{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
\n", "\n", "**This is a fixed-text formatted version of a Jupyter notebook**\n", "\n", "- Try online [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.11?urlpath=lab/tree/simulate_3d.ipynb)\n", "- You can contribute with your own notebooks in this\n", "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n", "- **Source files:**\n", "[simulate_3d.ipynb](../_static/notebooks/simulate_3d.ipynb) |\n", "[simulate_3d.py](../_static/notebooks/simulate_3d.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3D simulation and fitting\n", "\n", "This tutorial shows how to do a 3D map-based simulation and fit.\n", "\n", "For a tutorial on how to do a 3D map analyse of existing data, see the `analysis_3d` tutorial.\n", "\n", "This can be useful to do a performance / sensitivity study, or to evaluate the capabilities of Gammapy or a given analysis method. Note that is is a binned simulation as is e.g. done also in Sherpa for Chandra, not an event sampling and anbinned analysis as is done e.g. in the Fermi ST or ctools." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports and versions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import astropy.units as u\n", "from astropy.coordinates import SkyCoord, Angle\n", "from gammapy.irf import load_cta_irfs\n", "from gammapy.maps import WcsGeom, MapAxis, WcsNDMap, Map\n", "from gammapy.spectrum.models import PowerLaw\n", "from gammapy.image.models import SkyGaussian\n", "from gammapy.cube.models import SkyModel, SkyModels, BackgroundModel\n", "from gammapy.cube import MapDataset, PSFKernel\n", "from gammapy.cube import make_map_exposure_true_energy, make_map_background_irf\n", "from gammapy.utils.fitting import Fit\n", "from gammapy.data import FixedPointingInfo" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r\n", "Gammapy package:\r\n", "\r\n", "\tpath : /Users/adonath/github/adonath/gammapy/gammapy \r\n", "\tversion : 0.11 \r\n", "\r\n" ] } ], "source": [ "!gammapy info --no-envvar --no-dependencies --no-system" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulate" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "filename = (\n", " \"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\"\n", ")\n", "irfs = load_cta_irfs(filename)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SkyModel\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- ----- -------------- ---------- --------- ------\n", "\t lon_0 2.000e-01 nan deg -1.800e+02 1.800e+02 False\n", "\t lat_0 1.000e-01 nan deg -9.000e+01 9.000e+01 False\n", "\t sigma 3.000e-01 nan deg 0.000e+00 nan False\n", "\t index 3.000e+00 nan nan nan False\n", "\tamplitude 1.000e-11 nan cm-2 s-1 TeV-1 nan nan False\n", "\treference 1.000e+00 nan TeV nan nan True\n" ] } ], "source": [ "# Define sky model to simulate the data\n", "spatial_model = SkyGaussian(lon_0=\"0.2 deg\", lat_0=\"0.1 deg\", sigma=\"0.3 deg\")\n", "spectral_model = PowerLaw(\n", " index=3, amplitude=\"1e-11 cm-2 s-1 TeV-1\", reference=\"1 TeV\"\n", ")\n", "sky_model = SkyModel(\n", " spatial_model=spatial_model, spectral_model=spectral_model\n", ")\n", "print(sky_model)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Define map geometry\n", "axis = MapAxis.from_edges(\n", " np.logspace(-1.0, 1.0, 10), unit=\"TeV\", name=\"energy\", interp=\"log\"\n", ")\n", "geom = WcsGeom.create(\n", " skydir=(0, 0), binsz=0.02, width=(5, 4), coordsys=\"GAL\", axes=[axis]\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Define some observation parameters\n", "# We read in the pointing info from one of the 1dc event list files as an example\n", "pointing = FixedPointingInfo.read(\n", " \"$GAMMAPY_DATA/cta-1dc/data/baseline/gps/gps_baseline_110380.fits\"\n", ")\n", "\n", "livetime = 1 * u.hour\n", "offset_max = 2 * u.deg\n", "offset = Angle(\"2 deg\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "exposure = make_map_exposure_true_energy(\n", " pointing=pointing.radec, livetime=livetime, aeff=irfs[\"aeff\"], geom=geom\n", ")\n", "exposure.slice_by_idx({\"energy\": 3}).plot(add_cbar=True);" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Tried to get polar motions for times after IERS data is valid. Defaulting to polar motion from the 50-yr mean for those. This may affect precision at the 10s of arcsec level [astropy.coordinates.builtin_frames.utils]\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "background = make_map_background_irf(\n", " pointing=pointing, ontime=livetime, bkg=irfs[\"bkg\"], geom=geom\n", ")\n", "background.slice_by_idx({\"energy\": 3}).plot(add_cbar=True);" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "psf = irfs[\"psf\"].to_energy_dependent_table_psf(theta=offset)\n", "psf_kernel = PSFKernel.from_table_psf(psf, geom, max_radius=0.3 * u.deg)\n", "psf_kernel.psf_kernel_map.sum_over_axes().plot(stretch=\"log\");" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "energy = axis.edges * axis.unit\n", "edisp = irfs[\"edisp\"].to_energy_dispersion(\n", " offset, e_reco=energy, e_true=energy\n", ")\n", "edisp.plot_matrix();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have to compute `npred` maps, i.e. \"predicted counts per pixel\" given the model and the observation infos: exposure, background, PSF and EDISP. For this we use the `MapDataset` object:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "background_model = BackgroundModel(background)\n", "dataset = MapDataset(\n", " model=sky_model,\n", " exposure=exposure,\n", " background_model=background_model,\n", " psf=psf_kernel,\n", " edisp=edisp,\n", ")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "npred = dataset.npred()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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vtvyZaupYErGBrURlmkm3rQ1Th6Zi5b5WznfDiF3FUKpy102hqQUTQ8q+QcmY+uzj9rnElEtHIfGscJGr71gTck+pw+z7yLepL+4j5Nh6tP7DyhtTr1ZGK+sq50oL1R2D0AChgevnqBEyMFexBrFkbtJ9pJz6iDaXknbZMlr9UpX7Pgvf4Kcs33RwE3Cfe6ovXpR4D9GUyJsq7VAa4Cbwtr54rNftq0duNxnITPHRY9K1OttC1teG0DVC1tSu1p5Lhcr6NDI3dfjKG4SWpNX6G7JQYsncR+S+emIGJO39WFWv9S2/Ei8Dm1kRS+CpZC73U5Q7sGyfDBzpsI4ZxFgvMQSb0pZWVwpx2/lj0rV6u4DdhovUfYTuI3MgrM7tOjRFLOvS+qwpzVQvXOb1qfKYgU3XjSBEtj5odpJLhcdAG/hsh6LEO4WPqO33FBLXtu00mxhd6ttHzK7zaOKJh1R3zqVnU8l73ZEoLpKV0EhbptvHQupctmfX7VLnsj7fyocpZC7hW4626cCmtJtiYspjiT1GhbvIflM9cSLaBfARADuo+PcdzPxKK88OgLcCeCSAWwA8m5n/zlVn70hcDvDEErH9nqLCtfIuEnapbxd5+0i3aay4ltfVplbWVc6VN5QeiufOCV+onkGMQneRueuYr1375mHfEHzkLOtMHdi0+2qraq0uu80mFozvARR2v2PSfOnmmGnPZbW0R1Ylvgfgicx8OxFtAfgoEb2PmT8h8vw0gH9k5vsT0QUAfh3As10V9o7EAb+CDKnntiSu2SNme1MGNbW67OPafqr69pHAKsk7po0YUndZIUC6OrfbDfnnIU/Z1OUi8yZ5JVHbqlZT17EDm3bdLoSIPEaNu9I3VYkzMwO4vd7dql/2V+/pAP5tvf0OAJcQEdVll9BLEgfiFHNTEpfbMQRrH/epdrufrrrle+xkH5nXlV/7YjexTmLqcdUXU1dTuH5qqSq9KaHbx0M2iWnLpzQ1gnTFj2sI+eH2sZjy8pjr30GsKo/Bej1xU2sUziCio2L/Mma+TGYgoiGAawHcH8BrmfmTVh33BvBVAGDmCRF9E8C3AziuNbhSEieiNwF4KoCvM/ND6rR7AfhDALcB+Elmvt1TRUFBQcGKkTTt/jgzn+etjXkK4GFEdA8A7yKihzDz50UWTQ85h3Py/uMI4y0AzrfSXgTg5wG8AcD/HVOJSzE3VeGu8rE2R1MVbpd15dHa1voo82p91ewTW7m7fH67bV89rvokXHW1RUy9Q+ulgeA+L+36xxx3tUtwt6d9L2V5U0b7XphyJr+W1z6m1RUq7yory2nXJfU3rH2XQ+XywHjiMa+EWplvBfBhLHPiTQDOBgAiGgH4NgDfcNWzUiXOzB8honOs5CHmVyApiMH3A9K2tXIhogr53C7LImd0inYcVj5XffZ200k62rWOrctVPuZYLHyDkKF8If88FH9utxNrt2jXyvWw59A1kv61L3+TST6u8rZHDqXuFNumCVzRPgOszU7xgojOBLDPzLcS0REAP4hq4FLiKgD/EsDHATwLwIdcfjiwGZ74JQCuAPBNAM+JKeBTza48rnKuMkA4xntdBJ7iYfsIvAl5bxJx++pz/eRsgtHgivYA0qJbXP3xHXddy9AEn1CEiqu+ENn7pt43qbsNmfsGPUP77ZHtlnAWgMtrX3wA4E+Y+WoiejWAo8x8FYA3AriCiG5EpcAv8FW4dhJn5q8AeLwvDxFdAeAZALCztYXT6nQfEccSuCuvi8BT7I+Ycq6yvrplHrsObd9XLqa8XYevLte/IxcGGX9pM/E7i1HhIeJ3KWWJWPOyCbHLfE1iwmPy27Mx7b40JWRN8bsmDmnXwUXYISK36zh27BiI6A6R7UpmvlDpsgf5QgyZ+XMAHq6kv0JsnwTw47F1rp3EY1Bf9AsB4DtOP50Ht96qWiCATo4xBC7L2JMsfCTrU+6x5ByTJyY+3FWnVk4rq6U1Ue/OtADrUCKhs/W7suuf2cet8rFhgxIxoYs+T1Bbb6UJYibmhGZsyrpSLBZXqCEcx7QbiKs/NknLNrX2XTcAs3/uuefilltuOdVzanGwv0wbhF6QuA2bhHOocfNuK+mQwoY47iLwFGUdQ94pqrnpZJ0c1gvgJu4Ywh7WeaaO34+rDkPuWtsutR5S6VoeIBw+GaPcXdBme/oUceqMTc0igXJcU9AaWUM55mvTFUuuKWp72xVDbpfLAmZgUtZOAQAQ0dsBPAFVLOVNAF7JzG9sUpdN3i5iT1HjPgL3WSvAaghcO19Yee06XfldaTnqsclTI9uhVpEDKXmnM709jdh99kubgVKJmIlNsXHrGpGZNuz1T2DlscnYRdSyP67Lbvxw38CmdgPwEbnss0HIRnHd1GImTiWB4VYSG4BVR6f8RI56bDLT1LjMFyJwm1BTCDx0LEVZh8g7B+nG1BFTj50WIm6NiEP2ymCQ9i92Nltux/z2ZH9cSj2V1LV8S30KHAfiJyJpE5A0IjdgK08Mkdt1+VS5S3XHRKjYbcn0VCK3r4ksmwdc7JQu4CLvgXIsF4G7FLtsO3ZgU/ZLbodi0jU0IW6tvpBlohK5SJREaZOpTZguAo/JF/t7cpG6Ruh2W6l+uguSdGLgI3SbVGFtD60yth8dq45t+yRE5Bp8C2XZHrr8B2Cfm+y3va0huwo3sAdgNgi9JHGbvDVyTiFwnxWiqWSXvZEamSLLNx3YhJXPtS/rsNHkJhBD3iaPRsSxpK7B5E0VRzapNyV0IJ2cU/MD/lBHH5HLMlLBu4jYp9hlXS517XvwRCyRG7gGSUMqvOlNNgpclHhWyA9ZU90SGnGb7ZhJPCEitkl4XQObvvQ2cd0LNynrYApx+wg7RpG7fj+23ZJiv0hCty0XV9RLTjJPKSMVtkbK9rZWBvArartvso+aKrePaYOwLpIHdNtFSw8RueynQX4VjjKw2QVc5K0Re4jANTJOIXxzzG7Lp+ZlGVjHfXXbeW3Eqm1XPUv7DsUNzElQI+UQmccMftrtSNi+9kzsh4h8Nlssl5PMgW7UeVMih1LOReQhspd1aWQty2rHZJ1aXRDpMUTuQkyeNBQlnh0hks5N4H0a2DSIJW1XeopdIrdD77I+V10hzKxf6bQmYo10fb+9WDIHdKvF+Q9BtuFufil/Ck34iNxuWxJwiMiBZcUt64vxtUPK2yZybamAGCL3kXVeOwUlOqWgoKCgvyhKPDtsWyFWiadEm2gq3FUPrDKyrhRrJGXavY1QPHKsAgfSVLj2bqfZytun4GMgf0+2mgYq0ZQSTBBS5EB1Din2ykEe00ZEPzTlK+GKPnG1Y6fFRJwY+B4IYY67ZnnGzu50teVqQ1Pe9jXKa6PIhgqJZ8dAebnyAOkDj6sa3DTHDWIsFBs+AveVT7VQQvaJ3JbE7fLHQwOcPjvEpMt3k9+8TwUh88xfn12HPO82XvlBHtmOO5taxjVgCegTfOwytu3hmw6v5bPr0EjZtnN8T/