diff --git a/cosipy/data_builder/Time_series_builder.py b/cosipy/data_builder/Time_series_builder.py new file mode 100644 index 000000000..c0a8cdbd2 --- /dev/null +++ b/cosipy/data_builder/Time_series_builder.py @@ -0,0 +1,137 @@ +from threeML.utils.time_series.binned_spectrum_series import BinnedSpectrumSeries +from threeML.utils.spectrum.binned_spectrum import BinnedSpectrumWithDispersion +from threeML.utils.spectrum.binned_spectrum import BinnedSpectrum +from threeML.utils.spectrum.binned_spectrum_set import BinnedSpectrumSet +from threeML.utils.time_interval import TimeIntervalSet +from threeML.utils.OGIP.response import OGIPResponse +from cosipy.spacecraftfile import SpacecraftFile +from cosipy import BinnedData +import numpy as np +from astropy.time import Time +from astropy.coordinates import SkyCoord + + +def create_ogip_response(name, response_file, time_bins, l, b, ori_file): + ori = SpacecraftFile.parse_from_file(ori_file) + tmin = Time(time_bins[0],format = 'unix') + tmax = Time(time_bins[-1],format = 'unix') + + sc_orientation = ori.source_interval(tmin, tmax) + coord = SkyCoord( l = l, b=b, frame='galactic', unit='deg') + sc_orientation.get_target_in_sc_frame(target_name = name, target_coord=coord) + + sc_orientation.get_dwell_map(response=response_file, save=False) + sc_orientation.get_psr_rsp(response=response_file) + + sc_orientation.get_rmf(out_name=name) + sc_orientation.get_arf(out_name=name) + + return OGIPResponse(rsp_file = name + '.rmf', arf_file= name + '.arf') + + +def from_cosi_data(cls, + name, + yaml_file, + cosi_dataset, + response_file, + arf_file = None, + l=None, + b=None, + ori_file=None, + deadtime=None, + poly_order=-1, + verbose=True, + restore_poly_fit=None, + **kwargs): + """ + Builds a TimeSeries object from COSI data. + To be used as: cosi_data = from_cosi_data(TimeSeriesBuilder, ...) + + :param name: Name of the time series + :param yaml_file: Path to yaml file + :param cosi_dataset: COSIGRBData object (.hdf5 containing signal + background) + :param response_file: Path to response file (either a .hdf5 file or .rsp file) + :param arf_file: Path to arf file (only required when response is OGIP compatible and has a different name than the .rsp file) + :param l: Galactic longitude of the source (optional if response_file is OGIP compatible) + :param b: Galactic latitude of the source (optional if response_file is OGIP compatible) + :param ori: Path to orientation file (optional if response is OGIP compatible) + :param poly_order: Polynomial order for background fitting (optional) + :param verbose: Verbosity flag + :param restore_poly_fit: File to restore background fit from + """ + + # 1. Load and process the data using cosipy BinnedData + analysis = BinnedData(yaml_file) + analysis.load_binned_data_from_hdf5(binned_data=cosi_dataset) + time_energy_counts = analysis.binned_data.project(['Time', 'Em']).contents.todense() + time_bins = (analysis.binned_data.axes['Time'].edges).value + energy_bins = (analysis.binned_data.axes['Em'].edges).value + + # Calculate exposures (livetime) + bin_widths = np.diff(time_bins) + if deadtime is None: + deadtime = np.zeros(len(bin_widths)) + exposures = bin_widths - deadtime + + # 2. Setup Response + if (response_file.endswith(".rmf")): + if (arf_file == None): + response = OGIPResponse(rsp_file = response_file, arf_file= response_file[:-3] + "arf") + else: + response = OGIPResponse(rsp_file = response_file, arf_file= arf_file) + else: + if l is None or b is None: + raise ValueError("Galactic coordinates (l, b) are required when response_file is not OGIP compatible") + if ori_file is None: + raise ValueError("Orientation file is required when response_file is not OGIP compatible") + response = create_ogip_response(name, response_file, time_bins, l, b, ori_file) + + # 3. Build 3ML internal structures + intervals = [TimeIntervalSet.INTERVAL_TYPE(start, stop) + for start, stop in zip(time_bins[:-1], time_bins[1:])] + time_intervals = TimeIntervalSet(intervals) + + binned_spectrum_list = [] + for i in range(len(time_energy_counts)): + binned_spectrum = BinnedSpectrum( + counts=time_energy_counts[i], + exposure=exposures[i], + ebounds=energy_bins, + mission='COSI', + instrument=name, + is_poisson=True + ) + binned_spectrum_list.append(binned_spectrum) + + binned_spectrum_set = BinnedSpectrumSet( + binned_spectrum_list=binned_spectrum_list, + time_intervals=time_intervals, + reference_time=time_bins[0] + ) + + binned_spectrum_series = BinnedSpectrumSeries( + binned_spectrum_set, + first_channel=0, + mission='COSI', + instrument=name, + verbose=verbose + ) + + # cosi_data = COSIGRBData(time_energy_counts, time_bins, energy_bins, deadtime) + + return cls( + name, + binned_spectrum_series, + response = response, + # arf_file, + # l, + # b, + # ori_file, + poly_order=poly_order, + unbinned=False, + verbose=verbose, + restore_poly_fit=restore_poly_fit, + container_type=BinnedSpectrumWithDispersion, + **kwargs + ) + diff --git a/cosipy/data_builder/__init__.py b/cosipy/data_builder/__init__.py new file mode 100644 index 000000000..e3fb1f7d4 --- /dev/null +++ b/cosipy/data_builder/__init__.py @@ -0,0 +1 @@ +from .Time_series_builder import from_cosi_data \ No newline at end of file diff --git a/docs/tutorials/spectral_fits/continuum_fit/time_series_builder/SpectralFit_TSB.ipynb b/docs/tutorials/spectral_fits/continuum_fit/time_series_builder/SpectralFit_TSB.ipynb new file mode 100644 index 000000000..a9104fed9 --- /dev/null +++ b/docs/tutorials/spectral_fits/continuum_fit/time_series_builder/SpectralFit_TSB.ipynb @@ -0,0 +1,1856 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c9d1570e-1d9a-4f76-9503-6c6b7563a138", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/saurabh/Documents/PhD/condaenv/cosipy_env_python3.11/lib/python3.11/site-packages/threeML/io/package_data.py:4: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "                  performances in 3ML                                                                              \n",
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+       "                  performances in 3ML                                                                              \n",
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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=369895;file:///Users/saurabh/Documents/PhD/condaenv/cosipy_env_python3.11/lib/python3.11/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=847280;file:///Users/saurabh/Documents/PhD/condaenv/cosipy_env_python3.11/lib/python3.11/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 threeML.utils.data_builders.time_series_builder import TimeSeriesBuilder\n", + "from cosipy.util import fetch_wasabi_file\n", + "from cosipy import BinnedData\n", + "from cosipy.data_builder.Time_series_builder import from_cosi_data\n", + "from matplotlib import pyplot as plt\n", + "import astropy.units as u\n", + "from threeML import *\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "markdown", + "id": "b67144f9", + "metadata": {}, + "source": [ + "# 0. Files needed for this notebook\n", + "\n", + "From wasabi\n", + "- cosi-pipeline-public/COSI-SMEX/DC2/Responses/SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.nonsparse_nside8.area.good_chunks_unzip.h5.zip (please gunzip it)\n", + "- cosi-pipeline-public/COSI-SMEX/DC2/Data/Sources/GRB090206620_unbinned_data.fits.gz\n", + "- cosi-pipeline-public/COSI-SMEX/DC2/Data/Backgrounds/albedo_photons_3months_unbinned_data.fits.gz\n", + " - In this notebook, only the albedo gamma-ray background is considered for a tutorial.\n", + " - If you want to consider all of the background components, please replace it with cosi-pipeline-public/COSI-SMEX/DC2/Data/Backgrounds/total_bg_3months_unbinned_data.fits.gz\n", + " - Note that total_bg_3months_unbinned_data.fits.gz is 14.15 GB.