diff --git a/.gitignore b/.gitignore index 0e0d26a..d8393b6 100644 --- a/.gitignore +++ b/.gitignore @@ -8,4 +8,7 @@ site/* .cache/* .pytest_cache/* -.idea/* \ No newline at end of file +crnpy.egg-info/* +.idea/* +/.idea +.idea/* 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 7fc4fdf..0000000 --- a/.idea/crnpy.iml +++ /dev/null @@ -1,14 +0,0 @@ - - - - - - - - - - - - \ 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 @@ - - - - \ 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 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \ 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 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml deleted file mode 100644 index efc4ace..0000000 --- a/.idea/misc.xml +++ /dev/null @@ -1,4 +0,0 @@ - - - - \ 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 @@ - - - - - - - - \ 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 @@ - - - - - \ 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 @@ - - - - - - \ No newline at end of file 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: 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 index a7a73de..da3e199 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 = (xhigh_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. @@ -243,11 +241,13 @@ 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 """ # 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 @@ -278,20 +278,23 @@ 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: (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. + 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: @@ -307,7 +310,7 @@ def correction_incoming_flux(incoming_neutrons, incoming_Ref=None): - fi: incoming neutron flux correction factor $$ - f_i = \frac{I_{ref}}{I} + f_i = \frac{I}{I_{ref}} $$ where: @@ -318,20 +321,48 @@ def correction_incoming_flux(incoming_neutrons, incoming_Ref=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: - 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 + 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 - 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 +385,9 @@ 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 +395,25 @@ 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 +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
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 +453,32 @@ 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(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). @@ -442,8 +497,14 @@ 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(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': @@ -454,13 +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 @@ -483,7 +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. @@ -505,7 +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. @@ -525,7 +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) @@ -535,7 +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. @@ -562,13 +627,11 @@ 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 +661,25 @@ 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 +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 @@ -642,81 +706,305 @@ 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, 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: - 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) + 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. + 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). 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': + # 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('Input distances are not valid.') - + 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 + + 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 + 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(profiles) + theta_P = [] + r_stars = [] + for i in range(len(P)): + profile = P[i] + idx = profiles == 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('Air humidity values are out of range.') - + raise ValueError(f"Method {method} not recognized. Please use one of the following methods: 'Kohli_2015' or 'Schron_2017'") - # Combined and normalized weights - weights = Wd*W/np.nansum(Wd*W) - 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: @@ -738,7 +1026,6 @@ def exp_filter(sm,T=1): Soil Science Society of America Journal. """ - # Parameters t_delta = 1 sm_min = np.min(sm) @@ -746,18 +1033,18 @@ 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 +1054,7 @@ 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 +1075,10 @@ 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 +1089,102 @@ 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) + 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): @@ -817,6 +1194,7 @@ 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 +1224,7 @@ 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 +1235,10 @@ 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 +1250,8 @@ 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) @@ -882,26 +1262,30 @@ 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}) - # Interpolate nan values - incoming_flux = pd.Series(incoming_flux).interpolate(method='nearest', limit_direction='both') + # Set the Timestamp column as the index + df_nmdb.set_index('timestamp', inplace=True) - # Return only the values for the selected timestamps - return incoming_flux + # 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): @@ -927,7 +1311,7 @@ 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. @@ -940,10 +1324,11 @@ 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. + (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) @@ -951,68 +1336,19 @@ 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#) """ + # 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 - # 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 +1404,6 @@ 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 +1414,7 @@ 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 +1495,8 @@ 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 +1531,9 @@ 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 +1544,6 @@ 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 +1577,13 @@ 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 +1610,8 @@ 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/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/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 @@ - diff --git a/docs/examples/calibration/calibration.ipynb b/docs/examples/calibration/calibration.ipynb index 34984fd..9513800 100644 --- a/docs/examples/calibration/calibration.ipynb +++ b/docs/examples/calibration/calibration.ipynb @@ -188,18 +188,18 @@ "" ], "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", @@ -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)" ] }, { @@ -372,28 +375,28 @@ "" ], "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 +553,28 @@ "" ], "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 +627,7 @@ "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" ] }, { @@ -803,14 +805,22 @@ "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 0.2047467442102135\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, 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_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" ] }, { @@ -823,8 +833,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mean volumetric Water content during calibration survey: 0.263\n", - "Mean corrected counts during calibration: 1542 counts\n" + "Mean volumetric Water content during calibration survey: 0.205\n", + "Mean corrected counts during calibration: 1543 counts\n" ] } ], @@ -844,7 +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." ] }, { @@ -857,7 +867,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The solved value for N0 is: 2644\n" + "The solved value for N0 is: 2434\n" ] } ], @@ -869,7 +879,7 @@ "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" @@ -885,8 +895,18 @@ "\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." ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af515744-a25e-4fb0-8fd0-3559b0fea276", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -905,7 +925,7 @@ "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 @@ "" ], "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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" ] @@ -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": [ + "
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RecordNumDate Time(UTC)barometric_pressure_AvgP4_mbP1_mbT1_CRH1air_temperature_Avgrelative_humidity_AvgVbat...timestampxytotal_raw_countsincoming_fluxfifpabs_humidityfwtotal_corrected_neutrons
022018/05/01 14:15:00962.95962.8961.423.235.420.972.713.574...2018-05-01 14:15:00641605.4591244.282933e+06305101.1051.059591.00013613.2316301.071451308.372165
132018/05/01 14:16:00962.88962.7961.323.335.521.072.713.417...2018-05-01 14:16:00641604.6484294.282929e+06345101.1051.059591.00067513.3086981.071867348.762056
242018/05/01 14:17:00962.64962.5961.123.435.421.272.213.282...2018-05-01 14:17:00641604.4266954.282844e+06294101.1051.059591.00252413.3714011.072206296.751456
352018/05/01 14:18:00962.41962.2960.923.535.521.272.013.191...2018-05-01 14:18:00641599.5414174.282997e+06319101.1051.059591.00429913.3343611.072006321.356301
462018/05/01 14:19:00962.48962.1960.823.635.421.472.213.130...2018-05-01 14:19:00641587.9848004.283574e+06379101.1051.059591.00375913.5271871.073047382.376183
\n", + "

