo1WqcM3JkWl5j1ogRSW9LjpsEQQBA+4nABEF0KZXNzQCAQy0tKf1dsooU0DGDiMrmZryxeTOafT40eL0osVhg0eko/akd/KEQDFotrHo9mny+GNc+CkSzi3qPB/kmE5r9ftRloAdbrhEIhfDhjh04d+TIpJScjhKd2sfqDn8xdizUaXrdNy68MOnP3KjVwhMIQJKkjL5vgiCyG1KkCCLLYOYD0SpSe7SnSEmSBLVKhb42W0RD3WRhvajWVlYCaLUL1ms0PIAjlAmEQtCp1bDq9dztkFL7spMTFi/GTw0NpEilwG1ffIHz33gDm6qrM/o6wXA4xmwCQFqMJhgGrRZ9bbaktjVptZAA6jVGEL0cCqQIIstgTmHuVAMpBUVKo1bDoNHA5fcjGA4jLEnoJwdSYg1Ws8+Hwvvvx6p9++Lun6WxsB5S+SYTdBRItYs/FIJeo4FVr0e1fO4okMo+JEnCVxUVAIC+NhsFUkny8a5dANLfqy6akCRFKFKsXmqgw5HR140HV/upjo4gejUUSBFEluGRb8yppswxRUqcbACtN3xXIMAn7P3tdgTD4YiJz7vbtqHe48H727fH3X+0TXe+0Qi9RkMrsu3gD4WgY4FUlCJl0GioziJLqJdTuv5w3HG4bNw4CqSShH3/mzN4riRJikntY6nPhxUWZux1E8HVfrpGCKJXQ4EUQWQZzA67I4qUTq2OyddndUxswl4up8LUCOl938npeja5ga8S0RP+fJMJOrWaB3CEMoFwGHqNBgUmE/Y6nQBIkcpGKuTP5vxRo3jg217PtYXffgvr3//eFYeXtbDvv7gw4w4EMH3xYvxYVZWW1wjL6rno2revqQkAMDQ/Py2vkSrmOEY+BEH0LiiQIogsgytSHaiR0in0P7EZDGj2+/mEvZ8cSIl1Ujvq6gAAdQlqp+IpUpTalxi/XCM1sqiI15FQIJV9HJRNXtj3w6LTtfsdvG/lSrgCAf6d7Y1wRUoIpDZXV2N5RQWuef/9tLxGSA6kREXqT9Om4aTBg9NaI5UK8Yx8CILoXVAgRRBZhqejZhOyIhVNvtGIBq+XB0L9owKpsCRhozzBr0/gICem8LGaHx2l9rVLQK6RGiP0urEIgVR0gEp0D8w9Md9oBNBqPOALBhP2cys2mwEAPzc0ZP4AsxBJkvh5ExWpPY2NAFqNaT6Ra6g6Q1BerBEDqSP79MGXV14ZoVJ1JfFaSxAE0bugQIogsgy2up1yal8cRSrfZEKDxxNRIwW0BVLf7NuHyuZmOAyGxIqUEDBZdDqoVCpK7UsCpkgNFlKQeI0UKVJZQ6PXC71Gw93gDBoNJLRN4pUosVgAAD/10kDKEwzycUGskRIDy5kvv9zp12GfgSaLbMZJkSIIAuhAILV792688MILuOeee7BgwQI89NBDWLp0Kbwd6IXy1Vdf4eyzz0bfvn2hUqnw7rvvxmyzdetWzJo1Cw6HAxaLBZMmTcLevXv5816vF3PnzkVhYSGsVisuuOACVKUpL5sgugOuSKVqNtGOIsUm7Fa9Hg6DgQdSW2pqoAJw1vDhvOBeCVE5MciTTTKbaB9WI1UqT7oBSu3LRhq9XuQZjbzGkAVUiT4f9j1I9L3JZcQeaKIitVtWpNJFSEGR6m5IkSIIAkghkHr55Zdx9NFHY+jQobjtttvw7rvv4uuvv8Z//vMfnH766SgtLcVvfvMbVMj2scngcrkwfvx4PP7444rP//TTT5g6dSpGjhyJZcuW4ccff8Sf/vQnGOXUCwC45ZZb8MEHH+DNN9/E8uXLUVlZifPPPz/pYyCIbCPtipTRiAaPh68cG7VaFJnNvKfRweZmlFqtyDMaE7qUiYoUc8yKZ3++q76eGs3KMPvzMquVPyYGUr5QKGH6GNE1sECKwYKkRK6K7LuaaevvbEV8380ZDKSUUvu6G1KkCIIAAG0yG02YMAF6vR6//OUv8dZbb6G8vDzieZ/Ph1WrVuG1117DUUcdhSeeeAIXXXRRu/udOXMmZs6cGff5O+64A2eccQbuv/9+/tjQoUP5/51OJ5555hm88sorOOmkkwAAzz33HEaNGoXVq1fj2GOPTebtEUS38uGOHdCq1Th92DAAbSvgaauRMplQL6T2GbVaFFss3LWvsrkZfW02mHW6hMEbU6SGFRTg4tGjAQB6tVpRkTrs0UcxorAQ2+bNS+k95CLM/rxYUKTE9DG2DZu4E91Do9cLh+BamYwixQKJ9tz9chX2/gtNJjRFpfb95qij8MTatZhQVtbp1wkpuPZ1N3qNBlq1mizyCaKXk9So9I9//APffvstfvOb38QEUQBgMBgwffp0PPXUU9i2bRuGDBnS6QMLh8P46KOPMHz4cJx22mkoKSnBMcccE5H+t27dOgQCAZx88sn8sZEjR2LAgAFYtWpV3H37fD40NTVF/BBEd3H2q69i5ssvwykrOB1uyJtAkWoUUvsMGg3K7XZeEF7Z0pJcIBUKQQVgx7x5+NuMGQBkRSrOiv122QmwtxOQa6TE1fRU0seIrqHR54tUpOTvUiIzEB5I9dLJNHv//ez2iGDyQFMTDissxO+nTIEzDUFmNipSAFBqseCgrM4TBNE7SWpUOu2005LeYWFhISZOnNjhA2JUV1ejpaUF//jHP3D66afjs88+w3nnnYfzzz8fy5cvBwAcOnQIer0eeXl5EX9bWlqKQ4cOxd33vffeC4fDwX+UgkOC6GqYNXazzwe9RpO2GimrXg9PMMiDJKNWi7ElJdhYXQ1JktDg8aDAZEpKkTJotRF9qnRqNdmftwOrkQLaengxKJDKHpp8PtgFRSqZ1D4lx7reBAue+tls/BxIkgRPMAirXo98kyktKb7ZGkgNzs/nC1IEQfROOjwqVVdXY9OmTfjxxx8jftJFWB44zznnHNxyyy044ogj8Ic//AFnnXUWnnrqqU7te8GCBXA6nfxn37596ThkgkgZsTZm6nPPQZIkOH0+9LXZ0qZIsVx+VhBv1GoxurgYtW436j0eeIJBmLXapBQpfdT+lcwmxPc0adEiXPPeeym9j1wjGA7zlKSNN9yA7669lj+XzGSd6Bpcfj//rgCRQe5/1q9HyT//GbN9lVxn2J4iJUkSbyqbSzQJgRQ7B2xRwKTVcjU81MnFllAWuvYBwOC8vLTXgxEE0bNIOSl/3bp1mDNnDrZu3conTCqVCpIkQaVSIZSmCUFRURG0Wi1Gy7UYjFGjRmHFihUAgLKyMvj9fjQ2NkaoUlVVVShLkJdtMBhgEFYeCaK7iHb7avH7EZYk9LPZsC7FlJFEipT4WgZ5ggO0ToTcgQBMOh0sOh18oRBCwsRfxB8K8XQnhlJqnxgUrK2sxNrKSjxzzjkpvZdcIhQO85V0h9GIo/r25c+RIpU9uAIB7sQGRKb2/f7zz3kvNhb87qqvBwAUmEzt1kgd9+yzONDcjIqbb87MwXcTTT4fDBoNCs1mHlSx1GSTTgerXo+wJKHW7UapYLaSKtmqSA10OLB0z57uPgyCILqRlEelq6++GsOHD8c333yDn3/+Gbt37474N13o9XpMmjQJ27dvj3h8x44dGDhwIABg4sSJ0Ol0+PLLL/nz27dvx969ezF58uS0HQtBZArmfnfVEUcAaAt2+tps8AaDKa1i++MpUvLksM7thkalglat5ivvLX4/PIEAzDodd5KLp0qJk0iGkiIVnZI4tqQk6feQiwSFQCoaCqQyj+ruu3HNe+/B+ve/c6dKJVx+f0QgJX427POpEv6e9Y6aUFbWbmrfqv37sdfp7PB7yFaa/X7YDQbY9HoeTDInQ5NWiz42GwB0uo6I95HKskCqj82GQy0tOak2EgSRHCkrUj///DPeeustDJMdxjpDS0sLdgldz3fv3o0NGzagoKAAAwYMwK233opLLrkE06ZNw4knnohPPvkEH3zwAZYtWwYAcDgcuOaaazB//nwUFBTAbrfjxhtvxOTJk8mxj+gRsMCJ1c6wCUc/eQLiDgS4otQe8RQpFjTVejx8cij2QHEHAjDJqX3sNW0Kiq1PSZFSqJGKdhtM1OS3N5BMIJXI0IDoOOz79eyGDQCAbbW1Ee6JIq5AICK1T0y7ZCrrweZmDHA4ALQaKhg0GgzKy8NGub6xPZp9PsXvVk+F1ZXZDQZFRYpZ/h9sbsYRnXDvY6592aZI9bFaEQyHUed248FVqzD78MN7/cIRQfQ2Uh6VZsyYgR9++CEtL7527VpMmDABEyZMAADMnz8fEyZMwJ///GcAwHnnnYennnoK999/P8aNG4f//Oc/eOuttzB16lS+j4cffhhnnXUWLrjgAkybNg1lZWV4++2303J8BJFpGuRC7P5yIFXZ3Ayg1QULSM25L26NlBwg1Xs8fHJoFXqgeIJBmDqhSEWn9jFFalhBAW446ijUut29uk9SokCKBaakSGWGHVHOkYlq0Vx+f8SihZjax67fQ4KycrClBWVWK0wpNFXONVWKBVJFZjN8oRBqXK4IRYoHUmlSpLIukJIXvLbV1uK+lSsxR3AVJgiid5CyIvWf//wHc+bMwaZNmzB27FjohFQIAJg1a1bS+5o+fXq7E6yrr74aV199ddznjUYjHn/88bhNfQkim2mQV8xZIHVAtuLvI09AXH4/EGcFPZr2aqTq3O42RUpI7XPLqX28wWS8QCpOjVRMap/8929ceCF21NXhybVreQpQb4RS+7qP7bW1Eb87EzjIRddIiZ+NWqVCSJIiAgLWf82k0/HgQQnx+5Fr7n5NssJ20uDBUAH4386dGFVcDKD1/Ok1Gtj0+pha0FQJZqnZBBu3P9yxo5uPhCCI7iLlQGrVqlVYuXIlPv7445jn0mk2QRC9gXqPB2adjvevqZBXrFn6UDx1yOX3Y+Tjj+PdSy7BRNm8IBCnqSsLkOqE1D4WXDV6vQhLEkxaLZ9ExmswqaRIKab2yX9v0ev5inRlc3OvDKTCkgQJ8VfSjeTal1GiFak5776LS996C4E//SnicX8ohGA4rJjaV+/x8NSyg7JiDLRe031sNpi0Wp7OpoSY2pqOnkrZBFsgKbVaUWAyobK5GYNk4yeTPJ44jMZOB5ChLFWk+tvtOKpvX9z/zTcAEONqShBE7pPyqHTjjTdi9uzZOHjwIMLhcMQPBVFErlLrduPhVavSnqLW4PUi32jkE7jdjY0oMJngkAOreOrQzw0N2N/UhIdXr+aPxa2RElP75Bu9SauFCkCNPMkTg7l4fV+UFCm9RoNgOBxxXtgxW3Q6DM7PBwBsralJcBZyl/ZSkgykSGWU6KbQrkAAwXAYG6L6DPLgX1CkdGo11CoVDgjBk5jat7W2FsMLCmDUahMqUuLCRC4qUmyBxKTTwRMMttVIyde2w2BIqAQmQ7am9gHA5P79+f8Tvc9V+/bhinfe6YpDykrOfOUVvJzGFjkEkS2kPCrV1dXhlltuQWlpaSaOhyCykqveew/zP/us0ykq0dR7PMiXm+ECrQFSqcXCJ3TxFKlaOQASJ+DxaqSMWi10ajWqXS6ugKhUKph1Ou5iZtLpkG8yAWhNN7zt889x0ZtvRuzHp6B4sdcTVSlRkWKmGee/8UavdLZqr/8NpfZllj2NjZh9+OE4dejQiMcn/PvfEb83C9csg31H9svptsVmM0/tq3O7sb+pCUeUlfEAIh7iYkhnA4pso8nng10+Z2Y5xZHXSAmKVGeVuGx17QNae0kxqhO4Qs793//w0o8/dsh85+uKCp4G3lP5386dmN2LA0kid0l5VDr//POxdOnSTBwLQWQt3x88CCD9E95d9fUY6HBEBlJWK/892kqcwSZ04gQuniKlUql4uo1RCISsej1XpFhqn1atRoPXi/u/+Qb/3bIlYj++YFCxIS8QWQciKlLixGdPL2xc2a4iJRgaEOmn3uNBf5sNn86ejWKzOe52aw4cAACMket7GBYhkBpTUsIVKXYtH1ZY2JraFwjEVatdOaxIiS6EJq0W7kAgRpESHf06Sra69gHAEFl1B+Q00DjNh0cWFQFAjBqaDNMWL8Y5r73WsQPMAnqz2RCR+6RcIzV8+HAsWLAAK1aswLhx42LMJm666aa0HRxBZAssvScVF732ePTbb/HpTz/h1xMncgWqyedrVaTaMX5gEzpxkhYIhRQDKQAYnJ+PnfX1EYpSH5sNz8m20A6jESqVCvlGY9yVT38oFGPFzl5PdO5z+f3QqdVcrfr5ppswZOFC/HDoUMSkozfQXiClUqmg12hIkcoQjV4vT1m96Zhj8M62bVgvL4qIfLt/Pwbl5aFcrk1kiIrU0Px8LK+oANAWEDkMBph0OkhoXchQqpERU/tyrUZKTO0zy8ocGyONQmpfTSdbIGRzat+MIUPwp2nTYNRqcceSJajzeFCiYBDEjCl+amjAjBT2zwKztZWV6TjcboHGNyKX6ZBrn9VqxfLly7F8+fKI51QqFQVSRE6TzkDqq717AbSu2Fr1el60XmqxtGtFzlLyxEArEA4rpvYBwCB5ghitSAHAnPHjcWSfPgCAfJOJW7IDrSuJKjktzRcKoVDB/py9NqMlykZ6UF4ejFot9skT0t5EMhNAYwr22Z1h6e7dsOr1mNSvX8ZfKxsISxIavV6esvrHadNww1FHoeif/wQQqaa4AwEecIlY9HrslhvvDsrLw3tyg3gWSNkMBq68eAIBxUCKfUf7WK05l9rXLHzXWYpjg8cDh8HA1WiHwYBd9fWdep1sde0DWsfRv5x4IlbK43mNy4USi4V/p9mYy1T7VK8BFognSh/NdprjZFYQRC6QciC1e/fuTBwHQfQI0nkzy5MncX+cNg0atRrTBw3Cx7t2odRqhVathl6jiZvax26uYqCVSJFiK6SiWcS9M2bghR9+wL/POosHS8x5i+H0+fgE0xcMKtqfA7GpfdG1JsVmMw/+ehPJBlLMte+lH3/E4Lw8HDdgQFqPwxsM4qQXXgAAuG+/ndev5DLNPh8kICJAKjSbsXTOHJz4/PM42NLCAylfKKQYBJl1OrgCATgMBhSaTGj0elHv8fBaSbusSAGtY4MjZg9tqnGJxZLWhZjuJixJEQ3DzTod3IEA6j0eFApplOmokcpW1z4RNsZWu1wYA2Dck0+i0etFza23AmhTZVI9F7kQhIipnfFqeQmip5K9oxJBZAlifnc6J0LVbjfOPOwwPpk7SrYxL5VvyGxiokSL/HhEal8CRapAXpUXJ5VTBwzA02efzYMooLWJ7qubNvHfxeBHsY9UnNQ+S9REvdhi6XR6T08kmUDKIKf2+UMhXPHOO5j63HNpPw6xLoM1hXX5/XHrOXIBpqzmRylNfWUDFHHBwK9wbQPgynCByYQ8oxHBcBiF99+P6z78EFq1GgaNhisO8Zz7XIEANCoVHEZjj1YVomFjE1ek5FqxOo+HjzdAemqksjm1j1HucECtUuFnWcHcVV+PWrebp2Gzzz6eK2o8mnMgHVR8D51tzkwQ2UbKipQkSfjvf/+LpUuXorq6GuGoG/Hbb7+dtoMjiGxAnPykNZByuXgBMtC2oqmWAxuLvBquBLsxuZJUpNjEJnpSGc3hJSURv4s3faU+UvHMJixRtVTFZjMFUnFgqX0dKUJPllrh3O9rasKIoiJY770XNx9zDB4+/fSMvW5X8FN9PRq9Xt5PjcFq/fKFST3Q1uxa7AkVT5GyCIGUuJ9gOIwCkwkqlaottS9OkOTy+2HR63kNUa7AVHFRkapsbkZ9VCCVTvvzbHTtYxi1WgzOy8M2uQm0QaOBLxTCXcuW4amzzuKKVMqBVA4oUuJ72N/UxPskEkQukPKodPPNN+OKK67A7t27YbVa4XA4In4IItcQzRcS9YtJlRqXK8JJjFk0Hy3XsFj0+viKlHxjSlWRai+lYtaIETht6FC8ceGFACInh4qKlJL9eSCgrEhRap8iLJASbZHTbRUv7nuv08mvq9c3b07r62SSz376KSL4YQx79FEctWhRzONswhpd+2QzGGCRJ/0MpUUCoE2RypcVqYj9yAEEU5TjqS7s+8Bc7XKF6ECK1XjWezwoFAMpoxG+UKhTzpTZ7NonMrywEDvkejB2XXz+888AOp7aF69Bek9CVKQO9MJaWSK3SVmRevHFF/H222/jjDPOyMTxEETWIZovpHMi5PT5IhSi4YWFkO68k/9u1uni1kg1CwXIYUmCWqVKqEixSWJ7E5ERRUX4ZPZs7JPTv8TAMZEiFZPaF6VI5RuN2JBjhfbJwCaAiVbSWSAlTsSbfT7elDkd1Hs8sOh0MOt0ONjczIOIbJ+YMtyBAE576aXW5rd33KG4jWiMAsRP7QNa0/vEFCN/KBRhxMJg13GBycRTAhkV8neEra7vaWzEsUJzVsaBpiaUyAYy6e5D151ENzFmfaSC4TBGCzbydiHQLFY4x8mQzWYTIiUWC3bW1yMsSaj3eDCxTx+sP3gQoXCYj6WdSe0LhsM95jsrIiriuRAYEoRIyt9Ih8OBIUOGZOJYCCIryUQfGEmS4PR6+SRDCatej6YEZhNMZWI36ESKlEN+nUFC88hEiAX0jEQ1UmJqX7Pfz1fr+f7k+oneRiqKlJj+kupkqz3qZAMAm8GAFr+frwr3lElZlRz0RLsbisrdoajaC3YOlQLS6Jq9eKl97Drua7XGpCOdJivIeUYj8oxG7u4XzbqDBzGhTx+u2OQK0YqUw2jkRhzRqX1A56zfe0KNFAAUmkyoc7vR7PMhLEk4rLAQElqDenbtplrzJI4L8RbWsp0KpxMlFgt0anVOqbIEAXQgkLrrrrtw9913w5NDK2sEkQhx8nPTJ5+kZZ++UAiBcDhhIHVYQQHPt4+mxe/nphSsTiqRInVM//74/Ior8Ksjj0zq+Fjdh3jT8ytMNpVS+5xeL588MXKtPiRZkpkAMttoMUhPd7+hOrcbhSYTrHo9Wvx+rsZk+8SUUSWkhYrX5F3LlvH//xQVyDR4PLDp9YrvkU14GfFS+wbKwRNToz6dPRv/Ou003DplCj6ZPZtvNzgvL27D6a21tRhXUgJTAvOYnkh0IDW8sBBVLhf2NzVFBlJyINuZOilmiqLOckWq0GxGneDqeFhBAYBWRYYFUqm2OhAXoOLVzGY7FY2NvPF8b7wPELlNynfRiy++GA0NDSgpKcG4ceNw5JFHRvwQRK6RCSWFN/RMkL41rqQEm6qr+WRcpNnnQ5lcNM9WKeM1BGWcPGRI0hMRrki1E0gpmU04FdLScm0SmSxJBVKyWicGUplSpKx6PVoCAV7311PSbES1ab9QY/Gv1av5/3+K6lXUIPSQiqbIbI5IN4rn2se+Yyw189ShQ/F/xx6L+085JWK7YosF9V4vJEnC13LTXqB10uwOBFBsNuecKssm9Sz9cZRgnFOooEh15poOhsPQqFQRqZvZSIHJhFq3G+e/8QYA8BTHWrebBxCpBhK+qLTpnsg+2WAikRMtQfRUUg6k5syZg3Xr1mH27Nm44IILcM4550T8EESuwW58d0+fnrYcfbY6m0iRmlxeDm8wiNX798c81+L3o4+8Ss5SP9LZn0OtUsGg0UTc9JX2r2R/HleRaucG6vL7c65habKpfUyRYiv5GQmkZEWq2efjQdvBlpYeYa9cJQRSokmERq2G3WCAWqWKafra6PUqNtkFZEVKyKqIl9p3WGEhAGBIfn7C48szGtHo9eKDHTswbfFifLxzJ4BI58BcW42PVqTEtGFRkepnt0MFxFXskiEkST1CPWWtMpgD5xg5kKrrhCIlbv/mli0R7TiykV319Xjiu+8iHmP9CCmQInKRlCs/P/roI3z66aeYOnVqJo6HILIOFgCUWCwISVJaAhauSCUIpCb17Yu+NhseXbMGU4UGraFwGJ5gEP3lQKrR60VYkhCSpLipfR1BVJEkSWqtwYraf9KKlNx0NhQOKxov7Kyrw/DHHmt9LcFwo6eTdGqfrEiV2+2o93jSH0i53RhZWIhAOIwWvz9C/dpeV8d7mGUr+5qa+CSMpeS1+P1o9Hrx0nnn4cm1a/HXr7/GWcOH4xjZ8KHB641r919oNsem9il8p4/u1w9bfvObiDYFSjgMBvzc0MA/t5319ZiJSMOLXFNlW/x+6OTG4UDkopAYSBm1WgxwOLAzKtBNhZ5ishBtNjK8sBBatRq7GxvhDQZh1etTViXFQOqOJUtw/qhR7V6P3ckdS5bgjc2bMbq4GNMHDQLQeg8163Q59x0gCKADilR5eTnsdnsmjoUgshK2iswmB+m4EbCJbCJFSqNWY+6kSfhk166Ix9lKMCt+r/d4uCKUzo7xoorE3OdiFKmoGqmvKirgDQYVFSkgflrLFe+8k7bjziaSTe1jZhPFFguMWm1GFKkCkwk2uUbK6fPx6yc6JS4bqXA6Mb60FCqA158wZ8lyh4N/N6c8+yz/m0SK1ECHAw1eL5bs3g1ATu2L4yg3qri43ZSyPKMRTq+X1/Kwz48da4HJlHRq37I9e7B4w4Z2t+tuWqLcOcVzVCS0dQBalb1oxTAVgnEWYLKNCX36RCwEGbRazBg8GO9v3w5PIIB8ozGuIvXhjh1Q3X13jCofbRuf7W0k2Pdy/cGD/DF3IACTVptUZgJB9DRSHpkefPBB/P73v8eePXsycDgEkX14AgEYtVpu85uOQKo5Ki0mHn2sVjT5fBGpcyyQKpcnwg0eDw9k0qpICS5jPFCLo0ix509YvBhAbFE4C6RmvPCCYj8ZccKbSzfaULI1UnJqn91g4IYQjLAkYak84e8odW53W42UrEiV2+0waDQRRg7Zyp7GRgzJz0e+ycSDk70skLLb+fdIdPFr8Hji1khdPGYMzDodVu3bByB+al+y5BmN2Flfjy01NQCAJbt3Q5KkmNQ+XyjUbo+wE59/Hle9916Hj6WrcPn9ccevaIfDQpMpoo1EqoR6iCLFuGTMGO74ePqwYfhm377WdhcmEwLhMB8XgLa6p+XynGpVVCq3NxjE0Px8zD78cADIegt91hZA7PnmCQZhktsvuHMovZUggA4EUrNnz8bSpUsxdOhQ2Gw2FBQURPwQRK7hCQb5ahqQnkCKBQtsn/EolFd2xZsnm2TnG42w6HRo8HozokiJaRj+OPuPtj8fIdeUjCkpidkXAKw5cCDGXQ1oTZtkdKaWIttINbXPLjeLFYvKn1m/Hie98AK+VaiVSwZPIABPMBjh2tckp1+WWa0R9UfZyoGmJvS321Eg1DYx58Ho/k6MRKl9GrUaAx0Ovq94qX3Jwv72gVWrAADLKyrw8saNMal9QOKFAnHBJNtpSRBIRat7Nrk2r6P0lNQ+xqsXXADnH/4AAJg6YAB8oRCC4TBfMGKq1Es//gjrvffihMWL0V/O9FlXWRmxL28wCItej2dnzQKQ/YEUuz9VCuMKS+2jGikiF0m5Rupf//pXBg6DILIXTyDAV9OA9ARSbB+m9gIpeUW9zuNBqewgxtQsm8GAApMpY4qUeNNj+2/P/tys0+H6iRNxZJ8+MftiHGppiWjYCbSmQh1eWoofq6qwu7ERo6Ke76kk00g0QpHS62HR6yNsjqtlxUg0WUgFNpkvMJlQbrdjr9MJs06HsSUlKLVacailBTUuF0oeeABfXHEFZmRhn8AmuVi9QFCkqlpakGc0wqDVKqo8iVL7gNZFCubcp+RImQri344qKoJFr8etn3+O2447DiatFgatlrcU8MgTYyU6o9p0NUqBFOuJFo3NYIjoh5QqzLWvpyCmOYqBPgvs2TXw1tatAFpTok+Vv3f7BFdKoK1/Hxtrb/70U/zyiCOy0sFQkiQeSL2ycSMeOOUU9LHZeGqfSavt1HWQ7UQ3BSd6BykHUnPmzMnEcRBE1pIRRSoYhFatbneVlSlSrDD++Q0b8LBs+WzV65Evp8xkQpGyChP6eKl9apUKGpWKK1IHW1oUFQJxtf9A1ERBkiQcbGnBmOJibK2pidvYtCeStGtfAkWKre6Ldt2pIKaXTerXDzd98gk2Vldj6oABKLVYUOVy8XqG/27ZknWBlCRJ/NwwdzygtbcU66UmhlFfV1Tg+IEDW1P7EgVSgrrlS1AjlQzXH3UUDFotrv/wQ5h0OsydNAlXvfceDrW08PqtZBSphixXG0RcgQBPd2bsu+WWiLQ1RmcVqUTNxrMdsV6UpZqyYLNaSKtlSvz+qPHRGwzCKFybTbLrZqLWGd2FNxhEWJJwdL9+WHPgADbX1KDUaoUvFOKKVHUPSCXuCJuqqzHuySex/rrrMCFqIZHIbZJavnaleOGnuj1BdIa1lZVY+O23Gdt/JhQplurQHqIiBQBPrl2LH6qqAMiBlNHYGkhlQJESJz98/wqTGYNWC18wCEmSWpUNIU2PIZpqHIhSVt7euhVrKyvhD4UwwOHA7t6Y2ifUSEUrUixwYDVBqSKml5VYLNxZrL/d3mqS4PPhR/ma8mZhapnYvNokKB5VLhdXaUVL6GmLF8MTCMAXCiVUpFgvqVA4DH8oxBWjjqDXaHDp2LEAgJuOPpqnad23ciVfZEhm/OjpilSR2cw/ExGrXt9pRSqdY1tXIo7zXJGSr4Fd9fU447DDAIC7GkaPj9GBFBAbbGULTI26Tu4p2uzz8fdqipPaN/ejj7jpS09mxd69ANqs74neQ1Ij07Bhw/CPf/wDBwUXlmgkScLnn3+OmTNnYuHChWk7QIJoj0mLFuH/PvkkY/vPlCKVzMSNrWAyRUqcGHJFKkOufWI6TjxFCkCrghIIwBsMIiRJik6EI4qKsP+WWzCyqAgf7dyJt7Zs4c8xN699TU0os1qTWrF0BwI46fnnOxxcdBXJuvYFw2E0er2wyYqUeI0xJWpvBydPoiIFABPl1dL+djtXv1i90b4sPJ+iw6VRMECpamnhitQ1EyZE/M1nP/0EADi8tDTufgfn5WHNgQN4UK5rai/Ntj3sBgO8d9yBOUccwQMpAKiRPz8xtS8ePUmRSlQjFY3NYECL39/hHkiBUKhH1UiJiKle+UKNVLPPh2qXC8eVlwNoGweVFCmmllbcfDOA2PS/bIEFUmKPQ3a9K9VIBcNhPLF2LU576aWuP9g0w8apZL8TRO6Q1Mi0bNkyfPfddxg8eDCOOeYYzJ07F3/729/w4IMP4o9//CPOP/989O3bF1dffTXOPvts/P73v8/0cRMEgMiV6GUZcpJ0y+oRq2toSUOON1O52kOrViPPaOSKVIvfj5OHDMEDp5ySNYqURa+Hy+/n5yU63YfRz25HsdmMFXv34sI33+SfFzuvz8yaBZNOl1TDym/27cPSPXvw9Lp1Kb+vriSZQEoMPJUUKRZYdlaRYkE4s6a2CvVY7HN2ZWEhODs2m14fcX1UC+rnacOGQbrzTvzzlFNgNxjwXWUl+tvtGF9WFne/v50yBSMKC/HCDz8AQKcUKQab8A7Jz8eFo0dHPJdMal+2GwmItPj9cb/r0dj0eoQlqcMNiXtyap8IW8zwBIP8+8wUYqZE1QrNe4FWRZYpUn2sVqhVqqxVpNj4UWgyQa/RoNnna6sHlmukxECKtfYIKqSD9jSi75VE7yGpWdeIESPw1ltvYceOHbj44otx4MAB/Pe//8WiRYuwbNky9OvXD4sWLcKePXvwm9/8BpocGPCInkGdMPE48fnnO20TrYQrEIBFr+crTR1NUfm6ogLfyHbLrPg2GQpNJq5IuQIBjCgsxG+nTAHQusKZMUVKSMfxt6NItQiBVKIVOTHt77E1awC02v/mG40YW1ISt1g9Gq6yZGGdgAjrv5UokBItupkTo1gjtb2uDgBQ0cGUxwaPB0atlk/GrjriCEwoK8O0gQO5+sU+Z1cWFoKLipTYi0mskWIUmkxo8vlQ7/EkbHYNtNamnTJkCJ+UJpNqmyx6jQZvXnRRxGOppvZ1VL3pKlgqajLY5O06WifVk1P7gDazGdG1j42XRWYzT+FmY6dYRyqm9uk0GpRZrVmpHAOIuAfY9Ho0Cal9TJFiwXQwHMbZr77abceaTsKShIXy/awztYBEzySlkWnAgAH47W9/i3fffRfff/89tm3bhhUrVuDRRx/FWWedlXIA9dVXX+Hss89G3759oVKp8O6778bd9te//jVUKlWMa2B9fT0uv/xy2O125OXl4ZprrkFLD7DzJdLDwah88nQ3MgVaJ5cWnQ5qlQo2vT6mYWKyTFu8GMfJDUNZX41kEJ3KoleBudlEJhQpg6FtlS1BoMZUjWQCqWKhUWetEBwyVUrsXZUIXvcTp09QtsBd+xJ8LgXCexiUl8dTJQFgY1UVttTUYHRxMfY3NXVoch1tA17ucGD99dejyGyGWQ7aeCCVhYqUUmpfKBxGrdsdU4/D1LYKp5NP3hNRaDbDKe+/s6l9Srx+4YX45uqrW/efRGqf2Gw1lOWBlDMFwwNbJxehenJqH9CmVIo1UmLKG0uFY+0j9kcFUqJZT3+7PWsVqYhASk4Nd8epkYq2eW9KIQBZuXcvVnewHUQmcHq9/PjTkbFC9Cy6dWRyuVwYP348Hn/88YTbvfPOO1i9ejX69u0b89zll1+OzZs34/PPP8eHH36Ir776Ctddd12mDpnIMg5GBc3RjWDTQYvfzyf6Drk4v7N4gsGkV8CrXC785/vv8XNDQ0xdQlcpUokCNatej8rmZj4JTxhICQoCUxNdQnCYrCLV3EPy0YPhMNQqVcLrMjqQssoruQDwiGyicu6IEQiEw/B1wAyi0euNG3BGB8HZOAlwRilS3mAQtW43wpIUo0ixQGp3QwOfvCeiUDgv6Ujti+biMWMwWa6BSSa1T6wP9Geh8YeI0+ttV/VjpKJI7XU60ffBB7G9tpY/FpSkHp3a108OlMTUPjHljTmdjpX774m99HxRZhPldjv2d7AVQqYRAym7vBDHAkZWZ+wOBCBJEg8Gz5TNNhK5tY5+/HG8I1vFA8DU557D5GeeydTbSBlxTpDL9u6EMt0aSM2cORN//etfcd5558Xd5sCBA7jxxhvx8ssvQxc18dy6dSs++eQT/Oc//8ExxxyDqVOn4tFHH8Vrr72GyqjVDiI3iTYmyMQgJtr82g0G1Lnd3OUsWaInEJ4UUvvY5HDowoU41NISGUiZTAiEw3wgT6ciZdXr4Q4EEAqHeaCm1Gunzu3Gx7t24UW51iRejxwAOGnwYEzq2xfnjBgRka4YoUgloYqwgCLb05+S6X8jqkUmnQ6D8/Oxz+mELxjExupqXDl+PCb16wcgMtD5qb4+qebFiRrTWnQ6+EMhruRmY2qfaJbBrOKZmlkcFUix1Kl9TU1JBdmFgkKaztQ+JZJJ7asWLO6zOZAKhsNwBQIZUaTe2rIFB1tasGj9ev5YT1ekhstKEzsP3mAwws2uj6ysltvt6G+383Retq0YSKVTkfIFg1glp5ung+jUPlGRMut0MOl0CEsS/KEQKpubYdBo8B+50XA8t1ZfMIittbW4+dNP03ac6UbMUsnGxSgis2T1yBQOh3HFFVfg1ltvxZgxY2KeX7VqFfLy8nDUUUfxx04++WSo1Wp8m8AO2+fzoampKeKHyD4+/+knfF1RkXCb6ElJR9PuEiEqJg6DAU+sXYvxTz2VUmF4tMtSKql9n19xBW6fOpX/Lva7YRPkQ7Iy15leONGw4MYdCCQ0m6iSg9kPduwAkFglmj5oENZcey1mDB6MOo8HkiRFBKrJKlJsm44Wr3cVwXC43Qlg9PkaXVyMkCRhR10dttbUYExxcVt9nhCQD3v0UQx+5JF2j6HB40moSAGtDnj5RiNfLc4m6j0eWHQ66DUabjbB1M9oswM2sW/x+5NL7RMVqQwHUjq1GmqVKuE1W9XSwlUeXxZf2+w6TFaRYtdvMpPMdbI7cIVQBxTo4TVS/zzlFMwYPBjDCgoARKb2mbRaHkgVmEwYUVgYE0iJqX35Qi+1zrLw228x5dlnsVN4vc7Q4vdDq1ZDr9HAZjBE1EiJLUQ8wSAqm5vR12ZDicUCrVodt+E4S+PO5toj0bEvm4+TyAxZPTLdd9990Gq1uOmmmxSfP3ToEEpkKZyh1WpRUFCAQwm8/O+99144HA7+Uy6nXhDZxakvvYRpixcn3MYd1RQyHWl30YiKiVhcncqqYETtQzicktlEgcmEv82YgZMGDwYQmQLBJsisViy630hnYDdvfyiU0P6cKQbs5p6sEuAPheAKBFoDVaZICcXIiWA3q3RY0WeSZAIplUqFG446Cl9ccQWA1kAKAH6oqkKz348SiyVmIvofYbW+PRIpUmxiU+VyocxqhQTl4LTG5YpIrelKGoTURFZDJ07ORMSJfTKpfWLz6EwrUiqViitq8ah2ubh1ejYrUmK6ZTK0l9onNvH9WR7fxJqZYA937RtVXIwvrrwSJp0OWrU6QpEyarU8vbfAZMLIoqKItEbRtQ+IrF3tLCzleHk7C5bJwmp4VXI9sZL9OdA6bh9saUGZ7EJYZrXG1Dsz2P2FqZnZuMDAvg/9bDa0ZPk9iUg/WRtIrVu3Do888ggWL14c0YchHSxYsABOp5P/7EujtE2kn0Qr5MyanPXXyJQiZRVqpBipOCeJKYhOeZUu1RXwjy67DGcPH45fCwosmyCzWjFDGicbTN3yhUJtrn0K+w/Ln0+z3w+NSpXUMRQK/bE6okixVcpcCKQA4Ikzz8SMIUMAtE6myqxWnnKTZzTyoKDF78fuhgZc+8EH/G+bfT6uSCrR4PEkTO1jDHA4ACin91345ps4/403+GctIkkSvj94MGIynE7qheM3arU8AAdi65rM8kQVSC6gL5ffs9K+MoFBo0kYINV7PNx4IKsDKfn7l2xqXyLHU38oBO099+Der78G0JbiJY7lPT21T4QtBrDFNJVKxcdajVqNEYWF2Flfz79P0al9Vr2+Uz25RNh+D6QpK0es4WU1UmItmBhINfv9/PrpY7XG1DszWOYHM+4R76VK41F3wK7VfnY7KVK9kA6NTF9//TVmz56NyZMn48CBAwCAF198EStWrEjbgX399deorq7GgAEDoNVqodVqUVFRgd/+9rcYNGgQAKCsrAzV1dURfxcMBlFfX4+yBP1DDAYD7HZ7xA+RvSRq0MoCqQEOB47s0yftilR06tkA4Vq54aOPkt5PjVD7UO/xtJpNpDhxM2q1eP8Xv8BQOT0EEBQp+SaUCUXKFwwmNJtYdc01/AaZZzQmtfDBalPqPJ5IRSpqxf67Awdw59KlMQEyu7kmU0/VnSQbSEUzurgYq2RXKofBEKFILY3qlzbokUfQ58EH4+6rPkFqn9iwlhV9K6VfbZLHWaWUotc2bcKRTz8N7T33ZGQSEaFIydcZW6WOXoxQqVRclUpGkRIVrEyn9gGtixPxDEMkSUKj14syOc0rmwOpeKmV8VCrVLDodIrXB+undPuSJfAGgzjU0oICkyliLO/p9ucibLFITO9mCwVatRojiorgD4V4/aPYkBdova4lpGcRiS1apWtBSgykuCIVCECrVkOn0fDFCrdscMO27WOzxQ2kxJYAoXA4op9eJlx6O0KTzweNSoUSi4VqpHohKY9Mb731Fk477TSYTCZ8//338MmDndPpxN///ve0HdgVV1yBH3/8ERs2bOA/ffv2xa233opP5aLDyZMno7GxEeuEppxLlixBOBzGMccck7ZjIbqXsgcfxIR//1tRAWKBFNC6Updu+2ZfKISwJPGJvtjgc18KdtRiMFjv8XRIkVIikzVSoiKVyBVwYt++uHv6dABtxf7tkawiteDLL/GXr75C3n33Rfx9T1GkQh0MpEosFr4y7zAa21Kj/P6YhYVEtXrNPh8OtrRgaH6+4vOFZjN+uukm3D19Oo6SXVGVvkNswlInLAgwlgi9235K4LzVUeo9Hp76xBYK2HtWUpFYulmRYCQRD5VKhYGyKtUVE3WDRhM3NanZ70dIcCLM5kCKvYdUFm6YHXY0PwvXTJU8jo0oLIxUpDr4PcpGTDpda42UkN79i3Hj8K/TTsOFo0fzXnsNXi8kSYpx7etsP0MRTyYDKblGSkxj5zVScko327ZIbvERDIdx97JlEamN4vj29tatEaYU2RJINXq9PHOAXPt6HymPTH/961/x1FNPYdGiRREuescddxzWp5C3DwAtLS08SAKA3bt3Y8OGDdi7dy8KCwsxduzYiB+dToeysjKMGDECADBq1CicfvrpuPbaa7FmzRqsXLkS8+bNw6WXXqpolU70XDYcOoTnZVc4ETGQYo1h0wlLc2IT/fHCCj4QuVqWiAahQWid2926GpmGoEen0cCs0+FQSwsvZk8X+iQVKQB8op7s5C9GkZLPL6uRYgGqGGyKqWNMkcj2QKqjipRJq+WThGhFKpV+K9vkCQmru1JiSH4+/nzCCfw1lFL7WApNnULQ5guFeBH93gw0Cj3Y3IwyeXI5KC8PAPCd7MqqtBjB+i+VRDn6xWPVNdeg6ne/S3sKuRL6BKl9bMLIFKmOWN13Fd6OBFKCrb+IaCjEVInhhYWxilQPrpESYYtF4r1Lq1bj/449Flq1OiL9LRAOQwJiaqSA9LjDZVyRku3P2XsS35vYE9EhG2is2rcPdy1fjovefJPvU6wvvvi//8Xdy5dHvF42UOfxoNBshk1OuyR6Fynf4bdv345p06bFPO5wONCYhBWvyNq1azFhwgRMmDABADB//nxMmDABf/7zn5Pex8svv4yRI0dixowZOOOMMzB16lQ8/fTTKR0HkX0EFeotGhQmcTGKVJoHMTYoMkVqZFFRxPPJ5pZ7gkFeRF7v8bSu0qUplcii06HB40lrWh+gbDahZH8OtE7GgeQDS5teD61a3aZIyefXKKhgACBObUWr756iSHUmkGI4jEboNRpYdDpUtbSkVAfIFKLDZPvlRLDPIFqRCggTeiVFyh8KYYDDAb1Gg4oU7wFKeINBXPTmm1ypONjSwk0hxsnmQi/++CMA5b5x7HwnG0j1kZ3DuoJEqX0N0YFUFhbVM9h7SEUBLzSbFdXTlzZu5N/zLTU1AFoDKXcgwO8DgVAoZ1L7uGFKHOdWMdhggY5Yd8qt5NPRz1D+rrvTdK3F1EjJ9uemOIEUrz02GOD0erFMTlsW7zNVLhdfqBtdXIxd9fURr5cN1LrdKDSZyLWvl5LyyFRWVoZdu3bFPL5ixQoMkYulk2X69OmQJCnmZ3Ecp7Y9e/bgZtlUgFFQUIBXXnkFzc3NcDqdePbZZ2GN6nZP9DzYADlKCFyiLcSBKEUqA6l90bUAbFWU3diSde7zBIMoNJth0GjaUvvSFPiYdDo0eL1pTesDolL7wmGo0FoMrQQLpJK9salUKpRaLNhRVxehSFmEGy3Qev6VXMyYWpOuCUCm6HAgJUywmJJ5RFkZ1h08iKY451hp4l3ndkOnVidVL8TOffRihKgyxVOkjLKFc7w6h1TYWVeH/27ZgsvffhvBcBg1Lhc3YNBpNO0uGLC00a4KjlIhUWofWxxg13s2LxJ0RJEqNJkUr58mnw9nDR8OoDXzQIU2hZspWLmU2scUqWgTCYZYR6SUQhnPSv6lH39M2TQio4qUwYCwJKHO7eb3aJMwvosp3azR/VZZQRfT4w61tKCPzYYbjjqKB34PnHIKf71sgCtSBkPWHBPRdaQ8Ml177bX4v//7P3z77bdQqVSorKzEyy+/jN/97ne44YYbMnGMRC+ErepcLauVABT7TLi6KrVPmIgemD8fP//f/wFIXJ8iwgKnQrMZdcxsIk2KlEmrRViSMqZI+YLB1hXhBKk1yfTsieb4gQOxcM0aOH0+fn6jJwks9zwalmrWE8wm4gWfiWCTKZNWy8/7xD598ENVFZp8PvS12fDfiy7C3EmTcP6oUQCgmDbV4PWiwGRKKm3NEmeCJiqB8RQpvUbTOhlKQ80CC5LXHzyIg83NkADeZweINMhQYuqAAQCyNJBKoEixsYS5J6ZrLKv3eHDdBx/EtZfuCD4FpaQ9isxm3khZxB0I8JTNR9eswbCCAp76y66nXDKbYOnL8XpjiXVEXJFqp0YqGA7jinfewcB//SulY8lkjRT7t9rtjq2RCgZjFCl3IMADKXEcqXK5UCq3gGC1wGxhJVvUnzq3G0Vmc6silSZHRaLnkPLI9Ic//AGXXXYZZsyYgZaWFkybNg2/+tWvcP311+PGG2/MxDESvRA2mRos32ABKK52Zzq1T8mdqq/Nhj5WK7RqddL1Kh559XFYQQFW79+PsCSlLbWPvf+0B1JR9uftTWQeOOUUfC73QkqGv510Ev8/O7/R/WYaPJ6EBha50JBXCbOwUssYlJeHfU4nnF4vThg4EBeMHo3HzjgD8yZNAqAcSCVy7Iv3mtGqLpvg2w0GZUVKbhjqMBjS4prJlBl/KIS3t26FWqXCRKHm9Z1LLkn49/edfDJWX3NN0tbcXUki+/MGjwcqtPaiAdIXSN25dCkWrV+Pb9LY5sMbDEKtUqV0bSsFUmFJgjcYRLnghnrnCSdwFZZdT+0t5PQkmCIV7z0ZBUVKSfkzCYEWg333Q5KEq997L+ljyaQixcaTOrebH7NOrYZGpcK6ysqIbdkYv+HQIZRZrRFjWVVLC0otFliEHoNsYSVb1J86jweFJhNsej2/poneQ8p3eJVKhTvuuAP19fXYtGkTVq9ejZqaGtxzzz2ZOD6il8LS+Cb06QOgtW6gsrk5ZqWnM2YTosVsPFhgFt2TRqVSwS67EiUDc+k7Y9gwfPrTTwDS17eG3aTS2UMKiDWbaG8i89spU3ByCum9Q/LzeT2IkiIVCodR43ZzxzYlsrmOBOh8ap9oz93PbocrEMC+pqbIxrPy/xMpUsmgVqlg1uliFiMavV6o0Lqo0a4ilYZASlR5/7h0KU4YOJBfJ0BkE10ldBoNjunfv9PHkQn0Gk38GilZfTVotdBrNGmbJLLJZzrdxHyhEAwaTUoGHYUmU8z1wybw4md68pAh3HmRXdMd/R5lI6zFQzxFSqVSwaTVtqb2yddKRCAl/19cRBIVnOdk865kyKQixe7LtUJqn0oeYxauWQOgbbFOXPQ4Y9iwVsVOfu9Onw95RmPEPbjAZErrd6SziDVSQHq/a0T20+GRSa/XY/To0Tj66KOpJolIOxWNjdCq1RjocKDhttuw8PTT4Q0GYyZqnbE/v3vZMgx+5BH8+sMP4zYT5YqUQo1JSoGU7NIn9oBKlyLFbqyZNpvIRGoNW4nkipRwI1qxdy9a/H7e30jJ6jabLaKB1hXizphNiBMMVjvzc0NDxOPRk04R0To8GSw6Xcx3qNHrhcNoRLHFoqhI+eVJdV6aUvsaPB5+zlr8flw6dmzMNiOSMM/IRgxabdzgX1QPWdPVdMAmy6laRZc+8AD+LjfJjSZefU/C/VmtqPN4It4/m8CLk+QSi4Vf3+x6ihd09ESYIpUoODTL6ouS2QQ776Ii1dEFjEwqUmwMq/d4IhYN2fFrVCqcMHAgAESMUacOHQqg7T01+XywC86lQOviUTq/I50hFA6jQaiRArJHKSO6hpRnXuedd57iKpRKpYLRaMSwYcNw2WWXcYtygugIFU4nyu12aNRq5BmNfKCtj0r1ijabYEpGvLqUXfX1eHPzZiw4/njslVWvf69bh1+MHYsT5EbPItH25yIpK1JabcTKeroUqa5I7UtGkeoIhVGNVsXUvv1NTdBrNLhkzBhc/+GH2N3YiMnl5RF/r7S6HwyHsWrfPhwv36Tbo87thkGrjVEd00FnFSmxjm6QkOYqXkeJAqn9TU3t1hSJWBQmJ6xOrdBkimgszfDJipRBq02bItXXZkMoHMaB5mZeAyay/vrrs16NVMKg0aAhzqS1QQh60zlJZNdFqoFUtcuFO5Yswe3HHx/znC+qSWwysOt3r9PJXSTdCgtVYlNlMbUvpxSpYBDmBOmKZp0ObrnXFBA5tqtUqph+ex1dwOCufZlM7fN4IsYxVt/60vnnY6B8TZQK9YzF8v+dXi8KTSblQEqvz5qeTY1eLySA10gB2VO7RXQNKY9MDocDS5Yswfr166FSqaBSqfD9999jyZIlCAaDeP311zF+/HisXLkyE8dL9BJqXK6IYnG2UhttgS4GUmzC+JJsjazErz/8ELcvWRJjElERp/+NKxCAQaNRDMzsBkNcBzWgNQhjx+uVrW7FCXAqSkEieGpfmgMptgLMzCbiWZ93hjtPOAEAeI2EmNp3oLkZfW02OIxGFJnNEY07GUqK1PMbNmDa4sX4dv/+pI6h6J//xIR//7ujbyEhnbU/F895X5uNXzNKgVT0pKLB48GGQ4cwNSr4TIRFTu2r93hw08cfwx8KRQRSCVP7ZAvjzrK9rg4DHA6sv/56rLvuOsXGumadLunar2zCoNXya3Z/U1PENdrg9fIG24n60YQlKaWJb0cCqXA7xfJeuS4uFVggJaZTs4WqaOMdo1YLnVodaTaRazVSCVQ2kxxIxcuIYMEYo6MLGO40BlKBUAi+UCgmkAqGwxGLhuz6zxcWRMXvOFtcq3G74QuFEAyHuQLFsBkMvLFxd8Pq/liNFECKVG+jQ/bnl112GX7++We89dZbeOutt/DTTz9h9uzZGDp0KLZu3Yo5c+bgtttuy8TxEr2EZr8/wgmODbrRkwG3YKF6bP/+KLNaE9Y9sZW9zdXVqGxuxiVjxqDAZIprG9vi9yum9QGt/Wpe+OEHxUmHJEmw3Xsvjn/uOQBtqX3iBLg0TSmx0Y5I6UKlUrXaNTNFKgMrwqcMHYrgn/7EUx7Z5OyZ77/HgeZmXng/JD8/IpCSJKm13kRBlWDNg1emUFwv9iZJJ51VpKKD1+HySn60sqlRqWIUqe8PHUJYkriLXTIwJeShVavw6Jo1WLl3Lxp9PjgMhrjWvkydyDMa0eD1Yl1lJbbL7lsd4Zt9+3BceTlKLBYcKddI5gqi/Xn5ww/j2Gee4c81eL0RqX3xVtvv+PJLWP7+96TTWtl1kcpkO5HSHgqH0eD1pqyAl9vtUCFy0YpN4M06HR489VQ8fdZZAGRVSqi5y6XUPhYAJAoOmSIVLyMiOoiIXsBoLxBmuAIBqJCeQMoVlaYppq6L9yaWRSBmlojngd0Xq1pa+HVoNxgi0pkNGk3C5tZdCUt3LhQVKQqkehUpj0zPPPMMbr75ZqiFQU2tVuPGG2/E008/DZVKhXnz5mHTpk1pPVCid9Hs90f0vuGKlHDDkOSVWXGQdshNAOPBVvSXV1Tg2/37McDhQD+bDQfiWAO3RB2HCFO1NlVXxzx3QLZt3iw3mGRmE+KqWrHCSntHYO9faeW+s7BgxZ9B1yxR7WNpw8srKrDP6UQ/WakSAylJkhAIh2HT6xVT+1iRspKCFU2mbWo7GkixRrPDhZo6AJgku9cVCmpMPOOTbbW10KnVvMdXMtjk7w+boEhoS+0zK9RPAW2KVLHFgiafD0ctWoSRjz8e0cg3WQKhEA40N8c0vs4VmNlElYIDqdPr5SltNoMhroL01d69AADDX/+K7w8ebPc1O6JIJdr2ho8+wpNr16asgOs0GtiiVEue2qfTYf7kybh24kT+nFhzl3NmE7LKH+89GeVtlFIfxX0wWGD7wrnnAkg+1c/l96PYYoE3GEw6+IoHW2SJVqSAyKCKLZbFc2MtMpuhQmtqqRhIianN4iJfd1PjcgGITO0jRap3kfLIFAwGsW3btpjHt23bhhBzmDEaU3LzIYhoWqIUKbvBABUiU/v8oRDCkhQxYNvaqVtiK5yvb94MVyCAa488En1ttriNRMWc72gemzkTgPLq7VY5gAJaV+yZIiWSrsCE7bcwA6lOLBUpU2YTSvxTbra4vKICw+QgYEheHg+M2OeeZzQqrkiyzziZZsmZXjkMhsPQdGAsPH7AACyYOhX3CBbxAPD3GTPw0KmnYkxJScTjStf9ttpaDC0oSOk6swl1hkBrrr9TDqRY2l80zMEtemFA/9e/4qm1a5N+baBtoSQ/C63L0wFTpD7csYM/xiZdTrkWBGgNoLfGUfXGFhfz/3974EC7r9mRQCo6hVqEpU53JLBxRF2nLkGRUtqWHXNO2p8nUNmYTb4rEIBaDhpEohWpqpYWlFmtvL9Sg9fLU7IT4QoE+Pe2s2ly0YGUqFiKn+8o+fqNDqQ+/MUvsO6666BVq1FkNqMqKpDqL1jkA8gaRWpHXR0sOh3vdQVQjVRvI+WR8IorrsA111yDhx9+GCtWrMCKFSvw8MMP45prrsGVV14JAFi+fDnGjBmT9oMleg/NPh+swuCrllM9GhRWM8VB2t6OIsXymTdVV8Og0fDmj/Ea6yYKpAbLk3ylQVNUQ6pdLngCAX5jYQ030wVrXpmRQEpM7euiicyc8eP5/5mN9cC8POxvaoqoD8k3mRRT+9jkKzqQ8odC+GTXrogi7UNxAuh00dGVdJvBgL/PmBEzwbTq9bhl8mSuWDGUFKm9TmdEH7ZkYCllQTmQqvd4uCJlkV0xo1U8pkgpNcC97YsvUnp99j1MV/1gtsFStsRrk6lTTXIKJQAcUVaGbbW1ipNbv+AwmijgYTR3UpHaHaXsMiUqWaMdEXadVrW04NNdu/h7V1LT84xGNAqpfTmjSImpffECKdnd0eX3w6LTxSxMRytSh+RAii1ANHg8yL/vPpz96qsJj8Xl9/PvbWfT+6IDKbVsisGOl/HqBRfg+okTuakE48zhw3kqb4nFgmqXi1+7Vr2ef/6T5XtCoubWXcnW2lqMKi6GSqWCTqOBIYts2YmuIeXq9IcffhilpaW4//77UVVVBQAoLS3FLbfcwuuiTj31VJx++unpPVKiVxFdIwW09rHZJqzSKgVSNr0+4Q1eLJYvs1qhUqmQbzQqpuex44gXSNkU8qGf/O47zBgyJMImen9TE0KSxPfz/fXXp6Uon3GYnP7V2dQMJdgNvStrFMRJ1VhZedFrNJDQWsvE6gXyjUZsV7iRskngvqhA6uOdO3Hu66/jzhNOwF3TpwOAYopVOumqlCSlQKqyuTklxz6gTZFiE9gGr7ctkNLpEJYk+EKhiNVmXzDIU/uiYUHXgaYmeIPBCPt/JVhg0BONJJKBNaWtcrmgVasRDIdxqKUFQwsKWlP75Inw0IIChCUJB5qbMSzqnImTtO11dQlfzxcMwhcKoZ/NllIgJdZTffHzzxEpd0wd6YhqaJebNvd76CGEJAl3nnACSiwWxTTBPKORH3OioKOnITbkjTc2sBRQVyCgWKNrjA6kXC6UWiz8e3PWq6/CEwzynoVKhCUJnmCQf2/THUgBrfdmbzAYcY8ekp+Pp+RauHiY5WCTvUf295Xz5/N5QbYoUltrazFKSEW2tbOYS+QeKY9MGo0Gd9xxBw4ePIjGxkY0Njbi4MGDuP3226GRB9gBAwagf5Y2RCR6Bs0+X0xt0pTycnwjGAgoBVJ6jQb/27kzIuASESchbFW4wGRKqEhFB3SMaBm/2efDb/73P4x47DE0CL0zdsvmF+yGWGAycTUrHTAL2Uyk03JFqgtTa8T30S+q+eqIxx7jn3ue0ai4IskmgTUuV0R/MKZGikFslZzfDmSmXqqjfaRSJd9ojOnxVCm7HqaCVa9Hs8+HSrlmMFqRAmInXP5QCAatVrHmj/3N4U89hWGPPtru6+d6al+R2QxXIIAKpxNj5BSneo8HgVAInmCQp/axc1kjXJ+MZp8Po4qKcGSfPhHjoRJsQlfucKS0eMM+Y5teH5P2zOpdUmm+zWABf0j+rj25dq2ikgm0BVKSJOWUa59Jq0UgHIY3GIz7nnhqn6xIxewjKrUvWpFiSvugBIo0+4zZtZaJQIode6o9E42y2sQyDtjCTR+bje9fNG7pLiRJwtaaGowW0m2zpb8V0XV06g5vt9thj8pbJYh0oJRSN6KwEHsaG/mEVymQ2iLXJl3/4Yft7neW3OuswGSKmyKTKLVPo1bDrNPxQXOjoGrVezwYIa9SsTQ/pRtiOpjUty+emTULNx97bNr3LSpSmbA/T+b1gTaLb6DtPOfLNVLRAVCj18sVLDFAZgGWK2oCwkg0kehokNVVilR/uz3CeTIkKx2pBlJsNXWHrHRUu1xwBQLcbAJARJ1UKBxGSHZQtOj1MamILr8fkiTxz6E9c4T6HFek2Or/lpoaPj40er382mSpfWw7pb5dLX4/ju7XDzcfcwx21tcnnLQxlXKAwwGnz5e0as0m6UPy83FQwYjn5CFD8JcTT0xqXyJ2gyHiPVW7XPw9R8MCKRZ05UpqHwsKmv3+dlP7os2UGDFmEx4P8o3GCGe7u6dPx8HmZj52rausxF3LlvHn2fc4k4EUW4SMZywRD5a2p9SQmJENitShlhY45YUNhk1ejCJ6Dx1qPPPf//4Xb7zxBvbu3Qt/1CC+fv36tBwY0XsJhsPwBIMxSlCR2QxfKNRqeS6s+kS49skDtlJRvCRJaPH78dgZZ2DO+PE8MMg3GuEKBFDZ3Iw+crofI7pWKxrRplhMDzzY0oI+Vit+Nhh4jUEmGr4CrQrO1RMmZGTf7Gbl70KzCSWYWx0APLdhA4C2yXZ0kNfo9eKwggJsrqlBjdvNJ6VsUimmXYipfQdbWmLSqADg4VWr8Icvv8S+W26Ju3oej65qJNrPZouou6l2uRCSpA4pUtWCCsLUVJbaB0QGomwiwyY6JRZLRPuBZr8f/9u5k//+3IYNmJDA0rzB44FBo0lbs+psg6Wt7mlsxPkjR8Ko1bYGUrJaxMYv3ktHSZGSF3dYbaTT6407tvBAym5HWB7/7HECFxGvnK7Zz25HZZQiVed2Y96kSR26rsOShK8qKiIee1Q27YmmwGTCjro6fC1vnyupfUydafb54p5DQzupfSadDk3C5+IKBGDV6yNqJ8cUF8MXCqHG7UaJxYLz33gDe51ONHq9+Nfpp7cpUmlO7RPvxyw1Uxy/k8Gg0cAbDPJASslqPxtqpNiYO1BQ/qx6PVqyoL8V0XWkPDItXLgQV111FUpLS/H999/j6KOPRmFhIX7++WfMjDMgEkQqsBSs6AJkNnFgz7OBWwy43rr4YpwyZEhEyhbDGwwiJEmw6fUw6XTcdpvdSPo99BDe3LIl4m8SpfYBkatPosHEttpa5JtMKLFYYlL7ehLdkdoHAO9feikWn3MO/72/3Y4TBw0CAHwnO5WxVc7o9I5Gr5cHROJElE1WxdXCKpeLp5DGc/l7ePVq+EOhmKL7ZOgqk47+dntrA0v5XLDUvOjUyPYQJ9kDHA5+TTsMBn79iosULJBigWyxbF0s8v727Si323HKkCFx3TEZrJdSrrq+iumP+SYTd6ZrilKkdBoN8o3GuIqUTa9XrNGMRlSkgOTMKYC2vnelFkvEYoM/FEKz38/H4lQ5ViHlf1ycOr5fH3UUgDYFOldS+8ReQ/HeE1vAcgl9EkWiFSlXVL/D0cXFvD6SqcBs/Fuye3fr32Qgtc+i00UEc/eceCJKLZaUDZaYIucNBqFWqRQDTr1arahIra2sVFyAyARsLiJ+r20GAylSvYyUA6knnngCTz/9NB599FHo9Xr8/ve/x+eff46bbroJTqHRHkF0FDYIRq/+s8CKmQjwQEq4gZRZrTh/1Cjsb2rC+qg0IqXUA6DVapqxp7ER6yorI/4mkZIkFpbuaWzkbmO7GxuRbzSixGLJeGpfJmGrfl3dEPPsESMw54gj+O8qlQpL5szBBaNG8SCZ1QNEr0o6ZUUKQIQ6oqhIuVyYKK+Wxguk2E28VmFS2x5dZRvP+m2xAIr9m6oiJS5eTB80iF+7oiIlppKxc89SMEsslhib4p8aGlBisSSsRWTUyylKuYp4fvONRuQZjfjzsmV45NtvAUQGsoVmc4Q5DsPp9cImN0gGElsts2v+MLmRc3uBLIO5jFp0uogJOzuejjqE/m7KlJjH4qkyBSYTDBoNVzVyJbVPvJ8ksj/nrn1KipRsWAG09VNk3889//d/WH3NNRhWUIACkwlrDhyALxiE0+dDqcXCAyb2b2E6A6moY716wgQc+t3vUl4YYQt4zNhG6e9ZsBXNpEWLMOOFF1I7+A7C7gniwgLVSPU+Uh6Z9u7diynyYGgymdAs37CvuOIKvNqO1SZBJEONwioP0HbzPv655wC0TYijB282eZz49NMRk4x4gZTNYMAHv/gFAOCvX32FoxYt4hPwRK59QOsEkxXI72lsjFhxtRsM6GezoUJeYMhUal8mYTf0bOzjwhQpcVVSkiQ0er0YlJeHwwoK8O916/jjn8gOVuI1cailBUPy8pBnNEbUGIl0KpDqogCUBS8HhEBKrVKlnIrIvnMqAH2sVv54nrwoAETW7UQrUgMdDgzOz8fsww/HXSecAADYWV+PApMpYS0io8HrzVnrcyByrMqTAykAeP6HHwAgosaF9Vx6f/t2qO6+m/f3qvd4UGw28wWkRC6l7DnmfrkvycVOTzAIk07HHeYYzNCkM82/+9vtMGg0+PAXv8CDp56acFurXs8DqVxJ7RMX1OKm9skLWHEVKcFswhMMQkLbtTUwLw82gwEqlQrjSkqwuaaGp+sOLSjg90H2ubL7ajoCqXTd40RFSqk+ClCukWJtGzbGceFNN7VuN6x6fUTqoU1I9yd6BymPTGVlZaivrwfQ6s63evVqAMDu3bsz4npF9D7YoB9tpyxOCj2BAFr8fug1mhgTBDGNYIvQGDdeIAUAZw0fjlFFRXwArGhsRFiS4JLTaOJRaDLxVdpGrxcjCgt5A1arXo/xQtpKj0zt6yZFKhnYJNQdCODuZcvQ4PHAIxtj5JtM+NWRR2JTdTUkScKWmhqu0kTXSJVarSgym2Nc7xhsRb7DilQXBKAshY+palUuF4rNZp6+mizsO2fV6yOUITbpN2q1/DwCbWmV7Dv4txkz8PqFF+LF887D9XJq1l6nEwUmE/KNxriKlD8Uwu1ffslTYnMZNj7km0wxY5FovOAwGuH0+fCwfI+tc7vR4PVCQmsgwxWpBJO2Fr8fKrReHxadLqYlQDy8cmpftDuc0gp8qmy84QZU/e53OHP4cMyfPDnhthYhkMpJRaq91L54rn1Cah9LtVXablRREbbW1nIVf2h+fkwgJY6jnSHesXYEo1AjpVQfBbQ5G4psFe73XUGt2x2zqECKVO8j5ZHppJNOwvvvvw8AuOqqq3DLLbfglFNOwSWXXILzzjsv7QdI9D5q3W7oNZqYAMak0+Gz2bMBAJtranitQDRDBWtx0QBCqaZKRFwN3t3YCHcgAAmJlaRCk4lPwJv9fjgMBpTKK/k2vZ43GAR6ZmqfXlSksmgio9do+CRkU3U17lq+HPM+/phPuhwGA4bm56PZ70edx8MDjPGlpXyVXpIkVLlcKLNaE6adsdoCpXqV9uiqANRuMMCq1/P32eDxdCggYZMCs04XoQzZ5RXuvjZbRCAVbTaRZzSiTL7+i81mHjQUMkUqyoI7LEn458qVMP3tb7h3xQqsOXAgp1P7gLaAIM9ojBhbDBpNRD8lh9xzqUJQx8X6UXsSqX3M9U2lUsU4OybCEwgoK1Jx6ldTIS/KXS4RVr2eXzPZpoh3lFRS+9wJzCZYgNsSJzMDaE3p3FVfzxcnh+TnwxUIICxJ/HM16XStfZs6aSWeKOhJFdG1L94+Wa8txrbaWhz+1FNpef1kUQqkyLWv95HyHf7pp5/GHXfcAQCYO3cunn32WYwaNQp/+ctf8OSTT6b9AIneR7PPxydu0bB+DazruVKQIwZKYt0LsxhOpDAx1h88mFDBYoh1DOx42MBq1et5/Q2AlNWBbICt+nnSeJNMB6IzFLMw31pTw29gdoMBQ+SA+ueGBr4i+8sjjkCd280dzLzBIIrN5oiAWCQUDvMV/3XtWHcr0VWKlEqlwhFlZfhk1y4AsmlDBwISpoicMGhQRCDFrt0Gjwf3rVzJz3N0ap+IRq3mQRVL7Wvy+RAQJj+vb9qE33/xRYQtd3mOt9Rg40Oe0RgxCYtuSms3GOD0evm12+zz8frRYosFBo0GWrU6oSIl2mc7jMaEaYAi7PuulNqnVqlStrPuKFa9nqeDZtNCTmdIRpFqN7VPVKTkgErpPsVqori7nJyt4Q4EIhzxzDpdpxUpn9xPLh2wQDKhIqXVRihS8XpHZpJaj4cUKSK1QCoYDOKvf/0rDh06xB+79NJLsXDhQtx4443Q98DUJSL7iNc7A2ibhNS63WiUi66VuPHoowEgwsqZuU/FqxtZK5tMaFQqvL55c7sKFtCmSDFrdZusDLC/Y6+VqntatsBteNOY/54OxOuDKSSlVmtEbzE22Wvy+VDtcsFuMGBQXh5CkoQ6t5tva9Xr4ypSbOJ5XHk5lu3ZE9HgNxm6MiXy3BEjeINW5n6XKiqVChtvuAGLzzlHsVbp2iOPBNDWyDjabCKaPvJ1X2Ay8eNpFFSprfLkp9hshueOO/DCuefiD1OnpnzcPYmnzz4btxx7LIYXFuL/jjmGPx69bMQUKXadNvv9XBUtMpuhUqlgl13/4iGOpbYUbJmZa1+0O1yt2418ozHCmS2TWHMwtc+o1fLzl9D+XDabUOwjJShSiVL72P1yV309DBoNT91li0jseExabacDKX8oFLeeKVVYIOlrT5ESrk1mjHPGYYcBiHVzzQQ1LlesIiUYUBG9g5RGJq1Wi/vvvx/Bbu4mTeQ2iQIpg1YLm16PzdXVeGLt2ripKgtnzsSpQ4fi+R9+wPvbtwNoVS7yjMa4kz4W7Cw6+2xUu1x8Yp0ogCixWNAiT3BYGiCbwLO/+/HXv8a6665r/41nIazoN14/k+5CKZDSazQRgRTr1+IJBPDEd9+hyGzmCsmhlhY+QTTJaWxKDmlMpTqirKy1ue4996R0nF1p0pFvMsETDCIYDvMGnR1hbEkJTDqdYiB20ZgxANrSiRIpUkCbwsUUKQAR6X0bq6th1umw+le/glGrxRXjxydcuMgFzjjsMDx02mnQqtUYV1qKpXPmAEBMcOIwGiPMOd7ZuhX7m5pg0Gi4QUC53Z7QQMItp+gBcs87BUVq9f79+HVUA3OvYDYRDId5EX+dQipTJokwm8iR1D6VSsWDg3iLLBH25wrjrlGrRUiSEJC3AZRT+9hntbO+PiKV1CUHUir5GNKlSKWrabuoSMW7XxuiUvt+bmjA2JISvoh6KEmHys5Q63ajKGqctOr18MrjMNE7SHmJZ8aMGVi+fHkmjoUgACQOpIDWm8MTa9cCAJ4488y425VYLHAFAjjntdcAgNfDxGPF1Vdjw/XXRzTNBBIHUizVcPX+/QBaV33ZhJGlEI4rLeV1Uz0NdrNqSWMhcWdgN2rx+hCVpIhASr4B17jd2N3YiH42G//89zqdfEXXpNWiyGxWrIFiKuYY+XOOfr326EpFivcV8vk6nNonwq5j8fpn/2eBVLTZRDQsJZCZTQBt56/a5cL727fjsrFjeRpmb4QFm9Er73lGY4Rd+VPr1mFPYyP62+087Xlwfj5+Fiz+oxHHUqWUI5ffj8nPPIN/r1sXUfvmCQRgklP7gLbPuc7j6ZTRRKpYdLqcU6SAtprCRKl9oShbcxE2tnlk1QpIrEjtrKuLMDdhipRJrp9LSyCVIOhJFYOcUuqV7c+V0Gk0EWnCB1ta0Ndm4669yVr9d4Z4NVIAKL2vF5HyVT9z5kz84Q9/wMaNGzFx4kRYotKkZs2albaDI3on7mCw3UBqd2MjhhcW4tKxY+NuF22fXuVyoTSBHXR/ux397XauVLBAKlFN1ZiSEmhUKqzYuxdA62Sloz1WshG9kGKSDal97LpQuj7CkhShMrFtmGr5x2nT0MdqRaHJhN9+9hmekccqk06H/nY7ql2umMkAW9UcLQRSq/fv5+kj7eHvQkWKKTkVTicqGhtRMnp0p/bHAimxHiY6kIo2m4iGqSwRipQcSJ3y4osISxLvgdVbYcYL0RNGpbFqxd69Ea6kQ/Ly8NbWrZAkSbGmtL1AaumePfz/Jz3/PLbNmwegNf1ygMPRpuoGg7Do9ah1u7t0fLPq9by2NVdqpADwzyre4pR4LcQzmwBaA95EihRL5dtZX48j+/ThARirkWKvky5Fyp4mNdmYhNmETq1GIBzm135VSwuGFhTwtg3iwkAmCEsS6uLUSAGtY2R0LeFepxPeYBDD5b5uRG6Q8sj0m9/8BlVVVXjooYdw+eWX49xzz+U/5NpHpIP2FKkJZWUA2neOun7ixIjfmdV1e7AA7MeqKqiAhLUmRq0WxRYLdsktAWwGAy6W058G5eW1+1rZjlGr5ZbL2ZDaZxJu/NG4/P4IRUqn0UCjUvHeSgUmEwxaLd66+GJsr6vDVxUVfJ/lUX2YGFUuF7RqNW9oCgCXvfVWhDlCIgJpTHdpDzaJWbxhA9yBAH4l1zN1FDbJu0pojByjSLWT2sem9oVmM/8e1Xs8+Pynn/BjVRUA9PoUGKZIRa/m91Goq/yuspI3mwZa0wQrnE4sEwIikegaqejaDab2FJnN2F5Xx1f4a9xuFJvNfBLL6mm6us9XMsYMPRH2vYi3OCU+rhS4iooU+y4qjYlMmfeHQsgX0tqjg5SsU6Tk1MYGjyeiJYAIUyjZWMwWSgvNZqjQsXYVqdDo9SIsSYo1UkDrQuziDRsinjv2P//BiMceo1ZBOUbKgVQ4HI77E4ry9CeIjtBeIDV90KCk9jOiqAj3zpgBoHXl7lBLS0JFisFW8V7euBHDCwsTHgvQOhFi/Vlsej2mlJdDuvPOnFhpt+j13HAhG1L7EilSTUJhPptomHS6iEAKACbIlvSsONmk06FcXuXfG1VvUu1yocRiibhZOn2+iP5kieiO1L5Hvv0W40pLFSfiqaBSqdCyYAHunj6dP8augRhFKs4EiilSDoMBJq0WBo0G9R4P3tyyhW9z/IABnTrOng5TpKLdCvsIbRSePuss/vgtQu+lkwYPhkmrxQbZAOrdbdsiXPbaU6SafD5o1Wq8e8klAMBrpWpcLhRbLDGBVDqbriaD+Fq5lNrHiHcuxSyIqQrfjwhFSjakiGcAwsaufJOJL3hEO+KlI5BKp9kE+05UOJ1xU5RZYB2QF2LY/V2tUiE/QTuLdFEbpxUA+0yPf+45XPXee7x9AdCWbtgdDoNE5ujWkemrr77C2Wefjb59+0KlUuHdd9/lzwUCAdx2220YN24cLBYL+vbtiyuvvBKVsrMao76+Hpdffjnsdjvy8vJwzTXXoKULcmOJzNFeIDWmpAQAFM0BomHS+ikvvthujRTDbjDgTDl1S+wDFQ+H0YiddXUAYpsI93SU6mO6E5MQSLGJFVNFmv1+eAIBGDQaXptj0mp5ah+7Idv0eph1OvzEAilBkYou3K/3eFAoT0DGlpTggVNOAZBc2khYkhCWpC5P7QPaVNvOYtHrI1LGdBoNDBpN0mYTD556Kn4xdixKLBaoVCqUWCzc8Q8ADsyfj9OGDUvLsfZUtGo1XrvgArx8/vkRj7Ox6oiyMlw7cSLuP/lkXHXEERhZVMS3UalUGJKfj58aGvCnJUtw3uuv44FvvuHPJxNIOQwGTC4vB9DWP88VCKDEYmlTPgSHuK5cUEmm51JPJm4gJXyXFVP7hAA3nkU6g030+1qtPNDxKyhSrjSk9qVLfWcqXEVjY9yMEHY9BEIh+OU6XvZeE/UFTBcskIq+50eXAvwgK+8i0f30iJ5Nh0am5cuX4+yzz8awYcMwbNgwzJo1C19//XXK+3G5XBg/fjwef/zxmOfcbjfWr1+PP/3pT1i/fj3efvttbN++PaYG6/LLL8fmzZvx+eef48MPP8RXX32F63qoQxrRijsQgDlBigBLbUlmoGSTiJX79qHO7U5KkQKAs4YPBxBZGxMPZlOsVau7rL9KVyHeoLMhtU9UpFitCGu63CwrUibhmE06HSqbm6FC2yqnSqVCH6s1QpGyyCYh0YpUo9fLP9ONN9yAebIjVDKBFEuT6mpFCkBEHU26ESfk7ZlNjCgqwisXXMAD2352Ow40N6PC6cQFo0bxwvDeziVjx8ZMyPKMRtx/8sl49YILAAC3Hnccnj3nnJi/HVpQgB11dfirfA/2RStS8lhqMxjQ4vdHpBU1yT371CoVfj1xIhq93rZeVWZzRI0UgC537xTHn1xK7WMko0gpIX4uLr8/4WfCAymbLW5qX36UQ2RH8AWDaVOkWPZASJKSUqS44YZ8HroykIqnSDGUsheS7edG9AxSTmh96aWXcNVVV+H888/HTTfdBABYuXIlZsyYgcWLF+Oyyy5Lel8zZ87EzJkzFZ9zOBz4/PPPIx577LHHcPTRR2Pv3r0YMGAAtm7dik8++QTfffcdjjrqKADAo48+ijPOOAMPPPAA+grNUImeQ3uKlEmnw9H9+kX0YImHqFpJQNLueawYVFz9jQeboBebzV3WX6WrEG/Q2ZDax1P2BBWJBT8stU+8dkxaLfY6nSiK+mz62GzcIITtc4DDwVM0GWIgBbSmsBWZzckFUnLKSVdNAMVC7/bSUTuDaKPtD4WgVauTvu772Ww40NSELTU1uOLwwzN2jLmASqXCrccd1+52Q/Pz8diaNfz3kBAoeQTjHqtezw1Z2GMskALAnSvZBLTQbI6piWtvbE43uZ7aFy8AYopUvO+VqBS2p0ixRY6+NhsPdKJT+4otFkXX0lRIZ0Ne0RkyGUWKjbXsPHRlIBVdMxi9mMq+O6Ia7CRFKqdIeWT629/+hvvvvx+vv/46brrpJtx00014/fXX8Y9//AP3pNhjJVWcTidUKhXy5CL+VatWIS8vjwdRAHDyySdDrVbj22+/jbsfn8+HpqamiB8ie0jmZv3tr36Fy8aNa3dfV4wfH7G6l6zN8kmDB+Prq67CBaNGtbstK4bNNTUKyL7UPnZdqFUqrnIArQ5nrkAALVENLNn/B0YpNCx1RKNS8UCn3G5XVKQcUZ9ricXCV+0T0dWKlE6jwfuXXgqgrb4mE5h1Oq5QpJrO089mw3eVlTjQ3Iyj+/XL1CH2Kobm5/OJpEmrjeifE53aB0RO6MRAqtBsxv6mJvzfJ58AQETfoWafD5IkUWpfmmlPkYr3nlNRpI6Rv2cD8/LaaqRCIXiEQKrIbEat290pE4R01kiJBhvxFCkWWAfCYV7f1ZWKVI3LhTyjMSbAF4PJvjYbPzZxUZcUqdwi5ZHp559/xtlnnx3z+KxZs7B79+60HJQSXq8Xt912G37xi1/ALq9EHzp0CCVyvQxDq9WioKAAh+TiWyXuvfdeOBwO/lMu54cT2UE6Vz2LzGYskRteAq2TjmSZOmCAoqVwNMyxb5jgppUrZFtqH5tAsOL3zb/5Da498kjehLHa7earteL20Q6K7EYtpgHGVaSiXKPMOl1EQX88ulqRAlpTUt+/9FJcnkG1xyQUpqc6eSq1WtHo9UIF8LoconMMFcadk4cMwac//cSvz2jXPiBqZdzn4+oHm9x9LSu1DoOhrTeZ3w9/KISQJHXpOJDripQpjoLDznG89ywqUi3tKFJ/nDYN3117LY7p1w8atRoalSqmRqrIbOZ1Rh0lna59Zp2OjytxFSn5+aCQ2seudYfBkPFgpTpBO5VnZ83Cn+V2G6y+UKyLokAqt0h5ZCovL8eXX34Z8/gXX3yRsYAkEAjg4osvhiRJePLJJzu9vwULFsDpdPKfffv2peEoiXSR7vSR/oIbVroGepEFU6dizvjxeEd2vsolsi21j10XbCI/urgYT599NjcFqWxujijUZhOF6ECKpWOI76ncbo8xm3D6fDFKo1GrhTcJh9KuVqSA1nSws0eMyGiKaYQiFQympEixczmpX7+kjF+I9hksXNvXT5yIyuZmrJLvaUqKVLMwiWvwePh34byRIyP26zAaYdRqoVGp0OzztfUr6iZFypQF40+6YLWB8Rbq2Pc3nvMrG9eSUaRUKhWOkg29gNZ7YExqn5xK1xnLcF8aFSmVSsWvy7g1UkJqX/S1mQ4XwvaodrtREieQumrCBNx94omtxyGPlY0USOUsKc8qf/vb3+Kmm27Chg0bMGXKFACtNVKLFy/GI488kvYDZEFURUUFlixZwtUoACgrK0O1XGjOCAaDqK+vR1kC1yqDwQBDmhrHEekn3YEUG+yYgUS6OW3YsJx1HhMnMl1ZGxEPsX+KCAueKpubI2ykbz7mGGyqrsYvhV5IQFsgdZxgLTzA4YDT5+PpTpIkod7jUQ6kslSR6gpMWm2kIpXC4gQ7l8mavhDtUy6krZ42bBgMGg02VlfjxMGDFQOpGz/+GF9ddRUAoM7j4Q6P40pLccXhh+PFH3+ESavlATIzqWCT7K4cByw5qkh9fPnlWBvlQBzN6xdeiGP791d8Tq/RQIW2Gql4E3olDBoNN5tg4yAzTKhxu2HQanHDRx/hhXPPjUlrTkSqiyrtUWg242BLS7uKlJjaJ5oRddaFsD1Ya4xEiOo9M/MosVgokMoxUg6kbrjhBpSVleHBBx/EG2+8AQAYNWoUXn/9dZyj4CjUGVgQtXPnTixduhSFUd2gJ0+ejMbGRqxbtw4T5earS5YsQTgcxjFJGBEQ2UcgFEIwHE7rzVqtUmHLb34TkQJDJIe4+qzJgokMK0KOrmVidR4HmpoinBbPHjECB4cPj1FoWJBzlmxzD7QZjDj+8Q+cOGgQXr/wQrT4/RgclQ5q1Gp5ukYiukOR6gpMOh1//6nWSLFAKpcmxd2NOFZq1WqMKCrC1pqamLGUBVIsdQ+Q7f2Fwn42MRQn0KyR75RnngHQtSm+2VCXmQn62e3t9hlkjd2VUKlUrd9Dpkil0Pxdr9G0KVLyd7dIUKSeXrcO72/fjq/37k168TEsSQiEw2nN+ChMRZGKcu2zdECRkiQJq/bvx/jS0qSu8RqXq91SAVEZY4pUf7u9UymURPbRoav+vPPOw3nnndfpF29pacGuXbv477t378aGDRtQUFCAPn364MILL8T69evx4YcfIhQK8bqngoIC6PV6jBo1CqeffjquvfZaPPXUUwgEApg3bx4uvfRScuzroUSvLKWLUUnYmBOxxFsN7C7Y6rk/KrWO1XI4fb4Y62ClNDfmxni6oCRO7NsXh5eW4seqKizdswc75N5gw6MWcIxabUR6VDzYMeaaImXW6VAtm234Uwyk2KQo185JNsBMb4rNZtR7vTFjqZjyWu/xIN9oRJ3bHVHY309OORML4616PWrdbtTJK+rtWXOnk1wNpNKBSV7Qac+1LxqDVqtYIwW0KvrvbtsGANgjNJJtDxYYpPPaKJSdVm1xsodEs4l0pPa9vnkzfvHWW7h4zBi8fuGF/PFQOAyVShVzH0lGkTLrdDyAavB6YdXrYdHpeO9DIjdIOZAaMmQIvvvuuxh1qLGxEUceeSR+/vnnpPe1du1anHjiifz3+fPnAwDmzJmDu+66C++//z4A4IiotJylS5di+vTpAICXX34Z8+bNw4wZM6BWq3HBBRdg4cKFqb4tIkvIVCBFdIx0pmqkA41ajTW/+lWMjb14s03mZn7R6NE4Y8GCmInaQIcDP8oNFHfU1UGFWIMSo1ablGsfS/+LV1DeU4lJ7UvhGmHKISlS6WXHvHn8O5BvMqFRIZASJ9u76usxqqgIgXA4QpG65sgj8V1lZUR/L5vBwL8TT515JiYk0aQ8XVAgFZ8IRSqF8ySm9rFAyqDVwqbXY1N1NQ+Yt9fWJr1PtrBkT2PJRIHRiDyjMW69J0/tC4XgDgSgQlvtmEWvhzcYRCgcTjqTYrfcV/DtrVvRLJiwjHz8cfS12fBVRQVWXXMNju3fH5IkJRdICdkLTq8XDoMh6dRwoueQ8h1+z549CClE0z6fDwcOHEhpX9OnT09ot5mMFWdBQQFeeeWVlF6XyF7YzT+XCouJ9DJJwTZbDJ7irWCKqFQqxUkaS/kDgO11dRjgcMRci0aNJqkbIV8lzbHJoFlM7UuxLoKd81TcM4n2OUxY2MwzGFDR2BgTSIkq4K76ej4JFPvg2A0GvCI3AGbY9Hqs3r8fQObqTONBC2rxYYpUS4qW9KLZhDi25ZtMvLee3WDAoSQWixhNGQikDissTDhO6ERFyu+HSafjhhrsuvEEg0kH49UuF7RqNYLhML7Ztw+nDRsGbzCIXfX13Jn3X6tX47ULL4TT50MgHE6pRorVKxq0WlKkcoykAymmDgHAp59+CodQ4BoKhfDll19i0KBBaT04ovdBihTREcTc/M6kl0woK8MncrrxxurqmLQ+IHmziVy9liMUqRTrIgbm5eGz2bMxne4VGSPfZEKDgiIFANKdd8J27724Y8kS/Ou00wAgQn1SwmYwwBUIwKjVok8726abXGtwnk4sej1cLLUvhTFPr6BIAa1mOyyQGlFYyNN3k4EFUsksYiXL/MmTeVsLJUT7c3dUeiO75l1+f9KBVJXLhcn9+2PV/v34WVanNkWZmb2xeTPOHj6cL+YVC2quEmKKIWuEbdRqyWwix0j6DnjuuecCaF3JnSP05QEAnU6HQYMG4cEHH0zrwRG9j1ydfPZkzho+HPt7UNPqZHp/xePu6dNh0mrx52XLsKm6GmcouDH2+kCqE/bnAHDK0KGZOCxCJs9oVEztY7T4/Wjx+/Gb//0PQGR7CCXYwkQfq5UCmyzCYTDwzzklRUqjiamRAlpdAvs99BCA1rrQdQcPJr3PTChSWrU6YQqwVjCbcEcFk+x8KNVJra2sRFVLC04cPDjiu1HtcqGPzYZyux17Ghvx6a5dOP3ll6FRqRCSs6O0ajVmv/MOPvzFLwCgXUXKotPx+jF3IACT3B/LR6l9OUXSierhcBjhcBgDBgxAdXU1/z0cDsPn82H79u0466yzMnmsRC8gVyefPZkPfvELfH/99d19GEkTbXWeCjqNBkfLq437m5oi6kcYyQZS3Ekqx65lcZU11RopIvPkG41o8HjQHOVkxnhNTt2rbG6GRafjJhXxYCv6Re2svhNdi8NoxKubNgFIrZaMpZZFB1KiMpmqIsWutXQGUu2hizKbMCsoUkqB1KRFi3DWq6/ikdWrIx6vdbtRbDZjUF4efmpowLUffAAA+ItQx7/xhhsAAB/t3AkAivcHkVKrFTVuN4LhcIQiRTVSuUXKFb+7d+9Gkex4RRDphgIpoqO8e8kl2HTDDSn1VFGCXXthSVK03k1FkVKrVFln2NFZTEIBdar250TmcRiNCEkSV5ELo5w3Lxk7Fr+aMAEAMKygoF0FlylS7U0aia5F7G+XikOdQa7xjA6kRPrb7aj3eHgLh0v++19cKLe7UYKn9nVhPWi02YQ4Z2CLB0o24ywAc0al1zX7/bAbDJg+aBDe2roV+5qaMH3QIPz+uOP4NsMLC1FoMvH07/YWIfrb7QhLEg61tLQqUlotN/sgcoekA6lVq1bhww8/jHjshRdewODBg1FSUoLrrrsOPsr7JDoJSxmiQIpIlXNGjsSYkpJO70e89qKb8QIpKFLyzb0zqYbZiF5ODQJStz8nMg9TJyoaG6FRqRRVAmbjtHDmzHb3x+peogOyrmRQCn2SegviJP7IFJwU9RoNd9mLDqRYOwi2GFXrdqPa5cIbmzfjra1b4+6z0euFUavt0rYG0YqURWHcZtbjDEmS+HgcHXy2yPVUF40ezR9748ILoVWrefqrSqXClPJy7G5shE6tbrc+lP3d/qYmeFhqHylSOUfSNVJ/+ctfMH36dJ6+t3HjRlxzzTX45S9/iVGjRuGf//wn+vbti7vuuitTx0rkKN/s24dj+/eHWqUiRYrodsRUKKU+WqkoUrmW1ge0pgaFJAmhcLg1tS/H7N17OkwVqHA6UWAyKQby95x4IqYPGoRpAwcmvb/uCqQabrst55papwMWSM2bNCklS3qDVosqOW0vOpB655JL0Oj1Yp/c8Lza5YqojxVtwUX2NzW1W2uXbhIpUiyToCEqkGr2+/kiUHOUWsUCqWEFBfyxYjmg3HD99aiXbeHHlZTggx07kjLWYOfkQFMT3IEA+thsMMquiUTukPTotGHDBsyYMYP//tprr+GYY47BokWLMH/+fCxcuBBvJJB+ifTz6a5dmP322xHNE3saq/btw3HPPotF69YBaJ18alQqunES3YY4uVBK3TDKNQbttWeIvrnnCqwmyhcKtZpN0Hc1q7BGBVJK9LHZMPvww5PaH5uwRvdu6yryjMacayGQDhxysNCe62I0Bo0GDXJQEL3QY9RqUWa1ckWq2uXCdrkxOQAcaG5W3OdepxMDBSfnroCZTTDXPnGster10KhU/H0yWN2XVq2OaKoekvdh1ev59T65f3/+fKHZzFsMlMvvM5lFsnyjEUattlWRCgZ5ah8pUrlF0nfAhoYGlJaW8t+XL1+OmUJawKRJk7Bv3770Hh0Rl4PNzTj95Zfx8saNeHPLlu4+nA7zsZxr/LVsu+rO0XQooucgFtUHhb5SDBZotZfnnmqjzJ4CU6B8wSApUlkIWymvaGxMS10Tayg9a8SITu+LSB9HlJUhz2jEGYcdltLfGTQartTE69dYLARS+5uaoJHvxwfiuLfudToxoIsDKbbY6g+FYsZalUrF2wCIHJQDwcMKCiIUKdbzjy1CVP/ud/jiyisVX7dcVpmSqUtTqVTob7fz1D5mNkE1UrlF0oFUaWkpdu/eDQDw+/1Yv349jj32WP58c3MzdDm4+pqtvLttG3RqNUYVFfEgpCfC0gbYv7m6ik/0HKx6PZoXLMAdxx+PkwYPjnmeBVLtrSrm6rXMFCl/KERmE1kImwz+1NCQslqhxBXjx2Pr3LkYm4b6QyJ9nDxkCBpuuw3jy8pS+ju9oEjFG5/MOh1sej0OtrRgX1MTr8Gqi1J4GN0RSKlUKq7uuAMBmKMWdJh7pcjO+nqoABxeWorPfvoJcz/6CECbKQX77hRbLHHPDUvXi3culLY/0NzcZjbRgRopSZLw8o8/cvMPIrtIOpA644wz8Ic//AFff/01FixYALPZjOOPP54//+OPP2Io9QfpMvY0NqLc4cBhhYVwRq269CTYihGT3HN18kn0LKx6Pf560kmKxdPJBlLRlry5gl5I7SOziexDdE4bW1zc6f1p1WqMJKfenIHVOAKJa5EH5eXh54YG7G9qwqjiYmhUKl4nJOILBnGwpaXLAymgradddB8pAIqK1M66OgxwOHgj3SfWrgUQG0glYlSK36m+NhsONDe3pvYxRSrFQGr1/v2Y/c47+FeUZTuRHSQdSN1zzz3QarU44YQTsGjRIixatAh64aJ79tlnceqpp2bkIIlYatxulFgssBsMMUWTPQm2YlQj13lRIEVkO6koUrlqNgG0TqASWSgT3YM4oSQViYhG7PuW6F47tKAAb2/diqqWFvSxWlsDE4VAitVNdUsgJas7SotWxWZzTC+sHfX1OKywMMbJ8odDhwAkF0jpNRoMLyxMOtW1QG6Q7fL7YZEb8oYkSTFtPB5M/dpSW5v03xBdR9J3wKKiInz11VdwOp2wWq3QRK1Cvvnmm7B2UzFqb6Ta5WoNpPR6OL1evPDDD5g1YoSiXXM2U+/xwKDRoM7t5gWfFEgR2UwqipRSH6qejpjal6vBYk9GK5h/DM7P78YjIbIRsaYxoSLlcODdbdsAtDo25huNiorUXtnhrzsCKaPc005p3tDHasUPVVURj+2sq8PUAQMwVHDmA4AHVq0CAPRLMhV229y5Sddx58nnzRUIwGYw8PPvD4UivquJYKUPSuef6H5StltyOBwxQRQAFBQURChURGapdrlQYjbDZjDgh6oqzHn3Xfztq6+6+7BSpsHrxfDCQkhoHSQokCKynfYCqRa/H0+vWweX35+T17JBMNvI1feYK5R3sSU1kf3ok1Sk7pw+nf+/0GxGgcmkOJFnjxV1Q8PmiNS+qPdSZrXiYEsL/z0sSdhVX4/DCgowTlBqnV4vNlZV4d4ZM5J2pkzFDCvPaOQmHVa9np9/fwr1TsyOProvFpEdkG9tD6WKKVKCRJ1s8WM20eDxYIScf1/tclEgRWQ97QVSD69ahes//BCr9u/PSbWGTQRcfj8C4TB9X7OY7pjcEtmNmNpnSpCWm2c0chv0QpMJBSaT4hyD2YjbumEh3aTVwuX3wxsMxipSNhsOtbQgLNeDVcp1SocVFuLIPn24gcaq/fvhCQYxpbw8I8eYZzTyBti2jgZSSShSuxsasPH/27vz+KbqdH/gnyTN0qZJutCmC20psgrIIlrBDcd6ARXB6yw4jOi9/kRcrqLjMIMDOOOoKOPCyJ0RnRlx38ZR9DqKV0HGUXtR2aSA7GtpWkqXJG2Sps3390dzDidpkibQNkn7eb9efQ3NOTn5pnMSz3Oe7/d5gjJw1DsYSCUhb3s7qux2FFssAYHU3vr6OI4qdq3t7Wj2ejHc35/hREsLAylKeF0FUsrpGn3xXJYuxKSF3H3xPSa7hZMnI9NgYBsJ6kTKKGvV6pDFdJSk64sBaWkYMWAANldXd+qfZ/d4oNdoujxWTzCkpKDe/z0UXGxiaFYW2nw+bDh0CEDHtD7pcY1ajVf//d8BAO/v3g0A8nVId1Mut0jX6QLKtkdLmtoXrmfoVpsNg59+GpP++tczGCmdLgZSSeio3Y52IVCamQnlfyaDF1YmOmnh6jApkGJGipJAV4GUS/F45Ja9yUm6EGsMcwFD8ffYFVeg/pe/jPcwKAFJN0LC9ZBSWlBWBgAoNJsxZdAgHLXb5eyIxNHaKvcu622pWq0cXARfN5QPHowSiwUf7d0LANhz8iTUKpW8bnB4djZy0tLw582bYdbr5exbd8tUNMU26fVyRiqWUuZH7XboNBrUu1whG8G/7e8lqmFz9LjgXz2JtPl8mPzXv2LG668DAEozMjDO30Pi/40fj7owdysSlXRHuzQjAylqNTNSlBS6CqT2KTLDdv+0l75Ezkh10YuGiBKPdCEfTYW6O84/H1X33otBGRly/6TgG7Z2jycu0/qAjql9J8N8D6n8QZMU+K3euhVlhYXy+1epVLhg4EC0+XwYlp3dY9nb4IxUrFP7hBA4ZrfjHKsVnvb2gBt1khP+/0+iLV5B3Yt/9SRy1WuvoeLYMew8cQJAR0WmC4uL4Vu6FGUDB6LB5YqppGa8SfN9s1JT5VKlDKQo0UUKpLbabHi9slL+vakvBlL+98+pfUTJR/r8ZikyJZFITZ2l/YNLoDs8nk7lxHuLMiMVaj1qkdmMo3Y7hBDYYrPh+tGjA7aPtVoBdEz36ynKQOp01kh52tvhbmvDWf5MWqiCE1L7mEa3W14TRr2HgVSS8LS14X/375d/n1hQIN99UKlUGJCWBoHOX3KJTBprZmqq3KOCgRQlOulCxOX1Yu2+fQELfF/fvh0AsOiiizr27YPNaqULgXpmpIiSjhT0xNpIW2rloGxy+11NDU60tMRtap8hQkYK6AikDjU2wtHaitb2duQETd+7fPBgAOjRtjFnmpGSinlITYRD3cA70dICQ0oKfEL0yVkQiY6dFJPE9tpaAMDSSy7Bggsu6DQXNtt/t+hES0unL4tEJX0hZxoMMOl0cLa2MpCihJeiViNFrUaTx4P/fP99AIB3yRKkqNXYbLPh2hEj8Mjll2N4djZmRNm0MZkEF5voi5UJifqqc/xZmBpFafBomPV6aFQq+QZKu8+HsatWAYC8xKC3paakyAFJqOuGC4uL8cgXX+Afe/YA6FzFcsqgQXjzhz/EhT1UsQ9AQLbudAIpZ2srgFNj94SZ2jc8OxvbamrQ4HIlXT/RZMeMVJI41NgIALj7gguQmZraKZUuLaAc9ac/4UBDQ28P77TUu1wwpKQgVauFSa+Hg4EUJYl0nQ7rDx6Uf3/wn/8EAOyorcWonBwAwI3jxkU9fSaZaNRqpGm18oUYP69EyUPqLXbpoEExPU+lUiHDYJBnkihLoc8955xuG18sChQNdEMVvbli8GCMysnBT995B0DodgA/HjUKhT3Yb025bkmlUsWekQoKpEJlpOpdLgz1F+2qSbKiY30BA6kkUe9yQYXwKeiBii+Cl7dt66VRnZkGl0u+0DTpdAykKGmk63TYVVcn//67zz+HzelEtdMp3/Hty8x6PWwMpIiSjkqlwvF778Vfr7km5ucqm/KeUFyw333BBd02vlhIN62A0EGSVqPBbxWNhXMSoK/aGWekQjyvxevF+Lw8qAB5DT31HgZSSUJK16ojVJZ5YeZMAJAvcJTcbW2orK3FcYejx8YYqwa3W553bdLrcaK5GQK8MKPEl67T4WBDA9K0Wjz8gx8gTavFl0eOAECPNXZMJGa9HtUMpIiSUr7JJBfNiUWu0ShnPKQCB11dl/SkUbm58r/Drfm6cuhQ+d89VeI8FlK/LW+UhcGCA6ngjJQQAi1eL3LS0nBWVhab8sYB10gliXpF9iacG8eNw+uVlahtacFxhwNpWq2cwbr+73/Hmu+/BwCIBx7o8fFGo97lknssmHQ6+QuaF2aU6NJ1OrQLgVyjEYMyMtDi9eJ/9+9HicXSo9NEEoVFr8eekyejaupJRH1DodmMKv/NWKndyoG77orbeIZEUW0vVavF6pkzMSAtLW7fVb+68EIMysgAEHtGSio2ES6QcrW1yTegx+TmyuvpqfcwkEoSDW53QGO3cKzp6dhfX4/CJ59EgcmEqnvvBQA5iEokDW53wNQ+rrmgZCH1YMk1GmH13+V8bvNm/HTMmHgOq9dIazT5WSXqPwpNJmy12QB0TO3T+NdNxUu0fZNuGjeuZwfShWXl5fK/u7vYRLN/u1Gnw5jcXKzatOmMx0uxievUvs8//xwzZsxAQUEBVCoV1qxZE7BdCIGlS5ciPz8fqampKC8vx15/l2pJfX095syZA7PZjIyMDNx8881wxliNJhlEk5ECgNy0NHx59CgA4LjDAZvT2emDF6rqS28SQuD3X36JD/bsgTQhwKTXy6luVpyhRKcMpPIVC56Vc/b7Mov/M8pAiqj/KDSZUOXvy3SipQUD0tJ6rJFttN764Q/xP9dfH9cxxOJ0AimdRiOXmA/OSDV7vQA6qqeenZOD2ubmkL2mqOfENZBqbm7G2LFj8cc//jHk9uXLl+Ppp5/GqlWrsHHjRhiNRkydOhVuxUkyZ84c7NixA5988gk++OADfP7555g3b15vvYUe9z+7d2PwH/6Aky6XvJ4oEmt6OgBA679Tk//EEzA8/DAAYI7/bvmRpqYeGm10vq+rw8JPPwVw6s62cu7yhPz8uIyLKFpSye9zcnMxcsAA+Ty2JsAc/N7AjBRR/1NoNqPZ64X6wQfx9s6dCdFq5UejRuHqYcPiPYyoSddmsVTtS9fp5LYTwcUmlBkp6aZerKXt6czEdWrf9OnTMX369JDbhBBYsWIFFi9ejJn+IgovvfQSrFYr1qxZg9mzZ2PXrl1Yu3YtvvnmG0ycOBEAsHLlSlx55ZV4/PHHUVBQ0Gvvpacs/+orHGxsxMHGRtzmf4+RSAHJVcOGIc9oxJ83b0a7v9P1bRMn4tXt29EU54Ztyrslf7rqKgAdqXdDSgrafL6YGwUS9bZjdjsAoGzgQKhUKlw/ejSe3bRJvpHR10nVr0KVHCaivqlQkX3fXluLy2IsoU4d7SM0KlVAIOVua4NPiJA3ppytrTAp+k8FZ6Ra/BmpNK1WLiBS09yM4QMG9NRboCAJu0bq4MGDsNlsKFfMLbVYLCgrK0NFRQVmz56NiooKZGRkyEEUAJSXl0OtVmPjxo249tprQx7b4/HAowgm7P6LokSkvMMdVUbKv79apcIzV1+NFdOmoV0IeNra5LKl8e58LS1S/d1ll8lTpFLUavwsTr0oiGIlFZT4t7POAgB5imqoErx90TB/zxJNnKf1EFHvCS6kk91Pvu+6m06jCQikrnj5ZWw8dgyexYs7TZV0+jNSKpUKhpSUzmukFFP7pP/+1LKXVK9K2PLnNv+CRmtQTxar1Spvs9lsyFWUvwSAlJQUZGVlyfuEsmzZMlgsFvmnKIHLFR9WTMOLao2UP5CSPor6lBSkabUBTXwdIQKpE83N+N/9+898wFGQAqlfXnhhr7weUXf76zXX4Pi998p3AEf610b1l0BquD+QqkqgdgpE1LOUDXCB0M1hqWs6jQZeRSD1xZEj8Pp82Fdf32lfh8cjr4/SazSd10gppvZlGAzQqtWc2tfLEjYj1ZMWLVqEe/3V7ICOjFSiBlMHGxrkf0dTtU8KtkKliKUPY6iMVO7jjwMA2pYsgUatxnObNmHGsGEBC+m7S11LCyx6PcsmU9JK1+nkbCoA3Hn++ZhcVBRVOd6+QGo6zDufRP2HISUFj19xBWaNGIFddXVyZppio1VkpIR/6QUA7Kuvx9Cgv6nT65X/W2NISekUSElLNcx6PVQqFXKNRn4v97KEDaTy8vIAADU1NchXFB+oqanBOH8py7y8PNQG1cxva2tDfX29/PxQ9Ho99P6gIpHZPR6cdLlg0ung8M+T7UqxxYLfXXYZ5p17bqdteo0GWrUaDv8dDMn3dXXyvxvdbmjUatz6wQd456yzsPZnPzvzNxKkzl/th6ivUKtUmNgH1mRGy2IwQKNSoWzgwHgPhYh60c8nTwYAnNVPbhr1BOXUPqmxMQAcbGzstK9Tce0XKpCqstth1usDKsnWMJDqVQk7ta+0tBR5eXlYt26d/JjdbsfGjRsxadIkAMCkSZPQ2NiITYq6+evXr4fP50NZWVmvj7m7SdmoBy69FACQF8VCdpVKhcWXXBKyg7dKpYJJr+80tW/T8ePyv+taWrDfn17uqWk7DKSIkp/z/vux4cYb4z0MIqKkogyk9ium8ylnIEkcHo8cJKXrdHJfKUmVwxFQBMSans6MVC+La0bK6XRi37598u8HDx7E1q1bkZWVheLiYixYsAAPPfQQhg4ditLSUixZsgQFBQWYNWsWAGDkyJGYNm0abrnlFqxatQperxd33nknZs+e3Scq9kmVwWaPHo0bxo4NGRzFyqTTdZrat1fxQa5raZFf1xtlec6unGhuxtp9+3DD2LEdr+FyMZAiSnLS+jAiIopeQCDlD57KCgvDZqSkQMqs18MeFEgds9sDioBYjcaAa7pIPG1t2N/QgLP7Sf/DnhLXjNS3336L8ePHY/z48QCAe++9F+PHj8fSpUsBAAsXLsR//dd/Yd68eTjvvPPgdDqxdu1aGBTV61599VWMGDECl19+Oa688kpcdNFFeO655+LyfrqbNPc1w2DoliAK6PggBk/t21dfL891rmtpkaf62ZzOgPm7p+vaN9/E3DVr5LLnzEgRERFRf6QMpA40NCAnLQ3nWK0BgZTD48FWmw21zc2w+JeimPX6TjfC9zc0oMRikX/PNRpxpKkpqmu3+9etw6g//UkuoX6mDjQ04K6PPkKbz9ctx0sWcQ2kpkyZAiFEp58XXngBQMdUtAcffBA2mw1utxuffvophgU1XsvKysJrr70Gh8OBpqYmPP/880jvI71c7B4PNCpVtza9NIX4INa1tGCkv+dAXUsLttXUAOgI5GzdUP3ly6NHAZxKYTOQIiIiov5IGUjVu1zIMRpRmpEhT+376uhRmB99FOOffRZVDodcFdZiMARcv7V4vdheU4PzFDOwLhs0CMfsdnykmO0VjpS5+s5/zXemVm7ciJVff42P9u7tluMli4RdI0VAk9stV2LpLlLhCiVnayssBgMyDQbUtbRg54kTcn+cUOU4lZ7euBFv79wZdruy54GUwmYgRURERP2RTqOB15+1aW5thVGrRaHZjAa3G562NnzsD4IGZWQAAMb42/yYdTo0+Wf2AMDekyfRLgTGKoqrTRsyBGa9HtujCI6kxurbIrQLioXHHxx+q1h33x8wkEpgdo9H7v3UXcwhik00e71I9zdzq2tpQb3LhaH+ijzBQZeSTwjcvXYtfvS3v4Xc/sWRI5j26qvy74cbG9Hm86GBa6SIiIioH1JmpJq9Xhh1OmT6l6w0uN1o8ngwcsAAVN52G/4yYwbG+QOl4Kl9UuEJi+I6UaVS4azMTPnGdSSN/mNJ6+LP1J6TJwEAJ12ubjlesmAglcCaPB5YFOvBukOoYhPO1lYYdbqOQMrlQpPHI1eBCdW8V7LrxImIr/XMt99iw6FDADq6btc2N6PB5YLAqTshRERERP1Fp0BKq5X7hDa4XKhpboY1PR1GnQ43T5ggz0oKF0ilB7XGOSsrC7v9QU0kUuPe6m5q4CtVC2QgRQmjxzJSIab2pfsDqWN2O1rb2+UO5sGlNpUONzVFfK2Tiv4Io3NzcaipCSu//hoAmJEiIiKifkerVp8KpPw3spUZqRqnE9YQBcaC10iFC6QuLi5GxdGjEW+EA6cCnu4KpKQCacprv/6AgVQCq3e5AlK23SFUsYlmRSAlFYTISk1FmlbbKZByt7Xh5vfewyvffYcqRTo41OJCZQWaQrMZb+/cid99/jkABlJERETU/0TKSNW7XDjc1BSyb6hZr0ez14t2//oq6frMGBRITRsyBF6fTy70FY50LVjdTT1DpfVbzEhRQvjHnj34x969ODc/v1uPa9LpAu5SCCE6pvb510hJwY/FYAjZ/O2zgwfx/NateGDDBhxXfPg+PXCg02sptwdP5WMgRURERP1NQCDlv/6SMlL/d+wYDjQ04AelpZ2eJ81QkgIgZ2srtGo1dBpNwH5Ds7KQnZqKiigCqdSUlG4JfHxCwO7xYEBaGjNSFH8urxc/fvttZBoMuGncuG49tjS1T+ox4G5rgwDkjJTEotfDFCKQ+s0//wkAONrUhJ11dZiQn48pgwbheFBqWAiBZsVzpw0ZgnF5eSgfPBhAR28sIiIiov4kVLGJVK0Weo0GG6uqAHQ06A0WHEg1e72dpvUBHQUnzrFa8X2EdVI+/030IosloBLg6XJ4PBAASiyWTrOe+jq2pk9AX1dVocXrxVf/+Z8ozczs1mOb9Hr4hECL/8OrnGOrDKQyQmSkHB4Pvq6qwujcXBxubMRbO3bgh2efDZ1Gg6NB66Vc/gDt2auvxuzRo2HW6zFrxAj4hIDN6YRGzRieiIiI+pdQ5c+Bjil6UgW9LP9UPyVLiIxUqEAKAApMJhyJsI5durYrMpux9+RJ+ISA+gxa7UjrowrN5m7rS5UseDWbgDZXVyM1JQWj/b0DupN0R0MqOKGcY6sMpAaazR2BlKLjtfScRy+/HP/hz5QNslhQaDJ1Kp8pZaNyjcaAghlqlUouZEFERETUnwRnpKRgyKjV4mhTE9J1OmiDpusBoaf2RQqkIhWRkI5RZLFAIHJhsWhIWa2BJhO8Pl9AD9G+joFUAjpmt6PIYumRrI3J/6FTpoaBwIxUXno6NGp1p4yU9G+TXo+bJ0wAAGSmpuK8ggIcbGzETkU5dOm40p0WIiIiov5OCqSkJRBSsQijTodmr1deLxUsOJCqd7nCVnbOT0/HcYdDXsYBAN/X1WH8s8/imN1+KpAymwHgjKf3ST2kRvkTAGcamCUTBlIJqMrhkPs4dTeTlJFS3NEAOgKeXH+5zbnnnAOgI7hSFqaQ/p2u0+EcqxWf3HADFlxwAa4eNgwA8I1/bi9wKiMVXE2GiIiIqL+SAilpCYQ8tc//v6Gm9QGnAilpGt22mhqMCTNzKS89HS1er3xTGwCerKjAVpsNz3zzTedA6gzXNW2sqsJAsxlDs7IAoFObnb6MgVQCOma3Y6D/5O5uwVP7mhVrpIZkZeHTG27AI5dfLj8WMiPlD47KBw9GmlaLVK0W6Tod6hSVWpiRIiIiIgqkVavhaWvrdMNZ+t/MMIGUtN3h8cDd1obK2lqcW1AQcl+LP6ulLPyw19/e5ojdjt11dVDhVAYpOCMlhMCCtWvx3KZNUb2nTw4cwMXFxfLNemakKK5sTmfIHgLdIXhqX3BDt8sHD5anFHYqNhGm+RvQUd48IJBiRoqIiIgoQKpWC3dbW6cbzl1lpNQqFVJTUuBqa8N3NTVo8/kwMUwgFTwNEIBcFKzB5cLnhw9jjNWKEosFQOeMVGVtLf6wcSPuXru2y/fT5HZjq82G6UOGyNeH4ZoBT3zuOSxev77LYyYTBlIJqMHtDjtH9kyFndoXIuAJVbVPeQylAcGBFDNSRERERAGMWi2avd5ON5ylICTS9V+aVosWrxdfV1VBq1aHndoXHEgJIeSiYA1uN/55+DAuLSmRM1fBGanD/qDL7S8a0eL14rXt20O+Vm1zM4COImXSzfpQGSkhBDZVV+Phf/0r7PtLRgykEowQAo1ud9jU7plKTUmBRqUKKDah8j8eLLiPlLO1FSp0fJCDDUhLQ52iqRszUkRERESBpNYznTJS/uulcBkp4FQg9cmBA5hUVAR9iGs3oHOpdJvTCU97OwZlZKCythb7GxpwSUkJjFotNCpVp4yUsqWNy+vFwk8+wZx33sHBhoZOryU19B2QlnYqIxUikOqOxr+JiIFUgnG2tsInRI81rFWpVDD5m/JKr5eu00EVon9AqKl9Rp0uZK+BHKMRNYpSm8oiFkRERETUcV3kEwL1/sBCXiPlv17qKiPV3NqKfx46hCsGDw67n1yYwp9pkno7XVxcLAdXl5SUQKVSwazXd8pIHVW0tNlVVyc3Cg7Vm+qkfzZSdlpaxDVS+/1rtPoaBlIJpsF/MvfU1D6g4wOmnNoXLmuU7i/F6fOXz3S2tspp22AlFkvAB6zJ40G6TsfGu0RERER+UtZGmhInBVDZ/kxUpBlJaVotnvy//0OTx4Nz8/PD7mcKykhtq6lBuk4nP2doVpZcqdliMHTOSNntmJCfD41KhW+qquDyZ88ONTZ2ei1pWUd2aip0Gg10Gk3INVL1ioyU/QyrBCYSXuUmmEZ/INVTGSmgY8qePLUvQkM36fEW/wfI4Q+OQhmUkYHjDofchM3u8cipZSIiIiI6lYGSZvFIv5/lLx3uUpQsD6ZcWhGuYh8ApKjVSNNq5QDpUGMjBmdmojQzE8Cpqn5AxzTA4IzUkaYmDM/OxtDsbOyqq5ODoANhpval63TyNMN0nS7k1D7lY9v9GbK+gIFUgmnwn6w9GkgFTe0LN/0uuPqKs7U1ZKEJoCOQEji1QLHJ7Q7bKI6IiIioP5KuuWqCMlIXFxcDAMbm5YV9rhSs3H/RRXJGKZzs1FQ5W2RzOpGfno5LSkoAAAWKXqUWgwGNIdZIFZnNKDSZ8MGePaj2B307Tpzo9Do1Tidy0tLk34PX10uka0mtWo23duyIOPZkwkAqwUgBjqWHp/Ypi010lZGSPhCOCNmrs3NyAJyah2v3eHr0PRARERElGykDVeVwwKRYAlGamQnvkiX4QWlp2OdK0wEvHTSoy9cZaDbLlfqqnU7km0zIMBjw0Zw5+MuMGfJ+GQaDPBsKOFXhb6DZjEKzGfv9Wagis1m+xlOqcjhQqOh9mq7ThZza52xtRZpWi9smTsQbDKSopzT3QpEGkyLt6oxiap8ykAq3RiovPR0DzWZ841+Q2OTxMCNFREREpCBdWx1tauq0Hiqli3Xl+/wFG8KVPVcqNJtR5XAAAKodDuT7+5NOGzIEOYps1gBF5grouNbz+nzIMRrlQhJatRo3jh0rH0/puMOBQkWGy6TXh85I+a83zy0oQG1zM6589VVMfeUVCP86/GTFQCrBSCdfqBLj3SWWYhPKMUUKuoCOrNQe/4eca6SIiIiIAknXUUeammIuLCb1dcrzB0WRFJpMOGa3QwiBaqcz7HOC+4BKS0wyDQbcNG4cMg0GNN9/P0ozM9Hi9aLd5wt4flVQIBV2jZTHA5NOh8H+dVof7duH/92/P+S6q2QSugA9xY2U+uzJancBxSa8XljDfLg6ZaQ8HgzOyAh73BKLBd8ePw6go2hGsb9jNhERERGdqsp81G6XC0xE65MbbsC++vqQLWuCDTSbUWW3o8HtRmt7u5yRCpZjNOKEf8ogcKroWWZqKqYOGYIfnn02gMBrQuXSjdrm5oDryLBrpPzr7EuDriMrjh2L+e+QSJiRSjDNXm+P917q1EcqzOsF9wOIVGwC6AikpGITR/3za4mIiIiogz4lRV76EGtGqnzwYMyfODGqfQtNJjhaW7Hn5EkAQL4ia6SUk5YGR2urXHU5XBseaWmHMtvkEwJNbnfAvuEyUlILneBxVIeYLphMGEglmK6mz3UHZbGJSFP7pIAummITAFCSkYF6lwt1LS2wOZ2d7joQERER9XdSxb2e7BkqFYCQ1q5HykgBwAn/9D55al/Q+q1QzXYdHg8EAitNp+t08np/JekaUq3Ipuk1moBCF8kooQOp9vZ2LFmyBKWlpUhNTcVZZ52F3/3udwEL04QQWLp0KfLz85Gamory8nLs3bs3jqM+M70RSJn8FVWEEBH7SGnUaqSmpARmpCKMTZp/+3/HjgGAPA+WiIiIiDpI1fQiVeg7U9KsoG/8Sy7CZaSkIEjqJSUFNsHr3OWMlKIin7xvUCAVrvy5FIxl+YO0kTk5DKR60mOPPYZnnnkG//3f/41du3bhsccew/Lly7Fy5Up5n+XLl+Ppp5/GqlWrsHHjRhiNRkydOhXuJP0/pjlChqi7mPR6tAsBd1tbxD5SwKkUrRAiYkNe4FQg9cGePVABGBVFVRkiIiKi/kQqGjF79Ogeew0p67W9thZmvT5sETMpYJKa9550uWDW66HVaAL2Sw8xtU96jjIjZdRq0RyiqbDyZvzO229H9c9/jgyDQZ5KmKwSutjEV199hZkzZ+Kqq64CAAwaNAivv/46vv76awAd2agVK1Zg8eLFmDlzJgDgpZdegtVqxZo1azB79uy4jf10OSP0deou0tzcRrcbztbWiGXKpTsL7rY2tAsRcY2U1f+hfWvHDpxjtcp3HIiIiIiow6Z589Dc2tqjhcWMWi1S1GrsOnECgyIstbAEZaSqHY6QFf6k679QGangqX1hi034r2+l4hTBPaySUUJnpCZPnox169Zhz549AIBt27bhiy++wPTp0wEABw8ehM1mQ3l5ufwci8WCsrIyVFRUhD2ux+OB3W4P+EkUmQYDBvVwtTvpRD5qt0MAEQMe6QMhfSgiBXnZaWnQqFRocLsxIT+/W8dMRERE1BdMyM/HxSUlPfoaKpUKGQYDPO3tEculSxkpae28rbk55HoqU1AlZyB8INXsn8mkFGpWU18IpBI6I/WrX/0KdrsdI0aMgEajQXt7Ox5++GHMmTMHAGCz2QAAVqs14HlWq1XeFsqyZcvw29/+tucGfgZWXX11j7+GlIE61NgIILpASkrlRlojpVapMNBsxuGmJowcMKD7BkxEREREMckwGFDX0hJ2fRQAGHU6qHBqmp4tTM8pQ0oKNCpVwNS+2uZmqBBYNMOo06FdCHja22FIORVmSOXPlTL7wNS+hM5IvfXWW3j11Vfx2muvYfPmzXjxxRfx+OOP48UXXzyj4y5atAhNTU3yz9GjR7tpxMlBOpEPxxBIRZORAjpKYQLAuLy8bhgpEREREZ0OKVNUFKEdjVqlCqjmHC6QUqlUHevmFVP7jtntyEtPD1hPJV0nKiv3CSFCFizrCxmphA6kfvGLX+BXv/oVZs+ejTFjxuCGG27APffcg2XLlgEA8vwX6zU1NQHPq6mpkbeFotfrYTabA376Eylw2u3vLZCdlhZ2Xzkj5f/gRFojBZxaOHlZD1aiISIiIqLIpKDmrC6qKFsMhoCqfeHKsiv7kAIdgVRwz9Dg1jkA4GlvR5vP1+kakoFUD2tpaYE6aCGeRqOBz+cDAJSWliIvLw/r1q2Tt9vtdmzcuBGTJk3q1bEmk+zUVGQYDNjo7y0QKSNl0usDMlKRpvYBwKPl5Wi5/36k9OACSiIiIiKKTLp2OysrK+J+mQYDTvr7R9k9nrBFyExBhSRCBVJyUYqgflNA51lNmQYD3G1tchXDZJTQV7szZszAww8/jH/84x84dOgQ3n33XTz55JO49tprAXSkGRcsWICHHnoI77//PrZv3465c+eioKAAs2bNiu/gE5hKpcLw7GxU1tZCr9EgNSX8Url0rRbO1lbU+z9gGV00j1OrVEiNUE6diIiIiHren668EjOGDcPEgoKI+xWYTDjucMDnn4IXLpAKntpX29wsV2yWDPDPcjrR3Cw/1uIvhx7cbke6pkzmrFRCF5tYuXIllixZgttvvx21tbUoKCjArbfeiqVLl8r7LFy4EM3NzZg3bx4aGxtx0UUXYe3atTD0YLfovmBIVhY2VlXBmp4OlaLLdDCpj1S10wmjVtvl1D4iIiIiir/zCgvx/vXXd7lfocmEbTU1p2YfhctIBU3ta3C7O81qkvpX1SoCKSnjZAi6ca8MpCJVFkxkCR1ImUwmrFixAitWrAi7j0qlwoMPPogHH3yw9wbWBxT7S6wH30kIJq2RqnY4IlZ9ISIiIqLkU2Ay4cN9++SCE5Gm9ikDqXqXq1MgZdLpYEhJCQikXP5AKnjGUl/ISCX01D7qOVIFl5woA6njTmfIvgJERERElLyKLRbYnE7UOJ0AIgRS/nXzAOBtb4fd40FmUCClUqmQazRGlZHKDjENMNkwkOqnpIxUpPVRwKnGajVOp9yJmoiIiIj6hrF5efAJgamvvAIgfGGxdK1WXiMlZZFCFSwLDqRc/jVSwdecuUYjVACq/QFcMkroqX3Ucy4dNAi/vPBC/PvIkRH3S9fpINDRV2BQRkavjI2IiIiIesfo3FwAkCv3RcpISVP7GroKpFpa5N/DZaRS1GrkGo2odjjO8B3EDzNS/VS6TodHy8txfmFhl/sBHYFUV814iYiIiCi5GFJSsG3+fPn34JLmEpOiap9UzTlUz6lOGakwgRQA5JtMsDmd8AkBd1sbhBCn/0bigIEURSQFTyddri57SBERERFR8jnHasVAsxnDs7OhCdMLNF3RR0oKpEJmpNLSQq6RCtUeJz89HdVOJw40NCD14Yex4dChM30rvYpT+ygiZQlMZqSIiIiI+qbdd96J8A1xOq4Jm71e+ISIHEgZjXLhCuDUGqmQGan0dFSeOCFnupKtzQ4zUhSRMnhiIEVERETUN6VptSGzRhJpZpKztRUNLhf0Gk3I/XONRjhaW+UAyt3WBp1GA3WIvqX5JhOqHQ557VWyzX5iIEURMZAiIiIiIilb5PB4QvaQkkhVnk/4C0642tpCZqOAjoyUzemUe1gxI0V9ijJ4SraTm4iIiIi6R7oiIxUpkMr19yiV1km529rCttvJS0+H1+fD4cZGAMxIUR/DjBQRERERSUGO3eNBvdvdqRmvJDiQcnm94TNSJhMAYM/JkwAAY5JdazKQoohS1Gq5tGWB/2QnIiIiov5FGSBFykgNSEuT9wOAFq8XaWHWXuX7pwHura9Huk4Xch1VImPVPurSe7NnY1ddndywjYiIiIj6F2t6OtQqFY47HGhwuTB8wICQ++k0GmSlpsqNdp2trWEzTVJGam99fdJN6wMYSFEULi4pwcUlJfEeBhERERHFSYpaDavRiOMOR0dGKkQzXkmxxYIjTU0AgGavF8YwGSlDSgoyDAbsq6/HsOzsHhl3T+LUPiIiIiIi6lKBySQHUuHWSAEdgdRhRSAVaZ19oT8rlR3heImKgRQREREREXWpwGRClZSRihRImc2nAqkIU/sAYEhWFgCgJCOjW8faGxhIERERERFRlwpMJmyvrUW7EMjxF5UIpdBshs3pBBB5ah+gCKQslu4dbC9gIEVERERERF0qMJnktU+jIhQhyzUaUe9ywdve3pGRihBIXVRcDABJuUaKxSaIiIiIiKhL0nomjUoVMfCRSqXXtbR0uUZq1ogR2Hn77RjKQIqIiIiIiPoiqRWOTwikqMNPbLMqek5FKn8uGZmT032D7EUMpIiIiIiIqEvnFxbigoEDMW/ChIj7SRkpm9PZ5dS+ZMZAioiIiIiIuqRSqVBx881d7ldoNkOv0WBbTQ2avV4MiFCYIpmx2AQREREREXWbFLUao3Jz8cmBAwCAHH+Gqq9hIEVERERERN1q5IAB+OroUQCnpvr1NQykiIiIiIioW+UajWjxeuV/90UMpIiIiIiIqFspG/ZGat6bzBhIERERERFRt5LWRaVptdCn9M36dgkfSFVVVeFnP/sZsrOzkZqaijFjxuDbb7+VtwshsHTpUuTn5yM1NRXl5eXYu3dvHEdMRERERNS/SVkojUoV55H0nIQOpBoaGnDhhRdCq9Xio48+ws6dO/HEE08gMzNT3mf58uV4+umnsWrVKmzcuBFGoxFTp06F2+2O48iJiIiIiPqvfJMJAOBua4vzSHpOQufZHnvsMRQVFWH16tXyY6WlpfK/hRBYsWIFFi9ejJkzZwIAXnrpJVitVqxZswazZ8/u9TETEREREfV3Z+fkAAC8Pl+cR9JzEjoj9f7772PixIn40Y9+hNzcXIwfPx5//vOf5e0HDx6EzWZDeXm5/JjFYkFZWRkqKirCHtfj8cButwf8EBERERFR90jX6QD03Yp9QIIHUgcOHMAzzzyDoUOH4uOPP8Ztt92Gu+66Cy+++CIAwGazAQCsVmvA86xWq7wtlGXLlsFiscg/RUVFPfcmiIiIiIj6oS233opvb7kl3sPoMQkdSPl8PkyYMAGPPPIIxo8fj3nz5uGWW27BqlWrzui4ixYtQlNTk/xz1N8sjIiIiIiIuse4vDwUWSzxHkaPSehAKj8/H2effXbAYyNHjsSRI0cAAHl5eQCAmpqagH1qamrkbaHo9XqYzeaAHyIiIiIiomgldCB14YUXYvfu3QGP7dmzByUlJQA6Ck/k5eVh3bp18na73Y6NGzdi0qRJvTpWIiIiIiLqPxK6at8999yDyZMn45FHHsGPf/xjfP3113juuefw3HPPAQBUKhUWLFiAhx56CEOHDkVpaSmWLFmCgoICzJo1K76DJyIiIiKiPiuhA6nzzjsP7777LhYtWoQHH3wQpaWlWLFiBebMmSPvs3DhQjQ3N2PevHlobGzERRddhLVr18JgMMRx5ERERERE1JephBAi3oOIN7vdDovFgqamJq6XIiIiIiLqx6KNDRJ6jRQREREREVEiYiBFREREREQUIwZSREREREREMWIgRUREREREFCMGUkRERERERDFiIEVERERERBSjhO4j1VukCvB2uz3OIyEiIiIioniSYoKuukQxkALgcDgAAEVFRXEeCRERERERJQKHwwGLxRJ2OxvyAvD5fDh+/DhMJhNUKlW8h5OU7HY7ioqKcPToUTY1prjj+UhngucPJSOet3S6eO50JoSAw+FAQUEB1OrwK6GYkQKgVqsxcODAeA+jTzCbzfwQUsLg+UhngucPJSOet3S6eO4EipSJkrDYBBERERERUYwYSBEREREREcWIgRR1C71ejwceeAB6vT7eQyHi+UhnhOcPJSOet3S6eO6cPhabICIiIiIiihEzUkRERERERDFiIEVERERERBQjBlJEREREREQxYiBFREREREQUIwZSSWjZsmU477zzYDKZkJubi1mzZmH37t0B+7jdbtxxxx3Izs5Geno6rrvuOtTU1Mjbt23bhuuvvx5FRUVITU3FyJEj8Yc//KHTa23YsAETJkyAXq/HkCFD8MILL3Q5PiEEli5divz8fKSmpqK8vBx79+4Nua/H48G4ceOgUqmwdevWiMetrq7GT3/6UwwbNgxqtRoLFizotM8LL7wAlUoV8GMwGLocM52evnAuDho0qNM58+ijj3Z57K7G8/nnn2PGjBkoKCiASqXCmjVrujxmf9Nfzx9+lyW3vnDeAsA//vEPlJWVITU1FZmZmZg1a1aXx/7uu+9w8cUXw2AwoKioCMuXLw/YvmPHDlx33XXy52LFihVdHrM/6a/njtvtxk033YQxY8YgJSUl5P4bNmzo9J2nUqlgs9m6HHdcCUo6U6dOFatXrxaVlZVi69at4sorrxTFxcXC6XTK+8yfP18UFRWJdevWiW+//VZccMEFYvLkyfL2v/71r+Kuu+4SGzZsEPv37xcvv/yySE1NFStXrpT3OXDggEhLSxP33nuv2Llzp1i5cqXQaDRi7dq1Ecf36KOPCovFItasWSO2bdsmrrnmGlFaWipcLlenfe+66y4xffp0AUBs2bIl4nEPHjwo7rrrLvHiiy+KcePGibvvvrvTPqtXrxZms1lUV1fLPzabLeJx6fT1hXOxpKREPPjggwHnjHL8oUQzng8//FD8+te/Fu+8844AIN59991o/6z9Rn89f/hdltz6wnn79ttvi8zMTPHMM8+I3bt3ix07dog333wz4nGbmpqE1WoVc+bMEZWVleL1118Xqamp4tlnn5X3+frrr8V9990nXn/9dZGXlyeeeuqpaP+s/UJ/PXecTqeYP3++eO6558TUqVPFzJkzO+3z2WefCQBi9+7dAd977e3tXf1Z44qBVB9QW1srAIh//vOfQgghGhsbhVarFX/729/kfXbt2iUAiIqKirDHuf3228Vll10m/75w4UIxatSogH1+8pOfiKlTp4Y9hs/nE3l5eeL3v/+9/FhjY6PQ6/Xi9ddfD9j3ww8/FCNGjBA7duyIKpBSuvTSS8NefFgslqiPQ90rGc/FkpKSmP9jH+t4GEhFp7+cP0r8Lkt+yXbeer1eUVhYKP7yl7/E9D7/9Kc/iczMTOHxeOTHfvnLX4rhw4eH3P9MPxv9QX85d5RuvPHGiIFUQ0PDaR87Hji1rw9oamoCAGRlZQEANm3aBK/Xi/LycnmfESNGoLi4GBUVFRGPIx0DACoqKgKOAQBTp06NeIyDBw/CZrMFPM9isaCsrCzgeTU1Nbjlllvw8ssvIy0tLcp3Gh2n04mSkhIUFRVh5syZ2LFjR7cen8JLxnMRAB599FFkZ2dj/Pjx+P3vf4+2traI7/N0xkNd6y/nT7T4XZYcku283bx5M6qqqqBWqzF+/Hjk5+dj+vTpqKysjPg+KyoqcMkll0Cn0wWMZ/fu3WhoaIj4XAqtv5w7sRg3bhzy8/NxxRVX4Msvv+y24/YUBlJJzufzYcGCBbjwwgsxevRoAIDNZoNOp0NGRkbAvlarNexc06+++gpvvvkm5s2bJz9ms9lgtVo7HcNut8PlcoU8jnT8UM+TtgkhcNNNN2H+/PmYOHFi9G82CsOHD8fzzz+P9957D6+88gp8Ph8mT56MY8eOdevrUGfJeC4CwF133YU33ngDn332GW699VY88sgjWLhwYcT3ejrjocj60/kTDX6XJYdkPG8PHDgAAPjNb36DxYsX44MPPkBmZiamTJmC+vr6sO813HiUr0vR60/nTjTy8/OxatUq/P3vf8ff//53FBUVYcqUKdi8efMZHbenMZBKcnfccQcqKyvxxhtvnPYxKisrMXPmTDzwwAP4t3/7t6if9+qrryI9PV3++de//hXV81auXAmHw4FFixaF3Ud53Pnz50c9pkmTJmHu3LkYN24cLr30UrzzzjvIycnBs88+G/Ux6PQk47kIAPfeey+mTJmCc845B/Pnz8cTTzyBlStXwuPxADj9c5Fiw/MnEL/LkkMynrc+nw8A8Otf/xrXXXcdzj33XKxevRoqlQp/+9vfAACjRo2Sjzt9+vTY3xR1iedOoOHDh+PWW2/Fueeei8mTJ+P555/H5MmT8dRTT0V9jHhIifcA6PTdeeed+OCDD/D5559j4MCB8uN5eXlobW1FY2NjwF2Nmpoa5OXlBRxj586duPzyyzFv3jwsXrw4YFteXl5ApRjpGGazGampqbjmmmtQVlYmbyssLER1dbW8X35+fsDzxo0bBwBYv349KioqoNfrA449ceJEzJkzBy+++GJABT+z2Rz9HyWIVqvF+PHjsW/fvtM+BnUtWc/FUMrKytDW1oZDhw5h+PDhIc/FrsZDselv58/p4HdZ4knW81Z6/Oyzz5a36/V6DB48GEeOHAEAfPjhh/B6vQAgf6eFG4+0jaLX386d03X++efjiy++OKNj9Lh4L9Ki2Pl8PnHHHXeIgoICsWfPnk7bpcWKb7/9tvzY999/32mxYmVlpcjNzRW/+MUvQr7OwoULxejRowMeu/7666NarPj444/LjzU1NQUsVjx8+LDYvn27/PPxxx8LAOLtt98WR48ejepvEG6BdrC2tjYxfPhwcc8990R1XIpNsp+LobzyyitCrVaL+vr6sPvEOh6w2ERI/fX8UeJ3WfJJ9vNW+l1ZMKC1tVXk5uYGVOALJhWbaG1tlR9btGgRi03EoL+eO0rhik2EUl5eLq699tqo9o0XBlJJ6LbbbhMWi0Vs2LAhoERkS0uLvM/8+fNFcXGxWL9+vfj222/FpEmTxKRJk+Tt27dvFzk5OeJnP/tZwDFqa2vlfaTymb/4xS/Erl27xB//+Meoy2dmZGSI9957T3z33Xdi5syZYcufC9FRChhRVu3bsmWL2LJlizj33HPFT3/6U7FlyxaxY8cOeftvf/tb8fHHH4v9+/eLTZs2idmzZwuDwRCwD3WfZD8Xv/rqK/HUU0+JrVu3iv3794tXXnlF5OTkiLlz50Y8bjTjcTgc8vkKQDz55JNiy5Yt4vDhwzH9jfuy/nr+CMHvsmSW7OetEELcfffdorCwUHz88cfi+++/FzfffLPIzc2NeAOgsbFRWK1WccMNN4jKykrxxhtviLS0tIALaI/HI5/b+fn54r777hNbtmwRe/fujelv3Ff113NHCCF27NghtmzZImbMmCGmTJkinyeSp556SqxZs0bs3btXbN++Xdx9991CrVaLTz/9NNo/b1wwkEpCAEL+rF69Wt7H5XKJ22+/XWRmZoq0tDRx7bXXiurqann7Aw88EPIYJSUlAa/12WefiXHjxgmdTicGDx4c8Brh+Hw+sWTJEmG1WoVerxeXX3652L17d9j9YwmkuhrzggULRHFxsdDpdMJqtYorr7xSbN68ucvj0ulJ9nNx06ZNoqysTFgsFmEwGMTIkSPFI488Itxud5fH7mo8UinX4J8bb7yxy2P3F/35/OF3WfJK9vNWiI4sws9//nORm5srTCaTKC8vF5WVlV0ee9u2beKiiy4Ser1eFBYWikcffTRgu/Tf8+CfSy+9tMtj9wf9+dwpKSkJOW7JY489Js466yxhMBhEVlaWmDJlili/fn2Xx403lRBCRDsNkIiIiIiIiFi1j4iIiIiIKGYMpIiIiIiIiGLEQIqIiIiIiChGDKSIiIiIiIhixECKiIiIiIgoRgykiIiIiIiIYsRAioiIiIiIKEYMpIiIiIiIiGLEQIqIiIiIiChGDKSIiKhPuemmm6BSqaBSqaDVamG1WnHFFVfg+eefh8/ni/o4L7zwAjIyMnpuoERElNQYSBERUZ8zbdo0VFdX49ChQ/joo49w2WWX4e6778bVV1+Ntra2eA+PiIj6AAZSRETU5+j1euTl5aGwsBATJkzA/fffj/feew8fffQRXnjhBQDAk08+iTFjxsBoNKKoqAi33347nE4nAGDDhg34j//4DzQ1NcnZrd/85jcAAI/Hg/vuuw+FhYUwGo0oKyvDhg0b4vNGiYgobhhIERFRv/CDH/wAY8eOxTvvvAMAUKvVePrpp7Fjxw68+OKLWL9+PRYuXAgAmDx5MlasWAGz2Yzq6mpUV1fjvvvuAwDceeedqKiowBtvvIHvvvsOP/rRjzBt2jTs3bs3bu+NiIh6n0oIIeI9CCIiou5y0003obGxEWvWrOm0bfbs2fjuu++wc+fOTtvefvttzJ8/H3V1dQA61kgtWLAAjY2N8j5HjhzB4MGDceTIERQUFMiPl5eX4/zzz8cjjzzS7e+HiIgSU0q8B0BERNRbhBBQqVQAgE8//RTLli3D999/D7vdjra2NrjdbrS0tCAtLS3k87dv34729nYMGzYs4HGPx4Ps7OweHz8RESUOBlJERNRv7Nq1C6WlpTh06BCuvvpq3HbbbXj44YeRlZWFL774AjfffDNaW1vDBlJOpxMajQabNm2CRqMJ2Jaent4bb4GIiBIEAykiIuoX1q9fj+3bt+Oee+7Bpk2b4PP58MQTT0Ct7lgu/NZbbwXsr9Pp0N7eHvDY+PHj0d7ejtraWlx88cW9NnYiIko8DKSIiKjP8Xg8sNlsaG9vR01NDdauXYtly5bh6quvxty5c1FZWQmv14uVK1dixowZ+PLLL7Fq1aqAYwwaNAhOpxPr1q3D2LFjkZaWhmHDhmHOnDmYO3cunnjiCYwfPx4nTpzAunXrcM455+Cqq66K0zsmIqLexqp9RETU56xduxb5+fkYNGgQpk2bhs8++wxPP/003nvvPWg0GowdOxZPPvkkHnvsMYwePRqvvvoqli1bFnCMyZMnY/78+fjJT36CnJwcLF++HACwevVqzJ07Fz//+c8xfPhwzJo1C9988w2Ki4vj8VaJiChOWLWPiIiIiIgoRsxIERERERERxYiBFBERERERUYwYSBEREREREcWIgRQREREREVGMGEgRERERERHFiIEUERERERFRjBhIERERERERxYiBFBERERERUYwYSBEREREREcWIgRQREREREVGMGEgRERERERHF6P8D/eXj4myhZKgAAAAASUVORK5CYII=" 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kgtX74cc+fOxaRJkxAMBnH77bfj1FNPxZYtW2CxWFBZWYnKyko88MADGD16NCoqKvDrX/8alZWV+O9//wsACIVCOPPMM1FWVoZvvvkGBw8exJVXXgmdToe///3vSR1HZWUlysvLM/lWCYIgCIIgCILoQezbtw/9+/eP+3xWmU3U1NSgpKQEy5cvx7Rp0xS3efPNNzF79my4XC5otVp8/PHHOOuss1BZWYnS0lIAwFNPPYXbbrsNNTU1iu5DPp8PPp+P/+50OjFgwADs27cPdrs9M2+OIAiCIAiCIIisp6mpCeXl5WhsbIzIgosmq+zPnU4nAKCgoCDhNna7naftrVq1CuPGjeNBFACcdtppuOGGG7B582ZMmDAhZh/33nsv7r777pjH7XY7BVIEQRAEQRAEQbRb8pM1ZhPhcBg333wzjjvuOIwdO1Zxm9raWtxzzz247rrr+GOHDh2KCKIA8N8PHTqkuJ8FCxbA6XTyn3379qXpXRAEQRAEQRAE0RvIGkVq7ty52LRpE1asWKH4fFNTE84880yMHj0ad911V6dey2AwwGAwdGofBEEQBEEQBEH0XrJCkZo3bx4+/PBDLF26VLGgq7m5GaeffjpsNhveeecd6HQ6/lxZWRmqqqoitme/l5WVZfbACYIgCIIgCILolXRrICVJEubNm4d33nkHS5YsweDBg2O2aWpqwqmnngq9Xo/3338fRqMx4vnJkydj48aNqK6u5o99/vnnsNvtGD16dMbfA0EQBEEQBEEQvY9uTe2bO3cuXnnlFbz33nuw2Wy8psnhcMBkMvEgyu1246WXXkJTUxOampoAAMXFxdBoNDj11FMxevRoXHHFFbj//vtx6NAh/PGPf8TcuXMpfY8gCIIgCIIgiIzQrfbn8ZwwnnvuOfzyl7/EsmXLcOKJJypus3v3bgwaNAgAUFFRgRtuuAHLli2DxWLBnDlz8I9//CPphrxNTU1wOBzcEZAgCIIgCIIgiN5JsrFBVvWR6i4okCIIgiAIgiAIAkg+NsgKswmCIAiCIAiCIIieBAVSBEEQBEEQBEEQKUKBFEEQBEEQBEEQRIpQIEUQBEEQRNbz/vbtUN19NwKhUHcfCkEQBAAKpAiCIAiC6AE8tGoVAKDZ7+/mIyEIgmiFAimCIAiCILIedyAAAGihQIogiCyBAimCIAiCILIeFwVSBEFkGRRIEQRBEASR9TBFqtnn6+YjIQiCaIUCKYIgCIIgsh5vMAiAFCmCILIHCqQIgiAIgsh69BoNAAqkCILIHiiQIgiCIAgi6zFqtQDItY8giOyBAimCIAiCILIenbp1ykKKFEEQ2QIFUgRBEARBZD0hSQJAZhMEQWQPFEgRBEEQBJH1BMNhAG2mEwRBEN0NBVIEQRAEQWQ9ITmQ8oVC3XwkBEEQrVAgRRAEQRBE1kOKFEEQ2QYFUgRBEARBZD0USBEEkW1QIEUQBEEQRNbDzCYokCIIIlugQIogCIIgiKyHFCmCILINCqQIgiAIgsh6KJAiCCLboECKIAiCIIisJ0SBFEEQWQYFUgRBEARBZD2kSBEEkW1QIEUQBEEQRNaTSiAVDIfx4Y4dmT4kgiB6ORRIEQRBEASR1UiShJAkQa1SJRVI/XvtWpz96qtYvX9/FxwdQRC9FQqkCIIgCILIasKy9blFp0sqkGr0egEAPzc0ZPS4CILo3XRrIHXvvfdi0qRJsNlsKCkpwbnnnovt27dHbOP1ejF37lwUFhbCarXiggsuQFVVVcQ2e/fuxZlnngmz2YySkhLceuutCFIONUEQBEHkBCytz6rXJxVIsW2219Zm9LgIgujddGsgtXz5csydOxerV6/G559/jkAggFNPPRUul4tvc8stt+CDDz7Am2++ieXLl6OyshLnn38+fz4UCuHMM8+E3+/HN998g+effx6LFy/Gn//85+54SwRBEARBpBnWjNeSZCC1v7kZAFAtzCcIgiDSjbY7X/yTTz6J+H3x4sUoKSnBunXrMG3aNDidTjzzzDN45ZVXcNJJJwEAnnvuOYwaNQqrV6/Gsccei88++wxbtmzBF198gdLSUhxxxBG45557cNttt+Guu+6CXq/vjrdGEN3G7oYGSACG5Od396EQBEGkBVGRavB42t3++4MHAQCNPl9Gj4sgiN5NVtVIOZ1OAEBBQQEAYN26dQgEAjj55JP5NiNHjsSAAQOwatUqAMCqVaswbtw4lJaW8m1OO+00NDU1YfPmzYqv4/P50NTUFPFDELnCkIULMXThwu4+DIIgiLSRSmrfoZYW/CCXALBaKYIgiEyQNYFUOBzGzTffjOOOOw5jx44FABw6dAh6vR55eXkR25aWluLQoUN8GzGIYs+z55S499574XA4+E95eXma3w1BEARBEOmCNeNNxmyCBU/DCgrgpECKIIgMkjWB1Ny5c7Fp0ya89tprGX+tBQsWwOl08p99+/Zl/DUJgiAIgugYoiIVkiT+uxIs0OpjtfKgyh0I4Kinn8bwRx/N/MESBNFr6NYaKca8efPw4Ycf4quvvkL//v3542VlZfD7/WhsbIxQpaqqqlBWVsa3WbNmTcT+mKsf2yYag8EAg8GQ5ndBEARBEEQmYGYTVrnu2RcMQhunBtoTCAAAyqxW7KqvBwDM/d//sE6umyIIgkgX3apISZKEefPm4Z133sGSJUswePDgiOcnTpwInU6HL7/8kj+2fft27N27F5MnTwYATJ48GRs3bkR1dTXf5vPPP4fdbsfo0aO75o0QBEEQBJExgkJqH4CE6X3suTJBkVpz4ECGj5AgiN5ItypSc+fOxSuvvIL33nsPNpuN1zQ5HA6YTCY4HA5cc801mD9/PgoKCmC323HjjTdi8uTJOPbYYwEAp556KkaPHo0rrrgC999/Pw4dOoQ//vGPmDt3LqlOBEEQBJEDiKl9QOJAyiM/V2w2wxMMIhQOwydsL0kSfKEQvMEg8ozGDB41QRC5TrcqUk8++SScTiemT5+OPn368J/XX3+db/Pwww/jrLPOwgUXXIBp06ahrKwMb7/9Nn9eo9Hgww8/hEajweTJkzF79mxceeWV+Mtf/tIdb4kgCIIgiDQTSiGQYs8Vmc0AAFcgAE8wiKFySwhfKIQ5776L/PvuQyAUyuRhEwSR43SrIiXJOc+JMBqNePzxx/H444/H3WbgwIH43//+l85DIwiCIAgiS+CpfSkEUsUWCwCgxe+HLxjEQIcDPzU0wOn1Ym1lJQDgu8pKTCHnXoIgOkjWuPYRBJFeaKWVIIhcIaXUPtlsgilSLX4/fKEQSq1WAIDT5+MpfdRniiCIzkCBFEHkKG55MkEQBNHTiXbta0+R0ms0sMt10i1+P7zBIErkwKrJ54NK3rbJ58vcQRMEkfNQIEUQOYTYW4UCKYIgcoVUXPs8wSCMWi0PupxeL4LhMErkVD+n1wtWWECBFEEQnYECKYLIIURnKgqkCILIFVKtkTIJgVSdxwMAbYGUz4cWvx8AsElonUIQBJEqFEgRRA4hTi58VCNFEESOkKprn6hI1bndANoCqSYhkHp0zRr+PEEQRKpQIEUQOYRHmFwkmmgQBEH0JFJK7QsEYNRq+ba1cqBk0eth0mrh9HrRLKT0OSm9jyCIDkKBFEHkEGI6HwVSBEHkCimn9ul00KjVMGm1qJdT+4xaLRxGY0RqH4CIoIogCCIVKJAiiBzCJUwOKJAiCCJXYK59eo0GOrU6KbMJoDUVkNVIGWQnv31OJySAu/qR4QRBEB2FAimCyCFcpEgRBJGDMEVKq1bDqNUmZTYBtCpYLLXPqNXCYTBgV0MDAOCJM84AADQLC1AEQRCpQIEUQeQQlNpHEEQukkogFVeR0mqRbzJhS00NAGBIfj4ASu0jCKLjUCBFEDmEmNrno0CKIIgcgbn2aVSq5BQp2WjCGqVIHVFaimqXCwAwKC8PAClSBEF0HAqkCCKHoNQ+giBykWhFKlF7B2+0IiUHUgaNBpP69ePbFVssMGg0ONjcnMEjJwgil6FAiiByCJffDxVaJxsUSBEEkSuklNoXCMCo0QBoDaQavF4ArYpUX5uNb6dVq+ELhfDnZcsyd+AEQeQ0FEgRRA7hCgRg0evbnWgQBEH0JJhrnyZZswkhtY9h0etRYDJFbDtn/PgMHC1BEL0FCqQIIodw+f2w6HQUSBEEkVN02GxCDqgAwKTVxgRSk/v3h0alysAREwTRG6BAiiByCDcpUgRB5CDBcBgqAOpkzSaEGimgtT5Ko1Yj32iM2Nag1SIkSTxQIwiCSAUKpAgih/CFQjBqtRRIEQSRU4TCYWjUrVOWZAIpY1QgZZaVKZ1cO8Vg2yVyOa33eCjQIghCEQqkCCKH8IdC0CXhakUQBNGTCIbD0CYZSHkCgZgaKbOQ4gcA540cCaBVqQISu5wW3n8/5n70UccPniCInEXb3QdAEET6CIRC0Gk00IHszwmCyB1CkpR0IKWkSJmEQKrxttt4YMUVqaiFp2afDwdbWjDQ4QAAfPbzz2l6JwRB5BKkSBFEDuEPh6HXaGDQaCiQIggiZwiGw9wUIlEgFQqHEQiHYwIptWAo4TAaeYqfQd4uen9XvfceRjz2GA7IPaZsgvsfQRAEgxQpgsghAnJqH/WRIggilxBT+xItFLHHo80mwrJ9ejQstS+6RmrdwYMAgLe3bgUA2AyGzhw+QRA5CgVSBJFD+EMh6DUa6EmRIggih0jWbMIjPx6tSMULpIxxFClmLrFeDqhIkSIIQglK7SOIHCIQDkOn0ZBrH0EQOUWyZhNckYoym5DiKVJxaqTqPR4AwJaaGgBtilSTz4dZr76KnxsaOvxeCILIHSiQIogcIiArUhRIEQSRS6QaSDGliQVKg/LyFLdXUqSC4TDcgQAA4IeqKv4YADz7/ff4YMcOvCOn/BEE0buhQIogcgiyPycIIhcJSVJSZhMeOQBiNVJD8/MxJD8fD556quL2SjVSzT5fzHYssNrT2AigVZkiCILo1kDqq6++wtlnn42+fftCpVLh3XffjXi+paUF8+bNQ//+/WEymTB69Gg89dRTEdt4vV7MnTsXhYWFsFqtuOCCC1AlryARRG+DpfaRax9BELlERxUpm8GAn266CRP69FHcXkmRciYIpNhze5zOjrwNgiByjG4NpFwuF8aPH4/HH39c8fn58+fjk08+wUsvvYStW7fi5ptvxrx58/D+++/zbW655RZ88MEHePPNN7F8+XJUVlbi/PPP76q3QBBZhZ9S+wiCyEGiA6lAOIyQnG4n4omqkWoPpRoppjaphO2Y0uX0egEAlbItOkEQvZtude2bOXMmZs6cGff5b775BnPmzMH06dMBANdddx3+/e9/Y82aNZg1axacTieeeeYZvPLKKzjppJMAAM899xxGjRqF1atX49hjj+2Kt0EQWUNASO2jQIogiFwh2rUPaA1+zOrI9eBoRao9WAogC5QA4EBTEwCgn92O/fL/oxUpt7A9QRC9l6yukZoyZQref/99HDhwAJIkYenSpdixYwdOlXOd161bh0AggJNPPpn/zciRIzFgwACsWrUq7n59Ph+ampoifggiFyBFiiCIXCRakQJiez8BqQdSOo0GWrUaLjkwWnPgAM545RUAQF+bDQBQYrGgxe8H0KZIeSiQIggCWR5IPfrooxg9ejT69+8PvV6P008/HY8//jimTZsGADh06BD0ej3yotx4SktLcejQobj7vffee+FwOPhPeXl5Jt8GQXQZgXCYFCmCIHIOpUBKaYyLNptIBotOB5ccKLG+UQBQaDIBAE4cNAj7mpqwqbqaFCmCICLI+kBq9erVeP/997Fu3To8+OCDmDt3Lr744otO7XfBggVwOp38Z9++fWk6YoLoXkT7c6XVWoIgiJ5ItGsfoBxIpapIAYBFr+eBkVh3NdDhAABcNHo0AOCHQ4fg9HqhVql4LRZBEL2bbq2RSoTH48Htt9+Od955B2eeeSYA4PDDD8eGDRvwwAMP4OSTT0ZZWRn8fj8aGxsjVKmqqiqUlZXF3bfBYIBBbq5HELmEPxSCTqNBntEITzAIl98Pi9yQkiAIoqeSrCLlDgSgUamgk23Nk8Gi0/HUvpDQuLfMagUA6GUn1Fq3Gw1eL/rabKRIEQQBIIsVqUAggEAgAHVUIalGo0FYXjGaOHEidDodvvzyS/789u3bsXfvXkyePLlLj5cgsgGW2jdQXljYSxa9BJEy/1q9Gn//+uvuPgxCINlAyunzIc9oTGnfZiG1zy+49/1i3DgAwJF9+qDQbMaexkYEw2GU2+0USBEEAaCbFamWlhbs2rWL/757925s2LABBQUFGDBgAE444QTceuutMJlMGDhwIJYvX44XXngBDz30EADA4XDgmmuuwfz581FQUAC73Y4bb7wRkydPJsc+olfCzCZYSkqF04lRxcXdfFRENJIkocHrRYFcg0FkF7d8+ikA4A9Tp0KtUrWzNdEVhCQpxrVPKZBq9HrhSDGQsuj1XJFqlM0kAGB4YSGkO+8E0FovtaO+HgBQ7nBg9f79kCQJKro+CKJX062B1Nq1a3HiiSfy3+fPnw8AmDNnDhYvXozXXnsNCxYswOWXX476+noMHDgQf/vb3/DrX/+a/83DDz8MtVqNCy64AD6fD6eddhqeeOKJLn8vBJENBOTUvn52OzQqFSoaG7v7kAgFPtm1C2e88goWnn46bjzmmO4+HCIOlc3N6G+3d/dhEGhfkZIkCW9s3owKpzNlRUpM7RMDKZFCsxlfV1QAAMrtdkhotV9PpRaLIIjco1tHgOnTp0MS8pGjKSsrw3PPPZdwH0ajEY8//njcpr4E0ZtgqX1atRr97XbsoUAqK/mpoQEAcN/KlRRIZTFk2JI9tBdI/VhVhUvfegsAcESCGmklzDodT9WLF0gVmc1oltP/yuXg2hMIUCBFEL2crK2RIggidVhqHwAMzMtDBdVIZSX1Hg8AoMrlSriYRHQvPqFehuheQuFwXNe+FXv34oh//5tv+7O8UJEsFr2e10g1xFOkhDTccjl1muqkCIKgQIogcgRJkhAMh7lb1UCHA69u2oRat7ubj4yIpk7+TILhMJrkvjRE9kGKVPYgKlKGqEDqfzt3AgAuGTMGAFL+ThWbzdjX1ASgVZHKMxqx4qqrIrZhgdSsESP4/1mTXoIgei8USBFEjhCU3Sx18mTjpMGDAQAr9+7ttmMilKkXVr0p0M1eSJHKHoLhcFyziY3V1Th92DC8duGFAIBfHnFESvueNnAgfm5oQEVjIxo8HswZPx7HDRgQsY1dbply3siR6Cen9u2Xgy+CIHovFEgRRI4QYIGUrEhdcfjhUKtUqHK5MvJ6T373HR5atSoj+8516txuDJYt6uvkND8i+yBFKnsISRJXpLRqNTQqFbzBIELhMFbv34+JffoAAMJ//jOeO+eclPbNaqp21tej0etFvoJZBXNvtOr1GOBwQAVgN9WgEkSvhwIpgsgRAvLqOVOkNGo1is1mVLW0ZOT13tm2De9u25aRfecan/30EzxCPUWN242RRUUASJHKNpiyC5AilU2IqX1AqyrlDQbxQ1UVat1unDZ0KAB0yI681GIBAFS1tPDUvmjE/eo1GvS327E7xVosgiByDwqkCCJHiFakAKDUasWhDAVSNW53XIcroo1GrxenvfQSbvr4Y/7YPqcTo+X+XlRnkV2IAa+fAqmsQTSbANoCqWpZcR8kK7wdwaLXw6rXY6/TCU8wqBhIHdu/PwDw722JxUJqMkEQ3Wt/ThBE+ohWpIDWldZMpfbVuFy8ZoGID5vorTt4EEBrXUeVy4XhhYX8dyJ7EJ3YKLUve4inSLGFCKte36n9l1osvC2BTa6HEpk6YAA8d9zB67OMWi0plgRBUCBFELmCkiJVYDLxiXw6kSQJtW439VBJghr5/LPV672yJf2wggKoQIFUthERSNFEOWtoL5CydDKQ6me3Y21lJQDAFGdcE8c79voEQfRuaDmZIHIEJUUq32iM2xelM7T4/fCFQmjy+RCmPkgJYYEs+3w+2bULOrUa40tLYdBqSfXIMpyCdTZ9NtlDSJJiUvt8oRBcfj/0Gg3vn9dRLhkzBj9UVQFobdDbHgYKpAiCAAVSBJEzKClS+SZTRuqYamSDBAlAM/VBSggLpEJywLnmwAEc3a8fCs1mWtXOQhqEuhdSpLKHRIpUZ9P6AGCUbP4CAKYkAin67hIEAVAgRRA5Q1xFKgMF0aLTnJMCqYQwRbDe44EkSXAHAnDIxew0Gcs+RAWXFKnsIdOBVIHcZBeIn9onQt9dgiAACqQIImdQUqTyjEY4fT6EBEvndFAj1F2Rc19imPNbMBxGk88HVyDAU4eoYD37qJcXHmx6Pbn2ZREhoSEvkP5AqtBs5v9PJrXPqNFQIEUQBAVSBJErKCpS8iprU5pVoxpRkaJAKiEBYTJe63bDLQRSBpqMZR0NHg/sBgPMOh0FuVlElypSSab2kWJJEAQFUgSRI7DVc1GRcsg2vulOvxMVKUrtS4yoatR5PK2BlGChTIFUdtHg9aLAZCIjkCwjXiDVnKZASkznI7MJgiCShQIpgsgReGqfMNlglsAu2SJ4T2NjWswhqlwu9LfbAVBqX3sEwmEY5OC2xe+PUKRoVTv7aPB4kG80wqjVwkOfTdag5NrnDQax1+nkY1FnUAn7phopgiCShQIpgsgRAgqKlEWesLvk3jiDH3kEhz/1FKROWpbvbmzEyKIi6NRqSu1rB38oxNOGogMpg1YLL6WPZRUNXi/yTSZY9Xq+AEF0P0qKVGVzM1bt34/hBQVpfa1krNQpkCIIAqBAiiCyDkmSsL+pKeVgJxlFCmhVpR5bs6ZTx7i7oQFD8vK4mQURn0SBFE3Gso8Grxf5RiOsej1ahOa8RPcSVDCb2FlfDwAYXVyc1tcS1al40HeXIAiAAimCyDr+vW4dyh9+GP/buTOlv0tGkWK8vnlzyscVDIdxyX//i1319djT2IhBeXlwGI2U2tcOgXAYdoMBKrQFUizApclY9sFS+6x6PVpIkcoaQgqKFABo1WqcM3JkWl5j1ogRSW9LjpsEQQBA+4nABEF0KZXNzQCAQy0tKf1dsooU0DGDiMrmZryxeTOafT40eL0osVhg0eko/akd/KEQDFotrHo9mny+GNc+CkSzi3qPB/kmE5r9ftRloAdbrhEIhfDhjh04d+TIpJScjhKd2sfqDn8xdizUaXrdNy68MOnP3KjVwhMIQJKkjL5vgiCyG1KkCCLLYOYD0SpSe7SnSEmSBLVKhb42W0RD3WRhvajWVlYCaLUL1ms0PIAjlAmEQtCp1bDq9dztkFL7spMTFi/GTw0NpEilwG1ffIHz33gDm6qrM/o6wXA4xmwCQFqMJhgGrRZ9bbaktjVptZAA6jVGEL0cCqQIIstgTmHuVAMpBUVKo1bDoNHA5fcjGA4jLEnoJwdSYg1Ws8+Hwvvvx6p9++Lun6WxsB5S+SYTdBRItYs/FIJeo4FVr0e1fO4okMo+JEnCVxUVAIC+NhsFUkny8a5dANLfqy6akCRFKFKsXmqgw5HR140HV/upjo4gejUUSBFEluGRb8yppswxRUqcbACtN3xXIMAn7P3tdgTD4YiJz7vbtqHe48H727fH3X+0TXe+0Qi9RkMrsu3gD4WgY4FUlCJl0GioziJLqJdTuv5w3HG4bNw4CqSShH3/mzN4riRJikntY6nPhxUWZux1E8HVfrpGCKJXQ4EUQWQZzA67I4qUTq2OyddndUxswl4up8LUCOl938npeja5ga8S0RP+fJMJOrWaB3CEMoFwGHqNBgUmE/Y6nQBIkcpGKuTP5vxRo3jg217PtYXffgvr3//eFYeXtbDvv7gw4w4EMH3xYvxYVZWW1wjL6rno2revqQkAMDQ/Py2vkSrmOEY+BEH0LiiQIogsgytSHaiR0in0P7EZDGj2+/mEvZ8cSIl1Ujvq6gAAdQlqp+IpUpTalxi/XCM1sqiI15FQIJV9HJRNXtj3w6LTtfsdvG/lSrgCAf6d7Y1wRUoIpDZXV2N5RQWuef/9tLxGSA6kREXqT9Om4aTBg9NaI5UK8Yx8CILoXVAgRRBZhqejZhOyIhVNvtGIBq+XB0L9owKpsCRhozzBr0/gICem8LGaHx2l9rVLQK6RGiP0urEIgVR0gEp0D8w9Md9oBNBqPOALBhP2cys2mwEAPzc0ZP4AsxBJkvh5ExWpPY2NAFqNaT6Ra6g6Q1BerBEDqSP79MGXV14ZoVJ1JfFaSxAE0bugQIogsgy2up1yal8cRSrfZEKDxxNRIwW0BVLf7NuHyuZmOAyGxIqUEDBZdDqoVCpK7UsCpkgNFlKQeI0UKVJZQ6PXC71Gw93gDBoNJLRN4pUosVgAAD/10kDKEwzycUGskRIDy5kvv9zp12GfgSaLbMZJkSIIAuhAILV792688MILuOeee7BgwQI89NBDWLp0Kbwd6IXy1Vdf4eyzz0bfvn2hUqnw7rvvxmyzdetWzJo1Cw6HAxaLBZMmTcLevXv5816vF3PnzkVhYSGsVisuuOACVKUpL5sgugOuSKVqNtGOIsUm7Fa9Hg6DgQdSW2pqoAJw1vDhvOBeCVE5MciTTTKbaB9WI1UqT7oBSu3LRhq9XuQZjbzGkAVUiT4f9j1I9L3JZcQeaKIitVtWpNJFSEGR6m5IkSIIAkghkHr55Zdx9NFHY+jQobjtttvw7rvv4uuvv8Z//vMfnH766SgtLcVvfvMbVMj2scngcrkwfvx4PP7444rP//TTT5g6dSpGjhyJZcuW4ccff8Sf/vQnGOXUCwC45ZZb8MEHH+DNN9/E8uXLUVlZifPPPz/pYyCIbCPtipTRiAaPh68cG7VaFJnNvKfRweZmlFqtyDMaE7qUiYoUc8yKZ3++q76eGs3KMPvzMquVPyYGUr5QKGH6GNE1sECKwYKkRK6K7LuaaevvbEV8380ZDKSUUvu6G1KkCIIAAG0yG02YMAF6vR6//OUv8dZbb6G8vDzieZ/Ph1WrVuG1117DUUcdhSeeeAIXXXRRu/udOXMmZs6cGff5O+64A2eccQbuv/9+/tjQoUP5/51OJ5555hm88sorOOmkkwAAzz33HEaNGoXVq1fj2GOPTebtEUS38uGOHdCq1Th92DAAbSvgaauRMplQL6T2GbVaFFss3LWvsrkZfW02mHW6hMEbU6SGFRTg4tGjAQB6tVpRkTrs0UcxorAQ2+bNS+k95CLM/rxYUKTE9DG2DZu4E91Do9cLh+BamYwixQKJ9tz9chX2/gtNJjRFpfb95qij8MTatZhQVtbp1wkpuPZ1N3qNBlq1mizyCaKXk9So9I9//APffvstfvOb38QEUQBgMBgwffp0PPXUU9i2bRuGDBnS6QMLh8P46KOPMHz4cJx22mkoKSnBMcccE5H+t27dOgQCAZx88sn8sZEjR2LAgAFYtWpV3H37fD40NTVF/BBEd3H2q69i5ssvwykrOB1uyJtAkWoUUvsMGg3K7XZeEF7Z0pJcIBUKQQVgx7x5+NuMGQBkRSrOiv122QmwtxOQa6TE1fRU0seIrqHR54tUpOTvUiIzEB5I9dLJNHv//ez2iGDyQFMTDissxO+nTIEzDUFmNipSAFBqseCgrM4TBNE7SWpUOu2005LeYWFhISZOnNjhA2JUV1ejpaUF//jHP3D66afjs88+w3nnnYfzzz8fy5cvBwAcOnQIer0eeXl5EX9bWlqKQ4cOxd33vffeC4fDwX+UgkOC6GqYNXazzwe9RpO2GimrXg9PMMiDJKNWi7ElJdhYXQ1JktDg8aDAZEpKkTJotRF9qnRqNdmftwOrkQLaengxKJDKHpp8PtgFRSqZ1D4lx7reBAue+tls/BxIkgRPMAirXo98kyktKb7ZGkgNzs/nC1IEQfROOjwqVVdXY9OmTfjxxx8jftJFWB44zznnHNxyyy044ogj8Ic//AFnnXUWnnrqqU7te8GCBXA6nfxn37596ThkgkgZsTZm6nPPQZIkOH0+9LXZ0qZIsVx+VhBv1GoxurgYtW436j0eeIJBmLXapBQpfdT+lcwmxPc0adEiXPPeeym9j1wjGA7zlKSNN9yA7669lj+XzGSd6Bpcfj//rgCRQe5/1q9HyT//GbN9lVxn2J4iJUkSbyqbSzQJgRQ7B2xRwKTVcjU81MnFllAWuvYBwOC8vLTXgxEE0bNIOSl/3bp1mDNnDrZu3conTCqVCpIkQaVSIZSmCUFRURG0Wi1Gy7UYjFGjRmHFihUAgLKyMvj9fjQ2NkaoUlVVVShLkJdtMBhgEFYeCaK7iHb7avH7EZYk9LPZsC7FlJFEipT4WgZ5ggO0ToTcgQBMOh0sOh18oRBCwsRfxB8K8XQnhlJqnxgUrK2sxNrKSjxzzjkpvZdcIhQO85V0h9GIo/r25c+RIpU9uAIB7sQGRKb2/f7zz3kvNhb87qqvBwAUmEzt1kgd9+yzONDcjIqbb87MwXcTTT4fDBoNCs1mHlSx1GSTTgerXo+wJKHW7UapYLaSKtmqSA10OLB0z57uPgyCILqRlEelq6++GsOHD8c333yDn3/+Gbt37474N13o9XpMmjQJ27dvj3h8x44dGDhwIABg4sSJ0Ol0+PLLL/nz27dvx969ezF58uS0HQtBZArmfnfVEUcAaAt2+tps8AaDKa1i++MpUvLksM7thkalglat5ivvLX4/PIEAzDodd5KLp0qJk0iGkiIVnZI4tqQk6feQiwSFQCoaCqQyj+ruu3HNe+/B+ve/c6dKJVx+f0QgJX427POpEv6e9Y6aUFbWbmrfqv37sdfp7PB7yFaa/X7YDQbY9HoeTDInQ5NWiz42GwB0uo6I95HKskCqj82GQy0tOak2EgSRHCkrUj///DPeeustDJMdxjpDS0sLdgldz3fv3o0NGzagoKAAAwYMwK233opLLrkE06ZNw4knnohPPvkEH3zwAZYtWwYAcDgcuOaaazB//nwUFBTAbrfjxhtvxOTJk8mxj+gRsMCJ1c6wCUc/eQLiDgS4otQe8RQpFjTVejx8cij2QHEHAjDJqX3sNW0Kiq1PSZFSqJGKdhtM1OS3N5BMIJXI0IDoOOz79eyGDQCAbbW1Ee6JIq5AICK1T0y7ZCrrweZmDHA4ALQaKhg0GgzKy8NGub6xPZp9PsXvVk+F1ZXZDQZFRYpZ/h9sbsYRnXDvY6592aZI9bFaEQyHUed248FVqzD78MN7/cIRQfQ2Uh6VZsyYgR9++CEtL7527VpMmDABEyZMAADMnz8fEyZMwJ///GcAwHnnnYennnoK999/P8aNG4f//Oc/eOuttzB16lS+j4cffhhnnXUWLrjgAkybNg1lZWV4++2303J8BJFpGuRC7P5yIFXZ3Ayg1QULSM25L26NlBwg1Xs8fHJoFXqgeIJBmDqhSEWn9jFFalhBAW446ijUut29uk9SokCKBaakSGWGHVHOkYlq0Vx+f8SihZjax67fQ4KycrClBWVWK0wpNFXONVWKBVJFZjN8oRBqXK4IRYoHUmlSpLIukJIXvLbV1uK+lSsxR3AVJgiid5CyIvWf//wHc+bMwaZNmzB27FjohFQIAJg1a1bS+5o+fXq7E6yrr74aV199ddznjUYjHn/88bhNfQkim2mQV8xZIHVAtuLvI09AXH4/EGcFPZr2aqTq3O42RUpI7XPLqX28wWS8QCpOjVRMap/8929ceCF21NXhybVreQpQb4RS+7qP7bW1Eb87EzjIRddIiZ+NWqVCSJIiAgLWf82k0/HgQQnx+5Fr7n5NssJ20uDBUAH4386dGFVcDKD1/Ok1Gtj0+pha0FQJZqnZBBu3P9yxo5uPhCCI7iLlQGrVqlVYuXIlPv7445jn0mk2QRC9gXqPB2adjvevqZBXrFn6UDx1yOX3Y+Tjj+PdSy7BRNm8IBCnqSsLkOqE1D4WXDV6vQhLEkxaLZ9ExmswqaRIKab2yX9v0ev5inRlc3OvDKTCkgQJ8VfSjeTal1GiFak5776LS996C4E//SnicX8ohGA4rJjaV+/x8NSyg7JiDLRe031sNpi0Wp7OpoSY2pqOnkrZBFsgKbVaUWAyobK5GYNk4yeTPJ44jMZOB5ChLFWk+tvtOKpvX9z/zTcAEONqShBE7pPyqHTjjTdi9uzZOHjwIMLhcMQPBVFErlLrduPhVavSnqLW4PUi32jkE7jdjY0oMJngkAOreOrQzw0N2N/UhIdXr+aPxa2RElP75Bu9SauFCkCNPMkTg7l4fV+UFCm9RoNgOBxxXtgxW3Q6DM7PBwBsralJcBZyl/ZSkgykSGWU6KbQrkAAwXAYG6L6DPLgX1CkdGo11CoVDgjBk5jat7W2FsMLCmDUahMqUuLCRC4qUmyBxKTTwRMMttVIyde2w2BIqAQmQ7am9gHA5P79+f8Tvc9V+/bhinfe6YpDykrOfOUVvJzGFjkEkS2kPCrV1dXhlltuQWlpaSaOhyCykqveew/zP/us0ykq0dR7PMiXm+ECrQFSqcXCJ3TxFKlaOQASJ+DxaqSMWi10ajWqXS6ugKhUKph1Ou5iZtLpkG8yAWhNN7zt889x0ZtvRuzHp6B4sdcTVSlRkWKmGee/8UavdLZqr/8NpfZllj2NjZh9+OE4dejQiMcn/PvfEb83C9csg31H9svptsVmM0/tq3O7sb+pCUeUlfEAIh7iYkhnA4pso8nng10+Z2Y5xZHXSAmKVGeVuGx17QNae0kxqhO4Qs793//w0o8/dsh85+uKCp4G3lP5386dmN2LA0kid0l5VDr//POxdOnSTBwLQWQt3x88CCD9E95d9fUY6HBEBlJWK/892kqcwSZ04gQuniKlUql4uo1RCISsej1XpFhqn1atRoPXi/u/+Qb/3bIlYj++YFCxIS8QWQciKlLixGdPL2xc2a4iJRgaEOmn3uNBf5sNn86ejWKzOe52aw4cAACMket7GBYhkBpTUsIVKXYtH1ZY2JraFwjEVatdOaxIiS6EJq0W7kAgRpESHf06Sra69gHAEFl1B+Q00DjNh0cWFQFAjBqaDNMWL8Y5r73WsQPMAnqz2RCR+6RcIzV8+HAsWLAAK1aswLhx42LMJm666aa0HRxBZAssvScVF732ePTbb/HpTz/h1xMncgWqyedrVaTaMX5gEzpxkhYIhRQDKQAYnJ+PnfX1EYpSH5sNz8m20A6jESqVCvlGY9yVT38oFGPFzl5PdO5z+f3QqdVcrfr5ppswZOFC/HDoUMSkozfQXiClUqmg12hIkcoQjV4vT1m96Zhj8M62bVgvL4qIfLt/Pwbl5aFcrk1kiIrU0Px8LK+oANAWEDkMBph0OkhoXchQqpERU/tyrUZKTO0zy8ocGyONQmpfTSdbIGRzat+MIUPwp2nTYNRqcceSJajzeFCiYBDEjCl+amjAjBT2zwKztZWV6TjcboHGNyKX6ZBrn9VqxfLly7F8+fKI51QqFQVSRE6TzkDqq717AbSu2Fr1el60XmqxtGtFzlLyxEArEA4rpvYBwCB5ghitSAHAnPHjcWSfPgCAfJOJW7IDrSuJKjktzRcKoVDB/py9NqMlykZ6UF4ejFot9skT0t5EMhNAYwr22Z1h6e7dsOr1mNSvX8ZfKxsISxIavV6esvrHadNww1FHoeif/wQQqaa4AwEecIlY9HrslhvvDsrLw3tyg3gWSNkMBq68eAIBxUCKfUf7WK05l9rXLHzXWYpjg8cDh8HA1WiHwYBd9fWdep1sde0DWsfRv5x4IlbK43mNy4USi4V/p9mYy1T7VK8BFognSh/NdprjZFYQRC6QciC1e/fuTBwHQfQI0nkzy5MncX+cNg0atRrTBw3Cx7t2odRqhVathl6jiZvax26uYqCVSJFiK6SiWcS9M2bghR9+wL/POosHS8x5i+H0+fgE0xcMKtqfA7GpfdG1JsVmMw/+ehPJBlLMte+lH3/E4Lw8HDdgQFqPwxsM4qQXXgAAuG+/ndev5DLNPh8kICJAKjSbsXTOHJz4/PM42NLCAylfKKQYBJl1OrgCATgMBhSaTGj0elHv8fBaSbusSAGtY4MjZg9tqnGJxZLWhZjuJixJEQ3DzTod3IEA6j0eFApplOmokcpW1z4RNsZWu1wYA2Dck0+i0etFza23AmhTZVI9F7kQhIipnfFqeQmip5K9oxJBZAlifnc6J0LVbjfOPOwwPpk7SrYxL5VvyGxiokSL/HhEal8CRapAXpUXJ5VTBwzA02efzYMooLWJ7qubNvHfxeBHsY9UnNQ+S9REvdhi6XR6T08kmUDKIKf2+UMhXPHOO5j63HNpPw6xLoM1hXX5/XHrOXIBpqzmRylNfWUDFHHBwK9wbQPgynCByYQ8oxHBcBiF99+P6z78EFq1GgaNhisO8Zz7XIEANCoVHEZjj1YVomFjE1ek5FqxOo+HjzdAemqksjm1j1HucECtUuFnWcHcVV+PWrebp2Gzzz6eK2o8mnMgHVR8D51tzkwQ2UbKipQkSfjvf/+LpUuXorq6GuGoG/Hbb7+dtoMjiGxAnPykNZByuXgBMtC2oqmWAxuLvBquBLsxuZJUpNjEJnpSGc3hJSURv4s3faU+UvHMJixRtVTFZjMFUnFgqX0dKUJPllrh3O9rasKIoiJY770XNx9zDB4+/fSMvW5X8FN9PRq9Xt5PjcFq/fKFST3Q1uxa7AkVT5GyCIGUuJ9gOIwCkwkqlaottS9OkOTy+2HR63kNUa7AVHFRkapsbkZ9VCCVTvvzbHTtYxi1WgzOy8M2uQm0QaOBLxTCXcuW4amzzuKKVMqBVA4oUuJ72N/UxPskEkQukPKodPPNN+OKK67A7t27YbVa4XA4In4IItcQzRcS9YtJlRqXK8JJjFk0Hy3XsFj0+viKlHxjSlWRai+lYtaIETht6FC8ceGFACInh4qKlJL9eSCgrEhRap8iLJASbZHTbRUv7nuv08mvq9c3b07r62SSz376KSL4YQx79FEctWhRzONswhpd+2QzGGCRJ/0MpUUCoE2RypcVqYj9yAEEU5TjqS7s+8Bc7XKF6ECK1XjWezwoFAMpoxG+UKhTzpTZ7NonMrywEDvkejB2XXz+888AOp7aF69Bek9CVKQO9MJaWSK3SVmRevHFF/H222/jjDPOyMTxEETWIZovpHMi5PT5IhSi4YWFkO68k/9u1uni1kg1CwXIYUmCWqVKqEixSWJ7E5ERRUX4ZPZs7JPTv8TAMZEiFZPaF6VI5RuN2JBjhfbJwCaAiVbSWSAlTsSbfT7elDkd1Hs8sOh0MOt0ONjczIOIbJ+YMtyBAE576aXW5rd33KG4jWiMAsRP7QNa0/vEFCN/KBRhxMJg13GBycRTAhkV8neEra7vaWzEsUJzVsaBpiaUyAYy6e5D151ENzFmfaSC4TBGCzbydiHQLFY4x8mQzWYTIiUWC3bW1yMsSaj3eDCxTx+sP3gQoXCYj6WdSe0LhsM95jsrIiriuRAYEoRIyt9Ih8OBIUOGZOJYCCIryUQfGEmS4PR6+SRDCatej6YEZhNMZWI36ESKlEN+nUFC88hEiAX0jEQ1UmJqX7Pfz1fr+f7k+oneRiqKlJj+kupkqz3qZAMAm8GAFr+frwr3lElZlRz0RLsbisrdoajaC3YOlQLS6Jq9eKl97Drua7XGpCOdJivIeUYj8oxG7u4XzbqDBzGhTx+u2OQK0YqUw2jkRhzRqX1A56zfe0KNFAAUmkyoc7vR7PMhLEk4rLAQElqDenbtplrzJI4L8RbWsp0KpxMlFgt0anVOqbIEAXQgkLrrrrtw9913w5NDK2sEkQhx8nPTJ5+kZZ++UAiBcDhhIHVYQQHPt4+mxe/nphSsTiqRInVM//74/Ior8Ksjj0zq+Fjdh3jT8ytMNpVS+5xeL588MXKtPiRZkpkAMttoMUhPd7+hOrcbhSYTrHo9Wvx+rsZk+8SUUSWkhYrX5F3LlvH//xQVyDR4PLDp9YrvkU14GfFS+wbKwRNToz6dPRv/Ou003DplCj6ZPZtvNzgvL27D6a21tRhXUgJTAvOYnkh0IDW8sBBVLhf2NzVFBlJyINuZOilmiqLOckWq0GxGneDqeFhBAYBWRYYFUqm2OhAXoOLVzGY7FY2NvPF8b7wPELlNynfRiy++GA0NDSgpKcG4ceNw5JFHRvwQRK6RCSWFN/RMkL41rqQEm6qr+WRcpNnnQ5lcNM9WKeM1BGWcPGRI0hMRrki1E0gpmU04FdLScm0SmSxJBVKyWicGUplSpKx6PVoCAV7311PSbES1ab9QY/Gv1av5/3+K6lXUIPSQiqbIbI5IN4rn2se+Yyw189ShQ/F/xx6L+085JWK7YosF9V4vJEnC13LTXqB10uwOBFBsNuecKssm9Sz9cZRgnFOooEh15poOhsPQqFQRqZvZSIHJhFq3G+e/8QYA8BTHWrebBxCpBhK+qLTpnsg+2WAikRMtQfRUUg6k5syZg3Xr1mH27Nm44IILcM4550T8EESuwW58d0+fnrYcfbY6m0iRmlxeDm8wiNX798c81+L3o4+8Ss5SP9LZn0OtUsGg0UTc9JX2r2R/HleRaucG6vL7c65habKpfUyRYiv5GQmkZEWq2efjQdvBlpYeYa9cJQRSokmERq2G3WCAWqWKafra6PUqNtkFZEVKyKqIl9p3WGEhAGBIfn7C48szGtHo9eKDHTswbfFifLxzJ4BI58BcW42PVqTEtGFRkepnt0MFxFXskiEkST1CPWWtMpgD5xg5kKrrhCIlbv/mli0R7TiykV319Xjiu+8iHmP9CCmQInKRlCs/P/roI3z66aeYOnVqJo6HILIOFgCUWCwISVJaAhauSCUIpCb17Yu+NhseXbMGU4UGraFwGJ5gEP3lQKrR60VYkhCSpLipfR1BVJEkSWqtwYraf9KKlNx0NhQOKxov7Kyrw/DHHmt9LcFwo6eTdGqfrEiV2+2o93jSH0i53RhZWIhAOIwWvz9C/dpeV8d7mGUr+5qa+CSMpeS1+P1o9Hrx0nnn4cm1a/HXr7/GWcOH4xjZ8KHB641r919oNsem9il8p4/u1w9bfvObiDYFSjgMBvzc0MA/t5319ZiJSMOLXFNlW/x+6OTG4UDkopAYSBm1WgxwOLAzKtBNhZ5ishBtNjK8sBBatRq7GxvhDQZh1etTViXFQOqOJUtw/qhR7V6P3ckdS5bgjc2bMbq4GNMHDQLQeg8163Q59x0gCKADilR5eTnsdnsmjoUgshK2iswmB+m4EbCJbCJFSqNWY+6kSfhk166Ix9lKMCt+r/d4uCKUzo7xoorE3OdiFKmoGqmvKirgDQYVFSkgflrLFe+8k7bjziaSTe1jZhPFFguMWm1GFKkCkwk2uUbK6fPx6yc6JS4bqXA6Mb60FCqA158wZ8lyh4N/N6c8+yz/m0SK1ECHAw1eL5bs3g1ATu2L4yg3qri43ZSyPKMRTq+X1/Kwz48da4HJlHRq37I9e7B4w4Z2t+tuWqLcOcVzVCS0dQBalb1oxTAVgnEWYLKNCX36RCwEGbRazBg8GO9v3w5PIIB8ozGuIvXhjh1Q3X13jCofbRuf7W0k2Pdy/cGD/DF3IACTVptUZgJB9DRSHpkefPBB/P73v8eePXsycDgEkX14AgEYtVpu85uOQKo5Ki0mHn2sVjT5fBGpcyyQKpcnwg0eDw9k0qpICS5jPFCLo0ix509YvBhAbFE4C6RmvPCCYj8ZccKbSzfaULI1UnJqn91g4IYQjLAkYak84e8odW53W42UrEiV2+0waDQRRg7Zyp7GRgzJz0e+ycSDk70skLLb+fdIdPFr8Hji1khdPGYMzDodVu3bByB+al+y5BmN2Flfjy01NQCAJbt3Q5KkmNQ+XyjUbo+wE59/Hle9916Hj6WrcPn9ccevaIfDQpMpoo1EqoR6iCLFuGTMGO74ePqwYfhm377WdhcmEwLhMB8XgLa6p+XynGpVVCq3NxjE0Px8zD78cADIegt91hZA7PnmCQZhktsvuHMovZUggA4EUrNnz8bSpUsxdOhQ2Gw2FBQURPwQRK7hCQb5ahqQnkCKBQtsn/EolFd2xZsnm2TnG42w6HRo8HozokiJaRj+OPuPtj8fIdeUjCkpidkXAKw5cCDGXQ1oTZtkdKaWIttINbXPLjeLFYvKn1m/Hie98AK+VaiVSwZPIABPMBjh2tckp1+WWa0R9UfZyoGmJvS321Eg1DYx58Ho/k6MRKl9GrUaAx0Ovq94qX3Jwv72gVWrAADLKyrw8saNMal9QOKFAnHBJNtpSRBIRat7Nrk2r6P0lNQ+xqsXXADnH/4AAJg6YAB8oRCC4TBfMGKq1Es//gjrvffihMWL0V/O9FlXWRmxL28wCItej2dnzQKQ/YEUuz9VCuMKS+2jGikiF0m5Rupf//pXBg6DILIXTyDAV9OA9ARSbB+m9gIpeUW9zuNBqewgxtQsm8GAApMpY4qUeNNj+2/P/tys0+H6iRNxZJ8+MftiHGppiWjYCbSmQh1eWoofq6qwu7ERo6Ke76kk00g0QpHS62HR6yNsjqtlxUg0WUgFNpkvMJlQbrdjr9MJs06HsSUlKLVacailBTUuF0oeeABfXHEFZmRhn8AmuVi9QFCkqlpakGc0wqDVKqo8iVL7gNZFCubcp+RImQri344qKoJFr8etn3+O2447DiatFgatlrcU8MgTYyU6o9p0NUqBFOuJFo3NYIjoh5QqzLWvpyCmOYqBPgvs2TXw1tatAFpTok+Vv3f7BFdKoK1/Hxtrb/70U/zyiCOy0sFQkiQeSL2ycSMeOOUU9LHZeGqfSavt1HWQ7UQ3BSd6BykHUnPmzMnEcRBE1pIRRSoYhFatbneVlSlSrDD++Q0b8LBs+WzV65Evp8xkQpGyChP6eKl9apUKGpWKK1IHW1oUFQJxtf9A1ERBkiQcbGnBmOJibK2pidvYtCeStGtfAkWKre6Ldt2pIKaXTerXDzd98gk2Vldj6oABKLVYUOVy8XqG/27ZknWBlCRJ/NwwdzygtbcU66UmhlFfV1Tg+IEDW1P7EgVSgrrlS1AjlQzXH3UUDFotrv/wQ5h0OsydNAlXvfceDrW08PqtZBSphixXG0RcgQBPd2bsu+WWiLQ1RmcVqUTNxrMdsV6UpZqyYLNaSKtlSvz+qPHRGwzCKFybTbLrZqLWGd2FNxhEWJJwdL9+WHPgADbX1KDUaoUvFOKKVHUPSCXuCJuqqzHuySex/rrrMCFqIZHIbZJavnaleOGnuj1BdIa1lZVY+O23Gdt/JhQplurQHqIiBQBPrl2LH6qqAMiBlNHYGkhlQJESJz98/wqTGYNWC18wCEmSWpUNIU2PIZpqHIhSVt7euhVrKyvhD4UwwOHA7t6Y2ifUSEUrUixwYDVBqSKml5VYLNxZrL/d3mqS4PPhR/ma8mZhapnYvNokKB5VLhdXaUVL6GmLF8MTCMAXCiVUpFgvqVA4DH8oxBWjjqDXaHDp2LEAgJuOPpqnad23ciVfZEhm/OjpilSR2cw/ExGrXt9pRSqdY1tXIo7zXJGSr4Fd9fU447DDAIC7GkaPj9GBFBAbbGULTI26Tu4p2uzz8fdqipPaN/ejj7jpS09mxd69ANqs74neQ1Ij07Bhw/CPf/wDBwUXlmgkScLnn3+OmTNnYuHChWk7QIJoj0mLFuH/PvkkY/vPlCKVzMSNrWAyRUqcGHJFKkOufWI6TjxFCkCrghIIwBsMIiRJik6EI4qKsP+WWzCyqAgf7dyJt7Zs4c8xN699TU0os1qTWrF0BwI46fnnOxxcdBXJuvYFw2E0er2wyYqUeI0xJWpvBydPoiIFABPl1dL+djtXv1i90b4sPJ+iw6VRMECpamnhitQ1EyZE/M1nP/0EADi8tDTufgfn5WHNgQN4UK5rai/Ntj3sBgO8d9yBOUccwQMpAKiRPz8xtS8ePUmRSlQjFY3NYECL39/hHkiBUKhH1UiJiKle+UKNVLPPh2qXC8eVlwNoGweVFCmmllbcfDOA2PS/bIEFUmKPQ3a9K9VIBcNhPLF2LU576aWuP9g0w8apZL8TRO6Q1Mi0bNkyfPfddxg8eDCOOeYYzJ07F3/729/w4IMP4o9//CPOP/989O3bF1dffTXOPvts/P73v8/0cRMEgMiV6GUZcpJ0y+oRq2toSUOON1O52kOrViPPaOSKVIvfj5OHDMEDp5ySNYqURa+Hy+/n5yU63YfRz25HsdmMFXv34sI33+SfFzuvz8yaBZNOl1TDym/27cPSPXvw9Lp1Kb+vriSZQEoMPJUUKRZYdlaRYkE4s6a2CvVY7HN2ZWEhODs2m14fcX1UC+rnacOGQbrzTvzzlFNgNxjwXWUl+tvtGF9WFne/v50yBSMKC/HCDz8AQKcUKQab8A7Jz8eFo0dHPJdMal+2GwmItPj9cb/r0dj0eoQlqcMNiXtyap8IW8zwBIP8+8wUYqZE1QrNe4FWRZYpUn2sVqhVqqxVpNj4UWgyQa/RoNnna6sHlmukxECKtfYIKqSD9jSi75VE7yGpWdeIESPw1ltvYceOHbj44otx4MAB/Pe//8WiRYuwbNky9OvXD4sWLcKePXvwm9/8BpocGPCInkGdMPE48fnnO20TrYQrEIBFr+crTR1NUfm6ogLfyHbLrPg2GQpNJq5IuQIBjCgsxG+nTAHQusKZMUVKSMfxt6NItQiBVKIVOTHt77E1awC02v/mG40YW1ISt1g9Gq6yZGGdgAjrv5UokBItupkTo1gjtb2uDgBQ0cGUxwaPB0atlk/GrjriCEwoK8O0gQO5+sU+Z1cWFoKLipTYi0mskWIUmkxo8vlQ7/EkbHYNtNamnTJkCJ+UJpNqmyx6jQZvXnRRxGOppvZ1VL3pKlgqajLY5O06WifVk1P7gDazGdG1j42XRWYzT+FmY6dYRyqm9uk0GpRZrVmpHAOIuAfY9Ho0Cal9TJFiwXQwHMbZr77abceaTsKShIXy/awztYBEzySlkWnAgAH47W9/i3fffRfff/89tm3bhhUrVuDRRx/FWWedlXIA9dVXX+Hss89G3759oVKp8O6778bd9te//jVUKlWMa2B9fT0uv/xy2O125OXl4ZprrkFLD7DzJdLDwah88nQ3MgVaJ5cWnQ5qlQo2vT6mYWKyTFu8GMfJDUNZX41kEJ3KoleBudlEJhQpg6FtlS1BoMZUjWQCqWKhUWetEBwyVUrsXZUIXvcTp09QtsBd+xJ8LgXCexiUl8dTJQFgY1UVttTUYHRxMfY3NXVoch1tA17ucGD99dejyGyGWQ7aeCCVhYqUUmpfKBxGrdsdU4/D1LYKp5NP3hNRaDbDKe+/s6l9Srx+4YX45uqrW/efRGqf2Gw1lOWBlDMFwwNbJxehenJqH9CmVIo1UmLKG0uFY+0j9kcFUqJZT3+7PWsVqYhASk4Nd8epkYq2eW9KIQBZuXcvVnewHUQmcHq9/PjTkbFC9Cy6dWRyuVwYP348Hn/88YTbvfPOO1i9ejX69u0b89zll1+OzZs34/PPP8eHH36Ir776Ctddd12mDpnIMg5GBc3RjWDTQYvfzyf6Drk4v7N4gsGkV8CrXC785/vv8XNDQ0xdQlcpUokCNatej8rmZj4JTxhICQoCUxNdQnCYrCLV3EPy0YPhMNQqVcLrMjqQssoruQDwiGyicu6IEQiEw/B1wAyi0euNG3BGB8HZOAlwRilS3mAQtW43wpIUo0ixQGp3QwOfvCeiUDgv6Ujti+biMWMwWa6BSSa1T6wP9Geh8YeI0+ttV/VjpKJI7XU60ffBB7G9tpY/FpSkHp3a108OlMTUPjHljTmdjpX774m99HxRZhPldjv2d7AVQqYRAym7vBDHAkZWZ+wOBCBJEg8Gz5TNNhK5tY5+/HG8I1vFA8DU557D5GeeydTbSBlxTpDL9u6EMt0aSM2cORN//etfcd5558Xd5sCBA7jxxhvx8ssvQxc18dy6dSs++eQT/Oc//8ExxxyDqVOn4tFHH8Vrr72GyqjVDiI3iTYmyMQgJtr82g0G1Lnd3OUsWaInEJ4UUvvY5HDowoU41NISGUiZTAiEw3wgT6ciZdXr4Q4EEAqHeaCm1Gunzu3Gx7t24UW51iRejxwAOGnwYEzq2xfnjBgRka4YoUgloYqwgCLb05+S6X8jqkUmnQ6D8/Oxz+mELxjExupqXDl+PCb16wcgMtD5qb4+qebFiRrTWnQ6+EMhruRmY2qfaJbBrOKZmlkcFUix1Kl9TU1JBdmFgkKaztQ+JZJJ7asWLO6zOZAKhsNwBQIZUaTe2rIFB1tasGj9ev5YT1ekhstKEzsP3mAwws2uj6ysltvt6G+383Retq0YSKVTkfIFg1glp5ung+jUPlGRMut0MOl0CEsS/KEQKpubYdBo8B+50XA8t1ZfMIittbW4+dNP03ac6UbMUsnGxSgis2T1yBQOh3HFFVfg1ltvxZgxY2KeX7VqFfLy8nDUUUfxx04++WSo1Wp8m8AO2+fzoampKeKHyD4+/+knfF1RkXCb6ElJR9PuEiEqJg6DAU+sXYvxTz2VUmF4tMtSKql9n19xBW6fOpX/Lva7YRPkQ7Iy15leONGw4MYdCCQ0m6iSg9kPduwAkFglmj5oENZcey1mDB6MOo8HkiRFBKrJKlJsm44Wr3cVwXC43Qlg9PkaXVyMkCRhR10dttbUYExxcVt9nhCQD3v0UQx+5JF2j6HB40moSAGtDnj5RiNfLc4m6j0eWHQ66DUabjbB1M9oswM2sW/x+5NL7RMVqQwHUjq1GmqVKuE1W9XSwlUeXxZf2+w6TFaRYtdvMpPMdbI7cIVQBxTo4TVS/zzlFMwYPBjDCgoARKb2mbRaHkgVmEwYUVgYE0iJqX35Qi+1zrLw228x5dlnsVN4vc7Q4vdDq1ZDr9HAZjBE1EiJLUQ8wSAqm5vR12ZDicUCrVodt+E4S+PO5toj0bEvm4+TyAxZPTLdd9990Gq1uOmmmxSfP3ToEEpkKZyh1WpRUFCAQwm8/O+99144HA7+Uy6nXhDZxakvvYRpixcn3MYd1RQyHWl30YiKiVhcncqqYETtQzicktlEgcmEv82YgZMGDwYQmQLBJsisViy630hnYDdvfyiU0P6cKQbs5p6sEuAPheAKBFoDVaZICcXIiWA3q3RY0WeSZAIplUqFG446Cl9ccQWA1kAKAH6oqkKz348SiyVmIvofYbW+PRIpUmxiU+VyocxqhQTl4LTG5YpIrelKGoTURFZDJ07ORMSJfTKpfWLz6EwrUiqViitq8ah2ubh1ejYrUmK6ZTK0l9onNvH9WR7fxJqZYA937RtVXIwvrrwSJp0OWrU6QpEyarU8vbfAZMLIoqKItEbRtQ+IrF3tLCzleHk7C5bJwmp4VXI9sZL9OdA6bh9saUGZ7EJYZrXG1Dsz2P2FqZnZuMDAvg/9bDa0ZPk9iUg/WRtIrVu3Do888ggWL14c0YchHSxYsABOp5P/7EujtE2kn0Qr5MyanPXXyJQiZRVqpBipOCeJKYhOeZUu1RXwjy67DGcPH45fCwosmyCzWjFDGicbTN3yhUJtrn0K+w/Ln0+z3w+NSpXUMRQK/bE6okixVcpcCKQA4Ikzz8SMIUMAtE6myqxWnnKTZzTyoKDF78fuhgZc+8EH/G+bfT6uSCrR4PEkTO1jDHA4ACin91345ps4/403+GctIkkSvj94MGIynE7qheM3arU8AAdi65rM8kQVSC6gL5ffs9K+MoFBo0kYINV7PNx4IKsDKfn7l2xqXyLHU38oBO099+Der78G0JbiJY7lPT21T4QtBrDFNJVKxcdajVqNEYWF2Flfz79P0al9Vr2+Uz25RNh+D6QpK0es4WU1UmItmBhINfv9/PrpY7XG1DszWOYHM+4R76VK41F3wK7VfnY7KVK9kA6NTF9//TVmz56NyZMn48CBAwCAF198EStWrEjbgX399deorq7GgAEDoNVqodVqUVFRgd/+9rcYNGgQAKCsrAzV1dURfxcMBlFfX4+yBP1DDAYD7HZ7xA+RvSRq0MoCqQEOB47s0yftilR06tkA4Vq54aOPkt5PjVD7UO/xtJpNpDhxM2q1eP8Xv8BQOT0EEBQp+SaUCUXKFwwmNJtYdc01/AaZZzQmtfDBalPqPJ5IRSpqxf67Awdw59KlMQEyu7kmU0/VnSQbSEUzurgYq2RXKofBEKFILY3qlzbokUfQ58EH4+6rPkFqn9iwlhV9K6VfbZLHWaWUotc2bcKRTz8N7T33ZGQSEaFIydcZW6WOXoxQqVRclUpGkRIVrEyn9gGtixPxDEMkSUKj14syOc0rmwOpeKmV8VCrVLDodIrXB+undPuSJfAGgzjU0oICkyliLO/p9ucibLFITO9mCwVatRojiorgD4V4/aPYkBdova4lpGcRiS1apWtBSgykuCIVCECrVkOn0fDFCrdscMO27WOzxQ2kxJYAoXA4op9eJlx6O0KTzweNSoUSi4VqpHohKY9Mb731Fk477TSYTCZ8//338MmDndPpxN///ve0HdgVV1yBH3/8ERs2bOA/ffv2xa233opP5aLDyZMno7GxEeuEppxLlixBOBzGMccck7ZjIbqXsgcfxIR//1tRAWKBFNC6Updu+2ZfKISwJPGJvtjgc18KdtRiMFjv8XRIkVIikzVSoiKVyBVwYt++uHv6dABtxf7tkawiteDLL/GXr75C3n33Rfx9T1GkQh0MpEosFr4y7zAa21Kj/P6YhYVEtXrNPh8OtrRgaH6+4vOFZjN+uukm3D19Oo6SXVGVvkNswlInLAgwlgi9235K4LzVUeo9Hp76xBYK2HtWUpFYulmRYCQRD5VKhYGyKtUVE3WDRhM3NanZ70dIcCLM5kCKvYdUFm6YHXY0PwvXTJU8jo0oLIxUpDr4PcpGTDpda42UkN79i3Hj8K/TTsOFo0fzXnsNXi8kSYpx7etsP0MRTyYDKblGSkxj5zVScko327ZIbvERDIdx97JlEamN4vj29tatEaYU2RJINXq9PHOAXPt6HymPTH/961/x1FNPYdGiRREuescddxzWp5C3DwAtLS08SAKA3bt3Y8OGDdi7dy8KCwsxduzYiB+dToeysjKMGDECADBq1CicfvrpuPbaa7FmzRqsXLkS8+bNw6WXXqpolU70XDYcOoTnZVc4ETGQYo1h0wlLc2IT/fHCCj4QuVqWiAahQWid2926GpmGoEen0cCs0+FQSwsvZk8X+iQVKQB8op7s5C9GkZLPL6uRYgGqGGyKqWNMkcj2QKqjipRJq+WThGhFKpV+K9vkCQmru1JiSH4+/nzCCfw1lFL7WApNnULQ5guFeBH93gw0Cj3Y3IwyeXI5KC8PAPCd7MqqtBjB+i+VRDn6xWPVNdeg6ne/S3sKuRL6BKl9bMLIFKmOWN13Fd6OBFKCrb+IaCjEVInhhYWxilQPrpESYYtF4r1Lq1bj/449Flq1OiL9LRAOQwJiaqSA9LjDZVyRku3P2XsS35vYE9EhG2is2rcPdy1fjovefJPvU6wvvvi//8Xdy5dHvF42UOfxoNBshk1OuyR6Fynf4bdv345p06bFPO5wONCYhBWvyNq1azFhwgRMmDABADB//nxMmDABf/7zn5Pex8svv4yRI0dixowZOOOMMzB16lQ8/fTTKR0HkX0EFeotGhQmcTGKVJoHMTYoMkVqZFFRxPPJ5pZ7gkFeRF7v8bSu0qUplcii06HB40lrWh+gbDahZH8OtE7GgeQDS5teD61a3aZIyefXKKhgACBObUWr756iSHUmkGI4jEboNRpYdDpUtbSkVAfIFKLDZPvlRLDPIFqRCggTeiVFyh8KYYDDAb1Gg4oU7wFKeINBXPTmm1ypONjSwk0hxsnmQi/++CMA5b5x7HwnG0j1kZ3DuoJEqX0N0YFUFhbVM9h7SEUBLzSbFdXTlzZu5N/zLTU1AFoDKXcgwO8DgVAoZ1L7uGFKHOdWMdhggY5Yd8qt5NPRz1D+rrvTdK3F1EjJ9uemOIEUrz02GOD0erFMTlsW7zNVLhdfqBtdXIxd9fURr5cN1LrdKDSZyLWvl5LyyFRWVoZdu3bFPL5ixQoMkYulk2X69OmQJCnmZ3Ecp7Y9e/bgZtlUgFFQUIBXXnkFzc3NcDqdePbZZ2GN6nZP9DzYADlKCFyiLcSBKEUqA6l90bUAbFWU3diSde7zBIMoNJth0GjaUvvSFPiYdDo0eL1pTesDolL7wmGo0FoMrQQLpJK9salUKpRaLNhRVxehSFmEGy3Qev6VXMyYWpOuCUCm6HAgJUywmJJ5RFkZ1h08iKY451hp4l3ndkOnVidVL8TOffRihKgyxVOkjLKFc7w6h1TYWVeH/27ZgsvffhvBcBg1Lhc3YNBpNO0uGLC00a4KjlIhUWofWxxg13s2LxJ0RJEqNJkUr58mnw9nDR8OoDXzQIU2hZspWLmU2scUqWgTCYZYR6SUQhnPSv6lH39M2TQio4qUwYCwJKHO7eb3aJMwvosp3azR/VZZQRfT4w61tKCPzYYbjjqKB34PnHIKf71sgCtSBkPWHBPRdaQ8Ml177bX4v//7P3z77bdQqVSorKzEyy+/jN/97ne44YYbMnGMRC+ErepcLauVABT7TLi6KrVPmIgemD8fP//f/wFIXJ8iwgKnQrMZdcxsIk2KlEmrRViSMqZI+YLB1hXhBKk1yfTsieb4gQOxcM0aOH0+fn6jJwks9zwalmrWE8wm4gWfiWCTKZNWy8/7xD598ENVFZp8PvS12fDfiy7C3EmTcP6oUQCgmDbV4PWiwGRKKm3NEmeCJiqB8RQpvUbTOhlKQ80CC5LXHzyIg83NkADeZweINMhQYuqAAQCyNJBKoEixsYS5J6ZrLKv3eHDdBx/EtZfuCD4FpaQ9isxm3khZxB0I8JTNR9eswbCCAp76y66nXDKbYOnL8XpjiXVEXJFqp0YqGA7jinfewcB//SulY8lkjRT7t9rtjq2RCgZjFCl3IMADKXEcqXK5UCq3gGC1wGxhJVvUnzq3G0Vmc6silSZHRaLnkPLI9Ic//AGXXXYZZsyYgZaWFkybNg2/+tWvcP311+PGG2/MxDESvRA2mRos32ABKK52Zzq1T8mdqq/Nhj5WK7RqddL1Kh559XFYQQFW79+PsCSlLbWPvf+0B1JR9uftTWQeOOUUfC73QkqGv510Ev8/O7/R/WYaPJ6EBha50JBXCbOwUssYlJeHfU4nnF4vThg4EBeMHo3HzjgD8yZNAqAcSCVy7Iv3mtGqLpvg2w0GZUVKbhjqMBjS4prJlBl/KIS3t26FWqXCRKHm9Z1LLkn49/edfDJWX3NN0tbcXUki+/MGjwcqtPaiAdIXSN25dCkWrV+Pb9LY5sMbDEKtUqV0bSsFUmFJgjcYRLnghnrnCSdwFZZdT+0t5PQkmCIV7z0ZBUVKSfkzCYEWg333Q5KEq997L+ljyaQixcaTOrebH7NOrYZGpcK6ysqIbdkYv+HQIZRZrRFjWVVLC0otFliEHoNsYSVb1J86jweFJhNsej2/poneQ8p3eJVKhTvuuAP19fXYtGkTVq9ejZqaGtxzzz2ZOD6il8LS+Cb06QOgtW6gsrk5ZqWnM2YTosVsPFhgFt2TRqVSwS67EiUDc+k7Y9gwfPrTTwDS17eG3aTS2UMKiDWbaG8i89spU3ByCum9Q/LzeT2IkiIVCodR43ZzxzYlsrmOBOh8ap9oz93PbocrEMC+pqbIxrPy/xMpUsmgVqlg1uliFiMavV6o0Lqo0a4ilYZASlR5/7h0KU4YOJBfJ0BkE10ldBoNjunfv9PHkQn0Gk38GilZfTVotdBrNGmbJLLJZzrdxHyhEAwaTUoGHYUmU8z1wybw4md68pAh3HmRXdMd/R5lI6zFQzxFSqVSwaTVtqb2yddKRCAl/19cRBIVnOdk865kyKQixe7LtUJqn0oeYxauWQOgbbFOXPQ4Y9iwVsVOfu9Onw95RmPEPbjAZErrd6SziDVSQHq/a0T20+GRSa/XY/To0Tj66KOpJolIOxWNjdCq1RjocKDhttuw8PTT4Q0GYyZqnbE/v3vZMgx+5BH8+sMP4zYT5YqUQo1JSoGU7NIn9oBKlyLFbqyZNpvIRGoNW4nkipRwI1qxdy9a/H7e30jJ6jabLaKB1hXizphNiBMMVjvzc0NDxOPRk04R0To8GSw6Xcx3qNHrhcNoRLHFoqhI+eVJdV6aUvsaPB5+zlr8flw6dmzMNiOSMM/IRgxabdzgX1QPWdPVdMAmy6laRZc+8AD+LjfJjSZefU/C/VmtqPN4It4/m8CLk+QSi4Vf3+x6ihd09ESYIpUoODTL6ouS2QQ776Ii1dEFjEwqUmwMq/d4IhYN2fFrVCqcMHAgAESMUacOHQqg7T01+XywC86lQOviUTq/I50hFA6jQaiRArJHKSO6hpRnXuedd57iKpRKpYLRaMSwYcNw2WWXcYtygugIFU4nyu12aNRq5BmNfKCtj0r1ijabYEpGvLqUXfX1eHPzZiw4/njslVWvf69bh1+MHYsT5EbPItH25yIpK1JabcTKeroUqa5I7UtGkeoIhVGNVsXUvv1NTdBrNLhkzBhc/+GH2N3YiMnl5RF/r7S6HwyHsWrfPhwv36Tbo87thkGrjVEd00FnFSmxjm6QkOYqXkeJAqn9TU3t1hSJWBQmJ6xOrdBkimgszfDJipRBq02bItXXZkMoHMaB5mZeAyay/vrrs16NVMKg0aAhzqS1QQh60zlJZNdFqoFUtcuFO5Yswe3HHx/znC+qSWwysOt3r9PJXSTdCgtVYlNlMbUvpxSpYBDmBOmKZp0ObrnXFBA5tqtUqph+ex1dwOCufZlM7fN4IsYxVt/60vnnY6B8TZQK9YzF8v+dXi8KTSblQEqvz5qeTY1eLySA10gB2VO7RXQNKY9MDocDS5Yswfr166FSqaBSqfD9999jyZIlCAaDeP311zF+/HisXLkyE8dL9BJqXK6IYnG2UhttgS4GUmzC+JJsjazErz/8ELcvWRJjElERp/+NKxCAQaNRDMzsBkNcBzWgNQhjx+uVrW7FCXAqSkEieGpfmgMptgLMzCbiWZ93hjtPOAEAeI2EmNp3oLkZfW02OIxGFJnNEY07GUqK1PMbNmDa4sX4dv/+pI6h6J//xIR//7ujbyEhnbU/F895X5uNXzNKgVT0pKLB48GGQ4cwNSr4TIRFTu2r93hw08cfwx8KRQRSCVP7ZAvjzrK9rg4DHA6sv/56rLvuOsXGumadLunar2zCoNXya3Z/U1PENdrg9fIG24n60YQlKaWJb0cCqXA7xfJeuS4uFVggJaZTs4WqaOMdo1YLnVodaTaRazVSCVQ2kxxIxcuIYMEYo6MLGO40BlKBUAi+UCgmkAqGwxGLhuz6zxcWRMXvOFtcq3G74QuFEAyHuQLFsBkMvLFxd8Pq/liNFECKVG+jQ/bnl112GX7++We89dZbeOutt/DTTz9h9uzZGDp0KLZu3Yo5c+bgtttuy8TxEr2EZr8/wgmODbrRkwG3YKF6bP/+KLNaE9Y9sZW9zdXVqGxuxiVjxqDAZIprG9vi9yum9QGt/Wpe+OEHxUmHJEmw3Xsvjn/uOQBtqX3iBLg0TSmx0Y5I6UKlUrXaNTNFKgMrwqcMHYrgn/7EUx7Z5OyZ77/HgeZmXng/JD8/IpCSJKm13kRBlWDNg1emUFwv9iZJJ51VpKKD1+HySn60sqlRqWIUqe8PHUJYkriLXTIwJeShVavw6Jo1WLl3Lxp9PjgMhrjWvkydyDMa0eD1Yl1lJbbL7lsd4Zt9+3BceTlKLBYcKddI5gqi/Xn5ww/j2Gee4c81eL0RqX3xVtvv+PJLWP7+96TTWtl1kcpkO5HSHgqH0eD1pqyAl9vtUCFy0YpN4M06HR489VQ8fdZZAGRVSqi5y6XUPhYAJAoOmSIVLyMiOoiIXsBoLxBmuAIBqJCeQMoVlaYppq6L9yaWRSBmlojngd0Xq1pa+HVoNxgi0pkNGk3C5tZdCUt3LhQVKQqkehUpj0zPPPMMbr75ZqiFQU2tVuPGG2/E008/DZVKhXnz5mHTpk1pPVCid9Hs90f0vuGKlHDDkOSVWXGQdshNAOPBVvSXV1Tg2/37McDhQD+bDQfiWAO3RB2HCFO1NlVXxzx3QLZt3iw3mGRmE+KqWrHCSntHYO9faeW+s7BgxZ9B1yxR7WNpw8srKrDP6UQ/WakSAylJkhAIh2HT6xVT+1iRspKCFU2mbWo7GkixRrPDhZo6AJgku9cVCmpMPOOTbbW10KnVvMdXMtjk7w+boEhoS+0zK9RPAW2KVLHFgiafD0ctWoSRjz8e0cg3WQKhEA40N8c0vs4VmNlElYIDqdPr5SltNoMhroL01d69AADDX/+K7w8ebPc1O6JIJdr2ho8+wpNr16asgOs0GtiiVEue2qfTYf7kybh24kT+nFhzl3NmE7LKH+89GeVtlFIfxX0wWGD7wrnnAkg+1c/l96PYYoE3GEw6+IoHW2SJVqSAyKCKLZbFc2MtMpuhQmtqqRhIianN4iJfd1PjcgGITO0jRap3kfLIFAwGsW3btpjHt23bhhBzmDEaU3LzIYhoWqIUKbvBABUiU/v8oRDCkhQxYNvaqVtiK5yvb94MVyCAa488En1ttriNRMWc72gemzkTgPLq7VY5gAJaV+yZIiWSrsCE7bcwA6lOLBUpU2YTSvxTbra4vKICw+QgYEheHg+M2OeeZzQqrkiyzziZZsmZXjkMhsPQdGAsPH7AACyYOhX3CBbxAPD3GTPw0KmnYkxJScTjStf9ttpaDC0oSOk6swl1hkBrrr9TDqRY2l80zMEtemFA/9e/4qm1a5N+baBtoSQ/C63L0wFTpD7csYM/xiZdTrkWBGgNoLfGUfXGFhfz/3974EC7r9mRQCo6hVqEpU53JLBxRF2nLkGRUtqWHXNO2p8nUNmYTb4rEIBaDhpEohWpqpYWlFmtvL9Sg9fLU7IT4QoE+Pe2s2ly0YGUqFiKn+8o+fqNDqQ+/MUvsO6666BVq1FkNqMqKpDqL1jkA8gaRWpHXR0sOh3vdQVQjVRvI+WR8IorrsA111yDhx9+GCtWrMCKFSvw8MMP45prrsGVV14JAFi+fDnGjBmT9oMleg/NPh+swuCrllM9GhRWM8VB2t6OIsXymTdVV8Og0fDmj/Ea6yYKpAbLk3ylQVNUQ6pdLngCAX5jYQ030wVrXpmRQEpM7euiicyc8eP5/5mN9cC8POxvaoqoD8k3mRRT+9jkKzqQ8odC+GTXrogi7UNxAuh00dGVdJvBgL/PmBEzwbTq9bhl8mSuWDGUFKm9TmdEH7ZkYCllQTmQqvd4uCJlkV0xo1U8pkgpNcC97YsvUnp99j1MV/1gtsFStsRrk6lTTXIKJQAcUVaGbbW1ipNbv+AwmijgYTR3UpHaHaXsMiUqWaMdEXadVrW04NNdu/h7V1LT84xGNAqpfTmjSImpffECKdnd0eX3w6LTxSxMRytSh+RAii1ANHg8yL/vPpz96qsJj8Xl9/PvbWfT+6IDKbVsisGOl/HqBRfg+okTuakE48zhw3kqb4nFgmqXi1+7Vr2ef/6T5XtCoubWXcnW2lqMKi6GSqWCTqOBIYts2YmuIeXq9IcffhilpaW4//77UVVVBQAoLS3FLbfcwuuiTj31VJx++unpPVKiVxFdIwW09rHZJqzSKgVSNr0+4Q1eLJYvs1qhUqmQbzQqpuex44gXSNkU8qGf/O47zBgyJMImen9TE0KSxPfz/fXXp6Uon3GYnP7V2dQMJdgNvStrFMRJ1VhZedFrNJDQWsvE6gXyjUZsV7iRskngvqhA6uOdO3Hu66/jzhNOwF3TpwOAYopVOumqlCSlQKqyuTklxz6gTZFiE9gGr7ctkNLpEJYk+EKhiNVmXzDIU/uiYUHXgaYmeIPBCPt/JVhg0BONJJKBNaWtcrmgVasRDIdxqKUFQwsKWlP75Inw0IIChCUJB5qbMSzqnImTtO11dQlfzxcMwhcKoZ/NllIgJdZTffHzzxEpd0wd6YhqaJebNvd76CGEJAl3nnACSiwWxTTBPKORH3OioKOnITbkjTc2sBRQVyCgWKNrjA6kXC6UWiz8e3PWq6/CEwzynoVKhCUJnmCQf2/THUgBrfdmbzAYcY8ekp+Pp+RauHiY5WCTvUf295Xz5/N5QbYoUltrazFKSEW2tbOYS+QeKY9MGo0Gd9xxBw4ePIjGxkY0Njbi4MGDuP3226GRB9gBAwagf5Y2RCR6Bs0+X0xt0pTycnwjGAgoBVJ6jQb/27kzIuASESchbFW4wGRKqEhFB3SMaBm/2efDb/73P4x47DE0CL0zdsvmF+yGWGAycTUrHTAL2Uyk03JFqgtTa8T30S+q+eqIxx7jn3ue0ai4IskmgTUuV0R/MKZGikFslZzfDmSmXqqjfaRSJd9ojOnxVCm7HqaCVa9Hs8+HSrlmMFqRAmInXP5QCAatVrHmj/3N4U89hWGPPtru6+d6al+R2QxXIIAKpxNj5BSneo8HgVAInmCQp/axc1kjXJ+MZp8Po4qKcGSfPhHjoRJsQlfucKS0eMM+Y5teH5P2zOpdUmm+zWABf0j+rj25dq2ikgm0BVKSJOWUa59Jq0UgHIY3GIz7nnhqn6xIxewjKrUvWpFiSvugBIo0+4zZtZaJQIode6o9E42y2sQyDtjCTR+bje9fNG7pLiRJwtaaGowW0m2zpb8V0XV06g5vt9thj8pbJYh0oJRSN6KwEHsaG/mEVymQ2iLXJl3/4Yft7neW3OuswGSKmyKTKLVPo1bDrNPxQXOjoGrVezwYIa9SsTQ/pRtiOpjUty+emTULNx97bNr3LSpSmbA/T+b1gTaLb6DtPOfLNVLRAVCj18sVLDFAZgGWK2oCwkg0kehokNVVilR/uz3CeTIkKx2pBlJsNXWHrHRUu1xwBQLcbAJARJ1UKBxGSHZQtOj1MamILr8fkiTxz6E9c4T6HFek2Or/lpoaPj40er382mSpfWw7pb5dLX4/ju7XDzcfcwx21tcnnLQxlXKAwwGnz5e0as0m6UPy83FQwYjn5CFD8JcTT0xqXyJ2gyHiPVW7XPw9R8MCKRZ05UpqHwsKmv3+dlP7os2UGDFmEx4P8o3GCGe7u6dPx8HmZj52rausxF3LlvHn2fc4k4EUW4SMZywRD5a2p9SQmJENitShlhY45YUNhk1ejCJ6Dx1qPPPf//4Xb7zxBvbu3Qt/1CC+fv36tBwY0XsJhsPwBIMxSlCR2QxfKNRqeS6s+kS49skDtlJRvCRJaPH78dgZZ2DO+PE8MMg3GuEKBFDZ3Iw+crofI7pWKxrRplhMDzzY0oI+Vit+Nhh4jUEmGr4CrQrO1RMmZGTf7Gbl70KzCSWYWx0APLdhA4C2yXZ0kNfo9eKwggJsrqlBjdvNJ6VsUimmXYipfQdbWmLSqADg4VWr8Icvv8S+W26Ju3oej65qJNrPZouou6l2uRCSpA4pUtWCCsLUVJbaB0QGomwiwyY6JRZLRPuBZr8f/9u5k//+3IYNmJDA0rzB44FBo0lbs+psg6Wt7mlsxPkjR8Ko1bYGUrJaxMYv3ktHSZGSF3dYbaTT6407tvBAym5HWB7/7HECFxGvnK7Zz25HZZQiVed2Y96kSR26rsOShK8qKiIee1Q27YmmwGTCjro6fC1vnyupfUydafb54p5DQzupfSadDk3C5+IKBGDV6yNqJ8cUF8MXCqHG7UaJxYLz33gDe51ONHq9+Nfpp7cpUmlO7RPvxyw1Uxy/k8Gg0cAbDPJASslqPxtqpNiYO1BQ/qx6PVqyoL8V0XWkPDItXLgQV111FUpLS/H999/j6KOPRmFhIX7++WfMjDMgEkQqsBSs6AJkNnFgz7OBWwy43rr4YpwyZEhEyhbDGwwiJEmw6fUw6XTcdpvdSPo99BDe3LIl4m8SpfYBkatPosHEttpa5JtMKLFYYlL7ehLdkdoHAO9feikWn3MO/72/3Y4TBw0CAHwnO5WxVc7o9I5Gr5cHROJElE1WxdXCKpeLp5DGc/l7ePVq+EOhmKL7ZOgqk47+dntrA0v5XLDUvOjUyPYQJ9kDHA5+TTsMBn79iosULJBigWyxbF0s8v727Si323HKkCFx3TEZrJdSrrq+iumP+SYTd6ZrilKkdBoN8o3GuIqUTa9XrNGMRlSkgOTMKYC2vnelFkvEYoM/FEKz38/H4lQ5ViHlf1ycOr5fH3UUgDYFOldS+8ReQ/HeE1vAcgl9EkWiFSlXVL/D0cXFvD6SqcBs/Fuye3fr32Qgtc+i00UEc/eceCJKLZaUDZaYIucNBqFWqRQDTr1arahIra2sVFyAyARsLiJ+r20GAylSvYyUA6knnngCTz/9NB599FHo9Xr8/ve/x+eff46bbroJTqHRHkF0FDYIRq/+s8CKmQjwQEq4gZRZrTh/1Cjsb2rC+qg0IqXUA6DVapqxp7ER6yorI/4mkZIkFpbuaWzkbmO7GxuRbzSixGLJeGpfJmGrfl3dEPPsESMw54gj+O8qlQpL5szBBaNG8SCZ1QNEr0o6ZUUKQIQ6oqhIuVyYKK+Wxguk2E28VmFS2x5dZRvP+m2xAIr9m6oiJS5eTB80iF+7oiIlppKxc89SMEsslhib4p8aGlBisSSsRWTUyylKuYp4fvONRuQZjfjzsmV45NtvAUQGsoVmc4Q5DsPp9cImN0gGElsts2v+MLmRc3uBLIO5jFp0uogJOzuejjqE/m7KlJjH4qkyBSYTDBoNVzVyJbVPvJ8ksj/nrn1KipRsWAG09VNk3889//d/WH3NNRhWUIACkwlrDhyALxiE0+dDqcXCAyb2b2E6A6moY716wgQc+t3vUl4YYQt4zNhG6e9ZsBXNpEWLMOOFF1I7+A7C7gniwgLVSPU+Uh6Z9u7diynyYGgymdAs37CvuOIKvNqO1SZBJEONwioP0HbzPv655wC0TYijB282eZz49NMRk4x4gZTNYMAHv/gFAOCvX32FoxYt4hPwRK59QOsEkxXI72lsjFhxtRsM6GezoUJeYMhUal8mYTf0bOzjwhQpcVVSkiQ0er0YlJeHwwoK8O916/jjn8gOVuI1cailBUPy8pBnNEbUGIl0KpDqogCUBS8HhEBKrVKlnIrIvnMqAH2sVv54nrwoAETW7UQrUgMdDgzOz8fsww/HXSecAADYWV+PApMpYS0io8HrzVnrcyByrMqTAykAeP6HHwAgosaF9Vx6f/t2qO6+m/f3qvd4UGw28wWkRC6l7DnmfrkvycVOTzAIk07HHeYYzNCkM82/+9vtMGg0+PAXv8CDp56acFurXs8DqVxJ7RMX1OKm9skLWHEVKcFswhMMQkLbtTUwLw82gwEqlQrjSkqwuaaGp+sOLSjg90H2ubL7ajoCqXTd40RFSqk+ClCukWJtGzbGceFNN7VuN6x6fUTqoU1I9yd6BymPTGVlZaivrwfQ6s63evVqAMDu3bsz4npF9D7YoB9tpyxOCj2BAFr8fug1mhgTBDGNYIvQGDdeIAUAZw0fjlFFRXwArGhsRFiS4JLTaOJRaDLxVdpGrxcjCgt5A1arXo/xQtpKj0zt6yZFKhnYJNQdCODuZcvQ4PHAIxtj5JtM+NWRR2JTdTUkScKWmhqu0kTXSJVarSgym2Nc7xhsRb7DilQXBKAshY+palUuF4rNZp6+mizsO2fV6yOUITbpN2q1/DwCbWmV7Dv4txkz8PqFF+LF887D9XJq1l6nEwUmE/KNxriKlD8Uwu1ffslTYnMZNj7km0wxY5FovOAwGuH0+fCwfI+tc7vR4PVCQmsgwxWpBJO2Fr8fKrReHxadLqYlQDy8cmpftDuc0gp8qmy84QZU/e53OHP4cMyfPDnhthYhkMpJRaq91L54rn1Cah9LtVXablRREbbW1nIVf2h+fkwgJY6jnSHesXYEo1AjpVQfBbQ5G4psFe73XUGt2x2zqECKVO8j5ZHppJNOwvvvvw8AuOqqq3DLLbfglFNOwSWXXILzzjsv7QdI9D5q3W7oNZqYAMak0+Gz2bMBAJtranitQDRDBWtx0QBCqaZKRFwN3t3YCHcgAAmJlaRCk4lPwJv9fjgMBpTKK/k2vZ43GAR6ZmqfXlSksmgio9do+CRkU3U17lq+HPM+/phPuhwGA4bm56PZ70edx8MDjPGlpXyVXpIkVLlcKLNaE6adsdoCpXqV9uiqANRuMMCq1/P32eDxdCggYZMCs04XoQzZ5RXuvjZbRCAVbTaRZzSiTL7+i81mHjQUMkUqyoI7LEn458qVMP3tb7h3xQqsOXAgp1P7gLaAIM9ojBhbDBpNRD8lh9xzqUJQx8X6UXsSqX3M9U2lUsU4OybCEwgoK1Jx6ldTIS/KXS4RVr2eXzPZpoh3lFRS+9wJzCZYgNsSJzMDaE3p3FVfzxcnh+TnwxUIICxJ/HM16XStfZs6aSWeKOhJFdG1L94+Wa8txrbaWhz+1FNpef1kUQqkyLWv95HyHf7pp5/GHXfcAQCYO3cunn32WYwaNQp/+ctf8OSTT6b9AIneR7PPxydu0bB+DazruVKQIwZKYt0LsxhOpDAx1h88mFDBYoh1DOx42MBq1et5/Q2AlNWBbICt+nnSeJNMB6IzFLMw31pTw29gdoMBQ+SA+ueGBr4i+8sjjkCd280dzLzBIIrN5oiAWCQUDvMV/3XtWHcr0VWKlEqlwhFlZfhk1y4AsmlDBwISpoicMGhQRCDFrt0Gjwf3rVzJz3N0ap+IRq3mQRVL7Wvy+RAQJj+vb9qE33/xRYQtd3mOt9Rg40Oe0RgxCYtuSms3GOD0evm12+zz8frRYosFBo0GWrU6oSIl2mc7jMaEaYAi7PuulNqnVqlStrPuKFa9nqeDZtNCTmdIRpFqN7VPVKTkgErpPsVqori7nJyt4Q4EIhzxzDpdpxUpn9xPLh2wQDKhIqXVRihS8XpHZpJaj4cUKSK1QCoYDOKvf/0rDh06xB+79NJLsXDhQtx4443Q98DUJSL7iNc7A2ibhNS63WiUi66VuPHoowEgwsqZuU/FqxtZK5tMaFQqvL55c7sKFtCmSDFrdZusDLC/Y6+VqntatsBteNOY/54OxOuDKSSlVmtEbzE22Wvy+VDtcsFuMGBQXh5CkoQ6t5tva9Xr4ypSbOJ5XHk5lu3ZE9HgNxm6MiXy3BEjeINW5n6XKiqVChtvuAGLzzlHsVbp2iOPBNDWyDjabCKaPvJ1X2Ay8eNpFFSprfLkp9hshueOO/DCuefiD1OnpnzcPYmnzz4btxx7LIYXFuL/jjmGPx69bMQUKXadNvv9XBUtMpuhUqlgl13/4iGOpbYUbJmZa1+0O1yt2418ozHCmS2TWHMwtc+o1fLzl9D+XDabUOwjJShSiVL72P1yV309DBoNT91li0jseExabacDKX8oFLeeKVVYIOlrT5ESrk1mjHPGYYcBiHVzzQQ1LlesIiUYUBG9g5RGJq1Wi/vvvx/Bbu4mTeQ2iQIpg1YLm16PzdXVeGLt2ripKgtnzsSpQ4fi+R9+wPvbtwNoVS7yjMa4kz4W7Cw6+2xUu1x8Yp0ogCixWNAiT3BYGiCbwLO/+/HXv8a6665r/41nIazoN14/k+5CKZDSazQRgRTr1+IJBPDEd9+hyGzmCsmhlhY+QTTJaWxKDmlMpTqirKy1ue4996R0nF1p0pFvMsETDCIYDvMGnR1hbEkJTDqdYiB20ZgxANrSiRIpUkCbwsUUKQAR6X0bq6th1umw+le/glGrxRXjxydcuMgFzjjsMDx02mnQqtUYV1qKpXPmAEBMcOIwGiPMOd7ZuhX7m5pg0Gi4QUC53Z7QQMItp+gBcs87BUVq9f79+HVUA3OvYDYRDId5EX+dQipTJokwm8iR1D6VSsWDg3iLLBH25wrjrlGrRUiSEJC3AZRT+9hntbO+PiKV1CUHUir5GNKlSKWrabuoSMW7XxuiUvt+bmjA2JISvoh6KEmHys5Q63ajKGqctOr18MrjMNE7SHmJZ8aMGVi+fHkmjoUgACQOpIDWm8MTa9cCAJ4488y425VYLHAFAjjntdcAgNfDxGPF1Vdjw/XXRzTNBBIHUizVcPX+/QBaV33ZhJGlEI4rLeV1Uz0NdrNqSWMhcWdgN2rx+hCVpIhASr4B17jd2N3YiH42G//89zqdfEXXpNWiyGxWrIFiKuYY+XOOfr326EpFivcV8vk6nNonwq5j8fpn/2eBVLTZRDQsJZCZTQBt56/a5cL727fjsrFjeRpmb4QFm9Er73lGY4Rd+VPr1mFPYyP62+087Xlwfj5+Fiz+oxHHUqWUI5ffj8nPPIN/r1sXUfvmCQRgklP7gLbPuc7j6ZTRRKpYdLqcU6SAtprCRKl9oShbcxE2tnlk1QpIrEjtrKuLMDdhipRJrp9LSyCVIOhJFYOcUuqV7c+V0Gk0EWnCB1ta0Ndm4669yVr9d4Z4NVIAKL2vF5HyVT9z5kz84Q9/wMaNGzFx4kRYotKkZs2albaDI3on7mCw3UBqd2MjhhcW4tKxY+NuF22fXuVyoTSBHXR/ux397XauVLBAKlFN1ZiSEmhUKqzYuxdA62Sloz1WshG9kGKSDal97LpQuj7CkhShMrFtmGr5x2nT0MdqRaHJhN9+9hmekccqk06H/nY7ql2umMkAW9UcLQRSq/fv5+kj7eHvQkWKKTkVTicqGhtRMnp0p/bHAimxHiY6kIo2m4iGqSwRipQcSJ3y4osISxLvgdVbYcYL0RNGpbFqxd69Ea6kQ/Ly8NbWrZAkSbGmtL1AaumePfz/Jz3/PLbNmwegNf1ygMPRpuoGg7Do9ah1u7t0fLPq9by2NVdqpADwzyre4pR4LcQzmwBaA95EihRL5dtZX48j+/ThARirkWKvky5Fyp4mNdmYhNmETq1GIBzm135VSwuGFhTwtg3iwkAmCEsS6uLUSAGtY2R0LeFepxPeYBDD5b5uRG6Q8sj0m9/8BlVVVXjooYdw+eWX49xzz+U/5NpHpIP2FKkJZWUA2neOun7ixIjfmdV1e7AA7MeqKqiAhLUmRq0WxRYLdsktAWwGAy6W058G5eW1+1rZjlGr5ZbL2ZDaZxJu/NG4/P4IRUqn0UCjUvHeSgUmEwxaLd66+GJsr6vDVxUVfJ/lUX2YGFUuF7RqNW9oCgCXvfVWhDlCIgJpTHdpDzaJWbxhA9yBAH4l1zN1FDbJu0pojByjSLWT2sem9oVmM/8e1Xs8+Pynn/BjVRUA9PoUGKZIRa/m91Goq/yuspI3mwZa0wQrnE4sEwIikegaqejaDab2FJnN2F5Xx1f4a9xuFJvNfBLL6mm6us9XMsYMPRH2vYi3OCU+rhS4iooU+y4qjYlMmfeHQsgX0tqjg5SsU6Tk1MYGjyeiJYAIUyjZWMwWSgvNZqjQsXYVqdDo9SIsSYo1UkDrQuziDRsinjv2P//BiMceo1ZBOUbKgVQ4HI77E4ry9CeIjtBeIDV90KCk9jOiqAj3zpgBoHXl7lBLS0JFisFW8V7euBHDCwsTHgvQOhFi/Vlsej2mlJdDuvPOnFhpt+j13HAhG1L7EilSTUJhPptomHS6iEAKACbIlvSsONmk06FcXuXfG1VvUu1yocRiibhZOn2+iP5kieiO1L5Hvv0W40pLFSfiqaBSqdCyYAHunj6dP8augRhFKs4EiilSDoMBJq0WBo0G9R4P3tyyhW9z/IABnTrOng5TpKLdCvsIbRSePuss/vgtQu+lkwYPhkmrxQbZAOrdbdsiXPbaU6SafD5o1Wq8e8klAMBrpWpcLhRbLDGBVDqbriaD+Fq5lNrHiHcuxSyIqQrfjwhFSjakiGcAwsaufJOJL3hEO+KlI5BKp9kE+05UOJ1xU5RZYB2QF2LY/V2tUiE/QTuLdFEbpxUA+0yPf+45XPXee7x9AdCWbtgdDoNE5ujWkemrr77C2Wefjb59+0KlUuHdd9/lzwUCAdx2220YN24cLBYL+vbtiyuvvBKVsrMao76+Hpdffjnsdjvy8vJwzTXXoKULcmOJzNFeIDWmpAQAFM0BomHS+ikvvthujRTDbjDgTDl1S+wDFQ+H0YiddXUAYpsI93SU6mO6E5MQSLGJFVNFmv1+eAIBGDQaXptj0mp5ah+7Idv0eph1OvzEAilBkYou3K/3eFAoT0DGlpTggVNOAZBc2khYkhCWpC5P7QPaVNvOYtHrI1LGdBoNDBpN0mYTD556Kn4xdixKLBaoVCqUWCzc8Q8ADsyfj9OGDUvLsfZUtGo1XrvgArx8/vkRj7Ox6oiyMlw7cSLuP/lkXHXEERhZVMS3UalUGJKfj58aGvCnJUtw3uuv44FvvuHPJxNIOQwGTC4vB9DWP88VCKDEYmlTPgSHuK5cUEmm51JPJm4gJXyXFVP7hAA3nkU6g030+1qtPNDxKyhSrjSk9qVLfWcqXEVjY9yMEHY9BEIh+OU6XvZeE/UFTBcskIq+50eXAvwgK+8i0f30iJ5Nh0am5cuX4+yzz8awYcMwbNgwzJo1C19//XXK+3G5XBg/fjwef/zxmOfcbjfWr1+PP/3pT1i/fj3efvttbN++PaYG6/LLL8fmzZvx+eef48MPP8RXX32F63qoQxrRijsQgDlBigBLbUlmoGSTiJX79qHO7U5KkQKAs4YPBxBZGxMPZlOsVau7rL9KVyHeoLMhtU9UpFitCGu63CwrUibhmE06HSqbm6FC2yqnSqVCH6s1QpGyyCYh0YpUo9fLP9ONN9yAebIjVDKBFEuT6mpFCkBEHU26ESfk7ZlNjCgqwisXXMAD2352Ow40N6PC6cQFo0bxwvDeziVjx8ZMyPKMRtx/8sl49YILAAC3Hnccnj3nnJi/HVpQgB11dfirfA/2RStS8lhqMxjQ4vdHpBU1yT371CoVfj1xIhq93rZeVWZzRI0UgC537xTHn1xK7WMko0gpIX4uLr8/4WfCAymbLW5qX36UQ2RH8AWDaVOkWPZASJKSUqS44YZ8HroykIqnSDGUsheS7edG9AxSTmh96aWXcNVVV+H888/HTTfdBABYuXIlZsyYgcWLF+Oyyy5Lel8zZ87EzJkzFZ9zOBz4/PPPIx577LHHcPTRR2Pv3r0YMGAAtm7dik8++QTfffcdjjrqKADAo48+ijPOOAMPPPAA+grNUImeQ3uKlEmnw9H9+kX0YImHqFpJQNLueawYVFz9jQeboBebzV3WX6WrEG/Q2ZDax1P2BBWJBT8stU+8dkxaLfY6nSiK+mz62GzcIITtc4DDwVM0GWIgBbSmsBWZzckFUnLKSVdNAMVC7/bSUTuDaKPtD4WgVauTvu772Ww40NSELTU1uOLwwzN2jLmASqXCrccd1+52Q/Pz8diaNfz3kBAoeQTjHqtezw1Z2GMskALAnSvZBLTQbI6piWtvbE43uZ7aFy8AYopUvO+VqBS2p0ixRY6+NhsPdKJT+4otFkXX0lRIZ0Ne0RkyGUWKjbXsPHRlIBVdMxi9mMq+O6Ia7CRFKqdIeWT629/+hvvvvx+vv/46brrpJtx00014/fXX8Y9//AP3pNhjJVWcTidUKhXy5CL+VatWIS8vjwdRAHDyySdDrVbj22+/jbsfn8+HpqamiB8ie0jmZv3tr36Fy8aNa3dfV4wfH7G6l6zN8kmDB+Prq67CBaNGtbstK4bNNTUKyL7UPnZdqFUqrnIArQ5nrkAALVENLNn/B0YpNCx1RKNS8UCn3G5XVKQcUZ9ricXCV+0T0dWKlE6jwfuXXgqgrb4mE5h1Oq5QpJrO089mw3eVlTjQ3Iyj+/XL1CH2Kobm5/OJpEmrjeifE53aB0RO6MRAqtBsxv6mJvzfJ58AQETfoWafD5IkUWpfmmlPkYr3nlNRpI6Rv2cD8/LaaqRCIXiEQKrIbEat290pE4R01kiJBhvxFCkWWAfCYV7f1ZWKVI3LhTyjMSbAF4PJvjYbPzZxUZcUqdwi5ZHp559/xtlnnx3z+KxZs7B79+60HJQSXq8Xt912G37xi1/ALq9EHzp0CCVyvQxDq9WioKAAh+TiWyXuvfdeOBwO/lMu54cT2UE6Vz2LzGYskRteAq2TjmSZOmCAoqVwNMyxb5jgppUrZFtqH5tAsOL3zb/5Da498kjehLHa7earteL20Q6K7EYtpgHGVaSiXKPMOl1EQX88ulqRAlpTUt+/9FJcnkG1xyQUpqc6eSq1WtHo9UIF8LoconMMFcadk4cMwac//cSvz2jXPiBqZdzn4+oHm9x9LSu1DoOhrTeZ3w9/KISQJHXpOJDripQpjoLDznG89ywqUi3tKFJ/nDYN3117LY7p1w8atRoalSqmRqrIbOZ1Rh0lna59Zp2OjytxFSn5+aCQ2seudYfBkPFgpTpBO5VnZ83Cn+V2G6y+UKyLokAqt0h5ZCovL8eXX34Z8/gXX3yRsYAkEAjg4osvhiRJePLJJzu9vwULFsDpdPKfffv2peEoiXSR7vSR/oIbVroGepEFU6dizvjxeEd2vsolsi21j10XbCI/urgYT599NjcFqWxujijUZhOF6ECKpWOI76ncbo8xm3D6fDFKo1GrhTcJh9KuVqSA1nSws0eMyGiKaYQiFQympEixczmpX7+kjF+I9hksXNvXT5yIyuZmrJLvaUqKVLMwiWvwePh34byRIyP26zAaYdRqoVGp0OzztfUr6iZFypQF40+6YLWB8Rbq2Pc3nvMrG9eSUaRUKhWOkg29gNZ7YExqn5xK1xnLcF8aFSmVSsWvy7g1UkJqX/S1mQ4XwvaodrtREieQumrCBNx94omtxyGPlY0USOUsKc8qf/vb3+Kmm27Chg0bMGXKFACtNVKLFy/GI488kvYDZEFURUUFlixZwtUoACgrK0O1XGjOCAaDqK+vR1kC1yqDwQBDmhrHEekn3YEUG+yYgUS6OW3YsJx1HhMnMl1ZGxEPsX+KCAueKpubI2ykbz7mGGyqrsYvhV5IQFsgdZxgLTzA4YDT5+PpTpIkod7jUQ6kslSR6gpMWm2kIpXC4gQ7l8mavhDtUy6krZ42bBgMGg02VlfjxMGDFQOpGz/+GF9ddRUAoM7j4Q6P40pLccXhh+PFH3+ESavlATIzqWCT7K4cByw5qkh9fPnlWBvlQBzN6xdeiGP791d8Tq/RQIW2Gql4E3olDBoNN5tg4yAzTKhxu2HQanHDRx/hhXPPjUlrTkSqiyrtUWg242BLS7uKlJjaJ5oRddaFsD1Ya4xEiOo9M/MosVgokMoxUg6kbrjhBpSVleHBBx/EG2+8AQAYNWoUXn/9dZyj4CjUGVgQtXPnTixduhSFUd2gJ0+ejMbGRqxbtw4T5earS5YsQTgcxjFJGBEQ2UcgFEIwHE7rzVqtUmHLb34TkQJDJIe4+qzJgokMK0KOrmVidR4HmpoinBbPHjECB4cPj1FoWJBzlmxzD7QZjDj+8Q+cOGgQXr/wQrT4/RgclQ5q1Gp5ukYiukOR6gpMOh1//6nWSLFAKpcmxd2NOFZq1WqMKCrC1pqamLGUBVIsdQ+Q7f2Fwn42MRQn0KyR75RnngHQtSm+2VCXmQn62e3t9hlkjd2VUKlUrd9Dpkil0Pxdr9G0KVLyd7dIUKSeXrcO72/fjq/37k168TEsSQiEw2nN+ChMRZGKcu2zdECRkiQJq/bvx/jS0qSu8RqXq91SAVEZY4pUf7u9UymURPbRoav+vPPOw3nnndfpF29pacGuXbv477t378aGDRtQUFCAPn364MILL8T69evx4YcfIhQK8bqngoIC6PV6jBo1CqeffjquvfZaPPXUUwgEApg3bx4uvfRScuzroUSvLKWLUUnYmBOxxFsN7C7Y6rk/KrWO1XI4fb4Y62ClNDfmxni6oCRO7NsXh5eW4seqKizdswc75N5gw6MWcIxabUR6VDzYMeaaImXW6VAtm234Uwyk2KQo185JNsBMb4rNZtR7vTFjqZjyWu/xIN9oRJ3bHVHY309OORML4616PWrdbtTJK+rtWXOnk1wNpNKBSV7Qac+1LxqDVqtYIwW0KvrvbtsGANgjNJJtDxYYpPPaKJSdVm1xsodEs4l0pPa9vnkzfvHWW7h4zBi8fuGF/PFQOAyVShVzH0lGkTLrdDyAavB6YdXrYdHpeO9DIjdIOZAaMmQIvvvuuxh1qLGxEUceeSR+/vnnpPe1du1anHjiifz3+fPnAwDmzJmDu+66C++//z4A4IiotJylS5di+vTpAICXX34Z8+bNw4wZM6BWq3HBBRdg4cKFqb4tIkvIVCBFdIx0pmqkA41ajTW/+lWMjb14s03mZn7R6NE4Y8GCmInaQIcDP8oNFHfU1UGFWIMSo1ablGsfS/+LV1DeU4lJ7UvhGmHKISlS6WXHvHn8O5BvMqFRIZASJ9u76usxqqgIgXA4QpG65sgj8V1lZUR/L5vBwL8TT515JiYk0aQ8XVAgFZ8IRSqF8ySm9rFAyqDVwqbXY1N1NQ+Yt9fWJr1PtrBkT2PJRIHRiDyjMW69J0/tC4XgDgSgQlvtmEWvhzcYRCgcTjqTYrfcV/DtrVvRLJiwjHz8cfS12fBVRQVWXXMNju3fH5IkJRdICdkLTq8XDoMh6dRwoueQ8h1+z549CClE0z6fDwcOHEhpX9OnT09ot5mMFWdBQQFeeeWVlF6XyF7YzT+XCouJ9DJJwTZbDJ7irWCKqFQqxUkaS/kDgO11dRjgcMRci0aNJqkbIV8lzbHJoFlM7UuxLoKd81TcM4n2OUxY2MwzGFDR2BgTSIkq4K76ej4JFPvg2A0GvCI3AGbY9Hqs3r8fQObqTONBC2rxYYpUS4qW9KLZhDi25ZtMvLee3WDAoSQWixhNGQikDissTDhO6ERFyu+HSafjhhrsuvEEg0kH49UuF7RqNYLhML7Ztw+nDRsGbzCIXfX13Jn3X6tX47ULL4TT50MgHE6pRorVKxq0WlKkcoykAymmDgHAp59+CodQ4BoKhfDll19i0KBBaT04ovdBihTREcTc/M6kl0woK8MncrrxxurqmLQ+IHmziVy9liMUqRTrIgbm5eGz2bMxne4VGSPfZEKDgiIFANKdd8J27724Y8kS/Ou00wAgQn1SwmYwwBUIwKjVok8726abXGtwnk4sej1cLLUvhTFPr6BIAa1mOyyQGlFYyNN3k4EFUsksYiXL/MmTeVsLJUT7c3dUeiO75l1+f9KBVJXLhcn9+2PV/v34WVanNkWZmb2xeTPOHj6cL+YVC2quEmKKIWuEbdRqyWwix0j6DnjuuecCaF3JnSP05QEAnU6HQYMG4cEHH0zrwRG9j1ydfPZkzho+HPt7UNPqZHp/xePu6dNh0mrx52XLsKm6GmcouDH2+kCqE/bnAHDK0KGZOCxCJs9oVEztY7T4/Wjx+/Gb//0PQGR7CCXYwkQfq5UCmyzCYTDwzzklRUqjiamRAlpdAvs99BCA1rrQdQcPJr3PTChSWrU6YQqwVjCbcEcFk+x8KNVJra2sRFVLC04cPDjiu1HtcqGPzYZyux17Ghvx6a5dOP3ll6FRqRCSs6O0ajVmv/MOPvzFLwCgXUXKotPx+jF3IACT3B/LR6l9OUXSierhcBjhcBgDBgxAdXU1/z0cDsPn82H79u0466yzMnmsRC8gVyefPZkPfvELfH/99d19GEkTbXWeCjqNBkfLq437m5oi6kcYyQZS3Ekqx65lcZU11RopIvPkG41o8HjQHOVkxnhNTt2rbG6GRafjJhXxYCv6Re2svhNdi8NoxKubNgFIrZaMpZZFB1KiMpmqIsWutXQGUu2hizKbMCsoUkqB1KRFi3DWq6/ikdWrIx6vdbtRbDZjUF4efmpowLUffAAA+ItQx7/xhhsAAB/t3AkAivcHkVKrFTVuN4LhcIQiRTVSuUXKFb+7d+9Gkex4RRDphgIpoqO8e8kl2HTDDSn1VFGCXXthSVK03k1FkVKrVFln2NFZTEIBdar250TmcRiNCEkSV5ELo5w3Lxk7Fr+aMAEAMKygoF0FlylS7U0aia5F7G+XikOdQa7xjA6kRPrb7aj3eHgLh0v++19cKLe7UYKn9nVhPWi02YQ4Z2CLB0o24ywAc0al1zX7/bAbDJg+aBDe2roV+5qaMH3QIPz+uOP4NsMLC1FoMvH07/YWIfrb7QhLEg61tLQqUlotN/sgcoekA6lVq1bhww8/jHjshRdewODBg1FSUoLrrrsOPsr7JDoJSxmiQIpIlXNGjsSYkpJO70e89qKb8QIpKFLyzb0zqYbZiF5ODQJStz8nMg9TJyoaG6FRqRRVAmbjtHDmzHb3x+peogOyrmRQCn2SegviJP7IFJwU9RoNd9mLDqRYOwi2GFXrdqPa5cIbmzfjra1b4+6z0euFUavt0rYG0YqURWHcZtbjDEmS+HgcHXy2yPVUF40ezR9748ILoVWrefqrSqXClPJy7G5shE6tbrc+lP3d/qYmeFhqHylSOUfSNVJ/+ctfMH36dJ6+t3HjRlxzzTX45S9/iVGjRuGf//wn+vbti7vuuitTx0rkKN/s24dj+/eHWqUiRYrodsRUKKU+WqkoUrmW1ge0pgaFJAmhcLg1tS/H7N17OkwVqHA6UWAyKQby95x4IqYPGoRpAwcmvb/uCqQabrst55papwMWSM2bNCklS3qDVosqOW0vOpB655JL0Oj1Yp/c8Lza5YqojxVtwUX2NzW1W2uXbhIpUiyToCEqkGr2+/kiUHOUWsUCqWEFBfyxYjmg3HD99aiXbeHHlZTggx07kjLWYOfkQFMT3IEA+thsMMquiUTukPTotGHDBsyYMYP//tprr+GYY47BokWLMH/+fCxcuBBvJJB+ifTz6a5dmP322xHNE3saq/btw3HPPotF69YBaJ18alQqunES3YY4uVBK3TDKNQbttWeIvrnnCqwmyhcKtZpN0Hc1q7BGBVJK9LHZMPvww5PaH5uwRvdu6yryjMacayGQDhxysNCe62I0Bo0GDXJQEL3QY9RqUWa1ckWq2uXCdrkxOQAcaG5W3OdepxMDBSfnroCZTTDXPnGster10KhU/H0yWN2XVq2OaKoekvdh1ev59T65f3/+fKHZzFsMlMvvM5lFsnyjEUattlWRCgZ5ah8pUrlF0nfAhoYGlJaW8t+XL1+OmUJawKRJk7Bv3770Hh0Rl4PNzTj95Zfx8saNeHPLlu4+nA7zsZxr/LVsu+rO0XQooucgFtUHhb5SDBZotZfnnmqjzJ4CU6B8wSApUlkIWymvaGxMS10Tayg9a8SITu+LSB9HlJUhz2jEGYcdltLfGTQartTE69dYLARS+5uaoJHvxwfiuLfudToxoIsDKbbY6g+FYsZalUrF2wCIHJQDwcMKCiIUKdbzjy1CVP/ud/jiyisVX7dcVpmSqUtTqVTob7fz1D5mNkE1UrlF0oFUaWkpdu/eDQDw+/1Yv349jj32WP58c3MzdDm4+pqtvLttG3RqNUYVFfEgpCfC0gbYv7m6ik/0HKx6PZoXLMAdxx+PkwYPjnmeBVLtrSrm6rXMFCl/KERmE1kImwz+1NCQslqhxBXjx2Pr3LkYm4b6QyJ9nDxkCBpuuw3jy8pS+ju9oEjFG5/MOh1sej0OtrRgX1MTr8Gqi1J4GN0RSKlUKq7uuAMBmKMWdJh7pcjO+nqoABxeWorPfvoJcz/6CECbKQX77hRbLHHPDUvXi3culLY/0NzcZjbRgRopSZLw8o8/cvMPIrtIOpA644wz8Ic//AFff/01FixYALPZjOOPP54//+OPP2Io9QfpMvY0NqLc4cBhhYVwRq269CTYihGT3HN18kn0LKx6Pf560kmKxdPJBlLRlry5gl5I7SOziexDdE4bW1zc6f1p1WqMJKfenIHVOAKJa5EH5eXh54YG7G9qwqjiYmhUKl4nJOILBnGwpaXLAymgradddB8pAIqK1M66OgxwOHgj3SfWrgUQG0glYlSK36m+NhsONDe3pvYxRSrFQGr1/v2Y/c47+FeUZTuRHSQdSN1zzz3QarU44YQTsGjRIixatAh64aJ79tlnceqpp2bkIIlYatxulFgssBsMMUWTPQm2YlQj13lRIEVkO6koUrlqNgG0TqASWSgT3YM4oSQViYhG7PuW6F47tKAAb2/diqqWFvSxWlsDE4VAitVNdUsgJas7SotWxWZzTC+sHfX1OKywMMbJ8odDhwAkF0jpNRoMLyxMOtW1QG6Q7fL7YZEb8oYkSTFtPB5M/dpSW5v03xBdR9J3wKKiInz11VdwOp2wWq3QRK1Cvvnmm7B2UzFqb6Ta5WoNpPR6OL1evPDDD5g1YoSiXXM2U+/xwKDRoM7t5gWfFEgR2UwqipRSH6qejpjal6vBYk9GK5h/DM7P78YjIbIRsaYxoSLlcODdbdsAtDo25huNiorUXtnhrzsCKaPc005p3tDHasUPVVURj+2sq8PUAQMwVHDmA4AHVq0CAPRLMhV229y5Sddx58nnzRUIwGYw8PPvD4UivquJYKUPSuef6H5StltyOBwxQRQAFBQURChURGapdrlQYjbDZjDgh6oqzHn3Xfztq6+6+7BSpsHrxfDCQkhoHSQokCKynfYCqRa/H0+vWweX35+T17JBMNvI1feYK5R3sSU1kf3ok1Sk7pw+nf+/0GxGgcmkOJFnjxV1Q8PmiNS+qPdSZrXiYEsL/z0sSdhVX4/DCgowTlBqnV4vNlZV4d4ZM5J2pkzFDCvPaOQmHVa9np9/fwr1TsyOProvFpEdkG9tD6WKKVKCRJ1s8WM20eDxYIScf1/tclEgRWQ97QVSD69ahes//BCr9u/PSbWGTQRcfj8C4TB9X7OY7pjcEtmNmNpnSpCWm2c0chv0QpMJBSaT4hyD2YjbumEh3aTVwuX3wxsMxipSNhsOtbQgLNeDVcp1SocVFuLIPn24gcaq/fvhCQYxpbw8I8eYZzTyBti2jgZSSShSuxsasPH/27vz+KbqdH/gnyTN0qZJutCmC20psgrIIlrBDcd6ARXB6yw4jOi9/kRcrqLjMIMDOOOoKOPCyJ0RnRlx38ZR9DqKV0HGUXtR2aSA7GtpWkqXJG2Sps3390dzDidpkibQNkn7eb9efQ3NOTn5pnMSz3Oe7/d5gjJw1DsYSCUhb3s7qux2FFssAYHU3vr6OI4qdq3t7Wj2ejHc35/hREsLAylKeF0FUsrpGn3xXJYuxKSF3H3xPSa7hZMnI9NgYBsJ6kTKKGvV6pDFdJSk64sBaWkYMWAANldXd+qfZ/d4oNdoujxWTzCkpKDe/z0UXGxiaFYW2nw+bDh0CEDHtD7pcY1ajVf//d8BAO/v3g0A8nVId1Mut0jX6QLKtkdLmtoXrmfoVpsNg59+GpP++tczGCmdLgZSSeio3Y52IVCamQnlfyaDF1YmOmnh6jApkGJGipJAV4GUS/F45Ja9yUm6EGsMcwFD8ffYFVeg/pe/jPcwKAFJN0LC9ZBSWlBWBgAoNJsxZdAgHLXb5eyIxNHaKvcu622pWq0cXARfN5QPHowSiwUf7d0LANhz8iTUKpW8bnB4djZy0tLw582bYdbr5exbd8tUNMU26fVyRiqWUuZH7XboNBrUu1whG8G/7e8lqmFz9LjgXz2JtPl8mPzXv2LG668DAEozMjDO30Pi/40fj7owdysSlXRHuzQjAylqNTNSlBS6CqT2KTLDdv+0l75Ezkh10YuGiBKPdCEfTYW6O84/H1X33otBGRly/6TgG7Z2jycu0/qAjql9J8N8D6n8QZMU+K3euhVlhYXy+1epVLhg4EC0+XwYlp3dY9nb4IxUrFP7hBA4ZrfjHKsVnvb2gBt1khP+/0+iLV5B3Yt/9SRy1WuvoeLYMew8cQJAR0WmC4uL4Vu6FGUDB6LB5YqppGa8SfN9s1JT5VKlDKQo0UUKpLbabHi9slL+vakvBlL+98+pfUTJR/r8ZikyJZFITZ2l/YNLoDs8nk7lxHuLMiMVaj1qkdmMo3Y7hBDYYrPh+tGjA7aPtVoBdEz36ynKQOp01kh52tvhbmvDWf5MWqiCE1L7mEa3W14TRr2HgVSS8LS14X/375d/n1hQIN99UKlUGJCWBoHOX3KJTBprZmqq3KOCgRQlOulCxOX1Yu2+fQELfF/fvh0AsOiiizr27YPNaqULgXpmpIiSjhT0xNpIW2rloGxy+11NDU60tMRtap8hQkYK6AikDjU2wtHaitb2duQETd+7fPBgAOjRtjFnmpGSinlITYRD3cA70dICQ0oKfEL0yVkQiY6dFJPE9tpaAMDSSy7Bggsu6DQXNtt/t+hES0unL4tEJX0hZxoMMOl0cLa2MpCihJeiViNFrUaTx4P/fP99AIB3yRKkqNXYbLPh2hEj8Mjll2N4djZmRNm0MZkEF5voi5UJifqqc/xZmBpFafBomPV6aFQq+QZKu8+HsatWAYC8xKC3paakyAFJqOuGC4uL8cgXX+Afe/YA6FzFcsqgQXjzhz/EhT1UsQ9AQLbudAIpZ2srgFNj94SZ2jc8OxvbamrQ4HIlXT/RZMeMVJI41NgIALj7gguQmZraKZUuLaAc9ac/4UBDQ28P77TUu1wwpKQgVauFSa+Hg4EUJYl0nQ7rDx6Uf3/wn/8EAOyorcWonBwAwI3jxkU9fSaZaNRqpGm18oUYP69EyUPqLXbpoEExPU+lUiHDYJBnkihLoc8955xuG18sChQNdEMVvbli8GCMysnBT995B0DodgA/HjUKhT3Yb025bkmlUsWekQoKpEJlpOpdLgz1F+2qSbKiY30BA6kkUe9yQYXwKeiBii+Cl7dt66VRnZkGl0u+0DTpdAykKGmk63TYVVcn//67zz+HzelEtdMp3/Hty8x6PWwMpIiSjkqlwvF778Vfr7km5ucqm/KeUFyw333BBd02vlhIN62A0EGSVqPBbxWNhXMSoK/aGWekQjyvxevF+Lw8qAB5DT31HgZSSUJK16ojVJZ5YeZMAJAvcJTcbW2orK3FcYejx8YYqwa3W553bdLrcaK5GQK8MKPEl67T4WBDA9K0Wjz8gx8gTavFl0eOAECPNXZMJGa9HtUMpIiSUr7JJBfNiUWu0ShnPKQCB11dl/SkUbm58r/Drfm6cuhQ+d89VeI8FlK/LW+UhcGCA6ngjJQQAi1eL3LS0nBWVhab8sYB10gliXpF9iacG8eNw+uVlahtacFxhwNpWq2cwbr+73/Hmu+/BwCIBx7o8fFGo97lknssmHQ6+QuaF2aU6NJ1OrQLgVyjEYMyMtDi9eJ/9+9HicXSo9NEEoVFr8eekyejaupJRH1DodmMKv/NWKndyoG77orbeIZEUW0vVavF6pkzMSAtLW7fVb+68EIMysgAEHtGSio2ES6QcrW1yTegx+TmyuvpqfcwkEoSDW53QGO3cKzp6dhfX4/CJ59EgcmEqnvvBQA5iEokDW53wNQ+rrmgZCH1YMk1GmH13+V8bvNm/HTMmHgOq9dIazT5WSXqPwpNJmy12QB0TO3T+NdNxUu0fZNuGjeuZwfShWXl5fK/u7vYRLN/u1Gnw5jcXKzatOmMx0uxievUvs8//xwzZsxAQUEBVCoV1qxZE7BdCIGlS5ciPz8fqampKC8vx15/l2pJfX095syZA7PZjIyMDNx8881wxliNJhlEk5ECgNy0NHx59CgA4LjDAZvT2emDF6rqS28SQuD3X36JD/bsgTQhwKTXy6luVpyhRKcMpPIVC56Vc/b7Mov/M8pAiqj/KDSZUOXvy3SipQUD0tJ6rJFttN764Q/xP9dfH9cxxOJ0AimdRiOXmA/OSDV7vQA6qqeenZOD2ubmkL2mqOfENZBqbm7G2LFj8cc//jHk9uXLl+Ppp5/GqlWrsHHjRhiNRkydOhVuxUkyZ84c7NixA5988gk++OADfP7555g3b15vvYUe9z+7d2PwH/6Aky6XvJ4oEmt6OgBA679Tk//EEzA8/DAAYI7/bvmRpqYeGm10vq+rw8JPPwVw6s62cu7yhPz8uIyLKFpSye9zcnMxcsAA+Ty2JsAc/N7AjBRR/1NoNqPZ64X6wQfx9s6dCdFq5UejRuHqYcPiPYyoSddmsVTtS9fp5LYTwcUmlBkp6aZerKXt6czEdWrf9OnTMX369JDbhBBYsWIFFi9ejJn+IgovvfQSrFYr1qxZg9mzZ2PXrl1Yu3YtvvnmG0ycOBEAsHLlSlx55ZV4/PHHUVBQ0Gvvpacs/+orHGxsxMHGRtzmf4+RSAHJVcOGIc9oxJ83b0a7v9P1bRMn4tXt29EU54Ztyrslf7rqKgAdqXdDSgrafL6YGwUS9bZjdjsAoGzgQKhUKlw/ejSe3bRJvpHR10nVr0KVHCaivqlQkX3fXluLy2IsoU4d7SM0KlVAIOVua4NPiJA3ppytrTAp+k8FZ6Ra/BmpNK1WLiBS09yM4QMG9NRboCAJu0bq4MGDsNlsKFfMLbVYLCgrK0NFRQVmz56NiooKZGRkyEEUAJSXl0OtVmPjxo249tprQx7b4/HAowgm7P6LokSkvMMdVUbKv79apcIzV1+NFdOmoV0IeNra5LKl8e58LS1S/d1ll8lTpFLUavwsTr0oiGIlFZT4t7POAgB5imqoErx90TB/zxJNnKf1EFHvCS6kk91Pvu+6m06jCQikrnj5ZWw8dgyexYs7TZV0+jNSKpUKhpSUzmukFFP7pP/+1LKXVK9K2PLnNv+CRmtQTxar1Spvs9lsyFWUvwSAlJQUZGVlyfuEsmzZMlgsFvmnKIHLFR9WTMOLao2UP5CSPor6lBSkabUBTXwdIQKpE83N+N/9+898wFGQAqlfXnhhr7weUXf76zXX4Pi998p3AEf610b1l0BquD+QqkqgdgpE1LOUDXCB0M1hqWs6jQZeRSD1xZEj8Pp82Fdf32lfh8cjr4/SazSd10gppvZlGAzQqtWc2tfLEjYj1ZMWLVqEe/3V7ICOjFSiBlMHGxrkf0dTtU8KtkKliKUPY6iMVO7jjwMA2pYsgUatxnObNmHGsGEBC+m7S11LCyx6PcsmU9JK1+nkbCoA3Hn++ZhcVBRVOd6+QGo6zDufRP2HISUFj19xBWaNGIFddXVyZppio1VkpIR/6QUA7Kuvx9Cgv6nT65X/W2NISekUSElLNcx6PVQqFXKNRn4v97KEDaTy8vIAADU1NchXFB+oqanBOH8py7y8PNQG1cxva2tDfX29/PxQ9Ho99P6gIpHZPR6cdLlg0ung8M+T7UqxxYLfXXYZ5p17bqdteo0GWrUaDv8dDMn3dXXyvxvdbmjUatz6wQd456yzsPZnPzvzNxKkzl/th6ivUKtUmNgH1mRGy2IwQKNSoWzgwHgPhYh60c8nTwYAnNVPbhr1BOXUPqmxMQAcbGzstK9Tce0XKpCqstth1usDKsnWMJDqVQk7ta+0tBR5eXlYt26d/JjdbsfGjRsxadIkAMCkSZPQ2NiITYq6+evXr4fP50NZWVmvj7m7SdmoBy69FACQF8VCdpVKhcWXXBKyg7dKpYJJr+80tW/T8ePyv+taWrDfn17uqWk7DKSIkp/z/vux4cYb4z0MIqKkogyk9ium8ylnIEkcHo8cJKXrdHJfKUmVwxFQBMSans6MVC+La0bK6XRi37598u8HDx7E1q1bkZWVheLiYixYsAAPPfQQhg4ditLSUixZsgQFBQWYNWsWAGDkyJGYNm0abrnlFqxatQperxd33nknZs+e3Scq9kmVwWaPHo0bxo4NGRzFyqTTdZrat1fxQa5raZFf1xtlec6unGhuxtp9+3DD2LEdr+FyMZAiSnLS+jAiIopeQCDlD57KCgvDZqSkQMqs18MeFEgds9sDioBYjcaAa7pIPG1t2N/QgLP7Sf/DnhLXjNS3336L8ePHY/z48QCAe++9F+PHj8fSpUsBAAsXLsR//dd/Yd68eTjvvPPgdDqxdu1aGBTV61599VWMGDECl19+Oa688kpcdNFFeO655+LyfrqbNPc1w2DoliAK6PggBk/t21dfL891rmtpkaf62ZzOgPm7p+vaN9/E3DVr5LLnzEgRERFRf6QMpA40NCAnLQ3nWK0BgZTD48FWmw21zc2w+JeimPX6TjfC9zc0oMRikX/PNRpxpKkpqmu3+9etw6g//UkuoX6mDjQ04K6PPkKbz9ctx0sWcQ2kpkyZAiFEp58XXngBQMdUtAcffBA2mw1utxuffvophgU1XsvKysJrr70Gh8OBpqYmPP/880jvI71c7B4PNCpVtza9NIX4INa1tGCkv+dAXUsLttXUAOgI5GzdUP3ly6NHAZxKYTOQIiIiov5IGUjVu1zIMRpRmpEhT+376uhRmB99FOOffRZVDodcFdZiMARcv7V4vdheU4PzFDOwLhs0CMfsdnykmO0VjpS5+s5/zXemVm7ciJVff42P9u7tluMli4RdI0VAk9stV2LpLlLhCiVnayssBgMyDQbUtbRg54kTcn+cUOU4lZ7euBFv79wZdruy54GUwmYgRURERP2RTqOB15+1aW5thVGrRaHZjAa3G562NnzsD4IGZWQAAMb42/yYdTo0+Wf2AMDekyfRLgTGKoqrTRsyBGa9HtujCI6kxurbIrQLioXHHxx+q1h33x8wkEpgdo9H7v3UXcwhik00e71I9zdzq2tpQb3LhaH+ijzBQZeSTwjcvXYtfvS3v4Xc/sWRI5j26qvy74cbG9Hm86GBa6SIiIioH1JmpJq9Xhh1OmT6l6w0uN1o8ngwcsAAVN52G/4yYwbG+QOl4Kl9UuEJi+I6UaVS4azMTPnGdSSN/mNJ6+LP1J6TJwEAJ12ubjlesmAglcCaPB5YFOvBukOoYhPO1lYYdbqOQMrlQpPHI1eBCdW8V7LrxImIr/XMt99iw6FDADq6btc2N6PB5YLAqTshRERERP1Fp0BKq5X7hDa4XKhpboY1PR1GnQ43T5ggz0oKF0ilB7XGOSsrC7v9QU0kUuPe6m5q4CtVC2QgRQmjxzJSIab2pfsDqWN2O1rb2+UO5sGlNpUONzVFfK2Tiv4Io3NzcaipCSu//hoAmJEiIiKifkerVp8KpPw3spUZqRqnE9YQBcaC10iFC6QuLi5GxdGjEW+EA6cCnu4KpKQCacprv/6AgVQCq3e5AlK23SFUsYlmRSAlFYTISk1FmlbbKZByt7Xh5vfewyvffYcqRTo41OJCZQWaQrMZb+/cid99/jkABlJERETU/0TKSNW7XDjc1BSyb6hZr0ez14t2//oq6frMGBRITRsyBF6fTy70FY50LVjdTT1DpfVbzEhRQvjHnj34x969ODc/v1uPa9LpAu5SCCE6pvb510hJwY/FYAjZ/O2zgwfx/NateGDDBhxXfPg+PXCg02sptwdP5WMgRURERP1NQCDlv/6SMlL/d+wYDjQ04AelpZ2eJ81QkgIgZ2srtGo1dBpNwH5Ds7KQnZqKiigCqdSUlG4JfHxCwO7xYEBaGjNSFH8urxc/fvttZBoMuGncuG49tjS1T+ox4G5rgwDkjJTEotfDFCKQ+s0//wkAONrUhJ11dZiQn48pgwbheFBqWAiBZsVzpw0ZgnF5eSgfPBhAR28sIiIiov4kVLGJVK0Weo0GG6uqAHQ06A0WHEg1e72dpvUBHQUnzrFa8X2EdVI+/030IosloBLg6XJ4PBAASiyWTrOe+jq2pk9AX1dVocXrxVf/+Z8ozczs1mOb9Hr4hECL/8OrnGOrDKQyQmSkHB4Pvq6qwujcXBxubMRbO3bgh2efDZ1Gg6NB66Vc/gDt2auvxuzRo2HW6zFrxAj4hIDN6YRGzRieiIiI+pdQ5c+Bjil6UgW9LP9UPyVLiIxUqEAKAApMJhyJsI5durYrMpux9+RJ+ISA+gxa7UjrowrN5m7rS5UseDWbgDZXVyM1JQWj/b0DupN0R0MqOKGcY6sMpAaazR2BlKLjtfScRy+/HP/hz5QNslhQaDJ1Kp8pZaNyjcaAghlqlUouZEFERETUnwRnpKRgyKjV4mhTE9J1OmiDpusBoaf2RQqkIhWRkI5RZLFAIHJhsWhIWa2BJhO8Pl9AD9G+joFUAjpmt6PIYumRrI3J/6FTpoaBwIxUXno6NGp1p4yU9G+TXo+bJ0wAAGSmpuK8ggIcbGzETkU5dOm40p0WIiIiov5OCqSkJRBSsQijTodmr1deLxUsOJCqd7nCVnbOT0/HcYdDXsYBAN/X1WH8s8/imN1+KpAymwHgjKf3ST2kRvkTAGcamCUTBlIJqMrhkPs4dTeTlJFS3NEAOgKeXH+5zbnnnAOgI7hSFqaQ/p2u0+EcqxWf3HADFlxwAa4eNgwA8I1/bi9wKiMVXE2GiIiIqL+SAilpCYQ8tc//v6Gm9QGnAilpGt22mhqMCTNzKS89HS1er3xTGwCerKjAVpsNz3zzTedA6gzXNW2sqsJAsxlDs7IAoFObnb6MgVQCOma3Y6D/5O5uwVP7mhVrpIZkZeHTG27AI5dfLj8WMiPlD47KBw9GmlaLVK0W6Tod6hSVWpiRIiIiIgqkVavhaWvrdMNZ+t/MMIGUtN3h8cDd1obK2lqcW1AQcl+LP6ulLPyw19/e5ojdjt11dVDhVAYpOCMlhMCCtWvx3KZNUb2nTw4cwMXFxfLNemakKK5sTmfIHgLdIXhqX3BDt8sHD5anFHYqNhGm+RvQUd48IJBiRoqIiIgoQKpWC3dbW6cbzl1lpNQqFVJTUuBqa8N3NTVo8/kwMUwgFTwNEIBcFKzB5cLnhw9jjNWKEosFQOeMVGVtLf6wcSPuXru2y/fT5HZjq82G6UOGyNeH4ZoBT3zuOSxev77LYyYTBlIJqMHtDjtH9kyFndoXIuAJVbVPeQylAcGBFDNSRERERAGMWi2avd5ON5ylICTS9V+aVosWrxdfV1VBq1aHndoXHEgJIeSiYA1uN/55+DAuLSmRM1fBGanD/qDL7S8a0eL14rXt20O+Vm1zM4COImXSzfpQGSkhBDZVV+Phf/0r7PtLRgykEowQAo1ud9jU7plKTUmBRqUKKDah8j8eLLiPlLO1FSp0fJCDDUhLQ52iqRszUkRERESBpNYznTJS/uulcBkp4FQg9cmBA5hUVAR9iGs3oHOpdJvTCU97OwZlZKCythb7GxpwSUkJjFotNCpVp4yUsqWNy+vFwk8+wZx33sHBhoZOryU19B2QlnYqIxUikOqOxr+JiIFUgnG2tsInRI81rFWpVDD5m/JKr5eu00EVon9AqKl9Rp0uZK+BHKMRNYpSm8oiFkRERETUcV3kEwL1/sBCXiPlv17qKiPV3NqKfx46hCsGDw67n1yYwp9pkno7XVxcLAdXl5SUQKVSwazXd8pIHVW0tNlVVyc3Cg7Vm+qkfzZSdlpaxDVS+/1rtPoaBlIJpsF/MvfU1D6g4wOmnNoXLmuU7i/F6fOXz3S2tspp22AlFkvAB6zJ40G6TsfGu0RERER+UtZGmhInBVDZ/kxUpBlJaVotnvy//0OTx4Nz8/PD7mcKykhtq6lBuk4nP2doVpZcqdliMHTOSNntmJCfD41KhW+qquDyZ88ONTZ2ei1pWUd2aip0Gg10Gk3INVL1ioyU/QyrBCYSXuUmmEZ/INVTGSmgY8qePLUvQkM36fEW/wfI4Q+OQhmUkYHjDofchM3u8cipZSIiIiI6lYGSZvFIv5/lLx3uUpQsD6ZcWhGuYh8ApKjVSNNq5QDpUGMjBmdmojQzE8Cpqn5AxzTA4IzUkaYmDM/OxtDsbOyqq5ODoANhpval63TyNMN0nS7k1D7lY9v9GbK+gIFUgmnwn6w9GkgFTe0LN/0uuPqKs7U1ZKEJoCOQEji1QLHJ7Q7bKI6IiIioP5KuuWqCMlIXFxcDAMbm5YV9rhSs3H/RRXJGKZzs1FQ5W2RzOpGfno5LSkoAAAWKXqUWgwGNIdZIFZnNKDSZ8MGePaj2B307Tpzo9Do1Tidy0tLk34PX10uka0mtWo23duyIOPZkwkAqwUgBjqWHp/Ypi010lZGSPhCOCNmrs3NyAJyah2v3eHr0PRARERElGykDVeVwwKRYAlGamQnvkiX4QWlp2OdK0wEvHTSoy9cZaDbLlfqqnU7km0zIMBjw0Zw5+MuMGfJ+GQaDPBsKOFXhb6DZjEKzGfv9Wagis1m+xlOqcjhQqOh9mq7ThZza52xtRZpWi9smTsQbDKSopzT3QpEGkyLt6oxiap8ykAq3RiovPR0DzWZ841+Q2OTxMCNFREREpCBdWx1tauq0Hiqli3Xl+/wFG8KVPVcqNJtR5XAAAKodDuT7+5NOGzIEOYps1gBF5grouNbz+nzIMRrlQhJatRo3jh0rH0/puMOBQkWGy6TXh85I+a83zy0oQG1zM6589VVMfeUVCP86/GTFQCrBSCdfqBLj3SWWYhPKMUUKuoCOrNQe/4eca6SIiIiIAknXUUeammIuLCb1dcrzB0WRFJpMOGa3QwiBaqcz7HOC+4BKS0wyDQbcNG4cMg0GNN9/P0ozM9Hi9aLd5wt4flVQIBV2jZTHA5NOh8H+dVof7duH/92/P+S6q2QSugA9xY2U+uzJancBxSa8XljDfLg6ZaQ8HgzOyAh73BKLBd8ePw6go2hGsb9jNhERERGdqsp81G6XC0xE65MbbsC++vqQLWuCDTSbUWW3o8HtRmt7u5yRCpZjNOKEf8ogcKroWWZqKqYOGYIfnn02gMBrQuXSjdrm5oDryLBrpPzr7EuDriMrjh2L+e+QSJiRSjDNXm+P917q1EcqzOsF9wOIVGwC6AikpGITR/3za4mIiIiogz4lRV76EGtGqnzwYMyfODGqfQtNJjhaW7Hn5EkAQL4ia6SUk5YGR2urXHU5XBseaWmHMtvkEwJNbnfAvuEyUlILneBxVIeYLphMGEglmK6mz3UHZbGJSFP7pIAummITAFCSkYF6lwt1LS2wOZ2d7joQERER9XdSxb2e7BkqFYCQ1q5HykgBwAn/9D55al/Q+q1QzXYdHg8EAitNp+t08np/JekaUq3Ipuk1moBCF8kooQOp9vZ2LFmyBKWlpUhNTcVZZ52F3/3udwEL04QQWLp0KfLz85Gamory8nLs3bs3jqM+M70RSJn8FVWEEBH7SGnUaqSmpARmpCKMTZp/+3/HjgGAPA+WiIiIiDpI1fQiVeg7U9KsoG/8Sy7CZaSkIEjqJSUFNsHr3OWMlKIin7xvUCAVrvy5FIxl+YO0kTk5DKR60mOPPYZnnnkG//3f/41du3bhsccew/Lly7Fy5Up5n+XLl+Ppp5/GqlWrsHHjRhiNRkydOhXuJP0/pjlChqi7mPR6tAsBd1tbxD5SwKkUrRAiYkNe4FQg9cGePVABGBVFVRkiIiKi/kQqGjF79Ogeew0p67W9thZmvT5sETMpYJKa9550uWDW66HVaAL2Sw8xtU96jjIjZdRq0RyiqbDyZvzO229H9c9/jgyDQZ5KmKwSutjEV199hZkzZ+Kqq64CAAwaNAivv/46vv76awAd2agVK1Zg8eLFmDlzJgDgpZdegtVqxZo1azB79uy4jf10OSP0deou0tzcRrcbztbWiGXKpTsL7rY2tAsRcY2U1f+hfWvHDpxjtcp3HIiIiIiow6Z589Dc2tqjhcWMWi1S1GrsOnECgyIstbAEZaSqHY6QFf6k679QGangqX1hi034r2+l4hTBPaySUUJnpCZPnox169Zhz549AIBt27bhiy++wPTp0wEABw8ehM1mQ3l5ufwci8WCsrIyVFRUhD2ux+OB3W4P+EkUmQYDBvVwtTvpRD5qt0MAEQMe6QMhfSgiBXnZaWnQqFRocLsxIT+/W8dMRERE1BdMyM/HxSUlPfoaKpUKGQYDPO3tEculSxkpae28rbk55HoqU1AlZyB8INXsn8mkFGpWU18IpBI6I/WrX/0KdrsdI0aMgEajQXt7Ox5++GHMmTMHAGCz2QAAVqs14HlWq1XeFsqyZcvw29/+tucGfgZWXX11j7+GlIE61NgIILpASkrlRlojpVapMNBsxuGmJowcMKD7BkxEREREMckwGFDX0hJ2fRQAGHU6qHBqmp4tTM8pQ0oKNCpVwNS+2uZmqBBYNMOo06FdCHja22FIORVmSOXPlTL7wNS+hM5IvfXWW3j11Vfx2muvYfPmzXjxxRfx+OOP48UXXzyj4y5atAhNTU3yz9GjR7tpxMlBOpEPxxBIRZORAjpKYQLAuLy8bhgpEREREZ0OKVNUFKEdjVqlCqjmHC6QUqlUHevmFVP7jtntyEtPD1hPJV0nKiv3CSFCFizrCxmphA6kfvGLX+BXv/oVZs+ejTFjxuCGG27APffcg2XLlgEA8vwX6zU1NQHPq6mpkbeFotfrYTabA376Eylw2u3vLZCdlhZ2Xzkj5f/gRFojBZxaOHlZD1aiISIiIqLIpKDmrC6qKFsMhoCqfeHKsiv7kAIdgVRwz9Dg1jkA4GlvR5vP1+kakoFUD2tpaYE6aCGeRqOBz+cDAJSWliIvLw/r1q2Tt9vtdmzcuBGTJk3q1bEmk+zUVGQYDNjo7y0QKSNl0usDMlKRpvYBwKPl5Wi5/36k9OACSiIiIiKKTLp2OysrK+J+mQYDTvr7R9k9nrBFyExBhSRCBVJyUYqgflNA51lNmQYD3G1tchXDZJTQV7szZszAww8/jH/84x84dOgQ3n33XTz55JO49tprAXSkGRcsWICHHnoI77//PrZv3465c+eioKAAs2bNiu/gE5hKpcLw7GxU1tZCr9EgNSX8Url0rRbO1lbU+z9gGV00j1OrVEiNUE6diIiIiHren668EjOGDcPEgoKI+xWYTDjucMDnn4IXLpAKntpX29wsV2yWDPDPcjrR3Cw/1uIvhx7cbke6pkzmrFRCF5tYuXIllixZgttvvx21tbUoKCjArbfeiqVLl8r7LFy4EM3NzZg3bx4aGxtx0UUXYe3atTD0YLfovmBIVhY2VlXBmp4OlaLLdDCpj1S10wmjVtvl1D4iIiIiir/zCgvx/vXXd7lfocmEbTU1p2YfhctIBU3ta3C7O81qkvpX1SoCKSnjZAi6ca8MpCJVFkxkCR1ImUwmrFixAitWrAi7j0qlwoMPPogHH3yw9wbWBxT7S6wH30kIJq2RqnY4IlZ9ISIiIqLkU2Ay4cN9++SCE5Gm9ikDqXqXq1MgZdLpYEhJCQikXP5AKnjGUl/ISCX01D7qOVIFl5woA6njTmfIvgJERERElLyKLRbYnE7UOJ0AIgRS/nXzAOBtb4fd40FmUCClUqmQazRGlZHKDjENMNkwkOqnpIxUpPVRwKnGajVOp9yJmoiIiIj6hrF5efAJgamvvAIgfGGxdK1WXiMlZZFCFSwLDqRc/jVSwdecuUYjVACq/QFcMkroqX3Ucy4dNAi/vPBC/PvIkRH3S9fpINDRV2BQRkavjI2IiIiIesfo3FwAkCv3RcpISVP7GroKpFpa5N/DZaRS1GrkGo2odjjO8B3EDzNS/VS6TodHy8txfmFhl/sBHYFUV814iYiIiCi5GFJSsG3+fPn34JLmEpOiap9UzTlUz6lOGakwgRQA5JtMsDmd8AkBd1sbhBCn/0bigIEURSQFTyddri57SBERERFR8jnHasVAsxnDs7OhCdMLNF3RR0oKpEJmpNLSQq6RCtUeJz89HdVOJw40NCD14Yex4dChM30rvYpT+ygiZQlMZqSIiIiI+qbdd96J8A1xOq4Jm71e+ISIHEgZjXLhCuDUGqmQGan0dFSeOCFnupKtzQ4zUhSRMnhiIEVERETUN6VptSGzRhJpZpKztRUNLhf0Gk3I/XONRjhaW+UAyt3WBp1GA3WIvqX5JhOqHQ557VWyzX5iIEURMZAiIiIiIilb5PB4QvaQkkhVnk/4C0642tpCZqOAjoyUzemUe1gxI0V9ijJ4SraTm4iIiIi6R7oiIxUpkMr19yiV1km529rCttvJS0+H1+fD4cZGAMxIUR/DjBQRERERSUGO3eNBvdvdqRmvJDiQcnm94TNSJhMAYM/JkwAAY5JdazKQoohS1Gq5tGWB/2QnIiIiov5FGSBFykgNSEuT9wOAFq8XaWHWXuX7pwHura9Huk4Xch1VImPVPurSe7NnY1ddndywjYiIiIj6F2t6OtQqFY47HGhwuTB8wICQ++k0GmSlpsqNdp2trWEzTVJGam99fdJN6wMYSFEULi4pwcUlJfEeBhERERHFSYpaDavRiOMOR0dGKkQzXkmxxYIjTU0AgGavF8YwGSlDSgoyDAbsq6/HsOzsHhl3T+LUPiIiIiIi6lKBySQHUuHWSAEdgdRhRSAVaZ19oT8rlR3heImKgRQREREREXWpwGRClZSRihRImc2nAqkIU/sAYEhWFgCgJCOjW8faGxhIERERERFRlwpMJmyvrUW7EMjxF5UIpdBshs3pBBB5ah+gCKQslu4dbC9gIEVERERERF0qMJnktU+jIhQhyzUaUe9ywdve3pGRihBIXVRcDABJuUaKxSaIiIiIiKhL0nomjUoVMfCRSqXXtbR0uUZq1ogR2Hn77RjKQIqIiIiIiPoiqRWOTwikqMNPbLMqek5FKn8uGZmT032D7EUMpIiIiIiIqEvnFxbigoEDMW/ChIj7SRkpm9PZ5dS+ZMZAioiIiIiIuqRSqVBx881d7ldoNkOv0WBbTQ2avV4MiFCYIpmx2AQREREREXWbFLUao3Jz8cmBAwCAHH+Gqq9hIEVERERERN1q5IAB+OroUQCnpvr1NQykiIiIiIioW+UajWjxeuV/90UMpIiIiIiIqFspG/ZGat6bzBhIERERERFRt5LWRaVptdCn9M36dgkfSFVVVeFnP/sZsrOzkZqaijFjxuDbb7+VtwshsHTpUuTn5yM1NRXl5eXYu3dvHEdMRERERNS/SVkojUoV55H0nIQOpBoaGnDhhRdCq9Xio48+ws6dO/HEE08gMzNT3mf58uV4+umnsWrVKmzcuBFGoxFTp06F2+2O48iJiIiIiPqvfJMJAOBua4vzSHpOQufZHnvsMRQVFWH16tXyY6WlpfK/hRBYsWIFFi9ejJkzZwIAXnrpJVitVqxZswazZ8/u9TETEREREfV3Z+fkAAC8Pl+cR9JzEjoj9f7772PixIn40Y9+hNzcXIwfPx5//vOf5e0HDx6EzWZDeXm5/JjFYkFZWRkqKirCHtfj8cButwf8EBERERFR90jX6QD03Yp9QIIHUgcOHMAzzzyDoUOH4uOPP8Ztt92Gu+66Cy+++CIAwGazAQCsVmvA86xWq7wtlGXLlsFiscg/RUVFPfcmiIiIiIj6oS233opvb7kl3sPoMQkdSPl8PkyYMAGPPPIIxo8fj3nz5uGWW27BqlWrzui4ixYtQlNTk/xz1N8sjIiIiIiIuse4vDwUWSzxHkaPSehAKj8/H2effXbAYyNHjsSRI0cAAHl5eQCAmpqagH1qamrkbaHo9XqYzeaAHyIiIiIiomgldCB14YUXYvfu3QGP7dmzByUlJQA6Ck/k5eVh3bp18na73Y6NGzdi0qRJvTpWIiIiIiLqPxK6at8999yDyZMn45FHHsGPf/xjfP3113juuefw3HPPAQBUKhUWLFiAhx56CEOHDkVpaSmWLFmCgoICzJo1K76DJyIiIiKiPiuhA6nzzjsP7777LhYtWoQHH3wQpaWlWLFiBebMmSPvs3DhQjQ3N2PevHlobGzERRddhLVr18JgMMRx5ERERERE1JephBAi3oOIN7vdDovFgqamJq6XIiIiIiLqx6KNDRJ6jRQREREREVEiYiBFREREREQUIwZSREREREREMWIgRUREREREFCMGUkRERERERDFiIEVERERERBSjhO4j1VukCvB2uz3OIyEiIiIioniSYoKuukQxkALgcDgAAEVFRXEeCRERERERJQKHwwGLxRJ2OxvyAvD5fDh+/DhMJhNUKlW8h5OU7HY7ioqKcPToUTY1prjj+UhngucPJSOet3S6eO50JoSAw+FAQUEB1OrwK6GYkQKgVqsxcODAeA+jTzCbzfwQUsLg+UhngucPJSOet3S6eO4EipSJkrDYBBERERERUYwYSBEREREREcWIgRR1C71ejwceeAB6vT7eQyHi+UhnhOcPJSOet3S6eO6cPhabICIiIiIiihEzUkRERERERDFiIEVERERERBQjBlJEREREREQxYiBFREREREQUIwZSSWjZsmU477zzYDKZkJubi1mzZmH37t0B+7jdbtxxxx3Izs5Geno6rrvuOtTU1Mjbt23bhuuvvx5FRUVITU3FyJEj8Yc//KHTa23YsAETJkyAXq/HkCFD8MILL3Q5PiEEli5divz8fKSmpqK8vBx79+4Nua/H48G4ceOgUqmwdevWiMetrq7GT3/6UwwbNgxqtRoLFizotM8LL7wAlUoV8GMwGLocM52evnAuDho0qNM58+ijj3Z57K7G8/nnn2PGjBkoKCiASqXCmjVrujxmf9Nfzx9+lyW3vnDeAsA//vEPlJWVITU1FZmZmZg1a1aXx/7uu+9w8cUXw2AwoKioCMuXLw/YvmPHDlx33XXy52LFihVdHrM/6a/njtvtxk033YQxY8YgJSUl5P4bNmzo9J2nUqlgs9m6HHdcCUo6U6dOFatXrxaVlZVi69at4sorrxTFxcXC6XTK+8yfP18UFRWJdevWiW+//VZccMEFYvLkyfL2v/71r+Kuu+4SGzZsEPv37xcvv/yySE1NFStXrpT3OXDggEhLSxP33nuv2Llzp1i5cqXQaDRi7dq1Ecf36KOPCovFItasWSO2bdsmrrnmGlFaWipcLlenfe+66y4xffp0AUBs2bIl4nEPHjwo7rrrLvHiiy+KcePGibvvvrvTPqtXrxZms1lUV1fLPzabLeJx6fT1hXOxpKREPPjggwHnjHL8oUQzng8//FD8+te/Fu+8844AIN59991o/6z9Rn89f/hdltz6wnn79ttvi8zMTPHMM8+I3bt3ix07dog333wz4nGbmpqE1WoVc+bMEZWVleL1118Xqamp4tlnn5X3+frrr8V9990nXn/9dZGXlyeeeuqpaP+s/UJ/PXecTqeYP3++eO6558TUqVPFzJkzO+3z2WefCQBi9+7dAd977e3tXf1Z44qBVB9QW1srAIh//vOfQgghGhsbhVarFX/729/kfXbt2iUAiIqKirDHuf3228Vll10m/75w4UIxatSogH1+8pOfiKlTp4Y9hs/nE3l5eeL3v/+9/FhjY6PQ6/Xi9ddfD9j3ww8/FCNGjBA7duyIKpBSuvTSS8NefFgslqiPQ90rGc/FkpKSmP9jH+t4GEhFp7+cP0r8Lkt+yXbeer1eUVhYKP7yl7/E9D7/9Kc/iczMTOHxeOTHfvnLX4rhw4eH3P9MPxv9QX85d5RuvPHGiIFUQ0PDaR87Hji1rw9oamoCAGRlZQEANm3aBK/Xi/LycnmfESNGoLi4GBUVFRGPIx0DACoqKgKOAQBTp06NeIyDBw/CZrMFPM9isaCsrCzgeTU1Nbjlllvw8ssvIy0tLcp3Gh2n04mSkhIUFRVh5syZ2LFjR7cen8JLxnMRAB599FFkZ2dj/Pjx+P3vf4+2traI7/N0xkNd6y/nT7T4XZYcku283bx5M6qqqqBWqzF+/Hjk5+dj+vTpqKysjPg+KyoqcMkll0Cn0wWMZ/fu3WhoaIj4XAqtv5w7sRg3bhzy8/NxxRVX4Msvv+y24/YUBlJJzufzYcGCBbjwwgsxevRoAIDNZoNOp0NGRkbAvlarNexc06+++gpvvvkm5s2bJz9ms9lgtVo7HcNut8PlcoU8jnT8UM+TtgkhcNNNN2H+/PmYOHFi9G82CsOHD8fzzz+P9957D6+88gp8Ph8mT56MY8eOdevrUGfJeC4CwF133YU33ngDn332GW699VY88sgjWLhwYcT3ejrjocj60/kTDX6XJYdkPG8PHDgAAPjNb36DxYsX44MPPkBmZiamTJmC+vr6sO813HiUr0vR60/nTjTy8/OxatUq/P3vf8ff//53FBUVYcqUKdi8efMZHbenMZBKcnfccQcqKyvxxhtvnPYxKisrMXPmTDzwwAP4t3/7t6if9+qrryI9PV3++de//hXV81auXAmHw4FFixaF3Ud53Pnz50c9pkmTJmHu3LkYN24cLr30UrzzzjvIycnBs88+G/Ux6PQk47kIAPfeey+mTJmCc845B/Pnz8cTTzyBlStXwuPxADj9c5Fiw/MnEL/LkkMynrc+nw8A8Otf/xrXXXcdzj33XKxevRoqlQp/+9vfAACjRo2Sjzt9+vTY3xR1iedOoOHDh+PWW2/Fueeei8mTJ+P555/H5MmT8dRTT0V9jHhIifcA6PTdeeed+OCDD/D5559j4MCB8uN5eXlobW1FY2NjwF2Nmpoa5OXlBRxj586duPzyyzFv3jwsXrw4YFteXl5ApRjpGGazGampqbjmmmtQVlYmbyssLER1dbW8X35+fsDzxo0bBwBYv349KioqoNfrA449ceJEzJkzBy+++GJABT+z2Rz9HyWIVqvF+PHjsW/fvtM+BnUtWc/FUMrKytDW1oZDhw5h+PDhIc/FrsZDselv58/p4HdZ4knW81Z6/Oyzz5a36/V6DB48GEeOHAEAfPjhh/B6vQAgf6eFG4+0jaLX386d03X++efjiy++OKNj9Lh4L9Ki2Pl8PnHHHXeIgoICsWfPnk7bpcWKb7/9tvzY999/32mxYmVlpcjNzRW/+MUvQr7OwoULxejRowMeu/7666NarPj444/LjzU1NQUsVjx8+LDYvn27/PPxxx8LAOLtt98WR48ejepvEG6BdrC2tjYxfPhwcc8990R1XIpNsp+LobzyyitCrVaL+vr6sPvEOh6w2ERI/fX8UeJ3WfJJ9vNW+l1ZMKC1tVXk5uYGVOALJhWbaG1tlR9btGgRi03EoL+eO0rhik2EUl5eLq699tqo9o0XBlJJ6LbbbhMWi0Vs2LAhoERkS0uLvM/8+fNFcXGxWL9+vfj222/FpEmTxKRJk+Tt27dvFzk5OeJnP/tZwDFqa2vlfaTymb/4xS/Erl27xB//+Meoy2dmZGSI9957T3z33Xdi5syZYcufC9FRChhRVu3bsmWL2LJlizj33HPFT3/6U7FlyxaxY8cOeftvf/tb8fHHH4v9+/eLTZs2idmzZwuDwRCwD3WfZD8Xv/rqK/HUU0+JrVu3iv3794tXXnlF5OTkiLlz50Y8bjTjcTgc8vkKQDz55JNiy5Yt4vDhwzH9jfuy/nr+CMHvsmSW7OetEELcfffdorCwUHz88cfi+++/FzfffLPIzc2NeAOgsbFRWK1WccMNN4jKykrxxhtviLS0tIALaI/HI5/b+fn54r777hNbtmwRe/fujelv3Ff113NHCCF27NghtmzZImbMmCGmTJkinyeSp556SqxZs0bs3btXbN++Xdx9991CrVaLTz/9NNo/b1wwkEpCAEL+rF69Wt7H5XKJ22+/XWRmZoq0tDRx7bXXiurqann7Aw88EPIYJSUlAa/12WefiXHjxgmdTicGDx4c8Brh+Hw+sWTJEmG1WoVerxeXX3652L17d9j9YwmkuhrzggULRHFxsdDpdMJqtYorr7xSbN68ucvj0ulJ9nNx06ZNoqysTFgsFmEwGMTIkSPFI488Itxud5fH7mo8UinX4J8bb7yxy2P3F/35/OF3WfJK9vNWiI4sws9//nORm5srTCaTKC8vF5WVlV0ee9u2beKiiy4Ser1eFBYWikcffTRgu/Tf8+CfSy+9tMtj9wf9+dwpKSkJOW7JY489Js466yxhMBhEVlaWmDJlili/fn2Xx403lRBCRDsNkIiIiIiIiFi1j4iIiIiIKGYMpIiIiIiIiGLEQIqIiIiIiChGDKSIiIiIiIhixECKiIiIiIgoRgykiIiIiIiIYsRAioiIiIiIKEYMpIiIiIiIiGLEQIqIiIiIiChGDKSIiKhPuemmm6BSqaBSqaDVamG1WnHFFVfg+eefh8/ni/o4L7zwAjIyMnpuoERElNQYSBERUZ8zbdo0VFdX49ChQ/joo49w2WWX4e6778bVV1+Ntra2eA+PiIj6AAZSRETU5+j1euTl5aGwsBATJkzA/fffj/feew8fffQRXnjhBQDAk08+iTFjxsBoNKKoqAi33347nE4nAGDDhg34j//4DzQ1NcnZrd/85jcAAI/Hg/vuuw+FhYUwGo0oKyvDhg0b4vNGiYgobhhIERFRv/CDH/wAY8eOxTvvvAMAUKvVePrpp7Fjxw68+OKLWL9+PRYuXAgAmDx5MlasWAGz2Yzq6mpUV1fjvvvuAwDceeedqKiowBtvvIHvvvsOP/rRjzBt2jTs3bs3bu+NiIh6n0oIIeI9CCIiou5y0003obGxEWvWrOm0bfbs2fjuu++wc+fOTtvefvttzJ8/H3V1dQA61kgtWLAAjY2N8j5HjhzB4MGDceTIERQUFMiPl5eX4/zzz8cjjzzS7e+HiIgSU0q8B0BERNRbhBBQqVQAgE8//RTLli3D999/D7vdjra2NrjdbrS0tCAtLS3k87dv34729nYMGzYs4HGPx4Ps7OweHz8RESUOBlJERNRv7Nq1C6WlpTh06BCuvvpq3HbbbXj44YeRlZWFL774AjfffDNaW1vDBlJOpxMajQabNm2CRqMJ2Jaent4bb4GIiBIEAykiIuoX1q9fj+3bt+Oee+7Bpk2b4PP58MQTT0Ct7lgu/NZbbwXsr9Pp0N7eHvDY+PHj0d7ejtraWlx88cW9NnYiIko8DKSIiKjP8Xg8sNlsaG9vR01NDdauXYtly5bh6quvxty5c1FZWQmv14uVK1dixowZ+PLLL7Fq1aqAYwwaNAhOpxPr1q3D2LFjkZaWhmHDhmHOnDmYO3cunnjiCYwfPx4nTpzAunXrcM455+Cqq66K0zsmIqLexqp9RETU56xduxb5+fkYNGgQpk2bhs8++wxPP/003nvvPWg0GowdOxZPPvkkHnvsMYwePRqvvvoqli1bFnCMyZMnY/78+fjJT36CnJwcLF++HACwevVqzJ07Fz//+c8xfPhwzJo1C9988w2Ki4vj8VaJiChOWLWPiIiIiIgoRsxIERERERERxYiBFBERERERUYwYSBEREREREcWIgRQREREREVGMGEgRERERERHFiIEUERERERFRjBhIERERERERxYiBFBERERERUYwYSBEREREREcWIgRQREREREVGMGEgRERERERHF6P8D/eXj4myhZKgAAAAASUVORK5CYII="/></a>
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-<h3 id="uncertainty-estimation">Uncertainty estimation<a class="anchor-link" href="#uncertainty-estimation">&#182;</a></h3><p>Estimate the uncertainty of the volumetric water content using the <a href="https://soilwater.github.io/crnpy/reference/#crnpy.crnpy.uncertainty_vwc"><code>uncertainty_vwc()</code></a> function. Optionally see <a href="https://soilwater.github.io/crnpy/reference/#crnpy.crnpy.uncertainty_counts"><code>uncertainty_counts()</code></a> for estimating the uncertainty of the neutron counts.</p>
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+<h3 id="uncertainty-estimation">Uncertainty estimation<a class="anchor-link" href="#uncertainty-estimation">¶</a></h3><p>Estimate the uncertainty of the volumetric water content using the <a href="https://soilwater.github.io/crnpy/reference/#crnpy.crnpy.uncertainty_vwc"><code>uncertainty_vwc()</code></a> function. Optionally see <a href="https://soilwater.github.io/crnpy/reference/#crnpy.crnpy.uncertainty_counts"><code>uncertainty_counts()</code></a> for estimating the uncertainty of the neutron counts.</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Estimate the uncertainty of the volumetric water content</span>
-<span class="n">df</span><span class="p">[</span><span class="s1">&#39;sigma_VWC&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">uncertainty_vwc</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;total_raw_counts&#39;</span><span class="p">],</span> <span class="n">N0</span><span class="o">=</span><span class="n">N0_rdt</span><span class="p">,</span> <span class="n">bulk_density</span><span class="o">=</span><span class="mf">1.33</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;fp&#39;</span><span class="p">],</span> <span class="n">fi</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;fi&#39;</span><span class="p">],</span> <span class="n">fw</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;fw&#39;</span><span class="p">])</span>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Estimate the uncertainty of the volumetric water content</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">'sigma_VWC'</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">uncertainty_vwc</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'total_raw_counts'</span><span class="p">],</span> <span class="n">N0</span><span class="o">=</span><span class="n">N0_rdt</span><span class="p">,</span> <span class="n">bulk_density</span><span class="o">=</span><span class="mf">1.33</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'fp'</span><span class="p">],</span> <span class="n">fi</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'fi'</span><span class="p">],</span> <span class="n">fw</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'fw'</span><span class="p">])</span>
 
