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the feasibility of a measurement, to test whether fitted parameters really provide a good fit to the data etc. -Here we will see how to perform a 1D spectral simulation of a CTA +Here we will see how to perform a 1D spectral simulation of a CTAO observation, in particular, we will generate OFF observations following -the template background stored in the CTA IRFs. +the template background stored in the CTAO IRFs. **Objective: simulate a number of spectral ON-OFF observations of a -source with a power-law spectral model with CTA using the CTA 1DC +source with a power-law spectral model with CTAO using the CTA 1DC response, fit them with the assumed spectral model and check that the distribution of fitted parameters is consistent with the input values.** diff --git a/docs/dev/_downloads/1d3f6beaf08c01b5183c9c9291d0b28a/plot_piecewise_norm_spectral.zip b/docs/dev/_downloads/1d3f6beaf08c01b5183c9c9291d0b28a/plot_piecewise_norm_spectral.zip index c77b3721950..0ee77dd0f05 100644 Binary files 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b/docs/dev/_downloads/32b142b07467adc0ce252950fb293e8a/cta_sensitivity.py index 3d755d3dd02..78509d49128 100644 --- a/docs/dev/_downloads/32b142b07467adc0ce252950fb293e8a/cta_sensitivity.py +++ b/docs/dev/_downloads/32b142b07467adc0ce252950fb293e8a/cta_sensitivity.py @@ -2,12 +2,12 @@ Point source sensitivity ======================== -Estimate the CTA sensitivity for a point-like IRF at a fixed zenith angle and fixed offset. +Estimate the CTAO sensitivity for a point-like IRF at a fixed zenith angle and fixed offset. Introduction ------------ -This notebook explains how to estimate the CTA sensitivity for a +This notebook explains how to estimate the CTAO sensitivity for a point-like IRF at a fixed zenith angle and fixed offset, using the full containment IRFs distributed for the CTA 1DC. The significance is computed for a 1D analysis (ON-OFF regions) with the Li&Ma formula. @@ -81,7 +81,7 @@ # Load IRFs and prepare dataset # ----------------------------- # -# We extract the 1D IRFs from the full 3D IRFs provided by CTA. +# We extract the 1D IRFs from the full 3D IRFs provided by CTAO. # irfs = load_irf_dict_from_file( @@ -153,7 +153,7 @@ # # We assume an alpha of 0.2 (ratio between ON and OFF area). 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a/docs/dev/_downloads/51d6cfa441dab374164568c0a25aafdf/plot_snr_radius_evolution.pdf b/docs/dev/_downloads/51d6cfa441dab374164568c0a25aafdf/plot_snr_radius_evolution.pdf index d16efd8763d..db1f0c41353 100644 Binary files a/docs/dev/_downloads/51d6cfa441dab374164568c0a25aafdf/plot_snr_radius_evolution.pdf and b/docs/dev/_downloads/51d6cfa441dab374164568c0a25aafdf/plot_snr_radius_evolution.pdf differ diff --git a/docs/dev/_downloads/52215788338df5a5cca4ad9f70681d84/cta.py b/docs/dev/_downloads/52215788338df5a5cca4ad9f70681d84/cta.py index ec36908e90e..afc77daeb2b 100644 --- a/docs/dev/_downloads/52215788338df5a5cca4ad9f70681d84/cta.py +++ b/docs/dev/_downloads/52215788338df5a5cca4ad9f70681d84/cta.py @@ -1,58 +1,58 @@ """ -CTA with Gammapy -================ +CTAO with Gammapy +================= -Access and inspect CTA data and instrument response functions (IRFs) using Gammapy. +Access and inspect CTAO data and instrument response functions (IRFs) using Gammapy. Introduction ------------ -The `Cherenkov Telescope Array -(CTA) `__ is the next generation +The `Cherenkov Telescope Array Observatory (CTAO) `__ is the next generation ground-based observatory for gamma-ray astronomy. Gammapy is the core -library for the Cherenkov Telescope Array (CTA) science tools +library for the Cherenkov Telescope Array Observatory (CTAO) science tools (`2017ICRC…35..766D `__ and `CTAO Press -Release `__). +Release `__). -CTA will start taking data in the coming years. For now, to learn how to -analyse CTA data and to use Gammapy, if you are a member of the CTA -consortium, you can use the simulated dataset from the CTA first data -challenge which ran in 2017 and 2018. +CTAO will start taking data in the coming years. For now, to learn how to +analyse CTAO data and to use Gammapy, if you are a member of the CTAO +consortium, you can use the simulated dataset from: -- https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki (CTA - internal) +- the CTA first data challenge which ran in 2017 and 2018 (https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki), +- the CTAO Science Data Challenge of 2024 (https://ctaoobservatory.sharepoint.com/:f:/r/sites/share-open-data/Shared%20Documents/Reference%20datasets/Internal%20Science%20Data%20Challenge?csf=1&web=1&e=gNuFzI) Gammapy fully supports the FITS data formats (events, IRFs) used in CTA -1DC. The XML sky model format is not supported, but are also not needed +1DC and SDC. The XML sky model format is not supported, but are also not needed to analyse the data, you have to specify your model via the Gammapy YAML model format, or Python code, as shown below. -You can use Gammapy to simulate CTA data and evaluate CTA performance -using the CTA response files available here: +You can also use Gammapy to simulate CTAO data and evaluate CTAO performance +using the CTAO response files. Two sets of responses are available for different +array layouts: -- https://www.cta-observatory.org/science/cta-performance/ +- the Omega configuration (prod3b, 2016): https://zenodo.org/records/5163273, +- the Alpha configuration (prod5, 2021): https://zenodo.org/records/5499840. + +They are all fully supported by Gammapy. -The current FITS format `CTA-Performance-prod3b-v2-FITS.tar` is fully -supported by Gammapy, as shown below. Tutorial overview ----------------- -This notebook shows how to access CTA data and instrument response +This notebook shows how to access CTAO data and instrument response functions (IRFs) using Gammapy, and gives some examples how to quick -look the content of CTA files, especially to see the shape of CTA IRFs. +look the content of CTAO files, especially to see the shape of CTAO IRFs. At the end of the notebooks, we give several links to other tutorial -notebooks that show how to simulate CTA data and how to evaluate CTA -observability and sensitivity, or how to analyse CTA data. +notebooks that show how to simulate CTAO data and how to evaluate CTAO +observability and sensitivity, or how to analyse CTAO data. -Note that the FITS data and IRF format currently used by CTA is the one +Note that the FITS data and IRF format currently used by CTAO is the one documented at https://gamma-astro-data-formats.readthedocs.io/, and is -also used by H.E.S.S. and other imaging atmospheric Cherenkov telescopes +also used by H.E.S.S. and other Imaging Atmospheric Cherenkov Telescopes (IACTs). So if you see other Gammapy tutorials using e.g. H.E.S.S. -example data, know that they also apply to CTA, all you have to do is to -change the loaded data or IRFs to CTA. +example data, know that they also apply to CTAO, all you have to do is to +change the loaded data or IRFs to CTAO. Setup ----- @@ -95,18 +95,18 @@ # After download, follow the instructions how to `untar` the files, and # set a `CTADATA` environment variable to point to the data. # -# For convenience, since the 1DC data files are large, and not publicly +# **For convenience**, since the 1DC data files are large and not publicly # available to anyone, we have taken a tiny subset of the CTA 1DC data, # four observations with the southern array from the GPS survey, pointing -# near the Galactic center, and included them at `$GAMMAPY_DATA/cta-1dc` +# near the Galactic center, and **included them at `$GAMMAPY_DATA/cta-1dc`** # which you get via `gammapy download datasets`. # # Files # ~~~~~ # # Next we will show a quick overview of the files and how to load them, -# and some quick look plots showing the shape of the CTA IRFs. How to do -# CTA simulations and analyses is shown in other tutorials, see links at +# and some quick look plots showing the shape of the CTAO IRFs. How to do +# CTAO simulations and analyses is shown in other tutorials, see links at # the end of this notebook. # @@ -164,9 +164,9 @@ # equivalently via the `~gammapy.data.EventList` class by specifying the # EVENTS filename. # -# The quick-look `events.peek()` plot below shows that CTA has a field +# The quick-look `events.peek()` plot below shows that CTAO has a field # of view of a few degrees, and two energy thresholds, one significantly -# below 100 GeV where the CTA large-size telescopes (LSTs) detect events, +# below 100 GeV where the CTAO large-size telescopes (LSTs) detect events, # and a second one near 100 GeV where the mid-sized telescopes (MSTs) # start to detect events. # @@ -206,7 +206,7 @@ # IRFs # ---- # -# The CTA instrument response functions (IRFs) are given as FITS files in +# The CTAO instrument response functions (IRFs) are given as FITS files in # the `caldb` folder, the following IRFs are available: # # - effective area @@ -216,12 +216,12 @@ # # Notes: # -# - The IRFs contain the energy and offset dependence of the CTA response -# - CTA 1DC was based on an early version of the CTA FITS responses +# - The IRFs contain the energy and offset dependence of the CTAO response +# - CTA 1DC was based on an early version of the CTAO FITS responses # produced in 2017, improvements have been made since. # - The point spread function was approximated by a Gaussian shape # - The background is from hadronic and electron air shower events that -# pass CTA selection cuts. It was given as a function of field of view +# pass CTAO selection cuts. It was given as a function of field of view # coordinates, although it is radially symmetric. # - The energy dispersion in CTA 1DC is noisy at low energy, leading to # unreliable spectral points for some analyses. @@ -355,28 +355,28 @@ ###################################################################### -# CTA performance files -# --------------------- +# Latest CTAO performance files +# ----------------------------- # -# CTA 1DC is useful to learn how to analyse CTA data. But to do -# simulations and studies for CTA now, you should get the most recent CTA -# IRFs in FITS format from -# https://www.cta-observatory.org/science/cta-performance/ +# CTA 1DC is useful to learn how to analyse CTAO data. But to do +# simulations and studies for CTAO now, you should get the most recent CTAO +# IRFs in FITS format from https://www.ctao.org/for-scientists/performance/. # -# If you want to run the download and examples in the next code cells, -# remove the # to uncomment. +# If you want to use other response files, the following code cells (remove the # to uncomment) +# explain how to proceed. This example is made with the Alpha configuration (Prod5). # -# !curl -O https://www.cta-observatory.org/wp-content/uploads/2019/04/CTA-Performance-prod3b-v2-FITS.tar.gz - -# !tar xf CTA-Performance-prod3b-v2-FITS.tar.gz +# !curl -o cta-prod5-zenodo-fitsonly-v0.1.zip https://zenodo.org/records/5499840/files/cta-prod5-zenodo-fitsonly-v0.1.zip +# !unzip cta-prod5-zenodo-fitsonly-v0.1.zip +# !ls fits/ -# !ls caldb/data/cta/prod3b-v2/bcf +# !tar xf fits/CTA-Performance-prod5-v0.1-South-40deg.FITS.tar.gz -C fits/. +# !ls fits/*.fits.gz -# irfs1 = load_irf_dict_from_file("caldb/data/cta/prod3b-v2/bcf/South_z20_50h/irf_file.fits") +# irfs1 = load_irf_dict_from_file("fits/Prod5-South-40deg-SouthAz-14MSTs37SSTs.180000s-v0.1.fits.gz") # irfs1["aeff"].plot_energy_dependence() -# irfs2 = load_irf_dict_from_file("caldb/data/cta/prod3b-v2/bcf/South_z40_50h/irf_file.fits") +# irfs2 = load_irf_dict_from_file("fits/Prod5-South-40deg-SouthAz-14MSTs37SSTs.1800s-v0.1.fits.gz") # irfs2["aeff"].plot_energy_dependence() @@ -391,11 +391,11 @@ # - Use `~gammapy.data.EventList.pointing_radec` to find the pointing position of this # observation, and use `astropy.coordinates.SkyCoord` methods to find # the field of view offset of the highest-energy event. -# - What is the effective area and PSF 68% containment radius of CTA at 1 +# - What is the effective area and PSF 68% containment radius of CTAO at 1 # TeV for the `South_z20_50h` configuration used for the CTA 1DC # simulation? -# - Get the latest CTA FITS performance files from -# https://www.cta-observatory.org/science/cta-performance/ and run the +# - Get the latest CTAO FITS performance files from +# https://www.ctao.org/for-scientists/performance/ and run the # code example above. Make an effective area ratio plot of 40 deg # zenith versus 20 deg zenith for the `South_z40_50h` and # `South_z20_50h` configurations. @@ -412,7 +412,7 @@ # :doc:`/tutorials/starting/analysis_1` and # :doc:`/tutorials/starting/analysis_2` or any other # Gammapy analysis tutorial. -# - Learn how to evaluate CTA observability and sensitivity with +# - Learn how to evaluate CTAO observability and sensitivity with # :doc:`/tutorials/analysis-3d/simulate_3d`, # :doc:`/tutorials/analysis-1d/spectrum_simulation` # or :doc:`/tutorials/analysis-1d/cta_sensitivity`. diff --git a/docs/dev/_downloads/5375f8e9e54dd5a68d37e8a2fc4246b9/plot_spiral_arms.png b/docs/dev/_downloads/5375f8e9e54dd5a68d37e8a2fc4246b9/plot_spiral_arms.png index e9d675367af..77b3b66ab29 100644 Binary files a/docs/dev/_downloads/5375f8e9e54dd5a68d37e8a2fc4246b9/plot_spiral_arms.png and b/docs/dev/_downloads/5375f8e9e54dd5a68d37e8a2fc4246b9/plot_spiral_arms.png differ diff --git a/docs/dev/_downloads/5d70733460edff98657b0fd5d0eb69e4/modeling_2D.zip b/docs/dev/_downloads/5d70733460edff98657b0fd5d0eb69e4/modeling_2D.zip index 2bec2e2d8b4..1e8db596d6d 100644 Binary files a/docs/dev/_downloads/5d70733460edff98657b0fd5d0eb69e4/modeling_2D.zip and b/docs/dev/_downloads/5d70733460edff98657b0fd5d0eb69e4/modeling_2D.zip differ diff --git a/docs/dev/_downloads/5f7885af4d03116bd8091cb6252eae48/analysis_3d.zip b/docs/dev/_downloads/5f7885af4d03116bd8091cb6252eae48/analysis_3d.zip index f09c207f730..1ddfebed58b 100644 Binary files a/docs/dev/_downloads/5f7885af4d03116bd8091cb6252eae48/analysis_3d.zip and b/docs/dev/_downloads/5f7885af4d03116bd8091cb6252eae48/analysis_3d.zip differ diff --git a/docs/dev/_downloads/60399e3ce9aa0bfe3b7ca8f286fad1ce/plot_powerlaw_norm_spectral.zip b/docs/dev/_downloads/60399e3ce9aa0bfe3b7ca8f286fad1ce/plot_powerlaw_norm_spectral.zip index 769af26cd50..d8cb0af7060 100644 Binary files a/docs/dev/_downloads/60399e3ce9aa0bfe3b7ca8f286fad1ce/plot_powerlaw_norm_spectral.zip and b/docs/dev/_downloads/60399e3ce9aa0bfe3b7ca8f286fad1ce/plot_powerlaw_norm_spectral.zip differ diff --git a/docs/dev/_downloads/66ad9a060e067da15dee5cf2536ae998/spectral_analysis_rad_max.zip b/docs/dev/_downloads/66ad9a060e067da15dee5cf2536ae998/spectral_analysis_rad_max.zip index 8801cbea8b9..656fdc532af 100644 Binary files a/docs/dev/_downloads/66ad9a060e067da15dee5cf2536ae998/spectral_analysis_rad_max.zip and b/docs/dev/_downloads/66ad9a060e067da15dee5cf2536ae998/spectral_analysis_rad_max.zip differ diff --git a/docs/dev/_downloads/6717db513fe7901658f170b575b36cdd/Variability_estimation.zip b/docs/dev/_downloads/6717db513fe7901658f170b575b36cdd/Variability_estimation.zip index dcdf11516c0..d797a56bf55 100644 Binary files a/docs/dev/_downloads/6717db513fe7901658f170b575b36cdd/Variability_estimation.zip and b/docs/dev/_downloads/6717db513fe7901658f170b575b36cdd/Variability_estimation.zip differ diff --git a/docs/dev/_downloads/67d2e7080c8b88e8cbe4ee86b4306a9f/event_sampling_nrg_depend_models.ipynb b/docs/dev/_downloads/67d2e7080c8b88e8cbe4ee86b4306a9f/event_sampling_nrg_depend_models.ipynb index c0c7066aeac..3baa260ad36 100644 --- a/docs/dev/_downloads/67d2e7080c8b88e8cbe4ee86b4306a9f/event_sampling_nrg_depend_models.ipynb +++ b/docs/dev/_downloads/67d2e7080c8b88e8cbe4ee86b4306a9f/event_sampling_nrg_depend_models.ipynb @@ -191,7 +191,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Define an observation and make a dataset\n\nIn the following, we define an observation of 1 hr with CTA in the\nalpha-configuration for the south array, and we also create a dataset\nto be passed to the event sampler. The full `SkyModel` created above\nis passed to the dataset.\n\n\n" + "### Define an observation and make a dataset\n\nIn the following, we define an observation of 1 hr with CTAO in the\nalpha-configuration for the south array, and we also create a dataset\nto be passed to the event sampler. The full `SkyModel` created above\nis passed to the dataset.\n\n\n" ] }, { diff --git a/docs/dev/_downloads/682e4483582d9fdb778dbd09a795d104/plot_compound.zip b/docs/dev/_downloads/682e4483582d9fdb778dbd09a795d104/plot_compound.zip index c2a056fb278..0f0ca53fb56 100644 Binary files a/docs/dev/_downloads/682e4483582d9fdb778dbd09a795d104/plot_compound.zip and b/docs/dev/_downloads/682e4483582d9fdb778dbd09a795d104/plot_compound.zip differ diff --git a/docs/dev/_downloads/692bf70e2aa9caeb987bbb35050f71d2/astro_dark_matter.zip b/docs/dev/_downloads/692bf70e2aa9caeb987bbb35050f71d2/astro_dark_matter.zip index 301ae401124..6fe5993c3d1 100644 Binary files a/docs/dev/_downloads/692bf70e2aa9caeb987bbb35050f71d2/astro_dark_matter.zip and b/docs/dev/_downloads/692bf70e2aa9caeb987bbb35050f71d2/astro_dark_matter.zip differ diff --git a/docs/dev/_downloads/6a282de403487d7431bc34ed71c14d71/hess.py b/docs/dev/_downloads/6a282de403487d7431bc34ed71c14d71/hess.py index e86583a857b..6e9c40ec1c7 100644 --- a/docs/dev/_downloads/6a282de403487d7431bc34ed71c14d71/hess.py +++ b/docs/dev/_downloads/6a282de403487d7431bc34ed71c14d71/hess.py @@ -10,7 +10,7 @@ format `__. The H.E.S.S. data is private, and H.E.S.S. analysis is mostly documented -and discussed at https://hess-confluence.desy.de/ and in +and discussed in the internal Wiki pages and in H.E.S.S.-internal communication channels. However, in 2018, a small sub-set of archival H.E.S.S. data was publicly released, called the `H.E.S.S. DL3 @@ -26,9 +26,8 @@ H.E.S.S. members can find details on the DL3 FITS production on this `Confluence -page `__ -and access more detailed tutorials in this -`repository `__ +page `__ +and access more detailed tutorials in the BitBucket `hess-open-tools` repository. DL3 DR1 ------- @@ -146,6 +145,6 @@ # ---------- # # Now you know how to access and work with H.E.S.S. data. All other -# tutorials and documentation apply to H.E.S.S. and CTA or any other IACT +# tutorials and documentation apply to H.E.S.S. and CTAO or any other IACT # that provides DL3 data and IRFs in the standard format. # diff --git a/docs/dev/_downloads/6e47fca124ba94afacd2e1f76cf61081/plot_template_phase_temporal.zip b/docs/dev/_downloads/6e47fca124ba94afacd2e1f76cf61081/plot_template_phase_temporal.zip index cee3213ecb4..9e59776503f 100644 Binary files a/docs/dev/_downloads/6e47fca124ba94afacd2e1f76cf61081/plot_template_phase_temporal.zip and b/docs/dev/_downloads/6e47fca124ba94afacd2e1f76cf61081/plot_template_phase_temporal.zip differ diff --git a/docs/dev/_downloads/7005cb7d15e1b931b441b7a6bac95441/modeling_2D.ipynb b/docs/dev/_downloads/7005cb7d15e1b931b441b7a6bac95441/modeling_2D.ipynb index 05af45c5b88..743fe2ab918 100644 --- a/docs/dev/_downloads/7005cb7d15e1b931b441b7a6bac95441/modeling_2D.ipynb +++ b/docs/dev/_downloads/7005cb7d15e1b931b441b7a6bac95441/modeling_2D.ipynb @@ -58,7 +58,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Creating the config file\n\nNow, we create a config file for out analysis. You may load this from\ndisc if you have a pre-defined config file.\n\nHere, we use 3 simulated CTA runs of the galactic center.\n\n\n" + "## Creating the config file\n\nNow, we create a config file for out analysis. You may load this from\ndisc if you have a pre-defined config file.\n\nHere, we use 3 simulated CTAO runs of the galactic center.\n\n\n" ] }, { diff --git a/docs/dev/_downloads/70db13717c83b8e636f99ad0018eeb90/plot_template.zip b/docs/dev/_downloads/70db13717c83b8e636f99ad0018eeb90/plot_template.zip index 09022522065..8a29a476b54 100644 Binary files a/docs/dev/_downloads/70db13717c83b8e636f99ad0018eeb90/plot_template.zip and b/docs/dev/_downloads/70db13717c83b8e636f99ad0018eeb90/plot_template.zip differ diff --git a/docs/dev/_downloads/711c78a1a60c4f304b61eca7c205cd02/pulsar_analysis.py b/docs/dev/_downloads/711c78a1a60c4f304b61eca7c205cd02/pulsar_analysis.py index c031536ad75..c52a63c2c14 100644 --- a/docs/dev/_downloads/711c78a1a60c4f304b61eca7c205cd02/pulsar_analysis.py +++ b/docs/dev/_downloads/711c78a1a60c4f304b61eca7c205cd02/pulsar_analysis.py @@ -331,7 +331,7 @@ ###################################################################### -# Note that here we are lacking statistic because we only use one run of CTA. +# Note that here we are lacking statistic because we only use one run of CTAO. # # Phase-resolved spectrum # ----------------------- diff --git a/docs/dev/_downloads/73e595818811c6987149d60742eef584/make_reflected_regions.pdf b/docs/dev/_downloads/73e595818811c6987149d60742eef584/make_reflected_regions.pdf index 37ac4280a64..6113493f906 100644 Binary files a/docs/dev/_downloads/73e595818811c6987149d60742eef584/make_reflected_regions.pdf and b/docs/dev/_downloads/73e595818811c6987149d60742eef584/make_reflected_regions.pdf differ diff --git a/docs/dev/_downloads/73e7de11148f573eeb51f279652f7b9b/make_rectangular_reflected_background.pdf b/docs/dev/_downloads/73e7de11148f573eeb51f279652f7b9b/make_rectangular_reflected_background.pdf index 84418210e79..f5b1a279334 100644 Binary files a/docs/dev/_downloads/73e7de11148f573eeb51f279652f7b9b/make_rectangular_reflected_background.pdf and b/docs/dev/_downloads/73e7de11148f573eeb51f279652f7b9b/make_rectangular_reflected_background.pdf differ diff --git a/docs/dev/_downloads/74468ce448910c5a873be0e51cee13fb/plot_pwn_evolution.pdf b/docs/dev/_downloads/74468ce448910c5a873be0e51cee13fb/plot_pwn_evolution.pdf index 808b1f9fa7c..63f3c60bdcc 100644 Binary files a/docs/dev/_downloads/74468ce448910c5a873be0e51cee13fb/plot_pwn_evolution.pdf and b/docs/dev/_downloads/74468ce448910c5a873be0e51cee13fb/plot_pwn_evolution.pdf differ diff --git a/docs/dev/_downloads/760eb275a0d8a9d11ea857eb956132f2/plot_naima.zip b/docs/dev/_downloads/760eb275a0d8a9d11ea857eb956132f2/plot_naima.zip index 45933ab0f9f..2d7a725897e 100644 Binary files a/docs/dev/_downloads/760eb275a0d8a9d11ea857eb956132f2/plot_naima.zip and b/docs/dev/_downloads/760eb275a0d8a9d11ea857eb956132f2/plot_naima.zip differ diff --git a/docs/dev/_downloads/7757318506e3a6721d12df18db780f37/plot_sine_temporal.zip b/docs/dev/_downloads/7757318506e3a6721d12df18db780f37/plot_sine_temporal.zip index adf0948f5c8..1fac0d95b7a 100644 Binary files a/docs/dev/_downloads/7757318506e3a6721d12df18db780f37/plot_sine_temporal.zip and b/docs/dev/_downloads/7757318506e3a6721d12df18db780f37/plot_sine_temporal.zip differ diff --git a/docs/dev/_downloads/7e2933bb1c2bf7472d97b488698254dc/cta_data_analysis.py b/docs/dev/_downloads/7e2933bb1c2bf7472d97b488698254dc/cta_data_analysis.py index fdecc7637f1..6baa312c3d0 100644 --- a/docs/dev/_downloads/7e2933bb1c2bf7472d97b488698254dc/cta_data_analysis.py +++ b/docs/dev/_downloads/7e2933bb1c2bf7472d97b488698254dc/cta_data_analysis.py @@ -8,11 +8,11 @@ ------------ **This notebook shows an example how to make a sky image and spectrum -for simulated CTA data with Gammapy.** +for simulated CTAO data with Gammapy.