{ "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://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.15?urlpath=lab/tree/analysis_2.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", "[analysis_2.ipynb](../_static/notebooks/analysis_2.ipynb) |\n", "[analysis_2.py](../_static/notebooks/analysis_2.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Second analysis\n", "\n", "This notebook shows the same Crab analysis as performed in the [analysis_1 notebook](analysis_1.ipynb) but this time without the high level interface provided by the `Analysis` class. DL3 data release 1.\n", "\n", "As before, we will reduce the data to cube datasets and perform a simple 3D model fitting of the Crab nebula.\n", "\n", "The tutorial follows a typical analysis:\n", "\n", "- Observation selection\n", "- Data reduction\n", "- Model fitting\n", "- Estimating flux points\n", "\n", "but it gives more details on lower level API.\n", "\n", "First, we setup the analysis by performing required imports.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "import numpy as np\n", "from astropy import units as u\n", "from astropy.coordinates import SkyCoord\n", "from regions import CircleSkyRegion" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from gammapy.data import DataStore\n", "from gammapy.maps import WcsGeom, MapAxis\n", "from gammapy.cube import MapDatasetMaker, MapDataset, SafeMaskMaker\n", "from gammapy.modeling.models import (\n", " SkyModel,\n", " PowerLawSpectralModel,\n", " PointSpatialModel,\n", ")\n", "from gammapy.modeling import Fit\n", "from gammapy.spectrum import FluxPointsEstimator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Defining the datastore and selecting observations\n", "\n", "We first use the `~gammapy.data.DataStore` object to access the observations we want to analyse. Here the H.E.S.S. DL3 DR1. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data_store = DataStore.from_dir(\"$GAMMAPY_DATA/hess-dl3-dr1\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now define an observation filter to select only the relevant observations. \n", "Here we use a cone search which we define with a python dict.\n", "\n", "We then filter the `ObservationTable` with `~gammapy.data.ObservationTable.select_observations()`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "selection = dict(\n", " type=\"sky_circle\",\n", " frame=\"icrs\",\n", " lon=\"83.633 deg\",\n", " lat=\"22.014 deg\",\n", " radius=\"5 deg\",\n", ")\n", "selected_obs_table = data_store.obs_table.select_observations(selection)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now retrieve the relevant observations by passing their `obs_id` to the`~gammapy.data.DataStore.get_observations()` method." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "observations = data_store.get_observations(selected_obs_table[\"OBS_ID\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparing reduced datasets geometry\n", "\n", "Now we define a reference geometry for our analysis, We choose a WCS based geometry with a binsize of 0.02 deg and also define an energy axis: " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "energy_axis = MapAxis.from_edges(\n", " np.logspace(0.0, 1.0, 4), unit=\"TeV\", name=\"energy\", interp=\"log\"\n", ")\n", "geom = WcsGeom.create(\n", " skydir=(83.633, 22.014),\n", " binsz=0.02,\n", " width=(2, 2),\n", " coordsys=\"CEL\",\n", " proj=\"CAR\",\n", " axes=[energy_axis],\n", ")\n", "\n", "# Reduced IRFs are defined in true energy (i.e. not measured energy).\n", "energy_axis_true = MapAxis.from_edges(\n", " np.logspace(-0.3, 1.3, 10), unit=\"TeV\", name=\"energy\", interp=\"log\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can define the target dataset with this geometry." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "stacked = MapDataset.create(\n", " geom=geom, energy_axis_true=energy_axis_true, name=\"crab-stacked\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data reduction\n", "\n", "### Create the maker classes to be used\n", "\n", "The `~gammapy.cube.MapDatasetMaker` object is initialized as well as the `~gammapy.cube.SafeMaskMaker` that carries here a maximum offset selection." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "offset_max = 2.5 * u.deg\n", "maker = MapDatasetMaker()\n", "maker_safe_mask = SafeMaskMaker(methods=[\"offset-max\"], offset_max=offset_max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Perform the data reduction loop" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.69 s, sys: 108 ms, total: 1.8 s\n", "Wall time: 1.86 s\n" ] } ], "source": [ "%%time\n", "\n", "for obs in observations:\n", " # First a cutout of the target map is produced\n", " cutout = stacked.cutout(obs.pointing_radec, width=2 * offset_max)\n", " # A MapDataset is filled in this cutout geometry\n", " dataset = maker.run(cutout, obs)\n", " # The data quality cut is applied\n", " dataset = maker_safe_mask.run(dataset, obs)\n", " # The resulting dataset cutout is stacked onto the final one\n", " stacked.stack(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inspect the reduced dataset" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " ,\n", " )" ] }, "execution_count": 11, "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": [ "stacked.counts.sum_over_axes().smooth(0.05 * u.deg).plot(\n", " stretch=\"sqrt\", add_cbar=True\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save dataset to disk\n", "\n", "It is common to run the preparation step independent of the likelihood fit, because often the preparation of maps, PSF and energy dispersion is slow if you have a lot of data. We first create a folder:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "path = Path(\"analysis_2\")\n", "path.mkdir(exist_ok=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then write the maps and IRFs to disk by calling the dedicated `~gammapy.cube.MapDataset.write()` method:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "filename = path / \"crab-stacked-dataset.fits.gz\"\n", "stacked.write(filename, overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the model\n", "We first define the model, a `SkyModel`, as the combination of a point source `SpatialModel` with a powerlaw `SpectralModel`:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "target_position = SkyCoord(ra=83.63308, dec=22.01450, unit=\"deg\")\n", "spatial_model = PointSpatialModel(\n", " lon_0=target_position.ra, lat_0=target_position.dec, frame=\"icrs\"\n", ")\n", "\n", "spectral_model = PowerLawSpectralModel(\n", " index=2.702,\n", " amplitude=4.712e-11 * u.Unit(\"1 / (cm2 s TeV)\"),\n", " reference=1 * u.TeV,\n", ")\n", "\n", "sky_model = SkyModel(\n", " spatial_model=spatial_model, spectral_model=spectral_model, name=\"crab\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we assign this model to our reduced dataset:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "stacked.models = sky_model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit the model\n", "\n", "The `~gammapy.modeling.Fit` class is orchestrating the fit, connecting the `stats` method of the dataset to the minimizer. By default, it uses `iminuit`.\n", "\n", "Its contructor takes a list of dataset as argument." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------------------------\n", "| FCN = 1.485E+04 | Ncalls=129 (129 total) |\n", "| EDM = 2.43E-07 (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 2.98 s, sys: 152 ms, total: 3.13 s\n", "Wall time: 3.42 s\n" ] } ], "source": [ "%%time\n", "fit = Fit([stacked])\n", "result = fit.run(optimize_opts={\"print_level\": 1})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `FitResult` contains information on the fitted parameters." ] }, { "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", "
namevalueerrorunitminmaxfrozen
str9float64float64str14float64float64bool
lon_08.362e+013.127e-03degnannanFalse
lat_02.202e+012.764e-03deg-9.000e+019.000e+01False
index2.575e+001.036e-01nannanFalse
amplitude4.644e-113.971e-12cm-2 s-1 TeV-1nannanFalse
reference1.000e+000.000e+00TeVnannanTrue
norm9.555e-012.242e-020.000e+00nanFalse
tilt0.000e+000.000e+00nannanTrue
reference1.000e+000.000e+00TeVnannanTrue
" ], "text/plain": [ "\n", " name value error unit min max frozen\n", " str9 float64 float64 str14 float64 float64 bool \n", "--------- --------- --------- -------------- ---------- --------- ------\n", " lon_0 8.362e+01 3.127e-03 deg nan nan False\n", " lat_0 2.202e+01 2.764e-03 deg -9.000e+01 9.000e+01 False\n", " index 2.575e+00 1.036e-01 nan nan False\n", "amplitude 4.644e-11 3.971e-12 cm-2 s-1 TeV-1 nan nan False\n", "reference 1.000e+00 0.000e+00 TeV nan nan True\n", " norm 9.555e-01 2.242e-02 0.000e+00 nan False\n", " tilt 0.000e+00 0.000e+00 nan nan True\n", "reference 1.000e+00 0.000e+00 TeV nan nan True" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.parameters.to_table()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inspecting residuals\n", "\n", "For any fit it is usefull to inspect the residual images. We have a few option on the dataset object to handle this. First we can use `.plot_residuals()` to plot a residual image, summed over all energies: " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(, None)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "stacked.plot_residuals(method=\"diff/sqrt(model)\", vmin=-0.5, vmax=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition we can aslo specify a region in the map to show the spectral residuals:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "region = CircleSkyRegion(\n", " center=SkyCoord(\"83.63 deg\", \"22.14 deg\"), radius=0.5 * u.deg\n", ")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(,\n", " )" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "stacked.plot_residuals(\n", " region=region, method=\"diff/sqrt(model)\", vmin=-0.5, vmax=0.5\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also directly access the `.residuals()` to get a map, that we can plot interactively:" ] }, { "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": [ "residuals = stacked.residuals(method=\"diff\")\n", "residuals.smooth(\"0.08 deg\").plot_interactive(\n", " cmap=\"coolwarm\", vmin=-0.1, vmax=0.1, stretch=\"linear\", add_cbar=True\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inspecting fit statistic profiles\n", "\n", "To check the quality of the fit it is also useful to plot fit statistic profiles for specific parameters.\n", "For this we use `~gammapy.modeling.Fit.stat_profile()`." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "profile = fit.stat_profile(parameter=\"lon_0\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a good fit and error estimate the profile should be parabolic, if we plot it:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Delta TS')" ] }, "execution_count": 23, "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": [ "total_stat = result.total_stat\n", "plt.plot(profile[\"values\"], profile[\"stat\"] - total_stat)\n", "plt.xlabel(\"Lon (deg)\")\n", "plt.ylabel(\"Delta TS\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the fitted spectrum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Making a butterfly plot \n", "\n", "The `SpectralModel` component can be used to produce a, so-called, butterfly plot showing the enveloppe of the model taking into account parameter uncertainties.\n", "\n", "To do so, we have to copy the part of the covariance matrix stored on the `FitResult` on the model parameters:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "spec = sky_model.spectral_model\n", "\n", "# set covariance on the spectral model\n", "covar = result.parameters.get_subcovariance(spec.parameters)\n", "spec.parameters.covariance = covar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can actually do the plot using the `plot_error` method:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "energy_range = [1, 10] * u.TeV\n", "spec.plot(energy_range=energy_range, energy_power=2)\n", "ax = spec.plot_error(energy_range=energy_range, energy_power=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Computing flux points\n", "\n", "We can now compute some flux points using the `~gammapy.spectrum.FluxPointsEstimator`. \n", "\n", "Besides the list of datasets to use, we must provide it the energy intervals on which to compute flux points as well as the model component name. " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "e_edges = [1, 2, 4, 10] * u.TeV\n", "fpe = FluxPointsEstimator(datasets=[stacked], e_edges=e_edges, source=\"crab\")" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.93 s, sys: 50.2 ms, total: 1.98 s\n", "Wall time: 2.17 s\n" ] } ], "source": [ "%%time\n", "flux_points = fpe.run()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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