 <span class="c1"># Plot the VWC with uncertainty as a shaded area</span>
 <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>  <span class="c1"># Set the figure size</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">]</span><span class="o">-</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;sigma_VWC&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">]</span><span class="o">+</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;sigma_VWC&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;teal&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;Standard Deviation $\theta_v$&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Volumentric Water Content with Uncertainty&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Date&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;Volumetric Water Content ($m^3 \cdot m^{-3}$)&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'VWC'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'timestamp'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'VWC'</span><span class="p">]</span><span class="o">-</span><span class="n">df</span><span class="p">[</span><span class="s1">'sigma_VWC'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'VWC'</span><span class="p">]</span><span class="o">+</span><span class="n">df</span><span class="p">[</span><span class="s1">'sigma_VWC'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'teal'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">"Standard Deviation $\theta_v$"</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Volumentric Water Content with Uncertainty'</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Date"</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">"Volumetric Water Content ($m^3 \cdot m^{-3}$)"</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
-<div id="cell-20" class="clipboard-copy-txt"># Estimate the uncertainty of the volumetric water content
+<div class="clipboard-copy-txt" id="cell-20"># Estimate the uncertainty of the volumetric water content
 df['sigma_VWC'] = crnpy.uncertainty_vwc(df['total_raw_counts'], N0=N0_rdt, bulk_density=1.33, fp=df['fp'], fi=df['fi'], fw=df['fw'])
 
 # Plot the VWC with uncertainty as a shaded area
@@ -2984,43 +2215,40 @@ <h3 id="uncertainty-estimation">Uncertainty estimation<a class="anchor-link" hre
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" 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"/></a>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div> <!-- jp-Notebook -->
+</div> <!-- jupyter-wrapper -->
 
+        <style>
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+    <script id="__config" type="application/json">{"base": "../../..", "features": [], "search": "../../../assets/javascripts/workers/search.f886a092.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../../../assets/javascripts/bundle.220ee61c.min.js"></script>
+      <script src="../../../assets/javascripts/bundle.d7c377c4.min.js"></script>
       
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+        <script src="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js"></script>
       
-        
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+        <script src="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js"></script>
       
     
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diff --git a/site/index.html b/site/index.html
index 951bab7..2797b64 100644
--- a/site/index.html
+++ b/site/index.html
@@ -16,8 +16,9 @@
       
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+      
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+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.5.3">
     
     
       
@@ -25,12 +26,15 @@
       
     
     
-      <link rel="stylesheet" href="assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="assets/stylesheets/main.50c56a3b.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
@@ -95,9 +99,6 @@
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@@ -126,6 +127,7 @@
 
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+      
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@@ -146,8 +148,12 @@
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+    
+    
     
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+        
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@@ -156,12 +162,15 @@
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       <label class="md-search__icon md-icon" for="__search">
+        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
+        
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       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
+          
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
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@@ -185,7 +194,7 @@
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   </div>
   <div class="md-source__repository">
     GitHub
@@ -215,6 +224,7 @@
                     
 
 
+
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   <label class="md-nav__title" for="__drawer">
     <a href="." title="CRNPy Documentation" class="md-nav__button md-logo" aria-label="CRNPy Documentation" data-md-component="logo">
@@ -229,7 +239,7 @@
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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     GitHub
@@ -241,13 +251,12 @@
     
       
       
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@@ -257,12 +266,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Home
+          
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="." class="md-nav__link md-nav__link--active">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
       
         
@@ -282,35 +303,45 @@
       
         <li class="md-nav__item">
   <a href="#statement-of-need" class="md-nav__link">
-    Statement of Need
+    <span class="md-ellipsis">
+      Statement of Need
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#key-features" class="md-nav__link">
-    Key Features
+    <span class="md-ellipsis">
+      Key Features
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#installation" class="md-nav__link">
-    Installation
+    <span class="md-ellipsis">
+      Installation
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#authors" class="md-nav__link">
-    Authors
+    <span class="md-ellipsis">
+      Authors
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#community-guidelines" class="md-nav__link">
-    Community Guidelines
+    <span class="md-ellipsis">
+      Community Guidelines
+    </span>
   </a>
   
     <nav class="md-nav" aria-label="Community Guidelines">
@@ -318,21 +349,27 @@
         
           <li class="md-nav__item">
   <a href="#contributing" class="md-nav__link">
-    Contributing
+    <span class="md-ellipsis">
+      Contributing
+    </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
   <a href="#reporting-issues" class="md-nav__link">
-    Reporting Issues
+    <span class="md-ellipsis">
+      Reporting Issues
+    </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
   <a href="#seeking-support" class="md-nav__link">
-    Seeking Support
+    <span class="md-ellipsis">
+      Seeking Support
+    </span>
   </a>
   
 </li>
@@ -352,14 +389,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="sensor_description/" class="md-nav__link">
-        Sensor description
+        
+  
+  <span class="md-ellipsis">
+    Sensor description
+  </span>
+  
+
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@@ -367,14 +409,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="correction_routines/" class="md-nav__link">
-        Correction routines
+        
+  
+  <span class="md-ellipsis">
+    Correction routines
+  </span>
+  
+
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@@ -382,91 +429,126 @@
     
       
       
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+  
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+    
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-          <span class="md-nav__icon md-icon"></span>
-          Examples
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-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
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+  <span class="md-ellipsis">
+    Examples
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+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="examples/stationary/example_RDT_station/" class="md-nav__link">
-        Processing and analyzing data from a stationary detector
+        
+  
+  <span class="md-ellipsis">
+    Processing and analyzing data from a stationary detector
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="examples/rover/Hydroinnova_rover_example/" class="md-nav__link">
-        Processing and analyzing data from a roving detector
+        
+  
+  <span class="md-ellipsis">
+    Processing and analyzing data from a roving detector
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="examples/calibration/calibration/" class="md-nav__link">
-        Device-specific field calibration
+        
+  
+  <span class="md-ellipsis">
+    Device-specific field calibration
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="reference/" class="md-nav__link">
-        Reference
+        
+  
+  <span class="md-ellipsis">
+    Reference
+  </span>
+  
+
       </a>
     </li>
   
@@ -500,35 +582,45 @@
       
         <li class="md-nav__item">
   <a href="#statement-of-need" class="md-nav__link">
-    Statement of Need
+    <span class="md-ellipsis">
+      Statement of Need
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#key-features" class="md-nav__link">
-    Key Features
+    <span class="md-ellipsis">
+      Key Features
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#installation" class="md-nav__link">
-    Installation
+    <span class="md-ellipsis">
+      Installation
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#authors" class="md-nav__link">
-    Authors
+    <span class="md-ellipsis">
+      Authors
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#community-guidelines" class="md-nav__link">
-    Community Guidelines
+    <span class="md-ellipsis">
+      Community Guidelines
+    </span>
   </a>
   
     <nav class="md-nav" aria-label="Community Guidelines">
@@ -536,21 +628,27 @@
         
           <li class="md-nav__item">
   <a href="#contributing" class="md-nav__link">
-    Contributing
+    <span class="md-ellipsis">
+      Contributing
+    </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
   <a href="#reporting-issues" class="md-nav__link">
-    Reporting Issues
+    <span class="md-ellipsis">
+      Reporting Issues
+    </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
   <a href="#seeking-support" class="md-nav__link">
-    Seeking Support
+    <span class="md-ellipsis">
+      Seeking Support
+    </span>
   </a>
   
 </li>
@@ -582,7 +680,7 @@
 
 
 <h1 id="cosmic-ray-neutron-python-crnpy-library">Cosmic-Ray Neutron Python (CRNPy) Library</h1>
-<p><a class="glightbox" href="img/logo/crnpy-logo.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="CRNPy logo" src="img/logo/crnpy-logo.png" /></a></p>
+<p><a class="glightbox" href="img/logo/crnpy-logo.png" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="CRNPy logo" src="img/logo/crnpy-logo.png" /></a></p>
 <p><a href="https://github.com/soilwater/crnpy/actions/workflows/python-package.yml"><img alt="GitHub Workflow Status (building)" src="https://img.shields.io/github/actions/workflow/status/soilwater/crnpy/python-package.yml" /></a>
 <a href="https://github.com/soilwater/crnpy/actions/workflows/python-publish.yml"><img alt="GitHub Workflow Status (publish)" src="https://img.shields.io/github/actions/workflow/status/soilwater/crnpy/python-publish.yml?label=publish" /></a>
 <a href="https://pypi.org/project/crnpy/"><img alt="PyPI - Status" src="https://img.shields.io/pypi/v/crnpy" /></a>
@@ -632,12 +730,23 @@ <h3 id="seeking-support">Seeking Support</h3>
 
 
 
+
+
+  
+  
+
+
+
+
+
+
                 
 
               </article>
             </div>
           
           
+<script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script>
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@@ -665,22 +774,17 @@ <h3 id="seeking-support">Seeking Support</h3>
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diff --git a/site/reference/index.html b/site/reference/index.html
index 20342c8..0c0e61e 100644
--- a/site/reference/index.html
+++ b/site/reference/index.html
@@ -16,8 +16,9 @@
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+      
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-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.18">
+      <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.5.3">
     
     
       
@@ -25,12 +26,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.50c56a3b.min.css">
       
       
 
+
     
     
+      
+    
     
       
         
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@@ -126,6 +127,7 @@
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
+      
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@@ -146,8 +148,12 @@
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+      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+    
+    
     
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+        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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       <label class="md-search__icon md-icon" for="__search">
+        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
+        
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       </label>
       <nav class="md-search__options" aria-label="Search">
         
         <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
+          
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
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@@ -185,7 +194,7 @@
         <a href="https://github.com/soilwater/crnpy" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -215,6 +224,7 @@
                     
 
 
+
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
     <a href=".." title="CRNPy Documentation" class="md-nav__button md-logo" aria-label="CRNPy Documentation" data-md-component="logo">
@@ -229,7 +239,7 @@
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   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -241,14 +251,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href=".." class="md-nav__link">
-        Home
+        
+  
+  <span class="md-ellipsis">
+    Home
+  </span>
+  
+
       </a>
     </li>
   
@@ -256,14 +271,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="../sensor_description/" class="md-nav__link">
-        Sensor description
+        
+  
+  <span class="md-ellipsis">
+    Sensor description
+  </span>
+  
+
       </a>
     </li>
   
@@ -271,14 +291,19 @@
     
       
       
-      
-
+  
   
   
   
     <li class="md-nav__item">
       <a href="../correction_routines/" class="md-nav__link">
-        Correction routines
+        
+  
+  <span class="md-ellipsis">
+    Correction routines
+  </span>
+  
+
       </a>
     </li>
   
@@ -286,90 +311,119 @@
     
       
       
-      
-
   
   
   
+  
+    
+    
+    
+    
     
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-      
-      
-      
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-      
-      
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-          Examples
-          <span class="md-nav__icon md-icon"></span>
-        </label>
-      
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-        <label class="md-nav__title" for="__nav_4">
-          <span class="md-nav__icon md-icon"></span>
-          Examples
-        </label>
-        <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
+        
           
+          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_4">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="../examples/stationary/example_RDT_station/" class="md-nav__link">
-        Processing and analyzing data from a stationary detector
+        
+  
+  <span class="md-ellipsis">
+    Processing and analyzing data from a stationary detector
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="../examples/rover/Hydroinnova_rover_example/" class="md-nav__link">
-        Processing and analyzing data from a roving detector
+        
+  
+  <span class="md-ellipsis">
+    Processing and analyzing data from a roving detector
+  </span>
+  
+
       </a>
     </li>
   
 
-            
-          
+              
             
               
+                
+  
   
   
   
     <li class="md-nav__item">
       <a href="../examples/calibration/calibration/" class="md-nav__link">
-        Device-specific field calibration
+        
+  
+  <span class="md-ellipsis">
+    Device-specific field calibration
+  </span>
+  
+
       </a>
     </li>
   
 
+              
             
-          
-        </ul>
-      </nav>
+          </ul>
+        </nav>
+      
     </li>
   
 
     
       
       
-      
-
   
   
     
   
   
+  
     <li class="md-nav__item md-nav__item--active">
       
       <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
@@ -379,12 +433,24 @@
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          Reference
+          
+  
+  <span class="md-ellipsis">
+    Reference
+  </span>
+  
+
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="./" class="md-nav__link md-nav__link--active">
-        Reference
+        
+  
+  <span class="md-ellipsis">
+    Reference
+  </span>
+  
+
       </a>
       
         
@@ -404,503 +470,1231 @@
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy" class="md-nav__link">
-    crnpy.crnpy
+    <span class="md-ellipsis">
+      crnpy
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.abs_humidity" class="md-nav__link">
-    abs_humidity()
+    <span class="md-ellipsis">
+      abs_humidity()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.atmospheric_depth" class="md-nav__link">
+    <span class="md-ellipsis">
+      atmospheric_depth()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.biomass_to_bwe" class="md-nav__link">
-    biomass_to_bwe()
+    <span class="md-ellipsis">
+      biomass_to_bwe()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.correction_bwe" class="md-nav__link">
-    correction_bwe()
+    <span class="md-ellipsis">
+      correction_bwe()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.correction_humidity" class="md-nav__link">
-    correction_humidity()
+    <span class="md-ellipsis">
+      correction_humidity()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.correction_incoming_flux" class="md-nav__link">
-    correction_incoming_flux()
+    <span class="md-ellipsis">
+      correction_incoming_flux()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.correction_pressure" class="md-nav__link">
-    correction_pressure()
+    <span class="md-ellipsis">
+      correction_pressure()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.correction_road" class="md-nav__link">
-    correction_road()
+    <span class="md-ellipsis">
+      correction_road()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.counts_to_vwc" class="md-nav__link">
-    counts_to_vwc()
+    <span class="md-ellipsis">
+      counts_to_vwc()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.cutoff_rigidity" class="md-nav__link">
-    cutoff_rigidity()
+    <span class="md-ellipsis">
+      cutoff_rigidity()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.euclidean_distance" class="md-nav__link">
-    euclidean_distance()
+    <span class="md-ellipsis">
+      euclidean_distance()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.exp_filter" class="md-nav__link">
-    exp_filter()
+    <span class="md-ellipsis">
+      exp_filter()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.fill_missing_timestamps" class="md-nav__link">
-    fill_missing_timestamps()
+    <span class="md-ellipsis">
+      fill_missing_timestamps()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.find_neutron_monitor" class="md-nav__link">
-    find_neutron_monitor()
+    <span class="md-ellipsis">
+      find_neutron_monitor()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.get_incoming_neutron_flux" class="md-nav__link">
-    get_incoming_neutron_flux()
+    <span class="md-ellipsis">
+      get_incoming_neutron_flux()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.get_reference_neutron_flux" class="md-nav__link">
+    <span class="md-ellipsis">
+      get_reference_neutron_flux()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.idw" class="md-nav__link">
-    idw()
+    <span class="md-ellipsis">
+      idw()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.interpolate_2d" class="md-nav__link">
-    interpolate_2d()
+    <span class="md-ellipsis">
+      interpolate_2d()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.interpolate_incoming_flux" class="md-nav__link">
-    interpolate_incoming_flux()
+    <span class="md-ellipsis">
+      interpolate_incoming_flux()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.is_outlier" class="md-nav__link">
-    is_outlier()
+    <span class="md-ellipsis">
+      is_outlier()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.latlon_to_utm" class="md-nav__link">
-    latlon_to_utm()
+    <span class="md-ellipsis">
+      latlon_to_utm()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.lattice_water" class="md-nav__link">
-    lattice_water()
+    <span class="md-ellipsis">
+      lattice_water()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.location_factor" class="md-nav__link">
+    <span class="md-ellipsis">
+      location_factor()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.nrad_weight" class="md-nav__link">
-    nrad_weight()
+    <span class="md-ellipsis">
+      nrad_weight()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.remove_incomplete_intervals" class="md-nav__link">
-    remove_incomplete_intervals()
+    <span class="md-ellipsis">
+      remove_incomplete_intervals()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.rover_centered_coordinates" class="md-nav__link">
-    rover_centered_coordinates()
+    <span class="md-ellipsis">
+      rover_centered_coordinates()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.sensing_depth" class="md-nav__link">
-    sensing_depth()
+    <span class="md-ellipsis">
+      sensing_depth()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.smooth_1d" class="md-nav__link">
-    smooth_1d()
+    <span class="md-ellipsis">
+      smooth_1d()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.spatial_average" class="md-nav__link">
-    spatial_average()
+    <span class="md-ellipsis">
+      spatial_average()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.total_raw_counts" class="md-nav__link">
-    total_raw_counts()
+    <span class="md-ellipsis">
+      total_raw_counts()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.uncertainty_counts" class="md-nav__link">
-    uncertainty_counts()
+    <span class="md-ellipsis">
+      uncertainty_counts()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.crnpy.uncertainty_vwc" class="md-nav__link">
-    uncertainty_vwc()
+    <span class="md-ellipsis">
+      uncertainty_vwc()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#crnpy.data" class="md-nav__link">
-    crnpy.data
+    <span class="md-ellipsis">
+      data
+    </span>
   </a>
   
-    <nav class="md-nav" aria-label="crnpy.data">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#crnpy.data--crnpydata" class="md-nav__link">
-    crnpy.data
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.abs_humidity" class="md-nav__link">
+    <span class="md-ellipsis">
+      abs_humidity()
+    </span>
   </a>
   
 </li>
-        
-      </ul>
-    </nav>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.atmospheric_depth" class="md-nav__link">
+    <span class="md-ellipsis">
+      atmospheric_depth()
+    </span>
+  </a>
   
 </li>
       
-    </ul>
+        <li class="md-nav__item">
+  <a href="#crnpy.data.biomass_to_bwe" class="md-nav__link">
+    <span class="md-ellipsis">
+      biomass_to_bwe()
+    </span>
+  </a>
   
-</nav>
+</li>
       
-    </li>
-  
-
-    
-  </ul>
-</nav>
-                  </div>
-                </div>
-              </div>
-            
-            
-              
-              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
-                <div class="md-sidebar__scrollwrap">
-                  <div class="md-sidebar__inner">
-                    
-
-<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
-  
+        <li class="md-nav__item">
+  <a href="#crnpy.data.correction_bwe" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_bwe()
+    </span>
+  </a>
   
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.correction_humidity" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_humidity()
+    </span>
+  </a>
   
-    
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.correction_incoming_flux" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_incoming_flux()
+    </span>
+  </a>
   
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.correction_pressure" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_pressure()
+    </span>
+  </a>
   
-    <label class="md-nav__title" for="__toc">
-      <span class="md-nav__icon md-icon"></span>
-      Table of contents
-    </label>
-    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+</li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy" class="md-nav__link">
-    crnpy.crnpy
+  <a href="#crnpy.data.correction_road" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_road()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.abs_humidity" class="md-nav__link">
-    abs_humidity()
+  <a href="#crnpy.data.counts_to_vwc" class="md-nav__link">
+    <span class="md-ellipsis">
+      counts_to_vwc()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.biomass_to_bwe" class="md-nav__link">
-    biomass_to_bwe()
+  <a href="#crnpy.data.cutoff_rigidity" class="md-nav__link">
+    <span class="md-ellipsis">
+      cutoff_rigidity()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.correction_bwe" class="md-nav__link">
-    correction_bwe()
+  <a href="#crnpy.data.euclidean_distance" class="md-nav__link">
+    <span class="md-ellipsis">
+      euclidean_distance()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.correction_humidity" class="md-nav__link">
-    correction_humidity()
+  <a href="#crnpy.data.exp_filter" class="md-nav__link">
+    <span class="md-ellipsis">
+      exp_filter()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.correction_incoming_flux" class="md-nav__link">
-    correction_incoming_flux()
+  <a href="#crnpy.data.fill_missing_timestamps" class="md-nav__link">
+    <span class="md-ellipsis">
+      fill_missing_timestamps()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.correction_pressure" class="md-nav__link">
-    correction_pressure()
+  <a href="#crnpy.data.find_neutron_monitor" class="md-nav__link">
+    <span class="md-ellipsis">
+      find_neutron_monitor()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.correction_road" class="md-nav__link">
-    correction_road()
+  <a href="#crnpy.data.get_incoming_neutron_flux" class="md-nav__link">
+    <span class="md-ellipsis">
+      get_incoming_neutron_flux()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.counts_to_vwc" class="md-nav__link">
-    counts_to_vwc()
+  <a href="#crnpy.data.get_reference_neutron_flux" class="md-nav__link">
+    <span class="md-ellipsis">
+      get_reference_neutron_flux()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.cutoff_rigidity" class="md-nav__link">
-    cutoff_rigidity()
+  <a href="#crnpy.data.idw" class="md-nav__link">
+    <span class="md-ellipsis">
+      idw()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.euclidean_distance" class="md-nav__link">
-    euclidean_distance()
+  <a href="#crnpy.data.interpolate_2d" class="md-nav__link">
+    <span class="md-ellipsis">
+      interpolate_2d()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.exp_filter" class="md-nav__link">
-    exp_filter()
+  <a href="#crnpy.data.interpolate_incoming_flux" class="md-nav__link">
+    <span class="md-ellipsis">
+      interpolate_incoming_flux()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.fill_missing_timestamps" class="md-nav__link">
-    fill_missing_timestamps()
+  <a href="#crnpy.data.is_outlier" class="md-nav__link">
+    <span class="md-ellipsis">
+      is_outlier()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.find_neutron_monitor" class="md-nav__link">
-    find_neutron_monitor()
+  <a href="#crnpy.data.latlon_to_utm" class="md-nav__link">
+    <span class="md-ellipsis">
+      latlon_to_utm()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.get_incoming_neutron_flux" class="md-nav__link">
-    get_incoming_neutron_flux()
+  <a href="#crnpy.data.lattice_water" class="md-nav__link">
+    <span class="md-ellipsis">
+      lattice_water()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.idw" class="md-nav__link">
-    idw()
+  <a href="#crnpy.data.location_factor" class="md-nav__link">
+    <span class="md-ellipsis">
+      location_factor()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.interpolate_2d" class="md-nav__link">
-    interpolate_2d()
+  <a href="#crnpy.data.nrad_weight" class="md-nav__link">
+    <span class="md-ellipsis">
+      nrad_weight()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.interpolate_incoming_flux" class="md-nav__link">
-    interpolate_incoming_flux()
+  <a href="#crnpy.data.remove_incomplete_intervals" class="md-nav__link">
+    <span class="md-ellipsis">
+      remove_incomplete_intervals()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.is_outlier" class="md-nav__link">
-    is_outlier()
+  <a href="#crnpy.data.rover_centered_coordinates" class="md-nav__link">
+    <span class="md-ellipsis">
+      rover_centered_coordinates()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.latlon_to_utm" class="md-nav__link">
-    latlon_to_utm()
+  <a href="#crnpy.data.sensing_depth" class="md-nav__link">
+    <span class="md-ellipsis">
+      sensing_depth()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.lattice_water" class="md-nav__link">
-    lattice_water()
+  <a href="#crnpy.data.smooth_1d" class="md-nav__link">
+    <span class="md-ellipsis">
+      smooth_1d()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.nrad_weight" class="md-nav__link">
-    nrad_weight()
+  <a href="#crnpy.data.spatial_average" class="md-nav__link">
+    <span class="md-ellipsis">
+      spatial_average()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.remove_incomplete_intervals" class="md-nav__link">
-    remove_incomplete_intervals()
+  <a href="#crnpy.data.total_raw_counts" class="md-nav__link">
+    <span class="md-ellipsis">
+      total_raw_counts()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.rover_centered_coordinates" class="md-nav__link">
-    rover_centered_coordinates()
+  <a href="#crnpy.data.uncertainty_counts" class="md-nav__link">
+    <span class="md-ellipsis">
+      uncertainty_counts()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.sensing_depth" class="md-nav__link">
-    sensing_depth()
+  <a href="#crnpy.data.uncertainty_vwc" class="md-nav__link">
+    <span class="md-ellipsis">
+      uncertainty_vwc()
+    </span>
   </a>
   
 </li>
       
+    </ul>
+  
+</nav>
+      
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.smooth_1d" class="md-nav__link">
-    smooth_1d()
+  <a href="#crnpy.crnpy" class="md-nav__link">
+    <span class="md-ellipsis">
+      crnpy
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.spatial_average" class="md-nav__link">
-    spatial_average()
+  <a href="#crnpy.crnpy.abs_humidity" class="md-nav__link">
+    <span class="md-ellipsis">
+      abs_humidity()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.total_raw_counts" class="md-nav__link">
-    total_raw_counts()
+  <a href="#crnpy.crnpy.atmospheric_depth" class="md-nav__link">
+    <span class="md-ellipsis">
+      atmospheric_depth()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.uncertainty_counts" class="md-nav__link">
-    uncertainty_counts()
+  <a href="#crnpy.crnpy.biomass_to_bwe" class="md-nav__link">
+    <span class="md-ellipsis">
+      biomass_to_bwe()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.crnpy.uncertainty_vwc" class="md-nav__link">
-    uncertainty_vwc()
+  <a href="#crnpy.crnpy.correction_bwe" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_bwe()
+    </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#crnpy.data" class="md-nav__link">
-    crnpy.data
+  <a href="#crnpy.crnpy.correction_humidity" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_humidity()
+    </span>
   </a>
   
-    <nav class="md-nav" aria-label="crnpy.data">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#crnpy.data--crnpydata" class="md-nav__link">
-    crnpy.data
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.correction_incoming_flux" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_incoming_flux()
+    </span>
   </a>
   
 </li>
-        
-      </ul>
-    </nav>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.correction_pressure" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_pressure()
+    </span>
+  </a>
   
 </li>
       
-    </ul>
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.correction_road" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_road()
+    </span>
+  </a>
   
-</nav>
-                  </div>
-                </div>
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.counts_to_vwc" class="md-nav__link">
+    <span class="md-ellipsis">
+      counts_to_vwc()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.cutoff_rigidity" class="md-nav__link">
+    <span class="md-ellipsis">
+      cutoff_rigidity()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.euclidean_distance" class="md-nav__link">
+    <span class="md-ellipsis">
+      euclidean_distance()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.exp_filter" class="md-nav__link">
+    <span class="md-ellipsis">
+      exp_filter()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.fill_missing_timestamps" class="md-nav__link">
+    <span class="md-ellipsis">
+      fill_missing_timestamps()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.find_neutron_monitor" class="md-nav__link">
+    <span class="md-ellipsis">
+      find_neutron_monitor()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.get_incoming_neutron_flux" class="md-nav__link">
+    <span class="md-ellipsis">
+      get_incoming_neutron_flux()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.get_reference_neutron_flux" class="md-nav__link">
+    <span class="md-ellipsis">
+      get_reference_neutron_flux()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.idw" class="md-nav__link">
+    <span class="md-ellipsis">
+      idw()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.interpolate_2d" class="md-nav__link">
+    <span class="md-ellipsis">
+      interpolate_2d()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.interpolate_incoming_flux" class="md-nav__link">
+    <span class="md-ellipsis">
+      interpolate_incoming_flux()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.is_outlier" class="md-nav__link">
+    <span class="md-ellipsis">
+      is_outlier()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.latlon_to_utm" class="md-nav__link">
+    <span class="md-ellipsis">
+      latlon_to_utm()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.lattice_water" class="md-nav__link">
+    <span class="md-ellipsis">
+      lattice_water()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.location_factor" class="md-nav__link">
+    <span class="md-ellipsis">
+      location_factor()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.nrad_weight" class="md-nav__link">
+    <span class="md-ellipsis">
+      nrad_weight()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.remove_incomplete_intervals" class="md-nav__link">
+    <span class="md-ellipsis">
+      remove_incomplete_intervals()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.rover_centered_coordinates" class="md-nav__link">
+    <span class="md-ellipsis">
+      rover_centered_coordinates()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.sensing_depth" class="md-nav__link">
+    <span class="md-ellipsis">
+      sensing_depth()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.smooth_1d" class="md-nav__link">
+    <span class="md-ellipsis">
+      smooth_1d()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.spatial_average" class="md-nav__link">
+    <span class="md-ellipsis">
+      spatial_average()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.total_raw_counts" class="md-nav__link">
+    <span class="md-ellipsis">
+      total_raw_counts()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.uncertainty_counts" class="md-nav__link">
+    <span class="md-ellipsis">
+      uncertainty_counts()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.crnpy.uncertainty_vwc" class="md-nav__link">
+    <span class="md-ellipsis">
+      uncertainty_vwc()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data" class="md-nav__link">
+    <span class="md-ellipsis">
+      data
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.abs_humidity" class="md-nav__link">
+    <span class="md-ellipsis">
+      abs_humidity()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.atmospheric_depth" class="md-nav__link">
+    <span class="md-ellipsis">
+      atmospheric_depth()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.biomass_to_bwe" class="md-nav__link">
+    <span class="md-ellipsis">
+      biomass_to_bwe()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.correction_bwe" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_bwe()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.correction_humidity" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_humidity()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.correction_incoming_flux" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_incoming_flux()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.correction_pressure" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_pressure()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.correction_road" class="md-nav__link">
+    <span class="md-ellipsis">
+      correction_road()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.counts_to_vwc" class="md-nav__link">
+    <span class="md-ellipsis">
+      counts_to_vwc()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.cutoff_rigidity" class="md-nav__link">
+    <span class="md-ellipsis">
+      cutoff_rigidity()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.euclidean_distance" class="md-nav__link">
+    <span class="md-ellipsis">
+      euclidean_distance()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.exp_filter" class="md-nav__link">
+    <span class="md-ellipsis">
+      exp_filter()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.fill_missing_timestamps" class="md-nav__link">
+    <span class="md-ellipsis">
+      fill_missing_timestamps()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.find_neutron_monitor" class="md-nav__link">
+    <span class="md-ellipsis">
+      find_neutron_monitor()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.get_incoming_neutron_flux" class="md-nav__link">
+    <span class="md-ellipsis">
+      get_incoming_neutron_flux()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.get_reference_neutron_flux" class="md-nav__link">
+    <span class="md-ellipsis">
+      get_reference_neutron_flux()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.idw" class="md-nav__link">
+    <span class="md-ellipsis">
+      idw()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.interpolate_2d" class="md-nav__link">
+    <span class="md-ellipsis">
+      interpolate_2d()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.interpolate_incoming_flux" class="md-nav__link">
+    <span class="md-ellipsis">
+      interpolate_incoming_flux()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.is_outlier" class="md-nav__link">
+    <span class="md-ellipsis">
+      is_outlier()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.latlon_to_utm" class="md-nav__link">
+    <span class="md-ellipsis">
+      latlon_to_utm()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.lattice_water" class="md-nav__link">
+    <span class="md-ellipsis">
+      lattice_water()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.location_factor" class="md-nav__link">
+    <span class="md-ellipsis">
+      location_factor()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.nrad_weight" class="md-nav__link">
+    <span class="md-ellipsis">
+      nrad_weight()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.remove_incomplete_intervals" class="md-nav__link">
+    <span class="md-ellipsis">
+      remove_incomplete_intervals()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.rover_centered_coordinates" class="md-nav__link">
+    <span class="md-ellipsis">
+      rover_centered_coordinates()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.sensing_depth" class="md-nav__link">
+    <span class="md-ellipsis">
+      sensing_depth()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.smooth_1d" class="md-nav__link">
+    <span class="md-ellipsis">
+      smooth_1d()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.spatial_average" class="md-nav__link">
+    <span class="md-ellipsis">
+      spatial_average()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.total_raw_counts" class="md-nav__link">
+    <span class="md-ellipsis">
+      total_raw_counts()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.uncertainty_counts" class="md-nav__link">
+    <span class="md-ellipsis">
+      uncertainty_counts()
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#crnpy.data.uncertainty_vwc" class="md-nav__link">
+    <span class="md-ellipsis">
+      uncertainty_vwc()
+    </span>
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
               </div>
             
           
@@ -911,23 +1705,7156 @@
 
 
 
