{ "cells": [ { "cell_type": "markdown", "id": "74a86fb5-4e54-4e3f-b349-3e60fbdd0279", "metadata": { "tags": [] }, "source": [ "# FLUX Light-Curve example (GRB)" ] }, { "cell_type": "markdown", "id": "e7df3443-3ce1-43f3-90b5-1bceb7bc9af0", "metadata": {}, "source": [ "**We provide a companion notebook (`speclc_grbdc3_prep.ipynb`) that downloads the files and prepares them for analysis. The notebook creates in the working directory the following files:**\n", "- orientation file (DC3_final_530km_3_month_with_slew_1sbins_GalacticEarth_SAA.fits) \n", "- binned data (tsel_binned_data_local_grbdc3.hdf5, tsel_binned_data_local_galbk.hdf5,tsel_binned_data_galbk_grbdc3.hdf5)\n", "- corresponding yaml file with binning info (bin_grbdc3.yaml, bin_galbk.yaml, bin_galbk_grbdc3.yaml) \n", "- detector response \n", "(ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5) \n", "\n", "**The binned data are simulations of GRB 081207680 and Galactic photon background produced for Data Challenge 3. In the data preparation, the background file is cut in time 100s before and after the GRB time window (t_start=1836496300.0, t_stop=1836496388.1, duration=88s) to ensure enough statistics.**\n", "\n", "This notebook slices a COSI dataset into time bins, ensuring that a minimum signal-to-noise of 10 is reached in each bin. Perform a fit with 3ML in each of them. It finally examines the time series of flux and fitted spectral parameters. It saves: a txt file of the raw lightcurves (lc.dat); a plot at each iteration of the fit (fit_i.pdf); a txt file (spec_lc.dat) including the mid-point of each time bin with errors, the total counts, the fluxes with asymmetric errors, the fitted parameters with symmetric errors; two plots (raw_flux_counts_lc.pdf, specpars_bk_lc.pdf) of the final time series." ] }, { "cell_type": "code", "execution_count": 1, "id": "ce42ab82-3bbd-4729-8f84-a4e32eb3bb24", "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/sciaccaluga/Softwares/anaconda3/envs/cosi2/lib/python3.10/site-packages/astromodels/utils/file_utils.py:8: 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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       "                  available                                                                                        \n",
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       "                  require the C/C++ interface (currently HAWC)                                                     \n",
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       "                  software installed and configured?                                                               \n",
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       "                  software installed and configured?                                                               \n",
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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=700238;file:///home/sciaccaluga/Softwares/anaconda3/envs/cosi2/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=993459;file:///home/sciaccaluga/Softwares/anaconda3/envs/cosi2/lib/python3.10/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" }, { "data": { "text/plain": [ "'/home/sciaccaluga'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cosipy import BinnedData\n", "from cosipy.spacecraftfile import SpacecraftHistory\n", "from cosipy.response.FullDetectorResponse import FullDetectorResponse\n", "from cosipy.util import fetch_wasabi_file\n", "from cosipy.statistics import PoissonLikelihood\n", "from cosipy.background_estimation import FreeNormBinnedBackground\n", "from cosipy.interfaces import ThreeMLPluginInterface\n", "from cosipy.response import BinnedThreeMLModelFolding, BinnedInstrumentResponse, BinnedThreeMLPointSourceResponse\n", "from cosipy.data_io import EmCDSBinnedData\n", "\n", "from scoords import SpacecraftFrame\n", "\n", "from astropy.time import Time, TimeDelta\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 *\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", "from cosipy.pipeline.src.plotting import *\n", "\n", "import os\n", "import subprocess" ] }, { "cell_type": "markdown", "id": "bb816c15", "metadata": {}, "source": [ "## A function to create time slices with a minimum signal-to-noise.