{ "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/v0.18.2?urlpath=lab/tree/event_sampling.ipynb)\n", "- You can contribute with your own notebooks in this\n", "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/docs/tutorials).\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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prerequisites " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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 a `gammapy.data.Observations` and to create a `~gammapy.datasets.Dataset` object (for more info on `~gammapy.datasets.Dataset` objects, please visit this [link](analysis_2.ipynb#Preparing-reduced-datasets-geometry)). 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 two examples for sampling events: one chosing a point-like source and one using a template map. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "As usual, let's start with some general imports...\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from pathlib import Path\n", "import numpy as np\n", "import copy\n", "import astropy.units as u\n", "from astropy.io import fits\n", "from astropy.coordinates import SkyCoord\n", "from gammapy.data import DataStore, GTI, Observation\n", "from gammapy.datasets import MapDataset, MapDatasetEventSampler\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", " GaussianSpatialModel,\n", " TemplateSpatialModel,\n", " FoVBackgroundModel,\n", ")\n", "from regions import CircleSkyRegion" ] }, { "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": {}, "outputs": [], "source": [ "filename = (\n", " \"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\"\n", ")\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": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Invalid unit found in background table! Assuming (s-1 MeV-1 sr-1)\n" ] } ], "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: 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 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": {}, "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": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.22 s, sys: 334 ms, total: 1.56 s\n", "Wall time: 1.62 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](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": {}, "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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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": {}, "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 \n", " amplitude : 1.00e-12 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.000 deg \n", " lat_0 : 0.500 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 \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": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 168 ms, sys: 30 ms, total: 198 ms\n", "Wall time: 200 ms\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 and time of arrivals of the source and background events. 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": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Source events: 178\n", "Background events: 11804\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": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "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": {}, "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": {}, "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": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "counts = Map.create(\n", " frame=\"galactic\", skydir=(0, 0.0), binsz=0.02, npix=(100, 100)\n", ")\n", "counts.fill_events(events)\n", "counts.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](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." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "models_fit = Models.read(\"./event_sampling/point-pwl.yaml\")\n", "\n", "counts = Map.from_geom(geom)\n", "counts.fill_events(events)\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": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------------------------\n", "| FCN = 7.136e+04 | Ncalls=102 (102 total) |\n", "| EDM = 2.3e-05 (Goal: 0.0002) | up = 1.0 |\n", "------------------------------------------------------------------\n", "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n", "------------------------------------------------------------------\n", "| True | True | False | False |\n", "------------------------------------------------------------------\n", "| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n", "------------------------------------------------------------------\n", "| False | True | True | True | False |\n", "------------------------------------------------------------------\n", "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : minuit\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 102\n", "\ttotal stat : 71360.01\n", "\n", "CPU times: user 6.8 s, sys: 951 ms, total: 7.75 s\n", "Wall time: 8.09 s\n" ] } ], "source": [ "%%time\n", "fit = Fit([dataset])\n", "result = fit.run(optimize_opts={\"print_level\": 1})\n", "print(result)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=8\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
namevalueunitminmaxfrozenerror
str9float64str14float64float64boolfloat64
index1.9983e+00nannanFalse6.315e-02
amplitude1.0429e-12cm-2 s-1 TeV-1nannanFalse9.895e-14
reference1.0000e+00TeVnannanTrue0.000e+00
lon_01.3912e-03degnannanFalse3.396e-03
lat_05.0228e-01deg-9.000e+019.000e+01False3.295e-03
norm9.8943e-01nannanFalse9.154e-03
tilt0.0000e+00nannanTrue0.000e+00
reference1.0000e+00TeVnannanTrue0.000e+00
" ], "text/plain": [ "\n", " name value unit min max frozen error \n", " str9 float64 str14 float64 float64 bool float64 \n", "--------- ---------- -------------- ---------- --------- ------ ---------\n", " index 1.9983e+00 nan nan False 6.315e-02\n", "amplitude 1.0429e-12 cm-2 s-1 TeV-1 nan nan False 9.895e-14\n", "reference 1.0000e+00 TeV nan nan True 0.000e+00\n", " lon_0 1.3912e-03 deg nan nan False 3.396e-03\n", " lat_0 5.0228e-01 deg -9.000e+01 9.000e+01 False 3.295e-03\n", " norm 9.8943e-01 nan nan False 9.154e-03\n", " tilt 0.0000e+00 nan nan True 0.000e+00\n", "reference 1.0000e+00 TeV nan nan True 0.000e+00" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.parameters.to_table()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results looks great!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extended source using a template\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": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: FITSFixedWarning: 'datfix' made the change 'Set DATE-REF to '1858-11-17' from MJD-REF'. [astropy.wcs.wcs]\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": 19, "metadata": {}, "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 \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 \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": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.59 s, sys: 788 ms, total: 4.38 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": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "events.select_offset([0, 1] * u.deg).peek()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulate mutiple event list\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": 22, "metadata": {}, "outputs": [], "source": [ "tstarts = [1, 5, 7] * u.hr\n", "livetimes = [1, 1, 1] * u.hr" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6.06 s, sys: 1.3 s, total: 7.36 s\n", "Wall time: 7.03 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": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "ObservationTable length=3\n", "
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" ], "text/plain": [ "\n", "OBS_ID RA_PNT ... EVENTS_FILENAME EVENT_COUNT\n", " deg ... \n", "int64 float64 ... str31 int64 \n", "------ ----------------- ... ------------------------------- -----------\n", " 2 266.4049882865447 ... event_sampling/events_0002.fits 12232\n", " 0 266.4049882865447 ... event_sampling/events_0000.fits 11982\n", " 1 266.4049882865447 ... event_sampling/events_0001.fits 12171" ] }, "execution_count": 24, "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": [ "" ] }, { "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": "3.7.0" }, "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 }