{ "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/v0.13?urlpath=lab/tree/fermi_lat.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", "[fermi_lat.ipynb](../_static/notebooks/fermi_lat.ipynb) |\n", "[fermi_lat.py](../_static/notebooks/fermi_lat.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fermi-LAT data with Gammapy\n", "\n", "## Introduction\n", "\n", "This tutorial will show you how to work with Fermi-LAT data with Gammapy. As an example, we will look at the Galactic center region using the high-energy dataset that was used for the 3FHL catalog, in the energy range 10 GeV to 2 TeV.\n", "\n", "We note that support for Fermi-LAT data analysis in Gammapy is very limited. For most tasks, we recommend you use \n", "[Fermipy](http://fermipy.readthedocs.io/), which is based on the [Fermi Science Tools](https://fermi.gsfc.nasa.gov/ssc/data/analysis/software/) (Fermi ST).\n", "\n", "Using Gammapy with Fermi-LAT data could be an option for you if you want to do an analysis that is not easily possible with Fermipy and the Fermi Science Tools. For example a joint likelihood fit of Fermi-LAT data with data e.g. from H.E.S.S., MAGIC, VERITAS or some other instrument, or analysis of Fermi-LAT data with a complex spatial or spectral model that is not available in Fermipy or the Fermi ST.\n", "\n", "Besides Gammapy, you might want to look at are [Sherpa](http://cxc.harvard.edu/sherpa/) or [3ML](https://threeml.readthedocs.io/). Or just using Python to roll your own analyis using several existing analysis packages. E.g. it it possible to use Fermipy and the Fermi ST to evaluate the likelihood on Fermi-LAT data, and Gammapy to evaluate it e.g. for IACT data, and to do a joint likelihood fit using e.g. [iminuit](http://iminuit.readthedocs.io/) or [emcee](http://dfm.io/emcee).\n", "\n", "To use Fermi-LAT data with Gammapy, you first have to use the Fermi ST to prepare an event list (using ``gtselect`` and ``gtmktime``, exposure cube (using ``gtexpcube2`` and PSF (using ``gtpsf``). You can then use [gammapy.data.EventList](..\/api/gammapy.data.EventList.rst), [gammapy.maps](..\/maps/index.rst) and the [gammapy.irf.EnergyDependentTablePSF](..\/api/gammapy.irf.EnergyDependentTablePSF.rst) to read the Fermi-LAT maps and PSF, i.e. support for these high-level analysis products from the Fermi ST is built in. To do a 3D map analyis, you can use Fit for Fermi-LAT data in the same way that it's use for IACT data. This is illustrated in this notebook. A 1D region-based spectral analysis is also possible, this will be illustrated in a future tutorial.\n", "\n", "## Setup\n", "\n", "**IMPORTANT**: For this notebook you have to get the prepared ``3fhl`` dataset provided in your $GAMMAPY_DATA.\n", "\n", "Note that the ``3fhl`` dataset is high-energy only, ranging from 10 GeV to 2 TeV." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fermi_3fhl_events_selected.fits.gz\r\n", "fermi_3fhl_exposure_cube_hpx.fits.gz\r\n", "fermi_3fhl_psf_gc.fits.gz\r\n", "gll_iem_v06_cutout.fits\r\n", "iso_P8R2_SOURCE_V6_v06.txt\r\n" ] } ], "source": [ "# Check that you have the prepared Fermi-LAT dataset\n", "# We will use diffuse models from here\n", "!ls -1 $GAMMAPY_DATA/fermi_3fhl" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from astropy import units as u\n", "from astropy.coordinates import SkyCoord\n", "from gammapy.data import EventList\n", "from gammapy.irf import EnergyDependentTablePSF, EnergyDispersion\n", "from gammapy.maps import Map, MapAxis, WcsNDMap, WcsGeom\n", "from gammapy.spectrum.models import TableModel, PowerLaw\n", "from gammapy.image.models import SkyPointSource, SkyDiffuseConstant\n", "from gammapy.cube.models import SkyModel, SkyDiffuseCube, BackgroundModel\n", "from gammapy.cube import MapDataset, PSFKernel\n", "from gammapy.utils.fitting import Fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Events\n", "\n", "To load up the Fermi-LAT event list, use the [gammapy.data.EventList](..\/api/gammapy.data.EventList.rst) class:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EventList info:\n", "- Number of events: 697317\n", "- Median energy: 1.59e+04 MeV\n", "\n" ] } ], "source": [ "events = EventList.read(\n", " \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_events_selected.fits.gz\"\n", ")\n", "print(events)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The event data is stored in a [astropy.table.Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html) object. In case of the Fermi-LAT event list this contains all the additional information on positon, zenith angle, earth azimuth angle, event class, event type etc." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['ENERGY',\n", " 'RA',\n", " 'DEC',\n", " 'L',\n", " 'B',\n", " 'THETA',\n", " 'PHI',\n", " 'ZENITH_ANGLE',\n", " 'EARTH_AZIMUTH_ANGLE',\n", " 'TIME',\n", " 'EVENT_ID',\n", " 'RUN_ID',\n", " 'RECON_VERSION',\n", " 'CALIB_VERSION',\n", " 'EVENT_CLASS',\n", " 'EVENT_TYPE',\n", " 'CONVERSION_TYPE',\n", " 'LIVETIME',\n", " 'DIFRSP0',\n", " 'DIFRSP1',\n", " 'DIFRSP2',\n", " 'DIFRSP3',\n", " 'DIFRSP4']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.table.colnames" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=5\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
ENERGYRADEC
MeVdegdeg
float32float32float32
12856.5205139.64438-9.93702
14773.319177.0445460.55275
23273.527110.2132537.002018
41866.125334.8528717.577398
42463.074316.8667648.152477
" ], "text/plain": [ "\n", " ENERGY RA DEC \n", " MeV deg deg \n", " float32 float32 float32 \n", "---------- --------- ---------\n", "12856.5205 139.64438 -9.93702\n", " 14773.319 177.04454 60.55275\n", " 23273.527 110.21325 37.002018\n", " 41866.125 334.85287 17.577398\n", " 42463.074 316.86676 48.152477" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events.table[:5][[\"ENERGY\", \"RA\", \"DEC\"]]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2008-08-04 15:49:26.782\n", "2015-07-30 11:00:41.226\n" ] } ], "source": [ "print(events.time[0].iso)\n", "print(events.time[-1].iso)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean = 28905.451171875 MeV\n", "std = 61051.7421875 MeV\n", "min = 10000.03125 MeV\n", "max = 1998482.75 MeV\n", "n_bad = 0\n", "length = 697317\n" ] } ], "source": [ "energy = events.energy\n", "energy.info(\"stats\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a short analysis example we will count the number of events above a certain minimum energy: " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Events above 10 GeV: 697317\n", "Events above 100 GeV: 23628\n", "Events above 1000 GeV: 544\n" ] } ], "source": [ "for e_min in [10, 100, 1000] * u.GeV:\n", " n = (events.energy > e_min).sum()\n", " print(\"Events above {0:4.0f}: {1:5.0f}\".format(e_min, n))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Counts\n", "\n", "Let us start to prepare things for an 3D map analysis of the Galactic center region with Gammapy. The first thing we do is to define the map geometry. We chose a TAN projection centered on position ``(glon, glat) = (0, 0)`` with pixel size 0.1 deg, and four energy bins." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "gc_pos = SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\")\n", "energy_axis = MapAxis.from_edges(\n", " [10, 30, 100, 300, 2000], name=\"energy\", unit=\"GeV\", interp=\"log\"\n", ")\n", "counts = Map.create(\n", " skydir=gc_pos,\n", " npix=(100, 80),\n", " proj=\"TAN\",\n", " coordsys=\"GAL\",\n", " binsz=0.1,\n", " axes=[energy_axis],\n", ")\n", "# We put this call into the same Jupyter cell as the Map.create\n", "# because otherwise we could accidentally fill the counts\n", "# multiple times when executing the ``fill_by_coord`` multiple times.