** The dataset we will use is three observation runs on the Galactic Center. This is a tiny (and thus quick to process and play with and -learn) subset of the simulated CTA dataset that was produced for the +learn) subset of the simulated CTAO dataset that was produced for the first data challenge in August 2017. """ @@ -401,7 +401,7 @@ # What next? # ---------- # -# - This notebook showed an example of a first CTA analysis with Gammapy, +# - This notebook showed an example of a first CTAO analysis with Gammapy, # using simulated 1DC data. # - Let us know if you have any questions or issues! # diff --git a/docs/dev/_downloads/7f0f5ef8ba9bdfd9da1248f73ad010fe/overview.zip b/docs/dev/_downloads/7f0f5ef8ba9bdfd9da1248f73ad010fe/overview.zip index 87a8d1a8977..41874ec32dc 100644 Binary files a/docs/dev/_downloads/7f0f5ef8ba9bdfd9da1248f73ad010fe/overview.zip and b/docs/dev/_downloads/7f0f5ef8ba9bdfd9da1248f73ad010fe/overview.zip differ diff --git a/docs/dev/_downloads/800707065c222d9fe3c88a38845901ae/regionmap-2.pdf b/docs/dev/_downloads/800707065c222d9fe3c88a38845901ae/regionmap-2.pdf index df4cb07f1a2..4823582cd3d 100644 Binary files a/docs/dev/_downloads/800707065c222d9fe3c88a38845901ae/regionmap-2.pdf and b/docs/dev/_downloads/800707065c222d9fe3c88a38845901ae/regionmap-2.pdf differ diff --git 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files a/docs/dev/_downloads/a60ac3367f6b8b4daf62f7767737c935/regionmap-3.pdf and b/docs/dev/_downloads/a60ac3367f6b8b4daf62f7767737c935/regionmap-3.pdf differ diff --git a/docs/dev/_downloads/a61822e7cd2af8269e5acb058008a361/event_sampling_nrg_depend_models.py b/docs/dev/_downloads/a61822e7cd2af8269e5acb058008a361/event_sampling_nrg_depend_models.py index 36c5e4aac28..b9e9065e425 100644 --- a/docs/dev/_downloads/a61822e7cd2af8269e5acb058008a361/event_sampling_nrg_depend_models.py +++ b/docs/dev/_downloads/a61822e7cd2af8269e5acb058008a361/event_sampling_nrg_depend_models.py @@ -253,7 +253,7 @@ # Define an observation and make a dataset # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # -# In the following, we define an observation of 1 hr with CTA in the +# In the following, we define an observation of 1 hr with CTAO in the # alpha-configuration for the south array, and we also create a dataset # to be passed to the event sampler. 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For many uses cases, simulating directly\na reduced binned dataset is enough: the IRFs reduced in the correct\ngeometry are combined with a source model to predict an actual number of\ncounts per bin. The latter is then used to simulate a reduced dataset\nusing Poisson probability distribution.\n\nThis can be done to check the feasibility of a measurement, to test\nwhether fitted parameters really provide a good fit to the data etc.\n\nHere we will see how to perform a 1D spectral simulation of a CTA\nobservation, in particular, we will generate OFF observations following\nthe template background stored in the CTA IRFs.\n\n**Objective: simulate a number of spectral ON-OFF observations of a\nsource with a power-law spectral model with CTA using the CTA 1DC\nresponse, fit them with the assumed spectral model and check that the\ndistribution of fitted parameters is consistent with the input values.**\n\n## Proposed approach\n\nWe will use the following classes and functions:\n\n- `~gammapy.datasets.SpectrumDatasetOnOff`\n- `~gammapy.datasets.SpectrumDataset`\n- `~gammapy.irf.load_irf_dict_from_file`\n- `~gammapy.modeling.models.PowerLawSpectralModel`\n" + "\n# 1D spectrum simulation\n\nSimulate a number of spectral on-off observations of a source with a power-law spectral\nmodel using the CTA 1DC response and fit them with the assumed spectral model.\n\n## Prerequisites\n\n- Knowledge of spectral extraction and datasets used in gammapy, see\n for instance the :doc:`spectral analysis\n tutorial `\n\n## Context\n\nTo simulate a specific observation, it is not always necessary to\nsimulate the full photon list. For many uses cases, simulating directly\na reduced binned dataset is enough: the IRFs reduced in the correct\ngeometry are combined with a source model to predict an actual number of\ncounts per bin. The latter is then used to simulate a reduced dataset\nusing Poisson probability distribution.\n\nThis can be done to check the feasibility of a measurement, to test\nwhether fitted parameters really provide a good fit to the data etc.\n\nHere we will see how to perform a 1D spectral simulation of a CTAO\nobservation, in particular, we will generate OFF observations following\nthe template background stored in the CTAO IRFs.\n\n**Objective: simulate a number of spectral ON-OFF observations of a\nsource with a power-law spectral model with CTAO using the CTA 1DC\nresponse, fit them with the assumed spectral model and check that the\ndistribution of fitted parameters is consistent with the input values.**\n\n## Proposed approach\n\nWe will use the following classes and functions:\n\n- `~gammapy.datasets.SpectrumDatasetOnOff`\n- `~gammapy.datasets.SpectrumDataset`\n- `~gammapy.irf.load_irf_dict_from_file`\n- `~gammapy.modeling.models.PowerLawSpectralModel`\n" ] }, { diff --git a/docs/dev/_downloads/be73f34ea8068af3a10d3559a704a86c/plot_piecewise_norm_spatial.zip b/docs/dev/_downloads/be73f34ea8068af3a10d3559a704a86c/plot_piecewise_norm_spatial.zip index 302a597c406..9be7a7b8e7a 100644 Binary files a/docs/dev/_downloads/be73f34ea8068af3a10d3559a704a86c/plot_piecewise_norm_spatial.zip and b/docs/dev/_downloads/be73f34ea8068af3a10d3559a704a86c/plot_piecewise_norm_spatial.zip differ diff --git a/docs/dev/_downloads/bf0a16a362522ed59ca5852f58d8d53c/plot_super_exp_cutoff_powerlaw_4fgl_dr1.zip b/docs/dev/_downloads/bf0a16a362522ed59ca5852f58d8d53c/plot_super_exp_cutoff_powerlaw_4fgl_dr1.zip index 2580037f921..4f9c998cd1e 100644 Binary files a/docs/dev/_downloads/bf0a16a362522ed59ca5852f58d8d53c/plot_super_exp_cutoff_powerlaw_4fgl_dr1.zip and b/docs/dev/_downloads/bf0a16a362522ed59ca5852f58d8d53c/plot_super_exp_cutoff_powerlaw_4fgl_dr1.zip differ diff --git a/docs/dev/_downloads/bfe08b8785a83d00542b58588ba983b9/cta_data_analysis.ipynb b/docs/dev/_downloads/bfe08b8785a83d00542b58588ba983b9/cta_data_analysis.ipynb index 8042d807932..d09a50d0ab8 100644 --- a/docs/dev/_downloads/bfe08b8785a83d00542b58588ba983b9/cta_data_analysis.ipynb +++ b/docs/dev/_downloads/bfe08b8785a83d00542b58588ba983b9/cta_data_analysis.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# Basic image exploration and fitting\n\nDetect sources, produce a sky image and a spectrum using CTA-1DC data.\n\n## Introduction\n\n**This notebook shows an example how to make a sky image and spectrum\nfor simulated CTA data with Gammapy.**\n\nThe dataset we will use is three observation runs on the Galactic\nCenter. This is a tiny (and thus quick to process and play with and\nlearn) subset of the simulated CTA dataset that was produced for the\nfirst data challenge in August 2017.\n" + "\n# Basic image exploration and fitting\n\nDetect sources, produce a sky image and a spectrum using CTA-1DC data.\n\n## Introduction\n\n**This notebook shows an example how to make a sky image and spectrum\nfor simulated CTAO data with Gammapy.**\n\nThe dataset we will use is three observation runs on the Galactic\nCenter. This is a tiny (and thus quick to process and play with and\nlearn) subset of the simulated CTAO dataset that was produced for the\nfirst data challenge in August 2017.\n" ] }, { @@ -371,7 +371,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## What next?\n\n- This notebook showed an example of a first CTA analysis with Gammapy,\n using simulated 1DC data.\n- Let us know if you have any questions or issues!\n\n\n" + "## What next?\n\n- This notebook showed an example of a first CTAO analysis with Gammapy,\n using simulated 1DC data.\n- Let us know if you have any questions or issues!\n\n\n" ] } ], diff --git a/docs/dev/_downloads/bfe6aa14bdebe4f65370b05f400755e5/plot_cash_significance.pdf b/docs/dev/_downloads/bfe6aa14bdebe4f65370b05f400755e5/plot_cash_significance.pdf index a74c285fb9e..1efa6c3dea7 100644 Binary files a/docs/dev/_downloads/bfe6aa14bdebe4f65370b05f400755e5/plot_cash_significance.pdf and b/docs/dev/_downloads/bfe6aa14bdebe4f65370b05f400755e5/plot_cash_significance.pdf differ diff --git a/docs/dev/_downloads/c1da6aedff551cba26aafdf962f74127/plot_shell.zip b/docs/dev/_downloads/c1da6aedff551cba26aafdf962f74127/plot_shell.zip index f587c991ed0..c2cf9b61457 100644 Binary files a/docs/dev/_downloads/c1da6aedff551cba26aafdf962f74127/plot_shell.zip and b/docs/dev/_downloads/c1da6aedff551cba26aafdf962f74127/plot_shell.zip differ diff --git a/docs/dev/_downloads/c2775618fc6e3f5a7b28e723a9132470/plot_aeff.pdf b/docs/dev/_downloads/c2775618fc6e3f5a7b28e723a9132470/plot_aeff.pdf index f1198b4afb2..25d284ae498 100644 Binary files a/docs/dev/_downloads/c2775618fc6e3f5a7b28e723a9132470/plot_aeff.pdf and b/docs/dev/_downloads/c2775618fc6e3f5a7b28e723a9132470/plot_aeff.pdf differ diff --git a/docs/dev/_downloads/c2b61900ea448b3f1bbacb5848c7ae6e/plot_pulsar_spindown.pdf b/docs/dev/_downloads/c2b61900ea448b3f1bbacb5848c7ae6e/plot_pulsar_spindown.pdf index 9166eade300..2ca87413570 100644 Binary files a/docs/dev/_downloads/c2b61900ea448b3f1bbacb5848c7ae6e/plot_pulsar_spindown.pdf and b/docs/dev/_downloads/c2b61900ea448b3f1bbacb5848c7ae6e/plot_pulsar_spindown.pdf differ diff --git a/docs/dev/_downloads/c3e24f907cf5ba5950daf05c578e630c/plot_powerlaw.zip b/docs/dev/_downloads/c3e24f907cf5ba5950daf05c578e630c/plot_powerlaw.zip index dbe7e32cf3c..9b671e3386c 100644 Binary files a/docs/dev/_downloads/c3e24f907cf5ba5950daf05c578e630c/plot_powerlaw.zip and b/docs/dev/_downloads/c3e24f907cf5ba5950daf05c578e630c/plot_powerlaw.zip differ diff --git a/docs/dev/_downloads/c6bad13b7bd034ce07497e4d5025d5d2/edisp-1.pdf b/docs/dev/_downloads/c6bad13b7bd034ce07497e4d5025d5d2/edisp-1.pdf index 861f78bea81..5a73f1ecc3a 100644 Binary files a/docs/dev/_downloads/c6bad13b7bd034ce07497e4d5025d5d2/edisp-1.pdf and b/docs/dev/_downloads/c6bad13b7bd034ce07497e4d5025d5d2/edisp-1.pdf differ diff --git a/docs/dev/_downloads/c8b2c52022beac75de2a3a8b628986e3/plot_absorbed.zip b/docs/dev/_downloads/c8b2c52022beac75de2a3a8b628986e3/plot_absorbed.zip index 4ec5f28bdca..743f6032e0c 100644 Binary files a/docs/dev/_downloads/c8b2c52022beac75de2a3a8b628986e3/plot_absorbed.zip and b/docs/dev/_downloads/c8b2c52022beac75de2a3a8b628986e3/plot_absorbed.zip differ diff --git a/docs/dev/_downloads/c8dacdfbdf94c97cce7a5ee9555295b5/plot_cash_errors.pdf b/docs/dev/_downloads/c8dacdfbdf94c97cce7a5ee9555295b5/plot_cash_errors.pdf index 5171602acbf..a8a7d8cade9 100644 Binary files a/docs/dev/_downloads/c8dacdfbdf94c97cce7a5ee9555295b5/plot_cash_errors.pdf and b/docs/dev/_downloads/c8dacdfbdf94c97cce7a5ee9555295b5/plot_cash_errors.pdf differ diff --git a/docs/dev/_downloads/c9b72066179d4ce20b4ee80c56701372/hawc.ipynb b/docs/dev/_downloads/c9b72066179d4ce20b4ee80c56701372/hawc.ipynb index 8e6c523547c..fcbb4b7d097 100644 --- a/docs/dev/_downloads/c9b72066179d4ce20b4ee80c56701372/hawc.ipynb +++ b/docs/dev/_downloads/c9b72066179d4ce20b4ee80c56701372/hawc.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# HAWC with Gammapy\nExplore HAWC event lists and IRFs and perform the data reduction steps.\n\n[HAWC](https://www.hawc-observatory.org/)_ is an array of\nwater Cherenkov detectors located in Mexico that detects gamma-rays\nin the range between hundreds of GeV and hundreds of TeV.\nGammapy recently added support of HAWC high level data analysis,\nafter export to the current [open data level 3\nformat](https://gamma-astro-data-formats.readthedocs.io/)_.\n\nThe HAWC data is largely private. However, in 2022, a small\nsub-set of HAWC Pass4 event lists from the Crab nebula region\nwas publicly [released](https://data.hawc-observatory.org/datasets/crab_events_pass4/index.php)_.\nThis dataset is 1 GB in size, so only a subset will be used here as an example.\n\nThis notebook is a quick introduction to HAWC data analysis with Gammapy.\nIt briefly describes the HAWC data and how to access it, and then uses a\nsubset of it to produce a MapDataset, to show how the data reduction is performed.\n\nThe HAWC data release contains events where energy is estimated using\ntwo different algorithms, referred to as \"NN\" and \"GP\" (see this [paper](https://iopscience.iop.org/article/10.3847/1538-4357/ab2f7d)_\nfor a detailed description). These two event classes are not independent, meaning that\nevents are repeated between the NN and GP datasets. So these data should never\nbe analyzed jointly, and one of the two estimators needs to be chosen before\nproceeding.\n\nOnce the data has been reduced to a MapDataset, there are no differences\nin the way that HAWC data is handled with respect to data from any other\nobservatory, such as H.E.S.S. or CTA.\n\n\n## HAWC data access and reduction\n\nThis is how to access data and IRFs from the HAWC Crab event data release.\n" + "\n# HAWC with Gammapy\nExplore HAWC event lists and IRFs and perform the data reduction steps.\n\n[HAWC](https://www.hawc-observatory.org/)_ is an array of\nwater Cherenkov detectors located in Mexico that detects gamma-rays\nin the range between hundreds of GeV and hundreds of TeV.\nGammapy recently added support of HAWC high level data analysis,\nafter export to the current [open data level 3\nformat](https://gamma-astro-data-formats.readthedocs.io/)_.\n\nThe HAWC data is largely private. However, in 2022, a small\nsub-set of HAWC Pass4 event lists from the Crab nebula region\nwas publicly [released](https://data.hawc-observatory.org/datasets/crab_events_pass4/index.php)_.\nThis dataset is 1 GB in size, so only a subset will be used here as an example.\n\nThis notebook is a quick introduction to HAWC data analysis with Gammapy.\nIt briefly describes the HAWC data and how to access it, and then uses a\nsubset of it to produce a MapDataset, to show how the data reduction is performed.\n\nThe HAWC data release contains events where energy is estimated using\ntwo different algorithms, referred to as \"NN\" and \"GP\" (see this [paper](https://iopscience.iop.org/article/10.3847/1538-4357/ab2f7d)_\nfor a detailed description). These two event classes are not independent, meaning that\nevents are repeated between the NN and GP datasets. So these data should never\nbe analyzed jointly, and one of the two estimators needs to be chosen before\nproceeding.\n\nOnce the data has been reduced to a MapDataset, there are no differences\nin the way that HAWC data is handled with respect to data from any other\nobservatory, such as H.E.S.S. or CTAO.\n\n\n## HAWC data access and reduction\n\nThis is how to access data and IRFs from the HAWC Crab event data release.\n" ] }, { diff --git a/docs/dev/_downloads/cbf21326239e0fa794d51e4515e770f5/spectral_analysis_hli.zip b/docs/dev/_downloads/cbf21326239e0fa794d51e4515e770f5/spectral_analysis_hli.zip index 9983affdeb2..330a7fe4810 100644 Binary files a/docs/dev/_downloads/cbf21326239e0fa794d51e4515e770f5/spectral_analysis_hli.zip and b/docs/dev/_downloads/cbf21326239e0fa794d51e4515e770f5/spectral_analysis_hli.zip differ diff --git a/docs/dev/_downloads/cf72688370a27db6e3e5a36401e825da/plot_powerlaw2.zip b/docs/dev/_downloads/cf72688370a27db6e3e5a36401e825da/plot_powerlaw2.zip index 0451dbb8004..66128a3b53a 100644 Binary files a/docs/dev/_downloads/cf72688370a27db6e3e5a36401e825da/plot_powerlaw2.zip and b/docs/dev/_downloads/cf72688370a27db6e3e5a36401e825da/plot_powerlaw2.zip differ diff --git a/docs/dev/_downloads/d0da679c6d748017e74a51a72190ec8f/spectrum_simulation.zip b/docs/dev/_downloads/d0da679c6d748017e74a51a72190ec8f/spectrum_simulation.zip index f4d59b42fc9..5500e8368a9 100644 Binary files a/docs/dev/_downloads/d0da679c6d748017e74a51a72190ec8f/spectrum_simulation.zip and b/docs/dev/_downloads/d0da679c6d748017e74a51a72190ec8f/spectrum_simulation.zip differ diff --git a/docs/dev/_downloads/d13abf6a1bf7bbce4089786015c37bf1/cta_sensitivity.ipynb b/docs/dev/_downloads/d13abf6a1bf7bbce4089786015c37bf1/cta_sensitivity.ipynb index b7f7ad6b855..ea5b5238392 100644 --- a/docs/dev/_downloads/d13abf6a1bf7bbce4089786015c37bf1/cta_sensitivity.ipynb +++ b/docs/dev/_downloads/d13abf6a1bf7bbce4089786015c37bf1/cta_sensitivity.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# Point source sensitivity\n\nEstimate the CTA sensitivity for a point-like IRF at a fixed zenith angle and fixed offset.\n\n## Introduction\n\nThis notebook explains how to estimate the CTA sensitivity for a\npoint-like IRF at a fixed zenith angle and fixed offset, using the full\ncontainment IRFs distributed for the CTA 1DC. The significance is\ncomputed for a 1D analysis (ON-OFF regions) with the Li&Ma formula.\n\nWe use here an approximate approach with an energy dependent integration\nradius to take into account the variation of the PSF. We will first\ndetermine the 1D IRFs including a containment correction.\n\nWe will be using the following Gammapy class:\n\n- `~gammapy.estimators.SensitivityEstimator`\n" + "\n# Point source sensitivity\n\nEstimate the CTAO sensitivity for a point-like IRF at a fixed zenith angle and fixed offset.\n\n## Introduction\n\nThis notebook explains how to estimate the CTAO sensitivity for a\npoint-like IRF at a fixed zenith angle and fixed offset, using the full\ncontainment IRFs distributed for the CTA 1DC. The significance is\ncomputed for a 1D analysis (ON-OFF regions) with the Li&Ma formula.\n\nWe use here an approximate approach with an energy dependent integration\nradius to take into account the variation of the PSF. We will first\ndetermine the 1D IRFs including a containment correction.\n\nWe will be using the following Gammapy class:\n\n- `~gammapy.estimators.SensitivityEstimator`\n" ] }, { @@ -76,7 +76,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Load IRFs and prepare dataset\n\nWe extract the 1D IRFs from the full 3D IRFs provided by CTA.\n\n\n" + "## Load IRFs and prepare dataset\n\nWe extract the 1D IRFs from the full 3D IRFs provided by CTAO.\n\n\n" ] }, { @@ -166,7 +166,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Compute sensitivity\n\nWe impose a minimal number of expected signal counts of 10 per bin and a\nminimal significance of 5 per bin. The excess must also be larger than 5% of the background.\n\nWe assume an alpha of 0.2 (ratio between ON and OFF area). We then run the sensitivity estimator.\n\nThese are the conditions imposed in standard CTA sensitivity computations.\n\n" + "## Compute sensitivity\n\nWe impose a minimal number of expected signal counts of 10 per bin and a\nminimal significance of 5 per bin. The excess must also be larger than 5% of the background.\n\nWe assume an alpha of 0.2 (ratio between ON and OFF area). We then run the sensitivity estimator.\n\nThese are the conditions imposed in standard CTAO sensitivity computations.\n\n" ] }, { diff --git a/docs/dev/_downloads/d3f2b47ffbc735c23e8ffc373b698607/light_curve.zip b/docs/dev/_downloads/d3f2b47ffbc735c23e8ffc373b698607/light_curve.zip index 0c96274874b..58003949369 100644 Binary files a/docs/dev/_downloads/d3f2b47ffbc735c23e8ffc373b698607/light_curve.zip and b/docs/dev/_downloads/d3f2b47ffbc735c23e8ffc373b698607/light_curve.zip differ diff --git a/docs/dev/_downloads/d46f4cc1fb54f9148150a46cabce732a/plot_spiral_arm_models.pdf b/docs/dev/_downloads/d46f4cc1fb54f9148150a46cabce732a/plot_spiral_arm_models.pdf index ffe8e842287..bf6125d0f99 100644 Binary files a/docs/dev/_downloads/d46f4cc1fb54f9148150a46cabce732a/plot_spiral_arm_models.pdf and b/docs/dev/_downloads/d46f4cc1fb54f9148150a46cabce732a/plot_spiral_arm_models.pdf differ diff --git a/docs/dev/_downloads/d47b4b3bccd5fc1699bbc9406a3c4035/plot_edisp.pdf b/docs/dev/_downloads/d47b4b3bccd5fc1699bbc9406a3c4035/plot_edisp.pdf index 961a7ed1975..dfe59a898c8 100644 Binary files a/docs/dev/_downloads/d47b4b3bccd5fc1699bbc9406a3c4035/plot_edisp.pdf and b/docs/dev/_downloads/d47b4b3bccd5fc1699bbc9406a3c4035/plot_edisp.pdf differ diff --git a/docs/dev/_downloads/d58ab19eec70461756cbde5549642e51/modeling_2D.py b/docs/dev/_downloads/d58ab19eec70461756cbde5549642e51/modeling_2D.py index 95cd6898537..f1de504a39b 100644 --- a/docs/dev/_downloads/d58ab19eec70461756cbde5549642e51/modeling_2D.py +++ b/docs/dev/_downloads/d58ab19eec70461756cbde5549642e51/modeling_2D.py @@ -64,7 +64,7 @@ # 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 CTA runs of the galactic center. +# Here, we use 3 simulated CTAO runs of the galactic center. # config = AnalysisConfig() diff --git a/docs/dev/_downloads/d84a1f60bef1b2a4d70bc60ed632228b/plot_generalized_gaussian_temporal.zip b/docs/dev/_downloads/d84a1f60bef1b2a4d70bc60ed632228b/plot_generalized_gaussian_temporal.zip index ad1edc504ec..9bafb6dcea0 100644 Binary files a/docs/dev/_downloads/d84a1f60bef1b2a4d70bc60ed632228b/plot_generalized_gaussian_temporal.zip and b/docs/dev/_downloads/d84a1f60bef1b2a4d70bc60ed632228b/plot_generalized_gaussian_temporal.zip differ diff --git a/docs/dev/_downloads/db49dbe8386e235e780f90506f4c13d4/plot_gaussian_temporal.zip b/docs/dev/_downloads/db49dbe8386e235e780f90506f4c13d4/plot_gaussian_temporal.zip index 22ed752acdd..03ea8d44972 100644 Binary files a/docs/dev/_downloads/db49dbe8386e235e780f90506f4c13d4/plot_gaussian_temporal.zip and b/docs/dev/_downloads/db49dbe8386e235e780f90506f4c13d4/plot_gaussian_temporal.zip differ diff --git a/docs/dev/_downloads/dc84ab4fb2154102a7b86605311ec685/plot_linear_temporal.zip b/docs/dev/_downloads/dc84ab4fb2154102a7b86605311ec685/plot_linear_temporal.zip index 67f52b4842d..2d7057421b4 100644 Binary files a/docs/dev/_downloads/dc84ab4fb2154102a7b86605311ec685/plot_linear_temporal.zip and b/docs/dev/_downloads/dc84ab4fb2154102a7b86605311ec685/plot_linear_temporal.zip differ diff --git a/docs/dev/_downloads/dc889dafa0f60a201223634e8d485a12/mask_maps.zip b/docs/dev/_downloads/dc889dafa0f60a201223634e8d485a12/mask_maps.zip index b62e5d1f8ea..a93ecf4fabd 100644 Binary files a/docs/dev/_downloads/dc889dafa0f60a201223634e8d485a12/mask_maps.zip and b/docs/dev/_downloads/dc889dafa0f60a201223634e8d485a12/mask_maps.zip differ diff --git a/docs/dev/_downloads/ddf4900ca46e50deae170060d7a15571/flux_profiles.zip b/docs/dev/_downloads/ddf4900ca46e50deae170060d7a15571/flux_profiles.zip index 5554d402fc1..37e10a038be 100644 Binary files a/docs/dev/_downloads/ddf4900ca46e50deae170060d7a15571/flux_profiles.zip and b/docs/dev/_downloads/ddf4900ca46e50deae170060d7a15571/flux_profiles.zip differ diff --git a/docs/dev/_downloads/df937214d98218a68007eccea50ab49d/plot_logparabola.zip b/docs/dev/_downloads/df937214d98218a68007eccea50ab49d/plot_logparabola.zip index 19d39d50f56..9cac9b86eec 100644 Binary files a/docs/dev/_downloads/df937214d98218a68007eccea50ab49d/plot_logparabola.zip and b/docs/dev/_downloads/df937214d98218a68007eccea50ab49d/plot_logparabola.zip differ diff --git a/docs/dev/_downloads/e13573be09b72c90db0b9bda1249d8c4/Time_resolved_spectroscopy.zip b/docs/dev/_downloads/e13573be09b72c90db0b9bda1249d8c4/Time_resolved_spectroscopy.zip index cf9408dcba6..2d52d34636e 100644 Binary files a/docs/dev/_downloads/e13573be09b72c90db0b9bda1249d8c4/Time_resolved_spectroscopy.zip and b/docs/dev/_downloads/e13573be09b72c90db0b9bda1249d8c4/Time_resolved_spectroscopy.zip differ diff --git a/docs/dev/_downloads/e2a4ade170c5e85b14ae39a9784b12b1/cta.ipynb b/docs/dev/_downloads/e2a4ade170c5e85b14ae39a9784b12b1/cta.ipynb index a9226c29ff8..5a852e36c47 100644 --- a/docs/dev/_downloads/e2a4ade170c5e85b14ae39a9784b12b1/cta.ipynb +++ b/docs/dev/_downloads/e2a4ade170c5e85b14ae39a9784b12b1/cta.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# CTA with Gammapy\n\nAccess and inspect CTA data and instrument response functions (IRFs) using Gammapy.\n\n## Introduction\n\nThe [Cherenkov Telescope Array\n(CTA)](https://www.cta-observatory.org/)_ is the next generation\nground-based observatory for gamma-ray astronomy. Gammapy is the core\nlibrary for the Cherenkov Telescope Array (CTA) science tools\n([2017ICRC\u202635..766D](https://ui.adsabs.harvard.edu/abs/2017ICRC...35..766D)_\nand [CTAO Press\nRelease](https://www.cta-observatory.org/ctao-adopts-the-gammapy-software-package-for-science-analysis/)_).\n\nCTA will start taking data in the coming years. For now, to learn how to\nanalyse CTA data and to use Gammapy, if you are a member of the CTA\nconsortium, you can use the simulated dataset from the CTA first data\nchallenge which ran in 2017 and 2018.\n\n- https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki (CTA\n internal)\n\nGammapy fully supports the FITS data formats (events, IRFs) used in CTA\n1DC. The XML sky model format is not supported, but are also not needed\nto analyse the data, you have to specify your model via the Gammapy YAML\nmodel format, or Python code, as shown below.