{ "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?urlpath=lab/tree/simulate_3d.ipynb)\n", "- You can contribute with your own notebooks in this\n", "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/docs/tutorials).\n", "- **Source files:**\n", "[simulate_3d.ipynb](../_static/notebooks/simulate_3d.ipynb) |\n", "[simulate_3d.py](../_static/notebooks/simulate_3d.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3D simulation and fitting\n", "\n", "## Prerequisites\n", "\n", "- Knowledge of 3D extraction and datasets used in gammapy, see for instance the [first analysis tutorial](analysis_1.ipynb)\n", "\n", "## Context\n", "\n", "To simulate a specific observation, it is not always necessary to simulate the full photon list. For many uses cases, simulating directly a reduced binned dataset is enough: the IRFs reduced in the correct geometry are combined with a source model to predict an actual number of counts per bin. The latter is then used to simulate a reduced dataset using Poisson probability distribution.\n", "\n", "This can be done to check the feasibility of a measurement (performance / sensitivity study), to test whether fitted parameters really provide a good fit to the data etc.\n", "\n", "Here we will see how to perform a 3D simulation of a CTA observation, assuming both the spectral and spatial morphology of an observed source.\n", "\n", "**Objective: simulate a 3D observation of a source with CTA using the CTA 1DC response and fit it with the assumed source model.**\n", "\n", "## Proposed approach:\n", "\n", "Here we can't use the regular observation objects that are connected to a `DataStore`. Instead we will create a fake `~gammapy.data.Observation` that contain some pointing information and the CTA 1DC IRFs (that are loaded with `~gammapy.irf.load_cta_irfs`).\n", "\n", "Then we will create a `~gammapy.datasets.MapDataset` geometry and create it with the `~gammapy.makers.MapDatasetMaker`.\n", "\n", "Then we will be able to define a model consisting of a `~gammapy.modeling.models.PowerLawSpectralModel` and a `~gammapy.modeling.models.GaussianSpatialModel`. We will assign it to the dataset and fake the count data.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports and versions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import astropy.units as u\n", "from astropy.coordinates import SkyCoord\n", "from gammapy.irf import load_cta_irfs\n", "from gammapy.maps import WcsGeom, MapAxis\n", "from gammapy.modeling.models import (\n", " PowerLawSpectralModel,\n", " GaussianSpatialModel,\n", " SkyModel,\n", " Models,\n", " FoVBackgroundModel,\n", ")\n", "from gammapy.makers import MapDatasetMaker, SafeMaskMaker\n", "from gammapy.modeling import Fit\n", "from gammapy.data import Observation\n", "from gammapy.datasets import MapDataset" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r\n", "Gammapy package:\r\n", "\r\n", "\tversion : 0.18 \r\n", "\tpath : /Users/adonath/github/adonath/gammapy/gammapy \r\n", "\r\n" ] } ], "source": [ "!gammapy info --no-envvar --no-dependencies --no-system" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will simulate using the CTA-1DC IRFs shipped with gammapy. Note that for dedictaed CTA simulations, you can simply use [`Observation.from_caldb()`]() without having to externally load the IRFs" ] }, { "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": [ "# Loading IRFs\n", "irfs = load_cta_irfs(\n", " \"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\"\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Define the observation parameters (typically the observation duration and the pointing position):\n", "livetime = 2.0 * u.hr\n", "pointing = SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Define map geometry for binned simulation\n", "energy_reco = MapAxis.from_edges(\n", " np.logspace(-1.0, 1.0, 10), unit=\"TeV\", name=\"energy\", interp=\"log\"\n", ")\n", "geom = WcsGeom.create(\n", " skydir=(0, 0),\n", " binsz=0.02,\n", " width=(6, 6),\n", " frame=\"galactic\",\n", " axes=[energy_reco],\n", ")\n", "# It is usually useful to have a separate binning for the true energy axis\n", "energy_true = MapAxis.from_edges(\n", " np.logspace(-1.5, 1.5, 30), unit=\"TeV\", name=\"energy\", interp=\"log\"\n", ")\n", "\n", "empty = MapDataset.create(geom, name=\"dataset-simu\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Models\n", "\n", "Component 0: SkyModel\n", "\n", " Name : model-simu\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " index : 3.000 \n", " amplitude : 1.00e-11 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.200 deg \n", " lat_0 : 0.100 deg \n", " sigma : 0.300 deg \n", " e (frozen) : 0.000 \n", " phi (frozen) : 0.000 deg \n", "\n", "Component 1: FoVBackgroundModel\n", "\n", " Name : dataset-simu-bkg\n", " Datasets names : ['dataset-simu']\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": [ "# Define sky model to used simulate the data.\n", "# Here we use a Gaussian spatial model and a Power Law spectral model.\n", "spatial_model = GaussianSpatialModel(\n", " lon_0=\"0.2 deg\", lat_0=\"0.1 deg\", sigma=\"0.3 deg\", frame=\"galactic\"\n", ")\n", "spectral_model = PowerLawSpectralModel(\n", " index=3, amplitude=\"1e-11 cm-2 s-1 TeV-1\", reference=\"1 TeV\"\n", ")\n", "model_simu = SkyModel(\n", " spatial_model=spatial_model,\n", " spectral_model=spectral_model,\n", " name=\"model-simu\",\n", ")\n", "\n", "bkg_model = FoVBackgroundModel(dataset_name=\"dataset-simu\")\n", "\n", "models = Models([model_simu, bkg_model])\n", "print(models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, comes the main part of dataset simulation. We create an in-memory observation and an empty dataset. We then predict the number of counts for the given model, and Poission fluctuate it using `fake()` to make a simulated counts maps. Keep in mind that it is important to specify the `selection` of the maps that you want to produce " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Observation\n", "\n", "\tobs id : 0 \n", " \ttstart : 51544.00\n", "\ttstop : 51544.08\n", "\tduration : 7200.00 s\n", "\tpointing (icrs) : 266.4 deg, -28.9 deg\n", "\n", "\tdeadtime fraction : 0.0%\n", "\n" ] } ], "source": [ "# Create an in-memory observation\n", "obs = Observation.create(pointing=pointing, livetime=livetime, irfs=irfs)\n", "print(obs)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MapDataset\n", "----------\n", "\n", " Name : dataset-simu \n", "\n", " Total counts : nan \n", " Total background counts : 161250.95\n", " Total excess counts : nan\n", "\n", " Predicted counts : 161250.95\n", " Predicted background counts : 161250.95\n", " Predicted excess counts : nan\n", "\n", " Exposure min : 6.41e+07 m2 s\n", " Exposure max : 2.53e+10 m2 s\n", "\n", " Number of total bins : 0 \n", " Number of fit bins : 804492 \n", "\n", " Fit statistic type : cash\n", " Fit statistic value (-2 log(L)) : nan\n", "\n", " Number of models : 0 \n", " Number of parameters : 0\n", " Number of free parameters : 0\n", "\n", "\n" ] } ], "source": [ "# Make the MapDataset\n", "maker = MapDatasetMaker(selection=[\"exposure\", \"background\", \"psf\", \"edisp\"])\n", "\n", "maker_safe_mask = SafeMaskMaker(methods=[\"offset-max\"], offset_max=4.0 * u.deg)\n", "\n", "dataset = maker.run(empty, obs)\n", "dataset = maker_safe_mask.run(dataset, obs)\n", "print(dataset)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MapDataset\n", "----------\n", "\n", " Name : dataset-simu \n", "\n", " Total counts : 169151 \n", " Total background counts : 161250.95\n", " Total excess counts : 7900.05\n", "\n", " Predicted counts : 169690.08\n", " Predicted background counts : 161250.95\n", " Predicted excess counts : 8439.14\n", "\n", " Exposure min : 6.41e+07 m2 s\n", " Exposure max : 2.53e+10 m2 s\n", "\n", " Number of total bins : 810000 \n", " Number of fit bins : 804492 \n", "\n", " Fit statistic type : cash\n", " Fit statistic value (-2 log(L)) : 561449.47\n", "\n", " Number of models : 2 \n", " Number of parameters : 11\n", " Number of free parameters : 6\n", "\n", " Component 0: SkyModel\n", " \n", " Name : model-simu\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " index : 3.000 \n", " amplitude : 1.00e-11 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.200 deg \n", " lat_0 : 0.100 deg \n", " sigma : 0.300 deg \n", " e (frozen) : 0.000 \n", " phi (frozen) : 0.000 deg \n", " \n", " Component 1: FoVBackgroundModel\n", " \n", " Name : dataset-simu-bkg\n", " Datasets names : ['dataset-simu']\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": [ "# Add the model on the dataset and Poission fluctuate\n", "dataset.models = models\n", "dataset.fake()\n", "# Do a print on the dataset - there is now a counts maps\n", "print(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now use this dataset as you would in all standard analysis. You can plot the maps, or proceed with your custom analysis. \n", "In the next section, we show the standard 3D fitting as in [analysis_3d](analysis_3d.ipynb)." ] }, { "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": [ "# To plot, eg, counts:\n", "dataset.counts.smooth(0.05 * u.deg).plot_interactive(\n", " add_cbar=True, stretch=\"linear\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit\n", "\n", "In this section, we do a usual 3D fit with the same model used to simulated the data and see the stability of the simulations. Often, it is useful to simulate many such datasets and look at the distribution of the reconstructed parameters." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "models_fit = models.copy()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# We do not want to fit the background in this case, so we will freeze the parameters\n", "models_fit[\"dataset-simu-bkg\"].spectral_model.norm.frozen = True\n", "models_fit[\"dataset-simu-bkg\"].spectral_model.tilt.frozen = True" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DatasetModels\n", "\n", "Component 0: SkyModel\n", "\n", " Name : model-simu\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " index : 3.000 \n", " amplitude : 1.00e-11 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.200 deg \n", " lat_0 : 0.100 deg \n", " sigma : 0.300 deg \n", " e (frozen) : 0.000 \n", " phi (frozen) : 0.000 deg \n", "\n", "Component 1: FoVBackgroundModel\n", "\n", " Name : dataset-simu-bkg\n", " Datasets names : ['dataset-simu']\n", " Spectral model type : PowerLawNormSpectralModel\n", " Parameters:\n", " norm (frozen) : 1.000 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", "\n", "\n" ] } ], "source": [ "dataset.models = models_fit\n", "print(dataset.models)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------------------------\n", "| FCN = 5.614e+05 | Ncalls=128 (128 total) |\n", "| EDM = 1.19e-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", "CPU times: user 5.94 s, sys: 483 ms, total: 6.43 s\n", "Wall time: 6.65 s\n" ] } ], "source": [ "%%time\n", "fit = Fit([dataset])\n", "result = fit.run(optimize_opts={\"print_level\": 1})" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dataset.plot_residuals_spatial(method=\"diff/sqrt(model)\", vmin=-0.5, vmax=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare the injected and fitted models: " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True model: \n", " SkyModel\n", "\n", " Name : model-simu\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " index : 3.000 \n", " amplitude : 1.00e-11 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.200 deg \n", " lat_0 : 0.100 deg \n", " sigma : 0.300 deg \n", " e (frozen) : 0.000 \n", " phi (frozen) : 0.000 deg \n", "\n", " \n", "\n", " Fitted model: \n", " SkyModel\n", "\n", " Name : model-simu\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " index : 2.998 \n", " amplitude : 1.02e-11 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.200 deg \n", " lat_0 : 0.105 deg \n", " sigma : 0.307 deg \n", " e (frozen) : 0.000 \n", " phi (frozen) : 0.000 deg \n", "\n", "\n" ] } ], "source": [ "print(\n", " \"True model: \\n\",\n", " model_simu,\n", " \"\\n\\n Fitted model: \\n\",\n", " models_fit[\"model-simu\"],\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the errors on the fitted parameters from the parameter table" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=11\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
namevalueunitminmaxfrozenerror
str9float64str14float64float64boolfloat64
index2.9982e+00nannanFalse1.987e-02
amplitude1.0183e-11cm-2 s-1 TeV-1nannanFalse3.337e-13
reference1.0000e+00TeVnannanTrue0.000e+00
lon_02.0006e-01degnannanFalse6.057e-03
lat_01.0478e-01deg-9.000e+019.000e+01False6.035e-03
sigma3.0671e-01deg0.000e+00nanFalse4.083e-03
e0.0000e+000.000e+001.000e+00True0.000e+00
phi0.0000e+00degnannanTrue0.000e+00
norm1.0000e+00nannanTrue0.000e+00
tilt0.0000e+00nannanTrue0.000e+00
reference1.0000e+00TeVnannanTrue0.000e+00
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