\n", + "- cosi-pipeline-public/COSI-SMEX/DC3/Data/Orientation/DC3_final_530km_3_month_with_slew_1sbins_GalacticEarth.ori\n", + "\n", + "From docs/tutorials/spectral_fits/continuum_fit/grb\n", + "- TSB_GRB_inputs.yaml" + ] + }, + { + "cell_type": "markdown", + "id": "c49257c5", + "metadata": {}, + "source": [ + "You can download the orientation file, data and detector response from wasabi. You can skip this if you already have downloaded the files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2cff470c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"AcceptRanges\": \"bytes\",\n", + " \"LastModified\": \"Mon, 13 Nov 2023 20:36:46 GMT\",\n", + " \"ContentLength\": 880407105,\n", + " \"ETag\": \"\\\"74541f1c07785f2bb6488f1d3a710593-9\\\"\",\n", + " \"ContentType\": \"application/zip; charset=UTF-8\",\n", + " \"Metadata\": {}\n", + "}\n" + ] + } + ], + "source": [ + "# Response file:\n", + "# wasabi path: COSI-SMEX/DC2/Responses/SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.nonsparse_nside8.area.good_chunks_unzip.h5.zip\n", + "# File size: 850MB\n", + "fetch_wasabi_file('COSI-SMEX/DC2/Responses/SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.nonsparse_nside8.area.good_chunks_unzip.h5.zip')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0dbbea42", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"AcceptRanges\": \"bytes\",\n", + " \"LastModified\": \"Tue, 19 Dec 2023 21:44:46 GMT\",\n", + " \"ContentLength\": 1124753,\n", + " \"ETag\": \"\\\"89303a87d887749b2ed72e2b6f276977\\\"\",\n", + " \"ContentType\": \"application/gzip\",\n", + " \"Metadata\": {}\n", + "}\n" + ] + } + ], + "source": [ + "# Source file (GRB090206620):\n", + "# wasabi path: COSI-SMEX/DC2/Data/Sources/GRB090206620_unbinned_data.fits.gz\n", + "# File size: 619.22 MB\n", + "fetch_wasabi_file('COSI-SMEX/DC2/Data/Sources/GRB090206620_unbinned_data.fits.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f782f70a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"AcceptRanges\": \"bytes\",\n", + " \"LastModified\": \"Thu, 19 Oct 2023 15:39:14 GMT\",\n", + " \"ContentLength\": 2892418896,\n", + " \"ETag\": \"\\\"8b8853d7e0bc07e8cc30ff7978c2bde8-29\\\"\",\n", + " \"ContentType\": \"application/x-gzip; charset=UTF-8\",\n", + " \"Metadata\": {}\n", + "}\n" + ] + } + ], + "source": [ + "# Background file (albedo gamma):\n", + "# wasabi path: COSI-SMEX/DC2/Data/Backgrounds/albedo_photons_3months_unbinned_data.fits.gz\n", + "# File size: 2.69 GB\n", + "fetch_wasabi_file('COSI-SMEX/DC2/Data/Backgrounds/albedo_photons_3months_unbinned_data.fits.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "20b6b19c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"AcceptRanges\": \"bytes\",\n", + " \"LastModified\": \"Wed, 29 Jan 2025 15:29:17 GMT\",\n", + " \"ContentLength\": 1158035184,\n", + " \"ETag\": \"\\\"97bc6832b8133666693d3c7384fe4096-12\\\"\",\n", + " \"ContentType\": \"binary/octet-stream\",\n", + " \"Metadata\": {}\n", + "}\n" + ] + } + ], + "source": [ + "# Orientation file:\n", + "# wasabi path: COSI-SMEX/DC3/Data/Orientation/DC3_final_530km_3_month_with_slew_1sbins_GalacticEarth.ori\n", + "# File size: 2.69 GB\n", + "fetch_wasabi_file('COSI-SMEX/DC3/Data/Orientation/DC3_final_530km_3_month_with_slew_1sbins_GalacticEarth.ori')" + ] + }, + { + "cell_type": "markdown", + "id": "d18bb8a0", + "metadata": {}, + "source": [ + "# 1. Create binned event/background files in the Galactic coordinate system\n", + "\n", + " please modify \"path_data\" corresponding to your environment." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "56744587", + "metadata": {}, + "outputs": [], + "source": [ + "path_data = \"./\"" + ] + }, + { + "cell_type": "markdown", + "id": "a9b62fb0", + "metadata": {}, + "source": [ + "**Source**" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c85abc0b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.18 s, sys: 121 ms, total: 1.3 s\n", + "Wall time: 1.32 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "signal_filepath = path_data + \"GRB090206620_unbinned_data.fits.gz\"\n", + "\n", + "binned_signal = BinnedData(input_yaml = \"TSB_GRB_inputs.yaml\")\n", + "\n", + "binned_signal.get_binned_data(unbinned_data = signal_filepath, psichi_binning=\"galactic\")" + ] + }, + { + "cell_type": "markdown", + "id": "4110888a", + "metadata": {}, + "source": [ + "**Background**" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9c933ed1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "fill() discarded one or more values due to out-of-bounds coordinate in a dimension without under/overflow tracking\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 34.6 s, sys: 10.6 s, total: 45.2 s\n", + "Wall time: 1min 4s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "bkg_filepath = path_data + \"albedo_photons_3months_unbinned_data.fits.gz\"\n", + "\n", + "binned_bkg = BinnedData(input_yaml = \"TSB_GRB_inputs.yaml\")\n", + "\n", + "binned_bkg.get_binned_data(unbinned_data = bkg_filepath, psichi_binning=\"galactic\")" + ] + }, + { + "cell_type": "markdown", + "id": "65bd34b1", + "metadata": {}, + "source": [ + "Add the signal to the background" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0443e22a", + "metadata": {}, + "outputs": [], + "source": [ + "signal = binned_signal.binned_data\n", + "bkg = binned_bkg.binned_data\n", + "event = signal + bkg" + ] + }, + { + "cell_type": "markdown", + "id": "62430bfc", + "metadata": {}, + "source": [ + "Save the binned histograms" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "67878325", + "metadata": {}, + "outputs": [], + "source": [ + "signal.write(\"GRB090206620_binned.hdf5\", overwrite = True)\n", + "bkg.write(\"albedo_bkg_binned.hdf5\", overwrite = True)\n", + "event.write(\"GRB090206620_albedo_bkg_binned.hdf5\", overwrite = True)" + ] + }, + { + "cell_type": "markdown", + "id": "f0fba1fc", + "metadata": {}, + "source": [ + "Load the binned histograms" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bf49fd6c", + "metadata": {}, + "outputs": [], + "source": [ + "event = BinnedData(path_data + \"TSB_GRB_inputs.yaml\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d8ae8459", + "metadata": {}, + "outputs": [], + "source": [ + "event.load_binned_data_from_hdf5(path_data + \"GRB090206620_albedo_bkg_binned.hdf5\")" + ] + }, + { + "cell_type": "markdown", + "id": "933c5bf0-1c60-43e9-9153-da613cc03180", + "metadata": {}, + "source": [ + "# GRB090206620" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e68797c7-0f38-45fa-ae31-754b75d1f4db", + "metadata": {}, + "outputs": [], + "source": [ + "#if rmf/arf file already created, it can directly be loaded in the response, and ori file, l, b, can be left empty\n", + "cosi_tsb = from_cosi_data(\n", + " TimeSeriesBuilder,\n", + " name = 'GRB_090206620',\n", + " yaml_file=\"./TSB_GRB_inputs.yaml\",\n", + " cosi_dataset='./GRB090206620_albedo_bkg_binned.hdf5',\n", + " response_file= './SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.nonsparse_nside8.area.good_chunks_unzip.h5',#\"COSI_GRB_090206620_testing.rmf\",\n", + " ori_file=\"../wasabi_files/20280301_3_month.ori\",\n", + " l = 93.,\n", + " b= -53.,\n", + " # poly_order = 0 (optional)\n", + " verbose=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "623aca5d-70a3-4ad3-9b1a-0abf7a73c028", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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13:48:45 INFO      Interval set to 200.0-240.0 for GRB_090206620                         time_series_builder.py:290\n",
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", 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\u001b[0m\u001b]8;id=391329;file:///Users/saurabh/Documents/PhD/condaenv/cosipy_env_python3.11/lib/python3.11/site-packages/threeML/utils/time_series/binned_spectrum_series.py\u001b\\\u001b[2mbinned_spectrum_series.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=811704;file:///Users/saurabh/Documents/PhD/condaenv/cosipy_env_python3.11/lib/python3.11/site-packages/threeML/utils/time_series/binned_spectrum_series.py#389\u001b\\\u001b[2m389\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "150f2b4b821f4fd985e90436462e913a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fitting GRB_090206620 background: 0%| | 0/10 [00:0013:45:38 INFO None 0-order polynomial fit with the mle method time_series.py:458\n", + "\n" + ], + "text/plain": [ + "\u001b[38;5;46m13:45:38\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO \u001b[0m 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rate error 0.156\n", + "bkg. exposure 40.0\n", + "bkg. is poisson False\n", + "exposure 40.0\n", + "is poisson True\n", + "background profiled\n", + "significance 306.813723\n", + "src/bkg area ratio 1.0\n", + "src/bkg exposure ratio 1.0\n", + "src/bkg scale factor 1.0\n", + "response GRB_090206620.rmf" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spec" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "35997c5e-660e-4363-95b7-56410d7a273b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "spec.view_count_spectrum(scale_background=False);" + ] + }, + { + "cell_type": "markdown", + "id": "da239b6c", + "metadata": {}, + "source": [ + "## Perform spectral fit" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "068f2635-ca79-4d52-a9a5-c2dfe8583b3e", + "metadata": {}, + "outputs": [], + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1a480736-f94a-4fb2-975e-a159db2dda05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