\n\nYou can use Gammapy to simulate CTA data and evaluate CTA performance\nusing the CTA response files available here:\n\n- https://www.cta-observatory.org/science/cta-performance/\n\nThe current FITS format `CTA-Performance-prod3b-v2-FITS.tar` is fully\nsupported by Gammapy, as shown below.\n\n## Tutorial overview\n\nThis notebook shows how to access CTA data and instrument response\nfunctions (IRFs) using Gammapy, and gives some examples how to quick\nlook the content of CTA files, especially to see the shape of CTA IRFs.\n\nAt the end of the notebooks, we give several links to other tutorial\nnotebooks that show how to simulate CTA data and how to evaluate CTA\nobservability and sensitivity, or how to analyse CTA data.\n\nNote that the FITS data and IRF format currently used by CTA is the one\ndocumented at https://gamma-astro-data-formats.readthedocs.io/, and is\nalso used by H.E.S.S. and other imaging atmospheric Cherenkov telescopes\n(IACTs). So if you see other Gammapy tutorials using e.g. H.E.S.S.\nexample data, know that they also apply to CTA, all you have to do is to\nchange the loaded data or IRFs to CTA.\n\n## Setup\n" + "\n# CTAO with Gammapy\n\nAccess and inspect CTAO data and instrument response functions (IRFs) using Gammapy.\n\n## Introduction\n\nThe [Cherenkov Telescope Array Observatory (CTAO)](https://www.ctao.org/)_ is the next generation\nground-based observatory for gamma-ray astronomy. Gammapy is the core\nlibrary for the Cherenkov Telescope Array Observatory (CTAO) science tools\n([2017ICRC\u202635..766D](https://ui.adsabs.harvard.edu/abs/2017ICRC...35..766D)_\nand [CTAO Press\nRelease](https://www.ctao.org/news/ctao-adopts-the-gammapy-software-package-for-science-analysis/)_).\n\nCTAO will start taking data in the coming years. For now, to learn how to\nanalyse CTAO data and to use Gammapy, if you are a member of the CTAO\nconsortium, you can use the simulated dataset from:\n\n- the CTA first data challenge which ran in 2017 and 2018 (https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki),\n- the CTAO Science Data Challenge of 2024 (https://ctaoobservatory.sharepoint.com/:f:/r/sites/share-open-data/Shared%20Documents/Reference%20datasets/Internal%20Science%20Data%20Challenge?csf=1&web=1&e=gNuFzI)\n\nGammapy fully supports the FITS data formats (events, IRFs) used in CTA\n1DC and SDC. The XML sky model format is not supported, but are also not needed\nto analyse the data, you have to specify your model via the Gammapy YAML\nmodel format, or Python code, as shown below.\n\nYou can also use Gammapy to simulate CTAO data and evaluate CTAO performance\nusing the CTAO response files. Two sets of responses are available for different\narray layouts:\n\n- the Omega configuration (prod3b, 2016): https://zenodo.org/records/5163273,\n- the Alpha configuration (prod5, 2021): https://zenodo.org/records/5499840.\n\nThey are all fully supported by Gammapy.\n\n\n## Tutorial overview\n\nThis notebook shows how to access CTAO data and instrument response\nfunctions (IRFs) using Gammapy, and gives some examples how to quick\nlook the content of CTAO files, especially to see the shape of CTAO IRFs.\n\nAt the end of the notebooks, we give several links to other tutorial\nnotebooks that show how to simulate CTAO data and how to evaluate CTAO\nobservability and sensitivity, or how to analyse CTAO data.\n\nNote that the FITS data and IRF format currently used by CTAO is the one\ndocumented at https://gamma-astro-data-formats.readthedocs.io/, and is\nalso used by H.E.S.S. and other Imaging Atmospheric Cherenkov Telescopes\n(IACTs). So if you see other Gammapy tutorials using e.g. H.E.S.S.\nexample data, know that they also apply to CTAO, all you have to do is to\nchange the loaded data or IRFs to CTAO.\n\n## Setup\n" ] }, { @@ -40,7 +40,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## CTA 1DC\n\nThe CTA first data challenge (1DC) ran in 2017 and 2018. It is described\nin detail\n[here](https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki)_\nand a description of the data and how to download it is\n[here](https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki#Data-access)_.\n\nYou should download `caldb.tar.gz` (1.2 MB), `models.tar.gz` (0.9\nGB), `index.tar.gz` (0.5 MB), as well as optionally the simulated\nsurvey data you are interested in: Galactic plane survey `gps.tar.gz`\n(8.3 GB), Galactic center `gc.tar.gz` (4.4 MB), Extragalactic survey\n`egal.tar.gz` (2.5 GB), AGN monitoring `agn.wobble.tar.gz` (4.7 GB).\nAfter download, follow the instructions how to `untar` the files, and\nset a `CTADATA` environment variable to point to the data.\n\nFor convenience, since the 1DC data files are large, and not publicly\navailable to anyone, we have taken a tiny subset of the CTA 1DC data,\nfour observations with the southern array from the GPS survey, pointing\nnear the Galactic center, and included them at `$GAMMAPY_DATA/cta-1dc`\nwhich you get via `gammapy download datasets`.\n\n### Files\n\nNext we will show a quick overview of the files and how to load them,\nand some quick look plots showing the shape of the CTA IRFs. How to do\nCTA simulations and analyses is shown in other tutorials, see links at\nthe end of this notebook.\n\n\n" + "## CTA 1DC\n\nThe CTA first data challenge (1DC) ran in 2017 and 2018. It is described\nin detail\n[here](https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki)_\nand a description of the data and how to download it is\n[here](https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki#Data-access)_.\n\nYou should download `caldb.tar.gz` (1.2 MB), `models.tar.gz` (0.9\nGB), `index.tar.gz` (0.5 MB), as well as optionally the simulated\nsurvey data you are interested in: Galactic plane survey `gps.tar.gz`\n(8.3 GB), Galactic center `gc.tar.gz` (4.4 MB), Extragalactic survey\n`egal.tar.gz` (2.5 GB), AGN monitoring `agn.wobble.tar.gz` (4.7 GB).\nAfter download, follow the instructions how to `untar` the files, and\nset a `CTADATA` environment variable to point to the data.\n\n**For convenience**, since the 1DC data files are large and not publicly\navailable to anyone, we have taken a tiny subset of the CTA 1DC data,\nfour observations with the southern array from the GPS survey, pointing\nnear the Galactic center, and **included them at `$GAMMAPY_DATA/cta-1dc`**\nwhich you get via `gammapy download datasets`.\n\n### Files\n\nNext we will show a quick overview of the files and how to load them,\nand some quick look plots showing the shape of the CTAO IRFs. How to do\nCTAO simulations and analyses is shown in other tutorials, see links at\nthe end of this notebook.\n\n\n" ] }, { @@ -112,7 +112,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Events\n\nWe can load events data via the data store and observation, or\nequivalently via the `~gammapy.data.EventList` class by specifying the\nEVENTS filename.\n\nThe quick-look `events.peek()` plot below shows that CTA has a field\nof view of a few degrees, and two energy thresholds, one significantly\nbelow 100 GeV where the CTA large-size telescopes (LSTs) detect events,\nand a second one near 100 GeV where the mid-sized telescopes (MSTs)\nstart to detect events.\n\nNote that most events are \u201chadronic background\u201d due to cosmic ray\nshowers in the atmosphere that pass the gamma-hadron selection cuts for\nthis analysis configuration. Since this is simulated data, column\n`MC_ID` is available that gives an emission component identifier code,\nand the EVENTS header in `events.table.meta` can be used to look up\nwhich `MC_ID` corresponds to which emission component.\n\nEvents can be accessed from the observation object like:\n\n" + "## Events\n\nWe can load events data via the data store and observation, or\nequivalently via the `~gammapy.data.EventList` class by specifying the\nEVENTS filename.\n\nThe quick-look `events.peek()` plot below shows that CTAO has a field\nof view of a few degrees, and two energy thresholds, one significantly\nbelow 100 GeV where the CTAO large-size telescopes (LSTs) detect events,\nand a second one near 100 GeV where the mid-sized telescopes (MSTs)\nstart to detect events.\n\nNote that most events are \u201chadronic background\u201d due to cosmic ray\nshowers in the atmosphere that pass the gamma-hadron selection cuts for\nthis analysis configuration. Since this is simulated data, column\n`MC_ID` is available that gives an emission component identifier code,\nand the EVENTS header in `events.table.meta` can be used to look up\nwhich `MC_ID` corresponds to which emission component.\n\nEvents can be accessed from the observation object like:\n\n" ] }, { @@ -184,7 +184,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## IRFs\n\nThe CTA instrument response functions (IRFs) are given as FITS files in\nthe `caldb` folder, the following IRFs are available:\n\n- effective area\n- energy dispersion\n- point spread function\n- background\n\nNotes:\n\n- The IRFs contain the energy and offset dependence of the CTA response\n- CTA 1DC was based on an early version of the CTA FITS responses\n produced in 2017, improvements have been made since.\n- The point spread function was approximated by a Gaussian shape\n- The background is from hadronic and electron air shower events that\n pass CTA selection cuts. It was given as a function of field of view\n coordinates, although it is radially symmetric.\n- The energy dispersion in CTA 1DC is noisy at low energy, leading to\n unreliable spectral points for some analyses.\n- The CTA 1DC response files have the first node at field of view\n offset 0.5 deg, so to get the on-axis response at offset 0 deg,\n Gammapy has to extrapolate. Furthermore, because diffuse gamma-rays\n in the FOV were used to derive the IRFs, and the solid angle at small\n FOV offset circles is small, the IRFs at the center of the FOV are\n somewhat noisy. This leads to unstable analysis and simulation issues\n when using the DC1 IRFs for some analyses.\n\n\n" + "## IRFs\n\nThe CTAO instrument response functions (IRFs) are given as FITS files in\nthe `caldb` folder, the following IRFs are available:\n\n- effective area\n- energy dispersion\n- point spread function\n- background\n\nNotes:\n\n- The IRFs contain the energy and offset dependence of the CTAO response\n- CTA 1DC was based on an early version of the CTAO FITS responses\n produced in 2017, improvements have been made since.\n- The point spread function was approximated by a Gaussian shape\n- The background is from hadronic and electron air shower events that\n pass CTAO selection cuts. It was given as a function of field of view\n coordinates, although it is radially symmetric.\n- The energy dispersion in CTA 1DC is noisy at low energy, leading to\n unreliable spectral points for some analyses.\n- The CTA 1DC response files have the first node at field of view\n offset 0.5 deg, so to get the on-axis response at offset 0 deg,\n Gammapy has to extrapolate. Furthermore, because diffuse gamma-rays\n in the FOV were used to derive the IRFs, and the solid angle at small\n FOV offset circles is small, the IRFs at the center of the FOV are\n somewhat noisy. This leads to unstable analysis and simulation issues\n when using the DC1 IRFs for some analyses.\n\n\n" ] }, { @@ -328,7 +328,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## CTA performance files\n\nCTA 1DC is useful to learn how to analyse CTA data. But to do\nsimulations and studies for CTA now, you should get the most recent CTA\nIRFs in FITS format from\nhttps://www.cta-observatory.org/science/cta-performance/\n\nIf you want to run the download and examples in the next code cells,\nremove the # to uncomment.\n\n\n" + "## Latest CTAO performance files\n\nCTA 1DC is useful to learn how to analyse CTAO data. But to do\nsimulations and studies for CTAO now, you should get the most recent CTAO\nIRFs in FITS format from https://www.ctao.org/for-scientists/performance/.\n\nIf you want to use other response files, the following code cells (remove the # to uncomment)\nexplain how to proceed. This example is made with the Alpha configuration (Prod5).\n\n\n" ] }, { @@ -339,14 +339,14 @@ }, "outputs": [], "source": [ - "# !curl -O https://www.cta-observatory.org/wp-content/uploads/2019/04/CTA-Performance-prod3b-v2-FITS.tar.gz\n\n# !tar xf CTA-Performance-prod3b-v2-FITS.tar.gz\n\n# !ls caldb/data/cta/prod3b-v2/bcf\n\n# irfs1 = load_irf_dict_from_file(\"caldb/data/cta/prod3b-v2/bcf/South_z20_50h/irf_file.fits\")\n# irfs1[\"aeff\"].plot_energy_dependence()\n\n# irfs2 = load_irf_dict_from_file(\"caldb/data/cta/prod3b-v2/bcf/South_z40_50h/irf_file.fits\")\n# irfs2[\"aeff\"].plot_energy_dependence()" + "# !curl -o cta-prod5-zenodo-fitsonly-v0.1.zip https://zenodo.org/records/5499840/files/cta-prod5-zenodo-fitsonly-v0.1.zip\n# !unzip cta-prod5-zenodo-fitsonly-v0.1.zip\n# !ls fits/\n\n# !tar xf fits/CTA-Performance-prod5-v0.1-South-40deg.FITS.tar.gz -C fits/.\n# !ls fits/*.fits.gz\n\n# irfs1 = load_irf_dict_from_file(\"fits/Prod5-South-40deg-SouthAz-14MSTs37SSTs.180000s-v0.1.fits.gz\")\n# irfs1[\"aeff\"].plot_energy_dependence()\n\n# irfs2 = load_irf_dict_from_file(\"fits/Prod5-South-40deg-SouthAz-14MSTs37SSTs.1800s-v0.1.fits.gz\")\n# irfs2[\"aeff\"].plot_energy_dependence()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercises\n\n- Load the EVENTS file for `obs_id=111159` as a\n `~gammapy.data.EventList` object.\n- Use `~gammapy.data.EventList.table` to find the energy, sky coordinate and time of\n the highest-energy event.\n- Use `~gammapy.data.EventList.pointing_radec` to find the pointing position of this\n observation, and use `astropy.coordinates.SkyCoord` methods to find\n the field of view offset of the highest-energy event.\n- What is the effective area and PSF 68% containment radius of CTA at 1\n TeV for the `South_z20_50h` configuration used for the CTA 1DC\n simulation?\n- Get the latest CTA FITS performance files from\n https://www.cta-observatory.org/science/cta-performance/ and run the\n code example above. Make an effective area ratio plot of 40 deg\n zenith versus 20 deg zenith for the `South_z40_50h` and\n `South_z20_50h` configurations.\n\n\n" + "## Exercises\n\n- Load the EVENTS file for `obs_id=111159` as a\n `~gammapy.data.EventList` object.\n- Use `~gammapy.data.EventList.table` to find the energy, sky coordinate and time of\n the highest-energy event.\n- Use `~gammapy.data.EventList.pointing_radec` to find the pointing position of this\n observation, and use `astropy.coordinates.SkyCoord` methods to find\n the field of view offset of the highest-energy event.\n- What is the effective area and PSF 68% containment radius of CTAO at 1\n TeV for the `South_z20_50h` configuration used for the CTA 1DC\n simulation?\n- Get the latest CTAO FITS performance files from\n https://www.ctao.org/for-scientists/performance/ and run the\n code example above. Make an effective area ratio plot of 40 deg\n zenith versus 20 deg zenith for the `South_z40_50h` and\n `South_z20_50h` configurations.\n\n\n" ] }, { @@ -364,7 +364,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Next steps\n\n- Learn how to analyse data with\n :doc:`/tutorials/starting/analysis_1` and\n :doc:`/tutorials/starting/analysis_2` or any other\n Gammapy analysis tutorial.\n- Learn how to evaluate CTA observability and sensitivity with\n :doc:`/tutorials/analysis-3d/simulate_3d`,\n :doc:`/tutorials/analysis-1d/spectrum_simulation`\n or :doc:`/tutorials/analysis-1d/cta_sensitivity`.\n\n\n" + "## Next steps\n\n- Learn how to analyse data with\n :doc:`/tutorials/starting/analysis_1` and\n :doc:`/tutorials/starting/analysis_2` or any other\n Gammapy analysis tutorial.\n- Learn how to evaluate CTAO observability and sensitivity with\n :doc:`/tutorials/analysis-3d/simulate_3d`,\n :doc:`/tutorials/analysis-1d/spectrum_simulation`\n or :doc:`/tutorials/analysis-1d/cta_sensitivity`.\n\n\n" ] } ], diff --git a/docs/dev/_downloads/e4ae628be6bb0732f7ed85da29b2a1e9/hess.ipynb b/docs/dev/_downloads/e4ae628be6bb0732f7ed85da29b2a1e9/hess.ipynb index 824bf89972d..8344b482a1f 100644 --- a/docs/dev/_downloads/e4ae628be6bb0732f7ed85da29b2a1e9/hess.ipynb +++ b/docs/dev/_downloads/e4ae628be6bb0732f7ed85da29b2a1e9/hess.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# H.E.S.S. with Gammapy\nExplore H.E.S.S. event lists and IRFs.\n\n[H.E.S.S.](https://www.mpi-hd.mpg.de/hfm/HESS/)_ is an array of\ngamma-ray telescopes located in Namibia. Gammapy is regularly used and\nfully supports H.E.S.S. high level data analysis, after export to the\ncurrent [open data level 3\nformat](https://gamma-astro-data-formats.readthedocs.io/)_.\n\nThe H.E.S.S. data is private, and H.E.S.S. analysis is mostly documented\nand discussed at https://hess-confluence.desy.de/ and in\nH.E.S.S.-internal communication channels. However, in 2018, a small\nsub-set of archival H.E.S.S. data was publicly released, called the\n[H.E.S.S. DL3\nDR1](https://www.mpi-hd.mpg.de/hfm/HESS/pages/dl3-dr1/)_, the data\nlevel 3, data release number 1. This dataset is 50 MB in size and is\nused in many Gammapy analysis tutorials, and can be downloaded via\n[gammapy\ndownload](https://docs.gammapy.org/dev/getting-started/index.html#quickstart-setup)_.\n\nThis notebook is a quick introduction to this specific DR1 release. It\nbriefly describes H.E.S.S. data and instrument responses and show a\nsimple exploration of the data with the creation of theta-squared plot.\n\nH.E.S.S. members can find details on the DL3 FITS production on this\n[Confluence\npage](https://hess-confluence.desy.de/confluence/display/HESS/HESS+FITS+data)_\nand access more detailed tutorials in this\n[repository](https://bitbucket.org/hess_software/hess-open-source-tools/src/master/)_\n\n## DL3 DR1\n\nThis is how to access data and IRFs from the H.E.S.S. data level 3, data\nrelease 1.\n" + "\n# H.E.S.S. with Gammapy\nExplore H.E.S.S. event lists and IRFs.\n\n[H.E.S.S.](https://www.mpi-hd.mpg.de/hfm/HESS/)_ is an array of\ngamma-ray telescopes located in Namibia. Gammapy is regularly used and\nfully supports H.E.S.S. high level data analysis, after export to the\ncurrent [open data level 3\nformat](https://gamma-astro-data-formats.readthedocs.io/)_.\n\nThe H.E.S.S. data is private, and H.E.S.S. analysis is mostly documented\nand discussed in the internal Wiki pages and in\nH.E.S.S.-internal communication channels. However, in 2018, a small\nsub-set of archival H.E.S.S. data was publicly released, called the\n[H.E.S.S. DL3\nDR1](https://www.mpi-hd.mpg.de/hfm/HESS/pages/dl3-dr1/)_, the data\nlevel 3, data release number 1. This dataset is 50 MB in size and is\nused in many Gammapy analysis tutorials, and can be downloaded via\n[gammapy\ndownload](https://docs.gammapy.org/dev/getting-started/index.html#quickstart-setup)_.\n\nThis notebook is a quick introduction to this specific DR1 release. It\nbriefly describes H.E.S.S. data and instrument responses and show a\nsimple exploration of the data with the creation of theta-squared plot.\n\nH.E.S.S. members can find details on the DL3 FITS production on this\n[Confluence\npage](https://cchesswiki.in2p3.fr/en/hess/working_groups/analysis_and_reconstruction_working_group/ar_active_tasks/hess_fits_data)_\nand access more detailed tutorials in the BitBucket `hess-open-tools` repository.\n\n## DL3 DR1\n\nThis is how to access data and IRFs from the H.E.S.S. data level 3, data\nrelease 1.\n" ] }, { @@ -216,7 +216,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Next steps\n\nNow you know how to access and work with H.E.S.S. data. All other\ntutorials and documentation apply to H.E.S.S. and CTA or any other IACT\nthat provides DL3 data and IRFs in the standard format.\n\n\n" + "## Next steps\n\nNow you know how to access and work with H.E.S.S. data. All other\ntutorials and documentation apply to H.E.S.S. and CTAO or any other IACT\nthat provides DL3 data and IRFs in the standard format.\n\n\n" ] } ], diff --git a/docs/dev/_downloads/e62e3d07400cc367a5dd87acd8e51755/analysis_mwl.zip b/docs/dev/_downloads/e62e3d07400cc367a5dd87acd8e51755/analysis_mwl.zip index aa163a794ed..4e9026f9816 100644 Binary files a/docs/dev/_downloads/e62e3d07400cc367a5dd87acd8e51755/analysis_mwl.zip and b/docs/dev/_downloads/e62e3d07400cc367a5dd87acd8e51755/analysis_mwl.zip differ diff --git a/docs/dev/_downloads/e70cbbbc7e5076450d85dba47e48c39d/cta_data_analysis.zip b/docs/dev/_downloads/e70cbbbc7e5076450d85dba47e48c39d/cta_data_analysis.zip index 48649ccaccb..88243e48681 100644 Binary files a/docs/dev/_downloads/e70cbbbc7e5076450d85dba47e48c39d/cta_data_analysis.zip and b/docs/dev/_downloads/e70cbbbc7e5076450d85dba47e48c39d/cta_data_analysis.zip differ diff --git a/docs/dev/_downloads/e8a68734048b7e0ceca7060951d52b8d/regionmap-4.pdf 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diff --git a/docs/dev/_downloads/ebd45185ed3e93103504e3303364e3ef/plot_velocity_distributions.pdf b/docs/dev/_downloads/ebd45185ed3e93103504e3303364e3ef/plot_velocity_distributions.pdf index 39e60b5d73c..bae7a71b445 100644 Binary files a/docs/dev/_downloads/ebd45185ed3e93103504e3303364e3ef/plot_velocity_distributions.pdf and b/docs/dev/_downloads/ebd45185ed3e93103504e3303364e3ef/plot_velocity_distributions.pdf differ diff --git a/docs/dev/_downloads/ec6ec35ddc2c98b82ef77dc71701733a/pulsar_analysis.ipynb b/docs/dev/_downloads/ec6ec35ddc2c98b82ef77dc71701733a/pulsar_analysis.ipynb index d1f7e9c188f..5301b900b99 100644 --- a/docs/dev/_downloads/ec6ec35ddc2c98b82ef77dc71701733a/pulsar_analysis.ipynb +++ b/docs/dev/_downloads/ec6ec35ddc2c98b82ef77dc71701733a/pulsar_analysis.ipynb @@ -263,7 +263,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that here we are lacking statistic because we only use one run of CTA.\n\n## Phase-resolved spectrum\n\nWe can also make a phase-resolved spectrum.\nIn order to do that, we are going to use the `~gammapy.makers.PhaseBackgroundMaker` to create a\n`~gammapy.datasets.SpectrumDatasetOnOff` with the ON and OFF taken in the phase space.\nNote that this maker take the ON and OFF in the same spatial region.\n\nHere to create the `~gammapy.datasets.SpectrumDatasetOnOff`, we are going to redo the whole data reduction.\nHowever, note that one can use the `~gammapy.datasets.MapDatasetOnOff.to_spectrum_dataset()` method\n(with the `containment_correction` parameter set to True) if such a `~gammapy.datasets.MapDatasetOnOff`\nhas been created as shown above.\n\n" + "Note that here we are lacking statistic because we only use one run of CTAO.\n\n## Phase-resolved spectrum\n\nWe can also make a phase-resolved spectrum.\nIn order to do that, we are going to use the `~gammapy.makers.PhaseBackgroundMaker` to create a\n`~gammapy.datasets.SpectrumDatasetOnOff` with the ON and OFF taken in the phase space.\nNote that this maker take the ON and OFF in the same spatial region.\n\nHere to create the `~gammapy.datasets.SpectrumDatasetOnOff`, we are going to redo the whole data reduction.\nHowever, note that one can use the `~gammapy.datasets.MapDatasetOnOff.to_spectrum_dataset()` method\n(with the `containment_correction` parameter set to True) if such a `~gammapy.datasets.MapDatasetOnOff`\nhas been created as shown above.\n\n" ] }, { diff --git a/docs/dev/_downloads/f48159f5f5e15c2ab4d4c84f71fb7688/plot_edisp_kernel_param.pdf b/docs/dev/_downloads/f48159f5f5e15c2ab4d4c84f71fb7688/plot_edisp_kernel_param.pdf index 52b1489f8af..06f201ddf9c 100644 Binary files a/docs/dev/_downloads/f48159f5f5e15c2ab4d4c84f71fb7688/plot_edisp_kernel_param.pdf and b/docs/dev/_downloads/f48159f5f5e15c2ab4d4c84f71fb7688/plot_edisp_kernel_param.pdf differ diff --git a/docs/dev/_downloads/f49aa1b6b6eeb66ac89f34f562b16f06/observation_clustering.zip b/docs/dev/_downloads/f49aa1b6b6eeb66ac89f34f562b16f06/observation_clustering.zip index 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a/docs/dev/_modules/gammapy/data/data_store.html +++ b/docs/dev/_modules/gammapy/data/data_store.html @@ -744,11 +744,6 @@