\n", "counts.fill_by_coord(\n", " {\n", " \"skycoord\": events.radec,\n", " # The coord-based interface doesn't use Quantity,\n", " # so we need to pass energy in the same unit as\n", " # used for the map axis\n", " \"energy\": events.energy,\n", " }\n", ")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MapAxis\n", "\n", "\tname : energy \n", "\tunit : 'GeV' \n", "\tnbins : 4 \n", "\tnode type : edges \n", "\tedges min : 1.0e+01 GeV\n", "\tedges max : 2.0e+03 GeV\n", "\tinterp : log " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts.geom.axes[0]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "counts.sum_over_axes().smooth(2).plot(stretch=\"sqrt\", vmax=30);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exposure\n", "\n", "The Fermi-LAT datatset contains the energy-dependent exposure for the whole sky as a HEALPix map computed with ``gtexpcube2``. This format is supported by ``gammapy.maps`` directly.\n", "\n", "Interpolating the exposure cube from the Fermi ST to get an exposure cube matching the spatial geometry and energy axis defined above with Gammapy is easy. The only point to watch out for is how exactly you want the energy axis and binning handled.\n", "\n", "Below we just use the default behaviour, which is linear interpolation in energy on the original exposure cube. Probably log interpolation would be better, but it doesn't matter much here, because the energy binning is fine. Finally, we just copy the counts map geometry, which contains an energy axis with `node_type=\"edges\"`. This is non-ideal for exposure cubes, but again, acceptable because exposure doesn't vary much from bin to bin, so the exact way interpolation occurs in later use of that exposure cube doesn't matter a lot. Of course you could define any energy axis for your exposure cube that you like." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HpxGeom\n", "\n", "\taxes : skycoord, energy\n", "\tshape : (49152, 18)\n", "\tndim : 3\n", "\tnside : 64\n", "\tnested : False\n", "\tcoordsys : CEL\n", "\tprojection : HPX\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\n", "MapAxis\n", "\n", "\tname : energy \n", "\tunit : '' \n", "\tnbins : 18 \n", "\tnode type : center \n", "\tcenter min : 1.0e+04 \n", "\tcenter max : 2.0e+06 \n", "\tinterp : log \n", "\n" ] } ], "source": [ "exposure_hpx = Map.read(\n", " \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_exposure_cube_hpx.fits.gz\"\n", ")\n", "# Unit is not stored in the file, set it manually\n", "exposure_hpx.unit = \"cm2 s\"\n", "print(exposure_hpx.geom)\n", "print(exposure_hpx.geom.axes[0])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "exposure_hpx.plot();" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# For exposure, we choose a geometry with node_type='center',\n", "# whereas for counts it was node_type='edge'\n", "axis = MapAxis.from_nodes(\n", " counts.geom.axes[0].center, name=\"energy\", unit=\"GeV\", interp=\"log\"\n", ")\n", "geom = WcsGeom(wcs=counts.geom.wcs, npix=counts.geom.npix, axes=[axis])\n", "\n", "coord = counts.geom.get_coord()\n", "data = exposure_hpx.interp_by_coord(coord)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : lon, lat, energy\n", "\tshape : (100, 80, 4)\n", "\tndim : 3\n", "\tcoordsys : GAL\n", "\tprojection : TAN\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 10.0 deg x 8.0 deg\n", "\n", "MapAxis\n", "\n", "\tname : energy \n", "\tunit : 'GeV' \n", "\tnbins : 4 \n", "\tnode type : center \n", "\tcenter min : 1.7e+01 GeV\n", "\tcenter max : 7.7e+02 GeV\n", "\tinterp : log \n", "\n" ] } ], "source": [ "exposure = WcsNDMap(geom, data, unit=exposure_hpx.unit)\n", "print(exposure.geom)\n", "print(exposure.geom.axes[0])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Exposure is almost constant accross the field of view\n", "exposure.slice_by_idx({\"energy\": 0}).plot(add_cbar=True);" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3.26723811e+11, 3.26671386e+11, 3.26469134e+11])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Exposure varies very little with energy at these high energies\n", "energy = [10, 100, 1000] * u.GeV\n", "exposure.get_by_coord({\"skycoord\": gc_pos, \"energy\": energy})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Galactic diffuse background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Fermi-LAT collaboration provides a galactic diffuse emission model, that can be used as a background model for\n", "Fermi-LAT source analysis.\n", "\n", "Diffuse model maps are very large (100s of MB), so as an example here, we just load one that represents a small cutout for the Galactic center region." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : lon, lat, energy\n", "\tshape : (88, 48, 30)\n", "\tndim : 3\n", "\tcoordsys : GAL\n", "\tprojection : CAR\n", "\tcenter : 0.0 deg, -0.1 deg\n", "\twidth : 11.0 deg x 6.0 deg\n", "\n", "MapAxis\n", "\n", "\tname : energy \n", "\tunit : 'MeV' \n", "\tnbins : 30 \n", "\tnode type : center \n", "\tcenter min : 5.8e+01 MeV\n", "\tcenter max : 5.1e+05 MeV\n", "\tinterp : log \n", "\n" ] } ], "source": [ "diffuse_galactic_fermi = Map.read(\n", " \"$GAMMAPY_DATA/fermi_3fhl/gll_iem_v06_cutout.fits\"\n", ")\n", "# Unit is not stored in the file, set it manually\n", "diffuse_galactic_fermi.unit = \"cm-2 s-1 MeV-1 sr-1\"\n", "print(diffuse_galactic_fermi.geom)\n", "print(diffuse_galactic_fermi.geom.axes[0])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : lon, lat, energy\n", "\tshape : (100, 80, 4)\n", "\tndim : 3\n", "\tcoordsys : GAL\n", "\tprojection : TAN\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 10.0 deg x 8.0 deg\n", "\n", "MapAxis\n", "\n", "\tname : energy \n", "\tunit : 'GeV' \n", "\tnbins : 4 \n", "\tnode type : center \n", "\tcenter min : 1.7e+01 GeV\n", "\tcenter max : 7.7e+02 GeV\n", "\tinterp : log \n", "\n" ] } ], "source": [ "# Interpolate the diffuse emission model onto the counts geometry\n", "# The resolution of `diffuse_galactic_fermi` is low: bin size = 0.5 deg\n", "# We use ``interp=3`` which means cubic spline interpolation\n", "coord = counts.geom.get_coord()\n", "\n", "data = diffuse_galactic_fermi.interp_by_coord(\n", " {\n", " \"skycoord\": coord.skycoord,\n", " \"energy\": coord[\"energy\"]\n", " * counts.geom.get_axis_by_name(\"energy\").unit,\n", " },\n", " interp=3,\n", ")\n", "diffuse_galactic = WcsNDMap(\n", " exposure.geom, data, unit=diffuse_galactic_fermi.unit\n", ")\n", "\n", "print(diffuse_galactic.geom)\n", "print(diffuse_galactic.geom.axes[0])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "diffuse_galactic.slice_by_idx({\"energy\": 0}).plot(add_cbar=True);" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Flux (cm-2 s-1 MeV-1 sr-1)')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Exposure varies very little with energy at these high energies\n", "energy = np.logspace(1, 3, 10) * u.GeV\n", "dnde = diffuse_galactic.interp_by_coord(\n", " {\"skycoord\": gc_pos, \"energy\": energy}, interp=\"linear\", fill_value=None\n", ")\n", "plt.plot(energy.value, dnde, \"*\")\n", "plt.loglog()\n", "plt.xlabel(\"Energy (GeV)\")\n", "plt.ylabel(\"Flux (cm-2 s-1 MeV-1 sr-1)\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# TODO: show how one can fix the extrapolate to high energy\n", "# by computing and padding an extra plane e.g. at 1e3 TeV\n", "# that corresponds to a linear extrapolation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Isotropic diffuse background\n", "\n", "To load the isotropic diffuse model with Gammapy, use the [gammapy.spectrum.models.TableModel](..\/api/gammapy.spectrum.models.TableModel.rst). We are using `'fill_value': 'extrapolate'` to extrapolate the model above 500 GeV:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "filename = \"$GAMMAPY_DATA/fermi_3fhl/iso_P8R2_SOURCE_V6_v06.txt\"\n", "interp_kwargs = {\"fill_value\": None}\n", "diffuse_iso = TableModel.read_fermi_isotropic_model(\n", " filename=filename, interp_kwargs=interp_kwargs\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot the model in the energy range between 50 GeV and 2000 GeV:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "erange = [50, 2000] * u.GeV\n", "diffuse_iso.plot(erange, flux_unit=\"1 / (cm2 MeV s sr)\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## PSF\n", "\n", "Next we will tke a look at the PSF. It was computed using ``gtpsf``, in this case for the Galactic center position. Note that generally for Fermi-LAT, the PSF only varies little within a given regions of the sky, especially at high energies like what we have here. We use the [gammapy.irf.EnergyDependentTablePSF](..\/api/gammapy.irf.EnergyDependentTablePSF.rst) class to load the PSF and use some of it's methods to get some information about it." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EnergyDependentTablePSF\n", "-----------------------\n", "\n", "Axis info:\n", " rad : size = 300, min = 0.000 deg, max = 9.933 deg\n", " energy : size = 17, min = 10.000 GeV, max = 2000.000 GeV\n", "\n", "Containment info:\n", " 68.0% containment radius at 10 GeV: 0.16 deg\n", " 68.0% containment radius at 100 GeV: 0.10 deg\n", " 95.0% containment radius at 10 GeV: 0.71 deg\n", " 95.0% containment radius at 100 GeV: 0.43 deg\n", "\n" ] } ], "source": [ "psf = EnergyDependentTablePSF.read(\n", " \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_psf_gc.fits.gz\"\n", ")\n", "print(psf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get an idea of the size of the PSF we check how the containment radii of the Fermi-LAT PSF vari with energy and different containment fractions:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 5))\n", "psf.plot_containment_vs_energy(linewidth=2, fractions=[0.68, 0.95])\n", "plt.xlim(50, 2000)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition we can check how the actual shape of the PSF varies with energy and compare it against the mean PSF between 50 GeV and 2000 GeV:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 5))\n", "\n", "for energy in [100, 300, 1000] * u.GeV:\n", " psf_at_energy = psf.table_psf_at_energy(energy)\n", " psf_at_energy.plot_psf_vs_rad(label=\"PSF @ {:.0f}\".format(energy), lw=2)\n", "\n", "erange = [50, 2000] * u.GeV\n", "spectrum = PowerLaw(index=2.3)\n", "psf_mean = psf.table_psf_in_energy_band(energy_band=erange, spectrum=spectrum)\n", "psf_mean.plot_psf_vs_rad(label=\"PSF Mean\", lw=4, c=\"k\", ls=\"--\")\n", "\n", "plt.xlim(1e-3, 0.3)\n", "plt.ylim(1e3, 1e6)\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# Let's compute a PSF kernel matching the pixel size of our map\n", "psf_kernel = PSFKernel.from_table_psf(psf, counts.geom, max_radius=\"1 deg\")" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "psf_kernel.psf_kernel_map.sum_over_axes().plot(stretch=\"log\", add_cbar=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Energy Dispersion\n", "For simplicity we assume a diagonal energy dispersion:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "e_true = exposure.geom.axes[0].edges\n", "e_reco = counts.geom.axes[0].edges\n", "edisp = EnergyDispersion.from_diagonal_response(e_true=e_true, e_reco=e_reco)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Background\n", "\n", "Let's compute a background cube, with predicted number of background events per pixel from the diffuse Galactic and isotropic model components. For this, we use the use the [gammapy.cube.MapEvaluator](..\/api/gammapy.cube.MapEvaluator.rst) to multiply with the exposure and apply the PSF. The Fermi-LAT energy dispersion at high energies is small, we neglect it here." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background counts from Galactic diffuse: 13332.024923183082\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "model = SkyDiffuseCube(diffuse_galactic)\n", "\n", "background_gal = BackgroundModel.from_skymodel(\n", " model, exposure=exposure, psf=psf_kernel, edisp=edisp\n", ")\n", "\n", "background_gal.map.sum_over_axes().plot()\n", "print(\n", " \"Background counts from Galactic diffuse: \", background_gal.map.data.sum()\n", ")" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background counts from isotropic diffuse: 417.4189178478173\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "model = SkyModel(SkyDiffuseConstant(), diffuse_iso)\n", "\n", "background_iso = BackgroundModel.from_skymodel(\n", " model, exposure=exposure, edisp=edisp\n", ")\n", "\n", "background_iso.map.sum_over_axes().plot(add_cbar=True)\n", "print(\n", " \"Background counts from isotropic diffuse: \", background_iso.map.data.sum()\n", ")" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "background_total = background_iso + background_gal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Excess and flux\n", "\n", "Let's compute an excess and flux image, by subtracting the background, and summing over the energy axis." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Excess counts: 1866.5564\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "excess = counts.copy()\n", "excess.data -= background_total.evaluate().data\n", "excess.sum_over_axes().smooth(\"0.1 deg\").plot(\n", " cmap=\"coolwarm\", vmin=-5, vmax=5, add_cbar=True\n", ")\n", "print(\"Excess counts: \", excess.data.sum())" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "flux = excess.copy()\n", "flux.data /= exposure.data\n", "flux.unit = excess.unit / exposure.unit\n", "flux.sum_over_axes().smooth(\"0.1 deg\").plot(stretch=\"sqrt\", add_cbar=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit\n", "\n", "Finally, the big finale: let's do a 3D map fit for the source at the Galactic center, to measure it's position and spectrum. We keep the background normalisation free." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "model = SkyModel(\n", " SkyPointSource(\"0 deg\", \"0 deg\"),\n", " PowerLaw(index=2.5, amplitude=\"1e-11 cm-2 s-1 TeV-1\", reference=\"100 GeV\"),\n", ")\n", "\n", "dataset = MapDataset(\n", " model=model,\n", " counts=counts,\n", " exposure=exposure,\n", " background_model=background_total,\n", " psf=psf_kernel,\n", ")\n", "fit = Fit(dataset)\n", "result = fit.run()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : minuit\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 468\n", "\ttotal stat : 20055.56\n", "\n" ] } ], "source": [ "print(result)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=11\n", "
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namevalueerrorunitminmaxfrozen
str9float64float64str14float64float64bool
lon_0-2.497e-02nandeg-1.800e+021.800e+02False
lat_0-3.975e-02nandeg-9.000e+019.000e+01False
index2.683e+00nannannanFalse
amplitude6.034e-10nancm-2 s-1 TeV-1nannanFalse
reference1.000e+02nanGeVnannanTrue
norm3.585e+00nan0.000e+00nanFalse
tilt0.000e+00nannannanTrue
reference1.000e+00nanTeVnannanTrue
norm1.019e+00nan0.000e+00nanFalse
tilt0.000e+00nannannanTrue
reference1.000e+00nanTeVnannanTrue