5 rows × 47 columns

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" + ], + "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": 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", 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", 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" ] @@ -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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", 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", "text/plain": [ "
" ] @@ -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 @@ "" ], "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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", 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", + "image/png": 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", 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", 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", 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" ] @@ -560,7 +560,7 @@ "outputs": [ { "data": { - "image/png": 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wadIkTJkyBfv374/3uAmCIAiCIGRQ0QyCIJKRVoUUbtq0Cd9++y2GDBmi+vPTTjsNv/3tb7Fs2TK8/vrrWLt2LQYMGBDTQAmCIAiCIEJBRTMIgkhGWiW43n333YiOM5lM+N3vfteapyAIgiAIgogKCikkCCIZoSqFBEEQBEGkBORwEQSRjEQtuGpqalBdXQ0AqKiowMcff4xdu3bFfWAEQRAEQRDR4CeHiyCIJCQqwfXKK69g1KhRGD16NF566SVMmzYNxcXFuOqqq/DKK68kaowEQRAEQRBhIYeLIIhkJKocrueeew67du2C0+lE7969cejQIXTp0gV1dXU4++yzMXv27ESNkyAIgiAIIiRUpZAgiGQkKodLr9fDYrEgJycH/fv3R5cuXQAAmZmZ0Gg0CRkgQRAEQRBEJERbNOO1n36CZuHCRA6JIAgiOsGl0+ngcrkAAN988430eGNjY3xHRRBEwvnDl1/iw92723sYBEEQcYMJLp1GEzak0OP34+ZPPwUANHo8CR8bQRCdl6gE11dffQWTyQSgydViOBwOvPzyy/EdGUEQCWXpDz/gig8+aO9hEARBxA0muGxGY1iH65UtW6Svy2jjmCCIBBKV4FKGDpaVlQEAunbtijFjxsR3ZARBEARBEFHABJfVYAjrcBm0zUugchJcBEEkkJj6cE2aNCle42jBCy+8gMLCQpjNZowdOxYbN26M6Pfee+89aDQaXHbZZbLHBUHA/Pnz0a1bN1gsFkycOBH79+9PwMgJgiAIgmgP/IIAALAZDGEdrjSjUfqaHC6CIBJJTIJLECe2eLNy5UrMnTsXCxYswJYtWzBixAhMnjwZJ06cCPl7JSUluO+++zB+/PgWP1uyZAmee+45LFu2DBs2bIDNZsPkyZOlnDSCIAiCIDo2fEhhuCqFTu7nFQ4HAKDK4UjY2oYgiM5LTIIrUZUJly5dijlz5mDWrFkYPHgwli1bBqvVitdeey3o7/j9flx77bVYuHAh+vbtK/uZIAh45pln8NBDD2Hq1KkYPnw43nzzTRw7dgyffPJJQl4DQRAEQRBtCxNcaRHkcLl8Phi0WmSYTGj0eBAQBOT93//hweLithgqQRCdiJgEVyLweDzYvHkzJk6cKD2m1WoxceJErF+/PujvPfroo+jatStuvvnmFj87dOgQysrKZOfMzMzE2LFjQ57T7Xajvr5e9o8gUgG+dDJBEESq4AsEoAFg0evD5nC5fD5YDAakGY2wezzS8c9v2tQGIyUIojORdIKrsrISfr8f+fn5ssfz8/OlIh1K1q1bh1dffRXLly9X/Tn7vWjOCQCLFy9GZmam9K9Xr17RvBSCSFrCLUQIgiA6Ir5AAHqtFia9PqzD5fR6YdbrkWY0otHjkY5v9HgwRywXTxAEEQ9iElw6nS5e42g1DQ0NuP7667F8+XLk5eXF9dzz5s1DXV2d9K+0tDSu5yeI9iJcbgNBEERHxBcIQKfVwqTTReRwmfV62AwGNHo8snnxlZ9+AgCs2LoV/9yzJ6FjJggi9dHH8ss/iRNSPMnLy4NOp0N5ebns8fLychQUFLQ4/uDBgygpKcEll1wiPRYQw6X0ej327dsn/V55eTm6desmO+fIkSODjsVkMkl9xwgilSDBRRBEKuIXHS6zXo+aMEWxnD4fLKLDZfd6VQXaTf/6FwBAWLAgIeMlCKJzkHQhhUajEaNGjUIxl7QaCARQXFyM008/vcXxgwYNwo4dO7B161bp36WXXooJEyZg69at6NWrF4qKilBQUCA7Z319PTZs2KB6ToJIdUhwEQSRirCQQpaXFQrmcClDCgmCIOJNTA4X0CRcXn/9dZSVlaGoqAgjRozAsGHDYLVaW33OuXPn4sYbb8To0aNx2mmn4ZlnnoHdbsesWbMAADfccAN69OiBxYsXw2w2Y+jQobLfz8rKAgDZ4/fccw8WLVqEAQMGoKioCA8//DC6d+/eol8XQXQGSHARBJGK8IKrMYzgcnq9sBgMsBmNqHO5ZA5XkbiOIAiCiAcxC67p06dj27ZtGDNmDP79739j3759AIB+/fphxIgRWLlyZdTnnDlzJioqKjB//nyUlZVh5MiRWLVqlVT04vDhw9BqozPn7r//ftjtdtxyyy2ora3FmWeeiVWrVsFsNkc9PoLo6JDgIggiFWGCy2YwwO71hjzW5fdLDtexhgbJ4br0pJOw5fhx2bGNHo+sUTJBEEQ0xCy41q9fjzVr1mDMmDEAmkqpsxC/bdu2tfq8d9xxB+644w7Vn61Zsybk777xxhstHtNoNHj00Ufx6KOPtnpMBJEqkOAiCCIVicbhcrEcLrFoBnO4ci0WNLjdsgbIdS4XCS6CIFpNzIJr+PDh0OubT2MymTB69GiMHj061lMTBJEgSHARBJGK+AIB6DSaiAWXicvhcnGCq9HjgYNzyBxh3DKCIIhQxFw0Y8mSJZg/fz7cbnc8xkMQRBtQK1bvMkQZmksQBJHM+AWhKaTQaITH74cnRCEM5nDZWONj8dg8qxV+QUCFwyEdGy48kSAIIhQxr7YKCwtRX1+PwYMH48EHH8Snn35K/aoIIslh5ZLN+phNboIgiKSBDykEELJSYYsqhaLDlScW/TrW0CAde6C6OoGjJggi1YlZcF1++eUoKSnBGWecge+//x433ngjCgsL0aVLF0yaNCkeYyQIIs7UOJ0AmnaDCYIgUoUWgiuEM+Xy+WDS6aQ+XFJIoSi4jnOC64oPPkjgqAmCSHVi3t7euXMn1q9fjxEjRkiPlZSU4KeffsL27dtjPT1BEAmAOVyhwm0IgiA6GnyVQgAh87iYw8WOZfNirsUCQO5wEQRBxELMgmvMmDGw2+2yxwoLC1FYWIhp06bFenqCIBIAc7h8gQACggCtRtPOIyIIgogdJrhYuLQ7RIEgPqQQACodDmgAZIntYpSCi+ZKgiBaS8whhXfffTceeeQR1NbWxmE4BEG0BWwnFyCXiyCI1MEXCECn1cKo0wGAVAhDDaXgqnY6YdLrkW4yAQCONTbKjndS4QyCIFpJzA7XjBkzAAADBgzAtGnTMHbsWJxyyikYOnQojNSzgiCSEj6vwSM2/yQIgujoMIeLCa5wVQrNYpVCAKhyOmUC7HhDAwxaLbyBAICm0vA2WtcQBNEKYl5lHTp0CNu2bZMaHT/++OMoKSmBXq/HSSedRHlcBJGEePx+aSHh9vkAcUeXIAiiI8PKwpvETaRQgsutcLiqHA6piAbQFFKYYTKhSgzBdlL/QoIgWknMgqtPnz7o06cPLr30UumxhoYGbN26lcQWQSQpHr8faUYjalwuCikkCCJlUDpc4XK4THo9rGLRDBZSaNLpoNNocKyhAVlmM2xGIw7X1VHzY4IgWk1Cup6mp6dj/Pjx+P3vf5+I0xMEESNMcLGvCYIgUgEmuExhQgp9gQD8ggCzXg+L6IbVulww6XTQaDTShlS6yYR/zpwJACS4CIJoNQkRXARBJDe84AqVVE4QBNGRaOFwBZnfWM8ts14Pi+hw1bndUihihhhmnWEySYLsywMHEjp2giBSFxJcBNEJ8fr9UiUucrgIgkgVJIcrTA6XTHBxDhcrIHROYSEAIN1olEIOH/z660QOnSCIFIYEF0F0QiikkCCIVMQXCECn0cCgbVreRCK4DGLOlsvnk0IRp/TvDwAw6HSS4CIIgmgtcRNc48aNQ35+frxORxBEAvH4/bCJiwgSXARBpAp+0eHSabXQaTRBi2bwgguAFFbInDHW/NgfCERUCt7p9WLIiy9i54kTMb8GgiBSj7gJrpkzZ+KWW26J1+kIgkggshwuKnVMEESKwEIKgSbxFInDBUAKK2QOV6YYch0QBFgNBkwoLESfzMygz7u7ogK7Kyrw0qZN8XkhBEGkFHHrdnr33XfH61QEQSQYj9+PdAopJAgixeAFl1GnC1o0g200MYHFhJfS4QoIAgDgjF69cLCmpsV5dldUwO7xSM3k06mnIUEQKsRNcBEE0XHwBgKUw0UQRMohc7h0usgdLhZSKAowJrgE8XiDTgev4lyCIGDIiy8CAD4RS8dnkOAiCEIFKppBEJ0Qj98v5SVQWXiCIFIFVjQDaHK4og0pZN9nioIrz2oNeq4qp1P6ulr8Oj2CfC+CIDof5HARRCeEqhQSBJGKKHO4oi6aITpcaUYj3rv8ckwWqxWqCa4aTnDtqqgA0OyIfVNSArvXiwsHDIjDqyIIoqMTtcNVU1OD6upqAEBFRQU+/vhj7Nq1K+4DIwgiMQiCQDlcBEGkJH5BkOVwRV00Q9+8Dz1z6FAptFDtXLUul/T1p/v2AWjODTtnxQpc9M47VJSIIAgAUQquV155BaNGjcLo0aPx0ksvYdq0aSguLsZVV12FV155JVFjJAgijvgCAQBNO7oaUJVCgiBSh0iLZigFFyt2wRwuJeEE135xI5odw8awr6qqVa+DIIjUIqqQwueeew67du2C0+lE7969cejQIXTp0gV1dXU4++yzMXv27ESNkyCIOOEVBZdJp4NZr6ccLoIgUoZoi2YwR6urmKuVK/6vxKjTQUBTXy6deH5ecDHYfJpmNKLW5UKF3d76F0MQRMoQleDS6/WwWCywWCzo378/unTpAgDIzMyERkxSJQgiuWELEIMouFzkcBEEkSIoHa5Qgsug1UIrrl1YdcHu6emqxxvEc3r8fljEr+vc7hbHKcvNVzgcrX0pBEGkEFGFFOp0OrjEHZ1vvvlGeryxsTG+oyIIImGwBYiRBBdBEClGi6IZwfpw+f1SOCHQlPsFAN3S0lSPN4oCis2fb23bhjn//neL49jPWf+uE+RwEQSBKAXXV199BZO4C5TJdVx3OBx4+eWX4zsygiASAgkugiBSFR8X8hfO4eIFlyAKpLQgZd2VgusfO3bIHmcwgVcvul8UUkgQBBBlSCEvsni6du2Krl27xmVABEEkFhJcBBEf6lwuWAyGFotuov1Q5nAFm9+Ugmve+PFweL0Y1b276vFKwcVCB/VarUzUuX2+pn/iY9Vc6XiCIDovMffhqq+vx+uvv46ysjIUFRVhxIgRGDZsGKxBEk8JgmhfvCyHS6slwUUQMZD15JOY0r8/vrj22vYeCiHiV+Rw1avkWQFNgosvAV+Qlobll14a9LxMcLGiQ2rzJquK2ODxyJ6HIAgiZsE1ffp0bNu2DWPGjMG///1v7BN7UfTr1w8jRozAypUrYx4kQRDxgxwugogfqw4caO8hEByRFs1weL2wis2OI0HpcLF5ky8XlmU2w+P3o46rXkhVYAmCAOIguNavX481a9ZgzJgxAAC3240dO3Zg69at2LZtW8wDJAgivvCCy2IwkOAiCCJlUIYUBhM8dq8XtigEl0EZUqhy3hN2Oz7YvRs3jhgBAMizWml+JQgCQBwE1/Dhw6HnbHmTyYTRo0dj9OjRsZ6aIIgEQA5Xx2HL8eP4pqQE955+ensPhSA6BJE6XHaPB7YgBTLUYA4Xy90KNW/+VFYGAOhqs9H8ShAEgCirFKqxZMkSzJ8/H+4gcdIEQSQXJLg6DtNWrsTc//5XWuQRyQMr+00kF75AADqxt5ZJrw/62YnW4bKIG8tsvnR6vS2O+U3PngCAtYcPAyDBRRBEMzELrsLCQtTX12Pw4MF48MEH8emnn6K0tDQeYyMIIgGwpG/W+NhJC4KkJdtsBgDsPHGinUdCKKGFdPIhCAL8ghDS4XL5fFi+eTMa3O6oHC5WLr5RLIjB/tdoNHjq/PMBAF9edx0A4L8HDwJoElyUw0UQBBAHwXX55ZejpKQEZ5xxBr7//nvceOONKCwsRJcuXTBp0qRWn/eFF15AYWEhzGYzxo4di40bNwY99uOPP8bo0aORlZUFm82GkSNH4q233pIdc9NNN0Gj0cj+TZkypdXjI4iOiszhClE2mWh/+mRlAQA+FYsREcmDmsNBtC/MdeRzuHjB5fL58OwPP+CW//wHaw8fjsrhUgouO/f3/8O4cXA/9BAyTCZZi4Ask4nmV4IgAMQhh2vnzp1Yv349RohJogBQUlKCn376Cdu3b2/VOVeuXIm5c+di2bJlGDt2LJ555hlMnjwZ+/btU+33lZOTgz//+c8YNGgQjEYj/vOf/2DWrFno2rUrJk+eLB03ZcoUvP7669L3rIkzQXQmlCGFNdQnJmlhi/qvS0qwsJ3HQsghZzj58InuPe9w8Q7T7z/7DK9t3Sp9z/fhCoeNE1x8mCKrUsiEllmvl+ZYCtkmCIIRs8M1ZswY2BWd1AsLCzFt2jQsWLCgVedcunQp5syZg1mzZmHw4MFYtmwZrFYrXnvtNdXjzznnHEybNg0nn3wy+vXrh7vvvhvDhw/HunXrZMeZTCYUFBRI/7Kzs1s1PoLoyPCC66S8POyrqsLGo0cT9nyrDx3Cz1VVCTt/qlDe2IgD1dWyx2rF8tK1XJlpIjngHS7K50oO1AQXm+92V1TIxBYQ3efKqNPBqNOh0eNBpcMR9DgT53CR4CIIghGz4Lr77rvxyCOPoLa2Ng7DATweDzZv3oyJEydKj2m1WkycOBHr168P+/uCIKC4uBj79u3DWWedJfvZmjVr0LVrV5x00km47bbbUBVmEeh2u1FfXy/7RxAdHQ/X+Ph2sZ3D2FdekUJl4s25b76Jk194ISHnTiXm/PvfGPC3v6G8sVF6rEZcENaR4Eo6eIfLS3k6SYFScPFFM94WI25sBgP+MmECAKAihHBSI81oDCu4mNP18ZVXkuAiCEIi5pDCGTNmAAAGDBiAadOmYezYsTjllFMwdOhQGKNISGVUVlbC7/cjPz9f9nh+fj727t0b9Pfq6urQo0cPuN1u6HQ6vPjiizhfTGQFmsIJp0+fjqKiIhw8eBAPPvggLrjgAqxfvx46bkeKZ/HixVi4kAJ5iNTCyzlcOm3znsuvtbUYohKyGw/IAQjPr3V1AIC9lZXIT0sDAFQ7nci1WFBHVWCTDt7h8gYCoAD19ocJLp2Kw5VjsQAADtx1F7LMZjy8erVUzTBSmOBycH97kyIskQmubunp2FtZSRVGCYIAEAfBdejQIWzbtk1qdPz444+jpKQEer0eJ510UqvzuKIlPT0dW7duRWNjI4qLizF37lz07dsX55xzDgDgqquuko4dNmwYhg8fjn79+mHNmjU477zzVM85b948zJ07V/q+vr4evXr1SujrIIhE4/H7odVopEXJHWPG4PlNm1CfgEU9WwAR4WEVCZmr9WttLaqdTozv3RtrDx+GPxCQCWSifSGHK/lo4XDpdPALAvyBABxeLwrS0lAgbmb8Y9o0nNe3b1TnP1xXh0Vr1+KM3r0BAPeMHYs5o0bJjmECzGYwwEQOF0EQIjELrj59+qBPnz649NJLpccaGhqwdevWVomtvLw86HQ6lJeXyx4vLy9HQUFB0N/TarXo378/AGDkyJHYs2cPFi9eLAkuJX379kVeXh4OHDgQVHCZTCYqrEGkHB6/X1ZJ64Ezz8TzmzZJC/14QgU5IsciVkxj71nhs88CAIZ06YK1hw+jweNBlijKiPaHd7iCNdcl2ha/okqhmeud5fB6YeWqEl47fHirn+dXMYXi3tNPR+/MTNnP2NyaZjRSSCFBEBIJ2S5NT0/H+PHj8fvf/z7q3zUajRg1ahSKi4ulxwKBAIqLi3H66adHfJ5AIBCyGfORI0dQVVWFbt26RT1GgujIKAUXW8QnojBDqFwHQg4Lu6xWiNShYpgn5XElF7wj7CUnNylQOlxMYDm8Xjh9Pql5cWv57ciRAIBSMZ/bpJKOwB6zGY3okZ4ObyCAHYoNZIIgOh+tElyHxS7qkXI0ygpoc+fOxfLly7FixQrs2bMHt912G+x2O2bNmgUAuOGGGzBv3jzp+MWLF+N///sffvnlF+zZswd//etf8dZbb+E6sQlhY2Mj/vjHP+KHH35ASUkJiouLMXXqVPTv319WNp4gOgMevx8GLjTNajDAoNUmxI1igiua8sudFZbroXQaWV5dIkI+idYjE1zkcCUFSsHFXGOnisPVGh4To2GOiILLqCK42GM2gwEXDhgAk06Hrw8diul5CYLo+LRKcI0ZMwa33norNm3aFPSYuro6LF++HEOHDsVHH30U1flnzpyJp556CvPnz8fIkSOxdetWrFq1SiqkcfjwYRw/flw63m634/bbb8eQIUNwxhln4KOPPsI//vEPzJ49GwCg0+mwfft2XHrppRg4cCBuvvlmjBo1CmvXrqWQQaLT4Q0EZAsFjUaDbIslISGFVaKII8EVHtYviAnfgrQ0/HHcOMmBpLC15KKOHK6kI5TDFQ/BlSsW3tgsrj+UBTMA4MWLLsLVQ4c2bWTpdLAYDLJeYARBdE5atQravXs3HnvsMZx//vkwm80YNWoUunfvDrPZjJqaGuzevRu7du3CqaeeiiVLluDCCy+M+jnuuOMO3HHHHao/W7Nmjez7RYsWYdGiRUHPZbFY8OWXX0Y9BoJIRZQhhUBTWCEfUrh0/XpcNXQouqenx/RczOFS2wkm5DBBxYSv0+tFV5tNeu9o0ZZckMOVfLRwuERB5IyT4DKIn8WdJ04AUJ/Xhufn453LL5e+N+l0VKmQIIjWOVy5ublYunQpjh8/jueffx4DBgxAZWUl9u/fDwC49tprsXnzZqxfv75VYosgiMTh8fulhQMj22yWnBW3z4c//Pe/uPGTT2J+Lia4KHE8PGxRxnK42AKR5YTQoi254HPqyH1MDqSy8GK5d2VIoSVGwQUAM4cMAQBoNRpJ2IWCL01PEETnJaY4H4vFghkzZki9uAiCSH68ihwuQHS4xB175qS0Nmdo87FjGL18OX656y5JcDV6PBAEAZoo+950Jtycw+X1++ENBGA1GKRddFq0JRd1brfUl4lCCpMDf5iiGV1stpif4+KBA7Fy166Iewua9HpypwmCSEyVQoIgEs+eigqsLy2N+vd8gUBLh8tikTlc/P/RsqakBEBTngPL4QoIAi06wuDhcrhYjyer2MsHoJDCZKPe7Uae1QqAQgqThbAhhXHIJc2MMu+bHC6CIAASXATRYRn84osY99prUf+eLxBoEQqTbTZLuUNscdDaRYKBc2R+ra2VwnsaPZ5Wna+z4Pb5YDUYUONyST2eyOFKXup4wUUOV1IQqmhGlcMRlz52mVGew0SCiyAIkOAiiE6HmuDii2a4YxRcRi7naGtZGc7o3RsAYCfBFRK33498mw01TifsnOCiHK7kpM7lkqrWkcOVHAQrC7/u8GEcqq1t0aS4NbTG4aLPLkEQJLgIooPjEBfnkeITBHWHSxFSeLCmRsqJiAaWH1ZaX48alwu/6dEDADlc4fD4/ShIS4NfEHDCbgdADlcyU08OV9KhFFx6rRZ6rRbPiy1s4iK4onW49Hp46PogiE5PzILr8OHDEFSSRwVBiLpBMkEQkcMW4qwJZ6R4/f4WgivdZJIKW/C5Qu/s2BH1uNi5yxobAQBF2dkAILk2hDpunw+54gK+QhRcFr0eOq0WOo2GcriSDD6kkMRwciBVKVQ0dmfkp6XF/BzkcBEE0RpiFlxFRUWoqKho8Xh1dTWKiopiPT1BEEFIMxoBNJcRjxS1kEKrwQC/IMAbCMgWB+Xiwj8a2OKT/W7PjAwA5HCFwh8IwC8IyBZ3z6sVDaMp8T65+HjPHpyw26loRhTcu2oVnli3LqHP4Rc3f/n5jRXOKMzKwpju3WN+jmjzwCiHiyAIIMay8ACClnpubGyEOQ4JqgRBqMM+ddH2uPIFAi3KwvPJ5fzi4EB1ddTjYk4Mc96Y4KIcruCw95wt5lh1R1ah0KTX0y55klDtdOLy998HAAopjIJnNmwAANx/xhnQJqg9hDKkEGjO45r7m9/EpS1FtOcw6nRS1VGCIDovrRZcc+fOBdA0+Tz88MOwijceAPD7/diwYQNGjhwZ8wAJggiNM9ocriAOF9AkuJhgGtq1K441NLT43VUHDuDCAQOCLpqYMCitqwMA9EhPB0AOVyjYgl3pcLGCGeRwJQ9bjh+XvmZFM8L9bZb9+CMO19Xh8fPOS+jYOgJ2jwfpUYblRYqa4GLuYzzCCRmvT50qFRkKh0mvR10rexoSBJE6tFpw/fTTTwCaHK4dO3bAKIY3AYDRaMSIESNw3333xT5CgiBUYTut0e6ehhVc4vm6paW1CFe877//xbMbNmDTnDkYHSQ8hwm2442NMOp0Ul4S5XAFhy0UM8SFqDKk0KTTUQ5XkrCbC6EvSEuDQasN697e9tlnANBpBRfvztq9XpngimdDdDXBxebHHFEcx4ObothMphwugiCAGATX6tWrAQCzZs3Cs88+iwwxbIggiLaBOUytCSk0KxqAqjlc3dLTsenoUdlxXx48CKCpJHYw+MVFttkMrUYDs14fdTXFzkQwwWWiHK6ko8rhgE6jwYdXXolxvXoh02wmByMMFQ6H9DUvTteUlGDCihX49Z574lJBUE1wKT9bbQ3lcBEEAcShaMbrr79OYosg2pFoQwq9MThcrHpefYgFJu/EsJwkvVYrLXyIlrDy+5lBQgophyt5qHY6MaRrV1w2aBB0Wi0yTaaQGxBE87wByJ3uj/fsAQBsLSuLy/NIVQo5x6y9BZeR3GmCIBCHohkAUFxcjOLiYpw4cQIBxaLqtddei8dTEAShgO2axjukkJ23QBRcLOTnw927pWIODSFCqHhhEE5wffbzzzizd++oe9ukGmoOl16rlcpbk8OVPFQ5nbLwtGgcLn8gICtZ3lmo4QQp73D9KuZ5/umrrzCme3d0E/M9Wws5XARBJCsxz/wLFy7EpEmTUFxcjMrKStTU1Mj+EQSRGFgoYauqFIrOCUMZUqgB0NVmgzcQkHak2W40ADRE6XAZVASXPxDAxe++i5s//TSq8aci7L0x6nSw6PWodjoldwugHK5kolopuEymiAVXZw095B1ANp/Uu934Yv9+AMDeykpc/89/xvw8zCnWqQiudC7PvC2xGAwUTk0QROwO17Jly/DGG2/g+uuvj8d4CIKIAEEQJKHVqiqFiiR1ZUihSa+XKrBVO51IMxqxrbwct48ejZW7dsXF4WILL2UlxM4IvzOfbjLhUG2t9P4D5HAlE9VOJ3pxYfSZZnPEFetqXa64Fm/oKPAhyMzhqnQ4ZOX041E63RcIQKvRyCqoBsTeXLZ2ElxpRiNVaCUIInaHy+PxYNy4cfEYC0EQIfh03z78IrrG/OI7niGFdo8Hbr8fJp1OWhiyfKITdjt6ZGQg3WSKOIcrmxNcyuawbOGVqJ48HQlecM0cMgRAc8EM9jU5XMlBrcsla34bTQ5XpMIs1eCdPbbRonTm2RwUC2pzG6O95pl0ElwEQSAOgmv27Nl455134jEWgiBCMPW99zBm+XIAkIWotCakULko0Wu1sBoMqHW5JIdLKbjcPh9MOh3SjcaQIYUetZBCnS6owxWvktAdGV5wndqtG4DmxtYAlZZOJhxer8wticbB6KzNv+vdbnSxWqFBcz++thJcFn1cUtVbTZrRCI/fTw41QXRyYp6JXC4XXn75ZXz11VcYPnw4DIpJc+nSpbE+BUF0ethCjYkf3mGKukqh36+6C9zFakWFwwGDVgujisPl9vth1uuRbjKFDinkFhaZIUIKmWgkh0suuLrabADkhUlMOl3UwppIDA6vV7aID1cUgf9ZZ83lqRNdQYfXK81l7HrWaTTwC0LcBJdOMZ9svuUW7DhxIuZztxbWc6zR4+mU4aQEQTQRs+Davn07RopNAHfu3Cn7Ge1cE0R8KK2vl74WBEEK0dFpNK0KKVQWzQCALjYbKux25FqtMOl0yDSboQGkSoUevx8mvR5WgyHkc7p9PgzKy8OgvDxcMnAgADGkUOlwiQsvmiXUBRcvqo06XactuJBsOH0+mTjgwz2/KSnB0YYGXDNsmPTzssZG6etwzb9/OHIEBWlpKMzKiu+g25l6txsZYnER9h4wx/atadNwzccfQxBzrWJBzeE6uUsXnNylS8znbi1pohtKgosgOjcxCy7WAJkgiMRxWCyfDDQJILYYL0hLi0tIIdDscKUZjTDp9dBqNMi2WFDlcEi79CadDma9PqSr5vb7cUpBAd65/HLpsVBFMzQaDd7atg0jCwowLD8/qteSKvCCK0ulRL5Jr6eQpCQgIBarsfCCiwv3PGfFCgCQCa5dnLsSLqTw9FdfRbrRiPp58+I57Hanzu1GptmMaqezhcN1Vp8+uGzQoLjkOfkFIWgOV3vBBFeoMGyCIFKfuMxMa9euxXXXXYdx48bh6NGjAIC33noL69ati8fpCaLTU+VwSF/n/d//SUn6+WlpcSmaAYgOl8MhFc0AgJNyc7H5+HFpcWTS62HR68M6XCZF3oRaWXi28FpTUoIbPvkEN3zySVSvI5XgBVe3tDSc0asXHho/Xvq5UaulHK4E89Uvv4QVtexzYImioMnuigpYDQZoENrhYiXNQ4XrdlTq3W5kmkywGY0timaY9fq4VfILVTSjvUjnHC6CIDovMc9MH330ESZPngyLxYItW7bALe7i1NXV4fHHH495gARBtOzfwxyufJutdWXhVRYlOWYzapxOmWCa3K8f1pSUSAtKs14Pi8EQ1uEyKUIWQ+VwMXpnZkb1OlIJXnAZdDqs++1v8Zdzz5V+Tg5XYtldUYHz33oLZ772Gno9/bRUSlwJu+75kEJW0CTY3+dIfT16Z2bCZjSGzOE6msLtEerEkEKbwdDC4TLr9bAZDKkruMQcLtY0noQXQXROYp6ZFi1ahGXLlmH58uWyghlnnHEGtmzZEuvpCYJAk8DKNpth0euRZTajzu2GVqNBrtUadUihN8iixCo26PQEAjCKginPakWDxyO5KyaxMW+o52TVDHlUc7gUi0/l73QmeMGlhpEaHyeU3RUVAIBNx47hSH190A0FJpiUIYUevx814oIagEywldvtKEhLk4kNNfiw4VQjlMNl0uuRaTLFpWR+MgquXhkZ6JedjXd37sTB6mqkL16Mz8WGz2p8sX8/nt+4sQ1HSBBEWxDzzLRv3z6cddZZLR7PzMxEbW1trKcnCAJNVb4yzWbMPf30puRzlwsZJhOsYcL7AKBE8TkMtihhO/C8YLIYDPD4/dJCM6KQQrG4Bo9qWXjF4rMzF4UIJ7jCVcIjYmNbWZns+2Chfw+JOcvKohneQECq5gnIr+2yxsYmwcWJDTVS2flg85XNYGgumuH3Q6fRQK/Vont6Oo41NMRcOMMXCECXZIJLp9XinMJC7KusxC5R2P947FjQ4y985x3c+cUXbTW8pGN9aSnNdURKEvPMVFBQgAMHDrR4fN26dejbt2+spycIAs0hOWbRXap0OJBntYYtYPHPPXtQ9Oyz2FFeLj3mCwRgCOFw8YKJLSzZ7rNJpwsfUhjE4QpWNAMAJhQWhmymnOpE5HBRDlfCWF1SIvv+l5oaHOUqgzLe3LYNAFqUhQeaGoMz+M2DssZG5Nts0ucrGPxnKlhIY0elXiyakWY0ykIKzeL72CMjA06fL2aXKxkdLgDonp6O442NkihP4/q4xQtBEPDchg0dujhHjdOJca+9hoe+/rq9h0IQcSfmmWnOnDm4++67sWHDBmg0Ghw7dgxvv/027rvvPtx2223xGCNBdBh8gQDKuTLQ8YKF5DDBVeFwoKvNBovBEDK87/vSUgBABVd0I9guMFsQuniHS1wQ1TDBpdc3iTzxOevd7hbiS83h0mu18Cp2LdnC66WLLsKI/HypEEhnJKzDRTlcCWWrwuE6/dVX0fPpp4Meb1HkcAHy8u/sWj5QXY391dUYmJsbNqSQd41TqV9XQBCksvA2gwE/HDkCfyAgF1zp6QCa8t1iwZ/EgqussVGKNkjE33fHiRO4e9Uq/OXbb+N+7raCvS97KyvbeSQEEX9inpkeeOABXHPNNTjvvPPQ2NiIs846C7Nnz8att96KO++8Mx5jJIgOw5AXX0TBX/8a9/OysspMcJ2w25sEV5jwPiaU+IWe1++XFok8NoMBApoWi0wwWRQOl5mFFIo3xswnnsCY5ctl54nU4XJ4vRjcpQt+N3o0MsW8tM4K5XC1H16/P2io31e//KL6OO9Asc8K73Cxz92GI0fgCwRw3fDhTZsjIf6G/CI8XPn4joTd44EAINNkwqZjx+D0+fDJ3r0ywdVNFFxlMW5WJavD1S0tDb5AAAu/+QaAvOpsMJQbVOFwKnLjOiLsc+hPMYeXIIA4CK7S0lLMmzcP1dXV2LlzJ3744QdUVFTg0UcfxeHDh+MxRoLoEAiCgJ+rquJ+3i/278en+/YhSxRcHr8fZY2N6BJBSCETMSyUJSAI8AtC0JBCoGmxyASZ9Jj4+1JIIXdTZ3kJDNUcriAhhez8WWYzjjU04N0dO8K8G6kJ5XC1H6HC2M5/6y3Z9/1zcgAAQ7hGumxzoZwTXEw4MBGVZjRKmyXB4D/HqZTPxcrcpxmNOLtPHwBNC2oXVw2V9Z6LddMlWQXXWeLrZlRx+X7BiDa8komVZHz9kcKue+W9giBSgZg/mUVFRaisrITRaMTgwYNx2mmnIS0tDdXV1SgqKorHGAmiQ5Co/jkXvvMOgKawG7YjfKS+Hl2s1rAhhewGxm7wbNdU1eES8wpqnM6QIYUWUfSp5ZkIghBVDpdNFFz9srMBANd8/HHQ15LKsPdGp9Go/pzlcMVaVIBoCVvY3nXaaWGPtej1uGPMGFlILhMNZY2NyLVYYNLpcEws8e4QNxW0Gk14wcX9LJUEF3st6SYTnpg4EUDTPFTrcklCi/WqijWPM1kFV7bFgquHDpW+r06A4GLnVNtM6yiwa8VPgotIQWL+ZAZbADQ2NsIsTqYE0Rk4nuA+Ovk2myS4KhwO5FgsUkhhsM9hvcLhYi6JQUVw8Q4XX6UQaC6bzRwuAKrOmi8QgACo53CpVClkIm9gbq70eLShNKlAJDlcAhK/82v3ePClShGkVIYtbGedcgp+veeekMeqube8w5VtsaBbero0Fzg4F7ezOlyNnMPF3ju+8A/QVMnPZjDELrgEIeimRXuTzhXK4B2uwmeewZ+Li1scXxOl4GJRCNokff2RYCeHi0hh9OEPUWfu3LkAAI1Gg/nz58MqTpwA4Pf7sWHDBowcOTLmARJER+E4l3+QiJ3WgCBIggtoCsNhCxi33y/7GYMl77ObMRM9ag4XWxj6AoEWVQrf3rEDA3NzkWu1SrvSlSp5CCzPSOlwqZaF93qRLZ6rr+hwAcCvdXVS6FZngb03wRZLZm6huunYMcz617/w45w5UlPVePHW9u247bPP8MnMmZg6aFBcz52sMMGVbTaju5hLFAyPSlNv9lkqb2xEltkMg1YrzQW84DLpdGEdLqMYOhqqfHxHgxdcWo0GBq0Wbr8fVU4nuqWlScdlmEwp63ABzXlq1w0fjg1HjkiP/1pXh8fXrcNj550nOz5a0c021TpywRXJ4SInn0hBWj0z/fTTT/jpp58gCAJ27Nghff/TTz9h7969GDFiBN544404DpUgkhs+aT5eJbz5sL0p/fu3EFws5C/YTZblRLBwJU+IkEJ+B5adly9/vebGG6HVaNDVZgOg3ktGapCsdLg0GtWiGWwxatDp8PMddwAASlO4AWww/IIAvVYLTRDBxf4OTp8PXx44gJ+rqvBOAvLd2HW0bPNmAE0RDMt+/LFDl5oOBxNcWWZz2MW6WxRFPOxaL7fbkW02I8diQY3LhYPV1ah3u2UOV6h5wen1oou4cZmqDheAFq0tGPEQXMlapRAA5p15JvbdcQeGdOkiOVyhQoSjvQbYXJ+o0Pa2gL3mzhjlQKQ+rZ6ZVq9ejdWrV+PGG2/EF198IX2/evVqfPnll/j73/+OAQMGtHpgL7zwAgoLC2E2mzF27FhsDNF5/eOPP8bo0aORlZUFm82GkSNH4i1FsrMgCJg/fz66desGi8WCiRMnYn+Ibu8EES286IlXRTnmUL0/YwaGdO0qE1yZZrMUkhdMcNUHEVxqcf79cnJQlJUFAJJzwpe/Zju0bFE444MPWpwjmMMVrCy8jTt/jsUCIPpQmlQg3M48W7TzIvVQAhrLs+ppbAd+a1kZbvvsMzz53Xdxf6625ov9+/ED5ywwal0uaNB8zT9+7rlYcdll0s/5TQ+PSkghE8PHGhqQZTYj02zGp/v2of/f/oY3tm2TrvFwIYUOr1cSICS4WkcyO1wmvb4pSsBiQY3TiYBYOCQY0V4D7FwdWXAxZ7cz3gOI1Cfmmen1119HRkZGPMYisXLlSsydOxcLFizAli1bMGLECEyePBknTpxQPT4nJwd//vOfsX79emzfvh2zZs3CrFmz8OWXX0rHLFmyBM899xyWLVuGDRs2wGazYfLkyXDRB5uIE3wORrwqyrEbT7YoRvjFRJbZLC3m1MpIBwRBcibY2EIVzdBqNBhZUACg2e1KMxpx++jR+Pyaa6TjuogOlxpBHa5gRTM4V42FKkaSUJ5qhFso8nlzbEF1PAH93liYaI3LBY/fj6NiLlKy5sUoqXI40HPpUuxT6eNz4Tvv4PRXX23xeK3LhQyTSQrnnDd+PG4YMQIfXHEFAMj6w7lVWiowIeELBJBlNiODu6aVDle4kMJM0WVLRcHF5iqT+D5UiXmojHi0hkhmwcXIsVggoOm64wtjbC0rk21KtVpwdWA3mkWJ8NEiBJEqtDqHi6e4uBjFxcU4ceIEAopF1WuvvRb1+ZYuXYo5c+Zg1qxZAIBly5bhs88+w2uvvYYHHnigxfHnnHOO7Pu7774bK1aswLp16zB58mQIgoBnnnkGDz30EKZOnQoAePPNN5Gfn49PPvkEV111VdRjJAgl/GIqXiGFLPeK5ToVig4U0CRQ2CJRLeejUex/w48tVNEMoFkoZYi7/VqNBi9cdJHsGCvnSjG8fj8MXK+oiHK4FA6XTqtFltlMgksF3uFiC7FEFGmp5N77E3Y7fhVdtMwOUgDpp7IyHG1owAubNuG5Cy6I6HdYjzsluaIYqHQ4pA0PtRyuNE5gZZvNLcJCIxFcgiBgT2UlemdmwmowhGz10NGwezyw6PVSZUezXo86lwveQACZXA5iqjtcjF6ZmQCammLzYdwrd+7EQ2edJX0freBi95xQfRmTnQ927wbQJEbdXNsAgkgFYp6ZFi5ciEmTJqG4uBiVlZWoqamR/YsWj8eDzZs3Y6JYPhYAtFotJk6ciPXr14f9fUEQUFxcjH379uEscfI6dOgQysrKZOfMzMzE2LFjQ57T7Xajvr5e9o8ggsHf6OIVUlircLgK0tJwp1i+OtNkCulwsRt2ttksjS1U0Qyg2ckIV4yBFblYfsklAJpfeyiHS1mlkA+PY+RYLJ1WcIVykdj75PT5pB3sRDlcJ+flAWgqAnGUq7bXEWDvza9R5AHWuVyyhT+Dhbvx+TYeFYdL6dIqz8UqfIYSXEcbGrDl+HFcO2xY2GbmHY1Gj0cmSk06HU6ITiovdDuL4BqRnw+jTof1paVSBMPA3FxsOHpUNkdG7XCJ95yOLNaPNTTgPLGdUEUEzaEJoiMR8/bBsmXL8MYbb+D666+Px3hQWVkJv9+P/Px82eP5+fnYu3dv0N+rq6tDjx494Ha7odPp8OKLL+L8888HAJSVlUnnUJ6T/UyNxYsXY+HCha19KUQng7/Rxc3h4iqoMZ6aNAkXDxyILjabJOzUHC62uMuxWKSxhcrhAppDFvmdVzXW33wz9lVWSuLI5fMhw2QK6nAZdbqWOVyKkEI21ppOKriCuY4AZMVRGsW/ZSKEaaXDgcFdumBPZSXK7XZp0RfrQritYM2HlQJx3eHDQX+nzu2Wwll5cpngEhd+bDGs3EzQajSwGgxweL3IMptb9KcrFTfqQgku9hy9MzObGot34EWzEqXgMuv1UshYBu9wGY1xEVy6JBdcLJfrQHU1BojtMEZ164bNx4/LQtFbG1LYUcW6IAiwezxSFEeF3Y6ecU5XIYj2JOaZyePxYNy4cfEYS0ykp6dj69at2LRpEx577DHMnTsXa9asiemc8+bNQ11dnfSvtLQ0PoMlUhL+Rre6pCQu56xxOqGBfCfYqNNhUr9+ABDS4WI34GyLRfo6VA4XwAmuMA5XV5sN4/v0kYp4uMM4XKzcNcPj98MXCMhCCoEmh6C2gyzu40mkIYVOr1dycRKRq1HlcEgl+evdbmnR11HyQspF149vWeAPBDD+9deD/k6tyxU2pBBovrbVPjvMm8y2WFqca8/vfw9ALrje2bEDmoULpc8Ev7FiTnGHK6jgikeVQrHaZ7KTLc5zLIKhMCsLtS5XRDlcTq8XR1Wibdj12RHcaEEQ8Kf//Q/7q6qkxzx+P/yCIBVlSqXPAEEAcRBcs2fPxjvvvBOPsQAA8vLyoNPpUF5eLnu8vLwcBWJCvxparRb9+/fHyJEj8Yc//AEzZszA4sWLAUD6vWjPaTKZkJGRIftHEMFwcSWj7161Ki7nrHO7kc4l9CthDpGaw8V2yfmQwlBl4YHmkMJIiyTw/aGA4FUKlYKLCUSlw2UJU1jg8/37sWLr1ojG1pGItGgGn8PV6PGELCsdLYIgoNLhQC9xnrN7PM0OVwcp4nBEXIhWcEn3xxS5btvLy2UuVJ3brRpSaNLrkWY0SiGFniDXNtDsfmWZzRjdvTvGdO8u/WyQGKJp1uvhFwT4AgG8uW0bgGaXUsrVFJuZh/oMdDRahBRygqsz5nABTdfJP7ZvxytbtkCv1aJ7enqT4OJCCoNVG7zqo4/Q8+mnWzwuOVwdQHA5fT4s+f57nPryy9Jj7B7GQnlT6TNAEEAcQgpdLhdefvllfPXVVxg+fDgMih3rpUuXRnU+o9GIUaNGobi4GJeJpXkDgQCKi4txh9inJxICgQDc4uRdVFSEgoICFBcXS82Y6+vrsWHDBtx2221RjY8gguH0epFpMsU19rzR4wkZ3mfU6aDXakM6XFlmc8uQwiCC68ohQ/Dyli1SYnc4WgiuCB2uzcePA2hZgMOs14esVHaRuLmTZ7XiooEDIxpjRyCs4OL6cDV4PMg2m1HjcsHu9coWs7FQ53bDLwjoarPBrNfLxF1Hcbg2ib3hTtjtEAQBGo2mRT7XiGXL8Og55+Dhs88G0JTDNaxrV9Xz5VosUrifO8RmhY8TXIPy8rBxzhxsLy+XOW0sPOqTvXulsLdqpxMFaWmSw5VlNjeFFKbQYrNREToczuFif7fW4AsEoO8AFTWZ3F9dUoI8qxXZZjM8fr8kOEM1yV596BCApiq0/EYcuz7r3W7sKC/HMEUKRTJhV2waaTQaaa7JJcFFpCgxbwVt374dI0eOhFarxc6dO2UNkLe2cid67ty5WL58OVasWIE9e/bgtttug91ul6oW3nDDDZg3b550/OLFi/G///0Pv/zyC/bs2YO//vWveOutt3DdddcBADQaDe655x4sWrQIn376KXbs2IEbbrgB3bt3l0QdQcQKK+scT5S7w2r4AgHc/vnnLXJ6+BwuKaQwTNGM8/r2hbBggaxccyhMrXS4zhf75ClDCs16fUQ7tBe/+25E4+sohBNcOq0WRp0ODjGksLvYE40XQv5AIKb8NyYO8qxWKSeJ7bIf6QAFg3yBAHacOIFzi4rgDQSkcC1WaXH1jTdKx27kmnYHc7iApveCvS+Sw6VSOY05Znyu5fD8fJwrFgAAgPOKipBvs2F7ebm0UGZirsbpRLrRCL1W21Q0I8xnwBcI4LFvv0VJAnqxxRvlHMbPPUrBFRAEVbc+UjqKw8VvkNU4nVIOIROi6SZTUMHBNqmUn3V2vN3rxfBly+BXFClKJviwR5bjyN4T5nB1BKeOIKIhZodr9erV8RiHjJkzZ6KiogLz589HWVkZRo4ciVWrVklFLw4fPgwtN6na7XbcfvvtOHLkCCwWCwYNGoR//OMfmDlzpnTM/fffD7vdjltuuQW1tbU488wzsWrVKpg7SLljIvlhhSPiSSSC6+6xY/Hshg2Y8+9/46Mrr5SNB1APKQxWNCNaWutwMZQhhWa9HvVuN47U13eqhOlIFoqsXHijx4Oi7GzsqqhAg8eDbuLPH169GovXrUNg/vxWOQRVKoKL7TrvrqhI+sVsvduNgCBgUG4uvj50CDUuF7ItFvxaV4cciwVn9+kjHctfi8FyuICm3fZNx45BEISQOVxXDhmC93ftQtcQPeo0Gg3yrFbUuVxSyK4UUuhySYvuSHK4dp04gYdWr8Yn+/Zh05w5IY9tbxo9HvTmHPMrBg/GJ2IBLH6eYHNntdPZatc22a9RBp+f5RcEqQotC4VNNxqDFl5igqvC4ZDcIKBp7rUZDJJgbfR4kradAy+qD1RXo3dmpvQYy50kh4tINZK2ycEdd9wRNIRQWQxj0aJFWLRoUcjzaTQaPProo3j00UfjNUSCkOH0+VCQlhbXc0YiuJ6ZMgUunw/fc0VdXtq0CT+Ku/gspFAQhLBFM6JFKpohnjeUw+UXBPgDAVmehkUhzMx6PbaVl6PX009DWLBA9rN3d+yIy5iTkUgWihYxzK/B41F1uP7z888AxDDUVgh/5uTkKgTX6O7d8eOxY/ilpgYDc3NVy/knA+y6Yot73uHqk5kpE6FMcAmCgPogVQoBoKS2Fj9XVeGTvXvRTywmopbD9e7ll+NvF1wgLZyDkWk2o9xul8bCnMNGj0cSHBaDIWwuEwtBjFc11ETS6PEgjbtezujVS/U41nT9u8OH0XvYsFY9V0eoUgg0lYHfxLmsTGQs/OYbAHKHyx8I4J5Vq3C4vh5vT5/eLLjsdik/EGgSKFlmsyRc6oP0l0sGeIeLbfQwhyuHBBeRosRlZqqtrcVf//pXzJ49G7Nnz8bSpUtRF0UfFIJIBZxeL7LNZiw+7zwpLCJWIhFcANDFapUWmABw++ef4zUxpLerzQYBTbuK4YpmRIvS4fL4/dAALcQDez5vICCV7uZ/X+17ZRn52z77TPZ9PAtGtDeROlyNHg8cXi+6icKeT6zXKFyTaJEEl8UCGye4Boqlq8saG7Hku+9ge/zxFoUokgEmUvqIZaUlwVVXJz3GYJ+DRo8HAUEIGlLIzlnrcoUMKdRqNCHdLYYGwMpduySH594vv0SdyyX7nEcSUsheW7zy9xKJXTGHBQtX7pGRgf45OdJGUWvwdxCHa9nFF2PTnDmYUFiIq4YOxcDcXJzdpw/2iVX70o1GafPq3z//jOc3bcKn+/YhffFimcPF4/L5ZLm5ydzKgQ+pZPMOc/3STSYYQ+SwdXQcXi8+Ehs8E52LmGemH3/8Ef369cPTTz+N6upqVFdX4+mnn0a/fv2wZcuWeIyRIDoEDq8XFr0+ZMJztDR6PC3C7tTIMptlgounm+iGVDudUg5XqJ5P0aAWUmjS61uEtDHB5fH7pZvtyxdfLLkGDN7xUgoHtmhjfVpqgrxenoAg4LsQPZiShYgcLoMBleJ7wgQXH5rE3vFYBFe60QiTXg+rGJpU73ajL9cXZ7k4px+srm7VcyQSpcPFclx+ratDH0URGCae3hA3JYI5AV+J/SU9fn/IkMJI4f82RVlZ8AYCuOKDD1oKrjDzB3ttkcwN7Y1y0yiUSMwwmWIqa95RQgrTjEaM7t4dX994I969/HJoNBrcITa0Zz9n11ulQlix64+vxAk0RRfw953H161L1PBjhv8b3/755wgIgvSY1WBIuUqdPPf997+Y8cEHUr4e0XmIeWa69957cemll6KkpAQff/wxPv74Yxw6dAgXX3wx7rnnnjgMkSBix+P346oPP8TmGHZPw8Ea+YZqcBotynCcYGRbLGjweKRqaQy9Viu5bVUOh+RARVr2PRxqZeHVQq5kgku8sZ7F5dQozwdAKsfNYGFyrDDB8Qhclhc2bsSZr7+OXSdOhD22PYnU4WJ9pvJFwaW2OI1FcLFrxazX492dO+HwejEgNxc6jQYVDofkBB0Xx5FM1IkCnJW1r3W5IAiCFFLI8+OxY9hbWYm7xPYNwUIkh3Tt2tQziXe4YhBcvOsw+9RTMb53b5TW12N7ebkkniIpHMM2VzqCy1svtrZghMovtBoMcMQwd3YUwaUGHxXBhxQqK4QyMa7mcPEhhu8kcQi2ct463tAgvV6LXt8ij7HO5YJm4UJ8vn9/m44zERysqQHQMoKDSH3i4nD96U9/gp5bKOn1etx///348ccfYz09QcSFHeXlWLlrFy6MY884JXaPBzaDAWa9Hr5AIC5VoiINKWQ5KHUK18cXCEj5AVVOJ7x+Pww6XavLLithi0+3wuFSouZwqe3O8/kXv/vPf7B47Vrpe1bRkL3WSMLatom995J9WRppDhfbFWXha/zCPNaQwiqnU0rCLz50SBIYeVYr8qxWVNjtkjOazCGFORYLMkwm1LpcqHQ44PT5WoQUAsDIZcskp5CFTaqRZTajzu0OWRY+2jECTWHA5/fti72VldhTWSkVsrEYDGE3bJi7q9xgSTbcYhuDSEOsWWGY1tKRBRc/z/MhhQ0eDwrS0jCkSxcAzeF4SofL5fNhxskn46dbb5UeS9ZKhcpKlE6fT7rmjTpdi01LNt+8ncQiMhJcPh/+e/Cg9DXRuYh5ZsrIyMBhlZCd0tJSpIuhTATR3mwR+z6dsNuhWbgwIQsV3uECmgtIxOOc4WCuT41KbgfLmWAOV7zyt4CmRT4fbx+Nw6UsCQ/IBcTaw4fx4NdftzhHkbh4jmTRz8RHMhZ54InU4WKCK9tshkGrle0Us2taGYIUKbVcpbyhXF+qXIsFXWw2VDgc0t8nEnexral3u6HVaGA1GJBpMqHO7ZZ6cDGHS9m3aEBuLq4dNixksRuzXo/F69ZhvViURm1DIVLuGjtW+npU9+6yvC/2uYgmpNCbpAtqBnOplYKr+IYbsPf3v29xPCvW0lo6suBKV4Rd8g5XutGIh886C0Czu8w7XP5AAB6/HxaDQfbZbe1coKTS4cDGo0fjci6gyeHSaTRSVV2H1wuXzwezGI7OCy5/ICA9d0coEhOKr8UeagBSqtceERkxz0wzZ87EzTffjJUrV6K0tBSlpaV47733MHv2bFx99dXxGCNBxIyy+WksN/VgsORwZW+qaBn0/PNYun49ANExikAgsYUyCzViC2ODVosMkwl6rRbVTic8fn/cSsIz+PAP5qApidThUvu7sD5KXcTF6dLJk5ElVnsLB1vwJXsr1EhzuI6KQifLbG6xOGUV78paGe7XwDXZXjtrlvQ3y7VakWY0SgU7gORMyK93u5FhMskWbEfF94S1GOBDaU/t1k1ypUOxp7ISAPDCpk0AYnO4Fp17LoQFC1D3wAM4tVs3WRsJFjpmicDlYdd+sjtczIVRCq5zi4pwEhf+xmCVOFuLLxCIW7h0W9PC4WKCS6w6yjbO2GePzwFim3tmvV42j7R2LlAy+R//wNhXXonLuQAxGsRolJxlu8cDt98vbVbyguvD3btx07/+BQCqrUV4Vh86JHP1dldUSFUQkwH+2iSHq/MR88rrqaeewvTp03HDDTegsLAQhYWFuOmmmzBjxgw8+eST8RgjQcSM8iYeb8EVEAQ4xT4oyrymaKhzubCvqgqPiK0P3H5/RDvqrBx1jRg26BcEvD51KtwPPQSNRoMci6UppDAQiKvDBTQ5VUxEBRMOSodLLzbyVaL2d2GFDZxeL64YPBiZZnPESdWtDa9rayJ1uFiD3W7p6bBwgmvB6tWS2G614OJybbLMZpwilunOtVgk14U9nysJ8w/qRMEFNC/Y2EYAE/dF2dkAgFtHjYIGkTnIo7t3B9B8LcWSw8Vg42TjAZoLoETSh4u5u8meB8I3046ETu1wceKbz+FqFDdC+J5bOo1Glkfp5NxRADhw550A4ie49ovVE+OVM8haS7DIA97hAuSCa2tZmfR7oeb8bWVlOPfNN7GMS2UZ8uKLmLBiRVzGHA/4wlYkuDofMc9MRqMRzz77LGpqarB161Zs3bpVqlRoinMTWIJQ49Z//xuf7tsX8phECy52Pj6ksPiXXyThFCmsJPJwscl3pCGAvMPFJx+zvJ4ciyUhIYVAU4U3tuvqF4Swgsvh9QZ1FdRKa7MwSYfXC4v4eya9PqLwklQSXGwxlW02wyxWEnSIlQQf/fZbAEBBWlqLghZ8wYdQ8A4XALw9fTruHjtWctOcXm+z4GrFYuHX2tq4LQDVqHe7paIe7PpQFrr4+oYb8MW11yJDDDkMdS0yvvvtbzGxb18pDzCen5/f9OyJmj/9CUBzDh7bTAi1uGV/42QPKUyk4NpTUYEbP/lE5vIFm386Avx1qMzh4h0uADgpL09ytIHm8DQ2P/YQHd14fd6YMOKrosaCXfzcsddsVwguPo+RlcoHQodIsvsEe1/Y52dHEhVM4gVXLLmKRMek1TPT119/jcGDB6NevLitViuGDRuGYcOGwev1YsiQIVjLJbwTRKJ4ecsWTH3vvZDHKHeM4y24pDA5zuG64ZNPpEaWkbK7ogIApMa2kYYUsh3zKz/8UAqj4yv+5TKHK0jIXyywxSsQ3uG6+qOPUOVwBHUVHjjzTPzv+utlOTVMzDl9Pkl0mHS6iHLkmOBKhaIZbNHDyvyzxSlbYJzesyem9O/fQnBlP/kkZrz/ftgxsFwRRr+cHDwzZQo0Gk1TmBvvcLVCcBU++yy6/fWvUf9epNRzDpdJp4OLK+XO3tseGRmY0r8/Mk0m1LvdUmhTKIw6HfLFjQCdRhP3xrpZZjNeuPBCfHDFFQCaF83Brm9BEHC8oQE6jSbpQwqljagIcyit4nUWCRe/+y7e3LYNe8Q5E+jYDhd/XdmMRgQEAb5AAA1uN9KMRpngGpibi3q3WwpDZZ9H3iHKMpvjJrjYNRlJK45ICOZwsXsd7/KWcsIyVN4u21xhGxfsnmSJIEKkraghh6tT0+qZ6ZlnnsGcOXOQIe6k8GRmZuLWW2/F0qVLYxocQcSLRDtcds7hUhZoiCYMY6+YL8J2uL2BQEQhhXwxgKJnnwXQfJMEmvJwqsQcrrg7XOLiFQgvuH6uqsJn+/cHLWJh0OkwsW9faSewe3p6s+ASb9JA5A4X35A5mYlGcHVXCC72Xi2/5BLk22yykuEs5OzfP/8cdgxsJ10Ni16PBq5SXzIuFuoVIYXM4TKqVOXMYIIrAocLgOScxfuzw7h9zBj0F3vSsQVisB1wu9cLp8+HHhkZSR9S6PH7oY1CpEaTw8UqsvIhZx1ZcPHwYekeMbeJ3ww5Scx9YmJEGVIINLndcRNc4nlr4hQxYPd4ZILL7vEEDSl0er24e+xYLL/kEpyw24Ne8zWKfF322oPNae1Brcslub1UNKPz0eqZadu2bZgyZUrQn0+aNAmbN29u7ekJIiICEYoZh9crK5yQSIcrS9FEtSGKMIwSsbiH3euVREJrF3kWhcPFGh/Hu2hGplg2GwgvuICmXcpwi1x2sz29Z8+IHK46lytk+GAyCgSeSBaK7EbNSpmznkVMYGVbLE0Ni8Xr7S/ffgvjokURj4HtpKth0esl51Sn0QR9Px8sLsZZr78e8XPGEz6Hy6TXw+33wxOkamaGmCPj8vkiqmDJnzfRsI2SYAsydp3n22xJ73BFu8ETKqRQEAR8sGsX/IEA3D6f5Bb8IvY1AlJHcPHtNnyBAPQajWzTYGyPHgCaxabS4QJEwRWn5rrsmoxXiLbD54PNaIROq4VJpwuZw+UUH++eng4BCFosiV0PfnFNIAmuJGoOXutySfN3st+TiPjT6pmpvLwchhA3Kr1ejwrO6ieIRBCpcHJ6vbL8oEQ6XErBVRtFGAYbl8PrlQRFa5P0edcrkTlcGUZjWIeLv+lVOZ1hw7i+vuEGvD51KrK4/LAWOVyc4Drp+eeRu2RJ0PMl+80tkoUiq7THBILVYMDhujrp+soym2HjFqyRuFoMr98Pt98fdHFiNRikinO5VmtQd3HxunVYq9ImRPlciUDpcLl8PriDXO+Z3Gc0krYL7PhEOVw84YruSIIrLS3pc7iiLdJjMxph93hUowLu+uILXPnhh3h582Ycqq2VNtvY3BsQBAQEIe4hn23JO9OnY/rJJ8uuAbW5oVdmJvpmZ0s5v8ocLqB5ky0esI2uaO5loWAOFyD+zUMJLq8XFlFwAcHDCpnDVacoHhRJH8u2osblQhebDdoQm1ZE6tLqmalHjx7YuXNn0J9v374d3bp1a+3pCSIi+CRel88XtHiGw+vFiIICyd2xxyn5l8E7XBkmk8xNi+YmxSZhu8cTtcP11fXX46dbb5V6m/ALST6HKxFFM+q4RqxqwkEpQsM5XBOKinDTyJGycEV24wWa3hN+0a+268m7n8l+c4uknHUPccHBXle+zYZ1hw/jx2PHYNBqYRELabAFKP+e6zQaDH/pJfz+s89Uz31ILL0frB+VxWCQdpBPzsuL6f1k5dXjTZ3LJcvh4kMKlfDl2CNZkLE+d23hnoQLKUxlhyvTZII3EGhxfbl8PjwvXjfby8ulkuhmLgSRlQPvyA7X1cOG4aMrr5ScVLffL5tT2QxhE/ttsRB0tZBCJl7jAdu8i9dGJV+shrmafFl4vgqty+eDxWAIK7iYA1/rdqPe7cbVH30kG3syUOtySUWPqGhG56PVM9OFF16Ihx9+GC6VxaTT6cSCBQtw8cUXxzQ4gggHf0MZ+uKLmPree7L+JAyH14uTcnNhf/BB6fu4joNzuLQajWwH/dqPP474PGwStnu9kqCINIzpvL59MbKgANNPPhkH7rwTI8Wy3kCTK1HjdMLp88W9aEZ6BA6Xctc5ElcBkBfkcHLhX8GKZjzw1VfS13UdKEE5EoeL9SFjTu2S888HAPz34EFkmc3QaDSwGY3wiU1Q+WWGXxCw48QJvMiVTOYp/uUX6LVajO/TR/XnfKjSiPx81feTX/yrORRss+PeL78M8SojJyAI0g4/0LTgyhULCzAH1O3zqX5+eMGVGUGORy+xcXJb9PQJF1LIxtDVZusQOVxRCS5FP0EGvzitcDgk0dkrI0Oae1koWUcWXAylw6Xjir4ATZsE/bOzsb+6WjqO/z1AbNcRp/sc+zTHax5lRTOA5rYioUIKLXo98qxW6LVaHGtoaKrM+s03sk031muzvLER+0QhCiBu70E84AVXst+TiPjT6pnpoYceQnV1NQYOHIglS5bgX//6F/71r3/hySefxEknnYTq6mr8+c9/judYCaIF/GR6UIzlP66yA8YmbYNOB71WG3fB1cg5XEDzjjgA7IyiLG0sDhdPPzEBn5FjsUBA02Il3g6XSa+XxhppDkWkVcuyLRapt5gvEAhbFv7J776Tvubdz0gKbLQnkbxvA3NzsXLGDPx5/HgATTldvTMzsauiQlqoKqt+KWHJ9kpK6+vRMyMjqNvDwnVOys1FmtGoem6+WpxSLLACMOzvHo/d3Te2bsWY5cuxtawMvkAA1U6nJEbNOp1UcEA1pJAXXAr3VY3CrCwAwSsHxpNIHC6tRoNciyXpQwqjbbTOXFm2ycLgr7daLl+zZ0aGrAcgkBqCq0UOl/ia+ojC32owYEBuLg7V1MAXCKiGFPL9ERn+QABLvvsu6vufmxM/8cCu4nC5uM0R5gAJgiAJMa1Gg25paTje0IBnfvgBC9aswcpdu6RzHhTF5+qSEny2f3/zc8U5miUWapxOqc1GMglBom1o9cyUn5+P77//HkOHDsW8efMwbdo0TJs2DQ8++CCGDh2KdevWIV/sJUQQiUKtL4haeBm/oxZrc0012KTObnjZXAlftd5SwWDNk+1xyOHiYTv/ZY2NcS+aYdBqpYVfvAVXntUKp88nhYtEWxaekey7iZG+b1cOGSJbVLFCGkwoSX1tPB7Vz0YwccH3sFLjmJgP8c+ZM4PuzvLV4uqDLJgvHzwYAFRd6GhhOWU/V1VJ/XmYC2jS67HzxAk8tX49fub6+DB4hysjAoeLLXSDCdZ4wnb5QxXNyLFYYNTp4hpS+M89e/A+t4CNB9E6XFnBHC7uvah2OlHtdCLDZEKGySTN5akkuHiHi+8t9ta0abht9GhkWyzIsVjgFwQ4vV5JnJsVIYXK+9zGo0fxp6++whPr1kU1Hj68Lx7IHK4QOVwuhZDMFysvbjh6FACwvrRUOucvNTXSfMi3Y4lX77B4UOtyIdtiQbrRmFTjItqGmGamPn364PPPP0dlZSU2bNiAH374AZWVlfj8889RVFQUrzESRFDUdq/KVUrhJlxwiedn8eIzhwyRflbrckVcGt7l86F7ejoaPR6px0o8KqPlijeissbGuDtcRp0ueocrwpBCdgM9LIaLRNL4WO29ThXBpYQtUGzc4gVouh7VbujBdnv5Cn9qnCKGpxZlZwcVXCykB5CHc7LxAEBf0SkKVmksGli552MNDZL4YpsbJu6aVIMXnpEILpvRiGcmT8YX114by5Ajgn3eg42/ShRcBp0uriGF099/HzM//DBu5wPiKLjE6+ecwkLUiA5XrsUiLdaB1BJcwXK4irKz8eJFF0Gr0chEmcvng16rlb32UCGFfG+rSOALWMQDPt+Sd7jMXB8umeASX2uGyYQGj0eqTNkojsft86HO7UYvRZuiP5x+etI4SYIgoMblQpbZjDSjUbq/E52HuMxM2dnZGDNmDE477TRkZ2fH45QEERFqO+VqizknV+EuUQ4X79pcPXSo9LXH72+x4x8Mp9eLPuKi9KgYGhkPgcSaZlY6HHHP4eIXfqGEw5MTJ2J09+4AIg/xY4LrUXHHks/hYgtSQdwBHiUW6VFrFZCqgou5n/xmAtC0waAquIJc9/Vud8jQuj+OG4fy++6DWa8PKrj4z6IyJIx93orE+4Papki0sAXL/qoq6bm7iNdLuPeSd40jEVwAcPdvfiONP5GwsQUTXMzhMmi18AtCVH3+IiHaheB3hw8H/TxHW6SHuaxKwc6ut+7p6TjW0IDF69Y1tUHQ61uEFIYrPtMRCBZSqHaMy+eTtcxgWFVCClmLkje2bsU2zpEOhzOODhcTHiwKhAlD3uGy6PWwe724XGzYzu7dLKe3RCzyw14fK+jzwJlnAmi+Z/bKyJByWtsbh9cLXyCALLMZ6SaTJBaJzkPH3woiOjXrjxzBwNxc3D12rPSYstmjPxCA2++XJelGI7i8fn/Y4+1er8y16ZWZiVO7dcOdp50GoCl3KhJcPh96i7t0paJjEI+QQv5mnAiHyxsIQBCEkMLh/jPOwN/FQjqR9odhgovF5KuFFLJd4FAL57bIvYmFVgsu8f3gr20A2HT0qGr/t2BhLPyOsxo6rbY5P0qvh1NszM1TbrejnyhIlBsMbGHEdqDjUa6aibr91dXYXl4Os14vFbfYfPx4yN/lexolmyNi5BbbajB3h4073pUKj0ThftQ4nTjz9dfx0Ndfq/48WocrzWiEXqttcX2wBX/3tDRp8Xzvb34jC5tLJYcrXFl45TG8WGHYjEZ4AwGZC8p/Lkf+/e8Rj4c5W/HI4bKLwiObyzt1iEWi+JBCj9+P1SUlAOQO16ajR5sqF4qiDGieT3qkp2Nyv37SNcLmrGQI35P6JZLD1Wnp+DMT0anZVVGBUwoKMKGwUHpM6XCxm0Q0IYXHGhrwk7hom/ree7A9/njI45UOFwBsvuUW3DRyJICWO7Zq+AMBeAMByeFiYR/xEEh83k+8BZeBW/iFEw5sQR5pIrMy/00tpHCNeFNmYZNqqO3MNrjd2MxVuWtPYg4pFMU+K5387s6d0nt8ardu6JaWhin9+wd938PlcPGkcWGLPOWNjRgg5jgpr3cmeNOMRph0uqiagQeDPcf+6mp8e/gwRnfvLl3b1wwbFvb3h3TpEvMYEoExjMPFhxQCiHvhjGj+Nix3LliImifKPlwajQZdrNYWkQu8wwU0uVjXDR8uKz6QCmXhGcFCCnl4wcVHcDBsnNvNiDTSQgkTLPFwuFgBHpnDpVKlkId9n240osrpRKbJhKmDBknjYoIrx2KR3gedRiM9RzIUzqjh+iVSDlfnpOPPTESnpsHtRpbZLCs7rgxXUvYosRoMcIS5cQx76SWc+vLLAIAvDhwAAPwqhjGoYfd6VSu8sUaykUyuTBj2Fnfp/+/77wHEJ4eLd8niXTSDXyCGEw6ZZjP+7/zz8dJFF0V0brNeL5XyB9QdrlUHDqB7ejrO79tX9rv+MH24Xv3pJ5z9xhsRh2StPnQoLsUe1Ig5pFB8XzLNZtw+ejR+qamBAOD9GTOw+ZZbcOwPf8AVgwfD6fNJC1OecDlcPCx3qsHthiAm7QNNIYX9RUGtDClku+xGnQ7pJlNcdnfZc5TU1uKTvXtxpViQAwBuGDECwoIFIX9/w+zZ2HfHHTGPI97otVpoED6kMJzD9fDXX+PKDz6I+vmj+dsw5z5YERyP3x91CDMrjMDDrrFuouBiDmUat3BNZYdLLUxSJrhUQgptKhsjrfncsaboQHwcrhrO6QGai3vwVQqV4tHKhRQCTffIHLO5OaSQF1zcJpRURCgJwvdqFYIrHptORMei489MRKem0eNBmtEoW5woHS62w8c7XOF2vNRCngqffTZokroypJCRFoXgYqIgy2zGWVw/pEgr+oVCp9VKQivuDhe30+4LBKAPk0Nx37hxUeXCWLnXz762cC5lud2Ok3JzWwhJ/m+sJrjKGxth93pbiAM1BEHAuW++iRliTkG8iTWkkL/2uqenSwUs+GqZajveQNNC4Gh9veQehINtIjR4PHj0m29gffxxCIKAOrcbuVYrbAZDi5109vk06HRxWWx4/H588+uvyOcc0KsjcLV4bEYjBrZB1cFo0Wg0TY29g8w1dS4XMk0m6XoPNictWrsWH+zeHdFz8ueI5m/DipVYQwiuaOebgrS0FnO40uFimyRZYtN1Fs4MpIbgYn/bUDlc4UIKrXFyuBrDzKPRwsRRFhdSqMzhSufmsyUTJ2K4WPGaCa4MkwlpXMGUas414zdWo7n/JhoppNBioZDCTkpMM5PX68V5552H/VzPA4JoS5jgumjAADx45pl44IwzWjhcaoIr0hwufyAgFZwAmqvlKVELKQTCC64GtxuXvPsufq6qkpX2nXrSSdIxkToP4QgWrhEr0ThcscInT7PFQ1ljI/LT0qRxsF1YfldTbaHASs1HUsCBibJ4VNdTo9UhhYqiGUBzc1RA3g8u2LX42c8/w+334wrOIQpFBudwvSeWEff4/Whwu5FuNDYltitCClnYm0GrlRyuZ3/4IarEfZ6D1dU4Ul+Pv0yYID2WFyKktKPBV/7cX1Ul9RgCmhbNGSZT2PLx0cBvOkSzEGSOr9qcctWHH+LjPXuiFlz5NltLh0t8jd3S0gA0N+LNMpvhDQSkggRAaggujViF0CU60q0JKeR/zohkc0kJE+B6rTYuVQprOOEBNOdUywQXd8+bMXiw5GgyIZZhMsHGuZuNHg+MOh2MOp2sOJbk8iWB4OKFZrrJlBQikGhbYpqZDAYDtm/fHq+xEETUsAaKJr0ej513Hrqnp0sTOkPZFDIgCPjfL7+o9udR8l1pKaqdTkwbNAhA8OIXwRwuthAONrl+tn8//vPzz7j3yy9lJXBZtTWtRiMLl4wFtd3DeMDvtCdKcI3v3RtA82I/02SSGtuWNzaiwGaTct9Y6Cd7z9OMRlUXgO2KRiKijoo5KlkRNMltDTE7XNxiizXpBRQOV5Dcqy3Hj6NvdrYUrhUOKaSQa85d73aj0eNBusmETLO5xcKOb+LNHK57vvwS5775pmpVyXCw8/+mZ0/MP+ss7LjtNtXjDtx5J0rvvTfq87c3rJn4NyUlGPj88xj32msAmuauRo8HGSZTc5PgMPmhz/zwQ9iwWf4c0SwE2WdITfSxprTRCq7emZlS2W+Gy+eDBk3hhkCzw8U2FGpcruYqhSkguIDmsOlwDpfb71cNKZR+zv1t+IIokc43TIB3tdnim8PFOVwNbje8gYDqPYoP1WfXvE6rlTV25p1U9j6kJWFIIavymkYhhZ2SmGem6667Dq+++mo8xkIQUSGIiw9+Qs40m+Hx+2U3BqXD9ZO4q/7G1q1Bz8tY9O23AJpDWSqCLM6DOVzs5n/vl1+qLnp+OHJEGiNbtJj1eqmBa2sWo8EwczeieNIWDtdn11yD6vvvl14DE14vbtqEow0NKEhLk4RGiUJw5VgsqvkwbLGo3E0vravDx3v2yB5jJfqzk01wqThc43r1kr6OxOHaVl6OEVE0qWeLoXq3W3pfy+12COLPMjn3keHlQwpNJhwT389qpxO6Rx+VNU2OBCYQMs1mLJwwAUO7dlU9rl9ODnoqevN0BIw6Hdw+H/4hbmiy/n52j6fpfeYEl7JnlZJ7v/wybO6hzOGKYiHoUnGTlUQruIZ27Ypyu10qyAE0t/Vg1x7vcAFN70EqOVxAcy+q1oYU8mXjGYe4PGS1XE412HzRRWxCHyu1LhdsBoMUim4zGqXwWTWHSxkuDTSJQBZSKAgC3H6/9HrZxmoXqzWpHK5f6+okh5YVzYjn/Z1IfmKOLfL5fHjttdfw1VdfYdSoUbApqootXbo01qcgCFXYAl8muLg+LmZxclMKruPiYq+PWJxCiZpYe+Scc/DCpk2hHa4QuVYNHo9qYQ22qKhzuZodLoMhIYsGtZtZPGiRw5WAsSvHzHpG3fvllwCA0d27o5sYVsh2x9lNNttshkdlcREspPDqjz7Cd6Wl8Dz0kPTaktXhYqE2vJPFL3D53lrSbq9i8XGothaXn3xyxM/JF81gQooJqHSTSeqVw8NCCo06HdKMRvyoqA65pqQEI8XmypHAzh9pZcWOBgspPC5em8zxZmIo3WiMWHABTfMMc4fU4M8RTUihJLhCLGijFVyDxeqReysr0T09HX0yM1Erti1g1zt7P9h1X+N0Sm5vqggui+jgCFB/TcqiGco8OrWQwkM1NdBpNE3928THAoIgCXo12DXXxWZrdZVDHr4HFyDfLFJzuHjnjoVLN3g8yLNaERAEVDudcPMFN8T/u9hs0pyXDOF7uyoqMETcGGJzqCNIsS0iNYl5Ztq5cydOPfVUpKen4+eff8ZPP/0k/dsaxEEgiHjAh4wxpDAb7sagrFL42LnnApBXsePh87t+rqrCaT16IM9qRbbZHNrhCjNx8vHvv9bWQhAEadFf53bLcrj4sLB4wXbTOqLDpUSZ13Z6r17QabU4KTcXuyoqADRdHxo0u55KwoUU7uNCTpnDFe+eR0CTo+oXmzdHC1sA5Ss2urb/7nd48MwzZQsptXLuAUHAkfp6qTJmJFj0eug0GllIIdvESDcaZb2RGFLRDK0WmSaT5EIyou3LxRyuVF2ssHAyJriYIGJ/74wIHC6+iEy4sFn2fubbbFE5XMyZUDpcvJtvjlJw5XJN2vs99xyu+ugjlNvt0jX+/owZ+O63vwWgHlKYKoLLZjBI9zG112TkHCw1h0spuDx+PyocDjx/4YXolZGBwqwseP1+6B59FE+uWxd0HEyAd7Fa45PD5XTKnHd+o5K5VPwGG98zrwfncDHxdbShQR5SyDlcBp0OWo2m3RsfC4KA7eXlUisKNm9R4YzORcwO1+rVq+MxDoKIGjXBlamS16B0uO49/XQ8+u23QXdl+cVDhcOBUd27A2hKyq9spcPFj+NAdTUG/O1vWHHZZahSc7j0+pA9pVoLW5B0xBwuJUpng/1th3btir9v3oxTCgrgEPPqWBNNJdVBHK7CrCx8V1qKPRUVUqgac7hCFVvxBwLQaDQhd4tVf09cnLbmfWP5igUK92JYfj6GKcIEbSohhRV2Ozx+v9SQOBI0Go1U+MKt4nBZDYYWIoAPKVT2VgOai9G8sHEjCrOycNHAgSHHUC8W6EiVfB0lksPV0IA0o1FaeLMFWrpYNMOo0wUVXOkmU9BrXAk7f8+MjFY5XJ/v3w+H1yt9Dvk5NNpiJmw+Z9fEh7t34+KBAyWH7oohQ6RjedHJvk4VwZVmNEp/W7XrXKPRwKTTNRfNCCO4WO5U9/R03DJqFOavXg3jokUAgP/s348/nXmm6jjYnJdrscStLHw4hyuYK8rmsNmnniqFFx6tr5eFFPZTtKYwaLXtLrhKamtxrKFBCveOpl0MkTrEZWZau3YtrrvuOowbNw5Hjx4FALz11ltYF2LXhCBihS0meLdDCinkFg3shsFXcfIHArj/q69Ud+yUi2q2G5elUgyAEcrh+sPpp8vO+/WhQwCAtb/+iiqnE+nigorP4QKAu8eOxeOiGxcPWFhXvEMK28PhCuZsMKfmd599JolgvuIbw+XzSX+PMsXuPxs/v9PPHK5QgivjiScw9b33onwlsfUPYguyUOFijDSVfAa2qO0VhcMFQLpm2ftxjHO4rHp9i/eJDylUunFA08Jjy/HjuOOLL3Dxu++Gff46t1sWLplqsByuE3Y7BuTkoMHtRkAQpGuShdd5/H7c8cUXqufg/wbHwwiuWpcLVoMB2RZLdA4XtwDfJN77AXmua7R/J5tCcAHAf37+WXWDzKTXw6LXo8bpTD2HixPawV4Ty/NSK5rBN08GmkOocy0WWA0G8PEdfEuIjUePykQ3364kLkUzXC65w8XN5UqXTi23VFiwAPefcQa6paVBg6a5mQ8pnFBUBAAYnJcHAKrzf1uz5fhxAE1FfgB54SGi8xDzzPTRRx9h8uTJsFgs2LJlC9ziB7Wurg6PP/54zAMkiGDsrawEAFkvHbbLyZebdvp8MImhBQw20a3Ytq3FeZU39oNiTlCW2ay6mywIQpObEsThukbsD8QEVbEouErq6lDlcKBvdjZ8gYAkIJkwfGbKFMwbP179xbcC5jLEOwyrLXK4lHRLT8c706e3eJz3lqqdTtiMRtUbLnuvu6WltWyULf6d+AVraQQOl8PrxX9+/jmq1wHEJrgG5OQAaM5pCYVRp4Neq5XtqrLXFY3DBTQtGHaLoZtA84I+3WSCxWBosZHB3n+dRqMqDhs8HowSG41HAutFlaqY9HrUezzwCwJ6Z2ZCQJOrx0IK2Q45y3dSFkDwBwJw+XySQ8uK8wSDvZ/R9khz+f3SopjPb+W/jjbvUavRwGYwSL3kGME+e9kWS0qGFKYZjVKkRrDXxKpZRlI0o5prDiw1HTYYMP3kk6VIi3q3G2NfeQXDly2TzuPy+WDU6WAzGuMSUujknFBA7nDx96YDd96JH2bPDnoeg5gP2iAW72Ebf2lGI07cdx/uGzcOQNO8501AKHg0nLDbodNopHmaQgo7JzHPTIsWLcKyZcuwfPlyGLgPzhlnnIEtW7bEenqCCMquigp0S0uThSdkWyw4q08fvM81/HQoJnienSdOtHhMmY/w3JQpAJp2amtdrhaLG6fPBwEI6nApG1B+U1ICoOkG2ODxSO4Cq5YX7z5ZDLbQjfdCtT0cLgCYOXRoi8f40JvVJSWwBnG42AJjYG5uizBRJrjZ30sQBKkQRyT92yKt/sWIZaH41KRJ2Pa730miNxx8s1CgqSKjWa+POuwr3WjEJq7whczhUulz5xUXRBqNpkX4I9C08BjO7WazHeFgdAaHi12jrMpincslLdCYq79I7EOmbIXBRPVD48fjT2ecgfVhBFeDWNKfhYpGitvnQ35aGvRarczVYlURLxowAFerfE7DkWY0tpibP7jiCtVjs8xm7K2sbC4LH2VIb7ISLocLaHZv1PpwsWb3SsGVa7VKVXDz09LQ1WqV3C8WOl1SWysVtmFijrlpsaJshs1vVPIVRfvl5IS9F7LXz4cUAk0FM9i9QG3+FwQB//fddxH1YIwHFQ4H8qzWFv3EKKSwcxHzymjfvn0466yzWjyemZmJWkViNEHEk7LGRtWSz2N79JAl4YcSXMcaGqTYdv54oEkoFWZlYawYBpBlMuF/v/wC/V/+gh+PHcP28nIAzZNmMIeLF1yCIOCE3Y4ciwUn7HYEBAE9xXCO0ro66LXahAmW5Zdcgr9ffDH6i65IvGiPHC4AqrlSJ4thJECTA8ryXII5XCfl5kqLDQYTJGw3t8rpRL3bjYG5uUEFF18kINqbaCyCy6zXy4RKONKMRlmlsdL6evTKyJAlpkdChsmEI/X1yDSZkGux4FhDA7QaDawGg6rg8vj90nUSLKRQEAT8btQoZJhM+N/BgyGfv87tTmmHy6jTSdcoKxRQ+OyzeGfnThi0Wil8igll5aYBE2A5FgvyrNawIorNkVE7XGIoW57VKne4RMH18cyZYYsJqVFut0sRDIx+Qeat3RUVWLlrlxTSmEoOV20Yh0sSXCohhQBkIokJ+GyzWXJaTDodcq1W6Wds4wQAFn7zDYBmwWXR65s2F2MsZe4NBGSCi78353Cbp5Fg4ARXsLwvg9qGm9OJ+7/6Kmg4brypdDgkkQtwDhcJrk5FzDNTQUEBDhw40OLxdevWoW/fvrGeniCC4lIphQs03VB4EaW2+8f45969yFmyRPYYczgO3X03fr7jDulxPjRm3KuvYoQYdsGOj8ThcoluWM+MDOnm1ldM8j1UW6t604wXv+nZE7eMGhX14jocvMPlbUPBpcakfv2kr11iKKmRS5pmOSds57hvdjbqufLmv9TUYI3oQDLRwHJJTikoQIXDobrg4AVGtKWT2zIUqkd6uiw35oTdruo4hYPlIIzq3h0ZYl+tNKMRGlF0qeVwMRdO1eHyeFDldKKrzYZssznse1jncqW0w2XS6SQRxW8qrTpwQJaDyYrrVDkc+OeePbjqww8ByMPHbAZD2MavrKx4uhiiFSluvx8msVE773BVOBzIMpujLgkfC+z9ShXBZTMYwoYUMsGlFlIINIUcsjmv3u2GRa+HQaeThLpRp0OP9HSUNTai0eOR7kln9u4tPSfvcAUEIeZKrS0cLu6+Ge29ib1+j3gdBjtG2fierQ/ccXDsIqHC4ZCFfdsopLBTEvPMNGfOHNx9993YsGEDNBoNjh07hrfffhv33XcfbrvttniMkSBUcfp8qkIq22JBldOJB776CkBoh4vBL6LZYjHTZJKFavHnYDHhLp9PWsxE4nCxc/fKyJDKtLMS8IdqaxMWTphIeMEVStwmgremTcOaG2+Uvu9is0FYsADDxNwV3uH6fP9+mB97DIfr6pr/DmI455eio/LsDz9I52LHsP+Hde2KRlEYKOEFQrDCKsFoS8E1IDcXB6qrpe/r3e4WJfYjgYXEdEtLQ5rRCG8gID0WKqQQUM/paXC7Ue10ItdqRbrJFNYlTHWHq3t6upTD1EPh4vN/L97hmv7++1i5axeAZjcjV2z+6gsEQhYOYFXu0k2mqB0us14v5VExTtjtEeUVxhP2+lJFcKVxbmNrQgoBucPl4u6XzG0x6nQ4v18/eAMBrD50CGWNjcg0mdDVZpMcfuZist+NtVKhUnClG404t6gID5xxRtTnkkIKxc21UMcwfIEAPt23D4B69cdEUGG3yxwurUaDNLH5MdF5iPlqe+CBB3DNNdfgvPPOQ2NjI8466yzMnj0bt956K+68885Wn/eFF15AYWEhzGYzxo4di40bNwY9dvny5Rg/fjyys7ORnZ2NiRMntjj+pptugkajkf2bIubmEB0TtVK4QHNVwSe/+w6AuuDaOHs2zhOrGQHyHIhGjwc6jabF7uyU/v1xvsK1rbDbwzpcTEQ5vF5JnPG71llmM7LNZhyqqWlTsRIv2OtrFJP8E+nSKblu+HCcXVgY9Oe84GLO1Z6KCmkxwapzXSJWxuOrdTHRwHZBB4nhiiyfi4cXXMnscPXPzsZ+TnCx3J1oYeIq22yWfp/9b9HrpQIqDD6kUG0Xu8rphMvnQ67FEjSsrbyxEZ/9/DMufPttHKmvb5VQ7Ciwfj1Ac0ghg2/rwEKw+E0AQWwGy34ulWoPsbhziAt25nBFGjbGFrqsbxijwuFQLf8fKawYzMl5eXjivPPwyiWXBD121+23AwDqw4iTjgZ/PwmWl2bU6WAX5121zTql4OIbC/9u1Ci8PnUq+ufkINdiwdayMtSKJdtZ+CD/e2qNlFuDUnDptFoU33ADFk+cGPW5eIcrmJuqFFyPr12L+/73PwBtd61UOBzIU4RLpkUZvkt0fGJeGWk0Gvz5z3/GH//4Rxw4cACNjY0YPHgw0loRpsJYuXIl5s6di2XLlmHs2LF45plnMHnyZOzbtw9dxZ1rnjVr1uDqq6/GuHHjYDab8eSTT2LSpEnYtWsXevToIR03ZcoUvP7669L3phS+YXcGnD6f6m45L1oEQVCNbx/Towemn3yyVDHwWEODbPGSyyW4Mk7v1QtfXncddI8+KpXUrXA4JBEVrPqfVqOBRa+H3eORFj18VTiLwYA+WVnYWlbWIR0uFsrBRGsyiUZecLEF/691dfAFAjBotS3y2djfMs9qlRYcbCHJqmEerqvDady8AigcriB9kYLR1g7XCbtdcrbq3W70F0Nao4F9VrLMZkkA8A4X0LQhwkSYMm+D5+S8POwR83UyzWbVhchzGzbg7lWrZI+lssM1mBNcyoImvNDUa7Ww6PWy0CSvWPFUp9E0NaLmHPbsIDkyTp8PORYLemdmSg2XuyuEnhpsMc6HrgEtd/SjZe8dd2DniRPokZ4etichawVRH6bAREeDv5+EcriYox4uh4u/D2o0Grx08cXScSfl5WFfVRWyzWZkmExNgotzuFgOF4CYKxXyc3GsyIpmBLl3GrRaWZXCn7gKxm1VYEWZwwU0zZfkcHUuYr7qDx8+DEEQYDQaMXjwYJx22mmS2Dp8+HCrzrl06VLMmTMHs2bNwuDBg7Fs2TJYrVa89tprqse//fbbuP322zFy5EgMGjQIr7zyCgKBAIqLi2XHmUwmFBQUSP+yW7HQIJKHYA4XL1pqXK6gIYVFYigfIE8WPmG3B92d1Wg0sv4lMocrhNBgJeXZgp4vZW/W63GJ2Oi1rcNw4oFWo4FBq5Xi4pNJNJo4wcUqpx2qqZF29HtmZGDRhAnIsVhg93jwl2+/Re/MTIzt0aOFw5Uv9n1RFlkB5EULjov5EJHSpg6XKDAPii5Xg9vdKoeLObTpJpO0MGTnkRqMf/mldLzX75eF566/+Wbsuv12HLzrLvzf+edLj1sNBtVKeY+tXdtiDIloDp4sFHJzk/Lvo/w+w2SS9dlyer2odDiQY7FIOXVAy+qrPGwuHSJuaO5SVAhscLtVK0ey6nBKhyvWkEKtRoPh+fkR/Y3ZPYAJrlRphs3fTyISXCr3H9YYGUDQPC+g6X60v7paCtW1GAzNDpffn1CHKxZaE1LIO73xaOQcDkEQmjYgFNdytBVBiY5PzDNTUVERKrh+LIyqqioUcSFbkeLxeLB582ZM5OxlrVaLiRMnYv369RGdw+FwwOv1Ikexe71mzRp07doVJ510Em677TZUVVWFPI/b7UZ9fb3sH5E8BMvhOr9vX8w780wAwIe7d+Nf+/bBrxIiczK3i7ynokKaiEMJLiXf/vprcw5XiGpcXWw2nODE2SCump5Fr8e54mcltvpP7YdZr292uJJIcJl1OumGWy4KLr8gyMR678xMVDud+OqXXwA0OVgWLg+JLSQtej0yzeYWJbiB5sbIAHDzp58iV1GIJRTtIbge/PprAE2L1PQQ120wWIsBt8/XHFIonoc9x6s//SQdr9zV/k3PnhjcpQv6ZmfLxIVNDGtTClYmevtkZuKLa6/F5H79MHPIkKjH3VHgQ46Vi1Pl3yvdZJKFiTp9PpRzxVDYvBQ2pFCvR1FWFvRarSzPDwBu++wzjHr55RZltF1iw9kWDleMIYXRoNNqYdLppMVrqpSF5x2uYG0fjDqdJDSDhRSy+SuU4OpitaLa6USd6HyrOlyK9iatJe6CS8xPjDSkcB+37lPbPIs39W43vIFAC4eLQgo7HzHf4QVBUI3Jb2xshLkVVaQqKyvh9/uRryh1nJ+fjzLOCg7Fn/70J3Tv3l0m2qZMmYI333wTxcXFePLJJ/HNN9/gggsugD9EIvHixYuRmZkp/evVq1fUr4dIHMEcLo1GgysGDwbQvDPOSgbzsFAUALjnyy9x/ltvAYhccF09dCg+279fWsiEKszR1WaThR/y5W/Nej1GdesGABjMCbGOhFmvl0oYJ2tIYYAT3awqG9D8t+CLXVi5ym5sIWnS61tUwGQcra9HQVqaJJxDFShQ0paCK8diwbCuXaV+Ow0eT6tyodhi0OH1Nudzie/jAM69ZT3JQoUU8p9Dm9HYYiHi8vmk9+jZKVMwpX9/rLruuqDhcamAMjyKF5fKv1eGySQTSBe8/TbK7XZJFNsicbjEzSudViu79hmsQXbBX/8qe7zG6USW2QyTYlFb38ZFTawGAxrE3Nt4V2FtL/gNvGDujUmnk0KYIwopDDI3swiMerG/nczhEgWXFCocoyvkTYDDFawsPtAkVllIYZ3LhcN1dXhr2jTcPXZsi3YKiYC1S1CGBocKKTxcVydrbUOkBq3eip47dy6ApsXtww8/DCt3Mfn9fmzYsAEjR46MeYDR8sQTT+C9997DmjVrZILvqquukr4eNmwYhg8fjn79+mHNmjU477zzVM81b9486XUCQH19PYmuJCLUDYQtxlio4MoZM1oco9VoUPnHP2LQCy+g0uGQmoOesNulKneh6Judje9KS2EXhZ9aXyhGV5sNR+vrVQtsWMQwqk1z5shyNzoSMsGVRA6XSSyDrBRALKQQaN4ZZrv3/776aqz99Ves/fVXAM0Ol0GrbVGNjXG0oQE90tMxsagIX4t5gcE2o5S0peACgAsHDMCHu3fD6fWisZVFM87o1Qs3jRyJe37zGyz78UcAQI443+q1Wgzr2hU7TpyAy+eDzWhscriCLLL452elydmuvd3jQeYTT8AvCHjpooswddCgqMfaUXn83HOlHmtvT5+OoqwsPPHddy0dLqMR35eWSt9vLStDutGIPqJzqGy8roaTC7u2Ggwt8nT4nFN2XTu9XjR4PMi32XCwuloWUtjW1UotBgPq3e6Uyd8C5A5XMGcqXEhhsKIZSpjgqnO5cHJeHix6vXS9sGI2kVxHkZCIkEIm/EMdA0Cq/Nk/JwdH6uulqIdEwkSdMqQwzWiU9a7j6fPMM+iVkYHD996b8PERbUerV0Y/ieEigiBgx44dMHKTg9FoxIgRI3DfffdFfd68vDzodDqUi01lGeXl5SgoKAj5u0899RSeeOIJfPXVVxg+fHjIY/v27Yu8vDwcOHAgqOAymUxUWCOJCeZwAc2lp32BAG4YMQLnBekJl2u1SrvwbCc4UocrS3Q7qp3OsC5BF6sVm48dk5WQ16AphJDdBEd37x72OZMVWUhhkjpcPPy1w9wEdvO9oH9//Fpbi0O1tXh7+3YpP0Cj0TQ5XCqCizkK/E2fVfwKR1sLLla2nVXxPIlzpCLFpNfj9alTAUD6rPCvfeE552D6++/DKQquUA4Xj81gQI7FIrmI01aulMKBc1PY0VJj3vjx0tc6rVbK+1Q66Rkmk6woANDUFH6sWNiFCdraEMVceIeAr1DH4MXynV98gecvvFD6vOSnpclCCgVBgFvM+2krrAYDKh2OuBVjSAZ4wRWqx1SZuFGklkNsEos1AU1zXijB5QsEcLyxsSmkkBPdTKjZ4iC4BEGIu+By+3yoCTHXGnU6aczMUUo3GpFvs6Ha6WyRXxpvWH86taIZh2prg/5eKaWwpBytnp1Wr16N1atX48Ybb8QXX3whfb969Wp8+eWX+Pvf/44BAwZEfV6j0YhRo0bJCl6wAhinn3560N9bsmQJ/vKXv2DVqlUYPXp02Oc5cuQIqqqq0E0M5SI6HqEcrgyTCcxbCJejwhbQBWlp8AcCqAyTf3DrqFG4f9w4ZJvNaPB4sOnYMWknOhin9eiBfVVV2Hj0qNR8kk3AyeQItRaTXp9URTOY22jW66HTaGThhIA8pJCF65Tb7Ug3GqHTatFNrNB23T//iUqHQ1rwZFssqqEeLISKFx180YhQtLXgshkMON7YiJc3b8ZlgwZhfJ8+MZ2PXce8myf17BEXOt4IK5PZjEbkWa2oE5tR/0/MqwPkYbidEbYAV7qmbLPnZC4ceX91NU4V723ZZjO6WK24Z9Uq6XNQpsjF4h1fPn+RUedySUWGXti0CUCzI5xvs8mKZrD/21pwefz+lHK4eAEVqiAE0FTV8iSVcPQWfbjCbFCWNTYiy2yGRcz9CghCi5DCULmA4fALAgQEz0mLFubwefx+qR2M2jGs8TEfYcJCbk8k2OViLpZyw4gVzRizfDkufPtt6fFQGyNExybm2en1119HRkYGdu/ejVWrVuHTTz+V/WsNc+fOxfLly7FixQrs2bMHt912G+x2O2bNmgUAuOGGGzBv3jzp+CeffBIPP/wwXnvtNRQWFqKsrAxlZWVoFG8IjY2N+OMf/4gffvgBJSUlKC4uxtSpU9G/f39Mnjw51reAaAcEReEDJVqNRlqghXOfTu/ZEwBwsKYGn+3fDwEIKbiWXXwxnjz/fGlHbU1JiZSDFYwL+vcHAKw9fFj6vZGiYxts97IjkWwhhWyxp7boc/t8QUMK2cJjBCegj9TXSwueHunpOKKy89ggFp/gBdeKbdukG30o2sPhApqqKY6Ow4YTWxjyr1UqIS0u9kKFFPJY9HpJwFU6HLKd8FTJzWktkuBSPM4WmkMUYdAzhw5tOl6jwdiePXG8sRHfHT4M7cKF6PbXv2IHF0USLqSwzu3GaT164IYRIwAAb27bJi1Uu9psMoeLLfDbWnABqVOhEIjc4QKaQuTUQtrNnBAOF1LIyLVYpLnR5fPFtWgGizSIp8PFhH8wh8ug1UrPy7dwyRfnGeXmQ7ypsNuRZTa3mP/SjEZUO5348dgxfHHggHQfKOFcr0juH0THIebZ6dChQxgxYgSGDh2Kiy66CJdddhkuu+wyTJs2DdOmTWvVOWfOnImnnnoK8+fPx8iRI7F161asWrVKKqRx+PBhHOdK1L700kvweDyYMWMGunXrJv176qmnAAA6nQ7bt2/HpZdeioEDB+Lmm2/GqFGjsHbtWgoZ7KB4/H4ICB2+dqO4OAjWH4vxv+uvxzQxN2Tqe+8BCC24GGyh4/B60TdMiwEW1lNaVyfd3N67/HL866qrQuZ+dRSSrWgGW1jwO8NsIdjg8chCqPiQwkzxb9MvJwc/3XorgCZhwo4pyspCSW1ti8awrK+V8rVHkvjMbrRtVV2Nzx+MpVcSg31W+L5N/IINCF00g0en1UrJ5ZUOh1Rp73ejRmF8794xj7Ujw+YJpfBkRUf6cXNQF6tVNq+w8M9P9+2TKqEyweT1+2UNyy16PRyKkMI6lwuZJhNmnHwyAOCVLVukPLtMsWgGv7AH2lZwsbGnlMMVQdEM9pkKVrBJWTQj2N+Ed4dyrVbp/eQFl1ajgZnL7WoNCRFc4nUcyuFiz9vItXBhG7JqIeLxpMLhUG2RkG40ynLI2Ebeca7ibahCN0THI+YZ8a677kJRURGKi4tRVFSEjRs3oqqqCn/4wx8kwdMa7rjjDtxxxx2qP1uzZo3s+5KSkpDnslgs+DLC8B6iY8B2zkO5KWf16YOlP/wQ9gZhMxpb3KgjWYjyVYd6cZXW1NBrtTDr9ahzuzFUvDFkWyy49KSTwj5PR8Cs10s3h2RyuNiOty8QkBpeNng8cHq9yBQX8+zYo/X1GMq5BOyGfLyxUVrwFGZlwaUouw00Cy4mmsb16oXvS0tR6XBIoSvBaC+HC4hPXtSo7t2x+sYbcRYXmqhskuqNIqeHfa6O1NfjWEMDXrroIvwugjDxVIcJJaUsZ+8Xu14vGTgQSxWRG3lWKzJNJqzm7pVsIc7mR0sIh4td3xcPHIh+2dnol5ODRo8HGjT9rVkuDX/e9nC4Uklw8RuFoUqeA4A1yHsdaUghP0flWCxSiX2P3y9zxqwq4abR4I234NJqJeEfSdEMu3jNmvV6yRFLdGl4tabHQMuN4EM1NSjMypKJMLvHE/R1ER2PmGfE9evX4+uvv0ZeXh60Wi20Wi3OPPNMLF68GHfddZdUXIMg4olTsUhQg+38KnvHqKG8ifB9cIIxLD8fXcX+WpEcn240wuXzpWQ5ayZINIjfzTQW1K4L5jY1uN1S3yGgeexVTqeUuwVwgquhQWrAynpGldTWygRXg1jtb3h+PnItFvzh9NMlwRWOdhVccWoefE5hoex7KYeLCykMFdr7/owZWLlrFwBIoT4szCZcuG5ngVUwnSyGJzPYYq4gLQ0H77oLfTIzVUPrCrOysJmLDGGONPsbWUPlcInlwjUaDQbk5uL9XbtQlJWFNKMRGo2mKaSwHR0uZT5mKsDPB8HCadlcG+w+aOIdrhBFM3h3KMdikQRKvAVXIhwuRrBqqwatVppj7V6vdM2yPO92c7gU42WOM59TRg5XahHzHd7v9yNdXKTk5eXh2LFjAIA+ffpg3759sZ6eIFSJxOE6uUsXDMjJwW1jxkR8PqBp4RLpYuEZcSe5VwSCi50zWOhDR4bPmUqGXJtgfz9WcpwvuMIf240TUTaDAQatFuV2u3QME1yHamqk43yBABxeLzJMJmSazai8/35JgFRFEVLYVoKLLyCSqMp/LRyuQCBkDtcVQ4bgwyuvBNC0EMm1WPC3jRuRKYpYomkDSViwAOMUrUkuGjAA70yfjmuHDUPf7OygeUysTLwylMqpcKatXA8mBgspBJo2VRxeLxasWSOFvZlEh0sQiywA7SO4kmGzpy0xRRFS6ApRZIqfs3MsFul9VAoum0qPtmhIpOAK9h7otVqpimejxyNds1qNRiqHn0gq7PYWPbiAlsW82IYFv0EcS4ESIvmI+Q4/dOhQbNu2DQAwduxYLFmyBN999x0effRR9A1SipsgYiUSh8us1+PnO++UqnVFcj4A6BMmPJDn6mHD4H34YSn3JxSszCtbtKcS/O54MsAv9vib3cUDB6LB44GDKxLAJ6TzrpWGK7zCFjaZZjOyzWZZYjMLv+EdnCyzGRogIoeLhdkksjQxj4tbTNvC5De2FqXDFWmVQgb7jFw7bFhKFJVJJBqNBlcPGxa2YEShOK8NzM2FBsCzGzYA4Dav2GdYr5fNh26fD26/X5rj6rkG4SwsyqTXQ0BTFbr2zOHqbNcKi5YIJbj4UM9I/ibd0tIkIePmcrjY88QiAtxxFlx8uF2w98Cg0zU7XB6PrPoja+2SSII5XOyz0zc7G1qNRvrcnHA4pI0wcrhSi5gF10MPPYSAeDE/+uijOHToEMaPH4/PP/8czz33XMwDJAg1InG4ouGhs86SvlbbjQpFtM7E/LPPjur4jgATG8mQvwXIx3HZoEG4dtgwbJw9G0VZWWhwu2UVLvmbvzI0lAmufjk50mOFYuEMBgsB4d0ilmCuzIVRwxXnazkcZ4gOyX2nny6V+o43Socr2t47SjeGiB0mYjNNJggADlRXo9rpbM7h4hbVG44exYe7dwNoFljsM16nJrgUC3SAHK62gM05wQovRVo0A2gqrPL+jBnQabXS39Ph9cIXCDRHZ1gsqBYdoQPV1XgtypQRJtbCFbKKlJPFMFsgeDipXqttLgsvhhQygjWyD8XB6mqsOnAg4uOD5XCxkMLziopg0umkv1OjxyOFtpPDlVrEPCPyZdX79++PvXv3orq6GtnZ2UkRWkSkJpE4XNFw4YABWHPjjThnxYqE51ilUmI3gy3GkqEHFyAfh0Gnwz+mTwcAFB86hAbxJsauHX6xcpaiJxVznc7lcpSKsrNxqLYWT33/PYZ17SqFqwxQNBA2c3ktoWA32rZaLHZLT4ewYEFCn4O9/5LDFQhE5XDliDvX8VqYEc0i1hcIYOaQIVi5axf2V1VJYV5MtLD3/IoPPoCwYIEksFhIIT9/MbeAOaUVDgcmrFgBgARXW8A2JFyKEFCGWWxizfpphdrUuWnkSOlr9j6yuZL9Lbunp6Okthbbysow8u9/BwBcfvLJEUV4AM1VAuP1uR7MCa5g6029IoeLd/UzTSaZYxsJF7z9NvZXV8Pz0EOqUQlun09yWh1eLxo9HtWqx6O6dcOrl16Ka4cNw4e7d8ty7dimbyz5ckTy0eqV36FDh4L+LCcnh8QWkVDi7XABaC4JHqbEe2tJ5WpDbDGWjCGFPBniDZYPKeRROlxsh3EgJ6Z6Z2Tgf7/8gj/+73+44ZNPsL+qCha9XlYWHYCsN1Eo2M5zKs2ZGoXDF63DxT6LwRLhiehheaYNHg9eufRSAMDPVVUtQgozFe95negAsL/JB1dcIf2MLZxZXuq/ubzttnS7U7FoRiSwe0owJ539fepcLgQEIWIRzD6rTIyw3+uRno6j9fWySnpHuTLm4Yi34OqhmHPV4ItmNCpCCi0q+YrhYGF+6w4flj1+rKEBn+7bB/Njj6G0rg5Ac6l3taJaOq0Wvz3lFJj0epkT6fB6pRBECilMLVo9I/br1w99+vTBhAkTpH89xQayBJFo2A0mnruoI/Lz8cbUqbhKbBgab3656y74Ff2bUoVkCykMJbh8gQB8gUBEY2U7jPwNkxdGdS4XtpaXY3CXLi3CevjeRKEIt/PcUbGIu+uAmMMVxWKYvR/kcMUP1rqiwe1Gmtik+1hDgyRq2XuudCuUDlf/nBycW1SErw8dahZcotPyLbcIDZZTkwgsndThUuZKKknnnEcg8vslex+Z2OYdrmMNDbJQt2MNDTKnKRTxFlyhKp8y+KIZdo9H9twWvV5y8SKlq82GYw0NWLFtGyYUFQFo2lDqsXSpdMzeykr0ysyUhFe4KsbK0M8ssxlajYZCClOMVjtcX3/9NW688Ub88ssvuOWWW9CnTx8MGDAAt956K9577z2Uc13sCSLeKHdl44FGo8GNI0cmLPE622KJOj+so8AWackiJ1nYk7KZML97r7x21IqrMMHFl4v/47hx0tdpRiO+O3xYyovi4UsyhyLSZPaOhoXr5xRtSCErAEGCK36wsCYmsNKNRjSKPemAZoHEO/H+QEAqKsA//qNYjfiSgQMBNDtc35eWYmRBAfb+/vdt6nanakjhU+efj1tOPTXoz1nrk3PFhb8S9reuEB2pSP8m7B6o5nC5/X6pABTQ1L8wUpjgipcYjyQqQFY0QxFSyM9RkcJcqzVcTzu+ai3Q3HIhlMPFwxc3cYrRF9YYK0ISyUer7/LnnHMOzjnnHACAy+XC999/jzVr1mDNmjVYsWIFvF4vBg0ahF1ibxWCiCfKUsZE+8J2Gj0RODptwd7KSgDAKQoRlRmkqtXBu+5SjbO/ccQIPLdxo2whx4uvNKMRh+vqZCGHDFYqOxyh+uN0ZHiHK9qQQiaUU20B3Z5oNRp8cMUV+I0YiZJuMjU1AVcUueA3JSocDpyw26HXamW5re9Mn459VVW4Ucz7YT871tCA4fn5OCkvry1ekgT7LAdSLILgD9zmjhp5VivcDz0U9HOSFqPDpRRcLGy6+NAhaNA0n5ZF0OeS0ejxwGowBC3ykQj4ohnKkEIrN0cpWfLdd5jSv3+LthQ1Tqfk8JY3NiI/LQ37qqpkx7D35ITdjkyTKez7blKEFFr0epi4hs1EahCXu7zZbMa5556LM888ExMmTMAXX3yBv//979i7d288Tk8QLXD6fNBpNG1WSpsIDVukReLotAXnFBbire3bW7hWfAgKn+/RN0je3tNTpuD/Jk0K+jx+QYA3EFDNNYqmaEay5L7FEz6HK9qQQrYg84s700R8mDF4sPR1muhw1blcSDMaJVdR1pfur3/F9cOHo4vVKlskXzRwIC7izmvR62EUF4jKHLC2gIXOdcYFaqhNCfa+LF2/HkDsgquH6NR8KubqZUfZx6pREdLXFsgaH6vlcKm4SIIg4E9ffYW/fPstGubNkx73+v3wCwJG5ufj60OHcKi2Fp/u24db/vMf2e/f97//4ZeaGmSZzRG5ecqQQqvBIH2eiNQhpnJpHo8H3377LRYuXIgJEyYgKysLv/vd71BTU4Pnn38+ZGENgogFp9ebkovUjgorm35YjFlvb357yinwz5/fYjHCLwYj6UGl1WhUFzQshLBaDLeyqVyLpigEV0o6XFxCerQhhWyR0pY74Z2NdKMRDR4Pal0uWbigMkzrre3bVd1fHo1GI4UVRpJXE28ykmzDJ1lgG0Frxdy6SCNCpBwuheDi+xQCTc4mL7jCOYztIbhYDpcgCC3Kwlv0etVKgOyxRo8H+8RoCaA5laG/eL87Wl8via2LBgyQjsu32bB8yxbYgxRnUmLW6+ES7xUOcW1Dgiv1aLXgOvfcc5GdnY3bb78dJ06cwK233oqDBw9i3759WL58Oa6//nr07t07nmMlCAm719uiUzvRfnQTb8TJtOBRW6zzi8GzFSXgo+GLa6/FLaeeKt2Y1RYRfG+VUITrj9NRiSWk8LYxY/D4uefiAm4RQ8SXdJMJjaLg4jciTikokCoaMsIJLnY+oGWVw7aABJc6ynkpVofLqNNhaNeu0nFZZrPUx+pofT2yn3wSL2zcGPS89WLBlnhS9oc/4NjcuUF/zvJ5A4LQ5HApc7hUrplqrhny5uPHpa/5IkomnQ7/9/330s/u48I/X730Uti9XmwvL4/e4RJFmlGnk0IhidSg1YJr7dq1yM3NxbnnnovzzjsP559/PrqpJJ0TRCJoj50yIjgajQZPTpyIf111VXsPJSQsh+uyQYNiKsOebjLJmm6quWWRloVP2SqFfNGMKEMKzXo95o0fn5I965KFNKMRDW43at1umcOVaTbj8L334sc5c6THlP3p1GAub3s4XOxzTYJLjvIeGWmOm1ajgV6rbSG4AGDHbbdJX/MhhR/t2YN6txuvhmiGXFpf30LMx0p+Wposr1YJm3e8gUDLHK4gIYVVnODiXSa+/2fPjAxsOHoUALD11ltlnxEWyr7u8OGIInGY4PIFAvCKFXTJ4Uo9Wn03q62txcsvvwyr1Yonn3wS3bt3x7Bhw3DHHXfgww8/REVFRTzHSXRy/rZhA27+179k/TRIcCUX959xBi496aT2HkZI9FotVl17LVZcdlnM5+IXIcEcrk4dUsg1XfULAhXASDJYlUJlSCFjVPfu0tdzTz897PnYZyDSJrjxhIm8SD5vnQmtRiMTwCz0OxKMOp2q4OLJ5hyubWVlACDdo9U4VFuLQrEBd1vBNm2cXi+8gYDc4QpSNKNKLDICyJsPs2OtBgMuP/lkAE2REiMKCmQRFblWK/pkZkYXUujzySqGkuBKPVotuGw2G6ZMmYInnngCGzZsQGVlJZYsWQKr1YolS5agZ8+eGJqgfkZE5OytrMRzGzbg6w6eT/fIN9/gta1bsV1sN0CCi2gtk/v3j8suPF90Qy2Hyxyhw+VM0aIZFoMBLp9PWjREk8NFJJ40MYerLojgAoDxYlpAJItGVkq8PXO4oi3x3Rlg98kVl10W1T3TpNO1yOFiXD10KCb169cUUii6Qb+K+buVnFjhEQQBJbW1KGpjwcXmHVaSno8msBgM8Pj9LYrz8K+Bv6b46shXDxvW9Bg3x18ycCCuHz4cAKTQy0g+O/k2G36trZW1uyHBlXrEbVvVZrMhJycHOTk5yM7Ohl6vx549e+J1eqKVPLFuHVZs2wYAKL333rD9IJIVtrjdeeIETu3WjQQX0e6YwjlcUfThiiRHpqNhEasUsh3iSIqUEG1HuhhSWKnTYRiXl8PzxbXXqhYVUIMtDnO48vFtBRMEFFIYnOwonUfe4TIp3Ol3Lr8cAPDX77/H8cZGSUxZDQZUOhwQBKFFyPaR+no0ejwYoNJCI5HoFYKLF49MfDl9PtkcvuX4cXRPT0dAEPDijz/i1G7dMKGoSPosWAwG9BDDGAUuTPPTq6+WvmYl9CMJFx/TvTue3bBB6tslOVxRVml1+3z4bP9+TBfdNyK5aPWWYyAQwMaNG7FkyRJccMEFyMrKwrhx4/Diiy+ioKAAL7zwAn755Zd4jpVoBRUOh9R35fvS0nYeTethOz+svxIJLqK94UNnVHO4ogkpTMFwOxauY49zs1MiPqSbTKh2OvFzVZUsH5HHZjSiS4SbAWxBq6xk15ZcK7oORDPs79JLbJIcKUxwmXS6oPmuPTMy0OjxoN7txtGGBpxSUABvIIAG8Tl5dpw4AQAt+lolGpbDxd4HfqOMhb8qS9uvP3IEZ/buDavBgF9qanDum28CkIcUpptMmH/WWUHD09nnIJJ5b2RBAQBgs9hQvLU5XI9+8w0uf/997Ff0BSOSg1Y7XFlZWbDb7SgoKMCECRPw9NNP45xzzkG/fv3iOT4iRiodDgzr2hUltbXY3UHz6jx+vzQhsupBjR5Ph3XriNSADzVRLQsfRePjVAwpZAsGyeFKwdfYkUkzGuEVNw1GxGER3N6Cy/PQQ1RkRQXmUvWPIn8LaPr8Hm9sDPm5ZffggzU1cPl86JeTg+9KS1HlcLQILT1QXQ2zXo8+UQq/WGHXRIOKw8UiCyrsdtl64peaGpxTWNhizcSHFALAwgkTgj4vq9wbSX4uy2tjz9fakMK9otAqt9vb3EkkwtNqwfV///d/mDBhAgYOHBjP8RBxptLhQJ7VKiVId0Qq7HbpaxZTTg4X0d4wIfHG1KlS01ieaBofp2LRDJNeD4/fDzuFFCYlfFuNoiCNv6OhQZyb89spPDaaKpidiRyLBdVOZ9T3S5NeD18gEHJuYiJll+he9Ra/V2uG3OjxIN1ojKk6bGtQ5nDxr6eL1QqgKRKI4fX7cayhAb0zM1u4Uw8UFwNARBtk7L1hYYKhsBmN6GK1Yo8YwdPaohnsMxjJcxJtT6u3g2699VYSWx2AKlFw2YxGHGtowO2ffSZ9KDsKbPIuSEtDnfg1CS6ivZnYty8AYEJRkerPw+VwfbBrF0YuW5aygostGFhIITlcyQU/f0ab36MGCxUzpeC13JHZMHs2vrr++qh/j1UVjURw3fDJJwCA3qJ7pSa4HBFW7Is3oXK4unAOF+NoQwMENL0WXhrWOJ1SSkMkeVns/jAgQmexd2Ym9okOVWtDCo82NAAgwZWs0MyYwnj8ftS53ci1WGAzGPDuzp0AgIsHDsSFHaihKAuJ6JWRIU3kzhTtXUR0HIbl50NYsCDoz8OFFM79739xpL4emSZTSl7LRvH1k8OVnKRzIV/xCGn98MorUSpWqiOSh/45OVGHEwKRCS6lsx9KcCmbDrcVoQSX1WCAzWDACU5wHRav4d6ZmbINsx+OHAEArJwxQzWiQYnFYEDNn/4U8UZTltmMXWJIYWscLl8gIOVulTc2qh7z4e7dKK2rw70RtHkg4g8FPKcwx8Xdju7p6bKJrizIhzFZkQRXZqYUUpiqrgCROpjChBSyG3Gd252S17LS4aKiGclFepwXvxkmE4YEqXZIdDxMEQguAJjK9V7sGSKksL0crhZFMxShpwVpaTgmrpWAZsHVKyNDNn/vr66GSafDjMGDI37uLLM54lDXDJNJEnityeH6tbZWysmsDxLFdMUHH2Duf/8rE5hE20GCK4VhtnLPjAzZLktJbW07jah18A4XCykkwUUkO+EcLn7xkYrXMhXNSG4oJJsIRSQOFwD8c+ZM6escMZpGVXD5fO0aUtgQpKdYYVaW1EMMABavWwedRgOb0YjZp5wiPX6guhr9cnJkDY7jicxxbkVIIduMzjSZUB8mX3/Ov//dukESMUGCKwURBAHf/vqrtFPTIyNDFjJS2sHie5ng6pGejlqXC4IgkOAikh5WNIPv08LDu86pWKXQxBwurxc6jUZawBHJQaTl3onOCfu8hhNJfBGMDJMJWWazVE2Yp90crhAhhQCQbbHgg927sfPECVQ6HNhdUQG/OGf/Ydw4PH/BBdBqNPilpiahTZuZ42zU6aDTamHQaqMSXCySoFt6urQxrSRPLBISCHJPIhJLzIJr8eLFeO2111o8/tprr+HJJ5+M9fRElAQEAZe8+y7OfuMNXPvxx7AZDMg0mWQVCmtUJsNkpt7tRprRiG7p6bB7vagRJ5NUXKQSqQMrHqB206x2OrHu8GHp+1TcPDDqdPAGArB7PLAaDG1enYwITZbZjHcvvxwrZ8xo76EQSQgTXJkRFFT547hxOK+oCFaDAT0yMvDKTz/JNpp2nTghzQNtDQvpUysLDwD9xAqdX+zfL62N7uNynEYWFCAgCPhs//6EtqJhZfRZPm+0DheLJOiWlqYaUuj2+VApVmN0RtjMnIgvMQuuv//97xg0aFCLx4cMGYJly5bFenoiSj7Zuxef7d8PABAADO7SBRqNRsrbGpGfr2r3JzP1bjcyTCapr8WhmhoAqblIJVIHlivg5sLqGDeJVb0Yqej+GLmFDn1Wk5Orhg7FlUOGtPcwiCSEbRhlKvppqbHk/PPx1Q03QKPR4Pr/b+/O45uq0v+Bf5I2W9M23VdaVkFBCghSQRFGUMRR0VFkXIZlXAYVFRFxUAdcRgFHgdFxZMYR8auOODKOOqOD/thcEHWAspR9p9KF0r1pm6TN+f3Re683aZKmTUKb9vN+vfKS5t6cnNR70/vc55zn5OSguLZWmY+0u6QEF772Gj4/erRDhhXL71kmBVPuVTSfHjcOAGB1OJRroztycpTtl2ZnK0FZZkxMyPopZ7jkG8ltDbjk4kQZMTHK8EI1OeBMNJk8LkxNoRdwwFVcXIz09PQWzycnJ6OoqCjQ5qmN9pSUuPws3zWRA66L0tOVDFG4kAOuDOnL7rg0B40XcdSZyX/Yvy0ogPn557F6505lm1xeWNYV7zjKAVe1zcZS4URhRslw+RFwqckLG8tZFnXFPF0HLEwtz1U8W1cHnVbbYg6WITISF6Wno7i2Vgm44tyyenIxmMwQZrjkOVzqoYXtGVKYERPjMcMl/41JMZvDbmmgriLgoz8rKwtbtmxp8fyWLVuQkZERaPPURvJdnJeuugqLx4/H8+PHAwAeGDkSQPMdmrN1dWGV5aqx25szXFLAdbS8HAADLurc5OPzP4cOAQD+tmMHgOYhhscqKvDcFVfg1WuuAYAWGbCuwCXg6oIZPKKuTC8FR/4MKVSTb/LKWZZGqXIeAGw8cSI4nWsDea5sqdXq9ZpBrlToLeCa1K8f9BERGJqWFrJ+yr83YzuHFFodDmg1GiRHRXmcw1UvZRxTzGZmuDpIwFesd999N+bMmQOHw4ErrrgCALBhwwbMnz8fjzzySMAdpLYpq6/H5T17Yq7bOgvzRo/GvNGj8dK336KwpgbxS5ei7vHHw2IeVLXNhhi9HhaDAREajVLClQEXdWZykFEk3eHdUlCA7378EWadDk1C4LLsbOWP7IVdsJy2nNWqsduZ4SIKM20ZUqgmB2jyRb96RM1rP/95kHrnv0itFsbISPxYXd0ikJKdn5iI5d99h/L6emjgWjEQAGaNGIFZI0aEtJ9y3+SCHe3JcJl1Oph0Oo/LkagzXDuLi4PQY2qrgP8KPvrooygrK8N9990Huzwp0WjEY489hgULFgTcQWqb8vp6JJpMXrfHq7Z9cfQoJnuYf9fZyEMKNVKpVjmLx4CLOjP5gqVItcbLqDfewF+uvRYRGg0uSk9HtF4P6+OPd8k1qpQ5XF10nTGiriwywAyXPKxNXaTr6n79gtS7tonW63G2rg49vVQZfPaKK/BNQQG2FBTAYjCErPS7L/J1m5wRbE+Gy6zXKwvOCyFcChXJGa5UKcPlvp1CL+AhhRqNBkuXLkVpaSm+++477Nq1C+Xl5Vi4cGEw+kdtVFZX5zPgGpicrPy7yMMCyI/9v/8HzdNPY/HXX4ekf+0hB1zAT1+cAAMu6tzUGa5RPXoAaL67+MXRo0qwBXTdBYE5pJAofMnDzhJ8XE94YnEbUuipRPy5Jn/XxnsJHqN0Ojw1diwAKMU+zjX59+yQgqz2Zrj0EREQ+ClTJpOHradGR8MphNfFkSl0gjaDMTo6GhdffDEuvPBCGNqYgqbgKa+v9/kFeUmPHlgkfbGU1dXhnn//G5tV46pf+PZbAMDjGzeGtJ/+OFxWhgF/+hMKqquVgMus0ykBl4kBF3VicoarsKYGvePjMSc3F3FGI/5z6BBuHjiwg3sXeuoqhRxSSBRe5GrAAxIT2/S6GPcMlzSkcO2UKUHsXdvIGStvQwoBYHyfPgCA2wYPPid9cpfgIcPV6HT6vWaWnOGSb265B2vykMLczEwAwP8KC4PSb/Jfu/4Kzp07F88++yzMZjPmzp3rc99ly5a1q2PUNoU1NSirq1MKTPjy1LhxeH/vXjy5aROA5lLyZx59FGes1nPRVb+t3rkTh8rKAMBjhosXcdSZGVXrcCWaTEgwmZTjWS4z3JUZVEMK5SUdiCg8nKyqAgD0S0ho0+v0ERGI0etRKl1PlNfXY3RWFm7qwJtMx6TgsaC62us++ogIFM6di0RpceBzTclwqQIuoDnj5c+1jtXhUDJcQPO6W+rRE/KQwuEZGUg0mfD1yZOYIAWZdG60K8OVl5cHhxQt5+XleX3sVJVBbqtXX30VvXr1gtFoRG5uLn744Qev+77++usYM2YM4uPjER8fjwkTJrTYXwiBhQsXIj09HSaTCRMmTMBhab2qrmDwa68hZ+VK1NrtSvrclyS3L5WGxkb8dfv2UHWvXdRfMnI63KzX42RVFXRaLVLM5o7qGlGr1OdhZkyMy1yI1m6KdAUsC08Uvt676SbMGzWqXedutsWCvOJizPviC5yxWr0O5TvXTkpLyniTHhPTYWsiygs0y9M+9F4yVd5Y7XZlDpen18kZriidDmN69sRXp04Fpd/kv3b9FdwkZUYcDge0Wi1WrlyJ8847L2idev/99zF37lysXLkSubm5WLFiBSZOnIiDBw8ixUM1r82bN+PWW2/F6NGjYTQasXTpUlx11VXYu3cvMqX06QsvvICXX34Zb731Fnr37o3f/e53mDhxIvbt2wdjJ/kyCIQ8TrrO4fAr4JLneZl1OpTW1cH03HMu2yM7YL0Md+o+yJ9JHhowMDm5Sy4WS12HurrXgKQkl8pQbZ2IHo7k4Ttl9fWcw0UUZi7p0QOXSHNP2yrbYsF7+fkAgAiNpsOG6clK5s3Dc199hRsvuKBD+9GaH+66C+dJQzg9BU7rjhxBtsXiMhdfJme45ADZvVJhfWMjNGgeeTAiPR3LvvsuRJ+CvAnoqlqn02H37t3B6oti2bJluPvuuzFz5kwMHDgQK1euRFRUFFatWuVx/3fffRf33Xcfhg4divPPPx9/+9vf4HQ6sWHDBgDN2a0VK1bgySefxOTJk5GTk4P/+7//Q2FhIT766KOg97+jtSXDdbuHL8LXr7sOjU4nmlTrZ3SEWtVaEY+PGQMA2F9aCgC4JogBPlEoqIu6nJeQ4FKhq62llsOROgPNDBdR9yEvfgw0F2/o6AxXitmMP06ahHG9enVoP1pzcWamcqPKU8A16d13MejPf/b4Wn8yXCadDhqNBqnR0Sivr3dZI41CL+A0xh133IE33ngjGH0BANjtdmzfvh0TJkxQntNqtZgwYQK2bt3qVxt1dXVwOBxIkMYeHz9+HMXFxS5tWiwW5Obm+mzTZrOhurra5dEZOdxOLHMbMlw5qanYeuedKJs/H2Xz5+PIAw8oAVtHL8YqZ+2eHjdOuXgtleZvPTVuXEd1i8gv6pK72RYLLunRQ7nR0R2GFOoiIpR5CcxwEXUf7uXX21rpkFoGXJUeFjNWUzJcXgIuq8OhzOlKlv4OlUnXU3RuBHzbsbGxEatWrcL69esxfPhwmN3m1bS1aMbZs2fR1NSE1NRUl+dTU1Nx4MABv9p47LHHkJGRoQRYxdJQHk9tFvtYAG7x4sV4+umn29L9DrHNrdqMPxkueZ9Yg8Fl2ECCyYR9UhbJ6nC4LABYVFODX3/yCXJSUrD0yitRVFODjGXLsPe++zymuANV0dCACX36YKFUVREAhqSmYldJCYcTUliRz6MIKQjrDkMKASBNupPKgIuo+8hWZbgA/24Ckyv3gGt3SYmyrdHpbDHtw2q3I8qtaIZaSW2tMuogWfpvaV0dUlnQ6JwJOODKz8/HRRddBAA4dOiQy7aOWFRtyZIlWLNmDTZv3hzw3KwFCxa4VGGsrq5GVlZWoF0Mum2FhS5rNvgTcMkTND2tASR/OVpVQ/oA4Lmvv8a6I0ewv7QUS6+8Et9Iky4/PnAgJAGXpxL3m2fMQA3Xj6AwJf+R7C5LGvSIjcW+0lIOKSTqRuQL+zm5uUiKiuoWy2AEm3vA9ZmqyFtRTQ2y3IJa9yqF7hmuotpapVqsnOEq7WSVqbu6gP8KygU0giUpKQkREREoUUXzAFBSUoK0tDSfr33xxRexZMkSrF+/Hjk5Ocrz8utKSkqQnp7u0ubQoUO9tmcwGMJiTbFqmw0Wg0EZbudPwCWflJ7G8Mqvt7oNKfx/x44B+GluVZk05C8iRAU2KurrW5TPjjMafa6lQdSZfXrbbfhg374OuRnVER6+5BJ8cfSoUpaZiLq+QdIN2JsHDsSl2dkd3Jvw5B445Z85g15xcThRWYmC6uoWAVedvA6Xl6IZhTU16CNdT6kzXP7YUVSE/aWluF11XU1tF/CV8qlTpyC8LMx2qh1lJ/V6PYYPH64UvACgFMAYNWqU19e98MILePbZZ7Fu3TqMGDHCZVvv3r2Rlpbm0mZ1dTW+//57n22GC6tUmTBHGjLpT8Al3+mI8RBQmqWsV61bhqvGZkOq2Ywqmw1CCOSfOQOg+W5LMJTX1yttyj939GRbokDcPHCgy93dIWlp+P0VV3Rgj86tq/v1w9IJE/CEVPSGiLq+9JgYiEWLGGwFwD3gqrLZMFiq0v2jh3oCVrvda4Yrr6gIWwoKlMyWxWCATqv1O8M1/K9/xR3/+lf7P4ybqoYGZepKdxJwhqt3794oKipqUa69rKwMvXv3RpOfawiozZ07F9OnT8eIESMwcuRIrFixAlarFTNnzgQATJs2DZmZmVi8eDEAYOnSpVi4cCH+/ve/o1evXsq8rOjoaERHR0Oj0WDOnDn4/e9/j/POO08pC5+RkYEbbrghsF9AJ1ArVafZNH06/r5nDzJjYlp9zdQLL4RJp8MkVeU0mbchhVaHA33j41FitaLO4cA7UoXKU0EqJpL0wgsQAMSiRQCa53Bxsi2Fsw+mTOnoLnS4+Zde2tFdICIKK+6BU7XNhiGpqYjW61EgLUr91s6d+OLYMayePBn1jY2IMRg8Fs2Qgxs5Q6XRaJAUFeV3hksmhAjK6Iwr334b/ysshHPhwm4z2gMIQsDl7X9AbW1tu+dQTZ06FaWlpVi4cCGKi4sxdOhQrFu3Til6cerUKWhVw9hee+012O123HzzzS7tLFq0CE899RQAYP78+bBarbjnnntQWVmJyy67DOvWresSa3BZpcWOE0wmzB450q/XaDUa3HD++R63yRku9ZBCIQRq7XZkxMQgr7gYxbW1qLLZYIyMVE7+QFTU10OdJ21yOlHV0IB4BlxERETUjcjz7B3StI+qhgZYDAb0iI1VMlwzPv4YAHBa+jkrNtZj0Qy54vQw1bScZLO5zXO4au12j6Oi2up/UqG3M1Zrtyra0e6ASy4modFo8Lvf/Q5RUqoSAJqamvD999/7nB/VmtmzZ2P27Nket23evNnl5xMnTrTankajwTPPPINnnnmm3X3qrGqlyZLB4inDZWtqglMIZEjZs9PSMMKByckoaCXD5RQC1/7973hg5EhM8rB+1hs7duDfqoIrVrsdtqYmCIBDComIiKhb8ZThijUYkBUbq1xzJZhMKK+vx5cnTwIAesXFeRxSWOdwwBQZ6ZIcKaiqwp9LSvDk5Zcj3Y9RUUBzgBSMgEu2t7SUAZc/8vLyADRnPvbs2QO9at6QXq/HkCFDMG/evMB7SK2SF7wLFlNkJDRwzXDJ87nkgEu+wzIwORl5RUWwNzV5LdX+xdGj+O+RIwDQIuBqcjpx17//7fJcaV2dsugyM1xERETUnagDJyGES8CVLw0RPC8hAd+fPo0UsxlnrFZkWyxKUNWgynDVNza2qEgtB2R5xcU+Ay51jYYzViv6SuvbtleTqlBbcW1tQG2Fm3YHXHJ1wpkzZ+Lll19GjJ8RMgVfrd2OzNjYoLWn0WgQrde7lF+Xs109pPeRhxGel5AAgebyot76cLiszOW1akUeTrgzVivk+zCcw0VERETdiTrgqnM40CQELEYjUqOjsVEa1VXR0IBHRo3C/RdfjM+PHlUqFJp1OpRLVaSB5gyXe8C17/770XPFilaLntWrArczQSgjr76RX9XKYs5dTcBVCt98803s3LkTd9xxB0aPHo3Tp08DAN5++2188803AXeQWmcN8pBCoHlB5GpVwCVnuHpKpUhPSgFXqlRetE51Elntdtz1ySfKyujyXYwKDyeXe2EOef89UrVCDikkIiKi7kQdcMnXYjF6PSwGA6oaGlBqteJQWRkSTSb0jo/HLFV17mSz2aUgRr3DAZPbNWK2xYL06OhWp4Sor9GCEXCpq19XdbM1VQMOuP75z39i4sSJMJlM2LFjB2zSL7CqqgrPP/98wB2k1tVK5UCDyWI0ugRc8l2JFLMZhogInKisBABl/K064Fp/7BjeyMtD/NKlOF1drWSxTlZWtlhCwH2tLwCYvGYN7vzkEwAcUkhERETdi3uGC2ieXx9nNKLKZsO7e/YAAC6Q1jxTk4cYyjxluACgrzQk0ZfaIAdc6pFT1Qy42ub3v/89Vq5ciddffx061f/QSy+9FDt27Ai0efKh0enEkm++wYGzZwMeV+su1mBwufsgn3TRej2SoqJwXAq4UjxkuNTrgP3phx+UgOt/hYVYu2+fy/u4r/UV6zYhMyaIc9OIiIiIOrsIjQYaNAdc8nwsY2QkLEYjnELgZGUlMmJiMHnAgBavdQ+46hsbYYpsOYNoxpAhWHfkiM9qheqb4kHPcHFIYdscPHgQl19+eYvnLRYLKqWLcgqND/fvxwJpMefxvXsHtW2L25BCq1vAJWe45IX01AGX+t8HyspcJkYeLi93eR/3IYXudzy60xoNRERERBqNBvqICNibmpR5VKbISFikm9LHKiuRYjZ7vEZKdltjy1uG6wrpuvEHH1ku+Rot0WTCmTau2+VJjaoAG4cUtlFaWhqOSBXo1L755hv06dMn0ObJh3VHjmBgcjK23X23x7RyILxluMxSwNXQ2AizTqdkpOo8VDSMNxqx5dQpHK+owJ3DhgFwrVADeB5SCAD9gpyxIyIiIgoXcsClznDFSfPaj1dUeC0qFm80KnPoAaksvIeAq1dcHOKMRuwqKfHaB/l6LtticSnE0V61qgJsDLja6O6778ZDDz2E77//HhqNBoWFhXj33Xcxb9483HvvvcHoI3lxrKICOampGJ6REfS2W2S4pMAoSqdDopTV6h0fr9w1cQ+4NAA+ufVWlNbVocpmQ25mJgYlJ7dY2Vyd4bqyTx8MT09HUlQU8n7zGxQ8/HDQPxcRERFRZ6dkuKTrK5NOB4sUcB3zEXDFGAwuc6U8lYUHmrNoGTExPocKytd+GTExQZlzVSEFbZlBai+ctLssvOy3v/0tnE4nxo8fj7q6Olx++eUwGAyYN28eHnjggWD0kbw4VlGBS7OyQtJ2rFQJR2a12xGl00Gr0SBJOsn7JyYqd03cAy6zXo/LsrNxUXo6dhQVIS06GslmM866BVy1djt0Wi1qFixAhFYLIQScQsAQGekyF4yIiIiou/CU4YrUNudJrA4HErxUcXavMn26uhoDEhM97us+3wsA7v7kE3x6+DCOP/SQclM8IyZGmbsfiC0FBegVF4ekqChlPdfuIuAMl0ajwRNPPIHy8nLk5+fju+++Q2lpKZ599tlg9I+8aHI68WN1NXrGxYWkffcqhepKiElShuu8hAREarXQR0S0CLjkYGnuJZcAaE5Hp5jN2HD8uMsK6FaHA2a9Hgbpi0QXEaGsJUFERETUHXmaw6XOVMX5CLhq7HY4hUCt3Y4DZ8/iovR0j/t6Crj+lpeHotpa/HD6NKpsNmjQvASQp4xUQ2MjPty/v0UFak+OlJfjL9u3Y0hqKkyRkS7Xje7qHQ6/2gwnAQdcMr1ej4EDB2LkyJGIlkqFU+hU22wQCN3CwO5zuKwOhxJEyf89T5pnZdbpvAZct+fk4OScORiSloabLrgAZ6xW7Cwu/qndEJS0JyIiIgpnnjJc6oDL7GUUkFzdudZux67iYgjAe8AVFeUScDU5nZDLcOwoKsKBs2fRLyEBcW434WVr9+3DTf/4B97cubPVz3OsogIA8PiYMYjS6VwWVVardzgQ9fzzePn771ttM5wEJZXQ0NCA3bt348yZM3C6FUW4/vrrg/EW5EaeEBmqhYEtBgPqHA40Op2I1GqVYYLAT4vVZcbGAmie12X1EnABzdktALikRw+XvgOugRwRERERuc7h0uCntbk0AATg9dpJLmZWbbNhR1ER9BERGOilsFpSVJTLVI8zVivkvFK1zYbdJSXISU1tzprZbHAKAa2qMuKpqioAwPbCQvxaKo7mTZn0PgOTk/H5kSNeM1zrjx0DAGwrKvLZXrgJOOBat24dpk2bhrNnz7bYptFo0KQaPkbBUyEFLd5SyoFSn7AJJhOsqiDqqr598dzXX2NoWhqA5pNePUGz1uHwuH6WHBxWqCrdqAM5IiIiInLNcBkjI5US8CZpVJG30UEx0vVbjc2GLQUFGJKaqgRrnvZVr411uqZG+ffJqipsKSjAkvHjYTEaIdB8zaZeL/WItNRPsZQlW/Hdd3ht2zYcnD27xXuV19dDp9XCrNPBpNMpxUDcyW0mhWgEV0cJeEjhAw88gClTpqCoqAhOp9PlwWArdJQMV4gOSLkSjpxCrlWd3Jf37AmxaBHSpKGj8SaTEgACzSe5pzsv0Xo9tBqNy75WH18aRERERN2Reg6Xuqy7nGHydrNaDojK6uvx6eHDuK5/f6/vEa3Xo9ZuV+ZL5RUVQavRYEBiIj7Ytw/2pibcMmiQy014taPSMEF5vdWHP/8ch8rKWiwBJPcnMSoKGo0GUW5TUdTk69vyLrYwcsABV0lJCebOnYvU1NRg9If8JGeJQp3hkisVWt2GCarFua354D6kUKbRaFrsa2WGi4iIiMiFe4ZLJgdH3m5Wy/O8nti4EbV2O0b5qGYdo9dD4KdK01t//BE5qalIl8q2D0xORmZsrNeAS8lwSQGXzNOaXeX19UrdAVNkJBxOJxo9BGZKwBWEdb86k4ADrptvvhmbN28OQleoLeQD0qJK7QaTxe3k8jX0L95odMlaeQu4lH1VJxHncBERERG5Us/hMqkDLum/3q6d5IDrq5MnAfxU4MwTuY0aaVjh1h9/xKgePZQAa4iUTPEUcNU5HCisqcGIjAz8WF3tkrFyX3NVfi5RCrjkPnoaVihfT5Z5aCOcBTyH609/+hOmTJmCr7/+GoMHD4bOLeJ+8MEHA30L8qBGqu4XoQ1aoUkXMW4nl9XhQLSXuynxRiP2q+bw+Qy4TCaUuc3hSmdVSyIiIiKFHHDV2O3KNRmgynC1EnDJekgFzjyJVlU0rGpowIGzZ7HgssvwxdGjLq+1uI16AoACqWDG1EGDsK2wUCl2AQClVivgVqjjeEUF+knBn3oN1xi3xIGcUHDPmoW7gAOu9957D1988QWMRiM2b96sTOoDmoeQMeAKjXqHw2VMb7CZ3RY09pXhinPLWvkKuHrHxSljfgGWhSciIiJyJwdc1TabS6EKZytDCtXZsAWXXebzxry6wIY876pXXJxyDScHXJ4yXHImanzv3tBqNLjlgw+UbSVua3sBzfO9JvbtC0CV4fJQGl5u93hlJSrq62GIjIRJVTQkXAWcHnniiSfw9NNPo6qqCidOnMDx48eVxzFVtEvBVedwtLiLEUxRbgFXMOZwAcCAxEQcVGXD5IWPiYiIiKiZITISDY2NzRku1XWSTqo46O06S12RcNaIET7fQ53hkkcfJZpMSoAlB1zyfuqAS77uS4qKQrzRCJuqUN6hsjKX96lzOHDGakWf+HgAP11jWlUVEtXtjuvVCwBwy9q1MD//PH63aZPPzxEOAg647HY7pk6dCm2IhraRZ6EOuHQREYjUapW7D7U+MlHuC9j5CrgGJiejqLa2Od0MZriIiIiI3FkMBlTZbKhxy3AtHj8evx46VAle3KkzQclRUT7fI0Y1h0suUpEYFYUnL78cf77mGlxz3nkAgAitFtF6vceAK85odJkqMiwtDXnFxS7vI7edJPXHvU6AWp3DgZEZGUiKisK+0lIAwHNff+3zc4SDgKOk6dOn4/333w9GX6gNQh1wAXAp2+mruIVJp4O9qQlOIdDkdKK+sdHrvmOluxbyWN9qm63F+F0iIiKi7kyerlFts7lkuGaPHIk3Jk/2a4hda1NPkqKiEKHRoKCqSilSkSBluO69+GKX6ogWg8ElQKpqaIBWo3G53it4+GGMzMzEcdXUEeCnwEpeckiusF3hofR7nTTyadbw4ShUrQsmz10LVwHP4WpqasILL7yAzz//HDk5OS2KZixbtizQtyAP6hobz1nA5Whqgr2pyevQP/mEbGhsVEp8elr4GAAyYmKQFRuL/DNnUNXQAKvDgYyYmNB8ACIiIqIwFC9N14jQal0yXMFkiIxE/8RE7DlzBn3i4xGt13tdJNniVpG6sqEBcUajS+DXIzYWMXo9rG7VB+WAS/4c8hqyFR5Kv8sJhSi37NzpmhqfBUA6u4ADrj179mDYsGEAgPz8fJdt4T7BrTOrcysTGgqmyEjUORzKieMta6UOuBqkoYW+Sr1nWSwoqK5GQXV1889hfAIRERERBZs8P14XERHSkUAXpqRg/9mzsBgMStl2T9Kio12KYcgBFwB8d+edOCVVLTRLiymruQdcpshI6LRal/n/snop4Epzq2B9uKysewdcm7rARLZwdC6HFMqTGr3NtVIHXDXSSeUz4IqNbQ64pJMz22IJZreJiIiIwlq8yYQmIVBcWxuyNVeB5muy3SUlKI2PR4rZ7HW/tOhonJZulAPA2bo6ZSHj3B49kNujB4Dm6z/3gEsuJy8HXBqNBvEmU4shhY6mJjicTkTpdOjpdm3oab5XOGGlizBVfw4DLmWRZelOhjs54Kp3OJSTrNWAq6oKu0pKEKXTIZ1DComIiIgUcaprrklS8YpQyIiJQWFNDc5Yrb4DLrPZZW2sk1VVHm+YywGXes6VHCypp5u4V7gGfioTb4qMxICkJJdt7kFcuAk4w/XMM8/43L5w4cJA34I8OJcZrgpVJRpPTKoMlz8Bl5yW/u+RI7iqb19EssIlERERkUI9pK5/YmKbXnt1v36tViiUZcTEoMZux9GKClyckeGzP0WqgOtEZSWu69+/xX7Rej2cQqChsVEp2lFts8Gs07msCRZnNLospAz8tBRRlE6nXEfGG42ostkYcP3rX/9y+dnhcOD48eOIjIxE3759GXCFyDkNuKRJjfGtZLj8DbhSzGbU2u3YWVyMR0aNCnKviYiIiMKbt7Lv/vjv7bf7va9cuCz/zBn83EcmLcFkQrW0QLJWo8Gpqir0jItrsZ98/Wd1OJSAq6y+XimUITPrdC2Ka6gDLgD4euZMZMXGYsjKlajp7gFXXl5ei+eqq6sxY8YM3HjjjYE2T16cn5SEvgGcjP6I1utR0dCgpHzdTxaZMqSwDQEX0HzHo7eHk5WIiIioOwtVZUJ36ms7X0MK5f7U2O0wRETA1tSkzOFSUy+mLK+7dbyyssX1nqfiGu4B12XZ2Uqb3T7D5UlsbCyefvppXHfddfjVr34Virfo9v4xZUrI3yPBZMLRigpUNDTAGBnpsh6DmqcMl6/sm/qE7h3ioJGIiIgoHN05bJjPYX7BoC7I4WsYohJw2WxolK7xPBVTUwdcsmMVFRjgNizSrNOhVFr7S1bvFnCp22TA5UVVVRWqpCp0FJ4STSaU1dWhor7e63BC4KeF9eSAK8ptnK47dcDlfgISEREREfC3668P+XuoC6L5ynDJpemrbTY4pYIYntZn9RZwTerXz2U/s06HE61kuNRt1oR5lcKAA66XX37Z5WchBIqKivD2229j0qRJgTZPHSjBZEJZfT0qVGsteKLOcBXV1vo8YQG4VCVMbmVfIiIiIgoNdeVAf4cUyuvs+pPhstrtOGO1tpiTZvawQLIccJnc2o0xGFDrtm+4CTjgWr58ucvPWq0WycnJmD59OhYsWBBo89SBEqOiUGu3o8Rq9Tp/C3AtC3+0oqLVuWVajQZ35OS0qE5DREREROeOekSSPwFXtc2GCDng8pDhkoMwOeA6XlkJoGURELNOp6zzKvOW4YrR68P+mjHggOv48ePB6Ad1QvKK40fKy5Hq4yQ0REQgQqNBrd2Oo+XlGOHHeOO3WVCFiIiIqNPwNepIzoRV22wwREQA8Dxf3z3DdayiAgBaFM3wNC/LW8CVYDLhcHm5vx+jU+q0CyC9+uqr6NWrF4xGI3Jzc/HDDz943Xfv3r246aab0KtXL2g0GqxYsaLFPk899RQ0Go3L4/zzzw/hJwh/50uLzm0rLPSZ4dJoNIg1GFBts6G4thaZXMiYiIiIKKzopUDKE/UcLnkooKchhcbISGilm/AAUCYVxnAP5jwNKaxvbIQGUAI6WYLJhHJpiaJw1a4M19y5c/3ed9myZW1u//3338fcuXOxcuVK5ObmYsWKFZg4cSIOHjyIlJSUFvvX1dWhT58+mDJlCh5++GGv7Q4aNAjr169Xfo70UnWPmp2XmIgLkpKw/+xZxLVSnjTWYECVzXZO1gcjIiIiouBYMXEierWyTE+kVosUsxkFVVVKtsvTkEKNRoNovV4ZLlhjt8MUGYlIt2JqZmmtV6cQ0EpDFOVrSHmOmKzbBlye1t7yxP0X5q9ly5bh7rvvxsyZMwEAK1euxKeffopVq1bht7/9bYv9L774Ylx88cUA4HG7LDIyEmlpae3qU3d1XmIi9p896zPDBTRXuam22VDf2MiAi4iIiChMPHTJJX7td15CAnaVlCjZKk8ZLsB1uGC1zeZxTbFEqQR9WV2d0p63m/aJUsAlhMDxykp8dOAA7r7oIiXrFg7aFXBt2rQp2P1Q2O12bN++3aXghlarxYQJE7B169aA2j58+DAyMjJgNBoxatQoLF68GNnSomqe2Gw22FRlKKurqwN6/3CUER0NAD7LwgPNGa6KhgbYm5paVJchIiIiovDWLyEBb+3ahX8dOACdVgudlyGI7gGXp8Ao22IBAJyqqnIJuDxdQyaYTGh0OlFrt2NPSQke+eIL3JGTE1YBV1DmcFVWVuKll17CXXfdhbvuugvLly9v9xpcZ8+eRVNTE1JTU12eT01NRXFxcbv7mJubi9WrV2PdunV47bXXcPz4cYwZMwY1NTVeX7N48WJYLBblkZWV1e73D1fy+NrRrXx2i8GAktpaAL4XPSYiIiKi8KNehNni40a8OuCq8ZLhyoqNBQAUqJIZ3jJcSVI27IzV6nP+WGcWcMC1bds29O3bF8uXL0d5eTnKy8uxbNky9O3bFzt27AhGH4Ni0qRJmDJlCnJycjBx4kR89tlnqKysxD/+8Q+vr1mwYIGygHNVVRUKCgrOYY87h4Vjx2LR2LEYmZnpc79YgwHFUsBl4tw4IiIioi7l5oEDlX+flYpheBKt1yvrZlXb7R4DrmSzGfqICBSoEjTeAq4MqRhbYU2NMjcs3EZTBRxwPfzww7j++utx4sQJfPjhh/jwww9x/PhxXHvttZgzZ06b20tKSkJERARKSkpcni8pKQnq/Ku4uDj0798fR44c8bqPwWBAbGysy6O76ZeQgKfGjWt1Pl6swYASqxVA+J0ERERERORbanQ0nAsXAgBG9ejhdT/3DFeMh+IaWo0G6dHRKFSNNPNWByBTuv4+XVMDqxSUadtZJ6KjBCXD9dhjj7lU/IuMjMT8+fOxbdu2Nren1+sxfPhwbNiwQXnO6XRiw4YNGDVqVKDdVdTW1uLo0aNIT08PWpvdmcVgUO52cEghERERUdej0Whw4P778eltt3ndx6zTtVo0AwDSY2JQJI2OArxnuGINBkTr9ThdXQ2r3R6W15kBB1yxsbE4depUi+cLCgoQ0871mObOnYvXX38db731Fvbv3497770XVqtVqVo4bdo0l6IadrsdO3fuxM6dO2G323H69Gns3LnTJXs1b948fPnllzhx4gS+/fZb3HjjjYiIiMCtt97arj6SK/XJxCGFRERERF3TgKQkn9WrXTJcdrvHDBcApEdHK9NRAO8BFwBkxsTgdE0N6hyOsJu/BbSzSqHa1KlTceedd+LFF1/E6NGjAQBbtmzBo48+2u5gZurUqSgtLcXChQtRXFyMoUOHYt26dUohjVOnTkGrqudfWFiIYcOGKT+/+OKLePHFFzF27Fhs3rwZAPDjjz/i1ltvRVlZGZKTk3HZZZfhu+++Q3Jycjs/OampJ09ySCERERFR9+RPWXigOeD6RlUfoc7hQJyXYhyZsbE4XVOD9Ohoj+t/dXYBB1wvvvgiNBoNpk2bhsbGRgCATqfDvffeiyVLlrS73dmzZ2P27Nket8lBlKxXr14QQvhsb82aNe3uC7VOfTKFY6qXiIiIiAKnXvi41SGFqjlcdQ6H11FSmTExOFZRgVi9PiwzXO0eUpifnw+gec7VH//4R1RUVCjD+srLy7F8+XIYwqg+PgWGQwqJiIiIqEXRDB8ZrtK6OjiamgAA9a0MKfyxuhpWhyMsM1ztDrhycnKQm5uL119/HTU1NYiKisLgwYMxePBgREn18qn7sKhOJnkBOyIiIiLqXuSAq9HpRH1jo9cMV1p0NIDm9bWAVuZwxcaisKYGtXZ798pwffnllxg0aBAeeeQRpKenY/r06fj666+D2TcKI/Ldi6FpaWFXqpOIiIiIgiNar0d9YyMqGxoAwHvRDKm4nlyp0FfA1SM2Fg6nE8cqKrxmzDqzdgdcY8aMwapVq1BUVIRXXnkFJ06cwNixY9G/f38sXboUxcXFwewndXLnJyXhpgsuwIe33NLRXSEiIiKiDhItBVinq6sBwGuGS17QWN6vtSqFALC3tBQpYTiSLuCy8GazGTNnzsSXX36JQ4cOYcqUKXj11VeRnZ2N66+/Phh9pDAQrddj7S23oHd8fEd3hYiIiIg6SJa0UPHe0lIA8JqRSjGbYYyMxInKSgCtDymUpUpDEcNJwAGXWr9+/fD444/jySefRExMDD799NNgNk9ERERERJ1Y34QEAMD6Y8cANA8H9ESr0aB3XByOVVSg0emEw+n0Wngt1WyGPGElJQxrBQQt4Prqq68wY8YMpKWl4dFHH8UvfvELbNmyJVjNExERERFRJ5ccFYVovR7/PnQIsQaDMhzQkz7x8ThWWYl6hwOA96WFIrRaJEpDCVPDMOAKqH53YWEhVq9ejdWrV+PIkSMYPXo0Xn75Zdxyyy0wh+Evg4iIiIiI2k+j0aBPfDx2l5Tgkh49oPFRTC09Ohq7SkpQJwVcJh8VCNOio3G2rg4XpqQEvc+h1u6Aa9KkSVi/fj2SkpIwbdo0/PrXv8aAAQOC2TciIiIiIgozfaWAa2BSks/9ks1mnLFaUSFVNIw3Gr3u++4vfoFjFRVhWS+g3QGXTqfD2rVrce211yIiIiKYfSIiIiIiojDVRwqKBiYn+9wvOSoKpXV1KJXW4kryUYEwJzUVOampwevkOdTugOuTTz4JZj+IiIiIiKgLuCMnB4fKynBt//4+90s2m1HncOBUVZXyc1cU0BwuIiIiIiIitaFpafjk1ltb3S9ZymjtLS2FBr6HFIazoJaFJyIiIiIi8sdgaYjgJwcPIjEqChHarhmadM1PRUREREREnVpGTAwGJSdjb2kpEkymju5OyDDgIiIiIiKiDpEurdNlMRg6uCehw4CLiIiIiIg6RKKU2bJ00flbAAMuIiIiIiLqIHLAFcsMFxERERERUXDJc7c4pJCIiIiIiCjIEqXS8MxwERERERERBVlPiwUAUGWzdXBPQocBFxERERERdYif9e7d0V0IuciO7gAREREREXVPcUYjvpoxAxempHR0V0KGARcREREREXWYMT17dnQXQopDComIiIiIiEKEARcREREREVGIMOAiIiIiIiIKEQZcREREREREIcKAi4iIiIiIKEQYcBEREREREYUIAy4iIiIiIqIQYcBFREREREQUIgy4iIiIiIiIQoQBFxERERERUYgw4CIiIiIiIgqRyI7uQDgRQgAAqqurO7gnRERERETUkeSYQI4RvGHA1QY1NTUAgKysrA7uCRERERERdQY1NTWwWCxet2tEayEZKZxOJwoLCxETEwONRtPR3QlL1dXVyMrKQkFBAWJjYzu6O9TN8Xik9uKxQ+GIxy0FgsdPS0II1NTUICMjA1qt95lazHC1gVarRY8ePTq6G11CbGwsT1bqNHg8Unvx2KFwxOOWAsHjx5WvzJaMRTOIiIiIiIhChAEXERERERFRiDDgonPKYDBg0aJFMBgMHd0VIh6P1G48digc8bilQPD4aT8WzSAiIiIiIgoRZriIiIiIiIhChAEXERERERFRiDDgIiIiIiIiChEGXERERERERCHCgKuLWrx4MS6++GLExMQgJSUFN9xwAw4ePOiyT0NDA+6//34kJiYiOjoaN910E0pKSpTtu3btwq233oqsrCyYTCZccMEF+OMf/9jivTZv3oyLLroIBoMB/fr1w+rVq1vtnxACCxcuRHp6OkwmEyZMmIDDhw973Ndms2Ho0KHQaDTYuXOnz3aLiopw2223oX///tBqtZgzZ06LfVavXg2NRuPyMBqNrfaZ2q8rHI+9evVqcdwsWbKk1bZb689XX32F6667DhkZGdBoNPjoo49abbM76a7HDr/LwltXOG4B4NNPP0Vubi5MJhPi4+Nxww03tNr27t27MWbMGBiNRmRlZeGFF15w2b53717cdNNNynmxYsWKVtvsbrrr8dPQ0IAZM2Zg8ODBiIyM9Lj/5s2bW3zvaTQaFBcXt9rvDiWoS5o4caJ48803RX5+vti5c6e45pprRHZ2tqitrVX2mTVrlsjKyhIbNmwQ27ZtE5dccokYPXq0sv2NN94QDz74oNi8ebM4evSoePvtt4XJZBKvvPKKss+xY8dEVFSUmDt3rti3b5945ZVXREREhFi3bp3P/i1ZskRYLBbx0UcfiV27donrr79e9O7dW9TX17fY98EHHxSTJk0SAEReXp7Pdo8fPy4efPBB8dZbb4mhQ4eKhx56qMU+b775poiNjRVFRUXKo7i42Ge7FJiucDz27NlTPPPMMy7Hjbr/nvjTn88++0w88cQT4sMPPxQAxL/+9S9/f63dQnc9dvhdFt66wnG7du1aER8fL1577TVx8OBBsXfvXvH+++/7bLeqqkqkpqaK22+/XeTn54v33ntPmEwm8Ze//EXZ54cffhDz5s0T7733nkhLSxPLly/399fabXTX46e2tlbMmjVL/PWvfxUTJ04UkydPbrHPpk2bBABx8OBBl+++pqam1n6tHYoBVzdx5swZAUB8+eWXQgghKisrhU6nEx988IGyz/79+wUAsXXrVq/t3HfffeJnP/uZ8vP8+fPFoEGDXPaZOnWqmDhxotc2nE6nSEtLE3/4wx+U5yorK4XBYBDvvfeey76fffaZOP/888XevXv9CrjUxo4d6/UixWKx+N0OBV84Ho89e/Zs84VBW/vDgKt13eXYUeN3WfgLt+PW4XCIzMxM8be//a1Nn/PPf/6ziI+PFzabTXnuscceEwMGDPC4f6DnRnfRXY4ftenTp/sMuCoqKtrddkfgkMJuoqqqCgCQkJAAANi+fTscDgcmTJig7HP++ecjOzsbW7du9dmO3AYAbN261aUNAJg4caLPNo4fP47i4mKX11ksFuTm5rq8rqSkBHfffTfefvttREVF+flJ/VNbW4uePXsiKysLkydPxt69e4PaPvkWjscjACxZsgSJiYkYNmwY/vCHP6CxsdHn52xPf8i37nLs+IvfZeEh3I7bHTt24PTp09BqtRg2bBjS09MxadIk5Ofn+/ycW7duxeWXXw69Xu/Sn4MHD6KiosLna8m77nL8tMXQoUORnp6OK6+8Elu2bAlau6HCgKsbcDqdmDNnDi699FJceOGFAIDi4mLo9XrExcW57Juamup1HOy3336L999/H/fcc4/yXHFxMVJTU1u0UV1djfr6eo/tyO17ep28TQiBGTNmYNasWRgxYoT/H9YPAwYMwKpVq/Dxxx/jnXfegdPpxOjRo/Hjjz8G9X3Is3A8HgHgwQcfxJo1a7Bp0yb85je/wfPPP4/58+f7/Kzt6Q95152OHX/wuyw8hONxe+zYMQDAU089hSeffBL/+c9/EB8fj3HjxqG8vNzrZ/XWH/X7Utt0p+PHH+np6Vi5ciX++c9/4p///CeysrIwbtw47NixI6B2Q40BVzdw//33Iz8/H2vWrGl3G/n5+Zg8eTIWLVqEq666yu/Xvfvuu4iOjlYeX3/9tV+ve+WVV1BTU4MFCxZ43Ufd7qxZs/zu06hRozBt2jQMHToUY8eOxYcffojk5GT85S9/8bsNar9wPB4BYO7cuRg3bhxycnIwa9YsvPTSS3jllVdgs9kAtP94JP/x2HHF77LwEI7HrdPpBAA88cQTuOmmmzB8+HC8+eab0Gg0+OCDDwAAgwYNUtqdNGlS2z8U+YXHj6sBAwbgN7/5DYYPH47Ro0dj1apVGD16NJYvX+53Gx0hsqM7QKE1e/Zs/Oc//8FXX32FHj16KM+npaXBbrejsrLS5Q5JSUkJ0tLSXNrYt28fxo8fj3vuuQdPPvmky7a0tDSXqjhyG7GxsTCZTLj++uuRm5urbMvMzERRUZGyX3p6usvrhg4dCgDYuHEjtm7dCoPB4NL2iBEjcPvtt+Ott95yqVgYGxvr/y/FjU6nw7Bhw3DkyJF2t0H+Cdfj0ZPc3Fw0NjbixIkTGDBggMfjsbX+kP+627HTHvwu63zC9biVnx84cKCy3WAwoE+fPjh16hQA4LPPPoPD4QAA5fvMW3/kbdQ23e34aa+RI0fim2++CaiNkOvoSWQUGk6nU9x///0iIyNDHDp0qMV2ecLl2rVrlecOHDjQYsJlfn6+SElJEY8++qjH95k/f7648MILXZ679dZb/Zpw+eKLLyrPVVVVuUy4PHnypNizZ4/y+PzzzwUAsXbtWlFQUODX78DbRHN3jY2NYsCAAeLhhx/2q11qu3A/Hj155513hFarFeXl5V73aWt/wKIZLXTXY0eN32XhJ9yPW/lnddEDu90uUlJSXCoOupOLZtjtduW5BQsWsGhGG3XX40fNW9EMTyZMmCBuvPFGv/btKAy4uqh7771XWCwWsXnzZpeymXV1dco+s2bNEtnZ2WLjxo1i27ZtYtSoUWLUqFHK9j179ojk5GRxxx13uLRx5swZZR+5pOijjz4q9u/fL1599VW/S4rGxcWJjz/+WOzevVtMnjzZa1l4IZpLJMPPKoV5eXkiLy9PDB8+XNx2220iLy9P7N27V9n+9NNPi88//1wcPXpUbN++Xfzyl78URqPRZR8KrnA/Hr/99luxfPlysXPnTnH06FHxzjvviOTkZDFt2jSf7frTn5qaGuWYBSCWLVsm8vLyxMmTJ9v0O+6quuuxIwS/y8JZuB+3Qgjx0EMPiczMTPH555+LAwcOiDvvvFOkpKT4vFFQWVkpUlNTxa9+9SuRn58v1qxZI6Kiolwusm02m3Jsp6eni3nz5om8vDxx+PDhNv2Ou7LuevwIIcTevXtFXl6euO6668S4ceOUY0W2fPly8dFHH4nDhw+LPXv2iIceekhotVqxfv16f3+9HYIBVxcFwOPjzTffVPapr68X9913n4iPjxdRUVHixhtvFEVFRcr2RYsWeWyjZ8+eLu+1adMmMXToUKHX60WfPn1c3sMbp9Mpfve734nU1FRhMBjE+PHjxcGDB73u35aAq7U+z5kzR2RnZwu9Xi9SU1PFNddcI3bs2NFqu9R+4X48bt++XeTm5gqLxSKMRqO44IILxPPPPy8aGhpabbu1/sglbt0f06dPb7Xt7qA7Hzv8Lgtf4X7cCtGckXjkkUdESkqKiImJERMmTBD5+fmttr1r1y5x2WWXCYPBIDIzM8WSJUtctst/z90fY8eObbXt7qI7Hz89e/b02G/Z0qVLRd++fYXRaBQJCQli3LhxYuPGja2229E0Qgjh7/BDIiIiIiIi8h+rFBIREREREYUIAy4iIiIiIqIQYcBFREREREQUIgy4iIiIiIiIQoQBFxERERERUYgw4CIiIiIiIgoRBlxEREREREQhwoCLiIiIiIgoRBhwERERERERhQgDLiIi6pZmzJgBjUYDjUYDnU6H1NRUXHnllVi1ahWcTqff7axevRpxcXGh6ygREYU1BlxERNRtXX311SgqKsKJEyfw3//+Fz/72c/w0EMP4dprr0VjY2NHd4+IiLoABlxERNRtGQwGpKWlITMzExdddBEef/xxfPzxx/jvf/+L1atXAwCWLVuGwYMHw2w2IysrC/fddx9qa2sBAJs3b8bMmTNRVVWlZMueeuopAIDNZsO8efOQmZkJs9mM3NxcbN68uWM+KBERdRgGXERERCpXXHEFhgwZgg8//BAAoNVq8fLLL2Pv3r146623sHHjRsyfPx8AMHr0aKxYsQKxsbEoKipCUVER5s2bBwCYPXs2tm7dijVr1mD37t2YMmUKrr76ahw+fLjDPhsREZ17GiGE6OhOEBERnWszZsxAZWUlPvrooxbbfvnLX2L37t3Yt29fi21r167FrFmzcPbsWQDNc7jmzJmDyspKZZ9Tp06hT58+OHXqFDIyMpTnJ0yYgJEjR+L5558P+uchIqLOKbKjO0BERNTZCCGg0WgAAOvXr8fixYtx4MABVFdXo7GxEQ0NDairq0NUVJTH1+/ZswdNTU3o37+/y/M2mw2JiYkh7z8REXUeDLiIiIjc7N+/H71798aJEydw7bXX4t5778Vzzz2HhIQEfPPNN7jzzjtht9u9Bly1tbWIiIjA9u3bERER4bItOjr6XHwEIiLqJBhwERERqWzcuBF79uzBww8/jO3bt8PpdOKll16CVts87fkf//iHy/56vR5NTU0uzw0bNgxNTU04c+YMxowZc876TkREnQ8DLiIi6rZsNhuKi4vR1NSEkpISrFu3DosXL8a1116LadOmIT8/Hw6HA6+88gquu+46bNmyBStXrnRpo1evXqitrcWGDRswZMgQREVFoX///rj99tsxbdo0vPTSSxg2bBhKS0uxYcMG5OTk4Oc//3kHfWIiIjrXWKWQiIi6rXXr1iE9PR29evXC1VdfjU2bNuHll1/Gxx9/jIiICAwZMgTLli3D0qVLceGFF+Ldd9/F4sWLXdoYPXo0Zs2ahalTpyI5ORkvvPACAODNN9/EtGnT8Mgjj2DAgAG44YYb8L///Q/Z2dkd8VGJiKiDsEohERERERFRiDDDRUREREREFCIMuIiIiIiIiEKEARcREREREVGIMOAiIiIiIiIKEQZcREREREREIcKAi4iIiIiIKEQYcBEREREREYUIAy4iIiIiIqIQYcBFREREREQUIgy4iIiIiIiIQoQBFxERERERUYj8f5HrIpB5a7RnAAAAAElFTkSuQmCC", + "image/png": 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", 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", 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", 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", + "image/png": 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" ] @@ -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/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 %} - + Download {% include ".icons/material/download.svg" %} {% endif %} 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..f646fcf 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", @@ -19,6 +19,7 @@ "numpy", "pandas", "scipy", - "requests" + "requests", + "utm" ], ) \ No newline at end of file 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 @@ + - + @@ -21,12 +22,15 @@ - + + + + @@ -54,9 +58,6 @@ - - - @@ -80,6 +81,7 @@
@@ -100,8 +102,12 @@
+ + + - + + - + - - - + - - - + - - - + 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 index 68eaf3f..cd395b3 100644 Binary files a/site/assets/images/social/correction_routines.png and b/site/assets/images/social/correction_routines.png differ diff --git a/site/assets/images/social/examples/calibration/calibration.png b/site/assets/images/social/examples/calibration/calibration.png index 5c02539..7913baa 100644 Binary files a/site/assets/images/social/examples/calibration/calibration.png and b/site/assets/images/social/examples/calibration/calibration.png differ diff --git a/site/assets/images/social/examples/rover/Hydroinnova_rover_example.png b/site/assets/images/social/examples/rover/Hydroinnova_rover_example.png index b968aa5..d31c65f 100644 Binary files a/site/assets/images/social/examples/rover/Hydroinnova_rover_example.png and b/site/assets/images/social/examples/rover/Hydroinnova_rover_example.png differ diff --git a/site/assets/images/social/examples/stationary/example_RDT_station.png b/site/assets/images/social/examples/stationary/example_RDT_station.png index 22faac3..645c607 100644 Binary files a/site/assets/images/social/examples/stationary/example_RDT_station.png and b/site/assets/images/social/examples/stationary/example_RDT_station.png differ diff --git a/site/assets/images/social/index.png b/site/assets/images/social/index.png index eeeb8ff..fff3f7d 100644 Binary files a/site/assets/images/social/index.png and b/site/assets/images/social/index.png differ diff --git a/site/assets/images/social/reference.png b/site/assets/images/social/reference.png index 31d4e1a..5e1c011 100644 Binary files a/site/assets/images/social/reference.png and b/site/assets/images/social/reference.png differ diff --git a/site/correction_routines/index.html b/site/correction_routines/index.html index 6845b1f..b64309c 100644 --- a/site/correction_routines/index.html +++ b/site/correction_routines/index.html @@ -18,8 +18,9 @@ + - + @@ -27,12 +28,15 @@ - + + + + @@ -97,9 +101,6 @@ - - - @@ -128,6 +129,7 @@
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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 @@ + - + @@ -27,12 +28,15 @@ - + + + + @@ -56,7 +60,7 @@ - + @@ -72,7 +76,7 @@ - + @@ -97,9 +101,6 @@ - - - @@ -128,6 +129,7 @@
@@ -148,8 +150,12 @@
+ + + + + + + +
+
+
+ + + -
-
+ + +
  • + + + counts_to_vwc() + + + +
    • + +
  • + + + cutoff_rigidity() + + + +
    • + +
  • + + + euclidean_distance() + + + +
    • + +
  • + + + exp_filter() + + + +
    • + +
  • + + + fill_missing_timestamps() + + + +
    • + +
  • + + + find_neutron_monitor() + + + +
    • + +
  • + + + get_incoming_neutron_flux() + + + +
    • + +
  • + + + get_reference_neutron_flux() + + + +
    • + +
  • + + + idw() + + + +
    • + +
  • + + + interpolate_2d() + + + +
    • + +
  • + + + interpolate_incoming_flux() + + + +
    • + +
  • + + + is_outlier() + + + +
    • + +
  • + + + latlon_to_utm() + + + +
    • + +
  • + + + lattice_water() + + + +
    • + +
  • + + + location_factor() + + + +
    • + +
  • + + + nrad_weight() + + + +
    • + +
  • + + + remove_incomplete_intervals() + + + +
    • + +
  • + + + rover_centered_coordinates() + + + +
    • + +
  • + + + sensing_depth() + + + +
    • + +
  • + + + smooth_1d() + + + +
    • + +
  • + + + spatial_average() + + + +
    • + +
  • + + + total_raw_counts() + + + +
    • + +
  • + + + uncertainty_counts() + + + +
    • + +
  • + + + uncertainty_vwc() + + + +
    • + +
  • + + + data + + + +
    • + +
  • + + + abs_humidity() + + + +
    • + +
  • + + + atmospheric_depth() + + + +
    • + +
  • + + + biomass_to_bwe() + + + +
    • + +
  • + + + correction_bwe() + + + +
    • + +
  • + + + correction_humidity() + + + +
    • + +
  • + + + correction_incoming_flux() + + + +
    • + +
  • + + + correction_pressure() + + + +
    • + +
  • + + + correction_road() + + + +
    • + +
  • + + + counts_to_vwc() + + + +
    • + +
  • + + + cutoff_rigidity() + + + +
    • + +
  • + + + euclidean_distance() + + + +
    • + +
  • + + + exp_filter() + + + +
    • + +
  • + + + fill_missing_timestamps() + + + +
    • + +
  • + + + find_neutron_monitor() + + + +
    • + +
  • + + + get_incoming_neutron_flux() + + + +
    • + +
  • + + + get_reference_neutron_flux() + + + +
    • + +
  • + + + idw() + + + +
    • + +
  • + + + interpolate_2d() + + + +
    • + +
  • + + + interpolate_incoming_flux() + + + +
    • + +
  • + + + is_outlier() + + + +
    • + +
  • + + + latlon_to_utm() + + + +
    • + +
  • + + + lattice_water() + + + +
    • + +
  • + + + location_factor() + + + +
    • + +
  • + + + nrad_weight() + + + +
    • + +
  • + + + remove_incomplete_intervals() + + + +
    • + +
  • + + + rover_centered_coordinates() + + + +
    • + +
  • + + + sensing_depth() + + + +
    • + +
  • + + + smooth_1d() + + + +
    • + +
  • + + + spatial_average() + + + +
    • + +
  • + + + total_raw_counts() + + + +
    • + +
  • + + + uncertainty_counts() + + + +
    • + +
  • + + + uncertainty_vwc() + + + +
    • + + + + +
    + @@ -911,23 +1705,7156 @@ - + + + + + + +