Source code for gammapy.data.data_store

         - ``CALDB`` (example: ``CALDB = 1dc``)
         - ``IRF`` (example: ``IRF = South_z20_50h``)
 
-        This method is useful specifically if you want to load data simulated
-        with `ctobssim`_.
-
-        .. _ctobssim: http://cta.irap.omp.eu/ctools/users/reference_manual/ctobssim.html
-
         Parameters
         ----------
         events_paths : list of str or `~pathlib.Path`
@@ -1296,7 +1291,6 @@ 

Source code for gammapy.data.data_store

         yield dict(HDU_TYPE="bkg", HDU_CLASS="bkg_3d", HDU_NAME="BACKGROUND", **info)
 
 
-# TODO: load IRF file, and infer HDU_CLASS from IRF file contents!
 class CalDBIRF:
     """Helper class to work with IRFs in CALDB format."""
 
diff --git a/docs/dev/_modules/gammapy/irf/edisp/map.html b/docs/dev/_modules/gammapy/irf/edisp/map.html
index 6175fdd40b8..0b3b70d2b91 100644
--- a/docs/dev/_modules/gammapy/irf/edisp/map.html
+++ b/docs/dev/_modules/gammapy/irf/edisp/map.html
@@ -574,7 +574,7 @@ 

Source code for gammapy.irf.edisp.map

     --------
     ::
 
-        # Energy dispersion map for CTA data
+        # Energy dispersion map for CTAO data
         import numpy as np
         from astropy import units as u
         from astropy.coordinates import SkyCoord
diff --git a/docs/dev/_modules/gammapy/irf/effective_area.html b/docs/dev/_modules/gammapy/irf/effective_area.html
index 0c11b899503..6fce7fe1b0b 100644
--- a/docs/dev/_modules/gammapy/irf/effective_area.html
+++ b/docs/dev/_modules/gammapy/irf/effective_area.html
@@ -536,6 +536,7 @@
                   
   

Source code for gammapy.irf.effective_area

 # Licensed under a 3-clause BSD style license - see LICENSE.rst
+import warnings
 import numpy as np
 import astropy.units as u
 from astropy.visualization import quantity_support
@@ -544,6 +545,7 @@ 

Source code for gammapy.irf.effective_area

 from gammapy.maps.axes import UNIT_STRING_FORMAT
 from gammapy.visualization.utils import add_colorbar
 from .core import IRF
+from gammapy.utils.deprecation import GammapyDeprecationWarning
 
 __all__ = ["EffectiveAreaTable2D"]
 
@@ -792,7 +794,7 @@ 

Source code for gammapy.irf.effective_area

         energy_axis_true : `MapAxis`, optional
             Energy binning, analytic function is evaluated at log centers.
             Default is None.
-        instrument : {'HESS', 'HESS2', 'CTA'}
+        instrument : {'HESS', 'HESS2', 'CTAO'}
             Instrument name. Default is 'HESS'.
 
         Returns
@@ -805,9 +807,15 @@ 

Source code for gammapy.irf.effective_area

         pars = {
             "HESS": [6.85e9, 0.0891, 5e5],
             "HESS2": [2.05e9, 0.0891, 1e5],
-            "CTA": [1.71e11, 0.0891, 1e5],
+            "CTAO": [1.71e11, 0.0891, 1e5],
         }
 
+        if instrument == "CTA":
+            instrument = "CTAO"
+            warnings.warn(
+                "Since v1.3, the value 'CTA' is replaced by 'CTAO' for the argument instrument.",
+                GammapyDeprecationWarning,
+            )
         if instrument not in pars.keys():
             ss = f"Unknown instrument: {instrument}\n"
             ss += f"Valid instruments: {list(pars.keys())}"
diff --git a/docs/dev/_modules/gammapy/makers/map.html b/docs/dev/_modules/gammapy/makers/map.html
index 3488a149b38..398dcca835a 100644
--- a/docs/dev/_modules/gammapy/makers/map.html
+++ b/docs/dev/_modules/gammapy/makers/map.html
@@ -573,7 +573,7 @@ 

Source code for gammapy.makers.map

         Background evaluation oversampling factor in energy.
     background_interp_missing_data : bool, optional
         Interpolate missing values in background 3d map.
-        Default is True, have to be set to True for CTA IRF.
+        Default is True, have to be set to True for CTAO IRF.
     background_pad_offset : bool, optional
         Pad one bin in offset for 2d background map.
         This avoids extrapolation at edges and use the nearest value.
diff --git a/docs/dev/_modules/gammapy/modeling/models/spectral_cosmic_ray.html b/docs/dev/_modules/gammapy/modeling/models/spectral_cosmic_ray.html
index 6589190b9f1..786f2d8c5a0 100644
--- a/docs/dev/_modules/gammapy/modeling/models/spectral_cosmic_ray.html
+++ b/docs/dev/_modules/gammapy/modeling/models/spectral_cosmic_ray.html
@@ -539,7 +539,7 @@ 

Source code for gammapy.modeling.models.spectral_cosmic_ray

"""Simple models for cosmic ray spectra at Earth. For measurements, the "Database of Charged Cosmic Rays (CRDB)" is a great resource: -http://lpsc.in2p3.fr/cosmic-rays-db/ +https://lpsc.in2p3.fr/crdb/ """ import numpy as np from astropy import units as u @@ -576,7 +576,7 @@

