{ "cells": [ { "cell_type": "markdown", "id": "e1ee31e5", "metadata": {}, "source": [ "# Full detector response" ] }, { "cell_type": "markdown", "id": "59ac51fe", "metadata": {}, "source": [ "The detector response provides us with the the following information:\n", "- The effective area at a given energy for a given direction. This allows us to convert from counts to physical quantities like flux\n", "- The expected distribution of measured energy and other reconstructed quantities. This allows us to account for all sorts of detector effects when we do our analysis.\n", "\n", "This tutorial will show you how to handle the detector response and extract useful information from it." ] }, { "cell_type": "markdown", "id": "20ad36e2", "metadata": {}, "source": [ "## Dependencies" ] }, { "cell_type": "code", "execution_count": 1, "id": "09d73ed6", "metadata": {}, "outputs": [], "source": [ "%%capture\n", "import numpy as np\n", "import astropy.units as u\n", "from astropy.units import Quantity\n", "from astropy.coordinates import SkyCoord\n", "from astropy.io import fits\n", "from astropy.time import Time\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as ply\n", "\n", "from mhealpy import HealpixMap, HealpixBase\n", "import pandas as pd\n", "from pathlib import Path\n", "\n", "from scoords import Attitude, SpacecraftFrame\n", "from cosipy.response import FullDetectorResponse\n", "from cosipy.spacecraftfile import SpacecraftHistory\n", "from cosipy import test_data\n", "from cosipy.util import fetch_wasabi_file\n", "from histpy import Histogram\n", "import gc\n", "\n", "from threeML import Model, Powerlaw\n", "\n", "from cosipy.response import FullDetectorResponse\n", "\n", "import shutil\n", "import os" ] }, { "cell_type": "markdown", "id": "0bda281c", "metadata": {}, "source": [ "## File downloads" ] }, { "cell_type": "markdown", "id": "62a1fb46", "metadata": {}, "source": [ "You can skip this step if you've already downloaded the files. Make sure that paths point to the right files" ] }, { "cell_type": "code", "execution_count": 2, "id": "9657bb35", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "A file named 20280301_3_month_with_orbital_info.fits already exists with the specified checksum (5e69bc1d55fab9390f90635690f62896). Skipping.\n", "A file named SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5 already exists with the specified checksum (eb72400a1279325e9404110f909c7785). Skipping.\n" ] } ], "source": [ "data_dir = Path(\"\") # Current directory by default. Modify if you can want a different path\n", "\n", "ori_path = data_dir/\"20280301_3_month_with_orbital_info.fits\"\n", "response_path = data_dir/\"SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5\"\n", "\n", "# download orientation file ~684.38 MB\n", "fetch_wasabi_file(\"COSI-SMEX/develop/Data/Orientation/20280301_3_month_with_orbital_info.fits\", ori_path, checksum = '5e69bc1d55fab9390f90635690f62896')\n", "\n", "# download response file ~596.06 MB\n", "fetch_wasabi_file(\"COSI-SMEX/develop/Data/Responses/SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5\", checksum = 'eb72400a1279325e9404110f909c7785')" ] }, { "cell_type": "markdown", "id": "93beb034", "metadata": {}, "source": [ "## Opening a full detector response" ] }, { "cell_type": "markdown", "id": "d6cb984e", "metadata": {}, "source": [ "The response of the instrument in encoded in a series of matrices cointained in a file. You can open the file like this:" ] }, { "cell_type": "code", "execution_count": 3, "id": "efa29064", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5\n" ] } ], "source": [ "response = FullDetectorResponse.open(response_path)\n", "\n", "print(response.filename)\n", "\n", "response.close()" ] }, { "cell_type": "markdown", "id": "09b2ecec", "metadata": {}, "source": [ "Or if you don't want to worry about closing the file, use a context manager statement:" ] }, { "cell_type": "code", "execution_count": 4, "id": "361d36bb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FILENAME: '/Users/imartin5/software/cosipy/docs/tutorials/response/SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5'\n", "AXES:\n", " NuLambda:\n", " DESCRIPTION: 'Location of the simulated source in the spacecraft coordinates'\n", " TYPE: 'healpix'\n", " NPIX: 768\n", " NSIDE: 8\n", " SCHEME: 'RING'\n", " Ei:\n", " DESCRIPTION: 'Initial simulated energy'\n", " TYPE: 'log'\n", " UNIT: 'keV'\n", " NBINS: 10\n", " EDGES: [100.0 keV, 158.489 keV, 251.189 keV, 398.107 keV, 630.957 keV, 1000.0 keV, 1584.89 keV, 