{ "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/v0.17?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/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](https://docs.gammapy.org/dev/notebooks/analysis_2.html#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", " PointSpatialModel,\n", " GaussianSpatialModel,\n", " SkyDiffuseCube,\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": [ "IRF_FILE = (\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": [], "source": [ "irfs = load_cta_irfs(IRF_FILE)\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", "WCS_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 1.49 s, sys: 510 ms, total: 2 s\n", "Wall time: 2.03 s\n" ] } ], "source": [ "%%time\n", "empty = MapDataset.create(\n", " WCS_GEOM, energy_axis_true=ENERGY_AXIS_TRUE, migra_axis=MIGRA_AXIS\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](https://docs.gammapy.org/dev/notebooks/models.html)) 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", "sky_model_pntpwl = SkyModel(\n", " spectral_model=spectral_model_pwl,\n", " spatial_model=spatial_model_point,\n", " name=\"point-pwl\",\n", ")\n", "\n", "models_pntpwl = Models([sky_model_pntpwl])\n", "\n", "file_model = \"./event_sampling/point-pwl.yaml\"\n", "models_pntpwl.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": [ "Models\n", "\n", "Component 0: BackgroundModel\n", "\n", " Name : 5x1qxXyj-bkg\n", " Datasets names : ['5x1qxXyj']\n", " Parameters:\n", " norm : 1.000 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", "\n", "Component 1: SkyModel\n", "\n", " Name : point-pwl\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : PointSpatialModel\n", " Temporal model type : None\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", "\n" ] } ], "source": [ "dataset.models.extend(models_pntpwl)\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 1.08 s, sys: 96.6 ms, total: 1.17 s\n", "Wall time: 1.18 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 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'] == 2).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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "events.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[1].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", "text/plain": [ "
" ] }, "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](https://docs.gammapy.org/dev/notebooks/analysis_2.html#Fit-the-model) and [here](https://docs.gammapy.org/dev/notebooks/simulate_3d)). 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": [ "dataset = MapDataset.read(\"./event_sampling/dataset.fits\")\n", "models_sim_point = Models.read(\"./event_sampling/point-pwl.yaml\")\n", "\n", "counts = Map.from_geom(WCS_GEOM)\n", "counts.fill_events(events)\n", "\n", "dataset.counts = counts\n", "dataset.models.extend(models_sim_point)" ] }, { "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 = 7.137E+04 | Ncalls=100 (100 total) |\n", "| EDM = 2.26E-05 (Goal: 1E-05) | 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 : 100\n", "\ttotal stat : 71371.11\n", "\n", "CPU times: user 13.2 s, sys: 601 ms, total: 13.8 s\n", "Wall time: 13.9 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
norm9.894e-010.000e+00nanFalse9.154e-03
tilt0.000e+00nannanTrue0.000e+00
reference1.000e+00TeVnannanTrue0.000e+00
index1.998e+00nannanFalse6.349e-02
amplitude1.042e-12cm-2 s-1 TeV-1nannanFalse9.926e-14
reference1.000e+00TeVnannanTrue0.000e+00
lon_01.272e-03degnannanFalse3.447e-03
lat_05.022e-01deg-9.000e+019.000e+01False3.424e-03
" ], "text/plain": [ "\n", " name value unit min max frozen error \n", " str9 float64 str14 float64 float64 bool float64 \n", "--------- --------- -------------- ---------- --------- ------ ---------\n", " norm 9.894e-01 0.000e+00 nan False 9.154e-03\n", " tilt 0.000e+00 nan nan True 0.000e+00\n", "reference 1.000e+00 TeV nan nan True 0.000e+00\n", " index 1.998e+00 nan nan False 6.349e-02\n", "amplitude 1.042e-12 cm-2 s-1 TeV-1 nan nan False 9.926e-14\n", "reference 1.000e+00 TeV nan nan True 0.000e+00\n", " lon_0 1.272e-03 deg nan nan False 3.447e-03\n", " lat_0 5.022e-01 deg -9.000e+01 9.000e+01 False 3.424e-03" ] }, "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": [], "source": [ "diffuse = SkyDiffuseCube.read(\n", " \"$GAMMAPY_DATA/fermi-3fhl-gc/gll_iem_v06_gc.fits.gz\"\n", ")\n", "models_diffuse = Models([diffuse])\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": [ "Models\n", "\n", "Component 0: BackgroundModel\n", "\n", " Name : avWdQV1s-bkg\n", " Datasets names : ['avWdQV1s']\n", " Parameters:\n", " norm : 1.000 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", "\n", "Component 1: SkyDiffuseCube\n", "\n", " Name : gll_iem_v06_gc.fits\n", " Datasets names : None\n", " Parameters:\n", " norm : 1.000 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", "\n", "\n" ] } ], "source": [ "dataset = MapDataset.read(\"./event_sampling/dataset.fits\")\n", "dataset.models.extend(models_diffuse)\n", "print(dataset.models)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.66 s, sys: 494 ms, total: 3.15 s\n", "Wall time: 3.16 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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "events.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": [], "source": [ "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.extend(models_pntpwl)\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" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 4 }