Source code for gammapy.modeling.models.spectral_cosmic_ray

def create_cosmic_ray_spectral_model(particle="proton"): """Cosmic a cosmic ray spectral model at Earth. - These are the spectra assumed in this CTA study: + These are the spectra assumed in this CTAO study: Table 3 in https://ui.adsabs.harvard.edu/abs/2013APh....43..171B The spectrum given is a differential flux ``dnde`` in units of diff --git a/docs/dev/_sources/development/dev_howto.rst.txt b/docs/dev/_sources/development/dev_howto.rst.txt index 4f4d210c893..86450c64b9a 100644 --- a/docs/dev/_sources/development/dev_howto.rst.txt +++ b/docs/dev/_sources/development/dev_howto.rst.txt @@ -70,9 +70,12 @@ Notes: Gammapy names defined here, to the names used in the formats. Of course, where formats are not set in stone yet, we advocate and encourage the use of the names chosen here. -* Finally, we realise that eventually probably CTA will define this, and Gammapy - is only a prototype. So if CTA chooses something else, probably we will follow - suite and do one more backward-incompatible change at some point to align with CTA. +* Finally, CTAO has proposed a full data model associated to the needed quantities. And + the community is working to create a data format for very-high-energy data produced by gamma-ray + and neutrino experiments within the open initiative `Very-high-energy Open Data Format`_. This format + aims to respect the CTAO data model, to respect the `FAIR principles`_ and to follow as much as + possible the `IVOA`_ recommendations. In order to handle past, current and old formats, + Gammapy should follow the `FAIR4RS principles`_ by proposing a user-friendly interface. Clobber or overwrite? +++++++++++++++++++++ diff --git a/docs/dev/_sources/development/release.rst.txt b/docs/dev/_sources/development/release.rst.txt index 09774995204..bf558988b55 100644 --- a/docs/dev/_sources/development/release.rst.txt +++ b/docs/dev/_sources/development/release.rst.txt @@ -90,9 +90,8 @@ Steps for the day to announce the release: (decide on a case by case basis, if it's relevant to the group of people): * https://groups.google.com/forum/#!forum/astropy-dev - * https://lists.nasa.gov/mailman/listinfo/open-gamma-ray-astro - * CTA DATA list (cta-wp-dm@cta-observatory.org) - * hess-analysis + * CTAO AS WG list (cta-wg-as [at] cta-observatory [dot] org) + * hess-forum list (hess-forum [at] lsw.uni-heidelberg [dot] de) #. Make sure the release milestone and issue is closed on GitHub #. Update these release notes with any useful infos / steps that you learned while making the release (ideally try to script / automate the task or check, diff --git a/docs/dev/_sources/index.rst.txt b/docs/dev/_sources/index.rst.txt index 2fe2c37e2ed..1339b7d8163 100644 --- a/docs/dev/_sources/index.rst.txt +++ b/docs/dev/_sources/index.rst.txt @@ -18,7 +18,7 @@ Gammapy Gammapy is a community-developed, open-source Python package for gamma-ray -astronomy built on Numpy, Scipy and Astropy. **It is the core library for the** `CTA`_ **Science Tools** +astronomy built on Numpy, Scipy and Astropy. **It is the core library for the** `CTAO`_ **Science Tools** but can also be used to analyse data from existing imaging atmospheric Cherenkov telescopes (IACTs), such as `H.E.S.S.`_, `MAGIC`_ and `VERITAS`_. It also provides some support for `Fermi-LAT`_ and `HAWC`_ data analysis. diff --git a/docs/dev/_sources/sg_execution_times.rst.txt b/docs/dev/_sources/sg_execution_times.rst.txt index d7fa9ebb350..e03c6ef51ef 100644 --- a/docs/dev/_sources/sg_execution_times.rst.txt +++ b/docs/dev/_sources/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**09:53.580** total execution time for 84 files **from all galleries**: +**10:02.876** total execution time for 84 files **from all galleries**: .. container:: @@ -33,254 +33,254 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_tutorials_api_priors.py` (``../examples/tutorials/api/priors.py``) - - 01:21.171 + - 01:27.538 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-1d_spectral_analysis.py` (``../examples/tutorials/analysis-1d/spectral_analysis.py``) - - 00:44.699 + - 00:44.094 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-3d_analysis_3d.py` (``../examples/tutorials/analysis-3d/analysis_3d.py``) - - 00:37.983 + - 00:37.871 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-time_light_curve_flare.py` (``../examples/tutorials/analysis-time/light_curve_flare.py``) - - 00:34.207 + - 00:33.442 - 0.0 * - :ref:`sphx_glr_tutorials_api_astro_dark_matter.py` (``../examples/tutorials/api/astro_dark_matter.py``) - - 00:28.155 + - 00:27.997 - 0.0 - * - :ref:`sphx_glr_tutorials_analysis-time_Time_resolved_spectroscopy.py` (``../examples/tutorials/analysis-time/Time_resolved_spectroscopy.py``) - - 00:21.382 + * - :ref:`sphx_glr_tutorials_analysis-3d_event_sampling.py` (``../examples/tutorials/analysis-3d/event_sampling.py``) + - 00:26.961 - 0.0 - * - :ref:`sphx_glr_tutorials_analysis-3d_flux_profiles.py` (``../examples/tutorials/analysis-3d/flux_profiles.py``) - - 00:21.171 + * - :ref:`sphx_glr_tutorials_analysis-time_light_curve_simulation.py` (``../examples/tutorials/analysis-time/light_curve_simulation.py``) + - 00:21.166 - 0.0 - * - :ref:`sphx_glr_tutorials_analysis-2d_detect.py` (``../examples/tutorials/analysis-2d/detect.py``) - - 00:20.870 + * - :ref:`sphx_glr_tutorials_analysis-time_Time_resolved_spectroscopy.py` (``../examples/tutorials/analysis-time/Time_resolved_spectroscopy.py``) + - 00:21.126 - 0.0 - * - :ref:`sphx_glr_tutorials_analysis-3d_energy_dependent_estimation.py` (``../examples/tutorials/analysis-3d/energy_dependent_estimation.py``) - - 00:19.804 + * - :ref:`sphx_glr_tutorials_analysis-2d_detect.py` (``../examples/tutorials/analysis-2d/detect.py``) + - 00:20.552 - 0.0 - * - :ref:`sphx_glr_tutorials_analysis-time_light_curve_simulation.py` (``../examples/tutorials/analysis-time/light_curve_simulation.py``) - - 00:18.165 + * - :ref:`sphx_glr_tutorials_analysis-3d_flux_profiles.py` (``../examples/tutorials/analysis-3d/flux_profiles.py``) + - 00:20.547 - 0.0 - * - :ref:`sphx_glr_tutorials_analysis-3d_event_sampling.py` (``../examples/tutorials/analysis-3d/event_sampling.py``) - - 00:17.717 + * - :ref:`sphx_glr_tutorials_analysis-3d_energy_dependent_estimation.py` (``../examples/tutorials/analysis-3d/energy_dependent_estimation.py``) + - 00:19.657 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-3d_cta_data_analysis.py` (``../examples/tutorials/analysis-3d/cta_data_analysis.py``) - - 00:17.287 + - 00:16.841 - 0.0 * - :ref:`sphx_glr_tutorials_starting_analysis_1.py` (``../examples/tutorials/starting/analysis_1.py``) - - 00:15.384 + - 00:15.326 - 0.0 * - :ref:`sphx_glr_tutorials_api_makers.py` (``../examples/tutorials/api/makers.py``) - - 00:15.214 + - 00:15.013 - 0.0 * - :ref:`sphx_glr_tutorials_api_datasets.py` (``../examples/tutorials/api/datasets.py``) - - 00:14.906 + - 00:13.751 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-2d_ring_background.py` (``../examples/tutorials/analysis-2d/ring_background.py``) - - 00:12.115 + - 00:12.100 - 0.0 * - :ref:`sphx_glr_tutorials_api_fitting.py` (``../examples/tutorials/api/fitting.py``) - - 00:11.540 + - 00:11.367 - 0.0 * - :ref:`sphx_glr_tutorials_api_model_management.py` (``../examples/tutorials/api/model_management.py``) - - 00:10.396 + - 00:10.361 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-3d_simulate_3d.py` (``../examples/tutorials/analysis-3d/simulate_3d.py``) - - 00:10.129 + - 00:09.965 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-1d_spectral_analysis_hli.py` (``../examples/tutorials/analysis-1d/spectral_analysis_hli.py``) - - 00:08.080 + - 00:07.829 - 0.0 * - :ref:`sphx_glr_tutorials_starting_analysis_2.py` (``../examples/tutorials/starting/analysis_2.py``) - - 00:07.749 + - 00:07.665 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-time_light_curve.py` (``../examples/tutorials/analysis-time/light_curve.py``) - - 00:07.519 + - 00:07.502 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-1d_spectrum_simulation.py` (``../examples/tutorials/analysis-1d/spectrum_simulation.py``) - - 00:07.312 - - 0.0 - * - :ref:`sphx_glr_tutorials_data_hawc.py` (``../examples/tutorials/data/hawc.py``) - - 00:07.256 + - 00:07.149 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-1d_ebl.py` (``../examples/tutorials/analysis-1d/ebl.py``) - - 00:07.003 + - 00:06.985 - 0.0 * - :ref:`sphx_glr_tutorials_api_estimators.py` (``../examples/tutorials/api/estimators.py``) - - 00:06.947 + - 00:06.718 + - 0.0 + * - :ref:`sphx_glr_tutorials_data_hawc.py` (``../examples/tutorials/data/hawc.py``) + - 00:06.689 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-3d_analysis_mwl.py` (``../examples/tutorials/analysis-3d/analysis_mwl.py``) - - 00:06.759 + - 00:06.606 - 0.0 * - :ref:`sphx_glr_tutorials_data_fermi_lat.py` (``../examples/tutorials/data/fermi_lat.py``) - - 00:06.673 + - 00:06.263 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-time_pulsar_analysis.py` (``../examples/tutorials/analysis-time/pulsar_analysis.py``) - - 00:06.579 + - 00:06.235 - 0.0 * - :ref:`sphx_glr_tutorials_api_catalog.py` (``../examples/tutorials/api/catalog.py``) - - 00:06.157 + - 00:06.077 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-2d_modeling_2D.py` (``../examples/tutorials/analysis-2d/modeling_2D.py``) - - 00:05.661 - - 0.0 - * - :ref:`sphx_glr_tutorials_data_cta.py` (``../examples/tutorials/data/cta.py``) - - 00:05.525 + - 00:05.541 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-1d_sed_fitting.py` (``../examples/tutorials/analysis-1d/sed_fitting.py``) - - 00:05.362 + - 00:05.263 - 0.0 - * - :ref:`sphx_glr_tutorials_analysis-1d_spectral_analysis_rad_max.py` (``../examples/tutorials/analysis-1d/spectral_analysis_rad_max.py``) - - 00:04.474 + * - :ref:`sphx_glr_tutorials_data_cta.py` (``../examples/tutorials/data/cta.py``) + - 00:05.234 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-1d_extended_source_spectral_analysis.py` (``../examples/tutorials/analysis-1d/extended_source_spectral_analysis.py``) - - 00:04.330 + - 00:04.301 + - 0.0 + * - :ref:`sphx_glr_tutorials_analysis-1d_spectral_analysis_rad_max.py` (``../examples/tutorials/analysis-1d/spectral_analysis_rad_max.py``) + - 00:04.195 - 0.0 * - :ref:`sphx_glr_tutorials_api_observation_clustering.py` (``../examples/tutorials/api/observation_clustering.py``) - - 00:03.955 + - 00:03.875 - 0.0 * - :ref:`sphx_glr_tutorials_api_irfs.py` (``../examples/tutorials/api/irfs.py``) - - 00:03.852 + - 00:03.804 - 0.0 * - :ref:`sphx_glr_tutorials_data_hess.py` (``../examples/tutorials/data/hess.py``) - - 00:03.605 - - 0.0 - * - :ref:`sphx_glr_tutorials_api_models.py` (``../examples/tutorials/api/models.py``) - - 00:03.150 + - 00:03.259 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-3d_event_sampling_nrg_depend_models.py` (``../examples/tutorials/analysis-3d/event_sampling_nrg_depend_models.py``) - - 00:02.958 + - 00:02.891 - 0.0 * - :ref:`sphx_glr_tutorials_api_maps.py` (``../examples/tutorials/api/maps.py``) - - 00:02.746 + - 00:02.716 + - 0.0 + * - :ref:`sphx_glr_tutorials_api_models.py` (``../examples/tutorials/api/models.py``) + - 00:02.624 - 0.0 * - :ref:`sphx_glr_tutorials_scripts_survey_map.py` (``../examples/tutorials/scripts/survey_map.py``) - - 00:02.511 + - 00:02.499 - 0.0 * - :ref:`sphx_glr_tutorials_api_mask_maps.py` (``../examples/tutorials/api/mask_maps.py``) - - 00:02.337 + - 00:02.315 - 0.0 * - :ref:`sphx_glr_tutorials_starting_overview.py` (``../examples/tutorials/starting/overview.py``) - - 00:01.267 + - 00:01.360 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_absorbed.py` (``../examples/models/spectral/plot_absorbed.py``) - - 00:01.001 + - 00:01.055 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-1d_cta_sensitivity.py` (``../examples/tutorials/analysis-1d/cta_sensitivity.py``) - - 00:00.833 - - 0.0 - * - :ref:`sphx_glr_tutorials_analysis-time_Variability_estimation.py` (``../examples/tutorials/analysis-time/Variability_estimation.py``) - - 00:00.659 + - 00:00.825 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_constant_temporal.py` (``../examples/models/temporal/plot_constant_temporal.py``) - - 00:00.579 + - 00:00.752 + - 0.0 + * - :ref:`sphx_glr_tutorials_analysis-time_Variability_estimation.py` (``../examples/tutorials/analysis-time/Variability_estimation.py``) + - 00:00.596 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_template_spectral.py` (``../examples/models/spectral/plot_template_spectral.py``) - - 00:00.449 + - 00:00.446 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_naima.py` (``../examples/models/spectral/plot_naima.py``) - - 00:00.430 + - 00:00.419 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_constant.py` (``../examples/models/spatial/plot_constant.py``) - - 00:00.352 + - 00:00.369 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_exp_cutoff_powerlaw_norm_spectral.py` (``../examples/models/spectral/plot_exp_cutoff_powerlaw_norm_spectral.py``) - - 00:00.345 + - 00:00.343 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_broken_powerlaw.py` (``../examples/models/spectral/plot_broken_powerlaw.py``) + * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_powerlaw.py` (``../examples/models/spectral/plot_powerlaw.py``) - 00:00.331 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_compound.py` (``../examples/models/spectral/plot_compound.py``) - - 00:00.327 + * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_powerlaw2.py` (``../examples/models/spectral/plot_powerlaw2.py``) + - 00:00.324 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_smooth_broken_powerlaw.py` (``../examples/models/spectral/plot_smooth_broken_powerlaw.py``) - - 00:00.327 - - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_powerlaw2.py` (``../examples/models/spectral/plot_powerlaw2.py``) - - 00:00.326 + - 00:00.321 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_powerlaw.py` (``../examples/models/spectral/plot_powerlaw.py``) + * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_broken_powerlaw.py` (``../examples/models/spectral/plot_broken_powerlaw.py``) - 00:00.321 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_disk.py` (``../examples/models/spatial/plot_disk.py``) - - 00:00.317 + * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_compound.py` (``../examples/models/spectral/plot_compound.py``) + - 00:00.320 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_logparabola_norm_spectral.py` (``../examples/models/spectral/plot_logparabola_norm_spectral.py``) - - 00:00.299 + * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_disk.py` (``../examples/models/spatial/plot_disk.py``) + - 00:00.319 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_powerlaw_norm_spectral.py` (``../examples/models/spectral/plot_powerlaw_norm_spectral.py``) - 00:00.274 - 0.0 + * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_logparabola_norm_spectral.py` (``../examples/models/spectral/plot_logparabola_norm_spectral.py``) + - 00:00.271 + - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_constant_spectral.py` (``../examples/models/spectral/plot_constant_spectral.py``) - - 00:00.242 + - 00:00.239 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_template.py` (``../examples/models/spatial/plot_template.py``) - 00:00.229 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_piecewise_norm_spectral.py` (``../examples/models/spectral/plot_piecewise_norm_spectral.py``) - - 00:00.227 - - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_super_exp_cutoff_powerlaw_4fgl.py` (``../examples/models/spectral/plot_super_exp_cutoff_powerlaw_4fgl.py``) - - 00:00.226 + - 00:00.228 + - 0.0 + * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_piecewise_norm_spectral.py` (``../examples/models/spectral/plot_piecewise_norm_spectral.py``) + - 00:00.225 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_gen_gauss.py` (``../examples/models/spatial/plot_gen_gauss.py``) - 00:00.224 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_super_exp_cutoff_powerlaw_4fgl_dr1.py` (``../examples/models/spectral/plot_super_exp_cutoff_powerlaw_4fgl_dr1.py``) - - 00:00.223 + - 00:00.219 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_super_exp_cutoff_powerlaw_3fgl.py` (``../examples/models/spectral/plot_super_exp_cutoff_powerlaw_3fgl.py``) - - 00:00.222 + - 00:00.219 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_gauss_spectral.py` (``../examples/models/spectral/plot_gauss_spectral.py``) - - 00:00.221 + - 00:00.217 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_gauss.py` (``../examples/models/spatial/plot_gauss.py``) - - 00:00.215 + - 00:00.211 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_exp_cutoff_powerlaw.py` (``../examples/models/spectral/plot_exp_cutoff_powerlaw.py``) - - 00:00.209 + - 00:00.206 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_logparabola.py` (``../examples/models/spectral/plot_logparabola.py``) - - 00:00.208 + - 00:00.204 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_exp_cutoff_powerlaw_3fgl.py` (``../examples/models/spectral/plot_exp_cutoff_powerlaw_3fgl.py``) - - 00:00.206 + - 00:00.203 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_point.py` (``../examples/models/spatial/plot_point.py``) - - 00:00.203 + - 00:00.201 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_powerlaw_temporal.py` (``../examples/models/temporal/plot_powerlaw_temporal.py``) - - 00:00.193 + - 00:00.195 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_shell.py` (``../examples/models/spatial/plot_shell.py``) - - 00:00.186 + - 00:00.185 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_shell2.py` (``../examples/models/spatial/plot_shell2.py``) - 00:00.177 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_piecewise_norm_spatial.py` (``../examples/models/spatial/plot_piecewise_norm_spatial.py``) - - 00:00.146 + - 00:00.143 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_template_phase_temporal.py` (``../examples/models/temporal/plot_template_phase_temporal.py``) - - 00:00.123 + - 00:00.129 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_template_temporal.py` (``../examples/models/temporal/plot_template_temporal.py``) - 00:00.121 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_generalized_gaussian_temporal.py` (``../examples/models/temporal/plot_generalized_gaussian_temporal.py``) - - 00:00.114 + - 00:00.111 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_gaussian_temporal.py` (``../examples/models/temporal/plot_gaussian_temporal.py``) + * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_sine_temporal.py` (``../examples/models/temporal/plot_sine_temporal.py``) - 00:00.110 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_expdecay_temporal.py` (``../examples/models/temporal/plot_expdecay_temporal.py``) - - 00:00.110 + * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_gaussian_temporal.py` (``../examples/models/temporal/plot_gaussian_temporal.py``) + - 00:00.109 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_sine_temporal.py` (``../examples/models/temporal/plot_sine_temporal.py``) - - 00:00.108 + * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_expdecay_temporal.py` (``../examples/models/temporal/plot_expdecay_temporal.py``) + - 00:00.109 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_linear_temporal.py` (``../examples/models/temporal/plot_linear_temporal.py``) - - 00:00.104 + - 00:00.106 - 0.0 diff --git a/docs/dev/_sources/tutorials/analysis-1d/cta_sensitivity.rst.txt b/docs/dev/_sources/tutorials/analysis-1d/cta_sensitivity.rst.txt index 35b65565003..5274abf4534 100644 --- a/docs/dev/_sources/tutorials/analysis-1d/cta_sensitivity.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-1d/cta_sensitivity.rst.txt @@ -21,12 +21,12 @@ Point source sensitivity ======================== -Estimate the CTA sensitivity for a point-like IRF at a fixed zenith angle and fixed offset. +Estimate the CTAO sensitivity for a point-like IRF at a fixed zenith angle and fixed offset. Introduction ------------ -This notebook explains how to estimate the CTA sensitivity for a +This notebook explains how to estimate the CTAO sensitivity for a point-like IRF at a fixed zenith angle and fixed offset, using the full containment IRFs distributed for the CTA 1DC. The significance is computed for a 1D analysis (ON-OFF regions) with the Li&Ma formula. @@ -119,7 +119,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -195,7 +195,7 @@ degree. Load IRFs and prepare dataset ----------------------------- -We extract the 1D IRFs from the full 3D IRFs provided by CTA. +We extract the 1D IRFs from the full 3D IRFs provided by CTAO. .. GENERATED FROM PYTHON SOURCE LINES 86-97 @@ -339,7 +339,7 @@ minimal significance of 5 per bin. The excess must also be larger than 5% of the We assume an alpha of 0.2 (ratio between ON and OFF area). We then run the sensitivity estimator. -These are the conditions imposed in standard CTA sensitivity computations. +These are the conditions imposed in standard CTAO sensitivity computations. .. GENERATED FROM PYTHON SOURCE LINES 157-165 diff --git a/docs/dev/_sources/tutorials/analysis-1d/ebl.rst.txt b/docs/dev/_sources/tutorials/analysis-1d/ebl.rst.txt index 4cc66e6eba4..d95607951a5 100644 --- a/docs/dev/_sources/tutorials/analysis-1d/ebl.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-1d/ebl.rst.txt @@ -352,7 +352,7 @@ compound model. SkyModel - Name : YgI3kZSF + Name : Wqkl5UsP Datasets names : None Spectral model type : CompoundSpectralModel Spatial model type : @@ -367,7 +367,7 @@ compound model. SkyModel - Name : O4vAZEUJ + Name : dr-wsdJg Datasets names : None Spectral model type : PowerLawSpectralModel Spatial model type : diff --git a/docs/dev/_sources/tutorials/analysis-1d/extended_source_spectral_analysis.rst.txt b/docs/dev/_sources/tutorials/analysis-1d/extended_source_spectral_analysis.rst.txt index 6f33859439a..1a00aaa0865 100644 --- a/docs/dev/_sources/tutorials/analysis-1d/extended_source_spectral_analysis.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-1d/extended_source_spectral_analysis.rst.txt @@ -164,7 +164,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy diff --git a/docs/dev/_sources/tutorials/analysis-1d/sed_fitting.rst.txt b/docs/dev/_sources/tutorials/analysis-1d/sed_fitting.rst.txt index b6127303219..2233eee183b 100644 --- a/docs/dev/_sources/tutorials/analysis-1d/sed_fitting.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-1d/sed_fitting.rst.txt @@ -162,7 +162,7 @@ attribute:
Table length=5 - +
diff --git a/docs/dev/_sources/tutorials/analysis-1d/sg_execution_times.rst.txt b/docs/dev/_sources/tutorials/analysis-1d/sg_execution_times.rst.txt index da9322aa951..28fed85fb86 100644 --- a/docs/dev/_sources/tutorials/analysis-1d/sg_execution_times.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-1d/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**01:22.094** total execution time for 8 files **from tutorials/analysis-1d**: +**01:20.640** total execution time for 8 files **from tutorials/analysis-1d**: .. container:: @@ -33,26 +33,26 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_tutorials_analysis-1d_spectral_analysis.py` (``spectral_analysis.py``) - - 00:44.699 + - 00:44.094 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-1d_spectral_analysis_hli.py` (``spectral_analysis_hli.py``) - - 00:08.080 + - 00:07.829 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-1d_spectrum_simulation.py` (``spectrum_simulation.py``) - - 00:07.312 + - 00:07.149 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-1d_ebl.py` (``ebl.py``) - - 00:07.003 + - 00:06.985 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-1d_sed_fitting.py` (``sed_fitting.py``) - - 00:05.362 - - 0.0 - * - :ref:`sphx_glr_tutorials_analysis-1d_spectral_analysis_rad_max.py` (``spectral_analysis_rad_max.py``) - - 00:04.474 + - 00:05.263 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-1d_extended_source_spectral_analysis.py` (``extended_source_spectral_analysis.py``) - - 00:04.330 + - 00:04.301 + - 0.0 + * - :ref:`sphx_glr_tutorials_analysis-1d_spectral_analysis_rad_max.py` (``spectral_analysis_rad_max.py``) + - 00:04.195 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-1d_cta_sensitivity.py` (``cta_sensitivity.py``) - - 00:00.833 + - 00:00.825 - 0.0 diff --git a/docs/dev/_sources/tutorials/analysis-1d/spectral_analysis.rst.txt b/docs/dev/_sources/tutorials/analysis-1d/spectral_analysis.rst.txt index e489070b4d3..30c29ac6636 100644 --- a/docs/dev/_sources/tutorials/analysis-1d/spectral_analysis.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-1d/spectral_analysis.rst.txt @@ -206,7 +206,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -1216,7 +1216,7 @@ tutorial. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 44.699 seconds) + **Total running time of the script:** (0 minutes 44.094 seconds) .. _sphx_glr_download_tutorials_analysis-1d_spectral_analysis.py: diff --git a/docs/dev/_sources/tutorials/analysis-1d/spectral_analysis_hli.rst.txt b/docs/dev/_sources/tutorials/analysis-1d/spectral_analysis_hli.rst.txt index 13fec9316eb..be683fd7574 100644 --- a/docs/dev/_sources/tutorials/analysis-1d/spectral_analysis_hli.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-1d/spectral_analysis_hli.rst.txt @@ -150,7 +150,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -350,8 +350,8 @@ Here is what the configuration for our analysis looks like: parameters: selection_optional: all metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:29:44.930944' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T10:01:30.751715' origin: null @@ -571,8 +571,8 @@ One can export/import the `~gammapy.analysis.AnalysisConfig` to/from a YAML file parameters: selection_optional: all metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:29:44.955101' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T10:01:30.775845' origin: null @@ -1107,8 +1107,8 @@ This is how we can write the model back to file again: unit: TeV covariance: model-best-fit_covariance.dat metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:29:48.384963' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T10:01:34.151913' origin: null diff --git a/docs/dev/_sources/tutorials/analysis-1d/spectral_analysis_rad_max.rst.txt b/docs/dev/_sources/tutorials/analysis-1d/spectral_analysis_rad_max.rst.txt index d6c10352a32..6b0aac6beba 100644 --- a/docs/dev/_sources/tutorials/analysis-1d/spectral_analysis_rad_max.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-1d/spectral_analysis_rad_max.rst.txt @@ -206,7 +206,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -749,10 +749,10 @@ provide a good distribution of the background. result = super().__array_ufunc__(function, method, *arrays, **kwargs) model type name value unit ... min max frozen link prior --------- ---- --------- ---------- -------------- ... --- --- ------ ---- ----- - simulated amplitude 4.3947e-11 cm-2 s-1 TeV-1 ... nan nan False + simulated amplitude 4.1819e-11 cm-2 s-1 TeV-1 ... nan nan False simulated reference 1.0000e+00 TeV ... nan nan True - simulated alpha 2.5055e+00 ... nan nan False - simulated beta 1.5758e-01 ... nan nan False + simulated alpha 2.6903e+00 ... nan nan False + simulated beta 2.3367e-01 ... nan nan False diff --git a/docs/dev/_sources/tutorials/analysis-1d/spectrum_simulation.rst.txt b/docs/dev/_sources/tutorials/analysis-1d/spectrum_simulation.rst.txt index 04de2aafd0a..2922e90ebc4 100644 --- a/docs/dev/_sources/tutorials/analysis-1d/spectrum_simulation.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-1d/spectrum_simulation.rst.txt @@ -44,12 +44,12 @@ using Poisson probability distribution. This can be done to check the feasibility of a measurement, to test whether fitted parameters really provide a good fit to the data etc. -Here we will see how to perform a 1D spectral simulation of a CTA +Here we will see how to perform a 1D spectral simulation of a CTAO observation, in particular, we will generate OFF observations following -the template background stored in the CTA IRFs. +the template background stored in the CTAO IRFs. **Objective: simulate a number of spectral ON-OFF observations of a -source with a power-law spectral model with CTA using the CTA 1DC +source with a power-law spectral model with CTAO using the CTA 1DC response, fit them with the assumed spectral model and check that the distribution of fitted parameters is consistent with the input values.** @@ -142,7 +142,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -411,14 +411,14 @@ dealing with simulations based on observations of real off counts. SpectrumDatasetOnOff -------------------- - Name : bCK5aw7p + Name : 2n3Wh7vX - Total counts : 279 - Total background counts : 22.80 - Total excess counts : 256.20 + Total counts : 328 + Total background counts : 24.60 + Total excess counts : 303.40 - Predicted counts : 303.74 - Predicted background counts : 22.37 + Predicted counts : 306.36 + Predicted background counts : 24.99 Predicted excess counts : 281.37 Exposure min : 2.53e+08 m2 s @@ -428,7 +428,7 @@ dealing with simulations based on observations of real off counts. Number of fit bins : 9 Fit statistic type : wstat - Fit statistic value (-2 log(L)) : 9.90 + Fit statistic value (-2 log(L)) : 6.83 Number of models : 1 Number of parameters : 3 @@ -446,7 +446,7 @@ dealing with simulations based on observations of real off counts. amplitude : 2.50e-12 +/- 0.0e+00 1 / (cm2 s TeV) reference (frozen): 1.000 TeV - Total counts_off : 114 + Total counts_off : 123 Acceptance : 9 Acceptance off : 45 diff --git a/docs/dev/_sources/tutorials/analysis-2d/detect.rst.txt b/docs/dev/_sources/tutorials/analysis-2d/detect.rst.txt index 712861eb89c..6769bbc0c64 100644 --- a/docs/dev/_sources/tutorials/analysis-2d/detect.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-2d/detect.rst.txt @@ -124,7 +124,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -425,7 +425,7 @@ normalisation factor: SkyModel - Name : xKQKPkcV + Name : zTE1BNIs Datasets names : None Spectral model type : PowerLawSpectralModel Spatial model type : PointSpatialModel @@ -624,7 +624,7 @@ Here’s some suggestions what to do next: .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 20.870 seconds) + **Total running time of the script:** (0 minutes 20.552 seconds) .. _sphx_glr_download_tutorials_analysis-2d_detect.py: diff --git a/docs/dev/_sources/tutorials/analysis-2d/modeling_2D.rst.txt b/docs/dev/_sources/tutorials/analysis-2d/modeling_2D.rst.txt index 08b2b083ddc..367c3107aa0 100644 --- a/docs/dev/_sources/tutorials/analysis-2d/modeling_2D.