13:45:41 INFO      set the minimizer to minuit                                             joint_likelihood.py:1046\n",
+       "
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13:45:42 WARNING   These parameters returned a logLike = Nan: [ 2.81595084e+02             joint_likelihood.py:1006\n",
+       "                  2.45368678e-01  4.54656541e+02 -1.44069637e+01]                                                  \n",
+       "
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         WARNING   These parameters returned a logLike = Nan: [211.95239762  -1.39591867   joint_likelihood.py:1006\n",
+       "                  635.46999692  -2.4664374 ]                                                                       \n",
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         WARNING   81.89999999999999 percent of samples have been thrown away because they analysis_results.py:1739\n",
+       "                  failed the constraints on the parameters. This results might not be                              \n",
+       "                  suitable for error propagation. Enlarge the boundaries until you loose                           \n",
+       "                  less than 1 percent of the samples.                                                              \n",
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Best fit values:\n",
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resultunit
parameter
source.spectrum.main.Band.K(2.97 -0.20 +0.22) x 10^-21 / (keV s cm2)
source.spectrum.main.Band.alpha(-3.1 +/- 0.5) x 10^-1
source.spectrum.main.Band.xp(4.77 +/- 0.05) x 10^2keV
source.spectrum.main.Band.beta(-1.2 +/- 2.8) x 10
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\n",
+       "Correlation matrix:\n",
+       "\n",
+       "
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1.000.97-0.390.01
0.971.00-0.190.01
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\n",
+       "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)
GRB_09020662054.928514
total54.928514
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+       "Values of statistical measures:\n",
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statistical measures
AIC125.857028
BIC119.067369
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" + ], + "text/plain": [ + " statistical measures\n", + "AIC 125.857028\n", + "BIC 119.067369" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "( value negative_error positive_error \\\n", + " source.spectrum.main.Band.K 0.029678 -0.001828 0.002219 \n", + " source.spectrum.main.Band.alpha -0.305630 -0.048731 0.055730 \n", + " source.spectrum.main.Band.xp 476.594088 -5.271099 5.163861 \n", + " source.spectrum.main.Band.beta -11.831435 -1.453890 8.104623 \n", + " \n", + " error unit \n", + " source.spectrum.main.Band.K 0.002023 1 / (keV s cm2) \n", + " source.spectrum.main.Band.alpha 0.052231 \n", + " source.spectrum.main.Band.xp 5.217480 keV \n", + " source.spectrum.main.Band.beta 4.779257 ,\n", + " -log(likelihood)\n", + " GRB_090206620 54.928514\n", + " total 54.928514)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plugins = DataList(spec) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...)\n", + "\n", + "like = JointLikelihood(model, plugins, verbose = False)\n", + "\n", + "like.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "ef20bd66-c83e-466e-af8b-a14c92f5120e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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iJPS/B+l0knD1mrpopWNQUNmpVi0VyDnVqK4WR7g0bMh5kUREjzF4I6I8rQWI9lxYtYwLFkMk3n+gFkDEX7kiCVeuSsLff6dYDIFizSlKlYCTkxTo0V1cGjcWh3JP5GjenKXWAiQi0jB4IyLzrwUYHy9R365Vl5yy1FqAREQaBm9EZHa1ABMDAyX+/AWJWPhVho3BMcTqXL+eODdqJM716op9JpXXgbUAKTfduXNHdU5Ah4EiRYqoorCzZs1SNdbQzBxlKdARAHXIsMIR/UShfPnyqqE7ssio74babyiWawgWCA0ZMkQOHDigJt6j+CtqvqHUBIrhor4aCvWiJhk6HwDqreE8UOcNNdlQJw3lpbA/CveiqC1qzqG2G6AG3JgxY1SdNyya6N+/v9qOrg94HSiYi2OhIC/qyBnajteCWm4o9ovXhNp3aAwPaAGG58Y5o3UW7ksNNdzQxB7vpfa6UY8NddmSd63IyJUrV2Tw4MEZlgFJ3mTeYqHOG1mmK1eu6Jo0aaKuiaxRYlSULvbPP3VxV67owpd/rfPv1l1374nyunulyqS9PFFe3Y/94m/dMvWpUz6Jjo7WXbp0SV3nt8TERF3t2rV1a9as0W87deqUbtOmTerr3r1763bv3q2+Xrlypa5ly5b6/cqVK6c/53379unatm2b7vNs375d1717d/X1jh07dF27dlVfL1iwQDdixAj19blz53SdO3dWXwcEBOiqVaumCw8P192/f1/31FNP6eLj43W3bt3Sf15cvnxZV6lSJfU17qtSpYruwYMH6jH4GseAHj166F9PSEiILiYmJt3tQUFB6vWDr6+vrnTp0rqoqCh1u379+roLFy7oEhISdA0bNlTnm1rTpk11Tz/9tO7atWvq9tGjR9Vt7bUb4/Lly+o4GUn+fbHUn11m3ojIImoBokVXwf79VP/WmEOHJGbffon5+RfRhYU92jkhQWKPHVOX0A+niWOVyuL60kvi1rq1ONV+lu28bIBf23aS6Oef68dFH+Viu9M2cUe3AmTCkEnSoB0SLqk999xzMnv2bIPHR79RHCc9aMeErgRaVwG0t0JWClkmrdo/OhYgu4U2Wyg5glZayPjhgvt+//13dQ4aFHbH8+I4eBz2KVmypLoPj923b59qL4WsHlpNATKFWuN5Q9tRP0177WhnhS4NyKThgufReqGi8wAyfYa6LCBThy4GaM+Fa9xGf1NAtg/v9b3HvVG/+eYb1bkA7cXQPQGvHbXcktd2Q8YS7xOgy0OjRo3EGrA5IRFZFHtPTynQqZN4fbVQSp47K0U3rBf3/v3E4YknUuyHFawYdvXv2Eke1q4rwWPGSvTevZKUyz0GyXwgcEt6+DDXL+kFhJcvX1bN4o2BgOqVV15JsQ3tsapVq6ZaL40dOzbdxz548EBKly6tvsbQJIIiNKdH8IPADoHRsWPH1HClr69viv0B/UeTN4YHPA5DuDheevuj+j9aS3Xv3l21oJoyZYq6P73tyZ1B3+TERBVkGXM+GgSOO3fu1L8mrbcooO0Y2l2dP39etap6//331Xa0AsMwLIZbkzeNQn9XtO1C4LplyxbVsspaMPNmYui9tn37djVvAH9ZJW/mS0SZlydxeaGxuuimfSgJ1649qiO3b78qTYJSJFp9uaj1G9RFXF3EpfEL4oYVsK1aikOJEnybrQQyZKY8LvpyYl4ZAowFCxaobVrAgKbpaGye3G+//abaIiHzhd//yGZlZSU1gj4EMgigEESihyjm1mXm9u3bMm7cONWgPSMIvk6ePCl//vmnVK5cWTp37qyCPgRkhraj16mWmUOGLLPm84a4u7ur42P+HoJbzKfTHD9+XPUehbfeektl0gDvq/bcb7zxhgocYf/+/bJ79279YxDwosOCNWDwZmL4C6pv375qIioRZR8+9DC0ikuhd4dJIgoCHzyoArnYw0dEFx39aMeYWIk9eFBdwKlWTf3wqmP1amzfZcEMDW3mpapVq6rG9Bpkd/bs2ZNicv1XX32lJsYjO4WFDfiDPTUsVEBggWFBDDemhmAGmSoEaMgsYV98duBnftGiRfpAC4sYMIyI/bUABrRhRsBQKbJWCxculEqVKqU4fvL969atq7ZjAYQ23IlFD8hu1atXz+B2BFAYukS2a9SoUfL888+ne3ztfAzBUCkWHWzatMmo74NdsoA3+dd4rxC8ZfRclorDpibWpEkTlRbGvAQiyj0ORYuKe/fu4r3iayl5/i/xXrNaCrz1ltgXT/nhiJ6t4Z99Ln6tXxbfhs9JyPtTJObwYdHFJmv1RWRAq1atVCC1du1/JWyitT8SUkEj9D/++EPNz0oN2xD0ICAzBMERMlGAbBkyUghSIiMjJerxNIAlS5aooAzbMQ8OQ4+4H0OWyM4hQESAh9WgmDOnzZUD3Hfu3Dk17Ip5YpiPhk4PCHowF+/u3bsqEMKKUgSs6W2HoUOHquAu+SiSFjxdvHhRncO6devSDCGnHjqdOHGivPjiiym2IxjESlvAMbQhVazsxTmDdr/2/UHwrNFWsVoDZt5E1A8//lLCpEjMYQgPD5dJkyapyZqpxcXFyYoVK9RkTuyHv3SwpNrQBFUiMg92bm5qiBQX3YxPJf78ef3wavzFi/r9Eu/fl8jVa9TFrmBBcW3WTFxfaiUuLVqIg1cRk74GMj8Y0kPmbcSIEWrOFbJeCMAwNys1TOYfPXq0Ks+BsiCgBWEIaFatWqXKa6QXvCE4wecNgiYts/fw4UO1gAHHqFmzpvpsApTywDwwBDU4x88++0wNp+IYWGSBxy1dulTti5IaOOacOXNU2RCUChk/frw+kESZEWTUMNzYuHFjlVVLbzuGfVGiBOeCOX5akIXhXAxxInBEqRAMERtarJB86BTnkBqGP3v37q1KrpQoUUIf0M6fP18tWMAcOARsGnxPhg8frs4H54nFDNpQq6Wzw5JTsXH4awMTL5Onm9ML3j766CP1w460LiZdIiWLlSyY34AfkOzCfy5MAM3KnDfMrRgwYID6QcbKISLKuoT79yVm/34VzMUeO64KAqdhby/ODeqrQM71pdbiVLEC32ozgWAAE+iffPJJNX+MyBZ+ds0+84YJkadPn1YRvZ+fnyouiBeJvxQqVKig5gBg+XN66WZj4LGYq4BrBGIDBw40uB8yc/irBUuPMSkSkFpGwT/8JaX9NaVNUkWq2hD81YGgi4hMz7F0aSnYp4+6JIWHq/lx0cjKHTggupCQRzslJUncyd/UJeyTT8WxYsVHLb9eaiXOdeuKnRGTxImIcotZ/sbBnAFMVMQqTCx71pKDqAjt4eEhsbGxKlq9efOmWk2CdDDGwpGSzSgVmx4c15jgD+P6SGtrq1q0VDjG57GqBueK7B0kH2cnIstgX6iQuHVory66hASJO336UT25/Qck4eZN/X74OmIxLkvEvkgRcWnZUtwwvNqsqVFdHogMwVAjuhskh8UOM2fO5BtG5h28Yf4Axv5R1A/j+1gKjRUtmAyJ9h8aBHRYsYJsGGq4/Prrr6oNCcbekfXKi9Ul169fV0OlGI9PDnV64MaNG/rgzVgYh8d8B8wzwDUCUwSjhuY+BAQEqMmxyZd7E1HeQDbNpWFDdfGcOkXib9yUmAOPArm4U7+rbBwkBQdL9I8/qos4O4vL88+JS9MXxbHSU+LyXCNVaJjIGBjJwYXI4oI3TDzEhEMMS2JYND2YoImeZ7jghx1BD7JwWPWDxQQYysxt2vLs1LRtCK6yChWiV69erb+NCZjpzbdDHZ3k+xJR1iX6+kqin1+23jqXxo3VJSksTAVwsSd/k/jTp/8rQxIXJ7GHDquL4uYmntM+FKeaz+S4BAl6sjpk8Y9DIrJOZhe8IZhBQJZVGL7EihwEPRi+zAsIEJ2cnAwOu2r3ZxUWKBi7SAHDtcgsJs+8oYI0ERkvcu13Ej53Xv68ZdHREjphYq4cqtDoUeIxZnSuHIuILJvZBW/ZCdySw3BjXhXkQ4CIWjyGyodo9+clLP/GhYiyz/2tnmqxQV7AdI6Eq1cldMoHoouISHO/nYeHuLZ5Wdzat8tyFg2ZN8q97CuCePwsMJtJlsjsgjdzhuFRVMBOTZuHxsCKyPzhwzovP7BdatYUtw4dJP7cOYm/fkOivvtO4s89WnmuCwuT6I0/SPSPm1QA6d6nj2rtldMhVcoaDJsj+4rvAYM3skQW02EBBXHRdsSU0EoEiyRQtTo5LJrQ7iciwiIFLHQo+FZP8dm1U3y2bxO3V18V0aZdJCVJzJ69EtjjDfFr1kIiVq+WJAOZOrIMd+7cUX1NMU8bbaWwQhTV/DH3GgvvUCwX21H6SlO+fHm1HeWucB/6m2aU0UW7KHzGoHsBKi1odcJQnBY1Rhs1aqQW1WlQLBctrFADVOs+gNqgmHrz9NNPq6b0qKCgwT7YF4/BYzVIWKCiAhbmYfGgNrfb0HYkMlAIF0V5Ufnhhx9+0B8Hrw/Pi9fw8ccf59p7b7N0FuL69eu6pk2b5vnzXL58WdekSRPdrl270tx38eJFdd+6dev022JjY3U9evTQDRo0SJffrly5os4H10Rk/hL8/HSh8+brHtSpp7tXqkyKy/3KVXXB70/RxV2/burTtCjR0dG6S5cuqWtjxZw6pd5zXOdUYmKirnbt2ro1a9bot506dUq3adMmXe/evXW7d+9W21auXKlr2bKlfp9y5crpz3nfvn26tm3bpvsc27dv13Xv3l19vWPHDl3Xrl3V1wsWLNCNGDFCfX3u3Dld586d1dcBAQG6atWq6cLDw3X379/XPfXUU7r4+HjdrVu39J8X+KyrVKmS+hr3ValSRffgwQP1GHyNYwA+3/BaICQkRBcTE5Pu9qCgIPXawdfXV1e6dGldVFSUul2/fn3dhQsXdAkJCbqGDRuq87V10dn42dWYzbBpZosMsrOSMytQVw493bQh0GPHjqmiwIC2H+g9ir8mmjdvrmq6hYSESOnSpVU2EK1GUtfmISJKzcHHRzzeGymFhg2VmL37VMYt7sRJdR/myEWuWq0uLk2aiHvf3uLaqpXYpdMyibK3wlgXEyNBAwapr3HttXyp2D2ubp+dFb0o3I6i8W+//bZ+G9ol4oIKARoUk589e7bBY6BZPI6RHhwHxd0B7bDQmxTZOBSV13qUItOF7BbmZaNeHLJi+NzCBfehpBbOQYMsG54Xx8HjsE/JkiXVfXgsqjZgASAK5H///fdqu6enp7rG55+h7Zj3rbWKLFasmJpqhLJfuOB5tEb2PXv2VJm+7NRlpUfMJnhDgd2M5n3gG5+X80LQzBZBmObIkSPqAvjPoTWOnzx5sqrlhv8cCPaQJp81a5ZKfRMRGcPOyUlfDDj+8mWJWP2NRG/apC85Env0qLo4oK7k272kwBs9xMHLi29uLq8wTvL3l4DOr+ZoRS/6YRvz+x+fGambsaO3KRa8oUXjzz//nO5j0VweyQLA5yCCIiQaEPwgsEND+uPHj6vjIBGSfH9AfdL79++nOCYeh6FTHC+9/VEMH20b0T7y2rVrKqhDhYP0tieHNpOoXYoFhH/88Uea4yPoJSsI3goVKqQK8qb3nwBlMdCUNq9s3LjRqP3wl8XQoUPVhYgop5yqVZMis2aI5+SJErXxB4lYvUYSb91S9yXeuydh/5shYZ/PlQKdOor7O33FmdmKHK0w1jJvCNzsfXzSZN5yCnPfMLfspZcePR+KxkNQUJCcPXs2xb6//fabaveIzBcya8hmZSVJgc9MtGGsXbu2+uzE6BCKvGcGn6fjxo2TXbt2Zbgfgq+TJ0+quXqVK1eWzp07q6APAZmh7Vr3IWTmkInEKBVZ+YIF/ABgUQIatBq6IFLX2mQREVkbe09PKTigvxQ/eli8v/1GXFq0QJrl0Z2xsSqw82/TTvw7dpaoLVtE97hEEWUOQ6EIenFxqV9fBWyAa9zW7svOylN0/8HiBA36ZM+dO1f14dZaJWKBAYK4kSNHGjxGgwYNVCbNUDUDQLCkZc7wOagVjEeN0UWLFqmgcMWKFWo0CCNDyfcHLLTTSmhhqBSZuoULF+oX2aW3Py5YwIDhTtQ4RS1VvNb0tgOGbTHVaNSoUaptZUbHJysI3hC5lyhRIt378QM5cWLuFLskIjJXdvb24tqiuRT9do0K5AoOHCB2j+cUAfqtBr87Qh42aCRhcz6TxH//Nen5WiIt06Zd5wQ6AiGYQnef5P25U5s6daoaPsQwY2rYhqAnvR7bCI7QfQeQLcNwKzJ0qHwQFRWlti9ZskQFZdiOqT47d+5U92NIFNk5BIjIpGGKEubMaXPlAPedO3dODbsiAMR8NHQuQoCFuXh3795VQSNWpyJYTW87YFQKK2KTF5/XArWLFy+qc1i3bl2aIWTKojxZQkH5gqtNiWxDYmSkLmLtd7qHLVulWaV6r2w5XeDAwbqYEyd0SUlJOluTnRV7sefOqfcO17kBqzg7duyoe/LJJ3WNGjXStW/fXq26TL7aFD7//HPd4MGD9atNa9asqatVq5bu6aef1m3dujXDFa0DBgzQVahQQVenTh3dtWvX1PYbN27oKleurFaHduvWTRcWFqZ/zNKlS3UVK1ZUK021Y2PVqqOjo3pO7RIcHKzuwz7YF4/BYzUnT57UPfvss+ocUVUB55Le9vPnz2N4TP+6cEGVBjhx4oSuevXq6jV8+OGHufK+2/JqUzv8k9WAj8wD5lUMGDBAli9frlYOEZF1w6/ruFOn1IrU6F27MSkpxf2O1apKwT59xO3VLmJfoIDYAtQ6wwR6TK/B/DFjsMMCWerPrtkNmxrSrFkzlZYlIqJHKw1R/NdryWIp8dsJKfTeSLFP1jIv4fIVCZkwUR7WayChH30sCY8XPlBKmNuGVaXsrkCWyqyDNyYFiYgMcyhZUjzGjZUSp05KkYVfiHPduv/97gwNlYhly8X3hRcloFdvifn5F9ElJfGtNHMoJ4JVo8kvnOtNZl0qhIiIss7OxUUKdOmiLnHnzknk6jUS9dNWtUJVdDqJ/flndXEoX04K9u4tBbq/rla2Whtr+GMfiwRwIdugy8HPLIM3IiIr4VyzpjjP/Vw8prwvUes3SOSab1StOEi8dVsNpYbNnqP6rBbs21vVmLN0KFWB4WSU2fDx8cnTYu5EuRm44WcWP6/4Gc4qBm9ERFYG3RgKDR0iBQcNlJgDByRy1RrVsQHQxSHqu+/Uxfm5RmqBg+vLrVXXB0vk4OCg6oCidtgtzvEjC4LADT+7+BnOKgZvRERWCn1R3V5+WV3ir19XmTgU+9VFRqr70Vc16MRJsS9RQgr06K7mzbk810js3dzEkqB9IYrGolYakaVAxi07gRsweCMisrDm6tmF+W6uHTpI7MGDEr19hyQ+Xs2f9PChRMxfoL62K1BAiqxYLg5FiuTKeWen2Xu2nsfBIdsfhESWhsEbEZEFN1fPbbqoKAl6o2euHS87zd6JyIKDtzfffFM8PDxMfRpERGbVXD23Jdy5IyGjx4ouIiLFdqdnnpaCQ4eI45NPZvvYudHsnYhSYocFC8YOC0SUW5KioyX20CGJ/H69xB78+b87HBzEvU9vlT2zxhIjRJbILIv0xsXFmfoUiIhsChYpuLVtK0W/WSPe36xRdeGUxESJXLFSfF9sJpEbNrLYL5EZMMvgrUuXLjJv3jyVWSIiovzl2rKFFD94QDwmjBe7xz0XkwICJGT0GAno/KrEnT/PbwmRCZlt5u2nn36SQYMGSb9+/WTz5s0SHh5u6tMiIrIZCNoKjRguxY4cEtf27fXb406fFv+27SVk4iRJCg426TkS2SqznPMWFRUl+/fvl127dsmVK1f0FYibNGki7du3l7rJevjZMs55I6L8EnPkiIRO/VASbtzQb7MvUkQ8Jk6QAm/0UDXliMiGg7fkUDF7586dKpgLDg5WgVyxYsVUENemTRspng/1g8wVg7eUULcq7LPPJSkiQuzd3aX4kUMp7g/9ZPqjno+ZcG3ZUorMnplim1/bdpLo55/pYz2nTFY9JjXxN25KQPceYoxiu3akqIeF0hBh8+Zn+jjHChXE54cNKbYFvTtcYk+czPSx7j3fFI/Ro1Js+7dufaPO1+vLL8Tl+eeM2pesgy4uTiJWrpTwufP1hX7BqVZNKTx9ujjXqW3S8yOyFWZdKgTKly8vw4YNk8GDB8vx48dVIHfq1ClZuXKlrF69WurVq6cCuRdeeEEcHc3+5VAeQuCmZQV0BQumuT8pNFQVI81MUkhImm0I3Ix5rC46JtUDE4x63KN9k1KeR2SkcedroJxOUlCQcY8NC0u7zcjz1cXFGrUfWQ87Z2cpNHiwFOjUSUI//Z9Eb/lJbY//65z4v9JRdWnwmDxJHLy9TX2qRFbNYqIdVM7GsCkuQUFBsnfvXjWsikDu999/V/Xgtm3bZurTJBNCxk2xtzdY0R1lDtAGKDP2hQun2eZQzMeoc7Bzc031QEejnvPRvimnoCJ7aNT5Fi2adpuXl3GPNRD4GXu+ds4u6jr2+AkJGj7CqMeUPP17itthc+dJ5HfrMn0cWjZ5LfwyxTb/bt0l4e+/M32sx6j3VI00yj0OJUuq70dszzclZMpUSbjyaHFZ1PoNEr17j3iMGyvuvd4SO/5BTWTbwVtyXl5e8sYbb0iDBg3UqtTz589LmIEMAtkm+2LF0gyZgufUKeqSHcV278rW45wqVUwTsBgLAUd2g47UgU5WZPV8kYEzOrtoIPNnVIYwKCjttoAA4x6bbHgPLaZUhtPBcIBPWePy3HNSbO8e1TM1bM5nogsPF11oqIROmSpR674Xz08/EZcGDfi2Etl68KYtZsDw6bVr1wRT9lxdXaV58+amPjUim4QMnNHZRQOZP6MyhF5eabcVLSr2RvzRhgymxq9dBxXw4TmzG1RTSsiuFez3jrh1fEXC/jdDNb6H+EuXJKBLV3F79VU1F5TBMpENLVjQ/Pnnn2qY9OjRoxIbG6uCturVq6v5bi1atJACBQqIreGCBUkz0Z4fzJQR/ozkvdg/TqvMW3yyWnB2BQuqHqcF3+krdk5O+XAWRNbNrDNvfn5+snv3bnV5+PChCtgKFy4sHTt2VEEbFjMQEZH5cKlXV3x2bpeo79ZJ6KxZogsJVT1Twz7+RKK+Xy+Fp38iLi80NvVpElk0swzeDh48qLJsyLYlJSWJvb291K9fn6tK81n40mUSsWx5pvs5P/O0eK9elWJbYJ++Enf+QqaPLThwgBQaNDDFogPfpsYNgXuvWiHONWv+99hszrsi25Pk55emJErRDevVHEVN1JYtEjr9f5keC4tZUs+JDB4/UWIOHsz0sQU6d0ozDxNtqJLP09PYFyyoFgK4dfivYK65Qs0397d7iWuH9hI2c7ZErVsnotNJwvXrqnSO2ysdxGPqVHEsXcrUp0pkkcwyePv444/VdcmSJaVt27bqgtpulL/w17IxAVFiqbS/gBMDjStVgedIuUGXhVIV8Slue0x5X8Kmf6o+5IgMwc+GKsiSlJT25ywxIeXPV3RM9hdihIQYt5giNDTNNiyqSPP/Avtide6czywieNM4eHmpmonuPd+QkPenSvyZM/qajDEHDkqh90ZKwQH9xc7l0cplIrLg4K1ly5bspGAGME/FmMnkDt5eBrclGvFYPEfKDXZZKFWRcu6MY6VK6oLsBJEh+NlAAKQvK5Pih9YxTdkXo37+DZSRQbkZoxZieHqmPV7x4pKU6v8FMoUq4DR03hbAuVYt8dn2k0Rt3Chhn85Qq4d10dESNmOmRK7fIIU/+UhcueiMyPoWLFijOXPmyLFjxyQmJkZ1ihg4cKA0bmz8XBAuWCCyDda00AJZSRTURnkRBKQa1zYvi+e0D8WxbFmTnh+RJTDLxvSpJSQkyMaNG1Vwg5ZYycuCXL9+XebOnSt3794VS/P666+r17Vnzx6ZOHGiTJ8+XUINDKMQEVkLZCWxaKHYnt3i3OC/eYcxe/aKb7PmqiUcsnJEZMHBG8qCvPfee7Jo0SLx9fUVd3d3tepUg3lxWNyAAMjSlCtXTpydndXX6NkaHx8vAQEBpj4tIjIzhWfOEK8li9W1tXCqUV2Kbt4kRb5YoAprKzGxEv7Z5+LbopVE79uX4nc9EZn5nLfkvv32W9VBYdCgQaqrwqpVq+Sbb77R31+wYEF59tlnVYusAQMGZKvo7/r16+XSpUty+fJlCQ8Pl0mTJqlFEqnFxcXJihUrZN++fWq/ihUrSv/+/dVK2OxC1hDBJ47dqFEjqVChQraPRUTWye2lVvqv486dk8C+/Yx6XPHDv6RYwGOKFeQZwR+tBbq+Kq6tX5LwufMkYsVKkcRESbxzR4L69hOXFi2k8MfTxPHJJ406HpGtMPvg7eeff5batWvLm2++qf/PnlqpUqXU8Gl2YJgSDe4x56xSpUpy5vFqKENmzJghhw4dkm7dukmZMmVU/bnx48fLggULpGaykhVZMXr0aBk5cqScPXtW/v77b4Ovj4go+Spro1fBpspc5fcKcrXAAueAhUgZrAK3L1RIPD/8QDW2D5nygcQdP662x/78s/j++qsUGjxICg5/V+xtsBg7kUUGbyjUi2b0GXFzc5NIA3WRjOHt7S1btmxR11euXFHz6gxBZg7154YMGaIygPDyyy9Lnz59ZPHixeqiGTZsmMoWGtKrV680GUIHBwepW7eu/PDDDyoofO6557L1WojI+mGVtdHtyFL9MZjfK8hRszErCy2cqlSRohvXq1IioR99/ChYjIuT8C++lKgfN6kAz7V9O/6RSzbP7IM3BGYhISEZ7vPgwQPxNLDk3hiYc4bALTOHDx9WQRa6O2hcXFxUSZNly5ap+XjI3sFXX32VrXNJTEyU+/fvZ+uxRGQbUJg6uytOMZxp7JBmaqmHUfOKGkrt+Iq4tmyhgraIpctE4uMl8cEDCRo0WFyaNBHPTz4Sp6eeypfzITJHZr9goUaNGnL8+HE1x8wQBE0nT56UWrVq5el5YFgWWTEsmEiuWrVq6vrGjRtZOl5ERITs379fzbnDatpffvlFDdlm9DqwmAHlQbTL7du3s/lqiIjMm727u3hOmijFDh4Ql2ZN9dtjjx4Vv1atJfST6RZb947I6jNvPXr0UKtNR40apeaGITsFqI128eJFmT9/vtrWvXv3PD2PwMBAgxk6bVtWV4nir8sdO3bIvHnz1Iqq0qVLy9SpU+WpDP6a3LZtm5qfR0RkK5wqVhDvtd9KzN69EvrhR5J47x7qR0nEkqWqhRnai7l17syhVLIpZh+8YSUpgrcvvvhChg8frt+Oem+AvqeY9F+lSpU8L1ni5JSyoj9opT5wf1Ygg4eFDlmBIdvkRXyReUNtOCIiS+olm7ovcfT+AxIycVKmx7Fzc5NCo96T8EWL8UtXknz9JPjdERI8eqzYeXqqnqrpcW3ZUrXqSs6vbTtJ9PO36D6yZJvMPniDzp07qyBu69atqpxHWFiYCn4wZNmlSxd5Mh+WkWN+G+qwpYYSH9r9ea1o0aLqQkRkyb1kU/cllhjj+shiMYTH2DFSoNtrEjrtI4nZt//RHXFxovP3F10mnR1SQ+Bm6HktsY8s2ZZsB2+Yp7V582Y5cOCA3LlzR2WeMG9Lmx+2fft2VVKjbC61OilfvrwaNk0Phk6xoCCvYHjU39/f4HAqMKgiIjKul2zqvsTialwfWcyDA8dy5cR71UqJOfizBA0ZKjqt2oCdndgVLmyw0T06OxjTl9bS+8iSbchW8IZAbcyYMXLhwgW1yhNZMMxBS931oFChQtkqnJscAsRXX301w30QuH300Ufy8ccfS17RasChJEnyRQsoIaLdT0RE/0HmypjsFYoQJy9EbCysSC26+UcJ7NX7UdCl04kuPFw8p0wW9x49Mn18sd270u0jS2R1q021rgeoifbTTz+pchnJJe96kFOY64bCuOlJSkpSgduRI0ckLzVr1kwFiVg0kHzIFEFq9erV9WVCiIgo/6APKgI3p3r1Hm1ISJCQMeMkdMZM0SVrfE8ktp55y+uuB8k988wzalK+h4eH1KlTx2DghhpsmWXnMrJp0yZVukMbAj127JgqDgxdu3ZVwSgCtObNm6uabqg7h9Wh6Kf68OFDmTBhQg5fJRGRdUr09ZXEx79Pc5suJkaCBgx69Dy3b6sCvjE7H2XTIhZ+JfHnz0uhMaPF7vHCMmO49+4lds4u4lD8cb9VImsJ3vK660FyM2fOlHfffVemTJmiVmdqpTQQuH3yyScqK4cFDRnNh8vMhg0bVBCmQRZPy+S1bt1aBW8wefJklWHbu3evCvbQh3TWrFkqy0hERGlFrv1O9S3Na0n+/vrATRN7+Ii6ZFWh0aOkQJcuuXh2RGYQvOV114PkML/ss88+U22pxo0bJ4sWLVJz6jC/DQskOnXqpGrA5cTGjRuN2g8rSocOHaouRESUOfe3eqrG83mZeUPgZu/jI17Ll4qdq6vEnvxNwmbMVOVEwL5kSdWVwbFMGaOO61CMWTeywuAtedcDLEpIr+tBZtm5rKz0/Pzzz1XPUCyUQPYNQ6UdOnRQNd6IiMg8ORQvri55BQFbQOdX1bVL/Ue15JyfeUZcGjWUwN59VC24pH//lZCx48V75dfi0rBhpseMv3FT/Np1MOr5i+3akeL1IdMYNm9+po9zrFBBfH7YkGJb0LvDJfbEyUwf697zTfEYPSrNQgvWp7Md9tnteoDADRkvLFxI3vXg9OnTMnbs2FzveoCSI7Nnz5bg4GA1pInADZk4IiKyXci0Jb/WIIDz2b5dHKtVVbd1ISES0ONNidq8JfODJiaoFafGXCQx5aKIpMhI4x5roCtPUlCQcY8NC0v72IcPJeHGDVWahayfo7l1Pcis/RMK86KPKLJxyffFoonevXtn+fmIiMg6OZYuJT5bNkvQ4CESe+iwKuYbPHyEJNy+LYXeG5l+Sy0HR6Pqzj3a1z5NLTqjatYZKLhu7+Vl3GM9PNK9j/XpbIOdDo01s+nWrVu53vWgadP/GhBnBf4TZlRSxBqhOT3q6C1fvjzP24MREZnralYMVWJuXXrDs7r4eAl5f6pEffedfhu6NBSePStLK1HNmVafDsFfydM5L9NFVtweK7OuB9mR1X6fRERkuxCweYzJeO6znZOTFJ41QxyfLC9h0z9V26J++FES7t0X76+XGey+QGR1wRuGKzF0mlGJjL/++kt1JOjTp0+Wjs2yG0RElNswOlNoyGBxLFtWgpB0iImVuBMnxL9TF/H+ZrVquUVk1QsWVq1aJWfPns1wHwRvmc1fIyIiyk9o1+WzcaPYe3ur25jk7/9KJ4k7/Se/EWTdwZuxjeuxcIGIiMicONetIz7bt4rj457USYGB4v/66xK9Y6epT43IKNmOrtJdpYMaOfHxKvNWpEiR7B6eiIgoz2CY1GfrFnF+/vlHG2JiJWjQYAlfvERysI6PyLzmvKWu2YauBGjKnhraVoWGhqqm7ajFRkREZI6wUKHod99KyPgJagEDYEFDwq3bUvjTT8TOMUdr+kwu9vgJCRo+wqh9U69QDZs7TyK/W5fp41yeayReC79Msc2/W3dJ+PvvTB/rMeo9tUqYss7on8zkf4kg64bbhv46cXR0VKtQ0USeddeIiMicoVRI4XlzxaFcOQn/7HO1LWrtWkm8d1e8liwWewNdhMyR15dfiC4uVuycXfTbcFsVEs4GFAI25rEoLJxmW0CAcY/Nhf7ntsoxO/0/UYvt9ddfz/JKUiIiInODhASyQBhKDR4zVhXzRVFf/y6viveaNarYr7lzef65NNsQyBldbNhAIWCjCgZ7eaXdVrSo2BvoApFmP3f3bJ0bZbNI78OHD6VgwYLqQqbDIr1ERLkr9uRJCew3QLXTAvvixcR7zWrVbovypsiyajHmYJ+nPXCtTbYWLJQoUYKBGxERWR2XRo3EZ9tWcSj/qO4bGtsHdOkq0fv2m/rUrJJfuw7ysH4DdU3Gy9FszAsXLqhG9AEBAWqFqSETJ06U3BAeHi7Hjh3T908lIiLKC04VK4jP9m0S1LefxP3xh+iioyWoX3/x/GiaFHynb7619SLK1eANNdw+/vhjOXLkiFq0oC1g0Gi3cZ1bwZuvr6/MnDmTwRsREeU5By8vKbrhewkeNVqit21HKQUJnfqBJNy6JZ4ffiB2Dg45On6in5+Ez50nrq1fYvBG+RO8bdiwQQ4fPizt2rWTzp07y8CBA6Vbt27SokULVd/tu+++k7p168rgwYOzFJxlBNk9IiKi/GLn6ipFvlqoVqJGfLlQbYtcsVIS795V2+0LFOA3gywneNu/f788+eSTMmHCBP02LF6oXr26ujRq1EgGDRqkyoV06tTJqGNi9WpGhX+1TB4REVF+sbO3F8+JE8SxfDkJmTAJQ08Ss2+/BLz6mnivWcWsGVlO8Hb//v0UBXgRVGEoVYPA7vnnn5etW7caHbwVKlRI+vXrl25j+tu3b8u0adOyc7pEREQ54t6jhziUKi1BAweJLjxc4s+fF/8OHVVTe6dq1fju5lCSn5/8W7e++rrohvXiVKmi/r6oLVskdPr/Mj2GQzEfKbY7ZfOA4PETJebgwUwfW6BzJ/GcOiXFNt8Xm6Vbi67gwAFSaNBAsajgzcnJSVxc/isE6ObmJsHBwSn2KV68uBw/ftzoY1auXFktSkDgZ0hiYiJblhARkcm4vthEtdQKfLuPJN67J4kPHoh/51fFa9kScW3alN+ZbLAvWFCS8EVS0n+FfRP/SwaBLjom+8WGQ0KMKxgcGmpwUYkuIsLg/ultN+vgzcfHR/z8/PS3n3jiCTXXLfnQ5qVLl1Q2zViYOxcTE5Pu/QgGc2vxAxERUXY4VamimtoH9n1H4s/+pT7EA3v1lsIz/ifuPd/km5pFHuPGSticzyQpeTDkkDI0sXNzNapgsEMxH4Mt0IwqNuzpmfZ4xYtLUjr1bO1MXOc2W8EbhjZ//fVXfbCGhQqLFi1Sc+Aw3+38+fPqggUNxnrxxRczvB+BYNu2bbNzukRERLnGoVgxKfrjDxL87nCJ2bMXQ0OqP2rC7dviMXGCmidHxnHr0F5dMlKgSxd1yY4is2dm+1tR/MghMVfZCt4QlKEBvb+/vxQrVky6du0qZ86ckRMnTshvv/2m9qlWrZpatEBERGRt7N3cxGvZUtXIPmLZcrUt4qtFknj7jhSZP1fs3NxMfYpkxbIVvFWpUkVd9AdxdFQ12K5cuaIWM6ADA4I3+xz+9dGsWTP59ttvpWzZsjk6DhERUW5DrTfUfEMpEdSAw7yt6B07JOHBA/FetUIcihblm055IlvR1Z49e+TUqVNptletWlVatmwpNWrUyHHgBtlou0pERJSvCvbpLd6rV4nd40br8X/+Kf6vdJL4Gzf4nTADib6+Evb53Ed9VK1EtiKsWbNm6YdHiYiIbJ1ryxZSdPMm/eT4xDt3xL9jZ4k9fsLUp2bzEh93s8C1TQdv3t7eqnQHERERPeL8dA0ptn2bOFWvrm7rQkMl4M2eEvXDj1bxFlljBsumgrfGjRvLH3/8IXFxcbl/RjZmxIgR0qpVK3n55ZfVZdy4caY+JSIiyiaHUiWl6JZN4tKixaMN8fES/N4oFfRY+lQga8xg2VTwNmDAAHF1dZUpU6bIP//8k/tnZWPGjx8ve/fuVZc5c+aY+nSIiCiHhWexYMG999v6bQh6gke8J7rYWL63ZJrVpmhjFR8fLzdu3FALF5ydnaVw4cJpeo/i9vr163N+lkRERBbEztFRPD+drlaihn0yHSvwJHrzZkl8cF+8v35UWoQoXzNvSP2iPAhqvOGCwE3bnvyCWnCWICoqSlauXCljx46V9u3bq4LBu3fvNrgvhooXL14sXbp0UcOdqGX3+++/5+j5v/zyS3nllVdk9OjRcvPmzRwdi4iIzAMSGOh/6bV8qdi5uqptcSd/UwsZEh/8a+rTI1vLvG3cuFHyw5tvvikeHh55/jyhoaGyevVq1YKrUqVKquBwembMmCGHDh2Sbt26SZkyZVSQh2HPBQsWSM2aNbP83IMHD5by5cuLg4ODbNq0Sc15W7t2rRQoUCCHr4qIiMyBW9u24rDpBwns844k+ftLwt9/S/CoUaY+LbK14C2/5FeHBqye3bJli7pGoeGBAwca3A/9Wg8ePChDhgyRN954Q23DIoM+ffqobBwummHDhqkWYYb06tVLzRuE6o9XJWnB6q5du+TixYtSv379XH6VRERkKs7PPvuoJ+rbfSTh2jXRhYap7Qk3b4rzM8/wG0PWE7zlF8zZQ+CWmcOHD6sMWceOHfXbXFxc1FDrsmXLxNfXV2Xv4Kuvvsp2mt3SVyQREVFajmXLis9PmyVo4GCJ/fVXtS30g2niXL+BOJYuxbeMjGax3XMR4Ny9e1cFTPnl+vXraqjU/XEVbQ1agQEWcGRFeHi4mi+HeXRYAILhaGxLno1LLiAgQK5evaq/3L59OwevhoiI8pu9p6d4rV4pjlWrqttJgYES2Lu3JIU9ysRR7tPFxKS4tgZmn3lDtuvXX39V9dAKFSqktv37778yceJEffCCHqhTp05VWbG8FBgYaDBDp21DcJUVKHSMjN2dO3fUAhDMt0P3ioIFCxrcf9u2bWpuHhERWXZTe89pH0pgj0fTbxIuX1HZOO9v14idk5PYGhT9zavacbqYGAka8GgKFq6TLx7JCYdixcTh8UibKZh98LZ161YJCgrSB26wcOFCuXXrltSpU0fCwsLUAoK6deuqFZt5KTY2VpwM/MfCsKt2f1Zgle7y5cYvGcdwLQokaxC8Tp8+PUvPSUREpmdf2FNd2xUqJLrwcIk9elRCxk+QwnM/T1N2y9pFrv1O1cHLa0n+/hLQ+dVcOVah0aPEY8xoMRWzD94QpDVs2DBFWY8TJ05IixYt5MMPP5SEhARVdw4T/fM6eMP8NgxvpqZ1msD9ealo0aLqQkRE1sFz2gcSMnkK/vqXqI0/iMMTT4jHqPfElri/1VNcW7+Up5m3JH9/sffxydXMmymZffCGzJqXl5f+9rlz59RwY8uWLdVtDDfWq1dP9u/fn+fnguFRf39/g8OpwMCKiIiMoc2/cqxYUbwWzJegwUPU7fDPPheH0qXF/fVuNvNGYvgxL4cgvZYvVRk3XLtYSSWHbAVvM2fOlBdeeEFd0nP8+HE5cuSImpuWE1gcgABOgxps9vb2UqtWLf02BHAx+TARUasBFxkZmWLRAkqIaPcTEZFly8s5WOnNw3Lv308iv16htoWMHaf2ca79rEXNwzJXdo8zbbmRcbPo4A2FaUuUKJFh8IaVl3v27Mlx8PbEE0+oQLB///4qaDtw4IBUrlw5xRy4hw8fSpEiRSSvYWEE2n1h4YBW5w1DphiyxQpRrUwIERFZrvyag5XuPKzERAmdNNni5mGRFQybIqjJjdWfXbt2VXPbcK1l2BDIJYfMFwK6nEB3g4iICP0Q6LFjx8Tv8V9eeG6sAEWA1rx5c7VCNCQkREqXLq0CVASPEyZMyNHzExGR9c/Bymgeli4xUcI+ni5xv/2m9sN9hefPFQcj6pCawzwssoDgLb3VMKi/hqDnt99+M6rwrTHZrlGjRsnOnTvVbSxUaNu2rf7+s2fPqmHMBg0a5Oh5NmzYoIIwDYZ8cYHWrVvry3dMnjxZZdj27t2rgr0KFSqo8h7PPpu19DYREdnmHKyM5mF5r/1GAl7rJvF/nVPBXfj/ZkrRzT+KfTolpMg22emMLOfftGlTfcCGh2S2lBn7oN1TfrW4skUo1Is2Wyg3UqVKFVOfDhERGSnu/Hnxb9NOfPbsStMeC/PtVPP6u3fVbZfmzcR79Sqxc3Q023M2Z3EWet4ZMfonAQsEtIDtr7/+kmLFiknJkiXT7Id5aWgmjxpsHTp0yN2zJSIisnIY/kTBXv9OXUQXGiqxvxySkMnvS+FZM22uBhzlMHj74osvUmTh2rVrpxqyExERUe5yeuop8V6xXALefAuTyCXqu3WqN2qh4e/yrabs9TZFyyoGbkRERHnH5bnnpMjcz/S3w2bOkqgtW/iWU85Wm6LbwB9//KF6c2IVaO/evfVtotAJwdPTUw2jEhERUdYV6NJFEu/dV4EbBI8eKw4lSqjAjmxXtiMrNIvv1q2bTJo0SRYtWiSrVq3S33fz5k3p0qWLHDx4MLfOk4iIyCYVfHeYFOj55qMbcXES2G+AxF+/burTIksL3s6fPy8ffPCBatI+fPhwadWqVYr7UQ8NNdAwvEpERETZh0UKhf/3qVp1CljEENirtyQaaNdItiFbwduaNWtU3TOUqEAB2zJlyqTZB6UrkIHLKyhFcvfuXfH19c2z5yAiIjIHKBPitWSxOD39tLqNMiKBvftIUlSUqU+NLCV4Q0cDtMYqXLhwuvuglEhQUJDkFLJ3n376qYSHh+u3/fvvv2rBRK9evaR79+4ybdo01ayeiIjIWqFQr/eaVeJQqpS6jUK+wUOHqc4MZFvss7tQoUCBAhnug+4DuVGPZuvWrXL9+vUUvUwXLlwot27dktq1a0vFihXl0KFDqr8oERGRNcNiBdSAs3v8mRiz/4CEfvChGo0i25Gt4K1UqVJy5cqVDPe5ePGiaiqfUwjSqlWrpr+NVawnTpxQbbLmzZsnS5culXLlyjF4IyIim+BUtap4LV8m8rjjQuTqNRKxdJmpT4vMPXhDkd4LFy6kGzB9//338s8//6gAK6fCwsLEy8tLf/vcuXNqiLRly5bqNprV16tXT+7fv5/j5yIiIrIErk1ekMJzZutvh30yXaK37zDpOZGZ13nr0aOHmos2e/ZsOXDggMTFxantixcvVhk3BHaVKlWSV199Nccn6O7urgI4zZkzZ1TtOLTr0r8IR0dVZ46IiMhWuL/eTRLv3ZPwz+eq20Ej35OiJYqnaHRP1ilbmTfMd8O8M2TWEEyhdAjG29evX68Ct+bNm6shTWdn5xyfIIZejx8/LqGhoWrRAoLFypUrp5gD9/DhQylSpEiOn4uIiMiSFBr1nhR4vdujG7GxEtS3n8Tf/NvUp0Xm2mEBwRNqvY0cOVLNf0N2DFmyqhiLTzbMmVMoRfLhhx+qay3D1r9//zSrXxHQERER2VwNuNmzJPHfhxJ79KgkBQdLYK9e4rN9mzh4e5v69Mgc22MBWmA1bNhQ8kqzZs1k1KhRsnPnTnUb2b62bdvq7z979qxERkZKgwYN8uwciIiIzJWdk5N4LVsi/q92lYTLVyTx9h0J7POO+GxcL3ZubqY+PTLH4C0/dO7cWV0MefbZZ7nSlIiIbJq9h4d4r1kj/h07StJDX4n/808JGj5CvJYuETsHB1OfHpkqeJs5c2a2nmDixImSE+iggG4OGJJND8qHYD5c8eLFc/RcRERElsqxdCkVwAW82lV0kZESs3uPhH78iRT+aJqpT41MFbzt3r07S2PwWMCA65wGb+ig0LdvX+ndu3e6+/z444+ycuVKVayXiIjIVjk/XUMNoQa+3UckMVEiv14hjk88IQX7vWPqUyNTBG8oA2KMe/fuyapVq+TBgweSGxAEZlY5mpWliYiIHnFt1kwKz5whIePGq9uhH04Th9KlxK1NG75Ftha8Va9ePcP7Q0JCZPXq1bJjxw7VPuuZZ56RwYMHS37w9/fPtF0XERGRrXB/8w3VvD78iy+R4ZCgYe+Kzw8/iHOd2qY+NTKHBQso3YH6bhs2bFBzz8qXLy8DBw6Uxo0bZ/uYCAKTQy05Q5KSksTPz08OHjyYaXBJRERkSwqNHycJ9+5J9OYtIjGxEtinr/hs+0kcy5c39amRqYI3tKjatm2brFmzRoKDg8XHx0eGDx8ubdq0UR0QcgLDrhrMm0M5EFzSU7Ro0XzL8hEREVkCfH4W+WyOqgEXd+KEJAUGSmCv3lJ060/i4MXC9jYXvP3yyy/y9ddfq36iWAU6aNAgVUTXxcUlV05qwYIF+rls7733nqrrhqAwNQSJHh4eqgtDTgNGIiIia2Pn4iLeXy8T/86vSsL165Lw998S9E4/Kbp+ndi5upr69Cg/gjcMXy5ZskSuXr2quh1gJehbb72VolVVbkDtNk2fPn2kdu3aKbYRERGRcewLFxbvb9eI/yudJMnfX+J+/12C3xslRRZ9JXZMfFh38DZu3Dj5/fffVRoWWbB33nlHihUrlrdnJ6LKhBAREVH2OZYtK97frJaAV18TXXS0RG/fIQ5ly4rn+5P5tlpz8Hbq1CkVuKEQbmBgoMyZMyfTx2D/2bNn5/Qc1erVo0ePqh6qERERaqGCITmtKUdERGStnGvWlCKLF6lhU0lKkohFi8WhTBkp2PttU58a5eWwKeag/fvvv+piDARvOfXw4UMZPXq0qhuXUT233CgITEREZM3cXmolntM/kdDJ76vboVOmikOpUmo7WWHwhlIgpvDll1+qhRGtW7eW9u3bq1WtDuzTRkRElC3ItCXeu6cyb8jABQ8ZKg6bf1SZObKy4K1EiRJiClgkUbduXXn//Ud/JViTl19+OU3NvCFDhkiPHj1Mdk5ERGT9PCZNVAFc9Lbtag5cYO/HNeDKljX1qVF+FOnNa5jf9tRTT4k12rt3r/7rgIAA6datm7z44osmPSciIrJ+WGVaZN5cSXz4UOJO/S5Jfn6qBpzPT5vV6lQyb2ZfHA2dE27fvi3Wbv/+/VKjRg0pVaqUqU+FiIhsAOq8ea1YIY4VKqjbqAMX2H+g6GJjTX1qZOnBGwoA//nnn3Lo0KE8OT5aeq1cuVLGjh2r5tQh87V7926D+8bFxcnixYulS5cu0qpVK3VuKJ+SG/bt25dmGJWIiCgvodOC99pvxN7bW91GJ4bgseMyXCBIpmf2w6YnTpxQRXqnTZsmtWrVksqVK6uuDoZWm/bu3TvLxw8NDVW9VFECpVKlSun2UYUZM2aoIBLDm2XKlFFB3vjx41VHiJo5mOh58+ZNuXv3rjRr1izbxyAiIsoOx3LlxHv1Kgno9rroYmJUL1THMmXEY8J4vqFmyuyDt+R9TjPqcZrd4M3b21u2bNmirlFHbuDAgQb3u3Tpkhw8eFAtKHjjjTfUNmTK0AEC2ThcNMOGDZPz588bPE6vXr1kwIABaea+NW7cONc7VRARERnDuU5tKfLVlxLUfyDqgkn4F1+qIr7ubz76vCPzYvbBm9bnNK84OzurwC0zhw8fViVKOnbsqN+GXq4Yal22bJn4+vqq7B189dVXWVqQceDAARkzZkw2XwEREVHOubVpI54fTZPQDz5Ut0MmThKHUiXFlaNCZsfsgzdz6Wl6/fp1NVSaesi2WrVq6vrGjRv64