7xWS4hIpd9dPUdjmONweh/iCER3Q/AA5j5P9fLJ46Y+bZuu+aGj8Dt9JiwP03x+pS0i4ibELiEptJl/VpeH3Kq71jlTYPFMpoyDyl1HwyhDjH/XUkiXyB1sY9BVTblx90FmdddqfJF9kOSJKATORBH5qGBTFnWQCNz+3iI5CGOuyJagDCRxw5y5qXcnk+7J6KfAfACAP8EwD8FcB8AlwJ4Urddc/QHbvKGdSyWwDUy1pRyk+gUrX92vbK8hPYFzU3egE7gMeRt3n3k7bJX7LxaXzRoChyoCXq2TOh2PkPmNgFrSt4m5eEgbLHY55DTavFFngDLqlxrQxK5Ka+pcldkiqbMQ2GKvnhx7eaRQuQrwSGIE38hgEcB+CQAMPN/I6Lv6LRXAUiitAm9iQI3kKrZl19T2TLdHHOVk8dNHq0OLW8ITdU34CdwnwI3pKyRtyR4V31tSdyobrMvCd1F6lPoES2htmNVuTyfXMrcJnLArcoBtzLXyFSbri+hkbldjznmiyW36/Wp+FB+U8ZGJwTfcxLfY+YxUfUx148Lcj5lYpXQFLlErIXiUtNNCFyrTx4H3KQ8cGwb5Bq8jJm4EyJcm5x95D0aueuxVbnWP/v3Yw9gzqyXIXBD5qaMmke5PrY3bit0c51iVLk8nxxk7iJyQJ+mb+BaEdEmYFnOtypi7MCmK90mbFd6yg0BIl9WMHpP4n9FRC8HcISIfgjA/wPgvd12yw+NuG0SdRGoS2m76k6dem+3Y/czRoHHEL6ET3k0JfAQiUsClyRtyHvLShuNlgc7fYrcBU1ZG8K2CX0y0e0Wc8wgpMo1q8Wch03kQD4yB/xK00XmBj6FbhOmJHNTViNz28rwWSKuuHAf+ecicnmu7cG9jxN/KYCfBnA9gH8F4BpUDzVeOwbWy3Uc0Acem862DIUJpkyhd9Uj82tltPyh9KYEHuN7G5KWytu8S1L31euyVeyQQsBP2tMZMJ0sEq48hknVN58ql5643I8NRzTweeYx4s5F2KFjgB4yCCx74DYxu3xw3xopWl0hIrcJVvPJNcI2x7Q6JAbQ151JBnP1hdpQBEmcmWcAXl+/NgI+4nZFlrjCAn3euCThmFmUvoHMkMXiq8tGSH2ECNw3fT6WaG0FbhP3aKSnmfZtFW+3b8Mmcvs1qYnZkPVkMCdzU/dkggN7ZSDKSdhELq+dy2Yx11ATazlsFsg+eY65jgPxKttuK0TmklBN2RQi1/qu+e1aNEsoaiardu6jnUJE18Nzo2Pm7+2kRwHYH7whZZfKDYX+2XWFBjchymikmzrV3rUvERr4DNXjInB78k4MgZs8hnyHo7n6lmlbI53QZVmp3rXBUwnbRrGVuCHv/UlF3IbQJ3Vdk8mcyE09E6HIbXsldJ1TiRzonszl8YN+hquMgm/tFmBR8WpKWqb7iBxWfmCZ3DU/XULWl02J95HEATy1fn9h/X5F/f6TAO7srEeR8KlxSeyh2G1Y6VodgJvAU4/LY9q+Lx48twKXeWII3CZiY5PYZO17mX5sjRbrNG0Bery4IUdtIHMymZP4qN7enyyfl0wzpG3bKwfp4nraForLZpF9T1Xlpo+mzhAkUabmCxGyVOQzRzn7eEp0io+EZbpLebvaDd3gWiETiRPR2QDeCuCeqLp6GTP/npXnCQDeA+BYnXQlM7/aVaeTxOun0IOIHsfMjxOHXkpE/wWAs9KuYf/GfX63nT+Ud6ikp6rrEMH7zkMiJiZcq+cgPaMC1yyRLUHOUn2b/Z1tncTNtjYAqqlxGRpo9g9sk8kike9PKiI3ZD4eL56bbZ8YewWTed8kkZvrqxG5a8BTXmObzEOqXDt3Zz6Tx53Fmc9F5Ca/Zq3Icjb5y7q1YzFEril4X16tXyGh0xj5lPgEwC8x83VEdDcA1xLRB5j5i1a+v2bmpyrllxAzsHkqEf0AM38UAIjosQDaPz26JTS1bUPztV3euMw/UNIg3tsSuPZFCxG4k6i1NCsxdhXCGAI3dUnC3rbIWu7L7a3t5bK2GrdJ3BUXbvxuSeLjsaXEx/N6zHF5fjapDyxv3eCATBQLxb7udnoTr9yuU56/mgfNVflBf+AmcntfErlLrdvWh4vITRlAV9Oa8tYiUzo1OzhfdAoz3wzg5nr7NiK6AcC9AdgkHo0YEv9pAG8iom+r928F8FNNG8wBjYA1srbTtPKuaBNfdAocZUJ5QsrbIIbAY9Q34I5Akfm1AUU7DFAqZ0Ana6m+t7arvNvbi9umrHzRSJPiw/m1mE0xtFicJ7MD8rZ9cEPmw8GiEteuj4SxW0wfXT65S5Wb+lOIHIgn81yqXPOrgcWBSzuvr6xNpi4fXCNyrc+yPtfEotjJQFmQ9rT7M4joqNi/jJkv0zIS0TkAHo56IqWFxxDRZwF8DcAvM/MXXA3GRKdcC+ChRHR3AMTM3wyVKSgoKDhUiJ/ae5yZzwtlIqLTALwTwIuZ+VvW4esA3I+ZbyeipwB4N4AHuOqKWTvlFdY+AMBntHcJeVd2hRNKSBXuCj201XRMiCGUclDyaSo8VoHb5bT9hWMNVbi9b6wGaXVIDxxYVuFm32zLl0wHas98u658tFU3Qu6/BcCinzKZgiYTDGdTDMdjbE9mmEzBZ3wzAAAgAElEQVSqfwJGke+NF6syVoqtzFVM9Dyu8MNYNQ6sxloB4u0FW40Dfr/b3nfZMC4PXFPqEq4BTblv168huxrPGJ1CRFuoCPxtzHylfVySOjNfQ0SvI6IzmPm4Vl+MnXKH2N5FFbVyQ1q380MjYAPf4KRmoxgMlHSNwEPEHIpU0fprI8VCAdII3M7rCye0QwbtwUmNtI2FsmOlDbfrhre3ge0dYDR0E7mEHNmcsRjJ3AfG+6DJBFvjPYxGkwXf245r3xt7LmDdzHCA6lcxWe4GoKzd4RjcdBHuKqwViD5q2WwijiFyWZdNxrbdkUrkdp9jZ26afsqyMm82ZAwxpEoFvxHADcz8O4489wTw98zMRPQoVKd3i6vOGDvlt60GfgvAVSkdz40Q0boIXCvvInZZR4z/DSz3yZXPzmujCwLX8voGM6UKl3HgQ8sT1167u9X7zm5F5FvbqBN3qsLbWzWJjyoi394GBsNFIpeYzSryNtuT/Xk4yni/JvMt0HgfO+M9jE5OlmLTXQLfgGfzGxQmAA/m+zK2XLuWviiVFI8cWPzscoQj2oTtSg8RuV3GRcY2Wct6XERuE3No5iZEGRlhYxN5VjWeT4k/DsCFAK4nos/UaS8HcF8AYOZLATwLwM8R0QTACQAXMLPzvhSjxG2cAuC7GpTLjhDRAnERKq71U2Km0Mt27PbtvKnEHXUs8ptqk7cGbTErexLPjrRERjp57+7Ot4e7I2D3yJy4AWB3x6PGQ0pckPgBgY+r7ZMngNEIw8EJHBmNo87Zrl5iYtkq5t0VrZKLyA1ibJZYVR5L5IA+RV+WAXSiBvxPAoq1VuzIFGCZ9LX6bcSsOxSFjEq8jvLzdo2ZLwFwSWydMZ64nLk5BHAmgNfENtAFNLUtIQk4hcBd9Uq1DrhJ2SZ6X16tXMoxFym1sVFMmvTBJZFLT9zYJUaR2wS+uwvQ7nZF4Ls7cyIHFkl9e3uRyG1fXC5+AgCzaa3CpzV571UEPt6r6jm5V6n50RZ2BicwqAPB5blrU/jlvnZN7Do0f9y+tvJYqk9uEBtX3sZesRGKWjH1aZEqIdUt+2L3R1PTNpHLfBqRh4i9ERhV6NKGIkaJy4DzCSqvZq1nZJOqi6xjCdyXLttyWScAvATuO48mx1MIXCtrWwyuJWVdszGBecigrcQPCPwUs2NIvN4H6rTdRVtlewtzS8WjxJdU+E5F3OOtuZofjYA7q6GcLZwwlSxdC57NB1tNM4ZQ5eJWbJF4jK0ij4Xiy2NVuemzhqb2ikulx9grsrxU8SEil8clNJJ3pdnx4wNxLPS7Skd/p90b/DtmvlAmENEVdtqqQNZ2aMDSReAGocFRrd2UqfB2/tgybQhcyx+yXTQbRQ5s2iRu4sLNIKYk8QMCP+XUisDN++6RqrBU5rtH5iQ+GlVEbivx2axS4ICuwkcjYLy9qOStk97CCcxmM3XxLEPeo3rb+OPmd0uD+nszmBM1ELZV5LWNIXKgvcUSG8FiEzmwTOYuewXQSdqUaUvkmu8d8xBl7TyzoefP2Hyw3KkfCvHIbroTB01ta3mkqoaV1xedYqdJ+Ag8RoWvgsB9j1azOU5bXtYOMTSKfChIfDSaR5/s1IOY29vGQqkJ/JQjFUmfcmRO5MBciUs1bkjcELGE8cHNtrFPxjvV9vbW3EYZDKp3BTuzOw6qUIl8Mj/nmRzoxPJaLMCccDXi9JGpz/7IocpD7QO6Ak8lc9+65CEiN/X5JuzIelNWMbQHRVujrwtgEdHLUI2aHiEiE7dIAMYA1BlIq0JMLLhEaPamDZvcQzHi9n4b1Z6LwGPLm2PyJafbS0K3Z11KG2VHDmJKH/y00xbJHFhU5obEt7ZRXa0RFj+xGTCcAlvmRzQFdoUKrwczl1S4jVrNb89OYl9M1Tf/Lmaz+iY1W/TFzfUZDipbRZLjcFCTS2AQU0v3kaz8DEN+eZuBTxfRxZK5TdKyTh+Rw8qjDZy6olM0crfLyvLZ0