Continuum BG Estimation

codecov.yml

@@ -17,4 +17,4 @@ coverage:
 
 
 comment:
-  hide_project_coverage: false # Show both overall and delta coverage
+  hide_project_coverage: false # Show both overall and delta coverage

cosipy/__init__.py

@@ -17,3 +17,4 @@
 from .source_injector import SourceInjector
 
 from .background_estimation import LineBackgroundEstimation
+from .background_estimation import ContinuumEstimation

cosipy/background_estimation/ContinuumEstimation.py

@@ -0,0 +1,359 @@
+# Imports:
+import os
+from astropy.coordinates import SkyCoord
+from astropy import units as u
+from cosipy.response import FullDetectorResponse, DetectorResponse
+from cosipy import BinnedData
+from mhealpy import HealpixMap, HealpixBase
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.stats import norm
+import numpy.ma as ma
+from tqdm import tqdm
+import logging
+logger = logging.getLogger(__name__)
+
+class ContinuumEstimation:
+
+    def calc_psr(self, sc_orientation, detector_response, coord, nside=16):
+
+        """Calculates point source response (PSR) in Galactic coordinates.
+
+        Parameters
+        ----------
+        ori_file : str
+            Full path to orienation file.
+        sc_orientation : cosipy.spacecraftfile.SpacecraftFile
+            Spacecraft orientation object.  
+        detector_response : str
+            Full path to detector response file.
+        coord : astropy.coordinates.SkyCoord
+            The coordinates of the target object. 
+        nside : int, optional
+            nside of scatt map (default is 16). 
+
+        Returns
+        -------
+        :py:class:`PointSourceResponse` 
+        """
+
+        # Detector response:
+        dr = detector_response
+
+        # Scatt map:
+        scatt_map = sc_orientation.get_scatt_map(coord, nside = nside, coordsys = 'galactic')
+
+        # Calculate PSR:
+        with FullDetectorResponse.open(dr) as response:
+            psr = response.get_point_source_response(coord = coord, scatt_map = scatt_map)
+
+        return psr
+
+    def load_psr_from_file(self, psr_file):
+
+        """Loads point source response from h5 file.
+
+        Parameters
+        ----------
+        psr_file : str
+            Full path to precomputed response file (.h5 file).
+        """
+
+        logger.info("...loading the pre-computed point source response ...")
+        psr = DetectorResponse.open(psr_file)
+        logger.info("--> done")
+
+        return psr
+
+    def load_full_data(self, data_file, data_yaml):
+
+        """Loads binned data to be used as a template for the background estimate.
+
+        Parameters
+        ----------
+        data_file : str
+            Full path to binned data (must be .h5 file). 
+        data_yaml : str
+            Full path to the dataIO yaml file used for binning the data. 
+
+        Notes
+        -----
+        In practice, the data file used for estimating the background 
+        should be the full dataset.  
+
+        The full data binning needs to match the PSR. 
+        """
+
+        self.full_data = BinnedData(data_yaml)
+        self.full_data.load_binned_data_from_hdf5(data_file)
+
+        return
+
+    def mask_from_cumdist(self, psichi_map, containment, make_plots=False):
+
+        """
+        Determines masked pixels from cumulative distribution of
+        the point source response.
+
+        Parameters
+        ----------
+        psichi_map : histpy:Histogram
+            Point source response projected onto psichi. This can be 
+            either a slice of Em and Phi, or the full projection. Note
+            that psichi is a HealpixMap axis in histpy. 
+        containment : float
+            The percentage (non-inclusive) of the cumulative distribution 
+            to use for the mask, i.e. all pixels that fall below this value 
+            in the cumulative distribution will be masked. 
+        make_plots : bool
+            Option to plot cumulative distribution. 
+
+        Returns
+        -------
+        sorted_indices : array
+            Indices of sorted psichi array.
+        arm_mask : array
+            Boolean array specifying pixels in the psichi map that will be masked. 
+
+        Note
+        ----
+        The cumulative distribution is an estimate of the angular
+        resolution measure (ARM), which is a measure of the PSF
+        for Compton imaging. 
+        """
+
+        # Get healpix map:
+        h = psichi_map
+        m = HealpixMap(base = HealpixBase(npix = h.nbins), data = h.contents)
+
+        # Sort data in descending order:
+        sorted_data = np.sort(m)[::-1]
+
+        # Calculte the cummulative distribution
+        cumdist = np.cumsum(sorted_data) / sum(sorted_data)
+
+        # Get indices of sorted array
+        sorted_indices = np.argsort(h.contents.value)[::-1]
+
+        # Define mask based on fraction of total exposure (i.e. counts):
+        arm_mask = cumdist >= containment
+        arm_mask = ~arm_mask
+
+        # Plot cummulative distribution and corresponding masks:
+        if make_plots == True:
+            plt.plot(cumdist)
+            plt.title("Cumulative Distribution")
+            plt.xlabel("Pixel")
+            plt.ylabel("Fraction of Counts")
+            plt.savefig("cumdist.png")
+            plt.show()
+            plt.close()
+
+        return sorted_indices, arm_mask
+
+    def simple_inpainting(self, m_data, sorted_indices, arm_mask):
+
+        """Highly simplistic method for inpainting masked region in CDS.
+
+        This method relies on the input healpix map having a ring 
+        ordering. For each masked pixel, it searches to the left (i.e.
+        lower pixel numbers) until reaching the first non-zero pixel. 
+        It then search to the right (i.e. higher pixel numbers) until
+        again finding the first non-zero pixel. The mean of the two 
+        values is used for filling in the masked pixel. 
+
+        Parameters
+        ----------
+        m_data : array-like
+            HealpixMap object, containing projection of PSR onto psichi.
+        sorted_indices : array
+            Indices of sorted psichi array.
+        arm_mask : array
+            Boolean array specifying pixels in the psichi map that will be masked. 
+
+        Returns
+        -------
+        interp_list : array
+            Values for the inpainting, corresponding to the masked pixels. 
+        """
+
+        # Get mean of masked data for edge cases (simple solution for now):
+        # CK: It would be better if this were at least the mean of an 
+        # np masked array object, but a better method is anyways needed.
+        masked_mean = np.mean(m_data)
+
+        # Get interpolation values:
+        interp_list_low = []
+        interp_list_high = []
+        for i in range(0,len(sorted_indices[arm_mask])):
+
+            this_index = sorted_indices[arm_mask][i]
+
+            # Search left:
+            k = 1
+            search_left = True
+            while search_left == True:
+
+                if this_index-k < 0:
+                    logger.info("Edge case!")
+                    interp_list_low.append(masked_mean)
+                    search_left = False
+                    break
+
+                next_value = m_data[this_index-k]
+                if next_value == 0:
+                    k += 1
+                if next_value != 0:
+                    interp_list_low.append(next_value)
+                    search_left = False
+
+            # Search right:
+            j = 1
+            search_right = True
+            while search_right == True:
+
+                if this_index+j >= self.psr.axes['PsiChi'].nbins-1:
+                    logger.info("Edge case!")
+                    interp_list_high.append(masked_mean)
+                    search_right = False
+                    break
+
+                next_value = m_data[this_index+j]
+                if next_value == 0:
+                    j += 1
+                if next_value != 0:
+                    interp_list_high.append(next_value)
+                    search_right = False
+
+        interp_list_low = np.array(interp_list_low)
+        interp_list_high = np.array(interp_list_high)
+        interp_list = (interp_list_low + interp_list_high) / 2.0 
+
+        return interp_list
+
+    def continuum_bg_estimation(self, data_file, data_yaml, psr, \
+            containment=0.4, make_plots=False,\
+            e_loop="default", s_loop="default"):
+
+        """Estimates continuum background.
+
+        Parameters
+        ----------
+        data_file : str
+            Full path to binned data (must be .h5 file). 
+        data_yaml : str
+            Full path to the dataIO yaml file used for binning the data.  
+        psr : py:class:`PointSourceResponse`
+            Point source response object. 
+        containment : float, optional
+            The percentage (non-inclusive) of the cumulative distribution 
+            to use for the mask, i.e. all pixels that fall below this value 
+            in the cumulative distribution will be masked. Default is 0.4. 
+        make_plots : bool, optional
+            Option to make some plots of the data, response, and masks. 
+            Default is False.
+        e_loop : tuple, optional
+            Option to pass tuple specifying which energy range to 
+            loop over. This must coincide with the energy bins. The default
+            is all bins.
+        s_loop : tuple, optional
+            Option to pass tuple specifying which Phi anlge range to
+            loop over. This must coincide with the Phi  bins. The default
+            is all bins.
+
+        Returns
+        -------
+        estimated_bg : histpy:Histogram
+            Estimated background as histpy object. 
+        """
+
+        # Define psr attribute:
+        self.psr = psr
+
+        # Load data to be used for BG estimation:
+        self.load_full_data(data_file,data_yaml)
+        estimated_bg = self.full_data.binned_data.project('Em', 'Phi', 'PsiChi')
+
+        # Defaults for energy and scattering angle loops:
+        if e_loop == "default":
+            e_loop = (0,len(self.psr.axes['Em'].centers))
+        if s_loop == "default":
+            s_loop = (0,len(self.psr.axes['Phi'].centers))
+
+        # Progress bar:
+        e_tot = e_loop[1] - e_loop[0]
+        s_tot = s_loop[1] - s_loop[0]
+        num_lines = e_tot*s_tot
+        pbar = tqdm(total=num_lines)
+
+        # Loop through all bins of energy and phi:
+        for E in range(e_loop[0],e_loop[1]):
+            for s in range(s_loop[0],s_loop[1]):
+
+                pbar.update(1) # update progress bar
+                logger.info("Bin %s %s" %(str(E),str(s)))
+
+                # Get PSR slice:
+                h = self.psr.slice[{'Em':E, 'Phi':s}].project('PsiChi')
+
+                # Get mask:
+                sorted_indices, arm_mask = self.mask_from_cumdist(h, containment, make_plots=make_plots)       
+
+                # Mask data:
+                h_data = self.full_data.binned_data.project('Em', 'Phi', 'PsiChi').slice[{'Em':E, 'Phi':s}].project('PsiChi')
+                m_data = HealpixMap(base = HealpixBase(npix = h_data.nbins), data = h_data.contents.todense())
+                m_data[sorted_indices[arm_mask]] = 0
+
+                # Skip this iteration if map is all zeros:
+                if len(m_data[m_data[:] > 0]) == 0:
+                    logger.info("All zeros and so skipping iteration!")
+                    continue
+
+                # Get interpolated values:
+                interp_list = self.simple_inpainting(m_data, sorted_indices, arm_mask)
+
+                # Update estimated BG:
+                for p in range(len(sorted_indices[arm_mask])):
+                    estimated_bg[E,s,sorted_indices[arm_mask][p]] = interp_list[p]
+
+                # Option to make some plots:
+                if make_plots == True:
+
+                    # Plot true response:
+                    m_dummy = HealpixMap(base = HealpixBase(npix = h.nbins), data = h.contents)
+                    plot,ax = m_dummy.plot('mollview')
+                    plt.title("True Response")
+                    plt.show()
+                    plt.close()
+
+                    # Plot masked response:
+                    m_dummy[sorted_indices[arm_mask]] = 0
+                    plot,ax = m_dummy.plot('mollview')
+                    plt.title("Masked Response")
+                    plt.show()
+                    plt.close()
+
+                    # Plot true data:
+                    m_data_dummy = HealpixMap(base = HealpixBase(npix = h_data.nbins), data = h_data.contents.todense())
+                    plot,ax = m_data_dummy.plot('mollview')
+                    plt.title("True Data")
+                    plt.show()
+                    plt.close()
+
+                    # Plot masked data:
+                    plot,ax = m_data.plot('mollview')
+                    plt.title("Masked Data")
+                    plt.show()
+                    plt.close()
+
+                    # Plot masked data with interpolated values:
+                    m_data[sorted_indices[arm_mask]] = interp_list
+                    plot,ax = m_data.plot('mollview')
+                    plt.title("Interpolated Data (Estimated BG)")
+                    plt.show()
+                    plt.close()
+
+        # Close progress bar:
+        pbar.close()
+
+        return estimated_bg