    Reference

    + + +
    + + + + +
    + +

    crnpy is a Python package for processing cosmic ray neutron data.

    +

    Created by Joaquin Peraza and Andres Patrignani.

    + + + +
    + + + + + + + + + + +
    + + + + +

    + abs_humidity(relative_humidity, temp) + +

    + + +
    + +

    Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    relative_humidity + float + +
    +

    relative humidity (%)

    +
    +     		+ required +
    temp + float + +
    +

    temperature (Celsius)

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    Name TypeDescription
    float + +
    +

    actual vapor pressure (g m^-3)

    +
    +
    + +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + 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.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    elevation + float + +
    +

    Elevation in meters above sea level.

    +
    +     		+ required +
    latitude + float + +
    +

    Geographic latitude in decimal degrees. Value in range -90 to 90

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + 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.

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494) + +

    + + +
    + +

    Function to convert biomass to biomass water equivalent.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    biomass_dry + array or Series or DataFrame + +
    +

    Above ground dry biomass in kg m-2.

    +
    +     		+ required +
    biomass_fresh + array or Series or DataFrame + +
    +

    Above ground fresh biomass in kg m-2.

    +
    +     		+ required +
    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).

    +
    +     		+ 0.494 +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + array or Series or 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

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + 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.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    counts + array or Series or DataFrame + +
    +

    Array of ephithermal neutron counts.

    +
    +     		+ required +
    bwe + float + +
    +

    Biomass water equivalent kg m-2.

    +
    +     		+ required +
    r2_N0 + float + +
    +

    Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.

    +
    +     		+ 0.05 +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + array or Series or 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.

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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)
+
    +
    +
    + +
    + + +
    + + + + +

    + correction_humidity(abs_humidity, Aref) + +

    + + +
    + +

    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
      • +
    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    abs_humidity + list or array + +
    +

    Relative humidity readings.

    +
    +     		+ required +
    Aref + float + +
    +

    Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + list + +
    +

    fw correction factor.

    +
    +
    + +
    + References +

    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

    +
    +
    + Source code in crnpy/crnpy.py + 
    253
+254
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+256
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+279
+280
+281
+282
+283
+284
+285
+286
+287
+288
+289
    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:
+        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
+
    +
    +
    + +
    + + +
    + + + + +

    + 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) + +

    + + +
    + +

    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
      • +
    +

    $$ +f_i = \frac{I_{ref}}{I} +$$

    +

    where:

    +
      +
    • I: incoming neutron flux
      • +
    • Iref: reference incoming neutron flux
      • +
    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    incoming_neutrons + list or array + +
    +

    Incoming neutron flux readings.

    +
    +     		+ required +
    incoming_Ref + float + +
    +

    Reference incoming neutron flux. Baseline incoming neutron flux.

    +
    +     		+ None +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + list + +
    +

    fi correction factor.

    +
    +
    + +
    + References +

    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

    +
    +
    + Source code in crnpy/crnpy.py + 
    292
+293
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+295
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    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
+
+    $$
+    f_i = \frac{I_{ref}}{I}
+    $$
+
+    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.
+
+    Returns:
+        (list): fi correction factor.
+
+    References:
+        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
+
+
+    """
+    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
+
    +
    +
    + +
    + + +
    + + + + +

    + correction_pressure(pressure, Pref, L) + +

    + + +
    + +

    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.
      • +
    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    pressure + list or array + +
    +

    Atmospheric pressure readings. Long-term average pressure is recommended.

    +
    +     		+ required +
    Pref + float + +
    +

    Reference atmospheric pressure.

    +
    +     		+ required +
    L + float + +
    +

    Atmospheric attenuation coefficient.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + list + +
    +

    fp pressure correction factor.

    +
    +
    + +
    + References +

    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

    +
    +
    + Source code in crnpy/crnpy.py + 
    208
+209
+210
+211
+212
+213
+214
+215
+216
+217
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+241
+242
+243
+244
+245
+246
+247
+248
+249
+250
    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:
+        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
+
    +
    +
    + +
    + + +
    + + + + +

    + 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) + +

    + + +
    + +

    Function to correct for road effects in neutron counts. +following the approach described in Schrön et al., 2018.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    counts + array or Series or DataFrame + +
    +

    Array of ephithermal neutron counts.

    +
    +     		+ required +
    theta_N + float + +
    +

    Volumetric water content of the soil estimated from the uncorrected neutron counts.

    +
    +     		+ required +
    road_width + float + +
    +

    Width of the road in m.

    +
    +     		+ required +
    road_distance + float + +
    +

    Distance of the road from the sensor in m. Default is 0.0.

    +
    +     		+ 0.0 +
    theta_road + float + +
    +

    Volumetric water content of the road. Default is 0.12.

    +
    +     		+ 0.12 +
    p0-p9 + float + +
    +

    Parameters of the correction function. Default values are from Schrön et al., 2018.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + array or Series or 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

    +
    +
    + Source code in crnpy/crnpy.py + 
    559
+560
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=0.115) + +

    + + +
    + +

    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 $

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    counts + array or Series or DataFrame + +
    +

    Array of corrected and filtered neutron counts.

    +
    +     		+ required +
    N0 + float + +
    +

    Device-specific neutron calibration constant.

    +
    +     		+ required +
    Wlat + float + +
    +

    Lattice water content.

    +
    +     		+ required +
    Wsoc + float + +
    +

    Soil organic carbon content.

    +
    +     		+ required +
    bulk_density + float + +
    +

    Soil bulk density.

    +
    +     		+ required +
    a0 + float + +
    +

    Parameter given in Zreda et al., 2012. Default is 0.0808.

    +
    +     		+ 0.0808 +
    a1 + float + +
    +

    Parameter given in Zreda et al., 2012. Default is 0.372.

    +
    +     		+ 0.372 +
    a2 + float + +
    +

    Parameter given in Zreda et al., 2012. Default is 0.115.

    +
    +     		+ 0.115 +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + array or Series or 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

    +
    +
    + Source code in crnpy/crnpy.py + 
    591
+592
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+600
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+605
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+613
+614
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+616
+617
+618
+619
    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
+
    +
    +
    + +
    + + +
    + + + + +

    + 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.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    lat + float + +
    +

    Geographic latitude in decimal degrees. Value in range -90 to 90

    +
    +     		+ required +
    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.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + 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).

    +
    +
    + Source code in crnpy/crnpy.py + 
    1012
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+1056
    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)
+
    +
    +
    + +
    + + +
    + + + + +

    + euclidean_distance(px, py, x, y) + +

    + + +
    + +

    Function that computes the Euclidean distance between one point +in space and one or more points.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    px + float + +
    +

    x projected coordinate of the point.

    +
    +     		+ required +
    py + float + +
    +

    y projected coordinate of the point.

    +
    +     		+ required +
    x + (list, ndarray, series) + +
    +

    vector of x projected coordinates.

    +
    +     		+ required +
    y + (list, ndarray, series) + +
    +

    vector of y projected coordinates.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + ndarray + +
    +

    Numpy array of distances from the point (px,py) to all the points in x and y vectors.

    +
    +
    + +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + exp_filter(sm, T=1) + +

    + + +
    + +

    Exponential filter to estimate soil moisture in the rootzone from surface observtions.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    sm + list or array + +
    +

    Soil moisture in mm of water for the top layer of the soil profile.

    +
    +     		+ required +
    T + float + +
    +

    Characteristic time length in the same units as the measurement interval.

    +
    +     		+ 1 +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    Name TypeDescription
    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.

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + 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.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    df + DataFrame + +
    +

    Pandas DataFrame.

    +
    +     		+ required +
    timestamp_col + str + +
    +

    Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.

    +
    +     		+ 'timestamp' +
    freq + str + +
    +

    Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.

    +
    +     		+ 'H' +
    round_timestamp + bool + +
    +

    Whether to round timestamps to the nearest frequency. Default is True.

    +
    +     		+ True +
    verbose + bool + +
    +

    Prints the missing timestamps added to the DatFrame.

    +
    +     		+ False +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + DataFrame + +
    +

    DataFrame with filled missing timestamps.

    +
    +
    + +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False) + +

    + + +
    + +

    Search for potential reference neutron monitoring stations based on cutoff rigidity.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    Rc + float + +
    +

    Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.

    +
    +     		+ required +
    start_date + datetime + +
    +

    Start date for the period of interest.

    +
    +     		+ None +
    end_date + datetime + +
    +

    End date for the period of interest.

    +
    +     		+ None +
    verbose + bool + +
    +

    If True, print a expanded output of the incoming neutron flux data.

    +
    +     		+ False +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + 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

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + 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.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    start_date + datetime + +
    +

    Start date of the time series.

    +
    +     		+ required +
    end_date + datetime + +
    +

    End date of the time series.

    +
    +     		+ required +
    station + str + +
    +

    Neutron Monitor station to retrieve data from.

    +
    +     		+ required +
    utc_offset + int + +
    +

    UTC offset in hours. Default is 0.

    +
    +     		+ 0 +
    expand_window + int + +
    +

    Number of hours to expand the time window to retrieve extra data. Default is 0.

    +
    +     		+ 0 +
    verbose + bool + +
    +

    Print information about the request. Default is False.

    +
    +     		+ False +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + DataFrame + +
    +

    Neutron flux in counts per hour and timestamps.

    +
    +
    + +
    + References +

    Documentation available:https://www.nmdb.eu/nest/help.php#howto

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + 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).

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    station + str + +
    +

    Neutron Monitor station to retrieve data from.

    +
    +     		+ required +
    date + datetime + +
    +

    Date of the reference neutron flux. Default is 2011-05-01.

    +
    +     		+ to_datetime('2011-05-01') +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + 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.

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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()
+
    +
    +
    + +
    + + +
    + + + + +

    + idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1) + +

    + + +
    + +

    Function to interpolate data using inverse distance weight.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    x + list or array + +
    +

    UTM x coordinates in meters.

    +
    +     		+ required +
    y + list or array + +
    +

    UTM y coordinates in meters.

    +
    +     		+ required +
    z + list or array + +
    +

    Values to be interpolated.

    +
    +     		+ required +
    X_pred + list or array + +
    +

    UTM x coordinates where z values need to be predicted.

    +
    +     		+ required +
    Y_pred + list or array + +
    +

    UTM y coordinates where z values need to be predicted.

    +
    +     		+ required +
    neighborhood + float + +
    +

    Only points within this radius in meters are considered for the interpolation.

    +
    +     		+ 1000 +
    p + int + +
    +

    Exponent of the inverse distance weight formula. Typically, p=1 or p=2.

    +
    +     		+ 1 +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + array + +
    +

    Interpolated values.

    +
    +
    + +
    + References +

    https://en.wikipedia.org/wiki/Inverse_distance_weighting

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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)
+
    +
    +
    + +
    + + +
    + + + + +

    + interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000) + +

    + + +
    + +

    Function for interpolating irregular spatial data into a regular grid.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    x + list or array + +
    +

    UTM x coordinates in meters.

    +
    +     		+ required +
    y + list or array + +
    +

    UTM y coordinates in meters.

    +
    +     		+ required +
    z + list or array + +
    +

    Values to be interpolated.

    +
    +     		+ required +
    dx + float + +
    +

    Pixel width in meters.

    +
    +     		+ 100 +
    dy + float + +
    +

    Pixel height in meters.

    +
    +     		+ 100 +
    method + str + +
    +

    Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.

    +
    +     		+ 'cubic' +
    neighborhood + float + +
    +

    Only points within this radius in meters are considered for the interpolation.

    +
    +     		+ 1000 +
    + + + +

    Returns:

    + + + + + + + + + + + + + + + + + + + + + +
    Name TypeDescription
    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

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps) + +

    + + +
    + +

    Function to interpolate incoming neutron flux to match the timestamps of the observations.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    nmdb_timestamps + Series + +
    +

    Series of timestamps in datetime format from the NMDB.

    +
    +     		+ required +
    nmdb_counts + Series + +
    +

    Series of neutron counts from the NMDB

    +
    +     		+ required +
    crnp_timestamps + Series + +
    +

    Series of timestamps in datetime format from the station or device.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + Series + +
    +

    Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps

    +
    +
    + +
    + Source code in crnpy/crnpy.py + 
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    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.
+
+    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)
+
+    # Interpolate nan values
+    incoming_flux = pd.Series(incoming_flux).interpolate(method='nearest', limit_direction='both')
+
+    # Return only the values for the selected timestamps
+    return incoming_flux
+
    +
    +
    + +
    + + +
    + + + + +

    + 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.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    x + DataFrame or Series + +
    +

    Variable containing only the columns with neutron counts.

    +
    +     		+ required +
    method + str + +
    +

    Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad

    +
    +     		+ required +
    window + int + +
    +

    Window size for the moving central tendency. Default is 11.

    +
    +     		+ 11 +
    min_val + int or float + +
    +

    Minimum value for a reading to be considered valid. Default is None.

    +
    +     		+ None +
    max_val(int + or float + +
    +

    Maximum value for a reading to be considered valid. Default is None.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + DataFrame + +
    +

    Boolean indicating outliers.

    +
    +
    + +
    + References +

    Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + 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 +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.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    lat + (float, array) + +
    +

    Latitude in decimal degrees.

    +
    +     		+ required +
    lon + (float, array) + +
    +

    Longitude in decimal degrees.

    +
    +     		+ required +
    utm_zone_number + int + +
    +

    UTM zone number. If None, the zone number is automatically calculated.

    +
    +     		+ None +
    utm_zone_letter + str + +
    +

    UTM zone letter. If None, the zone letter is automatically calculated.

    +
    +     		+ None +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + (float, float) + +
    +

    Tuple of easting and northing coordinates in meters. First element is easting, second is northing.

    +
    +
    + +
    + References +

    Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87) +https://github.com/Turbo87/utm

    +

    https://www.maptools.com/tutorials/grid_zone_details#

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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): Tuple of easting and northing coordinates in meters. First element is easting, second is northing.
+
+    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#)
+    """
+    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
+
    +
    +
    + +
    + + +
    + + + + +

    + lattice_water(clay_content, total_carbon=None) + +

    + + +
    + +

    Estimate the amount of water in the lattice of clay minerals.

    + + + + + + + + + + + + + + + + + +
    img1img2
    $\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.
    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    clay_content + float + +
    +

    Clay content in the soil in percent.

    +
    +     		+ required +
    total_carbon + float + +
    +

    Total carbon content in the soil in percent. +If None, the amount of water is estimated based on clay content only.

    +
    +     		+ None +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + float + +
    +

    Amount of water in the lattice of clay minerals in percent

    +
    +
    + +
    + Source code in crnpy/crnpy.py + 
    1242
+1243
+1244
+1245
+1246
+1247
+1248
+1249
+1250
+1251
+1252
+1253
+1254
+1255
+1256
+1257
+1258
+1259
+1260
+1261
+1262
    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
+
    +
    +
    + +
    + + +
    + + + + +

    + 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.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    site_atmospheric_depth + float + +
    +

    Atmospheric depth at the site in g/cm2. Can be estimated using the function atmospheric_depth()

    +
    +     		+ required +
    site_Rc + float + +
    +

    Cutoff rigidity at the site in GV. Can be estimated using the function cutoff_rigidity()

    +
    +     		+ required +
    reference_atmospheric_depth + float + +
    +

    Atmospheric depth at the reference location in g/cm2.

    +
    +     		+ required +
    reference_Rc + float + +
    +

    Cutoff rigidity at the reference location in GV.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + 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.

    +
    +
    + Source code in crnpy/crnpy.py + 
    1104
+1105
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+1107
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+1109
+1110
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+1112
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+1114
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + 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.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    h + array or Series + +
    +

    Air Humidity from 0.1 to 50 in g/m^3. When h=0, the function will skip the distance weighting.

    +
    +     		+ required +
    theta + array or Series + +
    +

    Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)

    +
    +     		+ required +
    distances + array or Series + +
    +

    Distances from the location of each sample to the origin (0.5 - 600 m)

    +
    +     		+ required +
    depth + array or Series + +
    +

    Depths for each sample (m)

    +
    +     		+ required +
    rhob + array or Series + +
    +

    Bulk density in g/cm^3

    +
    +     		+ 1.4 +
    p + array or Series + +
    +

    Atmospheric pressure in hPa. Required for the 'Schron_2017' method.

    +
    +     		+ None +
    Hveg + array or Series + +
    +

    Vegetation height in m. Required for the 'Schron_2017' method.

    +
    +     		+ None +
    method + str + +
    +

    Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.

    +
    +     		+ None +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + array or Series or DataFrame + +
    +

    Distance weights for each sample.

    +
    +
    + +
    + 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.

    +
    +
    + Source code in crnpy/crnpy.py + 
    697
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    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 (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.
+
+    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)
+        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
+
    +
    +
    + +
    + + +
    + + + + +

    + remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False) + +

    + + +
    + +

    Function that removes rows with incomplete integration intervals.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    df + DataFrame + +
    +

    Pandas Dataframe with data from stationary or roving CRNP devices.

    +
    +     		+ required +
    timestamp_col + str + +
    +

    Name of the column with timestamps in datetime format.

    +
    +     		+ required +
    integration_time + int + +
    +

    Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.

    +
    +     		+ required +
    remove_first + bool + +
    +

    Remove first row. Default is False.

    +
    +     		+ False +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + DataFrame + +
    + +
    +
    + +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + rover_centered_coordinates(x, y) + +

    + + +
    + +

    Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    x + array + +
    +

    x coordinates.

    +
    +     		+ required +
    y + array + +
    +

    y coordinates.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + + + + + +
    Name TypeDescription
    x_est + array + +
    +

    Estimated x coordinates.

    +
    +
    y_est + array + +
    +

    Estimated y coordinates.

    +
    +
    + +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + 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.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    vwc + array or Series or DataFrame + +
    +

    Estimated volumetric water content for each timestamp.

    +
    +     		+ required +
    pressure + array or Series or DataFrame + +
    +

    Atmospheric pressure in hPa for each timestamp.

    +
    +     		+ required +
    p_ref + float + +
    +

    Reference pressure in hPa.

    +
    +     		+ required +
    bulk_density + float + +
    +

    Soil bulk density.

    +
    +     		+ required +
    Wlat + float + +
    +

    Lattice water content.

    +
    +     		+ required +
    method + str + +
    +

    Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.

    +
    +     		+ 'Schron_2017' +
    dist + list or array + +
    +

    List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.

    +
    +     		+ None +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + array or Series or 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

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + 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).

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    values + DataFrame or Serie + +
    +

    Dataframe containing the values to smooth.

    +
    +     		+ required +
    window + int + +
    +

    Window size for the Savitzky-Golay filter. Default is 5.

    +
    +     		+ 5 +
    method + str + +
    +

    Method to use for smoothing the data. Default is 'moving_median'. +Options are 'moving_average', 'moving_median' and 'savitzky_golay'.

    +
    +     		+ 'moving_median' +
    order + int + +
    +

    Order of the Savitzky-Golay filter. Default is 3.

    +
    +     		+ 3 +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + 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.

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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(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':
+        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
+
    +
    +
    + +
    + + +
    + + + + +

    + spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=False) + +

    + + +
    + +

    Moving buffer filter to smooth georeferenced two-dimensional data.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    x + list or array + +
    +

    UTM x coordinates in meters.

    +
    +     		+ required +
    y + list or array + +
    +

    UTM y coordinates in meters.

    +
    +     		+ required +
    z + list or array + +
    +

    Values to be smoothed.

    +
    +     		+ required +
    buffer + float + +
    +

    Radial buffer distance in meters.

    +
    +     		+ 100 +
    min_neighbours + int + +
    +

    Minimum number of neighbours to consider for the smoothing.

    +
    +     		+ 3 +
    method + str + +
    +

    One of 'mean' or 'median'.

    +
    +     		+ 'mean' +
    rnd + bool + +
    +

    Boolean to round the final result. Useful in case of z representing neutron counts.

    +
    +     		+ False +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + array + +
    +

    Smoothed version of z with the same dimension as z.

    +
    +
    + +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + total_raw_counts(counts) + +

    + + +
    + +

    Compute the sum of uncorrected neutron counts for all detectors.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    counts + DataFrame + +
    +

    Dataframe containing only the columns with neutron counts.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + DataFrame + +
    +

    Dataframe with the sum of uncorrected neutron counts for all detectors.

    +
    +
    + +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + 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} $

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    raw_counts + array + +
    +

    Raw neutron counts.

    +
    +     		+ required +
    metric + str + +
    +

    Either 'std' or 'cv' for standard deviation or coefficient of variation.

    +
    +     		+ 'std' +
    fp + float + +
    +

    Pressure correction factor.

    +
    +     		+ 1 +
    fw + float + +
    +

    Humidity correction factor.

    +
    +     		+ 1 +
    fi + float + +
    +

    Incoming neutron flux correction factor.

    +
    +     		+ 1 +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    Name TypeDescription
    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.

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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
+
    +
    +
    + +
    + + +
    + + + + +

    + uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808, a1=0.372, a2=0.115) + +

    + + +
    + +

    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: +$$ +\sigma_{\theta_g}(N) = \sigma_N \frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \sqrt{(N_{cor} - a_1 N_0)^4 + 8 \sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \sigma_N^4} +$$

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    raw_counts + array + +
    +

    Raw neutron counts.

    +
    +     		+ required +
    N0 + float + +
    +

    Calibration parameter N0.

    +
    +     		+ required +
    bulk_density + float + +
    +

    Bulk density in g cm-3.

    +
    +     		+ required +
    fp + float + +
    +

    Pressure correction factor.

    +
    +     		+ 1 +
    fw + float + +
    +

    Humidity correction factor.

    +
    +     		+ 1 +
    fi + float + +
    +

    Incoming neutron flux correction factor.

    +
    +     		+ 1 +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    Name TypeDescription
    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

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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
 
-
    Reference
    
+    return sigma_VWC
+
    +
    +
    + +
    + + + +
    + +
    +
    - + +
    -

    crnpy is a Python package for processing cosmic ray neutron data.

    -

    Created by Joaquin Peraza and Andres Patrignani.

    +

    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.
      • +
    @@ -941,12 +8868,14 @@

    Reference

    +
    -

    -abs_humidity(relative_humidity, temp) + +

    + abs_humidity(relative_humidity, temp)

    @@ -955,6 +8884,8 @@

    Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.

    + +

    Parameters:

    @@ -971,7 +8902,11 @@

    - + @@ -981,7 +8916,11 @@

    - + @@ -989,6 +8928,8 @@

    float

    relative humidity (%)

    		 +
    +

    relative humidity (%)

    +
    +     		required float

    temperature (Celsius)

    		 +
    +

    temperature (Celsius)

    +
    +     		required
    + +

    Returns:

    @@ -1001,37 +8942,41 @@

    - +
    float

    actual vapor pressure (g m^-3)

    		 +
    +

    actual vapor pressure (g m^-3)

    +
    +
    -
    - Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py - 
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    def abs_humidity(relative_humidity, temp):
+          
    
+            Source code in crnpy/crnpy.py
+            
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    def abs_humidity(relative_humidity, temp):
     """
     Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.
 
@@ -1043,30 +8988,215 @@ 
    
         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)
+    ### 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
+
    
+          
    
+
    + + + + +
    + + + + +

    + 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.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    elevation + float + +
    +

    Elevation in meters above sea level.

    +
    +     		+ required +
    latitude + float + +
    +

    Geographic latitude in decimal degrees. Value in range -90 to 90

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + 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.

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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.
 
-    # Vapor pressure Pascals
-    Pw = e_sat * relative_humidity / 100
+        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.
+    """
 
-    # Absolute humidity (g/m^3)
-    C = 2.16679  # g K/J;
-    abs_h = C * Pw / (temp + 273.15)
-    return abs_h
+    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
    -
    +
    +
    -

    -biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494) + +

    + biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494)

    @@ -1075,6 +9205,8 @@

    Function to convert biomass to biomass water equivalent.

    + +

    Parameters:

    @@ -1089,9 +9221,13 @@

    + - @@ -1099,9 +9235,13 @@

    + - @@ -1111,8 +9251,12 @@

    - + @@ -1120,6 +9264,8 @@

    biomass_dry - array or pd.Series or pd.DataFrame + array or Series or DataFrame + +
    +

    Above ground dry biomass in kg m-2.

    +

    Above ground dry biomass in kg m-2.

    		 required
    biomass_fresh - array or pd.Series or pd.DataFrame + array or Series or DataFrame + +
    +

    Above ground fresh biomass in kg m-2.

    +

    Above ground fresh biomass in kg m-2.

    		 required float

    Stoichiometric ratio of H2O to organic carbon molecules in the plant (assuming this is mostly cellulose) -Default is 0.494 (Wahbi & Avery, 2018).

    		 +
    +

    Stoichiometric ratio of H2O to organic carbon molecules in the plant (assuming this is mostly cellulose) +Default is 0.494 (Wahbi & Avery, 2018).

    +
    +     		0.494
    + +

    Returns:

    @@ -1131,9 +9277,13 @@

    + -
    - array or pd.Series or pd.DataFrame + array or Series or DataFrame + +
    +

    Biomass water equivalent in kg m-2.

    +

    Biomass water equivalent in kg m-2.
    @@ -1144,26 +9294,26 @@

    Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent. Springer, Cham. https://doi.org/10.1007/978-3-319-69539-6_2

    -
    - Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py - 
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    def biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494):
+          
    
+            Source code in crnpy/crnpy.py
+            
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    def biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494):
     """Function to convert biomass to biomass water equivalent.
 
     Args:
@@ -1182,17 +9332,19 @@ 
    
     """
     return (biomass_fresh - biomass_dry) + fWE * biomass_dry
 
    
-      
    
+
    +
    -

    -correction_bwe(counts, bwe, r2_N0=0.05) + +

    + correction_bwe(counts, bwe, r2_N0=0.05)

    @@ -1202,6 +9354,8 @@

    Function to correct for biomass effects in neutron counts. following the approach described in Baatz et al., 2015.

    + +

    Parameters:

    @@ -1216,9 +9370,13 @@

    + - @@ -1228,7 +9386,11 @@

    - + @@ -1238,7 +9400,11 @@

    - + @@ -1246,6 +9412,8 @@

    counts - array or pd.Series or pd.DataFrame + array or Series or DataFrame + +
    +

    Array of ephithermal neutron counts.

    +

    Array of ephithermal neutron counts.

    		 required float

    Biomass water equivalent kg m-2.

    		 +
    +

    Biomass water equivalent kg m-2.

    +
    +     		required float

    Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.

    		 +
    +

    Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.

    +
    +     		0.05
    + +

    Returns:

    @@ -1257,9 +9425,13 @@

    + -
    - array or pd.Series or pd.DataFrame + array or Series or DataFrame + +
    +

    Array of corrected neutron counts for biomass effects.

    +

    Array of corrected neutron counts for biomass effects.
    @@ -1270,27 +9442,27 @@

    An empiricalvegetation correction for soil water content quantification using cosmic ray probes, Water Resour. Res., 51, 2030–2046, doi:10.1002/ 2014WR016443.

    -
    - Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py - 
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    def correction_bwe(counts, bwe, r2_N0=0.05):
+          
    
+            Source code in crnpy/crnpy.py
+            
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    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.
 
@@ -1308,19 +9480,21 @@ 
    
         Water Resour. Res., 51, 2030–2046, doi:10.1002/ 2014WR016443.
     """
 
-    return counts/(1 - bwe*r2_N0)
+    return counts / (1 - bwe * r2_N0)
 
    
-      
    
+
    +
    -

    -correction_humidity(abs_humidity, Aref) + +

    + correction_humidity(abs_humidity, Aref)

    @@ -1347,6 +9521,8 @@

  • Aref: reference absolute humidity
    • + +

    Parameters:

    @@ -1363,17 +9539,11 @@

    - - - - - - @@ -1383,7 +9553,11 @@

    - + @@ -1391,6 +9565,8 @@

    list or array

    Relative humidity readings.

    		 - required -
    temp - list or array +
    +

    Relative humidity readings.

    +

    Temperature readings (Celsius).

    		 required float

    Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.

    		 +
    +

    Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.

    +
    +     		required
    + +

    Returns:

    @@ -1404,7 +9580,11 @@

    - +
    list

    fw correction factor.

    		 +
    +

    fw correction factor.

    +
    +
    @@ -1413,9 +9593,11 @@

    References

    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

    -
    - Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py - 
    255
+          
    
+            Source code in crnpy/crnpy.py
+            
    253
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@@ -1449,10 +9631,7 @@ 
    
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    def correction_humidity(abs_humidity, Aref):
+289
    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:
@@ -1478,7 +9657,6 @@ 
    
 
     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:
@@ -1488,20 +9666,22 @@ 
    
         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
 
    
-      
    
+
    +
    -

    -correction_incoming_flux(incoming_neutrons, incoming_Ref=None) + +

    + 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)

    @@ -1528,6 +9708,8 @@

  • Iref: reference incoming neutron flux
    • + +

    Parameters:

    @@ -1544,7 +9726,11 @@

    - + @@ -1554,7 +9740,11 @@

    - + @@ -1562,6 +9752,8 @@

    list or array

    Incoming neutron flux readings.

    		 +
    +

    Incoming neutron flux readings.

    +
    +     		required float

    Reference incoming neutron flux. Baseline incoming neutron flux.

    		 +
    +

    Reference incoming neutron flux. Baseline incoming neutron flux.

    +
    +     		None
    + +

    Returns:

    @@ -1575,7 +9767,11 @@

    - +
    list

    fi correction factor.

    		 +
    +

    fi correction factor.

    +
    +
    @@ -1584,9 +9780,11 @@

    References

    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

    -
    - Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py - 
    294
+          
    
+            Source code in crnpy/crnpy.py
+            
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@@ -1628,7 +9826,27 @@ 
    
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    def correction_incoming_flux(incoming_neutrons, incoming_Ref=None):
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    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:
@@ -1668,21 +9886,43 @@ 
    
         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
 
    
-      
    
+
    +
    -

    -correction_pressure(pressure, Pref, L) + +

    + correction_pressure(pressure, Pref, L)

    @@ -1711,6 +9951,8 @@

  • L: Atmospheric attenuation coefficient.
    • + +

    Parameters:

    @@ -1723,11 +9965,15 @@

    - + - + @@ -1737,7 +9983,11 @@

    - + @@ -1747,7 +9997,11 @@

    - + @@ -1755,6 +10009,8 @@

    atm_pressurepressure list or array

    Atmospheric pressure readings. Long-term average pressure is recommended.

    		 +
    +

    Atmospheric pressure readings. Long-term average pressure is recommended.

    +
    +     		required float

    Reference atmospheric pressure.

    		 +
    +

    Reference atmospheric pressure.

    +
    +     		required float

    Atmospheric attenuation coefficient.

    		 +
    +

    Atmospheric attenuation coefficient.

    +
    +     		required
    + +

    Returns:

    @@ -1768,7 +10024,11 @@

    - +
    list

    fp pressure correction factor.

    		 +
    +

    fp pressure correction factor.

    +
    +
    @@ -1777,9 +10037,11 @@

    References

    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

    -
    - Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py - 
    210
+          
    
+            Source code in crnpy/crnpy.py
+            
    208
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@@ -1819,9 +10081,7 @@ 
    
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    def correction_pressure(pressure, Pref, L):
+250
    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).
@@ -1849,7 +10109,7 @@ 
    
 
 
     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.
 
@@ -1861,21 +10121,23 @@ 
    
     """
 
     # 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
 
    
-      
    
+
    +
    -

    -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) + +

    + 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)

    @@ -1885,6 +10147,8 @@

    Function to correct for road effects in neutron counts. following the approach described in Schrön et al., 2018.

    + +

    Parameters:

    @@ -1899,9 +10163,13 @@

    + - @@ -1911,7 +10179,11 @@

    - + @@ -1921,7 +10193,11 @@

    - + @@ -1931,7 +10207,11 @@

    - + @@ -1941,7 +10221,11 @@

    - + @@ -1951,7 +10235,11 @@

    - + @@ -1959,61 +10247,69 @@

    counts - array or pd.Series or pd.DataFrame + array or Series or DataFrame + +
    +

    Array of ephithermal neutron counts.

    +

    Array of ephithermal neutron counts.

    		 required float

    Volumetric water content of the soil estimated from the uncorrected neutron counts.

    		 +
    +

    Volumetric water content of the soil estimated from the uncorrected neutron counts.

    +
    +     		required float

    Width of the road in m.

    		 +
    +

    Width of the road in m.

    +
    +     		required float

    Distance of the road from the sensor in m. Default is 0.0.

    		 +
    +

    Distance of the road from the sensor in m. Default is 0.0.

    +
    +     		0.0 float

    Volumetric water content of the road. Default is 0.12.

    		 +
    +

    Volumetric water content of the road. Default is 0.12.

    +
    +     		0.12 float

    Parameters of the correction function. Default values are from Schrön et al., 2018.

    		 +
    +

    Parameters of the correction function. Default values are from Schrön et al., 2018.

    +
    +     		required
    + +

    Returns:

    - - - - - - - - - -
    TypeDescription
    - 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

    -
    -
    - Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py -
    + + + + + + + + + 
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    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):
+
    		Description
    + array or Series or 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

    +
    +
    + Source code in crnpy/crnpy.py + 
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    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.
 
@@ -2033,7 +10329,7 @@ 
    
         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)
 
@@ -2043,17 +10339,19 @@ 
    
 
     return corrected_counts
    -
    +

    +
    -

    -counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=0.115) + +

    + counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=0.115)

    @@ -2064,6 +10362,8 @@

    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 $

    + +

    Parameters:

    @@ -2078,9 +10378,13 @@

    + - @@ -2090,7 +10394,11 @@

    - + @@ -2100,7 +10408,11 @@

    - + @@ -2110,7 +10422,11 @@

    - + @@ -2120,7 +10436,11 @@

    - + @@ -2130,7 +10450,11 @@

    - + @@ -2140,7 +10464,11 @@

    - + @@ -2150,7 +10478,11 @@

    - + @@ -2158,6 +10490,8 @@

    counts - array or pd.Series or pd.DataFrame + array or Series or DataFrame + +
    +

    Array of corrected and filtered neutron counts.

    +

    Array of corrected and filtered neutron counts.

    		 required float

    Device-specific neutron calibration constant.

    		 +
    +

    Device-specific neutron calibration constant.

    +
    +     		required float

    Lattice water content.

    		 +
    +

    Lattice water content.

    +
    +     		required float

    Soil organic carbon content.

    		 +
    +

    Soil organic carbon content.

    +
    +     		required float

    Soil bulk density.

    		 +
    +

    Soil bulk density.

    +
    +     		required float

    Parameter given in Zreda et al., 2012. Default is 0.0808.

    		 +
    +

    Parameter given in Zreda et al., 2012. Default is 0.0808.

    +
    +     		0.0808 float

    Parameter given in Zreda et al., 2012. Default is 0.372.

    		 +
    +

    Parameter given in Zreda et al., 2012. Default is 0.372.

    +
    +     		0.372 float

    Parameter given in Zreda et al., 2012. Default is 0.115.

    		 +
    +

    Parameter given in Zreda et al., 2012. Default is 0.115.

    +
    +     		0.115
    + +

    Returns:

    @@ -2169,9 +10503,13 @@

    + -
    - array or pd.Series or pd.DataFrame + array or Series or DataFrame + +
    +

    Volumetric water content in m3 m-3.

    +

    Volumetric water content in m3 m-3.
    @@ -2182,37 +10520,37 @@

    Land surface hydrology at an elusive scale with cosmic rays. Water Resour. Res. 46:W11505. doi.org/10.1029/2009WR008726

    -
    - Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py - 
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    def counts_to_vwc(counts, N0, Wlat, Wsoc ,bulk_density, a0=0.0808,a1=0.372,a2=0.115):
+          
    
+            Source code in crnpy/crnpy.py
+            
    591
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    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.
@@ -2239,20 +10577,22 @@ 
    
     """
 
     # 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
 
    
-      
    
+
    +
    -

    -cutoff_rigidity(lat, lon) + +

    + cutoff_rigidity(lat, lon)

    @@ -2263,6 +10603,8 @@

    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.

    + +

    Parameters:

    @@ -2279,7 +10621,11 @@

    - + @@ -2289,8 +10635,12 @@

    - + @@ -2298,6 +10648,8 @@

    float

    Geographic latitude in decimal degrees. Value in range -90 to 90

    		 +
    +

    Geographic latitude in decimal degrees. Value in range -90 to 90

    +
    +     		required 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.

    		 +
    +

    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.

    +
    +     		required
    + +

    Returns:

    @@ -2311,11 +10663,17 @@

    - +
    float

    Cutoff rigidity in GV. Error is about +/- 0.3 GV

    		 +
    +

    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)
@@ -2325,54 +10683,61 @@ 
    
 
 
    
   References
-  
    Smart, D. & Shea, Matthew. (2001). Geomagnetic Cutoff Rigidity Computer Program:
+  
    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).
    
 
    
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
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    def cutoff_rigidity(lat,lon):
+          
    
+            Source code in crnpy/crnpy.py
+            
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    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.
@@ -2393,6 +10758,10 @@ 
    
         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.
 
@@ -2403,28 +10772,30 @@ 
    
     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)
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-euclidean_distance(px, py, x, y)
+
+
    
+          euclidean_distance(px, py, x, y)
 
 
    
 
@@ -2434,6 +10805,8 @@ 
    
       
    Function that computes the Euclidean distance between one point
 in space and one or more points.
    
 
+
+
   
    Parameters:
    
   
@@ -2450,7 +10823,11 @@ 
    
           
    
-          
+          
@@ -2460,7 +10837,11 @@ 
    
           
    
-          
+          
@@ -2468,9 +10849,13 @@ 
    
         
    
+          
-          
@@ -2478,9 +10863,13 @@ 
    
         
    
+          
-          
@@ -2488,6 +10877,8 @@ 
    
     
   
    
     
    
                 float
           
    x projected coordinate of the point.
    		
+            
    
+              
    x projected coordinate of the point.
    
+            
    
+              		
           
               required
           
                 float
           
    y projected coordinate of the point.
    		
+            
    
+              
    y projected coordinate of the point.
    
+            
    
+              		
           
               required
           
    
           x
           
-                list, ndarray, pandas.series
+                (list, ndarray, series)
+          
+            
    
+              
    vector of x projected coordinates.
    
+            
    
           
    vector of x projected coordinates.
    		
           
               required
           
    
           y
           
-                list, ndarray, pandas.series
+                (list, ndarray, series)
+          
+            
    
+              
    vector of y projected coordinates.
    
+            
    
           
    vector of y projected coordinates.
    		
           
               required
           
    
 
+
+
   
    Returns:
    
   
@@ -2501,28 +10892,32 @@ 
    
           
    
-          
+          
-    
-  
    
     
    
                 ndarray
           
    Numpy array of distances from the point (px,py) to all the points in x and y vectors.
    		
+            
    
+              
    Numpy array of distances from the point (px,py) to all the points in x and y vectors.
    
+            
    
+              		
         
    
-
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
    
+  
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    def euclidean_distance(px, py, x, y):
+    
    
+
+          
    
+            Source code in crnpy/crnpy.py
+            
    1298
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    def euclidean_distance(px, py, x, y):
     """Function that computes the Euclidean distance between one point
     in space and one or more points.
 
@@ -2538,17 +10933,19 @@ 
    
     d = np.sqrt((px - x) ** 2 + (py - y) ** 2)
     return d
 
    
-      
    
+          
    
   
    
 
 
 
+
 
    
 
 
 
-
    
-exp_filter(sm, T=1)
+
+
    
+          exp_filter(sm, T=1)
 
 
    
 
@@ -2557,6 +10954,8 @@ 
    
   
       
    Exponential filter to estimate soil moisture in the rootzone from surface observtions.
    
 
+
+
   
    Parameters:
    
   
@@ -2573,7 +10972,11 @@ 
    
           
    
-          
+          
@@ -2583,7 +10986,11 @@ 
    
           
    
-          
+          
@@ -2591,6 +10998,8 @@ 
    
     
   
    
     
    
                 list or array
           
    Soil moisture in mm of water for the top layer of the soil profile.
    		
+            
    
+              
    Soil moisture in mm of water for the top layer of the soil profile.
    
+            
    
+              		
           
               required
           
                 float
           
    Characteristic time length in the same units as the measurement interval.
    		
+            
    
+              
    Characteristic time length in the same units as the measurement interval.
    
+            
    
+              		
           
                 1
           
    
 
+
+
   
    Returns:
    
   
@@ -2604,7 +11013,11 @@ 
    
 
    
-          
+          
    
     
    sm_subsurface          
                 list or array
           
    Subsurface soil moisture in the same units as the input.
    		
+            
    
+              
    Subsurface soil moisture in the same units as the input.
    
+            
    
+              		
         
    
     
   
    
@@ -2619,57 +11032,56 @@ 
    
 
    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.
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
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    def exp_filter(sm,T=1):
+          
    
+            Source code in crnpy/crnpy.py
+            
     962
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    def exp_filter(sm, T=1):
     """Exponential filter to estimate soil moisture in the rootzone from surface observtions.
 
     Args:
@@ -2691,7 +11103,6 @@ 
    
         Soil Science Society of America Journal.
     """
 
-
     # Parameters
     t_delta = 1
     sm_min = np.min(sm)
@@ -2699,18 +11110,18 @@ 
    
     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
 
@@ -2719,17 +11130,19 @@ 
    
 
     return sm_subsurface
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True, verbose=False)
+
+
    
+          fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True, verbose=False)
 
 
    
 
@@ -2738,6 +11151,8 @@ 
    
   
       
    Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.
    
 
+
+
   
    Parameters:
    
   
@@ -2752,9 +11167,13 @@ 
    
         
    
+          
-          
@@ -2764,7 +11183,11 @@ 
    
           
    
-          
+          
@@ -2774,7 +11197,11 @@ 
    
           
    
-          
+          
@@ -2784,7 +11211,11 @@ 
    
           
    
-          
+          
@@ -2794,7 +11225,11 @@ 
    
           
    
-          
+          
@@ -2802,6 +11237,8 @@ 
    
     
   
    
     
    
           df
           
-                pandas.DataFrame
+                DataFrame
+          
+            
    
+              
    Pandas DataFrame.
    
+            
    
           
    Pandas DataFrame.
    		
           
               required
           
                 str
           
    Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.
    		
+            
    
+              
    Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.
    
+            
    
+              		
           
                 'timestamp'
           
                 str
           
    Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.
    		
+            
    
+              
    Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.
    
+            
    
+              		
           
                 'H'
           
                 bool
           
    Whether to round timestamps to the nearest frequency. Default is True.
    		
+            
    
+              
    Whether to round timestamps to the nearest frequency. Default is True.
    
+            
    
+              		
           
                 True
           
                 bool
           
    Prints the missing timestamps added to the DatFrame.
    		
+            
    
+              
    Prints the missing timestamps added to the DatFrame.
    
+            
    
+              		
           
                 False
           
    
 
+
+
   
    Returns:
    
   
@@ -2813,16 +11250,21 @@ 
    
     
    
+          
-          
    
     
         
    
           
-                pandas.DataFrame
+                DataFrame
+          
+            
    
+              
    DataFrame with filled missing timestamps.
    
+            
    
           
    DataFrame with filled missing timestamps.
    		
         
    
         
   
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
     66
+          
    
+            Source code in crnpy/crnpy.py
+            
     65
+ 66
  67
  68
  69
@@ -2863,8 +11305,7 @@ 
    
 104
 105
 106
-107
-108
    def fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True, verbose=False):
+107
    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:
@@ -2895,9 +11336,9 @@ 
    
     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)
@@ -2908,17 +11349,19 @@ 
    
 
     return df
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False)
+
+
    
+          find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False)
 
 
    
 
@@ -2927,6 +11370,8 @@ 
    
   
       
    Search for potential reference neutron monitoring stations based on cutoff rigidity.
    
 
+
+
   
    Parameters:
    
   
@@ -2943,7 +11388,11 @@ 
    
           
    
-          
+          
@@ -2953,7 +11402,11 @@ 
    
           
    
-          
+          
@@ -2963,14 +11416,34 @@ 
    
           
    
-          
+          
+        
+          
+          
+          
+          
+        
    
     
    
                 float
           
    Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.
    		
+            
    
+              
    Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.
    
+            
    
+              		
           
               required
           
                 datetime
           
    Start date for the period of interest.
    		
+            
    
+              
    Start date for the period of interest.
    
+            
    
+              		
           
                 None
           
                 datetime
           
    End date for the period of interest.
    		
+            
    
+              
    End date for the period of interest.
    
+            
    
+              		
           
                 None
           
         
    verbose
+                bool
+          
+            
    
+              
    If True, print a expanded output of the incoming neutron flux data.
    
+            
    
+              		
+                False
+          
    
     
   
    
 
+
+
   
    Returns:
    
   
@@ -2984,12 +11457,18 @@ 
    
           
    
-          
+          
    
     
    
                 list
           
    List of top five stations with closes cutoff rigidity.
-User needs to select station according to site altitude.
    		
+            
    
+              
    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
@@ -3013,80 +11492,84 @@ 
    
   References
   
    https://www.nmdb.eu/nest/help.php#helpstations
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
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    def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):
+          
    
+            Source code in crnpy/crnpy.py
+            
    1145
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    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.
@@ -3116,7 +11599,7 @@ 
    
     """
 
     # 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()
@@ -3127,9 +11610,10 @@ 
    
             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
+                    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:
@@ -3141,23 +11625,26 @@ 
    
 
     # 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)
     return result
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expand_window=0, verbose=False)
+
+
    
+          get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expand_window=0, verbose=False)
 
 
    
 
@@ -3166,6 +11653,8 @@ 
    
   
       
    Function to retrieve neutron flux from the Neutron Monitor Database.
    
 
+
+
   
    Parameters:
    
   
@@ -3182,7 +11671,11 @@ 
    
           
    
-          
+          
@@ -3192,7 +11685,11 @@ 
    
           
    
-          
+          
@@ -3202,7 +11699,11 @@ 
    
           
    
-          
+          
@@ -3212,7 +11713,11 @@ 
    
           
    
-          
+          
@@ -3222,7 +11727,11 @@ 
    
           
    
-          
+          
@@ -3232,7 +11741,11 @@ 
    
           
    
-          
+          
@@ -3240,6 +11753,8 @@ 
    
     
   
    
     
    
                 datetime
           
    Start date of the time series.
    		
+            
    
+              
    Start date of the time series.
    
+            
    
+              		
           
               required
           
                 datetime
           
    End date of the time series.
    		
+            
    
+              
    End date of the time series.
    
+            
    
+              		
           
               required
           
                 str
           
    Neutron Monitor station to retrieve data from.
    		
+            
    
+              
    Neutron Monitor station to retrieve data from.
    
+            
    
+              		
           
               required
           
                 int
           
    UTC offset in hours. Default is 0.
    		
+            
    
+              
    UTC offset in hours. Default is 0.
    
+            
    
+              		
           
                 0
           
                 int
           
    Number of hours to expand the time window to retrieve extra data. Default is 0.
    		
+            
    
+              
    Number of hours to expand the time window to retrieve extra data. Default is 0.
    
+            
    
+              		
           
                 0
           
                 bool
           
    Print information about the request. Default is False.
    		
+            
    
+              
    Print information about the request. Default is False.
    
+            
    
+              		
           
                 False
           
    
 
+
+
   
    Returns:
    
   
@@ -3251,9 +11766,13 @@ 
    
     
    
+          
-          
    
     
         
    
           
-                pandas.DataFrame
+                DataFrame
+          
+            
    
+              
    Neutron flux in counts per hour and timestamps.
    
+            
    
           
    Neutron flux in counts per hour and timestamps.
    		
         
    
         
   
    
@@ -3262,28 +11781,9 @@ 
    
   References
   
    Documentation available:https://www.nmdb.eu/nest/help.php#howto
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
    339
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-353
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-356
-357
-358
+          
    
+            Source code in crnpy/crnpy.py
+            
    358
 359
 360
 361
@@ -3349,104 +11849,268 @@ 
    
 421
 422
 423
-424
    def get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expand_window=0, verbose=False):
+424
+425
+426
+427
+428
+429
+430
+431
+432
+433
+434
+435
+436
+437
+438
+439
+440
+441
    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.
+        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
+
    
+          
    
+  
    
+
+
+
+
+
    
+
+
+
+
+
    
+          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).
    
+
+
+
+  
    Parameters:
    
+  
+    
+      
+        
+        
+        
+        
+      
+    
+    
+        
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+        
+    
+  
    NameTypeDescriptionDefault
    station
+                str
+          
+            
    
+              
    Neutron Monitor station to retrieve data from.
    
+            
    
+              		
+              required
+          
    date
+                datetime
+          
+            
    
+              
    Date of the reference neutron flux. Default is 2011-05-01.
    
+            
    
+              		
+                to_datetime('2011-05-01')
+          
    
+
+
+
+  
    Returns:
    
+  
+    
+      
+        
+        
+      
+    
+    
+        
+          
+          
+        
+    
+  
    TypeDescription
    
+                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.
    
+
    
+          
    
+            Source code in crnpy/crnpy.py
+            
    444
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+463
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+468
    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:
-        (pandas.DataFrame): Neutron flux in counts per hour and timestamps.
+        (float): Reference neutron flux in counts per hour.
 