2511.89 keV, 3981.07 keV, 6309.57 keV, 10000.0 keV]\n", " Em:\n", " DESCRIPTION: 'Measured energy'\n", " TYPE: 'log'\n", " UNIT: 'keV'\n", " NBINS: 10\n", " EDGES: [100.0 keV, 158.489 keV, 251.189 keV, 398.107 keV, 630.957 keV, 1000.0 keV, 1584.89 keV, 2511.89 keV, 3981.07 keV, 6309.57 keV, 10000.0 keV]\n", " Phi:\n", " DESCRIPTION: 'Compton angle'\n", " TYPE: 'linear'\n", " UNIT: 'deg'\n", " NBINS: 36\n", " EDGES: [0.0 deg, 5.0 deg, 10.0 deg, 15.0 deg, 20.0 deg, 25.0 deg, 30.0 deg, 35.0 deg, 40.0 deg, 45.0 deg, 50.0 deg, 55.0 deg, 60.0 deg, 65.0 deg, 70.0 deg, 75.0 deg, 80.0 deg, 85.0 deg, 90.0 deg, 95.0 deg, 100.0 deg, 105.0 deg, 110.0 deg, 115.0 deg, 120.0 deg, 125.0 deg, 130.0 deg, 135.0 deg, 140.0 deg, 145.0 deg, 150.0 deg, 155.0 deg, 160.0 deg, 165.0 deg, 170.0 deg, 175.0 deg, 180.0 deg]\n", " PsiChi:\n", " DESCRIPTION: 'Location in the Compton Data Space'\n", " TYPE: 'healpix'\n", " NPIX: 768\n", " NSIDE: 8\n", " SCHEME: 'RING'\n", "\n" ] } ], "source": [ "with FullDetectorResponse.open(response_path) as response:\n", "\n", " print(repr(response))" ] }, { "cell_type": "markdown", "id": "453e9e99", "metadata": {}, "source": [ "Although opening a detector response does not load the matrices, it loads all the header information above. This allows us to pass it around for various analysis at a very low cost." ] }, { "cell_type": "markdown", "id": "dc780c30", "metadata": {}, "source": [ "## Detector response matrix" ] }, { "cell_type": "markdown", "id": "32d14b09", "metadata": {}, "source": [ "The full --i.e. all-sky-- detector response is encoded in a HEALPix grid. For each pixel there is a multidimensional matrix describing the response of the instrument for that particular direction in the spacefraft coordinates. For this response has the following grid:" ] }, { "cell_type": "code", "execution_count": 5, "id": "30c6f7ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NSIDE = 8\n", "SCHEME = RING\n", "NPIX = 768\n", "Pixel size = 7.33 deg\n" ] } ], "source": [ "with FullDetectorResponse.open(response_path) as response:\n", " \n", " print(f\"NSIDE = {response.nside}\")\n", " print(f\"SCHEME = {response.scheme}\")\n", " print(f\"NPIX = {response.npix}\")\n", " print(f\"Pixel size = {np.sqrt(response.pixarea()).to(u.deg):.2f}\")" ] }, { "cell_type": "markdown", "id": "d15b1afb", "metadata": {}, "source": [ "To retrieve the detector response matrix for a given pixel simply use the `[]` operator" ] }, { "cell_type": "code", "execution_count": 6, "id": "fd8fec2a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pixel 0 centered at \n" ] } ], "source": [ "with FullDetectorResponse.open(response_path) as response:\n", " \n", " print(f\"Pixel 0 centered at {response.pix2skycoord(0)}\")\n", " drm = response[0]" ] }, { "cell_type": "markdown", "id": "2128635c", "metadata": {}, "source": [ "Or better, get the interpolated matrix for a given direction. In this case, for the on-axis response:" ] }, { "cell_type": "code", "execution_count": 32, "id": "c05195b8", "metadata": {}, "outputs": [], "source": [ "with FullDetectorResponse.open(response_path) as response:\n", " \n", " drm = response.get_interp_response(SkyCoord(lon = 0*u.deg, lat = 90*u.deg, frame = SpacecraftFrame()))" ] }, { "cell_type": "markdown", "id": "1639499b", "metadata": {}, "source": [ "The matrix has multiple dimensions, including real photon initial energy, the measured energy, the Compton data space, and possibly other:" ] }, { "cell_type": "code", "execution_count": 33, "id": "988b8c01", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Ei', 'Em', 'Phi', 'PsiChi'], dtype='" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "drm.get_spectral_response().plot();" ] }, { "cell_type": "markdown", "id": "5b49c391", "metadata": {}, "source": [ "You can further project it into the initial energy to get the effective area:" ] }, { "cell_type": "code", "execution_count": 35, "id": "bdeda95d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax,plot = drm.get_effective_area().plot();\n", "\n", "ax.set_ylabel(f'Aeff [{drm.unit}]');" ] }, { "cell_type": "markdown", "id": "e878ab79", "metadata": {}, "source": [ "Get the interpolated effective area" ] }, { "cell_type": "code", "execution_count": 36, "id": "9f7d8256", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$99.27511 \\; \\mathrm{cm^{2}}$" ], "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "drm.get_effective_area(511*u.keV)" ] }, { "cell_type": "markdown", "id": "b3583357", "metadata": {}, "source": [ "Or the energy dispersion