{ "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/simulate_3d.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", "[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", ")\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 : 0.17 \r\n", "\tpath : /Users/terrier/Code/gammapy-dev/gammapy-docs/build/v0.17/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": [], "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", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SkyModel\n", "\n", " Name : model-simu\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : None\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" ] } ], "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", "print(model_simu)" ] }, { "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 predicted counts : 161422.07\n", " Total background counts : 161422.07\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 : 1 \n", " Number of parameters : 3\n", " Number of free parameters : 1\n", "\n", " Component 0: BackgroundModel\n", " \n", " Name : dataset-simu-bkg\n", " Datasets names : ['dataset-simu']\n", " Parameters:\n", " norm : 1.000 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", " \n", " \n" ] } ], "source": [ "# Make the MapDataset\n", "empty = MapDataset.create(geom, name=\"dataset-simu\")\n", "maker = MapDatasetMaker(selection=[\"exposure\", \"background\", \"psf\", \"edisp\"])\n", "maker_safe_mask = SafeMaskMaker(methods=[\"offset-max\"], offset_max=4.0 * u.deg)\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 : 170051 \n", " Total predicted counts : 170075.03\n", " Total background counts : 161422.07\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)) : 562945.54\n", "\n", " Number of models : 2 \n", " Number of parameters : 11\n", " Number of free parameters : 6\n", "\n", " Component 0: BackgroundModel\n", " \n", " Name : dataset-simu-bkg\n", " Datasets names : ['dataset-simu']\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 : model-simu\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : None\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" ] } ], "source": [ "# Add the model on the dataset and Poission fluctuate\n", "dataset.models.append(model_simu)\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": [ "# Make a copy of the dataset\n", "dataset_fit = dataset.copy(name=\"dataset-fit\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Models\n", "\n", "Component 0: BackgroundModel\n", "\n", " Name : dataset-simu-bkg\n", " Datasets names : ['dataset-simu']\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 : model-simu\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : None\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" ] } ], "source": [ "print(dataset_fit.models)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "dataset_fit.models[\"dataset-simu-bkg\"].datasets_names = [\"dataset-fit\"]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Models\n", "\n", "Component 0: SkyModel\n", "\n", " Name : model-fit\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : None\n", " Parameters:\n", " index : 2.000 \n", " amplitude : 1.00e-11 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.100 deg \n", " lat_0 : 0.100 deg \n", " sigma : 0.500 deg \n", " e (frozen) : 0.000 \n", " phi (frozen) : 0.000 deg \n", "\n", "Component 1: BackgroundModel\n", "\n", " Name : dataset-simu-bkg\n", " Datasets names : ['dataset-fit']\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 fit the data\n", "spatial_model1 = GaussianSpatialModel(\n", " lon_0=\"0.1 deg\", lat_0=\"0.1 deg\", sigma=\"0.5 deg\", frame=\"galactic\"\n", ")\n", "spectral_model1 = PowerLawSpectralModel(\n", " index=2, amplitude=\"1e-11 cm-2 s-1 TeV-1\", reference=\"1 TeV\"\n", ")\n", "model_fit = SkyModel(\n", " spatial_model=spatial_model1,\n", " spectral_model=spectral_model1,\n", " name=\"model-fit\",\n", ")\n", "\n", "dataset_fit.models = [model_fit, dataset_fit.models[0]]\n", "print(dataset_fit.models)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BackgroundModel\n", "\n", " Name : dataset-simu-bkg\n", " Datasets names : ['dataset-fit']\n", " Parameters:\n", " norm (frozen) : 1.000 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", "\n", "\n" ] } ], "source": [ "# We do not want to fit the background in this case, so we will freeze the parameters\n", "background_model = dataset_fit.background_model\n", "background_model.parameters[\"norm\"].value = 1.0\n", "background_model.parameters[\"norm\"].frozen = True\n", "background_model.parameters[\"tilt\"].frozen = True\n", "\n", "print(background_model)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MapDataset\n", "----------\n", "\n", " Name : dataset-fit \n", "\n", " Total counts : 170051 \n", " Total predicted counts : 164554.41\n", " Total background counts : 161422.07\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)) : 568045.77\n", "\n", " Number of models : 2 \n", " Number of parameters : 11\n", " Number of free parameters : 5\n", "\n", " Component 0: SkyModel\n", " \n", " Name : model-fit\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : None\n", " Parameters:\n", " index : 2.000 \n", " amplitude : 1.00e-11 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.100 deg \n", " lat_0 : 0.100 deg \n", " sigma : 0.500 deg \n", " e (frozen) : 0.000 \n", " phi (frozen) : 0.000 deg \n", " \n", " Component 1: BackgroundModel\n", " \n", " Name : dataset-simu-bkg\n", " Datasets names : ['dataset-fit']\n", " Parameters:\n", " norm (frozen) : 1.000 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", " \n", " \n" ] } ], "source": [ "print(dataset_fit)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------------------------\n", "| FCN = 5.629E+05 | Ncalls=191 (191 total) |\n", "| EDM = 1.8E-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", "CPU times: user 23.6 s, sys: 2.27 s, total: 25.9 s\n", "Wall time: 26.2 s\n" ] } ], "source": [ "%%time\n", "fit = Fit([dataset_fit])\n", "result = fit.run(optimize_opts={\"print_level\": 1})" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(, None)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dataset_fit.plot_residuals(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": 20, "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 : None\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-fit\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : None\n", " Parameters:\n", " index : 2.980 \n", " amplitude : 1.03e-11 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.199 deg \n", " lat_0 : 0.095 deg \n", " sigma : 0.299 deg \n", " e (frozen) : 0.000 \n", " phi (frozen) : 0.000 deg \n", "\n", "\n" ] } ], "source": [ "print(\"True model: \\n\", model_simu, \"\\n\\n Fitted model: \\n\", model_fit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the errors on the fitted parameters from the parameter table" ] }, { "cell_type": "code", "execution_count": 21, "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
index2.980e+00nannanFalse1.941e-02
amplitude1.030e-11cm-2 s-1 TeV-1nannanFalse3.291e-13
reference1.000e+00TeVnannanTrue0.000e+00
lon_01.986e-01degnannanFalse5.822e-03
lat_09.452e-02deg-9.000e+019.000e+01False5.823e-03
sigma2.991e-01deg0.000e+00nanFalse3.992e-03
e0.000e+000.000e+001.000e+00True0.000e+00
phi0.000e+00degnannanTrue0.000e+00
norm1.000e+000.000e+00nanTrue0.000e+00
tilt0.000e+00nannanTrue0.000e+00
reference1.000e+00TeVnannanTrue0.000e+00
" ], "text/plain": [ "\n", " name value unit min max frozen error \n", " str9 float64 str14 float64 float64 bool float64 \n", "--------- --------- -------------- ---------- --------- ------ ---------\n", " index 2.980e+00 nan nan False 1.941e-02\n", "amplitude 1.030e-11 cm-2 s-1 TeV-1 nan nan False 3.291e-13\n", "reference 1.000e+00 TeV nan nan True 0.000e+00\n", " lon_0 1.986e-01 deg nan nan False 5.822e-03\n", " lat_0 9.452e-02 deg -9.000e+01 9.000e+01 False 5.823e-03\n", " sigma 2.991e-01 deg 0.000e+00 nan False 3.992e-03\n", " e 0.000e+00 0.000e+00 1.000e+00 True 0.000e+00\n", " phi 0.000e+00 deg nan nan True 0.000e+00\n", " norm 1.000e+00 0.000e+00 nan True 0.000e+00\n", " tilt 0.000e+00 nan nan True 0.000e+00\n", "reference 1.000e+00 TeV nan nan True 0.000e+00" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.parameters.to_table()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": 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