     References:
-        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')
-    # 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)
-    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 = 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
+        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.
 
-    # 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')
+        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.
 
-    # 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)
+        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.
 
-    # 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
+    # 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()
 
    
-      
    
+          
   
    
 
 
    
 
+
 
    
 
 
 
-
    
-idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1)
+
+
    
+          idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1)
 
 
    
 
@@ -3455,6 +12119,8 @@ 
    
   
       
    Function to interpolate data using inverse distance weight.
    
 
+
+
   
    Parameters:
    
   
@@ -3471,7 +12137,11 @@ 
    
           
    
-          
+          
@@ -3481,7 +12151,11 @@ 
    
           
    
-          
+          
@@ -3491,7 +12165,11 @@ 
    
           
    
-          
+          
@@ -3501,7 +12179,11 @@ 
    
           
    
-          
+          
@@ -3511,7 +12193,11 @@ 
    
           
    
-          
+          
@@ -3521,7 +12207,11 @@ 
    
           
    
-          
+          
@@ -3531,7 +12221,11 @@ 
    
           
    
-          
+          
@@ -3539,6 +12233,8 @@ 
    
     
   
    
     
    
                 list or array
           
    UTM x coordinates in meters.
    		
+            
    
+              
    UTM x coordinates in meters.
    
+            
    
+              		
           
               required
           
                 list or array
           
    UTM y coordinates in meters.
    		
+            
    
+              
    UTM y coordinates in meters.
    
+            
    
+              		
           
               required
           
                 list or array
           
    Values to be interpolated.
    		
+            
    
+              
    Values to be interpolated.
    
+            
    
+              		
           
               required
           
                 list or array
           
    UTM x coordinates where z values need to be predicted.
    		
+            
    
+              
    UTM x coordinates where z values need to be predicted.
    
+            
    
+              		
           
               required
           
                 list or array
           
    UTM y coordinates where z values need to be predicted.
    		
+            
    
+              
    UTM y coordinates where z values need to be predicted.
    
+            
    
+              		
           
               required
           
                 float
           
    Only points within this radius in meters are considered for the interpolation.
    		
+            
    
+              
    Only points within this radius in meters are considered for the interpolation.
    
+            
    
+              		
           
                 1000
           
                 int
           
    Exponent of the inverse distance weight formula. Typically, p=1 or p=2.
    		
+            
    
+              
    Exponent of the inverse distance weight formula. Typically, p=1 or p=2.
    
+            
    
+              		
           
                 1
           
    
 
+
+
   
    Returns:
    
   
@@ -3552,7 +12248,11 @@ 
    
           
    
-          
+          
    
     
    
                 array
           
    Interpolated values.
    		
+            
    
+              
    Interpolated values.
    
+            
    
+              		
         
    
     
   
    
@@ -3561,48 +12261,48 @@ 
    
   References
   
    https://en.wikipedia.org/wiki/Inverse_distance_weighting
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
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    def idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1):
+          
    
+            Source code in crnpy/crnpy.py
+            
    1373
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    def idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1):
     """Function to interpolate data using inverse distance weight.
 
     Args:
@@ -3643,17 +12343,19 @@ 
    
 
     return np.reshape(Z_pred, s)
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000)
+
+
    
+          interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000)
 
 
    
 
@@ -3662,6 +12364,8 @@ 
    
   
       
    Function for interpolating irregular spatial data into a regular grid.
    
 
+
+
   
    Parameters:
    
   
@@ -3678,7 +12382,11 @@ 
    
           
    
-          
+          
@@ -3688,7 +12396,11 @@ 
    
           
    
-          
+          
@@ -3698,7 +12410,11 @@ 
    
           
    
-          
+          
@@ -3708,7 +12424,11 @@ 
    
           
    
-          
+          
@@ -3718,7 +12438,11 @@ 
    
           
    
-          
+          
@@ -3728,7 +12452,11 @@ 
    
           
    
-          
+          
@@ -3738,7 +12466,11 @@ 
    
           
    
-          
+          
@@ -3746,6 +12478,8 @@ 
    
     
   
    
     
    
                 list or array
           
    UTM x coordinates in meters.
    		
+            
    
+              
    UTM x coordinates in meters.
    
+            
    
+              		
           
               required
           
                 list or array
           
    UTM y coordinates in meters.
    		
+            
    
+              
    UTM y coordinates in meters.
    
+            
    
+              		
           
               required
           
                 list or array
           
    Values to be interpolated.
    		
+            
    
+              
    Values to be interpolated.
    
+            
    
+              		
           
               required
           
                 float
           
    Pixel width in meters.
    		
+            
    
+              
    Pixel width in meters.
    
+            
    
+              		
           
                 100
           
                 float
           
    Pixel height in meters.
    		
+            
    
+              
    Pixel height in meters.
    
+            
    
+              		
           
                 100
           
                 str
           
    Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.
    		
+            
    
+              
    Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.
    
+            
    
+              		
           
                 'cubic'
           
                 float
           
    Only points within this radius in meters are considered for the interpolation.
    		
+            
    
+              
    Only points within this radius in meters are considered for the interpolation.
    
+            
    
+              		
           
                 1000
           
    
 
+
+
   
    Returns:
    
   
@@ -3759,19 +12493,31 @@ 
    
 
    
-          
+          
-          
+          
-          
+          
    
     
    x_pred          
                 array
           
    2D array with x coordinates.
    		
+            
    
+              
    2D array with x coordinates.
    
+            
    
+              		
         
    
         
    
 y_pred          
                 array
           
    2D array with y coordinates.
    		
+            
    
+              
    2D array with y coordinates.
    
+            
    
+              		
         
    
         
    
 z_pred          
                 array
           
    2D array with interpolated values.
    		
+            
    
+              
    2D array with interpolated values.
    
+            
    
+              		
         
    
     
   
    
@@ -3780,56 +12526,57 @@ 
    
   References
   
    https://soilwater.github.io/pynotes-agriscience/notebooks/interpolation.html
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
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    def interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000):
+          
    
+            Source code in crnpy/crnpy.py
+            
    1415
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    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:
@@ -3857,7 +12604,8 @@ 
    
     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
@@ -3878,17 +12626,19 @@ 
    
 
     return X_pred, Y_pred, Z_pred
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps)
+
+
    
+          interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps)
 
 
    
 
@@ -3897,6 +12647,8 @@ 
    
   
       
    Function to interpolate incoming neutron flux to match the timestamps of the observations.
    
 
+
+
   
    Parameters:
    
   
@@ -3911,9 +12663,13 @@ 
    
         
    
+          
-          
@@ -3921,9 +12677,13 @@ 
    
         
    
+          
-          
@@ -3931,9 +12691,13 @@ 
    
         
    
+          
-          
@@ -3941,6 +12705,8 @@ 
    
     
   
    
     
    
           nmdb_timestamps
           
-                pd.Series
+                Series
+          
+            
    
+              
    Series of timestamps in datetime format from the NMDB.
    
+            
    
           
    Series of timestamps in datetime format from the NMDB.
    		
           
               required
           
    
           nmdb_counts
           
-                pd.Series
+                Series
+          
+            
    
+              
    Series of neutron counts from the NMDB
    
+            
    
           
    Series of neutron counts from the NMDB
    		
           
               required
           
    
           crnp_timestamps
           
-                pd.Series
+                Series
+          
+            
    
+              
    Series of timestamps in datetime format from the station or device.
    
+            
    
           
    Series of timestamps in datetime format from the station or device.
    		
           
               required
           
    
 
+
+
   
    Returns:
    
   
@@ -3952,39 +12718,43 @@ 
    
     
    
+          
-          
    
     
         
    
           
-                pd.Series
+                Series
+          
+            
    
+              
    Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps
    
+            
    
           
    Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps
    		
         
    
         
   
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
    881
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    def interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps):
+          
    
+            Source code in crnpy/crnpy.py
+            
    1216
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    def interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps):
     """Function to interpolate incoming neutron flux to match the timestamps of the observations.
 
     Args:
@@ -3996,7 +12766,7 @@ 
    
         (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])
@@ -4009,17 +12779,19 @@ 
    
     # Return only the values for the selected timestamps
     return incoming_flux
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-is_outlier(x, method, window=11, min_val=None, max_val=None)
+
+
    
+          is_outlier(x, method, window=11, min_val=None, max_val=None)
 
 
    
 
@@ -4028,6 +12800,8 @@ 
    
   
       
    Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.
    
 
+
+
   
    Parameters:
    
   
@@ -4042,9 +12816,13 @@ 
    
         
    
+          
-          
@@ -4054,7 +12832,11 @@ 
    
           
    
-          
+          
@@ -4064,7 +12846,11 @@ 
    
           
    
-          
+          
@@ -4074,7 +12860,11 @@ 
    
           
    
-          
+          
@@ -4084,7 +12874,11 @@ 
    
           
    
-          
+          
@@ -4092,6 +12886,8 @@ 
    
     
   
    
     
    
           x
           
-                pandas.DataFrame
+                DataFrame or Series
+          
+            
    
+              
    Variable containing only the columns with neutron counts.
    
+            
    
           
    Variable containing only the columns with neutron counts.
    		
           
               required
           
                 str
           
    Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad
    		
+            
    
+              
    Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad
    
+            
    
+              		
           
               required
           
                 int
           
    Window size for the moving central tendency. Default is 11.
    		
+            
    
+              
    Window size for the moving central tendency. Default is 11.
    
+            
    
+              		
           
                 11
           
                 int or float
           
    Minimum value for a reading to be considered valid. Default is None.
    		
+            
    
+              
    Minimum value for a reading to be considered valid. Default is None.
    
+            
    
+              		
           
                 None
           
                 or float
           
    Maximum value for a reading to be considered valid. Default is None.
    		
+            
    
+              
    Maximum value for a reading to be considered valid. Default is None.
    
+            
    
+              		
           
               required
           
    
 
+
+
   
    Returns:
    
   
@@ -4103,9 +12899,13 @@ 
    
     
    
+          
-          
    
     
         
    
           
-                pandas.DataFrame
+                DataFrame
+          
+            
    
+              
    Boolean indicating outliers.
    
+            
    
           
    Boolean indicating outliers.
    		
         
    
         
   
    
@@ -4114,9 +12914,11 @@ 
    
   References
   
    Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
    134
+          
    
+            Source code in crnpy/crnpy.py
+            
    132
+133
+134
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@@ -4187,13 +12989,11 @@ 
    
 202
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    def is_outlier(x, method, window=11, min_val=None, max_val=None):
+205
    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.
@@ -4222,70 +13022,413 @@ 
    
         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
+        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
+
    
+          
    
+  
    
+
+
+
+
+
    
+
+
+
+
+
    
+          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
+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.
    
+
+
+
+  
    Parameters:
    
+  
+    
+      
+        
+        
+        
+        
+      
+    
+    
+        
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+        
+    
+  
    NameTypeDescriptionDefault
    lat
+                (float, array)
+          
+            
    
+              
    Latitude in decimal degrees.
    
+            
    
+              		
+              required
+          
    lon
+                (float, array)
+          
+            
    
+              
    Longitude in decimal degrees.
    
+            
    
+              		
+              required
+          
    utm_zone_number
+                int
+          
+            
    
+              
    UTM zone number. If None, the zone number is automatically calculated.
    
+            
    
+              		
+                None
+          
    utm_zone_letter
+                str
+          
+            
    
+              
    UTM zone letter. If None, the zone letter is automatically calculated.
    
+            
    
+              		
+                None
+          
    
+
+
+
+  
    Returns:
    
+  
+    
+      
+        
+        
+      
+    
+    
+        
+          
+          
+        
+    
+  
    TypeDescription
    
+                (float, float)
+          
+            
    
+              
    Tuple of easting and northing coordinates in meters. First element is easting, second is northing.
    
+            
    
+          
    
+
+
    
+  References
+  
    Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87)
+https://github.com/Turbo87/utm
    
+
    https://www.maptools.com/tutorials/grid_zone_details#
    
+
    
+          
    
+            Source code in crnpy/crnpy.py
+            
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    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): Tuple of easting and northing coordinates in meters. First element is easting, second is northing.
+
+    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#)
+    """
+    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
+
    
+          
    
+  
    
+
+
    
+
+
+
    
+
+
+
+
+
    
+          lattice_water(clay_content, total_carbon=None)
+
+
    
+
+
+  
    
+  
+      
    Estimate the amount of water in the lattice of clay minerals.
    
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
    img1img2
    $\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.
    
+
+
+
+  
    Parameters:
    
+  
+    
+      
+        
+        
+        
+        
+      
+    
+    
+        
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+        
+    
+  
    NameTypeDescriptionDefault
    clay_content
+                float
+          
+            
    
+              
    Clay content in the soil in percent.
    
+            
    
+              		
+              required
+          
    total_carbon
+                float
+          
+            
    
+              
    Total carbon content in the soil in percent.
+If None, the amount of water is estimated based on clay content only.
    
+            
    
+              		
+                None
+          
    
 
-    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)
+  
    Returns:
    
+  
+    
+      
+        
+        
+      
+    
+    
+        
+          
+          
+        
+    
+  
    TypeDescription
    
+                float
+          
+            
    
+              
    Amount of water in the lattice of clay minerals in percent
    
+            
    
+          
    
 
-    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()
+          
    
+            Source code in crnpy/crnpy.py
+            
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    def lattice_water(clay_content, total_carbon=None):
+    r"""Estimate the amount of water in the lattice of clay minerals.
 
-        # Compute modified z-score
-        modified_z_score = 0.6745 * abs_diff / mad
-        idx_outliers = (modified_z_score < -3.5) | (modified_z_score > 3.5)
+    ![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.
 
-    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)
+    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:
-        raise TypeError('Outlier detection method not found.')
-
-    return idx_outliers | idx_range_outliers
+        lattice_water = -0.028 + 0.077 * clay_content + 0.459 * total_carbon
+    return lattice_water
 
    
-      
    
+          
   
    
 
 
    
 
+
 
    
 
 
 
-
    
-latlon_to_utm(lat, lon, utm_zone_number, missing_values=None)
+
+
    
+          location_factor(site_atmospheric_depth, site_Rc, reference_atmospheric_depth, reference_Rc)
 
 
    
 
 
   
    
   
-      
    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
-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.
    
+      
    Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.
    
+
+
 
   
    Parameters:
    
   
@@ -4299,31 +13442,57 @@ 
    
     
     
    
-          
+          
+          
-          
-          
+          
+          
-          
-          
+          
+          
+          
+        
+        
+          
+          
+          
-          
@@ -4331,6 +13500,8 @@ 
    
     
    
         
    latsite_atmospheric_depth
           
-                float, array
+                float
+          
+            
    
+              
    Atmospheric depth at the site in g/cm2. Can be estimated using the function atmospheric_depth()
    
+            
    
           
    Latitude in decimal degrees.
    		
           
               required
           
         
    
         
    lonsite_Rc
+                float
+          
           
-                float, array
+            
    
+              
    Cutoff rigidity at the site in GV. Can be estimated using the function cutoff_rigidity()
    
+            
    
           
    Longitude in decimal degrees.
    		
           
               required
           
         
    
         
    utm_zone_numberreference_atmospheric_depth
           
-                int
+                float
+          
+            
    
+              
    Atmospheric depth at the reference location in g/cm2.
    
+            
    
+              		
+              required
+          
    reference_Rc
+                float
+          
+            
    
+              
    Cutoff rigidity at the reference location in GV.
    
+            
    
           
    Universal Transverse Mercator (UTM) zone.
    		
           
               required
           
    
   
    
 
+
+
   
    Returns:
    
   
@@ -4342,229 +13513,123 @@ 
    
     
    
+          
-          
    
     
         
    
           
-                float, float
+                float
+          
+            
    
+              
    Location-dependent correction factor.
    
+            
    
           
    Tuple of easting and northing coordinates in meters. First element is easting, second is northing.
    		
         
    
         
   
    
 
 
    
   References
-  
    Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87)
-https://github.com/Turbo87/utm
    
-
    https://www.maptools.com/tutorials/grid_zone_details#
    
+  
    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.
    
 
    
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
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    def latlon_to_utm(lat, lon, utm_zone_number, missing_values=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): Universal Transverse Mercator (UTM) zone.
-
-    Returns:
-        (float, float): Tuple of easting and northing coordinates in meters. First element is easting, second is northing.
-
-    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#)
-    """
-
-
-    # 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
+          
    
+            Source code in crnpy/crnpy.py
+            
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    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.
 
-    # 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
+    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.
 
-    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))
+    Returns:
+        (float): Location-dependent correction factor.
 
-    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)))
+    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.
 
-    if np.any(lat < 0):
-        northing += 10_000_000
+    """
 
-    return easting, northing
+    # 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
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-lattice_water(clay_content, total_carbon=None)
+
+
    
+          nrad_weight(h, theta, distances, depth, rhob=1.4, method=None, p=None, Hveg=None)
 
 
    
 
 
   
    
   
-      
    Estimate the amount of water in the lattice of clay minerals.
    
-
-
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    img1img2
    $\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.
    
+      
    Function to compute distance weights corresponding to each soil sample.
    
+
+
 
   
    Parameters:
    
   
@@ -4578,22 +13643,113 @@ 
    
     
     
    
-          
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-          
@@ -4601,6 +13757,8 @@ 
    
     
    
         
    clay_contenth
           
-                float
+                array or Series
+          
+            
    
+              
    Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.
    
+            
    
           
    Clay content in the soil in percent.
    		
           
               required
           
         
    
         
    total_carbontheta
           
-                float
+                array or Series
+          
+            
    
+              
    Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)
    
+            
    
+              		
+              required
+          
    distances
+                array or Series
+          
+            
    
+              
    Distances from the location of each sample to the origin (0.5 - 600 m)
    
+            
    
+              		
+              required
+          
    depth
+                array or Series
+          
+            
    
+              
    Depths for each sample (m)
    
+            
    
+              		
+              required
+          
    rhob
+                array or Series
+          
+            
    
+              
    Bulk density in g/cm^3
    
+            
    
+              		
+                1.4
+          
    p
+                array or Series
+          
+            
    
+              
    Atmospheric pressure in hPa. Required for the 'Schron_2017' method.
    
+            
    
+              		
+                None
+          
    Hveg
+                array or Series
+          
+            
    
+              
    Vegetation height in m. Required for the 'Schron_2017' method.
    
+            
    
+              		
+                None
+          
    method
+                str
+          
+            
    
+              
    Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.
    
+            
    
           
    Total carbon content in the soil in percent.
-If None, the amount of water is estimated based on clay content only.
    		
           
                 None
           
    
   
    
 
+
+
   
    Returns:
    
   
@@ -4612,16 +13770,241 @@ 
    
     
    
+          
-          
    
     
         
    
           
-                float
+                array or Series or DataFrame
+          
+            
    
+              
    Distance weights for each sample.
    
+            
    
           
    Amount of water in the lattice of clay minerals in percent
    		
         
    
         
   
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
    907
+
    
+  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.
    
+
    
+          
    
+            Source code in crnpy/crnpy.py
+            
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@@ -4641,216 +14024,50 @@ 
    
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    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
-
    
-      
    
-  
    
-
-
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-
    
-
-
-
-
    
-nrad_weight(h, theta, distances, depth, rhob=1.4)
-
-
    
-
-
-  
    
-  
-      
    Function to compute distance weights corresponding to each soil sample.
    
-
-  
    Parameters:
    
-  
-    
-      
-        
-        
-        
-        
-      
-    
-    
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-          
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-    
-  
    NameTypeDescriptionDefault
    h
-                float
-          
    Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.
    		
-              required
-          
    theta
-                array or pd.Series or pd.DataFrame
-          
    Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)
    		
-              required
-          
    distances
-                array or pd.Series or pd.DataFrame
-          
    Distances from the location of each sample to the origin (0.5 - 600 m)
    		
-              required
-          
    depth
-                array or pd.Series or pd.DataFrame
-          
    Depths for each sample (m)
    		
-              required
-          
    rhob
-                float
-          
    Bulk density in g/cm^3
    		
-                1.4
-          
    
-
-  
    Returns:
    
-  
-    
-      
-        
-        
-      
-    
-    
-        
-          
-          
-        
-    
-  
    TypeDescription
    
-                array or pd.Series or pd.DataFrame
-          
    Distance weights for each sample.
    
-
-
    
-  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
    
-
    
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
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    def nrad_weight(h,theta,distances,depth,rhob=1.4):
+927
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    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.
@@ -4859,72 +14076,263 @@ 
    
         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.')
 
+        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.
+    """
 
-    # Combined and normalized weights
-    weights = Wd*W/np.nansum(Wd*W)
+    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.')
 
-    return weights
+        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
 
    
-      
    
+          
   
    
 
 
    
 
+
 
    
 
 
 
-
    
-remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False)
+
+
    
+          remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False)
 
 
    
 
@@ -4933,6 +14341,8 @@ 
    
   
       
    Function that removes rows with incomplete integration intervals.
    
 
+
+
   
    Parameters:
    
   
@@ -4947,9 +14357,13 @@ 
    
         
    
+          
-          
@@ -4959,7 +14373,11 @@ 
    
           
    
-          
+          
@@ -4969,7 +14387,11 @@ 
    
           
    
-          
+          
@@ -4979,7 +14401,11 @@ 
    
           
    
-          
+          
@@ -4987,6 +14413,8 @@ 
    
     
   
    
     
    
           df
           
-                pandas.DataFrame
+                DataFrame
+          
+            
    
+              
    Pandas Dataframe with data from stationary or roving CRNP devices.
    
+            
    
           
    Pandas Dataframe with data from stationary or roving CRNP devices.
    		
           
               required
           
                 str
           
    Name of the column with timestamps in datetime format.
    		
+            
    
+              
    Name of the column with timestamps in datetime format.
    
+            
    
+              		
           
               required
           
                 int
           
    Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.
    		
+            
    
+              
    Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.
    
+            
    
+              		
           
               required
           
                 bool
           
    Remove first row. Default is False.
    		
+            
    
+              
    Remove first row. Default is False.
    
+            
    
+              		
           
                 False
           
    
 
+
+
   
    Returns:
    
   
@@ -4998,16 +14426,21 @@ 
    
     
    
+          
-          
    
     
         
    
           
-                pandas.DataFrame
+                DataFrame
+          
+            
    
+              
+            
    
               		
         
    
         
   
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
    33
+          
    
+            Source code in crnpy/crnpy.py
+            
    32
+33
 34
 35
 36
@@ -5036,8 +14469,7 @@ 
    
 59
 60
 61
-62
-63
    def remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False):
+62
    def remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False):
     """Function that removes rows with incomplete integration intervals.
 
     Args:
@@ -5069,17 +14501,19 @@ 
    
 
     return df
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-rover_centered_coordinates(x, y)
+
+
    
+          rover_centered_coordinates(x, y)
 
 
    
 
@@ -5088,6 +14522,8 @@ 
    
   
       
    Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.
    
 
+
+
   
    Parameters:
    
   
@@ -5104,7 +14540,11 @@ 
    
           
    
-          
+          
@@ -5114,7 +14554,11 @@ 
    
           
    
-          
+          
@@ -5122,6 +14566,8 @@ 
    
     
   
    
     
    
                 array
           
    x coordinates.
    		
+            
    
+              
    x coordinates.
    
+            
    
+              		
           
               required
           
                 array
           
    y coordinates.
    		
+            
    
+              
    y coordinates.
    
+            
    
+              		
           
               required
           
    
 
+
+
   
    Returns:
    
   
@@ -5135,47 +14581,54 @@ 
    
 
    
-          
+          
-          
+          
    
     
    x_est          
                 array
           
    Estimated x coordinates.
    		
+            
    
+              
    Estimated x coordinates.
    
+            
    
+              		
         
    
         
    
 y_est          
                 array
           
    Estimated y coordinates.
    		
+            
    
+              
    Estimated y coordinates.
    
+            
    
+              		
         
    
     
   
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
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    def rover_centered_coordinates(x, y):
+          
    
+            Source code in crnpy/crnpy.py
+            
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    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:
@@ -5188,9 +14641,9 @@ 
    
     """
 
     # 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
@@ -5201,20 +14654,21 @@ 
    
     x_est = np.insert(x_est, 0, x[0])
     y_est = np.insert(y_est, 0, y[0])
 
-
     return x_est, y_est
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017')
+
+
    
+          sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017')
 
 
    
 
@@ -5225,6 +14679,8 @@ 
    
 The function offers several methods to compute the depth at which 86 % of the neutrons
 probe the soil profile.
    
 
+
+
   
    Parameters:
    
   
@@ -5239,9 +14695,13 @@ 
    
         
    
+          
-          
@@ -5249,9 +14709,13 @@ 
    
         
    
+          
-          
@@ -5261,7 +14725,11 @@ 
    
           
    
-          
+          
@@ -5271,7 +14739,11 @@ 
    
           
    
-          
+          
@@ -5281,7 +14753,11 @@ 
    
           
    
-          
+          
@@ -5291,7 +14767,11 @@ 
    
           
    
-          
+          
@@ -5301,7 +14781,11 @@ 
    
           
    
-          
+          
@@ -5309,6 +14793,8 @@ 
    
     
   
    
     
    
           vwc
           
-                array or pd.Series or pd.DataFrame
+                array or Series or DataFrame
+          
+            
    
+              
    Estimated volumetric water content for each timestamp.
    
+            
    
           
    Estimated volumetric water content for each timestamp.
    		
           
               required
           
    
           pressure
           
-                array or pd.Series or pd.DataFrame
+                array or Series or DataFrame
+          
+            
    
+              
    Atmospheric pressure in hPa for each timestamp.
    
+            
    
           
    Atmospheric pressure in hPa for each timestamp.
    		
           
               required
           
                 float
           
    Reference pressure in hPa.
    		
+            
    
+              
    Reference pressure in hPa.
    
+            
    
+              		
           
               required
           
                 float
           
    Soil bulk density.
    		
+            
    
+              
    Soil bulk density.
    
+            
    
+              		
           
               required
           
                 float
           
    Lattice water content.
    		
+            
    
+              
    Lattice water content.
    
+            
    
+              		
           
               required
           
                 str
           
    Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.
    		
+            
    
+              
    Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.
    
+            
    
+              		
           
                 'Schron_2017'
           
                 list or array
           
    List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.
    		
+            
    
+              
    List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.
    
+            
    
+              		
           
                 None
           
    
 
+
+
   
    Returns:
    
   
@@ -5320,9 +14806,13 @@ 
    
     
    
+          
-          
    
     
         
    
           
-                array or pd.Series or pd.DataFrame
+                array or Series or DataFrame
+          
+            
    
+              
    Estimated sensing depth in m.
    
+            
    
           
    Estimated sensing depth in m.
    		
         
    
         
   
    
@@ -5336,57 +14826,55 @@ 
    
 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
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
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    def sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017'):
-    # Convert docstring to google format
+          
    
+            Source code in crnpy/crnpy.py
+            
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    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.
@@ -5416,35 +14904,37 @@ 
    
     # 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
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-smooth_1d(values, window=5, order=3, method='moving_median')
+
+
    
+          smooth_1d(values, window=5, order=3, method='moving_median')
 
 
    
 
@@ -5453,6 +14943,8 @@ 
    
   
       
    Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).
    
 
+
+
   
    Parameters:
    
   
@@ -5467,9 +14959,13 @@ 
    
         
    
+          
-          
@@ -5479,7 +14975,11 @@ 
    
           
    
-          
+          
@@ -5489,8 +14989,12 @@ 
    
           
    
-          
+          
@@ -5500,7 +15004,11 @@ 
    
           
    
-          
+          
@@ -5508,6 +15016,8 @@ 
    
     
   
    
     
    
           values
           
-                pd.DataFrame or pd.Serie
+                DataFrame or Serie
+          
+            
    
+              
    Dataframe containing the values to smooth.
    
+            
    
           
    Dataframe containing the values to smooth.
    		
           
               required
           
                 int
           
    Window size for the Savitzky-Golay filter. Default is 5.
    		
+            
    
+              
    Window size for the Savitzky-Golay filter. Default is 5.
    
+            
    
+              		
           
                 5
           
                 str
           
    Method to use for smoothing the data. Default is 'moving_median'.
-Options are 'moving_average', 'moving_median' and 'savitzky_golay'.
    		
+            
    
+              
    Method to use for smoothing the data. Default is 'moving_median'.
+Options are 'moving_average', 'moving_median' and 'savitzky_golay'.
    
+            
    
+              		
           
                 'moving_median'
           
                 int
           
    Order of the Savitzky-Golay filter. Default is 3.
    		
+            
    
+              
    Order of the Savitzky-Golay filter. Default is 3.
    
+            
    
+              		
           
                 3
           
    
 
+
+
   
    Returns:
    
   
@@ -5519,9 +15029,13 @@ 
    
     
    
+          
-          
    
     
         
    
           
-                pd.DataFrame
+                DataFrame
+          
+            
    
+              
    DataFrame with smoothed values.
    
+            
    
           
    DataFrame with smoothed values.
    		
         
    
         
   
    
@@ -5531,47 +15045,56 @@ 
    
   
    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.
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
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    def smooth_1d(values, window=5, order=3, method='moving_median'):
+          
    
+            Source code in crnpy/crnpy.py
+            
    471
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    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:
@@ -5588,8 +15111,14 @@ 
    
         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':
@@ -5600,27 +15129,30 @@ 
    
             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
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=False)
+
+
    
+          spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=False)
 
 
    
 
@@ -5629,6 +15161,8 @@ 
    
   
       
    Moving buffer filter to smooth georeferenced two-dimensional data.
    
 
+
+
   
    Parameters:
    
   
@@ -5645,7 +15179,11 @@ 
    
           
    
-          
+          
@@ -5655,7 +15193,11 @@ 
    
           
    
-          
+          
@@ -5665,7 +15207,11 @@ 
    
           
    
-          
+          
@@ -5675,7 +15221,11 @@ 
    
           
    
-          
+          
@@ -5685,7 +15235,11 @@ 
    
           
    
-          
+          
@@ -5695,7 +15249,11 @@ 
    
           
    
-          
+          
@@ -5705,7 +15263,11 @@ 
    
           
    
-          
+          
@@ -5713,6 +15275,8 @@ 
    
     
   
    
     
    
                 list or array
           
    UTM x coordinates in meters.
    		
+            
    
+              
    UTM x coordinates in meters.
    
+            
    
+              		
           
               required
           
                 list or array
           
    UTM y coordinates in meters.
    		
+            
    
+              
    UTM y coordinates in meters.
    
+            
    
+              		
           
               required
           
                 list or array
           
    Values to be smoothed.
    		
+            
    
+              
    Values to be smoothed.
    
+            
    
+              		
           
               required
           
                 float
           
    Radial buffer distance in meters.
    		
+            
    
+              
    Radial buffer distance in meters.
    
+            
    
+              		
           
                 100
           
                 int
           
    Minimum number of neighbours to consider for the smoothing.
    		
+            
    
+              
    Minimum number of neighbours to consider for the smoothing.
    
+            
    
+              		
           
                 3
           
                 str
           
    One of 'mean' or 'median'.
    		
+            
    
+              
    One of 'mean' or 'median'.
    
+            
    
+              		
           
                 'mean'
           
                 bool
           
    Boolean to round the final result. Useful in case of z representing neutron counts.
    		
+            
    
+              
    Boolean to round the final result. Useful in case of z representing neutron counts.
    
+            
    
+              		
           
                 False
           
    
 
+
+
   
    Returns:
    
   
@@ -5726,70 +15290,73 @@ 
    
           
    
-          
+          
    
     
    
                 array
           
    Smoothed version of z with the same dimension as z.
    		
+            
    
+              
    Smoothed version of z with the same dimension as z.
    
+            
    
+              		
         
    
     
   
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
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    def spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=False):
+          
    
+            Source code in crnpy/crnpy.py
+            
    1315
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    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:
@@ -5826,7 +15393,6 @@ 
    
         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:
@@ -5837,7 +15403,7 @@ 
    
             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)
@@ -5847,17 +15413,19 @@ 
    
 
     return z_smooth
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-total_raw_counts(counts, nan_strategy=None, timestamp_col=None)
+
+
    
+          total_raw_counts(counts)
 
 
    
 
@@ -5866,6 +15434,8 @@ 
    
   
       
    Compute the sum of uncorrected neutron counts for all detectors.
    
 
+
+
   
    Parameters:
    
   
@@ -5880,26 +15450,22 @@ 
    
         
    
-          
-          
-        
-        
-          
-          
    
     
    
           counts
           
-                pandas.DataFrame
-          
    Dataframe containing only the columns with neutron counts.
    		
-              required
+                DataFrame
           
    nan_strategy
           
-                str
+            
    
+              
    Dataframe containing only the columns with neutron counts.
    
+            
    
           
    Strategy to use for NaN values. Options are 'interpolate', 'average', or None. Default is None.
    		
           
-                None
+              required
           
         
    
     
   
    
 
+
+
   
    Returns:
    
   
@@ -5911,16 +15477,21 @@ 
    
     
    
+          
-          
    
     
         
    
           
-                pandas.DataFrame
+                DataFrame
+          
+            
    
+              
    Dataframe with the sum of uncorrected neutron counts for all detectors.
    
+            
    
           
    Dataframe with the sum of uncorrected neutron counts for all detectors.
    		
         
    
         
   
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
    111
+          
    
+            Source code in crnpy/crnpy.py
+            
    110
+111
 112
 113
 114
@@ -5938,21 +15509,18 @@ 
    
 126
 127
 128
-129
-130
-131
    def total_raw_counts(counts, nan_strategy=None, timestamp_col=None):
+129
    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)
@@ -5962,17 +15530,19 @@ 
    
 
     return total_raw_counts
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-uncertainty_counts(raw_counts, metric='std', fp=1, fw=1, fi=1)
+
+
    
+          uncertainty_counts(raw_counts, metric='std', fp=1, fw=1, fi=1)
 
 
    
 
@@ -5984,6 +15554,8 @@ 
    
 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} $
    
 
+
+
   
    Parameters:
    
   
@@ -6000,7 +15572,11 @@ 
    
           
    
-          
+          
@@ -6010,7 +15586,11 @@ 
    
           
    
-          
+          
@@ -6020,7 +15600,11 @@ 
    
           
    
-          
+          
@@ -6030,7 +15614,11 @@ 
    
           
    
-          
+          
@@ -6040,7 +15628,11 @@ 
    
           
    
-          
+          
@@ -6048,6 +15640,8 @@ 
    
     
   
    
     
    
                 array
           
    Raw neutron counts.
    		
+            
    
+              
    Raw neutron counts.
    
+            
    
+              		
           
               required
           
                 str
           
    Either 'std' or 'cv' for standard deviation or coefficient of variation.
    		
+            
    
+              
    Either 'std' or 'cv' for standard deviation or coefficient of variation.
    
+            
    
+              		
           
                 'std'
           
                 float
           
    Pressure correction factor.
    		
+            
    
+              
    Pressure correction factor.
    
+            
    
+              		
           
                 1
           
                 float
           
    Humidity correction factor.
    		
+            
    
+              
    Humidity correction factor.
    
+            
    
+              		
           
                 1
           
                 float
           
    Incoming neutron flux correction factor.
    		
+            
    
+              
    Incoming neutron flux correction factor.
    
+            
    
+              		
           
                 1
           
    
 
+
+
   
    Returns:
    
   
@@ -6061,7 +15655,11 @@ 
    
 
    
-          
+          
    
     
    uncertainty          
                 float
           
    Uncertainty of raw counts.
    		
+            
    
+              
    Uncertainty of raw counts.
    
+            
    
+              		
         
    
     
   
    
@@ -6073,42 +15671,42 @@ 
    
 
    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.
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
    1215
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    def uncertainty_counts(raw_counts, metric="std", fp=1, fw=1, fi=1):
+          
    
+            Source code in crnpy/crnpy.py
+            
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    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).
@@ -6138,22 +15736,24 @@ 
    
     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
 
    
-      
    
+          
   
    
 
 
 
+
 
    
 
 
 
-
    
-uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808, a1=0.372, a2=0.115)
+
+
    
+          uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808, a1=0.372, a2=0.115)
 
 
    
 
@@ -6167,6 +15767,8 @@ 
    
 \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}
 $$
    
 
+
+
   
    Parameters:
    
   
@@ -6183,7 +15785,11 @@ 
    
           
    
-          
+          
@@ -6193,7 +15799,11 @@ 
    
           
    
-          
+          
@@ -6203,7 +15813,11 @@ 
    
           
    
-          
+          
@@ -6213,7 +15827,11 @@ 
    
           
    
-          
+          
@@ -6223,7 +15841,11 @@ 
    
           
    
-          
+          
@@ -6233,7 +15855,11 @@ 
    
           
    
-          
+          
@@ -6241,6 +15867,8 @@ 
    
     
   
    
     
    
                 array
           
    Raw neutron counts.
    		
+            
    
+              
    Raw neutron counts.
    
+            
    
+              		
           
               required
           
                 float
           
    Calibration parameter N0.
    		
+            
    
+              
    Calibration parameter N0.
    
+            
    
+              		
           
               required
           
                 float
           
    Bulk density in g cm-3.
    		
+            
    
+              
    Bulk density in g cm-3.
    
+            
    
+              		
           
               required
           
                 float
           
    Pressure correction factor.
    		
+            
    
+              
    Pressure correction factor.
    
+            
    
+              		
           
                 1
           
                 float
           
    Humidity correction factor.
    		
+            
    
+              
    Humidity correction factor.
    
+            
    
+              		
           
                 1
           
                 float
           
    Incoming neutron flux correction factor.
    		
+            
    
+              
    Incoming neutron flux correction factor.
    
+            
    
+              		
           
                 1
           
    
 
+
+
   
    Returns:
    
   
@@ -6254,7 +15882,11 @@ 
    
 
    
-          
+          
    
     
    sigma_VWC          
                 float
           
    Uncertainty in terms of volumetric water content.
    		
+            
    
+              
    Uncertainty in terms of volumetric water content.
    
+            
    
+              		
         
    
     
   
    
@@ -6264,39 +15896,40 @@ 
    
   
    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
    
 
-      
    
-        Source code in C:\Users\jperaza\AppData\Local\anaconda3\envs\crnpy\lib\site-packages\crnpy\crnpy.py
-        
    1251
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    def uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808,a1=0.372,a2=0.115):
+          
    
+            Source code in crnpy/crnpy.py
+            
    1531
+1532
+1533
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    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.
@@ -6323,12 +15956,13 @@ 
    
 
     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
 
    
-      
    
+          
   
    
 
 
@@ -6341,64 +15975,17 @@ 
    
 
 
 
-
    
-
-
-
-  
    
-  
-      
    crnpy.data
    
-
    Data module for crnpy.
    
-
    This module contains data for the crnpy package.
    
-
-  
    Attributes:
    
-  
-    
-      
-        
-        
-        
-      
-    
-    
-        
-          
-          
-          
-        
-        
-          
-          
-          
-        
-    
-  
    NameTypeDescription
    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
    
-
-  
-
-  
    
-
-
 
 
 
 
 
 
+  
+  
 
 
 
-  
    
-
-  
    
-
-
    
-
-
 
 
 
@@ -6408,6 +15995,7 @@ 
    crnpy.data
    
             
           
           
+
         
         
       
@@ -6435,22 +16023,17 @@ 
    crnpy.data
    
       
    
     
     
-    
+    
+    
     
     
-      
+      
       
-        
-          
-        
+        
       
-        
-          
-        
+        
       
-        
-          
-        
+        
       
     
   
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":"
    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.
     
    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.
    "},{"location":"#statement-of-need","title":"Statement of Need","text":"
    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.
    "},{"location":"#key-features","title":"Key Features","text":"
       
    •  
      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.
       
      •  
    •  
      Modular: The library is designed to be modular, allowing users to easily customize the processing workflow to their needs.
       
      •  
    "},{"location":"#installation","title":"Installation","text":"
    To install the CRNPy library, you can use Python's package manager. Open a terminal and type:
     
    pip install crnpy
     
    from the Jupyter notebook, type:
     
    !pip install crnpy
     
    Ideally dependencies should be installed automatically. If not, you can install them manually by typing:
     
    pip install -r requirements.txt
     
    The CRNPy library is compatible with Python 3.7 and above. See requirements.txt for a list of dependencies.
    "},{"location":"#authors","title":"Authors","text":"
    The CRNPy library was developed at the Kansas State University Soil Water Processes Lab by:
     
       
    •  
      Joaquin Peraza
       
      •  
    •  
      Andres Patrignani
       
      •  
     
    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. 
    "},{"location":"#community-guidelines","title":"Community Guidelines","text":""},{"location":"#contributing","title":"Contributing","text":"
    To contribute to the software, please first fork the repository and create your own branch from main. Ensure your code adheres to our established code structure and includes appropriate test/examples coverage. CRNPy source code is located in the /src/crnpy/ folder, and tests implemented using pytest are stored in the /src/tests/ folder. Submit a pull request with a clear and detailed description of your changes to include them in the main repository.
    "},{"location":"#reporting-issues","title":"Reporting Issues","text":"
    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.
    "},{"location":"#seeking-support","title":"Seeking Support","text":"
    If you need support, please first refer to the documentation. If you still require assistance, post a question on the issues page with the question tag. For private inquiries, you can reach us via email at jperaza@ksu.edu or andrespatrignani@ksu.edu.
    "},{"location":"correction_routines/","title":"Correction routines","text":""},{"location":"correction_routines/#overview","title":"Overview","text":"
    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.
    "},{"location":"correction_routines/#stationary-crnp-processing","title":"Stationary CRNP processing","text":"
     Dashed lines indicate optional steps. See the complete example notebook.
    "},{"location":"correction_routines/#roving-crnp-processing","title":"Roving CRNP processing","text":"
     Dashed lines indicate optional steps. See the complete example notebook.
    "},{"location":"correction_routines/#incoming-neutron-flux","title":"Incoming neutron flux","text":"
    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 & Smart., 2019).
     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. 
    Implementation
     
    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.
    "},{"location":"correction_routines/#atmospheric-corrections","title":"Atmospheric corrections","text":"
    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).
     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 
    Implementation
     
    See crnpy.crnpy.humidity_correction and crnpy.crnpy.pressure_correction documentation for the implementation details.
    "},{"location":"correction_routines/#biomass-correction","title":"Biomass correction","text":"
    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).
     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) 
    Implementation
     
    See crnpy.crnpy.bwe_correction and crnpy.crnpy.biomass_to_bwe documentation for the implementation details.
    "},{"location":"correction_routines/#road-correction","title":"Road correction","text":"
    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).
     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 
    Implementation
     
    See crnpy.crnpy.road_correction documentation for the implementation details.
    "},{"location":"correction_routines/#additional-corrections","title":"Additional corrections","text":"
    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.
     
    Implementation
     
    See crnpy.crnpy.estimate_lattice_water and crnpy.crnpy.counts_to_vwc documentation for the implementation details.
     
    References
     
    Klein, K.-L., Steigies, C., & Nmdb Team. (2009). WWW.NMDB.EU: The real-time Neutron Monitor database. EGU General Assembly Conference Abstracts, 5633.
     
    Shea, M., & Smart, D. (2019). Re-examination of the first five ground-level events. International Cosmic Ray Conference (ICRC2019), 36, 1149.
     
    Smart, D., & Shea, M. (2001). Geomagnetic cutoff rigidity computer program: Theory, software description and example.
     
    Andreasen, M., Jensen, K. H., Desilets, D., Franz, T. E., Zreda, M., Bogena, H. R., & 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.
     
    Rosolem, R., Shuttleworth, W. J., Zreda, M., Franz, T. E., Zeng, X., & 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.
     
    Zreda, M., Desilets, D., Ferr\u00e9, T., & Scott, R. L. (2008). Measuring soil moisture content non-invasively at intermediate spatial scale using cosmic-ray neutrons. Geophysical Research Letters, 35(21).
     
    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\u20132211.
     
    Wahbi, A., Heng, L., Dercon, G., Wahbi, A., & Avery, W. (2018). In situ destructive sampling. Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent, 5\u20139.
     
    Baatz, R., Bogena, H., Hendricks Franssen, H.-J., Huisman, J., Montzka, C., & Vereecken, H. (2015). An empirical vegetation correction for soil water content quantification using cosmic ray probes. Water Resources Research, 51(4), 2030\u20132046.
     
    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., & others. (2018). Cosmic-ray neutron rover surveys of field soil moisture and the influence of roads. Water Resources Research, 54(9), 6441\u20136459.
     
    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.
    "},{"location":"reference/","title":"Reference","text":"
    crnpy is a Python package for processing cosmic ray neutron data.
     
    Created by Joaquin Peraza and Andres Patrignani.
    "},{"location":"reference/#crnpy.crnpy.abs_humidity","title":"abs_humidity(relative_humidity, temp)","text":"
    Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.
     
    Parameters:
     Name Type Description Default relative_humidity float 
    relative humidity (%)
     required temp float 
    temperature (Celsius)
     required 
    Returns:
     Name Type Description float 
    actual vapor pressure (g m^-3)
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.biomass_to_bwe","title":"biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494)","text":"
    Function to convert biomass to biomass water equivalent.
     
    Parameters:
     Name Type Description Default biomass_dry array or pd.Series or pd.DataFrame 
    Above ground dry biomass in kg m-2.
     required biomass_fresh array or pd.Series or pd.DataFrame 
    Above ground fresh biomass in kg m-2.
     required 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).
     0.494 
    Returns:
     Type Description 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
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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 & 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
    "},{"location":"reference/#crnpy.crnpy.correction_bwe","title":"correction_bwe(counts, bwe, r2_N0=0.05)","text":"
    Function to correct for biomass effects in neutron counts. following the approach described in Baatz et al., 2015.
     
    Parameters:
     Name Type Description Default counts array or pd.Series or pd.DataFrame 
    Array of ephithermal neutron counts.
     required bwe float 
    Biomass water equivalent kg m-2.
     required r2_N0 float 
    Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.
     0.05 
    Returns:
     Type Description 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\u20132046, doi:10.1002/ 2014WR016443.
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.correction_humidity","title":"correction_humidity(abs_humidity, Aref)","text":"
    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
      •  
     
    Parameters:
     Name Type Description Default abs_humidity list or array 
    Relative humidity readings.
     required temp list or array 
    Temperature readings (Celsius).
     required Aref float 
    Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.
     required 
    Returns:
     Type Description list 
    fw correction factor.
     References 
    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
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.correction_incoming_flux","title":"correction_incoming_flux(incoming_neutrons, incoming_Ref=None)","text":"
    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
      •  
     
    $$ f_i = \\frac{I_{ref}}{I} $$
     
    where:
     
       
    • I: incoming neutron flux
      •  
    • Iref: reference incoming neutron flux
      •  
     
    Parameters:
     Name Type Description Default incoming_neutrons list or array 
    Incoming neutron flux readings.
     required incoming_Ref float 
    Reference incoming neutron flux. Baseline incoming neutron flux.
     None 
    Returns:
     Type Description list 
    fi correction factor.
     References 
    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
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.correction_pressure","title":"correction_pressure(pressure, Pref, L)","text":"
    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.
      •  
     
    Parameters:
     Name Type Description Default atm_pressure list or array 
    Atmospheric pressure readings. Long-term average pressure is recommended.
     required Pref float 
    Reference atmospheric pressure.
     required L float 
    Atmospheric attenuation coefficient.
     required 
    Returns:
     Type Description list 
    fp pressure correction factor.
     References 
    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
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.correction_road","title":"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)","text":"
    Function to correct for road effects in neutron counts. following the approach described in Schr\u00f6n et al., 2018.
     
    Parameters:
     Name Type Description Default counts array or pd.Series or pd.DataFrame 
    Array of ephithermal neutron counts.
     required theta_N float 
    Volumetric water content of the soil estimated from the uncorrected neutron counts.
     required road_width float 
    Width of the road in m.
     required road_distance float 
    Distance of the road from the sensor in m. Default is 0.0.
     0.0 theta_road float 
    Volumetric water content of the road. Default is 0.12.
     0.12 p0-p9 float 
    Parameters of the correction function. Default values are from Schr\u00f6n et al., 2018.
     required 
    Returns:
     Type Description array or pd.Series or pd.DataFrame 
    Array of corrected neutron counts for road effects.
     References 
    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
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.counts_to_vwc","title":"counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=0.115)","text":"
    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 $
     
    Parameters:
     Name Type Description Default counts array or pd.Series or pd.DataFrame 
    Array of corrected and filtered neutron counts.
     required N0 float 
    Device-specific neutron calibration constant.
     required Wlat float 
    Lattice water content.
     required Wsoc float 
    Soil organic carbon content.
     required bulk_density float 
    Soil bulk density.
     required a0 float 
    Parameter given in Zreda et al., 2012. Default is 0.0808.
     0.0808 a1 float 
    Parameter given in Zreda et al., 2012. Default is 0.372.
     0.372 a2 float 
    Parameter given in Zreda et al., 2012. Default is 0.115.
     0.115 
    Returns:
     Type Description array or pd.Series or pd.DataFrame 
    Volumetric water content in m3 m-3.
     References 
    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
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.cutoff_rigidity","title":"cutoff_rigidity(lat, lon)","text":"
    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.
     
    Parameters:
     Name Type Description Default lat float 
    Geographic latitude in decimal degrees. Value in range -90 to 90
     required 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.
     required 
    Returns:
     Type Description 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)\n>>> print(zq)\n2.52 GV (Value from NMD is 2.40 GV)\n
     References 
    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).
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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        >>> zq = cutoff_rigidity(39.68, -75.75)\n        >>> print(zq)\n        2.52 GV (Value from NMD is 2.40 GV)\n\n    References:\n        Smart, D. & 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., & 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 < 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
    "},{"location":"reference/#crnpy.crnpy.euclidean_distance","title":"euclidean_distance(px, py, x, y)","text":"
    Function that computes the Euclidean distance between one point in space and one or more points.
     
    Parameters:
     Name Type Description Default px float 
    x projected coordinate of the point.
     required py float 
    y projected coordinate of the point.
     required x list, ndarray, pandas.series 
    vector of x projected coordinates.
     required y list, ndarray, pandas.series 
    vector of y projected coordinates.
     required 
    Returns:
     Type Description ndarray 
    Numpy array of distances from the point (px,py) to all the points in x and y vectors.
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.exp_filter","title":"exp_filter(sm, T=1)","text":"
    Exponential filter to estimate soil moisture in the rootzone from surface observtions.
     
    Parameters:
     Name Type Description Default sm list or array 
    Soil moisture in mm of water for the top layer of the soil profile.
     required T float 
    Characteristic time length in the same units as the measurement interval.
     1 
    Returns:
     Name Type Description sm_subsurface list or array 
    Subsurface soil moisture in the same units as the input.
     References 
    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.
     
    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.
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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]) & ~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
    "},{"location":"reference/#crnpy.crnpy.fill_missing_timestamps","title":"fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True, verbose=False)","text":"
    Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.
     
    Parameters:
     Name Type Description Default df pandas.DataFrame 
    Pandas DataFrame.
     required timestamp_col str 
    Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.
     'timestamp' freq str 
    Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.
     'H' round_timestamp bool 
    Whether to round timestamps to the nearest frequency. Default is True.
     True verbose bool 
    Prints the missing timestamps added to the DatFrame.
     False 
    Returns:
     Type Description pandas.DataFrame 
    DataFrame with filled missing timestamps.
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.find_neutron_monitor","title":"find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False)","text":"
    Search for potential reference neutron monitoring stations based on cutoff rigidity.
     
    Parameters:
     Name Type Description Default Rc float 
    Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.
     required start_date datetime 
    Start date for the period of interest.
     None end_date datetime 
    End date for the period of interest.
     None 
    Returns:
     Type Description list 
    List of top five stations with closes cutoff rigidity. User needs to select station according to site altitude.
     
    Examples:
     
    >>> from crnpy import crnpy\n>>> Rc = 2.40 # 2.40 Newark, NJ, US\n>>> 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
     
    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
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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        >>> from crnpy import crnpy\n        >>> Rc = 2.40 # 2.40 Newark, NJ, US\n        >>> 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
    "},{"location":"reference/#crnpy.crnpy.get_incoming_neutron_flux","title":"get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expand_window=0, verbose=False)","text":"
    Function to retrieve neutron flux from the Neutron Monitor Database.
     
    Parameters:
     Name Type Description Default start_date datetime 
    Start date of the time series.
     required end_date datetime 
    End date of the time series.
     required station str 
    Neutron Monitor station to retrieve data from.
     required utc_offset int 
    UTC offset in hours. Default is 0.
     0 expand_window int 
    Number of hours to expand the time window to retrieve extra data. Default is 0.
     0 verbose bool 
    Print information about the request. Default is False.
     False 
    Returns:
     Type Description pandas.DataFrame 
    Neutron flux in counts per hour and timestamps.
     References 
    Documentation available:https://www.nmdb.eu/nest/help.php#howto
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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&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'\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 + '&'.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</code></pre><br>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
    "},{"location":"reference/#crnpy.crnpy.idw","title":"idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1)","text":"
    Function to interpolate data using inverse distance weight.
     
    Parameters:
     Name Type Description Default x list or array 
    UTM x coordinates in meters.
     required y list or array 
    UTM y coordinates in meters.
     required z list or array 
    Values to be interpolated.
     required X_pred list or array 
    UTM x coordinates where z values need to be predicted.
     required Y_pred list or array 
    UTM y coordinates where z values need to be predicted.
     required neighborhood float 
    Only points within this radius in meters are considered for the interpolation.
     1000 p int 
    Exponent of the inverse distance weight formula. Typically, p=1 or p=2.
     1 
    Returns:
     Type Description array 
    Interpolated values.
     References 
    https://en.wikipedia.org/wiki/Inverse_distance_weighting
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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 < 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
    "},{"location":"reference/#crnpy.crnpy.interpolate_2d","title":"interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000)","text":"
    Function for interpolating irregular spatial data into a regular grid.
     
    Parameters:
     Name Type Description Default x list or array 
    UTM x coordinates in meters.
     required y list or array 
    UTM y coordinates in meters.
     required z list or array 
    Values to be interpolated.
     required dx float 
    Pixel width in meters.
     100 dy float 
    Pixel height in meters.
     100 method str 
    Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.
     'cubic' neighborhood float 
    Only points within this radius in meters are considered for the interpolation.
     1000 
    Returns:
     Name Type Description 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
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.interpolate_incoming_flux","title":"interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps)","text":"
    Function to interpolate incoming neutron flux to match the timestamps of the observations.
     
    Parameters:
     Name Type Description Default nmdb_timestamps pd.Series 
    Series of timestamps in datetime format from the NMDB.
     required nmdb_counts pd.Series 
    Series of neutron counts from the NMDB
     required crnp_timestamps pd.Series 
    Series of timestamps in datetime format from the station or device.
     required 
    Returns:
     Type Description pd.Series 
    Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.is_outlier","title":"is_outlier(x, method, window=11, min_val=None, max_val=None)","text":"
    Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.
     
    Parameters:
     Name Type Description Default x pandas.DataFrame 
    Variable containing only the columns with neutron counts.
     required method str 
    Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad
     required window int 
    Window size for the moving central tendency. Default is 11.
     11 min_val int or float 
    Minimum value for a reading to be considered valid. Default is None.
     None max_val(int or float 
    Maximum value for a reading to be considered valid. Default is None.
     required 
    Returns:
     Type Description pandas.DataFrame 
    Boolean indicating outliers.
     References 
    Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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 < min_val) | (x > 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<low_fence ) | (x>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 < lb) | (x > ub)\n\n    elif method == 'zscore':\n        zscore = (x - x.mean())/x.std()\n        idx_outliers = (zscore < -3) | (zscore > 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 < -3) | (movzscore > 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 < -3.5) | (modified_z_score > 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 > (median + 3*mad)\n\n    else:\n        raise TypeError('Outlier detection method not found.')\n\n    return idx_outliers | idx_range_outliers\n
    "},{"location":"reference/#crnpy.crnpy.latlon_to_utm","title":"latlon_to_utm(lat, lon, utm_zone_number, missing_values=None)","text":"
    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 on an equirectangular world map with irregular zones in red and New York City's zone highlighted. See UTM zones for a full description.
     
    Parameters:
     Name Type Description Default lat float, array 
    Latitude in decimal degrees.
     required lon float, array 
    Longitude in decimal degrees.
     required utm_zone_number int 
    Universal Transverse Mercator (UTM) zone.
     required 
    Returns:
     Type Description float, float 
    Tuple of easting and northing coordinates in meters. First element is easting, second is northing.
     References 
    Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87) https://github.com/Turbo87/utm
     
    https://www.maptools.com/tutorials/grid_zone_details#
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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 < -80) | np.any(lat > 84):\n        raise \"One or more latitude values exceed the range -80 to 84\"\n\n    # Check longitude range\n    if np.any(lon < -180) | np.any(lon > 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 < 0):\n        northing += 10_000_000\n\n    return easting, northing\n
    "},{"location":"reference/#crnpy.crnpy.lattice_water","title":"lattice_water(clay_content, total_carbon=None)","text":"
    Estimate the amount of water in the lattice of clay minerals.
     $\\omega_{lat} = 0.097 * clay(\\%)$ $\\omega_{lat} = -0.028 + 0.077 * clay(\\%) + 0.459 * carbon(\\%)$ 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. 
    Parameters:
     Name Type Description Default clay_content float 
    Clay content in the soil in percent.
     required total_carbon float 
    Total carbon content in the soil in percent. If None, the amount of water is estimated based on clay content only.
     None 
    Returns:
     Type Description float 
    Amount of water in the lattice of clay minerals in percent
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.nrad_weight","title":"nrad_weight(h, theta, distances, depth, rhob=1.4)","text":"
    Function to compute distance weights corresponding to each soil sample.
     
    Parameters:
     Name Type Description Default h float 
    Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.
     required theta array or pd.Series or pd.DataFrame 
    Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)
     required distances array or pd.Series or pd.DataFrame 
    Distances from the location of each sample to the origin (0.5 - 600 m)
     required depth array or pd.Series or pd.DataFrame 
    Depths for each sample (m)
     required rhob float 
    Bulk density in g/cm^3
     1.4 
    Returns:
     Type Description array or pd.Series or pd.DataFrame 
    Distance weights for each sample.
     References 
    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
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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 >= 0.1) and (h<= 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]<=50) and (distances[i]>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]>50) and (distances[i]<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
    "},{"location":"reference/#crnpy.crnpy.remove_incomplete_intervals","title":"remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False)","text":"
    Function that removes rows with incomplete integration intervals.
     
    Parameters:
     Name Type Description Default df pandas.DataFrame 
    Pandas Dataframe with data from stationary or roving CRNP devices.
     required timestamp_col str 
    Name of the column with timestamps in datetime format.
     required integration_time int 
    Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.
     required remove_first bool 
    Remove first row. Default is False.
     False 
    Returns:
     Type Description pandas.DataFrame Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.rover_centered_coordinates","title":"rover_centered_coordinates(x, y)","text":"
    Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.
     
    Parameters:
     Name Type Description Default x array 
    x coordinates.
     required y array 
    y coordinates.
     required 
    Returns:
     Name Type Description x_est array 
    Estimated x coordinates.
     y_est array 
    Estimated y coordinates.
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.sensing_depth","title":"sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017')","text":"
    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.
     
    Parameters:
     Name Type Description Default vwc array or pd.Series or pd.DataFrame 
    Estimated volumetric water content for each timestamp.
     required pressure array or pd.Series or pd.DataFrame 
    Atmospheric pressure in hPa for each timestamp.
     required p_ref float 
    Reference pressure in hPa.
     required bulk_density float 
    Soil bulk density.
     required Wlat float 
    Lattice water content.
     required method str 
    Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.
     'Schron_2017' dist list or array 
    List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.
     None 
    Returns:
     Type Description 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
     
    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
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.smooth_1d","title":"smooth_1d(values, window=5, order=3, method='moving_median')","text":"
    Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).
     
    Parameters:
     Name Type Description Default values pd.DataFrame or pd.Serie 
    Dataframe containing the values to smooth.
     required window int 
    Window size for the Savitzky-Golay filter. Default is 5.
     5 method str 
    Method to use for smoothing the data. Default is 'moving_median'. Options are 'moving_average', 'moving_median' and 'savitzky_golay'.
     'moving_median' order int 
    Order of the Savitzky-Golay filter. Default is 3.
     3 
    Returns:
     Type Description 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
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.spatial_average","title":"spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=False)","text":"
    Moving buffer filter to smooth georeferenced two-dimensional data.
     
    Parameters:
     Name Type Description Default x list or array 
    UTM x coordinates in meters.
     required y list or array 
    UTM y coordinates in meters.
     required z list or array 
    Values to be smoothed.
     required buffer float 
    Radial buffer distance in meters.
     100 min_neighbours int 
    Minimum number of neighbours to consider for the smoothing.
     3 method str 
    One of 'mean' or 'median'.
     'mean' rnd bool 
    Boolean to round the final result. Useful in case of z representing neutron counts.
     False 
    Returns:
     Type Description array 
    Smoothed version of z with the same dimension as z.
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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 <= buffer\n\n\n        if np.isnan(z[k]):\n            z_new_val = np.nan\n        elif len(distances[idx_within_buffer]) > 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
    "},{"location":"reference/#crnpy.crnpy.total_raw_counts","title":"total_raw_counts(counts, nan_strategy=None, timestamp_col=None)","text":"
    Compute the sum of uncorrected neutron counts for all detectors.
     
    Parameters:
     Name Type Description Default counts pandas.DataFrame 
    Dataframe containing only the columns with neutron counts.
     required nan_strategy str 
    Strategy to use for NaN values. Options are 'interpolate', 'average', or None. Default is None.
     None 
    Returns:
     Type Description pandas.DataFrame 
    Dataframe with the sum of uncorrected neutron counts for all detectors.
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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] > 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
    "},{"location":"reference/#crnpy.crnpy.uncertainty_counts","title":"uncertainty_counts(raw_counts, metric='std', fp=1, fw=1, fi=1)","text":"
    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} $
     
    Parameters:
     Name Type Description Default raw_counts array 
    Raw neutron counts.
     required metric str 
    Either 'std' or 'cv' for standard deviation or coefficient of variation.
     'std' fp float 
    Pressure correction factor.
     1 fw float 
    Humidity correction factor.
     1 fi float 
    Incoming neutron flux correction factor.
     1 
    Returns:
     Name Type Description uncertainty float 
    Uncertainty of raw counts.
     References 
    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
     
    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.
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.uncertainty_vwc","title":"uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808, a1=0.372, a2=0.115)","text":"
    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: $$ \\sigma_{\\theta_g}(N) = \\sigma_N \\frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \\sqrt{(N_{cor} - a_1 N_0)^4 + 8 \\sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \\sigma_N^4} $$
     
    Parameters:
     Name Type Description Default raw_counts array 
    Raw neutron counts.
     required N0 float 
    Calibration parameter N0.
     required bulk_density float 
    Bulk density in g cm-3.
     required fp float 
    Pressure correction factor.
     1 fw float 
    Humidity correction factor.
     1 fi float 
    Incoming neutron flux correction factor.
     1 
    Returns:
     Name Type Description sigma_VWC float 
    Uncertainty in terms of volumetric water content.
     References 
    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
     Source code in C:\\Users\\jperaza\\AppData\\Local\\anaconda3\\envs\\crnpy\\lib\\site-packages\\crnpy\\crnpy.py 
    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
    "},{"location":"reference/#crnpy.data--crnpydata","title":"crnpy.data","text":"
    Data module for crnpy.
     
    This module contains data for the crnpy package.
     
    Attributes:
     Name Type Description 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
    "},{"location":"sensor_description/","title":"Sensor description","text":"
    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.
     
     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).
     
     Figure 2. Illustration showing the approximate sensing footprint of stationary detectors.
    "},{"location":"examples/calibration/calibration/","title":"Device-specific field calibration","text":"In\u00a0[1]: Copied! 
    # Importing required libraries\nimport crnpy\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.optimize import root\n
     # Importing required libraries import crnpy  import numpy as np import pandas as pd from scipy.optimize import root  In\u00a0[2]: Copied! 
    # 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
     # 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 
    3 rows \u00d7 21 columns
     In\u00a0[3]: Copied! 
    # 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
     # 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! 
    # 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
     # 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 
    3 rows \u00d7 28 columns
     In\u00a0[5]: Copied! 
    # 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'] >= deployment_date) & (df_station['timestamp'] <= calibration_end)\ndf_station = df_station[idx_period]\ndf_station.head(3)\n
     # 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) 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 
    3 rows \u00d7 28 columns
     
    This step is useful to trim large timeseries. For instance, the station data in our case extends until 2022-07-11 09:45:00, but the field calibration was conducted in 2021-10-22 16:00. 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 2021-09-22 12:00:00 until 2021-10-22 16:00. 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.
     In\u00a0[6]: Copied! 
    # 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
     # 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! 
    # 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
     # 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! 
    # 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
     # 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])  
    \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
     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! 
    # 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
     # 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! 
    # Apply correction factors\ndf_station['total_corrected_neutrons'] = df_station['total_raw_counts'] * df_station['fw'] / (df_station['fp'] * df_station['fi'])\n
     # 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! 
    # 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
     # 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! 
    # Determine the mean corrected counts during the calibration survey\nidx_cal_period = (df_station['timestamp'] >= calibration_start) & (df_station['timestamp'] <= 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
     # 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 corrected counts during calibration: {round(mean_cal_counts)} counts\")  
    Mean volumetric Water content during calibration survey: 0.263\nMean corrected counts during calibration: 1542 counts\n
     In\u00a0[13]: Copied! 
    # 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
     # 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}\")  
    The solved value for N0 is: 2644\n
    "},{"location":"examples/calibration/calibration/#device-specific-field-calibration","title":"Device-specific field calibration\u00b6","text":"
    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.
     
    $\\theta(N) =\\frac{a_0}{(\\frac{N}{N_0}) - a_1} - a_2 $ (Desilets et al., 2010).
     
    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.
     
    Figure 1. Horizontal layout and vertical layout used in this particular example calibration, it can be customized by the user depending on their needs.
     
    Download the following template  for collecting your own calibration soil samples:
    "},{"location":"examples/calibration/calibration/#read-calibration-field-survey-data","title":"Read calibration field survey data\u00b6","text":"
    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.
    "},{"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":"
    Using the function nrad_weight() the weights corresponding to each soil sample will be computed considering air-humidity, sample depth, and distance from station and bulk density.
    "},{"location":"examples/calibration/calibration/#solving-for-n_0","title":"Solving for $N_0$\u00b6","text":"
    Previous steps estimated the field volumetric water content of 0.263 and an average neutron count of 1542. Using scipy.optimize.root() $N_0$ is estimated given the observed value of $\\theta_v$ and neutron counts.
    "},{"location":"examples/calibration/calibration/#references","title":"References:\u00b6","text":"
    Desilets, D., Zreda, M., & Ferr\u00e9, T. P. (2010). Nature's neutron probe: Land surface hydrology at an elusive scale with cosmic rays. Water Resources Research, 46(11).
     
    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.
     
    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.
    "},{"location":"examples/rover/Hydroinnova_rover_example/","title":"Processing and analyzing data from a roving detector","text":"In\u00a0[1]: Copied! 
    # Import modules\nimport crnpy\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n
     # Import modules import crnpy  import numpy as np import pandas as pd import matplotlib.pyplot as plt  
    Load the data stored in the .csv file.
     In\u00a0[2]: Copied! 
    # 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
     # 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 
    3 rows \u00d7 38 columns
     
    Next, convert the latitude and longitude to UTM coordinates (x and y). A scatter plot is created to visualize the survey points.
     In\u00a0[3]: Copied! 
    # 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
     # 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()  
    The counts are normalized to counts per minute in case some observations covered a different timespan and total neutron counts are computed.
     In\u00a0[4]: Copied! 
    # 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
     # 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! 
    # 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
     # 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)  
    \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
     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! 
    # 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
     # 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! 
    # 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
     # 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! 
    # 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
     # 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! 
    # 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
     # 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! 
    # Apply correction factors\ndf['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])\n
     # Apply correction factors df['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])  
    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.
     In\u00a0[11]: Copied! 
    # 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
     # 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')  
    A gridded map of the Volumetric Water Content (VWC) is created using the interpolated x, y, and VWC values.
     In\u00a0[12]: Copied! 
    # 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
     # 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()  
    Finally, a contour map of the VWC is created and displayed.
     In\u00a0[13]: Copied! 
    # 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
     # 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":"
    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.
     
    First import the required packages.
    "},{"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":"
    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 station=\"NEWK\". The user is required to manually define this after considering the potential options suggested. See get_incoming_neutron_flux(), find_neutron_monitor() and correction_incoming_flux().
    "},{"location":"examples/rover/Hydroinnova_rover_example/#atmospheric-correction","title":"Atmospheric correction\u00b6","text":"
    NaN values in the atmospheric data are filled. The neutron counts are then corrected for atmospheric variables. See correction_humidity() and correction_pressure()
    "},{"location":"examples/stationary/example_RDT_station/","title":"Processing and analyzing data from a stationary detector","text":"In\u00a0[1]: Copied! 
    # Import required libraries\nimport crnpy\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n
     # Import required libraries import crnpy  import pandas as pd import numpy as np import matplotlib.pyplot as plt  In\u00a0[2]: Copied! 
    # 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
     # 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! 
    # 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
     # 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)  
    Removed a total of 48 rows.\nAdded a total of 60 missing timestamps.\n
     
    Utilize the total_raw_counts() function to calculate the total counts across all detectors. After calculating the total counts, identify outliers using the is_outlier() function.
     In\u00a0[4]: Copied! 
    # 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
     # 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! 
    # Compute total cunts\ndf['total_raw_counts'] = crnpy.total_raw_counts(df[cols_counts], nan_strategy='average')\n
     # Compute total cunts df['total_raw_counts'] = crnpy.total_raw_counts(df[cols_counts], nan_strategy='average')  In\u00a0[6]: Copied! 
    # 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
     # 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! 
    # 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
     # 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)  
    \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
     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! 
    # 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
     # 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! 
    # 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
     # 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! 
    # 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
     # 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! 
    # 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
     # 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! 
    # 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
     # 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]: 
    Text(0.5, 1.0, 'Correction Factors for Pressure, Humidity, and Incoming Flux')
     In\u00a0[13]: Copied! 
    # Apply correction factors\ndf['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])\n
     # Apply correction factors df['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])  In\u00a0[14]: Copied! 
    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
     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]: 
    Text(0.5, 1.0, 'Raw and Corrected Counts')
     
    Convert the smoothed neutron counts to Volumetric Water Content (VWC) using the counts_to_vwc(). 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. smooth_1d() is applied for smoothing the data using a Savitzky-Golay filter.
     In\u00a0[15]: Copied! 
    # Device N0 parameter\nN0_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.\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
     # 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.  # 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! 
    # 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
     # 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! 
    # 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
     # 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\")  
    Average sensing depth: 15.36 cm\n
     In\u00a0[18]: Copied! 
    # 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
     # 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! 
    # 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
     # 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! 
    # 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
     # 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":"
    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.
     
    The tutorial assumes that you are working within the Anaconda environment, which has all the necessary modules. Also, make sure that the CRNPy library is installed in your machine.
     
    The raw dataset to follow this example can be obtained at the .csv file.
     
    This device was installed in the vicinity of the KONA site of the National Ecological Observatory Network within the Konza Prairie Biological station (39.110596, -96.613050, 320 meters a.s.l.)
    "},{"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":"
    Find similar neutron monitors based on altitude and cutoff rigidity, which is estimated based on the geographic location using latitude and longitude values. See get_incoming_neutron_flux(), find_neutron_monitor() and correction_incoming_flux().
     
    In this particular example we selected the \"IRKT\" 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.
    "},{"location":"examples/stationary/example_RDT_station/#atmospheric-correction","title":"Atmospheric correction\u00b6","text":"
    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 correction_humidity() and correction_pressure().
    "},{"location":"examples/stationary/example_RDT_station/#sensing-depth-and-soil-water-storage","title":"Sensing depth and soil water storage\u00b6","text":"
    Estimate the sensing_depth()  at which 86 % of the neutrons probes the soil profile, and estimate the soil water storage() of the top 50 cm using and exponential filter.
    "},{"location":"examples/stationary/example_RDT_station/#uncertainty-estimation","title":"Uncertainty estimation\u00b6","text":"
    Estimate the uncertainty of the volumetric water content using the uncertainty_vwc() function. Optionally see uncertainty_counts() for estimating the uncertainty of the neutron counts.
    "}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Cosmic-Ray Neutron Python (CRNPy) Library","text":"
    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.
     
    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.
    "},{"location":"#statement-of-need","title":"Statement of Need","text":"
    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.
    "},{"location":"#key-features","title":"Key Features","text":"
       
    •  
      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.
       
      •  
    •  
      Modular: The library is designed to be modular, allowing users to easily customize the processing workflow to their needs.
       
      •  
    "},{"location":"#installation","title":"Installation","text":"
    To install the CRNPy library, you can use Python's package manager. Open a terminal and type:
     
    pip install crnpy
     
    from the Jupyter notebook, type:
     
    !pip install crnpy
     
    Ideally dependencies should be installed automatically. If not, you can install them manually by typing:
     
    pip install -r requirements.txt
     
    The CRNPy library is compatible with Python 3.7 and above. See requirements.txt for a list of dependencies.
    "},{"location":"#authors","title":"Authors","text":"
    The CRNPy library was developed at the Kansas State University Soil Water Processes Lab by:
     
       
    •  
      Joaquin Peraza
       
      •  
    •  
      Andres Patrignani
       
      •  
     
    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. 
    "},{"location":"#community-guidelines","title":"Community Guidelines","text":""},{"location":"#contributing","title":"Contributing","text":"
    To contribute to the software, please first fork the repository and create your own branch from main. Ensure your code adheres to our established code structure and includes appropriate test/examples coverage. CRNPy source code is located in the /src/crnpy/ folder, and tests implemented using pytest are stored in the /src/tests/ folder. Submit a pull request with a clear and detailed description of your changes to include them in the main repository.
    "},{"location":"#reporting-issues","title":"Reporting Issues","text":"
    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.
    "},{"location":"#seeking-support","title":"Seeking Support","text":"
    If you need support, please first refer to the documentation. If you still require assistance, post a question on the issues page with the question tag. For private inquiries, you can reach us via email at jperaza@ksu.edu or andrespatrignani@ksu.edu.
    "},{"location":"correction_routines/","title":"Correction routines","text":""},{"location":"correction_routines/#overview","title":"Overview","text":"
    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.
    "},{"location":"correction_routines/#stationary-crnp-processing","title":"Stationary CRNP processing","text":"
     Dashed lines indicate optional steps. See the complete example notebook.
    "},{"location":"correction_routines/#roving-crnp-processing","title":"Roving CRNP processing","text":"
     Dashed lines indicate optional steps. See the complete example notebook.
    "},{"location":"correction_routines/#incoming-neutron-flux","title":"Incoming neutron flux","text":"
    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 & Smart., 2019).
     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. 
    Implementation
     
    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.
    "},{"location":"correction_routines/#atmospheric-corrections","title":"Atmospheric corrections","text":"
    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).
     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 
    Implementation
     
    See crnpy.crnpy.humidity_correction and crnpy.crnpy.pressure_correction documentation for the implementation details.
    "},{"location":"correction_routines/#biomass-correction","title":"Biomass correction","text":"
    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).
     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) 
    Implementation
     
    See crnpy.crnpy.bwe_correction and crnpy.crnpy.biomass_to_bwe documentation for the implementation details.
    "},{"location":"correction_routines/#road-correction","title":"Road correction","text":"
    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).
     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 
    Implementation
     
    See crnpy.crnpy.road_correction documentation for the implementation details.
    "},{"location":"correction_routines/#additional-corrections","title":"Additional corrections","text":"
    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.
     
    Implementation
     
    See crnpy.crnpy.estimate_lattice_water and crnpy.crnpy.counts_to_vwc documentation for the implementation details.
     
    References
     
    Klein, K.-L., Steigies, C., & Nmdb Team. (2009). WWW.NMDB.EU: The real-time Neutron Monitor database. EGU General Assembly Conference Abstracts, 5633.
     
    Shea, M., & Smart, D. (2019). Re-examination of the first five ground-level events. International Cosmic Ray Conference (ICRC2019), 36, 1149.
     
    Smart, D., & Shea, M. (2001). Geomagnetic cutoff rigidity computer program: Theory, software description and example.
     
    Andreasen, M., Jensen, K. H., Desilets, D., Franz, T. E., Zreda, M., Bogena, H. R., & 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.
     
    Rosolem, R., Shuttleworth, W. J., Zreda, M., Franz, T. E., Zeng, X., & 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.
     
    Zreda, M., Desilets, D., Ferr\u00e9, T., & Scott, R. L. (2008). Measuring soil moisture content non-invasively at intermediate spatial scale using cosmic-ray neutrons. Geophysical Research Letters, 35(21).
     
    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\u20132211.
     
    Wahbi, A., Heng, L., Dercon, G., Wahbi, A., & Avery, W. (2018). In situ destructive sampling. Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent, 5\u20139.
     
    Baatz, R., Bogena, H., Hendricks Franssen, H.-J., Huisman, J., Montzka, C., & Vereecken, H. (2015). An empirical vegetation correction for soil water content quantification using cosmic ray probes. Water Resources Research, 51(4), 2030\u20132046.
     
    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., & others. (2018). Cosmic-ray neutron rover surveys of field soil moisture and the influence of roads. Water Resources Research, 54(9), 6441\u20136459.
     
    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.
    "},{"location":"reference/","title":"Reference","text":"
    crnpy is a Python package for processing cosmic ray neutron data.
     
    Created by Joaquin Peraza and Andres Patrignani.
     
    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.
      •  
    "},{"location":"reference/#crnpy.crnpy.abs_humidity","title":"abs_humidity(relative_humidity, temp)","text":"
    Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.
     
    Parameters:
     Name Type Description Default relative_humidity float 
    relative humidity (%)
     required temp float 
    temperature (Celsius)
     required 
    Returns:
     Name Type Description float 
    actual vapor pressure (g m^-3)
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.atmospheric_depth","title":"atmospheric_depth(elevation, latitude)","text":"
    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.
     
    Parameters:
     Name Type Description Default elevation float 
    Elevation in meters above sea level.
     required latitude float 
    Geographic latitude in decimal degrees. Value in range -90 to 90
     required 
    Returns:
     Type Description 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\u2010Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.
     Source code in crnpy/crnpy.py 
    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., & 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
    "},{"location":"reference/#crnpy.crnpy.biomass_to_bwe","title":"biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494)","text":"
    Function to convert biomass to biomass water equivalent.
     
    Parameters:
     Name Type Description Default biomass_dry array or Series or DataFrame 
    Above ground dry biomass in kg m-2.
     required biomass_fresh array or Series or DataFrame 
    Above ground fresh biomass in kg m-2.
     required 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).
     0.494 
    Returns:
     Type Description array or Series or 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
     Source code in crnpy/crnpy.py 
    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 & 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
    "},{"location":"reference/#crnpy.crnpy.correction_bwe","title":"correction_bwe(counts, bwe, r2_N0=0.05)","text":"
    Function to correct for biomass effects in neutron counts. following the approach described in Baatz et al., 2015.
     
    Parameters:
     Name Type Description Default counts array or Series or DataFrame 
    Array of ephithermal neutron counts.
     required bwe float 
    Biomass water equivalent kg m-2.
     required r2_N0 float 
    Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.
     0.05 
    Returns:
     Type Description array or Series or 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\u20132046, doi:10.1002/ 2014WR016443.
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.correction_humidity","title":"correction_humidity(abs_humidity, Aref)","text":"
    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
      •  
     
    Parameters:
     Name Type Description Default abs_humidity list or array 
    Relative humidity readings.
     required Aref float 
    Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.
     required 
    Returns:
     Type Description list 
    fw correction factor.
     References 
    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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.correction_incoming_flux","title":"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)","text":"
    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
      •  
     
    $$ f_i = \\frac{I_{ref}}{I} $$
     
    where:
     
       
    • I: incoming neutron flux
      •  
    • Iref: reference incoming neutron flux
      •  
     
    Parameters:
     Name Type Description Default incoming_neutrons list or array 
    Incoming neutron flux readings.
     required incoming_Ref float 
    Reference incoming neutron flux. Baseline incoming neutron flux.
     None 
    Returns:
     Type Description list 
    fi correction factor.
     References 
    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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.correction_pressure","title":"correction_pressure(pressure, Pref, L)","text":"
    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.
      •  
     
    Parameters:
     Name Type Description Default pressure list or array 
    Atmospheric pressure readings. Long-term average pressure is recommended.
     required Pref float 
    Reference atmospheric pressure.
     required L float 
    Atmospheric attenuation coefficient.
     required 
    Returns:
     Type Description list 
    fp pressure correction factor.
     References 
    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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.correction_road","title":"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)","text":"
    Function to correct for road effects in neutron counts. following the approach described in Schr\u00f6n et al., 2018.
     
    Parameters:
     Name Type Description Default counts array or Series or DataFrame 
    Array of ephithermal neutron counts.
     required theta_N float 
    Volumetric water content of the soil estimated from the uncorrected neutron counts.
     required road_width float 
    Width of the road in m.
     required road_distance float 
    Distance of the road from the sensor in m. Default is 0.0.
     0.0 theta_road float 
    Volumetric water content of the road. Default is 0.12.
     0.12 p0-p9 float 
    Parameters of the correction function. Default values are from Schr\u00f6n et al., 2018.
     required 
    Returns:
     Type Description array or Series or DataFrame 
    Array of corrected neutron counts for road effects.
     References 
    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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.counts_to_vwc","title":"counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=0.115)","text":"
    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 $
     
    Parameters:
     Name Type Description Default counts array or Series or DataFrame 
    Array of corrected and filtered neutron counts.
     required N0 float 
    Device-specific neutron calibration constant.
     required Wlat float 
    Lattice water content.
     required Wsoc float 
    Soil organic carbon content.
     required bulk_density float 
    Soil bulk density.
     required a0 float 
    Parameter given in Zreda et al., 2012. Default is 0.0808.
     0.0808 a1 float 
    Parameter given in Zreda et al., 2012. Default is 0.372.
     0.372 a2 float 
    Parameter given in Zreda et al., 2012. Default is 0.115.
     0.115 
    Returns:
     Type Description array or Series or DataFrame 
    Volumetric water content in m3 m-3.
     References 
    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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.cutoff_rigidity","title":"cutoff_rigidity(lat, lon)","text":"
    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.
     
    Parameters:
     Name Type Description Default lat float 
    Geographic latitude in decimal degrees. Value in range -90 to 90
     required 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.
     required 
    Returns:
     Type Description 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)\n>>> print(zq)\n2.52 GV (Value from NMD is 2.40 GV)\n
     References 
    Hawdon, A., McJannet, D., & Wallace, J. (2014). Calibration and correction procedures for cosmic\u2010ray 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).
     Source code in crnpy/crnpy.py 
    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        >>> zq = cutoff_rigidity(39.68, -75.75)\n        >>> print(zq)\n        2.52 GV (Value from NMD is 2.40 GV)\n\n    References:\n        Hawdon, A., McJannet, D., & 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. & 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., & 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 < 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
    "},{"location":"reference/#crnpy.crnpy.euclidean_distance","title":"euclidean_distance(px, py, x, y)","text":"
    Function that computes the Euclidean distance between one point in space and one or more points.
     
    Parameters:
     Name Type Description Default px float 
    x projected coordinate of the point.
     required py float 
    y projected coordinate of the point.
     required x (list, ndarray, series) 
    vector of x projected coordinates.
     required y (list, ndarray, series) 
    vector of y projected coordinates.
     required 
    Returns:
     Type Description ndarray 
    Numpy array of distances from the point (px,py) to all the points in x and y vectors.
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.exp_filter","title":"exp_filter(sm, T=1)","text":"
    Exponential filter to estimate soil moisture in the rootzone from surface observtions.
     
    Parameters:
     Name Type Description Default sm list or array 
    Soil moisture in mm of water for the top layer of the soil profile.
     required T float 
    Characteristic time length in the same units as the measurement interval.
     1 
    Returns:
     Name Type Description sm_subsurface list or array 
    Subsurface soil moisture in the same units as the input.
     References 
    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.
     
    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.
     Source code in crnpy/crnpy.py 
    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]) & ~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
    "},{"location":"reference/#crnpy.crnpy.fill_missing_timestamps","title":"fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True, verbose=False)","text":"
    Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.
     
    Parameters:
     Name Type Description Default df DataFrame 
    Pandas DataFrame.
     required timestamp_col str 
    Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.
     'timestamp' freq str 
    Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.
     'H' round_timestamp bool 
    Whether to round timestamps to the nearest frequency. Default is True.
     True verbose bool 
    Prints the missing timestamps added to the DatFrame.
     False 
    Returns:
     Type Description DataFrame 
    DataFrame with filled missing timestamps.
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.find_neutron_monitor","title":"find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False)","text":"
    Search for potential reference neutron monitoring stations based on cutoff rigidity.
     
    Parameters:
     Name Type Description Default Rc float 
    Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.
     required start_date datetime 
    Start date for the period of interest.
     None end_date datetime 
    End date for the period of interest.
     None verbose bool 
    If True, print a expanded output of the incoming neutron flux data.
     False 
    Returns:
     Type Description list 
    List of top five stations with closes cutoff rigidity. User needs to select station according to site altitude.
     
    Examples:
     
    >>> from crnpy import crnpy\n>>> Rc = 2.40 # 2.40 Newark, NJ, US\n>>> 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
     
    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
     Source code in crnpy/crnpy.py 
    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        >>> from crnpy import crnpy\n        >>> Rc = 2.40 # 2.40 Newark, NJ, US\n        >>> 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
    "},{"location":"reference/#crnpy.crnpy.get_incoming_neutron_flux","title":"get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expand_window=0, verbose=False)","text":"
    Function to retrieve neutron flux from the Neutron Monitor Database.
     
    Parameters:
     Name Type Description Default start_date datetime 
    Start date of the time series.
     required end_date datetime 
    End date of the time series.
     required station str 
    Neutron Monitor station to retrieve data from.
     required utc_offset int 
    UTC offset in hours. Default is 0.
     0 expand_window int 
    Number of hours to expand the time window to retrieve extra data. Default is 0.
     0 verbose bool 
    Print information about the request. Default is False.
     False 
    Returns:
     Type Description DataFrame 
    Neutron flux in counts per hour and timestamps.
     References 
    Documentation available:https://www.nmdb.eu/nest/help.php#howto
     Source code in crnpy/crnpy.py 
    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&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'\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 + '&'.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</code></pre><br>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
    "},{"location":"reference/#crnpy.crnpy.get_reference_neutron_flux","title":"get_reference_neutron_flux(station, date=pd.to_datetime('2011-05-01'))","text":"
    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).
     
    Parameters:
     Name Type Description Default station str 
    Neutron Monitor station to retrieve data from.
     required date datetime 
    Date of the reference neutron flux. Default is 2011-05-01.
     to_datetime('2011-05-01') 
    Returns:
     Type Description 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\u2010ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.
     
    Bogena, H. R., Schr\u00f6n, 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.
     Source code in crnpy/crnpy.py 
    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., & 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., & 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., ... & 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
    "},{"location":"reference/#crnpy.crnpy.idw","title":"idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1)","text":"
    Function to interpolate data using inverse distance weight.
     
    Parameters:
     Name Type Description Default x list or array 
    UTM x coordinates in meters.
     required y list or array 
    UTM y coordinates in meters.
     required z list or array 
    Values to be interpolated.
     required X_pred list or array 
    UTM x coordinates where z values need to be predicted.
     required Y_pred list or array 
    UTM y coordinates where z values need to be predicted.
     required neighborhood float 
    Only points within this radius in meters are considered for the interpolation.
     1000 p int 
    Exponent of the inverse distance weight formula. Typically, p=1 or p=2.
     1 
    Returns:
     Type Description array 
    Interpolated values.
     References 
    https://en.wikipedia.org/wiki/Inverse_distance_weighting
     Source code in crnpy/crnpy.py 
    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 < 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
    "},{"location":"reference/#crnpy.crnpy.interpolate_2d","title":"interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000)","text":"
    Function for interpolating irregular spatial data into a regular grid.
     
    Parameters:
     Name Type Description Default x list or array 
    UTM x coordinates in meters.
     required y list or array 
    UTM y coordinates in meters.
     required z list or array 
    Values to be interpolated.
     required dx float 
    Pixel width in meters.
     100 dy float 
    Pixel height in meters.
     100 method str 
    Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.
     'cubic' neighborhood float 
    Only points within this radius in meters are considered for the interpolation.
     1000 
    Returns:
     Name Type Description 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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.interpolate_incoming_flux","title":"interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps)","text":"
    Function to interpolate incoming neutron flux to match the timestamps of the observations.
     
    Parameters:
     Name Type Description Default nmdb_timestamps Series 
    Series of timestamps in datetime format from the NMDB.
     required nmdb_counts Series 
    Series of neutron counts from the NMDB
     required crnp_timestamps Series 
    Series of timestamps in datetime format from the station or device.
     required 
    Returns:
     Type Description Series 
    Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.is_outlier","title":"is_outlier(x, method, window=11, min_val=None, max_val=None)","text":"
    Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.
     
    Parameters:
     Name Type Description Default x DataFrame or Series 
    Variable containing only the columns with neutron counts.
     required method str 
    Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad
     required window int 
    Window size for the moving central tendency. Default is 11.
     11 min_val int or float 
    Minimum value for a reading to be considered valid. Default is None.
     None max_val(int or float 
    Maximum value for a reading to be considered valid. Default is None.
     required 
    Returns:
     Type Description DataFrame 
    Boolean indicating outliers.
     References 
    Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.
     Source code in crnpy/crnpy.py 
    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 < min_val) | (x > 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 < low_fence) | (x > 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 < lb) | (x > ub)\n\n    elif method == 'zscore':\n        zscore = (x - x.mean()) / x.std()\n        idx_outliers = (zscore < -3) | (zscore > 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 < -3) | (movzscore > 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 < -3.5) | (modified_z_score > 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 > (median + 3 * mad)\n\n    else:\n        raise TypeError('Outlier detection method not found.')\n\n    return idx_outliers | idx_range_outliers\n
    "},{"location":"reference/#crnpy.crnpy.latlon_to_utm","title":"latlon_to_utm(lat, lon, utm_zone_number=None, utm_zone_letter=None)","text":"
    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 on an equirectangular world map with irregular zones in red and New York City's zone highlighted. See UTM zones for a full description.
     
    Parameters:
     Name Type Description Default lat (float, array) 
    Latitude in decimal degrees.
     required lon (float, array) 
    Longitude in decimal degrees.
     required utm_zone_number int 
    UTM zone number. If None, the zone number is automatically calculated.
     None utm_zone_letter str 
    UTM zone letter. If None, the zone letter is automatically calculated.
     None 
    Returns:
     Type Description (float, float) 
    Tuple of easting and northing coordinates in meters. First element is easting, second is northing.
     References 
    Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87) https://github.com/Turbo87/utm
     
    https://www.maptools.com/tutorials/grid_zone_details#
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.lattice_water","title":"lattice_water(clay_content, total_carbon=None)","text":"
    Estimate the amount of water in the lattice of clay minerals.
     $\\omega_{lat} = 0.097 * clay(\\%)$ $\\omega_{lat} = -0.028 + 0.077 * clay(\\%) + 0.459 * carbon(\\%)$ 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. 
    Parameters:
     Name Type Description Default clay_content float 
    Clay content in the soil in percent.
     required total_carbon float 
    Total carbon content in the soil in percent. If None, the amount of water is estimated based on clay content only.
     None 
    Returns:
     Type Description float 
    Amount of water in the lattice of clay minerals in percent
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.location_factor","title":"location_factor(site_atmospheric_depth, site_Rc, reference_atmospheric_depth, reference_Rc)","text":"
    Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.
     
    Parameters:
     Name Type Description Default site_atmospheric_depth float 
    Atmospheric depth at the site in g/cm2. Can be estimated using the function atmospheric_depth()
     required site_Rc float 
    Cutoff rigidity at the site in GV. Can be estimated using the function cutoff_rigidity()
     required reference_atmospheric_depth float 
    Atmospheric depth at the reference location in g/cm2.
     required reference_Rc float 
    Cutoff rigidity at the reference location in GV.
     required 
    Returns:
     Type Description float 
    Location-dependent correction factor.
     References 
    McJannet, D. L., & 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.
     Source code in crnpy/crnpy.py 
    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., & 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
    "},{"location":"reference/#crnpy.crnpy.nrad_weight","title":"nrad_weight(h, theta, distances, depth, rhob=1.4, method=None, p=None, Hveg=None)","text":"
    Function to compute distance weights corresponding to each soil sample.
     
    Parameters:
     Name Type Description Default h array or Series 
    Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.
     required theta array or Series 
    Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)
     required distances array or Series 
    Distances from the location of each sample to the origin (0.5 - 600 m)
     required depth array or Series 
    Depths for each sample (m)
     required rhob array or Series 
    Bulk density in g/cm^3
     1.4 p array or Series 
    Atmospheric pressure in hPa. Required for the 'Schron_2017' method.
     None Hveg array or Series 
    Vegetation height in m. Required for the 'Schron_2017' method.
     None method str 
    Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.
     None 
    Returns:
     Type Description array or Series or DataFrame 
    Distance weights for each sample.
     References 
    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
     
    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.
     Source code in crnpy/crnpy.py 
    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 >= 0.1) and (h <= 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] <= 50) and (distances[i] > 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] > 50) and (distances[i] < 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 <= 1)\n        r1_idx = (r > 1) & (r <= 50)\n        r2_idx = (r > 50) & (r < 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
    "},{"location":"reference/#crnpy.crnpy.remove_incomplete_intervals","title":"remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False)","text":"
    Function that removes rows with incomplete integration intervals.
     
    Parameters:
     Name Type Description Default df DataFrame 
    Pandas Dataframe with data from stationary or roving CRNP devices.
     required timestamp_col str 
    Name of the column with timestamps in datetime format.
     required integration_time int 
    Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.
     required remove_first bool 
    Remove first row. Default is False.
     False 
    Returns:
     Type Description DataFrame Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.rover_centered_coordinates","title":"rover_centered_coordinates(x, y)","text":"
    Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.
     
    Parameters:
     Name Type Description Default x array 
    x coordinates.
     required y array 
    y coordinates.
     required 
    Returns:
     Name Type Description x_est array 
    Estimated x coordinates.
     y_est array 
    Estimated y coordinates.
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.sensing_depth","title":"sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017')","text":"
    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.
     
    Parameters:
     Name Type Description Default vwc array or Series or DataFrame 
    Estimated volumetric water content for each timestamp.
     required pressure array or Series or DataFrame 
    Atmospheric pressure in hPa for each timestamp.
     required p_ref float 
    Reference pressure in hPa.
     required bulk_density float 
    Soil bulk density.
     required Wlat float 
    Lattice water content.
     required method str 
    Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.
     'Schron_2017' dist list or array 
    List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.
     None 
    Returns:
     Type Description array or Series or 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
     
    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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.smooth_1d","title":"smooth_1d(values, window=5, order=3, method='moving_median')","text":"
    Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).
     
    Parameters:
     Name Type Description Default values DataFrame or Serie 
    Dataframe containing the values to smooth.
     required window int 
    Window size for the Savitzky-Golay filter. Default is 5.
     5 method str 
    Method to use for smoothing the data. Default is 'moving_median'. Options are 'moving_average', 'moving_median' and 'savitzky_golay'.
     'moving_median' order int 
    Order of the Savitzky-Golay filter. Default is 3.
     3 
    Returns:
     Type Description 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.
     Source code in crnpy/crnpy.py 
    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., & 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
    "},{"location":"reference/#crnpy.crnpy.spatial_average","title":"spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=False)","text":"
    Moving buffer filter to smooth georeferenced two-dimensional data.
     
    Parameters:
     Name Type Description Default x list or array 
    UTM x coordinates in meters.
     required y list or array 
    UTM y coordinates in meters.
     required z list or array 
    Values to be smoothed.
     required buffer float 
    Radial buffer distance in meters.
     100 min_neighbours int 
    Minimum number of neighbours to consider for the smoothing.
     3 method str 
    One of 'mean' or 'median'.
     'mean' rnd bool 
    Boolean to round the final result. Useful in case of z representing neutron counts.
     False 
    Returns:
     Type Description array 
    Smoothed version of z with the same dimension as z.
     Source code in crnpy/crnpy.py 
    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 <= buffer\n\n        if np.isnan(z[k]):\n            z_new_val = np.nan\n        elif len(distances[idx_within_buffer]) > 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
    "},{"location":"reference/#crnpy.crnpy.total_raw_counts","title":"total_raw_counts(counts)","text":"
    Compute the sum of uncorrected neutron counts for all detectors.
     
    Parameters:
     Name Type Description Default counts DataFrame 
    Dataframe containing only the columns with neutron counts.
     required 
    Returns:
     Type Description DataFrame 
    Dataframe with the sum of uncorrected neutron counts for all detectors.
     Source code in crnpy/crnpy.py 
    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] > 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
    "},{"location":"reference/#crnpy.crnpy.uncertainty_counts","title":"uncertainty_counts(raw_counts, metric='std', fp=1, fw=1, fi=1)","text":"
    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} $
     
    Parameters:
     Name Type Description Default raw_counts array 
    Raw neutron counts.
     required metric str 
    Either 'std' or 'cv' for standard deviation or coefficient of variation.
     'std' fp float 
    Pressure correction factor.
     1 fw float 
    Humidity correction factor.
     1 fi float 
    Incoming neutron flux correction factor.
     1 
    Returns:
     Name Type Description uncertainty float 
    Uncertainty of raw counts.
     References 
    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
     
    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.
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.crnpy.uncertainty_vwc","title":"uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808, a1=0.372, a2=0.115)","text":"
    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: $$ \\sigma_{\\theta_g}(N) = \\sigma_N \\frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \\sqrt{(N_{cor} - a_1 N_0)^4 + 8 \\sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \\sigma_N^4} $$
     
    Parameters:
     Name Type Description Default raw_counts array 
    Raw neutron counts.
     required N0 float 
    Calibration parameter N0.
     required bulk_density float 
    Bulk density in g cm-3.
     required fp float 
    Pressure correction factor.
     1 fw float 
    Humidity correction factor.
     1 fi float 
    Incoming neutron flux correction factor.
     1 
    Returns:
     Name Type Description sigma_VWC float 
    Uncertainty in terms of volumetric water content.
     References 
    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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.abs_humidity","title":"abs_humidity(relative_humidity, temp)","text":"
    Compute the actual vapor pressure (e) in g m^-3 using RH (%) and current temperature (c) observations.
     
    Parameters:
     Name Type Description Default relative_humidity float 
    relative humidity (%)
     required temp float 
    temperature (Celsius)
     required 
    Returns:
     Name Type Description float 
    actual vapor pressure (g m^-3)
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.atmospheric_depth","title":"atmospheric_depth(elevation, latitude)","text":"
    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.
     
    Parameters:
     Name Type Description Default elevation float 
    Elevation in meters above sea level.
     required latitude float 
    Geographic latitude in decimal degrees. Value in range -90 to 90
     required 
    Returns:
     Type Description 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\u2010Ray Soil and Snow Sensors Using the Global Neutron Monitor Network. Water Resources Research, 59(4), e2022WR033889.
     Source code in crnpy/crnpy.py 
    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., & 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
    "},{"location":"reference/#crnpy.data.biomass_to_bwe","title":"biomass_to_bwe(biomass_dry, biomass_fresh, fWE=0.494)","text":"
    Function to convert biomass to biomass water equivalent.
     
    Parameters:
     Name Type Description Default biomass_dry array or Series or DataFrame 
    Above ground dry biomass in kg m-2.
     required biomass_fresh array or Series or DataFrame 
    Above ground fresh biomass in kg m-2.
     required 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).
     0.494 
    Returns:
     Type Description array or Series or 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
     Source code in crnpy/crnpy.py 
    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 & 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
    "},{"location":"reference/#crnpy.data.correction_bwe","title":"correction_bwe(counts, bwe, r2_N0=0.05)","text":"
    Function to correct for biomass effects in neutron counts. following the approach described in Baatz et al., 2015.
     
    Parameters:
     Name Type Description Default counts array or Series or DataFrame 
    Array of ephithermal neutron counts.
     required bwe float 
    Biomass water equivalent kg m-2.
     required r2_N0 float 
    Ratio of neutron counts with biomass to neutron counts without biomass. Default is 0.05.
     0.05 
    Returns:
     Type Description array or Series or 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\u20132046, doi:10.1002/ 2014WR016443.
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.correction_humidity","title":"correction_humidity(abs_humidity, Aref)","text":"
    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
      •  
     
    Parameters:
     Name Type Description Default abs_humidity list or array 
    Relative humidity readings.
     required Aref float 
    Reference absolute humidity (g/m^3). The day of the instrument calibration is recommended.
     required 
    Returns:
     Type Description list 
    fw correction factor.
     References 
    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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.correction_incoming_flux","title":"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)","text":"
    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
      •  
     
    $$ f_i = \\frac{I_{ref}}{I} $$
     
    where:
     
       
    • I: incoming neutron flux
      •  
    • Iref: reference incoming neutron flux
      •  
     
    Parameters:
     Name Type Description Default incoming_neutrons list or array 
    Incoming neutron flux readings.
     required incoming_Ref float 
    Reference incoming neutron flux. Baseline incoming neutron flux.
     None 
    Returns:
     Type Description list 
    fi correction factor.
     References 
    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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.correction_pressure","title":"correction_pressure(pressure, Pref, L)","text":"
    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.
      •  
     
    Parameters:
     Name Type Description Default pressure list or array 
    Atmospheric pressure readings. Long-term average pressure is recommended.
     required Pref float 
    Reference atmospheric pressure.
     required L float 
    Atmospheric attenuation coefficient.
     required 
    Returns:
     Type Description list 
    fp pressure correction factor.
     References 
    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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.correction_road","title":"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)","text":"
    Function to correct for road effects in neutron counts. following the approach described in Schr\u00f6n et al., 2018.
     
    Parameters:
     Name Type Description Default counts array or Series or DataFrame 
    Array of ephithermal neutron counts.
     required theta_N float 
    Volumetric water content of the soil estimated from the uncorrected neutron counts.
     required road_width float 
    Width of the road in m.
     required road_distance float 
    Distance of the road from the sensor in m. Default is 0.0.
     0.0 theta_road float 
    Volumetric water content of the road. Default is 0.12.
     0.12 p0-p9 float 
    Parameters of the correction function. Default values are from Schr\u00f6n et al., 2018.
     required 
    Returns:
     Type Description array or Series or DataFrame 
    Array of corrected neutron counts for road effects.
     References 
    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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.counts_to_vwc","title":"counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=0.115)","text":"
    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 $
     
    Parameters:
     Name Type Description Default counts array or Series or DataFrame 
    Array of corrected and filtered neutron counts.
     required N0 float 
    Device-specific neutron calibration constant.
     required Wlat float 
    Lattice water content.
     required Wsoc float 
    Soil organic carbon content.
     required bulk_density float 
    Soil bulk density.
     required a0 float 
    Parameter given in Zreda et al., 2012. Default is 0.0808.
     0.0808 a1 float 
    Parameter given in Zreda et al., 2012. Default is 0.372.
     0.372 a2 float 
    Parameter given in Zreda et al., 2012. Default is 0.115.
     0.115 
    Returns:
     Type Description array or Series or DataFrame 
    Volumetric water content in m3 m-3.
     References 
    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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.cutoff_rigidity","title":"cutoff_rigidity(lat, lon)","text":"
    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.
     
    Parameters:
     Name Type Description Default lat float 
    Geographic latitude in decimal degrees. Value in range -90 to 90
     required 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.
     required 
    Returns:
     Type Description 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)\n>>> print(zq)\n2.52 GV (Value from NMD is 2.40 GV)\n
     References 
    Hawdon, A., McJannet, D., & Wallace, J. (2014). Calibration and correction procedures for cosmic\u2010ray 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).
     Source code in crnpy/crnpy.py 
    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        >>> zq = cutoff_rigidity(39.68, -75.75)\n        >>> print(zq)\n        2.52 GV (Value from NMD is 2.40 GV)\n\n    References:\n        Hawdon, A., McJannet, D., & 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. & 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., & 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 < 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
    "},{"location":"reference/#crnpy.data.euclidean_distance","title":"euclidean_distance(px, py, x, y)","text":"
    Function that computes the Euclidean distance between one point in space and one or more points.
     
    Parameters:
     Name Type Description Default px float 
    x projected coordinate of the point.
     required py float 
    y projected coordinate of the point.
     required x (list, ndarray, series) 
    vector of x projected coordinates.
     required y (list, ndarray, series) 
    vector of y projected coordinates.
     required 
    Returns:
     Type Description ndarray 
    Numpy array of distances from the point (px,py) to all the points in x and y vectors.
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.exp_filter","title":"exp_filter(sm, T=1)","text":"
    Exponential filter to estimate soil moisture in the rootzone from surface observtions.
     
    Parameters:
     Name Type Description Default sm list or array 
    Soil moisture in mm of water for the top layer of the soil profile.
     required T float 
    Characteristic time length in the same units as the measurement interval.
     1 
    Returns:
     Name Type Description sm_subsurface list or array 
    Subsurface soil moisture in the same units as the input.
     References 
    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.
     
    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.
     Source code in crnpy/crnpy.py 
    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]) & ~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
    "},{"location":"reference/#crnpy.data.fill_missing_timestamps","title":"fill_missing_timestamps(df, timestamp_col='timestamp', freq='H', round_timestamp=True, verbose=False)","text":"
    Helper function to fill rows with missing timestamps in datetime record. Rows are filled with NaN values.
     
    Parameters:
     Name Type Description Default df DataFrame 
    Pandas DataFrame.
     required timestamp_col str 
    Column with the timestamp. Must be in datetime format. Default column name is 'timestamp'.
     'timestamp' freq str 
    Timestamp frequency. 'H' for hourly, 'M' for minute, or None. Can also use '3H' for a 3 hour frequency. Default is 'H'.
     'H' round_timestamp bool 
    Whether to round timestamps to the nearest frequency. Default is True.
     True verbose bool 
    Prints the missing timestamps added to the DatFrame.
     False 
    Returns:
     Type Description DataFrame 
    DataFrame with filled missing timestamps.
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.find_neutron_monitor","title":"find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False)","text":"
    Search for potential reference neutron monitoring stations based on cutoff rigidity.
     
    Parameters:
     Name Type Description Default Rc float 
    Cutoff rigidity in GV. Values in range 1.0 to 3.0 GV.
     required start_date datetime 
    Start date for the period of interest.
     None end_date datetime 
    End date for the period of interest.
     None verbose bool 
    If True, print a expanded output of the incoming neutron flux data.
     False 
    Returns:
     Type Description list 
    List of top five stations with closes cutoff rigidity. User needs to select station according to site altitude.
     
    Examples:
     
    >>> from crnpy import crnpy\n>>> Rc = 2.40 # 2.40 Newark, NJ, US\n>>> 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
     
    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
     Source code in crnpy/crnpy.py 
    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        >>> from crnpy import crnpy\n        >>> Rc = 2.40 # 2.40 Newark, NJ, US\n        >>> 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
    "},{"location":"reference/#crnpy.data.get_incoming_neutron_flux","title":"get_incoming_neutron_flux(start_date, end_date, station, utc_offset=0, expand_window=0, verbose=False)","text":"
    Function to retrieve neutron flux from the Neutron Monitor Database.
     
    Parameters:
     Name Type Description Default start_date datetime 
    Start date of the time series.
     required end_date datetime 
    End date of the time series.
     required station str 
    Neutron Monitor station to retrieve data from.
     required utc_offset int 
    UTC offset in hours. Default is 0.
     0 expand_window int 
    Number of hours to expand the time window to retrieve extra data. Default is 0.
     0 verbose bool 
    Print information about the request. Default is False.
     False 
    Returns:
     Type Description DataFrame 
    Neutron flux in counts per hour and timestamps.
     References 
    Documentation available:https://www.nmdb.eu/nest/help.php#howto
     Source code in crnpy/crnpy.py 
    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&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'\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 + '&'.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</code></pre><br>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
    "},{"location":"reference/#crnpy.data.get_reference_neutron_flux","title":"get_reference_neutron_flux(station, date=pd.to_datetime('2011-05-01'))","text":"
    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).
     
    Parameters:
     Name Type Description Default station str 
    Neutron Monitor station to retrieve data from.
     required date datetime 
    Date of the reference neutron flux. Default is 2011-05-01.
     to_datetime('2011-05-01') 
    Returns:
     Type Description 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\u2010ray neutron soil moisture probes located across Australia. Water Resources Research, 50(6), 5029-5043.
     
    Bogena, H. R., Schr\u00f6n, 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.
     Source code in crnpy/crnpy.py 
    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., & 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., & 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., ... & 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
    "},{"location":"reference/#crnpy.data.idw","title":"idw(x, y, z, X_pred, Y_pred, neighborhood=1000, p=1)","text":"
    Function to interpolate data using inverse distance weight.
     
    Parameters:
     Name Type Description Default x list or array 
    UTM x coordinates in meters.
     required y list or array 
    UTM y coordinates in meters.
     required z list or array 
    Values to be interpolated.
     required X_pred list or array 
    UTM x coordinates where z values need to be predicted.
     required Y_pred list or array 
    UTM y coordinates where z values need to be predicted.
     required neighborhood float 
    Only points within this radius in meters are considered for the interpolation.
     1000 p int 
    Exponent of the inverse distance weight formula. Typically, p=1 or p=2.
     1 
    Returns:
     Type Description array 
    Interpolated values.
     References 
    https://en.wikipedia.org/wiki/Inverse_distance_weighting
     Source code in crnpy/crnpy.py 
    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 < 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
    "},{"location":"reference/#crnpy.data.interpolate_2d","title":"interpolate_2d(x, y, z, dx=100, dy=100, method='cubic', neighborhood=1000)","text":"
    Function for interpolating irregular spatial data into a regular grid.
     
    Parameters:
     Name Type Description Default x list or array 
    UTM x coordinates in meters.
     required y list or array 
    UTM y coordinates in meters.
     required z list or array 
    Values to be interpolated.
     required dx float 
    Pixel width in meters.
     100 dy float 
    Pixel height in meters.
     100 method str 
    Interpolation method. One of 'cubic', 'linear', 'nearest', or 'idw'.
     'cubic' neighborhood float 
    Only points within this radius in meters are considered for the interpolation.
     1000 
    Returns:
     Name Type Description 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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.interpolate_incoming_flux","title":"interpolate_incoming_flux(nmdb_timestamps, nmdb_counts, crnp_timestamps)","text":"
    Function to interpolate incoming neutron flux to match the timestamps of the observations.
     
    Parameters:
     Name Type Description Default nmdb_timestamps Series 
    Series of timestamps in datetime format from the NMDB.
     required nmdb_counts Series 
    Series of neutron counts from the NMDB
     required crnp_timestamps Series 
    Series of timestamps in datetime format from the station or device.
     required 
    Returns:
     Type Description Series 
    Series containing interpolated incoming neutron flux. Length of Series is the same as crnp_timestamps
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.is_outlier","title":"is_outlier(x, method, window=11, min_val=None, max_val=None)","text":"
    Function that tests whether values are outliers using a modified moving z-score based on the median absolute difference.
     
    Parameters:
     Name Type Description Default x DataFrame or Series 
    Variable containing only the columns with neutron counts.
     required method str 
    Outlier detection method. One of: range, iqr, moviqr, zscore, movzscore, modified_zscore, and scaled_mad
     required window int 
    Window size for the moving central tendency. Default is 11.
     11 min_val int or float 
    Minimum value for a reading to be considered valid. Default is None.
     None max_val(int or float 
    Maximum value for a reading to be considered valid. Default is None.
     required 
    Returns:
     Type Description DataFrame 
    Boolean indicating outliers.
     References 
    Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.
     Source code in crnpy/crnpy.py 
    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 < min_val) | (x > 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 < low_fence) | (x > 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 < lb) | (x > ub)\n\n    elif method == 'zscore':\n        zscore = (x - x.mean()) / x.std()\n        idx_outliers = (zscore < -3) | (zscore > 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 < -3) | (movzscore > 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 < -3.5) | (modified_z_score > 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 > (median + 3 * mad)\n\n    else:\n        raise TypeError('Outlier detection method not found.')\n\n    return idx_outliers | idx_range_outliers\n
    "},{"location":"reference/#crnpy.data.latlon_to_utm","title":"latlon_to_utm(lat, lon, utm_zone_number=None, utm_zone_letter=None)","text":"
    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 on an equirectangular world map with irregular zones in red and New York City's zone highlighted. See UTM zones for a full description.
     
    Parameters:
     Name Type Description Default lat (float, array) 
    Latitude in decimal degrees.
     required lon (float, array) 
    Longitude in decimal degrees.
     required utm_zone_number int 
    UTM zone number. If None, the zone number is automatically calculated.
     None utm_zone_letter str 
    UTM zone letter. If None, the zone letter is automatically calculated.
     None 
    Returns:
     Type Description (float, float) 
    Tuple of easting and northing coordinates in meters. First element is easting, second is northing.
     References 
    Code adapted from utm module created by Tobias Bieniek (Github username: Turbo87) https://github.com/Turbo87/utm
     
    https://www.maptools.com/tutorials/grid_zone_details#
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.lattice_water","title":"lattice_water(clay_content, total_carbon=None)","text":"
    Estimate the amount of water in the lattice of clay minerals.
     $\\omega_{lat} = 0.097 * clay(\\%)$ $\\omega_{lat} = -0.028 + 0.077 * clay(\\%) + 0.459 * carbon(\\%)$ 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. 
    Parameters:
     Name Type Description Default clay_content float 
    Clay content in the soil in percent.
     required total_carbon float 
    Total carbon content in the soil in percent. If None, the amount of water is estimated based on clay content only.
     None 
    Returns:
     Type Description float 
    Amount of water in the lattice of clay minerals in percent
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.location_factor","title":"location_factor(site_atmospheric_depth, site_Rc, reference_atmospheric_depth, reference_Rc)","text":"
    Function to estimate the location factor between two sites according to McJannet and Desilets, 2023.
     
    Parameters:
     Name Type Description Default site_atmospheric_depth float 
    Atmospheric depth at the site in g/cm2. Can be estimated using the function atmospheric_depth()
     required site_Rc float 
    Cutoff rigidity at the site in GV. Can be estimated using the function cutoff_rigidity()
     required reference_atmospheric_depth float 
    Atmospheric depth at the reference location in g/cm2.
     required reference_Rc float 
    Cutoff rigidity at the reference location in GV.
     required 
    Returns:
     Type Description float 
    Location-dependent correction factor.
     References 
    McJannet, D. L., & 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.
     Source code in crnpy/crnpy.py 
    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., & 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
    "},{"location":"reference/#crnpy.data.nrad_weight","title":"nrad_weight(h, theta, distances, depth, rhob=1.4, method=None, p=None, Hveg=None)","text":"
    Function to compute distance weights corresponding to each soil sample.
     
    Parameters:
     Name Type Description Default h array or Series 
    Air Humidity  from 0.1  to 50 in g/m^3. When h=0, the function will skip the distance weighting.
     required theta array or Series 
    Soil Moisture for each sample (0.02 - 0.50 m^3/m^3)
     required distances array or Series 
    Distances from the location of each sample to the origin (0.5 - 600 m)
     required depth array or Series 
    Depths for each sample (m)
     required rhob array or Series 
    Bulk density in g/cm^3
     1.4 p array or Series 
    Atmospheric pressure in hPa. Required for the 'Schron_2017' method.
     None Hveg array or Series 
    Vegetation height in m. Required for the 'Schron_2017' method.
     None method str 
    Method to compute the distance weights. Options are 'Kohli_2015' or 'Schron_2017'.
     None 
    Returns:
     Type Description array or Series or DataFrame 
    Distance weights for each sample.
     References 
    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
     
    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.
     Source code in crnpy/crnpy.py 
    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 >= 0.1) and (h <= 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] <= 50) and (distances[i] > 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] > 50) and (distances[i] < 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 <= 1)\n        r1_idx = (r > 1) & (r <= 50)\n        r2_idx = (r > 50) & (r < 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
    "},{"location":"reference/#crnpy.data.remove_incomplete_intervals","title":"remove_incomplete_intervals(df, timestamp_col, integration_time, remove_first=False)","text":"
    Function that removes rows with incomplete integration intervals.
     
    Parameters:
     Name Type Description Default df DataFrame 
    Pandas Dataframe with data from stationary or roving CRNP devices.
     required timestamp_col str 
    Name of the column with timestamps in datetime format.
     required integration_time int 
    Duration of the neutron counting interval in seconds. Typical values are 60 seconds and 3600 seconds.
     required remove_first bool 
    Remove first row. Default is False.
     False 
    Returns:
     Type Description DataFrame Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.rover_centered_coordinates","title":"rover_centered_coordinates(x, y)","text":"
    Function to estimate the intermediate locations between two points, assuming the measurements were taken at a constant speed.
     
    Parameters:
     Name Type Description Default x array 
    x coordinates.
     required y array 
    y coordinates.
     required 
    Returns:
     Name Type Description x_est array 
    Estimated x coordinates.
     y_est array 
    Estimated y coordinates.
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.sensing_depth","title":"sensing_depth(vwc, pressure, p_ref, bulk_density, Wlat, dist=None, method='Schron_2017')","text":"
    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.
     
    Parameters:
     Name Type Description Default vwc array or Series or DataFrame 
    Estimated volumetric water content for each timestamp.
     required pressure array or Series or DataFrame 
    Atmospheric pressure in hPa for each timestamp.
     required p_ref float 
    Reference pressure in hPa.
     required bulk_density float 
    Soil bulk density.
     required Wlat float 
    Lattice water content.
     required method str 
    Method to compute the sensing depth. Options are 'Schron_2017' or 'Franz_2012'.
     'Schron_2017' dist list or array 
    List of radial distances at which to estimate the sensing depth. Only used for the 'Schron_2017' method.
     None 
    Returns:
     Type Description array or Series or 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
     
    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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.smooth_1d","title":"smooth_1d(values, window=5, order=3, method='moving_median')","text":"
    Use a Savitzky-Golay filter to smooth the signal of corrected neutron counts or another one-dimensional array (e.g. computed volumetric water content).
     
    Parameters:
     Name Type Description Default values DataFrame or Serie 
    Dataframe containing the values to smooth.
     required window int 
    Window size for the Savitzky-Golay filter. Default is 5.
     5 method str 
    Method to use for smoothing the data. Default is 'moving_median'. Options are 'moving_average', 'moving_median' and 'savitzky_golay'.
     'moving_median' order int 
    Order of the Savitzky-Golay filter. Default is 3.
     3 
    Returns:
     Type Description 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.
     Source code in crnpy/crnpy.py 
    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., & 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
    "},{"location":"reference/#crnpy.data.spatial_average","title":"spatial_average(x, y, z, buffer=100, min_neighbours=3, method='mean', rnd=False)","text":"
    Moving buffer filter to smooth georeferenced two-dimensional data.
     
    Parameters:
     Name Type Description Default x list or array 
    UTM x coordinates in meters.
     required y list or array 
    UTM y coordinates in meters.
     required z list or array 
    Values to be smoothed.
     required buffer float 
    Radial buffer distance in meters.
     100 min_neighbours int 
    Minimum number of neighbours to consider for the smoothing.
     3 method str 
    One of 'mean' or 'median'.
     'mean' rnd bool 
    Boolean to round the final result. Useful in case of z representing neutron counts.
     False 
    Returns:
     Type Description array 
    Smoothed version of z with the same dimension as z.
     Source code in crnpy/crnpy.py 
    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 <= buffer\n\n        if np.isnan(z[k]):\n            z_new_val = np.nan\n        elif len(distances[idx_within_buffer]) > 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
    "},{"location":"reference/#crnpy.data.total_raw_counts","title":"total_raw_counts(counts)","text":"
    Compute the sum of uncorrected neutron counts for all detectors.
     
    Parameters:
     Name Type Description Default counts DataFrame 
    Dataframe containing only the columns with neutron counts.
     required 
    Returns:
     Type Description DataFrame 
    Dataframe with the sum of uncorrected neutron counts for all detectors.
     Source code in crnpy/crnpy.py 
    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] > 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
    "},{"location":"reference/#crnpy.data.uncertainty_counts","title":"uncertainty_counts(raw_counts, metric='std', fp=1, fw=1, fi=1)","text":"
    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} $
     
    Parameters:
     Name Type Description Default raw_counts array 
    Raw neutron counts.
     required metric str 
    Either 'std' or 'cv' for standard deviation or coefficient of variation.
     'std' fp float 
    Pressure correction factor.
     1 fw float 
    Humidity correction factor.
     1 fi float 
    Incoming neutron flux correction factor.
     1 
    Returns:
     Name Type Description uncertainty float 
    Uncertainty of raw counts.
     References 
    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
     
    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.
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"reference/#crnpy.data.uncertainty_vwc","title":"uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808, a1=0.372, a2=0.115)","text":"
    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: $$ \\sigma_{\\theta_g}(N) = \\sigma_N \\frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \\sqrt{(N_{cor} - a_1 N_0)^4 + 8 \\sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \\sigma_N^4} $$
     
    Parameters:
     Name Type Description Default raw_counts array 
    Raw neutron counts.
     required N0 float 
    Calibration parameter N0.
     required bulk_density float 
    Bulk density in g cm-3.
     required fp float 
    Pressure correction factor.
     1 fw float 
    Humidity correction factor.
     1 fi float 
    Incoming neutron flux correction factor.
     1 
    Returns:
     Name Type Description sigma_VWC float 
    Uncertainty in terms of volumetric water content.
     References 
    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
     Source code in crnpy/crnpy.py 
    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
    "},{"location":"sensor_description/","title":"Sensor description","text":"
    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.
     
     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).
     
     Figure 2. Illustration showing the approximate sensing footprint of stationary detectors.
    "},{"location":"examples/calibration/calibration/","title":"Device-specific field calibration","text":"In\u00a0[1]: Copied! 
    # Importing required libraries\nimport crnpy\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.optimize import root\n
     # Importing required libraries import crnpy  import numpy as np import pandas as pd from scipy.optimize import root  In\u00a0[2]: Copied! 
    # 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
     # 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 
    3 rows \u00d7 21 columns
     In\u00a0[3]: Copied! 
    # 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
     # 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! 
    # 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
     # 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 
    3 rows \u00d7 28 columns
     In\u00a0[5]: Copied! 
    # 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'] >= deployment_date) & (df_station['timestamp'] <= calibration_end)\ndf_station = df_station[idx_period]\ndf_station.head(3)\n
     # 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) 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 
    3 rows \u00d7 28 columns
     
    This step is useful to trim large timeseries. For instance, the station data in our case extends until 2022-07-11 09:45:00, but the field calibration was conducted in 2021-10-22 16:00. 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 2021-09-22 12:00:00 until 2021-10-22 16:00. 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.
     In\u00a0[6]: Copied! 
    # 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
     # 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! 
    # 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
     # 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! 
    # 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
     # 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])  
    \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
     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! 
    # 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
     # 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! 
    # Apply correction factors\ndf_station['total_corrected_neutrons'] = df_station['total_raw_counts'] * df_station['fw'] / (df_station['fp'] * df_station['fi'])\n
     # 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! 
    # 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
     # 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! 
    # Determine the mean corrected counts during the calibration survey\nidx_cal_period = (df_station['timestamp'] >= calibration_start) & (df_station['timestamp'] <= 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
     # 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 corrected counts during calibration: {round(mean_cal_counts)} counts\")  
    Mean volumetric Water content during calibration survey: 0.263\nMean corrected counts during calibration: 1542 counts\n
     In\u00a0[13]: Copied! 
    # 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
     # 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}\")  
    The solved value for N0 is: 2644\n
    "},{"location":"examples/calibration/calibration/#device-specific-field-calibration","title":"Device-specific field calibration\u00b6","text":"
    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.
     
    $\\theta(N) =\\frac{a_0}{(\\frac{N}{N_0}) - a_1} - a_2 $ (Desilets et al., 2010).
     
    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.
     
    Figure 1. Horizontal layout and vertical layout used in this particular example calibration, it can be customized by the user depending on their needs.
     
    Download the following template  for collecting your own calibration soil samples:
    "},{"location":"examples/calibration/calibration/#read-calibration-field-survey-data","title":"Read calibration field survey data\u00b6","text":"
    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.
    "},{"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":"
    Using the function nrad_weight() the weights corresponding to each soil sample will be computed considering air-humidity, sample depth, and distance from station and bulk density.
    "},{"location":"examples/calibration/calibration/#solving-for-n_0","title":"Solving for $N_0$\u00b6","text":"
    Previous steps estimated the field volumetric water content of 0.263 and an average neutron count of 1542. Using scipy.optimize.root() $N_0$ is estimated given the observed value of $\\theta_v$ and neutron counts.
    "},{"location":"examples/calibration/calibration/#references","title":"References:\u00b6","text":"
    Desilets, D., Zreda, M., & Ferr\u00e9, T. P. (2010). Nature's neutron probe: Land surface hydrology at an elusive scale with cosmic rays. Water Resources Research, 46(11).
     
    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.
     
    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.
    "},{"location":"examples/rover/Hydroinnova_rover_example/","title":"Processing and analyzing data from a roving detector","text":"In\u00a0[1]: Copied! 
    # Import modules\nimport crnpy\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n
     # Import modules import crnpy  import numpy as np import pandas as pd import matplotlib.pyplot as plt  
    Load the data stored in the .csv file.
     In\u00a0[2]: Copied! 
    # 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
     # 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 
    3 rows \u00d7 38 columns
     
    Next, convert the latitude and longitude to UTM coordinates (x and y). A scatter plot is created to visualize the survey points.
     In\u00a0[3]: Copied! 
    # 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
     # 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()  
    The counts are normalized to counts per minute in case some observations covered a different timespan and total neutron counts are computed.
     In\u00a0[4]: Copied! 
    # 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
     # 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! 
    # 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
     # 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)  
    \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
     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! 
    # 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
     # 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! 
    # 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
     # 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! 
    # 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
     # 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! 
    # 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
     # 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! 
    # Apply correction factors\ndf['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])\n
     # Apply correction factors df['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])  
    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.
     In\u00a0[11]: Copied! 
    # 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
     # 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')  
    A gridded map of the Volumetric Water Content (VWC) is created using the interpolated x, y, and VWC values.
     In\u00a0[12]: Copied! 
    # 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
     # 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()  
    Finally, a contour map of the VWC is created and displayed.
     In\u00a0[13]: Copied! 
    # 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
     # 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":"
    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.
     
    First import the required packages.
    "},{"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":"
    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 station=\"NEWK\". The user is required to manually define this after considering the potential options suggested. See get_incoming_neutron_flux(), find_neutron_monitor() and correction_incoming_flux().
    "},{"location":"examples/rover/Hydroinnova_rover_example/#atmospheric-correction","title":"Atmospheric correction\u00b6","text":"
    NaN values in the atmospheric data are filled. The neutron counts are then corrected for atmospheric variables. See correction_humidity() and correction_pressure()
    "},{"location":"examples/stationary/example_RDT_station/","title":"Processing and analyzing data from a stationary detector","text":"In\u00a0[1]: Copied! 
    # Import required libraries\nimport crnpy\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n
     # Import required libraries import crnpy  import pandas as pd import numpy as np import matplotlib.pyplot as plt  In\u00a0[2]: Copied! 
    # 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
     # 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! 
    # 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
     # 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)  
    Removed a total of 48 rows.\nAdded a total of 60 missing timestamps.\n
     
    Utilize the total_raw_counts() function to calculate the total counts across all detectors. After calculating the total counts, identify outliers using the is_outlier() function.
     In\u00a0[4]: Copied! 
    # 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
     # 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! 
    # Compute total cunts\ndf['total_raw_counts'] = crnpy.total_raw_counts(df[cols_counts], nan_strategy='average')\n
     # Compute total cunts df['total_raw_counts'] = crnpy.total_raw_counts(df[cols_counts], nan_strategy='average')  In\u00a0[6]: Copied! 
    # 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
     # 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! 
    # 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
     # 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)  
    \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
     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! 
    # 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
     # 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! 
    # 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
     # 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! 
    # 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
     # 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! 
    # 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
     # 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! 
    # 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
     # 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]: 
    Text(0.5, 1.0, 'Correction Factors for Pressure, Humidity, and Incoming Flux')
     In\u00a0[13]: Copied! 
    # Apply correction factors\ndf['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])\n
     # Apply correction factors df['total_corrected_neutrons'] = df['total_raw_counts'] * df['fw'] / (df['fp'] * df['fi'])  In\u00a0[14]: Copied! 
    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
     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]: 
    Text(0.5, 1.0, 'Raw and Corrected Counts')
     
    Convert the smoothed neutron counts to Volumetric Water Content (VWC) using the counts_to_vwc(). 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. smooth_1d() is applied for smoothing the data using a Savitzky-Golay filter.
     In\u00a0[15]: Copied! 
    # Device N0 parameter\nN0_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.\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
     # 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.  # 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! 
    # 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
     # 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! 
    # 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
     # 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\")  
    Average sensing depth: 15.36 cm\n
     In\u00a0[18]: Copied! 
    # 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
     # 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! 
    # 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
     # 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! 
    # 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
     # 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":"
    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.
     
    The tutorial assumes that you are working within the Anaconda environment, which has all the necessary modules. Also, make sure that the CRNPy library is installed in your machine.
     
    The raw dataset to follow this example can be obtained at the .csv file.
     
    This device was installed in the vicinity of the KONA site of the National Ecological Observatory Network within the Konza Prairie Biological station (39.110596, -96.613050, 320 meters a.s.l.)
    "},{"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":"
    Find similar neutron monitors based on altitude and cutoff rigidity, which is estimated based on the geographic location using latitude and longitude values. See get_incoming_neutron_flux(), find_neutron_monitor() and correction_incoming_flux().
     
    In this particular example we selected the \"IRKT\" 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.
    "},{"location":"examples/stationary/example_RDT_station/#atmospheric-correction","title":"Atmospheric correction\u00b6","text":"
    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 correction_humidity() and correction_pressure().
    "},{"location":"examples/stationary/example_RDT_station/#sensing-depth-and-soil-water-storage","title":"Sensing depth and soil water storage\u00b6","text":"
    Estimate the sensing_depth()  at which 86 % of the neutrons probes the soil profile, and estimate the soil water storage() of the top 50 cm using and exponential filter.
    "},{"location":"examples/stationary/example_RDT_station/#uncertainty-estimation","title":"Uncertainty estimation\u00b6","text":"
    Estimate the uncertainty of the volumetric water content using the uncertainty_vwc() function. Optionally see uncertainty_counts() for estimating the uncertainty of the neutron counts.
    "}]}
\ No newline at end of file
diff --git a/site/sitemap.xml b/site/sitemap.xml
index da75983..2b39ff3 100644
--- a/site/sitemap.xml
+++ b/site/sitemap.xml
@@ -2,37 +2,37 @@
 
     
          https://soilwater.github.io/crnpy/
-         2023-07-30
+         2024-01-07
          daily
     
     
          https://soilwater.github.io/crnpy/correction_routines/
-         2023-07-30
+         2024-01-07
          daily
     
     
          https://soilwater.github.io/crnpy/reference/
-         2023-07-30
+         2024-01-07
          daily
     
     
          https://soilwater.github.io/crnpy/sensor_description/
-         2023-07-30
+         2024-01-07
          daily
     
     
          https://soilwater.github.io/crnpy/examples/calibration/calibration/
-         2023-07-30
+         2024-01-07
          daily
     
     
          https://soilwater.github.io/crnpy/examples/rover/Hydroinnova_rover_example/
-         2023-07-30
+         2024-01-07
          daily
     
     
          https://soilwater.github.io/crnpy/examples/stationary/example_RDT_station/
-         2023-07-30
+         2024-01-07
          daily
     
 
\ No newline at end of file
diff --git a/site/sitemap.xml.gz b/site/sitemap.xml.gz
index 1a6c031..0d74e3f 100644
Binary files a/site/sitemap.xml.gz and b/site/sitemap.xml.gz differ
diff --git a/src/crnpy.egg-info/PKG-INFO b/src/crnpy.egg-info/PKG-INFO
index b9fbcc5..303b4b6 100644
--- a/src/crnpy.egg-info/PKG-INFO
+++ b/src/crnpy.egg-info/PKG-INFO
@@ -1,8 +1,8 @@
 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
@@ -13,6 +13,7 @@ License-File: LICENSE
 [![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 +33,6 @@ 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 +49,12 @@ 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.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 cda31c2..5f0a405 100644
--- a/src/crnpy/crnpy.py
+++ b/src/crnpy/crnpy.py
@@ -5,16 +5,16 @@
  Created by Joaquin Peraza and Andres Patrignani.
 """
 
+import crnpy.data as data
 import io
 import numbers
-# Import modules
-import sys
-import warnings
-
-import crnpy.data as data
 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
@@ -107,12 +107,11 @@ 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.
@@ -134,7 +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.
@@ -234,7 +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.
 
@@ -242,6 +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
     """
 
@@ -277,13 +278,14 @@ 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:
         (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
@@ -291,7 +293,9 @@ def correction_humidity(abs_humidity, Aref):
     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:
@@ -307,7 +311,7 @@ def correction_incoming_flux(incoming_neutrons, incoming_Ref=None):
     - fi: incoming neutron flux correction factor
 
     $$
-    f_i = \frac{I_{ref}}{I}
+    f_i = \frac{I}{I_{ref}}
     $$
 
     where:
@@ -318,20 +322,48 @@ def correction_incoming_flux(incoming_neutrons, incoming_Ref=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:
-        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
+        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
-    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
 
@@ -354,7 +386,7 @@ 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'
 
     # Expand the time window by 1 hour to ensure an extra observation is included in the request.
@@ -364,7 +396,6 @@ 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,
@@ -423,6 +454,33 @@ 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(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).
 
@@ -440,8 +498,14 @@ 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(values, pd.Series) and not isinstance(values, 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':
@@ -569,7 +633,6 @@ def counts_to_vwc(counts, N0, Wlat, Wsoc, bulk_density, a0=0.0808, a1=0.372, a2=
 
 
 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.
@@ -599,7 +662,7 @@ 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:
@@ -608,14 +671,14 @@ 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':
         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
 
 
@@ -645,118 +708,302 @@ 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, 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:
-        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)
+        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.
+        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).
         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':
+        # 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('Input distances are not valid.')
-
+                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
+
+        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
+        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(profiles)
+            theta_P = []
+            r_stars = []
+            for i in range(len(P)):
+                profile = P[i]
+                idx = profiles == 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('Air humidity values are out of range.')
-
-    # Combined and normalized weights
-    weights = Wd * W / np.nansum(Wd * W)
-
-    return weights
+        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):
@@ -830,6 +1077,10 @@ 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.
 
@@ -851,6 +1102,92 @@ 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 = 8.31432  # 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.
@@ -859,6 +1196,7 @@ 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.
@@ -902,6 +1240,7 @@ def find_neutron_monitor(Rc, start_date=None, end_date=None, verbose=False):
                     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:
@@ -925,26 +1264,31 @@ 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)
 
-    # Interpolate nan values
-    incoming_flux = pd.Series(incoming_flux).interpolate(method='nearest', limit_direction='both')
+    # 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
 
-    # Return only the values for the selected timestamps
-    return incoming_flux
+    # 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):
@@ -970,7 +1314,7 @@ 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.
@@ -983,10 +1327,11 @@ 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.
+        (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)
@@ -994,67 +1339,18 @@ 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#)
     """
+    # 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)
 
-    # 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):
diff --git a/src/tests/test_calibration_rdt.py b/src/tests/test_calibration_rdt.py
index fa04d59..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")
@@ -31,8 +33,7 @@ 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
 
@@ -63,25 +64,32 @@ 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(),
+                                         profiles=df_soil['ID'],
+                                         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
@@ -92,9 +100,28 @@ 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
new file mode 100644
index 0000000..01ee5c9
--- /dev/null
+++ b/src/tests/test_functions.py
@@ -0,0 +1,100 @@
+import crnpy
+import numpy as np
+import pandas as pd
+
+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.
+    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)
+    IDs = np.arange(0, 3500, 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 the weighted theta
+    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
+
+
+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)
+
+
+
+
+
+
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