matrix" ] }, { "cell_type": "code", "execution_count": 37, "id": "1dc1e36c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "drm.get_dispersion_matrix().plot();" ] }, { "cell_type": "markdown", "id": "c6c8e5ec", "metadata": {}, "source": [ "## Point source response and expected counts" ] }, { "cell_type": "markdown", "id": "a819e917", "metadata": {}, "source": [ "Once we have the response, the next step is usually to get the expected counts for a specific source. However, it is not trivial for the case of a spacecraft because the response we have here is the detector response. This response records the detector effects to given points viewed from the reference frame attached to the spacecraft (SC).\n", "\n", "A source with a fixed position on the sky is moving from the perspective of the spacecraft (detector). Therefore, we need to convert the coordinate of a source to the reference frame, which results in a moving point viewed from the spacecraft. By convolving the trajectory of the source in the spacecraft frame with the detector response, we will get the so-called point source response.\n", "\n", "See the spacecraft file tutorial for a discussion of the SC attitude history, transformations to/from galactic coordinates, and the dwell time map." ] }, { "cell_type": "code", "execution_count": 13, "id": "e7fcdf40", "metadata": {}, "outputs": [], "source": [ "# Read the full orientation\n", "ori = SpacecraftHistory.open(ori_path)\n", "\n", "# Define the target coordinates (Crab)\n", "target_coord = SkyCoord(184.5551, -05.7877, unit = \"deg\", frame = \"galactic\")\n", "\n", "# Get the dwell time map\n", "with FullDetectorResponse.open(response_path) as response:\n", " dwell_time_map = ori.get_dwell_map(target_coord = target_coord, base = response)" ] }, { "cell_type": "markdown", "id": "2bd5d351", "metadata": {}, "source": [ "We can now convolve the exposure map with the full detector response, and get a PointSourceResponse" ] }, { "cell_type": "code", "execution_count": 14, "id": "f57af9d3", "metadata": {}, "outputs": [], "source": [ "with FullDetectorResponse.open(response_path) as response:\n", " psr = response.get_point_source_response(exposure_map = dwell_time_map, coord = target_coord)" ] }, { "cell_type": "markdown", "id": "72a7e1d6", "metadata": {}, "source": [ "Note that a PointSourceResponse only depends on the path of the source, not on the spectrum of the source. It has units of area*time" ] }, { "cell_type": "code", "execution_count": 15, "id": "668755ae", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\mathrm{cm^{2}\\,s}$" ], "text/plain": [ "Unit(\"cm2 s\")" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.unit" ] }, { "cell_type": "markdown", "id": "8d1af159", "metadata": {}, "source": [ "Finally, we convolve a spectrum to get the expected excess for each *measured* energy bin:" ] }, { "cell_type": "code", "execution_count": 16, "id": "f67fd51f", "metadata": {}, "outputs": [], "source": [ "index = -2.2\n", "K = 10**-3 / u.cm / u.cm / u.s / u.keV\n", "piv = 100 * u.keV\n", "\n", "spectrum = Powerlaw()\n", "spectrum.index.value = index\n", "spectrum.K.value = K.value\n", "spectrum.piv.value = piv.value \n", "spectrum.K.unit = K.unit\n", "spectrum.piv.unit = piv.unit\n", " \n", "expectation = psr.get_expectation(spectrum)" ] }, { "cell_type": "code", "execution_count": 17, "id": "9974e5bc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Expected counts')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax, plot = expectation.project('Em').plot()\n", "\n", "ax.set_ylabel('Expected counts')" ] }, { "cell_type": "markdown", "id": "80d6936a", "metadata": {}, "source": [ "Try changing the spectrum and see how the expected excess changes." ] }, { "cell_type": "code", "execution_count": 18, "id": "de64ea68", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Expected counts')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spectrum.index.value = -1\n", "\n", "expectation = psr.get_expectation(spectrum)\n", "\n", "ax, plot = expectation.project('Em').plot()\n", "\n", "ax.set_ylabel('Expected counts')" ] }, { "cell_type": "markdown", "id": "c1a1b652", "metadata": {}, "source": [ "## Point source response in inertial coordinates" ] }, { "cell_type": "markdown", "id": "25e693f2", "metadata": {}, "source": [ "In the previous example we obtained the response for a point source as seen in the reference frame attached to the spacecraft (SC) frame. As the spacecraft rotates, a fixed source in the sky is seen by the detector from multiple directions, so binning the data on the spacecraft coordinate, without binning it simultaneously in time, can wash out the signal. As shown in this section, we can instead rotate the response and convolve it with the attitude history of the spacecraft, resulting in a point source response with a Compton data space binned in inertial coordinates.