-                  
+                  
+
+  
+  
+
+
+<h1 id="reference">Reference</h1>
+
+
+<div class="doc doc-object doc-module">
+
+
+
+<a id="crnpy.crnpy"></a>
+  <div class="doc doc-contents first">
+  
+      <p><code>crnpy</code> is a Python package for processing cosmic ray neutron data.</p>
+<p>Created by Joaquin Peraza and Andres Patrignani.</p>
+
+  
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
+
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.abs_humidity" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">abs_humidity</span><span class="p">(</span><span class="n">relative_humidity</span><span class="p">,</span> <span class="n">temp</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>relative_humidity</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>relative humidity (%)</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>temp</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>temperature (Celsius)</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+<th>Name</th>        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+<td><code>float</code></td>          <td>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>actual vapor pressure (g m^-3)</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">671</span>
+<span class="normal">672</span>
+<span class="normal">673</span>
+<span class="normal">674</span>
+<span class="normal">675</span>
+<span class="normal">676</span>
+<span class="normal">677</span>
+<span class="normal">678</span>
+<span class="normal">679</span>
+<span class="normal">680</span>
+<span class="normal">681</span>
+<span class="normal">682</span>
+<span class="normal">683</span>
+<span class="normal">684</span>
+<span class="normal">685</span>
+<span class="normal">686</span>
+<span class="normal">687</span>
+<span class="normal">688</span>
+<span class="normal">689</span>
+<span class="normal">690</span>
+<span class="normal">691</span>
+<span class="normal">692</span>
+<span class="normal">693</span>
+<span class="normal">694</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">abs_humidity</span><span class="p">(</span><span class="n">relative_humidity</span><span class="p">,</span> <span class="n">temp</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        relative_humidity (float): relative humidity (%)</span>
+<span class="sd">        temp (float): temperature (Celsius)</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        float: actual vapor pressure (g m^-3)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1">### Atmospheric water vapor factor</span>
+    <span class="c1"># Saturation vapor pressure</span>
+    <span class="n">e_sat</span> <span class="o">=</span> <span class="mf">0.611</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mf">17.502</span> <span class="o">*</span> <span class="n">temp</span> <span class="o">/</span> <span class="p">(</span>
+            <span class="n">temp</span> <span class="o">+</span> <span class="mf">240.97</span><span class="p">))</span> <span class="o">*</span> <span class="mi">1000</span>  <span class="c1"># in Pascals Eq. 3.8 p.41 Environmental Biophysics (Campbell and Norman)</span>
+
+    <span class="c1"># Vapor pressure Pascals</span>
+    <span class="n">Pw</span> <span class="o">=</span> <span class="n">e_sat</span> <span class="o">*</span> <span class="n">relative_humidity</span> <span class="o">/</span> <span class="mi">100</span>
+
+    <span class="c1"># Absolute humidity (g/m^3)</span>
+    <span class="n">C</span> <span class="o">=</span> <span class="mf">2.16679</span>  <span class="c1"># g K/J;</span>
+    <span class="n">abs_h</span> <span class="o">=</span> <span class="n">C</span> <span class="o">*</span> <span class="n">Pw</span> <span class="o">/</span> <span class="p">(</span><span class="n">temp</span> <span class="o">+</span> <span class="mf">273.15</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">abs_h</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.atmospheric_depth" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">atmospheric_depth</span><span class="p">(</span><span class="n">elevation</span><span class="p">,</span> <span class="n">latitude</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to estimate the atmospheric depth for any point on Earth according to McJannet and Desilets, 2023</p>
+<p>This function is required in the calculation of the location-dependent reference correction proposed by McJannet and Desilets, 2023.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>elevation</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Elevation in meters above sea level.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>latitude</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Geographic latitude in decimal degrees. Value in range -90 to 90</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Atmospheric depth in g/cm2</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Atmosphere, U. S. (1976). US standard atmosphere. National Oceanic and Atmospheric Administration.</p>
+<p>McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1059</span>
+<span class="normal">1060</span>
+<span class="normal">1061</span>
+<span class="normal">1062</span>
+<span class="normal">1063</span>
+<span class="normal">1064</span>
+<span class="normal">1065</span>
+<span class="normal">1066</span>
+<span class="normal">1067</span>
+<span class="normal">1068</span>
+<span class="normal">1069</span>
+<span class="normal">1070</span>
+<span class="normal">1071</span>
+<span class="normal">1072</span>
+<span class="normal">1073</span>
+<span class="normal">1074</span>
+<span class="normal">1075</span>
+<span class="normal">1076</span>
+<span class="normal">1077</span>
+<span class="normal">1078</span>
+<span class="normal">1079</span>
+<span class="normal">1080</span>
+<span class="normal">1081</span>
+<span class="normal">1082</span>
+<span class="normal">1083</span>
+<span class="normal">1084</span>
+<span class="normal">1085</span>
+<span class="normal">1086</span>
+<span class="normal">1087</span>
+<span class="normal">1088</span>
+<span class="normal">1089</span>
+<span class="normal">1090</span>
+<span class="normal">1091</span>
+<span class="normal">1092</span>
+<span class="normal">1093</span>
+<span class="normal">1094</span>
+<span class="normal">1095</span>
+<span class="normal">1096</span>
+<span class="normal">1097</span>
+<span class="normal">1098</span>
+<span class="normal">1099</span>
+<span class="normal">1100</span>
+<span class="normal">1101</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">atmospheric_depth</span><span class="p">(</span><span class="n">elevation</span><span class="p">,</span> <span class="n">latitude</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to estimate the atmospheric depth for any point on Earth according to McJannet and Desilets, 2023</span>
+
+<span class="sd">    This function is required in the calculation of the location-dependent reference correction proposed by McJannet and Desilets, 2023.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        elevation (float): Elevation in meters above sea level.</span>
+<span class="sd">        latitude (float): Geographic latitude in decimal degrees. Value in range -90 to 90</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (float): Atmospheric depth in g/cm2</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Atmosphere, U. S. (1976). US standard atmosphere. National Oceanic and Atmospheric Administration.</span>
+
+<span class="sd">        McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">density_of_rock</span> <span class="o">=</span> <span class="mi">2670</span>  <span class="c1"># Density of rock in kg/m3</span>
+    <span class="n">air_pressure_sea_level</span> <span class="o">=</span> <span class="mf">1013.25</span>  <span class="c1"># Air pressure at sea level in hPa</span>
+    <span class="n">air_molar_mass</span> <span class="o">=</span> <span class="mf">0.0289644</span>  <span class="c1"># Air molar mass in kg/mol</span>
+    <span class="n">universal_gas_constant</span> <span class="o">=</span> <span class="mf">8.3144598</span>  <span class="c1"># Universal gas constant in J/(mol*K)</span>
+    <span class="n">reference_temperature</span> <span class="o">=</span> <span class="mf">288.15</span>  <span class="c1"># Reference temperature Kelvin</span>
+    <span class="n">temperature_lapse_rate</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.0065</span>  <span class="c1"># Temperature lapse rate in K/m</span>
+
+    <span class="c1"># Gravity at sea-level calculation</span>
+    <span class="n">gravity_sea_level</span> <span class="o">=</span> <span class="mf">9.780327</span> <span class="o">*</span> <span class="p">(</span>
+            <span class="mi">1</span> <span class="o">+</span> <span class="mf">0.0053024</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">radians</span><span class="p">(</span><span class="n">latitude</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">-</span> <span class="mf">0.0000058</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">radians</span><span class="p">(</span><span class="n">latitude</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="c1"># Free air correction</span>
+    <span class="n">free_air</span> <span class="o">=</span> <span class="o">-</span><span class="mf">3.086</span> <span class="o">*</span> <span class="mi">10</span> <span class="o">**</span> <span class="o">-</span><span class="mi">6</span> <span class="o">*</span> <span class="n">elevation</span>
+    <span class="c1"># Bouguer correction</span>
+    <span class="n">bouguer_corr</span> <span class="o">=</span> <span class="mf">4.194</span> <span class="o">*</span> <span class="mi">10</span> <span class="o">**</span> <span class="o">-</span><span class="mi">10</span> <span class="o">*</span> <span class="n">density_of_rock</span> <span class="o">*</span> <span class="n">elevation</span>
+    <span class="c1"># Total gravity</span>
+    <span class="n">gravity</span> <span class="o">=</span> <span class="n">gravity_sea_level</span> <span class="o">+</span> <span class="n">free_air</span> <span class="o">+</span> <span class="n">bouguer_corr</span>
+
+    <span class="c1"># Air pressure calculation</span>
+    <span class="n">reference_air_pressure</span> <span class="o">=</span> <span class="n">air_pressure_sea_level</span> <span class="o">*</span> <span class="p">(</span>
+                <span class="mi">1</span> <span class="o">+</span> <span class="n">temperature_lapse_rate</span> <span class="o">/</span> <span class="n">reference_temperature</span> <span class="o">*</span> <span class="n">elevation</span><span class="p">)</span> <span class="o">**</span> <span class="p">((</span><span class="o">-</span><span class="n">gravity</span> <span class="o">*</span> <span class="n">air_molar_mass</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span>
+                <span class="n">universal_gas_constant</span> <span class="o">*</span> <span class="n">temperature_lapse_rate</span><span class="p">))</span>
+
+    <span class="c1"># Atmospheric depth calculation</span>
+    <span class="n">atmospheric_depth</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span> <span class="o">*</span> <span class="n">reference_air_pressure</span><span class="p">)</span> <span class="o">/</span> <span class="n">gravity</span>
+    <span class="k">return</span> <span class="n">atmospheric_depth</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.biomass_to_bwe" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">biomass_to_bwe</span><span class="p">(</span><span class="n">biomass_dry</span><span class="p">,</span> <span class="n">biomass_fresh</span><span class="p">,</span> <span class="n">fWE</span><span class="o">=</span><span class="mf">0.494</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to convert biomass to biomass water equivalent.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>biomass_dry</code></td>
+          <td>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Above ground dry biomass in kg m-2.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>biomass_fresh</code></td>
+          <td>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Above ground fresh biomass in kg m-2.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>fWE</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Stoichiometric ratio of H2O to organic carbon molecules in the plant (assuming this is mostly cellulose)
+Default is 0.494 (Wahbi &amp; Avery, 2018).</p>
+            </div>
+          </td>
+          <td>
+                <code>0.494</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Biomass water equivalent in kg m-2.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Wahbi, A., Avery, W. (2018). In Situ Destructive Sampling. In:
+Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent.
+Springer, Cham. https://doi.org/10.1007/978-3-319-69539-6_2</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">539</span>
+<span class="normal">540</span>
+<span class="normal">541</span>
+<span class="normal">542</span>
+<span class="normal">543</span>
+<span class="normal">544</span>
+<span class="normal">545</span>
+<span class="normal">546</span>
+<span class="normal">547</span>
+<span class="normal">548</span>
+<span class="normal">549</span>
+<span class="normal">550</span>
+<span class="normal">551</span>
+<span class="normal">552</span>
+<span class="normal">553</span>
+<span class="normal">554</span>
+<span class="normal">555</span>
+<span class="normal">556</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">biomass_to_bwe</span><span class="p">(</span><span class="n">biomass_dry</span><span class="p">,</span> <span class="n">biomass_fresh</span><span class="p">,</span> <span class="n">fWE</span><span class="o">=</span><span class="mf">0.494</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to convert biomass to biomass water equivalent.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        biomass_dry (array or pd.Series or pd.DataFrame): Above ground dry biomass in kg m-2.</span>
+<span class="sd">        biomass_fresh (array or pd.Series or pd.DataFrame): Above ground fresh biomass in kg m-2.</span>
+<span class="sd">        fWE (float): Stoichiometric ratio of H2O to organic carbon molecules in the plant (assuming this is mostly cellulose)</span>
+<span class="sd">            Default is 0.494 (Wahbi &amp; Avery, 2018).</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (array or pd.Series or pd.DataFrame): Biomass water equivalent in kg m-2.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Wahbi, A., Avery, W. (2018). In Situ Destructive Sampling. In:</span>
+<span class="sd">        Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent.</span>
+<span class="sd">        Springer, Cham. https://doi.org/10.1007/978-3-319-69539-6_2</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">biomass_fresh</span> <span class="o">-</span> <span class="n">biomass_dry</span><span class="p">)</span> <span class="o">+</span> <span class="n">fWE</span> <span class="o">*</span> <span class="n">biomass_dry</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.correction_bwe" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">correction_bwe</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">bwe</span><span class="p">,</span> <span class="n">r2_N0</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to correct for biomass effects in neutron counts.
+following the approach described in Baatz et al., 2015.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>counts</code></td>
+          <td>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Array of ephithermal neutron counts.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>bwe</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Biomass water equivalent kg m-2.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>r2_N0</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.</p>
+            </div>
+          </td>
+          <td>
+                <code>0.05</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Array of corrected neutron counts for biomass effects.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Baatz, R., H. R. Bogena, H.-J. Hendricks Franssen, J. A. Huisman, C. Montzka, and H. Vereecken (2015),
+An empiricalvegetation correction for soil water content quantification using cosmic ray probes,
+Water Resour. Res., 51, 2030–2046, doi:10.1002/ 2014WR016443.</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">518</span>
+<span class="normal">519</span>
+<span class="normal">520</span>
+<span class="normal">521</span>
+<span class="normal">522</span>
+<span class="normal">523</span>
+<span class="normal">524</span>
+<span class="normal">525</span>
+<span class="normal">526</span>
+<span class="normal">527</span>
+<span class="normal">528</span>
+<span class="normal">529</span>
+<span class="normal">530</span>
+<span class="normal">531</span>
+<span class="normal">532</span>
+<span class="normal">533</span>
+<span class="normal">534</span>
+<span class="normal">535</span>
+<span class="normal">536</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_bwe</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">bwe</span><span class="p">,</span> <span class="n">r2_N0</span><span class="o">=</span><span class="mf">0.05</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to correct for biomass effects in neutron counts.</span>
+<span class="sd">    following the approach described in Baatz et al., 2015.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        counts (array or pd.Series or pd.DataFrame): Array of ephithermal neutron counts.</span>
+<span class="sd">        bwe (float): Biomass water equivalent kg m-2.</span>
+<span class="sd">        r2_N0 (float): Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (array or pd.Series or pd.DataFrame): Array of corrected neutron counts for biomass effects.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Baatz, R., H. R. Bogena, H.-J. Hendricks Franssen, J. A. Huisman, C. Montzka, and H. Vereecken (2015),</span>
+<span class="sd">        An empiricalvegetation correction for soil water content quantification using cosmic ray probes,</span>
+<span class="sd">        Water Resour. Res., 51, 2030–2046, doi:10.1002/ 2014WR016443.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">return</span> <span class="n">counts</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">bwe</span> <span class="o">*</span> <span class="n">r2_N0</span><span class="p">)</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.correction_humidity" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">correction_humidity</span><span class="p">(</span><span class="n">abs_humidity</span><span class="p">,</span> <span class="n">Aref</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Correction factor for absolute humidity.</p>
+<p>This function corrects neutron counts for absolute humidity using the method described in Rosolem et al. (2013) and Anderson et al. (2017). The correction is performed using the following equation:</p>
+<p>$$
+C_{corrected} = C_{raw} \cdot f_w
+$$</p>
+<p>where:</p>
+<ul>
+<li>Ccorrected: corrected neutron counts</li>
+<li>Craw: raw neutron counts</li>
+<li>fw: absolute humidity correction factor</li>
+</ul>
+<p>$$
+f_w = 1 + 0.0054(A - A_{ref})
+$$</p>
+<p>where:</p>
+<ul>
+<li>A: absolute humidity</li>
+<li>Aref: reference absolute humidity</li>
+</ul>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>abs_humidity</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Relative humidity readings.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>Aref</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>list</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>fw correction factor.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">253</span>
+<span class="normal">254</span>
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+<span class="normal">289</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_humidity</span><span class="p">(</span><span class="n">abs_humidity</span><span class="p">,</span> <span class="n">Aref</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Correction factor for absolute humidity.</span>
+
+<span class="sd">    This function corrects neutron counts for absolute humidity using the method described in Rosolem et al. (2013) and Anderson et al. (2017). The correction is performed using the following equation:</span>
+
+<span class="sd">    $$</span>
+<span class="sd">    C_{corrected} = C_{raw} \cdot f_w</span>
+<span class="sd">    $$</span>
+
+<span class="sd">    where:</span>
+
+<span class="sd">    - Ccorrected: corrected neutron counts</span>
+<span class="sd">    - Craw: raw neutron counts</span>
+<span class="sd">    - fw: absolute humidity correction factor</span>
+
+<span class="sd">    $$</span>
+<span class="sd">    f_w = 1 + 0.0054(A - A_{ref})</span>
+<span class="sd">    $$</span>
+
+<span class="sd">    where:</span>
+
+<span class="sd">    - A: absolute humidity</span>
+<span class="sd">    - Aref: reference absolute humidity</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        abs_humidity (list or array): Relative humidity readings.</span>
+<span class="sd">        Aref (float): Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (list): fw correction factor.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">A</span> <span class="o">=</span> <span class="n">abs_humidity</span>
+    <span class="n">fw</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="mf">0.0054</span> <span class="o">*</span> <span class="p">(</span><span class="n">A</span> <span class="o">-</span> <span class="n">Aref</span><span class="p">)</span>  <span class="c1"># Zreda et al. 2017 Eq 6.</span>
+    <span class="k">return</span> <span class="n">fw</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.correction_incoming_flux" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">correction_incoming_flux</span><span class="p">(</span><span class="n">incoming_neutrons</span><span class="p">,</span> <span class="n">incoming_Ref</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fill_na</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Rc_method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Rc_site</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">site_atmdepth</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Rc_ref</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ref_atmdepth</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Correction factor for incoming neutron flux.</p>
+<p>This function corrects neutron counts for incoming neutron flux using the method described in Anderson et al. (2017). The correction is performed using the following equation:</p>
+<p>$$
+C_{corrected} = \frac{C_{raw}}{f_i}
+$$</p>
+<p>where:</p>
+<ul>
+<li>Ccorrected: corrected neutron counts</li>
+<li>Craw: raw neutron counts</li>
+<li>fi: incoming neutron flux correction factor</li>
+</ul>
+<p>$$
+f_i = \frac{I_{ref}}{I}
+$$</p>
+<p>where:</p>
+<ul>
+<li>I: incoming neutron flux</li>
+<li>Iref: reference incoming neutron flux</li>
+</ul>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>incoming_neutrons</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Incoming neutron flux readings.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>incoming_Ref</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Reference incoming neutron flux. Baseline incoming neutron flux.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>list</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>fi correction factor.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">292</span>
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+<span class="normal">355</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_incoming_flux</span><span class="p">(</span><span class="n">incoming_neutrons</span><span class="p">,</span> <span class="n">incoming_Ref</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fill_na</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Rc_method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Rc_site</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                             <span class="n">site_atmdepth</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Rc_ref</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ref_atmdepth</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Correction factor for incoming neutron flux.</span>
+
+<span class="sd">    This function corrects neutron counts for incoming neutron flux using the method described in Anderson et al. (2017). The correction is performed using the following equation:</span>
+
+<span class="sd">    $$</span>
+<span class="sd">    C_{corrected} = \frac{C_{raw}}{f_i}</span>
+<span class="sd">    $$</span>
+
+<span class="sd">    where:</span>
+
+<span class="sd">    - Ccorrected: corrected neutron counts</span>
+<span class="sd">    - Craw: raw neutron counts</span>
+<span class="sd">    - fi: incoming neutron flux correction factor</span>
+
+<span class="sd">    $$</span>
+<span class="sd">    f_i = \frac{I_{ref}}{I}</span>
+<span class="sd">    $$</span>
+
+<span class="sd">    where:</span>
+
+<span class="sd">    - I: incoming neutron flux</span>
+<span class="sd">    - Iref: reference incoming neutron flux</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        incoming_neutrons (list or array): Incoming neutron flux readings.</span>
+<span class="sd">        incoming_Ref (float): Reference incoming neutron flux. Baseline incoming neutron flux.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (list): fi correction factor.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</span>
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">incoming_Ref</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">incoming_neutrons</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="kc">None</span><span class="p">)):</span>
+        <span class="n">incoming_Ref</span> <span class="o">=</span> <span class="n">incoming_neutrons</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Reference incoming neutron flux not provided. Using first value of incoming neutron flux.&#39;</span><span class="p">)</span>
+    <span class="n">fi</span> <span class="o">=</span> <span class="n">incoming_neutrons</span> <span class="o">/</span> <span class="n">incoming_Ref</span>
+
+    <span class="k">if</span> <span class="n">Rc_method</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">Rc_ref</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Reference cutoff rigidity not provided.&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">Rc_site</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Site cutoff rigidity not provided.&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">Rc_method</span> <span class="o">==</span> <span class="s1">&#39;McJannetandDesilets2023&#39;</span><span class="p">:</span>
+            <span class="n">tau</span> <span class="o">=</span> <span class="n">location_factor</span><span class="p">(</span><span class="n">site_atmdepth</span><span class="p">,</span> <span class="n">Rc_site</span><span class="p">,</span> <span class="n">ref_atmdepth</span><span class="p">,</span> <span class="n">Rc_ref</span><span class="p">)</span>
+            <span class="n">fi</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">tau</span> <span class="o">*</span> <span class="n">fi</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">tau</span><span class="p">)</span>
+
+        <span class="k">elif</span> <span class="n">Rc_method</span> <span class="o">==</span> <span class="s1">&#39;Hawdonetal2014&#39;</span><span class="p">:</span>
+            <span class="n">Rc_corr</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.075</span> <span class="o">*</span> <span class="p">(</span><span class="n">Rc_site</span> <span class="o">-</span> <span class="n">Rc_ref</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.0</span>
+            <span class="n">fi</span> <span class="o">=</span> <span class="p">(</span><span class="n">fi</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span> <span class="o">*</span> <span class="n">Rc_corr</span> <span class="o">+</span> <span class="mf">1.0</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;Cutoff rigidity method not found. Valid options are: McJannetandDesilets2023, Hawdonetal2014.&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">fill_na</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fi</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">fill_na</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>  <span class="c1"># Use a value of 1 for days without data</span>
+
+    <span class="k">return</span> <span class="n">fi</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.correction_pressure" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">correction_pressure</span><span class="p">(</span><span class="n">pressure</span><span class="p">,</span> <span class="n">Pref</span><span class="p">,</span> <span class="n">L</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Correction factor for atmospheric pressure.</p>
+<p>This function corrects neutron counts for atmospheric pressure using the method described in Andreasen et al. (2017).
+The correction is performed using the following equation:</p>
+<p>$$
+C_{corrected} = \frac{C_{raw}}{fp}
+$$</p>
+<p>where:</p>
+<ul>
+<li>Ccorrected: corrected neutron counts</li>
+<li>Craw: raw neutron counts</li>
+<li>fp: pressure correction factor</li>
+</ul>
+<p>$$
+fp = e^{\frac{P_{ref} - P}{L}}
+$$</p>
+<p>where:</p>
+<ul>
+<li>P: atmospheric pressure</li>
+<li>Pref: reference atmospheric pressure</li>
+<li>L: Atmospheric attenuation coefficient.</li>
+</ul>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>pressure</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Atmospheric pressure readings. Long-term average pressure is recommended.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>Pref</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Reference atmospheric pressure.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>L</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Atmospheric attenuation coefficient.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>list</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>fp pressure correction factor.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">208</span>
+<span class="normal">209</span>
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+<span class="normal">248</span>
+<span class="normal">249</span>
+<span class="normal">250</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_pressure</span><span class="p">(</span><span class="n">pressure</span><span class="p">,</span> <span class="n">Pref</span><span class="p">,</span> <span class="n">L</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Correction factor for atmospheric pressure.</span>
+
+<span class="sd">    This function corrects neutron counts for atmospheric pressure using the method described in Andreasen et al. (2017).</span>
+<span class="sd">    The correction is performed using the following equation:</span>
+
+<span class="sd">    $$</span>
+<span class="sd">    C_{corrected} = \frac{C_{raw}}{fp}</span>
+<span class="sd">    $$</span>
+
+<span class="sd">    where:</span>
+
+<span class="sd">    - Ccorrected: corrected neutron counts</span>
+<span class="sd">    - Craw: raw neutron counts</span>
+<span class="sd">    - fp: pressure correction factor</span>
+
+<span class="sd">    $$</span>
+<span class="sd">    fp = e^{\frac{P_{ref} - P}{L}}</span>
+<span class="sd">    $$</span>
+
+<span class="sd">    where:</span>
+
+<span class="sd">    - P: atmospheric pressure</span>
+<span class="sd">    - Pref: reference atmospheric pressure</span>
+<span class="sd">    - L: Atmospheric attenuation coefficient.</span>
+
+
+<span class="sd">    Args:</span>
+<span class="sd">        pressure (list or array): Atmospheric pressure readings. Long-term average pressure is recommended.</span>
+<span class="sd">        Pref (float): Reference atmospheric pressure.</span>
+<span class="sd">        L (float): Atmospheric attenuation coefficient.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (list): fp pressure correction factor.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Compute pressure correction factor</span>
+    <span class="n">fp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">((</span><span class="n">Pref</span> <span class="o">-</span> <span class="n">pressure</span><span class="p">)</span> <span class="o">/</span> <span class="n">L</span><span class="p">)</span>  <span class="c1"># Zreda et al. 2017 Eq 5.</span>
+
+    <span class="k">return</span> <span class="n">fp</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.correction_road" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">correction_road</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">theta_N</span><span class="p">,</span> <span class="n">road_width</span><span class="p">,</span> <span class="n">road_distance</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">theta_road</span><span class="o">=</span><span class="mf">0.12</span><span class="p">,</span> <span class="n">p0</span><span class="o">=</span><span class="mf">0.42</span><span class="p">,</span> <span class="n">p1</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">p2</span><span class="o">=</span><span class="mf">1.06</span><span class="p">,</span> <span class="n">p3</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">p4</span><span class="o">=</span><span class="mf">0.16</span><span class="p">,</span> <span class="n">p6</span><span class="o">=</span><span class="mf">0.94</span><span class="p">,</span> <span class="n">p7</span><span class="o">=</span><span class="mf">1.1</span><span class="p">,</span> <span class="n">p8</span><span class="o">=</span><span class="mf">2.7</span><span class="p">,</span> <span class="n">p9</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to correct for road effects in neutron counts.
+following the approach described in Schrön et al., 2018.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>counts</code></td>
+          <td>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Array of ephithermal neutron counts.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>theta_N</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Volumetric water content of the soil estimated from the uncorrected neutron counts.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>road_width</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Width of the road in m.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>road_distance</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Distance of the road from the sensor in m. Default is 0.0.</p>
+            </div>
+          </td>
+          <td>
+                <code>0.0</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>theta_road</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Volumetric water content of the road. Default is 0.12.</p>
+            </div>
+          </td>
+          <td>
+                <code>0.12</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>p0-p9</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Parameters of the correction function. Default values are from Schrön et al., 2018.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Array of corrected neutron counts for road effects.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Schrön,M.,Rosolem,R.,Köhli,M., Piussi,L.,Schröter,I.,Iwema,J.,etal. (2018).Cosmic-ray neutron rover surveys
+of field soil moisture and the influence of roads.WaterResources Research,54,6441–6459.
+https://doi. org/10.1029/2017WR021719</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">559</span>
+<span class="normal">560</span>
+<span class="normal">561</span>
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+<span class="normal">585</span>
+<span class="normal">586</span>
+<span class="normal">587</span>
+<span class="normal">588</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_road</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">theta_N</span><span class="p">,</span> <span class="n">road_width</span><span class="p">,</span> <span class="n">road_distance</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">theta_road</span><span class="o">=</span><span class="mf">0.12</span><span class="p">,</span> <span class="n">p0</span><span class="o">=</span><span class="mf">0.42</span><span class="p">,</span> <span class="n">p1</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">p2</span><span class="o">=</span><span class="mf">1.06</span><span class="p">,</span> <span class="n">p3</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+                    <span class="n">p4</span><span class="o">=</span><span class="mf">0.16</span><span class="p">,</span> <span class="n">p6</span><span class="o">=</span><span class="mf">0.94</span><span class="p">,</span> <span class="n">p7</span><span class="o">=</span><span class="mf">1.10</span><span class="p">,</span> <span class="n">p8</span><span class="o">=</span><span class="mf">2.70</span><span class="p">,</span> <span class="n">p9</span><span class="o">=</span><span class="mf">0.01</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to correct for road effects in neutron counts.</span>
+<span class="sd">    following the approach described in Schrön et al., 2018.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        counts (array or pd.Series or pd.DataFrame): Array of ephithermal neutron counts.</span>
+<span class="sd">        theta_N (float): Volumetric water content of the soil estimated from the uncorrected neutron counts.</span>
+<span class="sd">        road_width (float): Width of the road in m.</span>
+<span class="sd">        road_distance (float): Distance of the road from the sensor in m. Default is 0.0.</span>
+<span class="sd">        theta_road (float): Volumetric water content of the road. Default is 0.12.</span>
+<span class="sd">        p0-p9 (float): Parameters of the correction function. Default values are from Schrön et al., 2018.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (array or pd.Series or pd.DataFrame): Array of corrected neutron counts for road effects.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Schrön,M.,Rosolem,R.,Köhli,M., Piussi,L.,Schröter,I.,Iwema,J.,etal. (2018).Cosmic-ray neutron rover surveys</span>
+<span class="sd">        of field soil moisture and the influence of roads.WaterResources Research,54,6441–6459.</span>
+<span class="sd">        https://doi. org/10.1029/2017WR021719</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">F1</span> <span class="o">=</span> <span class="n">p0</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p1</span> <span class="o">*</span> <span class="n">road_width</span><span class="p">))</span>
+    <span class="n">F2</span> <span class="o">=</span> <span class="o">-</span><span class="n">p2</span> <span class="o">-</span> <span class="n">p3</span> <span class="o">*</span> <span class="n">theta_road</span> <span class="o">-</span> <span class="p">((</span><span class="n">p4</span> <span class="o">+</span> <span class="n">theta_road</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">theta_N</span><span class="p">))</span>
+    <span class="n">F3</span> <span class="o">=</span> <span class="n">p6</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p7</span> <span class="o">*</span> <span class="p">(</span><span class="n">road_width</span> <span class="o">**</span> <span class="o">-</span><span class="n">p8</span><span class="p">)</span> <span class="o">*</span> <span class="n">road_distance</span> <span class="o">**</span> <span class="mi">4</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">p6</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p9</span> <span class="o">*</span> <span class="n">road_distance</span><span class="p">)</span>
+
+    <span class="n">C_roads</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">F1</span> <span class="o">*</span> <span class="n">F2</span> <span class="o">*</span> <span class="n">F3</span>
+
+    <span class="n">corrected_counts</span> <span class="o">=</span> <span class="n">counts</span> <span class="o">/</span> <span class="n">C_roads</span>
+
+    <span class="k">return</span> <span class="n">corrected_counts</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">counts_to_vwc</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">N0</span><span class="p">,</span> <span class="n">Wlat</span><span class="p">,</span> <span class="n">Wsoc</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">a0</span><span class="o">=</span><span class="mf">0.0808</span><span class="p">,</span> <span class="n">a1</span><span class="o">=</span><span class="mf">0.372</span><span class="p">,</span> <span class="n">a2</span><span class="o">=</span><span class="mf">0.115</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to convert corrected and filtered neutron counts into volumetric water content.</p>
+<p>This method implements soil moisture estimation using the non-linear relationship between neutron count and soil volumetric water content following the approach described in Desilets et al., 2010.</p>
+<p>$\theta(N) =\frac{a_0}{(\frac{N}{N_0}) - a_1} - a_2 $</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>counts</code></td>
+          <td>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Array of corrected and filtered neutron counts.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>N0</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Device-specific neutron calibration constant.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>Wlat</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Lattice water content.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>Wsoc</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Soil organic carbon content.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>bulk_density</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Soil bulk density.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>a0</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Parameter given in Zreda et al., 2012. Default is 0.0808.</p>
+            </div>
+          </td>
+          <td>
+                <code>0.0808</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>a1</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Parameter given in Zreda et al., 2012. Default is 0.372.</p>
+            </div>
+          </td>
+          <td>
+                <code>0.372</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>a2</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Parameter given in Zreda et al., 2012. Default is 0.115.</p>
+            </div>
+          </td>
+          <td>
+                <code>0.115</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Volumetric water content in m3 m-3.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Desilets, D., M. Zreda, and T.P.A. Ferré. 2010. Nature’s neutron probe:
+Land surface hydrology at an elusive scale with cosmic rays. Water Resour. Res. 46:W11505.
+doi.org/10.1029/2009WR008726</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">591</span>
+<span class="normal">592</span>
+<span class="normal">593</span>
+<span class="normal">594</span>
+<span class="normal">595</span>
+<span class="normal">596</span>
+<span class="normal">597</span>
+<span class="normal">598</span>
+<span class="normal">599</span>
+<span class="normal">600</span>
+<span class="normal">601</span>
+<span class="normal">602</span>
+<span class="normal">603</span>
+<span class="normal">604</span>
+<span class="normal">605</span>
+<span class="normal">606</span>
+<span class="normal">607</span>
+<span class="normal">608</span>
+<span class="normal">609</span>
+<span class="normal">610</span>
+<span class="normal">611</span>
+<span class="normal">612</span>
+<span class="normal">613</span>
+<span class="normal">614</span>
+<span class="normal">615</span>
+<span class="normal">616</span>
+<span class="normal">617</span>
+<span class="normal">618</span>
+<span class="normal">619</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">counts_to_vwc</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">N0</span><span class="p">,</span> <span class="n">Wlat</span><span class="p">,</span> <span class="n">Wsoc</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">a0</span><span class="o">=</span><span class="mf">0.0808</span><span class="p">,</span> <span class="n">a1</span><span class="o">=</span><span class="mf">0.372</span><span class="p">,</span> <span class="n">a2</span><span class="o">=</span><span class="mf">0.115</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Function to convert corrected and filtered neutron counts into volumetric water content.</span>
+
+<span class="sd">    This method implements soil moisture estimation using the non-linear relationship between neutron count and soil volumetric water content following the approach described in Desilets et al., 2010.</span>
+
+<span class="sd">    $\theta(N) =\frac{a_0}{(\frac{N}{N_0}) - a_1} - a_2 $</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        counts (array or pd.Series or pd.DataFrame): Array of corrected and filtered neutron counts.</span>
+<span class="sd">        N0 (float): Device-specific neutron calibration constant.</span>
+<span class="sd">        Wlat (float): Lattice water content.</span>
+<span class="sd">        Wsoc (float): Soil organic carbon content.</span>
+<span class="sd">        bulk_density (float): Soil bulk density.</span>
+<span class="sd">        a0 (float): Parameter given in Zreda et al., 2012. Default is 0.0808.</span>
+<span class="sd">        a1 (float): Parameter given in Zreda et al., 2012. Default is 0.372.</span>
+<span class="sd">        a2 (float): Parameter given in Zreda et al., 2012. Default is 0.115.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (array or pd.Series or pd.DataFrame): Volumetric water content in m3 m-3.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Desilets, D., M. Zreda, and T.P.A. Ferré. 2010. Nature’s neutron probe:</span>
+<span class="sd">        Land surface hydrology at an elusive scale with cosmic rays. Water Resour. Res. 46:W11505.</span>
+<span class="sd">        doi.org/10.1029/2009WR008726</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Convert neutron counts into vwc</span>
+    <span class="n">vwc</span> <span class="o">=</span> <span class="p">(</span><span class="n">a0</span> <span class="o">/</span> <span class="p">(</span><span class="n">counts</span> <span class="o">/</span> <span class="n">N0</span> <span class="o">-</span> <span class="n">a1</span><span class="p">)</span> <span class="o">-</span> <span class="n">a2</span> <span class="o">-</span> <span class="n">Wlat</span> <span class="o">-</span> <span class="n">Wsoc</span><span class="p">)</span> <span class="o">*</span> <span class="n">bulk_density</span>
+    <span class="k">return</span> <span class="n">vwc</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.cutoff_rigidity" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">cutoff_rigidity</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to estimate the approximate cutoff rigidity for any point on Earth according to the
+tabulated data of Smart and Shea, 2019. The returned value can be used to select the appropriate
+neutron monitor station to estimate the cosmic-ray neutron intensity at the location of interest.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>lat</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Geographic latitude in decimal degrees. Value in range -90 to 90</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>lon</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Geographic longitude in decimal degrees. Values in range from 0 to 360.
+Typical negative longitudes in the west hemisphere will fall in the range 180 to 360.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Cutoff rigidity in GV. Error is about +/- 0.3 GV</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+<p><strong>Examples:</strong></p>
+    <p>Estimate the cutoff rigidity for Newark, NJ, US</p>
+    <div class="highlight"><pre><span></span><code><span class="gp">&gt;&gt;&gt; </span><span class="n">zq</span> <span class="o">=</span> <span class="n">cutoff_rigidity</span><span class="p">(</span><span class="mf">39.68</span><span class="p">,</span> <span class="o">-</span><span class="mf">75.75</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">zq</span><span class="p">)</span>
+<span class="go">2.52 GV (Value from NMD is 2.40 GV)</span>
+</code></pre></div>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures
+for cosmic‐ray neutron soil moisture probes located across Australia. Water Resources Research,
+50(6), 5029-5043.</p>
+<p>Smart, D. &amp; Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program:
+Theory, Software Description and Example. NASA STI/Recon Technical Report N.</p>
+<p>Shea, M. A., &amp; Smart, D. F. (2019, July). Re-examination of the First Five Ground-Level Events.
+In International Cosmic Ray Conference (ICRC2019) (Vol. 36, p. 1149).</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1012</span>
+<span class="normal">1013</span>
+<span class="normal">1014</span>
+<span class="normal">1015</span>
+<span class="normal">1016</span>
+<span class="normal">1017</span>
+<span class="normal">1018</span>
+<span class="normal">1019</span>
+<span class="normal">1020</span>
+<span class="normal">1021</span>
+<span class="normal">1022</span>
+<span class="normal">1023</span>
+<span class="normal">1024</span>
+<span class="normal">1025</span>
+<span class="normal">1026</span>
+<span class="normal">1027</span>
+<span class="normal">1028</span>
+<span class="normal">1029</span>
+<span class="normal">1030</span>
+<span class="normal">1031</span>
+<span class="normal">1032</span>
+<span class="normal">1033</span>
+<span class="normal">1034</span>
+<span class="normal">1035</span>
+<span class="normal">1036</span>
+<span class="normal">1037</span>
+<span class="normal">1038</span>
+<span class="normal">1039</span>
+<span class="normal">1040</span>
+<span class="normal">1041</span>
+<span class="normal">1042</span>
+<span class="normal">1043</span>
+<span class="normal">1044</span>
+<span class="normal">1045</span>
+<span class="normal">1046</span>
+<span class="normal">1047</span>
+<span class="normal">1048</span>
+<span class="normal">1049</span>
+<span class="normal">1050</span>
+<span class="normal">1051</span>
+<span class="normal">1052</span>
+<span class="normal">1053</span>
+<span class="normal">1054</span>
+<span class="normal">1055</span>
+<span class="normal">1056</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">cutoff_rigidity</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to estimate the approximate cutoff rigidity for any point on Earth according to the</span>
+<span class="sd">    tabulated data of Smart and Shea, 2019. The returned value can be used to select the appropriate</span>
+<span class="sd">    neutron monitor station to estimate the cosmic-ray neutron intensity at the location of interest.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        lat (float): Geographic latitude in decimal degrees. Value in range -90 to 90</span>
+<span class="sd">        lon (float): Geographic longitude in decimal degrees. Values in range from 0 to 360.</span>
+<span class="sd">            Typical negative longitudes in the west hemisphere will fall in the range 180 to 360.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (float): Cutoff rigidity in GV. Error is about +/- 0.3 GV</span>
+
+<span class="sd">    Examples:</span>
+<span class="sd">        Estimate the cutoff rigidity for Newark, NJ, US</span>
+
+<span class="sd">        &gt;&gt;&gt; zq = cutoff_rigidity(39.68, -75.75)</span>
+<span class="sd">        &gt;&gt;&gt; print(zq)</span>
+<span class="sd">        2.52 GV (Value from NMD is 2.40 GV)</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures</span>
+<span class="sd">        for cosmic‐ray neutron soil moisture probes located across Australia. Water Resources Research,</span>
+<span class="sd">        50(6), 5029-5043.</span>
+
+<span class="sd">        Smart, D. &amp; Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program:</span>
+<span class="sd">        Theory, Software Description and Example. NASA STI/Recon Technical Report N.</span>
+
+<span class="sd">        Shea, M. A., &amp; Smart, D. F. (2019, July). Re-examination of the First Five Ground-Level Events.</span>
+<span class="sd">        In International Cosmic Ray Conference (ICRC2019) (Vol. 36, p. 1149).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">xq</span> <span class="o">=</span> <span class="n">lon</span>
+    <span class="n">yq</span> <span class="o">=</span> <span class="n">lat</span>
+
+    <span class="k">if</span> <span class="n">xq</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">xq</span> <span class="o">=</span> <span class="n">xq</span> <span class="o">*</span> <span class="o">-</span><span class="mi">1</span> <span class="o">+</span> <span class="mi">180</span>
+    <span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">cutoff_rigidity</span><span class="p">)</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">360</span><span class="p">,</span> <span class="n">Z</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">90</span><span class="p">,</span> <span class="o">-</span><span class="mi">90</span><span class="p">,</span> <span class="n">Z</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+    <span class="n">points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">X</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">Y</span><span class="o">.</span><span class="n">flatten</span><span class="p">()))</span><span class="o">.</span><span class="n">T</span>
+    <span class="n">values</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+    <span class="n">zq</span> <span class="o">=</span> <span class="n">griddata</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="p">(</span><span class="n">xq</span><span class="p">,</span> <span class="n">yq</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">zq</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.euclidean_distance" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">euclidean_distance</span><span class="p">(</span><span class="n">px</span><span class="p">,</span> <span class="n">py</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function that computes the Euclidean distance between one point
+in space and one or more points.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>px</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>x projected coordinate of the point.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>py</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>y projected coordinate of the point.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>x</code></td>
+          <td>
+                <code>(list, ndarray, <span title="pandas.series">series</span>)</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>vector of x projected coordinates.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>y</code></td>
+          <td>
+                <code>(list, ndarray, <span title="pandas.series">series</span>)</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>vector of y projected coordinates.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>ndarray</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Numpy array of distances from the point (px,py) to all the points in x and y vectors.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1298</span>
+<span class="normal">1299</span>
+<span class="normal">1300</span>
+<span class="normal">1301</span>
+<span class="normal">1302</span>
+<span class="normal">1303</span>
+<span class="normal">1304</span>
+<span class="normal">1305</span>
+<span class="normal">1306</span>
+<span class="normal">1307</span>
+<span class="normal">1308</span>
+<span class="normal">1309</span>
+<span class="normal">1310</span>
+<span class="normal">1311</span>
+<span class="normal">1312</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">euclidean_distance</span><span class="p">(</span><span class="n">px</span><span class="p">,</span> <span class="n">py</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function that computes the Euclidean distance between one point</span>
+<span class="sd">    in space and one or more points.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        px (float): x projected coordinate of the point.</span>
+<span class="sd">        py (float): y projected coordinate of the point.</span>
+<span class="sd">        x (list, ndarray, pandas.series): vector of x projected coordinates.</span>
+<span class="sd">        y (list, ndarray, pandas.series): vector of y projected coordinates.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (ndarray): Numpy array of distances from the point (px,py) to all the points in x and y vectors.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">px</span> <span class="o">-</span> <span class="n">x</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">py</span> <span class="o">-</span> <span class="n">y</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">d</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.exp_filter" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">exp_filter</span><span class="p">(</span><span class="n">sm</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Exponential filter to estimate soil moisture in the rootzone from surface observtions.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>sm</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Soil moisture in mm of water for the top layer of the soil profile.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>T</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Characteristic time length in the same units as the measurement interval.</p>
+            </div>
+          </td>
+          <td>
+                <code>1</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+<th>Name</th>        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+<td><code>sm_subsurface</code></td>          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Subsurface soil moisture in the same units as the input.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B. and Martin, E., 2008.
+From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model
+simulations. Hydrology and Earth System Sciences, 12(6), pp.1323-1337.</p>
+<p>Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.
+Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.</p>
+<p>Rossini, P. and Patrignani, A., 2021. Predicting rootzone soil moisture from surface observations in cropland using an exponential filter.
+Soil Science Society of America Journal.</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 962</span>
+<span class="normal"> 963</span>
+<span class="normal"> 964</span>
+<span class="normal"> 965</span>
+<span class="normal"> 966</span>
+<span class="normal"> 967</span>
+<span class="normal"> 968</span>
+<span class="normal"> 969</span>
+<span class="normal"> 970</span>
+<span class="normal"> 971</span>
+<span class="normal"> 972</span>
+<span class="normal"> 973</span>
+<span class="normal"> 974</span>
+<span class="normal"> 975</span>
+<span class="normal"> 976</span>
+<span class="normal"> 977</span>
+<span class="normal"> 978</span>
+<span class="normal"> 979</span>
+<span class="normal"> 980</span>
+<span class="normal"> 981</span>
+<span class="normal"> 982</span>
+<span class="normal"> 983</span>
+<span class="normal"> 984</span>
+<span class="normal"> 985</span>
+<span class="normal"> 986</span>
+<span class="normal"> 987</span>
+<span class="normal"> 988</span>
+<span class="normal"> 989</span>
+<span class="normal"> 990</span>
+<span class="normal"> 991</span>
+<span class="normal"> 992</span>
+<span class="normal"> 993</span>
+<span class="normal"> 994</span>
+<span class="normal"> 995</span>
+<span class="normal"> 996</span>
+<span class="normal"> 997</span>
+<span class="normal"> 998</span>
+<span class="normal"> 999</span>
+<span class="normal">1000</span>
+<span class="normal">1001</span>
+<span class="normal">1002</span>
+<span class="normal">1003</span>
+<span class="normal">1004</span>
+<span class="normal">1005</span>
+<span class="normal">1006</span>
+<span class="normal">1007</span>
+<span class="normal">1008</span>
+<span class="normal">1009</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">exp_filter</span><span class="p">(</span><span class="n">sm</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Exponential filter to estimate soil moisture in the rootzone from surface observtions.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        sm (list or array): Soil moisture in mm of water for the top layer of the soil profile.</span>
+<span class="sd">        T (float): Characteristic time length in the same units as the measurement interval.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        sm_subsurface (list or array): Subsurface soil moisture in the same units as the input.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B. and Martin, E., 2008.</span>
+<span class="sd">        From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model</span>
+<span class="sd">        simulations. Hydrology and Earth System Sciences, 12(6), pp.1323-1337.</span>
+
+<span class="sd">        Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.</span>
+<span class="sd">        Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.</span>
+
+<span class="sd">        Rossini, P. and Patrignani, A., 2021. Predicting rootzone soil moisture from surface observations in cropland using an exponential filter.</span>
+<span class="sd">        Soil Science Society of America Journal.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Parameters</span>
+    <span class="n">t_delta</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="n">sm_min</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">sm</span><span class="p">)</span>
+    <span class="n">sm_max</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">sm</span><span class="p">)</span>
+    <span class="n">ms</span> <span class="o">=</span> <span class="p">(</span><span class="n">sm</span> <span class="o">-</span> <span class="n">sm_min</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">sm_max</span> <span class="o">-</span> <span class="n">sm_min</span><span class="p">)</span>
+
+    <span class="c1"># Pre-allocate soil water index array and recursive constant K</span>
+    <span class="n">SWI</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">ms</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+    <span class="n">K</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">ms</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+
+    <span class="c1"># Initial conditions</span>
+    <span class="n">SWI</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">ms</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="c1"># Values from 2 to N</span>
+    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">SWI</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">ms</span><span class="p">[</span><span class="n">n</span><span class="p">])</span> <span class="o">&amp;</span> <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">ms</span><span class="p">[</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]):</span>
+            <span class="n">K</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">K</span><span class="p">[</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">t_delta</span> <span class="o">/</span> <span class="n">T</span><span class="p">))</span>
+            <span class="n">SWI</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">SWI</span><span class="p">[</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">ms</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">-</span> <span class="n">SWI</span><span class="p">[</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">continue</span>
+
+    <span class="c1"># Rootzone storage</span>
+    <span class="n">sm_subsurface</span> <span class="o">=</span> <span class="n">SWI</span> <span class="o">*</span> <span class="p">(</span><span class="n">sm_max</span> <span class="o">-</span> <span class="n">sm_min</span><span class="p">)</span> <span class="o">+</span> <span class="n">sm_min</span>
+
+    <span class="k">return</span> <span class="n">sm_subsurface</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.fill_missing_timestamps" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">fill_missing_timestamps</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="o">=</span><span class="s1">&#39;timestamp&#39;</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="n">round_timestamp</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>df</code></td>
+          <td>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Pandas DataFrame.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>timestamp_col</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.</p>
+            </div>
+          </td>
+          <td>
+                <code>&#39;timestamp&#39;</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>freq</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.</p>
+            </div>
+          </td>
+          <td>
+                <code>&#39;H&#39;</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>round_timestamp</code></td>
+          <td>
+                <code>bool</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to round timestamps to the nearest frequency. Default is True.</p>
+            </div>
+          </td>
+          <td>
+                <code>True</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>verbose</code></td>
+          <td>
+                <code>bool</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Prints the missing timestamps added to the DatFrame.</p>
+            </div>
+          </td>
+          <td>
+                <code>False</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>DataFrame with filled missing timestamps.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 65</span>
+<span class="normal"> 66</span>
+<span class="normal"> 67</span>
+<span class="normal"> 68</span>
+<span class="normal"> 69</span>
+<span class="normal"> 70</span>
+<span class="normal"> 71</span>
+<span class="normal"> 72</span>
+<span class="normal"> 73</span>
+<span class="normal"> 74</span>
+<span class="normal"> 75</span>
+<span class="normal"> 76</span>
+<span class="normal"> 77</span>
+<span class="normal"> 78</span>
+<span class="normal"> 79</span>
+<span class="normal"> 80</span>
+<span class="normal"> 81</span>
+<span class="normal"> 82</span>
+<span class="normal"> 83</span>
+<span class="normal"> 84</span>
+<span class="normal"> 85</span>
+<span class="normal"> 86</span>
+<span class="normal"> 87</span>
+<span class="normal"> 88</span>
+<span class="normal"> 89</span>
+<span class="normal"> 90</span>
+<span class="normal"> 91</span>
+<span class="normal"> 92</span>
+<span class="normal"> 93</span>
+<span class="normal"> 94</span>
+<span class="normal"> 95</span>
+<span class="normal"> 96</span>
+<span class="normal"> 97</span>
+<span class="normal"> 98</span>
+<span class="normal"> 99</span>
+<span class="normal">100</span>
+<span class="normal">101</span>
+<span class="normal">102</span>
+<span class="normal">103</span>
+<span class="normal">104</span>
+<span class="normal">105</span>
+<span class="normal">106</span>
+<span class="normal">107</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fill_missing_timestamps</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="o">=</span><span class="s1">&#39;timestamp&#39;</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="n">round_timestamp</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.</span>
+
+<span class="sd">     Args:</span>
+<span class="sd">         df (pandas.DataFrame): Pandas DataFrame.</span>
+<span class="sd">         timestamp_col (str, optional): Column with the timestamp. Must be in datetime format. Default column name is &#39;timestamp&#39;.</span>
+<span class="sd">         freq (str, optional): Timestamp frequency. &#39;H&#39; for hourly, &#39;M&#39; for minute, or None. Can also use &#39;3H&#39; for a 3 hour frequency. Default is &#39;H&#39;.</span>
+<span class="sd">         round_timestamp (bool, optional): Whether to round timestamps to the nearest frequency. Default is True.</span>
+<span class="sd">         verbose (bool, optional): Prints the missing timestamps added to the DatFrame.</span>
+
+<span class="sd">     Returns:</span>
+<span class="sd">         (pandas.DataFrame): DataFrame with filled missing timestamps.</span>
+
+<span class="sd">     &quot;&quot;&quot;</span>
+
+    <span class="c1"># Check format of timestamp column</span>
+    <span class="k">if</span> <span class="n">df</span><span class="p">[</span><span class="n">timestamp_col</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="s1">&#39;datetime64[ns]&#39;</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;timestamp_col must be datetime64. Use `pd.to_datetime()` to fix this issue.&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Round timestamps to nearest frequency. This steps must preced the filling of rows.</span>
+    <span class="k">if</span> <span class="n">round_timestamp</span><span class="p">:</span>
+        <span class="n">df</span><span class="p">[</span><span class="n">timestamp_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">timestamp_col</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">freq</span><span class="p">)</span>
+
+    <span class="c1"># Fill in rows with missing timestamps</span>
+    <span class="n">start_date</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">timestamp_col</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">end_date</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">timestamp_col</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">date_range</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="n">freq</span><span class="p">)</span>
+    <span class="n">counter</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">date</span> <span class="ow">in</span> <span class="n">date_range</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">date</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="n">timestamp_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Adding missing date:&#39;</span><span class="p">,</span> <span class="n">date</span><span class="p">)</span>
+            <span class="n">new_line</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="n">timestamp_col</span><span class="p">:</span> <span class="n">date</span><span class="p">},</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>  <span class="c1"># By default fills columns with np.nan</span>
+            <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df</span><span class="p">,</span> <span class="n">new_line</span><span class="p">])</span>
+            <span class="n">counter</span> <span class="o">+=</span> <span class="mi">1</span>
+
+    <span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="n">timestamp_col</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="c1"># Notify user about the number of rows that have been removed</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Added a total of </span><span class="si">{</span><span class="n">counter</span><span class="si">}</span><span class="s2"> missing timestamps.&quot;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">df</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.find_neutron_monitor" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">find_neutron_monitor</span><span class="p">(</span><span class="n">Rc</span><span class="p">,</span> <span class="n">start_date</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">end_date</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Search for potential reference neutron monitoring stations based on cutoff rigidity.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>Rc</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>start_date</code></td>
+          <td>
+                <code>datetime</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Start date for the period of interest.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>end_date</code></td>
+          <td>
+                <code>datetime</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>End date for the period of interest.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>verbose</code></td>
+          <td>
+                <code>bool</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>If True, print a expanded output of the incoming neutron flux data.</p>
+            </div>
+          </td>
+          <td>
+                <code>False</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>list</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>List of top five stations with closes cutoff rigidity.
+User needs to select station according to site altitude.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+<p><strong>Examples:</strong></p>
+    <div class="highlight"><pre><span></span><code><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">crnpy</span> <span class="kn">import</span> <span class="n">crnpy</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Rc</span> <span class="o">=</span> <span class="mf">2.40</span> <span class="c1"># 2.40 Newark, NJ, US</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">crnpy</span><span class="o">.</span><span class="n">find_neutron_monitor</span><span class="p">(</span><span class="n">Rc</span><span class="p">)</span>
+<span class="go">Select a station with an altitude similar to that of your location. For more information go to: &#39;https://www.nmdb.eu/nest/help.php#helpstations</span>
+</code></pre></div>
+    <p>Your cutoff rigidity is 2.4 GV
+        STID                          NAME     R  Altitude_m
+40   NEWK                        Newark  2.40          50
+33   MOSC                        Moscow  2.43         200
+27   KIEL                          Kiel  2.36          54
+28  KIEL2                        KielRT  2.36          54
+31   MCRL  Mobile Cosmic Ray Laboratory  2.46         200
+32   MGDN                       Magadan  2.10         220
+42   NVBK                   Novosibirsk  2.91         163
+26   KGSN                      Kingston  1.88          65
+9    CLMX                        Climax  3.00        3400
+57   YKTK                       Yakutsk  1.65         105</p>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>https://www.nmdb.eu/nest/help.php#helpstations</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1145</span>
+<span class="normal">1146</span>
+<span class="normal">1147</span>
+<span class="normal">1148</span>
+<span class="normal">1149</span>
+<span class="normal">1150</span>
+<span class="normal">1151</span>
+<span class="normal">1152</span>
+<span class="normal">1153</span>
+<span class="normal">1154</span>
+<span class="normal">1155</span>
+<span class="normal">1156</span>
+<span class="normal">1157</span>
+<span class="normal">1158</span>
+<span class="normal">1159</span>
+<span class="normal">1160</span>
+<span class="normal">1161</span>
+<span class="normal">1162</span>
+<span class="normal">1163</span>
+<span class="normal">1164</span>
+<span class="normal">1165</span>
+<span class="normal">1166</span>
+<span class="normal">1167</span>
+<span class="normal">1168</span>
+<span class="normal">1169</span>
+<span class="normal">1170</span>
+<span class="normal">1171</span>
+<span class="normal">1172</span>
+<span class="normal">1173</span>
+<span class="normal">1174</span>
+<span class="normal">1175</span>
+<span class="normal">1176</span>
+<span class="normal">1177</span>
+<span class="normal">1178</span>
+<span class="normal">1179</span>
+<span class="normal">1180</span>
+<span class="normal">1181</span>
+<span class="normal">1182</span>
+<span class="normal">1183</span>
+<span class="normal">1184</span>
+<span class="normal">1185</span>
+<span class="normal">1186</span>
+<span class="normal">1187</span>
+<span class="normal">1188</span>
+<span class="normal">1189</span>
+<span class="normal">1190</span>
+<span class="normal">1191</span>
+<span class="normal">1192</span>
+<span class="normal">1193</span>
+<span class="normal">1194</span>
+<span class="normal">1195</span>
+<span class="normal">1196</span>
+<span class="normal">1197</span>
+<span class="normal">1198</span>
+<span class="normal">1199</span>
+<span class="normal">1200</span>
+<span class="normal">1201</span>
+<span class="normal">1202</span>
+<span class="normal">1203</span>
+<span class="normal">1204</span>
+<span class="normal">1205</span>
+<span class="normal">1206</span>
+<span class="normal">1207</span>
+<span class="normal">1208</span>
+<span class="normal">1209</span>
+<span class="normal">1210</span>
+<span class="normal">1211</span>
+<span class="normal">1212</span>
+<span class="normal">1213</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">find_neutron_monitor</span><span class="p">(</span><span class="n">Rc</span><span class="p">,</span> <span class="n">start_date</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">end_date</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Search for potential reference neutron monitoring stations based on cutoff rigidity.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        Rc (float): Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.</span>
+<span class="sd">        start_date (datetime): Start date for the period of interest.</span>
+<span class="sd">        end_date (datetime): End date for the period of interest.</span>
+<span class="sd">        verbose (bool): If True, print a expanded output of the incoming neutron flux data.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (list): List of top five stations with closes cutoff rigidity.</span>
+<span class="sd">            User needs to select station according to site altitude.</span>
+
+<span class="sd">    Examples:</span>
+<span class="sd">        &gt;&gt;&gt; from crnpy import crnpy</span>
+<span class="sd">        &gt;&gt;&gt; Rc = 2.40 # 2.40 Newark, NJ, US</span>
+<span class="sd">        &gt;&gt;&gt; crnpy.find_neutron_monitor(Rc)</span>
+<span class="sd">        Select a station with an altitude similar to that of your location. For more information go to: &#39;https://www.nmdb.eu/nest/help.php#helpstations</span>
+
+<span class="sd">        Your cutoff rigidity is 2.4 GV</span>
+<span class="sd">                STID                          NAME     R  Altitude_m</span>
+<span class="sd">        40   NEWK                        Newark  2.40          50</span>
+<span class="sd">        33   MOSC                        Moscow  2.43         200</span>
+<span class="sd">        27   KIEL                          Kiel  2.36          54</span>
+<span class="sd">        28  KIEL2                        KielRT  2.36          54</span>
+<span class="sd">        31   MCRL  Mobile Cosmic Ray Laboratory  2.46         200</span>
+<span class="sd">        32   MGDN                       Magadan  2.10         220</span>
+<span class="sd">        42   NVBK                   Novosibirsk  2.91         163</span>
+<span class="sd">        26   KGSN                      Kingston  1.88          65</span>
+<span class="sd">        9    CLMX                        Climax  3.00        3400</span>
+<span class="sd">        57   YKTK                       Yakutsk  1.65         105</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        https://www.nmdb.eu/nest/help.php#helpstations</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Load file with list of neutron monitoring stations</span>
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">neutron_detectors</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;STID&quot;</span><span class="p">,</span> <span class="s2">&quot;NAME&quot;</span><span class="p">,</span> <span class="s2">&quot;R&quot;</span><span class="p">,</span> <span class="s2">&quot;Altitude_m&quot;</span><span class="p">])</span>
+
+    <span class="c1"># Sort stations by closest cutoff rigidity</span>
+    <span class="n">idx_R</span> <span class="o">=</span> <span class="p">(</span><span class="n">stations</span><span class="p">[</span><span class="s1">&#39;R&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">Rc</span><span class="p">)</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">start_date</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">end_date</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">stations</span><span class="p">[</span><span class="s2">&quot;Period available&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
+            <span class="n">station</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">idx_R</span><span class="p">[</span><span class="n">i</span><span class="p">]][</span><span class="s2">&quot;STID&quot;</span><span class="p">]</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">station</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">idx_R</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="k">pass</span>
+
+        <span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">stations</span><span class="p">[</span><span class="s2">&quot;Period available&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="kc">True</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No stations available for the selected period!&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">stations</span> <span class="o">=</span> <span class="n">stations</span><span class="p">[</span><span class="n">stations</span><span class="p">[</span><span class="s2">&quot;Period available&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="kc">True</span><span class="p">]</span>
+            <span class="n">idx_R</span> <span class="o">=</span> <span class="p">(</span><span class="n">stations</span><span class="p">[</span><span class="s1">&#39;R&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">Rc</span><span class="p">)</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">idx_R</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">10</span><span class="p">]]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">idx_R</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">rename_axis</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+
+    <span class="c1"># Print results</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Select a station with an altitude similar to that of your location. For more information go to: &#39;https://www.nmdb.eu/nest/help.php#helpstations&quot;&quot;&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Your cutoff rigidity is </span><span class="si">{</span><span class="n">Rc</span><span class="si">}</span><span class="s2"> GV&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">result</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.get_incoming_neutron_flux" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">station</span><span class="p">,</span> <span class="n">utc_offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">expand_window</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to retrieve neutron flux from the Neutron Monitor Database.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>start_date</code></td>
+          <td>
+                <code>datetime</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Start date of the time series.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>end_date</code></td>
+          <td>
+                <code>datetime</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>End date of the time series.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>station</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Neutron Monitor station to retrieve data from.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>utc_offset</code></td>
+          <td>
+                <code>int</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTC offset in hours. Default is 0.</p>
+            </div>
+          </td>
+          <td>
+                <code>0</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>expand_window</code></td>
+          <td>
+                <code>int</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of hours to expand the time window to retrieve extra data. Default is 0.</p>
+            </div>
+          </td>
+          <td>
+                <code>0</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>verbose</code></td>
+          <td>
+                <code>bool</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Print information about the request. Default is False.</p>
+            </div>
+          </td>
+          <td>
+                <code>False</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Neutron flux in counts per hour and timestamps.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Documentation available:https://www.nmdb.eu/nest/help.php#howto</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">358</span>
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+<span class="normal">441</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">station</span><span class="p">,</span> <span class="n">utc_offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">expand_window</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to retrieve neutron flux from the Neutron Monitor Database.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        start_date (datetime): Start date of the time series.</span>
+<span class="sd">        end_date (datetime): End date of the time series.</span>
+<span class="sd">        station (str): Neutron Monitor station to retrieve data from.</span>
+<span class="sd">        utc_offset (int): UTC offset in hours. Default is 0.</span>
+<span class="sd">        expand_window (int): Number of hours to expand the time window to retrieve extra data. Default is 0.</span>
+<span class="sd">        verbose (bool): Print information about the request. Default is False.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (pandas.DataFrame): Neutron flux in counts per hour and timestamps.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Documentation available:https://www.nmdb.eu/nest/help.php#howto</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Example: get_incoming_neutron_flux(station=&#39;IRKT&#39;,start_date=&#39;2020-04-10 11:00:00&#39;,end_date=&#39;2020-06-18 17:00:00&#39;)</span>
+    <span class="c1"># Template url = &#39;http://nest.nmdb.eu/draw_graph.php?formchk=1&amp;stations[]=KERG&amp;output=ascii&amp;tabchoice=revori&amp;dtype=corr_for_efficiency&amp;date_choice=bydate&amp;start_year=2009&amp;start_month=09&amp;start_day=01&amp;start_hour=00&amp;start_min=00&amp;end_year=2009&amp;end_month=09&amp;end_day=05&amp;end_hour=23&amp;end_min=59&amp;yunits=0&#39;</span>
+
+    <span class="c1"># Expand the time window by 1 hour to ensure an extra observation is included in the request.</span>
+    <span class="n">start_date</span> <span class="o">-=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">expand_window</span><span class="p">)</span>
+    <span class="n">end_date</span> <span class="o">+=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">expand_window</span><span class="p">)</span>
+
+    <span class="c1"># Convert local time to UTC</span>
+    <span class="n">start_date</span> <span class="o">=</span> <span class="n">start_date</span> <span class="o">-</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">utc_offset</span><span class="p">)</span>
+    <span class="n">end_date</span> <span class="o">=</span> <span class="n">end_date</span> <span class="o">-</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">utc_offset</span><span class="p">)</span>
+    <span class="n">root</span> <span class="o">=</span> <span class="s1">&#39;http://www.nmdb.eu/nest/draw_graph.php?&#39;</span>
+    <span class="n">url_par</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;formchk=1&#39;</span><span class="p">,</span>
+               <span class="s1">&#39;stations[]=&#39;</span> <span class="o">+</span> <span class="n">station</span><span class="p">,</span>
+               <span class="s1">&#39;output=ascii&#39;</span><span class="p">,</span>
+               <span class="s1">&#39;tabchoice=revori&#39;</span><span class="p">,</span>
+               <span class="s1">&#39;dtype=corr_for_efficiency&#39;</span><span class="p">,</span>
+               <span class="s1">&#39;tresolution=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="mi">60</span><span class="p">),</span>
+               <span class="s1">&#39;date_choice=bydate&#39;</span><span class="p">,</span>
+               <span class="s1">&#39;start_year=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">year</span><span class="p">),</span>
+               <span class="s1">&#39;start_month=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">month</span><span class="p">),</span>
+               <span class="s1">&#39;start_day=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">day</span><span class="p">),</span>
+               <span class="s1">&#39;start_hour=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">hour</span><span class="p">),</span>
+               <span class="s1">&#39;start_min=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">minute</span><span class="p">),</span>
+               <span class="s1">&#39;end_year=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">year</span><span class="p">),</span>
+               <span class="s1">&#39;end_month=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">month</span><span class="p">),</span>
+               <span class="s1">&#39;end_day=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">day</span><span class="p">),</span>
+               <span class="s1">&#39;end_hour=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">hour</span><span class="p">),</span>
+               <span class="s1">&#39;end_min=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">minute</span><span class="p">),</span>
+               <span class="s1">&#39;yunits=0&#39;</span><span class="p">]</span>
+
+    <span class="n">url</span> <span class="o">=</span> <span class="n">root</span> <span class="o">+</span> <span class="s1">&#39;&amp;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">url_par</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Retrieving data from </span><span class="si">{</span><span class="n">url</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="n">r</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">)</span><span class="o">.</span><span class="n">content</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Subtract 1 hour to restore the last date included in the request.</span>
+    <span class="n">end_date</span> <span class="o">-=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="s1">&#39;1H&#39;</span><span class="p">)</span>
+    <span class="n">start</span> <span class="o">=</span> <span class="n">r</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s2">&quot;RCORR_E</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="mi">8</span>
+    <span class="n">end</span> <span class="o">=</span> <span class="n">r</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&lt;/code&gt;&lt;/pre&gt;&lt;br&gt;Total&#39;</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">s</span> <span class="o">=</span> <span class="n">r</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
+    <span class="n">s2</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">row</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;;&#39;</span><span class="p">,</span> <span class="s1">&#39;,&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">s</span><span class="p">])</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">df_flux</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">io</span><span class="o">.</span><span class="n">StringIO</span><span class="p">(</span><span class="n">s2</span><span class="p">),</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">,</span> <span class="s1">&#39;counts&#39;</span><span class="p">])</span>
+    <span class="k">except</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Error retrieving data from </span><span class="si">{</span><span class="n">url</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="kc">None</span>
+
+    <span class="c1"># Check if all values from selected detector are NaN. If yes, warn the user</span>
+    <span class="k">if</span> <span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;counts&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">all</span><span class="p">():</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Data for selected neutron detectors appears to be unavailable for the selected period&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Convert timestamp to datetime and apply UTC offset</span>
+    <span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">])</span>
+    <span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">utc_offset</span><span class="p">)</span>
+
+    <span class="c1"># Print acknowledgement to inform users about restrictions and to acknowledge the NMDB database</span>
+    <span class="n">acknowledgement</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;Data retrieved via NMDB are the property of the individual data providers. These data are free for non commercial</span>
+<span class="s2">use to within the restriction imposed by the providers. If you use such data for your research or applications, please acknowledge</span>
+<span class="s2">the origin by a sentence like &#39;We acknowledge the NMDB database (www.nmdb.eu) founded under the European Union&#39;s FP7 programme </span>
+<span class="s2">(contract no. 213007), and the PIs of individual neutron monitors at: IGY Jungfraujoch </span>
+<span class="s2">(Physikalisches Institut, University of Bern, Switzerland)&quot;&quot;&quot;</span>
+
+    <span class="k">return</span> <span class="n">df_flux</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.get_reference_neutron_flux" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">get_reference_neutron_flux</span><span class="p">(</span><span class="n">station</span><span class="p">,</span> <span class="n">date</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="s1">&#39;2011-05-01&#39;</span><span class="p">))</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to retrieve reference neutron flux from the Neutron Monitor Database. Default date is 2011-05-01, following previous studies (Zreda et a., 2012, Hawdon et al., 2014, Bogena et al., 2022).</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>station</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Neutron Monitor station to retrieve data from.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>date</code></td>
+          <td>
+                <code>datetime</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Date of the reference neutron flux. Default is 2011-05-01.</p>
+            </div>
+          </td>
+          <td>
+                <code><span title="pandas.to_datetime">to_datetime</span>(&#39;2011-05-01&#39;)</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Reference neutron flux in counts per hour.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., &amp; Rosolem, R. (2012). COSMOS: The cosmic-ray soil moisture observing system. Hydrology and Earth System Sciences, 16(11), 4079-4099.</p>
+<p>Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures for cosmic‐ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.</p>
+<p>Bogena, H. R., Schrön, M., Jakobi, J., Ney, P., Zacharias, S., Andreasen, M., ... &amp; Vereecken, H. (2022). COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors. Earth System Science Data, 14(3), 1125-1151.</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">444</span>
+<span class="normal">445</span>
+<span class="normal">446</span>
+<span class="normal">447</span>
+<span class="normal">448</span>
+<span class="normal">449</span>
+<span class="normal">450</span>
+<span class="normal">451</span>
+<span class="normal">452</span>
+<span class="normal">453</span>
+<span class="normal">454</span>
+<span class="normal">455</span>
+<span class="normal">456</span>
+<span class="normal">457</span>
+<span class="normal">458</span>
+<span class="normal">459</span>
+<span class="normal">460</span>
+<span class="normal">461</span>
+<span class="normal">462</span>
+<span class="normal">463</span>
+<span class="normal">464</span>
+<span class="normal">465</span>
+<span class="normal">466</span>
+<span class="normal">467</span>
+<span class="normal">468</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">get_reference_neutron_flux</span><span class="p">(</span><span class="n">station</span><span class="p">,</span> <span class="n">date</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="s2">&quot;2011-05-01&quot;</span><span class="p">)):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to retrieve reference neutron flux from the Neutron Monitor Database. Default date is 2011-05-01, following previous studies (Zreda et a., 2012, Hawdon et al., 2014, Bogena et al., 2022).</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        station (str): Neutron Monitor station to retrieve data from.</span>
+<span class="sd">        date (datetime): Date of the reference neutron flux. Default is 2011-05-01.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (float): Reference neutron flux in counts per hour.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., &amp; Rosolem, R. (2012). COSMOS: The cosmic-ray soil moisture observing system. Hydrology and Earth System Sciences, 16(11), 4079-4099.</span>
+
+<span class="sd">        Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures for cosmic‐ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.</span>
+
+<span class="sd">        Bogena, H. R., Schrön, M., Jakobi, J., Ney, P., Zacharias, S., Andreasen, M., ... &amp; Vereecken, H. (2022). COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors. Earth System Science Data, 14(3), 1125-1151.</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+
+    <span class="c1"># Get flux for 2011-05-01</span>
+    <span class="n">df_flux</span> <span class="o">=</span> <span class="n">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">station</span><span class="p">,</span> <span class="n">date</span><span class="p">,</span> <span class="n">date</span> <span class="o">+</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="mi">24</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">df_flux</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Reference neutron flux for </span><span class="si">{</span><span class="n">station</span><span class="si">}</span><span class="s2"> not available. Returning NaN.&quot;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;counts&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.idw" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">idw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span><span class="p">,</span> <span class="n">neighborhood</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to interpolate data using inverse distance weight.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>x</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM x coordinates in meters.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>y</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM y coordinates in meters.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>z</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Values to be interpolated.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>X_pred</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM x coordinates where z values need to be predicted.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>Y_pred</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM y coordinates where z values need to be predicted.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>neighborhood</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Only points within this radius in meters are considered for the interpolation.</p>
+            </div>
+          </td>
+          <td>
+                <code>1000</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>p</code></td>
+          <td>
+                <code>int</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Exponent of the inverse distance weight formula. Typically, p=1 or p=2.</p>
+            </div>
+          </td>
+          <td>
+                <code>1</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Interpolated values.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p><a href="https://en.wikipedia.org/wiki/Inverse_distance_weighting">https://en.wikipedia.org/wiki/Inverse_distance_weighting</a></p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1373</span>
+<span class="normal">1374</span>
+<span class="normal">1375</span>
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+<span class="normal">1404</span>
+<span class="normal">1405</span>
+<span class="normal">1406</span>
+<span class="normal">1407</span>
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+<span class="normal">1409</span>
+<span class="normal">1410</span>
+<span class="normal">1411</span>
+<span class="normal">1412</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">idw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span><span class="p">,</span> <span class="n">neighborhood</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to interpolate data using inverse distance weight.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        x (list or array): UTM x coordinates in meters.</span>
+<span class="sd">        y (list or array): UTM y coordinates in meters.</span>
+<span class="sd">        z (list or array): Values to be interpolated.</span>
+<span class="sd">        X_pred (list or array): UTM x coordinates where z values need to be predicted.</span>
+<span class="sd">        Y_pred (list or array): UTM y coordinates where z values need to be predicted.</span>
+<span class="sd">        neighborhood (float): Only points within this radius in meters are considered for the interpolation.</span>
+<span class="sd">        p (int): Exponent of the inverse distance weight formula. Typically, p=1 or p=2.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (array): Interpolated values.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        [https://en.wikipedia.org/wiki/Inverse_distance_weighting](https://en.wikipedia.org/wiki/Inverse_distance_weighting)</span>
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Flatten arrays to handle 1D and 2D arrays with the same code</span>
+    <span class="n">s</span> <span class="o">=</span> <span class="n">X_pred</span><span class="o">.</span><span class="n">shape</span>  <span class="c1"># Save shape</span>
+    <span class="n">X_pred</span> <span class="o">=</span> <span class="n">X_pred</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+    <span class="n">Y_pred</span> <span class="o">=</span> <span class="n">Y_pred</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+
+    <span class="c1"># Pre-allocate output array</span>
+    <span class="n">Z_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full_like</span><span class="p">(</span><span class="n">X_pred</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">X_pred</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
+        <span class="c1"># Distance between current and observed points</span>
+        <span class="n">d</span> <span class="o">=</span> <span class="n">euclidean_distance</span><span class="p">(</span><span class="n">X_pred</span><span class="p">[</span><span class="n">n</span><span class="p">],</span> <span class="n">Y_pred</span><span class="p">[</span><span class="n">n</span><span class="p">],</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+        <span class="c1"># Select points within neighborhood only for interpolation</span>
+        <span class="n">idx_neighbors</span> <span class="o">=</span> <span class="n">d</span> <span class="o">&lt;</span> <span class="n">neighborhood</span>
+
+        <span class="c1"># Compute interpolated value at point of interest</span>
+        <span class="n">Z_pred</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">z</span><span class="p">[</span><span class="n">idx_neighbors</span><span class="p">]</span> <span class="o">/</span> <span class="n">d</span><span class="p">[</span><span class="n">idx_neighbors</span><span class="p">]</span> <span class="o">**</span> <span class="n">p</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">d</span><span class="p">[</span><span class="n">idx_neighbors</span><span class="p">]</span> <span class="o">**</span> <span class="n">p</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">Z_pred</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">interpolate_2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">,</span> <span class="n">neighborhood</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function for interpolating irregular spatial data into a regular grid.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>x</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM x coordinates in meters.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>y</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM y coordinates in meters.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>z</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Values to be interpolated.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>dx</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Pixel width in meters.</p>
+            </div>
+          </td>
+          <td>
+                <code>100</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>dy</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Pixel height in meters.</p>
+            </div>
+          </td>
+          <td>
+                <code>100</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>method</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.</p>
+            </div>
+          </td>
+          <td>
+                <code>&#39;cubic&#39;</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>neighborhood</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Only points within this radius in meters are considered for the interpolation.</p>
+            </div>
+          </td>
+          <td>
+                <code>1000</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+<th>Name</th>        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+<td><code>x_pred</code></td>          <td>
+                <code>array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>2D array with x coordinates.</p>
+            </div>
+          </td>
+        </tr>
+        <tr>
+<td><code>y_pred</code></td>          <td>
+                <code>array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>2D array with y coordinates.</p>
+            </div>
+          </td>
+        </tr>
+        <tr>
+<td><code>z_pred</code></td>          <td>
+                <code>array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>2D array with interpolated values.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p><a href="https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html">https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html</a></p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1415</span>
+<span class="normal">1416</span>
+<span class="normal">1417</span>
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+<span class="normal">1460</span>
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+<span class="normal">1462</span>
+<span class="normal">1463</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">interpolate_2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">,</span> <span class="n">neighborhood</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function for interpolating irregular spatial data into a regular grid.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        x (list or array): UTM x coordinates in meters.</span>
+<span class="sd">        y (list or array): UTM y coordinates in meters.</span>
+<span class="sd">        z (list or array): Values to be interpolated.</span>
+<span class="sd">        dx (float): Pixel width in meters.</span>
+<span class="sd">        dy (float): Pixel height in meters.</span>
+<span class="sd">        method (str): Interpolation method. One of &#39;cubic&#39;, &#39;linear&#39;, &#39;nearest&#39;, or &#39;idw&#39;.</span>
+<span class="sd">        neighborhood (float): Only points within this radius in meters are considered for the interpolation.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        x_pred (array): 2D array with x coordinates.</span>
+<span class="sd">        y_pred (array): 2D array with y coordinates.</span>
+<span class="sd">        z_pred (array): 2D array with interpolated values.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        [https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html](https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Drop NaN values in x y and z</span>
+    <span class="n">idx_nan</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="o">~</span><span class="n">idx_nan</span><span class="p">]</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="o">~</span><span class="n">idx_nan</span><span class="p">]</span>
+    <span class="n">z</span> <span class="o">=</span> <span class="n">z</span><span class="p">[</span><span class="o">~</span><span class="n">idx_nan</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="n">idx_nan</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
+        <span class="nb">print</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;WARNING: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2">, and </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">z</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2"> NaN values were dropped from x, y, and z.&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Create 2D grid for interpolation</span>
+    <span class="n">Nx</span> <span class="o">=</span> <span class="nb">round</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">/</span> <span class="n">dx</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
+    <span class="n">Ny</span> <span class="o">=</span> <span class="nb">round</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">y</span><span class="p">))</span> <span class="o">/</span> <span class="n">dy</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
+    <span class="n">X_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">Nx</span><span class="p">)</span>
+    <span class="n">Y_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">y</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">y</span><span class="p">),</span> <span class="n">Ny</span><span class="p">)</span>
+    <span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">X_vec</span><span class="p">,</span> <span class="n">Y_vec</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">method</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="s1">&#39;nearest&#39;</span><span class="p">,</span> <span class="s1">&#39;cubic&#39;</span><span class="p">]:</span>
+        <span class="n">points</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">))</span>
+        <span class="n">Z_pred</span> <span class="o">=</span> <span class="n">griddata</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="p">(</span><span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span><span class="p">),</span> <span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;idw&#39;</span><span class="p">:</span>
+        <span class="n">Z_pred</span> <span class="o">=</span> <span class="n">idw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span><span class="p">,</span> <span class="n">neighborhood</span><span class="p">)</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="sa">f</span><span class="s2">&quot;Method </span><span class="si">{</span><span class="n">method</span><span class="si">}</span><span class="s2"> does not exist. Provide either &#39;cubic&#39;, &#39;linear&#39;, &#39;nearest&#39;, or &#39;idw&#39;.&quot;</span>
+
+    <span class="k">return</span> <span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span><span class="p">,</span> <span class="n">Z_pred</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.interpolate_incoming_flux" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">interpolate_incoming_flux</span><span class="p">(</span><span class="n">nmdb_timestamps</span><span class="p">,</span> <span class="n">nmdb_counts</span><span class="p">,</span> <span class="n">crnp_timestamps</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to interpolate incoming neutron flux to match the timestamps of the observations.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>nmdb_timestamps</code></td>
+          <td>
+                <code><span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Series of timestamps in datetime format from the NMDB.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>nmdb_counts</code></td>
+          <td>
+                <code><span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Series of neutron counts from the NMDB</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>crnp_timestamps</code></td>
+          <td>
+                <code><span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Series of timestamps in datetime format from the station or device.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code><span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1216</span>
+<span class="normal">1217</span>
+<span class="normal">1218</span>
+<span class="normal">1219</span>
+<span class="normal">1220</span>
+<span class="normal">1221</span>
+<span class="normal">1222</span>
+<span class="normal">1223</span>
+<span class="normal">1224</span>
+<span class="normal">1225</span>
+<span class="normal">1226</span>
+<span class="normal">1227</span>
+<span class="normal">1228</span>
+<span class="normal">1229</span>
+<span class="normal">1230</span>
+<span class="normal">1231</span>
+<span class="normal">1232</span>
+<span class="normal">1233</span>
+<span class="normal">1234</span>
+<span class="normal">1235</span>
+<span class="normal">1236</span>
+<span class="normal">1237</span>
+<span class="normal">1238</span>
+<span class="normal">1239</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">interpolate_incoming_flux</span><span class="p">(</span><span class="n">nmdb_timestamps</span><span class="p">,</span> <span class="n">nmdb_counts</span><span class="p">,</span> <span class="n">crnp_timestamps</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to interpolate incoming neutron flux to match the timestamps of the observations.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        nmdb_timestamps (pd.Series): Series of timestamps in datetime format from the NMDB.</span>
+<span class="sd">        nmdb_counts (pd.Series): Series of neutron counts from the NMDB</span>
+<span class="sd">        crnp_timestamps (pd.Series): Series of timestamps in datetime format from the station or device.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (pd.Series): Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">incoming_flux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">timestamp</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">crnp_timestamps</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">timestamp</span> <span class="ow">in</span> <span class="n">nmdb_timestamps</span><span class="o">.</span><span class="n">values</span><span class="p">:</span>
+            <span class="n">idx</span> <span class="o">=</span> <span class="n">timestamp</span> <span class="o">==</span> <span class="n">nmdb_timestamps</span>
+            <span class="n">incoming_flux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">incoming_flux</span><span class="p">,</span> <span class="n">nmdb_counts</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">incoming_flux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">incoming_flux</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+
+    <span class="c1"># Interpolate nan values</span>
+    <span class="n">incoming_flux</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">incoming_flux</span><span class="p">)</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">,</span> <span class="n">limit_direction</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Return only the values for the selected timestamps</span>
+    <span class="k">return</span> <span class="n">incoming_flux</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.is_outlier" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">is_outlier</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">min_val</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_val</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>x</code></td>
+          <td>
+                <code><span title="pandas.DataFrame">DataFrame</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Variable containing only the columns with neutron counts.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>method</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>window</code></td>
+          <td>
+                <code>int</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Window size for the moving central tendency. Default is 11.</p>
+            </div>
+          </td>
+          <td>
+                <code>11</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>min_val</code></td>
+          <td>
+                <code>int or float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Minimum value for a reading to be considered valid. Default is None.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>max_val(int</code></td>
+          <td>
+                <code>or float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Maximum value for a reading to be considered valid. Default is None.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Boolean indicating outliers.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">132</span>
+<span class="normal">133</span>
+<span class="normal">134</span>
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+<span class="normal">165</span>
+<span class="normal">166</span>
+<span class="normal">167</span>
+<span class="normal">168</span>
+<span class="normal">169</span>
+<span class="normal">170</span>
+<span class="normal">171</span>
+<span class="normal">172</span>
+<span class="normal">173</span>
+<span class="normal">174</span>
+<span class="normal">175</span>
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+<span class="normal">179</span>
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+<span class="normal">198</span>
+<span class="normal">199</span>
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+<span class="normal">203</span>
+<span class="normal">204</span>
+<span class="normal">205</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">is_outlier</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">min_val</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_val</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        x (pd.DataFrame or pd.Series): Variable containing only the columns with neutron counts.</span>
+<span class="sd">        method (str): Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad</span>
+<span class="sd">        window (int, optional): Window size for the moving central tendency. Default is 11.</span>
+<span class="sd">        min_val (int or float): Minimum value for a reading to be considered valid. Default is None.</span>
+<span class="sd">        max_val(int or float): Maximum value for a reading to be considered valid. Default is None.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (pandas.DataFrame): Boolean indicating outliers.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;x must of type pandas.Series&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Separate this method to allow usage together with other methods below</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">min_val</span><span class="p">,</span> <span class="n">numbers</span><span class="o">.</span><span class="n">Number</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">max_val</span><span class="p">,</span> <span class="n">numbers</span><span class="o">.</span><span class="n">Number</span><span class="p">):</span>
+        <span class="n">idx_range_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">&lt;</span> <span class="n">min_val</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">x</span> <span class="o">&gt;</span> <span class="n">max_val</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">idx_range_outliers</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full_like</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+    <span class="c1"># Apply other methods in addition to a range check</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;iqr&#39;</span><span class="p">:</span>
+        <span class="n">q1</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span>
+        <span class="n">q3</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span>
+        <span class="n">iqr</span> <span class="o">=</span> <span class="n">q3</span> <span class="o">-</span> <span class="n">q1</span>
+        <span class="n">high_fence</span> <span class="o">=</span> <span class="n">q3</span> <span class="o">+</span> <span class="p">(</span><span class="mf">1.5</span> <span class="o">*</span> <span class="n">iqr</span><span class="p">)</span>
+        <span class="n">low_fence</span> <span class="o">=</span> <span class="n">q1</span> <span class="o">-</span> <span class="p">(</span><span class="mf">1.5</span> <span class="o">*</span> <span class="n">iqr</span><span class="p">)</span>
+        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">&lt;</span> <span class="n">low_fence</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">x</span> <span class="o">&gt;</span> <span class="n">high_fence</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;moviqr&#39;</span><span class="p">:</span>
+        <span class="n">q1</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span>
+        <span class="n">q3</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span>
+        <span class="n">iqr</span> <span class="o">=</span> <span class="n">q3</span> <span class="o">-</span> <span class="n">q1</span>
+        <span class="n">ub</span> <span class="o">=</span> <span class="n">q3</span> <span class="o">+</span> <span class="p">(</span><span class="mf">1.5</span> <span class="o">*</span> <span class="n">iqr</span><span class="p">)</span>  <span class="c1"># Upper boundary</span>
+        <span class="n">lb</span> <span class="o">=</span> <span class="n">q1</span> <span class="o">-</span> <span class="p">(</span><span class="mf">1.5</span> <span class="o">*</span> <span class="n">iqr</span><span class="p">)</span>  <span class="c1"># Lower boundary</span>
+        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">&lt;</span> <span class="n">lb</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">x</span> <span class="o">&gt;</span> <span class="n">ub</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;zscore&#39;</span><span class="p">:</span>
+        <span class="n">zscore</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span> <span class="o">/</span> <span class="n">x</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
+        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">zscore</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mi">3</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">zscore</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;movzscore&#39;</span><span class="p">:</span>
+        <span class="n">movmean</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+        <span class="n">movstd</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
+        <span class="n">movzscore</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">movmean</span><span class="p">)</span> <span class="o">/</span> <span class="n">movstd</span>
+        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">movzscore</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mi">3</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">movzscore</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;modified_zscore&#39;</span><span class="p">:</span>
+        <span class="c1"># Compute median absolute difference</span>
+        <span class="n">movmedian</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
+        <span class="n">abs_diff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">movmedian</span><span class="p">)</span>
+        <span class="n">mad</span> <span class="o">=</span> <span class="n">abs_diff</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
+
+        <span class="c1"># Compute modified z-score</span>
+        <span class="n">modified_z_score</span> <span class="o">=</span> <span class="mf">0.6745</span> <span class="o">*</span> <span class="n">abs_diff</span> <span class="o">/</span> <span class="n">mad</span>
+        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">modified_z_score</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mf">3.5</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">modified_z_score</span> <span class="o">&gt;</span> <span class="mf">3.5</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;scaled_mad&#39;</span><span class="p">:</span>
+        <span class="c1"># Returns true for elements more than three scaled MAD from the median. </span>
+        <span class="n">c</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">erfcinv</span><span class="p">(</span><span class="mi">3</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">median</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">mad</span> <span class="o">=</span> <span class="n">c</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">median</span><span class="p">))</span>
+        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="n">x</span> <span class="o">&gt;</span> <span class="p">(</span><span class="n">median</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">mad</span><span class="p">)</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Outlier detection method not found.&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">idx_outliers</span> <span class="o">|</span> <span class="n">idx_range_outliers</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.latlon_to_utm" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">latlon_to_utm</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">,</span> <span class="n">utm_zone_number</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">utm_zone_letter</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.</p>
+<p>Function only applies to non-polar coordinates.
+If further functionality is required, consider using the utm module. See references for more information.</p>
+<p><a class="glightbox" href="https://upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Universal_Transverse_Mercator_zones.svg/1920px-Universal_Transverse_Mercator_zones.svg.png" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="UTM zones" src="https://upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Universal_Transverse_Mercator_zones.svg/1920px-Universal_Transverse_Mercator_zones.svg.png" /></a>
+UTM zones on an equirectangular world map with irregular zones in red and New York City's zone highlighted. See <a href="https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#UTM_zones">UTM zones</a> for a full description.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>lat</code></td>
+          <td>
+                <code>(float, array)</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Latitude in decimal degrees.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>lon</code></td>
+          <td>
+                <code>(float, array)</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Longitude in decimal degrees.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>utm_zone_number</code></td>
+          <td>
+                <code>int</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM zone number. If None, the zone number is automatically calculated.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>utm_zone_letter</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM zone letter. If None, the zone letter is automatically calculated.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>(float, float)</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Tuple of easting and northing coordinates in meters. First element is easting, second is northing.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87)
+<a href="https://github.com/Turbo87/utm">https://github.com/Turbo87/utm</a></p>
+<p><a href="https://www.maptools.com/tutorials/grid_zone_details#">https://www.maptools.com/tutorials/grid_zone_details#</a></p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1265</span>
+<span class="normal">1266</span>
+<span class="normal">1267</span>
+<span class="normal">1268</span>
+<span class="normal">1269</span>
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+<span class="normal">1273</span>
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+<span class="normal">1276</span>
+<span class="normal">1277</span>
+<span class="normal">1278</span>
+<span class="normal">1279</span>
+<span class="normal">1280</span>
+<span class="normal">1281</span>
+<span class="normal">1282</span>
+<span class="normal">1283</span>
+<span class="normal">1284</span>
+<span class="normal">1285</span>
+<span class="normal">1286</span>
+<span class="normal">1287</span>
+<span class="normal">1288</span>
+<span class="normal">1289</span>
+<span class="normal">1290</span>
+<span class="normal">1291</span>
+<span class="normal">1292</span>
+<span class="normal">1293</span>
+<span class="normal">1294</span>
+<span class="normal">1295</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">latlon_to_utm</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">,</span> <span class="n">utm_zone_number</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">utm_zone_letter</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.</span>
+
+<span class="sd">    Function only applies to non-polar coordinates.</span>
+<span class="sd">    If further functionality is required, consider using the utm module. See references for more information.</span>
+
+<span class="sd">    ![UTM zones](https://upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Universal_Transverse_Mercator_zones.svg/1920px-Universal_Transverse_Mercator_zones.svg.png)</span>
+<span class="sd">    UTM zones on an equirectangular world map with irregular zones in red and New York City&#39;s zone highlighted. See [UTM zones](https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#UTM_zones) for a full description.</span>
+
+
+<span class="sd">    Args:</span>
+<span class="sd">        lat (float, array): Latitude in decimal degrees.</span>
+<span class="sd">        lon (float, array): Longitude in decimal degrees.</span>
+<span class="sd">        utm_zone_number (int): UTM zone number. If None, the zone number is automatically calculated.</span>
+<span class="sd">        utm_zone_letter (str): UTM zone letter. If None, the zone letter is automatically calculated.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (float, float): Tuple of easting and northing coordinates in meters. First element is easting, second is northing.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">         Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87)</span>
+<span class="sd">         [https://github.com/Turbo87/utm](https://github.com/Turbo87/utm)</span>
+
+<span class="sd">         [https://www.maptools.com/tutorials/grid_zone_details#](https://www.maptools.com/tutorials/grid_zone_details#)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">utm_zone_number</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">utm_zone_letter</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">easting</span><span class="p">,</span> <span class="n">northing</span><span class="p">,</span> <span class="n">zone_number</span><span class="p">,</span> <span class="n">zone_letter</span> <span class="o">=</span> <span class="n">utm</span><span class="o">.</span><span class="n">from_latlon</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">easting</span><span class="p">,</span> <span class="n">northing</span><span class="p">,</span> <span class="n">zone_number</span><span class="p">,</span> <span class="n">zone_letter</span> <span class="o">=</span> <span class="n">utm</span><span class="o">.</span><span class="n">from_latlon</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">,</span> <span class="n">utm_zone_number</span><span class="p">,</span> <span class="n">utm_zone_letter</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">easting</span><span class="p">,</span> <span class="n">northing</span><span class="p">,</span> <span class="n">zone_number</span><span class="p">,</span> <span class="n">zone_letter</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.lattice_water" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">lattice_water</span><span class="p">(</span><span class="n">clay_content</span><span class="p">,</span> <span class="n">total_carbon</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Estimate the amount of water in the lattice of clay minerals.</p>
+<table>
+<thead>
+<tr>
+<th style="text-align: center;"><a class="glightbox" href="../img/lattice_water_simple.png" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="img1" src="../img/lattice_water_simple.png" /></a></th>
+<th style="text-align: center;"><a class="glightbox" href="../img/lattice_water_multiple.png" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="img2" src="../img/lattice_water_multiple.png" /></a></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align: center;">$\omega_{lat} = 0.097 * clay(\%)$</td>
+<td style="text-align: center;">$\omega_{lat} = -0.028 + 0.077 * clay(\%) + 0.459 * carbon(\%)$</td>
+</tr>
+<tr>
+<td style="text-align: center;">Linear regression [lattice water (%) as a function of clay (%)] done with data from Kansas Sate University - Soil Water Processes Lab.</td>
+<td style="text-align: center;">Multiple linear regression [lattice water (%) as a function of clay (%) and soil carbon (%)] done with data from Soil Water Processes Lab.</td>
+</tr>
+</tbody>
+</table>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>clay_content</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Clay content in the soil in percent.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>total_carbon</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Total carbon content in the soil in percent.
+If None, the amount of water is estimated based on clay content only.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Amount of water in the lattice of clay minerals in percent</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1242</span>
+<span class="normal">1243</span>
+<span class="normal">1244</span>
+<span class="normal">1245</span>
+<span class="normal">1246</span>
+<span class="normal">1247</span>
+<span class="normal">1248</span>
+<span class="normal">1249</span>
+<span class="normal">1250</span>
+<span class="normal">1251</span>
+<span class="normal">1252</span>
+<span class="normal">1253</span>
+<span class="normal">1254</span>
+<span class="normal">1255</span>
+<span class="normal">1256</span>
+<span class="normal">1257</span>
+<span class="normal">1258</span>
+<span class="normal">1259</span>
+<span class="normal">1260</span>
+<span class="normal">1261</span>
+<span class="normal">1262</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">lattice_water</span><span class="p">(</span><span class="n">clay_content</span><span class="p">,</span> <span class="n">total_carbon</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Estimate the amount of water in the lattice of clay minerals.</span>
+
+<span class="sd">    ![img1](img/lattice_water_simple.png) | ![img2](img/lattice_water_multiple.png)</span>
+<span class="sd">    :-------------------------:|:-------------------------:</span>
+<span class="sd">    $\omega_{lat} = 0.097 * clay(\%)$ | $\omega_{lat} = -0.028 + 0.077 * clay(\%) + 0.459 * carbon(\%)$</span>
+<span class="sd">    Linear regression [lattice water (%) as a function of clay (%)] done with data from Kansas Sate University - Soil Water Processes Lab. |  Multiple linear regression [lattice water (%) as a function of clay (%) and soil carbon (%)] done with data from Soil Water Processes Lab.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        clay_content (float): Clay content in the soil in percent.</span>
+<span class="sd">        total_carbon (float, optional): Total carbon content in the soil in percent.</span>
+<span class="sd">            If None, the amount of water is estimated based on clay content only.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (float): Amount of water in the lattice of clay minerals in percent</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">total_carbon</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">lattice_water</span> <span class="o">=</span> <span class="mf">0.097</span> <span class="o">*</span> <span class="n">clay_content</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">lattice_water</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.028</span> <span class="o">+</span> <span class="mf">0.077</span> <span class="o">*</span> <span class="n">clay_content</span> <span class="o">+</span> <span class="mf">0.459</span> <span class="o">*</span> <span class="n">total_carbon</span>
+    <span class="k">return</span> <span class="n">lattice_water</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.location_factor" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">location_factor</span><span class="p">(</span><span class="n">site_atmospheric_depth</span><span class="p">,</span> <span class="n">site_Rc</span><span class="p">,</span> <span class="n">reference_atmospheric_depth</span><span class="p">,</span> <span class="n">reference_Rc</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>site_atmospheric_depth</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Atmospheric depth at the site in g/cm2. Can be estimated using the function <code>atmospheric_depth()</code></p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>site_Rc</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Cutoff rigidity at the site in GV. Can be estimated using the function <code>cutoff_rigidity()</code></p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>reference_atmospheric_depth</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Atmospheric depth at the reference location in g/cm2.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>reference_Rc</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Cutoff rigidity at the reference location in GV.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Location-dependent correction factor.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1104</span>
+<span class="normal">1105</span>
+<span class="normal">1106</span>
+<span class="normal">1107</span>
+<span class="normal">1108</span>
+<span class="normal">1109</span>
+<span class="normal">1110</span>
+<span class="normal">1111</span>
+<span class="normal">1112</span>
+<span class="normal">1113</span>
+<span class="normal">1114</span>
+<span class="normal">1115</span>
+<span class="normal">1116</span>
+<span class="normal">1117</span>
+<span class="normal">1118</span>
+<span class="normal">1119</span>
+<span class="normal">1120</span>
+<span class="normal">1121</span>
+<span class="normal">1122</span>
+<span class="normal">1123</span>
+<span class="normal">1124</span>
+<span class="normal">1125</span>
+<span class="normal">1126</span>
+<span class="normal">1127</span>
+<span class="normal">1128</span>
+<span class="normal">1129</span>
+<span class="normal">1130</span>
+<span class="normal">1131</span>
+<span class="normal">1132</span>
+<span class="normal">1133</span>
+<span class="normal">1134</span>
+<span class="normal">1135</span>
+<span class="normal">1136</span>
+<span class="normal">1137</span>
+<span class="normal">1138</span>
+<span class="normal">1139</span>
+<span class="normal">1140</span>
+<span class="normal">1141</span>
+<span class="normal">1142</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">location_factor</span><span class="p">(</span><span class="n">site_atmospheric_depth</span><span class="p">,</span> <span class="n">site_Rc</span><span class="p">,</span> <span class="n">reference_atmospheric_depth</span><span class="p">,</span> <span class="n">reference_Rc</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.</span>
+
+
+<span class="sd">    Args:</span>
+<span class="sd">        site_atmospheric_depth (float): Atmospheric depth at the site in g/cm2. Can be estimated using the function `atmospheric_depth()`</span>
+<span class="sd">        site_Rc (float): Cutoff rigidity at the site in GV. Can be estimated using the function `cutoff_rigidity()`</span>
+<span class="sd">        reference_atmospheric_depth (float): Atmospheric depth at the reference location in g/cm2.</span>
+<span class="sd">        reference_Rc (float): Cutoff rigidity at the reference location in GV.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (float): Location-dependent correction factor.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Renamed variables based on the provided table</span>
+    <span class="n">c0</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.0009</span>  <span class="c1"># from C39</span>
+    <span class="n">c1</span> <span class="o">=</span> <span class="mf">1.7699</span>  <span class="c1"># from C40</span>
+    <span class="n">c2</span> <span class="o">=</span> <span class="mf">0.0064</span>  <span class="c1"># from C41</span>
+    <span class="n">c3</span> <span class="o">=</span> <span class="mf">1.8855</span>  <span class="c1"># from C42</span>
+    <span class="n">c4</span> <span class="o">=</span> <span class="mf">0.000013</span>  <span class="c1"># from C43</span>
+    <span class="n">c5</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.2237</span>  <span class="c1"># from C44</span>
+    <span class="n">epsilon</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># from C45</span>
+
+    <span class="c1"># Translated formula with renamed variables from McJannet and Desilets, 2023</span>
+    <span class="n">tau_new</span> <span class="o">=</span> <span class="n">epsilon</span> <span class="o">*</span> <span class="p">(</span><span class="n">c0</span> <span class="o">*</span> <span class="n">reference_atmospheric_depth</span> <span class="o">+</span> <span class="n">c1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span>
+            <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span>
+        <span class="o">-</span><span class="p">(</span><span class="n">c2</span> <span class="o">*</span> <span class="n">reference_atmospheric_depth</span> <span class="o">+</span> <span class="n">c3</span><span class="p">)</span> <span class="o">*</span> <span class="n">reference_Rc</span> <span class="o">**</span> <span class="p">(</span><span class="n">c4</span> <span class="o">*</span> <span class="n">reference_atmospheric_depth</span> <span class="o">+</span> <span class="n">c5</span><span class="p">)))</span>
+
+    <span class="n">norm_factor</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">tau_new</span>
+
+    <span class="c1"># Calculate the result using the provided parameters</span>
+    <span class="n">tau</span> <span class="o">=</span> <span class="n">epsilon</span> <span class="o">*</span> <span class="n">norm_factor</span> <span class="o">*</span> <span class="p">(</span><span class="n">c0</span> <span class="o">*</span> <span class="n">site_atmospheric_depth</span> <span class="o">+</span> <span class="n">c1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span>
+                <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">c2</span> <span class="o">*</span> <span class="n">site_atmospheric_depth</span> <span class="o">+</span> <span class="n">c3</span><span class="p">)</span> <span class="o">*</span> <span class="n">site_Rc</span> <span class="o">**</span> <span class="p">(</span><span class="n">c4</span> <span class="o">*</span> <span class="n">site_atmospheric_depth</span> <span class="o">+</span> <span class="n">c5</span><span class="p">)))</span>
+    <span class="k">return</span> <span class="n">tau</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.nrad_weight" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">nrad_weight</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">distances</span><span class="p">,</span> <span class="n">depth</span><span class="p">,</span> <span class="n">rhob</span><span class="o">=</span><span class="mf">1.4</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Hveg</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to compute distance weights corresponding to each soil sample.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>h</code></td>
+          <td>
+                <code><span title="numpy.array">array</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>theta</code></td>
+          <td>
+                <code><span title="numpy.array">array</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>distances</code></td>
+          <td>
+                <code><span title="numpy.array">array</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Distances from the location of each sample to the origin (0.5 - 600 m)</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>depth</code></td>
+          <td>
+                <code><span title="numpy.array">array</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Depths for each sample (m)</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>rhob</code></td>
+          <td>
+                <code><span title="numpy.array">array</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Bulk density in g/cm^3</p>
+            </div>
+          </td>
+          <td>
+                <code>1.4</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>p</code></td>
+          <td>
+                <code><span title="numpy.array">array</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Atmospheric pressure in hPa. Required for the 'Schron_2017' method.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>Hveg</code></td>
+          <td>
+                <code><span title="numpy.array">array</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Vegetation height in m. Required for the 'Schron_2017' method.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>method</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Distance weights for each sample.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015).
+Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray
+neutrons. Water Resour. Res. 51, 5772–5790. doi:10.1002/2015WR017169</p>
+<p>Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L.,
+Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M., Rebmann, C.,
+Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.: Improving calibration and
+validation of cosmic-ray neutron sensors in the light of spatial sensitivity,
+Hydrol. Earth Syst. Sci., 21, 5009–5030, https://doi.org/10.5194/hess-21-5009-2017, 2017.</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">697</span>
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+<span class="normal">957</span>
+<span class="normal">958</span>
+<span class="normal">959</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">nrad_weight</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">distances</span><span class="p">,</span> <span class="n">depth</span><span class="p">,</span> <span class="n">rhob</span><span class="o">=</span><span class="mf">1.4</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Hveg</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to compute distance weights corresponding to each soil sample.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        h (np.array or pd.Series): Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.</span>
+<span class="sd">        theta (np.array or pd.Series): Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)</span>
+<span class="sd">        distances (np.array or pd.Series): Distances from the location of each sample to the origin (0.5 - 600 m)</span>
+<span class="sd">        depth (np.array or pd.Series): Depths for each sample (m)</span>
+<span class="sd">        rhob (np.array or pd.Series): Bulk density in g/cm^3</span>
+<span class="sd">        p (np.array or pd.Series): Atmospheric pressure in hPa. Required for the &#39;Schron_2017&#39; method.</span>
+<span class="sd">        Hveg (np.array or pd.Series): Vegetation height in m. Required for the &#39;Schron_2017&#39; method.</span>
+<span class="sd">        method (str): Method to compute the distance weights. Options are &#39;Kohli_2015&#39; or &#39;Schron_2017&#39;.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (array or pd.Series or pd.DataFrame): Distance weights for each sample.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015).</span>
+<span class="sd">        Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray</span>
+<span class="sd">        neutrons. Water Resour. Res. 51, 5772–5790. doi:10.1002/2015WR017169</span>
+
+<span class="sd">        Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L.,</span>
+<span class="sd">        Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M., Rebmann, C.,</span>
+<span class="sd">        Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.: Improving calibration and</span>
+<span class="sd">        validation of cosmic-ray neutron sensors in the light of spatial sensitivity,</span>
+<span class="sd">        Hydrol. Earth Syst. Sci., 21, 5009–5030, https://doi.org/10.5194/hess-21-5009-2017, 2017.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;Kohli_2015&#39;</span><span class="p">:</span>
+
+        <span class="c1"># Table A1. Parameters for Fi and D86</span>
+        <span class="n">p10</span> <span class="o">=</span> <span class="mi">8735</span><span class="p">;</span>
+        <span class="n">p11</span> <span class="o">=</span> <span class="mf">17.1758</span><span class="p">;</span>
+        <span class="n">p12</span> <span class="o">=</span> <span class="mi">11720</span><span class="p">;</span>
+        <span class="n">p13</span> <span class="o">=</span> <span class="mf">0.00978</span><span class="p">;</span>
+        <span class="n">p14</span> <span class="o">=</span> <span class="mi">7045</span><span class="p">;</span>
+        <span class="n">p15</span> <span class="o">=</span> <span class="mf">0.003632</span><span class="p">;</span>
+        <span class="n">p20</span> <span class="o">=</span> <span class="mf">2.7925e-2</span><span class="p">;</span>
+        <span class="n">p21</span> <span class="o">=</span> <span class="mf">5.0399</span><span class="p">;</span>
+        <span class="n">p22</span> <span class="o">=</span> <span class="mf">2.8544e-2</span><span class="p">;</span>
+        <span class="n">p23</span> <span class="o">=</span> <span class="mf">0.002455</span><span class="p">;</span>
+        <span class="n">p24</span> <span class="o">=</span> <span class="mf">6.851e-5</span><span class="p">;</span>
+        <span class="n">p25</span> <span class="o">=</span> <span class="mf">9.2926</span><span class="p">;</span>
+        <span class="n">p30</span> <span class="o">=</span> <span class="mi">247970</span><span class="p">;</span>
+        <span class="n">p31</span> <span class="o">=</span> <span class="mf">17.63</span><span class="p">;</span>
+        <span class="n">p32</span> <span class="o">=</span> <span class="mi">374655</span><span class="p">;</span>
+        <span class="n">p33</span> <span class="o">=</span> <span class="mf">0.00191</span><span class="p">;</span>
+        <span class="n">p34</span> <span class="o">=</span> <span class="mi">195725</span><span class="p">;</span>
+        <span class="n">p40</span> <span class="o">=</span> <span class="mf">5.4818e-2</span><span class="p">;</span>
+        <span class="n">p41</span> <span class="o">=</span> <span class="mf">15.921</span><span class="p">;</span>
+        <span class="n">p42</span> <span class="o">=</span> <span class="mf">0.6373</span><span class="p">;</span>
+        <span class="n">p43</span> <span class="o">=</span> <span class="mf">5.99e-2</span><span class="p">;</span>
+        <span class="n">p44</span> <span class="o">=</span> <span class="mf">5.425e-4</span><span class="p">;</span>
+        <span class="n">p50</span> <span class="o">=</span> <span class="mi">1383702</span><span class="p">;</span>
+        <span class="n">p51</span> <span class="o">=</span> <span class="mf">4.156</span><span class="p">;</span>
+        <span class="n">p52</span> <span class="o">=</span> <span class="mi">5325</span><span class="p">;</span>
+        <span class="n">p53</span> <span class="o">=</span> <span class="mf">0.00238</span><span class="p">;</span>
+        <span class="n">p54</span> <span class="o">=</span> <span class="mf">0.0156</span><span class="p">;</span>
+        <span class="n">p55</span> <span class="o">=</span> <span class="mf">0.130</span><span class="p">;</span>
+        <span class="n">p56</span> <span class="o">=</span> <span class="mi">1521</span><span class="p">;</span>
+        <span class="n">p60</span> <span class="o">=</span> <span class="mf">6.031e-5</span><span class="p">;</span>
+        <span class="n">p61</span> <span class="o">=</span> <span class="mf">98.5</span><span class="p">;</span>
+        <span class="n">p62</span> <span class="o">=</span> <span class="mf">1.0466e-3</span><span class="p">;</span>
+        <span class="n">p70</span> <span class="o">=</span> <span class="mi">11747</span><span class="p">;</span>
+        <span class="n">p71</span> <span class="o">=</span> <span class="mf">41.66</span><span class="p">;</span>
+        <span class="n">p72</span> <span class="o">=</span> <span class="mi">4521</span><span class="p">;</span>
+        <span class="n">p73</span> <span class="o">=</span> <span class="mf">0.01998</span><span class="p">;</span>
+        <span class="n">p74</span> <span class="o">=</span> <span class="mf">0.00604</span><span class="p">;</span>
+        <span class="n">p75</span> <span class="o">=</span> <span class="mi">2534</span><span class="p">;</span>
+        <span class="n">p76</span> <span class="o">=</span> <span class="mf">0.00475</span><span class="p">;</span>
+        <span class="n">p80</span> <span class="o">=</span> <span class="mf">1.543e-2</span><span class="p">;</span>
+        <span class="n">p81</span> <span class="o">=</span> <span class="mf">10.06</span><span class="p">;</span>
+        <span class="n">p82</span> <span class="o">=</span> <span class="mf">1.807e-2</span><span class="p">;</span>
+        <span class="n">p83</span> <span class="o">=</span> <span class="mf">0.0011</span><span class="p">;</span>
+        <span class="n">p84</span> <span class="o">=</span> <span class="mf">8.81e-5</span><span class="p">;</span>
+        <span class="n">p85</span> <span class="o">=</span> <span class="mf">0.0405</span><span class="p">;</span>
+        <span class="n">p86</span> <span class="o">=</span> <span class="mf">20.24</span><span class="p">;</span>
+        <span class="n">p90</span> <span class="o">=</span> <span class="mf">8.321</span><span class="p">;</span>
+        <span class="n">p91</span> <span class="o">=</span> <span class="mf">0.14249</span><span class="p">;</span>
+        <span class="n">p92</span> <span class="o">=</span> <span class="mf">0.96655</span><span class="p">;</span>
+        <span class="n">p93</span> <span class="o">=</span> <span class="mf">26.42</span><span class="p">;</span>
+        <span class="n">p94</span> <span class="o">=</span> <span class="mf">0.0567</span><span class="p">;</span>
+
+        <span class="c1"># Numerical determination of the penetration depth (86%) (Eq. 8)</span>
+        <span class="n">D86</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">rhob</span> <span class="o">*</span> <span class="p">(</span><span class="n">p90</span> <span class="o">+</span> <span class="n">p91</span> <span class="o">*</span> <span class="p">(</span><span class="n">p92</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">distances</span> <span class="o">/</span> <span class="mi">100</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">p93</span> <span class="o">+</span> <span class="n">theta</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">p94</span> <span class="o">+</span> <span class="n">theta</span><span class="p">))</span>
+
+        <span class="c1"># Depth weights (Eq. 7)</span>
+        <span class="n">Wd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span> <span class="o">*</span> <span class="n">depth</span> <span class="o">/</span> <span class="n">D86</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">h</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">W</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># skip distance weighting</span>
+
+        <span class="k">elif</span> <span class="p">(</span><span class="n">h</span> <span class="o">&gt;=</span> <span class="mf">0.1</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">h</span> <span class="o">&lt;=</span> <span class="mi">50</span><span class="p">):</span>
+            <span class="c1"># Functions for Fi (Appendix A in Köhli et al., 2015)</span>
+            <span class="n">F1</span> <span class="o">=</span> <span class="n">p10</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">p13</span> <span class="o">*</span> <span class="n">h</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p11</span> <span class="o">*</span> <span class="n">theta</span><span class="p">)</span> <span class="o">+</span> <span class="n">p12</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">p15</span> <span class="o">*</span> <span class="n">h</span><span class="p">)</span> <span class="o">-</span> <span class="n">p14</span> <span class="o">*</span> <span class="n">theta</span>
+            <span class="n">F2</span> <span class="o">=</span> <span class="p">((</span><span class="o">-</span><span class="n">p20</span> <span class="o">+</span> <span class="n">p24</span> <span class="o">*</span> <span class="n">h</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p21</span> <span class="o">*</span> <span class="n">theta</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">p25</span> <span class="o">*</span> <span class="n">theta</span><span class="p">))</span> <span class="o">+</span> <span class="n">p22</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">h</span> <span class="o">*</span> <span class="n">p23</span><span class="p">)</span>
+            <span class="n">F3</span> <span class="o">=</span> <span class="p">(</span><span class="n">p30</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">p33</span> <span class="o">*</span> <span class="n">h</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p31</span> <span class="o">*</span> <span class="n">theta</span><span class="p">)</span> <span class="o">+</span> <span class="n">p32</span> <span class="o">-</span> <span class="n">p34</span> <span class="o">*</span> <span class="n">theta</span><span class="p">)</span>
+            <span class="n">F4</span> <span class="o">=</span> <span class="n">p40</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p41</span> <span class="o">*</span> <span class="n">theta</span><span class="p">)</span> <span class="o">+</span> <span class="n">p42</span> <span class="o">-</span> <span class="n">p43</span> <span class="o">*</span> <span class="n">theta</span> <span class="o">+</span> <span class="n">p44</span> <span class="o">*</span> <span class="n">h</span>
+            <span class="n">F5</span> <span class="o">=</span> <span class="n">p50</span> <span class="o">*</span> <span class="p">(</span><span class="mf">0.02</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">p55</span> <span class="o">/</span> <span class="p">(</span><span class="n">h</span> <span class="o">-</span> <span class="n">p55</span> <span class="o">+</span> <span class="n">p56</span> <span class="o">*</span> <span class="n">theta</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">p54</span> <span class="o">-</span> <span class="n">theta</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span>
+                <span class="o">-</span><span class="n">p51</span> <span class="o">*</span> <span class="p">(</span><span class="n">theta</span> <span class="o">-</span> <span class="n">p54</span><span class="p">))</span> <span class="o">+</span> <span class="n">p52</span> <span class="o">*</span> <span class="p">(</span><span class="mf">0.7</span> <span class="o">-</span> <span class="n">h</span> <span class="o">*</span> <span class="n">theta</span> <span class="o">*</span> <span class="n">p53</span><span class="p">)</span>
+            <span class="n">F6</span> <span class="o">=</span> <span class="n">p60</span> <span class="o">*</span> <span class="p">(</span><span class="n">h</span> <span class="o">+</span> <span class="n">p61</span><span class="p">)</span> <span class="o">+</span> <span class="n">p62</span> <span class="o">*</span> <span class="n">theta</span>
+            <span class="n">F7</span> <span class="o">=</span> <span class="p">(</span><span class="n">p70</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">p76</span> <span class="o">*</span> <span class="n">h</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p71</span> <span class="o">*</span> <span class="n">theta</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">h</span> <span class="o">*</span> <span class="n">p74</span><span class="p">))</span> <span class="o">+</span> <span class="n">p72</span> <span class="o">-</span> <span class="n">p75</span> <span class="o">*</span> <span class="n">theta</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">+</span> <span class="n">h</span> <span class="o">*</span> <span class="n">p73</span><span class="p">)</span>
+            <span class="n">F8</span> <span class="o">=</span> <span class="p">((</span><span class="o">-</span><span class="n">p80</span> <span class="o">+</span> <span class="n">p84</span> <span class="o">*</span> <span class="n">h</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p81</span> <span class="o">*</span> <span class="n">theta</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">p85</span> <span class="o">*</span> <span class="n">h</span> <span class="o">+</span> <span class="n">p86</span> <span class="o">*</span> <span class="n">theta</span><span class="p">))</span> <span class="o">+</span> <span class="n">p82</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">+</span> <span class="n">h</span> <span class="o">*</span> <span class="n">p83</span><span class="p">)</span>
+
+            <span class="c1"># Distance weights (Eq. 3)</span>
+            <span class="n">W</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">distances</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">distances</span><span class="p">)):</span>
+                <span class="k">if</span> <span class="p">(</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">50</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">):</span>
+                    <span class="n">W</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">F1</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">F2</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> <span class="o">+</span> <span class="n">F3</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">F4</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+                <span class="k">elif</span> <span class="p">(</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">50</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">600</span><span class="p">):</span>
+                    <span class="n">W</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">F5</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">F6</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> <span class="o">+</span> <span class="n">F7</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">F8</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Input distances are not valid.&#39;</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Air humidity values are out of range.&#39;</span><span class="p">)</span>
+
+        <span class="c1"># Combined and normalized weights</span>
+        <span class="n">weights</span> <span class="o">=</span> <span class="n">Wd</span> <span class="o">*</span> <span class="n">W</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">Wd</span> <span class="o">*</span> <span class="n">W</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">weights</span>
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;Schron_2017&#39;</span><span class="p">:</span>
+        <span class="c1"># Horizontal distance weights According to Eq. 6 and Table A1 in Schrön et al. (2017)</span>
+        <span class="c1"># Method for calculating the horizontal distance weights from 0 to 1m</span>
+        <span class="k">def</span> <span class="nf">WrX</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+            <span class="n">x00</span> <span class="o">=</span> <span class="mf">3.7</span>
+            <span class="n">a00</span> <span class="o">=</span> <span class="mi">8735</span><span class="p">;</span>
+            <span class="n">a01</span> <span class="o">=</span> <span class="mf">22.689</span><span class="p">;</span>
+            <span class="n">a02</span> <span class="o">=</span> <span class="mi">11720</span><span class="p">;</span>
+            <span class="n">a03</span> <span class="o">=</span> <span class="mf">0.00978</span><span class="p">;</span>
+            <span class="n">a04</span> <span class="o">=</span> <span class="mi">9306</span><span class="p">;</span>
+            <span class="n">a05</span> <span class="o">=</span> <span class="mf">0.003632</span>
+            <span class="n">a10</span> <span class="o">=</span> <span class="mf">2.7925e-2</span><span class="p">;</span>
+            <span class="n">a11</span> <span class="o">=</span> <span class="mf">6.6577</span><span class="p">;</span>
+            <span class="n">a12</span> <span class="o">=</span> <span class="mf">0.028544</span><span class="p">;</span>
+            <span class="n">a13</span> <span class="o">=</span> <span class="mf">0.002455</span><span class="p">;</span>
+            <span class="n">a14</span> <span class="o">=</span> <span class="mf">6.851e-5</span><span class="p">;</span>
+            <span class="n">a15</span> <span class="o">=</span> <span class="mf">12.2755</span>
+            <span class="n">a20</span> <span class="o">=</span> <span class="mi">247970</span><span class="p">;</span>
+            <span class="n">a21</span> <span class="o">=</span> <span class="mf">23.289</span><span class="p">;</span>
+            <span class="n">a22</span> <span class="o">=</span> <span class="mi">374655</span><span class="p">;</span>
+            <span class="n">a23</span> <span class="o">=</span> <span class="mf">0.00191</span><span class="p">;</span>
+            <span class="n">a24</span> <span class="o">=</span> <span class="mi">258552</span>
+            <span class="n">a30</span> <span class="o">=</span> <span class="mf">5.4818e-2</span><span class="p">;</span>
+            <span class="n">a31</span> <span class="o">=</span> <span class="mf">21.032</span><span class="p">;</span>
+            <span class="n">a32</span> <span class="o">=</span> <span class="mf">0.6373</span><span class="p">;</span>
+            <span class="n">a33</span> <span class="o">=</span> <span class="mf">0.0791</span><span class="p">;</span>
+            <span class="n">a34</span> <span class="o">=</span> <span class="mf">5.425e-4</span>
+
+            <span class="n">x0</span> <span class="o">=</span> <span class="n">x00</span>
+            <span class="n">A0</span> <span class="o">=</span> <span class="p">(</span><span class="n">a00</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a03</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a01</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">+</span> <span class="n">a02</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a05</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="n">a04</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span>
+            <span class="n">A1</span> <span class="o">=</span> <span class="p">((</span><span class="o">-</span><span class="n">a10</span> <span class="o">+</span> <span class="n">a14</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a11</span> <span class="o">*</span> <span class="n">y</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a15</span> <span class="o">*</span> <span class="n">y</span><span class="p">))</span> <span class="o">+</span> <span class="n">a12</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="n">a13</span><span class="p">)</span>
+            <span class="n">A2</span> <span class="o">=</span> <span class="p">(</span><span class="n">a20</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a23</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a21</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">+</span> <span class="n">a22</span> <span class="o">-</span> <span class="n">a24</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span>
+            <span class="n">A3</span> <span class="o">=</span> <span class="n">a30</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a31</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">+</span> <span class="n">a32</span> <span class="o">-</span> <span class="n">a33</span> <span class="o">*</span> <span class="n">y</span> <span class="o">+</span> <span class="n">a34</span> <span class="o">*</span> <span class="n">x</span>
+
+            <span class="k">return</span> <span class="p">((</span><span class="n">A0</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">A1</span> <span class="o">*</span> <span class="n">r</span><span class="p">))</span> <span class="o">+</span> <span class="n">A2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">A3</span> <span class="o">*</span> <span class="n">r</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x0</span> <span class="o">*</span> <span class="n">r</span><span class="p">)))</span>
+
+        <span class="c1"># Method for calculating the horizontal distance weights from 1 to 50m</span>
+        <span class="k">def</span> <span class="nf">WrA</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+            <span class="n">a00</span> <span class="o">=</span> <span class="mi">8735</span><span class="p">;</span>
+            <span class="n">a01</span> <span class="o">=</span> <span class="mf">22.689</span><span class="p">;</span>
+            <span class="n">a02</span> <span class="o">=</span> <span class="mi">11720</span><span class="p">;</span>
+            <span class="n">a03</span> <span class="o">=</span> <span class="mf">0.00978</span><span class="p">;</span>
+            <span class="n">a04</span> <span class="o">=</span> <span class="mi">9306</span><span class="p">;</span>
+            <span class="n">a05</span> <span class="o">=</span> <span class="mf">0.003632</span>
+            <span class="n">a10</span> <span class="o">=</span> <span class="mf">2.7925e-2</span><span class="p">;</span>
+            <span class="n">a11</span> <span class="o">=</span> <span class="mf">6.6577</span><span class="p">;</span>
+            <span class="n">a12</span> <span class="o">=</span> <span class="mf">0.028544</span><span class="p">;</span>
+            <span class="n">a13</span> <span class="o">=</span> <span class="mf">0.002455</span><span class="p">;</span>
+            <span class="n">a14</span> <span class="o">=</span> <span class="mf">6.851e-5</span><span class="p">;</span>
+            <span class="n">a15</span> <span class="o">=</span> <span class="mf">12.2755</span>
+            <span class="n">a20</span> <span class="o">=</span> <span class="mi">247970</span><span class="p">;</span>
+            <span class="n">a21</span> <span class="o">=</span> <span class="mf">23.289</span><span class="p">;</span>
+            <span class="n">a22</span> <span class="o">=</span> <span class="mi">374655</span><span class="p">;</span>
+            <span class="n">a23</span> <span class="o">=</span> <span class="mf">0.00191</span><span class="p">;</span>
+            <span class="n">a24</span> <span class="o">=</span> <span class="mi">258552</span>
+            <span class="n">a30</span> <span class="o">=</span> <span class="mf">5.4818e-2</span><span class="p">;</span>
+            <span class="n">a31</span> <span class="o">=</span> <span class="mf">21.032</span><span class="p">;</span>
+            <span class="n">a32</span> <span class="o">=</span> <span class="mf">0.6373</span><span class="p">;</span>
+            <span class="n">a33</span> <span class="o">=</span> <span class="mf">0.0791</span><span class="p">;</span>
+            <span class="n">a34</span> <span class="o">=</span> <span class="mf">5.425e-4</span>
+
+            <span class="n">A0</span> <span class="o">=</span> <span class="p">(</span><span class="n">a00</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a03</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a01</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">+</span> <span class="n">a02</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a05</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="n">a04</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span>
+            <span class="n">A1</span> <span class="o">=</span> <span class="p">((</span><span class="o">-</span><span class="n">a10</span> <span class="o">+</span> <span class="n">a14</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a11</span> <span class="o">*</span> <span class="n">y</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a15</span> <span class="o">*</span> <span class="n">y</span><span class="p">))</span> <span class="o">+</span> <span class="n">a12</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="n">a13</span><span class="p">)</span>
+            <span class="n">A2</span> <span class="o">=</span> <span class="p">(</span><span class="n">a20</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a23</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a21</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">+</span> <span class="n">a22</span> <span class="o">-</span> <span class="n">a24</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span>
+            <span class="n">A3</span> <span class="o">=</span> <span class="n">a30</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a31</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">+</span> <span class="n">a32</span> <span class="o">-</span> <span class="n">a33</span> <span class="o">*</span> <span class="n">y</span> <span class="o">+</span> <span class="n">a34</span> <span class="o">*</span> <span class="n">x</span>
+
+            <span class="k">return</span> <span class="n">A0</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">A1</span> <span class="o">*</span> <span class="n">r</span><span class="p">)</span> <span class="o">+</span> <span class="n">A2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">A3</span> <span class="o">*</span> <span class="n">r</span><span class="p">)</span>
+
+        <span class="c1"># Method for calculating the horizontal distance weights from 50 to 600m</span>
+        <span class="k">def</span> <span class="nf">WrB</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+            <span class="n">b00</span> <span class="o">=</span> <span class="mi">39006</span><span class="p">;</span>
+            <span class="n">b01</span> <span class="o">=</span> <span class="mi">15002337</span><span class="p">;</span>
+            <span class="n">b02</span> <span class="o">=</span> <span class="mf">2009.24</span><span class="p">;</span>
+            <span class="n">b03</span> <span class="o">=</span> <span class="mf">0.01181</span><span class="p">;</span>
+            <span class="n">b04</span> <span class="o">=</span> <span class="mf">3.146</span><span class="p">;</span>
+            <span class="n">b05</span> <span class="o">=</span> <span class="mf">16.7417</span><span class="p">;</span>
+            <span class="n">b06</span> <span class="o">=</span> <span class="mi">3727</span>
+            <span class="n">b10</span> <span class="o">=</span> <span class="mf">6.031e-5</span><span class="p">;</span>
+            <span class="n">b11</span> <span class="o">=</span> <span class="mf">98.5</span><span class="p">;</span>
+            <span class="n">b12</span> <span class="o">=</span> <span class="mf">0.0013826</span>
+            <span class="n">b20</span> <span class="o">=</span> <span class="mi">11747</span><span class="p">;</span>
+            <span class="n">b21</span> <span class="o">=</span> <span class="mf">55.033</span><span class="p">;</span>
+            <span class="n">b22</span> <span class="o">=</span> <span class="mi">4521</span><span class="p">;</span>
+            <span class="n">b23</span> <span class="o">=</span> <span class="mf">0.01998</span><span class="p">;</span>
+            <span class="n">b24</span> <span class="o">=</span> <span class="mf">0.00604</span><span class="p">;</span>
+            <span class="n">b25</span> <span class="o">=</span> <span class="mf">3347.4</span><span class="p">;</span>
+            <span class="n">b26</span> <span class="o">=</span> <span class="mf">0.00475</span>
+            <span class="n">b30</span> <span class="o">=</span> <span class="mf">1.543e-2</span><span class="p">;</span>
+            <span class="n">b31</span> <span class="o">=</span> <span class="mf">13.29</span><span class="p">;</span>
+            <span class="n">b32</span> <span class="o">=</span> <span class="mf">1.807e-2</span><span class="p">;</span>
+            <span class="n">b33</span> <span class="o">=</span> <span class="mf">0.0011</span><span class="p">;</span>
+            <span class="n">b34</span> <span class="o">=</span> <span class="mf">8.81e-5</span><span class="p">;</span>
+            <span class="n">b35</span> <span class="o">=</span> <span class="mf">0.0405</span><span class="p">;</span>
+            <span class="n">b36</span> <span class="o">=</span> <span class="mf">26.74</span>
+
+            <span class="n">B0</span> <span class="o">=</span> <span class="p">(</span><span class="n">b00</span> <span class="o">-</span> <span class="n">b01</span> <span class="o">/</span> <span class="p">(</span><span class="n">b02</span> <span class="o">*</span> <span class="n">y</span> <span class="o">+</span> <span class="n">x</span> <span class="o">-</span> <span class="mf">0.13</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">b03</span> <span class="o">-</span> <span class="n">y</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">b04</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">-</span> <span class="n">b05</span> <span class="o">*</span> <span class="n">x</span> <span class="o">*</span> <span class="n">y</span> <span class="o">+</span> <span class="n">b06</span>
+            <span class="n">B1</span> <span class="o">=</span> <span class="n">b10</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">b11</span><span class="p">)</span> <span class="o">+</span> <span class="n">b12</span> <span class="o">*</span> <span class="n">y</span>
+            <span class="n">B2</span> <span class="o">=</span> <span class="p">(</span><span class="n">b20</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">b26</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">b21</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">x</span> <span class="o">*</span> <span class="n">b24</span><span class="p">))</span> <span class="o">+</span> <span class="n">b22</span> <span class="o">-</span> <span class="n">b25</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="n">b23</span><span class="p">)</span>
+            <span class="n">B3</span> <span class="o">=</span> <span class="p">((</span><span class="o">-</span><span class="n">b30</span> <span class="o">+</span> <span class="n">b34</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">b31</span> <span class="o">*</span> <span class="n">y</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">b35</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="n">b36</span> <span class="o">*</span> <span class="n">y</span><span class="p">))</span> <span class="o">+</span> <span class="n">b32</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="n">b33</span><span class="p">)</span>
+
+            <span class="k">return</span> <span class="n">B0</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">B1</span> <span class="o">*</span> <span class="n">r</span><span class="p">)</span> <span class="o">+</span> <span class="n">B2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">B3</span> <span class="o">*</span> <span class="n">r</span><span class="p">)</span>
+
+        <span class="k">def</span> <span class="nf">rscaled</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">Hveg</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+            <span class="n">Fp</span> <span class="o">=</span> <span class="mf">0.4922</span> <span class="o">/</span> <span class="p">(</span><span class="mf">0.86</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p</span> <span class="o">/</span> <span class="mf">1013.25</span><span class="p">))</span>
+            <span class="n">Fveg</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="mf">0.17</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">0.41</span> <span class="o">*</span> <span class="n">Hveg</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">9.25</span> <span class="o">*</span> <span class="n">y</span><span class="p">))</span>
+            <span class="k">return</span> <span class="n">r</span> <span class="o">/</span> <span class="n">Fp</span> <span class="o">/</span> <span class="n">Fveg</span>
+
+        <span class="c1"># Rename variables to be consistent with the revised paper</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="n">distances</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="n">h</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">theta</span>
+        <span class="n">bd</span> <span class="o">=</span> <span class="n">rhob</span>
+
+        <span class="k">if</span> <span class="n">Hveg</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">p</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">r</span> <span class="o">=</span> <span class="n">rscaled</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">Hveg</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+        <span class="n">Wr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">r</span><span class="p">))</span>
+
+        <span class="c1"># See Eq. 6 in Schron et al. (2017)</span>
+        <span class="n">r0_idx</span> <span class="o">=</span> <span class="p">(</span><span class="n">r</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">r1_idx</span> <span class="o">=</span> <span class="p">(</span><span class="n">r</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">r</span> <span class="o">&lt;=</span> <span class="mi">50</span><span class="p">)</span>
+        <span class="n">r2_idx</span> <span class="o">=</span> <span class="p">(</span><span class="n">r</span> <span class="o">&gt;</span> <span class="mi">50</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">r</span> <span class="o">&lt;</span> <span class="mi">600</span><span class="p">)</span>
+        <span class="n">Wr</span><span class="p">[</span><span class="n">r0_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">WrX</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="n">r0_idx</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="n">r0_idx</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">r0_idx</span><span class="p">])</span>
+        <span class="n">Wr</span><span class="p">[</span><span class="n">r1_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">WrA</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="n">r1_idx</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="n">r1_idx</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">r1_idx</span><span class="p">])</span>
+        <span class="n">Wr</span><span class="p">[</span><span class="n">r2_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">WrB</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="n">r2_idx</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="n">r2_idx</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">r2_idx</span><span class="p">])</span>
+
+        <span class="c1"># Vertical distance weights</span>
+        <span class="k">def</span> <span class="nf">D86</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">bd</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+            <span class="k">return</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">bd</span> <span class="o">*</span> <span class="p">(</span><span class="mf">8.321</span> <span class="o">+</span> <span class="mf">0.14249</span> <span class="o">*</span> <span class="p">(</span><span class="mf">0.96655</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">0.01</span> <span class="o">*</span> <span class="n">r</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mf">0.0429</span> <span class="o">+</span> <span class="n">y</span><span class="p">))</span>
+
+        <span class="k">def</span> <span class="nf">Wd</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">bd</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span> <span class="o">*</span> <span class="n">d</span> <span class="o">/</span> <span class="n">D86</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">bd</span><span class="p">,</span> <span class="n">y</span><span class="p">))</span>
+
+        <span class="c1"># Calculate the vertical distance weights</span>
+        <span class="n">Wd</span> <span class="o">=</span> <span class="n">Wd</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">bd</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+        <span class="c1"># Combined and normalized weights</span>
+        <span class="c1"># Combined and normalized weights</span>
+        <span class="n">weights</span> <span class="o">=</span> <span class="n">Wd</span> <span class="o">*</span> <span class="n">Wr</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">Wd</span> <span class="o">*</span> <span class="n">Wr</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">weights</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.remove_incomplete_intervals" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">remove_incomplete_intervals</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="p">,</span> <span class="n">integration_time</span><span class="p">,</span> <span class="n">remove_first</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function that removes rows with incomplete integration intervals.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>df</code></td>
+          <td>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Pandas Dataframe with data from stationary or roving CRNP devices.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>timestamp_col</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Name of the column with timestamps in datetime format.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>integration_time</code></td>
+          <td>
+                <code>int</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>remove_first</code></td>
+          <td>
+                <code>bool</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Remove first row. Default is False.</p>
+            </div>
+          </td>
+          <td>
+                <code>False</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">32</span>
+<span class="normal">33</span>
+<span class="normal">34</span>
+<span class="normal">35</span>
+<span class="normal">36</span>
+<span class="normal">37</span>
+<span class="normal">38</span>
+<span class="normal">39</span>
+<span class="normal">40</span>
+<span class="normal">41</span>
+<span class="normal">42</span>
+<span class="normal">43</span>
+<span class="normal">44</span>
+<span class="normal">45</span>
+<span class="normal">46</span>
+<span class="normal">47</span>
+<span class="normal">48</span>
+<span class="normal">49</span>
+<span class="normal">50</span>
+<span class="normal">51</span>
+<span class="normal">52</span>
+<span class="normal">53</span>
+<span class="normal">54</span>
+<span class="normal">55</span>
+<span class="normal">56</span>
+<span class="normal">57</span>
+<span class="normal">58</span>
+<span class="normal">59</span>
+<span class="normal">60</span>
+<span class="normal">61</span>
+<span class="normal">62</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">remove_incomplete_intervals</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="p">,</span> <span class="n">integration_time</span><span class="p">,</span> <span class="n">remove_first</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function that removes rows with incomplete integration intervals.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        df (pandas.DataFrame): Pandas Dataframe with data from stationary or roving CRNP devices.</span>
+<span class="sd">        timestamp_col (str): Name of the column with timestamps in datetime format.</span>
+<span class="sd">        integration_time (int): Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.</span>
+<span class="sd">        remove_first (bool, optional): Remove first row. Default is False.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (pandas.DataFrame): </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Check format of timestamp column</span>
+    <span class="k">if</span> <span class="n">df</span><span class="p">[</span><span class="n">timestamp_col</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="s1">&#39;datetime64[ns]&#39;</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;timestamp_col must be datetime64. Use `pd.to_datetime()` to fix this issue.&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Check if differences in timestamps are below or above the provided integration time</span>
+    <span class="n">idx_delta</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">timestamp_col</span><span class="p">]</span><span class="o">.</span><span class="n">diff</span><span class="p">()</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">total_seconds</span><span class="p">()</span> <span class="o">!=</span> <span class="n">integration_time</span>
+
+    <span class="k">if</span> <span class="n">remove_first</span><span class="p">:</span>
+        <span class="n">idx_delta</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="c1"># Select rows that meet the specified integration time</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="o">~</span><span class="n">idx_delta</span><span class="p">]</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="c1"># Notify user about the number of rows that have been removed</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Removed a total of </span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">idx_delta</span><span class="p">)</span><span class="si">}</span><span class="s2"> rows.&quot;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">df</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.rover_centered_coordinates" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">rover_centered_coordinates</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>x</code></td>
+          <td>
+                <code>array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>x coordinates.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>y</code></td>
+          <td>
+                <code>array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>y coordinates.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+<th>Name</th>        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+<td><code>x_est</code></td>          <td>
+                <code>array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Estimated x coordinates.</p>
+            </div>
+          </td>
+        </tr>
+        <tr>
+<td><code>y_est</code></td>          <td>
+                <code>array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Estimated y coordinates.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1466</span>
+<span class="normal">1467</span>
+<span class="normal">1468</span>
+<span class="normal">1469</span>
+<span class="normal">1470</span>
+<span class="normal">1471</span>
+<span class="normal">1472</span>
+<span class="normal">1473</span>
+<span class="normal">1474</span>
+<span class="normal">1475</span>
+<span class="normal">1476</span>
+<span class="normal">1477</span>
+<span class="normal">1478</span>
+<span class="normal">1479</span>
+<span class="normal">1480</span>
+<span class="normal">1481</span>
+<span class="normal">1482</span>
+<span class="normal">1483</span>
+<span class="normal">1484</span>
+<span class="normal">1485</span>
+<span class="normal">1486</span>
+<span class="normal">1487</span>
+<span class="normal">1488</span>
+<span class="normal">1489</span>
+<span class="normal">1490</span>
+<span class="normal">1491</span>
+<span class="normal">1492</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">rover_centered_coordinates</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        x (array): x coordinates.</span>
+<span class="sd">        y (array): y coordinates.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        x_est (array): Estimated x coordinates.</span>
+<span class="sd">        y_est (array): Estimated y coordinates.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Make it datatype agnostic</span>
+    <span class="k">if</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)):</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">values</span>
+    <span class="k">if</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)):</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">values</span>
+
+    <span class="c1"># Do the average of the two points</span>
+    <span class="n">x_est</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="n">x</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="n">y_est</span> <span class="o">=</span> <span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="n">y</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="mi">2</span>
+
+    <span class="c1"># Add the first point to match the length of the original array</span>
+    <span class="n">x_est</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">x_est</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">y_est</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">y_est</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+    <span class="k">return</span> <span class="n">x_est</span><span class="p">,</span> <span class="n">y_est</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.sensing_depth" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">sensing_depth</span><span class="p">(</span><span class="n">vwc</span><span class="p">,</span> <span class="n">pressure</span><span class="p">,</span> <span class="n">p_ref</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">Wlat</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;Schron_2017&#39;</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function that computes the estimated sensing depth of the cosmic-ray neutron probe.
+The function offers several methods to compute the depth at which 86 % of the neutrons
+probe the soil profile.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>vwc</code></td>
+          <td>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Estimated volumetric water content for each timestamp.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>pressure</code></td>
+          <td>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Atmospheric pressure in hPa for each timestamp.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>p_ref</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Reference pressure in hPa.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>bulk_density</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Soil bulk density.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>Wlat</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Lattice water content.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>method</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.</p>
+            </div>
+          </td>
+          <td>
+                <code>&#39;Schron_2017&#39;</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>dist</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Estimated sensing depth in m.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Franz, T.E., Zreda, M., Ferre, T.P.A., Rosolem, R., Zweck, C., Stillman, S., Zeng, X. and Shuttleworth, W.J., 2012.
+Measurement depth of the cosmic ray soil moisture probe affected by hydrogen from various sources.
+Water Resources Research, 48(8). doi.org/10.1029/2012WR011871</p>
+<p>Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., et al. (2017).
+Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity.
+Hydrol. Earth Syst. Sci. 21, 5009–5030. doi.org/10.5194/hess-21-5009-2017</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">622</span>
+<span class="normal">623</span>
+<span class="normal">624</span>
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+<span class="normal">664</span>
+<span class="normal">665</span>
+<span class="normal">666</span>
+<span class="normal">667</span>
+<span class="normal">668</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">sensing_depth</span><span class="p">(</span><span class="n">vwc</span><span class="p">,</span> <span class="n">pressure</span><span class="p">,</span> <span class="n">p_ref</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">Wlat</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;Schron_2017&#39;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function that computes the estimated sensing depth of the cosmic-ray neutron probe.</span>
+<span class="sd">    The function offers several methods to compute the depth at which 86 % of the neutrons</span>
+<span class="sd">    probe the soil profile.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        vwc (array or pd.Series or pd.DataFrame): Estimated volumetric water content for each timestamp.</span>
+<span class="sd">        pressure (array or pd.Series or pd.DataFrame): Atmospheric pressure in hPa for each timestamp.</span>
+<span class="sd">        p_ref (float): Reference pressure in hPa.</span>
+<span class="sd">        bulk_density (float): Soil bulk density.</span>
+<span class="sd">        Wlat (float): Lattice water content.</span>
+<span class="sd">        method (str): Method to compute the sensing depth. Options are &#39;Schron_2017&#39; or &#39;Franz_2012&#39;.</span>
+<span class="sd">        dist (list or array): List of radial distances at which to estimate the sensing depth. Only used for the &#39;Schron_2017&#39; method.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (array or pd.Series or pd.DataFrame): Estimated sensing depth in m.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Franz, T.E., Zreda, M., Ferre, T.P.A., Rosolem, R., Zweck, C., Stillman, S., Zeng, X. and Shuttleworth, W.J., 2012.</span>
+<span class="sd">        Measurement depth of the cosmic ray soil moisture probe affected by hydrogen from various sources.</span>
+<span class="sd">        Water Resources Research, 48(8). doi.org/10.1029/2012WR011871</span>
+
+<span class="sd">        Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., et al. (2017).</span>
+<span class="sd">        Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity.</span>
+<span class="sd">        Hydrol. Earth Syst. Sci. 21, 5009–5030. doi.org/10.5194/hess-21-5009-2017</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Determine sensing depth (D86)</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;Schron_2017&#39;</span><span class="p">:</span>
+        <span class="c1"># See Appendix A of Schrön et al. (2017)</span>
+        <span class="n">Fp</span> <span class="o">=</span> <span class="mf">0.4922</span> <span class="o">/</span> <span class="p">(</span><span class="mf">0.86</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">pressure</span> <span class="o">/</span> <span class="n">p_ref</span><span class="p">))</span>
+        <span class="n">Fveg</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">dist</span><span class="p">:</span>
+            <span class="c1"># Compute r_star</span>
+            <span class="n">r_start</span> <span class="o">=</span> <span class="n">d</span> <span class="o">/</span> <span class="n">Fp</span>
+
+            <span class="c1"># Compute soil depth that accounts for 86% of the neutron flux</span>
+            <span class="n">D86</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">bulk_density</span> <span class="o">*</span> <span class="p">(</span><span class="mf">8.321</span> <span class="o">+</span> <span class="mf">0.14249</span> <span class="o">*</span> <span class="p">(</span><span class="mf">0.96655</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">0.01</span> <span class="o">*</span> <span class="n">r_start</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="p">(</span><span class="n">Wlat</span> <span class="o">+</span> <span class="n">vwc</span><span class="p">))</span> <span class="o">/</span> <span class="p">(</span>
+                        <span class="mf">0.0429</span> <span class="o">+</span> <span class="p">(</span><span class="n">Wlat</span> <span class="o">+</span> <span class="n">vwc</span><span class="p">)))</span>
+            <span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">D86</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;Franz_2012&#39;</span><span class="p">:</span>
+        <span class="n">results</span> <span class="o">=</span> <span class="mf">5.8</span> <span class="o">/</span> <span class="p">(</span><span class="n">bulk_density</span> <span class="o">*</span> <span class="n">Wlat</span> <span class="o">+</span> <span class="n">vwc</span> <span class="o">+</span> <span class="mf">0.0829</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Method not recognized. Please select either &quot;Schron_2017&quot; or &quot;Franz_2012&quot;.&#39;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">results</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.smooth_1d" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">smooth_1d</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;moving_median&#39;</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>values</code></td>
+          <td>
+                <code><span title="pandas.DataFrame">DataFrame</span> or <span title="pandas.Serie">Serie</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Dataframe containing the values to smooth.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>window</code></td>
+          <td>
+                <code>int</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Window size for the Savitzky-Golay filter. Default is 5.</p>
+            </div>
+          </td>
+          <td>
+                <code>5</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>method</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Method to use for smoothing the data. Default is 'moving_median'.
+Options are 'moving_average', 'moving_median' and 'savitzky_golay'.</p>
+            </div>
+          </td>
+          <td>
+                <code>&#39;moving_median&#39;</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>order</code></td>
+          <td>
+                <code>int</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Order of the Savitzky-Golay filter. Default is 3.</p>
+            </div>
+          </td>
+          <td>
+                <code>3</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>DataFrame with smoothed values.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.
+Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.
+doi.org/10.3389/frwa.2020.00009</p>
+<p>Savitzky, A., &amp; Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures.
+Analytical chemistry, 36(8), 1627-1639.</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">471</span>
+<span class="normal">472</span>
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+<span class="normal">497</span>
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+<span class="normal">501</span>
+<span class="normal">502</span>
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+<span class="normal">504</span>
+<span class="normal">505</span>
+<span class="normal">506</span>
+<span class="normal">507</span>
+<span class="normal">508</span>
+<span class="normal">509</span>
+<span class="normal">510</span>
+<span class="normal">511</span>
+<span class="normal">512</span>
+<span class="normal">513</span>
+<span class="normal">514</span>
+<span class="normal">515</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">smooth_1d</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;moving_median&#39;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        values (pd.DataFrame or pd.Serie): Dataframe containing the values to smooth.</span>
+<span class="sd">        window (int): Window size for the Savitzky-Golay filter. Default is 5.</span>
+<span class="sd">        method (str): Method to use for smoothing the data. Default is &#39;moving_median&#39;.</span>
+<span class="sd">            Options are &#39;moving_average&#39;, &#39;moving_median&#39; and &#39;savitzky_golay&#39;.</span>
+<span class="sd">        order (int): Order of the Savitzky-Golay filter. Default is 3.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (pd.DataFrame): DataFrame with smoothed values.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.</span>
+<span class="sd">        Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.</span>
+<span class="sd">        doi.org/10.3389/frwa.2020.00009</span>
+
+<span class="sd">        Savitzky, A., &amp; Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures.</span>
+<span class="sd">        Analytical chemistry, 36(8), 1627-1639.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Input must be a pandas Series or DataFrame&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;moving_average&#39;</span><span class="p">:</span>
+        <span class="n">corrected_counts</span> <span class="o">=</span> <span class="n">values</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_periods</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;moving_median&#39;</span><span class="p">:</span>
+        <span class="n">corrected_counts</span> <span class="o">=</span> <span class="n">values</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_periods</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
+
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;savitzky_golay&#39;</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">values</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Dataframe contains NaN values. Please remove NaN values before smoothing the data.&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">values</span><span class="p">)</span> <span class="o">==</span> <span class="n">pd</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">series</span><span class="o">.</span><span class="n">Series</span><span class="p">:</span>
+            <span class="n">filtered</span> <span class="o">=</span> <span class="n">savgol_filter</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> <span class="n">order</span><span class="p">)</span>
+            <span class="n">corrected_counts</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">filtered</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;smoothed&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="n">values</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">values</span><span class="p">)</span> <span class="o">==</span> <span class="n">pd</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">frame</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">values</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+                <span class="n">values</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">savgol_filter</span><span class="p">(</span><span class="n">values</span><span class="p">[</span><span class="n">col</span><span class="p">],</span> <span class="n">window</span><span class="p">,</span> <span class="n">order</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="s1">&#39;Invalid method. Please select a valid filtering method., options are: moving_average, moving_median, savitzky_golay&#39;</span><span class="p">)</span>
+    <span class="n">corrected_counts</span> <span class="o">=</span> <span class="n">corrected_counts</span><span class="o">.</span><span class="n">ffill</span><span class="p">(</span><span class="n">limit</span><span class="o">=</span><span class="n">window</span><span class="p">)</span><span class="o">.</span><span class="n">bfill</span><span class="p">(</span><span class="n">limit</span><span class="o">=</span><span class="n">window</span><span class="p">)</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">corrected_counts</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">spatial_average</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">min_neighbours</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">,</span> <span class="n">rnd</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Moving buffer filter to smooth georeferenced two-dimensional data.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>x</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM x coordinates in meters.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>y</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM y coordinates in meters.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>z</code></td>
+          <td>
+                <code>list or array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Values to be smoothed.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>buffer</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Radial buffer distance in meters.</p>
+            </div>
+          </td>
+          <td>
+                <code>100</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>min_neighbours</code></td>
+          <td>
+                <code>int</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Minimum number of neighbours to consider for the smoothing.</p>
+            </div>
+          </td>
+          <td>
+                <code>3</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>method</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>One of 'mean' or 'median'.</p>
+            </div>
+          </td>
+          <td>
+                <code>&#39;mean&#39;</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>rnd</code></td>
+          <td>
+                <code>bool</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Boolean to round the final result. Useful in case of z representing neutron counts.</p>
+            </div>
+          </td>
+          <td>
+                <code>False</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Smoothed version of z with the same dimension as z.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1315</span>
+<span class="normal">1316</span>
+<span class="normal">1317</span>
+<span class="normal">1318</span>
+<span class="normal">1319</span>
+<span class="normal">1320</span>
+<span class="normal">1321</span>
+<span class="normal">1322</span>
+<span class="normal">1323</span>
+<span class="normal">1324</span>
+<span class="normal">1325</span>
+<span class="normal">1326</span>
+<span class="normal">1327</span>
+<span class="normal">1328</span>
+<span class="normal">1329</span>
+<span class="normal">1330</span>
+<span class="normal">1331</span>
+<span class="normal">1332</span>
+<span class="normal">1333</span>
+<span class="normal">1334</span>
+<span class="normal">1335</span>
+<span class="normal">1336</span>
+<span class="normal">1337</span>
+<span class="normal">1338</span>
+<span class="normal">1339</span>
+<span class="normal">1340</span>
+<span class="normal">1341</span>
+<span class="normal">1342</span>
+<span class="normal">1343</span>
+<span class="normal">1344</span>
+<span class="normal">1345</span>
+<span class="normal">1346</span>
+<span class="normal">1347</span>
+<span class="normal">1348</span>
+<span class="normal">1349</span>
+<span class="normal">1350</span>
+<span class="normal">1351</span>
+<span class="normal">1352</span>
+<span class="normal">1353</span>
+<span class="normal">1354</span>
+<span class="normal">1355</span>
+<span class="normal">1356</span>
+<span class="normal">1357</span>
+<span class="normal">1358</span>
+<span class="normal">1359</span>
+<span class="normal">1360</span>
+<span class="normal">1361</span>
+<span class="normal">1362</span>
+<span class="normal">1363</span>
+<span class="normal">1364</span>
+<span class="normal">1365</span>
+<span class="normal">1366</span>
+<span class="normal">1367</span>
+<span class="normal">1368</span>
+<span class="normal">1369</span>
+<span class="normal">1370</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">spatial_average</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">min_neighbours</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">,</span> <span class="n">rnd</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Moving buffer filter to smooth georeferenced two-dimensional data.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        x (list or array): UTM x coordinates in meters.</span>
+<span class="sd">        y (list or array): UTM y coordinates in meters.</span>
+<span class="sd">        z (list or array): Values to be smoothed.</span>
+<span class="sd">        buffer (float): Radial buffer distance in meters.</span>
+<span class="sd">        min_neighbours (int): Minimum number of neighbours to consider for the smoothing.</span>
+<span class="sd">        method (str): One of &#39;mean&#39; or &#39;median&#39;.</span>
+<span class="sd">        rnd (bool): Boolean to round the final result. Useful in case of z representing neutron counts.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (array): Smoothed version of z with the same dimension as z.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Convert input data to Numpy arrays</span>
+    <span class="k">if</span> <span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="s2">&quot;Input values cannot be converted to Numpy arrays.&quot;</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="sa">f</span><span class="s2">&quot;The number of x and y must be equal. Input x has </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="si">}</span><span class="s2"> values and y has </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="si">}</span><span class="s2"> values.&quot;</span>
+
+    <span class="c1"># Compute distances</span>
+    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+    <span class="n">z_smooth</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
+        <span class="n">px</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+        <span class="n">py</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+
+        <span class="n">distances</span> <span class="o">=</span> <span class="n">euclidean_distance</span><span class="p">(</span><span class="n">px</span><span class="p">,</span> <span class="n">py</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="n">idx_within_buffer</span> <span class="o">=</span> <span class="n">distances</span> <span class="o">&lt;=</span> <span class="n">buffer</span>
+
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">z</span><span class="p">[</span><span class="n">k</span><span class="p">]):</span>
+            <span class="n">z_new_val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">distances</span><span class="p">[</span><span class="n">idx_within_buffer</span><span class="p">])</span> <span class="o">&gt;</span> <span class="n">min_neighbours</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;mean&#39;</span><span class="p">:</span>
+                <span class="n">z_new_val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">z</span><span class="p">[</span><span class="n">idx_within_buffer</span><span class="p">])</span>
+            <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;median&#39;</span><span class="p">:</span>
+                <span class="n">z_new_val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">z</span><span class="p">[</span><span class="n">idx_within_buffer</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="sa">f</span><span class="s2">&quot;Method </span><span class="si">{</span><span class="n">method</span><span class="si">}</span><span class="s2"> does not exist. Provide either &#39;mean&#39; or &#39;median&#39;.&quot;</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">z_new_val</span> <span class="o">=</span> <span class="n">z</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>  <span class="c1"># If there are not enough neighbours, keep the original value</span>
+
+        <span class="c1"># Append smoothed value to array</span>
+        <span class="n">z_smooth</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">z_smooth</span><span class="p">,</span> <span class="n">z_new_val</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">rnd</span><span class="p">:</span>
+        <span class="n">z_smooth</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">z_smooth</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">z_smooth</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.total_raw_counts" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">total_raw_counts</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Compute the sum of uncorrected neutron counts for all detectors.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>counts</code></td>
+          <td>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Dataframe containing only the columns with neutron counts.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Dataframe with the sum of uncorrected neutron counts for all detectors.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">110</span>
+<span class="normal">111</span>
+<span class="normal">112</span>
+<span class="normal">113</span>
+<span class="normal">114</span>
+<span class="normal">115</span>
+<span class="normal">116</span>
+<span class="normal">117</span>
+<span class="normal">118</span>
+<span class="normal">119</span>
+<span class="normal">120</span>
+<span class="normal">121</span>
+<span class="normal">122</span>
+<span class="normal">123</span>
+<span class="normal">124</span>
+<span class="normal">125</span>
+<span class="normal">126</span>
+<span class="normal">127</span>
+<span class="normal">128</span>
+<span class="normal">129</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">total_raw_counts</span><span class="p">(</span><span class="n">counts</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Compute the sum of uncorrected neutron counts for all detectors.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        counts (pandas.DataFrame): Dataframe containing only the columns with neutron counts.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (pandas.DataFrame): Dataframe with the sum of uncorrected neutron counts for all detectors.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">counts</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">counts</span> <span class="o">=</span> <span class="n">counts</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">counts</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+    <span class="c1"># Compute sum of counts</span>
+    <span class="n">total_raw_counts</span> <span class="o">=</span> <span class="n">counts</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="c1"># Replace zeros with NaN</span>
+    <span class="n">total_raw_counts</span> <span class="o">=</span> <span class="n">total_raw_counts</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">total_raw_counts</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.uncertainty_counts" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">uncertainty_counts</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s1">&#39;std&#39;</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fi</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to estimate the uncertainty of raw counts.</p>
+<p>Measurements of proportional neutron detector systems are governed by counting statistics that follow a Poissonian probability distribution (Zreda et al., 2012).
+The expected uncertainty in the neutron count rate $N$ is defined by the standard deviation $ \sqrt{N} $ (Jakobi et al., 2020).
+The CV% can be expressed as $ N^{-1/2} $</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>raw_counts</code></td>
+          <td>
+                <code>array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Raw neutron counts.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>metric</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Either 'std' or 'cv' for standard deviation or coefficient of variation.</p>
+            </div>
+          </td>
+          <td>
+                <code>&#39;std&#39;</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>fp</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Pressure correction factor.</p>
+            </div>
+          </td>
+          <td>
+                <code>1</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>fw</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Humidity correction factor.</p>
+            </div>
+          </td>
+          <td>
+                <code>1</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>fi</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Incoming neutron flux correction factor.</p>
+            </div>
+          </td>
+          <td>
+                <code>1</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+<th>Name</th>        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+<td><code>uncertainty</code></td>          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Uncertainty of raw counts.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Jakobi J, Huisman JA, Schrön M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With
+Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010</p>
+<p>Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System,
+Hydrol. Earth Syst. Sci., 16, 4079–4099, https://doi.org/10.5194/hess-16-4079-2012, 2012.</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1495</span>
+<span class="normal">1496</span>
+<span class="normal">1497</span>
+<span class="normal">1498</span>
+<span class="normal">1499</span>
+<span class="normal">1500</span>
+<span class="normal">1501</span>
+<span class="normal">1502</span>
+<span class="normal">1503</span>
+<span class="normal">1504</span>
+<span class="normal">1505</span>
+<span class="normal">1506</span>
+<span class="normal">1507</span>
+<span class="normal">1508</span>
+<span class="normal">1509</span>
+<span class="normal">1510</span>
+<span class="normal">1511</span>
+<span class="normal">1512</span>
+<span class="normal">1513</span>
+<span class="normal">1514</span>
+<span class="normal">1515</span>
+<span class="normal">1516</span>
+<span class="normal">1517</span>
+<span class="normal">1518</span>
+<span class="normal">1519</span>
+<span class="normal">1520</span>
+<span class="normal">1521</span>
+<span class="normal">1522</span>
+<span class="normal">1523</span>
+<span class="normal">1524</span>
+<span class="normal">1525</span>
+<span class="normal">1526</span>
+<span class="normal">1527</span>
+<span class="normal">1528</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">uncertainty_counts</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s2">&quot;std&quot;</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fi</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to estimate the uncertainty of raw counts.</span>
+
+<span class="sd">    Measurements of proportional neutron detector systems are governed by counting statistics that follow a Poissonian probability distribution (Zreda et al., 2012).</span>
+<span class="sd">    The expected uncertainty in the neutron count rate $N$ is defined by the standard deviation $ \sqrt{N} $ (Jakobi et al., 2020).</span>
+<span class="sd">    The CV% can be expressed as $ N^{-1/2} $</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        raw_counts (array): Raw neutron counts.</span>
+<span class="sd">        metric (str): Either &#39;std&#39; or &#39;cv&#39; for standard deviation or coefficient of variation.</span>
+<span class="sd">        fp (float): Pressure correction factor.</span>
+<span class="sd">        fw (float): Humidity correction factor.</span>
+<span class="sd">        fi (float): Incoming neutron flux correction factor.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        uncertainty (float): Uncertainty of raw counts.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Jakobi J, Huisman JA, Schrön M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With</span>
+<span class="sd">        Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010</span>
+
+<span class="sd">        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System,</span>
+<span class="sd">        Hydrol. Earth Syst. Sci., 16, 4079–4099, https://doi.org/10.5194/hess-16-4079-2012, 2012.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">s</span> <span class="o">=</span> <span class="n">fw</span> <span class="o">/</span> <span class="p">(</span><span class="n">fp</span> <span class="o">*</span> <span class="n">fi</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">metric</span> <span class="o">==</span> <span class="s2">&quot;std&quot;</span><span class="p">:</span>
+        <span class="n">uncertainty</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">)</span> <span class="o">*</span> <span class="n">s</span>
+    <span class="k">elif</span> <span class="n">metric</span> <span class="o">==</span> <span class="s2">&quot;cv&quot;</span><span class="p">:</span>
+        <span class="n">uncertainty</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">)</span> <span class="o">*</span> <span class="n">s</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="sa">f</span><span class="s2">&quot;Metric </span><span class="si">{</span><span class="n">metric</span><span class="si">}</span><span class="s2"> does not exist. Provide either &#39;std&#39; or &#39;cv&#39; for standard deviation or coefficient of variation.&quot;</span>
+    <span class="k">return</span> <span class="n">uncertainty</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.crnpy.uncertainty_vwc" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">uncertainty_vwc</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">,</span> <span class="n">N0</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fi</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">a0</span><span class="o">=</span><span class="mf">0.0808</span><span class="p">,</span> <span class="n">a1</span><span class="o">=</span><span class="mf">0.372</span><span class="p">,</span> <span class="n">a2</span><span class="o">=</span><span class="mf">0.115</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to estimate the uncertainty propagated to volumetric water content.</p>
+<p>The uncertainty of the volumetric water content is estimated by propagating the uncertainty of the raw counts.
+Following Eq. 10 in Jakobi et al. (2020), the uncertainty of the volumetric water content can be expressed as:
+$$
+\sigma_{\theta_g}(N) = \sigma_N \frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \sqrt{(N_{cor} - a_1 N_0)^4 + 8 \sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \sigma_N^4}
+$$</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>raw_counts</code></td>
+          <td>
+                <code>array</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Raw neutron counts.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>N0</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Calibration parameter N0.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>bulk_density</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Bulk density in g cm-3.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>fp</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Pressure correction factor.</p>
+            </div>
+          </td>
+          <td>
+                <code>1</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>fw</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Humidity correction factor.</p>
+            </div>
+          </td>
+          <td>
+                <code>1</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>fi</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Incoming neutron flux correction factor.</p>
+            </div>
+          </td>
+          <td>
+                <code>1</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+<th>Name</th>        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+<td><code>sigma_VWC</code></td>          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Uncertainty in terms of volumetric water content.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Jakobi J, Huisman JA, Schrön M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With
+Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1531</span>
+<span class="normal">1532</span>
+<span class="normal">1533</span>
+<span class="normal">1534</span>
+<span class="normal">1535</span>
+<span class="normal">1536</span>
+<span class="normal">1537</span>
+<span class="normal">1538</span>
+<span class="normal">1539</span>
+<span class="normal">1540</span>
+<span class="normal">1541</span>
+<span class="normal">1542</span>
+<span class="normal">1543</span>
+<span class="normal">1544</span>
+<span class="normal">1545</span>
+<span class="normal">1546</span>
+<span class="normal">1547</span>
+<span class="normal">1548</span>
+<span class="normal">1549</span>
+<span class="normal">1550</span>
+<span class="normal">1551</span>
+<span class="normal">1552</span>
+<span class="normal">1553</span>
+<span class="normal">1554</span>
+<span class="normal">1555</span>
+<span class="normal">1556</span>
+<span class="normal">1557</span>
+<span class="normal">1558</span>
+<span class="normal">1559</span>
+<span class="normal">1560</span>
+<span class="normal">1561</span>
+<span class="normal">1562</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">uncertainty_vwc</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">,</span> <span class="n">N0</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fi</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">a0</span><span class="o">=</span><span class="mf">0.0808</span><span class="p">,</span> <span class="n">a1</span><span class="o">=</span><span class="mf">0.372</span><span class="p">,</span> <span class="n">a2</span><span class="o">=</span><span class="mf">0.115</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Function to estimate the uncertainty propagated to volumetric water content.</span>
+
+<span class="sd">    The uncertainty of the volumetric water content is estimated by propagating the uncertainty of the raw counts.</span>
+<span class="sd">    Following Eq. 10 in Jakobi et al. (2020), the uncertainty of the volumetric water content can be expressed as:</span>
+<span class="sd">    $$</span>
+<span class="sd">    \sigma_{\theta_g}(N) = \sigma_N \frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \sqrt{(N_{cor} - a_1 N_0)^4 + 8 \sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \sigma_N^4}</span>
+<span class="sd">    $$</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        raw_counts (array): Raw neutron counts.</span>
+<span class="sd">        N0 (float): Calibration parameter N0.</span>
+<span class="sd">        bulk_density (float): Bulk density in g cm-3.</span>
+<span class="sd">        fp (float): Pressure correction factor.</span>
+<span class="sd">        fw (float): Humidity correction factor.</span>
+<span class="sd">        fi (float): Incoming neutron flux correction factor.</span>
 
-  
-  
+<span class="sd">    Returns:</span>
+<span class="sd">        sigma_VWC (float): Uncertainty in terms of volumetric water content.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Jakobi J, Huisman JA, Schrön M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With</span>
+<span class="sd">        Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010</span>
+<span class="sd">    &quot;&quot;&quot;</span>
 
+    <span class="n">Ncorr</span> <span class="o">=</span> <span class="n">raw_counts</span> <span class="o">*</span> <span class="n">fw</span> <span class="o">/</span> <span class="p">(</span><span class="n">fp</span> <span class="o">*</span> <span class="n">fi</span><span class="p">)</span>
+    <span class="n">sigma_N</span> <span class="o">=</span> <span class="n">uncertainty_counts</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s2">&quot;std&quot;</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="n">fp</span><span class="p">,</span> <span class="n">fw</span><span class="o">=</span><span class="n">fw</span><span class="p">,</span> <span class="n">fi</span><span class="o">=</span><span class="n">fi</span><span class="p">)</span>
+    <span class="n">sigma_GWC</span> <span class="o">=</span> <span class="n">sigma_N</span> <span class="o">*</span> <span class="p">((</span><span class="n">a0</span> <span class="o">*</span> <span class="n">N0</span><span class="p">)</span> <span class="o">/</span> <span class="p">((</span><span class="n">Ncorr</span> <span class="o">-</span> <span class="n">a1</span> <span class="o">*</span> <span class="n">N0</span><span class="p">)</span> <span class="o">**</span> <span class="mi">4</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span>
+        <span class="p">(</span><span class="n">Ncorr</span> <span class="o">-</span> <span class="n">a1</span> <span class="o">*</span> <span class="n">N0</span><span class="p">)</span> <span class="o">**</span> <span class="mi">4</span> <span class="o">+</span> <span class="mi">8</span> <span class="o">*</span> <span class="n">sigma_N</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">Ncorr</span> <span class="o">-</span> <span class="n">a1</span> <span class="o">*</span> <span class="n">N0</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">15</span> <span class="o">*</span> <span class="n">sigma_N</span> <span class="o">**</span> <span class="mi">4</span><span class="p">)</span>
+    <span class="n">sigma_VWC</span> <span class="o">=</span> <span class="n">sigma_GWC</span> <span class="o">*</span> <span class="n">bulk_density</span>
 
-<h1 id="reference">Reference</h1>
+    <span class="k">return</span> <span class="n">sigma_VWC</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+
+  </div>
+
+  </div>
 
+</div>
 
 <div class="doc doc-object doc-module">
 
 
-<a id="crnpy.crnpy"></a>
+
+<a id="crnpy.data"></a>
   <div class="doc doc-contents first">
   
-      <p><code>crnpy</code> is a Python package for processing cosmic ray neutron data.</p>
-<p>Created by Joaquin Peraza and Andres Patrignani.</p>
+      <p><code>crnpy</code> is a package for processing observations from cosmic ray neutron detectors.</p>
+<p>Modules exported by this package:</p>
+<ul>
+<li><code>crnpy</code>: Provide several functions to process observations from cosmic ray neutron detectors.</li>
+</ul>
 
   
 
@@ -941,12 +8868,14 @@ <h1 id="reference">Reference</h1>
 
 
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.abs_humidity" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">abs_humidity</span><span class="p">(</span><span class="n">relative_humidity</span><span class="p">,</span> <span class="n">temp</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.abs_humidity" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">abs_humidity</span><span class="p">(</span><span class="n">relative_humidity</span><span class="p">,</span> <span class="n">temp</span><span class="p">)</span></code>
 
 </h2>
 
@@ -955,6 +8884,8 @@ <h2 id="crnpy.crnpy.abs_humidity" class="doc doc-heading">
   
       <p>Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -971,7 +8902,11 @@ <h2 id="crnpy.crnpy.abs_humidity" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>relative humidity (%)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>relative humidity (%)</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -981,7 +8916,11 @@ <h2 id="crnpy.crnpy.abs_humidity" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>temperature (Celsius)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>temperature (Celsius)</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -989,6 +8928,8 @@ <h2 id="crnpy.crnpy.abs_humidity" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -1001,37 +8942,41 @@ <h2 id="crnpy.crnpy.abs_humidity" class="doc doc-heading">
         <tr>
 <td><code>float</code></td>          <td>
           </td>
-          <td><p>actual vapor pressure (g m^-3)</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>actual vapor pressure (g m^-3)</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">619</span>
-<span class="normal">620</span>
-<span class="normal">621</span>
-<span class="normal">622</span>
-<span class="normal">623</span>
-<span class="normal">624</span>
-<span class="normal">625</span>
-<span class="normal">626</span>
-<span class="normal">627</span>
-<span class="normal">628</span>
-<span class="normal">629</span>
-<span class="normal">630</span>
-<span class="normal">631</span>
-<span class="normal">632</span>
-<span class="normal">633</span>
-<span class="normal">634</span>
-<span class="normal">635</span>
-<span class="normal">636</span>
-<span class="normal">637</span>
-<span class="normal">638</span>
-<span class="normal">639</span>
-<span class="normal">640</span>
-<span class="normal">641</span>
-<span class="normal">642</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">abs_humidity</span><span class="p">(</span><span class="n">relative_humidity</span><span class="p">,</span> <span class="n">temp</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">671</span>
+<span class="normal">672</span>
+<span class="normal">673</span>
+<span class="normal">674</span>
+<span class="normal">675</span>
+<span class="normal">676</span>
+<span class="normal">677</span>
+<span class="normal">678</span>
+<span class="normal">679</span>
+<span class="normal">680</span>
+<span class="normal">681</span>
+<span class="normal">682</span>
+<span class="normal">683</span>
+<span class="normal">684</span>
+<span class="normal">685</span>
+<span class="normal">686</span>
+<span class="normal">687</span>
+<span class="normal">688</span>
+<span class="normal">689</span>
+<span class="normal">690</span>
+<span class="normal">691</span>
+<span class="normal">692</span>
+<span class="normal">693</span>
+<span class="normal">694</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">abs_humidity</span><span class="p">(</span><span class="n">relative_humidity</span><span class="p">,</span> <span class="n">temp</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.</span>
 
@@ -1043,30 +8988,215 @@ <h2 id="crnpy.crnpy.abs_humidity" class="doc doc-heading">
 <span class="sd">        float: actual vapor pressure (g m^-3)</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="c1">### Atmospheric water vapor factor</span>
-    <span class="c1"># Saturation vapor pressure</span>
-    <span class="n">e_sat</span> <span class="o">=</span> <span class="mf">0.611</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mf">17.502</span> <span class="o">*</span> <span class="n">temp</span> <span class="o">/</span> <span class="p">(</span>
-                <span class="n">temp</span> <span class="o">+</span> <span class="mf">240.97</span><span class="p">))</span> <span class="o">*</span> <span class="mi">1000</span>  <span class="c1"># in Pascals Eq. 3.8 p.41 Environmental Biophysics (Campbell and Norman)</span>
+    <span class="c1">### Atmospheric water vapor factor</span>
+    <span class="c1"># Saturation vapor pressure</span>
+    <span class="n">e_sat</span> <span class="o">=</span> <span class="mf">0.611</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mf">17.502</span> <span class="o">*</span> <span class="n">temp</span> <span class="o">/</span> <span class="p">(</span>
+            <span class="n">temp</span> <span class="o">+</span> <span class="mf">240.97</span><span class="p">))</span> <span class="o">*</span> <span class="mi">1000</span>  <span class="c1"># in Pascals Eq. 3.8 p.41 Environmental Biophysics (Campbell and Norman)</span>
+
+    <span class="c1"># Vapor pressure Pascals</span>
+    <span class="n">Pw</span> <span class="o">=</span> <span class="n">e_sat</span> <span class="o">*</span> <span class="n">relative_humidity</span> <span class="o">/</span> <span class="mi">100</span>
+
+    <span class="c1"># Absolute humidity (g/m^3)</span>
+    <span class="n">C</span> <span class="o">=</span> <span class="mf">2.16679</span>  <span class="c1"># g K/J;</span>
+    <span class="n">abs_h</span> <span class="o">=</span> <span class="n">C</span> <span class="o">*</span> <span class="n">Pw</span> <span class="o">/</span> <span class="p">(</span><span class="n">temp</span> <span class="o">+</span> <span class="mf">273.15</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">abs_h</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.data.atmospheric_depth" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">atmospheric_depth</span><span class="p">(</span><span class="n">elevation</span><span class="p">,</span> <span class="n">latitude</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to estimate the atmospheric depth for any point on Earth according to McJannet and Desilets, 2023</p>
+<p>This function is required in the calculation of the location-dependent reference correction proposed by McJannet and Desilets, 2023.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>elevation</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Elevation in meters above sea level.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>latitude</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Geographic latitude in decimal degrees. Value in range -90 to 90</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Atmospheric depth in g/cm2</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Atmosphere, U. S. (1976). US standard atmosphere. National Oceanic and Atmospheric Administration.</p>
+<p>McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1059</span>
+<span class="normal">1060</span>
+<span class="normal">1061</span>
+<span class="normal">1062</span>
+<span class="normal">1063</span>
+<span class="normal">1064</span>
+<span class="normal">1065</span>
+<span class="normal">1066</span>
+<span class="normal">1067</span>
+<span class="normal">1068</span>
+<span class="normal">1069</span>
+<span class="normal">1070</span>
+<span class="normal">1071</span>
+<span class="normal">1072</span>
+<span class="normal">1073</span>
+<span class="normal">1074</span>
+<span class="normal">1075</span>
+<span class="normal">1076</span>
+<span class="normal">1077</span>
+<span class="normal">1078</span>
+<span class="normal">1079</span>
+<span class="normal">1080</span>
+<span class="normal">1081</span>
+<span class="normal">1082</span>
+<span class="normal">1083</span>
+<span class="normal">1084</span>
+<span class="normal">1085</span>
+<span class="normal">1086</span>
+<span class="normal">1087</span>
+<span class="normal">1088</span>
+<span class="normal">1089</span>
+<span class="normal">1090</span>
+<span class="normal">1091</span>
+<span class="normal">1092</span>
+<span class="normal">1093</span>
+<span class="normal">1094</span>
+<span class="normal">1095</span>
+<span class="normal">1096</span>
+<span class="normal">1097</span>
+<span class="normal">1098</span>
+<span class="normal">1099</span>
+<span class="normal">1100</span>
+<span class="normal">1101</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">atmospheric_depth</span><span class="p">(</span><span class="n">elevation</span><span class="p">,</span> <span class="n">latitude</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to estimate the atmospheric depth for any point on Earth according to McJannet and Desilets, 2023</span>
+
+<span class="sd">    This function is required in the calculation of the location-dependent reference correction proposed by McJannet and Desilets, 2023.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        elevation (float): Elevation in meters above sea level.</span>
+<span class="sd">        latitude (float): Geographic latitude in decimal degrees. Value in range -90 to 90</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (float): Atmospheric depth in g/cm2</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Atmosphere, U. S. (1976). US standard atmosphere. National Oceanic and Atmospheric Administration.</span>
 
-    <span class="c1"># Vapor pressure Pascals</span>
-    <span class="n">Pw</span> <span class="o">=</span> <span class="n">e_sat</span> <span class="o">*</span> <span class="n">relative_humidity</span> <span class="o">/</span> <span class="mi">100</span>
+<span class="sd">        McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="c1"># Absolute humidity (g/m^3)</span>
-    <span class="n">C</span> <span class="o">=</span> <span class="mf">2.16679</span>  <span class="c1"># g K/J;</span>
-    <span class="n">abs_h</span> <span class="o">=</span> <span class="n">C</span> <span class="o">*</span> <span class="n">Pw</span> <span class="o">/</span> <span class="p">(</span><span class="n">temp</span> <span class="o">+</span> <span class="mf">273.15</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">abs_h</span>
+    <span class="n">density_of_rock</span> <span class="o">=</span> <span class="mi">2670</span>  <span class="c1"># Density of rock in kg/m3</span>
+    <span class="n">air_pressure_sea_level</span> <span class="o">=</span> <span class="mf">1013.25</span>  <span class="c1"># Air pressure at sea level in hPa</span>
+    <span class="n">air_molar_mass</span> <span class="o">=</span> <span class="mf">0.0289644</span>  <span class="c1"># Air molar mass in kg/mol</span>
+    <span class="n">universal_gas_constant</span> <span class="o">=</span> <span class="mf">8.3144598</span>  <span class="c1"># Universal gas constant in J/(mol*K)</span>
+    <span class="n">reference_temperature</span> <span class="o">=</span> <span class="mf">288.15</span>  <span class="c1"># Reference temperature Kelvin</span>
+    <span class="n">temperature_lapse_rate</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.0065</span>  <span class="c1"># Temperature lapse rate in K/m</span>
+
+    <span class="c1"># Gravity at sea-level calculation</span>
+    <span class="n">gravity_sea_level</span> <span class="o">=</span> <span class="mf">9.780327</span> <span class="o">*</span> <span class="p">(</span>
+            <span class="mi">1</span> <span class="o">+</span> <span class="mf">0.0053024</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">radians</span><span class="p">(</span><span class="n">latitude</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">-</span> <span class="mf">0.0000058</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">radians</span><span class="p">(</span><span class="n">latitude</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="c1"># Free air correction</span>
+    <span class="n">free_air</span> <span class="o">=</span> <span class="o">-</span><span class="mf">3.086</span> <span class="o">*</span> <span class="mi">10</span> <span class="o">**</span> <span class="o">-</span><span class="mi">6</span> <span class="o">*</span> <span class="n">elevation</span>
+    <span class="c1"># Bouguer correction</span>
+    <span class="n">bouguer_corr</span> <span class="o">=</span> <span class="mf">4.194</span> <span class="o">*</span> <span class="mi">10</span> <span class="o">**</span> <span class="o">-</span><span class="mi">10</span> <span class="o">*</span> <span class="n">density_of_rock</span> <span class="o">*</span> <span class="n">elevation</span>
+    <span class="c1"># Total gravity</span>
+    <span class="n">gravity</span> <span class="o">=</span> <span class="n">gravity_sea_level</span> <span class="o">+</span> <span class="n">free_air</span> <span class="o">+</span> <span class="n">bouguer_corr</span>
+
+    <span class="c1"># Air pressure calculation</span>
+    <span class="n">reference_air_pressure</span> <span class="o">=</span> <span class="n">air_pressure_sea_level</span> <span class="o">*</span> <span class="p">(</span>
+                <span class="mi">1</span> <span class="o">+</span> <span class="n">temperature_lapse_rate</span> <span class="o">/</span> <span class="n">reference_temperature</span> <span class="o">*</span> <span class="n">elevation</span><span class="p">)</span> <span class="o">**</span> <span class="p">((</span><span class="o">-</span><span class="n">gravity</span> <span class="o">*</span> <span class="n">air_molar_mass</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span>
+                <span class="n">universal_gas_constant</span> <span class="o">*</span> <span class="n">temperature_lapse_rate</span><span class="p">))</span>
+
+    <span class="c1"># Atmospheric depth calculation</span>
+    <span class="n">atmospheric_depth</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span> <span class="o">*</span> <span class="n">reference_air_pressure</span><span class="p">)</span> <span class="o">/</span> <span class="n">gravity</span>
+    <span class="k">return</span> <span class="n">atmospheric_depth</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.biomass_to_bwe" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">biomass_to_bwe</span><span class="p">(</span><span class="n">biomass_dry</span><span class="p">,</span> <span class="n">biomass_fresh</span><span class="p">,</span> <span class="n">fWE</span><span class="o">=</span><span class="mf">0.494</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.biomass_to_bwe" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">biomass_to_bwe</span><span class="p">(</span><span class="n">biomass_dry</span><span class="p">,</span> <span class="n">biomass_fresh</span><span class="p">,</span> <span class="n">fWE</span><span class="o">=</span><span class="mf">0.494</span><span class="p">)</span></code>
 
 </h2>
 
@@ -1075,6 +9205,8 @@ <h2 id="crnpy.crnpy.biomass_to_bwe" class="doc doc-heading">
   
       <p>Function to convert biomass to biomass water equivalent.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -1089,9 +9221,13 @@ <h2 id="crnpy.crnpy.biomass_to_bwe" class="doc doc-heading">
         <tr>
           <td><code>biomass_dry</code></td>
           <td>
-                <code>array or pd.Series or pd.DataFrame</code>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Above ground dry biomass in kg m-2.</p>
+            </div>
           </td>
-          <td><p>Above ground dry biomass in kg m-2.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -1099,9 +9235,13 @@ <h2 id="crnpy.crnpy.biomass_to_bwe" class="doc doc-heading">
         <tr>
           <td><code>biomass_fresh</code></td>
           <td>
-                <code>array or pd.Series or pd.DataFrame</code>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Above ground fresh biomass in kg m-2.</p>
+            </div>
           </td>
-          <td><p>Above ground fresh biomass in kg m-2.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -1111,8 +9251,12 @@ <h2 id="crnpy.crnpy.biomass_to_bwe" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Stoichiometric ratio of H2O to organic carbon molecules in the plant (assuming this is mostly cellulose)
-Default is 0.494 (Wahbi &amp; Avery, 2018).</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Stoichiometric ratio of H2O to organic carbon molecules in the plant (assuming this is mostly cellulose)
+Default is 0.494 (Wahbi &amp; Avery, 2018).</p>
+            </div>
+          </td>
           <td>
                 <code>0.494</code>
           </td>
@@ -1120,6 +9264,8 @@ <h2 id="crnpy.crnpy.biomass_to_bwe" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -1131,9 +9277,13 @@ <h2 id="crnpy.crnpy.biomass_to_bwe" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code>array or pd.Series or pd.DataFrame</code>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Biomass water equivalent in kg m-2.</p>
+            </div>
           </td>
-          <td><p>Biomass water equivalent in kg m-2.</p></td>
         </tr>
     </tbody>
   </table>
@@ -1144,26 +9294,26 @@ <h2 id="crnpy.crnpy.biomass_to_bwe" class="doc doc-heading">
 Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent.
 Springer, Cham. https://doi.org/10.1007/978-3-319-69539-6_2</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">488</span>
-<span class="normal">489</span>
-<span class="normal">490</span>
-<span class="normal">491</span>
-<span class="normal">492</span>
-<span class="normal">493</span>
-<span class="normal">494</span>
-<span class="normal">495</span>
-<span class="normal">496</span>
-<span class="normal">497</span>
-<span class="normal">498</span>
-<span class="normal">499</span>
-<span class="normal">500</span>
-<span class="normal">501</span>
-<span class="normal">502</span>
-<span class="normal">503</span>
-<span class="normal">504</span>
-<span class="normal">505</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">biomass_to_bwe</span><span class="p">(</span><span class="n">biomass_dry</span><span class="p">,</span> <span class="n">biomass_fresh</span><span class="p">,</span> <span class="n">fWE</span><span class="o">=</span><span class="mf">0.494</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">539</span>
+<span class="normal">540</span>
+<span class="normal">541</span>
+<span class="normal">542</span>
+<span class="normal">543</span>
+<span class="normal">544</span>
+<span class="normal">545</span>
+<span class="normal">546</span>
+<span class="normal">547</span>
+<span class="normal">548</span>
+<span class="normal">549</span>
+<span class="normal">550</span>
+<span class="normal">551</span>
+<span class="normal">552</span>
+<span class="normal">553</span>
+<span class="normal">554</span>
+<span class="normal">555</span>
+<span class="normal">556</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">biomass_to_bwe</span><span class="p">(</span><span class="n">biomass_dry</span><span class="p">,</span> <span class="n">biomass_fresh</span><span class="p">,</span> <span class="n">fWE</span><span class="o">=</span><span class="mf">0.494</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to convert biomass to biomass water equivalent.</span>
 
 <span class="sd">    Args:</span>
@@ -1182,17 +9332,19 @@ <h2 id="crnpy.crnpy.biomass_to_bwe" class="doc doc-heading">
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="p">(</span><span class="n">biomass_fresh</span> <span class="o">-</span> <span class="n">biomass_dry</span><span class="p">)</span> <span class="o">+</span> <span class="n">fWE</span> <span class="o">*</span> <span class="n">biomass_dry</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.correction_bwe" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">correction_bwe</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">bwe</span><span class="p">,</span> <span class="n">r2_N0</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.correction_bwe" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">correction_bwe</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">bwe</span><span class="p">,</span> <span class="n">r2_N0</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span></code>
 
 </h2>
 
@@ -1202,6 +9354,8 @@ <h2 id="crnpy.crnpy.correction_bwe" class="doc doc-heading">
       <p>Function to correct for biomass effects in neutron counts.
 following the approach described in Baatz et al., 2015.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -1216,9 +9370,13 @@ <h2 id="crnpy.crnpy.correction_bwe" class="doc doc-heading">
         <tr>
           <td><code>counts</code></td>
           <td>
-                <code>array or pd.Series or pd.DataFrame</code>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Array of ephithermal neutron counts.</p>
+            </div>
           </td>
-          <td><p>Array of ephithermal neutron counts.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -1228,7 +9386,11 @@ <h2 id="crnpy.crnpy.correction_bwe" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Biomass water equivalent kg m-2.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Biomass water equivalent kg m-2.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1238,7 +9400,11 @@ <h2 id="crnpy.crnpy.correction_bwe" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.</p>
+            </div>
+          </td>
           <td>
                 <code>0.05</code>
           </td>
@@ -1246,6 +9412,8 @@ <h2 id="crnpy.crnpy.correction_bwe" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -1257,9 +9425,13 @@ <h2 id="crnpy.crnpy.correction_bwe" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code>array or pd.Series or pd.DataFrame</code>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Array of corrected neutron counts for biomass effects.</p>
+            </div>
           </td>
-          <td><p>Array of corrected neutron counts for biomass effects.</p></td>
         </tr>
     </tbody>
   </table>
@@ -1270,27 +9442,27 @@ <h2 id="crnpy.crnpy.correction_bwe" class="doc doc-heading">
 An empiricalvegetation correction for soil water content quantification using cosmic ray probes,
 Water Resour. Res., 51, 2030–2046, doi:10.1002/ 2014WR016443.</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">468</span>
-<span class="normal">469</span>
-<span class="normal">470</span>
-<span class="normal">471</span>
-<span class="normal">472</span>
-<span class="normal">473</span>
-<span class="normal">474</span>
-<span class="normal">475</span>
-<span class="normal">476</span>
-<span class="normal">477</span>
-<span class="normal">478</span>
-<span class="normal">479</span>
-<span class="normal">480</span>
-<span class="normal">481</span>
-<span class="normal">482</span>
-<span class="normal">483</span>
-<span class="normal">484</span>
-<span class="normal">485</span>
-<span class="normal">486</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_bwe</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">bwe</span><span class="p">,</span> <span class="n">r2_N0</span><span class="o">=</span><span class="mf">0.05</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">518</span>
+<span class="normal">519</span>
+<span class="normal">520</span>
+<span class="normal">521</span>
+<span class="normal">522</span>
+<span class="normal">523</span>
+<span class="normal">524</span>
+<span class="normal">525</span>
+<span class="normal">526</span>
+<span class="normal">527</span>
+<span class="normal">528</span>
+<span class="normal">529</span>
+<span class="normal">530</span>
+<span class="normal">531</span>
+<span class="normal">532</span>
+<span class="normal">533</span>
+<span class="normal">534</span>
+<span class="normal">535</span>
+<span class="normal">536</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_bwe</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">bwe</span><span class="p">,</span> <span class="n">r2_N0</span><span class="o">=</span><span class="mf">0.05</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to correct for biomass effects in neutron counts.</span>
 <span class="sd">    following the approach described in Baatz et al., 2015.</span>
 
@@ -1308,19 +9480,21 @@ <h2 id="crnpy.crnpy.correction_bwe" class="doc doc-heading">
 <span class="sd">        Water Resour. Res., 51, 2030–2046, doi:10.1002/ 2014WR016443.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">return</span> <span class="n">counts</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">bwe</span><span class="o">*</span><span class="n">r2_N0</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">counts</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">bwe</span> <span class="o">*</span> <span class="n">r2_N0</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.correction_humidity" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">correction_humidity</span><span class="p">(</span><span class="n">abs_humidity</span><span class="p">,</span> <span class="n">Aref</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.correction_humidity" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">correction_humidity</span><span class="p">(</span><span class="n">abs_humidity</span><span class="p">,</span> <span class="n">Aref</span><span class="p">)</span></code>
 
 </h2>
 
@@ -1347,6 +9521,8 @@ <h2 id="crnpy.crnpy.correction_humidity" class="doc doc-heading">
 <li>Aref: reference absolute humidity</li>
 </ul>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -1363,17 +9539,11 @@ <h2 id="crnpy.crnpy.correction_humidity" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>Relative humidity readings.</p></td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr>
-          <td><code>temp</code></td>
           <td>
-                <code>list or array</code>
+            <div class="doc-md-description">
+              <p>Relative humidity readings.</p>
+            </div>
           </td>
-          <td><p>Temperature readings (Celsius).</p></td>
           <td>
               <em>required</em>
           </td>
@@ -1383,7 +9553,11 @@ <h2 id="crnpy.crnpy.correction_humidity" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1391,6 +9565,8 @@ <h2 id="crnpy.crnpy.correction_humidity" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -1404,7 +9580,11 @@ <h2 id="crnpy.crnpy.correction_humidity" class="doc doc-heading">
           <td>
                 <code>list</code>
           </td>
-          <td><p>fw correction factor.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>fw correction factor.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
@@ -1413,9 +9593,11 @@ <h2 id="crnpy.crnpy.correction_humidity" class="doc doc-heading">
   <summary>References</summary>
   <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">255</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">253</span>
+<span class="normal">254</span>
+<span class="normal">255</span>
 <span class="normal">256</span>
 <span class="normal">257</span>
 <span class="normal">258</span>
@@ -1449,10 +9631,7 @@ <h2 id="crnpy.crnpy.correction_humidity" class="doc doc-heading">
 <span class="normal">286</span>
 <span class="normal">287</span>
 <span class="normal">288</span>
-<span class="normal">289</span>
-<span class="normal">290</span>
-<span class="normal">291</span>
-<span class="normal">292</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_humidity</span><span class="p">(</span><span class="n">abs_humidity</span><span class="p">,</span> <span class="n">Aref</span><span class="p">):</span>
+<span class="normal">289</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_humidity</span><span class="p">(</span><span class="n">abs_humidity</span><span class="p">,</span> <span class="n">Aref</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Correction factor for absolute humidity.</span>
 
 <span class="sd">    This function corrects neutron counts for absolute humidity using the method described in Rosolem et al. (2013) and Anderson et al. (2017). The correction is performed using the following equation:</span>
@@ -1478,7 +9657,6 @@ <h2 id="crnpy.crnpy.correction_humidity" class="doc doc-heading">
 
 <span class="sd">    Args:</span>
 <span class="sd">        abs_humidity (list or array): Relative humidity readings.</span>
-<span class="sd">        temp (list or array): Temperature readings (Celsius).</span>
 <span class="sd">        Aref (float): Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.</span>
 
 <span class="sd">    Returns:</span>
@@ -1488,20 +9666,22 @@ <h2 id="crnpy.crnpy.correction_humidity" class="doc doc-heading">
 <span class="sd">        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">A</span> <span class="o">=</span> <span class="n">abs_humidity</span>
-    <span class="n">fw</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="mf">0.0054</span><span class="o">*</span><span class="p">(</span><span class="n">A</span> <span class="o">-</span> <span class="n">Aref</span><span class="p">)</span> <span class="c1"># Zreda et al. 2017 Eq 6.</span>
+    <span class="n">fw</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="mf">0.0054</span> <span class="o">*</span> <span class="p">(</span><span class="n">A</span> <span class="o">-</span> <span class="n">Aref</span><span class="p">)</span>  <span class="c1"># Zreda et al. 2017 Eq 6.</span>
     <span class="k">return</span> <span class="n">fw</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.correction_incoming_flux" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">correction_incoming_flux</span><span class="p">(</span><span class="n">incoming_neutrons</span><span class="p">,</span> <span class="n">incoming_Ref</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.correction_incoming_flux" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">correction_incoming_flux</span><span class="p">(</span><span class="n">incoming_neutrons</span><span class="p">,</span> <span class="n">incoming_Ref</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fill_na</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Rc_method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Rc_site</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">site_atmdepth</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Rc_ref</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ref_atmdepth</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
 </h2>
 
@@ -1528,6 +9708,8 @@ <h2 id="crnpy.crnpy.correction_incoming_flux" class="doc doc-heading">
 <li>Iref: reference incoming neutron flux</li>
 </ul>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -1544,7 +9726,11 @@ <h2 id="crnpy.crnpy.correction_incoming_flux" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>Incoming neutron flux readings.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Incoming neutron flux readings.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1554,7 +9740,11 @@ <h2 id="crnpy.crnpy.correction_incoming_flux" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Reference incoming neutron flux. Baseline incoming neutron flux.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Reference incoming neutron flux. Baseline incoming neutron flux.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -1562,6 +9752,8 @@ <h2 id="crnpy.crnpy.correction_incoming_flux" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -1575,7 +9767,11 @@ <h2 id="crnpy.crnpy.correction_incoming_flux" class="doc doc-heading">
           <td>
                 <code>list</code>
           </td>
-          <td><p>fi correction factor.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>fi correction factor.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
@@ -1584,9 +9780,11 @@ <h2 id="crnpy.crnpy.correction_incoming_flux" class="doc doc-heading">
   <summary>References</summary>
   <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">294</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">292</span>
+<span class="normal">293</span>
+<span class="normal">294</span>
 <span class="normal">295</span>
 <span class="normal">296</span>
 <span class="normal">297</span>
@@ -1628,7 +9826,27 @@ <h2 id="crnpy.crnpy.correction_incoming_flux" class="doc doc-heading">
 <span class="normal">333</span>
 <span class="normal">334</span>
 <span class="normal">335</span>
-<span class="normal">336</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_incoming_flux</span><span class="p">(</span><span class="n">incoming_neutrons</span><span class="p">,</span> <span class="n">incoming_Ref</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="normal">336</span>
+<span class="normal">337</span>
+<span class="normal">338</span>
+<span class="normal">339</span>
+<span class="normal">340</span>
+<span class="normal">341</span>
+<span class="normal">342</span>
+<span class="normal">343</span>
+<span class="normal">344</span>
+<span class="normal">345</span>
+<span class="normal">346</span>
+<span class="normal">347</span>
+<span class="normal">348</span>
+<span class="normal">349</span>
+<span class="normal">350</span>
+<span class="normal">351</span>
+<span class="normal">352</span>
+<span class="normal">353</span>
+<span class="normal">354</span>
+<span class="normal">355</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_incoming_flux</span><span class="p">(</span><span class="n">incoming_neutrons</span><span class="p">,</span> <span class="n">incoming_Ref</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fill_na</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Rc_method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Rc_site</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                             <span class="n">site_atmdepth</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Rc_ref</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ref_atmdepth</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Correction factor for incoming neutron flux.</span>
 
 <span class="sd">    This function corrects neutron counts for incoming neutron flux using the method described in Anderson et al. (2017). The correction is performed using the following equation:</span>
@@ -1668,21 +9886,43 @@ <h2 id="crnpy.crnpy.correction_incoming_flux" class="doc doc-heading">
         <span class="n">incoming_Ref</span> <span class="o">=</span> <span class="n">incoming_neutrons</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Reference incoming neutron flux not provided. Using first value of incoming neutron flux.&#39;</span><span class="p">)</span>
     <span class="n">fi</span> <span class="o">=</span> <span class="n">incoming_neutrons</span> <span class="o">/</span> <span class="n">incoming_Ref</span>
-    <span class="n">fi</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>  <span class="c1"># Use a value of 1 for days without data</span>
+
+    <span class="k">if</span> <span class="n">Rc_method</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">Rc_ref</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Reference cutoff rigidity not provided.&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">Rc_site</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Site cutoff rigidity not provided.&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">Rc_method</span> <span class="o">==</span> <span class="s1">&#39;McJannetandDesilets2023&#39;</span><span class="p">:</span>
+            <span class="n">tau</span> <span class="o">=</span> <span class="n">location_factor</span><span class="p">(</span><span class="n">site_atmdepth</span><span class="p">,</span> <span class="n">Rc_site</span><span class="p">,</span> <span class="n">ref_atmdepth</span><span class="p">,</span> <span class="n">Rc_ref</span><span class="p">)</span>
+            <span class="n">fi</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">tau</span> <span class="o">*</span> <span class="n">fi</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">tau</span><span class="p">)</span>
+
+        <span class="k">elif</span> <span class="n">Rc_method</span> <span class="o">==</span> <span class="s1">&#39;Hawdonetal2014&#39;</span><span class="p">:</span>
+            <span class="n">Rc_corr</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.075</span> <span class="o">*</span> <span class="p">(</span><span class="n">Rc_site</span> <span class="o">-</span> <span class="n">Rc_ref</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.0</span>
+            <span class="n">fi</span> <span class="o">=</span> <span class="p">(</span><span class="n">fi</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span> <span class="o">*</span> <span class="n">Rc_corr</span> <span class="o">+</span> <span class="mf">1.0</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;Cutoff rigidity method not found. Valid options are: McJannetandDesilets2023, Hawdonetal2014.&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">fill_na</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fi</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">fill_na</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>  <span class="c1"># Use a value of 1 for days without data</span>
 
     <span class="k">return</span> <span class="n">fi</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.correction_pressure" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">correction_pressure</span><span class="p">(</span><span class="n">pressure</span><span class="p">,</span> <span class="n">Pref</span><span class="p">,</span> <span class="n">L</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.correction_pressure" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">correction_pressure</span><span class="p">(</span><span class="n">pressure</span><span class="p">,</span> <span class="n">Pref</span><span class="p">,</span> <span class="n">L</span><span class="p">)</span></code>
 
 </h2>
 
@@ -1711,6 +9951,8 @@ <h2 id="crnpy.crnpy.correction_pressure" class="doc doc-heading">
 <li>L: Atmospheric attenuation coefficient.</li>
 </ul>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -1723,11 +9965,15 @@ <h2 id="crnpy.crnpy.correction_pressure" class="doc doc-heading">
     </thead>
     <tbody>
         <tr>
-          <td><code>atm_pressure</code></td>
+          <td><code>pressure</code></td>
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>Atmospheric pressure readings. Long-term average pressure is recommended.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Atmospheric pressure readings. Long-term average pressure is recommended.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1737,7 +9983,11 @@ <h2 id="crnpy.crnpy.correction_pressure" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Reference atmospheric pressure.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Reference atmospheric pressure.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1747,7 +9997,11 @@ <h2 id="crnpy.crnpy.correction_pressure" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Atmospheric attenuation coefficient.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Atmospheric attenuation coefficient.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1755,6 +10009,8 @@ <h2 id="crnpy.crnpy.correction_pressure" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -1768,7 +10024,11 @@ <h2 id="crnpy.crnpy.correction_pressure" class="doc doc-heading">
           <td>
                 <code>list</code>
           </td>
-          <td><p>fp pressure correction factor.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>fp pressure correction factor.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
@@ -1777,9 +10037,11 @@ <h2 id="crnpy.crnpy.correction_pressure" class="doc doc-heading">
   <summary>References</summary>
   <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">210</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">208</span>
+<span class="normal">209</span>
+<span class="normal">210</span>
 <span class="normal">211</span>
 <span class="normal">212</span>
 <span class="normal">213</span>
@@ -1819,9 +10081,7 @@ <h2 id="crnpy.crnpy.correction_pressure" class="doc doc-heading">
 <span class="normal">247</span>
 <span class="normal">248</span>
 <span class="normal">249</span>
-<span class="normal">250</span>
-<span class="normal">251</span>
-<span class="normal">252</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_pressure</span><span class="p">(</span><span class="n">pressure</span><span class="p">,</span> <span class="n">Pref</span><span class="p">,</span> <span class="n">L</span><span class="p">):</span>
+<span class="normal">250</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_pressure</span><span class="p">(</span><span class="n">pressure</span><span class="p">,</span> <span class="n">Pref</span><span class="p">,</span> <span class="n">L</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Correction factor for atmospheric pressure.</span>
 
 <span class="sd">    This function corrects neutron counts for atmospheric pressure using the method described in Andreasen et al. (2017).</span>
@@ -1849,7 +10109,7 @@ <h2 id="crnpy.crnpy.correction_pressure" class="doc doc-heading">
 
 
 <span class="sd">    Args:</span>
-<span class="sd">        atm_pressure (list or array): Atmospheric pressure readings. Long-term average pressure is recommended.</span>
+<span class="sd">        pressure (list or array): Atmospheric pressure readings. Long-term average pressure is recommended.</span>
 <span class="sd">        Pref (float): Reference atmospheric pressure.</span>
 <span class="sd">        L (float): Atmospheric attenuation coefficient.</span>
 
@@ -1861,21 +10121,23 @@ <h2 id="crnpy.crnpy.correction_pressure" class="doc doc-heading">
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="c1"># Compute pressure correction factor</span>
-    <span class="n">fp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">((</span><span class="n">Pref</span> <span class="o">-</span> <span class="n">pressure</span><span class="p">)</span> <span class="o">/</span> <span class="n">L</span><span class="p">)</span> <span class="c1"># Zreda et al. 2017 Eq 5.</span>
+    <span class="n">fp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">((</span><span class="n">Pref</span> <span class="o">-</span> <span class="n">pressure</span><span class="p">)</span> <span class="o">/</span> <span class="n">L</span><span class="p">)</span>  <span class="c1"># Zreda et al. 2017 Eq 5.</span>
 
     <span class="k">return</span> <span class="n">fp</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.correction_road" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">correction_road</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">theta_N</span><span class="p">,</span> <span class="n">road_width</span><span class="p">,</span> <span class="n">road_distance</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">theta_road</span><span class="o">=</span><span class="mf">0.12</span><span class="p">,</span> <span class="n">p0</span><span class="o">=</span><span class="mf">0.42</span><span class="p">,</span> <span class="n">p1</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">p2</span><span class="o">=</span><span class="mf">1.06</span><span class="p">,</span> <span class="n">p3</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">p4</span><span class="o">=</span><span class="mf">0.16</span><span class="p">,</span> <span class="n">p6</span><span class="o">=</span><span class="mf">0.94</span><span class="p">,</span> <span class="n">p7</span><span class="o">=</span><span class="mf">1.1</span><span class="p">,</span> <span class="n">p8</span><span class="o">=</span><span class="mf">2.7</span><span class="p">,</span> <span class="n">p9</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.correction_road" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">correction_road</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">theta_N</span><span class="p">,</span> <span class="n">road_width</span><span class="p">,</span> <span class="n">road_distance</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">theta_road</span><span class="o">=</span><span class="mf">0.12</span><span class="p">,</span> <span class="n">p0</span><span class="o">=</span><span class="mf">0.42</span><span class="p">,</span> <span class="n">p1</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">p2</span><span class="o">=</span><span class="mf">1.06</span><span class="p">,</span> <span class="n">p3</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">p4</span><span class="o">=</span><span class="mf">0.16</span><span class="p">,</span> <span class="n">p6</span><span class="o">=</span><span class="mf">0.94</span><span class="p">,</span> <span class="n">p7</span><span class="o">=</span><span class="mf">1.1</span><span class="p">,</span> <span class="n">p8</span><span class="o">=</span><span class="mf">2.7</span><span class="p">,</span> <span class="n">p9</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span></code>
 
 </h2>
 
@@ -1885,6 +10147,8 @@ <h2 id="crnpy.crnpy.correction_road" class="doc doc-heading">
       <p>Function to correct for road effects in neutron counts.
 following the approach described in Schrön et al., 2018.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -1899,9 +10163,13 @@ <h2 id="crnpy.crnpy.correction_road" class="doc doc-heading">
         <tr>
           <td><code>counts</code></td>
           <td>
-                <code>array or pd.Series or pd.DataFrame</code>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Array of ephithermal neutron counts.</p>
+            </div>
           </td>
-          <td><p>Array of ephithermal neutron counts.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -1911,7 +10179,11 @@ <h2 id="crnpy.crnpy.correction_road" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Volumetric water content of the soil estimated from the uncorrected neutron counts.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Volumetric water content of the soil estimated from the uncorrected neutron counts.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1921,7 +10193,11 @@ <h2 id="crnpy.crnpy.correction_road" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Width of the road in m.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Width of the road in m.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1931,7 +10207,11 @@ <h2 id="crnpy.crnpy.correction_road" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Distance of the road from the sensor in m. Default is 0.0.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Distance of the road from the sensor in m. Default is 0.0.</p>
+            </div>
+          </td>
           <td>
                 <code>0.0</code>
           </td>
@@ -1941,7 +10221,11 @@ <h2 id="crnpy.crnpy.correction_road" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Volumetric water content of the road. Default is 0.12.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Volumetric water content of the road. Default is 0.12.</p>
+            </div>
+          </td>
           <td>
                 <code>0.12</code>
           </td>
@@ -1951,7 +10235,11 @@ <h2 id="crnpy.crnpy.correction_road" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Parameters of the correction function. Default values are from Schrön et al., 2018.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Parameters of the correction function. Default values are from Schrön et al., 2018.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -1959,61 +10247,69 @@ <h2 id="crnpy.crnpy.correction_road" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
       <tr>
         <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr>
-          <td>
-                <code>array or pd.Series or pd.DataFrame</code>
-          </td>
-          <td><p>Array of corrected neutron counts for road effects.</p></td>
-        </tr>
-    </tbody>
-  </table>
-
-<details class="references" open>
-  <summary>References</summary>
-  <p>Schrön,M.,Rosolem,R.,Köhli,M., Piussi,L.,Schröter,I.,Iwema,J.,etal. (2018).Cosmic-ray neutron rover surveys
-of field soil moisture and the influence of roads.WaterResources Research,54,6441–6459.
-https://doi. org/10.1029/2017WR021719</p>
-</details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">508</span>
-<span class="normal">509</span>
-<span class="normal">510</span>
-<span class="normal">511</span>
-<span class="normal">512</span>
-<span class="normal">513</span>
-<span class="normal">514</span>
-<span class="normal">515</span>
-<span class="normal">516</span>
-<span class="normal">517</span>
-<span class="normal">518</span>
-<span class="normal">519</span>
-<span class="normal">520</span>
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-<span class="normal">522</span>
-<span class="normal">523</span>
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-<span class="normal">532</span>
-<span class="normal">533</span>
-<span class="normal">534</span>
-<span class="normal">535</span>
-<span class="normal">536</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_road</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">theta_N</span><span class="p">,</span> <span class="n">road_width</span><span class="p">,</span> <span class="n">road_distance</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">theta_road</span><span class="o">=</span><span class="mf">0.12</span><span class="p">,</span> <span class="n">p0</span><span class="o">=</span><span class="mf">0.42</span><span class="p">,</span> <span class="n">p1</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">p2</span><span class="o">=</span><span class="mf">1.06</span><span class="p">,</span> <span class="n">p3</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">p4</span><span class="o">=</span><span class="mf">0.16</span><span class="p">,</span> <span class="n">p6</span><span class="o">=</span><span class="mf">0.94</span><span class="p">,</span> <span class="n">p7</span><span class="o">=</span><span class="mf">1.10</span><span class="p">,</span> <span class="n">p8</span><span class="o">=</span><span class="mf">2.70</span><span class="p">,</span> <span class="n">p9</span><span class="o">=</span><span class="mf">0.01</span><span class="p">):</span>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Array of corrected neutron counts for road effects.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Schrön,M.,Rosolem,R.,Köhli,M., Piussi,L.,Schröter,I.,Iwema,J.,etal. (2018).Cosmic-ray neutron rover surveys
+of field soil moisture and the influence of roads.WaterResources Research,54,6441–6459.
+https://doi. org/10.1029/2017WR021719</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">559</span>
+<span class="normal">560</span>
+<span class="normal">561</span>
+<span class="normal">562</span>
+<span class="normal">563</span>
+<span class="normal">564</span>
+<span class="normal">565</span>
+<span class="normal">566</span>
+<span class="normal">567</span>
+<span class="normal">568</span>
+<span class="normal">569</span>
+<span class="normal">570</span>
+<span class="normal">571</span>
+<span class="normal">572</span>
+<span class="normal">573</span>
+<span class="normal">574</span>
+<span class="normal">575</span>
+<span class="normal">576</span>
+<span class="normal">577</span>
+<span class="normal">578</span>
+<span class="normal">579</span>
+<span class="normal">580</span>
+<span class="normal">581</span>
+<span class="normal">582</span>
+<span class="normal">583</span>
+<span class="normal">584</span>
+<span class="normal">585</span>
+<span class="normal">586</span>
+<span class="normal">587</span>
+<span class="normal">588</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">correction_road</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">theta_N</span><span class="p">,</span> <span class="n">road_width</span><span class="p">,</span> <span class="n">road_distance</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">theta_road</span><span class="o">=</span><span class="mf">0.12</span><span class="p">,</span> <span class="n">p0</span><span class="o">=</span><span class="mf">0.42</span><span class="p">,</span> <span class="n">p1</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">p2</span><span class="o">=</span><span class="mf">1.06</span><span class="p">,</span> <span class="n">p3</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+                    <span class="n">p4</span><span class="o">=</span><span class="mf">0.16</span><span class="p">,</span> <span class="n">p6</span><span class="o">=</span><span class="mf">0.94</span><span class="p">,</span> <span class="n">p7</span><span class="o">=</span><span class="mf">1.10</span><span class="p">,</span> <span class="n">p8</span><span class="o">=</span><span class="mf">2.70</span><span class="p">,</span> <span class="n">p9</span><span class="o">=</span><span class="mf">0.01</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to correct for road effects in neutron counts.</span>
 <span class="sd">    following the approach described in Schrön et al., 2018.</span>
 
@@ -2033,7 +10329,7 @@ <h2 id="crnpy.crnpy.correction_road" class="doc doc-heading">
 <span class="sd">        of field soil moisture and the influence of roads.WaterResources Research,54,6441–6459.</span>
 <span class="sd">        https://doi. org/10.1029/2017WR021719</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">F1</span> <span class="o">=</span> <span class="n">p0</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p1</span><span class="o">*</span><span class="n">road_width</span><span class="p">))</span>
+    <span class="n">F1</span> <span class="o">=</span> <span class="n">p0</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p1</span> <span class="o">*</span> <span class="n">road_width</span><span class="p">))</span>
     <span class="n">F2</span> <span class="o">=</span> <span class="o">-</span><span class="n">p2</span> <span class="o">-</span> <span class="n">p3</span> <span class="o">*</span> <span class="n">theta_road</span> <span class="o">-</span> <span class="p">((</span><span class="n">p4</span> <span class="o">+</span> <span class="n">theta_road</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">theta_N</span><span class="p">))</span>
     <span class="n">F3</span> <span class="o">=</span> <span class="n">p6</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p7</span> <span class="o">*</span> <span class="p">(</span><span class="n">road_width</span> <span class="o">**</span> <span class="o">-</span><span class="n">p8</span><span class="p">)</span> <span class="o">*</span> <span class="n">road_distance</span> <span class="o">**</span> <span class="mi">4</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">p6</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p9</span> <span class="o">*</span> <span class="n">road_distance</span><span class="p">)</span>
 
@@ -2043,17 +10339,19 @@ <h2 id="crnpy.crnpy.correction_road" class="doc doc-heading">
 
     <span class="k">return</span> <span class="n">corrected_counts</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">counts_to_vwc</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">N0</span><span class="p">,</span> <span class="n">Wlat</span><span class="p">,</span> <span class="n">Wsoc</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">a0</span><span class="o">=</span><span class="mf">0.0808</span><span class="p">,</span> <span class="n">a1</span><span class="o">=</span><span class="mf">0.372</span><span class="p">,</span> <span class="n">a2</span><span class="o">=</span><span class="mf">0.115</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.counts_to_vwc" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">counts_to_vwc</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">N0</span><span class="p">,</span> <span class="n">Wlat</span><span class="p">,</span> <span class="n">Wsoc</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">a0</span><span class="o">=</span><span class="mf">0.0808</span><span class="p">,</span> <span class="n">a1</span><span class="o">=</span><span class="mf">0.372</span><span class="p">,</span> <span class="n">a2</span><span class="o">=</span><span class="mf">0.115</span><span class="p">)</span></code>
 
 </h2>
 
@@ -2064,6 +10362,8 @@ <h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
 <p>This method implements soil moisture estimation using the non-linear relationship between neutron count and soil volumetric water content following the approach described in Desilets et al., 2010.</p>
 <p>$\theta(N) =\frac{a_0}{(\frac{N}{N_0}) - a_1} - a_2 $</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -2078,9 +10378,13 @@ <h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
         <tr>
           <td><code>counts</code></td>
           <td>
-                <code>array or pd.Series or pd.DataFrame</code>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Array of corrected and filtered neutron counts.</p>
+            </div>
           </td>
-          <td><p>Array of corrected and filtered neutron counts.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -2090,7 +10394,11 @@ <h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Device-specific neutron calibration constant.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Device-specific neutron calibration constant.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -2100,7 +10408,11 @@ <h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Lattice water content.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Lattice water content.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -2110,7 +10422,11 @@ <h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Soil organic carbon content.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Soil organic carbon content.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -2120,7 +10436,11 @@ <h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Soil bulk density.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Soil bulk density.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -2130,7 +10450,11 @@ <h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Parameter given in Zreda et al., 2012. Default is 0.0808.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Parameter given in Zreda et al., 2012. Default is 0.0808.</p>
+            </div>
+          </td>
           <td>
                 <code>0.0808</code>
           </td>
@@ -2140,7 +10464,11 @@ <h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Parameter given in Zreda et al., 2012. Default is 0.372.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Parameter given in Zreda et al., 2012. Default is 0.372.</p>
+            </div>
+          </td>
           <td>
                 <code>0.372</code>
           </td>
@@ -2150,7 +10478,11 @@ <h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Parameter given in Zreda et al., 2012. Default is 0.115.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Parameter given in Zreda et al., 2012. Default is 0.115.</p>
+            </div>
+          </td>
           <td>
                 <code>0.115</code>
           </td>
@@ -2158,6 +10490,8 @@ <h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -2169,9 +10503,13 @@ <h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code>array or pd.Series or pd.DataFrame</code>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Volumetric water content in m3 m-3.</p>
+            </div>
           </td>
-          <td><p>Volumetric water content in m3 m-3.</p></td>
         </tr>
     </tbody>
   </table>
@@ -2182,37 +10520,37 @@ <h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
 Land surface hydrology at an elusive scale with cosmic rays. Water Resour. Res. 46:W11505.
 doi.org/10.1029/2009WR008726</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">538</span>
-<span class="normal">539</span>
-<span class="normal">540</span>
-<span class="normal">541</span>
-<span class="normal">542</span>
-<span class="normal">543</span>
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-<span class="normal">560</span>
-<span class="normal">561</span>
-<span class="normal">562</span>
-<span class="normal">563</span>
-<span class="normal">564</span>
-<span class="normal">565</span>
-<span class="normal">566</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">counts_to_vwc</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">N0</span><span class="p">,</span> <span class="n">Wlat</span><span class="p">,</span> <span class="n">Wsoc</span> <span class="p">,</span><span class="n">bulk_density</span><span class="p">,</span> <span class="n">a0</span><span class="o">=</span><span class="mf">0.0808</span><span class="p">,</span><span class="n">a1</span><span class="o">=</span><span class="mf">0.372</span><span class="p">,</span><span class="n">a2</span><span class="o">=</span><span class="mf">0.115</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">591</span>
+<span class="normal">592</span>
+<span class="normal">593</span>
+<span class="normal">594</span>
+<span class="normal">595</span>
+<span class="normal">596</span>
+<span class="normal">597</span>
+<span class="normal">598</span>
+<span class="normal">599</span>
+<span class="normal">600</span>
+<span class="normal">601</span>
+<span class="normal">602</span>
+<span class="normal">603</span>
+<span class="normal">604</span>
+<span class="normal">605</span>
+<span class="normal">606</span>
+<span class="normal">607</span>
+<span class="normal">608</span>
+<span class="normal">609</span>
+<span class="normal">610</span>
+<span class="normal">611</span>
+<span class="normal">612</span>
+<span class="normal">613</span>
+<span class="normal">614</span>
+<span class="normal">615</span>
+<span class="normal">616</span>
+<span class="normal">617</span>
+<span class="normal">618</span>
+<span class="normal">619</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">counts_to_vwc</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">N0</span><span class="p">,</span> <span class="n">Wlat</span><span class="p">,</span> <span class="n">Wsoc</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">a0</span><span class="o">=</span><span class="mf">0.0808</span><span class="p">,</span> <span class="n">a1</span><span class="o">=</span><span class="mf">0.372</span><span class="p">,</span> <span class="n">a2</span><span class="o">=</span><span class="mf">0.115</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Function to convert corrected and filtered neutron counts into volumetric water content.</span>
 
 <span class="sd">    This method implements soil moisture estimation using the non-linear relationship between neutron count and soil volumetric water content following the approach described in Desilets et al., 2010.</span>
@@ -2239,20 +10577,22 @@ <h2 id="crnpy.crnpy.counts_to_vwc" class="doc doc-heading">
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="c1"># Convert neutron counts into vwc</span>
-    <span class="n">vwc</span> <span class="o">=</span> <span class="p">(</span><span class="n">a0</span> <span class="o">/</span> <span class="p">(</span><span class="n">counts</span><span class="o">/</span><span class="n">N0</span><span class="o">-</span><span class="n">a1</span><span class="p">)</span> <span class="o">-</span> <span class="n">a2</span> <span class="o">-</span> <span class="n">Wlat</span> <span class="o">-</span> <span class="n">Wsoc</span><span class="p">)</span> <span class="o">*</span> <span class="n">bulk_density</span>
+    <span class="n">vwc</span> <span class="o">=</span> <span class="p">(</span><span class="n">a0</span> <span class="o">/</span> <span class="p">(</span><span class="n">counts</span> <span class="o">/</span> <span class="n">N0</span> <span class="o">-</span> <span class="n">a1</span><span class="p">)</span> <span class="o">-</span> <span class="n">a2</span> <span class="o">-</span> <span class="n">Wlat</span> <span class="o">-</span> <span class="n">Wsoc</span><span class="p">)</span> <span class="o">*</span> <span class="n">bulk_density</span>
     <span class="k">return</span> <span class="n">vwc</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.cutoff_rigidity" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">cutoff_rigidity</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.cutoff_rigidity" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">cutoff_rigidity</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">)</span></code>
 
 </h2>
 
@@ -2263,6 +10603,8 @@ <h2 id="crnpy.crnpy.cutoff_rigidity" class="doc doc-heading">
 tabulated data of Smart and Shea, 2019. The returned value can be used to select the appropriate
 neutron monitor station to estimate the cosmic-ray neutron intensity at the location of interest.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -2279,7 +10621,11 @@ <h2 id="crnpy.crnpy.cutoff_rigidity" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Geographic latitude in decimal degrees. Value in range -90 to 90</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Geographic latitude in decimal degrees. Value in range -90 to 90</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -2289,8 +10635,12 @@ <h2 id="crnpy.crnpy.cutoff_rigidity" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Geographic longitude in decimal degrees. Values in range from 0 to 360.
-Typical negative longitudes in the west hemisphere will fall in the range 180 to 360.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Geographic longitude in decimal degrees. Values in range from 0 to 360.
+Typical negative longitudes in the west hemisphere will fall in the range 180 to 360.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -2298,6 +10648,8 @@ <h2 id="crnpy.crnpy.cutoff_rigidity" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -2311,11 +10663,17 @@ <h2 id="crnpy.crnpy.cutoff_rigidity" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Cutoff rigidity in GV. Error is about +/- 0.3 GV</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Cutoff rigidity in GV. Error is about +/- 0.3 GV</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
+
+
 <p><strong>Examples:</strong></p>
     <p>Estimate the cutoff rigidity for Newark, NJ, US</p>
     <div class="highlight"><pre><span></span><code><span class="gp">&gt;&gt;&gt; </span><span class="n">zq</span> <span class="o">=</span> <span class="n">cutoff_rigidity</span><span class="p">(</span><span class="mf">39.68</span><span class="p">,</span> <span class="o">-</span><span class="mf">75.75</span><span class="p">)</span>
@@ -2325,54 +10683,61 @@ <h2 id="crnpy.crnpy.cutoff_rigidity" class="doc doc-heading">
 
 <details class="references" open>
   <summary>References</summary>
-  <p>Smart, D. &amp; Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program:
+  <p>Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures
+for cosmic‐ray neutron soil moisture probes located across Australia. Water Resources Research,
+50(6), 5029-5043.</p>
+<p>Smart, D. &amp; Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program:
 Theory, Software Description and Example. NASA STI/Recon Technical Report N.</p>
 <p>Shea, M. A., &amp; Smart, D. F. (2019, July). Re-examination of the First Five Ground-Level Events.
 In International Cosmic Ray Conference (ICRC2019) (Vol. 36, p. 1149).</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">770</span>
-<span class="normal">771</span>
-<span class="normal">772</span>
-<span class="normal">773</span>
-<span class="normal">774</span>
-<span class="normal">775</span>
-<span class="normal">776</span>
-<span class="normal">777</span>
-<span class="normal">778</span>
-<span class="normal">779</span>
-<span class="normal">780</span>
-<span class="normal">781</span>
-<span class="normal">782</span>
-<span class="normal">783</span>
-<span class="normal">784</span>
-<span class="normal">785</span>
-<span class="normal">786</span>
-<span class="normal">787</span>
-<span class="normal">788</span>
-<span class="normal">789</span>
-<span class="normal">790</span>
-<span class="normal">791</span>
-<span class="normal">792</span>
-<span class="normal">793</span>
-<span class="normal">794</span>
-<span class="normal">795</span>
-<span class="normal">796</span>
-<span class="normal">797</span>
-<span class="normal">798</span>
-<span class="normal">799</span>
-<span class="normal">800</span>
-<span class="normal">801</span>
-<span class="normal">802</span>
-<span class="normal">803</span>
-<span class="normal">804</span>
-<span class="normal">805</span>
-<span class="normal">806</span>
-<span class="normal">807</span>
-<span class="normal">808</span>
-<span class="normal">809</span>
-<span class="normal">810</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">cutoff_rigidity</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span><span class="n">lon</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1012</span>
+<span class="normal">1013</span>
+<span class="normal">1014</span>
+<span class="normal">1015</span>
+<span class="normal">1016</span>
+<span class="normal">1017</span>
+<span class="normal">1018</span>
+<span class="normal">1019</span>
+<span class="normal">1020</span>
+<span class="normal">1021</span>
+<span class="normal">1022</span>
+<span class="normal">1023</span>
+<span class="normal">1024</span>
+<span class="normal">1025</span>
+<span class="normal">1026</span>
+<span class="normal">1027</span>
+<span class="normal">1028</span>
+<span class="normal">1029</span>
+<span class="normal">1030</span>
+<span class="normal">1031</span>
+<span class="normal">1032</span>
+<span class="normal">1033</span>
+<span class="normal">1034</span>
+<span class="normal">1035</span>
+<span class="normal">1036</span>
+<span class="normal">1037</span>
+<span class="normal">1038</span>
+<span class="normal">1039</span>
+<span class="normal">1040</span>
+<span class="normal">1041</span>
+<span class="normal">1042</span>
+<span class="normal">1043</span>
+<span class="normal">1044</span>
+<span class="normal">1045</span>
+<span class="normal">1046</span>
+<span class="normal">1047</span>
+<span class="normal">1048</span>
+<span class="normal">1049</span>
+<span class="normal">1050</span>
+<span class="normal">1051</span>
+<span class="normal">1052</span>
+<span class="normal">1053</span>
+<span class="normal">1054</span>
+<span class="normal">1055</span>
+<span class="normal">1056</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">cutoff_rigidity</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to estimate the approximate cutoff rigidity for any point on Earth according to the</span>
 <span class="sd">    tabulated data of Smart and Shea, 2019. The returned value can be used to select the appropriate</span>
 <span class="sd">    neutron monitor station to estimate the cosmic-ray neutron intensity at the location of interest.</span>
@@ -2393,6 +10758,10 @@ <h2 id="crnpy.crnpy.cutoff_rigidity" class="doc doc-heading">
 <span class="sd">        2.52 GV (Value from NMD is 2.40 GV)</span>
 
 <span class="sd">    References:</span>
+<span class="sd">        Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures</span>
+<span class="sd">        for cosmic‐ray neutron soil moisture probes located across Australia. Water Resources Research,</span>
+<span class="sd">        50(6), 5029-5043.</span>
+
 <span class="sd">        Smart, D. &amp; Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program:</span>
 <span class="sd">        Theory, Software Description and Example. NASA STI/Recon Technical Report N.</span>
 
@@ -2403,28 +10772,30 @@ <h2 id="crnpy.crnpy.cutoff_rigidity" class="doc doc-heading">
     <span class="n">yq</span> <span class="o">=</span> <span class="n">lat</span>
 
     <span class="k">if</span> <span class="n">xq</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">xq</span> <span class="o">=</span> <span class="n">xq</span><span class="o">*-</span><span class="mi">1</span> <span class="o">+</span> <span class="mi">180</span>
+        <span class="n">xq</span> <span class="o">=</span> <span class="n">xq</span> <span class="o">*</span> <span class="o">-</span><span class="mi">1</span> <span class="o">+</span> <span class="mi">180</span>
     <span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">cutoff_rigidity</span><span class="p">)</span>
     <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">360</span><span class="p">,</span> <span class="n">Z</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
     <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">90</span><span class="p">,</span> <span class="o">-</span><span class="mi">90</span><span class="p">,</span> <span class="n">Z</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
     <span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-    <span class="n">points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> <span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">Y</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span> <span class="p">)</span><span class="o">.</span><span class="n">T</span>
+    <span class="n">points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">X</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">Y</span><span class="o">.</span><span class="n">flatten</span><span class="p">()))</span><span class="o">.</span><span class="n">T</span>
     <span class="n">values</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-    <span class="n">zq</span> <span class="o">=</span> <span class="n">griddata</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="p">(</span><span class="n">xq</span><span class="p">,</span><span class="n">yq</span><span class="p">))</span>
+    <span class="n">zq</span> <span class="o">=</span> <span class="n">griddata</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="p">(</span><span class="n">xq</span><span class="p">,</span> <span class="n">yq</span><span class="p">))</span>
 
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">zq</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">zq</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.euclidean_distance" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">euclidean_distance</span><span class="p">(</span><span class="n">px</span><span class="p">,</span> <span class="n">py</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.euclidean_distance" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">euclidean_distance</span><span class="p">(</span><span class="n">px</span><span class="p">,</span> <span class="n">py</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></code>
 
 </h2>
 
@@ -2434,6 +10805,8 @@ <h2 id="crnpy.crnpy.euclidean_distance" class="doc doc-heading">
       <p>Function that computes the Euclidean distance between one point
 in space and one or more points.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -2450,7 +10823,11 @@ <h2 id="crnpy.crnpy.euclidean_distance" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>x projected coordinate of the point.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>x projected coordinate of the point.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -2460,7 +10837,11 @@ <h2 id="crnpy.crnpy.euclidean_distance" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>y projected coordinate of the point.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>y projected coordinate of the point.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -2468,9 +10849,13 @@ <h2 id="crnpy.crnpy.euclidean_distance" class="doc doc-heading">
         <tr>
           <td><code>x</code></td>
           <td>
-                <code>list, ndarray, pandas.<span title="pandas.series">series</span></code>
+                <code>(list, ndarray, <span title="pandas.series">series</span>)</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>vector of x projected coordinates.</p>
+            </div>
           </td>
-          <td><p>vector of x projected coordinates.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -2478,9 +10863,13 @@ <h2 id="crnpy.crnpy.euclidean_distance" class="doc doc-heading">
         <tr>
           <td><code>y</code></td>
           <td>
-                <code>list, ndarray, pandas.<span title="pandas.series">series</span></code>
+                <code>(list, ndarray, <span title="pandas.series">series</span>)</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>vector of y projected coordinates.</p>
+            </div>
           </td>
-          <td><p>vector of y projected coordinates.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -2488,6 +10877,8 @@ <h2 id="crnpy.crnpy.euclidean_distance" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -2501,28 +10892,32 @@ <h2 id="crnpy.crnpy.euclidean_distance" class="doc doc-heading">
           <td>
                 <code>ndarray</code>
           </td>
-          <td><p>Numpy array of distances from the point (px,py) to all the points in x and y vectors.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Numpy array of distances from the point (px,py) to all the points in x and y vectors.</p>
+            </div>
+          </td>
         </tr>
-    </tbody>
-  </table>
-
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1017</span>
-<span class="normal">1018</span>
-<span class="normal">1019</span>
-<span class="normal">1020</span>
-<span class="normal">1021</span>
-<span class="normal">1022</span>
-<span class="normal">1023</span>
-<span class="normal">1024</span>
-<span class="normal">1025</span>
-<span class="normal">1026</span>
-<span class="normal">1027</span>
-<span class="normal">1028</span>
-<span class="normal">1029</span>
-<span class="normal">1030</span>
-<span class="normal">1031</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">euclidean_distance</span><span class="p">(</span><span class="n">px</span><span class="p">,</span> <span class="n">py</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    </tbody>
+  </table>
+
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1298</span>
+<span class="normal">1299</span>
+<span class="normal">1300</span>
+<span class="normal">1301</span>
+<span class="normal">1302</span>
+<span class="normal">1303</span>
+<span class="normal">1304</span>
+<span class="normal">1305</span>
+<span class="normal">1306</span>
+<span class="normal">1307</span>
+<span class="normal">1308</span>
+<span class="normal">1309</span>
+<span class="normal">1310</span>
+<span class="normal">1311</span>
+<span class="normal">1312</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">euclidean_distance</span><span class="p">(</span><span class="n">px</span><span class="p">,</span> <span class="n">py</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function that computes the Euclidean distance between one point</span>
 <span class="sd">    in space and one or more points.</span>
 
@@ -2538,17 +10933,19 @@ <h2 id="crnpy.crnpy.euclidean_distance" class="doc doc-heading">
     <span class="n">d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">px</span> <span class="o">-</span> <span class="n">x</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">py</span> <span class="o">-</span> <span class="n">y</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">d</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.exp_filter" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">exp_filter</span><span class="p">(</span><span class="n">sm</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.exp_filter" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">exp_filter</span><span class="p">(</span><span class="n">sm</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
 
 </h2>
 
@@ -2557,6 +10954,8 @@ <h2 id="crnpy.crnpy.exp_filter" class="doc doc-heading">
   
       <p>Exponential filter to estimate soil moisture in the rootzone from surface observtions.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -2573,7 +10972,11 @@ <h2 id="crnpy.crnpy.exp_filter" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>Soil moisture in mm of water for the top layer of the soil profile.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Soil moisture in mm of water for the top layer of the soil profile.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -2583,7 +10986,11 @@ <h2 id="crnpy.crnpy.exp_filter" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Characteristic time length in the same units as the measurement interval.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Characteristic time length in the same units as the measurement interval.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -2591,6 +10998,8 @@ <h2 id="crnpy.crnpy.exp_filter" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -2604,7 +11013,11 @@ <h2 id="crnpy.crnpy.exp_filter" class="doc doc-heading">
 <td><code>sm_subsurface</code></td>          <td>
                 <code>list or array</code>
           </td>
-          <td><p>Subsurface soil moisture in the same units as the input.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Subsurface soil moisture in the same units as the input.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
@@ -2619,57 +11032,56 @@ <h2 id="crnpy.crnpy.exp_filter" class="doc doc-heading">
 <p>Rossini, P. and Patrignani, A., 2021. Predicting rootzone soil moisture from surface observations in cropland using an exponential filter.
 Soil Science Society of America Journal.</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">719</span>
-<span class="normal">720</span>
-<span class="normal">721</span>
-<span class="normal">722</span>
-<span class="normal">723</span>
-<span class="normal">724</span>
-<span class="normal">725</span>
-<span class="normal">726</span>
-<span class="normal">727</span>
-<span class="normal">728</span>
-<span class="normal">729</span>
-<span class="normal">730</span>
-<span class="normal">731</span>
-<span class="normal">732</span>
-<span class="normal">733</span>
-<span class="normal">734</span>
-<span class="normal">735</span>
-<span class="normal">736</span>
-<span class="normal">737</span>
-<span class="normal">738</span>
-<span class="normal">739</span>
-<span class="normal">740</span>
-<span class="normal">741</span>
-<span class="normal">742</span>
-<span class="normal">743</span>
-<span class="normal">744</span>
-<span class="normal">745</span>
-<span class="normal">746</span>
-<span class="normal">747</span>
-<span class="normal">748</span>
-<span class="normal">749</span>
-<span class="normal">750</span>
-<span class="normal">751</span>
-<span class="normal">752</span>
-<span class="normal">753</span>
-<span class="normal">754</span>
-<span class="normal">755</span>
-<span class="normal">756</span>
-<span class="normal">757</span>
-<span class="normal">758</span>
-<span class="normal">759</span>
-<span class="normal">760</span>
-<span class="normal">761</span>
-<span class="normal">762</span>
-<span class="normal">763</span>
-<span class="normal">764</span>
-<span class="normal">765</span>
-<span class="normal">766</span>
-<span class="normal">767</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">exp_filter</span><span class="p">(</span><span class="n">sm</span><span class="p">,</span><span class="n">T</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 962</span>
+<span class="normal"> 963</span>
+<span class="normal"> 964</span>
+<span class="normal"> 965</span>
+<span class="normal"> 966</span>
+<span class="normal"> 967</span>
+<span class="normal"> 968</span>
+<span class="normal"> 969</span>
+<span class="normal"> 970</span>
+<span class="normal"> 971</span>
+<span class="normal"> 972</span>
+<span class="normal"> 973</span>
+<span class="normal"> 974</span>
+<span class="normal"> 975</span>
+<span class="normal"> 976</span>
+<span class="normal"> 977</span>
+<span class="normal"> 978</span>
+<span class="normal"> 979</span>
+<span class="normal"> 980</span>
+<span class="normal"> 981</span>
+<span class="normal"> 982</span>
+<span class="normal"> 983</span>
+<span class="normal"> 984</span>
+<span class="normal"> 985</span>
+<span class="normal"> 986</span>
+<span class="normal"> 987</span>
+<span class="normal"> 988</span>
+<span class="normal"> 989</span>
+<span class="normal"> 990</span>
+<span class="normal"> 991</span>
+<span class="normal"> 992</span>
+<span class="normal"> 993</span>
+<span class="normal"> 994</span>
+<span class="normal"> 995</span>
+<span class="normal"> 996</span>
+<span class="normal"> 997</span>
+<span class="normal"> 998</span>
+<span class="normal"> 999</span>
+<span class="normal">1000</span>
+<span class="normal">1001</span>
+<span class="normal">1002</span>
+<span class="normal">1003</span>
+<span class="normal">1004</span>
+<span class="normal">1005</span>
+<span class="normal">1006</span>
+<span class="normal">1007</span>
+<span class="normal">1008</span>
+<span class="normal">1009</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">exp_filter</span><span class="p">(</span><span class="n">sm</span><span class="p">,</span> <span class="n">T</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Exponential filter to estimate soil moisture in the rootzone from surface observtions.</span>
 
 <span class="sd">    Args:</span>
@@ -2691,7 +11103,6 @@ <h2 id="crnpy.crnpy.exp_filter" class="doc doc-heading">
 <span class="sd">        Soil Science Society of America Journal.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-
     <span class="c1"># Parameters</span>
     <span class="n">t_delta</span> <span class="o">=</span> <span class="mi">1</span>
     <span class="n">sm_min</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">sm</span><span class="p">)</span>
@@ -2699,18 +11110,18 @@ <h2 id="crnpy.crnpy.exp_filter" class="doc doc-heading">
     <span class="n">ms</span> <span class="o">=</span> <span class="p">(</span><span class="n">sm</span> <span class="o">-</span> <span class="n">sm_min</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">sm_max</span> <span class="o">-</span> <span class="n">sm_min</span><span class="p">)</span>
 
     <span class="c1"># Pre-allocate soil water index array and recursive constant K</span>
-    <span class="n">SWI</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">ms</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-    <span class="n">K</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">ms</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+    <span class="n">SWI</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">ms</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+    <span class="n">K</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">ms</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
 
     <span class="c1"># Initial conditions</span>
     <span class="n">SWI</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">ms</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
     <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
 
     <span class="c1"># Values from 2 to N</span>
-    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">SWI</span><span class="p">)):</span>
-        <span class="k">if</span> <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">ms</span><span class="p">[</span><span class="n">n</span><span class="p">])</span> <span class="o">&amp;</span> <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">ms</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
-            <span class="n">K</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">K</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">t_delta</span><span class="o">/</span><span class="n">T</span><span class="p">))</span>
-            <span class="n">SWI</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">SWI</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="n">n</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">ms</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">-</span> <span class="n">SWI</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">SWI</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">ms</span><span class="p">[</span><span class="n">n</span><span class="p">])</span> <span class="o">&amp;</span> <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">ms</span><span class="p">[</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]):</span>
+            <span class="n">K</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">K</span><span class="p">[</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">t_delta</span> <span class="o">/</span> <span class="n">T</span><span class="p">))</span>
+            <span class="n">SWI</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">SWI</span><span class="p">[</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">ms</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">-</span> <span class="n">SWI</span><span class="p">[</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">])</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">continue</span>
 
@@ -2719,17 +11130,19 @@ <h2 id="crnpy.crnpy.exp_filter" class="doc doc-heading">
 
     <span class="k">return</span> <span class="n">sm_subsurface</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.fill_missing_timestamps" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">fill_missing_timestamps</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="o">=</span><span class="s1">&#39;timestamp&#39;</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="n">round_timestamp</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.fill_missing_timestamps" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">fill_missing_timestamps</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="o">=</span><span class="s1">&#39;timestamp&#39;</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="n">round_timestamp</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h2>
 
@@ -2738,6 +11151,8 @@ <h2 id="crnpy.crnpy.fill_missing_timestamps" class="doc doc-heading">
   
       <p>Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -2752,9 +11167,13 @@ <h2 id="crnpy.crnpy.fill_missing_timestamps" class="doc doc-heading">
         <tr>
           <td><code>df</code></td>
           <td>
-                <code>pandas.<span title="pandas.DataFrame">DataFrame</span></code>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Pandas DataFrame.</p>
+            </div>
           </td>
-          <td><p>Pandas DataFrame.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -2764,7 +11183,11 @@ <h2 id="crnpy.crnpy.fill_missing_timestamps" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;timestamp&#39;</code>
           </td>
@@ -2774,7 +11197,11 @@ <h2 id="crnpy.crnpy.fill_missing_timestamps" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;H&#39;</code>
           </td>
@@ -2784,7 +11211,11 @@ <h2 id="crnpy.crnpy.fill_missing_timestamps" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Whether to round timestamps to the nearest frequency. Default is True.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Whether to round timestamps to the nearest frequency. Default is True.</p>
+            </div>
+          </td>
           <td>
                 <code>True</code>
           </td>
@@ -2794,7 +11225,11 @@ <h2 id="crnpy.crnpy.fill_missing_timestamps" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Prints the missing timestamps added to the DatFrame.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Prints the missing timestamps added to the DatFrame.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -2802,6 +11237,8 @@ <h2 id="crnpy.crnpy.fill_missing_timestamps" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -2813,16 +11250,21 @@ <h2 id="crnpy.crnpy.fill_missing_timestamps" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code>pandas.<span title="pandas.DataFrame">DataFrame</span></code>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>DataFrame with filled missing timestamps.</p>
+            </div>
           </td>
-          <td><p>DataFrame with filled missing timestamps.</p></td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 66</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 65</span>
+<span class="normal"> 66</span>
 <span class="normal"> 67</span>
 <span class="normal"> 68</span>
 <span class="normal"> 69</span>
@@ -2863,8 +11305,7 @@ <h2 id="crnpy.crnpy.fill_missing_timestamps" class="doc doc-heading">
 <span class="normal">104</span>
 <span class="normal">105</span>
 <span class="normal">106</span>
-<span class="normal">107</span>
-<span class="normal">108</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fill_missing_timestamps</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="o">=</span><span class="s1">&#39;timestamp&#39;</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="n">round_timestamp</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="normal">107</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fill_missing_timestamps</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="o">=</span><span class="s1">&#39;timestamp&#39;</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="n">round_timestamp</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.</span>
 
 <span class="sd">     Args:</span>
@@ -2895,9 +11336,9 @@ <h2 id="crnpy.crnpy.fill_missing_timestamps" class="doc doc-heading">
     <span class="k">for</span> <span class="n">date</span> <span class="ow">in</span> <span class="n">date_range</span><span class="p">:</span>
         <span class="k">if</span> <span class="n">date</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="n">timestamp_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Adding missing date:&#39;</span><span class="p">,</span><span class="n">date</span><span class="p">)</span>
-            <span class="n">new_line</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="n">timestamp_col</span><span class="p">:</span><span class="n">date</span><span class="p">},</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="c1"># By default fills columns with np.nan</span>
-            <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df</span><span class="p">,</span><span class="n">new_line</span><span class="p">])</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Adding missing date:&#39;</span><span class="p">,</span> <span class="n">date</span><span class="p">)</span>
+            <span class="n">new_line</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="n">timestamp_col</span><span class="p">:</span> <span class="n">date</span><span class="p">},</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>  <span class="c1"># By default fills columns with np.nan</span>
+            <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df</span><span class="p">,</span> <span class="n">new_line</span><span class="p">])</span>
             <span class="n">counter</span> <span class="o">+=</span> <span class="mi">1</span>
 
     <span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="n">timestamp_col</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@@ -2908,17 +11349,19 @@ <h2 id="crnpy.crnpy.fill_missing_timestamps" class="doc doc-heading">
 
     <span class="k">return</span> <span class="n">df</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.find_neutron_monitor" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">find_neutron_monitor</span><span class="p">(</span><span class="n">Rc</span><span class="p">,</span> <span class="n">start_date</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">end_date</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.find_neutron_monitor" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">find_neutron_monitor</span><span class="p">(</span><span class="n">Rc</span><span class="p">,</span> <span class="n">start_date</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">end_date</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h2>
 
@@ -2927,6 +11370,8 @@ <h2 id="crnpy.crnpy.find_neutron_monitor" class="doc doc-heading">
   
       <p>Search for potential reference neutron monitoring stations based on cutoff rigidity.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -2943,7 +11388,11 @@ <h2 id="crnpy.crnpy.find_neutron_monitor" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -2953,7 +11402,11 @@ <h2 id="crnpy.crnpy.find_neutron_monitor" class="doc doc-heading">
           <td>
                 <code>datetime</code>
           </td>
-          <td><p>Start date for the period of interest.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Start date for the period of interest.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -2963,14 +11416,34 @@ <h2 id="crnpy.crnpy.find_neutron_monitor" class="doc doc-heading">
           <td>
                 <code>datetime</code>
           </td>
-          <td><p>End date for the period of interest.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>End date for the period of interest.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
         </tr>
+        <tr>
+          <td><code>verbose</code></td>
+          <td>
+                <code>bool</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>If True, print a expanded output of the incoming neutron flux data.</p>
+            </div>
+          </td>
+          <td>
+                <code>False</code>
+          </td>
+        </tr>
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -2984,12 +11457,18 @@ <h2 id="crnpy.crnpy.find_neutron_monitor" class="doc doc-heading">
           <td>
                 <code>list</code>
           </td>
-          <td><p>List of top five stations with closes cutoff rigidity.
-User needs to select station according to site altitude.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>List of top five stations with closes cutoff rigidity.
+User needs to select station according to site altitude.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
+
+
 <p><strong>Examples:</strong></p>
     <div class="highlight"><pre><span></span><code><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">crnpy</span> <span class="kn">import</span> <span class="n">crnpy</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Rc</span> <span class="o">=</span> <span class="mf">2.40</span> <span class="c1"># 2.40 Newark, NJ, US</span>
@@ -3013,80 +11492,84 @@ <h2 id="crnpy.crnpy.find_neutron_monitor" class="doc doc-heading">
   <summary>References</summary>
   <p>https://www.nmdb.eu/nest/help.php#helpstations</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">813</span>
-<span class="normal">814</span>
-<span class="normal">815</span>
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-<span class="normal">871</span>
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-<span class="normal">876</span>
-<span class="normal">877</span>
-<span class="normal">878</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">find_neutron_monitor</span><span class="p">(</span><span class="n">Rc</span><span class="p">,</span> <span class="n">start_date</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">end_date</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1145</span>
+<span class="normal">1146</span>
+<span class="normal">1147</span>
+<span class="normal">1148</span>
+<span class="normal">1149</span>
+<span class="normal">1150</span>
+<span class="normal">1151</span>
+<span class="normal">1152</span>
+<span class="normal">1153</span>
+<span class="normal">1154</span>
+<span class="normal">1155</span>
+<span class="normal">1156</span>
+<span class="normal">1157</span>
+<span class="normal">1158</span>
+<span class="normal">1159</span>
+<span class="normal">1160</span>
+<span class="normal">1161</span>
+<span class="normal">1162</span>
+<span class="normal">1163</span>
+<span class="normal">1164</span>
+<span class="normal">1165</span>
+<span class="normal">1166</span>
+<span class="normal">1167</span>
+<span class="normal">1168</span>
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+<span class="normal">1177</span>
+<span class="normal">1178</span>
+<span class="normal">1179</span>
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+<span class="normal">1181</span>
+<span class="normal">1182</span>
+<span class="normal">1183</span>
+<span class="normal">1184</span>
+<span class="normal">1185</span>
+<span class="normal">1186</span>
+<span class="normal">1187</span>
+<span class="normal">1188</span>
+<span class="normal">1189</span>
+<span class="normal">1190</span>
+<span class="normal">1191</span>
+<span class="normal">1192</span>
+<span class="normal">1193</span>
+<span class="normal">1194</span>
+<span class="normal">1195</span>
+<span class="normal">1196</span>
+<span class="normal">1197</span>
+<span class="normal">1198</span>
+<span class="normal">1199</span>
+<span class="normal">1200</span>
+<span class="normal">1201</span>
+<span class="normal">1202</span>
+<span class="normal">1203</span>
+<span class="normal">1204</span>
+<span class="normal">1205</span>
+<span class="normal">1206</span>
+<span class="normal">1207</span>
+<span class="normal">1208</span>
+<span class="normal">1209</span>
+<span class="normal">1210</span>
+<span class="normal">1211</span>
+<span class="normal">1212</span>
+<span class="normal">1213</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">find_neutron_monitor</span><span class="p">(</span><span class="n">Rc</span><span class="p">,</span> <span class="n">start_date</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">end_date</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Search for potential reference neutron monitoring stations based on cutoff rigidity.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        Rc (float): Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.</span>
 <span class="sd">        start_date (datetime): Start date for the period of interest.</span>
 <span class="sd">        end_date (datetime): End date for the period of interest.</span>
+<span class="sd">        verbose (bool): If True, print a expanded output of the incoming neutron flux data.</span>
 
 <span class="sd">    Returns:</span>
 <span class="sd">        (list): List of top five stations with closes cutoff rigidity.</span>
@@ -3116,7 +11599,7 @@ <h2 id="crnpy.crnpy.find_neutron_monitor" class="doc doc-heading">
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="c1"># Load file with list of neutron monitoring stations</span>
-    <span class="n">stations</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">neutron_detectors</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;STID&quot;</span><span class="p">,</span><span class="s2">&quot;NAME&quot;</span><span class="p">,</span><span class="s2">&quot;R&quot;</span><span class="p">,</span><span class="s2">&quot;Altitude_m&quot;</span><span class="p">])</span>
+    <span class="n">stations</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">neutron_detectors</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;STID&quot;</span><span class="p">,</span> <span class="s2">&quot;NAME&quot;</span><span class="p">,</span> <span class="s2">&quot;R&quot;</span><span class="p">,</span> <span class="s2">&quot;Altitude_m&quot;</span><span class="p">])</span>
 
     <span class="c1"># Sort stations by closest cutoff rigidity</span>
     <span class="n">idx_R</span> <span class="o">=</span> <span class="p">(</span><span class="n">stations</span><span class="p">[</span><span class="s1">&#39;R&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">Rc</span><span class="p">)</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</span>
@@ -3127,9 +11610,10 @@ <h2 id="crnpy.crnpy.find_neutron_monitor" class="doc doc-heading">
             <span class="n">station</span> <span class="o">=</span> <span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">idx_R</span><span class="p">[</span><span class="n">i</span><span class="p">]][</span><span class="s2">&quot;STID&quot;</span><span class="p">]</span>
             <span class="k">try</span><span class="p">:</span>
                 <span class="k">if</span> <span class="n">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">station</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">idx_R</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+                    <span class="n">stations</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">idx_R</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
             <span class="k">except</span><span class="p">:</span>
                 <span class="k">pass</span>
+
         <span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">stations</span><span class="p">[</span><span class="s2">&quot;Period available&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="kc">True</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;No stations available for the selected period!&quot;</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
@@ -3141,23 +11625,26 @@ <h2 id="crnpy.crnpy.find_neutron_monitor" class="doc doc-heading">
 
     <span class="c1"># Print results</span>
     <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;&quot;Select a station with an altitude similar to that of your location. For more information go to: &#39;https://www.nmdb.eu/nest/help.php#helpstations&quot;&quot;&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Select a station with an altitude similar to that of your location. For more information go to: &#39;https://www.nmdb.eu/nest/help.php#helpstations&quot;&quot;&quot;</span><span class="p">)</span>
     <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
     <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Your cutoff rigidity is </span><span class="si">{</span><span class="n">Rc</span><span class="si">}</span><span class="s2"> GV&quot;</span><span class="p">)</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">result</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.get_incoming_neutron_flux" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">station</span><span class="p">,</span> <span class="n">utc_offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">expand_window</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.get_incoming_neutron_flux" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">station</span><span class="p">,</span> <span class="n">utc_offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">expand_window</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h2>
 
@@ -3166,6 +11653,8 @@ <h2 id="crnpy.crnpy.get_incoming_neutron_flux" class="doc doc-heading">
   
       <p>Function to retrieve neutron flux from the Neutron Monitor Database.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -3182,7 +11671,11 @@ <h2 id="crnpy.crnpy.get_incoming_neutron_flux" class="doc doc-heading">
           <td>
                 <code>datetime</code>
           </td>
-          <td><p>Start date of the time series.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Start date of the time series.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -3192,7 +11685,11 @@ <h2 id="crnpy.crnpy.get_incoming_neutron_flux" class="doc doc-heading">
           <td>
                 <code>datetime</code>
           </td>
-          <td><p>End date of the time series.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>End date of the time series.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -3202,7 +11699,11 @@ <h2 id="crnpy.crnpy.get_incoming_neutron_flux" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Neutron Monitor station to retrieve data from.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Neutron Monitor station to retrieve data from.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -3212,7 +11713,11 @@ <h2 id="crnpy.crnpy.get_incoming_neutron_flux" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>UTC offset in hours. Default is 0.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTC offset in hours. Default is 0.</p>
+            </div>
+          </td>
           <td>
                 <code>0</code>
           </td>
@@ -3222,7 +11727,11 @@ <h2 id="crnpy.crnpy.get_incoming_neutron_flux" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Number of hours to expand the time window to retrieve extra data. Default is 0.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Number of hours to expand the time window to retrieve extra data. Default is 0.</p>
+            </div>
+          </td>
           <td>
                 <code>0</code>
           </td>
@@ -3232,7 +11741,11 @@ <h2 id="crnpy.crnpy.get_incoming_neutron_flux" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Print information about the request. Default is False.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Print information about the request. Default is False.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -3240,6 +11753,8 @@ <h2 id="crnpy.crnpy.get_incoming_neutron_flux" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -3251,9 +11766,13 @@ <h2 id="crnpy.crnpy.get_incoming_neutron_flux" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code>pandas.<span title="pandas.DataFrame">DataFrame</span></code>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Neutron flux in counts per hour and timestamps.</p>
+            </div>
           </td>
-          <td><p>Neutron flux in counts per hour and timestamps.</p></td>
         </tr>
     </tbody>
   </table>
@@ -3262,28 +11781,9 @@ <h2 id="crnpy.crnpy.get_incoming_neutron_flux" class="doc doc-heading">
   <summary>References</summary>
   <p>Documentation available:https://www.nmdb.eu/nest/help.php#howto</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">339</span>
-<span class="normal">340</span>
-<span class="normal">341</span>
-<span class="normal">342</span>
-<span class="normal">343</span>
-<span class="normal">344</span>
-<span class="normal">345</span>
-<span class="normal">346</span>
-<span class="normal">347</span>
-<span class="normal">348</span>
-<span class="normal">349</span>
-<span class="normal">350</span>
-<span class="normal">351</span>
-<span class="normal">352</span>
-<span class="normal">353</span>
-<span class="normal">354</span>
-<span class="normal">355</span>
-<span class="normal">356</span>
-<span class="normal">357</span>
-<span class="normal">358</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">358</span>
 <span class="normal">359</span>
 <span class="normal">360</span>
 <span class="normal">361</span>
@@ -3349,104 +11849,268 @@ <h2 id="crnpy.crnpy.get_incoming_neutron_flux" class="doc doc-heading">
 <span class="normal">421</span>
 <span class="normal">422</span>
 <span class="normal">423</span>
-<span class="normal">424</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">station</span><span class="p">,</span> <span class="n">utc_offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">expand_window</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="normal">424</span>
+<span class="normal">425</span>
+<span class="normal">426</span>
+<span class="normal">427</span>
+<span class="normal">428</span>
+<span class="normal">429</span>
+<span class="normal">430</span>
+<span class="normal">431</span>
+<span class="normal">432</span>
+<span class="normal">433</span>
+<span class="normal">434</span>
+<span class="normal">435</span>
+<span class="normal">436</span>
+<span class="normal">437</span>
+<span class="normal">438</span>
+<span class="normal">439</span>
+<span class="normal">440</span>
+<span class="normal">441</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">,</span> <span class="n">station</span><span class="p">,</span> <span class="n">utc_offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">expand_window</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to retrieve neutron flux from the Neutron Monitor Database.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        start_date (datetime): Start date of the time series.</span>
 <span class="sd">        end_date (datetime): End date of the time series.</span>
 <span class="sd">        station (str): Neutron Monitor station to retrieve data from.</span>
-<span class="sd">        utc_offset (int): UTC offset in hours. Default is 0.</span>
-<span class="sd">        expand_window (int): Number of hours to expand the time window to retrieve extra data. Default is 0.</span>
-<span class="sd">        verbose (bool): Print information about the request. Default is False.</span>
+<span class="sd">        utc_offset (int): UTC offset in hours. Default is 0.</span>
+<span class="sd">        expand_window (int): Number of hours to expand the time window to retrieve extra data. Default is 0.</span>
+<span class="sd">        verbose (bool): Print information about the request. Default is False.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (pandas.DataFrame): Neutron flux in counts per hour and timestamps.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">        Documentation available:https://www.nmdb.eu/nest/help.php#howto</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Example: get_incoming_neutron_flux(station=&#39;IRKT&#39;,start_date=&#39;2020-04-10 11:00:00&#39;,end_date=&#39;2020-06-18 17:00:00&#39;)</span>
+    <span class="c1"># Template url = &#39;http://nest.nmdb.eu/draw_graph.php?formchk=1&amp;stations[]=KERG&amp;output=ascii&amp;tabchoice=revori&amp;dtype=corr_for_efficiency&amp;date_choice=bydate&amp;start_year=2009&amp;start_month=09&amp;start_day=01&amp;start_hour=00&amp;start_min=00&amp;end_year=2009&amp;end_month=09&amp;end_day=05&amp;end_hour=23&amp;end_min=59&amp;yunits=0&#39;</span>
+
+    <span class="c1"># Expand the time window by 1 hour to ensure an extra observation is included in the request.</span>
+    <span class="n">start_date</span> <span class="o">-=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">expand_window</span><span class="p">)</span>
+    <span class="n">end_date</span> <span class="o">+=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">expand_window</span><span class="p">)</span>
+
+    <span class="c1"># Convert local time to UTC</span>
+    <span class="n">start_date</span> <span class="o">=</span> <span class="n">start_date</span> <span class="o">-</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">utc_offset</span><span class="p">)</span>
+    <span class="n">end_date</span> <span class="o">=</span> <span class="n">end_date</span> <span class="o">-</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">utc_offset</span><span class="p">)</span>
+    <span class="n">root</span> <span class="o">=</span> <span class="s1">&#39;http://www.nmdb.eu/nest/draw_graph.php?&#39;</span>
+    <span class="n">url_par</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;formchk=1&#39;</span><span class="p">,</span>
+               <span class="s1">&#39;stations[]=&#39;</span> <span class="o">+</span> <span class="n">station</span><span class="p">,</span>
+               <span class="s1">&#39;output=ascii&#39;</span><span class="p">,</span>
+               <span class="s1">&#39;tabchoice=revori&#39;</span><span class="p">,</span>
+               <span class="s1">&#39;dtype=corr_for_efficiency&#39;</span><span class="p">,</span>
+               <span class="s1">&#39;tresolution=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="mi">60</span><span class="p">),</span>
+               <span class="s1">&#39;date_choice=bydate&#39;</span><span class="p">,</span>
+               <span class="s1">&#39;start_year=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">year</span><span class="p">),</span>
+               <span class="s1">&#39;start_month=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">month</span><span class="p">),</span>
+               <span class="s1">&#39;start_day=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">day</span><span class="p">),</span>
+               <span class="s1">&#39;start_hour=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">hour</span><span class="p">),</span>
+               <span class="s1">&#39;start_min=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">minute</span><span class="p">),</span>
+               <span class="s1">&#39;end_year=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">year</span><span class="p">),</span>
+               <span class="s1">&#39;end_month=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">month</span><span class="p">),</span>
+               <span class="s1">&#39;end_day=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">day</span><span class="p">),</span>
+               <span class="s1">&#39;end_hour=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">hour</span><span class="p">),</span>
+               <span class="s1">&#39;end_min=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">minute</span><span class="p">),</span>
+               <span class="s1">&#39;yunits=0&#39;</span><span class="p">]</span>
+
+    <span class="n">url</span> <span class="o">=</span> <span class="n">root</span> <span class="o">+</span> <span class="s1">&#39;&amp;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">url_par</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Retrieving data from </span><span class="si">{</span><span class="n">url</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="n">r</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">)</span><span class="o">.</span><span class="n">content</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Subtract 1 hour to restore the last date included in the request.</span>
+    <span class="n">end_date</span> <span class="o">-=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="s1">&#39;1H&#39;</span><span class="p">)</span>
+    <span class="n">start</span> <span class="o">=</span> <span class="n">r</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s2">&quot;RCORR_E</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="mi">8</span>
+    <span class="n">end</span> <span class="o">=</span> <span class="n">r</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&lt;/code&gt;&lt;/pre&gt;&lt;br&gt;Total&#39;</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">s</span> <span class="o">=</span> <span class="n">r</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
+    <span class="n">s2</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">row</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;;&#39;</span><span class="p">,</span> <span class="s1">&#39;,&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">s</span><span class="p">])</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">df_flux</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">io</span><span class="o">.</span><span class="n">StringIO</span><span class="p">(</span><span class="n">s2</span><span class="p">),</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">,</span> <span class="s1">&#39;counts&#39;</span><span class="p">])</span>
+    <span class="k">except</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Error retrieving data from </span><span class="si">{</span><span class="n">url</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="kc">None</span>
+
+    <span class="c1"># Check if all values from selected detector are NaN. If yes, warn the user</span>
+    <span class="k">if</span> <span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;counts&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">all</span><span class="p">():</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Data for selected neutron detectors appears to be unavailable for the selected period&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Convert timestamp to datetime and apply UTC offset</span>
+    <span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">])</span>
+    <span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">utc_offset</span><span class="p">)</span>
+
+    <span class="c1"># Print acknowledgement to inform users about restrictions and to acknowledge the NMDB database</span>
+    <span class="n">acknowledgement</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;Data retrieved via NMDB are the property of the individual data providers. These data are free for non commercial</span>
+<span class="s2">use to within the restriction imposed by the providers. If you use such data for your research or applications, please acknowledge</span>
+<span class="s2">the origin by a sentence like &#39;We acknowledge the NMDB database (www.nmdb.eu) founded under the European Union&#39;s FP7 programme </span>
+<span class="s2">(contract no. 213007), and the PIs of individual neutron monitors at: IGY Jungfraujoch </span>
+<span class="s2">(Physikalisches Institut, University of Bern, Switzerland)&quot;&quot;&quot;</span>
+
+    <span class="k">return</span> <span class="n">df_flux</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.data.get_reference_neutron_flux" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">get_reference_neutron_flux</span><span class="p">(</span><span class="n">station</span><span class="p">,</span> <span class="n">date</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="s1">&#39;2011-05-01&#39;</span><span class="p">))</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Function to retrieve reference neutron flux from the Neutron Monitor Database. Default date is 2011-05-01, following previous studies (Zreda et a., 2012, Hawdon et al., 2014, Bogena et al., 2022).</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>station</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Neutron Monitor station to retrieve data from.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>date</code></td>
+          <td>
+                <code>datetime</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Date of the reference neutron flux. Default is 2011-05-01.</p>
+            </div>
+          </td>
+          <td>
+                <code><span title="pandas.to_datetime">to_datetime</span>(&#39;2011-05-01&#39;)</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Reference neutron flux in counts per hour.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., &amp; Rosolem, R. (2012). COSMOS: The cosmic-ray soil moisture observing system. Hydrology and Earth System Sciences, 16(11), 4079-4099.</p>
+<p>Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures for cosmic‐ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.</p>
+<p>Bogena, H. R., Schrön, M., Jakobi, J., Ney, P., Zacharias, S., Andreasen, M., ... &amp; Vereecken, H. (2022). COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors. Earth System Science Data, 14(3), 1125-1151.</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">444</span>
+<span class="normal">445</span>
+<span class="normal">446</span>
+<span class="normal">447</span>
+<span class="normal">448</span>
+<span class="normal">449</span>
+<span class="normal">450</span>
+<span class="normal">451</span>
+<span class="normal">452</span>
+<span class="normal">453</span>
+<span class="normal">454</span>
+<span class="normal">455</span>
+<span class="normal">456</span>
+<span class="normal">457</span>
+<span class="normal">458</span>
+<span class="normal">459</span>
+<span class="normal">460</span>
+<span class="normal">461</span>
+<span class="normal">462</span>
+<span class="normal">463</span>
+<span class="normal">464</span>
+<span class="normal">465</span>
+<span class="normal">466</span>
+<span class="normal">467</span>
+<span class="normal">468</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">get_reference_neutron_flux</span><span class="p">(</span><span class="n">station</span><span class="p">,</span> <span class="n">date</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="s2">&quot;2011-05-01&quot;</span><span class="p">)):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to retrieve reference neutron flux from the Neutron Monitor Database. Default date is 2011-05-01, following previous studies (Zreda et a., 2012, Hawdon et al., 2014, Bogena et al., 2022).</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        station (str): Neutron Monitor station to retrieve data from.</span>
+<span class="sd">        date (datetime): Date of the reference neutron flux. Default is 2011-05-01.</span>
 