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-2d/modeling_2D.rst.txt @@ -124,7 +124,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -165,7 +165,7 @@ 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 CTA runs of the galactic center. +Here, we use 3 simulated CTAO runs of the galactic center. .. GENERATED FROM PYTHON SOURCE LINES 69-76 @@ -342,8 +342,8 @@ analysis as *3D*, and define the geometry of the analysis. parameters: selection_optional: all metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:30:26.582357' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T10:02:11.313439' origin: null diff --git a/docs/dev/_sources/tutorials/analysis-2d/ring_background.rst.txt b/docs/dev/_sources/tutorials/analysis-2d/ring_background.rst.txt index 33a019cfee3..1783cc9b138 100644 --- a/docs/dev/_sources/tutorials/analysis-2d/ring_background.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-2d/ring_background.rst.txt @@ -125,7 +125,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -325,8 +325,8 @@ MSH 1552 parameters: selection_optional: all metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:30:32.416068' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T10:02:17.016713' origin: null @@ -476,8 +476,8 @@ create the config parameters: selection_optional: all metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:30:32.473564' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T10:02:17.073732' origin: null @@ -776,7 +776,7 @@ to be a standard normal distribution. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 12.115 seconds) + **Total running time of the script:** (0 minutes 12.100 seconds) .. _sphx_glr_download_tutorials_analysis-2d_ring_background.py: diff --git a/docs/dev/_sources/tutorials/analysis-2d/sg_execution_times.rst.txt b/docs/dev/_sources/tutorials/analysis-2d/sg_execution_times.rst.txt index 134e740ea86..c89d61bb124 100644 --- a/docs/dev/_sources/tutorials/analysis-2d/sg_execution_times.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-2d/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:38.646** total execution time for 3 files **from tutorials/analysis-2d**: +**00:38.193** total execution time for 3 files **from tutorials/analysis-2d**: .. container:: @@ -33,11 +33,11 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_tutorials_analysis-2d_detect.py` (``detect.py``) - - 00:20.870 + - 00:20.552 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-2d_ring_background.py` (``ring_background.py``) - - 00:12.115 + - 00:12.100 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-2d_modeling_2D.py` (``modeling_2D.py``) - - 00:05.661 + - 00:05.541 - 0.0 diff --git a/docs/dev/_sources/tutorials/analysis-3d/analysis_3d.rst.txt b/docs/dev/_sources/tutorials/analysis-3d/analysis_3d.rst.txt index 1fc1ebff839..d9dd1c29921 100644 --- a/docs/dev/_sources/tutorials/analysis-3d/analysis_3d.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-3d/analysis_3d.rst.txt @@ -96,7 +96,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -295,8 +295,8 @@ the analysis, we will programmatically build a config file from scratch. parameters: selection_optional: all metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:30:44.740422' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T10:02:29.341815' origin: null AnalysisConfig @@ -409,8 +409,8 @@ the analysis, we will programmatically build a config file from scratch. parameters: selection_optional: all metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:30:44.748175' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T10:02:29.349613' origin: null @@ -1065,21 +1065,21 @@ Data reduction Dataset 0: Type : MapDataset - Name : tnFttnKH + Name : X-JIar1n Instrument : CTA Models : Dataset 1: Type : MapDataset - Name : Cdd-kKNp + Name : -mXcSLBO Instrument : CTA Models : Dataset 2: Type : MapDataset - Name : o86DlVQX + Name : pQ5z7iuR Instrument : CTA Models : @@ -1112,7 +1112,7 @@ You can access each one by name or by index, eg: MapDataset ---------- - Name : tnFttnKH + Name : X-JIar1n Total counts : 40481 Total background counts : 36014.51 @@ -1180,9 +1180,9 @@ map, omit the region argument. name counts excess ... n_fit_bins stat_type stat_sum ... -------- ------ ------------------ ... ---------- --------- -------- - tnFttnKH 40481 4466.4930435940405 ... 693940 cash nan - Cdd-kKNp 40525 4510.505523195905 ... 693940 cash nan - o86DlVQX 40235 4220.480554966147 ... 693940 cash nan + X-JIar1n 40481 4466.4930435940405 ... 693940 cash nan + -mXcSLBO 40525 4510.505523195905 ... 693940 cash nan + pQ5z7iuR 40235 4220.480554966147 ... 693940 cash nan Models Component 0: SkyModel @@ -1203,8 +1203,8 @@ map, omit the region argument. Component 1: FoVBackgroundModel - Name : tnFttnKH-bkg - Datasets names : ['tnFttnKH'] + Name : X-JIar1n-bkg + Datasets names : ['X-JIar1n'] Spectral model type : PowerLawNormSpectralModel Parameters: norm : 1.000 +/- 0.00 @@ -1213,8 +1213,8 @@ map, omit the region argument. Component 2: FoVBackgroundModel - Name : Cdd-kKNp-bkg - Datasets names : ['Cdd-kKNp'] + Name : -mXcSLBO-bkg + Datasets names : ['-mXcSLBO'] Spectral model type : PowerLawNormSpectralModel Parameters: norm : 1.000 +/- 0.00 @@ -1223,8 +1223,8 @@ map, omit the region argument. Component 3: FoVBackgroundModel - Name : o86DlVQX-bkg - Datasets names : ['o86DlVQX'] + Name : pQ5z7iuR-bkg + Datasets names : ['pQ5z7iuR'] Spectral model type : PowerLawNormSpectralModel Parameters: norm : 1.000 +/- 0.00 @@ -1339,8 +1339,8 @@ Check best-fit parameters and error estimates: Component 1: FoVBackgroundModel - Name : tnFttnKH-bkg - Datasets names : ['tnFttnKH'] + Name : X-JIar1n-bkg + Datasets names : ['X-JIar1n'] Spectral model type : PowerLawNormSpectralModel Parameters: norm : 1.118 +/- 0.01 @@ -1349,8 +1349,8 @@ Check best-fit parameters and error estimates: Component 2: FoVBackgroundModel - Name : Cdd-kKNp-bkg - Datasets names : ['Cdd-kKNp'] + Name : -mXcSLBO-bkg + Datasets names : ['-mXcSLBO'] Spectral model type : PowerLawNormSpectralModel Parameters: norm : 1.119 +/- 0.01 @@ -1359,8 +1359,8 @@ Check best-fit parameters and error estimates: Component 3: FoVBackgroundModel - Name : o86DlVQX-bkg - Datasets names : ['o86DlVQX'] + Name : pQ5z7iuR-bkg + Datasets names : ['pQ5z7iuR'] Spectral model type : PowerLawNormSpectralModel Parameters: norm : 1.111 +/- 0.01 @@ -1489,7 +1489,7 @@ Exercises .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 37.983 seconds) + **Total running time of the script:** (0 minutes 37.871 seconds) .. _sphx_glr_download_tutorials_analysis-3d_analysis_3d.py: diff --git a/docs/dev/_sources/tutorials/analysis-3d/analysis_mwl.rst.txt b/docs/dev/_sources/tutorials/analysis-3d/analysis_mwl.rst.txt index a2b68d30770..6f6723cd037 100644 --- a/docs/dev/_sources/tutorials/analysis-3d/analysis_mwl.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-3d/analysis_mwl.rst.txt @@ -123,7 +123,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy diff --git a/docs/dev/_sources/tutorials/analysis-3d/cta_data_analysis.rst.txt b/docs/dev/_sources/tutorials/analysis-3d/cta_data_analysis.rst.txt index a9d9d087386..73112269581 100644 --- a/docs/dev/_sources/tutorials/analysis-3d/cta_data_analysis.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-3d/cta_data_analysis.rst.txt @@ -27,11 +27,11 @@ Introduction ------------ **This notebook shows an example how to make a sky image and spectrum -for simulated CTA data with Gammapy.** +for simulated CTAO data with Gammapy.** The dataset we will use is three observation runs on the Galactic Center. This is a tiny (and thus quick to process and play with and -learn) subset of the simulated CTA dataset that was produced for the +learn) subset of the simulated CTAO dataset that was produced for the first data challenge in August 2017. .. GENERATED FROM PYTHON SOURCE LINES 21-26 @@ -118,7 +118,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -818,7 +818,7 @@ profile to compute the flux and flux error.
Table length=5 -
e_refe_mine_maxdndednde_errpdnde_errndnde_ulsqrt_tsis_ul
GeVGeVGeV1 / (cm2 GeV s)1 / (cm2 GeV s)1 / (cm2 GeV s)1 / (cm2 GeV s)
float64float64float64float64float64float64float64float32bool
+
@@ -904,7 +904,7 @@ Exercises What next? ---------- -- This notebook showed an example of a first CTA analysis with Gammapy, +- This notebook showed an example of a first CTAO analysis with Gammapy, using simulated 1DC data. - Let us know if you have any questions or issues! @@ -912,7 +912,7 @@ What next? .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 17.287 seconds) + **Total running time of the script:** (0 minutes 16.841 seconds) .. _sphx_glr_download_tutorials_analysis-3d_cta_data_analysis.py: diff --git a/docs/dev/_sources/tutorials/analysis-3d/energy_dependent_estimation.rst.txt b/docs/dev/_sources/tutorials/analysis-3d/energy_dependent_estimation.rst.txt index 021954020bb..53f4e60d448 100644 --- a/docs/dev/_sources/tutorials/analysis-3d/energy_dependent_estimation.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-3d/energy_dependent_estimation.rst.txt @@ -126,7 +126,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -482,7 +482,7 @@ Plotting the results .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 19.804 seconds) + **Total running time of the script:** (0 minutes 19.657 seconds) .. _sphx_glr_download_tutorials_analysis-3d_energy_dependent_estimation.py: diff --git a/docs/dev/_sources/tutorials/analysis-3d/event_sampling.rst.txt b/docs/dev/_sources/tutorials/analysis-3d/event_sampling.rst.txt index cabea0e7fd5..dbfb597f67e 100644 --- a/docs/dev/_sources/tutorials/analysis-3d/event_sampling.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-3d/event_sampling.rst.txt @@ -174,7 +174,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -1077,7 +1077,7 @@ Exercises .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 17.717 seconds) + **Total running time of the script:** (0 minutes 26.961 seconds) .. _sphx_glr_download_tutorials_analysis-3d_event_sampling.py: diff --git a/docs/dev/_sources/tutorials/analysis-3d/event_sampling_nrg_depend_models.rst.txt b/docs/dev/_sources/tutorials/analysis-3d/event_sampling_nrg_depend_models.rst.txt index 84411ad169b..8df35384b09 100644 --- a/docs/dev/_sources/tutorials/analysis-3d/event_sampling_nrg_depend_models.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-3d/event_sampling_nrg_depend_models.rst.txt @@ -125,7 +125,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -457,7 +457,7 @@ passed through the map. Define an observation and make a dataset ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -In the following, we define an observation of 1 hr with CTA in the +In the following, we define an observation of 1 hr with CTAO in the alpha-configuration for the south array, and we also create a dataset to be passed to the event sampler. The full `SkyModel` created above is passed to the dataset. diff --git a/docs/dev/_sources/tutorials/analysis-3d/flux_profiles.rst.txt b/docs/dev/_sources/tutorials/analysis-3d/flux_profiles.rst.txt index 998e603a7bd..63164bb52f7 100644 --- a/docs/dev/_sources/tutorials/analysis-3d/flux_profiles.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-3d/flux_profiles.rst.txt @@ -131,7 +131,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -374,11 +374,11 @@ We can see the full configuration by printing the estimator object: -------------------- energy_edges : [ 10. 2000.] GeV - fit : + fit : n_jobs : None n_sigma : 1 n_sigma_ul : 2 - norm : Parameter(name='norm', value=1.0, factor=1.0, scale=1.0, unit=Unit(dimensionless), min=nan, max=nan, frozen=False, prior=None, id=0x7fcdbe622130) + norm : Parameter(name='norm', value=1.0, factor=1.0, scale=1.0, unit=Unit(dimensionless), min=nan, max=nan, frozen=False, prior=None, id=0x7f4fd4b3cbb0) null_value : 0 parallel_backend : None reoptimize : False @@ -826,7 +826,7 @@ And now plot the points together with the likelihood profiles: .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 21.171 seconds) + **Total running time of the script:** (0 minutes 20.547 seconds) .. _sphx_glr_download_tutorials_analysis-3d_flux_profiles.py: diff --git a/docs/dev/_sources/tutorials/analysis-3d/sg_execution_times.rst.txt b/docs/dev/_sources/tutorials/analysis-3d/sg_execution_times.rst.txt index 5c61928aef8..be6af331d03 100644 --- a/docs/dev/_sources/tutorials/analysis-3d/sg_execution_times.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-3d/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**02:13.808** total execution time for 8 files **from tutorials/analysis-3d**: +**02:21.339** total execution time for 8 files **from tutorials/analysis-3d**: .. container:: @@ -33,26 +33,26 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_tutorials_analysis-3d_analysis_3d.py` (``analysis_3d.py``) - - 00:37.983 + - 00:37.871 + - 0.0 + * - :ref:`sphx_glr_tutorials_analysis-3d_event_sampling.py` (``event_sampling.py``) + - 00:26.961 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-3d_flux_profiles.py` (``flux_profiles.py``) - - 00:21.171 + - 00:20.547 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-3d_energy_dependent_estimation.py` (``energy_dependent_estimation.py``) - - 00:19.804 - - 0.0 - * - :ref:`sphx_glr_tutorials_analysis-3d_event_sampling.py` (``event_sampling.py``) - - 00:17.717 + - 00:19.657 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-3d_cta_data_analysis.py` (``cta_data_analysis.py``) - - 00:17.287 + - 00:16.841 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-3d_simulate_3d.py` (``simulate_3d.py``) - - 00:10.129 + - 00:09.965 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-3d_analysis_mwl.py` (``analysis_mwl.py``) - - 00:06.759 + - 00:06.606 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-3d_event_sampling_nrg_depend_models.py` (``event_sampling_nrg_depend_models.py``) - - 00:02.958 + - 00:02.891 - 0.0 diff --git a/docs/dev/_sources/tutorials/analysis-3d/simulate_3d.rst.txt b/docs/dev/_sources/tutorials/analysis-3d/simulate_3d.rst.txt index 5932d7e56f4..46fdd086d3a 100644 --- a/docs/dev/_sources/tutorials/analysis-3d/simulate_3d.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-3d/simulate_3d.rst.txt @@ -303,9 +303,9 @@ the `selection` of the maps that you want to produce Name : dataset-simu - Total counts : 170109 + Total counts : 170025 Total background counts : 161250.95 - Total excess counts : 8858.05 + Total excess counts : 8774.05 Predicted counts : 169871.64 Predicted background counts : 161250.95 @@ -318,7 +318,7 @@ the `selection` of the maps that you want to produce Number of fit bins : 804492 Fit statistic type : cash - Fit statistic value (-2 log(L)) : 562258.99 + Fit statistic value (-2 log(L)) : 561885.48 Number of models : 2 Number of parameters : 11 @@ -537,12 +537,12 @@ Compare the injected and fitted models: Spatial model type : GaussianSpatialModel Temporal model type : Parameters: - index : 3.013 +/- 0.02 + index : 3.022 +/- 0.02 amplitude : 1.01e-11 +/- 3.3e-13 1 / (cm2 s TeV) reference (frozen): 1.000 TeV - lon_0 : 0.201 +/- 0.01 deg + lon_0 : 0.197 +/- 0.01 deg lat_0 : 0.096 +/- 0.01 deg - sigma : 0.298 +/- 0.00 deg + sigma : 0.303 +/- 0.00 deg e (frozen): 0.000 phi (frozen): 0.000 deg @@ -572,12 +572,12 @@ Get the errors on the fitted parameters from the parameter table type name value unit ... max frozen link prior ---- --------- ---------- -------------- ... --------- ------ ---- ----- - index 3.0132e+00 ... nan False - amplitude 1.0059e-11 cm-2 s-1 TeV-1 ... nan False + index 3.0222e+00 ... nan False + amplitude 1.0051e-11 cm-2 s-1 TeV-1 ... nan False reference 1.0000e+00 TeV ... nan True - lon_0 2.0138e-01 deg ... nan False - lat_0 9.6064e-02 deg ... 9.000e+01 False - sigma 2.9839e-01 deg ... nan False + lon_0 1.9666e-01 deg ... nan False + lat_0 9.5761e-02 deg ... 9.000e+01 False + sigma 3.0341e-01 deg ... nan False e 0.0000e+00 ... 1.000e+00 True phi 0.0000e+00 deg ... nan True norm 1.0000e+00 ... nan True @@ -588,11 +588,6 @@ Get the errors on the fitted parameters from the parameter table -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** (0 minutes 10.129 seconds) - - .. _sphx_glr_download_tutorials_analysis-3d_simulate_3d.py: .. only:: html diff --git a/docs/dev/_sources/tutorials/analysis-time/Time_resolved_spectroscopy.rst.txt b/docs/dev/_sources/tutorials/analysis-time/Time_resolved_spectroscopy.rst.txt index 6dee6ab8a91..e7ddfc3ef0b 100644 --- a/docs/dev/_sources/tutorials/analysis-time/Time_resolved_spectroscopy.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-time/Time_resolved_spectroscopy.rst.txt @@ -275,238 +275,238 @@ modelled. Dataset 0: Type : SpectrumDatasetOnOff - Name : dqj7OCOt + Name : NiQZ4ry2 Instrument : HESS Models : Dataset 1: Type : SpectrumDatasetOnOff - Name : IuqqlS6d + Name : vqjXpIdP Instrument : HESS Models : Dataset 2: Type : SpectrumDatasetOnOff - Name : zybaXCrP + Name : dyMZ9coR Instrument : HESS Models : Dataset 3: Type : SpectrumDatasetOnOff - Name : t3X_lqk4 + Name : 8xmVEej2 Instrument : HESS Models : Dataset 4: Type : SpectrumDatasetOnOff - Name : Drq3iIdo + Name : 85Zdag8M Instrument : HESS Models : Dataset 5: Type : SpectrumDatasetOnOff - Name : oHjZxCgK + Name : UGeBFzTk Instrument : HESS Models : Dataset 6: Type : SpectrumDatasetOnOff - Name : k2qlefkU + Name : Vd5qKleU Instrument : HESS Models : Dataset 7: Type : SpectrumDatasetOnOff - Name : Y3Tybbz8 + Name : 9f-K2Z0A Instrument : HESS Models : Dataset 8: Type : SpectrumDatasetOnOff - Name : cmn7uMUy + Name : iBp2fXaH Instrument : HESS Models : Dataset 9: Type : SpectrumDatasetOnOff - Name : IcD0Fs8r + Name : h90YWjxn Instrument : HESS Models : Dataset 10: Type : SpectrumDatasetOnOff - Name : uQu94Q85 + Name : Jcm1BeoC Instrument : HESS Models : Dataset 11: Type : SpectrumDatasetOnOff - Name : Sgzyu_un + Name : 9k5GaK3O Instrument : HESS Models : Dataset 12: Type : SpectrumDatasetOnOff - Name : FHqp5P2a + Name : Wa8Jxt7n Instrument : HESS Models : Dataset 13: Type : SpectrumDatasetOnOff - Name : HOX1lSkv + Name : ahWhUVcg Instrument : HESS Models : Dataset 14: Type : SpectrumDatasetOnOff - Name : 9TIUrtyj + Name : iPNbqlev Instrument : HESS Models : Dataset 15: Type : SpectrumDatasetOnOff - Name : wV3fdmcq + Name : OuyApKFP Instrument : HESS Models : Dataset 16: Type : SpectrumDatasetOnOff - Name : tHIZ3J_y + Name : RF47DVOT Instrument : HESS Models : Dataset 17: Type : SpectrumDatasetOnOff - Name : Cd7aIGC1 + Name : TQ_RB2xH Instrument : HESS Models : Dataset 18: Type : SpectrumDatasetOnOff - Name : y-wvy6QT + Name : nNJG0ula Instrument : HESS Models : Dataset 19: Type : SpectrumDatasetOnOff - Name : xhrPjoTk + Name : AaS585B_ Instrument : HESS Models : Dataset 20: Type : SpectrumDatasetOnOff - Name : wI5bx3R4 + Name : Aw_vqXrZ Instrument : HESS Models : Dataset 21: Type : SpectrumDatasetOnOff - Name : u-XbpT7u + Name : HFlgxHdy Instrument : HESS Models : Dataset 22: Type : SpectrumDatasetOnOff - Name : 0nEJ-_kP + Name : yaEezWwp Instrument : HESS Models : Dataset 23: Type : SpectrumDatasetOnOff - Name : syqVZIjW + Name : S_T9qRu- Instrument : HESS Models : Dataset 24: Type : SpectrumDatasetOnOff - Name : yuStLMOW + Name : 6mnBDRFY Instrument : HESS Models : Dataset 25: Type : SpectrumDatasetOnOff - Name : XfMW_aU2 + Name : Vjs8V2_r Instrument : HESS Models : Dataset 26: Type : SpectrumDatasetOnOff - Name : nTwpP0gs + Name : 9_8FW5W3 Instrument : HESS Models : Dataset 27: Type : SpectrumDatasetOnOff - Name : 93g21J34 + Name : XyI7F1GI Instrument : HESS Models : Dataset 28: Type : SpectrumDatasetOnOff - Name : py-xXhuo + Name : jEgqTth- Instrument : HESS Models : Dataset 29: Type : SpectrumDatasetOnOff - Name : Dg4XeSeE + Name : Wkqz_A7n Instrument : HESS Models : Dataset 30: Type : SpectrumDatasetOnOff - Name : yfEQie9j + Name : v0pC1bcZ Instrument : HESS Models : Dataset 31: Type : SpectrumDatasetOnOff - Name : ACi5Ugo8 + Name : Z_j6iSF8 Instrument : HESS Models : Dataset 32: Type : SpectrumDatasetOnOff - Name : jvlAXiw- + Name : W6_g1hPS Instrument : HESS Models : Dataset 33: Type : SpectrumDatasetOnOff - Name : azCzxhr4 + Name : aYXklO6J Instrument : HESS Models : @@ -929,7 +929,7 @@ Exercises .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 21.382 seconds) + **Total running time of the script:** (0 minutes 21.126 seconds) .. _sphx_glr_download_tutorials_analysis-time_Time_resolved_spectroscopy.py: diff --git a/docs/dev/_sources/tutorials/analysis-time/Variability_estimation.rst.txt b/docs/dev/_sources/tutorials/analysis-time/Variability_estimation.rst.txt index 3067c248cf7..a99ffb77f8a 100644 --- a/docs/dev/_sources/tutorials/analysis-time/Variability_estimation.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-time/Variability_estimation.rst.txt @@ -438,7 +438,7 @@ with the original light curve start and end points. .. code-block:: none - + diff --git a/docs/dev/_sources/tutorials/analysis-time/light_curve.rst.txt b/docs/dev/_sources/tutorials/analysis-time/light_curve.rst.txt index c963f9c4b5d..b3e3ab53a78 100644 --- a/docs/dev/_sources/tutorials/analysis-time/light_curve.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-time/light_curve.rst.txt @@ -132,7 +132,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy diff --git a/docs/dev/_sources/tutorials/analysis-time/light_curve_flare.rst.txt b/docs/dev/_sources/tutorials/analysis-time/light_curve_flare.rst.txt index 236dabf5c86..41997a91602 100644 --- a/docs/dev/_sources/tutorials/analysis-time/light_curve_flare.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-time/light_curve_flare.rst.txt @@ -149,7 +149,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -626,7 +626,7 @@ Here, we rebin 4 adjacent bins together, to get 30 min bins .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 34.207 seconds) + **Total running time of the script:** (0 minutes 33.442 seconds) .. _sphx_glr_download_tutorials_analysis-time_light_curve_flare.py: diff --git a/docs/dev/_sources/tutorials/analysis-time/light_curve_simulation.rst.txt b/docs/dev/_sources/tutorials/analysis-time/light_curve_simulation.rst.txt index ebb92e0d5f2..22d4466eb47 100644 --- a/docs/dev/_sources/tutorials/analysis-time/light_curve_simulation.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-time/light_curve_simulation.rst.txt @@ -167,7 +167,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -413,16 +413,16 @@ quick look into our datasets. name counts excess ... n_fit_bins stat_type stat_sum ... --------- ------ ------------------ ... ---------- --------- ------------------- - dataset-0 828 807.6812310184489 ... 9 cash -6642.086210032157 - dataset-1 353 343.76419591747674 ... 9 cash -2254.1954009303604 - dataset-2 278 268.3947637541758 ... 9 cash -1589.0039992789837 - dataset-3 364 349.2227134679628 ... 9 cash -2358.492346502915 - dataset-4 189 174.22271346796282 ... 9 cash -995.3847936004798 - dataset-5 167 148.52839183495354 ... 9 cash -811.0332843024476 - dataset-6 34 19.22271346796283 ... 9 cash -65.98862211011613 - dataset-7 38 18.78952750835169 ... 9 cash -89.08811786589854 - dataset-8 29 13.114416978060046 ... 9 cash -53.923361548601605 - dataset-9 41 23.636688324856333 ... 9 cash -87.87723932823582 + dataset-0 830 809.6812310184489 ... 9 cash -6642.047923092727 + dataset-1 341 331.76419591747674 ... 9 cash -2127.3614605107423 + dataset-2 323 313.3947637541758 ... 9 cash -2022.9576749783714 + dataset-3 330 315.2227134679628 ... 9 cash -2020.1188859247495 + dataset-4 212 197.22271346796282 ... 9 cash -1160.1822508893981 + dataset-5 176 157.52839183495354 ... 9 cash -902.7032124181009 + dataset-6 34 19.22271346796283 ... 9 cash -64.93180377532649 + dataset-7 45 25.78952750835169 ... 9 cash -73.95429248223132 + dataset-8 18 2.1144169780600457 ... 9 cash -10.450785462745976 + dataset-9 33 15.636688324856333 ... 9 cash -60.633254644563365 @@ -572,10 +572,10 @@ that ``t_ref`` should be fixed by default for the Name : dataset_lc Number of total flux points : 30 - Number of fit bins : 24 + Number of fit bins : 22 Fit statistic type : chi2 - Fit statistic value (-2 log(L)) : 1524.09 + Fit statistic value (-2 log(L)) : 1541.35 Number of models : 1 Number of parameters : 5 @@ -623,10 +623,10 @@ Fit the dataset type name value unit error min max frozen link prior ---- --------- ---------- -------------- --------- --- --- ------ ---- ----- - index 2.9834e+00 2.780e-02 nan nan False - amplitude 1.0194e-11 cm-2 s-1 TeV-1 3.477e-13 nan nan False + index 2.9532e+00 2.725e-02 nan nan False + amplitude 1.0390e-11 cm-2 s-1 TeV-1 3.492e-13 nan nan False reference 1.0000e+00 TeV 0.000e+00 nan nan True - t0 5.9005e+00 h 2.237e-01 nan nan False + t0 5.9005e+00 h 2.249e-01 nan nan False t_ref 5.8909e+04 d 0.000e+00 nan nan True @@ -739,10 +739,10 @@ Perform a joint fit type name value unit error min max frozen link prior ---- --------- ---------- -------------- --------- --- --- ------ ---- ----- - index 2.9983e+00 3.161e-02 nan nan False - amplitude 1.0341e-11 cm-2 s-1 TeV-1 3.600e-13 nan nan False + index 2.9838e+00 3.120e-02 nan nan False + amplitude 1.0556e-11 cm-2 s-1 TeV-1 3.601e-13 nan nan False reference 1.0000e+00 TeV 0.000e+00 nan nan True - t0 5.7898e+00 h 2.000e-01 nan nan False + t0 5.7745e+00 h 1.936e-01 nan nan False t_ref 5.8909e+04 d 0.000e+00 nan nan True @@ -768,7 +768,7 @@ Exercises .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 18.165 seconds) + **Total running time of the script:** (0 minutes 21.166 seconds) .. _sphx_glr_download_tutorials_analysis-time_light_curve_simulation.py: diff --git a/docs/dev/_sources/tutorials/analysis-time/pulsar_analysis.rst.txt b/docs/dev/_sources/tutorials/analysis-time/pulsar_analysis.rst.txt index 43fadd71df1..106d22844d8 100644 --- a/docs/dev/_sources/tutorials/analysis-time/pulsar_analysis.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-time/pulsar_analysis.rst.txt @@ -117,7 +117,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -674,7 +674,7 @@ Finally, we can run an `~gammapy.estimators.ExcessMapEstimator` to compute the e .. GENERATED FROM PYTHON SOURCE LINES 334-348 -Note that here we are lacking statistic because we only use one run of CTA. +Note that here we are lacking statistic because we only use one run of CTAO. Phase-resolved spectrum ----------------------- diff --git a/docs/dev/_sources/tutorials/analysis-time/sg_execution_times.rst.txt b/docs/dev/_sources/tutorials/analysis-time/sg_execution_times.rst.txt index 118fafac69e..0fee51cbb7d 100644 --- a/docs/dev/_sources/tutorials/analysis-time/sg_execution_times.rst.txt +++ b/docs/dev/_sources/tutorials/analysis-time/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**01:28.511** total execution time for 6 files **from tutorials/analysis-time**: +**01:30.067** total execution time for 6 files **from tutorials/analysis-time**: .. container:: @@ -33,20 +33,20 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_tutorials_analysis-time_light_curve_flare.py` (``light_curve_flare.py``) - - 00:34.207 - - 0.0 - * - :ref:`sphx_glr_tutorials_analysis-time_Time_resolved_spectroscopy.py` (``Time_resolved_spectroscopy.py``) - - 00:21.382 + - 00:33.442 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-time_light_curve_simulation.py` (``light_curve_simulation.py``) - - 00:18.165 + - 00:21.166 + - 0.0 + * - :ref:`sphx_glr_tutorials_analysis-time_Time_resolved_spectroscopy.py` (``Time_resolved_spectroscopy.py``) + - 00:21.126 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-time_light_curve.py` (``light_curve.py``) - - 00:07.519 + - 00:07.502 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-time_pulsar_analysis.py` (``pulsar_analysis.py``) - - 00:06.579 + - 00:06.235 - 0.0 * - :ref:`sphx_glr_tutorials_analysis-time_Variability_estimation.py` (``Variability_estimation.py``) - - 00:00.659 + - 00:00.596 - 0.0 diff --git a/docs/dev/_sources/tutorials/api/astro_dark_matter.rst.txt b/docs/dev/_sources/tutorials/api/astro_dark_matter.rst.txt index 2028d253954..6d630566b62 100644 --- a/docs/dev/_sources/tutorials/api/astro_dark_matter.rst.txt +++ b/docs/dev/_sources/tutorials/api/astro_dark_matter.rst.txt @@ -106,7 +106,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -478,7 +478,7 @@ Finally flux maps for decay can be produced like this: .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 28.155 seconds) + **Total running time of the script:** (0 minutes 27.997 seconds) .. _sphx_glr_download_tutorials_api_astro_dark_matter.py: diff --git a/docs/dev/_sources/tutorials/api/catalog.rst.txt b/docs/dev/_sources/tutorials/api/catalog.rst.txt index 5880fa01714..4971013c3a6 100644 --- a/docs/dev/_sources/tutorials/api/catalog.rst.txt +++ b/docs/dev/_sources/tutorials/api/catalog.rst.txt @@ -118,7 +118,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy diff --git a/docs/dev/_sources/tutorials/api/datasets.rst.txt b/docs/dev/_sources/tutorials/api/datasets.rst.txt index bd6fb537a85..a63a8617838 100644 --- a/docs/dev/_sources/tutorials/api/datasets.rst.txt +++ b/docs/dev/_sources/tutorials/api/datasets.rst.txt @@ -103,7 +103,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -297,7 +297,7 @@ To see the geometry of each map, we can use: .. code-block:: none - {'geom': , 'geom_exposure': , 'geom_psf': , 'geom_edisp': } + {'geom': , 'geom_exposure': , 'geom_psf': , 'geom_edisp': } @@ -1883,7 +1883,7 @@ Or slice all datasets by a given energy range: .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 14.906 seconds) + **Total running time of the script:** (0 minutes 13.751 seconds) .. _sphx_glr_download_tutorials_api_datasets.py: diff --git a/docs/dev/_sources/tutorials/api/estimators.rst.txt b/docs/dev/_sources/tutorials/api/estimators.rst.txt index bc9d39d2e29..eb365a354ce 100644 --- a/docs/dev/_sources/tutorials/api/estimators.rst.txt +++ b/docs/dev/_sources/tutorials/api/estimators.rst.txt @@ -551,7 +551,7 @@ Typically, it is the best fit model: SkyModel - Name : wYsZT8AS + Name : J04Tt_Fm Datasets names : None Spectral model type : PowerLawSpectralModel Spatial model type : diff --git a/docs/dev/_sources/tutorials/api/fitting.rst.txt b/docs/dev/_sources/tutorials/api/fitting.rst.txt index 3a06fbc0ff6..448a5df98c1 100644 --- a/docs/dev/_sources/tutorials/api/fitting.rst.txt +++ b/docs/dev/_sources/tutorials/api/fitting.rst.txt @@ -107,7 +107,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -1119,7 +1119,7 @@ in. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 11.540 seconds) + **Total running time of the script:** (0 minutes 11.367 seconds) .. _sphx_glr_download_tutorials_api_fitting.py: diff --git a/docs/dev/_sources/tutorials/api/makers.rst.txt b/docs/dev/_sources/tutorials/api/makers.rst.txt index a3eb13dc139..ad37d5daa3e 100644 --- a/docs/dev/_sources/tutorials/api/makers.rst.txt +++ b/docs/dev/_sources/tutorials/api/makers.rst.txt @@ -98,7 +98,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -174,7 +174,7 @@ object. This is illustrated in the following example: MapDataset ---------- - Name : Nj35nLaI + Name : AMw5riZy Total counts : 0 Total background counts : 0.00 @@ -345,7 +345,7 @@ observational data using the `~gammapy.makers.MapDatasetMaker`: MapDataset ---------- - Name : g1hz85F8 + Name : UftOlEWP Total counts : 2016 Total background counts : 1866.72 @@ -597,7 +597,7 @@ dataset per observation is stacked into a larger map. MapDataset ---------- - Name : NJF1frkD + Name : HTbP0aHQ Total counts : 7972 Total background counts : 7555.42 @@ -672,30 +672,30 @@ allow to use multiple processes on run. Dataset 0: Type : MapDataset - Name : 3AyTdMXN + Name : cj-kBoNZ Instrument : HESS - Models : ['3AyTdMXN-bkg'] + Models : ['cj-kBoNZ-bkg'] Dataset 1: Type : MapDataset - Name : n7MOliCm + Name : 5_Wwdc5s Instrument : HESS - Models : ['n7MOliCm-bkg'] + Models : ['5_Wwdc5s-bkg'] Dataset 2: Type : MapDataset - Name : Oz_zRyNs + Name : HPcD7_1w Instrument : HESS - Models : ['Oz_zRyNs-bkg'] + Models : ['HPcD7_1w-bkg'] Dataset 3: Type : MapDataset - Name : QR_nggnK + Name : ynurRGl_ Instrument : HESS - Models : ['QR_nggnK-bkg'] + Models : ['ynurRGl_-bkg'] @@ -957,7 +957,7 @@ use `get_by_coord` .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 15.214 seconds) + **Total running time of the script:** (0 minutes 15.013 seconds) .. _sphx_glr_download_tutorials_api_makers.py: diff --git a/docs/dev/_sources/tutorials/api/maps.rst.txt b/docs/dev/_sources/tutorials/api/maps.rst.txt index b428cc79df7..ca479725068 100644 --- a/docs/dev/_sources/tutorials/api/maps.rst.txt +++ b/docs/dev/_sources/tutorials/api/maps.rst.txt @@ -130,7 +130,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy diff --git a/docs/dev/_sources/tutorials/api/mask_maps.rst.txt b/docs/dev/_sources/tutorials/api/mask_maps.rst.txt index e053ddf17a2..b815ac7d555 100644 --- a/docs/dev/_sources/tutorials/api/mask_maps.rst.txt +++ b/docs/dev/_sources/tutorials/api/mask_maps.rst.txt @@ -163,7 +163,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy diff --git a/docs/dev/_sources/tutorials/api/model_management.rst.txt b/docs/dev/_sources/tutorials/api/model_management.rst.txt index e8c55474cce..a92dba6480e 100644 --- a/docs/dev/_sources/tutorials/api/model_management.rst.txt +++ b/docs/dev/_sources/tutorials/api/model_management.rst.txt @@ -136,7 +136,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -1567,7 +1567,7 @@ associated models, ie, with yaml and fits files. eg: .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 10.396 seconds) + **Total running time of the script:** (0 minutes 10.361 seconds) .. _sphx_glr_download_tutorials_api_model_management.py: diff --git a/docs/dev/_sources/tutorials/api/models.rst.txt b/docs/dev/_sources/tutorials/api/models.rst.txt index 7c2f6e67f9a..1ec12e8cece 100644 --- a/docs/dev/_sources/tutorials/api/models.rst.txt +++ b/docs/dev/_sources/tutorials/api/models.rst.txt @@ -95,7 +95,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -1198,7 +1198,7 @@ generated: .. code-block:: none - Yq6N6Dy3 + TaIMLVwq @@ -1392,7 +1392,7 @@ existing FITS file: SkyModel - Name : arA_C38k + Name : 6u_OBXEK Datasets names : None Spectral model type : PowerLawNormSpectralModel Spatial model type : TemplateSpatialModel @@ -1530,7 +1530,7 @@ multiple model components, Gammapy has a `~gammapy.modeling.models.Models` class Component 1: SkyModel - Name : arA_C38k + Name : 6u_OBXEK Datasets names : None Spectral model type : PowerLawNormSpectralModel Spatial model type : TemplateSpatialModel @@ -1616,7 +1616,7 @@ To see which models are available you can use the ``.names`` attribute: .. code-block:: none - ['my-source', 'arA_C38k'] + ['my-source', '6u_OBXEK'] @@ -1717,7 +1717,7 @@ format: value: 2.0 unit: d scale: utc - - name: arA_C38k + - name: 6u_OBXEK type: SkyModel spectral: type: PowerLawNormSpectralModel @@ -1786,8 +1786,8 @@ format: unit: d scale: utc metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:36:16.315310' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T10:08:07.202917' origin: null @@ -1864,8 +1864,8 @@ amplitudes: .. code-block:: none - Parameter(name='index', value=2.3, factor=2.3, scale=1.0, unit=Unit(dimensionless), min=1.0, max=5.0, frozen=False, prior=None, id=0x7fcdd50a4a60) - Parameter(name='index', value=2.3, factor=2.3, scale=1.0, unit=Unit(dimensionless), min=1.0, max=5.0, frozen=False, prior=None, id=0x7fcdd50a4a60) + Parameter(name='index', value=2.3, factor=2.3, scale=1.0, unit=Unit(dimensionless), min=1.0, max=5.0, frozen=False, prior=None, id=0x7f4fd525a6d0) + Parameter(name='index', value=2.3, factor=2.3, scale=1.0, unit=Unit(dimensionless), min=1.0, max=5.0, frozen=False, prior=None, id=0x7f4fd525a6d0) @@ -1904,7 +1904,7 @@ In the YAML files the shared parameter is flagged by the additional value: 2.3 min: 1.0 max: 5.0 - link: index@4oIhnYmE + link: index@xQxKkcrV - name: amplitude value: 2.7e-12 unit: cm-2 s-1 TeV-1 @@ -1920,7 +1920,7 @@ In the YAML files the shared parameter is flagged by the additional value: 2.3 min: 1.0 max: 5.0 - link: index@4oIhnYmE + link: index@xQxKkcrV - name: amplitude value: 1.0e-12 unit: cm-2 s-1 TeV-1 @@ -1928,8 +1928,8 @@ In the YAML files the shared parameter is flagged by the additional value: 1.0 unit: TeV metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:36:16.500393' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T10:08:07.385517' origin: null diff --git a/docs/dev/_sources/tutorials/api/observation_clustering.rst.txt b/docs/dev/_sources/tutorials/api/observation_clustering.rst.txt index a5682c067e2..e4e9c2839f9 100644 --- a/docs/dev/_sources/tutorials/api/observation_clustering.rst.txt +++ b/docs/dev/_sources/tutorials/api/observation_clustering.rst.txt @@ -201,7 +201,7 @@ be adjusted according to each specific science case. .. code-block:: none - {'group_high_zenith': , 'group_low_zenith': } + {'group_high_zenith': , 'group_low_zenith': } @@ -481,7 +481,7 @@ Finally, ``observations.group_by_label`` creates a dictionary containing ``t`` .. code-block:: none - {'group_1': , 'group_2': } + {'group_1': , 'group_2': } diff --git a/docs/dev/_sources/tutorials/api/priors.rst.txt b/docs/dev/_sources/tutorials/api/priors.rst.txt index e0620c2107a..105f97238ab 100644 --- a/docs/dev/_sources/tutorials/api/priors.rst.txt +++ b/docs/dev/_sources/tutorials/api/priors.rst.txt @@ -135,7 +135,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -371,8 +371,8 @@ The parameters table will mention the type of prior associated to each model model type name value ... max frozen link prior ----------- ---- --------- ---------- ... --- ------ ---- ------------- - prior-model index 2.1652e+00 ... nan False GaussianPrior - prior-model amplitude 7.8406e-12 ... nan False + prior-model index 2.1128e+00 ... nan False GaussianPrior + prior-model amplitude 9.6402e-12 ... nan False prior-model reference 1.0000e+00 ... nan True @@ -781,14 +781,14 @@ The only parameter is ``sigma`` and the evaluation method return the squared inv {'spectral': {'type': 'PowerLawSpectralModel', 'parameters': [{'name': 'index', 'value': 2.0, 'prior': {'type': 'MyCustomPrior', 'parameters': [{'name': 'sigma', 'value': 0.5, 'unit': ''}], 'weight': 1}}, {'name': 'amplitude', 'value': 1e-12, 'unit': 'cm-2 s-1 TeV-1'}, {'name': 'reference', 'value': 1.0, 'unit': 'TeV'}]}} - [<__main__.MyCustomPrior object at 0x7fcdb1654940>, None, None] + [<__main__.MyCustomPrior object at 0x7f4fd5374a30>, None, None] .. rst-class:: sphx-glr-timing - **Total running time of the script:** (1 minutes 21.171 seconds) + **Total running time of the script:** (1 minutes 27.538 seconds) .. _sphx_glr_download_tutorials_api_priors.py: diff --git a/docs/dev/_sources/tutorials/api/sg_execution_times.rst.txt b/docs/dev/_sources/tutorials/api/sg_execution_times.rst.txt index 1d627c8342d..e2a0b3ff9a0 100644 --- a/docs/dev/_sources/tutorials/api/sg_execution_times.rst.txt +++ b/docs/dev/_sources/tutorials/api/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**03:10.525** total execution time for 13 files **from tutorials/api**: +**03:14.156** total execution time for 13 files **from tutorials/api**: .. container:: @@ -33,41 +33,41 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_tutorials_api_priors.py` (``priors.py``) - - 01:21.171 + - 01:27.538 - 0.0 * - :ref:`sphx_glr_tutorials_api_astro_dark_matter.py` (``astro_dark_matter.py``) - - 00:28.155 + - 00:27.997 - 0.0 * - :ref:`sphx_glr_tutorials_api_makers.py` (``makers.py``) - - 00:15.214 + - 00:15.013 - 0.0 * - :ref:`sphx_glr_tutorials_api_datasets.py` (``datasets.py``) - - 00:14.906 + - 00:13.751 - 0.0 * - :ref:`sphx_glr_tutorials_api_fitting.py` (``fitting.py``) - - 00:11.540 + - 00:11.367 - 0.0 * - :ref:`sphx_glr_tutorials_api_model_management.py` (``model_management.py``) - - 00:10.396 + - 00:10.361 - 0.0 * - :ref:`sphx_glr_tutorials_api_estimators.py` (``estimators.py``) - - 00:06.947 + - 00:06.718 - 0.0 * - :ref:`sphx_glr_tutorials_api_catalog.py` (``catalog.py``) - - 00:06.157 + - 00:06.077 - 0.0 * - :ref:`sphx_glr_tutorials_api_observation_clustering.py` (``observation_clustering.py``) - - 00:03.955 + - 00:03.875 - 0.0 * - :ref:`sphx_glr_tutorials_api_irfs.py` (``irfs.py``) - - 00:03.852 - - 0.0 - * - :ref:`sphx_glr_tutorials_api_models.py` (``models.py``) - - 00:03.150 + - 00:03.804 - 0.0 * - :ref:`sphx_glr_tutorials_api_maps.py` (``maps.py``) - - 00:02.746 + - 00:02.716 + - 0.0 + * - :ref:`sphx_glr_tutorials_api_models.py` (``models.py``) + - 00:02.624 - 0.0 * - :ref:`sphx_glr_tutorials_api_mask_maps.py` (``mask_maps.py``) - - 00:02.337 + - 00:02.315 - 0.0 diff --git a/docs/dev/_sources/tutorials/data/cta.rst.txt b/docs/dev/_sources/tutorials/data/cta.rst.txt index 3901983597a..2769ecd217c 100644 --- a/docs/dev/_sources/tutorials/data/cta.rst.txt +++ b/docs/dev/_sources/tutorials/data/cta.rst.txt @@ -18,60 +18,60 @@ .. _sphx_glr_tutorials_data_cta.py: -CTA with Gammapy -================ +CTAO with Gammapy +================= -Access and inspect CTA data and instrument response functions (IRFs) using Gammapy. +Access and inspect CTAO data and instrument response functions (IRFs) using Gammapy. Introduction ------------ -The `Cherenkov Telescope Array -(CTA) `__ is the next generation +The `Cherenkov Telescope Array Observatory (CTAO) `__ is the next generation ground-based observatory for gamma-ray astronomy. Gammapy is the core -library for the Cherenkov Telescope Array (CTA) science tools +library for the Cherenkov Telescope Array Observatory (CTAO) science tools (`2017ICRC…35..766D `__ and `CTAO Press -Release `__). +Release `__). -CTA will start taking data in the coming years. For now, to learn how to -analyse CTA data and to use Gammapy, if you are a member of the CTA -consortium, you can use the simulated dataset from the CTA first data -challenge which ran in 2017 and 2018. +CTAO will start taking data in the coming years. For now, to learn how to +analyse CTAO data and to use Gammapy, if you are a member of the CTAO +consortium, you can use the simulated dataset from: -- https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki (CTA - internal) +- the CTA first data challenge which ran in 2017 and 2018 (https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki), +- the CTAO Science Data Challenge of 2024 (https://ctaoobservatory.sharepoint.com/:f:/r/sites/share-open-data/Shared%20Documents/Reference%20datasets/Internal%20Science%20Data%20Challenge?csf=1&web=1&e=gNuFzI) Gammapy fully supports the FITS data formats (events, IRFs) used in CTA -1DC. The XML sky model format is not supported, but are also not needed +1DC and SDC. The XML sky model format is not supported, but are also not needed to analyse the data, you have to specify your model via the Gammapy YAML model format, or Python code, as shown below. -You can use Gammapy to simulate CTA data and evaluate CTA performance -using the CTA response files available here: +You can also use Gammapy to simulate CTAO data and evaluate CTAO performance +using the CTAO response files. Two sets of responses are available for different +array layouts: -- https://www.cta-observatory.org/science/cta-performance/ +- the Omega configuration (prod3b, 2016): https://zenodo.org/records/5163273, +- the Alpha configuration (prod5, 2021): https://zenodo.org/records/5499840. + +They are all fully supported by Gammapy. -The current FITS format `CTA-Performance-prod3b-v2-FITS.tar` is fully -supported by Gammapy, as shown below. Tutorial overview ----------------- -This notebook shows how to access CTA data and instrument response +This notebook shows how to access CTAO data and instrument response functions (IRFs) using Gammapy, and gives some examples how to quick -look the content of CTA files, especially to see the shape of CTA IRFs. +look the content of CTAO files, especially to see the shape of CTAO IRFs. At the end of the notebooks, we give several links to other tutorial -notebooks that show how to simulate CTA data and how to evaluate CTA -observability and sensitivity, or how to analyse CTA data. +notebooks that show how to simulate CTAO data and how to evaluate CTAO +observability and sensitivity, or how to analyse CTAO data. -Note that the FITS data and IRF format currently used by CTA is the one +Note that the FITS data and IRF format currently used by CTAO is the one documented at https://gamma-astro-data-formats.readthedocs.io/, and is -also used by H.E.S.S. and other imaging atmospheric Cherenkov telescopes +also used by H.E.S.S. and other Imaging Atmospheric Cherenkov Telescopes (IACTs). So if you see other Gammapy tutorials using e.g. H.E.S.S. -example data, know that they also apply to CTA, all you have to do is to -change the loaded data or IRFs to CTA. +example data, know that they also apply to CTAO, all you have to do is to +change the loaded data or IRFs to CTAO. Setup ----- @@ -131,7 +131,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -183,18 +183,18 @@ survey data you are interested in: Galactic plane survey `gps.tar.gz` After download, follow the instructions how to `untar` the files, and set a `CTADATA` environment variable to point to the data. -For convenience, since the 1DC data files are large, and not publicly +**For convenience**, since the 1DC data files are large and not publicly available to anyone, we have taken a tiny subset of the CTA 1DC data, four observations with the southern array from the GPS survey, pointing -near the Galactic center, and included them at `$GAMMAPY_DATA/cta-1dc` +near the Galactic center, and **included them at `$GAMMAPY_DATA/cta-1dc`** which you get via `gammapy download datasets`. Files ~~~~~ Next we will show a quick overview of the files and how to load them, -and some quick look plots showing the shape of the CTA IRFs. How to do -CTA simulations and analyses is shown in other tutorials, see links at +and some quick look plots showing the shape of the CTAO IRFs. How to do +CTAO simulations and analyses is shown in other tutorials, see links at the end of this notebook. @@ -350,9 +350,9 @@ We can load events data via the data store and observation, or equivalently via the `~gammapy.data.EventList` class by specifying the EVENTS filename. -The quick-look `events.peek()` plot below shows that CTA has a field +The quick-look `events.peek()` plot below shows that CTAO has a field of view of a few degrees, and two energy thresholds, one significantly -below 100 GeV where the CTA large-size telescopes (LSTs) detect events, +below 100 GeV where the CTAO large-size telescopes (LSTs) detect events, and a second one near 100 GeV where the mid-sized telescopes (MSTs) start to detect events. @@ -462,7 +462,7 @@ And show a summary plot: IRFs ---- -The CTA instrument response functions (IRFs) are given as FITS files in +The CTAO instrument response functions (IRFs) are given as FITS files in the `caldb` folder, the following IRFs are available: - effective area @@ -472,12 +472,12 @@ the `caldb` folder, the following IRFs are available: Notes: -- The IRFs contain the energy and offset dependence of the CTA response -- CTA 1DC was based on an early version of the CTA FITS responses +- The IRFs contain the energy and offset dependence of the CTAO response +- CTA 1DC was based on an early version of the CTAO FITS responses produced in 2017, improvements have been made since. - The point spread function was approximated by a Gaussian shape - The background is from hadronic and electron air shower events that - pass CTA selection cuts. It was given as a function of field of view + pass CTAO selection cuts. It was given as a function of field of view coordinates, although it is radially symmetric. - The energy dispersion in CTA 1DC is noisy at low energy, leading to unreliable spectral points for some analyses. @@ -523,7 +523,7 @@ Notes: /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) - {'aeff': , 'psf': , 'edisp': , 'bkg': } + {'aeff': , 'psf': , 'edisp': , 'bkg': } @@ -781,35 +781,35 @@ understands. -.. GENERATED FROM PYTHON SOURCE LINES 358-369 +.. GENERATED FROM PYTHON SOURCE LINES 358-368 -CTA performance files ---------------------- +Latest CTAO performance files +----------------------------- -CTA 1DC is useful to learn how to analyse CTA data. But to do -simulations and studies for CTA now, you should get the most recent CTA -IRFs in FITS format from -https://www.cta-observatory.org/science/cta-performance/ +CTA 1DC is useful to learn how to analyse CTAO data. But to do +simulations and studies for CTAO now, you should get the most recent CTAO +IRFs in FITS format from https://www.ctao.org/for-scientists/performance/. -If you want to run the download and examples in the next code cells, -remove the # to uncomment. +If you want to use other response files, the following code cells (remove the # to uncomment) +explain how to proceed. This example is made with the Alpha configuration (Prod5). -.. GENERATED FROM PYTHON SOURCE LINES 369-383 +.. GENERATED FROM PYTHON SOURCE LINES 368-383 .. code-block:: Python - # !curl -O https://www.cta-observatory.org/wp-content/uploads/2019/04/CTA-Performance-prod3b-v2-FITS.tar.gz - - # !tar xf CTA-Performance-prod3b-v2-FITS.tar.gz + # !curl -o cta-prod5-zenodo-fitsonly-v0.1.zip https://zenodo.org/records/5499840/files/cta-prod5-zenodo-fitsonly-v0.1.zip + # !unzip cta-prod5-zenodo-fitsonly-v0.1.zip + # !ls fits/ - # !ls caldb/data/cta/prod3b-v2/bcf + # !tar xf fits/CTA-Performance-prod5-v0.1-South-40deg.FITS.tar.gz -C fits/. + # !ls fits/*.fits.gz - # irfs1 = load_irf_dict_from_file("caldb/data/cta/prod3b-v2/bcf/South_z20_50h/irf_file.fits") + # irfs1 = load_irf_dict_from_file("fits/Prod5-South-40deg-SouthAz-14MSTs37SSTs.180000s-v0.1.fits.gz") # irfs1["aeff"].plot_energy_dependence() - # irfs2 = load_irf_dict_from_file("caldb/data/cta/prod3b-v2/bcf/South_z40_50h/irf_file.fits") + # irfs2 = load_irf_dict_from_file("fits/Prod5-South-40deg-SouthAz-14MSTs37SSTs.1800s-v0.1.fits.gz") # irfs2["aeff"].plot_energy_dependence() @@ -832,11 +832,11 @@ Exercises - Use `~gammapy.data.EventList.pointing_radec` to find the pointing position of this observation, and use `astropy.coordinates.SkyCoord` methods to find the field of view offset of the highest-energy event. -- What is the effective area and PSF 68% containment radius of CTA at 1 +- What is the effective area and PSF 68% containment radius of CTAO at 1 TeV for the `South_z20_50h` configuration used for the CTA 1DC simulation? -- Get the latest CTA FITS performance files from - https://www.cta-observatory.org/science/cta-performance/ and run the +- Get the latest CTAO FITS performance files from + https://www.ctao.org/for-scientists/performance/ and run the code example above. Make an effective area ratio plot of 40 deg zenith versus 20 deg zenith for the `South_z40_50h` and `South_z20_50h` configurations. @@ -866,7 +866,7 @@ Next steps :doc:`/tutorials/starting/analysis_1` and :doc:`/tutorials/starting/analysis_2` or any other Gammapy analysis tutorial. -- Learn how to evaluate CTA observability and sensitivity with +- Learn how to evaluate CTAO observability and sensitivity with :doc:`/tutorials/analysis-3d/simulate_3d`, :doc:`/tutorials/analysis-1d/spectrum_simulation` or :doc:`/tutorials/analysis-1d/cta_sensitivity`. diff --git a/docs/dev/_sources/tutorials/data/fermi_lat.rst.txt b/docs/dev/_sources/tutorials/data/fermi_lat.rst.txt index 787db6116ba..febd79764eb 100644 --- a/docs/dev/_sources/tutorials/data/fermi_lat.rst.txt +++ b/docs/dev/_sources/tutorials/data/fermi_lat.rst.txt @@ -140,7 +140,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy diff --git a/docs/dev/_sources/tutorials/data/hawc.rst.txt b/docs/dev/_sources/tutorials/data/hawc.rst.txt index a9560212fc2..458c9957afc 100644 --- a/docs/dev/_sources/tutorials/data/hawc.rst.txt +++ b/docs/dev/_sources/tutorials/data/hawc.rst.txt @@ -47,7 +47,7 @@ proceeding. Once the data has been reduced to a MapDataset, there are no differences in the way that HAWC data is handled with respect to data from any other -observatory, such as H.E.S.S. or CTA. +observatory, such as H.E.S.S. or CTAO. HAWC data access and reduction @@ -108,7 +108,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy diff --git a/docs/dev/_sources/tutorials/data/hess.rst.txt b/docs/dev/_sources/tutorials/data/hess.rst.txt index 8be2f223987..1820344468e 100644 --- a/docs/dev/_sources/tutorials/data/hess.rst.txt +++ b/docs/dev/_sources/tutorials/data/hess.rst.txt @@ -29,7 +29,7 @@ current `open data level 3 format `__. The H.E.S.S. data is private, and H.E.S.S. analysis is mostly documented -and discussed at https://hess-confluence.desy.de/ and in +and discussed in the internal Wiki pages and in H.E.S.S.-internal communication channels. However, in 2018, a small sub-set of archival H.E.S.S. data was publicly released, called the `H.E.S.S. DL3 @@ -45,9 +45,8 @@ simple exploration of the data with the creation of theta-squared plot. H.E.S.S. members can find details on the DL3 FITS production on this `Confluence -page `__ -and access more detailed tutorials in this -`repository `__ +page `__ +and access more detailed tutorials in the BitBucket `hess-open-tools` repository. DL3 DR1 ------- @@ -55,7 +54,7 @@ DL3 DR1 This is how to access data and IRFs from the H.E.S.S. data level 3, data release 1. -.. GENERATED FROM PYTHON SOURCE LINES 40-51 +.. GENERATED FROM PYTHON SOURCE LINES 39-50 .. code-block:: Python @@ -77,12 +76,12 @@ release 1. -.. GENERATED FROM PYTHON SOURCE LINES 52-54 +.. GENERATED FROM PYTHON SOURCE LINES 51-53 Check setup ----------- -.. GENERATED FROM PYTHON SOURCE LINES 54-60 +.. GENERATED FROM PYTHON SOURCE LINES 53-59 .. code-block:: Python @@ -111,7 +110,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -144,7 +143,7 @@ Check setup -.. GENERATED FROM PYTHON SOURCE LINES 61-69 +.. GENERATED FROM PYTHON SOURCE LINES 60-68 A useful way to organize the relevant files are the index tables. The observation index table contains information on each particular run, @@ -155,11 +154,11 @@ the directory in which they can be found, in this case `$GAMMAPY_DATA/hess-dl3-dr1`: -.. GENERATED FROM PYTHON SOURCE LINES 71-72 +.. GENERATED FROM PYTHON SOURCE LINES 70-71 Create and get info on the data store -.. GENERATED FROM PYTHON SOURCE LINES 72-77 +.. GENERATED FROM PYTHON SOURCE LINES 71-76 .. code-block:: Python @@ -193,11 +192,11 @@ Create and get info on the data store -.. GENERATED FROM PYTHON SOURCE LINES 78-79 +.. GENERATED FROM PYTHON SOURCE LINES 77-78 Preview an excerpt from the observation table -.. GENERATED FROM PYTHON SOURCE LINES 79-82 +.. GENERATED FROM PYTHON SOURCE LINES 78-81 .. code-block:: Python @@ -221,11 +220,11 @@ Preview an excerpt from the observation table -.. GENERATED FROM PYTHON SOURCE LINES 83-84 +.. GENERATED FROM PYTHON SOURCE LINES 82-83 Get a single observation -.. GENERATED FROM PYTHON SOURCE LINES 84-87 +.. GENERATED FROM PYTHON SOURCE LINES 83-86 .. code-block:: Python @@ -239,11 +238,11 @@ Get a single observation -.. GENERATED FROM PYTHON SOURCE LINES 88-89 +.. GENERATED FROM PYTHON SOURCE LINES 87-88 Select and peek events -.. GENERATED FROM PYTHON SOURCE LINES 89-92 +.. GENERATED FROM PYTHON SOURCE LINES 88-91 .. code-block:: Python @@ -262,11 +261,11 @@ Select and peek events -.. GENERATED FROM PYTHON SOURCE LINES 93-94 +.. GENERATED FROM PYTHON SOURCE LINES 92-93 Peek the effective area -.. GENERATED FROM PYTHON SOURCE LINES 94-97 +.. GENERATED FROM PYTHON SOURCE LINES 93-96 .. code-block:: Python @@ -292,11 +291,11 @@ Peek the effective area -.. GENERATED FROM PYTHON SOURCE LINES 98-99 +.. GENERATED FROM PYTHON SOURCE LINES 97-98 Peek the energy dispersion -.. GENERATED FROM PYTHON SOURCE LINES 99-102 +.. GENERATED FROM PYTHON SOURCE LINES 98-101 .. code-block:: Python @@ -315,11 +314,11 @@ Peek the energy dispersion -.. GENERATED FROM PYTHON SOURCE LINES 103-104 +.. GENERATED FROM PYTHON SOURCE LINES 102-103 Peek the psf -.. GENERATED FROM PYTHON SOURCE LINES 104-106 +.. GENERATED FROM PYTHON SOURCE LINES 103-105 .. code-block:: Python @@ -337,11 +336,11 @@ Peek the psf -.. GENERATED FROM PYTHON SOURCE LINES 107-108 +.. GENERATED FROM PYTHON SOURCE LINES 106-107 Peek the background rate -.. GENERATED FROM PYTHON SOURCE LINES 108-111 +.. GENERATED FROM PYTHON SOURCE LINES 107-110 .. code-block:: Python @@ -360,7 +359,7 @@ Peek the background rate -.. GENERATED FROM PYTHON SOURCE LINES 112-118 +.. GENERATED FROM PYTHON SOURCE LINES 111-117 Theta squared event distribution -------------------------------- @@ -369,7 +368,7 @@ As a quick look plot it can be helpful to plot the quadratic offset (theta squared) distribution of the events. -.. GENERATED FROM PYTHON SOURCE LINES 118-133 +.. GENERATED FROM PYTHON SOURCE LINES 117-132 .. code-block:: Python @@ -400,7 +399,7 @@ As a quick look plot it can be helpful to plot the quadratic offset -.. GENERATED FROM PYTHON SOURCE LINES 134-142 +.. GENERATED FROM PYTHON SOURCE LINES 133-141 Exercises --------- @@ -411,13 +410,13 @@ Exercises background IRF. -.. GENERATED FROM PYTHON SOURCE LINES 145-152 +.. GENERATED FROM PYTHON SOURCE LINES 144-151 Next steps ---------- Now you know how to access and work with H.E.S.S. data. All other -tutorials and documentation apply to H.E.S.S. and CTA or any other IACT +tutorials and documentation apply to H.E.S.S. and CTAO or any other IACT that provides DL3 data and IRFs in the standard format. diff --git a/docs/dev/_sources/tutorials/data/sg_execution_times.rst.txt b/docs/dev/_sources/tutorials/data/sg_execution_times.rst.txt index 664b7b56494..de66aeeb866 100644 --- a/docs/dev/_sources/tutorials/data/sg_execution_times.rst.txt +++ b/docs/dev/_sources/tutorials/data/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:23.059** total execution time for 4 files **from tutorials/data**: +**00:21.446** total execution time for 4 files **from tutorials/data**: .. container:: @@ -33,14 +33,14 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_tutorials_data_hawc.py` (``hawc.py``) - - 00:07.256 + - 00:06.689 - 0.0 * - :ref:`sphx_glr_tutorials_data_fermi_lat.py` (``fermi_lat.py``) - - 00:06.673 + - 00:06.263 - 0.0 * - :ref:`sphx_glr_tutorials_data_cta.py` (``cta.py``) - - 00:05.525 + - 00:05.234 - 0.0 * - :ref:`sphx_glr_tutorials_data_hess.py` (``hess.py``) - - 00:03.605 + - 00:03.259 - 0.0 diff --git a/docs/dev/_sources/tutorials/index.rst.txt b/docs/dev/_sources/tutorials/index.rst.txt index fa609617354..fc5283f019e 100644 --- a/docs/dev/_sources/tutorials/index.rst.txt +++ b/docs/dev/_sources/tutorials/index.rst.txt @@ -141,7 +141,7 @@ event lists according to different criteria, as well as to get a quick look of t .. raw:: html -
+
.. only:: html @@ -152,7 +152,7 @@ event lists according to different criteria, as well as to get a quick look of t .. raw:: html -
CTA with Gammapy
+
CTAO with Gammapy
@@ -241,7 +241,7 @@ time-dependent analysis with light-curves. .. raw:: html -
+