\n", "\n", "We use a scatt map, which tracks the amount of time the spacecraft spent in a given orientation. See spacecraft file tutorial for more details." ] }, { "cell_type": "code", "execution_count": 19, "id": "c4565f25", "metadata": {}, "outputs": [], "source": [ "scatt_map = ori.get_scatt_map(nside = 16, earth_occ = False)" ] }, { "cell_type": "markdown", "id": "fe624ff7", "metadata": {}, "source": [ "Now we can let cosipy perform the convolution with the scatt map and get the point source response:" ] }, { "cell_type": "code", "execution_count": 20, "id": "a69762fd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.04 s, sys: 38.7 ms, total: 2.08 s\n", "Wall time: 2.08 s\n" ] } ], "source": [ "%%time\n", "\n", "from astropy.coordinates import SkyCoord\n", "\n", "coord = SkyCoord.from_name('Crab').galactic\n", "\n", "with FullDetectorResponse.open(response_path) as response:\n", " psr = response.get_point_source_response(coord = coord, scatt_map = scatt_map)" ] }, { "cell_type": "markdown", "id": "2a47abab", "metadata": {}, "source": [ "This is how a slice of the response looks like in galactic coordinates:" ] }, { "cell_type": "code", "execution_count": 21, "id": "b3578b8b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "psichi_slice = psr.slice[{'Ei':4, 'Phi':4}].project('PsiChi')\n", "\n", "ax,plot = psichi_slice.plot(ax_kw = {'coord':'G'})\n", "\n", "ax.scatter([coord.l.deg], [coord.b.deg], transform = ax.get_transform('world'), marker = 'x', color = 'red')" ] }, { "cell_type": "markdown", "id": "6c07d2ac", "metadata": {}, "source": [ "And here in ICRS (RA/Dec)" ] }, { "cell_type": "code", "execution_count": 22, "id": "faae6f4f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax,plot = psichi_slice.plot(ax_kw = {'coord':'C'})\n", "\n", "ax.scatter([coord.icrs.ra.deg], [coord.icrs.dec.deg], transform = ax.get_transform('world'), marker = 'x', color = 'red')" ] }, { "cell_type": "markdown", "id": "126565d8", "metadata": {}, "source": [ "You can also use it the same way as a point source response obtained from a exposure map. e.g." ] }, { "cell_type": "code", "execution_count": 23, "id": "45dc99dc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Expected counts')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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jg5KaPn26nDp1yvQ10tv3dVlLZ3Patm0rqe3ChQsydOhQyZ49u7z//vuJLt4uUKCAedbeTs5ov6b4PZsAAIDvcmlvOA1F5cuXtyui1l5GWhc0ZcoU+fzzz6V48eKyfPlySYs+TRqUtE5Jm0smJdhoXyaVO3fuFBwhAADw+bCkM0d695iNdsyOjo6Wxo0bm6/1brJq1arJmTNnJKVoobUugemt/zZ619vw4cPNuYkTJ5rGlI7cunXLtAuIX2+lBd6qevXqKTZuAADgB8twurylgclGu2xrI8jKlSv//zcOCDB3mbli8eLFcvPmzdi7zrZu3SoXL140f+7QoYNpJjlz5kxZuXKlLFiwQAoV+t/eLWPHjjVbpLRq1coEqbh3qmXNmlXq1atn/nzkyBFzx1uTJk2kcOHCEhkZKb/++qv89ddfZtmwbNmyLo0bAAD4HpfCUrFixeS3336T3r17m5C0du1aeeihh+xqmM6fPy958uRxaVAagPT1Nps3bzYPpR23NSw5cuzYMfOsy3/xlwALFiwYG5b0Tj0NdvqeWnul34MuG+rynRZwAwAAJCss6ezO22+/bZ5tM0ganOLSjXA1QLli4cKF971m1KhR5pHU19l6LFn1UgIAAEhWWNK2AEOGDJFffvnFfK2F3S1btrTrb6R1QbovGwAAgF82pWzfvr15OKL7saXFnXAAAAAecTecdrDW2SMreoccna4BAIBfhqUvv/zyvmHJttUIAACA34WlxND+R3qXGQAAgDdzOc2kS5fO6Tntnr1nzx6XWwcAAAB4XYG3bjIb/zZ9R0Xc9+7dM1uOaIfsNm3auGeUAAAAnh6WdDuQuLNK+nXcY7FvGBAgJUqUMBvq9ujRw30jBQAA8OSwFLfhY/369aVz587Ss2fPlBoXAACA9/ZZ0u1InG05AgAAIP4elnSfNQAAAH/gcgfv69evmwLvQ4cOyc2bNyU6OjrBNVrb9NFHHyV3jAAAAN4Vlo4fPy6vvPKKhIWFOSzyTkx7AQAAAJ8NS5988oncuHFDnnvuOWndurXkz59fMmTI4P7RAQAAeGNY2r9/v9StW1d69erl/hEBAAB4ewdv7aVUuHBh948GAADAF8JSlSpV5PDhw+4fDQAAgC+Epf79+8uJEyfku+++c/+IAAAAvL1m6euvv5ZSpUrJ559/LsuWLZPg4GDJnj27w2tff/315I4RAADAu8LSihUrYv989uxZ83DWOoCwBAAA/HK7EwAAAH/AdicAAADuLvAGAADwFy7NLK1cuTLR17Zo0cKVjwAAAPDesDRhwoT77vume8bpNYQlAADgd2HJ2R1ut27dkiNHjsjatWulTp06Urt27eSODwAAwPvCUsuWLS3Pt2vXTl555RVp3769q+MCAADw3QLvhx9+2MwszZkzJyXeHgAAwPvvhitYsKAcP348pd4eAADAe8OSFnfv2bNHMmXKlBJvDwAA4Nk1S7t373Z4PDo6Wi5fviyrVq2SQ4cOSfPmzZM7PgAAAO8LS4MHD7ZsHaAzS4888ogMHDgwOWMDAI8QcS1S5g1cL94iY5YAqdapjJSsWSithwL4b1jq0aOHw7CUPn16yZEjh5QvX14qVKjgjvEBQJqGDpFIiYkRCb8a6UX/EpGyY9FRwhKQlmHphRdecNfnA4DH0tkZDR13b0eJN82CabjzpjEDPhmWAMAf6DKWty1l6XKhd82CAT4els6dOydr1qyRY8eOme7d2bNnl+DgYGnatKkUKuT6/8GEh4fL/Pnz5cCBA3Lw4EEJCwuTkSNH3rcZpo1e/9lnn8nmzZslMjLSLAv2799fypYtm+DaLVu2yJdffimnT5+W3LlzS6tWreS5556TgAByJAAASEZYWrRokQkkegecFnTbbNq0Sb766it56aWXpFOnTi69d2hoqISEhEhQUJAJX7t27Ur0a+/duycjRowwPZ66du0quXLlkqVLl5qi9FmzZknRokVjr92+fbu88cYbUqVKFXP+xIkTMnfuXLl27ZoMHTrUpbEDAADf4lJY+u2332T69OkmiHTu3FmqVq0qgYGBcvXqVdm5c6csXLhQPvnkEylSpIjUqlUrye+v77VkyRLzrC0I+vbtm+jXbty4Ufbt2yfvvPOONGjQwBxr1KiRdOvWzcwgvfXWW7HXfvrpp1K6dGmZPHly7ExStmzZ5JtvvpGOHTtK8eLFkzx2AADgW1xqSrlgwQJ54IEHZPbs2dK9e3epWLGi6ditd8Dp1zqDkzNnTnOdK7SZpQYlV+jMVt68eeWJJ56IPabLaw0bNjRLbnfu3DHHTp06ZR5t27a1W3J78sknzUyZhi4AAACXwtLRo0dN+ChQoIDD87p8puePHDmS6n/D+pllypQxbQzi0rql27dvyz///BN7nYpfx5QvXz7Jnz+/+R4BAABcWoa7e/euZM2a1fIaPa/XpTZdCqxcuXKC47aZqitXrpilN32Oezz+tbbz8WmH8rjntDAcAAD4LpfCkhZJb926Vfr06ePwrrGoqChT1xS3mDq16N1vjvaksx3T88q2HOfsWr0jz5Fly5aZ4nMAAOAfXApLuuebFkcPGzZM+vXrZ7eUpQXZM2fONMtdert+asucOXNsEIrLdkzPxw1Jzq61XRdfu3btpE6dOnYzS+PGjXPb+AEAgA+EJb1TbM+ePWZ26cUXXzTBIk+ePOaWe5250QLpunXrmutSmxZ3O1pCi7/sFndZTmus4l+rNU6OaE2TPgAAgH9wKSxlyJBB3n33XVm5cqV5aFPKCxcumKaUekdcixYtzOxTWtDi7r1795p+S3GLvLW5ZZYsWWKXBvU6dfjwYbt97LQm6dKlS2YGCQAAIFltqjUU6SOtaLDRzuGFCxeOrZ2qX7++ue1fu3fb+ixdv35dNmzYILVr145dfitZsqQUK1ZMfvrpJxOMNAAqbWCpmwTr+wAAAHjsnh6LFy+Wmzdvxi6f6ZLfxYsXzZ87dOggOXLkMLVROrOl/Zxs26toQPr+++9lwoQJpo+SrYO3zjTF3wBYa6p0GxXt1t24cWPTwVubYbZp00ZKlCiRBt81AADwmQ7ev/zyiwwZMsRh/Y7O+EyZMsU0fHz88cddGpgGoPPnz8d+rTNF+lDNmjUzYckRnSGaOHGiKUDXwKU1VOXKlTOhSGeS4tKZJi3O1rvbpk6daoKVNtXs2bOnS2MGAAC+x6WwpLMvGoicFTrrcQ06ep2rYUm3TLmfUaNGmUd82j1c94fTx/3Uq1fPPAAAANzWwVsLuuMWRTuid5PpdQAAAH4XlsLCwsx+a1Z0SSs0NNTVcQEAAHhvWNIgZNtjzRk9r8thAAAAfheWdO81LfI+fvy4w/O6/KZ3r1WpUiW54wMAAPC+Au9nnnnG3Jk2YMAA6dq1q1SrVk3y589vmjn+8ccf5k427VWkd5YBAAD4XVgqXbq0jB492vQy0tvu424sq1udZM2aVd5++21zHQAAgF82pdTmj7oct2LFCrN5rjaQ1Bol7WnUsmXL+xaAAwAA+HwHb908t1u3bu4bDQAAgC8UeAMAAPgLwhIAAIAFwhIAAIAFwhIAAIAFwhIAAIAFwhIAAIAFwhIAAEBy+yxduHBBXBUUFOTyawEAALwiLHXu3Nns9ZZU+poNGza4Mi4AAADvCUvNmzdPEJbOnj0re/fulRw5ckhwcLDkzZtXrl69KseOHTNbn1SqVEkefPDBlBo3AACA54SlUaNG2X198uRJGTBggHTv3t08dONcm4iICPn6669l6dKlMnToUPePGAAAwNP3hpsxY4bZMLdPnz4Jzmlw6tu3r9lc97PPPpP33nvPHeMEACRBxLVImTdwvdf8nWXMEiDVOpWRkjULpfVQAPeEpX379smTTz5peU358uVlyZIlrrw9ACAZoUMkUmJiRMKvRnrR32Ok7Fh0lLAE3wlL9+7dkzNnzlhe8++//0qM/rQCAFKNzs5o6Lh7O8qrZsH014U3jRn+xaWwVLlyZdm0aZOsW7dOGjdunOD82rVrZfPmzVKzZk13jBEAkEi6jOVtS1m6XOhds2DwNy6FpX79+pk74caOHSvz5s2TRx55RPLkySPXrl2Tv/76S44fPy7ZsmWTl156yf0jBgAA8PSwVKJECfnkk0/ko48+kj179ph2AfFnnoYMGWKuAwAA8LuwpEqVKiXTpk0z3b11Jkl7K2nPpdKlS9O1GwAA+AyXw1Lc7UzY0gQAAPiqZIWlu3fvyo4dO+Tvv/+W27dvS48ePczxyMhICQ8Pl1y5ckn69OzVCwAA/DAsbdmyRSZNmiTXr183LQJ0OxRbWNJluf79+8sbb7whTZs2ded4AQAAUpVL0z56x9tbb70lGTNmlJdfflmaNGlid75ChQpSuHBh014AAADA72aWvvrqK1PMPWvWLMmdO7fcuHEjwTVly5aVgwcPumOMAAAA3jWzdODAAalbt64JSs4UKFBArl69mpyxAQAAeGdY0sJubTppRVsJaB0TAACA34WlBx98UA4dOmR5zf79+6VYsWKujgsAAMB7w1L9+vVl3759snz5cofnv/vuOzl58qQ0atQoueMDAADwvgLvrl27mjvdJk6caDbNvXPnjjk+Y8YMM6OkQSo4OFieeuopd48XAADA88OS1itNnz5dpkyZIhs2bJB79+6Z4/Pnzzd1Sg0bNpRXX31VMmXK5NKgNHzNmTNHVq9eLWFhYWYLld69e0v16tUtX9e5c2c5f/68w3PaykBnvGyeeOIJh9f17dtXunfv7tK4AQCA73G5KWXOnDlNr6XBgweb+iVtH5A9e3YpV66c5M2bN1mDmjBhgmzcuFE6deokRYoUkRUrVsjw4cNl6tSpUqlSJaev055PERERdsc0PM2ePdth0KpWrZq0aNHC7liZMmWSNXYAAOBbkr03nG5pUrNmTfeM5v/aEqxbt0769esnTz/9tDnWvHlz6dmzp1nm04cz9erVc9gTSjnqJF60aFFp1qyZ28YOAAB8j0sF3g0aNIgNIc7MnTvXLMclldZCZciQQdq1axd7LHPmzNK6dWtTD3XhwoUkvZ/WVBUqVEgeeeQRh+d1Hzt9AAAAuC0s6V5w+kjMdUl19OhRs/SmS3pxlS9f3jwfO3Ys0e915MgROX36dILtWGxWrlxpZpZ01unZZ5+VNWvWJHm8AADAtyV7Gc4Z3WBXZ4SS6sqVKxIYGJjguO3Y5cuXE/1etvDjaAnu4YcfNjNfOuukn/nDDz/I2LFj5datW9K+fXun76mfr9fbaBgDAAC+K9FhSWdh4s8AxT+m9M64ixcvyqpVq6RkyZJJHpAuiekGvfHZ7qxL7JKZjmP9+vWmYLtEiRIJzn/66ad2X7dq1crccTdz5kxp2bKl06C3bNkyCQkJSeR3AwAA/CYs6R1qtu1L9Hnr1q3m4WzpTcPG888/n+QB6et0O5X4bL2cEjtbtXv3brl06ZK5oy4xNKBpX6jJkyfL4cOHnd51p7VUderUsZtZGjduXKI+AwAA+HBYev3112P//N5775mNdPURnxZna1sBXebS56TS5TYNOfHZlr7y5cuX6CW49OnTO61Xcrb5r9I2CM7o5yd2DAAAwI/Cki5NxZ210dv0HYWl5NLO37t27TK1Q3GLvLWlgO38/egslN5VV6VKlSQFm7Nnz5rn3LlzuzR2AADge1y6G27kyJEpEpRsbQmio6NNbVDc8KP70FWoUEGCgoLMMW0h4Ky4evv27XLz5k2Hhd224vP4wsPD5fvvvzd9o8qWLeu27wcAAPjh3XC//fab/PLLLzJkyBCHMzd6x5huhdK2bVt5/PHHk/TeGoj0LjUttNZQo9uUaCG5duIeMWJE7HXjx483M1ybN292uASnBeG64a8jeufbli1bpHbt2iZ86RKfhjENYG+88YbDAnMAAOCfXApLS5YsMYHI2RKXHtdwo9clNSypUaNGmRCjd9TpDFGpUqXk/fffN8tq96PLd9u2bTOfmyNHDofXaINK3ez3559/NvVJWbJkMX2cNIw99thjSR4vAADwXS6FJW0MqbMyVjR8aGhxhd7x1r9/f/NwZtq0aQ6Pa52Tdu22ovvE3W9TXgAAAJdrlsLCwu5bBK21P6GhofwtAwAA/wtLGoT++ecfy2v0vCutAwAAALw+LFWuXNkUeR8/ftzpMp02rExMjREAAIDP1Sw988wz5i60AQMGSNeuXaVatWqSP39+00zyjz/+kAULFpgu3927d3f/iAEAADw9LJUuXVpGjx5ttkDRfdLi7pWm251kzZpV3n77bXMdAACA34UlW/NIXY5bsWKFHDp0yNzirzVK5cqVM92+6YINAAD8OiypPHnySLdu3dw3GgAAAF8o8I5PGztq92sAAABf4/LMki67zZkzR9avX2/6KWlB94YNG2I3vf3yyy+ld