{ "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://static.mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.19?urlpath=lab/tree/tutorials/analysis/3D/event_sampling.ipynb)\n", "- You may download all the notebooks in the documentation as a\n", "[tar file](../../../_downloads/notebooks-0.19.tar).\n", "- **Source files:**\n", "[event_sampling.ipynb](../../../_static/notebooks/event_sampling.ipynb) |\n", "[event_sampling.py](../../../_static/notebooks/event_sampling.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Event sampling\n", "\n", "## Prerequisites \n", "\n", "To understand how to generate a Model and a MapDataset, and how to fit the data, please refer to the `~gammapy.modeling.models.SkyModel` and [simulate_3d](simulate_3d.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Context \n", "\n", "This tutorial describes how to sample events from an observation of a one (or more) gamma-ray source(s). The main aim of the tutorial will be to set the minimal configuration needed to deal with the Gammapy event-sampler and how to obtain an output photon event list.\n", "\n", "The core of the event sampling lies into the Gammapy `~gammapy.datasets.MapDatasetEventSampler` class, which is based on the inverse cumulative distribution function [(Inverse CDF)](https://en.wikipedia.org/wiki/Cumulative_distribution_function#Inverse_distribution_function_(quantile_function)). \n", "\n", "The `~gammapy.datasets.MapDatasetEventSampler` takes in input a `~gammapy.datasets.Dataset` object containing the spectral, spatial and temporal properties of the source(s) of interest.\n", "\n", "The `~gammapy.datasets.MapDatasetEventSampler` class evaluates the map of predicted counts (`npred`) per bin of the given Sky model, and the `npred` map is then used to sample the events. In particular, the output of the event-sampler will be a set of events having information about their true coordinates, true energies and times of arrival. \n", "\n", "To these events, IRF corrections (i.e. PSF and energy dispersion) can also further applied in order to obtain reconstructed coordinates and energies of the sampled events. \n", "\n", "At the end of this process, you will obtain an event-list in FITS format. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Objective\n", "Describe the process of sampling events from a given Sky model and obtaining an output event-list." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Proposed approach\n", "\n", "In this section, we will show how to define an observation and to create a Dataset object. These are both necessary for the event sampling. \n", "Then, we will define the Sky model from which we sample events. \n", "\n", "In this tutorial, we propose examples for sampling events of:\n", "\n", "* [a point-like source](#sampling-the-source-and-background-events)\n", "* [a time variable point-like source](#time-variable-source-using-a-lightcurve)\n", "* [an extended source using a template map](#extended-source-using-a-template)\n", "* [a set of observations](#simulate-mutiple-event-lists)\n", "\n", "We will work with the following functions and classes:\n", "\n", "* `~gammapy.data.Observations`\n", "* `~gammapy.datasets.Dataset`\n", "* `~gammapy.modeling.models.SkyModel`\n", "* `~gammapy.datasets.MapDatasetEventSampler`\n", "* `~gammapy.data.EventListgammapy.data.EventList`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup \n", "As usual, let's start with some general imports..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:48.971674Z", "iopub.status.busy": "2021-11-22T21:03:48.970932Z", "iopub.status.idle": "2021-11-22T21:03:49.134830Z", "shell.execute_reply": "2021-11-22T21:03:49.135020Z" } }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:49.138112Z", "iopub.status.busy": "2021-11-22T21:03:49.137791Z", "iopub.status.idle": "2021-11-22T21:03:49.650850Z", "shell.execute_reply": "2021-11-22T21:03:49.651021Z" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from pathlib import Path\n", "import numpy as np\n", "import astropy.units as u\n", "from astropy.io import fits\n", "from astropy.table import Table\n", "from astropy.time import Time\n", "from astropy.coordinates import SkyCoord, Angle\n", "from regions import CircleSkyRegion\n", "from gammapy.data import DataStore, Observation\n", "from gammapy.datasets import MapDataset, MapDatasetEventSampler\n", "from gammapy.estimators import LightCurveEstimator\n", "from gammapy.maps import MapAxis, WcsGeom, Map\n", "from gammapy.irf import load_cta_irfs\n", "from gammapy.makers import MapDatasetMaker\n", "from gammapy.modeling import Fit\n", "from gammapy.modeling.models import (\n", " Model,\n", " Models,\n", " SkyModel,\n", " PowerLawSpectralModel,\n", " PowerLawNormSpectralModel,\n", " PointSpatialModel,\n", " TemplateSpatialModel,\n", " ExpDecayTemporalModel,\n", " LightCurveTemplateTemporalModel,\n", " FoVBackgroundModel,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define an Observation\n", "\n", "You can firstly create a `gammapy.data.Observations` object that contains the pointing position, the GTIs and the IRF you want to consider. \n", "\n", "Hereafter, we chose the IRF of the South configuration used for the CTA DC1 and we set the pointing position of the simulated field at the Galactic Center. We also fix the exposure time to 1 hr.\n", "\n", "Let's start with some initial settings:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:49.653430Z", "iopub.status.busy": "2021-11-22T21:03:49.653130Z", "iopub.status.idle": "2021-11-22T21:03:49.654418Z", "shell.execute_reply": "2021-11-22T21:03:49.654580Z" } }, "outputs": [], "source": [ "filename = \"$GAMMAPY_DATA/cta-caldb/Prod5-South-20deg-AverageAz-14MSTs37SSTs.180000s-v0.1.fits.gz\"\n", "\n", "pointing = SkyCoord(0.0, 0.0, frame=\"galactic\", unit=\"deg\")\n", "livetime = 1 * u.hr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you can create the observation:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:49.656579Z", "iopub.status.busy": "2021-11-22T21:03:49.656260Z", "iopub.status.idle": "2021-11-22T21:03:49.725408Z", "shell.execute_reply": "2021-11-22T21:03:49.725610Z" } }, "outputs": [], "source": [ "irfs = load_cta_irfs(filename)\n", "observation = Observation.create(\n", " obs_id=1001, pointing=pointing, livetime=livetime, irfs=irfs\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define the MapDataset\n", "\n", "Let's generate the `~gammapy.datasets.Dataset` object (for more info on `~gammapy.datasets.Dataset` objects, please visit the [link](../../starting/analysis_2.ipynb#Preparing-reduced-datasets-geometry)): we define the energy axes (true and reconstruncted), the migration axis and the geometry of the observation. \n", "\n", "*This is a crucial point for the correct configuration of the event sampler. Indeed the spatial and energetic binning should be treaten carefully and... the finer the better. For this reason, we suggest to define the energy axes (true and reconstructed) by setting a minimum binning of least 10-20 bins per decade for all the sources of interest. The spatial binning may instead be different from source to source and, at first order, it should be adopted a binning significantly smaller than the expected source size.*\n", "\n", "For the examples that will be shown hereafter, we set the geometry of the dataset to a field of view of 2degx2deg and we bin the spatial map with pixels of 0.02 deg." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:49.729558Z", "iopub.status.busy": "2021-11-22T21:03:49.729239Z", "iopub.status.idle": "2021-11-22T21:03:49.730528Z", "shell.execute_reply": "2021-11-22T21:03:49.730779Z" } }, "outputs": [], "source": [ "energy_axis = MapAxis.from_energy_bounds(\n", " \"0.1 TeV\", \"100 TeV\", nbin=10, per_decade=True\n", ")\n", "energy_axis_true = MapAxis.from_energy_bounds(\n", " \"0.03 TeV\", \"300 TeV\", nbin=20, per_decade=True, name=\"energy_true\"\n", ")\n", "migra_axis = MapAxis.from_bounds(\n", " 0.5, 2, nbin=150, node_type=\"edges\", name=\"migra\"\n", ")\n", "\n", "geom = WcsGeom.create(\n", " skydir=pointing,\n", " width=(2, 2),\n", " binsz=0.02,\n", " frame=\"galactic\",\n", " axes=[energy_axis],\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following, the dataset is created by selecting the effective area, background model, the PSF and the Edisp from the IRF. The dataset thus produced can be saved into a FITS file just using the `write()` function. We put it into the `evt_sampling` sub-folder:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:49.735223Z", "iopub.status.busy": "2021-11-22T21:03:49.734907Z", "iopub.status.idle": "2021-11-22T21:03:50.775536Z", "shell.execute_reply": "2021-11-22T21:03:50.775717Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 846 ms, sys: 161 ms, total: 1.01 s\n", "Wall time: 1.04 s\n" ] } ], "source": [ "%%time\n", "empty = MapDataset.create(\n", " geom,\n", " energy_axis_true=energy_axis_true,\n", " migra_axis=migra_axis,\n", " name=\"my-dataset\",\n", ")\n", "maker = MapDatasetMaker(selection=[\"exposure\", \"background\", \"psf\", \"edisp\"])\n", "dataset = maker.run(empty, observation)\n", "\n", "Path(\"event_sampling\").mkdir(exist_ok=True)\n", "dataset.write(\"./event_sampling/dataset.fits\", overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define the Sky model: a point-like source\n", "\n", "Now let's define a Sky model (see how to create it [here](../../api/models.ipynb)) for a point-like source centered 0.5 deg far from the Galactic Center and with a power-law spectrum. We then save the model into a yaml file." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:50.782478Z", "iopub.status.busy": "2021-11-22T21:03:50.782180Z", "iopub.status.idle": "2021-11-22T21:03:50.794634Z", "shell.execute_reply": "2021-11-22T21:03:50.794816Z" } }, "outputs": [], "source": [ "spectral_model_pwl = PowerLawSpectralModel(\n", " index=2, amplitude=\"1e-12 TeV-1 cm-2 s-1\", reference=\"1 TeV\"\n", ")\n", "spatial_model_point = PointSpatialModel(\n", " lon_0=\"0 deg\", lat_0=\"0.5 deg\", frame=\"galactic\"\n", ")\n", "\n", "sky_model_pntpwl = SkyModel(\n", " spectral_model=spectral_model_pwl,\n", " spatial_model=spatial_model_point,\n", " name=\"point-pwl\",\n", ")\n", "\n", "bkg_model = FoVBackgroundModel(dataset_name=\"my-dataset\")\n", "\n", "models = Models([sky_model_pntpwl, bkg_model])\n", "\n", "file_model = \"./event_sampling/point-pwl.yaml\"\n", "models.write(file_model, overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling the source and background events\n", "\n", "Now, we can finally add the `~gammapy.modeling.models.SkyModel` we want to event-sample to the `~gammapy.datasets.Dataset` container:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:50.796949Z", "iopub.status.busy": "2021-11-22T21:03:50.796635Z", "iopub.status.idle": "2021-11-22T21:03:50.797925Z", "shell.execute_reply": "2021-11-22T21:03:50.798114Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DatasetModels\n", "\n", "Component 0: SkyModel\n", "\n", " Name : point-pwl\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : PointSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " index : 2.000 +/- 0.00 \n", " amplitude : 1.00e-12 +/- 0.0e+00 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.000 +/- 0.00 deg \n", " lat_0 : 0.500 +/- 0.00 deg \n", "\n", "Component 1: FoVBackgroundModel\n", "\n", " Name : my-dataset-bkg\n", " Datasets names : ['my-dataset']\n", " Spectral model type : PowerLawNormSpectralModel\n", " Parameters:\n", " norm : 1.000 +/- 0.00 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", "\n", "\n" ] } ], "source": [ "dataset.models = models\n", "print(dataset.models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next step shows how to sample the events with the `~gammapy.datasets.MapDatasetEventSampler` class. The class requests a random number seed generator (that we set with `random_state=0`), the `~gammapy.datasets.Dataset` and the `gammapy.data.Observations` object. From the latter, the `~gammapy.datasets.MapDatasetEventSampler` class takes all the meta data information." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:50.802963Z", "iopub.status.busy": "2021-11-22T21:03:50.800598Z", "iopub.status.idle": "2021-11-22T21:03:53.442094Z", "shell.execute_reply": "2021-11-22T21:03:53.442318Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.15 s, sys: 453 ms, total: 2.61 s\n", "Wall time: 2.64 s\n" ] } ], "source": [ "%%time\n", "sampler = MapDatasetEventSampler(random_state=0)\n", "events = sampler.run(dataset, observation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output of the event-sampler is an event list with coordinates, energies (true and reconstructed) and time of arrivals of the source and background events. `events` is a `~gammapy.data.EventListgammapy.data.EventList` object (more details [here](https://docs.gammapy.org/dev/tutorials/data/cta.html#Events)).\n", "Source and background events are flagged by the MC_ID identifier (where 0 is the default identifier for the background)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:53.444232Z", "iopub.status.busy": "2021-11-22T21:03:53.443939Z", "iopub.status.idle": "2021-11-22T21:03:53.445714Z", "shell.execute_reply": "2021-11-22T21:03:53.445887Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Source events: 138\n", "Background events: 15319\n" ] } ], "source": [ "print(f\"Source events: {(events.table['MC_ID'] == 1).sum()}\")\n", "print(f\"Background events: {(events.table['MC_ID'] == 0).sum()}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can inspect the properties of the simulated events as follows:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:53.447739Z", "iopub.status.busy": "2021-11-22T21:03:53.447457Z", "iopub.status.idle": "2021-11-22T21:03:54.025788Z", "shell.execute_reply": "2021-11-22T21:03:54.025979Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "events.select_offset([0, 1] * u.deg).peek()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, the `~gammapy.datasets.MapDatasetEventSampler` fills the metadata keyword `OBJECT` in the event list using the first model of the SkyModel object. You can change it with the following commands:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:54.028156Z", "iopub.status.busy": "2021-11-22T21:03:54.027831Z", "iopub.status.idle": "2021-11-22T21:03:54.029002Z", "shell.execute_reply": "2021-11-22T21:03:54.029254Z" } }, "outputs": [], "source": [ "events.table.meta[\"OBJECT\"] = dataset.models[0].name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's write the event list and its GTI extension to a FITS file. We make use of `fits` library in `astropy`:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:54.031694Z", "iopub.status.busy": "2021-11-22T21:03:54.031392Z", "iopub.status.idle": "2021-11-22T21:03:54.049224Z", "shell.execute_reply": "2021-11-22T21:03:54.049431Z" } }, "outputs": [], "source": [ "primary_hdu = fits.PrimaryHDU()\n", "hdu_evt = fits.BinTableHDU(events.table)\n", "hdu_gti = fits.BinTableHDU(dataset.gti.table, name=\"GTI\")\n", "hdu_all = fits.HDUList([primary_hdu, hdu_evt, hdu_gti])\n", "hdu_all.writeto(\"./event_sampling/events_0001.fits\", overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate a skymap\n", "A skymap of the simulated events can be obtained with:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:54.051354Z", "iopub.status.busy": "2021-11-22T21:03:54.051050Z", "iopub.status.idle": "2021-11-22T21:03:54.308735Z", "shell.execute_reply": "2021-11-22T21:03:54.308936Z" }, "nbsphinx-thumbnail": { "tooltip": "Check out the process of sampling events from a given sky model and obtain a simulated events list." } }, "outputs": [ { "data": { "image/png": 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RuZjMyCykF3ZzsPlFhnZLEJYF6lJgsl+Z0qi1lQxkbWJfk/GJVgqgdr1krKTmOZewaqZr1Rfl2Dptkv7leCWDzNnkJLMvEd2oedk3dq9P21ophrJd+j0+C+ujaLHS1rLZxrYa27VSR+M9S+2PcvzrBWtWbfbJdW4n3RHwdEeHw+FohDPLacdcAEsa2kjh3g6bUFiWtGvKuuE5W05kO8vGuvuKbFKD5VnNiW/I5ZT5WQIRORufHEOHtQm7ZNp/L5kdlh1VY4C1zCbRx4jC5i1hjlxMo5Z1kx5Xk72biB1vP8EoNTRdSzXCwIjL1aIdcpJ4aR9d4iHJ9b+jYXzl8Jelw+FwNMCZpcPhcBTCX5bTipyQhgzDsZwcaQByNKLLIGgtDEXWBZeBzblpm6zfktOBtMKISqbC0nkgp7XpMaPJQYo+aEHdlgq9nB6m/UTE9EwrNVPrV4YdpWYFS7FeS4OM22TdHJmCqZkGrDAgKzkhNyYNTSmMXW2N8CXN8WMlYawVz2CaNrs8qaLZzjTcmaXD4XAUYrC84UMnpEFEDOSDoU0BAbEeqP/a5kJuLPVraxloTkcsUQLPBYJLJlxSF8gSC9EYctMYrHRCbSwaU5NsXzJAzWnTpMC+XlknGTIy661wspyYiOzHOl6KqG6+XDiFNKGLCHmPNJESOT7TCZjuEz7XzGurBs/TeHT0zKK2RO9wIQ2HwzHMGKxp+NAxy1/ZjfjcpePL6S93U6hEhGbfkTamkgp+psCCYr/LiSRY+1iMuItZNoxfs5nlmHATmqphamOQY0zHH+u0rwzhXk3XNnfsXNXNnHCv1dZi1RDrtW0R0h6c7mfJ32nCxLH/mi1XGUuExT61VOGU4Z/98zaY5VN5dPSPi9oSvdOZpcPhGGYMFrMcupflo49V9jEZsA3U2ZUVfK0F+1osLufl7fQRPjX7neXxlCUL1iTbNgtblsVAcmPKHV+KSEh7o5YOWpI+GRGvw8v+8xYAwH1vq6YCY4nHNeKDq7d0jaUj2JET2jVSI7WxyesubYg5O2pTKuzaxJsMIW8nJf/SGt1Ndk3NdtwRPRG29Ygcm66NQWHVXUImreQ7Dl51x1n9PBgRHUtE3yWiO4norLDuACK6hoi+SES7hXVvJaJbiWgjEV1HRIckfZxERN8Lfycl668lokX9PB+HwzGV2F741x/0zWZJRLMB/DeAlwHYBOBGACcCWAVgHYCDABzIzOcR0e7M/NOw328BeDszH0tECwCMAjgC1U/PTQAOZ+YHiehaACcz8925cexFxMcly5rnU7KpnJ3NsgdK5pf2IyE9ubn4O0vmS7N5NZUqSL9bZRlGBAMBbC919L7mPLhWhEGXeG5Y+ZSRyh6Jt30IALCSVtbG1GH2hbGB6TaLYWqMzJptyPjFtK11rrkY35JUSYutl0Dum7M/NtmiNQY+AmDnVspKHMijo6cWtSV6V19slv1kls8HcCczf5+ZfwHgswCOBzAb4z8RBADxRRkwH9WLEQCOAXAlM29l5gcBXAng2LBtK4AnpvwsHA5HnzBYzLKfNssDAdyTLG8C8AIAH0TFLB8C8Ia4kYhOBfBHAHYB8OJMHwcCADO/eqoG7nA4pgOD5eDp5zT8dQCOYeY3h+WVAJ7PzO9o2O8NYb+TiOgMALsy81+GbX8O4FFm/lBDH+sAxJfpPECfksYg34sv7t5fTvW0SoolCuDWVLpEYabJJJBzSnSOr+xrTSsltKmepSyfojHQP5Nud8Yj3c/mSqLaGM1UwswUVY4zly5oOe601MgIGe7VC6xUzFSDNJoUOIRNRSdXrrpjhHyetNTLkpruOWxasABbt25N97qMmVf20scRRxzAo6NvKWpL9J4dbhq+CcBTk+WFAH5UsN9nAbxqMn0w80pmns/M88uG6nA4JorFixcj/r+Fv55elAAAZmDbtrK/PqGf0/AbASwhosUAfgjgBCTT7hREtISZvxcWXwEgfr8CwF8T0Z5heTmAs3sZxAIAx0EP7ZFpYzVWpBjfZd3oqJ+o1vMW22ItmZxjxwoalgwqPY4VzL3uZ6sBAB/cd21tTFLVO+fYsMJyVLYlWLTVR9p/dBRFJhkDz2P/q5aNt10vrp2lTq6JY9ScckooThznqlVhm3xGMsxVnnPOqSKPJ69LOqb4zEVGKcPX5DiARAtU2VYbq3DQWcH1mm7pGFpUSt/e3jSciN4J4M2o/B+3AngjMz/eSx99e1ky8zYiOg3VC282gAuY+Xaj+WlE9FIAvwTwIICTQh9bieh9qF68APBeZt46xUN3OBz9BgN4op2XJREdCOAPABzCzI8R0b+gImsX9tJPX4PSmfnLAL5c0O70zLYLAFzQ1pjSoOzSkIl0u0x7k/tq9rAOQxWsSAujiVgujmNV+EuPmQaqA8DS3dZCohZEL5a1a9KU2qnZH2uMVSxroUOR4Ujl+osV5ifFPDp9KWPq7Bu3iX61RAVZE0dj9PKYEVLhXQv3ys1e5Jgsmb5caFIulRPoZt7xfyLaRMfC9Zdj1RIVRtCiRFuLzBLVu24uEf0Sld+ixATYhb4GpTscDkcxeHvZH7A3EY0mf12eIWb+IYC1AP4XwL0AHmLmDb0OZ+jSHXNoYpZWbfB0n1xan8U4ZOD02qRhtKPW7F2GZ72r/wIvZmR0wSRXE+nV2GNcx6IKZoSW4tkkipEywlgx0fIMp+icY9hHXoc4RkrGKNlstHvKuj0aOqwtE8guj2OJ6Wo2xY7XW0lZjCgRGKnta0QN5GZR8ZpZXn1t/Xq0le3YE7O8P+cNDz6O4wEsBvATAJ8johXMXKKb3IEzS4fDMXhgtOkNfymAu5j5x8z8SwCXAVCMXXkMnUTb0n2JN7xmPJZSY1/y50ZjAtY+uVQ6KeXfWW94oLVjWhJtqv1LtNXEXGX/kuXKWD6gblfLXZcmubKcNFjJuTelZ2r314pRzcW9NokYayyxSWpO69+6d5oEn5zF5O6Hma6ZaWulsebY6AoAd7SR7rhsXx696neK2tI+52bjLInoBaj8HM8D8Bgqx84oM3+slzH5NNzhcAweWvSGM/MNRHQpgJtRSRl9G8D5vfYzdMwylpXICVDIX9tcTKAlf9aLIG7OftRkW8rZtOTYVIk543glY5KQArOAXcZAsqtbAoMFgCvXVyzWKhGhQcrbSVmx1CYq7YNxmxTjSCFFc3PPxFpDIq+k5IjFkFP0IqYC0cZi3um+0j5eEiGRohUhjaX78OhXXlPUlg74Bxf/dTgcQ4x2Q4cmDX9ZOhyOwQMz8MRgif8WTcOJ6OkAljDzVUQ0F8BOzPzwlI9uCjCPiJegbMora3Vr+oGWEyUX7mKlw+UcPNLxkpuaWil6WoqnFGqwjPglYh9a//I4EhtETXDAvi5y2gzUp5WWs6wXJ5SGpv5yGpglghpWamSn/zR5ojBkKPccSdOD9kxLk4x1rSVamYaP7M2jl/9mUVtafNFgCGkQ0e8BuBTAP4RVCwF8YQrH5HA4hh0xzrLkr09oZJZEtBGVcO8NzHxoWHcrMz936ofXPqJSeo5lQdmWbi9xqmi/xpYit8VO0+9SsEM6MLS61ZLRaIyjyXmSqyvdi8RchAyfygXOWw6FVK6spEqhRNO4NyS1cSQrl2PTjmdVzLSC1NN+pJPJCvzOQc6IrH6A8cB8rcqpxXabkh1aCR167l48+q8vL2pLSz4zMA6enzPzLygowBDRThhXLnc4HI72wZiRDp7/IKI/RZWE/jIAbwfwb1M7rKmHZlOsBR5PIAwoZ+uTjKMm8BoYzVjSSbQtSfEEFvWyU1iB0xqjaQqm1xiOTBO0QmMAW3DCSoPU2soQmZQxWXVoJIPVwoEs29zyxH5qhVLl2LRk/XI2kK0iWWBzjed0xlo93TSyxDSES1bgjNDqn8djbhZi2DE4vSMAk5l1TB7cWpxlWyh5WZ4F4BRUGnC/j0o16JNTOSiHwzHkmKne8B0J0maZE/ItCTCX9h1pQ0wZgWYfSvsvKXVhCWdoTMcKHi5Jh5OlBVQpMoM15q6XxeZSSCZZksJo7aMxypqQ8gREeTVbbm1MBkvUUmFN8RYxxhRxf/lcTcQmqh2/KQg9vY6tV3d89p48+s//t6gtLf3X6bVZEtGtyNgmmTk3E3E4HI6JozfVob4gNw2PQU6xeO+68Pm7UEXKZhbUmMawson5pZC2sVwcpCVMK6GVuugwG2Mfrd+cQK0FWcM8hxUG88552y2vrBYTKEWMNVZnsUHJ6nKphTlRXst+nSvSFmExsmj7W69cY8sTrbHQ+MxF9l9jv8n+a0U8a84m2lSETxvj5qT/dsR/MXNelsz8AwAgohcy8wuTTWcR0X8CeO9UD87hcAwxZsrLMsF8InoRM18HAER0FIAZWyVxK6pfxPirmWZK7BdZXFi2ys9qzEl6Y3vJBpHxg5rnUrKFErug5VnV5NxkhpC0veZiD7PsyhBMlva7LuZnsLheRD1KxG0ltPNpEhppKt6l7StnLk1jkH3FY0lWHe/hqrBiv8SrD3HPmmTWgPq9k/bstLBZfCZaFf+dgd7wUwBcQERPDss/AfCmKRuRw+FwxFK4A4TGlyUz3wRgKRHtjsp7/tDUD8vhcAw9eIYxSyL6C7EMAGDmGWmzXICqbrgmIKClDgJ2WiJgp4RpU8faNFtMTaX2ozYGGXaSm27WUjozgdlSLEReAy1QW/ahXR85pZbT7pKg99qYM+dRorlYC/spmOZb01fNLGKFGeUU8eXxIrTUTOscOyYUMTatnxIhEM1RBOjPadxnzTzgErSEGWizfCT5PgeVl7w1h5fD4XDUMMNChwAAzPyhdJmI1gK4fMpGNMV4DOEXUQn9sOTJJGPqChCWYSeZkBIZVhR/7TcbYR1pm6b+0zFb8l4y0ByoB21LybZOOFXCLC3HRU4aTDJiee5dzggR0F8S0GvVuVFr/BQKmqTrrFAb1cHTQ+B6hNW/mkIq2kpoDiR5DXPjbxIa0Zx2HSflKuCSG40de8VMe1kqmAfgoLYH4nA4HB0MoIOnRKItzeSZDWAfAO/rtTLaoCDW4Im/mmlITGqXA+xA6lzdng0aUxJtZP8R8Tid0AzY9lOLKQDN6YGaaEVJHfIIK6VTk72TNskSWTeJkhCliBLBCys1VR4vRVOFRu36x+DzyOTjsiZeIY+dS4GVx5TXXVZ/BJrFSbTZQGe54JxbT3dcshuP/t1zitrScTcMjERbKle8DcB9zDxYr3yHw7GDYfBsliXMch0zr2xaN1MQhTQiehF90AQKmqAxG608gna83LaaIIXCImrMVTAewK5WaJ07UGcusb+iFEmxXFIRUp6jJhBh2RJ78TxHaOIqkn3W5PWU8Vtpg5odVaaZSnutJqRhBetPRhwjhSVPlyuZArTFLOfz6IefVdSWXnnTwDDLZ6cLQfy3lSB9h8PhUDGTvOFEdDaAKPr707gawC8wgQLlgwKZ7pjzPMtf0Fzd7Vx8ZUTsTzKnEqEOyTA6v/ZCUDaFZBEdRqYwQOllj3W8o7BsyuZkHJ6MhxwxvgOKlFr4TFmpZTPLSahFNMVopsdukkUD6qmn8twjtPHLMgzWtQCAEeM4nWdEk8gLn/I5zdmbY/SBtM9r11SmN0p2bf3vDKOQxt8A+Bsi+htmPruPY3I4HMOOAfSG55jlwcz8HQCfI6LD5HZmvnlKR+ZwOIYbAyakYTp4iOh8Zn4LEX1V2czM/OKpHdrUQNYNT8MrLIO1xC3XvbTzfeWLrgJga0dq2ohWal5OQVsGOEsnTjolktOmnMK1FRYij6+ZHkoqEspptxyb5miQ90aeq6aAZJ2Hdp+t6pQ5x5pczoVuWX1IB6F2HyJizff9lEQFawzSgVcSoqQ9c9ZxSsLLVqCl6o4HzeHRv1pU1Jbe8N3pdfAw81vC15cz8+PpNiKaM6WjcjgcjgGzWZaEDt3MzIc1rZspkMyyJEynJPxEMhst5CP+4lt6hlpokqWfWBL6YTkytEB8qx9Z4Q+oM5ccO82F/aTbNViB2bl9JPvRjmtVfMwFaFuhSbJd0/hSlKjelwh1dPQsRdpsybG0GuNyXS8JBCNokVm+d2FRW1r5P9Neg2c/AAei8oYfisoTDgC7o0p5dDgcjqnBADp4cjbLkwCcDOAIAKPJpocBXMjMl0356KYAMd3RqgUD2HJcWoByU5C4xrYs21WT/TA31tyvfwwDohAGpNlpLbtmyZjWiuuyItO/Vec7Z5OTbDFNB5UB4BI5IQqrZnpOtEKy3Vzqq5XEoNlISwO+c8il01riJyX9WbMmazbSSlD6ol149C/2K2pLp9zTyCyJaA9UJbyfgyp9+03M/I1expSzWV4E4CIieg0zf76XTh0Oh2PSaNdm+VEAX2Hm1xLRLpjA7LiobjgRvQJVJk/HsTNTxX8js5Q1wgHbppj7Je2FCUimUUsjU/YpFW1N2ZzlkZ9QGqVYb/UD6Cl5ltdeik2kkEH7sj6QJqFWE0U2POm58Wss3RIlKZF1K4l2sMZgnU8KKzW1xKsv2fXmTI0luU+uaujIPOCSZ7XALJ++C4++a++itvT792aZZajycAuAg7jkhWdgVuNAiM4D8HoA70Blt3wdgKdP9IAOh8PRjFCwrOQP2JuIRpO/t4jODgLwYwCfJqJvE9Eniajnoosl3vAxZh5JPncDcBkzL+/1YIOA6A2fCFuM8ZUxtlLbP+edrh2nYB9ZiuKMR6ry7Uup0jHRvLOWDS4nV9bk+dfskHJbzqYY2UiMH4ze9RKWFWEJkAB5kQqtrxQyhjXn1beuS7pPk31WO64sQSFnOem9y3m9LZTYZyHaWDJuuagNoCWb5dN25tE/2bOoLZ324yZmeQSAbwJ4ITPfQEQfBfBTZv7zXsbUyCxRiYsDwKNEdACAXwJY3MtBHA6Hozcw8MS2sr9mbAKwiZlvCMuXAug59LGEWf45gI8BeAmAc6uzwD8y81/0erBBQJRoy2WmyGX5y6rJcVm2Jo3xSeSYiCW+0Is3M2V6QLedtoT1yHZyvLnYScubbMWPWv2kY8pFGOSETKwxyeOk91dmQVnXvZdnokSYOBtLinw/uXPfIOJmc1lX8njWMgCsCxEXH1y9pR2b5VN34tE/3q2oLb3zoRJv+NcBvJmZv0tE5wCYz8xn9DKmkho87wtfP09EX0Ll5Dm4l4M4HA5Hb2hdou0dAD4TPOHfB/DGXjvoqQYPM/8cwM+J6HMAntbrwRwOh6MYLb4smXkjqpjxCaModKi2E9E9zPzUyRy48DjHooqPmg3gk8z8fiJagKo08SIAdwP4HWZ+kIiOBnAyM5/c0GdXULolBpCiRHdylXA+ZAUoxBRXKo+XTDMnglzQtYQlgAHYSuC50CErDVFzPsltuYBqawqaM22UOHRk21wIT7o9PbZlGtggnpV0m2V2ySnJl+hyWmFLuZAky4SijT99/ltJd1w4i0ffUSZBQWc91pd0xxIHj4YJxyqVgohmo7KRvhzAIQBOJKJDAJwF4GpmXgLg6rDscDh2NGzfXvbXJ+TSHf8N+kuRALyYmXuOU+ppYES/BuAcZj4mLEcB4pMBHM3M9xLR/gCuZeZfJaKjALyemU/P9RtDh1IxiQhL7sty9CDpJwpSWFJq6X4lKZERMiC7SZYLGHfgRMbKQvU8l1po1YJJIdtKJ0hOeixCBubnHBo58RC5v6yNrlU6bGJOuXNuCuLXxts2rNRUyUJT5jeZ0CFZ212bbaQJA60wywOJR982u6gt/fkT016DZ+0Et7WFAwHckyxvAvACAE9h5nsBILww9w3fr4daMMHhcMxIzDSJtukCEb0OwDHM/OawvBLA8wGsZOY9knYPMnM2epWI1gF4NQDMBualFdhyqWHSRqMFCFuBu72IC+eqRlphSyXMz2KuWmqkZRfU7GJNjCnXv2X3LZEry51r53hivWbzi6wzFxAfIdnzRFIWz1hbMfuVkdkXzCjkecnwrxRWqFIK61i50C3rHuWEQEYAfHnBAmzdujU94mW9VoM94gDiG2UejoFZ78G0M8vpxiYAqRNpIYAfAbiPiPZPpuFbmjoKN2olUE3Dp2KwDoejwuLFi/HAAw9M2kw3YMRyoF+WNwJYQkSLAfwQwAkA3gBgAYCTALw/fH5xIp3LNEJgnC1Ie87LVlTMANfb72X5qytLIgC2rSnaG1coRgTJKCPDWSPYQ0wjBMbtp1aKXm5MVoB2Ki4Sr088phQQznqXY5vwGc9jhbJPjY0qjMxiOzmmasnDaVUMZXVHq8KkxqZj27G3dz83GpvXnhdrTPKe1SqLZsRD5HWPbXOyfSXCKWkSRht1spmB7U+00FGLGNiXJTNvI6LTAFyBKnToAma+nYjeD+BfiOgUAP+LStjD4XDsQGAA2wbsZVmS7nglgNcx80/C8p4APhu91DMN0RsekSshsFkIFshYyhSWTU6LgSsVWkghmUFc3pARVWiKw0v7kSIVk0kXzKV4lqTmWTJuJQW4SoRAIpqEj7V+e/EqlzwLEk3HsY6l7VsSm5krpWFFheSiEoB2hDQO24/464VWzt3WDo7Ncu/4ogSAEAC+79QNyeFwOAbPZlkSlL6diDqpjUT0dPQhKN3hcAwvmIHthX/9QgmzfBeA64joP8LybwAodOoPPnLhKNJxMbKx+kzDOCxVdW1ZVhq0nAU5jU2IKdh+yvTbCvTOhShZgeWa6aHmFBLTtVR1O6rbrE8cAGn/Vp8AOue6KpxIvP7a+C0nREmojxXYDtRDtOQUNDeWXJ0noDsxQt5Hq85611jE/ZRj7HJeim3R7DIyUj9+p60RvJ/T8NxwFHD2z9EKBo1ZlqgOfYWIDgNwJKrsnXcy8/1TPjKHwzG0YMyglyURHczM3wkvSqCKcQSApxHR05j55qkf3tRBU922ApAjOuxI+ZXvtBHLXQHsQjvQSrNTa/E0GPVzjiRrn/S7ZJCRtUR20osu52qFpUxEezEecyz0F+/Zmo3jbSSzszQktTYywD8ifSZkrSaLoWrX1FQaj8vJyUtnX7z+Fwtmm/bTVGupq77Rxu7+O/1utMdvJVjkUkjbwgBWws0yyz9CNd3+kLKNAbx4SkbkcDgcrctZTh4loUNzmPnxpnUzBQtnE58+R68qKO1TJareMhUyF15hhZDIUKXlCTNrYnFNgcNA3Va5QbHJWcfThDok89aYRkRORR3Q0x8tRib3kfvl2mrMTPav3V8ZvG31r4mHWFU8c3ZySzQkV/lT9qH1v1Y8W70w/JJZQXqd2hDSWLoP8ZdfVdZ24Sf7EzpU4g3XottcsMLhcEwZmAdOoS0r0bYfKuWf9ajSDOMvxe4AzmPmGVlaItbgicgFRUvkfn2b6lZr6yx2qLEgKQVn2ZG08efsmvI4TawaqNt2cymGpWMpEffQ7JCW7J205+UiDGTbtYmXWgb7Wywr9aBHD7PF4rQxWcHtuXOWzL5EnMSSc9PSWTv9GAzZQhtB6SN7E//bb5W1XfTp6Q9KPwaVduRCVHbLePI/BfCnUzssh8MxzBhEb3iJzfI1zPz5Po1nyhHTHXP2I2tbLk5RMjCZqgfYqYS92I/kPjkJNYvRpMxpucGcrLHl2vQiNVeSTmmJP2j2UyuFVGWUYrkpVTLd1lk2ZhLpsbUUSyB/fWRNcGn/TMdi1VFfp9S3j7DuncZCax56sd2KN26DWT53L+LLji1r+yv/NDg2y8OJaI+4QER7EtFfTt2QHA7HsIMxeBk8Jczy28x8qFh3MzMfZu0zyJB1w1M0FQXT7EeWsIVmryphjIBet9qyyeUKTlkMWY3ZE9tyLEteJ8lwtGgBKXWWSsoB3QzX8gDnRHMt8ZPO8ZRrWpKtJJmdZuNL+wLqdmRNDAPQC8fJ+zmR+EWNLVrPgCwdAdRLpKTya1pfadv1j7bDLJ+zgPhzy8vaHnLJ9NssI2YT0a6hDC6IaC6AXad2WA6HY5jBmJl6lusBXE1En0Z1Dm8CcNGUjsrhcAw3ZmJQOgAQ0csBvASVR3wDM18x1QObKsSg9AgttEduk8bvdHooqyHKNun0zHIkyKldOl2TYSFWBb+SKX7OzCCnXLlA7VwAvByLpdlZogPZaRuPmwl6l1Nd2a8WyG5N63OmE0vrsWSfXpxnVpB6rr+SELSme6fBTMtVntM184BLnjX5afghexB/5uiytod9cXCm4WDmfwfw71M8FofD4ehg0Jhl48uSiI4E8DEAzwKwC6oSD48w8+5TPLYphRr+I1hVZCCSxaUMJzouJPNTg9LF8WQ9Zg0y3S72IZ0pOXXvHKOx2FWRuIcx1i7pLiOgWaZIajWE5Lg1FmexTou9A+MMPvYn7++6T4xrW5Oon2MxV60ejWxbwixr4WrhM73Wsn6UJfWXokRURba1EhTicnoNOjOplgQ1ZmoNno+jKhb2OQBHAFgF4JlTOSiHw+HoZ1hQCUpCh0aZ+QgiGmPmkbDuemY+KrvjgIJCKdzcr3pNKFUwQE00ocNSROjQWuUqWaErOZk1GYKTCw+S+1gCIemxI0pqWjfZv7R6MU0pf+l2yZZztXiaRDcicvY7aZPTatdI5ILrm85VExyRoWDy/ubq6VhCvpqEWslsQ/Yrj6vZ6dNA+bN/Pnmb5bOeTHzBr5W1PeqKwbFZPkpEuwDYSERrANwLYNI1gR0Oh8PCIE7DS5jl0wFsAbAzgHcCeDKATzDznVM/vPYRg9JzwbjSE2mxrxSWF7mrjbGcq/JosV2rXnY6Pkuwoxf0UoFSO3crlVOy6pwIR02uLLn+0dYZg9BLylbcEtIBl4Z0QFkbfYMipGGx3U7/itBF0/XO2RjlmErYumSl2j6Wx1yLdpDjz0VtpMy4DW/4r+5OfH4hVzz6qwPCLJn5B+HrYwDeM7XDcTgcDrQeZ0lEswGMAvghM//mRPrIlZW4FZkqjtF+OdPwGKpfzDh4Lc2uwywavLIprOJU65U2kvnl4vykfahjRyqIr2wqa6C1rbG5TBxhiQCIZINxHxmNoMWuStuuhtXiHkkBZY0lkRCYiLa+keu7++xqI/rJ3TPpBc/FTEbIayrjdnPMviM0Ej4lK03XRbujFOwoEV+WY9LSfvEocLg91J7QcujQ6QDuQCUxOSHkmOWE3r4Oh8MxWbRpsySihQBeAeCvUJXLmRDMl2Uy/XY4HI6+o4fQob2JaDRZPp+Zz0+WPwLgTABPmsx4hjYoPWdDkKEXOedNZ11ou1ao6Ywp03zLYSFrawPoTLfl9GyNmNZqgeASmjE/9rNCHFtqU+auV5w2jylhNnJ8lu5nOuZ4jpYGpqbWE8fbpM+ZIvYvnTi50J6me5iOT7ax1IjSsdSOq6yT90KGiK1QzBfSKSPD4LocVoaJwQq1AnpzBJagR/Hf+y0HDxH9JoAtzHwTER09mTF5ULrD4Rg8MLDtl6309EIAv0VExwGYA2B3IlrPzNbvk4mhC0qPoUOakrk0YEfUUtAyQhdS/zDHUiJyYUCSucTxSn1IDVagc4qmUCSNRViOqhyLi06ziy+uPq3Kh9a69LiaXmaTkEnOqSLrh+faWiIc2vWxwqS0a2o5y7Rg+9g2MnqZqKBpY8qAfmts8lhAs85rus8I2tGzfOY84g8VVvl61bfLQocCs1zdujc8gQelOxyOvmPQhDRKg9LvQ2WvnPFB6bEGT0T6K2qlB+aUwCOsdLscS7SYX465ylAizebUFICssZQmQQ2t1rhlx9RCn6Tiu8VeANtOKsODgLqNsiSdT7t/2vHSfnqZs1myfbka5Na11ILsredF3ndtzBORgJNMNnf/V7QUlP6MecRrCo19r7118ILSH4cHpTscjn5gJqU7EtHxABYy87lh+QYA+4TNZzLzpf0ZYrvI1Q1vEojQbHKSLZTYHyOkrUyzyUlvryVIrCHHKCVkfZho45K1ZtK2lt0xZ/+S63Nediu4W6unY6X+aSyul/vc1CYn4JGruSP71rzqgF4TPCc8bI1FQs4oeqlYGtEl0Zb0c3gLNsuD5hD/1aKytm/47vQzyzNRecEjdgXwPFT2yk8DmJEvS4fDMTMwaMwy97LchZnvSZavY+YHADxARDuMgycn1RaR9ag2CGhkU/UEo8yNz/I4l9jSJNvV7LTSo7pepFemDDO2kXW8tRIXJaw2bZe2jf3GsXTY9PX1tjXvvbT/Zjy5JSUuahERRruusTTYfzX5t7hPZM8jBdfUsj+WMOSSqA15TeVsJ922AlVO4WTBA1iDJ/ey3DNdYObTksV94HA4HFOIGSP+S0SfAXAtM/+jWP/7AI5m5hP7ML7WkRP/tbyKOdvNRDzPMlMkJ6VmeeShtJX7WGzOktZKkYups2Iw4/oNGW9157hiOefhlvukLEjaVpvshOk4rf7VaAFjHy3rxyrONpExaRlOTTJ0Odk1iZysnsVUm7zsd7Rgs1y0K/G79y9r+6YfTL/N8p0AvkBEbwBwc1h3OCrb5aumeFwOh2OYMZOm4cy8BcBRRPRiAM8Oq/8fM1/Tl5E5HI6hBbeX7tgaiuqG70iIQem9GMFLlK9LgtJlfyWOJCt4W07d03opVtDwBqFhWIJcAL41Rm38VrpjU+0cDbm2lvNDC0qv6XwqYTRS/MJKo9R0S61QMS3cyRqTrOQI2EId0lSTS5GUIWGqOAx05CqKAu2kOz59Z+Kz9ipr+/b7pn8a7nA4HNMCxuCFDg0ts1SDiMOnNNrnKuNZtVJibRjLWdF13Ezoiha6UwqLIeRYhBXsrtXdtqA5kHoJrbJYYY7RyH5kSJQmW1eS+ievf5OgRrq/leqpHUdWBY3Q2K4VelS7boojLKrCy9lFSQ0kKT6jPQdtOXiethPxGU8ua/sHW51ZOhyOIcagOXiGnllqLMX6lc+xvBz7kf032TVzAcix37VijBpDiwxEsolcuJGVSpemGEZIIWItZU6ydcmKtNAYS7Ahd88sdl4yG7BCuoDxc9ps2Hu1dEE5I5Fjk3bnFFY4Vi6cqZcQnxJYlSyt85Fow2b51NnEf1iY+rL6YWeWDodjSDHUNksiIgAfBXAcquSzk5n55rDtBFS56Bcz80fCuq8A2B/VC/3rAE5l5ieIaFcAF6OK+XwAwOuZ+W4iWgTgQmY+OjcOKdGWImdTSlHiQc95wbXgaiDPNHKeVDkmCxrjWCdqaEfkhBaaxD1yAc5W+QHtekk7mravZUOUaYOaN1l6mjUh34iSeuFyTJZnPhf10MSQgebnM0KzHTdFAmjHLrF9p/eqDSGNhbOI37FrWduzHu8Ps5w11QdI8HIAS8LfWwD8fbLtBFQiHUcS0W5h3e8w81IAz0GVXvm6sP4UAA8y8zMBfBjAB/owdofD0Wds57K/fqGf0/DjUTFHBvBNItqDiPZn5nsBxF8hjt+Z+afJGHfBeA3z4wGcE75fCuDjgbU+AWBr0yAWzAJWzLHlrSaKXlLaJHORtj7+xL6d7yvfvgWAXfJCg+WN1STg1gdGKe1gkXGuDNvXKCwlekVloTI1nVIsy+uVQl6PHKORkFEIOcFmLR4R0Gtod5YtWbrkfKxzrV2L9JiSsYqx9BIPrG2XReU6DDkyzoL01ggpLZi26SVeNoc2S+G2hX4yywMBpCpGm8I6ALgMwCiAUWZ+ODYgoisAbAHwMMYl4Tr9MPM2AA8B2IuZ72HmV0/pGTgcjr5h+/ayv36hny9LzYbBAMDMFzHzocz8oa6NzMegslvuCuDFTf2YByZaR0SPENEjWwcsHMHh2NFw1113If6/hb91vfbBALZtL/vrF6bUwUNEpwL4vbB4I4BrmPmfw7bvolIvuregn5MAPI+ZTwts8xxm/gYR7QRgM4B9uPBEZLpjL2lqcdqmKXVH5KZnVuhILtwootRpoO2TqyponXNTypvWv1yvwXJMadNyS8NTG39TXZ2JaGxq/VtVPFNIR1RTkL02ppLrLvfNPXsyXbPESdQUTmaFoLURlH4AEZ9S2PYvsQM4eJj5XGZexszLAHwBwCqqcCSAh6wXJRHtRkT7h+87ofKgfydsvhzASeH7a1G9gIcrWNTh2MHBALYX/vUL/XTwfBnVS+9OVGblN2bazgdweQgTmg3gGgAxHPhTANYR0Z2oHDon6F3oWDgfWLt0nCVqjgsZ2hMN2RuEwyQHLfA49it/oXM1rq1aMlYdnLRNhGRdqXZkFLaAwXJzgdqyzWYjmD+FxbK00CTL8aJdnxU9XJ+moO4c5D2UfQJ1R45sk2OP8hnLJRDU1mdU4a1j5wLkLeQY+RiAncu7ymLQLGZ9e1kG9ndqYdv7UIUSadsex3gYkcPh2AERmeUgYejSHaVSeiptFtGUBqepVvdic7LQSbfMyK1JoY4OM0z7EernskZOipztDRgPY4ohTOk4O32I9RrLbdpH22YyzMxsQB5XCyaX1yOmju53XvdxNVh2vFQdPqZEWrMCLZ22JHA9QrJmWe9ee06bbNIlQemW/Rbovh5tpDvuR8QrC9uu7ZPN0tMdHQ7HwIEBbJvuQQgMHbOM3nAtrUwySfmLLZfTfaz0wFzqn2QNOYGCpn1zQgslogxrhVBEE+MEbKHa1Kbb5LntrFe84Zb3VRu/VWNcE+qwvLvS9grUxUd68WzLdMpcsL11jtnKn+LccgzZYsva8yTvWZxdLE1mF0A3G41sffn17aQ7PoWIS50Rf+fM0uFwDCvcZjkAiDZLDZbUmBU3B0xMSCPCKhGhwWJoWtVH6f2W7Cs9P4u55GTkpIRa7vo0yYTlzlkyM+06WXZNGdGQMkvJoiVb1OJoZYqfPOfU9q3ZhgFbpi4dQ4l90BLdyJ1zk1CHVpYkQv5f5J7tEbRjs9yXiEu9uJ9wZulwOIYZg8Ysh/Zlmcv8iL+clgiwhrjPumDfoWDfWaG0kbF6ltBC7jirFHYSsVnYrkqEIuI62W/c3tWHFOYwmDhQZ4PW+FObZWQwcgy56yKZnzU2oLkuuSZaEtmtFhcKdNtp5X3usLbru9tq52NJ2Gk2UemJj+dlCZwAiX1T2Je1c5bnKmNA0/Gn1//welc9YxAdPP3MDXc4HI4itJnBQ0RPJaKvEtEdRHQ7EZ0+kTENLbN0OByDjRan4dsA/DEz30xETwJwExFdycz/1UsnQ/eynItKfVgNmRBTRllvJRdAHbfdN7ala306farVZhHH7fSphNHIKdGYmAqlY6vViQmfMZA93S6nglLjsRfRCTk9BOx0Opkymm6fSC1xmb4qj5dzCllTX6DujLFCh7RjxXOPNZA65ouGGjZpv5pTrnZvxrrX52pExTbSfJMLM7L6TZ9t+Ty2gbZelkGD4t7w/WEiugOV1KO/LB0Ox8xGj6FDexPRaLJ8PjOfrzUM5WcOBXBDr2Ma2pelFtISQ0qiZIes3yLTCIG6oX8/g9Vpx5YMMjKNbI0Z0ZfGvizJtzi2dL0lFyfDRTQhjYjoUIoMpyso3XDwbBBOqDQQPPZjVXdMEfuJaZ9WIHtOSCPnWKs5oAzHS45RyWdCin5oY+lsK3D6rTZY9YjiaCthflZihQxTa0sV3UIPL8v7S0KHQsmazwP4w6QSQzGG9mXpcDgGF217w4loZ1Qvys8w82UT6WNoX5YaI7DqamtiCR0ERlMSXhFDOtbHX2aZmhc+c6IGVmpeaoe06gFpEmoyLXBEsFstVKYWZiRYXXqd4rYNy8I4w7lLtqWOqbuJut5i8lYVw3SblbKopv4Z11BjvdIWbaWfaoHmtdo4qMNKk9VmAbJ/M8QqaSsZZexf7mONoY3QIaA9m2Wo0fUpAHcw899OtB8PHXI4HAOHlsV/XwhgJYAXE9HG8Hdcr2MaOmY5F5kURWHzkd6/5Qrz7NjgCkRbm6B5k2Ua3BrBPCJzS1P0rHS7nASZVQIhJw4rzzUyjpQlaumYaVvNpmjJ3nX2TZjTiMG8LYap9Sv7T5mxDGCPy5Zwh3ZMi7lqz4hVgz17fcSsJmdT7NhgjbECdaGRCEsKbqrQojf8Oui1u3rC0L0sHQ7H4MOFNAYAC2cTn57UDdeEZCMsT2dONMFkgsjH1aX7bFaYTZMX0yrYBdQZgWYri9DiQ+Xxm9rkWFAvZQyaBH1zbXPMybqvGuOzvOoloielIiVpv2sVu7Lcx2Ll1rUGbBGMnMhw07lqtt0xtCPRtgcR/3ph2y+5kIbD4RhWDGJu+NC9LLduFzbBjH3QKnuq2QTXCYFUaU/SIJlktD+OKT/lFmOVXvh0fJKlaPGd0mNbUt5AXp8cy5K2XE3GTfZvrcuxUkuurEkiDqjbVbWZQxOz1EqBSOYtbZirlH1GLu5uo6EWrSH61faN44sxwyXZQ/I4UnxDKyPdJgZtGj50L0uHwzH4GESbpb8sHQ7HQGLQXpZD5+CJNXhyCuMybcxKH0y3Wc6CrnU9KKNbkP2uu+6lAIAPLr9qfEwNgezalL1JqVubhmuOBO34QD0YOuc8yKmEy33kfbT20UwP8n43OeCAsum+FZLUGUtmneU0S/uylMs10Y0IaQ6R9yyF9dznnEFp0PvZP5+8g+dJRMUem2vdweNwOIYZg+bgGTpm+Su7EZ+7VA8el0G2VvpYroZNjj1awdA5ZlNjrAaLy9W9kUHG2pgiLOdWji1GVidr86T9WKFNuXCjiFy9GxkOZbEf7Zwls+wc/1G7bUTuvKyU15xTayIhVlIKTjLN3GzAqnCZopdaTun+bdTg2Y2Ilxa2vd6ZpcPhGGa4zXKaEW2WETmbWYT8hU1tfjUR2/Cp/brnaoqn++Zso52+wuctwWa58kVXwUIuXTCORYpjSNtiDrkqlU3MTMrgpWOyJMc0tmUFaEdodloL6jNhnGPOTigZcVOapbav1m9pULoqCCK2aWPpJXBd7jMyD7jkWZNnlvOJ+DmFbb/lzNLhcAwznFlOMyKzzKUjSraQs4dJG6IlmArUWegGRV5N6xOoM5qSNLW1hjBuDjm5MgslY5FtJdPRxIWtY2ssyGTpis1V1iOX91kTdy5Jc4yI5xIl+eRxIrTzk15qja3Lao5xmxTS0CpCltRil/tYz5xlj28j3XE+ER9c2PZmZ5YOh2NYMYjpjkPHLImIAT1Vz0rjy7GKJk+nFt/XZF/L2SylrS+y1RyDlWPjkJoJjNc3t2L3OmNS2K5li7sl6f+Dq7d0tZXQGH5nm7BVRqamedstr32u/yYGC9izjNpYje8lY0whx5KL8bVYeokgSK14nsKmLa++vB9Atx34jhaY5VwifmZh29ucWTocjmHGoNks/WXpcDgGEoP2shy6afheRFyqJy/rlmiGemtaFp0raY2YpprW2vSpSeFHm8pbqZcydCmFld6oBUfLaZg0BaRTMnluMjSJhVpT2lZefy2RwDJLyL5Sc0gcd6znHR0l8jgpTDNL+FyXmB5WhnOR45eVIq2gbm05vb85Rw6QVwAq1S/V+snVNUodaW0Epc8h4qcXtv1vn4Y7HI5hhjPLaUZkliWhMVZqWE7UoCR1UYYG5YKue0mNlMexGEeJEnhPNWDEcTRninQkWKr0Wj+W8EUK6fzJaW1aiQM5tXnrOmmhSVZ4kdSUTJllZNiUMGwg7+yzAtdztXGaHG3aMUvCpdLns42g9F2J+IDCtnc7s3Q4HMOMQWOWQ/uylHVpADvAOVePOTKaFdd3t9Xkyzq/6uEz2tnOjLYsJZSlKTBbYx6y9nRO8qxJRkwGdwPAmvBpBWznUv/M0KTkexynpaqeIrLOMwWjXCXsel3HamCsOdEQGUZTImQSt0lGmV6DaOeUjFi7/rEGu5WSWiLIknt+LObdhsRgKVz81+FwOAoxaC/LobNZxqD0ErEBaY+yamBryNWukf2vWhW2j9X7l151y86WE2TNeVpLKjTKdpbor2RqgB2QnZMrs1htrkZ3bR/RvxZhsNm4tiVVEeX112rwNEHbx7r+XcfORDUAetpmhLyG2vNj2WflPbPYdBtB6TsT8d6FbTe7zdLhcAwzPN1xmpGTaOMgd7Y0yJ3J2EBZgiFtY4mrpr/+TeUqct5Yy56meWPlmCzBDm3cvZRCkMwvMuS05nWTvFcuTrQXId8mNqQxfFnqIpcCKyGZa1qpUcZTxvutydFFlIicyDFN5J6VeP4lLKm5FGmcaxtxljsR8ZML2251ZulwOIYZbrOcZsiyEjnGJGMbNfuVxXpy2SwQbbSMkQhLjisndGHFMGoM1rJvWUXPtDFI5q2xLLlvqUBFCi1LR2PW6dhyEQAlEnASpfY8bVvunJvEMLTrb+1jXROt31SApdNGZDaVIB1LGxJts4l4t8K2P3Vm6XA4hhmDxiz9ZelwOAYSg/ay7Ns0nIgOBvBpAIcBeBczr022nQDgTAAXM/NHwrrDAVwIYC6ALwM4nZmZiHYFcDGAwwE8AOD1zHw3ES0CcCEzH50bx8LZxKfPyVc6tPQItQBh2Q+Lmji5NL4I6fDRavxY9ao1NNWVTnUIrZAVGeqTnnN05KwWpoGInAPAqhuem+bLfjUHjDznptrjgJ5eKvu30jPls6ClqFoCIJqzznr2pBMqbWsF1+dMA1KEoyTErcT0kJqL2nDwzCLinQvb/qJP0/BZU32ABFsB/AGAtcq2EwA8D8CRRBRNFX8P4C0AloS/Y8P6UwA8yMzPBPBhAB+YykE7HI7+I2bwlPyVgIiOJaLvEtGdRHTWRMbUdwcPEZ0D4GeCWX4RwKtR/Ti+BcBuAL7KXJXhIKITARzNzL9PRFcAOIeZv0FEOwHYDGAfAAsBfJSZX507fnTwyHARwP7ltOoya/vkIB0icQyWdFvavwy2toLJ07YROVGJiTg5Ov0WiHrkAsqB3mr95JilxUJz6uTWfU7vgzU+2XZEmW1ISKdfTlYvJ83XJASSu5aW6IkmwRef9yjyIaXnrBCuNoLSiYhLmdz2BmZJRLMB/DeAlwHYBOBGACcy83/1MqZ+MsscLgMwCmCUmR8GcCCqk4rYFNYhfN4DAMy8DcBDAPZi5nuaXpQOh2PmoEVm+XwAdzLz95n5FwA+C+D4XsczEMxSafM8AH/DzC8Ny78O4ExmfiUR3Q7gGGbeFLb9D4DnM/MDmf7WoWKumDVr1rxDDz20tfMZFNx1111YvHjxdA9jSrCjntuOel7f/va3sX379pRfX8bMK3vpg4i+AqA043EOgMeT5fOZ+fykr9cCOJaZ3xyWVwJ4ATOf1suYptQbTkSnAvi9sHgcM/+ocNdNqKbVEQsB/CjZ9lQAm8I0/Mmo7KEmwo1aGcb0yOjo6PzCccwYENEjDzzwwA53XsCOe2478nkx86TOi5mPbW5VDM0k0DNLnNJpODOfy8zLwl/pixLMfC+Ah4noSCIiAKsAfDFsvhzASeH7awFcw8MWWe9wOHpBJFgRKfkqRj9Dh/ZDZZfcHZWp4WcADmHmnxrtj8B46NC/A3hHCB2aA2AdgENRMcoTmPn7PYxj0r96g4gd9byAHffc/Lz6gzAD/W8ALwHwQ1QOnjcw8+299NO3oHRm3ozuqXVT+1EAz1HWPw7gdZMYymWT2HeQsaOeF7DjnpufVx/AzNuI6DQAVwCYDeCCXl+UwBDmhjscDsdEMCihQw6HwzHQ8Jelw+FwFGDGvyyJ6AIi2kJEtyXrDiCia4joizF9koh2JaJLQrrTDSGXPLY/iYi+F/5OStZfm7brJ7T0LOO83kpEtxLRRiK6jogOSfoYuPOyYJzvAiK6Moz/SiLaM6w/mogunKZxziGibxHRLUR0OxG9J6w/h4h+GO7DRiI6LqzfhYg+He7RLUR0dNLX0UQ0SkRrknWfCu3GiOjS5D4TEf1duD5jRHRYss/dfTr9LsyUe9YamHlG/wH4DVTiHLcl694P4NkAXgngrWHd2wGcF76fAOCS8H0BgO+Hzz3D9z3DtmsBLJqGc5oN4H8AHARgFwC3ADjEOK/dk/1+C8BXBvW8JnC+awCcFdqcBeAD4fvRqERTpmOsBGC38H1nADcAOBLAOQBWK+1PBfDp8H1fADcBmBWWL0EV7fEhAAcr9/Nvk/M/DlVUCIXj3ZC0u9vv2dT/zXhmycxfQz0ofTbGs6FiQOrxAC4K3y8F8JIQw3kMgCuZeSszPwjgSoyLdmwF8MQUDt+ClZ5VOy/uDr2aj/Fg20E8LwvW+ab37CIArwrff4EqzbXv4Ao/C4s7h7+cl/QQAFeHfbcA+AmAmMc8C+OaEV33Mzybc5O+j0elysXM/E0AexDR/mHbjyd/Zj1jxtyztjDjX5YGPg7gHwC8FeP6AmpOebo+oJOHzsyvZuZ0W79gjUk7LxDRqSHtcw0qZadcH9N5XhassT6FqwQFhM99w/frmfn0vo8ygIhmE9FGAFtQ/SDdEDadFqbIF8TpJyrGdTwR7UREi1FJC8YA6U8CuB4V07wj6f/TqARiDgbwsbA6dz+f1/Y5FmBG3bM2sEO+LJn5B8z8G8z8Sq6EOQA75amVVKiWoY7JOC9wlSn1DAB/AuDPcn1MwVjbwEwaK5j5CWZehipu+PlE9BxUkoLPALAMwL2optYAcAGqF8kogI+gejluC/1cwcyHMfMfi/7fCOAAAHcAeH1YPWjXaNDGM+XYIV+WBjopT9SdU95KKlTLmOiYPovxac8gnpcFa6z3xalm+NwyDWMzwcw/QWX/PZaZ7wsv0e0A/hHVNBXMvI2Z38lVyu/xAPYA8L2Cvp9AZdN8TVg1aPdzRt6zyWCYXpZWTvkVAJYT0Z5h6rQ8rJtO3AhgCREtJqJdUDmkLtcaElFa2fcVGP9HHMTzsmCdb3rPTsK4PsC0gYj2IaI9wve5AF4K4DuJ/RAAfhvAbaHNPCKaH76/DMA2NnQUg8f7mfE7Kkfed8LmywGsCm2OBPBQnO5OE2bMPWsN0+1hmuwfgH9GNe35Japfu1OMdnMAfA7AnQC+BeCgZNubwvo7Abxxus8pjOk4VPms/4OqDIfV7qMAbgewEcBXATx7kM+rl/NFZVO+GtUPwNUAFgzAOEcAfBuV9u1tAP4irF8H4Naw/nIA+4f1iwB8F9WU+ioAT8/0PQvAf4Z+bgPwGQTvOKpp77nh+twK4IgBuBYz4p619efpjg6Hw1GAYZqGOxwOx4ThL0uHw+EogL8sHQ6HowD+snQ4HI4C+MvS4XA4CuAvS4fD4SiAvyyHCET0FCL6JyL6PhHdRETfIKLfbthnESXydz0e72QiOiBZ/iQlEnIN+x5NRF+ayHEL+z+AiC4N35dFSbUe+ziHiFa3PzrHIMJflkOCkBHyBQBfY+aDmPlwVFkXxXWRJoCTUeU4AwCY+c1sZK/0G8z8I2Z+bVhchirA2uEw4S/L4cGLAfyCmc+LK7gS5vgY0GGQXyeim8PfUbKDXBsiOjMRuH0/VYXtjwDwGarEcOdSJTp8RGh/bOjjFiK6uvQkiOjEcJzbiOgDyfqfEdFfhf6+SURPCeufEZZvJKL3EtHPknO5LaTqvRfA68M4Xy8ZY2i3KHx/F1WCt1cB+NWkzTOI6CuBsX+diA4uPSfHDMF0pxD5X3/+UEm3fTizfR6AOeH7EgCj4fsiBGHlTJuXo1LTmReWF4TPa5Gk5cVlAPugkvdanLYX4zkawJfEugMA/G/YfycA1wB4VdjGAF4Zvq8B8Gfh+5cAnBi+vxXAz5TzOhnAx5PjnINEyBdV6uEiVPJqt4brsDuqNNLVoc3VAJaE7y9ApT0w7ffd/9r761spXMdggYjOBfAiVGzzeahEbD9ORMtQCQP/irKb1ealqNTAHwUAZpZizBJHojIH3FXYPuJ5AK5l5h+Hc/gMKqX8L6ASl402zpsAvCx8/zWMKzH9E4C1hcfS8OsA/jWeJxFdHj53A3AUgM9V1g4AwK6TOI5jAOEvy+HB7RiX+wIzn0pEe6PSWQSAdwK4D8BSVOaZx5U+rDaE3rQMe22f7mfhlxxoHaoX+WSe7W3oNlHNSb5r454F4CdcaVw6dlC4zXJ4cA2AOUT0tmTdvOT7kwHcy5Ue40pUJSwkrDYbALyJiOYBVdGqsP5hAE9S+vkGgP9DlXJ42r4JN4T99iai2QBOBPAfDft8E+M/EicYbeQ470ZV1wlUFQZbHNZ/DcBvB/vrk1BJqIGrUhB3EdHrwj5EREsLz8kxQ+AvyyFBYF2vQvWyuYuIvoWqRsqfhCafAHASEX0T1fT6EaUbtQ0zfwWVLNkoVeUWonPkQgDnRQdPMpYfA3gLgMuI6BZUIrcaXkJEm+IfKrvh2aik6G4BcDMzN+kl/iGAPwrnuz/0OjBfBXBIdPAA+DyABeFc3oZKhgzMfHMY68bQ5utJH78L4JRwPrejqkXj2IHgEm2OHRqB7T7GzExEJ6By9viLzNEz3Gbp2NFxOCqnFKGqrPim6R2OY6bCmaXD4XAUwG2WDofDUQB/WTocDkcB/GXpcDgcBfCXpcPhcBTAX5YOh8NRgP8PIWrgcD9N9dQAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "counts = Map.from_geom(geom)\n", "\n", "counts.fill_events(events)\n", "counts.sum_over_axes().plot(add_cbar=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fit the simulated data\n", "We can now check the sake of the event sampling by fitting the data (a tutorial of source fitting is [here](../../starting/analysis_2.ipynb#Fit-the-model) and [here](simulate_3d.ipynb). We make use of the same `~gammapy.modeling.models.Models` adopted for the simulation. \n", "Hence, we firstly read the `~gammapy.datasets.Dataset` and the model file, and we fill the `~gammapy.datasets.Dataset` with the sampled events. We set the `counts` map to the `dataset`:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:54.310897Z", "iopub.status.busy": "2021-11-22T21:03:54.310617Z", "iopub.status.idle": "2021-11-22T21:03:54.324960Z", "shell.execute_reply": "2021-11-22T21:03:54.325161Z" } }, "outputs": [], "source": [ "models_fit = Models.read(\"./event_sampling/point-pwl.yaml\")\n", "\n", "dataset.counts = counts\n", "dataset.models = models_fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's fit the data and look at the results:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:03:54.327465Z", "iopub.status.busy": "2021-11-22T21:03:54.327148Z", "iopub.status.idle": "2021-11-22T21:04:04.719161Z", "shell.execute_reply": "2021-11-22T21:04:04.719395Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : migrad\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 102\n", "\ttotal stat : 76401.38\n", "\n", "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : migrad\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 102\n", "\ttotal stat : 76401.38\n", "\n", "\n", "CPU times: user 8.88 s, sys: 1.71 s, total: 10.6 s\n", "Wall time: 10.4 s\n" ] } ], "source": [ "%%time\n", "fit = Fit()\n", "result = fit.run(dataset)\n", "print(result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results looks great!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Time variable source using a lightcurve\n", "\n", "The event sampler can also handle temporal variability of the simulated sources. In this example, we show how \n", "to sample a source characterized by an exponential decay, with decay time of 2800 seconds, during the observation. \n", "\n", "First of all, let's create a lightcurve:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:04.722575Z", "iopub.status.busy": "2021-11-22T21:04:04.722288Z", "iopub.status.idle": "2021-11-22T21:04:04.723513Z", "shell.execute_reply": "2021-11-22T21:04:04.723745Z" } }, "outputs": [], "source": [ "t0 = 2800 * u.s\n", "t_ref = Time(\"2000-01-01T00:01:04.184\")\n", "\n", "times = t_ref + livetime * np.linspace(0, 1, 100)\n", "expdecay_model = ExpDecayTemporalModel(t_ref=t_ref.mjd * u.d, t0=t0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we defined the time axis starting from the reference time `t_ref` up to the requested exposure (`livetime`). The bin size of the time-axis is quite arbitrary but, as above for spatial and energy binnings, the finer the better." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we can create the sky model. Just for the sake of the example, let's boost the flux of the simulated source of an order of magnitude:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:04.742372Z", "iopub.status.busy": "2021-11-22T21:04:04.742054Z", "iopub.status.idle": "2021-11-22T21:04:04.745420Z", "shell.execute_reply": "2021-11-22T21:04:04.745614Z" } }, "outputs": [], "source": [ "spectral_model_pwl.amplitude.value = 2e-11\n", "\n", "sky_model_pntpwl = SkyModel(\n", " spectral_model=spectral_model_pwl,\n", " spatial_model=spatial_model_point,\n", " temporal_model=expdecay_model,\n", " name=\"point-pwl\",\n", ")\n", "\n", "bkg_model = FoVBackgroundModel(dataset_name=\"my-dataset\")\n", "\n", "models = Models([sky_model_pntpwl, bkg_model])\n", "\n", "file_model = \"./event_sampling/point-pwl_decay.yaml\"\n", "models.write(file_model, overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For simplicity, we use the same dataset defined for the previous example:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:04.747737Z", "iopub.status.busy": "2021-11-22T21:04:04.747442Z", "iopub.status.idle": "2021-11-22T21:04:04.748756Z", "shell.execute_reply": "2021-11-22T21:04:04.748935Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DatasetModels\n", "\n", "Component 0: SkyModel\n", "\n", " Name : point-pwl\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : PointSpatialModel\n", " Temporal model type : ExpDecayTemporalModel\n", " Parameters:\n", " index : 2.000 +/- 0.00 \n", " amplitude : 2.00e-11 +/- 0.0e+00 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.000 +/- 0.00 deg \n", " lat_0 : 0.500 +/- 0.00 deg \n", " t0 : 2800.000 +/- 0.00 s \n", " t_ref (frozen) : 51544.001 d \n", "\n", "Component 1: FoVBackgroundModel\n", "\n", " Name : my-dataset-bkg\n", " Datasets names : ['my-dataset']\n", " Spectral model type : PowerLawNormSpectralModel\n", " Parameters:\n", " norm : 1.000 +/- 0.00 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", "\n", "\n" ] } ], "source": [ "dataset.models = models\n", "print(dataset.models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now, let's simulate the variable source:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:04.753910Z", "iopub.status.busy": "2021-11-22T21:04:04.753582Z", "iopub.status.idle": "2021-11-22T21:04:07.080995Z", "shell.execute_reply": "2021-11-22T21:04:07.081186Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.94 s, sys: 385 ms, total: 2.33 s\n", "Wall time: 2.33 s\n" ] } ], "source": [ "%%time\n", "sampler = MapDatasetEventSampler(random_state=0)\n", "events = sampler.run(dataset, observation)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:07.083047Z", "iopub.status.busy": "2021-11-22T21:04:07.082752Z", "iopub.status.idle": "2021-11-22T21:04:07.084678Z", "shell.execute_reply": "2021-11-22T21:04:07.084894Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Source events: 1523\n", "Background events: 15246\n" ] } ], "source": [ "print(f\"Source events: {(events.table['MC_ID'] == 1).sum()}\")\n", "print(f\"Background events: {(events.table['MC_ID'] == 0).sum()}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now inspect the properties of the simulated source. To do that, we adopt the `select_region` function that extracts only the events into a given `SkyRegion` of a `~gammapy.data.event_list.EventList` object:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:07.087933Z", "iopub.status.busy": "2021-11-22T21:04:07.087642Z", "iopub.status.idle": "2021-11-22T21:04:07.095236Z", "shell.execute_reply": "2021-11-22T21:04:07.095393Z" } }, "outputs": [], "source": [ "src_position = SkyCoord(0.0, 0.5, frame=\"galactic\", unit=\"deg\")\n", "\n", "on_region_radius = Angle(\"0.15 deg\")\n", "on_region = CircleSkyRegion(center=src_position, radius=on_region_radius)\n", "\n", "src_events = events.select_region(on_region)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can have a quick look to the data with the `peek` function:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:07.103330Z", "iopub.status.busy": "2021-11-22T21:04:07.102961Z", "iopub.status.idle": "2021-11-22T21:04:07.507970Z", "shell.execute_reply": "2021-11-22T21:04:07.508250Z" } }, "outputs": [ { "data": { "image/png": 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sA1yWzhd9IiIOjYjnJH2DJJkH+O/uBZlmZmbWOh5/fRifnZss3xq/4d9L+qY/uUvJcbbUYHmZwfesX1oFYOqXPlxyfPg3r1/x+KZnty7pGzxnw5Ljtx29skzftLt3L+k7etfS9eGzXyktQzj4vJXXWmur0hmz243/Z8nx+7/zpZLjbMm8Px9bupnWssfWKTlexMYrHpeXGRw78a6S48e+vVfJ8XNXvWXF49uuLI3hgNWOpJq7y2YALzmlLH0sKwG4xVUr43rt0NJ/x1rlAMvLDv65LMZyB2TKEGZLEgJc/+ZlNZ9b87pl/xa1rtVTjGkeu4pc64BXWkld5bz9a/RdCFzYyLjMzMzMzPLinTCtxIs1+mqVKGyGWuULzczMzFpVnnPAzczMzMw6Xl0JuKS3pFVJkDRU0ro9PcfMzMzMzFbVYwIu6XjgcpKqJJCU/ruyiTGZmZmZmbWteuaAn0iyjfwdABHxqKSNaz/FzMzMrLoRq7/EZ0bdBEDX4OdL+p7edL2S4022WLlCqbz6yNeu+kjJ8fIJpZVBzr19ZV2HdWevXtL36jtL9/J78v5tq8Z7906l1Swe+/YOJcfbn7qygsqa7366pO/GTUurkaxVdu0lW61Mx0YfcX/VGKC8qkhpTP8sq0byj1NPLjk+4Msrq3v0ptJH+bn3n31ylTN7VqvaSk8VRcr1p9JJEdfNqicBfz0i3uguoyJpMN590sysUPtMmcmCJaXlykYNG1pQNGZm1hv1JOB/kvQVYKikA4DPAVc3NywzM6tlwZKlzJtyUNFhmJlZH9SzCHMysBi4H/gMSR3v/2xmUGZmZmZm7arHEfCIeBP4cfplHcx1uc3MzMz6r2oCLul+asz1jogdqvWZmZmZmVlltUbAD06/n5h+/1n6/WPAq02LyMzMzMysjVVNwCPicQBJ+0TEPpmuyZJuAf672cGZmdnANWrYULomX1Ox/ZbJ+xYQkbWSJ5cO5+S/JiUE3zX6sZK+G28q/ZB9+YYrSwvu/44Hq/ZVki09WF4+7x2nfqfkeNMv37XicXkpundddVbJ8T9OPaW0/7CV/WvydM1zy2VL8/VUAq/kdWbcWdJ3fw/PzV67VjnAWs/rrzxK/LWCeqqgrC3pnRHxFwBJewNrNzcsMzMb6Kol2ZWScjMzW6meKigTgR9ImidpHnAe8OmmRmVmLUvShZIWSXog0zZc0vWSHk2/b5DpO13SHEmPSHp/MVGbmZnlp8cEPCLujogdgR2AHSNip4i4p/mhmVmLuhg4sKxtMnBDRIwFbkiPkbQtcDSwXfqc8yQNyi9UMzOz/PU4BUXSf5UdAxARngNuZquIiJsldZU1TwDGp48vAW4CTkvbp0XE68BcSXOA3YHbcgnWrANIOryO016LiGubHoyZAfXNAX8l83hNkuooDzUnHDNrU5tExEKAiFgoaeO0fRRwe+a8+WnbKiRNAiYBjB49uomhmrWdHwNXAapxzrtJNtpbhaQDgXOBQcAFETGlynm7kbyfj4qIy/sVsVmbq2cjnv/NHks6G5jRtIjMrJNUSggq7j8QEVOBqQDjxo2rukeBma3idxFRc+2WpJ9XaR8E/AA4gOQP5LskzYiIByuc9y3gusaEbNbe6hkBL7cWsGWjAzGztva0pJHp6PdIYFHaPh/YInPe5sBTuUdn1sYi4uP9OGd3YE5EPAYgaRrJ1LEHy877PPBrYLd649pgyKt8aKv7AHjoxU1L+spLCw56dmW68vjur5T2fbs0lSkv+Zctt3fAObeW9JW+am1/vvJLdZ/72qG79+LKvSvN15s4+vOavSmNaL1Xzxzw7I6Yg4ARwDeaGZSZtZ0ZwHHAlPT7VZn2X0o6B9gMGAvcWfEKZtYvVeaCvwDcHxGLKvRBMiXsyczxfGCPsuuOAj4E7EsvEnCzTlbPCPjBmcfLgKcjonbVezPrWJIuJVlwuZGk+cBXSRLv6ZImAk8ARwJExGxJ00lG05YBJ0bE8kICN2t/E4G9gBvT4/Ekc7a3lvTfEfGzCs+pZ5rYd4HTImJ5d6GGarJrOdYbObTuwM3aTT0J+P9ExCeyDZJ+Vt5mZgYQEcdU6dqvyvlnAmc2LyIzS70JvD0ingaQtAnwQ5IR7ZuBSgl4PdPExgHT0uR7I+CDkpZFxJXlF8uu5Ri53QZey2Edq54EfLvsgaTBwK7NCcfMzMyapKs7+U4tAraOiOck/avKc+4CxkoaAywgqdv/0ewJETGm+7Gki4HfVkq+zWylqgm4pNOBrwBDJb3Y3Qy8QfrXq5mZmbWMP0v6LdC9ou4I4GZJawNLKj0hIpZJOomkuskg4MJ06tgJaf/5zQ/brP1UTcAj4pvANyV9MyJOzzEmMzMza7wTgcOBd5IMqF0C/DoiAnhvtSelG/RcW9ZWMfGOiE/WG8xzL67DZX/YG4Dt93yspC9b9QRgrX+unFteXmFks7+ULRs5tfprlj+3vKJItvJHT9512FlVr1Xe14pc+aS5ao2AbxMRDwOXSdqlvN/b0ZuZmbWOiAhJs4AXIuKPktYC1gFeKjg0s45Taw74KSQrlf+3Ql+QlBsyMzOzFiDpeJLf68OBt5KUGDyfKgukzax5ak1BmZQ+/EBEvJbtk7RmU6MyMzOzRjuRZGOdOwAi4lFJGxcbkllnqqcKyq1A+RSUSm1m1qYkbQBsERH3FR1Lp9lnykwWLFm6SvuoYa6hbL32ekS80V2rO61q5lKAZgWoNQd8U5KPp4ZK2pmVxfjXI9mO3szamKSbgENJ/j9xL7BY0p8i4pRaz7PGWrBkKfOmHFR0GNYe/iSpu7rZAcDngKsLjsmsI9UaAX8/8EmSovvnZNpfIilPaGbtbf2IeFHSvwEXRcRXJXkE3Kx1TSbZDfN+4DMklU0uKDQisw5Vaw74JcAlkj4cEb/OMSYzGxgGSxoJfAQ4o+hgzKx/IuJN4MfpV+HesckmzDo1+UCtvPzfZoe+peR4zRl3Vr1Oebm88mv1ppxeb84tL2FYb58Z1DEHPCJ+Lekgkh0x18y0/3czAzOzwn2dZPONv0TEXZK2BB4tOCYz6yVJ91NjrndE7JBjOGZGHQm4pPNJ5ny/l+SjqiOA6n+Kmlm7WJj9xRwRj0k6p9YTzGxAOjj9fmL6/Wfp948Br+YfjpmtVsc5e0fEscDzEfF1YC9gi+aGZWYDwPfqbDOzASwiHo+Ix4F9IuLLEXF/+jWZZL2XmeWsnjKE3fWvXpW0GfAsMKZ5IZlZkSTtBewNjJCUrXiyHjComKjMrAHWlvTOiPgLgKS9gbULjsmsI9WTgP9W0jDgLOAeknlkA2IBh5k1xRok21MPBtbNtL9IMgXNzFrTROBCSeuT/C5/Afh0sSGZdaZ6FmF+I334a0m/JVmIuU1TozKzwkTEn0jqBV+cfmxtZm0gIu4GdpS0HqCIeKHomLr1pvpIntcya5Z65oCvEBGvp29Y/3Sbtb8hkqZK+oOkmd1f/bmgpJMlzZb0gKRLJa0pabik6yU9mn7foFE3YGYg6eDscUS8WJ58l59jZs1VzxSUStTzKf0n6UDgXJJ5pxdExBRJw4FfAV3APOAjEfG8pPHAJyPik3nEZtYBLgPOJ6l+tLy/F5M0CvgCsG1ELJU0HTga2Ba4IX1/TybZLOS0/r6ema1wlqQF1P7d/f8Bv80pHrOO19cEvGo90UaRNAj4AXAAMB+4S9IMkt05/cvarPmWRcQPG3zNwSTbYP+LpLzpU8DpwPi0/xLgJvyeNmukpynd0boS1/g3y1HVBFzS1VROtAVs2LSIVtodmBMRj6XxTAMmpF/j03Oyv6zfIFlQYmaNcbWkzwG/AV7vboyI5/pysYhYIOls4AmS6kp/iIg/SNokIham5yyUtHEDYjezVESMLzoGMytVawT87D72Ncoo4MnM8XxgD6DiL+uIuBW4NYe4zDrFcen37J7KAWzZl4ulc7snkJQxXQJcJunjvXj+JGASwOjRo/sSgpmZ2YBQNQFPKyEUqdJctaZPfTGzREQ0ut7//sDciFgMIOkKknrjT0samf5BPRJYVCWeqcBUgHHjxvn/BWZm1rL6Ogc8D/Mp3XFzc5L5onX9ss6S9DPg8OaEaTbwzJ07F0mvZJquiIhP9OYako6t1B4RP+1jWE8Ae0pai2QKyn7ALOAVktH2Ken3q/p4fTOrQdKQiHi9pzYza76BnIDfBYyVNAZYQFIt4aPAcHr5yzpNPD4BIMkjZ9b2xowZw7PPPtvfHe52yzxekyRhvgfoUwIeEXdIujy9xjLgryQj2usA0yVNJEnSj+xP0GZW1W3ALnW0mVmTDdgEPCKWSToJuI6kDOGFETFb0hT8y9qs6SLi89njdPe8n/Xzml8FvlrW/DpJct/R9pkykwVLlq7SPmrY0AKisXYiaVOSdVVDJe3Myime65FUIzKznPWYgEu6HjgyIpakxxsA0yLi/U2OjYi4Fri2rO1Z/MvarAivAmOLDqJdLViylHlTDio6DGtP7ycp4bs5peUIXwS+0tOTK+3JUdY/AfgG8CbJp1tfjIi/NCRyszZVzwj4Rt3JN0C66Y3LhJm1ubJSpIOAtwPTi4vIzPoiIi4BLpH04Yj4dW+eW21Pjoh4MHPaDcCMiAhJO5D8f2KbBoVv1pbqScDflDQ6Ip4AkPQWXI3ErBNky40uAx6PiPlFBWNm/XaLpJ8Am0XEByRtC+wVET+p8Zxqe3KsSMAj4uXM+WvjHMGsR6vVcc4ZwF8k/SytJnIzyc51ZtbG0lKkDwPrAhuQbHZlZq3rIpJ1VZulx38HvtjDcyrtyTGq/CRJH5L0MHAN8Ol+R2rW5npMwCPi9yQrpH9F8rHSrhFxXbMDM7NiSfoIcCfJQuePAHdIOqLYqMysHzaKiOkkc7WJiGXA8h6eU9eeHBHxm4jYBjiMZD545YtJkyTNkjRr8eLFdQdu1m5qbUW/TUQ8LKm7PNFT6ffR6ZSUe5ofnpkV6Axgt4hYBCBpBPBH4PJCozKzvnpF0oakCbSkPYEXenhOtT05KoqImyW9VdJGEfFMhX5vqGVG7Tngp5Bs+/y/FfoC2LcpEZnZQLFad/Kdepb6pq1ZDyqVHHS5QcvBKcAM4K2SbgFGAD19qlVtT44VJG0F/CNdhLkLsAbJ/y/MrIpaW9FPSh9+ICJey/ZJWrOpUZnZQPB7SdcBl6bHR1FWFtT6xiUHrQgRcY+k9wBvI5la8khE/KuH51Tbk+OEtP984MPAsZL+RbLL7VER4dFtsxrqqYJyK6vuklWpzczaQDqatUlEfEnS4cA7SX5Z3wb8otDgzKzPJB0J/D5NoP8T2EXS//Q0pbTKnhznZx5/C/hWM2I2a1e15oB75yyzzvRd0s05IuIK4AoASePSvkOKCszM+uX/RcRlkt5JsjnP2cAPgT2KDcus89QaAc/unPW/rEzA69o5y8xaVldE3FfeGBGzJHUVEI+1mFHDhtI1+ZqK7bdM9vKhAnVXPDkI+GFEXCXpawXGY9axas0B7/POWWbW0mqt8fBKQetRtSS7UlJuuVog6UfA/sC3JA3BC6vNClHPHPBdJd3QvR29pA2A/4iI/2xqZGZWlLskHR8RP842SpoI3F1QTNYGKo2Me1S8+SSNiYi5JPX8DwTOjoglkkYCXyo2OrPOVE8C/oGIWDHlJCKel/RBwAm4WXv6IvAbSR9jZcI9jqS02IeKCspaX6VE26Piubgc2BW4OiL2626MiIXAwsKiMutg9STggyQNiYjXASQNBYY0NywzK0pEPA3sLem9wPZp8zURMbPAsMys71aT9FVga0mnlHdGxDkFxGTW0epJwH8O3CDpIpINeD4NXNLUqMyscBFxI3Bj0XGYWb8dTbJF/GBg3WJDMTOoIwGPiG9Luh/Yj6QSyjci4rqmR2ZmbUfSMOACkpH17j/oHwF+BXQB84CPRMTzxURo1pYOjIhvpZ9m/3fRwZhZnaufI+J3EXFqRPyHk28z64dzSTYC2QbYEXgImAzcEBFjgRvSYzNrnE+l3w8rMggzW6nHEXBJewLfA95OsghrEPBKRKzX5NjMrI1IWg94N8n+AkTEG8AbkiYA49PTLgFuAk7LP0KztvWQpHnACEnZGv8CIiJ2KCYss85Vzxzw75PMH7uMpBLCscBWzQzKzNrSlsBi4CJJO5JUWPl3km3vF0JSlUHSxgXGaNZ2IuKYdHfr64BDi47HzOqfgjIHGBQRyyPiIuC9zQ3LzNrQYGAXkh34dgZeoRfTTSRNkjRL0qzFixc3K0azthQR/yTZcn5dYB3g6Yh4PCIeLzYys85UTwL+qqQ1gHslfVvSycDaTY7LzNrPfGB+RNyRHl9OkpA/nW4IQvp9UaUnR8TUiBgXEeNGjBiRS8Bm7UDSYEnfBp4kmeb1c+DJ9Hf66sVGZ9aZ6knAP0Ey7/skkhGrLYAPNzMoM2s/6Qjck5LeljbtBzwIzACOS9uOA64qIDyzdnYWMBzYMiJ2TT+BeiswDDi7yMDMOlU9ZQi7P55aCny9ueGYWZv7PPCL9FO1x0iqM6wGTE+3un8COLLA+Mza0cHA1hER3Q0R8aKkzwIPk6zFMLMcVU3A09rfUa3fq6bNrLci4l6Sxdzl9qvQZmaNEdnkO9O4XFLV3/Nm1jy1RsAPzi0KMzMza5YHJR0bET/NNkr6OMkIuJnlrGoC7pXRZmbWbKOGDaVr8jUV22+ZvG8BEbWlE4ErJH2apPxnALsBQ4EPFRmYWafyRjxmZlaYakl2paTc+iYiFgB7SNoX2I5kA57fRcQNxUZm1rm8EY+ZmVkHiIiZwMyi4zCz+hJwImKOpEERsZxkF7tbmxyXmVnL22fKTBYsWbpK+6hhQwuIxszMBop6EvCSjXiAhXgjHjOzHi1YspR5Uw4qOgwzMxtg6t2IZzW8EY+ZmVnHkXSgpEckzZE0uUL/xyTdl37dKmnHIuI0ayW92YjnNbwRj5mZWceQNAj4AXAAMB+4S9KMiHgwc9pc4D0R8bykDwBTgT3yj9asdVQdAZc0QdKJmeM7JD2Wfh2RT3hmZmZWoN2BORHxWES8AUwDJmRPiIhbI+L59PB2YPOcYzRrObWmoHwZmJE5HkJSN3Q88NkmxmRmZmYDwyjgyczx/LStmonA76p1SpokaZakWYsXL25QiGatp9YUlDUiIvum+0tEPAs8K8mLMM3MrGm8Qc+AoQptFbevl/RekgT8ndUuFhFTSaaoMG7cuIrXMesEtRLwDbIHEXFS5nBEc8IxMzPzBj0DyHyS4gvdNgeeKj9J0g7ABcAH0sE6M6uh1hSUOyQdX94o6TPAnc0LyczMzAaIu4CxksakJYmPpnR6KpJGA1cAn4iIvxcQo1nLqTUCfjJwpaSPAvekbbuSzAU/rMlxmZmZWcEiYpmkk4DrgEHAhRExW9IJaf/5wH8BGwLnSQJYFhHjiorZrBVUTcAjYhGwt6R9ge3S5mvSrWzNzMysA0TEtcC1ZW3nZx7/G/Bvecdl1sp63IgnImZGxPfSrz4n30r8X1rI/z5Ju2T6jpZ0j6QvpsdrSbpG0sOSZkuakjl3iKRfpde5Q1JX2t4l6aa+xmdmZmZmlod6dsJslA8AY9OvScAPM31Hk5Q43FPSOmnb2RGxDbAzsE9a3B+SFdbPR8RWwHeAb+URvJn1n6RBkv4q6bfp8XBJ10t6NP2+QU/XMDMza3V5JuATgJ9G4nZgmKSRaV93maMAFBGvRsSNAGnh/3tYWdh/AnBJ+vhyYD8lk86WA8/lcB9m1nf/DjyUOZ4M3BARY4Eb0mMzM7O2lmcCXquY/xXALGBWRLyUfZKkYcAhJL+cS64TEcuAF4ANI+LJiDi8adGbWb9I2hw4iKRUWbfsH9SX4AXeZmbWAWpVQWm0qsX8I+ISVv4SXvkEaTBwKfB/EfFYT9cxswHtuyQ77K6badskIhYCRMRCSRsXEZiZmVmemjoCLulESfdKupekcH+PxfzLTAUejYjvZtpWbAqQJujr08PUE0mvdH/17g7MWtPcuXMH1M+9pIOBRRFxdz+u4S2szcysLTQ1AY+IH0TEThGxE3AlcGxaDWVP4IXuka9KJP0PSXL9xbKuGcBx6eMjgJkRUXMEPCLW7v7q252YtZYxY8YMtJ/7fYBDJc0DpgH7Svo58HT3WpD0+6JqF4iIqRExLiLGjRjhzXjNzKx15TkH/FrgMWAO8GPgc9VOTOeKngFsC9yTjqJ31xj9CbChpDnAKXjRltmAFxGnR8TmEdFFUvVoZkR8nNI/qI8DriooRDMzs9zkNgc8HaU+sc5z51N5rjcR8RpwZANDM7PiTAGmS5oIPIHf22Zm1gHyXIRpZkZE3ATclD5+FtivyHjMzMzy5gTczKyf9pkykwVLlq7SPmrY0AKiMTOzgc4JuJlZPy1YspR5Uw4qOgwzM2sReS7CNDMzMzPreB4BNzOzljFq2FC6Jl+zStstk/dd5dxKU4OqnWtmlicn4GZm1jIqJc/lCXm3SlODqp1rZpYnT0ExMzMzM8uRE3AzMzMzsxw5ATczMzMzy5ETcDMzMzOzHDkBNzMzMzPLkaugmJn1QrXSdmZmZvVyAm5m1gve9dI6jaQDgXOBQcAFETGlrH8b4CJgF+CMiDg7/yjNWosTcDMzM6tI0iDgB8ABwHzgLkkzIuLBzGnPAV8ADss/QrPW5DngZmZmVs3uwJyIeCwi3gCmAROyJ0TEooi4C/hXEQGatSKPgJuZVVBprjd4vrd1nFHAk5nj+cAeBcVi1jacgJuZVeC53u1p1LChFbejHzVsaMVt7g1VaIs+X0yaBEwCGD16dF8vY9bynICbmVnHqJZkV0rKDUhGvLfIHG8OPNXXi0XEVGAqwLhx4/qcyJu1OifgZpYLSVsAPwU2Bd4EpkbEuZKGA78CuoB5wEci4vmi4rTWU2tU2/rtLmCspDHAAuBo4KPFhmTW+pyAm1lelgH/ERH3SFoXuFvS9cAngRsiYoqkycBk4LS8gvJc79bnqSPNExHLJJ0EXEdShvDCiJgt6YS0/3xJmwKzgPWANyV9Edg2Il4sKm6zgc4JuJnlIiIWAgvTxy9JeohkgdcEYHx62iXATeSYgHuut1ltEXEtcG1Z2/mZx/8kmZpiZnVyGUIzy52kLmBn4A5gkzQ5707SNy4wNDMzs6ZzAm5muZK0DvBr4Iu9+Yha0iRJsyTNWrx4cfMCNDMzazIn4GaWG0mrkyTfv4iIK9LmpyWNTPtHAosqPTcipkbEuIgYN2LEiHwCNjMzawIn4GaWC0kCfgI8FBHnZLpmAMelj48Drso7NjMzszx5EaaZ5WUf4BPA/ZLuTdu+AkwBpkuaCDwBHNm0ACpUPHG1EzMzy5sTcDPLRUT8hcq76gHsl0cMrnhiZmYDgaegmJmZmZnlyAm4mZmZmVmOnICbmZmZmeXICbiZmZmZWY6cgJuZmZmZ5cgJuJmZmZlZjlyG0MzMrIpqteNvmbxvQRGZWTtwAm5mZlZFpdrxXZOvKSgaM2sXnoJiZmZmZpYjJ+BmZmZmZjnyFBQzM+t4o4YNrTi1ZNSwob0613PDzaweTsDNzKzj9SZxrnbuPlNmOjE3s7o4ATeztlOpcgVUHs00a5RqSbYXbZpZOSfgZtZ2KlWuMDMzGyg6LgHfcMMN6erqKjqMXpk7dy5jxowpOow+c/z5mzt3bhQdg5mZmVXWcQn4M888o6Jj6C1Jrzz77LNrFx1HXzl+M+tklRZtel64WWfruATczAYeSQcC5wKDgAsiYkrBIZk1TKVEu5Xmhff0/pSktP+DwKvAJyPintwDNWshrgNuZoWSNAj4AfABYFvgGEnbFhuVmUHd788PAGPTr0nAD3MN0qwFOQFvDVcUHUA/OX6rZXdgTkQ8FhFvANOACQXHZGaJet6fE4CfRuJ2YJikkXkHatZKnIC3gIj4RNEx9Ifjtx6MAp7MHM9P28ysePW8P/0eNuslzwE3s6JVWhi9ShUXSZNIPt4GeFnSIzUv+q0GRJafjYBnig4iB77PjDp+Rt/SiGD6qZ73Z13vYej9+7gX1gdeaNC51fortZe3ZY9r9TXyvTAQ7738uJPvvfL7OCL8lfMXcCDwCDAHmJy2bQbMBK4C1knbTgEeBO4DbgDekrnGccCj6ddxmfabgK4BdF/DgevTOK8HNkjbxwMXNymONYE7gb8Bs4Gvp+1fAxYA96ZfH0zb1wAuAu5PnzM+c63xwCzg2xVe53vAy5ljAf+X3v99wC6ZvnlF/9wN1C9gL+C6zPHpwOlFx5Xzv8GsomPwffo+q9xLj+9P4EfAMZnjR4CROcc5tVHnVuuv1F7elj3uoa9hPyMD8d5r/Vt02r1X+/IUlJzVWNDyBeDzwAXAx9PT/wqMi4gdgMuBb6fXGA58FdiDZH7eVyVtkOd9lKtxX5OBGyJiLMkfEZNzCOd1YN+I2BHYCThQ0p5p33ciYqf069q07XiAiHgHcADwv5K63xufBd4FDJK0TfcLSBoHDCt7XS9E6pu7gLGSxkhaAzgamFFwTGaWqOf9OQM4Vok9gRciYmHOcV7dwHOr9VdqL2+7us6+RhqI915+3Mn3XpET8PxVW9AyCHgz/RJARNwYEa+mz7sd2Dx9/H7g+oh4LiKeJxlZPjDtew5YnsudlKp2XxOAS9JzLgEOSx+/Qf0fG/VKJF5OD1dPv2ptTLMtyR8HRMQiYAkwLu1bLX3uiv8u6R8bZwFfLrtOrYVIi/tzT+0sIpYBJwHXAQ8B0yNidrFRmRlUf39KOkHSCelp1wKPkXz692PgcwXEWXci1tO51fortZe3ZY9r9TXSQLz38uNOvvdqPAc8f5UWq+xBktD9jCQp/WiF500EflfjGqMAIuLwBsdbr2r3tUn3SEhELJS0cfr4VuDWZgWTJsl3A1sBP4iIOyR9ADhJ0rEk00r+I/0D5m/ABEnTgC2AXdPvd5J8InErcGNEPJRe/iRgRno/2Zet9t9lYUTs1qRbbQvppxHX9nhi+5padAA58X22oErvz4g4P/M4gBPzjqvFtdXPSC918r2v4AQ8fxUXq0TE48C7Kz5B+jjJiOx7al2jMeH12YCKKSKWAztJGgb8RtL2JFNCvpHG9Q3gf4FPAxcCbydJyh8nSbiXpde5jmTkBwBJmwFHkswNLzeg/g2sdURER/xC8n2aJTr5Z6ST7z3LU1DyN59kdLXb5sBT1U6WtD9wBnBoRLzel2vkpFpMT3dPw0i/L8ozqIhYQrIw9cCIeDoilkfEmyQfk+6enrMsIk5O54VPIJnb/WiVS+5MMqo+R9I8YC1Jc9K+gfjfxczMzAYYJ+D5q3vBmaSdSVaXH5rOTe52HfA+SRukiy/fR2aUtiDV7msGScUW0u9XNTsQSSPSkW8kDQX2Bx4u2xjiQ8AD6TlrSVo7fXwAsCwiHqx07Yi4JiI2jYiuiOgCXo2IrdLugbAQyQYoSQdKekTSHEmrLEaW9DFJ96Vft0rasYg4G6GOe52Q3ue9kmZJemcRcfZXT/eZOW83ScslHZFnfGY2cHkKSs4iYpmk7gUtg4ALayw4OwtYB7gsnWv8REQcGhHPSfoGSdIL8N8R8VyzY6+l2n1JmgJMlzQReIJk+kazjQQuSeeBr0ayaOi3kn4maSeSaSHzgM+k528MXCfpTZIyhX3deOda4IMkC5FeBT7V5zuwtpKpEnQAyScld0maUfaH3lzgPRHxfLpeYSrJOoqWUue93kCyjiIk7QBMB7ZZ9WoDV5332X3etyh+kMTMBhCl9QrNzKxJJO0FfC0i3p8enw4QEd+scv4GwAMR0XK7CfbhXvci+YP97flF2X/13qekLwL/AnYDfhsRl+ccqg1w6Sew55FUB7spIn5RcEi5kbQlyTTb9SOioz4h8hQUM7Pm6+1W3dmqR62mrnuV9CFJDwPXkCyGbjU93qekUSTT3c7HOoqkCyUtkvRAWXulaUuHA5dHxPHAobkH22C9ufe0dPHEYiItlhNwM7Pm681W3e8lScBPa2pEzVPXvUbEbyJiG5K9Ab7R7KCaoJ77/C5wWlqVyTrLxazcnwOouWHd5qz8Y64dflYupv5771hOwM3Mmq+uCjnpfOgLgAkR8WxOsTVar6oBRcTNwFslbdTswBqsnvscB0xLKyYdAZwn6bBcorNCpT/X5Wuzqm1YN5+VG+21fF7Wy3vvWC3/H9rMrAX0WP1I0mjgCuATEfH3AmJslHrudSulK8sl7QKsAbTaHxw93mdEjMlUTLoc+FxEXJl7pDZQVJu2dAXwYUk/pHlbthet4r1L2lDS+cDO3esoOoWroJiZNVmNKkEnpP3nA/8FbEgySgpJOcxxRcXcV3Xe64dJSnb+C1gKHBUtVhGgzvs0y6q2Ed8rtH/VrGr3/ixwQt7BDASugmJmZmbWYJK6SCrfbJ8e96pCUCvr5Huvl6egtBBJm0j6paTHJN0t6TZJH+rhOV3lK5F78XqfVLL1evfxBfUumpA0XtJv+/K6dV5/M0mXp493kvTBPlzja5JObXx0ZmZmq6h7I7421Mn3XpET8BaRzpe8Erg5IraMiF1JfoA3r/nE/vkksCIBj4h/q7ZDZN4i4qlMzdCdSDbAMTMzK5ykS4HbgLdJmi9pYkQsA7qnLT1EsklctY34WlYn33tveApKi5C0H/BfEfGeKv1dwM+AtdOmkyLi1uzHQNXOSZ//ZZIdIN8kqT88i6SU0AKSOZp7pe2nRsQsSQcC/x/J3MdnImK/snjGp+ceXNZ+DPAVkvlg10TEaWn7y8C5wMHp602IiKclvRX4Rfo6vwNOiYh1uu8L2IVk58mhaazfBN4OvBwRZ6fXfgA4OCLmSToDOJZkMchi4O6IODt9nR8AI0h2sTw+Ih6u+B/DzMzMrB88At46tgPuqdG/CDggInYBjgL+r95z0m2vDwP2iIgdgW+nu7XNAj4WETtFxNLui0gaAfwY+HB6fl3by6fTWb4F7Esyar1bpiTX2sDt6fVuBo5P288Fzo2I3ahQyiwtZ/RfwK/SOH9V4/W7PzXYmWTjg90y3VOBz6efLJxKsiuZmZmZWcO5CkqLkvQD4J3AG2lyujrwfUk7kRTy37rC06qdsz9wUUS8ChAR5fU7y+1JMhVmbp3nd9uNZJvdxek9/AJ4N8nUmjdIRrQB7gYOSB/vRfLHAcAvgbPrfK1K3gX8pvs+Jc1Iv68D7A1cllafABjSj9cxMzMzq8oj4K1jNsl0CwAi4kRgP5IpEwAnA08DO5Js/rBGhWtUO0dU2ZWvit6en31eNf/KlCFbTv/+OFxG6c/2mpnHleJeDViSjqB3f729H69v1muSNpd0laRHJf1D0rnpYqXu/ksl3SfpZEnbSLpX0l/T6VO9eZ2SxdWZ9q9IekPSJ8raP5a+7n2SbpW0Y5XrStJMSetV6Ovzgud0kfVtkmanMRyV6ZsmaWxfrmtmViQn4K1jJrCmpM9m2tbKPF4fWBgRb5LM5R5U4RrVzvkD8GlJawFIGp62vwSsW+E6twHvkTSm7Pye3JE+byMl29IeA/yph+fcTlIzGJLpI5WUxzmP9I8VJZt8jEnbbwY+JGmopHWBQwAi4kVgrqQj0+eoWpJh1gzpIusrgCsjYizJp1PrAGem/ZsCe0fEDhHxHZJPha6KiJ0j4h+9fLlPkllcnV7/48D7SbaI/g9JB2S65wLviYgdSLaMn1rluh8E/pa+nxrpVeDYiNiOZHvr70oalvb9EPhyg1/PzKzpnIC3iHR0+DCSBHaupDuBS4DT0lPOA46TdDvJL+9XKlym4jkR8XuSckCzJN1LMgcakkWY56cjbUMzsSwGJgFXSPobUG3e9X7pCuj5kuYDXcDpwI3A34B7IuKqHm79i8Ap6f2OBF6ocM6NwLZpnEcBvwaGp/fyWeDvadz3pLHem57z58w1PgZMTO9nNh2+Ra7lbl/gtYi4CCAilpN8YtX9h/EfgI3Tn/Gvkrwv/k3SjZLWlnSNpL9JeqB7hFjSrpL+pKRk6XWSRko6guTTr190v68l7Q8cB3wwIuYA7wO+nk5VIyJujYjn0zhvp3rlpY8BK97Pks6Q9IikPwJvy7S/VdLv07j+LGmbTPvtku6S9N9KFmYTEX+PiEfTx0+RrGXp/uTvz8D+kjyd0sxaiqug2ICWJh9LIyIkHQ0cExFOjq2tSPoCMCYiTi5r/ytJcvwipZtafI200o+kDwMHRsTxad/6JKPGfyKpJrQ4TcrfHxGflnQTaTWjPsR5KrBNRPxbhb7Hge0j4iUlC54vBvYgmU52D3B+Gu8NwAkR8aikPYBvRsS+SvYN+EVEXKpkN8mzI2KdstfYnWTgYbv0kzwkXQ9Mjoi7e3s/ZmZF8Qi4DXS7AvdKug/4HPAfBcdj1gzV1lXUs97ifpJR4G9JeldEvEAy4rw9cH36SdB/0s89AyS9F5jIyk/dyg2PiJfSxysWPKdTUioteL4X+BHJJ1uQLLi+LH38ywqvP5KkjOqnupPv1CLKptSYDWSSlqefQHV/TS46JiiJazNJd6SPn5C0OBNrV9lzxku6raxtsKSn00/dzpL0T3nTu1X4Yzsb0CLizySLRs3a2WxWrnUAQMlixi2AfwAbV3tiRPw9HXH+IPBNSX8AfgPMjoi9GhGcpB2AC4APRMSzVU5bJmm1THJcc8FzL19/PeAa4D8j4vay7jVJ9g4waxVLe/se6ImkwelmN/2RjWuP9LqfBMZFxElVnnMzsLmkroiYl7btDzwQEQuBL0mqNCW243kE3MyseDcAa0k6FkDJIuX/BS7uLptZjZKKJq9GxM9JynTuAjwCjJC0V3rO6pK2S59SbXF1teuPJlkg+omI+HuNUx8Btkwf92XBc8UF10oqwfwG+GlEdI+QZ21N8geMWUuTNE/S1yXdI+n+zPqItSVdmK6P+KukCWn7JyVdJulq4A+S1pI0XUm1oF+lo9jjJE2U9J3M6xwv6Zw+xLfK+o30D+7LSPYW6XY0cGm//jE6gBNwM7OCpYusPwQcKelRkoXDr5HsGtuTdwB3plM6zgD+J92g6gjgW0oWFt9LMvUDqiyuruG/gA2B89LnVJs7fg0wPr2fvix4/iKVF1x/hGS/gE9mPgbfCUDSJiSjdgvruA+zgWJo2RSUbPL6TLpZ3g9ZWRDhDGBmuufHe4GzJHXvaL0XcFxE7EsyTfP5TMWiXdNzpgGHSlo9Pf4UcFEf4q62Yd2lpH80SxpC8mncr/tw/Y7iRZhmZtZv6Rztn0bEAT2eXPn5vV5wLelk4MWI+ElfXtOsCJJeLl9gnLbPA/aJiAXpAuUzI2L/9I/eNUn2uAAYTlI2dA+SEqGfSp9/JcnO0Temx/cAkyJilqQfA9cCDwE/S5P5HuPqnoICTAYWk3zS1W1I954Z6cDBwcDbgU9HxKGZa3yNdNF4/f9K7c9zwM3MrN8iYqGkH0tar4+1wHcl2alXwBLg03U8ZwnJwkyzdvF6+j27IZ2AD0dENvklTdKz86trbXZ3Acknag/Tt9HvntZvTCMZBX87nn5SF09BMTOzhoiI6X3diCci/hwRO6abDb07rUne03MuasDCM7OB7jrg8+kfp0jaucp5fyGZsoWkbUmmpwEQEXeQLOr+KH1IkOvYsO5S4OMkexrM6O31O5ETcDMzM7P8lM8Bn9LD+d8AVgfuk/RAelzJeSSLr+8jKRd6H6Wb100HbslsrNVbVTesi4gHSfYfmBkRrnpSB88BNzMzM2txafWk1SPiNUlvJamutHW6KBslm119JyJuqPL8inPTGxDX1/Ac8FV4BNzMzMys9a0F/CUdof4N8NmIeEPSMEl/J1nkXDH5Tr2Yjsg3bGMrSWeRTE3xqHgZj4CbmZmZmeXII+BmZmZmZjlyAm5mZmZmliMn4GZmZmZmOeq4jXg22mij6OrqKjoMs6a6++67n4mIEUXH0Sx+H1snaPf3sVkn67gEvKuri1mzZhUdhllTSXq86Biaye9j6wTt/j4262SegmJmDSXpQkmL0g0jsu2fl/SIpNmSvp1pP13SnLTv/flHbGZmlq+OGwE3s6a7GPg+8NPuBknvJdk1bYeIeF3Sxmn7tsDRwHbAZsAfJW0dEctzj9rMzCwnHgE3s4aKiJuB58qaPwtMiYjX03MWpe0TgGkR8XpEzAXmALvnFqyZmVkBnICbWR62Bt4l6Q5Jf5K0W9o+Cngyc978tM3MzKxteQqKmeVhMLABsCewGzBd0paAKpxbcXteSZOASQCjR49uUphmZmbN5xFwM8vDfOCKSNwJvAlslLZvkTlvc+CpSheIiKkRMS4ixo0Y4cpsZmbWupyAm1kergT2BZC0NbAG8AwwAzha0hBJY4CxwJ1FBWlmZpaHjp+Css+UmSxYshSAUcOGcsvkfQuOyKy1SboUGA9sJGk+8FXgQuDCtDThG8BxERHAbEnTgQeBZcCJRVRAOepHtwHwq8/slfdLm5lZB+q4BPzhf75E1+RrVhyPGjaUeVMOAihpN7O+iYhjqnR9vMr5ZwJnNi8iMzOzgaXjEvB/LX9zRcJtZmZmZpY3zwE3MzMzM8uRE3AzMzMzsxw5ATczMzMzy5ETcDMzMzOzHDkBNzMzMzPLkRNwMzMzM7McOQE3MzMzM8uRE3AzMzMzsxy1dAIu6TBJP5Z0laT3FR2PmZmZmVlPBlwCLulCSYskPVDWfqCkRyTNkTQZICKujIjjgU8CRxUQrpmZmZlZrwy4BBy4GDgw2yBpEPAD4APAtsAxkrbNnPKfab+ZmZmZ2YA24BLwiLgZeK6seXdgTkQ8FhFvANOACUp8C/hdRNyTd6xmZmZmZr014BLwKkYBT2aO56dtnwf2B46QdEK1J0uaJGmWpFnNDdPMzMzMrLbBRQdQJ1Voi4j4P+D/enpyREwFpgIMGTk2qp03athQuiZfU3J8y+R9ex+tmZmZmVkVrZKAzwe2yBxvDjzV6BcpT7azybiZmZmZWSO0SgJ+FzBW0hhgAXA08NFiQzKzVnbUj25b8fjBhS+u0varz+yVe0xmZtYZBtwccEmXArcBb5M0X9LEiFgGnARcBzwETI+I2b287iGSpjY+YjMzMzOz+g24EfCIOKZK+7XAtf247tXA1UNGjj2+r9cws9bTPapdPqKdPa52jpmZWTMMuBFwMzMzM7N25gTczMzMzCxHTsDNzMzMzHLUMQm4F2Ga5UPShZIWSXqgQt+pkkLSRpm20yXNkfSIpPfnG62ZmVn+BtwizGbpyyLM8o15KvV7ox6zVVwMfB/4abZR0hbAAcATmbZtScqKbgdsBvxR0tYRsTy3aM3MzHLWMQl4X/SUXHujHrNVRcTNkroqdH0H+DJwVaZtAjAtIl4H5kqaA+xOUorUzMysLXXMFBQzK46kQ4EFEfG3sq5RwJOZ4/lpm5mZWdvyCLiZNZWktYAzgPdV6q7QFlWuMwmYBDB69OiGxWdmZpY3J+ANss+UmSxYsnTFseeHm63wVmAM8DdJAJsD90janWTEe4vMuZsDT1W6SERMBaYCjBs3rmKSbmZm1go6JgGXdAhwyBqbbtWU6y9YspR5Uw5acez54WaJiLgf2Lj7WNI8YFxEPCNpBvBLSeeQLMIcC9xZSKBmZmY56Zg54BFxdURMKjoOs3Yn6VKSRZRvkzRf0sRq50bEbGA68CDwe+BEV0AxM7N21zEj4M2QLVM4atjQgqMxGxgi4pge+rvKjs8EzmxmTGZmZgOJE/B+8BxvMzMzM+utjpmCYmZmZmY2EDgBNzMzMzPLUcdMQWl2FRQzGziO+tHKjTQfXPjiKm0Av/rMXrnGZGZm1q1jEvCIuBq4esjIsccXHYuZDSxOxs3MLE8dk4DnLVshpfvYizbN8pFNqLtHvp1km5nZQOEEvEnKk21vzGNmZmZm4EWYZmZmZma58gh4i9lnykwWLFm64rg3U1v681wzMzMzawwn4C0gmziPGjaUeVMOWtHXm6ktC5Ys7fNzzczMzKwxnIC3gPLE2czMzMxaV8ck4AO5Drinhpi1PldbMTOzenVMAj6Q64B7aoiZmZlZ5+iYBLxdud64mZmZWWtxAp6TbKLcyCTZ9cbNzMzMWosT8JxkE+WekuRKo9pmZmZm1h6cgBegpwTbU0jMzMzM2pcT8AI4wTYzMzPrXN6K3szMzMwsR07AzczMzMxy5ATczMzMzCxHHTMHfCDvhDlQZHfkdD1xMzMzs+bomAR8IO+E2Uj9qTee3ZHT9cStryRdCBwMLIqI7dO2s4BDgDeAfwCfioglad/pwERgOfCFiLiukfF4a3gzMxtoOiYB7xSuN24DwMXA94GfZtquB06PiGWSvgWcDpwmaVvgaGA7YDPgj5K2jojlOcdsZmaWGyfgbcz1xq0IEXGzpK6ytj9kDm8HjkgfTwCmRcTrwFxJc4DdgdvyiNXMzKwITsDbmBNsG6A+DfwqfTyKJCHvNj9tMzMza1tOwM0sN5LOAJYBv+huqnBaVHnuJGASwOjRo5sSX28d9aOVA/UPLnxxlTbPPzczs0qcgFtdshVSwFVSrPckHUeyOHO/iOhOsucDW2RO2xx4qtLzI2IqMBVg3LhxFZN0MzOzVuAE3OqSrZACrpJivSPpQOA04D0R8WqmawbwS0nnkCzCHAvcWUCIfZId4e4e+faot5mZ9cQJuJk1lKRLgfHARpLmA18lqXoyBLheEsDtEXFCRMyWNB14kGRqyomugGJmZu3OCbhVVFSJQk91aX0RcUyF5p/UOP9M4MzmRWRmZjawOAG3iopKej3VxczMzNrdakUHYGZmZmbWSTpmBFzSIcAha2y6VdGhtKXs1BFPGzFbVT2LNL2Q08ysM3RMAh4RVwNXDxk59viiY2lH2akj+0yZucr8cSfkZmZmZomOScAtP+XJtudxm5mZma3kOeBmZi3iqB/dVrLTZl/PMTOzYnkE3PokrzKFlV7H01nMzMyslTkBtz7JKwn2dBYzMzNrN56CYmZmZmaWIyfgZmZmZmY5cgJuZtYDL2w0M7NGyiUBl7SBpB3yeC0zazy/h83MzBqnaQm4pJskrSdpOPA34CJJ5zTr9cyssfweNjMza45mVkFZPyJelPRvwEUR8VVJ9zXx9awFZbewh+aVM7Q+8Xu4F7x9vJmZ1auZCfhgSSOBjwBnNPF1rIVlt7C3AcfvYTMzsyZoZgL+deA64C8RcZekLYFHm/h61iKyo94e8R7Q/B42MzNrgmYm4AsjYsWirYh4zPNHDTzq3UL8HjYzM2uCZibg3wN2qaPN2lxe29Zbw/k93E/Z0oUPLnxxlbZylc7x3HIzs/bT8ARc0l7A3sAISadkutYDBjX69Wzgy2vbemsMv4fNzMyaqxkj4GsA66TXXjfT/iJwRBNery6SDgEOWWPTrYoKwaxVDMj3cCvKjl53j2rXGtGu5xwzM2t9DU/AI+JPwJ8kXRwRjzf6+n0VEVcDVw8ZOfb4omMxG8gG6nu4U3kai5lZ+2nmHPAhkqYCXdnXiQjPRzBrDX4Pm5mZNUEzE/DLgPOBC4DlTXwdM2uOPr2HJV0IHAwsiojt07bhwK9Ikvl5wEci4vm073RgYvoaX4iI6xp3C63P01jMzNpPMxPwZRHxwyZe38yaq6/v4YuB7wM/zbRNBm6IiCmSJqfHp0naFjga2A7YDPijpK0jwn+0N4kTdDOz4q3WxGtfLelzkkZKGt791cTXM7PG6tN7OCJuBp4ra54AXJI+vgQ4LNM+LSJej4i5wBxg98aEb2ZmNjA1cwT8uPT7lzJtAWzZxNc0s8Zp5Ht4k4hYCBARCyVtnLaPAm7PnDc/bStcPYsfPYpsZmZ90bQEPCLGNOvaZr21z5SZLFiydMXxqGFDXZ+8Bzm9h1XppSueKE0CJgGMHj26mTGZmZk1VdMScEnHVmqPiJ9WajdrpgVLljJvykErjrM7c1plDX4PPy1pZDr6PRJYlLbPB7bInLc58FSV150KTAUYN25cxSS9kXq7+NHMzKxezZyCslvm8ZrAfsA9lC7MMrOBq5Hv4RkkU1qmpN+vyrT/UtI5JIswxwJ39jVgMzOzVtDMKSifzx5LWh/4WbNez8waq6/vYUmXAuOBjSTNB75KknhPlzQReAI4Mn2N2ZKmAw8Cy4ATXQHFzMzaXTNHwMu9SjK6ZWatqa73cEQcU6Vrvyrnnwmc2Y+4zMzMWkoz54BfzcrFVIOAtwPTm/V6ZtZYfg+bmZk1RzNHwM/OPF4GPB4R85v4embWWH4Pm5mZNUHTNuKJiD8BDwPrAhsAbzTrtcys8fwe7lxH/ei2VWqem5lZ4zRzCspHgLOAm0hq/X5P0pci4vJmvaa1v1HDhq4oIeha3s3l93Bj1VPC0GUOzcw6QzOnoJwB7BYRiwAkjQD+CPiXt/VZNuF2Le+m83vYzMysCZqZgK/W/Ys79SxNnPJiZg3n9/AA41F0M7P20MwE/PeSrgMuTY+PAq5t4utZh8lOR6nWX22KSvlzPZ2lIr+H20R2PveDC19cpc1Ju5lZvhqegEvaCtgkIr4k6XDgnSTzR28DftHo17PO1VPCXCs5L3+up7Os5PewmZlZczVjBPy7wFcAIuIK4AoASePSvkOa8Jpm1jjfxe/htpId4e4e+faot5lZcZoxn7MrIu4rb4yIWUBXE17PzBrL72EzM7MmasYI+Jo1+oY24fXMrLH8Hi7j0WIzM2ukZoyA3yXp+PJGSROBu5vwembWWH4Pm5mZNVEzRsC/CPxG0sdY+ct6HLAG8KEmvJ6ZNdYX8XvYzMysaRqegEfE08Dekt4LbJ82XxMRMxv9WpK2JNksZP2IOKLR17fO4bKEK+X5HjYzM+tETasDHhE3Ajf29nmSLgQOBhZFxPaZ9gOBc4FBwAURMSUiHgMmSvLOfNYvLku4qr6+h83MzKy2Zm7E01cXA98HftrdIGkQ8APgAGA+yRzVGRHxYCERmpm1keymPNDzZj0uZWhm1j8DblvpiLgZeK6seXdgTkQ8FhFvANOACbkHZ2ZmZmbWTwNxBLySUcCTmeP5wB6SNgTOBHaWdHpEfLPSkyVNAiYBrLHpVs2O1QaI7LzuUcM6snqeWV3KR7I9wm1m1lytkoCrQltExLPACT09OSKmAlMBhowcGw2OzQaoTl1EaWZmZgPbgJuCUsV8YIvM8ebAUwXFYmZmZmbWZ62SgN8FjJU0RtIawNHAjIJjMjMzMzPrtQGXgEu6FLgNeJuk+ZImRsQy4CTgOuAhYHpEzC4yTjPrPUknS5ot6QFJl0paU9JwSddLejT9vkHRcZqZmTXTgJsDHhHHVGm/Fri2r9eVdAhwiBdhmhVD0ijgC8C2EbFU0nSST7O2BW6IiCmSJgOTgdMKDLWteWGlmVnxBtwIeLNExNURManoOMw63GBgqKTBwFokazkmAJek/ZcAhxUTmpmZWT46JgE3s2JFxALgbOAJYCHwQkT8AdgkIham5ywENi4uSjMzs+ZzAm5muUjndk8AxgCbAWtL+ngvnj9J0ixJsxYvXtysMM3MzJpuwM0BbxbPAbdG2WfKTBYsWVqxb9SwoSX1x8vPLe/vMPsDcyNiMYCkK4C9gacljYyIhZJGAosqPTlbz3/cuHGu529mZi2rYxLwiLgauHrIyLHHFx2LtbYFS5Yyb8pBFfu6d96sdm55f4d5AthT0lrAUmA/YBbwCnAcMCX9flVhEVpV3btjAjy48MVV2ry408ysfh2TgJtZsSLiDkmXA/cAy4C/koxorwNMlzSRJEk/srgozczMms8JuJnlJiK+Cny1rPl1ktFwG8CyI9zdI9/VRr176jcz63ROwM3MrIQTZzOz5nIVFDMzMzOzHHXMCLiroFhfVapkYmZmZtZXHZOAuwqK9VWtqidmVhzPNTezVuUpKGZmZmZmOXICbmZmZmaWIyfgZmZmVRz1o9tKNhwyM2sEJ+BmZmZmZjlyAm5mZmZmlqOOqYLiMoSWh1HDhtI1+ZqSYzMzM7OsjknAXYbQ8nDL5H2LDsHMzMwGuI5JwM3MrHmyCxUfXPjiKm3get1mZt2cgJuZWa+0UyLtzXzMrAhOwM3MrN+yCayTWjOz2pyAm5lZ7vqapPc01cVJv5m1ApchNDMzMzPLUceMgLsMofVGtpxgXqUE95kykwVLlpbE4Koq1qmqjZB7qouZtYOOScBdhtB6o4jEd8GSpcybctCK42w9cTPLj6e5mFmzeQqKmZmZmVmOOmYE3MzMrB6NnubiqTJmVs4j4GZmZmZmOXICbmZmZmaWIyfgZpYbScMkXS7pYUkPSdpL0nBJ10t6NP2+QdFxmpmZNZPngJtZns4Ffh8RR0haA1gL+ApwQ0RMkTQZmAycVmSQ1r6y1UzAVU7MrBgeATezXEhaD3g38BOAiHgjIpYAE4BL0tMuAQ4rIj4zM7O8eATczPKyJbAYuEjSjsDdwL8Dm0TEQoCIWChp4wJjtAaoNoI8EOprl7+GK5SYWRE6JgH3Tpg2EGR32Ow+7uumP9mdM1tk18zBwC7A5yPiDknnkkw3qYukScAkgNGjRzcnQitUTwk69C5RdnJtZgNVxyTg3gnTBoLyJLk/u11md85skV0z5wPzI+KO9PhykgT8aUkj09HvkcCiSk+OiKnAVIBx48ZFHgFbY/VUX7s82TYza1cdk4CbWbEi4p+SnpT0toh4BNgPeDD9Og6Ykn6/qsAwrUC93QDHI9tm1qqcgJtZnj4P/CKtgPIY8CmSxeDTJU0EngCOLDA+MzOzpnMCbma5iYh7gXEVuvbLORSzphoIC07NbOByAm5mZlaFE2UzawYn4GZmZg3W2/nsZtZZvBGPmZmZmVmOnICbmZn1w1E/us0lFM2sV5yAm5mZmZnlyHPAzcysbfS2+ojnZZtZETwCbmZmZmaWo44ZAZd0CHDIGptuVXQoZiuMGjZ0xTbyo4YNrdrXfVy+lb1Zq2rWyLOrj5hZK+iYBDwirgauHjJy7PFFx2LWrVZCXd6XTcbN2p2TZjNrZ56CYmZmZmaWIyfgZmZmZmY56pgpKGZmZkXwdBozK+cE3MzMrBfKN92pp9yhmVmWp6CYmZmZmeXII+BmZma9UD667XKHZtZbHgE3MzMzM8uRE3AzMzMzsxx5CoqZmbUlTwkxs4HKI+BmZmZmZjlyAm5mZmZmliMn4GZmZmZmOXICbmZmZmaWIyfgZpYbSYMk/VXSb9Pj4ZKul/Ro+n2DomM0MzNrNifgZpanfwceyhxPBm6IiLHADemxmZlZW+uYMoSSDgEOWWPTrYoOxazhRg0bStfka4oOoyZJmwMHAWcCp6TNE4Dx6eNLgJuA0/KOzaw/XO7QzHqrYxLwiLgauHrIyLHHFx2LWaPdMnnfkmN9q6BAavsu8GVg3UzbJhGxECAiFkrauIjAzMzM8uQpKGbWdJIOBhZFxN39uMYkSbMkzVq8eHEDozMzM8uXE3Azy8M+wKGS5gHTgH0l/Rx4WtJIgPT7omoXiIipETEuIsaNGDEij5jNzMyawgm4mTVdRJweEZtHRBdwNDAzIj4OzACOS087DriqoBDNzMxy4wTczIo0BThA0qPAAemxmZlZW+uYRZhmNjBExE0k1U6IiGeB/YqMx8zMLG8eATczMzMzy5ETcDMzMzOzHCkiio4hV5IWA4836HLrAy806Nxq/ZXay9tqHWcfbwQ8U2e8PfG9N+bcZt37WyKibUuF1PE+buR/77w59mIMxNjb+n1s1tEiwl99/AKmNurcav2V2svbah2XPZ7le++Me+/0r0b+93bsjt1f/vKXvxr95Sko/XN1A8+t1l+pvbyt1nFvYuwN33tjzm3WvZuZmdkA1XFTUDqZpFkRMa7oOIrQyffeiVr5v7djL0Yrx25mrccj4J1latEBFKiT770TtfJ/b8dejFaO3cxajEfAzczMzMxy5BFwMzMzM7McOQE3s7Yi6UBJj0iaI2ly0fEASLpQ0iJJD2Tahku6XtKj6fcNMn2np/E/Iun9mfZdJd2f9v2fJDU57i0k3SjpIUmzJf17C8W+pqQ7Jf0tjf3rrRK7mbU/J+Bm1jYkDQJ+AHwA2BY4RtK2xUYFwMXAgWVtk4EbImIscEN6TBrv0cB26XPOS+8L4IfAJGBs+lV+zUZbBvxHRLwd2BM4MY2vFWJ/Hdg3InYEdgIOlLRni8RuZm3OCXgHk7S2pEsk/VjSx4qOJ0+StpT0E0mXFx2LNdTuwJyIeCwi3gCmARMKjomIuBl4rqx5AnBJ+vgS4LBM+7SIeD0i5gJzgN0ljQTWi4jbIlm889PMc5oV98KIuCd9/BLwEDCqRWKPiHg5PVw9/YpWiN3M2p8T8DZT6aPutL3Sx/KHA5dHxPHAobkH22C9ufc0QZtYTKTWRKOAJzPH89O2gWiTiFgISaILbJy2V7uHUenj8vZcSOoCdgbuoEVilzRI0r3AIuD6iGiZ2M2svTkBbz8XU/bxaI2P5Tdn5S+c5TnG2CwXU/+9W3uqNDe31Uo9VbuHwu5N0jrAr4EvRsSLtU6t0FZY7BGxPCJ2Ivl/3e6Stq9x+oCK3czamxPwNlPlo+5qH8vPJ/nFBG3ws9DLe7f2NB/YInO8OfBUQbH05Ol0egPp90Vpe7V7yL5fs+1NJWl1kuT7FxFxRdrcErF3i4glwE0kf6C3VOxm1p5aPumyulT7aPUK4MOSfkj7bmNe8d4lbSjpfGBnSacXE5o1wV3AWEljJK1BsqhuRsExVTMDOC59fBxwVab9aElDJI0hWfR3Zzpd4iVJe6ZVOI7NPKcp0tf5CfBQRJzTYrGPkDQsfTwU2B94uBViN7P2N7joACwXFT9CjYhXgE/lHUzOqt37s8AJeQdjzRURyySdBFwHDAIujIjZBYeFpEuB8cBGkuYDXwWmANMlTQSeAI4EiIjZkqYDD5JUITkxIrqniH2WZKrVUOB36Vcz7QN8Arg/nUsN8JUWiX0kcEk6DW01YHpE/FbSbS0Qu5m1Oe+E2YbSxVK/jYjt0+O9gK9FxPvT49MBIuKbhQXZJJ1872ZmZtYaPAWlM7TSx/KN1sn3bmZmZgOQE/A2k37UfRvwNknzJU2MiGVA98fyD5F8FFv4x/KN1sn3bmZmZq3DU1DMzMzMzHLkEXAzMzMzsxw5ATczMzMzy5ETcDMzMzOzHDkBNzNrYemmUvemX/+UtCB9/LKk85r0ml+UdGwDrzdN0thGXc/MbKDzIkwzszYh6WvAyxFxdhNfYzBwD7BLWmWoEdd8D/DxiDi+EdczMxvoPAJuZtaGJI2X9Nv08dckXSLpD5LmSTpc0rcl3S/p95JWT8/bVdKfJN0t6TpJIytcel/gnu7kW9IXJD0o6T5J09K2tSVdKOkuSX+VNCFtHyTp7PR175P0+fSafwb2T5N7M7O25wS8Q0hanvmY+l5Jk4uOCUri2kzSHenjJyQtzsTaVfac8el20tm2wZKeljRS0lnpR/Gn5nozZgPbW4GDgAnAz4EbI+IdwFLgoDQJ/x5wRETsClwInFnhOvsAd2eOJwM7R8QOwAlp2xnAzIjYDXgvcJaktYFJwJjM+b8AiIg3gTnAjg28XzOzAcujDZ1jaUTs1MgLShrcgI+gs3HtkV73k8C4iDipynNuBjaX1BUR89K2/YEHImIh8CVJr/QzLrN287uI+Jek+4FBwO/T9vuBLuBtwPbA9ZJIz1lY4TojSTa16nYf8AtJVwJXpm3vAw7N/BG8JjCa5H16fvf/NyLiucx1FgGbUZrcm5m1JSfgHU7SPOAS4BBgdeDIiHg4Ha36HvAOkp+Tr0XEVWlyfBDJL9S1JR0MXAxsQ/JLuQs4kWQka/uIODl9neOBt0fEKb2M763AD4ARwKvA8Wl8lwFHAd9KTz0auLQP/wRmneJ1SEabJf0rVi4AepPkPS5gdkTs1cN1lpK8/7sdBLwbOBT4f5K2S6/14Yh4JPtEJZl9tYVHa6bXNjNre56C0jmGlk1BOSrT90xE7AL8EOgesar2ETLAXsBxEbEv8Dng+fTj5G8Au6bnTCMZAVs9Pf4UcFEf4p4KfD79SPxUoLuqw6UkSTeShgAfBH7dh+ubWeIRYISkvQAkrZ4m0+UeArZKz1kN2CIibgS+DAwD1gGuAz6fJtxI2jl97h+AE7rneksanrnu1sDsRt+UmdlA5BHwzlFrCsoV6fe7gcPTx9U+Qga4PvPR8TuBcwEi4gFJ96WPX5E0EzhY0kPA6hFxf28ClrQOsDdwWfp7HGBIev27JK0j6W3A24HbI+L53lzfzFaKiDckHQH8n6T1SX4/fJdVk+LfAT9LHw8Cfp6eL+A7EbFE0jfS596XJuHzgIOBC0gS7fsk/Qv4MfB9SZuQ/D+q0pQXM7O24wTcIP1oGljOyp+Jah8h7wFk51eL6i4AvgI8TN9Gv1cDltT4w2EaySj42/H0EzMi4muZxzcBN5W3p8frVHnOvSTTSWq9xuOSnpU0NiIeJfkjvPycpcBnKrQvA05Jv7I+Cvyo1uuambUTT0Gxaqp9hFzuL8BH0nO2JZkzDkBE3AFsQfLLtdcJckS8CMyVdGR6fUnKVkm4FPg4SVm0Gb29vpn12WSSxZiNsoRkLYqZWUdwAt45yueAT+nh/G+QLMq8T9ID6XEl55HMG70POI2kIsILmf7pwC39mB7yMWCipL+RfBQ+obsjIh4kWZg5MyJc9cQsJxHxSETc3MDrXdSoTX3MzFqBd8K0fpE0iGR+92tpxZIbgK0j4o20/7ck80JvqPL8l7Mfhzcwrq/R5B0BzczMzPrCI+DWX2sBf0lHqH8DfDZdzDVM0t9JFlZVTL5TL3ZvxNOogCSdRTI1xaPiZmZmNuB4BNzMzMzMLEceATczMzMzy5ETcDMzMzOzHDkBNzMzMzPLkRNwMzMzM7McOQE3MzMzM8uRE3AzMzMzsxz9/0UsD7e09OXvAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "src_events.peek()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the right figure of the bottom panel, it is shown the source lightcurve that follows a decay trend as expected." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extended source using a template\n", "\n", "The event sampler can also work with a template model.\n", "Here we use the interstellar emission model map of the Fermi 3FHL, which can be found in the GAMMAPY data repository.\n", "\n", "We proceed following the same steps showed above and we finally have a look at the event's properties:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:07.510804Z", "iopub.status.busy": "2021-11-22T21:04:07.510460Z", "iopub.status.idle": "2021-11-22T21:04:07.544319Z", "shell.execute_reply": "2021-11-22T21:04:07.544510Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Template file already exits, and overwrite is False\n" ] } ], "source": [ "template_model = TemplateSpatialModel.read(\n", " \"$GAMMAPY_DATA/fermi-3fhl-gc/gll_iem_v06_gc.fits.gz\", normalize=False\n", ")\n", "# we make the model brighter artificially so that it becomes visible over the background\n", "diffuse = SkyModel(\n", " spectral_model=PowerLawNormSpectralModel(norm=5),\n", " spatial_model=template_model,\n", " name=\"template-model\",\n", ")\n", "\n", "bkg_model = FoVBackgroundModel(dataset_name=\"my-dataset\")\n", "\n", "models_diffuse = Models([diffuse, bkg_model])\n", "\n", "file_model = \"./event_sampling/diffuse.yaml\"\n", "models_diffuse.write(file_model, overwrite=True)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:07.546766Z", "iopub.status.busy": "2021-11-22T21:04:07.546440Z", "iopub.status.idle": "2021-11-22T21:04:07.547717Z", "shell.execute_reply": "2021-11-22T21:04:07.547892Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DatasetModels\n", "\n", "Component 0: SkyModel\n", "\n", " Name : template-model\n", " Datasets names : None\n", " Spectral model type : PowerLawNormSpectralModel\n", " Spatial model type : TemplateSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " norm : 5.000 +/- 0.00 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", "\n", "Component 1: FoVBackgroundModel\n", "\n", " Name : my-dataset-bkg\n", " Datasets names : ['my-dataset']\n", " Spectral model type : PowerLawNormSpectralModel\n", " Parameters:\n", " norm : 1.000 +/- 0.00 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", "\n", "\n" ] } ], "source": [ "dataset.models = models_diffuse\n", "print(dataset.models)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:07.552821Z", "iopub.status.busy": "2021-11-22T21:04:07.549818Z", "iopub.status.idle": "2021-11-22T21:04:11.961166Z", "shell.execute_reply": "2021-11-22T21:04:11.961405Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.91 s, sys: 764 ms, total: 4.67 s\n", "Wall time: 4.41 s\n" ] } ], "source": [ "%%time\n", "sampler = MapDatasetEventSampler(random_state=0)\n", "events = sampler.run(dataset, observation)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:11.963388Z", "iopub.status.busy": "2021-11-22T21:04:11.963106Z", "iopub.status.idle": "2021-11-22T21:04:12.365181Z", "shell.execute_reply": "2021-11-22T21:04:12.365450Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "events.select_offset([0, 1] * u.deg).peek()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulate multiple event lists\n", "\n", "In some user case, you may want to sample events from a number of observations. \n", "In this section, we show how to simulate a set of event lists. For simplicity we consider only one point-like source, observed three times for 1 hr and assuming the same pointing position.\n", "\n", "Let's firstly define the time start and the livetime of each observation:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:12.367457Z", "iopub.status.busy": "2021-11-22T21:04:12.367165Z", "iopub.status.idle": "2021-11-22T21:04:12.368280Z", "shell.execute_reply": "2021-11-22T21:04:12.368504Z" } }, "outputs": [], "source": [ "tstarts = [1, 5, 7] * u.hr\n", "livetimes = [1, 1, 1] * u.hr" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:12.369814Z", "iopub.status.busy": "2021-11-22T21:04:12.369528Z", "iopub.status.idle": "2021-11-22T21:04:21.012378Z", "shell.execute_reply": "2021-11-22T21:04:21.012615Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 7.48 s, sys: 1.46 s, total: 8.94 s\n", "Wall time: 8.64 s\n" ] } ], "source": [ "%%time\n", "for idx, tstart in enumerate(tstarts):\n", "\n", " observation = Observation.create(\n", " obs_id=idx,\n", " pointing=pointing,\n", " tstart=tstart,\n", " livetime=livetimes[idx],\n", " irfs=irfs,\n", " )\n", "\n", " dataset = maker.run(empty, observation)\n", " dataset.models = models\n", "\n", " sampler = MapDatasetEventSampler(random_state=idx)\n", " events = sampler.run(dataset, observation)\n", " events.table.write(\n", " f\"./event_sampling/events_{idx:04d}.fits\", overwrite=True\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can now load the event list with `Datastore.from_events_files()` and make your own analysis following the instructions in the [`analysis_2`](analysis_2.ipynb) tutorial." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:21.014740Z", "iopub.status.busy": "2021-11-22T21:04:21.014452Z", "iopub.status.idle": "2021-11-22T21:04:21.024525Z", "shell.execute_reply": "2021-11-22T21:04:21.024707Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "\n", "OBS_ID TSTART TSTOP ONTIME LIVETIME ... OBJECT CALDB IRF EVENTS_FILENAME EVENT_COUNT\n", " s s s s ... \n", "int64 float64 float64 float64 float64 ... str9 str13 str13 str31 int64 \n", "------ ----------------- ----------------- ------- -------- ... --------- ------------- ------------- ------------------------------- -----------\n", " 2 25199.99999979045 28800.00000020955 3600.0 3600.0 ... point-pwl NOT AVAILABLE NOT AVAILABLE event_sampling/events_0002.fits 15133\n", " 0 3599.999999790452 7200.000000209548 3600.0 3600.0 ... point-pwl NOT AVAILABLE NOT AVAILABLE event_sampling/events_0000.fits 15627\n", " 1 18000.00000020955 21600.0 3600.0 3600.0 ... point-pwl NOT AVAILABLE NOT AVAILABLE event_sampling/events_0001.fits 15226" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = Path(\"./event_sampling/\")\n", "paths = list(path.rglob(\"events*.fits\"))\n", "data_store = DataStore.from_events_files(paths)\n", "data_store.obs_table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For completeness, `data_store` is a `~gammapy.data.Datastore` object. You can find more information about it [here](https://docs.gammapy.org/dev/tutorials/data/cta.html#Datastore)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "- Try to sample events for an extended source (e.g. a radial gaussian morphology);\n", "- Change the spatial model and the spectrum of the simulated Sky model;\n", "- Include a temporal model in the simulation" ] } ], "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": "3.9.0" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 4 }