\n", "In this ways the binning in time is coarser in the weak tails of the light-curve and finer around the peak. The function returns the edges of the time slices." ] }, { "cell_type": "code", "execution_count": 2, "id": "ffa74cdc-ac8f-4d44-b1d6-78e0380a54a5", "metadata": {}, "outputs": [], "source": [ "def make_minsn_tslices(yaml_path, hdf5_path, min_sn):\n", " # \n", " data = BinnedData(yaml_path)\n", " data.load_binned_data_from_hdf5(hdf5_path)\n", " #\n", " tstart=np.min(data.binned_data.axes['Time'].edges.value)\n", " tstop=np.max(data.binned_data.axes['Time'].edges.value)\n", " max_slices=data.binned_data.axes['Time'].nbins\n", " step=(tstop-tstart)/max_slices\n", " tmins = np.array([], dtype=float)\n", " tmaxs = np.array([], dtype=float)\n", " #\n", " tmax=tstart\n", " for i in range (max_slices):\n", " #\n", " tmin=tmax\n", " tmax_i=tstart+(i+1)*step\n", " #\n", " sou_min = np.where(data.binned_data.axes['Time'].edges.value >= tmin)[0][0]\n", " sou_max = np.where(data.binned_data.axes['Time'].edges.value <= tmax_i)[0][-1]\n", " data_sliced=data.binned_data.slice[{'Time':slice(sou_min,sou_max)}].project('Em')\n", " #\n", " #\n", " signal=np.sum(data_sliced.to_dense(copy=False).contents)\n", " noise=np.sqrt(signal)\n", " if signal == 0:\n", " raise ValueError(\"Process halted: Bin with 0 counts. Consider a larger min_sn\")\n", " #\n", " #\n", " sn=signal/noise\n", " if (sn >= min_sn and tmax_i=tstop):\n", " tmaxs[-1] = tmax_i\n", " return(tmins, tmaxs)\n", " " ] }, { "cell_type": "markdown", "id": "8d1c0168-9823-4eb7-930e-5dc61d6448ca", "metadata": {}, "source": [ "## Download and read in binned data\n", "Run `speclc_grbdc3_prep.ipynb` to obtain the data to run this tutorial, if you haven't yet. Products will be saved in the current directory." ] }, { "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 if they have already been downloaded, or the directory to download the files into. Define a directory where you want to save the outputs. Default is current directory." ] }, { "cell_type": "code", "execution_count": 4, "id": "cdd53b2a-5176-42cf-bb2c-feb3387fc0a4", "metadata": {}, "outputs": [], "source": [ "indir=str(\".\")\n", "data_path = Path(indir)\n", "odir= str(\".\")" ] }, { "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": 5, "id": "3b5faaa1-1874-4d43-a6ae-7e1b0aaabb26", "metadata": {}, "outputs": [], "source": [ "grb = BinnedData(data_path / \"bin_grbdc3.yaml\")\n", "grb_bkg = BinnedData(data_path / \"bin_galbk_grbdc3.yaml\")\n", "bkg = BinnedData(data_path / \"bin_galbk.yaml\")" ] }, { "cell_type": "markdown", "id": "cf8b5ab1-7452-493e-b516-73fa72e455e5", "metadata": {}, "source": [ "Load binned .hdf5 files" ] }, { "cell_type": "code", "execution_count": 6, "id": "620159d2-f01a-453e-9e4c-075c99740086", "metadata": {}, "outputs": [], "source": [ "grb.load_binned_data_from_hdf5(binned_data=data_path / \"tsel_binned_data_local_grbdc3.hdf5\")\n", "grb_bkg.load_binned_data_from_hdf5(binned_data=data_path / \"tsel_binned_data_local_galbk_grbdc3.hdf5\")\n", "bkg.load_binned_data_from_hdf5(binned_data=data_path / \"tsel_binned_data_local_galbk.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 = FullDetectorResponse.open(data_path / \"ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5\") \n", "ori = SpacecraftHistory.open(data_path/\"DC3_final_530km_3_month_with_slew_1sbins_GalacticEarth_SAA.fits\")" ] }, { "cell_type": "markdown", "id": "0ce73e23", "metadata": {}, "source": [ "Define the model to be fitted and initialize the parameters of the fits to something reasonable. In this example, for better results, we keep the indices and the pivot energy frozen. Hence, the free parameters are the normalization and the background parameter." ] }, { "cell_type": "code", "execution_count": 8, "id": "926aaf1c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n" ], "text/plain": [ " * description: Band model from Band et al., 1993, parametrized with the peak energy\n", " * formula: $K \\begin{cases} \\left(\\frac{x}{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}{piv}\\right)^{\\beta}\n", " * \\exp (\\beta-\\alpha)\\left[\\frac{(\\alpha-\\beta) x_{p}}{piv(2+\\alpha)}\\right]^{\\alpha-\\beta}\n", " * &x>(\\alpha-\\beta) \\frac{x_{p}}{(\\alpha+2)} \\end{cases} $\n", " * parameters:\n", " * K:\n", " * value: 0.1\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", " * delta: 1.0e-05\n", " * free: true\n", " * alpha:\n", " * value: -0.58\n", " * desc: low-energy photon index\n", " * min_value: -1.5\n", " * max_value: 3.0\n", " * unit: ''\n", " * is_normalization: false\n", " * delta: 0.1\n", " * free: false\n", " * xp:\n", " * value: 298.09999999999997\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", " * delta: 50.0\n", " * free: false\n", " * beta:\n", " * value: -2.09\n", " * desc: high-energy photon index\n", " * min_value: -15.0\n", " * max_value: -1.6\n", " * unit: ''\n", " * is_normalization: false\n", " * delta: 0.2\n", " * free: 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", " * delta: 10.0\n", " * free: false" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Model to be fitted\n", "# # Setting parameters to something reasonable helps the fitting to converge\\n\",\n", "\n", "beta = -2.09\n", "\n", "l = 177.42\n", "b = -9.41\n", "\n", "alpha = -0.58 \n", "xp = 298.1 * u.keV\n", "piv = 500. * u.keV\n", "K = 0.1 / u.cm / u.cm / u.s / u.keV\n", "\n", "spectrum = Band()\n", "\n", "spectrum.beta.min_value = -15.0\n", "spectrum.alpha.value = alpha\n", "spectrum.alpha.free=False\n", "spectrum.beta.value = beta\n", "spectrum.beta.free=False\n", "spectrum.xp.value = xp.value\n", "spectrum.xp.free=False\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", "spectrum" ] }, { "cell_type": "markdown", "id": "692e1656", "metadata": {}, "source": [ "Move to the product directory." ] }, { "cell_type": "code", "execution_count": 9, "id": "66517a53", "metadata": {}, "outputs": [], "source": [ "os.chdir(odir)" ] }, { "cell_type": "markdown", "id": "393f276e", "metadata": {}, "source": [ "Find time window of the GRB data and make a raw lightcurve to be plotted as a comparison." ] }, { "cell_type": "code", "execution_count": 10, "id": "6c5c6c84", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "(, )" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dE6LgsEATq2q5zhxOVVJcsooLSxzaGReg5gwLImFhYZo8ebI2bNiggwcP2tsnTJigqVOnVrpcfHy8YmJijCoLaFLcPd0kWWWzSfmXrNdMsSpxSzJ/8EySFJes7PN5FUxhXICaMvT6SGBgoPr166chQ4bIx8dHn332mTZs2KAOHTooKiqqwmUiIyMVHh5uf2yxWDR37lwjywQardAxIUrc4njkXZB1NZj89GgcDaf8tXdxkbx8PSQxLkBtGRZE9u7dqwULFmjjxo266aabJElDhgyRzWbTihUrNGzYMLVr185pOX9/f/n7+xtVFtCkBIcFOh1dx0bv+8nZEZjFy9dD45dESGJcgNoy7GbVbdu2KSQkxB5CyoWHh6uwsFDJyclGdQ0AAJoIw4JIVlaWysrKnNpLSq6etiwtLTWqawAA0EQYFkSCgoKUnJyslJQUh/a9e/fK1dVV3bt3N6prAADQRBh2j8jYsWP1+eefKzo6Wg8//LB8fHx06NAhff7553rggQe4DwQAABgXRPr376+lS5dqzZo12rZtm65cuaLAwEBNnTpV48aNM6pbAADQhBj68d3evXtrwYIFRnYBAACaMMPuEQEAAKgKQQQAAJiGIAIAAExDEAEAAKYhiAAAANMQRAAAgGkIIgAAwDQEEQAAYBqCCAAAMA1BBAAAmIYgAgAATEMQAQAApiGIAAAA0xBEAACAadzMLgDmO3M4VUlxySouLKlyXndPN4WOCVFwWGCt1lnd5RuT6r4+BVnWBqoITYWR20FBllWx0ftqtM7mtF2i+SCIQElxyco+n1fNua1K3JJc5Q6r8nVWb/nGpGavz9WdOiAZsx1cfX9ZZbNJ+ZeuDb9Vr7M5bZdoPthjwn505OIiefl6VDpfQdbVnV91zpxUtM6aLN+YVPf1kf5zZAlIxmwHoWNClLjF8axGddfZnLZLNB8EEdh5+Xpo/JKISqfHRu/7yRFYzdZZm+Ubk6peH6Ay9bkdBIcFOp25qOk6m9N2iaaPm1UBAIBpCCIAAMA0BBEAAGAagggAADANQQQAAJiGIAIAAExDEAEAAKYhiAAAANMQRAAAgGkIIgAAwDQEEQAAYBqCCAAAMA1BBAAAmIYgAgAATEMQAQAApiGIAAAA07gZ3cHp06e1Zs0aHT9+XEVFRerUqZMefPBBjR492uiuAQBAI2doEDly5Ihmz56tkJAQPf744/Ly8tKPP/6o9PR0I7sFAABNhGFBJC8vT3/84x/1q1/9Sm+++aZcXbkKBAAAHBmWDj7++GNdunRJU6dOlaurqwoKClRWVmZUdwAAoAky7IxIYmKi2rRpo4yMDL300ktKSUmRl5eXhg8frujoaHl4eFS4XEZGhjIzM+2PLRaLUSUCTs4cTlVSXLKKC0vsbQVZVkP6KsiyKjZ6X637qOvyjaUPXNWQ7z2gMTEsiJw7d06lpaWaM2eORo4cqaeeekrHjh3T1q1blZubq9dee63C5eLj4xUTE2NUWcB1JcUlK/t8XoXT3D3rZ3O5uh6rbDYp/5K1gmnGLt8QNaLmGuK9BzRGhr27CwoKVFhYqFGjRum5556TJA0ZMkTFxcWKj4/XpEmTFBQU5LRcZGSkwsPD7Y8tFovmzp1rVJmAg/KjURcXycv3P2ft3D3dFDompF76CB0TosQtjke+Nemjrss3RI2ouYZ47wGNkWFBpPzSy9133+3QPmzYMMXHx+vEiRMVBhF/f3/5+/sbVRZQLV6+Hhq/JMKQdQeHBSo4LNC05RtLH6iYke89oDEy7GZVPz8/SVKHDh0c2n19fSVJOTk5RnUNAACaCMOCSM+ePSXJ6TtDMjIyJEnt27c3qmsAANBEGBZEhg4dKknasWOHQ/uOHTvUqlUrDRgwwKiuAQBAE2HYPSI/+9nPNGLECO3cuVOlpaXq37+/jh07pv379+uxxx7jPhAAAGDsV7zPmjVLAQEB2rVrlz755BMFBAQoOjpajzzyiJHdAgCAJsLQIOLm5qYnn3xSTz75pJHdAACAJoofgAEAAKYhiAAAANMQRAAAgGkIIgAAwDQEEQAAYBqCCAAAMA1BBAAAmIYgAgAATEMQAQAApiGIAAAA0xBEAACAaQgiAADANAQRAABgGoIIAAAwjZvZBcAcBVlWxUbvs//bLGcOpyopLlnFhSUO7e6ebgodE6LgsEDDa2gsrwVQV9e+lyubXttlyzXktomWgSDSwrh7ukmyymaT8i9ZK5jWsJLikpV9Pq+CKVYlbkk2dGfX2F4LoLau916ufP7aLdsQ2yZaFva2LUzomBAlbqn8DERDK6/DxUXy8vWQdPXIzGaTU431rbG9FkBtVfZershP3981Wbahtk20LASRFiY4LLBRHsl4+Xpo/JIISVJs9L5qHpnVTWN9LYCaqst7uSbLNtS2iZaFm1UBAIBpCCIAAMA0BBEAAGAagggAADANQQQAAJiGIAIAAExDEAEAAKYhiAAAANMQRAAAgGkIIgAAwDQEEQAAYBqCCAAAMA1BBAAAmIYgAgAATEMQAQAApiGIAAAA0zRoEFm3bp0GDx6sxx9/vCG7BQAAjVSDBZGLFy9qw4YN8vLyaqguAQBAI+fWUB397W9/U+/evVVWVqbs7OyG6hYAADRiDXJG5NixYzpw4IBmzJjREN0BAIAmwvAzIqWlpVq0aJFGjhyp7t27Vzl/RkaGMjMz7Y8tFouR5aEWCrKsio3eV+U8aLyuHUPGyllV7/H6eM1+2gfjgJbK8CCyfft2XbhwQQsXLqzW/PHx8YqJiTG2KNSKu6ebJKtsNin/UvV2mleXQWNxvTFkrGr+Hq/Na1ZVH4wDWhpD3/HZ2dl69913NXHiRLVv375ay0RGRio8PNz+2GKxaO7cuQZViJoIHROixC3JKi4sqdb87p5uCh0TYnBVqInKxpCxuqom7/HavmbX64NxQEtkaBBZtWqV2rZtq6ioqGov4+/vL39/fwOrQm0FhwUqOCzQ7DJQB4zh9TXE68MYAI4MCyIpKSn64IMPNGPGDGVkZNjbi4qKVFJSotTUVLVp00Y+Pj5GlQAAABo5w4JIRkaGysrKtGjRIi1atMhp+qOPPqrRo0dr5syZRpUAAAAaOcOCSHBwsN566y2n9lWrVik/P18zZ85Up06djOoeAAA0AYYFkfbt22vQoEFO7Vu2bJGkCqcBAICWhR+9AwAApmnwD6wvXry4obsEAACNFGdEAACAaQgiAADANAQRAABgGoIIAAAwDUEEAACYhiACAABMQxABAACmIYgAAADTEEQAAIBpCCIAAMA0BBEAAGAagggAADANQQQAAJiGIAIAAEzjZnYBZjpzOFVJcckqLixxaHf3dFPomBAFhwXWaD4za2xKCrKsio3eZ/93dTTH1wHNU23e300Z2ybqqkUHkaS4ZGWfz6tgilWJW5LtG1B15zOzxqbA3dNNklU2m5R/yVrBtMo1p9cBzVNd3t9NGdsm6qr5bh3VUJ7gXVwkL18PSVePYGw2OaT76s5nZo1NQeiYECVuqfzI6Xqa0+uA5qku7++mjG0TddWig0g5L18PjV8SIUmKjd7ndDRT0/nMrLExCw4LrPPRUXN4HdA81cf7uylj20RtcbMqAAAwDUEEAACYhiACAABMQxABAACmIYgAAADTEEQAAIBpCCIAAMA0BBEAAGAagggAADANQQQAAJiGIAIAAExDEAEAAKYhiAAAANMQRAAAgGkIIgAAwDQEEQAAYBo3o1Z86tQp7d69W1988YXS0tLk4+OjPn36aMqUKQoKCjKqWwAA0IQYdkYkNjZWBw4c0G233aaZM2fqwQcf1JdffqkpU6bozJkzRnULAACaEMPOiDzyyCN69dVX5e7ubm+LiIjQk08+qY0bN+qVV14xqmsAANBEGBZE+vbt69QWFBSkrl27ymKxGNUtAABoQgwLIhWx2WzKyspS165dK50nIyNDmZmZ9sdmhZaCLKtio/fZ/220M4dTlRSXrOLCEqc6WqrqjkFDjxUAoP40aBD517/+pfT0dE2aNKnSeeLj4xUTE9NwRf2Eu6ebJKtsNin/krWCacZIiktW9vm8KupqGao7BmaNFQCg/jTY3tpisWjhwoXq06eP7rvvvkrni4yMVHh4uMNyc+fObYgSJUmhY0KUuMX5zIS7p5tCx4QY1m95fy4ukpevR4P23dhUdwzMGisAQP1pkCCSmZmpF198UW3atNGbb76pVq1aVTqvv7+//P39G6KsCgWHBSo4LNC0/r18PTR+SYRp/TcG1R0Ds8cKAFB3hgeR3NxcvfDCC8rNzdWSJUtMDRkAAKBxMTSIWK1W/f73v1dKSoreeeed696kCgAAWh7Dgkhpaan+8Ic/6MSJE/rjH/+oW2+91aiuAABAE2VYEFm6dKkOHjyogQMHKicnR3v27HGYPnz4cKO6BgAATYRhQeS7776TJB06dEiHDh1ymk4QAQAAhgWRxYsXG7VqAADQTBj2o3cAAABVIYgAAADTEEQAAIBpCCIAAMA0BBEAAGAagggAADANQQQAAJiGIAIAAExDEAEAAKYhiAAAANMQRAAAgGkIIgAAwDQEEQAAYBqCCAAAMI2b2QU0dQVZVsVG77vuPO6ebgodE6LgsEDD6jhzOFVJcckqLixxqA0A6tu1+73r7Weqs3+srobYj8IcBJFacvd0k2SVzSblX6rqD75ViVuSDd2AkuKSlX0+r8JpV2sFgLq53n7v2v1MzfaP1WX8fhTm4C9ULYWOCVHiFsczEBUpyLq6MVY1X12Vr9/FRfLy9bC3lx9FAEBdVbbf++l+prr7x+pqqP0ozEEQqaXgsMBqJfPY6H31eERQNS9fD41fEtFg/QFoOaq736vufNXV0PtRNCxuVgUAAKYhiAAAANMQRAAAgGkIIgAAwDQEEQAAYBqCCAAAMA1BBAAAmIYgAgAATEMQAQAApiGIAAAA0xBEAACAaQgiAADANAQRAABgGoIIAAAwDUEEAACYhiACAABM42bkyouKirR69Wrt2bNHOTk56t69u6ZMmaLbb7/dyG4BAEATYegZkXnz5mnz5s265557NHPmTLm6uuqFF17QV199ZWS3AACgiTAsiJw8eVJ79+7VU089pWnTpikyMlJ/+ctf1LFjRy1btsyobgEAQBNiWBA5cOCAWrVqpcjISHubh4eHRo4cqRMnTujChQtGdQ0AAJoIw+4RSU5OVufOndWmTRuH9l69ekmSvvvuOwUEBDgtl5GRoczMTPtji8ViVIkNqiDLqtjofdedXpd11mZ5AGhKqtqPovZuaOehX78VbkrfhgWRzMxM+fn5ObWXt2VkZFS4XHx8vGJiYowqq8G5e7pJsspmk/IvVR0Wrs5f+3VWZ3kAaEpquh9F02LYXy2r1Sp3d3en9tatW9unVyQyMlLh4f9JZRaLRXPnzjWkxhvaeTj83wihY0KUuCVZxYUlVc7r7umm0DEhtV5ndZcHgKakJvtR1I6RfwerYlgQ8fDwUHFxsVN7UVGRfXpF/P395e/vb1RZDhriNFRwWKCCwwIb/ToBoLFin9e8GXazqp+fn8O9HuXK2xoqbAAAgMbLsCDSo0cPnTt3Tnl5eQ7tJ0+etE8HAAAtm2FB5K677lJpaani4+PtbUVFRdq5c6d69+5d4SdmAABAy2LYPSK9e/fW0KFDtXLlSl2+fFk333yzdu/erbS0NL344otGdQsAAJoQQz/rOWfOHAUEBOijjz5Sbm6uunXrpvnz56t///5GdgsAAJoIQ4OIh4eHpk2bpmnTphnZDQAAaKIM/dE7AACA6yGIAAAA0xBEAACAaQgiAADANAQRAABgGoIIAAAwDUEEAACYxtDvEakPVqtVkmSxWEyuBAAA1FSXLl3k6elZ6fRGH0TS0tIkSXPnzjW5EgAAUFN///vf1bNnz0qnu9hsNlsD1lNjly9f1pEjRxQYGKjWrVvX67otFovmzp2rl19+WV26dKnXdaP2GJfGiXFpvBibxolxuarJnxFp3769hg8fbmgfXbp0uW5agzkYl8aJcWm8GJvGiXG5Pm5WBQAApiGIAAAA07ToIOLn56cnnnhCfn5+ZpeCazAujRPj0ngxNo0T41I9jf5mVQAA0Hy16DMiAADAXAQRAABgGoIIAAAwDUEEAACYptF/oVl9Kyoq0urVq7Vnzx7l5OSoe/fumjJlim6//XazS2sxTp06pd27d+uLL75QWlqafHx81KdPH02ZMkVBQUEO8549e1ZLlizR8ePH5ebmpjvuuEPR0dFq3769OcW3IOvWrdOqVasUHBystWvXOkw7fvy4li9frm+//VZt2rTR0KFDNXXqVN1www0mVdv8nT59WmvWrNHx48dVVFSkTp066cEHH9To0aPt8zAuDS8lJUWrV6/W8ePHdeXKFQUEBGjYsGEaO3asw7eJMjaVa3Gfmnn99df173//W2PGjFHnzp21a9cuffPNN1q0aJF+8YtfmF1ei/DKK6/o+PHjGjp0qLp3767MzExt27ZNBQUFWrZsmbp16yZJunjxoiZPnixvb29FRUWpoKBAmzZtUkBAgFasWCF3d3eTn0nzdfHiRT322GNycXFRx44dHYJIcnKynn32WXXp0kWRkZG6ePGi3n//fQ0YMEALFiwwserm68iRI5o9e7ZCQkIUEREhLy8v/fjjj7LZbHr22WclMS5muHDhgp588kl5e3srMjJSPj4+OnHihHbt2qXw8HDNmzdPEmNTJVsLcuLECdugQYNssbGx9rbCwkLb2LFjbc8884yJlbUsX331la2oqMih7YcffrDdfffdtjfeeMPe9vbbb9uGDRtmS0tLs7cdPXrUNmjQINv27dsbrN6W6LXXXrM999xzthkzZtgmTpzoMG3WrFm2X//617bc3Fx72wcffGAbNGiQ7fPPP2/oUpu93Nxc26hRo2xz5syxlZaWVjof49Lw1q1bZxs0aJDtzJkzDu1z5861DRo0yHblyhWbzcbYVKVF3SNy4MABtWrVSpGRkfY2Dw8PjRw5UidOnNCFCxdMrK7l6Nu3r9PZjKCgIHXt2lUWi8XeduDAAQ0cOFABAQH2ttDQUAUFBWn//v0NVm9Lc+zYMR04cEAzZsxwmpaXl6fExEQNHz5cbdq0sbffe++98vLyYlwM8PHHH+vSpUuaOnWqXF1dVVBQoLKyMod5GBdz5OXlSZJ8fX0d2v38/OTq6io3NzfGphpaVBBJTk5W586dHd4MktSrVy9J0nfffWdGWZBks9mUlZWldu3aSZLS09OVlZVV4Q9F9erVS8nJyQ1dYotQWlqqRYsWaeTIkerevbvT9DNnzqi0tNRpXNzd3RUSEsK4GCAxMVFt2rRRRkaGfvOb3+jee+/V/fffr7fffltWq1US42KWAQMGSJLmz5+v5ORkXbhwQXv37tX27dsVFRUlLy8vxqYaWtTNqpmZmRV+1W55W0ZGRkOXhP/vX//6l9LT0zVp0iRJV8dKUqXjdeXKFRUVFal169YNWmdzt337dl24cEELFy6scHpV4/Lll18aWl9LdO7cOZWWlmrOnDkaOXKknnrqKR07dkxbt25Vbm6uXnvtNcbFJGFhYZo8ebI2bNiggwcP2tsnTJigqVOnSmKbqY4WFUSsVmuFNziW/zErP7pAw7JYLFq4cKH69Omj++67T9J/xqKq8SKI1J/s7Gy9++67mjhxYqWfSqpqXIqKiowssUUqKChQYWGhRo0apeeee06SNGTIEBUXFys+Pl6TJk1iXEwUGBiofv36aciQIfLx8dFnn32mDRs2qEOHDoqKimJsqqFFBREPDw8VFxc7tZe/ETw8PBq6pBYvMzNTL774otq0aaM333xTrVq1kvSfsWC8Gs6qVavUtm1bRUVFVTpPVeNCMKx/5a/53Xff7dA+bNgwxcfH68SJE/aPiTIuDWvv3r1asGCBNm7cqJtuuknS1ZBos9m0YsUKDRs2jG2mGlrUPSJ+fn7202TXKm/z9/dv6JJatNzcXL3wwgvKzc3Vn//8Z4fXv/w0ZmXj5ePjwwZcj1JSUvTBBx9o9OjRysjIUGpqqlJTU1VUVKSSkhKlpqbqypUrVY4L21D9K3/NO3To4NBefoNkTk4O42KSbdu2KSQkxB5CyoWHh6uwsFDJycmMTTW0qCDSo0cPnTt3zn6nc7mTJ0/ap6NhWK1W/f73v1dKSor+9Kc/qWvXrg7Tb7zxRrVv316nT592WvbUqVOMVT3LyMhQWVmZFi1apEcffdT+38mTJ5WSkqJHH31UMTExCg4OVqtWrZzGpbi4WMnJyYyLAcpvckxPT3doL7+nrX379oyLSbKyspw+wSRJJSUlkq7e/M3YVK1FBZG77rpLpaWlio+Pt7cVFRVp586d6t27t8PHRGGc0tJS/eEPf9CJEyf0+uuv69Zbb61wviFDhujQoUMOH6tOSkpSSkqKhg4d2lDltgjBwcF66623nP4LDg5WQECA3nrrLY0cOVLe3t4KDQ3Vnj17lJ+fb1/+o48+UkFBAeNigPLXdMeOHQ7tO3bsUKtWrTRgwADGxSRBQUFKTk5WSkqKQ/vevXvl6uqq7t27MzbV0OK+WfW1115TQkKCHnnkEd18883avXu3Tp06pYULF6p///5ml9ciLF68WHFxcRo4cKAiIiKcpg8fPlzS1W8tnDJliry9vTV69GgVFBTovffe04033qiVK1dyaaYBzJw5U9nZ2Q7frHr69GlNnz7d6Vsi+/Xrp7ffftvEapuvP/3pT9q5c6eGDh2q/v3769ixY9q/f78ee+wxPfXUU5IYFzMcO3ZMv/3tb+Xj46OHH35YPj4+OnTokD7//HM98MADeuGFFyQxNlVpcUHEarXaf2smNzdX3bp105QpU/TLX/7S7NJajJkzZ+rYsWOVTk9ISLD/+/vvv3f6rZnp06c7XS+HMSoKIpL01Vdf2X8344YbbtDQoUP19NNP87sZBikpKdH69eu1a9cuZWRkKCAgQA899JAeeeQRh/kYl4Z38uRJrVmzRsnJybpy5YoCAwN13333ady4cXJz+8/nQRibyrW4IAIAABqPFnWPCAAAaFwIIgAAwDQEEQAAYBqCCAAAMA1BBAAAmIYgAgAATEMQAQAApiGIAAAA07hVPQsAAObKyMhQXFycTp06pW+++UYFBQVatGiRBgwYUO11JCYmav369Tpz5oxKS0vVuXNnRUVF6d5777XPY7VatXDhQp06dUoXL15UWVmZOnXqpBEjRuihhx5y+LbUn67322+/VVlZmYKCgjRu3DjdfffdFdbx448/6vHHH1dRUZFWrlypn//85w7Tjx49qpiYGH377bdyd3fXbbfdpmnTpikwMNBpXfn5+Vq7dq3279+vzMxMtWvXTn369NFLL70kT09PSVe/in7Tpk1KTk5Wdna2vL291aNHDz3++OPq27dvtV+/n9q7d69iY2NlsVjk5eWl8PBwPfPMM2rfvn2N1kMQAeCk/Gv4r/26/cZo165dmjdvnv1xRESE/vCHP9R4PcuXL1dsbKz98RNPPKFJkybVR4moJykpKYqNjVXnzp3VrVs3nThxokbLf/rpp3rppZfUp08fPfHEE3JxcdH+/fv11ltvKTs72/51+VarVWfPntWvfvUrdezYUa6urvr666+1ZMkSnTp1Sq+++qrDenfu3Kn58+crNDRUU6dOVatWrfTDDz/o4sWLldby17/+Va1atapw2qFDhzRnzhz97Gc/09NPP628vDzFxcUpOjpaq1evdvgjn5ubq5kzZyo9PV0PPvigbr75Zl2+fFlfffWViouL7UHk3LlzcnV11ahRo9ShQwfl5OToX//6l2bMmKH58+crLCysRq+lJP3zn//UO++8o9tuu03Tp09Xenq64uLidPr0aS1fvlweHh7VXhdBBGjmBg8eXKP5G3v4qMidd96pHj16qFu3brVaPjQ0VK1bt1ZaWpp2795dz9WhOmbOnKmOHTtqzpw5FU7v2bOnPvzwQ/n4+Ojf//63UyCoyj/+8Q/5+fnpL3/5i/0HMyMjIzVhwgTt2rXLHkR8fHy0fPlyh2VHjRqlNm3a6B//+IemT58uPz8/SVJqaqoWLlyohx9+WM8991y16jhy5IiOHj2qcePGad26dU7Tly9frsDAQC1dulTu7u6SpPDwcE2ZMkUbNmxQdHS0fd6VK1cqLS1Nq1atUqdOneztv/nNbxzW+cADD+iBBx5waHvooYc0duxYbdmypcZBpLi4WCtXrlS/fv30zjvvyMXFRZJ066236ve//70+/PBDRUVFVXt9BBGgmXviiSec2uLi4pSbm1vhNEl66aWXVFhYaGxh9WjQoEG6//77a718aGioQkND9cUXXxBEGqm6/jhcfn6+2rZt6/Cr3W5ubmrXrl21lu/YsaOkq2chyoPI9u3bVVZWpsmTJ9v78PLysv9h/qmSkhItXrxYo0eP1s033+w0/cqVKzp79qzGjRtnDyGS1KNHD3Xp0kX79u2zB5GcnBzt3LlTUVFR6tSpk4qLi2Wz2ar9q+Senp5q166dcnNznabt2bNHmzdv1tmzZ+Xh4aHbb79dzz77rAICAiRJZ86cUW5uriIiIhye68CBA+Xl5aW9e/cSRAD8R0WXGHbv3q3c3NxKLz+U73CA5qJ///6KjY3VqlWrdN9998nFxUUff/yxTp8+XeHlvOLiYuXl5clqter06dPatGmTOnbs6BAgkpKSdMstt+jw4cNatmyZ0tPT1bZtWz300EOaNGmSXF0dPw+yZcsW5eTkaOLEiRWeeSwqKpKkCi9reHh46Pvvv1dmZqb8/Px0/PhxFRUVqXPnznrllVf06aefqqysTH369NFvf/tbhYSEOK0jLy9PxcXFys7O1kcffaTvv/9eEyZMcJhn3bp1Wr16tYYOHaoHHnhAly9f1tatWzVjxgytXr1abdu2VXFx8XXrTE5OVllZmdPzrwxBBICTiu4RKb8fY/bs2fLx8dG6det05swZtW3bViNGjLDveHft2qXNmzcrJSVF7du3V1RUlMaNG+fUh81m086dO7Vjxw77zYNdu3bVr3/9a40cObJenkdGRoY2btyow4cPKz09Xa1bt1aHDh3Uv39/PfPMM/L29q6XftD4Pf7440pNTdX69evtl0Q8PT31xhtvaNCgQU7zJyQk6PXXX7c//vnPf64XX3zR4WbV8nsv/vSnP2ncuHHq3r27EhIStG7dOpWWlurpp5+2z5uZmam1a9dq2rRpatOmTYU1dujQQd7e3jp+/LhDe3Z2tiwWi6Sr72k/Pz+dO3dO0tXLM506ddKcOXOUl5enmJgY/fd//7fWrl0rf39/h/W89tprOnLkiCTJ3d1dkZGRmjhxon16Wlqa1qxZoylTpjgElMGDB2vy5Mn65z//qQkTJqhz585ycXHR8ePHNWLECPt8P/zwgy5fvizp6hmb6p5tIogAqJGEhAQdPXpUgwYNUt++ffXZZ59p3bp1stls8vb21rp163TnnXeqf//+SkhI0LJly+Tr66v77rvPvg6bzaY333xTH3/8sTp37qxhw4bJ3d1dR48e1fz583X27FlNnz69TnUWFhZq+vTpSktL0+23367BgweruLhYqamp2rNnj8aOHUsQMUlJSYnTJYGSkhIVFxfb/5CV8/HxqfaR9fW4u7srKChId911lwYPHqzS0lJ98MEHmjt3rt555x316dPHYf4BAwbonXfeUW5urpKSkvTdd985Xa4sKChQWVmZnn76aft9GXfddZdycnIUFxenCRMm2C8pLV++XJ06dXK6V+Narq6uioyMVGxsrFasWKERI0YoPz9fy5Yts5+FsFqt9r7LLVy40N5PSEiInn32WW3btk1Tp051WP/TTz+tRx99VBcvXtTu3btVUlKi0tJS+/SEhASVlZVp6NChDuPQoUMHde7cWV988YUmTJig9u3ba+jQodq9e7e6dOmiwYMHKz09XYsWLZKbm5tKSkrsdVYHQQRAjXz++edaunSpevXqJenqpZ9x48Zpy5YtuuGGG7R69Wr7jXNjx47V+PHjtWnTJocg8uGHH+rjjz/WiBEjNGvWLPtRZnFxsV555RW9//77GjZsmHr27FnrOpOSkpSamqoxY8ZoxowZDtPy8/Mr/BgmGsbx48crvLnz66+/1t69ex3a3n///Qo/tlpTf/nLX3Ty5EmtWrXKHmwiIiI0ceJELV68WCtWrHCYv0OHDurQoYOkq+Fi/fr1ev755xUbG2u/R8TDw0MFBQUaNmyYw7J33323Pv/8c3377bfq37+/Tpw4oT179mjhwoVVhqrJkycrOztb7733njZu3ChJuv322zVy5Eht375dXl5ekmS/FyQ8PNzh/pk+ffooMDBQX3/9tdO6r71cM3z4cE2ZMkXz5s3Tm2++KenqGR6bzabx48dXWNu128ysWbNktVr1t7/9TX/729/s6+zUqZMSEhLsdVYHWyKAGhk+fLg9hEhXbyK84447tHPnTo0fP97h7v2AgAD17dtXX375pUpKSuw7sn/84x/y8vLSb3/7W4edm7u7u6ZOnapDhw7p448/rlMQKVfRdey63viIuunRo4feeecdh7alS5eqQ4cOTpfxysNAXRQXF2vHjh0aP368QxBwc3NTWFiYtm3bpuLiYocbRH/qrrvu0t///nd9+umnGjVqlCTZL5H4+vo6zFv+OCcnR5K0bNky/eIXv1BgYKBSU1MlyX7GITMzUxcuXLDfl+Xu7q4XX3xRU6dOVUpKijp06KCgoCC98cYbcnV1td+jUn7Z5ad9l7dVdBPqtdzd3RUeHq6NGzfKarXKw8NDZWVlcnFx0YIFCyoMTNeGC29vb82bN08XLlxQamqqOnbsqI4dO+rZZ59V+/bt1bZt2+v2fy2CCIAa6dGjh1Nb+RFiZdNKS0uVlZWlG2+8UYWFhTpz5oz8/f3tR3zXKj9V/MMPP9Spzn79+snPz08bN27Ud999p4EDB6p///7q0qVLpZ9qQMNo27atQkNDndr8/Pyc2utDdna2SktLHS5DlCstLVVZWZnKysquu47ySw15eXn2tp49e+rcuXPKyMhwCOAZGRmSZP/Oj4sXLyotLU2PPvqo03pnz54tb29v7dy506H92jMypaWl+uKLL9SrVy97iC4P6eV9XSsjI0O33HLLdZ9P+XOy2WzKz8+Xh4eHbr75ZtlsNgUGBiooKKjK5aWrBxvlISonJ0fffvuthgwZUq1lyxFEANRIRTfalX850/WmlZSUSLq6s7LZbEpPT1dMTEyl/dT148Pe3t5avny5Vq9erUOHDunw4cOSpJtuukm/+c1v9NBDD9Vp/Wi8Lly4oMLCQnXp0kXS1TME3t7e+uSTTzR58mT7mY/8/HwdPHhQt9xyi/3M2eXLl9WuXTunsPrhhx9KksNZuoiICO3du1c7duyw349RVlamXbt2ycfHxz5v+WWMa/3v//6vtm7dqmnTptnrrMymTZuUmZnpcDnrlltuUY8ePfTpp5/q8uXL9tBz5MgRXbx40eHjs1lZWU5nTnJycnTgwAHddNNN9mmDBw/WypUrtWbNGr3yyisOr4HNZtOVK1euewPqypUrVVpaqjFjxlz3+fwUQQRAgyoPKz179tTf//53Q/sKCAjQnDlzVFZWpv/7v//T0aNHtXXrVi1cuFBt27Z1uraPxm3t2rWSpLNnz0qSPvroI3311VeSrn4qptxbb73l8KmvVq1aaezYsVq1apWeeeYZ3XvvvSorK9OOHTuUnp6ul19+2b7snj17FB8frzvvvFOdOnVSfn6+jhw5osTERA0cOFC33Xabfd4777xTt912mzZs2KDLly+rR48e+uSTT/TVV19p1qxZ9vs4fvnLXzo9l/JLJ/3793f4ivc9e/bowIED6tevn7y8vJSYmKj9+/frgQce0F133eWwjujoaP3ud79TdHS0IiMjlZubq82bNysoKMh++UiS/ud//kc33nijevfuLV9fX124cEE7d+5UZmamw0eXb775Zk2ePNn+RWmDBg3SDTfcoPPnz+uTTz7Rgw8+aL90tmHDBn3//ffq3bu3WrVqpU8++URHjx7VlClTHC7dVgdBBECDuuGGG9SlSxdZLBbl5OTU6Fpybbm6uiokJEQhISHq06ePZsyYoYMHDxJEmpjVq1c7PL72csa1QaQiEydOVGBgoOLi4hQTE6Pi4mJ1795db7zxhsMf+F/84hc6ceKE9u7dq6ysLLVq1UpBQUGKjo7Www8/7LBOFxcXvfXWW1q1apX27dun3bt3KygoSC+//LKGDx9eq+fYuXNnXblyRWvXrpXVatUtt9yi3/3ud4qMjHSa97/+67+0YMECrV69WitXrpSnp6cGDRqkZ555xuE+qBEjRmjfvn3avHmzcnNz1bZtW/Xu3Vuvvvqq+vXr57DOxx57TEFBQdqyZYv9jOWNN96o22+/XXfeead9vu7du+uTTz7RwYMHVVZWpu7du+v111/X0KFDa/ycCSIAGtzo0aP19ttva8GCBZo9e7bTHfbnz5+Xi4tLnT4t8f3336tdu3ZONztmZWVJUrW/gRINY/HixVXOU92fH6hsXffcc4/uueee6y7785//3OH7Q6pyww03aObMmZo5c2a1l5Gk+++/v8JvA+7du7f++te/Vns95d8KfD0PP/ywU4i6niFDhlR5n8cdd9yhO+64o9rrvB6CCIAGFxkZqRMnTmj37t06fvy4QkND5efnp6ysLP3www86efKkXn311ToFkaNHj2rZsmXq27evgoKC5OPjo/Pnz+vgwYNq3bo194gAjQRBBECDc3Fx0Zw5c/SrX/1KH374oQ4dOqSCggL5+vqqc+fOmjZtmsO1+Nr45S9/qbS0NH355ZdKSEhQQUGB/P39FRERofHjx6tr167182QA1ImLzWazmV0EANTGtV87X5cfvSv3xRdf6LnnntMTTzxR6e/wAKhfdf/eXAAw2bx58zR48OAKf7ysOpYvX67BgwdX+6fcAdQfLs0AaLJ69OihJ554wv64W7dutVpPaGiow82rAwYMqGtpAKqJSzMAAMA0XJoBAACmIYgAAADTEEQAAIBpCCIAAMA0BBEAAGAagggAADANQQQAAJiGIAIAAExDEAEAAKb5f2ezDdVBAz1WAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grb_bkg.get_raw_lightcurve(binned_data=data_path / \"tsel_binned_data_local_grbdc3.hdf5\", output_name=\"lc\")\n", "time, rate = np.loadtxt(\"lc.dat\", skiprows=1, unpack=True)\n", "grb_bkg.binned_data.project('Time').plot()" ] }, { "cell_type": "markdown", "id": "1451322e-a68f-4700-a025-0b93baf49d74", "metadata": { "scrolled": true }, "source": [ "Now we use the make_minsn_tslices function to create N time slices with a minimum signal to noise of 10.\n", "You can customize the requirement of a minimum signal to noise to create less or more bins.\n", "We used them to slice the data in time. " ] }, { "cell_type": "code", "execution_count": 11, "id": "dcbe7732-a780-488e-ade7-9cc052300dfb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "min_sn=10\n", "yaml_path=data_path / \"bin_galbk_grbdc3.yaml\"\n", "hdf5_path=data_path / \"tsel_binned_data_local_galbk_grbdc3.hdf5\"\n", "tmins,tmaxs=make_minsn_tslices(yaml_path,hdf5_path,min_sn)\n", "len(tmins)" ] }, { "cell_type": "markdown", "id": "0e4dfa67", "metadata": {}, "source": [ "Now we used them to slice the data in time. We perform N spectral fits to determine fluxes and spectral parameters as a function of time. We save all the time series in a text file." ] }, { "cell_type": "code", "execution_count": 13, "id": "c08e2fdc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
16:03:59 INFO      set the minimizer to minuit                                             joint_likelihood.py:1046\n",
       "
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16:04:01 WARNING   50.86000000000001 percent of samples have been thrown away because they analysis_results.py:1739\n",
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16:04:11 WARNING   28.28 percent of samples have been thrown away because they failed the  analysis_results.py:1739\n",
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16:04:21 WARNING   49.64 percent of samples have been thrown away because they failed the  analysis_results.py:1739\n",
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parameter
source.spectrum.main.Band.K(7.5 -0.7 +0.8) x 10^-41 / (keV s cm2)
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total745.4151714817089
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16:04:30 WARNING   50.36000000000001 percent of samples have been thrown away because they analysis_results.py:1739\n",
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       "                  less than 1 percent of the samples.                                                              \n",
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Best fit values:\n",
       "\n",
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resultunit
parameter
source.spectrum.main.Band.K(2.43 -0.20 +0.22) x 10^-41 / (keV s cm2)
bkg_gal(0.0 +/- 2.8) x 10^-6Hz
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       "Correlation matrix:\n",
       "\n",
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       "Values of -log(likelihood) at the minimum:\n",
       "\n",
       "
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-log(likelihood)