EcSZ+ZfBfCrRPSrzPyyFfYpCto6JxJSrcs0Wd5O9w1mppJxKnx1arzksk/sMnaEhiSnkAqXvvhQvAOLdsr2NrC1OxDkvTNX4kaFm3cAOOWUOYlv7QLYArCDOYHbt+IZgFqJYwoMx8CRPWC7HswcbcEbY27eZzPQZIIjswl4Nl93xby2RsurJGrXZsFSUdS4yz7RCDU0KBlS5l0Rue+Yy/8G3NYJAnm09kKDmnZfOntmZNq0+5XDp8QfyMx/A+BPiegR9nFmvq7TnnmgKWWX522nxSyCZdftg13eN/gZW8fCsYQ7RlsVburQCN2Q+ZZlpxg/vGLzLWGVGC9cEPlpp1WFjTKnI6jI27ykCrdJ3PzUJ6j+DJ4EhjvAqdvA6M7Fk7Bmd86394HdIxjO7jiY5bk/WbxJSdvEdyM0TdhqPETUTYkcCMeWN7VXXMpbHtOO22rb5HeRNBx5XO1pKtsVjgjo0S3Z0NPHs/0igBcA+G3lGAN4Yic9ioRPhQO6Z+7zx311u2LQfQRuQ+triJ9TBjG11Qlt8nGVcXnnhtCHo2VPfChInbZHlSVyMFgpolIkgRs75cipqKYbHKlf21hU49rP0fyIxgD26jInqnw71gQhI6Mn07mXXhN4NTC6jdH45EH/p9a7UemkXI+JZbX4BjDt41o+X5qNVahyIF2ZhxAiewNbTadM+jHlO0FfPXFmfkG9+WRmPimPEdFup70KwCZgn+dtp9l5JWJJX9v3zchcJYGH6jQK225Hs1KML35A5oPFEEPjix9ElxwoceGJn3Lq3D45YlYwPg1zApdqfAvVV9J8Lc3PfoaDBU0OCHyMBeW+BeAUi7xNOCIwn+G5XQ2GDrfHC2pcnjMNFqNTeGZdO2GZyEgVTY3HRKzYn8UqVHlK9ErsMTuPRr6+dVkAd8SJnWbbKrKNyJ9DGjaYxGPO92ORaQUFBQWHEzyLe60BPk/8nqgWKz9CRA/HXGzeHdV/4bXCqGuX76w5q67BTF8cd2hxKs1CCXnhOVW4q6xmqfjySvtEqlGjxl1rp9BoUIcI7sx98e3tRW/c+OMHX5sjqJ4rcgqqNdV2MbdUjBoH5lpLDmwaFV5bKRI7DMym89CTyXSuxMd7dR/rkMTxCKPR+MBS2RfnOJkAU2GZyH8oJkpFU9fmWjrtigg1Lj+XLgc826jxhX5gcfDSNRnIzqvZIcCyJRJaM0WW74xC+2qnAPgRABcBuA8AudrWbahCD9cC2y0FFsna5YVrvrYvJtxlpaQSeMiGsZFK4K4n9dh1ahEpWhl7gJOsd1P2YOKPWcRqNJqT+e7O3Bs3XjntoCJroCJx6YnvYO6N29EpZlDTTK0cQ79FTwFMgN16Nufuzpy4gbpv44Up/sPReGFpAdtKktdiqlwnaaeYHkhLBXCTvV1P6rGDfgWIHHCTeVMi9xG8L+xQy+eqI2SfxIQnZkNfo1OY+XIAlxPRM5n5nSvsUxRCk32AZbU+sNKBeBVut7NuAvf54C4V7tqXJG32lwY3hSIHKkU+GGA+1X17a74i4UhM4NndqcMIj2BO4obQpS9uBja3sfgJ2Z64iSWXP3NzfArQXnXzGI/nyhuYTyYy/atPZms0wb4VD2+PG9jXxPjiTRAzgGnnB9xl2i6kldqfg3JYJlRfHrNthyeaYxIpUSmyjk61ck+VOACAmd9JRP8HqpmbuyL91V12zIeYgcsUAh9Y76HJQE0slBDaErhPhce2a4cWyjRNndJocKBq557LUCyoUivzA5tEKvFdzBW4eTdWin173sfip2W2DYFP67LbVV3b+3NrZ1TnPejn1ryfg8HSzco+d+1dXq8m0SextkpMW0C3RN6EHF2DlzHEL4/5lLdN7jLNpGdT44x+kzgRXYrqv++/APAGVGvdfqrjfgURWslQwiZpU95OA3SFPlCO2/W42nTtLxxLIPAQtLpkmnxQgjyuhRvaL0lwo1GdydgTg+EiQR7YFiPMVXa9iqEk3IVjJsTQhBkC8z/n9pWe1vmnoux+VS8Ji2dUtymXuh3N12exbRT5z0NaKDLcMOSL29c25vefg8iB5j55EyK3SRZYns1p59OIWZsMBEfaWib7IN+DkrtADE08lpmfC+AfmflVAB4D4Oxuu+WHRpzSSZUqXFPZmgIPETwhjsDtuux6FtIVQj1oz5Eeq8I1otYsAm3ftKH54QuK/IABSShxS+2SIWVD5uY1FGnb9fa2OLYljpl3Q/ym3EjkHYn94bwfS3cjMbOzfhiztFK0axP7b2aoXGPX9Y75zEJ1aBgO9O+IQah/zn904a55MXBsu35DQyz/I7aFlSbABtDLt4JR4jGvNSBmASyzluedRHQvVI8JOre7LsXBRdauvICbwCGO22W0emPVtysN8P8YmxJ4DAm4ysf45+qPXCXH+jUyhGz72CPrZYh4IN6lBtuat3eQXyNw8TO2O7xE5pUa1wZ6Y60pn0USA5ftYuAb8PQdB/wWS9PIFU2R22nSJrEHL21FDiz75HZ9oQFN+ZF0NtkH3PtVDK8monsA+E1UT2FmAK/vtFceyLutBpvYYwg8tGCVaxDUbjcmDWhGtrETemT9sWRkT/6xjztvHrWS1RnfXtxAxhxIrTRQ8mifsCyrvWS9w+WLIG80Sp+1827yOdllYywXXx1trJo2RO5sE2GP3CZyIP7ByybNIPTUH/um0MlkH0Y/o1MMmPk19eY7iehqVCNSD+y0V5FwkbUrH7D4c7fz+8IQQ/HiscdCxBDrg7chmFho/rmzf5pUX5btnn2bjGV6DNson3yMDzKIq9+skWKqbBqhkjqouS4iT/HItUthD1xqseE2AQPuqfehOPFudfJmx4knUQEz7zHzNwH8adMGieh8IvpbIrqRiF5apz2YiD5ORJcThWlMqm0NMREqwCJB+2wWVx9SjzUl8BgVHqu6V4qlL76JJpH7Mn1mpWt5lhqpX8qwVuiHF/nDTL2G2ufoqyP6XtOwfBuP3HlM2bfTQovJaWVMPvuYPT9E88y1/3fZsMGeeNPzbDRmQERDAK8F8GQADwLwE0T0IFSLbT0NwFEAP5xSpyRrTZnLfPI9Zo1wX5u50XYg04e2vrmEOrtY/UIbrSRJVpL1VEljVBEmM+u1Xx9jVPHgM/E+VeqdOvokFJX17vv9hZSwD7EDmG0HNTeByLU0jchdZD7A8u/RlWbqtuuziTwLuI5OiXkFQERnE9FfEtENRPQFIvoFJQ8R0e/XQvdz2iqyEjGeuHpaDcs9CsCNzPxlACCiPwLwdFTX3fyigzeImMHMlHw2fEQfW3YpvcGPqEkkis8PT/HVgeo7OXN8N2czYGimuANzgpSvyT6wU5PqAemi3pavESqitrUWcEDKB5N99jEnblnvbDHvZB9Y6J9yk6mPyedq2ucor4WWpylcg5ptwgzXYa2o+bE82Aks2yuATiTabEzNVpFtaAtiZUU+lT0B8EvMfB0R3Q3AtUT0AWb+osjzZAAPqF/fD+AP6ncVvrVT3gv9GhOAb2/QeaBai+WrYv+munO/B+DPAPw3AJemVBiyVuy8gHuyT2x7qcdSwwi7QNu21H+MLgU+E0p4YUalWf9kv36ZNBm5AswJGVZ5YL6e+F79Pq239zC/KfByh201XvfV/LOwszWFjyjbknRMnlDkSojIAZ3MUyJWXOk+MjdlIMqlPhRC1mG30wrMwDTPwCYz3wzg5nr7NiK6ARUvShJ/OoC3MjMD+AQR3YOIzqrLLsGnxH+r4TEfNJXNzPxf4bnTFBQUFKwVHfjdRHQOgIcD+KR1SBO790ZN/jZ8a6f8Vase6rgJixOF7gPga6FCRHQFgGcAwClbW8FQP02d2yrcVz7nsaYqvOmknhywBaxRZlMrfXhgm5iVA/fn63ZPptVCVPtjYGsP8xmVwFyF72F5BGOGxbhwk9/oKqPC97Goxk19E2A6rtqeTBYfCmGexzaZ/1Owz1VeA+26GLBSJhVOdRtQ076yMcebroDYRI1DOeabcu/LI9O0urVFtY4dOwYiukMkXcnMFwaaXwQDPIt2kM8goqNi/zJmXnomMRGdBuCdAF7MzN+yD+u90NHUE2+KTwN4ABGdC+B/ArgAwHNCheqLfiEAnH366Yxbb10gax8kRYTCCSWyzfZqgKaDmTkIXSMqky5th8kE2JL2xAGB1+Q9Hs8fzLBl7JNxXdse5haKeRnbZITln6+xSUzZfVRz0AyRn8ACsY/3qr6M96uX6bDpj7BabDdoar3b1yQ32lgjMcebErkLPiIH4j1pH5FrNwWZppG1q75zzz0Xt9xyy6nKoSQkfAeOM/N5vgxEtIWKwN/GzFcqWZLE7kpJnJknRHQxgPejuu5vYuYvpNThWnzKjj7xIWXGZchr96rzBio85Sk9OfIYuAb1XFFU0xnAk+rBw4sEXpP3eA8Y7wAn6+Vghycwv5pmtqX5NGeoFsCaYnH2pjlmBiyBuRLfA3CH2K5VOdfEPRZkDghlLv4pTCZzcT5ZvlFJtJmw18XMztjjTYm86WQgtQ/QyV0j3hCBa2oc9XG5cFZOcL1MfQ4QEQF4I4AbmPl3HNmuAnBxHfjx/QC+6fLDgdUrcTDzNQCuaVOH/Qfchm8SkGax2HVpN4qYcKqFYxkJvE1IYQx4BmCwSNAjOMYtZ/MysxkwlARuCH08nqvg8R5w8gRwqlnvBFi+cjNU2soQ+L51XA6KTjF/KMQegDsBnKz3T1ZtjcfVzcO0Dywq81qRs1Dc9sse7DTvMlqny0gVLY+vzVUSecpEoBRoCtu1kJZvSn8XiHdTgngcKlfheiL6TJ32cgD3BQBmvhQVPz4FwI2ovuDP81UYs4rhBwD8ODPfWu+fDuCPmPlHGp5ENqSEGgJxMeGuKfg5kIvAc3rjs9li2dkMGHjshX3rkZXDA3Ic1+p3a5G85aJYO/K2aYcQ7mPum0ujTHvGprFmTqL6jt8J4ASwd2dF3idP1H0Zz+2U8d78H0OtyieTKujAqHDXDctclxSkRnrEKvQupvDnJnK1fcSrce1YSI3bx3Kqceb50Er7uvijCLi1dVTKC2PrjFHiZxgCrxv4RyL6jtgGuoBPFbvCDV0E3vTDbqLCNwE2aRtMa/KW+aQiNWS3LTzx/QmwPZmBDuyT7YogbfI2S78a7BDmNokhaEPg2m3Z5AUq8ra98DuA/RPAyZPAnXfURF6T+cl6/baTe/MbTX0HWnBXBJlPLRK31bq5Pj6sY72ktlP0s/UD7oHO2OZDHriW3qUaX8V1a4oYEp8R0X2Z+X8AABHdD10u3RuJUGy4HPiMJfDQQliu/YVjHdsobVT4dFa1Jf1ZoLZFxLbPD5cPj59OKl7cGdVP0RmP5wR+8uTCut1L2DEK2zx6zcSLGxKXSn2G+c/TqHCjxE8C+yeBO++cE/idd9YEXhM3UN9k5hbP/ri27yfL58cWYduE7CPoNstO51DjTW2XddoqWqy3q6w5Zit4kz6AW9k3BXNWOyU7Ykj8/wXwUSIyIYePB/CC7rqUBknWMXm17dgHPQTrTyTwmEesxdSfmsdAs1KkjWJslf3J/AHCksTHY2A4qtX4eG/+IIaTJ3CwxriLxGfT6nmYZGwU+XxN+2s5w7Invgfw3pysT54A7jxRE3m9fbJW58CCzcLjuQrft95dVorth9vpq0YXg52+B0t0bau44LJUDvpVv8vnenZhqfRaiTPzn9dz9x+N6pq9hJmPd94zD1IWvzKIJXAbKSo8BakEniOvBkPkZnDTQKpRrhWqIXJgTuj7Y2A8AnYGJ2HW6FZXNLQbNRVsj4HtPYDMuuAyMuWgAOYkXj9Lc3+8SOBSgR8Q+N6cxA+U+LhyVcZY8MSnipUiBzA1UrdPKQdSSLGNPbIKa6WJvREzkOlS411hw5/O5p12/0Bm/hux+IqJU7xvba9c1333/IhR4b7ww9RnZeayUVLXMPHVnwOGyKX6tgnN8K7ph/SRx+MqfWu0JwicMF9TXDQE1AOM04pYd4/MH2JsnghkXzRzFzkoO8F88HRPIfM7FokcODg+Hc+wX/fZvPbFuciBTlt5amldIGayT2w9TepwWSubpMbtOgzJy29Ovmn3yBZi2AV8SvwXUdkmv60cYwBP7KRHEfANXBpit9GEwGN4s8nCVqn15YhG4Zm/P8YXl6Q9sMhbimtD3ouPcJtgODBEXl9N8+23DfXJpPbR9xdJXN4EgLkZqZL4eK6wJYFLe+VgYPMEeDypSHu8eBNyqXBXaKFMk+Rljmvk3wQ51lrJHXqYGj+e6o2HrBNtkg/qcq4B0LZgrjTHpsI37d743k9m5pPyGBHtKkVWipAKd4UfxqrvtjZKLh88BbHltR+utFQkQe3XBD6ZzAkbmKvvwXj+8IjhANgdjOfXWMqX2UxMgZ9W29v7yyr8QMULOSqXkJWTiw4m9exXtonxwE+eFMr85EGHzSGpwm0y1yb9aIO9oeuZC5tI5K56AMd3C8uEHEvuvrLymLkBZF2CVqCvStzgYwDs9Wy1tJVA+5BSnrFpl/PlDyGVdJsQeO71UkyEig0ZTmf2jRo3Snw6ASaDeR8MkZt3g12MF6/1jKtKdo9U+0bGb+8pJC6e1Sk7YhS5UfTjvfkMUWOr2PbKeO6J71nkbdsovhBDk7auxyx2TeSpyLVsrYvIfdPqpW2yqo+DN/vBPl5P/J6oVs46QkQPx5wD7w7glBX0zQuXCjfpvqeK5HpWpgurXGI2NzQ1PhVEblwNw7N74/m+fN/FGCTN9N0j88Lb23MVvr1Tk7hC4AOrI8D8hrBA5Htiluac0Pnk+CDCUFPgxlaRkSn7liIHllW4z0qJRbKPHOGTNyXyTbNV7DK+suanNsOyGs/Ju30NMfwRABehWnzltzHnwW+hmiZaUFBQcOjR2+gUZr4cwOVE9ExmfucK+5QEn5XiGuR0ISnvmqyUJpBhhPJfguuLqVkqclwSWPTHtfYAYHs2wXB2BzA7UlewXR0wNsj2NjDaq5W4DE8cLktPY0qadVpmM2u1QjHNfryH6cnJgeoGlpW4UeN747nytif6SIvJpTrta9i15dLWFsmpxlPbUPNiUTHHTMPXLBWz7VogqxUyTrvvAjGe+COJ6IPW2im/xMz/ptuuuZHD8mjTlg8pVkoTks5B7OYH6bqx8KyeLGERt92PPbE9GVREaZPfzmyGrdkdFYGbdUy2awIf788J/GBQc7h8krPZcpSLXHDLIvL9cUXO+4LEbQK348S1kEpzHjaxa4ObLnSh4NrM2PSVTw1t7NJSsYnaJmk7pDBku7QBo792isGTmfnAPqnXTnkKgLWRONB8ve+YSJQQcqrwXG00JXcnGYhj0hs366sYct8TZWy1NptVJLk9AbYnYwxHNaNONAKv310nIzsjH/BwsCrhBNPx7GDMVL5Mf7WIFDlz014MK6Q+7cHgVWLV66R0GTsuiTgHss/Y7OvApsCQiHaYeQ8AiOgIqnnSa4dGyBq5NyX8w6jCpaViR6rIL+pgsGiruBQ5MCfykUL6W6N5ecPRB4R+MKBZq+/RcN64hOyYGdicTQ8YeCqIeF/YJGYbmNsmGonvKzaKtFBiVXiqldKVNRJzvOm0/La2Sk61rM3i7EKNM9D7EMM/BPBBInozqvP5KQCXd9qrBGi85puS37q9DVThMTC+uOuY3e5shoPFhAaCzOAg8z1RjyTE6agiyZ3tOYkbQh+NJtUEoRGEHx7oZF2xmaCjqeqJIGuzdK49wUeGFJoJoZKktTjxhXvJGlV4CnITuYZcD5DwWSaapbIy9F2JM/NvENH1AJ6E6rq+hpnf33nPPMj56DTfoGgKcqnwJoOcTWZvAlWfpRpXfVJURLg1WlTTLjI39UglPhpVKnhYlzVpxganATAczDAYzJZCFWV9cn8yWX53kTmwbJnY5WRdPmKOVeHRVkLHajyUJ7X9po91W2gTedU4sEz2QF6i77snDmZ+H4D3ddyXJIQe8NDFQOcqVHgqckewmO2FNkSasSdGIywQ+WxW2SlShct9eQOY1uQtFzmMFeJmWxKuT5WbcjaBGwtFI3B7KdpNV+G5/e8m9boegpG7byE13omdwn47cd2IebLPowH8BwDfjfl6oXcw89077lsSQuo8x4Cm2m7GAchVhRrasNW4RuYLKlzu119u21ufWuQ9Gs2tjS2FxM11NPW4big2yfpUuSFq0185oSeGwCWJa30xaGsldEXCsW2sQ42nIkTOXa2bAuR9xmYXiFHil6B6Kv2fAjgPwHMB3L/LTsWiK6GrKvsMKjynJZIDWty4/EHblob9wzWzOAGABemORnNfmQaKEp8sk7hsLxScYt5tJe4idJN/MnHndxG4q/1NUeF9QJMBTtsX9+VZai+9i0EcBjvlRiIaMvMUwJuJ6GMd98sLn5US8xzNXFjF9Pqu1bkhWvnUH/VHN6jIz5C1rcpNfwxpGyKXk4ykFWOm8EsSt9W4gSRMX8SIHWVi2ynasRQFrhG4psKbqOpN9cZT+9V2kNNH7qEIlC7VeK8HNgHcSUTbAD5DRL8B4GYAp3bbrTDacljKgOZhUuEuSwVYJnKT30D+oM2qhmYFQ/PAiMGgInozqDkQ9U0HelnbC/cpcbMtiVWSsB1lYgh3aqXHELjmg/cduaybVQ5whtQ4MFfkXRB5TjuFiN4E4KkAvs7MD1GOPwHAewAcq5OuZOZX++qMIfELUV2riwG8BMDZAJ4Z3+28iIkDXwUn5lDhIfLuWoUbaGuNS0vFqd4w//FIVW1ept6tkU7UU4cf7oNcs9smXBc5x+TRFLivfdMHG12r6S7hHNjOoMa7GuA0cD1zMwcYyLme+FtQWdRv9eT5a2Z+amyFMSGGX6k3TwB4VWzFq0AMl7lWOuwKqSrchS7VueZ5G2gTgDSf/GAfiz8ek18jczloOVC29x190vqukbNN8JqyluRtp2v1y+1N98FjiXKT1Li3fgDGMH8AABbcSURBVCx73pKcQ7ZKtp9QxjhxZv4IEZ2Tp7YKvqVor4fnnwkzf2/OjhQUFBRsKlb8D+kxRPRZVI/E/GVm/oIvs0+JR8v5VUO7w2aZjamlKYmrsFJylwtBm44vVXjIVplYahtYVuUsjtnKXL774BpkdMV2a/l9T+3R3mU7Bl2tVrgqSyV1kDNHv2KjVLS0GK+7q5mciZ74GUR0VOxfxsyXJTR3HYD7MfPt9RpV7wbwAF8B31K0X3Ed2wSY37srPnyFY4UH6IOVImEPcoaI3O6jJLvBYG6tmLLSelE9cSxHphgMRT9sSAJ1kbA9MKlZKFoZWa8LscvSrgMuX9uVt22fcz00wlk/wpYKxL4MTcyJhBDD48x8XtN2mPlbYvsaInodEZ3BzMddZQ7NZB+DEP+patuVd8NU+CrgInIgMgxtNidoSeb2DUNT33LbJ3w0svWp7pi8PgJf+cSWBCJeJTqbGVq/h6rWFryCsp97FcNVPhSifqLa3zMzE9GjUJ3GLb4yvZzsY6vwHFZKW+SaYr9qgrfJFXCvdCjzhiwWjcy1m0EotNDVZ3tbs1dCeUMEHvoX0DXakGbbQc6UtlPU+LojcBoh40MhiOjtAJ6Ayna5CcArAWwBADNfCuBZAH6OiCaogkkuYGbv/4BeTvZJhSH5VQtgjZTakHRXBJ9K5KYvUT/Guu6JVR8Qp/J9fTawiVse95G4K03WKeEj8E0kpi4Ic2WePZaVNZCmxnMtd8KcZKcE6uKfCBy/BJVwjkZvJ/v4EPLLtbybgE2zWUJrj0er8oNC1ZsppxE7oP+rcVkaGnHLbbtPTe2TTX7SfVdt5Gg7RY1rA5oh2FaMrCOnL76JN2mD2Mk+A2zIZB8D20rJzX+xfvgqVipcBTQ1DiwTuYHPHkn6wivtTiLL+4jYt59in2wCVmGrtK3TNxCdAy41vgr09kHJBiJK5SQ2bLLPJiPVStkEFR5D5AahlQ995GG342o31FdfWupxH/msS4XnQgyRd6nGmyJFmdsRK1m73NdVDIno6QDuw8yvrfc/CeDM+vCvMPM7VtA/vW/raniNWDfJxxA5EEfmOYghRNKxeYA4q2bdaHvN+jKgmJ2AM6Gvqxj+CqqoFIMdAN+Hyg9/M4C1kbhBWyulbWhhXyNSfPCpYheRA2Eyd6WZ9CYE41P6sel9IHCDTSJiV1+0SJUuolRClsoA+Sb+cMbolC7gI/FtZv6q2P8oM98C4BYi2tiBzU3gw9xRKatGiMiBeDIH/OStpacilcyBw22fuBAiztw32dwIzci0VXyu0GPu8TM2T5c7zHyx2D0Ta0boA+qD5RJL7Jt4A9BUObAcxQJ0R94uNCVv4PASeF/hs1dWOcC5yXaKjx4+SUQ/YycS0b8C8KnuupSGEL9twkSgdaAt8ccQLM900pvO5i9X3XLWZFvI+g47gbf9XDdREOREF6dnolNiXuuAT4m/BMC7ieg5qBZlAYBHovLGf7TrjhUUFBRsBPpqpzDz1wE8loieCODBdfKfMfOHVtKzRKTegbsa1Iz1w/ugiGJD/1zWCrDslWttdIXYmOU+KPB1IyXKqMvBTdsXN7Zpl25Hnwc2AQA1aW8Ucd+VLJI+kD3gHvA0sH/UXU2SSp1s0kcC7zrccN0DmW3DDLsIU9xkTzxq7ZRNhYsHVv00n67QlMBzEn/qRByfKpfQyDaV2NvODuwjgefCuok6BVrUSSh8MOfvndHTyT6bik0g6LuCldIGIVXuwqqmvd+VyfuwotNIlQ33xHtLJzEdd9kubf3wVWCTiL7pF9gVvbIudNmfVX9eOdpLXQYiRZBoQkf7LeW8bl2GFfc1OqUXsL8DfYgP7yOarG9iYBPnKm+Mm3QT2URsqq1iWyhNLJVcYGy2J76ynxMRPZCIPk5Ee0T0y9axC4joOiJ6cUxdGyRSNw6bpOBdMIq4C4KVdRcCv+tBirhcPwUTnRLzWgdWqcS/AeBF0GPML0C1LsvbiOg0Zr49pkL5IdkKvKsIlhQ/vGm+PhBxLmhEG6vUN42kV61qN1VFrworU+PFE6/AzF9n5k8D0O5XMtwzuyOyLk48bGS8qi+yraZdr4I8cH1Pu/DF2yC2ui4s1RnHvdaBTaGZKwEcBXCUmW9bd2cM1jGoediI/66G8vn54Rrc3OTrxvV64jGvdWAjBjaZ+XIAl7uOE9EVAJ4BAKdubQHIu/zsJn+BCgruqoiZtGNsUxd/Hjt2DER0h0i6kpkvTO3LXdZOIaIXEtFn6te9mtbDzBcy86nMfOqZp27sKritURY3OhxY5eeQq63D+t0599xzYbijfiUT+KYvgNXpR8fMr2Xmh9Wvr3XVTgkrLCjoBoeV3FMxiXyFQERvIqKvE9HnHceJiH6fiG4kos8R0SNCda4yxPCeRHQTgF8E8G+I6CYiunsXbfVxbZXyYzlc2HSftyAejMrWiXlF4C0AzvccfzKAB9SvFwD4g1CFK/PEmfl/AbjPqtorKCgoyIVcTgkzf4SIzvFkeTqAtzIzA/gEEd2DiM5i5ptdBXqpFXrZaRRlVnA44AozPKzf70QlfgYRHRWvFyQ2d28A8rGYN9VpTmxEdEobrNsPT4mxTa2joKBgM5CgxI8z83ktmtIozRuB3jsSTyXtuwo/ruJGUG42BX1BzlUNGXGDlplwE4Czxf59AHiDQsrPMgFdPcigoKANNvnmelh+MxkHNkO4CsBz6yiVRwP4ps8PB3qoxCU0Vd7HyJSCgoLNhfHEc4CI3g7gCai885sAvBLAFgAw86UArgHwFAA3ArgTwPNCdfaaxLvEutYRLygocKOLR6/FIGN0yk8EjjOAF6bUeZcn8XX+Fd3kv8EFBV1Be2jypmOTu9tbEl/V0rMFBQV3beS0U7pAb0k8BkXoFhT0B5u6PvqKo1OS0UsSX3dseEFBLDaVmArSsMkfYS9JvKCgoGCVKCReoMIotDLAWbAJMN/D8s9hEcUTLygoKFgxcj8prZB4QUFBQU9RlHjBocFsVqyfgrsmSnTKBqMQU0FBf2Ar4lU8m7go8YKCgoIVIrcfDhQS7yV41q/1U8o/ioK2WHdUyrrbd6Eo8TVihjJrs6Bg09DluildqHCgkPihwXR2eNZHLlgNVqEuN1XBdo1V+OFAmXbfCRhl6n1BQR8wzXiDWee9apPvk70k8YKCTcdhVsfauW3i+WZbAzxjXV2gkHhLuKbOl4HGgj7BRcKbSM4udOWHA4XECw4Rynovhw99ImqDkB+e85SKEi84QFHndw2skhT7SMB9xCZf5kLihwirvEmUG9LhwGG5CXRppWx6dMqh+RlOERdytInf2b7+kPra7y5Rrsn60cVHMIt8hUBE5xPR3xLRjUT0UuX4RUT0D0T0mfr1/FCdRYkXFBQUeJDLEyeiIYDXAvghADcB+DQRXcXMX7Sy/jEzXxxb76FR4gUFBWlYx78GbbbmbOYIW+y+O9HIpMQfBeBGZv4yM48B/BGAp7ftWyFxuL9EXU4PPiwo9sEcq74W5dqvDgkkfgYRHRWvF4hq7g3gq2L/pjrNxjOJ6HNE9A4iOjvUt97aKbGzNnOvn+Kaeq8N9MWm+dJTUQYcC3IhdlKP62bS1WzNVU23N0gc2DzOzOc5jmmUZY/JvhfA25l5j4h+FsDlAJ7oa/DQ/dxX/QEXFEUI9O8a9K2/LjAWWXDm2G7bRiY75SYAUlnfB8DXFtpivoWZ9+rd1wN4ZKjS3pE4O7Z9sC/upn5/c/2w1vEDPSykUHA4kPvrmInEPw3gAUR0LhFtA7gAwFUyAxGdJXafBuCGUKW9tVMMNFtlCmDYoC7Nimi7rniKpVLQT/TtpnlYbri2iOvytHLUzcwTIroYwPtRUdSbmPkLRPRqAEeZ+SoALyKip6FycL4B4KJQvb0n8XUgxRdPRZ8Jvs99Lwij7cJXrsgUtS1HHeuYC5Jz2j0zXwPgGivtFWL7ZQBellJnL39y9gXVbJU+e+M5lNK61NZhUXmxOKwqvO155RzUjIHWXE4OyDXZpwvcZZR47iiVrlFU7ebjrnbDWhe0y+waD+viIynT7juCpsZT1k9Y5e9vXesvFzXeHfp4bduo8C7Ot8trmPuf+CYr8d6SeAyafJCxk35cfxdzfjH7TIZ97nsIh/ncUpASH54ycS7l8q6CPDOGGHaCXtspqRZJ3yyVHFinLXMYLaF1EvhdPSJllWuIr7Lutug1iWvo0/M3Yx6w0Hci7Hv/DQ4DCYbQl6f7hGxTQ/a5lqctD4XIDC021McRTWPG26DEhi+ir08D2iTy6rovqfW3tVJSQwvb5m2LDfoqLKF3JF5QUFCwSpTolA4Quis2nY5/kN5ycDM32qqwTVGUZrXITemPD5vUx8Py+XcJc4pdzQ8pA5sd464wYNnWjtk0O2dTLZa7AuHZ8J1zX69Hzse1bbonvrKfEBH9ZL1G7ueI6GNE9FBx7AIiuo6IXhxbX8pFlXfnVX0YKbG2q5pFt4k/yE1R5pvSDxt96dN0tvpZmqvEJivxVeqgYwD+OTN/L4DXALhMHLsAwPcBeDQRnZajsbZ34hRL5TB/eVeFdZLoJhJlLoTOretz73pQU5KnHZWS89Q2mcRXZqcw88fE7idQraVrYKICkyIEpY1iLmDqXWnVVkzbh0IcNlvFxiptlk0n73X3b93tA8vE2OVT7V3Y9IHNdXniPw3gfWL/SgBHAfwhM9/mKzja2cFZj3jEAvHav3dybA8d6S6+0JagJc8tRlvZUKtj4KgjZclbVx0p2GQyl2izFHAfH7GXddavh/VC10YrmzrAz5F1ALqSNcVtla2p7ZnnWBtsuidOrF3lLhsk+hcAXgfgB5j5lsgyVwB4BgDs7Oyc8pCHPKTDHs5x7NgxnHvuuStpa5U4jOd1GM8JOJzntcpzuv766zEej+8USVcy84UpdRARx+qIGXCt5/FsnaBTEieiFwL4mXr3KQDOAPAuAE9m5i911nAmENEdzHzquvuRG4fxvA7jOQGH87z6dk5E9OeouCsGx5n5/C77Y2NlSpyI7gvgQwCea/njG4u+fdlicRjP6zCeE3A4z+swntM6sUpP/BUAvh3A66gylier/ttRUFBQcNiwyuiU5wN4/qray4Qr192BjnAYz+swnhNwOM/rMJ7T2rDygc2CgoKCgnzoSZBZQUFBQYGGQuIFBQUFPUYh8RpEdDYR/SUR3UBEXyCiX6jT70VEHyKi9+RaEmCVIKI3EdHXiejzIq3X5wQARHQ+Ef0tEd1IRC+t0x5MRB8nosuJ2kwR6g5EtEtEnyKiz9bfs1fV6W8homNE9Jn69bA6/XQiele95tCniOghoq7kNYe6QoPz+jYieq/I/zxR10vq83r2us6nV2Dm8qrGBc4C8Ih6+24AvgTgQQB+DcCDAfyfAH523f1scF6PB/AIAJ8XaX0/pyGA/w7guwBsA/hs/Vm9EcCZAH4ewPnr7qej7wTgtHp7C8AnATwawFsAPEvJ/5sAXllvPxDAB8Wxd9fX4o9MnT06r5cD+PV6+0wA36g/y9MA/CdUQRfvWffn1YfXRqqVdYCZb2bm6+rt2wDcAODeqH4kZn2bvjz57QDM/BFUPxCJXp8TgEcBuJGZv8zMY1Qk9nRU52VmSW/keXGF2+vdrfrliy54EIAP1mX/BsA5RPSd9bFGaw51gQbnxQDuRlW88WmovqMTLJ5TQQQKiSsgonMAPByVmrgEwH8E8LMA/nB9vcqKvp/TvQF8VezfVKf9HoA/A/AYAH+xhn5FgYiGRPQZAF8H8AFm/mR96N/XtsnvEtFOnfZZ1EtOENGjANwP88XjzJpDRzmw5tAqkHhelwD4bgBfA3A9gF9g5ll9HtejOq8/XvEp9BIlxNBC7RH/FYB/z8yHIp61vildzcyrWXSmYxDRjwP4Ea7mHoCILgTwKGb++fX2LA1EdA9Uy1D8PIBbAPwvVJbCZQD+OzO/mojujurm9HBU5PZAAM9n5s+up9dhRJ7XswA8DsAvAvinAD4A4KHM/K319Lq/KEpcgIi2ALwTwNsOC4EfUtwE4Gyxfx9Uiq5XYOZbAXwYlX9/c21J7AF4MyrLCMz8LWZ+HjM/DMBzUfnHx9bV5xjEnBeA56FajIqZ+UZU5/TAtXS45ygkXqP25t4I4AZm/p1196fAi08DeAARnUtE26geKnLVmvsUBSI6s1aqIKIjAH4QwN8Q0Vl1GgH4UQCfr/fvUZ8jUM14/sgmqtXU8wLwPwA8qT72nQD+GYAvr7rfhwGH4hmbmfA4ABcCuL729QDg5cx8zRr71BpE9HYATwBwBhHdhCrS4Y3r7VU7MPOEiC4G8H5Ug5lvYuYvrLlbsTgLwOVENEQlov6Ema+uQz7PRDWw9xlU4xVA5Ru/lYimAL6Iai3+TUTqeb0GwFuI6Pr62L9m5uPr6HjfUTzxgoKCgh6j2CkFBQUFPUYh8YKCgoIeo5B4QUFBQY9RSLygoKCgxygkXlBQUNBjFBIvKCgo6DEKiRcsgIi+k4j+ExF9mYiurZd2/bFAmXPkUreJ7V1ERPcS+28gogdFln0CEV3dpN1YENHH6vdziOg5DcpfRESX5O9ZQUGFQuIFB6hn1b0b1azA72LmR6KaDXkff8lWuAjAAYkz8/OZ+YsdtpcEZn5svXkOgGQSLyjoGoXECySeCGDMzJeaBGb+CjP/B+BAjf51vWD/dUT0WLsCXx4i+hUiur5+EMCv1YsgnQfgbfUDA44Q0YeJ6Lw6//l1HZ8log/GngQRPYmI/mvd1pvMynlE9HdE9Kq6zuuJ6IF1+plE9IE6/T8S0VeI6Iz6mFle9dcA/O91P19iK2wiupqInlBvP4+IvkREf4VqJjBEO+8kok/Xr4NjBQVNUUi8QOLBAK7zHP86gB9i5kcAeDaA34/NQ0RPRrV2xvcz80MB/AYzvwPVkqM/ycwPY+YTppJ6qvbrATyzzv/jMSdARLuoHkTwbGb+HlRLS/ycyHK87tsfAPjlOu2VAD5Up78LwH2Vql8K4K/rfv6up/2zALwKFXn/EKr1wA1+D8DvMvP3AXgmgDfEnFNBgQ9l7ZQCJ4jotQB+AJU6/z5UC/1fQtUjtqYA/jelmCvPDwJ4MzPfCQDMbD+owsajUdk6xyLzG/wzAMeY+Uv1/uUAXgjg/6v3zeqU16Jep7s+xx+r2/lzIvrHyLY0fD+ADzPzPwAAEf0xFq/BgyrXCgBwdyK62yasBV7QXxQSL5D4AiqFCABg5hfWtsLROuklAP4ewENR/Ys7qdThykNIe1pLan5Zzoe9+n2K+fe/yVNxJlj8J7srtl39HgB4jPzHUVDQFsVOKZD4EIBdIpL2wyli+9sA3MzMM1QrPg6VOlx5/gLATxHRKQBARP+kTr8N1TNNbXwcwD8nonOt/CGYR5jdv96/ENVDPnz4KID/q27nhwGcruSx+/l3AB5GRAMiOhvzdbI/CeAJRPTtVK1PL22gvwBwsdmp/60UFLRCIfGCA3C1pOWPoiLPY0T0KVR2xL+us7wOwL8kok+gsgjuUKpR8zDzn6Na8/tovdSv8aPfAuBSM7Ap+vIPAF4A4Eoi+izcj+p6EhHdZF6onoDzPAB/Wi9zOgNwqaOswasA/DARXQfgyQBuRkXaEp8DMKkHWV8C4L+gepDB9QB+C/VYAjPfDODforoJ/WcsjjG8CMB5VD2q7IuYL8taUNAYZSnagrs86uiVab1O+WMA/EH9JJ2Cgo1H8cQLCqpolD8hogGAMYCfWXN/CgqiUZR4QUFBQY9RPPGCgoKCHqOQeEFBQUGPUUi8oKCgoMcoJF5QUFDQYxQSLygoKOgx/n/Y9d2Y8kBY8AAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "npred.sum_over_axes().plot(add_cbar=True);" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# This one line is the core of how to simulate data when\n", "# using binned simulation / analysis: you Poisson fluctuate\n", "# npred to obtain simulated observed counts.\n", "# Compute counts as a Poisson fluctuation\n", "rng = np.random.RandomState(seed=42)\n", "counts = rng.poisson(npred.data)\n", "counts_map = WcsNDMap(geom, counts)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "counts_map.sum_over_axes().plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit\n", "\n", "Now let's analyse the simulated data.\n", "Here we just fit it again with the same model we had before, but you could do any analysis you like here, e.g. fit a different model, or do a region-based analysis, ..." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SkyModel\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- ----- -------------- ---------- --------- ------\n", "\t lon_0 1.000e-01 nan deg -1.800e+02 1.800e+02 False\n", "\t lat_0 1.000e-01 nan deg -9.000e+01 9.000e+01 False\n", "\t sigma 5.000e-01 nan deg 0.000e+00 nan False\n", "\t index 2.000e+00 nan nan nan False\n", "\tamplitude 1.000e-11 nan cm-2 s-1 TeV-1 nan nan False\n", "\treference 1.000e+00 nan TeV nan nan True\n" ] } ], "source": [ "# Define sky model to fit the data\n", "spatial_model = SkyGaussian(lon_0=\"0.1 deg\", lat_0=\"0.1 deg\", sigma=\"0.5 deg\")\n", "spectral_model = PowerLaw(\n", " index=2, amplitude=\"1e-11 cm-2 s-1 TeV-1\", reference=\"1 TeV\"\n", ")\n", "model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)\n", "print(model)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BackgroundModel\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- ----- ---- --------- --- ------\n", "\t