diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 00000000..35d7ecc8 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,31 @@ +name: docs + +on: [push, pull_request, workflow_dispatch] + +permissions: + contents: write + +jobs: + docs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v5 + with: + python-version: '3.11' + - name: Install dependencies + run: | + sudo apt install pandoc + pip install sphinx sphinx_rtd_theme nbsphinx mock + - name: Sphinx build + run: | + make -C docs html + - name: Deploy to GitHub Pages + uses: peaceiris/actions-gh-pages@v3 + if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/docs' }} + with: + publish_branch: gh-pages + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: docs/_build/html + force_orphan: true + diff --git a/README.md b/README.md index 7b53f064..ad62580b 100644 --- a/README.md +++ b/README.md @@ -1,26 +1,10 @@ # cosipy -The COSI high-level data analysis tools -## Installation from source +The cosipy library is [COSI](https://cosi.ssl.berkeley.edu)'s high-level analysis software. -Meant for developers. Currently the only option. +The main repository is hosted in https://github.com/cositools/cosipy -1. Clone the repository into your local machine +For the cosipy installation and usage instructions please refer to the main [cosipy documentation](https://cositools.github.io/cosipy/). -``` -git clone git@github.com:cositools/cosipy.git -``` -2. Move to the cosipy folder -``` -cd cosipy -``` - -3. Create a new conda environment. Optional but highly encouranged. - -4. Install it: - -``` -pip install -e . -``` diff --git a/cosipy/response/FullDetectorResponse.py b/cosipy/response/FullDetectorResponse.py index 3a6e21d0..06c7ce9a 100644 --- a/cosipy/response/FullDetectorResponse.py +++ b/cosipy/response/FullDetectorResponse.py @@ -835,7 +835,7 @@ def get_point_source_response(self, Effective time spent by the source at each pixel location in spacecraft coordinates coord : :py:class:`astropy.coordinates.SkyCoord` Source coordinate - scatt_map : :py:cass:`SpacecraftAttitudeMap` + scatt_map : :py:class:`SpacecraftAttitudeMap` Spacecraft attitude map Returns diff --git a/cosipy/spacecraftfile/SpacecraftFile.py b/cosipy/spacecraftfile/SpacecraftFile.py index 4a7a7e06..b9dde9ab 100644 --- a/cosipy/spacecraftfile/SpacecraftFile.py +++ b/cosipy/spacecraftfile/SpacecraftFile.py @@ -20,13 +20,20 @@ def __init__(self, time, x_pointings = None, y_pointings = None, z_pointings = N instrument = "COSI", frame = "galactic"): """ - Time: astropy.Time; The time stamp for each pointings. Note this is NOT the time duration. - x_pointings: astropy.coordinates.SkyCoord; The pointings (galactic system) of the x axis of the local coordinate system attached to the spacecraft. - y_pointings: astropy.coordinates.SkyCoord; The pointings (galactic system) of the y axis of the local coordinate system attached to the spacecraft. - z_pointings: astropy.coordinates.SkyCoord; The pointings (galactic system) of the z axis of the local coordinate system attached to the spacecraft. - attitude: numpy.ndarray: the attitude of the spacecraft. - instrument: string; the instrument name. - frame: string; the frame on which the analysis will be based. + Time : astropy.Time + The time stamps for each pointings. Note this is NOT the time duration. + x_pointings : astropy.coordinates.SkyCoord or NoneType, optional + The pointings (galactic system) of the x axis of the local coordinate system attached to the spacecraft (the default is None, which implies no input for the x pointings). + y_pointings : astropy.coordinates.SkyCoord or NoneType, optional + The pointings (galactic system) of the y axis of the local coordinate system attached to the spacecraft (the default is None, which implies no input for the y pointings). + z_pointings : astropy.coordinates.SkyCoord or NoneType, optional + The pointings (galactic system) of the z axis of the local coordinate system attached to the spacecraft (the default is None, which implies no input for the z pointings). + attitude: numpy.ndarray or NoneType, optional + The attitude of the spacecraft (the default is None, which implies no input for the attitude of the spacecraft). + instrument : str, optional + The instrument name (the default is "COSI") + frame : str, optional + The frame on which the analysis will be based (the default is "galactic"). """ # check if the inputs are valid @@ -76,7 +83,19 @@ def __init__(self, time, x_pointings = None, y_pointings = None, z_pointings = N @classmethod def parse_from_file(cls, file): - #parses timestamps, axis positions from file and returns to __init__ + """ + Parses timestamps, axis positions from file and returns to __init__. + + Parameters + ---------- + file : str + The file path of the pointings. + + Returns + ------- + cosipy.spacecraftfile.SpacecraftFile + The SpacecraftFile object. + """ time_stamps = np.loadtxt(file, usecols = 1, delimiter = ' ', skiprows = 1, comments=("#","EN")) axis_1 = np.loadtxt(file, usecols = (3,2), delimiter = ' ', skiprows = 1, comments=("#","EN")) @@ -95,11 +114,13 @@ def get_time(self, time_array = None): Parameters ---------- - time_array: None or np.array; If None, the time array will be taken from the instance + time_array : numpy.ndarray or NoneType, optional + The time array (the default is None, which implies the time array will be taken from the instance). Returns ------- astropy.Time + The time stamps of the orientation. """ if time_array == None: @@ -112,15 +133,17 @@ def get_time(self, time_array = None): def get_time_delta(self, time_array = None): """ - Return an array of the time period between neighbouring time points + Return an array of the time period between neighbouring time points. Parameters ---------- - time_array: None or np.array; If None, the time array will be taken from the instance + time_array : numpy.ndarray or NoneType, optional + The time delta array (the default is None, which implies the time array will be taken from the instance). Returns ------- - astropy.Time + time_delta : astropy.Time + The time difference between the neighbouring time stamps. """ if time_array == None: @@ -135,7 +158,7 @@ def get_time_delta(self, time_array = None): def interpolate_direction(self, trigger, idx, direction): """ - Linearly interpolates position at a given time between two timestamps + Linearly interpolates position at a given time between two timestamps. Parameters ---------- @@ -230,14 +253,17 @@ def get_attitude(self, x_pointings = None, y_pointings = None, z_pointings = Non Parameters ---------- - x_pointings : SkyCoord object; the pointings of the x axis of the spacecraft. - y_pointings : SkyCoord object; the pointings of the y axis of the spacecraft. - z_pointings : SkyCoord object; the pointings of the z axis of the spacecraft. - frame : :py:class:`astropy.coordinates.BaseCoordinateFrame`; Inertial reference frame + x_pointings : astropy.coordinates.SkyCoord or NoneType, optional + The pointings (galactic system) of the x axis of the local coordinate system attached to the spacecraft (the default is None, which implies that the x pointings will be taken from the instance). + y_pointings : astropy.coordinates.SkyCoord or NoneType, optional + The pointings (galactic system) of the y axis of the local coordinate system attached to the spacecraft (the default is None, which implies that the y pointings will be taken from the instance). + z_pointings : astropy.coordinates.SkyCoord or NoneType, optional + The pointings (galactic system) of the z axis of the local coordinate system attached to the spacecraft (the default is None, which implies that the z pointings will be taken from the instance). Returns ------- cosipy.attitude + The attitude of the spacecraft. """ if self.attitude is None: # the attitude is None, we will calculate from the x, y and z pointings @@ -275,13 +301,21 @@ def get_target_in_sc_frame(self, target_name, target_coord, attitude = None, qui Parameters ---------- - target_name: str; the name of the target object - target_coord: astropy.Skycoord; the coordinates of the target object - attitude: None or cosipy.Attitude; If not, the attitude will be taken from the instance + target_name : str + The name of the target object. + target_coord : astropy.Skycoord + The coordinates of the target object. + attitude: cosipy.Attitude or NoneType, optional + The attitude of the spacecraft (the default is None, which implies the attitude will be taken from the instance). + quiet : bool, default=False + Setting `True` to stop printing the messages. + save : bool, default=False + Setting `True` to save the target coordinates in the spacecraft frame. Returns ------- - SkyCoord object + astropy.coordinates.SkyCoord + The target coordinates in the spacecraft frame. """ if attitude != None: @@ -324,13 +358,21 @@ def get_dwell_map(self, response, dts = None, dt_format = None, src_path = None, Parameters ---------- - response : str; .h5 file, the response for the observation - dts : None or numpy.ndarray; the elapsed time for each pointing. It must has the same size as the pointings - src_path : SkyCoord object; the movement of source in the detector frame. + response : str or pathlib.Path + The path to the response. + dts : None or numpy.ndarray, optional + The elapsed time for each pointing. It must has the same size as the pointings (the default is None, which implies that the `dts` will be read from the instance). + ds_format : None or str, optional + The time format for `dts`. If the `dts` is provided by the `dts` argument, you must provide the time format using this argument (the default is None, which implies that there is no input for the time format for `dts`). + src_path : astropy.coordinates.SkyCoord or NoneType, optional + The movement of source in the detector frame (the default is None, which implies that the `src_path` will be read from the instance). + save : bool, default=False + Set True to save the dwell time map. Returns ------- mhealpy.containers.healpix_map.HealpixMap + The dwell time map. """ # Define the response @@ -376,6 +418,15 @@ def get_scatt_map(self, ): """ Bin the spacecraft attitude history into a 4D histogram that contains the accumulated time the axes of the spacecraft where looking at a given direction. + + Paremeters + ---------- + nside : int + The nside of the scatt map. + scheme : str, optional + The scheme of the scatt map (the default is "ring") + coordsys : str, optional + The coordinate system used in the scatt map (the default is "galactic). """ # Get orientations @@ -402,22 +453,31 @@ def get_psr_rsp(self, response = None, dwell_map = None, dts = None): Parameters ---------- - response : .h5 file; the response for the observation - dwell_map : None or str; the time dwell map for the source, you can load saved dwell time map - using this parameterif you've saved it before - dts : numpy array or the file path; the elapsed time for each pointing. It must has the same - size as the pointings. If you have saved this array, you can load it using this parameter + response : str or pathlib.Path, optional + The response for the observation (the defaul is None, which implies that the `response` will be read from the instance). + dwell_map : None or str, optional + The time dwell map for the source, you can load saved dwell time map using this parameter if you've saved it before (the defaul is None, which implies that the `dwell_map` will be read from the instance). + dts : numpy.ndarray or str, optional + The elapsed time for each pointing. It must has the same size as the pointings. If you have saved this array, you can load it using this parameter (the defaul is None, which implies that the `dts` will be read from the instance). Returns ------- - Ei_edges : numpy array; the edges of the incident energy - Ei_lo : numpy array; the lower edges of the incident energy - Ei_hi : numpy array; the upper edges of the incident energy - Em_edges : numpy array; the edges of the measured energy - Em_lo : numpy array; the lower edges of the measured energy - Em_hi : numpy array; the upper edges of the measured energy - areas " numpy array; the effective area of each energy bin - matrix : numpy.array; the energy dispersion matrix + Ei_edges : numpy.ndarray + The edges of the incident energy. + Ei_lo : numpy.ndarray + The lower edges of the incident energy. + Ei_hi : numpy.ndarray + The upper edges of the incident energy. + Em_edges : numpy.ndarray + The edges of the measured energy. + Em_lo : numpy.ndarray + The lower edges of the measured energy. + Em_hi : numpy.ndarray + The upper edges of the measured energy. + areas : numpy.ndarray + The effective area of each energy bin. + matrix : numpy.ndarray + The energy dispersion matrix. """ if response == None: @@ -466,11 +526,12 @@ def get_psr_rsp(self, response = None, dwell_map = None, dts = None): def get_arf(self, out_name = None): """ - Converts the point source response to an arf file that can be read by XSPEC + Converts the point source response to an arf file that can be read by XSPEC. Parameters ---------- - out_name: str; the name of the arf file to save + out_name: str or NoneType + The name of the arf file to save. (the default is None, which implies that the saving name will be the target name of the instance). Returns ------- @@ -523,12 +584,13 @@ def get_arf(self, out_name = None): def get_rmf(self, out_name = None): """ - Converts the point source response to an rmf file that can be read by XSPEC + Converts the point source response to an rmf file that can be read by XSPEC. Parameters ---------- - out_name: str; the name of the rmf file to save - + out_name: str or NoneType + The name of the arf file to save. (the default is None, which implies that the saving name will be the target name of the instance). + Returns ------- None @@ -667,17 +729,24 @@ def get_pha(self, src_counts, errors, rmf_file = None, arf_file = None, bkg_file Parameters ---------- - src_counts : np.array; the counts in each energy band. If you have src_counts with unit counts/kev/s, you must - convert it to counts by multiplying it with exposure time and the energy band width - errors : np.array; the error for counts. It has the same unit requirement as src_counts - rmf_file : str; the rmf file name - arf_file : str; the arf file name - bkg_file : str; the background file name. If the src_counts is source counts only, you don't need to edit this parameter - exposure_time : number; the exposure time for this source observation - dts : numpy array or str; it's used to calculate the exposure time. It has the same effect as exposure_time. If both - exposure_time and dts are given, dts will write over the exposure_time - telescope : str; the name of the telecope. Default is COSI. - instrument : str; the name of the instrument. Default is COSI. + src_counts : numpy.ndarray + The counts in each energy band. If you have src_counts with unit counts/kev/s, you must convert it to counts by multiplying it with exposure time and the energy band width. + errors : numpy.ndarray + The error for counts. It has the same unit requirement as src_counts. + rmf_file : str or NoneType, optional + The rmf file name to be written into the pha file (the default is None, which implies that it uses the rmf file generate by function `get_rmf`) + arf_file : str or NoneType, optional + The arf file name to be written into the pha file (the default is None, which implies that it uses the arf file generate by function `get_arf`) + bkg_file : str or NoneType, optional + The background file name (the default is None, which implied the `src_counts` is source counts only). + exposure_time : NoneType or float, optional + The exposure time for this source observation (the default is None, which implied that the exposure time will be calculated by `dts`). + dts : numpy.ndarray or NoneType, optional + It's used to calculate the exposure time. It has the same effect as exposure_time. If both exposure_time and dts are given, dts will write over the exposure_time (the default is None, which implies that the `dts` will be read from the instance). + telescope : str, optional + The name of the telecope (the default is "COSI"). + instrument : str, optional + The instrument name (the default is "COSI"). Returns ------- @@ -774,9 +843,12 @@ def plot_arf(self, file_name = None, save_name = None, dpi = 300): Parameters ---------- - file_name: str; the directory if the arf fits file. - save_name: str; the name of the saved image of effective area - dpi: int; the dpi of the saved image + file_name: str or NoneType, optional + The directory if the arf fits file (the default is None, which implies the file name will be read from the instance). + save_name: str or NoneType, optional + The name of the saved image of effective area (the default is None, which implies the file name will be read from the instance). + dpi: int, optional + The dpi of the saved image (the default is 300). Returns ------- @@ -827,10 +899,13 @@ def plot_rmf(self, file_name = None, save_name = None, dpi = 300): Parameters ---------- - file_name: str; the directory if the rmf fits file. - save_name: str; the name of the saved image of effective area - dpi: int; the dpi of the saved image - + file_name: str or NoneType, optional + The directory if the arf fits file (the default is None, which implies the file name will be read from the instance). + save_name: str or NoneType, optional + The name of the saved image of effective area (the default is None, which implies the file name will be read from the instance). + dpi: int, optional + The dpi of the saved image (the default is 300). + Returns ------- None diff --git a/cosipy/threeml/COSILike.py b/cosipy/threeml/COSILike.py index e0daa5aa..3fa27e6a 100644 --- a/cosipy/threeml/COSILike.py +++ b/cosipy/threeml/COSILike.py @@ -34,36 +34,34 @@ logger = logging.getLogger(__name__) class COSILike(PluginPrototype): - + """ + COSI 3ML plugin. + + Parameters + ---------- + name : str + Plugin name e.g. "cosi". Needs to have a distinct name with respect to other plugins in the same analysis + dr : str + Path to full detector response + data : histpy.Histogram + Binned data. Note: Eventually this should be a cosipy data class + bkg : histpy.Histogram + Binned background model. Note: Eventually this should be a cosipy data class + sc_orientation : cosipy.spacecraftfile.SpacecraftFile + Contains the information of the orientation: timestamps (astropy.Time) and attitudes (scoord.Attitude) that describe + the spacecraft for the duration of the data included in the analysis + nuisance_param : astromodels.core.parameter.Parameter, optional + Background parameter + coordsys : str, optional + Coordinate system ('galactic' or 'spacecraftframe') to perform fit in, which should match coordinate system of data + and background. This only needs to be specified if the binned data and background do not have a coordinate system + attached to them + precomputed_psr_file : str, optional + Full path to precomputed point source response in Galactic coordinates + """ def __init__(self, name, dr, data, bkg, sc_orientation, nuisance_param=None, coordsys=None, precomputed_psr_file=None, **kwargs): - """ - COSI 3ML plugin - - Parameters - ---------- - name : str - Plugin name e.g. "cosi". Needs to have a distinct name with respect to other plugins in the same analysis - dr : Path - Path to full detector response - data: histpy.Histogram - Binned data. Note: Eventually this should be a cosipy data class - bkg: histpy.Histogram - Binned background model. Note: Eventually this should be a cosipy data class - sc_orientation: cosipy.spacecraftfile.SpacecraftFile - Contains the information of the orientation: timestamps (astropy.Time) and attitudes (scoord.Attitude) that describe - the spacecraft for the duration of the data included in the analysis - nuisance_param: astromodels.core.parameter.Parameter - Background parameter (optional) - coordsys: str - Coordinate system ('galactic' or 'spacecraftframe') to perform fit in, which should match coordinate system of data - and background. This only needs to be specified if the binned data and background do not have a coordinate system - attached to them - precomputed_psr_file: str - Full path to precomputed point source response in Galactic coordinates (optional) - """ - # create the hash for the nuisance parameters. We have none for now. self._nuisance_parameters = collections.OrderedDict() @@ -121,13 +119,13 @@ def __init__(self, name, dr, data, bkg, sc_orientation, print("--> done") def set_model(self, model): - """ Set the model to be used in the joint minimization. Parameters ---------- - model: astromodels.core.model.Model; any model supported by astromodels + model : astromodels.core.model.Model + Any model supported by astromodels """ # Get point sources and extended sources from model: @@ -194,7 +192,7 @@ def set_model(self, model): print("... Calculating point source responses ...") self._psr = {} - self._source_location = {} # Shoule the poition information be in the point source response? (HY) + self._source_location = {} # Should the poition information be in the point source response? (HY) for name, source in point_sources.items(): coord = source.position.sky_coord @@ -240,20 +238,19 @@ def set_model(self, model): # Convolve with spectrum # See also the Detector Response and Source Injector tutorials spectrum = source.spectrum.main.shape - - total_expectation = self._psr[name].get_expectation(spectrum).project(['Em', 'Phi', 'PsiChi']) - # should it be like self._psr[name].get_expectation(spectrum) (without 'project')? (HY) + + total_expectation = self._psr[name].get_expectation(spectrum) + + # Save expected counts for source: + self._expected_counts[name] = copy.deepcopy(total_expectation) # Need to check if self._signal type is dense (i.e. 'Quantity') or sparse (i.e. 'COO'). if type(total_expectation.contents) == u.quantity.Quantity: - total_expectation = total_expectation.contents.value + total_expectation = total_expectation.project(['Em', 'Phi', 'PsiChi']).contents.value elif type(total_expectation.contents) == COO: - total_expectation = total_expectation.contents.todense() + total_expectation = total_expectation.project(['Em', 'Phi', 'PsiChi']).contents.todense() else: raise RuntimeError("Expectation is an unknown object") - - # Save expected counts for source: - self._expected_counts[name] = copy.deepcopy(total_expectation) # Add source to signal and update source counter: if self.src_counter == 0: @@ -266,13 +263,13 @@ def set_model(self, model): self._model = model def get_log_like(self): - """ - Return the value of the log-likelihood. + Calculate the log-likelihood. Returns ---------- - log_like: float + log_like : float + Value of the log-likelihood """ # Recompute the expectation if any parameter in the model changed @@ -309,25 +306,25 @@ def get_log_like(self): return log_like def inner_fit(self): - """ - This fits nuisance parameters. + Required for 3ML fit. """ return self.get_log_like() def _get_dwell_time_map(self, coord): - """ - Get the dwell time map of the source in the spacecraft frame. + Get the dwell time map of the source in the inertial (spacecraft) frame. Parameters ---------- - coord: astropy.coordinates.SkyCoord; the coordinate of the target source. + coord : astropy.coordinates.SkyCoord + Coordinates of the target source Returns ------- - dwell_time_map: mhealpy.containers.healpix_map.HealpixMap + dwell_time_map : mhealpy.containers.healpix_map.HealpixMap + Dwell time map """ self._sc_orientation.get_target_in_sc_frame(target_name = self._name, target_coord = coord) @@ -336,18 +333,12 @@ def _get_dwell_time_map(self, coord): return dwell_time_map def _get_scatt_map(self): - """ Get the spacecraft attitude map of the source in the inertial (spacecraft) frame. - Parameters - ---------- - nside: int; resolution of scatt map - coordsys: BaseFrameRepresentation or str; coordinate system of map - Returns ------- - scatt_map: cosipy.spacecraftfile.scatt_map.SpacecraftAttitudeMap + scatt_map : cosipy.spacecraftfile.scatt_map.SpacecraftAttitudeMap """ scatt_map = self._sc_orientation.get_scatt_map(nside = self._dr.nside * 2, coordsys = 'galactic') @@ -355,13 +346,13 @@ def _get_scatt_map(self): return scatt_map def set_inner_minimization(self, flag: bool): - """ - Turn on the minimization of the internal COSI parameters. + Turn on the minimization of the internal COSI (nuisance) parameters. Parameters ---------- - flag: bool; turns on and off the minimization of the internal parameters + flag : bool + Turns on and off the minimization of the internal parameters """ self._fit_nuisance_params: bool = bool(flag) diff --git a/cosipy/ts_map/fast_norm_fit.py b/cosipy/ts_map/fast_norm_fit.py index 64011056..78ca9e91 100644 --- a/cosipy/ts_map/fast_norm_fit.py +++ b/cosipy/ts_map/fast_norm_fit.py @@ -240,4 +240,4 @@ def solve(self, data, bkg, unit_excess): #Assumed to be a numerical error ts = 0 - return (ts, norm, norm_err, failed) + return (ts, norm, norm_err, failed, iteration) diff --git a/cosipy/ts_map/fast_ts_fit.py b/cosipy/ts_map/fast_ts_fit.py index b9536fce..c1dcd6fe 100644 --- a/cosipy/ts_map/fast_ts_fit.py +++ b/cosipy/ts_map/fast_ts_fit.py @@ -1,5 +1,8 @@ -from histpy import Histogram +from histpy import Histogram, Axis, Axes +import h5py as h5 +import sys from cosipy import SpacecraftFile +from cosipy.response import PointSourceResponse import healpy as hp from mhealpy import HealpixMap import numpy as np @@ -10,21 +13,29 @@ from pathlib import Path from cosipy.response import FullDetectorResponse import time +import scipy.stats class FastTSMap(): - def __init__(self, data, bkg_model, orientation, response_path, frame = "local", scheme = "RING"): + def __init__(self, data, bkg_model, response_path, orientation = None, cds_frame = "local", scheme = "RING"): """ - Initialize the instance + Initialize the instance if a TS map fit. Parameters ---------- - data: histpy.Histogram; observed data, whichincludes counts from both signal and background - bkg_model: histpy.Histogram; background model, which includes the background counts to model the background in observed data - orientation: cosipy.SpacecraftFile; the orientation of the spacecraft when data are collected - response_path: pathlib.Path; the path to the response file - frame: str; "local" or "galactic", it's the frame of the data, bkg_model and the response + data : histpy.Histogram + Observed data, which includes counts from both signal and background. + bkg_model : histpy.Histogram + Background model, which includes the background counts to model the background in the observed data. + response_path : str or pathlib.Path + The path to the response file. + orientation : cosipy.SpacecraftFile or NoneType, optional + The orientation of the spacecraft when data are collected (the default is None, which implies the orientation file is not needed). + cds_frame : str, optional + "local" or "galactic", it's the Compton data space (CDS) frame of the data, bkg_model and the response. In other words, they should have the same cds frame (the default is "local", which implied that a local frame that attached to the spacecraft). + scheme : str, optional + The scheme of the CDS of data (the default is "RING", which implies a "RING" scheme of the data). Returns ------- @@ -32,27 +43,29 @@ def __init__(self, data, bkg_model, orientation, response_path, frame = "local", self._data = data.project(["Em", "PsiChi", "Phi"]) self._bkg_model = bkg_model.project(["Em", "PsiChi", "Phi"]) - if not isinstance(orientation, SpacecraftFile): - raise TypeError("The orientation must be a cosipy.SpacecraftFile object!") self._orientation = orientation self._response_path = Path(response_path) - self._frame = frame + self._cds_frame = cds_frame self._scheme = scheme @staticmethod def slice_energy_channel(hist, channel_start, channel_stop): """ - Slice one or more bins along first axis + Slice one or more bins along first axis of the `hist`. Parameters ---------- - hist: histpy.Histogram; the hist to be sliced - channel_start: int; the start of the slice (inclusive) - channel_stop: int; the stop of the slice (exclusive) + hist : histpy.Histogram + The histogram object to be sliced. + channel_start : int + The start of the slice (inclusive) + channel_stop : int + The stop of the slice (exclusive) Returns ------- - sliced_hist: histpy.Histogram: the sliced hist + sliced_hist : histpy.Histogram + The sliced histogram. """ sliced_hist = hist.slice[channel_start:channel_stop,:] @@ -63,15 +76,21 @@ def slice_energy_channel(hist, channel_start, channel_stop): def get_hypothesis_coords(nside, scheme = "RING", coordsys = "galactic"): """ - Get a list of hypothesis coordinates + Get a list of hypothesis coordinates. Parameters ---------- - nside: int; the nside of the map + nside : int + The nside of the map. + scheme : str, optional + The scheme of the map where the hypothesis coordinates are generated (the default is "RING", which implies the "RING" scheme is used to get the hypothesis coordinates). + coordsys : str, optional + The coordinate system used in the map where the hypothesis coordinates are generated (the default is "galactic", which implies the galactic coordinates system is used). Returns ------- - hypothesis_coords: list; the list of the hypothesis coordinates at the center of each pixel + hypothesis_coords : list + The list of the hypothesis coordinates at the center of each pixel """ data_array = np.zeros(hp.nside2npix(nside)) @@ -88,16 +107,19 @@ def get_hypothesis_coords(nside, scheme = "RING", coordsys = "galactic"): def get_cds_array(hist, energy_channel): """ - Get the flattened cds array from data. + Get the flattened cds array from input Histogram. Parameters ----------- - hist: histpy.Histogram; input data - energy_channel: list; [lower_channel, upper_chanel] - + hist : histpy.Histogram + The input Histogram. + energy_channel : list + The format is `[lower_channel, upper_chanel]`. The lower_channel is inclusive while the upper_channel is exclusive. + Returns ------- - cds_array + cds_array : numpy.ndarray + The flattended Compton data space (CDS) array. """ if not isinstance(hist, Histogram): @@ -114,73 +136,244 @@ def get_cds_array(hist, energy_channel): cds_array = np.array(hist_cds.to_dense()[:]).flatten() # here [:] is equivalent to [:, :] return cds_array + + @staticmethod + def get_psr_in_galactic(hypothesis_coord, response_path, spectrum): + + """ + Get the point source response (psr) in galactic. Please be aware that you must use a galactic response! + To do: to make the weight parameter not hardcoded + + Parameters + ---------- + hypothesis_coord : astropy.coordinates.SkyCoord + The hypothesis coordinate. + response_path : str or path.lib.Path + The path to the response. + spectrum : astromodel spectrum + The spectrum of the source to be placed at the hypothesis coordinate. + + Returns + ------- + psr : histpy.Histogram + The point source response of the spectrum at the hypothesis coordinate. + """ + # Open the response + # Notes from Israel: Inside it contains a single histogram with all the regular axes for a Compton Data Space (CDS) analysis, in galactic coordinates. Since there is no class yet to handle it, this is how to read in the HDF5 manually. + with h5.File(response_path) as f: + + axes_group = f['hist/axes'] + axes = [] + for axis in axes_group.values(): + # Get class. Backwards compatible with version + # with only Axis + axis_cls = Axis + if '__class__' in axis.attrs: + class_module, class_name = axis.attrs['__class__'] + axis_cls = getattr(sys.modules[class_module], class_name) + axes += [axis_cls._open(axis)] + axes = Axes(axes) + + # get the pixel number of the hypothesis coordinate + map_temp = HealpixMap(base = axes[0]) + hypothesis_coord_pix_number = map_temp.ang2pix(hypothesis_coord) + + # get the expectation for the hypothesis coordinate (a point source) + with h5.File(response_path) as f: + pix = hypothesis_coord_pix_number + psr = PointSourceResponse(axes[1:], f['hist/contents'][pix+1], unit = f['hist'].attrs['unit']) + + return psr + + + @staticmethod + def get_ei_cds_array(hypothesis_coord, energy_channel, response_path, spectrum, cds_frame, orientation = None): + + """ + Get the expected counts in CDS in local or galactic frame. + + Parameters + ---------- + hypothesis_coord : astropy.coordinates.SkyCoord + The hypothesis coordinate. + energy_channel : list + The format is `[lower_channel, upper_chanel]`. The lower_channel is inclusive while the upper_channel is exclusive. + response_path : str or pathlib.Path + The path to the response file. + spectrum : astromodel spectrum + The spectrum of the source. + cds_frame : str, optional + "local" or "galactic", it's the Compton data space (CDS) frame of the data, bkg_model and the response. In other words, they should have the same cds frame. + orientation : cosipy.SpacecraftFile or NoneType, optional + The orientation of the spacecraft when data are collected (the default is None, which implies the orientation file is not needed). + + Returns + ------- + cds_array : numpy.ndarray + The flattended Compton data space (CDS) array. + """ + + # check inputs, will complete later + + # the local and galactic frame works very differently, so we need to compuate the point source response (psr) accordingly + if cds_frame == "local": + + if orientation == None: + raise TypeError("The when the data are binned in local frame, orientation must be provided to compute the expected counts.") + + # convert the hypothesis coord to the local frame (Spacecraft frame) + hypothesis_in_sc_frame = orientation.get_target_in_sc_frame(target_name = "Hypothesis", + target_coord = hypothesis_coord, + quiet = True) + # get the dwell time map: the map of the time spent on each pixel in the local frame + dwell_time_map = orientation.get_dwell_map(response = response_path) + + # convolve the response with the dwell_time_map to get the point source response + with FullDetectorResponse.open(response_path) as response: + psr = response.get_point_source_response(dwell_time_map) + + elif cds_frame == "galactic": + + psr = FastTSMap.get_psr_in_galactic(hypothesis_coord = hypothesis_coord, response_path = response_path, spectrum = spectrum) + + else: + raise ValueError("The point source response must be calculated in the local and galactic frame. Others are not supported (yet)!") + + # convolve the point source reponse with the spectrum to get the expected counts + expectation = psr.get_expectation(spectrum) + + # slice energy channals and project it to CDS + ei_cds_array = FastTSMap.get_cds_array(expectation, energy_channel) + + return ei_cds_array @staticmethod def fast_ts_fit(hypothesis_coord, energy_channel, data_cds_array, bkg_model_cds_array, - orientation, response_path, spectrum, + orientation, response_path, spectrum, cds_frame, ts_nside, ts_scheme): + + """ + Perform a TS fit on a single location at `hypothesis_coord`. + + Parameters + ---------- + hypothesis_coord : astropy.coordinates.SkyCoord + The hypothesis coordinate. + energy_channel : list + The format is `[lower_channel, upper_chanel]`. The lower_channel is inclusive while the upper_channel is exclusive. + data_cds_array : numpy.ndarray + The flattened Compton data space (CDS) array of the data. + bkg_model_cds_array : numpy.ndarray + The flattened Compton data space (CDS) array of the background model. + orientation : cosipy.SpacecraftFile + The orientation of the spacecraft when data are collected. + response_path : str or pathlib.Path + The path to the response file. + spectrum : astromodel spectrum + The spectrum of the source. + cds_frame : str + "local" or "galactic", it's the Compton data space (CDS) frame of the data, bkg_model and the response. In other words, they should have the same cds frame . + ts_nside : int + The nside of the ts map. + ts_scheme : str + The scheme of the Ts map. + + Rsturns + ------- + list + The list of the resulting TS fit: [pix number, ts value, norm, norm_err, failed, iterations, time_ei_cds_array, time_fit, time_fast_ts_fit] + """ + + start_fast_ts_fit = time.time() - # get the pix number + # get the pix number of the ts map data_array = np.zeros(hp.nside2npix(ts_nside)) ts_temp = HealpixMap(data = data_array, scheme = ts_scheme, coordsys = "galactic") pix = ts_temp.ang2pix(hypothesis_coord) - # get the expected counts for the hypothesis_coord - hypothesis_in_sc_frame = orientation.get_target_in_sc_frame(target_name = "Hypothesis", - target_coord = hypothesis_coord, - quiet = True) - - dwell_time_map = orientation.get_dwell_map(response = response_path) - - with FullDetectorResponse.open(response_path) as response: - psr = response.get_point_source_response(dwell_time_map) - - expectation = psr.get_expectation(spectrum) - ei_cds_array = FastTSMap.get_cds_array(expectation, energy_channel) + # get the expected counts in the flattened cds array + start_ei_cds_array = time.time() + ei_cds_array = FastTSMap.get_ei_cds_array(hypothesis_coord = hypothesis_coord, cds_frame = cds_frame, + energy_channel = energy_channel, orientation = orientation, + response_path = response_path, spectrum = spectrum) + end_ei_cds_array = time.time() + time_ei_cds_array = end_ei_cds_array - start_ei_cds_array # start the fit + start_fit = time.time() fit = fnf(max_iter=1000) result = fit.solve(data_cds_array, bkg_model_cds_array, ei_cds_array) + end_fit = time.time() + time_fit = end_fit - start_fit + + end_fast_ts_fit = time.time() + time_fast_ts_fit = end_fast_ts_fit - start_fast_ts_fit - return [pix, result[0], result[1], result[2], result[3]] + return [pix, result[0], result[1], result[2], result[3], result[4], time_ei_cds_array, time_fit, time_fast_ts_fit] - def parallel_ts_fit(self, hypothesis_coords, energy_channel, spectrum, ts_scheme = "RING"): + def parallel_ts_fit(self, hypothesis_coords, energy_channel, spectrum, ts_scheme = "RING", start_method = "fork", cpu_cores = None): """ Perform parallel computation on all the hypothesis coordinates. Parameters ---------- - hypothesis_coords: list; a list of the hypothesis coordinates - energy_channel: list; the energy channel you want to use: [lower_channel, upper_channel] - spectrum: astromodels; the model to be placed at the hypothesis coordinates - ts_scheme: str; "RING" or "NESTED" + hypothesis_coords : list + A list of the hypothesis coordinates + energy_channel : list + the energy channel you want to use: [lower_channel, upper_channel]. lower_channel is inclusive while upper_channel is exclusive. + spectrum : astromodel spectrum + The spectrum of the source. + ts_scheme : str, optional + The scheme of the TS map (the default is "RING", which implies a "RING" scheme of the TS map). + start_method : str, optional + The starting method of the parallel computation (the default is "fork", which implies using the fork method to start parallel computation). + cpu_cores : int or NoneType, optional + The number of cpu cores you wish to use for the parallel computation (the default is None, which implies using all the available number of cores -1 to perform the parallel computation). Returns ------- - ts_values + results : numpy.ndarray + The result of the ts fit over all the hypothesis coordinates. """ # decide the ts_nside from the list of hypothesis coordinates ts_nside = hp.npix2nside(len(hypothesis_coords)) - # get the data_cds_array - data_cds_array = FastTSMap.get_cds_array(self._data, energy_channel) - bkg_model_cds_array = FastTSMap.get_cds_array(self._bkg_model, energy_channel) - - if (data_cds_array.flatten()[bkg_model_cds_array.flatten()==0]!=0).sum() != 0: - raise ValueError("You have data!=0 but bkg=0, check your inputs!") + # get the flattened data_cds_array + data_cds_array = FastTSMap.get_cds_array(self._data, energy_channel).flatten() + bkg_model_cds_array = FastTSMap.get_cds_array(self._bkg_model, energy_channel).flatten() + + if (data_cds_array[bkg_model_cds_array ==0]!=0).sum() != 0: + #raise ValueError("You have data!=0 but bkg=0, check your inputs!") + # let's try to set the data bin to zero when the corresponding bkg bin isn't zero. + # Need further investigate, why bkg = 0 but data!=0 happens? ==> it's more like an issue related to simulated data instead of code + # This first happened in GRB fitting, but got fixed somehow <== I now understand it's caused by using different PsiChi binning in the same fit + # But it also happened to Crab while the PsiChi binning are both galactic for Crab and the Albedo, why???? ?_? + data_cds_array[bkg_model_cds_array == 0] =0 + + # set up the number of cores to use for the parallel computation + total_cores = multiprocessing.cpu_count() + if cpu_cores == None or cpu_cores >= total_cores: + # if you don't specify the number of cpu cores to use or the specified number of cpu cores is the same as the total number of cores you have + # it will use the [total_cores - 1] number of cores to run the parallel computation. + cores = total_cores - 1 + print(f"You have total {total_cores} CPU cores, using {cores} CPU cores for parallel computation.") + else: + cores = cpu_cores + print(f"You have total {total_cores} CPU cores, using {cores} CPU cores for parallel computation.") + start = time.time() - - cores = multiprocessing.cpu_count() - pool = multiprocessing.Pool(processes=cores) - results = pool.starmap(FastTSMap.fast_ts_fit, product(hypothesis_coords, [energy_channel], [data_cds_array], [bkg_model_cds_array], - [self._orientation], [self._response_path], [spectrum], [ts_nside], [ts_scheme])) + multiprocessing.set_start_method(start_method, force = True) + pool = multiprocessing.Pool(processes = cores) + results = pool.starmap(FastTSMap.fast_ts_fit, product(hypothesis_coords, [energy_channel], [data_cds_array], [bkg_model_cds_array], + [self._orientation], [self._response_path], [spectrum], [self._cds_frame], + [ts_nside], [ts_scheme])) pool.close() pool.join() @@ -191,7 +384,103 @@ def parallel_ts_fit(self, hypothesis_coords, energy_channel, spectrum, ts_scheme elapsed_minutes = elapsed_seconds/60 print(f"The time used for the parallel TS map computation is {elapsed_minutes} minutes") + results = np.array(results) + self.result_array = results return results + + @staticmethod + def _plot_ts(result_array, skycoord = None, containment = None): + + """ + Plot the containment region of the TS map. + + Parameters + ---------- + result_array : numpy.ndarray + The result array from parallel ts fit. + skyoord : astropy.coordinates.SkyCoord or NoneType, optional + The true location of the source (the default is None, which implies that there are no coordiantes to be printed on the TS map). + containment: NoneType or float, optional + The containment level of the source (the default is None, which will plot raw TS values). + + Returns + ------- + None + """ + + + if skycoord != None: + lon = skycoord.l.deg + lat = skycoord.b.deg + + # sort the array by the pixel number + result_array = result_array[result_array[:, 0].argsort()] + + # get the ts value colum + m_ts = result_array[:,1] + + # plot the ts map with containment region + if containment != None: + critical = FastTSMap.get_chi_critical_value(containment = containment) + percentage = containment*100 + max_ts = np.max(m_ts[:]) + min_ts = np.min(m_ts[:]) + hp.mollview(m_ts[:], max = max_ts, min = max_ts-critical, title = f"Containment {percentage}%") + elif containment == None: + hp.mollview(m_ts[:]) + + + if skycoord != None: + hp.projtext(lon, lat, "x", lonlat=True, coord = "G", label = f"True location at l={lon}, b={lat}", color = "fuchsia"); + #hp.projtext(40, -17, "True Location", lonlat=True, coord = "G", label = "True location at l=51, b=-17", color = "fuchsia") + hp.projtext(0, 0, "o", lonlat=True, coord = "G", color = "red"); + hp.projtext(350, 0, "(l=0, b=0)", lonlat=True, coord = "G", color = "red"); + + return + + def plot_ts(self, skycoord = None, containment = 0.9): + + """ + Plot the containment region of the TS map. + + Parameters + ---------- + skyoord : astropy.coordinates.SkyCoord or NoneType, optional + The true location of the source (the default is None, which implies that there are no coordiantes to be printed on the TS map). + containment: NoneType or float, optional + The containment level of the source (the default is 0.9, which implies plot the 90% containment region). + + Returns + ------- + None + """ + + + result_array = self.result_array + + FastTSMap._plot_ts(result_array = result_array, skycoord = skycoord, containment = containment) + + return + + @staticmethod + def get_chi_critical_value(containment = 0.90): + + """ + Get the critical value of the chi^2 distribution based ob the confidence level. + + Parameters + ---------- + containment : float, optional + The confidence level of the chi^2 distribution (the default is 0.9, which implies that the 90% containment region). + + Returns + ------- + float + The critical value corresponds to the confidence level. + """ + + return scipy.stats.chi2.ppf(containment, df=2) + \ No newline at end of file diff --git a/docs/api/coordinates.rst b/docs/api/coordinates.rst deleted file mode 100644 index a18a4a6b..00000000 --- a/docs/api/coordinates.rst +++ /dev/null @@ -1,4 +0,0 @@ -Coordinates -=========== - -The cosipy library uses :py:mod:`astropy.coordinates`. diff --git a/docs/api/data_io.rst b/docs/api/data_io.rst new file mode 100644 index 00000000..070dc7d7 --- /dev/null +++ b/docs/api/data_io.rst @@ -0,0 +1,8 @@ +Data IO +======= + +.. automodule:: cosipy.data_io + :imported-members: + :members: + :undoc-members: + diff --git a/docs/api/image_deconvolution.rst b/docs/api/image_deconvolution.rst new file mode 100644 index 00000000..b2c43491 --- /dev/null +++ b/docs/api/image_deconvolution.rst @@ -0,0 +1,7 @@ +Image deconvolution +=================== + +.. automodule:: cosipy.image_deconvolution + :imported-members: + :members: + :undoc-members: diff --git a/docs/api/index.rst b/docs/api/index.rst index bde41e45..bccae241 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -1,9 +1,23 @@ API === +This is cosipy's Application Programming Interface (API). It is an exhaustive list of all available classes and their properties, as well as the inputs and outputs of each method. + +If you are instead interested in an overview on how to use cosipy, see out `tutorial series instead <../tutorials/index.html>`_. + +.. warning:: + Under construction. The description of some methods is still missing. If you need the description of a particular class, please open an `issue `_ so we can prioritize it. + .. toctree:: :maxdepth: 2 :caption: Contents: response - coordinates + data_io + spacecraftfile + threeml + ts_map + image_deconvolution + + + diff --git a/docs/api/spacecraftfile.rst b/docs/api/spacecraftfile.rst new file mode 100644 index 00000000..e4026261 --- /dev/null +++ b/docs/api/spacecraftfile.rst @@ -0,0 +1,9 @@ +Spacecraft File +=============== + +.. automodule:: cosipy.spacecraftfile + :imported-members: + :members: + :undoc-members: + + diff --git a/docs/api/threeml.rst b/docs/api/threeml.rst new file mode 100644 index 00000000..17b56f72 --- /dev/null +++ b/docs/api/threeml.rst @@ -0,0 +1,10 @@ +COSILike (3ML plugin) +===================== + +ThreeML plugin + +.. automodule:: cosipy.threeml + :imported-members: + :members: + :undoc-members: + diff --git a/docs/api/ts_map.rst b/docs/api/ts_map.rst new file mode 100644 index 00000000..0b344778 --- /dev/null +++ b/docs/api/ts_map.rst @@ -0,0 +1,7 @@ +TS Map +====== + +.. automodule:: cosipy.ts_map + :imported-members: + :members: + :undoc-members: diff --git a/docs/conf.py b/docs/conf.py index 49dc103f..57a82fdb 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -10,11 +10,9 @@ # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # -# import os -# import sys -# sys.path.insert(0, os.path.abspath('.')) - -import cosipy +import os +import sys +sys.path.insert(0, os.path.abspath('..')) # -- Project information ----------------------------------------------------- @@ -23,7 +21,8 @@ author = 'COSI Team' # The full version, including alpha/beta/rc tags -release = cosipy.__version__ +with open('../cosipy/_version.py') as f: + release = f.readline() # -- General configuration --------------------------------------------------- @@ -50,16 +49,46 @@ # The master toctree document. master_doc = 'index' -# intersphinx +# mock dependencies so we don't have to install them +autodoc_mock_imports = ["histpy", + 'threeML', + 'astromodels', + 'past', + 'numpy', + 'h5py', + 'astropy', + 'healpy', + 'mhealpy', + 'sparse', + 'matplotlib', + 'yaml', + 'scoords', + 'pandas', + 'tqdm', + 'scipy'] + +# There seems to be a conflict between unittest.mock (used by sphinx) and metaclasses +# The cosipy.threeml.custom_functions.Band_Eflux includes a metaclass from +# astromodels.functions.function, so we mock that one manually with the mock package +import mock + +MOCK_MODULES = ['astromodels.functions.function'] +for mod_name in MOCK_MODULES: + sys.modules[mod_name] = mock.Mock() + +# intersphinx for mocked dependencies intersphinx_mapping = { 'histpy': ('https://histpy.readthedocs.io/en/latest', None), + 'threeML': ('https://threeml.readthedocs.io/en/latest/', None), + 'astromodels': ('https://astromodels.readthedocs.io/en/latest/', None), + 'numpy': ('https://numpy.org/doc/stable/', None), 'h5py' : ('https://docs.h5py.org/en/stable/', None), 'astropy' : ('https://docs.astropy.org/en/stable', None), 'python' : ('https://docs.python.org/3', None), 'mhealpy' : ('https://mhealpy.readthedocs.io/en/latest/', None), 'sparse' : ('https://sparse.pydata.org/en/stable/', None), - 'gammapy' : ('https://docs.gammapy.org/dev', None), + 'matplotlib' : ('https://matplotlib.org/stable/', None), 'scipy' : ('https://scipy.github.io/devdocs', None), } @@ -87,29 +116,3 @@ # Extensions to theme docs -# Fix issue with Napoleon RTD that displays "Variables" instead of "Attributes" -# credit - https://michaelgoerz.net/notes/extending-sphinx-napoleon-docstring-sections.html - -from sphinx.ext.napoleon.docstring import GoogleDocstring - -# first, we define new methods for any new sections and add them to the class -def parse_keys_section(self, section): - return self._format_fields('Keys', self._consume_fields()) -GoogleDocstring._parse_keys_section = parse_keys_section - -def parse_attributes_section(self, section): - return self._format_fields('Attributes', self._consume_fields()) -GoogleDocstring._parse_attributes_section = parse_attributes_section - -def parse_class_attributes_section(self, section): - return self._format_fields('Class Attributes', self._consume_fields()) -GoogleDocstring._parse_class_attributes_section = parse_class_attributes_section - -# we now patch the parse method to guarantee that the the above methods are -# assigned to the _section dict -def patched_parse(self): - self._sections['keys'] = self._parse_keys_section - self._sections['class attributes'] = self._parse_class_attributes_section - self._unpatched_parse() -GoogleDocstring._unpatched_parse = GoogleDocstring._parse -GoogleDocstring._parse = patched_parse diff --git a/docs/index.rst b/docs/index.rst index 7b30e5ea..bcd86f08 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,11 +1,34 @@ Welcome to cosipy's documentation! ================================== +The cosipy library is `COSI `_'s high-level analysis software. It allows you to extract imaging and spectral information from the data, as well as to perform statistical model comparisons. The cosipy products are meant to be ready for interpretation. + +The main repository is hosted at https://github.com/cositools/cosipy + +In the following sections you will find: + +- Installation instructions +- A tutorial series explaining the basics of various components of cosipy +- Further usage examples +- The Application Programming Interface (API), describes the various available classes, their properties, and usage. + +.. warning:: + While many features are already available, cosipy is still actively under development. COSI is scheduled to launch in 2027. In preparation, the cosipy team will be releasing alpha versions with approximately an annual cadence. Your feedback will be greatly appreciated! Note, however, that these are not stable releases and various components can be modified or deprecated shortly. + + +Contributing +------------ + +Cosipy is open-source and anyone can contribute. It doesn't matter if you are part of the COSI team or an external contributor. + +The preferred communication channel is the GitHub repository:: if you find a problem, please report it by opening an issue; if you have a question or an idea on how to collaborate, please open a discussion; if you have code to contribute, please fork the repository and open a pull request. + + .. toctree:: :maxdepth: 2 :caption: Contents: install - tutorials/Intro.ipynb tutorials/index + tutorials/other_examples api/index diff --git a/docs/install.rst b/docs/install.rst index 9cc67d5b..9225aae5 100644 --- a/docs/install.rst +++ b/docs/install.rst @@ -1,9 +1,40 @@ Installation ============ -For developers --------------- +Using pip +--------- +Optional but recommended step: install a conda environment:: + + conda create -n python=3.10 pip + conda activate + +Note: currently cosipy is not compatible with Python 3.11 and 3.12, mainly due to +installation issues with a dependency (astromodels, see issues `#201 `_ and `#204 `_) + +Install with pip:: + + pip install cosipy==0.0.2a1 + +Note: you need to specify the alpha release 0.0.2a1, otherwise pip will fall back to +the latest regular release (which is currently unusable). This will be updated when +we have our next regular release. + + +From source (for developers) +---------------------------- + +Optional but recommended step: install a conda environment:: + + conda create -n python=3.10 pip + conda activate + +Also optional but recommended: before installing cosipy, install the main +dependencies from the source (similar +procedure as for cosipy below). These are histpy, mhealpy, scoords, threeml and +astromodels. The reason is that these libraries might be changing rapidly to +accommodate new features in cosipy. + Do the following (preferably inside a conda environment):: git clone git@github.com:cositools/cosipy.git @@ -16,6 +47,9 @@ having to run ``pip`` again. Testing ....... +.. warning:: + Under construction. Unit tests are not ready. + When you make a change, check that it didn't break something by running:: pytest --cov=cosipy --cov-report term --cov-report html:tests/coverage_report @@ -31,14 +65,15 @@ You can install ``pytest`` and ``pytest-cov`` with:: Compiling the docs ------------------ -You need sphinx, nbsphinx and sphinx_rtd_theme. Using conda:: +You need pandoc, sphinx, nbsphinx, sphinx_rtd_theme and mock. Using conda:: - conda install -c conda-forge nbsphinx sphinx_rtd_theme + conda install -c conda-forge pandoc nbsphinx sphinx_rtd_theme mock -Onece you have this requirements, run:: +Once you have these requirements, run:: cd docs make html To read the documentation, open ``docs/_build/html/index.html`` in a browser. + diff --git a/docs/tutorials/Parallel_TS_map_computation.ipynb b/docs/tutorials/Parallel_TS_map_computation.ipynb deleted file mode 100644 index 5b9fc3a4..00000000 --- a/docs/tutorials/Parallel_TS_map_computation.ipynb +++ /dev/null @@ -1,247 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "808c5247-e5ef-4435-ae3d-8a21df9e1058", - "metadata": {}, - "outputs": [], - "source": [ - "from threeML import Powerlaw\n", - "from cosipy import FastTSMap, SpacecraftFile\n", - "import astropy.units as u\n", - "from histpy import Histogram\n", - "from astropy.time import Time\n", - "import numpy as np\n", - "from astropy.coordinates import SkyCoord\n", - "from pathlib import Path\n", - "from mhealpy import HealpixMap" - ] - }, - { - "cell_type": "markdown", - "id": "fc125d78-7305-42f2-bbb5-b22c146174e1", - "metadata": {}, - "source": [ - "# Define a powerlaw spectrum" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d56dd795-845f-4db6-876d-6b929a7df5e8", - "metadata": {}, - "outputs": [], - "source": [ - "# define a powerlaw spectrum\n", - "index = -2.2\n", - "K = 10 / u.cm / u.cm / u.s / u.keV\n", - "piv = 100 * u.keV\n", - "spectrum = Powerlaw()\n", - "spectrum.index.value = index\n", - "spectrum.K.value = K.value\n", - "spectrum.piv.value = piv.value \n", - "spectrum.K.unit = K.unit\n", - "spectrum.piv.unit = piv.unit" - ] - }, - { - "cell_type": "markdown", - "id": "9f37efab-d2de-4678-8d93-fb678371b52a", - "metadata": {}, - "source": [ - "# Read data, background model and orientation" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "5d98d53e-fa3b-4657-8b16-e48ce1b2ee6a", - "metadata": {}, - "outputs": [], - "source": [ - "# open bkg model\n", - "\n", - "bkg_model = Histogram.open(\"../../cosipy/test_data/new_healpix_rsp_Binned_Bkg_2s_model.hdf5\")\n", - "bkg_model = bkg_model.project(['Em', 'PsiChi', 'Phi'])\n", - "\n", - "# read the signal and bkg to assemble data = bkg + signal\n", - "signal_original = Histogram.open(\"../../cosipy/test_data/new_healpix_rsp_Binned_protoGRB.hdf5\")\n", - "signal = signal_original.project(['Em', 'PsiChi', 'Phi'])\n", - "\n", - "bkg_original = Histogram.open(\"../../cosipy/test_data/new_healpix_rsp_Binned_Cosmic2s.hdf5\")\n", - "bkg = bkg_original.project(['Em', 'PsiChi', 'Phi'])\n", - "\n", - "signal = 1*signal\n", - "data_used = bkg + signal" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "4ab267c2-662b-4933-bb21-745f19441a75", - "metadata": {}, - "outputs": [], - "source": [ - "# get orientation\n", - "# Note this orientation is hard-coded for this specific GRB\n", - "\n", - "time = Time(np.array([1835481433.0, 1835481433.5, 1835481434.0, 1835481434.5, 1835481435.0]), format=\"unix\")\n", - "\n", - "x_pointings = SkyCoord(b=[53.30823215719789, 53.30823215719789, 53.25629494101732, 53.25629494101732, 53.20436773279772]*u.deg,\n", - " l = [51.00102125784474, 51.00102125784474, 51.035133117383225, 51.035133117383225, 51.0692916774301]*u.deg,\n", - " frame = \"galactic\")\n", - "\n", - "z_pointings = SkyCoord(l = [51.00102125784474, 51.00102125784474, 51.035133117383225, 51.035133117383225, 51.0692916774301]*u.deg, \n", - " b = [-36.69176784280211, -36.69176784280211, -36.74370505898268, -36.74370505898268, -36.79563226720228]*u.deg,\n", - " frame = \"galactic\")\n", - "\n", - "ori = SpacecraftFile(time = time, x_pointings = x_pointings, z_pointings = z_pointings)" - ] - }, - { - "cell_type": "markdown", - "id": "03e76f5e-a255-4e5d-b501-581295a92787", - "metadata": {}, - "source": [ - "# Start TS map fit" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "1baef583-a4c2-474b-a5bc-ed16f2c552cf", - "metadata": {}, - "outputs": [], - "source": [ - "response_path = Path(\"/home/sheng2/astrohe_yong/COSI/Response/Continuum_Flat_100to10000keV_10logEbins_HealPix03.binnedimaging.imagingresponse_nside8.area.h5\")\n", - "\n", - "# here let's create a FastTSMap object\n", - "ts = FastTSMap(data = data_used, bkg_model = bkg_model, orientation = ori, \n", - " response_path = response_path, frame = \"local\", scheme = \"RING\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "af11cb6a-b572-4594-95fa-e7b3e857e7a8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# get a list of hypothesis coordinates to fit. The models will be put on these coordinates\n", - "hypothesis_coords = FastTSMap.get_hypothesis_coords(nside = 16)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "06c7d26a-4518-4333-94df-42d58c0c8366", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 6 µs, sys: 3 µs, total: 9 µs\n", - "Wall time: 18.8 µs\n", - "The time used for the parallel TS map computation is 3.094292132059733 minutes\n" - ] - } - ], - "source": [ - "# This is the actual parallel fit\n", - "# It uses as much as cores available now ---> Please let me know if it significantly slow down your machine.\n", - "# energy channel is [lower_channel, upper_channel]. Lower channel is inclusive while the upper channel is exclusive\n", - "ts_results = ts.parallel_ts_fit(hypothesis_coords = hypothesis_coords, energy_channel = [2,3], spectrum = spectrum, ts_scheme = \"RING\")" - ] - }, - { - "cell_type": "markdown", - "id": "e9ed5290-a3d2-4292-80d4-9a724fea5ca2", - "metadata": {}, - "source": [ - "# Plot the fitted TS map" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "11fd0558-5032-4182-b07c-d0deb1e56937", - "metadata": {}, - "outputs": [], - "source": [ - "ts_array = np.array(ts_results)[:,0:2] # get the pix number and TS value column\n", - "ts_array = ts_array[ts_array[:, 0].argsort()] # arrange the row by the pix number to make sure the ts values are ordered correctly" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "3a78fdb2-d821-4f34-a5be-7eba6a8c67a4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(51, -17, '⬋here lon=51$^\\\\circ$,lat=-17$^\\\\circ$')" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "array = ts_array[:,1] # get the array of ts value\n", - "m = HealpixMap(data = array, scheme = \"RING\", dtype = float, coordsys = \"G\")\n", - "plot, ax = m.plot(ax_kw = {'coord':'G'})\n", - "lon = 51\n", - "lat = -17\n", - "ax.text(lon, lat, fr\"⬋here lon={lon}$^\\circ$,lat={lat}$^\\circ$\", size = 7, transform = ax.get_transform('galactic'), color = \"red\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca0d6fd4-376a-44b5-ab4e-05620ac45b42", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "cosipy_spacecraftfile_new", - "language": "python", - "name": "cosipy_spacecraftfile_new" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/tutorials/image_deconvolution/511keV/ScAttBinning/511keV-DC2-ScAtt-ImageDeconvolution.ipynb b/docs/tutorials/image_deconvolution/511keV/ScAttBinning/511keV-DC2-ScAtt-ImageDeconvolution.ipynb index 2d25a249..57be11d7 100644 --- a/docs/tutorials/image_deconvolution/511keV/ScAttBinning/511keV-DC2-ScAtt-ImageDeconvolution.ipynb +++ b/docs/tutorials/image_deconvolution/511keV/ScAttBinning/511keV-DC2-ScAtt-ImageDeconvolution.ipynb @@ -385,7 +385,7 @@ "tags": [] }, "source": [ - "# 0. Files needed for this notebook\n", + "## 0. Files needed for this notebook\n", "\n", "From wasabi\n", "- cosi-pipeline-public/COSI-SMEX/DC2/Responses/SMEXv12.511keV.HEALPixO4.binnedimaging.imagingresponse.nonsparse_nside16.area.h5\n", @@ -405,7 +405,7 @@ "id": "6c259412", "metadata": {}, "source": [ - "# 1. Read the response matrix" + "## 1. Read the response matrix" ] }, { @@ -495,7 +495,7 @@ "id": "26d6eb3a", "metadata": {}, "source": [ - "# 2. Read binned 511keV binned files (source and background)" + "## 2. Read binned 511keV binned files (source and background)" ] }, { @@ -520,7 +520,7 @@ "data_bkg = BinnedData(\"inputs_511keV_DC2.yaml\")\n", "data_bkg.load_binned_data_from_hdf5(\"511keV_scatt_binning_DC2_bkg.hdf5\")\n", "\n", - "# signal + background\n", + "## signal + background\n", "data_511keV = BinnedData(\"inputs_511keV_DC2.yaml\")\n", "data_511keV.load_binned_data_from_hdf5(\"511keV_scatt_binning_DC2_event.hdf5\")" ] @@ -532,7 +532,7 @@ "tags": [] }, "source": [ - "# 3. Load the coordsys conversion matrix" + "## 3. Load the coordsys conversion matrix" ] }, { @@ -561,7 +561,7 @@ "id": "31ec05ad-90b7-4fad-9ad0-98cfd6483d41", "metadata": {}, "source": [ - "# 4. Imaging deconvolution" + "## 4. Imaging deconvolution" ] }, { @@ -569,7 +569,7 @@ "id": "6e88ca7f", "metadata": {}, "source": [ - "## Brief overview of the image deconvolution\n", + "### Brief overview of the image deconvolution\n", "\n", "Basically, we have to maximize the following likelihood function\n", "\n", @@ -632,7 +632,7 @@ "id": "e0a2582e", "metadata": {}, "source": [ - "## 4-1. Prepare DataLoader containing all neccesary datasets" + "### 4-1. Prepare DataLoader containing all neccesary datasets" ] }, { @@ -710,7 +710,7 @@ "id": "2a662f5e", "metadata": {}, "source": [ - "## 4-2. Load the response file\n", + "### 4-2. Load the response file\n", "\n", "The response file will be loaded on the CPU memory. It requires a few GB. In the actual COSI satellite analysis, the response could be much larger, perhaps ~1TB wiht finer bin size. \n", "\n", @@ -769,7 +769,7 @@ "id": "b1a0269e", "metadata": {}, "source": [ - "## 4-3. Initialize the instance of the image deconvolution class\n", + "### 4-3. Initialize the instance of the image deconvolution class\n", "\n", "First, we prepare an instance of the ImageDeconvolution class and then register the dataset and parameters for the deconvolution. After that, you can start the calculation." ] @@ -1774,7 +1774,7 @@ "id": "f577c7ac", "metadata": {}, "source": [ - "## Log-likelihood\n", + "### Log-likelihood\n", "\n", "Plotting the log-likelihood vs the number of iterations" ] @@ -1815,7 +1815,7 @@ "id": "3f085706", "metadata": {}, "source": [ - "## Alpha (the factor used for the acceleration)\n", + "### Alpha (the factor used for the acceleration)\n", "\n", "Plotting $\\alpha$ vs the number of iterations. $\\alpha$ is a parameter to accelerate the EM algorithm (see the beginning of Section 4). If it is too large, reconstructed images may have artifacts." ] @@ -1856,7 +1856,7 @@ "id": "b3298aa5", "metadata": {}, "source": [ - "## Background normalization\n", + "### Background normalization\n", "\n", "Plotting the background nomalization factor vs the number of iterations. If the background model is accurate and the image is reconstructed perfectly, this factor should be close to 1." ] @@ -1897,7 +1897,7 @@ "id": "58e0d3a6", "metadata": {}, "source": [ - "## The reconstructed images" + "### The reconstructed images" ] }, { diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index e95e6314..50a9f8ab 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -1,11 +1,76 @@ Tutorials ========= -Tutorial for various components of the `cosipy` library. These are Python -notebooks that you can execute interactively. +This is a series of tutorials explaining step by step the various components of the `cosipy` library and how to use it. Although they are rendered as a webpage here, these are interactive Python notebooks (ipynb) that you can execute and modify, distributed as part of the cosipy repository. +If you are interested instead of the description of each class and method, please see our `API <../api/index.html>`_ section. + +List of tutorials and contents, as a link to the corresponding Python notebook in the repository: + +1. Data format and handling `(ipynb) `_ + + - Data format, binned and unbinned + - Binning the data in both local and galactic coordinates + - Combining files. + - Inspecting and plotting the data + +2. Spacecraft orientation and location `(ipynb) `_ + + - SC file format and manipulation it —e.g. get a time range, rebin it. + - The dwell time map and how to obtain it + - The scatt map and how to obtain it + +3. Detector response and signal expectation `(ipynb) `_ + + - Explanation of the detector response format and meaning + - Visualizing the response + - Convolving the detector response with a point source model (location + spectrum) + spacecraft file to obtain the expected signal counts. Both in SC and galactic coordinates. + +4. TS Map: localizing a GRB `(ipynb) `_ + - TS calculation + - Meaning of the TS map and how to compute confidence contours + - Computing a TS map, getting the best location and estimating the error + +5. Fitting the spectrum of a GRB `(ipynb) `_ + + - Introduction to 3ML and astromodels + - Likelihood analysis. + - Mechanics of background estimation. + - Fitting a simple power law, assuming you know the time of the GRB + - Plotting the result + - Comparing the result with the data + +6. Fitting the spectrum of the Crab `(ipynb) `_ + + - Analysing a continuous source transiting in the sky. + +7. Extended source model fitting `(ipynb) `_ + + - Obtaining the extended source response as a convolution of multiple point sources + - Pre-computing a response in galactic coordinates for all-sky + - Fitting an extended source + +8. Image deconvolution `(ipynb) `_ + - Explain the RL algorithm. Reference the previous example. Explain the difference with a TS map. + - Scatt binning and its advantages/disadvantages + - Fitting the 511 diffuse emission. + +9. TODO: Source injector + - Nice to have: allow theorist to test the sensitivity of their models + +.. warning:: + Under construction. Some of the explanations described above might be missing. However, the notebooks are fully functional. If you have a question not yet covered by the tutorials, please discuss `issue `_ so we can prioritize it. + .. toctree:: :maxdepth: 1 - DetectorResponse.ipynb - + Data format and handling + Spacecraft orientation and location + Detector response and signal expectation + TS Map: localizing a GRB + Fitting the spectrum of a GRB + Fitting the spectrum of the Crab + Extended source model fitting + Image deconvolution + + diff --git a/docs/tutorials/other_examples.rst b/docs/tutorials/other_examples.rst new file mode 100644 index 00000000..c460132e --- /dev/null +++ b/docs/tutorials/other_examples.rst @@ -0,0 +1,11 @@ +Other examples +============== + +.. warning:: + Under construction. + +.. toctree:: + :maxdepth: 1 + + spectral_fits/line_fit/SpectralFit_Line.ipynb + diff --git a/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb b/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb index a20d26a8..3cfc935c 100644 --- a/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb +++ b/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb @@ -15,7 +15,7 @@ "id": "e7df3443-3ce1-43f3-90b5-1bceb7bc9af0", "metadata": {}, "source": [ - "**To run this, you need the detector response (SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.nonsparse_nside8.area.good_chunks_unzip.h5), binned data (crab_bkg_binned_data.hdf5, crab_binned_data.hdf5, & bkg_binned_data.hdf5), and orientation file (20280301_3_month.ori), which are available on wasabi in cosi-pipeline-public/COSI-SMEX/DC2/Responses (response), cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame (binned data), and cosi-pipeline-public/COSI-SMEX/DC2/Data/Orientation (orientation), and can be downloaded using the first cell of this notebook. The binned data are simulations of the Crab Nebula and albedo photon background produced using the COSI SMEX mass model.**" + "**To run this, you need the detector response (SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.nonsparse_nside8.area.good_chunks_unzip.h5.zip), binned data (crab_bkg_binned_data.hdf5, crab_binned_data.hdf5, & bkg_binned_data.hdf5), and orientation file (20280301_3_month.ori), which are available on wasabi in cosi-pipeline-public/COSI-SMEX/DC2/Responses (response), cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame (binned data), and cosi-pipeline-public/COSI-SMEX/DC2/Data/Orientation (orientation). The binned data are simulations of the Crab Nebula and albedo photon background produced using the COSI SMEX mass model. The detector response needs to be unzipped before running the notebook.**" ] }, { @@ -85,49 +85,6 @@ "import os" ] }, - { - "cell_type": "markdown", - "id": "bd26a834-d17a-46e9-98de-800ff3b4f9e1", - "metadata": {}, - "source": [ - "## Read in binned data" - ] - }, - { - "cell_type": "markdown", - "id": "f952ade2-142e-4202-ac80-72befdb990a8", - "metadata": {}, - "source": [ - "Download the data, detector response, and orientation file from wasabi. You can skip this cell if you already have downloaded the files" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "22a50794-9081-4812-b69c-0f1da4c0ab86", - "metadata": {}, - "outputs": [], - "source": [ - "file_list = ['bkg_binned_data.hdf5','crab_binned_data.hdf5','crab_bkg_binned_data.hdf5']\n", - "response_file = 'SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.nonsparse_nside8.area.good_chunks_unzip.h5.zip'\n", - "ori_file = '20280301_3_month.ori'\n", - "\n", - "for each in file_list:\n", - " os.system(\"AWS_ACCESS_KEY_ID=GBAL6XATQZNRV3GFH9Y4 AWS_SECRET_ACCESS_KEY=GToOczY5hGX3sketNO2fUwiq4DJoewzIgvTCHoOv aws s3api get-object --bucket cosi-pipeline-public --key COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame/%s --endpoint-url=https://s3.us-west-1.wasabisys.com %s\" %(each,each))\n", - " \n", - "os.system(\"AWS_ACCESS_KEY_ID=GBAL6XATQZNRV3GFH9Y4 AWS_SECRET_ACCESS_KEY=GToOczY5hGX3sketNO2fUwiq4DJoewzIgvTCHoOv aws s3api get-object --bucket cosi-pipeline-public --key COSI-SMEX/DC2/Responses/%s --endpoint-url=https://s3.us-west-1.wasabisys.com %s\" %(response_file,response_file))\n", - "\n", - "os.system(\"AWS_ACCESS_KEY_ID=GBAL6XATQZNRV3GFH9Y4 AWS_SECRET_ACCESS_KEY=GToOczY5hGX3sketNO2fUwiq4DJoewzIgvTCHoOv aws s3api get-object --bucket cosi-pipeline-public --key COSI-SMEX/DC2/Data/Orientation/%s --endpoint-url=https://s3.us-west-1.wasabisys.com %s\" %(ori_file,ori_file))" - ] - }, - { - "cell_type": "markdown", - "id": "81e1372b-6569-4e1c-8786-182cb24e563e", - "metadata": {}, - "source": [ - "**Note: `SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.nonsparse_nside8.area.good_chunks_unzip.h5.zip` will need to be unzipped before running the rest of the notebook.**" - ] - }, { "cell_type": "markdown", "id": "8d1c0168-9823-4eb7-930e-5dc61d6448ca", @@ -141,7 +98,7 @@ "id": "a57e30ec-9301-441c-a627-6ad0355aca22", "metadata": {}, "source": [ - "Define the path to the directory containing the data, detector response, orientation file, and yaml files." + "Define the path to the directory containing the data, detector response, orientation file, and yaml files" ] }, { @@ -13316,11 +13273,7 @@ " binned_energy = np.append(binned_energy, (binned_energy_edges[i+1] + binned_energy_edges[i]) / 2)\n", " bin_sizes = np.append(bin_sizes, binned_energy_edges[i+1] - binned_energy_edges[i])\n", "\n", - "with FullDetectorResponse.open(dr) as response:\n", - " scatt_map = cosi._get_scatt_map()\n", - " psr = response.get_point_source_response(coord=source.position.sky_coord, scatt_map=scatt_map)\n", - " \n", - "expectation = psr.get_expectation(spectrum)" + "expectation = cosi._expected_counts['source']" ] }, { diff --git a/docs/tutorials/spectral_fits/continuum_fit/grb/20280301_first_2hrs.ori b/docs/tutorials/spectral_fits/continuum_fit/grb/20280301_first_2hrs.ori deleted file mode 100644 index 31e4a9d1..00000000 --- a/docs/tutorials/spectral_fits/continuum_fit/grb/20280301_first_2hrs.ori 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example (GRB)" - ] - }, - { - "cell_type": "markdown", - "id": "e7df3443-3ce1-43f3-90b5-1bceb7bc9af0", - "metadata": {}, - "source": [ - "**To run this, you need the binned data (grb_bkg_binned_data.hdf5, grb_binned_data.hdf5, & bkg_binned_data_full.hdf5) and detector response (FlatContinuumIsotropic.LowRes.binnedimaging.imagingresponse.area.nside8.cosipy.h5), which are available on wasabi in cosi-pipeline-public/ComptonSphere/mini-DC2 and can be downloaded using the first cell of this notebook. The binned data are simulations of a GRB and background produced using the Compton sphere mass model.**" - ] - }, - { - "cell_type": "markdown", - "id": "ba543558-7de9-494c-8b72-8cdd368676e9", - "metadata": {}, - "source": [ - "This notebook fits the spectrum of a GRB simulated using MEGAlib and combined with background which is constant in time.\n", - "\n", - "[3ML](https://threeml.readthedocs.io/) is a high-level interface that allows multiple datasets from different instruments to be used coherently to fit the parameters of source model. A source model typically consists of a list of sources with parametrized spectral shapes, sky locations and, for extended sources, shape. Polarization is also possible. A \"coherent\" analysis, in this context, means that the source model parameters are fitted using all available datasets simultanously, rather than performing individual fits and finding a well-suited common model a posteriori. \n", - "\n", - "In order for a dataset to be included in 3ML, each instrument needs to provide a \"plugin\". Each plugin is responsible for reading the data, convolving the source model (provided by 3ML) with the instrument response, and returning a likelihood. In our case, we'll compute a binned Poisson likelihood:\n", - "\n", - "$$\n", - "\\log \\mathcal{L}(\\mathbf{x}) = \\sum_i \\log \\frac{\\lambda_i(\\mathbf{x})^{d_i} \\exp (-\\lambda_i)}{d_i!}\n", - "$$\n", - "\n", - "where $d_i$ are the counts on each bin and $\\lambda_i$ are the expected counts given a source model with parameters $\\mathbf{x}$. \n", - "\n", - "In this example, we will fit a single point source with a known location. We'll assume the background is known and fixed up to a scaling factor. Finally, we will fit a Band function:\n", - "\n", - "$$\n", - "f(x) = K \\begin{cases} \\left(\\frac{x}{E_{piv}}\\right)^{\\alpha} \\exp \\left(-\\frac{(2+\\alpha)\n", - " * x}{x_{p}}\\right) & x \\leq (\\alpha-\\beta) \\frac{x_{p}}{(\\alpha+2)} \\\\ \\left(\\frac{x}{E_{piv}}\\right)^{\\beta}\n", - " * \\exp (\\beta-\\alpha)\\left[\\frac{(\\alpha-\\beta) x_{p}}{E_{piv}(2+\\alpha)}\\right]^{\\alpha-\\beta}\n", - " * &x>(\\alpha-\\beta) \\frac{x_{p}}{(\\alpha+2)} \\end{cases}\n", - "$$\n", - "\n", - "\n", - "where $K$ (normalization), $\\alpha$ & $\\beta$ (spectral indeces), and $x_p$ (peak energy) are the free parameters, while $E_{piv}$ is the pivot energy which is fixed (and arbitrary).\n", - "\n", - "Considering these assumptions:\n", - "\n", - "$$\n", - "\\lambda_i(\\mathbf{x}) = B*b_i + s_i(\\mathbf{x})\n", - "$$\n", - "\n", - "where $B*b_i$ are the estimated counts due to background in each bin of the Compton data space with $B$ the amplitude and $b_i$ the shape of the background, and $s_i$ are the corresponding expected counts from the source, the goal is then to find the values of $\\mathbf{x} = [K, \\alpha, \\beta, x_p]$ and $B$ that maximize $\\mathcal{L}$. These are the best estimations of the parameters.\n", - "\n", - "The final module needs to also fit the time-dependent background, handle multiple point-like and extended sources, as well as all the spectral models supported by 3ML. Eventually, it will also fit the polarization angle. However, this simple example already contains all the necessary pieces to do a fit." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ce42ab82-3bbd-4729-8f84-a4e32eb3bb24", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "                  performances in 3ML                                                                              \n",
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Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=45006;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=942860;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", - "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mperformances in 3ML \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cosipy import COSILike, BinnedData\n", - "from cosipy.spacecraftfile import SpacecraftFile\n", - "from cosipy.response.FullDetectorResponse import FullDetectorResponse\n", - "\n", - "from scoords import SpacecraftFrame\n", - "\n", - "from astropy.time import Time\n", - "import astropy.units as u\n", - "from astropy.coordinates import SkyCoord\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "from threeML import Band, PointSource, Model, JointLikelihood, DataList\n", - "from cosipy import Band_Eflux\n", - "from astromodels import Parameter\n", - "\n", - "from pathlib import Path\n", - "\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "id": "8d1c0168-9823-4eb7-930e-5dc61d6448ca", - "metadata": {}, - "source": [ - "## Read in binned data" - ] - }, - { - "cell_type": "markdown", - "id": "bdfa74d8-b860-4ba0-9b0f-30cd171545a2", - "metadata": {}, - "source": [ - "Download the data and detector response from wasabi. You can skip this cell if you already have downloaded the files" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "534d0ea4-557f-4043-b1c0-4990146ed2b1", - "metadata": {}, - "outputs": [], - "source": [ - "file_list = ['bkg_binned_data_full.hdf5','grb_binned_data.hdf5','grb_bkg_binned_data.hdf5','FlatContinuumIsotropic.LowRes.binnedimaging.imagingresponse.area.nside8.cosipy.h5.zip']\n", - "\n", - "for each in file_list:\n", - " os.system(\"AWS_ACCESS_KEY_ID=GBAL6XATQZNRV3GFH9Y4 AWS_SECRET_ACCESS_KEY=GToOczY5hGX3sketNO2fUwiq4DJoewzIgvTCHoOv aws s3api get-object --bucket cosi-pipeline-public --key ComptonSphere/mini-DC2/%s --endpoint-url=https://s3.us-west-1.wasabisys.com %s\" %(each,each))" - ] - }, - { - "cell_type": "markdown", - "id": "a4c4ab40-c6a1-4885-8216-fb631dab99e7", - "metadata": {}, - "source": [ - "**Note: `FlatContinuumIsotropic.LowRes.binnedimaging.imagingresponse.area.nside8.cosipy.h5.zip` will need to be unzipped before running the rest of the notebook.**" - ] - }, - { - "cell_type": "markdown", - "id": "dc364649-56e4-4bb1-8403-74e90cf3ed05", - "metadata": {}, - "source": [ - "Define the path to the directory containing the data, detector response, orientation file, and yaml files." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "cdd53b2a-5176-42cf-bb2c-feb3387fc0a4", - "metadata": {}, - "outputs": [], - "source": [ - "data_path = Path(\"/path/to/files\")" - ] - }, - { - "cell_type": "markdown", - "id": "d898bbd7-9ed0-4a27-bd5a-67414178733d", - "metadata": {}, - "source": [ - "Read in the spacecraft orientation file & select the beginning and end times of the GRB" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ed2c03a0-63e3-4044-9e16-50f0f17996af", - "metadata": {}, - "outputs": [], - "source": [ - "ori = SpacecraftFile.parse_from_file(data_path / \"20280301_first_2hrs.ori\")\n", - "tmin = Time(1835481433.0,format = 'unix')\n", - "tmax = Time(1835481435.0,format = 'unix')\n", - "sc_orientation = ori.source_interval(tmin, tmax)" - ] - }, - { - "cell_type": "markdown", - "id": "f579870f-c854-450d-84e8-f1d5ef0753d1", - "metadata": {}, - "source": [ - "Create BinnedData objects for the GRB only, GRB+background, and background only. The GRB only simulation is not used for the spectral fit, but can be used to compare the fitted spectrum to the source simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3b5faaa1-1874-4d43-a6ae-7e1b0aaabb26", - "metadata": {}, - "outputs": [], - "source": [ - "grb = BinnedData(data_path / \"grb.yaml\")\n", - "grb_bkg = BinnedData(data_path / \"grb.yaml\")\n", - "bkg = BinnedData(data_path / \"background.yaml\")" - ] - }, - { - "cell_type": "markdown", - "id": "cf8b5ab1-7452-493e-b516-73fa72e455e5", - "metadata": {}, - "source": [ - "Load binned .hdf5 files" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "620159d2-f01a-453e-9e4c-075c99740086", - "metadata": {}, - "outputs": [], - "source": [ - "grb.load_binned_data_from_hdf5(binned_data=data_path / \"grb_binned_data.hdf5\")\n", - "grb_bkg.load_binned_data_from_hdf5(binned_data=data_path / \"grb_bkg_binned_data.hdf5\")\n", - "bkg.load_binned_data_from_hdf5(binned_data=data_path / \"bkg_binned_data_full.hdf5\")" - ] - }, - { - "cell_type": "markdown", - "id": "a6bdaee8-45d7-41df-9835-413c1e397c12", - "metadata": {}, - "source": [ - "Define the path to the detector response" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "acccab93-7f9c-4167-a8f9-eedcf74b8a05", - "metadata": {}, - "outputs": [], - "source": [ - "dr = str(data_path / \"FlatContinuumIsotropic.LowRes.binnedimaging.imagingresponse.area.nside8.cosipy.h5\") # path to detector response" - ] - }, - { - "cell_type": "markdown", - "id": "31b5dbd7-8a50-43db-af66-7b8601f7e2fd", - "metadata": { - "tags": [] - }, - "source": [ - "## Perform spectral fit" - ] - }, - { - "cell_type": "markdown", - "id": "7441f3f1-ebe6-467f-b8ab-1baa70f20b15", - "metadata": {}, - "source": [ - "Set background parameter, which is used to fit the amplitude of the background, and instantiate the COSI 3ML plugin" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a9f21e74-5f62-4030-9815-6c77ebaab16f", - "metadata": {}, - "outputs": [], - "source": [ - "bkg_par = Parameter(\"background_cosi\", # background parameter\n", - " 2.8e-4, # initial value of parameter\n", - " min_value=0, # minimum value of parameter\n", - " max_value=5, # maximum value of parameter\n", - " delta=0.05, # initial step used by fitting engine\n", - " desc=\"Background parameter for cosi\")\n", - "\n", - "cosi = COSILike(\"cosi\", # COSI 3ML plugin\n", - " dr = dr, # detector response\n", - " data = grb_bkg.binned_data.project('Em', 'Phi', 'PsiChi'), # data (source+background)\n", - " bkg = bkg.binned_data.project('Em', 'Phi', 'PsiChi'), # background model \n", - " sc_orientation = sc_orientation, # spacecraft orientation\n", - " nuisance_param = bkg_par, # background parameter\n", - " coordsys='spacecraftframe') # coordinate system" - ] - }, - { - "cell_type": "markdown", - "id": "e6d55283-abb0-4295-9e5c-80a5c717f0ba", - "metadata": {}, - "source": [ - "Define a point source at the known location with a Band function spectrum and add it to the model" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "98b2d026-c24d-4cfe-8b7b-41415fce5d16", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Now converting to the Spacecraft frame...\n", - "Conversion completed!\n" - ] - } - ], - "source": [ - "l = 51.\n", - "b = -17.\n", - "\n", - "alpha = -1. # Setting parameters to something reasonable helps the fitting to converge\n", - "beta = -3.\n", - "xp = 1000 * u.keV\n", - "piv = 500 * u.keV\n", - "K = 0.00247 / u.cm / u.cm / u.s / u.keV\n", - "\n", - "spectrum = Band(alpha=alpha,\n", - " beta=beta,\n", - " xp=xp.value,\n", - " K=K.value,\n", - " piv=piv.value)\n", - "\n", - "spectrum.xp.unit = xp.unit\n", - "spectrum.K.unit = K.unit\n", - "spectrum.piv.unit = piv.unit\n", - "\n", - "source = PointSource(\"source\", # Name of source (arbitrary, but needs to be unique)\n", - " l = l, # Longitude (deg)\n", - " b = b, # Latitude (deg)\n", - " spectral_shape = spectrum) # Spectral model\n", - "\n", - "# Optional: free the position parameters\n", - "#source.position.l.free = True\n", - "#source.position.b.free = True\n", - "\n", - "model = Model(source) # Model with single source. If we had multiple sources, we would do Model(source1, source2, ...)\n", - "\n", - "# Optional: if you want to call get_log_like manually, then you also need to set the model manually\n", - "# 3ML does this internally during the fit though\n", - "cosi.set_model(model)" - ] - }, - { - "cell_type": "markdown", - "id": "27ded6d5-4551-4623-8483-b3f4e8b02040", - "metadata": {}, - "source": [ - "Gather all plugins and combine with the model in a JointLikelihood object, then perform maximum likelihood fit" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d56d3ad6-7226-437a-a037-57fbcd80d196", - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
15:40:07 INFO      set the minimizer to minuit                                             joint_likelihood.py:1042\n",
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resultunit
parameter
source.spectrum.main.Band.K(2.67 -0.09 +0.10) x 10^-31 / (cm2 keV s)
source.spectrum.main.Band.alpha(-6.98 +/- 0.33) x 10^-1
source.spectrum.main.Band.xp(1.004 -0.023 +0.024) x 10^3keV
source.spectrum.main.Band.beta-2.45 +/- 0.05
background_cosi(3.174 +/- 0.025) x 10^-4
\n", - "
" - ], - "text/plain": [ - " result unit\n", - "parameter \n", - "source.spectrum.main.Band.K (2.67 -0.09 +0.10) x 10^-3 1 / (cm2 keV s)\n", - "source.spectrum.main.Band.alpha (-6.98 +/- 0.33) x 10^-1 \n", - "source.spectrum.main.Band.xp (1.004 -0.023 +0.024) x 10^3 keV\n", - "source.spectrum.main.Band.beta -2.45 +/- 0.05 \n", - "background_cosi (3.174 +/- 0.025) x 10^-4 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n",
-       "Correlation matrix:\n",
-       "\n",
-       "
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1.000.95-0.940.510.03
0.951.00-0.840.420.18
-0.94-0.841.00-0.63-0.02
0.510.42-0.631.000.01
0.030.18-0.020.011.00
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-       "Values of -log(likelihood) at the minimum:\n",
-       "\n",
-       "
\n" - ], - "text/plain": [ - "\n", - "\u001b[1;4;38;5;49mValues of -\u001b[0m\u001b[1;4;38;5;49mlog\u001b[0m\u001b[1;4;38;5;49m(\u001b[0m\u001b[1;4;38;5;49mlikelihood\u001b[0m\u001b[1;4;38;5;49m)\u001b[0m\u001b[1;4;38;5;49m at the minimum:\u001b[0m\n", - "\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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-log(likelihood)
cosi-42410.316355
total-42410.316355
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" - ], - "text/plain": [ - " -log(likelihood)\n", - "cosi -42410.316355\n", - "total -42410.316355" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n",
-       "Values of statistical measures:\n",
-       "\n",
-       "
\n" - ], - "text/plain": [ - "\n", - "\u001b[1;4;38;5;49mValues of statistical measures:\u001b[0m\n", - "\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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statistical measures
AIC-84822.632709
BIC-84820.632709
\n", - "
" - ], - "text/plain": [ - " statistical measures\n", - "AIC -84822.632709\n", - "BIC -84820.632709" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "None\n", - " * source (point source):\n", - " * position:\n", - " * l:\n", - " * value: 51.0\n", - " * desc: Galactic longitude\n", - " * min_value: 0.0\n", - " * max_value: 360.0\n", - " * unit: deg\n", - " * is_normalization: false\n", - " * b:\n", - " * value: -17.0\n", - " * desc: Galactic latitude\n", - " * min_value: -90.0\n", - " * max_value: 90.0\n", - " * unit: deg\n", - " * is_normalization: false\n", - " * equinox: J2000\n", - " * spectrum:\n", - " * main:\n", - " * Band:\n", - " * K:\n", - " * value: 0.002668934589726522\n", - " * desc: Differential flux at the pivot energy\n", - " * min_value: 1.0e-99\n", - " * max_value: null\n", - " * unit: keV-1 s-1 cm-2\n", - " * is_normalization: true\n", - " * alpha:\n", - " * value: -0.69761536655642\n", - " * desc: low-energy photon index\n", - " * min_value: -1.5\n", - " * max_value: 3.0\n", - " * unit: ''\n", - " * is_normalization: false\n", - " * xp:\n", - " * value: 1003.7774760386509\n", - " * desc: peak in the x * x * N (nuFnu if x is a energy)\n", - " * min_value: 10.0\n", - " * max_value: null\n", - " * unit: keV\n", - " * is_normalization: false\n", - " * beta:\n", - " * value: -2.4487564706936693\n", - " * desc: high-energy photon index\n", - " * min_value: -5.0\n", - " * max_value: -1.6\n", - " * unit: ''\n", - " * is_normalization: false\n", - " * piv:\n", - " * value: 500.0\n", - " * desc: pivot energy\n", - " * min_value: null\n", - " * max_value: null\n", - " * unit: keV\n", - " * is_normalization: false\n", - " * polarization: {}\n", - "\n" - ] - } - ], - "source": [ - "results = like.results\n", - "\n", - "print(results.display())\n", - "\n", - "parameters = {par.name:results.get_variates(par.path)\n", - " for par in results.optimized_model[\"source\"].parameters.values()\n", - " if par.free}\n", - "\n", - "results_err = results.propagate(results.optimized_model[\"source\"].spectrum.main.shape.evaluate_at, **parameters)\n", - "\n", - "print(results.optimized_model[\"source\"])" - ] - }, - { - "cell_type": "markdown", - "id": "5eaec533-b5b3-45c4-94df-75453e2df3bf", - "metadata": {}, - "source": [ - "Evaluate the flux and errors at a range of energies for the fitted and injected spectra, and the simulated source flux" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "cc7d6f50-06cd-450a-83d9-115b67d83b30", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Now converting to the Spacecraft frame...\n", - "Conversion completed!\n" - ] - } - ], - "source": [ - "energy = np.geomspace(100*u.keV,5*u.MeV).to_value(u.keV)\n", - "\n", - "flux_lo = np.zeros_like(energy)\n", - "flux_median = np.zeros_like(energy)\n", - "flux_hi = np.zeros_like(energy)\n", - "flux_inj = np.zeros_like(energy)\n", - "\n", - "for i, e in enumerate(energy):\n", - " flux = results_err(e)\n", - " flux_median[i] = flux.median\n", - " flux_lo[i], flux_hi[i] = flux.equal_tail_interval(cl=0.68)\n", - " flux_inj[i] = spectrum_inj.evaluate_at(e)\n", - " \n", - "binned_energy_edges = grb.binned_data.axes['Em'].edges\n", - "binned_energy = np.array([])\n", - "bin_sizes = np.array([])\n", - "\n", - "for i in range(len(binned_energy_edges)-1):\n", - " binned_energy = np.append(binned_energy, (binned_energy_edges[i+1] + binned_energy_edges[i]) / 2)\n", - " bin_sizes = np.append(bin_sizes, binned_energy_edges[i+1] - binned_energy_edges[i])\n", - "\n", - "with FullDetectorResponse.open(dr) as response:\n", - " drm = response.get_interp_response(SkyCoord(lon = l*u.deg, lat = b*u.deg, frame = SpacecraftFrame()))\n", - " dwell_time_map = cosi._get_dwell_time_map(source.position.sky_coord)\n", - " psr = response.get_point_source_response(dwell_time_map)\n", - " \n", - "expectation = psr.get_expectation(spectrum)" - ] - }, - { - "cell_type": "markdown", - "id": "8cb8c4aa-ef51-4f19-93dc-2ac7d7d2f189", - "metadata": {}, - "source": [ - "Plot the fitted and injected spectra" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "f8dbd36f-4b16-4bec-8835-8f6f876ab169", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots()\n", - "\n", - "ax.plot(energy, energy*energy*flux_median, label = \"Best fit\")\n", - "ax.fill_between(energy, energy*energy*flux_lo, energy*energy*flux_hi, alpha = .5, label = \"Best fit (errors)\")\n", - "ax.plot(energy, energy*energy*flux_inj, color = 'black', ls = \":\", label = \"Injected\")\n", - "\n", - "ax.set_xscale(\"log\")\n", - "ax.set_yscale(\"log\")\n", - "\n", - "ax.set_xlabel(\"Energy (keV)\")\n", - "ax.set_ylabel(r\"$E^2 \\frac{dN}{dE}$ (keV cm$^{-2}$ s$^{-1}$)\")\n", - "\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "20a08b36-44d2-4fef-a82e-def1dfd7b9d9", - "metadata": {}, - "source": [ - "Plot the fitted spectrum convolved with the response, as well as the simulated source counts" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "7d1dd8d1-f86d-4e63-8286-db1d5bc14b04", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots()\n", - "\n", - "ax.stairs(expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n", - "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n", - "ax.stairs(grb.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n", - "ax.errorbar(binned_energy, grb.binned_data.project('Em').todense().contents, yerr=np.sqrt(grb.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n", - "\n", - "ax.set_xscale(\"log\")\n", - "ax.set_yscale(\"log\")\n", - "\n", - "ax.set_xlabel(\"Energy (keV)\")\n", - "ax.set_ylabel(\"Counts\")\n", - "\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "00234bec-2a9f-4557-8a41-0d1a9b71e9c9", - "metadata": {}, - "source": [ - "Plot the fitted spectrum convolved with the response plus the fitted background, as well as the simulated source+background counts" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "06df3b27-d2ed-4214-bda7-d4fda667e145", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots()\n", - "\n", - "ax.stairs(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.project('Em').todense().contents), binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n", - "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.project('Em').todense().contents), yerr=np.sqrt(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.project('Em').todense().contents)), color='purple', linewidth=0, elinewidth=1)\n", - "ax.stairs(grb_bkg.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n", - "ax.errorbar(binned_energy, grb_bkg.binned_data.project('Em').todense().contents, yerr=np.sqrt(grb_bkg.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n", - "\n", - "ax.set_xscale(\"log\")\n", - "ax.set_yscale(\"log\")\n", - "\n", - "ax.set_xlabel(\"Energy (keV)\")\n", - "ax.set_ylabel(\"Counts\")\n", - "\n", - "ax.legend()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb b/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb new file mode 100644 index 00000000..113021f6 --- /dev/null +++ b/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb @@ -0,0 +1,7761 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "74a86fb5-4e54-4e3f-b349-3e60fbdd0279", + "metadata": { + "tags": [] + }, + "source": [ + "# Spectral fitting example (GRB)" + ] + }, + { + "cell_type": "markdown", + "id": "e7df3443-3ce1-43f3-90b5-1bceb7bc9af0", + "metadata": {}, + "source": [ + "**To run this, you need the detector response (SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.nonsparse_nside8.area.good_chunks_unzip.h5.zip), binned data (grb_bkg_binned_data.hdf5, grb_binned_data.hdf5, & bkg_binned_data_1s_local.hdf5), and orientation file (20280301_3_month.ori), which are available on wasabi in cosi-pipeline-public/COSI-SMEX/DC2/Responses (response), cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/grb_spectral_fit_local_frame (binned data), and cosi-pipeline-public/COSI-SMEX/DC2/Data/Orientation (orientation). The binned data are simulations of GRB090206620 and albedo photon background produced using the COSI SMEX mass model. The detector response needs to be unzipped before running the notebook.**" + ] + }, + { + "cell_type": "markdown", + "id": "ba543558-7de9-494c-8b72-8cdd368676e9", + "metadata": {}, + "source": [ + "This notebook fits the spectrum of a GRB simulated using MEGAlib and combined with background.\n", + "\n", + "[3ML](https://threeml.readthedocs.io/) is a high-level interface that allows multiple datasets from different instruments to be used coherently to fit the parameters of source model. A source model typically consists of a list of sources with parametrized spectral shapes, sky locations and, for extended sources, shape. Polarization is also possible. A \"coherent\" analysis, in this context, means that the source model parameters are fitted using all available datasets simultanously, rather than performing individual fits and finding a well-suited common model a posteriori. \n", + "\n", + "In order for a dataset to be included in 3ML, each instrument needs to provide a \"plugin\". Each plugin is responsible for reading the data, convolving the source model (provided by 3ML) with the instrument response, and returning a likelihood. In our case, we'll compute a binned Poisson likelihood:\n", + "\n", + "$$\n", + "\\log \\mathcal{L}(\\mathbf{x}) = \\sum_i \\log \\frac{\\lambda_i(\\mathbf{x})^{d_i} \\exp (-\\lambda_i)}{d_i!}\n", + "$$\n", + "\n", + "where $d_i$ are the counts on each bin and $\\lambda_i$ are the expected counts given a source model with parameters $\\mathbf{x}$. \n", + "\n", + "In this example, we will fit a single point source with a known location. We'll assume the background is known and fixed up to a scaling factor. Finally, we will fit a Band function:\n", + "\n", + "$$\n", + "f(x) = K \\begin{cases} \\left(\\frac{x}{E_{piv}}\\right)^{\\alpha} \\exp \\left(-\\frac{(2+\\alpha)\n", + " * x}{x_{p}}\\right) & x \\leq (\\alpha-\\beta) \\frac{x_{p}}{(\\alpha+2)} \\\\ \\left(\\frac{x}{E_{piv}}\\right)^{\\beta}\n", + " * \\exp (\\beta-\\alpha)\\left[\\frac{(\\alpha-\\beta) x_{p}}{E_{piv}(2+\\alpha)}\\right]^{\\alpha-\\beta}\n", + " * &x>(\\alpha-\\beta) \\frac{x_{p}}{(\\alpha+2)} \\end{cases}\n", + "$$\n", + "\n", + "\n", + "where $K$ (normalization), $\\alpha$ & $\\beta$ (spectral indeces), and $x_p$ (peak energy) are the free parameters, while $E_{piv}$ is the pivot energy which is fixed (and arbitrary).\n", + "\n", + "Considering these assumptions:\n", + "\n", + "$$\n", + "\\lambda_i(\\mathbf{x}) = B*b_i + s_i(\\mathbf{x})\n", + "$$\n", + "\n", + "where $B*b_i$ are the estimated counts due to background in each bin of the Compton data space with $B$ the amplitude and $b_i$ the shape of the background, and $s_i$ are the corresponding expected counts from the source, the goal is then to find the values of $\\mathbf{x} = [K, \\alpha, \\beta, x_p]$ and $B$ that maximize $\\mathcal{L}$. These are the best estimations of the parameters.\n", + "\n", + "The final module needs to also fit the time-dependent background, handle multiple point-like and extended sources, as well as all the spectral models supported by 3ML. Eventually, it will also fit the polarization angle. However, this simple example already contains all the necessary pieces to do a fit." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ce42ab82-3bbd-4729-8f84-a4e32eb3bb24", + "metadata": {}, + "outputs": [], + "source": [ + "from cosipy import COSILike, BinnedData\n", + "from cosipy.spacecraftfile import SpacecraftFile\n", + "from cosipy.response.FullDetectorResponse import FullDetectorResponse\n", + "\n", + "from scoords import SpacecraftFrame\n", + "\n", + "from astropy.time import Time\n", + "import astropy.units as u\n", + "from astropy.coordinates import SkyCoord\n", + "from astropy.stats import poisson_conf_interval\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "from threeML import Band, PointSource, Model, JointLikelihood, DataList\n", + "from cosipy import Band_Eflux\n", + "from astromodels import Parameter\n", + "\n", + "from pathlib import Path\n", + "\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "id": "8d1c0168-9823-4eb7-930e-5dc61d6448ca", + "metadata": {}, + "source": [ + "## Read in binned data" + ] + }, + { + "cell_type": "markdown", + "id": "dc364649-56e4-4bb1-8403-74e90cf3ed05", + "metadata": {}, + "source": [ + "Define the path to the directory containing the data, detector response, orientation file, and yaml files" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cdd53b2a-5176-42cf-bb2c-feb3387fc0a4", + "metadata": {}, + "outputs": [], + "source": [ + "data_path = Path(\"/path/to/files\")" + ] + }, + { + "cell_type": "markdown", + "id": "d898bbd7-9ed0-4a27-bd5a-67414178733d", + "metadata": {}, + "source": [ + "Read in the spacecraft orientation file & select the beginning and end times of the GRB" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ed2c03a0-63e3-4044-9e16-50f0f17996af", + "metadata": {}, + "outputs": [], + "source": [ + "ori = SpacecraftFile.parse_from_file(data_path / \"20280301_3_month.ori\")\n", + "tmin = Time(1842597410.0,format = 'unix')\n", + "tmax = Time(1842597450.0,format = 'unix')\n", + "sc_orientation = ori.source_interval(tmin, tmax)" + ] + }, + { + "cell_type": "markdown", + "id": "f579870f-c854-450d-84e8-f1d5ef0753d1", + "metadata": {}, + "source": [ + "Create BinnedData objects for the GRB only, GRB+background, and background only. The GRB only simulation is not used for the spectral fit, but can be used to compare the fitted spectrum to the source simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "3b5faaa1-1874-4d43-a6ae-7e1b0aaabb26", + "metadata": {}, + "outputs": [], + "source": [ + "grb = BinnedData(data_path / \"grb.yaml\")\n", + "grb_bkg = BinnedData(data_path / \"grb.yaml\")\n", + "bkg = BinnedData(data_path / \"background.yaml\")" + ] + }, + { + "cell_type": "markdown", + "id": "cf8b5ab1-7452-493e-b516-73fa72e455e5", + "metadata": {}, + "source": [ + "Load binned .hdf5 files" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "620159d2-f01a-453e-9e4c-075c99740086", + "metadata": {}, + "outputs": [], + "source": [ + "grb.load_binned_data_from_hdf5(binned_data=data_path / \"grb_binned_data.hdf5\")\n", + "grb_bkg.load_binned_data_from_hdf5(binned_data=data_path / \"grb_bkg_binned_data.hdf5\")\n", + "bkg.load_binned_data_from_hdf5(binned_data=data_path / \"bkg_binned_data_1s_local.hdf5\")" + ] + }, + { + "cell_type": "markdown", + "id": "a6bdaee8-45d7-41df-9835-413c1e397c12", + "metadata": {}, + "source": [ + "Define the path to the detector response" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "acccab93-7f9c-4167-a8f9-eedcf74b8a05", + "metadata": {}, + "outputs": [], + "source": [ + "dr = str(data_path / \"SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.nonsparse_nside8.area.good_chunks_unzip.h5\") # path to detector response" + ] + }, + { + "cell_type": "markdown", + "id": "31b5dbd7-8a50-43db-af66-7b8601f7e2fd", + "metadata": { + "tags": [] + }, + "source": [ + "## Perform spectral fit" + ] + }, + { + "cell_type": "markdown", + "id": "2210f6ff-c988-455a-be15-882d0b795072", + "metadata": {}, + "source": [ + "Define time window of binned background simulation to use for background model" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "a29ec8c4-edea-40bf-8a3e-8038ba47bf8e", + "metadata": {}, + "outputs": [], + "source": [ + "bkg_tmin = 1842597310.0\n", + "bkg_tmax = 1842597550.0\n", + "bkg_min = np.where(bkg.binned_data.axes['Time'].edges.value == bkg_tmin)[0][0]\n", + "bkg_max = np.where(bkg.binned_data.axes['Time'].edges.value == bkg_tmax)[0][0]" + ] + }, + { + "cell_type": "markdown", + "id": "7441f3f1-ebe6-467f-b8ab-1baa70f20b15", + "metadata": {}, + "source": [ + "Set background parameter, which is used to fit the amplitude of the background, and instantiate the COSI 3ML plugin" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "a9f21e74-5f62-4030-9815-6c77ebaab16f", + "metadata": {}, + "outputs": [], + "source": [ + "bkg_par = Parameter(\"background_cosi\", # background parameter\n", + " 0.1, # initial value of parameter\n", + " min_value=0, # minimum value of parameter\n", + " max_value=5, # maximum value of parameter\n", + " delta=1e-3, # initial step used by fitting engine\n", + " desc=\"Background parameter for cosi\")\n", + "\n", + "cosi = COSILike(\"cosi\", # COSI 3ML plugin\n", + " dr = dr, # detector response\n", + " data = grb_bkg.binned_data.project('Em', 'Phi', 'PsiChi'), # data (source+background)\n", + " bkg = bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em', 'Phi', 'PsiChi'), # background model \n", + " sc_orientation = sc_orientation, # spacecraft orientation\n", + " nuisance_param = bkg_par) # background parameter" + ] + }, + { + "cell_type": "markdown", + "id": "e6d55283-abb0-4295-9e5c-80a5c717f0ba", + "metadata": {}, + "source": [ + "Define a point source at the known location with a Band function spectrum and add it to the model" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "98b2d026-c24d-4cfe-8b7b-41415fce5d16", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now converting to the Spacecraft frame...\n", + "Conversion completed!\n" + ] + } + ], + "source": [ + "l = 93.\n", + "b = -53.\n", + "\n", + "alpha = -1 # Setting parameters to something reasonable helps the fitting to converge\\n\",\n", + "beta = -3\n", + "xp = 450. * u.keV\n", + "piv = 500. * u.keV\n", + "K = 1 / u.cm / u.cm / u.s / u.keV\n", + "\n", + "spectrum = Band()\n", + "\n", + "spectrum.beta.min_value = -15.0\n", + "\n", + "spectrum.alpha.value = alpha\n", + "spectrum.beta.value = beta\n", + "spectrum.xp.value = xp.value\n", + "spectrum.K.value = K.value\n", + "spectrum.piv.value = piv.value\n", + "\n", + "spectrum.xp.unit = xp.unit\n", + "spectrum.K.unit = K.unit\n", + "spectrum.piv.unit = piv.unit\n", + "\n", + "source = PointSource(\"source\", # Name of source (arbitrary, but needs to be unique)\n", + " l = l, # Longitude (deg)\n", + " b = b, # Latitude (deg)\n", + " spectral_shape = spectrum) # Spectral model\n", + "\n", + "# Optional: free the position parameters\n", + "#source.position.l.free = True\n", + "#source.position.b.free = True\n", + "\n", + "model = Model(source) # Model with single source. If we had multiple sources, we would do Model(source1, source2, ...)\n", + "\n", + "# Optional: if you want to call get_log_like manually, then you also need to set the model manually\n", + "# 3ML does this internally during the fit though\n", + "cosi.set_model(model)" + ] + }, + { + "cell_type": "markdown", + "id": "27ded6d5-4551-4623-8483-b3f4e8b02040", + "metadata": {}, + "source": [ + "Gather all plugins and combine with the model in a JointLikelihood object, then perform maximum likelihood fit" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "d56d3ad6-7226-437a-a037-57fbcd80d196", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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12:43:03 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
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source.spectrum.main.Band.K(3.10 -0.20 +0.21) x 10^-21 / (cm2 keV s)
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source.spectrum.main.Band.K(3.10 -0.20 +0.21) x 10^-21 / (cm2 keV s)
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statistical measures
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" + ], + "text/plain": [ + " statistical measures\n", + "AIC 85838.098697\n", + "BIC 85840.098697" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n", + " * source (point source):\n", + " * position:\n", + " * l:\n", + " * value: 93.0\n", + " * desc: Galactic longitude\n", + " * min_value: 0.0\n", + " * max_value: 360.0\n", + " * unit: deg\n", + " * is_normalization: false\n", + " * b:\n", + " * value: -53.0\n", + " * desc: Galactic latitude\n", + " * min_value: -90.0\n", + " * max_value: 90.0\n", + " * unit: deg\n", + " * is_normalization: false\n", + " * equinox: J2000\n", + " * spectrum:\n", + " * main:\n", + " * Band:\n", + " * K:\n", + " * value: 0.030991288726256453\n", + " * desc: Differential flux at the pivot energy\n", + " * min_value: 1.0e-99\n", + " * max_value: null\n", + " * unit: keV-1 s-1 cm-2\n", + " * is_normalization: true\n", + " * alpha:\n", + " * value: -0.27668340897900645\n", + " * desc: low-energy photon index\n", + " * min_value: -1.5\n", + " * max_value: 3.0\n", + " * unit: ''\n", + " * is_normalization: false\n", + " * xp:\n", + " * value: 474.671132780481\n", + " * desc: peak in the x * x * N (nuFnu if x is a energy)\n", + " * min_value: 10.0\n", + " * max_value: null\n", + " * unit: keV\n", + " * is_normalization: false\n", + " * beta:\n", + " * value: -6.753586360838797\n", + " * desc: high-energy photon index\n", + " * min_value: -15.0\n", + " * max_value: -1.6\n", + " * unit: ''\n", + " * is_normalization: false\n", + " * piv:\n", + " * value: 500.0\n", + " * desc: pivot energy\n", + " * min_value: null\n", + " * max_value: null\n", + " * unit: keV\n", + " * is_normalization: false\n", + " * polarization: {}\n", + "\n" + ] + } + ], + "source": [ + "results = like.results\n", + "\n", + "print(results.display())\n", + "\n", + "parameters = {par.name:results.get_variates(par.path)\n", + " for par in results.optimized_model[\"source\"].parameters.values()\n", + " if par.free}\n", + "\n", + "results_err = results.propagate(results.optimized_model[\"source\"].spectrum.main.shape.evaluate_at, **parameters)\n", + "\n", + "print(results.optimized_model[\"source\"])" + ] + }, + { + "cell_type": "markdown", + "id": "5eaec533-b5b3-45c4-94df-75453e2df3bf", + "metadata": {}, + "source": [ + "Evaluate the flux and errors at a range of energies for the fitted and injected spectra, and the simulated source flux" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "cc7d6f50-06cd-450a-83d9-115b67d83b30", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now converting to the Spacecraft frame...\n", + "Conversion completed!\n" + ] + } + ], + "source": [ + "energy = np.geomspace(100*u.keV,10*u.MeV).to_value(u.keV)\n", + "\n", + "flux_lo = np.zeros_like(energy)\n", + "flux_median = np.zeros_like(energy)\n", + "flux_hi = np.zeros_like(energy)\n", + "flux_inj = np.zeros_like(energy)\n", + "\n", + "for i, e in enumerate(energy):\n", + " flux = results_err(e)\n", + " flux_median[i] = flux.median\n", + " flux_lo[i], flux_hi[i] = flux.equal_tail_interval(cl=0.68)\n", + " flux_inj[i] = spectrum_inj.evaluate_at(e)\n", + " \n", + "binned_energy_edges = grb.binned_data.axes['Em'].edges.value\n", + "binned_energy = np.array([])\n", + "bin_sizes = np.array([])\n", + "\n", + "for i in range(len(binned_energy_edges)-1):\n", + " binned_energy = np.append(binned_energy, (binned_energy_edges[i+1] + binned_energy_edges[i]) / 2)\n", + " bin_sizes = np.append(bin_sizes, binned_energy_edges[i+1] - binned_energy_edges[i])\n", + "\n", + "expectation = cosi._expected_counts['source']" + ] + }, + { + "cell_type": "markdown", + "id": "8cb8c4aa-ef51-4f19-93dc-2ac7d7d2f189", + "metadata": {}, + "source": [ + "Plot the fitted and injected spectra" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "f8dbd36f-4b16-4bec-8835-8f6f876ab169", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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bUcf+kVzbqxx5afIH+LgEoFaa3iYOrnZ4hrjiFeKKZ4grnqGmPzt7OciHbBuktlfhF+WJX5Qn3ePDMBqNlOZqyTpRSPaJQrJPFphHMkdGXoOHoxeaikJS/lfImW82ExDjRfiAADafWM/fnn2K8PBw3nvvvQ7/iLsQoi5Jti7Bw8Oj3oJspaWlgKlgm7A9rVbL/fffz+HDh4mOjjY/8KCv0VN4vowQ7878wi8YDAZeve1Dwjy6mF/rWxXO/XFPE+LZmUD3TubjSrUCr1A3uoSF4NXJDe8wN7w7ueHkYS9JVTumUChwD3DBPcCFbld3wmg0UnJBS/bJQqJOBDP62AQqS0yFWY1GyDlVRM6pIj7a/ilgWhJgV+2M0Wg0/5xcrKwjPzft38Evk698URMNuLnLlS8SrZokW5cQGRlJYmIiOp2u1rqt1NRUoHYh06aQacSGq6io4MCBA/zyyy/07duXMWPGmM85OTnx008/UVBQQH5uATtXHaPgbCmF6SUY9EZ8SzozJPxqOnlF4GrnVatdd0dPxgy4Fp9wd3zC3c2JlUegM0qVLE7v6BQKBR6BLngEmpIvg8FIbnIx6QcvcO5gLppsU+X9PiFD0BlquFCaSdracj7fup3okSF0GRnCviO7mDNnDnfeeSf33HNPvUsThLCUzp074+joiKOjI1VVVfTr14+VK1fWKU/TUKtXr2bYsGF07dq13vP79u3jwQcfRK1Ws2TJEt58803efvttoqKirvhagNmzZzN48GBuu+22JsVnaXPmzGHgwIHcfvvtFm9bkq1LGDlyJOvXr2f79u21iphu2rQJX19fevTo0az2ZRqxflVVVSiVSuzs/lhwfvDgQUaNGgWYalgN6DGEvJRi8lI15KVqCLQPR6MswU3hzZEfTuP4p2rmQzpfzZDOVwPgHuiMT2d3fDu74/P7f07u1t9AVrQPSqWCwBgvAmO8GHJHN4ozy0g/eAH/g56MSrkWnV6HUqGkNK+Cw1+ncPjrFD478RbHjx/nueeeIyYmhhtuuMHWtyHauYsbURuNRq677jpWr17N448/3qS2Vq9eja+v7yUTpjVr1nDPPffwzDPPAHDNNdc0+LWZmZl8//33vPbaa42K6a8DIJc61pjXXzR37lxGjhzJrbfeavH9WDtksrVv3z4qKyvNtZDS09PZtm0bAHFxcTg6OhIXF8fAgQNZvnw5Wq2WkJAQNm/ezP79+0lISKizNqixZGSrtv/973+88sorHD58mG+++YZrr70WgKryGvzVoSiVSgwGAz/+bytflO+o9doZQ+bg7OBmXluFAjyDXUw1lCI98O3sjne4O/ZOHfLHXViJ5+9r9/pMiUJbXMW5w7mk7csh83g+GE1TiLkXTAVsne1dcU4PJeNYPsE9fVAqFVy4cIELFy7Qu3dvG9+JaI+qqqooLy/Hy+uPEf1ly5bx+eefo9PpCAwM5F//+hedOnVi/fr1/P3vf0epVKLT6Vi8eDF5eXkcOHCAJ598koSEhDpb7ixZsoS1a9fi7OzMp59+yrZt2+jbty8bNmxg3759l30tmDabv/nmm81T7DU1NTz//PNs2bKF6upqunXrxvvvv4+npyfTp0/H3d2d06dPc/78ed59911mzZrFqFGj+OWXX5g9ezYRERHMnDmT8vJyHB0def311xk+fDhnz55l4MCBPPnkk/z000/ceOONREdH17nfqVOn4u/vT0REBJs3b65TuLy5Otx2PQDTpk0jJyen3nNr164lKMi0/YdWq2XlypW1tuu566676t2up6k60nY9JSUl/Pzzz/z88888++yztX67+O9//2seun3s7llMG3YfeWeKKc40TdV8e+zfeDn5EukTU2ubGgA3fyd8Iz3wi/TAL8oDn3B37J2vXCBTCGsoL6wkZVcmyTsyKc4qJ68smyzNOfqEDAHA2cuBrleF8uXPH/HKa0sYNWoU77zzziXr9onWp76tW1rDmq0/TyOmpaUxYMAAfvzxR9RqNZ999hnbtm1jxYoVqFQqPvnkE7788ku+/fZb+vTpw4oVKxg2bBgGg4GSkhI8PT0ZPXo0Tz/9NJMnT663v+nTpzNw4ECeeOIJc/8bNmygV69eV3zt2LFjefrpp82/WP/jH//AYDCQkJAAwEsvvUR+fj5vvvkm06dP5+jRo+zYsQNXV1e2bdvGmDFj2LFjByNGjKC6upro6GhWrlzJNddcw65du7jllltISUkhLy+PiIgIPv30U+644w6AS94vmLbe02q1LFmypFa8sl1PE3z++ecNus7Z2ZmnnnqKp556ysoRdQwPPPAAX3zxBQCTJk2ie9ee5KUUcyG5iMpDph/eALcQ8o6VkayrXRR2auxdgOmJQP9oT/yjPfGLMo1cXXwEX4jWwMXbkT5Touh9XST5qRpO78jkzJ5OVJfrANAWVfHLV6dYsc60QfCePXvw8fGxZciiHbk4jajT6Xj44YeZO3cur732Gt988w0HDhxgwIABQO2nt8eOHcusWbO4+eabGT9+PH379rV6nBkZGbX2jv3mm28oKSnhyy+/BEw7dVysawmmQRJXV1fz1127dmXEiBGAadDC3t7ePI05YsQI/P39OXr0KEFBQTg6OtZah3W5+w0MDGTHjtqzJ5bQIZOt1qC9TiO+8cYbbN26lQsXLrBv375a5/r3HmhOtlbM/TeDfMZwcVzVaLTnjRvX4uLgZr5eoVLgE+7+e3LlgV+0J+4BzvJkl2gTFAqFubRE3F3dOHc4j+QdmZz/NQ/0Rib3uoOtp9cT4tmZQ/93jpprVXTq749SqWDXrl04ODgwaNAgW9+GaKPUajU33XQTzzzzDK+99hpGo5GEhARmzJhR59rly5fz22+/sXXrVu69917uvPNOnn32WavG5+zsTEVFhflro9HIe++9V+sBqD/7c6L116///ATwn1089tc9Ty93v5WVlTg5OdVpq7kk2bKRtr5APi8vj/z8fLp3717r+FdffcWuXbsASDp8BkOBHTmnCslJKqLyjCuju0wi0qcbUY69+fMEtkKhwNffh4CuXvh39cI/2hPfCHcpCiraBZWdiojBgUQMDqS8sJITP6bjutmF0dGTqNRVkHOyiJyTRbj5O9FzQmf+lvA3Dhw8wPjx4/nss89k5Es0yZYtW8zLU6ZMmcKbb77J9ddfj7e3NzU1NRw/fpx+/fpx6tQpevbsSc+ePVGr1fz444+AqcRRfSWQGuJKr+3duzenTp1i2LBh5viWL19OXFwczs7OaLVa0tLS6Nmz5xX76tatG1VVVWzZsoUxY8awZ88ecnNziY2NJS8vr871l7pfgJMnT9KnT58m3PHlSbIlGqW8vJyBAwdy6tQpRowYwc6dOwEw6Azkp2mI9u/BLnbh4uDGR89+Q7TfH09thnpGcOfA35+KUYB3mBsBXbzw7+pJQBcv3PydZNRKtHsu3o4Mui2GfjdEk7wzk+PfnzWXkSjNreA/r3/DgYMHAMjMyKy1wFm0Xq2lFtbNN9+Mo6MjNTU1dO7cmfffN01X33333RQUFDB69GgUCgU6nY7777+ffv368dxzz3H69Gns7e1xdnZmxYoVADz00EPMmTOHpUuX1rvI/XKu9Nqbb76Zjz/+2DzSNm/ePBYuXMiQIUPMnwNz585tULJlb2/PV199xZNPPmleIP/FF1/g4uJSb7J1qfs1Go1s3ryZ+fPnN/g+G6pDLpBvTVrjAnmDwcDx48fZsWMHUVFR5gWMF3Xp0oWUlBTs7ezZ/skhCs+Uk5tcbNp/rvwC1fpqAtxCUCr+eHRW7aDCP9qTgBgvAmK88I/ykEXsQgBGg5GMo/kc+y6NrOMFVOuq2Hd2K9+f+JxbBs7gvkfvpfekCBxcTe+X9PR0wsPDbRx1x9bcxdLC9DkzaNAgvv32W0JDQ20dDmAq7fTpp5/yySef1DknC+TbqNa8ZuvIkSP0798fgJtuuolrr70WXbWe3ORisk8WEu3ZC6OvmijfHuz/4jdc7P9YZ+XjEgCYFrIHxngR0M2bwBgvfDu7o1RLoVAh/kqhVNCprx+d+vpReL6U49+fxXGXI8Mi41EqlBz59gwnfjxL70mR+PZzpHvP7sTFxbFw4UJGjhxp6/CFaBKlUsm//vUvzp4922qSLY1GwyuvvGKVtmVky8ZsNbKVmprKZ599xvbt23nsscdqFVvU6/V4e3tTUlKCt4cPH81aT/4ZDQa96UelvsWILt6OBHb3JrCbqeijZ7ArCqVMCQrRFNqiSn79NpVTm8+Z33cAXx//kO+PmZ7Weuqpp3jjjTdsFGHHJiNbHY+MbIkrqqqqQqVS1aprdfr0aZ5//nnA9AjtlOumkndGQ/ZvBWSdKGBs5A3YKx3o6hfLhaSiWsmVQqHA1c+JoO7eBHXzJrC7t6y3EsKCnL0cGTa9B7GTIjj8vxSSd2RgNEAntyj83YIp0uYxZdDt6Kr18hCJEG2AJFvt2Lp163jjjTfYu3cvP/zwg3nLG4ChQ4ehUqnQ6/Xs+fEAnzzwE7pqg/n8xO631mrLPcCZwO7epgSruzeuvpZ/NFYIUZubnxOjHoqlz5RIDn+dAoqr6N9pBOeKzpC6sZCc3dsZclc3IocGsXPnTvbs2cOsWbNktEWIVkaSLRtpiTVbRUVFbN26FYCtW7cSG9WfrOMFZP1WQPaJAh4e9hyhnhH4ugTWSrQAXP2cCO7hTVAPH4J7eOPiI8mVELbiEejC6Mf60GdKJAe/TEb1s2k0S1tcxdZ3jnBsUyoJXz7ObyePs3LlSrZs2SKL6IVoRSTZspGWqLM1MNa0PUigTzCnNmXxVdLOWuf7hQ4z/9nZy4Hgnj4E9fAmuIcPbv7OCCFaF69QN+Jn9afgbAm//DeJjKP5AOzZs5cTp04A4OnhSadOnWwZphDiLyTZaqfWv7iPC6eLeGXKGrxd/Oqcd3C1I7iHD8G9fAju6YN7oFRmF6Kt8OnszoR5gzh3OJc9q0/QlV4suOZt1h7+gImhd5OyK4suI0LMD6lcqsK2aF/+vDfhpSxYsICePXty6623XvKay9m2bRvV1dWMHz++Sa99+umnOXDgQJP6bssk2WqnXHxMazYuJlpqBxWB3bwJ7uVDSE8fvMPc5GlBIdq4sH7+BPf04eiGNFTfKpkz5p8A7Hj/GCd+TGfEA73QO1USHx/P4sWLmTJlio0jFra2aNGiZr1+27ZtlJWVNSnZ6sik8FE71amPH4HdvOh/UzSTFwzh7pXxTJg7kN6TIvDp7C6JlhDthNpeRf8bo7l52SjCBvibj+enlvC/+buZdu2dHD9+nKlTp/Luu+/aMNL2bfny5YSGhhIaGsq2bdtqnUtLSzOfmzlzZp3XTpkyxXz+r1avXs3y5csbFcvo0aOZO3cuI0eOJCoqikceecR8bvr06bzzzjsA1NTUMG/ePAYPHkzfvn257bbbKC4uBkw1px544AFiY2Pp06cPM2bM4Ndff+X999/n448/pm/fvubE7YcffmDEiBEMGDCAIUOG1NrIOSEhgejoaK666io2bNjQqPtoT2Rky0asvUC+y6gQuowKsUrbQojWx83PifFzBpBxNI/dH/5GaW4FNbpqii6UAODt6cMtt9xi4yjbr5KSEjIzMwFTuZ0/0+v15nNFRUV1XpuXl2c+/1fl5eWUlJQ0Op4zZ86Yp/x69OjB3r17GTp0aK1rli5diqurKz///DMAL730Ei+88AJvvvkms2bNwtXVlSNHjqBUKsnLy8PPz49HHnmEsrIyli1bBphqNi5cuJBNmzbh7u5OSkoKV111FWfPnmXTpk2sW7eOX3/9FScnp1r1HDsaSbZspK1vRC2EaJ1Ce/tx87JRHNuYxuGvU5g56kX2nt2Co9qJk1/n4HmvN/ZO8k+/pbm7uxMSYvoF18HBodY5lUplPlffXpd+fn7m83/l4uJSJ3lriNtuuw2VSoWTkxN9+/blzJkzdZKtb775hpKSEr780lQot7q6mqioKAA2bNjAwYMHUSqV5hjrs2nTJlJSUmqVFgI4f/48W7du5dZbb8XV1RWAGTNm8PLLLzf6XtoDeccJIUQ7o1Ir6Ts1iugRwWxfcZRhirEAJO/IJPNoPqOf6INfF3emTJnCQw891KFHHCxl9uzZzJ49u95zERERZGRkXPK169atu+S56dOnNymeP9daU6lU6HS6OtcYjUbee+89xowZ06Q+LrYxYcIEPv7443rPCRNZsyWEEO2Uq48TE/8+mGH39UBlZ/rnXltcxXcv/8wjt8xi06ZN3HjjjebdJETHMmXKFJYvX45WqwVAq9Xy22+/mc8tXboUg8FUgzEvLw8wjeBpNBpzG+PHj2fTpk0cP37cfOzitOTYsWP5/PPPKS8vR6/Xs3r16pa4rVZJki0hhGjHFAoFPcaFc9OrI/CNdAdMIw6/HjoCmEY9rr/+ehtGKFraxTIg8+bNo2/fvgwZMoTevXsTFxfHr7/+CsDrr7+OVqulV69e9O3bl/nz5wNwww03cODAAfMC+S5duvDvf/+bBx54gD59+tC9e3fefPNNACZPnszkyZPp06cPY8aMoXfv3ja539ZANqK2MVttRC2E6HgMBiNH16dy6Mtk9DoD+89upaiygKf/9jSD7+iGSi2/fzdEW96IeuLEidxzzz3cdttttg6lTWnu37m8s4QQooNQKhX0nRrFDf8cjleoG3ERY7i2+y38timdr+fuouRCOWAqN3CxBIBoP+Lj4yktLWXSpEm2DqXDkWRLCCE6GK9QN27853D6TInkYmF5TXY5X8/bzTuLP+C+++5jwIABHD582LaBCotKTExk586duLm52TqUDkeeRrSRltiIWgghLkWpVjLothg6Dw7gp9cOoS2qorKiihf/uQAw1U/av38//fr1s3GkQrR9MrJlI/Hx8SxZsqTeasJCCNFS/CI9uXnpSEJifVAr1Tw9Zgmdvbsyssc47rrlHluH16rJkueO4+JTmU0lI1tCCNHB2TvbMWHeIH793xkOfgXPxi/FYNTz1bO7Gf1Yb8IHBABQUVGBk5OTjaO1PTs7OxQKhbmqumzy3X4ZjUaqq6vJy8tDqVRib2/fpHYk2RJCCIFCoaDfjdEExHiR+MYhqst11FTo+Gn5IXqOD8e1v57x48ezevVqrrnmGluHa1MqlYrQ0FAyMjI4e/asrcMRLcDZ2ZmwsDBzRf3GktIPNialH4QQrY22uIofXj1AwVnTnnyVNRW8sm0OGflnUalUrF+/nmuvvdbGUdqeXq+npqbG1mEIK1OpVKjV6maNYMrIlhBCiFqcPR2Y+tJQ9qw5wanE8ygUCnwdg8jgLDHR3Rg+fLitQ2wVVCoVKpXK1mGINkAWyAshhKhDqVIyYkYvrn6iD87Ozjw6fD7X9bqDGbHzyNhfZOvwhGhTZGSrmaqrq3nttdc4cOAA5eXldO7cmccff5zY2FhbhyaEEM0WNSwY3wh3vv/nL0xR3gXA3tUnKDpfStzd3anR11BRUYGXl5eNIxWi9ZKRrWbS6/UEBQXx3nvv8d133zF16lTmz59PZWWlrUMTQgiL8Ahy5cYlI/CL8gDAaISTief5/pVfuOO2Oxk6dCipqak2jlKI1kuSrWZycnJi+vTpBAQEoFQqufbaazEYDGRkZNg6NCGEsBh7ZzuuezGOiLhA87FVn7/L/779mqSkJOLj42WxuBCX0OGmEbVaLWvWrCE5OZnk5GQ0Gg3Tp09nxowZ9V67atUqtm7dSmlpKWFhYdx5552MHTv2ku2np6dTVVVFcHCwNW9DCCFanFKlZOyT/TgQeJpfvz1DXOex7E/fzoXSTJ576EVUClksLkR9OlyypdFoWL9+PVFRUYwcOZINGzZc8tqEhAROnTrFww8/TKdOnUhMTGThwoUYDAbGjRtX5/rKykoWL17MPffcg7OzszVvQwghbGbgtK64Bzqza5WSeeOWkZx7HPvkIA5+lUzsxAgc3ZpW+FGI9qrDJVuBgYFs3LgRhUJBcXHxJZOtvXv3cuDAARYsWEB8fDwA/fv3JycnhxUrVjBmzJhaj/zqdDpeeOEFwsPDufvuuy/Zf35+PgUFBeav09PTLXRnQgjRcrqOCsXd35kfXztIX/uh1FToOLYhjfKCSnpPjqRCVUJISIitwxSiVehwa7YUCkWDCpPt3LkTJycnRo8eXev4xIkTyc/P58SJE+ZjBoOBxYsXo1QqmTt37mXbX7duHQ8++KD5v5dffrnJ9yKEELYU2M2b618ehqufaQsfg95Iyq4sVixcTVRUNCveW2HjCIVoHTrcyFZDpaWlER4ejlpd+1sUFRVlPn+xvMOyZcsoKChg6dKlda7/qylTptQqCJieni4JlxCizXIPcOGGfwxj0ysHyEvRkFOSwcK1c9AZanjs8ccICQxlyo3X2TpMIWyqw41sNZRGo8Hd3b3OcTc3NwBKSkzbWOTk5LBhwwZOnDjBlClTuOaaa7jmmms4cuRIve36+voSExPD+fPn+eijj1i3bp31bkIIIVqAg4s9kxfEET7AnwC3EMbGTAVgaLfRBOqjKUgvsXGEQtiWjGxdRkOmGwMDA9mxY0ej246Pjyc+Pt68N6IQQrRlKrWS+Nn92fnBMW7iPkI9IxgYNpJzv+SjMKooy6ugU18/lGr5HV90PJJsXYKHhwcajabO8dLSUoB6R72EEKIjUygUjHq4Nw4udii+V4ARijPK0FfrMRqMFF0oIWZkGE7uDrYOVYgWJcnWJURGRpKYmIhOp6u1DutileSIiIhmtZ+YmEhiYiJlZWXNakcIIVqbIXd1x87ZjsNfp2A0GCnNreBoYhJvbHuescPG8/zCBHwjPGwdphAtRsZzL2HkyJFUVFSwffv2Wsc3bdqEr68vPXr0aFb78fHxLFmyhJkzZzarHSGEaI363xjNkLu6obJTotPXsOTbefx25ihvfbKMfyx8hTN7s9FV620dphAtokOObO3bt4/Kykq0Wi1geiJw27ZtAMTFxeHo6EhcXBwDBw5k+fLlaLVaQkJC2Lx5M/v37ychIaFWja2mkJEtIUR712tCZ+wcVez9+CR9QoZwJv8kbo4e9I0cTHFmGdrCSjoPCsDNX4pAi/ZNYTQajbYOoqVNmzaNnJyces+tXbuWoKAgwLRdz8qVK2tt13PXXXdddruexrq4QH7lypXExMRYrF0hhGgtUvdls/vD3/ju0Fd09etF54BoooYF4+LtiEIB/l29CO7pg1J55YeShGiLOmSy1ZpIsiWE6AjOHcpl56rjVBRXAaBUK4gaFoyrr6kgqrOXAxGDAnF0l61+RPvTIacRWwOZRhRCdCRh/f0Z/WgsO1f+Rll+BQadkZTdWaQoD9CjRw96RMVycss5OvX2wzdSFs+L9kVGtmxMRraEEB1JzqkCdvzrOJqccr4/8Tn/O7oGTzdvVv/jc0IDwwDwDHElvL8/aofmrY0VorWQpxGFEEK0mMBuPox8OBbXAAeOZx8AoLi0kG+//5/5muLMMk4knqPkQrmtwhTCoiTZEkII0aKCunlz1YN9mH/bq4R4hHNjn/sY6j6RwnN/bOtTU6EjZVcW53/Nw6Az2DBaIZpP1mzZiKzZEkJ0ZME9fRl7/yAcHf6PssxqANIP5GLQG80FT41GyE0ppjRXS+dBATh7OdoyZCGaTNZs2Zis2RJCdGTpBy9w+H9nyE/9Y3s092g7ImPDau1Pq1QqCOrhTUCMV4P2rRWiNZFpRCGEEDYT1t+f3tdF4N/FE4CckgweW34Xr7yzuNZ1BoORzOMFnN6eQVV5jQ0iFaLpJNkSQghhMwqFgohBgfQYH4Z7Z3teTXyWgvILfLnjYz5cs4q/Tr6U5VdyMvEcBWdLLtGiEK2PrNmyEVmzJYQQJgqlgsi4IAx6I3cff4j31r9KqGcEoYoYMo/lExLrW2vqUF9j4OyBCxRnl0uJCNEmyJotG5M1W0IIYaLXGUjemcnqlR/TSdcDB7VpQbxPZ3c69fOrd62WnaOK8AEBeAS5tHS4QjSYTCMKIYRoFVRqJdHDg7nnvrvoMjjMfLzgbAnpB3IxGuqODdRU6knZncW5w7lSIkK0WpJsCSGEaDXU9iq6jAwhJNaH8EEBoACj0ci2fYmk7s/GUE/CBZB3RsPJzecpL6ps4YiFuDJZsyWEEKJVsXNUEz08BH2NgcoaLS++M5dD5/dQpM1niuEWIuICUarqjhVUllaTtPU8Qd29CYzxRqGUEhGidZBky0ZkgbwQQlyag6sd0cOD2bz7Rw6d3wPA2kMf0CdkCAa9kcihQajs6iZcRgNk/VaIJsdUCNXR1b6lQxeiDostkD948CCHDh3i+PHj5ObmotFocHR0xNPTk8jISPr27cvQoUPx8fGxRHfthiyQF0KIS9PklPP4QzP5JvFL7o97mtjAQQA4ezsQNSwYtf2ln0RUqZWE9vbFN9KjpcIVol7NSra0Wi1fffUV69evJzc311wPxd7eHnd3d6qqqigvL8dgMC1aVKlUDB8+nGnTphEbG2uZO2jjJNkSQojLu5BSyM8/HsFD7cuZ3Vnoa0yfKU4e9kSNCMHuCqUfPINdCOvvj52jTOYI22jyT963337LRx99RFFREdHR0UyZMoWePXsSExODs7Oz+Tqj0UhGRgYnTpzgl19+YdeuXezcuZPhw4fz+OOPExwcbJEbEUII0T4FRHszsKYPWb8VED0yhDO7s9BV6anQVJOyI4OoESHYO13646w4q5zygnOE9ffHM8S1BSMXwqTJI1tXX30148aN4/bbbyciIqLBr6uqqiIxMZF///vfXHPNNUyfPr0p3bcbMrIlhBANc+5QLnmpGipLq/l27XoKNQXERYzB3llN9MgQHFzsrtiGb2d3Qvv4orKTQqii5TR5ZOvf//43ISEhjX6dg4MDkyZNYsKECeTl5TW1eyGEEB1Mp75+VFfU8Mm6/+PtH5eiVKjwcwsmyrcbp7dnED0iGCd3h8u2kX+2hNK8CsIHBuDm59RCkYuOrsl1tpqSaP2ZSqUiMDCwWW0IIYToOBRKBZFDgigsz8VgNKAz1LD1zDoAdJV6kndkUl545TpbVeU1JO/IIONY/iXrdglhSbJaUAghRJuhVCv515r3SE49Tb9ug7lr4v2c3XsBbXEV+moDKbsyiRwafMVRK6MRLiQVUXrBVCLCyePyI2JCNIfsjWgjf66zdfToUVmzJYQQjVBRUkXyjkxqKvXoawyk7s2iLN80qqVQKoiIC8QjsGH7JSpVCoJ7+uDfxbPe/ReFaC6LbtdTWlrKpk2bLNlkuxUfH8+SJUuYOXOmrUMRQog2x8ndgejhwajUSlR2SqKGB+MeYHoS3mgwkro3m6KM0ga1ZdAbyTiaT/KOTKrKa6wZtuigLJpsXbhwgSVLlliySSGEEKJezl6OdB4cgEIB+cW5fJu8BrcgR9NJI5z9+QL5aZoGt1eaV8HJxHMUnC2xUsSio2rUmq0LFy5c9nx+fn6zghFCCCEawzPYlaSiwzz69MNoyopxdXZlQvSt5oTp/OE89DoDAV28GtSevsbA2QMXKM4qk0KowmIa9VM0bdq0y85nG41Gme8WQgjRoiJ6hVFWYZoyXL/ta+6ecj9KtZK8lGIAso4VoK82ENTDu8GfUcVZ5ZQX/l4INVgKoYrmaVSy5ebmxoMPPki/fv3qPZ+ens7zzz9vkcCEEEKIhoiLi+P5559n37ZfeOaeF3BydCYk1gmVnZKck4WA6clDfY2B0D6+DU64air1nNmTjW+EO6F9/FCpLbryRnQgjUq2unbtSnFxMWFhYfWer66uRh5uFEII0dKef/55DPONJO/IRFtUhUKhIKi7N2p7JRlHTEtc8lM16Kv1hA8MQKFs+CxMfloJpbkVdB4UgKuvFEIVjdeoNP36668nKCjokucDAgKYN29es4Nqa7755hvuv/9+rr76aj788ENbhyOEEB2OUqlEbacialhwrX0S/aI8CR8YAL/nVkUZZaTuzcagMzSq/aryGk5vzyDzuBRCFY3XqGRr1KhRjB8//pLn3dzcuPbaa5sdVFvj4+PDjBkzGDlypK1DEUKIDs3eSU3UiGD01PDuZ69xoSAH7zA3IuOCzKNZJRe0pOzOQletb1TbRiPknCoiaet5KkqqrBG+aKdkAtoCRo4cyfDhw3FxaVgBPSGEENaTU5DJjBdu5aP//YsX33kWg8GAR5ALUcODUapNCVd5QSUpOzOpqdQ1un1tURWntpwnN6VYls6IBml2sjV69OgrloRoTbRaLStWrGD27Nlcd911jBo16pJTf1qtlrfeeosbbriB+Ph4ZsyYwebNm1s4YiGEEI3h5eWFpqQYgF9PHSQp7QQAbn5OdBkZgtre9NFXoakmeUcm1drGFzI16Iyc/zWPlN1ZVFc0PmETHUuzk622ltVrNBrWr19PTU3NFaf9EhIS2LRpE9OnT+fVV1+lW7duLFy4kJ9++qmFohVCCNFYXl5efPLJJ/Tu3ZuN//mR7lG9zOecvRzpclUodr+v66oqq+H09kwqS6qb1FdJjpaTiecaXK1edEwdrlpbYGAgGzduRKFQUFxczIYNG+q9bu/evRw4cIAFCxYQHx8PQP/+/cnJyWHFihWMGTMGlUrV6P7z8/MpKCgwf52ent60GxFCCHFJo0eP5tChQyiVSlL3ZlOcVW4+5+hmT9erQkjZlUVVWQ01FTpO78ggengwzl6Oje5LV6UndV8OPuFaOvX1RWXX+M8G0b51uGSrofVVdu7ciZOTE6NHj651fOLEiSxatIgTJ04QGxvb6P7XrVvH6tWrG/06IYQQjXPxF+KIwYGc3p5BedEfi9rtne3oMiqEM3uyqSiuQl9tIHlnJpFDg3Dzc25SfwXpJZTlawkfEICbf9PaEO2TLJC/hLS0NMLDw1Gra+ejUVFR5vMX6XQ6qqqqMBgM6PV6qqqq0Ovrf8plypQprFy50vxfQkKC9W5CCCEESrWSyGHB7DqyhbzCP9YY2zmq6TIyBFdf02iWQWfkzO5sNH8aBWusqnIdyTszOf9rXqPLS4j2S5KtS9BoNLi7u9c57ubmBkBJyR8blX788ceMGzeO77//nk8++YRx48bx448/1tuur68vMTExnD9/no8++oh169ZZ5waEEEIApn19b73jFma9/AivrVlc65zKTknU8GDcA00jUUaDkdT92RSkN30zaqMRclOKObn5HOUFFc2KXbQPHW4asTEaOuU4Y8YMZsyY0ai24+PjiY+PJykpiQcffLAp4QkhhGgAtVrN7t27AUjcs4mjkw7Ru2t/83mlSklkXBDpB3MpOl8KRjh3MBd9jQH/aM8m91tZWkPS9gwCungR1NMHZSOq1ov2RUa2LsHDwwONRlPneGmp6YmT+ka9hBBCtD4+Pj68/vrr+Pv789///peJt9Qtzq1QKggf6I9vpIf5WObRfLJPFDTrqXujAXKSiji1+RzaosomtyPatmaPbN199924ura/HdEjIyNJTExEp9PVWreVmpoKQERERLPaT0xMJDExkbKysma1I4QQ4sruuOMOrrvuOvMvytVaHbnJxbWuUSgUhPbxRW2vJOdUEWCqGK+vMRDSu+EbWNenQlNN0tYMArt7ERjj3ai9GUXb1+yRrQceeKBdVk4fOXIkFRUVbN++vdbxTZs24evrS48ePZrVfnx8PEuWLGHmzJnNakcIIcSVKRSKWjMSob198Qiq+9mlUCgI6uFDSG9f87G8MxrSD+ZibOaeiAaDkazfCknaer7Jdb1E22T1NVtGo5GMjAwcHBzw9/e3dncNsm/fPiorK9FqtYCp1tW2bdsAiIuLw9HRkbi4OAYOHMjy5cvRarWEhISwefNm9u/fT0JCQpNqbP2ZjGwJIYTtKBQKqj012JXaU1NW96lB/2hPVHZKzh3MBaDoXCn6aj0RQwJRqpo3TlFeVMXJLecI6eWLX5RHs0bMRNugMFqoBPzOnTvZvXs3jz/+uPmJvezsbObNm2cu3DlmzBgSEhJQKm27VGzatGnk5OTUe27t2rUEBQUBpu16Vq5cydatWyktLSUsLIy77rqLsWPHWiyWiwvkV65cSUxMjMXaFUIIUb/Kykr++c9/8s9//pMXFyzkun63XXLLneKsMs7+nIPx93zM1deJyKFBqOws8znm7u9M+EB/7J3tLNKeaJ0slmw988wz5ObmsmbNGvOx+fPns3v3bvr3709JSQlnzpzh6aefZvLkyZbosl2QZEsIIVrWoUOHGDRoEAaDAUdHRw7sOURNuj36S9TFKs3VkrovG4PO9HHp5OlA1PBg7BwsUyleba+kU19/vMPcLNKeaH0sNsSUlpZG9+7dzV+XlZWxb98+xowZw+uvv86//vUvwsPD2bhxo6W6bNMSExOZN28eb7/9tq1DEUKIDqV///7MnDkTtVrN7NmziezWmYghgVxqNs/N35noESGoLm5gXVxF8o6MJm1gXR9dtYG0n3NI3ZeNrrr+gtiibbNYslVcXIyPj4/562PHjqHX681Tbmq1moEDB5KZmWmpLts0WSAvhBC289JLL3Ho0CEWL16Mk5MTHkEuhPbxu+T1Lt6OdBkVip2jaTSrqvT3DaxLLbfQvSijjBM/nUOT0/QK9qJ1sliy5eLiUquq+uHDh1EqlfTp08d8TK1WU1kpdUaEEELYlpubW539bf2jPS9bxNTJ3Z4uV4Vi72JaX1VToSN5Ryba4qpLvqaxaip0pOzK4tzhXNnupx2xWLIVFhbGnj17KCkpoaysjM2bN9OlSxfzYnmAnJwcvLy8LNVlmybTiEII0boYDAZC+/jiGXzpckYOLnZ0vSoERw97AHRVepJ3ZFKWb9ltefLOaGS7n3bEYsnWTTfdRH5+PjfeeCM333wzBQUFTJ061Xxer9dz7NgxoqOjLdVlmybTiEII0ToYjUa++OILunXrxsmTJ+k8OBBnL4dLXn9xA2sX74sbWBtI2Z1l8em/i9v9ZP1W0OwaX8K2LJZsjR49mr/97W9ERETQqVMnHnroISZNmmQ+f/DgQSorKxk8eLCluhRCCCGa7aOPPmLatGkkJyfzzDPPoFIriRoWjL3zpUtRqu1VRI0Ixi3g9w2s9UZS92ZTeL7UorEZDZB90lQItaLEctOVomVZrPSDaBop/SCEELZVUVFBTEwM58+fJz4+nm+++QYXFxe0mipOb8tAX3PptVMGg5H0Xy5QnPlHgerQvn74/WmPRUtRqhSE9PLBL9pTCqG2MVavIC+EEEK0Zk5OTrz33nsoFAomTpxoTmScPRyIGBLImT1Z5qKmf6VUKug8OIDzh5UUnDU9JJbxax76aj0BMV4WTYoMeiPnj+SjydZKIdQ2RpItG5HteoQQovW4VLFtj0AXOvX159yh3Eu+VqFQ0KmfHyp7JbmniwHIPlGIrtpASKyPxUehSnK1nEw8R2hfP3zC3K/8AmFzMo1oYzKNKIQQrV/G0Twu/J5IXc6F00VkHS8wf+0d7kZYP38USutM+3mFuhLWzx+1harZC+uw7SaFQgghRCt08uRJPv74Y/PXIbG+eIa4XvF1AV296NTvj+KohemlpO3PwaC3Ts0sUyHUdDTZUgi1NZNkSwghhPiTZ555htjYWB588EFSU1MB01RhxKAAXC5TEuIi3wgPOv9p+x9Ndjln9mRfdqF9c9RU6knZnUX6wQvoa2S7n9ZIki0hhBDiT9RqNXq9nurqal599VXzcaVaSdTwy5eEuMgrxJXIYcEoVaaMqyyvgpRdmeiqrJcM5aeVcDLxPKV5Ugi1tZE1Wzby5wXyR48elTVbQgjRSpSUlNCvXz+mT5/OnDlzcHZ2rnW+ISUhLiovrOTM7izztY5udkQND2lQwtZUCgX4d/EkuKcPSpWMqbQGkmzZmCyQF0KI1qempgY7u0uXVtBkl3Nm76VLQvxZhaaKlN1Z6CpNo1p2TmqiRwTj6GZvqXDr5eRhT+eBATh7OVq1H3FljU55DQYDqamp5Ofn1zmn0+n49ddfLRGXEEIIYTOXS7QAPIJcCO3td9lrLnLycKCrlTewrk+FppqkrRlknyyU7X5srFHJVk5ODtOnT+e+++7j5ptvZt68eWg0GvP5kpISZs2aZekYhRBCCJsqKyujoqL2Wij/aE/8oz0b9Pr6NrBO2Wn5Daz/ymAwkvVbAae3Z1BZVm3VvsSlNSrZWrFiBX5+fnzxxRd8+OGH1NTU8Pjjj9ca5ZJZSSGEEO2FwWDgo48+omvXrixdurTO+dA+vngEuTSorb9uYK2vsc4G1vUpK6jkVOJ58s4UW70vUVejkq0jR47w6KOP4u/vT2RkJMuWLaN379488cQTXLhwAUD2axJCCNFupKam8tBDD5Gdnc0rr7xCVlZWrfMKhYKIIYE4e165JAT8aQNr/9obWBdlWHYD6/rodQbOHc4jeVcm1RU6q/cn/tCoZKuysrLWPLZCoeDZZ59l0KBBzJw5k8zMTIsHKIQQQthKdHQ0jz76KABjxoypM5UIoFIriRoWhJ1Tw54wVKmVRA4L+qNIqhHO/nyB/FTN5V9oISU5Wk7+lE7heesneMKkUclWWFgYSUlJdY7PmTOHuLg45s2bZ7HAhBBCiNbghRdeYO/evaxfv56oqKh6r7F3tiN6WBAqdcM+Vi9uYO3T+Y+9Dc//mkdOUpFFYr4SXbWBtP05pO3PRlcthVCtrVGFPkaNGsVPP/3E+PHj65ybPXs2RqORdevWWSy49kw2ohZCiLbBx8cHHx+fK17n7OVI58EBpO7NpiHLl80bWNspyU0uBiD7twL0NXqCe1p+A+v6FJ4vozS/kvAB/ngENmztmWg8qbNlY1JnSwgh2pcLp4vIOFq3PNLl5CQVkf3bHxtY+3R2p1M/vxZdB+0X5UForC/KBo7OiYaT76gQQgjRCL/++iv//Oc/L3k+oKsXflEejWozMMaLTn3/qNtVcLaEsz9fwKBvufGQvDMaTiSeo6xAtvuxtGYnW6NHjzY/iSiEEEK0Z7NmzaJfv37Mnz+fn3/++ZLXderjh3ug8yXP18c30oPOgwPg98Gs4swyUvdmoddZZwPr+lSV1XB6ewaZx/MxSCFUi2l2siWzkEIIITqKPy/3eOutty55nUKpIHJIIE4ejduSxyvUjcihQSh+38C6NLeClJ3W3cD6r4wGyDlVRNKW82g11q1y31HINKIQQgjRQPfffz9xcXG88cYbrFq16rLXquxURA8Pxs5R1ag+PAJdiB4RjMrO9BGtLaoieUfL18bSFleRtOU8OUlFMrDSTJJsCSGEEA1kb2/Pnj17eOqpp3B0vPIGz/bOdkQNC0apbtxCd1cfJ7qMCkHtYErUKkurSbbBljsGvZHMY/mc3p5BVVlNi/bdnkiy1UzFxcU8++yzjB8/njvuuINffvnF1iEJIYSwosY+Ieji7UjnQYE09sHCPzawNlVpqtbqSN5u/Q2s61OWX8nJxHMtVni1vZFkq5lef/11vL29WbduHY899hgvvPBCrc25hRBCtG9lZWUYDJdfxO4V4kpwL99Gt+3gakfXUaE4uv+xgXXyDutvYF0fvc5A+qFczuzJoqZStvtpDEm2mkGr1bJz507uu+8+HB0dGTFiBNHR0ezatcvWoQkhhLCyyspKXn/9dSIiIvjqq6+ueH1gjBe+Ee5XvO6v7JzUdBn1xwbWBp2BlF1ZaLKtv4F1fYqzyjnx0zmKMqUod0N1qGRLq9WyYsUKZs+ezXXXXceoUaP48MMPL3ntW2+9xQ033EB8fDwzZsxg8+bNta7JyMjAycmJgIAA87HIyEjS0tKseh9CCCFsb/v27cyePZv8/HwWLFiAXn/lJwbD+vnj7t+4khDwpw2sA37fwNpgJHVfNoXnShrdliXoqvSk7s3m7C8X0NfIdj9X0uxk6+6778bV1dUSsVidRqNh/fr11NTUMHLkyMtem5CQwKZNm5g+fTqvvvoq3bp1Y+HChfz000/mayoqKnBxqb29gYuLS70blQohhGhfxo8fz/DhwwHo168fpaVX3thZoVQQEReIk3vjSkLA7xtYDw3CM/SPDazTD+SSm1Lc6LYspSC9hJOJ5yjNk8+9y2nU3oj1eeCBBywRR4sIDAxk48aNKBQKiouL2bBhQ73X7d27lwMHDrBgwQLi4+MB6N+/Pzk5OaxYsYIxY8agUqlwcnKivLz2MG55eTlOTk6XjCE/P5+Cgj+2ZEhPT7fAnQkhhGhpCoWCd955B6VSSe/evRv8OrW9iqjhwSRtPU9NZeNGhZRKBZ0HBZBhpyQ/zTSqlXk0H321nsDu3i26vc9FVeU6kndk4N/Fk+CePihVHWrSrEGanWy1JQ39Idy5cydOTk6MHj261vGJEyeyaNEiTpw4QWxsLKGhoVRUVJCbm4u/vz8AaWlp9W7UfdG6detYvXp1U29BCCFEK9K3b98mvc7BxVQS4vSODAy6xtWwUigUhPb1Q+2gIudUEWAqQqqr0hPat2X3U7zIaIQLp4spuaCl8+BAnD0cWjyG1sziyZbBYCAvL4+8vDx0uvqfVmjqD2dLSUtLIzw8HLW69rcnKirKfD42NhZnZ2dGjBjBRx99xKxZszh48CDJycksXLjwkm1PmTLFPOwMppGtl19+2To3IoQQotVy8XYkYlAgqfuyaWzNUIVCQVAPH1T2KjJ/3/Q6P60EXY2B8IEBKJUtn3ABVGiqSdp8nqCePgR09bRJ4tcaWSzZMhqNfPrpp3z++eeUlFx+wd62bdss1a1VaDQagoOD6xx3c3MDqHV/s2fPZvHixUyePBlfX19efPFFPD09L9m2r68vvr6+JCYmkpiYSFmZPM0hhBDtxZ49e8jKyuLmm29u0PWeIa6ExPqS8XvC1Fj+0Z6o7VWkH7wARijOKENfbSAiLhCV2jbTeQaDqRBqSXY54YMCcHCxs0kcrYnFkq1//etf/Oc//8HLy4trr70WHx8fVKrGbVHQmjQ0G/f09GTp0qWNbj8+Pp74+HiSkpJ48MEHG/16IYQQrYdOp+OGG25gw4YN+Pj4cM0115h/Qb+SgK5eVJXXkHemaTUavcPcUNkpSdufg9FgpDRXy5ldWUQOC0Jtb7vP4dL8Ck4mnqNTHz98Oje+5EV7YrFka9OmTXTq1IkPPvgAZ+fGP9bamnh4eNRbmPTikybu7s3/oZGRLSGEaD/UarX5s6GgoID/+7//Y9asWQ1+fac+flSX16DJ0Tapf48g036KZ/ZkY9AZKC+sJHlHpmlvRifbLc/W1xg4e+ACmpxywvr5m7cf6mgsNsZYUVHB0KFD23yiBaZaWenp6XXWnKWmpgIQERHR7D7i4+NZsmQJM2fObHZbQgghbG/hwoVERkayatUqnnjiiUa91lQSIghnz6YvLHf1/ct+iiXVv+9p2LL7KdanKKOMEz+l26wQq61ZLNmKjo6uVdKgLRs5ciQVFRVs37691vFNmzbh6+tLjx49bBSZEEKI1io6Oprk5GTuv//+Og9YNYRKrSRqWBD2zRiJcvZ0oOtVIdg7/7Gf4mkb7af4VzWVelJ2Z3HucC4G3eW3N2pvLDa2eM899/D888+TlJRETEyMpZq1uH379lFZWYlWaxqqTU9PNy/Yj4uLw9HRkbi4OAYOHMjy5cvRarWEhISwefNm9u/fT0JCgkXWosk0ohBCtD9KZfPGMOyd7YgaHszpbRnom5iQOLja0/WqUFJ2Z1FZUo2uSk/KzkwihwXh6nPpOpAtJe+MhtJcLZ0HBZq3IGrvFEZjYx84vbRt27bx+uuvM3z4cKKioupUV79owoQJluqy0aZNm0ZOTk6959auXUtQUBBg2q5n5cqVbN26ldLSUsLCwrjrrrsYO3asReO5uEB+5cqVrTpJFUII0XjV1dXY2ze+Wrwmu5wze7MwNmMASFet58yeLLSFplEthUpBxJBAPALr/2xuaQolBHbzJqibNwoblapoKRZLtqqrq3n11VdJTEzkYpN/faLPaDSiUChafemHliTJlhBCtD8lJSW8+eabvPvuuxw4cIDQ0NBGt5F3pphzh/OaFYdeZyBtXzalub9vp6OA8IEBeHdq2JOSLcHF25HOgwJwdGt8UtpWWGwa8Z133uGnn34iKiqKq666qs2XfrA2mUYUQoj2a+nSpeaC1S+//DLvv/9+o9vwi/KkqqyGC8nFTY5DpVYSOSyY9F8uUJxZZtpP8ZcL6GsM+EV6NLldSyovrOTk5nOExvriF+Vp63CswmIjW1OmTCEwMJD33nuvSQsDOyoZ2RJCiPanoKCAiIgItFotDz30EO+++26TqqkbjUZS92ZTnNW8p/iMRiPnD+dRcPaPotxBPbwJiPFqVVXe3QOdCR8Q0KyHBFojiz2NWF1dTb9+/STREkII0eH5+PiwevVqTp48yXvvvdfkhEahUBAxOBAXr+btNahQKOjUz4+Arl7mY9knCsk8lo8Fl243W0mOlpOJ5yjKKLV1KBZlscwoJiaGjIwMSzXX7sk0ohBCtG833nijRdpRqpVEDQ/m1JbzVGvr33O4IRQKBcG9fFDZK8k6birVlJeiQV9tIKy/f6tZpK6r0pO6Lwef8HI69fVDZdf2lyRZbGTrwQcf5Oeff2bPnj2WarJdk6KmQgghGsrOUU308GBUds3/2A7o6kVYf3/z14XnSknbn4NB37pqXxWkl3Iy8RyleU2rqt+aWGxk68CBA/Tt25f58+fTr18/oqOj6y39oFAouPfeey3VrRBCCNHqGY1GEhMTCQ4OpmfPnk1qw8nDgci4QFJ2N68kBIBPZ3dUdkrO/pKD0fB7qYk92UTGBVkkobOUqnIdyTsy8e/iSXBPH5Sq1hNbY1hsgfxVV13VsA6l9EMtskBeCCHat4yMDO6880527NjB5MmTWb9+fbPay0/TkH4w1yKxleZqSd2bjUFvSgWcvRyIGhbcKvcwdPKwp/PgQJw9mrd+zRYsNrL15ptvWqopIYQQot3w9fU17627YcMGDh48yIABA5reXoQHlWU1XEgqanZsbv7ORI8M4cyeLPTVBrRFVSTvyCRqRHCreyKwQlNN0pbzBPXwIaCrZ6t6ivJKLPad7Nu3r6Wa6hBkgbwQQnQMjo6OLFiwgGXLlrFo0SL69evX7DZDevlQXV5DUUbzP0NcvB3pMjKElN1Z6Cr1VJZWk7w9g+gRITi42jW7fUsy6I1kHsunJKec8IEBOLi0rvguxaLb9YjGk2lEIYRo/3Q601OEliyPZNAbSN6RSVlBpUXaqyqvIWVnpvmJR7WDiugRwTi10mk7lZ2STn388OnsbutQrshiK82OHTvGO++8Q0FBQb3nCwoKeOedd/jtt98s1aUQQgjRJqjVaovXoVSqlEQODbLY6JODix1drwo1b5ujq9KTvCOT8kLLJHOWpq8xcPbABc7szaamsuklMVqCxZKttWvXsnv3bnx8fOo97+Pjw549e/j8888t1aUQQgjRZhkMzS+1cLEkhKUWtNs5qekyKgTn34uo6msMpOzMpDS39ZZfKM4s4+RP5yjOar3LciyWbJ06dYrevXtf9po+ffpw4sQJS3UphBBCtDkFBQXMnTuX0aNHW6R6u6ObPVFDg1CqLLNg3DR9GIKrnxNgWid1Zk+WaW/FVqqmSs+ZPdmkH7iAvkZv63DqsNiYZnFxMb6+vpe9xtvbm6Ki5j890R7IAnkhhOiYbr75ZnMJpP/9738WqTTv6utE50EBpO3PwRIrsVV2SqKGBXH25wtosssxGiBtfw5hA/zxCW+9a6Tyz5ZQmqclfGAAbn7Otg7HzGIjW66uruTmXr7ux4ULF3BycrJUl22aVJAXQoiO6ZlnngHAwcGBs2fPWqxdr1A3gntdftCjMZQqJRFDAvHq5GY+du5gLrkpxRbrwxouFkLNOJLXaqriWyzZ6tmzJzt27ODChQv1nr9w4QI7d+6kV69elupSCCGEaHOuvfZalixZQkpKCrNnz7Zo24ExXvhFeVisPYVSQfhAf3wj/2gz82g+2ScLW9UG1n9lNMKF5GJObT6Ptsj2C/wtlmxNmzaNqqoqHn/8cTZt2kR+fj4A+fn5fP/99zz22GNUV1dz6623WqpLIYQQos1RKBTMnTuX0NBQq7TfqY8fHkF1t8trKoVCQWgfXwJivMzHck4Wknk0v1UnXAAVJdVcSC62dRiWW7PVp08fnnzySd5++22WLFkCmP6CLv5FKBQKZs6cKcVPhRBCCCtSKBVEDAnk9PYMtEVVlmlToSC4pw9qeyWZx0wlnvLOaNDXGAjr749C2XaquduCRYt+3HjjjfTt25dvvvmGU6dOUVZWhqurK927d2fq1KlERkZasjshhBCizTMajWzYsIFx48bh6OhokTZVaiVRw4JJ2nreXKTUEvy7eKGyU3HukGmNduG5UvQ6A50HBbTZTaJbgsU3PoqMjLT4HLQQQgjRHv3yyy88+uijHDx4kDfffJMnn3zSYm3bO6mJHhHM6W0Z6Kott1Dcp7M7KjslZ382PfmoySrnzJ5sIocGoVJLwlUf+a4IIYQQNmJnZ8fBgwcBWLx4MVqtZYuHOrk7EDEkCKWFp/k8Q1yJHBZsru1VlldBys5MdNWtr8ZVa9C6tvTuQKTOlhBCiL59+3LLLbeQnJzMyy+/bJXySO4BznTq50f6wcuXZ2pKu1Ejgkndk42+xoC2qIrkHZlEDw/GzknSiz+TjahtTDaiFkKIjk2j0eDm5oZSad3Jpsxj+eQkWb6weIWmipRdWeiqTKNa9i5qokeE4OBimT0bm8s7zI2IwYE2jUGmEYUQQggb8vDwsHqiBRDcywevUFeLt+vk4UCXq0KwdzaNZlWX6zi9PYMKjWWehGwPJNkSQgghOgCFQkHnQQG4+Fjmicc/c3S1p8uoUBzdTKNZuko9yTsyKS+osHhfbVGzki2dznKPkwohhBAdXVZWFo8//jjPP/+8VdpXqpREDQ2yyhSfvbOaLqNCcfZyAEBfYyBlVxYlFyy76L8talaydeONN/Luu+9adG8nIYQQoiMqLy+nV69evPfee7z22mtkZ2dbpR87RzVRw4NQ21t+ckvtoCJ6RAiufqaF/ga9kdQ9WRRldOyHwZr1nS4vL+fzzz9n+vTpPProo2zYsMHij60KIYQQHYGLiwv33nsvACqVikOHDlmtL2uVhABQ2ZkKqnoEm7YMMhrh7M855KdpLN5XW9GsZOt///sfTzzxBJGRkZw4cYJly5Zxww03sGTJEo4ePWqpGFu9b775hvvvv5+rr76aDz/80NbhCCGEaKOee+45nnnmGVJTU5k0aZJV+3IPcKZTfz+rtK1UKYgYHIh3uJv52PnDeVywwtOQbYHFSj8kJSXx3XffsXnzZkpLS00bV4aGMmnSJK655hq8vb0t0U2rtHPnTpRKJT/88AOdO3dmxowZDX6tlH4QQghhS9YqCQGmrYiyjheQ+6fNoP27eBLcyweFomX2U2wNpR8sVnUsJiaGmJgYnnjiCXbs2MHGjRs5fPgw//rXv1i5ciVDhw5l0qRJxMXFtcgjri1p5MiRAOzatcvGkQghhBCNE9zLh6ryGqusq1IoFAT38kFlryL7N9MG1rnJxeirDXTq79diCZetWbzEq52dHWPHjmXs2LHk5uby/fff8/3337Nr1y52796Nt7c3X3/9taW7RavVsmbNGpKTk0lOTkaj0TB9+vR6R5m0Wi2rVq1i69atlJaWEhYWxp133snYsWMtHpcQQgjRVHq9nj179ph/qbeGiyUhqit0lBdUWqX9wBgv1PZKzh/OA6AgvQR9jZ7wQYHmLX/aM6sOMfn7+3Pvvffy6quvEhsbi9FopLCw0Cp9aTQa1q9fT01NzRV/KBMSEti0aRPTp0/n1VdfpVu3bixcuJCffvrJKrEJIYQQjbVhwwZiY2O56qqrOH78uFX7smZJiIt8IzzoPDiQi4NZxVnlpO7NQq+z3CbZrZXVNi+qqKhg69atfPfddxw/fhyj0YijoyNXXXWVVfoLDAxk48aNKBQKiouL2bBhQ73X7d27lwMHDrBgwQLi4+MB6N+/Pzk5OaxYsYIxY8agUqkAmDVr1iV/wG+77TYeeOCBRseZn59PQUGB+ev09PRGtyGEEKL9O3XqFCdPngRgwYIFVpkV+rOLJSFOb8tAV22dBMgr1BWVXRBp+3Iw6I2U5po2sI4aHozaXmWVPlsDiydbR44c4bvvvmPbtm1UVVVhNBrp1q0bkyZNIj4+HmdnZ0t3CdDged+dO3fi5OTE6NGjax2fOHEiixYt4sSJE8TGxgLwxhtvWDhKWLduHatXr7Z4u0IIIdqXxx57jOXLlxMREcHMmTNbpM+LJSFSdmditNKAk3uAC9EjgjnTgTawtshd5eXlsWnTJr7//nuysrIwGo24u7szefJkJk2aRGRkpCW6sYi0tDTCw8NRq2vfelRUlPn8xWSroXQ6HXq9HoPBgF6vp6qqCrVabR4h+7MpU6YwfPhw89fp6em8/PLLTbgTIYQQ7ZmzszM///wzISEhLbqQ3D3AmbB+/qQfzLVaHy4+TnQZFWLewLqypJrT2zNMG1i7to4NrC2pWcnWli1b+P777zlw4AAGgwGlUsmgQYOYOHEiI0eOrJPQtAYajYbg4OA6x93cTLVASkpKGt3mxx9/XGu06pNPPuG5557j2muvrXOtr68vvr6+JCYmkpiYSFlZx66qK4QQ4tJCQ0Nt0q9vhAeVZTVWrYvl5OFA16tCSdmVSbVWR7VW93vCFYyTh4PV+rWFZmVDCxcuBEzrpSZOnMi1116Lv7+/RQKzJkv/hjBjxoxG1dYCiI+PJz4+3lxnSwghhGhNQnr5UG2lkhAXObjamRKu3VlUllSjqzJtYB05LAhXHyer9dvSmpVsjR07lkmTJjFgwABLxWN1Hh4eaDR1twwoLS0FwN3dvUXikJEtIYQQjZGamsqiRYu46667zA94WZNCoaDzwACqtTrKCy1fEuIiOyc1XUaFcGZ3FtqiKvQ1Bs7syiIiLhD3ABer9duSmpVsLViwwFJxtJjIyEgSExPR6XS1pjlTU1MBiIiIaJE4ZGRLCCFEQx04cIChQ4ei0+k4efIkY8eObZF1XEq1kqhhQSRtPU9Vuc5q/ajtVUSPDCFtXzaluRW/b2CdTfigALxC3a7cQCtn0TpbOp2Ozz//nIceeogJEyZw9dVXm88lJyezfPlyzp8/b8kuG23kyJFUVFSwffv2Wsc3bdqEr68vPXr0sFFkQgghRP369+9P9+7dAdPnaVpaWov1beeoJmpYMCo76+7+olIriRwajGetDawvkJ/a9jewttgK9qqqKubMmcPx48fx8PDAxcWFyso/hh2DgoL47rvvcHNzs9pIzr59+6isrESr1QKmJ/22bdsGQFxcHI6OjsTFxTFw4ECWL1+OVqslJCSEzZs3s3//fhISEup9gtAaZBpRCCFEQymVSpYsWcL+/fuZPXs2Hh4eLdq/k4cDkXGBpOzOslpJCDBtYN15SCDnD+VRkG56YO38r3noqvUExHi12e19LLYR9apVq/jkk094+OGHuf322/noo4/4+OOPzckOwNNPP01JSQkffPCBJbqsY9q0aeTk5NR7bu3atQQFBQGm7XpWrlxZa7ueu+66yybb9chG1EIIIdqK/FQN6YesVxLiIqPRSNZvBeSeLjYf84v2ICTWt9EJV7vaiHrLli307duXO+64A6j/ib/g4GCSk5Mt1WUdn3/+eYOuc3Z25qmnnuKpp56yWixCCCFEe+Mb6UFVeQ05ViwJAaYcIqSXL2p7FVnHTbuu5KVo0FcbCOvvj0LZtka4LJZs5ebmXnFPQmdnZ8rLyy3VZZsm04hCCCGao7q6mrS0tBafFQnu5UOVlUtCXBTQ1Qu1vYpzv4+mFZ4rRV9joPPgAJQq664hsySLRerk5FRvSYU/y8rKavF55tYqPj6eJUuWtNgWDEIIIdoHo9HI6tWriYmJYcKECVRXV7do/wqFgs6DAnD1cWyR/nw6uxMxJBDF7xmLJrucM7uz0NfoW6R/S7BYstWzZ0927959yZGa3Nxc9u3bR58+fSzVpRBCCNHhKBQK/vOf/3D27FnOnj3Lhx9+2OIxKFVKIocG4eDSMlvreIa4EjUsGKXaNH1Yll9J8o4saiqtV47CkiyWbN12222UlpYye/Zsjh07hl5vyjgrKys5ePAgc+bMQafTceutt1qqyzYtMTGRefPm8fbbb9s6FCGEEG3MxT11x48fz6BBg2wSg52jmujhwajtW2Y6z83fmegRIah+769CY9rAuqq8pkX6bw6LPY0I8O233/Lmm29iMNR9LlSpVDJ79mwmT55sqe7aBXkaUQghRFMcP36cXr162ToMSvO0pOzMwmCwWDpxWZWl1aTsyqKmwjSqZeeoImpECE7u9vVe366eRgSYOnUqffr04dtvv+XkyZOUlJTg4uJC9+7dueGGG1qsOrsQQgjR3rWGRAvAzc+ZsAH+nP3lQov05+hmT9erQkjZlUVVWQ01lXqSd2QQNSwYF++WWUfWWBZNtgA6d+4sJRWEEEKIDsQn3J2q8hqyTxS2SH/2znZ0uSqUM7uzqCiuQl9tIGVnJpFDg3Dzd26RGBqjxZ+brG+KUQghhBBNd+LECe655x6Kiqxb/+pygnv44BPecvsY2jmo6DIyBFdfJwAMeiNn9mRRnNn6SipZLNnasGHDFa/R6/UsWrTIUl22abJAXgghhCWsWrWKXr168cknn/Daa6/ZNJawAQG4+Tm1WH8qOyVRw4PwCPp9P0UDpO3PoeBsSYvF0BAWS7Zee+01du7cecnzRqORl156qdb2PR2Z1NkSQghhCfHx8ajVplVBa9euRaezXTkEpVJB5NCgSy5Wt0qfKiURQwLxDvtjVO3coVwunLbdKN9fWbTO1qJFizhy5EidcxcTra1btzJlyhRLdSmEEEJ0eJ07d+bZZ5/l1Vdf5ciRI+bEy1bU9iqihgdj56hqsT4VSgVhA/zxi/6jcHrW8QKyjhdgwaILTWaxZGvJkiUEBwfz3HPPcebMGfNxo9HIyy+/zObNm5kyZQqzZ8+2VJdCCCGEwFR365lnnsHZuXUsDndwsatVhLQlKBQKQmJ9CerhbT524XQRSVvOt1hZikuxWLLl6urKa6+9hqurK8888wzZ2dnmEa3ExESuu+465syZY6nuhBBCCNGKuXg7EjEoEEUL7hmtUCgI7OZNaF8/87HMYwVsfedX9DrbPaBn0aKmAOfPn+exxx7Dzc2Nrl27snXrViZPnswzzzxjyW7avD9vRH306FEpaiqEEMJitFotWq0WX19fW4fChdNFZBzNb/F+C8+Xkn7gAvye5Yx5si+RcUEtHgdYofRDp06dWLp0KYWFhWzbto1JkyZJolUPWSAvhBDC0qqqqnjnnXeIiopqNZ+9AV298I/2bPF+vTu5ETk0CKVaQZ8pkTZLtKAZI1urV6++7PkjR46QkpLCDTfcgFL5R06nUCi49957m9JluyTb9QghhLCU4uJiIiMjKSoqQqlUcuLEiVbx2WI0Gkndm01xVnmL9+3oZkeP8eEoWnI+8y+a/MjCRx991KDr1qxZU+trSbaEEEII6/D09OSZZ55h/vz5XH/99TZ/MvEihUJB58GBJG/PoLyoqkX7dvZytGmiBc1Itt58801LxiGEEEIIC3jyyScZP348AwYMsHUotajUSqKGB3Nqy3mqtbarBWYLTU62+vbta8EwhBBCCGEJLi4urS7RusjOUU308GBOb89AV91xtu9r8b0RhRBCCNFxOXk4EDEkCKXStlN7LanJydbcuXNJSkpq0murqqr4z3/+wzfffNPU7oUQQgjRAAcOHCAhIcHWYdTiHuBMp35+V76wnWjyNGJubi4PP/wwffv25ZprrmHUqFG4uLhc9jWnTp3ixx9/JDExkYqKCubPn9/U7tu8P9fZEkIIIaxh1qxZ5jXW8fHxjB492rYB/YlvhAdVZTXkJLWePQytpcmlH4xGI9999x1r1qzhwoULKJVKwsLC6Nq1K15eXri5uVFVVUVJSQkZGRkkJSVRXl6OQqHg6quv5sEHHyQoyHY1L1oLKf0ghBDCWj7++GNzBYBp06axdu1aG0dUm9Fo5OzPORSet97Ag3eYGxGDA63WfkM0eWRLoVAwadIkrr32Wvbu3cv333/PkSNH+PHHH+tcq1QqiYyMZOTIkUyePLlVVLQVQggh2rs777yTjz/+mJtuuon777/f1uHUoVAoCB8YQLVWR1lBpa3DsZpmF+BQKpUMHz6c4cOHA3D27Fny8vIoKSnB3t4eT09PIiIicHV1bXawQgghhGg4lUpFYmKircO4LKVKSdSwYE5tPU9VWY2tw7EKi1c769y5M507d7Z0s0IIIYRop9QOKqKHBZO07Xy7LAkhpR+EEEKIDqSkpAS9Xm/rMOpwdLc37WXYDktCSLIlhBBCdADl5eW88sorREREtLqF8he5+TkTNsDf1mFYnCRbzVRdXc0///lPbrrpJiZMmMAjjzzCsWPHbB2WEEIIUcv+/fuZN28ehYWFvPDCC+h0rXPLHJ9wd4J6eNs6DIuSZKuZ9Ho9QUFBvPfee3z33XdMnTqV+fPnU1nZfp+qEEII0faMGTOGq6++GoVCwdChQ1t1ncfgHj54h7nZOgyLkWSrmZycnJg+fToBAQEolUquvfZaDAYDGRkZtg5NCCGEqOXtt9/m+PHjfPzxx3h6eto6nMsKHxiAm6+TrcOwCIs/jWgrWq2WNWvWkJycTHJyMhqNhunTpzNjxox6r121ahVbt26ltLSUsLAw7rzzTsaOHdvsONLT06mqqiI4OLjZbQkhhBCW1LNnT1uH0GBKpYLIYUEkbT1PZWnbLgnRbka2NBoN69evp6amhpEjR1722oSEBDZt2sT06dN59dVX6datGwsXLuSnn35qVgyVlZUsXryYe+65B2dn52a1JYQQQnR0ansVUcOCUTuobB1Ks1h9ZGv06NFs27bN2t0QGBjIxo0bUSgUFBcXs2HDhnqv27t3LwcOHGDBggXEx8cD0L9/f3JyclixYgVjxoxBpTL9pc6aNYvjx4/X285tt93GAw88YP5ap9PxwgsvEB4ezt13333JOPPz8ykoKDB/nZ6e3uh7FUIIISxhx44dZGdnc+utt9o6lEtydLMnMi6IlJ2ZGAxN2mHQ5qyebDVx68VGUygaVpdj586dODk51dmMc+LEiSxatIgTJ04QGxsLwBtvvNGgNg0GA4sXL0apVDJ37tzLxrJu3TpWr17doHaFEEIIa9DpdEyePJkffvgBb29vrr32Wtzd3W0d1iW5+TkRNsCfs79csHUoTdKoZGvu3Ll06dKFrl270qVLlwZtJP3nxOPTTz9l2rRp2NnZNT5SC0lLSyM8PBy1uvatR0VFmc9fTLYaatmyZRQUFLB06dI67f7VlClTzFsbgWlk6+WXX25Uf0IIIURzqNVq/Pz8ACgsLOTDDz9k1qxZtg3qCnzC3akqqyH7ZKGtQ2m0RiVbvr6+/PLLL6xdu5aamhpcXV3p0qVLrQQsLCysVoL155GtlStXMnHiRLy8vAD473//y9SpU3FyarmnDTQaTb2L193cTI+YlpSUNKq9nJwcNmzYgL29PVOmTDEff/XVV+nTp0+d6319ffH19SUxMZHExMRW/eitEEKI9uvFF1/k559/5u9//zt33nmnrcNpkOCePlSV11B4rtTWoTRKo5KtZ555BjDVlkpPTycpKYmUlBROnTrFhg0b0Gq1ODo6smnTpnpf/9cpxdWrVzNq1ChzslVUVMRjjz3Gf/7zn6bcS4M1dMqxIQIDA9mxY0ejXxcfH098fDxJSUk8+OCDFotHCCGEaIioqChOnTpl0c/ElhA+wJ9qbQ1l+W2nnmWT1mypVCoiIyOJjIwE4MyZM+zYsYNvvvkGjUbT4Hb+mnwZjUays7ObElKDeXh41BtjaakpS26pOWsZ2RJCCGFrbS3RAlCqlEQNDebU1vNUlbWNkhBNXiB/4sQJtm/fzs6dO8nNzaVv377MmDGj1nqk1igyMpLExER0Ol2t9VWpqakAREREtEgcMrIlhBCitamsrMTR0dHWYVyR2kFF9LBgkradR1dtsHU4V9SoZOvgwYPs2LGDnTt3Ul5ezpAhQ7jvvvsYOnQorq6uDWpjz5499OvXz2ZFP0eOHMn69evZvn17rSKmmzZtwtfXlx49etgkLiGEEMJWiouLee2111ixYgWHDx+mU6dOtg7pihzd7YkcGkTKzqxWXxKiUcnW7Nmz8fX15a677uK6665r0FOFfx6i7NKlC6+//jo6nQ5nZ2eqq6v59NNPiY2NpUuXLg1O2C5l3759VFZWotVqAdOTfhdrfMXFxeHo6EhcXBwDBw5k+fLlaLVaQkJC2Lx5M/v37ychIcFcY8vaZBpRCCFEa7F8+XLzk/EvvfQSH3zwgY0jahg3P+c2URJCYWxEIawHHniAs2fPotPpcHNzq/UkYteuXevNhP9a1FSn03H27FlOnz7N6dOnSU5OJiUlhaqqKhQKBUajsclFUKdNm0ZOTk6959auXWsuVaHValm5cmWt7Xruuusui2zX01gXpxFXrlxJTExMi/cvhBBCFBYWEhERQUVFBY888ghvvvlmm1rPlfVbwSVLQniHuRExOLCFI6qtUckWXD5ZcnJyIjo6mrffftt8fUMqyBuNRs6fP29+uvHRRx9t0s20RZJsCSGEaA3Wr19Pr169WmztsqWl/ZxTb0mI1pBsNXqBvFqtJjo6mujoaCZOnAjUTZbqs3TpUqKiooiOjiYqKgoXFxfzOYVCQVhYGGFhYYwbN66Jt9K2yDSiEEKI1uS6666zdQjN0ppLQjQ42WpuslRTU8N3331Heno6NTU1BAQEmNu6mLzZatG8LcjTiEIIIYTltOaSEA1OtnQ6XbOSpfnz5wOmfQQPHTrEwoULyc/Pp6ioiC+//NJcEDUyMpL33nuv+XcmhBBCiCYxGo18//33hIWF0atXL1uH02BqBxXRw4NJ2tq6SkI0ONl67rnngOYnS0qlkjVr1vDII48wadIk4I9Rr3/961906dKlmbfUNsg0ohBCiNbo3Llz3Hbbbezdu5eJEyeyceNGW4fUKI5ura8kRKPXbFkiWTp58qR5pAvAzs6OqVOn4uzsTGJiYmNDapNkGlEIIURrFBAQQGZmJgDfffcdBw8eZMCAATaOqnFaW0kIZVNedPLkSfr372/++mKy9Le//e2SpRf+LDo6mh9//LHO8Z49e3Ly5MmmhCSEEEIIC3BwcGDBggX06NGDr776qtbnfVviE+5OUHdvW4cBNDHZam6yNGfOHD777DPefPNNsrKyANP05LffftugQqlCCCGEsJ7p06dz9OhRbrzxxjZVb+uvgnv64N3JzdZhNG1vxDlz5vDEE09QXFzMLbfcQnBwcKOSpS5duvDee+/x+uuvc/vtt+Ps7Ixer0en0/H00083JaQ2R9ZsCSGEaK1aajeVluAR5HLli6ys0UVNLzpz5gyvv/46x44dq5MsXay/BVcuapqVlUVycjIKhYKYmBgCAgKaEk6bJUVNhRBCtAUGgwGlskkTYh1ek0a2AKKionjnnXeumCxdKZcLDg7uUPW1hBBCiLYkNzeXJUuWsHfvXnbv3i0JVxM0Odm66ErJ0vbt25vbhRBCCCFsZNq0aebP8q+++opbbrnFxhG1PY1OTw0GA6mpqeTn59c5p9Pp+PXXXy0RlxBCCCFagXnz5gHg5OREdna2jaNpmxo1spWTk8Ozzz5Leno6CoWCuLg4nnvuOTw8PAAoKSlh1qxZV9x4WgghhBBtwzXXXMNrr73G7bffTlBQkK3DaZMalWytWLECPz8/li1bRllZGe+++y6PP/44b7zxBr6+vsCV12gJE3kaUQghRFugUCiYPXu2rcNo0xo1jXjkyBEeffRR/P39iYyMZNmyZfTu3ZsnnniCCxdMVVrbcj2OlhQfH8+SJUuYOXOmrUMRQgghhBU1KtmqrKysVUdLoVDw7LPPMmjQIGbOnGku7y+EEEKI9slgMPDll19SWVlp61DajEYlW2FhYSQlJdU5PmfOHOLi4syL6IQQQgjR/uzbt49+/fpxyy238P7779s6nDajUcnWqFGj+Omnn+o9N3v2bMaMGSNrtoQQQoh2ytnZmaNHjwLwj3/8A61Wa+OI2oYmV5AXliEV5IUQQrQlt912G2fOnGHx4sWMGzdO1mo3QLOKmmZlZUn1dyGEEKIDWblyJa6urpJkNUKzau7fcccdfPLJJ5aKRQghhBCtnJubmyRajdSskS2j0Yher691bOPGjRw/fpy5c+c2K7D2TupsCSGEaC+MRqMkYJdh8d0kc3Nz+f777+s999lnn/H4449buss2SepsCSGEaOvOnz/PQw89xPz5820dSqvW7I2oG6OqqorffvutJbsUQgghhBWUl5cTGxuLRqPB0dGRmTNnyjruS7D4yJYQQggh2j8XFxfuv/9+AOzt7Tly5IiNI2q9WnRkSwghhBDtx7x583BycmL27Nl4e3vbOpxWS5ItIYQQQjSJn58fL7/8sq3DaPWanWx98803pKWl0a1bN2JiYigvL7dEXEIIIYQQ7UKzkq2IiAjOnTvHtm3b2LZtW63HPhctWkRkZCRRUVFERkYSEBDQ7GBbqxdffJHDhw9TVVVFYGAgDz30EMOGDbN1WEIIIUSL0ul07NixgzFjxtg6lFal2dv1VFdXk5KSQnJyMklJSZw+fZq0tDR0Op2pg98TMBcXFxwcHCgsLGTbtm3NDrw1SUtLIzQ0FDs7O06cOMGcOXP473//i4eHxxVfK9v1CCGEaA++/fZbnnnmGVJSUjhy5AixsbG2DqnVaPY0or29PT169KBHjx7mYzqdjjNnznD69GlzApaamkpZWVm7LHoWERFh/rNKpaKmpob8/PwGJVtCCCFEe5CamkpycjIAzz//PN98841tA2pFrLJAXq1WExMTQ0xMDNdddx1gSsDOnj1LUlKSNbpEq9WyZs0akpOTSU5ORqPRMH36dGbMmFHvtatWrWLr1q2UlpYSFhbGnXfeydixY5vc/6JFi9ixYwfV1dXExcURGRnZnNsRQggh2pRHHnmEZcuW0aVLF55++mlbh9OqtNjTiGq1mujoaKKjo63SvkajYf369URFRTFy5Eg2bNhwyWsTEhI4deoUDz/8MJ06dSIxMZGFCxdiMBgYN25ck/pfsGABOp2OQ4cOkZ6e3i5H8IQQQohLcXJy4sCBAwQGBspn4F+0m9IPgYGBbNy4EYVCQXFx8SWTrb1793LgwAEWLFhAfHw8AP379ycnJ4cVK1YwZswYVCoVALNmzeL48eP1tnPbbbfxwAMP1DqmVqsZPHgwX331FaGhoQwdOrTO6/Lz8ykoKDB/nZ6e3qT7FUIIIVqboKAgW4fQKrWbZKuhWfTOnTtxcnJi9OjRtY5PnDiRRYsWceLECfOivjfeeKNJsRgMBjIzM+s9t27dOlavXt2kdoUQQgjR9nS47XrS0tIIDw9Hra6dZ0ZFRZnPN0ZBQQHbtm2joqICnU7Hli1bOHz4MH379q33+ilTprBy5UrzfwkJCU26DyGEEKI1S05O5s477+THH3+0dSg2125GthpKo9HUu1Gmm5sbACUlJY1u88svv+SVV15BoVAQGhrKiy++eMm1ab6+vvj6+pKYmEhiYiJlZWWN7k8IIYRozX755ReGDh2KXq/n9OnTjBs3rkOv4+pwyRY0fMqxIXx8fHjnnXca/br4+Hji4+PNdbaEEEKI9mLAgAH06tWLI0eOcPbsWdLS0jr0U/odLtny8PBAo9HUOV5aWgqAu7t7i8QhI1tCCCHaK6VSyZIlSzh48CBPPvmkefaoo+pwyVZkZCSJiYnodLpa67ZSU1OB2gVKrUlGtoQQQrRnEyZMYMKECbYOo1XocAvkR44cSUVFBdu3b691fNOmTfj6+taqhC+EEEII0VztamRr3759VFZWotVqAVMNq4v7MMbFxeHo6EhcXBwDBw5k+fLlaLVaQkJC2Lx5M/v37ychIcFcY8vaZBpRCCFER1JZWUlqamqHHNRo9kbUrcm0adPIycmp99zatWvNxda0Wi0rV66stV3PXXfd1azteppKNqIWQgjRnhmNRlatWsWiRYtQKpWcPn0aBwcHW4fVotrVyNbnn3/eoOucnZ156qmneOqpp6wckRBCCNGxKRQKvv76azIyMgBYtWoVjz/+uI2jalntKtlqS2QaUQghREfx0ksvsWnTJiZPnsyIESNsHU6La1fTiG2RTCMKIYToCJKSkjrs51yHexpRCCGEEC2voyZaINOINiPTiEIIIUTHICNbNhIfH8+SJUuYOXOmrUMRQgghWtSRI0e4/fbbKSwstHUoLUKSLSGEEEK0mA8++IC+ffvy3//+l2XLltk6nBYhyZYQQgghWsyECROwt7cH4Ouvv0av19s4IuuTNVs2Imu2hBBCdERhYWHMnz8fDw8PHnnkkRbbucWWpPSDjUnpByGEEKJ9k2lEIYQQQggrkmRLCCGEEDZVVlZGXl6ercOwGkm2hBBCCGETlZWVvP7660RGRvL000/bOhyrkWRLCCGEEDZRVVXFSy+9RF5eHv/+9785efKkrUOyCnka0UbkaUQhhBAdnYeHB3PnzmXevHnccsstODo62jokq5CnEW1MnkYUQgjRkZWXl5OSkkKfPn1sHYrVyDSiEEIIIWzGxcWlXSdaIMmWEEIIIYRVSbIlhBBCiFZj3759zJs3z9ZhWJQkW0IIIYRoFZ544gmGDh3KK6+8wtatW20djsVIsiWEEEKIViEuLs785w8++MCGkViWJFtCCCGEaBVuv/12JkyYwMqVK/n4449tHY7FSJ0tG5E6W0IIIURtKpWK77//3tZhWJwkWzYSHx9PfHy8uc6WEEIIIdonmUYUQgghRKtVVFSEXq+3dRjNIsmWEEIIIVqd0tJSXn75ZSIiIvjss89sHU6zSLIlhBBCiFbn4MGDPP/882g0Gl588UVqampsHVKTSbIlhBBCiFZn9OjRjB07FpVKxdVXX015ebmtQ2oyWSAvhBBCiFbp7bffRq1W06VLF1uH0iwysmVBx48f56qrrmLNmjW2DkUIIYRo87p3797mEy2QZMtiDAYDb7/9Nt27d7d1KEIIIYRoRSTZspB169YRGxtLWFiYrUMRQggh2h2j0UhiYmKbfDKx3azZ0mq1rFmzhuTkZJKTk9FoNEyfPp0ZM2bUe+2qVavYunUrpaWlhIWFceeddzJ27Ngm9a3RaPjyyy95//33eeutt5p7K0IIIYT4E51Ox4QJE9i8eTNeXl5MmjQJDw8PW4fVYO1mZEuj0bB+/XpqamoYOXLkZa9NSEhg06ZNTJ8+nVdffZVu3bqxcOFCfvrppyb1/cEHHzBt2jRcXV2b9HohhBBCXJparSY0NBQwFTldvXq1bQNqpHYzshUYGMjGjRtRKBQUFxezYcOGeq/bu3cvBw4cYMGCBcTHxwPQv39/cnJyWLFiBWPGjEGlUgEwa9Ysjh8/Xm87t912Gw888ABJSUmcPn2aOXPmNCjO/Px8CgoKzF+np6c35jaFEEKIDmnBggUcOHCAhIQEpk2bZutwGqXdJFsKhaJB1+3cuRMnJydGjx5d6/jEiRNZtGgRJ06cIDY2FoA33njjiu0dOXKEs2fPMnXqVAAqKipQKpVkZGTw97//vc7169ata3MZuRBCCGFrkZGRHDt2rMGf961Ju0m2GiotLY3w8HDU6tq3HhUVZT5/MdlqiMmTJ9dK3N59910CAwO544476r1+ypQpDB8+3Px1eno6L7/8ciPuQAghhOiY2mKiBR0w2dJoNAQHB9c57ubmBkBJSUmj2nN2dsbZ2dn8tYODA87OzpdcuOfr64uvry+JiYkkJiZSVlbWqP6EEEIIYaLVamt9BrdW7WaBfGNYMzOeP38+99577xWvi4+PZ8mSJcycOdNqsQghhBDtUWFhIfPmzSM0NJRz587ZOpwr6nAjWx4eHmg0mjrHS0tLAXB3d2+ROGRkSwghhGiaN954g1deeQWARYsWsWrVKhtHdHkdbmQrMjKS9PR0dDpdreOpqakAREREtEgcMrIlhBBCNM3f/vY3PDw8cHBwwMvLC6PRaOuQLqvDJVsjR46koqKC7du31zq+adMmfH196dGjh40iE0IIIURDeHl58dlnn5GcnMzSpUtb/cL5djWNuG/fPiorK9FqtYDpSb9t27YBEBcXh6OjI3FxcQwcOJDly5ej1WoJCQlh8+bN7N+/n4SEBHONLWuTaUQhhBCi6SZOnGjrEBpMYWztY2+NMG3aNHJycuo9t3btWoKCggDT0wsrV66stV3PXXfd1eTtepojKSmJBx98kJUrVxITE9Pi/QshhBDCutpVstWW/Hlk6+jRo5JsCSGEEE1kNBr59ttviYiIoE+fPrYOp44Ot2artZAF8kIIIUTzpaenM2jQIG644QbmzZtn63DqJcmWEEIIIdqsoKAg8vPzAdPDbgcOHLBxRHVJsiWEEEKINsve3p4XXniBPn36sG7dOgYMGGDrkOpoV08jtiXyNKIQQghhGffccw/33nsvSmXrHEOSZMtG4uPjiY+PNz+NKIQQQoimaamyTU3VOlNAIYQQQohm0Ov1tg7BTJItIYQQQrQb2dnZPPHEEwwdOhSDwWDrcACZRrQZWbMlhBBCWN7tt99u3pLviy++4NZbb7VxRDKyZTNSZ0sIIYSwvPnz5wPg4uJiLglhazKyJYQQQoh2Y9y4cbz11lvceuut+Pv72zocQJItIYQQQrQjCoWi1c0ayTSiEEIIIYQVSbIlhBBCCGFFMo1oI/I0ohBCCNExSLJlI1JBXgghhOgYZBpRCCGEEMKKJNkSQgghhLAiSbaEEEIIIaxIki0hhBBCCCuSZEsIIYQQworkaUQbkdIPQgghRMcgyZaNSOkHIYQQomOQaUQhhBBCCCuSZEsIIYQQwook2RJCCCGEsCJJtoQQQgghrEiSLSGEEEIIK5JkSwghhBDCiiTZEkIIIYSwIqmzZWNVVVUApKen2zgSIYQQQjRWeHg4jo6Ol71Gki0by8nJAeDll1+2cSRCCCGEaKyVK1cSExNz2WsURqPR2ELxiHoUFxfz888/88033/DUU0816DVvv/02M2fOvOJ16enpvPzyyyQkJBAeHt7cUNuFhn7vbKGlY7NWf5ZqtzntNOW1jX1NQ66X92Bdrfk9CPI+tGQ71n4ftpbPQhnZagM8PT0ZP348W7ZsuWJmfJGrq2uDrwXTD0Jjrm/PGvu9a0ktHZu1+rNUu81ppymvbexrGnO9vAf/0JrfgyDvQ0u2Y+33YVv6LJQF8q1EfHy8Va4VtbXm711Lx2at/izVbnPaacprG/ua1vyz1Jq19u+bvA8t146134et/Wfpz2QasR27uMl1Q+aThRCWJ+9BIWyvNbwPZWSrHfPx8WH69On4+PjYOhQhOiR5Dwphe63hfSgjW0IIIYQQViQjW0IIIYQQViTJlhBCCCGEFUmyJYQQQghhRZJsCSGEEEJYkSRbQgghhBBWJMlWB1ZdXc0///lPbrrpJiZMmMAjjzzCsWPHbB2WEB3Kiy++yNSpU5kwYQLTp09nz549tg5JiA7r+PHjXHXVVaxZs8ai7Urphw6soqKCtWvXcu211+Ln58cPP/zAe++9xxdffHHFfZ6EEJaRlpZGaGgodnZ2nDhxgjlz5vDf//4XDw8PW4cmRIdiMBh49NFHUSgUDB06lHvvvddibcvIVgfm5OTE9OnTCQgIQKlUcu2112IwGMjIyLB1aEJ0GBEREdjZ2QGgUqmoqakhPz/fxlEJ0fGsW7eO2NhYwsLCLN62bETdhmi1WtasWUNycjLJycloNBqmT5/OjBkz6r121apVbN26ldLSUsLCwrjzzjsZO3bsJdtPT0+nqqqK4OBga96GEG2Wtd6DixYtYseOHVRXVxMXF0dkZGRL3I4QbZI13ocajYYvv/yS999/n7feesviMUuy1YZoNBrWr19PVFQUI0eOZMOGDZe8NiEhgVOnTvHwww/TqVMnEhMTWbhwIQaDgXHjxtW5vrKyksWLF3PPPffg7OxszdsQos2y1ntwwYIF6HQ6Dh06RHp6OgqFwtq3IkSbZY334QcffMC0adNwdXW1SsySbLUhgYGBbNy4EYVCQXFx8SV/wPbu3cuBAwdYsGCBeVf0/v37k5OTw4oVKxgzZgwqlcp8vU6n44UXXiA8PJy77767Re5FiLbIWu9BALVazeDBg/nqq68IDQ1l6NChVr8fIdoiS78Pk5KSOH36NHPmzLFazLJmqw1RKBQN+o13586dODk5MXr06FrHJ06cSH5+PidOnDAfMxgMLF68GKVSydy5c+U3aiEuwxrvwb8yGAxkZmY2N1Qh2i1Lvw+PHDnC2bNnmTp1Ktdddx1btmzh008/ZfHixRaLWUa22qG0tDTCw8NRq2v/9UZFRZnPx8bGArBs2TIKCgpYunRpneuFEE3T0PdgQUEBx44dY8iQIdjZ2bFjxw4OHz7Mww8/bIuwhWhXGvo+nDx5cq2E7N133yUwMJA77rjDYrHIp2s7pNFo6l3k7ubmBkBJSQkAOTk5bNiwAXt7e6ZMmWK+7tVXX6VPnz4tE6wQ7VBD34MAX375Ja+88goKhYLQ0FBefPFFoqOjWyxWIdqrhr4PnZ2da61VdnBwwNnZ2aLlVyTZaqcaMsQaGBjIjh07WiAaITqehrwHfXx8eOedd1ogGiE6pqYsjZk/f77F45A1W+2Qh4cHGo2mzvHS0lIA3N3dWzokIToUeQ8KYXut6X0oyVY7FBkZSXp6Ojqdrtbx1NRUwFREUQhhPfIeFML2WtP7UJKtdmjkyJFUVFSwffv2Wsc3bdqEr68vPXr0sFFkQnQM8h4UwvZa0/tQ1my1Mfv27aOyshKtVguYqr5v27YNgLi4OBwdHYmLi2PgwIEsX74crVZLSEgImzdvZv/+/SQkJNSp7yOEaDh5Dwphe23tfSgbUbcx06ZNIycnp95za9euJSgoCDBtUbBy5cpaWxTcddddl92uRwhxZfIeFML22tr7UJItIYQQQggrkjVbQgghhBBWJMmWEEIIIYQVSbIlhBBCCGFFkmwJIYQQQliRJFtCCCGEEFYkyZYQQgghhBVJsiWEEEIIYUWSbAkhhBBCWJEkW0II0Yq9/vrrXHfddeZtSQA+/PBDRo0axeHDh20Y2R8WL17MLbfcQlVVla1DEaJVkr0RhRAtJjs7m1tvvfWy10RHR/Phhx+2UESt2/nz51m3bh0PPfQQzs7OVu3r22+/5bXXXmPKlCk8/fTTl732gQce4PTp06xcuZKYmBjuvfdeEhMT+eKLL7jrrrusGqcQbZEkW0KIFhcSEsK4cePqPefj49PC0bReH330Efb29kydOtXqfcXHx/Puu++yZcsWZs6ciYODQ73XnTlzhtOnT9OlSxdiYmIACA0NZcSIEXz22WfcdNNNODk5WT1eIdoSSbaEEC0uJCSEGTNm2DqMVq24uJgdO3YwevRoq49qAbi4uHDVVVfxww8/sH37dsaPH1/vdRs2bABg4sSJtY6PHz+e7du3s3nzZiZPnmz1eIVoS2TNlhCiVRs1ahRPPvkkxcXFLFmyhClTphAfH88jjzxyyTVLWq2WDz/8kHvuuYf4+HgmTpzI008/zdGjR+tc++STTzJq1Ciqq6v5v//7P26//XauvvrqWlOZ27dv58EHHyQ+Pp6pU6fy6quvUlpayrRp05g2bZr5usWLFzNq1ChOnjxZb1wrVqxg1KhR7Nix44r3vXnzZqqrqxk9evQVr73ozJkz3HDDDVx33XWcOHHCfDwrK4tXXnmFm2++mbFjx3L99dfzj3/8g5ycnFqvnzRpEgDff/99ve3X1NSQmJiIvb19nWQsLi4OJycnvvvuuwbHK0RHIcmWEKLVKysr47HHHuPMmTOMGzeOUaNGkZSUxNNPP01qamqta0tKSnj00UdZvXo17u7uXH/99ebrn3rqKXbu3FlvHwkJCXz33Xf06dOHW265heDgYAA2btzI888/T2ZmJtdccw0TJkzgt99+Y/bs2eh0ulptTJkyBfhj9OfPdDodP/zwA97e3gwbNuyK93zw4EEAevbseeVvEHDkyBFmzpyJSqXinXfeoUePHgCcOHGCBx54gE2bNhETE8PNN99Mnz59+Omnn3j44YfJysoyt9G3b19CQ0M5dOgQ2dnZdfrYvXs3Go2GUaNG4ebmVuucnZ0dXbt25eTJk1RUVDQoZiE6CplGFEK0uMzMzEsugu/ZsydDhgypdSwlJYXrr7+eWbNmoVSafkfs378/r776Kl9//XWtBd1vvPEGaWlpzJs3r9ZUV2FhIQ899BBLly5l8ODBddYkFRQU8NFHH+Hu7m4+VlpayltvvYWzszOrVq0yJ2APPvggzz77LElJSQQGBpqvj42NJSIigs2bN/PEE0/UWru0Z88eCgsLueOOO1Crr/xP7/Hjx/Hz88PLy+uK1+7cuZOFCxcSHBzMsmXL8Pf3B0wJ3osvvojBYGDlypVER0ebX3P06FGeeuop3nrrLZYsWWI+PnHiRD744AM2bdrEfffdV6ufjRs3An+MgP1VTEwMR44c4eTJk/Tv3/+KcQvRUcjIlhCixWVmZrJ69ep6/9u/f3+d652cnHjkkUfMiRbAhAkTUKlUnDp1ynysuLiYrVu3MmDAgDpriry9vbn99tspLi42jxr92X333Vcr0QLYtWsXFRUVTJ482ZxoAajVau6///56723KlClotVq2bNlS6/iGDRtQKBQNWs9UU1NDcXFxgxKtDRs2sGDBArp06cI777xjTrTAlODl5ORw++2310q0AHr37s3w4cPZt28f5eXl5uMXv6/ff/89RqPRfDw/P58DBw4QGBh4yUTqYrx5eXlXjFuIjkRGtoQQLW7w4MEsW7aswdeHhobWWSSuVqvx9vamrKzMfOzUqVPo9Xqqq6vrHTnLyMgAID09vc5UXvfu3etcf+bMGQD+v737C2nqDeMA/t2mVszjYq4MREuTmGUgSVk0gy1q1DIKsj8GddOFZAQlFV3mVWFlSpRdWFCBVKRSI+e0sItsEYk0M4IupK2aGtEKImV/fhdyTr95zqYzVqLfz93e97zveXeuHt73Oc8pKCiQ9eXn50Oj0cjaN2/ejIaGBtjtdmkHaHh4GC9fvpSO6Sbi9/sBQHZUN97du3fx7NkzrF27FtXV1Zg7d25E/5s3bwAAHz58UHweX79+RSgUgsfjgdFoBAAYDAYUFxeju7sbPT09KCoqAjCWxxUMBrF161aoVCrF9YjBqrh+IhrDYIuIpj2tVqvYrtFoEAqFpN/fv38HALjdbrjd7qjz/fr1S9am1+tlbeKOz/z582V9arUaOp1O1i4IAsxmMxwOBwYGBrBkyRI8evQIwWBw0m/piUecExUJFRP+i4uLZYEWMHYMCgAdHR0x5xn/PGw2G7q7u9HW1hYRbKnVamzZsiXqPOJ6o5WNIJqtGGwR0YwhBmV79uxBZWVlXGOVdmvE+b59+ybrC4VC8Pv9WLBggaxv+/btcDgcsNvtqKysRFtbG9LS0rBhw4ZJrUUQBCQlJUnBYzSnTp3CzZs3UV9fD7VajZ07d0b0i7uBZ8+enVRSvmjdunXQ6/V4+vQpjh07hvfv38Pr9WLNmjXIyMiIOk5cr1JwSjSbMWeLiGYMo9EIlUolHZ/9qaVLlwIYS1Yf7+3btwgGg4rjCgoKkJubi/b2drhcLnz69AmbNm2Ka8cnJycHPp9P9sbj/wmCgNraWixbtgy1tbVobm6O6BffSIz3eSQlJcFqtWJkZASPHz+WyjlES4wXeTweAEBubm5c9yOa6RhsEdGMkZ6eDrPZjL6+PjQ1NUUkeIv6+/sVjxGVmEwmzJs3D3a7PaJEQiAQQGNjY8yxpaWl8Pv9qKmpAYC4C30WFhZidHRUyhuLRhAEXLx4EUajEZcuXcL9+/cj1p+RkYE7d+6gt7dXNjYQCCjWHgN+B1atra3o6uqCTqeDyWSKuZb+/n6kp6cjKytrgn9HNLvwGJGI/rpYpR8A/FF1+ePHj8Pj8eDq1atob2/HihUrkJqaiqGhIbx79w5erxctLS2KOU7jCYKAI0eOoKamBocOHYLFYoFWq4XL5UJKSgoMBkPUZHGr1Ypr167hy5cvWL58ubRLNlklJSW4d+8eXr16JX0WJ9Y6L1y4gKqqKtTV1SEcDmPXrl1ISUlBdXU1Tp48iaNHj6KoqAg5OTkAgMHBQbx+/Ro6nQ63b9+WzZmdnY2VK1dKuW/btm1DcnJy1DV8/PgRnz9/xo4dO+L6n0SzAYMtIvrrxNIP0fxJsJWWloYrV66gubkZT548QWdnJ0KhEPR6PfLy8nDw4EHFxPZoSktLIQgCbt26BYfDAa1Wi/Xr16OiogJlZWXIzMxUHJeamgqTyYTOzs4pfb6msLAQ2dnZcDqdKC8vn/B6cYerqqoK9fX1CIfDKCsrQ35+Pq5fv46mpia4XC643W4kJyfDYDCgpKQEGzdujDqnzWaTgq3xpTTGczqdAH4XdiWi31RhpX12IiKKyev1ory8HGazGWfOnFG85sCBAxgcHERLS8uUvm/44MEDnD9/Hg0NDVL+1XQUCASwf/9+LFq0CHV1df96OUTTDnO2iIhi+PHjB0ZHRyPaRkZGcPnyZQBjx31Knj9/joGBAVit1il/SNpms2Hx4sW4cePGlMb/LU6nEz6fD4cPH/7XSyGalniMSEQUQ29vL86dO4fVq1dj4cKF8Pv96Onpgc/nw6pVq2CxWCKub21txdDQEB4+fIg5c+Zg3759U763RqPB6dOn8eLFC/z8+XPKQVuiqVQqnDhxYsLcMqLZiseIREQxeDweNDY2oq+vT6q3lZmZCYvFgr1798rKOezevRvDw8PIyspCRUVFXPWtiGhmYrBFRERElEDM2SIiIiJKIAZbRERERAnEYIuIiIgogRhsERERESUQgy0iIiKiBGKwRURERJRADLaIiIiIEojBFhEREVEC/QcPc+nWno7Q4gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax = plt.subplots()\n", + "\n", + "ax.plot(energy, energy*energy*flux_median, label = \"Best fit\")\n", + "ax.fill_between(energy, energy*energy*flux_lo, energy*energy*flux_hi, alpha = .5, label = \"Best fit (errors)\")\n", + "ax.plot(energy, energy*energy*flux_inj, color = 'black', ls = \":\", label = \"Injected\")\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "\n", + "ax.set_xlabel(\"Energy (keV)\")\n", + "ax.set_ylabel(r\"$E^2 \\frac{dN}{dE}$ (keV cm$^{-2}$ s$^{-1}$)\")\n", + "\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "20a08b36-44d2-4fef-a82e-def1dfd7b9d9", + "metadata": {}, + "source": [ + "Plot the fitted spectrum convolved with the response, as well as the simulated source counts" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "7d1dd8d1-f86d-4e63-8286-db1d5bc14b04", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit_poisson_error = np.zeros((2,len(expectation.project('Em').todense().contents)))\n", + "fit_gaussian_error = np.zeros(len(expectation.project('Em').todense().contents))\n", + "inj_poisson_error = np.zeros((2,len(grb.binned_data.project('Em').todense().contents)))\n", + "inj_gaussian_error = np.zeros(len(grb.binned_data.project('Em').todense().contents))\n", + "\n", + "for i, counts in enumerate(expectation.project('Em').todense().contents):\n", + " if counts > 5:\n", + " fit_gaussian_error[i] = np.sqrt(counts)\n", + " else:\n", + " poisson_error = poisson_conf_interval(counts, interval=\"frequentist-confidence\", sigma=1)\n", + " fit_poisson_error[0][i] = poisson_error[0]\n", + " fit_poisson_error[1][i] = poisson_error[1]\n", + "\n", + "for i, counts in enumerate(grb.binned_data.project('Em').todense().contents):\n", + " if counts > 5:\n", + " inj_gaussian_error[i] = np.sqrt(counts)\n", + " else:\n", + " poisson_error = poisson_conf_interval(counts, interval=\"frequentist-confidence\", sigma=1)\n", + " inj_poisson_error[0][i] = poisson_error[0]\n", + " inj_poisson_error[1][i] = poisson_error[1]\n", + " \n", + "fig,ax = plt.subplots()\n", + "\n", + "ax.stairs(expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n", + "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=fit_poisson_error, color='purple', linewidth=0, elinewidth=1)\n", + "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=fit_gaussian_error, color='purple', linewidth=0, elinewidth=1)\n", + "ax.stairs(grb.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n", + "ax.errorbar(binned_energy, grb.binned_data.project('Em').todense().contents, yerr=inj_poisson_error, color='black', linewidth=0, elinewidth=1)\n", + "ax.errorbar(binned_energy, grb.binned_data.project('Em').todense().contents, yerr=inj_gaussian_error, color='black', linewidth=0, elinewidth=1)\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "\n", + "ax.set_xlabel(\"Energy (keV)\")\n", + "ax.set_ylabel(\"Counts\")\n", + "\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "00234bec-2a9f-4557-8a41-0d1a9b71e9c9", + "metadata": {}, + "source": [ + "Plot the fitted spectrum convolved with the response plus the fitted background, as well as the simulated source+background counts" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "06df3b27-d2ed-4214-bda7-d4fda667e145", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit_bkg_poisson_error = np.zeros((2,len(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents))))\n", + "fit_bkg_gaussian_error = np.zeros(len(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents)))\n", + "inj_bkg_poisson_error = np.zeros((2,len(grb_bkg.binned_data.project('Em').todense().contents)))\n", + "inj_bkg_gaussian_error = np.zeros(len(grb_bkg.binned_data.project('Em').todense().contents))\n", + "\n", + "for i, counts in enumerate(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents)):\n", + " if counts > 5:\n", + " fit_bkg_gaussian_error[i] = np.sqrt(counts)\n", + " else:\n", + " poisson_error = poisson_conf_interval(counts, interval=\"frequentist-confidence\", sigma=1)\n", + " fit_bkg_poisson_error[0][i] = poisson_error[0]\n", + " fit_bkg_poisson_error[1][i] = poisson_error[1]\n", + "\n", + "for i, counts in enumerate(grb_bkg.binned_data.project('Em').todense().contents):\n", + " if counts > 5:\n", + " inj_bkg_gaussian_error[i] = np.sqrt(counts)\n", + " else:\n", + " poisson_error = poisson_conf_interval(counts, interval=\"frequentist-confidence\", sigma=1)\n", + " inj_bkg_poisson_error[0][i] = poisson_error[0]\n", + " inj_bkg_poisson_error[1][i] = poisson_error[1]\n", + " \n", + "fig,ax = plt.subplots()\n", + "\n", + "ax.stairs(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents), binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n", + "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents), yerr=fit_bkg_poisson_error, color='purple', linewidth=0, elinewidth=1)\n", + "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents), yerr=fit_bkg_gaussian_error, color='purple', linewidth=0, elinewidth=1)\n", + "ax.stairs(grb_bkg.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n", + "ax.errorbar(binned_energy, grb_bkg.binned_data.project('Em').todense().contents, yerr=inj_bkg_poisson_error, color='black', linewidth=0, elinewidth=1)\n", + "ax.errorbar(binned_energy, grb_bkg.binned_data.project('Em').todense().contents, yerr=inj_bkg_gaussian_error, color='black', linewidth=0, elinewidth=1)\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "\n", + "ax.set_xlabel(\"Energy (keV)\")\n", + "ax.set_ylabel(\"Counts\")\n", + "\n", + "ax.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/spectral_fits/continuum_fit/grb/background.yaml b/docs/tutorials/spectral_fits/continuum_fit/grb/background.yaml index ed9b313d..6df54174 100644 --- a/docs/tutorials/spectral_fits/continuum_fit/grb/background.yaml +++ b/docs/tutorials/spectral_fits/continuum_fit/grb/background.yaml @@ -3,12 +3,12 @@ data_file: "/path/to/background/tra/file" # full path ori_file: "/path/to/ori/file" # full path to orientation file -unbinned_output: 'hdf5' # 'fits' or 'hdf5' -time_bins: 0.5 # time bin size in seconds. Takes int or list of bin edges. -energy_bins: [100., 200., 500., 1000., 2000., 5000.] # Takes list. Needs to match response. -phi_pix_size: 10 # binning of Compton scattering angle [deg] +unbinned_output: 'fits' # 'fits' or 'hdf5' +time_bins: 1 # time bin size in seconds. Takes int or list of bin edges. +energy_bins: [100., 158.489, 251.189, 398.107, 630.957, 1000., 1584.89, 2511.89, 3981.07, 6309.57, 10000.] # Takes list. Needs to match response. +phi_pix_size: 5 # binning of Compton scattering angle [deg] nside: 8 # healpix binning of psi chi local scheme: 'ring' # healpix binning of psi chi local -tmin: 1835478000.0 # Min time cut in seconds. -tmax: 1835485200.0 # Max time cut in seconds. +tmin: 1835487300.0 # Min time cut in seconds. +tmax: 1843467255.0 # Max time cut in seconds. #----------# diff --git a/docs/tutorials/spectral_fits/continuum_fit/grb/grb.yaml b/docs/tutorials/spectral_fits/continuum_fit/grb/grb.yaml index 0d16e144..92c47d82 100644 --- a/docs/tutorials/spectral_fits/continuum_fit/grb/grb.yaml +++ b/docs/tutorials/spectral_fits/continuum_fit/grb/grb.yaml @@ -1,14 +1,14 @@ #----------# # Data I/O: -data_file: "/path/to/grb/tra/file" # full path +data_file: "/path/to/background/tra/file" # full path ori_file: "/path/to/ori/file" # full path to orientation file -unbinned_output: 'hdf5' # 'fits' or 'hdf5' -time_bins: 0.5 # time bin size in seconds. Takes int or list of bin edges. -energy_bins: [100., 200., 500., 1000., 2000., 5000.] # Takes list. Needs to match response. -phi_pix_size: 10 # binning of Compton scattering angle [deg] +unbinned_output: 'fits' # 'fits' or 'hdf5' +time_bins: 1 # time bin size in seconds. Takes int or list of bin edges. +energy_bins: [100., 158.489, 251.189, 398.107, 630.957, 1000., 1584.89, 2511.89, 3981.07, 6309.57, 10000.] # Takes list. Needs to match response. +phi_pix_size: 5 # binning of Compton scattering angle [deg] nside: 8 # healpix binning of psi chi local scheme: 'ring' # healpix binning of psi chi local -tmin: 1835481433.0 # Min time cut in seconds. -tmax: 1835481435.0 # Max time cut in seconds. +tmin: 1842597410.0 # Min time cut in seconds. +tmax: 1842597450.0 # Max time cut in seconds. #----------# diff --git a/docs/tutorials/ts_map/Crab_bkg_galactic_inputs.yaml b/docs/tutorials/ts_map/Crab_bkg_galactic_inputs.yaml new file mode 100644 index 00000000..6cecbf28 --- /dev/null +++ b/docs/tutorials/ts_map/Crab_bkg_galactic_inputs.yaml @@ -0,0 +1,15 @@ +#----------# +# Data I/O: + +# data files available on the COSI Sharepoint: https://drive.google.com/drive/folders/1UdLfuLp9Fyk4dNussn1wt7WEOsTWrlQ6 +data_file: "/zfs/astrohe/yong/COSI/cosipy_workshop2023/DC2/Data/Sources/crab_3months_unbinned_data.fits.gz" # full path +ori_file: "" # full path +unbinned_output: 'fits' # 'fits' or 'hdf5' +time_bins: 0.5 # time bin size in seconds. Takes int, float, or list of bin edges. +energy_bins: [100, 158.489, 251.189, 398.107, 630.957, 1000, 1584.89, 2511.89, 3981.07, 6309.57, 10000] # Takes list. Needs to match response. +phi_pix_size: 5 # binning of Compton scattering anlge [deg] +nside: 8 # 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. +#----------# diff --git a/docs/tutorials/ts_map/GRB_Orientation.ori b/docs/tutorials/ts_map/GRB_Orientation.ori new file mode 100644 index 00000000..563968e6 --- /dev/null +++ b/docs/tutorials/ts_map/GRB_Orientation.ori @@ -0,0 +1,7 @@ +Type OrientationsGalactic +OG 1835481433.0 53.30823215719789 51.00102125784474 -36.69176784280211 51.00102125784474 +OG 1835481433.5 53.30823215719789 51.00102125784474 -36.69176784280211 51.00102125784474 +OG 1835481434.0 53.25629494101732 51.035133117383225 -36.74370505898268 51.035133117383225 +OG 1835481434.5 53.25629494101732 51.035133117383225 -36.74370505898268 51.035133117383225 +OG 1835481435.0 53.20436773279772 51.0692916774301 -36.79563226720228 51.0692916774301 +# EN \ No newline at end of file diff --git a/docs/tutorials/ts_map/Parallel_TS_map_computation_DC2.ipynb b/docs/tutorials/ts_map/Parallel_TS_map_computation_DC2.ipynb new file mode 100644 index 00000000..f9b8ffd6 --- /dev/null +++ b/docs/tutorials/ts_map/Parallel_TS_map_computation_DC2.ipynb @@ -0,0 +1,1002 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "54329e0c-b926-45f1-bd4a-f6a1c6e68d52", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Parallel TS Map computation" + ] + }, + { + "cell_type": "markdown", + "id": "0ead2b89-59f2-49dd-ae7b-30068d9ba290", + "metadata": {}, + "source": [ + "## Fast flux and TS Map calculation" + ] + }, + { + "cell_type": "markdown", + "id": "b39635ea-ab7c-488e-b09f-26730d79024e", + "metadata": { + "tags": [] + }, + "source": [ + "### Possion distribution" + ] + }, + { + "cell_type": "markdown", + "id": "76e69ae4-f016-4f4d-a9af-db88ca8b184e", + "metadata": {}, + "source": [ + "A discrete random variable $X$ is said to have Poisson distribution, with parameter $\\lambda>0$:\n", + "$$\n", + "f(k;\\lambda)=\\text{Pr}(X=k)=\\frac{\\lambda^ke^{-\\lambda}}{k!},\n", + "$$\n", + "where:\n", + "- $k$ is the number of occurrences ($k=0,1,2,...$)\n", + "- $e$ is the Euler's number\n", + "- $\\lambda$ is equal to the expectation and variance of $X$: $\\lambda=\\text{E}(X)=\\text{Var}(X)$" + ] + }, + { + "cell_type": "markdown", + "id": "0a2f765f-79b2-4f9d-88a4-5b6d9c42e9be", + "metadata": { + "tags": [] + }, + "source": [ + "### Maximum Poisson log-likelihood ratio test statistic (TS)" + ] + }, + { + "cell_type": "markdown", + "id": "4c74dc69-dae9-43b7-9bbc-b7b5bc7677e0", + "metadata": {}, + "source": [ + "Here, we will examine two contradictory hypotheses:\n", + "- There are source photons emitted from a sky location (pixel) with likelihood $L(f)$, where $f$ is the source flux.\n", + "- There are only background photons emitted from a sky location (pixel) with likelihood $L(0)$, where $f=0$ since no source is present." + ] + }, + { + "cell_type": "markdown", + "id": "ea18a011-38f3-409f-a1b0-49ad666ee6c7", + "metadata": {}, + "source": [ + "The log-likelihood ratio test statistic is defined as:\n", + "$$\n", + "L L R(f)=2 \\log \\frac{L(f)}{L(0)}=2 \\sum_{i=1}^N \\log \\frac{P\\left(b_i+e_i f, d_i\\right)}{P\\left(b_i, d_i\\right)}\n", + "$$\n", + "$$\n", + "T S=\\max L L R(f)=L L R(F)\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "19a5f5b2-8a25-461b-8380-cfd1af01940a", + "metadata": {}, + "source": [ + "- $P(\\lambda, n)$ is the Poisson probability for n photons with mean $\\lambda$ (also called expectation)\n", + " - $\\lambda=b_i+e_i f$\n", + " - $b_i$ is the background counts\n", + " - $e_i$ is the expected excess per flux unit obtained from the detector response and source model (spectrum and location)\n", + " - $f$ is the free parameter representing the flux from the source\n", + " - $d_i$ is the measured count data, including both source and background photons\n", + " - $F$ is the best estimated flux norm that maximizes $L L R(f)$" + ] + }, + { + "cell_type": "markdown", + "id": "98b7b13f-992f-40e1-9fcf-d05cd2a10b49", + "metadata": {}, + "source": [ + "One good news is that $L L R(f)$ has analytic derivatives at all orders. What's more, the second-order derivative is always negative. Therefore, $L L R(f)$ has only one maximum, which can be solved by Newton-Raphson's method.\n", + "$$\n", + "L L R^{\\prime}(f)=2 \\sum\\left(d_i \\frac{e_i}{b_i+e_i f}-e_i\\right)\n", + "$$\n", + "$$\n", + "L L R^{\\prime \\prime}(f)=-2 \\sum\\left(d_i \\frac{e_i^2}{\\left(b_i+e_i f\\right)^2}\\right)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4e1443e2-dfd9-47fd-a9db-1956b78ce33f", + "metadata": { + "tags": [] + }, + "source": [ + "### Parallel Computation" + ] + }, + { + "cell_type": "markdown", + "id": "bc922c16-5e70-44b6-9da6-e6afadb0ae14", + "metadata": {}, + "source": [ + "The way we generate a TS map is to iterate through all pixels in an all-sky map. Although this generally works, it needs a tremendous amount of time when we want an all-sky map with a good resolution (3072 pixels or higher). A solution to speed it up is implementing parallel computation in our method. The idea is very simple: **The computation of pixels is independent of each other. Thus, we can perform the computations together, depending on the number of available CPU cores per user.**" + ] + }, + { + "cell_type": "markdown", + "id": "9954578b-7d9f-43fc-9ad4-02a20d1e36ac", + "metadata": {}, + "source": [ + "Here let me describe the steps in the computation for a single pixel:" + ] + }, + { + "cell_type": "markdown", + "id": "0f887e07-6590-4814-9dc6-7ceb1c581393", + "metadata": {}, + "source": [ + "#### Step 1: Data Preparation" + ] + }, + { + "cell_type": "markdown", + "id": "53d7afaa-e1af-47e2-a10b-f538d095708e", + "metadata": {}, + "source": [ + "We need several data files to perform the TS map calculation\n", + "- Measured (observational) data in *hd5f* format (in this case, the measured data is simulated)\n", + "- Background model in *hd5f* format\n", + "- Response in *h5* format (we have both detector and galactic responses)\n", + "- Orientation file in *ori* format (needed when using detector response)\n", + " \n", + "With those files, we can then:\n", + "\n", + "- Read all the data files\n", + "- Generate a null all-sky map with a customized number of pixels\n", + "- Choose a pixel from the all-sky map\n", + "- Convolve the response with the pixel coordinate and spectrum to get the expected excess per flux unit $e_i$" + ] + }, + { + "cell_type": "markdown", + "id": "a039e477-d5ad-4c35-b0a6-6b51b19a5f2d", + "metadata": {}, + "source": [ + "#### Step 2: Data Projection\n", + "The data themselves have multiple axes. However, we only need Compton data space in a specific energy range. So, we will process the data to obtain the portion needed for the TS map.