C0rTp8+LQkJCdKwYcNM98WKVHS30NjCQg4iIso/Bfu9Iwl37kjk1ytEEhMlaOBgKbp5kzg/XYPfBjNi9sGbuUDQZChDp21DYJXdhQotW7YUR8fMvxXbtm1T8/OIiIjyiucHUyXxwQOJ2bVbdJGREti7t/hs28Y33IyYffBmbLCS3TlvxoqNjRUnJyeDw67a/dmRleLDGLLF3Ljkmbfp06dn63mJiIgMsXNwEK8vFoj/Q1+J//NPSXroqwI4tNUi82BRCxbSC9qwpDmvgzfMb4uPjzdYPkS7P68VLVpUXYiIiPKSnZubeK9eKf4dO0nirduScPmKhE3/lG+6mbDYBQsRERFy7do12bRpk9SrV0/VXstLGB719/dPs12bg8agioiIrImDt7d4f/ONBHTqLEnBwRJ/5lG1B9aAMz2LXrDwwgsvyEsvvST9+/fP87ZSWg24yMjIFIsWUEJEu5+IiMiaOFWsIF6rVkpA9x6YH6S2Ra3fIC5sYm9SZt9hITNly5ZVgdu6devy9HlQQDcxMVEtGkg+ZLpr1y7Vwis7K02JiIjMnUv9euK1YL7+dtQ330r0/gMmPSdbZ/aZN2MULlxY7ty5k+3HY+gVw7DaEOixY8fEz89Pfd21a1cpWLCgCtCaN2+uarqFhIRI6dKlZc+ePfLw4UOZMGFCrr0WIiIic+P2SgcpcPKkRK1eo4r4Bg8fIY47totTpYqmPjWbZPHBG7Jfp06dUgFWdm3YsEEFYZojR46oC7Ru3Vp/7MmTJ6sMGzoiINirUKGCzJo1y2xq0REREeWVAt1ffxS8Yd5beLgEvdNPfHZsE3sPD77p+czsgzdktwzBECZqq6FlFbJuyJBl18aNG43aDytKhw4dqi5ERES2BFUdwKF8eUm8dUsSbt6U4BEjxWvlCrGzt/hZWBbF7IM3NIPXfmCS01a74D4UuR00aJAJzo6IiMi2eH44VYJHjRZdSKjE7D8g4Z/PFY9xY019WjbF7IO3iRMnGtxub2+vGrlXrlyZZTqIiIjyiUPJkuK1eJEE9uyFBt0SPn+BONWoIW7t2vJ7kE/MPnhr25Y/DERERObE9cUXxeP9yRL2yaMuP8Ej3xPHCk+KU9Wqpj41m8BBaiIiIsqygoMGiluXzuprXVSUBPbrL0khIXwn84HZZ94058+fl927d8uNGzf0hXKfeuopefnll6UmiwUSERHlK8w5LzxntiRcvyHxFy6oNlpBw94V72/WqP6oZOPB25dffqlqsSVfpICvr169Kjt37pTXXntN3n33XVOfJhERkU2xd3MTrxXLxb9te0kKCpLYQ4clbNZs8Zw8ydSnZtXMPnhDtu3HH39UnRT69OkjtWvXFi8vLwkODlbtqtC4HvejPVWbNm1MfbpEREQ2xbFMGfFaukQCeryBOl4S8dUitYChQKeOpj41q2X2c962bt0qPj4+smTJEmnVqpVqEI/MGwI4lAjBdjSF/+mnn0x9qkRERDbJ5fnnxHPah/rbIaPHSPzFR72/yQaDt3/++UeaNm2abgcFbMf92I+IiIhMw71vHynwejf1tS4mRgLf6SeJQUH8dthi8GYMQ0V8iYiIKJ8XMMz4nzjVftQyMvHePQkaNER0CQn8Ntha8Pbkk0/K4cOHJSoqyuD92I77sR8RERGZjp2rq3gvXyb2Pj7qdtzx4xL6uBYc2VDw1rFjR/H395chQ4bIoUOHJORxDRlc4za24/5OnTqZ+lSJiIhsnurAsHypiJOTei8iv14hUT/8aPPvi02tNm3Xrp1cv35dNm/eLNOmTUtRKgRw/eqrr7ITAxERkZlwqV9fCk//REImPGpxGTxhojhWfkqca9Uy9alZBbMP3mDkyJHSrFkzg0V6UR6kFn8YiIiIzIr7Wz0l7vwFiVq7ViQ2VoL6DRCf3TvF4fGQKll58AYI0BikERERWY7Cn3wkCVevStzvv0viv/9K0MBBUnTDerFzdjb1qVk0s5/zRkRERJYJQZrXsiViX6K4uh136ncJnfaRqU/L4tlbQk/ThQsXSmBgoMH7AwIC1P0XL17M93MjIiKijDkUKybeXy8XeZxti1zzjUSu+55vmzUHbxs2bJBjx46pzgqGoLvC8ePHZePGjfl+bkRERJQ559q1pfDM/+lvh7w/RWL/OM23zlqDtytXrkjNmjUz3Adz4S5dYhsOIiIic+Xevbu4v9P30Y24OAkaOFASHz409WlZJLMP3lDPDdm1jGiN6omIiMh8eX4wVZyfe059neTrJ4H9B4ouNtbUp2VxzD54Q+9SPz+/DPfx9fUVNze3fDsnIiIiyjo7JyfxWrpYHEqXVrfjz5yRkMnv62u3kpUEb9WrV5cjR46oAM0QbD969Kg8/fTT+X5uRERElDUO3t7itfJr1UoLotZvUIsYyIqCt9dff11iY2Nl2LBhsmfPHrW6FHCNor1Dhw6VuLg46d69u6lPlYiIiIzg/PTTUviz2frboR9Ok9iTJ/neWUuR3meffVYFbosWLZKZM2emaY+Fr4cPH672IyIiIstQoEsXib9wUSKWLBVJSJCggYPFZ/cucSxdytSnZvbMPniDbt26SZ06dWTr1q1q9WlERISaC1etWjXVkL5ChQqmPkUiIiLKIo9JEyX+0iWJPXJUkgIDJah/f/HZvEnsOI/d8oM3qFixoowePdrUp0FERES5xM7RUbwWfSV+7TtI4u07En/uvASPnyhFvpivRtbIQue8ERERkfWyL1JEvFd8rc+2RW/eLJFfrzD1aZk1swvexo4dK5cvX87WY6Ojo2Xt2rWyefPmXD8vIiIiyhtO1apJkfnz9LdDP5kuMUd/5dttKcEbivIOGTJERo4cKbt27VLz2zKDvqbz5s1TK1O/+eYbVbTXEvzzzz/y7rvvSps2baRXr15y5swZU58SERGRSbh1aC8Fh7/76EZiogQNHiIJd+7wu2EJc96+/vprVQJk9erVMmvWLJkzZ46ULVtWqlSpIkWKFFELFVAaJCwsTO7evStXr16VqKgosbe3l5YtW0r//v2lePHiYu4SEhJk8uTJ0rVrV1mwYIEK3D744AOVOfT09DT16REREeU7j3FjJf7SZYk9eFB0ISES+E5/8dn2k9gXKMDvhjkHb9C2bVuVjTp58qTKvp09e1b27duXZj8EbFhp+uKLL0r79u0zbaNlTu7cuSPh4eHy2muvqdv16tWTp556ShUc7tChg6lPj4iIKN/ZOTiI15cLxL9DR0n4+29JuHxZQkaPkSKLF3EBg7kHb4BVJs8995y6wK1bt8Tf319l3JydnaVw4cLy5JNPqkxcTiBrt379etXYHnPtEFBNmjRJBZCpIeO3YsUKFUhiP6yARaavfv362Xru1O1AcBtDqURERLbK3tNTdWBAAKeLiJDo7TvE6ZlnpNCwoaY+NbNhdnPe0lO+fHkVJGFotEmTJvLMM8/kOHCD0NBQNUR7+/ZtqVSpUob7zpgxQzZu3CgvvfSSjBgxQmX+xo8fL+fOncvy8z7xxBPq/Dds2KCGUH/77TeVYYyJicnBqyEiIrJ8Tk89JUW+XKC/HTZjpsT88otJz8mcmG3mLb94e3vLli1b1DUKAA8cONDgfsjMHTx4UC2meOONN9S2l19+Wfr06SOLFy9WFw06Qpw/f97gcbAwYcCAAeLo6Cj/+9//ZP78+fLtt9+qOX0tWrQQHx+fPHqlRERElsOtdWspNHaMhH/2OYamJGjYcCm2Y7s4VnhSbJ3NB28YgkXglpnDhw+Lg4ODdOzYUb/NxcVFzbVbtmyZ+Pr66hdKfPXVV0a9+Rh2/fLLL/W3ERi2bt063f3RzzUwMFB/G9lCIiIia1Vo5AiJv3BBYvbsFV1oqAT26y8+27eKfS6MvFkymw/ejHX9+nUpU6aMuLu7p9iOFl1w48aNLK9yvXnzpjom5roh+4frhg0bprv/tm3b1BAvERGRLbCzt5ciC+aL/yudJOHaNXUJHvmeeC1fpu6zVQzejISMl6EMnbYNWbGswkpalEVJSkpSq00//fTTDPdH1q9x48YpMm/Tp0/P8vMSERFZCmTZ0IEBLbR0YWEqCxe+4AvxGPWe2CoGb0aKjY0VJycng8Ou2v1ZNXz4cHUxFkqhWFI5FCIiotyAeW5eixZKYK/eav4b5sE51aiu5sXZItvNOWYR5rfFx8cbLB+i3U9ERER5w7V5c/GYNFF/O3j4SIm/ft0m324Gb0bC8GjyxQIabRszYkRERHmr4NAh4vbKo0L2qAEX2LefJIWG2tzbzuDNSKgBd+/ePYmMjExTQkS7n4iIiPK2gH/huZ+L4+PFgon//CNB744QXWKiTb3tDN6M1KxZM0lMTFQrPpMPmWLRQfXq1S2inyoREZGlsy9QQLxXfi12hQur27E///yoFpwN4YIFEdm0aZNEIP36eAj02LFj4ufnp75G43h0QkCA1rx5c1XTLSQkREqXLi179uyRhw8fyoQJE0z7XSQiIrIhjk88IV5LFkvgmz1FkpIk/IsvxalGDXHr0F5sAYM3EdWiCkGY5siRI+oCKJqrteGaPHmyyrDt3btXBXsVKlSQWbNmybPPPmuq7x8REZFNcm3ygnhOnSKhH32sbgePGi2OFSuI0+MhVWvG4E1E9Ss1BlaUDh06VF2IiIjItNwH9Je48xckevNm0UVFqQ4MxXbuEPsiRaz6W8M5b0RERGSxCxiKzJ4pTs88o24n3r4jQUOHiS4hQawZgzciIiKyWHZubuK1YrnYP+54FHvkqITNnCXWjMEbERERWTTH0qXFa9kSEcdHs8EiFi+RqC1bxFoxeCMiIiKL59KokXh+NE1/O3jsOIk7f16sEYM3IiIisgruvd+WAm/0eHQjJlaC+g2QpBDr68DA4I2IiIispwPDp9PFqU4ddTvx/n0J+98MsTYM3oiIiMhq2Lm4iPfypWJfrJi6HX/unFgbBm9ERERkVRxKlBCv5ctEnJz022L27RdrweCNiIiIrI5LvbpS+H+f6m8nhYWJtWDwRkRERFbJ/c03xK3ba+rrAq91FWvB4I2IiIisVsF+74i1YfBGREREZEEYvBERERFZEAZvRERERBaEwRsRERGRBWHwRkRERGRBGLwRERERWRBHU5+ALbp8+bLs2bNHzpw5Iw8fPhQPDw+pUaOG9O/fX8qWLWvq0yMiIiIzxuDNBNatWyfnz5+X5s2bS8WKFSUwMFC2bNmigrfFixdLhQoVjDpOeHi4ur5x40YenzEREeWmhDt3JNjZWULu3BFHZ2eLeHMt8Zwt9bzLlSsnrq6u6d5vp9PpdPl6RqQCt6pVq4pTsp5rd+/elb59+0rTpk1l6tSpRr1La9askRUrVsjy5culSpUqfGeJiIgs3Isvvpjp5zozbybwzDPPpNmG4dLy5cvL7du3jT7O008/nctnRkRERKaEwA2Zt4wweDMTSIAGBwerAC49AQEBaohVk/xrIiIisnzGjKQxeDMT+/fvF39/f3nnnfR7sG3btk1Wr16dr+dFRERE5oXBmxnAUOm8efPUitM2bdqku1/Hjh2lcePGKR43ffr0fDpLIiIiMgcM3kwMQ58TJkwQd3d3+eSTT8TBwSHdfYsWLaouREREZLtYpNeEIiIiZPz48er6s88+Y2BGRCaT6OsrYZ/PVddEZN4YvJlIbGysTJw4UZUImTlzZoYLFYiI8lqin5+Ez52nronIvHHY1AQSExNl2rRpcvHiRfnf//7Hkh9ERERkNAZvJvDVV1/JsWPH5Pnnn1ddEvbt25fi/tatW5vitIiIiMgCMHgzAa2d1fHjx9UlNQZvRERElB4GbybwxRdfmOJpiYiIyApwwQIRERGRBWHwRkRERGRBGLwRERERWRAGb0REREQWhMEbERERkQVh8EZERERkQRi8ERERkdVKtMK+vQzeiIiIyGolWmHfXrMv0vvvv//KH3/8Ic7OztKkSRMpUKCAqU+JiIiIyGTMJnj79ttvZfv27bJixQopVKiQ2nbmzBmZOHGixMbGqttr1qyRJUuWiIeHh4nPloiIiMjGh02PHj0qJUuW1AdugEAtKSlJ+vbtK506dZL79+/LDz/8YNLzJCIiIjIlswneHj58KOXKldPfDggIkCtXrkiXLl2kd+/eMmrUKKlbt64cOXLEpOdJREREZEpmM2waFRWVIuv2119/iZ2dnTz//PP6bU899ZRcvnxZLB1e6/r16+XSpUvq9YSHh8ukSZOkbdu2pj41IiIiMnNmk3krUqSIyr5pfv/9d3FycpLq1avrt8XFxamAztKFhobK6tWr5fbt21KpUiWxVda4fNtc8b0mIrIeZhO8Va1aVX799Vc5fvy4Wl36yy+/SO3atdUq0+QrT729vcXS4TVs2bJFzd8bMmSI2CprXL5trvheExFZD7MZNn3rrbfkxIkTMnnyZHUbGbZevXqlyLqdO3dOlQuxdAhIrSEIJSIiIhsO3qpUqaJWl+7du1fdbt68eYoh0+vXr6tMXKtWrcRWYRFHYGCg/jaGXYmIiMi2mE3wBpj/ld4csBo1asinn34qtmzbtm1qrhyRLc3Vi1z7nbi/1VMcihc39ekQEZkFswreKGMdO3aUxo0bp8i8TZ8+nW8bWf1cPdfWLzF4y2O6mJgU10RkvkwWvO3Zsyfbj23Tpo3YoqJFi6oLEVFuSoqOlqCh76qvcV38yCGxd3Pjm5yHmFUmiwzeZsyYkeWyHzqdTj3GVoM3IqK8kHDliiQ9eKC+xjVuO9euzTc7DzGrTBYZvKFnKRERmZ5j1apiX6qUCtxwjdtEZL5MFryxmwARkXnAEKnXooUS0PlVdc0hUyLzxgULJrJp0yaJiIjQl/44duyY+D0uVtu1a1cpWLCgqU6NMsG5KmSN7FxdU1wTkfli8GYiGzZsSNEO7MiRI+oCrVu3ZvBmxjhXhYiITMmsgjdfX1/55ptv5PTp06ogbUJCQpp9sGABrbMs3caNG019CkRERGSBzCZ4e/DggQwaNEgNJZYvX17i4+OlePHiqpUUepoikEMBXw4nEhER5T9OGTEfZtOYftWqVRIZGSnz5s1TX0O7du1k7dq1aogRxWmjo6Pl448/NvWpEhFl+iEX9vlcdU1kbVNGcE2mZTbBG4ZKGzVqJM8++2yKum6AwrTTpk1TXy9btsxk50hEZAx+yBGRTQRvoaGh8sQTT+hvOzg4SEyyNi0YPq1Xr56cOHHCRGdIREREZHpmE7x5enqmCNZwO/lqTC2gw5w4IiIiIlvt22s2wVuZMmXk/v37+tvVqlWT33//XS1kgJCQEDl8+LCUKlXKhGdJREREltq3Nyk6WqyB2QRvDRs2lDNnzkh4eLi63a1bN4mKipK+ffvKwIED5c0335SgoCBVwJaIiIgoO317rYHZBG+dO3eWL774Qg2NQu3ateXDDz9U5UL+/vtv8fLykhEjRsgrr7xi6lMlIiIiC+rbC9bUt9ds6ry5u7tL9erVU2xr3ry5uhARERFllb2V9u01m8wbERERUW6zs8K+vY7m1BrLWBhKJSIiIrJFZhO8vf7666pvaWaspbcpWefybXPF95qIyHqYTfD28ssvGwzeUNft5s2bqr8pui+UKFHCJOdHebt8u/iRQ1YzF8Hc8L0mIrIuZhO8TZ48Od370CZr/fr18v3338uECROydNw///xTtd66cOGC+Pn5qU4Orq6uUrhwYalQoYIKCJ977jnx9vaW/BQXFycrVqyQffv2qfIoFStWlP79+0v9+vXFVpdvO9euberTskp8r4mIrIvZBG8ZQUbujTfekJMnT8qiRYtk+vTpGe6PBvabNm2S7du3q7l0Wo9UtNjy8PCQ2NhY+eeff1RGb//+/eLo6CjPP/+8Grp95pln8uU1zZgxQw4dOqTq2aFA8e7du2X8+PGyYMECqVmzptjK8m0Ebta0fNsc8b0mIrIuFhG8aapUqSI7duzIcJ+tW7fKqlWrJDg4WGWz+vXrJzVq1JCqVatKgQIF9PshoLt3755cunRJdXL49ddf5ejRo9K4cWMZNmxYnnZywHMePHhQhgwZooJSbdi4T58+snjxYnWxdta6fNsc8b0mIrIuFhW8oX1WYmJihvvMnz9fWrVqpYIiDItmlM0rW7asuiBwQjYOWbi1a9eqoUwEUnkFbb5QjLhjx476bS4uLtK+fXtZtmyZyhbawopaa1y+ba4s9b3mQgsiIgsM3pKSksTf31/27Nkjx44dkzp16mS4/zfffKMCsqxC8NShQwdp27ZtlsqWZMf169fVUCkKEyeHfq5w48YNg8FbQECABAYG6m/fvn1bXeOcMYcuI3jftm3blmIbgkfMCczM6NGj1UWDOXrauWYGmdC6devqbyNzOnjwYPV1FZ3IN2Inbdu0laup1qoULFhQrqRqYzJu3Dg17zEzCIKXLl2aYlu9evXk4cOHmT529uzZqhWb5urVq9KyZcsU+6R33sjglixZUn8bgfjHH3+c6XNWrlxZfv755xTbevbsqYL8zAwYMEB1IkkOP1upGTpn/KHSrFkz/T4Yxn/rrbfEGMhaJ/fRRx/J8uXLM31c06ZN5bvvvkuxrUWLFnLt2rU0+7roRDaISEmxk/Odu8jZyZOk/7Bh+vuxiMnYOaLIdCNzr1m3bp2appAZLJD6448/UmwbNGiQ7Ny5M8PHae93ahgBwCKszCxZskT9PtJg3m6nTp3EGJcvX5ZChQrpb8+dO1ddMtOpalVJPfPY1L8jMmLOvyPSk/p3BBj6/WeK3xGGpP4dcfqP0/KEkeecH78jUvvggw9UO83UvyMy+qwx1e8IQJJpzpw5aX5HpP65NuvgDd+wjEqFYJgTv5AwpJmR1IHbu+++q34xoEeqMZARy8shU0AAZmiBhLYNQZohCL5Wr16dZjsWYkRGRmb4nIYCWgTFyGZmJiwsLM33wpjHQeqgEvMRtccWcXIS8Skufv5+cj8+PsV+yT98NBgKN+Z50QM3NfxSNuax6KebXEJCQprHpXfeqbPC+JA25jk9PT3TbMPPgDGPxQKc1Aw9ztA5I9ucHG4b+301dB7GPNbQzzb+WDL02GednKSkz6M/YhDAXbj16I+V5O+3seeL72Pq73N2Xyt+vjJ7rPZ+p/bgwQN9/+aM4P9J6v9Hxp6vNsc3+f9fYx4bbOAPRlP/jsiIOf+OSE/y3xFaVjnUwO8/U/yOMCT174i4+EffK0O/s03xOyK11H8Yab8jMvqsMdXvCO3n1dDvCGOYTfBWq1Ytg8EbtuE/KaLRdu3aSZEiRbJ03PPnz6s30tjgLT/gP4QTfphSwYIK7X5D8Fcw5uQlz7xh8UaxYsUyzbz5+PgY3Fa6dOlMzxeLPFJ/T4x5XPLXpHFzc9M/ttjjz5hiPsWktIHMW2r43hvzvOiDm5qxJWaSz4sELGZJ/ZzpnbfWlzf5azDmfA1lWYsWLWrUYw39Ujf0OEPnjGxzcrht7PfV0HkY81i8LkOv39AHTJhO5F/RqcAN1wnly6W4H++3seeL72Pq77MxjzX0c4Ofr8weq73fqeEPQ2Myb/h/kvr/kbGvNfXvUfz/Neax6nfrQz+z+h2REXP+HZEe7XdE8vI9S3yKSXf83rcz7e8IQ1L/jnB2ck73d7Ypfkdk9jOh/Y7I6LPGVL8jwFA8Y2zyyE6X+s80K4OMHr6h5cqVU+ltvFykrZGNQ+o8eZo0v/Tu3Vt90zA/L7lbt27J22+/LWPGjDFqiASvBylxpKJN8TpyKu78efFv00589uwS53xa5Wur522J5wyxv/+uFrUU/WmzuFhQGR1LfL8TfX0lcu134v5WT3GwgTm3phR35oz4d/hvzrPPjm0WUSrJEn+uLfm8xdZ7m2JI8eLFi+qvCPyFiPQ45llgXHrmzJmZZq1yG4ZHk89d02jbDP3lQWSLLHWhhSVCwOYxZjQDt3ws3wMslUTZYTbDpnkJQdu0adOkYcOGYm9vr4YlT506JRs3blT11RA0pZ40mJcqVaokZ86cUUFl8kULKCGi3U/miysgiSgnWL6HLDZ4694do/xZh7kU6LaQFSgdgi4KyYO5Jk2aqAuGHLGyBUEcVm3mB6zewWvAAgStzhuyf7t27ZLq1avbRJkQS8VWU0SUG5hVJosM3gxNtcNqD23oEBMNMcSJVUzaCh0MN6aeVJgZBGpohZUezBnD8m0sT8+v4A0BWvPmzVUpiZCQEDWxEaVQMJyb1fZflL/YaoqIbBVHHcyHyYI3DFkmh+XzqBOE+jMIqNAVAUOcqPOGvqRff/21xMTEGFWvKPVKEKw4zQgWL2zZskXyu5crMmx79+5Vq89QUHjWrFmq1yqZL7aaIiJbxFEH82I2CxZQlBJDh/PmzVP9RRG4Aa7R6xNBG4I37JcVGC49d+6cWqCQHmT38nvRLTKCQ4cOlZ9++kkOHDigsnANGjTI13Og7M9VAbb1ovQwQ0G2MOpApmM2wRu6JyDQSl0rS4PhUtyPHqRZgUrYWL2JoBBVppGFSx6oYeEC2mKhlAiRMThXhbKSocBtytsSJ2Gfz1XXlHe4Qta8mM1qU6y8zKxLgDH7GCoM+NVXX6m2GWj/g3Yirq6uqs4ajqVl3bK7gIKIKDnOi8xfiX5+Ej53nri2follTvIQV8iaF7PJvD355JOqt1h6LSXu3r2rerthv6zC3DIMt6IbAYr2onIyWlCgYvMTTzwhU6ZMMbo3HRFRRpihIGvFUQfzYTaZt169eqkgql+/fqrzAea9ITuG3l+Ys4YyGpjzhg4E2S0xopUHgfjH/c0MtakiIsouZiiIyGaCNwRVkyZNkgULFsiPP/4omzZt0t+HYU0Us504caK88MILufJ8DNqIKK8wQ0FENhG8QZs2beTFF1+Uo0ePyo0bN/QdCNBxAEFb8m4E6Rk7dqzK3lWrVi3Lzx8dHa2CRgyrvvrqq9l8FURERGQuHIoVk0KjR6lra2FWwRsgcHr55ZfVJTtQ9HbIkCFSq1YtdQwEg2hMnxH0Pd23b5+aU4fWWajBRkRERNbTt9eamF3wllMo5otWV6tXr1ZFb9GztGzZslKlShU1hw6BHOrJYZUpFkFcvXpVoqKiVD05LFro378/21MRERGR2TJZ8IZ2UIDMGLJt2m1jh1czgjZX2OfkyZNqocPZs2dVZi01BGzobIBzwCIJ1IMjIiIiMmcmC95mzJihVoCiDRaCN+12RrBwAftkFrwB9kNRX60h/a1bt8Tf319l3JydnVW/U5QdyWxIlYiIiMicmCx4w8pRrdl88tt5pXz58upCREREZMlMFrxhaDOj20RERERkAwsWzF1AQICqY3f58mW5cuWKKk+C2na1a9cWW2ONy7fNFd9rIiLrYTbBm6+vr9y7d0/NgUPvUUhKSpLvv/9eNa13cXGR119/XT+HzVJhheu6deukTJkyarEEypTYKmtcvm2u+F4TEVkPswneVqxYIcePH5effvpJv+3bb7+VlStX6m9j1eiiRYuyVYDXXKBkyY4dO8TDw0MOHTokH3zwgalPiYiIiCyI2TSmP3/+vNStW1ccHR31K0s3b96sGsf/8MMPsnTpUnFzc5P169eLJcPKWgRuRERERBadeUNnhBIlSuhvX79+XW3r27evFCtWTF3QIgvZN1ueLxcYGKi/ffv2bZOeDxEREdlw8Ib5bbhoEKShVludOnX023x8fCQoKEhs1bZt21TnCCJbwYUWRERmHLwVL15crcDUoDk9asBh2FSDrJM5FdVFsBkfH2/UvigMnFkR4sx07NhRGjdunCLzNn369Bwdk8iccaEFEZEZB29NmzZVCxSmTp2qAh3MgevSpUuKfRCslCpVSszFX3/9JSNHjjRqX7y2cuXK5ej50L6LLbyIiCwfs8pkFcFbjx495Pfff5cjR46o2xUrVpR33nlHf//Dhw9VZq5nz55iLpAVnDRpklH7ap0kiIiImFUmqwje3N3dZcmSJfL333+r28hSOTg4pNjnk08+kapVq4q5QEDGzhBERERkk8GbBoVrDcFK1OSrUYmIiIhskdkFb1iUgKHTO3fuSExMjEyYMEFtR9mQBw8eqOFUdFuwZGvWrFHXt27dUtd79+6Vc+fOqa979+5t0nOjzHGuChERmZJZBW9btmyRr776Sr+CE6szteAtODhYhg4dKmPGjJFXXnlFLBm6SSS3a9cu/dcM3swf56oQmQ9dTEyKayJbYDbBG/qXzp8/X7WP6tOnj5w8eVLVNdM8+eSTKuuGEiKWHrxpizKIiCj7kqKjJWjou+prXBc/ckjs3dz4luYRjjqYD7Npj4UG9Kj1tmDBAnn++eelSJEiBufDaUONRERk2xKuXJGkBw/U17jGbcr7UQdck2mZTfB248YNadSokepfmh7UOMPwKRGROWOGIn84Vq0q9o9rf+Iat4lsgdkEb2hErzWlTw8CNycnp3w7JyKi7GCGIn9giNRr0UL1Na45ZEq2wmyCt7Jly+pXXBqSkJCgOhqkV0qEiIhsj52ra4prIltgNsHbSy+9JNevX5dVq1aluS8xMVEWLVok//77r7Rp08Yk50dERERkDsxmtWnXrl3l+PHjqgba/v37VX9T+PDDD+XKlSuqPVb9+vWlffv2pj5VIiIiIpMxm8wb5rt99tlnqndpWFiY/PPPP2oe3KFDhyQ8PFzefPNNmTFjhqr9RkRERGSrzCbzBliMMGDAAOnfv7/qsIAgDj1PtT6n6LCwevVqmTx5sqlPlYiIiMgkzCp40yC7hoBN4+vrq4ZT0UYK898YvBEREZGtMnnwhhWmaBd19epVlV2rWbOmDBkyRJ544gnV2/Trr7+Wn376SbXMQp03DKsSERER2SqTBm8I2EaPHq3vZQpYtIDtCxculEmTJqmOCgjaMOcNbbG0hQxEREREtsikwdu6detU4DZw4ED9KtIdO3bI8uXL5d1331VFed9++2156623xMXFxZSnSkRERGQWTBq8XbhwQerUqZNiKBSB2h9//CFnz55Vw6fdu3cXa3L69GlVCgXDxf7+/uLl5aXeg379+qkMIxEREZHZBm/IrLVq1SrN9ipVqqjgzRoL8i5ZskStom3WrJnqKoEVtJs3b5YTJ06ouX/e3t6mPkUiIiIyYyYN3rBy1FAjetfHbU48PT3F2gwbNkwtyrC3/6/EXoMGDWTEiBEqiEOpFCIiIiKzXW1qa5599lmD2zw8POT27dsmOSciIiKyHCYP3vbt2ycXL15Mse3+/fvqety4cQZrwM2ePVusSVRUlERHR2eaaQwICJDAwED9bQZ7REREtsfkwRsCNS1YS+3UqVNptllje6wffvhBrbpt0aJFhvtt27ZNdZggIiIi22XS4G3Dhg1iyZKSklLUqMsI6tMZCjyxMAMBWfPmzaVu3boZHqNjx47SuHHjFJm36dOnZ+PMiYiIyFKZNHgrUaKEWLK//vpLRo4cadS+3377bYqWX1rwNWXKFKlQoYJMmDAh02OglAjLiRAREdk2kw+bWjK08EIXCGOkLgGCfq1jxowRd3d3mTVrlhQoUCCPzpKIiIisCYO3HEBA1rZt2yw/LjQ0VAVuGHKdN28es2lERERkNAZv+QyrSsePH69Wji5YsEAV6iUiIiIyFoO3fPbJJ5/I5cuXpV27dmrOW/JyHyhY3KRJk/w+JSIiIrIgDN7y2Y0bN9T1rl271CX1Ag4Gb0RERJQRBm/5bOPGjfn9lERERGRF/muwSUREZGEcihWTQqNHqWsiW8HMGxERWSyH4sXFY8xoU58GUb5i5o2IiIjIgjB4IyIiIrIgDN6IiIiILAjnvFmw2NhYdZ28VhwRERFZNvRCd3V1Tfd+Bm8W7OHDh+p6+vTppj4VIiIiyiXLly+XKlWqpHs/gzcL1qBBA6lUqZKMGjVKnJ2djXrMl19+KcOHD890P2TzEBROmTJF/QVAxr93tnR+efWcuXXcnBwnu4/NyuP4/zFn+H8yf96P3Dxufv+f/DKLjzFm//z4fMzsuAzeLFjhwoWlWLFi8swzzxj9mIIFC2YYzRv6AcrK/tYsq++dLZxfXj1nbh03J8fJ7mOz8jj+f8wZ/p/Mn/cjN4+b3/8nC2bxMVnZ35Sfj1ywYOFatWqVp/uT5bx3pji/vHrO3DpuTo6T3cdm5XHm/jNl7sz9/cvv8zP3/485PVZ2HtvKSj8j7XQ6nc7UJ0Hm5+rVqzJgwIBMx92JiP8fiWzJVTP4fGTmjQzy9vaWPn36qGsiMi3+fyQyH95m8PnIzBsRERGRBWHmjYiIiMiCMHgjIiIisiAM3ihb4uLiZObMmfLaa69JmzZtZPDgwXLhwgW+m0QmMmfOHOncubP6/9i7d285duwYvxdEJobPxaZNm8qaNWty9bic80bZEh0dLRs2bJC2bduKj4+P/PLLLzJ//ny1rUCBAnxXifIZCoeWLFlSFey+fPmyjB49WtavXy+enp78XhCZQFJSkgwdOlRQ1OP5559Xf1TlFmbeKFvc3NzUapvixYuLvb29tGzZUhwdHeXu3bt8R4lMAAVDtU4rdnZ2Eh8fLwEBAfxeEJnI9u3bpVq1annShYEdFmxEVFSU+iv80qVL6q/y8PBwmTRpksqcGRoSXbFihezbt0/tV7FiRenfv7/Ur18/3eMjaMO+pUuXzuNXQmT58ur/49y5c2XXrl3qMY0aNZIKFSrk0ysislxRefD/MTQ0VH744QdZvHixarmV25h5sxH4QVq9erUaWkE/1IzMmDFDNm7cKC+99JKMGDFCZdbGjx8v586dM7h/bGys6vPWs2dP1VqEiEzz/xFDpXv37pV58+apDxNk4Igo//8/ooBvt27dpFChQpIXGLzZCBQT3LJli/pLYMiQIenuh788Dh48KAMHDlRj9R07dlRz2UqUKKH+gkgtISFBPvjgA5VxwzAqEZnu/yM4ODhI3bp15fTp03LixAl+O4jy+f/jtWvX5MqVK9KhQwfJKwzebATmwhhTDfrw4cPqlz9+KDUuLi7Svn17uXjxovj6+qaYjImMG/66nzx5Mv/KJzLh/8fUEhMT5f79+/yeEOXz/8ezZ8+qqURdu3ZVK8B//vlnWbduncra5RbOeaMUrl+/LmXKlBF3d/cU2zHpEm7cuKEWKcBnn30mgYGB6hqLFYjINP8fIyIiVJatcePG6oPo6NGjcubMGZUhIKL8/f+I4A6L+DRffPGFWgmOqUW5hZ+4lAKCMUN/gWjbtNVrDx8+lB07dqgPiuR/hcyePVtq1arFd5UoH/8/IvuN/4+Y64ayBJjGMHXqVHnqqaf4fSDK5/+Prq6u6pI8O4cKDbk5/43BG6VZfODk5JTmXdFKEOB+wBj/kSNH+O4RmcH/R2QCFixYwO8FkRn8f0wN04pyG+e8UQr4CwH1oQwtj9buJ6L8wf+PRObDxYw+Hxm8UZr0L1LDqWnbihYtyneMKJ/w/yOR+fA2o89HBm+UAmrc3Lt3TyIjI9MskdbuJ6L8wf+PROajkhl9PjJ4oxSaNWumSgxs27YtRUoYVdurV6+uX2lKRHmP/x+JzEczM/p85IIFG7Jp0yZVUkBL8R47dkz8/PzU16hHg+4I+AFs3ry5LFu2TEJCQtSqtT179qjVpRMmTDDxKyCyHvz/SGQ+NlnY56OdDuvKySa8/vrr6ofMkA0bNqg6NNqKGa13G36Y0R8RvdsaNGiQz2dMZL34/5HIfLxuYZ+PDN6IiIiILAjnvBERERFZEAZvRERERBaEwRsRERGRBWHwRkRERGRBGLwRERERWRAGb0REREQWhMEbERERkQVh8EZERERkQRi8EREREVkQBm9EREREFoTBGxEREZEFcTT1CRARWYJ///1XunfvnuE+JUqUkI0bN+bbORGRbWLwRkSUBaVLl5aXXnrJ4H0FCxbke0lEeY7BGxFRFoO3d955h+8ZEZkM57wREeWBF198UUaMGCFBQUHy6aefyiuvvCKtWrWSwYMHy5kzZww+JioqSlauXClvv/222rddu3YyZswYOXfuXJp9cWw8R2xsrCxfvlx69OghzZs3V4/XHD58WAYMGKCO1alTJ5k9e7aEh4fL66+/ri6aTz75RB3r0qVLBs9rxYoV6v4DBw7kyntDRDnD4I2IKI9ERETIsGHD5NatW9K6dWsVAF29elXGjh0rf//9d4p9w8LCZMiQIbJ69WopVKiQCraw/7Vr12TkyJFy9OhRg88xdepU2bNnj9SuXVtee+01KVmypNq+c+dOdd+9e/fk5ZdfljZt2sjFixdl9OjRkpCQkOIYHTt21D8mtcTERNm1a5d4enqq8yEi0+OwKRFRFty/fz9Fdiu5GjVqSMOGDfW3b9y4IZ07d5b33ntP7O0f/a1cp04dlQHbvHmzCuI08+fPl3/++UfGjx8vHTp00G8PDg5W2bM5c+ZIgwYNxMXFJcVzBgYGyqpVq8TDw0O/Ddm1L774Qtzc3GTZsmVStmxZtR3HwXMigMTiCk2tWrWkfPnycvDgQXn33XfV4zSnTp0Sf39/6datmzg7O/NnhcgMMPNGRJTF4A3ZMUOX3377LcW+CIIwTKoFboAMmIODg1y5ckW/LSQkRH755RcV2CUP3KBIkSLyxhtvqH1Onz6d5nz69u2bInCDX3/9VaKjo9Wwqxa4gaOjo/Tv39/g60L2DcO2COCS27Fjh7rGsC8RmQdm3oiIsgDZr88++8yofcuUKSMFChRI+UvX0VG8vLzUkKoGgRyGJ+Pj4w1m9TD0Cbdv35bnn38+xX3VqlVLs//NmzfVdc2aNdPcV716dRU8poah1aVLl6pgTQsgMV/v+PHj8vTTT6vMHBGZBwZvRER5xN3d3eB2BE9JSUkp5rvB+fPn1SU9MTExabYhEEwtMjJSn7VLDVlAzF9LDfPssOAB8+cwH69ChQqye/duFVQy60ZkXhi8ERGZSZCHIsBY4JAVdnZ26R4P8+VSQ9AYGhoqPj4+ae7DIgkEb9u3b1eLJLCAAcdCUEdE5oNz3oiITKxq1aoqCMNq0NxQsWJFdW0oi3f58mWVTUtvwQUeu3//frVQAcO1KEjs6uqaK+dFRLmDwRsRkYl5e3ur7NaFCxfk+++/F51Ol2Yf1GAzNGxqyAsvvKAWSyBzhgUWGpQIQc22jGDhAoZxZ86cqW6nXkBBRKbHYVMiolwqFQI9e/ZMU87DGKi/dvfuXVm8eLHs3btXZcHQbgtlOrCgAVmwLVu2GJUFw/w1lPxAeRGUB2nRooUa/jx58qQq91G0aFGDw62AenRLliyRgIAAqVKlilSuXDnLr4WI8haDNyKibJQKSQ/qoWUneEO5j0WLFqn6bz///LPqZoD5aViQUKlSJendu7fBhQbpwSIDBHHffvutmseG4K1x48aqdAnOEW2+DMF+TZo0kX379jHrRmSm7HSG8vNERGSVkMF788031TDtRx99ZHAfBIoPHz5UgWR6K2aJyHQ4542IyAqhy0JcXFyKbeiDunDhQvU1smuGYGgVnR7QD5WBG5F54rApEZEVOnv2rMyaNUvq168vxYoVU+VB/vzzT5VRQycHzINL7qeffhI/Pz9VpBfz4jB3j4jME4M3IiIrhI4I9erVU+VC0FoLMM+tX79+0qNHjxQtu2DdunVqcQTaaU2cOFFKlSplojMnosxwzhsRERGRBeGcNyIiIiILwuCNiIiIyIIweCMiykWovtS/f39VdFczYsQIefHFF83mfUYB30GDBpn6NIgomxi8ERHlIhTEvXbtmloYkJc+/vhjFRCimG9GIiMjVX/Sdu3aqVIh0LdvX9Xj9ODBg3l6jkSUNxi8ERHlEnREWLVqldSsWVO1t8pL7du3V9e7du3KcD8EdwjaWrZsqe/8ULduXdX2Cm2+WKedyPIweCMiyiW//fabqqPWpk2bPH9PUautZMmSqnabr69vuvtpwZ0W7CXvYYpeqng8EVkWBm9ERLkEgRIavjdt2tSo/TFsiYwYhjHRCD55gV3UWkN/Utz/xhtvyPLlyyUmJka/D54HQ6HI9qWXfUOnBAyPVqxYUapWrZrivmbNmqnr3bt3Z/PVEpGpMHgjIsoFGH48c+aMKnKLhvCZ2bRpk5q3Vr16dfnyyy+laNGi+k4HI0eOVMV1GzVqJF27dlUdEtBgHosg4uPj9cdAhg/FdhGAGRr+TC/rBjgmLqdPn87hKyei/MYOC0REueD27dsSFhYmDRs2zHRfZNEQjKG/6AcffKCfi3br1i1ZsGCBypTNmzdPPD099Y9Zu3atLFu2TAV96JAAxYsXV+2vMFyL4U/MZdMkJCTI/v37VasrDJEaUqVKFTl69Kg8ePCAHRWILAgzb0REuQB9QaFIkSLp7pOYmKj6jSJww5AoMm9a4AZbt25V+yDzljxwgzfffFMKFy6cZoWollXbuXNniu0nTpyQoKAgady4sXh4eBg8Hy8vL3WNtlhEZDmYeSMiygXIukFGQ6ZTp06VX3/9VXr16iUDBgxIc/+lS5fU9alTpwwOZzo6OsqdO3dSbHvhhRdUUIcMWkREhBQsWDBFMGdoyFSjBXVoWk9EloPBGxFRLtAyaHFxcenu89dff6lhTMxlyygARGbOWAjoMCy6ceNGVRakc+fOEhgYqIZSMayK5vTp0eq+ubq6Gv18RGR6DN6IiHIBsl/JAzBDMI8Niw7GjRsnc+bMkWeeeSbF/e7u7vpCvwUKFDD6uTt06KCCN2TbELzt27dPDb+2bdtWLWhIj3auqYdoici8cc4bEVEuKF++vAqUUg9rJofCuPPnzxcnJycVwGFFaXJYeQoXL17M8nOjKPDVq1fl5s2b+pIlKCWSEdR5Q+auXLlyWXo+IjItBm9ERLkAc92wShQBFGqvpadSpUr6AG7s2LFy7tw5/X3Imjk4OKgVp4YK74aHh6vWW4Zoc9vmzp2rVr5i5WmJEiXSPQ+UHLl+/bpaccphUyLLwuCNiCiXoPRHVFRUppkzBHkI4DBPDhk4zIWDChUqqGHV+/fvS8+ePWXKlCmyePFi+fzzz2XMmDHSpUsX2bZtm8FjtmjRQtzc3PTZvIwWKgCCRszPwzkTkWVh8EZElEsw9wyZM9RXy4wWwCHrNX78eNVVAVBCZNGiRSqowurTH374QQ4dOqRWhHbr1k1dDMEcuebNm+tXkWYWlGFeHLJ/mQ2tEpH5sdOxKzERUa6ZPn26qrGGoCsriw7yE4ZfEQSiRRbacBGRZWHmjYgoF/Xv31+V4EAnBHO1YcMGNS+vX79+pj4VIsoGBm9ERLkIiwQmT55stlk3bVgV5+jj42PqUyGibOCwKREREZEFYeaNiIiIyIIweCMiIiKyIAzeiIiIiCwIgzciIiIiC8LgjYiIiMiCMHgjIiIisiAM3oiIiIgsCIM3IiIiIgvC4I2IiIhILMf/AX1iMDbymWciAAAAAElFTkSuQmCC", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display_spectrum_model_counts(like, show_background = True);" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "3a86e2cd-1dfc-48e7-a748-7ed72a7d50b8", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "77c1f1a91f9c4a59ad367c3e7fc57003", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "processing MLE analyses: 0%| | 0/1 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_spectra(like.results, flux_unit=\"keV/(s cm2)\", ene_min=100, ene_max=10000);" + ] + }, + { + "cell_type": "markdown", + "id": "56940123", + "metadata": {}, + "source": [ + "# Comparison with injected spectrum in MEGAlib" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "04b04e27-a119-41e4-be61-5edbf924293c", + "metadata": {}, + "outputs": [], + "source": [ + "alpha_inj = -0.360\n", + "beta_inj = -11.921\n", + "E0_inj = 288.016 * u.keV\n", + "xp_inj = E0_inj * (alpha_inj + 2)\n", + "piv_inj = 1. * u.keV\n", + "K_inj = 0.283 / u.cm / u.cm / u.s / u.keV\n", + "\n", + "spectrum_inj = Band()\n", + "\n", + "spectrum_inj.beta.min_value = -15.0\n", + "\n", + "spectrum_inj.alpha.value = alpha_inj\n", + "spectrum_inj.beta.value = beta_inj\n", + "spectrum_inj.xp.value = xp_inj.value\n", + "spectrum_inj.K.value = K_inj.value\n", + "spectrum_inj.piv.value = piv_inj.value\n", + "\n", + "spectrum_inj.xp.unit = xp_inj.unit\n", + "spectrum_inj.K.unit = K_inj.unit\n", + "spectrum_inj.piv.unit = piv_inj.unit" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "b844bb8f-a247-4de2-908f-22f012bf9a27", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Best fit values:\n",
+       "\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;4;38;5;49mBest fit values:\u001b[0m\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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resultunit
parameter
source.spectrum.main.Band.K(2.97 -0.20 +0.22) x 10^-21 / (keV s cm2)
source.spectrum.main.Band.alpha(-3.1 +/- 0.5) x 10^-1
source.spectrum.main.Band.xp(4.77 +/- 0.05) x 10^2keV
source.spectrum.main.Band.beta(-1.2 +/- 2.8) x 10
\n", + "
" + ], + "text/plain": [ + " result unit\n", + "parameter \n", + "source.spectrum.main.Band.K (2.97 -0.20 +0.22) x 10^-2 1 / (keV s cm2)\n", + "source.spectrum.main.Band.alpha (-3.1 +/- 0.5) x 10^-1 \n", + "source.spectrum.main.Band.xp (4.77 +/- 0.05) x 10^2 keV\n", + "source.spectrum.main.Band.beta (-1.2 +/- 2.8) x 10 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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+       "Correlation matrix:\n",
+       "\n",
+       "
\n" + ], + "text/plain": [ + "\n", + "\u001b[1;4;38;5;49mCorrelation matrix:\u001b[0m\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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1.000.97-0.390.01
0.971.00-0.190.01
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0.010.01-0.021.00
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\n",
+       "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)
GRB_09020662054.928514
total54.928514
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" + ], + "text/plain": [ + " -log(likelihood)\n", + "GRB_090206620 54.928514\n", + "total 54.928514" + ] + }, + "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
AIC125.857028
BIC119.067369
\n", + "
" + ], + "text/plain": [ + " statistical measures\n", + "AIC 125.857028\n", + "BIC 119.067369" + ] + }, + "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: 0.02967818127136365\n", + " * desc: Differential flux at the pivot energy\n", + " * min_value: 1.0e-50\n", + " * max_value: null\n", + " * unit: keV-1 s-1 cm-2\n", + " * is_normalization: true\n", + " * alpha:\n", + " * value: -0.3056296844848436\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: 476.5940878272197\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: -11.83143486647813\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": "code", + "execution_count": 28, + "id": "e6576cca-e184-4c74-85fc-34e54abe8e5f", + "metadata": {}, + "outputs": [], + "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 = event.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 = spec._expected_counts['source']" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "5760b5cb-d82c-44f7-8bcb-62981177dce2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "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", + "# ax.set_ylim(100,2000)\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": "code", + "execution_count": null, + "id": "0fd08179", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cosipy_env_python3.11 (3.11.14)", + "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": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/spectral_fits/continuum_fit/time_series_builder/TSB_GRB_inputs.yaml b/docs/tutorials/spectral_fits/continuum_fit/time_series_builder/TSB_GRB_inputs.yaml new file mode 100644 index 000000000..4dee8cfc9 --- /dev/null +++ b/docs/tutorials/spectral_fits/continuum_fit/time_series_builder/TSB_GRB_inputs.yaml @@ -0,0 +1,16 @@ +#----------# +# Data I/O: + +# data files available on the COSI Sharepoint: https://drive.google.com/drive/folders/1UdLfuLp9Fyk4dNussn1wt7WEOsTWrlQ6 +data_file: "albedo_photons_3months_unbinned_data.fits.gz" #"GalacticScan.inc1.id1.crab2hr.extracted.tra.gz" # full path +ori_file: "../wasabi_files/20280301_3_month.ori" # full path +unbinned_output: 'fits' # 'fits' or 'hdf5' +time_bins: 1 # 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. ] #[1500., 1550., 1600., 1650., 1700., 1750., 1800., 1850., 1900., 1950., 2000.] #[100., 200., 500., 1000., 2000., 5000.] # Takes list. Needs to match response. +phi_pix_size: 3 # binning of Compton scattering anlge [deg] +nside: 16 # healpix binning of psi chi local +scheme: 'ring' # healpix binning of psi chi local +tmin: 1842597210.0 # Min time cut in seconds. +tmax: 1842597650.0 # Max time cut in seconds. +#----------#