cosipy/background_estimation/__init__.py

@@ -1 +1,2 @@
 from .LineBackgroundEstimation import LineBackgroundEstimation
+from .ContinuumEstimation import ContinuumEstimation

cosipy/test_data/inputs_crab_continuum_bg_estimation_testing.yaml

@@ -0,0 +1,17 @@
+#----------#
+# Data I/O:
+
+# data files available on the COSI Sharepoint: https://drive.google.com/drive/folders/1UdLfuLp9Fyk4dNussn1wt7WEOsTWrlQ6
+# The tra file is the first 10 seconds of the crab sim from mini-DC2,
+# and the ori file is the corresponding ori from mini-DC2.
+data_file: "GalacticScan.inc1.id1.crab2hr.extracted.testsample.tra.gz" # full path
+ori_file: "/project/majello/astrohe/ckarwin/COSI/COSIpy_Development/Continuum_BG_Estimation/Run_1/Data_Files/20280301_3_month.ori" # full path
+unbinned_output: 'fits' # 'fits' or 'hdf5'
+time_bins: 2659985.0  # time bin size in seconds. Takes int, float, or list of bin edges.
+energy_bins: [100., 1000., 10000.] # Takes list. Needs to match response.
+phi_pix_size: 45 # binning of Compton scattering anlge [deg]
+nside: 1 # healpix binning of psi chi local
+scheme: 'ring' # healpix binning of psi chi local
+tmin: 1835487300.0 # Min time cut in seconds.
+tmax: 1843467255.0 # Max time cut in seconds.
+#----------#