 <span class="sd">    Returns:</span>
-<span class="sd">        (pandas.DataFrame): Neutron flux in counts per hour and timestamps.</span>
+<span class="sd">        (float): Reference neutron flux in counts per hour.</span>
 
 <span class="sd">    References:</span>
-<span class="sd">        Documentation available:https://www.nmdb.eu/nest/help.php#howto</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="c1"># Example: get_incoming_flux(station=&#39;IRKT&#39;,start_date=&#39;2020-04-10 11:00:00&#39;,end_date=&#39;2020-06-18 17:00:00&#39;)</span>
-    <span class="c1"># Template url = &#39;http://nest.nmdb.eu/draw_graph.php?formchk=1&amp;stations[]=KERG&amp;output=ascii&amp;tabchoice=revori&amp;dtype=corr_for_efficiency&amp;date_choice=bydate&amp;start_year=2009&amp;start_month=09&amp;start_day=01&amp;start_hour=00&amp;start_min=00&amp;end_year=2009&amp;end_month=09&amp;end_day=05&amp;end_hour=23&amp;end_min=59&amp;yunits=0&#39;</span>
-
-
-    <span class="c1"># Expand the time window by 1 hour to ensure an extra observation is included in the request.</span>
-    <span class="n">start_date</span> <span class="o">-=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">expand_window</span><span class="p">)</span>
-    <span class="n">end_date</span> <span class="o">+=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">expand_window</span><span class="p">)</span>
-
-    <span class="c1"># Convert local time to UTC</span>
-    <span class="n">start_date</span> <span class="o">=</span> <span class="n">start_date</span> <span class="o">-</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">utc_offset</span><span class="p">)</span>
-    <span class="n">end_date</span> <span class="o">=</span> <span class="n">end_date</span> <span class="o">-</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">utc_offset</span><span class="p">)</span>
-    <span class="n">date_format</span> <span class="o">=</span> <span class="s1">&#39;%Y-%m-</span><span class="si">%d</span><span class="s1"> %H:%M:%S&#39;</span>
-    <span class="n">root</span> <span class="o">=</span> <span class="s1">&#39;http://www.nmdb.eu/nest/draw_graph.php?&#39;</span>
-    <span class="n">url_par</span> <span class="o">=</span> <span class="p">[</span> <span class="s1">&#39;formchk=1&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;stations[]=&#39;</span> <span class="o">+</span> <span class="n">station</span><span class="p">,</span>
-                <span class="s1">&#39;output=ascii&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;tabchoice=revori&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;dtype=corr_for_efficiency&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;tresolution=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="mi">60</span><span class="p">),</span>
-                <span class="s1">&#39;date_choice=bydate&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;start_year=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">year</span><span class="p">),</span>
-                <span class="s1">&#39;start_month=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">month</span><span class="p">),</span>
-                <span class="s1">&#39;start_day=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">day</span><span class="p">),</span>
-                <span class="s1">&#39;start_hour=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">hour</span><span class="p">),</span>
-                <span class="s1">&#39;start_min=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">start_date</span><span class="o">.</span><span class="n">minute</span><span class="p">),</span>
-                <span class="s1">&#39;end_year=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">year</span><span class="p">),</span>
-                <span class="s1">&#39;end_month=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">month</span><span class="p">),</span>
-                <span class="s1">&#39;end_day=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">day</span><span class="p">),</span>
-                <span class="s1">&#39;end_hour=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">hour</span><span class="p">),</span>
-                <span class="s1">&#39;end_min=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">end_date</span><span class="o">.</span><span class="n">minute</span><span class="p">),</span>
-                <span class="s1">&#39;yunits=0&#39;</span><span class="p">]</span>
-
-    <span class="n">url</span> <span class="o">=</span> <span class="n">root</span> <span class="o">+</span> <span class="s1">&#39;&amp;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">url_par</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Retrieving data from </span><span class="si">{</span><span class="n">url</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-    <span class="n">r</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">)</span><span class="o">.</span><span class="n">content</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span>
-
-    <span class="c1"># Subtract 1 hour to restore the last date included in the request.</span>
-    <span class="n">end_date</span> <span class="o">-=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="s1">&#39;1H&#39;</span><span class="p">)</span>
-    <span class="n">start</span> <span class="o">=</span> <span class="n">r</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s2">&quot;RCORR_E</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="mi">8</span>
-    <span class="n">end</span> <span class="o">=</span> <span class="n">r</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&lt;/code&gt;&lt;/pre&gt;&lt;br&gt;Total&#39;</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
-    <span class="n">s</span> <span class="o">=</span> <span class="n">r</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
-    <span class="n">s2</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">row</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;;&#39;</span><span class="p">,</span><span class="s1">&#39;,&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">s</span><span class="p">])</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">df_flux</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">io</span><span class="o">.</span><span class="n">StringIO</span><span class="p">(</span><span class="n">s2</span><span class="p">),</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">,</span><span class="s1">&#39;counts&#39;</span><span class="p">])</span>
-    <span class="k">except</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Error retrieving data from </span><span class="si">{</span><span class="n">url</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="kc">None</span>
+<span class="sd">        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., &amp; Rosolem, R. (2012). COSMOS: The cosmic-ray soil moisture observing system. Hydrology and Earth System Sciences, 16(11), 4079-4099.</span>
 
-    <span class="c1"># Check if all values from selected detector are NaN. If yes, warn the user</span>
-    <span class="k">if</span> <span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;counts&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">all</span><span class="p">():</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Data for selected neutron detectors appears to be unavailable for the selected period&#39;</span><span class="p">)</span>
+<span class="sd">        Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures for cosmic‐ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.</span>
 
-    <span class="c1"># Convert timestamp to datetime and apply UTC offset</span>
-    <span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">])</span>
-    <span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">utc_offset</span><span class="p">)</span>
+<span class="sd">        Bogena, H. R., Schrön, M., Jakobi, J., Ney, P., Zacharias, S., Andreasen, M., ... &amp; Vereecken, H. (2022). COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors. Earth System Science Data, 14(3), 1125-1151.</span>
 
-    <span class="c1"># Print acknowledgement to inform users about restrictions and to acknowledge the NMDB database</span>
-    <span class="n">acknowledgement</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;Data retrieved via NMDB are the property of the individual data providers. These data are free for non commercial</span>
-<span class="s2">use to within the restriction imposed by the providers. If you use such data for your research or applications, please acknowledge</span>
-<span class="s2">the origin by a sentence like &#39;We acknowledge the NMDB database (www.nmdb.eu) founded under the European Union&#39;s FP7 programme </span>
-<span class="s2">(contract no. 213007), and the PIs of individual neutron monitors at: IGY Jungfraujoch </span>
-<span class="s2">(Physikalisches Institut, University of Bern, Switzerland)&quot;&quot;&quot;</span>
+<span class="sd">&quot;&quot;&quot;</span>
 
-    <span class="k">return</span> <span class="n">df_flux</span>
+    <span class="c1"># Get flux for 2011-05-01</span>
+    <span class="n">df_flux</span> <span class="o">=</span> <span class="n">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">station</span><span class="p">,</span> <span class="n">date</span><span class="p">,</span> <span class="n">date</span> <span class="o">+</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="mi">24</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">df_flux</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Reference neutron flux for </span><span class="si">{</span><span class="n">station</span><span class="si">}</span><span class="s2"> not available. Returning NaN.&quot;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">df_flux</span><span class="p">[</span><span class="s1">&#39;counts&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.idw" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">idw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span><span class="p">,</span> <span class="n">neighborhood</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.idw" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">idw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span><span class="p">,</span> <span class="n">neighborhood</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
 
 </h2>
 
@@ -3455,6 +12119,8 @@ <h2 id="crnpy.crnpy.idw" class="doc doc-heading">
   
       <p>Function to interpolate data using inverse distance weight.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -3471,7 +12137,11 @@ <h2 id="crnpy.crnpy.idw" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>UTM x coordinates in meters.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM x coordinates in meters.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -3481,7 +12151,11 @@ <h2 id="crnpy.crnpy.idw" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>UTM y coordinates in meters.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM y coordinates in meters.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -3491,7 +12165,11 @@ <h2 id="crnpy.crnpy.idw" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>Values to be interpolated.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Values to be interpolated.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -3501,7 +12179,11 @@ <h2 id="crnpy.crnpy.idw" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>UTM x coordinates where z values need to be predicted.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM x coordinates where z values need to be predicted.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -3511,7 +12193,11 @@ <h2 id="crnpy.crnpy.idw" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>UTM y coordinates where z values need to be predicted.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM y coordinates where z values need to be predicted.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -3521,7 +12207,11 @@ <h2 id="crnpy.crnpy.idw" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Only points within this radius in meters are considered for the interpolation.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Only points within this radius in meters are considered for the interpolation.</p>
+            </div>
+          </td>
           <td>
                 <code>1000</code>
           </td>
@@ -3531,7 +12221,11 @@ <h2 id="crnpy.crnpy.idw" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Exponent of the inverse distance weight formula. Typically, p=1 or p=2.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Exponent of the inverse distance weight formula. Typically, p=1 or p=2.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -3539,6 +12233,8 @@ <h2 id="crnpy.crnpy.idw" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -3552,7 +12248,11 @@ <h2 id="crnpy.crnpy.idw" class="doc doc-heading">
           <td>
                 <code>array</code>
           </td>
-          <td><p>Interpolated values.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Interpolated values.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
@@ -3561,48 +12261,48 @@ <h2 id="crnpy.crnpy.idw" class="doc doc-heading">
   <summary>References</summary>
   <p><a href="https://en.wikipedia.org/wiki/Inverse_distance_weighting">https://en.wikipedia.org/wiki/Inverse_distance_weighting</a></p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1093</span>
-<span class="normal">1094</span>
-<span class="normal">1095</span>
-<span class="normal">1096</span>
-<span class="normal">1097</span>
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-<span class="normal">1100</span>
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-<span class="normal">1131</span>
-<span class="normal">1132</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">idw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span><span class="p">,</span> <span class="n">neighborhood</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1373</span>
+<span class="normal">1374</span>
+<span class="normal">1375</span>
+<span class="normal">1376</span>
+<span class="normal">1377</span>
+<span class="normal">1378</span>
+<span class="normal">1379</span>
+<span class="normal">1380</span>
+<span class="normal">1381</span>
+<span class="normal">1382</span>
+<span class="normal">1383</span>
+<span class="normal">1384</span>
+<span class="normal">1385</span>
+<span class="normal">1386</span>
+<span class="normal">1387</span>
+<span class="normal">1388</span>
+<span class="normal">1389</span>
+<span class="normal">1390</span>
+<span class="normal">1391</span>
+<span class="normal">1392</span>
+<span class="normal">1393</span>
+<span class="normal">1394</span>
+<span class="normal">1395</span>
+<span class="normal">1396</span>
+<span class="normal">1397</span>
+<span class="normal">1398</span>
+<span class="normal">1399</span>
+<span class="normal">1400</span>
+<span class="normal">1401</span>
+<span class="normal">1402</span>
+<span class="normal">1403</span>
+<span class="normal">1404</span>
+<span class="normal">1405</span>
+<span class="normal">1406</span>
+<span class="normal">1407</span>
+<span class="normal">1408</span>
+<span class="normal">1409</span>
+<span class="normal">1410</span>
+<span class="normal">1411</span>
+<span class="normal">1412</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">idw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span><span class="p">,</span> <span class="n">neighborhood</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to interpolate data using inverse distance weight.</span>
 
 <span class="sd">    Args:</span>
@@ -3643,17 +12343,19 @@ <h2 id="crnpy.crnpy.idw" class="doc doc-heading">
 
     <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">Z_pred</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">interpolate_2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">,</span> <span class="n">neighborhood</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.interpolate_2d" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">interpolate_2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">,</span> <span class="n">neighborhood</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span></code>
 
 </h2>
 
@@ -3662,6 +12364,8 @@ <h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
   
       <p>Function for interpolating irregular spatial data into a regular grid.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -3678,7 +12382,11 @@ <h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>UTM x coordinates in meters.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM x coordinates in meters.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -3688,7 +12396,11 @@ <h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>UTM y coordinates in meters.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM y coordinates in meters.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -3698,7 +12410,11 @@ <h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>Values to be interpolated.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Values to be interpolated.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -3708,7 +12424,11 @@ <h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Pixel width in meters.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Pixel width in meters.</p>
+            </div>
+          </td>
           <td>
                 <code>100</code>
           </td>
@@ -3718,7 +12438,11 @@ <h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Pixel height in meters.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Pixel height in meters.</p>
+            </div>
+          </td>
           <td>
                 <code>100</code>
           </td>
@@ -3728,7 +12452,11 @@ <h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;cubic&#39;</code>
           </td>
@@ -3738,7 +12466,11 @@ <h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Only points within this radius in meters are considered for the interpolation.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Only points within this radius in meters are considered for the interpolation.</p>
+            </div>
+          </td>
           <td>
                 <code>1000</code>
           </td>
@@ -3746,6 +12478,8 @@ <h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -3759,19 +12493,31 @@ <h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
 <td><code>x_pred</code></td>          <td>
                 <code>array</code>
           </td>
-          <td><p>2D array with x coordinates.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>2D array with x coordinates.</p>
+            </div>
+          </td>
         </tr>
         <tr>
 <td><code>y_pred</code></td>          <td>
                 <code>array</code>
           </td>
-          <td><p>2D array with y coordinates.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>2D array with y coordinates.</p>
+            </div>
+          </td>
         </tr>
         <tr>
 <td><code>z_pred</code></td>          <td>
                 <code>array</code>
           </td>
-          <td><p>2D array with interpolated values.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>2D array with interpolated values.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
@@ -3780,56 +12526,57 @@ <h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
   <summary>References</summary>
   <p><a href="https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html">https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html</a></p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1135</span>
-<span class="normal">1136</span>
-<span class="normal">1137</span>
-<span class="normal">1138</span>
-<span class="normal">1139</span>
-<span class="normal">1140</span>
-<span class="normal">1141</span>
-<span class="normal">1142</span>
-<span class="normal">1143</span>
-<span class="normal">1144</span>
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-<span class="normal">1177</span>
-<span class="normal">1178</span>
-<span class="normal">1179</span>
-<span class="normal">1180</span>
-<span class="normal">1181</span>
-<span class="normal">1182</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">interpolate_2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">,</span> <span class="n">neighborhood</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1415</span>
+<span class="normal">1416</span>
+<span class="normal">1417</span>
+<span class="normal">1418</span>
+<span class="normal">1419</span>
+<span class="normal">1420</span>
+<span class="normal">1421</span>
+<span class="normal">1422</span>
+<span class="normal">1423</span>
+<span class="normal">1424</span>
+<span class="normal">1425</span>
+<span class="normal">1426</span>
+<span class="normal">1427</span>
+<span class="normal">1428</span>
+<span class="normal">1429</span>
+<span class="normal">1430</span>
+<span class="normal">1431</span>
+<span class="normal">1432</span>
+<span class="normal">1433</span>
+<span class="normal">1434</span>
+<span class="normal">1435</span>
+<span class="normal">1436</span>
+<span class="normal">1437</span>
+<span class="normal">1438</span>
+<span class="normal">1439</span>
+<span class="normal">1440</span>
+<span class="normal">1441</span>
+<span class="normal">1442</span>
+<span class="normal">1443</span>
+<span class="normal">1444</span>
+<span class="normal">1445</span>
+<span class="normal">1446</span>
+<span class="normal">1447</span>
+<span class="normal">1448</span>
+<span class="normal">1449</span>
+<span class="normal">1450</span>
+<span class="normal">1451</span>
+<span class="normal">1452</span>
+<span class="normal">1453</span>
+<span class="normal">1454</span>
+<span class="normal">1455</span>
+<span class="normal">1456</span>
+<span class="normal">1457</span>
+<span class="normal">1458</span>
+<span class="normal">1459</span>
+<span class="normal">1460</span>
+<span class="normal">1461</span>
+<span class="normal">1462</span>
+<span class="normal">1463</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">interpolate_2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">,</span> <span class="n">neighborhood</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function for interpolating irregular spatial data into a regular grid.</span>
 
 <span class="sd">    Args:</span>
@@ -3857,7 +12604,8 @@ <h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
     <span class="n">z</span> <span class="o">=</span> <span class="n">z</span><span class="p">[</span><span class="o">~</span><span class="n">idx_nan</span><span class="p">]</span>
 
     <span class="k">if</span> <span class="n">idx_nan</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;WARNING: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2">, and </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">z</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2"> NaN values were dropped from x, y, and z.&quot;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;WARNING: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2">, and </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">z</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2"> NaN values were dropped from x, y, and z.&quot;</span><span class="p">)</span>
 
     <span class="c1"># Create 2D grid for interpolation</span>
     <span class="n">Nx</span> <span class="o">=</span> <span class="nb">round</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">/</span> <span class="n">dx</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
@@ -3878,17 +12626,19 @@ <h2 id="crnpy.crnpy.interpolate_2d" class="doc doc-heading">
 
     <span class="k">return</span> <span class="n">X_pred</span><span class="p">,</span> <span class="n">Y_pred</span><span class="p">,</span> <span class="n">Z_pred</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.interpolate_incoming_flux" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">interpolate_incoming_flux</span><span class="p">(</span><span class="n">nmdb_timestamps</span><span class="p">,</span> <span class="n">nmdb_counts</span><span class="p">,</span> <span class="n">crnp_timestamps</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.interpolate_incoming_flux" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">interpolate_incoming_flux</span><span class="p">(</span><span class="n">nmdb_timestamps</span><span class="p">,</span> <span class="n">nmdb_counts</span><span class="p">,</span> <span class="n">crnp_timestamps</span><span class="p">)</span></code>
 
 </h2>
 
@@ -3897,6 +12647,8 @@ <h2 id="crnpy.crnpy.interpolate_incoming_flux" class="doc doc-heading">
   
       <p>Function to interpolate incoming neutron flux to match the timestamps of the observations.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -3911,9 +12663,13 @@ <h2 id="crnpy.crnpy.interpolate_incoming_flux" class="doc doc-heading">
         <tr>
           <td><code>nmdb_timestamps</code></td>
           <td>
-                <code><span title="pandas">pd</span>.<span title="pandas.Series">Series</span></code>
+                <code><span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Series of timestamps in datetime format from the NMDB.</p>
+            </div>
           </td>
-          <td><p>Series of timestamps in datetime format from the NMDB.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -3921,9 +12677,13 @@ <h2 id="crnpy.crnpy.interpolate_incoming_flux" class="doc doc-heading">
         <tr>
           <td><code>nmdb_counts</code></td>
           <td>
-                <code><span title="pandas">pd</span>.<span title="pandas.Series">Series</span></code>
+                <code><span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Series of neutron counts from the NMDB</p>
+            </div>
           </td>
-          <td><p>Series of neutron counts from the NMDB</p></td>
           <td>
               <em>required</em>
           </td>
@@ -3931,9 +12691,13 @@ <h2 id="crnpy.crnpy.interpolate_incoming_flux" class="doc doc-heading">
         <tr>
           <td><code>crnp_timestamps</code></td>
           <td>
-                <code><span title="pandas">pd</span>.<span title="pandas.Series">Series</span></code>
+                <code><span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Series of timestamps in datetime format from the station or device.</p>
+            </div>
           </td>
-          <td><p>Series of timestamps in datetime format from the station or device.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -3941,6 +12705,8 @@ <h2 id="crnpy.crnpy.interpolate_incoming_flux" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -3952,39 +12718,43 @@ <h2 id="crnpy.crnpy.interpolate_incoming_flux" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code><span title="pandas">pd</span>.<span title="pandas.Series">Series</span></code>
+                <code><span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps</p>
+            </div>
           </td>
-          <td><p>Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps</p></td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">881</span>
-<span class="normal">882</span>
-<span class="normal">883</span>
-<span class="normal">884</span>
-<span class="normal">885</span>
-<span class="normal">886</span>
-<span class="normal">887</span>
-<span class="normal">888</span>
-<span class="normal">889</span>
-<span class="normal">890</span>
-<span class="normal">891</span>
-<span class="normal">892</span>
-<span class="normal">893</span>
-<span class="normal">894</span>
-<span class="normal">895</span>
-<span class="normal">896</span>
-<span class="normal">897</span>
-<span class="normal">898</span>
-<span class="normal">899</span>
-<span class="normal">900</span>
-<span class="normal">901</span>
-<span class="normal">902</span>
-<span class="normal">903</span>
-<span class="normal">904</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">interpolate_incoming_flux</span><span class="p">(</span><span class="n">nmdb_timestamps</span><span class="p">,</span> <span class="n">nmdb_counts</span><span class="p">,</span> <span class="n">crnp_timestamps</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1216</span>
+<span class="normal">1217</span>
+<span class="normal">1218</span>
+<span class="normal">1219</span>
+<span class="normal">1220</span>
+<span class="normal">1221</span>
+<span class="normal">1222</span>
+<span class="normal">1223</span>
+<span class="normal">1224</span>
+<span class="normal">1225</span>
+<span class="normal">1226</span>
+<span class="normal">1227</span>
+<span class="normal">1228</span>
+<span class="normal">1229</span>
+<span class="normal">1230</span>
+<span class="normal">1231</span>
+<span class="normal">1232</span>
+<span class="normal">1233</span>
+<span class="normal">1234</span>
+<span class="normal">1235</span>
+<span class="normal">1236</span>
+<span class="normal">1237</span>
+<span class="normal">1238</span>
+<span class="normal">1239</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">interpolate_incoming_flux</span><span class="p">(</span><span class="n">nmdb_timestamps</span><span class="p">,</span> <span class="n">nmdb_counts</span><span class="p">,</span> <span class="n">crnp_timestamps</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to interpolate incoming neutron flux to match the timestamps of the observations.</span>
 
 <span class="sd">    Args:</span>
@@ -3996,7 +12766,7 @@ <h2 id="crnpy.crnpy.interpolate_incoming_flux" class="doc doc-heading">
 <span class="sd">        (pd.Series): Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">incoming_flux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
-    <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">timestamp</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">crnp_timestamps</span><span class="p">):</span>
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">timestamp</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">crnp_timestamps</span><span class="p">):</span>
         <span class="k">if</span> <span class="n">timestamp</span> <span class="ow">in</span> <span class="n">nmdb_timestamps</span><span class="o">.</span><span class="n">values</span><span class="p">:</span>
             <span class="n">idx</span> <span class="o">=</span> <span class="n">timestamp</span> <span class="o">==</span> <span class="n">nmdb_timestamps</span>
             <span class="n">incoming_flux</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">incoming_flux</span><span class="p">,</span> <span class="n">nmdb_counts</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
@@ -4009,17 +12779,19 @@ <h2 id="crnpy.crnpy.interpolate_incoming_flux" class="doc doc-heading">
     <span class="c1"># Return only the values for the selected timestamps</span>
     <span class="k">return</span> <span class="n">incoming_flux</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.is_outlier" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">is_outlier</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">min_val</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_val</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.is_outlier" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">is_outlier</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">min_val</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_val</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
 </h2>
 
@@ -4028,6 +12800,8 @@ <h2 id="crnpy.crnpy.is_outlier" class="doc doc-heading">
   
       <p>Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -4042,9 +12816,13 @@ <h2 id="crnpy.crnpy.is_outlier" class="doc doc-heading">
         <tr>
           <td><code>x</code></td>
           <td>
-                <code>pandas.<span title="pandas.DataFrame">DataFrame</span></code>
+                <code><span title="pandas.DataFrame">DataFrame</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Variable containing only the columns with neutron counts.</p>
+            </div>
           </td>
-          <td><p>Variable containing only the columns with neutron counts.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -4054,7 +12832,11 @@ <h2 id="crnpy.crnpy.is_outlier" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -4064,7 +12846,11 @@ <h2 id="crnpy.crnpy.is_outlier" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Window size for the moving central tendency. Default is 11.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Window size for the moving central tendency. Default is 11.</p>
+            </div>
+          </td>
           <td>
                 <code>11</code>
           </td>
@@ -4074,7 +12860,11 @@ <h2 id="crnpy.crnpy.is_outlier" class="doc doc-heading">
           <td>
                 <code>int or float</code>
           </td>
-          <td><p>Minimum value for a reading to be considered valid. Default is None.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Minimum value for a reading to be considered valid. Default is None.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -4084,7 +12874,11 @@ <h2 id="crnpy.crnpy.is_outlier" class="doc doc-heading">
           <td>
                 <code>or float</code>
           </td>
-          <td><p>Maximum value for a reading to be considered valid. Default is None.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Maximum value for a reading to be considered valid. Default is None.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -4092,6 +12886,8 @@ <h2 id="crnpy.crnpy.is_outlier" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -4103,9 +12899,13 @@ <h2 id="crnpy.crnpy.is_outlier" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code>pandas.<span title="pandas.DataFrame">DataFrame</span></code>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Boolean indicating outliers.</p>
+            </div>
           </td>
-          <td><p>Boolean indicating outliers.</p></td>
         </tr>
     </tbody>
   </table>
@@ -4114,9 +12914,11 @@ <h2 id="crnpy.crnpy.is_outlier" class="doc doc-heading">
   <summary>References</summary>
   <p>Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">134</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">132</span>
+<span class="normal">133</span>
+<span class="normal">134</span>
 <span class="normal">135</span>
 <span class="normal">136</span>
 <span class="normal">137</span>
@@ -4187,13 +12989,11 @@ <h2 id="crnpy.crnpy.is_outlier" class="doc doc-heading">
 <span class="normal">202</span>
 <span class="normal">203</span>
 <span class="normal">204</span>
-<span class="normal">205</span>
-<span class="normal">206</span>
-<span class="normal">207</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">is_outlier</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">min_val</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_val</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="normal">205</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">is_outlier</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">min_val</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_val</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.</span>
 
 <span class="sd">    Args:</span>
-<span class="sd">        x (pandas.DataFrame): Variable containing only the columns with neutron counts.</span>
+<span class="sd">        x (pd.DataFrame or pd.Series): Variable containing only the columns with neutron counts.</span>
 <span class="sd">        method (str): Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad</span>
 <span class="sd">        window (int, optional): Window size for the moving central tendency. Default is 11.</span>
 <span class="sd">        min_val (int or float): Minimum value for a reading to be considered valid. Default is None.</span>
@@ -4222,70 +13022,413 @@ <h2 id="crnpy.crnpy.is_outlier" class="doc doc-heading">
         <span class="n">iqr</span> <span class="o">=</span> <span class="n">q3</span> <span class="o">-</span> <span class="n">q1</span>
         <span class="n">high_fence</span> <span class="o">=</span> <span class="n">q3</span> <span class="o">+</span> <span class="p">(</span><span class="mf">1.5</span> <span class="o">*</span> <span class="n">iqr</span><span class="p">)</span>
         <span class="n">low_fence</span> <span class="o">=</span> <span class="n">q1</span> <span class="o">-</span> <span class="p">(</span><span class="mf">1.5</span> <span class="o">*</span> <span class="n">iqr</span><span class="p">)</span>
-        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="o">&lt;</span><span class="n">low_fence</span> <span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">x</span><span class="o">&gt;</span><span class="n">high_fence</span> <span class="p">)</span>
+        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">&lt;</span> <span class="n">low_fence</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">x</span> <span class="o">&gt;</span> <span class="n">high_fence</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;moviqr&#39;</span><span class="p">:</span>
+        <span class="n">q1</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span>
+        <span class="n">q3</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span>
+        <span class="n">iqr</span> <span class="o">=</span> <span class="n">q3</span> <span class="o">-</span> <span class="n">q1</span>
+        <span class="n">ub</span> <span class="o">=</span> <span class="n">q3</span> <span class="o">+</span> <span class="p">(</span><span class="mf">1.5</span> <span class="o">*</span> <span class="n">iqr</span><span class="p">)</span>  <span class="c1"># Upper boundary</span>
+        <span class="n">lb</span> <span class="o">=</span> <span class="n">q1</span> <span class="o">-</span> <span class="p">(</span><span class="mf">1.5</span> <span class="o">*</span> <span class="n">iqr</span><span class="p">)</span>  <span class="c1"># Lower boundary</span>
+        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">&lt;</span> <span class="n">lb</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">x</span> <span class="o">&gt;</span> <span class="n">ub</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;zscore&#39;</span><span class="p">:</span>
+        <span class="n">zscore</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span> <span class="o">/</span> <span class="n">x</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
+        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">zscore</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mi">3</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">zscore</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;movzscore&#39;</span><span class="p">:</span>
+        <span class="n">movmean</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+        <span class="n">movstd</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
+        <span class="n">movzscore</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">movmean</span><span class="p">)</span> <span class="o">/</span> <span class="n">movstd</span>
+        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">movzscore</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mi">3</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">movzscore</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;modified_zscore&#39;</span><span class="p">:</span>
+        <span class="c1"># Compute median absolute difference</span>
+        <span class="n">movmedian</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
+        <span class="n">abs_diff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">movmedian</span><span class="p">)</span>
+        <span class="n">mad</span> <span class="o">=</span> <span class="n">abs_diff</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
+
+        <span class="c1"># Compute modified z-score</span>
+        <span class="n">modified_z_score</span> <span class="o">=</span> <span class="mf">0.6745</span> <span class="o">*</span> <span class="n">abs_diff</span> <span class="o">/</span> <span class="n">mad</span>
+        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">modified_z_score</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mf">3.5</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">modified_z_score</span> <span class="o">&gt;</span> <span class="mf">3.5</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;scaled_mad&#39;</span><span class="p">:</span>
+        <span class="c1"># Returns true for elements more than three scaled MAD from the median. </span>
+        <span class="n">c</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">erfcinv</span><span class="p">(</span><span class="mi">3</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">median</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">mad</span> <span class="o">=</span> <span class="n">c</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">median</span><span class="p">))</span>
+        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="n">x</span> <span class="o">&gt;</span> <span class="p">(</span><span class="n">median</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">mad</span><span class="p">)</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Outlier detection method not found.&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">idx_outliers</span> <span class="o">|</span> <span class="n">idx_range_outliers</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.data.latlon_to_utm" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">latlon_to_utm</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">,</span> <span class="n">utm_zone_number</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">utm_zone_letter</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.</p>
+<p>Function only applies to non-polar coordinates.
+If further functionality is required, consider using the utm module. See references for more information.</p>
+<p><a class="glightbox" href="https://upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Universal_Transverse_Mercator_zones.svg/1920px-Universal_Transverse_Mercator_zones.svg.png" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="UTM zones" src="https://upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Universal_Transverse_Mercator_zones.svg/1920px-Universal_Transverse_Mercator_zones.svg.png" /></a>
+UTM zones on an equirectangular world map with irregular zones in red and New York City's zone highlighted. See <a href="https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#UTM_zones">UTM zones</a> for a full description.</p>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>lat</code></td>
+          <td>
+                <code>(float, array)</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Latitude in decimal degrees.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>lon</code></td>
+          <td>
+                <code>(float, array)</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Longitude in decimal degrees.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>utm_zone_number</code></td>
+          <td>
+                <code>int</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM zone number. If None, the zone number is automatically calculated.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>utm_zone_letter</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM zone letter. If None, the zone letter is automatically calculated.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+
+
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>(float, float)</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Tuple of easting and northing coordinates in meters. First element is easting, second is northing.</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87)
+<a href="https://github.com/Turbo87/utm">https://github.com/Turbo87/utm</a></p>
+<p><a href="https://www.maptools.com/tutorials/grid_zone_details#">https://www.maptools.com/tutorials/grid_zone_details#</a></p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1265</span>
+<span class="normal">1266</span>
+<span class="normal">1267</span>
+<span class="normal">1268</span>
+<span class="normal">1269</span>
+<span class="normal">1270</span>
+<span class="normal">1271</span>
+<span class="normal">1272</span>
+<span class="normal">1273</span>
+<span class="normal">1274</span>
+<span class="normal">1275</span>
+<span class="normal">1276</span>
+<span class="normal">1277</span>
+<span class="normal">1278</span>
+<span class="normal">1279</span>
+<span class="normal">1280</span>
+<span class="normal">1281</span>
+<span class="normal">1282</span>
+<span class="normal">1283</span>
+<span class="normal">1284</span>
+<span class="normal">1285</span>
+<span class="normal">1286</span>
+<span class="normal">1287</span>
+<span class="normal">1288</span>
+<span class="normal">1289</span>
+<span class="normal">1290</span>
+<span class="normal">1291</span>
+<span class="normal">1292</span>
+<span class="normal">1293</span>
+<span class="normal">1294</span>
+<span class="normal">1295</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">latlon_to_utm</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">,</span> <span class="n">utm_zone_number</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">utm_zone_letter</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.</span>
+
+<span class="sd">    Function only applies to non-polar coordinates.</span>
+<span class="sd">    If further functionality is required, consider using the utm module. See references for more information.</span>
+
+<span class="sd">    ![UTM zones](https://upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Universal_Transverse_Mercator_zones.svg/1920px-Universal_Transverse_Mercator_zones.svg.png)</span>
+<span class="sd">    UTM zones on an equirectangular world map with irregular zones in red and New York City&#39;s zone highlighted. See [UTM zones](https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#UTM_zones) for a full description.</span>
+
+
+<span class="sd">    Args:</span>
+<span class="sd">        lat (float, array): Latitude in decimal degrees.</span>
+<span class="sd">        lon (float, array): Longitude in decimal degrees.</span>
+<span class="sd">        utm_zone_number (int): UTM zone number. If None, the zone number is automatically calculated.</span>
+<span class="sd">        utm_zone_letter (str): UTM zone letter. If None, the zone letter is automatically calculated.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (float, float): Tuple of easting and northing coordinates in meters. First element is easting, second is northing.</span>
+
+<span class="sd">    References:</span>
+<span class="sd">         Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87)</span>
+<span class="sd">         [https://github.com/Turbo87/utm](https://github.com/Turbo87/utm)</span>
+
+<span class="sd">         [https://www.maptools.com/tutorials/grid_zone_details#](https://www.maptools.com/tutorials/grid_zone_details#)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">utm_zone_number</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">utm_zone_letter</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">easting</span><span class="p">,</span> <span class="n">northing</span><span class="p">,</span> <span class="n">zone_number</span><span class="p">,</span> <span class="n">zone_letter</span> <span class="o">=</span> <span class="n">utm</span><span class="o">.</span><span class="n">from_latlon</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">easting</span><span class="p">,</span> <span class="n">northing</span><span class="p">,</span> <span class="n">zone_number</span><span class="p">,</span> <span class="n">zone_letter</span> <span class="o">=</span> <span class="n">utm</span><span class="o">.</span><span class="n">from_latlon</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">,</span> <span class="n">utm_zone_number</span><span class="p">,</span> <span class="n">utm_zone_letter</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">easting</span><span class="p">,</span> <span class="n">northing</span><span class="p">,</span> <span class="n">zone_number</span><span class="p">,</span> <span class="n">zone_letter</span>
+</code></pre></div></td></tr></table></div>
+          </details>
+  </div>
+
+</div>
+
+
+<div class="doc doc-object doc-function">
+
+
+
+
+<h2 id="crnpy.data.lattice_water" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">lattice_water</span><span class="p">(</span><span class="n">clay_content</span><span class="p">,</span> <span class="n">total_carbon</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+</h2>
+
+
+  <div class="doc doc-contents ">
+  
+      <p>Estimate the amount of water in the lattice of clay minerals.</p>
+<table>
+<thead>
+<tr>
+<th style="text-align: center;"><a class="glightbox" href="../img/lattice_water_simple.png" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="img1" src="../img/lattice_water_simple.png" /></a></th>
+<th style="text-align: center;"><a class="glightbox" href="../img/lattice_water_multiple.png" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="img2" src="../img/lattice_water_multiple.png" /></a></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align: center;">$\omega_{lat} = 0.097 * clay(\%)$</td>
+<td style="text-align: center;">$\omega_{lat} = -0.028 + 0.077 * clay(\%) + 0.459 * carbon(\%)$</td>
+</tr>
+<tr>
+<td style="text-align: center;">Linear regression [lattice water (%) as a function of clay (%)] done with data from Kansas Sate University - Soil Water Processes Lab.</td>
+<td style="text-align: center;">Multiple linear regression [lattice water (%) as a function of clay (%) and soil carbon (%)] done with data from Soil Water Processes Lab.</td>
+</tr>
+</tbody>
+</table>
+
+
+
+  <p><strong>Parameters:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Name</th>
+        <th>Type</th>
+        <th>Description</th>
+        <th>Default</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td><code>clay_content</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Clay content in the soil in percent.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>total_carbon</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Total carbon content in the soil in percent.
+If None, the amount of water is estimated based on clay content only.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+    </tbody>
+  </table>
 
-    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;moviqr&#39;</span><span class="p">:</span>
-        <span class="n">q1</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span>
-        <span class="n">q3</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span>
-        <span class="n">iqr</span> <span class="o">=</span> <span class="n">q3</span> <span class="o">-</span> <span class="n">q1</span>
-        <span class="n">ub</span> <span class="o">=</span> <span class="n">q3</span> <span class="o">+</span> <span class="p">(</span><span class="mf">1.5</span> <span class="o">*</span> <span class="n">iqr</span><span class="p">)</span> <span class="c1"># Upper boundary</span>
-        <span class="n">lb</span> <span class="o">=</span> <span class="n">q1</span> <span class="o">-</span> <span class="p">(</span><span class="mf">1.5</span> <span class="o">*</span> <span class="n">iqr</span><span class="p">)</span> <span class="c1"># Lower boundary</span>
-        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">&lt;</span> <span class="n">lb</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">x</span> <span class="o">&gt;</span> <span class="n">ub</span><span class="p">)</span>
 
-    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;zscore&#39;</span><span class="p">:</span>
-        <span class="n">zscore</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span><span class="o">/</span><span class="n">x</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
-        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">zscore</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mi">3</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">zscore</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">)</span>
 
-    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;movzscore&#39;</span><span class="p">:</span>
-        <span class="n">movmean</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-        <span class="n">movstd</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
-        <span class="n">movzscore</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">movmean</span><span class="p">)</span><span class="o">/</span><span class="n">movstd</span>
-        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">movzscore</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mi">3</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">movzscore</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">)</span>
+  <p><strong>Returns:</strong></p>
+  <table>
+    <thead>
+      <tr>
+        <th>Type</th>
+        <th>Description</th>
+      </tr>
+    </thead>
+    <tbody>
+        <tr>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Amount of water in the lattice of clay minerals in percent</p>
+            </div>
+          </td>
+        </tr>
+    </tbody>
+  </table>
 
-    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;modified_zscore&#39;</span><span class="p">:</span>
-        <span class="c1"># Compute median absolute difference</span>
-        <span class="n">movmedian</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
-        <span class="n">abs_diff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">movmedian</span><span class="p">)</span>
-        <span class="n">mad</span> <span class="o">=</span> <span class="n">abs_diff</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1242</span>
+<span class="normal">1243</span>
+<span class="normal">1244</span>
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+<span class="normal">1261</span>
+<span class="normal">1262</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">lattice_water</span><span class="p">(</span><span class="n">clay_content</span><span class="p">,</span> <span class="n">total_carbon</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Estimate the amount of water in the lattice of clay minerals.</span>
 
-        <span class="c1"># Compute modified z-score</span>
-        <span class="n">modified_z_score</span> <span class="o">=</span> <span class="mf">0.6745</span> <span class="o">*</span> <span class="n">abs_diff</span> <span class="o">/</span> <span class="n">mad</span>
-        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="p">(</span><span class="n">modified_z_score</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mf">3.5</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">modified_z_score</span> <span class="o">&gt;</span> <span class="mf">3.5</span><span class="p">)</span>
+<span class="sd">    ![img1](img/lattice_water_simple.png) | ![img2](img/lattice_water_multiple.png)</span>
+<span class="sd">    :-------------------------:|:-------------------------:</span>
+<span class="sd">    $\omega_{lat} = 0.097 * clay(\%)$ | $\omega_{lat} = -0.028 + 0.077 * clay(\%) + 0.459 * carbon(\%)$</span>
+<span class="sd">    Linear regression [lattice water (%) as a function of clay (%)] done with data from Kansas Sate University - Soil Water Processes Lab. |  Multiple linear regression [lattice water (%) as a function of clay (%) and soil carbon (%)] done with data from Soil Water Processes Lab.</span>
 
-    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;scaled_mad&#39;</span><span class="p">:</span>
-        <span class="c1"># Returns true for elements more than three scaled MAD from the median. </span>
-        <span class="n">c</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">erfcinv</span><span class="p">(</span><span class="mi">3</span><span class="o">/</span><span class="mi">2</span><span class="p">))</span>
-        <span class="n">median</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="n">mad</span> <span class="o">=</span> <span class="n">c</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">median</span><span class="p">))</span>
-        <span class="n">idx_outliers</span> <span class="o">=</span> <span class="n">x</span> <span class="o">&gt;</span> <span class="p">(</span><span class="n">median</span> <span class="o">+</span> <span class="mi">3</span><span class="o">*</span><span class="n">mad</span><span class="p">)</span>
+<span class="sd">    Args:</span>
+<span class="sd">        clay_content (float): Clay content in the soil in percent.</span>
+<span class="sd">        total_carbon (float, optional): Total carbon content in the soil in percent.</span>
+<span class="sd">            If None, the amount of water is estimated based on clay content only.</span>
 
+<span class="sd">    Returns:</span>
+<span class="sd">        (float): Amount of water in the lattice of clay minerals in percent</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">total_carbon</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">lattice_water</span> <span class="o">=</span> <span class="mf">0.097</span> <span class="o">*</span> <span class="n">clay_content</span>
     <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Outlier detection method not found.&#39;</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">idx_outliers</span> <span class="o">|</span> <span class="n">idx_range_outliers</span>
+        <span class="n">lattice_water</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.028</span> <span class="o">+</span> <span class="mf">0.077</span> <span class="o">*</span> <span class="n">clay_content</span> <span class="o">+</span> <span class="mf">0.459</span> <span class="o">*</span> <span class="n">total_carbon</span>
+    <span class="k">return</span> <span class="n">lattice_water</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.latlon_to_utm" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">latlon_to_utm</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">,</span> <span class="n">utm_zone_number</span><span class="p">,</span> <span class="n">missing_values</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.location_factor" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">location_factor</span><span class="p">(</span><span class="n">site_atmospheric_depth</span><span class="p">,</span> <span class="n">site_Rc</span><span class="p">,</span> <span class="n">reference_atmospheric_depth</span><span class="p">,</span> <span class="n">reference_Rc</span><span class="p">)</span></code>
 
 </h2>
 
 
   <div class="doc doc-contents ">
   
-      <p>Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.</p>
-<p>Function only applies to non-polar coordinates.
-If further functionality is required, consider using the utm module. See references for more information.</p>
-<p><a class="glightbox" href="https://upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Universal_Transverse_Mercator_zones.svg/1920px-Universal_Transverse_Mercator_zones.svg.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="UTM zones" src="https://upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Universal_Transverse_Mercator_zones.svg/1920px-Universal_Transverse_Mercator_zones.svg.png" /></a>
-UTM zones on an equirectangular world map with irregular zones in red and New York City's zone highlighted. See <a href="https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#UTM_zones">UTM zones</a> for a full description.</p>
+      <p>Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.</p>
+
+
 
   <p><strong>Parameters:</strong></p>
   <table>
@@ -4299,31 +13442,57 @@ <h2 id="crnpy.crnpy.latlon_to_utm" class="doc doc-heading">
     </thead>
     <tbody>
         <tr>
-          <td><code>lat</code></td>
+          <td><code>site_atmospheric_depth</code></td>
           <td>
-                <code>float, array</code>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Atmospheric depth at the site in g/cm2. Can be estimated using the function <code>atmospheric_depth()</code></p>
+            </div>
           </td>
-          <td><p>Latitude in decimal degrees.</p></td>
           <td>
               <em>required</em>
           </td>
         </tr>
         <tr>
-          <td><code>lon</code></td>
+          <td><code>site_Rc</code></td>
+          <td>
+                <code>float</code>
+          </td>
           <td>
-                <code>float, array</code>
+            <div class="doc-md-description">
+              <p>Cutoff rigidity at the site in GV. Can be estimated using the function <code>cutoff_rigidity()</code></p>
+            </div>
           </td>
-          <td><p>Longitude in decimal degrees.</p></td>
           <td>
               <em>required</em>
           </td>
         </tr>
         <tr>
-          <td><code>utm_zone_number</code></td>
+          <td><code>reference_atmospheric_depth</code></td>
           <td>
-                <code>int</code>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Atmospheric depth at the reference location in g/cm2.</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>reference_Rc</code></td>
+          <td>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Cutoff rigidity at the reference location in GV.</p>
+            </div>
           </td>
-          <td><p>Universal Transverse Mercator (UTM) zone.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -4331,6 +13500,8 @@ <h2 id="crnpy.crnpy.latlon_to_utm" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -4342,229 +13513,123 @@ <h2 id="crnpy.crnpy.latlon_to_utm" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code>float, float</code>
+                <code>float</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Location-dependent correction factor.</p>
+            </div>
           </td>
-          <td><p>Tuple of easting and northing coordinates in meters. First element is easting, second is northing.</p></td>
         </tr>
     </tbody>
   </table>
 
 <details class="references" open>
   <summary>References</summary>
-  <p>Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87)
-<a href="https://github.com/Turbo87/utm">https://github.com/Turbo87/utm</a></p>
-<p><a href="https://www.maptools.com/tutorials/grid_zone_details#">https://www.maptools.com/tutorials/grid_zone_details#</a></p>
+  <p>McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 930</span>
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-<span class="normal">1015</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">latlon_to_utm</span><span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">lon</span><span class="p">,</span> <span class="n">utm_zone_number</span><span class="p">,</span> <span class="n">missing_values</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.</span>
-
-<span class="sd">    Function only applies to non-polar coordinates.</span>
-<span class="sd">    If further functionality is required, consider using the utm module. See references for more information.</span>
-
-<span class="sd">    ![UTM zones](https://upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Universal_Transverse_Mercator_zones.svg/1920px-Universal_Transverse_Mercator_zones.svg.png)</span>
-<span class="sd">    UTM zones on an equirectangular world map with irregular zones in red and New York City&#39;s zone highlighted. See [UTM zones](https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#UTM_zones) for a full description.</span>
-
-
-<span class="sd">    Args:</span>
-<span class="sd">        lat (float, array): Latitude in decimal degrees.</span>
-<span class="sd">        lon (float, array): Longitude in decimal degrees.</span>
-<span class="sd">        utm_zone_number (int): Universal Transverse Mercator (UTM) zone.</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        (float, float): Tuple of easting and northing coordinates in meters. First element is easting, second is northing.</span>
-
-<span class="sd">    References:</span>
-<span class="sd">         Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87)</span>
-<span class="sd">         [https://github.com/Turbo87/utm](https://github.com/Turbo87/utm)</span>
-
-<span class="sd">         [https://www.maptools.com/tutorials/grid_zone_details#](https://www.maptools.com/tutorials/grid_zone_details#)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-
-    <span class="c1"># Define constants</span>
-    <span class="n">R</span> <span class="o">=</span> <span class="mi">6_378_137</span>  <span class="c1"># Earth&#39;s radius at the Equator in meters</span>
-
-    <span class="c1"># Convert input data to Numpy arrays</span>
-    <span class="k">if</span> <span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">lat</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">lon</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">lat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">lat</span><span class="p">)</span>
-            <span class="n">lon</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">lon</span><span class="p">)</span>
-        <span class="k">except</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="s2">&quot;Input values cannot be converted to Numpy arrays.&quot;</span>
-
-    <span class="c1"># Check latitude range</span>
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">lat</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mi">80</span><span class="p">)</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">lat</span> <span class="o">&gt;</span> <span class="mi">84</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="s2">&quot;One or more latitude values exceed the range -80 to 84&quot;</span>
-
-    <span class="c1"># Check longitude range</span>
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">lon</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mi">180</span><span class="p">)</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">lon</span> <span class="o">&gt;</span> <span class="mi">180</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="s2">&quot;One or more longitude values exceed the range -180 to 180&quot;</span>
-
-    <span class="c1"># Constants</span>
-    <span class="n">K0</span> <span class="o">=</span> <span class="mf">0.9996</span>
-    <span class="n">E</span> <span class="o">=</span> <span class="mf">0.00669438</span>
-    <span class="n">E_P2</span> <span class="o">=</span> <span class="n">E</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">E</span><span class="p">)</span>
-
-    <span class="n">M1</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">E</span> <span class="o">/</span> <span class="mi">4</span> <span class="o">-</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">E</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="mi">64</span> <span class="o">-</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">E</span> <span class="o">**</span> <span class="mi">3</span> <span class="o">/</span> <span class="mi">256</span><span class="p">)</span>
-    <span class="n">M2</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="n">E</span> <span class="o">/</span> <span class="mi">8</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">E</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="mi">32</span> <span class="o">+</span> <span class="mi">45</span> <span class="o">*</span> <span class="n">E</span> <span class="o">**</span> <span class="mi">3</span> <span class="o">/</span> <span class="mi">1024</span><span class="p">)</span>
-    <span class="n">M3</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span> <span class="o">*</span> <span class="n">E</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="mi">256</span> <span class="o">+</span> <span class="mi">45</span> <span class="o">*</span> <span class="n">E</span> <span class="o">**</span> <span class="mi">3</span> <span class="o">/</span> <span class="mi">1024</span><span class="p">)</span>
-    <span class="n">M4</span> <span class="o">=</span> <span class="p">(</span><span class="mi">35</span> <span class="o">*</span> <span class="n">E</span> <span class="o">**</span> <span class="mi">3</span> <span class="o">/</span> <span class="mi">3072</span><span class="p">)</span>
-
-    <span class="c1"># Trigonometric operations</span>
-    <span class="n">lat_rad</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">radians</span><span class="p">(</span><span class="n">lat</span><span class="p">)</span>
-    <span class="n">lon_rad</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">radians</span><span class="p">(</span><span class="n">lon</span><span class="p">)</span>
-
-    <span class="n">lat_sin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">lat_rad</span><span class="p">)</span>
-    <span class="n">lat_cos</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">lat_rad</span><span class="p">)</span>
-    <span class="n">lat_tan</span> <span class="o">=</span> <span class="n">lat_sin</span> <span class="o">/</span> <span class="n">lat_cos</span>
-    <span class="n">lat_tan2</span> <span class="o">=</span> <span class="n">lat_tan</span> <span class="o">*</span> <span class="n">lat_tan</span>
-    <span class="n">lat_tan4</span> <span class="o">=</span> <span class="n">lat_tan2</span> <span class="o">*</span> <span class="n">lat_tan2</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1104</span>
+<span class="normal">1105</span>
+<span class="normal">1106</span>
+<span class="normal">1107</span>
+<span class="normal">1108</span>
+<span class="normal">1109</span>
+<span class="normal">1110</span>
+<span class="normal">1111</span>
+<span class="normal">1112</span>
+<span class="normal">1113</span>
+<span class="normal">1114</span>
+<span class="normal">1115</span>
+<span class="normal">1116</span>
+<span class="normal">1117</span>
+<span class="normal">1118</span>
+<span class="normal">1119</span>
+<span class="normal">1120</span>
+<span class="normal">1121</span>
+<span class="normal">1122</span>
+<span class="normal">1123</span>
+<span class="normal">1124</span>
+<span class="normal">1125</span>
+<span class="normal">1126</span>
+<span class="normal">1127</span>
+<span class="normal">1128</span>
+<span class="normal">1129</span>
+<span class="normal">1130</span>
+<span class="normal">1131</span>
+<span class="normal">1132</span>
+<span class="normal">1133</span>
+<span class="normal">1134</span>
+<span class="normal">1135</span>
+<span class="normal">1136</span>
+<span class="normal">1137</span>
+<span class="normal">1138</span>
+<span class="normal">1139</span>
+<span class="normal">1140</span>
+<span class="normal">1141</span>
+<span class="normal">1142</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">location_factor</span><span class="p">(</span><span class="n">site_atmospheric_depth</span><span class="p">,</span> <span class="n">site_Rc</span><span class="p">,</span> <span class="n">reference_atmospheric_depth</span><span class="p">,</span> <span class="n">reference_Rc</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.</span>
 
-    <span class="c1"># Find central meridian.</span>
-    <span class="n">central_lon</span> <span class="o">=</span> <span class="p">(</span><span class="n">utm_zone_number</span> <span class="o">*</span> <span class="mi">6</span> <span class="o">-</span> <span class="mi">180</span><span class="p">)</span> <span class="o">-</span> <span class="mi">3</span>  <span class="c1"># Zones are every 6 degrees.</span>
-    <span class="n">central_lon_rad</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">radians</span><span class="p">(</span><span class="n">central_lon</span><span class="p">)</span>
 
-    <span class="n">n</span> <span class="o">=</span> <span class="n">R</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">E</span> <span class="o">*</span> <span class="n">lat_sin</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
-    <span class="n">c</span> <span class="o">=</span> <span class="n">E_P2</span> <span class="o">*</span> <span class="n">lat_cos</span> <span class="o">**</span> <span class="mi">2</span>
+<span class="sd">    Args:</span>
+<span class="sd">        site_atmospheric_depth (float): Atmospheric depth at the site in g/cm2. Can be estimated using the function `atmospheric_depth()`</span>
+<span class="sd">        site_Rc (float): Cutoff rigidity at the site in GV. Can be estimated using the function `cutoff_rigidity()`</span>
+<span class="sd">        reference_atmospheric_depth (float): Atmospheric depth at the reference location in g/cm2.</span>
+<span class="sd">        reference_Rc (float): Cutoff rigidity at the reference location in GV.</span>
 
-    <span class="k">with</span> <span class="n">np</span><span class="o">.</span><span class="n">errstate</span><span class="p">(</span><span class="n">divide</span><span class="o">=</span><span class="s1">&#39;ignore&#39;</span><span class="p">,</span> <span class="n">invalid</span><span class="o">=</span><span class="s1">&#39;ignore&#39;</span><span class="p">):</span>
-        <span class="n">a</span> <span class="o">=</span> <span class="n">lat_cos</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">remainder</span><span class="p">(((</span><span class="n">lon_rad</span> <span class="o">-</span> <span class="n">central_lon_rad</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-    <span class="n">m</span> <span class="o">=</span> <span class="n">R</span> <span class="o">*</span> <span class="p">(</span><span class="n">M1</span> <span class="o">*</span> <span class="n">lat_rad</span> <span class="o">-</span> <span class="n">M2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">lat_rad</span><span class="p">)</span> <span class="o">+</span> <span class="n">M3</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">4</span> <span class="o">*</span> <span class="n">lat_rad</span><span class="p">)</span> <span class="o">-</span> <span class="n">M4</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">6</span> <span class="o">*</span> <span class="n">lat_rad</span><span class="p">))</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        (float): Location-dependent correction factor.</span>
 
-    <span class="n">easting</span> <span class="o">=</span> <span class="n">K0</span> <span class="o">*</span> <span class="n">n</span> <span class="o">*</span> <span class="p">(</span><span class="n">a</span> <span class="o">+</span> <span class="n">a</span> <span class="o">**</span> <span class="mi">3</span> <span class="o">/</span> <span class="mi">6</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">lat_tan2</span> <span class="o">+</span> <span class="n">c</span><span class="p">)</span> <span class="o">+</span> <span class="n">a</span> <span class="o">**</span> <span class="mi">5</span> <span class="o">/</span> <span class="mi">120</span> <span class="o">*</span> <span class="p">(</span>
-                <span class="mi">5</span> <span class="o">-</span> <span class="mi">18</span> <span class="o">*</span> <span class="n">lat_tan2</span> <span class="o">+</span> <span class="n">lat_tan4</span> <span class="o">+</span> <span class="mi">72</span> <span class="o">*</span> <span class="n">c</span> <span class="o">-</span> <span class="mi">58</span> <span class="o">*</span> <span class="n">E_P2</span><span class="p">))</span> <span class="o">+</span> <span class="mi">500_000</span>
-    <span class="n">northing</span> <span class="o">=</span> <span class="n">K0</span> <span class="o">*</span> <span class="p">(</span><span class="n">m</span> <span class="o">+</span> <span class="n">n</span> <span class="o">*</span> <span class="n">lat_tan</span> <span class="o">*</span> <span class="p">(</span>
-                <span class="n">a</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">a</span> <span class="o">**</span> <span class="mi">4</span> <span class="o">/</span> <span class="mi">24</span> <span class="o">*</span> <span class="p">(</span><span class="mi">5</span> <span class="o">-</span> <span class="n">lat_tan2</span> <span class="o">+</span> <span class="mi">9</span> <span class="o">*</span> <span class="n">c</span> <span class="o">+</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">c</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">a</span> <span class="o">**</span> <span class="mi">6</span> <span class="o">/</span> <span class="mi">720</span> <span class="o">*</span> <span class="p">(</span>
-                    <span class="mi">61</span> <span class="o">-</span> <span class="mi">58</span> <span class="o">*</span> <span class="n">lat_tan2</span> <span class="o">+</span> <span class="n">lat_tan4</span> <span class="o">+</span> <span class="mi">600</span> <span class="o">*</span> <span class="n">c</span> <span class="o">-</span> <span class="mi">330</span> <span class="o">*</span> <span class="n">E_P2</span><span class="p">)))</span>
+<span class="sd">    References:</span>
+<span class="sd">        McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.</span>
 
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">lat</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">):</span>
-        <span class="n">northing</span> <span class="o">+=</span> <span class="mi">10_000_000</span>
+<span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">return</span> <span class="n">easting</span><span class="p">,</span> <span class="n">northing</span>
+    <span class="c1"># Renamed variables based on the provided table</span>
+    <span class="n">c0</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.0009</span>  <span class="c1"># from C39</span>
+    <span class="n">c1</span> <span class="o">=</span> <span class="mf">1.7699</span>  <span class="c1"># from C40</span>
+    <span class="n">c2</span> <span class="o">=</span> <span class="mf">0.0064</span>  <span class="c1"># from C41</span>
+    <span class="n">c3</span> <span class="o">=</span> <span class="mf">1.8855</span>  <span class="c1"># from C42</span>
+    <span class="n">c4</span> <span class="o">=</span> <span class="mf">0.000013</span>  <span class="c1"># from C43</span>
+    <span class="n">c5</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.2237</span>  <span class="c1"># from C44</span>
+    <span class="n">epsilon</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># from C45</span>
+
+    <span class="c1"># Translated formula with renamed variables from McJannet and Desilets, 2023</span>
+    <span class="n">tau_new</span> <span class="o">=</span> <span class="n">epsilon</span> <span class="o">*</span> <span class="p">(</span><span class="n">c0</span> <span class="o">*</span> <span class="n">reference_atmospheric_depth</span> <span class="o">+</span> <span class="n">c1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span>
+            <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span>
+        <span class="o">-</span><span class="p">(</span><span class="n">c2</span> <span class="o">*</span> <span class="n">reference_atmospheric_depth</span> <span class="o">+</span> <span class="n">c3</span><span class="p">)</span> <span class="o">*</span> <span class="n">reference_Rc</span> <span class="o">**</span> <span class="p">(</span><span class="n">c4</span> <span class="o">*</span> <span class="n">reference_atmospheric_depth</span> <span class="o">+</span> <span class="n">c5</span><span class="p">)))</span>
+
+    <span class="n">norm_factor</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">tau_new</span>
+
+    <span class="c1"># Calculate the result using the provided parameters</span>
+    <span class="n">tau</span> <span class="o">=</span> <span class="n">epsilon</span> <span class="o">*</span> <span class="n">norm_factor</span> <span class="o">*</span> <span class="p">(</span><span class="n">c0</span> <span class="o">*</span> <span class="n">site_atmospheric_depth</span> <span class="o">+</span> <span class="n">c1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span>
+                <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">c2</span> <span class="o">*</span> <span class="n">site_atmospheric_depth</span> <span class="o">+</span> <span class="n">c3</span><span class="p">)</span> <span class="o">*</span> <span class="n">site_Rc</span> <span class="o">**</span> <span class="p">(</span><span class="n">c4</span> <span class="o">*</span> <span class="n">site_atmospheric_depth</span> <span class="o">+</span> <span class="n">c5</span><span class="p">)))</span>
+    <span class="k">return</span> <span class="n">tau</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.lattice_water" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">lattice_water</span><span class="p">(</span><span class="n">clay_content</span><span class="p">,</span> <span class="n">total_carbon</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.nrad_weight" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">nrad_weight</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">distances</span><span class="p">,</span> <span class="n">depth</span><span class="p">,</span> <span class="n">rhob</span><span class="o">=</span><span class="mf">1.4</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Hveg</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
 </h2>
 
 
   <div class="doc doc-contents ">
   
-      <p>Estimate the amount of water in the lattice of clay minerals.</p>
-<table>
-<thead>
-<tr>
-<th align="center"><a class="glightbox" href="../img/lattice_water_simple.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="img1" src="../img/lattice_water_simple.png" /></a></th>
-<th align="center"><a class="glightbox" href="../img/lattice_water_multiple.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="img2" src="../img/lattice_water_multiple.png" /></a></th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">$\omega_{lat} = 0.097 * clay(\%)$</td>
-<td align="center">$\omega_{lat} = -0.028 + 0.077 * clay(\%) + 0.459 * carbon(\%)$</td>
-</tr>
-<tr>
-<td align="center">Linear regression [lattice water (%) as a function of clay (%)] done with data from Kansas Sate University - Soil Water Processes Lab.</td>
-<td align="center">Multiple linear regression [lattice water (%) as a function of clay (%) and soil carbon (%)] done with data from Soil Water Processes Lab.</td>
-</tr>
-</tbody>
-</table>
+      <p>Function to compute distance weights corresponding to each soil sample.</p>
+
+
 
   <p><strong>Parameters:</strong></p>
   <table>
@@ -4578,22 +13643,113 @@ <h2 id="crnpy.crnpy.lattice_water" class="doc doc-heading">
     </thead>
     <tbody>
         <tr>
-          <td><code>clay_content</code></td>
+          <td><code>h</code></td>
           <td>
-                <code>float</code>
+                <code><span title="numpy.array">array</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.</p>
+            </div>
           </td>
-          <td><p>Clay content in the soil in percent.</p></td>
           <td>
               <em>required</em>
           </td>
         </tr>
         <tr>
-          <td><code>total_carbon</code></td>
+          <td><code>theta</code></td>
           <td>
-                <code>float</code>
+                <code><span title="numpy.array">array</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>distances</code></td>
+          <td>
+                <code><span title="numpy.array">array</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Distances from the location of each sample to the origin (0.5 - 600 m)</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>depth</code></td>
+          <td>
+                <code><span title="numpy.array">array</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Depths for each sample (m)</p>
+            </div>
+          </td>
+          <td>
+              <em>required</em>
+          </td>
+        </tr>
+        <tr>
+          <td><code>rhob</code></td>
+          <td>
+                <code><span title="numpy.array">array</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Bulk density in g/cm^3</p>
+            </div>
+          </td>
+          <td>
+                <code>1.4</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>p</code></td>
+          <td>
+                <code><span title="numpy.array">array</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Atmospheric pressure in hPa. Required for the 'Schron_2017' method.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>Hveg</code></td>
+          <td>
+                <code><span title="numpy.array">array</span> or <span title="pandas.Series">Series</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Vegetation height in m. Required for the 'Schron_2017' method.</p>
+            </div>
+          </td>
+          <td>
+                <code>None</code>
+          </td>
+        </tr>
+        <tr>
+          <td><code>method</code></td>
+          <td>
+                <code>str</code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.</p>
+            </div>
           </td>
-          <td><p>Total carbon content in the soil in percent.
-If None, the amount of water is estimated based on clay content only.</p></td>
           <td>
                 <code>None</code>
           </td>
@@ -4601,6 +13757,8 @@ <h2 id="crnpy.crnpy.lattice_water" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -4612,16 +13770,241 @@ <h2 id="crnpy.crnpy.lattice_water" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code>float</code>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Distance weights for each sample.</p>
+            </div>
           </td>
-          <td><p>Amount of water in the lattice of clay minerals in percent</p></td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">907</span>
+<details class="references" open>
+  <summary>References</summary>
+  <p>Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015).
+Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray
+neutrons. Water Resour. Res. 51, 5772–5790. doi:10.1002/2015WR017169</p>
+<p>Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L.,
+Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M., Rebmann, C.,
+Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.: Improving calibration and
+validation of cosmic-ray neutron sensors in the light of spatial sensitivity,
+Hydrol. Earth Syst. Sci., 21, 5009–5030, https://doi.org/10.5194/hess-21-5009-2017, 2017.</p>
+</details>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">697</span>
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@@ -4641,216 +14024,50 @@ <h2 id="crnpy.crnpy.lattice_water" class="doc doc-heading">
 <span class="normal">924</span>
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-<span class="normal">927</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">lattice_water</span><span class="p">(</span><span class="n">clay_content</span><span class="p">,</span> <span class="n">total_carbon</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Estimate the amount of water in the lattice of clay minerals.</span>
-
-<span class="sd">    ![img1](img/lattice_water_simple.png) | ![img2](img/lattice_water_multiple.png)</span>
-<span class="sd">    :-------------------------:|:-------------------------:</span>
-<span class="sd">    $\omega_{lat} = 0.097 * clay(\%)$ | $\omega_{lat} = -0.028 + 0.077 * clay(\%) + 0.459 * carbon(\%)$</span>
-<span class="sd">    Linear regression [lattice water (%) as a function of clay (%)] done with data from Kansas Sate University - Soil Water Processes Lab. |  Multiple linear regression [lattice water (%) as a function of clay (%) and soil carbon (%)] done with data from Soil Water Processes Lab.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        clay_content (float): Clay content in the soil in percent.</span>
-<span class="sd">        total_carbon (float, optional): Total carbon content in the soil in percent.</span>
-<span class="sd">            If None, the amount of water is estimated based on clay content only.</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        (float): Amount of water in the lattice of clay minerals in percent</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">total_carbon</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">lattice_water</span> <span class="o">=</span> <span class="mf">0.097</span> <span class="o">*</span> <span class="n">clay_content</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">lattice_water</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.028</span> <span class="o">+</span> <span class="mf">0.077</span> <span class="o">*</span> <span class="n">clay_content</span> <span class="o">+</span> <span class="mf">0.459</span> <span class="o">*</span> <span class="n">total_carbon</span>
-    <span class="k">return</span> <span class="n">lattice_water</span>
-</code></pre></div></td></tr></table></div>
-      </details>
-  </div>
-
-</div>
-
-<div class="doc doc-object doc-function">
-
-
-
-<h2 id="crnpy.crnpy.nrad_weight" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">nrad_weight</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">distances</span><span class="p">,</span> <span class="n">depth</span><span class="p">,</span> <span class="n">rhob</span><span class="o">=</span><span class="mf">1.4</span><span class="p">)</span></code>
-
-</h2>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>Function to compute distance weights corresponding to each soil sample.</p>
-
-  <p><strong>Parameters:</strong></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr>
-          <td><code>h</code></td>
-          <td>
-                <code>float</code>
-          </td>
-          <td><p>Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.</p></td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr>
-          <td><code>theta</code></td>
-          <td>
-                <code>array or pd.Series or pd.DataFrame</code>
-          </td>
-          <td><p>Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)</p></td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr>
-          <td><code>distances</code></td>
-          <td>
-                <code>array or pd.Series or pd.DataFrame</code>
-          </td>
-          <td><p>Distances from the location of each sample to the origin (0.5 - 600 m)</p></td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr>
-          <td><code>depth</code></td>
-          <td>
-                <code>array or pd.Series or pd.DataFrame</code>
-          </td>
-          <td><p>Depths for each sample (m)</p></td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr>
-          <td><code>rhob</code></td>
-          <td>
-                <code>float</code>
-          </td>
-          <td><p>Bulk density in g/cm^3</p></td>
-          <td>
-                <code>1.4</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-  <p><strong>Returns:</strong></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr>
-          <td>
-                <code>array or pd.Series or pd.DataFrame</code>
-          </td>
-          <td><p>Distance weights for each sample.</p></td>
-        </tr>
-    </tbody>
-  </table>
-
-<details class="references" open>
-  <summary>References</summary>
-  <p>Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015).
-Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray
-neutrons. Water Resour. Res. 51, 5772–5790. doi:10.1002/2015WR017169</p>
-</details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">645</span>
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-<span class="normal">710</span>
-<span class="normal">711</span>
-<span class="normal">712</span>
-<span class="normal">713</span>
-<span class="normal">714</span>
-<span class="normal">715</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">nrad_weight</span><span class="p">(</span><span class="n">h</span><span class="p">,</span><span class="n">theta</span><span class="p">,</span><span class="n">distances</span><span class="p">,</span><span class="n">depth</span><span class="p">,</span><span class="n">rhob</span><span class="o">=</span><span class="mf">1.4</span><span class="p">):</span>
+<span class="normal">927</span>
+<span class="normal">928</span>
+<span class="normal">929</span>
+<span class="normal">930</span>
+<span class="normal">931</span>
+<span class="normal">932</span>
+<span class="normal">933</span>
+<span class="normal">934</span>
+<span class="normal">935</span>
+<span class="normal">936</span>
+<span class="normal">937</span>
+<span class="normal">938</span>
+<span class="normal">939</span>
+<span class="normal">940</span>
+<span class="normal">941</span>
+<span class="normal">942</span>
+<span class="normal">943</span>
+<span class="normal">944</span>
+<span class="normal">945</span>
+<span class="normal">946</span>
+<span class="normal">947</span>
+<span class="normal">948</span>
+<span class="normal">949</span>
+<span class="normal">950</span>
+<span class="normal">951</span>
+<span class="normal">952</span>
+<span class="normal">953</span>
+<span class="normal">954</span>
+<span class="normal">955</span>
+<span class="normal">956</span>
+<span class="normal">957</span>
+<span class="normal">958</span>
+<span class="normal">959</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">nrad_weight</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">distances</span><span class="p">,</span> <span class="n">depth</span><span class="p">,</span> <span class="n">rhob</span><span class="o">=</span><span class="mf">1.4</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Hveg</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to compute distance weights corresponding to each soil sample.</span>
 
 <span class="sd">    Args:</span>
-<span class="sd">        h (float): Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.</span>
-<span class="sd">        theta (array or pd.Series or pd.DataFrame): Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)</span>
-<span class="sd">        distances (array or pd.Series or pd.DataFrame): Distances from the location of each sample to the origin (0.5 - 600 m)</span>
-<span class="sd">        depth (array or pd.Series or pd.DataFrame): Depths for each sample (m)</span>
-<span class="sd">        rhob (float): Bulk density in g/cm^3</span>
+<span class="sd">        h (np.array or pd.Series): Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.</span>
+<span class="sd">        theta (np.array or pd.Series): Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)</span>
+<span class="sd">        distances (np.array or pd.Series): Distances from the location of each sample to the origin (0.5 - 600 m)</span>
+<span class="sd">        depth (np.array or pd.Series): Depths for each sample (m)</span>
+<span class="sd">        rhob (np.array or pd.Series): Bulk density in g/cm^3</span>
+<span class="sd">        p (np.array or pd.Series): Atmospheric pressure in hPa. Required for the &#39;Schron_2017&#39; method.</span>
+<span class="sd">        Hveg (np.array or pd.Series): Vegetation height in m. Required for the &#39;Schron_2017&#39; method.</span>
+<span class="sd">        method (str): Method to compute the distance weights. Options are &#39;Kohli_2015&#39; or &#39;Schron_2017&#39;.</span>
 
 <span class="sd">    Returns:</span>
 <span class="sd">        (array or pd.Series or pd.DataFrame): Distance weights for each sample.</span>
@@ -4859,72 +14076,263 @@ <h2 id="crnpy.crnpy.nrad_weight" class="doc doc-heading">
 <span class="sd">        Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015).</span>
 <span class="sd">        Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray</span>
 <span class="sd">        neutrons. Water Resour. Res. 51, 5772–5790. doi:10.1002/2015WR017169</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="c1"># Table A1. Parameters for Fi and D86</span>
-    <span class="n">p10</span> <span class="o">=</span> <span class="mi">8735</span><span class="p">;</span>       <span class="n">p11</span> <span class="o">=</span> <span class="mf">17.1758</span><span class="p">;</span> <span class="n">p12</span> <span class="o">=</span> <span class="mi">11720</span><span class="p">;</span>      <span class="n">p13</span> <span class="o">=</span> <span class="mf">0.00978</span><span class="p">;</span>   <span class="n">p14</span> <span class="o">=</span> <span class="mi">7045</span><span class="p">;</span>      <span class="n">p15</span> <span class="o">=</span> <span class="mf">0.003632</span><span class="p">;</span>
-    <span class="n">p20</span> <span class="o">=</span> <span class="mf">2.7925e-2</span><span class="p">;</span>  <span class="n">p21</span> <span class="o">=</span> <span class="mf">5.0399</span><span class="p">;</span>  <span class="n">p22</span> <span class="o">=</span> <span class="mf">2.8544e-2</span><span class="p">;</span>  <span class="n">p23</span> <span class="o">=</span> <span class="mf">0.002455</span><span class="p">;</span>  <span class="n">p24</span> <span class="o">=</span> <span class="mf">6.851e-5</span><span class="p">;</span>  <span class="n">p25</span> <span class="o">=</span> <span class="mf">9.2926</span><span class="p">;</span>
-    <span class="n">p30</span> <span class="o">=</span> <span class="mi">247970</span><span class="p">;</span>     <span class="n">p31</span> <span class="o">=</span> <span class="mf">17.63</span><span class="p">;</span>   <span class="n">p32</span> <span class="o">=</span> <span class="mi">374655</span><span class="p">;</span>     <span class="n">p33</span> <span class="o">=</span> <span class="mf">0.00191</span><span class="p">;</span>   <span class="n">p34</span> <span class="o">=</span> <span class="mi">195725</span><span class="p">;</span>
-    <span class="n">p40</span> <span class="o">=</span> <span class="mf">5.4818e-2</span><span class="p">;</span>  <span class="n">p41</span> <span class="o">=</span> <span class="mf">15.921</span><span class="p">;</span>  <span class="n">p42</span> <span class="o">=</span> <span class="mf">0.6373</span><span class="p">;</span>     <span class="n">p43</span> <span class="o">=</span> <span class="mf">5.99e-2</span><span class="p">;</span>   <span class="n">p44</span> <span class="o">=</span> <span class="mf">5.425e-4</span><span class="p">;</span>
-    <span class="n">p50</span> <span class="o">=</span> <span class="mi">1383702</span><span class="p">;</span>    <span class="n">p51</span> <span class="o">=</span> <span class="mf">4.156</span><span class="p">;</span>   <span class="n">p52</span> <span class="o">=</span> <span class="mi">5325</span><span class="p">;</span>       <span class="n">p53</span> <span class="o">=</span> <span class="mf">0.00238</span><span class="p">;</span>   <span class="n">p54</span> <span class="o">=</span> <span class="mf">0.0156</span><span class="p">;</span>    <span class="n">p55</span> <span class="o">=</span> <span class="mf">0.130</span><span class="p">;</span>     <span class="n">p56</span> <span class="o">=</span> <span class="mi">1521</span><span class="p">;</span>
-    <span class="n">p60</span> <span class="o">=</span> <span class="mf">6.031e-5</span><span class="p">;</span>   <span class="n">p61</span> <span class="o">=</span> <span class="mf">98.5</span><span class="p">;</span>    <span class="n">p62</span> <span class="o">=</span> <span class="mf">1.0466e-3</span><span class="p">;</span>
-    <span class="n">p70</span> <span class="o">=</span> <span class="mi">11747</span><span class="p">;</span>      <span class="n">p71</span> <span class="o">=</span> <span class="mf">41.66</span><span class="p">;</span>   <span class="n">p72</span> <span class="o">=</span> <span class="mi">4521</span><span class="p">;</span>       <span class="n">p73</span> <span class="o">=</span> <span class="mf">0.01998</span><span class="p">;</span>   <span class="n">p74</span> <span class="o">=</span> <span class="mf">0.00604</span><span class="p">;</span>   <span class="n">p75</span> <span class="o">=</span> <span class="mi">2534</span><span class="p">;</span>      <span class="n">p76</span> <span class="o">=</span> <span class="mf">0.00475</span><span class="p">;</span>
-    <span class="n">p80</span> <span class="o">=</span> <span class="mf">1.543e-2</span><span class="p">;</span>   <span class="n">p81</span> <span class="o">=</span> <span class="mf">10.06</span><span class="p">;</span>   <span class="n">p82</span> <span class="o">=</span> <span class="mf">1.807e-2</span><span class="p">;</span>   <span class="n">p83</span> <span class="o">=</span> <span class="mf">0.0011</span><span class="p">;</span>    <span class="n">p84</span> <span class="o">=</span> <span class="mf">8.81e-5</span><span class="p">;</span>   <span class="n">p85</span> <span class="o">=</span> <span class="mf">0.0405</span><span class="p">;</span>    <span class="n">p86</span> <span class="o">=</span> <span class="mf">20.24</span><span class="p">;</span>
-    <span class="n">p90</span> <span class="o">=</span> <span class="mf">8.321</span><span class="p">;</span>      <span class="n">p91</span> <span class="o">=</span> <span class="mf">0.14249</span><span class="p">;</span> <span class="n">p92</span> <span class="o">=</span> <span class="mf">0.96655</span><span class="p">;</span>    <span class="n">p93</span> <span class="o">=</span> <span class="mf">26.42</span><span class="p">;</span>     <span class="n">p94</span> <span class="o">=</span> <span class="mf">0.0567</span><span class="p">;</span>
-
-
-    <span class="c1"># Numerical determination of the penetration depth (86%) (Eq. 8)</span>
-    <span class="n">D86</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">rhob</span><span class="o">*</span><span class="p">(</span><span class="n">p90</span><span class="o">+</span><span class="n">p91</span><span class="o">*</span><span class="p">(</span><span class="n">p92</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="o">*</span><span class="n">distances</span><span class="o">/</span><span class="mi">100</span><span class="p">))</span><span class="o">*</span><span class="p">(</span><span class="n">p93</span><span class="o">+</span><span class="n">theta</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">p94</span><span class="o">+</span><span class="n">theta</span><span class="p">))</span>
-
-    <span class="c1"># Depth weights (Eq. 7)</span>
-    <span class="n">Wd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">depth</span><span class="o">/</span><span class="n">D86</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">h</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">W</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># skip distance weighting</span>
-
-    <span class="k">elif</span> <span class="p">(</span><span class="n">h</span> <span class="o">&gt;=</span> <span class="mf">0.1</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">h</span><span class="o">&lt;=</span> <span class="mi">50</span><span class="p">):</span>
-        <span class="c1"># Functions for Fi (Appendix A in Köhli et al., 2015)</span>
-        <span class="n">F1</span> <span class="o">=</span> <span class="n">p10</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="n">p13</span><span class="o">*</span><span class="n">h</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p11</span><span class="o">*</span><span class="n">theta</span><span class="p">)</span><span class="o">+</span><span class="n">p12</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="n">p15</span><span class="o">*</span><span class="n">h</span><span class="p">)</span><span class="o">-</span><span class="n">p14</span><span class="o">*</span><span class="n">theta</span>
-        <span class="n">F2</span> <span class="o">=</span> <span class="p">((</span><span class="o">-</span><span class="n">p20</span><span class="o">+</span><span class="n">p24</span><span class="o">*</span><span class="n">h</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p21</span><span class="o">*</span><span class="n">theta</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="n">p25</span><span class="o">*</span><span class="n">theta</span><span class="p">))</span><span class="o">+</span><span class="n">p22</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="n">h</span><span class="o">*</span><span class="n">p23</span><span class="p">)</span>
-        <span class="n">F3</span> <span class="o">=</span> <span class="p">(</span><span class="n">p30</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="n">p33</span><span class="o">*</span><span class="n">h</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p31</span><span class="o">*</span><span class="n">theta</span><span class="p">)</span><span class="o">+</span><span class="n">p32</span><span class="o">-</span><span class="n">p34</span><span class="o">*</span><span class="n">theta</span><span class="p">)</span>
-        <span class="n">F4</span> <span class="o">=</span> <span class="n">p40</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p41</span><span class="o">*</span><span class="n">theta</span><span class="p">)</span><span class="o">+</span><span class="n">p42</span><span class="o">-</span><span class="n">p43</span><span class="o">*</span><span class="n">theta</span><span class="o">+</span><span class="n">p44</span><span class="o">*</span><span class="n">h</span>
-        <span class="n">F5</span> <span class="o">=</span> <span class="n">p50</span><span class="o">*</span><span class="p">(</span><span class="mf">0.02</span><span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="n">p55</span><span class="o">/</span><span class="p">(</span><span class="n">h</span><span class="o">-</span><span class="n">p55</span><span class="o">+</span><span class="n">p56</span><span class="o">*</span><span class="n">theta</span><span class="p">))</span><span class="o">*</span><span class="p">(</span><span class="n">p54</span><span class="o">-</span><span class="n">theta</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p51</span><span class="o">*</span><span class="p">(</span><span class="n">theta</span><span class="o">-</span><span class="n">p54</span><span class="p">))</span><span class="o">+</span><span class="n">p52</span><span class="o">*</span><span class="p">(</span><span class="mf">0.7</span><span class="o">-</span><span class="n">h</span><span class="o">*</span><span class="n">theta</span><span class="o">*</span><span class="n">p53</span><span class="p">)</span>
-        <span class="n">F6</span> <span class="o">=</span> <span class="n">p60</span><span class="o">*</span><span class="p">(</span><span class="n">h</span><span class="o">+</span><span class="n">p61</span><span class="p">)</span><span class="o">+</span><span class="n">p62</span><span class="o">*</span><span class="n">theta</span>
-        <span class="n">F7</span> <span class="o">=</span> <span class="p">(</span><span class="n">p70</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">p76</span><span class="o">*</span><span class="n">h</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p71</span><span class="o">*</span><span class="n">theta</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">h</span><span class="o">*</span><span class="n">p74</span><span class="p">))</span><span class="o">+</span><span class="n">p72</span><span class="o">-</span><span class="n">p75</span><span class="o">*</span><span class="n">theta</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="mi">2</span><span class="o">+</span><span class="n">h</span><span class="o">*</span><span class="n">p73</span><span class="p">)</span>
-        <span class="n">F8</span> <span class="o">=</span> <span class="p">((</span><span class="o">-</span><span class="n">p80</span><span class="o">+</span><span class="n">p84</span><span class="o">*</span><span class="n">h</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p81</span><span class="o">*</span><span class="n">theta</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="n">p85</span><span class="o">*</span><span class="n">h</span><span class="o">+</span><span class="n">p86</span><span class="o">*</span><span class="n">theta</span><span class="p">))</span><span class="o">+</span><span class="n">p82</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="mi">2</span><span class="o">+</span><span class="n">h</span><span class="o">*</span><span class="n">p83</span><span class="p">)</span>
-
-        <span class="c1"># Distance weights (Eq. 3)</span>
-        <span class="n">W</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">distances</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">distances</span><span class="p">)):</span>
-            <span class="k">if</span> <span class="p">(</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">&lt;=</span><span class="mi">50</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">&gt;</span><span class="mf">0.5</span><span class="p">):</span>
-                <span class="n">W</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">=</span><span class="n">F1</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">F2</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span><span class="o">+</span><span class="n">F3</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">F4</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-            <span class="k">elif</span> <span class="p">(</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">50</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">&lt;</span><span class="mi">600</span><span class="p">):</span>
-                <span class="n">W</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">=</span><span class="n">F5</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">F6</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span><span class="o">+</span><span class="n">F7</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">F8</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Input distances are not valid.&#39;</span><span class="p">)</span>
-
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Air humidity values are out of range.&#39;</span><span class="p">)</span>
 
+<span class="sd">        Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L.,</span>
+<span class="sd">        Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M., Rebmann, C.,</span>
+<span class="sd">        Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.: Improving calibration and</span>
+<span class="sd">        validation of cosmic-ray neutron sensors in the light of spatial sensitivity,</span>
+<span class="sd">        Hydrol. Earth Syst. Sci., 21, 5009–5030, https://doi.org/10.5194/hess-21-5009-2017, 2017.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="c1"># Combined and normalized weights</span>
-    <span class="n">weights</span> <span class="o">=</span> <span class="n">Wd</span><span class="o">*</span><span class="n">W</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">Wd</span><span class="o">*</span><span class="n">W</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;Kohli_2015&#39;</span><span class="p">:</span>
+
+        <span class="c1"># Table A1. Parameters for Fi and D86</span>
+        <span class="n">p10</span> <span class="o">=</span> <span class="mi">8735</span><span class="p">;</span>
+        <span class="n">p11</span> <span class="o">=</span> <span class="mf">17.1758</span><span class="p">;</span>
+        <span class="n">p12</span> <span class="o">=</span> <span class="mi">11720</span><span class="p">;</span>
+        <span class="n">p13</span> <span class="o">=</span> <span class="mf">0.00978</span><span class="p">;</span>
+        <span class="n">p14</span> <span class="o">=</span> <span class="mi">7045</span><span class="p">;</span>
+        <span class="n">p15</span> <span class="o">=</span> <span class="mf">0.003632</span><span class="p">;</span>
+        <span class="n">p20</span> <span class="o">=</span> <span class="mf">2.7925e-2</span><span class="p">;</span>
+        <span class="n">p21</span> <span class="o">=</span> <span class="mf">5.0399</span><span class="p">;</span>
+        <span class="n">p22</span> <span class="o">=</span> <span class="mf">2.8544e-2</span><span class="p">;</span>
+        <span class="n">p23</span> <span class="o">=</span> <span class="mf">0.002455</span><span class="p">;</span>
+        <span class="n">p24</span> <span class="o">=</span> <span class="mf">6.851e-5</span><span class="p">;</span>
+        <span class="n">p25</span> <span class="o">=</span> <span class="mf">9.2926</span><span class="p">;</span>
+        <span class="n">p30</span> <span class="o">=</span> <span class="mi">247970</span><span class="p">;</span>
+        <span class="n">p31</span> <span class="o">=</span> <span class="mf">17.63</span><span class="p">;</span>
+        <span class="n">p32</span> <span class="o">=</span> <span class="mi">374655</span><span class="p">;</span>
+        <span class="n">p33</span> <span class="o">=</span> <span class="mf">0.00191</span><span class="p">;</span>
+        <span class="n">p34</span> <span class="o">=</span> <span class="mi">195725</span><span class="p">;</span>
+        <span class="n">p40</span> <span class="o">=</span> <span class="mf">5.4818e-2</span><span class="p">;</span>
+        <span class="n">p41</span> <span class="o">=</span> <span class="mf">15.921</span><span class="p">;</span>
+        <span class="n">p42</span> <span class="o">=</span> <span class="mf">0.6373</span><span class="p">;</span>
+        <span class="n">p43</span> <span class="o">=</span> <span class="mf">5.99e-2</span><span class="p">;</span>
+        <span class="n">p44</span> <span class="o">=</span> <span class="mf">5.425e-4</span><span class="p">;</span>
+        <span class="n">p50</span> <span class="o">=</span> <span class="mi">1383702</span><span class="p">;</span>
+        <span class="n">p51</span> <span class="o">=</span> <span class="mf">4.156</span><span class="p">;</span>
+        <span class="n">p52</span> <span class="o">=</span> <span class="mi">5325</span><span class="p">;</span>
+        <span class="n">p53</span> <span class="o">=</span> <span class="mf">0.00238</span><span class="p">;</span>
+        <span class="n">p54</span> <span class="o">=</span> <span class="mf">0.0156</span><span class="p">;</span>
+        <span class="n">p55</span> <span class="o">=</span> <span class="mf">0.130</span><span class="p">;</span>
+        <span class="n">p56</span> <span class="o">=</span> <span class="mi">1521</span><span class="p">;</span>
+        <span class="n">p60</span> <span class="o">=</span> <span class="mf">6.031e-5</span><span class="p">;</span>
+        <span class="n">p61</span> <span class="o">=</span> <span class="mf">98.5</span><span class="p">;</span>
+        <span class="n">p62</span> <span class="o">=</span> <span class="mf">1.0466e-3</span><span class="p">;</span>
+        <span class="n">p70</span> <span class="o">=</span> <span class="mi">11747</span><span class="p">;</span>
+        <span class="n">p71</span> <span class="o">=</span> <span class="mf">41.66</span><span class="p">;</span>
+        <span class="n">p72</span> <span class="o">=</span> <span class="mi">4521</span><span class="p">;</span>
+        <span class="n">p73</span> <span class="o">=</span> <span class="mf">0.01998</span><span class="p">;</span>
+        <span class="n">p74</span> <span class="o">=</span> <span class="mf">0.00604</span><span class="p">;</span>
+        <span class="n">p75</span> <span class="o">=</span> <span class="mi">2534</span><span class="p">;</span>
+        <span class="n">p76</span> <span class="o">=</span> <span class="mf">0.00475</span><span class="p">;</span>
+        <span class="n">p80</span> <span class="o">=</span> <span class="mf">1.543e-2</span><span class="p">;</span>
+        <span class="n">p81</span> <span class="o">=</span> <span class="mf">10.06</span><span class="p">;</span>
+        <span class="n">p82</span> <span class="o">=</span> <span class="mf">1.807e-2</span><span class="p">;</span>
+        <span class="n">p83</span> <span class="o">=</span> <span class="mf">0.0011</span><span class="p">;</span>
+        <span class="n">p84</span> <span class="o">=</span> <span class="mf">8.81e-5</span><span class="p">;</span>
+        <span class="n">p85</span> <span class="o">=</span> <span class="mf">0.0405</span><span class="p">;</span>
+        <span class="n">p86</span> <span class="o">=</span> <span class="mf">20.24</span><span class="p">;</span>
+        <span class="n">p90</span> <span class="o">=</span> <span class="mf">8.321</span><span class="p">;</span>
+        <span class="n">p91</span> <span class="o">=</span> <span class="mf">0.14249</span><span class="p">;</span>
+        <span class="n">p92</span> <span class="o">=</span> <span class="mf">0.96655</span><span class="p">;</span>
+        <span class="n">p93</span> <span class="o">=</span> <span class="mf">26.42</span><span class="p">;</span>
+        <span class="n">p94</span> <span class="o">=</span> <span class="mf">0.0567</span><span class="p">;</span>
+
+        <span class="c1"># Numerical determination of the penetration depth (86%) (Eq. 8)</span>
+        <span class="n">D86</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">rhob</span> <span class="o">*</span> <span class="p">(</span><span class="n">p90</span> <span class="o">+</span> <span class="n">p91</span> <span class="o">*</span> <span class="p">(</span><span class="n">p92</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">distances</span> <span class="o">/</span> <span class="mi">100</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">p93</span> <span class="o">+</span> <span class="n">theta</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">p94</span> <span class="o">+</span> <span class="n">theta</span><span class="p">))</span>
+
+        <span class="c1"># Depth weights (Eq. 7)</span>
+        <span class="n">Wd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span> <span class="o">*</span> <span class="n">depth</span> <span class="o">/</span> <span class="n">D86</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">h</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">W</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># skip distance weighting</span>
+
+        <span class="k">elif</span> <span class="p">(</span><span class="n">h</span> <span class="o">&gt;=</span> <span class="mf">0.1</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">h</span> <span class="o">&lt;=</span> <span class="mi">50</span><span class="p">):</span>
+            <span class="c1"># Functions for Fi (Appendix A in Köhli et al., 2015)</span>
+            <span class="n">F1</span> <span class="o">=</span> <span class="n">p10</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">p13</span> <span class="o">*</span> <span class="n">h</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p11</span> <span class="o">*</span> <span class="n">theta</span><span class="p">)</span> <span class="o">+</span> <span class="n">p12</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">p15</span> <span class="o">*</span> <span class="n">h</span><span class="p">)</span> <span class="o">-</span> <span class="n">p14</span> <span class="o">*</span> <span class="n">theta</span>
+            <span class="n">F2</span> <span class="o">=</span> <span class="p">((</span><span class="o">-</span><span class="n">p20</span> <span class="o">+</span> <span class="n">p24</span> <span class="o">*</span> <span class="n">h</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p21</span> <span class="o">*</span> <span class="n">theta</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">p25</span> <span class="o">*</span> <span class="n">theta</span><span class="p">))</span> <span class="o">+</span> <span class="n">p22</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">h</span> <span class="o">*</span> <span class="n">p23</span><span class="p">)</span>
+            <span class="n">F3</span> <span class="o">=</span> <span class="p">(</span><span class="n">p30</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">p33</span> <span class="o">*</span> <span class="n">h</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p31</span> <span class="o">*</span> <span class="n">theta</span><span class="p">)</span> <span class="o">+</span> <span class="n">p32</span> <span class="o">-</span> <span class="n">p34</span> <span class="o">*</span> <span class="n">theta</span><span class="p">)</span>
+            <span class="n">F4</span> <span class="o">=</span> <span class="n">p40</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p41</span> <span class="o">*</span> <span class="n">theta</span><span class="p">)</span> <span class="o">+</span> <span class="n">p42</span> <span class="o">-</span> <span class="n">p43</span> <span class="o">*</span> <span class="n">theta</span> <span class="o">+</span> <span class="n">p44</span> <span class="o">*</span> <span class="n">h</span>
+            <span class="n">F5</span> <span class="o">=</span> <span class="n">p50</span> <span class="o">*</span> <span class="p">(</span><span class="mf">0.02</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">p55</span> <span class="o">/</span> <span class="p">(</span><span class="n">h</span> <span class="o">-</span> <span class="n">p55</span> <span class="o">+</span> <span class="n">p56</span> <span class="o">*</span> <span class="n">theta</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">p54</span> <span class="o">-</span> <span class="n">theta</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span>
+                <span class="o">-</span><span class="n">p51</span> <span class="o">*</span> <span class="p">(</span><span class="n">theta</span> <span class="o">-</span> <span class="n">p54</span><span class="p">))</span> <span class="o">+</span> <span class="n">p52</span> <span class="o">*</span> <span class="p">(</span><span class="mf">0.7</span> <span class="o">-</span> <span class="n">h</span> <span class="o">*</span> <span class="n">theta</span> <span class="o">*</span> <span class="n">p53</span><span class="p">)</span>
+            <span class="n">F6</span> <span class="o">=</span> <span class="n">p60</span> <span class="o">*</span> <span class="p">(</span><span class="n">h</span> <span class="o">+</span> <span class="n">p61</span><span class="p">)</span> <span class="o">+</span> <span class="n">p62</span> <span class="o">*</span> <span class="n">theta</span>
+            <span class="n">F7</span> <span class="o">=</span> <span class="p">(</span><span class="n">p70</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">p76</span> <span class="o">*</span> <span class="n">h</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p71</span> <span class="o">*</span> <span class="n">theta</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">h</span> <span class="o">*</span> <span class="n">p74</span><span class="p">))</span> <span class="o">+</span> <span class="n">p72</span> <span class="o">-</span> <span class="n">p75</span> <span class="o">*</span> <span class="n">theta</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">+</span> <span class="n">h</span> <span class="o">*</span> <span class="n">p73</span><span class="p">)</span>
+            <span class="n">F8</span> <span class="o">=</span> <span class="p">((</span><span class="o">-</span><span class="n">p80</span> <span class="o">+</span> <span class="n">p84</span> <span class="o">*</span> <span class="n">h</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p81</span> <span class="o">*</span> <span class="n">theta</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">p85</span> <span class="o">*</span> <span class="n">h</span> <span class="o">+</span> <span class="n">p86</span> <span class="o">*</span> <span class="n">theta</span><span class="p">))</span> <span class="o">+</span> <span class="n">p82</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">+</span> <span class="n">h</span> <span class="o">*</span> <span class="n">p83</span><span class="p">)</span>
+
+            <span class="c1"># Distance weights (Eq. 3)</span>
+            <span class="n">W</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">distances</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">distances</span><span class="p">)):</span>
+                <span class="k">if</span> <span class="p">(</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">50</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">):</span>
+                    <span class="n">W</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">F1</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">F2</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> <span class="o">+</span> <span class="n">F3</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">F4</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+                <span class="k">elif</span> <span class="p">(</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">50</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">600</span><span class="p">):</span>
+                    <span class="n">W</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">F5</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">F6</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> <span class="o">+</span> <span class="n">F7</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">F8</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">distances</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Input distances are not valid.&#39;</span><span class="p">)</span>
 
-    <span class="k">return</span> <span class="n">weights</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Air humidity values are out of range.&#39;</span><span class="p">)</span>
+
+        <span class="c1"># Combined and normalized weights</span>
+        <span class="n">weights</span> <span class="o">=</span> <span class="n">Wd</span> <span class="o">*</span> <span class="n">W</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">Wd</span> <span class="o">*</span> <span class="n">W</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">weights</span>
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;Schron_2017&#39;</span><span class="p">:</span>
+        <span class="c1"># Horizontal distance weights According to Eq. 6 and Table A1 in Schrön et al. (2017)</span>
+        <span class="c1"># Method for calculating the horizontal distance weights from 0 to 1m</span>
+        <span class="k">def</span> <span class="nf">WrX</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+            <span class="n">x00</span> <span class="o">=</span> <span class="mf">3.7</span>
+            <span class="n">a00</span> <span class="o">=</span> <span class="mi">8735</span><span class="p">;</span>
+            <span class="n">a01</span> <span class="o">=</span> <span class="mf">22.689</span><span class="p">;</span>
+            <span class="n">a02</span> <span class="o">=</span> <span class="mi">11720</span><span class="p">;</span>
+            <span class="n">a03</span> <span class="o">=</span> <span class="mf">0.00978</span><span class="p">;</span>
+            <span class="n">a04</span> <span class="o">=</span> <span class="mi">9306</span><span class="p">;</span>
+            <span class="n">a05</span> <span class="o">=</span> <span class="mf">0.003632</span>
+            <span class="n">a10</span> <span class="o">=</span> <span class="mf">2.7925e-2</span><span class="p">;</span>
+            <span class="n">a11</span> <span class="o">=</span> <span class="mf">6.6577</span><span class="p">;</span>
+            <span class="n">a12</span> <span class="o">=</span> <span class="mf">0.028544</span><span class="p">;</span>
+            <span class="n">a13</span> <span class="o">=</span> <span class="mf">0.002455</span><span class="p">;</span>
+            <span class="n">a14</span> <span class="o">=</span> <span class="mf">6.851e-5</span><span class="p">;</span>
+            <span class="n">a15</span> <span class="o">=</span> <span class="mf">12.2755</span>
+            <span class="n">a20</span> <span class="o">=</span> <span class="mi">247970</span><span class="p">;</span>
+            <span class="n">a21</span> <span class="o">=</span> <span class="mf">23.289</span><span class="p">;</span>
+            <span class="n">a22</span> <span class="o">=</span> <span class="mi">374655</span><span class="p">;</span>
+            <span class="n">a23</span> <span class="o">=</span> <span class="mf">0.00191</span><span class="p">;</span>
+            <span class="n">a24</span> <span class="o">=</span> <span class="mi">258552</span>
+            <span class="n">a30</span> <span class="o">=</span> <span class="mf">5.4818e-2</span><span class="p">;</span>
+            <span class="n">a31</span> <span class="o">=</span> <span class="mf">21.032</span><span class="p">;</span>
+            <span class="n">a32</span> <span class="o">=</span> <span class="mf">0.6373</span><span class="p">;</span>
+            <span class="n">a33</span> <span class="o">=</span> <span class="mf">0.0791</span><span class="p">;</span>
+            <span class="n">a34</span> <span class="o">=</span> <span class="mf">5.425e-4</span>
+
+            <span class="n">x0</span> <span class="o">=</span> <span class="n">x00</span>
+            <span class="n">A0</span> <span class="o">=</span> <span class="p">(</span><span class="n">a00</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a03</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a01</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">+</span> <span class="n">a02</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a05</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="n">a04</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span>
+            <span class="n">A1</span> <span class="o">=</span> <span class="p">((</span><span class="o">-</span><span class="n">a10</span> <span class="o">+</span> <span class="n">a14</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a11</span> <span class="o">*</span> <span class="n">y</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a15</span> <span class="o">*</span> <span class="n">y</span><span class="p">))</span> <span class="o">+</span> <span class="n">a12</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="n">a13</span><span class="p">)</span>
+            <span class="n">A2</span> <span class="o">=</span> <span class="p">(</span><span class="n">a20</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a23</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a21</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">+</span> <span class="n">a22</span> <span class="o">-</span> <span class="n">a24</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span>
+            <span class="n">A3</span> <span class="o">=</span> <span class="n">a30</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a31</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">+</span> <span class="n">a32</span> <span class="o">-</span> <span class="n">a33</span> <span class="o">*</span> <span class="n">y</span> <span class="o">+</span> <span class="n">a34</span> <span class="o">*</span> <span class="n">x</span>
+
+            <span class="k">return</span> <span class="p">((</span><span class="n">A0</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">A1</span> <span class="o">*</span> <span class="n">r</span><span class="p">))</span> <span class="o">+</span> <span class="n">A2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">A3</span> <span class="o">*</span> <span class="n">r</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x0</span> <span class="o">*</span> <span class="n">r</span><span class="p">)))</span>
+
+        <span class="c1"># Method for calculating the horizontal distance weights from 1 to 50m</span>
+        <span class="k">def</span> <span class="nf">WrA</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+            <span class="n">a00</span> <span class="o">=</span> <span class="mi">8735</span><span class="p">;</span>
+            <span class="n">a01</span> <span class="o">=</span> <span class="mf">22.689</span><span class="p">;</span>
+            <span class="n">a02</span> <span class="o">=</span> <span class="mi">11720</span><span class="p">;</span>
+            <span class="n">a03</span> <span class="o">=</span> <span class="mf">0.00978</span><span class="p">;</span>
+            <span class="n">a04</span> <span class="o">=</span> <span class="mi">9306</span><span class="p">;</span>
+            <span class="n">a05</span> <span class="o">=</span> <span class="mf">0.003632</span>
+            <span class="n">a10</span> <span class="o">=</span> <span class="mf">2.7925e-2</span><span class="p">;</span>
+            <span class="n">a11</span> <span class="o">=</span> <span class="mf">6.6577</span><span class="p">;</span>
+            <span class="n">a12</span> <span class="o">=</span> <span class="mf">0.028544</span><span class="p">;</span>
+            <span class="n">a13</span> <span class="o">=</span> <span class="mf">0.002455</span><span class="p">;</span>
+            <span class="n">a14</span> <span class="o">=</span> <span class="mf">6.851e-5</span><span class="p">;</span>
+            <span class="n">a15</span> <span class="o">=</span> <span class="mf">12.2755</span>
+            <span class="n">a20</span> <span class="o">=</span> <span class="mi">247970</span><span class="p">;</span>
+            <span class="n">a21</span> <span class="o">=</span> <span class="mf">23.289</span><span class="p">;</span>
+            <span class="n">a22</span> <span class="o">=</span> <span class="mi">374655</span><span class="p">;</span>
+            <span class="n">a23</span> <span class="o">=</span> <span class="mf">0.00191</span><span class="p">;</span>
+            <span class="n">a24</span> <span class="o">=</span> <span class="mi">258552</span>
+            <span class="n">a30</span> <span class="o">=</span> <span class="mf">5.4818e-2</span><span class="p">;</span>
+            <span class="n">a31</span> <span class="o">=</span> <span class="mf">21.032</span><span class="p">;</span>
+            <span class="n">a32</span> <span class="o">=</span> <span class="mf">0.6373</span><span class="p">;</span>
+            <span class="n">a33</span> <span class="o">=</span> <span class="mf">0.0791</span><span class="p">;</span>
+            <span class="n">a34</span> <span class="o">=</span> <span class="mf">5.425e-4</span>
+
+            <span class="n">A0</span> <span class="o">=</span> <span class="p">(</span><span class="n">a00</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a03</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a01</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">+</span> <span class="n">a02</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a05</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="n">a04</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span>
+            <span class="n">A1</span> <span class="o">=</span> <span class="p">((</span><span class="o">-</span><span class="n">a10</span> <span class="o">+</span> <span class="n">a14</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a11</span> <span class="o">*</span> <span class="n">y</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a15</span> <span class="o">*</span> <span class="n">y</span><span class="p">))</span> <span class="o">+</span> <span class="n">a12</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="n">a13</span><span class="p">)</span>
+            <span class="n">A2</span> <span class="o">=</span> <span class="p">(</span><span class="n">a20</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a23</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a21</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">+</span> <span class="n">a22</span> <span class="o">-</span> <span class="n">a24</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span>
+            <span class="n">A3</span> <span class="o">=</span> <span class="n">a30</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">a31</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">+</span> <span class="n">a32</span> <span class="o">-</span> <span class="n">a33</span> <span class="o">*</span> <span class="n">y</span> <span class="o">+</span> <span class="n">a34</span> <span class="o">*</span> <span class="n">x</span>
+
+            <span class="k">return</span> <span class="n">A0</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">A1</span> <span class="o">*</span> <span class="n">r</span><span class="p">)</span> <span class="o">+</span> <span class="n">A2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">A3</span> <span class="o">*</span> <span class="n">r</span><span class="p">)</span>
+
+        <span class="c1"># Method for calculating the horizontal distance weights from 50 to 600m</span>
+        <span class="k">def</span> <span class="nf">WrB</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+            <span class="n">b00</span> <span class="o">=</span> <span class="mi">39006</span><span class="p">;</span>
+            <span class="n">b01</span> <span class="o">=</span> <span class="mi">15002337</span><span class="p">;</span>
+            <span class="n">b02</span> <span class="o">=</span> <span class="mf">2009.24</span><span class="p">;</span>
+            <span class="n">b03</span> <span class="o">=</span> <span class="mf">0.01181</span><span class="p">;</span>
+            <span class="n">b04</span> <span class="o">=</span> <span class="mf">3.146</span><span class="p">;</span>
+            <span class="n">b05</span> <span class="o">=</span> <span class="mf">16.7417</span><span class="p">;</span>
+            <span class="n">b06</span> <span class="o">=</span> <span class="mi">3727</span>
+            <span class="n">b10</span> <span class="o">=</span> <span class="mf">6.031e-5</span><span class="p">;</span>
+            <span class="n">b11</span> <span class="o">=</span> <span class="mf">98.5</span><span class="p">;</span>
+            <span class="n">b12</span> <span class="o">=</span> <span class="mf">0.0013826</span>
+            <span class="n">b20</span> <span class="o">=</span> <span class="mi">11747</span><span class="p">;</span>
+            <span class="n">b21</span> <span class="o">=</span> <span class="mf">55.033</span><span class="p">;</span>
+            <span class="n">b22</span> <span class="o">=</span> <span class="mi">4521</span><span class="p">;</span>
+            <span class="n">b23</span> <span class="o">=</span> <span class="mf">0.01998</span><span class="p">;</span>
+            <span class="n">b24</span> <span class="o">=</span> <span class="mf">0.00604</span><span class="p">;</span>
+            <span class="n">b25</span> <span class="o">=</span> <span class="mf">3347.4</span><span class="p">;</span>
+            <span class="n">b26</span> <span class="o">=</span> <span class="mf">0.00475</span>
+            <span class="n">b30</span> <span class="o">=</span> <span class="mf">1.543e-2</span><span class="p">;</span>
+            <span class="n">b31</span> <span class="o">=</span> <span class="mf">13.29</span><span class="p">;</span>
+            <span class="n">b32</span> <span class="o">=</span> <span class="mf">1.807e-2</span><span class="p">;</span>
+            <span class="n">b33</span> <span class="o">=</span> <span class="mf">0.0011</span><span class="p">;</span>
+            <span class="n">b34</span> <span class="o">=</span> <span class="mf">8.81e-5</span><span class="p">;</span>
+            <span class="n">b35</span> <span class="o">=</span> <span class="mf">0.0405</span><span class="p">;</span>
+            <span class="n">b36</span> <span class="o">=</span> <span class="mf">26.74</span>
+
+            <span class="n">B0</span> <span class="o">=</span> <span class="p">(</span><span class="n">b00</span> <span class="o">-</span> <span class="n">b01</span> <span class="o">/</span> <span class="p">(</span><span class="n">b02</span> <span class="o">*</span> <span class="n">y</span> <span class="o">+</span> <span class="n">x</span> <span class="o">-</span> <span class="mf">0.13</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">b03</span> <span class="o">-</span> <span class="n">y</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">b04</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">-</span> <span class="n">b05</span> <span class="o">*</span> <span class="n">x</span> <span class="o">*</span> <span class="n">y</span> <span class="o">+</span> <span class="n">b06</span>
+            <span class="n">B1</span> <span class="o">=</span> <span class="n">b10</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">b11</span><span class="p">)</span> <span class="o">+</span> <span class="n">b12</span> <span class="o">*</span> <span class="n">y</span>
+            <span class="n">B2</span> <span class="o">=</span> <span class="p">(</span><span class="n">b20</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">b26</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">b21</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">x</span> <span class="o">*</span> <span class="n">b24</span><span class="p">))</span> <span class="o">+</span> <span class="n">b22</span> <span class="o">-</span> <span class="n">b25</span> <span class="o">*</span> <span class="n">y</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="n">b23</span><span class="p">)</span>
+            <span class="n">B3</span> <span class="o">=</span> <span class="p">((</span><span class="o">-</span><span class="n">b30</span> <span class="o">+</span> <span class="n">b34</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">b31</span> <span class="o">*</span> <span class="n">y</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">b35</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="n">b36</span> <span class="o">*</span> <span class="n">y</span><span class="p">))</span> <span class="o">+</span> <span class="n">b32</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="n">b33</span><span class="p">)</span>
+
+            <span class="k">return</span> <span class="n">B0</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">B1</span> <span class="o">*</span> <span class="n">r</span><span class="p">)</span> <span class="o">+</span> <span class="n">B2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">B3</span> <span class="o">*</span> <span class="n">r</span><span class="p">)</span>
+
+        <span class="k">def</span> <span class="nf">rscaled</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">Hveg</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+            <span class="n">Fp</span> <span class="o">=</span> <span class="mf">0.4922</span> <span class="o">/</span> <span class="p">(</span><span class="mf">0.86</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">p</span> <span class="o">/</span> <span class="mf">1013.25</span><span class="p">))</span>
+            <span class="n">Fveg</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="mf">0.17</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">0.41</span> <span class="o">*</span> <span class="n">Hveg</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">9.25</span> <span class="o">*</span> <span class="n">y</span><span class="p">))</span>
+            <span class="k">return</span> <span class="n">r</span> <span class="o">/</span> <span class="n">Fp</span> <span class="o">/</span> <span class="n">Fveg</span>
+
+        <span class="c1"># Rename variables to be consistent with the revised paper</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="n">distances</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="n">h</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">theta</span>
+        <span class="n">bd</span> <span class="o">=</span> <span class="n">rhob</span>
+
+        <span class="k">if</span> <span class="n">Hveg</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">p</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">r</span> <span class="o">=</span> <span class="n">rscaled</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">Hveg</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+        <span class="n">Wr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">r</span><span class="p">))</span>
+
+        <span class="c1"># See Eq. 6 in Schron et al. (2017)</span>
+        <span class="n">r0_idx</span> <span class="o">=</span> <span class="p">(</span><span class="n">r</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">r1_idx</span> <span class="o">=</span> <span class="p">(</span><span class="n">r</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">r</span> <span class="o">&lt;=</span> <span class="mi">50</span><span class="p">)</span>
+        <span class="n">r2_idx</span> <span class="o">=</span> <span class="p">(</span><span class="n">r</span> <span class="o">&gt;</span> <span class="mi">50</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">r</span> <span class="o">&lt;</span> <span class="mi">600</span><span class="p">)</span>
+        <span class="n">Wr</span><span class="p">[</span><span class="n">r0_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">WrX</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="n">r0_idx</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="n">r0_idx</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">r0_idx</span><span class="p">])</span>
+        <span class="n">Wr</span><span class="p">[</span><span class="n">r1_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">WrA</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="n">r1_idx</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="n">r1_idx</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">r1_idx</span><span class="p">])</span>
+        <span class="n">Wr</span><span class="p">[</span><span class="n">r2_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">WrB</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="n">r2_idx</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="n">r2_idx</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">r2_idx</span><span class="p">])</span>
+
+        <span class="c1"># Vertical distance weights</span>
+        <span class="k">def</span> <span class="nf">D86</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">bd</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+            <span class="k">return</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">bd</span> <span class="o">*</span> <span class="p">(</span><span class="mf">8.321</span> <span class="o">+</span> <span class="mf">0.14249</span> <span class="o">*</span> <span class="p">(</span><span class="mf">0.96655</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">0.01</span> <span class="o">*</span> <span class="n">r</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mf">0.0429</span> <span class="o">+</span> <span class="n">y</span><span class="p">))</span>
+
+        <span class="k">def</span> <span class="nf">Wd</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">bd</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span> <span class="o">*</span> <span class="n">d</span> <span class="o">/</span> <span class="n">D86</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">bd</span><span class="p">,</span> <span class="n">y</span><span class="p">))</span>
+
+        <span class="c1"># Calculate the vertical distance weights</span>
+        <span class="n">Wd</span> <span class="o">=</span> <span class="n">Wd</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">bd</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+        <span class="c1"># Combined and normalized weights</span>
+        <span class="c1"># Combined and normalized weights</span>
+        <span class="n">weights</span> <span class="o">=</span> <span class="n">Wd</span> <span class="o">*</span> <span class="n">Wr</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">Wd</span> <span class="o">*</span> <span class="n">Wr</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">weights</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.remove_incomplete_intervals" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">remove_incomplete_intervals</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="p">,</span> <span class="n">integration_time</span><span class="p">,</span> <span class="n">remove_first</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.remove_incomplete_intervals" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">remove_incomplete_intervals</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="p">,</span> <span class="n">integration_time</span><span class="p">,</span> <span class="n">remove_first</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h2>
 
@@ -4933,6 +14341,8 @@ <h2 id="crnpy.crnpy.remove_incomplete_intervals" class="doc doc-heading">
   
       <p>Function that removes rows with incomplete integration intervals.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -4947,9 +14357,13 @@ <h2 id="crnpy.crnpy.remove_incomplete_intervals" class="doc doc-heading">
         <tr>
           <td><code>df</code></td>
           <td>
-                <code>pandas.<span title="pandas.DataFrame">DataFrame</span></code>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Pandas Dataframe with data from stationary or roving CRNP devices.</p>
+            </div>
           </td>
-          <td><p>Pandas Dataframe with data from stationary or roving CRNP devices.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -4959,7 +14373,11 @@ <h2 id="crnpy.crnpy.remove_incomplete_intervals" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Name of the column with timestamps in datetime format.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Name of the column with timestamps in datetime format.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -4969,7 +14387,11 @@ <h2 id="crnpy.crnpy.remove_incomplete_intervals" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -4979,7 +14401,11 @@ <h2 id="crnpy.crnpy.remove_incomplete_intervals" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Remove first row. Default is False.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Remove first row. Default is False.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -4987,6 +14413,8 @@ <h2 id="crnpy.crnpy.remove_incomplete_intervals" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -4998,16 +14426,21 @@ <h2 id="crnpy.crnpy.remove_incomplete_intervals" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code>pandas.<span title="pandas.DataFrame">DataFrame</span></code>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              
+            </div>
           </td>
-          <td></td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">33</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">32</span>
+<span class="normal">33</span>
 <span class="normal">34</span>
 <span class="normal">35</span>
 <span class="normal">36</span>
@@ -5036,8 +14469,7 @@ <h2 id="crnpy.crnpy.remove_incomplete_intervals" class="doc doc-heading">
 <span class="normal">59</span>
 <span class="normal">60</span>
 <span class="normal">61</span>
-<span class="normal">62</span>
-<span class="normal">63</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">remove_incomplete_intervals</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="p">,</span> <span class="n">integration_time</span><span class="p">,</span> <span class="n">remove_first</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="normal">62</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">remove_incomplete_intervals</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="p">,</span> <span class="n">integration_time</span><span class="p">,</span> <span class="n">remove_first</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function that removes rows with incomplete integration intervals.</span>
 
 <span class="sd">    Args:</span>
@@ -5069,17 +14501,19 @@ <h2 id="crnpy.crnpy.remove_incomplete_intervals" class="doc doc-heading">
 
     <span class="k">return</span> <span class="n">df</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.rover_centered_coordinates" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">rover_centered_coordinates</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.rover_centered_coordinates" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">rover_centered_coordinates</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></code>
 
 </h2>
 
@@ -5088,6 +14522,8 @@ <h2 id="crnpy.crnpy.rover_centered_coordinates" class="doc doc-heading">
   
       <p>Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -5104,7 +14540,11 @@ <h2 id="crnpy.crnpy.rover_centered_coordinates" class="doc doc-heading">
           <td>
                 <code>array</code>
           </td>
-          <td><p>x coordinates.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>x coordinates.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -5114,7 +14554,11 @@ <h2 id="crnpy.crnpy.rover_centered_coordinates" class="doc doc-heading">
           <td>
                 <code>array</code>
           </td>
-          <td><p>y coordinates.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>y coordinates.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -5122,6 +14566,8 @@ <h2 id="crnpy.crnpy.rover_centered_coordinates" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -5135,47 +14581,54 @@ <h2 id="crnpy.crnpy.rover_centered_coordinates" class="doc doc-heading">
 <td><code>x_est</code></td>          <td>
                 <code>array</code>
           </td>
-          <td><p>Estimated x coordinates.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Estimated x coordinates.</p>
+            </div>
+          </td>
         </tr>
         <tr>
 <td><code>y_est</code></td>          <td>
                 <code>array</code>
           </td>
-          <td><p>Estimated y coordinates.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Estimated y coordinates.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1185</span>
-<span class="normal">1186</span>
-<span class="normal">1187</span>
-<span class="normal">1188</span>
-<span class="normal">1189</span>
-<span class="normal">1190</span>
-<span class="normal">1191</span>
-<span class="normal">1192</span>
-<span class="normal">1193</span>
-<span class="normal">1194</span>
-<span class="normal">1195</span>
-<span class="normal">1196</span>
-<span class="normal">1197</span>
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-<span class="normal">1199</span>
-<span class="normal">1200</span>
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-<span class="normal">1209</span>
-<span class="normal">1210</span>
-<span class="normal">1211</span>
-<span class="normal">1212</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">rover_centered_coordinates</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1466</span>
+<span class="normal">1467</span>
+<span class="normal">1468</span>
+<span class="normal">1469</span>
+<span class="normal">1470</span>
+<span class="normal">1471</span>
+<span class="normal">1472</span>
+<span class="normal">1473</span>
+<span class="normal">1474</span>
+<span class="normal">1475</span>
+<span class="normal">1476</span>
+<span class="normal">1477</span>
+<span class="normal">1478</span>
+<span class="normal">1479</span>
+<span class="normal">1480</span>
+<span class="normal">1481</span>
+<span class="normal">1482</span>
+<span class="normal">1483</span>
+<span class="normal">1484</span>
+<span class="normal">1485</span>
+<span class="normal">1486</span>
+<span class="normal">1487</span>
+<span class="normal">1488</span>
+<span class="normal">1489</span>
+<span class="normal">1490</span>
+<span class="normal">1491</span>
+<span class="normal">1492</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">rover_centered_coordinates</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.</span>
 
 <span class="sd">    Args:</span>
@@ -5188,9 +14641,9 @@ <h2 id="crnpy.crnpy.rover_centered_coordinates" class="doc doc-heading">
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="c1"># Make it datatype agnostic</span>
-    <span class="k">if</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)):</span>
+    <span class="k">if</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)):</span>
         <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">values</span>
-    <span class="k">if</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)):</span>
+    <span class="k">if</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)):</span>
         <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">values</span>
 
     <span class="c1"># Do the average of the two points</span>
@@ -5201,20 +14654,21 @@ <h2 id="crnpy.crnpy.rover_centered_coordinates" class="doc doc-heading">
     <span class="n">x_est</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">x_est</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
     <span class="n">y_est</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">y_est</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 
-
     <span class="k">return</span> <span class="n">x_est</span><span class="p">,</span> <span class="n">y_est</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.sensing_depth" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">sensing_depth</span><span class="p">(</span><span class="n">vwc</span><span class="p">,</span> <span class="n">pressure</span><span class="p">,</span> <span class="n">p_ref</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">Wlat</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;Schron_2017&#39;</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.sensing_depth" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">sensing_depth</span><span class="p">(</span><span class="n">vwc</span><span class="p">,</span> <span class="n">pressure</span><span class="p">,</span> <span class="n">p_ref</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">Wlat</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;Schron_2017&#39;</span><span class="p">)</span></code>
 
 </h2>
 
@@ -5225,6 +14679,8 @@ <h2 id="crnpy.crnpy.sensing_depth" class="doc doc-heading">
 The function offers several methods to compute the depth at which 86 % of the neutrons
 probe the soil profile.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -5239,9 +14695,13 @@ <h2 id="crnpy.crnpy.sensing_depth" class="doc doc-heading">
         <tr>
           <td><code>vwc</code></td>
           <td>
-                <code>array or pd.Series or pd.DataFrame</code>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Estimated volumetric water content for each timestamp.</p>
+            </div>
           </td>
-          <td><p>Estimated volumetric water content for each timestamp.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -5249,9 +14709,13 @@ <h2 id="crnpy.crnpy.sensing_depth" class="doc doc-heading">
         <tr>
           <td><code>pressure</code></td>
           <td>
-                <code>array or pd.Series or pd.DataFrame</code>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Atmospheric pressure in hPa for each timestamp.</p>
+            </div>
           </td>
-          <td><p>Atmospheric pressure in hPa for each timestamp.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -5261,7 +14725,11 @@ <h2 id="crnpy.crnpy.sensing_depth" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Reference pressure in hPa.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Reference pressure in hPa.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -5271,7 +14739,11 @@ <h2 id="crnpy.crnpy.sensing_depth" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Soil bulk density.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Soil bulk density.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -5281,7 +14753,11 @@ <h2 id="crnpy.crnpy.sensing_depth" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Lattice water content.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Lattice water content.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -5291,7 +14767,11 @@ <h2 id="crnpy.crnpy.sensing_depth" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;Schron_2017&#39;</code>
           </td>
@@ -5301,7 +14781,11 @@ <h2 id="crnpy.crnpy.sensing_depth" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.</p>
+            </div>
+          </td>
           <td>
                 <code>None</code>
           </td>
@@ -5309,6 +14793,8 @@ <h2 id="crnpy.crnpy.sensing_depth" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -5320,9 +14806,13 @@ <h2 id="crnpy.crnpy.sensing_depth" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code>array or pd.Series or pd.DataFrame</code>
+                <code>array or <span title="pandas.Series">Series</span> or <span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Estimated sensing depth in m.</p>
+            </div>
           </td>
-          <td><p>Estimated sensing depth in m.</p></td>
         </tr>
     </tbody>
   </table>
@@ -5336,57 +14826,55 @@ <h2 id="crnpy.crnpy.sensing_depth" class="doc doc-heading">
 Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity.
 Hydrol. Earth Syst. Sci. 21, 5009–5030. doi.org/10.5194/hess-21-5009-2017</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">570</span>
-<span class="normal">571</span>
-<span class="normal">572</span>
-<span class="normal">573</span>
-<span class="normal">574</span>
-<span class="normal">575</span>
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-<span class="normal">596</span>
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-<span class="normal">598</span>
-<span class="normal">599</span>
-<span class="normal">600</span>
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-<span class="normal">602</span>
-<span class="normal">603</span>
-<span class="normal">604</span>
-<span class="normal">605</span>
-<span class="normal">606</span>
-<span class="normal">607</span>
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-<span class="normal">609</span>
-<span class="normal">610</span>
-<span class="normal">611</span>
-<span class="normal">612</span>
-<span class="normal">613</span>
-<span class="normal">614</span>
-<span class="normal">615</span>
-<span class="normal">616</span>
-<span class="normal">617</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">sensing_depth</span><span class="p">(</span><span class="n">vwc</span><span class="p">,</span> <span class="n">pressure</span><span class="p">,</span> <span class="n">p_ref</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">Wlat</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;Schron_2017&#39;</span><span class="p">):</span>
-    <span class="c1"># Convert docstring to google format</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">622</span>
+<span class="normal">623</span>
+<span class="normal">624</span>
+<span class="normal">625</span>
+<span class="normal">626</span>
+<span class="normal">627</span>
+<span class="normal">628</span>
+<span class="normal">629</span>
+<span class="normal">630</span>
+<span class="normal">631</span>
+<span class="normal">632</span>
+<span class="normal">633</span>
+<span class="normal">634</span>
+<span class="normal">635</span>
+<span class="normal">636</span>
+<span class="normal">637</span>
+<span class="normal">638</span>
+<span class="normal">639</span>
+<span class="normal">640</span>
+<span class="normal">641</span>
+<span class="normal">642</span>
+<span class="normal">643</span>
+<span class="normal">644</span>
+<span class="normal">645</span>
+<span class="normal">646</span>
+<span class="normal">647</span>
+<span class="normal">648</span>
+<span class="normal">649</span>
+<span class="normal">650</span>
+<span class="normal">651</span>
+<span class="normal">652</span>
+<span class="normal">653</span>
+<span class="normal">654</span>
+<span class="normal">655</span>
+<span class="normal">656</span>
+<span class="normal">657</span>
+<span class="normal">658</span>
+<span class="normal">659</span>
+<span class="normal">660</span>
+<span class="normal">661</span>
+<span class="normal">662</span>
+<span class="normal">663</span>
+<span class="normal">664</span>
+<span class="normal">665</span>
+<span class="normal">666</span>
+<span class="normal">667</span>
+<span class="normal">668</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">sensing_depth</span><span class="p">(</span><span class="n">vwc</span><span class="p">,</span> <span class="n">pressure</span><span class="p">,</span> <span class="n">p_ref</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">Wlat</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;Schron_2017&#39;</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function that computes the estimated sensing depth of the cosmic-ray neutron probe.</span>
 <span class="sd">    The function offers several methods to compute the depth at which 86 % of the neutrons</span>
 <span class="sd">    probe the soil profile.</span>
@@ -5416,35 +14904,37 @@ <h2 id="crnpy.crnpy.sensing_depth" class="doc doc-heading">
     <span class="c1"># Determine sensing depth (D86)</span>
     <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;Schron_2017&#39;</span><span class="p">:</span>
         <span class="c1"># See Appendix A of Schrön et al. (2017)</span>
-        <span class="n">Fp</span> <span class="o">=</span> <span class="mf">0.4922</span> <span class="o">/</span> <span class="p">(</span><span class="mf">0.86</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">pressure</span> <span class="o">/</span> <span class="n">p_ref</span><span class="p">));</span>
+        <span class="n">Fp</span> <span class="o">=</span> <span class="mf">0.4922</span> <span class="o">/</span> <span class="p">(</span><span class="mf">0.86</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">pressure</span> <span class="o">/</span> <span class="n">p_ref</span><span class="p">))</span>
         <span class="n">Fveg</span> <span class="o">=</span> <span class="mi">0</span>
         <span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">dist</span><span class="p">:</span>
             <span class="c1"># Compute r_star</span>
-            <span class="n">r_start</span> <span class="o">=</span> <span class="n">d</span><span class="o">/</span><span class="n">Fp</span>
+            <span class="n">r_start</span> <span class="o">=</span> <span class="n">d</span> <span class="o">/</span> <span class="n">Fp</span>
 
             <span class="c1"># Compute soil depth that accounts for 86% of the neutron flux</span>
-            <span class="n">D86</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span> <span class="n">bulk_density</span> <span class="o">*</span> <span class="p">(</span><span class="mf">8.321</span><span class="o">+</span><span class="mf">0.14249</span><span class="o">*</span><span class="p">(</span><span class="mf">0.96655</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">0.01</span><span class="o">*</span><span class="n">r_start</span><span class="p">))</span><span class="o">*</span><span class="p">(</span><span class="mi">20</span><span class="o">+</span><span class="p">(</span><span class="n">Wlat</span><span class="o">+</span><span class="n">vwc</span><span class="p">))</span> <span class="o">/</span> <span class="p">(</span><span class="mf">0.0429</span><span class="o">+</span><span class="p">(</span><span class="n">Wlat</span><span class="o">+</span><span class="n">vwc</span><span class="p">)))</span>
+            <span class="n">D86</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">bulk_density</span> <span class="o">*</span> <span class="p">(</span><span class="mf">8.321</span> <span class="o">+</span> <span class="mf">0.14249</span> <span class="o">*</span> <span class="p">(</span><span class="mf">0.96655</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">0.01</span> <span class="o">*</span> <span class="n">r_start</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="p">(</span><span class="n">Wlat</span> <span class="o">+</span> <span class="n">vwc</span><span class="p">))</span> <span class="o">/</span> <span class="p">(</span>
+                        <span class="mf">0.0429</span> <span class="o">+</span> <span class="p">(</span><span class="n">Wlat</span> <span class="o">+</span> <span class="n">vwc</span><span class="p">)))</span>
             <span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">D86</span><span class="p">)</span>
 
     <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;Franz_2012&#39;</span><span class="p">:</span>
-        <span class="n">results</span> <span class="o">=</span> <span class="mf">5.8</span><span class="o">/</span><span class="p">(</span><span class="n">bulk_density</span><span class="o">*</span><span class="n">Wlat</span><span class="o">+</span><span class="n">vwc</span><span class="o">+</span><span class="mf">0.0829</span><span class="p">)</span>
+        <span class="n">results</span> <span class="o">=</span> <span class="mf">5.8</span> <span class="o">/</span> <span class="p">(</span><span class="n">bulk_density</span> <span class="o">*</span> <span class="n">Wlat</span> <span class="o">+</span> <span class="n">vwc</span> <span class="o">+</span> <span class="mf">0.0829</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Method not recognized. Please select either &quot;Schron_2017&quot; or &quot;Franz_2012&quot;.&#39;</span><span class="p">)</span>
-
     <span class="k">return</span> <span class="n">results</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.smooth_1d" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">smooth_1d</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;moving_median&#39;</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.smooth_1d" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">smooth_1d</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;moving_median&#39;</span><span class="p">)</span></code>
 
 </h2>
 
@@ -5453,6 +14943,8 @@ <h2 id="crnpy.crnpy.smooth_1d" class="doc doc-heading">
   
       <p>Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -5467,9 +14959,13 @@ <h2 id="crnpy.crnpy.smooth_1d" class="doc doc-heading">
         <tr>
           <td><code>values</code></td>
           <td>
-                <code>pd.DataFrame or pd.Serie</code>
+                <code><span title="pandas.DataFrame">DataFrame</span> or <span title="pandas.Serie">Serie</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Dataframe containing the values to smooth.</p>
+            </div>
           </td>
-          <td><p>Dataframe containing the values to smooth.</p></td>
           <td>
               <em>required</em>
           </td>
@@ -5479,7 +14975,11 @@ <h2 id="crnpy.crnpy.smooth_1d" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Window size for the Savitzky-Golay filter. Default is 5.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Window size for the Savitzky-Golay filter. Default is 5.</p>
+            </div>
+          </td>
           <td>
                 <code>5</code>
           </td>
@@ -5489,8 +14989,12 @@ <h2 id="crnpy.crnpy.smooth_1d" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Method to use for smoothing the data. Default is 'moving_median'.
-Options are 'moving_average', 'moving_median' and 'savitzky_golay'.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Method to use for smoothing the data. Default is 'moving_median'.
+Options are 'moving_average', 'moving_median' and 'savitzky_golay'.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;moving_median&#39;</code>
           </td>
@@ -5500,7 +15004,11 @@ <h2 id="crnpy.crnpy.smooth_1d" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Order of the Savitzky-Golay filter. Default is 3.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Order of the Savitzky-Golay filter. Default is 3.</p>
+            </div>
+          </td>
           <td>
                 <code>3</code>
           </td>
@@ -5508,6 +15016,8 @@ <h2 id="crnpy.crnpy.smooth_1d" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -5519,9 +15029,13 @@ <h2 id="crnpy.crnpy.smooth_1d" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>DataFrame with smoothed values.</p>
+            </div>
           </td>
-          <td><p>DataFrame with smoothed values.</p></td>
         </tr>
     </tbody>
   </table>
@@ -5531,47 +15045,56 @@ <h2 id="crnpy.crnpy.smooth_1d" class="doc doc-heading">
   <p>Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.
 Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.
 doi.org/10.3389/frwa.2020.00009</p>
+<p>Savitzky, A., &amp; Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures.
+Analytical chemistry, 36(8), 1627-1639.</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">428</span>
-<span class="normal">429</span>
-<span class="normal">430</span>
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-<span class="normal">456</span>
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-<span class="normal">462</span>
-<span class="normal">463</span>
-<span class="normal">464</span>
-<span class="normal">465</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">smooth_1d</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;moving_median&#39;</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">471</span>
+<span class="normal">472</span>
+<span class="normal">473</span>
+<span class="normal">474</span>
+<span class="normal">475</span>
+<span class="normal">476</span>
+<span class="normal">477</span>
+<span class="normal">478</span>
+<span class="normal">479</span>
+<span class="normal">480</span>
+<span class="normal">481</span>
+<span class="normal">482</span>
+<span class="normal">483</span>
+<span class="normal">484</span>
+<span class="normal">485</span>
+<span class="normal">486</span>
+<span class="normal">487</span>
+<span class="normal">488</span>
+<span class="normal">489</span>
+<span class="normal">490</span>
+<span class="normal">491</span>
+<span class="normal">492</span>
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+<span class="normal">499</span>
+<span class="normal">500</span>
+<span class="normal">501</span>
+<span class="normal">502</span>
+<span class="normal">503</span>
+<span class="normal">504</span>
+<span class="normal">505</span>
+<span class="normal">506</span>
+<span class="normal">507</span>
+<span class="normal">508</span>
+<span class="normal">509</span>
+<span class="normal">510</span>
+<span class="normal">511</span>
+<span class="normal">512</span>
+<span class="normal">513</span>
+<span class="normal">514</span>
+<span class="normal">515</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">smooth_1d</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;moving_median&#39;</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).</span>
 
 <span class="sd">    Args:</span>
@@ -5588,8 +15111,14 @@ <h2 id="crnpy.crnpy.smooth_1d" class="doc doc-heading">
 <span class="sd">        Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.</span>
 <span class="sd">        Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.</span>
 <span class="sd">        doi.org/10.3389/frwa.2020.00009</span>
+
+<span class="sd">        Savitzky, A., &amp; Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures.</span>
+<span class="sd">        Analytical chemistry, 36(8), 1627-1639.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Input must be a pandas Series or DataFrame&#39;</span><span class="p">)</span>
+
     <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;moving_average&#39;</span><span class="p">:</span>
         <span class="n">corrected_counts</span> <span class="o">=</span> <span class="n">values</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_periods</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
     <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;moving_median&#39;</span><span class="p">:</span>
@@ -5600,27 +15129,30 @@ <h2 id="crnpy.crnpy.smooth_1d" class="doc doc-heading">
             <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Dataframe contains NaN values. Please remove NaN values before smoothing the data.&#39;</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">values</span><span class="p">)</span> <span class="o">==</span> <span class="n">pd</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">series</span><span class="o">.</span><span class="n">Series</span><span class="p">:</span>
-            <span class="n">filtered</span> <span class="o">=</span> <span class="n">savgol_filter</span><span class="p">(</span><span class="n">values</span><span class="p">,</span><span class="n">window</span><span class="p">,</span><span class="n">order</span><span class="p">)</span>
-            <span class="n">corrected_counts</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">filtered</span><span class="p">,</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;smoothed&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="n">values</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
+            <span class="n">filtered</span> <span class="o">=</span> <span class="n">savgol_filter</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> <span class="n">order</span><span class="p">)</span>
+            <span class="n">corrected_counts</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">filtered</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;smoothed&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="n">values</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
         <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">values</span><span class="p">)</span> <span class="o">==</span> <span class="n">pd</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">frame</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
             <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">values</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
-                <span class="n">values</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">savgol_filter</span><span class="p">(</span><span class="n">values</span><span class="p">[</span><span class="n">col</span><span class="p">],</span><span class="n">window</span><span class="p">,</span><span class="n">order</span><span class="p">)</span>
+                <span class="n">values</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">savgol_filter</span><span class="p">(</span><span class="n">values</span><span class="p">[</span><span class="n">col</span><span class="p">],</span> <span class="n">window</span><span class="p">,</span> <span class="n">order</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Invalid method. Please select a valid filtering method., options are: moving_average, moving_median, savitzky_golay&#39;</span><span class="p">)</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="s1">&#39;Invalid method. Please select a valid filtering method., options are: moving_average, moving_median, savitzky_golay&#39;</span><span class="p">)</span>
     <span class="n">corrected_counts</span> <span class="o">=</span> <span class="n">corrected_counts</span><span class="o">.</span><span class="n">ffill</span><span class="p">(</span><span class="n">limit</span><span class="o">=</span><span class="n">window</span><span class="p">)</span><span class="o">.</span><span class="n">bfill</span><span class="p">(</span><span class="n">limit</span><span class="o">=</span><span class="n">window</span><span class="p">)</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="k">return</span> <span class="n">corrected_counts</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">spatial_average</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">min_neighbours</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">,</span> <span class="n">rnd</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.spatial_average" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">spatial_average</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">min_neighbours</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">,</span> <span class="n">rnd</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h2>
 
@@ -5629,6 +15161,8 @@ <h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
   
       <p>Moving buffer filter to smooth georeferenced two-dimensional data.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -5645,7 +15179,11 @@ <h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>UTM x coordinates in meters.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM x coordinates in meters.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -5655,7 +15193,11 @@ <h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>UTM y coordinates in meters.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>UTM y coordinates in meters.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -5665,7 +15207,11 @@ <h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
           <td>
                 <code>list or array</code>
           </td>
-          <td><p>Values to be smoothed.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Values to be smoothed.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -5675,7 +15221,11 @@ <h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Radial buffer distance in meters.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Radial buffer distance in meters.</p>
+            </div>
+          </td>
           <td>
                 <code>100</code>
           </td>
@@ -5685,7 +15235,11 @@ <h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
           <td>
                 <code>int</code>
           </td>
-          <td><p>Minimum number of neighbours to consider for the smoothing.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Minimum number of neighbours to consider for the smoothing.</p>
+            </div>
+          </td>
           <td>
                 <code>3</code>
           </td>
@@ -5695,7 +15249,11 @@ <h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>One of 'mean' or 'median'.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>One of 'mean' or 'median'.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;mean&#39;</code>
           </td>
@@ -5705,7 +15263,11 @@ <h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
           <td>
                 <code>bool</code>
           </td>
-          <td><p>Boolean to round the final result. Useful in case of z representing neutron counts.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Boolean to round the final result. Useful in case of z representing neutron counts.</p>
+            </div>
+          </td>
           <td>
                 <code>False</code>
           </td>
@@ -5713,6 +15275,8 @@ <h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -5726,70 +15290,73 @@ <h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
           <td>
                 <code>array</code>
           </td>
-          <td><p>Smoothed version of z with the same dimension as z.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Smoothed version of z with the same dimension as z.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1034</span>
-<span class="normal">1035</span>
-<span class="normal">1036</span>
-<span class="normal">1037</span>
-<span class="normal">1038</span>
-<span class="normal">1039</span>
-<span class="normal">1040</span>
-<span class="normal">1041</span>
-<span class="normal">1042</span>
-<span class="normal">1043</span>
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-<span class="normal">1045</span>
-<span class="normal">1046</span>
-<span class="normal">1047</span>
-<span class="normal">1048</span>
-<span class="normal">1049</span>
-<span class="normal">1050</span>
-<span class="normal">1051</span>
-<span class="normal">1052</span>
-<span class="normal">1053</span>
-<span class="normal">1054</span>
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-<span class="normal">1057</span>
-<span class="normal">1058</span>
-<span class="normal">1059</span>
-<span class="normal">1060</span>
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-<span class="normal">1062</span>
-<span class="normal">1063</span>
-<span class="normal">1064</span>
-<span class="normal">1065</span>
-<span class="normal">1066</span>
-<span class="normal">1067</span>
-<span class="normal">1068</span>
-<span class="normal">1069</span>
-<span class="normal">1070</span>
-<span class="normal">1071</span>
-<span class="normal">1072</span>
-<span class="normal">1073</span>
-<span class="normal">1074</span>
-<span class="normal">1075</span>
-<span class="normal">1076</span>
-<span class="normal">1077</span>
-<span class="normal">1078</span>
-<span class="normal">1079</span>
-<span class="normal">1080</span>
-<span class="normal">1081</span>
-<span class="normal">1082</span>
-<span class="normal">1083</span>
-<span class="normal">1084</span>
-<span class="normal">1085</span>
-<span class="normal">1086</span>
-<span class="normal">1087</span>
-<span class="normal">1088</span>
-<span class="normal">1089</span>
-<span class="normal">1090</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">spatial_average</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">min_neighbours</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">,</span> <span class="n">rnd</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1315</span>
+<span class="normal">1316</span>
+<span class="normal">1317</span>
+<span class="normal">1318</span>
+<span class="normal">1319</span>
+<span class="normal">1320</span>
+<span class="normal">1321</span>
+<span class="normal">1322</span>
+<span class="normal">1323</span>
+<span class="normal">1324</span>
+<span class="normal">1325</span>
+<span class="normal">1326</span>
+<span class="normal">1327</span>
+<span class="normal">1328</span>
+<span class="normal">1329</span>
+<span class="normal">1330</span>
+<span class="normal">1331</span>
+<span class="normal">1332</span>
+<span class="normal">1333</span>
+<span class="normal">1334</span>
+<span class="normal">1335</span>
+<span class="normal">1336</span>
+<span class="normal">1337</span>
+<span class="normal">1338</span>
+<span class="normal">1339</span>
+<span class="normal">1340</span>
+<span class="normal">1341</span>
+<span class="normal">1342</span>
+<span class="normal">1343</span>
+<span class="normal">1344</span>
+<span class="normal">1345</span>
+<span class="normal">1346</span>
+<span class="normal">1347</span>
+<span class="normal">1348</span>
+<span class="normal">1349</span>
+<span class="normal">1350</span>
+<span class="normal">1351</span>
+<span class="normal">1352</span>
+<span class="normal">1353</span>
+<span class="normal">1354</span>
+<span class="normal">1355</span>
+<span class="normal">1356</span>
+<span class="normal">1357</span>
+<span class="normal">1358</span>
+<span class="normal">1359</span>
+<span class="normal">1360</span>
+<span class="normal">1361</span>
+<span class="normal">1362</span>
+<span class="normal">1363</span>
+<span class="normal">1364</span>
+<span class="normal">1365</span>
+<span class="normal">1366</span>
+<span class="normal">1367</span>
+<span class="normal">1368</span>
+<span class="normal">1369</span>
+<span class="normal">1370</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">spatial_average</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">min_neighbours</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">,</span> <span class="n">rnd</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Moving buffer filter to smooth georeferenced two-dimensional data.</span>
 
 <span class="sd">    Args:</span>
@@ -5826,7 +15393,6 @@ <h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
         <span class="n">distances</span> <span class="o">=</span> <span class="n">euclidean_distance</span><span class="p">(</span><span class="n">px</span><span class="p">,</span> <span class="n">py</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
         <span class="n">idx_within_buffer</span> <span class="o">=</span> <span class="n">distances</span> <span class="o">&lt;=</span> <span class="n">buffer</span>
 
-
         <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">z</span><span class="p">[</span><span class="n">k</span><span class="p">]):</span>
             <span class="n">z_new_val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
         <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">distances</span><span class="p">[</span><span class="n">idx_within_buffer</span><span class="p">])</span> <span class="o">&gt;</span> <span class="n">min_neighbours</span><span class="p">:</span>
@@ -5837,7 +15403,7 @@ <h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
             <span class="k">else</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="sa">f</span><span class="s2">&quot;Method </span><span class="si">{</span><span class="n">method</span><span class="si">}</span><span class="s2"> does not exist. Provide either &#39;mean&#39; or &#39;median&#39;.&quot;</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">z_new_val</span> <span class="o">=</span> <span class="n">z</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="c1"># If there are not enough neighbours, keep the original value</span>
+            <span class="n">z_new_val</span> <span class="o">=</span> <span class="n">z</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>  <span class="c1"># If there are not enough neighbours, keep the original value</span>
 
         <span class="c1"># Append smoothed value to array</span>
         <span class="n">z_smooth</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">z_smooth</span><span class="p">,</span> <span class="n">z_new_val</span><span class="p">)</span>
@@ -5847,17 +15413,19 @@ <h2 id="crnpy.crnpy.spatial_average" class="doc doc-heading">
 
     <span class="k">return</span> <span class="n">z_smooth</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.total_raw_counts" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">total_raw_counts</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">nan_strategy</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.total_raw_counts" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">total_raw_counts</span><span class="p">(</span><span class="n">counts</span><span class="p">)</span></code>
 
 </h2>
 
@@ -5866,6 +15434,8 @@ <h2 id="crnpy.crnpy.total_raw_counts" class="doc doc-heading">
   
       <p>Compute the sum of uncorrected neutron counts for all detectors.</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -5880,26 +15450,22 @@ <h2 id="crnpy.crnpy.total_raw_counts" class="doc doc-heading">
         <tr>
           <td><code>counts</code></td>
           <td>
-                <code>pandas.<span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td><p>Dataframe containing only the columns with neutron counts.</p></td>
-          <td>
-              <em>required</em>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
           </td>
-        </tr>
-        <tr>
-          <td><code>nan_strategy</code></td>
           <td>
-                <code>str</code>
+            <div class="doc-md-description">
+              <p>Dataframe containing only the columns with neutron counts.</p>
+            </div>
           </td>
-          <td><p>Strategy to use for NaN values. Options are 'interpolate', 'average', or None. Default is None.</p></td>
           <td>
-                <code>None</code>
+              <em>required</em>
           </td>
         </tr>
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -5911,16 +15477,21 @@ <h2 id="crnpy.crnpy.total_raw_counts" class="doc doc-heading">
     <tbody>
         <tr>
           <td>
-                <code>pandas.<span title="pandas.DataFrame">DataFrame</span></code>
+                <code><span title="pandas.DataFrame">DataFrame</span></code>
+          </td>
+          <td>
+            <div class="doc-md-description">
+              <p>Dataframe with the sum of uncorrected neutron counts for all detectors.</p>
+            </div>
           </td>
-          <td><p>Dataframe with the sum of uncorrected neutron counts for all detectors.</p></td>
         </tr>
     </tbody>
   </table>
 
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">111</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">110</span>
+<span class="normal">111</span>
 <span class="normal">112</span>
 <span class="normal">113</span>
 <span class="normal">114</span>
@@ -5938,21 +15509,18 @@ <h2 id="crnpy.crnpy.total_raw_counts" class="doc doc-heading">
 <span class="normal">126</span>
 <span class="normal">127</span>
 <span class="normal">128</span>
-<span class="normal">129</span>
-<span class="normal">130</span>
-<span class="normal">131</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">total_raw_counts</span><span class="p">(</span><span class="n">counts</span><span class="p">,</span> <span class="n">nan_strategy</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">timestamp_col</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="normal">129</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">total_raw_counts</span><span class="p">(</span><span class="n">counts</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Compute the sum of uncorrected neutron counts for all detectors.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        counts (pandas.DataFrame): Dataframe containing only the columns with neutron counts.</span>
-<span class="sd">        nan_strategy (str): Strategy to use for NaN values. Options are &#39;interpolate&#39;, &#39;average&#39;, or None. Default is None.</span>
 
 <span class="sd">    Returns:</span>
 <span class="sd">        (pandas.DataFrame): Dataframe with the sum of uncorrected neutron counts for all detectors.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="k">if</span> <span class="n">counts</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">counts</span> <span class="o">=</span> <span class="n">counts</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">counts</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">counts</span> <span class="o">=</span> <span class="n">counts</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">counts</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 
     <span class="c1"># Compute sum of counts</span>
     <span class="n">total_raw_counts</span> <span class="o">=</span> <span class="n">counts</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
@@ -5962,17 +15530,19 @@ <h2 id="crnpy.crnpy.total_raw_counts" class="doc doc-heading">
 
     <span class="k">return</span> <span class="n">total_raw_counts</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.uncertainty_counts" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">uncertainty_counts</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s1">&#39;std&#39;</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fi</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.uncertainty_counts" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">uncertainty_counts</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s1">&#39;std&#39;</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fi</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
 
 </h2>
 
@@ -5984,6 +15554,8 @@ <h2 id="crnpy.crnpy.uncertainty_counts" class="doc doc-heading">
 The expected uncertainty in the neutron count rate $N$ is defined by the standard deviation $ \sqrt{N} $ (Jakobi et al., 2020).
 The CV% can be expressed as $ N^{-1/2} $</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -6000,7 +15572,11 @@ <h2 id="crnpy.crnpy.uncertainty_counts" class="doc doc-heading">
           <td>
                 <code>array</code>
           </td>
-          <td><p>Raw neutron counts.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Raw neutron counts.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -6010,7 +15586,11 @@ <h2 id="crnpy.crnpy.uncertainty_counts" class="doc doc-heading">
           <td>
                 <code>str</code>
           </td>
-          <td><p>Either 'std' or 'cv' for standard deviation or coefficient of variation.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Either 'std' or 'cv' for standard deviation or coefficient of variation.</p>
+            </div>
+          </td>
           <td>
                 <code>&#39;std&#39;</code>
           </td>
@@ -6020,7 +15600,11 @@ <h2 id="crnpy.crnpy.uncertainty_counts" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Pressure correction factor.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Pressure correction factor.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -6030,7 +15614,11 @@ <h2 id="crnpy.crnpy.uncertainty_counts" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Humidity correction factor.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Humidity correction factor.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -6040,7 +15628,11 @@ <h2 id="crnpy.crnpy.uncertainty_counts" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Incoming neutron flux correction factor.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Incoming neutron flux correction factor.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -6048,6 +15640,8 @@ <h2 id="crnpy.crnpy.uncertainty_counts" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -6061,7 +15655,11 @@ <h2 id="crnpy.crnpy.uncertainty_counts" class="doc doc-heading">
 <td><code>uncertainty</code></td>          <td>
                 <code>float</code>
           </td>
-          <td><p>Uncertainty of raw counts.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Uncertainty of raw counts.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
@@ -6073,42 +15671,42 @@ <h2 id="crnpy.crnpy.uncertainty_counts" class="doc doc-heading">
 <p>Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System,
 Hydrol. Earth Syst. Sci., 16, 4079–4099, https://doi.org/10.5194/hess-16-4079-2012, 2012.</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1215</span>
-<span class="normal">1216</span>
-<span class="normal">1217</span>
-<span class="normal">1218</span>
-<span class="normal">1219</span>
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-<span class="normal">1247</span>
-<span class="normal">1248</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">uncertainty_counts</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s2">&quot;std&quot;</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fi</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1495</span>
+<span class="normal">1496</span>
+<span class="normal">1497</span>
+<span class="normal">1498</span>
+<span class="normal">1499</span>
+<span class="normal">1500</span>
+<span class="normal">1501</span>
+<span class="normal">1502</span>
+<span class="normal">1503</span>
+<span class="normal">1504</span>
+<span class="normal">1505</span>
+<span class="normal">1506</span>
+<span class="normal">1507</span>
+<span class="normal">1508</span>
+<span class="normal">1509</span>
+<span class="normal">1510</span>
+<span class="normal">1511</span>
+<span class="normal">1512</span>
+<span class="normal">1513</span>
+<span class="normal">1514</span>
+<span class="normal">1515</span>
+<span class="normal">1516</span>
+<span class="normal">1517</span>
+<span class="normal">1518</span>
+<span class="normal">1519</span>
+<span class="normal">1520</span>
+<span class="normal">1521</span>
+<span class="normal">1522</span>
+<span class="normal">1523</span>
+<span class="normal">1524</span>
+<span class="normal">1525</span>
+<span class="normal">1526</span>
+<span class="normal">1527</span>
+<span class="normal">1528</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">uncertainty_counts</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s2">&quot;std&quot;</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fi</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function to estimate the uncertainty of raw counts.</span>
 
 <span class="sd">    Measurements of proportional neutron detector systems are governed by counting statistics that follow a Poissonian probability distribution (Zreda et al., 2012).</span>
@@ -6138,22 +15736,24 @@ <h2 id="crnpy.crnpy.uncertainty_counts" class="doc doc-heading">
     <span class="k">if</span> <span class="n">metric</span> <span class="o">==</span> <span class="s2">&quot;std&quot;</span><span class="p">:</span>
         <span class="n">uncertainty</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">)</span> <span class="o">*</span> <span class="n">s</span>
     <span class="k">elif</span> <span class="n">metric</span> <span class="o">==</span> <span class="s2">&quot;cv&quot;</span><span class="p">:</span>
-        <span class="n">uncertainty</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span>  <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">)</span> <span class="o">*</span> <span class="n">s</span>
+        <span class="n">uncertainty</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">)</span> <span class="o">*</span> <span class="n">s</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="k">raise</span> <span class="sa">f</span><span class="s2">&quot;Metric </span><span class="si">{</span><span class="n">metric</span><span class="si">}</span><span class="s2"> does not exist. Provide either &#39;std&#39; or &#39;cv&#39; for standard deviation or coefficient of variation.&quot;</span>
     <span class="k">return</span> <span class="n">uncertainty</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
 
+
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="crnpy.crnpy.uncertainty_vwc" class="doc doc-heading">
-<code class="highlight language-python"><span class="n">uncertainty_vwc</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">,</span> <span class="n">N0</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fi</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">a0</span><span class="o">=</span><span class="mf">0.0808</span><span class="p">,</span> <span class="n">a1</span><span class="o">=</span><span class="mf">0.372</span><span class="p">,</span> <span class="n">a2</span><span class="o">=</span><span class="mf">0.115</span><span class="p">)</span></code>
+
+<h2 id="crnpy.data.uncertainty_vwc" class="doc doc-heading">
+          <code class="highlight language-python"><span class="n">uncertainty_vwc</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">,</span> <span class="n">N0</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fi</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">a0</span><span class="o">=</span><span class="mf">0.0808</span><span class="p">,</span> <span class="n">a1</span><span class="o">=</span><span class="mf">0.372</span><span class="p">,</span> <span class="n">a2</span><span class="o">=</span><span class="mf">0.115</span><span class="p">)</span></code>
 
 </h2>
 
@@ -6167,6 +15767,8 @@ <h2 id="crnpy.crnpy.uncertainty_vwc" class="doc doc-heading">
 \sigma_{\theta_g}(N) = \sigma_N \frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \sqrt{(N_{cor} - a_1 N_0)^4 + 8 \sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \sigma_N^4}
 $$</p>
 
+
+
   <p><strong>Parameters:</strong></p>
   <table>
     <thead>
@@ -6183,7 +15785,11 @@ <h2 id="crnpy.crnpy.uncertainty_vwc" class="doc doc-heading">
           <td>
                 <code>array</code>
           </td>
-          <td><p>Raw neutron counts.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Raw neutron counts.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -6193,7 +15799,11 @@ <h2 id="crnpy.crnpy.uncertainty_vwc" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Calibration parameter N0.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Calibration parameter N0.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -6203,7 +15813,11 @@ <h2 id="crnpy.crnpy.uncertainty_vwc" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Bulk density in g cm-3.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Bulk density in g cm-3.</p>
+            </div>
+          </td>
           <td>
               <em>required</em>
           </td>
@@ -6213,7 +15827,11 @@ <h2 id="crnpy.crnpy.uncertainty_vwc" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Pressure correction factor.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Pressure correction factor.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -6223,7 +15841,11 @@ <h2 id="crnpy.crnpy.uncertainty_vwc" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Humidity correction factor.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Humidity correction factor.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -6233,7 +15855,11 @@ <h2 id="crnpy.crnpy.uncertainty_vwc" class="doc doc-heading">
           <td>
                 <code>float</code>
           </td>
-          <td><p>Incoming neutron flux correction factor.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Incoming neutron flux correction factor.</p>
+            </div>
+          </td>
           <td>
                 <code>1</code>
           </td>
@@ -6241,6 +15867,8 @@ <h2 id="crnpy.crnpy.uncertainty_vwc" class="doc doc-heading">
     </tbody>
   </table>
 
+
+
   <p><strong>Returns:</strong></p>
   <table>
     <thead>
@@ -6254,7 +15882,11 @@ <h2 id="crnpy.crnpy.uncertainty_vwc" class="doc doc-heading">
 <td><code>sigma_VWC</code></td>          <td>
                 <code>float</code>
           </td>
-          <td><p>Uncertainty in terms of volumetric water content.</p></td>
+          <td>
+            <div class="doc-md-description">
+              <p>Uncertainty in terms of volumetric water content.</p>
+            </div>
+          </td>
         </tr>
     </tbody>
   </table>
@@ -6264,39 +15896,40 @@ <h2 id="crnpy.crnpy.uncertainty_vwc" class="doc doc-heading">
   <p>Jakobi J, Huisman JA, Schrön M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With
 Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010</p>
 </details>
-      <details class="quote">
-        <summary>Source code in <code>C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1251</span>
-<span class="normal">1252</span>
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-<span class="normal">1280</span>
-<span class="normal">1281</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">uncertainty_vwc</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">,</span> <span class="n">N0</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fi</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">a0</span><span class="o">=</span><span class="mf">0.0808</span><span class="p">,</span><span class="n">a1</span><span class="o">=</span><span class="mf">0.372</span><span class="p">,</span><span class="n">a2</span><span class="o">=</span><span class="mf">0.115</span><span class="p">):</span>
+          <details class="quote">
+            <summary>Source code in <code>crnpy/crnpy.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">1531</span>
+<span class="normal">1532</span>
+<span class="normal">1533</span>
+<span class="normal">1534</span>
+<span class="normal">1535</span>
+<span class="normal">1536</span>
+<span class="normal">1537</span>
+<span class="normal">1538</span>
+<span class="normal">1539</span>
+<span class="normal">1540</span>
+<span class="normal">1541</span>
+<span class="normal">1542</span>
+<span class="normal">1543</span>
+<span class="normal">1544</span>
+<span class="normal">1545</span>
+<span class="normal">1546</span>
+<span class="normal">1547</span>
+<span class="normal">1548</span>
+<span class="normal">1549</span>
+<span class="normal">1550</span>
+<span class="normal">1551</span>
+<span class="normal">1552</span>
+<span class="normal">1553</span>
+<span class="normal">1554</span>
+<span class="normal">1555</span>
+<span class="normal">1556</span>
+<span class="normal">1557</span>
+<span class="normal">1558</span>
+<span class="normal">1559</span>
+<span class="normal">1560</span>
+<span class="normal">1561</span>
+<span class="normal">1562</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">uncertainty_vwc</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">,</span> <span class="n">N0</span><span class="p">,</span> <span class="n">bulk_density</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fi</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">a0</span><span class="o">=</span><span class="mf">0.0808</span><span class="p">,</span> <span class="n">a1</span><span class="o">=</span><span class="mf">0.372</span><span class="p">,</span> <span class="n">a2</span><span class="o">=</span><span class="mf">0.115</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Function to estimate the uncertainty propagated to volumetric water content.</span>
 
 <span class="sd">    The uncertainty of the volumetric water content is estimated by propagating the uncertainty of the raw counts.</span>
@@ -6323,12 +15956,13 @@ <h2 id="crnpy.crnpy.uncertainty_vwc" class="doc doc-heading">
 
     <span class="n">Ncorr</span> <span class="o">=</span> <span class="n">raw_counts</span> <span class="o">*</span> <span class="n">fw</span> <span class="o">/</span> <span class="p">(</span><span class="n">fp</span> <span class="o">*</span> <span class="n">fi</span><span class="p">)</span>
     <span class="n">sigma_N</span> <span class="o">=</span> <span class="n">uncertainty_counts</span><span class="p">(</span><span class="n">raw_counts</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s2">&quot;std&quot;</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="n">fp</span><span class="p">,</span> <span class="n">fw</span><span class="o">=</span><span class="n">fw</span><span class="p">,</span> <span class="n">fi</span><span class="o">=</span><span class="n">fi</span><span class="p">)</span>
-    <span class="n">sigma_GWC</span> <span class="o">=</span> <span class="n">sigma_N</span> <span class="o">*</span> <span class="p">((</span><span class="n">a0</span><span class="o">*</span><span class="n">N0</span><span class="p">)</span> <span class="o">/</span> <span class="p">((</span><span class="n">Ncorr</span> <span class="o">-</span> <span class="n">a1</span><span class="o">*</span><span class="n">N0</span><span class="p">)</span><span class="o">**</span><span class="mi">4</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">Ncorr</span> <span class="o">-</span> <span class="n">a1</span> <span class="o">*</span> <span class="n">N0</span><span class="p">)</span><span class="o">**</span><span class="mi">4</span> <span class="o">+</span> <span class="mi">8</span> <span class="o">*</span> <span class="n">sigma_N</span><span class="o">**</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">Ncorr</span> <span class="o">-</span> <span class="n">a1</span> <span class="o">*</span> <span class="n">N0</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">15</span> <span class="o">*</span> <span class="n">sigma_N</span><span class="o">**</span><span class="mi">4</span><span class="p">)</span>
+    <span class="n">sigma_GWC</span> <span class="o">=</span> <span class="n">sigma_N</span> <span class="o">*</span> <span class="p">((</span><span class="n">a0</span> <span class="o">*</span> <span class="n">N0</span><span class="p">)</span> <span class="o">/</span> <span class="p">((</span><span class="n">Ncorr</span> <span class="o">-</span> <span class="n">a1</span> <span class="o">*</span> <span class="n">N0</span><span class="p">)</span> <span class="o">**</span> <span class="mi">4</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span>
+        <span class="p">(</span><span class="n">Ncorr</span> <span class="o">-</span> <span class="n">a1</span> <span class="o">*</span> <span class="n">N0</span><span class="p">)</span> <span class="o">**</span> <span class="mi">4</span> <span class="o">+</span> <span class="mi">8</span> <span class="o">*</span> <span class="n">sigma_N</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">Ncorr</span> <span class="o">-</span> <span class="n">a1</span> <span class="o">*</span> <span class="n">N0</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">15</span> <span class="o">*</span> <span class="n">sigma_N</span> <span class="o">**</span> <span class="mi">4</span><span class="p">)</span>
     <span class="n">sigma_VWC</span> <span class="o">=</span> <span class="n">sigma_GWC</span> <span class="o">*</span> <span class="n">bulk_density</span>
 
     <span class="k">return</span> <span class="n">sigma_VWC</span>
 </code></pre></div></td></tr></table></div>
-      </details>
+          </details>
   </div>
 
 </div>
@@ -6341,64 +15975,17 @@ <h2 id="crnpy.crnpy.uncertainty_vwc" class="doc doc-heading">
 
 </div>
 
-<div class="doc doc-object doc-module">
-
-
-<a id="crnpy.data"></a>
-  <div class="doc doc-contents first">
-  
-      <h3 id="crnpy.data--crnpydata">crnpy.data</h3>
-<p>Data module for crnpy.</p>
-<p>This module contains data for the crnpy package.</p>
-
-  <p><strong>Attributes:</strong></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr>
-          <td><code>cutoff_rigidity</code></td>
-          <td>
-                <code>list</code>
-          </td>
-          <td><p>Cutoff rigidity values for the whole world. See <a class="autorefs autorefs-internal" href="#crnpy.crnpy.cutoff_rigidity">crnpy.crnpy.cutoff_rigidity</a></p></td>
-        </tr>
-        <tr>
-          <td><code>neutron_detectors</code></td>
-          <td>
-                <code>list</code>
-          </td>
-          <td><p>Neutron detector locations. See <a class="autorefs autorefs-internal" href="#crnpy.crnpy.find_neutron_monitor">crnpy.crnpy.find_neutron_monitor</a></p></td>
-        </tr>
-    </tbody>
-  </table>
-
-  
-
-  <div class="doc doc-children">
-
-
 
 
 
 
 
 
+  
+  
 
 
 
-  </div>
-
-  </div>
-
-</div>
-
-
 
 
 
@@ -6408,6 +15995,7 @@ <h3 id="crnpy.data--crnpydata">crnpy.data</h3>
             </div>
           
           
+<script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script>
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@@ -6435,22 +16023,17 @@ <h3 id="crnpy.data--crnpydata">crnpy.data</h3>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.f886a092.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.220ee61c.min.js"></script>
+      <script src="../assets/javascripts/bundle.d7c377c4.min.js"></script>
       
