{ "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/v0.19?urlpath=lab/tree/tutorials/starting/analysis_2.ipynb)\n", "- You may download all the notebooks in the documentation as a\n", "[tar file](../../_downloads/notebooks-0.19.tar).\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": [ "# Low level API\n", "\n", "## Prerequisites:\n", "\n", "- Understanding the gammapy data workflow, in particular what are DL3 events and instrument response functions (IRF).\n", "- Understanding of the data reduction and modeling fitting process as shown in the [analysis with the high level interface tutorial](analysis_1.ipynb)\n", "\n", "## Context\n", "\n", "This notebook is an introduction to gammapy analysis this time using the lower level classes and functions\n", "the library.\n", "This allows to understand what happens during two main gammapy analysis steps, data reduction and modeling/fitting. \n", "\n", "**Objective: Create a 3D dataset of the Crab using the H.E.S.S. DL3 data release 1 and perform a simple model fitting of the Crab nebula using the lower level gammapy API.**\n", "\n", "## Proposed approach:\n", "\n", "Here, we have to interact with the data archive (with the `~gammapy.data.DataStore`) to retrieve a list of selected observations (`~gammapy.data.Observations`). Then, we define the geometry of the `~gammapy.datasets.MapDataset` object we want to produce and the maker object that reduce an observation\n", "to a dataset. \n", "\n", "We can then proceed with data reduction with a loop over all selected observations to produce datasets in the relevant geometry and stack them together (i.e. sum them all).\n", "\n", "In practice, we have to:\n", "- Create a `~gammapy.data.DataStore` poiting to the relevant data \n", "- Apply an observation selection to produce a list of observations, a `~gammapy.data.Observations` object.\n", "- Define a geometry of the Map we want to produce, with a sky projection and an energy range.\n", " - Create a `~gammapy.maps.MapAxis` for the energy\n", " - Create a `~gammapy.maps.WcsGeom` for the geometry\n", "- Create the necessary makers : \n", " - the map dataset maker : `~gammapy.makers.MapDatasetMaker`\n", " - the background normalization maker, here a `~gammapy.makers.FoVBackgroundMaker`\n", " - and usually the safe range maker : `~gammapy.makers.SafeRangeMaker`\n", "- Perform the data reduction loop. And for every observation:\n", " - Apply the makers sequentially to produce the current `~gammapy.datasets.MapDataset`\n", " - Stack it on the target one.\n", "- Define the `~gammapy.modeling.models.SkyModel` to apply to the dataset.\n", "- Create a `~gammapy.modeling.Fit` object and run it to fit the model parameters\n", "- Apply a `~gammapy.estimators.FluxPointsEstimator` to compute flux points for the spectral part of the fit.\n", "\n", "## Setup\n", "First, we setup the analysis by performing required imports.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:27.403960Z", "iopub.status.busy": "2021-11-22T21:08:27.403103Z", "iopub.status.idle": "2021-11-22T21:08:27.570669Z", "shell.execute_reply": "2021-11-22T21:08:27.570874Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:27.572844Z", "iopub.status.busy": "2021-11-22T21:08:27.572528Z", "iopub.status.idle": "2021-11-22T21:08:27.819534Z", "shell.execute_reply": "2021-11-22T21:08:27.819714Z" } }, "outputs": [], "source": [ "from pathlib import Path\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": { "execution": { "iopub.execute_input": "2021-11-22T21:08:27.821992Z", "iopub.status.busy": "2021-11-22T21:08:27.821701Z", "iopub.status.idle": "2021-11-22T21:08:28.093952Z", "shell.execute_reply": "2021-11-22T21:08:28.094125Z" } }, "outputs": [], "source": [ "from gammapy.data import DataStore\n", "from gammapy.datasets import MapDataset\n", "from gammapy.maps import WcsGeom, MapAxis\n", "from gammapy.makers import MapDatasetMaker, SafeMaskMaker, FoVBackgroundMaker\n", "from gammapy.modeling.models import (\n", " SkyModel,\n", " PowerLawSpectralModel,\n", " PointSpatialModel,\n", " FoVBackgroundModel,\n", ")\n", "from gammapy.modeling import Fit\n", "from gammapy.estimators 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": { "execution": { "iopub.execute_input": "2021-11-22T21:08:28.096725Z", "iopub.status.busy": "2021-11-22T21:08:28.096072Z", "iopub.status.idle": "2021-11-22T21:08:28.111503Z", "shell.execute_reply": "2021-11-22T21:08:28.111660Z" } }, "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": { "execution": { "iopub.execute_input": "2021-11-22T21:08:28.115415Z", "iopub.status.busy": "2021-11-22T21:08:28.115118Z", "iopub.status.idle": "2021-11-22T21:08:28.116236Z", "shell.execute_reply": "2021-11-22T21:08:28.116467Z" } }, "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": { "execution": { "iopub.execute_input": "2021-11-22T21:08:28.119285Z", "iopub.status.busy": "2021-11-22T21:08:28.119003Z", "iopub.status.idle": "2021-11-22T21:08:28.123279Z", "shell.execute_reply": "2021-11-22T21:08:28.123458Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "No HDU found matching: OBS_ID = 23523, HDU_TYPE = rad_max, HDU_CLASS = None\n", "No HDU found matching: OBS_ID = 23526, HDU_TYPE = rad_max, HDU_CLASS = None\n", "No HDU found matching: OBS_ID = 23559, HDU_TYPE = rad_max, HDU_CLASS = None\n", "No HDU found matching: OBS_ID = 23592, HDU_TYPE = rad_max, HDU_CLASS = None\n" ] } ], "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": { "execution": { "iopub.execute_input": "2021-11-22T21:08:28.126457Z", "iopub.status.busy": "2021-11-22T21:08:28.126163Z", "iopub.status.idle": "2021-11-22T21:08:28.127236Z", "shell.execute_reply": "2021-11-22T21:08:28.127445Z" } }, "outputs": [], "source": [ "energy_axis = MapAxis.from_energy_bounds(1.0, 10.0, 4, unit=\"TeV\")\n", "\n", "geom = WcsGeom.create(\n", " skydir=(83.633, 22.014),\n", " binsz=0.02,\n", " width=(2, 2),\n", " frame=\"icrs\",\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_energy_bounds(\n", " 0.5, 20, 10, unit=\"TeV\", name=\"energy_true\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can define the target dataset with this geometry." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:28.131710Z", "iopub.status.busy": "2021-11-22T21:08:28.131362Z", "iopub.status.idle": "2021-11-22T21:08:28.136371Z", "shell.execute_reply": "2021-11-22T21:08:28.136552Z" } }, "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.datasets.MapDatasetMaker` object is initialized as well as the `~gammapy.makers.SafeMaskMaker` that carries here a maximum offset selection." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:28.138584Z", "iopub.status.busy": "2021-11-22T21:08:28.138284Z", "iopub.status.idle": "2021-11-22T21:08:28.139455Z", "shell.execute_reply": "2021-11-22T21:08:28.139610Z" } }, "outputs": [], "source": [ "offset_max = 2.5 * u.deg\n", "maker = MapDatasetMaker()\n", "maker_safe_mask = SafeMaskMaker(\n", " methods=[\"offset-max\", \"aeff-max\"], offset_max=offset_max\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:28.141910Z", "iopub.status.busy": "2021-11-22T21:08:28.141610Z", "iopub.status.idle": "2021-11-22T21:08:28.149093Z", "shell.execute_reply": "2021-11-22T21:08:28.149269Z" } }, "outputs": [], "source": [ "circle = CircleSkyRegion(\n", " center=SkyCoord(\"83.63 deg\", \"22.14 deg\"), radius=0.2 * u.deg\n", ")\n", "exclusion_mask = ~geom.region_mask(regions=[circle])\n", "maker_fov = FoVBackgroundMaker(method=\"fit\", exclusion_mask=exclusion_mask)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Perform the data reduction loop" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:28.166029Z", "iopub.status.busy": "2021-11-22T21:08:28.157619Z", "iopub.status.idle": "2021-11-22T21:08:29.828137Z", "shell.execute_reply": "2021-11-22T21:08:29.828366Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Background norm obs 23523: 0.99\n", "Background norm obs 23526: 1.08\n", "Background norm obs 23559: 0.99\n", "Background norm obs 23592: 1.10\n", "CPU times: user 1.61 s, sys: 57.9 ms, total: 1.67 s\n", "Wall time: 1.68 s\n" ] } ], "source": [ "%%time\n", "\n", "for obs in observations:\n", " # First a cutout of the target map is produced\n", " cutout = stacked.cutout(\n", " obs.pointing_radec, width=2 * offset_max, name=f\"obs-{obs.obs_id}\"\n", " )\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", " # fit background model\n", " dataset = maker_fov.run(dataset)\n", " print(\n", " f\"Background norm obs {obs.obs_id}: {dataset.background_model.spectral_model.norm.value:.2f}\"\n", " )\n", " # The resulting dataset cutout is stacked onto the final one\n", " stacked.stack(dataset)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:29.830239Z", "iopub.status.busy": "2021-11-22T21:08:29.829912Z", "iopub.status.idle": "2021-11-22T21:08:29.833464Z", "shell.execute_reply": "2021-11-22T21:08:29.833692Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MapDataset\n", "----------\n", "\n", " Name : crab-stacked \n", "\n", " Total counts : 2479 \n", " Total background counts : 2112.97\n", " Total excess counts : 366.03\n", "\n", " Predicted counts : 2112.97\n", " Predicted background counts : 2112.97\n", " Predicted excess counts : nan\n", "\n", " Exposure min : 3.75e+08 m2 s\n", " Exposure max : 3.48e+09 m2 s\n", "\n", " Number of total bins : 40000 \n", " Number of fit bins : 40000 \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": [ "print(stacked)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inspect the reduced dataset" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:29.836449Z", "iopub.status.busy": "2021-11-22T21:08:29.836146Z", "iopub.status.idle": "2021-11-22T21:08:30.083213Z", "shell.execute_reply": "2021-11-22T21:08:30.083401Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": 14, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:30.085469Z", "iopub.status.busy": "2021-11-22T21:08:30.085139Z", "iopub.status.idle": "2021-11-22T21:08:30.086401Z", "shell.execute_reply": "2021-11-22T21:08:30.086650Z" } }, "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.datasets.MapDataset.write()` method:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:30.155706Z", "iopub.status.busy": "2021-11-22T21:08:30.131101Z", "iopub.status.idle": "2021-11-22T21:08:30.209167Z", "shell.execute_reply": "2021-11-22T21:08:30.209383Z" } }, "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": 16, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:30.212917Z", "iopub.status.busy": "2021-11-22T21:08:30.212604Z", "iopub.status.idle": "2021-11-22T21:08:30.220116Z", "shell.execute_reply": "2021-11-22T21:08:30.220318Z" } }, "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", ")\n", "\n", "bkg_model = FoVBackgroundModel(dataset_name=\"crab-stacked\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we assign this model to our reduced dataset:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:30.222328Z", "iopub.status.busy": "2021-11-22T21:08:30.221999Z", "iopub.status.idle": "2021-11-22T21:08:30.223395Z", "shell.execute_reply": "2021-11-22T21:08:30.223577Z" } }, "outputs": [], "source": [ "stacked.models = [sky_model, bkg_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 constructor takes a list of dataset as argument." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:30.225764Z", "iopub.status.busy": "2021-11-22T21:08:30.225442Z", "iopub.status.idle": "2021-11-22T21:08:33.644769Z", "shell.execute_reply": "2021-11-22T21:08:33.645017Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.04 s, sys: 429 ms, total: 3.47 s\n", "Wall time: 3.42 s\n" ] } ], "source": [ "%%time\n", "fit = Fit(optimize_opts={\"print_level\": 1})\n", "result = fit.run([stacked])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `FitResult` contains information about the optimization and parameter error calculation." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:33.646893Z", "iopub.status.busy": "2021-11-22T21:08:33.646580Z", "iopub.status.idle": "2021-11-22T21:08:33.647875Z", "shell.execute_reply": "2021-11-22T21:08:33.648111Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : migrad\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 139\n", "\ttotal stat : 16240.97\n", "\n", "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : migrad\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 139\n", "\ttotal stat : 16240.97\n", "\n", "\n" ] } ], "source": [ "print(result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fitted parameters are visible from the `~astropy.modeling.models.Models` object." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:33.651646Z", "iopub.status.busy": "2021-11-22T21:08:33.651306Z", "iopub.status.idle": "2021-11-22T21:08:33.652704Z", "shell.execute_reply": "2021-11-22T21:08:33.652876Z" } }, "outputs": [ { "data": { "text/html": [ "
Table length=8\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
modeltypenamevalueuniterrorminmaxfrozenlink
str16str8str9float64str14float64float64float64boolstr1
crabspectralindex2.5999e+001.004e-01nannanFalse
crabspectralamplitude4.5906e-11cm-2 s-1 TeV-13.704e-12nannanFalse
crabspectralreference1.0000e+00TeV0.000e+00nannanTrue
crabspatiallon_08.3619e+01deg3.105e-03nannanFalse
crabspatiallat_02.2024e+01deg2.950e-03-9.000e+019.000e+01False
crab-stacked-bkgspectralnorm9.3477e-012.192e-02nannanFalse
crab-stacked-bkgspectraltilt0.0000e+000.000e+00nannanTrue
crab-stacked-bkgspectralreference1.0000e+00TeV0.000e+00nannanTrue
" ], "text/plain": [ "\n", " model type name value unit error min max frozen link\n", " str16 str8 str9 float64 str14 float64 float64 float64 bool str1\n", "---------------- -------- --------- ---------- -------------- --------- ---------- --------- ------ ----\n", " crab spectral index 2.5999e+00 1.004e-01 nan nan False \n", " crab spectral amplitude 4.5906e-11 cm-2 s-1 TeV-1 3.704e-12 nan nan False \n", " crab spectral reference 1.0000e+00 TeV 0.000e+00 nan nan True \n", " crab spatial lon_0 8.3619e+01 deg 3.105e-03 nan nan False \n", " crab spatial lat_0 2.2024e+01 deg 2.950e-03 -9.000e+01 9.000e+01 False \n", "crab-stacked-bkg spectral norm 9.3477e-01 2.192e-02 nan nan False \n", "crab-stacked-bkg spectral tilt 0.0000e+00 0.000e+00 nan nan True \n", "crab-stacked-bkg spectral reference 1.0000e+00 TeV 0.000e+00 nan nan True " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stacked.models.to_parameters_table()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inspecting residuals\n", "\n", "For any fit it is useful to inspect the residual images. We have a few options on the dataset object to handle this. First we can use `.plot_residuals_spatial()` to plot a residual image, summed over all energies:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:33.655919Z", "iopub.status.busy": "2021-11-22T21:08:33.655610Z", "iopub.status.idle": "2021-11-22T21:08:33.757137Z", "shell.execute_reply": "2021-11-22T21:08:33.757336Z" }, "nbsphinx-thumbnail": { "tooltip": "Introduction to Gammapy analysis using the low level API." } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/mambaforge/envs/gammapy-dev/lib/python3.9/site-packages/astropy/visualization/wcsaxes/core.py:211: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.\n", " return super().imshow(X, *args, origin=origin, **kwargs)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "stacked.plot_residuals_spatial(method=\"diff/sqrt(model)\", vmin=-0.5, vmax=0.5);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition, we can also specify a region in the map to show the spectral residuals:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:33.759726Z", "iopub.status.busy": "2021-11-22T21:08:33.759377Z", "iopub.status.idle": "2021-11-22T21:08:33.760482Z", "shell.execute_reply": "2021-11-22T21:08:33.760783Z" } }, "outputs": [], "source": [ "region = CircleSkyRegion(\n", " center=SkyCoord(\"83.63 deg\", \"22.14 deg\"), radius=0.5 * u.deg\n", ")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:33.776230Z", "iopub.status.busy": "2021-11-22T21:08:33.765035Z", "iopub.status.idle": "2021-11-22T21:08:33.992904Z", "shell.execute_reply": "2021-11-22T21:08:33.993112Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/mambaforge/envs/gammapy-dev/lib/python3.9/site-packages/astropy/visualization/wcsaxes/core.py:211: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.\n", " return super().imshow(X, *args, origin=origin, **kwargs)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "stacked.plot_residuals(\n", " kwargs_spatial=dict(method=\"diff/sqrt(model)\", vmin=-0.5, vmax=0.5),\n", " kwargs_spectral=dict(region=region),\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": 24, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:33.995397Z", "iopub.status.busy": "2021-11-22T21:08:33.995056Z", "iopub.status.idle": "2021-11-22T21:08:34.143936Z", "shell.execute_reply": "2021-11-22T21:08:34.143674Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c45d8772c5074cdd92521f5388f835b3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(SelectionSlider(continuous_update=False, description='Select energy:', layout=Layout(wid…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "residuals = stacked.residuals(method=\"diff\")\n", "residuals.smooth(\"0.08 deg\").plot_interactive(\n", " cmap=\"coolwarm\", vmin=-0.2, vmax=0.2, stretch=\"linear\", add_cbar=True\n", ");" ] }, { "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 envelope of the model taking into account parameter uncertainties:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:34.146558Z", "iopub.status.busy": "2021-11-22T21:08:34.146252Z", "iopub.status.idle": "2021-11-22T21:08:34.147210Z", "shell.execute_reply": "2021-11-22T21:08:34.147401Z" } }, "outputs": [], "source": [ "spec = sky_model.spectral_model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can actually do the plot using the `plot_error` method:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:34.189175Z", "iopub.status.busy": "2021-11-22T21:08:34.156358Z", "iopub.status.idle": "2021-11-22T21:08:34.308223Z", "shell.execute_reply": "2021-11-22T21:08:34.308504Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "energy_bounds = [1, 10] * u.TeV\n", "spec.plot(energy_bounds=energy_bounds, energy_power=2)\n", "ax = spec.plot_error(energy_bounds=energy_bounds, energy_power=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Computing flux points\n", "\n", "We can now compute some flux points using the `~gammapy.estimators.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": 27, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:34.310608Z", "iopub.status.busy": "2021-11-22T21:08:34.310264Z", "iopub.status.idle": "2021-11-22T21:08:34.311502Z", "shell.execute_reply": "2021-11-22T21:08:34.311699Z" } }, "outputs": [], "source": [ "energy_edges = [1, 2, 4, 10] * u.TeV\n", "fpe = FluxPointsEstimator(energy_edges=energy_edges, source=\"crab\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:34.313653Z", "iopub.status.busy": "2021-11-22T21:08:34.313328Z", "iopub.status.idle": "2021-11-22T21:08:35.392830Z", "shell.execute_reply": "2021-11-22T21:08:35.393031Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.07 s, sys: 103 ms, total: 1.18 s\n", "Wall time: 1.08 s\n" ] } ], "source": [ "%%time\n", "flux_points = fpe.run(datasets=[stacked])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:35.402913Z", "iopub.status.busy": "2021-11-22T21:08:35.401956Z", "iopub.status.idle": "2021-11-22T21:08:35.573292Z", "shell.execute_reply": "2021-11-22T21:08:35.573581Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "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": [ "ax = spec.plot_error(energy_bounds=energy_bounds, energy_power=2)\n", "flux_points.plot(ax=ax, energy_power=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.9.0" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1.0, "eqLabelWithNumbers": true, "eqNumInitial": 1.0, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "nbsphinx": { "orphan": true }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "0d12b19797154cfc97357dc103ab75b5": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_3bfc00912b794d09866acaf3a0097d1d", "msg_id": "", "outputs": [ { "name": "stderr", "output_type": "stream", "text": "/Users/adonath/software/mambaforge/envs/gammapy-dev/lib/python3.9/site-packages/astropy/visualization/wcsaxes/core.py:211: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.\n return super().imshow(X, *args, origin=origin, **kwargs)\n" }, { "data": { "image/png": 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\n", 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