cosi936.034617074883
total936.034617074883
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       "Values of statistical measures:\n",
       "\n",
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statistical measures
AIC1876.0692862337776
BIC1896.7643785653738
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" ], "text/plain": [ " statistical measures\n", "AIC 1876.0692862337776\n", "BIC 1896.7643785653738" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bd741760dd484659ac0af80e068f3c5f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "processing MLE analyses: 0%| | 0/1 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#\n", "#Initialize empty vectors that will be filled at each step.\n", "#\n", "tmins_lc=np.array([])\n", "tmaxs_lc=np.array([])\n", "tmeds_lc=np.array([])\n", "e_tmeds_lc=np.array([])\n", "#\n", "cts_lc=np.array([])\n", "fls=np.array([])\n", "e_low_fls=np.array([])\n", "e_hi_fls=np.array([])\n", "#\n", "pars_bk=np.array([])\n", "epars_bk=np.array([])\n", "#\n", "par_epar=np.array([])\n", "#\n", "\n", "# sc orientation for background\n", "tmin_bk = Time(bkg.tmin, format='unix')\n", "tmax_bk = Time(bkg.tmax, format='unix')\n", "sc_orientation_bk = ori.select_interval(tmin_bk, tmax_bk)\n", "\n", "for i in range(len(tmaxs)):\n", " #\n", " #Slice the orientation file into the time interval\n", " #\n", " ori_min = Time(tmins[i],format = 'unix')\n", " ori_max = Time(tmaxs[i],format = 'unix')\n", " sc_orientation = ori.select_interval(ori_min, ori_max)\n", " #\n", " #\n", " #Find the index of time axes corresponding to the limits of the tmins[i],tmaxs[i].\n", " #These are used to slice the data in time in the fit setup.\n", " #\n", " #\n", " sou_min = np.where(grb_bkg.binned_data.axes['Time'].edges.value >= tmins[i])[0][0]\n", " sou_max = np.where(grb_bkg.binned_data.axes['Time'].edges.value <= tmaxs[i])[0][-1]\n", " #\n", " #Save the time bin edges and middle points for plotting later.\n", " #\n", " tmin_lc=grb_bkg.binned_data.axes['Time'].edges.value[sou_min]\n", " tmins_lc=np.append(tmins_lc,tmin_lc)\n", " #\n", " tmax_lc=grb_bkg.binned_data.axes['Time'].edges.value[sou_max]\n", " tmaxs_lc=np.append(tmaxs_lc,tmax_lc)\n", " #\n", " dt=(tmax_lc-tmin_lc) #total duration of the time bin\n", " tmed_lc=tmin_lc+0.5*dt#midpoint of time bins\n", " tmeds_lc=np.append(tmeds_lc,tmed_lc)#midpoints of the time bin\n", " hdt=dt/2 #half duration of the time bin i.e symmetric error for t_med\n", " e_tmeds_lc=np.append(e_tmeds_lc,hdt)#half duration of the time bin i.e symmetric error for t_med\n", "\n", " data = EmCDSBinnedData(grb_bkg.binned_data.slice[{'Time':slice(sou_min,sou_max)}].project('Em', 'Phi', 'PsiChi'))\n", "\n", " bkg_wrap = FreeNormBinnedBackground({'bkg_gal': bkg.binned_data.project('Em', 'Phi', 'PsiChi')},\n", " sc_history = sc_orientation_bk,\n", " copy = False)\n", "\n", " instrument_response = BinnedInstrumentResponse(dr, data)\n", "\n", " psr = BinnedThreeMLPointSourceResponse(data = data,\n", " instrument_response = instrument_response,\n", " sc_history=sc_orientation,\n", " energy_axis = dr.axes['Ei'],\n", " polarization_axis = dr.axes['Pol'] if 'Pol' in dr.axes.labels else None,\n", " nside = 2*data.axes['PsiChi'].nside)\n", "\n", " response = BinnedThreeMLModelFolding(data = data, point_source_response = psr)\n", "\n", " like_fun = PoissonLikelihood(data, response, bkg_wrap)\n", " \n", " cosi = ThreeMLPluginInterface('cosi',\n", " like_fun,\n", " response,\n", " bkg_wrap)\n", " \n", " cosi.bkg_parameter[\"bkg_gal\"] = Parameter('bkg_gal', # background parameter\n", " 1, # initial value of parameter\n", " min_value=0, # minimum value of parameter\n", " max_value= 20, # maximum value of parameter\n", " delta=0.05, # initial step used by fitting engine\n", " unit = u.Hz\n", " )\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", " 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) \n", " plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...)\n", " #\n", " like = JointLikelihood(model, plugins, verbose = False)\n", " like.fit()\n", " results = like.results\n", " #\n", " #\n", " #Obtain counts in time slices:\n", " #\n", " cts=np.sum(grb_bkg.binned_data.slice[{'Time':slice(sou_min,sou_max)}])\n", " cts_lc=np.append(cts_lc,cts)\n", " #\n", " #Obtain parameters:\n", " #\n", " #bk\n", " #\n", " par_bk=results.get_variates(\"bkg_gal\").median\n", " pars_bk=np.append(pars_bk,par_bk)\n", " epar_bk=results.get_variates(\"bkg_gal\").std\n", " epars_bk=np.append(epars_bk,epar_bk) \n", " #\n", " #These are dictionaries of parameters values and errors.\n", " #\n", " par_bf= {par.name:results.get_variates(par.path).median\n", " for par in results.optimized_model[\"source\"].parameters.values()\n", " if par.free}\n", " #\n", " epar_bf= {par.name:results.get_variates(par.path).std\n", " for par in results.optimized_model[\"source\"].parameters.values()\n", " if par.free}\n", " #\n", " par_list = list(par_bf.keys()) #This is a list of the parameter names.\n", " for j in range(len(par_list)):\n", " par_epar=np.append(par_epar,par_bf[par_list[j]])\n", " par_epar=np.append(par_epar,epar_bf[par_list[j]]) \n", " #\n", " #Obtain fluxes:\n", " #Here I use the 3ML method to ge integrated flux in an energy range.\n", " #I use the energy range from the data 100--10000 keV\n", " #\n", " threeML_config.point_source.integrate_flux_method = \"trapz\"\n", " result_fl=results.get_flux(\n", " ene_min=100. * u.keV,\n", " ene_max= 10000.* u.keV,\n", " confidence_level=0.95,\n", " sum_sources=True,\n", " flux_unit=\"1/(cm2 s)\"\n", " )\n", " #\n", " fl=result_fl[\"flux\"].values[0].value\n", " fls=np.append(fls,fl)\n", " e_low_fl=np.abs(result_fl[\"low bound\"].values[0].value-fl)\n", " e_low_fls=np.append(e_low_fls, e_low_fl)\n", " e_hi_fl=result_fl[\"hi bound\"].values[0].value-fl\n", " e_hi_fls=np.append(e_hi_fls, e_hi_fl)\n", " #\n", " #\n", " #Save a plot of the current fit.\n", " #\n", " sliced_data=grb_bkg.binned_data.slice[{'Time':slice(sou_min,sou_max)}]\n", " expectation = response.expectation()\n", " bkg_expectation = bkg_wrap.expectation()\n", " cts_exp=expectation.project('Em').to_dense(copy=False).contents + bkg_expectation.project('Em').to_dense(copy=False).contents\n", " plot_filename=str(\"fit_\"+str(i)+\".pdf\")\n", " plot_fit(sliced_data, cts_exp, plot_filename)\n", "#\n", "#Save lc in in a text file:\n", "#\n", "lc=np.vstack((tmeds_lc,e_tmeds_lc,cts_lc,fls,e_low_fls,e_hi_fls,pars_bk,epars_bk)).T\n", "nbins=len(tmeds_lc)\n", "npars=2*len(par_list)\n", "lc_par=par_epar.reshape(nbins,npars)\n", "lc_all=np.hstack((lc,lc_par))\n", "fl_list=['t','e_t','cts','fl','e+_fl','e-_fl','bk','e_bk']\n", "par_list = list(par_bf.keys())\n", "header=fl_list+par_list\n", "np.savetxt(\"spec_lc.dat\", lc_all, delimiter=\" \",header=str(header))\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "id": "e9088224", "metadata": {}, "source": [ "## Plotting the time series.\n", "\n", "Now we plot the time-series of fluxes, counts and fitted parameters. We convert the time in mjd for plotting. \n", "We use the raw lightcurve and the average flux injected as comparison. We plot the counts in each time bins to check that the fits had a reasonable statistics. In the future, we may be able to compute a goodness of fit in each time bin with 3ML." ] }, { "cell_type": "code", "execution_count": 14, "id": "e45ac8c4", "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "time_mjd=Time(time,format='unix').mjd\n", "tmeds_mjd=Time(tmeds_lc, format='unix').mjd\n", "e_tmeds_mjd=TimeDelta(e_tmeds_lc, format='sec').to('day').value\n", "\n", "cm = 1/2.54\n", "fig, axs = plt.subplots(3, 1, sharex=True, figsize=(21*cm, 29.7*cm))\n", "#\n", "# Raw Lightcurve\n", "axs[0].scatter (time_mjd, rate)\n", "axs[0].set_title('Raw lightcurve')\n", "axs[0].set_ylabel('Rate (cts/s)')\n", "#\n", "# Flux Lightcurve\n", "axs[1].errorbar(tmeds_mjd, fls, xerr=e_tmeds_mjd, yerr=[e_low_fls,e_hi_fls],fmt='o', capsize=1)\n", "axs[1].set_title('Flux[0.1-10 MeV] LightCurve')\n", "axs[1].set_ylabel('Flux (Photons/s/cm2)')\n", "#\n", "#Counts in log scale. To check that the fits have reasonable statistics.\n", "axs[2].set_yscale('log')\n", "axs[2].step(tmeds_mjd, cts_lc, where='mid',color='purple')\n", "axs[2].errorbar (tmeds_mjd, cts_lc,xerr=e_tmeds_mjd,fmt='o', capsize=1)\n", "axs[2].set_title('Counts')\n", "axs[2].set_xlabel('Time (MJD)')\n", "axs[2].axhline(y=100, color='red', linestyle='--')\n", "#\n", "#\n", "# Adjust spacing between subplots\n", "plt.tight_layout()\n", "plt.savefig(\"raw_flux_counts_lc.pdf\", dpi=300)" ] }, { "cell_type": "code", "execution_count": 15, "id": "1c99dbd5", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#\n", "#Plot the free parameters as a function of time.\n", "#\n", "npar=len(par_list)\n", "cm = 1/2.54 \n", "fig, axs = plt.subplots(npar+1, 1, sharex=True, figsize=(21*cm, 29.7*cm))\n", "\n", "h=np.arange(0, 2*npar, 2)\n", "for i in range(npar):\n", " axs[i].errorbar(tmeds_mjd, lc_par[:,h[i]], xerr=e_tmeds_mjd, yerr=lc_par[:,h[i]+1], fmt='o', capsize=5)\n", " axs[i].set_ylabel(par_list[i])\n", "\n", "axs[npar].errorbar(tmeds_mjd, pars_bk, xerr=e_tmeds_mjd, yerr=epars_bk, fmt='o')\n", "axs[npar].set_title('Bakground COSI')\n", "axs[npar].set_xlabel('Time (MJD)')\n", "axs[npar].set_ylabel('Bk parameter')\n", "\n", "# Adjust spacing between subplots\n", "plt.tight_layout()\n", "plt.savefig(\"specpars_bk_lc.pdf\", dpi=300)" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:base] *", "language": "python", "name": "conda-base-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.9" } }, "nbformat": 4, "nbformat_minor": 5 }