+/e7LMGAAD8b2ZJZ5Jeeukls8dagQIFpHjx4qaw20YLu3U7Ed2jDQAAwO/Cks4aadNJveNt1qxZptg7/nYlWvy9c+dOd40TAAAgTbgUlrThZK1ataRRo0ZOrylUqJDpuwQAAOB3YenKlStSokQJy2syZswoERERro4LAADAe8PSAw88IBcvXrS85u+//5bAwEBXxwUAAOC9d8NpPZIuxWlg0gLv+E6dOiX//e9/pVWrVu4YIwDAD0Rci5R5A9eLN8mYJUCqdSojJWsWSuuhwNPC0rPPPitbtmwxe8P16dPHtA6whSS9C2727NmSKVMms28cAAD3CxwikaI3VYdfjfSyv6xI2bHoKGHJx7m8N9z//M//yPjx4+Xdd981x7R1QM+ePc1ztmzZzPmiRYu6e7wAAB+jMzMaOO7ejhJvmwnTgOdt40YqNqWsW7euLFiwQFauXGmaUGrvpezZs0uFChXYGw4AkGi6hOWNy1i6ZOh9M2FI9b3htNC7c+fOyXkLAAAA398bLioqSsLCwswzAACAL3F5Zik6OloWL14sK1asMIXdWquk+8Np/6WWLVvKU089JQEByZq4AgAASHMupZnw8HAZNmyYqVXSgKTtA/LmzStXr141wenTTz+VTZs2yaRJkyRr1qzuHzUAAIAnh6UvvvhC9u/fL02aNJG+fftKUFBQ7LkLFy7I559/LuvWrZM5c+bIwIED3TleAAAAz69Z2rBhg5QtW1befPNNu6Ck9Ou33nrLnNfrAAAA/C4saRPKatWqWV7z2GOPmXYCAAAA3sylsFSkSBG5du2a5TXXr1+XwoULuzouAAAA7w1LHTt2lPXr18vJkycdnj9+/Lg536lTp+SODwAAwPsKvHVm6dFHHzX7wrVo0UIqVaokefLkMbNNe/bskVWrVkmNGjXMzNLu3bvtXlulShV3jR0AAMAzw9LgwYNNywDtrfTTTz/Jzz//HHtOj6nffvvNPOLbuHFjcsYLAADg+WGpR48eJiwBAAD4OpfC0gsvvOD+kQAAAPjq3nDO3Lt3LyXfHgAAwDPD0ocffih37tyxvObcuXN07wYAAP4Zln788UezzYmz1gG61UmvXr3k4MGDyR0fAACA94UlbRnwzz//mMD0ww8/xB6PiIiQd999V8aOHSuZM2eWDz74wJ1jBQAA8I4C7+7du5s+SxqKpk2bJr///rs89dRT8tFHH8mZM2ekVq1a8vrrr0vu3LndP2IAAABPD0uqQoUKMmfOHJkyZYqsXr1atm/fLpkyZZJXXnlFnnzyyWQNSuuh9L31fcPCwqR06dLSu3dvqV69uuXrvvjiCwkJCUlwXMe1du3aBMe1P9T8+fPl/Pnzkj9/ftOZvEOHDskaOwAA8C0uhyUVHh4uFy9ejG1GmT59esmSJUuyBzVhwgTTvFK3S9Fu4StWrJDhw4fL1KlTTbfw+xk6dKhkzZo19msdl6O6q8mTJ0v9+vWlS5cusnfvXvP+t2/flmeeeSbZ3wMAAPDzsLRlyxZ5//335caNG/Kf//xH6tWrJxMnTjTHdFlu2LBhkj179iS/74EDB0yBeL9+/eTpp582x5o3by49e/aUGTNmmMf9aACyWgKMjIyU2bNnm+VCXUpUbdu2Na0O5s6dK+3atZOcOXMmeewAAMD3uNw6YPTo0ebP48ePl1dffdUskX355ZcmqOgmus8//7z89ddfSX7vTZs2SYYMGUxgsdFi8datW8v+/fvlwoULiXqfW7duxW69Et/OnTslNDRU2rdvb3dclw+1SH3btm1JHjcAAPBNLrcOqFq1qglHdevWjT2eI0cOGTNmjLz22msmjOgeckl19OhRs/QWf1aqfPny5vnYsWP3fQ9dVmvZsqXZ5Fdnjq5evZrgM1S5cuXsjpctW9Ys2R05ciTJ4wYAAL7JpWU4bRnQrVs3p/vDtWnTRipXrizvvPNOkt/7ypUrEhgYmOC47djly5edvlaXzvSuvIoVK0rGjBlNHdKSJUtMv6dZs2bFBjD9DJ29ypMnj93r9TUPPPCAOe+Mfn7c86dPn07y9wgAAHw8LCWmALpo0aKJqi9yVE+kocXRHW22885oQXhcDRo0MDNSOrukoUlbHtjeIyDA8beun2P1GcuWLXN4xx0AAPBNybobzsrdu3dNCwBnocQZrU/S18Zn215FzydF06ZN5ZNPPpE///wzNizpe0RFRTm8Xj/H6jO0lqpOnTp2M0vjxo1L0pgAAIAP1ixpHdD3339vd0zveps+fbrD67/55htTlJ1UutzmaBnMdixfvnxJfs8CBQqYu/bifkZ0dLRcu3bN7joNaXqdo2VAG/18rW2yPYoXL57k8QAAAO+R6Gkfbdx48+ZNu2N6d5oGqIEDB7ptQMHBwbJr1y5zN1vcIm9tKWA7nxR6R5yOvUyZMrHHbH8+dOiQaR9go19r+4C41wIAYCXiWqTMG7jea/6SMmYJkGqdykjJmoXSeiheI8WW4VyldUbaVVtrg2x9lnRpbPny5aZreFBQkDmmLQS0gWTcmZ3r168n6K+0dOlSc7xmzZqxx3SrFi3k1rv64oYl/VqbasY9BgCAs9AhEinapSb8qvNaV88TKTsWHSUseXNY0kDUsGFDmTlzpgk5hQsXlpUrV5rZoREjRsRep/2ddu/eLZs3b7Yr8G7UqJGUKlXKFGprnydtcKkzRfH7NvXq1cts1fLWW29JjRo1ZM+ePWZ7Fd0kWIMUAABWdHZGQ8fd245rYD11FkzDnTeN2RN4XFhSo0aNMjNIq1atMkt/Gn60M3iVKlXuW8y9b98+09hSZ6P0PXR26rnnnkuwDYs2oNTi8wULFsjWrVtNXZMuJ8a/ow4AAEd0GcvblrJ0udC7ZsE8g0eGJZ356d+/v3k4M23atATHdP+4pNAtTvQBAADg1g7eAAAA/iJJM0ta06N3wNmcOXPGPOv2JvHZzgEAAPhNWNIA5CgEab8lR5xthwIAAOBzYUkLoQEAAPxNosNSwYIFU3YkAAAAHogCbwAAAAuEJQAAAG/rs4T/dWL7Ofnze+/rDgsAgC8hLHkwDUqhZ2+J9+6ZBACA9+M3mgezzShpB4aseTKLt+1oDQCALyAseQENSt2mN0rrYQAA4Jco8AYAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALAQIB7ozp07MmfOHFm9erWEhYVJ6dKlpXfv3lK9enXL123atEnWr18vhw4dkqtXr0qBAgWkVq1a0qNHD8mZM6fdtZ07d5bz588neI927drJsGHD3P49AQAA7+SRYWnChAmyceNG6dSpkxQpUkRWrFghw4cPl6lTp0qlSpWcvm7SpEkSGBgozZo1k6CgIDl+/LgsWbJEtm/fbsJX5syZ7a4vU6aMdOnSxe6Yfh4AAIDHhqUDBw7IunXrpF+/fvL000+bY82bN5eePXvKjBkzzMOZd955R6pWrWp3rGzZsvLuu+/KmjVrpE2bNnbn8uXLZ4IVAACA19Qs6VJahgwZzHKYjc4ItW7dWvbv3y8XLlxw+tr4QUk98cQT5vnUqVMOX3P37l2JiIhwy9gBAIDv8biZpaNHj5qlsOzZs9sdL1++vHk+duyYWWJLrCtXrpjn3LlzJzi3c+dOM7MUHR0tBQsWNMt++gAAAPDYsKThRuuO4rMdu3z5cpLeb968eWamqn79+nbHS5UqZeqfihYtKjdu3DB1UR9//LF5f10CdEbP2wKYOn36dJLGAwAAvIvHhaXIyEjJmDFjguOZMmWKPZ9YWqf0yy+/mNonDUVxvffee3Zft2rVSl577TVZuHChdOjQwdxJ58iyZcskJCQk0WMAAADezePCktYnaR2Ro3YCtvOJsWfPHnn//felRo0a0qdPn/teny5dOtNO4Pfff5fdu3c7LfzWWqo6derYzSyNGzcuUWMCAADex+PCki63Xbp0KcFx29KX3sF2P1rXNHLkSLPUpnfIBQQk7tu0zSbpspwz+vmJGQMAAPANHheWgoODZdeuXXLr1i27Im9tKWA7b+XMmTOmqWSePHlk4sSJki1btkR/9tmzZ50WgwMA4CsirkXKvIHrxVtkzBIg1TqVkZI1C6XJ53tcWGrQoIHMnz/f1AbZ+izpEtzy5culQoUKsXfCaQuB27dvS/Hixe1mn4YOHSrp06c3DSqdhR6dOdIgpoXfNlFRUfLtt9+aeilHLQgAAPB2GjpEIiUmRiT8auJrgNNepOxYdJSwZKOBqGHDhjJz5ky5fv26FC5cWFauXGm2JhkxYkTsdePHjze1RZs3b449pgXaOjukIeuvv/4yDxudabJtl7J161aZO3euuUOuUKFCZksVLQY/efKk9O3b1+HdeAAAeDudndHQcfd2lHjTLFhMjKTpmD1uZkmNGjXKzCCtWrVKbt68aWqPtFi7SpUq961VUt99912Cc/paW1jS99MZKQ1IGsi0pkm3PhkzZowJagAA+CJdxkqrpSxX6XJhWs+CeWRY0jve+vfvbx7OTJs2LcGxuLNMVnQLlPitAwAAALxiuxMAAABPQlgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwECAe6M6dOzJnzhxZvXq1hIWFSenSpaV3795SvXr1+7720qVLMn36dPnjjz/k3r17UrVqVXn55ZflwQcfTHDtzz//LPPnz5fz589L/vz5pWPHjtKhQ4cU+q4AAIA38siZpQkTJsjChQuladOmMmjQIEmfPr0MHz5c9u7da/m68PBwGTx4sOzevVu6d+8uL7zwghw9etSEpdDQULtrf/zxR5k4caKULFnSvObhhx+WqVOnyrfffpvC3x0AAPAmHjezdODAAVm3bp3069dPnn76aXOsefPm0rNnT5kxY4Z5OLN06VL5999/5fPPP5fy5cubYzVr1jSvXbBggfTt29cci4yMlNmzZ0utWrVk7Nix5ljbtm3NTNTcuXOlXbt2kjNnzlT5fgEAgGfzuJmlTZs2SYYMGUxgscmcObO0bt1a9u/fLxcuXHD62o0bN0q5cuVig5IqXry4PProo7Jhw4bYYzt37jQzTe3bt7d7/ZNPPikRERGybds2t39fAADAO3lcWNJlsyJFikj27NntjtsC0LFjxxy+TmeFTpw4YcJSfPraM2fOmGU622eo+NeWLVvWLPkdOXLEbd8PAADwbh63DHflyhUJDAxMcNx27PLlyw5fd+PGDVMYfr/XFitWzHyGzl7lyZPH7rqMGTPKAw88YM47o+8R9/zp06eT8N0BAABv43FhSeuJNLTElylTptjzzl6nEvNafQ4IcPyt67XOPkMtW7ZMQkJCEvW9AAAA7+dxYUnrk+7evZvguM4a2c47e51KzGv1OSoqyuH76LXOPkNpLVWdOnXsZpbGjRsnKSFbrsx2zwAA+JtsHvC70OPCki6Zaa+k+GxLX/ny5XP4Ol0+01khR0to8V+rnxEdHS3Xrl2zW4rToKXLeY6W8mz0PZyNwd3aj///oQwAAH/U3gN+F3pcgXdwcLC5/f/WrVsJWgrYzjuihdmlSpWSQ4cOJTinr9WmlNmyZTNflylTxjzHv1a/1kJx23kAAACPC0sNGjQwsz5aGxR3aWz58uVSoUIFCQoKMse0hUD84ur69eubwBM3BP3999+ya9cu87422kpAZ6K0MWVc+nWWLFlM/yUAAACPXIbTQNSwYUOZOXOmXL9+XQoXLiwrV640W5KMGDEi9rrx48ebTt2bN2+265OkW5jodV27djV3vGkncF1q069ttCapV69eMmXKFHnrrbekRo0asmfPHrO9Sp8+fUyQAgAA8MiwpEaNGmVmkFatWiU3b940y2vvv/++VKlSxfJ1usymW5bo3nDaidu2N9zAgQMld+7cdtdqsNI74rSz99atW6VAgQLmuk6dOqXwdwcAALxJupiYmJi0HoQ3O3z4sJmNmjVrlmlqCQAAfIvH1SwBAAB4EsISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACAt3Xw9iaRkZHmOf4+dQAAwPMVL17c7AtrhbCUTLpnnRo3blxy3woAAKSyxOzAwXYnyaSb/f7++++ydOlSGTx4cKJf9/HHH8vLL79seY3OVmkIGz16tEm+SNzfmz+NLSU+013vmZz3ceW1SX0NP4O+9zOYFuNLqc/zh5/DjxN5bUr/LmRmKRXoBr3NmjWT9evXJ2lvuBw5ciT6ev2HZN+5pP+9+cPYUuIz3fWeyXkfV16b1NfwM+h7P4NpMb6U+jx/+DnMkcT3T8vfhRR4u0mTJk1S9Hp4/t9bWowtJT7TXe+ZnPdx5bX8DKYOT/4ZTIvxpdTn+cPPYRMP/99SXCzDebDDhw9Lnz59ErWeCoCfQcAXHfaA34XMLHmwwMBA6dmzp3kGwM8g4I8CPeB3ITNLAAAAFphZAgAAsEBYAgAAsEBY8mJ37tyR9957Tzp27CgtWrSQl156Sfbt25fWwwL8ygcffCDt27c3P4M9evSQrVu3pvWQAL+1b98+qV+/vnz11VdufV9qlrxYRESELFiwQFq2bCn58+eXDRs2yEcffWSOZcuWLa2HB/gFbZhXqFAhyZQpkxw8eFBeffVVmT9/vuTKlSuthwb4lXv37kn//v0lJiZGateubf7jxV2YWfJiWbNmNXcIBAUFSfr06aVx48YSEBAg//zzT1oPDfAb2ihPg5JKly6d3L17Vy5fvpzWwwL8zk8//STly5dPkS7f7A2XisLDw81/cR44cMD8F2hYWJiMHDnSzAw5WmKbM2eOrF692lxXunRp6d27t1SvXt3p+2tI0msLFy6cwt8J4J1S6mfwww8/lOXLl5vXPP7441KqVKlU+o4A7xOeAj+HoaGhsmjRIpkxY4bZRsXdmFlKRfqPGRISYqbtg4ODLa+dMGGCLFy4UJo2bSqDBg0yM0fDhw+XvXv3Orw+MjLS7J3zzDPPmBbyAFLvZ1CX3latWiVTpkwx/yeuM0wAUu/nUBtWdurUSXLmzCkpIgapJjIyMuby5cvmzwcPHoypV69ezPLlyxNct3//fnNu3rx5scdu374d07Vr15iXXnopwfV3796NGT58eMyYMWNi7t27l8LfBeC9UupnMK4RI0bE/PbbbykwesA3RLr55/Dw4cMxvXr1iomKijJfjx8/PiYkJMStY2ZmKRVpXUNiOpBu2rRJMmTIIO3atYs9ljlzZmndurXs379fLly4YFfQpjNK+l+yo0aN4r9ogVT+GYwvOjpazpw5w78DkEo/h7t37zZlKB06dDB3purG9vPmzTOzUu5CzZIHOnr0qBQpUkSyZ89ud1wL19SxY8dMUbeaNGmSXLlyxTxrcTeA1PsZvHnzpmzbtk3q1KljfgH8+uuvsmvXLunbty//DEAq/RxqmNIbnGymTZtm7lDVshR34berB9Lw4yh1247Z7rQ5f/68/Pzzz+b/pOMm74kTJ0rlypVTccSAf/4M6oyu/gxqrZLerqw3V7z55ptSpkyZVB8z4K8/h1myZDGPuLNPere4O+uXCEseSIu1M2bMmOC47fZkPa8KFiwomzdvTvXxAb4usT+D+l+8U6dOTfXxAf4gMpE/h/FpSYq7UbPkgTQVa68WR7dQ2s4D4GcQ8GWZPeh3IWHJA+kUo04/xmc7li9fvjQYFeA/+BkE0l6gB/0uJCx5IO078e+//8qtW7fsjmsDL9t5APwMAr4s2IN+FxKWPFCDBg3M7cfLli2zm3bUDsEVKlSIvRMOAD+DgK9q4EG/CynwTmWLFy82txvbphF1h/KLFy+aP2uPCO2+rf8jaNiwocycOVOuX79u7rBZuXKlufttxIgRqT1kwKfwMwikvcVe9rswnXamTNVP9HOdO3c2/9COLFiwwPSGsFX52/bD0f9B6V5Tuh9OjRo1UnnEgG/hZxBIe5297HchYQkAAMACNUsAAAAWCEsAAAAWCEsAAAAWCEsAAAAWCEsAAAAWCEsAAAAWCEsAAAAWCEsAAAAWCEsAAAAWCEsAAAAWCEsA8H+eeOIJu4fuS2WzYsUKc0yfPcGPP/5oN9Z33303rYcE+KyAtB4AAP9z7tw56dKli+U1BQsWlIULF6bamOJ+bosWLcyfM2TIkKKf9fvvv8uwYcOkevXqMnnyZMtr33nnHVm7dq28+eab0rRpUylbtqz07NnTbC76/fffp+g4AX9HWAKQZgoXLmx+8TuSI0cOSQsall544YVU+axq1apJUFCQ/Pnnn3LhwgXzZ0c0EP3666/m70RnkVS5cuXMQ4MnYQlIWYQlAGkallIrmHii9OnTS8uWLSUkJERWrlwpPXr0cHidzijpkmCrVq0kc+bMqT5OwN9RswTAK+iMyqBBg+TSpUtmSapt27bSvHlzGT58uJw9e9Zcc+rUKRk1apS0bt3anNMlq6tXr6bouC5evGhCTpMmTWTjxo2xx69duyYff/yxPP3009K4cWMz3tGjR8uJEyfsXq8BKF26dKYWKiYmxuFnLF++3Dzr9wUg9RGWAHiNsLAwGTBggFl60jBUtWpV2b59u7z66qsmhPTv318iIiJMANElqk2bNsmYMWNSbDwazvQzNTB98MEH0qBBA3P8zJkz0rt3b1m0aJE8+OCD8tRTT8njjz9uapT69esnBw4csFv2e+yxx0zg27VrV4LP0O/r0KFDUqZMGXnooYdS7HsB4BzLcADSjIaKL774wuG5ihUrSs2aNe2OHT9+XDp37iwDBw6MPfbhhx/K0qVLzbHnn39eOnXqZI7rLM2IESNMmDp8+LApiHan/fv3m/cPCAgwM0jBwcGx58aPH29mtCZNmiQ1atSIPf7cc89Jnz59ZOLEiWbpzUZnjHbs2GFmkB599FG7z2FWCUh7zCwBSNOwpKHB0eO///1vguuzZs1qZmzi0iUulStXLunYsWPscV3asp3TkOVO27ZtkyFDhkjOnDnl008/tQtKR44ckX379pmZr7hBSRUtWlTatGljZoviLsfVq1fPjF9nwm7duhV7PCoqSlavXi2ZMmVyWggPIOUxswQgzWiY0NmXxCpSpIhkyZLF7lhgYKB5LlWqlAlIjs5dvnxZ3GXDhg3yxx9/SOnSpc3SW548eezO25bYtGbJ0azZ33//HfusY1a2MKR3tWkx93/+8x9zfOvWrXL9+nVTD6XBDEDaICwB8BrZs2dPcMzWC8nqnM7QuHP5LTo6WipVqpQgKKkbN27Ezj7pwxmtrYpLl+I0LOmymy0ssQQHeAbCEgAkQd++fWXLli0m2GgY04LzuGyhbfDgwdKhQ4dEv6/OVGlR+sGDB+XkyZNmJkkLwgsVKpSgjglA6qJmCQCSQJfMtIC7Vq1asmDBApk+fbrd+fLly8fOQCWVrTXAL7/8IqtWrTIzWLbWAgDSDmEJAFwITOPGjZPatWubLVn0bjibChUqmMe6devMI7579+7J7t27Hb6v1iZpTZYWdesSnDattG29AiDtsAwHwCNbB6hnnnnGYztWZ8yYUcaOHStvvfWW6aekrQq0aabSY6+88orp8aTLddojSb8P7cekd8qFhoaaQu74dAmvfv36ZlZJC7u1dYKzLVAApB7CEoA0bx3gjPZM8tSwFDcwvf322yYUaWDSWiVtRDlnzhyzTKd7uml3bp0l0rvzKleuHNu80tlSnIYlpUtwANJeuhhn/fUBwA+3VKlSpYpMmzZNvIV2M+/SpYtZrtOtXgC4HzNLABCH1hNpaFJr1qzx2JmtH3/8USZPnpzWwwD8AmEJAP5Pz549HfZp8kS6fUvc8WpdFICUwTIcAACABVoHAAAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAiHP/D5QsPvex7gJWAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "expectation = psr.get_expectation(spectrum)\n", "\n", "ax, plot = expectation.project('Em').plot()\n", "\n", "ax.set_ylabel('Expected counts')" ] }, { "cell_type": "markdown", "id": "eb702d77", "metadata": {}, "source": [ "## XSPEC support" ] }, { "cell_type": "markdown", "id": "3527d9e4", "metadata": {}, "source": [ "You can also convert the point source response to XSPEC readable files (arf, rmf and pha) if you want to do spectral fitting or simulation in XSPEC." ] }, { "cell_type": "markdown", "id": "5cdab7ad-7115-4443-852d-bb616190c207", "metadata": {}, "source": [ "
\n", "Warning: RspArfRmfConverter currently only supports local spacecraft coordinate. Therefore, it is limited to short-duration events.\n", "
" ] }, { "cell_type": "code", "execution_count": 24, "id": "67f043ee-bd57-46b2-bf39-7b5d696e9b7c", "metadata": {}, "outputs": [], "source": [ "from cosipy.response import RspArfRmfConverter" ] }, { "cell_type": "code", "execution_count": 25, "id": "9097214d-a2f5-4ab6-ad81-6dee7d7f52ec", "metadata": {}, "outputs": [], "source": [ "response = FullDetectorResponse.open(response_path)\n", "rsp_converter = RspArfRmfConverter(response, ori, coord)" ] }, { "cell_type": "code", "execution_count": 26, "id": "9246e074-ed42-4454-885b-1b6a784e8741", "metadata": {}, "outputs": [], "source": [ "Ei_edges, Ei_lo, Ei_hi, Em_edges, Em_lo, Em_hi, arf, rmf = rsp_converter.get_psr_rsp()" ] }, { "cell_type": "code", "execution_count": 30, "id": "89823005", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rsp_converter.write_arf(\"Crab.arf\", overwrite = True)\n", "rsp_converter.plot_arf()" ] }, { "cell_type": "code", "execution_count": 31, "id": "eab980df", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'numpy.float32' object is not iterable", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[31]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m rsp_converter.write_rmf(\u001b[33m\"\u001b[39m\u001b[33mCrab.rmf\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43mrsp_converter\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot_rmf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/software/cosipy/cosipy/response/rsp_to_arf_rmf.py:465\u001b[39m, in \u001b[36mRspArfRmfConverter.plot_rmf\u001b[39m\u001b[34m(self, ax, file_name)\u001b[39m\n\u001b[32m 463\u001b[39m matrix = data[i][\u001b[32m5\u001b[39m] \u001b[38;5;66;03m# get the probabilities of this row (incident energy)\u001b[39;00m\n\u001b[32m 464\u001b[39m indices = []\n\u001b[32m--> \u001b[39m\u001b[32m465\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mf_chan\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 466\u001b[39m \u001b[43m \u001b[49m\u001b[43mchannels\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_chan\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43margwhere\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf_chan\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtolist\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# generate the cha\u001b[39;49;00m\n\u001b[32m 467\u001b[39m \u001b[43m \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannels\u001b[49m \u001b[38;5;66;03m# append the channels together\u001b[39;00m\n", "\u001b[31mTypeError\u001b[39m: 'numpy.float32' object is not iterable" ] } ], "source": [ "rsp_converter.write_rmf(\"Crab.rmf\", overwrite = True)\n", "rsp_converter.plot_rmf()" ] }, { "cell_type": "code", "execution_count": null, "id": "659022af", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:cosipy]", "language": "python", "name": "conda-env-cosipy-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": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 5 }