" ], "text/plain": [ "\n", " name value error unit min max frozen\n", " str9 float64 float64 str14 float64 float64 bool \n", "--------- ---------- ------- -------------- ---------- --------- ------\n", " lon_0 -2.497e-02 nan deg -1.800e+02 1.800e+02 False\n", " lat_0 -3.975e-02 nan deg -9.000e+01 9.000e+01 False\n", " index 2.683e+00 nan nan nan False\n", "amplitude 6.034e-10 nan cm-2 s-1 TeV-1 nan nan False\n", "reference 1.000e+02 nan GeV nan nan True\n", " norm 3.585e+00 nan 0.000e+00 nan False\n", " tilt 0.000e+00 nan nan nan True\n", "reference 1.000e+00 nan TeV nan nan True\n", " norm 1.019e+00 nan 0.000e+00 nan False\n", " tilt 0.000e+00 nan nan nan True\n", "reference 1.000e+00 nan TeV nan nan True" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.parameters.to_table()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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7d/eVpQp4WxDVGEM6F6IIyeEqae4BaGgmMRq2YAXBRb1YuVBIQmq81msNGzhM5rd5jZbUVCTzxUgJNcpgSDhlkFL/zhY1aPF9UacU2lwKjEG0l0TiS4xj9wAPCAgIOGyEEMqSYKDWbCMSgac9mtN67mycUNKkmC+zlaQWAAyoa7voQnOCa4ctFU7WeGOTk1ENEpsSa7uKOEHE1rD7jGw6oF2UsGw5C7KoFlvQkuDhZBJ3pU+ss5pGWGydsHa3gK3ChudMNyc6f6MGNYCmprZ2gZPAcyjghrc17nQk5eeUjIN6TnKPpaEvABS0lKXxNHtjXNLd8EnClCy9jFqidXxCecuo1b01Ze1z93q7pWPqYl5MC1Adck7IVmSViufE3g5zp4XTvZLovHcq9YJm68rsISfhtcu5foDvhRAC2FKO6fs0kwKgMXVinatG5dZFTTiMkrjcWFu8AEv3epBQ1/lmUrumF25Lgp/vuwiTAUpWYG9nGXAx8GCBBwQEBBxLBBZKQEBAwHGECQ0dlgiL2JfGS4iAE1icrJIQyrAibnZCJd8+CcZhCXb/xdWslcRTqKCkUm5JXsY1vW99fQQ3ln5ErmyDEk/+mtbtc7N96ZS5uT6cwyXnJAEQ+a7yEau/sYSAT9QWtdZg1CZrgdphLSHsXeFBgxJ71bxaIaBqfJxYq5d6uzniZByXrUvCkxO2zI/PfCK6yNRVZldd5qCkJCeHWOS0rCXNPPFZuIMiZBkpUkrIrqYtn2o4qaSy/pF1931QaGiuVh7uw0hx7V6wu+7XFXOnx1o+HvswEKsxskRDv3libqwccuRtASePM7/Na4nV/loT5b/PzklzwXUX1luxonAIAJu53gPjQ1Y8Jg7XzOaAvqO8Bic+dJMu6F5/KzAGiJLQ0CEgICDgWCJY4AEBAQHHEiYkMZeFyFbojq8D0E7knAHfbSqPe+A7qA+rxV3nxW0vFpR+u9fdeQ2FaCgqMiv5BpQFIm3aAGBK3ezzBeyRRfuqhu7rpMo2kBBBM1JmSzdXVzpKXdn4mK+VVN0mlQstsaocf/7EN4KoKwzOM0dqbeDIrW9SG7LIh2sS5sSzWp13xyOSBajIwolrmogOzGyQ0AnfS4bcgwmF0xa1TyOFhBrLQnjk3BqM2T1yDzpTLeNOCn1vnswzVvZqfCDzxmuppBDLIt4/txQrI3ffRakPqLeXm0Q+dENTymE2+SSe8XrbQBei2Uu2YBF4LrjBSZSVfkw6VpYIkLnKOaxD0xYtWBfMVKpuUOtw0zjClZhH82clICAg4MjAWeAH+bvhmYy5xxjzIWPMo8aYzxhj/vatjOzYWeDGlmiOnOUjojV78T5VC5lab9kF4jysX0yVZ/JLXnKCjoV8SHSIRaYErM0tSU7mjkdUASpiUFtTEvJJ5hNzYhEB9STprLUWW9Bgq85dC1uitQbOs3ZXOn7WyJbEUHtKjYQzPX+RUEWdn0+2WrMFiSVOnLLVJNfSoNZfnNiT5DQnYdlqF8ubmyrzdS/SyE6pfZtop6dUncmeR1T4JCxx9hPeplZt8iGcpORKxp7ndyfQfZyIl+QrX38/VZEu8bhqDabpvlUznXdKxNc08eV9i1+Xe8FJarb2pz5hOUqZk0/t6azed2n3VxR6fvbSml4wjGsS6k3IPU+c1jhrukvSurDL5YG7gSzNAi8A/M/W2keMMT0AnzTG/La19rM3c7Jj9wAPCAgIOEwsk4Virb0E4JLf3jXGPArgbgDhAR4QEBCwdNwmHrgx5gKANwD42M2e4xg+wA2q2LuV3q3lpE1aqdst3be5jJpdMnELORTCSchB6dzDWpkxJUxZuEo0nGMwx5bO693KRQk6AKh866oaR5g+t+uvha8vrvT83cIlNMcJlbez2JMIRMXzrcPc6/Ml04vK6/OYkqDEveZwjHxGKyL3lq5l5vbXXPn5MBV3WmeN7HgWAiHOPV2r6k7TfadwjoyPk3VdKLd5tf+MOyfxuSeUOJR1U1Ene962FG6RkFk3Vc6+hGgAYHX3WTdWSvxNqCv8Tsu17eOwCq9nCU1xqzyRTQA0bDG0pINPa0zu26LvBaD877giYTQK3Ymw1HahY2Y9eJ7jWUiS1kqXEsUrseOUs7BYXfjKf4do3XOpvbzOz4Nl4UWwUE4aYz5B/3+XtfZdc+czpgvgvQB+1FpSiHuROIYP8ICAgIDDxYuwwK9aa9+077mMSeEe3v+vtfbXbmVc4QEeEBAQsA+W2dDBGGMA/GsAj1prf/5Wz3fsHuDWRBg3XTm38e4TtyZj9ypJ5sMFi7SK2Q1LmRvs2RR1917fmy/gIXObrgys6ubcOg6RMFtAXEVmTrBan5Qht4gFsvKYemrVdadNXT348Gzf9VMP6fF+ATKPfEohEGHMRDWWjV6L9SyG7Whjto87sU+IBbGaudAJs2Dsgiz+orAJoG79qFSOcIPEhFLP2GAWCnPCJQyULtDN5uvi8nzubp6O3BznLdIAX8CzZledS9lZ47ptXWimmWuIpt3XVmzJ9UtuI6YQUE/nvfShmYRCYxxiEBVFU+q1FMSCGTYcY8VgcSm/zCEHCGohFB864fkpiOc+SlxLtmIPNUX+rLHnwnNoi98b+fnkcA3zyGU7LudbEQJAnLt5SXevY7kwwPIKeb4WwDsAfMoY86d+309aaz9wMyc7dg/wgICAgEOFAaJ4aSyUD7szLgfH7gFujZkl0sRyLiixxnxd6bjCVXycDJKKN9Y/5iRhJ3LWTYt+fTlJuaiqkzvuMGKpGqXxJSQQJDxyTlyy8JRoc7NFMn3sy7PtK5/4HADgblpona5WpaY+CcdWYz9TYaqRlcSiXmuL3EbhEbMo0+ZY55XR8knCksbCfOC23y18buCFyVu3zd4IV41KhWRC92JCFaZiVbIeOjeuNtV8kmtR4ov3cdem3FvjcUYdl3JN2EZ0XHfkPKPmjoqURSMdlxl6yzxRCz5O9brbiTs+S0jbPdeEaDLw1aAsnLZxD43Fc9YNW/XzD6Maz5s8v2nq7vEKNfuuVWX6aWnFOr8sslb/jnT9+XXfuNL7lvkOWU2r3gqvd7kfi6xuAEh3fIek5y7OXd+tIagRBgQEBBxPGCwzhLJUhAd4QEBAwA0QLPAlQtxCaVfFCbJmQe2efIJHWjUBQL+ikl8frqi1+6IfWgnB1HmnizmmaSGtpfT1UYO4sXDuYZ1jS5xrXxJsqBHwYEH7s5wSVJ1775ttn5w4V9O09ZiUyrtniR/isRfEc84id1xVE1Ka58+zwBU3LWbIfLKwVm1efTiD5Qfq5duOs5xPyZWnEEm1IPQ1yNkVd6GNXkx66xU1iI7ce2ttyMgtLzM3x5OGJjF3rW7vTH1zXUosrjVV2Ko91YSfhDtsxKpMFMLwoRNLYRN+PR67cELMVOEFCWHWAOdrkevuEPc8WZDcrZXqU8sy4foXCbUapHUTC8+90mvu7Gqz5zKh0FbzLgAvaBZN921Wt8GhK/os0aKXZuUA0Mt13lcmbr1HGc3lEmBgYEywwAMCAgKOHwxgQkOHgICAgOOJEEJZEqIyx8rmkwCA3fV7AdTbYUlYBaiHKwScQReWxwjq6rN7mYiLTd4Tc0yYP5559gkzBDLiBg+iFX/84jLjhm8T1qDu61QdPuNsj1J15bMHXj/bTs/dDwCYpOSyUrgk23HcY1MSR5fGN+01/ecwS2delY5L7XsZ8XGpPFrmOK91tacO8L57eAZlNnA4Q+Z7y2j597Sg9m7RfEn2tJxXuOPxT2I6l79GZtxk1Aas8uGO7ZS05XMKIfh1w5x5DtEwE6lI3eeWxJRKWWUyc/NeZaRgyCGQvg8RUK1DsXJKr2vtbgBATiGORcqAMRazowQ1CQiSLRC5Ag7d8VxKOCuhMCPztDncw/M1+ywKg4n6JEsRjEjCYKtyIRRmapHwIVptx3lvtjSEsxS4Sp7lnnNJOHYP8ICAgIDDRrDAlwRT5Eiecpznjrc2R2tqge9YTRwmsfv15upIFmgSC62ue026zHDnZ33jWnNe0hEvW24qGyRaxMJPolFd1XjW8wnXmESNqHgOu/F6bUwAsNW9S8fSPj03vuaUqv+mztqNc21Cm0709aztPreMSaOceNpiTe9VlcoNhGdiTzr8eveesRtDRBzfkiw8STq3qOkwN5sWMSq23lhvu5m48zagcykeDgBkPmFcsyTJ6qu8hc/Hr6fUvNcvhxPjZ/T8Y309z9SaZ8t1BrJKYxFm20MMS5KThqz6UUc9A+lAxVWlbA1PjG+mTcn7mrc0EzFb3GC66b2kWn3DgnDwNCYvlpK/nISUtc+Cau1YvbDVyRU3JrrZzL+XNcRjrWnay2fly21qDODI0ggPdVR7daMwxtxljPmgMeb9XqUrICAg4EjAGHPgv8PGYVvgC7tRAPheAD8C4AEAfx3AvzzkcQUEBATsicBCwb7dKGI4j7vCjXQCDGY82cgL+HAykRNnY5/4W0vJZeMyXd9Gq8531u1x5VzhCbVG4xAMtzcTDDJtd8XcWjktH8+cdQmdmD1K8SWJye4tl4dLEo1DKBVxg4uWhpkEJSXOxFXl42vnkrCIXXx7uNTd+Otmfv0g0rBCz4cVWlR+bimcIsJPvUznx1p1zKRVm+hHA4AhzreEE1oU1miMlC8sGHbPzLaZEy8hnt5EQyTSvg/QcEdrS19nsJ63LmdqDUZhFdnmUnyOF8m5+F5ywlKkH05c/sxsX/TkF2fbG/e/CgDw7Jk3zPbtFroWJJFrSQe/QWXx8t1igS4O13SstJ/TY8p4sbCXgFsY8ndoJkpnOCHOTbZFB36xdvls33Awt++WEJKY83hBN4ovAHgPgG0A3/NSjSkgICBgIUISU7GgG8UOgK97KcYSEBAQcCOESkyPm+lGYYyZ+UTrqysozj8IAJg2XbabFQRbsTIHRE+aQwENCltEsbiHi7tYS0swbg3GqnoN4nyLqzim1lXM+ZbWWh0qieY2WtNsPnebU2ZfuMfc1b43Vr5rY+hCBCVxjLklmJRXcyhl3NRwj4y/Ueo1Mcum8ln4Wld7zHO/AeVhM/OE2QKiHrnXWAVNo/MziDRsIKEZDkFxCEJc+FoIKdX7EnmWRkoKexzikBCGKfO5Y9xY/flTuj+0zSGSxtSpCDb6VF5OoaupZ2zw+eMJqQ16lgrrffPxMm/Rxcdm+575zx+eba+/3O2/67/W+X/+1Ktn233r1gOrBnLbP5GRYAmJirYlxMJzzbUY0tINUM35uvIksZ78emTt9d1KGS2jwq0bDs216fsu962a6L7HH3+89vyw1s7rU9wIBkfWAj9sFspNdaOw1nbk78L5u258QEBAQACA+++/v/b8uLmzGJgoOtDfYeOwLfBb7kZRxSl21p2Ik3B3OanStZq4Egu7UamllU1IiznzFXucwBqpKE/r6hNu4/KzekxPLYL87P2z7c11t90vqUsNWavSHaaR6FjYUhOrRaoUgTp3uevFjmoewOCanv/KUwCApEFWYVe750il37CjVXw72YnZtljIDaufyRbu1IsSFcTLNWZe7ApQfjYnbLkBsSm9pcRCRU0d68Rb2yyGxVabzOsonU/MAuoZ1ESduLOL578zt5kTg/UkpAN3uZl6a5654wWJNnFj6fbAVcDGz6h2e9zRcZtT9/qxUsUiVV1Gm15HfJc6BnV1DdqmW292pPctbVOnpb7b3966MtvXWlO98In3HLiZN2t0NxJ3rdwFSKqK3Xu9l8vdkUpeI+TZLEBONRYjX7dRa3bNDZjlvi7gsQNA5T26+OTpfT/zRcOg1jHpKOGwWShL7UYREBAQcPthFipAHgUcu0rMgICAgMPGSxEeOQiO3QPcwszCDZJAScm9a5CrbH2HYm7IyolDcbm4KXLr2lOz7d0PfhAA8Plf/9Rs3wNf/+Bse+Nb1VVOVp2oUK2hK7mHIpw1pDBcmej0i6sYsbhPruGeduGSn5xw5WRW6suHy4tPz/ZFbX09uu/lAIDxmoZ9BqWORdqPcbKKpQCkrJ753h0KBzUTOs4nplh0KCVXV+7fpKWJxe2K+POl1/s2XCqv21Pfkmu3pBAKy23beS59i7jVkhDMG3r8brYxdwyD52IcuXFzso1LvgeVzmvTh+SqNnkAACAASURBVKwa3fmwDKBl95zE7k0pudp3973a0eR3lJOet/XNrEd6zInXvWK2HW+4MFmxfpY+VeeiW7mQI4u4bRZ6Ly5P5yUahhMuj3ffsW6q3yGWODALONu18ncOkRgRTKOQKLXFa/j55nOu55f1dR9StF0d/1JgEHjgAQEBAccT5siyUMIDPCAgIGAfGAOYkMRcDuKqwGr/2dq+iFgozPcVFger7nG2f5zNsxiyrrZ7ap5ybnX3DLUp61CbMCoZjirP86byb86Qi9vIHFvOts+OoRBGjTnhzy9tpQBg0FZGSWPNbRdfUrZD/ox2Qu959kzjLHOMlU0wO2ZBqAAA+pNm7TqAunJjTZnQu8XcCZ1fF+7xbq7zOip0LtteTXCF3PJoQfs2lhVgNcTBArZY1qCwhF8D220tpWf2kLT2miR6/dzGS2QNOB0/2wdgXOr2VuJDKHe/crYvoVZ3g5YLcUwjXVdd4lQbzyqKT+3BrKjcGotP6OtVT9fIuOvOP2wp44jDcBKmE746ABTUgvBy7o6bkN56VFN+dGugSyqcXdD3jSZp7FvlFQt05gEN0/Eay2pKoW5dSDgRALpbGjJMrl1yxy/9YRtK6QMCAgKOLwILJSAgIOCYIrBQlgNjSzR3XGigaLkQQN7UUMCEWmMlXiEtKqgjOTEPRNCf3bhkRSs9V974ZgDAK1b1/FGXOnb3lLkgrc7YFWdXUdzylMIK/LmiiMhFSQxRKeTX2cXfOfsKP2YNK6SPf0E/y7MUujsafirWqHgDbt44bMJhAWGfJAvaYgFAQYUg7ci50xu7T872lYmGCJ5MHwIAXO6T7EClc2E9eSYjZoScEwDa3l3nsn9bUns1z1ZgV5yLenI/lprUAbnqq6VjdsTMaCKVSbltrGLJCnkriYYjWtaFS7hQKSHGiZSqd1jNj9ur+WIx6U4PANGmFuXYpmf0nFF2Ub+rjJNx7O7rdqEsmEnO4SrHylqLVK2RFQLbPowVk6pgixoyrCaOxdKhRiHc3IKRtk/UxgTUm4aMyvmmJ6zoWflEYkKl+pOuNrcQ7KXoedM4wmqEBxqVMeY+Y8w3+u2W1/IOCAgI+MpAZA72d8i4oQVujPlbAH4AwAaAlwE4D9dw4W23d2h7wALwv7AiIMTWzchQGbNxVktKVjfHssSCY0t4SiXVO6ccd7qW6qN2TfU2WO4cIrgD1DnTSeabw5JVW9LvpyT8am2+Gnq8JJk4scnv3W64JJa9S49ZZUtky3FkoymVylPLNdtwYxlSQ6RBQeXjhddgpxXDpe4Mmfdkosk6bqDM8yLISM9bkqBFpfPTgSauen2XrOJ7yQldmctaeTfNuySKmU/cMprkbA+vzo05pkbBnYmzVtlCH5EwWGuiFmjmRcZGXU0y5uSNNCc7/lxq9U4puV41XUKyl2v9AutdG5/wZFkC5qTL2h4W+vrVAdUHeNmCUUfXLbePE3E4a3XMPK8yhxVZqFP+vi3ggXPyfq+y+UWYeAu9pCRv1ea6BDfGlNsSLgvR0WShHMQC/yE4DZMdALDWfhHAksUGAgICAo4ojHEx8IP83fBU5heNMVeMMZ9extAO8gCfWKsKSsaYBDXtzYCAgIA7HMYc7O/G+H8AfMuyhnWQJObvG2N+EkDLGPNNAP5HAP9xWQN40ahKRJs+yeQ5rqwKJ93fAWDqubtFV91HVvsTV7OW4CK+b5m615u9+UQJAIyYW+unkt3LkpJooqDH4QMuOW56V5XdX/55bcCFUIzlMmVNollfvs6JzdEpTWxlHa+1vKC1GgBkXht7nOnxY3K7JYSSJaTgR1rOnMTbMe6zpidfO9vH9wU+B3b3ioZwWOd84tvi9RIN93RITzvru3DQtKvzP0w00CUSAcwTZyVRE/mxUj62F2mIpvRceA6L5BGFGPqOa5/0NfEXnTg/2+bQkbynyW4/tXITJcy9Em9yvy278Cc1SZmvuLU5JgVFTnS3SrduepTcHzU0NCTa7SyL0Cv1uuS7NY30M/u5hlMkzJVFuq5iWiOJ4W1/48n84/qAzOvP83coI0XLYeGVEylMmVG7xMSrQO6l739LWFIS01r7B74b2VJwkFH9BIDnAXwKwA8C+ACAn1rWAAICAgKONF5cCOWkMeYT9PcDt3NoN7TArbUVgF/wfwEBAQFfeTh4EvOqtfZNt3MojD0f4MaYT2GfWLe19uHbMqIboapQ7Th1wdi7qhwWiWJ15fq5CwdMSTWOXTLJijegDABmJsxcUVKyy4nDu52oCz/xIvjstjeJxyyfJUp6QD1kJg0PKioJr7Wj8hIBPD7mMYsw/gRaRr3TIWZEz80Rc6eZhZL69nAx8Z2Z8S2hk7VMj+/Fi0umJYRxaazMEGaUrGZuLKciVZJjBcHN2IUFEirV58kq2m6Mo7aGtliZUEI/XN7diPQeSziHGRDcVT32LAa+Jm7bJ2ERM9CwS9pazH0ufchqSCyU7VTHLeGCTqLH830R5cSK28+tkQSAr1vYinWued6kmQmzbM61da5X4L5LvQHfC123212nsjnOKbRIoUEJwaxGpPhZ6XesFi7x5+WGF7yG5bvJIRSWnpAlMKWGEf1SQ37y1elQOGw5OJ564N/q//0h/+97/L9/DcBw/u0BAQEBdyAMjl8lprX2SQAwxnyttfZr6aWfMMb8EYD/9XYPbiGMgWm7X3Dr+bys591IdHsUzfOUuXWUoJZoASVFvCWRDihZRcmmSeOB2fbO1FkCnJiUKjdALfNJRY2GyTqZ2nkLnqsbR4mz5GqVmJQY3Jo6z2Aw1fPHkX7ARtOd6wRV2S3Sza41tyVLqJe5uWCruzvVeSmJID6O5ptJszci3GL2NljMSVpysVUdd7VCVkTKrkdq1Q7JQlzxlve5yeP6mbtqYea+grff1uMbuSYeY8+5Zh15brmWr3qNbPJwLFnI3Ax52nYe0ZVEk5xXR5pwFWtzLVOrdL11fbbdzHW+Z8eQVSrJ1UFJ2vDkuYkIWAKd/05FYlB9V9XMPPLN7r2z7ecL5y30aX5PNHWuHrryIQDAzvu0P3k51es/8ZY3zrare1xdRXRCk+ugXtZDXw2cV9SKkCpw17yX0oj4WvW6VkqX3F7Z1ArgZcCiLoJ3KzDG/FsAfwkuVn4RwN+31v7rmz3fQVgoHWPMW307NBhj3gLgJpuDBgQEBBw3LK+U3lr7V5dyIo+DPMC/H8AvGmOEp7QF4PuWOYiAgICAI40jqoVyEBbKJwG8zhizAsBYaxdnaw4LSQJ7xrmj1rvtGSV92lSmLFcXk/vISRMpBWc+85Tct8i3CVth95iEsbj0d2fiXNlWqp8VR+rqtiLnlnOndtaNFlEf5sVyOEVCJ7UkJiXhxl5Pe3es15LEHAKJ/Zh0fMw9LsmFFnQoHCXj4s9n7XDm4su46jx3vW5JsokuOACMiiZtu7EMpnovdqkrfDd1c7k91n0sfLXu24y1dlQPPbmu23HsxKAarUuzfZa13fv1JDkA5F5WAQCurr/MfSa18msPr822UwqhpBPHw263NRSQlxp66U98LQInBpt0j/wajPmcFO6R5GqTkrQsACX3ol3Rd2SkIZpk7MY3WlGe966lrvN+jbYS/fzzpWrOf/Fn/hkA4OLvaoiK8bJnNcx2//e7cFKzrdfP11W03D2YUlwlI4JBa+pDP5mSB/i7vXrdhcyqj/7ewrHcNIyp8/CPEA6ihfL3XvB/AIC19qWJgQcEBAQcNo4hC0UwoO0mHDvl0dsznICAgIAjiOPGQhFYa3+O/2+M+acA/sNtG9ENoe6MZPvjCbUJa6jL1TTut6cz1DJs7kq/veJCMVfsudk+Vm0TV7S9cWG2T1qnAXVO9zh3781LUigklkmZ+q7zFBaZ0HvlXIu0lgGg5cNEXCrP4YxO6rvSN4kvTdzr0pfwT1MNVaQUlhDlPQ7hCFsEACrPiWaWiiU1xHo4x83hiOcC8xbMohAQAAy9XvU4Z6U66mTu/2WWzEamIYKO705eNmh855QxlO669WCe1xCKKXTebe62TUohrlTDCtcK58Kfip/Xc1KbtIiUA+G3V8b63l6mPPBxsSDcFOnxqWdY1eoTcgor+HDIRotalyVaqyBMo85IQzzNzYuzbePVNTOaq5WmrjubufMy26N9VcNRz/6BapMvQue0lviXK759XGOxfr/oz5esB06hOeu3WQWT19UZ/zzYfaLecvHWYZbGQlk2bqahQxvAAzd8V0BAQMCdAIPjm8R8QUVmDOAUgJ+5nYPaF0WO6JLnea47SybvaPXhKNNffBGm6hi1PpK+bs/0gztcGUeWoLckn4mVFxsRt3ow1SSeaLnnBSU2SYO67xNyey2D1CerksbipsfDzHOXK9KKJkvlROaSRRvpfJNYQC1rTuJez5RbPfWc8ylZwgUJby3qyNNLiTvNCVefvG0Rp56TjMJ95o42bE1Ld54s0ddXyLPq+C4xLbJU1wq1cKWSkis1N1OtXmytunGfbGgkMLnylI7PNwqerKtnJnxoANiZunld75CHQUmuskHCWXa+mLlFyeHTHff6qUzX5frgmbljcmrWjbaud+Hf57FapZzETK3vBDVW7reIwQEAfAPgeKqVmsKzB4BW6tZ4u1QPp0p13f9XP/ttAIDf/9H3z/adf5vO9alvU+G958+8BoCKnQF1zvfW2F3jONc1Omno9mnRx1/cFGrWoWv99a/SnX/05cVvfpGwx/UBDq3IBIACwGVr9+j7FRAQEHDH4eiyUA7ys/IPrbVP+r9nrLWFMeY9Nz4sICAg4A7B8vTAl4qDWOCv4f/4hg5v3OO9tx/GwHi3T1zc66sXZi9vE8dWwga7VDLNv6TjbF4bm5OIkljs59QmrSRXmRrxirs/ocTbZl9/H0fe228Q3bpFEtnNTJKcVFJOIRLhCXPSZi3T5rmrE5dMSnN1hSecLPLJz5w4truFuuVFNb8UuHx6ZzTPVz6zouNbqQlHucRYm/S8WUSr8KEtbn+X031hCYDZ+UlEa8XMlyLUkrs+dLKbaau950YadhjnTvjp9IaGRe5ee2K2Lc2wt1rKjb6+q2Ptj91YL5GA1M6KzjVzsqVBchGxhILOYSt2710fauKtdV3DOZXXZ7crGpbYbeq4tyu33odTXTcsNiVrpEttzpKujrVquuvqr90z2/dcpGX/Ex/6S1Pl8Q9Izzz7S98JAPjGf69pMdvQsTx/4S/Mti+V7vvapuT4SqyhnYGXBbg20S+GMbp9puHWxcmSOP3U1k447dHJJTcMO45NjY0x7zTG7AJ42Biz4/92AVwG8P69jgsICAi4kyBaKAf5O2zs+QC31v4ja20PwM9aa1f8X89ae8Ja+85DHGNAQEDASwsTHezvkLGfHvgrrbWfA/CrxpivfuHr1tpHbuvI9oBNMowvuFZdmyv3AQB2K3UJuWWZtM4aUrunQYvea+cvfy1RBTrJ5m8VymwZgsrHKa8hbvFmX8955apye7e3nau3sqKu9JlTnBhxx18nBcJBSi3L/LWsNNVlbJCyoLAduOUac953EhdO2Cl0Ljhs0kzceRMzzxYBgKln12wNiMUypBgQkSRa/lydiEq+SW9bWl6xgp4wOwBgMPHt20gKgHnI0hW+IgXEYoEUAIebdsb6+sUr7vzbqxpuM6fum233PKd8WOlFMbtoMHbbFykENS00xNJIddynV9b9tej4+8ReaiRu/3pTy8Pb1ROz7XjbsWuyTOenaCk7hplKgh0KQUgYbo0YOWmP9LJ96IrVJEfUskzCaCvE1y4SkjiI3fUNHqIQldF5uTzR68pLN5ZTHWXBnLr2hdl2tubCf1Xvgp6roHvsvxu8xhtj/b5GO47Jkz/5BJYLU+OjHyXsFwP/OwB+AMDPLXjNAviG2zKigICAgKOGIxoD308PXHq5vd1aO+bXjDHzQtuHhDJKZklLsbyLPbrcJF64qUn9J/iX1JA1LWgWmhhMvQXbJL5z0aTOJNSAeLNw1lxMzW+ThASKErcAKuIFL6AIYzDWYzhJutp219Kgij22kKWiLScxL270++zIWWDX+jq+mNZkO/PNdWn+xGJiNDMl4Y6n+vqlXC3Edua1zXs6virT94pl+9yuegOXt4jz7ldbR0+JFWowHHuxJ26+ywJNwtlN08UNnKeF74JDnPszVrnXnR1n9U45ya1OGADvzYyoSpDuJd/DK36NdRrzOvQAsJK5+8lNk0cb1CDZr8Gdlbtn+5j/f1/+eQBARjzv59ZfqefytQCsY5+3qAGyP3+bKjXP9tSC3kqcZS0do4C6Tn3hPYAJfZdEGx8ANke03vx6HVRaKXoi1u9Qb+CSk+vUrPrzu5oofqJy21GPGiEnpPTh73s51ET+UmCWpwe+bBzkZ+UjB9wXEBAQcMfBwsCa6EB/h439YuBnAdwNoGWMeQMwCyiuoBbxDAgICLjDcUQt8P1i4N8M4G8AOA/g52n/LoCfvI1j2hexLbHqXa1p2zenpbBIgxu6+iRfk/STM9qWNlncZJaFgDpD50p3r2lrLjPVaFK+pjzh4err3et0n9utiLbdWDPypBPKixTeK2RXPKIQCWt7C3YpITnybaZYLGtrrNfy3JYLrVzd1gHyZ610XAilon3bfSpv9+Nb6erxHIIZkNfaafkkJDWvHaTz4SquiO61Sbt8gXnQJ23wpyvH8z3V0nDRept41t7WYLeXk7+n191cnelqefjKlnKvk10XTmiQRZWeoDBa73UAgJJ1symclBfE8/ZhmtUWJXGJp5365CaLhF3vqXSDgBOqCYUzGn2XEExJCuAMXbfICXCS8mpXE7aSfK+1WRtrOAp+irm+gokCDV83kdH3rp3oXE8y/VwRqeIWhlzCL/r07RUNecYLjFrWCE+o7qHqeb3xV1EpPf54/gQ3gWNXSm+tfTeAdxtjvsta+95DHFNAQEDAEcLxZKEAAKy17zXG/GW4iswm7Q8NHQICAu58GBzLEAoAwBjzL+Fi3l8P4F8B+G8B/MltHtfe45mO0f7UHwIANl7v3OZppi4hu2fiSo9S0keuSCFv6hgnGbEVptTpXdwm8xyVNm9rGXf8Kurt7BP7HJbgbfH2O8TfqSiGsOs9wZ1dCoEQi6Xymf90Va+PS92HU68WuKAM3R3v9bxLYq5MdHul466VXdbtbf2s55514YaVNaWGnD2jc9UmPeoVHw4ZTnV5cau30z0XhjrbVcYPt1wT/jjzuLl1lvW5d2knBtR55sITblGbM2gEAIBTYUyo5d1gRbnVxfoFAHVmCIdjJhN3L0YTnayi1NfrTCg3Fw2SaBiT5vy1kVtD1ymt1Ex03tcbbt55XY9KXURXTjmli3VillTUHk7moqKS9JLCNVu5W/tjYi+dwxN0vBs/c8NZTfFM8bS7vomGYHLijK91VM5gbN01nhpTuGpTy+LLVccyaRUa2rp/TcM5ItGwMtT2bay9nrfdHFTJfE3ArcHM1txRw0FG9RZr7fcC2LTW/jSArwFwzw2OCQgICLgjcJRL6Q8iZiVZgqEx5i4A1wDcf/uGtD+qyQRbj3wKALBy4SEAQOOMJhPrVpvn2JIu9zBTU6zrkyatXH/xG5EmUJrXXOeSL/+bD8z2PfEBFR16y09rRdnZ73CJp2tras2XpMc99EbLrp4eRCmH9cbgxafVKi1ytbraLZegOUt8ZBbOeuqy71YyUavy3rM6Fyd6zgIcTdQ6Ywt7Z+B+y5uUa8xzPdd06t67eY00wGM9/4MP6IH3rTsvhfnK3J3ndMNZVS1LCWWydqV7TS15fEV52qbtLDyx2IB6kinue6vtebXUTj6k3sbuqks+sqjUM5Eu6S9ccWvkyqa+fnqdkqwtr61eLf7CtohfLlZ+SYm/go4TYaxJruPvNPW+dFM3bzbSY7YmrbntHaow7sW6nqUZdkwK0CuleiYTX8HKtRTDhn5HJj5qmlAz7LVI+ferT3/WbWyqHnsr03vZa6v3W3SdNc7NtC0lt0Vorruj37G0pUnK7ZYT0RqTSFtMnlfkG46nz2vHoWXh2CUxCb9ujFkD8LMAHoH7QfqF2zqqgICAgCOERS0BjwIOksSU7jvvNcb8Olwi85X7HBIQEBBwB8GgOqINHV5UT0xr7QTAxBjzqwDmyaqHgDIvsfWE075ee9a52Kc6WnpbUKNe4+MSU2pHxa2n+l5AKKk0gdYdapPW/NN/CqAeNmF85O9/aLb9tgcuAABe9iYNoeSrqpH87HXfsJUSl90WhSi8WNJ4qGNpErd5vetc+CymZBaV8vcHbv9TT2gyaTJRV/OBe5wrvrmjoYDHvqghoC97Avj5C5p0SlJ1G5std/zVZ9V95rE+9DJNAj5QfA4A0Ln8Jb1Yq9dqn3VJLDtR9xevntNLQ/Xon+m1PKcuevZ2p0H9W/abZ/tWW5ok/Nr+LwMAhl96bLavfVbL00/2XOKMNbpFqxrQUvjBkMTIMpJgMBL20LF2mjqv622dl1bi3rSaamjsREbhiIYLMXAzbU6uppE7nkWryppOvPuXudkpiZxlpUvyNcaafOf2avGqG0utFgL6Hcr8ue611JT5krYpqx5zpfzFrl4fZ+erXK819vG55G5t5WdImzy55ptMU61Fuqrf7f59bvt5Qwnnrs7biefduquu7d9o+cXC4uha4Dcb2LmpqzHGfIsx5vPGmC8ZY37C77vLGPNBY8z7jTHdG50jICAg4FBhjm4p/c1+4mKu2j4wzmz55wDeDuDVAP6qMebVAP4nAD8CR1H86zc5noCAgIDbBgtzoL8bYZEReyvYTwvlP2Lxg9oAOLFg/43w5wF8yVr7mD//rwD4drhO95X/u+EMFMMCX36/456efM1nAADdVdUirjqk8eyz0g3uWt/WEIF0sOdfTmnLBADTLdJNvgGml5xb3pwqA4BL4de9gtoalXQPqfv2F5UaO8OZs8qnFeYDu8pcki1e6/Z1UmcjJIm71sGA+NRUN7991V1rSqp5p8925947JBpNTHrl95xQt7fzZ78HAHj2t/5otu/648p8uPwRKtX2ePl3fmy2vXLeLa+nP6ohkOuf0Xl9+Lt/BADw8//Lh+fOAwDv/t+/3o3jN39HxzTU441f1lxe3k50/A+edXNwZk3dc0N65Fe23XE7A52/k6saNjjdUo1qkXPo5rqvRcp/617xMCKN62yg627YcaGNK4mGgFYbysyQNSYcaaDOOJHQSXNH+dZRX8eyOnDbzXUNS/S7yupqey59c0tZQNzVPp+4z5U2hwBgEtLwHmk4p/8l973tUVil8YCyf+yg748nvQliXUlbvkZMWgv0hIqH7lrz6xrmWxaWYV2TEftNAC4C+Lgx5j9Yaz97s+fcLwb+T2/ytb1wN4Cn6f8XAfwFOHbLewBsA/iemzhvQEBAwG2DXV4p/V5G7PIf4Nba37/Zk+6BRda1tdY+CeDrlvxZAQEBAUvDkpKYexmxN40XxUK5RVxEvYLzPIDF9I4XwBjzHgB/BQBWsgYAFxKpps4VK3sa0Smp9VQynO9enlAX62buXTbL6mhUyHOXKxx4zf+grMlHf/nzs+3eg+rKxR23zYyXNim0nWj67uCxhmgeK8ht7fvmE8Q8WV3RX/1J4Vz0MZXPc/FH5Bs9dHrKsun2tEii2TT+nHr8PXfr5z/3vCuKefIxda+/+Gm9PRJCmZJL3Giqq9vNdH9+xbEAvvA+ZSuUQ9YenMeX3qcxpLh9cd9jBv/gx/3W4tasV6cuTHbmPmU75GcuzLYfKx076NqurpWNloZQpP1ZOyPlRvoC7/q2coOBjq+R6HYXGq6R9WaIhVNS2brQ07KxHpNuXpptt/1x66t6zGnqr5L4MKEUuQDAGBp6aybuvGVT2VGWKHHxFTfXjef1M9Pzyp4SmospdC3bJn3H1lyMQ4qrAKA4e0E/vySVwid9+7SMQlNN/b5UWy70Ea1pyHN4QsluUeXuy+kJFXjRvIpSKDNfHn/8cRhjOK74a9bad+BF4kVUWZ40xnyC/v8ua+27ZIiLTv1ix8I4zAf4xwE8aIy5H8AzAL4bBwyZ+Al/BwC8euO0xcFD0wEBAV/BuP/++3Ht2rXOjd+5P7hq9wa4aq190x6v3bQRuxcO7QFurS2MMT8M4DfhEpe/aK39zIs9j0kNug86a6T7MvfrXJJ4TdrXBJn8Ilt6PaFfUolryS/7CxGddyJZ575NrZuVe0/PtodXNFkSd9wa6Q60fPtCmzjbsbOABpWuJbbqzpx21uz6OiURqXxbKqm3h4uTKSc33LWkr1ZvZGOVSulX3FhqlmKm3sj5k85aH4/UUnvmi5q4Kr0geDHRY65c1GTW01uqwfwK/54bWd1n3qIJ5fN/TpNZ2085zjFb5Ywv/KqzwH7rQ8r3fWTj7bPt1/3Zv3Abp5Tb/NTGa2fbn3zUJWevXlfr8MJ5vS+yRHbJbmOJgc2t+fUyJm+oD5JT8GtsGul9jTJq1ux53qtd/fwOeXFF7D6YxbpYpIuT7rPxUdPj2fHUusw29LNiaUO2SWt5Qy2kwmveV+TZppefnG3nW87LTVeo/L6nEgccO26m7nvL30cWo4p9InR410OzfVc6ui7a1ovPTfSak1w9Zttw88aJ0eXogS9NzOqmjdi9cMNRGWN+25fSy//XjTG/eTMfZq39gLX2Fdbal1lr/7ebOUdAQEDAYUIKeW6VRmitLQCIEfsogH93M0Ys4yAW+Elr7Swwaq3dNMac3u+AgICAgDsJ1ZLkZK21HwDwgRu+8YA4yAO8Msbca619CgCMMffhFgPvt4KsneGhb3XucOQTJ/HTmljMn9WQUuT93vj+V8z2TduaIJFSeubgslJa0naORzIkXu8pdfsj6o9WjX3J8mc+OtvXpMRO/xUu2Xw10VADl8W/Qmm+M7QSddV3vYpgQZ3iudH5iRUXrlhpqxWQxBrCEOFAPn6Y6wlS/97zd2ms4CKFi65dcqGpa80VGQAAIABJREFUmPi+Vanjf+KSfm7zguqzL8L6V7kQxlf96H8/21ecU7d37THHqhpt6Tp/5kPz5dEf+vqf0s8889Oz7avf4ZLOG9+t57+eq4svrexObuj1s3b7tS03F1evarhofV3d/m7XnaCmgd7UUECbcmY5vIQChRIMfX26kUsypoWGSFgNUJJ0CenYS5s0ALOi9+auzs8qfVbm6xIySoxWLQ3xFGfdvYo7VATNeuI+CSk1FQBQXdOy+ummD6E8oI+SiIgCrW0NKYqiZHVWE5OTFQ1PWi+J8Xxb18+IFD2N11Yf9y7M9nUKJSrIVaVLr4g8WJHOS4GDPMD/LoAPG2OEVvh1AH7g9g0pICAg4GjhRSQxDxUHUSP8z8aYrwbwZjgazI9Za6/e4LCAgICAOwJHWcxqv1L6V1prP+cf3oDSXe71IZVHbv/wFowrjtE+57LcdurczmpEmWhy66OWy0oXxBO/3lUWz07plNCmpU7DtbZm0GPfxupMR7n3a3erqxp1NbQiKKiM15BCW+uU49tu3KUMAcN1wt5rZSU6/tWXJgAniVnSbWiIJfXH7VLDhtFUXenEh2uYzsotz6SqnvpR4OWv0rlI0rj2L1DngY9GOu5H3/T9AIC3/Vt97+ZHPj7bXnv9qwEAV75K1QR3NE+Ou1pu+8Hv1Pk9+7Deg6Ev+3/qj5Sl0r1Lw1Un3+pYXNsnlc98ItP78jUvd2EBbm22OyUmk2dJlKXuW+mqW372hLvWcz0Nlbyi+vRsu/PxP5xtV7turFGXQhTs4guP+aSWr5ckByEsjbKpxw97GnYQxAMq1V8kfUoxIuaBV54ZEpOsgn1G+ftx073OqoG2qZz0xkkXUqy6NGbifuPJL+q4H3f3q72m38fR6VqvOwBASvUTMYUZx5X73O2p3usT1PV+beyVET+rKpbLwrF7gAP4O3Chkp9b8JoF8A23ZUQBAQEBRwzH7gFurZU499utpdIvAMaY5oJDDgUmSZCdv9v/x1kyESXWovMXZtujcw8CAC53Xjbbt5MTD9tbuNsTvZznd9Tq2vGG/bkNtRhe+1rdXh9pYqgxdBZe+pxWidkxeQY+OdqbKk89T+ebr27kKjokTZcBYNBynzvtkFZzSe2mImcJjQtNslIfW5ztOEuQ25x9blcTuk9dcpZggzjK7ZbO64lTzgLs9kh/eWPx8nnkKTeG8uHvm+176H7lYUsD3U3SROOWY5Om80xa6zq+tKfc557P3r78m/W+tU6pJVdecIniSaL3emWqx7d8NexuRuePdV57nh9/zwm9vlNNTZadqFxiLqKEd4vEnvqffnS2/elf+SQAIN/V9556WO/RuTdcAACsPkzJ855WZcoat8zjJgteqjptpnNhKQkpYlhY0arUbKLnb11y1ZHl46rdPiLt9dY5Z+1H5zTxaNc1uZ103FzYoa7VBollTa7ouZKeux/TDR3LMFHLfmXi3rtGgnCc0N2ybl0MqRp5g6pl4213jy997JaYeXOwMDUN9qOEg4zqIwfcFxAQEHBHYllyssvGfjHws3DiKy1jzBugdfwrANp7HRcQEBBwR8EeTxbKNwP4G3D1+j8HfYDvAPjJ2zusfRAnwEnngpmBd2sphCJhEwB4suWSZZsjLh3WEEErcUnQHpWUb5L7efmKSxJeeV5v3uA+5ajevaGu5NkNF0K5i1zddKCJs2nXhQvY/e1V+npWuHDLymc1ATZ5TMMxpx5yXHYWZaqoo3e+JtvqnnP384nvCm/o+gv12lEUbn+nreNLac1Ke7WMXNZzJzRxeaan4RwJ02SRJqOmTXWVJcm1QW26WlMt3+5ccTrgObnfg0saAhH07tVkXutlmrAc+SQol3FzT8NF2s5riYZI1nwYqznQz0+/qIk9qTUQIScAMB3N/o43NQQweILaxnnsPqvhhrOvd3PIye+ExKKw4a6xoK7zlq5rmvlaCNK8L2NdF9PE2Vp5RMJmI9Jj3/XhDpqTzssu6Gc94MJRV89qCKykVn4nGi70klF5vSXt9eycJu1xwn1fdpsaFkmtfvdEuzwmzvmgSeu5dNctbeoAoI15KYHWBjf2ujz3+s3gOMbA3w3g3caY77LWvvcQxxQQEBBwhGCOrAV+kBj4GxdoofzD2zimgICAgCMDC+dVHuTvsHGQSsy3W2tnIROvhfLfAPipfY65fTAGVcO5mKIiyBn43baGNXan7n0V/Xoyz1rCKa1U+bynejoll3xYgnWfx+rdYXvEKodeF3mVSuXX5t3nVq4uX4daa9kF3F1u6db0r/fXlMc+STQVMbZtfy3KDR8Xes5ndpxb2Wlo3GS1o9sP3ucW32pb3dMh8cjz3M1Fg0IoKy1SM2yQhvXUueUTqCs7STWMlRpHaur19Zjmk9qUZPqUuuOC1gkNIew87erIuI0XThI32pPdk0rHx4qTmTCCSGpgbVPDVfFnnZzz5Dnl/A+39b5d/KgLG1x5RMMep79aQxiXPjwf7mEMnlZSl/VxLK5fMBF1nZcxTTVEZUjlcujbAk5pLUg4DgBiX4LPchExKQAaH4aLT2p5vj2jug7bp13obhvUtpAeVOmqW49rFPZguQXW65Zr6T2n0hedBoWLPFe9pHARh35EgmA10+/rCq0h+NDc6kMXdN8faejrVrC/ruZLh4M8wGNjTMNaOwEAY0wLQOMGxwQEBATcMTiqIZSDPMD/DYDfNcb8EtyP6PcBePdtHdU+qKIYo1WXGIm8+I3whgHgaqUWuDT9jcnqLiq1HgaF+x3KyfplvufpdXd8QZ1xTvTUklttzlvYl8bKbWaHShKmJ0mAKi3Uqhr75rbxAw/P9rnuQw75KWfpDFK91t2Suqz4JAtbJ9z0+FrfnYsbLZ/rqVUpPOwJ8bG5avO+c26/pYq+2Oi8soXX3vWJI6rq3MrUQk4TZ62t5Xr94M4q/oNTShhzwnD3GWfhjq8p37hJDYqHLZf42jRqVZqEkteR+9zVkSa4qj/8rdn27/+EU0tO6b4/+O1aSzB43h3Peuc3sroZd79V12jvfmftpufV6rVU9ShWZUQWOHeVyq3zAlmDvEfNpqURMM9vwXrg626OmDs+6en4Yv9ZpyoViWPPb9ZxiKsvr6nnUgxIVN17GZay53FPvbTolKtGzSkhyyqA0impZ8gzpWbN1XV3D0w2X19xK3ipKIIHwUG0UP6JMeZTAN4Gx0T5GWvtTemBBwQEBBxHHGcLHNba3wDwG7d5LAEBAQFHEsfWAjfGvBnA/wngVQAyuHZoA2vtyr4H3iZYE2GUuY8uvZjStlVeaZ/K4hPv4jP3e1ioq7g7ca5Wrd0U6UKfWXVhD9Zv7qTqvq6l6spNrAtR7EzVveSbLtv9Sl3dikrVC+vGkK9o2GT1PnUFhbscgdq0FfpeCZc0Ymo+S1ZDM/XNcZua4NpINAnXr5wrW5BQEOuVZz5xxvOTUhJwbPS6pbF0QhrXlpKfknAdt0ij+x5toxWfclIJ2ycu6GdRs+lzEtuhwQxParjl6dKVfV/cUvdcrh8AHlxzia/2pgpkPfXHmkQV5DskjJbovTrzWhdi2Pn8E3PH7IVXv0M16c+97c2zbXuPC81M2xoiqliP24c+IkoSimwDAKx6zvdzsSa3+fuw6ofdmegxFbV32z3t6ibGKa1L4pl3xy4s0exrqKLL4S6fEI53NIQkbdYA1ckHgPSEC21Fa8rtti1db1Xb3a8qppZrlD5sRe5ckiQHAEMl/NNNtz/fmeeG3wqsrYcjjxIOYoH/X3C9234VwJsAfC+Al9/OQQUEBAQcJRz3EMqXjDGxtbYE8EvGmKCFEhAQ8BUD9jyPEg7yAB8aYzIAf2qM+ScALgHo3OCY24aozLF+1WkMD1ddSX2/oe6fsD0YHMowdMl56fZXxLxopMSTbrjMf2qUecJZcdYtblmXba9SfZ1DHBKG4bEwi2RauXGlEXUk76qrm1XOfRTWAQCksY4r8uyaAX3m5lDfKyGEXkyqcaWGJSaRCz21EnXVedFmkfusLh3P4Rwe127HsQmYj8yYVO69u7GyDVQKQMNFW5W+HlEI5Myb3BwZGuAzqZbSP3XVueJXNnWuLxBN/NTQ8cyjZ5T7ffJVpBP/rAuNmZjKy8+p27/6gJur0ZYyQy7+7uKS7fu+xTGmzr3962b7dl/zF2fbwo/nsEVJjJrSr1cOJXQn84wX0a4HNBwHKDuoOdqcOwYANjsuXLVZaNilGel3KPJ1A2mubJLGdWWkYOrWZdXREFDyilfq6zukme9lAQanLiwcS+q7zRtiNPEazeDmm1U6bVvDZOm6u4bhM/Pt924NBtURjYEfpHToHXBx7x8GMABwD4Dvup2DCggICDgqsHAhlIP8HTYOQiOUsrgRgJ/e772HgskY9hEXwem9+nUAgOJutd5alIyZ+sTidq6/0jlZ2zLhESXj2pSkbERTf4xawlsTarLaVAtww7jqwISsda53KryFLaJSQL2psOTlmtTImDnr7cSdq2k0KXQqVrEl47nuT9u7Z/vGU+bQigfAVX664EQUqBWrpcXJW6lqbPdVCKlINGF8paEWrPCvGxk16q3Us5A5YKsxjqjzinXnHVNHHOby9xuOa59bnT8WLCu8Z0XS6Tjdpe5Ivrmuaekbum9962z7Nfc4q3nnc4/N9mUruoayu53nd+/XLa7Pu/Z5EqZq+K8Y1RoUyby3wTx6SfICOm+FJcG2jDThjbsvHRJ1apDnk+Y1KX/3WbSvO3VjnbIwGlnwA+MIAw1KfmcJrbvcfX5B3O3pCU0oN9Z0Lqxf5CUlUWPitDPXXcCcd/FMBtQ1S7w9ADgp+uyf1S5Ay0J13GLgnvu9Z+THWvvwXq8FBAQE3DGw2nLwqGE/C/xbD20UAQEBAUcUEkI5ithPTnZeUegIwEQR4k69nwTzjQ210ZIQAYvvcGKu6ROWvYYev0rCVnI8h02u9snVpHBM3oznPiuJNBwi59od67lGuR7fa7iEaDNWl7GbqEvZNM6FjYiDK0JFgIY7UgpFcNn80IdTrk402WQb1CDZOu5uRuX9nEwqPN+4T+4rJ97ElfeD2Rcybxyaiin01DDufkyJDywcYEBbyU1J6Iiv+9SKG0uNs06vb592nPP4xP36+TSXLZ8YW6dWejXhLL+dUhn42r2a5GQ01/x6nOgaa5Eet4QDWGObIWX/TfB90TWQ+755LJLWGl6dbRdeLzzPOvS6JhZ711yYyGzo/FzPtOWZrNtpout2vKoa30l7OHf+nN5bdeaFrSxpNCS5hmaMFxwz1KqOQyy5X4NXoRlpbki+3nZJ6bw/Hza6VRxVFsoNk5jGmDcbYz5ujOkbY6bGmNIYEiMICAgIuMNReSbKjf4OG6GQJyAgIOAGOKoW+LEr5LGNFkZv+iYA6sKz1vTEqvsmmXtmODRJL1tK7LuJulxNctWFAcBsjWZG+sZ0U3enjjnB5eWsDChc8qpBPHIqmW57/jWHTdZK6ujtw0S7TVXY29Y+G7MYHXd37zZZhXE+nDSlEMYwcuGACTMcKtKt9myJCZR5kkHDAitTddslhsKqdROj4Y7EzKvFMQsj8veVWTB8D8axu9+jUsfCcglrjeHcMRzuetK60Ema6GduxMqtFmXIlEIsnc2ndHzPKn9c0DuvoaX2KQ1TNU46dobpkuoeKffJtXBNAFMHhHe/QqXsvPB2ui6cMY2pK31HxzKJ3T1oFsTjTvW9xpfoM7e6RxIL8h3jENOU6i6qZP5ecoinxrjx94Nb2iWpriHRPGdlQ14Xci2jiZ4zIkVM0dTv0r3Ax289EmytQVndfuvaGPPfAfgHcLIlf95a+4kbHXPsCnkCAgICDhuHZIF/GsBfAfB/H/SAgzzA3wEXK/9hAD+Gl7iQpzAJLrecZRTDd+QhS2tK3GCx4DgxmLCGtd/m5rtyTkA516sNtpSpSo4y02LZcrKMec4SH0vo9V5GFWeec55yMpAgTXm5yo6Fswr/+WyJtjO9LrFAuamxVH+6a+n48en1m0jfK9Z4ZqhKj6yjiqoHi8hZZWxpMX+99MJQ7O0w33e2jxKbzE2WSk62qpmRLdfFCR621Aa5O571ztstSoz706aFji8azqd90gtqoacPUH0B6VFXHeclTZpqgXP3pcS6e8RVu3xdUcONu0Oa99xdaORtqWGlnhPfwybcNaRUXTkmvW/xBnhMzenu3OsRWeAsrCUJx0lHE74FJZ+TUj9X5nNKHvM0m58X/j73m3reQeWrVqFo0PepSN0ctF/9an3D+25oxB4Ih6FGaK19FFA9/IPgxRTyjHEUCnkCAgICDhGuJ+ZLPYrF2K+Q59sBnLfW/nP//48BkODSj1tr/79DGF9AQEDAS44XEUI5aYxhs/9d1tp3yX+MMb8D4Oz8Yfi71tr3v9hx7WeB/zgc+0TQAPDn4OLfvwTgJXmAW0QYlM5VKn2pObvHrIedLQhHsACUhDW4jRrzcRve/ezFJEBFjXqLQl1FSRJGFDYoqvnpLak8nsMtkuTkEI6EIgDA+HFxWKZNwlNSns4hktrnHrBjNosmLdIz52SY8HIBYJiszL03tTo+3m4uaOCcUxJOkmANcvsLCo2VPjhSEA99UlAIx88xN3jOTDX3OksVFE09Vzt3POnWQBOz3Di7vPAaAMCwq8ky5j5zQlFEvnguO5WGYyQMxYn2gpsGm/kwIScJJeRWWGoazfr11XzoR/T0ASC28+dnZJCWbNRKjwSm4rELt3CTXA6xpFNqqebB9QMsxyDJY0lWAnUJhlJICbTGmbonDc3N/V+18FpuBS/iAX7VWvumvc9jv3EpA/LY7wGeWWufpv9/2Fp7DcA1Y0xIYgYEBHxFwFocCgvlZrCfWbbO/7HW/jD99xQCAgICvkJg7cH+bgXGmO80xlwE8DUA/pMx5oa9h/ezwD9mjPlb1tpfeMGH/CCAP7m1od48KmvQz53DVvlfxRYp+DGPum1FX5haf8XqPIx85n5UcmsycqV96MTuUYrPLBThfzcTCuEQu0W0s1lPnPUVmD0zu1YKNQgLpFmSWiB1Wh96HXEef0zhIAkzMee9Q8qDEqIwWKywJ+GcrKQWWcQwYLdYIAqGL3x9xt+vSG2Q2re1KnffmLNe1sIKbl5zM6/mCACxZ89wiGRcqKvOEgZ6fmJ+eGZFyQp969o1Xlx1biOXg+4frZHEh8RGxBIZULhDwkQse8BhQAmRsIQCt5frGleWz2qCHIaT+oF4D261MIUkfAEAg4i6JcaORcOhuWZHP39903HiYyrPj0cUtuGQom8bx/x+ZrewDvrsXBz68l8+/t4UxNgZxG7cUVuvb1k4jCSmtfZ9AN73Yo7Z7wH+YwD+vTHmewA84ve9ES7c9R03NcKAgICAY4bjKmZ1BcBbjDHfAOA1fvd/stZ+8FBGFhAQEHAUsITwyO3CQXjgHwRwZB7aBhZNzyQpvHtW7FEeLi4Zu5SLwK46h1Mk3GEXFOwAQIfat60kzu0X5gpQV12T1ljMIimh7p+48Mybic38uDkcxOEOdjUFScTNG3xrLSqeaRfq6krZe0Vj4pZp4sLz53OIp6BwhhQbmYiYJeSCT3yjBi4kyqjQpz10Ze15V+/FFBpumXpmB7viGw0tBZc57hca4tiecNm9+7fb1NluxxoWKL3EQZFoEckoVfbRoHLbvC54LO1IzyXhip1Kj98c67jaqbuWhBpWsMLejm+o0Cb9uIQKjGzs1xUxbiZW523i1TnXB1pSngy0VL5sufNz8Q2Hs6S5xpSaa4yNzmW05t7bamrYhdUEa2vUN5JobV6c7auorH/gWyQOY241qOtKGnzwSm9R0VLiQ2tcQLYMWADl4sjiS44DaaEEBAQEfCXj2FrgRw2xqbCRuoSJWAfbU00K9SlZZVKXNGFuc0WJLbGaOGnELc/GxfwvPnO3W7Fa4FJizo12c7IaS2+V7hVLkzGmC6xuACh8GyrmqXMjYRHuSsjCN8SDN5hPkrKlIuNmb0V0twEtg2Z95lFTxbQ4CThdwH3OoHMssgFstfJ27JOj7akmxkxG8+rFsPgzme/cnDhu8k5LS8Zjo6QqOa5DCe9OsU2f78Y6ybjNGFmglU+i071sUMKaPSsR/+JS+XFBZffCmad1xfMm3kRzTNxxaj1W+RL7kpt1k9Wai5gUeU5cNj/qOHG0Qabzw5/fMr6xN10fe2l94z5/t63HJ9TsuzdVkbDO2HlJ8bbuixJq3N3y15JqncuErkW+O5yIX410jUhSnesnloVjV4kZEBAQEIDjHQMPCAgI+EqGBVCFGPhyYFChaYfyHwDArlGOLSu5idvKHc2Zmy3JJlb4KyjZJK5wSglCVjPkxJy42MwHtlS9Je/lsv+9QgiCKbntkphi95bHLe4lKwjGtXEX/lr0+msdv31ohvnaHGIpfZKrpGQXh3Nm9wRaFs5hhyb09YYvxeaSaXbLx631ufGt9p+ZbedezY65yynz0722dZdCBazDXi2QFWC3W8rHM2r3NWm2547htVa/f6SD7kMAk5IUCOkeSeiEdeQ5nNKLXTiIE4MFKRvK/eCEc+26/BoZdrUNme1pS7RridvPSUpWgVwxLrS0Mr5Cn6lrfDt1NX3DSkOX7YifdrpeJ10Xrpl2FrefGzVcCIVrIljfXtYVJ+JZ2kFIA6xdviwcVQt8uenafWCM+WvGmP/i/z5ijHkdvfbdxphHjDE/eljjCQgICDgIXCn9wf4OG4f2AAfwOIC/aK19GMDPAHgXvfbdcEJZbzbGdBcdHBAQEPBSwVp7oL/DxqGFUKy13IbtowDO0//Fz7LAjZXTRUFNmB/scrLLpV3p9ZTMSBExfWaL1LvKu/MKawKoszy4uYGci8MqixTeaiGKWjjE7ec2YYsaRvD4+LNkrBxWmVaL36ug433Z+zSmjuI01qnn647tfCgBqM+FhE5Ksg+4rL458swB/ahaOEWU/aQMHAASZl54Rs5eDSXGLeeij9PFtoAwO2pKdxwOilzYgkvWW4k2OVj1DSnGEd+r+fJ8QEvkmefNwkgiA9FNiYdeY/Q0/DUp44fnRfj7Ne43MalK6+YgT+Z5+gAwyd3+vVQ8m77bfWv3sh5PIZyrqedukzJnlOi19ondInUVrCzJrCeRDcgsMXoSloZw943DVdy2T9YDSzwsC1/xIZQX4PsB/Ab9/9cAfALAJ6y1u4sPCQgICHhpUFUH+ztsHHoS0xjz9XAP8LfKPmvtuwG8e59jZpmK9dXVWaWeWCUZVWPlEVc3it43VQySpTbxFWUjSsDkCyz4lJJ58QKrG9DESUY/iTk1751ZpWRBs7CVaIdzdSJX5Mna4CRqk7TNu/F8I9+dXPnx2nJNrSdDDZoXWS3CPQeAwidnF4lKAWopApjptbPV0kzVUkp9U1zWEOfjJRGbp9SgmprjjlN3fB5Ro+Sa1ebmTVpwAXXPTCw4nmv24oaZW1cpaXynhXoAJ8euqnHS0PEPUm15xpDrGlJT52k5bzdx9SbPhXhmfK020Xs88JWau1MdK59dvhuLvD0AaPlq4nUoNzvL9Vozz6k3pPFd0bqY+MrlcUFWteFEOjUU98nHNrRqVix8AGj3nZWfU1XndluTr5Lo5u/zmNw4SXKyZ/b444/Xnh/W2hcthb0MpcHbhdtqgRtjfsgY86f+7y5jzMMA/hWAb/fa4geCtbYjfxfuufv2DTggIOCOwv333197ftzseSp7sL/Dxm21wH07NmnJdi9cqOQd1tov3M7PDQgICFgmqvJomuCHGUL5ewBOAPgXvutysV/rob1hZ2I+4haye1jrVF5JJ3hqwbQgCVgTH0o1rDDj8FbsHlLClBJfVeQ/i5IyFYdu/PYivrcb43wJPZfdSziI3dMap92nDjjxmCeU2JsJSJH7S/x5Ec5i95OvT5K/PFdppWGXKSX0Rr58vCAHj/WwKx9nmlRcqq7jkrkcUwjF0rmGPkRTUCiCZQ0amA8HcbhE7kEz5jZvyi2WcNEoUoNtJbmu55+4snZjF5e/87aEnDghnRe6PfZhMk4s1sTX/D0YE7e8IrGnfiHhKj1nM9Xr78YuRFETJqOGWlIq391VnndNgKqYb0vI9QFSYt+lz9xrXkU8jTvVZ1MNoaS7bo651D9qkQSA/1yeX15XQjbo2MWc+JuFfYms64PgMFkofxPA3zyszwsICAhYFo5qDPzYVWIGBAQEHDaqI2qCH7sHuDUxhh1XvjvjgJLHVMvKRvPl64zYH9grVB+5R+7h9di3ziJXn9033i882DpLRF1JDr3oWKnE33NgbaznzyjbLyGGGjOFW7157W8Oa4j77M7f8OfR45MFZfXcuov5urOww1RZnhG16ZpmlB/y5BvmWfO8ybiZb8yMHGE2FBTC4eP7uSj8EWMn1e1uGs8dwyEG4WR3Ii3D7k40pz7166oP0vA2J2fbRc9doCgNAsCwXMyPl9ANt3dLqRWe7N8t9bNasYYdIj/vorwJ1JUN5fy87nitSc1EK1c1w5bRdSEtzWrd42kN5P9/e1ceLNlZ1X/nLt39lswkkCiBoBMEoQAhRmRXIgEqKCARFQpEAuWCBYUiFBAphZRlFcrmEgQispRCAbKIFahAEEGUNUSSARIpZEIcTDGJhszMW/p13z7+8X2nv9+dvv2WeZ333h3Or+rV9HKX7/vu7Ttn+Z3fiYyhwX5iNBEjaDFy5jn0mG/Qlo9DMMOc5nK3sAZ94u/zGtvvhX/Ptdfx91wSi2YWCB15ZnrImaF1D3CHw+HYUaiicgt8NhhJhqXI0zWrrZclq7MqJxsQ57WKxZQgssQR852LEetWh+OyJdclHnit6a4lT8kF4HP1YscZtjR5f7OaONnElpQ0NHTl/deihZ01WF9Asx44J8vmVkN1JDeZZZ6zJe66t6bOLtywttyXLFTYbiTLzNxm8ya6dN3mhYSj4lzYkmOBo2FMTg+GNH9KfC0R59re+zR6AAAdIUlEQVTAnlk+bhQ8ub5A8jZ4fe8YJgvUqhqzKZZm7bzx3tvXSVb1PloXS6TXm2WnufSil1CSt7IC4olHC74spohZxU5JLAzWWfn+5IZT9ML7UW+cOxJ1h+lamZjUsJjU7QZSM24grTd3dWIRseOxgTInJpm/34lJSk6MLmbJm7Bq31l35AFqy7On0LoHuMPhcOwkQgjFLXCHw+FoH9T1wO8UWAiAhZpGOXPCg1vXVM4LJFd5OUu8WsnTsawNGrc542NxAqUTy9rnqd3T6aOUGDPua8VlyCTEY9zjaSEWHfPAiadNrvZStRCPny4pi0nZWtQ0yMmqyKpJvm8NTRYIudq1cEpMIu0b3ZrOX6Rk1O15SEJzknRBU5Itj6En1hPvU5JwLWpr82+KS/ytFmBU02OnoYppqyehpaVuChHYWnPisak8nN13BrdUM3Q0hYtYr9rcfSuJB+ql7paUrrW/o7E06cxzaGc1ykQsd0kMi3TOrSy+pvNOr4+VYY0qSn7PU/s6kxhYKdJviDXcFleofVq8xziEYtruALDcC8dYrZrF5Qyc/J6veCyxPoQv9ozgFrjD4XC0EAov5HE4HI52Qr2UfqawbDaXjRvYvari9+yGNTEHWEuZu9Ybi4TZEOzW83GtlJvd6vljyX0so6obK62tLia33UIgzHjhcMdYNoBZKDVudZjrsSFpXOukAh/rPnOpvLUx4xDPakF83MhC6PaS6h7zbTkEY5zi7iDNf9hNrvLK/uAqM9uAQwSd2B6tyrnV3eSt2iPmxXzJnPuoNki3d9UgS7BEfOqRprEYpzqbwumfy8K8WeOcQ1clsVuMpdFbTbUGXJ4+jHIBa4spxDSksdhx+/QZa28PYjhpjWUPiIVjPPKlPN13ukhhxqhyyKX8tfsurhXXDIwodLYWQyDMMmLw/ZTHEAe3h+NzWcivxs4ilUgLF/HvtazpgYfrdWeEUPZqIc9u6YE7HA5HK7DZbjzbjZOLyGtF5MbYdvLDInL6Rvu0zgIX6NjyGcQqrowWjq2fUbTquNFwvZHw+pVdxt3mfbi6kBOOpo3NCaxRkSyl/Gjky5IlylaLJW7Ywmbrw/jE3Ki4Q8nVTjw/V1ouDSebPdct/GRhm8DUsMa3pmRatOpWu2n+89SlZn4liT2NYgWsUmLQGtaGMXYm5sKJrXwUhbW40S9rn3cmhb0WyuQN2BryWjILvleE+6eW+KQKV7P0WCPcrG4AKBDWmgW82DNjLr/Eqkvp0rUs0+vjvdA96I5hWh9OVNs9cscgreXxVSKSN4CvsemBr5JVPiBrvaomRdY40W+dllhM7GiemhKbNdwnPXD28rjxdJNlzAlT83Tt+kzMK17PFRZBG7HHOB+PMykMt13sEA/8agCXqupQRP4UwKUAXr7eDm6BOxwOxwYYqW7qbztQ1U+ojqvvTmw72YjWWeAOh8Ox09gFGuHzALxvo41a+QC3JGa2gV9TSIPGdq3RcHBAilpjVdLYjgJAI0qw9fPkNq8Rz9hCJ5yYO967axrLopUcp+9Zd9lcXU628ViNiz6fkx63JlezWy3HsVLiskjl7RZO4QTR8pBDAOFW4LAGr5+FBWrl+7QW0kvhOkt+Hh9REpQa7dq02dXmcMS45JrWZ1+ewjWn5eZKp7Xk49u61fjSmHzNDaob6MZTOf/W2quJowwAQ/pZHdUQGlme0mD5jrXw+dIghRJ6Ba9LMfH9kO67MoaZynzKWOMYWSe/FpqKet5Zw/oAaa4cIjI9diCtO4f7OvQb4jCWNavm5HktdINwDws1gOaQpQl38VwqDsMZJ75BNmI7UAWqzbNQzhSRa+j9Fap6hb0RkU8CuFvDfq9U1Y/EbV4JYAjg3RudrJUPcIfD4dhJ6OZZKLet16hGVR+33s4i8hwATwJwoW7C7PcHuMPhcKwDnUF8ezMQkYsQkpaPUdXljbYHWvkAFwyi624KfHWt5+T2myvN7brYvS0astXM7e0MQtijxmUlNgSHO7KYYWf3cY0y/8cWz47H5zZoVIodvULmuDL32dqnLQzvGH/WVBbPYYcutRkb5pPuJx+/H1kYzLxYKJJG9Pwo8thz1kan0BLxcY9WsVP6gHjetbUKY6xJBZAEgq0xr1VJpehjFUEqAxhQuMZ43CXNv4lTz+cvssnQEdcU1MMKYT8O0ZQgbjfdbxZaY+141nRfHoRtuWyfFfgQ7+f5ksISzDKJoZPTO6TxTWGFlSr8VjhEwveotdLjMddbwuXxe24PmE287tIxu5IYOyO6SGM9cDo+M3bG+9C5asqGVpdBoS2+X02VlMc6K2zBAt8OLgfQBXB1bDv5BVV9/no7tPAB7nA4HDuLnXiAq+q9t7pP6x7gIwiWI3952MBtbmpoy3xtTnyatc5WAFsM/U44T61RMJotFWuour+6Le1PVqkJZs0jJeNKbu5axo47ugHHl7WOaS52Lu5gslpRknI0mXirVbk1JOTY0ulGb6S2FhlVMjbcSvXE3+Tx2ZKrWWVxXp0he5GcfA4/pkVJ3khRTnpezAfm626eEXfR4YStNW5mPfUBiZubtcrViVzhyxaobWOiUgAwoDW0htsZeY5zRbovTiviWGh8c/lk9x9uNl3rRBQbRDO3+7S1VCFr3uxSlio1mwTTViqqWaBKUPNC2Nvj+5r1vm092cvl+8les/Y5i9OxR2TgdTfhL66KnQW2mMTcUbTuAe5wOBw7i+1XWd5Z8Ae4w+FwrAfdu1oorXuAK7JxYkbG5eVV7XvDuMkpuZ+cjCo0uLesz8wunfFVOYnJyBp4xvVkGSX5LDlJkYQetRGr4qVgAaqqQbiKS845LGDj7lOyrE9lxpY448TbXEbJrPiaQx08lrUyuO3sEtfEsmrHDSGEbie5vFzi3yRMxSGA+X5o+dWhBsojugbW9o1FkeaoqbJpX6/lKYlK1OlxGKmWhGyoGVhFClWsVpy8lThmEtMapbF2SG/b1m2Qra9xzbIAvK6DsSZ9WksOh1iCnpse8/FtXvtXj4w/W7j95jS+xVCrMDgtzY914q38vdY+kO7L/WUYy/5RklIoGvTOAaA7CInWjK4b65QbuA2bZKTDHkMwTSJ2DJNimCXcAnc4HI4WQrFjLJQtwx/gDofDsR7UH+CzgyYXrhNDJ6YaCNTDKeNdyOVjHvgqgotd5OSeEyMlq6xkn9gY1EndMvxA6pr+f/kPjT9jPq+5oGvEZhgI8dNjiGMah9V4vDUWCJWy96MLzeEJnncKN1HYIGPubpgXs2z49fEslISze90hxg+7xcYGKIbp+z61zlotwmsONy2uJRe80z86cUwOlxQr8ftjSWO72JdkC/pnRvZQLZyWYOyYEpNSAUAqGz9KneiXBqRBHUMnnW4KaxTEvChIJ32swEfkoqrhuhTE2GGu/ljugH6pzDKxUnZuQ8Ywfvg0jexiLYzVNNiBepjMWB4se8DhlPk87N9bTSEkVhisMWJMHbRM369IWmMDs6P4XLZWrOLJv5dCmjXJtw9FVe3Nppjte4A7HA7HDsK70jscDkdb4SyU2UFEx53frRSeC0ZYNc1aNOkUhT8LETBbhAsmLAM+LevNanarMXRRU90jWEsuDsFw0Y65+zVVOMrAm6t4LDuD9pksea4p6JHqXN4QmuEQhqEEtVyj28MKnLh4hQs2Cm6pFjuVcyd7Kan1lQn3D1P5d28lhUOyKjan6KZmAAPa37glneMkK0Btyix0M4/UsZxDHKaKx9eVmUipACu598xYsXACSzGsUdHWkOQGjMnEaz1H4QgDX8shFcqYAl9GIRIOg1nIbGVAobNaQ4fYlb6T2B7lYmLJ5HFdmLmxklOjjxjC4HDbKCcJApmcy5BDKMRCMTZXrVUfsab4dzo+Vy0Mtn4Yw34DTUVj24XHwB0Oh6OV8EKemSFDhdNHoRTY9Ki54Wt/NJnM6WRc8tzcUNXAoj6WcBxWbIlSUqVBIIlFkWoiW9Eynq9SsmeOLCWzeiriSzN32lpH9SkbxtxlK+XPapx48jbivFjYq8Y5j5bitPUxkaycvp/Gtx0UMaFKliiXatscpaAy6C6VckdrmNuwWRIVAFb3h7U6naw7Tnha0rlHPHKpJsfKCdmmWgBuZMxJxv1FsPxNgx2oi3wNdIH2i+Xj5NmUVOo9Fouaknw275JzkGwNs3jb+PyjSW9ridrnSRRWA4DeIKwRa7tz2b/pw9eahWcNyXnykPhY7N0W8XpNa9w9blpMv+EVLtuP27Jeut33jCZLfjtQBarhJDliL6B1D3CHw+HYabgF7nA4HG2EqsfAZwXREbprMfkVvSulpEpW6+Qe3T8qzWX0sxB64URKTTs8ajRzqGGaFrG5yk3trIBUNs/d2znJl8/F7uEF82JTaKgS6x5Outk0lnkNrnB3mDS8mSduIQx2X2vt0WKIJqPEHodobH4cNiiGVNJdprFyCbuBk4CDGC5YHaWELHe7t7XiJCorK67EdVnZl9bKuqcDqeS6N0hJ0hLMzQ5zXCtITZHmbTzobDTZegxIPHdWS+T9+0LXDRYWoHDSMH1v9xhfiwFpg3fysD8bgJw8txL7vDOpIMjHXamtX3q9UBpnnmoaqkm1wSBTHcBqhFURk9sF6eSjOem/mofrxTzupvuxTyHLtSody7TPs4bQJdAsZzELeCWmw+FwtBijDfrv7hb8Ae5wOBzrwUvpZwkZc0uNb1zj+FLYwNxaLoVnZUFjnAy1mQFgjJISHDZJrvQcCfcbS4OZDaOMecax6zxxZPl77tRtYEaIqfVxiIZ5sb1+CKHMLaWGEmtz1Cl+bmFifDXVu8hcyIlFU+aTKo6shsjj57CJNZXgJgnMXrE1nsaZN/57RQwGZv+YsiLv36N1NbW+UYdCWA1rzZx6DjGYymOTLAOQGllwy7aaiiWPdRTWZYnay60M07Wei6EHbohQDaj5Q5XF+aXzNzGFmhQOeQ68VsPatnPxmBzimgzBrFJYoz+cDJEs5tT1Hs2NPEwxsU/Kjnwu47yvDpsfSxaybGLe8Fg5rDILKBQjL6V3OByOFkKB0cgf4DNDsvzC/7Rc+WWJEiBZmJxAY1h1Y601GllCi7H9GVvwnB/JK9LTjjxi5iMbHxoAVopQVXj7XOLgNoEtVdaANquQj98kFDQqqPVXPqndPU1vfNDQnq4vVFEXhbu0IOumSJWSy6OUhDRLiDm6TVYR86zZ2jX++2DEnPVJAShOVtXbt0VuNYlx9akNmVngnExcJrGqTvQ8rOIXqFuVK9GqrsjqrImAjdjatabApNFNFbILZRSToopGvkaW5Jyj+5LnujQK9zs3kGbOus2BPSsuaDTLu2l9AaCM1i5b4MMqzWV5GOZ6LE/3Qoeue9NvbyFPiXZrtg0AR9cWJ7bNaS52j/B1b0rYTvu9bwceQnE4HI4WQqFQT2I6HA5HC+FJzFlCx8JJVqq9TO7bUkUCRNHVYleexao6sPJzcmmRuMP7jt8Svqcy65X5M8evuXy6jJxo5kY36SI3aXQDqdR6fi0JMOUkEGVhIxZNWqOwQBY5siWFbTjhOOZJU/6qoJJuK9VmvnGTK1pLfE4JG9h6slYzJ7PsevRAndSFy/47E2PN6Fi2lhzWqLvVtmMak0xpWWZgTr+JVXWkWYLB2uNx2IiTcbVWc3FcfI8xjH/P8y+4VVsMgVibOqDOo7bQCZ+/W5LOuWmf032fZc1hNAOHa2y/YUE89yGHLWIYk0NEHC5r4K8vjpIIGYcnR2U2cSxGJ4ZzusT5X8VkK7lZl9ID/gB3OByOVkJVUVWuheJwOBythDoLZVaQMbvCQhjTOLDmqluZOVDnQQ8jJ5yZH51hclU5dGJgpbVlSaEb7QS3rcehAAqxmLvPIRxt1C0mBsAgZeurGBrplylTz/x1xHBKScyJklp79WLmPycOLbuvvRiaMYYFAJSs/R1fF6MU1ulw2X2jxjW53ayQF5fIStKBup648fq5jVqftMGPF4HfzsqROSa5wRy2KDSNz8Ipi3kK4Ug+yXZoaucFAN3YSo65+8LhJPK2LQTB+3P7MysbH1F7vg5do4UsXDdeqwGxg2zbhSJ9f0aW5BpGkZ20mqVwD0gZ0Rg/PFdWHhxLKND17ZVp3hZ6Oq1Ia8nsKdbnt3NxyJNVGqvK5CImFQqBdF2m1hfE3940/v5Jw2PgDofD0VY4C2VmGEmGpU6ooGtKZvWo440lydh64Yo5S4wNyWrmbiG6OFnZtSRJt3qVBIqqLDZs7dJnNY6qWRdprNxg2RIvow4n2DgZEyyAYdacODRrt19MNokFUvUgz9+0oAGgtKa2nbuk8XGFqq01We3cCLdDVakmBsUNa3kNx9xoap7LFnjv2JHw2ZGbx5/N7U9Ni3H2AwAAS0XSCO+O0vmtUxAngTmhazrjLHxWkLc1jM2mKxJwYkvPrEY+ZkmeGVud3dhwW2rdZPbR6yicRZZmjd8ez8trrQV5BlFTfU6Tt7b/2HfHr1d7wVupOulasjfSiYls9rzYM7TrNk3ErRs58ywmxmBNe6sGXaFKTJ7r2mjyccScdhtLvUsPEQGiNzIvaS1mAYW3VHM4HI52QhWjHWjoICJ/DOAXEUqtjgC4RFX/Z719WvcAFxF0OmbhTP6PzHHXTrSgyor0SbiLS7RmhbrcMF2sjIkLth57ZAELxXWty0qXjlXVLBnbluKyNFazwLsU3yyySVnWTsEWfrp81iexQxSzoiDZzXzSAi8oLmqVnF2q5MxpfEWcS0nJnJy7yNTWNcZVhbROaK3yuJwdobHmJAO8ECxk2X/W+DNZTNZ22Q3r0iWaZkm5jSJWCvL4OPdRdoK1nFEMm3uGZuM+lmn8BV3LPL5mC1yE1y3tV5pGDvfEpOuWxbgvW+Bc7NqJnhNTC7VgOdZwLr7ueUUx5ijTW7+uk78XpTEVwnOVie+5J2YnUhL5WjBGfI9Wk9W+vC6lxeFrekTp+16s2uxOWWu7Gh3ynGaFHQqhvFZV/xAARORFAP4IwPPX26F1D/DDhw/j4oufuiPnOnToEM4999wdOddO4lSc16k4J8DnNYPzbD/2sUNJTFU9Sm8XUGPRN0P2aqugvQARWVLV5qByi3EqzutUnBPg89oLEJGrAJy54YYBPQDcQeYKVb1iC+f6EwC/DuAOAD+nqreuu70/wKejTTfZVnAqzutUnBPg8zrVICKfBHC3hq9eqaofoe0uBdBT1Vetezx/gE/HqXqTnYrzOhXnBPi8flAhIj8K4KOq+sD1tmuugHEYPrTbA7iTcCrO61ScE+Dz+oGBiNyH3j4FwI0b7uMWuMPhcOw+ROSDAO6LQKj5DoDnq+p3193HH+AOh8PRTngI5QSISC4i/yEiV8b3dxeRT4nIR0RksmXIHoeI3FNE/kVEbhCRr4vI78bP2z6vi0TkP0XkWyLyivjZnp+TiPRE5Esicl28HpfFz18tIt8Vka/Gv5+Pn5ci8i4RORiv4aV0rAtE5BoR+bPdmg+NZUvzit89SEQ+H7c/KCK9+Pmemdeeh6r6H/0B+H0A7wFwZXz/GgAPAPBkBJdm18e4xfmcDeD8+Po0AN8EcP82zwtADuC/ANwLoRb9urbMCaH6bDG+LgF8EcDDAbwawEsbtn8mgPfG1/MAbgJwIL5/H0JX4tcDuF/L5lUAuB7Ag+P7uwLI99q89vqfW+AEETkHwC8AeBt9nCPEpEaoi5O0Aqp6i6peG18fA3ADgHug3fN6KIBvqeq3VXUNwHsRSpD3/Jw0wLqGlPFvvTimAlgQkQLhobYGwAo+svj9rs/3JOb1BADXq+p1cf//VR0L7eyZee11+AO8jj8H8DLU2r7icgBvRShp/fvdGNSsICIHAPwkgnXU5nndA8B/0/vD8bNWzCmG6b6KoHdxtap+MX71QhG5XkTeLiJnxM8+AGAJwC0AbgbwOlU1vdi3AfgcgExVb9jBKTRii/P6cQAqIh8XkWtF5GV0qD01r70Mf4BHiMiTABxR1a/w56r6HVX9WVV9crRgW4kYE/4ggN9T1aMtn1eTVaZtmZOqVqp6HoBzADxURB4I4M0AfgzAeQgP69fHzR8KoAJwdwDnAniJiNwrHufjqnq+qr5kp+fQhC3OqwDwaADPiv9eLCIXxuPsqXntZfgDPOFRAJ4iIjchuOSPFZE9a8VtBSJSIjy8362qpwL/9jCAe9L7cwCsq9q2F6Gq3wfwaQAXqer34gNwBOBvEB7cQIiBX6WqA1U9AuDfATxkVwa8SWxyXocBfEZVb1PVZQAfA3D+rgy4xfAHeISqXqqq56jqAQDPAPApVf21XR7WtiEiAuBvAdygqm/Y7fHMCF8GcB8ROVeCDOAzAPzTLo9pUxCRs0Tk9Ph6DsDjANwoImfTZhcD+Fp8fTOCMSEisoCQGNywwGOncRLz+jiAB4nIfIzvPwbAN3ZyzKcCWqdG6NgyHgXg2QAOxvgkAPyBqn5sF8e0LajqUEReiPAQyAG8XVW/vsvD2izOBvAuEckRDKj3q+qVIvJ3InIeQvLuJgC/Hbd/E4B3IDz4BMA7VPX6nR/2htjSvFT1dhF5A8J/xgrgY6r60d0ZenvhhTwOh8PRUngIxeFwOFoKf4A7HA5HS+EPcIfD4Wgp/AHucDgcLYU/wB0Oh6Ol8Ae4w+FwtBT+AHfUICI/LCLvEZFvi8hXotznxRvsc0BEvrbeNuvse4mI3J3ev01E7r/JfS8w2d87CyLyufjvARF55knsf4mIXD77kTkc/gB3EGLV5j8C+FdVvZeq/hRCleM5d+JpL0HQ+QAAqOpvqOqeqchT1UfGlwcQytodjj0Df4A7GI8FsKaqb7EPokDUXwFjK/SzUT3uWhF55IkHWG8bEXlZFO6/TkReIyK/jKDr8e4o9j8nIp8WkYfE7S+Kx7hORP55s5MQkQslNOU4GBXwuvHzm0TksnjMgyJyv/j5WSJydfz8rSLyHRE5M35nEqmvAfAzcZwvPtGyFpErReSC+Pq5IvJNEfkMQiUs6DwfFJEvx7/xdw7HycAf4A7GAwBcu873RwA8XlXPB/B0AH+52W1E5IkAngrgYar6YAB/pqofAHANgGep6nmqumIHEZGzEMSPnha3/5XNTEBCV5d3Ani6qv4EglzE79Amt8WxvRnAS+Nnr0LQvjkfwIcB/EjDoV8B4LNxnG9c5/xnA7gM4cH9eIRGE4a/APBGVf1pAE9DXXfe4dgyXAvFMRUi8iYEqc+1+NApAVwetS0qBE3nEzFtm8ch6HgsAwBpWk/DwxFCOYc2ub3hvgAOqeo34/t3AXgBgtY7kLqhfwXAL8XXj0YQWoKqXiUit2/yXE14GIBPq+qtACAi70N9De4fIlUAgH0ictpelr517G34A9zB+DqCZQgAUNUXxFDCNfGjFwP4HoAHI3hvqw3HmLaNYP0OLSdiq9vzfuuhH/+tkO7/k+n6MkTdg+3R62njzgA8gj0Nh2M78BCKg/EpAD0R4ZDDPL3eD+CWqO38bAQlwBMxbZtPAHieiMwDgIjcJX5+DKFX54n4PIDHiMi5J2y/EW4EcEBE7h3fPxvAZzbY598A/Go8zxMAnNGwzYnjvAnAeSKSicg9kXSuvwjgAhG5qwQddg79fALAC+1N9FIcjpOGP8AdY2iQpnwqwoPzkIh8CSEE8fK4yV8DeI6IfAEhLLDUcJjGbVT1KgTN7muirK3Fn98J4C2WxKSx3ArgtwB8SESuQ2h024QLReSw/SG0jHsugH8QkYMI7fHeMmVfw2UAniAi1wJ4IkLnmBPDGtcDGMaE6osRGiscAnAQwOsQcweqegtCI9/PA/gk6jmFFwF4iIT2Yt9AaP3mcJw0XE7W8QOPyFKpos74IwC8ObYGczj2NDwG7nAE1sn7RSRD6Pr+m7s8HodjU3AL3OFwOFoKj4E7HA5HS+EPcIfD4Wgp/AHucDgcLYU/wB0Oh6Ol8Ae4w+FwtBT+AHc4HI6W4v8B7JevL/+AcL0AAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "residual = counts - dataset.npred()\n", "residual.sum_over_axes().smooth(\"0.1 deg\").plot(\n", " cmap=\"coolwarm\", vmin=-3, vmax=3, add_cbar=True\n", ");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "- Fit the position and spectrum of the source [SNR G0.9+0.1](http://gamma-sky.net/#/cat/tev/110).\n", "- Make maps and fit the position and spectrum of the [Crab nebula](http://gamma-sky.net/#/cat/tev/25)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "In this tutorial you have seen how to work with Fermi-LAT data with Gammapy. You have to use the Fermi ST to prepare the exposure cube and PSF, and then you can use Gammapy for any event or map analysis using the same methods that are used to analyse IACT data.\n", "\n", "This works very well at high energies (here above 10 GeV), where the exposure and PSF is almost constant spatially and only varies a little with energy. It is not expected to give good results for low-energy data, where the Fermi-LAT PSF is very large. If you are interested to help us validate down to what energy Fermi-LAT analysis with Gammapy works well (e.g. by re-computing results from 3FHL or other published analysis results), or to extend the Gammapy capabilities (e.g. to work with energy-dependent multi-resolution maps and PSF), that would be very welcome!" ] } ], "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 }, "toc": { "base_numbering": 1, "nav_menu": { "height": "237px", "width": "253px" }, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }