{ "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/v0.18.1?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 : 0.18.1 \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 : 169978 \n", " Total background counts : 161250.95\n", " Total excess counts : 8727.05\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)) : 565057.78\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", 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" ] }, "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 = 5.651e+05 | Ncalls=129 (129 total) |\n", "| EDM = 8.83e-06 (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 5.6 s, sys: 342 ms, total: 5.94 s\n", "Wall time: 6 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 : 3.017 \n", " amplitude : 9.69e-12 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.205 deg \n", " lat_0 : 0.101 deg \n", " sigma : 0.298 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
index3.0175e+00nannanFalse2.037e-02
amplitude9.6860e-12cm-2 s-1 TeV-1nannanFalse3.290e-13
reference1.0000e+00TeVnannanTrue0.000e+00
lon_02.0456e-01degnannanFalse5.887e-03
lat_01.0071e-01deg-9.000e+019.000e+01False5.927e-03
sigma2.9817e-01deg0.000e+00nanFalse4.057e-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
" ], "text/plain": [ "\n", " name value unit min max frozen error \n", " str9 float64 str14 float64 float64 bool float64 \n", "--------- ---------- -------------- ---------- --------- ------ ---------\n", " index 3.0175e+00 nan nan False 2.037e-02\n", "amplitude 9.6860e-12 cm-2 s-1 TeV-1 nan nan False 3.290e-13\n", "reference 1.0000e+00 TeV nan nan True 0.000e+00\n", " lon_0 2.0456e-01 deg nan nan False 5.887e-03\n", " lat_0 1.0071e-01 deg -9.000e+01 9.000e+01 False 5.927e-03\n", " sigma 2.9817e-01 deg 0.000e+00 nan False 4.057e-03\n", " e 0.0000e+00 0.000e+00 1.000e+00 True 0.000e+00\n", " phi 0.0000e+00 deg nan nan True 0.000e+00\n", " norm 1.0000e+00 nan nan True 0.000e+00\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": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.parameters.to_table()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], 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