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diff --git a/site/search/search_index.json b/site/search/search_index.json
index 40a8489..9741780 100644
--- a/site/search/search_index.json
+++ b/site/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Cosmic-Ray Neutron Python (CRNPy) Library","text":"<p>Welcome to the homepage of the CRNPy (Cosmic-Ray Neutron Python) library, an open-source Python library designed for the processing and conversion of raw neutron counts from cosmic-ray neutron probes (CRNP) into soil moisture data.</p> <p>This library has been developed with the intent of providing a comprehensive yet easy-to-use workflow for processing raw data from a variety of CRNP, encompassing multiple manufacturers and models.</p>"},{"location":"#statement-of-need","title":"Statement of Need","text":"<p>CRNPs are a valuable tool for non-invasive soil moisture estimation at the hectometer scale (e.g., typical agricultural fields), filling the gap between point-level sensors and large-scale (i.e., several kilometers) remote sensors onboard orbiting satellites. However, cleaning, processing, and analyzing CRNP data involves multiple corrections and filtering steps spread across multiple peer-reviewed manuscripts. CRNPy simplifies these steps by providing a complete, user-friendly, and well-documented library with minimal dependencies that includes examples to convert raw CRNP data into soil moisture. The library is designed to be accessible to both researchers and instrument manufacturers. Unlike other similar libraries, CRNPy does not require any specific naming convention for the input data or large external data sources, or reanalysis data.</p>"},{"location":"#key-features","title":"Key Features","text":"<ul> <li> <p>Versatile and instrument agnostic: CRNPy can handle data from various CRNP manufacturers and models. It has been successfully tested on both roving and stationary CRNP.</p> </li> <li> <p>Modular: The library is designed to be modular, allowing users to easily customize the processing workflow to their needs.</p> </li> </ul>"},{"location":"#installation","title":"Installation","text":"<p>To install the CRNPy library, you can use Python's package manager. Open a terminal and type:</p> <p><code>pip install crnpy</code></p> <p>from the Jupyter notebook, type:</p> <p><code>!pip install crnpy</code></p> <p>Ideally dependencies should be installed automatically. If not, you can install them manually by typing:</p> <p><code>pip install -r requirements.txt</code></p> <p>The CRNPy library is compatible with Python 3.7 and above. See requirements.txt for a list of dependencies.</p>"},{"location":"#authors","title":"Authors","text":"<p>The CRNPy library was developed at the Kansas State University Soil Water Processes Lab by:</p> <ul> <li> <p>Joaquin Peraza</p> </li> <li> <p>Andres Patrignani</p> </li> </ul> <p>The Soil Water Processes Lab at Kansas State University combines a range of experimental and computational approaches to tackle pressing issues in soil and water research. The development of the CRNPy library is a step forward to creating reproducible data processing workflows across the scientific community using cosmic-ray neutrons probes for soil moisture sensing. </p>"},{"location":"#community-guidelines","title":"Community Guidelines","text":""},{"location":"#contributing","title":"Contributing","text":"<p>To contribute to the software, please first fork the repository and create your own branch from <code>main</code>. Ensure your code adheres to our established code structure and includes appropriate test/examples coverage. CRNPy source code is located in the <code>/src/crnpy/</code> folder, and tests implemented using <code>pytest</code> are stored in the <code>/src/tests/</code> folder. Submit a pull request with a clear and detailed description of your changes to include them in the main repository.</p>"},{"location":"#reporting-issues","title":"Reporting Issues","text":"<p>If you encounter any issues or problems with the software, please report them on our issues page. Include a detailed description of the issue, steps to reproduce the problem, any error messages you received, and details about your operating system and software version.</p>"},{"location":"#seeking-support","title":"Seeking Support","text":"<p>If you need support, please first refer to the documentation. If you still require assistance, post a question on the issues page with the <code>question</code> tag. For private inquiries, you can reach us via email at jperaza@ksu.edu or andrespatrignani@ksu.edu.</p>"},{"location":"correction_routines/","title":"Correction routines","text":""},{"location":"correction_routines/#overview","title":"Overview","text":"<p>The CRNPy library provides the tools to correct and process neutron counts recorded with both stationary and roving cosmic-ray neutron probes. Below are two flowcharts describing the typical steps from the correction of raw neutron counts to the conversion into volumetric soil water content.</p>"},{"location":"correction_routines/#stationary-crnp-processing","title":"Stationary CRNP processing","text":"<p> Dashed lines indicate optional steps. See the complete example notebook.</p>"},{"location":"correction_routines/#roving-crnp-processing","title":"Roving CRNP processing","text":"<p> Dashed lines indicate optional steps. See the complete example notebook.</p>"},{"location":"correction_routines/#incoming-neutron-flux","title":"Incoming neutron flux","text":"<p>The CRNPy library includes a complete set of methods for correcting the raw observed neutron counts for natural variation in the incoming neutron flux, including a set of tools for searching and downloading data from reference neutron monitors from the NMDB database (www.nmdb.eu) with similar the cut-off rigidity as the study location (Klein et al., 2009; Shea &amp; Smart., 2019).</p> Incoming neutron flux correction factor $fi = \\frac{I_{m}}{I_{0}}$ $ fi $: Incoming neutron flux correction factor $ I_{m} $: Measured incoming neutron flux $ I_{0} $: Reference incoming neutron flux at a given time. <p>Implementation</p> <p>See  crnpy.crnpy.cutoff_rigidity, crnpy.crnpy.find_neutron_monitor, crnpy.crnpy.get_incoming_neutron_flux, crnpy.crnpy.interpolate_incoming_flux and crnpy.crnpy.incoming_flux_correction documentation for the implementation details.</p>"},{"location":"correction_routines/#atmospheric-corrections","title":"Atmospheric corrections","text":"<p>The CRNPy library also provides functions for correcting raw neutron counts for atmospheric pressure, air humidity, and air temperature (Andreasen et al., 2017; Rosolem et al., 2013).</p> Pressure correction Atmospheric water correction $fp = exp(\\frac{P_{0} - P}{L})$ $fw = 1 + 0.0054*(A - Aref)$ $fp$: Atmospheric pressure correction factor $fw$: Atmospheric water correction factor $P_{0}$: Reference atmospheric pressure (for e.g. long-term average) $A$: Atmospheric absolute humidity (g/m3) $P$: Measured atmospheric pressure $Aref$: Reference atmospheric absolute humidity (g/m3) $L$: Mass attenuation factor for high-energy neutrons in air <p>Implementation</p> <p>See crnpy.crnpy.humidity_correction and crnpy.crnpy.pressure_correction documentation for the implementation details.</p>"},{"location":"correction_routines/#biomass-correction","title":"Biomass correction","text":"<p>The library provides a function for correcting neutron counts for the effects of above-ground biomass by combining an approach for estimating biomass water equivalent (BWE) from in-situ biomass samples and the BWE correction factor (Baatz et al., 2015).</p> Biomass correction $fb = 1 - bwe*r2_N0$ $fb$: Biomass correction factor $bwe$: Biomass water equivalent $r2_N0$: Ratio of the neutron counts reduction ($counts kg^-1$) to the neutron calibration constant (N0) <p>Implementation</p> <p>See crnpy.crnpy.bwe_correction and crnpy.crnpy.biomass_to_bwe documentation for the implementation details.</p>"},{"location":"correction_routines/#road-correction","title":"Road correction","text":"<p>The lCRNPY library includes functions to correct for the effect of roads during rover surveys which account for the field soil water content and the road water content following the approach proposed by Schr\u00f6n et al., (2018).</p> Road correction $fr = 1 + F1 \\cdot F2 \\cdot F3$ $fr$: Road correction factor $F1$: Road geometry term $F2$: Road moisture term $F3$: Road distance term <p>Implementation</p> <p>See crnpy.crnpy.road_correction documentation for the implementation details.</p>"},{"location":"correction_routines/#additional-corrections","title":"Additional corrections","text":"<p>Other correction routines includes corrections for soil lattice water and total soil carbon that are known to affect the attentuation of epithermal cosmic-ray neutrons. A function to estimate the soil lattice water content based on clay content and soil organic carbon content was developed using soil samples collected across the state of Kansas.</p> <p>Implementation</p> <p>See crnpy.crnpy.estimate_lattice_water and crnpy.crnpy.counts_to_vwc documentation for the implementation details.</p> <p>References</p> <p>Klein, K.-L., Steigies, C., &amp; Nmdb Team. (2009). WWW.NMDB.EU: The real-time Neutron Monitor database. EGU General Assembly Conference Abstracts, 5633.</p> <p>Shea, M., &amp; Smart, D. (2019). Re-examination of the first five ground-level events. International Cosmic Ray Conference (ICRC2019), 36, 1149.</p> <p>Smart, D., &amp; Shea, M. (2001). Geomagnetic cutoff rigidity computer program: Theory, software description and example.</p> <p>Andreasen, M., Jensen, K. H., Desilets, D., Franz, T. E., Zreda, M., Bogena, H. R., &amp; Looms, M. C. (2017). Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone Journal, 16(8), 1\u201311.</p> <p>Rosolem, R., Shuttleworth, W. J., Zreda, M., Franz, T. E., Zeng, X., &amp; Kurc, S. A. (2013). The effect of atmospheric water vapor on neutron count in the cosmic-ray soil moisture observing system. Journal of Hydrometeorology, 14(5), 1659\u20131671.</p> <p>Zreda, M., Desilets, D., Ferr\u00e9, T., &amp; Scott, R. L. (2008). Measuring soil moisture content non-invasively at intermediate spatial scale using cosmic-ray neutrons. Geophysical Research Letters, 35(21).</p> <p>Dong, J., &amp; Ochsner, T. E. (2018). Soil texture often exerts a stronger influence than precipitation on mesoscale soil moisture patterns. Water Resources Research, 54(3), 2199\u20132211.</p> <p>Wahbi, A., Heng, L., Dercon, G., Wahbi, A., &amp; Avery, W. (2018). In situ destructive sampling. Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent, 5\u20139.</p> <p>Baatz, R., Bogena, H., Hendricks Franssen, H.-J., Huisman, J., Montzka, C., &amp; Vereecken, H. (2015). An empirical vegetation correction for soil water content quantification using cosmic ray probes. Water Resources Research, 51(4), 2030\u20132046.</p> <p>Schr\u00f6n, M., Rosolem, R., K\u00f6hli, M., Piussi, L., Schr\u00f6ter, I., Iwema, J., K\u00f6gler, S., Oswald, S. E., Wollschl\u00e4ger, U., Samaniego, L., &amp; others. (2018). Cosmic-ray neutron rover surveys of field soil moisture and the influence of roads. Water Resources Research, 54(9), 6441\u20136459.</p> <p>Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079\u20134099, https://doi.org/10.5194/hess-16-4079-2012, 2012.</p>"},{"location":"reference/","title":"Reference","text":"<p><code>crnpy</code> is a Python package for processing cosmic ray neutron data.</p> <p>Created by Joaquin Peraza and Andres Patrignani.</p>"},{"location":"reference/#crnpy.crnpy.abs_humidity","title":"<code>abs_humidity(relative_humidity, temp)</code>","text":"<p>Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.</p> <p>Parameters:</p> Name Type Description Default <code>relative_humidity</code> <code>float</code> <p>relative humidity (%)</p> required <code>temp</code> <code>float</code> <p>temperature (Celsius)</p> required <p>Returns:</p> Name Type Description <code>float</code> <p>actual vapor pressure (g m^-3)</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def abs_humidity(relative_humidity, temp):\n\"\"\"\n    Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.\n\n    Args:\n        relative_humidity (float): relative humidity (%)\n        temp (float): temperature (Celsius)\n\n    Returns:\n        float: actual vapor pressure (g m^-3)\n    \"\"\"\n\n    ### Atmospheric water vapor factor\n    # Saturation vapor pressure\n    e_sat = 0.611 * np.exp(17.502 * temp / (\n                temp + 240.97)) * 1000  # in Pascals Eq. 3.8 p.41 Environmental Biophysics (Campbell and Norman)\n\n    # Vapor pressure Pascals\n    Pw = e_sat * relative_humidity / 100\n\n    # Absolute humidity (g/m^3)\n    C = 2.16679  # g K/J;\n    abs_h = C * Pw / (temp + 273.15)\n    return abs_h\n</code></pre>"},{"location":"reference/#crnpy.crnpy.biomass_to_bwe","title":"<code>biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494)</code>","text":"<p>Function to convert biomass to biomass water equivalent.</p> <p>Parameters:</p> Name Type Description Default <code>biomass_dry</code> <code>array or pd.Series or pd.DataFrame</code> <p>Above ground dry biomass in kg m-2.</p> required <code>biomass_fresh</code> <code>array or pd.Series or pd.DataFrame</code> <p>Above ground fresh biomass in kg m-2.</p> required <code>fWE</code> <code>float</code> <p>Stoichiometric ratio of H2O to organic carbon molecules in the plant (assuming this is mostly cellulose) Default is 0.494 (Wahbi &amp; Avery, 2018).</p> <code>0.494</code> <p>Returns:</p> Type Description <code>array or pd.Series or pd.DataFrame</code> <p>Biomass water equivalent in kg m-2.</p> References <p>Wahbi, A., Avery, W. (2018). In Situ Destructive Sampling. In: Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent. Springer, Cham. https://doi.org/10.1007/978-3-319-69539-6_2</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494):\n\"\"\"Function to convert biomass to biomass water equivalent.\n\n    Args:\n        biomass_dry (array or pd.Series or pd.DataFrame): Above ground dry biomass in kg m-2.\n        biomass_fresh (array or pd.Series or pd.DataFrame): Above ground fresh biomass in kg m-2.\n        fWE (float): Stoichiometric ratio of H2O to organic carbon molecules in the plant (assuming this is mostly cellulose)\n            Default is 0.494 (Wahbi &amp; Avery, 2018).\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Biomass water equivalent in kg m-2.\n\n    References:\n        Wahbi, A., Avery, W. (2018). In Situ Destructive Sampling. In:\n        Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent.\n        Springer, Cham. https://doi.org/10.1007/978-3-319-69539-6_2\n    \"\"\"\n    return (biomass_fresh - biomass_dry) + fWE * biomass_dry\n</code></pre>"},{"location":"reference/#crnpy.crnpy.correction_bwe","title":"<code>correction_bwe(counts, bwe, r2_N0=0.05)</code>","text":"<p>Function to correct for biomass effects in neutron counts. following the approach described in Baatz et al., 2015.</p> <p>Parameters:</p> Name Type Description Default <code>counts</code> <code>array or pd.Series or pd.DataFrame</code> <p>Array of ephithermal neutron counts.</p> required <code>bwe</code> <code>float</code> <p>Biomass water equivalent kg m-2.</p> required <code>r2_N0</code> <code>float</code> <p>Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.</p> <code>0.05</code> <p>Returns:</p> Type Description <code>array or pd.Series or pd.DataFrame</code> <p>Array of corrected neutron counts for biomass effects.</p> References <p>Baatz, R., H. R. Bogena, H.-J. Hendricks Franssen, J. A. Huisman, C. Montzka, and H. Vereecken (2015), An empiricalvegetation correction for soil water content quantification using cosmic ray probes, Water Resour. Res., 51, 2030\u20132046, doi:10.1002/ 2014WR016443.</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def correction_bwe(counts, bwe, r2_N0=0.05):\n\"\"\"Function to correct for biomass effects in neutron counts.\n    following the approach described in Baatz et al., 2015.\n\n    Args:\n        counts (array or pd.Series or pd.DataFrame): Array of ephithermal neutron counts.\n        bwe (float): Biomass water equivalent kg m-2.\n        r2_N0 (float): Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Array of corrected neutron counts for biomass effects.\n\n    References:\n        Baatz, R., H. R. Bogena, H.-J. Hendricks Franssen, J. A. Huisman, C. Montzka, and H. Vereecken (2015),\n        An empiricalvegetation correction for soil water content quantification using cosmic ray probes,\n        Water Resour. Res., 51, 2030\u20132046, doi:10.1002/ 2014WR016443.\n    \"\"\"\n\n    return counts/(1 - bwe*r2_N0)\n</code></pre>"},{"location":"reference/#crnpy.crnpy.correction_humidity","title":"<code>correction_humidity(abs_humidity, Aref)</code>","text":"<p>Correction factor for absolute humidity.</p> <p>This function corrects neutron counts for absolute humidity using the method described in Rosolem et al. (2013) and Anderson et al. (2017). The correction is performed using the following equation:</p> <p>$$ C_{corrected} = C_{raw} \\cdot f_w $$</p> <p>where:</p> <ul> <li>Ccorrected: corrected neutron counts</li> <li>Craw: raw neutron counts</li> <li>fw: absolute humidity correction factor</li> </ul> <p>$$ f_w = 1 + 0.0054(A - A_{ref}) $$</p> <p>where:</p> <ul> <li>A: absolute humidity</li> <li>Aref: reference absolute humidity</li> </ul> <p>Parameters:</p> Name Type Description Default <code>abs_humidity</code> <code>list or array</code> <p>Relative humidity readings.</p> required <code>temp</code> <code>list or array</code> <p>Temperature readings (Celsius).</p> required <code>Aref</code> <code>float</code> <p>Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.</p> required <p>Returns:</p> Type Description <code>list</code> <p>fw correction factor.</p> References <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def correction_humidity(abs_humidity, Aref):\nr\"\"\"Correction factor for absolute humidity.\n\n    This function corrects neutron counts for absolute humidity using the method described in Rosolem et al. (2013) and Anderson et al. (2017). The correction is performed using the following equation:\n\n    $$\n    C_{corrected} = C_{raw} \\cdot f_w\n    $$\n\n    where:\n\n    - Ccorrected: corrected neutron counts\n    - Craw: raw neutron counts\n    - fw: absolute humidity correction factor\n\n    $$\n    f_w = 1 + 0.0054(A - A_{ref})\n    $$\n\n    where:\n\n    - A: absolute humidity\n    - Aref: reference absolute humidity\n\n    Args:\n        abs_humidity (list or array): Relative humidity readings.\n        temp (list or array): Temperature readings (Celsius).\n        Aref (float): Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.\n\n    Returns:\n        (list): fw correction factor.\n\n    References:\n        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086\n    \"\"\"\n    A = abs_humidity\n    fw = 1 + 0.0054*(A - Aref) # Zreda et al. 2017 Eq 6.\n    return fw\n</code></pre>"},{"location":"reference/#crnpy.crnpy.correction_incoming_flux","title":"<code>correction_incoming_flux(incoming_neutrons, incoming_Ref=None)</code>","text":"<p>Correction factor for incoming neutron flux.</p> <p>This function corrects neutron counts for incoming neutron flux using the method described in Anderson et al. (2017). The correction is performed using the following equation:</p> <p>$$ C_{corrected} = \\frac{C_{raw}}{f_i} $$</p> <p>where:</p> <ul> <li>Ccorrected: corrected neutron counts</li> <li>Craw: raw neutron counts</li> <li>fi: incoming neutron flux correction factor</li> </ul> <p>$$ f_i = \\frac{I_{ref}}{I} $$</p> <p>where:</p> <ul> <li>I: incoming neutron flux</li> <li>Iref: reference incoming neutron flux</li> </ul> <p>Parameters:</p> Name Type Description Default <code>incoming_neutrons</code> <code>list or array</code> <p>Incoming neutron flux readings.</p> required <code>incoming_Ref</code> <code>float</code> <p>Reference incoming neutron flux. Baseline incoming neutron flux.</p> <code>None</code> <p>Returns:</p> Type Description <code>list</code> <p>fi correction factor.</p> References <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def correction_incoming_flux(incoming_neutrons, incoming_Ref=None):\nr\"\"\"Correction factor for incoming neutron flux.\n\n    This function corrects neutron counts for incoming neutron flux using the method described in Anderson et al. (2017). The correction is performed using the following equation:\n\n    $$\n    C_{corrected} = \\frac{C_{raw}}{f_i}\n    $$\n\n    where:\n\n    - Ccorrected: corrected neutron counts\n    - Craw: raw neutron counts\n    - fi: incoming neutron flux correction factor\n\n    $$\n    f_i = \\frac{I_{ref}}{I}\n    $$\n\n    where:\n\n    - I: incoming neutron flux\n    - Iref: reference incoming neutron flux\n\n    Args:\n        incoming_neutrons (list or array): Incoming neutron flux readings.\n        incoming_Ref (float): Reference incoming neutron flux. Baseline incoming neutron flux.\n\n    Returns:\n        (list): fi correction factor.\n\n    References:\n        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086\n\n\n    \"\"\"\n    if incoming_Ref is None and not isinstance(incoming_neutrons, type(None)):\n        incoming_Ref = incoming_neutrons[0]\n        warnings.warn('Reference incoming neutron flux not provided. Using first value of incoming neutron flux.')\n    fi = incoming_neutrons / incoming_Ref\n    fi.fillna(1.0, inplace=True)  # Use a value of 1 for days without data\n\n    return fi\n</code></pre>"},{"location":"reference/#crnpy.crnpy.correction_pressure","title":"<code>correction_pressure(pressure, Pref, L)</code>","text":"<p>Correction factor for atmospheric pressure.</p> <p>This function corrects neutron counts for atmospheric pressure using the method described in Andreasen et al. (2017). The correction is performed using the following equation:</p> <p>$$ C_{corrected} = \\frac{C_{raw}}{fp} $$</p> <p>where:</p> <ul> <li>Ccorrected: corrected neutron counts</li> <li>Craw: raw neutron counts</li> <li>fp: pressure correction factor</li> </ul> <p>$$ fp = e^{\\frac{P_{ref} - P}{L}} $$</p> <p>where:</p> <ul> <li>P: atmospheric pressure</li> <li>Pref: reference atmospheric pressure</li> <li>L: Atmospheric attenuation coefficient.</li> </ul> <p>Parameters:</p> Name Type Description Default <code>atm_pressure</code> <code>list or array</code> <p>Atmospheric pressure readings. Long-term average pressure is recommended.</p> required <code>Pref</code> <code>float</code> <p>Reference atmospheric pressure.</p> required <code>L</code> <code>float</code> <p>Atmospheric attenuation coefficient.</p> required <p>Returns:</p> Type Description <code>list</code> <p>fp pressure correction factor.</p> References <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def correction_pressure(pressure, Pref, L):\nr\"\"\"Correction factor for atmospheric pressure.\n\n    This function corrects neutron counts for atmospheric pressure using the method described in Andreasen et al. (2017).\n    The correction is performed using the following equation:\n\n    $$\n    C_{corrected} = \\frac{C_{raw}}{fp}\n    $$\n\n    where:\n\n    - Ccorrected: corrected neutron counts\n    - Craw: raw neutron counts\n    - fp: pressure correction factor\n\n    $$\n    fp = e^{\\frac{P_{ref} - P}{L}}\n    $$\n\n    where:\n\n    - P: atmospheric pressure\n    - Pref: reference atmospheric pressure\n    - L: Atmospheric attenuation coefficient.\n\n\n    Args:\n        atm_pressure (list or array): Atmospheric pressure readings. Long-term average pressure is recommended.\n        Pref (float): Reference atmospheric pressure.\n        L (float): Atmospheric attenuation coefficient.\n\n    Returns:\n        (list): fp pressure correction factor.\n\n    References:\n        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086\n    \"\"\"\n\n    # Compute pressure correction factor\n    fp = np.exp((Pref - pressure) / L) # Zreda et al. 2017 Eq 5.\n\n    return fp\n</code></pre>"},{"location":"reference/#crnpy.crnpy.correction_road","title":"<code>correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0.12, p0=0.42, p1=0.5, p2=1.06, p3=4, p4=0.16, p6=0.94, p7=1.1, p8=2.7, p9=0.01)</code>","text":"<p>Function to correct for road effects in neutron counts. following the approach described in Schr\u00f6n et al., 2018.</p> <p>Parameters:</p> Name Type Description Default <code>counts</code> <code>array or pd.Series or pd.DataFrame</code> <p>Array of ephithermal neutron counts.</p> required <code>theta_N</code> <code>float</code> <p>Volumetric water content of the soil estimated from the uncorrected neutron counts.</p> required <code>road_width</code> <code>float</code> <p>Width of the road in m.</p> required <code>road_distance</code> <code>float</code> <p>Distance of the road from the sensor in m. Default is 0.0.</p> <code>0.0</code> <code>theta_road</code> <code>float</code> <p>Volumetric water content of the road. Default is 0.12.</p> <code>0.12</code> <code>p0-p9</code> <code>float</code> <p>Parameters of the correction function. Default values are from Schr\u00f6n et al., 2018.</p> required <p>Returns:</p> Type Description <code>array or pd.Series or pd.DataFrame</code> <p>Array of corrected neutron counts for road effects.</p> References <p>Schr\u00f6n,M.,Rosolem,R.,K\u00f6hli,M., Piussi,L.,Schr\u00f6ter,I.,Iwema,J.,etal. (2018).Cosmic-ray neutron rover surveys of field soil moisture and the influence of roads.WaterResources Research,54,6441\u20136459. https://doi. org/10.1029/2017WR021719</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0.12, p0=0.42, p1=0.5, p2=1.06, p3=4, p4=0.16, p6=0.94, p7=1.10, p8=2.70, p9=0.01):\n\"\"\"Function to correct for road effects in neutron counts.\n    following the approach described in Schr\u00f6n et al., 2018.\n\n    Args:\n        counts (array or pd.Series or pd.DataFrame): Array of ephithermal neutron counts.\n        theta_N (float): Volumetric water content of the soil estimated from the uncorrected neutron counts.\n        road_width (float): Width of the road in m.\n        road_distance (float): Distance of the road from the sensor in m. Default is 0.0.\n        theta_road (float): Volumetric water content of the road. Default is 0.12.\n        p0-p9 (float): Parameters of the correction function. Default values are from Schr\u00f6n et al., 2018.\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Array of corrected neutron counts for road effects.\n\n    References:\n        Schr\u00f6n,M.,Rosolem,R.,K\u00f6hli,M., Piussi,L.,Schr\u00f6ter,I.,Iwema,J.,etal. (2018).Cosmic-ray neutron rover surveys\n        of field soil moisture and the influence of roads.WaterResources Research,54,6441\u20136459.\n        https://doi. org/10.1029/2017WR021719\n    \"\"\"\n    F1 = p0 * (1-np.exp(-p1*road_width))\n    F2 = -p2 - p3 * theta_road - ((p4 + theta_road) / (theta_N))\n    F3 = p6 * np.exp(-p7 * (road_width ** -p8) * road_distance ** 4) + (1 - p6) * np.exp(-p9 * road_distance)\n\n    C_roads = 1 + F1 * F2 * F3\n\n    corrected_counts = counts / C_roads\n\n    return corrected_counts\n</code></pre>"},{"location":"reference/#crnpy.crnpy.counts_to_vwc","title":"<code>counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=0.115)</code>","text":"<p>Function to convert corrected and filtered neutron counts into volumetric water content.</p> <p>This method implements soil moisture estimation using the non-linear relationship between neutron count and soil volumetric water content following the approach described in Desilets et al., 2010.</p> <p>$\\theta(N) =\\frac{a_0}{(\\frac{N}{N_0}) - a_1} - a_2 $</p> <p>Parameters:</p> Name Type Description Default <code>counts</code> <code>array or pd.Series or pd.DataFrame</code> <p>Array of corrected and filtered neutron counts.</p> required <code>N0</code> <code>float</code> <p>Device-specific neutron calibration constant.</p> required <code>Wlat</code> <code>float</code> <p>Lattice water content.</p> required <code>Wsoc</code> <code>float</code> <p>Soil organic carbon content.</p> required <code>bulk_density</code> <code>float</code> <p>Soil bulk density.</p> required <code>a0</code> <code>float</code> <p>Parameter given in Zreda et al., 2012. Default is 0.0808.</p> <code>0.0808</code> <code>a1</code> <code>float</code> <p>Parameter given in Zreda et al., 2012. Default is 0.372.</p> <code>0.372</code> <code>a2</code> <code>float</code> <p>Parameter given in Zreda et al., 2012. Default is 0.115.</p> <code>0.115</code> <p>Returns:</p> Type Description <code>array or pd.Series or pd.DataFrame</code> <p>Volumetric water content in m3 m-3.</p> References <p>Desilets, D., M. Zreda, and T.P.A. Ferr\u00e9. 2010. Nature\u2019s neutron probe: Land surface hydrology at an elusive scale with cosmic rays. Water Resour. Res. 46:W11505. doi.org/10.1029/2009WR008726</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def counts_to_vwc(counts, N0, Wlat, Wsoc ,bulk_density, a0=0.0808,a1=0.372,a2=0.115):\nr\"\"\"Function to convert corrected and filtered neutron counts into volumetric water content.\n\n    This method implements soil moisture estimation using the non-linear relationship between neutron count and soil volumetric water content following the approach described in Desilets et al., 2010.\n\n    $\\theta(N) =\\frac{a_0}{(\\frac{N}{N_0}) - a_1} - a_2 $\n\n    Args:\n        counts (array or pd.Series or pd.DataFrame): Array of corrected and filtered neutron counts.\n        N0 (float): Device-specific neutron calibration constant.\n        Wlat (float): Lattice water content.\n        Wsoc (float): Soil organic carbon content.\n        bulk_density (float): Soil bulk density.\n        a0 (float): Parameter given in Zreda et al., 2012. Default is 0.0808.\n        a1 (float): Parameter given in Zreda et al., 2012. Default is 0.372.\n        a2 (float): Parameter given in Zreda et al., 2012. Default is 0.115.\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Volumetric water content in m3 m-3.\n\n    References:\n        Desilets, D., M. Zreda, and T.P.A. Ferr\u00e9. 2010. Nature\u2019s neutron probe:\n        Land surface hydrology at an elusive scale with cosmic rays. Water Resour. Res. 46:W11505.\n        doi.org/10.1029/2009WR008726\n    \"\"\"\n\n    # Convert neutron counts into vwc\n    vwc = (a0 / (counts/N0-a1) - a2 - Wlat - Wsoc) * bulk_density\n    return vwc\n</code></pre>"},{"location":"reference/#crnpy.crnpy.cutoff_rigidity","title":"<code>cutoff_rigidity(lat, lon)</code>","text":"<p>Function to estimate the approximate cutoff rigidity for any point on Earth according to the tabulated data of Smart and Shea, 2019. The returned value can be used to select the appropriate neutron monitor station to estimate the cosmic-ray neutron intensity at the location of interest.</p> <p>Parameters:</p> Name Type Description Default <code>lat</code> <code>float</code> <p>Geographic latitude in decimal degrees. Value in range -90 to 90</p> required <code>lon</code> <code>float</code> <p>Geographic longitude in decimal degrees. Values in range from 0 to 360. Typical negative longitudes in the west hemisphere will fall in the range 180 to 360.</p> required <p>Returns:</p> Type Description <code>float</code> <p>Cutoff rigidity in GV. Error is about +/- 0.3 GV</p> <p>Examples:</p> <p>Estimate the cutoff rigidity for Newark, NJ, US</p> <pre><code>&gt;&gt;&gt; zq = cutoff_rigidity(39.68, -75.75)\n&gt;&gt;&gt; print(zq)\n2.52 GV (Value from NMD is 2.40 GV)\n</code></pre> References <p>Smart, D. &amp; Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program: Theory, Software Description and Example. NASA STI/Recon Technical Report N.</p> <p>Shea, M. A., &amp; Smart, D. F. (2019, July). Re-examination of the First Five Ground-Level Events. In International Cosmic Ray Conference (ICRC2019) (Vol. 36, p. 1149).</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def cutoff_rigidity(lat,lon):\n\"\"\"Function to estimate the approximate cutoff rigidity for any point on Earth according to the\n    tabulated data of Smart and Shea, 2019. The returned value can be used to select the appropriate\n    neutron monitor station to estimate the cosmic-ray neutron intensity at the location of interest.\n\n    Args:\n        lat (float): Geographic latitude in decimal degrees. Value in range -90 to 90\n        lon (float): Geographic longitude in decimal degrees. Values in range from 0 to 360.\n            Typical negative longitudes in the west hemisphere will fall in the range 180 to 360.\n\n    Returns:\n        (float): Cutoff rigidity in GV. Error is about +/- 0.3 GV\n\n    Examples:\n        Estimate the cutoff rigidity for Newark, NJ, US\n\n        &gt;&gt;&gt; zq = cutoff_rigidity(39.68, -75.75)\n        &gt;&gt;&gt; print(zq)\n        2.52 GV (Value from NMD is 2.40 GV)\n\n    References:\n        Smart, D. &amp; Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program:\n        Theory, Software Description and Example. NASA STI/Recon Technical Report N.\n\n        Shea, M. A., &amp; Smart, D. F. (2019, July). Re-examination of the First Five Ground-Level Events.\n        In International Cosmic Ray Conference (ICRC2019) (Vol. 36, p. 1149).\n    \"\"\"\n    xq = lon\n    yq = lat\n\n    if xq &lt; 0:\n        xq = xq*-1 + 180\n    Z = np.array(data.cutoff_rigidity)\n    x = np.linspace(0, 360, Z.shape[1])\n    y = np.linspace(90, -90, Z.shape[0])\n    X, Y = np.meshgrid(x, y)\n    points = np.array( (X.flatten(), Y.flatten()) ).T\n    values = Z.flatten()\n    zq = griddata(points, values, (xq,yq))\n\n    return np.round(zq,2)\n</code></pre>"},{"location":"reference/#crnpy.crnpy.euclidean_distance","title":"<code>euclidean_distance(px, py, x, y)</code>","text":"<p>Function that computes the Euclidean distance between one point in space and one or more points.</p> <p>Parameters:</p> Name Type Description Default <code>px</code> <code>float</code> <p>x projected coordinate of the point.</p> required <code>py</code> <code>float</code> <p>y projected coordinate of the point.</p> required <code>x</code> <code>list, ndarray, pandas.series</code> <p>vector of x projected coordinates.</p> required <code>y</code> <code>list, ndarray, pandas.series</code> <p>vector of y projected coordinates.</p> required <p>Returns:</p> Type Description <code>ndarray</code> <p>Numpy array of distances from the point (px,py) to all the points in x and y vectors.</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def euclidean_distance(px, py, x, y):\n\"\"\"Function that computes the Euclidean distance between one point\n    in space and one or more points.\n\n    Args:\n        px (float): x projected coordinate of the point.\n        py (float): y projected coordinate of the point.\n        x (list, ndarray, pandas.series): vector of x projected coordinates.\n        y (list, ndarray, pandas.series): vector of y projected coordinates.\n\n    Returns:\n        (ndarray): Numpy array of distances from the point (px,py) to all the points in x and y vectors.\n    \"\"\"\n    d = np.sqrt((px - x) ** 2 + (py - y) ** 2)\n    return d\n</code></pre>"},{"location":"reference/#crnpy.crnpy.exp_filter","title":"<code>exp_filter(sm, T=1)</code>","text":"<p>Exponential filter to estimate soil moisture in the rootzone from surface observtions.</p> <p>Parameters:</p> Name Type Description Default <code>sm</code> <code>list or array</code> <p>Soil moisture in mm of water for the top layer of the soil profile.</p> required <code>T</code> <code>float</code> <p>Characteristic time length in the same units as the measurement interval.</p> <code>1</code> <p>Returns:</p> Name Type Description <code>sm_subsurface</code> <code>list or array</code> <p>Subsurface soil moisture in the same units as the input.</p> References <p>Albergel, C., R\u00fcdiger, C., Pellarin, T., Calvet, J.C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B. and Martin, E., 2008. From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations. Hydrology and Earth System Sciences, 12(6), pp.1323-1337.</p> <p>Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020. Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.</p> <p>Rossini, P. and Patrignani, A., 2021. Predicting rootzone soil moisture from surface observations in cropland using an exponential filter. Soil Science Society of America Journal.</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def exp_filter(sm,T=1):\n\"\"\"Exponential filter to estimate soil moisture in the rootzone from surface observtions.\n\n    Args:\n        sm (list or array): Soil moisture in mm of water for the top layer of the soil profile.\n        T (float): Characteristic time length in the same units as the measurement interval.\n\n    Returns:\n        sm_subsurface (list or array): Subsurface soil moisture in the same units as the input.\n\n    References:\n        Albergel, C., R\u00fcdiger, C., Pellarin, T., Calvet, J.C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B. and Martin, E., 2008.\n        From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model\n        simulations. Hydrology and Earth System Sciences, 12(6), pp.1323-1337.\n\n        Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.\n        Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.\n\n        Rossini, P. and Patrignani, A., 2021. Predicting rootzone soil moisture from surface observations in cropland using an exponential filter.\n        Soil Science Society of America Journal.\n    \"\"\"\n\n\n    # Parameters\n    t_delta = 1\n    sm_min = np.min(sm)\n    sm_max = np.max(sm)\n    ms = (sm - sm_min) / (sm_max - sm_min)\n\n    # Pre-allocate soil water index array and recursive constant K\n    SWI = np.ones_like(ms)*np.nan\n    K = np.ones_like(ms)*np.nan\n\n    # Initial conditions\n    SWI[0] = ms[0]\n    K[0] = 1\n\n    # Values from 2 to N\n    for n in range(1,len(SWI)):\n        if ~np.isnan(ms[n]) &amp; ~np.isnan(ms[n-1]):\n            K[n] = K[n-1] / (K[n-1] + np.exp(-t_delta/T))\n            SWI[n] = SWI[n-1] + K[n]*(ms[n] - SWI[n-1])\n        else:\n            continue\n\n    # Rootzone storage\n    sm_subsurface = SWI * (sm_max - sm_min) + sm_min\n\n    return sm_subsurface\n</code></pre>"},{"location":"reference/#crnpy.crnpy.fill_missing_timestamps","title":"<code>fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True, verbose=False)</code>","text":"<p>Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>pandas.DataFrame</code> <p>Pandas DataFrame.</p> required <code>timestamp_col</code> <code>str</code> <p>Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.</p> <code>'timestamp'</code> <code>freq</code> <code>str</code> <p>Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.</p> <code>'H'</code> <code>round_timestamp</code> <code>bool</code> <p>Whether to round timestamps to the nearest frequency. Default is True.</p> <code>True</code> <code>verbose</code> <code>bool</code> <p>Prints the missing timestamps added to the DatFrame.</p> <code>False</code> <p>Returns:</p> Type Description <code>pandas.DataFrame</code> <p>DataFrame with filled missing timestamps.</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True, verbose=False):\n\"\"\"Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.\n\n     Args:\n         df (pandas.DataFrame): Pandas DataFrame.\n         timestamp_col (str, optional): Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.\n         freq (str, optional): Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.\n         round_timestamp (bool, optional): Whether to round timestamps to the nearest frequency. Default is True.\n         verbose (bool, optional): Prints the missing timestamps added to the DatFrame.\n\n     Returns:\n         (pandas.DataFrame): DataFrame with filled missing timestamps.\n\n     \"\"\"\n\n    # Check format of timestamp column\n    if df[timestamp_col].dtype != 'datetime64[ns]':\n        raise TypeError('timestamp_col must be datetime64. Use `pd.to_datetime()` to fix this issue.')\n\n    # Round timestamps to nearest frequency. This steps must preced the filling of rows.\n    if round_timestamp:\n        df[timestamp_col] = df[timestamp_col].dt.round(freq)\n\n    # Fill in rows with missing timestamps\n    start_date = df[timestamp_col].iloc[0]\n    end_date = df[timestamp_col].iloc[-1]\n    date_range = pd.date_range(start_date, end_date, freq=freq)\n    counter = 0\n    for date in date_range:\n        if date not in df[timestamp_col].values:\n            if verbose:\n                print('Adding missing date:',date)\n            new_line = pd.DataFrame({timestamp_col:date}, index=[-1]) # By default fills columns with np.nan\n            df = pd.concat([df,new_line])\n            counter += 1\n\n    df.sort_values(by=timestamp_col, inplace=True)\n    df.reset_index(drop=True, inplace=True)\n\n    # Notify user about the number of rows that have been removed\n    print(f\"Added a total of {counter} missing timestamps.\")\n\n    return df\n</code></pre>"},{"location":"reference/#crnpy.crnpy.find_neutron_monitor","title":"<code>find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False)</code>","text":"<p>Search for potential reference neutron monitoring stations based on cutoff rigidity.</p> <p>Parameters:</p> Name Type Description Default <code>Rc</code> <code>float</code> <p>Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.</p> required <code>start_date</code> <code>datetime</code> <p>Start date for the period of interest.</p> <code>None</code> <code>end_date</code> <code>datetime</code> <p>End date for the period of interest.</p> <code>None</code> <p>Returns:</p> Type Description <code>list</code> <p>List of top five stations with closes cutoff rigidity. User needs to select station according to site altitude.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; from crnpy import crnpy\n&gt;&gt;&gt; Rc = 2.40 # 2.40 Newark, NJ, US\n&gt;&gt;&gt; crnpy.find_neutron_monitor(Rc)\nSelect a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\n</code></pre> <p>Your cutoff rigidity is 2.4 GV         STID                          NAME     R  Altitude_m 40   NEWK                        Newark  2.40          50 33   MOSC                        Moscow  2.43         200 27   KIEL                          Kiel  2.36          54 28  KIEL2                        KielRT  2.36          54 31   MCRL  Mobile Cosmic Ray Laboratory  2.46         200 32   MGDN                       Magadan  2.10         220 42   NVBK                   Novosibirsk  2.91         163 26   KGSN                      Kingston  1.88          65 9    CLMX                        Climax  3.00        3400 57   YKTK                       Yakutsk  1.65         105</p> References <p>https://www.nmdb.eu/nest/help.php#helpstations</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):\n\"\"\"Search for potential reference neutron monitoring stations based on cutoff rigidity.\n\n    Args:\n        Rc (float): Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.\n        start_date (datetime): Start date for the period of interest.\n        end_date (datetime): End date for the period of interest.\n\n    Returns:\n        (list): List of top five stations with closes cutoff rigidity.\n            User needs to select station according to site altitude.\n\n    Examples:\n        &gt;&gt;&gt; from crnpy import crnpy\n        &gt;&gt;&gt; Rc = 2.40 # 2.40 Newark, NJ, US\n        &gt;&gt;&gt; crnpy.find_neutron_monitor(Rc)\n        Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\n\n        Your cutoff rigidity is 2.4 GV\n                STID                          NAME     R  Altitude_m\n        40   NEWK                        Newark  2.40          50\n        33   MOSC                        Moscow  2.43         200\n        27   KIEL                          Kiel  2.36          54\n        28  KIEL2                        KielRT  2.36          54\n        31   MCRL  Mobile Cosmic Ray Laboratory  2.46         200\n        32   MGDN                       Magadan  2.10         220\n        42   NVBK                   Novosibirsk  2.91         163\n        26   KGSN                      Kingston  1.88          65\n        9    CLMX                        Climax  3.00        3400\n        57   YKTK                       Yakutsk  1.65         105\n\n    References:\n        https://www.nmdb.eu/nest/help.php#helpstations\n    \"\"\"\n\n    # Load file with list of neutron monitoring stations\n    stations = pd.DataFrame(data.neutron_detectors, columns=[\"STID\",\"NAME\",\"R\",\"Altitude_m\"])\n\n    # Sort stations by closest cutoff rigidity\n    idx_R = (stations['R'] - Rc).abs().argsort()\n\n    if start_date is not None and end_date is not None:\n        stations[\"Period available\"] = False\n        for i in range(10):\n            station = stations.iloc[idx_R[i]][\"STID\"]\n            try:\n                if get_incoming_neutron_flux(start_date, end_date, station, verbose=verbose) is not None:\n                    stations.iloc[idx_R[i],-1] = True\n            except:\n                pass\n        if sum(stations[\"Period available\"] == True) == 0:\n            print(\"No stations available for the selected period!\")\n        else:\n            stations = stations[stations[\"Period available\"] == True]\n            idx_R = (stations['R'] - Rc).abs().argsort()\n            result = stations.iloc[idx_R.iloc[:10]]\n    else:\n        result = stations.reindex(idx_R).head(10).rename_axis(None)\n\n    # Print results\n    print('')\n    print(\"\"\"Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\"\"\")\n    print('')\n    print(f\"Your cutoff rigidity is {Rc} GV\")\n    print(result)\n    return result\n</code></pre>"},{"location":"reference/#crnpy.crnpy.get_incoming_neutron_flux","title":"<code>get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expand_window=0, verbose=False)</code>","text":"<p>Function to retrieve neutron flux from the Neutron Monitor Database.</p> <p>Parameters:</p> Name Type Description Default <code>start_date</code> <code>datetime</code> <p>Start date of the time series.</p> required <code>end_date</code> <code>datetime</code> <p>End date of the time series.</p> required <code>station</code> <code>str</code> <p>Neutron Monitor station to retrieve data from.</p> required <code>utc_offset</code> <code>int</code> <p>UTC offset in hours. Default is 0.</p> <code>0</code> <code>expand_window</code> <code>int</code> <p>Number of hours to expand the time window to retrieve extra data. Default is 0.</p> <code>0</code> <code>verbose</code> <code>bool</code> <p>Print information about the request. Default is False.</p> <code>False</code> <p>Returns:</p> Type Description <code>pandas.DataFrame</code> <p>Neutron flux in counts per hour and timestamps.</p> References <p>Documentation available:https://www.nmdb.eu/nest/help.php#howto</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expand_window=0, verbose=False):\n\"\"\"Function to retrieve neutron flux from the Neutron Monitor Database.\n\n    Args:\n        start_date (datetime): Start date of the time series.\n        end_date (datetime): End date of the time series.\n        station (str): Neutron Monitor station to retrieve data from.\n        utc_offset (int): UTC offset in hours. Default is 0.\n        expand_window (int): Number of hours to expand the time window to retrieve extra data. Default is 0.\n        verbose (bool): Print information about the request. Default is False.\n\n    Returns:\n        (pandas.DataFrame): Neutron flux in counts per hour and timestamps.\n\n    References:\n        Documentation available:https://www.nmdb.eu/nest/help.php#howto\n    \"\"\"\n\n    # Example: get_incoming_flux(station='IRKT',start_date='2020-04-10 11:00:00',end_date='2020-06-18 17:00:00')\n    # Template url = 'http://nest.nmdb.eu/draw_graph.php?formchk=1&amp;stations[]=KERG&amp;output=ascii&amp;tabchoice=revori&amp;dtype=corr_for_efficiency&amp;date_choice=bydate&amp;start_year=2009&amp;start_month=09&amp;start_day=01&amp;start_hour=00&amp;start_min=00&amp;end_year=2009&amp;end_month=09&amp;end_day=05&amp;end_hour=23&amp;end_min=59&amp;yunits=0'\n\n\n    # Expand the time window by 1 hour to ensure an extra observation is included in the request.\n    start_date -= pd.Timedelta(hours=expand_window)\n    end_date += pd.Timedelta(hours=expand_window)\n\n    # Convert local time to UTC\n    start_date = start_date - pd.Timedelta(hours=utc_offset)\n    end_date = end_date - pd.Timedelta(hours=utc_offset)\n    date_format = '%Y-%m-%d %H:%M:%S'\n    root = 'http://www.nmdb.eu/nest/draw_graph.php?'\n    url_par = [ 'formchk=1',\n                'stations[]=' + station,\n                'output=ascii',\n                'tabchoice=revori',\n                'dtype=corr_for_efficiency',\n                'tresolution=' + str(60),\n                'date_choice=bydate',\n                'start_year=' + str(start_date.year),\n                'start_month=' + str(start_date.month),\n                'start_day=' + str(start_date.day),\n                'start_hour=' + str(start_date.hour),\n                'start_min=' + str(start_date.minute),\n                'end_year=' + str(end_date.year),\n                'end_month=' + str(end_date.month),\n                'end_day=' + str(end_date.day),\n                'end_hour=' + str(end_date.hour),\n                'end_min=' + str(end_date.minute),\n                'yunits=0']\n\n    url = root + '&amp;'.join(url_par)\n\n    if verbose:\n        print(f\"Retrieving data from {url}\")\n\n    r = requests.get(url).content.decode('utf-8')\n\n    # Subtract 1 hour to restore the last date included in the request.\n    end_date -= pd.Timedelta('1H')\n    start = r.find(\"RCORR_E\\n\") + 8\n    end = r.find('\\n&lt;/code&gt;&lt;/pre&gt;&lt;br&gt;Total') - 1\n    s = r[start:end]\n    s2 = ''.join([row.replace(';',',') for row in s])\n    try:\n        df_flux = pd.read_csv(io.StringIO(s2), names=['timestamp','counts'])\n    except:\n        if verbose:\n            print(f\"Error retrieving data from {url}\")\n        return None\n\n    # Check if all values from selected detector are NaN. If yes, warn the user\n    if df_flux['counts'].isna().all():\n        warnings.warn('Data for selected neutron detectors appears to be unavailable for the selected period')\n\n    # Convert timestamp to datetime and apply UTC offset\n    df_flux['timestamp'] = pd.to_datetime(df_flux['timestamp'])\n    df_flux['timestamp'] = df_flux['timestamp'] + pd.Timedelta(hours=utc_offset)\n\n    # Print acknowledgement to inform users about restrictions and to acknowledge the NMDB database\n    acknowledgement = \"\"\"Data retrieved via NMDB are the property of the individual data providers. These data are free for non commercial\nuse to within the restriction imposed by the providers. If you use such data for your research or applications, please acknowledge\nthe origin by a sentence like 'We acknowledge the NMDB database (www.nmdb.eu) founded under the European Union's FP7 programme \n(contract no. 213007), and the PIs of individual neutron monitors at: IGY Jungfraujoch \n(Physikalisches Institut, University of Bern, Switzerland)\"\"\"\n\n    return df_flux\n</code></pre>"},{"location":"reference/#crnpy.crnpy.idw","title":"<code>idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1)</code>","text":"<p>Function to interpolate data using inverse distance weight.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>list or array</code> <p>UTM x coordinates in meters.</p> required <code>y</code> <code>list or array</code> <p>UTM y coordinates in meters.</p> required <code>z</code> <code>list or array</code> <p>Values to be interpolated.</p> required <code>X_pred</code> <code>list or array</code> <p>UTM x coordinates where z values need to be predicted.</p> required <code>Y_pred</code> <code>list or array</code> <p>UTM y coordinates where z values need to be predicted.</p> required <code>neighborhood</code> <code>float</code> <p>Only points within this radius in meters are considered for the interpolation.</p> <code>1000</code> <code>p</code> <code>int</code> <p>Exponent of the inverse distance weight formula. Typically, p=1 or p=2.</p> <code>1</code> <p>Returns:</p> Type Description <code>array</code> <p>Interpolated values.</p> References <p>https://en.wikipedia.org/wiki/Inverse_distance_weighting</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1):\n\"\"\"Function to interpolate data using inverse distance weight.\n\n    Args:\n        x (list or array): UTM x coordinates in meters.\n        y (list or array): UTM y coordinates in meters.\n        z (list or array): Values to be interpolated.\n        X_pred (list or array): UTM x coordinates where z values need to be predicted.\n        Y_pred (list or array): UTM y coordinates where z values need to be predicted.\n        neighborhood (float): Only points within this radius in meters are considered for the interpolation.\n        p (int): Exponent of the inverse distance weight formula. Typically, p=1 or p=2.\n\n    Returns:\n        (array): Interpolated values.\n\n    References:\n        [https://en.wikipedia.org/wiki/Inverse_distance_weighting](https://en.wikipedia.org/wiki/Inverse_distance_weighting)\n\n\n    \"\"\"\n\n    # Flatten arrays to handle 1D and 2D arrays with the same code\n    s = X_pred.shape  # Save shape\n    X_pred = X_pred.flatten()\n    Y_pred = Y_pred.flatten()\n\n    # Pre-allocate output array\n    Z_pred = np.full_like(X_pred, np.nan)\n\n    for n in range(X_pred.size):\n        # Distance between current and observed points\n        d = euclidean_distance(X_pred[n], Y_pred[n], x, y)\n\n        # Select points within neighborhood only for interpolation\n        idx_neighbors = d &lt; neighborhood\n\n        # Compute interpolated value at point of interest\n        Z_pred[n] = np.sum(z[idx_neighbors] / d[idx_neighbors] ** p) / np.sum(1 / d[idx_neighbors] ** p)\n\n    return np.reshape(Z_pred, s)\n</code></pre>"},{"location":"reference/#crnpy.crnpy.interpolate_2d","title":"<code>interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000)</code>","text":"<p>Function for interpolating irregular spatial data into a regular grid.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>list or array</code> <p>UTM x coordinates in meters.</p> required <code>y</code> <code>list or array</code> <p>UTM y coordinates in meters.</p> required <code>z</code> <code>list or array</code> <p>Values to be interpolated.</p> required <code>dx</code> <code>float</code> <p>Pixel width in meters.</p> <code>100</code> <code>dy</code> <code>float</code> <p>Pixel height in meters.</p> <code>100</code> <code>method</code> <code>str</code> <p>Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.</p> <code>'cubic'</code> <code>neighborhood</code> <code>float</code> <p>Only points within this radius in meters are considered for the interpolation.</p> <code>1000</code> <p>Returns:</p> Name Type Description <code>x_pred</code> <code>array</code> <p>2D array with x coordinates.</p> <code>y_pred</code> <code>array</code> <p>2D array with y coordinates.</p> <code>z_pred</code> <code>array</code> <p>2D array with interpolated values.</p> References <p>https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000):\n\"\"\"Function for interpolating irregular spatial data into a regular grid.\n\n    Args:\n        x (list or array): UTM x coordinates in meters.\n        y (list or array): UTM y coordinates in meters.\n        z (list or array): Values to be interpolated.\n        dx (float): Pixel width in meters.\n        dy (float): Pixel height in meters.\n        method (str): Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.\n        neighborhood (float): Only points within this radius in meters are considered for the interpolation.\n\n    Returns:\n        x_pred (array): 2D array with x coordinates.\n        y_pred (array): 2D array with y coordinates.\n        z_pred (array): 2D array with interpolated values.\n\n    References:\n        [https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html](https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html)\n    \"\"\"\n\n    # Drop NaN values in x y and z\n    idx_nan = np.isnan(x) | np.isnan(y) | np.isnan(z)\n    x = x[~idx_nan]\n    y = y[~idx_nan]\n    z = z[~idx_nan]\n\n    if idx_nan.any():\n        print(f\"WARNING: {np.isnan(x).sum()}, {np.isnan(y).sum()}, and {np.isnan(z).sum()} NaN values were dropped from x, y, and z.\")\n\n    # Create 2D grid for interpolation\n    Nx = round((np.max(x) - np.min(x)) / dx) + 1\n    Ny = round((np.max(y) - np.min(y)) / dy) + 1\n    X_vec = np.linspace(np.min(x), np.max(x), Nx)\n    Y_vec = np.linspace(np.min(y), np.max(y), Ny)\n    X_pred, Y_pred = np.meshgrid(X_vec, Y_vec)\n\n    if method in ['linear', 'nearest', 'cubic']:\n        points = list(zip(x, y))\n        Z_pred = griddata(points, z, (X_pred, Y_pred), method=method)\n\n    elif method == 'idw':\n        Z_pred = idw(x, y, z, X_pred, Y_pred, neighborhood)\n\n    else:\n        raise f\"Method {method} does not exist. Provide either 'cubic', 'linear', 'nearest', or 'idw'.\"\n\n    return X_pred, Y_pred, Z_pred\n</code></pre>"},{"location":"reference/#crnpy.crnpy.interpolate_incoming_flux","title":"<code>interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps)</code>","text":"<p>Function to interpolate incoming neutron flux to match the timestamps of the observations.</p> <p>Parameters:</p> Name Type Description Default <code>nmdb_timestamps</code> <code>pd.Series</code> <p>Series of timestamps in datetime format from the NMDB.</p> required <code>nmdb_counts</code> <code>pd.Series</code> <p>Series of neutron counts from the NMDB</p> required <code>crnp_timestamps</code> <code>pd.Series</code> <p>Series of timestamps in datetime format from the station or device.</p> required <p>Returns:</p> Type Description <code>pd.Series</code> <p>Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps):\n\"\"\"Function to interpolate incoming neutron flux to match the timestamps of the observations.\n\n    Args:\n        nmdb_timestamps (pd.Series): Series of timestamps in datetime format from the NMDB.\n        nmdb_counts (pd.Series): Series of neutron counts from the NMDB\n        crnp_timestamps (pd.Series): Series of timestamps in datetime format from the station or device.\n\n    Returns:\n        (pd.Series): Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps\n    \"\"\"\n    incoming_flux = np.array([])\n    for k,timestamp in enumerate(crnp_timestamps):\n        if timestamp in nmdb_timestamps.values:\n            idx = timestamp == nmdb_timestamps\n            incoming_flux = np.append(incoming_flux, nmdb_counts.loc[idx])\n        else:\n            incoming_flux = np.append(incoming_flux, np.nan)\n\n    # Interpolate nan values\n    incoming_flux = pd.Series(incoming_flux).interpolate(method='nearest', limit_direction='both')\n\n    # Return only the values for the selected timestamps\n    return incoming_flux\n</code></pre>"},{"location":"reference/#crnpy.crnpy.is_outlier","title":"<code>is_outlier(x, method, window=11, min_val=None, max_val=None)</code>","text":"<p>Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>pandas.DataFrame</code> <p>Variable containing only the columns with neutron counts.</p> required <code>method</code> <code>str</code> <p>Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad</p> required <code>window</code> <code>int</code> <p>Window size for the moving central tendency. Default is 11.</p> <code>11</code> <code>min_val</code> <code>int or float</code> <p>Minimum value for a reading to be considered valid. Default is None.</p> <code>None</code> <code>max_val(int</code> <code>or float</code> <p>Maximum value for a reading to be considered valid. Default is None.</p> required <p>Returns:</p> Type Description <code>pandas.DataFrame</code> <p>Boolean indicating outliers.</p> References <p>Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def is_outlier(x, method, window=11, min_val=None, max_val=None):\n\"\"\"Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.\n\n    Args:\n        x (pandas.DataFrame): Variable containing only the columns with neutron counts.\n        method (str): Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad\n        window (int, optional): Window size for the moving central tendency. Default is 11.\n        min_val (int or float): Minimum value for a reading to be considered valid. Default is None.\n        max_val(int or float): Maximum value for a reading to be considered valid. Default is None.\n\n    Returns:\n        (pandas.DataFrame): Boolean indicating outliers.\n\n    References:\n        Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.\n    \"\"\"\n\n    if not isinstance(x, pd.Series):\n        raise TypeError('x must of type pandas.Series')\n\n    # Separate this method to allow usage together with other methods below\n    if isinstance(min_val, numbers.Number) and isinstance(max_val, numbers.Number):\n        idx_range_outliers = (x &lt; min_val) | (x &gt; max_val)\n    else:\n        idx_range_outliers = np.full_like(x, False)\n\n    # Apply other methods in addition to a range check\n    if method == 'iqr':\n        q1 = x.quantile(0.25)\n        q3 = x.quantile(0.75)\n        iqr = q3 - q1\n        high_fence = q3 + (1.5 * iqr)\n        low_fence = q1 - (1.5 * iqr)\n        idx_outliers = (x&lt;low_fence ) | (x&gt;high_fence )\n\n    elif method == 'moviqr':\n        q1 = x.rolling(window, center=True).quantile(0.25)\n        q3 = x.rolling(window, center=True).quantile(0.75)\n        iqr = q3 - q1\n        ub = q3 + (1.5 * iqr) # Upper boundary\n        lb = q1 - (1.5 * iqr) # Lower boundary\n        idx_outliers = (x &lt; lb) | (x &gt; ub)\n\n    elif method == 'zscore':\n        zscore = (x - x.mean())/x.std()\n        idx_outliers = (zscore &lt; -3) | (zscore &gt; 3)\n\n    elif method == 'movzscore':\n        movmean = x.rolling(window=window, center=True).mean()\n        movstd = x.rolling(window=window, center=True).std()\n        movzscore = (x - movmean)/movstd\n        idx_outliers = (movzscore &lt; -3) | (movzscore &gt; 3)\n\n    elif method == 'modified_zscore':\n        # Compute median absolute difference\n        movmedian = x.rolling(window, center=True).median()\n        abs_diff = np.abs(x - movmedian)\n        mad = abs_diff.rolling(window, center=True).median()\n\n        # Compute modified z-score\n        modified_z_score = 0.6745 * abs_diff / mad\n        idx_outliers = (modified_z_score &lt; -3.5) | (modified_z_score &gt; 3.5)\n\n    elif method == 'scaled_mad':\n        # Returns true for elements more than three scaled MAD from the median. \n        c = -1 / (np.sqrt(2)*erfcinv(3/2))\n        median = np.nanmedian(x)\n        mad = c*np.nanmedian(np.abs(x - median))\n        idx_outliers = x &gt; (median + 3*mad)\n\n    else:\n        raise TypeError('Outlier detection method not found.')\n\n    return idx_outliers | idx_range_outliers\n</code></pre>"},{"location":"reference/#crnpy.crnpy.latlon_to_utm","title":"<code>latlon_to_utm(lat, lon, utm_zone_number, missing_values=None)</code>","text":"<p>Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.</p> <p>Function only applies to non-polar coordinates. If further functionality is required, consider using the utm module. See references for more information.</p> <p> UTM zones on an equirectangular world map with irregular zones in red and New York City's zone highlighted. See UTM zones for a full description.</p> <p>Parameters:</p> Name Type Description Default <code>lat</code> <code>float, array</code> <p>Latitude in decimal degrees.</p> required <code>lon</code> <code>float, array</code> <p>Longitude in decimal degrees.</p> required <code>utm_zone_number</code> <code>int</code> <p>Universal Transverse Mercator (UTM) zone.</p> required <p>Returns:</p> Type Description <code>float, float</code> <p>Tuple of easting and northing coordinates in meters. First element is easting, second is northing.</p> References <p>Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87) https://github.com/Turbo87/utm</p> <p>https://www.maptools.com/tutorials/grid_zone_details#</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def latlon_to_utm(lat, lon, utm_zone_number, missing_values=None):\n\"\"\"Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.\n\n    Function only applies to non-polar coordinates.\n    If further functionality is required, consider using the utm module. See references for more information.\n\n    ![UTM zones](https://upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Universal_Transverse_Mercator_zones.svg/1920px-Universal_Transverse_Mercator_zones.svg.png)\n    UTM zones on an equirectangular world map with irregular zones in red and New York City's zone highlighted. See [UTM zones](https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#UTM_zones) for a full description.\n\n\n    Args:\n        lat (float, array): Latitude in decimal degrees.\n        lon (float, array): Longitude in decimal degrees.\n        utm_zone_number (int): Universal Transverse Mercator (UTM) zone.\n\n    Returns:\n        (float, float): Tuple of easting and northing coordinates in meters. First element is easting, second is northing.\n\n    References:\n         Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87)\n         [https://github.com/Turbo87/utm](https://github.com/Turbo87/utm)\n\n         [https://www.maptools.com/tutorials/grid_zone_details#](https://www.maptools.com/tutorials/grid_zone_details#)\n    \"\"\"\n\n\n    # Define constants\n    R = 6_378_137  # Earth's radius at the Equator in meters\n\n    # Convert input data to Numpy arrays\n    if (type(lat) is not np.ndarray) or (type(lon) is not np.ndarray):\n        try:\n            lat = np.array(lat)\n            lon = np.array(lon)\n        except:\n            raise \"Input values cannot be converted to Numpy arrays.\"\n\n    # Check latitude range\n    if np.any(lat &lt; -80) | np.any(lat &gt; 84):\n        raise \"One or more latitude values exceed the range -80 to 84\"\n\n    # Check longitude range\n    if np.any(lon &lt; -180) | np.any(lon &gt; 180):\n        raise \"One or more longitude values exceed the range -180 to 180\"\n\n    # Constants\n    K0 = 0.9996\n    E = 0.00669438\n    E_P2 = E / (1 - E)\n\n    M1 = (1 - E / 4 - 3 * E ** 2 / 64 - 5 * E ** 3 / 256)\n    M2 = (3 * E / 8 + 3 * E ** 2 / 32 + 45 * E ** 3 / 1024)\n    M3 = (15 * E ** 2 / 256 + 45 * E ** 3 / 1024)\n    M4 = (35 * E ** 3 / 3072)\n\n    # Trigonometric operations\n    lat_rad = np.radians(lat)\n    lon_rad = np.radians(lon)\n\n    lat_sin = np.sin(lat_rad)\n    lat_cos = np.cos(lat_rad)\n    lat_tan = lat_sin / lat_cos\n    lat_tan2 = lat_tan * lat_tan\n    lat_tan4 = lat_tan2 * lat_tan2\n\n    # Find central meridian.\n    central_lon = (utm_zone_number * 6 - 180) - 3  # Zones are every 6 degrees.\n    central_lon_rad = np.radians(central_lon)\n\n    n = R / np.sqrt(1 - E * lat_sin ** 2)\n    c = E_P2 * lat_cos ** 2\n\n    with np.errstate(divide='ignore', invalid='ignore'):\n        a = lat_cos * (np.remainder(((lon_rad - central_lon_rad) + np.pi), (2 * np.pi)) - np.pi)\n    m = R * (M1 * lat_rad - M2 * np.sin(2 * lat_rad) + M3 * np.sin(4 * lat_rad) - M4 * np.sin(6 * lat_rad))\n\n    easting = K0 * n * (a + a ** 3 / 6 * (1 - lat_tan2 + c) + a ** 5 / 120 * (\n                5 - 18 * lat_tan2 + lat_tan4 + 72 * c - 58 * E_P2)) + 500_000\n    northing = K0 * (m + n * lat_tan * (\n                a ** 2 / 2 + a ** 4 / 24 * (5 - lat_tan2 + 9 * c + 4 * c ** 2) + a ** 6 / 720 * (\n                    61 - 58 * lat_tan2 + lat_tan4 + 600 * c - 330 * E_P2)))\n\n    if np.any(lat &lt; 0):\n        northing += 10_000_000\n\n    return easting, northing\n</code></pre>"},{"location":"reference/#crnpy.crnpy.lattice_water","title":"<code>lattice_water(clay_content, total_carbon=None)</code>","text":"<p>Estimate the amount of water in the lattice of clay minerals.</p> $\\omega_{lat} = 0.097 * clay(\\%)$ $\\omega_{lat} = -0.028 + 0.077 * clay(\\%) + 0.459 * carbon(\\%)$ Linear regression [lattice water (%) as a function of clay (%)] done with data from Kansas Sate University - Soil Water Processes Lab. Multiple linear regression [lattice water (%) as a function of clay (%) and soil carbon (%)] done with data from Soil Water Processes Lab. <p>Parameters:</p> Name Type Description Default <code>clay_content</code> <code>float</code> <p>Clay content in the soil in percent.</p> required <code>total_carbon</code> <code>float</code> <p>Total carbon content in the soil in percent. If None, the amount of water is estimated based on clay content only.</p> <code>None</code> <p>Returns:</p> Type Description <code>float</code> <p>Amount of water in the lattice of clay minerals in percent</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def lattice_water(clay_content, total_carbon=None):\nr\"\"\"Estimate the amount of water in the lattice of clay minerals.\n\n    ![img1](img/lattice_water_simple.png) | ![img2](img/lattice_water_multiple.png)\n    :-------------------------:|:-------------------------:\n    $\\omega_{lat} = 0.097 * clay(\\%)$ | $\\omega_{lat} = -0.028 + 0.077 * clay(\\%) + 0.459 * carbon(\\%)$\n    Linear regression [lattice water (%) as a function of clay (%)] done with data from Kansas Sate University - Soil Water Processes Lab. |  Multiple linear regression [lattice water (%) as a function of clay (%) and soil carbon (%)] done with data from Soil Water Processes Lab.\n\n    Args:\n        clay_content (float): Clay content in the soil in percent.\n        total_carbon (float, optional): Total carbon content in the soil in percent.\n            If None, the amount of water is estimated based on clay content only.\n\n    Returns:\n        (float): Amount of water in the lattice of clay minerals in percent\n    \"\"\"\n    if total_carbon is None:\n        lattice_water = 0.097 * clay_content\n    else:\n        lattice_water = -0.028 + 0.077 * clay_content + 0.459 * total_carbon\n    return lattice_water\n</code></pre>"},{"location":"reference/#crnpy.crnpy.nrad_weight","title":"<code>nrad_weight(h, theta, distances, depth, rhob=1.4)</code>","text":"<p>Function to compute distance weights corresponding to each soil sample.</p> <p>Parameters:</p> Name Type Description Default <code>h</code> <code>float</code> <p>Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.</p> required <code>theta</code> <code>array or pd.Series or pd.DataFrame</code> <p>Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)</p> required <code>distances</code> <code>array or pd.Series or pd.DataFrame</code> <p>Distances from the location of each sample to the origin (0.5 - 600 m)</p> required <code>depth</code> <code>array or pd.Series or pd.DataFrame</code> <p>Depths for each sample (m)</p> required <code>rhob</code> <code>float</code> <p>Bulk density in g/cm^3</p> <code>1.4</code> <p>Returns:</p> Type Description <code>array or pd.Series or pd.DataFrame</code> <p>Distance weights for each sample.</p> References <p>K\u00f6hli, M., Schr\u00f6n, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015). Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray neutrons. Water Resour. Res. 51, 5772\u20135790. doi:10.1002/2015WR017169</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def nrad_weight(h,theta,distances,depth,rhob=1.4):\n\"\"\"Function to compute distance weights corresponding to each soil sample.\n\n    Args:\n        h (float): Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.\n        theta (array or pd.Series or pd.DataFrame): Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)\n        distances (array or pd.Series or pd.DataFrame): Distances from the location of each sample to the origin (0.5 - 600 m)\n        depth (array or pd.Series or pd.DataFrame): Depths for each sample (m)\n        rhob (float): Bulk density in g/cm^3\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Distance weights for each sample.\n\n    References:\n        K\u00f6hli, M., Schr\u00f6n, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015).\n        Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray\n        neutrons. Water Resour. Res. 51, 5772\u20135790. doi:10.1002/2015WR017169\n    \"\"\"\n\n    # Table A1. Parameters for Fi and D86\n    p10 = 8735;       p11 = 17.1758; p12 = 11720;      p13 = 0.00978;   p14 = 7045;      p15 = 0.003632;\n    p20 = 2.7925e-2;  p21 = 5.0399;  p22 = 2.8544e-2;  p23 = 0.002455;  p24 = 6.851e-5;  p25 = 9.2926;\n    p30 = 247970;     p31 = 17.63;   p32 = 374655;     p33 = 0.00191;   p34 = 195725;\n    p40 = 5.4818e-2;  p41 = 15.921;  p42 = 0.6373;     p43 = 5.99e-2;   p44 = 5.425e-4;\n    p50 = 1383702;    p51 = 4.156;   p52 = 5325;       p53 = 0.00238;   p54 = 0.0156;    p55 = 0.130;     p56 = 1521;\n    p60 = 6.031e-5;   p61 = 98.5;    p62 = 1.0466e-3;\n    p70 = 11747;      p71 = 41.66;   p72 = 4521;       p73 = 0.01998;   p74 = 0.00604;   p75 = 2534;      p76 = 0.00475;\n    p80 = 1.543e-2;   p81 = 10.06;   p82 = 1.807e-2;   p83 = 0.0011;    p84 = 8.81e-5;   p85 = 0.0405;    p86 = 20.24;\n    p90 = 8.321;      p91 = 0.14249; p92 = 0.96655;    p93 = 26.42;     p94 = 0.0567;\n\n\n    # Numerical determination of the penetration depth (86%) (Eq. 8)\n    D86 = 1/rhob*(p90+p91*(p92+np.exp(-1*distances/100))*(p93+theta)/(p94+theta))\n\n    # Depth weights (Eq. 7)\n    Wd = np.exp(-2*depth/D86)\n\n    if h == 0:\n        W = 1 # skip distance weighting\n\n    elif (h &gt;= 0.1) and (h&lt;= 50):\n        # Functions for Fi (Appendix A in K\u00f6hli et al., 2015)\n        F1 = p10*(1+p13*h)*np.exp(-p11*theta)+p12*(1+p15*h)-p14*theta\n        F2 = ((-p20+p24*h)*np.exp(-p21*theta/(1+p25*theta))+p22)*(1+h*p23)\n        F3 = (p30*(1+p33*h)*np.exp(-p31*theta)+p32-p34*theta)\n        F4 = p40*np.exp(-p41*theta)+p42-p43*theta+p44*h\n        F5 = p50*(0.02-1/p55/(h-p55+p56*theta))*(p54-theta)*np.exp(-p51*(theta-p54))+p52*(0.7-h*theta*p53)\n        F6 = p60*(h+p61)+p62*theta\n        F7 = (p70*(1-p76*h)*np.exp(-p71*theta*(1-h*p74))+p72-p75*theta)*(2+h*p73)\n        F8 = ((-p80+p84*h)*np.exp(-p81*theta/(1+p85*h+p86*theta))+p82)*(2+h*p83)\n\n        # Distance weights (Eq. 3)\n        W = np.ones_like(distances)*np.nan\n        for i in range(len(distances)):\n            if (distances[i]&lt;=50) and (distances[i]&gt;0.5):\n                W[i]=F1[i]*(np.exp(-F2[i]*distances[i]))+F3[i]*np.exp(-F4[i]*distances[i])\n\n            elif (distances[i]&gt;50) and (distances[i]&lt;600):\n                W[i]=F5[i]*(np.exp(-F6[i]*distances[i]))+F7[i]*np.exp(-F8[i]*distances[i])\n\n            else:\n                raise ValueError('Input distances are not valid.')\n\n    else:\n        raise ValueError('Air humidity values are out of range.')\n\n\n    # Combined and normalized weights\n    weights = Wd*W/np.nansum(Wd*W)\n\n    return weights\n</code></pre>"},{"location":"reference/#crnpy.crnpy.remove_incomplete_intervals","title":"<code>remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False)</code>","text":"<p>Function that removes rows with incomplete integration intervals.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>pandas.DataFrame</code> <p>Pandas Dataframe with data from stationary or roving CRNP devices.</p> required <code>timestamp_col</code> <code>str</code> <p>Name of the column with timestamps in datetime format.</p> required <code>integration_time</code> <code>int</code> <p>Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.</p> required <code>remove_first</code> <code>bool</code> <p>Remove first row. Default is False.</p> <code>False</code> <p>Returns:</p> Type Description <code>pandas.DataFrame</code> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False):\n\"\"\"Function that removes rows with incomplete integration intervals.\n\n    Args:\n        df (pandas.DataFrame): Pandas Dataframe with data from stationary or roving CRNP devices.\n        timestamp_col (str): Name of the column with timestamps in datetime format.\n        integration_time (int): Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.\n        remove_first (bool, optional): Remove first row. Default is False.\n\n    Returns:\n        (pandas.DataFrame): \n    \"\"\"\n\n    # Check format of timestamp column\n    if df[timestamp_col].dtype != 'datetime64[ns]':\n        raise TypeError('timestamp_col must be datetime64. Use `pd.to_datetime()` to fix this issue.')\n\n    # Check if differences in timestamps are below or above the provided integration time\n    idx_delta = df[timestamp_col].diff().dt.total_seconds() != integration_time\n\n    if remove_first:\n        idx_delta[0] = True\n\n    # Select rows that meet the specified integration time\n    df = df[~idx_delta]\n    df.reset_index(drop=True, inplace=True)\n\n    # Notify user about the number of rows that have been removed\n    print(f\"Removed a total of {sum(idx_delta)} rows.\")\n\n    return df\n</code></pre>"},{"location":"reference/#crnpy.crnpy.rover_centered_coordinates","title":"<code>rover_centered_coordinates(x, y)</code>","text":"<p>Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>array</code> <p>x coordinates.</p> required <code>y</code> <code>array</code> <p>y coordinates.</p> required <p>Returns:</p> Name Type Description <code>x_est</code> <code>array</code> <p>Estimated x coordinates.</p> <code>y_est</code> <code>array</code> <p>Estimated y coordinates.</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def rover_centered_coordinates(x, y):\n\"\"\"Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.\n\n    Args:\n        x (array): x coordinates.\n        y (array): y coordinates.\n\n    Returns:\n        x_est (array): Estimated x coordinates.\n        y_est (array): Estimated y coordinates.\n    \"\"\"\n\n    # Make it datatype agnostic\n    if(isinstance(x, pd.Series)):\n        x = x.values\n    if(isinstance(y, pd.Series)):\n        y = y.values\n\n    # Do the average of the two points\n    x_est = (x[1:] + x[:-1]) / 2\n    y_est = (y[1:] + y[:-1]) / 2\n\n    # Add the first point to match the length of the original array\n    x_est = np.insert(x_est, 0, x[0])\n    y_est = np.insert(y_est, 0, y[0])\n\n\n    return x_est, y_est\n</code></pre>"},{"location":"reference/#crnpy.crnpy.sensing_depth","title":"<code>sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017')</code>","text":"<p>Function that computes the estimated sensing depth of the cosmic-ray neutron probe. The function offers several methods to compute the depth at which 86 % of the neutrons probe the soil profile.</p> <p>Parameters:</p> Name Type Description Default <code>vwc</code> <code>array or pd.Series or pd.DataFrame</code> <p>Estimated volumetric water content for each timestamp.</p> required <code>pressure</code> <code>array or pd.Series or pd.DataFrame</code> <p>Atmospheric pressure in hPa for each timestamp.</p> required <code>p_ref</code> <code>float</code> <p>Reference pressure in hPa.</p> required <code>bulk_density</code> <code>float</code> <p>Soil bulk density.</p> required <code>Wlat</code> <code>float</code> <p>Lattice water content.</p> required <code>method</code> <code>str</code> <p>Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.</p> <code>'Schron_2017'</code> <code>dist</code> <code>list or array</code> <p>List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.</p> <code>None</code> <p>Returns:</p> Type Description <code>array or pd.Series or pd.DataFrame</code> <p>Estimated sensing depth in m.</p> References <p>Franz, T.E., Zreda, M., Ferre, T.P.A., Rosolem, R., Zweck, C., Stillman, S., Zeng, X. and Shuttleworth, W.J., 2012. Measurement depth of the cosmic ray soil moisture probe affected by hydrogen from various sources. Water Resources Research, 48(8). doi.org/10.1029/2012WR011871</p> <p>Schr\u00f6n, M., K\u00f6hli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., et al. (2017). Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity. Hydrol. Earth Syst. Sci. 21, 5009\u20135030. doi.org/10.5194/hess-21-5009-2017</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017'):\n    # Convert docstring to google format\n\"\"\"Function that computes the estimated sensing depth of the cosmic-ray neutron probe.\n    The function offers several methods to compute the depth at which 86 % of the neutrons\n    probe the soil profile.\n\n    Args:\n        vwc (array or pd.Series or pd.DataFrame): Estimated volumetric water content for each timestamp.\n        pressure (array or pd.Series or pd.DataFrame): Atmospheric pressure in hPa for each timestamp.\n        p_ref (float): Reference pressure in hPa.\n        bulk_density (float): Soil bulk density.\n        Wlat (float): Lattice water content.\n        method (str): Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.\n        dist (list or array): List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Estimated sensing depth in m.\n\n    References:\n        Franz, T.E., Zreda, M., Ferre, T.P.A., Rosolem, R., Zweck, C., Stillman, S., Zeng, X. and Shuttleworth, W.J., 2012.\n        Measurement depth of the cosmic ray soil moisture probe affected by hydrogen from various sources.\n        Water Resources Research, 48(8). doi.org/10.1029/2012WR011871\n\n        Schr\u00f6n, M., K\u00f6hli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., et al. (2017).\n        Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity.\n        Hydrol. Earth Syst. Sci. 21, 5009\u20135030. doi.org/10.5194/hess-21-5009-2017\n    \"\"\"\n\n    # Determine sensing depth (D86)\n    if method == 'Schron_2017':\n        # See Appendix A of Schr\u00f6n et al. (2017)\n        Fp = 0.4922 / (0.86 - np.exp(-1 * pressure / p_ref));\n        Fveg = 0\n        results = []\n        for d in dist:\n            # Compute r_star\n            r_start = d/Fp\n\n            # Compute soil depth that accounts for 86% of the neutron flux\n            D86 = 1/ bulk_density * (8.321+0.14249*(0.96655 + np.exp(-0.01*r_start))*(20+(Wlat+vwc)) / (0.0429+(Wlat+vwc)))\n            results.append(D86)\n\n    elif method == 'Franz_2012':\n        results = 5.8/(bulk_density*Wlat+vwc+0.0829)\n    else:\n        raise ValueError('Method not recognized. Please select either \"Schron_2017\" or \"Franz_2012\".')\n\n    return results\n</code></pre>"},{"location":"reference/#crnpy.crnpy.smooth_1d","title":"<code>smooth_1d(values, window=5, order=3, method='moving_median')</code>","text":"<p>Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).</p> <p>Parameters:</p> Name Type Description Default <code>values</code> <code>pd.DataFrame or pd.Serie</code> <p>Dataframe containing the values to smooth.</p> required <code>window</code> <code>int</code> <p>Window size for the Savitzky-Golay filter. Default is 5.</p> <code>5</code> <code>method</code> <code>str</code> <p>Method to use for smoothing the data. Default is 'moving_median'. Options are 'moving_average', 'moving_median' and 'savitzky_golay'.</p> <code>'moving_median'</code> <code>order</code> <code>int</code> <p>Order of the Savitzky-Golay filter. Default is 3.</p> <code>3</code> <p>Returns:</p> Type Description <code>pd.DataFrame</code> <p>DataFrame with smoothed values.</p> References <p>Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020. Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9. doi.org/10.3389/frwa.2020.00009</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def smooth_1d(values, window=5, order=3, method='moving_median'):\n\"\"\"Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).\n\n    Args:\n        values (pd.DataFrame or pd.Serie): Dataframe containing the values to smooth.\n        window (int): Window size for the Savitzky-Golay filter. Default is 5.\n        method (str): Method to use for smoothing the data. Default is 'moving_median'.\n            Options are 'moving_average', 'moving_median' and 'savitzky_golay'.\n        order (int): Order of the Savitzky-Golay filter. Default is 3.\n\n    Returns:\n        (pd.DataFrame): DataFrame with smoothed values.\n\n    References:\n        Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.\n        Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.\n        doi.org/10.3389/frwa.2020.00009\n    \"\"\"\n\n    if method == 'moving_average':\n        corrected_counts = values.rolling(window=window, center=True, min_periods=1).mean()\n    elif method == 'moving_median':\n        corrected_counts = values.rolling(window=window, center=True, min_periods=1).median()\n\n    elif method == 'savitzky_golay':\n        if values.isna().any():\n            print('Dataframe contains NaN values. Please remove NaN values before smoothing the data.')\n\n        if type(values) == pd.core.series.Series:\n            filtered = savgol_filter(values,window,order)\n            corrected_counts = pd.DataFrame(filtered,columns=['smoothed'], index=values.index)\n        elif type(values) == pd.core.frame.DataFrame:\n            for col in values.columns:\n                values[col] = savgol_filter(values[col],window,order)\n    else:\n        raise ValueError('Invalid method. Please select a valid filtering method., options are: moving_average, moving_median, savitzky_golay')\n    corrected_counts = corrected_counts.ffill(limit=window).bfill(limit=window).copy()\n    return corrected_counts\n</code></pre>"},{"location":"reference/#crnpy.crnpy.spatial_average","title":"<code>spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=False)</code>","text":"<p>Moving buffer filter to smooth georeferenced two-dimensional data.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>list or array</code> <p>UTM x coordinates in meters.</p> required <code>y</code> <code>list or array</code> <p>UTM y coordinates in meters.</p> required <code>z</code> <code>list or array</code> <p>Values to be smoothed.</p> required <code>buffer</code> <code>float</code> <p>Radial buffer distance in meters.</p> <code>100</code> <code>min_neighbours</code> <code>int</code> <p>Minimum number of neighbours to consider for the smoothing.</p> <code>3</code> <code>method</code> <code>str</code> <p>One of 'mean' or 'median'.</p> <code>'mean'</code> <code>rnd</code> <code>bool</code> <p>Boolean to round the final result. Useful in case of z representing neutron counts.</p> <code>False</code> <p>Returns:</p> Type Description <code>array</code> <p>Smoothed version of z with the same dimension as z.</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=False):\n\"\"\"Moving buffer filter to smooth georeferenced two-dimensional data.\n\n    Args:\n        x (list or array): UTM x coordinates in meters.\n        y (list or array): UTM y coordinates in meters.\n        z (list or array): Values to be smoothed.\n        buffer (float): Radial buffer distance in meters.\n        min_neighbours (int): Minimum number of neighbours to consider for the smoothing.\n        method (str): One of 'mean' or 'median'.\n        rnd (bool): Boolean to round the final result. Useful in case of z representing neutron counts.\n\n    Returns:\n        (array): Smoothed version of z with the same dimension as z.\n    \"\"\"\n\n    # Convert input data to Numpy arrays\n    if (type(x) is not np.ndarray) or (type(y) is not np.ndarray):\n        try:\n            x = np.array(x)\n            y = np.array(y)\n        except:\n            raise \"Input values cannot be converted to Numpy arrays.\"\n\n    if len(x) != len(y):\n        raise f\"The number of x and y must be equal. Input x has {len(x)} values and y has {len(y)} values.\"\n\n    # Compute distances\n    N = len(x)\n    z_smooth = np.array([])\n    for k in range(N):\n        px = x[k]\n        py = y[k]\n\n        distances = euclidean_distance(px, py, x, y)\n        idx_within_buffer = distances &lt;= buffer\n\n\n        if np.isnan(z[k]):\n            z_new_val = np.nan\n        elif len(distances[idx_within_buffer]) &gt; min_neighbours:\n            if method == 'mean':\n                z_new_val = np.nanmean(z[idx_within_buffer])\n            elif method == 'median':\n                z_new_val = np.nanmedian(z[idx_within_buffer])\n            else:\n                raise f\"Method {method} does not exist. Provide either 'mean' or 'median'.\"\n        else:\n            z_new_val = z[k] # If there are not enough neighbours, keep the original value\n\n        # Append smoothed value to array\n        z_smooth = np.append(z_smooth, z_new_val)\n\n    if rnd:\n        z_smooth = np.round(z_smooth, 0)\n\n    return z_smooth\n</code></pre>"},{"location":"reference/#crnpy.crnpy.total_raw_counts","title":"<code>total_raw_counts(counts, nan_strategy=None, timestamp_col=None)</code>","text":"<p>Compute the sum of uncorrected neutron counts for all detectors.</p> <p>Parameters:</p> Name Type Description Default <code>counts</code> <code>pandas.DataFrame</code> <p>Dataframe containing only the columns with neutron counts.</p> required <code>nan_strategy</code> <code>str</code> <p>Strategy to use for NaN values. Options are 'interpolate', 'average', or None. Default is None.</p> <code>None</code> <p>Returns:</p> Type Description <code>pandas.DataFrame</code> <p>Dataframe with the sum of uncorrected neutron counts for all detectors.</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def total_raw_counts(counts, nan_strategy=None, timestamp_col=None):\n\"\"\"Compute the sum of uncorrected neutron counts for all detectors.\n\n    Args:\n        counts (pandas.DataFrame): Dataframe containing only the columns with neutron counts.\n        nan_strategy (str): Strategy to use for NaN values. Options are 'interpolate', 'average', or None. Default is None.\n\n    Returns:\n        (pandas.DataFrame): Dataframe with the sum of uncorrected neutron counts for all detectors.\n    \"\"\"\n\n    if counts.shape[0] &gt; 1:\n        counts = counts.apply(lambda x: x.fillna(counts.mean(axis=1)),axis=0)\n\n    # Compute sum of counts\n    total_raw_counts = counts.sum(axis=1)\n\n    # Replace zeros with NaN\n    total_raw_counts = total_raw_counts.replace(0, np.nan)\n\n    return total_raw_counts\n</code></pre>"},{"location":"reference/#crnpy.crnpy.uncertainty_counts","title":"<code>uncertainty_counts(raw_counts, metric='std', fp=1, fw=1, fi=1)</code>","text":"<p>Function to estimate the uncertainty of raw counts.</p> <p>Measurements of proportional neutron detector systems are governed by counting statistics that follow a Poissonian probability distribution (Zreda et al., 2012). The expected uncertainty in the neutron count rate $N$ is defined by the standard deviation $ \\sqrt{N} $ (Jakobi et al., 2020). The CV% can be expressed as $ N^{-1/2} $</p> <p>Parameters:</p> Name Type Description Default <code>raw_counts</code> <code>array</code> <p>Raw neutron counts.</p> required <code>metric</code> <code>str</code> <p>Either 'std' or 'cv' for standard deviation or coefficient of variation.</p> <code>'std'</code> <code>fp</code> <code>float</code> <p>Pressure correction factor.</p> <code>1</code> <code>fw</code> <code>float</code> <p>Humidity correction factor.</p> <code>1</code> <code>fi</code> <code>float</code> <p>Incoming neutron flux correction factor.</p> <code>1</code> <p>Returns:</p> Name Type Description <code>uncertainty</code> <code>float</code> <p>Uncertainty of raw counts.</p> References <p>Jakobi J, Huisman JA, Schr\u00f6n M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010</p> <p>Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079\u20134099, https://doi.org/10.5194/hess-16-4079-2012, 2012.</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def uncertainty_counts(raw_counts, metric=\"std\", fp=1, fw=1, fi=1):\n\"\"\"Function to estimate the uncertainty of raw counts.\n\n    Measurements of proportional neutron detector systems are governed by counting statistics that follow a Poissonian probability distribution (Zreda et al., 2012).\n    The expected uncertainty in the neutron count rate $N$ is defined by the standard deviation $ \\sqrt{N} $ (Jakobi et al., 2020).\n    The CV% can be expressed as $ N^{-1/2} $\n\n    Args:\n        raw_counts (array): Raw neutron counts.\n        metric (str): Either 'std' or 'cv' for standard deviation or coefficient of variation.\n        fp (float): Pressure correction factor.\n        fw (float): Humidity correction factor.\n        fi (float): Incoming neutron flux correction factor.\n\n    Returns:\n        uncertainty (float): Uncertainty of raw counts.\n\n    References:\n        Jakobi J, Huisman JA, Schr\u00f6n M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With\n        Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010\n\n        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System,\n        Hydrol. Earth Syst. Sci., 16, 4079\u20134099, https://doi.org/10.5194/hess-16-4079-2012, 2012.\n\n    \"\"\"\n\n    s = fw / (fp * fi)\n    if metric == \"std\":\n        uncertainty = np.sqrt(raw_counts) * s\n    elif metric == \"cv\":\n        uncertainty = 1 /  np.sqrt(raw_counts) * s\n    else:\n        raise f\"Metric {metric} does not exist. Provide either 'std' or 'cv' for standard deviation or coefficient of variation.\"\n    return uncertainty\n</code></pre>"},{"location":"reference/#crnpy.crnpy.uncertainty_vwc","title":"<code>uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808, a1=0.372, a2=0.115)</code>","text":"<p>Function to estimate the uncertainty propagated to volumetric water content.</p> <p>The uncertainty of the volumetric water content is estimated by propagating the uncertainty of the raw counts. Following Eq. 10 in Jakobi et al. (2020), the uncertainty of the volumetric water content can be expressed as: $$ \\sigma_{\\theta_g}(N) = \\sigma_N \\frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \\sqrt{(N_{cor} - a_1 N_0)^4 + 8 \\sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \\sigma_N^4} $$</p> <p>Parameters:</p> Name Type Description Default <code>raw_counts</code> <code>array</code> <p>Raw neutron counts.</p> required <code>N0</code> <code>float</code> <p>Calibration parameter N0.</p> required <code>bulk_density</code> <code>float</code> <p>Bulk density in g cm-3.</p> required <code>fp</code> <code>float</code> <p>Pressure correction factor.</p> <code>1</code> <code>fw</code> <code>float</code> <p>Humidity correction factor.</p> <code>1</code> <code>fi</code> <code>float</code> <p>Incoming neutron flux correction factor.</p> <code>1</code> <p>Returns:</p> Name Type Description <code>sigma_VWC</code> <code>float</code> <p>Uncertainty in terms of volumetric water content.</p> References <p>Jakobi J, Huisman JA, Schr\u00f6n M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010</p> Source code in <code>C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py</code> <pre><code>def uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808,a1=0.372,a2=0.115):\nr\"\"\"Function to estimate the uncertainty propagated to volumetric water content.\n\n    The uncertainty of the volumetric water content is estimated by propagating the uncertainty of the raw counts.\n    Following Eq. 10 in Jakobi et al. (2020), the uncertainty of the volumetric water content can be expressed as:\n    $$\n    \\sigma_{\\theta_g}(N) = \\sigma_N \\frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \\sqrt{(N_{cor} - a_1 N_0)^4 + 8 \\sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \\sigma_N^4}\n    $$\n\n    Args:\n        raw_counts (array): Raw neutron counts.\n        N0 (float): Calibration parameter N0.\n        bulk_density (float): Bulk density in g cm-3.\n        fp (float): Pressure correction factor.\n        fw (float): Humidity correction factor.\n        fi (float): Incoming neutron flux correction factor.\n\n    Returns:\n        sigma_VWC (float): Uncertainty in terms of volumetric water content.\n\n    References:\n        Jakobi J, Huisman JA, Schr\u00f6n M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With\n        Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010\n    \"\"\"\n\n    Ncorr = raw_counts * fw / (fp * fi)\n    sigma_N = uncertainty_counts(raw_counts, metric=\"std\", fp=fp, fw=fw, fi=fi)\n    sigma_GWC = sigma_N * ((a0*N0) / ((Ncorr - a1*N0)**4)) * np.sqrt((Ncorr - a1 * N0)**4 + 8 * sigma_N**2 * (Ncorr - a1 * N0)**2 + 15 * sigma_N**4)\n    sigma_VWC = sigma_GWC * bulk_density\n\n    return sigma_VWC\n</code></pre>"},{"location":"reference/#crnpy.data--crnpydata","title":"crnpy.data","text":"<p>Data module for crnpy.</p> <p>This module contains data for the crnpy package.</p> <p>Attributes:</p> Name Type Description <code>cutoff_rigidity</code> <code>list</code> <p>Cutoff rigidity values for the whole world. See crnpy.crnpy.cutoff_rigidity</p> <code>neutron_detectors</code> <code>list</code> <p>Neutron detector locations. See crnpy.crnpy.find_neutron_monitor</p>"},{"location":"sensor_description/","title":"Sensor description","text":"<p>The cosmic-ray neutron sensing technology is an innovative and non-invasive method for monitoring soil moisture. The sensing principle is based on the detection of neutrons that are generated when cosmic rays interact with atoms in the Earth's atmosphere. These neutrons, in turn, interact with hydrogen pools in the land surface, and thus, the intensity of neutrons detected near the ground surface is inversely related to the amunt of water in land biomass and the top layers of the soil. Typically, cosmic-ray neutron probes have a large sensing footprint, covering an area of approximately 12 hectares (about 30 acres) and can measure soil moisture up to a depth of 70 cm (about 28 inches), although typically most field applications span a depth between 5 and 40 cm. This makes the technology particularly useful for capturing spatially-averaged soil moisture over large areas, bridging the gap between point measurements and remote sensing. In hydrology, cosmic-ray neutron sensors are used for watershed studies, understanding water balance, and improving hydrological models. In agriculture, they are employed for irrigation management, crop yield prediction, and understanding the effects of soil moisture on plant growth. The technology's ability to provide continuous, real-time data without disturbing the soil makes it a valuable tool for both hydrological and agricultural research.</p> <p> Figure 1. A) Stationary detector manufactured by Radiation Detection Technologies, Inc. (Manhattan, KS). B) Stationary detector manufactured by Hydroinnova, Inc. (Albuquerque, NM). C) Roving detector manufactured by Hydroinnova, Inc. (Albuquerque, NM).</p> <p> Figure 2. Illustration showing the approximate sensing footprint of stationary detectors.</p>"},{"location":"examples/calibration/calibration/","title":"Device-specific field calibration","text":"In\u00a0[1]: Copied! <pre># Importing required libraries\nimport crnpy\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.optimize import root\n</pre> # Importing required libraries import crnpy  import numpy as np import pandas as pd from scipy.optimize import root  In\u00a0[2]: Copied! <pre># Load the soil samples data and the CRNP dataset using pandas\nurl_soil_samples = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/soil_data.csv\"\ndf_soil = pd.read_csv(url_soil_samples)\ndf_soil.head(3)\n</pre> # Load the soil samples data and the CRNP dataset using pandas url_soil_samples = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/soil_data.csv\" df_soil = pd.read_csv(url_soil_samples) df_soil.head(3)  Out[2]: field date core_number distance_from_station latitude longitude top_depth bottom_depth core_diameter wet_mass_with_bag ... can_number mass_empty_can wet_mass_with_can dry_mass_with_can mass_water theta_g volume bulk_density theta_v Observation 0 Flickner 22-Oct 1 5 N38.23459 W97.57101 0 5 30.49 45.31 ... 1 52.10 92.03 85.31 6.72 0.202 36.514864 0.909 0.184403 NaN 1 Flickner 22-Oct 1 5 N38.23459 W97.57101 5 10 30.49 69.53 ... 2 51.85 115.97 103.85 12.12 0.233 36.514864 1.424 0.332585 NaN 2 Flickner 22-Oct 1 5 N38.23459 W97.57101 10 25 30.49 214.90 ... 3 51.56 260.97 219.77 41.20 0.245 109.544592 1.536 0.376856 NaN <p>3 rows \u00d7 21 columns</p> In\u00a0[3]: Copied! <pre># Define start and end of field survey calibration\ncalibration_start = pd.to_datetime(\"2021-10-22 08:00\")\ncalibration_end = pd.to_datetime(\"2021-10-22 16:00\")\n</pre> # Define start and end of field survey calibration calibration_start = pd.to_datetime(\"2021-10-22 08:00\") calibration_end = pd.to_datetime(\"2021-10-22 16:00\")  In\u00a0[4]: Copied! <pre># Load the station data\nurl_station_data = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/station_data.csv\"\ndf_station = pd.read_csv(url_station_data, skiprows=[0,2,3])\n\n#  Parse dates (you can also use the option `parse_dates=['TIMESTAMP]` in pd.read_csv()\ndf_station['timestamp'] = pd.to_datetime(df_station['TIMESTAMP'], format='%Y-%m-%d %H:%M:%S')\n\ndf_station.head(3)\n</pre> # Load the station data url_station_data = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/station_data.csv\" df_station = pd.read_csv(url_station_data, skiprows=[0,2,3])  #  Parse dates (you can also use the option `parse_dates=['TIMESTAMP]` in pd.read_csv() df_station['timestamp'] = pd.to_datetime(df_station['TIMESTAMP'], format='%Y-%m-%d %H:%M:%S')  df_station.head(3)  Out[4]: TIMESTAMP RECORD station farm field latitude longitude altitude battery_voltage_Min PTemp_Avg ... wind_speed_gust_Max air_temperature_Avg vapor_pressure_Avg barometric_pressure_Avg relative_humidity_Avg humidity_sensor_temperature_Avg tilt_north_south_Avg tilt_west_east_Avg NDVI_Avg timestamp 0 2021-09-22 12:00:00 0 KS003 Flickner Rainfed South 38.23461 -97.57095 455 13.52 29.04 ... 4.20 22.30 9.20 973 41.30 26.4 -1.000 1.000 0.311 2021-09-22 12:00:00 1 2021-09-22 13:00:00 1 KS003 Flickner Rainfed South 38.23461 -97.57095 455 13.53 29.98 ... 9.02 22.90 9.08 972 32.63 26.8 -0.975 0.950 0.308 2021-09-22 13:00:00 2 2021-09-22 14:00:00 2 KS003 Flickner Rainfed South 38.23461 -97.57095 455 13.53 30.43 ... 5.54 23.38 8.68 971 30.25 27.2 -0.775 0.625 0.31 2021-09-22 14:00:00 <p>3 rows \u00d7 28 columns</p> In\u00a0[5]: Copied! <pre># Define date in which the probe was deployed in the field (i.e., first record)\ndeployment_date = df_station['timestamp'].iloc[0]\n\n# Filter station data from the first record to the end of the field survey calibration\n# This is important since we are considering the incoming flux on the first day as the reference value\nidx_period = (df_station['timestamp'] &gt;= deployment_date) &amp; (df_station['timestamp'] &lt;= calibration_end)\ndf_station = df_station[idx_period]\ndf_station.head(3)\n</pre> # Define date in which the probe was deployed in the field (i.e., first record) deployment_date = df_station['timestamp'].iloc[0]  # Filter station data from the first record to the end of the field survey calibration # This is important since we are considering the incoming flux on the first day as the reference value idx_period = (df_station['timestamp'] &gt;= deployment_date) &amp; (df_station['timestamp'] &lt;= calibration_end) df_station = df_station[idx_period] df_station.head(3)  Out[5]: TIMESTAMP RECORD station farm field latitude longitude altitude battery_voltage_Min PTemp_Avg ... wind_speed_gust_Max air_temperature_Avg vapor_pressure_Avg barometric_pressure_Avg relative_humidity_Avg humidity_sensor_temperature_Avg tilt_north_south_Avg tilt_west_east_Avg NDVI_Avg timestamp 0 2021-09-22 12:00:00 0 KS003 Flickner Rainfed South 38.23461 -97.57095 455 13.52 29.04 ... 4.20 22.30 9.20 973 41.30 26.4 -1.000 1.000 0.311 2021-09-22 12:00:00 1 2021-09-22 13:00:00 1 KS003 Flickner Rainfed South 38.23461 -97.57095 455 13.53 29.98 ... 9.02 22.90 9.08 972 32.63 26.8 -0.975 0.950 0.308 2021-09-22 13:00:00 2 2021-09-22 14:00:00 2 KS003 Flickner Rainfed South 38.23461 -97.57095 455 13.53 30.43 ... 5.54 23.38 8.68 971 30.25 27.2 -0.775 0.625 0.31 2021-09-22 14:00:00 <p>3 rows \u00d7 28 columns</p> <p>This step is useful to trim large timeseries. For instance, the station data in our case extends until <code>2022-07-11 09:45:00</code>, but the field calibration was conducted in <code>2021-10-22 16:00</code>. Since all the station observation after the date of the field calibration are not relevent, we decided to only work with the data that we need from <code>2021-09-22 12:00:00</code> until <code>2021-10-22 16:00</code>. This could help getting data of incoming neutron flux from a single reference neutron monitor. So, if you are running this code shortly after the calibration field survey, then there is no need to filter station data.</p> In\u00a0[6]: Copied! <pre># Compute total neutron counts by adding the counts from both probe detectors\ndf_station['total_raw_counts'] = crnpy.total_raw_counts(df_station[['counts_1_Tot','counts_2_Tot']],\n                                                    nan_strategy='average')\n</pre> # Compute total neutron counts by adding the counts from both probe detectors df_station['total_raw_counts'] = crnpy.total_raw_counts(df_station[['counts_1_Tot','counts_2_Tot']],                                                     nan_strategy='average')  In\u00a0[7]: Copied! <pre># Atmospheric corrections\n\n# Fill NaN values in atmospheric data\ndf_station[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']] = df_station[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')\n\n# Calculate absolute humidity\ndf_station['abs_humidity'] = crnpy.abs_humidity(df_station['relative_humidity_Avg'], df_station['air_temperature_Avg'])\n\n# Compute correction factor for atmospheric pressure\n# Reference atmospheric pressure for the location is 976 Pa\n# Using an atmospheric attentuation coefficient of 130 g/cm2\ndf_station['fp'] = crnpy.correction_pressure(pressure=df_station['barometric_pressure_Avg'],\n                                             Pref=976, L=130)\n\n# Compute correction factor for air humidity\ndf_station['fw'] = crnpy.correction_humidity(abs_humidity=df_station['abs_humidity'],\n                                             Aref=0)\n</pre> # Atmospheric corrections  # Fill NaN values in atmospheric data df_station[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']] = df_station[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')  # Calculate absolute humidity df_station['abs_humidity'] = crnpy.abs_humidity(df_station['relative_humidity_Avg'], df_station['air_temperature_Avg'])  # Compute correction factor for atmospheric pressure # Reference atmospheric pressure for the location is 976 Pa # Using an atmospheric attentuation coefficient of 130 g/cm2 df_station['fp'] = crnpy.correction_pressure(pressure=df_station['barometric_pressure_Avg'],                                              Pref=976, L=130)  # Compute correction factor for air humidity df_station['fw'] = crnpy.correction_humidity(abs_humidity=df_station['abs_humidity'],                                              Aref=0)  In\u00a0[8]: Copied! <pre># Find the cutoff rigidity for the location\ncutoff_rigidity = crnpy.cutoff_rigidity(39.1, -96.6)\n\n# Filtering the time window from experiment setup to the end of the calibration\ncrnpy.find_neutron_monitor(cutoff_rigidity,\n                           start_date=df_station['timestamp'].iloc[0],\n                           end_date=df_station['timestamp'].iloc[-1])\n</pre> # Find the cutoff rigidity for the location cutoff_rigidity = crnpy.cutoff_rigidity(39.1, -96.6)  # Filtering the time window from experiment setup to the end of the calibration crnpy.find_neutron_monitor(cutoff_rigidity,                            start_date=df_station['timestamp'].iloc[0],                            end_date=df_station['timestamp'].iloc[-1])  <pre>\nSelect a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\n\nYour cutoff rigidity is 2.87 GV\n     STID     NAME     R  Altitude_m  Period available\n13   DRBS  Dourbes  3.18         225              True\n40   NEWK   Newark  2.40          50              True\n28  KIEL2   KielRT  2.36          54              True\n</pre> Out[8]: STID NAME R Altitude_m Period available 13 DRBS Dourbes 3.18 225 True 40 NEWK Newark 2.40 50 True 28 KIEL2 KielRT 2.36 54 True In\u00a0[9]: Copied! <pre># Incoming neutron flux correction\n\n# Download data for the reference neutron monitor and add it to the DataFrame\nnmdb = crnpy.get_incoming_neutron_flux(deployment_date,\n                                        calibration_end,\n                                        station=\"DRBS\",\n                                        utc_offset=-6)\n\n# Interpolate incoming neutron flux to match the timestamps in our station data\ndf_station['incoming_flux'] = crnpy.interpolate_incoming_flux(nmdb['timestamp'], nmdb['counts'], df_station['timestamp'])\n\n# Compute correction factor for incoming neutron flux\ndf_station['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df_station['incoming_flux'],\n                                                  incoming_Ref=df_station['incoming_flux'].iloc[0])\n</pre> # Incoming neutron flux correction  # Download data for the reference neutron monitor and add it to the DataFrame nmdb = crnpy.get_incoming_neutron_flux(deployment_date,                                         calibration_end,                                         station=\"DRBS\",                                         utc_offset=-6)  # Interpolate incoming neutron flux to match the timestamps in our station data df_station['incoming_flux'] = crnpy.interpolate_incoming_flux(nmdb['timestamp'], nmdb['counts'], df_station['timestamp'])  # Compute correction factor for incoming neutron flux df_station['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df_station['incoming_flux'],                                                   incoming_Ref=df_station['incoming_flux'].iloc[0])   In\u00a0[10]: Copied! <pre># Apply correction factors\ndf_station['total_corrected_neutrons'] = df_station['total_raw_counts'] * df_station['fw'] / (df_station['fp'] * df_station['fi'])\n</pre> # Apply correction factors df_station['total_corrected_neutrons'] = df_station['total_raw_counts'] * df_station['fw'] / (df_station['fp'] * df_station['fi'])  In\u00a0[11]: Copied! <pre># Compute the weights of each sample for the field average\nnrad_weights = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'], df_soil['distance_from_station'], (df_soil['bottom_depth']+df_soil['top_depth'])/2, rhob=df_soil['bulk_density'].mean())\n\n# Apply distance weights to volumetric water content and bulk density\nfield_theta_v = np.sum(df_soil['theta_v']*nrad_weights)\nfield_bulk_density = np.sum(df_soil['bulk_density']*nrad_weights)\n</pre> # Compute the weights of each sample for the field average nrad_weights = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'], df_soil['distance_from_station'], (df_soil['bottom_depth']+df_soil['top_depth'])/2, rhob=df_soil['bulk_density'].mean())  # Apply distance weights to volumetric water content and bulk density field_theta_v = np.sum(df_soil['theta_v']*nrad_weights) field_bulk_density = np.sum(df_soil['bulk_density']*nrad_weights)  In\u00a0[12]: Copied! <pre># Determine the mean corrected counts during the calibration survey\nidx_cal_period = (df_station['timestamp'] &gt;= calibration_start) &amp; (df_station['timestamp'] &lt;= calibration_end)\nmean_cal_counts = df_station.loc[idx_cal_period, 'total_corrected_neutrons'].mean()\n\nprint(f\"Mean volumetric Water content during calibration survey: {round(field_theta_v,3)}\")\nprint(f\"Mean corrected counts during calibration: {round(mean_cal_counts)} counts\")\n</pre> # Determine the mean corrected counts during the calibration survey idx_cal_period = (df_station['timestamp'] &gt;= calibration_start) &amp; (df_station['timestamp'] &lt;= calibration_end) mean_cal_counts = df_station.loc[idx_cal_period, 'total_corrected_neutrons'].mean()  print(f\"Mean volumetric Water content during calibration survey: {round(field_theta_v,3)}\") print(f\"Mean corrected counts during calibration: {round(mean_cal_counts)} counts\")  <pre>Mean volumetric Water content during calibration survey: 0.263\nMean corrected counts during calibration: 1542 counts\n</pre> In\u00a0[13]: Copied! <pre># Define the function for which we want to find the roots\nVWC_func = lambda N0 : crnpy.counts_to_vwc(mean_cal_counts, N0, bulk_density=field_bulk_density, Wlat=0.03, Wsoc=0.01) - field_theta_v\n\n# Make an initial guess for N0\nN0_initial_guess = 1000\n\n# Find the root\nsol = int(root(VWC_func, N0_initial_guess).x)\n\n# Print the solution\nprint(f\"The solved value for N0 is: {sol}\")\n</pre> # Define the function for which we want to find the roots VWC_func = lambda N0 : crnpy.counts_to_vwc(mean_cal_counts, N0, bulk_density=field_bulk_density, Wlat=0.03, Wsoc=0.01) - field_theta_v  # Make an initial guess for N0 N0_initial_guess = 1000  # Find the root sol = int(root(VWC_func, N0_initial_guess).x)  # Print the solution print(f\"The solved value for N0 is: {sol}\")  <pre>The solved value for N0 is: 2644\n</pre>"},{"location":"examples/calibration/calibration/#device-specific-field-calibration","title":"Device-specific field calibration\u00b6","text":"<p>The calibration of a cosmic-ray neutron probe (CRNP) is an essential step to ensure accurate soil moisture measurements. The CRNP operates by counting fast neutrons produced from cosmic rays, which are predominantly moderated by water molecules in the soil. The parameter $N_0$ is typically considered a device-specific constant that represents the neutron count rate in the absence of soil moisture conditions.</p> <p>$\\theta(N) =\\frac{a_0}{(\\frac{N}{N_0}) - a_1} - a_2 $ (Desilets et al., 2010).</p> <p>For the calibration of the stationary detector, a total of 14 undisturbed soil cores were collected at radial distances of 5, 50, and 100 m from the detector. In this example each soil sample was split into four depth segments: 0-5 cm, 5-10 cm, 10-25 cm, and 25-40 cm. Soil samples were processed and soil moisture was determined using the thermo-gravimetric method.</p> <p>Figure 1. Horizontal layout and vertical layout used in this particular example calibration, it can be customized by the user depending on their needs.</p> <p>Download the following template  for collecting your own calibration soil samples:</p>"},{"location":"examples/calibration/calibration/#read-calibration-field-survey-data","title":"Read calibration field survey data\u00b6","text":"<p>For each sample it is required to know the bulk density ($\\rho_\\beta$) and the volumetric water content ($\\theta_v$). See the details of the calculation used in the filled example.</p>"},{"location":"examples/calibration/calibration/#read-data-from-crnp","title":"Read data from CRNP\u00b6","text":""},{"location":"examples/calibration/calibration/#correct-neutron-counts","title":"Correct neutron counts\u00b6","text":""},{"location":"examples/calibration/calibration/#determine-field-average-soil-moisture-and-bulk-density","title":"Determine field-average soil moisture and bulk density\u00b6","text":"<p>Using the function <code>nrad_weight()</code> the weights corresponding to each soil sample will be computed considering air-humidity, sample depth, and distance from station and bulk density.</p>"},{"location":"examples/calibration/calibration/#solving-for-n_0","title":"Solving for $N_0$\u00b6","text":"<p>Previous steps estimated the field volumetric water content of <code>0.263</code> and an average neutron count of <code>1542</code>. Using <code>scipy.optimize.root()</code> $N_0$ is estimated given the observed value of $\\theta_v$ and neutron counts.</p>"},{"location":"examples/calibration/calibration/#references","title":"References:\u00b6","text":"<p>Desilets, D., Zreda, M., &amp; Ferr\u00e9, T. P. (2010). Nature's neutron probe: Land surface hydrology at an elusive scale with cosmic rays. Water Resources Research, 46(11).</p> <p>Dong, J., &amp; Ochsner, T. E. (2018). Soil texture often exerts a stronger influence than precipitation on mesoscale soil moisture patterns. Water Resources Research, 54(3), 2199-2211.</p> <p>Patrignani, A., Ochsner, T. E., Montag, B., &amp; Bellinger, S. (2021). A novel lithium foil cosmic-ray neutron detector for measuring field-scale soil moisture. Frontiers in Water, 3, 673185.</p>"},{"location":"examples/rover/Hydroinnova_rover_example/","title":"Processing and analyzing data from a roving detector","text":"In\u00a0[1]: Copied! <pre># Import modules\nimport crnpy\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n</pre> # Import modules import crnpy  import numpy as np import pandas as pd import matplotlib.pyplot as plt  <p>Load the data stored in the .csv file.</p> In\u00a0[2]: Copied! <pre># Load sample dataset\nfilepath = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/rover/gypsum_transect_01_may_2018.KSU\"\ncol_names = 'RecordNum,Date Time(UTC),barometric_pressure_Avg,P4_mb,P1_mb,T1_C,RH1,air_temperature_Avg,relative_humidity_Avg,Vbat,N1Cts,N2Cts,N3Cts,N4Cts,N5Cts,N6Cts,N7Cts,N8Cts,N1ETsec,N3ETsec,N5ETsec,N7ETsec,N1T(C),N1RH,N5T(C),N5RH,GpsUTC,LatDec,LongDec,Alt,Qual,NumSats,HDOP,Speed_kmh,COG,SpeedQuality,strDate'.split(',')\n\ndf = pd.read_csv(filepath, skiprows=20, names=col_names)\ndf['LongDec'] = df['LongDec'] * -1 # Raw data is in absolute values\n\n# Parse timestamps and set as index\ndf['timestamp'] = pd.to_datetime(df['Date Time(UTC)'])\n\n# Remove rows with missing coordinates\ndf['LatDec'].replace(0.0, np.nan, inplace=True)\ndf['LongDec'].replace(0.0, np.nan, inplace=True)\ndf.dropna(axis=0, subset=['LatDec','LongDec'], inplace=True)\ndf.reset_index(drop=True, inplace=True)\n\n# Display a few rows to visualize dataset\ndf.head(3)\n</pre> # Load sample dataset filepath = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/rover/gypsum_transect_01_may_2018.KSU\" col_names = 'RecordNum,Date Time(UTC),barometric_pressure_Avg,P4_mb,P1_mb,T1_C,RH1,air_temperature_Avg,relative_humidity_Avg,Vbat,N1Cts,N2Cts,N3Cts,N4Cts,N5Cts,N6Cts,N7Cts,N8Cts,N1ETsec,N3ETsec,N5ETsec,N7ETsec,N1T(C),N1RH,N5T(C),N5RH,GpsUTC,LatDec,LongDec,Alt,Qual,NumSats,HDOP,Speed_kmh,COG,SpeedQuality,strDate'.split(',')  df = pd.read_csv(filepath, skiprows=20, names=col_names) df['LongDec'] = df['LongDec'] * -1 # Raw data is in absolute values  # Parse timestamps and set as index df['timestamp'] = pd.to_datetime(df['Date Time(UTC)'])  # Remove rows with missing coordinates df['LatDec'].replace(0.0, np.nan, inplace=True) df['LongDec'].replace(0.0, np.nan, inplace=True) df.dropna(axis=0, subset=['LatDec','LongDec'], inplace=True) df.reset_index(drop=True, inplace=True)  # Display a few rows to visualize dataset df.head(3)  Out[2]: RecordNum Date Time(UTC) barometric_pressure_Avg P4_mb P1_mb T1_C RH1 air_temperature_Avg relative_humidity_Avg Vbat ... LongDec Alt Qual NumSats HDOP Speed_kmh COG SpeedQuality strDate timestamp 0 2 2018/05/01 14:15:00 962.95 962.8 961.4 23.2 35.4 20.9 72.7 13.574 ... -97.37195 393.2 2.0 10 0.8 0.00 270.7 A 10518.0 2018-05-01 14:15:00 1 3 2018/05/01 14:16:00 962.88 962.7 961.3 23.3 35.5 21.0 72.7 13.417 ... -97.37197 387.2 2.0 11 0.8 0.00 270.7 A 10518.0 2018-05-01 14:16:00 2 4 2018/05/01 14:17:00 962.64 962.5 961.1 23.4 35.4 21.2 72.2 13.282 ... -97.37199 388.2 1.0 11 0.8 3.89 356.3 A 10518.0 2018-05-01 14:17:00 <p>3 rows \u00d7 38 columns</p> <p>Next, convert the latitude and longitude to UTM coordinates (x and y). A scatter plot is created to visualize the survey points.</p> In\u00a0[3]: Copied! <pre># Convert Lat and Lon to X and Y\ndf['x'],df['y'] = crnpy.latlon_to_utm(df['LatDec'], df['LongDec'], 14, missing_values=0.0)\n\n# Create figure of survey points\nplt.figure(figsize = (5,5))\nplt.scatter(df['x'], df['y'], marker='o', edgecolor='k', facecolor='w', label = \"Observation end\")\n\n# Estimate the center of the observation, assuming each timestamp is observation end time.\ndf['x'],df['y'] = crnpy.rover_centered_coordinates(df['x'], df['y']) \nplt.scatter(df['x'], df['y'], marker='+', label=\"Observation center\")\n\nplt.ticklabel_format(scilimits = (-5,5))\nplt.xlabel('Easting')\nplt.ylabel('Northing')\nplt.legend(loc=[1.1,.8])\nplt.show()\n</pre> # Convert Lat and Lon to X and Y df['x'],df['y'] = crnpy.latlon_to_utm(df['LatDec'], df['LongDec'], 14, missing_values=0.0)  # Create figure of survey points plt.figure(figsize = (5,5)) plt.scatter(df['x'], df['y'], marker='o', edgecolor='k', facecolor='w', label = \"Observation end\")  # Estimate the center of the observation, assuming each timestamp is observation end time. df['x'],df['y'] = crnpy.rover_centered_coordinates(df['x'], df['y'])  plt.scatter(df['x'], df['y'], marker='+', label=\"Observation center\")  plt.ticklabel_format(scilimits = (-5,5)) plt.xlabel('Easting') plt.ylabel('Northing') plt.legend(loc=[1.1,.8]) plt.show()  <p>The counts are normalized to counts per minute in case some observations covered a different timespan and total neutron counts are computed.</p> In\u00a0[4]: Copied! <pre># Define columns names\ncounts_colums = ['N1Cts', 'N2Cts', 'N3Cts','N4Cts', 'N5Cts', 'N6Cts', 'N7Cts', 'N8Cts']\ncont_times_col = ['N1ETsec', 'N1ETsec', 'N3ETsec','N3ETsec', 'N5ETsec', 'N5ETsec', 'N7ETsec', 'N7ETsec']\n\n# Compute total neutron counts\ndf['total_raw_counts'] = crnpy.total_raw_counts(df[counts_colums])\n</pre> # Define columns names counts_colums = ['N1Cts', 'N2Cts', 'N3Cts','N4Cts', 'N5Cts', 'N6Cts', 'N7Cts', 'N8Cts'] cont_times_col = ['N1ETsec', 'N1ETsec', 'N3ETsec','N3ETsec', 'N5ETsec', 'N5ETsec', 'N7ETsec', 'N7ETsec']  # Compute total neutron counts df['total_raw_counts'] = crnpy.total_raw_counts(df[counts_colums])  In\u00a0[5]: Copied! <pre># Define transect start and end dates\nstart_date = df.iloc[0]['timestamp']\nend_date = df.iloc[-1]['timestamp']\n\n#Find stations with cutoff rigidity similar to estimated by lat,lon\ncrnpy.find_neutron_monitor(crnpy.cutoff_rigidity(df['LatDec'][0], df['LongDec'][0]), \n                             start_date=start_date, end_date=end_date)\n</pre> # Define transect start and end dates start_date = df.iloc[0]['timestamp'] end_date = df.iloc[-1]['timestamp']  #Find stations with cutoff rigidity similar to estimated by lat,lon crnpy.find_neutron_monitor(crnpy.cutoff_rigidity(df['LatDec'][0], df['LongDec'][0]),                               start_date=start_date, end_date=end_date)  <pre>\nSelect a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\n\nYour cutoff rigidity is 2.99 GV\n    STID                          NAME     R  Altitude_m  Period available\n13  DRBS                       Dourbes  3.18         225              True\n31  MCRL  Mobile Cosmic Ray Laboratory  2.46         200              True\n33  MOSC                        Moscow  2.43         200              True\n40  NEWK                        Newark  2.40          50              True\n20  IRK3                     Irkustk 3  3.64        3000              True\n21  IRKT                       Irkustk  3.64         435              True\n</pre> Out[5]: STID NAME R Altitude_m Period available 13 DRBS Dourbes 3.18 225 True 31 MCRL Mobile Cosmic Ray Laboratory 2.46 200 True 33 MOSC Moscow 2.43 200 True 40 NEWK Newark 2.40 50 True 20 IRK3 Irkustk 3 3.64 3000 True 21 IRKT Irkustk 3.64 435 True In\u00a0[6]: Copied! <pre># Download incoming neutron flux data from the Neutron Monitor Database (NMDB).\n# Use utc_offset for Central Standard Time.\nnmdb = crnpy.get_incoming_neutron_flux(start_date, end_date, station=\"NEWK\", utc_offset=-6)\n</pre> # Download incoming neutron flux data from the Neutron Monitor Database (NMDB). # Use utc_offset for Central Standard Time. nmdb = crnpy.get_incoming_neutron_flux(start_date, end_date, station=\"NEWK\", utc_offset=-6)  In\u00a0[7]: Copied! <pre># Interpolate incoming neutron flux to match the timestamps in our rover data\ndf['incoming_flux'] = crnpy.interpolate_incoming_flux(nmdb['timestamp'], nmdb['counts'], df['timestamp'])\n</pre> # Interpolate incoming neutron flux to match the timestamps in our rover data df['incoming_flux'] = crnpy.interpolate_incoming_flux(nmdb['timestamp'], nmdb['counts'], df['timestamp'])  In\u00a0[8]: Copied! <pre># Compute correction factor for incoming neutron flux\ndf['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df['incoming_flux'],\n                                          incoming_Ref=df['incoming_flux'].iloc[0])\n</pre> # Compute correction factor for incoming neutron flux df['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df['incoming_flux'],                                           incoming_Ref=df['incoming_flux'].iloc[0])  In\u00a0[9]: Copied! <pre># Fill NaN values in atmospheric data\ndf[['barometric_pressure_Avg', 'relative_humidity_Avg', 'air_temperature_Avg']] = df[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')\n\n# Compute the pressure correction factor\ndf['fp'] = crnpy.correction_pressure(pressure=df['barometric_pressure_Avg'], Pref=df['barometric_pressure_Avg'].mean(), L=130)\n\n# Estimate the absolute humidity and compute the vapor pressure correction factor\ndf['abs_humidity'] = crnpy.abs_humidity(df['relative_humidity_Avg'], df['air_temperature_Avg'])\ndf['fw'] = crnpy.correction_humidity(df['abs_humidity'], Aref=0)\n</pre> # Fill NaN values in atmospheric data df[['barometric_pressure_Avg', 'relative_humidity_Avg', 'air_temperature_Avg']] = df[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')  # Compute the pressure correction factor df['fp'] = crnpy.correction_pressure(pressure=df['barometric_pressure_Avg'], Pref=df['barometric_pressure_Avg'].mean(), L=130)  # Estimate the absolute humidity and compute the vapor pressure correction factor df['abs_humidity'] = crnpy.abs_humidity(df['relative_humidity_Avg'], df['air_temperature_Avg']) df['fw'] = crnpy.correction_humidity(df['abs_humidity'], Aref=0)  In\u00a0[10]: Copied! <pre># Apply correction factors\ndf['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])\n</pre> # Apply correction factors df['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])  <p>The corrected counts are smoothed using a 2D smoothing function. The smoothed counts are then converted to volumetric water content (VWC) using the counts_to_vwc function.</p> In\u00a0[11]: Copied! <pre># Smooth variable\ndf['corrected_neutrons_smoothed'] = crnpy.spatial_average(df['x'],\n                                        df['y'],\n                                        df['total_corrected_neutrons'],\n                                        buffer= 800, method='median', rnd=True)\n\n# Estimate lattice water based on texture\nlattice_water = crnpy.lattice_water(clay_content=0.35)\n\n# Estimate Soil Volumetric Water Content\ndf['VWC'] = crnpy.counts_to_vwc(df['corrected_neutrons_smoothed'], N0=550, bulk_density=1.3, Wlat=lattice_water, Wsoc=0.01)\n\n# Drop VWC NaN values before interpolating values\ndf = df.dropna(subset = ['VWC'])\n\n# Interpolate variable using IDW (https://en.wikipedia.org/wiki/Inverse_distance_weighting)\nX_pred, Y_pred, Z_pred = crnpy.interpolate_2d(df['x'],\n                                        df['y'],\n                                        df['VWC'],\n                                        dx=250, dy=250, method='idw')\n</pre> # Smooth variable df['corrected_neutrons_smoothed'] = crnpy.spatial_average(df['x'],                                         df['y'],                                         df['total_corrected_neutrons'],                                         buffer= 800, method='median', rnd=True)  # Estimate lattice water based on texture lattice_water = crnpy.lattice_water(clay_content=0.35)  # Estimate Soil Volumetric Water Content df['VWC'] = crnpy.counts_to_vwc(df['corrected_neutrons_smoothed'], N0=550, bulk_density=1.3, Wlat=lattice_water, Wsoc=0.01)  # Drop VWC NaN values before interpolating values df = df.dropna(subset = ['VWC'])  # Interpolate variable using IDW (https://en.wikipedia.org/wiki/Inverse_distance_weighting) X_pred, Y_pred, Z_pred = crnpy.interpolate_2d(df['x'],                                         df['y'],                                         df['VWC'],                                         dx=250, dy=250, method='idw')  <p>A gridded map of the Volumetric Water Content (VWC) is created using the interpolated x, y, and VWC values.</p> In\u00a0[12]: Copied! <pre># Show interpolated grid\ncmap = 'RdYlBu'\n\nplt.figure(figsize=(6,6))\nplt.title('Gridded map')\nplt.pcolormesh(X_pred, Y_pred, Z_pred, cmap=cmap)\nplt.colorbar(label=r\"Volumetric Water Content $(cm^3 \\cdot cm^{-3})$\", location='right')\nplt.scatter(df['x'], df['y'], marker='o', edgecolor='k', facecolor='w')\nplt.ticklabel_format(scilimits=(-5,5))\nplt.xlabel('Easting', size=14)\nplt.ylabel('Northing', size=14)\nplt.show()\n</pre> # Show interpolated grid cmap = 'RdYlBu'  plt.figure(figsize=(6,6)) plt.title('Gridded map') plt.pcolormesh(X_pred, Y_pred, Z_pred, cmap=cmap) plt.colorbar(label=r\"Volumetric Water Content $(cm^3 \\cdot cm^{-3})$\", location='right') plt.scatter(df['x'], df['y'], marker='o', edgecolor='k', facecolor='w') plt.ticklabel_format(scilimits=(-5,5)) plt.xlabel('Easting', size=14) plt.ylabel('Northing', size=14) plt.show()  <p>Finally, a contour map of the VWC is created and displayed.</p> In\u00a0[13]: Copied! <pre># Show contour map\ncmap = 'RdYlBu'\n\nplt.figure(figsize=(7,6))\nplt.title('Contours')\nplt.contourf(X_pred, Y_pred, Z_pred, cmap=cmap)\nplt.ticklabel_format(scilimits=(-5,5))\nplt.colorbar(label=r\"Volumetric Water Content $(cm^3 \\cdot cm^{-3})$\", location='right')\nplt.scatter(df['x'], df['y'], marker='o', edgecolor='k', facecolor='w')\nplt.show()\n</pre> # Show contour map cmap = 'RdYlBu'  plt.figure(figsize=(7,6)) plt.title('Contours') plt.contourf(X_pred, Y_pred, Z_pred, cmap=cmap) plt.ticklabel_format(scilimits=(-5,5)) plt.colorbar(label=r\"Volumetric Water Content $(cm^3 \\cdot cm^{-3})$\", location='right') plt.scatter(df['x'], df['y'], marker='o', edgecolor='k', facecolor='w') plt.show()"},{"location":"examples/rover/Hydroinnova_rover_example/#processing-and-analyzing-data-from-a-roving-detector","title":"Processing and analyzing data from a roving detector\u00b6","text":"<p>This tutorial demonstrates how to process and analyze data from a rover using the Cosmic Ray Neutron Python (CRNPy) library. The steps include loading the data, converting geographical coordinates to Cartesian coordinates, normalizing neutron counts, correcting for atmospheric effects, and visualizing the results.</p> <p>First import the required packages.</p>"},{"location":"examples/rover/Hydroinnova_rover_example/#neutron-count-correction","title":"Neutron count correction\u00b6","text":""},{"location":"examples/rover/Hydroinnova_rover_example/#incommng-neutron-flux","title":"Incommng neutron flux\u00b6","text":"<p>Find stations with a cutoff rigidity similar to the estimated value based on the latitude and longitude, ensuring that the reference station is under a similar earth electromagnetic field. Note that the station is hardcoded in the second line as <code>station=\"NEWK\"</code>. The user is required to manually define this after considering the potential options suggested. See <code>get_incoming_neutron_flux()</code>, <code>find_neutron_monitor()</code> and <code>correction_incoming_flux()</code>.</p>"},{"location":"examples/rover/Hydroinnova_rover_example/#atmospheric-correction","title":"Atmospheric correction\u00b6","text":"<p>NaN values in the atmospheric data are filled. The neutron counts are then corrected for atmospheric variables. See <code>correction_humidity()</code> and <code>correction_pressure()</code></p>"},{"location":"examples/stationary/example_RDT_station/","title":"Processing and analyzing data from a stationary detector","text":"In\u00a0[1]: Copied! <pre># Import required libraries\nimport crnpy\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n</pre> # Import required libraries import crnpy  import pandas as pd import numpy as np import matplotlib.pyplot as plt  In\u00a0[2]: Copied! <pre># Load observations from a stationary detector\nfilepath = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/stationary/rdt.csv\"\n\n# Read the DataFrame\ndf = pd.read_csv(filepath, names=['timestamp','barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg','DP','BattVolt','counts_1_Tot','counts_2_Tot','counts_3_Tot'])\n\n# Parse timestamps\ndf['timestamp'] = pd.to_datetime(df['timestamp'])\n\n# Display a few rows of the dataset\ndf.head(3)\n</pre> # Load observations from a stationary detector filepath = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/stationary/rdt.csv\"  # Read the DataFrame df = pd.read_csv(filepath, names=['timestamp','barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg','DP','BattVolt','counts_1_Tot','counts_2_Tot','counts_3_Tot'])  # Parse timestamps df['timestamp'] = pd.to_datetime(df['timestamp'])  # Display a few rows of the dataset df.head(3)  Out[2]: timestamp barometric_pressure_Avg relative_humidity_Avg air_temperature_Avg DP BattVolt counts_1_Tot counts_2_Tot counts_3_Tot 0 2020-04-10 09:47:00 983.8 29.0 9.6 -7.4 14.4 848 716.0 742 1 2020-04-10 10:47:00 982.3 25.0 10.9 -7.9 14.3 436 7200.0 796 2 2020-04-10 11:17:00 980.8 25.0 11.5 -7.4 13.7 389 396.0 354 In\u00a0[3]: Copied! <pre># Remove rows with incomplete intervals\ndf = crnpy.remove_incomplete_intervals(df, timestamp_col='timestamp', integration_time=3600, remove_first=True)\n\n# Fill missing timestamps to create a conplete record\ndf = crnpy.fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True)\n</pre> # Remove rows with incomplete intervals df = crnpy.remove_incomplete_intervals(df, timestamp_col='timestamp', integration_time=3600, remove_first=True)  # Fill missing timestamps to create a conplete record df = crnpy.fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True)  <pre>Removed a total of 48 rows.\nAdded a total of 60 missing timestamps.\n</pre> <p>Utilize the <code>total_raw_counts()</code> function to calculate the total counts across all detectors. After calculating the total counts, identify outliers using the <code>is_outlier()</code> function.</p> In\u00a0[4]: Copied! <pre># Flag and fill outliers\ncols_counts = ['counts_1_Tot','counts_2_Tot','counts_3_Tot']\nfor col in cols_counts:\n    \n    # Find outliers\n    idx_outliers = crnpy.is_outlier(df[col], method='scaled_mad', min_val=500, max_val=2000)\n    df.loc[idx_outliers,col] = np.nan\n    \n    # Fill missing values with linear interpolation and round to nearest integer\n    df[col] = df[col].interpolate(method='linear', limit=3, limit_direction='both').round()\n</pre> # Flag and fill outliers cols_counts = ['counts_1_Tot','counts_2_Tot','counts_3_Tot'] for col in cols_counts:          # Find outliers     idx_outliers = crnpy.is_outlier(df[col], method='scaled_mad', min_val=500, max_val=2000)     df.loc[idx_outliers,col] = np.nan          # Fill missing values with linear interpolation and round to nearest integer     df[col] = df[col].interpolate(method='linear', limit=3, limit_direction='both').round()  In\u00a0[5]: Copied! <pre># Compute total cunts\ndf['total_raw_counts'] = crnpy.total_raw_counts(df[cols_counts], nan_strategy='average')\n</pre> # Compute total cunts df['total_raw_counts'] = crnpy.total_raw_counts(df[cols_counts], nan_strategy='average')  In\u00a0[6]: Copied! <pre># Plot total counts\n# Create a new figure and plot the data\nfig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size\nax.plot(df['timestamp'], df['total_raw_counts'], label='Raw Counts', color='black', linewidth=.8)\n# Set the labels for the x-axis and y-axis\nax.set_xlabel(\"Date\")\nax.set_ylabel(\"Total Raw Counts\")\n\n# Set the title of the plot\nax.set_title('Total Raw Counts Over Time')\n\n# Adjust size of axes labels\nax.tick_params(axis='both', which='major')\n\n# Show the plot\nplt.show()\n</pre> # Plot total counts # Create a new figure and plot the data fig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size ax.plot(df['timestamp'], df['total_raw_counts'], label='Raw Counts', color='black', linewidth=.8) # Set the labels for the x-axis and y-axis ax.set_xlabel(\"Date\") ax.set_ylabel(\"Total Raw Counts\")  # Set the title of the plot ax.set_title('Total Raw Counts Over Time')  # Adjust size of axes labels ax.tick_params(axis='both', which='major')  # Show the plot plt.show()  In\u00a0[7]: Copied! <pre># Define study start and end dates\nstart_date = df.iloc[0]['timestamp']\nend_date = df.iloc[-1]['timestamp']\n\n# Define geographic coordiantes\nlat = 39.110596\nlon = -96.613050\n\n# Find stations with cutoff rigidity similar to estimated by lat,lon\ncrnpy.find_neutron_monitor(crnpy.cutoff_rigidity(lat, lon), start_date=start_date, end_date=end_date, verbose=False)\n</pre> # Define study start and end dates start_date = df.iloc[0]['timestamp'] end_date = df.iloc[-1]['timestamp']  # Define geographic coordiantes lat = 39.110596 lon = -96.613050  # Find stations with cutoff rigidity similar to estimated by lat,lon crnpy.find_neutron_monitor(crnpy.cutoff_rigidity(lat, lon), start_date=start_date, end_date=end_date, verbose=False)  <pre>\nSelect a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\n\nYour cutoff rigidity is 2.87 GV\n     STID     NAME     R  Altitude_m  Period available\n13   DRBS  Dourbes  3.18         225              True\n40   NEWK   Newark  2.40          50              True\n28  KIEL2   KielRT  2.36          54              True\n21   IRKT  Irkustk  3.64         435              True\n</pre> Out[7]: STID NAME R Altitude_m Period available 13 DRBS Dourbes 3.18 225 True 40 NEWK Newark 2.40 50 True 28 KIEL2 KielRT 2.36 54 True 21 IRKT Irkustk 3.64 435 True In\u00a0[8]: Copied! <pre># Download incoming neutron flux data from the Neutron Monitor Database (NMDB).\n# Use utc_offset for Central Standard Time.\nnmdb = crnpy.get_incoming_neutron_flux(start_date, end_date, station=\"IRKT\", utc_offset=-6)\n</pre> # Download incoming neutron flux data from the Neutron Monitor Database (NMDB). # Use utc_offset for Central Standard Time. nmdb = crnpy.get_incoming_neutron_flux(start_date, end_date, station=\"IRKT\", utc_offset=-6)  In\u00a0[9]: Copied! <pre># Interpolate incoming neutron flux to match the timestamps in our station data\ndf['incoming_flux'] = crnpy.interpolate_incoming_flux(nmdb['timestamp'], nmdb['counts'], df['timestamp'])\n</pre> # Interpolate incoming neutron flux to match the timestamps in our station data df['incoming_flux'] = crnpy.interpolate_incoming_flux(nmdb['timestamp'], nmdb['counts'], df['timestamp'])  In\u00a0[10]: Copied! <pre># Compute correction factor for incoming neutron flux\ndf['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df['incoming_flux'],\n                                          incoming_Ref=df['incoming_flux'].iloc[0])\n</pre> # Compute correction factor for incoming neutron flux df['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df['incoming_flux'],                                           incoming_Ref=df['incoming_flux'].iloc[0])  In\u00a0[11]: Copied! <pre># Fill NaN values in atmospheric data and neutron counts\ndf[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']] = df[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')\n\n# Compute the pressure correction factor \ndf['fp'] = crnpy.correction_pressure(pressure=df['barometric_pressure_Avg'], Pref=df['barometric_pressure_Avg'].mean(), L=130)\n\n# Calculate the absolute humidity (g cm^-3) and the vapor pressure correction factor\ndf['abs_humidity'] = crnpy.abs_humidity(df['relative_humidity_Avg'], df['air_temperature_Avg'])\ndf['fw'] = crnpy.correction_humidity(abs_humidity=df['abs_humidity'], Aref=0)\n</pre> # Fill NaN values in atmospheric data and neutron counts df[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']] = df[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')  # Compute the pressure correction factor  df['fp'] = crnpy.correction_pressure(pressure=df['barometric_pressure_Avg'], Pref=df['barometric_pressure_Avg'].mean(), L=130)  # Calculate the absolute humidity (g cm^-3) and the vapor pressure correction factor df['abs_humidity'] = crnpy.abs_humidity(df['relative_humidity_Avg'], df['air_temperature_Avg']) df['fw'] = crnpy.correction_humidity(abs_humidity=df['abs_humidity'], Aref=0)  In\u00a0[12]: Copied! <pre># Plot all the correction factors\nfig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size\nax.plot(df['timestamp'], df['fp'], label='Pressure',color='tomato', linewidth=1)\nax.plot(df['timestamp'], df['fw'], label='Humidity', color='navy', linewidth=1)\nax.plot(df['timestamp'], df['fi'], label='Incoming Flux', color='olive', linewidth=1)\nax.set_xlabel(\"Date\")\nax.set_ylabel('Correction Factor')\nax.legend()\nax.set_title('Correction Factors for Pressure, Humidity, and Incoming Flux')\n</pre> # Plot all the correction factors fig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size ax.plot(df['timestamp'], df['fp'], label='Pressure',color='tomato', linewidth=1) ax.plot(df['timestamp'], df['fw'], label='Humidity', color='navy', linewidth=1) ax.plot(df['timestamp'], df['fi'], label='Incoming Flux', color='olive', linewidth=1) ax.set_xlabel(\"Date\") ax.set_ylabel('Correction Factor') ax.legend() ax.set_title('Correction Factors for Pressure, Humidity, and Incoming Flux')  Out[12]: <pre>Text(0.5, 1.0, 'Correction Factors for Pressure, Humidity, and Incoming Flux')</pre> In\u00a0[13]: Copied! <pre># Apply correction factors\ndf['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])\n</pre> # Apply correction factors df['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])  In\u00a0[14]: Copied! <pre>fig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size\n\n# Subplot 1: Raw and Corrected Counts\nax.plot(df['timestamp'], df['total_raw_counts'], label='Raw Counts', color='black', linestyle='dashed', linewidth=.8)\nax.plot(df['timestamp'], df['total_corrected_neutrons'], label='Corrected Counts', color='teal', linewidth=.8)\nax.set_xlabel(\"Date\")\nax.set_ylabel('Counts')\nax.legend()\nax.set_title('Raw and Corrected Counts')\n</pre> fig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size  # Subplot 1: Raw and Corrected Counts ax.plot(df['timestamp'], df['total_raw_counts'], label='Raw Counts', color='black', linestyle='dashed', linewidth=.8) ax.plot(df['timestamp'], df['total_corrected_neutrons'], label='Corrected Counts', color='teal', linewidth=.8) ax.set_xlabel(\"Date\") ax.set_ylabel('Counts') ax.legend() ax.set_title('Raw and Corrected Counts')  Out[14]: <pre>Text(0.5, 1.0, 'Raw and Corrected Counts')</pre> <p>Convert the smoothed neutron counts to Volumetric Water Content (VWC) using the <code>counts_to_vwc()</code>. The function considers the smoothed neutron counts, $N_0$ specific parameter, soil bulk density, weight percent of latent water (Wlat), and weight percent of soil organic carbon (Wsoc). After conversion, plot the VWC against the timestamp for visual analysis. <code>smooth_1d()</code> is applied for smoothing the data using a Savitzky-Golay filter.</p> In\u00a0[15]: Copied! <pre># Device N0 parameter\nN0_rdt = 3767 # Patrignani, A., Ochsner, T. E., Montag, B., &amp; Bellinger, S. (2021). A novel lithium foil cosmic-ray neutron detector for measuring field-scale soil moisture. Frontiers in Water, 3, 673185.\n\n# Estimate lattice water (%) based on texture\nlattice_water = crnpy.lattice_water(clay_content=0.35)\n\ndf['VWC'] = crnpy.counts_to_vwc(df['total_corrected_neutrons'], N0=N0_rdt, bulk_density=1.33, Wlat=lattice_water, Wsoc=0.01)\n\n# Drop any NaN beofre smoothing\ndf = df.dropna(subset=['VWC']).copy().reset_index()\n\n# Filter using the Savitzky-Golay filter, drop NaN values and timestamps\ndf['VWC'] = crnpy.smooth_1d(df['VWC'], window=11, order=3, method=\"savitzky_golay\")\n</pre> # Device N0 parameter N0_rdt = 3767 # Patrignani, A., Ochsner, T. E., Montag, B., &amp; Bellinger, S. (2021). A novel lithium foil cosmic-ray neutron detector for measuring field-scale soil moisture. Frontiers in Water, 3, 673185.  # Estimate lattice water (%) based on texture lattice_water = crnpy.lattice_water(clay_content=0.35)  df['VWC'] = crnpy.counts_to_vwc(df['total_corrected_neutrons'], N0=N0_rdt, bulk_density=1.33, Wlat=lattice_water, Wsoc=0.01)  # Drop any NaN beofre smoothing df = df.dropna(subset=['VWC']).copy().reset_index()  # Filter using the Savitzky-Golay filter, drop NaN values and timestamps df['VWC'] = crnpy.smooth_1d(df['VWC'], window=11, order=3, method=\"savitzky_golay\")  In\u00a0[16]: Copied! <pre># Plot the obtained time series of volumetric water content\n# Create a new figure and plot the data\nfig, ax = plt.subplots(figsize=(10, 4))  # Set the figure size\nax.plot(df['timestamp'], df['VWC'], color='teal', linewidth=1.0)\n\n# Set the labels for the x-axis and y-axis\nax.set_xlabel(\"Date\")\nax.set_ylabel(r\"Volumetric Water Content ($m^3 \\cdot m^{-3}$)\")\n\n# Set the title of the plot\nax.set_title('Soil Moisture')\n\n# Show the plot\nplt.show()\n</pre> # Plot the obtained time series of volumetric water content # Create a new figure and plot the data fig, ax = plt.subplots(figsize=(10, 4))  # Set the figure size ax.plot(df['timestamp'], df['VWC'], color='teal', linewidth=1.0)  # Set the labels for the x-axis and y-axis ax.set_xlabel(\"Date\") ax.set_ylabel(r\"Volumetric Water Content ($m^3 \\cdot m^{-3}$)\")  # Set the title of the plot ax.set_title('Soil Moisture')  # Show the plot plt.show()  In\u00a0[17]: Copied! <pre># Estimate sensing depth\ndf['sensing_depth'] = crnpy.sensing_depth(df['VWC'], df['barometric_pressure_Avg'], df['barometric_pressure_Avg'].mean(), bulk_density=1.33, Wlat=lattice_water, method = \"Franz_2012\")\nprint(f\"Average sensing depth: {np.round(df['sensing_depth'].mean(),2)} cm\")\n</pre> # Estimate sensing depth df['sensing_depth'] = crnpy.sensing_depth(df['VWC'], df['barometric_pressure_Avg'], df['barometric_pressure_Avg'].mean(), bulk_density=1.33, Wlat=lattice_water, method = \"Franz_2012\") print(f\"Average sensing depth: {np.round(df['sensing_depth'].mean(),2)} cm\")  <pre>Average sensing depth: 15.36 cm\n</pre> In\u00a0[18]: Copied! <pre># Compute the storage using the exponential filter\nsurface = df['VWC']\nsubsurface = crnpy.exp_filter(df['VWC'])\n\nz_surface = 150 # Average depth in mm obtained from previous cell using crnpy.sensing_depth()\nz_subsurface = 350 # Arbitrary subsurface depth in mm\ndf['storage'] = np.sum([surface*z_surface, subsurface*z_subsurface], axis=0)\n</pre> # Compute the storage using the exponential filter surface = df['VWC'] subsurface = crnpy.exp_filter(df['VWC'])  z_surface = 150 # Average depth in mm obtained from previous cell using crnpy.sensing_depth() z_subsurface = 350 # Arbitrary subsurface depth in mm df['storage'] = np.sum([surface*z_surface, subsurface*z_subsurface], axis=0)  In\u00a0[19]: Copied! <pre># Plot the obtained time series of soil water storage\n# Create a new figure and plot the data\nfig, ax = plt.subplots(figsize=(10, 4))  # Set the figure size\nax.plot(df['timestamp'], df['storage'], color='teal', linewidth=1.0)\n\n# Set the labels for the x-axis and y-axis\nax.set_xlabel(\"Date\")\nax.set_ylabel(\"Storage (mm)\")\n\n# Set the title of the plot\nax.set_title('Soil Water Storage')\n\n# Show the plot\nplt.show()\n</pre> # Plot the obtained time series of soil water storage # Create a new figure and plot the data fig, ax = plt.subplots(figsize=(10, 4))  # Set the figure size ax.plot(df['timestamp'], df['storage'], color='teal', linewidth=1.0)  # Set the labels for the x-axis and y-axis ax.set_xlabel(\"Date\") ax.set_ylabel(\"Storage (mm)\")  # Set the title of the plot ax.set_title('Soil Water Storage')  # Show the plot plt.show()  In\u00a0[20]: Copied! <pre># Estimate the uncertainty of the volumetric water content\ndf['sigma_VWC'] = crnpy.uncertainty_vwc(df['total_raw_counts'], N0=N0_rdt, bulk_density=1.33, fp=df['fp'], fi=df['fi'], fw=df['fw'])\n\n# Plot the VWC with uncertainty as a shaded area\nfig, ax = plt.subplots(figsize=(10, 4))  # Set the figure size\nax.plot(df['timestamp'], df['VWC'], color='black', linewidth=1.0)\nax.fill_between(df['timestamp'], df['VWC']-df['sigma_VWC'], df['VWC']+df['sigma_VWC'], color='teal', alpha=0.5, label = r\"Standard Deviation $\\theta_v$\")\nax.set_title('Volumentric Water Content with Uncertainty')\nax.set_xlabel(\"Date\")\nax.set_ylabel(r\"Volumetric Water Content ($m^3 \\cdot m^{-3}$)\")\nplt.legend()\nplt.show()\n</pre> # Estimate the uncertainty of the volumetric water content df['sigma_VWC'] = crnpy.uncertainty_vwc(df['total_raw_counts'], N0=N0_rdt, bulk_density=1.33, fp=df['fp'], fi=df['fi'], fw=df['fw'])  # Plot the VWC with uncertainty as a shaded area fig, ax = plt.subplots(figsize=(10, 4))  # Set the figure size ax.plot(df['timestamp'], df['VWC'], color='black', linewidth=1.0) ax.fill_between(df['timestamp'], df['VWC']-df['sigma_VWC'], df['VWC']+df['sigma_VWC'], color='teal', alpha=0.5, label = r\"Standard Deviation $\\theta_v$\") ax.set_title('Volumentric Water Content with Uncertainty') ax.set_xlabel(\"Date\") ax.set_ylabel(r\"Volumetric Water Content ($m^3 \\cdot m^{-3}$)\") plt.legend() plt.show()"},{"location":"examples/stationary/example_RDT_station/#processing-and-analyzing-data-from-a-stationary-detector","title":"Processing and analyzing data from a stationary detector\u00b6","text":"<p>This tutorial demonstrates how to process and analyze neutron counts of epithermal neutrons recorded with a stationary cosmic-ray neutron probe consisting of three lithium-foil detectors manufactured by Radiation Detection Technologies, Inc (RDT). The tutorial covers all the steps from reading the file with raw data obtained from the probe in the field, outlier removal, atmospheric corrections, and conversion of corrected neutron counts into volumetric soil water content.</p> <p>The tutorial assumes that you are working within the <code>Anaconda</code> environment, which has all the necessary modules. Also, make sure that the <code>CRNPy</code> library is installed in your machine.</p> <p>The raw dataset to follow this example can be obtained at the .csv file.</p> <p>This device was installed in the vicinity of the KONA site of the National Ecological Observatory Network within the Konza Prairie Biological station (<code>39.110596, -96.613050, 320 meters a.s.l.</code>)</p>"},{"location":"examples/stationary/example_RDT_station/#neutron-count-correction","title":"Neutron count correction\u00b6","text":""},{"location":"examples/stationary/example_RDT_station/#incomming-neutron-flux","title":"Incomming neutron flux\u00b6","text":"<p>Find similar neutron monitors based on altitude and cutoff rigidity, which is estimated based on the geographic location using latitude and longitude values. See <code>get_incoming_neutron_flux()</code>, <code>find_neutron_monitor()</code> and <code>correction_incoming_flux()</code>.</p> <p>In this particular example we selected the <code>\"IRKT\"</code> monitor since it has a similar cuoff rigidity and altitude as our site. The reference neutron monitor will likely be different for your location. Note that the function only reports neutron monitors based on cutoff rigidity and leaves to the user the selection of a reference neutron monitor based on the altitude of the location.</p>"},{"location":"examples/stationary/example_RDT_station/#atmospheric-correction","title":"Atmospheric correction\u00b6","text":"<p>This section is about correcting the atmospheric variables. First, fill the missing values in the atmospheric data, then correct the count for atmospheric variables using <code>correction_humidity()</code> and <code>correction_pressure()</code>.</p>"},{"location":"examples/stationary/example_RDT_station/#sensing-depth-and-soil-water-storage","title":"Sensing depth and soil water storage\u00b6","text":"<p>Estimate the <code>sensing_depth()</code>  at which 86 % of the neutrons probes the soil profile, and estimate the soil water <code>storage()</code> of the top 50 cm using and exponential filter.</p>"},{"location":"examples/stationary/example_RDT_station/#uncertainty-estimation","title":"Uncertainty estimation\u00b6","text":"<p>Estimate the uncertainty of the volumetric water content using the <code>uncertainty_vwc()</code> function. Optionally see <code>uncertainty_counts()</code> for estimating the uncertainty of the neutron counts.</p>"}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Cosmic-Ray Neutron Python (CRNPy) Library","text":"<p>Welcome to the homepage of the CRNPy (Cosmic-Ray Neutron Python) library, an open-source Python library designed for the processing and conversion of raw neutron counts from cosmic-ray neutron probes (CRNP) into soil moisture data.</p> <p>This library has been developed with the intent of providing a comprehensive yet easy-to-use workflow for processing raw data from a variety of CRNP, encompassing multiple manufacturers and models.</p>"},{"location":"#statement-of-need","title":"Statement of Need","text":"<p>CRNPs are a valuable tool for non-invasive soil moisture estimation at the hectometer scale (e.g., typical agricultural fields), filling the gap between point-level sensors and large-scale (i.e., several kilometers) remote sensors onboard orbiting satellites. However, cleaning, processing, and analyzing CRNP data involves multiple corrections and filtering steps spread across multiple peer-reviewed manuscripts. CRNPy simplifies these steps by providing a complete, user-friendly, and well-documented library with minimal dependencies that includes examples to convert raw CRNP data into soil moisture. The library is designed to be accessible to both researchers and instrument manufacturers. Unlike other similar libraries, CRNPy does not require any specific naming convention for the input data or large external data sources, or reanalysis data.</p>"},{"location":"#key-features","title":"Key Features","text":"<ul> <li> <p>Versatile and instrument agnostic: CRNPy can handle data from various CRNP manufacturers and models. It has been successfully tested on both roving and stationary CRNP.</p> </li> <li> <p>Modular: The library is designed to be modular, allowing users to easily customize the processing workflow to their needs.</p> </li> </ul>"},{"location":"#installation","title":"Installation","text":"<p>To install the CRNPy library, you can use Python's package manager. Open a terminal and type:</p> <p><code>pip install crnpy</code></p> <p>from the Jupyter notebook, type:</p> <p><code>!pip install crnpy</code></p> <p>Ideally dependencies should be installed automatically. If not, you can install them manually by typing:</p> <p><code>pip install -r requirements.txt</code></p> <p>The CRNPy library is compatible with Python 3.7 and above. See requirements.txt for a list of dependencies.</p>"},{"location":"#authors","title":"Authors","text":"<p>The CRNPy library was developed at the Kansas State University Soil Water Processes Lab by:</p> <ul> <li> <p>Joaquin Peraza</p> </li> <li> <p>Andres Patrignani</p> </li> </ul> <p>The Soil Water Processes Lab at Kansas State University combines a range of experimental and computational approaches to tackle pressing issues in soil and water research. The development of the CRNPy library is a step forward to creating reproducible data processing workflows across the scientific community using cosmic-ray neutrons probes for soil moisture sensing. </p>"},{"location":"#community-guidelines","title":"Community Guidelines","text":""},{"location":"#contributing","title":"Contributing","text":"<p>To contribute to the software, please first fork the repository and create your own branch from <code>main</code>. Ensure your code adheres to our established code structure and includes appropriate test/examples coverage. CRNPy source code is located in the <code>/src/crnpy/</code> folder, and tests implemented using <code>pytest</code> are stored in the <code>/src/tests/</code> folder. Submit a pull request with a clear and detailed description of your changes to include them in the main repository.</p>"},{"location":"#reporting-issues","title":"Reporting Issues","text":"<p>If you encounter any issues or problems with the software, please report them on our issues page. Include a detailed description of the issue, steps to reproduce the problem, any error messages you received, and details about your operating system and software version.</p>"},{"location":"#seeking-support","title":"Seeking Support","text":"<p>If you need support, please first refer to the documentation. If you still require assistance, post a question on the issues page with the <code>question</code> tag. For private inquiries, you can reach us via email at jperaza@ksu.edu or andrespatrignani@ksu.edu.</p>"},{"location":"correction_routines/","title":"Correction routines","text":""},{"location":"correction_routines/#overview","title":"Overview","text":"<p>The CRNPy library provides the tools to correct and process neutron counts recorded with both stationary and roving cosmic-ray neutron probes. Below are two flowcharts describing the typical steps from the correction of raw neutron counts to the conversion into volumetric soil water content.</p>"},{"location":"correction_routines/#stationary-crnp-processing","title":"Stationary CRNP processing","text":"<p> Dashed lines indicate optional steps. See the complete example notebook.</p>"},{"location":"correction_routines/#roving-crnp-processing","title":"Roving CRNP processing","text":"<p> Dashed lines indicate optional steps. See the complete example notebook.</p>"},{"location":"correction_routines/#incoming-neutron-flux","title":"Incoming neutron flux","text":"<p>The CRNPy library includes a complete set of methods for correcting the raw observed neutron counts for natural variation in the incoming neutron flux, including a set of tools for searching and downloading data from reference neutron monitors from the NMDB database (www.nmdb.eu) with similar the cut-off rigidity as the study location (Klein et al., 2009; Shea &amp; Smart., 2019).</p> Incoming neutron flux correction factor $fi = \\frac{I_{m}}{I_{0}}$ $ fi $: Incoming neutron flux correction factor $ I_{m} $: Measured incoming neutron flux $ I_{0} $: Reference incoming neutron flux at a given time. <p>Implementation</p> <p>See  crnpy.crnpy.cutoff_rigidity, crnpy.crnpy.find_neutron_monitor, crnpy.crnpy.get_incoming_neutron_flux, crnpy.crnpy.interpolate_incoming_flux and crnpy.crnpy.incoming_flux_correction documentation for the implementation details.</p>"},{"location":"correction_routines/#atmospheric-corrections","title":"Atmospheric corrections","text":"<p>The CRNPy library also provides functions for correcting raw neutron counts for atmospheric pressure, air humidity, and air temperature (Andreasen et al., 2017; Rosolem et al., 2013).</p> Pressure correction Atmospheric water correction $fp = exp(\\frac{P_{0} - P}{L})$ $fw = 1 + 0.0054*(A - Aref)$ $fp$: Atmospheric pressure correction factor $fw$: Atmospheric water correction factor $P_{0}$: Reference atmospheric pressure (for e.g. long-term average) $A$: Atmospheric absolute humidity (g/m3) $P$: Measured atmospheric pressure $Aref$: Reference atmospheric absolute humidity (g/m3) $L$: Mass attenuation factor for high-energy neutrons in air <p>Implementation</p> <p>See crnpy.crnpy.humidity_correction and crnpy.crnpy.pressure_correction documentation for the implementation details.</p>"},{"location":"correction_routines/#biomass-correction","title":"Biomass correction","text":"<p>The library provides a function for correcting neutron counts for the effects of above-ground biomass by combining an approach for estimating biomass water equivalent (BWE) from in-situ biomass samples and the BWE correction factor (Baatz et al., 2015).</p> Biomass correction $fb = 1 - bwe*r2_N0$ $fb$: Biomass correction factor $bwe$: Biomass water equivalent $r2_N0$: Ratio of the neutron counts reduction ($counts kg^-1$) to the neutron calibration constant (N0) <p>Implementation</p> <p>See crnpy.crnpy.bwe_correction and crnpy.crnpy.biomass_to_bwe documentation for the implementation details.</p>"},{"location":"correction_routines/#road-correction","title":"Road correction","text":"<p>The lCRNPY library includes functions to correct for the effect of roads during rover surveys which account for the field soil water content and the road water content following the approach proposed by Schr\u00f6n et al., (2018).</p> Road correction $fr = 1 + F1 \\cdot F2 \\cdot F3$ $fr$: Road correction factor $F1$: Road geometry term $F2$: Road moisture term $F3$: Road distance term <p>Implementation</p> <p>See crnpy.crnpy.road_correction documentation for the implementation details.</p>"},{"location":"correction_routines/#additional-corrections","title":"Additional corrections","text":"<p>Other correction routines includes corrections for soil lattice water and total soil carbon that are known to affect the attentuation of epithermal cosmic-ray neutrons. A function to estimate the soil lattice water content based on clay content and soil organic carbon content was developed using soil samples collected across the state of Kansas.</p> <p>Implementation</p> <p>See crnpy.crnpy.estimate_lattice_water and crnpy.crnpy.counts_to_vwc documentation for the implementation details.</p> <p>References</p> <p>Klein, K.-L., Steigies, C., &amp; Nmdb Team. (2009). WWW.NMDB.EU: The real-time Neutron Monitor database. EGU General Assembly Conference Abstracts, 5633.</p> <p>Shea, M., &amp; Smart, D. (2019). Re-examination of the first five ground-level events. International Cosmic Ray Conference (ICRC2019), 36, 1149.</p> <p>Smart, D., &amp; Shea, M. (2001). Geomagnetic cutoff rigidity computer program: Theory, software description and example.</p> <p>Andreasen, M., Jensen, K. H., Desilets, D., Franz, T. E., Zreda, M., Bogena, H. R., &amp; Looms, M. C. (2017). Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone Journal, 16(8), 1\u201311.</p> <p>Rosolem, R., Shuttleworth, W. J., Zreda, M., Franz, T. E., Zeng, X., &amp; Kurc, S. A. (2013). The effect of atmospheric water vapor on neutron count in the cosmic-ray soil moisture observing system. Journal of Hydrometeorology, 14(5), 1659\u20131671.</p> <p>Zreda, M., Desilets, D., Ferr\u00e9, T., &amp; Scott, R. L. (2008). Measuring soil moisture content non-invasively at intermediate spatial scale using cosmic-ray neutrons. Geophysical Research Letters, 35(21).</p> <p>Dong, J., &amp; Ochsner, T. E. (2018). Soil texture often exerts a stronger influence than precipitation on mesoscale soil moisture patterns. Water Resources Research, 54(3), 2199\u20132211.</p> <p>Wahbi, A., Heng, L., Dercon, G., Wahbi, A., &amp; Avery, W. (2018). In situ destructive sampling. Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent, 5\u20139.</p> <p>Baatz, R., Bogena, H., Hendricks Franssen, H.-J., Huisman, J., Montzka, C., &amp; Vereecken, H. (2015). An empirical vegetation correction for soil water content quantification using cosmic ray probes. Water Resources Research, 51(4), 2030\u20132046.</p> <p>Schr\u00f6n, M., Rosolem, R., K\u00f6hli, M., Piussi, L., Schr\u00f6ter, I., Iwema, J., K\u00f6gler, S., Oswald, S. E., Wollschl\u00e4ger, U., Samaniego, L., &amp; others. (2018). Cosmic-ray neutron rover surveys of field soil moisture and the influence of roads. Water Resources Research, 54(9), 6441\u20136459.</p> <p>Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079\u20134099, https://doi.org/10.5194/hess-16-4079-2012, 2012.</p>"},{"location":"reference/","title":"Reference","text":"<p><code>crnpy</code> is a Python package for processing cosmic ray neutron data.</p> <p>Created by Joaquin Peraza and Andres Patrignani.</p> <p><code>crnpy</code> is a package for processing observations from cosmic ray neutron detectors.</p> <p>Modules exported by this package:</p> <ul> <li><code>crnpy</code>: Provide several functions to process observations from cosmic ray neutron detectors.</li> </ul>"},{"location":"reference/#crnpy.crnpy.abs_humidity","title":"<code>abs_humidity(relative_humidity, temp)</code>","text":"<p>Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.</p> <p>Parameters:</p> Name Type Description Default <code>relative_humidity</code> <code>float</code> <p>relative humidity (%)</p> required <code>temp</code> <code>float</code> <p>temperature (Celsius)</p> required <p>Returns:</p> Name Type Description <code>float</code> <p>actual vapor pressure (g m^-3)</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def abs_humidity(relative_humidity, temp):\n    \"\"\"\n    Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.\n\n    Args:\n        relative_humidity (float): relative humidity (%)\n        temp (float): temperature (Celsius)\n\n    Returns:\n        float: actual vapor pressure (g m^-3)\n    \"\"\"\n\n    ### Atmospheric water vapor factor\n    # Saturation vapor pressure\n    e_sat = 0.611 * np.exp(17.502 * temp / (\n            temp + 240.97)) * 1000  # in Pascals Eq. 3.8 p.41 Environmental Biophysics (Campbell and Norman)\n\n    # Vapor pressure Pascals\n    Pw = e_sat * relative_humidity / 100\n\n    # Absolute humidity (g/m^3)\n    C = 2.16679  # g K/J;\n    abs_h = C * Pw / (temp + 273.15)\n    return abs_h\n</code></pre>"},{"location":"reference/#crnpy.crnpy.atmospheric_depth","title":"<code>atmospheric_depth(elevation, latitude)</code>","text":"<p>Function to estimate the atmospheric depth for any point on Earth according to McJannet and Desilets, 2023</p> <p>This function is required in the calculation of the location-dependent reference correction proposed by McJannet and Desilets, 2023.</p> <p>Parameters:</p> Name Type Description Default <code>elevation</code> <code>float</code> <p>Elevation in meters above sea level.</p> required <code>latitude</code> <code>float</code> <p>Geographic latitude in decimal degrees. Value in range -90 to 90</p> required <p>Returns:</p> Type Description <code>float</code> <p>Atmospheric depth in g/cm2</p> References <p>Atmosphere, U. S. (1976). US standard atmosphere. National Oceanic and Atmospheric Administration.</p> <p>McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic\u2010Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def atmospheric_depth(elevation, latitude):\n    \"\"\"Function to estimate the atmospheric depth for any point on Earth according to McJannet and Desilets, 2023\n\n    This function is required in the calculation of the location-dependent reference correction proposed by McJannet and Desilets, 2023.\n\n    Args:\n        elevation (float): Elevation in meters above sea level.\n        latitude (float): Geographic latitude in decimal degrees. Value in range -90 to 90\n\n    Returns:\n        (float): Atmospheric depth in g/cm2\n\n    References:\n        Atmosphere, U. S. (1976). US standard atmosphere. National Oceanic and Atmospheric Administration.\n\n        McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic\u2010Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.\n    \"\"\"\n\n    density_of_rock = 2670  # Density of rock in kg/m3\n    air_pressure_sea_level = 1013.25  # Air pressure at sea level in hPa\n    air_molar_mass = 0.0289644  # Air molar mass in kg/mol\n    universal_gas_constant = 8.3144598  # Universal gas constant in J/(mol*K)\n    reference_temperature = 288.15  # Reference temperature Kelvin\n    temperature_lapse_rate = -0.0065  # Temperature lapse rate in K/m\n\n    # Gravity at sea-level calculation\n    gravity_sea_level = 9.780327 * (\n            1 + 0.0053024 * np.sin(np.radians(latitude)) ** 2 - 0.0000058 * np.sin(2 * np.radians(latitude)) ** 2)\n    # Free air correction\n    free_air = -3.086 * 10 ** -6 * elevation\n    # Bouguer correction\n    bouguer_corr = 4.194 * 10 ** -10 * density_of_rock * elevation\n    # Total gravity\n    gravity = gravity_sea_level + free_air + bouguer_corr\n\n    # Air pressure calculation\n    reference_air_pressure = air_pressure_sea_level * (\n                1 + temperature_lapse_rate / reference_temperature * elevation) ** ((-gravity * air_molar_mass) / (\n                universal_gas_constant * temperature_lapse_rate))\n\n    # Atmospheric depth calculation\n    atmospheric_depth = (10 * reference_air_pressure) / gravity\n    return atmospheric_depth\n</code></pre>"},{"location":"reference/#crnpy.crnpy.biomass_to_bwe","title":"<code>biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494)</code>","text":"<p>Function to convert biomass to biomass water equivalent.</p> <p>Parameters:</p> Name Type Description Default <code>biomass_dry</code> <code>array or Series or DataFrame</code> <p>Above ground dry biomass in kg m-2.</p> required <code>biomass_fresh</code> <code>array or Series or DataFrame</code> <p>Above ground fresh biomass in kg m-2.</p> required <code>fWE</code> <code>float</code> <p>Stoichiometric ratio of H2O to organic carbon molecules in the plant (assuming this is mostly cellulose) Default is 0.494 (Wahbi &amp; Avery, 2018).</p> <code>0.494</code> <p>Returns:</p> Type Description <code>array or Series or DataFrame</code> <p>Biomass water equivalent in kg m-2.</p> References <p>Wahbi, A., Avery, W. (2018). In Situ Destructive Sampling. In: Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent. Springer, Cham. https://doi.org/10.1007/978-3-319-69539-6_2</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494):\n    \"\"\"Function to convert biomass to biomass water equivalent.\n\n    Args:\n        biomass_dry (array or pd.Series or pd.DataFrame): Above ground dry biomass in kg m-2.\n        biomass_fresh (array or pd.Series or pd.DataFrame): Above ground fresh biomass in kg m-2.\n        fWE (float): Stoichiometric ratio of H2O to organic carbon molecules in the plant (assuming this is mostly cellulose)\n            Default is 0.494 (Wahbi &amp; Avery, 2018).\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Biomass water equivalent in kg m-2.\n\n    References:\n        Wahbi, A., Avery, W. (2018). In Situ Destructive Sampling. In:\n        Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent.\n        Springer, Cham. https://doi.org/10.1007/978-3-319-69539-6_2\n    \"\"\"\n    return (biomass_fresh - biomass_dry) + fWE * biomass_dry\n</code></pre>"},{"location":"reference/#crnpy.crnpy.correction_bwe","title":"<code>correction_bwe(counts, bwe, r2_N0=0.05)</code>","text":"<p>Function to correct for biomass effects in neutron counts. following the approach described in Baatz et al., 2015.</p> <p>Parameters:</p> Name Type Description Default <code>counts</code> <code>array or Series or DataFrame</code> <p>Array of ephithermal neutron counts.</p> required <code>bwe</code> <code>float</code> <p>Biomass water equivalent kg m-2.</p> required <code>r2_N0</code> <code>float</code> <p>Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.</p> <code>0.05</code> <p>Returns:</p> Type Description <code>array or Series or DataFrame</code> <p>Array of corrected neutron counts for biomass effects.</p> References <p>Baatz, R., H. R. Bogena, H.-J. Hendricks Franssen, J. A. Huisman, C. Montzka, and H. Vereecken (2015), An empiricalvegetation correction for soil water content quantification using cosmic ray probes, Water Resour. Res., 51, 2030\u20132046, doi:10.1002/ 2014WR016443.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def correction_bwe(counts, bwe, r2_N0=0.05):\n    \"\"\"Function to correct for biomass effects in neutron counts.\n    following the approach described in Baatz et al., 2015.\n\n    Args:\n        counts (array or pd.Series or pd.DataFrame): Array of ephithermal neutron counts.\n        bwe (float): Biomass water equivalent kg m-2.\n        r2_N0 (float): Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Array of corrected neutron counts for biomass effects.\n\n    References:\n        Baatz, R., H. R. Bogena, H.-J. Hendricks Franssen, J. A. Huisman, C. Montzka, and H. Vereecken (2015),\n        An empiricalvegetation correction for soil water content quantification using cosmic ray probes,\n        Water Resour. Res., 51, 2030\u20132046, doi:10.1002/ 2014WR016443.\n    \"\"\"\n\n    return counts / (1 - bwe * r2_N0)\n</code></pre>"},{"location":"reference/#crnpy.crnpy.correction_humidity","title":"<code>correction_humidity(abs_humidity, Aref)</code>","text":"<p>Correction factor for absolute humidity.</p> <p>This function corrects neutron counts for absolute humidity using the method described in Rosolem et al. (2013) and Anderson et al. (2017). The correction is performed using the following equation:</p> <p>$$ C_{corrected} = C_{raw} \\cdot f_w $$</p> <p>where:</p> <ul> <li>Ccorrected: corrected neutron counts</li> <li>Craw: raw neutron counts</li> <li>fw: absolute humidity correction factor</li> </ul> <p>$$ f_w = 1 + 0.0054(A - A_{ref}) $$</p> <p>where:</p> <ul> <li>A: absolute humidity</li> <li>Aref: reference absolute humidity</li> </ul> <p>Parameters:</p> Name Type Description Default <code>abs_humidity</code> <code>list or array</code> <p>Relative humidity readings.</p> required <code>Aref</code> <code>float</code> <p>Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.</p> required <p>Returns:</p> Type Description <code>list</code> <p>fw correction factor.</p> References <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def correction_humidity(abs_humidity, Aref):\n    r\"\"\"Correction factor for absolute humidity.\n\n    This function corrects neutron counts for absolute humidity using the method described in Rosolem et al. (2013) and Anderson et al. (2017). The correction is performed using the following equation:\n\n    $$\n    C_{corrected} = C_{raw} \\cdot f_w\n    $$\n\n    where:\n\n    - Ccorrected: corrected neutron counts\n    - Craw: raw neutron counts\n    - fw: absolute humidity correction factor\n\n    $$\n    f_w = 1 + 0.0054(A - A_{ref})\n    $$\n\n    where:\n\n    - A: absolute humidity\n    - Aref: reference absolute humidity\n\n    Args:\n        abs_humidity (list or array): Relative humidity readings.\n        Aref (float): Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.\n\n    Returns:\n        (list): fw correction factor.\n\n    References:\n        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086\n    \"\"\"\n    A = abs_humidity\n    fw = 1 + 0.0054 * (A - Aref)  # Zreda et al. 2017 Eq 6.\n    return fw\n</code></pre>"},{"location":"reference/#crnpy.crnpy.correction_incoming_flux","title":"<code>correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None, Rc_method=None, Rc_site=None, site_atmdepth=None, Rc_ref=None, ref_atmdepth=None)</code>","text":"<p>Correction factor for incoming neutron flux.</p> <p>This function corrects neutron counts for incoming neutron flux using the method described in Anderson et al. (2017). The correction is performed using the following equation:</p> <p>$$ C_{corrected} = \\frac{C_{raw}}{f_i} $$</p> <p>where:</p> <ul> <li>Ccorrected: corrected neutron counts</li> <li>Craw: raw neutron counts</li> <li>fi: incoming neutron flux correction factor</li> </ul> <p>$$ f_i = \\frac{I_{ref}}{I} $$</p> <p>where:</p> <ul> <li>I: incoming neutron flux</li> <li>Iref: reference incoming neutron flux</li> </ul> <p>Parameters:</p> Name Type Description Default <code>incoming_neutrons</code> <code>list or array</code> <p>Incoming neutron flux readings.</p> required <code>incoming_Ref</code> <code>float</code> <p>Reference incoming neutron flux. Baseline incoming neutron flux.</p> <code>None</code> <p>Returns:</p> Type Description <code>list</code> <p>fi correction factor.</p> References <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None, Rc_method=None, Rc_site=None,\n                             site_atmdepth=None, Rc_ref=None, ref_atmdepth=None):\n    r\"\"\"Correction factor for incoming neutron flux.\n\n    This function corrects neutron counts for incoming neutron flux using the method described in Anderson et al. (2017). The correction is performed using the following equation:\n\n    $$\n    C_{corrected} = \\frac{C_{raw}}{f_i}\n    $$\n\n    where:\n\n    - Ccorrected: corrected neutron counts\n    - Craw: raw neutron counts\n    - fi: incoming neutron flux correction factor\n\n    $$\n    f_i = \\frac{I_{ref}}{I}\n    $$\n\n    where:\n\n    - I: incoming neutron flux\n    - Iref: reference incoming neutron flux\n\n    Args:\n        incoming_neutrons (list or array): Incoming neutron flux readings.\n        incoming_Ref (float): Reference incoming neutron flux. Baseline incoming neutron flux.\n\n    Returns:\n        (list): fi correction factor.\n\n    References:\n        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086\n\n\n    \"\"\"\n    if incoming_Ref is None and not isinstance(incoming_neutrons, type(None)):\n        incoming_Ref = incoming_neutrons[0]\n        warnings.warn('Reference incoming neutron flux not provided. Using first value of incoming neutron flux.')\n    fi = incoming_neutrons / incoming_Ref\n\n    if Rc_method is not None:\n        if Rc_ref is None:\n            raise ValueError('Reference cutoff rigidity not provided.')\n        if Rc_site is None:\n            raise ValueError('Site cutoff rigidity not provided.')\n\n        if Rc_method == 'McJannetandDesilets2023':\n            tau = location_factor(site_atmdepth, Rc_site, ref_atmdepth, Rc_ref)\n            fi = 1 / (tau * fi + 1 - tau)\n\n        elif Rc_method == 'Hawdonetal2014':\n            Rc_corr = -0.075 * (Rc_site - Rc_ref) + 1.0\n            fi = (fi - 1.0) * Rc_corr + 1.0\n\n        else:\n            raise ValueError(\n                'Cutoff rigidity method not found. Valid options are: McJannetandDesilets2023, Hawdonetal2014.')\n\n    if fill_na is not None:\n        fi.fillna(fill_na, inplace=True)  # Use a value of 1 for days without data\n\n    return fi\n</code></pre>"},{"location":"reference/#crnpy.crnpy.correction_pressure","title":"<code>correction_pressure(pressure, Pref, L)</code>","text":"<p>Correction factor for atmospheric pressure.</p> <p>This function corrects neutron counts for atmospheric pressure using the method described in Andreasen et al. (2017). The correction is performed using the following equation:</p> <p>$$ C_{corrected} = \\frac{C_{raw}}{fp} $$</p> <p>where:</p> <ul> <li>Ccorrected: corrected neutron counts</li> <li>Craw: raw neutron counts</li> <li>fp: pressure correction factor</li> </ul> <p>$$ fp = e^{\\frac{P_{ref} - P}{L}} $$</p> <p>where:</p> <ul> <li>P: atmospheric pressure</li> <li>Pref: reference atmospheric pressure</li> <li>L: Atmospheric attenuation coefficient.</li> </ul> <p>Parameters:</p> Name Type Description Default <code>pressure</code> <code>list or array</code> <p>Atmospheric pressure readings. Long-term average pressure is recommended.</p> required <code>Pref</code> <code>float</code> <p>Reference atmospheric pressure.</p> required <code>L</code> <code>float</code> <p>Atmospheric attenuation coefficient.</p> required <p>Returns:</p> Type Description <code>list</code> <p>fp pressure correction factor.</p> References <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def correction_pressure(pressure, Pref, L):\n    r\"\"\"Correction factor for atmospheric pressure.\n\n    This function corrects neutron counts for atmospheric pressure using the method described in Andreasen et al. (2017).\n    The correction is performed using the following equation:\n\n    $$\n    C_{corrected} = \\frac{C_{raw}}{fp}\n    $$\n\n    where:\n\n    - Ccorrected: corrected neutron counts\n    - Craw: raw neutron counts\n    - fp: pressure correction factor\n\n    $$\n    fp = e^{\\frac{P_{ref} - P}{L}}\n    $$\n\n    where:\n\n    - P: atmospheric pressure\n    - Pref: reference atmospheric pressure\n    - L: Atmospheric attenuation coefficient.\n\n\n    Args:\n        pressure (list or array): Atmospheric pressure readings. Long-term average pressure is recommended.\n        Pref (float): Reference atmospheric pressure.\n        L (float): Atmospheric attenuation coefficient.\n\n    Returns:\n        (list): fp pressure correction factor.\n\n    References:\n        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086\n    \"\"\"\n\n    # Compute pressure correction factor\n    fp = np.exp((Pref - pressure) / L)  # Zreda et al. 2017 Eq 5.\n\n    return fp\n</code></pre>"},{"location":"reference/#crnpy.crnpy.correction_road","title":"<code>correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0.12, p0=0.42, p1=0.5, p2=1.06, p3=4, p4=0.16, p6=0.94, p7=1.1, p8=2.7, p9=0.01)</code>","text":"<p>Function to correct for road effects in neutron counts. following the approach described in Schr\u00f6n et al., 2018.</p> <p>Parameters:</p> Name Type Description Default <code>counts</code> <code>array or Series or DataFrame</code> <p>Array of ephithermal neutron counts.</p> required <code>theta_N</code> <code>float</code> <p>Volumetric water content of the soil estimated from the uncorrected neutron counts.</p> required <code>road_width</code> <code>float</code> <p>Width of the road in m.</p> required <code>road_distance</code> <code>float</code> <p>Distance of the road from the sensor in m. Default is 0.0.</p> <code>0.0</code> <code>theta_road</code> <code>float</code> <p>Volumetric water content of the road. Default is 0.12.</p> <code>0.12</code> <code>p0-p9</code> <code>float</code> <p>Parameters of the correction function. Default values are from Schr\u00f6n et al., 2018.</p> required <p>Returns:</p> Type Description <code>array or Series or DataFrame</code> <p>Array of corrected neutron counts for road effects.</p> References <p>Schr\u00f6n,M.,Rosolem,R.,K\u00f6hli,M., Piussi,L.,Schr\u00f6ter,I.,Iwema,J.,etal. (2018).Cosmic-ray neutron rover surveys of field soil moisture and the influence of roads.WaterResources Research,54,6441\u20136459. https://doi. org/10.1029/2017WR021719</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0.12, p0=0.42, p1=0.5, p2=1.06, p3=4,\n                    p4=0.16, p6=0.94, p7=1.10, p8=2.70, p9=0.01):\n    \"\"\"Function to correct for road effects in neutron counts.\n    following the approach described in Schr\u00f6n et al., 2018.\n\n    Args:\n        counts (array or pd.Series or pd.DataFrame): Array of ephithermal neutron counts.\n        theta_N (float): Volumetric water content of the soil estimated from the uncorrected neutron counts.\n        road_width (float): Width of the road in m.\n        road_distance (float): Distance of the road from the sensor in m. Default is 0.0.\n        theta_road (float): Volumetric water content of the road. Default is 0.12.\n        p0-p9 (float): Parameters of the correction function. Default values are from Schr\u00f6n et al., 2018.\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Array of corrected neutron counts for road effects.\n\n    References:\n        Schr\u00f6n,M.,Rosolem,R.,K\u00f6hli,M., Piussi,L.,Schr\u00f6ter,I.,Iwema,J.,etal. (2018).Cosmic-ray neutron rover surveys\n        of field soil moisture and the influence of roads.WaterResources Research,54,6441\u20136459.\n        https://doi. org/10.1029/2017WR021719\n    \"\"\"\n    F1 = p0 * (1 - np.exp(-p1 * road_width))\n    F2 = -p2 - p3 * theta_road - ((p4 + theta_road) / (theta_N))\n    F3 = p6 * np.exp(-p7 * (road_width ** -p8) * road_distance ** 4) + (1 - p6) * np.exp(-p9 * road_distance)\n\n    C_roads = 1 + F1 * F2 * F3\n\n    corrected_counts = counts / C_roads\n\n    return corrected_counts\n</code></pre>"},{"location":"reference/#crnpy.crnpy.counts_to_vwc","title":"<code>counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=0.115)</code>","text":"<p>Function to convert corrected and filtered neutron counts into volumetric water content.</p> <p>This method implements soil moisture estimation using the non-linear relationship between neutron count and soil volumetric water content following the approach described in Desilets et al., 2010.</p> <p>$\\theta(N) =\\frac{a_0}{(\\frac{N}{N_0}) - a_1} - a_2 $</p> <p>Parameters:</p> Name Type Description Default <code>counts</code> <code>array or Series or DataFrame</code> <p>Array of corrected and filtered neutron counts.</p> required <code>N0</code> <code>float</code> <p>Device-specific neutron calibration constant.</p> required <code>Wlat</code> <code>float</code> <p>Lattice water content.</p> required <code>Wsoc</code> <code>float</code> <p>Soil organic carbon content.</p> required <code>bulk_density</code> <code>float</code> <p>Soil bulk density.</p> required <code>a0</code> <code>float</code> <p>Parameter given in Zreda et al., 2012. Default is 0.0808.</p> <code>0.0808</code> <code>a1</code> <code>float</code> <p>Parameter given in Zreda et al., 2012. Default is 0.372.</p> <code>0.372</code> <code>a2</code> <code>float</code> <p>Parameter given in Zreda et al., 2012. Default is 0.115.</p> <code>0.115</code> <p>Returns:</p> Type Description <code>array or Series or DataFrame</code> <p>Volumetric water content in m3 m-3.</p> References <p>Desilets, D., M. Zreda, and T.P.A. Ferr\u00e9. 2010. Nature\u2019s neutron probe: Land surface hydrology at an elusive scale with cosmic rays. Water Resour. Res. 46:W11505. doi.org/10.1029/2009WR008726</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=0.115):\n    r\"\"\"Function to convert corrected and filtered neutron counts into volumetric water content.\n\n    This method implements soil moisture estimation using the non-linear relationship between neutron count and soil volumetric water content following the approach described in Desilets et al., 2010.\n\n    $\\theta(N) =\\frac{a_0}{(\\frac{N}{N_0}) - a_1} - a_2 $\n\n    Args:\n        counts (array or pd.Series or pd.DataFrame): Array of corrected and filtered neutron counts.\n        N0 (float): Device-specific neutron calibration constant.\n        Wlat (float): Lattice water content.\n        Wsoc (float): Soil organic carbon content.\n        bulk_density (float): Soil bulk density.\n        a0 (float): Parameter given in Zreda et al., 2012. Default is 0.0808.\n        a1 (float): Parameter given in Zreda et al., 2012. Default is 0.372.\n        a2 (float): Parameter given in Zreda et al., 2012. Default is 0.115.\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Volumetric water content in m3 m-3.\n\n    References:\n        Desilets, D., M. Zreda, and T.P.A. Ferr\u00e9. 2010. Nature\u2019s neutron probe:\n        Land surface hydrology at an elusive scale with cosmic rays. Water Resour. Res. 46:W11505.\n        doi.org/10.1029/2009WR008726\n    \"\"\"\n\n    # Convert neutron counts into vwc\n    vwc = (a0 / (counts / N0 - a1) - a2 - Wlat - Wsoc) * bulk_density\n    return vwc\n</code></pre>"},{"location":"reference/#crnpy.crnpy.cutoff_rigidity","title":"<code>cutoff_rigidity(lat, lon)</code>","text":"<p>Function to estimate the approximate cutoff rigidity for any point on Earth according to the tabulated data of Smart and Shea, 2019. The returned value can be used to select the appropriate neutron monitor station to estimate the cosmic-ray neutron intensity at the location of interest.</p> <p>Parameters:</p> Name Type Description Default <code>lat</code> <code>float</code> <p>Geographic latitude in decimal degrees. Value in range -90 to 90</p> required <code>lon</code> <code>float</code> <p>Geographic longitude in decimal degrees. Values in range from 0 to 360. Typical negative longitudes in the west hemisphere will fall in the range 180 to 360.</p> required <p>Returns:</p> Type Description <code>float</code> <p>Cutoff rigidity in GV. Error is about +/- 0.3 GV</p> <p>Examples:</p> <p>Estimate the cutoff rigidity for Newark, NJ, US</p> <pre><code>&gt;&gt;&gt; zq = cutoff_rigidity(39.68, -75.75)\n&gt;&gt;&gt; print(zq)\n2.52 GV (Value from NMD is 2.40 GV)\n</code></pre> References <p>Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures for cosmic\u2010ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.</p> <p>Smart, D. &amp; Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program: Theory, Software Description and Example. NASA STI/Recon Technical Report N.</p> <p>Shea, M. A., &amp; Smart, D. F. (2019, July). Re-examination of the First Five Ground-Level Events. In International Cosmic Ray Conference (ICRC2019) (Vol. 36, p. 1149).</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def cutoff_rigidity(lat, lon):\n    \"\"\"Function to estimate the approximate cutoff rigidity for any point on Earth according to the\n    tabulated data of Smart and Shea, 2019. The returned value can be used to select the appropriate\n    neutron monitor station to estimate the cosmic-ray neutron intensity at the location of interest.\n\n    Args:\n        lat (float): Geographic latitude in decimal degrees. Value in range -90 to 90\n        lon (float): Geographic longitude in decimal degrees. Values in range from 0 to 360.\n            Typical negative longitudes in the west hemisphere will fall in the range 180 to 360.\n\n    Returns:\n        (float): Cutoff rigidity in GV. Error is about +/- 0.3 GV\n\n    Examples:\n        Estimate the cutoff rigidity for Newark, NJ, US\n\n        &gt;&gt;&gt; zq = cutoff_rigidity(39.68, -75.75)\n        &gt;&gt;&gt; print(zq)\n        2.52 GV (Value from NMD is 2.40 GV)\n\n    References:\n        Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures\n        for cosmic\u2010ray neutron soil moisture probes located across Australia. Water Resources Research,\n        50(6), 5029-5043.\n\n        Smart, D. &amp; Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program:\n        Theory, Software Description and Example. NASA STI/Recon Technical Report N.\n\n        Shea, M. A., &amp; Smart, D. F. (2019, July). Re-examination of the First Five Ground-Level Events.\n        In International Cosmic Ray Conference (ICRC2019) (Vol. 36, p. 1149).\n    \"\"\"\n    xq = lon\n    yq = lat\n\n    if xq &lt; 0:\n        xq = xq * -1 + 180\n    Z = np.array(data.cutoff_rigidity)\n    x = np.linspace(0, 360, Z.shape[1])\n    y = np.linspace(90, -90, Z.shape[0])\n    X, Y = np.meshgrid(x, y)\n    points = np.array((X.flatten(), Y.flatten())).T\n    values = Z.flatten()\n    zq = griddata(points, values, (xq, yq))\n\n    return np.round(zq, 2)\n</code></pre>"},{"location":"reference/#crnpy.crnpy.euclidean_distance","title":"<code>euclidean_distance(px, py, x, y)</code>","text":"<p>Function that computes the Euclidean distance between one point in space and one or more points.</p> <p>Parameters:</p> Name Type Description Default <code>px</code> <code>float</code> <p>x projected coordinate of the point.</p> required <code>py</code> <code>float</code> <p>y projected coordinate of the point.</p> required <code>x</code> <code>(list, ndarray, series)</code> <p>vector of x projected coordinates.</p> required <code>y</code> <code>(list, ndarray, series)</code> <p>vector of y projected coordinates.</p> required <p>Returns:</p> Type Description <code>ndarray</code> <p>Numpy array of distances from the point (px,py) to all the points in x and y vectors.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def euclidean_distance(px, py, x, y):\n    \"\"\"Function that computes the Euclidean distance between one point\n    in space and one or more points.\n\n    Args:\n        px (float): x projected coordinate of the point.\n        py (float): y projected coordinate of the point.\n        x (list, ndarray, pandas.series): vector of x projected coordinates.\n        y (list, ndarray, pandas.series): vector of y projected coordinates.\n\n    Returns:\n        (ndarray): Numpy array of distances from the point (px,py) to all the points in x and y vectors.\n    \"\"\"\n    d = np.sqrt((px - x) ** 2 + (py - y) ** 2)\n    return d\n</code></pre>"},{"location":"reference/#crnpy.crnpy.exp_filter","title":"<code>exp_filter(sm, T=1)</code>","text":"<p>Exponential filter to estimate soil moisture in the rootzone from surface observtions.</p> <p>Parameters:</p> Name Type Description Default <code>sm</code> <code>list or array</code> <p>Soil moisture in mm of water for the top layer of the soil profile.</p> required <code>T</code> <code>float</code> <p>Characteristic time length in the same units as the measurement interval.</p> <code>1</code> <p>Returns:</p> Name Type Description <code>sm_subsurface</code> <code>list or array</code> <p>Subsurface soil moisture in the same units as the input.</p> References <p>Albergel, C., R\u00fcdiger, C., Pellarin, T., Calvet, J.C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B. and Martin, E., 2008. From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations. Hydrology and Earth System Sciences, 12(6), pp.1323-1337.</p> <p>Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020. Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.</p> <p>Rossini, P. and Patrignani, A., 2021. Predicting rootzone soil moisture from surface observations in cropland using an exponential filter. Soil Science Society of America Journal.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def exp_filter(sm, T=1):\n    \"\"\"Exponential filter to estimate soil moisture in the rootzone from surface observtions.\n\n    Args:\n        sm (list or array): Soil moisture in mm of water for the top layer of the soil profile.\n        T (float): Characteristic time length in the same units as the measurement interval.\n\n    Returns:\n        sm_subsurface (list or array): Subsurface soil moisture in the same units as the input.\n\n    References:\n        Albergel, C., R\u00fcdiger, C., Pellarin, T., Calvet, J.C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B. and Martin, E., 2008.\n        From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model\n        simulations. Hydrology and Earth System Sciences, 12(6), pp.1323-1337.\n\n        Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.\n        Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.\n\n        Rossini, P. and Patrignani, A., 2021. Predicting rootzone soil moisture from surface observations in cropland using an exponential filter.\n        Soil Science Society of America Journal.\n    \"\"\"\n\n    # Parameters\n    t_delta = 1\n    sm_min = np.min(sm)\n    sm_max = np.max(sm)\n    ms = (sm - sm_min) / (sm_max - sm_min)\n\n    # Pre-allocate soil water index array and recursive constant K\n    SWI = np.ones_like(ms) * np.nan\n    K = np.ones_like(ms) * np.nan\n\n    # Initial conditions\n    SWI[0] = ms[0]\n    K[0] = 1\n\n    # Values from 2 to N\n    for n in range(1, len(SWI)):\n        if ~np.isnan(ms[n]) &amp; ~np.isnan(ms[n - 1]):\n            K[n] = K[n - 1] / (K[n - 1] + np.exp(-t_delta / T))\n            SWI[n] = SWI[n - 1] + K[n] * (ms[n] - SWI[n - 1])\n        else:\n            continue\n\n    # Rootzone storage\n    sm_subsurface = SWI * (sm_max - sm_min) + sm_min\n\n    return sm_subsurface\n</code></pre>"},{"location":"reference/#crnpy.crnpy.fill_missing_timestamps","title":"<code>fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True, verbose=False)</code>","text":"<p>Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>Pandas DataFrame.</p> required <code>timestamp_col</code> <code>str</code> <p>Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.</p> <code>'timestamp'</code> <code>freq</code> <code>str</code> <p>Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.</p> <code>'H'</code> <code>round_timestamp</code> <code>bool</code> <p>Whether to round timestamps to the nearest frequency. Default is True.</p> <code>True</code> <code>verbose</code> <code>bool</code> <p>Prints the missing timestamps added to the DatFrame.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>DataFrame with filled missing timestamps.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True, verbose=False):\n    \"\"\"Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.\n\n     Args:\n         df (pandas.DataFrame): Pandas DataFrame.\n         timestamp_col (str, optional): Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.\n         freq (str, optional): Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.\n         round_timestamp (bool, optional): Whether to round timestamps to the nearest frequency. Default is True.\n         verbose (bool, optional): Prints the missing timestamps added to the DatFrame.\n\n     Returns:\n         (pandas.DataFrame): DataFrame with filled missing timestamps.\n\n     \"\"\"\n\n    # Check format of timestamp column\n    if df[timestamp_col].dtype != 'datetime64[ns]':\n        raise TypeError('timestamp_col must be datetime64. Use `pd.to_datetime()` to fix this issue.')\n\n    # Round timestamps to nearest frequency. This steps must preced the filling of rows.\n    if round_timestamp:\n        df[timestamp_col] = df[timestamp_col].dt.round(freq)\n\n    # Fill in rows with missing timestamps\n    start_date = df[timestamp_col].iloc[0]\n    end_date = df[timestamp_col].iloc[-1]\n    date_range = pd.date_range(start_date, end_date, freq=freq)\n    counter = 0\n    for date in date_range:\n        if date not in df[timestamp_col].values:\n            if verbose:\n                print('Adding missing date:', date)\n            new_line = pd.DataFrame({timestamp_col: date}, index=[-1])  # By default fills columns with np.nan\n            df = pd.concat([df, new_line])\n            counter += 1\n\n    df.sort_values(by=timestamp_col, inplace=True)\n    df.reset_index(drop=True, inplace=True)\n\n    # Notify user about the number of rows that have been removed\n    print(f\"Added a total of {counter} missing timestamps.\")\n\n    return df\n</code></pre>"},{"location":"reference/#crnpy.crnpy.find_neutron_monitor","title":"<code>find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False)</code>","text":"<p>Search for potential reference neutron monitoring stations based on cutoff rigidity.</p> <p>Parameters:</p> Name Type Description Default <code>Rc</code> <code>float</code> <p>Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.</p> required <code>start_date</code> <code>datetime</code> <p>Start date for the period of interest.</p> <code>None</code> <code>end_date</code> <code>datetime</code> <p>End date for the period of interest.</p> <code>None</code> <code>verbose</code> <code>bool</code> <p>If True, print a expanded output of the incoming neutron flux data.</p> <code>False</code> <p>Returns:</p> Type Description <code>list</code> <p>List of top five stations with closes cutoff rigidity. User needs to select station according to site altitude.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; from crnpy import crnpy\n&gt;&gt;&gt; Rc = 2.40 # 2.40 Newark, NJ, US\n&gt;&gt;&gt; crnpy.find_neutron_monitor(Rc)\nSelect a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\n</code></pre> <p>Your cutoff rigidity is 2.4 GV         STID                          NAME     R  Altitude_m 40   NEWK                        Newark  2.40          50 33   MOSC                        Moscow  2.43         200 27   KIEL                          Kiel  2.36          54 28  KIEL2                        KielRT  2.36          54 31   MCRL  Mobile Cosmic Ray Laboratory  2.46         200 32   MGDN                       Magadan  2.10         220 42   NVBK                   Novosibirsk  2.91         163 26   KGSN                      Kingston  1.88          65 9    CLMX                        Climax  3.00        3400 57   YKTK                       Yakutsk  1.65         105</p> References <p>https://www.nmdb.eu/nest/help.php#helpstations</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):\n    \"\"\"Search for potential reference neutron monitoring stations based on cutoff rigidity.\n\n    Args:\n        Rc (float): Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.\n        start_date (datetime): Start date for the period of interest.\n        end_date (datetime): End date for the period of interest.\n        verbose (bool): If True, print a expanded output of the incoming neutron flux data.\n\n    Returns:\n        (list): List of top five stations with closes cutoff rigidity.\n            User needs to select station according to site altitude.\n\n    Examples:\n        &gt;&gt;&gt; from crnpy import crnpy\n        &gt;&gt;&gt; Rc = 2.40 # 2.40 Newark, NJ, US\n        &gt;&gt;&gt; crnpy.find_neutron_monitor(Rc)\n        Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\n\n        Your cutoff rigidity is 2.4 GV\n                STID                          NAME     R  Altitude_m\n        40   NEWK                        Newark  2.40          50\n        33   MOSC                        Moscow  2.43         200\n        27   KIEL                          Kiel  2.36          54\n        28  KIEL2                        KielRT  2.36          54\n        31   MCRL  Mobile Cosmic Ray Laboratory  2.46         200\n        32   MGDN                       Magadan  2.10         220\n        42   NVBK                   Novosibirsk  2.91         163\n        26   KGSN                      Kingston  1.88          65\n        9    CLMX                        Climax  3.00        3400\n        57   YKTK                       Yakutsk  1.65         105\n\n    References:\n        https://www.nmdb.eu/nest/help.php#helpstations\n    \"\"\"\n\n    # Load file with list of neutron monitoring stations\n    stations = pd.DataFrame(data.neutron_detectors, columns=[\"STID\", \"NAME\", \"R\", \"Altitude_m\"])\n\n    # Sort stations by closest cutoff rigidity\n    idx_R = (stations['R'] - Rc).abs().argsort()\n\n    if start_date is not None and end_date is not None:\n        stations[\"Period available\"] = False\n        for i in range(10):\n            station = stations.iloc[idx_R[i]][\"STID\"]\n            try:\n                if get_incoming_neutron_flux(start_date, end_date, station, verbose=verbose) is not None:\n                    stations.iloc[idx_R[i], -1] = True\n            except:\n                pass\n\n        if sum(stations[\"Period available\"] == True) == 0:\n            print(\"No stations available for the selected period!\")\n        else:\n            stations = stations[stations[\"Period available\"] == True]\n            idx_R = (stations['R'] - Rc).abs().argsort()\n            result = stations.iloc[idx_R.iloc[:10]]\n    else:\n        result = stations.reindex(idx_R).head(10).rename_axis(None)\n\n    # Print results\n    print('')\n    print(\n        \"\"\"Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\"\"\")\n    print('')\n    print(f\"Your cutoff rigidity is {Rc} GV\")\n    print(result)\n    return result\n</code></pre>"},{"location":"reference/#crnpy.crnpy.get_incoming_neutron_flux","title":"<code>get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expand_window=0, verbose=False)</code>","text":"<p>Function to retrieve neutron flux from the Neutron Monitor Database.</p> <p>Parameters:</p> Name Type Description Default <code>start_date</code> <code>datetime</code> <p>Start date of the time series.</p> required <code>end_date</code> <code>datetime</code> <p>End date of the time series.</p> required <code>station</code> <code>str</code> <p>Neutron Monitor station to retrieve data from.</p> required <code>utc_offset</code> <code>int</code> <p>UTC offset in hours. Default is 0.</p> <code>0</code> <code>expand_window</code> <code>int</code> <p>Number of hours to expand the time window to retrieve extra data. Default is 0.</p> <code>0</code> <code>verbose</code> <code>bool</code> <p>Print information about the request. Default is False.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>Neutron flux in counts per hour and timestamps.</p> References <p>Documentation available:https://www.nmdb.eu/nest/help.php#howto</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expand_window=0, verbose=False):\n    \"\"\"Function to retrieve neutron flux from the Neutron Monitor Database.\n\n    Args:\n        start_date (datetime): Start date of the time series.\n        end_date (datetime): End date of the time series.\n        station (str): Neutron Monitor station to retrieve data from.\n        utc_offset (int): UTC offset in hours. Default is 0.\n        expand_window (int): Number of hours to expand the time window to retrieve extra data. Default is 0.\n        verbose (bool): Print information about the request. Default is False.\n\n    Returns:\n        (pandas.DataFrame): Neutron flux in counts per hour and timestamps.\n\n    References:\n        Documentation available:https://www.nmdb.eu/nest/help.php#howto\n    \"\"\"\n\n    # Example: get_incoming_neutron_flux(station='IRKT',start_date='2020-04-10 11:00:00',end_date='2020-06-18 17:00:00')\n    # Template url = 'http://nest.nmdb.eu/draw_graph.php?formchk=1&amp;stations[]=KERG&amp;output=ascii&amp;tabchoice=revori&amp;dtype=corr_for_efficiency&amp;date_choice=bydate&amp;start_year=2009&amp;start_month=09&amp;start_day=01&amp;start_hour=00&amp;start_min=00&amp;end_year=2009&amp;end_month=09&amp;end_day=05&amp;end_hour=23&amp;end_min=59&amp;yunits=0'\n\n    # Expand the time window by 1 hour to ensure an extra observation is included in the request.\n    start_date -= pd.Timedelta(hours=expand_window)\n    end_date += pd.Timedelta(hours=expand_window)\n\n    # Convert local time to UTC\n    start_date = start_date - pd.Timedelta(hours=utc_offset)\n    end_date = end_date - pd.Timedelta(hours=utc_offset)\n    root = 'http://www.nmdb.eu/nest/draw_graph.php?'\n    url_par = ['formchk=1',\n               'stations[]=' + station,\n               'output=ascii',\n               'tabchoice=revori',\n               'dtype=corr_for_efficiency',\n               'tresolution=' + str(60),\n               'date_choice=bydate',\n               'start_year=' + str(start_date.year),\n               'start_month=' + str(start_date.month),\n               'start_day=' + str(start_date.day),\n               'start_hour=' + str(start_date.hour),\n               'start_min=' + str(start_date.minute),\n               'end_year=' + str(end_date.year),\n               'end_month=' + str(end_date.month),\n               'end_day=' + str(end_date.day),\n               'end_hour=' + str(end_date.hour),\n               'end_min=' + str(end_date.minute),\n               'yunits=0']\n\n    url = root + '&amp;'.join(url_par)\n\n    if verbose:\n        print(f\"Retrieving data from {url}\")\n\n    r = requests.get(url).content.decode('utf-8')\n\n    # Subtract 1 hour to restore the last date included in the request.\n    end_date -= pd.Timedelta('1H')\n    start = r.find(\"RCORR_E\\n\") + 8\n    end = r.find('\\n&lt;/code&gt;&lt;/pre&gt;&lt;br&gt;Total') - 1\n    s = r[start:end]\n    s2 = ''.join([row.replace(';', ',') for row in s])\n    try:\n        df_flux = pd.read_csv(io.StringIO(s2), names=['timestamp', 'counts'])\n    except:\n        if verbose:\n            print(f\"Error retrieving data from {url}\")\n        return None\n\n    # Check if all values from selected detector are NaN. If yes, warn the user\n    if df_flux['counts'].isna().all():\n        warnings.warn('Data for selected neutron detectors appears to be unavailable for the selected period')\n\n    # Convert timestamp to datetime and apply UTC offset\n    df_flux['timestamp'] = pd.to_datetime(df_flux['timestamp'])\n    df_flux['timestamp'] = df_flux['timestamp'] + pd.Timedelta(hours=utc_offset)\n\n    # Print acknowledgement to inform users about restrictions and to acknowledge the NMDB database\n    acknowledgement = \"\"\"Data retrieved via NMDB are the property of the individual data providers. These data are free for non commercial\nuse to within the restriction imposed by the providers. If you use such data for your research or applications, please acknowledge\nthe origin by a sentence like 'We acknowledge the NMDB database (www.nmdb.eu) founded under the European Union's FP7 programme \n(contract no. 213007), and the PIs of individual neutron monitors at: IGY Jungfraujoch \n(Physikalisches Institut, University of Bern, Switzerland)\"\"\"\n\n    return df_flux\n</code></pre>"},{"location":"reference/#crnpy.crnpy.get_reference_neutron_flux","title":"<code>get_reference_neutron_flux(station, date=pd.to_datetime('2011-05-01'))</code>","text":"<p>Function to retrieve reference neutron flux from the Neutron Monitor Database. Default date is 2011-05-01, following previous studies (Zreda et a., 2012, Hawdon et al., 2014, Bogena et al., 2022).</p> <p>Parameters:</p> Name Type Description Default <code>station</code> <code>str</code> <p>Neutron Monitor station to retrieve data from.</p> required <code>date</code> <code>datetime</code> <p>Date of the reference neutron flux. Default is 2011-05-01.</p> <code>to_datetime('2011-05-01')</code> <p>Returns:</p> Type Description <code>float</code> <p>Reference neutron flux in counts per hour.</p> References <p>Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., &amp; Rosolem, R. (2012). COSMOS: The cosmic-ray soil moisture observing system. Hydrology and Earth System Sciences, 16(11), 4079-4099.</p> <p>Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures for cosmic\u2010ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.</p> <p>Bogena, H. R., Schr\u00f6n, M., Jakobi, J., Ney, P., Zacharias, S., Andreasen, M., ... &amp; Vereecken, H. (2022). COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors. Earth System Science Data, 14(3), 1125-1151.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def get_reference_neutron_flux(station, date=pd.to_datetime(\"2011-05-01\")):\n    \"\"\"Function to retrieve reference neutron flux from the Neutron Monitor Database. Default date is 2011-05-01, following previous studies (Zreda et a., 2012, Hawdon et al., 2014, Bogena et al., 2022).\n\n    Args:\n        station (str): Neutron Monitor station to retrieve data from.\n        date (datetime): Date of the reference neutron flux. Default is 2011-05-01.\n\n    Returns:\n        (float): Reference neutron flux in counts per hour.\n\n    References:\n        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., &amp; Rosolem, R. (2012). COSMOS: The cosmic-ray soil moisture observing system. Hydrology and Earth System Sciences, 16(11), 4079-4099.\n\n        Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures for cosmic\u2010ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.\n\n        Bogena, H. R., Schr\u00f6n, M., Jakobi, J., Ney, P., Zacharias, S., Andreasen, M., ... &amp; Vereecken, H. (2022). COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors. Earth System Science Data, 14(3), 1125-1151.\n\n\"\"\"\n\n    # Get flux for 2011-05-01\n    df_flux = get_incoming_neutron_flux(station, date, date + pd.Timedelta(hours=24))\n    if df_flux is None:\n        warnings.warn(f\"Reference neutron flux for {station} not available. Returning NaN.\")\n    else:\n        return df_flux['counts'].median()\n</code></pre>"},{"location":"reference/#crnpy.crnpy.idw","title":"<code>idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1)</code>","text":"<p>Function to interpolate data using inverse distance weight.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>list or array</code> <p>UTM x coordinates in meters.</p> required <code>y</code> <code>list or array</code> <p>UTM y coordinates in meters.</p> required <code>z</code> <code>list or array</code> <p>Values to be interpolated.</p> required <code>X_pred</code> <code>list or array</code> <p>UTM x coordinates where z values need to be predicted.</p> required <code>Y_pred</code> <code>list or array</code> <p>UTM y coordinates where z values need to be predicted.</p> required <code>neighborhood</code> <code>float</code> <p>Only points within this radius in meters are considered for the interpolation.</p> <code>1000</code> <code>p</code> <code>int</code> <p>Exponent of the inverse distance weight formula. Typically, p=1 or p=2.</p> <code>1</code> <p>Returns:</p> Type Description <code>array</code> <p>Interpolated values.</p> References <p>https://en.wikipedia.org/wiki/Inverse_distance_weighting</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1):\n    \"\"\"Function to interpolate data using inverse distance weight.\n\n    Args:\n        x (list or array): UTM x coordinates in meters.\n        y (list or array): UTM y coordinates in meters.\n        z (list or array): Values to be interpolated.\n        X_pred (list or array): UTM x coordinates where z values need to be predicted.\n        Y_pred (list or array): UTM y coordinates where z values need to be predicted.\n        neighborhood (float): Only points within this radius in meters are considered for the interpolation.\n        p (int): Exponent of the inverse distance weight formula. Typically, p=1 or p=2.\n\n    Returns:\n        (array): Interpolated values.\n\n    References:\n        [https://en.wikipedia.org/wiki/Inverse_distance_weighting](https://en.wikipedia.org/wiki/Inverse_distance_weighting)\n\n\n    \"\"\"\n\n    # Flatten arrays to handle 1D and 2D arrays with the same code\n    s = X_pred.shape  # Save shape\n    X_pred = X_pred.flatten()\n    Y_pred = Y_pred.flatten()\n\n    # Pre-allocate output array\n    Z_pred = np.full_like(X_pred, np.nan)\n\n    for n in range(X_pred.size):\n        # Distance between current and observed points\n        d = euclidean_distance(X_pred[n], Y_pred[n], x, y)\n\n        # Select points within neighborhood only for interpolation\n        idx_neighbors = d &lt; neighborhood\n\n        # Compute interpolated value at point of interest\n        Z_pred[n] = np.sum(z[idx_neighbors] / d[idx_neighbors] ** p) / np.sum(1 / d[idx_neighbors] ** p)\n\n    return np.reshape(Z_pred, s)\n</code></pre>"},{"location":"reference/#crnpy.crnpy.interpolate_2d","title":"<code>interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000)</code>","text":"<p>Function for interpolating irregular spatial data into a regular grid.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>list or array</code> <p>UTM x coordinates in meters.</p> required <code>y</code> <code>list or array</code> <p>UTM y coordinates in meters.</p> required <code>z</code> <code>list or array</code> <p>Values to be interpolated.</p> required <code>dx</code> <code>float</code> <p>Pixel width in meters.</p> <code>100</code> <code>dy</code> <code>float</code> <p>Pixel height in meters.</p> <code>100</code> <code>method</code> <code>str</code> <p>Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.</p> <code>'cubic'</code> <code>neighborhood</code> <code>float</code> <p>Only points within this radius in meters are considered for the interpolation.</p> <code>1000</code> <p>Returns:</p> Name Type Description <code>x_pred</code> <code>array</code> <p>2D array with x coordinates.</p> <code>y_pred</code> <code>array</code> <p>2D array with y coordinates.</p> <code>z_pred</code> <code>array</code> <p>2D array with interpolated values.</p> References <p>https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000):\n    \"\"\"Function for interpolating irregular spatial data into a regular grid.\n\n    Args:\n        x (list or array): UTM x coordinates in meters.\n        y (list or array): UTM y coordinates in meters.\n        z (list or array): Values to be interpolated.\n        dx (float): Pixel width in meters.\n        dy (float): Pixel height in meters.\n        method (str): Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.\n        neighborhood (float): Only points within this radius in meters are considered for the interpolation.\n\n    Returns:\n        x_pred (array): 2D array with x coordinates.\n        y_pred (array): 2D array with y coordinates.\n        z_pred (array): 2D array with interpolated values.\n\n    References:\n        [https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html](https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html)\n    \"\"\"\n\n    # Drop NaN values in x y and z\n    idx_nan = np.isnan(x) | np.isnan(y) | np.isnan(z)\n    x = x[~idx_nan]\n    y = y[~idx_nan]\n    z = z[~idx_nan]\n\n    if idx_nan.any():\n        print(\n            f\"WARNING: {np.isnan(x).sum()}, {np.isnan(y).sum()}, and {np.isnan(z).sum()} NaN values were dropped from x, y, and z.\")\n\n    # Create 2D grid for interpolation\n    Nx = round((np.max(x) - np.min(x)) / dx) + 1\n    Ny = round((np.max(y) - np.min(y)) / dy) + 1\n    X_vec = np.linspace(np.min(x), np.max(x), Nx)\n    Y_vec = np.linspace(np.min(y), np.max(y), Ny)\n    X_pred, Y_pred = np.meshgrid(X_vec, Y_vec)\n\n    if method in ['linear', 'nearest', 'cubic']:\n        points = list(zip(x, y))\n        Z_pred = griddata(points, z, (X_pred, Y_pred), method=method)\n\n    elif method == 'idw':\n        Z_pred = idw(x, y, z, X_pred, Y_pred, neighborhood)\n\n    else:\n        raise f\"Method {method} does not exist. Provide either 'cubic', 'linear', 'nearest', or 'idw'.\"\n\n    return X_pred, Y_pred, Z_pred\n</code></pre>"},{"location":"reference/#crnpy.crnpy.interpolate_incoming_flux","title":"<code>interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps)</code>","text":"<p>Function to interpolate incoming neutron flux to match the timestamps of the observations.</p> <p>Parameters:</p> Name Type Description Default <code>nmdb_timestamps</code> <code>Series</code> <p>Series of timestamps in datetime format from the NMDB.</p> required <code>nmdb_counts</code> <code>Series</code> <p>Series of neutron counts from the NMDB</p> required <code>crnp_timestamps</code> <code>Series</code> <p>Series of timestamps in datetime format from the station or device.</p> required <p>Returns:</p> Type Description <code>Series</code> <p>Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps):\n    \"\"\"Function to interpolate incoming neutron flux to match the timestamps of the observations.\n\n    Args:\n        nmdb_timestamps (pd.Series): Series of timestamps in datetime format from the NMDB.\n        nmdb_counts (pd.Series): Series of neutron counts from the NMDB\n        crnp_timestamps (pd.Series): Series of timestamps in datetime format from the station or device.\n\n    Returns:\n        (pd.Series): Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps\n    \"\"\"\n    incoming_flux = np.array([])\n    for k, timestamp in enumerate(crnp_timestamps):\n        if timestamp in nmdb_timestamps.values:\n            idx = timestamp == nmdb_timestamps\n            incoming_flux = np.append(incoming_flux, nmdb_counts.loc[idx])\n        else:\n            incoming_flux = np.append(incoming_flux, np.nan)\n\n    # Interpolate nan values\n    incoming_flux = pd.Series(incoming_flux).interpolate(method='nearest', limit_direction='both')\n\n    # Return only the values for the selected timestamps\n    return incoming_flux\n</code></pre>"},{"location":"reference/#crnpy.crnpy.is_outlier","title":"<code>is_outlier(x, method, window=11, min_val=None, max_val=None)</code>","text":"<p>Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>DataFrame or Series</code> <p>Variable containing only the columns with neutron counts.</p> required <code>method</code> <code>str</code> <p>Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad</p> required <code>window</code> <code>int</code> <p>Window size for the moving central tendency. Default is 11.</p> <code>11</code> <code>min_val</code> <code>int or float</code> <p>Minimum value for a reading to be considered valid. Default is None.</p> <code>None</code> <code>max_val(int</code> <code>or float</code> <p>Maximum value for a reading to be considered valid. Default is None.</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>Boolean indicating outliers.</p> References <p>Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def is_outlier(x, method, window=11, min_val=None, max_val=None):\n    \"\"\"Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.\n\n    Args:\n        x (pd.DataFrame or pd.Series): Variable containing only the columns with neutron counts.\n        method (str): Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad\n        window (int, optional): Window size for the moving central tendency. Default is 11.\n        min_val (int or float): Minimum value for a reading to be considered valid. Default is None.\n        max_val(int or float): Maximum value for a reading to be considered valid. Default is None.\n\n    Returns:\n        (pandas.DataFrame): Boolean indicating outliers.\n\n    References:\n        Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.\n    \"\"\"\n\n    if not isinstance(x, pd.Series):\n        raise TypeError('x must of type pandas.Series')\n\n    # Separate this method to allow usage together with other methods below\n    if isinstance(min_val, numbers.Number) and isinstance(max_val, numbers.Number):\n        idx_range_outliers = (x &lt; min_val) | (x &gt; max_val)\n    else:\n        idx_range_outliers = np.full_like(x, False)\n\n    # Apply other methods in addition to a range check\n    if method == 'iqr':\n        q1 = x.quantile(0.25)\n        q3 = x.quantile(0.75)\n        iqr = q3 - q1\n        high_fence = q3 + (1.5 * iqr)\n        low_fence = q1 - (1.5 * iqr)\n        idx_outliers = (x &lt; low_fence) | (x &gt; high_fence)\n\n    elif method == 'moviqr':\n        q1 = x.rolling(window, center=True).quantile(0.25)\n        q3 = x.rolling(window, center=True).quantile(0.75)\n        iqr = q3 - q1\n        ub = q3 + (1.5 * iqr)  # Upper boundary\n        lb = q1 - (1.5 * iqr)  # Lower boundary\n        idx_outliers = (x &lt; lb) | (x &gt; ub)\n\n    elif method == 'zscore':\n        zscore = (x - x.mean()) / x.std()\n        idx_outliers = (zscore &lt; -3) | (zscore &gt; 3)\n\n    elif method == 'movzscore':\n        movmean = x.rolling(window=window, center=True).mean()\n        movstd = x.rolling(window=window, center=True).std()\n        movzscore = (x - movmean) / movstd\n        idx_outliers = (movzscore &lt; -3) | (movzscore &gt; 3)\n\n    elif method == 'modified_zscore':\n        # Compute median absolute difference\n        movmedian = x.rolling(window, center=True).median()\n        abs_diff = np.abs(x - movmedian)\n        mad = abs_diff.rolling(window, center=True).median()\n\n        # Compute modified z-score\n        modified_z_score = 0.6745 * abs_diff / mad\n        idx_outliers = (modified_z_score &lt; -3.5) | (modified_z_score &gt; 3.5)\n\n    elif method == 'scaled_mad':\n        # Returns true for elements more than three scaled MAD from the median. \n        c = -1 / (np.sqrt(2) * erfcinv(3 / 2))\n        median = np.nanmedian(x)\n        mad = c * np.nanmedian(np.abs(x - median))\n        idx_outliers = x &gt; (median + 3 * mad)\n\n    else:\n        raise TypeError('Outlier detection method not found.')\n\n    return idx_outliers | idx_range_outliers\n</code></pre>"},{"location":"reference/#crnpy.crnpy.latlon_to_utm","title":"<code>latlon_to_utm(lat, lon, utm_zone_number=None, utm_zone_letter=None)</code>","text":"<p>Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.</p> <p>Function only applies to non-polar coordinates. If further functionality is required, consider using the utm module. See references for more information.</p> <p> UTM zones on an equirectangular world map with irregular zones in red and New York City's zone highlighted. See UTM zones for a full description.</p> <p>Parameters:</p> Name Type Description Default <code>lat</code> <code>(float, array)</code> <p>Latitude in decimal degrees.</p> required <code>lon</code> <code>(float, array)</code> <p>Longitude in decimal degrees.</p> required <code>utm_zone_number</code> <code>int</code> <p>UTM zone number. If None, the zone number is automatically calculated.</p> <code>None</code> <code>utm_zone_letter</code> <code>str</code> <p>UTM zone letter. If None, the zone letter is automatically calculated.</p> <code>None</code> <p>Returns:</p> Type Description <code>(float, float)</code> <p>Tuple of easting and northing coordinates in meters. First element is easting, second is northing.</p> References <p>Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87) https://github.com/Turbo87/utm</p> <p>https://www.maptools.com/tutorials/grid_zone_details#</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def latlon_to_utm(lat, lon, utm_zone_number=None, utm_zone_letter=None):\n    \"\"\"Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.\n\n    Function only applies to non-polar coordinates.\n    If further functionality is required, consider using the utm module. See references for more information.\n\n    ![UTM zones](https://upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Universal_Transverse_Mercator_zones.svg/1920px-Universal_Transverse_Mercator_zones.svg.png)\n    UTM zones on an equirectangular world map with irregular zones in red and New York City's zone highlighted. See [UTM zones](https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#UTM_zones) for a full description.\n\n\n    Args:\n        lat (float, array): Latitude in decimal degrees.\n        lon (float, array): Longitude in decimal degrees.\n        utm_zone_number (int): UTM zone number. If None, the zone number is automatically calculated.\n        utm_zone_letter (str): UTM zone letter. If None, the zone letter is automatically calculated.\n\n    Returns:\n        (float, float): Tuple of easting and northing coordinates in meters. First element is easting, second is northing.\n\n    References:\n         Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87)\n         [https://github.com/Turbo87/utm](https://github.com/Turbo87/utm)\n\n         [https://www.maptools.com/tutorials/grid_zone_details#](https://www.maptools.com/tutorials/grid_zone_details#)\n    \"\"\"\n    if utm_zone_number is None or utm_zone_letter is None:\n        easting, northing, zone_number, zone_letter = utm.from_latlon(lat, lon)\n    else:\n        easting, northing, zone_number, zone_letter = utm.from_latlon(lat, lon, utm_zone_number, utm_zone_letter)\n\n    return easting, northing, zone_number, zone_letter\n</code></pre>"},{"location":"reference/#crnpy.crnpy.lattice_water","title":"<code>lattice_water(clay_content, total_carbon=None)</code>","text":"<p>Estimate the amount of water in the lattice of clay minerals.</p> $\\omega_{lat} = 0.097 * clay(\\%)$ $\\omega_{lat} = -0.028 + 0.077 * clay(\\%) + 0.459 * carbon(\\%)$ Linear regression [lattice water (%) as a function of clay (%)] done with data from Kansas Sate University - Soil Water Processes Lab. Multiple linear regression [lattice water (%) as a function of clay (%) and soil carbon (%)] done with data from Soil Water Processes Lab. <p>Parameters:</p> Name Type Description Default <code>clay_content</code> <code>float</code> <p>Clay content in the soil in percent.</p> required <code>total_carbon</code> <code>float</code> <p>Total carbon content in the soil in percent. If None, the amount of water is estimated based on clay content only.</p> <code>None</code> <p>Returns:</p> Type Description <code>float</code> <p>Amount of water in the lattice of clay minerals in percent</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def lattice_water(clay_content, total_carbon=None):\n    r\"\"\"Estimate the amount of water in the lattice of clay minerals.\n\n    ![img1](img/lattice_water_simple.png) | ![img2](img/lattice_water_multiple.png)\n    :-------------------------:|:-------------------------:\n    $\\omega_{lat} = 0.097 * clay(\\%)$ | $\\omega_{lat} = -0.028 + 0.077 * clay(\\%) + 0.459 * carbon(\\%)$\n    Linear regression [lattice water (%) as a function of clay (%)] done with data from Kansas Sate University - Soil Water Processes Lab. |  Multiple linear regression [lattice water (%) as a function of clay (%) and soil carbon (%)] done with data from Soil Water Processes Lab.\n\n    Args:\n        clay_content (float): Clay content in the soil in percent.\n        total_carbon (float, optional): Total carbon content in the soil in percent.\n            If None, the amount of water is estimated based on clay content only.\n\n    Returns:\n        (float): Amount of water in the lattice of clay minerals in percent\n    \"\"\"\n    if total_carbon is None:\n        lattice_water = 0.097 * clay_content\n    else:\n        lattice_water = -0.028 + 0.077 * clay_content + 0.459 * total_carbon\n    return lattice_water\n</code></pre>"},{"location":"reference/#crnpy.crnpy.location_factor","title":"<code>location_factor(site_atmospheric_depth, site_Rc, reference_atmospheric_depth, reference_Rc)</code>","text":"<p>Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.</p> <p>Parameters:</p> Name Type Description Default <code>site_atmospheric_depth</code> <code>float</code> <p>Atmospheric depth at the site in g/cm2. Can be estimated using the function <code>atmospheric_depth()</code></p> required <code>site_Rc</code> <code>float</code> <p>Cutoff rigidity at the site in GV. Can be estimated using the function <code>cutoff_rigidity()</code></p> required <code>reference_atmospheric_depth</code> <code>float</code> <p>Atmospheric depth at the reference location in g/cm2.</p> required <code>reference_Rc</code> <code>float</code> <p>Cutoff rigidity at the reference location in GV.</p> required <p>Returns:</p> Type Description <code>float</code> <p>Location-dependent correction factor.</p> References <p>McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic\u2010Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def location_factor(site_atmospheric_depth, site_Rc, reference_atmospheric_depth, reference_Rc):\n    \"\"\"\n    Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.\n\n\n    Args:\n        site_atmospheric_depth (float): Atmospheric depth at the site in g/cm2. Can be estimated using the function `atmospheric_depth()`\n        site_Rc (float): Cutoff rigidity at the site in GV. Can be estimated using the function `cutoff_rigidity()`\n        reference_atmospheric_depth (float): Atmospheric depth at the reference location in g/cm2.\n        reference_Rc (float): Cutoff rigidity at the reference location in GV.\n\n    Returns:\n        (float): Location-dependent correction factor.\n\n    References:\n        McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic\u2010Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.\n\n    \"\"\"\n\n    # Renamed variables based on the provided table\n    c0 = -0.0009  # from C39\n    c1 = 1.7699  # from C40\n    c2 = 0.0064  # from C41\n    c3 = 1.8855  # from C42\n    c4 = 0.000013  # from C43\n    c5 = -1.2237  # from C44\n    epsilon = 1  # from C45\n\n    # Translated formula with renamed variables from McJannet and Desilets, 2023\n    tau_new = epsilon * (c0 * reference_atmospheric_depth + c1) * (\n            1 - np.exp(\n        -(c2 * reference_atmospheric_depth + c3) * reference_Rc ** (c4 * reference_atmospheric_depth + c5)))\n\n    norm_factor = 1 / tau_new\n\n    # Calculate the result using the provided parameters\n    tau = epsilon * norm_factor * (c0 * site_atmospheric_depth + c1) * (\n                1 - np.exp(-(c2 * site_atmospheric_depth + c3) * site_Rc ** (c4 * site_atmospheric_depth + c5)))\n    return tau\n</code></pre>"},{"location":"reference/#crnpy.crnpy.nrad_weight","title":"<code>nrad_weight(h, theta, distances, depth, rhob=1.4, method=None, p=None, Hveg=None)</code>","text":"<p>Function to compute distance weights corresponding to each soil sample.</p> <p>Parameters:</p> Name Type Description Default <code>h</code> <code>array or Series</code> <p>Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.</p> required <code>theta</code> <code>array or Series</code> <p>Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)</p> required <code>distances</code> <code>array or Series</code> <p>Distances from the location of each sample to the origin (0.5 - 600 m)</p> required <code>depth</code> <code>array or Series</code> <p>Depths for each sample (m)</p> required <code>rhob</code> <code>array or Series</code> <p>Bulk density in g/cm^3</p> <code>1.4</code> <code>p</code> <code>array or Series</code> <p>Atmospheric pressure in hPa. Required for the 'Schron_2017' method.</p> <code>None</code> <code>Hveg</code> <code>array or Series</code> <p>Vegetation height in m. Required for the 'Schron_2017' method.</p> <code>None</code> <code>method</code> <code>str</code> <p>Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.</p> <code>None</code> <p>Returns:</p> Type Description <code>array or Series or DataFrame</code> <p>Distance weights for each sample.</p> References <p>K\u00f6hli, M., Schr\u00f6n, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015). Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray neutrons. Water Resour. Res. 51, 5772\u20135790. doi:10.1002/2015WR017169</p> <p>Schr\u00f6n, M., K\u00f6hli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M., Rebmann, C., Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.: Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity, Hydrol. Earth Syst. Sci., 21, 5009\u20135030, https://doi.org/10.5194/hess-21-5009-2017, 2017.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def nrad_weight(h, theta, distances, depth, rhob=1.4, method=None, p=None, Hveg=None):\n    \"\"\"Function to compute distance weights corresponding to each soil sample.\n\n    Args:\n        h (np.array or pd.Series): Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.\n        theta (np.array or pd.Series): Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)\n        distances (np.array or pd.Series): Distances from the location of each sample to the origin (0.5 - 600 m)\n        depth (np.array or pd.Series): Depths for each sample (m)\n        rhob (np.array or pd.Series): Bulk density in g/cm^3\n        p (np.array or pd.Series): Atmospheric pressure in hPa. Required for the 'Schron_2017' method.\n        Hveg (np.array or pd.Series): Vegetation height in m. Required for the 'Schron_2017' method.\n        method (str): Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Distance weights for each sample.\n\n    References:\n        K\u00f6hli, M., Schr\u00f6n, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015).\n        Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray\n        neutrons. Water Resour. Res. 51, 5772\u20135790. doi:10.1002/2015WR017169\n\n        Schr\u00f6n, M., K\u00f6hli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L.,\n        Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M., Rebmann, C.,\n        Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.: Improving calibration and\n        validation of cosmic-ray neutron sensors in the light of spatial sensitivity,\n        Hydrol. Earth Syst. Sci., 21, 5009\u20135030, https://doi.org/10.5194/hess-21-5009-2017, 2017.\n    \"\"\"\n\n    if method == 'Kohli_2015':\n\n        # Table A1. Parameters for Fi and D86\n        p10 = 8735;\n        p11 = 17.1758;\n        p12 = 11720;\n        p13 = 0.00978;\n        p14 = 7045;\n        p15 = 0.003632;\n        p20 = 2.7925e-2;\n        p21 = 5.0399;\n        p22 = 2.8544e-2;\n        p23 = 0.002455;\n        p24 = 6.851e-5;\n        p25 = 9.2926;\n        p30 = 247970;\n        p31 = 17.63;\n        p32 = 374655;\n        p33 = 0.00191;\n        p34 = 195725;\n        p40 = 5.4818e-2;\n        p41 = 15.921;\n        p42 = 0.6373;\n        p43 = 5.99e-2;\n        p44 = 5.425e-4;\n        p50 = 1383702;\n        p51 = 4.156;\n        p52 = 5325;\n        p53 = 0.00238;\n        p54 = 0.0156;\n        p55 = 0.130;\n        p56 = 1521;\n        p60 = 6.031e-5;\n        p61 = 98.5;\n        p62 = 1.0466e-3;\n        p70 = 11747;\n        p71 = 41.66;\n        p72 = 4521;\n        p73 = 0.01998;\n        p74 = 0.00604;\n        p75 = 2534;\n        p76 = 0.00475;\n        p80 = 1.543e-2;\n        p81 = 10.06;\n        p82 = 1.807e-2;\n        p83 = 0.0011;\n        p84 = 8.81e-5;\n        p85 = 0.0405;\n        p86 = 20.24;\n        p90 = 8.321;\n        p91 = 0.14249;\n        p92 = 0.96655;\n        p93 = 26.42;\n        p94 = 0.0567;\n\n        # Numerical determination of the penetration depth (86%) (Eq. 8)\n        D86 = 1 / rhob * (p90 + p91 * (p92 + np.exp(-1 * distances / 100)) * (p93 + theta) / (p94 + theta))\n\n        # Depth weights (Eq. 7)\n        Wd = np.exp(-2 * depth / D86)\n\n        if h == 0:\n            W = 1  # skip distance weighting\n\n        elif (h &gt;= 0.1) and (h &lt;= 50):\n            # Functions for Fi (Appendix A in K\u00f6hli et al., 2015)\n            F1 = p10 * (1 + p13 * h) * np.exp(-p11 * theta) + p12 * (1 + p15 * h) - p14 * theta\n            F2 = ((-p20 + p24 * h) * np.exp(-p21 * theta / (1 + p25 * theta)) + p22) * (1 + h * p23)\n            F3 = (p30 * (1 + p33 * h) * np.exp(-p31 * theta) + p32 - p34 * theta)\n            F4 = p40 * np.exp(-p41 * theta) + p42 - p43 * theta + p44 * h\n            F5 = p50 * (0.02 - 1 / p55 / (h - p55 + p56 * theta)) * (p54 - theta) * np.exp(\n                -p51 * (theta - p54)) + p52 * (0.7 - h * theta * p53)\n            F6 = p60 * (h + p61) + p62 * theta\n            F7 = (p70 * (1 - p76 * h) * np.exp(-p71 * theta * (1 - h * p74)) + p72 - p75 * theta) * (2 + h * p73)\n            F8 = ((-p80 + p84 * h) * np.exp(-p81 * theta / (1 + p85 * h + p86 * theta)) + p82) * (2 + h * p83)\n\n            # Distance weights (Eq. 3)\n            W = np.ones_like(distances) * np.nan\n            for i in range(len(distances)):\n                if (distances[i] &lt;= 50) and (distances[i] &gt; 0.5):\n                    W[i] = F1[i] * (np.exp(-F2[i] * distances[i])) + F3[i] * np.exp(-F4[i] * distances[i])\n\n                elif (distances[i] &gt; 50) and (distances[i] &lt; 600):\n                    W[i] = F5[i] * (np.exp(-F6[i] * distances[i])) + F7[i] * np.exp(-F8[i] * distances[i])\n\n                else:\n                    raise ValueError('Input distances are not valid.')\n\n        else:\n            raise ValueError('Air humidity values are out of range.')\n\n        # Combined and normalized weights\n        weights = Wd * W / np.nansum(Wd * W)\n        return weights\n    elif method == 'Schron_2017':\n        # Horizontal distance weights According to Eq. 6 and Table A1 in Schr\u00f6n et al. (2017)\n        # Method for calculating the horizontal distance weights from 0 to 1m\n        def WrX(r, x, y):\n            x00 = 3.7\n            a00 = 8735;\n            a01 = 22.689;\n            a02 = 11720;\n            a03 = 0.00978;\n            a04 = 9306;\n            a05 = 0.003632\n            a10 = 2.7925e-2;\n            a11 = 6.6577;\n            a12 = 0.028544;\n            a13 = 0.002455;\n            a14 = 6.851e-5;\n            a15 = 12.2755\n            a20 = 247970;\n            a21 = 23.289;\n            a22 = 374655;\n            a23 = 0.00191;\n            a24 = 258552\n            a30 = 5.4818e-2;\n            a31 = 21.032;\n            a32 = 0.6373;\n            a33 = 0.0791;\n            a34 = 5.425e-4\n\n            x0 = x00\n            A0 = (a00 * (1 + a03 * x) * np.exp(-a01 * y) + a02 * (1 + a05 * x) - a04 * y)\n            A1 = ((-a10 + a14 * x) * np.exp(-a11 * y / (1 + a15 * y)) + a12) * (1 + x * a13)\n            A2 = (a20 * (1 + a23 * x) * np.exp(-a21 * y) + a22 - a24 * y)\n            A3 = a30 * np.exp(-a31 * y) + a32 - a33 * y + a34 * x\n\n            return ((A0 * (np.exp(-A1 * r)) + A2 * np.exp(-A3 * r)) * (1 - np.exp(-x0 * r)))\n\n        # Method for calculating the horizontal distance weights from 1 to 50m\n        def WrA(r, x, y):\n            a00 = 8735;\n            a01 = 22.689;\n            a02 = 11720;\n            a03 = 0.00978;\n            a04 = 9306;\n            a05 = 0.003632\n            a10 = 2.7925e-2;\n            a11 = 6.6577;\n            a12 = 0.028544;\n            a13 = 0.002455;\n            a14 = 6.851e-5;\n            a15 = 12.2755\n            a20 = 247970;\n            a21 = 23.289;\n            a22 = 374655;\n            a23 = 0.00191;\n            a24 = 258552\n            a30 = 5.4818e-2;\n            a31 = 21.032;\n            a32 = 0.6373;\n            a33 = 0.0791;\n            a34 = 5.425e-4\n\n            A0 = (a00 * (1 + a03 * x) * np.exp(-a01 * y) + a02 * (1 + a05 * x) - a04 * y)\n            A1 = ((-a10 + a14 * x) * np.exp(-a11 * y / (1 + a15 * y)) + a12) * (1 + x * a13)\n            A2 = (a20 * (1 + a23 * x) * np.exp(-a21 * y) + a22 - a24 * y)\n            A3 = a30 * np.exp(-a31 * y) + a32 - a33 * y + a34 * x\n\n            return A0 * np.exp(-A1 * r) + A2 * np.exp(-A3 * r)\n\n        # Method for calculating the horizontal distance weights from 50 to 600m\n        def WrB(r, x, y):\n            b00 = 39006;\n            b01 = 15002337;\n            b02 = 2009.24;\n            b03 = 0.01181;\n            b04 = 3.146;\n            b05 = 16.7417;\n            b06 = 3727\n            b10 = 6.031e-5;\n            b11 = 98.5;\n            b12 = 0.0013826\n            b20 = 11747;\n            b21 = 55.033;\n            b22 = 4521;\n            b23 = 0.01998;\n            b24 = 0.00604;\n            b25 = 3347.4;\n            b26 = 0.00475\n            b30 = 1.543e-2;\n            b31 = 13.29;\n            b32 = 1.807e-2;\n            b33 = 0.0011;\n            b34 = 8.81e-5;\n            b35 = 0.0405;\n            b36 = 26.74\n\n            B0 = (b00 - b01 / (b02 * y + x - 0.13)) * (b03 - y) * np.exp(-b04 * y) - b05 * x * y + b06\n            B1 = b10 * (x + b11) + b12 * y\n            B2 = (b20 * (1 - b26 * x) * np.exp(-b21 * y * (1 - x * b24)) + b22 - b25 * y) * (2 + x * b23)\n            B3 = ((-b30 + b34 * x) * np.exp(-b31 * y / (1 + b35 * x + b36 * y)) + b32) * (2 + x * b33)\n\n            return B0 * np.exp(-B1 * r) + B2 * np.exp(-B3 * r)\n\n        def rscaled(r, p, Hveg, y):\n            Fp = 0.4922 / (0.86 - np.exp(-p / 1013.25))\n            Fveg = 1 - 0.17 * (1 - np.exp(-0.41 * Hveg)) * (1 + np.exp(-9.25 * y))\n            return r / Fp / Fveg\n\n        # Rename variables to be consistent with the revised paper\n        r = distances\n        x = h\n        y = theta\n        bd = rhob\n\n        if Hveg is not None and p is not None:\n            r = rscaled(r, p, Hveg, y)\n\n        Wr = np.zeros(len(r))\n\n        # See Eq. 6 in Schron et al. (2017)\n        r0_idx = (r &lt;= 1)\n        r1_idx = (r &gt; 1) &amp; (r &lt;= 50)\n        r2_idx = (r &gt; 50) &amp; (r &lt; 600)\n        Wr[r0_idx] = WrX(r[r0_idx], x[r0_idx], y[r0_idx])\n        Wr[r1_idx] = WrA(r[r1_idx], x[r1_idx], y[r1_idx])\n        Wr[r2_idx] = WrB(r[r2_idx], x[r2_idx], y[r2_idx])\n\n        # Vertical distance weights\n        def D86(r, bd, y):\n            return 1 / bd * (8.321 + 0.14249 * (0.96655 + np.exp(-0.01 * r)) * (20 + y) / (0.0429 + y))\n\n        def Wd(d, r, bd, y):\n            return np.exp(-2 * d / D86(r, bd, y))\n\n        # Calculate the vertical distance weights\n        Wd = Wd(d, r, bd, y)\n\n        # Combined and normalized weights\n        # Combined and normalized weights\n        weights = Wd * Wr / np.nansum(Wd * Wr)\n\n        return weights\n</code></pre>"},{"location":"reference/#crnpy.crnpy.remove_incomplete_intervals","title":"<code>remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False)</code>","text":"<p>Function that removes rows with incomplete integration intervals.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>Pandas Dataframe with data from stationary or roving CRNP devices.</p> required <code>timestamp_col</code> <code>str</code> <p>Name of the column with timestamps in datetime format.</p> required <code>integration_time</code> <code>int</code> <p>Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.</p> required <code>remove_first</code> <code>bool</code> <p>Remove first row. Default is False.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> Source code in <code>crnpy/crnpy.py</code> <pre><code>def remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False):\n    \"\"\"Function that removes rows with incomplete integration intervals.\n\n    Args:\n        df (pandas.DataFrame): Pandas Dataframe with data from stationary or roving CRNP devices.\n        timestamp_col (str): Name of the column with timestamps in datetime format.\n        integration_time (int): Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.\n        remove_first (bool, optional): Remove first row. Default is False.\n\n    Returns:\n        (pandas.DataFrame): \n    \"\"\"\n\n    # Check format of timestamp column\n    if df[timestamp_col].dtype != 'datetime64[ns]':\n        raise TypeError('timestamp_col must be datetime64. Use `pd.to_datetime()` to fix this issue.')\n\n    # Check if differences in timestamps are below or above the provided integration time\n    idx_delta = df[timestamp_col].diff().dt.total_seconds() != integration_time\n\n    if remove_first:\n        idx_delta[0] = True\n\n    # Select rows that meet the specified integration time\n    df = df[~idx_delta]\n    df.reset_index(drop=True, inplace=True)\n\n    # Notify user about the number of rows that have been removed\n    print(f\"Removed a total of {sum(idx_delta)} rows.\")\n\n    return df\n</code></pre>"},{"location":"reference/#crnpy.crnpy.rover_centered_coordinates","title":"<code>rover_centered_coordinates(x, y)</code>","text":"<p>Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>array</code> <p>x coordinates.</p> required <code>y</code> <code>array</code> <p>y coordinates.</p> required <p>Returns:</p> Name Type Description <code>x_est</code> <code>array</code> <p>Estimated x coordinates.</p> <code>y_est</code> <code>array</code> <p>Estimated y coordinates.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def rover_centered_coordinates(x, y):\n    \"\"\"Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.\n\n    Args:\n        x (array): x coordinates.\n        y (array): y coordinates.\n\n    Returns:\n        x_est (array): Estimated x coordinates.\n        y_est (array): Estimated y coordinates.\n    \"\"\"\n\n    # Make it datatype agnostic\n    if (isinstance(x, pd.Series)):\n        x = x.values\n    if (isinstance(y, pd.Series)):\n        y = y.values\n\n    # Do the average of the two points\n    x_est = (x[1:] + x[:-1]) / 2\n    y_est = (y[1:] + y[:-1]) / 2\n\n    # Add the first point to match the length of the original array\n    x_est = np.insert(x_est, 0, x[0])\n    y_est = np.insert(y_est, 0, y[0])\n\n    return x_est, y_est\n</code></pre>"},{"location":"reference/#crnpy.crnpy.sensing_depth","title":"<code>sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017')</code>","text":"<p>Function that computes the estimated sensing depth of the cosmic-ray neutron probe. The function offers several methods to compute the depth at which 86 % of the neutrons probe the soil profile.</p> <p>Parameters:</p> Name Type Description Default <code>vwc</code> <code>array or Series or DataFrame</code> <p>Estimated volumetric water content for each timestamp.</p> required <code>pressure</code> <code>array or Series or DataFrame</code> <p>Atmospheric pressure in hPa for each timestamp.</p> required <code>p_ref</code> <code>float</code> <p>Reference pressure in hPa.</p> required <code>bulk_density</code> <code>float</code> <p>Soil bulk density.</p> required <code>Wlat</code> <code>float</code> <p>Lattice water content.</p> required <code>method</code> <code>str</code> <p>Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.</p> <code>'Schron_2017'</code> <code>dist</code> <code>list or array</code> <p>List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.</p> <code>None</code> <p>Returns:</p> Type Description <code>array or Series or DataFrame</code> <p>Estimated sensing depth in m.</p> References <p>Franz, T.E., Zreda, M., Ferre, T.P.A., Rosolem, R., Zweck, C., Stillman, S., Zeng, X. and Shuttleworth, W.J., 2012. Measurement depth of the cosmic ray soil moisture probe affected by hydrogen from various sources. Water Resources Research, 48(8). doi.org/10.1029/2012WR011871</p> <p>Schr\u00f6n, M., K\u00f6hli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., et al. (2017). Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity. Hydrol. Earth Syst. Sci. 21, 5009\u20135030. doi.org/10.5194/hess-21-5009-2017</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017'):\n    \"\"\"Function that computes the estimated sensing depth of the cosmic-ray neutron probe.\n    The function offers several methods to compute the depth at which 86 % of the neutrons\n    probe the soil profile.\n\n    Args:\n        vwc (array or pd.Series or pd.DataFrame): Estimated volumetric water content for each timestamp.\n        pressure (array or pd.Series or pd.DataFrame): Atmospheric pressure in hPa for each timestamp.\n        p_ref (float): Reference pressure in hPa.\n        bulk_density (float): Soil bulk density.\n        Wlat (float): Lattice water content.\n        method (str): Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.\n        dist (list or array): List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Estimated sensing depth in m.\n\n    References:\n        Franz, T.E., Zreda, M., Ferre, T.P.A., Rosolem, R., Zweck, C., Stillman, S., Zeng, X. and Shuttleworth, W.J., 2012.\n        Measurement depth of the cosmic ray soil moisture probe affected by hydrogen from various sources.\n        Water Resources Research, 48(8). doi.org/10.1029/2012WR011871\n\n        Schr\u00f6n, M., K\u00f6hli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., et al. (2017).\n        Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity.\n        Hydrol. Earth Syst. Sci. 21, 5009\u20135030. doi.org/10.5194/hess-21-5009-2017\n    \"\"\"\n\n    # Determine sensing depth (D86)\n    if method == 'Schron_2017':\n        # See Appendix A of Schr\u00f6n et al. (2017)\n        Fp = 0.4922 / (0.86 - np.exp(-1 * pressure / p_ref))\n        Fveg = 0\n        results = []\n        for d in dist:\n            # Compute r_star\n            r_start = d / Fp\n\n            # Compute soil depth that accounts for 86% of the neutron flux\n            D86 = 1 / bulk_density * (8.321 + 0.14249 * (0.96655 + np.exp(-0.01 * r_start)) * (20 + (Wlat + vwc)) / (\n                        0.0429 + (Wlat + vwc)))\n            results.append(D86)\n\n    elif method == 'Franz_2012':\n        results = 5.8 / (bulk_density * Wlat + vwc + 0.0829)\n    else:\n        raise ValueError('Method not recognized. Please select either \"Schron_2017\" or \"Franz_2012\".')\n    return results\n</code></pre>"},{"location":"reference/#crnpy.crnpy.smooth_1d","title":"<code>smooth_1d(values, window=5, order=3, method='moving_median')</code>","text":"<p>Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).</p> <p>Parameters:</p> Name Type Description Default <code>values</code> <code>DataFrame or Serie</code> <p>Dataframe containing the values to smooth.</p> required <code>window</code> <code>int</code> <p>Window size for the Savitzky-Golay filter. Default is 5.</p> <code>5</code> <code>method</code> <code>str</code> <p>Method to use for smoothing the data. Default is 'moving_median'. Options are 'moving_average', 'moving_median' and 'savitzky_golay'.</p> <code>'moving_median'</code> <code>order</code> <code>int</code> <p>Order of the Savitzky-Golay filter. Default is 3.</p> <code>3</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>DataFrame with smoothed values.</p> References <p>Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020. Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9. doi.org/10.3389/frwa.2020.00009</p> <p>Savitzky, A., &amp; Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 36(8), 1627-1639.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def smooth_1d(values, window=5, order=3, method='moving_median'):\n    \"\"\"Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).\n\n    Args:\n        values (pd.DataFrame or pd.Serie): Dataframe containing the values to smooth.\n        window (int): Window size for the Savitzky-Golay filter. Default is 5.\n        method (str): Method to use for smoothing the data. Default is 'moving_median'.\n            Options are 'moving_average', 'moving_median' and 'savitzky_golay'.\n        order (int): Order of the Savitzky-Golay filter. Default is 3.\n\n    Returns:\n        (pd.DataFrame): DataFrame with smoothed values.\n\n    References:\n        Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.\n        Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.\n        doi.org/10.3389/frwa.2020.00009\n\n        Savitzky, A., &amp; Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures.\n        Analytical chemistry, 36(8), 1627-1639.\n    \"\"\"\n\n    if not isinstance(x, pd.Series) and not isinstance(x, pd.DataFrame):\n        raise ValueError('Input must be a pandas Series or DataFrame')\n\n    if method == 'moving_average':\n        corrected_counts = values.rolling(window=window, center=True, min_periods=1).mean()\n    elif method == 'moving_median':\n        corrected_counts = values.rolling(window=window, center=True, min_periods=1).median()\n\n    elif method == 'savitzky_golay':\n        if values.isna().any():\n            print('Dataframe contains NaN values. Please remove NaN values before smoothing the data.')\n\n        if type(values) == pd.core.series.Series:\n            filtered = savgol_filter(values, window, order)\n            corrected_counts = pd.DataFrame(filtered, columns=['smoothed'], index=values.index)\n        elif type(values) == pd.core.frame.DataFrame:\n            for col in values.columns:\n                values[col] = savgol_filter(values[col], window, order)\n    else:\n        raise ValueError(\n            'Invalid method. Please select a valid filtering method., options are: moving_average, moving_median, savitzky_golay')\n    corrected_counts = corrected_counts.ffill(limit=window).bfill(limit=window).copy()\n    return corrected_counts\n</code></pre>"},{"location":"reference/#crnpy.crnpy.spatial_average","title":"<code>spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=False)</code>","text":"<p>Moving buffer filter to smooth georeferenced two-dimensional data.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>list or array</code> <p>UTM x coordinates in meters.</p> required <code>y</code> <code>list or array</code> <p>UTM y coordinates in meters.</p> required <code>z</code> <code>list or array</code> <p>Values to be smoothed.</p> required <code>buffer</code> <code>float</code> <p>Radial buffer distance in meters.</p> <code>100</code> <code>min_neighbours</code> <code>int</code> <p>Minimum number of neighbours to consider for the smoothing.</p> <code>3</code> <code>method</code> <code>str</code> <p>One of 'mean' or 'median'.</p> <code>'mean'</code> <code>rnd</code> <code>bool</code> <p>Boolean to round the final result. Useful in case of z representing neutron counts.</p> <code>False</code> <p>Returns:</p> Type Description <code>array</code> <p>Smoothed version of z with the same dimension as z.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=False):\n    \"\"\"Moving buffer filter to smooth georeferenced two-dimensional data.\n\n    Args:\n        x (list or array): UTM x coordinates in meters.\n        y (list or array): UTM y coordinates in meters.\n        z (list or array): Values to be smoothed.\n        buffer (float): Radial buffer distance in meters.\n        min_neighbours (int): Minimum number of neighbours to consider for the smoothing.\n        method (str): One of 'mean' or 'median'.\n        rnd (bool): Boolean to round the final result. Useful in case of z representing neutron counts.\n\n    Returns:\n        (array): Smoothed version of z with the same dimension as z.\n    \"\"\"\n\n    # Convert input data to Numpy arrays\n    if (type(x) is not np.ndarray) or (type(y) is not np.ndarray):\n        try:\n            x = np.array(x)\n            y = np.array(y)\n        except:\n            raise \"Input values cannot be converted to Numpy arrays.\"\n\n    if len(x) != len(y):\n        raise f\"The number of x and y must be equal. Input x has {len(x)} values and y has {len(y)} values.\"\n\n    # Compute distances\n    N = len(x)\n    z_smooth = np.array([])\n    for k in range(N):\n        px = x[k]\n        py = y[k]\n\n        distances = euclidean_distance(px, py, x, y)\n        idx_within_buffer = distances &lt;= buffer\n\n        if np.isnan(z[k]):\n            z_new_val = np.nan\n        elif len(distances[idx_within_buffer]) &gt; min_neighbours:\n            if method == 'mean':\n                z_new_val = np.nanmean(z[idx_within_buffer])\n            elif method == 'median':\n                z_new_val = np.nanmedian(z[idx_within_buffer])\n            else:\n                raise f\"Method {method} does not exist. Provide either 'mean' or 'median'.\"\n        else:\n            z_new_val = z[k]  # If there are not enough neighbours, keep the original value\n\n        # Append smoothed value to array\n        z_smooth = np.append(z_smooth, z_new_val)\n\n    if rnd:\n        z_smooth = np.round(z_smooth, 0)\n\n    return z_smooth\n</code></pre>"},{"location":"reference/#crnpy.crnpy.total_raw_counts","title":"<code>total_raw_counts(counts)</code>","text":"<p>Compute the sum of uncorrected neutron counts for all detectors.</p> <p>Parameters:</p> Name Type Description Default <code>counts</code> <code>DataFrame</code> <p>Dataframe containing only the columns with neutron counts.</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>Dataframe with the sum of uncorrected neutron counts for all detectors.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def total_raw_counts(counts):\n    \"\"\"Compute the sum of uncorrected neutron counts for all detectors.\n\n    Args:\n        counts (pandas.DataFrame): Dataframe containing only the columns with neutron counts.\n\n    Returns:\n        (pandas.DataFrame): Dataframe with the sum of uncorrected neutron counts for all detectors.\n    \"\"\"\n\n    if counts.shape[0] &gt; 1:\n        counts = counts.apply(lambda x: x.fillna(counts.mean(axis=1)), axis=0)\n\n    # Compute sum of counts\n    total_raw_counts = counts.sum(axis=1)\n\n    # Replace zeros with NaN\n    total_raw_counts = total_raw_counts.replace(0, np.nan)\n\n    return total_raw_counts\n</code></pre>"},{"location":"reference/#crnpy.crnpy.uncertainty_counts","title":"<code>uncertainty_counts(raw_counts, metric='std', fp=1, fw=1, fi=1)</code>","text":"<p>Function to estimate the uncertainty of raw counts.</p> <p>Measurements of proportional neutron detector systems are governed by counting statistics that follow a Poissonian probability distribution (Zreda et al., 2012). The expected uncertainty in the neutron count rate $N$ is defined by the standard deviation $ \\sqrt{N} $ (Jakobi et al., 2020). The CV% can be expressed as $ N^{-1/2} $</p> <p>Parameters:</p> Name Type Description Default <code>raw_counts</code> <code>array</code> <p>Raw neutron counts.</p> required <code>metric</code> <code>str</code> <p>Either 'std' or 'cv' for standard deviation or coefficient of variation.</p> <code>'std'</code> <code>fp</code> <code>float</code> <p>Pressure correction factor.</p> <code>1</code> <code>fw</code> <code>float</code> <p>Humidity correction factor.</p> <code>1</code> <code>fi</code> <code>float</code> <p>Incoming neutron flux correction factor.</p> <code>1</code> <p>Returns:</p> Name Type Description <code>uncertainty</code> <code>float</code> <p>Uncertainty of raw counts.</p> References <p>Jakobi J, Huisman JA, Schr\u00f6n M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010</p> <p>Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079\u20134099, https://doi.org/10.5194/hess-16-4079-2012, 2012.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def uncertainty_counts(raw_counts, metric=\"std\", fp=1, fw=1, fi=1):\n    \"\"\"Function to estimate the uncertainty of raw counts.\n\n    Measurements of proportional neutron detector systems are governed by counting statistics that follow a Poissonian probability distribution (Zreda et al., 2012).\n    The expected uncertainty in the neutron count rate $N$ is defined by the standard deviation $ \\sqrt{N} $ (Jakobi et al., 2020).\n    The CV% can be expressed as $ N^{-1/2} $\n\n    Args:\n        raw_counts (array): Raw neutron counts.\n        metric (str): Either 'std' or 'cv' for standard deviation or coefficient of variation.\n        fp (float): Pressure correction factor.\n        fw (float): Humidity correction factor.\n        fi (float): Incoming neutron flux correction factor.\n\n    Returns:\n        uncertainty (float): Uncertainty of raw counts.\n\n    References:\n        Jakobi J, Huisman JA, Schr\u00f6n M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With\n        Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010\n\n        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System,\n        Hydrol. Earth Syst. Sci., 16, 4079\u20134099, https://doi.org/10.5194/hess-16-4079-2012, 2012.\n\n    \"\"\"\n\n    s = fw / (fp * fi)\n    if metric == \"std\":\n        uncertainty = np.sqrt(raw_counts) * s\n    elif metric == \"cv\":\n        uncertainty = 1 / np.sqrt(raw_counts) * s\n    else:\n        raise f\"Metric {metric} does not exist. Provide either 'std' or 'cv' for standard deviation or coefficient of variation.\"\n    return uncertainty\n</code></pre>"},{"location":"reference/#crnpy.crnpy.uncertainty_vwc","title":"<code>uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808, a1=0.372, a2=0.115)</code>","text":"<p>Function to estimate the uncertainty propagated to volumetric water content.</p> <p>The uncertainty of the volumetric water content is estimated by propagating the uncertainty of the raw counts. Following Eq. 10 in Jakobi et al. (2020), the uncertainty of the volumetric water content can be expressed as: $$ \\sigma_{\\theta_g}(N) = \\sigma_N \\frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \\sqrt{(N_{cor} - a_1 N_0)^4 + 8 \\sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \\sigma_N^4} $$</p> <p>Parameters:</p> Name Type Description Default <code>raw_counts</code> <code>array</code> <p>Raw neutron counts.</p> required <code>N0</code> <code>float</code> <p>Calibration parameter N0.</p> required <code>bulk_density</code> <code>float</code> <p>Bulk density in g cm-3.</p> required <code>fp</code> <code>float</code> <p>Pressure correction factor.</p> <code>1</code> <code>fw</code> <code>float</code> <p>Humidity correction factor.</p> <code>1</code> <code>fi</code> <code>float</code> <p>Incoming neutron flux correction factor.</p> <code>1</code> <p>Returns:</p> Name Type Description <code>sigma_VWC</code> <code>float</code> <p>Uncertainty in terms of volumetric water content.</p> References <p>Jakobi J, Huisman JA, Schr\u00f6n M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808, a1=0.372, a2=0.115):\n    r\"\"\"Function to estimate the uncertainty propagated to volumetric water content.\n\n    The uncertainty of the volumetric water content is estimated by propagating the uncertainty of the raw counts.\n    Following Eq. 10 in Jakobi et al. (2020), the uncertainty of the volumetric water content can be expressed as:\n    $$\n    \\sigma_{\\theta_g}(N) = \\sigma_N \\frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \\sqrt{(N_{cor} - a_1 N_0)^4 + 8 \\sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \\sigma_N^4}\n    $$\n\n    Args:\n        raw_counts (array): Raw neutron counts.\n        N0 (float): Calibration parameter N0.\n        bulk_density (float): Bulk density in g cm-3.\n        fp (float): Pressure correction factor.\n        fw (float): Humidity correction factor.\n        fi (float): Incoming neutron flux correction factor.\n\n    Returns:\n        sigma_VWC (float): Uncertainty in terms of volumetric water content.\n\n    References:\n        Jakobi J, Huisman JA, Schr\u00f6n M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With\n        Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010\n    \"\"\"\n\n    Ncorr = raw_counts * fw / (fp * fi)\n    sigma_N = uncertainty_counts(raw_counts, metric=\"std\", fp=fp, fw=fw, fi=fi)\n    sigma_GWC = sigma_N * ((a0 * N0) / ((Ncorr - a1 * N0) ** 4)) * np.sqrt(\n        (Ncorr - a1 * N0) ** 4 + 8 * sigma_N ** 2 * (Ncorr - a1 * N0) ** 2 + 15 * sigma_N ** 4)\n    sigma_VWC = sigma_GWC * bulk_density\n\n    return sigma_VWC\n</code></pre>"},{"location":"reference/#crnpy.data.abs_humidity","title":"<code>abs_humidity(relative_humidity, temp)</code>","text":"<p>Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.</p> <p>Parameters:</p> Name Type Description Default <code>relative_humidity</code> <code>float</code> <p>relative humidity (%)</p> required <code>temp</code> <code>float</code> <p>temperature (Celsius)</p> required <p>Returns:</p> Name Type Description <code>float</code> <p>actual vapor pressure (g m^-3)</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def abs_humidity(relative_humidity, temp):\n    \"\"\"\n    Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.\n\n    Args:\n        relative_humidity (float): relative humidity (%)\n        temp (float): temperature (Celsius)\n\n    Returns:\n        float: actual vapor pressure (g m^-3)\n    \"\"\"\n\n    ### Atmospheric water vapor factor\n    # Saturation vapor pressure\n    e_sat = 0.611 * np.exp(17.502 * temp / (\n            temp + 240.97)) * 1000  # in Pascals Eq. 3.8 p.41 Environmental Biophysics (Campbell and Norman)\n\n    # Vapor pressure Pascals\n    Pw = e_sat * relative_humidity / 100\n\n    # Absolute humidity (g/m^3)\n    C = 2.16679  # g K/J;\n    abs_h = C * Pw / (temp + 273.15)\n    return abs_h\n</code></pre>"},{"location":"reference/#crnpy.data.atmospheric_depth","title":"<code>atmospheric_depth(elevation, latitude)</code>","text":"<p>Function to estimate the atmospheric depth for any point on Earth according to McJannet and Desilets, 2023</p> <p>This function is required in the calculation of the location-dependent reference correction proposed by McJannet and Desilets, 2023.</p> <p>Parameters:</p> Name Type Description Default <code>elevation</code> <code>float</code> <p>Elevation in meters above sea level.</p> required <code>latitude</code> <code>float</code> <p>Geographic latitude in decimal degrees. Value in range -90 to 90</p> required <p>Returns:</p> Type Description <code>float</code> <p>Atmospheric depth in g/cm2</p> References <p>Atmosphere, U. S. (1976). US standard atmosphere. National Oceanic and Atmospheric Administration.</p> <p>McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic\u2010Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def atmospheric_depth(elevation, latitude):\n    \"\"\"Function to estimate the atmospheric depth for any point on Earth according to McJannet and Desilets, 2023\n\n    This function is required in the calculation of the location-dependent reference correction proposed by McJannet and Desilets, 2023.\n\n    Args:\n        elevation (float): Elevation in meters above sea level.\n        latitude (float): Geographic latitude in decimal degrees. Value in range -90 to 90\n\n    Returns:\n        (float): Atmospheric depth in g/cm2\n\n    References:\n        Atmosphere, U. S. (1976). US standard atmosphere. National Oceanic and Atmospheric Administration.\n\n        McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic\u2010Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.\n    \"\"\"\n\n    density_of_rock = 2670  # Density of rock in kg/m3\n    air_pressure_sea_level = 1013.25  # Air pressure at sea level in hPa\n    air_molar_mass = 0.0289644  # Air molar mass in kg/mol\n    universal_gas_constant = 8.3144598  # Universal gas constant in J/(mol*K)\n    reference_temperature = 288.15  # Reference temperature Kelvin\n    temperature_lapse_rate = -0.0065  # Temperature lapse rate in K/m\n\n    # Gravity at sea-level calculation\n    gravity_sea_level = 9.780327 * (\n            1 + 0.0053024 * np.sin(np.radians(latitude)) ** 2 - 0.0000058 * np.sin(2 * np.radians(latitude)) ** 2)\n    # Free air correction\n    free_air = -3.086 * 10 ** -6 * elevation\n    # Bouguer correction\n    bouguer_corr = 4.194 * 10 ** -10 * density_of_rock * elevation\n    # Total gravity\n    gravity = gravity_sea_level + free_air + bouguer_corr\n\n    # Air pressure calculation\n    reference_air_pressure = air_pressure_sea_level * (\n                1 + temperature_lapse_rate / reference_temperature * elevation) ** ((-gravity * air_molar_mass) / (\n                universal_gas_constant * temperature_lapse_rate))\n\n    # Atmospheric depth calculation\n    atmospheric_depth = (10 * reference_air_pressure) / gravity\n    return atmospheric_depth\n</code></pre>"},{"location":"reference/#crnpy.data.biomass_to_bwe","title":"<code>biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494)</code>","text":"<p>Function to convert biomass to biomass water equivalent.</p> <p>Parameters:</p> Name Type Description Default <code>biomass_dry</code> <code>array or Series or DataFrame</code> <p>Above ground dry biomass in kg m-2.</p> required <code>biomass_fresh</code> <code>array or Series or DataFrame</code> <p>Above ground fresh biomass in kg m-2.</p> required <code>fWE</code> <code>float</code> <p>Stoichiometric ratio of H2O to organic carbon molecules in the plant (assuming this is mostly cellulose) Default is 0.494 (Wahbi &amp; Avery, 2018).</p> <code>0.494</code> <p>Returns:</p> Type Description <code>array or Series or DataFrame</code> <p>Biomass water equivalent in kg m-2.</p> References <p>Wahbi, A., Avery, W. (2018). In Situ Destructive Sampling. In: Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent. Springer, Cham. https://doi.org/10.1007/978-3-319-69539-6_2</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494):\n    \"\"\"Function to convert biomass to biomass water equivalent.\n\n    Args:\n        biomass_dry (array or pd.Series or pd.DataFrame): Above ground dry biomass in kg m-2.\n        biomass_fresh (array or pd.Series or pd.DataFrame): Above ground fresh biomass in kg m-2.\n        fWE (float): Stoichiometric ratio of H2O to organic carbon molecules in the plant (assuming this is mostly cellulose)\n            Default is 0.494 (Wahbi &amp; Avery, 2018).\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Biomass water equivalent in kg m-2.\n\n    References:\n        Wahbi, A., Avery, W. (2018). In Situ Destructive Sampling. In:\n        Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent.\n        Springer, Cham. https://doi.org/10.1007/978-3-319-69539-6_2\n    \"\"\"\n    return (biomass_fresh - biomass_dry) + fWE * biomass_dry\n</code></pre>"},{"location":"reference/#crnpy.data.correction_bwe","title":"<code>correction_bwe(counts, bwe, r2_N0=0.05)</code>","text":"<p>Function to correct for biomass effects in neutron counts. following the approach described in Baatz et al., 2015.</p> <p>Parameters:</p> Name Type Description Default <code>counts</code> <code>array or Series or DataFrame</code> <p>Array of ephithermal neutron counts.</p> required <code>bwe</code> <code>float</code> <p>Biomass water equivalent kg m-2.</p> required <code>r2_N0</code> <code>float</code> <p>Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.</p> <code>0.05</code> <p>Returns:</p> Type Description <code>array or Series or DataFrame</code> <p>Array of corrected neutron counts for biomass effects.</p> References <p>Baatz, R., H. R. Bogena, H.-J. Hendricks Franssen, J. A. Huisman, C. Montzka, and H. Vereecken (2015), An empiricalvegetation correction for soil water content quantification using cosmic ray probes, Water Resour. Res., 51, 2030\u20132046, doi:10.1002/ 2014WR016443.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def correction_bwe(counts, bwe, r2_N0=0.05):\n    \"\"\"Function to correct for biomass effects in neutron counts.\n    following the approach described in Baatz et al., 2015.\n\n    Args:\n        counts (array or pd.Series or pd.DataFrame): Array of ephithermal neutron counts.\n        bwe (float): Biomass water equivalent kg m-2.\n        r2_N0 (float): Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Array of corrected neutron counts for biomass effects.\n\n    References:\n        Baatz, R., H. R. Bogena, H.-J. Hendricks Franssen, J. A. Huisman, C. Montzka, and H. Vereecken (2015),\n        An empiricalvegetation correction for soil water content quantification using cosmic ray probes,\n        Water Resour. Res., 51, 2030\u20132046, doi:10.1002/ 2014WR016443.\n    \"\"\"\n\n    return counts / (1 - bwe * r2_N0)\n</code></pre>"},{"location":"reference/#crnpy.data.correction_humidity","title":"<code>correction_humidity(abs_humidity, Aref)</code>","text":"<p>Correction factor for absolute humidity.</p> <p>This function corrects neutron counts for absolute humidity using the method described in Rosolem et al. (2013) and Anderson et al. (2017). The correction is performed using the following equation:</p> <p>$$ C_{corrected} = C_{raw} \\cdot f_w $$</p> <p>where:</p> <ul> <li>Ccorrected: corrected neutron counts</li> <li>Craw: raw neutron counts</li> <li>fw: absolute humidity correction factor</li> </ul> <p>$$ f_w = 1 + 0.0054(A - A_{ref}) $$</p> <p>where:</p> <ul> <li>A: absolute humidity</li> <li>Aref: reference absolute humidity</li> </ul> <p>Parameters:</p> Name Type Description Default <code>abs_humidity</code> <code>list or array</code> <p>Relative humidity readings.</p> required <code>Aref</code> <code>float</code> <p>Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.</p> required <p>Returns:</p> Type Description <code>list</code> <p>fw correction factor.</p> References <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def correction_humidity(abs_humidity, Aref):\n    r\"\"\"Correction factor for absolute humidity.\n\n    This function corrects neutron counts for absolute humidity using the method described in Rosolem et al. (2013) and Anderson et al. (2017). The correction is performed using the following equation:\n\n    $$\n    C_{corrected} = C_{raw} \\cdot f_w\n    $$\n\n    where:\n\n    - Ccorrected: corrected neutron counts\n    - Craw: raw neutron counts\n    - fw: absolute humidity correction factor\n\n    $$\n    f_w = 1 + 0.0054(A - A_{ref})\n    $$\n\n    where:\n\n    - A: absolute humidity\n    - Aref: reference absolute humidity\n\n    Args:\n        abs_humidity (list or array): Relative humidity readings.\n        Aref (float): Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.\n\n    Returns:\n        (list): fw correction factor.\n\n    References:\n        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086\n    \"\"\"\n    A = abs_humidity\n    fw = 1 + 0.0054 * (A - Aref)  # Zreda et al. 2017 Eq 6.\n    return fw\n</code></pre>"},{"location":"reference/#crnpy.data.correction_incoming_flux","title":"<code>correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None, Rc_method=None, Rc_site=None, site_atmdepth=None, Rc_ref=None, ref_atmdepth=None)</code>","text":"<p>Correction factor for incoming neutron flux.</p> <p>This function corrects neutron counts for incoming neutron flux using the method described in Anderson et al. (2017). The correction is performed using the following equation:</p> <p>$$ C_{corrected} = \\frac{C_{raw}}{f_i} $$</p> <p>where:</p> <ul> <li>Ccorrected: corrected neutron counts</li> <li>Craw: raw neutron counts</li> <li>fi: incoming neutron flux correction factor</li> </ul> <p>$$ f_i = \\frac{I_{ref}}{I} $$</p> <p>where:</p> <ul> <li>I: incoming neutron flux</li> <li>Iref: reference incoming neutron flux</li> </ul> <p>Parameters:</p> Name Type Description Default <code>incoming_neutrons</code> <code>list or array</code> <p>Incoming neutron flux readings.</p> required <code>incoming_Ref</code> <code>float</code> <p>Reference incoming neutron flux. Baseline incoming neutron flux.</p> <code>None</code> <p>Returns:</p> Type Description <code>list</code> <p>fi correction factor.</p> References <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None, Rc_method=None, Rc_site=None,\n                             site_atmdepth=None, Rc_ref=None, ref_atmdepth=None):\n    r\"\"\"Correction factor for incoming neutron flux.\n\n    This function corrects neutron counts for incoming neutron flux using the method described in Anderson et al. (2017). The correction is performed using the following equation:\n\n    $$\n    C_{corrected} = \\frac{C_{raw}}{f_i}\n    $$\n\n    where:\n\n    - Ccorrected: corrected neutron counts\n    - Craw: raw neutron counts\n    - fi: incoming neutron flux correction factor\n\n    $$\n    f_i = \\frac{I_{ref}}{I}\n    $$\n\n    where:\n\n    - I: incoming neutron flux\n    - Iref: reference incoming neutron flux\n\n    Args:\n        incoming_neutrons (list or array): Incoming neutron flux readings.\n        incoming_Ref (float): Reference incoming neutron flux. Baseline incoming neutron flux.\n\n    Returns:\n        (list): fi correction factor.\n\n    References:\n        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086\n\n\n    \"\"\"\n    if incoming_Ref is None and not isinstance(incoming_neutrons, type(None)):\n        incoming_Ref = incoming_neutrons[0]\n        warnings.warn('Reference incoming neutron flux not provided. Using first value of incoming neutron flux.')\n    fi = incoming_neutrons / incoming_Ref\n\n    if Rc_method is not None:\n        if Rc_ref is None:\n            raise ValueError('Reference cutoff rigidity not provided.')\n        if Rc_site is None:\n            raise ValueError('Site cutoff rigidity not provided.')\n\n        if Rc_method == 'McJannetandDesilets2023':\n            tau = location_factor(site_atmdepth, Rc_site, ref_atmdepth, Rc_ref)\n            fi = 1 / (tau * fi + 1 - tau)\n\n        elif Rc_method == 'Hawdonetal2014':\n            Rc_corr = -0.075 * (Rc_site - Rc_ref) + 1.0\n            fi = (fi - 1.0) * Rc_corr + 1.0\n\n        else:\n            raise ValueError(\n                'Cutoff rigidity method not found. Valid options are: McJannetandDesilets2023, Hawdonetal2014.')\n\n    if fill_na is not None:\n        fi.fillna(fill_na, inplace=True)  # Use a value of 1 for days without data\n\n    return fi\n</code></pre>"},{"location":"reference/#crnpy.data.correction_pressure","title":"<code>correction_pressure(pressure, Pref, L)</code>","text":"<p>Correction factor for atmospheric pressure.</p> <p>This function corrects neutron counts for atmospheric pressure using the method described in Andreasen et al. (2017). The correction is performed using the following equation:</p> <p>$$ C_{corrected} = \\frac{C_{raw}}{fp} $$</p> <p>where:</p> <ul> <li>Ccorrected: corrected neutron counts</li> <li>Craw: raw neutron counts</li> <li>fp: pressure correction factor</li> </ul> <p>$$ fp = e^{\\frac{P_{ref} - P}{L}} $$</p> <p>where:</p> <ul> <li>P: atmospheric pressure</li> <li>Pref: reference atmospheric pressure</li> <li>L: Atmospheric attenuation coefficient.</li> </ul> <p>Parameters:</p> Name Type Description Default <code>pressure</code> <code>list or array</code> <p>Atmospheric pressure readings. Long-term average pressure is recommended.</p> required <code>Pref</code> <code>float</code> <p>Reference atmospheric pressure.</p> required <code>L</code> <code>float</code> <p>Atmospheric attenuation coefficient.</p> required <p>Returns:</p> Type Description <code>list</code> <p>fp pressure correction factor.</p> References <p>M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def correction_pressure(pressure, Pref, L):\n    r\"\"\"Correction factor for atmospheric pressure.\n\n    This function corrects neutron counts for atmospheric pressure using the method described in Andreasen et al. (2017).\n    The correction is performed using the following equation:\n\n    $$\n    C_{corrected} = \\frac{C_{raw}}{fp}\n    $$\n\n    where:\n\n    - Ccorrected: corrected neutron counts\n    - Craw: raw neutron counts\n    - fp: pressure correction factor\n\n    $$\n    fp = e^{\\frac{P_{ref} - P}{L}}\n    $$\n\n    where:\n\n    - P: atmospheric pressure\n    - Pref: reference atmospheric pressure\n    - L: Atmospheric attenuation coefficient.\n\n\n    Args:\n        pressure (list or array): Atmospheric pressure readings. Long-term average pressure is recommended.\n        Pref (float): Reference atmospheric pressure.\n        L (float): Atmospheric attenuation coefficient.\n\n    Returns:\n        (list): fp pressure correction factor.\n\n    References:\n        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086\n    \"\"\"\n\n    # Compute pressure correction factor\n    fp = np.exp((Pref - pressure) / L)  # Zreda et al. 2017 Eq 5.\n\n    return fp\n</code></pre>"},{"location":"reference/#crnpy.data.correction_road","title":"<code>correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0.12, p0=0.42, p1=0.5, p2=1.06, p3=4, p4=0.16, p6=0.94, p7=1.1, p8=2.7, p9=0.01)</code>","text":"<p>Function to correct for road effects in neutron counts. following the approach described in Schr\u00f6n et al., 2018.</p> <p>Parameters:</p> Name Type Description Default <code>counts</code> <code>array or Series or DataFrame</code> <p>Array of ephithermal neutron counts.</p> required <code>theta_N</code> <code>float</code> <p>Volumetric water content of the soil estimated from the uncorrected neutron counts.</p> required <code>road_width</code> <code>float</code> <p>Width of the road in m.</p> required <code>road_distance</code> <code>float</code> <p>Distance of the road from the sensor in m. Default is 0.0.</p> <code>0.0</code> <code>theta_road</code> <code>float</code> <p>Volumetric water content of the road. Default is 0.12.</p> <code>0.12</code> <code>p0-p9</code> <code>float</code> <p>Parameters of the correction function. Default values are from Schr\u00f6n et al., 2018.</p> required <p>Returns:</p> Type Description <code>array or Series or DataFrame</code> <p>Array of corrected neutron counts for road effects.</p> References <p>Schr\u00f6n,M.,Rosolem,R.,K\u00f6hli,M., Piussi,L.,Schr\u00f6ter,I.,Iwema,J.,etal. (2018).Cosmic-ray neutron rover surveys of field soil moisture and the influence of roads.WaterResources Research,54,6441\u20136459. https://doi. org/10.1029/2017WR021719</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0.12, p0=0.42, p1=0.5, p2=1.06, p3=4,\n                    p4=0.16, p6=0.94, p7=1.10, p8=2.70, p9=0.01):\n    \"\"\"Function to correct for road effects in neutron counts.\n    following the approach described in Schr\u00f6n et al., 2018.\n\n    Args:\n        counts (array or pd.Series or pd.DataFrame): Array of ephithermal neutron counts.\n        theta_N (float): Volumetric water content of the soil estimated from the uncorrected neutron counts.\n        road_width (float): Width of the road in m.\n        road_distance (float): Distance of the road from the sensor in m. Default is 0.0.\n        theta_road (float): Volumetric water content of the road. Default is 0.12.\n        p0-p9 (float): Parameters of the correction function. Default values are from Schr\u00f6n et al., 2018.\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Array of corrected neutron counts for road effects.\n\n    References:\n        Schr\u00f6n,M.,Rosolem,R.,K\u00f6hli,M., Piussi,L.,Schr\u00f6ter,I.,Iwema,J.,etal. (2018).Cosmic-ray neutron rover surveys\n        of field soil moisture and the influence of roads.WaterResources Research,54,6441\u20136459.\n        https://doi. org/10.1029/2017WR021719\n    \"\"\"\n    F1 = p0 * (1 - np.exp(-p1 * road_width))\n    F2 = -p2 - p3 * theta_road - ((p4 + theta_road) / (theta_N))\n    F3 = p6 * np.exp(-p7 * (road_width ** -p8) * road_distance ** 4) + (1 - p6) * np.exp(-p9 * road_distance)\n\n    C_roads = 1 + F1 * F2 * F3\n\n    corrected_counts = counts / C_roads\n\n    return corrected_counts\n</code></pre>"},{"location":"reference/#crnpy.data.counts_to_vwc","title":"<code>counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=0.115)</code>","text":"<p>Function to convert corrected and filtered neutron counts into volumetric water content.</p> <p>This method implements soil moisture estimation using the non-linear relationship between neutron count and soil volumetric water content following the approach described in Desilets et al., 2010.</p> <p>$\\theta(N) =\\frac{a_0}{(\\frac{N}{N_0}) - a_1} - a_2 $</p> <p>Parameters:</p> Name Type Description Default <code>counts</code> <code>array or Series or DataFrame</code> <p>Array of corrected and filtered neutron counts.</p> required <code>N0</code> <code>float</code> <p>Device-specific neutron calibration constant.</p> required <code>Wlat</code> <code>float</code> <p>Lattice water content.</p> required <code>Wsoc</code> <code>float</code> <p>Soil organic carbon content.</p> required <code>bulk_density</code> <code>float</code> <p>Soil bulk density.</p> required <code>a0</code> <code>float</code> <p>Parameter given in Zreda et al., 2012. Default is 0.0808.</p> <code>0.0808</code> <code>a1</code> <code>float</code> <p>Parameter given in Zreda et al., 2012. Default is 0.372.</p> <code>0.372</code> <code>a2</code> <code>float</code> <p>Parameter given in Zreda et al., 2012. Default is 0.115.</p> <code>0.115</code> <p>Returns:</p> Type Description <code>array or Series or DataFrame</code> <p>Volumetric water content in m3 m-3.</p> References <p>Desilets, D., M. Zreda, and T.P.A. Ferr\u00e9. 2010. Nature\u2019s neutron probe: Land surface hydrology at an elusive scale with cosmic rays. Water Resour. Res. 46:W11505. doi.org/10.1029/2009WR008726</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=0.115):\n    r\"\"\"Function to convert corrected and filtered neutron counts into volumetric water content.\n\n    This method implements soil moisture estimation using the non-linear relationship between neutron count and soil volumetric water content following the approach described in Desilets et al., 2010.\n\n    $\\theta(N) =\\frac{a_0}{(\\frac{N}{N_0}) - a_1} - a_2 $\n\n    Args:\n        counts (array or pd.Series or pd.DataFrame): Array of corrected and filtered neutron counts.\n        N0 (float): Device-specific neutron calibration constant.\n        Wlat (float): Lattice water content.\n        Wsoc (float): Soil organic carbon content.\n        bulk_density (float): Soil bulk density.\n        a0 (float): Parameter given in Zreda et al., 2012. Default is 0.0808.\n        a1 (float): Parameter given in Zreda et al., 2012. Default is 0.372.\n        a2 (float): Parameter given in Zreda et al., 2012. Default is 0.115.\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Volumetric water content in m3 m-3.\n\n    References:\n        Desilets, D., M. Zreda, and T.P.A. Ferr\u00e9. 2010. Nature\u2019s neutron probe:\n        Land surface hydrology at an elusive scale with cosmic rays. Water Resour. Res. 46:W11505.\n        doi.org/10.1029/2009WR008726\n    \"\"\"\n\n    # Convert neutron counts into vwc\n    vwc = (a0 / (counts / N0 - a1) - a2 - Wlat - Wsoc) * bulk_density\n    return vwc\n</code></pre>"},{"location":"reference/#crnpy.data.cutoff_rigidity","title":"<code>cutoff_rigidity(lat, lon)</code>","text":"<p>Function to estimate the approximate cutoff rigidity for any point on Earth according to the tabulated data of Smart and Shea, 2019. The returned value can be used to select the appropriate neutron monitor station to estimate the cosmic-ray neutron intensity at the location of interest.</p> <p>Parameters:</p> Name Type Description Default <code>lat</code> <code>float</code> <p>Geographic latitude in decimal degrees. Value in range -90 to 90</p> required <code>lon</code> <code>float</code> <p>Geographic longitude in decimal degrees. Values in range from 0 to 360. Typical negative longitudes in the west hemisphere will fall in the range 180 to 360.</p> required <p>Returns:</p> Type Description <code>float</code> <p>Cutoff rigidity in GV. Error is about +/- 0.3 GV</p> <p>Examples:</p> <p>Estimate the cutoff rigidity for Newark, NJ, US</p> <pre><code>&gt;&gt;&gt; zq = cutoff_rigidity(39.68, -75.75)\n&gt;&gt;&gt; print(zq)\n2.52 GV (Value from NMD is 2.40 GV)\n</code></pre> References <p>Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures for cosmic\u2010ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.</p> <p>Smart, D. &amp; Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program: Theory, Software Description and Example. NASA STI/Recon Technical Report N.</p> <p>Shea, M. A., &amp; Smart, D. F. (2019, July). Re-examination of the First Five Ground-Level Events. In International Cosmic Ray Conference (ICRC2019) (Vol. 36, p. 1149).</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def cutoff_rigidity(lat, lon):\n    \"\"\"Function to estimate the approximate cutoff rigidity for any point on Earth according to the\n    tabulated data of Smart and Shea, 2019. The returned value can be used to select the appropriate\n    neutron monitor station to estimate the cosmic-ray neutron intensity at the location of interest.\n\n    Args:\n        lat (float): Geographic latitude in decimal degrees. Value in range -90 to 90\n        lon (float): Geographic longitude in decimal degrees. Values in range from 0 to 360.\n            Typical negative longitudes in the west hemisphere will fall in the range 180 to 360.\n\n    Returns:\n        (float): Cutoff rigidity in GV. Error is about +/- 0.3 GV\n\n    Examples:\n        Estimate the cutoff rigidity for Newark, NJ, US\n\n        &gt;&gt;&gt; zq = cutoff_rigidity(39.68, -75.75)\n        &gt;&gt;&gt; print(zq)\n        2.52 GV (Value from NMD is 2.40 GV)\n\n    References:\n        Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures\n        for cosmic\u2010ray neutron soil moisture probes located across Australia. Water Resources Research,\n        50(6), 5029-5043.\n\n        Smart, D. &amp; Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program:\n        Theory, Software Description and Example. NASA STI/Recon Technical Report N.\n\n        Shea, M. A., &amp; Smart, D. F. (2019, July). Re-examination of the First Five Ground-Level Events.\n        In International Cosmic Ray Conference (ICRC2019) (Vol. 36, p. 1149).\n    \"\"\"\n    xq = lon\n    yq = lat\n\n    if xq &lt; 0:\n        xq = xq * -1 + 180\n    Z = np.array(data.cutoff_rigidity)\n    x = np.linspace(0, 360, Z.shape[1])\n    y = np.linspace(90, -90, Z.shape[0])\n    X, Y = np.meshgrid(x, y)\n    points = np.array((X.flatten(), Y.flatten())).T\n    values = Z.flatten()\n    zq = griddata(points, values, (xq, yq))\n\n    return np.round(zq, 2)\n</code></pre>"},{"location":"reference/#crnpy.data.euclidean_distance","title":"<code>euclidean_distance(px, py, x, y)</code>","text":"<p>Function that computes the Euclidean distance between one point in space and one or more points.</p> <p>Parameters:</p> Name Type Description Default <code>px</code> <code>float</code> <p>x projected coordinate of the point.</p> required <code>py</code> <code>float</code> <p>y projected coordinate of the point.</p> required <code>x</code> <code>(list, ndarray, series)</code> <p>vector of x projected coordinates.</p> required <code>y</code> <code>(list, ndarray, series)</code> <p>vector of y projected coordinates.</p> required <p>Returns:</p> Type Description <code>ndarray</code> <p>Numpy array of distances from the point (px,py) to all the points in x and y vectors.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def euclidean_distance(px, py, x, y):\n    \"\"\"Function that computes the Euclidean distance between one point\n    in space and one or more points.\n\n    Args:\n        px (float): x projected coordinate of the point.\n        py (float): y projected coordinate of the point.\n        x (list, ndarray, pandas.series): vector of x projected coordinates.\n        y (list, ndarray, pandas.series): vector of y projected coordinates.\n\n    Returns:\n        (ndarray): Numpy array of distances from the point (px,py) to all the points in x and y vectors.\n    \"\"\"\n    d = np.sqrt((px - x) ** 2 + (py - y) ** 2)\n    return d\n</code></pre>"},{"location":"reference/#crnpy.data.exp_filter","title":"<code>exp_filter(sm, T=1)</code>","text":"<p>Exponential filter to estimate soil moisture in the rootzone from surface observtions.</p> <p>Parameters:</p> Name Type Description Default <code>sm</code> <code>list or array</code> <p>Soil moisture in mm of water for the top layer of the soil profile.</p> required <code>T</code> <code>float</code> <p>Characteristic time length in the same units as the measurement interval.</p> <code>1</code> <p>Returns:</p> Name Type Description <code>sm_subsurface</code> <code>list or array</code> <p>Subsurface soil moisture in the same units as the input.</p> References <p>Albergel, C., R\u00fcdiger, C., Pellarin, T., Calvet, J.C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B. and Martin, E., 2008. From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations. Hydrology and Earth System Sciences, 12(6), pp.1323-1337.</p> <p>Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020. Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.</p> <p>Rossini, P. and Patrignani, A., 2021. Predicting rootzone soil moisture from surface observations in cropland using an exponential filter. Soil Science Society of America Journal.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def exp_filter(sm, T=1):\n    \"\"\"Exponential filter to estimate soil moisture in the rootzone from surface observtions.\n\n    Args:\n        sm (list or array): Soil moisture in mm of water for the top layer of the soil profile.\n        T (float): Characteristic time length in the same units as the measurement interval.\n\n    Returns:\n        sm_subsurface (list or array): Subsurface soil moisture in the same units as the input.\n\n    References:\n        Albergel, C., R\u00fcdiger, C., Pellarin, T., Calvet, J.C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B. and Martin, E., 2008.\n        From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model\n        simulations. Hydrology and Earth System Sciences, 12(6), pp.1323-1337.\n\n        Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.\n        Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.\n\n        Rossini, P. and Patrignani, A., 2021. Predicting rootzone soil moisture from surface observations in cropland using an exponential filter.\n        Soil Science Society of America Journal.\n    \"\"\"\n\n    # Parameters\n    t_delta = 1\n    sm_min = np.min(sm)\n    sm_max = np.max(sm)\n    ms = (sm - sm_min) / (sm_max - sm_min)\n\n    # Pre-allocate soil water index array and recursive constant K\n    SWI = np.ones_like(ms) * np.nan\n    K = np.ones_like(ms) * np.nan\n\n    # Initial conditions\n    SWI[0] = ms[0]\n    K[0] = 1\n\n    # Values from 2 to N\n    for n in range(1, len(SWI)):\n        if ~np.isnan(ms[n]) &amp; ~np.isnan(ms[n - 1]):\n            K[n] = K[n - 1] / (K[n - 1] + np.exp(-t_delta / T))\n            SWI[n] = SWI[n - 1] + K[n] * (ms[n] - SWI[n - 1])\n        else:\n            continue\n\n    # Rootzone storage\n    sm_subsurface = SWI * (sm_max - sm_min) + sm_min\n\n    return sm_subsurface\n</code></pre>"},{"location":"reference/#crnpy.data.fill_missing_timestamps","title":"<code>fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True, verbose=False)</code>","text":"<p>Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>Pandas DataFrame.</p> required <code>timestamp_col</code> <code>str</code> <p>Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.</p> <code>'timestamp'</code> <code>freq</code> <code>str</code> <p>Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.</p> <code>'H'</code> <code>round_timestamp</code> <code>bool</code> <p>Whether to round timestamps to the nearest frequency. Default is True.</p> <code>True</code> <code>verbose</code> <code>bool</code> <p>Prints the missing timestamps added to the DatFrame.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>DataFrame with filled missing timestamps.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True, verbose=False):\n    \"\"\"Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.\n\n     Args:\n         df (pandas.DataFrame): Pandas DataFrame.\n         timestamp_col (str, optional): Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.\n         freq (str, optional): Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.\n         round_timestamp (bool, optional): Whether to round timestamps to the nearest frequency. Default is True.\n         verbose (bool, optional): Prints the missing timestamps added to the DatFrame.\n\n     Returns:\n         (pandas.DataFrame): DataFrame with filled missing timestamps.\n\n     \"\"\"\n\n    # Check format of timestamp column\n    if df[timestamp_col].dtype != 'datetime64[ns]':\n        raise TypeError('timestamp_col must be datetime64. Use `pd.to_datetime()` to fix this issue.')\n\n    # Round timestamps to nearest frequency. This steps must preced the filling of rows.\n    if round_timestamp:\n        df[timestamp_col] = df[timestamp_col].dt.round(freq)\n\n    # Fill in rows with missing timestamps\n    start_date = df[timestamp_col].iloc[0]\n    end_date = df[timestamp_col].iloc[-1]\n    date_range = pd.date_range(start_date, end_date, freq=freq)\n    counter = 0\n    for date in date_range:\n        if date not in df[timestamp_col].values:\n            if verbose:\n                print('Adding missing date:', date)\n            new_line = pd.DataFrame({timestamp_col: date}, index=[-1])  # By default fills columns with np.nan\n            df = pd.concat([df, new_line])\n            counter += 1\n\n    df.sort_values(by=timestamp_col, inplace=True)\n    df.reset_index(drop=True, inplace=True)\n\n    # Notify user about the number of rows that have been removed\n    print(f\"Added a total of {counter} missing timestamps.\")\n\n    return df\n</code></pre>"},{"location":"reference/#crnpy.data.find_neutron_monitor","title":"<code>find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False)</code>","text":"<p>Search for potential reference neutron monitoring stations based on cutoff rigidity.</p> <p>Parameters:</p> Name Type Description Default <code>Rc</code> <code>float</code> <p>Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.</p> required <code>start_date</code> <code>datetime</code> <p>Start date for the period of interest.</p> <code>None</code> <code>end_date</code> <code>datetime</code> <p>End date for the period of interest.</p> <code>None</code> <code>verbose</code> <code>bool</code> <p>If True, print a expanded output of the incoming neutron flux data.</p> <code>False</code> <p>Returns:</p> Type Description <code>list</code> <p>List of top five stations with closes cutoff rigidity. User needs to select station according to site altitude.</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; from crnpy import crnpy\n&gt;&gt;&gt; Rc = 2.40 # 2.40 Newark, NJ, US\n&gt;&gt;&gt; crnpy.find_neutron_monitor(Rc)\nSelect a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\n</code></pre> <p>Your cutoff rigidity is 2.4 GV         STID                          NAME     R  Altitude_m 40   NEWK                        Newark  2.40          50 33   MOSC                        Moscow  2.43         200 27   KIEL                          Kiel  2.36          54 28  KIEL2                        KielRT  2.36          54 31   MCRL  Mobile Cosmic Ray Laboratory  2.46         200 32   MGDN                       Magadan  2.10         220 42   NVBK                   Novosibirsk  2.91         163 26   KGSN                      Kingston  1.88          65 9    CLMX                        Climax  3.00        3400 57   YKTK                       Yakutsk  1.65         105</p> References <p>https://www.nmdb.eu/nest/help.php#helpstations</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):\n    \"\"\"Search for potential reference neutron monitoring stations based on cutoff rigidity.\n\n    Args:\n        Rc (float): Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.\n        start_date (datetime): Start date for the period of interest.\n        end_date (datetime): End date for the period of interest.\n        verbose (bool): If True, print a expanded output of the incoming neutron flux data.\n\n    Returns:\n        (list): List of top five stations with closes cutoff rigidity.\n            User needs to select station according to site altitude.\n\n    Examples:\n        &gt;&gt;&gt; from crnpy import crnpy\n        &gt;&gt;&gt; Rc = 2.40 # 2.40 Newark, NJ, US\n        &gt;&gt;&gt; crnpy.find_neutron_monitor(Rc)\n        Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\n\n        Your cutoff rigidity is 2.4 GV\n                STID                          NAME     R  Altitude_m\n        40   NEWK                        Newark  2.40          50\n        33   MOSC                        Moscow  2.43         200\n        27   KIEL                          Kiel  2.36          54\n        28  KIEL2                        KielRT  2.36          54\n        31   MCRL  Mobile Cosmic Ray Laboratory  2.46         200\n        32   MGDN                       Magadan  2.10         220\n        42   NVBK                   Novosibirsk  2.91         163\n        26   KGSN                      Kingston  1.88          65\n        9    CLMX                        Climax  3.00        3400\n        57   YKTK                       Yakutsk  1.65         105\n\n    References:\n        https://www.nmdb.eu/nest/help.php#helpstations\n    \"\"\"\n\n    # Load file with list of neutron monitoring stations\n    stations = pd.DataFrame(data.neutron_detectors, columns=[\"STID\", \"NAME\", \"R\", \"Altitude_m\"])\n\n    # Sort stations by closest cutoff rigidity\n    idx_R = (stations['R'] - Rc).abs().argsort()\n\n    if start_date is not None and end_date is not None:\n        stations[\"Period available\"] = False\n        for i in range(10):\n            station = stations.iloc[idx_R[i]][\"STID\"]\n            try:\n                if get_incoming_neutron_flux(start_date, end_date, station, verbose=verbose) is not None:\n                    stations.iloc[idx_R[i], -1] = True\n            except:\n                pass\n\n        if sum(stations[\"Period available\"] == True) == 0:\n            print(\"No stations available for the selected period!\")\n        else:\n            stations = stations[stations[\"Period available\"] == True]\n            idx_R = (stations['R'] - Rc).abs().argsort()\n            result = stations.iloc[idx_R.iloc[:10]]\n    else:\n        result = stations.reindex(idx_R).head(10).rename_axis(None)\n\n    # Print results\n    print('')\n    print(\n        \"\"\"Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\"\"\")\n    print('')\n    print(f\"Your cutoff rigidity is {Rc} GV\")\n    print(result)\n    return result\n</code></pre>"},{"location":"reference/#crnpy.data.get_incoming_neutron_flux","title":"<code>get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expand_window=0, verbose=False)</code>","text":"<p>Function to retrieve neutron flux from the Neutron Monitor Database.</p> <p>Parameters:</p> Name Type Description Default <code>start_date</code> <code>datetime</code> <p>Start date of the time series.</p> required <code>end_date</code> <code>datetime</code> <p>End date of the time series.</p> required <code>station</code> <code>str</code> <p>Neutron Monitor station to retrieve data from.</p> required <code>utc_offset</code> <code>int</code> <p>UTC offset in hours. Default is 0.</p> <code>0</code> <code>expand_window</code> <code>int</code> <p>Number of hours to expand the time window to retrieve extra data. Default is 0.</p> <code>0</code> <code>verbose</code> <code>bool</code> <p>Print information about the request. Default is False.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>Neutron flux in counts per hour and timestamps.</p> References <p>Documentation available:https://www.nmdb.eu/nest/help.php#howto</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expand_window=0, verbose=False):\n    \"\"\"Function to retrieve neutron flux from the Neutron Monitor Database.\n\n    Args:\n        start_date (datetime): Start date of the time series.\n        end_date (datetime): End date of the time series.\n        station (str): Neutron Monitor station to retrieve data from.\n        utc_offset (int): UTC offset in hours. Default is 0.\n        expand_window (int): Number of hours to expand the time window to retrieve extra data. Default is 0.\n        verbose (bool): Print information about the request. Default is False.\n\n    Returns:\n        (pandas.DataFrame): Neutron flux in counts per hour and timestamps.\n\n    References:\n        Documentation available:https://www.nmdb.eu/nest/help.php#howto\n    \"\"\"\n\n    # Example: get_incoming_neutron_flux(station='IRKT',start_date='2020-04-10 11:00:00',end_date='2020-06-18 17:00:00')\n    # Template url = 'http://nest.nmdb.eu/draw_graph.php?formchk=1&amp;stations[]=KERG&amp;output=ascii&amp;tabchoice=revori&amp;dtype=corr_for_efficiency&amp;date_choice=bydate&amp;start_year=2009&amp;start_month=09&amp;start_day=01&amp;start_hour=00&amp;start_min=00&amp;end_year=2009&amp;end_month=09&amp;end_day=05&amp;end_hour=23&amp;end_min=59&amp;yunits=0'\n\n    # Expand the time window by 1 hour to ensure an extra observation is included in the request.\n    start_date -= pd.Timedelta(hours=expand_window)\n    end_date += pd.Timedelta(hours=expand_window)\n\n    # Convert local time to UTC\n    start_date = start_date - pd.Timedelta(hours=utc_offset)\n    end_date = end_date - pd.Timedelta(hours=utc_offset)\n    root = 'http://www.nmdb.eu/nest/draw_graph.php?'\n    url_par = ['formchk=1',\n               'stations[]=' + station,\n               'output=ascii',\n               'tabchoice=revori',\n               'dtype=corr_for_efficiency',\n               'tresolution=' + str(60),\n               'date_choice=bydate',\n               'start_year=' + str(start_date.year),\n               'start_month=' + str(start_date.month),\n               'start_day=' + str(start_date.day),\n               'start_hour=' + str(start_date.hour),\n               'start_min=' + str(start_date.minute),\n               'end_year=' + str(end_date.year),\n               'end_month=' + str(end_date.month),\n               'end_day=' + str(end_date.day),\n               'end_hour=' + str(end_date.hour),\n               'end_min=' + str(end_date.minute),\n               'yunits=0']\n\n    url = root + '&amp;'.join(url_par)\n\n    if verbose:\n        print(f\"Retrieving data from {url}\")\n\n    r = requests.get(url).content.decode('utf-8')\n\n    # Subtract 1 hour to restore the last date included in the request.\n    end_date -= pd.Timedelta('1H')\n    start = r.find(\"RCORR_E\\n\") + 8\n    end = r.find('\\n&lt;/code&gt;&lt;/pre&gt;&lt;br&gt;Total') - 1\n    s = r[start:end]\n    s2 = ''.join([row.replace(';', ',') for row in s])\n    try:\n        df_flux = pd.read_csv(io.StringIO(s2), names=['timestamp', 'counts'])\n    except:\n        if verbose:\n            print(f\"Error retrieving data from {url}\")\n        return None\n\n    # Check if all values from selected detector are NaN. If yes, warn the user\n    if df_flux['counts'].isna().all():\n        warnings.warn('Data for selected neutron detectors appears to be unavailable for the selected period')\n\n    # Convert timestamp to datetime and apply UTC offset\n    df_flux['timestamp'] = pd.to_datetime(df_flux['timestamp'])\n    df_flux['timestamp'] = df_flux['timestamp'] + pd.Timedelta(hours=utc_offset)\n\n    # Print acknowledgement to inform users about restrictions and to acknowledge the NMDB database\n    acknowledgement = \"\"\"Data retrieved via NMDB are the property of the individual data providers. These data are free for non commercial\nuse to within the restriction imposed by the providers. If you use such data for your research or applications, please acknowledge\nthe origin by a sentence like 'We acknowledge the NMDB database (www.nmdb.eu) founded under the European Union's FP7 programme \n(contract no. 213007), and the PIs of individual neutron monitors at: IGY Jungfraujoch \n(Physikalisches Institut, University of Bern, Switzerland)\"\"\"\n\n    return df_flux\n</code></pre>"},{"location":"reference/#crnpy.data.get_reference_neutron_flux","title":"<code>get_reference_neutron_flux(station, date=pd.to_datetime('2011-05-01'))</code>","text":"<p>Function to retrieve reference neutron flux from the Neutron Monitor Database. Default date is 2011-05-01, following previous studies (Zreda et a., 2012, Hawdon et al., 2014, Bogena et al., 2022).</p> <p>Parameters:</p> Name Type Description Default <code>station</code> <code>str</code> <p>Neutron Monitor station to retrieve data from.</p> required <code>date</code> <code>datetime</code> <p>Date of the reference neutron flux. Default is 2011-05-01.</p> <code>to_datetime('2011-05-01')</code> <p>Returns:</p> Type Description <code>float</code> <p>Reference neutron flux in counts per hour.</p> References <p>Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., &amp; Rosolem, R. (2012). COSMOS: The cosmic-ray soil moisture observing system. Hydrology and Earth System Sciences, 16(11), 4079-4099.</p> <p>Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures for cosmic\u2010ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.</p> <p>Bogena, H. R., Schr\u00f6n, M., Jakobi, J., Ney, P., Zacharias, S., Andreasen, M., ... &amp; Vereecken, H. (2022). COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors. Earth System Science Data, 14(3), 1125-1151.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def get_reference_neutron_flux(station, date=pd.to_datetime(\"2011-05-01\")):\n    \"\"\"Function to retrieve reference neutron flux from the Neutron Monitor Database. Default date is 2011-05-01, following previous studies (Zreda et a., 2012, Hawdon et al., 2014, Bogena et al., 2022).\n\n    Args:\n        station (str): Neutron Monitor station to retrieve data from.\n        date (datetime): Date of the reference neutron flux. Default is 2011-05-01.\n\n    Returns:\n        (float): Reference neutron flux in counts per hour.\n\n    References:\n        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., &amp; Rosolem, R. (2012). COSMOS: The cosmic-ray soil moisture observing system. Hydrology and Earth System Sciences, 16(11), 4079-4099.\n\n        Hawdon, A., McJannet, D., &amp; Wallace, J. (2014). Calibration and correction procedures for cosmic\u2010ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.\n\n        Bogena, H. R., Schr\u00f6n, M., Jakobi, J., Ney, P., Zacharias, S., Andreasen, M., ... &amp; Vereecken, H. (2022). COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors. Earth System Science Data, 14(3), 1125-1151.\n\n\"\"\"\n\n    # Get flux for 2011-05-01\n    df_flux = get_incoming_neutron_flux(station, date, date + pd.Timedelta(hours=24))\n    if df_flux is None:\n        warnings.warn(f\"Reference neutron flux for {station} not available. Returning NaN.\")\n    else:\n        return df_flux['counts'].median()\n</code></pre>"},{"location":"reference/#crnpy.data.idw","title":"<code>idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1)</code>","text":"<p>Function to interpolate data using inverse distance weight.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>list or array</code> <p>UTM x coordinates in meters.</p> required <code>y</code> <code>list or array</code> <p>UTM y coordinates in meters.</p> required <code>z</code> <code>list or array</code> <p>Values to be interpolated.</p> required <code>X_pred</code> <code>list or array</code> <p>UTM x coordinates where z values need to be predicted.</p> required <code>Y_pred</code> <code>list or array</code> <p>UTM y coordinates where z values need to be predicted.</p> required <code>neighborhood</code> <code>float</code> <p>Only points within this radius in meters are considered for the interpolation.</p> <code>1000</code> <code>p</code> <code>int</code> <p>Exponent of the inverse distance weight formula. Typically, p=1 or p=2.</p> <code>1</code> <p>Returns:</p> Type Description <code>array</code> <p>Interpolated values.</p> References <p>https://en.wikipedia.org/wiki/Inverse_distance_weighting</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1):\n    \"\"\"Function to interpolate data using inverse distance weight.\n\n    Args:\n        x (list or array): UTM x coordinates in meters.\n        y (list or array): UTM y coordinates in meters.\n        z (list or array): Values to be interpolated.\n        X_pred (list or array): UTM x coordinates where z values need to be predicted.\n        Y_pred (list or array): UTM y coordinates where z values need to be predicted.\n        neighborhood (float): Only points within this radius in meters are considered for the interpolation.\n        p (int): Exponent of the inverse distance weight formula. Typically, p=1 or p=2.\n\n    Returns:\n        (array): Interpolated values.\n\n    References:\n        [https://en.wikipedia.org/wiki/Inverse_distance_weighting](https://en.wikipedia.org/wiki/Inverse_distance_weighting)\n\n\n    \"\"\"\n\n    # Flatten arrays to handle 1D and 2D arrays with the same code\n    s = X_pred.shape  # Save shape\n    X_pred = X_pred.flatten()\n    Y_pred = Y_pred.flatten()\n\n    # Pre-allocate output array\n    Z_pred = np.full_like(X_pred, np.nan)\n\n    for n in range(X_pred.size):\n        # Distance between current and observed points\n        d = euclidean_distance(X_pred[n], Y_pred[n], x, y)\n\n        # Select points within neighborhood only for interpolation\n        idx_neighbors = d &lt; neighborhood\n\n        # Compute interpolated value at point of interest\n        Z_pred[n] = np.sum(z[idx_neighbors] / d[idx_neighbors] ** p) / np.sum(1 / d[idx_neighbors] ** p)\n\n    return np.reshape(Z_pred, s)\n</code></pre>"},{"location":"reference/#crnpy.data.interpolate_2d","title":"<code>interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000)</code>","text":"<p>Function for interpolating irregular spatial data into a regular grid.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>list or array</code> <p>UTM x coordinates in meters.</p> required <code>y</code> <code>list or array</code> <p>UTM y coordinates in meters.</p> required <code>z</code> <code>list or array</code> <p>Values to be interpolated.</p> required <code>dx</code> <code>float</code> <p>Pixel width in meters.</p> <code>100</code> <code>dy</code> <code>float</code> <p>Pixel height in meters.</p> <code>100</code> <code>method</code> <code>str</code> <p>Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.</p> <code>'cubic'</code> <code>neighborhood</code> <code>float</code> <p>Only points within this radius in meters are considered for the interpolation.</p> <code>1000</code> <p>Returns:</p> Name Type Description <code>x_pred</code> <code>array</code> <p>2D array with x coordinates.</p> <code>y_pred</code> <code>array</code> <p>2D array with y coordinates.</p> <code>z_pred</code> <code>array</code> <p>2D array with interpolated values.</p> References <p>https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000):\n    \"\"\"Function for interpolating irregular spatial data into a regular grid.\n\n    Args:\n        x (list or array): UTM x coordinates in meters.\n        y (list or array): UTM y coordinates in meters.\n        z (list or array): Values to be interpolated.\n        dx (float): Pixel width in meters.\n        dy (float): Pixel height in meters.\n        method (str): Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.\n        neighborhood (float): Only points within this radius in meters are considered for the interpolation.\n\n    Returns:\n        x_pred (array): 2D array with x coordinates.\n        y_pred (array): 2D array with y coordinates.\n        z_pred (array): 2D array with interpolated values.\n\n    References:\n        [https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html](https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html)\n    \"\"\"\n\n    # Drop NaN values in x y and z\n    idx_nan = np.isnan(x) | np.isnan(y) | np.isnan(z)\n    x = x[~idx_nan]\n    y = y[~idx_nan]\n    z = z[~idx_nan]\n\n    if idx_nan.any():\n        print(\n            f\"WARNING: {np.isnan(x).sum()}, {np.isnan(y).sum()}, and {np.isnan(z).sum()} NaN values were dropped from x, y, and z.\")\n\n    # Create 2D grid for interpolation\n    Nx = round((np.max(x) - np.min(x)) / dx) + 1\n    Ny = round((np.max(y) - np.min(y)) / dy) + 1\n    X_vec = np.linspace(np.min(x), np.max(x), Nx)\n    Y_vec = np.linspace(np.min(y), np.max(y), Ny)\n    X_pred, Y_pred = np.meshgrid(X_vec, Y_vec)\n\n    if method in ['linear', 'nearest', 'cubic']:\n        points = list(zip(x, y))\n        Z_pred = griddata(points, z, (X_pred, Y_pred), method=method)\n\n    elif method == 'idw':\n        Z_pred = idw(x, y, z, X_pred, Y_pred, neighborhood)\n\n    else:\n        raise f\"Method {method} does not exist. Provide either 'cubic', 'linear', 'nearest', or 'idw'.\"\n\n    return X_pred, Y_pred, Z_pred\n</code></pre>"},{"location":"reference/#crnpy.data.interpolate_incoming_flux","title":"<code>interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps)</code>","text":"<p>Function to interpolate incoming neutron flux to match the timestamps of the observations.</p> <p>Parameters:</p> Name Type Description Default <code>nmdb_timestamps</code> <code>Series</code> <p>Series of timestamps in datetime format from the NMDB.</p> required <code>nmdb_counts</code> <code>Series</code> <p>Series of neutron counts from the NMDB</p> required <code>crnp_timestamps</code> <code>Series</code> <p>Series of timestamps in datetime format from the station or device.</p> required <p>Returns:</p> Type Description <code>Series</code> <p>Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps):\n    \"\"\"Function to interpolate incoming neutron flux to match the timestamps of the observations.\n\n    Args:\n        nmdb_timestamps (pd.Series): Series of timestamps in datetime format from the NMDB.\n        nmdb_counts (pd.Series): Series of neutron counts from the NMDB\n        crnp_timestamps (pd.Series): Series of timestamps in datetime format from the station or device.\n\n    Returns:\n        (pd.Series): Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps\n    \"\"\"\n    incoming_flux = np.array([])\n    for k, timestamp in enumerate(crnp_timestamps):\n        if timestamp in nmdb_timestamps.values:\n            idx = timestamp == nmdb_timestamps\n            incoming_flux = np.append(incoming_flux, nmdb_counts.loc[idx])\n        else:\n            incoming_flux = np.append(incoming_flux, np.nan)\n\n    # Interpolate nan values\n    incoming_flux = pd.Series(incoming_flux).interpolate(method='nearest', limit_direction='both')\n\n    # Return only the values for the selected timestamps\n    return incoming_flux\n</code></pre>"},{"location":"reference/#crnpy.data.is_outlier","title":"<code>is_outlier(x, method, window=11, min_val=None, max_val=None)</code>","text":"<p>Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>DataFrame or Series</code> <p>Variable containing only the columns with neutron counts.</p> required <code>method</code> <code>str</code> <p>Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad</p> required <code>window</code> <code>int</code> <p>Window size for the moving central tendency. Default is 11.</p> <code>11</code> <code>min_val</code> <code>int or float</code> <p>Minimum value for a reading to be considered valid. Default is None.</p> <code>None</code> <code>max_val(int</code> <code>or float</code> <p>Maximum value for a reading to be considered valid. Default is None.</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>Boolean indicating outliers.</p> References <p>Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def is_outlier(x, method, window=11, min_val=None, max_val=None):\n    \"\"\"Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.\n\n    Args:\n        x (pd.DataFrame or pd.Series): Variable containing only the columns with neutron counts.\n        method (str): Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad\n        window (int, optional): Window size for the moving central tendency. Default is 11.\n        min_val (int or float): Minimum value for a reading to be considered valid. Default is None.\n        max_val(int or float): Maximum value for a reading to be considered valid. Default is None.\n\n    Returns:\n        (pandas.DataFrame): Boolean indicating outliers.\n\n    References:\n        Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.\n    \"\"\"\n\n    if not isinstance(x, pd.Series):\n        raise TypeError('x must of type pandas.Series')\n\n    # Separate this method to allow usage together with other methods below\n    if isinstance(min_val, numbers.Number) and isinstance(max_val, numbers.Number):\n        idx_range_outliers = (x &lt; min_val) | (x &gt; max_val)\n    else:\n        idx_range_outliers = np.full_like(x, False)\n\n    # Apply other methods in addition to a range check\n    if method == 'iqr':\n        q1 = x.quantile(0.25)\n        q3 = x.quantile(0.75)\n        iqr = q3 - q1\n        high_fence = q3 + (1.5 * iqr)\n        low_fence = q1 - (1.5 * iqr)\n        idx_outliers = (x &lt; low_fence) | (x &gt; high_fence)\n\n    elif method == 'moviqr':\n        q1 = x.rolling(window, center=True).quantile(0.25)\n        q3 = x.rolling(window, center=True).quantile(0.75)\n        iqr = q3 - q1\n        ub = q3 + (1.5 * iqr)  # Upper boundary\n        lb = q1 - (1.5 * iqr)  # Lower boundary\n        idx_outliers = (x &lt; lb) | (x &gt; ub)\n\n    elif method == 'zscore':\n        zscore = (x - x.mean()) / x.std()\n        idx_outliers = (zscore &lt; -3) | (zscore &gt; 3)\n\n    elif method == 'movzscore':\n        movmean = x.rolling(window=window, center=True).mean()\n        movstd = x.rolling(window=window, center=True).std()\n        movzscore = (x - movmean) / movstd\n        idx_outliers = (movzscore &lt; -3) | (movzscore &gt; 3)\n\n    elif method == 'modified_zscore':\n        # Compute median absolute difference\n        movmedian = x.rolling(window, center=True).median()\n        abs_diff = np.abs(x - movmedian)\n        mad = abs_diff.rolling(window, center=True).median()\n\n        # Compute modified z-score\n        modified_z_score = 0.6745 * abs_diff / mad\n        idx_outliers = (modified_z_score &lt; -3.5) | (modified_z_score &gt; 3.5)\n\n    elif method == 'scaled_mad':\n        # Returns true for elements more than three scaled MAD from the median. \n        c = -1 / (np.sqrt(2) * erfcinv(3 / 2))\n        median = np.nanmedian(x)\n        mad = c * np.nanmedian(np.abs(x - median))\n        idx_outliers = x &gt; (median + 3 * mad)\n\n    else:\n        raise TypeError('Outlier detection method not found.')\n\n    return idx_outliers | idx_range_outliers\n</code></pre>"},{"location":"reference/#crnpy.data.latlon_to_utm","title":"<code>latlon_to_utm(lat, lon, utm_zone_number=None, utm_zone_letter=None)</code>","text":"<p>Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.</p> <p>Function only applies to non-polar coordinates. If further functionality is required, consider using the utm module. See references for more information.</p> <p> UTM zones on an equirectangular world map with irregular zones in red and New York City's zone highlighted. See UTM zones for a full description.</p> <p>Parameters:</p> Name Type Description Default <code>lat</code> <code>(float, array)</code> <p>Latitude in decimal degrees.</p> required <code>lon</code> <code>(float, array)</code> <p>Longitude in decimal degrees.</p> required <code>utm_zone_number</code> <code>int</code> <p>UTM zone number. If None, the zone number is automatically calculated.</p> <code>None</code> <code>utm_zone_letter</code> <code>str</code> <p>UTM zone letter. If None, the zone letter is automatically calculated.</p> <code>None</code> <p>Returns:</p> Type Description <code>(float, float)</code> <p>Tuple of easting and northing coordinates in meters. First element is easting, second is northing.</p> References <p>Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87) https://github.com/Turbo87/utm</p> <p>https://www.maptools.com/tutorials/grid_zone_details#</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def latlon_to_utm(lat, lon, utm_zone_number=None, utm_zone_letter=None):\n    \"\"\"Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.\n\n    Function only applies to non-polar coordinates.\n    If further functionality is required, consider using the utm module. See references for more information.\n\n    ![UTM zones](https://upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Universal_Transverse_Mercator_zones.svg/1920px-Universal_Transverse_Mercator_zones.svg.png)\n    UTM zones on an equirectangular world map with irregular zones in red and New York City's zone highlighted. See [UTM zones](https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#UTM_zones) for a full description.\n\n\n    Args:\n        lat (float, array): Latitude in decimal degrees.\n        lon (float, array): Longitude in decimal degrees.\n        utm_zone_number (int): UTM zone number. If None, the zone number is automatically calculated.\n        utm_zone_letter (str): UTM zone letter. If None, the zone letter is automatically calculated.\n\n    Returns:\n        (float, float): Tuple of easting and northing coordinates in meters. First element is easting, second is northing.\n\n    References:\n         Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87)\n         [https://github.com/Turbo87/utm](https://github.com/Turbo87/utm)\n\n         [https://www.maptools.com/tutorials/grid_zone_details#](https://www.maptools.com/tutorials/grid_zone_details#)\n    \"\"\"\n    if utm_zone_number is None or utm_zone_letter is None:\n        easting, northing, zone_number, zone_letter = utm.from_latlon(lat, lon)\n    else:\n        easting, northing, zone_number, zone_letter = utm.from_latlon(lat, lon, utm_zone_number, utm_zone_letter)\n\n    return easting, northing, zone_number, zone_letter\n</code></pre>"},{"location":"reference/#crnpy.data.lattice_water","title":"<code>lattice_water(clay_content, total_carbon=None)</code>","text":"<p>Estimate the amount of water in the lattice of clay minerals.</p> $\\omega_{lat} = 0.097 * clay(\\%)$ $\\omega_{lat} = -0.028 + 0.077 * clay(\\%) + 0.459 * carbon(\\%)$ Linear regression [lattice water (%) as a function of clay (%)] done with data from Kansas Sate University - Soil Water Processes Lab. Multiple linear regression [lattice water (%) as a function of clay (%) and soil carbon (%)] done with data from Soil Water Processes Lab. <p>Parameters:</p> Name Type Description Default <code>clay_content</code> <code>float</code> <p>Clay content in the soil in percent.</p> required <code>total_carbon</code> <code>float</code> <p>Total carbon content in the soil in percent. If None, the amount of water is estimated based on clay content only.</p> <code>None</code> <p>Returns:</p> Type Description <code>float</code> <p>Amount of water in the lattice of clay minerals in percent</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def lattice_water(clay_content, total_carbon=None):\n    r\"\"\"Estimate the amount of water in the lattice of clay minerals.\n\n    ![img1](img/lattice_water_simple.png) | ![img2](img/lattice_water_multiple.png)\n    :-------------------------:|:-------------------------:\n    $\\omega_{lat} = 0.097 * clay(\\%)$ | $\\omega_{lat} = -0.028 + 0.077 * clay(\\%) + 0.459 * carbon(\\%)$\n    Linear regression [lattice water (%) as a function of clay (%)] done with data from Kansas Sate University - Soil Water Processes Lab. |  Multiple linear regression [lattice water (%) as a function of clay (%) and soil carbon (%)] done with data from Soil Water Processes Lab.\n\n    Args:\n        clay_content (float): Clay content in the soil in percent.\n        total_carbon (float, optional): Total carbon content in the soil in percent.\n            If None, the amount of water is estimated based on clay content only.\n\n    Returns:\n        (float): Amount of water in the lattice of clay minerals in percent\n    \"\"\"\n    if total_carbon is None:\n        lattice_water = 0.097 * clay_content\n    else:\n        lattice_water = -0.028 + 0.077 * clay_content + 0.459 * total_carbon\n    return lattice_water\n</code></pre>"},{"location":"reference/#crnpy.data.location_factor","title":"<code>location_factor(site_atmospheric_depth, site_Rc, reference_atmospheric_depth, reference_Rc)</code>","text":"<p>Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.</p> <p>Parameters:</p> Name Type Description Default <code>site_atmospheric_depth</code> <code>float</code> <p>Atmospheric depth at the site in g/cm2. Can be estimated using the function <code>atmospheric_depth()</code></p> required <code>site_Rc</code> <code>float</code> <p>Cutoff rigidity at the site in GV. Can be estimated using the function <code>cutoff_rigidity()</code></p> required <code>reference_atmospheric_depth</code> <code>float</code> <p>Atmospheric depth at the reference location in g/cm2.</p> required <code>reference_Rc</code> <code>float</code> <p>Cutoff rigidity at the reference location in GV.</p> required <p>Returns:</p> Type Description <code>float</code> <p>Location-dependent correction factor.</p> References <p>McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic\u2010Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def location_factor(site_atmospheric_depth, site_Rc, reference_atmospheric_depth, reference_Rc):\n    \"\"\"\n    Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.\n\n\n    Args:\n        site_atmospheric_depth (float): Atmospheric depth at the site in g/cm2. Can be estimated using the function `atmospheric_depth()`\n        site_Rc (float): Cutoff rigidity at the site in GV. Can be estimated using the function `cutoff_rigidity()`\n        reference_atmospheric_depth (float): Atmospheric depth at the reference location in g/cm2.\n        reference_Rc (float): Cutoff rigidity at the reference location in GV.\n\n    Returns:\n        (float): Location-dependent correction factor.\n\n    References:\n        McJannet, D. L., &amp; Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic\u2010Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.\n\n    \"\"\"\n\n    # Renamed variables based on the provided table\n    c0 = -0.0009  # from C39\n    c1 = 1.7699  # from C40\n    c2 = 0.0064  # from C41\n    c3 = 1.8855  # from C42\n    c4 = 0.000013  # from C43\n    c5 = -1.2237  # from C44\n    epsilon = 1  # from C45\n\n    # Translated formula with renamed variables from McJannet and Desilets, 2023\n    tau_new = epsilon * (c0 * reference_atmospheric_depth + c1) * (\n            1 - np.exp(\n        -(c2 * reference_atmospheric_depth + c3) * reference_Rc ** (c4 * reference_atmospheric_depth + c5)))\n\n    norm_factor = 1 / tau_new\n\n    # Calculate the result using the provided parameters\n    tau = epsilon * norm_factor * (c0 * site_atmospheric_depth + c1) * (\n                1 - np.exp(-(c2 * site_atmospheric_depth + c3) * site_Rc ** (c4 * site_atmospheric_depth + c5)))\n    return tau\n</code></pre>"},{"location":"reference/#crnpy.data.nrad_weight","title":"<code>nrad_weight(h, theta, distances, depth, rhob=1.4, method=None, p=None, Hveg=None)</code>","text":"<p>Function to compute distance weights corresponding to each soil sample.</p> <p>Parameters:</p> Name Type Description Default <code>h</code> <code>array or Series</code> <p>Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.</p> required <code>theta</code> <code>array or Series</code> <p>Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)</p> required <code>distances</code> <code>array or Series</code> <p>Distances from the location of each sample to the origin (0.5 - 600 m)</p> required <code>depth</code> <code>array or Series</code> <p>Depths for each sample (m)</p> required <code>rhob</code> <code>array or Series</code> <p>Bulk density in g/cm^3</p> <code>1.4</code> <code>p</code> <code>array or Series</code> <p>Atmospheric pressure in hPa. Required for the 'Schron_2017' method.</p> <code>None</code> <code>Hveg</code> <code>array or Series</code> <p>Vegetation height in m. Required for the 'Schron_2017' method.</p> <code>None</code> <code>method</code> <code>str</code> <p>Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.</p> <code>None</code> <p>Returns:</p> Type Description <code>array or Series or DataFrame</code> <p>Distance weights for each sample.</p> References <p>K\u00f6hli, M., Schr\u00f6n, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015). Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray neutrons. Water Resour. Res. 51, 5772\u20135790. doi:10.1002/2015WR017169</p> <p>Schr\u00f6n, M., K\u00f6hli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M., Rebmann, C., Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.: Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity, Hydrol. Earth Syst. Sci., 21, 5009\u20135030, https://doi.org/10.5194/hess-21-5009-2017, 2017.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def nrad_weight(h, theta, distances, depth, rhob=1.4, method=None, p=None, Hveg=None):\n    \"\"\"Function to compute distance weights corresponding to each soil sample.\n\n    Args:\n        h (np.array or pd.Series): Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.\n        theta (np.array or pd.Series): Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)\n        distances (np.array or pd.Series): Distances from the location of each sample to the origin (0.5 - 600 m)\n        depth (np.array or pd.Series): Depths for each sample (m)\n        rhob (np.array or pd.Series): Bulk density in g/cm^3\n        p (np.array or pd.Series): Atmospheric pressure in hPa. Required for the 'Schron_2017' method.\n        Hveg (np.array or pd.Series): Vegetation height in m. Required for the 'Schron_2017' method.\n        method (str): Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Distance weights for each sample.\n\n    References:\n        K\u00f6hli, M., Schr\u00f6n, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015).\n        Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray\n        neutrons. Water Resour. Res. 51, 5772\u20135790. doi:10.1002/2015WR017169\n\n        Schr\u00f6n, M., K\u00f6hli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L.,\n        Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M., Rebmann, C.,\n        Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.: Improving calibration and\n        validation of cosmic-ray neutron sensors in the light of spatial sensitivity,\n        Hydrol. Earth Syst. Sci., 21, 5009\u20135030, https://doi.org/10.5194/hess-21-5009-2017, 2017.\n    \"\"\"\n\n    if method == 'Kohli_2015':\n\n        # Table A1. Parameters for Fi and D86\n        p10 = 8735;\n        p11 = 17.1758;\n        p12 = 11720;\n        p13 = 0.00978;\n        p14 = 7045;\n        p15 = 0.003632;\n        p20 = 2.7925e-2;\n        p21 = 5.0399;\n        p22 = 2.8544e-2;\n        p23 = 0.002455;\n        p24 = 6.851e-5;\n        p25 = 9.2926;\n        p30 = 247970;\n        p31 = 17.63;\n        p32 = 374655;\n        p33 = 0.00191;\n        p34 = 195725;\n        p40 = 5.4818e-2;\n        p41 = 15.921;\n        p42 = 0.6373;\n        p43 = 5.99e-2;\n        p44 = 5.425e-4;\n        p50 = 1383702;\n        p51 = 4.156;\n        p52 = 5325;\n        p53 = 0.00238;\n        p54 = 0.0156;\n        p55 = 0.130;\n        p56 = 1521;\n        p60 = 6.031e-5;\n        p61 = 98.5;\n        p62 = 1.0466e-3;\n        p70 = 11747;\n        p71 = 41.66;\n        p72 = 4521;\n        p73 = 0.01998;\n        p74 = 0.00604;\n        p75 = 2534;\n        p76 = 0.00475;\n        p80 = 1.543e-2;\n        p81 = 10.06;\n        p82 = 1.807e-2;\n        p83 = 0.0011;\n        p84 = 8.81e-5;\n        p85 = 0.0405;\n        p86 = 20.24;\n        p90 = 8.321;\n        p91 = 0.14249;\n        p92 = 0.96655;\n        p93 = 26.42;\n        p94 = 0.0567;\n\n        # Numerical determination of the penetration depth (86%) (Eq. 8)\n        D86 = 1 / rhob * (p90 + p91 * (p92 + np.exp(-1 * distances / 100)) * (p93 + theta) / (p94 + theta))\n\n        # Depth weights (Eq. 7)\n        Wd = np.exp(-2 * depth / D86)\n\n        if h == 0:\n            W = 1  # skip distance weighting\n\n        elif (h &gt;= 0.1) and (h &lt;= 50):\n            # Functions for Fi (Appendix A in K\u00f6hli et al., 2015)\n            F1 = p10 * (1 + p13 * h) * np.exp(-p11 * theta) + p12 * (1 + p15 * h) - p14 * theta\n            F2 = ((-p20 + p24 * h) * np.exp(-p21 * theta / (1 + p25 * theta)) + p22) * (1 + h * p23)\n            F3 = (p30 * (1 + p33 * h) * np.exp(-p31 * theta) + p32 - p34 * theta)\n            F4 = p40 * np.exp(-p41 * theta) + p42 - p43 * theta + p44 * h\n            F5 = p50 * (0.02 - 1 / p55 / (h - p55 + p56 * theta)) * (p54 - theta) * np.exp(\n                -p51 * (theta - p54)) + p52 * (0.7 - h * theta * p53)\n            F6 = p60 * (h + p61) + p62 * theta\n            F7 = (p70 * (1 - p76 * h) * np.exp(-p71 * theta * (1 - h * p74)) + p72 - p75 * theta) * (2 + h * p73)\n            F8 = ((-p80 + p84 * h) * np.exp(-p81 * theta / (1 + p85 * h + p86 * theta)) + p82) * (2 + h * p83)\n\n            # Distance weights (Eq. 3)\n            W = np.ones_like(distances) * np.nan\n            for i in range(len(distances)):\n                if (distances[i] &lt;= 50) and (distances[i] &gt; 0.5):\n                    W[i] = F1[i] * (np.exp(-F2[i] * distances[i])) + F3[i] * np.exp(-F4[i] * distances[i])\n\n                elif (distances[i] &gt; 50) and (distances[i] &lt; 600):\n                    W[i] = F5[i] * (np.exp(-F6[i] * distances[i])) + F7[i] * np.exp(-F8[i] * distances[i])\n\n                else:\n                    raise ValueError('Input distances are not valid.')\n\n        else:\n            raise ValueError('Air humidity values are out of range.')\n\n        # Combined and normalized weights\n        weights = Wd * W / np.nansum(Wd * W)\n        return weights\n    elif method == 'Schron_2017':\n        # Horizontal distance weights According to Eq. 6 and Table A1 in Schr\u00f6n et al. (2017)\n        # Method for calculating the horizontal distance weights from 0 to 1m\n        def WrX(r, x, y):\n            x00 = 3.7\n            a00 = 8735;\n            a01 = 22.689;\n            a02 = 11720;\n            a03 = 0.00978;\n            a04 = 9306;\n            a05 = 0.003632\n            a10 = 2.7925e-2;\n            a11 = 6.6577;\n            a12 = 0.028544;\n            a13 = 0.002455;\n            a14 = 6.851e-5;\n            a15 = 12.2755\n            a20 = 247970;\n            a21 = 23.289;\n            a22 = 374655;\n            a23 = 0.00191;\n            a24 = 258552\n            a30 = 5.4818e-2;\n            a31 = 21.032;\n            a32 = 0.6373;\n            a33 = 0.0791;\n            a34 = 5.425e-4\n\n            x0 = x00\n            A0 = (a00 * (1 + a03 * x) * np.exp(-a01 * y) + a02 * (1 + a05 * x) - a04 * y)\n            A1 = ((-a10 + a14 * x) * np.exp(-a11 * y / (1 + a15 * y)) + a12) * (1 + x * a13)\n            A2 = (a20 * (1 + a23 * x) * np.exp(-a21 * y) + a22 - a24 * y)\n            A3 = a30 * np.exp(-a31 * y) + a32 - a33 * y + a34 * x\n\n            return ((A0 * (np.exp(-A1 * r)) + A2 * np.exp(-A3 * r)) * (1 - np.exp(-x0 * r)))\n\n        # Method for calculating the horizontal distance weights from 1 to 50m\n        def WrA(r, x, y):\n            a00 = 8735;\n            a01 = 22.689;\n            a02 = 11720;\n            a03 = 0.00978;\n            a04 = 9306;\n            a05 = 0.003632\n            a10 = 2.7925e-2;\n            a11 = 6.6577;\n            a12 = 0.028544;\n            a13 = 0.002455;\n            a14 = 6.851e-5;\n            a15 = 12.2755\n            a20 = 247970;\n            a21 = 23.289;\n            a22 = 374655;\n            a23 = 0.00191;\n            a24 = 258552\n            a30 = 5.4818e-2;\n            a31 = 21.032;\n            a32 = 0.6373;\n            a33 = 0.0791;\n            a34 = 5.425e-4\n\n            A0 = (a00 * (1 + a03 * x) * np.exp(-a01 * y) + a02 * (1 + a05 * x) - a04 * y)\n            A1 = ((-a10 + a14 * x) * np.exp(-a11 * y / (1 + a15 * y)) + a12) * (1 + x * a13)\n            A2 = (a20 * (1 + a23 * x) * np.exp(-a21 * y) + a22 - a24 * y)\n            A3 = a30 * np.exp(-a31 * y) + a32 - a33 * y + a34 * x\n\n            return A0 * np.exp(-A1 * r) + A2 * np.exp(-A3 * r)\n\n        # Method for calculating the horizontal distance weights from 50 to 600m\n        def WrB(r, x, y):\n            b00 = 39006;\n            b01 = 15002337;\n            b02 = 2009.24;\n            b03 = 0.01181;\n            b04 = 3.146;\n            b05 = 16.7417;\n            b06 = 3727\n            b10 = 6.031e-5;\n            b11 = 98.5;\n            b12 = 0.0013826\n            b20 = 11747;\n            b21 = 55.033;\n            b22 = 4521;\n            b23 = 0.01998;\n            b24 = 0.00604;\n            b25 = 3347.4;\n            b26 = 0.00475\n            b30 = 1.543e-2;\n            b31 = 13.29;\n            b32 = 1.807e-2;\n            b33 = 0.0011;\n            b34 = 8.81e-5;\n            b35 = 0.0405;\n            b36 = 26.74\n\n            B0 = (b00 - b01 / (b02 * y + x - 0.13)) * (b03 - y) * np.exp(-b04 * y) - b05 * x * y + b06\n            B1 = b10 * (x + b11) + b12 * y\n            B2 = (b20 * (1 - b26 * x) * np.exp(-b21 * y * (1 - x * b24)) + b22 - b25 * y) * (2 + x * b23)\n            B3 = ((-b30 + b34 * x) * np.exp(-b31 * y / (1 + b35 * x + b36 * y)) + b32) * (2 + x * b33)\n\n            return B0 * np.exp(-B1 * r) + B2 * np.exp(-B3 * r)\n\n        def rscaled(r, p, Hveg, y):\n            Fp = 0.4922 / (0.86 - np.exp(-p / 1013.25))\n            Fveg = 1 - 0.17 * (1 - np.exp(-0.41 * Hveg)) * (1 + np.exp(-9.25 * y))\n            return r / Fp / Fveg\n\n        # Rename variables to be consistent with the revised paper\n        r = distances\n        x = h\n        y = theta\n        bd = rhob\n\n        if Hveg is not None and p is not None:\n            r = rscaled(r, p, Hveg, y)\n\n        Wr = np.zeros(len(r))\n\n        # See Eq. 6 in Schron et al. (2017)\n        r0_idx = (r &lt;= 1)\n        r1_idx = (r &gt; 1) &amp; (r &lt;= 50)\n        r2_idx = (r &gt; 50) &amp; (r &lt; 600)\n        Wr[r0_idx] = WrX(r[r0_idx], x[r0_idx], y[r0_idx])\n        Wr[r1_idx] = WrA(r[r1_idx], x[r1_idx], y[r1_idx])\n        Wr[r2_idx] = WrB(r[r2_idx], x[r2_idx], y[r2_idx])\n\n        # Vertical distance weights\n        def D86(r, bd, y):\n            return 1 / bd * (8.321 + 0.14249 * (0.96655 + np.exp(-0.01 * r)) * (20 + y) / (0.0429 + y))\n\n        def Wd(d, r, bd, y):\n            return np.exp(-2 * d / D86(r, bd, y))\n\n        # Calculate the vertical distance weights\n        Wd = Wd(d, r, bd, y)\n\n        # Combined and normalized weights\n        # Combined and normalized weights\n        weights = Wd * Wr / np.nansum(Wd * Wr)\n\n        return weights\n</code></pre>"},{"location":"reference/#crnpy.data.remove_incomplete_intervals","title":"<code>remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False)</code>","text":"<p>Function that removes rows with incomplete integration intervals.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>Pandas Dataframe with data from stationary or roving CRNP devices.</p> required <code>timestamp_col</code> <code>str</code> <p>Name of the column with timestamps in datetime format.</p> required <code>integration_time</code> <code>int</code> <p>Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.</p> required <code>remove_first</code> <code>bool</code> <p>Remove first row. Default is False.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> Source code in <code>crnpy/crnpy.py</code> <pre><code>def remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False):\n    \"\"\"Function that removes rows with incomplete integration intervals.\n\n    Args:\n        df (pandas.DataFrame): Pandas Dataframe with data from stationary or roving CRNP devices.\n        timestamp_col (str): Name of the column with timestamps in datetime format.\n        integration_time (int): Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.\n        remove_first (bool, optional): Remove first row. Default is False.\n\n    Returns:\n        (pandas.DataFrame): \n    \"\"\"\n\n    # Check format of timestamp column\n    if df[timestamp_col].dtype != 'datetime64[ns]':\n        raise TypeError('timestamp_col must be datetime64. Use `pd.to_datetime()` to fix this issue.')\n\n    # Check if differences in timestamps are below or above the provided integration time\n    idx_delta = df[timestamp_col].diff().dt.total_seconds() != integration_time\n\n    if remove_first:\n        idx_delta[0] = True\n\n    # Select rows that meet the specified integration time\n    df = df[~idx_delta]\n    df.reset_index(drop=True, inplace=True)\n\n    # Notify user about the number of rows that have been removed\n    print(f\"Removed a total of {sum(idx_delta)} rows.\")\n\n    return df\n</code></pre>"},{"location":"reference/#crnpy.data.rover_centered_coordinates","title":"<code>rover_centered_coordinates(x, y)</code>","text":"<p>Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>array</code> <p>x coordinates.</p> required <code>y</code> <code>array</code> <p>y coordinates.</p> required <p>Returns:</p> Name Type Description <code>x_est</code> <code>array</code> <p>Estimated x coordinates.</p> <code>y_est</code> <code>array</code> <p>Estimated y coordinates.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def rover_centered_coordinates(x, y):\n    \"\"\"Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.\n\n    Args:\n        x (array): x coordinates.\n        y (array): y coordinates.\n\n    Returns:\n        x_est (array): Estimated x coordinates.\n        y_est (array): Estimated y coordinates.\n    \"\"\"\n\n    # Make it datatype agnostic\n    if (isinstance(x, pd.Series)):\n        x = x.values\n    if (isinstance(y, pd.Series)):\n        y = y.values\n\n    # Do the average of the two points\n    x_est = (x[1:] + x[:-1]) / 2\n    y_est = (y[1:] + y[:-1]) / 2\n\n    # Add the first point to match the length of the original array\n    x_est = np.insert(x_est, 0, x[0])\n    y_est = np.insert(y_est, 0, y[0])\n\n    return x_est, y_est\n</code></pre>"},{"location":"reference/#crnpy.data.sensing_depth","title":"<code>sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017')</code>","text":"<p>Function that computes the estimated sensing depth of the cosmic-ray neutron probe. The function offers several methods to compute the depth at which 86 % of the neutrons probe the soil profile.</p> <p>Parameters:</p> Name Type Description Default <code>vwc</code> <code>array or Series or DataFrame</code> <p>Estimated volumetric water content for each timestamp.</p> required <code>pressure</code> <code>array or Series or DataFrame</code> <p>Atmospheric pressure in hPa for each timestamp.</p> required <code>p_ref</code> <code>float</code> <p>Reference pressure in hPa.</p> required <code>bulk_density</code> <code>float</code> <p>Soil bulk density.</p> required <code>Wlat</code> <code>float</code> <p>Lattice water content.</p> required <code>method</code> <code>str</code> <p>Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.</p> <code>'Schron_2017'</code> <code>dist</code> <code>list or array</code> <p>List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.</p> <code>None</code> <p>Returns:</p> Type Description <code>array or Series or DataFrame</code> <p>Estimated sensing depth in m.</p> References <p>Franz, T.E., Zreda, M., Ferre, T.P.A., Rosolem, R., Zweck, C., Stillman, S., Zeng, X. and Shuttleworth, W.J., 2012. Measurement depth of the cosmic ray soil moisture probe affected by hydrogen from various sources. Water Resources Research, 48(8). doi.org/10.1029/2012WR011871</p> <p>Schr\u00f6n, M., K\u00f6hli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., et al. (2017). Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity. Hydrol. Earth Syst. Sci. 21, 5009\u20135030. doi.org/10.5194/hess-21-5009-2017</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017'):\n    \"\"\"Function that computes the estimated sensing depth of the cosmic-ray neutron probe.\n    The function offers several methods to compute the depth at which 86 % of the neutrons\n    probe the soil profile.\n\n    Args:\n        vwc (array or pd.Series or pd.DataFrame): Estimated volumetric water content for each timestamp.\n        pressure (array or pd.Series or pd.DataFrame): Atmospheric pressure in hPa for each timestamp.\n        p_ref (float): Reference pressure in hPa.\n        bulk_density (float): Soil bulk density.\n        Wlat (float): Lattice water content.\n        method (str): Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.\n        dist (list or array): List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.\n\n    Returns:\n        (array or pd.Series or pd.DataFrame): Estimated sensing depth in m.\n\n    References:\n        Franz, T.E., Zreda, M., Ferre, T.P.A., Rosolem, R., Zweck, C., Stillman, S., Zeng, X. and Shuttleworth, W.J., 2012.\n        Measurement depth of the cosmic ray soil moisture probe affected by hydrogen from various sources.\n        Water Resources Research, 48(8). doi.org/10.1029/2012WR011871\n\n        Schr\u00f6n, M., K\u00f6hli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., et al. (2017).\n        Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity.\n        Hydrol. Earth Syst. Sci. 21, 5009\u20135030. doi.org/10.5194/hess-21-5009-2017\n    \"\"\"\n\n    # Determine sensing depth (D86)\n    if method == 'Schron_2017':\n        # See Appendix A of Schr\u00f6n et al. (2017)\n        Fp = 0.4922 / (0.86 - np.exp(-1 * pressure / p_ref))\n        Fveg = 0\n        results = []\n        for d in dist:\n            # Compute r_star\n            r_start = d / Fp\n\n            # Compute soil depth that accounts for 86% of the neutron flux\n            D86 = 1 / bulk_density * (8.321 + 0.14249 * (0.96655 + np.exp(-0.01 * r_start)) * (20 + (Wlat + vwc)) / (\n                        0.0429 + (Wlat + vwc)))\n            results.append(D86)\n\n    elif method == 'Franz_2012':\n        results = 5.8 / (bulk_density * Wlat + vwc + 0.0829)\n    else:\n        raise ValueError('Method not recognized. Please select either \"Schron_2017\" or \"Franz_2012\".')\n    return results\n</code></pre>"},{"location":"reference/#crnpy.data.smooth_1d","title":"<code>smooth_1d(values, window=5, order=3, method='moving_median')</code>","text":"<p>Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).</p> <p>Parameters:</p> Name Type Description Default <code>values</code> <code>DataFrame or Serie</code> <p>Dataframe containing the values to smooth.</p> required <code>window</code> <code>int</code> <p>Window size for the Savitzky-Golay filter. Default is 5.</p> <code>5</code> <code>method</code> <code>str</code> <p>Method to use for smoothing the data. Default is 'moving_median'. Options are 'moving_average', 'moving_median' and 'savitzky_golay'.</p> <code>'moving_median'</code> <code>order</code> <code>int</code> <p>Order of the Savitzky-Golay filter. Default is 3.</p> <code>3</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>DataFrame with smoothed values.</p> References <p>Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020. Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9. doi.org/10.3389/frwa.2020.00009</p> <p>Savitzky, A., &amp; Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 36(8), 1627-1639.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def smooth_1d(values, window=5, order=3, method='moving_median'):\n    \"\"\"Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).\n\n    Args:\n        values (pd.DataFrame or pd.Serie): Dataframe containing the values to smooth.\n        window (int): Window size for the Savitzky-Golay filter. Default is 5.\n        method (str): Method to use for smoothing the data. Default is 'moving_median'.\n            Options are 'moving_average', 'moving_median' and 'savitzky_golay'.\n        order (int): Order of the Savitzky-Golay filter. Default is 3.\n\n    Returns:\n        (pd.DataFrame): DataFrame with smoothed values.\n\n    References:\n        Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.\n        Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.\n        doi.org/10.3389/frwa.2020.00009\n\n        Savitzky, A., &amp; Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures.\n        Analytical chemistry, 36(8), 1627-1639.\n    \"\"\"\n\n    if not isinstance(x, pd.Series) and not isinstance(x, pd.DataFrame):\n        raise ValueError('Input must be a pandas Series or DataFrame')\n\n    if method == 'moving_average':\n        corrected_counts = values.rolling(window=window, center=True, min_periods=1).mean()\n    elif method == 'moving_median':\n        corrected_counts = values.rolling(window=window, center=True, min_periods=1).median()\n\n    elif method == 'savitzky_golay':\n        if values.isna().any():\n            print('Dataframe contains NaN values. Please remove NaN values before smoothing the data.')\n\n        if type(values) == pd.core.series.Series:\n            filtered = savgol_filter(values, window, order)\n            corrected_counts = pd.DataFrame(filtered, columns=['smoothed'], index=values.index)\n        elif type(values) == pd.core.frame.DataFrame:\n            for col in values.columns:\n                values[col] = savgol_filter(values[col], window, order)\n    else:\n        raise ValueError(\n            'Invalid method. Please select a valid filtering method., options are: moving_average, moving_median, savitzky_golay')\n    corrected_counts = corrected_counts.ffill(limit=window).bfill(limit=window).copy()\n    return corrected_counts\n</code></pre>"},{"location":"reference/#crnpy.data.spatial_average","title":"<code>spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=False)</code>","text":"<p>Moving buffer filter to smooth georeferenced two-dimensional data.</p> <p>Parameters:</p> Name Type Description Default <code>x</code> <code>list or array</code> <p>UTM x coordinates in meters.</p> required <code>y</code> <code>list or array</code> <p>UTM y coordinates in meters.</p> required <code>z</code> <code>list or array</code> <p>Values to be smoothed.</p> required <code>buffer</code> <code>float</code> <p>Radial buffer distance in meters.</p> <code>100</code> <code>min_neighbours</code> <code>int</code> <p>Minimum number of neighbours to consider for the smoothing.</p> <code>3</code> <code>method</code> <code>str</code> <p>One of 'mean' or 'median'.</p> <code>'mean'</code> <code>rnd</code> <code>bool</code> <p>Boolean to round the final result. Useful in case of z representing neutron counts.</p> <code>False</code> <p>Returns:</p> Type Description <code>array</code> <p>Smoothed version of z with the same dimension as z.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=False):\n    \"\"\"Moving buffer filter to smooth georeferenced two-dimensional data.\n\n    Args:\n        x (list or array): UTM x coordinates in meters.\n        y (list or array): UTM y coordinates in meters.\n        z (list or array): Values to be smoothed.\n        buffer (float): Radial buffer distance in meters.\n        min_neighbours (int): Minimum number of neighbours to consider for the smoothing.\n        method (str): One of 'mean' or 'median'.\n        rnd (bool): Boolean to round the final result. Useful in case of z representing neutron counts.\n\n    Returns:\n        (array): Smoothed version of z with the same dimension as z.\n    \"\"\"\n\n    # Convert input data to Numpy arrays\n    if (type(x) is not np.ndarray) or (type(y) is not np.ndarray):\n        try:\n            x = np.array(x)\n            y = np.array(y)\n        except:\n            raise \"Input values cannot be converted to Numpy arrays.\"\n\n    if len(x) != len(y):\n        raise f\"The number of x and y must be equal. Input x has {len(x)} values and y has {len(y)} values.\"\n\n    # Compute distances\n    N = len(x)\n    z_smooth = np.array([])\n    for k in range(N):\n        px = x[k]\n        py = y[k]\n\n        distances = euclidean_distance(px, py, x, y)\n        idx_within_buffer = distances &lt;= buffer\n\n        if np.isnan(z[k]):\n            z_new_val = np.nan\n        elif len(distances[idx_within_buffer]) &gt; min_neighbours:\n            if method == 'mean':\n                z_new_val = np.nanmean(z[idx_within_buffer])\n            elif method == 'median':\n                z_new_val = np.nanmedian(z[idx_within_buffer])\n            else:\n                raise f\"Method {method} does not exist. Provide either 'mean' or 'median'.\"\n        else:\n            z_new_val = z[k]  # If there are not enough neighbours, keep the original value\n\n        # Append smoothed value to array\n        z_smooth = np.append(z_smooth, z_new_val)\n\n    if rnd:\n        z_smooth = np.round(z_smooth, 0)\n\n    return z_smooth\n</code></pre>"},{"location":"reference/#crnpy.data.total_raw_counts","title":"<code>total_raw_counts(counts)</code>","text":"<p>Compute the sum of uncorrected neutron counts for all detectors.</p> <p>Parameters:</p> Name Type Description Default <code>counts</code> <code>DataFrame</code> <p>Dataframe containing only the columns with neutron counts.</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>Dataframe with the sum of uncorrected neutron counts for all detectors.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def total_raw_counts(counts):\n    \"\"\"Compute the sum of uncorrected neutron counts for all detectors.\n\n    Args:\n        counts (pandas.DataFrame): Dataframe containing only the columns with neutron counts.\n\n    Returns:\n        (pandas.DataFrame): Dataframe with the sum of uncorrected neutron counts for all detectors.\n    \"\"\"\n\n    if counts.shape[0] &gt; 1:\n        counts = counts.apply(lambda x: x.fillna(counts.mean(axis=1)), axis=0)\n\n    # Compute sum of counts\n    total_raw_counts = counts.sum(axis=1)\n\n    # Replace zeros with NaN\n    total_raw_counts = total_raw_counts.replace(0, np.nan)\n\n    return total_raw_counts\n</code></pre>"},{"location":"reference/#crnpy.data.uncertainty_counts","title":"<code>uncertainty_counts(raw_counts, metric='std', fp=1, fw=1, fi=1)</code>","text":"<p>Function to estimate the uncertainty of raw counts.</p> <p>Measurements of proportional neutron detector systems are governed by counting statistics that follow a Poissonian probability distribution (Zreda et al., 2012). The expected uncertainty in the neutron count rate $N$ is defined by the standard deviation $ \\sqrt{N} $ (Jakobi et al., 2020). The CV% can be expressed as $ N^{-1/2} $</p> <p>Parameters:</p> Name Type Description Default <code>raw_counts</code> <code>array</code> <p>Raw neutron counts.</p> required <code>metric</code> <code>str</code> <p>Either 'std' or 'cv' for standard deviation or coefficient of variation.</p> <code>'std'</code> <code>fp</code> <code>float</code> <p>Pressure correction factor.</p> <code>1</code> <code>fw</code> <code>float</code> <p>Humidity correction factor.</p> <code>1</code> <code>fi</code> <code>float</code> <p>Incoming neutron flux correction factor.</p> <code>1</code> <p>Returns:</p> Name Type Description <code>uncertainty</code> <code>float</code> <p>Uncertainty of raw counts.</p> References <p>Jakobi J, Huisman JA, Schr\u00f6n M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010</p> <p>Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079\u20134099, https://doi.org/10.5194/hess-16-4079-2012, 2012.</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def uncertainty_counts(raw_counts, metric=\"std\", fp=1, fw=1, fi=1):\n    \"\"\"Function to estimate the uncertainty of raw counts.\n\n    Measurements of proportional neutron detector systems are governed by counting statistics that follow a Poissonian probability distribution (Zreda et al., 2012).\n    The expected uncertainty in the neutron count rate $N$ is defined by the standard deviation $ \\sqrt{N} $ (Jakobi et al., 2020).\n    The CV% can be expressed as $ N^{-1/2} $\n\n    Args:\n        raw_counts (array): Raw neutron counts.\n        metric (str): Either 'std' or 'cv' for standard deviation or coefficient of variation.\n        fp (float): Pressure correction factor.\n        fw (float): Humidity correction factor.\n        fi (float): Incoming neutron flux correction factor.\n\n    Returns:\n        uncertainty (float): Uncertainty of raw counts.\n\n    References:\n        Jakobi J, Huisman JA, Schr\u00f6n M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With\n        Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010\n\n        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System,\n        Hydrol. Earth Syst. Sci., 16, 4079\u20134099, https://doi.org/10.5194/hess-16-4079-2012, 2012.\n\n    \"\"\"\n\n    s = fw / (fp * fi)\n    if metric == \"std\":\n        uncertainty = np.sqrt(raw_counts) * s\n    elif metric == \"cv\":\n        uncertainty = 1 / np.sqrt(raw_counts) * s\n    else:\n        raise f\"Metric {metric} does not exist. Provide either 'std' or 'cv' for standard deviation or coefficient of variation.\"\n    return uncertainty\n</code></pre>"},{"location":"reference/#crnpy.data.uncertainty_vwc","title":"<code>uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808, a1=0.372, a2=0.115)</code>","text":"<p>Function to estimate the uncertainty propagated to volumetric water content.</p> <p>The uncertainty of the volumetric water content is estimated by propagating the uncertainty of the raw counts. Following Eq. 10 in Jakobi et al. (2020), the uncertainty of the volumetric water content can be expressed as: $$ \\sigma_{\\theta_g}(N) = \\sigma_N \\frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \\sqrt{(N_{cor} - a_1 N_0)^4 + 8 \\sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \\sigma_N^4} $$</p> <p>Parameters:</p> Name Type Description Default <code>raw_counts</code> <code>array</code> <p>Raw neutron counts.</p> required <code>N0</code> <code>float</code> <p>Calibration parameter N0.</p> required <code>bulk_density</code> <code>float</code> <p>Bulk density in g cm-3.</p> required <code>fp</code> <code>float</code> <p>Pressure correction factor.</p> <code>1</code> <code>fw</code> <code>float</code> <p>Humidity correction factor.</p> <code>1</code> <code>fi</code> <code>float</code> <p>Incoming neutron flux correction factor.</p> <code>1</code> <p>Returns:</p> Name Type Description <code>sigma_VWC</code> <code>float</code> <p>Uncertainty in terms of volumetric water content.</p> References <p>Jakobi J, Huisman JA, Schr\u00f6n M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010</p> Source code in <code>crnpy/crnpy.py</code> <pre><code>def uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808, a1=0.372, a2=0.115):\n    r\"\"\"Function to estimate the uncertainty propagated to volumetric water content.\n\n    The uncertainty of the volumetric water content is estimated by propagating the uncertainty of the raw counts.\n    Following Eq. 10 in Jakobi et al. (2020), the uncertainty of the volumetric water content can be expressed as:\n    $$\n    \\sigma_{\\theta_g}(N) = \\sigma_N \\frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \\sqrt{(N_{cor} - a_1 N_0)^4 + 8 \\sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \\sigma_N^4}\n    $$\n\n    Args:\n        raw_counts (array): Raw neutron counts.\n        N0 (float): Calibration parameter N0.\n        bulk_density (float): Bulk density in g cm-3.\n        fp (float): Pressure correction factor.\n        fw (float): Humidity correction factor.\n        fi (float): Incoming neutron flux correction factor.\n\n    Returns:\n        sigma_VWC (float): Uncertainty in terms of volumetric water content.\n\n    References:\n        Jakobi J, Huisman JA, Schr\u00f6n M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With\n        Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010\n    \"\"\"\n\n    Ncorr = raw_counts * fw / (fp * fi)\n    sigma_N = uncertainty_counts(raw_counts, metric=\"std\", fp=fp, fw=fw, fi=fi)\n    sigma_GWC = sigma_N * ((a0 * N0) / ((Ncorr - a1 * N0) ** 4)) * np.sqrt(\n        (Ncorr - a1 * N0) ** 4 + 8 * sigma_N ** 2 * (Ncorr - a1 * N0) ** 2 + 15 * sigma_N ** 4)\n    sigma_VWC = sigma_GWC * bulk_density\n\n    return sigma_VWC\n</code></pre>"},{"location":"sensor_description/","title":"Sensor description","text":"<p>The cosmic-ray neutron sensing technology is an innovative and non-invasive method for monitoring soil moisture. The sensing principle is based on the detection of neutrons that are generated when cosmic rays interact with atoms in the Earth's atmosphere. These neutrons, in turn, interact with hydrogen pools in the land surface, and thus, the intensity of neutrons detected near the ground surface is inversely related to the amunt of water in land biomass and the top layers of the soil. Typically, cosmic-ray neutron probes have a large sensing footprint, covering an area of approximately 12 hectares (about 30 acres) and can measure soil moisture up to a depth of 70 cm (about 28 inches), although typically most field applications span a depth between 5 and 40 cm. This makes the technology particularly useful for capturing spatially-averaged soil moisture over large areas, bridging the gap between point measurements and remote sensing. In hydrology, cosmic-ray neutron sensors are used for watershed studies, understanding water balance, and improving hydrological models. In agriculture, they are employed for irrigation management, crop yield prediction, and understanding the effects of soil moisture on plant growth. The technology's ability to provide continuous, real-time data without disturbing the soil makes it a valuable tool for both hydrological and agricultural research.</p> <p> Figure 1. A) Stationary detector manufactured by Radiation Detection Technologies, Inc. (Manhattan, KS). B) Stationary detector manufactured by Hydroinnova, Inc. (Albuquerque, NM). C) Roving detector manufactured by Hydroinnova, Inc. (Albuquerque, NM).</p> <p> Figure 2. Illustration showing the approximate sensing footprint of stationary detectors.</p>"},{"location":"examples/calibration/calibration/","title":"Device-specific field calibration","text":"In\u00a0[1]: Copied! <pre># Importing required libraries\nimport crnpy\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.optimize import root\n</pre> # Importing required libraries import crnpy  import numpy as np import pandas as pd from scipy.optimize import root  In\u00a0[2]: Copied! <pre># Load the soil samples data and the CRNP dataset using pandas\nurl_soil_samples = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/soil_data.csv\"\ndf_soil = pd.read_csv(url_soil_samples)\ndf_soil.head(3)\n</pre> # Load the soil samples data and the CRNP dataset using pandas url_soil_samples = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/soil_data.csv\" df_soil = pd.read_csv(url_soil_samples) df_soil.head(3)  Out[2]: field date core_number distance_from_station latitude longitude top_depth bottom_depth core_diameter wet_mass_with_bag ... can_number mass_empty_can wet_mass_with_can dry_mass_with_can mass_water theta_g volume bulk_density theta_v Observation 0 Flickner 22-Oct 1 5 N38.23459 W97.57101 0 5 30.49 45.31 ... 1 52.10 92.03 85.31 6.72 0.202 36.514864 0.909 0.184403 NaN 1 Flickner 22-Oct 1 5 N38.23459 W97.57101 5 10 30.49 69.53 ... 2 51.85 115.97 103.85 12.12 0.233 36.514864 1.424 0.332585 NaN 2 Flickner 22-Oct 1 5 N38.23459 W97.57101 10 25 30.49 214.90 ... 3 51.56 260.97 219.77 41.20 0.245 109.544592 1.536 0.376856 NaN <p>3 rows \u00d7 21 columns</p> In\u00a0[3]: Copied! <pre># Define start and end of field survey calibration\ncalibration_start = pd.to_datetime(\"2021-10-22 08:00\")\ncalibration_end = pd.to_datetime(\"2021-10-22 16:00\")\n</pre> # Define start and end of field survey calibration calibration_start = pd.to_datetime(\"2021-10-22 08:00\") calibration_end = pd.to_datetime(\"2021-10-22 16:00\")  In\u00a0[4]: Copied! <pre># Load the station data\nurl_station_data = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/station_data.csv\"\ndf_station = pd.read_csv(url_station_data, skiprows=[0,2,3])\n\n#  Parse dates (you can also use the option `parse_dates=['TIMESTAMP]` in pd.read_csv()\ndf_station['timestamp'] = pd.to_datetime(df_station['TIMESTAMP'], format='%Y-%m-%d %H:%M:%S')\n\ndf_station.head(3)\n</pre> # Load the station data url_station_data = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/station_data.csv\" df_station = pd.read_csv(url_station_data, skiprows=[0,2,3])  #  Parse dates (you can also use the option `parse_dates=['TIMESTAMP]` in pd.read_csv() df_station['timestamp'] = pd.to_datetime(df_station['TIMESTAMP'], format='%Y-%m-%d %H:%M:%S')  df_station.head(3)  Out[4]: TIMESTAMP RECORD station farm field latitude longitude altitude battery_voltage_Min PTemp_Avg ... wind_speed_gust_Max air_temperature_Avg vapor_pressure_Avg barometric_pressure_Avg relative_humidity_Avg humidity_sensor_temperature_Avg tilt_north_south_Avg tilt_west_east_Avg NDVI_Avg timestamp 0 2021-09-22 12:00:00 0 KS003 Flickner Rainfed South 38.23461 -97.57095 455 13.52 29.04 ... 4.20 22.30 9.20 973 41.30 26.4 -1.000 1.000 0.311 2021-09-22 12:00:00 1 2021-09-22 13:00:00 1 KS003 Flickner Rainfed South 38.23461 -97.57095 455 13.53 29.98 ... 9.02 22.90 9.08 972 32.63 26.8 -0.975 0.950 0.308 2021-09-22 13:00:00 2 2021-09-22 14:00:00 2 KS003 Flickner Rainfed South 38.23461 -97.57095 455 13.53 30.43 ... 5.54 23.38 8.68 971 30.25 27.2 -0.775 0.625 0.31 2021-09-22 14:00:00 <p>3 rows \u00d7 28 columns</p> In\u00a0[5]: Copied! <pre># Define date in which the probe was deployed in the field (i.e., first record)\ndeployment_date = df_station['timestamp'].iloc[0]\n\n# Filter station data from the first record to the end of the field survey calibration\n# This is important since we are considering the incoming flux on the first day as the reference value\nidx_period = (df_station['timestamp'] &gt;= deployment_date) &amp; (df_station['timestamp'] &lt;= calibration_end)\ndf_station = df_station[idx_period]\ndf_station.head(3)\n</pre> # Define date in which the probe was deployed in the field (i.e., first record) deployment_date = df_station['timestamp'].iloc[0]  # Filter station data from the first record to the end of the field survey calibration # This is important since we are considering the incoming flux on the first day as the reference value idx_period = (df_station['timestamp'] &gt;= deployment_date) &amp; (df_station['timestamp'] &lt;= calibration_end) df_station = df_station[idx_period] df_station.head(3)  Out[5]: TIMESTAMP RECORD station farm field latitude longitude altitude battery_voltage_Min PTemp_Avg ... wind_speed_gust_Max air_temperature_Avg vapor_pressure_Avg barometric_pressure_Avg relative_humidity_Avg humidity_sensor_temperature_Avg tilt_north_south_Avg tilt_west_east_Avg NDVI_Avg timestamp 0 2021-09-22 12:00:00 0 KS003 Flickner Rainfed South 38.23461 -97.57095 455 13.52 29.04 ... 4.20 22.30 9.20 973 41.30 26.4 -1.000 1.000 0.311 2021-09-22 12:00:00 1 2021-09-22 13:00:00 1 KS003 Flickner Rainfed South 38.23461 -97.57095 455 13.53 29.98 ... 9.02 22.90 9.08 972 32.63 26.8 -0.975 0.950 0.308 2021-09-22 13:00:00 2 2021-09-22 14:00:00 2 KS003 Flickner Rainfed South 38.23461 -97.57095 455 13.53 30.43 ... 5.54 23.38 8.68 971 30.25 27.2 -0.775 0.625 0.31 2021-09-22 14:00:00 <p>3 rows \u00d7 28 columns</p> <p>This step is useful to trim large timeseries. For instance, the station data in our case extends until <code>2022-07-11 09:45:00</code>, but the field calibration was conducted in <code>2021-10-22 16:00</code>. Since all the station observation after the date of the field calibration are not relevent, we decided to only work with the data that we need from <code>2021-09-22 12:00:00</code> until <code>2021-10-22 16:00</code>. This could help getting data of incoming neutron flux from a single reference neutron monitor. So, if you are running this code shortly after the calibration field survey, then there is no need to filter station data.</p> In\u00a0[6]: Copied! <pre># Compute total neutron counts by adding the counts from both probe detectors\ndf_station['total_raw_counts'] = crnpy.total_raw_counts(df_station[['counts_1_Tot','counts_2_Tot']],\n                                                    nan_strategy='average')\n</pre> # Compute total neutron counts by adding the counts from both probe detectors df_station['total_raw_counts'] = crnpy.total_raw_counts(df_station[['counts_1_Tot','counts_2_Tot']],                                                     nan_strategy='average')  In\u00a0[7]: Copied! <pre># Atmospheric corrections\n\n# Fill NaN values in atmospheric data\ndf_station[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']] = df_station[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')\n\n# Calculate absolute humidity\ndf_station['abs_humidity'] = crnpy.abs_humidity(df_station['relative_humidity_Avg'], df_station['air_temperature_Avg'])\n\n# Compute correction factor for atmospheric pressure\n# Reference atmospheric pressure for the location is 976 Pa\n# Using an atmospheric attentuation coefficient of 130 g/cm2\ndf_station['fp'] = crnpy.correction_pressure(pressure=df_station['barometric_pressure_Avg'],\n                                             Pref=976, L=130)\n\n# Compute correction factor for air humidity\ndf_station['fw'] = crnpy.correction_humidity(abs_humidity=df_station['abs_humidity'],\n                                             Aref=0)\n</pre> # Atmospheric corrections  # Fill NaN values in atmospheric data df_station[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']] = df_station[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')  # Calculate absolute humidity df_station['abs_humidity'] = crnpy.abs_humidity(df_station['relative_humidity_Avg'], df_station['air_temperature_Avg'])  # Compute correction factor for atmospheric pressure # Reference atmospheric pressure for the location is 976 Pa # Using an atmospheric attentuation coefficient of 130 g/cm2 df_station['fp'] = crnpy.correction_pressure(pressure=df_station['barometric_pressure_Avg'],                                              Pref=976, L=130)  # Compute correction factor for air humidity df_station['fw'] = crnpy.correction_humidity(abs_humidity=df_station['abs_humidity'],                                              Aref=0)  In\u00a0[8]: Copied! <pre># Find the cutoff rigidity for the location\ncutoff_rigidity = crnpy.cutoff_rigidity(39.1, -96.6)\n\n# Filtering the time window from experiment setup to the end of the calibration\ncrnpy.find_neutron_monitor(cutoff_rigidity,\n                           start_date=df_station['timestamp'].iloc[0],\n                           end_date=df_station['timestamp'].iloc[-1])\n</pre> # Find the cutoff rigidity for the location cutoff_rigidity = crnpy.cutoff_rigidity(39.1, -96.6)  # Filtering the time window from experiment setup to the end of the calibration crnpy.find_neutron_monitor(cutoff_rigidity,                            start_date=df_station['timestamp'].iloc[0],                            end_date=df_station['timestamp'].iloc[-1])  <pre>\nSelect a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\n\nYour cutoff rigidity is 2.87 GV\n     STID     NAME     R  Altitude_m  Period available\n13   DRBS  Dourbes  3.18         225              True\n40   NEWK   Newark  2.40          50              True\n28  KIEL2   KielRT  2.36          54              True\n</pre> Out[8]: STID NAME R Altitude_m Period available 13 DRBS Dourbes 3.18 225 True 40 NEWK Newark 2.40 50 True 28 KIEL2 KielRT 2.36 54 True In\u00a0[9]: Copied! <pre># Incoming neutron flux correction\n\n# Download data for the reference neutron monitor and add it to the DataFrame\nnmdb = crnpy.get_incoming_neutron_flux(deployment_date,\n                                        calibration_end,\n                                        station=\"DRBS\",\n                                        utc_offset=-6)\n\n# Interpolate incoming neutron flux to match the timestamps in our station data\ndf_station['incoming_flux'] = crnpy.interpolate_incoming_flux(nmdb['timestamp'], nmdb['counts'], df_station['timestamp'])\n\n# Compute correction factor for incoming neutron flux\ndf_station['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df_station['incoming_flux'],\n                                                  incoming_Ref=df_station['incoming_flux'].iloc[0])\n</pre> # Incoming neutron flux correction  # Download data for the reference neutron monitor and add it to the DataFrame nmdb = crnpy.get_incoming_neutron_flux(deployment_date,                                         calibration_end,                                         station=\"DRBS\",                                         utc_offset=-6)  # Interpolate incoming neutron flux to match the timestamps in our station data df_station['incoming_flux'] = crnpy.interpolate_incoming_flux(nmdb['timestamp'], nmdb['counts'], df_station['timestamp'])  # Compute correction factor for incoming neutron flux df_station['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df_station['incoming_flux'],                                                   incoming_Ref=df_station['incoming_flux'].iloc[0])   In\u00a0[10]: Copied! <pre># Apply correction factors\ndf_station['total_corrected_neutrons'] = df_station['total_raw_counts'] * df_station['fw'] / (df_station['fp'] * df_station['fi'])\n</pre> # Apply correction factors df_station['total_corrected_neutrons'] = df_station['total_raw_counts'] * df_station['fw'] / (df_station['fp'] * df_station['fi'])  In\u00a0[11]: Copied! <pre># Compute the weights of each sample for the field average\nnrad_weights = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'], df_soil['distance_from_station'], (df_soil['bottom_depth']+df_soil['top_depth'])/2, rhob=df_soil['bulk_density'].mean())\n\n# Apply distance weights to volumetric water content and bulk density\nfield_theta_v = np.sum(df_soil['theta_v']*nrad_weights)\nfield_bulk_density = np.sum(df_soil['bulk_density']*nrad_weights)\n</pre> # Compute the weights of each sample for the field average nrad_weights = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'], df_soil['distance_from_station'], (df_soil['bottom_depth']+df_soil['top_depth'])/2, rhob=df_soil['bulk_density'].mean())  # Apply distance weights to volumetric water content and bulk density field_theta_v = np.sum(df_soil['theta_v']*nrad_weights) field_bulk_density = np.sum(df_soil['bulk_density']*nrad_weights)  In\u00a0[12]: Copied! <pre># Determine the mean corrected counts during the calibration survey\nidx_cal_period = (df_station['timestamp'] &gt;= calibration_start) &amp; (df_station['timestamp'] &lt;= calibration_end)\nmean_cal_counts = df_station.loc[idx_cal_period, 'total_corrected_neutrons'].mean()\n\nprint(f\"Mean volumetric Water content during calibration survey: {round(field_theta_v,3)}\")\nprint(f\"Mean corrected counts during calibration: {round(mean_cal_counts)} counts\")\n</pre> # Determine the mean corrected counts during the calibration survey idx_cal_period = (df_station['timestamp'] &gt;= calibration_start) &amp; (df_station['timestamp'] &lt;= calibration_end) mean_cal_counts = df_station.loc[idx_cal_period, 'total_corrected_neutrons'].mean()  print(f\"Mean volumetric Water content during calibration survey: {round(field_theta_v,3)}\") print(f\"Mean corrected counts during calibration: {round(mean_cal_counts)} counts\")  <pre>Mean volumetric Water content during calibration survey: 0.263\nMean corrected counts during calibration: 1542 counts\n</pre> In\u00a0[13]: Copied! <pre># Define the function for which we want to find the roots\nVWC_func = lambda N0 : crnpy.counts_to_vwc(mean_cal_counts, N0, bulk_density=field_bulk_density, Wlat=0.03, Wsoc=0.01) - field_theta_v\n\n# Make an initial guess for N0\nN0_initial_guess = 1000\n\n# Find the root\nsol = int(root(VWC_func, N0_initial_guess).x)\n\n# Print the solution\nprint(f\"The solved value for N0 is: {sol}\")\n</pre> # Define the function for which we want to find the roots VWC_func = lambda N0 : crnpy.counts_to_vwc(mean_cal_counts, N0, bulk_density=field_bulk_density, Wlat=0.03, Wsoc=0.01) - field_theta_v  # Make an initial guess for N0 N0_initial_guess = 1000  # Find the root sol = int(root(VWC_func, N0_initial_guess).x)  # Print the solution print(f\"The solved value for N0 is: {sol}\")  <pre>The solved value for N0 is: 2644\n</pre>"},{"location":"examples/calibration/calibration/#device-specific-field-calibration","title":"Device-specific field calibration\u00b6","text":"<p>The calibration of a cosmic-ray neutron probe (CRNP) is an essential step to ensure accurate soil moisture measurements. The CRNP operates by counting fast neutrons produced from cosmic rays, which are predominantly moderated by water molecules in the soil. The parameter $N_0$ is typically considered a device-specific constant that represents the neutron count rate in the absence of soil moisture conditions.</p> <p>$\\theta(N) =\\frac{a_0}{(\\frac{N}{N_0}) - a_1} - a_2 $ (Desilets et al., 2010).</p> <p>For the calibration of the stationary detector, a total of 14 undisturbed soil cores were collected at radial distances of 5, 50, and 100 m from the detector. In this example each soil sample was split into four depth segments: 0-5 cm, 5-10 cm, 10-25 cm, and 25-40 cm. Soil samples were processed and soil moisture was determined using the thermo-gravimetric method.</p> <p>Figure 1. Horizontal layout and vertical layout used in this particular example calibration, it can be customized by the user depending on their needs.</p> <p>Download the following template  for collecting your own calibration soil samples:</p>"},{"location":"examples/calibration/calibration/#read-calibration-field-survey-data","title":"Read calibration field survey data\u00b6","text":"<p>For each sample it is required to know the bulk density ($\\rho_\\beta$) and the volumetric water content ($\\theta_v$). See the details of the calculation used in the filled example.</p>"},{"location":"examples/calibration/calibration/#read-data-from-crnp","title":"Read data from CRNP\u00b6","text":""},{"location":"examples/calibration/calibration/#correct-neutron-counts","title":"Correct neutron counts\u00b6","text":""},{"location":"examples/calibration/calibration/#determine-field-average-soil-moisture-and-bulk-density","title":"Determine field-average soil moisture and bulk density\u00b6","text":"<p>Using the function <code>nrad_weight()</code> the weights corresponding to each soil sample will be computed considering air-humidity, sample depth, and distance from station and bulk density.</p>"},{"location":"examples/calibration/calibration/#solving-for-n_0","title":"Solving for $N_0$\u00b6","text":"<p>Previous steps estimated the field volumetric water content of <code>0.263</code> and an average neutron count of <code>1542</code>. Using <code>scipy.optimize.root()</code> $N_0$ is estimated given the observed value of $\\theta_v$ and neutron counts.</p>"},{"location":"examples/calibration/calibration/#references","title":"References:\u00b6","text":"<p>Desilets, D., Zreda, M., &amp; Ferr\u00e9, T. P. (2010). Nature's neutron probe: Land surface hydrology at an elusive scale with cosmic rays. Water Resources Research, 46(11).</p> <p>Dong, J., &amp; Ochsner, T. E. (2018). Soil texture often exerts a stronger influence than precipitation on mesoscale soil moisture patterns. Water Resources Research, 54(3), 2199-2211.</p> <p>Patrignani, A., Ochsner, T. E., Montag, B., &amp; Bellinger, S. (2021). A novel lithium foil cosmic-ray neutron detector for measuring field-scale soil moisture. Frontiers in Water, 3, 673185.</p>"},{"location":"examples/rover/Hydroinnova_rover_example/","title":"Processing and analyzing data from a roving detector","text":"In\u00a0[1]: Copied! <pre># Import modules\nimport crnpy\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n</pre> # Import modules import crnpy  import numpy as np import pandas as pd import matplotlib.pyplot as plt  <p>Load the data stored in the .csv file.</p> In\u00a0[2]: Copied! <pre># Load sample dataset\nfilepath = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/rover/gypsum_transect_01_may_2018.KSU\"\ncol_names = 'RecordNum,Date Time(UTC),barometric_pressure_Avg,P4_mb,P1_mb,T1_C,RH1,air_temperature_Avg,relative_humidity_Avg,Vbat,N1Cts,N2Cts,N3Cts,N4Cts,N5Cts,N6Cts,N7Cts,N8Cts,N1ETsec,N3ETsec,N5ETsec,N7ETsec,N1T(C),N1RH,N5T(C),N5RH,GpsUTC,LatDec,LongDec,Alt,Qual,NumSats,HDOP,Speed_kmh,COG,SpeedQuality,strDate'.split(',')\n\ndf = pd.read_csv(filepath, skiprows=20, names=col_names)\ndf['LongDec'] = df['LongDec'] * -1 # Raw data is in absolute values\n\n# Parse timestamps and set as index\ndf['timestamp'] = pd.to_datetime(df['Date Time(UTC)'])\n\n# Remove rows with missing coordinates\ndf['LatDec'].replace(0.0, np.nan, inplace=True)\ndf['LongDec'].replace(0.0, np.nan, inplace=True)\ndf.dropna(axis=0, subset=['LatDec','LongDec'], inplace=True)\ndf.reset_index(drop=True, inplace=True)\n\n# Display a few rows to visualize dataset\ndf.head(3)\n</pre> # Load sample dataset filepath = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/rover/gypsum_transect_01_may_2018.KSU\" col_names = 'RecordNum,Date Time(UTC),barometric_pressure_Avg,P4_mb,P1_mb,T1_C,RH1,air_temperature_Avg,relative_humidity_Avg,Vbat,N1Cts,N2Cts,N3Cts,N4Cts,N5Cts,N6Cts,N7Cts,N8Cts,N1ETsec,N3ETsec,N5ETsec,N7ETsec,N1T(C),N1RH,N5T(C),N5RH,GpsUTC,LatDec,LongDec,Alt,Qual,NumSats,HDOP,Speed_kmh,COG,SpeedQuality,strDate'.split(',')  df = pd.read_csv(filepath, skiprows=20, names=col_names) df['LongDec'] = df['LongDec'] * -1 # Raw data is in absolute values  # Parse timestamps and set as index df['timestamp'] = pd.to_datetime(df['Date Time(UTC)'])  # Remove rows with missing coordinates df['LatDec'].replace(0.0, np.nan, inplace=True) df['LongDec'].replace(0.0, np.nan, inplace=True) df.dropna(axis=0, subset=['LatDec','LongDec'], inplace=True) df.reset_index(drop=True, inplace=True)  # Display a few rows to visualize dataset df.head(3)  Out[2]: RecordNum Date Time(UTC) barometric_pressure_Avg P4_mb P1_mb T1_C RH1 air_temperature_Avg relative_humidity_Avg Vbat ... LongDec Alt Qual NumSats HDOP Speed_kmh COG SpeedQuality strDate timestamp 0 2 2018/05/01 14:15:00 962.95 962.8 961.4 23.2 35.4 20.9 72.7 13.574 ... -97.37195 393.2 2.0 10 0.8 0.00 270.7 A 10518.0 2018-05-01 14:15:00 1 3 2018/05/01 14:16:00 962.88 962.7 961.3 23.3 35.5 21.0 72.7 13.417 ... -97.37197 387.2 2.0 11 0.8 0.00 270.7 A 10518.0 2018-05-01 14:16:00 2 4 2018/05/01 14:17:00 962.64 962.5 961.1 23.4 35.4 21.2 72.2 13.282 ... -97.37199 388.2 1.0 11 0.8 3.89 356.3 A 10518.0 2018-05-01 14:17:00 <p>3 rows \u00d7 38 columns</p> <p>Next, convert the latitude and longitude to UTM coordinates (x and y). A scatter plot is created to visualize the survey points.</p> In\u00a0[3]: Copied! <pre># Convert Lat and Lon to X and Y\ndf['x'],df['y'] = crnpy.latlon_to_utm(df['LatDec'], df['LongDec'], 14, missing_values=0.0)\n\n# Create figure of survey points\nplt.figure(figsize = (5,5))\nplt.scatter(df['x'], df['y'], marker='o', edgecolor='k', facecolor='w', label = \"Observation end\")\n\n# Estimate the center of the observation, assuming each timestamp is observation end time.\ndf['x'],df['y'] = crnpy.rover_centered_coordinates(df['x'], df['y']) \nplt.scatter(df['x'], df['y'], marker='+', label=\"Observation center\")\n\nplt.ticklabel_format(scilimits = (-5,5))\nplt.xlabel('Easting')\nplt.ylabel('Northing')\nplt.legend(loc=[1.1,.8])\nplt.show()\n</pre> # Convert Lat and Lon to X and Y df['x'],df['y'] = crnpy.latlon_to_utm(df['LatDec'], df['LongDec'], 14, missing_values=0.0)  # Create figure of survey points plt.figure(figsize = (5,5)) plt.scatter(df['x'], df['y'], marker='o', edgecolor='k', facecolor='w', label = \"Observation end\")  # Estimate the center of the observation, assuming each timestamp is observation end time. df['x'],df['y'] = crnpy.rover_centered_coordinates(df['x'], df['y'])  plt.scatter(df['x'], df['y'], marker='+', label=\"Observation center\")  plt.ticklabel_format(scilimits = (-5,5)) plt.xlabel('Easting') plt.ylabel('Northing') plt.legend(loc=[1.1,.8]) plt.show()  <p>The counts are normalized to counts per minute in case some observations covered a different timespan and total neutron counts are computed.</p> In\u00a0[4]: Copied! <pre># Define columns names\ncounts_colums = ['N1Cts', 'N2Cts', 'N3Cts','N4Cts', 'N5Cts', 'N6Cts', 'N7Cts', 'N8Cts']\ncont_times_col = ['N1ETsec', 'N1ETsec', 'N3ETsec','N3ETsec', 'N5ETsec', 'N5ETsec', 'N7ETsec', 'N7ETsec']\n\n# Compute total neutron counts\ndf['total_raw_counts'] = crnpy.total_raw_counts(df[counts_colums])\n</pre> # Define columns names counts_colums = ['N1Cts', 'N2Cts', 'N3Cts','N4Cts', 'N5Cts', 'N6Cts', 'N7Cts', 'N8Cts'] cont_times_col = ['N1ETsec', 'N1ETsec', 'N3ETsec','N3ETsec', 'N5ETsec', 'N5ETsec', 'N7ETsec', 'N7ETsec']  # Compute total neutron counts df['total_raw_counts'] = crnpy.total_raw_counts(df[counts_colums])  In\u00a0[5]: Copied! <pre># Define transect start and end dates\nstart_date = df.iloc[0]['timestamp']\nend_date = df.iloc[-1]['timestamp']\n\n#Find stations with cutoff rigidity similar to estimated by lat,lon\ncrnpy.find_neutron_monitor(crnpy.cutoff_rigidity(df['LatDec'][0], df['LongDec'][0]), \n                             start_date=start_date, end_date=end_date)\n</pre> # Define transect start and end dates start_date = df.iloc[0]['timestamp'] end_date = df.iloc[-1]['timestamp']  #Find stations with cutoff rigidity similar to estimated by lat,lon crnpy.find_neutron_monitor(crnpy.cutoff_rigidity(df['LatDec'][0], df['LongDec'][0]),                               start_date=start_date, end_date=end_date)  <pre>\nSelect a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\n\nYour cutoff rigidity is 2.99 GV\n    STID                          NAME     R  Altitude_m  Period available\n13  DRBS                       Dourbes  3.18         225              True\n31  MCRL  Mobile Cosmic Ray Laboratory  2.46         200              True\n33  MOSC                        Moscow  2.43         200              True\n40  NEWK                        Newark  2.40          50              True\n20  IRK3                     Irkustk 3  3.64        3000              True\n21  IRKT                       Irkustk  3.64         435              True\n</pre> Out[5]: STID NAME R Altitude_m Period available 13 DRBS Dourbes 3.18 225 True 31 MCRL Mobile Cosmic Ray Laboratory 2.46 200 True 33 MOSC Moscow 2.43 200 True 40 NEWK Newark 2.40 50 True 20 IRK3 Irkustk 3 3.64 3000 True 21 IRKT Irkustk 3.64 435 True In\u00a0[6]: Copied! <pre># Download incoming neutron flux data from the Neutron Monitor Database (NMDB).\n# Use utc_offset for Central Standard Time.\nnmdb = crnpy.get_incoming_neutron_flux(start_date, end_date, station=\"NEWK\", utc_offset=-6)\n</pre> # Download incoming neutron flux data from the Neutron Monitor Database (NMDB). # Use utc_offset for Central Standard Time. nmdb = crnpy.get_incoming_neutron_flux(start_date, end_date, station=\"NEWK\", utc_offset=-6)  In\u00a0[7]: Copied! <pre># Interpolate incoming neutron flux to match the timestamps in our rover data\ndf['incoming_flux'] = crnpy.interpolate_incoming_flux(nmdb['timestamp'], nmdb['counts'], df['timestamp'])\n</pre> # Interpolate incoming neutron flux to match the timestamps in our rover data df['incoming_flux'] = crnpy.interpolate_incoming_flux(nmdb['timestamp'], nmdb['counts'], df['timestamp'])  In\u00a0[8]: Copied! <pre># Compute correction factor for incoming neutron flux\ndf['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df['incoming_flux'],\n                                          incoming_Ref=df['incoming_flux'].iloc[0])\n</pre> # Compute correction factor for incoming neutron flux df['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df['incoming_flux'],                                           incoming_Ref=df['incoming_flux'].iloc[0])  In\u00a0[9]: Copied! <pre># Fill NaN values in atmospheric data\ndf[['barometric_pressure_Avg', 'relative_humidity_Avg', 'air_temperature_Avg']] = df[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')\n\n# Compute the pressure correction factor\ndf['fp'] = crnpy.correction_pressure(pressure=df['barometric_pressure_Avg'], Pref=df['barometric_pressure_Avg'].mean(), L=130)\n\n# Estimate the absolute humidity and compute the vapor pressure correction factor\ndf['abs_humidity'] = crnpy.abs_humidity(df['relative_humidity_Avg'], df['air_temperature_Avg'])\ndf['fw'] = crnpy.correction_humidity(df['abs_humidity'], Aref=0)\n</pre> # Fill NaN values in atmospheric data df[['barometric_pressure_Avg', 'relative_humidity_Avg', 'air_temperature_Avg']] = df[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')  # Compute the pressure correction factor df['fp'] = crnpy.correction_pressure(pressure=df['barometric_pressure_Avg'], Pref=df['barometric_pressure_Avg'].mean(), L=130)  # Estimate the absolute humidity and compute the vapor pressure correction factor df['abs_humidity'] = crnpy.abs_humidity(df['relative_humidity_Avg'], df['air_temperature_Avg']) df['fw'] = crnpy.correction_humidity(df['abs_humidity'], Aref=0)  In\u00a0[10]: Copied! <pre># Apply correction factors\ndf['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])\n</pre> # Apply correction factors df['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])  <p>The corrected counts are smoothed using a 2D smoothing function. The smoothed counts are then converted to volumetric water content (VWC) using the counts_to_vwc function.</p> In\u00a0[11]: Copied! <pre># Smooth variable\ndf['corrected_neutrons_smoothed'] = crnpy.spatial_average(df['x'],\n                                        df['y'],\n                                        df['total_corrected_neutrons'],\n                                        buffer= 800, method='median', rnd=True)\n\n# Estimate lattice water based on texture\nlattice_water = crnpy.lattice_water(clay_content=0.35)\n\n# Estimate Soil Volumetric Water Content\ndf['VWC'] = crnpy.counts_to_vwc(df['corrected_neutrons_smoothed'], N0=550, bulk_density=1.3, Wlat=lattice_water, Wsoc=0.01)\n\n# Drop VWC NaN values before interpolating values\ndf = df.dropna(subset = ['VWC'])\n\n# Interpolate variable using IDW (https://en.wikipedia.org/wiki/Inverse_distance_weighting)\nX_pred, Y_pred, Z_pred = crnpy.interpolate_2d(df['x'],\n                                        df['y'],\n                                        df['VWC'],\n                                        dx=250, dy=250, method='idw')\n</pre> # Smooth variable df['corrected_neutrons_smoothed'] = crnpy.spatial_average(df['x'],                                         df['y'],                                         df['total_corrected_neutrons'],                                         buffer= 800, method='median', rnd=True)  # Estimate lattice water based on texture lattice_water = crnpy.lattice_water(clay_content=0.35)  # Estimate Soil Volumetric Water Content df['VWC'] = crnpy.counts_to_vwc(df['corrected_neutrons_smoothed'], N0=550, bulk_density=1.3, Wlat=lattice_water, Wsoc=0.01)  # Drop VWC NaN values before interpolating values df = df.dropna(subset = ['VWC'])  # Interpolate variable using IDW (https://en.wikipedia.org/wiki/Inverse_distance_weighting) X_pred, Y_pred, Z_pred = crnpy.interpolate_2d(df['x'],                                         df['y'],                                         df['VWC'],                                         dx=250, dy=250, method='idw')  <p>A gridded map of the Volumetric Water Content (VWC) is created using the interpolated x, y, and VWC values.</p> In\u00a0[12]: Copied! <pre># Show interpolated grid\ncmap = 'RdYlBu'\n\nplt.figure(figsize=(6,6))\nplt.title('Gridded map')\nplt.pcolormesh(X_pred, Y_pred, Z_pred, cmap=cmap)\nplt.colorbar(label=r\"Volumetric Water Content $(cm^3 \\cdot cm^{-3})$\", location='right')\nplt.scatter(df['x'], df['y'], marker='o', edgecolor='k', facecolor='w')\nplt.ticklabel_format(scilimits=(-5,5))\nplt.xlabel('Easting', size=14)\nplt.ylabel('Northing', size=14)\nplt.show()\n</pre> # Show interpolated grid cmap = 'RdYlBu'  plt.figure(figsize=(6,6)) plt.title('Gridded map') plt.pcolormesh(X_pred, Y_pred, Z_pred, cmap=cmap) plt.colorbar(label=r\"Volumetric Water Content $(cm^3 \\cdot cm^{-3})$\", location='right') plt.scatter(df['x'], df['y'], marker='o', edgecolor='k', facecolor='w') plt.ticklabel_format(scilimits=(-5,5)) plt.xlabel('Easting', size=14) plt.ylabel('Northing', size=14) plt.show()  <p>Finally, a contour map of the VWC is created and displayed.</p> In\u00a0[13]: Copied! <pre># Show contour map\ncmap = 'RdYlBu'\n\nplt.figure(figsize=(7,6))\nplt.title('Contours')\nplt.contourf(X_pred, Y_pred, Z_pred, cmap=cmap)\nplt.ticklabel_format(scilimits=(-5,5))\nplt.colorbar(label=r\"Volumetric Water Content $(cm^3 \\cdot cm^{-3})$\", location='right')\nplt.scatter(df['x'], df['y'], marker='o', edgecolor='k', facecolor='w')\nplt.show()\n</pre> # Show contour map cmap = 'RdYlBu'  plt.figure(figsize=(7,6)) plt.title('Contours') plt.contourf(X_pred, Y_pred, Z_pred, cmap=cmap) plt.ticklabel_format(scilimits=(-5,5)) plt.colorbar(label=r\"Volumetric Water Content $(cm^3 \\cdot cm^{-3})$\", location='right') plt.scatter(df['x'], df['y'], marker='o', edgecolor='k', facecolor='w') plt.show()"},{"location":"examples/rover/Hydroinnova_rover_example/#processing-and-analyzing-data-from-a-roving-detector","title":"Processing and analyzing data from a roving detector\u00b6","text":"<p>This tutorial demonstrates how to process and analyze data from a rover using the Cosmic Ray Neutron Python (CRNPy) library. The steps include loading the data, converting geographical coordinates to Cartesian coordinates, normalizing neutron counts, correcting for atmospheric effects, and visualizing the results.</p> <p>First import the required packages.</p>"},{"location":"examples/rover/Hydroinnova_rover_example/#neutron-count-correction","title":"Neutron count correction\u00b6","text":""},{"location":"examples/rover/Hydroinnova_rover_example/#incommng-neutron-flux","title":"Incommng neutron flux\u00b6","text":"<p>Find stations with a cutoff rigidity similar to the estimated value based on the latitude and longitude, ensuring that the reference station is under a similar earth electromagnetic field. Note that the station is hardcoded in the second line as <code>station=\"NEWK\"</code>. The user is required to manually define this after considering the potential options suggested. See <code>get_incoming_neutron_flux()</code>, <code>find_neutron_monitor()</code> and <code>correction_incoming_flux()</code>.</p>"},{"location":"examples/rover/Hydroinnova_rover_example/#atmospheric-correction","title":"Atmospheric correction\u00b6","text":"<p>NaN values in the atmospheric data are filled. The neutron counts are then corrected for atmospheric variables. See <code>correction_humidity()</code> and <code>correction_pressure()</code></p>"},{"location":"examples/stationary/example_RDT_station/","title":"Processing and analyzing data from a stationary detector","text":"In\u00a0[1]: Copied! <pre># Import required libraries\nimport crnpy\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n</pre> # Import required libraries import crnpy  import pandas as pd import numpy as np import matplotlib.pyplot as plt  In\u00a0[2]: Copied! <pre># Load observations from a stationary detector\nfilepath = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/stationary/rdt.csv\"\n\n# Read the DataFrame\ndf = pd.read_csv(filepath, names=['timestamp','barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg','DP','BattVolt','counts_1_Tot','counts_2_Tot','counts_3_Tot'])\n\n# Parse timestamps\ndf['timestamp'] = pd.to_datetime(df['timestamp'])\n\n# Display a few rows of the dataset\ndf.head(3)\n</pre> # Load observations from a stationary detector filepath = \"https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/stationary/rdt.csv\"  # Read the DataFrame df = pd.read_csv(filepath, names=['timestamp','barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg','DP','BattVolt','counts_1_Tot','counts_2_Tot','counts_3_Tot'])  # Parse timestamps df['timestamp'] = pd.to_datetime(df['timestamp'])  # Display a few rows of the dataset df.head(3)  Out[2]: timestamp barometric_pressure_Avg relative_humidity_Avg air_temperature_Avg DP BattVolt counts_1_Tot counts_2_Tot counts_3_Tot 0 2020-04-10 09:47:00 983.8 29.0 9.6 -7.4 14.4 848 716.0 742 1 2020-04-10 10:47:00 982.3 25.0 10.9 -7.9 14.3 436 7200.0 796 2 2020-04-10 11:17:00 980.8 25.0 11.5 -7.4 13.7 389 396.0 354 In\u00a0[3]: Copied! <pre># Remove rows with incomplete intervals\ndf = crnpy.remove_incomplete_intervals(df, timestamp_col='timestamp', integration_time=3600, remove_first=True)\n\n# Fill missing timestamps to create a conplete record\ndf = crnpy.fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True)\n</pre> # Remove rows with incomplete intervals df = crnpy.remove_incomplete_intervals(df, timestamp_col='timestamp', integration_time=3600, remove_first=True)  # Fill missing timestamps to create a conplete record df = crnpy.fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True)  <pre>Removed a total of 48 rows.\nAdded a total of 60 missing timestamps.\n</pre> <p>Utilize the <code>total_raw_counts()</code> function to calculate the total counts across all detectors. After calculating the total counts, identify outliers using the <code>is_outlier()</code> function.</p> In\u00a0[4]: Copied! <pre># Flag and fill outliers\ncols_counts = ['counts_1_Tot','counts_2_Tot','counts_3_Tot']\nfor col in cols_counts:\n    \n    # Find outliers\n    idx_outliers = crnpy.is_outlier(df[col], method='scaled_mad', min_val=500, max_val=2000)\n    df.loc[idx_outliers,col] = np.nan\n    \n    # Fill missing values with linear interpolation and round to nearest integer\n    df[col] = df[col].interpolate(method='linear', limit=3, limit_direction='both').round()\n</pre> # Flag and fill outliers cols_counts = ['counts_1_Tot','counts_2_Tot','counts_3_Tot'] for col in cols_counts:          # Find outliers     idx_outliers = crnpy.is_outlier(df[col], method='scaled_mad', min_val=500, max_val=2000)     df.loc[idx_outliers,col] = np.nan          # Fill missing values with linear interpolation and round to nearest integer     df[col] = df[col].interpolate(method='linear', limit=3, limit_direction='both').round()  In\u00a0[5]: Copied! <pre># Compute total cunts\ndf['total_raw_counts'] = crnpy.total_raw_counts(df[cols_counts], nan_strategy='average')\n</pre> # Compute total cunts df['total_raw_counts'] = crnpy.total_raw_counts(df[cols_counts], nan_strategy='average')  In\u00a0[6]: Copied! <pre># Plot total counts\n# Create a new figure and plot the data\nfig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size\nax.plot(df['timestamp'], df['total_raw_counts'], label='Raw Counts', color='black', linewidth=.8)\n# Set the labels for the x-axis and y-axis\nax.set_xlabel(\"Date\")\nax.set_ylabel(\"Total Raw Counts\")\n\n# Set the title of the plot\nax.set_title('Total Raw Counts Over Time')\n\n# Adjust size of axes labels\nax.tick_params(axis='both', which='major')\n\n# Show the plot\nplt.show()\n</pre> # Plot total counts # Create a new figure and plot the data fig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size ax.plot(df['timestamp'], df['total_raw_counts'], label='Raw Counts', color='black', linewidth=.8) # Set the labels for the x-axis and y-axis ax.set_xlabel(\"Date\") ax.set_ylabel(\"Total Raw Counts\")  # Set the title of the plot ax.set_title('Total Raw Counts Over Time')  # Adjust size of axes labels ax.tick_params(axis='both', which='major')  # Show the plot plt.show()  In\u00a0[7]: Copied! <pre># Define study start and end dates\nstart_date = df.iloc[0]['timestamp']\nend_date = df.iloc[-1]['timestamp']\n\n# Define geographic coordiantes\nlat = 39.110596\nlon = -96.613050\n\n# Find stations with cutoff rigidity similar to estimated by lat,lon\ncrnpy.find_neutron_monitor(crnpy.cutoff_rigidity(lat, lon), start_date=start_date, end_date=end_date, verbose=False)\n</pre> # Define study start and end dates start_date = df.iloc[0]['timestamp'] end_date = df.iloc[-1]['timestamp']  # Define geographic coordiantes lat = 39.110596 lon = -96.613050  # Find stations with cutoff rigidity similar to estimated by lat,lon crnpy.find_neutron_monitor(crnpy.cutoff_rigidity(lat, lon), start_date=start_date, end_date=end_date, verbose=False)  <pre>\nSelect a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations\n\nYour cutoff rigidity is 2.87 GV\n     STID     NAME     R  Altitude_m  Period available\n13   DRBS  Dourbes  3.18         225              True\n40   NEWK   Newark  2.40          50              True\n28  KIEL2   KielRT  2.36          54              True\n21   IRKT  Irkustk  3.64         435              True\n</pre> Out[7]: STID NAME R Altitude_m Period available 13 DRBS Dourbes 3.18 225 True 40 NEWK Newark 2.40 50 True 28 KIEL2 KielRT 2.36 54 True 21 IRKT Irkustk 3.64 435 True In\u00a0[8]: Copied! <pre># Download incoming neutron flux data from the Neutron Monitor Database (NMDB).\n# Use utc_offset for Central Standard Time.\nnmdb = crnpy.get_incoming_neutron_flux(start_date, end_date, station=\"IRKT\", utc_offset=-6)\n</pre> # Download incoming neutron flux data from the Neutron Monitor Database (NMDB). # Use utc_offset for Central Standard Time. nmdb = crnpy.get_incoming_neutron_flux(start_date, end_date, station=\"IRKT\", utc_offset=-6)  In\u00a0[9]: Copied! <pre># Interpolate incoming neutron flux to match the timestamps in our station data\ndf['incoming_flux'] = crnpy.interpolate_incoming_flux(nmdb['timestamp'], nmdb['counts'], df['timestamp'])\n</pre> # Interpolate incoming neutron flux to match the timestamps in our station data df['incoming_flux'] = crnpy.interpolate_incoming_flux(nmdb['timestamp'], nmdb['counts'], df['timestamp'])  In\u00a0[10]: Copied! <pre># Compute correction factor for incoming neutron flux\ndf['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df['incoming_flux'],\n                                          incoming_Ref=df['incoming_flux'].iloc[0])\n</pre> # Compute correction factor for incoming neutron flux df['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df['incoming_flux'],                                           incoming_Ref=df['incoming_flux'].iloc[0])  In\u00a0[11]: Copied! <pre># Fill NaN values in atmospheric data and neutron counts\ndf[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']] = df[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')\n\n# Compute the pressure correction factor \ndf['fp'] = crnpy.correction_pressure(pressure=df['barometric_pressure_Avg'], Pref=df['barometric_pressure_Avg'].mean(), L=130)\n\n# Calculate the absolute humidity (g cm^-3) and the vapor pressure correction factor\ndf['abs_humidity'] = crnpy.abs_humidity(df['relative_humidity_Avg'], df['air_temperature_Avg'])\ndf['fw'] = crnpy.correction_humidity(abs_humidity=df['abs_humidity'], Aref=0)\n</pre> # Fill NaN values in atmospheric data and neutron counts df[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']] = df[['barometric_pressure_Avg','relative_humidity_Avg', 'air_temperature_Avg']].interpolate(method='pchip', limit=24, limit_direction='both')  # Compute the pressure correction factor  df['fp'] = crnpy.correction_pressure(pressure=df['barometric_pressure_Avg'], Pref=df['barometric_pressure_Avg'].mean(), L=130)  # Calculate the absolute humidity (g cm^-3) and the vapor pressure correction factor df['abs_humidity'] = crnpy.abs_humidity(df['relative_humidity_Avg'], df['air_temperature_Avg']) df['fw'] = crnpy.correction_humidity(abs_humidity=df['abs_humidity'], Aref=0)  In\u00a0[12]: Copied! <pre># Plot all the correction factors\nfig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size\nax.plot(df['timestamp'], df['fp'], label='Pressure',color='tomato', linewidth=1)\nax.plot(df['timestamp'], df['fw'], label='Humidity', color='navy', linewidth=1)\nax.plot(df['timestamp'], df['fi'], label='Incoming Flux', color='olive', linewidth=1)\nax.set_xlabel(\"Date\")\nax.set_ylabel('Correction Factor')\nax.legend()\nax.set_title('Correction Factors for Pressure, Humidity, and Incoming Flux')\n</pre> # Plot all the correction factors fig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size ax.plot(df['timestamp'], df['fp'], label='Pressure',color='tomato', linewidth=1) ax.plot(df['timestamp'], df['fw'], label='Humidity', color='navy', linewidth=1) ax.plot(df['timestamp'], df['fi'], label='Incoming Flux', color='olive', linewidth=1) ax.set_xlabel(\"Date\") ax.set_ylabel('Correction Factor') ax.legend() ax.set_title('Correction Factors for Pressure, Humidity, and Incoming Flux')  Out[12]: <pre>Text(0.5, 1.0, 'Correction Factors for Pressure, Humidity, and Incoming Flux')</pre> In\u00a0[13]: Copied! <pre># Apply correction factors\ndf['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])\n</pre> # Apply correction factors df['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])  In\u00a0[14]: Copied! <pre>fig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size\n\n# Subplot 1: Raw and Corrected Counts\nax.plot(df['timestamp'], df['total_raw_counts'], label='Raw Counts', color='black', linestyle='dashed', linewidth=.8)\nax.plot(df['timestamp'], df['total_corrected_neutrons'], label='Corrected Counts', color='teal', linewidth=.8)\nax.set_xlabel(\"Date\")\nax.set_ylabel('Counts')\nax.legend()\nax.set_title('Raw and Corrected Counts')\n</pre> fig, ax = plt.subplots(figsize=(10, 3))  # Set the figure size  # Subplot 1: Raw and Corrected Counts ax.plot(df['timestamp'], df['total_raw_counts'], label='Raw Counts', color='black', linestyle='dashed', linewidth=.8) ax.plot(df['timestamp'], df['total_corrected_neutrons'], label='Corrected Counts', color='teal', linewidth=.8) ax.set_xlabel(\"Date\") ax.set_ylabel('Counts') ax.legend() ax.set_title('Raw and Corrected Counts')  Out[14]: <pre>Text(0.5, 1.0, 'Raw and Corrected Counts')</pre> <p>Convert the smoothed neutron counts to Volumetric Water Content (VWC) using the <code>counts_to_vwc()</code>. The function considers the smoothed neutron counts, $N_0$ specific parameter, soil bulk density, weight percent of latent water (Wlat), and weight percent of soil organic carbon (Wsoc). After conversion, plot the VWC against the timestamp for visual analysis. <code>smooth_1d()</code> is applied for smoothing the data using a Savitzky-Golay filter.</p> In\u00a0[15]: Copied! <pre># Device N0 parameter\nN0_rdt = 3767 # Patrignani, A., Ochsner, T. E., Montag, B., &amp; Bellinger, S. (2021). A novel lithium foil cosmic-ray neutron detector for measuring field-scale soil moisture. Frontiers in Water, 3, 673185.\n\n# Estimate lattice water (%) based on texture\nlattice_water = crnpy.lattice_water(clay_content=0.35)\n\ndf['VWC'] = crnpy.counts_to_vwc(df['total_corrected_neutrons'], N0=N0_rdt, bulk_density=1.33, Wlat=lattice_water, Wsoc=0.01)\n\n# Drop any NaN beofre smoothing\ndf = df.dropna(subset=['VWC']).copy().reset_index()\n\n# Filter using the Savitzky-Golay filter, drop NaN values and timestamps\ndf['VWC'] = crnpy.smooth_1d(df['VWC'], window=11, order=3, method=\"savitzky_golay\")\n</pre> # Device N0 parameter N0_rdt = 3767 # Patrignani, A., Ochsner, T. E., Montag, B., &amp; Bellinger, S. (2021). A novel lithium foil cosmic-ray neutron detector for measuring field-scale soil moisture. Frontiers in Water, 3, 673185.  # Estimate lattice water (%) based on texture lattice_water = crnpy.lattice_water(clay_content=0.35)  df['VWC'] = crnpy.counts_to_vwc(df['total_corrected_neutrons'], N0=N0_rdt, bulk_density=1.33, Wlat=lattice_water, Wsoc=0.01)  # Drop any NaN beofre smoothing df = df.dropna(subset=['VWC']).copy().reset_index()  # Filter using the Savitzky-Golay filter, drop NaN values and timestamps df['VWC'] = crnpy.smooth_1d(df['VWC'], window=11, order=3, method=\"savitzky_golay\")  In\u00a0[16]: Copied! <pre># Plot the obtained time series of volumetric water content\n# Create a new figure and plot the data\nfig, ax = plt.subplots(figsize=(10, 4))  # Set the figure size\nax.plot(df['timestamp'], df['VWC'], color='teal', linewidth=1.0)\n\n# Set the labels for the x-axis and y-axis\nax.set_xlabel(\"Date\")\nax.set_ylabel(r\"Volumetric Water Content ($m^3 \\cdot m^{-3}$)\")\n\n# Set the title of the plot\nax.set_title('Soil Moisture')\n\n# Show the plot\nplt.show()\n</pre> # Plot the obtained time series of volumetric water content # Create a new figure and plot the data fig, ax = plt.subplots(figsize=(10, 4))  # Set the figure size ax.plot(df['timestamp'], df['VWC'], color='teal', linewidth=1.0)  # Set the labels for the x-axis and y-axis ax.set_xlabel(\"Date\") ax.set_ylabel(r\"Volumetric Water Content ($m^3 \\cdot m^{-3}$)\")  # Set the title of the plot ax.set_title('Soil Moisture')  # Show the plot plt.show()  In\u00a0[17]: Copied! <pre># Estimate sensing depth\ndf['sensing_depth'] = crnpy.sensing_depth(df['VWC'], df['barometric_pressure_Avg'], df['barometric_pressure_Avg'].mean(), bulk_density=1.33, Wlat=lattice_water, method = \"Franz_2012\")\nprint(f\"Average sensing depth: {np.round(df['sensing_depth'].mean(),2)} cm\")\n</pre> # Estimate sensing depth df['sensing_depth'] = crnpy.sensing_depth(df['VWC'], df['barometric_pressure_Avg'], df['barometric_pressure_Avg'].mean(), bulk_density=1.33, Wlat=lattice_water, method = \"Franz_2012\") print(f\"Average sensing depth: {np.round(df['sensing_depth'].mean(),2)} cm\")  <pre>Average sensing depth: 15.36 cm\n</pre> In\u00a0[18]: Copied! <pre># Compute the storage using the exponential filter\nsurface = df['VWC']\nsubsurface = crnpy.exp_filter(df['VWC'])\n\nz_surface = 150 # Average depth in mm obtained from previous cell using crnpy.sensing_depth()\nz_subsurface = 350 # Arbitrary subsurface depth in mm\ndf['storage'] = np.sum([surface*z_surface, subsurface*z_subsurface], axis=0)\n</pre> # Compute the storage using the exponential filter surface = df['VWC'] subsurface = crnpy.exp_filter(df['VWC'])  z_surface = 150 # Average depth in mm obtained from previous cell using crnpy.sensing_depth() z_subsurface = 350 # Arbitrary subsurface depth in mm df['storage'] = np.sum([surface*z_surface, subsurface*z_subsurface], axis=0)  In\u00a0[19]: Copied! <pre># Plot the obtained time series of soil water storage\n# Create a new figure and plot the data\nfig, ax = plt.subplots(figsize=(10, 4))  # Set the figure size\nax.plot(df['timestamp'], df['storage'], color='teal', linewidth=1.0)\n\n# Set the labels for the x-axis and y-axis\nax.set_xlabel(\"Date\")\nax.set_ylabel(\"Storage (mm)\")\n\n# Set the title of the plot\nax.set_title('Soil Water Storage')\n\n# Show the plot\nplt.show()\n</pre> # Plot the obtained time series of soil water storage # Create a new figure and plot the data fig, ax = plt.subplots(figsize=(10, 4))  # Set the figure size ax.plot(df['timestamp'], df['storage'], color='teal', linewidth=1.0)  # Set the labels for the x-axis and y-axis ax.set_xlabel(\"Date\") ax.set_ylabel(\"Storage (mm)\")  # Set the title of the plot ax.set_title('Soil Water Storage')  # Show the plot plt.show()  In\u00a0[20]: Copied! <pre># Estimate the uncertainty of the volumetric water content\ndf['sigma_VWC'] = crnpy.uncertainty_vwc(df['total_raw_counts'], N0=N0_rdt, bulk_density=1.33, fp=df['fp'], fi=df['fi'], fw=df['fw'])\n\n# Plot the VWC with uncertainty as a shaded area\nfig, ax = plt.subplots(figsize=(10, 4))  # Set the figure size\nax.plot(df['timestamp'], df['VWC'], color='black', linewidth=1.0)\nax.fill_between(df['timestamp'], df['VWC']-df['sigma_VWC'], df['VWC']+df['sigma_VWC'], color='teal', alpha=0.5, label = r\"Standard Deviation $\\theta_v$\")\nax.set_title('Volumentric Water Content with Uncertainty')\nax.set_xlabel(\"Date\")\nax.set_ylabel(r\"Volumetric Water Content ($m^3 \\cdot m^{-3}$)\")\nplt.legend()\nplt.show()\n</pre> # Estimate the uncertainty of the volumetric water content df['sigma_VWC'] = crnpy.uncertainty_vwc(df['total_raw_counts'], N0=N0_rdt, bulk_density=1.33, fp=df['fp'], fi=df['fi'], fw=df['fw'])  # Plot the VWC with uncertainty as a shaded area fig, ax = plt.subplots(figsize=(10, 4))  # Set the figure size ax.plot(df['timestamp'], df['VWC'], color='black', linewidth=1.0) ax.fill_between(df['timestamp'], df['VWC']-df['sigma_VWC'], df['VWC']+df['sigma_VWC'], color='teal', alpha=0.5, label = r\"Standard Deviation $\\theta_v$\") ax.set_title('Volumentric Water Content with Uncertainty') ax.set_xlabel(\"Date\") ax.set_ylabel(r\"Volumetric Water Content ($m^3 \\cdot m^{-3}$)\") plt.legend() plt.show()"},{"location":"examples/stationary/example_RDT_station/#processing-and-analyzing-data-from-a-stationary-detector","title":"Processing and analyzing data from a stationary detector\u00b6","text":"<p>This tutorial demonstrates how to process and analyze neutron counts of epithermal neutrons recorded with a stationary cosmic-ray neutron probe consisting of three lithium-foil detectors manufactured by Radiation Detection Technologies, Inc (RDT). The tutorial covers all the steps from reading the file with raw data obtained from the probe in the field, outlier removal, atmospheric corrections, and conversion of corrected neutron counts into volumetric soil water content.</p> <p>The tutorial assumes that you are working within the <code>Anaconda</code> environment, which has all the necessary modules. Also, make sure that the <code>CRNPy</code> library is installed in your machine.</p> <p>The raw dataset to follow this example can be obtained at the .csv file.</p> <p>This device was installed in the vicinity of the KONA site of the National Ecological Observatory Network within the Konza Prairie Biological station (<code>39.110596, -96.613050, 320 meters a.s.l.</code>)</p>"},{"location":"examples/stationary/example_RDT_station/#neutron-count-correction","title":"Neutron count correction\u00b6","text":""},{"location":"examples/stationary/example_RDT_station/#incomming-neutron-flux","title":"Incomming neutron flux\u00b6","text":"<p>Find similar neutron monitors based on altitude and cutoff rigidity, which is estimated based on the geographic location using latitude and longitude values. See <code>get_incoming_neutron_flux()</code>, <code>find_neutron_monitor()</code> and <code>correction_incoming_flux()</code>.</p> <p>In this particular example we selected the <code>\"IRKT\"</code> monitor since it has a similar cuoff rigidity and altitude as our site. The reference neutron monitor will likely be different for your location. Note that the function only reports neutron monitors based on cutoff rigidity and leaves to the user the selection of a reference neutron monitor based on the altitude of the location.</p>"},{"location":"examples/stationary/example_RDT_station/#atmospheric-correction","title":"Atmospheric correction\u00b6","text":"<p>This section is about correcting the atmospheric variables. First, fill the missing values in the atmospheric data, then correct the count for atmospheric variables using <code>correction_humidity()</code> and <code>correction_pressure()</code>.</p>"},{"location":"examples/stationary/example_RDT_station/#sensing-depth-and-soil-water-storage","title":"Sensing depth and soil water storage\u00b6","text":"<p>Estimate the <code>sensing_depth()</code>  at which 86 % of the neutrons probes the soil profile, and estimate the soil water <code>storage()</code> of the top 50 cm using and exponential filter.</p>"},{"location":"examples/stationary/example_RDT_station/#uncertainty-estimation","title":"Uncertainty estimation\u00b6","text":"<p>Estimate the uncertainty of the volumetric water content using the <code>uncertainty_vwc()</code> function. Optionally see <code>uncertainty_counts()</code> for estimating the uncertainty of the neutron counts.</p>"}]}
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z=+41))WqOendCIhzn_zalJj8D>SFojwRj|B=le@*qcad2bVa;Pvp52$u`;eM;_t&p
z@)GamChy5I_$Zhi7jff#_a?+J3@IBAt?D>Mss-yNY*;L!$kNwXZlVxVfonELZc)pr
z3WqX`#tzB@rM2-*e@qPuO|Bb7E+*glm}1{zC3o5GDap1;_8csWwzBC)xf)IcKeAHB
zj=5MVR~~T#`Wu=?BG5iett9NdalrSCOK<`T7Yp|c$>12NtbjF{Zkp{q)51lyoekHn
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IN=*d-0I2Sm`Tzg`

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zb!U_Y7b1n2*`btwzd%vNdD5iTmpHH9SdSeg`To+{=nTXLT@gQ}Ssa1WSQ%Fr@%P~)
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diff --git a/src/crnpy.egg-info/PKG-INFO b/src/crnpy.egg-info/PKG-INFO
index b9fbcc5..dde5303 100644
--- a/src/crnpy.egg-info/PKG-INFO
+++ b/src/crnpy.egg-info/PKG-INFO
@@ -1,18 +1,23 @@
 Metadata-Version: 2.1
 Name: crnpy
-Version: 0.5.1
+Version: 0.6.0
 Summary: A Python package for the estimation and processing of soil moisture data from cosmic-ray neutron counts.
-Home-page: https://github.com/soilwater/crnpy
+Home-page: https://soilwater.github.io/crnpy/
 Author: Joaquin Peraza, Andres Patrignani
 Author-email: jperaza@ksu.edu, andrespatrignani@ksu.edu
 License: MIT
 Description-Content-Type: text/markdown
 License-File: LICENSE
+Requires-Dist: numpy
+Requires-Dist: pandas
+Requires-Dist: scipy
+Requires-Dist: requests
 
 [![GitHub Workflow Status (building)](https://img.shields.io/github/actions/workflow/status/soilwater/crnpy/python-package.yml)](https://github.com/soilwater/crnpy/actions/workflows/python-package.yml)
 [![GitHub Workflow Status (publish)](https://img.shields.io/github/actions/workflow/status/soilwater/crnpy/python-publish.yml?label=publish)](https://github.com/soilwater/crnpy/actions/workflows/python-publish.yml)
 [![PyPI - Status](https://img.shields.io/pypi/v/crnpy)](https://pypi.org/project/crnpy/)
 [![GitHub commits since latest release (by SemVer including pre-releases)](https://img.shields.io/github/commits-since/soilwater/crnpy/latest/main)](https://github.com/soilwater/crnpy)
+![JOSS submission status](https://joss.theoj.org/papers/e65c1bb5fee58c39289efc4547d1fd10/status.svg)
 
 # Cosmic-Ray Neutron Python (CRNPy) Library
 
@@ -32,7 +37,6 @@ CRNPs are a valuable tool for non-invasive soil moisture estimation at the hecto
 - Modular: The library is designed to be modular, allowing users to easily customize the processing workflow to their needs.
 
 
-
 ## Installation
 To install the CRNPy library, you can use Python's package manager. Open a terminal and type:
 
@@ -49,6 +53,12 @@ Ideally dependencies should be installed automatically. If not, you can install
 The CRNPy library is compatible with Python 3.7 and above.
 See [requirements.txt](https://github.com/soilwater/crnpy/blob/main/requirements.txt) for a list of dependencies.
 
+## Examples
+
+- [https://soilwater.github.io/crnpy/examples/stationary/example_RDT_station/](Processing and analyzing data from a stationary detector)
+- [https://soilwater.github.io/crnpy/examples/rover/Hydroinnova_rover_example/](Processing and analyzing data from a roving detector)
+- [https://soilwater.github.io/crnpy/examples/calibration/calibration/](Device-specific field calibration)
+
 ## Authors
 The CRNPy library was developed at the Kansas State University Soil Water Processes Lab by:
 
diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index 65aeb38..8536df7 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -5,20 +5,19 @@
  Created by Joaquin Peraza and Andres Patrignani.
 """
 
-# Import modules
-import sys
-import warnings
+import crnpy.data as data
+import io
 import numbers
 import numpy as np
 import pandas as pd
 import requests
-import io
+import sys
 import utm
+import warnings
 
-from scipy.signal import savgol_filter
 from scipy.interpolate import griddata
+from scipy.signal import savgol_filter
 from scipy.special import erfcinv
-import crnpy.data as data
 
 # Define python version
 python_version = (3, 7)  # tuple of (major, minor) version requirement
@@ -42,24 +41,24 @@ def remove_incomplete_intervals(df, timestamp_col, integration_time, remove_firs
     Returns:
         (pandas.DataFrame): 
     """
-    
+
     # Check format of timestamp column
     if df[timestamp_col].dtype != 'datetime64[ns]':
         raise TypeError('timestamp_col must be datetime64. Use `pd.to_datetime()` to fix this issue.')
 
     # Check if differences in timestamps are below or above the provided integration time
     idx_delta = df[timestamp_col].diff().dt.total_seconds() != integration_time
-    
+
     if remove_first:
         idx_delta[0] = True
-        
+
     # Select rows that meet the specified integration time
     df = df[~idx_delta]
     df.reset_index(drop=True, inplace=True)
 
     # Notify user about the number of rows that have been removed
     print(f"Removed a total of {sum(idx_delta)} rows.")
-    
+
     return df
 
 
@@ -94,17 +93,17 @@ def fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_times
     for date in date_range:
         if date not in df[timestamp_col].values:
             if verbose:
-                print('Adding missing date:',date)
-            new_line = pd.DataFrame({timestamp_col:date}, index=[-1]) # By default fills columns with np.nan
-            df = pd.concat([df,new_line])
+                print('Adding missing date:', date)
+            new_line = pd.DataFrame({timestamp_col: date}, index=[-1])  # By default fills columns with np.nan
+            df = pd.concat([df, new_line])
             counter += 1
 
     df.sort_values(by=timestamp_col, inplace=True)
     df.reset_index(drop=True, inplace=True)
-    
+
     # Notify user about the number of rows that have been removed
     print(f"Added a total of {counter} missing timestamps.")
-    
+
     return df
 
 
@@ -119,14 +118,14 @@ def total_raw_counts(counts):
     """
 
     if counts.shape[0] > 1:
-        counts = counts.apply(lambda x: x.fillna(counts.mean(axis=1)),axis=0)
+        counts = counts.apply(lambda x: x.fillna(counts.mean(axis=1)), axis=0)
 
     # Compute sum of counts
     total_raw_counts = counts.sum(axis=1)
-    
+
     # Replace zeros with NaN
     total_raw_counts = total_raw_counts.replace(0, np.nan)
-    
+
     return total_raw_counts
 
 
@@ -146,7 +145,7 @@ def is_outlier(x, method, window=11, min_val=None, max_val=None):
     References:
         Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.
     """
-        
+
     if not isinstance(x, pd.Series):
         raise TypeError('x must of type pandas.Series')
 
@@ -163,24 +162,24 @@ def is_outlier(x, method, window=11, min_val=None, max_val=None):
         iqr = q3 - q1
         high_fence = q3 + (1.5 * iqr)
         low_fence = q1 - (1.5 * iqr)
-        idx_outliers = (x<low_fence ) | (x>high_fence )
+        idx_outliers = (x < low_fence) | (x > high_fence)
 
     elif method == 'moviqr':
         q1 = x.rolling(window, center=True).quantile(0.25)
         q3 = x.rolling(window, center=True).quantile(0.75)
         iqr = q3 - q1
-        ub = q3 + (1.5 * iqr) # Upper boundary
-        lb = q1 - (1.5 * iqr) # Lower boundary
+        ub = q3 + (1.5 * iqr)  # Upper boundary
+        lb = q1 - (1.5 * iqr)  # Lower boundary
         idx_outliers = (x < lb) | (x > ub)
 
     elif method == 'zscore':
-        zscore = (x - x.mean())/x.std()
+        zscore = (x - x.mean()) / x.std()
         idx_outliers = (zscore < -3) | (zscore > 3)
 
     elif method == 'movzscore':
         movmean = x.rolling(window=window, center=True).mean()
         movstd = x.rolling(window=window, center=True).std()
-        movzscore = (x - movmean)/movstd
+        movzscore = (x - movmean) / movstd
         idx_outliers = (movzscore < -3) | (movzscore > 3)
 
     elif method == 'modified_zscore':
@@ -195,10 +194,10 @@ def is_outlier(x, method, window=11, min_val=None, max_val=None):
 
     elif method == 'scaled_mad':
         # Returns true for elements more than three scaled MAD from the median. 
-        c = -1 / (np.sqrt(2)*erfcinv(3/2))
+        c = -1 / (np.sqrt(2) * erfcinv(3 / 2))
         median = np.nanmedian(x)
-        mad = c*np.nanmedian(np.abs(x - median))
-        idx_outliers = x > (median + 3*mad)
+        mad = c * np.nanmedian(np.abs(x - median))
+        idx_outliers = x > (median + 3 * mad)
 
     else:
         raise TypeError('Outlier detection method not found.')
@@ -246,7 +245,7 @@ def correction_pressure(pressure, Pref, L):
     """
 
     # Compute pressure correction factor
-    fp = np.exp((Pref - pressure) / L) # Zreda et al. 2017 Eq 5.
+    fp = np.exp((Pref - pressure) / L)  # Zreda et al. 2017 Eq 5.
 
     return fp
 
@@ -286,12 +285,12 @@ def correction_humidity(abs_humidity, Aref):
         M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086
     """
     A = abs_humidity
-    fw = 1 + 0.0054*(A - Aref) # Zreda et al. 2017 Eq 6.
+    fw = 1 + 0.0054 * (A - Aref)  # Zreda et al. 2017 Eq 6.
     return fw
 
 
-
-def correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None, Rc_method=None, Rc_site=None, site_atmdepth=None, Rc_ref=None, ref_atmdepth=None):
+def correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None, Rc_method=None, Rc_site=None,
+                             site_atmdepth=None, Rc_ref=None, ref_atmdepth=None):
     r"""Correction factor for incoming neutron flux.
 
     This function corrects neutron counts for incoming neutron flux using the method described in Anderson et al. (2017). The correction is performed using the following equation:
@@ -318,13 +317,25 @@ def correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None,
     Args:
         incoming_neutrons (list or array): Incoming neutron flux readings.
         incoming_Ref (float): Reference incoming neutron flux. Baseline incoming neutron flux.
+        fill_na (float): Value to fill missing data. If None, missing data remains as NaN.
+        Rc_method (str): Optional to correct for differences in cutoff rigidity between the site and the reference station. Possible values are 'McJannetandDesilets2023' or 'Hawdonetal2014'. If None, no correction is performed.
+        Rc_site (float): Cutoff rigidity at the monitoring site.
+        site_atmdepth (float): Atmospheric depth at the monitoring site.
+        Rc_ref (float): Cutoff rigidity at the reference station.
+        ref_atmdepth (float): Atmospheric depth at the reference station.
 
     Returns:
         (list): fi correction factor.
 
     References:
+        Hawdon, A., D. McJannet, and J. Wallace (2014), Calibration and correction procedures for cosmic-ray neutron soil moisture probes located across Australia, Water Resour. Res., 50, 5029–5043, doi:10.1002/2013WR015138.
+
         M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086
 
+        McJannet, D. L., & Desilets, D. (2023). Incoming neutron flux corrections for cosmic-ray soil and snow sensors using the global neutron monitor network. Water Resources Research, 59, e2022WR033889. https://doi.org/10.1029/2022WR033889
+
+
+
 
     """
     if incoming_Ref is None and not isinstance(incoming_neutrons, type(None)):
@@ -339,17 +350,16 @@ def correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None,
             raise ValueError('Site cutoff rigidity not provided.')
 
         if Rc_method == 'McJannetandDesilets2023':
-            if latitude is None or elevation is None:
-                raise ValueError('Latitude and elevation are required inputs for McJannet and Desilets (2023) method.')
             tau = location_factor(site_atmdepth, Rc_site, ref_atmdepth, Rc_ref)
             fi = 1 / (tau * fi + 1 - tau)
 
         elif Rc_method == 'Hawdonetal2014':
-            Rc_corr = -0.075 * (Rc_site - Rc_ref)+ 1.0
+            Rc_corr = -0.075 * (Rc_site - Rc_ref) + 1.0
             fi = (fi - 1.0) * Rc_corr + 1.0
 
         else:
-            raise ValueError('Cutoff rigidity method not found. Valid options are: McJannetandDesilets2023, Hawdonetal2014.')
+            raise ValueError(
+                'Cutoff rigidity method not found. Valid options are: McJannetandDesilets2023, Hawdonetal2014.')
 
     if fill_na is not None:
         fi.fillna(fill_na, inplace=True)  # Use a value of 1 for days without data
@@ -378,7 +388,6 @@ def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expan
     # Example: get_incoming_neutron_flux(station='IRKT',start_date='2020-04-10 11:00:00',end_date='2020-06-18 17:00:00')
     # Template url = 'http://nest.nmdb.eu/draw_graph.php?formchk=1&stations[]=KERG&output=ascii&tabchoice=revori&dtype=corr_for_efficiency&date_choice=bydate&start_year=2009&start_month=09&start_day=01&start_hour=00&start_min=00&end_year=2009&end_month=09&end_day=05&end_hour=23&end_min=59&yunits=0'
 
-
     # Expand the time window by 1 hour to ensure an extra observation is included in the request.
     start_date -= pd.Timedelta(hours=expand_window)
     end_date += pd.Timedelta(hours=expand_window)
@@ -387,24 +396,24 @@ def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expan
     start_date = start_date - pd.Timedelta(hours=utc_offset)
     end_date = end_date - pd.Timedelta(hours=utc_offset)
     root = 'http://www.nmdb.eu/nest/draw_graph.php?'
-    url_par = [ 'formchk=1',
-                'stations[]=' + station,
-                'output=ascii',
-                'tabchoice=revori',
-                'dtype=corr_for_efficiency',
-                'tresolution=' + str(60),
-                'date_choice=bydate',
-                'start_year=' + str(start_date.year),
-                'start_month=' + str(start_date.month),
-                'start_day=' + str(start_date.day),
-                'start_hour=' + str(start_date.hour),
-                'start_min=' + str(start_date.minute),
-                'end_year=' + str(end_date.year),
-                'end_month=' + str(end_date.month),
-                'end_day=' + str(end_date.day),
-                'end_hour=' + str(end_date.hour),
-                'end_min=' + str(end_date.minute),
-                'yunits=0']
+    url_par = ['formchk=1',
+               'stations[]=' + station,
+               'output=ascii',
+               'tabchoice=revori',
+               'dtype=corr_for_efficiency',
+               'tresolution=' + str(60),
+               'date_choice=bydate',
+               'start_year=' + str(start_date.year),
+               'start_month=' + str(start_date.month),
+               'start_day=' + str(start_date.day),
+               'start_hour=' + str(start_date.hour),
+               'start_min=' + str(start_date.minute),
+               'end_year=' + str(end_date.year),
+               'end_month=' + str(end_date.month),
+               'end_day=' + str(end_date.day),
+               'end_hour=' + str(end_date.hour),
+               'end_min=' + str(end_date.minute),
+               'yunits=0']
 
     url = root + '&'.join(url_par)
 
@@ -418,9 +427,9 @@ def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expan
     start = r.find("RCORR_E\n") + 8
     end = r.find('\n</code></pre><br>Total') - 1
     s = r[start:end]
-    s2 = ''.join([row.replace(';',',') for row in s])
+    s2 = ''.join([row.replace(';', ',') for row in s])
     try:
-        df_flux = pd.read_csv(io.StringIO(s2), names=['timestamp','counts'])
+        df_flux = pd.read_csv(io.StringIO(s2), names=['timestamp', 'counts'])
     except:
         if verbose:
             print(f"Error retrieving data from {url}")
@@ -443,7 +452,8 @@ def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expan
 
     return df_flux
 
-def get_reference_neutron_flux(station, date = pd.to_datetime("2011-05-01")):
+
+def get_reference_neutron_flux(station, date=pd.to_datetime("2011-05-01")):
     """Function to retrieve reference neutron flux from the Neutron Monitor Database. Default date is 2011-05-01, following previous studies (Zreda et a., 2012, Hawdon et al., 2014, Bogena et al., 2022).
 
     Args:
@@ -469,6 +479,7 @@ def get_reference_neutron_flux(station, date = pd.to_datetime("2011-05-01")):
     else:
         return df_flux['counts'].median()
 
+
 def smooth_1d(values, window=5, order=3, method='moving_median'):
     """Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).
 
@@ -494,7 +505,6 @@ def smooth_1d(values, window=5, order=3, method='moving_median'):
     if not isinstance(x, pd.Series) and not isinstance(x, pd.DataFrame):
         raise ValueError('Input must be a pandas Series or DataFrame')
 
-
     if method == 'moving_average':
         corrected_counts = values.rolling(window=window, center=True, min_periods=1).mean()
     elif method == 'moving_median':
@@ -505,13 +515,14 @@ def smooth_1d(values, window=5, order=3, method='moving_median'):
             print('Dataframe contains NaN values. Please remove NaN values before smoothing the data.')
 
         if type(values) == pd.core.series.Series:
-            filtered = savgol_filter(values,window,order)
-            corrected_counts = pd.DataFrame(filtered,columns=['smoothed'], index=values.index)
+            filtered = savgol_filter(values, window, order)
+            corrected_counts = pd.DataFrame(filtered, columns=['smoothed'], index=values.index)
         elif type(values) == pd.core.frame.DataFrame:
             for col in values.columns:
-                values[col] = savgol_filter(values[col],window,order)
+                values[col] = savgol_filter(values[col], window, order)
     else:
-        raise ValueError('Invalid method. Please select a valid filtering method., options are: moving_average, moving_median, savitzky_golay')
+        raise ValueError(
+            'Invalid method. Please select a valid filtering method., options are: moving_average, moving_median, savitzky_golay')
     corrected_counts = corrected_counts.ffill(limit=window).bfill(limit=window).copy()
     return corrected_counts
 
@@ -534,7 +545,8 @@ def correction_bwe(counts, bwe, r2_N0=0.05):
         Water Resour. Res., 51, 2030–2046, doi:10.1002/ 2014WR016443.
     """
 
-    return counts/(1 - bwe*r2_N0)
+    return counts / (1 - bwe * r2_N0)
+
 
 def biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494):
     """Function to convert biomass to biomass water equivalent.
@@ -556,7 +568,8 @@ def biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494):
     return (biomass_fresh - biomass_dry) + fWE * biomass_dry
 
 
-def correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0.12, p0=0.42, p1=0.5, p2=1.06, p3=4, p4=0.16, p6=0.94, p7=1.10, p8=2.70, p9=0.01):
+def correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0.12, p0=0.42, p1=0.5, p2=1.06, p3=4,
+                    p4=0.16, p6=0.94, p7=1.10, p8=2.70, p9=0.01):
     """Function to correct for road effects in neutron counts.
     following the approach described in Schrön et al., 2018.
 
@@ -576,7 +589,7 @@ def correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0
         of field soil moisture and the influence of roads.WaterResources Research,54,6441–6459.
         https://doi. org/10.1029/2017WR021719
     """
-    F1 = p0 * (1-np.exp(-p1*road_width))
+    F1 = p0 * (1 - np.exp(-p1 * road_width))
     F2 = -p2 - p3 * theta_road - ((p4 + theta_road) / (theta_N))
     F3 = p6 * np.exp(-p7 * (road_width ** -p8) * road_distance ** 4) + (1 - p6) * np.exp(-p9 * road_distance)
 
@@ -586,7 +599,8 @@ def correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0
 
     return corrected_counts
 
-def counts_to_vwc(counts, N0, Wlat, Wsoc ,bulk_density, a0=0.0808,a1=0.372,a2=0.115):
+
+def counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=0.115):
     r"""Function to convert corrected and filtered neutron counts into volumetric water content.
 
     This method implements soil moisture estimation using the non-linear relationship between neutron count and soil volumetric water content following the approach described in Desilets et al., 2010.
@@ -613,11 +627,10 @@ def counts_to_vwc(counts, N0, Wlat, Wsoc ,bulk_density, a0=0.0808,a1=0.372,a2=0.
     """
 
     # Convert neutron counts into vwc
-    vwc = (a0 / (counts/N0-a1) - a2 - Wlat - Wsoc) * bulk_density
+    vwc = (a0 / (counts / N0 - a1) - a2 - Wlat - Wsoc) * bulk_density
     return vwc
 
 
-
 def sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017'):
     """Function that computes the estimated sensing depth of the cosmic-ray neutron probe.
     The function offers several methods to compute the depth at which 86 % of the neutrons
@@ -653,18 +666,20 @@ def sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='S
         results = []
         for d in dist:
             # Compute r_star
-            r_start = d/Fp
+            r_start = d / Fp
 
             # Compute soil depth that accounts for 86% of the neutron flux
-            D86 = 1/ bulk_density * (8.321+0.14249*(0.96655 + np.exp(-0.01*r_start))*(20+(Wlat+vwc)) / (0.0429+(Wlat+vwc)))
+            D86 = 1 / bulk_density * (8.321 + 0.14249 * (0.96655 + np.exp(-0.01 * r_start)) * (20 + (Wlat + vwc)) / (
+                        0.0429 + (Wlat + vwc)))
             results.append(D86)
 
     elif method == 'Franz_2012':
-        results = 5.8/(bulk_density*Wlat+vwc+0.0829)
+        results = 5.8 / (bulk_density * Wlat + vwc + 0.0829)
     else:
         raise ValueError('Method not recognized. Please select either "Schron_2017" or "Franz_2012".')
     return results
 
+
 def abs_humidity(relative_humidity, temp):
     """
     Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.
@@ -680,7 +695,7 @@ def abs_humidity(relative_humidity, temp):
     ### Atmospheric water vapor factor
     # Saturation vapor pressure
     e_sat = 0.611 * np.exp(17.502 * temp / (
-                temp + 240.97)) * 1000  # in Pascals Eq. 3.8 p.41 Environmental Biophysics (Campbell and Norman)
+            temp + 240.97)) * 1000  # in Pascals Eq. 3.8 p.41 Environmental Biophysics (Campbell and Norman)
 
     # Vapor pressure Pascals
     Pw = e_sat * relative_humidity / 100
@@ -691,7 +706,7 @@ def abs_humidity(relative_humidity, temp):
     return abs_h
 
 
-def nrad_weight(h,theta,distances,depth,rhob=1.4, method=None, p=None, Hveg=None):
+def nrad_weight(h, theta, distances, depth, rhob=1.4, method=None, p=None, Hveg=None):
     """Function to compute distance weights corresponding to each soil sample.
 
     Args:
@@ -719,48 +734,90 @@ def nrad_weight(h,theta,distances,depth,rhob=1.4, method=None, p=None, Hveg=None
         Hydrol. Earth Syst. Sci., 21, 5009–5030, https://doi.org/10.5194/hess-21-5009-2017, 2017.
     """
 
-    if method=='Kohli_2015':
+    if method == 'Kohli_2015':
 
         # Table A1. Parameters for Fi and D86
-        p10 = 8735;       p11 = 17.1758; p12 = 11720;      p13 = 0.00978;   p14 = 7045;      p15 = 0.003632;
-        p20 = 2.7925e-2;  p21 = 5.0399;  p22 = 2.8544e-2;  p23 = 0.002455;  p24 = 6.851e-5;  p25 = 9.2926;
-        p30 = 247970;     p31 = 17.63;   p32 = 374655;     p33 = 0.00191;   p34 = 195725;
-        p40 = 5.4818e-2;  p41 = 15.921;  p42 = 0.6373;     p43 = 5.99e-2;   p44 = 5.425e-4;
-        p50 = 1383702;    p51 = 4.156;   p52 = 5325;       p53 = 0.00238;   p54 = 0.0156;    p55 = 0.130;     p56 = 1521;
-        p60 = 6.031e-5;   p61 = 98.5;    p62 = 1.0466e-3;
-        p70 = 11747;      p71 = 41.66;   p72 = 4521;       p73 = 0.01998;   p74 = 0.00604;   p75 = 2534;      p76 = 0.00475;
-        p80 = 1.543e-2;   p81 = 10.06;   p82 = 1.807e-2;   p83 = 0.0011;    p84 = 8.81e-5;   p85 = 0.0405;    p86 = 20.24;
-        p90 = 8.321;      p91 = 0.14249; p92 = 0.96655;    p93 = 26.42;     p94 = 0.0567;
-
+        p10 = 8735;
+        p11 = 17.1758;
+        p12 = 11720;
+        p13 = 0.00978;
+        p14 = 7045;
+        p15 = 0.003632;
+        p20 = 2.7925e-2;
+        p21 = 5.0399;
+        p22 = 2.8544e-2;
+        p23 = 0.002455;
+        p24 = 6.851e-5;
+        p25 = 9.2926;
+        p30 = 247970;
+        p31 = 17.63;
+        p32 = 374655;
+        p33 = 0.00191;
+        p34 = 195725;
+        p40 = 5.4818e-2;
+        p41 = 15.921;
+        p42 = 0.6373;
+        p43 = 5.99e-2;
+        p44 = 5.425e-4;
+        p50 = 1383702;
+        p51 = 4.156;
+        p52 = 5325;
+        p53 = 0.00238;
+        p54 = 0.0156;
+        p55 = 0.130;
+        p56 = 1521;
+        p60 = 6.031e-5;
+        p61 = 98.5;
+        p62 = 1.0466e-3;
+        p70 = 11747;
+        p71 = 41.66;
+        p72 = 4521;
+        p73 = 0.01998;
+        p74 = 0.00604;
+        p75 = 2534;
+        p76 = 0.00475;
+        p80 = 1.543e-2;
+        p81 = 10.06;
+        p82 = 1.807e-2;
+        p83 = 0.0011;
+        p84 = 8.81e-5;
+        p85 = 0.0405;
+        p86 = 20.24;
+        p90 = 8.321;
+        p91 = 0.14249;
+        p92 = 0.96655;
+        p93 = 26.42;
+        p94 = 0.0567;
 
         # Numerical determination of the penetration depth (86%) (Eq. 8)
-        D86 = 1/rhob*(p90+p91*(p92+np.exp(-1*distances/100))*(p93+theta)/(p94+theta))
+        D86 = 1 / rhob * (p90 + p91 * (p92 + np.exp(-1 * distances / 100)) * (p93 + theta) / (p94 + theta))
 
         # Depth weights (Eq. 7)
-        Wd = np.exp(-2*depth/D86)
+        Wd = np.exp(-2 * depth / D86)
 
         if h == 0:
-            W = 1 # skip distance weighting
+            W = 1  # skip distance weighting
 
-        elif (h >= 0.1) and (h<= 50):
+        elif (h >= 0.1) and (h <= 50):
             # Functions for Fi (Appendix A in Köhli et al., 2015)
-            F1 = p10*(1+p13*h)*np.exp(-p11*theta)+p12*(1+p15*h)-p14*theta
-            F2 = ((-p20+p24*h)*np.exp(-p21*theta/(1+p25*theta))+p22)*(1+h*p23)
-            F3 = (p30*(1+p33*h)*np.exp(-p31*theta)+p32-p34*theta)
-            F4 = p40*np.exp(-p41*theta)+p42-p43*theta+p44*h
-            F5 = p50*(0.02-1/p55/(h-p55+p56*theta))*(p54-theta)*np.exp(-p51*(theta-p54))+p52*(0.7-h*theta*p53)
-            F6 = p60*(h+p61)+p62*theta
-            F7 = (p70*(1-p76*h)*np.exp(-p71*theta*(1-h*p74))+p72-p75*theta)*(2+h*p73)
-            F8 = ((-p80+p84*h)*np.exp(-p81*theta/(1+p85*h+p86*theta))+p82)*(2+h*p83)
+            F1 = p10 * (1 + p13 * h) * np.exp(-p11 * theta) + p12 * (1 + p15 * h) - p14 * theta
+            F2 = ((-p20 + p24 * h) * np.exp(-p21 * theta / (1 + p25 * theta)) + p22) * (1 + h * p23)
+            F3 = (p30 * (1 + p33 * h) * np.exp(-p31 * theta) + p32 - p34 * theta)
+            F4 = p40 * np.exp(-p41 * theta) + p42 - p43 * theta + p44 * h
+            F5 = p50 * (0.02 - 1 / p55 / (h - p55 + p56 * theta)) * (p54 - theta) * np.exp(
+                -p51 * (theta - p54)) + p52 * (0.7 - h * theta * p53)
+            F6 = p60 * (h + p61) + p62 * theta
+            F7 = (p70 * (1 - p76 * h) * np.exp(-p71 * theta * (1 - h * p74)) + p72 - p75 * theta) * (2 + h * p73)
+            F8 = ((-p80 + p84 * h) * np.exp(-p81 * theta / (1 + p85 * h + p86 * theta)) + p82) * (2 + h * p83)
 
             # Distance weights (Eq. 3)
-            W = np.ones_like(distances)*np.nan
+            W = np.ones_like(distances) * np.nan
             for i in range(len(distances)):
-                if (distances[i]<=50) and (distances[i]>0.5):
-                    W[i]=F1[i]*(np.exp(-F2[i]*distances[i]))+F3[i]*np.exp(-F4[i]*distances[i])
+                if (distances[i] <= 50) and (distances[i] > 0.5):
+                    W[i] = F1[i] * (np.exp(-F2[i] * distances[i])) + F3[i] * np.exp(-F4[i] * distances[i])
 
-                elif (distances[i]>50) and (distances[i]<600):
-                    W[i]=F5[i]*(np.exp(-F6[i]*distances[i]))+F7[i]*np.exp(-F8[i]*distances[i])
+                elif (distances[i] > 50) and (distances[i] < 600):
+                    W[i] = F5[i] * (np.exp(-F6[i] * distances[i])) + F7[i] * np.exp(-F8[i] * distances[i])
 
                 else:
                     raise ValueError('Input distances are not valid.')
@@ -768,19 +825,36 @@ def nrad_weight(h,theta,distances,depth,rhob=1.4, method=None, p=None, Hveg=None
         else:
             raise ValueError('Air humidity values are out of range.')
 
-
         # Combined and normalized weights
-        weights = Wd*W/np.nansum(Wd*W)
+        weights = Wd * W / np.nansum(Wd * W)
         return weights
-    elif method=='Schron_2017':
+    elif method == 'Schron_2017':
         # Horizontal distance weights According to Eq. 6 and Table A1 in Schrön et al. (2017)
         # Method for calculating the horizontal distance weights from 0 to 1m
         def WrX(r, x, y):
             x00 = 3.7
-            a00 = 8735; a01 = 22.689; a02 = 11720; a03 = 0.00978; a04 = 9306; a05 = 0.003632
-            a10 = 2.7925e-2; a11 = 6.6577; a12 = 0.028544; a13 = 0.002455; a14 = 6.851e-5; a15 = 12.2755
-            a20 = 247970; a21 = 23.289; a22 = 374655; a23 = 0.00191; a24 = 258552
-            a30 = 5.4818e-2; a31 = 21.032; a32 = 0.6373; a33 = 0.0791; a34 = 5.425e-4
+            a00 = 8735;
+            a01 = 22.689;
+            a02 = 11720;
+            a03 = 0.00978;
+            a04 = 9306;
+            a05 = 0.003632
+            a10 = 2.7925e-2;
+            a11 = 6.6577;
+            a12 = 0.028544;
+            a13 = 0.002455;
+            a14 = 6.851e-5;
+            a15 = 12.2755
+            a20 = 247970;
+            a21 = 23.289;
+            a22 = 374655;
+            a23 = 0.00191;
+            a24 = 258552
+            a30 = 5.4818e-2;
+            a31 = 21.032;
+            a32 = 0.6373;
+            a33 = 0.0791;
+            a34 = 5.425e-4
 
             x0 = x00
             A0 = (a00 * (1 + a03 * x) * np.exp(-a01 * y) + a02 * (1 + a05 * x) - a04 * y)
@@ -792,10 +866,28 @@ def WrX(r, x, y):
 
         # Method for calculating the horizontal distance weights from 1 to 50m
         def WrA(r, x, y):
-            a00 = 8735; a01 = 22.689; a02 = 11720; a03 = 0.00978; a04 = 9306; a05 = 0.003632
-            a10 = 2.7925e-2; a11 = 6.6577; a12 = 0.028544; a13 = 0.002455; a14 = 6.851e-5; a15 = 12.2755
-            a20 = 247970; a21 = 23.289; a22 = 374655; a23 = 0.00191; a24 = 258552
-            a30 = 5.4818e-2; a31 = 21.032; a32 = 0.6373; a33 = 0.0791; a34 = 5.425e-4
+            a00 = 8735;
+            a01 = 22.689;
+            a02 = 11720;
+            a03 = 0.00978;
+            a04 = 9306;
+            a05 = 0.003632
+            a10 = 2.7925e-2;
+            a11 = 6.6577;
+            a12 = 0.028544;
+            a13 = 0.002455;
+            a14 = 6.851e-5;
+            a15 = 12.2755
+            a20 = 247970;
+            a21 = 23.289;
+            a22 = 374655;
+            a23 = 0.00191;
+            a24 = 258552
+            a30 = 5.4818e-2;
+            a31 = 21.032;
+            a32 = 0.6373;
+            a33 = 0.0791;
+            a34 = 5.425e-4
 
             A0 = (a00 * (1 + a03 * x) * np.exp(-a01 * y) + a02 * (1 + a05 * x) - a04 * y)
             A1 = ((-a10 + a14 * x) * np.exp(-a11 * y / (1 + a15 * y)) + a12) * (1 + x * a13)
@@ -806,10 +898,30 @@ def WrA(r, x, y):
 
         # Method for calculating the horizontal distance weights from 50 to 600m
         def WrB(r, x, y):
-            b00 = 39006; b01 = 15002337; b02 = 2009.24; b03 = 0.01181; b04 = 3.146; b05 = 16.7417; b06 = 3727
-            b10 = 6.031e-5; b11 = 98.5; b12 = 0.0013826
-            b20 = 11747; b21 = 55.033; b22 = 4521; b23 = 0.01998; b24 = 0.00604; b25 = 3347.4; b26 = 0.00475
-            b30 = 1.543e-2; b31 = 13.29; b32 = 1.807e-2; b33 = 0.0011; b34 = 8.81e-5; b35 = 0.0405; b36 = 26.74
+            b00 = 39006;
+            b01 = 15002337;
+            b02 = 2009.24;
+            b03 = 0.01181;
+            b04 = 3.146;
+            b05 = 16.7417;
+            b06 = 3727
+            b10 = 6.031e-5;
+            b11 = 98.5;
+            b12 = 0.0013826
+            b20 = 11747;
+            b21 = 55.033;
+            b22 = 4521;
+            b23 = 0.01998;
+            b24 = 0.00604;
+            b25 = 3347.4;
+            b26 = 0.00475
+            b30 = 1.543e-2;
+            b31 = 13.29;
+            b32 = 1.807e-2;
+            b33 = 0.0011;
+            b34 = 8.81e-5;
+            b35 = 0.0405;
+            b36 = 26.74
 
             B0 = (b00 - b01 / (b02 * y + x - 0.13)) * (b03 - y) * np.exp(-b04 * y) - b05 * x * y + b06
             B1 = b10 * (x + b11) + b12 * y
@@ -859,7 +971,7 @@ def Wd(d, r, bd, y):
         return weights
 
 
-def exp_filter(sm,T=1):
+def exp_filter(sm, T=1):
     """Exponential filter to estimate soil moisture in the rootzone from surface observtions.
 
     Args:
@@ -881,7 +993,6 @@ def exp_filter(sm,T=1):
         Soil Science Society of America Journal.
     """
 
-
     # Parameters
     t_delta = 1
     sm_min = np.min(sm)
@@ -889,18 +1000,18 @@ def exp_filter(sm,T=1):
     ms = (sm - sm_min) / (sm_max - sm_min)
 
     # Pre-allocate soil water index array and recursive constant K
-    SWI = np.ones_like(ms)*np.nan
-    K = np.ones_like(ms)*np.nan
+    SWI = np.ones_like(ms) * np.nan
+    K = np.ones_like(ms) * np.nan
 
     # Initial conditions
     SWI[0] = ms[0]
     K[0] = 1
 
     # Values from 2 to N
-    for n in range(1,len(SWI)):
-        if ~np.isnan(ms[n]) & ~np.isnan(ms[n-1]):
-            K[n] = K[n-1] / (K[n-1] + np.exp(-t_delta/T))
-            SWI[n] = SWI[n-1] + K[n]*(ms[n] - SWI[n-1])
+    for n in range(1, len(SWI)):
+        if ~np.isnan(ms[n]) & ~np.isnan(ms[n - 1]):
+            K[n] = K[n - 1] / (K[n - 1] + np.exp(-t_delta / T))
+            SWI[n] = SWI[n - 1] + K[n] * (ms[n] - SWI[n - 1])
         else:
             continue
 
@@ -910,7 +1021,7 @@ def exp_filter(sm,T=1):
     return sm_subsurface
 
 
-def cutoff_rigidity(lat,lon):
+def cutoff_rigidity(lat, lon):
     """Function to estimate the approximate cutoff rigidity for any point on Earth according to the
     tabulated data of Smart and Shea, 2019. The returned value can be used to select the appropriate
     neutron monitor station to estimate the cosmic-ray neutron intensity at the location of interest.
@@ -945,16 +1056,16 @@ def cutoff_rigidity(lat,lon):
     yq = lat
 
     if xq < 0:
-        xq = xq*-1 + 180
+        xq = xq * -1 + 180
     Z = np.array(data.cutoff_rigidity)
     x = np.linspace(0, 360, Z.shape[1])
     y = np.linspace(90, -90, Z.shape[0])
     X, Y = np.meshgrid(x, y)
-    points = np.array( (X.flatten(), Y.flatten()) ).T
+    points = np.array((X.flatten(), Y.flatten())).T
     values = Z.flatten()
-    zq = griddata(points, values, (xq,yq))
+    zq = griddata(points, values, (xq, yq))
 
-    return np.round(zq,2)
+    return np.round(zq, 2)
 
 
 def atmospheric_depth(elevation, latitude):
@@ -984,7 +1095,7 @@ def atmospheric_depth(elevation, latitude):
 
     # Gravity at sea-level calculation
     gravity_sea_level = 9.780327 * (
-                1 + 0.0053024 * np.sin(np.radians(latitude)) ** 2 - 0.0000058 * np.sin(2 * np.radians(latitude)) ** 2)
+            1 + 0.0053024 * np.sin(np.radians(latitude)) ** 2 - 0.0000058 * np.sin(2 * np.radians(latitude)) ** 2)
     # Free air correction
     free_air = -3.086 * 10 ** -6 * elevation
     # Bouguer correction
@@ -993,14 +1104,15 @@ def atmospheric_depth(elevation, latitude):
     gravity = gravity_sea_level + free_air + bouguer_corr
 
     # Air pressure calculation
-    reference_air_pressure = air_pressure_sea_level * (1 + temperature_lapse_rate / reference_temperature * elevation) ** ((-gravity * air_molar_mass) / (universal_gas_constant * temperature_lapse_rate))
+    reference_air_pressure = air_pressure_sea_level * (
+                1 + temperature_lapse_rate / reference_temperature * elevation) ** ((-gravity * air_molar_mass) / (
+                universal_gas_constant * temperature_lapse_rate))
 
     # Atmospheric depth calculation
     atmospheric_depth = (10 * reference_air_pressure) / gravity
     return atmospheric_depth
 
 
-
 def location_factor(site_atmospheric_depth, site_Rc, reference_atmospheric_depth, reference_Rc):
     """
     Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.
@@ -1031,14 +1143,17 @@ def location_factor(site_atmospheric_depth, site_Rc, reference_atmospheric_depth
 
     # Translated formula with renamed variables from McJannet and Desilets, 2023
     tau_new = epsilon * (c0 * reference_atmospheric_depth + c1) * (
-                1 - np.exp(-(c2 * reference_atmospheric_depth + c3) * reference_Rc ** (c4 * reference_atmospheric_depth + c5)))
+            1 - np.exp(
+        -(c2 * reference_atmospheric_depth + c3) * reference_Rc ** (c4 * reference_atmospheric_depth + c5)))
 
-    norm_factor = 1/tau_new
+    norm_factor = 1 / tau_new
 
     # Calculate the result using the provided parameters
-    tau = epsilon * norm_factor * (c0 * site_atmospheric_depth + c1) * (1 - np.exp(-(c2 * site_atmospheric_depth + c3) * site_Rc**(c4 * site_atmospheric_depth + c5)))
+    tau = epsilon * norm_factor * (c0 * site_atmospheric_depth + c1) * (
+                1 - np.exp(-(c2 * site_atmospheric_depth + c3) * site_Rc ** (c4 * site_atmospheric_depth + c5)))
     return tau
 
+
 def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):
     """Search for potential reference neutron monitoring stations based on cutoff rigidity.
 
@@ -1076,7 +1191,7 @@ def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):
     """
 
     # Load file with list of neutron monitoring stations
-    stations = pd.DataFrame(data.neutron_detectors, columns=["STID","NAME","R","Altitude_m"])
+    stations = pd.DataFrame(data.neutron_detectors, columns=["STID", "NAME", "R", "Altitude_m"])
 
     # Sort stations by closest cutoff rigidity
     idx_R = (stations['R'] - Rc).abs().argsort()
@@ -1087,9 +1202,10 @@ def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):
             station = stations.iloc[idx_R[i]]["STID"]
             try:
                 if get_incoming_neutron_flux(start_date, end_date, station, verbose=verbose) is not None:
-                    stations.iloc[idx_R[i],-1] = True
+                    stations.iloc[idx_R[i], -1] = True
             except:
                 pass
+
         if sum(stations["Period available"] == True) == 0:
             print("No stations available for the selected period!")
         else:
@@ -1101,7 +1217,8 @@ def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):
 
     # Print results
     print('')
-    print("""Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations""")
+    print(
+        """Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations""")
     print('')
     print(f"Your cutoff rigidity is {Rc} GV")
     print(result)
@@ -1120,7 +1237,7 @@ def interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps):
         (pd.Series): Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps
     """
     incoming_flux = np.array([])
-    for k,timestamp in enumerate(crnp_timestamps):
+    for k, timestamp in enumerate(crnp_timestamps):
         if timestamp in nmdb_timestamps.values:
             idx = timestamp == nmdb_timestamps
             incoming_flux = np.append(incoming_flux, nmdb_counts.loc[idx])
@@ -1189,6 +1306,7 @@ def latlon_to_utm(lat, lon, utm_zone_number=None, utm_zone_letter=None):
 
     return easting, northing, zone_number, zone_letter
 
+
 def euclidean_distance(px, py, x, y):
     """Function that computes the Euclidean distance between one point
     in space and one or more points.
@@ -1243,7 +1361,6 @@ def spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=Fa
         distances = euclidean_distance(px, py, x, y)
         idx_within_buffer = distances <= buffer
 
-
         if np.isnan(z[k]):
             z_new_val = np.nan
         elif len(distances[idx_within_buffer]) > min_neighbours:
@@ -1254,7 +1371,7 @@ def spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=Fa
             else:
                 raise f"Method {method} does not exist. Provide either 'mean' or 'median'."
         else:
-            z_new_val = z[k] # If there are not enough neighbours, keep the original value
+            z_new_val = z[k]  # If there are not enough neighbours, keep the original value
 
         # Append smoothed value to array
         z_smooth = np.append(z_smooth, z_new_val)
@@ -1335,7 +1452,8 @@ def interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000):
     z = z[~idx_nan]
 
     if idx_nan.any():
-        print(f"WARNING: {np.isnan(x).sum()}, {np.isnan(y).sum()}, and {np.isnan(z).sum()} NaN values were dropped from x, y, and z.")
+        print(
+            f"WARNING: {np.isnan(x).sum()}, {np.isnan(y).sum()}, and {np.isnan(z).sum()} NaN values were dropped from x, y, and z.")
 
     # Create 2D grid for interpolation
     Nx = round((np.max(x) - np.min(x)) / dx) + 1
@@ -1370,9 +1488,9 @@ def rover_centered_coordinates(x, y):
     """
 
     # Make it datatype agnostic
-    if(isinstance(x, pd.Series)):
+    if (isinstance(x, pd.Series)):
         x = x.values
-    if(isinstance(y, pd.Series)):
+    if (isinstance(y, pd.Series)):
         y = y.values
 
     # Do the average of the two points
@@ -1383,7 +1501,6 @@ def rover_centered_coordinates(x, y):
     x_est = np.insert(x_est, 0, x[0])
     y_est = np.insert(y_est, 0, y[0])
 
-
     return x_est, y_est
 
 
@@ -1417,13 +1534,13 @@ def uncertainty_counts(raw_counts, metric="std", fp=1, fw=1, fi=1):
     if metric == "std":
         uncertainty = np.sqrt(raw_counts) * s
     elif metric == "cv":
-        uncertainty = 1 /  np.sqrt(raw_counts) * s
+        uncertainty = 1 / np.sqrt(raw_counts) * s
     else:
         raise f"Metric {metric} does not exist. Provide either 'std' or 'cv' for standard deviation or coefficient of variation."
     return uncertainty
 
 
-def uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808,a1=0.372,a2=0.115):
+def uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808, a1=0.372, a2=0.115):
     r"""Function to estimate the uncertainty propagated to volumetric water content.
 
     The uncertainty of the volumetric water content is estimated by propagating the uncertainty of the raw counts.
@@ -1450,17 +1567,8 @@ def uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808,a1
 
     Ncorr = raw_counts * fw / (fp * fi)
     sigma_N = uncertainty_counts(raw_counts, metric="std", fp=fp, fw=fw, fi=fi)
-    sigma_GWC = sigma_N * ((a0*N0) / ((Ncorr - a1*N0)**4)) * np.sqrt((Ncorr - a1 * N0)**4 + 8 * sigma_N**2 * (Ncorr - a1 * N0)**2 + 15 * sigma_N**4)
+    sigma_GWC = sigma_N * ((a0 * N0) / ((Ncorr - a1 * N0) ** 4)) * np.sqrt(
+        (Ncorr - a1 * N0) ** 4 + 8 * sigma_N ** 2 * (Ncorr - a1 * N0) ** 2 + 15 * sigma_N ** 4)
     sigma_VWC = sigma_GWC * bulk_density
 
     return sigma_VWC
-
-
-
-
-
-
-
-
-
-

From b8e984dfc6e2cef9b2ed2cb4948cbaf3074e0e62 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Tue, 9 Jan 2024 01:17:58 -0300
Subject: [PATCH 11/24] fixed examples + expanded tests

#11
---
 build/lib/crnpy/crnpy.py          | 21 +++++++++++++++++++--
 setup.py                          |  3 ++-
 src/crnpy.egg-info/PKG-INFO       |  1 +
 src/crnpy.egg-info/requires.txt   |  1 +
 src/crnpy/crnpy.py                |  9 +++++++--
 src/tests/test_calibration_rdt.py |  3 +--
 src/tests/test_functions.py       | 17 +++++++++++++++++
 src/tests/test_hydroinnova.py     |  2 +-
 8 files changed, 49 insertions(+), 8 deletions(-)
 create mode 100644 src/tests/test_functions.py

diff --git a/build/lib/crnpy/crnpy.py b/build/lib/crnpy/crnpy.py
index bc923a1..507f688 100644
--- a/build/lib/crnpy/crnpy.py
+++ b/build/lib/crnpy/crnpy.py
@@ -317,13 +317,25 @@ def correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None,
     Args:
         incoming_neutrons (list or array): Incoming neutron flux readings.
         incoming_Ref (float): Reference incoming neutron flux. Baseline incoming neutron flux.
+        fill_na (float): Value to fill missing data. If None, missing data remains as NaN.
+        Rc_method (str): Optional to correct for differences in cutoff rigidity between the site and the reference station. Possible values are 'McJannetandDesilets2023' or 'Hawdonetal2014'. If None, no correction is performed.
+        Rc_site (float): Cutoff rigidity at the monitoring site.
+        site_atmdepth (float): Atmospheric depth at the monitoring site.
+        Rc_ref (float): Cutoff rigidity at the reference station.
+        ref_atmdepth (float): Atmospheric depth at the reference station.
 
     Returns:
         (list): fi correction factor.
 
     References:
+        Hawdon, A., D. McJannet, and J. Wallace (2014), Calibration and correction procedures for cosmic-ray neutron soil moisture probes located across Australia, Water Resour. Res., 50, 5029–5043, doi:10.1002/2013WR015138.
+
         M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086
 
+        McJannet, D. L., & Desilets, D. (2023). Incoming neutron flux corrections for cosmic-ray soil and snow sensors using the global neutron monitor network. Water Resources Research, 59, e2022WR033889. https://doi.org/10.1029/2022WR033889
+
+
+
 
     """
     if incoming_Ref is None and not isinstance(incoming_neutrons, type(None)):
@@ -694,7 +706,7 @@ def abs_humidity(relative_humidity, temp):
     return abs_h
 
 
-def nrad_weight(h, theta, distances, depth, rhob=1.4, method=None, p=None, Hveg=None):
+def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=None, Hveg=None):
     """Function to compute distance weights corresponding to each soil sample.
 
     Args:
@@ -723,7 +735,6 @@ def nrad_weight(h, theta, distances, depth, rhob=1.4, method=None, p=None, Hveg=
     """
 
     if method == 'Kohli_2015':
-
         # Table A1. Parameters for Fi and D86
         p10 = 8735;
         p11 = 17.1758;
@@ -1287,6 +1298,12 @@ def latlon_to_utm(lat, lon, utm_zone_number=None, utm_zone_letter=None):
 
          [https://www.maptools.com/tutorials/grid_zone_details#](https://www.maptools.com/tutorials/grid_zone_details#)
     """
+    # utm module requires numpy arrays
+    if not isinstance(lat, np.ndarray):
+        lat = np.array(lat)
+    if not isinstance(lon, np.ndarray):
+        lon = np.array(lon)
+
     if utm_zone_number is None or utm_zone_letter is None:
         easting, northing, zone_number, zone_letter = utm.from_latlon(lat, lon)
     else:
diff --git a/setup.py b/setup.py
index 5f2cc7d..f646fcf 100644
--- a/setup.py
+++ b/setup.py
@@ -19,6 +19,7 @@
         "numpy",
         "pandas",
         "scipy",
-        "requests"
+        "requests",
+        "utm"
     ],
 )
\ No newline at end of file
diff --git a/src/crnpy.egg-info/PKG-INFO b/src/crnpy.egg-info/PKG-INFO
index dde5303..a12720e 100644
--- a/src/crnpy.egg-info/PKG-INFO
+++ b/src/crnpy.egg-info/PKG-INFO
@@ -12,6 +12,7 @@ Requires-Dist: numpy
 Requires-Dist: pandas
 Requires-Dist: scipy
 Requires-Dist: requests
+Requires-Dist: utm
 
 [![GitHub Workflow Status (building)](https://img.shields.io/github/actions/workflow/status/soilwater/crnpy/python-package.yml)](https://github.com/soilwater/crnpy/actions/workflows/python-package.yml)
 [![GitHub Workflow Status (publish)](https://img.shields.io/github/actions/workflow/status/soilwater/crnpy/python-publish.yml?label=publish)](https://github.com/soilwater/crnpy/actions/workflows/python-publish.yml)
diff --git a/src/crnpy.egg-info/requires.txt b/src/crnpy.egg-info/requires.txt
index 4ecb749..07118c5 100644
--- a/src/crnpy.egg-info/requires.txt
+++ b/src/crnpy.egg-info/requires.txt
@@ -2,3 +2,4 @@ numpy
 pandas
 scipy
 requests
+utm
diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index 8536df7..507f688 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -706,7 +706,7 @@ def abs_humidity(relative_humidity, temp):
     return abs_h
 
 
-def nrad_weight(h, theta, distances, depth, rhob=1.4, method=None, p=None, Hveg=None):
+def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=None, Hveg=None):
     """Function to compute distance weights corresponding to each soil sample.
 
     Args:
@@ -735,7 +735,6 @@ def nrad_weight(h, theta, distances, depth, rhob=1.4, method=None, p=None, Hveg=
     """
 
     if method == 'Kohli_2015':
-
         # Table A1. Parameters for Fi and D86
         p10 = 8735;
         p11 = 17.1758;
@@ -1299,6 +1298,12 @@ def latlon_to_utm(lat, lon, utm_zone_number=None, utm_zone_letter=None):
 
          [https://www.maptools.com/tutorials/grid_zone_details#](https://www.maptools.com/tutorials/grid_zone_details#)
     """
+    # utm module requires numpy arrays
+    if not isinstance(lat, np.ndarray):
+        lat = np.array(lat)
+    if not isinstance(lon, np.ndarray):
+        lon = np.array(lon)
+
     if utm_zone_number is None or utm_zone_letter is None:
         easting, northing, zone_number, zone_letter = utm.from_latlon(lat, lon)
     else:
diff --git a/src/tests/test_calibration_rdt.py b/src/tests/test_calibration_rdt.py
index fa04d59..b951b11 100644
--- a/src/tests/test_calibration_rdt.py
+++ b/src/tests/test_calibration_rdt.py
@@ -31,8 +31,7 @@ def calibration_example():
     df_station = df_station[idx_period]
 
     # Compute total neutron counts by adding the counts from both probe detectors
-    df_station['total_raw_counts'] = crnpy.total_raw_counts(df_station[['counts_1_Tot', 'counts_2_Tot']],
-                                                                    nan_strategy='average')
+    df_station['total_raw_counts'] = crnpy.total_raw_counts(df_station[['counts_1_Tot', 'counts_2_Tot']])
 
     # Atmospheric corrections
 
diff --git a/src/tests/test_functions.py b/src/tests/test_functions.py
new file mode 100644
index 0000000..39054c6
--- /dev/null
+++ b/src/tests/test_functions.py
@@ -0,0 +1,17 @@
+import crnpy
+
+
+def test_correction_bwe():
+    r2_N0 = 6.4/1210
+    N = 420
+    Nv = 574 # Eg. 1 from Table 3. in Baatz et al. 2015
+    BWE = 51.5
+    Nv_ = crnpy.correction_bwe(N, BWE, r2_N0)
+    assert abs(Nv - Nv_) < 5 # 5 counts of difference is acceptable
+
+def test_correction_humidity():
+    # From Rosolem et al. 2013 Fig 2.
+    fp = crnpy.correction_humidity(20, 0)
+    assert round(fp,1) == 1.1
+
+def test_correction_pressure():
\ No newline at end of file
diff --git a/src/tests/test_hydroinnova.py b/src/tests/test_hydroinnova.py
index 56ce68c..f25a101 100644
--- a/src/tests/test_hydroinnova.py
+++ b/src/tests/test_hydroinnova.py
@@ -22,7 +22,7 @@ def hydroinnova_example_mean_value():
     df.reset_index(drop=True, inplace=True)
 
     # Convert Lat and Lon to X and Y
-    df['x'], df['y'] = crnpy.latlon_to_utm(df['LatDec'], df['LongDec'], 14, missing_values=0.0)
+    df['x'], df['y'], zone_letter, zone_number = crnpy.latlon_to_utm(df['LatDec'], df['LongDec'])
     df['x'], df['y'] = crnpy.rover_centered_coordinates(df['x'], df['y'])  # Estimate the center of the observation
 
     # Define columns names

From dc24a229844f5869303b896a2c36a4593acf1207 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Sat, 20 Jan 2024 16:30:41 -0600
Subject: [PATCH 12/24] Test functions

#11 #9 #7
---
 build/lib/crnpy/crnpy.py    |  6 +--
 src/tests/test_functions.py | 98 +++++++++++++++++++++++++++++++++++--
 2 files changed, 97 insertions(+), 7 deletions(-)

diff --git a/build/lib/crnpy/crnpy.py b/build/lib/crnpy/crnpy.py
index 507f688..e44618b 100644
--- a/build/lib/crnpy/crnpy.py
+++ b/build/lib/crnpy/crnpy.py
@@ -306,7 +306,7 @@ def correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None,
     - fi: incoming neutron flux correction factor
 
     $$
-    f_i = \frac{I_{ref}}{I}
+    f_i = \frac{I}{I_{ref}}
     $$
 
     where:
@@ -961,7 +961,7 @@ def Wd(d, r, bd, y):
             return np.exp(-2 * d / D86(r, bd, y))
 
         # Calculate the vertical distance weights
-        Wd = Wd(d, r, bd, y)
+        Wd = Wd(depth, r, bd, y)
 
         # Combined and normalized weights
         # Combined and normalized weights
@@ -1088,7 +1088,7 @@ def atmospheric_depth(elevation, latitude):
     density_of_rock = 2670  # Density of rock in kg/m3
     air_pressure_sea_level = 1013.25  # Air pressure at sea level in hPa
     air_molar_mass = 0.0289644  # Air molar mass in kg/mol
-    universal_gas_constant = 8.3144598  # Universal gas constant in J/(mol*K)
+    universal_gas_constant = 8.31432  # Universal gas constant in J/(mol*K)
     reference_temperature = 288.15  # Reference temperature Kelvin
     temperature_lapse_rate = -0.0065  # Temperature lapse rate in K/m
 
diff --git a/src/tests/test_functions.py b/src/tests/test_functions.py
index 39054c6..7952002 100644
--- a/src/tests/test_functions.py
+++ b/src/tests/test_functions.py
@@ -1,5 +1,6 @@
 import crnpy
-
+import numpy as np
+import pandas as pd
 
 def test_correction_bwe():
     r2_N0 = 6.4/1210
@@ -11,7 +12,96 @@ def test_correction_bwe():
 
 def test_correction_humidity():
     # From Rosolem et al. 2013 Fig 2.
-    fp = crnpy.correction_humidity(20, 0)
-    assert round(fp,1) == 1.1
+    fw = crnpy.correction_humidity(20, 0)
+    assert round(fw,1) == 1.1
+
+def test_correction_inncoming_neutrons():
+    incomming_neutrons = crnpy.get_incoming_neutron_flux(pd.to_datetime('2013-01-01 12:00:00'), pd.to_datetime('2013-01-05 12:00:00'), station='IRKT')
+    fi = crnpy.correction_incoming_flux(incomming_neutrons['counts'], 210)
+    print(incomming_neutrons['counts'])
+    # check that fi is between 0.9 and 1.1
+    assert (fi > 0.9).all() and (fi < 1.1).all()
+    #check that highest count has the highest correction factor
+    min = np.argmin(incomming_neutrons['counts'])
+    assert fi[min] == np.min(fi)
+    #check that lowest count has the lowest correction factor
+    max = np.argmax(incomming_neutrons['counts'])
+    assert fi[max] == np.max(fi)
+
+def test_correction_pressure():
+    fp = crnpy.correction_pressure(1013, 1013, 130)
+    assert round(fp,1) == 1.0
+
+def test_count_to_vwc():
+    # From Patrigani et al. 2021
+    N0 = 3767
+    N = 2580
+    vwc = crnpy.counts_to_vwc(N, N0, 0.033, 0, 1.33)
+    assert round(vwc,2) == 0.15
+
+def test_weighting():
+    # create vector from 0 to 350 m with .1 spacing
+    x = np.arange(0, 350, 0.1)
+    y = np.repeat(0, len(x))
+    h = np.repeat(10, len(x))
+
+
+    p = np.repeat(1013.25, len(x))
+    Hveg = np.repeat(0, len(x))
+    bd = 1.1
+    SM = 0.02
+    sm = np.repeat(SM, len(x))
+
+    # calculate weighting function using Schrön et al. 2017 method
+    weights = crnpy.nrad_weight(h, sm, x, y, bd, method="Schron_2017", p=p, Hveg=Hveg)
+    # check that the sum of the weights is 1
+    Max = 0.01147
+    Avg = 0.00029
+    Min = 0.00000
+    assert round(np.sum(weights),5) == 1
+    # Compare to the values in supplementary material of Schrön et al. 2017
+    # check that the max weight is 0.01
+    assert round(np.max(weights),3) == round(Max,3)
+    # check that the avg weight is 0.00029
+    assert round(np.mean(weights),5) == round(Avg,5)
+    # check that the min weight is 0.00000
+    assert round(np.min(weights),5) == round(Min,5)
+
+def test_correction_inncoming_neutrons_RcMethods():
+    # Test Hawdon 2014 Method using Baldry location from the original paper
+    RcJUNG = 4.50 #4.49
+    Rc_local = crnpy.cutoff_rigidity(-32.87, 148.54)
+    reference_counts = 159
+    # GEt JUNG for entire 2012
+    reference_data = crnpy.get_incoming_neutron_flux(pd.to_datetime('2012-01-01 12:00:00'), pd.to_datetime('2013-01-01 12:00:00'), station='JUNG')
+    reference_neutron_flux = reference_data['counts']
+    assert np.abs(Rc_local - 4.7) < 0.5 # check that the calculated local cutoff rigidity is close to the measured value reported in the paper
+    fi = crnpy.correction_incoming_flux(reference_neutron_flux, reference_counts, Rc_method='Hawdonetal2014', Rc_site=Rc_local, Rc_ref=RcJUNG)
+    paper_fi_range = [0.87, 1.04]
+    assert round(np.min(fi),2) == round(paper_fi_range[0],2)
+    assert round(np.max(fi),2) == round(paper_fi_range[1],2)
+
+    # Test McJannet and Desilets 2023 Method using data from the supplementary material
+    CRNSelevation = 604 #m
+    CRNSlatitude = -22.282 #deg
+    CRNSlongitude = 133.251 #deg
+    CutoffRigidity = 10.08 #GV
+    # Simulate a 1:1 relationship between incoming neutron flux and counts to get Tau
+    # This is not a realistic scenario but it is useful for testing
+    reference_counts = np.array([2400])
+    reference_neutron_flux = np.array([1200])
+    site_atmdepth = crnpy.atmospheric_depth(CRNSelevation, CRNSlatitude)
+    assert round(site_atmdepth,2) == 963.47
+    JUNG_atmdepth = 665.18 # From supplementary material of McJannet and Desilets 2023
+    fi = crnpy.correction_incoming_flux(reference_neutron_flux, reference_counts, Rc_method='McJannetandDesilets2023', Rc_site=CutoffRigidity, Rc_ref=RcJUNG, site_atmdepth=site_atmdepth, ref_atmdepth=JUNG_atmdepth)
+    f0 = 0.5
+    #if the formula for fi is fi = 1 / (tau * f0 + 1 - tau) get tau
+    tau = (1/fi[0] - 1) / (f0 - 1)
+    Tau_paper = 0.4753 # From supplementary material of McJannet and Desilets 2023
+    assert round(tau,2) == round(Tau_paper,2)
+
+
+
+
+
 
-def test_correction_pressure():
\ No newline at end of file

From d1a2ff4cee2a8dfddf23b9a332c0cffb4e458409 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Sat, 20 Jan 2024 16:30:48 -0600
Subject: [PATCH 13/24] Update crnpy.py

#8
---
 src/crnpy/crnpy.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index 507f688..6c6b9b9 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -241,6 +241,8 @@ def correction_pressure(pressure, Pref, L):
         (list): fp pressure correction factor.
 
     References:
+        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079–4099, https://doi.org/10.5194/hess-16-4079-2012, 2012.
+
         M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086
     """
 
@@ -282,6 +284,8 @@ def correction_humidity(abs_humidity, Aref):
         (list): fw correction factor.
 
     References:
+        Rosolem, R., W. J. Shuttleworth, M. Zreda, T. E. Franz, X. Zeng, and S. A. Kurc, 2013: The Effect of Atmospheric Water Vapor on Neutron Count in the Cosmic-Ray Soil Moisture Observing System. J. Hydrometeor., 14, 1659–1671, https://doi.org/10.1175/JHM-D-12-0120.1.
+
         M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086
     """
     A = abs_humidity
@@ -306,7 +310,7 @@ def correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None,
     - fi: incoming neutron flux correction factor
 
     $$
-    f_i = \frac{I_{ref}}{I}
+    f_i = \frac{I}{I_{ref}}
     $$
 
     where:
@@ -333,10 +337,6 @@ def correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None,
         M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086
 
         McJannet, D. L., & Desilets, D. (2023). Incoming neutron flux corrections for cosmic-ray soil and snow sensors using the global neutron monitor network. Water Resources Research, 59, e2022WR033889. https://doi.org/10.1029/2022WR033889
-
-
-
-
     """
     if incoming_Ref is None and not isinstance(incoming_neutrons, type(None)):
         incoming_Ref = incoming_neutrons[0]
@@ -961,7 +961,7 @@ def Wd(d, r, bd, y):
             return np.exp(-2 * d / D86(r, bd, y))
 
         # Calculate the vertical distance weights
-        Wd = Wd(d, r, bd, y)
+        Wd = Wd(depth, r, bd, y)
 
         # Combined and normalized weights
         # Combined and normalized weights
@@ -1088,7 +1088,7 @@ def atmospheric_depth(elevation, latitude):
     density_of_rock = 2670  # Density of rock in kg/m3
     air_pressure_sea_level = 1013.25  # Air pressure at sea level in hPa
     air_molar_mass = 0.0289644  # Air molar mass in kg/mol
-    universal_gas_constant = 8.3144598  # Universal gas constant in J/(mol*K)
+    universal_gas_constant = 8.31432  # Universal gas constant in J/(mol*K)
     reference_temperature = 288.15  # Reference temperature Kelvin
     temperature_lapse_rate = -0.0065  # Temperature lapse rate in K/m
 
@@ -1098,7 +1098,7 @@ def atmospheric_depth(elevation, latitude):
     # Free air correction
     free_air = -3.086 * 10 ** -6 * elevation
     # Bouguer correction
-    bouguer_corr = 4.194 * 10 ** -10 * density_of_rock * elevation
+    bouguer_corr = 4.193 * 10 ** -10 * density_of_rock * elevation
     # Total gravity
     gravity = gravity_sea_level + free_air + bouguer_corr
 

From a70f0cfe031aa15afe01a7bb4691c9be01aa5ba5 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Sun, 21 Jan 2024 20:45:14 -0600
Subject: [PATCH 14/24] Examples upgraded to new 0.6.0 version

---
 build/lib/crnpy/crnpy.py                      |  47 ++--
 docs/examples/calibration/calibration.ipynb   |  63 +++--
 .../rover/Hydroinnova_rover_example.ipynb     | 254 ++++++++++++++++--
 .../stationary/example_RDT_station.ipynb      |  26 +-
 src/crnpy/crnpy.py                            |  37 +--
 5 files changed, 328 insertions(+), 99 deletions(-)

diff --git a/build/lib/crnpy/crnpy.py b/build/lib/crnpy/crnpy.py
index e44618b..0b974c2 100644
--- a/build/lib/crnpy/crnpy.py
+++ b/build/lib/crnpy/crnpy.py
@@ -241,6 +241,8 @@ def correction_pressure(pressure, Pref, L):
         (list): fp pressure correction factor.
 
     References:
+        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079–4099, https://doi.org/10.5194/hess-16-4079-2012, 2012.
+
         M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086
     """
 
@@ -282,6 +284,8 @@ def correction_humidity(abs_humidity, Aref):
         (list): fw correction factor.
 
     References:
+        Rosolem, R., W. J. Shuttleworth, M. Zreda, T. E. Franz, X. Zeng, and S. A. Kurc, 2013: The Effect of Atmospheric Water Vapor on Neutron Count in the Cosmic-Ray Soil Moisture Observing System. J. Hydrometeor., 14, 1659–1671, https://doi.org/10.1175/JHM-D-12-0120.1.
+
         M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086
     """
     A = abs_humidity
@@ -333,10 +337,6 @@ def correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None,
         M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086
 
         McJannet, D. L., & Desilets, D. (2023). Incoming neutron flux corrections for cosmic-ray soil and snow sensors using the global neutron monitor network. Water Resources Research, 59, e2022WR033889. https://doi.org/10.1029/2022WR033889
-
-
-
-
     """
     if incoming_Ref is None and not isinstance(incoming_neutrons, type(None)):
         incoming_Ref = incoming_neutrons[0]
@@ -473,7 +473,7 @@ def get_reference_neutron_flux(station, date=pd.to_datetime("2011-05-01")):
 """
 
     # Get flux for 2011-05-01
-    df_flux = get_incoming_neutron_flux(station, date, date + pd.Timedelta(hours=24))
+    df_flux = get_incoming_neutron_flux(date, date + pd.Timedelta(hours=24), station=station)
     if df_flux is None:
         warnings.warn(f"Reference neutron flux for {station} not available. Returning NaN.")
     else:
@@ -502,7 +502,7 @@ def smooth_1d(values, window=5, order=3, method='moving_median'):
         Analytical chemistry, 36(8), 1627-1639.
     """
 
-    if not isinstance(x, pd.Series) and not isinstance(x, pd.DataFrame):
+    if not isinstance(values, pd.Series) and not isinstance(x, pd.DataFrame):
         raise ValueError('Input must be a pandas Series or DataFrame')
 
     if method == 'moving_average':
@@ -1098,7 +1098,7 @@ def atmospheric_depth(elevation, latitude):
     # Free air correction
     free_air = -3.086 * 10 ** -6 * elevation
     # Bouguer correction
-    bouguer_corr = 4.194 * 10 ** -10 * density_of_rock * elevation
+    bouguer_corr = 4.193 * 10 ** -10 * density_of_rock * elevation
     # Total gravity
     gravity = gravity_sea_level + free_air + bouguer_corr
 
@@ -1228,26 +1228,29 @@ def interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps):
     """Function to interpolate incoming neutron flux to match the timestamps of the observations.
 
     Args:
-        nmdb_timestamps (pd.Series): Series of timestamps in datetime format from the NMDB.
-        nmdb_counts (pd.Series): Series of neutron counts from the NMDB
-        crnp_timestamps (pd.Series): Series of timestamps in datetime format from the station or device.
+        nmdb_timestamps (pd.Series or np.array): Series or array of timestamps in datetime format from the NMDB
+        nmdb_counts (pd.Series or np.array): Series or array of incoming neutron flux counts from the NMDB
+        crnp_timestamps (pd.Series or np.array): Series or array of timestamps in datetime format from the CRNP device
 
     Returns:
         (pd.Series): Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps
     """
-    incoming_flux = np.array([])
-    for k, timestamp in enumerate(crnp_timestamps):
-        if timestamp in nmdb_timestamps.values:
-            idx = timestamp == nmdb_timestamps
-            incoming_flux = np.append(incoming_flux, nmdb_counts.loc[idx])
-        else:
-            incoming_flux = np.append(incoming_flux, np.nan)
+    # Create a DataFrame from nmdb timestamps and counts
+    df_nmdb = pd.DataFrame({'timestamp': nmdb_timestamps, 'counts': nmdb_counts})
+
+    # Set the Timestamp column as the index
+    df_nmdb.set_index('timestamp', inplace=True)
+
+    # Reindex the DataFrame to the timestamps from the CRNP device using the nearest method
+    # This will match each CRNP timestamp with the nearest NMDB timestamp
+    interpolated_flux = df_nmdb.reindex(crnp_timestamps, method='nearest')['counts'].values
+
+    #drop NaN values
+    interpolated_flux = interpolated_flux[~np.isnan(interpolated_flux)]
 
-    # Interpolate nan values
-    incoming_flux = pd.Series(incoming_flux).interpolate(method='nearest', limit_direction='both')
+    assert len(interpolated_flux) == len(crnp_timestamps), "Length of interpolated flux does not match length of CRNP timestamps"
 
-    # Return only the values for the selected timestamps
-    return incoming_flux
+    return interpolated_flux
 
 
 def lattice_water(clay_content, total_carbon=None):
@@ -1290,7 +1293,7 @@ def latlon_to_utm(lat, lon, utm_zone_number=None, utm_zone_letter=None):
         utm_zone_letter (str): UTM zone letter. If None, the zone letter is automatically calculated.
 
     Returns:
-        (float, float): Tuple of easting and northing coordinates in meters. First element is easting, second is northing.
+        (float, float, int, str): Tuple of easting, northing, zone number and zone letter. First element is easting, second is northing, third is zone number and fourth is zone letter.
 
     References:
          Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87)
diff --git a/docs/examples/calibration/calibration.ipynb b/docs/examples/calibration/calibration.ipynb
index 34984fd..50b075f 100644
--- a/docs/examples/calibration/calibration.ipynb
+++ b/docs/examples/calibration/calibration.ipynb
@@ -188,18 +188,18 @@
        "</div>"
       ],
       "text/plain": [
-       "      field    date  core_number  distance_from_station   latitude  longitude   \n",
-       "0  Flickner  22-Oct            1                      5  N38.23459  W97.57101  \\\n",
+       "      field    date  core_number  distance_from_station   latitude  longitude  \\\n",
+       "0  Flickner  22-Oct            1                      5  N38.23459  W97.57101   \n",
        "1  Flickner  22-Oct            1                      5  N38.23459  W97.57101   \n",
        "2  Flickner  22-Oct            1                      5  N38.23459  W97.57101   \n",
        "\n",
-       "   top_depth  bottom_depth  core_diameter  wet_mass_with_bag  ...  can_number   \n",
-       "0          0             5          30.49              45.31  ...           1  \\\n",
+       "   top_depth  bottom_depth  core_diameter  wet_mass_with_bag  ...  can_number  \\\n",
+       "0          0             5          30.49              45.31  ...           1   \n",
        "1          5            10          30.49              69.53  ...           2   \n",
        "2         10            25          30.49             214.90  ...           3   \n",
        "\n",
-       "   mass_empty_can  wet_mass_with_can  dry_mass_with_can  mass_water  theta_g   \n",
-       "0           52.10              92.03              85.31        6.72    0.202  \\\n",
+       "   mass_empty_can  wet_mass_with_can  dry_mass_with_can  mass_water  theta_g  \\\n",
+       "0           52.10              92.03              85.31        6.72    0.202   \n",
        "1           51.85             115.97             103.85       12.12    0.233   \n",
        "2           51.56             260.97             219.77       41.20    0.245   \n",
        "\n",
@@ -372,28 +372,28 @@
        "</div>"
       ],
       "text/plain": [
-       "             TIMESTAMP  RECORD station      farm          field  latitude   \n",
-       "0  2021-09-22 12:00:00       0   KS003  Flickner  Rainfed South  38.23461  \\\n",
+       "             TIMESTAMP  RECORD station      farm          field  latitude  \\\n",
+       "0  2021-09-22 12:00:00       0   KS003  Flickner  Rainfed South  38.23461   \n",
        "1  2021-09-22 13:00:00       1   KS003  Flickner  Rainfed South  38.23461   \n",
        "2  2021-09-22 14:00:00       2   KS003  Flickner  Rainfed South  38.23461   \n",
        "\n",
-       "   longitude  altitude  battery_voltage_Min  PTemp_Avg  ...   \n",
-       "0  -97.57095       455                13.52      29.04  ...  \\\n",
+       "   longitude  altitude  battery_voltage_Min  PTemp_Avg  ...  \\\n",
+       "0  -97.57095       455                13.52      29.04  ...   \n",
        "1  -97.57095       455                13.53      29.98  ...   \n",
        "2  -97.57095       455                13.53      30.43  ...   \n",
        "\n",
-       "   wind_speed_gust_Max  air_temperature_Avg  vapor_pressure_Avg   \n",
-       "0                 4.20                22.30                9.20  \\\n",
+       "   wind_speed_gust_Max  air_temperature_Avg  vapor_pressure_Avg  \\\n",
+       "0                 4.20                22.30                9.20   \n",
        "1                 9.02                22.90                9.08   \n",
        "2                 5.54                23.38                8.68   \n",
        "\n",
-       "   barometric_pressure_Avg  relative_humidity_Avg   \n",
-       "0                      973                  41.30  \\\n",
+       "   barometric_pressure_Avg  relative_humidity_Avg  \\\n",
+       "0                      973                  41.30   \n",
        "1                      972                  32.63   \n",
        "2                      971                  30.25   \n",
        "\n",
-       "   humidity_sensor_temperature_Avg  tilt_north_south_Avg  tilt_west_east_Avg   \n",
-       "0                             26.4                -1.000               1.000  \\\n",
+       "   humidity_sensor_temperature_Avg  tilt_north_south_Avg  tilt_west_east_Avg  \\\n",
+       "0                             26.4                -1.000               1.000   \n",
        "1                             26.8                -0.975               0.950   \n",
        "2                             27.2                -0.775               0.625   \n",
        "\n",
@@ -550,28 +550,28 @@
        "</div>"
       ],
       "text/plain": [
-       "             TIMESTAMP  RECORD station      farm          field  latitude   \n",
-       "0  2021-09-22 12:00:00       0   KS003  Flickner  Rainfed South  38.23461  \\\n",
+       "             TIMESTAMP  RECORD station      farm          field  latitude  \\\n",
+       "0  2021-09-22 12:00:00       0   KS003  Flickner  Rainfed South  38.23461   \n",
        "1  2021-09-22 13:00:00       1   KS003  Flickner  Rainfed South  38.23461   \n",
        "2  2021-09-22 14:00:00       2   KS003  Flickner  Rainfed South  38.23461   \n",
        "\n",
-       "   longitude  altitude  battery_voltage_Min  PTemp_Avg  ...   \n",
-       "0  -97.57095       455                13.52      29.04  ...  \\\n",
+       "   longitude  altitude  battery_voltage_Min  PTemp_Avg  ...  \\\n",
+       "0  -97.57095       455                13.52      29.04  ...   \n",
        "1  -97.57095       455                13.53      29.98  ...   \n",
        "2  -97.57095       455                13.53      30.43  ...   \n",
        "\n",
-       "   wind_speed_gust_Max  air_temperature_Avg  vapor_pressure_Avg   \n",
-       "0                 4.20                22.30                9.20  \\\n",
+       "   wind_speed_gust_Max  air_temperature_Avg  vapor_pressure_Avg  \\\n",
+       "0                 4.20                22.30                9.20   \n",
        "1                 9.02                22.90                9.08   \n",
        "2                 5.54                23.38                8.68   \n",
        "\n",
-       "   barometric_pressure_Avg  relative_humidity_Avg   \n",
-       "0                      973                  41.30  \\\n",
+       "   barometric_pressure_Avg  relative_humidity_Avg  \\\n",
+       "0                      973                  41.30   \n",
        "1                      972                  32.63   \n",
        "2                      971                  30.25   \n",
        "\n",
-       "   humidity_sensor_temperature_Avg  tilt_north_south_Avg  tilt_west_east_Avg   \n",
-       "0                             26.4                -1.000               1.000  \\\n",
+       "   humidity_sensor_temperature_Avg  tilt_north_south_Avg  tilt_west_east_Avg  \\\n",
+       "0                             26.4                -1.000               1.000   \n",
        "1                             26.8                -0.975               0.950   \n",
        "2                             27.2                -0.775               0.625   \n",
        "\n",
@@ -624,8 +624,7 @@
    "outputs": [],
    "source": [
     "# Compute total neutron counts by adding the counts from both probe detectors\n",
-    "df_station['total_raw_counts'] = crnpy.total_raw_counts(df_station[['counts_1_Tot','counts_2_Tot']],\n",
-    "                                                    nan_strategy='average')\n"
+    "df_station['total_raw_counts'] = crnpy.total_raw_counts(df_station[['counts_1_Tot','counts_2_Tot']])\n"
    ]
   },
   {
@@ -824,7 +823,7 @@
      "output_type": "stream",
      "text": [
       "Mean volumetric Water content during calibration survey: 0.263\n",
-      "Mean corrected counts during calibration: 1542 counts\n"
+      "Mean corrected counts during calibration: 1543 counts\n"
      ]
     }
    ],
@@ -857,7 +856,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The solved value for N0 is: 2644\n"
+      "The solved value for N0 is: 2645\n"
      ]
     }
    ],
@@ -869,7 +868,7 @@
     "N0_initial_guess = 1000\n",
     "\n",
     "# Find the root\n",
-    "sol = int(root(VWC_func, N0_initial_guess).x)\n",
+    "sol = int(root(VWC_func, N0_initial_guess).x[0])\n",
     "\n",
     "# Print the solution\n",
     "print(f\"The solved value for N0 is: {sol}\")\n"
@@ -905,7 +904,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/rover/Hydroinnova_rover_example.ipynb b/docs/examples/rover/Hydroinnova_rover_example.ipynb
index 089ce8a..761b23c 100644
--- a/docs/examples/rover/Hydroinnova_rover_example.ipynb
+++ b/docs/examples/rover/Hydroinnova_rover_example.ipynb
@@ -164,18 +164,18 @@
        "</div>"
       ],
       "text/plain": [
-       "   RecordNum        Date Time(UTC)  barometric_pressure_Avg  P4_mb  P1_mb   \n",
-       "0          2   2018/05/01 14:15:00                   962.95  962.8  961.4  \\\n",
+       "   RecordNum        Date Time(UTC)  barometric_pressure_Avg  P4_mb  P1_mb  \\\n",
+       "0          2   2018/05/01 14:15:00                   962.95  962.8  961.4   \n",
        "1          3   2018/05/01 14:16:00                   962.88  962.7  961.3   \n",
        "2          4   2018/05/01 14:17:00                   962.64  962.5  961.1   \n",
        "\n",
-       "   T1_C   RH1  air_temperature_Avg  relative_humidity_Avg    Vbat  ...   \n",
-       "0  23.2  35.4                 20.9                   72.7  13.574  ...  \\\n",
+       "   T1_C   RH1  air_temperature_Avg  relative_humidity_Avg    Vbat  ...  \\\n",
+       "0  23.2  35.4                 20.9                   72.7  13.574  ...   \n",
        "1  23.3  35.5                 21.0                   72.7  13.417  ...   \n",
        "2  23.4  35.4                 21.2                   72.2  13.282  ...   \n",
        "\n",
-       "    LongDec    Alt  Qual  NumSats  HDOP  Speed_kmh    COG  SpeedQuality   \n",
-       "0 -97.37195  393.2   2.0       10   0.8       0.00  270.7             A  \\\n",
+       "    LongDec    Alt  Qual  NumSats  HDOP  Speed_kmh    COG  SpeedQuality  \\\n",
+       "0 -97.37195  393.2   2.0       10   0.8       0.00  270.7             A   \n",
        "1 -97.37197  387.2   2.0       11   0.8       0.00  270.7             A   \n",
        "2 -97.37199  388.2   1.0       11   0.8       3.89  356.3             A   \n",
        "\n",
@@ -229,7 +229,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]
@@ -240,7 +240,7 @@
    ],
    "source": [
     "# Convert Lat and Lon to X and Y\n",
-    "df['x'],df['y'] = crnpy.latlon_to_utm(df['LatDec'], df['LongDec'], 14, missing_values=0.0)\n",
+    "df['x'],df['y'],zone_n, zone_letter = crnpy.latlon_to_utm(df['LatDec'], df['LongDec'])\n",
     "\n",
     "# Create figure of survey points\n",
     "plt.figure(figsize = (5,5))\n",
@@ -453,7 +453,7 @@
    "source": [
     "# Compute correction factor for incoming neutron flux\n",
     "df['fi'] = crnpy.correction_incoming_flux(incoming_neutrons=df['incoming_flux'],\n",
-    "                                          incoming_Ref=df['incoming_flux'].iloc[0])\n"
+    "                                          incoming_Ref=crnpy.get_reference_neutron_flux(station=\"NEWK\"))\n"
    ]
   },
   {
@@ -495,6 +495,230 @@
     "df['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "678b96e9-8d18-475e-8e56-1c458f224bdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>RecordNum</th>\n",
+       "      <th>Date Time(UTC)</th>\n",
+       "      <th>barometric_pressure_Avg</th>\n",
+       "      <th>P4_mb</th>\n",
+       "      <th>P1_mb</th>\n",
+       "      <th>T1_C</th>\n",
+       "      <th>RH1</th>\n",
+       "      <th>air_temperature_Avg</th>\n",
+       "      <th>relative_humidity_Avg</th>\n",
+       "      <th>Vbat</th>\n",
+       "      <th>...</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>total_raw_counts</th>\n",
+       "      <th>incoming_flux</th>\n",
+       "      <th>fi</th>\n",
+       "      <th>fp</th>\n",
+       "      <th>abs_humidity</th>\n",
+       "      <th>fw</th>\n",
+       "      <th>total_corrected_neutrons</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2018/05/01 14:15:00</td>\n",
+       "      <td>962.95</td>\n",
+       "      <td>962.8</td>\n",
+       "      <td>961.4</td>\n",
+       "      <td>23.2</td>\n",
+       "      <td>35.4</td>\n",
+       "      <td>20.9</td>\n",
+       "      <td>72.7</td>\n",
+       "      <td>13.574</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2018-05-01 14:15:00</td>\n",
+       "      <td>641605.459124</td>\n",
+       "      <td>4.282933e+06</td>\n",
+       "      <td>305</td>\n",
+       "      <td>101.105</td>\n",
+       "      <td>1.05959</td>\n",
+       "      <td>1.000136</td>\n",
+       "      <td>13.231630</td>\n",
+       "      <td>1.071451</td>\n",
+       "      <td>308.372165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2018/05/01 14:16:00</td>\n",
+       "      <td>962.88</td>\n",
+       "      <td>962.7</td>\n",
+       "      <td>961.3</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>35.5</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>72.7</td>\n",
+       "      <td>13.417</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2018-05-01 14:16:00</td>\n",
+       "      <td>641604.648429</td>\n",
+       "      <td>4.282929e+06</td>\n",
+       "      <td>345</td>\n",
+       "      <td>101.105</td>\n",
+       "      <td>1.05959</td>\n",
+       "      <td>1.000675</td>\n",
+       "      <td>13.308698</td>\n",
+       "      <td>1.071867</td>\n",
+       "      <td>348.762056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2018/05/01 14:17:00</td>\n",
+       "      <td>962.64</td>\n",
+       "      <td>962.5</td>\n",
+       "      <td>961.1</td>\n",
+       "      <td>23.4</td>\n",
+       "      <td>35.4</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>72.2</td>\n",
+       "      <td>13.282</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2018-05-01 14:17:00</td>\n",
+       "      <td>641604.426695</td>\n",
+       "      <td>4.282844e+06</td>\n",
+       "      <td>294</td>\n",
+       "      <td>101.105</td>\n",
+       "      <td>1.05959</td>\n",
+       "      <td>1.002524</td>\n",
+       "      <td>13.371401</td>\n",
+       "      <td>1.072206</td>\n",
+       "      <td>296.751456</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2018/05/01 14:18:00</td>\n",
+       "      <td>962.41</td>\n",
+       "      <td>962.2</td>\n",
+       "      <td>960.9</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>35.5</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>13.191</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2018-05-01 14:18:00</td>\n",
+       "      <td>641599.541417</td>\n",
+       "      <td>4.282997e+06</td>\n",
+       "      <td>319</td>\n",
+       "      <td>101.105</td>\n",
+       "      <td>1.05959</td>\n",
+       "      <td>1.004299</td>\n",
+       "      <td>13.334361</td>\n",
+       "      <td>1.072006</td>\n",
+       "      <td>321.356301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2018/05/01 14:19:00</td>\n",
+       "      <td>962.48</td>\n",
+       "      <td>962.1</td>\n",
+       "      <td>960.8</td>\n",
+       "      <td>23.6</td>\n",
+       "      <td>35.4</td>\n",
+       "      <td>21.4</td>\n",
+       "      <td>72.2</td>\n",
+       "      <td>13.130</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2018-05-01 14:19:00</td>\n",
+       "      <td>641587.984800</td>\n",
+       "      <td>4.283574e+06</td>\n",
+       "      <td>379</td>\n",
+       "      <td>101.105</td>\n",
+       "      <td>1.05959</td>\n",
+       "      <td>1.003759</td>\n",
+       "      <td>13.527187</td>\n",
+       "      <td>1.073047</td>\n",
+       "      <td>382.376183</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 47 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   RecordNum        Date Time(UTC)  barometric_pressure_Avg  P4_mb  P1_mb  \\\n",
+       "0          2   2018/05/01 14:15:00                   962.95  962.8  961.4   \n",
+       "1          3   2018/05/01 14:16:00                   962.88  962.7  961.3   \n",
+       "2          4   2018/05/01 14:17:00                   962.64  962.5  961.1   \n",
+       "3          5   2018/05/01 14:18:00                   962.41  962.2  960.9   \n",
+       "4          6   2018/05/01 14:19:00                   962.48  962.1  960.8   \n",
+       "\n",
+       "   T1_C   RH1  air_temperature_Avg  relative_humidity_Avg    Vbat  ...  \\\n",
+       "0  23.2  35.4                 20.9                   72.7  13.574  ...   \n",
+       "1  23.3  35.5                 21.0                   72.7  13.417  ...   \n",
+       "2  23.4  35.4                 21.2                   72.2  13.282  ...   \n",
+       "3  23.5  35.5                 21.2                   72.0  13.191  ...   \n",
+       "4  23.6  35.4                 21.4                   72.2  13.130  ...   \n",
+       "\n",
+       "            timestamp              x             y  total_raw_counts  \\\n",
+       "0 2018-05-01 14:15:00  641605.459124  4.282933e+06               305   \n",
+       "1 2018-05-01 14:16:00  641604.648429  4.282929e+06               345   \n",
+       "2 2018-05-01 14:17:00  641604.426695  4.282844e+06               294   \n",
+       "3 2018-05-01 14:18:00  641599.541417  4.282997e+06               319   \n",
+       "4 2018-05-01 14:19:00  641587.984800  4.283574e+06               379   \n",
+       "\n",
+       "   incoming_flux       fi        fp  abs_humidity        fw  \\\n",
+       "0        101.105  1.05959  1.000136     13.231630  1.071451   \n",
+       "1        101.105  1.05959  1.000675     13.308698  1.071867   \n",
+       "2        101.105  1.05959  1.002524     13.371401  1.072206   \n",
+       "3        101.105  1.05959  1.004299     13.334361  1.072006   \n",
+       "4        101.105  1.05959  1.003759     13.527187  1.073047   \n",
+       "\n",
+       "   total_corrected_neutrons  \n",
+       "0                308.372165  \n",
+       "1                348.762056  \n",
+       "2                296.751456  \n",
+       "3                321.356301  \n",
+       "4                382.376183  \n",
+       "\n",
+       "[5 rows x 47 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "8fbd8cfc-6b69-4a1c-a662-77531c3df4f1",
@@ -505,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "6e09f8aa-3a13-4c5d-9b2f-9976c778f2e7",
    "metadata": {},
    "outputs": [],
@@ -542,13 +766,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "2a595e5e-5ace-4db3-afc2-e11ca26fa0da",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x600 with 2 Axes>"
       ]
@@ -582,13 +806,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "858a04a9-6c7a-457f-865d-58419d821054",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Ovfl5vWtyAWN+r42+1zGDA8D19cAn69er/a0B9Q8qn37yCYL9PDFmcIBFH7h1YclQIU7LVmjRsrXZ5nd3d1cTc5ro0KEDOBwOKioq1I5XVFTA19e32c+mp6dj9erVyM7OxoABAzSOycvLw4ULF7Br1y6d9oaHh+Pp06e4cuUKevY0/4OivrDn/04bwtgb1L7DlzBJlI3+Q2IglUpx//59SKVS9B8Sg0mibOw7fIlhS9WRyeSYv7UAvPh4iDMzERERgbZt2yIiIgLizEzw4uOxIKMQMpmc0XW1nfeYMWNw/MQJlJaWms0Ofc55zuajuFJehZRFi5rEoTg7O0OQkoLLZZXIKy432R59yCsuZ5U9ZXdrAQD9+vXT+L7yuHKcJfDz8QQAVdmBxiiPK8c5mj0A4OfdWm3txqhs8jbfjTq6yyPVv/uERsCnSyBShULI5ep/23K5HEKRCEHcQERGay41wRQNr98cjjPWvhkGSVYW+AkJatcnfkICJFlZWJMUypiQk8nkyD1dih25F5F7upTxa6294uLigsGDB6slJsjlcuTk5CAyMlLr59asWYOVK1fiwIEDGDJkiNZxW7ZsweDBgxESEqLTlpMnT8LZ2RmdOnUy7CTMDIk5C2EtIdUQa4iE5s47MzMTPB4P8+bNg0wmM4sd+pxz6Z37AMwjVoy5eLNNPBkrCszllQOAmMie4AZ2glCYqlEYiERCBAf5WGSLlY32AEBMX19wfT0gTNVik1CIYD9PxPRt3rOhjbtth+s1TinoOBwO3hAsQ5ZEggQ+X004JfD5yJJIsHz1oiY9sc1Bw/83xw3tit2CWJw5loeoqCi4u7sjKioKRcfzGd8heG7mbowU7Mfra3MwUrAfz83cbfaHeHth7ty52Lx5M7Zt24Zz587h3XffxYMHD5CUlAQAmDp1qlqCRFpaGhYvXoytW7eCy+WivLwc5eXlTWL0qqursWfPHkyfPr3JmlKpFOvXr8epU6dw6dIlbN++HXPmzMHrr78OLy8v856wgZCYMxJDb1Rs8LZYQyToPG+BAJcvX0ZeXp5Z7ND3nAHmPRjGXrzZ4FFpiLlFgTFwOM5IXzEZEkkW+PxGHhV+AiSSLKxdPsliW2PmsMdUMWys18kcIlwp6CJH87BgwxYU/nZKTTidOXMKGTs2gseP0zmXd00uIzY1FnS/fzkJOaKx+M/8kcgRjcWFTRMZFXLW3JWxByZPnoz09HQsWbIEAwcOxMmTJ3HgwAFVUkRJSQnKyspU47/44gvU1dVhwoQJ8PPzU73S09PV5t25cycUCgUSExObrOnq6oqdO3di2LBh6Nu3L1JTUzFnzhx8+eWX5j1ZI3CYmDlrwwZvS0ORoClmxRwiQe/zbvBHyKQd+p6zj1cbCFNTNcbNGCNWlBdvXnw8djSK05skaj4msKF4YsoeU1CKgkmielEgSElRnY9IKIQkq/58LB1TNI4Xij0ZyZi3ZBeioqJUx4ODfLAnIxnjeE0D2h3JHuCZ12n+1jx1m/w8zRYfq43oLo+Qf90NkaN5CIuNw9nCI7h3qwJeHX0w89WBFvHINcYpfKgqho7DcW4S88kEjXcnlH/Pyl0ZfkICFmTkIyGCq/NvqD4pJYNxG22F5ORkJCcna3wvNzdX7ecrV67oNefMmTMxc+ZMje89//zzOHLE9GQkS0BizgQaXgh0YQ0h1RhriAS9z9vPzyx26HvOomlDkJjGjFgx9eLNRvFkqCgw5xarml28UCTEDUae9ALKKirh5+OJmMieen83DbcKmfD4mGqPORg3tCsSIrjIKy5H2d1a+Hm3RkxfX6vaxOFw0D+i/v+RhnF1umDKK9cQQ67jxqDcndjRzK5MVFQU8orLmxWTTuFDgb8SbQiiMSTmLAQbvC3WEAm6zlsoFKJz584YOHAgpFIp43boe87jhnYFx9mJEQ8GExdvNnlUGtrENlEA/OVRie4NoF6cVekYr427bYczIhYa2mMqTAkNfb1O+opwfePlGqP0zjX8mQ0Y+z03/r40zcGGXRnC/iEx9xcymdyop2l9LwJs8bZYWiQ0d95CoRASiQQAVMGk5rBD33NmSqwwdfFmo3gy11aUqWgTFzKZDNL8AlSU34SPbydERoc1u53HlKAjtMMWAdcYpTBr7npujMeZiV0ZS3m6CduFxByAfZJCzFuyC1dKnjXs5QZ2QvqKyYzGubDF22JpkdDcee/6KBYdPFqZ3Q59z5kJscLkljpbxVNzWPLG05yHSCL+AUsWrkLJ1WuqY4FBAViR9nGzgfYk6NiJpX4nDR/QDf1/WdPDvam7MiTkCH1weDG3T1KIiUkbwOPFY8euBoHqwlRMTNqgV+CyIS56tnhbLC0S2HDeljpnNmypOwK6hFxS4jvg8XjYtXO3mjc4KfEdnZmTbBJ05o7pargOW7H078KU76Lx74stuzKEfeOkUCgU1jbCElRXV8PDwwOVlzepKrDLZHL0CF2A/iFhEIub3nT5/AQUnS7E7wVpOv/QLHGxJWyHhtmszcXp2RuWEgTNCTmZTIbQPi8gZMBAiMViDX/XfJw+cwoFxT83u+XKFjGnxJzXGEN/b8bGzJkC234fhqA4elhjF5xgP0+sSQrVO4Go+v5DeAa/jaqqKp1dE0xFec+MnLTFLB0gnj6phXT3WxY5F0fApEeB1atXw8nJCR9++KHWMZs3b0ZMTAy8vLzg5eWF2NhYFBQUqI2pqKjAG2+8AX9/f7Ru3RpjxozBH3/8oXr/7t27mDVrFnr27IlWrVohMDAQH3zwAaqqjA11ridPegFXSm4iJUVbDbQUXL5agTzpBZPWIRwPSxUiZRNs8exI8wtQcvUaUlJStNY2vHqlBNL8Ai0zsBOn8KGMf8fmmNNcWENAMoVT+FCMn/t3/HHqE4217JS/B1v6fRDswuht1sLCQmzatElrrzMlubm5SExMRFRUFNzc3JCWloZRo0ahuLgYnTt3hkKhAJ/PR8uWLZGZmQl3d3esW7cOsbGxOHv2LNq0aYPS0lKUlpYiPT0dffr0wdWrV/HOO++gtLQU//3vf409BZRVVALQI1D9r3EEYQhs2Fq2R3Td1CvK62Nf+/XrB5lMhry8PJSVlcHPzw8xMTGqv2vluObWYaM3iKltVxINlofDccaLMyZa2wzCDjFKzNXU1GDKlCnYvHkzVq1a1ezY7du3q/381VdfYe/evcjJycHUqVPxxx9/4MiRIygqKkLfvn0B1Fdu9vX1xY4dOzB9+nT069cPe/fuVc3RrVs3pKam4vXXX8fTp0/RooVxmrRhP8VmA9V19FOkLVZCG7aYwGAMbBIGPr71PRM3bNiATZs2qRUP5XK5qgKhynG2iD6Zl7o+a4uwVWAThLUxykXw/vvvIz4+HrGxsQZ/tra2Fk+ePIG3tzcA4PHjxwAAN7dntYecnZ3h6uqK/Px8rfMo99mNFXIAO/spEoStwTZxEBkdhg4d2yMlJQX9+/dXb5/Uvz8WLVqEjh076NXQne1be8ZkW9o6bP+dEIQ1MFjM7dy5EydOnIBIJDJqwYULF8Lf318lBHv16oXAwEAIBALcu3cPdXV1SEtLw/Xr19VaPDXk9u3bWLlypdYWHEC9SKyurlZ7NYZt/R0JgmgefW/kzk7OiI+Ph1gsRkREBNq2bVvfgUMsxssvvwwnZyfzGmpB9ImzsrdYLBJ0BKGOQSrl2rVrmD17NrZv367mSdOX1atXY+fOnfj2229Vn2/ZsiX27duH33//Hd7e3mjdujUOHTqEuLi4JsHLQH2GTXx8PPr06YNly5ZpXUskEsHDw0P1CggI0DhO2U/xzKkC9UD104VW66dIELYCGwWCNL8AN2/ewiItHTgWLVqEmxW3bC4BQhfaBBsbf0dMQIKOIJ5h0B7l8ePHcfPmTTz//POqYzKZDL/88gs2bNiAx48fa031T09Px+rVq5Gdnd0kaWLw4ME4efIkqqqqUFdXh44dOyI8PBxDhgxRG3f//n2MGTMG7dq1w7fffouWLVtqtVUgEGDu3Lmqn6urq5sVdGzrp8h2ZDI5BfY7OGwVCQ0TIDShbwKEEluL01ImSLD198Mktva7IQhzYZCYGzlyJM6cOaN2LCkpCb169cLChQu1Crk1a9YgNTUVP/74YxOB1hAPDw8AwB9//IFjx45h5cqVqveqq6sxevRouLq64rvvvtPpGXR1dYWrq6u+p8ZoP0V7R1O9JK6vB9a+GWaXJTcIdqCvJ0aZ2KArscmWEyB04QhCTgkJOoIwcJu1Xbt26Nevn9qrTZs2aN++veppd+rUqRAIBKrPpKWlYfHixdi6dSu4XC7Ky8tRXl6Ompoa1Zg9e/YgNzcXly5dQmZmJl566SXw+XyMGjUKQL2QGzVqFB48eIAtW7agurpaNY9MJmPiezAaR8tkVRbD7T8kRj2wfEgMJomyse/wJWubSFgANouFyOgwBAYFQCgUaklsEiGIG6hXAoQS2tJjN/T7IRwdxtt5lZSUqMWpfPHFF6irq8OECRPUxi1dulQV81ZWVoa5c+eioqICfn5+mDp1KhYvXqwae+LECRw9ehQA0L17d7V5Ll++DC6Xy/RpEBqQyeSYv7UAvPh4tTZVEREREGdmgp+QgAUZ+UiI4NKWqx1jDSFnyM2aw+FgRdrHSEp8B3w+HwKB4FkHDpEIEokEGTs2Ntv9gTAfSi8a0wLMHj10jb+j+/L71jGEYD0O3c6LCRzJM5d7uhQjBfshlUo1bl9JpVJERUUhRzTWIWqrOSpsF3NKJOIfsGThKpRcvaY6FsQNxPLVi5rty9oc9iYWjMXQ30dz3xvTos5WfkfGnPf96vsI7tSX2nkRTWDcM0fYL2V3awHo0THjr3EEYU14/DjEjR0FaX4BKspvwse3EyKjw0zyyNmj90dfTBFdys82/O7MtTWqaS02QVvChDkgMWckjuSRU+LnXf90prNjhjfzT3GOgC1kCLM5Vk4THA4H0cMiGZ3TkQSdObZCLQXbfk8k4ghz4nBiTnHsCBStXUy6KTmikAOAmL6+4Pp6QJiaqhYzB/wVWC4UItjPEzF9fa1opW1CGcK2BduEAlPYm+Bgw+/J3r5Tgp2w67HfgiiOHla9DP2co8LhOGPtm2GQZGWBn9CoY0ZCAiRZWViTFMo6bxLboQxh28RebtJ32w5XvewRa52foWvKZDLk/yzF3l2ZyP9ZavVKDYRt4XCeOU00FGjNeewcWcgpGTe0K3YLYjF/ax6ioqJUx4P9PLFbEEteJAOhDGHbhu3xWZqwV9GmDw3P3Vy/M6aSdQKDArAi7WOjk3UIx4LEXCM0CTsSceqMG9oVCRFc1sd32QJ5xeW4Ul6FHVpaTwlSUhAVFYW84nKrZwjbWrycJWGzqHNk8dYcjb8XU393xn7PEvEPSEp8BzweD7t27laV0REKhUhKfAcZOzaSoCN0QmKuGUjEaYfDcba6uLAHKEPYvmBa1LFViOVfb9qBJ7rLIytYwhyNvXaW+O5lMhmWLFwFHo8HsVis7pkXi8Hn87H0o1TEjR1FdRGJZiExRxBWhDKE7RNjRR0bxZsm4aZrHJPCrvH6lhCNlvo9SPMLUHL1Gnbt3K3ZMy8QICoqCtL8Asazsgn7wuHEXF5RGcYMDqAtQYIVUIawfaOPqGOTgNNXuOk7j7HCqzk78q+72bwXUElF+U0Auj3zynEEoQ2HUzSvLD+A52bupgxBghVQhrBj0DizkU0ZpPnX3VQva85tzFhbx8e3E4BnHvjGKI8rxxGENhzuDpGdnU0lHwhWocwQPnOsPkPY3d0dUVFRKDqez5oMYUp+YAZHEHC61mTSDlsXdJHRYQgMCoBQKIRcLld7Ty6XQyQSIYgbiMjoMCtZSNgKDrfNGhoaSiUfCNZBGcKEJWCD+GHaBlveduVwOFiR9jGSEt8Bn8+HQCBQZbOKRCJIJBJk7NhIyQ+EThxOzAHsK/lAEABlCJsDmUzGaG9WW4UNIs6cmBqjZ014/Dhk7NiIJQtXqdXuDOIGUlkSQm8cUswBVPKBIOwdKsRaj70LuYbYqqjj8eMQN3YUPXgQRuOwezhU8oEg7BdlIdaQAQPVkkpCBgxEUuI7kIh/sLaJFsHcQi7nSInaiy3YYoIEh8NB9LBIjJ+cgOhhkSTkCINwSM8clXwgCPvFkQuxWkrAaBNuDY+PjAi0iC3NYa7adwTBNhxOzBUUFODTTz6BJCsLuwWxFGBOECzH0OxPey/Eam2Pk74euMbjrC3uSNgR9ozDibmXXnqJmsIThB1jT4VYrS3cGmLqNqohXjttazElCEnYEfaGw4m575aOoQ4QBGHHNCzE2lyLNLYXYrUnIcfUfObYxrVGuzCCYBqHE3Mx/fxIyBGEHdOwEGvDmDnANgqx2rOIYxJzxedp+/6NEXmG/C5JRBKm4HBijiAI+8aWC7GyRcixWcRpQmmvOePyzP27sdWyKgQ7IDFHEITdYWuFWNki4gDbE3INYVs2rTHYckcLwnqQmCMIollstS8rjx+HUS+PxJaN/8aVS1fB7RqEt96ZChcXF2ubpoJEnPmwZWFHXjrCUEjMEQTBWkxpSq+pA8TmzzNY0wGCLULO3kScJnKOlNicoAMo65bQH8oEIAhCK7bqlaMOEM3Dxq4N5sbWzzX/uhukN9jxAECwDxJzBEHYFY07QERERKBt27aqDhA8Hg9LP0qFTCaztqkWx9EEXGMc+dwJ+4bEHEEQGrG2V87YLVZlB4iUlBStHSCuXimBNL+AASvZjyN64ZqDvgfCHqGYOcIoZDI58orLUXa3Fn7erRHT15fq99kR1hZypmALHSAsES9HooUgHAcSc4TB7Dt8CfO3FuBKeZXqGNfXA2vfDKMWaYTVsZcOEKZAQq55bDUhgiC0Qa4UwiD2Hb6ESaJs9B8SoxZY3n9IDCaJsrHv8CVrm0iYiC175QD1DhByuVztPVvoAGEKtJ2qP/Q9EfYEiTlCb2QyOeZvLQAvPh7izEz1wPLMTPDi47EgoxAymVz3ZAQrsXUhBzzrACGRSMDn89UeOvh8PiQSCZavXsTKDhCmQOLEcOg7sy0+//xzcLlcuLm5ITw8HAUF2uNeN2/ejJiYGHh5ecHLywuxsbFNxr/xxhtwcnJSe40ZM0ZtzN27dzFlyhS4u7vD09MTb731FmpqasxyfqZAYo7Qm7ziclwpr0LKokWaA8tTUnC5rBJ5xeVWspCwF0ypLwc86wBx6vRJREVFwd3dHVFRUTh95pTVO0CYI16ORAlh7+zatQtz587F0qVLceLECYSEhGD06NG4eVNz7Gtubi4SExNx6NAhSKVSBAQEYNSoUbhx44bauDFjxqCsrEz12rFjh9r7U6ZMQXFxMf73v/9BIpHgl19+wcyZM812nsZCMXOE3pTdrQWgO7BcOY6wLezBK9cQHj8OcWNHQZpfgIrym/Dx7YTI6DDyyBFqUPycbbBu3TrMmDEDSUlJAICNGzciKysLW7duxUcffdRk/Pbt29V+/uqrr7B3717k5ORg6tSpquOurq7w9fXVuOa5c+dw4MABFBYWYsiQIQCAzz77DC+//DLS09Ph7+/P1OmZDHnmCL3x824N4FkAeWOUx5XjCMORyeTIPV2KHbkXkXu61GJb1vYm5JRwOBxED4vE+MkJiB4WSUKO0Ah9j9ajurpa7fX48eMmY+rq6nD8+HHExsaqjjk7OyM2NhZSqVSvdWpra/HkyRN4e3urHc/NzUWnTp3Qs2dPvPvuu7hz547qPalUCk9PT5WQA4DY2Fg4Ozvj6NGjhp6qWSHPHKE3MX19wfX1gDA1FeLMTLWtVrlcDpFQiGA/T8T01fyUQzQPZQnbP0xusZIAYRby0GlmQGw3uLRuy/i8dbU1kO4GAgIC1I4vXboUy5YtUzt2+/ZtyGQy+Pj4qB338fHB+fPn9Vpv4cKF8Pf3VxOEY8aMwbhx4xAcHIw///wTKSkpiIuLg1QqBYfDQXl5OTp1Us96b9GiBby9vVFezq5wIhJzhN5wOM5Y+2YYJomywE9IgCAlBf369UNRURFEQiEkWVnYLYh1qHpzhnq0FEcPazyuzBLmxcdjx6JFqu9VmJqKSaL679Vcgs5evXL2DAk586D8XknUWY5r167B3d1d9bOrqyvja6xevRo7d+5Ebm4u3NyePVC99tprqn/3798fAwYMQLdu3ZCbm4uRI0cyboc5ITFHGMS4oV2xWxCL+VvzEBUVpToe7OdpVsHBBpgQPQ3nUAq7xlnCSo+nMkuYn5CABRn5SIjgMi6UScgRRFOs5aWTyWQ4W3gE925VwKujD/qERthdaEBj3N3d1cScJjp06AAOh4OKigq14xUVFVrj3ZSkp6dj9erVyM7OxoABA5od27VrV3To0AEXL17EyJEj4evr2yTB4unTp7h7967OdS0NiTnCYMYN7YqECK7DdIAwl+BRzpu3eQ+ulFdhRzNZwlFRUcgrLsfwAaYH3NqCgPOuyTU5o5UgTMHSgk76owRfi5ah4vozr6tPl0C8IViGyNE8i9nBRlxcXDB48GDk5OSAz+cDqA/tycnJQXJystbPrVmzBqmpqfjxxx/V4t60cf36ddy5cwd+fn4AgMjISFRWVuL48eMYPHgwAODgwYOQy+UIDw83/cQYxD7vvoTZ4XCcMXyAPxKHd8fwAf4k5Eyg3LsLAPNkCTuFD23yImwb2mK1HJb6rqU/SrAm+S2EDgpRq4sYOigEa5LfgvRHiUXsYDNz587F5s2bsW3bNpw7dw7vvvsuHjx4oMpunTp1KgQCgWp8WloaFi9ejK1bt4LL5aK8vBzl5eWqGnE1NTWYP38+jhw5gitXriAnJwcJCQno3r07Ro8eDQDo3bs3xowZgxkzZqCgoACHDx9GcnIyXnvtNVZlsgLkmSMIjVhS9Pj5eALQ3X5KV5awvQk1c3rnZDKZ3ZcsIZjB3B46mUyGr0XLEM/jIVMsVguzyBSLkcDnY9vq5QiLtV5tRDYwefJk3Lp1C0uWLEF5eTkGDhyIAwcOqJIiSkpK1HY2vvjiC9TV1WHChAlq8ygTLDgcDk6fPo1t27ahsrIS/v7+GDVqFFauXKkWt7d9+3YkJydj5MiRcHZ2xvjx4/Hpp59a5qQNwEmhUCisbYQlqK6uhoeHB+7tSYJ7axdrm0OwGEuLIplMjh6hC9A/JAxicdMsYT4/AUWnC/F7QZrdekCbg2lBJxH/gCULV6Hk6jXVscCgAKxI+9isxYSZymQlz5x1MJegO3PkMBa//iqkUqnGhzmpVIqoqCis/M+36NZ3AP42qBuqqqp0xpmZivKe+faXuWbLZt00c7hFzsURcLg7w568Sxat30XYDtbahuRwnJG+YjIkkizw+QmN2k8lQCLJwtrlkxxSyAH1HjrvmlxG5pKIf0BS4jsIGTBQ7XsOGTAQSYnvQCL+gZF1CEJf7t2qD+rXFWahHEcQmnC4u8PMT3/GSMF+PDdzNzWFJwBYT8Q1ZBwvFHsyknHmVIFa+6mi04XYk5GMcbxQq9rHBkwVdTKZDEsWrgKPx4NYLFbvLSwWg8fjYelHqZDJZMwZzTDklbMe5vruvTrWbxPqKsauHEcQmnA4MXfjxg1IpVL0HxKDSaJsEnQOjrVFXEPG8ULxR+EaHBQLsH3TuzgoFuD3gjQSco0wVtRJ8wtQcvUaUlJSNGcNCwS4eqUE0nztzbsJx8Ycgq5PaAR8ugQiVSiEXK6+YySXyyEUieAbEIQ+oU23YAlCicOJOdWTeGYmePHxWJBRSFuuDggbvHGa4HCcMTy6NxLHR2J4dG+H3VrVB0NFXUV5fb0oXdtZynEEoQmmBR2Hw8EbgmXIkkiQwOerbf8n8PnIkkgw7aOllKBDNIvD3imU9bsul1Uir5hdbTkI88JGEUcYj76izse3vi2Pru0s5TgmYbKNF2F9mBZ0kaN5WLBhCwp/O6UWZnHs5Gks2LDF4evMEbpx6NIkptTvImwTEnL2i1LQact+jYwOQ2BQAIRCIcQNSkAAf/UWFokQxA1EZHSYBaw1HIqXYxdMlyyJHM1DWGycw3WAIJjBocWcvvW7CIKwHbTVp+NwOFiR9jGSEt8Bn8+HQCB41ltYJIJEIkHGjo1083Qw5HIZSs//htrK22jt2QH+vQbB2dk6/w9wOBz0j6AHTsJwHFbMyeVyiIRCBPt5IqYvu3qsEeaBvHKOgzZBx+PHIWPHRixZuEqtt3AQNxAZOzaatc4cwS7hBAAXCw9CumM9Km+Wqo55dvJHZOKH6B46wmp2EYShOJyYu3//PoqLiyESCiHJysJuQSwFmROEDSOTyZEnvYCyikr4+XgiJrInOBznZgVd3NhRjHeAUHaVKCstx+1bd9ChY3v4+ftaddtWKZ5q7t3Ew+p7aN3OC228O1lFRLFNOF0sPIgDny5EfDwPixbtVXlpU1OFyPp0IcZ8kEaCjrAZHE7MdelS3wcz2M8TuwWxGDe0q5UtIghm0CZq7G3NhuyTFGLekl24UvIsA5Ub2AnpKyZjHC+02S3X6GGRjNmhqatEixYt8PTpUwQGBSBxwXKLB7FrEk9KmywtotgmnORyGaQ71iM+nofMzEYttDLFSEjgI3/nJ+g6eJhVPYcEoS8O55L68oNhyBGNxYVNE0nIORCNt1hlMjly889hx14pcvPPmb08jbnX2ycpRI/QBRjBF2HK219gBF+EHqELsE9SyOg61l6z8foTkzagf0iYWjmH/iFhmJi0QWUHkx0kNKHsKjGgv3qT9Li4ODg5OaFTRx+LN0tXiqfosOc12tQtwB8HPl2Ii4UHzW5LY+HUsFhzZqYY8fE8SHd+ArnccsWaS8//hsqbpVi0SHPNwZQUASorbqD0/G8Ws4kgTMHhxNzEmK4YPsCftlbxl8A4XYoduRet1uLMGjZYWoSYez19RQ2TWGPNhshkcsxbsgs8XjzE4sxG3RwywePFY/7S3Wr/P5lD0DXsKpGZ2diO+q4Sd+7cwcvx8di2erlFukuoiyfNNt29ewcvvxxvERHFRuFUW3kbgO6ag8pxBMF2SNFYATaIqH2HL+G5mbsxUrAfr6/NsUqLM0vZ0NArZ2kRYu71jBE1pmKNNRuTJ72AKyU3kZKySEs3hxRcvlqBPOkFtfeYFnT6dJW4fPky4saMQfm1qzhbeITR9TWhSzypbIobYxERxUbh1NqzAwDdNQeV45hCJpPhzJHD+GX/Ppw5cpjVreMI24LEnIVhi4iaJMpG/yEx6gLDgi3OrGGDpUWIJdYzVtSYgjXWbExZRSUA3QKhrKKyyRa3RxVzW4v6dpVo1aoVAMs0S9dXPCltMreIspZwag7/XoPg2ckfqalaWmgJRfD06Qz/XoMYW1P6owTvjQzH4tdfxbo572Dx66/ivZHhFt1+J+wXEnMWhA0iSiaTY/7WAvDi4yFuvAVjoRZnlrShoVfO0iLEEusZImqYwhprNsbPxxOAboHwx6VyjVvcuTvXMuKl07erxMOHDwFYplm6vuJJaZO5RZQ1hJMunJ05iEz8EFlZEiQkNGqhlcBHVpYEka/NZiz5QfqjBGuS30LoIPW4ytBBIRaPpyTsExJzFoINIgoA8orLcaW8CimLtAgMC7Q4s5YNlhYhllhPX1GjHMcE1lizMTGRPcEN7AShMFWjQBCJhPDt5Illad82u8VtqqBr2FVCsx0iBAcH44cDB0xqlm5I94dn4knbd/OXTT8csIiIsrRw0pfuoSMw5oM05BccV2uhlV94gtHsWplMhq9FyxDP4yFT3CgBRCxGPI9nsXhKwn4hMWch2CCigGety3QKDDO2OLOWDZYWIZZYTx9RExzkg5jInkavwYY1G8PhOCN9xWRIJFng8xPUBAKfnwCJJAsyuRxjx/J0bnGbIuiUXSUkEgkSEhrbwYdEIoF3+/b4PivLYs3Sn4mnLO02ebfH999nWUxEWUo4GWPXlPRv8WrKRox+bxVeTdmIKWv3MWrP2cIjqLhegkVa4ipTBAKLxVMS9guJOQvBBhEFPGtdplNgmLHFmaVsaFyOxNIixBLr6SNq1i6fxGj2tjXW1MQ4Xij2ZCTjzKkCNYFQdLoQSxe8ilu3qy2ypa7sKnH6jHqT9B9++AEKhQI3Km5ZvFn6M/F0QqNNf14vs7iIsoRwMgZnZw669BmC56LGoEufIXqLW337sirjJHVd+y0RT0nYLw5XNNhaNBQwERFNt1os1Sc2pq8vuL4eEKamQpyZ2bTZuAVanFnCBk2tu5QiZGLSBvD5CRAIUhr05hRCIsnCnoxkxkSIpdZTipp5S3aptagKDvLBnoxkjOOFmnoqrFhTmx0JcYObFC7eLT4KQP8tbm3FhfWlYVeJxh0gZNwXrNLvtXvoCHQdPIw1HSCAZ8LJkVDGSeq69lsinpKwX0jMWQg2iCigXmCsfTMMk0RZ4CckQJDSQGAY2OJMJpMjr7gcZXdr4efdGjF9ffX6HJM2aKK5HqyWFiGWWk+bqDGnd8waa2qCw3HG8OjeascabnE3+/D01zhThNwzOzR3lci/brpoGhkRaFDcnBJHFE9so09oBHy6BCJVKESmWNzk2i8UiUyKpyQIAHBSKBQKaxthCaqrq+Hh4YF7e5Lg3tqlyfvGChNDUGaz8uLjtQoYS3Wl2Hf4EuZvLcCV8irVsWA/T6xJCtXLBk2f5/p6YO2bYXqfg6k2aKM5MafE0m2orN32ytGQyeToEboA/UPCIBY3fXji8xNQdLoQvxekgcNxZkTMaSP/uhsj8xgj5gjzoe82K/AsmzWex0OKQKC69gtFImRJJHpvw9fev4+/DeqGqqoquLu7m2K+TpT3zLe/zIVL67aMz19XW4NNM4db5FwcARJzYEaY6Iu5BIwxGCtgG4rSlEWLnl2YUlMNFqVMi2h9hBzhGCgLNvN48Vq3uJWeUVsQcwB7BJ1cLkPp+d9QW3kbrT07WG3b1loYIuSUSH+U4GvRMlRcf/Y79A0IwrSPluodT0lijtCGw4s5JoWJvljCC2guZDI5npu5G/2HxGjcLuYnJKDoeD4ubJpo8XMiIUc0Zp+kEPOW7MKVkpuqY8FBPli7fJLaFretiDnA+oLuYuFBSHesR+XNUtUxz07+iEz80OrJDJbCGDEH1JcpOVt4BPduVcCrow/6hEYYFE9JYo7QhkPHzDWu/aYUJsrab/yEBCzIyEdCBJfxbMDhA/wZm8+SKEus7GimxEpUVBTyissteo4k5AhNWDuuj2khZ20uFh7EgU8XIj6eh0WL9qoeflNThcj6dKFVy4xYCmOFHFAfV9k/gq5VBPM4tJhjqzBhM2wpsdIQWxdyFE9nXjQlSNgyxiZDmIpcLoN0x3rEx/OQmSlWe/jNzBQjIYGP/J2foOvgYQ615UoQbMCh7xhsFCZshw116hpi60Jun6RQY7upfZJCa5tGsBhTvEPGUnr+N1TeLMWiRVqK36YIUFlxA6Xnf7O4bZbCGt87QeiDQ4s5tgkTW6BhiRWNhXAtVGLFKXyoXQi5iUkbmm03RRBsobbyNgDdD7/KcQRBWA6HFnNsESa2hLJGnCSrvkacWvX/hARIsrKwJinUrNuEti7igPqt1XlLdoHHi9fZboogNGFpL1Frzw4AdD/8KscRBGE5HFrMsUGY2CLjhnbFbkEszhzLU2+jdDzf7LXy7EHIAUCe9AKulNy0SLspwn6xpKDz7zUInp38kZoq1PjwKxSK4OnTGf69BlnMJktiie9aJpPhzJHD+GX/Ppw5chgymczsaxL2gUMnQADPhMn8rXnqFfr9PC1axNfWGDe0KxIiuBYtsWIvQg541kZK33ZTBGFtnJ05iEz8EFmfLkRCAh8pKQ2K3wpFyMqSYMwHaZT8YCSa6tD5dAnEG4JlFu3rS9gmDi/mAOsIE3vAUiVW7EnEKTG03RRBaMOS2a3dQ0dgzAdpyN+xXu3h19Ons12XJTG3V65hh4hFe3Y9K/kiFGJN8lt6d4ggHBeHKxpceXkT3Nu1anas4uhhC1lF6MIehRxgeLspwjKYo3iwJWrNWbpUiaN1gDCnmJPJZHhvZDhCB4Vo7N2awOfj2MnT+Dz7CB7X1lLRYEIjDueZ25NZgG7cTs3W8tIkIEjgWRZ7FXFKOBxnpK+YjIlJG8DnJ2htN0VCzrJ41+SatRuEveDszEGXPkOsbYZdcLbwCCqul2DRnl2aS74IBIiKisLZwiPo1neAlawkmOLJkycoLy9HbW0tOnbsCG9vb0bmdbg7xYwPtxhVy8seSmHYCo7yPY/jhWJPRjLOnCpQTyQ5XajWN5SwLN41uYzOF93lEaPzaYLqn5kPc3+3925VANAdP6scR9ge9+/fxxdffIFhw4bB3d0dXC4XvXv3RseOHREUFIQZM2agsNC0UlQO55m7ceMGSkpKIBSmYmLSBoNvmkqhQZ46zTiKEGMKa7ebIjRDHjrCUnh19AGgO35WOY6wLdatW4fU1FR069YNY8eORUpKCvz9/dGqVSvcvXsXRUVFyMvLw6hRoxAeHo7PPvsMPXr0MHgdh4uZU+7PMxGX5KiCjgQb4SgwKejsMXbOEjT0jFnj/MztmWscM6dQKJCXl4eysjL4+Phg3T//ieOnzlDMnI2SmJiIjz/+GH379m123OPHj5GRkQEXFxe8+eabBq/jcJ45JcpaXlFRUciTXjCqd6MjeOlIuBGODHnodNNY7BgruPQRTcoxlhJ1lti+5nA4eEOwDGuS30JYeDjKSktRWlqqer9FixbgJb0NDsd+E0zsmR07dug1ztXVFe+8847R6zismAOYq+VlT6KOxBtBqMOUoIvu8sgi3jlzo0vgWEIAWVrUmZvI0TwkTH8P4s2fg8fjYe/evc/Kk6SmIvOrf6HnwMEIiRpmbVMJA7h37x4UCgW8vb1x69Yt5OXloWfPnjq9dMbg0GKO6Vpe+oo6SwkmfcQliTeC0I2teOjMVXOOrQkW9iLqZDIZpD/sB2/sWLXyJBEREcjMzEQCn49tq5djrfh/VraU0JevvvoKQqEQADB//nxs374dISEhWLp0KWbPno3p06czup7Dijm5XA6RSIjgIB/ERPZkdG62CCS22EEQ9oAyy9UWRJ2psFW8acMcos6S34G+5UnOHzct45GwHJ9++imKi4vx8OFDBAYG4vLly+jYsSOqqqowbNgwEnOmcv/+fRQXF1MtL4IgjMIULx1bt1ptTbxpw1Y9dfqWJ6m8c9NiNhGm0aJFC7Rq1QqtWrVC9+7d0bFjRwCAh4cHnJycmF+P8RlZTpcuXQAAwUE+VMuLIAijYPO2q66tVnsRbs1ha6JO3/Iknu07WdQuwng4HA4ePXoENzc3/Pzzz6rjNTU1ZlnP4cTc5vVv6ewAQRAEoQu2CzrCdr6HPqER8OkSiFShUGNLL6FIBN+AIPQaTM4HWyE7Oxuurq4A6r1xSmpra/Hll18yvp5Jamb16tVwcnLChx9+qHXM5s2bERMTAy8vL3h5eSE2NhYFBQVqYyoqKvDGG2/A398frVu3xpgxY/DHH3+ojXn06BHef/99tG/fHm3btsX48eNRUWF4ReyJCWEYHt2bhBxBECZjTLcIS3SEIGwLZXmSLIkECXw+pFIp7t+/D6lUigQ+H1kSCaZ9tBQcO+5/a2803k4tLy8HAHTq1AmhocyLcqMVTWFhITZt2oQBA5rvFZebm4vExEQcOnQIUqkUAQEBGDVqFG7cuAEAUCgU4PP5uHTpEjIzM/Hbb78hKCgIsbGxePDggWqeOXPmYP/+/dizZw9+/vlnlJaWYty4ccaaTxAEwQgk6AgmiBzNw4INW1D42ym19n7HTp7Ggg1bEDmaZ20TCRMYNWqUWec3apu1pqYGU6ZMwebNm7Fq1apmx27fvl3t56+++gp79+5FTk4Opk6dij/++ANHjhxBUVGRqvbKF198AV9fX+zYsQPTp09HVVUVtmzZgm+++QYjRowAAGRkZKB37944cuSIxhgDbezJLKBtVoIgGMWYLVe2JkMQ1iNyNA9hsXE4W3gE925VwKujD/qERlDBYDvA3M22jFIz77//PuLj4xEbG2vwZ2tra/HkyRN4e3sDqG9hAQBubs8uas7OznB1dUV+fj4A4Pjx43jy5Inaer169UJgYCCkUqnGdR4/fozq6mq1FwDM+HALRvBF6BG6APsklOZNEI6ATCZHbv457NgrRW7+OchkcsbXIA8dwQQcDgf9I4bihbHj0D9iKAm5Bnz++efgcrlwc3NDeHh4k5CthugK8Xry5AkWLlyI/v37o02bNvD398fUqVPVum8AAJfLhZOTk9pr9erVBttujgzWhhgs5nbu3IkTJ05AJBIZteDChQvh7++vEmZKUSYQCHDv3j3U1dUhLS0N169fR1lZGYD6vWYXFxd4enqqzeXj46Pah26MSCSCh4eH6hUQEAAAuHHjBqRSKfqHhGFi0gYSdARh5+yTFKJH6AKM4Isw5e0vzPow512Ta7CoI0FHELrZtWsX5s6di6VLl+LEiRMICQnB6NGjcfOm5nItukK8amtrceLECSxevBgnTpzAvn37cOHCBbzyyitN5lqxYgXKyspUr1mzZpn1XI3BIDF37do1zJ49G9u3b1fzpOnL6tWrsXPnTnz77beqz7ds2RL79u3D77//Dm9vb7Ru3RqHDh1CXFxck+KJhiAQCFBVVaV6Xbt2DQDQtm1bREREQCzOBI8Xj/lLd5vlKZ0gCOuzT1KIiUkb0D8kTC2o3NwPcyToCIJZ1q1bhxkzZiApKQl9+vTBxo0b0bp1a2zdulXj+O3bt+O9997DwIED0atXL3z11VeQy+XIyckBUJ+g8L///Q+TJk1Cz549ERERgQ0bNuD48eMoKVEvadOuXTv4+vqqXm3atDH7+RqKQWrp+PHjuHnzJp5//nm0aNECLVq0wM8//4xPP/0ULVq0gEwm0/rZ9PR0rF69Gj/99FOTpInBgwfj5MmTqKysRFlZGQ4cOIA7d+6ga9euAABfX1/U1dWhsrJS7XMVFRXw9fXVuJ6rqyvc3d3VXmon7uwMgSAFl69WIE96wZCvgSAIG0Amk2Pekl3g8eIhFmciIiLCog9zJOgIghnq6upw/PhxtVArZ2dnxMbGag21akzjEC9NVFVVwcnJqcku4OrVq9G+fXsMGjQIa9euxdOnTw0+B3NvlxuUADFy5EicOXNG7VhSUhJ69eqFhQsXajV2zZo1SE1NxY8//oghQ4ZonV9Zi+WPP/7AsWPHsHLlSgD1Yq9ly5bIycnB+PHjAQAXLlxASUkJIiMjDTkFNZRVtcsqKo2egyCYpnFPXWrLZhx50gu4UnITO3Yt0tgiSSBIQVRUFPKkFzA8urdZbDA0McIWkyKUItTW7CbYgTKeXYmrq6uqPpuS27dvQyaTwcfHR+24j48Pzp8/r9c6jUO8GvPo0SMsXLgQiYmJas6fDz74AM8//zy8vb3x66+/QiAQoKysDOvWrdNrXSW//fabQeMNxSAx165duybtRtq0aYP27durjk+dOhWdO3dWxdSlpaVhyZIl+Oabb8DlclUxbm3btkXbtm0BAHv27EHHjh0RGBiIM2fOYPbs2eDz+apUXg8PD7z11luYO3cuvL294e7ujlmzZiEyMtKgTNbGKKtq+/l4Gj2HoyOTyZEnvYCyikr4+XhSlrAJNBZxjY+TqDMM5UOarhZJ5n6YY3NxYVOI9HsAaX4B9h6+CR/fToiMDlM90JOwsx+GhQagdbt2jM9be/8+NgGqeHYlS5cuxbJlyxhdSxnilZubqzFE7MmTJ5g0aRIUCgW++OILtffmzp2r+veAAQPg4uKCt99+GyKRqInotCaMd4AoKSlRewr+4osvUFdXhwkTJqiNa/gLKysrw9y5c1FRUQE/Pz9MnToVixcvVhv/z3/+E87Ozhg/fjweP36M0aNH41//+pfRdsrlcohEQgQH+SAmsqfR8zgy+ySFmLdkF66UPAtA5QZ2QvqKydQmzQC0iTht45gUdYqjh+1WJCof0nS1SLLEw5whgo7t3rnoLo8gEf+A0IWrUHL1mup4YFAAVqR9DB4/rsmWMZvPh7Au165dU/OEaRJIHTp0AIfDadIooLlQKyXKEK/s7GyNdXGVQu7q1as4ePBgk5CsxoSHh+Pp06e4cuUKevZkj3ZwUpi7+AlLqK6uhoeHB65fv46SkhKIREJIJFnUn9VIlIHlPF48UlIWoV+/figqKoJQmErfq57oK+K0YawI07auvYk6mUyOHqEL0D8kDGJxZpMWSXx+AopOF+L3gjSLeZMN8dCxTQApBZpE/AOSEt8Bj8dDSkpKg799ISQSCTJ2bASPH6d1Hradly1Re/8+/jaoG6qqqnSKDlNR3jO/+e1Ps3nmDDmX8PBwhIWF4bPPPgNQ/zccGBiI5ORkfPTRRxo/0zDES9MDnVLI/fHHHzh06BA6duyo047t27dj6tSpuH37Nry8vHSOb0h1dTUyMjJQXl6O4OBghISEoH///mjdurVB82jC4cSckuAgH6xdPokEhxGw8SZpa5gq5JToK8AMWc+eRF3Dhw6B4JnwsObDnK0JuoZeNplMhtA+LyBkwECINfQQ5fP5OH3mFAqKf9YZ8M2Gc7M1HFnM7dq1C9OmTcOmTZsQFhaG9evXY/fu3Th//jx8fHyaDfEaOvTZNU0Z4vXkyRNMmDABJ06cgEQiUYvH8/b2houLC6RSKY4ePYoXX3wR7dq1g1QqxZw5cxAXF4dt27YZfM6xsbE4deoUQkNDUVJSggsX6pMvu3XrhpCQEOzatcvgOZUwvs3Kdjavf4s6QJgIGwLLbRWmRFzj+TQJMGPXsqet13G8UOzJSMa8JbsQFRWlOh4c5GM177Eyy1UfUWftLdfG26XS/AKUXL2GXTt3a/nbFyAqKgrS/AJEDzM+OY0gGjN58mTcunULS5YsQXl5OQYOHIgDBw6oRJihIV43btzAd999BwAYOHCg2phDhw5h+PDhcHV1xc6dO7Fs2TI8fvwYwcHBmDNnjlocnSFIpVLk5uaqerM+fvwYZ86cwcmTJ3Hq1Cmj5lTicGJuYkIY3Nu1srYZNg1bAsttjcbiSiaTI6+4HGV3a+Hn3RoxfX2NfsCwpEi0NcbxQpEQN5h1iTr6xtFZK/5MU6mUivL6+Fhdf/vKcbrmJ+8cYQjJyclITk7W+F5ubq7az1euXGl2Li6Xq7PF1vPPP48jR44YYmKzDBgwAC1aPJNdrq6uGDJkSLNVPvTF4cQcYTpsCiy3FRqLrX2HL2H+1gJcKa9SHeP6emDtm2EYN7Srpc3Tir146TgcZ1Z6iY3t6arEXGJIW807H99OAHT/7SvH6bMOCTrCUVizZg2WLFmC//73v4xnwtI+I2EwMZE9wQ3sBKEwFXK5esFVyhJuiiYhN0mUjf5DYtS7EgyJwSRRNvYdvmQlSzXDtNePUMeYnq5Kors8Ur2YQNdckdFhCAwKgFAo1PK3L0IQNxCR0WGM2EMQ9gSXy0V1dTX69OmDlJQUfPfdd6ruVKZCYo4wGA7HGekrJkMiyQKfn6AmSPj8BEgkWVi7fJLVt7HYgKat1flbC8CLj4c4s1FXgsxM8OLjsSCjkHUt5kjQmRdTBJ0SbcKu4XFdL11wOBysSPsYEokEfD6/0d8+HxKJBMtXLzKo2j11viAchfHjx+PKlSsYOnQofv31V0ybNg1cLhcdO3ZU1dU1FtpmJYyCjYHltkBecTmulFdhxyItySMpfyWPFJdj+AB/K1mpGXvZcmUrTBYXNqdA4vHjkLFjI5YsXKX2tx/EDdRZloTQjUwmw9nCI7h3qwJeHX3QJzTC7K2gCMtQVFQEqVSKkJAQ1bErV67gt99+w+nTp02am8QcYTRsDSxnC5q8WWV3awHokTzy1zi2YU+JEWzEVrpF8PhxiBs7CtL8AlSUN+0AYSgUO1eP9EcJvhYtQ8X1Z43efboE4g3BMkSO5lnRMoIJQkND8eDBA7VjXC4XXC4Xr776qklz012XMAllYHni+EgMj+5NQu4vtG1L+nnXF4dUBoo3RpU84m16EUnCNmFiy9UScDgcRA+LxPjJCYgeFmmy98jRt1ulP0qwJvkthA4KUdu+Dh0UgjXJb0H6o8TaJhImMnv2bCxbtgyVlZWMz013XoKwIDF9fcH19YAwVUvyiFCIYD9PxPRtvkWNtaEYOvPiXZNrM6KOMB2ZTIavRcsQz+MhUyxWi6XNFIsRz+Nh2+rlkMll1jaVMIEJEyYgOzsbPXr0wMyZM7FlyxacOHECdXV1Js/tcGJuT2YBcvPPsS7AnLAfmhM6HI4z1r4ZBklWFvgJjZJHEhIgycrCmqRQm/BwkqAzP44m6BzVO3e28AgqrpdgUUqKxljaFIEA5deu4vzxQitZSDDB5cuXIRaLMWvWLNy5cwdCoRChoaFo166dxr6xhuBwMXMzPtwCgBrCE9Zj3NCu2C2IxfyteerJI36e2C2IZVWdOV1QUoT5sZU4OqZwxPi5e7fqG8jriqWtvKO7GDPBXm7cuIGuXbvilVdeUR27f/8+Tp48SQkQhnLjxg2UlJRAKEzFxKQNlHlJWIVxQ7siIYLLWAcIwr5xNEHnaHh1rG9JVVRUhNDQUOTl5aGsrAx+fn6IiYlRxdJ6ttevGDPBTt5//30kJyerifZ27drB39+/SUsxQ3E4Maeq6SXOBJ+fgPlLdyMhbjDdRAnGcAofqtcWJIfjzLryI8ZA3jnzQ0LOvukTGgGfLoFInjULd27fVmtFxeVy0b5DB/gGBKHXYHI82DIXLlzA8OHDmxzPzs7G/v37IZEYn+TisApG2RD+8tUK5EkvWNscws4gccMMiqOHVS+CsFc4HA4i48bi+LFj6Nevn1osbb9+/XD82DFEjOGB40z15mwZd3d33Lt3r8nxmJgYk3vAOpxnriHUEJ4wJ/p66OwBc3jnGn93+n6XJKQJW0Mmk0H6w37weDxkZmaqkiAiIiKQmZmJhIQEHDkgwfh3ZlvZUsIUxowZg/T0dOzcuVPtuLOzs8kZrQ7rmQOoITxhfhxJWDApXE2Zy948ebTFav+oslm1dIZJSUmhbFY7YOXKlfj5558xfvx4nDlzBgDw6NEjpKWlUTarsVBDeMJSOJKHzlTMIQgdSVATtgllszoGAQEBOHLkCN59912EhITA1dUVT58+hYeHB/bv32/S3A4n5u7fv4/i4mKIREJIJFnYk5FMyQ+E2VEKCnsXdaZst5rruyFRR7CdhtmsERERTd6nbFb7ISgoCN9//z1KSkpw8uRJtGzZEuHh4fD29jZpXocTc126dAFADeEJ62AuL11DoWKLgtESNpOoI9iKMps1VShEplisttUql8shFIkom9XOCAwMRGBgIGPzOZxLavP6t3BQLMDvBWkk5Air4BQ+lFFB0Xgua4sVQ4WZpcWnLcXUUbycY8DhcPCGYBmyJBIk8Plq2awJfD6yJBJM+2gpZbMSWnE4z9zEhDC4t2tlbTMIwmQvXXOizRa2da1tG3nqCDYROZqHBRu24GvRMrXOML4BQViwYQsiR/NQe/++FS0k2IzDiTmCYBPGiC5DxIe1ki90xc5ZW8g1hEQd+3DEll5AvaALi43D2cIjuHerAl4dfdAnNAIcDnnkiOZxODGXJ72A6vsP4efjiZjInpT8QLACc4outmXTsskWgmAbHA4H/SPowcKeiYqKwp9//omKigrG5nQ4MTf2b+tU/+YGdkL6iskUO0fYFIZmjFpLPDVc19qCUiaT6+yDyzYPHcXLEYR9MnnyZNy+fZvROR1OzGVnZyM8PBxFRUUQClMxMWkDZbUSrMAcgoctXjBr2rHv8CXM31qAK+VVqmNcXw+sfTMM44Z2bTK+sQglCIJgktmzme/k4XB7jKGhoWjbti0iIiIgFmeCx4vH/KW7IZPJrW0aQegtHvQRR2wRctZk3+FLmCTKRv8hMWoZgv2HxGCSKBv7Dl9q9vO2lPlKEITj4nBiriHOzs4QCFJw+WoF8qQXrG0OQRhEcyKDBEj91ur8rQXgxcdDnJmJiIiIZw9ymZngxcdjQUahXg9ySlFH3ytBEGzEocUc8KxNSllFpXUNIYi/sIV4OFsgr7gcV8qrkKKl36UgJQWXyyqRV1xu0Lwk6p4hk8mQ/7MUe3dlIv9nKWQymbVNsggymQxnjhzGL/v34cyRw3qdtzGfIQh9cbiYucYo26T4+Xha1xAbRSaTI096AWUVlZQhzCCGxM81ToggoVFP2d1aALr7XSrHGYoprcvsAYn4ByxZuAolV6+pjgUGBWCZKAXe7b1RUX4TPr6dEBkdZvXSGjKZjLFyH9IfJfhatAwV10tUx3y6BOINwTJEjuYx9hmCMASHvuvK5XKIREIEB/kgJrKntc2xOfZJCtEjdAFG8EWY8vYXGMEXoUfoAuyTFFrbNLvAGA8dCbln+Hm3BvDsga0xqge5v8bpi0wmR+7pUuzIvYjc/HNmibc1JJPVGt4xifgHJCW+g5ABA9ViETt19MHMqbPAHz0Zb0+r/29onxcgEf9gdpu0If1RgvdGhmPx669i3Zx3sPj1V/HeyHBIf5QYNdea5LcQOihE7bxDB4VgTfJbGuc05jOEfXDv3j3cvXsXAHDr1i3s27cPxcXFZlnL4cRcQUGB6o+Jz0+ARJKFtcsnkTfJQPZJCjExaQP6h4SpB5aHhGFi0gYSdAxhrS1XNcFyutSqCULG2hLT1xdcXw8IU1Mhl6t/Ri6XQyQUItjPEzF9ffW2Zd/hS3hu5m6MFOzH62tzrP4AIxH/gNA+L1hUPMlkMixZuAo8Hg9isVgVi1haWorjx49jzJgxateEkAEDkZT4jsE2RXd5ZLKtTAopmUyGr0XLEM/jIbPBeUdERCBTLEY8j4dtq5eriWljPkPYB1999RUGDx6MIUOG4IsvvsCrr76KnJwcvPbaa/jqq68YX89JoVAoGJ+VhVRXV8PDw0PtWHCQD9Yun0RlSQxEJpOjR+gC9A8Jg1ic2aQpNJ+fgKLThfi9II1EsoE0t21tKa+brlIelqwZZ2hZEU2fnyTKBi8+HoKUFPTr1w9FRUUQCYWQZGVhtyBWr3kaz5WyaJFqLqEwFRJJFqMljvTxzCm9YzweDykNzk0oFEIikSBjx0bw+HGM2NOQ/J+l4I+eDKlUioiICAD1oqV79+7o378/xBoaxfP5fJw+cwoFxT8btL1pShcImUyG90aGI3RQiMbm9Ql8Po6dPI3Ps4/oZdOZI4ex+PVX1c67IVKpFFFRUVj5n29VRX+N+Uxz1N6/j78N6oaqqiq4u7vrHG8KynvmN7/9idbt2jE+vyXPxRoMGDAAR48excOHDxEYGIjLly+jY8eOqKqqwrBhw3Dy5ElG13O4O+3+b+Zi+6Z3cVAswO8FaQ4t5GQyOXLzz2HHXqlB20V50gu4UnITKSlaAssNyBA21gamsbYdMpkcK9aK0bnfbK3b1paIz9JVyuPbWy6ssUVXWREAGDe0K3YLYnHmWB6ioqLg7u6OqKgoFB3PN0jINZsZa4USR9q8Y/X2iMHj8bD0o1SzeHwqym8CUI9FzMvLw5UrV5CSkqLlmiDA1SslkOYXMG6PNs4WHkHF9RIs0mJTikCA8mtXcbbwiF7z3btVX61fVwymcpyxnyHsgxYtWqBVq1bw9vZG9+7d0bFjRwCAh4cHnJycmF+P8RlZTkxkT7i3a2W19dmSMLBPUoh5S3bhSslN1TF9O2IoM391BpbryBA2xQYmsbYd+ySFeHfeNty6XQ0ej4dFjbw+DQtbG9PLVV8aCxblDVBZyoPPT8D8pbuREDfY7P/P6rQlIQELMvKREMHVacu4oV2REMHV2QGiOZSZsTu0ZcYKUhAVFYU86QUMj+5t+AkbiDS/ACVXr2HXzt1axVNUVBSk+QWIHhbJ6No+vp0A1MccKr1NZWVlAHRfE5RC0BIwLaS8OvoAUD/vhihjMJXjjP0MYR9wOBw8evQIbm5u+Pnnn1XHa2pqzLKew3nmrAlbEgZMjXdTZv7qDCxvJkOYLTF31rZjn6QQE974DA8fyTF27FhkWtHro7OUhwVrMjJdVoTDccbwAf5IHN4dwwf4GyxG9c6MtVCJI03eMU32mEM8RUaHITAoAEKhUBWL6OfnB0D3NUEpBC1BQyHVnE36Cqk+oRHw6RKI1AbnrUQul0MoFMHTpzPK5f7IOVKi32dEIvgGBKFPaFOhR9g22dnZcHV1BQC1EK/a2lp8+eWXjK9HYs5CWFs0KJHJ5Ji3ZBd4vHiIxcYJh5jInuAGdoJQqCWwXEeGMBM2MIG17VCuHxkZgZqamma2qNRFlLni1fQVLKWHC8weM2fusiKGondmrIVKHDX0jjVnjznEE4fDwYq0jyGRSMDn8yGVSjFw4ED4+/sjVVuyiUiEIG4gIqPDGLdHG0wLKQ6HgzcEy5AlkSDhr/NWXscTEvjIypIg8rXZcHauj7/LOVKC3MIbeH58cv1nEhp9hs9HlkSCaR8tBYfDQc6REp2vnwuv6bCSYAvatlM7deqE0FDmd3xIzFkAa4uGhjAR78bhOCN9xWRIJFng8xPULlD6ZAgzGXNnCta2Q7n+mDH1QerWFlHmKuVh67YAembGMljiyLsmt9n3NXnH1Owxs3ji8eOQsWMjTp0+iaioKHh5eaG0tBQSiQQJCY2vCXxIJBIsX73IovXmmhVfjYSUvkSO5mHBhi0o/O2UWgxmfuEJjPkgDd1DRzT5TPfQERjzQRryC46rf6ag/jO1HgNUnjwAkMtluH72GH7/9QCunz0GuZwyXe2F6upqfPLJJxAIBPjyyy9x9OhR1NYy80DqcDFz1kB5096xy/rxNkzFu43jhWJPRjLmLdmFqKgo1fHgIB+dWX1M2WAq1rZDOa/yKU1XXI25hUtDwdIwTg0wvpSHPdgC1D/ArH0zDJNEWeAnJGjMjN2TkWyx+Feldywp8R3w+XwIBIJn9ohEqmxWc4onHj8OcWNHQZpfoCoQfPfOXSwTCNWuCUHcQLNl1upCKb6+Fi1Ts8k3IAgLNmwxqmBv5Ggeatr1Ren531BbeRutPTvAv9cglUdOE91DR6Dr4GE6P3Ox8CCkO9aj8map6phnJ39EJn6oUSgStsW4ceNw6tQphIaGYv/+/bhwod5R0K1bN4SEhGDXrl1Gz01izgJYWzQ0pGG8W7PCQY/tonG8UCTEDTY4oYNJG0zB2nYo53V3dweXy4VQKNRY1kFoIeGij2DZLYi1iGBhky1KlJmx87fmqT/A+HnWZ8YynCzjXZPbbIkSpXdsycJVVhNPHA6nSYJFfMIYNYFn7Q4QkaN5CIuNY6wDRM6REjg7c9ClzxCDPqfrMxcLD+LApwsRH8/DokV7Vf+/p6YKkfXpQoz5IA2BfS23TU0wj1QqRW5uruoB/vHjxzhz5gxOnjyJU6dOmTS3w9WZq7y8yeLZrLn55zCCL9JZa+igWGB2zxwbasSxwQY22NFw/alTp2HSpEng8XhqXpbU1FR8//33BpXRMBVNtd2C/TyxJinUYjaw0RYlMplcY2asOcq16FNvTiaTsUo8MYUpNebMQcOtUKaRy2XYPu9VRIcNRmamhpp4CXzkF57AxBX/xua3R1KdORslMjIS//rXvzBo0CDG5ybPnAVomDCgSTRYsqWYMt5tYtIG8PkJEAgaeDxEQlXxU3OKKDbYwAY7Gq4PAKmpqdi0aZOal8XHu61FhRzATCkPe7RFiTIz1hLo8s7V29PUO0YwizmFHACUnv8NlTdLsWjRXs018VLqS82U/X7arHYQ5mXNmjVYsmQJ/vvf/6oyXZnC4cRcnvQCqu8/tGiNN2uLhsaYEu9mTzawwY6G6+/f/6y1UEfP1ng/vjdSJj9vFeFiScGiCzbZQjgW5hZxSmorbwPQHYrzsOqORewhzAOXy0V1dTX69OmDyZMnIyIiAoMGDUJAQIDJczvcNmtDGhaGtUQxX03Faa3ZUowNBYzZYAMb7JDJ5Phl617WeJ8IwzFnVwx9tlvtDWtvs1pKyAHA9bPH8K3wHZ2hOLx//BOS/5tD26w2SlhYGCoqKjBs2DCUlJTg1KlTqK6uhre3NwYNGoSffvrJ6LkdzjOXnZ2N8PBwter6/3h/DP773XGzdwAwNmHAXHA4zhapVs92G9hgh/MxKXmfbBzF0cMWaXNGmB9LCjkA8O81CJ6d/JGaKtQYM6csSOz33ACL2kUwS1FREaRSKUJCQlTHrly5gt9++w2nT5u2he5wj/6hoaFqNd4GD34e6Rt+sFgxX6VoSBwfieHRvcn7QlisaT1hu+iqO0cwh6WFHFCf6RqZ+CGysjQUF25YkNjJ9hNbHJnQ0FA8ePBA7RiXy8Wrr76KpUuXmjS3QysJhUKB0tKyv5pVW795NuFYKI4eJiFH6A0JOvNjDSGnRGtx4WYKEhO2xezZs7Fs2TJUVlYyPrfDbbM2JC8vD6Wlpdi7V3MGkaWbZxOOAQk4wlj0yW4lbBd9iwsTtsmECRMAAD169MCrr76K8PBwDBo0CP369YOLi4tJczu0mCsrKwPAjmK+hP1C4s1xUP6uKXbOdKK7PLJoEoQ1vXINMaYgMWEbXL58GadOnVIVCRYKhbhy5QpatGiBnj17mhQ359BizsfHB4D1OxEQBGFfUDIEQRCNuXHjBrp27YpXXnlFdez+/fs4efIkJUAYSkFBgSqw9J//XIcWLVogVVvzbAsW8yXsE/LKOS7miol0pNi56C6PLLIOW7xyRPN8/vnn4HK5cHNzQ3h4OAoKCrSO3bx5M2JiYuDl5QUvLy/ExsY2Ga9QKLBkyRL4+fmhVatWiI2NxR9//KE25u7du5gyZQrc3d3h6emJt956CzU1NUbZ//777+Po0aNqx9q1awd/f39MnTrVqDmVOJyYe+mll1SBpcVnjuHDd15CVlYW+PwEtQwiPj8BEkkW1i6fRBmnhFGQkCMA+v+AIJhg165dmDt3LpYuXYoTJ04gJCQEo0ePxs2bNzWOz83NRWJiIg4dOgSpVIqAgACMGjUKN27cUI1Zs2YNPv30U2zcuBFHjx5FmzZtMHr0aDx69OwhYsqUKSguLsb//vc/SCQS/PLLL5g5c6ZR53DhwgUMHz68yfHs7GwkJiYaNacShysavP+buU06QLCtmC9h+9ANnNAEU1uvjpYEYe7YOVvxzNXV1mDTzOEOWTQ4PDwcoaGh2LChvv2hXC5HQEAAZs2ahY8++kjn52UyGby8vLBhwwZMnToVCoUC/v7++Mc//oF58+YBAKqqquDj44Ovv/4ar732Gs6dO4c+ffqgsLAQQ4bUxzEeOHAAL7/8Mq5fvw5/f8Nqg/r6+kIikajmUnL27Fm88MILuH37tkHzNYRcTqgv5vtH4RocFAuwfdO7OCgW4PeCNBJyhFGQkCMIZjHndqutCDl7pbq6Wu31+PHjJmPq6upw/PhxxMbGqo45OzsjNjYWUqlUr3Vqa2vx5MkTeHt7A6hPRigvL1eb08PDA+Hh4ao5pVIpPD091cRXbGwsnJ2dm2yX6sOYMWOQnp7e5LizszPq6uoMnq8hDpcAMfZv61T/btjlwdodAAj7gIQc0RyUGEHYGpGdH6Gde0vG571fXS/QG/clXbp0KZYtW6Z27Pbt25DJZKqkRSU+Pj44f/68XustXLgQ/v7+KvFWXl6umqPxnMr3ysvL0alTJ7X3W7RoAW9vb9UYQ1i5ciXCwsIwfvx4LFu2DP3798ejR4+QlpaGAQNM6+7hcJ657Oxss3d5IBwTEnIEYT7M4Z0jr5z1uXbtGqqqqlQvgUDA+BqrV6/Gzp078e2338LNzXo9fwMCAnDkyBE8fPgQISEhaNWqFdq1a4f9+/dj7dq1Js3tcJ65xu28+PwEzF+6GwlxgynRgSDsGJlMjrzicpTdrYWfd2vE9PW1yt88eecI4hnu7u46Y+Y6dOgADoeDiooKteMVFRXw9fVt9rPp6elYvXo1srOz1bxfys9VVFTAz89Pbc6BAweqxjROsHj69Cnu3r2rc11tBAUF4fvvv0dJSQlOnjyJli1bIjw8XLX9aywOrV6UXR4uX61AnvSCtc0hbBjyyrGbfYcv4bmZuzFSsB+vr83BSMF+PDdzN/YdvmRt04zCkcqTmAvyytkOLi4uGDx4MHJyclTH5HI5cnJyEBkZqfVza9aswcqVK3HgwIEmSQfBwcHw9fVVm7O6uhpHjx5VzRkZGYnKykocP35cNebgwYOQy+UIDw836ZwCAwPxyiuvIC4uzmQhBzi4mAOoywNhOiTk2M2+w5cwSZSN/kNi1MoP9R8Sg0mibJsVdI6IperOEexj7ty52Lx5M7Zt24Zz587h3XffxYMHD5CUlAQAmDp1qtoWbVpaGhYvXoytW7eCy+WivLwc5eXlqhpxTk5O+PDDD7Fq1Sp89913OHPmDKZOnQp/f3/w+XwAQO/evTFmzBjMmDEDBQUFOHz4MJKTk/Haa6/pnclaUmLYQ0PD0imG4PBijro8NI+y8GnjF1EPfRfsRiaTY/7WAvDi4yHOzERERMSzMIvMTPDi47EgoxAymVz3ZAxC/98QhGFMnjwZ6enpWLJkCQYOHIiTJ0/iwIEDqgSGkpISVYtOAPjiiy9QV1eHCRMmwM/PT/VqmE26YMECzJo1CzNnzkRoaChqampw4MABtbi67du3o1evXhg5ciRefvllREdH48svv9Tb7tDQULz99tsoLNQem19VVYXNmzejX79+2Lt3ryFfiwqHqzPXsKaNXC4Hn5+AotOF+L0gjWLmGmDszcZRYoHoZmwb5J4uxUjBfkilUo0t+6RSKaKiorD/5RBE+3nCfVwvi9lm6t8KG2rNyWQySPMLUFF+Ez6+nRAZHQYOx7xN4ZmsOWdrW63WqDN3+WYx2rkzX2fufvV9BHfqa5FzsSZ37txBamoqtm7dCjc3NwwePBj+/v5wc3PDvXv3cPbsWRQXF+P555/H4sWL8fLLLxu1jsMlQBQUFCA8PBxFRUUQiYSQSLKwJyOZhNxfGCNS1ALLT5eqBZbbo7gjIWc7lN2tBfAsnKIxyuPltfU1nqr3nbeYoLP1RAiJ+AcsWbgKJVevqY4FBgVgRdrH4PHjrGiZ/oyMCARge6KOsB3at2+PdevWITU1FVlZWcjPz8fVq1fx8OFDdOjQAVOmTMHo0aO1XqP0xeHE3EsvvaT6d3CQD/ZkJFNxYBgvUPYdvoT5WwtwpbxKdYzr64G1b4Zh3NCuqnlt6aZFYs1+8PNuDaA+nEKTZ04ZZuHb2kV1zJKCzhS8a3Kt5p2TiH9AUuI74PF42LVzN/r164eioiIIhUIkJb6DjB0bzSboors8YrwjxMiIQBJ0hFlp1aoVJkyYgAkTJphlfofbZtXUzsuRMUW4KAPLefHxSFm06NkFPTUVkqws7BbEYtzQrmqfYZOoI9Fm/8hkcjw3czf6D4mBODMTzs7P/t7lcjn4CQk4nXcIx8cNBsfZSe2zlhJ0pvxNWEPMyWQyhPZ5ASEDBkIsFjf9Tvl8nD5zCgXFP5tty9Vc7b3YLuhom5XQhsMpmeiW9/BaFzmGtbwL52NSh72hm5rIYGxgORsSKNhgA2EZOBxnrH0zDJKsLPATEtSyWfkJCZBkZWHlkKAmQg6o99ARTZHmF6Dk6jWkpKSoCTlAWe5JgKtXSiDNL7CShcaj3HYlCFvD4bZZNaHtxs4mL5ImrClI8orLcaW8CjsWLdJ8QU9JQVRUFPKKyzF8QNMUbmttv9qKiGNLgVt7YNzQrtgtiMX8rXmIiopSHQ/288S2Eb0xlttR62dtZcvVklSU1xdR1RWHqBxna1AcHWGLkJhrhsY3fraIOzYIEn0Dy5XjtGEpUceG70xfdMUhEoYzbmhXJERw1QRyyO9VGj1yjTG3oLO1RAgf3/pelbriEJXjbBUSdYQtQY/6BmDNemtsq/HWMLBcE6r6fX+N04U5z40t35k+UIFb88HhOGP4AH8kDu+O4QP89RJySti65WqNThCR0WEIDAqAUCiEXK4eRiGXyyESiRDEDURkdJjZbLBk8eCREYG0/UowQklJCTSlKSgUCoOLCzfG4cRcXlEZduReRO7pUkYKhZpb3LFJwDUkpq8vuL4eEKamar6gC4UI9vNETF/D+tcxeb5s/e60wdYCt0Q91fvOm03U2dL/pxwOByvSPoZEIgGfz1ePQ+TzIZFIsHz1IrPXm7M0lhR1crkM188ew++/HsD1s8cgl8sAAKez/7TI+oR5CA4Oxq1bt5ocv3v3LoKDg02a2+G2WV9ZfkD1b3NsXTEVf8f2i7sysHySqD6wXJCSospmFQmFqmxWY+O8Gp6/MVtQbP/+NGFqHCKhP6aIMoqjA3j8OGTs2IglC1epxSEGcQPNWpaEDZh7+/Vi4UFId6xH5c1S1bE27p0QGDIFnn6m1SIjrItCoYCTU9MdgZqaGrWuE8bgcGIuOztbVTRYmJqKSSLNJTSYxhbFhS6aCyxn8js1RNjZ8vfMVBwiYX5I0NULurixoyzeAYItmEPUXSw8iAOfLkR8PA+LFu1VPSDXF5xdjx4R7zK2FmE55s6dC6C+H+zixYvRuvWz8COZTIajR49i4MCBJq3hcHXmmrTzSkhA0fF8XNg0kbIFjcRamZeNhZ0tCzlA/9ZTOaKx5JkzEHNtjzIt6IzxQrOhrZe1MFe9OWMwVdTJ5TJsn/cqosMGIzOzaf2+hIQEHPz5KGrv36I6czbGiy++CAD4+eefERkZCReXZ0XKXVxcwOVyMW/ePPTo0cPoNRzOM9cQ2rpiBmVguaWxdfHWmIZxiJoK3Bobh+goWCNJQbmmo3vpCNO7SJSe/w2VN0uxaNFejWEWKSkpkEiitHyaYDOHDh0CACQlJeGTTz4xi3h1eFcUbV0RbEGfArdrkkLJg9wAZVKCtbNNmbLDmAcUa2S0EpoxJUGitvI2AN1hFoTtkpGRYTYvpEN75gDDS2gQhDmxVByirWJt0aYPpnrrbK3uHKGOsbF0rT07ANBdv4+wbXJycpCTk4ObN282qQSxdetWo+d1aDEnl8shpK0rgmVoKnBryx0gbEGAmQPagnVsDN129e81CJ6d/JGaKtQYMycUiuDR0Q9Vt8rMYS5hAZYvX44VK1ZgyJAh8PPz05jZaiwOJ+YKCgqeZbMKhZBIJJg3boDN3igJ+8RacYhM4qgirjHGiDryztkHhgg6Z2cOIhM/RNanC5GQwEdKikCVzSoUipCVJcHIt5cje+MSM1tNmIuNGzfi66+/xt///nfG53a4bNaGBAcHo723N+6U/knZrATBECTimkdfUWeImKOMVvajr6jTVGfO06czIl+bjcC+Ydg0czhls9oo7du3R0FBAbp168b43A7nmZNIJKiuroafnx9iYmJQUFBA2awEwQAk4piFvHP2hb5euu6hI9B18DCUnv8NtZW30dqzA/x7DYKzMwd1tTUWsJQwF9OnT8c333yDxYsXMz63w4m5mJgYtacAymYlCNMgEWcY5ig47F2T69DeOVtBX0Hn7MxBlz5DLGARYUkePXqEL7/8EtnZ2RgwYABatmyp9v66deuMntvhxFxjKJuVIIyDRJzx6CvoyDtnf5i7HRjBXk6fPq3q9NA4O9nUZAiHFnNUiJUgjIOEnOlQSzB2Et3lEQDzx+KZWmSYsD2UxYPNgcOJuYbZrEw0hCcIR4JEHLPoI+jIO2c5lEKu4b/NKer0KTJMgs++yMvLw6ZNm3Dp0iXs2bMHnTt3xv/7f/8PwcHBiI6ONnpekxTM6tWr4eTkhA8//FDrmM2bNyMmJgZeXl7w8vJCbGwsCgoK1MbU1NQgOTkZXbp0QatWrdCnTx9s3LhRbUx5eTn+/ve/w9fXF23atMHzzz+PvXv3GmzzSy+9BHd3d0RFRaHoeD4VYiUIPSEhxzz6eOZIyDVPdJdHaiKM6TmU75m6hrGMjAhUvYaFBljFBoIZ9u7di9GjR6NVq1Y4ceIEHj9+DACoqqqCUCg0aW6jPXOFhYXYtGkTBgwY0Oy43NxcJCYmIioqCm5ubkhLS8OoUaNQXFyMzp07AwDmzp2LgwcP4j//+Q+4XC5++uknvPfee/D398crr7wCAJg6dSoqKyvx3XffoUOHDvjmm28wadIkHDt2DIMGDdLb7u+WjkF17RObL8RKEAShhJIfDPekGSPONH3GVkqjENZn1apV2LhxI6ZOnYqdO3eqjg8dOhSrVq0yaW6jlExNTQ2mTJmCzZs3w8vLq9mx27dvx3vvvYeBAweiV69e+OqrryCXy5GTk6Ma8+uvv2LatGkYPnw4uFwuZs6ciZCQEDUP3q+//opZs2YhLCwMXbt2xccffwxPT08cP37cmFMgCIKwKhQvZx60edEaetiY9LI1nteaXjyC3Vy4cAEvvPBCk+MeHh6orKw0aW6jPHPvv/8+4uPjERsba7CarK2txZMnT+Dt7a06FhUVhe+++w5vvvkm/P39kZubi99//x3//Oc/1cbs2rUL8fHx8PT0xO7du/Ho0SMMHz7coPVfWX5A9W+urwfWvhlG26wEoQPaYrUO+myxkldOM9YWVPqsT149x8LX1xcXL14El8tVO56fn4+uXU3TIQaLuZ07d+LEiRMoLCw0asGFCxfC398fsbGxqmOfffYZZs6ciS5duqBFixZwdnbG5s2b1RTs7t27MXnyZLRv3x4tWrRA69at8e2336J79+4a13n8+LFqPxqor2YNANnZ2c/aeaWmYpIoi+LmCIKwKOSVIwDDBef9avL42TIzZszA7NmzsXXrVjg5OaG0tBRSqRTz5s0zuZCwQWLu2rVrmD17Nv73v//Bzc3wJ4rVq1dj586dyM3NVfv8Z599hiNHjuC7775DUFAQfvnlF7z//vtqom/x4sWorKxEdnY2OnToALFYjEmTJiEvLw/9+/dvspZIJMLy5cubHA8NDUXbtm0REREBcWYm+AkJWJCRj4QILsXPEYSdI5MrIK2oQnltHXxbuyDSxwMcZ+aaXRMEQWjjo48+glwux8iRI1FbW4sXXngBrq6umDdvHmbNmmXS3Ab1ZhWLxXj11VfB4XBUx2QyGZycnODs7IzHjx+rvdeQ9PR0rFq1CtnZ2Rgy5Fll64cPH8LDwwPffvst4uPjVcenT5+O69ev48CBA/jzzz/RvXt3FBUVoW/fvqoxsbGx6N69e5PMV0CzZy4gIKBJHzipVIqoqCjkiMZSOy+C0II9bLPuv3ILHx+7ipKqB6pjgR5tsGpIEMZyO1rMDiZ7s9IWq2NhyX6m1JuVeUpKStClSxc8ffoUFy9eRE1NDfr06YM2bdrg2rVrCAzUXapGGwZ55kaOHIkzZ86oHUtKSkKvXr2wcOFCrUJuzZo1SE1NxY8//qgm5ADgyZMnePLkCZyd1b1iHA4HcrkcQH2cHYBmxzTG1dUVrq6uOs+J2nkRhP2z/8otTDt4Drz4eOxatAj9+vVThVpMy8rCthGwqKAjCG141+Rqfa/Fg4eWM4RgnODgYJSVlaFTp07o06eP6vidO3cQHBwMmUxm9NwGibl27dqpxI+SNm3aoH379qrjU6dORefOnSESiQAAaWlpWLJkCb755htwuVyUl5cDANq2bYu2bdvC3d0dw4YNw/z589GqVSsEBQXh559/xr///W9Vn7JevXqhe/fuePvtt5Geno727dtDLBbjf//7HyQSidEnD1A7L4Kwd2RyBT4+dhW8+HiIMzNVD4UNQy0W5x3Cy4EdzL7lymSsHHnldNNQGJnz+2pOgBGEEm0boTU1NUaFrjWE8Q4QJSUlah60L774AnV1dZgwYYLauKVLl2LZsmUA6pMqBAIBpkyZgrt37yIoKAipqal45513AAAtW7bE999/j48++ghjx45FTU0Nunfvjm3btuHll1822la5XA6RSIjgIB+88OZ4OGmImVMcPWz0/ARB1NNYxFhy21ZaUYWSqgfYtWhRE+++s7MzBCkpiIqSQFpRhWg/T0bXNla8mVooWFOWpLmyO2UyGaT5Bagovwkf306IjA7TuktjLMrz0fccNIkr5TGmRB0JOEJf5s6dC6C+/+qSJUvQuvUz55FMJsPRo0dVPVuNxWQxl5ub2+zPV65c0TmHr68vMjIymh3To0cPozo+NEatnZdICIkkC3sykrUmPzS8qJKwIxwRU4WXJkFjiMgxdf3y2joAaLKroER5XDnOWNieoWoOgScR/4AlC1eh5Oo11bHAoACsSPsYPH6c0fNqK9mRf91Np826RJap3joScYSh/PbbbwDqPXNnzpyBi4uL6j0XFxeEhIRg3rx5Jq3hcL1ZX3rpJdW/g4N8sCcjGeN4oXp9loSdZmQyOfKKy1F2t5Y6a9gh7uN6GSWomBI3ynmMFXW+resvnEVFRYiIiGjyvjLUQjnOULusQXMixJDaZQ3HGirsJOIfkJT4Dng8Hnbt3P0sDlEoRFLiO8jYsdEgQcdEzTVDhZa+3jpLCDh97ikKEx84COtw6NAhAPU5Bp988olZEj4Myma1ZZSZOfu/mYvq+w/h5+OJmMieJosORxd1+w5fwvytBbhSXqU6RsWY7RN9xZS5RY6hok4mV+D5fccREvOiWswcUB9qwU9IwOm8Qzg+brBeMXPmPj9Ts1hNFUX6iDqZTIbQPi8gZMBAiMXipt8pn4/TZ06hoPhnnVuuhtrbnH2mii5N3yvTQs6Ue0Z1bR28JmZQNivRBIfzzMVE9oR7u1aMzefI3rp9hy9hkigbvPh47GiUIUjFmI2HrZ5OXR46S3mqDPXUcZydsGpIEKZlZYGfkABBSorq/1WRUAhJVha2jeitU8ixZRvV3IkP+sSnSfMLUHL1Gnbt3K05DlEgQFRUFKT5BYgeFqlzLaa423a4SeLLuyZX7ftlUsg52v2B0ExOTg5ycnJw8+bNJtU4tm7davS8DifmlMhkcuRJL6CsopIxL51S2DnCH61MJsf8rQXNZghSMWbDYbuns7Ggs6bAMUTUjeV2xLYRwMd5hxAV9SwDPsijDbaN6N1sWRJLnqM5Eh9MmUuboKsovwlAdxyicpymuc1hFxOQgCPMxfLly7FixQoMGTIEfn5+cHJiLnveIcXcPkkh5i3ZhSslzy403MBOSF8xWe/4ueZwBFGXV1yOK+VV2NFshmAU8orLqRizntiKp5MtHiol+oq6sdyOeDmwg0EdINh2rpYuR6LNS+fj2wmA7jhE5bjG85kTU71zTGDP137CeDZu3Iivv/4af//73xmf2+FcJt8dOIGJSRvQPyQMUqkU9+/fh1QqRf+QMExM2oB9EuN6zmrC1KdsNqMssqzryZyKMetHY09nRESEWts5Xnw8FmQUQibTXCSbqBdeusQXx9kJ0X6emNCtE6L9PLUKOX3mYhpd1wtdQs6cQqnx3JHRYQgMCoBQKGyyVVRf8kmEIG4gIqPDVJ93hKbyiqOHScgRWqmrq0NUVJRZ5nY4Mfdx6l7wePEQixvdMMWZ4PHiMX/pbkZvmE7hQ+1S1CmLLCufwBtDxZgNQ+npTGnG03m5rBJ5xeVWstB2UAqxhi9DP0s0paEg43A4WJH2MSQSCfh8vtqDMZ/Ph0QiwfLViyAta2MVEWdpD6Y5RJxMJkfu6VLsyL2I3NOl9CBnB0yfPh3ffPONWeZ2uG3Wkuu3sWuPlhum4K+tQekFDI/uzei6TuFD7eqJLaavL7i+HhCmpmrMEBQJhQj280RMX18rWmk7kKfTvLBdoLHZK6dpreguj8DjxyFjx0YsWbhKzdvgGxCEBRu2wHMIz+w2sAFzXNc1xc4GerTBooFdGF+LsByPHj3Cl19+iezsbAwYMAAtW7ZUe1/Z9coYHE7MAXrcMCsqzbKuPcXScTjOWPtmGCaJtGcI7hbEUvKDnjT0dDYXg0SeToINKMUjjx+HuLGjIM0vwC9n78Grow/6hEYw3gHCGCwRO8f0tbx633m1PsKNY2ffNrF9JWFdTp8+rer00HhXy9RkCIerMwcAUqlU4w1TKpUiKioKB8UCxj1zjbEHQQdofoIM9vPEmqRQVgTr2woymRzPzdyN/kNitNZCKzqejwubJpJAtjNM9coBlvXMsQV9PHPmFHNMXMMbJ+3oqokY//LLOPDjj1RnjmiCw3nmArt0gFCYCrFYw9bgX31aYyJ7mt0Oe/HSjRvaFQkRXFbWRbMlyNNJGIsjCjl9MYd3zhwiTomuPsL/mDcPB3780eT1CfvD4cTcqkXjMe29L8HnJ0AgaHDD1KNPqzmwh1g6DseZyo8wwLihXbFbEIv5W/PUYpCC/TxZU5aEYBYmvHKE5TCnkAN09xHu3du8O0aE+amsrMSWLVtw7tw5AECfPn3w1ltvqXYOjcXhxJyXRxvMnjkKu8SH1W+YBvZpZRJ7EHQEM5CnkyD0Q98kCKa8c6Zeo/Upbq2rj7BSABC2ybFjxzB69Gi0atUKYWH1ZXv++c9/QigU4qeffsLzzz9v9NwOJ+bG/u1ZtohPJ08kjgvHK2OeZ6QDBEEwAXk6CX2hLVbdWFvIGdJLONLHA4EebbRWCfi/9HSj7SCsz5w5c/DKK69g8+bNaNGiXn49ffoU06dPx4cffohffvnF6LkdTr1kZ2er6iGFhUfjk00/4W5lDQk5giAIQg1T6sdV7ztvkJADnvURlvzVR1itfl9CgsPHy33++efgcrlwc3NDeHg4CgoKtI4tLi7G+PHjweVy4eTkhPXr1zcZo3yv8ev9999XjRk+fHiT99955x2j7D927BgWLlyoEnIA0KJFCyxYsADHjh0zak7VPCZ92gYJDQ1VKxTM5ydg/tLdSIgbTIKOQazdLN7a6xOELuyxmLg9YYyIM1S8aaK5PsKbhvXE2z9fMHkNW2TXrl2YO3cuNm7ciPDwcKxfvx6jR4/GhQsX0KlTpybja2tr0bVrV0ycOBFz5szROGdhYSFkMpnq56KiIrz00kuYOHGi2rgZM2ZgxYoVqp9btzauRJS7uztKSkrQq5d63ctr166hXTvTMoYdTsw1hMlCwTKZHHnSCyirqISfj6fVt22tKWas3Sze2usTBBNQ8oP1sJaQU6Ktj/CDpzIAjinm1q1bhxkzZiApKQlAfZ/TrKwsbN26FR999FGT8aGhoQgNrY+B1/Q+AHTs2FHt59WrV6Nbt24YNmyY2vHWrVvD19f0AviTJ0/GW2+9hfT0dFXM/uHDhzF//nwkJiaaNLfDuyqYKBS8T1KIHqELMIIvwpS3v8AIvgg9Qhcw2ufVIHsOX8JzM3djpGA/Xl+bg5GC/Xhu5m7sO3zJImtPEmWj/5AY9d63Q2IwSZRtdhusvT5BEOzBmHg5aws5Jfr2EbZ1qqur1V6PHz9uMqaurg7Hjx9HbGys6pizszNiY2MhlUoZsaOurg7/+c9/8OabbzYp4Lt9+3Z06NAB/fr1g0AgQG2tcZ140tPTMW7cOEydOhVcLhdcLhdvvPEGJkyYgLS0NJPsd2jPHNCgsr6Pp1Gf3ycpxMSkDeDx4rFjV4Nq3cJUTEzaYPEMWaWY0VQ9fJIoy6wlLho3i1cG7yqbxfMTErAgIx8JEVyzeAmtvT5BEJaFDW29zCHk2ITXg3y4O7difN4WDx4CAAICAtSOL126FMuWLVM7dvv2bchkMvj4+Kgd9/HxwfnzzHz/YrEYlZWVeOONN9SO/+1vf0NQUBD8/f1x+vRpLFy4EBcuXMC+ffsMXsPFxQWffPIJRCIR/vzzTwBAt27djN62bYhDizlTCwXLZHLMW7ILPF68WhFia8XjWVvMKJvF72imWXxUVBTyisvNkq1p7fUtja6YK3OVu7HVUjrmiFGz5vdAmazMY8jv095FnKW4du2aWgcIV1dXq9ixZcsWxMXFwd9f/d4wc+ZM1b/79+8PPz8/jBw5En/++Se6deum19wHDx5EcnIyjhw5And3d7Ru3Rr9+/cHAFRVVaFv377YuHEjYmJijLbf4cRcQUEBwsPDGSkUnCe9gCslN7FjlxbxwFA8nt72WFnMWLtZvLXXNxfGihBtn9N1w9JnPXN3MLGV5AB76eRCkJCzFu7u7jrbeXXo0AEcDgcVFRVqxysqKhiJZbt69Sqys7P18raFh4cDAC5evKi3mFu/fj1mzJih8Tw9PDzw9ttvY926dSTmDOGll15S/dvUQsHKODud4sGEeDyD7LGymLF2s3hrr88ElhAxTK5hDjFjK0KuIYZ+D7Z4jvYMCTl24+LigsGDByMnJwd8Ph9A/c5aTk4OkpOTTZ4/IyMDnTp1Qnx8vM6xJ0+eBAD4+fnpPf+pU6eajYkbNWoU0k2sIehwYm7/N3NRff8hIxmnyjg7neLByHg8g+2xspiJ6esLrq+H1oKXIqEQwX6eiOlr+pMUG9c3Bnu5qTOx9WoP34WtbkHbI0z3ZAVIyFmTuXPnYtq0aRgyZAjCwsKwfv16PHjwQJXdOnXqVHTu3BkikQhAfULD2bNnVf++ceMGTp48ibZt26J79+6qeeVyOTIyMjBt2jS1+m8A8Oeff+Kbb77Byy+/jPbt2+P06dOYM2cOXnjhBQwYMEBv2ysqKtCyZUut77do0QK3bt3Sez6Nc5j0aRskKqwHThWVoKyiEnnSCyYJupjInuAGdoJQmKoWMwcYHo/HxE3A2mLG2s3irb2+vtiDaNGEKV46e/pO7HXrNedICWNzjYwINGruxp8DTE+C0Pf31JyQO/Njuerf/Uez52HRnpg8eTJu3bqFJUuWoLy8HAMHDsSBAwdUSRElJSVq97zS0lIMGjRI9XN6ejrS09MxbNgw5Obmqo5nZ2ejpKQEb775ZpM1XVxckJ2drRKOAQEBGD9+PD7++GODbO/cuTOKiorURGRDTp8+bZCnTxNOCoVCYdIMNkJ1dTU8PDwQ2KUDSq7fVh3nBnZC+orJRm+1NsxmFQgaiIcG8Xj6zs3Exb9hNqs2MWPuWmua6rwF+3liTVKo1erMWXJ9bdiTYNGX5v6fdpTvQ9N3oOvcddWYs2TyA5MizlQ0iTkAGsWcPp45poWcEnMJuuq6pwj6f4dRVVWlM87M5LX+umdWXt4E93bMZ7NW338Iz+C3LXIu1mbWrFnIzc1FYWEh3NzU/3YfPnyIsLAwvPjii/j000+NXsPhxNyYMaOxdOkytRIihoquxuyTFGLekl24UnJTdSw4yAdrl08yaE6mnuTZIGas3YHB2us3xFFEC6Gdhn/btiLk2CTilBgi5gDrCDpzeuZIzNkmFRUVeP7558HhcJCcnIyePet3686fP4/PP/8cMpkMJ06caFJ6xRAcTszdu3cPnp6equNyuRx8fgKKThfi94I0o2/4THSAYHJbhk1ixhEhAUdoQnH0MOvFHBtFnBJtYg6wrncOqBd05t5iJTFnu1y9ehXvvvsufvzxRyhll5OTE0aPHo3PP/8cwcHBJs3vcDFz5iohwuE4W6T8iL5wOM52UUvN1iARx36s2XqPzUKOzSLOWO62Hc5YIoT7uF7NCjqKlSOaIygoCN9//z3u3buHixcvQqFQoEePHvDy8mJkfocTc5qwdAkRwv4gEWcbaAqJMDVu1lKQkGsebYkQugQdZSATlsTLy0vVM5ZJaN8Nli8hQtgHTuFDVS+C/SiTlfqHhKn37Q0Jw8SkDVbrpaxEl1fOXNiDkLMU7uN6WdsEgtCIw3nm5HJ5k59NaelFOB4k3mwPtrXeMxRzeeUcRcgxud1KEGyEfVctM5OY+JraUzmfnwCJJAtrl09i5UWcYA/khbNdlK33UlK0t967fLUCedILVrGvOa+cLQg5uVyG62eP4fdfD+D62WOQy2WMzW0Ixn5X9HdN2DoO55k7W3QcUVFRqp9NbelFOEbmrKUv9tYM0rdH2NZ6ryHW2F5lUshdLDwI6Y71qLxZqjrm2ckfkYkfonvoCMbWMRWmvHO6EiEIwho4nJj7LXelqgME3SRNR1NNO66vB9a+GWbVAr1MYmkhZ8tB+tpQChZrbXWxrfWevjDtlWN6W/Vi4UEc+HQh4uN5WLRor6p+Z2qqEFmfLsSYD9IsLuia6whB262EtXjy5AnGjBmDjRs3okePHozP73AqRllCJHF8JIZH9yYhZwLKbhP9h8SoB5QPicEkUTb2Hb5kbRNtjmdB+uGsDNI3hLtth6tejY9Zmoat99gUN2vJ7VWmhZxcLoN0x3rEx/OQmSlGREQE2rZti4iICGRmihEfz4N05ydW23I1FEMe2igRgjCUli1b4vTp02abn5QMi7CluA2ZTI75WwvAi4+HODNT7UIuzswELz4eCzIKIZPJdU/GYiz5O5HJ5Ji7VAwejwexWP3mKBZngseLx/ylu1n/neoj2Cwt6jgcZ6SvmAyJJAt8foLDxc2aI9Gh9PxvqLxZikWLUjTGIaakCFBZcQOl539jfG1dNCeErZU1TBCvv/46tmzZYpa5HW6blWCGvOJyXCmvwo5FWgLKU/4qxFxcbrPFiy0trvOkF1By9Rp27dxttuLW5sSYm6Qlt73G8UKxJyMZ85bsYkXcrK2LitrK+h7XuuIQlePYhLb/7wypOaf0zlH8HKEvT58+xdatW5GdnY3BgwejTZs2au+vW7fO6LlJzBFGUXa3FoAeAeV/jSN0w+YgfV2YIkwsGU83jheKhLjBDpdcMjIikHHvXGvPDgB0xyEqx5lKc628GqMtZk4fDC0ibK6ECJlcAWlFFcpr6+Db2gWRPh6Mr0FYlqKiIjz//PMAgN9//13tPScnJ5PmJjFHGIWfd2sAegSU/zWOaRpePO0lfsVWg/SZ8jBZykvHhtZ7tu6VAwD/XoPg2ckfqalCZGaK1bzJcrkcQqEInj6d4d9rkEnrGCLiDMGSrb4MZf+VW/j42FWUVD1QHQv0aINFA7swtgZheQ4dOmS2uUnMEXrR+EIVIlcg0KMNhKmpEGdmNrmQi4RCBHm0QcjvVai+WK3XGg1FmSEXxup95xkXdNaIX3wWpC+EWNz05sjG4tZMixLKNjQfTHvnnJ05iEz8EFmfLkRCAh8pKQJVNqtQKEJWlgRjPkiDszPHaHvNDRPbrUqY2nbdf+UWph08B158PHYtWvTsO01NxdsSiUlzE9YnLy8PmzZtwqVLl7Bnzx507twZ/+///T8EBwcjOjra6HlJzBHNou3CxHF2wqohQZiWlQV+QgIEKSmqi45IKIQkKwvbRvQGx1l/17Gjx54og/QnJm0An8+HQPDs5igSCSGRZGFPRjJrtgTN5V0y57arvjabU1Ba0yvHtKDrHjoCYz5IQ/6O9WpxiJ4+nY0uS2IJEdeQ5gQdAKNFHWD4NU0mV+DjY1dViWVqnUoyMxH/8ss48OOPBs1JsIe9e/fi73//O6ZMmYITJ07g8ePHAICqqioIhUJ8//33Rs/tpFAoFEwZymaqq6vh4eGBysub4N6ulbXN0QpbGj7rexHStB0Q5NEGK4cEYSy3o7nM0whT3jlrZxX/O/shlixchZKr11THgoN8sHb5JFbUmbOkGGlOVFlTFJki9vS12xY6PyiRy2UoPf8baitvo7VnB/j3GmSwR45JEWdMzFxzv1Omrsu6rqv5ZZUY+/0pSKVSjaEW2dnZeOmll1BVVQV3d3dGbNKGue+Z1fcfwjP4bYucC1sYNGgQ5syZg6lTp6Jdu3Y4deoUunbtit9++w1xcXEoLy83em7yzLEMY9z7TGLok+RYbke8HNihSaCuIR45Qp2psa0Qd/YXSPML8OBKLquC9C0toNgaW2aoXUqhwIbzMUcyhLMzB136DDHaHjbQ3BY/U9dlXV678to6ANqToHr3Zl8WO6E/Fy5cwAsvvNDkuIeHByorK02am8QcAcC0LU6OsxOi/TyZM4YAh8PBK4MfA4MjrW2KCjYIEVuFvjt12CLgDIHpB+3GOwnV+87Dt7ULAO1JUOfOnWNsfcLy+Pr64uLFi+ByuWrH8/Pz0bWraR2THE7MyWRy5Oafc6iyBM1hT3FqTCRCWHuLVQmbkgBIiNgf5vDO6VrPFtCVgGPOnRP3cb0wWiYH98QVrYll/5eebpa1CcswY8YMzJ49G1u3boWTkxNKS0shlUoxb948LF682KS5HU7MDRq+GCXXnxWxtPWel8ZiTyKOMB8k5AhjsBXxpglrCjoOxxlr3wzDJJHmxDJKfrBtPvroI8jlcowcORK1tbV44YUX4Orqinnz5mHWrFkmze1wLqk+/QbbfM9LU7FnIWfKubHFK8cGrNVDlbAcTAqukRGBai9bR9f/++a8Vowb2hW7BbE4cywPUVFRcHd3R1RUFIqO52PbP14027qE+XFycsKiRYtw9+5dFBUV4ciRI7h16xZWrlxp+tyOls167949eHp6qo7L5XLw+QkoOl2I3wvSWLHlas4ECHsWckqM3WolMVcPiTh2YK5s1sYYst1qK0LNlA4QDdEV7mDOa7VMJkdecTnK7tbCz7s1Yvr64sHjp/CamEHZrDZKSUkJAgICNHZ7KCkpQWCg8X9fDrfNaos9LwmCIMyFUqA1FHW2Ito0wZSQ0wdzb7naal9rQjPBwcEoKytDp06d1I7fuXMHwcHBkMlkRs9tfTcUC2Bbz0tzeYgcwSsHOM55mgPyyjku9rRNyhT6/D2QR5/QF4VCodErV1NTAzc30zzxDueZ0wRbe14SlkVx9LBDX5hJyBG2jiW9cg2xdn1Qgt3MnTsXQH3M3OLFi9G69bOe5TKZDEePHsXAgQNNWsPhxJxcLm/yMxt7XjINeasIgrBnrCXklGh6ECSBRwDAb7/9BqDeM3fmzBm4uLio3nNxcUFISAjmzZtn0hoOJ+YSE1/DkiVLWd3zkmkcUcgxUXPOkSCvHGHLmFPI6SpV0hz6ePpJ8Nk/hw4dAgAkJSXhk08+MUvCh8OJubNFx9UaQgcH+WBPRrLD1ZkjNOOIW60k5AhbxtoeOVMx5HrjdP8hgAzzGUOYlYyM+t/d2bNnUVJSgrq6OrX3X3nlFaPndjgx91vuSpwqKnGYDhCO6JUjCMIxsHUhRzgWly9fBp/Px5kzZ+Dk5ARlZThlUoQp2awOJ+Y4HGcqP+Ig0FZrU8gLZxtYqsacLWNJIWfKVitBKPnggw8QHByMnJwcBAcHo6CgAHfu3ME//vEPpJvYqs3hxJwj9WYlr5xx2OpWKwk1wlEgjxxhi0ilUhw8eBAdOnSAs7MznJ2dER0dDZFIhA8++ECVKGEMDifmqDcrYY+QkCMcBRJyhK0ik8nQrl07AECHDh1QWlqKnj17IigoCBcuXDBpbvt0STWDo/RmJa9cPY7wPZCQIxwFawo5+jsjTKVfv344deoUACA8PBxr1qzB4cOHsWLFCnTt2tWkuR3OM7djx05Vb9aIiAiIxZng8xMwf+luJMQNtostV2sImDM/ljf7fv/RvhayhBlsdauV0I1MJoM0vwAV5Tfh49sJkdFh4HA41jbL4bEFjxvFzhGm8PHHH+PBgwcAgBUrVoDH4yEmJgbt27fHrl27TJrb4cScrfRmZXtFcV3iTdt4WxN1hH0hEf+AJQtXoeTqNdWxwKAArEj7GDx+nBUtUye6yyOzJ0EYI55MsckWxJo+NPTQGSrsTPHukYi0fUaPHq36d/fu3XH+/HncvXsXXl5eGtt8GYLDiTlNsK03K1sxVMBpm8PSgs7es1q9a3JpC0gPJOIfkJT4Dng8Hnbt3K0qHC4UCpGU+A4ydmy0a0HHhJhSzqGPXfYi3ppD+XfXWGiZ4+/xbtvhuC+/z/i8hPm5fPkygoODNb7n7e3NyBpOCmWhEzunuroaHh4eqKqqalJ9WSqVIioqCgfFAtZ45gDjK4ObY5uVCSHXEGt46IwRdLa01UqCTjsymQyhfV5AyICBEIvFah56uVwOPp+P02dOoaD4Z9ZtuRoj6CwppPKvuzmEcGMD96vvI7hTX433MaZR3jMrL2+Ce7tWzM9//yE8g9+2yLlYG2dnZwQFBeHFF19Uvbp06cLoGg7nmXPU3qymwLSQU85JW67MQh467UjzC1By9Rp27dytJdRCgKioKEjzCxA9LNJKVmqG7UJJaR/FIhKEZg4ePIjc3Fzk5uZix44dqKurQ9euXTFixAiVuPPx8TFpDYcTc47Ym9UU9BVyMoUCpx8/xB35U7R3boEBrq3A0REDYGlBZ2vbrTKZHHnSCwbVRCRBp5mK8psAnoVUNEZ5XDmOMAxbiUUkCGswfPhwDB8+HADw6NEj/Prrrypxt23bNjx58gS9evVCcXGx0Ws4nHpR9mZ1d3dHVFQUik4XsrY3q7FbfJYWLD8/vI+/3S7B7NvXsOJuGWbfvoa/3S7Bzw91x3eYw+vHJNZKQtknKUSP0AUYwRdhyttfYARfhB6hC/QqoeNdk0vB0o3w8e0EACgqKtL4vvK4chyhP8pYxJABA9XKPoUMGIikxHcgEf9gbRMJgjW4ublhxIgR+Pjjj7F8+XJ88MEHaNu2Lc6fNy08yuHE3G+5K3FQLMD2Te/ioFiA3wvSNAq5u22Ha3w5EvoIrZ8f3seSO6UY8lKs2oV8cOxILLlTyjpBZwt15/ZJCjExaQP6h4SZVBORBN0zIqPDEBgUAKFQqCXUQoQgbiAio8OsZKFtIpPJsGThKvB4PIjFYkRERKBt27Z/lX0Sg8fjYelHqSb1nCQIe6Curg6//PILli9fjhdffBGenp545513cO/ePWzYsAGXL182aX6HS4DQFMxpqkgz503TWkkQ+ggsmUKBv90uwZCXYiHOzGwSVJ7wyis4kZ2D7R0CdW65KrHEtiubEyFkMjl6hC5A/5AwiMVNv1M+PwFFpwvxe0Ga3mEBjvYQoo2G2awCgaBBqIUIEomEddmstkD+z1LwR0+GVCpFREREk/eVyWXiH3exLhbRFqEECNtkxIgROHr0KIKDgzFs2DDExMRg2LBh8PPzY2wNh/PM3WsTzbi3zd48ePp6yk4/foiyx4+QsmiRxqDylEWLUPr4EU4/fsj42qbAZu9cnvQCrpTcREqK5u9UIEjB5asVyJPq3/qFPHT18PhxyNixEadOn1QLtTh95hQJOSOhWESC0E1eXh7at2+PESNGYOTIkXjppZcYFXKAAyZAEM1jiJi6I38KQPeFXDnOUBscMdtVWetQ13dqaE1EpaCz9QcNU+Hx4xA3dhRlXTJEw1hETZ45ikU0jcYZwv0GsKd0FqE/lZWVyMvLQ25uLtLS0pCYmIjnnnsOw4YNw/DhwzFs2DB07NjRpDUczjNnKZi6abK5zll75/pnAV1B5cpxhnLmx3LVi2kM9c5ZKhGiDXc4AN3fqZ+Pp1Hzk5cO4HA4iB4WifGTExA9LJKEnAlQLKL5kIh/QGifF8AfPRlvT5sF/ujJGB5G3mNbpE2bNhgzZgxWr16No0eP4vbt21izZg1at26NNWvWoEuXLlof4PWFxByhwlDRNMC1Ffxc3ZC6apXGC7kwNRX+rm4Y4Gp6vIW5RB3b0H1zNL0mIgk6gik4HA5WpH0MiUQCPp+vlrDD5/MhkUiwfPUiEswGoi1DuF/f/tY2jWCANm3awNvbG97e3vDy8kKLFi1w7tw5k+YkMUeoMHRbk+PkhPfaeiMrKwsJr7yidtFJeOUVZGVl4d223nonPxBAx4d5zdwcEyCRZGHt8kkm10QkQUcwBcUiMktzGcI7duywtnlW5fPPPweXy4WbmxvCw8NRUFCgdWxxcTHGjx8PLpcLJycnrF+/vsmYZcuWwcnJSe3Vq5d6gtyjR4/w/vvvo3379mjbti3Gjx+PiooKg+yWy+UoKCjAmjVrEBcXB09PT0RFReFf//oXfH198fnnn+PSpUsGzdkYipkj1Og/2tcgD9iwVu2wor0//pWdg6isLNVxf1c3rGjvj2Gt2pnDTLtmamwrYMdGLFuwGFFRUarjwUE+rK2JSDg2FIvIHLq6lTgqu3btwty5c7Fx40aEh4dj/fr1GD16NC5cuIBOnZrGZNbW1qJr166YOHEi5syZo3Xevn37Ijs7W/VzixbqsmjOnDnIysrCnj174OHhgeTkZIwbNw6HD+sfeuPp6YkHDx7A19cXL774Iv75z39i+PDh6Natm95z6ILEnBm523a4VTwgpmZrGiPoot3aGtwBwtZQHD1ssRjGqbGtMKVwjcEdIAyBukUQTKKMRSRMQ1eGsKOybt06zJgxA0lJSQCAjRs3IisrC1u3bsVHH33UZHxoaChCQ+sffDW9r6RFixbw9dW8K1VVVYUtW7bgm2++wYgRIwAAGRkZ6N27N44cOaIx6UcTa9euxYsvvojnnntOr/HG4HBiTiaTIf9nKT09MgzHyQmD3Fpb2wy7gsNxxvBo82avkaAjCHahK0PY3qiurlb72dXVFa6urmrH6urqcPz4cQgEAtUxZ2dnxMbWF6s3hT/++AP+/v5wc3NDZGQkRCIRAgMDAQDHjx/HkydPEBsbqxrfq1cvBAYGaq2tqIm3337bJBv1weHE3PCwOFy/dkP1M/UP1Iyh3jlL4IilSgiCcCwaJkGJxeImhcMtjeLYEShauzA/b20dACAgIEDt+NKlS7Fs2TK1Y7dv34ZMJmvSjN7Hx8ekNljh4eH4+uuv0bNnT5SVlWH58uWIiYlBUVER2rVrh/Lycri4uMDT07PJuuXl7Lo/OtwGfL++/S3aP9CWvR4knhwDSoYgCPbQXIZwYmKitc1jnGvXrqGqqkr1auh9MzdxcXGYOHEiBgwYgNGjR+P7779HZWUldu/ebTEbmMLhxNyOHTtsqn+gtRq9K7FXQWdMSy/A+r8PgiDsH20ZwsVnNdeftGXc3d3VXo23WAGgQ4cO4HA4TbJIKyoqtMa7GYOnpyeee+45XLx4EQDg6+uLuro6VFZWmnVdJnA4Mae5RZIAV6+UQJqvPc3ZkbFXQUc8g7xzBMEuePw4FJ79BeIfd2HTts8g/nEXDh393tpmWQUXFxcMHjwYOTk5qmNyuRw5OTmIjGQu6aampgZ//vmnqtXW4MGD0bJlS7V1L1y4gJKSEkbXZQKHi5nThD31DzRX31E2xtARBEHYM40zhO9X37eiNdZl7ty5mDZtGoYMGYKwsDCsX78eDx48UGW3Tp06FZ07d4ZIJAJQnzRx9uxZ1b9v3LiBkydPom3btujevTsAYN68eRg7diyCgoJQWlqKpUuXgsPhqLazPTw88NZbb2Hu3Lnw9vaGu7s7Zs2ahcjISKOSU0QiEXx8fPDmm2+qHd+6dStu3bqFhQsXGv39OJxnThPm7h9oy3FzDek/2pe8dLDfrVbyzhEEwVYmT56M9PR0LFmyBAMHDsTJkydx4MABVVJESUkJysrKVONLS0sxaNAgDBo0CGVlZUhPT8egQYMwffp01Zjr168jMTERPXv2xKRJk9C+fXscOXJErU/qP//5T/B4PIwfPx4vvPACfH19sW/fPqPOYdOmTU2KEgP1te42btxo1JxKHM4zp7HtlFAIH59OKCstR/7PUipXooOGgs5S3joSkQRBEI5NcnIykpOTNb6Xm5ur9jOXy4VCoWh2vp07d+pc083NDZ9//jk+//xzve3URnl5uWoLtyEdO3ZUE6LG4HBiLjExEUuWLEG/fv1QVFSEDz74ACdPnsTTp0/xbtJsAOYpV2KtAsIEQRBMweQ1zF52LAhCXwICAnD48GEEBwerHT98+DD8/f1NmtukbdbVq1fDyckJH374odYxmzdvRkxMDLy8vODl5YXY2Ngm/dRqamqQnJyMLl26oFWrVujTp49Gl6NUKsWIESPQpk0buLu744UXXsDDhw8Nsrmo+IxadtCxY8cwZswYi5QrMebiZamOA46EsZmsBOGIeNfkql5Mz0sQjsSMGTPw4YcfIiMjA1evXsXVq1exdetWzJkzBzNmzDBpbqM9c4WFhdi0aRMGDBjQ7Ljc3FwkJiYiKioKbm5uSEtLw6hRo1BcXIzOnTsDqA9sPHjwIP7zn/+Ay+Xip59+wnvvvQd/f3+88sorAOqF3JgxYyAQCPDZZ5+hRYsWOHXqlMG96nILfkDR6XMoKy3HMoEQYaFhaoUZleVK+Hw+ln6Uirixo2jLlWiCJVt7EYS9Qh1ICEdi/vz5uHPnDt577z3U1dUXTXZzc8PChQtNrq9nlGeupqYGU6ZMwebNm+Hl5dXs2O3bt+O9997DwIED0atXL3z11VeqlGIlv/76K6ZNm4bhw4eDy+Vi5syZCAkJUfPgzZkzBx988AE++ugj9O3bVxWwqKkmTXMos4P8/H1RUV6BlJQUi5YroQsXQRAEQTgeTk5OSEtLw61bt3DkyBGcOnUKd+/exZIlS0ye2ygx9/777yM+Pl6tX5m+1NbW4smTJ/D29lYdi4qKwnfffYcbN25AoVDg0KFD+P333zFq1CgAwM2bN3H06FF06tQJUVFR8PHxwbBhw5Cfn691ncePH6O6ulrt1RBdzYzNWa7EUEHHVg8QlSohCPvG0K1QmUyO3Pxz2LFXitz8c5DJLN9+St2e+l7ce3dlIv9nqVUKwxtjQ/51N40v6Q03C1hMmJu2bdsiNDQU/fr1M9ghpQ2Dt1l37tyJEydOoLCw0KgFFy5cCH9/fzUh+Nlnn2HmzJno0qULWrRoAWdnZ2zevBkvvPACAODSpUsAgGXLliE9PR0DBw7Ev//9b4wcORJFRUXo0aNHk3VEIhGWL1+u1Q5dzYwtUa6EYkYIwjrIZDJI8wtQUX4TPr6dbDaDnU3nsU9SiHlLduFKybMHYG5gJ6SvmIxxvFCtnzPXVqtE/AOWLFyFkqvXVMcs3Ytbmw2JC5YjcjTPIjYQ1mXu3LlYuXIl2rRpg7lz5zY7dt26dUavY5Bn7tq1a5g9eza2b98ONzfDnxBWr16NnTv/f3tnHtbUmbbxO4kFFQRXNhVQ27qLVEAIolhpLSVKilXGamXQ1vpVu2jdAKt1IXGrdVqtdjoV7aboFIOGVmekakGjoFVxK9YFsQpoRwUV65Kc7w9NJJCQ9SQnyfO7rlyXnLx5z/Mmx5w7z/ssm7B161at13/22Wc4cOAAtm3bhsOHD+Pjjz/G5MmTsWvXLgBPyom89dZbSE1NRWhoKD755BN07doV69at03mutLQ0rX5vly5d0nq+bjNjXeVKpFIpgoIDETUgwuR1EtyFkh8cB7a8KnLZTwjvMRDiocl4K+UdiIcmI7zHQIMJT1zw8tTF3HWwQY68GCNTV6F3SIRWMlnvkAiMTF2FHLl5P/7NRS77CamjJyGkT1+b9uI2xoY+ffpi6ZQJUOyUs24DYX+OHDmCBw8eaP6t73H06FGLzsNjDBViqYNMJsMrr7yi9ctPqVSCx+OBz+fj3r17en8VLl++HIsWLcKuXbsQFhamOX737l14e3tj69atSEhI0Bx/44038Mcff2DHjh24cOECOnfujG+++QZjx47VjElOTkaTJk3w3XffGbS9pqYG3t7euHD1JFp4tQDw5D+bSCRCWlqaplyJVCqFXC5H1sa1rP+CM9Y7Z2yhWrY6QOjCltus1qozZ0jMKZUqFJysRMX1Wvi3bo6Ynn4QCPT/5uHqFri5cCWmky2vSt3/8+np6Zr/8xKJpNH/81zw8tS3x5x1mIox309KpQrPhM9E75AIyGS5WjHIKpUKYnEiTpQU40zREr3/l6x53SmVSoT3GIiQPn21ktue2CNGyfFjKDq5lzUv5t6LT+HtIf0RHhqCXB02JIrFOHS0BKt3HTDJhtpbt/BaaBdUV1fDy8uLDdM1qO+ZN7akwqu5m/Xnr72PViOzbLIWLvDgwQO89NJLWLt2rc7dREsxyTM3ZMgQHD9+HEePHtU8wsLCMGbMGBw9elTvRbl06VIsXLgQO3bs0BJywKMFPnjwoEESgkAg0HjMgoODERAQgNLSUq0xZ86cQVBQkClL0EJfM+OS48dsIuQA47/ErCUalCoGX+eexydbz6Cw4iaUKqO1vBbWEnJKhsGRv2qxq7YGR/6qhdL43xZWJ2ffeTw7cTOGpG3H2GX5GJK2Hc9O3IycfeftZpMrwpZXRalUYu6sRRCJRJDJZIiMjISnp6cmg10kEmHe7MwGHjcueHmssQ5TMfaHZoGiFGXlV5GenqEnmSwdFy5WoUBRqmcG66IoLEL5xUs2T26rG9t2qvgAqv4oR4YeG9LT0lB56SJOFR+wqg0Ed3nqqadQUlLC2vwmxcy1aNGiQcKAh4cH2rRpozlevz/akiVLMHfuXHz//fcIDg5GZeUjEeDp6QlPT094eXlh0KBBmDFjBpo1a4agoCDs3bsXX3/9tWb/mMfjYcaMGZg3bx5CQkLQt29fbNiwAb/99hv+/e9/W/QGiMTxiB/2IitxJ8bGs9gqfm572TXMOXQR5dV3Hh34sQL+7k2xZEBnDAtu1/iLWWDv3Vv4/PZ1VNz7S3PM370p3vZsjUHNWtjUlpx95zFKuguihARszMh44unIzMQoaR42p8UhKbqzTW2yNVzwytUXKtYsGaS+yWdv2qz3Ji8UCqEoLNL0w2TTHnMxZx1sUlF1E4DhZDL1OF1YM27OHslthX9ohx3duFZllA3qcYRrMHbsWHz11VdYvHix1ee2em/W+v3R1qxZg/v37+PVV1+Fv7+/5rF8+XLNmE2bNiE8PBxjxoxBjx49sHjxYmRmZmLSpEmaMe+//z7S0tIwdepUhISEID8/H//973/RpUsXi21WlysZkZyIAYOirPKlbGo8C9s30u1l15Dy82mExAzW8i70ixuClPxTWCX7ndXz12fv3VuY+78rCHshroE9c/93BXvv2q6htFKpwox1RRAlJECWm6vt6cjNhSghATOziu2emecKsOlVMecmby8vT2PYMxNfF/6+LQE8SRqrj/q4ehzb1E1ua8weayW31RdyANCqna9RNqjHEa7Bw4cPsWbNGoSFheGtt97CtGnTtB6WYHE7r/r90Or/XVZWZnAOPz8/ZGVlGRw3e/ZszJ492wTr7EPdeJbsTZu14llSR08yewuX1z/arCbvShWDOYcuasRKXe9C7rZtSBw+HGt25WPAjgoIeDyz4tOUDIOSe3fxP9VDtOE3QR/3ZhDweHrHfn77+iNvR2P2NPU02x5d6IuXKzhZibLKamzM0LNNlJ4OoVCIgpOViO2j3XKFigdbFzaFijkZ7FwTToD9M/HrExPVFcGBPpBIMnXGzEmlEnQK8kVMVFeb2FM3uU1XzJw1k9t0CTkA6BEeCd8OgciUSHTGzEmkUvh1DEKP8IafH+G8nDhxAs899xyAR2FideHpuV8ai9U9c66OreJZTEFRVY3y6jtI1yNW0jMycOXeXyi5Z1prNDV7797Ca3+W470/L2HB9Qq89+clvPZnuV7vWsm9u6i49xdr9phKxfVaAEZsEz0eVxdLhRzX6nLpw1aZnGx6VczJYLe1l8cYuJaJLxDwsXxBMuTyPCQmJmp52sXiRMjleVg2f1SjiUTWtUeABUvmQC6XQywW17NHDLlcjvmLM1jdFhcIBPh72kfIk8uRWM+GRLEYeXI5UmbPc8hyOIT57N69W+/j559/tmhuEnNWhovbMpW1j9qGGBIrzXt5muwFM2e79H+qh0bZox7HNv6tmwMwYpvo8ThrkSMvxjPhM/G8WIoxb63B82IpngmfafMyDmr0xW3asgQGm0LFnJs814STueswB1NDP9q2aYEdO3ZoJZMVHyzElqwpjdaZM+dchrBFcps+r5yaqKEizFz1FYqPHNPuB360BDNXfUV15lyQ8vJy6CsgUl5ebtHcJOasjLnbMtZKgNC1lej3OK3ckFjxMyH9vPdQvwbbpXW9kLnbtiEhIQFrbl9vkKHaht/EKHva8JtYbYu1MWJ6+iHYzxuSzEzdN2yJBJ38WyKmp7YtlnjljK3LZW/Pna0zOdkWKqbe5Lng5bHGOszFGJGlvpYjowZiz549+PHHHzF//nxERUWi8upNq5zDHETieBSf+gWyndn4YsNnkO3MRtHJvTYtJRM1VITP8w9i4bdbMe2TtVj47Vas3nWAhJyL0qlTJ1y7dq3B8f/973/o1KmTRXObVGfOkdFVZ44NCvcqIB6aDIVCoTOeRaFQQCgUQrYzWyvTzJr15urXmlOqGDyXcxghMYO1YtSAx3WXEhNRUrAbh5P6QcA3ft++sOImhv14zOBa/9G2I0KbPvFqKRkGr/1Zjn5xQ5C7bVvDGkzDh+PXXfn4rm0g+r7kb7Q9hmisxlzdbNa0OnW7pBIJ5Hm6s1nNFXPG1uVaPO9VzPpoi8kV9S1FfXO1Z70uXXXdgoIDMX9xhlVuxqZ2TmDbHnOxZQcIXd9RltaY40IGtbkY8syxAdWZc2z4fD6qqqrQrp129YiLFy+iR48euHPnjtlzW5wAQWhjTvAt22VJBHweFoUFISUvD+LERJ1iZcPz3U0ScoDx27f1t0sFPB7e9myNuXl5SBw+HOn1SoHk5eVhQZsAqwo5QyRFd8bmtDjMWFcAoVCoOd7Jv6XVy5Ko63JtzNZfl0soFCJ5/GoMGybCxuw6748kEyNTVxm1dWUu6jIR9iyBwWbJIOBJBjtX7DEXU9dhCbpKKBl7LRcoShE7oHuD+RwVewg5wnFRZ6ryeDx8+OGHaN68jnNDqcTBgwfRt29fi85BYs7KqLdlUkdPglgs1ttZQn0TMEXImZPJqmZYcDtseB6YU7AbQuGTNjJB3h7Y8Hx3s+rM1d2+bSyrTr2tWpdBzVpgQZsAfL4rH8K8PM3xAPemWNAmwOp15oxp45UU3RmJkcFGdYCwZIvV2Lpczz0XquXteJREkwuxOBEz5m1GYnw/nbYplSoUKEpRUXUT/r4tERPV1azgc3tnctpSqBgD1+yxB/UFnTVqzBGEs3PkyBEAAMMwOH78ONzcnng63dzcEBISgunTp1t0DhJzLKCOZ5k7a5GWlycoOFArnsUWhYLrMiy4HV4ObAtFVTUqa+/Dr7kbony9TfbIqYny9UagtwckmZk6t28lmZkIcG+KPu7NdL5+ULMWGNDUU2dJE1vEyulCIOA3KD9iberW5WpMBE+Y8IbJ3g5zm53Xp/XtPZwrgUFwg7qCzthruW6NOUf2yNkapVKJU8UHcONaFVq180Vw9572Nokwg927dwMAUlNT8emnn6JFC+uHelECBEvYM/i2MS+UgM/DAP+WeLWLDwb4tzRbyKnnWhQWBPnj7VKt9Pvhw5GXl4f/82ytt94c8GjLNbRpc8Q190Jo0+aNjuUKlpYjqVuXS1fChUQiQZMmTTBmzBidr9fn7bB2s/OEvnc5l8lJcAO1IDN0LatrzPV8YSKue8aSkDMBxU453h7SHx+OfQUrpk7Ch2NfwbRhz9vbLMICsrKycPToUYwdOxZCoRCXL18GAHzzzTcoLCy0aG4ScyzSWGcJW3vl2OLR9m13lBTu0cqq+3VXvtnbpfbyytmKunW5xOKGdbny8vLw8OFDnD59WufrdXk7lEoVps/NhkiUAJmsXhcLWS5EogTMmLfZpGxYgYDPyUxOghtc94w1cC2LIZfnYd6SBU51jdgiXk6xU46lUyYgPDRE6z19ro/u7WzCMfjhhx8wdOhQNGvWDL/++ivu3bsHAKiuroZEIrFobhJzdsBZhJyaYcHt8GtSP/yjbUfMbe2Pf7TtiO/aBtq8v6otsFa3hyRROLZkTcHxY0VaIvhESTGyv5pslLejbkV9tpqdj4trZpMSGIRjct0zVnMtHys5RteIFVAqlVgv/QgJIhFyZTKEh4fj0KFDuHDhAia//ba9zSMsYNGiRVi7di2+/PJLPPXUU5rj0dHR+PXXXy2a2+Vi5tSV7O2VkeZsQk6NgM/DuMTOOL6z0qJ5nN0rV5ckUTgS4/vpTFbg83kYmboKYnEi0tLqZB9LJZDL87Ala4pWUgObgejj4poh/tQvnMvkJLjBdc9YxP4tFsUjp9E1YgVOFR9A1R/lyNiSDZlMhg8++MCotpgE9yktLcXAgQMbHPf29sbNmzctmtvlxFxsRDz+uHRZ83dgUEcsWDLH6X49eiV1a1Bvzhb0HupntqBjS8gZk8lqDGz0YBUI+A2SGIAnnrvpc7O1S6UE+eosS2JOILppdlImJ9E4rnCN2GKL9ca1KgDAuXPn8Prrr0MkEmHjxo3o1asXDh48iLi4ONZtINjBz88PZ8+eRXBwsNbxwsJCdO5sWfkrlxNzvXr2xpbN/65Tt0uC1NGTbLId4KxeufqoRZkuUedKnjdLacxzVx+2m52ra88RhKtiq9pyrdr5AgBmzpyp6fGt/v8cHs5esXCCfd5880289957WLduHXg8Hq5cuQKFQoHp06fjww8/tGhulxNzGzduRMuWLQGo63bJIBaLMW92JuKHvWjT6unODheEm7W8cvZCn+dO17jlC5JN2po1FRJ0hKtiyyLBPcIj0aqtD65cuYIffvihQQws4bjMnj0bKpUKQ4YMQW1tLQYOHAh3d3dMnz4d77zzjkVzu9xVoq+S/cWycigKi+xkFcEGji7kTKWxpAprdYxwxR8lhGtj624PAoEAA4aJAeiPgSUcEx6Ph4yMDFy/fh0nTpzAgQMHcO3aNSxcuNDiuV3OM6cLtivZ2+sGaK+4OS7gakJOjSlbs+ZCHjrCVbBX266IIfHYnvVPvTGwhGPj5uaGHj16WHVOEnNgt5I9eTJsj6sKOTXGbs0SBKEbe/de7REeCd8OgciUSJBbr8c34dj89ddfKCkpwdWrVxuUnho+fLjZ87qcmKNK9s6Nqws5giAsw95CDni01fr3tI+wdMoEJIrFSH/c47uoiEKBHJkdO3Zg3Lhx+PPPPxs8x+PxoFQqzZ7b5eT+6NGjbVbJnrxyhLNC1zbhjHBByKmJGirCzFVfofjIk2LML7zwgr3NIizgnXfewciRI1FRUQGVSqX1sETIAS7omTtx8rhW3a6g4EBWypJY+2bHHNxn1utcKW6OTa8cGzXmCILgDlwScmqihooQERePU8UHcONaFZp5tEDmRN09mwnuU1VVhWnTpsHX19fqc7ucmNtT9BNOlJymKuVOBm2v2h5KhCCcBS4KOTUCgQC9Ix/9mKy9dcvO1hCW8Oqrr2LPnj3o0qWL1ed2OTFniyrltAVlW0jIEQRhLlwWcoRzsWrVKowcORIFBQXo3bu3Vn9WAHj33XfNntvlxBzbkJCzLSTk7At55whHhoQcYUs2btyI//znP2jatCn27NkDHo+neY7H45GY4wok5GyLMwo5pVLFao044hFKpZKawrs4JOQIW5ORkYH58+dj9uzZVi83Q2LOSpCQIywlR16M6XOzUVb+pHh1cKAPli9Itkr3BrZwNO+cXPYT5s5ahPKLlzTHAoM6YsGSOaz3ZyYIwnW5f/8+kpOTWakbSD/5LaT17T2sCzlzM1mdHWfK0s2RF2Nk6ir0DonQKp3TOyQCI1NXIUdebG8TG8UW/w+sgVz2E1JHT0JIn75a73NIn75IHT0JctlP9jaRsAHklSPsQUpKCrKzs1mZm8cwDMPKzByjpqYG3t7euHD1JFp4tbB4PlveuCwVc84keupjq61WNkuTKJUqPBM+E71DIiCT5Wr9alOpVBCLE3GipBhnipY4zJYrFz11SqUS4T0GIqRPX8jqVdV/9D6LUXL8GIpO7qUtVyfGkYVc7a1beC20C6qrq+Hl5cXqudT3zBtbUuHV3M3689feR6uRWTZZC1d499138fXXXyMkJAR9+vRpkACxYsUKs+embVYTsbX3gYRc49Tk/ObwsXMFilKUlV/FxuyMBu53Pp+PtLR0CIVCFChKHaZNFxe3XhWFRSi/eAnZmzbreZ/TIBQKoSgsYj3jnbAPjizkCMfn+PHjCA0NBfCkjaiauskQ5uByYq7VnUJ48Zs1OG7oxmOPLSRH2l5VqhgoqqpRWXsffs3dEOXrDQHfsovTVaiougkA6NWrl87n1cfV4xwF9f8Zroi6qspHsYiG3mf1OIIgCGuye/du1uZ2OTGnD0eI9+Eq28uuYc6hiyivvqM5FujtgUVhQRgW3M6OljkG/r4tATz6pRYZGdngefUvOPU4R4Mros7XzweA4fdZPY5wLsgrRzgzJOY4ijW8crbYYt1edg0pP5+GKCEB2RkZ6NWrF06cOAFJZiZS8vKw4XmwLugcfatVGPEM/H1bITMzE7m5DWPmpFIJOgX5Iiaqqx2ttJz6P5hsLe6iBkQgMKgjJBKJzpg5qVSKoOBARA2IsKldBPuQkCO4wIIFCxp9fu7cuWbPTWKOgzjK9qpSxWDOoYsQJSRAVkeEREZGQpabC3FiIj4s2I2XA9vSlqse1OVIKqpuQC6XY/jw4cioI4qlUgnk8jxsyZriMMkPxlJX3NlC2AkEAixYMgepoydBLBYjLS2tzvsshVwuR9bGtZT84GSQkCO4wtatW7X+fvDgAS5cuIAmTZqgS5cuJOaIhtjCK6eoqkZ59R1kZ+gJ3E9Ph1Aoh6KqGgP8W7JqC9veOebgPqtntKrLkYhECdiYnYFz585h5syZEAqFmjGdgnyxJWsKp+vMWQNbee1E4nhkbVyLubMWab3PQcGByNq4lurMWQAVYiaIxjly5EiDYzU1Nfj73/+OV155xaK5ScxxDEfxygFAZe19AIYDytXjiCcolSpMn5sNkShBU44kMjISf/vb37B3717MmDEdVy6X4bRCCjc31/tvyqbXTiSOR/ywF0l4WAG110uxU4710o9Q9Ue55jkuFGImrxzBdby8vDB//nwMGzYMr7/+utnzONe+jYPjSEIOAPwe1x6qn2KtRn3cj4UaRbpwpDIs6nIk6enaXk2BQIDnn38eq1atRmXVDewv+t2OVnIDNpKTBAIBBgyKwojkRAwYFEVCzkQK/2iqJeSWTpmA8NAQrULMfexYiLmufYTzsHr1agQHB6Np06bo378/ioqK9I49efIkRowYgeDgYPB4PKxcubLBGKlUivDwcLRo0QI+Pj4Qi8UoLS3VGhMbGwsej6f1mDRpklXXVV1djerqaovmcL2f/C6ArURNlK83Ar09IMnM1IqZAx4HlEskCPL2QJSvt03scSSctRwJW3AlI9bVqS+QlEol1ks/QoJIhNw6SSWRkZHIlcmQKBZj9gwJWoQOx6CgB3axkXAOsrOzMW3aNKxduxb9+/fHypUrMXToUJSWlsLHp2EGem1tLTp37oyRI0di6tSpOufcu3cvJk+ejPDwcDx8+BDp6el48cUXcerUKXh4eGjGvfnmm1rJC82bNzdrDZ9++qnW3wzDoKKiAt988w3i4y3zYJOYcyJs7ZkS8HlYFBaElLw8iBMTkZae/iSgXCKBPC8PG57vTskPOnD2ciRsYeukCeIJukTSqeIDqPqjHBlbsnXGzaY/LsR8qvgABIJoDOjwl81tJJyDFStW4M0330RqaioAYO3atcjLy8O6deswe/bsBuPDw8MRHv4o1ljX8wCwY8cOrb/Xr18PHx8fHD58GAMHDtQcb968Ofz8/CxewyeffKL1N5/PR7t27ZCSkoK0tDSL5iYx5+DYe2txWHA7bHgemFOwG0KhXHM8yNsDG57v7lR15qyZBBET1RXBgT6QSDJ1tvBylnIkbGLvUieuhD6RdONaFQDDHmb1uMI/mrIm6EjIOS/379/H4cOHtQQPn89HXFwcFAqF1c6j3ups3bq11vHvvvsO3377Lfz8/DBs2DB8+OGHZnnnLly4YBU7dUFijkPw+kc7XNwc8EjQvRzY1u4dIGxRa079+Vgq6gQCPpYvSMbI1FUQixORlpbeoBzJexNfRIGiFDFRXZ2uLAkb2Lvwt7OKycZEUqt2vgAMe5jV49jCmYWcUqnEqeIDuHGtCs08LO8rzjVqamq0/nZ3d4e7u7vWsT///BNKpRK+vtrXka+vL377zToODZVKhffffx/R0dFaP05ee+01BAUFISAgACUlJZg1axZKS0uRk5NjlfNaC5cTc8yhA8DgwfY2w+kQ8Hmslx/hEtYQdUmicGzJmoLpc7O1ymS4u7uBYRis/GInVn6xE8GBPli+INnpy5M4Os7oKTQkknqER8K3QyAyJRKtmDng0c1RIpXCr2MQeoQ/EXrW9s45s5DTlSVsa2q2nQFYyKivuf8QANCxY0et4/PmzcNHH31k9fMZYvLkyThx4gQKCwu1jk+cOFHz7969e8Pf3x9DhgzBuXPn0KVLF4PzTps2zWgbVqxYYbzB9XA5MQcAD/cXoOBkJSqu18K/dXMMHD+CPB8Ojr06QFi69ZokCkdifD8UKEqxbcevWLl2J1544QVkZMx50k1DkomRqatcot6cM+Ho4s4YkSQQCPD3tI+wdMoEJIrFSK9TiFkilSJPLsfMVV81yBZWz812DJ0jo84SThCJkLElG7169cLBgwcRFxdnb9OsyqVLl+Dl5aX5u75XDgDatm0LgUCAqqoqreNVVVVWiWWbMmUK5HI5fvnlF3To0KHRsf379wcAnD171igxp6u2nC54PMt2slxOzG07UIZ53/2KssonacDB//gFy8ZHICm6MwDLt9AswVG3Wl0ZS710AgEfMVFdMf7drzBsmEgrhi4yMhIyWS7E4kTMmLcZifH96IeHg+JIGbmmeLuihoowc9VXWC/9SMvD7NcxCDNXfYWooSKD5zFX1DmrV65ulnDODz9g37592L59u5bocRa8vLwMrsvNzQ39+vVDfn4+xGIxgEee3/z8fEyZMsXsczMMg3feeQdbt27Fnj170KlTJ4OvOXr0KADA39/fqHPs3r3bbPtMweXEXMrHuzFMJMLGen1ER0nzsDktDknRnbXElD2FHWEcXOnLaomoU9ed25itp5tGWjqEQiEKFKWIHdDdKvYShC7MEUhRQ0WIiIvXxHa1aueLHuGRRtfvM0fUOauQA55kCUdP/j88++yzKCsrs7dJdmfatGlISUlBWFgYIiIisHLlSty5c0eT3Tpu3Di0b98eUqkUwKOkiVOnTmn+ffnyZRw9ehSenp54+umnATzaWv3++++Rm5uLFi1aoLKyEgDg7e2NZs2a4dy5c/j+++/x8ssvo02bNigpKcHUqVMxcOBA9OnTx6x13Lx5E1999RVOnz4NAOjZsyfGjx8Pb2/LSni53E/8l4YOhSw3F5GRkfD09NT0ERUlJGBmVjGUSpXWePKSEaZizjVDdedcB3snajSGJQJJIBCgd2Q0Bg5LQu/IaLMKMRtb7NeZhRzwJPs3PT0dvXv31hRj3rVrl50tsx/JyclYvnw55s6di759++Lo0aPYsWOHJimivLwcFRUVmvFXrlxBaGgoQkNDUVFRgeXLlyM0NBRvvPGGZsyaNWtQXV2N2NhY+Pv7ax7Z2dkAHnkEd+3ahRdffBHdunXDBx98gBEjRmD79u1mreHQoUPo0qULPvnkE1y/fh3Xr1/HihUr0KVLF/z6668WvDsAj2EYxqIZHISamhp4e3tj165dGDJkSIPnFQoFhEIh8qXDENsnQOcctvTSGSMI7F2WhAtwxSunD2OvmT2Fp/G8WAqFQqEzK1B9ff4sS9PyzOm6TsibzH24uNXKRYGky1PHRTutzbH9v2Dh+L8hPj4esjqJJer7WHV1NetbrupzXXw9Gl4sJUAEfbPPJmvhCjExMXj66afx5ZdfokmTR+/pw4cP8cYbb+D8+fP45ZdfzJ7b5bZZu3fXvUWl8Xxcr9X7WjaarRPOTWOivO61ZLDunESCTv4tMYD/p0Ghb63yKQR7tL69hzOCjsviqP72K5dttS48TUeC+mEXhONy6NAhLSEHAE2aNMHMmTMRFhZm0dwud5Wo96nro6m437rxQoC07coduO6VMwRzcJ/mwT+kwLKxIZDLH3XTqNvjUpyYCHleHpamhpuU/KCem+Am9t5udaT+pY5kqzWo/t81APrDLgjHxMvLC+XlDcvMXLp0CS1aWFZD0OXE3PJly6BSacfF1fV8xPQ0nOZsixskeVVcj6TozticFofjhwogFArh5eUFoVCIE4cLNck55lBXNBIE4EoeLsekbjFmwnlITk7GhAkTkJ2djUuXLuHSpUvYtGkT3njjDYwePdqiuV1um3XHzp16+4huTosz2vPB9pYr3Xhdk6TozkiMDNaqgxjT089q5UhoC5Zb2Gu7dUCHv0jQcZjGijETjsvy5cvB4/Ewbtw4PHz4qGjyU089hf/7v//D4sWLLZrb5RIgNnwwuEGduU7+LbE0NdwszwdbN0VjxZyrJ0E4+larvSFRZ3/sFTvHhpir23rK1PIkhDZ1iwarizEfPHgQL7zwAiVAOBgnTpzQ2jKvra3FuXPnAABdunQxq89rfVxOzN3YkgoP9yZW9XxY+4ZoilfO1cUcQIKOC5AoNB9nEXO6Wk/5dgjE39M+arRwMKEffe28SMw5Fnw+H+Hh4XjjjTfwt7/9zeL4OJ3nsPqMDoBAwEdsnwCMjn0asX0CLN7CsueWKAkZErRcoG5cHsXoOQbWbKWl9iKFh4ZoJe+Eh4Zg6ZQJUOyUW+1crkTUUBE+zz+Ihd9uxbRP1iLjn9/Z2yTCDPbu3YuePXvigw8+gL+/P1JSUlBQUGDVc7ikZ86ruRsr57CGd8LcGyAJGhK2XIe8d/pxZO+cUqnE20P6Izw0pEF8l0qlQqJYjENHS7B61wHacrWQ2lu38FpoF/LMOSh37tzB5s2bsX79ehQUFODpp5/GhAkTkJKSYnGPWZf0zLGFpZ4IS17vldTN5cUMCVpuQ5467mEN75y69VSGjppofD4f6WlpqLx0EaeKD1h8LoJwZDw8PJCamoq9e/fizJkzGDlyJFavXo3AwEAMHz7corldLpuVC7B5U/NK6ubSoqYm5zeXF7VchrJpnQ916ylDrejU4wiCAJ5++mmkp6cjKCgIaWlpyMvLs2g+EnNWhgveBxJ0JOi4jr27qSiVKhQoSlFRdRP+vi0RE9XVauVfzMGeHSEsLVNStyaarlZ06lpp6nEE4er88ssvWLduHX744Qfw+XyMGjUKEyZMsGhOEnNOilrMuKqoI0HHfewl6HLkxZg+Nxtl5Vc1x4IDfbB8QTKSROE2t8fRaawmmkqlgkQqhV/HIPQIbyj0CMJVuHLlCtavX4/169fj7NmzEAqF+PTTTzFq1Ch4eHhYPD/FzOmA1z+6wcNRcWVB46pC1pGwddZrjrwYI1NXoXdIhFbWZe+QCIxMXYUcebHNbHEWBAIB/p72EfLkciSKxVrva6JYjDy5HCmz51HyA+GyxMfHIygoCJ999hleeeUVnD59GoWFhUhNTbWKkANcNJvVe/Bgs+bgwhaqubiysHF0QatUqljrCMEl2P7RpFSq8Ez4TPQOiYBMltvAgyQWJ+JESTHOFC2xy/trr21WNZZmtuqqiebXMRApsx2rzpw6KYSLHTIom9UxGT58OCZMmACRSMTajxqX22blhZnv6uf1j3ZYQefKcXSOvOWas+88Zqwr0upYEuznjWXjI8zu1cpV2N52LVCUoqz8KjZmZ+jMukxLS4dQKESBohSxA7qzZoc+7Bk3Zy3q+wZUKsfyFdTN7uWaqBvQ4S/cqrFebUDCdmzbto31czjfz3uC0IEjCtmcfecxSroLvcNitLcEw2IwSroLOfvO29tEq8Pmj6WKqpsADGddqse5GpaUKVEXDY54rq/WtRrxXF+HKBo8oMNfetevfq6xMWxir/MSjoXLeeYI9lCqGCiqqlFZex9+zd0Q5esNAZ9nb7M0OJKHTqlUYca6IogSEiDLfbIlGBkZCVluLsSJiZiZVYjEyGCn23Jly0Pn79sSgOGsS/U4wjiUSiXWSz9CgkiklQARGRmJXJkMiWIxNiyej4i4eE7GzZkqlPSNZ8ODRyKOMBbnugsQdmN72TU8l3MYw348hjf3nMawH4/huZzD2F52zd6maeEoHrqCk5Uoq6xGeoaeLcH0dFyouImCk5V2stDxiInqiuBAH0gkmVCpVFrPqVQqSKUSdAryRUxUVztZ6Jg4atFga3u8TJlPqVTi+IF9+GV7Do4f2AelUsmqbYTzQ2KOsJjtZdeQ8vNphMQM1tpiCYkZjJSfT5OgM4OK67UAjNgSfDzO2WBju1Ug4GP5gmTI5XkQixO1rlWxOBFyeR6WzR9l93pz9sQcAeFoRYPZFkqGtmQVO+V4e0h/fDj2FayYOgkfjn0Fbw/pr9mKJhFHmAOJORNx5DIlbKBUMZhz6KJmOzAyMhKenp6a7UBRQgI+PHQRSo4FQtfk/Kb14Br+rZsDeLL1Vx/NluDjcYRxJInCsSVrCo4fK4JQKISXlxeEQiFOlBRjS9YUqjNnBnWLBuuCK0WD7eHtqn9OdWxheGiI1o+J8NAQLJ0yATcPbbWpfYTz4HKlSW5e+AJeLZpZNJejZrSyIVoKK25i2I/HoFAodMYhKRQKCIVCbH85BAP8W1r9/Gxh79g6pVKFZyduRu+wGK2YOeBxGY3ERJw4XIjSL0Y6XcxcXdj68cS1DhB1sXdGq6mxX0qlEm8P6Y/w0BCdRYMTxWIcOlqC1bsO2C1mjgveLqVSifAeA9GnT1+d75NYLEbJ8WMoOrlX7/t0q+YWOvn0pNIkRAO48e1FOCyVtfcBGN5iUY9zFOp77mztvRMI+Fg2PgLyvDyIE+ttCSYmQp6Xh6Wp4ZwRIGzBVlFhgYCP2AHdMXpEFGIHdHf699EUTBU+XC8azAUhBwCKwiKUX7ykN7YwLS0NF8vKoSgsspOFhCND2ayERfg1dwNgOENQPc6RUQs6W3ntkqI7Y3NaHGasK4BQKNQc7+TfEpvT4pyuzlxj2LuXqy1xxHpzUUNFmLnqK6yXfqR1rfp1DMLMVV8ZXTRYqVTiVPEB3LhWhVbtfNEjPNIiEWiMkFMqlVAUFqGq8ip8/XwQNSACABocs1SMVlU+ah9n6IevehxBmAKJOcIiony9EejtAUlmps7tQKlEgiBvD0T5etvRSutiyxInSdGdkRgZ7BIdIAzhSoLOEYkaKkJEXHyjYqwxsaarg4Rvh0D8Pc28DhLGCDm57CfMnbUI5RcvaY75+LSDilHhz2v/0xwLDOqIBUvmQCSON9kONb5+PgAM//BVjyMIU3C9OwJhVQR8HhaFBTW6HbgwLIhT9easgS23XgUCPmL7BGB07NOI7RPgkkJODZfjVZVKFfYUnsbGHxTYU3gaSqXK8ItYt0mJwr0K/JCdi8K9igYlMPRh7takQCBA78hoDByWhN6R0VpCrrEsTkOJAaYWHTZWyKWOnoSQPtqFjsPDI/C/P69DKpU+yczv0xepoydBLvvJ5PdETdSACAQGdYREItFTGkeKoOBAjWeQIEyBEiDMgMs3lMZgU3xsL7uGOYcuorz6juZYkLcHFoYFYVhwO9bOywXsnSxhK7jUI5bXP5pTSQw58mJMn5uNsvInW2TBgT5YviDZrAxZa2yz6vI6meJhsmYRXLVYSxCJkJGejl69euHEiRPIlEiQJ5fDq3UbxAijrJJAYezWaniPgQjp0xcyPckIJ06cwO+//w6BQGB0goIh1AJSJBIhLS1N8z5IpVLI5XJkbVyr87NRbwWXXbiI9yfNpAQIogEk5syAxJxuuN4BwhKMWRubos7eQoprPWJ12mOBeLLIFnkxRqaugkiUgPT0DM0NWiLJhFyeZ3bJE0sEXV3RkF5HPEkkkkZFQ12sJeYMZrsmJmLHjh3Ys2cPoqMbbqOrM+IXfrsVvSMb32Y31qNYuFcB8dBkg1n4u3fvRmxsrNYx2c5sDBgUZdR5dKFLZAcFB2L+4gydn4mu8STmiPq47H4NF7dEHB0Bn4cB/i3xahcfDPBvabaQU6oYFFbcxL/PXUVhxU2716izd3eLnH3n8ezEzRiSth1jl+VjSNp2PDtxs816s3KtR6xee0IiMDJ1FXLkxTazRalUYfrcbIhECZDJ6tVZlOVCJErAjHmbbfr9olQqMXfWIohEIshksno2ySASiTBvdqbBLVdrZYEa7BCRno6HDx+ipqZG5+vZKDpsbDJCRUVFg2OWJiiIxPEoPvULZDuz8cWGzyDbmY2ik3v1Crm6W8GXL1+26NyE8+KSYi5HXoxnwmfiebEUY95ag+fFUjwTPtOmNwFCN/YWTrrsMba7BRueT3sLqfo9YnUVhZ6ZVWwzsdKoPXYQTwWKUpSVX0V6up62a2npuHCxCgWKUpvYAzwpgZHOkRIYxnaIKC7W/f1rbNFhU8Rn3WSExs7p7+/f4Jg1EhQEAgEGDIrCiOREDBgUpXPbVp8oJwhduJyY27bjV4xMXYXeIRF2/1VPaMO1tmD27m7BBSHFtR6xBu2xsXiqqLoJwAgPz+NxpmBuay+ulcAwtkPETzt26EwMkEil8OsYhB7hDbdDzcWYZIROnTohJiZG6xgbCQr6klQaE+UEUR+Xu0LmZP7AqS0R4hH2Fk66UFRVo7z6TqNC5mL1HSiqqvXMYBlcEFJc6xFrtD1miCdz8PdtCcAID8/jcbbAWK+TMR4ma2y19giPhG+HQGTqEU4SqRSt2/ngwOMiw+YUHTan0PGCJXMgl8shrnfO4cOHQy6X480330Rtbe3j3r1iyOVyzF+cYdXix3LZTwjvMRDiocl4K+UdiIcmI7zHQMhlPxkU5QRRF5cTc+V//MmpLRFbwcX+o3Wxt3DShTndLaz5PnNBSHGtR6zR9thIPMVEdUVwoA8kkkw9Hh4JOgX5Iiaqq1nzm+Od41oJDGM6RLz50WLMWr0OxUeOafXMPXS0xKSiw6YgEscja+NaHCs5qnXO4kNFaNO2NdLT0zXHSo4fMyppxBT0lUZRl0E5f/YCAP3XOkHUxSWLBnPlVz3xBC62BbN3d4u6wqWx87MppGJ6+iHYz7vRotCd/FsipqcfazaYZI+F4slUBAI+li9IxsjUVRCLE5GWll6n3IREk81qy8xjtdcpdfQkiMVivSUwbNley9gOEYaKDuvCEu+hSByP+GEv2qQDRF3qx8Opr2N1kopYLMamb/6NjoEdIJFIGpRPIYj6uKSYM3hztOGWCPEIewsnXdi7uwUXhJS6R+wo6aOi0Gl1ylxIJRLI8/KwOS3OZmLFGHtsLZ6SROHYkjUF0+dma7ddC/I1uyyJpai9TnNnLdKyKSg40OoeJmMxpkOEuuiwLVEnI9THkvIjhlDHw2Vv2qw3SUUoFGLWh9OwdNEnGlEeGBjImk2EY+NydeYCO7RFSOijGLmGhSITcaKkGGeKljR6M3DEOnNc32ZVqhg8l3MYITGDdQoXcWIiSgp243BSP5vWrlMnZYgSEnQKhw3Pd9dZFNlaNefU2az6zm+rHq266rp18m+JpanhnKkzV98eW7f+YrOIsbk153T1HTXHw2TNAsLWxFrlU2zND9m5eCvlHdy6dUtnhuqtW7fg5eWFLzZ8Bnd3N6ozRxjE5TxzizJGIOXtf3JmS4R4hLotWEqefo/Lhue727wI8bDgdtjwPDCnYDeEwicthYK8PfQKOWuSFN0Zm9PiMGNdgbbXx7+lzYSc2g4u9Yg1xh71jy5biTqBgI/YAd1tci5j0ed1IuyLKX1aBwyK0mwFqztAEER9XM4zd/PCF9i190SD1judgnyxbP4oo7ZEyDPHHlxtC2Zqdwtrd4OwdwcIZ8DWnjprYo32XpbARc+co3rlAOPaielqHXar5hY6+fQkzxzRAJfzzAGPYlwS4/txpq8j8YRhwe3wcmBbzrUFU3e3MJaanN+sKugEAj5i+wRYbT5XxNaeOmvS+vYeuwq6AR3+4qSgc1S4mKRCODYWqZfFixeDx+Ph/fff1zvmyy+/RExMDFq1aoVWrVohLi4ORUXalcdv376NKVOmoEOHDmjWrBl69OiBtWvX6pyPYRjEx8eDx+NBJpOZbbt6S2T0iCjEDuhOQo5DWKstmD1hs08rYRnMwX0O6V0nnuDIXjk1+kqjsFEGhXB+zPbMFRcX44svvkCfPn0aHbdnzx6MHj0aQqEQTZs2xZIlS/Diiy/i5MmTaN++PQBg2rRp+Pnnn/Htt98iODgY//nPf/D2228jICAAw4cP15pv5cqV4PEc7+Zub7ySujnMVqujQ0KOIAhj0FcahTxyhKmY5Y66ffs2xowZgy+//BKtWrVqdOx3332Ht99+G3379kW3bt3wr3/9CyqVCvn5+Zox+/fvR0pKCmJjYxEcHIyJEyciJCSkgQfv6NGj+Pjjj7Fu3TpzzHZ5SGSwD73HBFuY296L4DbG9GklCEOY5ZmbPHkyEhISEBcXh0WLFpn02traWjx48ACtW7fWHBMKhdi2bRvGjx+PgIAA7NmzB2fOnMEnn3yi9brXXnsNq1evhp+f4bpa9+7dw7179zR/19TUmGQnQZgKCTnH4uH+AhSq2lLcLMEZDAn2Jnfu2sYQwuEwWcxt2rQJv/76K4qLzWtIP2vWLAQEBCAuLk5z7LPPPsPEiRPRoUMHNGnSBHw+H19++SUGDhyoGTN16lQIhUIkJiYadR6pVIr58+ebZaMzQ9ut7EBCzrHQVacuONAHyxck26XIL+F6kKeVsCYm/Qy9dOkS3nvvPXz33Xdo2tT0zKbFixdj06ZN2Lp1q9brP/vsMxw4cADbtm3D4cOH8fHHH2Py5MnYtWsXAGDbtm34+eefsXLlSqPPlZaWhurqas3j0qVLhl9kBBQ43ThKFYPCipv497mrKKy4CaXK+Svf2ELIKZUq7Cm5go17zmJPyRUolSrDLyJ0oi7E3DssRqsnZu+QCIxMXYUcuXk/VG0BCQDHp/XtPfQ5ElbHpDpzMpkMr7zyitaevlKpBI/HA5/Px7179/Tu9y9fvhyLFi3Crl27EBYWpjl+9+5deHt7Y+vWrUhISNAcf+ONN/DHH39gx44deP/99/Hpp59q1eJRKpXg8/mIiYnBnj17DNpet86cV4tmxi65Ac4m5qzppdNVIy7Q2wOL7Fwjjk1sIeR0epH8vLFsfIRdui84MkqlCs9O3IzeYTG6O40Y2QXGntizRAlXypM4YjarNQRcza27aNnpLaozRzTApG+rIUOG4Pjx4zh69KjmERYWhjFjxuDo0aN6hdzSpUuxcOFC7NixQ0vIAcCDBw/w4MGDBv3pBAIBVKpH3ofZs2ejpKRE67wA8MknnyArK8uUJRD18ErqpvUwF3Xbq5CYwVrejpCYwUj5+TS2l12zotXcwFZCTqcXKSwGo6S7kLPvPOs2OBMFJytRVlmN9IwMPT0x03HhYhUKFKV2spBwNsgTR9gCk+R2ixYt0KtXL61jHh4eaNOmjeb4uHHj0L59e0ilUgDAkiVLMHfuXHz//fcIDg5GZWUlAMDT0xOenp7w8vLCoEGDMGPGDDRr1gxBQUHYu3cvvv76a6xYsQIA4OfnpzPpITAwEJ06dTJ91YRe6gsUYzx3ShWDOYcuQpSQoOXtiIyMhCw3F+LERHxYsBsvB7Z1yJpxurDV1uqMdUWNvq8zswqRGBnMWS8S16i4XgsADb7H1KiPV1TdtJVJJmPvAsKEcZCAI2yJ1e8A5eXlqKio0Py9Zs0a3L9/H6+++ir8/f01j+XLl2vGbNq0CeHh4RgzZgx69OiBxYsXIzMzE5MmTbK2eYSJGOO1U1RVo7z6jn5vR3o6LlbfgaKqWs8MjoWtkh0MepHS03Gh4iYKTlbaxB5nwL91cwBPel/WR33c37elrUwiTITrW6zkiWOP1atXIzg4GE2bNkX//v0blC+ry8mTJzFixAgEBweDx+Ppjbk3NOdff/2FyZMno02bNvD09MSIESNQVVVlzWVZBYs3wuvHq9X/u6yszOAcfn5+Jm+X2qOlrLPFy5mKvkzYytr7AAx7O9TjHBlbZq0a7UV6PI4wTExPPwT7eUOSmakzZk4qlaBTkC9iorra0UrCESEBxy7Z2dmYNm0a1q5di/79+2PlypUYOnQoSktL4ePj02B8bW0tOnfujJEjR2Lq1Klmzzl16lTk5eVhy5Yt8Pb2xpQpU5CUlIR9+7ilB2hvhrAYv+ZuAAx7O9TjHBVblx8x2ov0eBxhGIGAj2XjIyDPy4M4MVErDlEsToRcnodl80dxftuahAO3oM+DfVasWIE333wTqampmpafzZs319tEIDw8HMuWLcPf/vY3uLu7mzVndXU1vvrqK6xYsQLPP/88+vXrh6ysLOzfvx8HDhxgba3mwO1vLIJz6Np2jfL1ho9HUyxatEiTtKJGpVIhMzMTvp7NEOXrbWtzrYKlySHmUteLpOt9lUok6OTfEjE9DRfRJgBe/2jw+kdjxLTXsSVrCo6XFGn1xDxRUowtWVOozhxhNLSlajk1NTVaj7rF/tXcv38fhw8f1qpPy+fzERcXB4VCYdZ5jZnz8OHDePDggdaYbt26ITAw0OzzsoX18405DnPoABgH9xBxBbXAufHv01AxDH788UeIxWKkpaWhV69eOHHiBKRSKX788Ue0bWbZe26smLJWqRUuFAFWe5FGSR95kdLS05+8rxIJ5Hl52JwWx3kvkq3g9Y82emySKByJ8f1QoCh12A4Q9kiEqBuvZkqZEq7HuZmKKwm4U/lV8OBbv8XYHZUSANCxY0et4/PmzcNHH32kdezPP/+EUqmEr6+v1nFfX1/89pt53/nGzFlZWQk3Nze0bNmywRh1MidXcDkxR1ifY89648/ae5BKpfjiiy8gFAo1z3Xq1AmZmZlIT0+HoqoaA/xb6pzDWuLJnGxcNuywFknRnbE5LQ4z1hVov6/+LbE5Lc6l6syZItaMQSDgI3ZAd6vOaWvUosIe2a1qgaYWdc4m2HThSiLOVly6dEmrzpy+LVGicUjMERajDsCfMmUKZsyYgYKCAlRUVMDf3x8xMTGora1Fenq6VgKErURT3fPoE3ZcE3D1SYrujMTIYBScrETF9Vr4t26OmJ5+DuVFMgVrizZXwJ7lSpxdxJGAYxcvLy+DRYPbtm0LgUDQIIu0qqrKqF7t5s7p5+eH+/fv4+bNm1reOUvOyxYk5giLqRuoHxkZidjYWK3n1YH6nYc+Da8+AbY2TwPXRVtjCAR8xNrxvWMTEm/WgerPWRcuiTilUoUCRSnOlV21tyl2wc3NDf369UN+fj7EYjGAR3HD+fn5mDJlCmtz9uvXD0899RTy8/MxYsQIAEBpaSnKy8sRFRVl8bqsCYk5wmIMlnugQH2iHiTg2IEEneVwScQBQI68GNPnZqOs3DWFnJpp06YhJSUFYWFhiIiIwMqVK3Hnzh2kpqYCaNiw4P79+zh16pTm35cvX8bRo0fh6emJp59+2qg5vb29MWHCBEybNg2tW7eGl5cX3nnnHURFRSEyMtIO74J+SMwRFkOB+kRjkHCzLfaMo3NUuCbg1OTIizEydRVEogRszM5AYGAg2rdvb2+z7EJycjKuXbuGuXPnorKyEn379sWOHTs0CQzl5eVajoQrV64gNDRU8/fy5cuxfPlyDBo0SFMP19CcwKO2oXw+HyNGjMC9e/cwdOhQfP7557ZZtAnwGHtU37UD6qbBN7akwouyWVlBV0P4Tv4tsTQ13KUC9V0ZEm7cggSdfrgq4NQolSo8Ez4TvUMiIJM92vFQ38ds0Zxefa6fAp5mLZs1/spZm6zFFSDPHGE1XC1Q39Uh4cZ9aNu1IVwXcWoKFKUoK7+KjdkN2/kRRH1IzBFWxZkD9V0VEm2ODQk6xxFwdamouglAfzs/gqiLS4s5pVJFXiSCeAyJNufFVQWdI4o4Nb7tHm09qqsEEERjuKyY0xXfFeznjWXjIyyO76p/U2QOcqshL0EAJN5cDVcRdI4s4OqiOn0STZo0QWZmJnLrVQkgiPq45NWRs+88Rkl3oXdYjFaj7d5hMRgl3YWcfefNnlvXDVLdE7LugyDsBV2Drouz9hJVr8sZ1sYc3Afm4D5cvXkXDx8+RF5eHsRiseZeRRC6cDnPnFKpwox1RRAlJGjVRIuMjIQsNxfixETMzCpEYmSwSVuupt4cdY0nDx7BFiTeiLpw0UvnDELMUureA9TF2CUSSYM2iQRRH5cTc/tPV6GsshobMxpmCPH5fKSlp0MoFKLgZKXRgfzWulHy+keToCOsCok4Qh+2FHQk1AxT/7tfXYx9/759OHPmDPbt24dz587hjTfesJOFBJdxuW3Wyht3AejPEFIfV/cbNYS1b5Z08yUshbbzCWNhQ2TV3fJ0lq1PttH1I15djF2el4cRSUlwd3fHSy+9ZAfrCEfA5Txzfq2aAdCfIaTuI6p2cTcGWzdL8tARpkCijbAEUztGkDizLo191ydFd8bmtDjMWFdA26xEo7icmBN297W4j6gtbp4k6FwTEmaEvSCRZhlsfV/XLcZ+rqIGEz/dy8p5CMfG5cScpX1EbXmzJUHnGpCAI4jGsdb3oDX/r9nyu1ldjP25p9uSmCN04nJiDtDvuu7k3xKb0+L01pmzx02XBJ1zQgKOIBqHje+9+nOa+v+QvosJruKSYg4wvY+oPW++JOicAxJwBKEfe3zHGRJ39L1LOAouK+YA4/qIcuUGTILO8eDKtUPoRqlUoUBRioqqm/D3bYmYqK7Uzs/GcO07jWv2EISxuLSYqw/Xb74k6LgB168TwjA58mJMn5uNsvKrmmPBgT5YviAZSaJwO1rmuNB3E0HYD5cTc7ywSPBaNLO3GWZDgs4+kIBzHnLkxRiZugoiUQI2ZmdoEqAkkkyMTF2FLVlTSNAZwNbfQTU5vxkc45XUzQaWEAQ3cTkx5wyQoLMtJOScB6VShelzsyESJUAmq9fOT5YLsTgRM+ZtRmJ8P9pyrYc9vnOMEXH6xpK4I1wJEnMEoQcuiTiK77IOBYpSlJVfxcZsPe380h6381OUInZAdztZyR3s+aPRFCFHEK4OiTkHhaveOaVSZXSGMJfhkpCj+C7rUVF1E4AR7fwej3NV7P3dYqmQI68c4Wo43l2W4Cw5+87j2YmbMSRtO8Yuy8eQtO14duJm5Ow7b2/TTIJrQm5k6ir0DomAQqHArVu3oFAo0DskAiNTVyFHXmxvEx0Kf9+WAJ607auPpp3f43GuBnNwn8MLOYJwRUjMEVYhZ995jJLuQu+wGG3RERaDUdJdDiHouNacvn58V2RkJDw9PTXxXSJRAmbM2wylUmVvUx2GmKiuCA70gUSSCZVK+31TqVSQSiXoFOSLmKiudrLQ9qgFnL1FHEBCjiDMhcScA8MV4aFUqjBjXRFECQmQ5dYTHbm5ECUkYGZWMadFB1fey7qo47vS0/XHd124WIUCRamdLHQ8BAI+li9IhlyeB7E4UeuHh1icCLk8D8vmj3LI0ABT4JKAU2MtIUdbrIQrQjFzhMUUnKxEWWU1NmboER3pj4PKT1YaLNJsD7go5ACK72KLJFE4tmRNwfS52drt/IJ8nbosCZeEW13IG0cQlkNizsHhQiJExfVaAEaIjsfjuAJXRZyauvFdkZGRDZ539fguS0gShSMxvp/TZwjb+7vBECTkCMI6ONc3l4tib1Hi37o5ACOCyh+P4wL2fs+MgeK72EUg4CN2QHeMHhGF2AHdnUrIcW0LVRdsCDnaYiVcFef59nJx7ClOhN184O72FDIzdYsOiUSCpu5uEHbzsZOFT+BakkNjUHwXYSqOIOIA8sgRhLWhbVYnwl5brvt/u4p79x8gLy8PYrEYaWlpmhZJUqkUeXl5YBgGCp4PBuG6ze1T4ygiri6uGt9FmAYJOOelbu1Or+ZP2dscgqOQmHMy7CHo1LFw33zzDebMmaMtOjp1wjfffIOxY8c+CtTvYFPTNDiikFPjKvFdhOlwXcSRgLOMnH3nMWNdEcoqq+1tCsFx6G7ghNhauKhj4bp06YKzZ89i9+7d+P7777F79278/vvv6Ny5MwDA7/ofNrVLjSMLOTXOHN9FEERDdNXu3LVrl73NIjgKeeacFFt66GJ6+iHYzxuSzEzIcnMRGxureU4TqO/fEjE9/WxiT12cQcgRhC647pUjzKd+7U51yafwcAqrIHTjcmLuhscAPPRsYfLrWt/eY31jWEYtZNj+0hcI+Fg2PgKjpHkQJyYiLT29TsycBHJ5Hjanxdncm0RCjiAIR6Sx2p0EoQu6SozkumesvU0wG1uImqTozticFofjhwogFArh5eUFoVCIE4cKsTktDknRnVm3gSAI18ZZYvQM1e4kiPq4nGeuPkqlEorCIlRVXoWvnw+iBkRAIBDoHKsWdI7qpWPbQ5cU3RmJkcGazCv/1s0R09PPLvFd5JUjCMJRqVu7U1fBcIKoj0uLObnsJ8ydtQjlFy9pjgUGdcSCJXMgEsfrfd11z1gSdHoQCPh2b9lFQo4gCEdGHYecmZmJ3DoxcwShD5e9QuSyn5A6ehJC+vTVKsYa0qcvUkdPglz2U6Ovv+4Z65Bbr45UNNccnHltBKGGkh/04wxbreo45Ly8PAwfPlxzjyoqKrK3aQRH4TEMw9jbCFtQU1MDb29vXLh6Es09miO8x0CE9OkLmUym9atHpVJBLBaj5PgxFJ3cq3fLtT6O6KlzthsCCTnCVXCU/7v2ElbO0tbr5bk/Iv9YBR4+fKh1vLq6Gl5eXqyeW33P/CngaXjwjbsPmsIdlRLxV87aZC2ugEt65hSFRSi/eAnp6ekN3Nd8Ph9paWm4WFYORaHxv4Ic1UvHBkqlCntKrmDjnrPYU3IFSqXK8IsIwsoolSrsKTyNjT8osKfwNF2HhMPx4nMdNELO3d3dztYQXMYlY+aqKq8C0J8ppD6uHmcsjpggYe04Ol0Vy4P9vLFsfASrGa3klSPqkiMvxvS52Sgrf/J/ODjQB8sXJFMLNBvildTNKbY97cVbQ7thdlYxhr70EnJycvCf//wHIpHI3mYRHMQlPXO+fo8avp84cULn8+rj6nGm4mheOmsJIV0VyxUKBXqHxWCUdBdy9p23ynnqQ0LuCeSNeiTkRqauQu+QCO3rMCQCI1NXIUdebG8TCZZxFgF58Pc/8eDhQ2RkZOCpp55CTEyMvU0iOArFzFkpZk4XjuShs9Q7p1Sq8OzEzegdFqNVsRx4/J4mJuLE4UKUfjHS6qVKSMw9grxRj67DZ8JnondIBGQyHdehOBEnSopxpmiJw7ZEc5R4OTUUN2c+G/ecxdhl+bh16xY8PT019zGKmSPq45jfZhYiEAiwYMkcyOVyiMVirV/vYrEYcrkc8xdnWCTkAMfy0FkqiNQVy9N1VCzn8/lIS0/HhYqbKDhZadF56kNC7hHkjXpEgaIUZeVXkZ6u5zpMS8eFi1UoUJTayULCGihVDAorbuLf566isOImlKqGPgl7iEhrxwvXrTdHEI3hkjFzACASxyNr41rMnbUIQqFQczwoOBBZG9c2WmfOFBy1Jp2pGKpYrj6uHkdYD6VShelzsyESJWh5oyIjIyGT5UIsTsSMeZuRGN/PYb1RxlJRdROAEdfh43GEYZRKFScKgavZXnYNcw5dRHn1Hc2xQG8PLAoLwrDgdlpja3J+s5mHjo144fp9rwlCH879zW4AkTgexad+gWxnNr7Y8BlkO7NRdHKv1YScGkfy0JmLoV+Q6uPqcYT1cGRvlLVj/Px9WwIw4jp8PM7RsPUWa86+83h24mYMSduOscvyMSRtO56duJm1+FdDbC+7hpSfTyMkZrB2fdCYwUj5+TS2l12zi11sxQur683J8x71vaY6c4Q+XDJmroVXC7vY4AgeOnNvFvaKmaNtVmDjDwqMeWuNJq6mPrdu3YKXlxe+WTMJ7f1boaLqJvx9WyImqqvBz0KpVKFAUWrUa0wZC5gX42foHM4eM2dLMacWKKKEBKRnZKBXr144ceIEJJmZkOflGd1z2VrbnUoVg+dyDiMkZrDe75iSgt04nNQPAj5P67Vseuds8d1X3+tHMXNEfRzv28yBcdSuEcZQ/xekVhxiYiLkeXlYmhru0kKOrUxTY71R0+dtwvNiKca8tQbPi6V4Jnxmo7F0OfJiPBM+06jXmDJWPd7UGD9jziEQ8LF8QTLk8jyIxfWuQ3Ei5PI8LJs/yujr0B7ZwfrOaUshp1SqMGNdEUQJCZDl5iIyMhKenp6Ptu5zcyFKSMDMrGKbZksrqqpRXn2n0bjci9V3oKiq1jMDO9giXjgpujPO/HMUts17yVJzCSeFxJwdsJWgs/WNKCm6MzanxeH4oQIIhUJ4eXlBKBTisGIv3h3eC61bNLVbqQx7l+wwVeyYQkxUVwQH+kAiyYRKpb0ulUoFiUSCJk2aIDwi2iThZKzYMlWY1Y/x0xIKslyIRAmYMW+z1mdkyjmSROHYkjUFx48VaV2HJ0qKsSVritGZvWx+Zlw6py7sldDUGJW19wEYjodUj6sLm8kQtooXFgj4iOnlb9EchPNCYs5KKJVKFO5V4IfsXBTuVUCpVDY6nm1BZ+5NwVJvl/oXZL50GN5L7AXfVh648r9b+EfucbvF29j7Bsl2pqlhb5QcffuGIDd3m1HCyRSxZY4wMzXGz5xzJInC8XvxUvwsS8N3X/wffpal4UzREpOEnK2zgw2e04b/b7iY0OTX3A2AYQ+0elx92BJ0FC9McAESc1ZALvsJ4T0GQjw0GW+lvAPx0GSE9xgIueynRl/HlqCzd5kKgYCP67f+wqfbTiIierBVA4JNxd7vhTlCxBz0eaMOF+8HAHz22SqjkyNMEVvmJF+YmnFqboKHQMBH7IDuGD0iCrEDupu0tWqLz8ykc9p4W5OLAiXK1xuB3h6QZOr2QEslEgR5eyDK19tmNgHaGaf67Ork3xIxPf1sapczsnr1agQHB6Np06bo37+/wYSQLVu2oFu3bmjatCl69+6NH3/8Uet5Ho+n87Fs2TLNmODg4AbPL168mJX1WQKJOQuRy35C6uhJCOnTVzu7qk9fpI6eZHNBZ48bkS4buBBvw4X3wpaZprq8UUvmjQRgWqkOU8SWOaVATM04tXW5EXtkBxs8p423NbkoUAR8HhaFBTUal7swLKhB8kNd2PDO2SNe2BXJzs7GtGnTMG/ePPz6668ICQnB0KFDcfWq7rab+/fvx+jRozFhwgQcOXIEYrEYYrFY63unoqJC67Fu3TrweDyMGDFCa64FCxZojXvnnXdYXas50NVlAUqlEnNnLYJIJIJMJqsnFmQQiUSYNzvTpluu1rgROUIBYWNs5ELJDlsLkfreqPb+rQGYVqrDFLFlTikQQzF+UqkEnYJ8ERPV1WR7rIE9atUZfU4bbWtyVaAMC26HDc93x7GC3Voe6JKC3djwfPcGdeZ0wYag0xcvfOJwodFZv0TjrFixAm+++SZSU1PRo0cPrF27Fs2bN8e6det0jv/HP/6Bl156CTNmzED37t2xcOFCPPfcc1i1apVmjJ+fn9YjNzcXgwcPRufO2p9XixYttMZ5eHiwulZzcJmiweoKLLdu3bbanIrCIpRfvIR1X2Xh9u2G87777rt44YUXkL9zD6IGRDQ61y30Q6s7hRbbdK7s0a+UwMBA1NTUNHg+MDBQM+65kGC98zA6goiNtqGixjgbKmrw3NNtzToH79Zdw3ZY6b2wBK8WzQAABw8eRHh4w3gt9TaBV4tmqDFiTaYS0isQgR3aYv78j7Bx46YGZRMWLJiPoI7tENIrUHN+U14DwOT5AWBB2isY939fICHhZXzwwXR0794dp0+fxscfL8eOHTvx9Zq3cKf2ntlrsAR7fGZGn7P5U6ix4P+mKcSFdsD6abH48JtfIBTKNceDfL2xflos4kI7GGVLzf2HVrVrUEAr7BW1RNHVGlTdvQ/fZm6I8PECn88z/lwsvIdxoR1waGUA9p+uQuWNu/Br1QzC7r4QCPhW+8zU89iyotgdFTs7F+p56383u7u7w93dXevY/fv3cfjwYaSlpWmO8fl8xMXFQaFQ6JxfoVBg2rRpWseGDh0KmUymc3xVVRXy8vKwYcOGBs8tXrwYCxcuRGBgIF577TVMnToVTZpwTD4xLsKlS5cYAPSgBz3oQQ96OPTj0qVLrN8z7969y/j5+bG6Dk9PzwbH5s2b18CWy5cvMwCY/fv3ax2fMWMGExERodP+p556ivn++++1jq1evZrx8fHROX7JkiVMq1atmLt372od//jjj5ndu3czx44dY9asWcO0bNmSmTp1qgnvpG3gmLRkj4CAAFy6dAktWrQAj6c/psLa1NTUoGPHjrh06ZLTFEZ0xjUBzrkuWpNjQGtyDOy9JoZhcOvWLQQEBLB+rqZNm+LChQu4f589TzDDMA3ux/W9crZi3bp1GDNmDJo2bap1vK53r0+fPnBzc8Nbb70FqVRqN1t14TJijs/no0OHDnY7v5eXl9N8oalxxjUBzrkuWpNjQGtyDOy5Jm9vb5udq2nTpg3EjT1o27YtBAIBqqqqtI5XVVXBz093Eo6fn5/R4wsKClBaWors7GyDtvTv3x8PHz5EWVkZunbtasIq2IUSIAiCIAiC4Cxubm7o168f8vPzNcdUKhXy8/MRFRWl8zVRUVFa4wHgv//9r87xX331Ffr164eQkBCDthw9ehR8Ph8+Pj4mroJdXMYzRxAEQRCEYzJt2jSkpKQgLCwMERERWLlyJe7cuYPU1FQAwLhx49C+fXtIpVIAwHvvvYdBgwbh448/RkJCAjZt2oRDhw7hn//8p9a8NTU12LJlCz7++OMG51QoFDh48CAGDx6MFi1aQKFQYOrUqRg7dixatWrF/qJNgMQcy7i7u2PevHmc2lu3FGdcE+Cc66I1OQa0JsfAGdfkKCQnJ+PatWuYO3cuKisr0bdvX+zYsQO+vr4AgPLycq1Md6FQiO+//x5z5sxBeno6nnnmGchksgblfzZt2gSGYTB69OgG53R3d8emTZvw0Ucf4d69e+jUqROmTp3aIEuWC/AYxoY5zgRBEARBEIRVoZg5giAIgiAIB4bEHEEQBEEQhANDYo4gCIIgCMKBITFHEARBEAThwJCYM5HLly9j7NixaNOmDZo1a4bevXvj0KFDescXFhYiOjpaM75bt2745JNP9I5fvHgxeDwe3n//fRas1w0ba/roo4/A4/G0Ht26dWN7KRrY+pxMndeasLGm4ODgBp8Tj8fD5MmT2V4OAHbWpFQq8eGHH6JTp05o1qwZunTpgoULF9qsnyUba7p16xbef/99BAUFoVmzZhAKhSguLmZ7KVpYcu3v27cPTZo0Qd++fRs8t3r1agQHB6Np06bo37+/pg+tLWBjTb/88guGDRuGgIAA8Hg8vb1ACcKaUGkSE7hx4waio6MxePBg/PTTT2jXrh1+//33RuvNeHh4YMqUKejTpw88PDxQWFiIt956Cx4eHpg4caLW2OLiYnzxxRfo06cP20vRwOaaevbsiV27dmn+tlVjYrbWZM68XF9TcXExlEql5jUnTpzACy+8gJEjRzrsmpYsWYI1a9Zgw4YN6NmzJw4dOoTU1FR4e3vj3Xffdcg1vfHGGzhx4gS++eYbBAQE4Ntvv0VcXBxOnTqF9u3bs7omc9el5ubNmxg3bhyGDBnSoCJ/dnY2pk2bhrVr16J///5YuXIlhg4ditLSUtaLsrK1pjt37iAkJATjx49HUlISW+YThDb2bAzraMyaNYsZMGCAxfO88sorzNixY7WO3bp1i3nmmWeY//73v8ygQYOY9957z+LzGANba5o3bx4TEhJi8bzmwNaarDWvObB57dXlvffeY7p06cKoVCqLz2UIttaUkJDAjB8/XmtMUlISM2bMGIvPZQg21lRbW8sIBAJGLpdrjXnuueeYjIwMi89lDJasKzk5mZkzZ47O74SIiAhm8uTJmr+VSiUTEBDASKVSS8w1CrbWVBcAzNatW80zkCBMgLZZTWDbtm0ICwvDyJEj4ePjg9DQUHz55ZcmzXHkyBHs378fgwYN0jo+efJkJCQkIC4uzpomG4TNNf3+++8ICAhA586dMWbMGJSXl1vTdL2wtSZrzGsubH5Oau7fv49vv/0W48ePb9D8mg3YWpNQKER+fj7OnDkDADh27BgKCwsRHx9vVft1wcaaHj58CKVS2aBHZrNmzVBYWGg12xvD3HVlZWXh/PnzmDdvXoPn7t+/j8OHD2t95/H5fMTFxUGhUFjVfl2wsSaCsBv2VpOOhLu7O+Pu7s6kpaUxv/76K/PFF18wTZs2ZdavX2/wte3bt2fc3NwYPp/PLFiwQOu5jRs3Mr169WLu3r3LMAxjU88cW2v68ccfmc2bNzPHjh1jduzYwURFRTGBgYFMTU0NW0vRwNaaLJnXUthaU12ys7MZgUDAXL582Zqm64WtNSmVSmbWrFkMj8djmjRpwvB4PEYikbC1DC3YWlNUVBQzaNAg5vLly8zDhw+Zb775huHz+cyzzz7L1lK0MGddZ86cYXx8fJjS0lKGYRp66y9fvswAYPbv36/1uhkzZjARERGsrKMubKypPiDPHGEjSMyZwFNPPcVERUVpHXvnnXeYyMhIg689f/48U1JSwvzzn/9kWrduzXz//fcMwzBMeXk54+Pjwxw7dkwz1pZijo016eLGjRuMl5cX869//ctimw3B1posmddSbPE5vfjii4xIJLKKvcbA1po2btzIdOjQgdm4cSNTUlLCfP3110zr1q1tIrrZWtPZs2eZgQMHMgAYgUDAhIeHM2PGjGG6detm9TXowtR1PXz4kAkLC2PWrFmjOcY1McfGmupDYo6wFSTmTCAwMJCZMGGC1rHPP/+cCQgIMGmehQsXan5Rb926VfMFrX4AYHg8HiMQCJiHDx9azX5dsLEmfYSFhTGzZ8822UZTYWtN1prXHNj+nMrKyhg+n8/IZDKL7DQFttbUoUMHZtWqVQ3GdO3a1XxjjYTtz+n27dvMlStXGIZhmFGjRjEvv/yy+caagKnrunHjRoPvNR6PpzmWn5/P3Lt3jxEIBA3Ezrhx45jhw4eztRQNbKypPiTmCFtB2awmEB0djdLSUq1jZ86cQVBQkEnzqFQq3Lt3DwAwZMgQHD9+XOv51NRUdOvWDbNmzYJAILDMaAOwsSZd3L59G+fOncPrr79ulp2mwNaarDWvObD9OWVlZcHHxwcJCQkW2WkKbK2ptrZWq+E2AAgEAqhUKvONNRK2PycPDw94eHjgxo0b2LlzJ5YuXWqRvcZi6rq8vLwafK99/vnn+Pnnn/Hvf/8bnTp1gpubG/r164f8/HyIxWIAj9adn5+PKVOmsLKOurCxJoKwG/ZWk45EUVER06RJEyYzM5P5/fffme+++45p3rw58+2332rGzJ49m3n99dc1f69atYrZtm0bc+bMGebMmTPMv/71L6ZFixaNZqHZcpuVrTV98MEHzJ49e5gLFy4w+/btY+Li4pi2bdsyV69eddg1GTOvo62JYR7FmAUGBjKzZs1ifR11YWtNKSkpTPv27Rm5XM5cuHCBycnJYdq2bcvMnDnTYde0Y8cO5qeffmLOnz/P/Oc//2FCQkKY/v37M/fv32d9Teauqz66tiQ3bdrEuLu7M+vXr2dOnTrFTJw4kWnZsiVTWVnJ1lI0sLWmW7duMUeOHGGOHDnCAGBWrFjBHDlyhLl48SJbSyEI2mY1le3btzO9evVi3N3dmW7dujH//Oc/tZ5PSUlhBg0apPn7008/ZXr27Mk0b96c8fLyYkJDQ5nPP/+cUSqVes9hSzHHMOysKTk5mfH392fc3NyY9u3bM8nJyczZs2dttSTWPidD87IJW2vauXMnA0AT1G1L2FhTTU0N89577zGBgYFM06ZNmc6dOzMZGRnMvXv3HHZN2dnZTOfOnRk3NzfGz8+PmTx5MnPz5k2brEeNqeuqj774ss8++4wJDAxk3NzcmIiICObAgQNWtlw/bKxp9+7dDIAGj5SUFOsvgCAew2MYG5VFJwiCIAiCIKwO1ZkjCIIgCIJwYEjMEQRBEARBODAk5giCIAiCIBwYEnMEQRAEQRAODIk5giAIgiAIB4bEHEEQBEEQhANDYo4gCIIgCMKBITFHEARBEFbil19+wbBhwxAQEAAejweZTGbS68vKysDj8Ro8Dhw4wI7BhFNAYo4gCIIgrMSdO3cQEhKC1atXWzTPrl27UFFRoXn069fPShYSzkgTextAEARBEM5CfHw84uPj9T5/7949ZGRkYOPGjbh58yZ69eqFJUuWIDY2VmtcmzZt4Ofnx7K1hLNAnjmCIAiCsBFTpkyBQqHApk2bUFJSgpEjR+Kll17C77//rjVu+PDh8PHxwYABA7Bt2zY7WUs4CtSblSAIgiBYgMfjYevWrRCLxQCA8vJydO7cGeXl5QgICNCMi4uLQ0REBCQSCf788098/fXXiI6OBp/Pxw8//IClS5dCJpNh+PDhdloJwXVom5UgCIIgbMDx48ehVCrx7LPPah2/d+8e2rRpAwBo27Ytpk2bpnkuPDwcV65cwbJly0jMEXohMUcQBEEQNuD27dsQCAQ4fPgwBAKB1nOenp56X9e/f3/897//Zds8woEhMUcQBEEQNiA0NBRKpRJXr15FTEyM0a87evQo/P39WbSMcHRIzBEEQRCElbh9+zbOnj2r+fvChQs4evQoWrdujWeffRZjxozBuHHj8PHHHyM0NBTXrl1Dfn4++vTpg4SEBGzYsAFubm4IDQ0FAOTk5GDdunX417/+Za8lEQ4AiTmCIAiCsBKHDh3C4MGDNX+r499SUlKwfv16ZGVlYdGiRfjggw9w+fJltG3bFpGRkRCJRJrXLFy4EBcvXkSTJk3QrVs3ZGdn49VXX7X5WgjHgbJZCYIgCIIgHBiqM0cQBEEQBOHAkJgjCIIgCIJwYEjMEQRBEARBODAk5giCIAiCIBwYEnMEQRAEQRAODIk5giAIgiAIB4bEHEEQBEEQhANDYo4gCIIgCMKBITFHEARBEAThwJCYIwiCIAiCcGBIzBEEQRAEQTgwJOYIgiAIgiAcmP8Hqqzix5lmEAMAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 700x600 with 2 Axes>"
       ]
@@ -627,7 +851,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/stationary/example_RDT_station.ipynb b/docs/examples/stationary/example_RDT_station.ipynb
index c287b57..852a9a9 100644
--- a/docs/examples/stationary/example_RDT_station.ipynb
+++ b/docs/examples/stationary/example_RDT_station.ipynb
@@ -28,7 +28,7 @@
     "\n",
     "import pandas as pd\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n"
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -111,13 +111,13 @@
        "</div>"
       ],
       "text/plain": [
-       "            timestamp  barometric_pressure_Avg  relative_humidity_Avg   \n",
-       "0 2020-04-10 09:47:00                    983.8                   29.0  \\\n",
+       "            timestamp  barometric_pressure_Avg  relative_humidity_Avg  \\\n",
+       "0 2020-04-10 09:47:00                    983.8                   29.0   \n",
        "1 2020-04-10 10:47:00                    982.3                   25.0   \n",
        "2 2020-04-10 11:17:00                    980.8                   25.0   \n",
        "\n",
-       "   air_temperature_Avg   DP  BattVolt  counts_1_Tot  counts_2_Tot   \n",
-       "0                  9.6 -7.4      14.4           848         716.0  \\\n",
+       "   air_temperature_Avg   DP  BattVolt  counts_1_Tot  counts_2_Tot  \\\n",
+       "0                  9.6 -7.4      14.4           848         716.0   \n",
        "1                 10.9 -7.9      14.3           436        7200.0   \n",
        "2                 11.5 -7.4      13.7           389         396.0   \n",
        "\n",
@@ -204,7 +204,7 @@
    "outputs": [],
    "source": [
     "# Compute total cunts\n",
-    "df['total_raw_counts'] = crnpy.total_raw_counts(df[cols_counts], nan_strategy='average')\n"
+    "df['total_raw_counts'] = crnpy.total_raw_counts(df[cols_counts])\n"
    ]
   },
   {
@@ -215,7 +215,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x300 with 1 Axes>"
       ]
@@ -450,7 +450,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x300 with 1 Axes>"
       ]
@@ -500,7 +500,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x300 with 1 Axes>"
       ]
@@ -560,7 +560,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -640,7 +640,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -689,7 +689,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -730,7 +730,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,
diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index 6c6b9b9..0b974c2 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -473,7 +473,7 @@ def get_reference_neutron_flux(station, date=pd.to_datetime("2011-05-01")):
 """
 
     # Get flux for 2011-05-01
-    df_flux = get_incoming_neutron_flux(station, date, date + pd.Timedelta(hours=24))
+    df_flux = get_incoming_neutron_flux(date, date + pd.Timedelta(hours=24), station=station)
     if df_flux is None:
         warnings.warn(f"Reference neutron flux for {station} not available. Returning NaN.")
     else:
@@ -502,7 +502,7 @@ def smooth_1d(values, window=5, order=3, method='moving_median'):
         Analytical chemistry, 36(8), 1627-1639.
     """
 
-    if not isinstance(x, pd.Series) and not isinstance(x, pd.DataFrame):
+    if not isinstance(values, pd.Series) and not isinstance(x, pd.DataFrame):
         raise ValueError('Input must be a pandas Series or DataFrame')
 
     if method == 'moving_average':
@@ -1228,26 +1228,29 @@ def interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps):
     """Function to interpolate incoming neutron flux to match the timestamps of the observations.
 
     Args:
-        nmdb_timestamps (pd.Series): Series of timestamps in datetime format from the NMDB.
-        nmdb_counts (pd.Series): Series of neutron counts from the NMDB
-        crnp_timestamps (pd.Series): Series of timestamps in datetime format from the station or device.
+        nmdb_timestamps (pd.Series or np.array): Series or array of timestamps in datetime format from the NMDB
+        nmdb_counts (pd.Series or np.array): Series or array of incoming neutron flux counts from the NMDB
+        crnp_timestamps (pd.Series or np.array): Series or array of timestamps in datetime format from the CRNP device
 
     Returns:
         (pd.Series): Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps
     """
-    incoming_flux = np.array([])
-    for k, timestamp in enumerate(crnp_timestamps):
-        if timestamp in nmdb_timestamps.values:
-            idx = timestamp == nmdb_timestamps
-            incoming_flux = np.append(incoming_flux, nmdb_counts.loc[idx])
-        else:
-            incoming_flux = np.append(incoming_flux, np.nan)
+    # Create a DataFrame from nmdb timestamps and counts
+    df_nmdb = pd.DataFrame({'timestamp': nmdb_timestamps, 'counts': nmdb_counts})
+
+    # Set the Timestamp column as the index
+    df_nmdb.set_index('timestamp', inplace=True)
+
+    # Reindex the DataFrame to the timestamps from the CRNP device using the nearest method
+    # This will match each CRNP timestamp with the nearest NMDB timestamp
+    interpolated_flux = df_nmdb.reindex(crnp_timestamps, method='nearest')['counts'].values
+
+    #drop NaN values
+    interpolated_flux = interpolated_flux[~np.isnan(interpolated_flux)]
 
-    # Interpolate nan values
-    incoming_flux = pd.Series(incoming_flux).interpolate(method='nearest', limit_direction='both')
+    assert len(interpolated_flux) == len(crnp_timestamps), "Length of interpolated flux does not match length of CRNP timestamps"
 
-    # Return only the values for the selected timestamps
-    return incoming_flux
+    return interpolated_flux
 
 
 def lattice_water(clay_content, total_carbon=None):
@@ -1290,7 +1293,7 @@ def latlon_to_utm(lat, lon, utm_zone_number=None, utm_zone_letter=None):
         utm_zone_letter (str): UTM zone letter. If None, the zone letter is automatically calculated.
 
     Returns:
-        (float, float): Tuple of easting and northing coordinates in meters. First element is easting, second is northing.
+        (float, float, int, str): Tuple of easting, northing, zone number and zone letter. First element is easting, second is northing, third is zone number and fourth is zone letter.
 
     References:
          Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87)

From 51fa09e42270d2345ee17297eb9cbe748234e9e9 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Wed, 24 Jan 2024 09:49:55 -0600
Subject: [PATCH 15/24] Update main.html

---
 docs/overrides/main.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/overrides/main.html b/docs/overrides/main.html
index b5632e3..a866fed 100644
--- a/docs/overrides/main.html
+++ b/docs/overrides/main.html
@@ -2,7 +2,7 @@
 
 {% block content %}
 {% if page.nb_url %}
-    <a href="{{ page.nb_url }}" title="Download Notebook" class="md-content__button md-icon md-button" style="padding: 0.25em 0.5em; margin: 1em 0.5em;">
+    <a href="{{ page.nb_url }}" title="Download Notebook" class="md-content__button md-icon md-button" style="position: absolute; right: 25%; padding: 0.25em 0.5em; margin: 1em 0.5em;">
        Download {% include ".icons/material/download.svg" %}
     </a>
 {% endif %}

From 4636770e7f4191191f0823e80abdcb8e96542670 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Sun, 3 Mar 2024 14:11:48 -0700
Subject: [PATCH 16/24] Code formatting of new methods

#11
---
 src/crnpy/crnpy.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index 0b974c2..7d3638d 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -670,7 +670,7 @@ def sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='S
 
             # Compute soil depth that accounts for 86% of the neutron flux
             D86 = 1 / bulk_density * (8.321 + 0.14249 * (0.96655 + np.exp(-0.01 * r_start)) * (20 + (Wlat + vwc)) / (
-                        0.0429 + (Wlat + vwc)))
+                    0.0429 + (Wlat + vwc)))
             results.append(D86)
 
     elif method == 'Franz_2012':
@@ -1104,8 +1104,8 @@ def atmospheric_depth(elevation, latitude):
 
     # Air pressure calculation
     reference_air_pressure = air_pressure_sea_level * (
-                1 + temperature_lapse_rate / reference_temperature * elevation) ** ((-gravity * air_molar_mass) / (
-                universal_gas_constant * temperature_lapse_rate))
+            1 + temperature_lapse_rate / reference_temperature * elevation) ** ((-gravity * air_molar_mass) / (
+            universal_gas_constant * temperature_lapse_rate))
 
     # Atmospheric depth calculation
     atmospheric_depth = (10 * reference_air_pressure) / gravity
@@ -1149,7 +1149,7 @@ def location_factor(site_atmospheric_depth, site_Rc, reference_atmospheric_depth
 
     # Calculate the result using the provided parameters
     tau = epsilon * norm_factor * (c0 * site_atmospheric_depth + c1) * (
-                1 - np.exp(-(c2 * site_atmospheric_depth + c3) * site_Rc ** (c4 * site_atmospheric_depth + c5)))
+            1 - np.exp(-(c2 * site_atmospheric_depth + c3) * site_Rc ** (c4 * site_atmospheric_depth + c5)))
     return tau
 
 
@@ -1245,10 +1245,11 @@ def interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps):
     # This will match each CRNP timestamp with the nearest NMDB timestamp
     interpolated_flux = df_nmdb.reindex(crnp_timestamps, method='nearest')['counts'].values
 
-    #drop NaN values
+    # drop NaN values
     interpolated_flux = interpolated_flux[~np.isnan(interpolated_flux)]
 
-    assert len(interpolated_flux) == len(crnp_timestamps), "Length of interpolated flux does not match length of CRNP timestamps"
+    assert len(interpolated_flux) == len(
+        crnp_timestamps), "Length of interpolated flux does not match length of CRNP timestamps"
 
     return interpolated_flux
 

From cfb568498a67ac538f08fb29ea10f918ff2d8f5d Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Sun, 7 Apr 2024 12:31:02 -0600
Subject: [PATCH 17/24] Fixed rscaled with default Hveg to at least account for
 pressure without biomass data

Addressing 2nd sets of comments in #9
---
 src/crnpy/crnpy.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index 7d3638d..90eb130 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -929,7 +929,7 @@ def WrB(r, x, y):
 
             return B0 * np.exp(-B1 * r) + B2 * np.exp(-B3 * r)
 
-        def rscaled(r, p, Hveg, y):
+        def rscaled(r, p, y, Hveg = 0):
             Fp = 0.4922 / (0.86 - np.exp(-p / 1013.25))
             Fveg = 1 - 0.17 * (1 - np.exp(-0.41 * Hveg)) * (1 + np.exp(-9.25 * y))
             return r / Fp / Fveg
@@ -940,8 +940,7 @@ def rscaled(r, p, Hveg, y):
         y = theta
         bd = rhob
 
-        if Hveg is not None and p is not None:
-            r = rscaled(r, p, Hveg, y)
+        r = rscaled(r, p, y, Hveg)
 
         Wr = np.zeros(len(r))
 

From 154f85dac9172978d6f8a6233a264d8d13e8ab72 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Sun, 7 Apr 2024 21:51:57 -0600
Subject: [PATCH 18/24] =?UTF-8?q?Fixed=20Weighting=20according=20to=20Schr?=
 =?UTF-8?q?=C3=B6n=20et=20al.=20(2017)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Addressing the comments in #9, the method was updated to use the iterative routine.

Examples and tests, were also updated.
---
 build/lib/crnpy/crnpy.py                    | 101 +++++++++++++-------
 docs/examples/calibration/calibration.ipynb |  32 +++++--
 src/crnpy/crnpy.py                          |  85 ++++++++++------
 src/tests/test_calibration_rdt.py           |  46 ++++++---
 src/tests/test_functions.py                 |  21 ++--
 5 files changed, 189 insertions(+), 96 deletions(-)

diff --git a/build/lib/crnpy/crnpy.py b/build/lib/crnpy/crnpy.py
index 0b974c2..2b1f749 100644
--- a/build/lib/crnpy/crnpy.py
+++ b/build/lib/crnpy/crnpy.py
@@ -670,7 +670,7 @@ def sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='S
 
             # Compute soil depth that accounts for 86% of the neutron flux
             D86 = 1 / bulk_density * (8.321 + 0.14249 * (0.96655 + np.exp(-0.01 * r_start)) * (20 + (Wlat + vwc)) / (
-                        0.0429 + (Wlat + vwc)))
+                    0.0429 + (Wlat + vwc)))
             results.append(D86)
 
     elif method == 'Franz_2012':
@@ -706,7 +706,7 @@ def abs_humidity(relative_humidity, temp):
     return abs_h
 
 
-def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=None, Hveg=None):
+def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=None, Hveg=0, tol=0.01):
     """Function to compute distance weights corresponding to each soil sample.
 
     Args:
@@ -718,9 +718,11 @@ def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=Non
         p (np.array or pd.Series): Atmospheric pressure in hPa. Required for the 'Schron_2017' method.
         Hveg (np.array or pd.Series): Vegetation height in m. Required for the 'Schron_2017' method.
         method (str): Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.
+        tol (float): Tolerance for the iterative solution. Default is 0.01. Required for the 'Schron_2017' method.
 
     Returns:
-        (array or pd.Series or pd.DataFrame): Distance weights for each sample.
+        theta_new (np.array or pd.Series): Weighted soil moisture values.
+        weights (np.array or pd.Series): Distance weights for each sample. For the 'Schron_2017' method, the weights are computed for each distance.
 
     References:
         Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015).
@@ -826,7 +828,10 @@ def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=Non
 
         # Combined and normalized weights
         weights = Wd * W / np.nansum(Wd * W)
-        return weights
+
+        theta_new = np.sum(theta * weights)
+
+        return theta_new, weights
     elif method == 'Schron_2017':
         # Horizontal distance weights According to Eq. 6 and Table A1 in Schrön et al. (2017)
         # Method for calculating the horizontal distance weights from 0 to 1m
@@ -929,45 +934,70 @@ def WrB(r, x, y):
 
             return B0 * np.exp(-B1 * r) + B2 * np.exp(-B3 * r)
 
-        def rscaled(r, p, Hveg, y):
+        # Wrapper method for calculating the horizontal distance weights
+        def Wr(r, x, y):
+            if r <= 1:
+                return WrX(r, x, y)
+            elif r <= 50:
+                return WrA(r, x, y)
+            elif r <= 600:
+                return WrB(r, x, y)
+            else:
+                raise ValueError("r must be between 1 and 600m when using 'Schron_2017' method")
+
+        def rscaled(r, p, y, Hveg = 0):
             Fp = 0.4922 / (0.86 - np.exp(-p / 1013.25))
             Fveg = 1 - 0.17 * (1 - np.exp(-0.41 * Hveg)) * (1 + np.exp(-9.25 * y))
             return r / Fp / Fveg
 
         # Rename variables to be consistent with the revised paper
         r = distances
-        x = h
-        y = theta
-        bd = rhob
-
-        if Hveg is not None and p is not None:
-            r = rscaled(r, p, Hveg, y)
-
-        Wr = np.zeros(len(r))
+        theta_ = np.mean(theta) # Start with the mean value of theta as initial guess
+        bd = np.mean(rhob) # Neutrons are impacted by the bulk density across the whole area and not just the sample area. https://github.com/soilwater/crnpy/issues/9#issuecomment-2003813777
 
-        # See Eq. 6 in Schron et al. (2017)
-        r0_idx = (r <= 1)
-        r1_idx = (r > 1) & (r <= 50)
-        r2_idx = (r > 50) & (r < 600)
-        Wr[r0_idx] = WrX(r[r0_idx], x[r0_idx], y[r0_idx])
-        Wr[r1_idx] = WrA(r[r1_idx], x[r1_idx], y[r1_idx])
-        Wr[r2_idx] = WrB(r[r2_idx], x[r2_idx], y[r2_idx])
-
-        # Vertical distance weights
+        # Vertical distance weights functions
         def D86(r, bd, y):
             return 1 / bd * (8.321 + 0.14249 * (0.96655 + np.exp(-0.01 * r)) * (20 + y) / (0.0429 + y))
 
         def Wd(d, r, bd, y):
             return np.exp(-2 * d / D86(r, bd, y))
 
-        # Calculate the vertical distance weights
-        Wd = Wd(depth, r, bd, y)
-
-        # Combined and normalized weights
-        # Combined and normalized weights
-        weights = Wd * Wr / np.nansum(Wd * Wr)
-
-        return weights
+        step = 0
+        diff = 1
+        while diff > tol:
+            step += 1
+            print(f"Step {step}, diff = {diff}", end="\r")
+            # Calculate the scaled distance and D86
+            r = rscaled(distances, p, theta_, Hveg)
+
+            # Calculate the vertical average for each profile
+            P = np.unique(distances)
+            theta_P = []
+            r_stars = []
+            for i in range(len(P)):
+                profile = P[i]
+                idx = distances == profile
+                depths_P = depth[idx]
+                r_P = r[idx]
+                theta_Pi = theta[idx]
+                # Calculate the vertical distance weights
+                Wd_P = Wd(depths_P, r_P, bd, theta_Pi)
+                # Calculate the vertical average of theta
+                theta_P_i = np.sum(Wd_P * theta_Pi) / np.sum(Wd_P)
+                theta_P.append(theta_P_i)
+                r_stars.append(np.mean(r_P))
+
+            # Calculate the horizontal distance weights
+            Wrs = np.array([Wr(r_star, theta_p, Hveg) for r_star, theta_p in zip(r_stars, theta_P)])
+            theta_new = np.sum(Wrs * theta_P) / np.sum(Wrs)
+            diff = np.abs(theta_new - theta_)
+            theta_ = theta_new
+
+        print(f"Solution converged after {step} steps, the average soil moisture is {theta_new}")
+
+        return theta_new, [theta_P, r_stars, Wrs]
+    else:
+        raise ValueError(f"Method {method} not recognized. Please use one of the following methods: 'Kohli_2015' or 'Schron_2017'")
 
 
 def exp_filter(sm, T=1):
@@ -1104,8 +1134,8 @@ def atmospheric_depth(elevation, latitude):
 
     # Air pressure calculation
     reference_air_pressure = air_pressure_sea_level * (
-                1 + temperature_lapse_rate / reference_temperature * elevation) ** ((-gravity * air_molar_mass) / (
-                universal_gas_constant * temperature_lapse_rate))
+            1 + temperature_lapse_rate / reference_temperature * elevation) ** ((-gravity * air_molar_mass) / (
+            universal_gas_constant * temperature_lapse_rate))
 
     # Atmospheric depth calculation
     atmospheric_depth = (10 * reference_air_pressure) / gravity
@@ -1149,7 +1179,7 @@ def location_factor(site_atmospheric_depth, site_Rc, reference_atmospheric_depth
 
     # Calculate the result using the provided parameters
     tau = epsilon * norm_factor * (c0 * site_atmospheric_depth + c1) * (
-                1 - np.exp(-(c2 * site_atmospheric_depth + c3) * site_Rc ** (c4 * site_atmospheric_depth + c5)))
+            1 - np.exp(-(c2 * site_atmospheric_depth + c3) * site_Rc ** (c4 * site_atmospheric_depth + c5)))
     return tau
 
 
@@ -1245,10 +1275,11 @@ def interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps):
     # This will match each CRNP timestamp with the nearest NMDB timestamp
     interpolated_flux = df_nmdb.reindex(crnp_timestamps, method='nearest')['counts'].values
 
-    #drop NaN values
+    # drop NaN values
     interpolated_flux = interpolated_flux[~np.isnan(interpolated_flux)]
 
-    assert len(interpolated_flux) == len(crnp_timestamps), "Length of interpolated flux does not match length of CRNP timestamps"
+    assert len(interpolated_flux) == len(
+        crnp_timestamps), "Length of interpolated flux does not match length of CRNP timestamps"
 
     return interpolated_flux
 
diff --git a/docs/examples/calibration/calibration.ipynb b/docs/examples/calibration/calibration.ipynb
index 50b075f..4933c82 100644
--- a/docs/examples/calibration/calibration.ipynb
+++ b/docs/examples/calibration/calibration.ipynb
@@ -802,14 +802,22 @@
    "execution_count": 11,
    "id": "457e6db7-a5ab-4424-a93b-7b0f4a46fe45",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Solution converged after 2 steps, the average soil moisture is 0.2197191482091855\n"
+     ]
+    }
+   ],
    "source": [
-    "# Compute the weights of each sample for the field average\n",
-    "nrad_weights = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'], df_soil['distance_from_station'], (df_soil['bottom_depth']+df_soil['top_depth'])/2, rhob=df_soil['bulk_density'].mean())\n",
+    "# Weight the samples for the field average\n",
+    "#field_theta_v, w = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'], df_soil['distance_from_station'], (df_soil['bottom_depth']+df_soil['top_depth'])/2, rhob=df_soil['bulk_density'].mean(), method=\"Kohli_2015\")\n",
+    "field_theta_v, w = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'], df_soil['distance_from_station'], (df_soil['bottom_depth']+df_soil['top_depth'])/2, rhob=df_soil['bulk_density'].mean(),p=df_station['barometric_pressure_Avg'].mean(), method = \"Schron_2017\")\n",
     "\n",
     "# Apply distance weights to volumetric water content and bulk density\n",
-    "field_theta_v = np.sum(df_soil['theta_v']*nrad_weights)\n",
-    "field_bulk_density = np.sum(df_soil['bulk_density']*nrad_weights)\n"
+    "field_bulk_density = np.mean(df_soil['bulk_density'])\n"
    ]
   },
   {
@@ -822,7 +830,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean volumetric Water content during calibration survey: 0.263\n",
+      "Mean volumetric Water content during calibration survey: 0.22\n",
       "Mean corrected counts during calibration: 1543 counts\n"
      ]
     }
@@ -856,7 +864,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The solved value for N0 is: 2645\n"
+      "The solved value for N0 is: 2470\n"
      ]
     }
    ],
@@ -886,6 +894,14 @@
     "\n",
     "Patrignani, A., Ochsner, T. E., Montag, B., & Bellinger, S. (2021). A novel lithium foil cosmic-ray neutron detector for measuring field-scale soil moisture. Frontiers in Water, 3, 673185."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af515744-a25e-4fb0-8fd0-3559b0fea276",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -909,4 +925,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index 90eb130..2b1f749 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -706,7 +706,7 @@ def abs_humidity(relative_humidity, temp):
     return abs_h
 
 
-def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=None, Hveg=None):
+def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=None, Hveg=0, tol=0.01):
     """Function to compute distance weights corresponding to each soil sample.
 
     Args:
@@ -718,9 +718,11 @@ def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=Non
         p (np.array or pd.Series): Atmospheric pressure in hPa. Required for the 'Schron_2017' method.
         Hveg (np.array or pd.Series): Vegetation height in m. Required for the 'Schron_2017' method.
         method (str): Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.
+        tol (float): Tolerance for the iterative solution. Default is 0.01. Required for the 'Schron_2017' method.
 
     Returns:
-        (array or pd.Series or pd.DataFrame): Distance weights for each sample.
+        theta_new (np.array or pd.Series): Weighted soil moisture values.
+        weights (np.array or pd.Series): Distance weights for each sample. For the 'Schron_2017' method, the weights are computed for each distance.
 
     References:
         Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015).
@@ -826,7 +828,10 @@ def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=Non
 
         # Combined and normalized weights
         weights = Wd * W / np.nansum(Wd * W)
-        return weights
+
+        theta_new = np.sum(theta * weights)
+
+        return theta_new, weights
     elif method == 'Schron_2017':
         # Horizontal distance weights According to Eq. 6 and Table A1 in Schrön et al. (2017)
         # Method for calculating the horizontal distance weights from 0 to 1m
@@ -929,6 +934,17 @@ def WrB(r, x, y):
 
             return B0 * np.exp(-B1 * r) + B2 * np.exp(-B3 * r)
 
+        # Wrapper method for calculating the horizontal distance weights
+        def Wr(r, x, y):
+            if r <= 1:
+                return WrX(r, x, y)
+            elif r <= 50:
+                return WrA(r, x, y)
+            elif r <= 600:
+                return WrB(r, x, y)
+            else:
+                raise ValueError("r must be between 1 and 600m when using 'Schron_2017' method")
+
         def rscaled(r, p, y, Hveg = 0):
             Fp = 0.4922 / (0.86 - np.exp(-p / 1013.25))
             Fveg = 1 - 0.17 * (1 - np.exp(-0.41 * Hveg)) * (1 + np.exp(-9.25 * y))
@@ -936,37 +952,52 @@ def rscaled(r, p, y, Hveg = 0):
 
         # Rename variables to be consistent with the revised paper
         r = distances
-        x = h
-        y = theta
-        bd = rhob
-
-        r = rscaled(r, p, y, Hveg)
-
-        Wr = np.zeros(len(r))
+        theta_ = np.mean(theta) # Start with the mean value of theta as initial guess
+        bd = np.mean(rhob) # Neutrons are impacted by the bulk density across the whole area and not just the sample area. https://github.com/soilwater/crnpy/issues/9#issuecomment-2003813777
 
-        # See Eq. 6 in Schron et al. (2017)
-        r0_idx = (r <= 1)
-        r1_idx = (r > 1) & (r <= 50)
-        r2_idx = (r > 50) & (r < 600)
-        Wr[r0_idx] = WrX(r[r0_idx], x[r0_idx], y[r0_idx])
-        Wr[r1_idx] = WrA(r[r1_idx], x[r1_idx], y[r1_idx])
-        Wr[r2_idx] = WrB(r[r2_idx], x[r2_idx], y[r2_idx])
-
-        # Vertical distance weights
+        # Vertical distance weights functions
         def D86(r, bd, y):
             return 1 / bd * (8.321 + 0.14249 * (0.96655 + np.exp(-0.01 * r)) * (20 + y) / (0.0429 + y))
 
         def Wd(d, r, bd, y):
             return np.exp(-2 * d / D86(r, bd, y))
 
-        # Calculate the vertical distance weights
-        Wd = Wd(depth, r, bd, y)
-
-        # Combined and normalized weights
-        # Combined and normalized weights
-        weights = Wd * Wr / np.nansum(Wd * Wr)
-
-        return weights
+        step = 0
+        diff = 1
+        while diff > tol:
+            step += 1
+            print(f"Step {step}, diff = {diff}", end="\r")
+            # Calculate the scaled distance and D86
+            r = rscaled(distances, p, theta_, Hveg)
+
+            # Calculate the vertical average for each profile
+            P = np.unique(distances)
+            theta_P = []
+            r_stars = []
+            for i in range(len(P)):
+                profile = P[i]
+                idx = distances == profile
+                depths_P = depth[idx]
+                r_P = r[idx]
+                theta_Pi = theta[idx]
+                # Calculate the vertical distance weights
+                Wd_P = Wd(depths_P, r_P, bd, theta_Pi)
+                # Calculate the vertical average of theta
+                theta_P_i = np.sum(Wd_P * theta_Pi) / np.sum(Wd_P)
+                theta_P.append(theta_P_i)
+                r_stars.append(np.mean(r_P))
+
+            # Calculate the horizontal distance weights
+            Wrs = np.array([Wr(r_star, theta_p, Hveg) for r_star, theta_p in zip(r_stars, theta_P)])
+            theta_new = np.sum(Wrs * theta_P) / np.sum(Wrs)
+            diff = np.abs(theta_new - theta_)
+            theta_ = theta_new
+
+        print(f"Solution converged after {step} steps, the average soil moisture is {theta_new}")
+
+        return theta_new, [theta_P, r_stars, Wrs]
+    else:
+        raise ValueError(f"Method {method} not recognized. Please use one of the following methods: 'Kohli_2015' or 'Schron_2017'")
 
 
 def exp_filter(sm, T=1):
diff --git a/src/tests/test_calibration_rdt.py b/src/tests/test_calibration_rdt.py
index b951b11..fdeb644 100644
--- a/src/tests/test_calibration_rdt.py
+++ b/src/tests/test_calibration_rdt.py
@@ -62,25 +62,30 @@ def calibration_example():
                 df_station['fp'])
 
     # Compute the weights of each sample for the field average
-    nrad_weights = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'],
-                                     df_soil['distance_from_station'],
-                                     (df_soil['bottom_depth'] + df_soil['top_depth']) / 2,
-                                     rhob=df_soil['bulk_density'].mean())
+    field_theta_v_1, w = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'],
+                                         df_soil['distance_from_station'],
+                                         (df_soil['bottom_depth'] + df_soil['top_depth']) / 2,
+                                         rhob=df_soil['bulk_density'].mean(), method="Kohli_2015")
+
+    field_theta_v_2, w = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'],
+                                         df_soil['distance_from_station'],
+                                         (df_soil['bottom_depth'] + df_soil['top_depth']) / 2,
+                                         rhob=df_soil['bulk_density'].mean(),
+                                         p=df_station['barometric_pressure_Avg'].mean(), method="Schron_2017")
 
     # Apply distance weights to volumetric water content and bulk density
-    field_theta_v = np.sum(df_soil['theta_v'] * nrad_weights)
-    field_bulk_density = np.sum(df_soil['bulk_density'] * nrad_weights)
+    field_bulk_density = np.sum(df_soil['bulk_density'].mean())
 
     # Determine the mean corrected counts during the calibration survey
     idx_cal_period = (df_station['TIMESTAMP'] >= calibration_start) & (df_station['TIMESTAMP'] <= calibration_end)
     mean_cal_counts = df_station.loc[idx_cal_period, 'total_corrected_neutrons'].mean()
 
-    print(f"Mean volumetric Water content during calibration survey: {round(field_theta_v, 3)}")
+    print(f"Mean volumetric Water content during calibration survey: {round(field_theta_v_2, 3)}")
     print(f"Mean corrected counts during calibration: {round(mean_cal_counts)} counts")
 
     # Define the function for which we want to find the roots
     VWC_func = lambda N0: crnpy.counts_to_vwc(mean_cal_counts, N0, bulk_density=field_bulk_density, Wlat=0.03,
-                                              Wsoc=0.01) - field_theta_v
+                                              Wsoc=0.01) - field_theta_v_1
 
     # Make an initial guess for N0
     N0_initial_guess = 1000
@@ -91,9 +96,28 @@ def calibration_example():
     # Print the solution
     print(f"The solved value for N0 is: {sol}")
 
-    return sol
+    # Compute close and far soil moisture to compare both weight methods
+    close_sm = df_soil[(df_soil['distance_from_station'] < 10) & (df_soil['bottom_depth'] < 10)]['theta_v'].mean()
+    far_sm = df_soil[(df_soil['distance_from_station'] > 10) & (df_soil['bottom_depth'] < 10)]['theta_v'].mean()
+    return sol, field_theta_v_1, field_theta_v_2, close_sm, far_sm
 
 def test_calibration():
-    solved_N0 = calibration_example()
+    solved_N0, field_sm_Kohli, field_sm_Schron, close_sm, far_sm  = calibration_example()
     print("The expected value for N0 is between 2500 and 2800")
-    assert solved_N0 > 2500 and solved_N0 < 2800, f"Calibration test failed. Solved N0: {solved_N0}, expected value between 2500 and 2800."
\ No newline at end of file
+
+    assert solved_N0 > 2500 and solved_N0 < 2800, f"Calibration test failed. Solved N0: {solved_N0}, expected value between 2500 and 2800."
+    print("The expected value for field soil moisture using Kohli et al. (2015) or Schron et al. (2017) is between 0.25 and 0.30")
+    assert field_sm_Kohli > 0.25 and field_sm_Kohli < 0.30, f"Calibration test failed. Field soil moisture using Kohli et al. (2015): {field_sm_Kohli}, expected value between 0.25 and 0.30."
+    assert field_sm_Schron > 0.2 and field_sm_Schron < 0.30, f"Calibration test failed. Field soil moisture using Schron et al. (2017): {field_sm_Schron}, expected value between 0.25 and 0.30."
+
+    print(f"Close soil moisture: {close_sm}")
+    print(f"Far soil moisture: {far_sm}")
+    print(f"Field soil moisture using Kohli et al. (2015): {field_sm_Kohli}")
+    print(f"Field soil moisture using Schron et al. (2017): {field_sm_Schron}")
+
+    # Schron et al. (2017) method should more influenced by close soil moisture samples according to CRNP sensitivity assumptions
+
+    dif_Kohli = abs(field_sm_Kohli - close_sm)
+    dif_Schron = abs(field_sm_Schron - close_sm)
+
+    assert dif_Schron < dif_Kohli, f"Calibration test failed. Schron et al. (2017) method should be more influenced by close soil moisture samples according to CRNP sensitivity assumptions."
\ No newline at end of file
diff --git a/src/tests/test_functions.py b/src/tests/test_functions.py
index 7952002..820fd50 100644
--- a/src/tests/test_functions.py
+++ b/src/tests/test_functions.py
@@ -45,27 +45,18 @@ def test_weighting():
     y = np.repeat(0, len(x))
     h = np.repeat(10, len(x))
 
-
     p = np.repeat(1013.25, len(x))
     Hveg = np.repeat(0, len(x))
     bd = 1.1
     SM = 0.02
     sm = np.repeat(SM, len(x))
 
-    # calculate weighting function using Schrön et al. 2017 method
-    weights = crnpy.nrad_weight(h, sm, x, y, bd, method="Schron_2017", p=p, Hveg=Hveg)
-    # check that the sum of the weights is 1
-    Max = 0.01147
-    Avg = 0.00029
-    Min = 0.00000
-    assert round(np.sum(weights),5) == 1
-    # Compare to the values in supplementary material of Schrön et al. 2017
-    # check that the max weight is 0.01
-    assert round(np.max(weights),3) == round(Max,3)
-    # check that the avg weight is 0.00029
-    assert round(np.mean(weights),5) == round(Avg,5)
-    # check that the min weight is 0.00000
-    assert round(np.min(weights),5) == round(Min,5)
+    # calculate the weighted theta
+    theta_adj, w = crnpy.nrad_weight(h, sm, x, y, bd, method="Schron_2017", p=p, Hveg=Hveg)
+
+    # check that theta_adj is = SM +- 1e-3
+    assert np.abs(theta_adj - SM) < 1e-3
+
 
 def test_correction_inncoming_neutrons_RcMethods():
     # Test Hawdon 2014 Method using Baldry location from the original paper

From 472a25c4204020ffcb0781228e42e0735a6fc1b2 Mon Sep 17 00:00:00 2001
From: Andres Patrignani <andrespatrignani@users.campus>
Date: Fri, 19 Apr 2024 13:25:01 -0500
Subject: [PATCH 19/24] Minor fixes in code style

---
 build/lib/crnpy/__init__.py                 |   21 -
 build/lib/crnpy/crnpy.py                    | 1613 -------------------
 build/lib/crnpy/data.py                     |   67 -
 docs/examples/calibration/calibration.ipynb |   17 +-
 src/crnpy.egg-info/PKG-INFO                 |    5 -
 5 files changed, 12 insertions(+), 1711 deletions(-)
 delete mode 100644 build/lib/crnpy/__init__.py
 delete mode 100644 build/lib/crnpy/crnpy.py
 delete mode 100644 build/lib/crnpy/data.py

diff --git a/build/lib/crnpy/__init__.py b/build/lib/crnpy/__init__.py
deleted file mode 100644
index ae4e374..0000000
--- a/build/lib/crnpy/__init__.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# crnpy/__init__.py
-""" `crnpy` is a package for processing observations from cosmic ray neutron detectors.
-
-Modules exported by this package:
-
-- `crnpy`: Provide several functions to process observations from cosmic ray neutron detectors.
-"""
-
-# GEt version from setup.py
-from pathlib import Path
-from importlib.metadata import version, PackageNotFoundError
-
-try:
-    __version__ = version(__name__)
-except PackageNotFoundError:
-    __version__ = "unknown"
-
-# Import all functions from crnpy
-
-from .crnpy import *
-
diff --git a/build/lib/crnpy/crnpy.py b/build/lib/crnpy/crnpy.py
deleted file mode 100644
index 2b1f749..0000000
--- a/build/lib/crnpy/crnpy.py
+++ /dev/null
@@ -1,1613 +0,0 @@
-# crnpy/crnpy.py
-"""
-`crnpy` is a Python package for processing cosmic ray neutron data.
-
- Created by Joaquin Peraza and Andres Patrignani.
-"""
-
-import crnpy.data as data
-import io
-import numbers
-import numpy as np
-import pandas as pd
-import requests
-import sys
-import utm
-import warnings
-
-from scipy.interpolate import griddata
-from scipy.signal import savgol_filter
-from scipy.special import erfcinv
-
-# Define python version
-python_version = (3, 7)  # tuple of (major, minor) version requirement
-python_version_str = str(python_version[0]) + "." + str(python_version[1])
-
-# produce an error message if the python version is less than required
-if sys.version_info < python_version:
-    msg = "Module only runs on python version >= %s" % python_version_str
-    raise Exception(msg)
-
-
-def remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False):
-    """Function that removes rows with incomplete integration intervals.
-    
-    Args:
-        df (pandas.DataFrame): Pandas Dataframe with data from stationary or roving CRNP devices.
-        timestamp_col (str): Name of the column with timestamps in datetime format.
-        integration_time (int): Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.
-        remove_first (bool, optional): Remove first row. Default is False.
-        
-    Returns:
-        (pandas.DataFrame): 
-    """
-
-    # Check format of timestamp column
-    if df[timestamp_col].dtype != 'datetime64[ns]':
-        raise TypeError('timestamp_col must be datetime64. Use `pd.to_datetime()` to fix this issue.')
-
-    # Check if differences in timestamps are below or above the provided integration time
-    idx_delta = df[timestamp_col].diff().dt.total_seconds() != integration_time
-
-    if remove_first:
-        idx_delta[0] = True
-
-    # Select rows that meet the specified integration time
-    df = df[~idx_delta]
-    df.reset_index(drop=True, inplace=True)
-
-    # Notify user about the number of rows that have been removed
-    print(f"Removed a total of {sum(idx_delta)} rows.")
-
-    return df
-
-
-def fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True, verbose=False):
-    """Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.
-
-     Args:
-         df (pandas.DataFrame): Pandas DataFrame.
-         timestamp_col (str, optional): Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.
-         freq (str, optional): Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.
-         round_timestamp (bool, optional): Whether to round timestamps to the nearest frequency. Default is True.
-         verbose (bool, optional): Prints the missing timestamps added to the DatFrame.
-
-     Returns:
-         (pandas.DataFrame): DataFrame with filled missing timestamps.
-
-     """
-
-    # Check format of timestamp column
-    if df[timestamp_col].dtype != 'datetime64[ns]':
-        raise TypeError('timestamp_col must be datetime64. Use `pd.to_datetime()` to fix this issue.')
-
-    # Round timestamps to nearest frequency. This steps must preced the filling of rows.
-    if round_timestamp:
-        df[timestamp_col] = df[timestamp_col].dt.round(freq)
-
-    # Fill in rows with missing timestamps
-    start_date = df[timestamp_col].iloc[0]
-    end_date = df[timestamp_col].iloc[-1]
-    date_range = pd.date_range(start_date, end_date, freq=freq)
-    counter = 0
-    for date in date_range:
-        if date not in df[timestamp_col].values:
-            if verbose:
-                print('Adding missing date:', date)
-            new_line = pd.DataFrame({timestamp_col: date}, index=[-1])  # By default fills columns with np.nan
-            df = pd.concat([df, new_line])
-            counter += 1
-
-    df.sort_values(by=timestamp_col, inplace=True)
-    df.reset_index(drop=True, inplace=True)
-
-    # Notify user about the number of rows that have been removed
-    print(f"Added a total of {counter} missing timestamps.")
-
-    return df
-
-
-def total_raw_counts(counts):
-    """Compute the sum of uncorrected neutron counts for all detectors.
-
-    Args:
-        counts (pandas.DataFrame): Dataframe containing only the columns with neutron counts.
-
-    Returns:
-        (pandas.DataFrame): Dataframe with the sum of uncorrected neutron counts for all detectors.
-    """
-
-    if counts.shape[0] > 1:
-        counts = counts.apply(lambda x: x.fillna(counts.mean(axis=1)), axis=0)
-
-    # Compute sum of counts
-    total_raw_counts = counts.sum(axis=1)
-
-    # Replace zeros with NaN
-    total_raw_counts = total_raw_counts.replace(0, np.nan)
-
-    return total_raw_counts
-
-
-def is_outlier(x, method, window=11, min_val=None, max_val=None):
-    """Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.
-
-    Args:
-        x (pd.DataFrame or pd.Series): Variable containing only the columns with neutron counts.
-        method (str): Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad
-        window (int, optional): Window size for the moving central tendency. Default is 11.
-        min_val (int or float): Minimum value for a reading to be considered valid. Default is None.
-        max_val(int or float): Maximum value for a reading to be considered valid. Default is None.
-
-    Returns:
-        (pandas.DataFrame): Boolean indicating outliers.
-
-    References:
-        Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.
-    """
-
-    if not isinstance(x, pd.Series):
-        raise TypeError('x must of type pandas.Series')
-
-    # Separate this method to allow usage together with other methods below
-    if isinstance(min_val, numbers.Number) and isinstance(max_val, numbers.Number):
-        idx_range_outliers = (x < min_val) | (x > max_val)
-    else:
-        idx_range_outliers = np.full_like(x, False)
-
-    # Apply other methods in addition to a range check
-    if method == 'iqr':
-        q1 = x.quantile(0.25)
-        q3 = x.quantile(0.75)
-        iqr = q3 - q1
-        high_fence = q3 + (1.5 * iqr)
-        low_fence = q1 - (1.5 * iqr)
-        idx_outliers = (x < low_fence) | (x > high_fence)
-
-    elif method == 'moviqr':
-        q1 = x.rolling(window, center=True).quantile(0.25)
-        q3 = x.rolling(window, center=True).quantile(0.75)
-        iqr = q3 - q1
-        ub = q3 + (1.5 * iqr)  # Upper boundary
-        lb = q1 - (1.5 * iqr)  # Lower boundary
-        idx_outliers = (x < lb) | (x > ub)
-
-    elif method == 'zscore':
-        zscore = (x - x.mean()) / x.std()
-        idx_outliers = (zscore < -3) | (zscore > 3)
-
-    elif method == 'movzscore':
-        movmean = x.rolling(window=window, center=True).mean()
-        movstd = x.rolling(window=window, center=True).std()
-        movzscore = (x - movmean) / movstd
-        idx_outliers = (movzscore < -3) | (movzscore > 3)
-
-    elif method == 'modified_zscore':
-        # Compute median absolute difference
-        movmedian = x.rolling(window, center=True).median()
-        abs_diff = np.abs(x - movmedian)
-        mad = abs_diff.rolling(window, center=True).median()
-
-        # Compute modified z-score
-        modified_z_score = 0.6745 * abs_diff / mad
-        idx_outliers = (modified_z_score < -3.5) | (modified_z_score > 3.5)
-
-    elif method == 'scaled_mad':
-        # Returns true for elements more than three scaled MAD from the median. 
-        c = -1 / (np.sqrt(2) * erfcinv(3 / 2))
-        median = np.nanmedian(x)
-        mad = c * np.nanmedian(np.abs(x - median))
-        idx_outliers = x > (median + 3 * mad)
-
-    else:
-        raise TypeError('Outlier detection method not found.')
-
-    return idx_outliers | idx_range_outliers
-
-
-def correction_pressure(pressure, Pref, L):
-    r"""Correction factor for atmospheric pressure.
-
-    This function corrects neutron counts for atmospheric pressure using the method described in Andreasen et al. (2017).
-    The correction is performed using the following equation:
-
-    $$
-    C_{corrected} = \frac{C_{raw}}{fp}
-    $$
-
-    where:
-
-    - Ccorrected: corrected neutron counts
-    - Craw: raw neutron counts
-    - fp: pressure correction factor
-
-    $$
-    fp = e^{\frac{P_{ref} - P}{L}}
-    $$
-
-    where:
-
-    - P: atmospheric pressure
-    - Pref: reference atmospheric pressure
-    - L: Atmospheric attenuation coefficient.
-
-
-    Args:
-        pressure (list or array): Atmospheric pressure readings. Long-term average pressure is recommended.
-        Pref (float): Reference atmospheric pressure.
-        L (float): Atmospheric attenuation coefficient.
-
-    Returns:
-        (list): fp pressure correction factor.
-
-    References:
-        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079–4099, https://doi.org/10.5194/hess-16-4079-2012, 2012.
-
-        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086
-    """
-
-    # Compute pressure correction factor
-    fp = np.exp((Pref - pressure) / L)  # Zreda et al. 2017 Eq 5.
-
-    return fp
-
-
-def correction_humidity(abs_humidity, Aref):
-    r"""Correction factor for absolute humidity.
-
-    This function corrects neutron counts for absolute humidity using the method described in Rosolem et al. (2013) and Anderson et al. (2017). The correction is performed using the following equation:
-
-    $$
-    C_{corrected} = C_{raw} \cdot f_w
-    $$
-
-    where:
-
-    - Ccorrected: corrected neutron counts
-    - Craw: raw neutron counts
-    - fw: absolute humidity correction factor
-
-    $$
-    f_w = 1 + 0.0054(A - A_{ref})
-    $$
-
-    where:
-
-    - A: absolute humidity
-    - Aref: reference absolute humidity
-
-    Args:
-        abs_humidity (list or array): Relative humidity readings.
-        Aref (float): Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.
-
-    Returns:
-        (list): fw correction factor.
-
-    References:
-        Rosolem, R., W. J. Shuttleworth, M. Zreda, T. E. Franz, X. Zeng, and S. A. Kurc, 2013: The Effect of Atmospheric Water Vapor on Neutron Count in the Cosmic-Ray Soil Moisture Observing System. J. Hydrometeor., 14, 1659–1671, https://doi.org/10.1175/JHM-D-12-0120.1.
-
-        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086
-    """
-    A = abs_humidity
-    fw = 1 + 0.0054 * (A - Aref)  # Zreda et al. 2017 Eq 6.
-    return fw
-
-
-def correction_incoming_flux(incoming_neutrons, incoming_Ref=None, fill_na=None, Rc_method=None, Rc_site=None,
-                             site_atmdepth=None, Rc_ref=None, ref_atmdepth=None):
-    r"""Correction factor for incoming neutron flux.
-
-    This function corrects neutron counts for incoming neutron flux using the method described in Anderson et al. (2017). The correction is performed using the following equation:
-
-    $$
-    C_{corrected} = \frac{C_{raw}}{f_i}
-    $$
-
-    where:
-
-    - Ccorrected: corrected neutron counts
-    - Craw: raw neutron counts
-    - fi: incoming neutron flux correction factor
-
-    $$
-    f_i = \frac{I}{I_{ref}}
-    $$
-
-    where:
-
-    - I: incoming neutron flux
-    - Iref: reference incoming neutron flux
-
-    Args:
-        incoming_neutrons (list or array): Incoming neutron flux readings.
-        incoming_Ref (float): Reference incoming neutron flux. Baseline incoming neutron flux.
-        fill_na (float): Value to fill missing data. If None, missing data remains as NaN.
-        Rc_method (str): Optional to correct for differences in cutoff rigidity between the site and the reference station. Possible values are 'McJannetandDesilets2023' or 'Hawdonetal2014'. If None, no correction is performed.
-        Rc_site (float): Cutoff rigidity at the monitoring site.
-        site_atmdepth (float): Atmospheric depth at the monitoring site.
-        Rc_ref (float): Cutoff rigidity at the reference station.
-        ref_atmdepth (float): Atmospheric depth at the reference station.
-
-    Returns:
-        (list): fi correction factor.
-
-    References:
-        Hawdon, A., D. McJannet, and J. Wallace (2014), Calibration and correction procedures for cosmic-ray neutron soil moisture probes located across Australia, Water Resour. Res., 50, 5029–5043, doi:10.1002/2013WR015138.
-
-        M. Andreasen, K.H. Jensen, D. Desilets, T.E. Franz, M. Zreda, H.R. Bogena, and M.C. Looms. 2017. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications. Vadose Zone J. 16(8). doi:10.2136/vzj2017.04.0086
-
-        McJannet, D. L., & Desilets, D. (2023). Incoming neutron flux corrections for cosmic-ray soil and snow sensors using the global neutron monitor network. Water Resources Research, 59, e2022WR033889. https://doi.org/10.1029/2022WR033889
-    """
-    if incoming_Ref is None and not isinstance(incoming_neutrons, type(None)):
-        incoming_Ref = incoming_neutrons[0]
-        warnings.warn('Reference incoming neutron flux not provided. Using first value of incoming neutron flux.')
-    fi = incoming_neutrons / incoming_Ref
-
-    if Rc_method is not None:
-        if Rc_ref is None:
-            raise ValueError('Reference cutoff rigidity not provided.')
-        if Rc_site is None:
-            raise ValueError('Site cutoff rigidity not provided.')
-
-        if Rc_method == 'McJannetandDesilets2023':
-            tau = location_factor(site_atmdepth, Rc_site, ref_atmdepth, Rc_ref)
-            fi = 1 / (tau * fi + 1 - tau)
-
-        elif Rc_method == 'Hawdonetal2014':
-            Rc_corr = -0.075 * (Rc_site - Rc_ref) + 1.0
-            fi = (fi - 1.0) * Rc_corr + 1.0
-
-        else:
-            raise ValueError(
-                'Cutoff rigidity method not found. Valid options are: McJannetandDesilets2023, Hawdonetal2014.')
-
-    if fill_na is not None:
-        fi.fillna(fill_na, inplace=True)  # Use a value of 1 for days without data
-
-    return fi
-
-
-def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expand_window=0, verbose=False):
-    """Function to retrieve neutron flux from the Neutron Monitor Database.
-
-    Args:
-        start_date (datetime): Start date of the time series.
-        end_date (datetime): End date of the time series.
-        station (str): Neutron Monitor station to retrieve data from.
-        utc_offset (int): UTC offset in hours. Default is 0.
-        expand_window (int): Number of hours to expand the time window to retrieve extra data. Default is 0.
-        verbose (bool): Print information about the request. Default is False.
-
-    Returns:
-        (pandas.DataFrame): Neutron flux in counts per hour and timestamps.
-
-    References:
-        Documentation available:https://www.nmdb.eu/nest/help.php#howto
-    """
-
-    # Example: get_incoming_neutron_flux(station='IRKT',start_date='2020-04-10 11:00:00',end_date='2020-06-18 17:00:00')
-    # Template url = 'http://nest.nmdb.eu/draw_graph.php?formchk=1&stations[]=KERG&output=ascii&tabchoice=revori&dtype=corr_for_efficiency&date_choice=bydate&start_year=2009&start_month=09&start_day=01&start_hour=00&start_min=00&end_year=2009&end_month=09&end_day=05&end_hour=23&end_min=59&yunits=0'
-
-    # Expand the time window by 1 hour to ensure an extra observation is included in the request.
-    start_date -= pd.Timedelta(hours=expand_window)
-    end_date += pd.Timedelta(hours=expand_window)
-
-    # Convert local time to UTC
-    start_date = start_date - pd.Timedelta(hours=utc_offset)
-    end_date = end_date - pd.Timedelta(hours=utc_offset)
-    root = 'http://www.nmdb.eu/nest/draw_graph.php?'
-    url_par = ['formchk=1',
-               'stations[]=' + station,
-               'output=ascii',
-               'tabchoice=revori',
-               'dtype=corr_for_efficiency',
-               'tresolution=' + str(60),
-               'date_choice=bydate',
-               'start_year=' + str(start_date.year),
-               'start_month=' + str(start_date.month),
-               'start_day=' + str(start_date.day),
-               'start_hour=' + str(start_date.hour),
-               'start_min=' + str(start_date.minute),
-               'end_year=' + str(end_date.year),
-               'end_month=' + str(end_date.month),
-               'end_day=' + str(end_date.day),
-               'end_hour=' + str(end_date.hour),
-               'end_min=' + str(end_date.minute),
-               'yunits=0']
-
-    url = root + '&'.join(url_par)
-
-    if verbose:
-        print(f"Retrieving data from {url}")
-
-    r = requests.get(url).content.decode('utf-8')
-
-    # Subtract 1 hour to restore the last date included in the request.
-    end_date -= pd.Timedelta('1H')
-    start = r.find("RCORR_E\n") + 8
-    end = r.find('\n</code></pre><br>Total') - 1
-    s = r[start:end]
-    s2 = ''.join([row.replace(';', ',') for row in s])
-    try:
-        df_flux = pd.read_csv(io.StringIO(s2), names=['timestamp', 'counts'])
-    except:
-        if verbose:
-            print(f"Error retrieving data from {url}")
-        return None
-
-    # Check if all values from selected detector are NaN. If yes, warn the user
-    if df_flux['counts'].isna().all():
-        warnings.warn('Data for selected neutron detectors appears to be unavailable for the selected period')
-
-    # Convert timestamp to datetime and apply UTC offset
-    df_flux['timestamp'] = pd.to_datetime(df_flux['timestamp'])
-    df_flux['timestamp'] = df_flux['timestamp'] + pd.Timedelta(hours=utc_offset)
-
-    # Print acknowledgement to inform users about restrictions and to acknowledge the NMDB database
-    acknowledgement = """Data retrieved via NMDB are the property of the individual data providers. These data are free for non commercial
-use to within the restriction imposed by the providers. If you use such data for your research or applications, please acknowledge
-the origin by a sentence like 'We acknowledge the NMDB database (www.nmdb.eu) founded under the European Union's FP7 programme 
-(contract no. 213007), and the PIs of individual neutron monitors at: IGY Jungfraujoch 
-(Physikalisches Institut, University of Bern, Switzerland)"""
-
-    return df_flux
-
-
-def get_reference_neutron_flux(station, date=pd.to_datetime("2011-05-01")):
-    """Function to retrieve reference neutron flux from the Neutron Monitor Database. Default date is 2011-05-01, following previous studies (Zreda et a., 2012, Hawdon et al., 2014, Bogena et al., 2022).
-
-    Args:
-        station (str): Neutron Monitor station to retrieve data from.
-        date (datetime): Date of the reference neutron flux. Default is 2011-05-01.
-
-    Returns:
-        (float): Reference neutron flux in counts per hour.
-
-    References:
-        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., & Rosolem, R. (2012). COSMOS: The cosmic-ray soil moisture observing system. Hydrology and Earth System Sciences, 16(11), 4079-4099.
-
-        Hawdon, A., McJannet, D., & Wallace, J. (2014). Calibration and correction procedures for cosmic‐ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.
-
-        Bogena, H. R., Schrön, M., Jakobi, J., Ney, P., Zacharias, S., Andreasen, M., ... & Vereecken, H. (2022). COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors. Earth System Science Data, 14(3), 1125-1151.
-
-"""
-
-    # Get flux for 2011-05-01
-    df_flux = get_incoming_neutron_flux(date, date + pd.Timedelta(hours=24), station=station)
-    if df_flux is None:
-        warnings.warn(f"Reference neutron flux for {station} not available. Returning NaN.")
-    else:
-        return df_flux['counts'].median()
-
-
-def smooth_1d(values, window=5, order=3, method='moving_median'):
-    """Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).
-
-    Args:
-        values (pd.DataFrame or pd.Serie): Dataframe containing the values to smooth.
-        window (int): Window size for the Savitzky-Golay filter. Default is 5.
-        method (str): Method to use for smoothing the data. Default is 'moving_median'.
-            Options are 'moving_average', 'moving_median' and 'savitzky_golay'.
-        order (int): Order of the Savitzky-Golay filter. Default is 3.
-
-    Returns:
-        (pd.DataFrame): DataFrame with smoothed values.
-
-    References:
-        Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.
-        Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.
-        doi.org/10.3389/frwa.2020.00009
-
-        Savitzky, A., & Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures.
-        Analytical chemistry, 36(8), 1627-1639.
-    """
-
-    if not isinstance(values, pd.Series) and not isinstance(x, pd.DataFrame):
-        raise ValueError('Input must be a pandas Series or DataFrame')
-
-    if method == 'moving_average':
-        corrected_counts = values.rolling(window=window, center=True, min_periods=1).mean()
-    elif method == 'moving_median':
-        corrected_counts = values.rolling(window=window, center=True, min_periods=1).median()
-
-    elif method == 'savitzky_golay':
-        if values.isna().any():
-            print('Dataframe contains NaN values. Please remove NaN values before smoothing the data.')
-
-        if type(values) == pd.core.series.Series:
-            filtered = savgol_filter(values, window, order)
-            corrected_counts = pd.DataFrame(filtered, columns=['smoothed'], index=values.index)
-        elif type(values) == pd.core.frame.DataFrame:
-            for col in values.columns:
-                values[col] = savgol_filter(values[col], window, order)
-    else:
-        raise ValueError(
-            'Invalid method. Please select a valid filtering method., options are: moving_average, moving_median, savitzky_golay')
-    corrected_counts = corrected_counts.ffill(limit=window).bfill(limit=window).copy()
-    return corrected_counts
-
-
-def correction_bwe(counts, bwe, r2_N0=0.05):
-    """Function to correct for biomass effects in neutron counts.
-    following the approach described in Baatz et al., 2015.
-
-    Args:
-        counts (array or pd.Series or pd.DataFrame): Array of ephithermal neutron counts.
-        bwe (float): Biomass water equivalent kg m-2.
-        r2_N0 (float): Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.
-
-    Returns:
-        (array or pd.Series or pd.DataFrame): Array of corrected neutron counts for biomass effects.
-
-    References:
-        Baatz, R., H. R. Bogena, H.-J. Hendricks Franssen, J. A. Huisman, C. Montzka, and H. Vereecken (2015),
-        An empiricalvegetation correction for soil water content quantification using cosmic ray probes,
-        Water Resour. Res., 51, 2030–2046, doi:10.1002/ 2014WR016443.
-    """
-
-    return counts / (1 - bwe * r2_N0)
-
-
-def biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494):
-    """Function to convert biomass to biomass water equivalent.
-
-    Args:
-        biomass_dry (array or pd.Series or pd.DataFrame): Above ground dry biomass in kg m-2.
-        biomass_fresh (array or pd.Series or pd.DataFrame): Above ground fresh biomass in kg m-2.
-        fWE (float): Stoichiometric ratio of H2O to organic carbon molecules in the plant (assuming this is mostly cellulose)
-            Default is 0.494 (Wahbi & Avery, 2018).
-
-    Returns:
-        (array or pd.Series or pd.DataFrame): Biomass water equivalent in kg m-2.
-
-    References:
-        Wahbi, A., Avery, W. (2018). In Situ Destructive Sampling. In:
-        Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent.
-        Springer, Cham. https://doi.org/10.1007/978-3-319-69539-6_2
-    """
-    return (biomass_fresh - biomass_dry) + fWE * biomass_dry
-
-
-def correction_road(counts, theta_N, road_width, road_distance=0.0, theta_road=0.12, p0=0.42, p1=0.5, p2=1.06, p3=4,
-                    p4=0.16, p6=0.94, p7=1.10, p8=2.70, p9=0.01):
-    """Function to correct for road effects in neutron counts.
-    following the approach described in Schrön et al., 2018.
-
-    Args:
-        counts (array or pd.Series or pd.DataFrame): Array of ephithermal neutron counts.
-        theta_N (float): Volumetric water content of the soil estimated from the uncorrected neutron counts.
-        road_width (float): Width of the road in m.
-        road_distance (float): Distance of the road from the sensor in m. Default is 0.0.
-        theta_road (float): Volumetric water content of the road. Default is 0.12.
-        p0-p9 (float): Parameters of the correction function. Default values are from Schrön et al., 2018.
-
-    Returns:
-        (array or pd.Series or pd.DataFrame): Array of corrected neutron counts for road effects.
-
-    References:
-        Schrön,M.,Rosolem,R.,Köhli,M., Piussi,L.,Schröter,I.,Iwema,J.,etal. (2018).Cosmic-ray neutron rover surveys
-        of field soil moisture and the influence of roads.WaterResources Research,54,6441–6459.
-        https://doi. org/10.1029/2017WR021719
-    """
-    F1 = p0 * (1 - np.exp(-p1 * road_width))
-    F2 = -p2 - p3 * theta_road - ((p4 + theta_road) / (theta_N))
-    F3 = p6 * np.exp(-p7 * (road_width ** -p8) * road_distance ** 4) + (1 - p6) * np.exp(-p9 * road_distance)
-
-    C_roads = 1 + F1 * F2 * F3
-
-    corrected_counts = counts / C_roads
-
-    return corrected_counts
-
-
-def counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=0.115):
-    r"""Function to convert corrected and filtered neutron counts into volumetric water content.
-
-    This method implements soil moisture estimation using the non-linear relationship between neutron count and soil volumetric water content following the approach described in Desilets et al., 2010.
-
-    $\theta(N) =\frac{a_0}{(\frac{N}{N_0}) - a_1} - a_2 $
-
-    Args:
-        counts (array or pd.Series or pd.DataFrame): Array of corrected and filtered neutron counts.
-        N0 (float): Device-specific neutron calibration constant.
-        Wlat (float): Lattice water content.
-        Wsoc (float): Soil organic carbon content.
-        bulk_density (float): Soil bulk density.
-        a0 (float): Parameter given in Zreda et al., 2012. Default is 0.0808.
-        a1 (float): Parameter given in Zreda et al., 2012. Default is 0.372.
-        a2 (float): Parameter given in Zreda et al., 2012. Default is 0.115.
-
-    Returns:
-        (array or pd.Series or pd.DataFrame): Volumetric water content in m3 m-3.
-
-    References:
-        Desilets, D., M. Zreda, and T.P.A. Ferré. 2010. Nature’s neutron probe:
-        Land surface hydrology at an elusive scale with cosmic rays. Water Resour. Res. 46:W11505.
-        doi.org/10.1029/2009WR008726
-    """
-
-    # Convert neutron counts into vwc
-    vwc = (a0 / (counts / N0 - a1) - a2 - Wlat - Wsoc) * bulk_density
-    return vwc
-
-
-def sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017'):
-    """Function that computes the estimated sensing depth of the cosmic-ray neutron probe.
-    The function offers several methods to compute the depth at which 86 % of the neutrons
-    probe the soil profile.
-
-    Args:
-        vwc (array or pd.Series or pd.DataFrame): Estimated volumetric water content for each timestamp.
-        pressure (array or pd.Series or pd.DataFrame): Atmospheric pressure in hPa for each timestamp.
-        p_ref (float): Reference pressure in hPa.
-        bulk_density (float): Soil bulk density.
-        Wlat (float): Lattice water content.
-        method (str): Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.
-        dist (list or array): List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.
-
-    Returns:
-        (array or pd.Series or pd.DataFrame): Estimated sensing depth in m.
-
-    References:
-        Franz, T.E., Zreda, M., Ferre, T.P.A., Rosolem, R., Zweck, C., Stillman, S., Zeng, X. and Shuttleworth, W.J., 2012.
-        Measurement depth of the cosmic ray soil moisture probe affected by hydrogen from various sources.
-        Water Resources Research, 48(8). doi.org/10.1029/2012WR011871
-
-        Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., et al. (2017).
-        Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity.
-        Hydrol. Earth Syst. Sci. 21, 5009–5030. doi.org/10.5194/hess-21-5009-2017
-    """
-
-    # Determine sensing depth (D86)
-    if method == 'Schron_2017':
-        # See Appendix A of Schrön et al. (2017)
-        Fp = 0.4922 / (0.86 - np.exp(-1 * pressure / p_ref))
-        Fveg = 0
-        results = []
-        for d in dist:
-            # Compute r_star
-            r_start = d / Fp
-
-            # Compute soil depth that accounts for 86% of the neutron flux
-            D86 = 1 / bulk_density * (8.321 + 0.14249 * (0.96655 + np.exp(-0.01 * r_start)) * (20 + (Wlat + vwc)) / (
-                    0.0429 + (Wlat + vwc)))
-            results.append(D86)
-
-    elif method == 'Franz_2012':
-        results = 5.8 / (bulk_density * Wlat + vwc + 0.0829)
-    else:
-        raise ValueError('Method not recognized. Please select either "Schron_2017" or "Franz_2012".')
-    return results
-
-
-def abs_humidity(relative_humidity, temp):
-    """
-    Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.
-
-    Args:
-        relative_humidity (float): relative humidity (%)
-        temp (float): temperature (Celsius)
-
-    Returns:
-        float: actual vapor pressure (g m^-3)
-    """
-
-    ### Atmospheric water vapor factor
-    # Saturation vapor pressure
-    e_sat = 0.611 * np.exp(17.502 * temp / (
-            temp + 240.97)) * 1000  # in Pascals Eq. 3.8 p.41 Environmental Biophysics (Campbell and Norman)
-
-    # Vapor pressure Pascals
-    Pw = e_sat * relative_humidity / 100
-
-    # Absolute humidity (g/m^3)
-    C = 2.16679  # g K/J;
-    abs_h = C * Pw / (temp + 273.15)
-    return abs_h
-
-
-def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=None, Hveg=0, tol=0.01):
-    """Function to compute distance weights corresponding to each soil sample.
-
-    Args:
-        h (np.array or pd.Series): Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.
-        theta (np.array or pd.Series): Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)
-        distances (np.array or pd.Series): Distances from the location of each sample to the origin (0.5 - 600 m)
-        depth (np.array or pd.Series): Depths for each sample (m)
-        rhob (np.array or pd.Series): Bulk density in g/cm^3
-        p (np.array or pd.Series): Atmospheric pressure in hPa. Required for the 'Schron_2017' method.
-        Hveg (np.array or pd.Series): Vegetation height in m. Required for the 'Schron_2017' method.
-        method (str): Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.
-        tol (float): Tolerance for the iterative solution. Default is 0.01. Required for the 'Schron_2017' method.
-
-    Returns:
-        theta_new (np.array or pd.Series): Weighted soil moisture values.
-        weights (np.array or pd.Series): Distance weights for each sample. For the 'Schron_2017' method, the weights are computed for each distance.
-
-    References:
-        Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S. (2015).
-        Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray
-        neutrons. Water Resour. Res. 51, 5772–5790. doi:10.1002/2015WR017169
-
-        Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L.,
-        Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M., Rebmann, C.,
-        Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.: Improving calibration and
-        validation of cosmic-ray neutron sensors in the light of spatial sensitivity,
-        Hydrol. Earth Syst. Sci., 21, 5009–5030, https://doi.org/10.5194/hess-21-5009-2017, 2017.
-    """
-
-    if method == 'Kohli_2015':
-        # Table A1. Parameters for Fi and D86
-        p10 = 8735;
-        p11 = 17.1758;
-        p12 = 11720;
-        p13 = 0.00978;
-        p14 = 7045;
-        p15 = 0.003632;
-        p20 = 2.7925e-2;
-        p21 = 5.0399;
-        p22 = 2.8544e-2;
-        p23 = 0.002455;
-        p24 = 6.851e-5;
-        p25 = 9.2926;
-        p30 = 247970;
-        p31 = 17.63;
-        p32 = 374655;
-        p33 = 0.00191;
-        p34 = 195725;
-        p40 = 5.4818e-2;
-        p41 = 15.921;
-        p42 = 0.6373;
-        p43 = 5.99e-2;
-        p44 = 5.425e-4;
-        p50 = 1383702;
-        p51 = 4.156;
-        p52 = 5325;
-        p53 = 0.00238;
-        p54 = 0.0156;
-        p55 = 0.130;
-        p56 = 1521;
-        p60 = 6.031e-5;
-        p61 = 98.5;
-        p62 = 1.0466e-3;
-        p70 = 11747;
-        p71 = 41.66;
-        p72 = 4521;
-        p73 = 0.01998;
-        p74 = 0.00604;
-        p75 = 2534;
-        p76 = 0.00475;
-        p80 = 1.543e-2;
-        p81 = 10.06;
-        p82 = 1.807e-2;
-        p83 = 0.0011;
-        p84 = 8.81e-5;
-        p85 = 0.0405;
-        p86 = 20.24;
-        p90 = 8.321;
-        p91 = 0.14249;
-        p92 = 0.96655;
-        p93 = 26.42;
-        p94 = 0.0567;
-
-        # Numerical determination of the penetration depth (86%) (Eq. 8)
-        D86 = 1 / rhob * (p90 + p91 * (p92 + np.exp(-1 * distances / 100)) * (p93 + theta) / (p94 + theta))
-
-        # Depth weights (Eq. 7)
-        Wd = np.exp(-2 * depth / D86)
-
-        if h == 0:
-            W = 1  # skip distance weighting
-
-        elif (h >= 0.1) and (h <= 50):
-            # Functions for Fi (Appendix A in Köhli et al., 2015)
-            F1 = p10 * (1 + p13 * h) * np.exp(-p11 * theta) + p12 * (1 + p15 * h) - p14 * theta
-            F2 = ((-p20 + p24 * h) * np.exp(-p21 * theta / (1 + p25 * theta)) + p22) * (1 + h * p23)
-            F3 = (p30 * (1 + p33 * h) * np.exp(-p31 * theta) + p32 - p34 * theta)
-            F4 = p40 * np.exp(-p41 * theta) + p42 - p43 * theta + p44 * h
-            F5 = p50 * (0.02 - 1 / p55 / (h - p55 + p56 * theta)) * (p54 - theta) * np.exp(
-                -p51 * (theta - p54)) + p52 * (0.7 - h * theta * p53)
-            F6 = p60 * (h + p61) + p62 * theta
-            F7 = (p70 * (1 - p76 * h) * np.exp(-p71 * theta * (1 - h * p74)) + p72 - p75 * theta) * (2 + h * p73)
-            F8 = ((-p80 + p84 * h) * np.exp(-p81 * theta / (1 + p85 * h + p86 * theta)) + p82) * (2 + h * p83)
-
-            # Distance weights (Eq. 3)
-            W = np.ones_like(distances) * np.nan
-            for i in range(len(distances)):
-                if (distances[i] <= 50) and (distances[i] > 0.5):
-                    W[i] = F1[i] * (np.exp(-F2[i] * distances[i])) + F3[i] * np.exp(-F4[i] * distances[i])
-
-                elif (distances[i] > 50) and (distances[i] < 600):
-                    W[i] = F5[i] * (np.exp(-F6[i] * distances[i])) + F7[i] * np.exp(-F8[i] * distances[i])
-
-                else:
-                    raise ValueError('Input distances are not valid.')
-
-        else:
-            raise ValueError('Air humidity values are out of range.')
-
-        # Combined and normalized weights
-        weights = Wd * W / np.nansum(Wd * W)
-
-        theta_new = np.sum(theta * weights)
-
-        return theta_new, weights
-    elif method == 'Schron_2017':
-        # Horizontal distance weights According to Eq. 6 and Table A1 in Schrön et al. (2017)
-        # Method for calculating the horizontal distance weights from 0 to 1m
-        def WrX(r, x, y):
-            x00 = 3.7
-            a00 = 8735;
-            a01 = 22.689;
-            a02 = 11720;
-            a03 = 0.00978;
-            a04 = 9306;
-            a05 = 0.003632
-            a10 = 2.7925e-2;
-            a11 = 6.6577;
-            a12 = 0.028544;
-            a13 = 0.002455;
-            a14 = 6.851e-5;
-            a15 = 12.2755
-            a20 = 247970;
-            a21 = 23.289;
-            a22 = 374655;
-            a23 = 0.00191;
-            a24 = 258552
-            a30 = 5.4818e-2;
-            a31 = 21.032;
-            a32 = 0.6373;
-            a33 = 0.0791;
-            a34 = 5.425e-4
-
-            x0 = x00
-            A0 = (a00 * (1 + a03 * x) * np.exp(-a01 * y) + a02 * (1 + a05 * x) - a04 * y)
-            A1 = ((-a10 + a14 * x) * np.exp(-a11 * y / (1 + a15 * y)) + a12) * (1 + x * a13)
-            A2 = (a20 * (1 + a23 * x) * np.exp(-a21 * y) + a22 - a24 * y)
-            A3 = a30 * np.exp(-a31 * y) + a32 - a33 * y + a34 * x
-
-            return ((A0 * (np.exp(-A1 * r)) + A2 * np.exp(-A3 * r)) * (1 - np.exp(-x0 * r)))
-
-        # Method for calculating the horizontal distance weights from 1 to 50m
-        def WrA(r, x, y):
-            a00 = 8735;
-            a01 = 22.689;
-            a02 = 11720;
-            a03 = 0.00978;
-            a04 = 9306;
-            a05 = 0.003632
-            a10 = 2.7925e-2;
-            a11 = 6.6577;
-            a12 = 0.028544;
-            a13 = 0.002455;
-            a14 = 6.851e-5;
-            a15 = 12.2755
-            a20 = 247970;
-            a21 = 23.289;
-            a22 = 374655;
-            a23 = 0.00191;
-            a24 = 258552
-            a30 = 5.4818e-2;
-            a31 = 21.032;
-            a32 = 0.6373;
-            a33 = 0.0791;
-            a34 = 5.425e-4
-
-            A0 = (a00 * (1 + a03 * x) * np.exp(-a01 * y) + a02 * (1 + a05 * x) - a04 * y)
-            A1 = ((-a10 + a14 * x) * np.exp(-a11 * y / (1 + a15 * y)) + a12) * (1 + x * a13)
-            A2 = (a20 * (1 + a23 * x) * np.exp(-a21 * y) + a22 - a24 * y)
-            A3 = a30 * np.exp(-a31 * y) + a32 - a33 * y + a34 * x
-
-            return A0 * np.exp(-A1 * r) + A2 * np.exp(-A3 * r)
-
-        # Method for calculating the horizontal distance weights from 50 to 600m
-        def WrB(r, x, y):
-            b00 = 39006;
-            b01 = 15002337;
-            b02 = 2009.24;
-            b03 = 0.01181;
-            b04 = 3.146;
-            b05 = 16.7417;
-            b06 = 3727
-            b10 = 6.031e-5;
-            b11 = 98.5;
-            b12 = 0.0013826
-            b20 = 11747;
-            b21 = 55.033;
-            b22 = 4521;
-            b23 = 0.01998;
-            b24 = 0.00604;
-            b25 = 3347.4;
-            b26 = 0.00475
-            b30 = 1.543e-2;
-            b31 = 13.29;
-            b32 = 1.807e-2;
-            b33 = 0.0011;
-            b34 = 8.81e-5;
-            b35 = 0.0405;
-            b36 = 26.74
-
-            B0 = (b00 - b01 / (b02 * y + x - 0.13)) * (b03 - y) * np.exp(-b04 * y) - b05 * x * y + b06
-            B1 = b10 * (x + b11) + b12 * y
-            B2 = (b20 * (1 - b26 * x) * np.exp(-b21 * y * (1 - x * b24)) + b22 - b25 * y) * (2 + x * b23)
-            B3 = ((-b30 + b34 * x) * np.exp(-b31 * y / (1 + b35 * x + b36 * y)) + b32) * (2 + x * b33)
-
-            return B0 * np.exp(-B1 * r) + B2 * np.exp(-B3 * r)
-
-        # Wrapper method for calculating the horizontal distance weights
-        def Wr(r, x, y):
-            if r <= 1:
-                return WrX(r, x, y)
-            elif r <= 50:
-                return WrA(r, x, y)
-            elif r <= 600:
-                return WrB(r, x, y)
-            else:
-                raise ValueError("r must be between 1 and 600m when using 'Schron_2017' method")
-
-        def rscaled(r, p, y, Hveg = 0):
-            Fp = 0.4922 / (0.86 - np.exp(-p / 1013.25))
-            Fveg = 1 - 0.17 * (1 - np.exp(-0.41 * Hveg)) * (1 + np.exp(-9.25 * y))
-            return r / Fp / Fveg
-
-        # Rename variables to be consistent with the revised paper
-        r = distances
-        theta_ = np.mean(theta) # Start with the mean value of theta as initial guess
-        bd = np.mean(rhob) # Neutrons are impacted by the bulk density across the whole area and not just the sample area. https://github.com/soilwater/crnpy/issues/9#issuecomment-2003813777
-
-        # Vertical distance weights functions
-        def D86(r, bd, y):
-            return 1 / bd * (8.321 + 0.14249 * (0.96655 + np.exp(-0.01 * r)) * (20 + y) / (0.0429 + y))
-
-        def Wd(d, r, bd, y):
-            return np.exp(-2 * d / D86(r, bd, y))
-
-        step = 0
-        diff = 1
-        while diff > tol:
-            step += 1
-            print(f"Step {step}, diff = {diff}", end="\r")
-            # Calculate the scaled distance and D86
-            r = rscaled(distances, p, theta_, Hveg)
-
-            # Calculate the vertical average for each profile
-            P = np.unique(distances)
-            theta_P = []
-            r_stars = []
-            for i in range(len(P)):
-                profile = P[i]
-                idx = distances == profile
-                depths_P = depth[idx]
-                r_P = r[idx]
-                theta_Pi = theta[idx]
-                # Calculate the vertical distance weights
-                Wd_P = Wd(depths_P, r_P, bd, theta_Pi)
-                # Calculate the vertical average of theta
-                theta_P_i = np.sum(Wd_P * theta_Pi) / np.sum(Wd_P)
-                theta_P.append(theta_P_i)
-                r_stars.append(np.mean(r_P))
-
-            # Calculate the horizontal distance weights
-            Wrs = np.array([Wr(r_star, theta_p, Hveg) for r_star, theta_p in zip(r_stars, theta_P)])
-            theta_new = np.sum(Wrs * theta_P) / np.sum(Wrs)
-            diff = np.abs(theta_new - theta_)
-            theta_ = theta_new
-
-        print(f"Solution converged after {step} steps, the average soil moisture is {theta_new}")
-
-        return theta_new, [theta_P, r_stars, Wrs]
-    else:
-        raise ValueError(f"Method {method} not recognized. Please use one of the following methods: 'Kohli_2015' or 'Schron_2017'")
-
-
-def exp_filter(sm, T=1):
-    """Exponential filter to estimate soil moisture in the rootzone from surface observtions.
-
-    Args:
-        sm (list or array): Soil moisture in mm of water for the top layer of the soil profile.
-        T (float): Characteristic time length in the same units as the measurement interval.
-
-    Returns:
-        sm_subsurface (list or array): Subsurface soil moisture in the same units as the input.
-
-    References:
-        Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B. and Martin, E., 2008.
-        From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model
-        simulations. Hydrology and Earth System Sciences, 12(6), pp.1323-1337.
-
-        Franz, T.E., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L. and Wagner, W., 2020.
-        Practical data products from cosmic-ray neutron sensing for hydrological applications. Frontiers in Water, 2, p.9.
-
-        Rossini, P. and Patrignani, A., 2021. Predicting rootzone soil moisture from surface observations in cropland using an exponential filter.
-        Soil Science Society of America Journal.
-    """
-
-    # Parameters
-    t_delta = 1
-    sm_min = np.min(sm)
-    sm_max = np.max(sm)
-    ms = (sm - sm_min) / (sm_max - sm_min)
-
-    # Pre-allocate soil water index array and recursive constant K
-    SWI = np.ones_like(ms) * np.nan
-    K = np.ones_like(ms) * np.nan
-
-    # Initial conditions
-    SWI[0] = ms[0]
-    K[0] = 1
-
-    # Values from 2 to N
-    for n in range(1, len(SWI)):
-        if ~np.isnan(ms[n]) & ~np.isnan(ms[n - 1]):
-            K[n] = K[n - 1] / (K[n - 1] + np.exp(-t_delta / T))
-            SWI[n] = SWI[n - 1] + K[n] * (ms[n] - SWI[n - 1])
-        else:
-            continue
-
-    # Rootzone storage
-    sm_subsurface = SWI * (sm_max - sm_min) + sm_min
-
-    return sm_subsurface
-
-
-def cutoff_rigidity(lat, lon):
-    """Function to estimate the approximate cutoff rigidity for any point on Earth according to the
-    tabulated data of Smart and Shea, 2019. The returned value can be used to select the appropriate
-    neutron monitor station to estimate the cosmic-ray neutron intensity at the location of interest.
-
-    Args:
-        lat (float): Geographic latitude in decimal degrees. Value in range -90 to 90
-        lon (float): Geographic longitude in decimal degrees. Values in range from 0 to 360.
-            Typical negative longitudes in the west hemisphere will fall in the range 180 to 360.
-
-    Returns:
-        (float): Cutoff rigidity in GV. Error is about +/- 0.3 GV
-
-    Examples:
-        Estimate the cutoff rigidity for Newark, NJ, US
-
-        >>> zq = cutoff_rigidity(39.68, -75.75)
-        >>> print(zq)
-        2.52 GV (Value from NMD is 2.40 GV)
-
-    References:
-        Hawdon, A., McJannet, D., & Wallace, J. (2014). Calibration and correction procedures
-        for cosmic‐ray neutron soil moisture probes located across Australia. Water Resources Research,
-        50(6), 5029-5043.
-
-        Smart, D. & Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program:
-        Theory, Software Description and Example. NASA STI/Recon Technical Report N.
-
-        Shea, M. A., & Smart, D. F. (2019, July). Re-examination of the First Five Ground-Level Events.
-        In International Cosmic Ray Conference (ICRC2019) (Vol. 36, p. 1149).
-    """
-    xq = lon
-    yq = lat
-
-    if xq < 0:
-        xq = xq * -1 + 180
-    Z = np.array(data.cutoff_rigidity)
-    x = np.linspace(0, 360, Z.shape[1])
-    y = np.linspace(90, -90, Z.shape[0])
-    X, Y = np.meshgrid(x, y)
-    points = np.array((X.flatten(), Y.flatten())).T
-    values = Z.flatten()
-    zq = griddata(points, values, (xq, yq))
-
-    return np.round(zq, 2)
-
-
-def atmospheric_depth(elevation, latitude):
-    """Function to estimate the atmospheric depth for any point on Earth according to McJannet and Desilets, 2023
-
-    This function is required in the calculation of the location-dependent reference correction proposed by McJannet and Desilets, 2023.
-
-    Args:
-        elevation (float): Elevation in meters above sea level.
-        latitude (float): Geographic latitude in decimal degrees. Value in range -90 to 90
-
-    Returns:
-        (float): Atmospheric depth in g/cm2
-
-    References:
-        Atmosphere, U. S. (1976). US standard atmosphere. National Oceanic and Atmospheric Administration.
-
-        McJannet, D. L., & Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.
-    """
-
-    density_of_rock = 2670  # Density of rock in kg/m3
-    air_pressure_sea_level = 1013.25  # Air pressure at sea level in hPa
-    air_molar_mass = 0.0289644  # Air molar mass in kg/mol
-    universal_gas_constant = 8.31432  # Universal gas constant in J/(mol*K)
-    reference_temperature = 288.15  # Reference temperature Kelvin
-    temperature_lapse_rate = -0.0065  # Temperature lapse rate in K/m
-
-    # Gravity at sea-level calculation
-    gravity_sea_level = 9.780327 * (
-            1 + 0.0053024 * np.sin(np.radians(latitude)) ** 2 - 0.0000058 * np.sin(2 * np.radians(latitude)) ** 2)
-    # Free air correction
-    free_air = -3.086 * 10 ** -6 * elevation
-    # Bouguer correction
-    bouguer_corr = 4.193 * 10 ** -10 * density_of_rock * elevation
-    # Total gravity
-    gravity = gravity_sea_level + free_air + bouguer_corr
-
-    # Air pressure calculation
-    reference_air_pressure = air_pressure_sea_level * (
-            1 + temperature_lapse_rate / reference_temperature * elevation) ** ((-gravity * air_molar_mass) / (
-            universal_gas_constant * temperature_lapse_rate))
-
-    # Atmospheric depth calculation
-    atmospheric_depth = (10 * reference_air_pressure) / gravity
-    return atmospheric_depth
-
-
-def location_factor(site_atmospheric_depth, site_Rc, reference_atmospheric_depth, reference_Rc):
-    """
-    Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.
-
-
-    Args:
-        site_atmospheric_depth (float): Atmospheric depth at the site in g/cm2. Can be estimated using the function `atmospheric_depth()`
-        site_Rc (float): Cutoff rigidity at the site in GV. Can be estimated using the function `cutoff_rigidity()`
-        reference_atmospheric_depth (float): Atmospheric depth at the reference location in g/cm2.
-        reference_Rc (float): Cutoff rigidity at the reference location in GV.
-
-    Returns:
-        (float): Location-dependent correction factor.
-
-    References:
-        McJannet, D. L., & Desilets, D. (2023). Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.
-
-    """
-
-    # Renamed variables based on the provided table
-    c0 = -0.0009  # from C39
-    c1 = 1.7699  # from C40
-    c2 = 0.0064  # from C41
-    c3 = 1.8855  # from C42
-    c4 = 0.000013  # from C43
-    c5 = -1.2237  # from C44
-    epsilon = 1  # from C45
-
-    # Translated formula with renamed variables from McJannet and Desilets, 2023
-    tau_new = epsilon * (c0 * reference_atmospheric_depth + c1) * (
-            1 - np.exp(
-        -(c2 * reference_atmospheric_depth + c3) * reference_Rc ** (c4 * reference_atmospheric_depth + c5)))
-
-    norm_factor = 1 / tau_new
-
-    # Calculate the result using the provided parameters
-    tau = epsilon * norm_factor * (c0 * site_atmospheric_depth + c1) * (
-            1 - np.exp(-(c2 * site_atmospheric_depth + c3) * site_Rc ** (c4 * site_atmospheric_depth + c5)))
-    return tau
-
-
-def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):
-    """Search for potential reference neutron monitoring stations based on cutoff rigidity.
-
-    Args:
-        Rc (float): Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.
-        start_date (datetime): Start date for the period of interest.
-        end_date (datetime): End date for the period of interest.
-        verbose (bool): If True, print a expanded output of the incoming neutron flux data.
-
-    Returns:
-        (list): List of top five stations with closes cutoff rigidity.
-            User needs to select station according to site altitude.
-
-    Examples:
-        >>> from crnpy import crnpy
-        >>> Rc = 2.40 # 2.40 Newark, NJ, US
-        >>> crnpy.find_neutron_monitor(Rc)
-        Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations
-
-        Your cutoff rigidity is 2.4 GV
-                STID                          NAME     R  Altitude_m
-        40   NEWK                        Newark  2.40          50
-        33   MOSC                        Moscow  2.43         200
-        27   KIEL                          Kiel  2.36          54
-        28  KIEL2                        KielRT  2.36          54
-        31   MCRL  Mobile Cosmic Ray Laboratory  2.46         200
-        32   MGDN                       Magadan  2.10         220
-        42   NVBK                   Novosibirsk  2.91         163
-        26   KGSN                      Kingston  1.88          65
-        9    CLMX                        Climax  3.00        3400
-        57   YKTK                       Yakutsk  1.65         105
-
-    References:
-        https://www.nmdb.eu/nest/help.php#helpstations
-    """
-
-    # Load file with list of neutron monitoring stations
-    stations = pd.DataFrame(data.neutron_detectors, columns=["STID", "NAME", "R", "Altitude_m"])
-
-    # Sort stations by closest cutoff rigidity
-    idx_R = (stations['R'] - Rc).abs().argsort()
-
-    if start_date is not None and end_date is not None:
-        stations["Period available"] = False
-        for i in range(10):
-            station = stations.iloc[idx_R[i]]["STID"]
-            try:
-                if get_incoming_neutron_flux(start_date, end_date, station, verbose=verbose) is not None:
-                    stations.iloc[idx_R[i], -1] = True
-            except:
-                pass
-
-        if sum(stations["Period available"] == True) == 0:
-            print("No stations available for the selected period!")
-        else:
-            stations = stations[stations["Period available"] == True]
-            idx_R = (stations['R'] - Rc).abs().argsort()
-            result = stations.iloc[idx_R.iloc[:10]]
-    else:
-        result = stations.reindex(idx_R).head(10).rename_axis(None)
-
-    # Print results
-    print('')
-    print(
-        """Select a station with an altitude similar to that of your location. For more information go to: 'https://www.nmdb.eu/nest/help.php#helpstations""")
-    print('')
-    print(f"Your cutoff rigidity is {Rc} GV")
-    print(result)
-    return result
-
-
-def interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps):
-    """Function to interpolate incoming neutron flux to match the timestamps of the observations.
-
-    Args:
-        nmdb_timestamps (pd.Series or np.array): Series or array of timestamps in datetime format from the NMDB
-        nmdb_counts (pd.Series or np.array): Series or array of incoming neutron flux counts from the NMDB
-        crnp_timestamps (pd.Series or np.array): Series or array of timestamps in datetime format from the CRNP device
-
-    Returns:
-        (pd.Series): Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps
-    """
-    # Create a DataFrame from nmdb timestamps and counts
-    df_nmdb = pd.DataFrame({'timestamp': nmdb_timestamps, 'counts': nmdb_counts})
-
-    # Set the Timestamp column as the index
-    df_nmdb.set_index('timestamp', inplace=True)
-
-    # Reindex the DataFrame to the timestamps from the CRNP device using the nearest method
-    # This will match each CRNP timestamp with the nearest NMDB timestamp
-    interpolated_flux = df_nmdb.reindex(crnp_timestamps, method='nearest')['counts'].values
-
-    # drop NaN values
-    interpolated_flux = interpolated_flux[~np.isnan(interpolated_flux)]
-
-    assert len(interpolated_flux) == len(
-        crnp_timestamps), "Length of interpolated flux does not match length of CRNP timestamps"
-
-    return interpolated_flux
-
-
-def lattice_water(clay_content, total_carbon=None):
-    r"""Estimate the amount of water in the lattice of clay minerals.
-
-    ![img1](img/lattice_water_simple.png) | ![img2](img/lattice_water_multiple.png)
-    :-------------------------:|:-------------------------:
-    $\omega_{lat} = 0.097 * clay(\%)$ | $\omega_{lat} = -0.028 + 0.077 * clay(\%) + 0.459 * carbon(\%)$
-    Linear regression [lattice water (%) as a function of clay (%)] done with data from Kansas Sate University - Soil Water Processes Lab. |  Multiple linear regression [lattice water (%) as a function of clay (%) and soil carbon (%)] done with data from Soil Water Processes Lab.
-
-    Args:
-        clay_content (float): Clay content in the soil in percent.
-        total_carbon (float, optional): Total carbon content in the soil in percent.
-            If None, the amount of water is estimated based on clay content only.
-
-    Returns:
-        (float): Amount of water in the lattice of clay minerals in percent
-    """
-    if total_carbon is None:
-        lattice_water = 0.097 * clay_content
-    else:
-        lattice_water = -0.028 + 0.077 * clay_content + 0.459 * total_carbon
-    return lattice_water
-
-
-def latlon_to_utm(lat, lon, utm_zone_number=None, utm_zone_letter=None):
-    """Convert geographic coordinates (lat, lon) to projected coordinates (utm) using the Military Grid Reference System.
-
-    Function only applies to non-polar coordinates.
-    If further functionality is required, consider using the utm module. See references for more information.
-
-    ![UTM zones](https://upload.wikimedia.org/wikipedia/commons/thumb/b/b7/Universal_Transverse_Mercator_zones.svg/1920px-Universal_Transverse_Mercator_zones.svg.png)
-    UTM zones on an equirectangular world map with irregular zones in red and New York City's zone highlighted. See [UTM zones](https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#UTM_zones) for a full description.
-
-
-    Args:
-        lat (float, array): Latitude in decimal degrees.
-        lon (float, array): Longitude in decimal degrees.
-        utm_zone_number (int): UTM zone number. If None, the zone number is automatically calculated.
-        utm_zone_letter (str): UTM zone letter. If None, the zone letter is automatically calculated.
-
-    Returns:
-        (float, float, int, str): Tuple of easting, northing, zone number and zone letter. First element is easting, second is northing, third is zone number and fourth is zone letter.
-
-    References:
-         Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87)
-         [https://github.com/Turbo87/utm](https://github.com/Turbo87/utm)
-
-         [https://www.maptools.com/tutorials/grid_zone_details#](https://www.maptools.com/tutorials/grid_zone_details#)
-    """
-    # utm module requires numpy arrays
-    if not isinstance(lat, np.ndarray):
-        lat = np.array(lat)
-    if not isinstance(lon, np.ndarray):
-        lon = np.array(lon)
-
-    if utm_zone_number is None or utm_zone_letter is None:
-        easting, northing, zone_number, zone_letter = utm.from_latlon(lat, lon)
-    else:
-        easting, northing, zone_number, zone_letter = utm.from_latlon(lat, lon, utm_zone_number, utm_zone_letter)
-
-    return easting, northing, zone_number, zone_letter
-
-
-def euclidean_distance(px, py, x, y):
-    """Function that computes the Euclidean distance between one point
-    in space and one or more points.
-
-    Args:
-        px (float): x projected coordinate of the point.
-        py (float): y projected coordinate of the point.
-        x (list, ndarray, pandas.series): vector of x projected coordinates.
-        y (list, ndarray, pandas.series): vector of y projected coordinates.
-
-    Returns:
-        (ndarray): Numpy array of distances from the point (px,py) to all the points in x and y vectors.
-    """
-    d = np.sqrt((px - x) ** 2 + (py - y) ** 2)
-    return d
-
-
-def spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=False):
-    """Moving buffer filter to smooth georeferenced two-dimensional data.
-
-    Args:
-        x (list or array): UTM x coordinates in meters.
-        y (list or array): UTM y coordinates in meters.
-        z (list or array): Values to be smoothed.
-        buffer (float): Radial buffer distance in meters.
-        min_neighbours (int): Minimum number of neighbours to consider for the smoothing.
-        method (str): One of 'mean' or 'median'.
-        rnd (bool): Boolean to round the final result. Useful in case of z representing neutron counts.
-
-    Returns:
-        (array): Smoothed version of z with the same dimension as z.
-    """
-
-    # Convert input data to Numpy arrays
-    if (type(x) is not np.ndarray) or (type(y) is not np.ndarray):
-        try:
-            x = np.array(x)
-            y = np.array(y)
-        except:
-            raise "Input values cannot be converted to Numpy arrays."
-
-    if len(x) != len(y):
-        raise f"The number of x and y must be equal. Input x has {len(x)} values and y has {len(y)} values."
-
-    # Compute distances
-    N = len(x)
-    z_smooth = np.array([])
-    for k in range(N):
-        px = x[k]
-        py = y[k]
-
-        distances = euclidean_distance(px, py, x, y)
-        idx_within_buffer = distances <= buffer
-
-        if np.isnan(z[k]):
-            z_new_val = np.nan
-        elif len(distances[idx_within_buffer]) > min_neighbours:
-            if method == 'mean':
-                z_new_val = np.nanmean(z[idx_within_buffer])
-            elif method == 'median':
-                z_new_val = np.nanmedian(z[idx_within_buffer])
-            else:
-                raise f"Method {method} does not exist. Provide either 'mean' or 'median'."
-        else:
-            z_new_val = z[k]  # If there are not enough neighbours, keep the original value
-
-        # Append smoothed value to array
-        z_smooth = np.append(z_smooth, z_new_val)
-
-    if rnd:
-        z_smooth = np.round(z_smooth, 0)
-
-    return z_smooth
-
-
-def idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1):
-    """Function to interpolate data using inverse distance weight.
-
-    Args:
-        x (list or array): UTM x coordinates in meters.
-        y (list or array): UTM y coordinates in meters.
-        z (list or array): Values to be interpolated.
-        X_pred (list or array): UTM x coordinates where z values need to be predicted.
-        Y_pred (list or array): UTM y coordinates where z values need to be predicted.
-        neighborhood (float): Only points within this radius in meters are considered for the interpolation.
-        p (int): Exponent of the inverse distance weight formula. Typically, p=1 or p=2.
-
-    Returns:
-        (array): Interpolated values.
-
-    References:
-        [https://en.wikipedia.org/wiki/Inverse_distance_weighting](https://en.wikipedia.org/wiki/Inverse_distance_weighting)
-
-
-    """
-
-    # Flatten arrays to handle 1D and 2D arrays with the same code
-    s = X_pred.shape  # Save shape
-    X_pred = X_pred.flatten()
-    Y_pred = Y_pred.flatten()
-
-    # Pre-allocate output array
-    Z_pred = np.full_like(X_pred, np.nan)
-
-    for n in range(X_pred.size):
-        # Distance between current and observed points
-        d = euclidean_distance(X_pred[n], Y_pred[n], x, y)
-
-        # Select points within neighborhood only for interpolation
-        idx_neighbors = d < neighborhood
-
-        # Compute interpolated value at point of interest
-        Z_pred[n] = np.sum(z[idx_neighbors] / d[idx_neighbors] ** p) / np.sum(1 / d[idx_neighbors] ** p)
-
-    return np.reshape(Z_pred, s)
-
-
-def interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000):
-    """Function for interpolating irregular spatial data into a regular grid.
-
-    Args:
-        x (list or array): UTM x coordinates in meters.
-        y (list or array): UTM y coordinates in meters.
-        z (list or array): Values to be interpolated.
-        dx (float): Pixel width in meters.
-        dy (float): Pixel height in meters.
-        method (str): Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.
-        neighborhood (float): Only points within this radius in meters are considered for the interpolation.
-
-    Returns:
-        x_pred (array): 2D array with x coordinates.
-        y_pred (array): 2D array with y coordinates.
-        z_pred (array): 2D array with interpolated values.
-
-    References:
-        [https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html](https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html)
-    """
-
-    # Drop NaN values in x y and z
-    idx_nan = np.isnan(x) | np.isnan(y) | np.isnan(z)
-    x = x[~idx_nan]
-    y = y[~idx_nan]
-    z = z[~idx_nan]
-
-    if idx_nan.any():
-        print(
-            f"WARNING: {np.isnan(x).sum()}, {np.isnan(y).sum()}, and {np.isnan(z).sum()} NaN values were dropped from x, y, and z.")
-
-    # Create 2D grid for interpolation
-    Nx = round((np.max(x) - np.min(x)) / dx) + 1
-    Ny = round((np.max(y) - np.min(y)) / dy) + 1
-    X_vec = np.linspace(np.min(x), np.max(x), Nx)
-    Y_vec = np.linspace(np.min(y), np.max(y), Ny)
-    X_pred, Y_pred = np.meshgrid(X_vec, Y_vec)
-
-    if method in ['linear', 'nearest', 'cubic']:
-        points = list(zip(x, y))
-        Z_pred = griddata(points, z, (X_pred, Y_pred), method=method)
-
-    elif method == 'idw':
-        Z_pred = idw(x, y, z, X_pred, Y_pred, neighborhood)
-
-    else:
-        raise f"Method {method} does not exist. Provide either 'cubic', 'linear', 'nearest', or 'idw'."
-
-    return X_pred, Y_pred, Z_pred
-
-
-def rover_centered_coordinates(x, y):
-    """Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.
-
-    Args:
-        x (array): x coordinates.
-        y (array): y coordinates.
-
-    Returns:
-        x_est (array): Estimated x coordinates.
-        y_est (array): Estimated y coordinates.
-    """
-
-    # Make it datatype agnostic
-    if (isinstance(x, pd.Series)):
-        x = x.values
-    if (isinstance(y, pd.Series)):
-        y = y.values
-
-    # Do the average of the two points
-    x_est = (x[1:] + x[:-1]) / 2
-    y_est = (y[1:] + y[:-1]) / 2
-
-    # Add the first point to match the length of the original array
-    x_est = np.insert(x_est, 0, x[0])
-    y_est = np.insert(y_est, 0, y[0])
-
-    return x_est, y_est
-
-
-def uncertainty_counts(raw_counts, metric="std", fp=1, fw=1, fi=1):
-    """Function to estimate the uncertainty of raw counts.
-
-    Measurements of proportional neutron detector systems are governed by counting statistics that follow a Poissonian probability distribution (Zreda et al., 2012).
-    The expected uncertainty in the neutron count rate $N$ is defined by the standard deviation $ \sqrt{N} $ (Jakobi et al., 2020).
-    The CV% can be expressed as $ N^{-1/2} $
-
-    Args:
-        raw_counts (array): Raw neutron counts.
-        metric (str): Either 'std' or 'cv' for standard deviation or coefficient of variation.
-        fp (float): Pressure correction factor.
-        fw (float): Humidity correction factor.
-        fi (float): Incoming neutron flux correction factor.
-
-    Returns:
-        uncertainty (float): Uncertainty of raw counts.
-
-    References:
-        Jakobi J, Huisman JA, Schrön M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With
-        Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010
-
-        Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System,
-        Hydrol. Earth Syst. Sci., 16, 4079–4099, https://doi.org/10.5194/hess-16-4079-2012, 2012.
-
-    """
-
-    s = fw / (fp * fi)
-    if metric == "std":
-        uncertainty = np.sqrt(raw_counts) * s
-    elif metric == "cv":
-        uncertainty = 1 / np.sqrt(raw_counts) * s
-    else:
-        raise f"Metric {metric} does not exist. Provide either 'std' or 'cv' for standard deviation or coefficient of variation."
-    return uncertainty
-
-
-def uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808, a1=0.372, a2=0.115):
-    r"""Function to estimate the uncertainty propagated to volumetric water content.
-
-    The uncertainty of the volumetric water content is estimated by propagating the uncertainty of the raw counts.
-    Following Eq. 10 in Jakobi et al. (2020), the uncertainty of the volumetric water content can be expressed as:
-    $$
-    \sigma_{\theta_g}(N) = \sigma_N \frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \sqrt{(N_{cor} - a_1 N_0)^4 + 8 \sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \sigma_N^4}
-    $$
-
-    Args:
-        raw_counts (array): Raw neutron counts.
-        N0 (float): Calibration parameter N0.
-        bulk_density (float): Bulk density in g cm-3.
-        fp (float): Pressure correction factor.
-        fw (float): Humidity correction factor.
-        fi (float): Incoming neutron flux correction factor.
-
-    Returns:
-        sigma_VWC (float): Uncertainty in terms of volumetric water content.
-
-    References:
-        Jakobi J, Huisman JA, Schrön M, Fiedler J, Brogi C, Vereecken H and Bogena HR (2020) Error Estimation for Soil Moisture Measurements With
-        Cosmic Ray Neutron Sensing and Implications for Rover Surveys. Front. Water 2:10. doi: 10.3389/frwa.2020.00010
-    """
-
-    Ncorr = raw_counts * fw / (fp * fi)
-    sigma_N = uncertainty_counts(raw_counts, metric="std", fp=fp, fw=fw, fi=fi)
-    sigma_GWC = sigma_N * ((a0 * N0) / ((Ncorr - a1 * N0) ** 4)) * np.sqrt(
-        (Ncorr - a1 * N0) ** 4 + 8 * sigma_N ** 2 * (Ncorr - a1 * N0) ** 2 + 15 * sigma_N ** 4)
-    sigma_VWC = sigma_GWC * bulk_density
-
-    return sigma_VWC
diff --git a/build/lib/crnpy/data.py b/build/lib/crnpy/data.py
deleted file mode 100644
index 8e08bd0..0000000
--- a/build/lib/crnpy/data.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# crnpy/data.py
-"""## crnpy.data
-Data module for crnpy.
-
-This module contains data for the crnpy package.
-
-Attributes:
-    cutoff_rigidity (list): Cutoff rigidity values for the whole world. See [crnpy.crnpy.cutoff_rigidity][]
-    neutron_detectors (list): Neutron detector locations. See [crnpy.crnpy.find_neutron_monitor][]
-
-"""
-
-cutoff_rigidity = [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-                   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-                   [0.0, 0.0, 0.02, 0.02, 0.02, 0.03, 0.03, 0.04, 0.03, 0.03, 0.04, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-                   [0.04, 0.07, 0.09, 0.08, 0.13, 0.08, 0.16, 0.14, 0.13, 0.15, 0.17, 0.13, 0.08, 0.04, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02, 0.05, 0.04],
-                   [0.18, 0.28, 0.34, 0.32, 0.35, 0.41, 0.38, 0.42, 0.47, 0.47, 0.46, 0.43, 0.33, 0.21, 0.11, 0.04, 0.0, 0.0, 0.0, 0.0, 0.02, 0.08, 0.15, 0.2, 0.18],
-                   [0.6, 0.66, 0.74, 0.75, 0.76, 0.79, 0.81, 0.87, 0.91, 0.98, 1.01, 0.96, 0.72, 0.55, 0.3, 0.19, 0.07, 0.04, 0.05, 0.06, 0.15, 0.31, 0.45, 0.58, 0.6],
-                   [1.13, 1.3, 1.35, 1.39, 1.39, 1.44, 1.47, 1.56, 1.72, 1.76, 1.86, 1.8, 1.42, 1.08, 0.74, 0.48, 0.32, 0.21, 0.22, 0.31, 0.49, 0.7, 0.94, 1.14, 1.13],
-                   [2.1, 2.18, 2.22, 2.3, 2.3, 2.38, 2.45, 2.59, 2.75, 2.96, 3.1, 2.97, 2.31, 1.85, 1.39, 0.93, 0.65, 0.5, 0.5, 0.65, 0.94, 1.38, 1.75, 2.02, 2.1],
-                   [3.4, 3.49, 3.49, 3.53, 3.58, 3.69, 3.81, 4.05, 4.3, 4.61, 4.72, 4.54, 3.59, 2.91, 2.27, 1.6, 1.18, 0.93, 0.9, 1.21, 1.71, 2.4, 2.96, 3.24, 3.4],
-                   [4.98, 5.09, 5.07, 5.12, 5.25, 5.39, 5.53, 5.77, 6.03, 6.3, 6.35, 5.96, 4.96, 4.31, 3.42, 2.68, 1.96, 1.57, 1.56, 1.94, 2.85, 3.92, 4.6, 4.97, 4.98],
-                   [7.33, 7.26, 7.11, 7.08, 7.32, 7.67, 7.99, 8.34, 8.93, 9.3, 9.22, 8.63, 6.72, 5.65, 4.88, 4.06, 2.99, 2.46, 2.43, 3.0, 4.24, 5.47, 6.56, 7.24, 7.33],
-                   [9.87, 9.73, 9.83, 10.05, 10.51, 10.87, 11.13, 11.02, 11.29, 11.51, 11.16, 10.3, 9.33, 8.11, 6.43, 5.3, 4.31, 3.54, 3.5, 4.29, 5.87, 8.43, 9.54, 9.86, 9.87],
-                   [11.73, 11.77, 11.79, 12.13, 12.61, 13.1, 13.58, 13.84, 13.86, 13.66, 13.19, 12.51, 10.83, 9.78, 8.85, 7.24, 5.65, 4.51, 4.48, 5.76, 8.89, 10.72, 11.4, 11.71, 11.73],
-                   [13.45, 13.68, 13.9, 14.17, 14.62, 15.05, 15.32, 15.35, 15.19, 14.81, 14.22, 13.52, 12.32, 11.61, 10.73, 9.53, 7.8, 6.22, 6.14, 8.09, 10.88, 12.19, 12.93, 13.35, 13.45],
-                   [14.31, 14.63, 14.9, 15.26, 15.76, 16.23, 16.49, 16.44, 16.15, 15.65, 14.99, 14.29, 13.2, 12.61, 11.9, 10.93, 8.99, 6.99, 7.04, 9.87, 12.07, 13.07, 13.73, 14.18, 14.31],
-                   [14.71, 15.11, 15.47, 15.93, 16.49, 17.0, 17.25, 17.15, 16.78, 16.21, 15.54, 14.88, 13.9, 13.38, 12.79, 11.96, 10.64, 9.38, 9.64, 11.59, 12.74, 13.54, 14.1, 14.57, 14.71],
-                   [14.7, 15.16, 15.62, 16.18, 16.82, 17.36, 17.62, 17.5, 17.08, 16.5, 15.87, 15.29, 14.43, 13.95, 13.44, 12.83, 11.92, 11.1, 11.33, 12.25, 13.07, 13.66, 14.1, 14.54, 14.7],
-                   [14.3, 14.8, 15.36, 16.03, 16.75, 17.33, 17.6, 17.48, 17.07, 16.52, 15.97, 15.49, 14.77, 14.34, 13.89, 13.38, 12.76, 12.16, 12.12, 12.57, 13.13, 13.48, 13.75, 14.14, 14.3],
-                   [13.56, 14.08, 14.72, 15.5, 16.29, 16.9, 17.18, 17.1, 16.73, 16.24, 15.81, 15.46, 14.9, 14.53, 14.12, 13.68, 13.17, 12.66, 12.43, 12.65, 12.95, 13.04, 13.11, 13.41, 13.56],
-                   [12.55, 13.06, 13.78, 14.65, 15.48, 16.08, 16.38, 16.35, 16.06, 15.66, 15.35, 15.15, 14.78, 14.48, 14.14, 13.75, 13.31, 12.83, 12.52, 12.53, 12.57, 12.4, 12.26, 12.43, 12.55],
-                   [11.33, 11.84, 12.6, 13.51, 14.33, 14.89, 15.18, 15.23, 15.02, 14.71, 14.54, 14.5, 14.39, 14.2, 13.95, 13.63, 13.25, 12.79, 12.41, 12.24, 12.02, 11.56, 11.15, 11.23, 11.33],
-                   [9.92, 10.36, 11.19, 12.09, 12.8, 13.32, 13.56, 13.67, 13.54, 13.32, 13.29, 13.42, 13.69, 13.66, 13.54, 13.32, 13.0, 12.58, 12.14, 11.79, 11.34, 10.67, 9.87, 9.8, 9.92],
-                   [8.19, 8.64, 9.37, 10.07, 10.6, 10.89, 11.19, 11.36, 11.19, 10.53, 10.67, 11.73, 12.6, 12.84, 12.91, 12.83, 12.6, 12.21, 11.73, 11.21, 10.45, 9.49, 8.49, 8.13, 8.19],
-                   [6.81, 7.15, 7.68, 8.25, 8.34, 8.09, 7.89, 7.96, 8.02, 8.03, 8.55, 9.35, 10.25, 11.34, 12.03, 12.14, 12.04, 11.72, 11.19, 10.4, 9.5, 8.25, 7.18, 6.86, 6.81],
-                   [5.53, 5.6, 5.94, 5.96, 5.73, 5.45, 5.41, 5.33, 5.45, 5.58, 5.93, 6.53, 8.68, 8.75, 10.03, 11.22, 11.31, 11.1, 10.47, 9.62, 8.42, 7.08, 6.22, 5.61, 5.53],
-                   [4.42, 4.27, 4.34, 4.39, 4.17, 3.97, 3.6, 3.52, 3.56, 3.74, 4.22, 4.84, 6.02, 7.21, 8.43, 9.01, 10.42, 10.27, 9.66, 8.7, 7.3, 6.17, 5.2, 4.49, 4.42],
-                   [3.55, 3.42, 3.41, 3.38, 2.9, 2.57, 2.26, 2.12, 2.19, 2.3, 2.69, 3.29, 4.53, 5.17, 6.06, 7.75, 9.07, 9.23, 8.68, 7.62, 6.57, 5.46, 4.34, 3.68, 3.55],
-                   [2.87, 2.78, 2.47, 2.24, 1.96, 1.6, 1.28, 1.22, 1.17, 1.29, 1.55, 2.03, 3.06, 3.94, 4.57, 5.53, 7.23, 8.03, 7.82, 7.02, 5.8, 4.42, 3.56, 3.11, 2.87],
-                   [2.32, 2.07, 1.84, 1.59, 1.24, 0.94, 0.71, 0.58, 0.57, 0.66, 0.85, 1.18, 2.02, 2.66, 3.34, 4.17, 5.04, 5.92, 6.28, 5.52, 4.5, 3.6, 2.93, 2.49, 2.32],
-                   [1.85, 1.57, 1.36, 1.07, 0.81, 0.54, 0.34, 0.2, 0.22, 0.26, 0.36, 0.61, 1.18, 1.68, 2.3, 3.04, 3.87, 4.37, 4.51, 4.24, 3.64, 2.9, 2.31, 1.92, 1.85],
-                   [1.43, 1.17, 0.95, 0.69, 0.49, 0.28, 0.11, 0.05, 0.03, 0.04, 0.12, 0.25, 0.67, 1.04, 1.53, 2.02, 2.62, 3.15, 3.46, 3.24, 2.78, 2.29, 1.94, 1.47, 1.43],
-                   [1.06, 0.84, 0.66, 0.46, 0.2, 0.12, 0.03, 0.0, 0.0, 0.0, 0.02, 0.06, 0.34, 0.59, 0.92, 1.32, 1.73, 2.06, 2.34, 2.31, 2.02, 1.7, 1.39, 1.14, 1.06],
-                   [0.76, 0.55, 0.39, 0.24, 0.09, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.11, 0.32, 0.51, 0.78, 1.03, 1.24, 1.42, 1.46, 1.37, 1.16, 1.0, 0.79, 0.76],
-                   [0.48, 0.35, 0.22, 0.09, 0.04, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03, 0.17, 0.24, 0.43, 0.6, 0.72, 0.8, 0.84, 0.85, 0.72, 0.65, 0.51, 0.48],
-                   [0.26, 0.17, 0.12, 0.06, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03, 0.05, 0.09, 0.2, 0.32, 0.38, 0.44, 0.43, 0.44, 0.41, 0.37, 0.29, 0.26],
-                   [0.12, 0.07, 0.06, 0.02, 0.02, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03, 0.03, 0.08, 0.07, 0.11, 0.11, 0.18, 0.16, 0.16, 0.16, 0.16, 0.12],
-                   [0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04]]
-
-neutron_detectors = [["AATA", "Alma-Ata A", 5.9, 897], ["AATB", "Alma-Ata B", 5.9, 3340], ["AHMD", "Ahmedabad", 15.94, 50], ["APTY", "Apatity", 0.65, 181],
-                     ["ARNM", "Aragats", 7.1, 3200], ["ATHN", "Athens", 8.53, 260], ["BKSN", "Baksan", 5.7, 1700], ["CALG", "Calgary", 1.08, 1123],
-                     ["CALM", "NM de Castilla la Mancha", 6.95, 708], ["CLMX", "Climax", 3.0, 3400], ["DJON", "Daejeon", 11.2, 200],
-                     ["DOMB", "Dome C mini NM (bare)", 0.01, 3233], ["DOMC", "Dome C mini NM", 0.01, 3233], ["DRBS", "Dourbes", 3.18, 225],
-                     ["ESOI", "Emilio Segre Obs. Israel", 10.75, 2055], ["FSMT", "Forth Smith", 0.3, 180], ["HRMS", "Hermanus", 4.58, 26],
-                     ["HUAN", "Huancayo", 12.92, 3400], ["INVK", "Inuvik", 0.3, 21], ["IRK2", "Irkustk 2", 3.64, 2000], ["IRK3", "Irkustk 3", 3.64, 3000],
-                     ["IRKT", "Irkustk", 3.64, 435], ["JBGO", "JangBogo", 0.3, 29], ["JUNG", "IGY Jungfraujoch", 4.49, 3570],
-                     ["JUNG1", "NM64 Jungfraujoch", 4.49, 3475], ["KERG", "Kerguelen", 1.14, 33], ["KGSN", "Kingston", 1.88, 65],
-                     ["KIEL", "Kiel", 2.36, 54], ["KIEL2", "KielRT", 2.36, 54], ["LMKS", "Lomnicky Stit", 3.84, 2634], ["MCMU", "Mc Murdo", 0.3, 48],
-                     ["MCRL", "Mobile Cosmic Ray Laboratory", 2.46, 200], ["MGDN", "Magadan", 2.1, 220], ["MOSC", "Moscow", 2.43, 200], ["MRNY", "Mirny", 0.03, 30],
-                     ["MWSN", "Mawson", 0.22, 30], ["MXCO", "Mexico", 8.28, 2274], ["NAIN", "Nain", 0.3, 46], ["NANM", "Nor-Amberd", 7.1, 2000],
-                     ["NEU3", "Neumayer III mini neutron monitor", 0.1, 40], ["NEWK", "Newark", 2.4, 50], ["NRLK", "Norilsk", 0.63, 0],
-                     ["NVBK", "Novosibirsk", 2.91, 163], ["OULU", "Oulu", 0.81, 15], ["PSNM", "Doi Inthanon (Princess Sirindhorn NM)", 16.8, 2565],
-                     ["PTFM", "Potchefstroom", 6.98, 1351], ["PWNK", "Peawanuck", 0.3, 53], ["ROME", "Rome", 6.27, 0], ["SANB", "Sanae D", 0.73, 52],
-                     ["SNAE", "Sanae IV", 0.73, 856], ["SOPB", "South Pole Bare", 0.1, 2820], ["SOPO", "South Pole", 0.1, 2820], ["TERA", "Terre Adelie", 0.01, 32],
-                     ["THUL", "Thule", 0.3, 26], ["TSMB", "Tsumeb", 9.15, 1240], ["TXBY", "Tixie Bay", 0.48, 0], ["UFSZ", "Zugspitze", 4.1, 2650],
-                     ["YKTK", "Yakutsk", 1.65, 105], ["ZUGS", "Zugspitze", 4.24, 2960]]
diff --git a/docs/examples/calibration/calibration.ipynb b/docs/examples/calibration/calibration.ipynb
index 4933c82..2c21921 100644
--- a/docs/examples/calibration/calibration.ipynb
+++ b/docs/examples/calibration/calibration.ipynb
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "130e6b13",
    "metadata": {
     "collapsed": false,
@@ -37,7 +37,8 @@
     },
     "pycharm": {
      "name": "#%%\n"
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -814,7 +815,13 @@
    "source": [
     "# Weight the samples for the field average\n",
     "#field_theta_v, w = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'], df_soil['distance_from_station'], (df_soil['bottom_depth']+df_soil['top_depth'])/2, rhob=df_soil['bulk_density'].mean(), method=\"Kohli_2015\")\n",
-    "field_theta_v, w = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'], df_soil['distance_from_station'], (df_soil['bottom_depth']+df_soil['top_depth'])/2, rhob=df_soil['bulk_density'].mean(),p=df_station['barometric_pressure_Avg'].mean(), method = \"Schron_2017\")\n",
+    "field_theta_v, w = crnpy.nrad_weight(df_station['abs_humidity'].mean(), \n",
+    "                                     df_soil['theta_v'],\n",
+    "                                     df_soil['distance_from_station'], \n",
+    "                                     (df_soil['bottom_depth']+df_soil['top_depth'])/2,\n",
+    "                                     rhob = df_soil['bulk_density'].mean(),\n",
+    "                                     p = df_station['barometric_pressure_Avg'].mean(),\n",
+    "                                     method = \"Schron_2017\")\n",
     "\n",
     "# Apply distance weights to volumetric water content and bulk density\n",
     "field_bulk_density = np.mean(df_soil['bulk_density'])\n"
@@ -920,9 +927,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/src/crnpy.egg-info/PKG-INFO b/src/crnpy.egg-info/PKG-INFO
index a12720e..303b4b6 100644
--- a/src/crnpy.egg-info/PKG-INFO
+++ b/src/crnpy.egg-info/PKG-INFO
@@ -8,11 +8,6 @@ Author-email: jperaza@ksu.edu, andrespatrignani@ksu.edu
 License: MIT
 Description-Content-Type: text/markdown
 License-File: LICENSE
-Requires-Dist: numpy
-Requires-Dist: pandas
-Requires-Dist: scipy
-Requires-Dist: requests
-Requires-Dist: utm
 
 [![GitHub Workflow Status (building)](https://img.shields.io/github/actions/workflow/status/soilwater/crnpy/python-package.yml)](https://github.com/soilwater/crnpy/actions/workflows/python-package.yml)
 [![GitHub Workflow Status (publish)](https://img.shields.io/github/actions/workflow/status/soilwater/crnpy/python-publish.yml?label=publish)](https://github.com/soilwater/crnpy/actions/workflows/python-publish.yml)

From a69d27dfd26af5456c242ba32f76388b3c845f22 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Sun, 21 Apr 2024 09:31:11 -0600
Subject: [PATCH 20/24] Delete .idea directory

---
 .idea/.gitignore                              |  8 ---
 .idea/crnpy.iml                               | 17 -----
 .../inspectionProfiles/profiles_settings.xml  |  6 --
 .idea/markdown-navigator-enh.xml              | 29 --------
 .idea/markdown-navigator.xml                  | 72 -------------------
 .idea/misc.xml                                |  4 --
 .idea/modules.xml                             |  8 ---
 .idea/other.xml                               |  6 --
 .idea/vcs.xml                                 |  6 --
 9 files changed, 156 deletions(-)
 delete mode 100644 .idea/.gitignore
 delete mode 100644 .idea/crnpy.iml
 delete mode 100644 .idea/inspectionProfiles/profiles_settings.xml
 delete mode 100644 .idea/markdown-navigator-enh.xml
 delete mode 100644 .idea/markdown-navigator.xml
 delete mode 100644 .idea/misc.xml
 delete mode 100644 .idea/modules.xml
 delete mode 100644 .idea/other.xml
 delete mode 100644 .idea/vcs.xml

diff --git a/.idea/.gitignore b/.idea/.gitignore
deleted file mode 100644
index 13566b8..0000000
--- a/.idea/.gitignore
+++ /dev/null
@@ -1,8 +0,0 @@
-# Default ignored files
-/shelf/
-/workspace.xml
-# Editor-based HTTP Client requests
-/httpRequests/
-# Datasource local storage ignored files
-/dataSources/
-/dataSources.local.xml
diff --git a/.idea/crnpy.iml b/.idea/crnpy.iml
deleted file mode 100644
index 68d8a3d..0000000
--- a/.idea/crnpy.iml
+++ /dev/null
@@ -1,17 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$">
-      <excludeFolder url="file://$MODULE_DIR$/venv" />
-    </content>
-    <orderEntry type="jdk" jdkName="Python 3.11 (crnpy)" jdkType="Python SDK" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-  <component name="PyDocumentationSettings">
-    <option name="format" value="NUMPY" />
-    <option name="myDocStringFormat" value="NumPy" />
-  </component>
-  <component name="SonarLintModuleSettings">
-    <option name="uniqueId" value="7a23bc73-9054-4e8d-9f5a-f0d1afe24c71" />
-  </component>
-</module>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
deleted file mode 100644
index 105ce2d..0000000
--- a/.idea/inspectionProfiles/profiles_settings.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <settings>
-    <option name="USE_PROJECT_PROFILE" value="false" />
-    <version value="1.0" />
-  </settings>
-</component>
\ No newline at end of file
diff --git a/.idea/markdown-navigator-enh.xml b/.idea/markdown-navigator-enh.xml
deleted file mode 100644
index 12fb99d..0000000
--- a/.idea/markdown-navigator-enh.xml
+++ /dev/null
@@ -1,29 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="MarkdownEnhProjectSettings">
-    <AnnotatorSettings targetHasSpaces="true" linkCaseMismatch="true" wikiCaseMismatch="true" wikiLinkHasDashes="true" notUnderWikiHome="true" targetNotWikiPageExt="true" notUnderSourceWikiHome="true" targetNameHasAnchor="true" targetPathHasAnchor="true" wikiLinkHasSlash="true" wikiLinkHasSubdir="true" wikiLinkHasOnlyAnchor="true" linkTargetsWikiHasExt="true" linkTargetsWikiHasBadExt="true" notUnderSameRepo="true" targetNotUnderVcs="false" linkNeedsExt="true" linkHasBadExt="true" linkTargetNeedsExt="true" linkTargetHasBadExt="true" wikiLinkNotInWiki="true" imageTargetNotInRaw="true" repoRelativeAcrossVcsRoots="true" multipleWikiTargetsMatch="true" unresolvedLinkReference="true" linkIsIgnored="true" anchorIsIgnored="true" anchorIsUnresolved="true" anchorLineReferenceIsUnresolved="true" anchorLineReferenceFormat="true" anchorHasDuplicates="true" abbreviationDuplicates="true" abbreviationNotUsed="true" attributeIdDuplicateDefinition="true" attributeIdNotUsed="true" footnoteDuplicateDefinition="true" footnoteUnresolved="true" footnoteDuplicates="true" footnoteNotUsed="true" macroDuplicateDefinition="true" macroUnresolved="true" macroDuplicates="true" macroNotUsed="true" referenceDuplicateDefinition="true" referenceUnresolved="true" referenceDuplicates="true" referenceNotUsed="true" referenceUnresolvedNumericId="true" enumRefDuplicateDefinition="true" enumRefUnresolved="true" enumRefDuplicates="true" enumRefNotUsed="true" enumRefLinkUnresolved="true" enumRefLinkDuplicates="true" simTocUpdateNeeded="true" simTocTitleSpaceNeeded="true" />
-    <HtmlExportSettings updateOnSave="false" parentDir="" targetDir="" cssDir="css" scriptDir="js" plainHtml="false" imageDir="" copyLinkedImages="false" imagePathType="0" targetPathType="2" targetExt="" useTargetExt="false" noCssNoScripts="false" useElementStyleAttribute="false" linkToExportedHtml="true" exportOnSettingsChange="true" regenerateOnProjectOpen="false" linkFormatType="HTTP_ABSOLUTE" />
-    <LinkMapSettings>
-      <textMaps />
-    </LinkMapSettings>
-  </component>
-  <component name="MarkdownNavigatorHistory">
-    <PasteImageHistory checkeredTransparentBackground="false" filename="image" directory="" onPasteImageTargetRef="3" onPasteLinkText="0" onPasteImageElement="1" onPasteLinkElement="1" onPasteReferenceElement="2" cornerRadius="20" borderColor="0" transparentColor="16777215" borderWidth="1" trimTop="0" trimBottom="0" trimLeft="0" trimRight="0" transparent="false" roundCorners="false" showPreview="true" bordered="false" scaled="false" cropped="false" hideInapplicableOperations="false" preserveLinkFormat="false" scale="50" scalingInterpolation="1" transparentTolerance="0" saveAsDefaultOnOK="false" linkFormat="0" addHighlights="false" showHighlightCoordinates="true" showHighlights="false" mouseSelectionAddsHighlight="false" outerFilled="false" outerFillColor="0" outerFillTransparent="true" outerFillAlpha="30">
-      <highlightList />
-      <directories />
-      <filenames />
-    </PasteImageHistory>
-    <CopyImageHistory checkeredTransparentBackground="false" filename="image" directory="" onPasteImageTargetRef="3" onPasteLinkText="0" onPasteImageElement="1" onPasteLinkElement="1" onPasteReferenceElement="2" cornerRadius="20" borderColor="0" transparentColor="16777215" borderWidth="1" trimTop="0" trimBottom="0" trimLeft="0" trimRight="0" transparent="false" roundCorners="false" showPreview="true" bordered="false" scaled="false" cropped="false" hideInapplicableOperations="false" preserveLinkFormat="false" scale="50" scalingInterpolation="1" transparentTolerance="0" saveAsDefaultOnOK="false" linkFormat="0" addHighlights="false" showHighlightCoordinates="true" showHighlights="false" mouseSelectionAddsHighlight="false" outerFilled="false" outerFillColor="0" outerFillTransparent="true" outerFillAlpha="30">
-      <highlightList />
-      <directories />
-      <filenames />
-    </CopyImageHistory>
-    <PasteLinkHistory onPasteImageTargetRef="3" onPasteTargetRef="1" onPasteLinkText="0" onPasteImageElement="1" onPasteLinkElement="1" onPasteWikiElement="2" onPasteReferenceElement="2" hideInapplicableOperations="false" preserveLinkFormat="false" useHeadingForLinkText="false" linkFormat="0" saveAsDefaultOnOK="false" />
-    <TableToJsonHistory>
-      <entries />
-    </TableToJsonHistory>
-    <TableSortHistory>
-      <entries />
-    </TableSortHistory>
-  </component>
-</project>
\ No newline at end of file
diff --git a/.idea/markdown-navigator.xml b/.idea/markdown-navigator.xml
deleted file mode 100644
index ea474b2..0000000
--- a/.idea/markdown-navigator.xml
+++ /dev/null
@@ -1,72 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="FlexmarkProjectSettings">
-    <FlexmarkHtmlSettings flexmarkSpecExampleRendering="0" flexmarkSpecExampleRenderHtml="false">
-      <flexmarkSectionLanguages>
-        <option name="1" value="Markdown" />
-        <option name="2" value="HTML" />
-        <option name="3" value="flexmark-ast:1" />
-      </flexmarkSectionLanguages>
-    </FlexmarkHtmlSettings>
-  </component>
-  <component name="MarkdownProjectSettings">
-    <PreviewSettings splitEditorLayout="SPLIT" splitEditorPreview="PREVIEW" useGrayscaleRendering="false" zoomFactor="1.0" maxImageWidth="0" synchronizePreviewPosition="true" highlightPreviewType="LINE" highlightFadeOut="5" highlightOnTyping="true" synchronizeSourcePosition="true" verticallyAlignSourceAndPreviewSyncPosition="true" showSearchHighlightsInPreview="true" showSelectionInPreview="true" lastLayoutSetsDefault="false">
-      <PanelProvider>
-        <provider providerId="com.vladsch.md.nav.editor.swing.html.panel" providerName="Default - Swing" />
-      </PanelProvider>
-    </PreviewSettings>
-    <ParserSettings gitHubSyntaxChange="false" correctedInvalidSettings="false" emojiShortcuts="1" emojiImages="0">
-      <PegdownExtensions>
-        <option name="ATXHEADERSPACE" value="true" />
-        <option name="FENCED_CODE_BLOCKS" value="true" />
-        <option name="INTELLIJ_DUMMY_IDENTIFIER" value="true" />
-        <option name="RELAXEDHRULES" value="true" />
-        <option name="STRIKETHROUGH" value="true" />
-        <option name="TABLES" value="true" />
-        <option name="TASKLISTITEMS" value="true" />
-      </PegdownExtensions>
-      <ParserOptions>
-        <option name="ATTRIBUTES_EXT" value="true" />
-        <option name="COMMONMARK_LISTS" value="true" />
-        <option name="EMOJI_SHORTCUTS" value="true" />
-        <option name="GFM_TABLE_RENDERING" value="true" />
-        <option name="PRODUCTION_SPEC_PARSER" value="true" />
-        <option name="SIM_TOC_BLANK_LINE_SPACER" value="true" />
-      </ParserOptions>
-    </ParserSettings>
-    <HtmlSettings headerTopEnabled="false" headerBottomEnabled="false" bodyTopEnabled="false" bodyBottomEnabled="false" addPageHeader="false" addAnchorLinks="false" anchorLinksWrapText="false" imageUriSerials="false" addDocTypeHtml="true" noParaTags="false" defaultUrlTitle="false" migratedPlantUml="true" migratedAnchorLinks="true" plantUmlConversion="0">
-      <GeneratorProvider>
-        <provider providerId="com.vladsch.md.nav.editor.text.html.generator" providerName="Unmodified HTML Generator" />
-      </GeneratorProvider>
-      <headerTop />
-      <headerBottom />
-      <bodyTop />
-      <bodyBottom />
-      <fencedCodeConversions>
-        <option name="c4plantuml" value="NONE" />
-        <option name="ditaa" value="NONE" />
-        <option name="erd" value="NONE" />
-        <option name="graphviz" value="NONE" />
-        <option name="latex" value="KATEX" />
-        <option name="math" value="KATEX" />
-        <option name="mermaid" value="NONE" />
-        <option name="nomnoml" value="NONE" />
-        <option name="plantuml" value="NONE" />
-        <option name="puml" value="NONE" />
-        <option name="svgbob" value="NONE" />
-        <option name="umlet" value="NONE" />
-        <option name="vega" value="NONE" />
-        <option name="vegalite" value="NONE" />
-        <option name="wavedrom" value="NONE" />
-      </fencedCodeConversions>
-    </HtmlSettings>
-    <CssSettings previewScheme="UI_SCHEME" cssUri="" isCssUriEnabled="false" isCssUriSerial="true" isCssTextEnabled="false" isDynamicPageWidth="true">
-      <StylesheetProvider>
-        <provider providerId="com.vladsch.md.nav.editor.text.html.css" providerName="No Stylesheet" />
-      </StylesheetProvider>
-      <ScriptProviders />
-      <cssText />
-      <cssUriHistory />
-    </CssSettings>
-  </component>
-</project>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
deleted file mode 100644
index aedf446..0000000
--- a/.idea/misc.xml
+++ /dev/null
@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (crnpy)" project-jdk-type="Python SDK" />
-</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
deleted file mode 100644
index 7b7e509..0000000
--- a/.idea/modules.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/crnpy.iml" filepath="$PROJECT_DIR$/.idea/crnpy.iml" />
-    </modules>
-  </component>
-</project>
\ No newline at end of file
diff --git a/.idea/other.xml b/.idea/other.xml
deleted file mode 100644
index a708ec7..0000000
--- a/.idea/other.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="PySciProjectComponent">
-    <option name="PY_SCI_VIEW_SUGGESTED" value="true" />
-  </component>
-</project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
deleted file mode 100644
index 94a25f7..0000000
--- a/.idea/vcs.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$" vcs="Git" />
-  </component>
-</project>
\ No newline at end of file

From fe8893655de2b039c735d033e8a413197eaf701b Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Sun, 21 Apr 2024 09:31:32 -0600
Subject: [PATCH 21/24] Delete .DS_Store

---
 .DS_Store | Bin 6148 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 .DS_Store

diff --git a/.DS_Store b/.DS_Store
deleted file mode 100644
index 8ceb77b03969e4447ffb693919f548c0470f50d9..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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From 709b08e7e53d9dcefe7032c9c95b8799b89100dd Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Sun, 21 Apr 2024 09:39:06 -0600
Subject: [PATCH 22/24] =?UTF-8?q?Add=20the=20profile=20ID=20in=20the=20Sch?=
 =?UTF-8?q?r=C3=B6n=20et=20al.=20(2017)=20method?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

#9 Added the parameter`profiles`, which is the ID that identifies each unique soil profile P when running the weighting algorithm.
---
 .gitignore                                  |  1 +
 build/lib/crnpy/crnpy.py                    | 10 +++++++---
 docs/examples/calibration/calibration.ipynb | 17 ++++++++++-------
 src/crnpy/crnpy.py                          | 10 +++++++---
 src/tests/test_calibration_rdt.py           |  6 +++++-
 src/tests/test_functions.py                 |  4 +++-
 6 files changed, 33 insertions(+), 15 deletions(-)

diff --git a/.gitignore b/.gitignore
index e88baf2..5ef7b49 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,3 +10,4 @@ site/*
 .pytest_cache/*
 crnpy.egg-info/*
 .idea/*
+/.idea
diff --git a/build/lib/crnpy/crnpy.py b/build/lib/crnpy/crnpy.py
index 2b1f749..da3e199 100644
--- a/build/lib/crnpy/crnpy.py
+++ b/build/lib/crnpy/crnpy.py
@@ -706,7 +706,7 @@ def abs_humidity(relative_humidity, temp):
     return abs_h
 
 
-def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=None, Hveg=0, tol=0.01):
+def nrad_weight(h, theta, distances, depth, profiles=None, rhob=1.4, method="Kohli_2015", p=None, Hveg=0, tol=0.01):
     """Function to compute distance weights corresponding to each soil sample.
 
     Args:
@@ -714,6 +714,7 @@ def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=Non
         theta (np.array or pd.Series): Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)
         distances (np.array or pd.Series): Distances from the location of each sample to the origin (0.5 - 600 m)
         depth (np.array or pd.Series): Depths for each sample (m)
+        profiles (np.array or pd.Series): Soil profiles id for each sample. Required for the 'Schron_2017' method.
         rhob (np.array or pd.Series): Bulk density in g/cm^3
         p (np.array or pd.Series): Atmospheric pressure in hPa. Required for the 'Schron_2017' method.
         Hveg (np.array or pd.Series): Vegetation height in m. Required for the 'Schron_2017' method.
@@ -950,6 +951,9 @@ def rscaled(r, p, y, Hveg = 0):
             Fveg = 1 - 0.17 * (1 - np.exp(-0.41 * Hveg)) * (1 + np.exp(-9.25 * y))
             return r / Fp / Fveg
 
+        if profiles is None:
+            raise ValueError("Profile ID's must be provided when using 'Schron_2017' method")
+
         # Rename variables to be consistent with the revised paper
         r = distances
         theta_ = np.mean(theta) # Start with the mean value of theta as initial guess
@@ -971,12 +975,12 @@ def Wd(d, r, bd, y):
             r = rscaled(distances, p, theta_, Hveg)
 
             # Calculate the vertical average for each profile
-            P = np.unique(distances)
+            P = np.unique(profiles)
             theta_P = []
             r_stars = []
             for i in range(len(P)):
                 profile = P[i]
-                idx = distances == profile
+                idx = profiles == profile
                 depths_P = depth[idx]
                 r_P = r[idx]
                 theta_Pi = theta[idx]
diff --git a/docs/examples/calibration/calibration.ipynb b/docs/examples/calibration/calibration.ipynb
index 4933c82..a97dedf 100644
--- a/docs/examples/calibration/calibration.ipynb
+++ b/docs/examples/calibration/calibration.ipynb
@@ -232,7 +232,10 @@
    "source": [
     "# Define start and end of field survey calibration\n",
     "calibration_start = pd.to_datetime(\"2021-10-22 08:00\")\n",
-    "calibration_end = pd.to_datetime(\"2021-10-22 16:00\")\n"
+    "calibration_end = pd.to_datetime(\"2021-10-22 16:00\")\n",
+    "\n",
+    "# Create an ID for each soil profile using their respective latitude and longitude\n",
+    "df_soil['ID'] = df_soil['latitude'].astype(str) +'_'+ df_soil['longitude'].astype(str)"
    ]
   },
   {
@@ -807,14 +810,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solution converged after 2 steps, the average soil moisture is 0.2197191482091855\n"
+      "Solution converged after 2 steps, the average soil moisture is 0.2047467442102135\n"
      ]
     }
    ],
    "source": [
     "# Weight the samples for the field average\n",
     "#field_theta_v, w = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'], df_soil['distance_from_station'], (df_soil['bottom_depth']+df_soil['top_depth'])/2, rhob=df_soil['bulk_density'].mean(), method=\"Kohli_2015\")\n",
-    "field_theta_v, w = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'], df_soil['distance_from_station'], (df_soil['bottom_depth']+df_soil['top_depth'])/2, rhob=df_soil['bulk_density'].mean(),p=df_station['barometric_pressure_Avg'].mean(), method = \"Schron_2017\")\n",
+    "field_theta_v, w = crnpy.nrad_weight(df_station['abs_humidity'].mean(), df_soil['theta_v'], df_soil['distance_from_station'], (df_soil['bottom_depth']+df_soil['top_depth'])/2, profiles=df_soil['ID'], rhob=df_soil['bulk_density'].mean(),p=df_station['barometric_pressure_Avg'].mean(), method = \"Schron_2017\")\n",
     "\n",
     "# Apply distance weights to volumetric water content and bulk density\n",
     "field_bulk_density = np.mean(df_soil['bulk_density'])\n"
@@ -830,7 +833,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean volumetric Water content during calibration survey: 0.22\n",
+      "Mean volumetric Water content during calibration survey: 0.205\n",
       "Mean corrected counts during calibration: 1543 counts\n"
      ]
     }
@@ -851,7 +854,7 @@
    "source": [
     "## Solving for $N_0$\n",
     "\n",
-    "Previous steps estimated the field volumetric water content of `0.263` and an average neutron count of `1542`. Using [`scipy.optimize.root()`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html) $N_0$ is estimated given the observed value of $\\theta_v$ and neutron counts."
+    "Previous steps estimated the field volumetric water content of `0.205` and an average neutron count of `1543`. Using [`scipy.optimize.root()`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html) $N_0$ is estimated given the observed value of $\\theta_v$ and neutron counts."
    ]
   },
   {
@@ -864,7 +867,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The solved value for N0 is: 2470\n"
+      "The solved value for N0 is: 2434\n"
      ]
     }
    ],
@@ -925,4 +928,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index 2b1f749..93e49e3 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -706,7 +706,7 @@ def abs_humidity(relative_humidity, temp):
     return abs_h
 
 
-def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=None, Hveg=0, tol=0.01):
+def nrad_weight(h, theta, distances, depth, profiles=None, rhob=1.4, method="Kohli_2015", p=None, Hveg=0, tol=0.01):
     """Function to compute distance weights corresponding to each soil sample.
 
     Args:
@@ -714,6 +714,7 @@ def nrad_weight(h, theta, distances, depth, rhob=1.4, method="Kohli_2015", p=Non
         theta (np.array or pd.Series): Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)
         distances (np.array or pd.Series): Distances from the location of each sample to the origin (0.5 - 600 m)
         depth (np.array or pd.Series): Depths for each sample (m)
+        profiles (np.array or pd.Series): Soil profiles ID for each sample. Required for the 'Schron_2017' method.
         rhob (np.array or pd.Series): Bulk density in g/cm^3
         p (np.array or pd.Series): Atmospheric pressure in hPa. Required for the 'Schron_2017' method.
         Hveg (np.array or pd.Series): Vegetation height in m. Required for the 'Schron_2017' method.
@@ -950,6 +951,9 @@ def rscaled(r, p, y, Hveg = 0):
             Fveg = 1 - 0.17 * (1 - np.exp(-0.41 * Hveg)) * (1 + np.exp(-9.25 * y))
             return r / Fp / Fveg
 
+        if profiles is None:
+            raise ValueError("Profile ID's must be provided when using 'Schron_2017' method")
+
         # Rename variables to be consistent with the revised paper
         r = distances
         theta_ = np.mean(theta) # Start with the mean value of theta as initial guess
@@ -971,12 +975,12 @@ def Wd(d, r, bd, y):
             r = rscaled(distances, p, theta_, Hveg)
 
             # Calculate the vertical average for each profile
-            P = np.unique(distances)
+            P = np.unique(profiles)
             theta_P = []
             r_stars = []
             for i in range(len(P)):
                 profile = P[i]
-                idx = distances == profile
+                idx = profiles == profile
                 depths_P = depth[idx]
                 r_P = r[idx]
                 theta_Pi = theta[idx]
diff --git a/src/tests/test_calibration_rdt.py b/src/tests/test_calibration_rdt.py
index fdeb644..45aa659 100644
--- a/src/tests/test_calibration_rdt.py
+++ b/src/tests/test_calibration_rdt.py
@@ -10,6 +10,8 @@ def calibration_example():
     url_soil_samples = "https://raw.githubusercontent.com/soilwater/crnpy/main/docs/examples/calibration/soil_data.csv"
     df_soil = pd.read_csv(url_soil_samples)
 
+    df_soil['ID'] = df_soil['latitude'].astype(str) +'_'+ df_soil['longitude'].astype(str)
+
     # Define start and end of field survey calibration
     calibration_start = pd.to_datetime("2021-10-22 08:00")
     calibration_end = pd.to_datetime("2021-10-22 16:00")
@@ -71,7 +73,9 @@ def calibration_example():
                                          df_soil['distance_from_station'],
                                          (df_soil['bottom_depth'] + df_soil['top_depth']) / 2,
                                          rhob=df_soil['bulk_density'].mean(),
-                                         p=df_station['barometric_pressure_Avg'].mean(), method="Schron_2017")
+                                         p=df_station['barometric_pressure_Avg'].mean(),
+                                         profiles=df_soil['ID'],
+                                         method="Schron_2017")
 
     # Apply distance weights to volumetric water content and bulk density
     field_bulk_density = np.sum(df_soil['bulk_density'].mean())
diff --git a/src/tests/test_functions.py b/src/tests/test_functions.py
index 820fd50..01ee5c9 100644
--- a/src/tests/test_functions.py
+++ b/src/tests/test_functions.py
@@ -42,6 +42,7 @@ def test_count_to_vwc():
 def test_weighting():
     # create vector from 0 to 350 m with .1 spacing
     x = np.arange(0, 350, 0.1)
+    IDs = np.arange(0, 3500, 1)
     y = np.repeat(0, len(x))
     h = np.repeat(10, len(x))
 
@@ -51,8 +52,9 @@ def test_weighting():
     SM = 0.02
     sm = np.repeat(SM, len(x))
 
+
     # calculate the weighted theta
-    theta_adj, w = crnpy.nrad_weight(h, sm, x, y, bd, method="Schron_2017", p=p, Hveg=Hveg)
+    theta_adj, w = crnpy.nrad_weight(h, sm, x, y, profiles=IDs, rhob = bd, method="Schron_2017", p=p, Hveg=Hveg)
 
     # check that theta_adj is = SM +- 1e-3
     assert np.abs(theta_adj - SM) < 1e-3

From 82e60936e71ca5d96b8171539256fe20b93d5452 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Sun, 21 Apr 2024 09:42:07 -0600
Subject: [PATCH 23/24] =?UTF-8?q?Added=20Schr=C3=B6n=20et=20al.=202017=20r?=
 =?UTF-8?q?eference?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 docs/examples/calibration/calibration.ipynb | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/examples/calibration/calibration.ipynb b/docs/examples/calibration/calibration.ipynb
index a97dedf..9513800 100644
--- a/docs/examples/calibration/calibration.ipynb
+++ b/docs/examples/calibration/calibration.ipynb
@@ -895,7 +895,9 @@
     "\n",
     "Dong, J., & Ochsner, T. E. (2018). Soil texture often exerts a stronger influence than precipitation on mesoscale soil moisture patterns. Water Resources Research, 54(3), 2199-2211.\n",
     "\n",
-    "Patrignani, A., Ochsner, T. E., Montag, B., & Bellinger, S. (2021). A novel lithium foil cosmic-ray neutron detector for measuring field-scale soil moisture. Frontiers in Water, 3, 673185."
+    "Patrignani, A., Ochsner, T. E., Montag, B., & Bellinger, S. (2021). A novel lithium foil cosmic-ray neutron detector for measuring field-scale soil moisture. Frontiers in Water, 3, 673185.\n",
+    "\n",
+    "Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M., Rebmann, C., Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.: Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity, Hydrol. Earth Syst. Sci., 21, 5009–5030, https://doi.org/10.5194/hess-21-5009-2017, 2017."
    ]
   },
   {

From 0692b6b177027dbfeca0e2c38d20de45d68803d5 Mon Sep 17 00:00:00 2001
From: Joaquin Peraza <joaquin@peraza.uy>
Date: Sun, 21 Apr 2024 10:05:06 -0600
Subject: [PATCH 24/24] Fixed bug in `smooth_1d()`

---
 src/crnpy/crnpy.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/crnpy/crnpy.py b/src/crnpy/crnpy.py
index ce46eb1..5f0a405 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -503,7 +503,7 @@ def smooth_1d(values, window=5, order=3, method='moving_median'):
         Analytical chemistry, 36(8), 1627-1639.
     """
 
-    if not isinstance(values, pd.Series) and not isinstance(x, pd.DataFrame):
+    if not isinstance(values, pd.Series) and not isinstance(values, pd.DataFrame):
         raise ValueError('Input must be a pandas Series or DataFrame')
 
     if method == 'moving_average':