\n", + "- Slice the energy range we want\n", + "- Project to Compton data space (CDS).\n", + "\n", + " CDS is a 3D data space (Compton scattering angle, Psi, and Chi); here, I use a 2D slice (PsiChi) to represent CDS in the image below." + ] + }, + { + "cell_type": "markdown", + "id": "aa0e9b71-7ac2-44db-a791-9f5e59c6ad6c", + "metadata": {}, + "source": [ + "#### Steps 3: Newton-Raphson's Method\n", + "With the data we obtained from Step 2, we can construct the log-likelihood ratio function and find its global maximum. The returned maximum will be feedback to the pixel we picked as the TS value or the flux norm. At this point, the calculation of a pixel is completed." + ] + }, + { + "attachments": { + "4acba1f5-5083-4b35-9183-e711c3f39490.jpg": { + "image/jpeg": 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+ } + }, + "cell_type": "markdown", + "id": "71c3a35c-b75c-417f-9beb-d563de5a696f", + "metadata": {}, + "source": [ + "![Xnip2024-01-11_00-36-07.jpg](attachment:4acba1f5-5083-4b35-9183-e711c3f39490.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "69c142ef-ea14-44bc-8ae5-5907ee3b30ff", + "metadata": {}, + "source": [ + "## Importing modules" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9485f7bb-ca6e-440e-8d2d-4b4f29d32781", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "# import necessary modules\n", + "from threeML import Powerlaw\n", + "from cosipy import FastTSMap, SpacecraftFile\n", + "from cosipy.response import FullDetectorResponse\n", + "import astropy.units as u\n", + "from histpy import Histogram\n", + "from astropy.time import Time\n", + "import numpy as np\n", + "from astropy.coordinates import SkyCoord\n", + "from pathlib import Path\n", + "from mhealpy import HealpixMap\n", + "from matplotlib import pyplot as plt\n", + "import sys" + ] + }, + { + "cell_type": "markdown", + "id": "71cd1f65-552d-4741-abab-f9c85415d276", + "metadata": {}, + "source": [ + "## Example 1: Fit the GRB using the Compton Data Space (CDS) in local coordinates (Spacecraft frame)" + ] + }, + { + "cell_type": "markdown", + "id": "fc125d78-7305-42f2-bbb5-b22c146174e1", + "metadata": {}, + "source": [ + "### Define a powerlaw spectrum" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d56dd795-845f-4db6-876d-6b929a7df5e8", + "metadata": {}, + "outputs": [], + "source": [ + "# define a powerlaw spectrum\n", + "index = -2.2\n", + "K = 10 / u.cm / u.cm / u.s / u.keV\n", + "piv = 100 * u.keV\n", + "spectrum = Powerlaw()\n", + "spectrum.index.value = index\n", + "spectrum.K.value = K.value\n", + "spectrum.piv.value = piv.value \n", + "spectrum.K.unit = K.unit\n", + "spectrum.piv.unit = piv.unit" + ] + }, + { + "cell_type": "markdown", + "id": "9f37efab-d2de-4678-8d93-fb678371b52a", + "metadata": {}, + "source": [ + "### Read signal data, background model and orientation" + ] + }, + { + "cell_type": "markdown", + "id": "d8a21d25-73ab-49f3-a0ca-1111fffb7bf7", + "metadata": {}, + "source": [ + "The data (signal and background) are simulated using the Compton Sphere massmodel, so we will use a Compton Sphere response for data analysis." + ] + }, + { + "cell_type": "markdown", + "id": "4d4fe262-2aa8-4b51-a638-c4c18c62c37a", + "metadata": {}, + "source": [ + "Data availability:\n", + "\n", + "- `new_healpix_rsp_Binned_Bkg_2s_model.hdf5` is the background model. It's available at the same location as this notebook.\n", + "- `new_healpix_rsp_Binned_protoGRB.hdf5` is the GRB signal simulated with the Compton Sphere massmodel in mini-DC2. It's available at the same location as this notebook.\n", + "- `new_healpix_rsp_Binned_Cosmic2s.hdf5` is the background component simulated with the Compton Sphere massmodel in mini-DC2. It's available at the same location as this notebook.\n", + "- `GRB_Orientation.ori` is the orientation file of the observation (simulation) of the GRB. It's available in the same folder as this notebook.\n", + "- `Continuum_Flat_100to10000keV_10logEbins_HealPix03.binnedimaging.imagingresponse_nside8.area.h5` is the Compton Sphere response we will use for GRB. Since it's too large to upload to GitHub, it's available on Wasabi at *cosi-pipeline-public/ComptonSphere/mini-DC2*. Please unzip the response file before using it." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5d98d53e-fa3b-4657-8b16-e48ce1b2ee6a", + "metadata": {}, + "outputs": [], + "source": [ + "# Read all the files needed\n", + "\n", + "bkg_model = Histogram.open(\"new_healpix_rsp_Binned_Bkg_2s_model.hdf5\")\n", + "bkg_model = bkg_model.project(['Em', 'PsiChi', 'Phi'])\n", + "\n", + "# read the signal and bkg to assemble data = bkg + signal\n", + "signal_original = Histogram.open(\"new_healpix_rsp_Binned_protoGRB.hdf5\")\n", + "signal = signal_original.project(['Em', 'PsiChi', 'Phi'])\n", + "\n", + "bkg_original = Histogram.open(\"new_healpix_rsp_Binned_Cosmic2s.hdf5\")\n", + "bkg = bkg_original.project(['Em', 'PsiChi', 'Phi'])\n", + "\n", + "# We will use full signal counts now (100%). Note that the full signal is very very strong GRB.\n", + "signal = 1*signal\n", + "data_used = bkg + signal" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "804937e9-026c-4b45-b8e5-e46dc025c950", + "metadata": {}, + "outputs": [], + "source": [ + "# Read GRB orientation from file\n", + "ori = SpacecraftFile.parse_from_file(\"GRB_Orientation.ori\")" + ] + }, + { + "cell_type": "markdown", + "id": "73f084fb-84a5-4ed6-8418-7fab64f85192", + "metadata": {}, + "source": [ + "### Start TS map fit" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1baef583-a4c2-474b-a5bc-ed16f2c552cf", + "metadata": {}, + "outputs": [], + "source": [ + "# define the path to the response\n", + "response_path = Path(\"/home/sheng2/astrohe_yong/COSI/Response/Continuum_Flat_100to10000keV_10logEbins_HealPix03.binnedimaging.imagingresponse_nside8.area.h5\")\n", + "\n", + "# here let's create a FastTSMap object for fitting the ts map in the following cells\n", + "ts = FastTSMap(data = data_used, bkg_model = bkg_model, orientation = ori, \n", + " response_path = response_path, cds_frame = \"local\", scheme = \"RING\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "af11cb6a-b572-4594-95fa-e7b3e857e7a8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# get a list of hypothesis coordinates to fit. The models will be put on these locations for get the expected counts from the source spectrum.\n", + "# note that this nside is also the nside of the final TS map\n", + "hypothesis_coords = FastTSMap.get_hypothesis_coords(nside = 16)" + ] + }, + { + "cell_type": "markdown", + "id": "26cb7be2-dae9-4c50-8946-f9b98c9c4ada", + "metadata": {}, + "source": [ + "Below is the actual parallel fit:\n", + "- In default, the maximum number of cores it can use is `max_number-1`. You can also customize the number of cores you want to use by the `cpu_cores` parameter.\n", + "- energy channel is `[lower_channel, upper_channel]`. Lower channel is inclusive while the upper channel is exclusive\n", + "- This might take long in a personal computer and consume a lot of memories (up to 30-40 GB)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "06c7d26a-4518-4333-94df-42d58c0c8366", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You have total 56 CPU cores, using 40 CPU cores for parallel computation.\n", + "The time used for the parallel TS map computation is 2.7677059610684713 minutes\n" + ] + } + ], + "source": [ + "ts_results = ts.parallel_ts_fit(hypothesis_coords = hypothesis_coords, energy_channel = [2,3], spectrum = spectrum, ts_scheme = \"RING\", cpu_cores = 40)" + ] + }, + { + "cell_type": "markdown", + "id": "e9ed5290-a3d2-4292-80d4-9a724fea5ca2", + "metadata": {}, + "source": [ + "### Plot the fitted TS map" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5ed7fc2c-5794-406e-ac41-199a4fbc8f6e", + "metadata": {}, + "outputs": [], + "source": [ + "# This the true location of the GRB\n", + "coord = SkyCoord(l=51, b = -17, unit = (u.deg, u.deg), frame = \"galactic\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b32c1fcc-1068-4c64-a7d7-45f751dc405a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "ts.plot_ts(skycoord = coord)" + ] + }, + { + "cell_type": "markdown", + "id": "45dd4305-79b3-47ee-a410-1e034086035f", + "metadata": {}, + "source": [ + "The image above plots the raw TS values, which is also an image of the GRB. However, for the purpose of localization, we are more interested in the confidence level of the imaged GRB. Thus, you can plot the 90% confidence level of the GRB location by setting `containment` parameter to the percetage you want to plot." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "11fd0558-5032-4182-b07c-d0deb1e56937", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ts.plot_ts(skycoord = coord, containment = 0.9)" + ] + }, + { + "cell_type": "markdown", + "id": "b470a40e-2e3f-4189-a671-4bda530fe7c9", + "metadata": {}, + "source": [ + "As you can see, the GRB region shrinks only to a single pixel. This is caused by the fact that the GRB signal is very very strong in this case. In the next section, we will manipulate the strength of the GRB signal to see how the front source signal affects the TS values and the 90% confidence region." + ] + }, + { + "cell_type": "markdown", + "id": "69b8f13d-e7f3-4de5-bcf5-05cb33a81da2", + "metadata": {}, + "source": [ + "## Example 2: Fit a fainter GRB using the Compton Data Space (CDS) in local coordinates (Spacecraft frame)" + ] + }, + { + "cell_type": "markdown", + "id": "4c574065-8901-448c-8a74-1d718439a95c", + "metadata": {}, + "source": [ + "Here we will set up a scaling factor to manipulate the strength of the signal to see the affects on the final TS map. Since all the steps are exactly the same execpt the scaling factor, I will put the main codes in a single cell for simplicity.\n", + "\n", + "**If you encounter any errors, please try to restart the notebook kernel or the whole session.**" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "54378c2c-00f1-4c77-8e58-87d4252c954a", + "metadata": {}, + "outputs": [], + "source": [ + "scaling_factor = 0.02" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "68d167dc-410f-4119-8a7c-73eb976ddf7f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The time used for the parallel TS map computation is 2.9340662002563476 minutes\n" + ] + } + ], + "source": [ + "# define a powerlaw spectrum\n", + "index = -2.2\n", + "K = 10 / u.cm / u.cm / u.s / u.keV\n", + "piv = 100 * u.keV\n", + "spectrum = Powerlaw()\n", + "spectrum.index.value = index\n", + "spectrum.K.value = K.value\n", + "spectrum.piv.value = piv.value \n", + "spectrum.K.unit = K.unit\n", + "spectrum.piv.unit = piv.unit\n", + "\n", + "# open bkg model\n", + "bkg_model = Histogram.open(\"new_healpix_rsp_Binned_Bkg_2s_model.hdf5\")\n", + "bkg_model = bkg_model.project(['Em', 'PsiChi', 'Phi'])\n", + "\n", + "# read the signal and bkg to assemble data = bkg + signal\n", + "signal_original = Histogram.open(\"new_healpix_rsp_Binned_protoGRB.hdf5\")\n", + "signal = signal_original.project(['Em', 'PsiChi', 'Phi'])\n", + "\n", + "bkg_original = Histogram.open(\"new_healpix_rsp_Binned_Cosmic2s.hdf5\")\n", + "bkg = bkg_original.project(['Em', 'PsiChi', 'Phi'])\n", + "\n", + "# We scale the signal here\n", + "signal = scaling_factor*signal\n", + "data_used = bkg + signal\n", + "\n", + "# Read GRB orientation from file\n", + "ori = SpacecraftFile.parse_from_file(\"GRB_Orientation.ori\")\n", + "\n", + "# define the path to the response\n", + "response_path = Path(\"/home/sheng2/astrohe_yong/COSI/Response/Continuum_Flat_100to10000keV_10logEbins_HealPix03.binnedimaging.imagingresponse_nside8.area.h5\")\n", + "\n", + "# here let's create a FastTSMap object for fitting the ts map in the following cells\n", + "ts = FastTSMap(data = data_used, bkg_model = bkg_model, orientation = ori, \n", + " response_path = response_path, cds_frame = \"local\", scheme = \"RING\")\n", + "\n", + "# get a list of hypothesis coordinates to fit. The models will be put on these locations for get the expected counts from the source\n", + "# note that this nside is also the nside of the final TS map\n", + "hypothesis_coords = FastTSMap.get_hypothesis_coords(nside = 16)\n", + "\n", + "ts_results = ts.parallel_ts_fit(hypothesis_coords = hypothesis_coords, energy_channel = [2,3], spectrum = spectrum, ts_scheme = \"RING\", cpu_cores = 40)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bad4ff02-14b4-4f28-9e8b-6d9ec949318c", + "metadata": {}, + "outputs": [], + "source": [ + "# This the true location of the GRB\n", + "coord = SkyCoord(l=51, b = -17, unit = (u.deg, u.deg), frame = \"galactic\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c4196616-528f-4bc5-ae4c-2b25a2793e86", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the raw ts values\n", + "ts.plot_ts(skycoord = coord)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "18790cb4-f97a-4927-a4db-14ce9e85d1f7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the 90% confidence region\n", + "ts.plot_ts(skycoord = coord, containment = 0.9)" + ] + }, + { + "cell_type": "markdown", + "id": "64eab521-9a79-41b7-8e13-50f11896d8bb", + "metadata": {}, + "source": [ + "## Example 3: Fit Crab using the Compton Data Space (CDS) in galactic coordinates" + ] + }, + { + "cell_type": "markdown", + "id": "b31e885f-cc29-49ca-b0f7-d14f99fddb10", + "metadata": {}, + "source": [ + "The Crab case is similar to the GRB one. The difference is that the Crab data (signal and background) are binned in the galactic coordiates instead of the spacecraft coordinates. Therefore, we will need to use the galatic response for Crab. In addition, the orientation file is not needed since Crab is a fixed source in galactic coordinates. What's more, unlike the GRB case, the Crab and background are simulated using the SMEX massmodel, so we will use the SMEX response to analyze the Crab data." + ] + }, + { + "cell_type": "markdown", + "id": "c56d38fa-ffa3-4102-af9f-573189164182", + "metadata": {}, + "source": [ + "### Read data and background model" + ] + }, + { + "cell_type": "markdown", + "id": "36ba7f06-fff3-4b7c-9453-33b4eda9f3f4", + "metadata": {}, + "source": [ + "Data availability:\n", + "- `Crab_bkg_galactic_inputs.yaml` is the configuration file used to bin the data. It's available at the same location as this folder.\n", + "- `crab_3months_unbinned_data.fits.gz` is the unbinned Crab data (signal) simulated with balloon massmodel. It's available on Wasabi at *cosi-pipeline-public/COSI-SMEX/DC2/Data/Sources*.\n", + "- `albedo_photons_3months_unbinned_data.fits.gz` is the unbinned background data simulated with balloon massmodel (this is also the background model we will use). It's available on Wasabi at *cosi-pipeline-public/COSI-SMEX/DC2/Data/Backgrounds*.\n", + "- `Albedo_galactic_CDS_binned.hdf5` is the binned background data as well as background model. It's available on Wasabi at *cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/ts_maps*.\n", + "- `Crab_galactic_CDS_binned.hdf5` is the binned Crab data. It's available on Wasabi at *cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/ts_maps*.\n", + "- `psr_gal_DC2.h5` is the point source response rotated to galactic coordiantes. It's available on Wasabi at *cosi-pipeline-public/COSI-SMEX/DC2/Responses/PointSourceReponse/psr_gal_continuum_DC2.h5.zip*. You will get the response file by unzipping *psr_gal_continuum_DC2.h5.zip*.\n" + ] + }, + { + "cell_type": "markdown", + "id": "92247366-573f-46e1-8a8a-c1e5db2b5399", + "metadata": {}, + "source": [ + "### Bin data (optional)" + ] + }, + { + "cell_type": "markdown", + "id": "2f19cae4-ac54-4b62-8ee1-99e1db7d7b56", + "metadata": {}, + "source": [ + "If you want to binned the data by yourself, you can run this **Bin data** section. Otherwise, you can use the binned data downloaded from Wasabi, which is faster." + ] + }, + { + "cell_type": "markdown", + "id": "4c9917d4-278a-4003-bc06-c8957523e4ff", + "metadata": {}, + "source": [ + "### Getting the binned Crab data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5fd8cbec-61a0-4e1f-af66-a6d44c2e4ff7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "binning data...\n", + "Time unit: s\n", + "Em unit: keV\n", + "Phi unit: deg\n", + "PsiChi unit: None\n" + ] + } + ], + "source": [ + "# Here is the code I used to bin the Crab data if you want to generate it by yourself.\n", + "from cosipy import BinnedData\n", + "# \"Crab_bkg_galactic_inputs.yaml\" can be used for both Crab and background binning since the only useful information in the yaml file is the binning of CDS\n", + "analysis = BinnedData(\"Crab_bkg_galactic_inputs.yaml\")\n", + "analysis.get_binned_data(unbinned_data = \"crab_3months_unbinned_data.fits.gz\", \n", + " make_binning_plots=False, \n", + " output_name = \"Crab_galactic_CDS_binned\", \n", + " psichi_binning = \"galactic\")\n", + "\n", + "# After you generate the binned data files, it should be saved to the same directory of this notebook" + ] + }, + { + "cell_type": "markdown", + "id": "55891a90-167a-4b45-94cc-4815709126ef", + "metadata": {}, + "source": [ + "### Getting the binned background data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ae1ec395-d217-4a57-8b95-384aee92d38d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "binning data...\n", + "Time unit: s\n", + "Em unit: keV\n", + "Phi unit: deg\n", + "PsiChi unit: None\n" + ] + } + ], + "source": [ + "# Here is the code I used to bin the background data if you want to generate it by yourself.\n", + "from cosipy import BinnedData\n", + "# \"Crab_bkg_galactic_inputs.yaml\" can be used for both Crab and background binning since the only useful information in the yaml file is the binning of CDS\n", + "analysis = BinnedData(\"Crab_bkg_galactic_inputs.yaml\")\n", + "analysis.get_binned_data(unbinned_data = \"albedo_photons_3months_unbinned_data.fits.gz\", \n", + " make_binning_plots = False,\n", + " output_name = \"Albedo_galactic_CDS_binned\",\n", + " psichi_binning = \"galactic\")" + ] + }, + { + "cell_type": "markdown", + "id": "993bbc9e-b056-4369-b7f7-3f59f0e26e4f", + "metadata": {}, + "source": [ + "### Read data and background" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "720de2f7-222e-4d29-815a-a85c07abca46", + "metadata": {}, + "outputs": [], + "source": [ + "# Read background model\n", + "bkg_model = Histogram.open(\"../Albedo_galactic_CDS_binned.hdf5\") # please make sure you adjust the path to the files by yourself.\n", + "bkg_model = bkg_model.project(['Em', 'PsiChi', 'Phi'])\n", + "\n", + "# Read the signal and bkg to assemble data = bkg + signal\n", + "signal_original = Histogram.open(\"../Crab_galactic_CDS_binned.hdf5\")\n", + "signal = signal_original.project(['Em', 'PsiChi', 'Phi'])\n", + "\n", + "# Here the background is the same as the background model since they are simulations, thus we know the background very well.\n", + "bkg_original = Histogram.open(\"../Albedo_galactic_CDS_binned.hdf5\")\n", + "bkg = bkg_original.project(['Em', 'PsiChi', 'Phi'])\n", + "\n", + "# Assemble the signal and background\n", + "data_used = bkg + signal" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "109c9aaf-be38-4a10-9c93-add2fdb5bfa9", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Counts')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the counts distribution\n", + "ax,plot = bkg.project(\"Em\").draw(label = \"background\", color = \"purple\")\n", + "#data.project(\"Em\").draw(ax, label = \"data\", color = \"green\")\n", + "data_used.project(\"Em\").draw(ax, label = \"data(Crab+bkg)\", color = \"green\")\n", + "signal.project(\"Em\").draw(ax, label = \"Crab\", color = \"pink\")\n", + "bkg_model.project(\"Em\").draw(ax, label = \"background model\", color = \"blue\")\n", + "\n", + "ax.legend()\n", + "ax.set_xscale(\"log\")\n", + "ax.set_ylabel(\"Counts\")" + ] + }, + { + "cell_type": "markdown", + "id": "e48658c5-9b31-48eb-8644-e4556f83011f", + "metadata": {}, + "source": [ + "### Start TS map fit" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "72ba732c-37d3-4a43-afbe-24292fbbd3e6", + "metadata": {}, + "outputs": [], + "source": [ + "# define a powerlaw spectrum\n", + "index = -3\n", + "K = 10**-3 / u.cm / u.cm / u.s / u.keV\n", + "piv = 100 * u.keV\n", + "\n", + "spectrum = Powerlaw()\n", + "spectrum.index.value = index\n", + "spectrum.K.value = K.value\n", + "spectrum.piv.value = piv.value \n", + "spectrum.K.unit = K.unit\n", + "spectrum.piv.unit = piv.unit" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "de748dc4-97a9-4b44-8531-e56a7be242d5", + "metadata": {}, + "outputs": [], + "source": [ + "# the galactic response file is available on wasabi\n", + "response_path = Path(\"/zfs/astrohe/yong/COSI/cosipy_workshop2023/DC2/Responses/PointSourceReponse/psr_gal_DC2.h5\")\n", + "\n", + "# here let's create a FastTSMap object\n", + "ts = FastTSMap(data = data_used, bkg_model = bkg_model, response_path = response_path, cds_frame = \"galactic\", scheme = \"RING\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "73ba79d0-a686-453e-b081-46d9462d338f", + "metadata": {}, + "outputs": [], + "source": [ + "# get a list of hypothesis coordinates to fit. The models will be put on these locations for get the expected counts from the source\n", + "# note that this nside is also the nside of the final TS map\n", + "hypothesis_coords = FastTSMap.get_hypothesis_coords(nside = 16)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "19d55399-0ef5-41dc-9bd6-29281375ccb5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You have total 56 CPU cores, using 40 CPU cores for parallel computation.\n", + "The time used for the parallel TS map computation is 0.5140911261240642 minutes\n" + ] + } + ], + "source": [ + "# Perform the parallel fit\n", + "ts_results = ts.parallel_ts_fit(hypothesis_coords = hypothesis_coords, energy_channel = [1,2], spectrum = spectrum, ts_scheme = \"RING\", cpu_cores = 40)" + ] + }, + { + "cell_type": "markdown", + "id": "a6f94572-b73d-425f-9c81-04d508420ab8", + "metadata": {}, + "source": [ + "### Plot results" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f967ebe1-a1ef-4b4c-a11b-840f33b0f055", + "metadata": {}, + "outputs": [], + "source": [ + "# This the true location of Crab\n", + "coord = SkyCoord(l=184.5551, b = -05.7877, unit = (u.deg, u.deg), frame = \"galactic\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d5a70e14-3eeb-41da-9eb5-8631779ac98e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the raw ts values\n", + "ts.plot_ts(skycoord = coord)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0f87ce79-1584-4739-91d8-84016b4293b0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the 90% confidence region\n", + "ts.plot_ts(skycoord = coord, containment = 0.9)" + ] + }, + { + "cell_type": "markdown", + "id": "ebb53a69-63be-48b3-9668-f9dae007267c", + "metadata": {}, + "source": [ + "## Improvements in progress" + ] + }, + { + "cell_type": "markdown", + "id": "9fe82127-8feb-4ca6-8fc8-881cd8657c2c", + "metadata": {}, + "source": [ + "The current method can generate the TS map for a GRB and Crab. However, the computation time needed on a personal laptop is still long and requires a massive amount of RAM (~30-40 GB). The future improvements will include:\n", + "- Optimization of the speed\n", + " - Faster algorithm for Newton-Raphson's method\n", + " - GPU computation\n", + "- Optimization of the RAM usage\n", + " - Share memories among parallel processes" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cosipy_spacecraftfile_new", + "language": "python", + "name": "cosipy_spacecraftfile_new" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/cosipy/test_data/new_healpix_rsp_Binned_Bkg_2s_model.hdf5 b/docs/tutorials/ts_map/new_healpix_rsp_Binned_Bkg_2s_model.hdf5 similarity index 100% rename from cosipy/test_data/new_healpix_rsp_Binned_Bkg_2s_model.hdf5 rename to docs/tutorials/ts_map/new_healpix_rsp_Binned_Bkg_2s_model.hdf5 diff --git a/cosipy/test_data/new_healpix_rsp_Binned_Cosmic2s.hdf5 b/docs/tutorials/ts_map/new_healpix_rsp_Binned_Cosmic2s.hdf5 similarity index 100% rename from cosipy/test_data/new_healpix_rsp_Binned_Cosmic2s.hdf5 rename to docs/tutorials/ts_map/new_healpix_rsp_Binned_Cosmic2s.hdf5 diff --git a/cosipy/test_data/new_healpix_rsp_Binned_protoGRB.hdf5 b/docs/tutorials/ts_map/new_healpix_rsp_Binned_protoGRB.hdf5 similarity index 100% rename from cosipy/test_data/new_healpix_rsp_Binned_protoGRB.hdf5 rename to docs/tutorials/ts_map/new_healpix_rsp_Binned_protoGRB.hdf5