norm 1.000e+00 nan 0.000e+00 nan True\n", "\t tilt 0.000e+00 nan nan nan True\n", "\treference 1.000e+00 nan TeV nan nan True\n" ] } ], "source": [ "# We do not want to fit the background in this case, so we will freeze the parameters\n", "background_model.parameters[\"norm\"].value = 1.0\n", "background_model.parameters[\"norm\"].frozen = True\n", "background_model.parameters[\"tilt\"].frozen = True\n", "\n", "print(background_model)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "dataset = MapDataset(\n", " model=model,\n", " exposure=exposure,\n", " counts=counts_map,\n", " background_model=background_model,\n", " psf=psf_kernel,\n", " edisp=edisp,\n", ")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 16.2 s, sys: 374 ms, total: 16.6 s\n", "Wall time: 8.33 s\n" ] } ], "source": [ "%%time\n", "fit = Fit(dataset)\n", "result = fit.run(optimize_opts={\"print_level\": 1})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "True model:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SkyModel\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- ----- -------------- ---------- --------- ------\n", "\t lon_0 2.000e-01 nan deg -1.800e+02 1.800e+02 False\n", "\t lat_0 1.000e-01 nan deg -9.000e+01 9.000e+01 False\n", "\t sigma 3.000e-01 nan deg 0.000e+00 nan False\n", "\t index 3.000e+00 nan nan nan False\n", "\tamplitude 1.000e-11 nan cm-2 s-1 TeV-1 nan nan False\n", "\treference 1.000e+00 nan TeV nan nan True\n" ] } ], "source": [ "print(sky_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Best-fit model:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SkyModel\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- ----- -------------- ---------- --------- ------\n", "\t lon_0 1.859e-01 nan deg -1.800e+02 1.800e+02 False\n", "\t lat_0 8.768e-02 nan deg -9.000e+01 9.000e+01 False\n", "\t sigma 2.963e-01 nan deg 0.000e+00 nan False\n", "\t index 3.056e+00 nan nan nan False\n", "\tamplitude 9.060e-12 nan cm-2 s-1 TeV-1 nan nan False\n", "\treference 1.000e+00 nan TeV nan nan True\n" ] } ], "source": [ "print(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get the errors on the model, we can check the covariance table:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=9\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
namelon_0lat_0sigmaindexamplitudereferencenormtilt
str9float64float64float64float64float64float64float64float64
lon_07.777e-053.533e-07-1.008e-07-4.077e-064.958e-170.000e+000.000e+000.000e+00
lat_03.533e-077.652e-051.832e-063.182e-064.912e-170.000e+000.000e+000.000e+00
sigma-1.008e-071.832e-063.720e-05-5.426e-077.917e-160.000e+000.000e+000.000e+00
index-4.077e-063.182e-06-5.426e-079.344e-04-1.196e-140.000e+000.000e+000.000e+00
amplitude4.958e-174.912e-177.917e-16-1.196e-142.128e-250.000e+000.000e+000.000e+00
reference0.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+00
norm0.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+00
tilt0.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+00
reference0.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+000.000e+00
" ], "text/plain": [ "\n", " name lon_0 lat_0 sigma ... reference norm tilt \n", " str9 float64 float64 float64 ... float64 float64 float64 \n", "--------- ---------- --------- ---------- ... --------- --------- ---------\n", " lon_0 7.777e-05 3.533e-07 -1.008e-07 ... 0.000e+00 0.000e+00 0.000e+00\n", " lat_0 3.533e-07 7.652e-05 1.832e-06 ... 0.000e+00 0.000e+00 0.000e+00\n", " sigma -1.008e-07 1.832e-06 3.720e-05 ... 0.000e+00 0.000e+00 0.000e+00\n", " index -4.077e-06 3.182e-06 -5.426e-07 ... 0.000e+00 0.000e+00 0.000e+00\n", "amplitude 4.958e-17 4.912e-17 7.917e-16 ... 0.000e+00 0.000e+00 0.000e+00\n", "reference 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00\n", " norm 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00\n", " tilt 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00\n", "reference 0.000e+00 0.000e+00 0.000e+00 ... 0.000e+00 0.000e+00 0.000e+00" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.parameters.covariance_to_table()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.055576702983643 0.03056715401514759\n" ] } ], "source": [ "# Or, to see the value of and error on an individual parameter, say index:\n", "print(result.parameters[\"index\"].value, result.parameters.error(\"index\"))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# TODO: show e.g. how to make a residual image" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.0" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }