.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "tutorials/analysis-2d/modeling_2D.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_tutorials_analysis-2d_modeling_2D.py: 2D map fitting ============== Source modelling and fitting in stacked observations using the high level interface. Prerequisites ------------- - To understand how a general modelling and fitting works in gammapy, please refer to the :doc:`/tutorials/analysis-3d/analysis_3d` tutorial. Context ------- We often want the determine the position and morphology of an object. To do so, we don’t necessarily have to resort to a full 3D fitting but can perform a simple image fitting, in particular, in an energy range where the PSF does not vary strongly, or if we want to explore a possible energy dependence of the morphology. Objective --------- To localize a source and/or constrain its morphology. Proposed approach ----------------- The first step here, as in most analysis with DL3 data, is to create reduced datasets. For this, we will use the `Analysis` class to create a single set of stacked maps with a single bin in energy (thus, an *image* which behaves as a *cube*). This, we will then model with a spatial model of our choice, while keeping the spectral model fixed to an integrated power law. .. GENERATED FROM PYTHON SOURCE LINES 38-43 .. code-block:: Python # %matplotlib inline import astropy.units as u import matplotlib.pyplot as plt .. GENERATED FROM PYTHON SOURCE LINES 44-49 Setup ----- As usual, we’ll start with some general imports… .. GENERATED FROM PYTHON SOURCE LINES 49-52 .. code-block:: Python from IPython.display import display from gammapy.analysis import Analysis, AnalysisConfig .. GENERATED FROM PYTHON SOURCE LINES 53-55 Check setup ----------- .. GENERATED FROM PYTHON SOURCE LINES 55-60 .. code-block:: Python from gammapy.utils.check import check_tutorials_setup check_tutorials_setup() .. rst-class:: sphx-glr-script-out .. code-block:: none System: python_executable : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/bin/python python_version : 3.9.20 machine : x86_64 system : Linux Gammapy package: version : 1.3 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy Other packages: numpy : 1.26.4 scipy : 1.13.1 astropy : 5.2.2 regions : 0.8 click : 8.1.7 yaml : 6.0.2 IPython : 8.18.1 jupyterlab : not installed matplotlib : 3.9.2 pandas : not installed healpy : 1.17.3 iminuit : 2.30.1 sherpa : 4.16.1 naima : 0.10.0 emcee : 3.1.6 corner : 2.2.3 ray : 2.39.0 Gammapy environment variables: GAMMAPY_DATA : /home/runner/work/gammapy-docs/gammapy-docs/gammapy-datasets/1.3 .. GENERATED FROM PYTHON SOURCE LINES 61-69 Creating the config file ------------------------ Now, we create a config file for out analysis. You may load this from disc if you have a pre-defined config file. Here, we use 3 simulated CTAO runs of the galactic center. .. GENERATED FROM PYTHON SOURCE LINES 69-76 .. code-block:: Python config = AnalysisConfig() # Selecting the observations config.observations.datastore = "$GAMMAPY_DATA/cta-1dc/index/gps/" config.observations.obs_ids = [110380, 111140, 111159] .. GENERATED FROM PYTHON SOURCE LINES 77-81 Technically, gammapy implements 2D analysis as a special case of 3D analysis (one bin in energy). So, we must specify the type of analysis as *3D*, and define the geometry of the analysis. .. GENERATED FROM PYTHON SOURCE LINES 81-108 .. code-block:: Python config.datasets.type = "3d" config.datasets.geom.wcs.skydir = { "lon": "0 deg", "lat": "0 deg", "frame": "galactic", } # The WCS geometry - centered on the galactic center config.datasets.geom.wcs.width = {"width": "8 deg", "height": "6 deg"} config.datasets.geom.wcs.binsize = "0.02 deg" # The FoV radius to use for cutouts config.datasets.geom.selection.offset_max = 2.5 * u.deg config.datasets.safe_mask.methods = ["offset-max"] config.datasets.safe_mask.parameters = {"offset_max": "2.5 deg"} config.datasets.background.method = "fov_background" config.fit.fit_range = {"min": "0.1 TeV", "max": "30.0 TeV"} # We now fix the energy axis for the counts map - (the reconstructed energy binning) config.datasets.geom.axes.energy.min = "0.1 TeV" config.datasets.geom.axes.energy.max = "10 TeV" config.datasets.geom.axes.energy.nbins = 1 config.datasets.geom.wcs.binsize_irf = "0.2 deg" print(config) .. rst-class:: sphx-glr-script-out .. code-block:: none AnalysisConfig general: log: level: info filename: null filemode: null format: null datefmt: null outdir: . n_jobs: 1 datasets_file: null models_file: null observations: datastore: /home/runner/work/gammapy-docs/gammapy-docs/gammapy-datasets/1.3/cta-1dc/index/gps obs_ids: - 110380 - 111140 - 111159 obs_file: null obs_cone: frame: null lon: null lat: null radius: null obs_time: start: null stop: null required_irf: - aeff - edisp - psf - bkg datasets: type: 3d stack: true geom: wcs: skydir: frame: galactic lon: 0.0 deg lat: 0.0 deg binsize: 0.02 deg width: width: 8.0 deg height: 6.0 deg binsize_irf: 0.2 deg selection: offset_max: 2.5 deg axes: energy: min: 0.1 TeV max: 10.0 TeV nbins: 1 energy_true: min: 0.5 TeV max: 20.0 TeV nbins: 16 map_selection: - counts - exposure - background - psf - edisp background: method: fov_background exclusion: null parameters: {} safe_mask: methods: - offset-max parameters: offset_max: 2.5 deg on_region: frame: null lon: null lat: null radius: null containment_correction: true fit: fit_range: min: 0.1 TeV max: 30.0 TeV flux_points: energy: min: null max: null nbins: null source: source parameters: selection_optional: all excess_map: correlation_radius: 0.1 deg parameters: {} energy_edges: min: null max: null nbins: null light_curve: time_intervals: start: null stop: null energy_edges: min: null max: null nbins: null source: source parameters: selection_optional: all metadata: creator: Gammapy 1.3 date: '2024-11-26T10:11:33.120258' origin: null .. GENERATED FROM PYTHON SOURCE LINES 109-112 Getting the reduced dataset --------------------------- .. GENERATED FROM PYTHON SOURCE LINES 115-118 We now use the config file and create a single `MapDataset` containing `counts`, `background`, `exposure`, `psf` and `edisp` maps. .. GENERATED FROM PYTHON SOURCE LINES 120-127 .. code-block:: Python analysis = Analysis(config) analysis.get_observations() analysis.get_datasets() print(analysis.datasets["stacked"]) .. rst-class:: sphx-glr-script-out .. code-block:: none /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/astropy/units/core.py:2097: UnitsWarning: '1/s/MeV/sr' did not parse as fits unit: Numeric factor not supported by FITS If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html warnings.warn(msg, UnitsWarning) /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/astropy/units/core.py:2097: UnitsWarning: '1/s/MeV/sr' did not parse as fits unit: Numeric factor not supported by FITS If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html warnings.warn(msg, UnitsWarning) /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/astropy/units/core.py:2097: UnitsWarning: '1/s/MeV/sr' did not parse as fits unit: Numeric factor not supported by FITS If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html warnings.warn(msg, UnitsWarning) MapDataset ---------- Name : stacked Total counts : 85625 Total background counts : 85624.99 Total excess counts : 0.01 Predicted counts : 85625.00 Predicted background counts : 85624.99 Predicted excess counts : nan Exposure min : 8.46e+08 m2 s Exposure max : 2.14e+10 m2 s Number of total bins : 120000 Number of fit bins : 96602 Fit statistic type : cash Fit statistic value (-2 log(L)) : nan Number of models : 0 Number of parameters : 0 Number of free parameters : 0 .. GENERATED FROM PYTHON SOURCE LINES 128-133 The counts and background maps have only one bin in reconstructed energy. The exposure and IRF maps are in true energy, and hence, have a different binning based upon the binning of the IRFs. We need not bother about them presently. .. GENERATED FROM PYTHON SOURCE LINES 133-141 .. code-block:: Python print(analysis.datasets["stacked"].counts) print(analysis.datasets["stacked"].background) print(analysis.datasets["stacked"].exposure) .. rst-class:: sphx-glr-script-out .. code-block:: none WcsNDMap geom : WcsGeom axes : ['lon', 'lat', 'energy'] shape : (400, 300, 1) ndim : 3 unit : dtype : float32 WcsNDMap geom : WcsGeom axes : ['lon', 'lat', 'energy'] shape : (400, 300, 1) ndim : 3 unit : dtype : float32 WcsNDMap geom : WcsGeom axes : ['lon', 'lat', 'energy_true'] shape : (400, 300, 16) ndim : 3 unit : m2 s dtype : float32 .. GENERATED FROM PYTHON SOURCE LINES 142-144 We can have a quick look of these maps in the following way: .. GENERATED FROM PYTHON SOURCE LINES 144-149 .. code-block:: Python analysis.datasets["stacked"].counts.reduce_over_axes().plot(vmax=10, add_cbar=True) plt.show() .. image-sg:: /tutorials/analysis-2d/images/sphx_glr_modeling_2D_001.png :alt: modeling 2D :srcset: /tutorials/analysis-2d/images/sphx_glr_modeling_2D_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 150-158 Modelling --------- Now, we define a model to be fitted to the dataset. **The important thing to note here is the dummy spectral model - an integrated powerlaw with only free normalisation**. Here, we use its YAML definition to load it: .. GENERATED FROM PYTHON SOURCE LINES 158-196 .. code-block:: Python model_config = """ components: - name: GC-1 type: SkyModel spatial: type: PointSpatialModel frame: galactic parameters: - name: lon_0 value: 0.02 unit: deg - name: lat_0 value: 0.01 unit: deg spectral: type: PowerLaw2SpectralModel parameters: - name: amplitude value: 1.0e-12 unit: cm-2 s-1 - name: index value: 2.0 unit: '' frozen: true - name: emin value: 0.1 unit: TeV frozen: true - name: emax value: 10.0 unit: TeV frozen: true """ analysis.set_models(model_config) .. GENERATED FROM PYTHON SOURCE LINES 197-199 We will freeze the parameters of the background .. GENERATED FROM PYTHON SOURCE LINES 199-207 .. code-block:: Python analysis.datasets["stacked"].background_model.parameters["tilt"].frozen = True # To run the fit analysis.run_fit() # To see the best fit values along with the errors display(analysis.models.to_parameters_table()) .. rst-class:: sphx-glr-script-out .. code-block:: none model type name value unit ... max frozen link prior ----------- ---- --------- ----------- -------- ... --------- ------ ---- ----- GC-1 amplitude 4.1626e-11 cm-2 s-1 ... nan False GC-1 index 2.0000e+00 ... nan True GC-1 emin 1.0000e-01 TeV ... nan True GC-1 emax 1.0000e+01 TeV ... nan True GC-1 lon_0 -5.4638e-02 deg ... nan False GC-1 lat_0 -4.6395e-02 deg ... 9.000e+01 False stacked-bkg norm 9.9441e-01 ... nan False stacked-bkg tilt 0.0000e+00 ... nan True stacked-bkg reference 1.0000e+00 TeV ... nan True .. _sphx_glr_download_tutorials_analysis-2d_modeling_2D.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v1.3?urlpath=lab/tree/notebooks/1.3/tutorials/analysis-2d/modeling_2D.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: modeling_2D.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: modeling_2D.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: modeling_2D.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_