.. only:: html diff --git a/docs/dev/_sources/tutorials/scripts/sg_execution_times.rst.txt b/docs/dev/_sources/tutorials/scripts/sg_execution_times.rst.txt index 7a260c3e989..8bb569250f1 100644 --- a/docs/dev/_sources/tutorials/scripts/sg_execution_times.rst.txt +++ b/docs/dev/_sources/tutorials/scripts/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:02.511** total execution time for 1 file **from tutorials/scripts**: +**00:02.499** total execution time for 1 file **from tutorials/scripts**: .. container:: @@ -33,5 +33,5 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_tutorials_scripts_survey_map.py` (``survey_map.py``) - - 00:02.511 + - 00:02.499 - 0.0 diff --git a/docs/dev/_sources/tutorials/starting/analysis_1.rst.txt b/docs/dev/_sources/tutorials/starting/analysis_1.rst.txt index 97936c3062a..26eab45b488 100644 --- a/docs/dev/_sources/tutorials/starting/analysis_1.rst.txt +++ b/docs/dev/_sources/tutorials/starting/analysis_1.rst.txt @@ -138,7 +138,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy @@ -310,8 +310,8 @@ Here is what the configuration for our analysis looks like: parameters: selection_optional: all metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:53.021569' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:41.447240' origin: null @@ -628,8 +628,8 @@ One can export/import the `~gammapy.modeling.AnalysisConfig` to/from a YAML file parameters: selection_optional: all metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:53.048716' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:41.473779' origin: null @@ -1183,8 +1183,8 @@ This is how we can write the model back to file again: unit: TeV covariance: model-best-fit_covariance.dat metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:28:01.911387' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:50.284234' origin: null @@ -1337,7 +1337,7 @@ You can see how to perform a 1D spectral analysis of the same data in .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 15.384 seconds) + **Total running time of the script:** (0 minutes 15.326 seconds) .. _sphx_glr_download_tutorials_starting_analysis_1.py: diff --git a/docs/dev/_sources/tutorials/starting/analysis_2.rst.txt b/docs/dev/_sources/tutorials/starting/analysis_2.rst.txt index a775591b76c..146a8689aca 100644 --- a/docs/dev/_sources/tutorials/starting/analysis_2.rst.txt +++ b/docs/dev/_sources/tutorials/starting/analysis_2.rst.txt @@ -154,7 +154,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy diff --git a/docs/dev/_sources/tutorials/starting/overview.rst.txt b/docs/dev/_sources/tutorials/starting/overview.rst.txt index 71e340c8efb..eae65b64331 100644 --- a/docs/dev/_sources/tutorials/starting/overview.rst.txt +++ b/docs/dev/_sources/tutorials/starting/overview.rst.txt @@ -137,7 +137,7 @@ Check setup Gammapy package: - version : 1.3.dev1143+ge26e20052 + version : 1.3.dev1180+gb03e9b8c7 path : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy diff --git a/docs/dev/_sources/tutorials/starting/sg_execution_times.rst.txt b/docs/dev/_sources/tutorials/starting/sg_execution_times.rst.txt index 081a836e261..51f51a9d858 100644 --- a/docs/dev/_sources/tutorials/starting/sg_execution_times.rst.txt +++ b/docs/dev/_sources/tutorials/starting/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:24.400** total execution time for 3 files **from tutorials/starting**: +**00:24.351** total execution time for 3 files **from tutorials/starting**: .. container:: @@ -33,11 +33,11 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_tutorials_starting_analysis_1.py` (``analysis_1.py``) - - 00:15.384 + - 00:15.326 - 0.0 * - :ref:`sphx_glr_tutorials_starting_analysis_2.py` (``analysis_2.py``) - - 00:07.749 + - 00:07.665 - 0.0 * - :ref:`sphx_glr_tutorials_starting_overview.py` (``overview.py``) - - 00:01.267 + - 00:01.360 - 0.0 diff --git a/docs/dev/_sources/user-guide/astro/darkmatter/index.rst.txt b/docs/dev/_sources/user-guide/astro/darkmatter/index.rst.txt index 2509f74c846..daf81c33cbb 100644 --- a/docs/dev/_sources/user-guide/astro/darkmatter/index.rst.txt +++ b/docs/dev/_sources/user-guide/astro/darkmatter/index.rst.txt @@ -84,7 +84,7 @@ gamLike `GamLike`_ contains likelihood functions for most leading gamma-ray indirect searches for dark matter, including Fermi-LAT observations of dwarfs and the Galactic Centre (GC), HESS observations of the GC, and projected sensitivities -for CTA observations of the GC. It is released in tandem with the `GAMBIT`_ +for CTAO observations of the GC. It is released in tandem with the `GAMBIT`_ module `DarkBit`_. DarkBit can be used for directly computing observables and likelihoods, for any combination of parameter values in some underlying particle model. diff --git a/docs/dev/_sources/user-guide/howto.rst.txt b/docs/dev/_sources/user-guide/howto.rst.txt index d7ce187f7d5..a058d500afb 100644 --- a/docs/dev/_sources/user-guide/howto.rst.txt +++ b/docs/dev/_sources/user-guide/howto.rst.txt @@ -179,7 +179,7 @@ It is possible to combine Gammapy with astrophysical modeling codes, if they provide a Python interface. Usually this requires some glue code to be written, e.g. `~gammapy.modeling.models.NaimaSpectralModel` is an example of a Gammapy wrapper class around the Naima spectral model and radiation classes, which then -allows modeling and fitting of Naima models within Gammapy (e.g. using CTA, +allows modeling and fitting of Naima models within Gammapy (e.g. using CTAO, H.E.S.S. or Fermi-LAT data). .. accordion-footer:: @@ -327,7 +327,8 @@ Note that you can create your own style with matplotlib (see `here `__ and `here `__) -The CTA observatory released a document describing best practices for **data visualisation in a way friendly to +.. TODO: update the link +The CTAO observatory released a document describing best practices for **data visualisation in a way friendly to color-blind people**: `CTAO document `_. To use them, you should add into your notebooks or scripts the following lines after the Gammapy imports: diff --git a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_constant.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_constant.rst.txt index 9404cf8f729..b9e21c707b4 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_constant.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_constant.rst.txt @@ -110,8 +110,8 @@ Here is an example YAML file using the model: type: ConstantSpatialModel parameters: [] metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:38.032406' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:26.664097' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_disk.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_disk.rst.txt index cf09e1e834b..deb1083eb7e 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_disk.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_disk.rst.txt @@ -232,8 +232,8 @@ Here is an example YAML file using the model: - name: edge_width value: 0.01 metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:38.469500' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:27.102251' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_gauss.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_gauss.rst.txt index c2ca39466a1..61ca01005a7 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_gauss.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_gauss.rst.txt @@ -190,8 +190,8 @@ Here is an example YAML file using the model: value: 0.0 unit: deg metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:38.801929' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:27.426205' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_gen_gauss.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_gen_gauss.rst.txt index 3f09d938019..5ec0643c90a 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_gen_gauss.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_gen_gauss.rst.txt @@ -182,8 +182,8 @@ Here is an example YAML file using the model: value: 0.0 unit: deg metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:39.145591' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:27.769762' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_piecewise_norm_spatial.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_piecewise_norm_spatial.rst.txt index a42da6499d0..55662a89a0a 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_piecewise_norm_spatial.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_piecewise_norm_spatial.rst.txt @@ -141,8 +141,8 @@ Here is an example YAML file using the model: - -1.785 unit: deg metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:39.402378' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:28.022676' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_point.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_point.rst.txt index 71f29a3d874..ad3ccd4c5b9 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_point.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_point.rst.txt @@ -133,8 +133,8 @@ Here is an example YAML file using the model: value: 0.0 unit: deg metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:39.725954' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:28.343796' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_shell.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_shell.rst.txt index b9906b68c13..04f6c6e9b32 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_shell.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_shell.rst.txt @@ -145,8 +145,8 @@ Here is an example YAML file using the model: value: 0.2 unit: deg metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:40.024408' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:28.638011' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_shell2.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_shell2.rst.txt index 34c05251cfe..b20597494e8 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_shell2.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_shell2.rst.txt @@ -157,8 +157,8 @@ Here is an example YAML file using the model: - name: eta value: 0.2 metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:40.321851' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:28.935576' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_template.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_template.rst.txt index b02d651b13d..7a7250cd00b 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spatial/plot_template.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spatial/plot_template.rst.txt @@ -129,8 +129,8 @@ Here is an example YAML file using the model: normalize: true unit: 1 / sr metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:40.671093' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:29.276248' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spatial/sg_execution_times.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spatial/sg_execution_times.rst.txt index 7612b503216..e9583949294 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spatial/sg_execution_times.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spatial/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:02.050** total execution time for 9 files **from user-guide/model-gallery/spatial**: +**00:02.058** total execution time for 9 files **from user-guide/model-gallery/spatial**: .. container:: @@ -33,10 +33,10 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_constant.py` (``plot_constant.py``) - - 00:00.352 + - 00:00.369 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_disk.py` (``plot_disk.py``) - - 00:00.317 + - 00:00.319 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_template.py` (``plot_template.py``) - 00:00.229 @@ -45,17 +45,17 @@ Computation times - 00:00.224 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_gauss.py` (``plot_gauss.py``) - - 00:00.215 + - 00:00.211 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_point.py` (``plot_point.py``) - - 00:00.203 + - 00:00.201 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_shell.py` (``plot_shell.py``) - - 00:00.186 + - 00:00.185 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_shell2.py` (``plot_shell2.py``) - 00:00.177 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spatial_plot_piecewise_norm_spatial.py` (``plot_piecewise_norm_spatial.py``) - - 00:00.146 + - 00:00.143 - 0.0 diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_absorbed.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_absorbed.rst.txt index ceb7e1f691f..06c68975aa7 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_absorbed.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_absorbed.rst.txt @@ -185,8 +185,8 @@ Here is an example YAML file using the model: filename: /home/runner/work/gammapy-docs/gammapy-docs/gammapy-datasets/dev/ebl/ebl_dominguez11.fits.gz operator: mul metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:41.844159' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:30.500622' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_broken_powerlaw.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_broken_powerlaw.rst.txt index 0f907e1addb..dd06cc40994 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_broken_powerlaw.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_broken_powerlaw.rst.txt @@ -111,8 +111,8 @@ Here is an example YAML file using the model: value: 1.0 unit: TeV metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:42.317479' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:30.951806' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_compound.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_compound.rst.txt index 205289eb261..cbaf9d20751 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_compound.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_compound.rst.txt @@ -126,8 +126,8 @@ Here is an example YAML file using the model: value: 1.0 operator: add metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:42.773695' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:31.379851' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_constant_spectral.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_constant_spectral.rst.txt index dde04296dac..3adb73aad3b 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_constant_spectral.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_constant_spectral.rst.txt @@ -92,8 +92,8 @@ Here is an example YAML file using the model: value: 1.0 unit: cm-2 s-1 TeV-1 metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:43.142852' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:31.729956' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_exp_cutoff_powerlaw.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_exp_cutoff_powerlaw.rst.txt index 7ceb40b5d15..e0be1b77981 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_exp_cutoff_powerlaw.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_exp_cutoff_powerlaw.rst.txt @@ -110,8 +110,8 @@ Here is an example YAML file using the model: - name: alpha value: 1.0 metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:43.469253' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:32.041904' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_exp_cutoff_powerlaw_3fgl.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_exp_cutoff_powerlaw_3fgl.rst.txt index 4588f559e89..3fbfb0d449d 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_exp_cutoff_powerlaw_3fgl.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_exp_cutoff_powerlaw_3fgl.rst.txt @@ -109,8 +109,8 @@ Here is an example YAML file using the model: value: 10.0 unit: TeV metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:43.792100' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:32.345959' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_exp_cutoff_powerlaw_norm_spectral.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_exp_cutoff_powerlaw_norm_spectral.rst.txt index 1f0f3967e02..0252fc863c6 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_exp_cutoff_powerlaw_norm_spectral.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_exp_cutoff_powerlaw_norm_spectral.rst.txt @@ -146,8 +146,8 @@ Here is an example YAML file using the model: value: 1.0 operator: mul metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:44.255235' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:32.797325' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_gauss_spectral.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_gauss_spectral.rst.txt index 327033b177a..7913a2950ea 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_gauss_spectral.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_gauss_spectral.rst.txt @@ -108,8 +108,8 @@ Here is an example YAML file using the model: value: 0.2 unit: TeV metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:44.607609' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:33.129108' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_logparabola.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_logparabola.rst.txt index ba1f525398f..00dc094a15b 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_logparabola.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_logparabola.rst.txt @@ -118,8 +118,8 @@ Here is an example YAML file using the model: - name: beta value: 0.5 metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:44.929801' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:33.437280' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_logparabola_norm_spectral.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_logparabola_norm_spectral.rst.txt index f2732f65727..20d0fbbbf7e 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_logparabola_norm_spectral.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_logparabola_norm_spectral.rst.txt @@ -135,8 +135,8 @@ Here is an example YAML file using the model: value: 0.0 operator: mul metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:45.343004' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:33.811055' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_naima.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_naima.rst.txt index 5922cf550ef..4e79d568039 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_naima.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_naima.rst.txt @@ -169,8 +169,8 @@ Here is an example YAML file using the model: interp: lin scale_method: scale10 metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:45.910796' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:34.342318' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_piecewise_norm_spectral.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_piecewise_norm_spectral.rst.txt index 40252a5bc89..cf70fd315a6 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_piecewise_norm_spectral.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_piecewise_norm_spectral.rst.txt @@ -134,8 +134,8 @@ Here is an example YAML file using the model: unit: TeV operator: mul metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:46.268480' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:34.692427' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_powerlaw.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_powerlaw.rst.txt index d86c64a38a5..2660cbd9f62 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_powerlaw.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_powerlaw.rst.txt @@ -104,8 +104,8 @@ Here is an example YAML file using the model: value: 1.0 unit: TeV metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:46.708533' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:35.127536' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_powerlaw2.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_powerlaw2.rst.txt index eb7ace6fe7c..5b852864dbd 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_powerlaw2.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_powerlaw2.rst.txt @@ -111,8 +111,8 @@ Here is an example YAML file using the model: value: 10.0 unit: TeV metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:47.154660' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:35.564616' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_powerlaw_norm_spectral.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_powerlaw_norm_spectral.rst.txt index e2976ba382d..8caa647683d 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_powerlaw_norm_spectral.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_powerlaw_norm_spectral.rst.txt @@ -132,8 +132,8 @@ Here is an example YAML file using the model: unit: TeV operator: mul metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:47.550055' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:35.950968' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_smooth_broken_powerlaw.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_smooth_broken_powerlaw.rst.txt index 62b698a1316..95e7fa182d7 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_smooth_broken_powerlaw.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_smooth_broken_powerlaw.rst.txt @@ -114,8 +114,8 @@ Here is an example YAML file using the model: - name: beta value: 1.0 metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:47.996746' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:36.383601' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_super_exp_cutoff_powerlaw_3fgl.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_super_exp_cutoff_powerlaw_3fgl.rst.txt index ae274418cbc..f26b83a99a3 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_super_exp_cutoff_powerlaw_3fgl.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_super_exp_cutoff_powerlaw_3fgl.rst.txt @@ -125,8 +125,8 @@ Here is an example YAML file using the model: - name: index_2 value: 2.0 metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:48.346622' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:36.716755' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_super_exp_cutoff_powerlaw_4fgl.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_super_exp_cutoff_powerlaw_4fgl.rst.txt index 5511514b932..df18a47857a 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_super_exp_cutoff_powerlaw_4fgl.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_super_exp_cutoff_powerlaw_4fgl.rst.txt @@ -124,8 +124,8 @@ Here is an example YAML file using the model: - name: index_2 value: 2.0 metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:48.693127' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:37.049222' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_super_exp_cutoff_powerlaw_4fgl_dr1.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_super_exp_cutoff_powerlaw_4fgl_dr1.rst.txt index c8ba8f82a02..83bdd9cb36f 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_super_exp_cutoff_powerlaw_4fgl_dr1.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_super_exp_cutoff_powerlaw_4fgl_dr1.rst.txt @@ -126,8 +126,8 @@ Here is an example YAML file using the model: - name: index_2 value: 2.0 metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:49.031173' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:37.372911' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_template_spectral.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_template_spectral.rst.txt index 651a7d9c07c..478b51212d9 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/plot_template_spectral.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/plot_template_spectral.rst.txt @@ -116,7 +116,7 @@ The following shows how to implement extrapolation of a template spectral model: .. code-block:: none - + @@ -203,8 +203,8 @@ Here is an example YAML file using the model: - 3.9e-39 unit: 1 / (cm2 MeV s) metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:49.594229' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:37.920370' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/spectral/sg_execution_times.rst.txt b/docs/dev/_sources/user-guide/model-gallery/spectral/sg_execution_times.rst.txt index 65912997aac..0bfb7867893 100644 --- a/docs/dev/_sources/user-guide/model-gallery/spectral/sg_execution_times.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/spectral/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:06.414** total execution time for 20 files **from user-guide/model-gallery/spectral**: +**00:06.385** total execution time for 20 files **from user-guide/model-gallery/spectral**: .. container:: @@ -33,62 +33,62 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_absorbed.py` (``plot_absorbed.py``) - - 00:01.001 + - 00:01.055 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_template_spectral.py` (``plot_template_spectral.py``) - - 00:00.449 + - 00:00.446 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_naima.py` (``plot_naima.py``) - - 00:00.430 + - 00:00.419 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_exp_cutoff_powerlaw_norm_spectral.py` (``plot_exp_cutoff_powerlaw_norm_spectral.py``) - - 00:00.345 + - 00:00.343 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_broken_powerlaw.py` (``plot_broken_powerlaw.py``) + * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_powerlaw.py` (``plot_powerlaw.py``) - 00:00.331 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_compound.py` (``plot_compound.py``) - - 00:00.327 + * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_powerlaw2.py` (``plot_powerlaw2.py``) + - 00:00.324 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_smooth_broken_powerlaw.py` (``plot_smooth_broken_powerlaw.py``) - - 00:00.327 - - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_powerlaw2.py` (``plot_powerlaw2.py``) - - 00:00.326 + - 00:00.321 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_powerlaw.py` (``plot_powerlaw.py``) + * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_broken_powerlaw.py` (``plot_broken_powerlaw.py``) - 00:00.321 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_logparabola_norm_spectral.py` (``plot_logparabola_norm_spectral.py``) - - 00:00.299 + * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_compound.py` (``plot_compound.py``) + - 00:00.320 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_powerlaw_norm_spectral.py` (``plot_powerlaw_norm_spectral.py``) - 00:00.274 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_constant_spectral.py` (``plot_constant_spectral.py``) - - 00:00.242 + * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_logparabola_norm_spectral.py` (``plot_logparabola_norm_spectral.py``) + - 00:00.271 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_piecewise_norm_spectral.py` (``plot_piecewise_norm_spectral.py``) - - 00:00.227 + * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_constant_spectral.py` (``plot_constant_spectral.py``) + - 00:00.239 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_super_exp_cutoff_powerlaw_4fgl.py` (``plot_super_exp_cutoff_powerlaw_4fgl.py``) - - 00:00.226 + - 00:00.228 + - 0.0 + * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_piecewise_norm_spectral.py` (``plot_piecewise_norm_spectral.py``) + - 00:00.225 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_super_exp_cutoff_powerlaw_4fgl_dr1.py` (``plot_super_exp_cutoff_powerlaw_4fgl_dr1.py``) - - 00:00.223 + - 00:00.219 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_super_exp_cutoff_powerlaw_3fgl.py` (``plot_super_exp_cutoff_powerlaw_3fgl.py``) - - 00:00.222 + - 00:00.219 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_gauss_spectral.py` (``plot_gauss_spectral.py``) - - 00:00.221 + - 00:00.217 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_exp_cutoff_powerlaw.py` (``plot_exp_cutoff_powerlaw.py``) - - 00:00.209 + - 00:00.206 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_logparabola.py` (``plot_logparabola.py``) - - 00:00.208 + - 00:00.204 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_spectral_plot_exp_cutoff_powerlaw_3fgl.py` (``plot_exp_cutoff_powerlaw_3fgl.py``) - - 00:00.206 + - 00:00.203 - 0.0 diff --git a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_constant_temporal.rst.txt b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_constant_temporal.rst.txt index 496032bd1f4..458b2bdbb09 100644 --- a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_constant_temporal.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_constant_temporal.rst.txt @@ -111,8 +111,8 @@ Here is an example YAML file using the model: parameters: [] scale: utc metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:50.373108' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:38.860103' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_expdecay_temporal.rst.txt b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_expdecay_temporal.rst.txt index 4d97ec01229..0459286f278 100644 --- a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_expdecay_temporal.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_expdecay_temporal.rst.txt @@ -121,8 +121,8 @@ Here is an example YAML file using the model: unit: d scale: utc metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:50.606922' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:39.083718' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_gaussian_temporal.rst.txt b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_gaussian_temporal.rst.txt index ea68e8645fe..86448f2416d 100644 --- a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_gaussian_temporal.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_gaussian_temporal.rst.txt @@ -120,8 +120,8 @@ Here is an example YAML file using the model: unit: h scale: utc metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:50.840677' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:39.303371' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_generalized_gaussian_temporal.rst.txt b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_generalized_gaussian_temporal.rst.txt index 8275606a559..9c9eebddeee 100644 --- a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_generalized_gaussian_temporal.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_generalized_gaussian_temporal.rst.txt @@ -132,8 +132,8 @@ Here is an example YAML file using the model: value: 0.6666666666666666 scale: utc metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:51.075404' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:39.523805' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_linear_temporal.rst.txt b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_linear_temporal.rst.txt index ebd338aee6f..5e5b9829374 100644 --- a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_linear_temporal.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_linear_temporal.rst.txt @@ -117,12 +117,12 @@ Here is an example YAML file using the model: value: 0.5 unit: d-1 - name: t_ref - value: 60594.2526759211 + value: 60594.31643099465 unit: d scale: utc metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:51.301305' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:39.742070' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_powerlaw_temporal.rst.txt b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_powerlaw_temporal.rst.txt index 85bd569fc2c..2176872f44a 100644 --- a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_powerlaw_temporal.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_powerlaw_temporal.rst.txt @@ -113,15 +113,15 @@ Here is an example YAML file using the model: - name: alpha value: -2.0 - name: t_ref - value: 60594.252678526995 + value: 60594.31643351969 unit: d - name: t0 value: 1.0 unit: d scale: utc metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:51.616529' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:40.049560' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_sine_temporal.rst.txt b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_sine_temporal.rst.txt index 487aac99e61..40795141c68 100644 --- a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_sine_temporal.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_sine_temporal.rst.txt @@ -119,12 +119,12 @@ Here is an example YAML file using the model: value: 0.7853981633974483 unit: d-1 rad - name: t_ref - value: 60594.2526820428 + value: 60594.316436978916 unit: d scale: utc metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:51.834275' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:40.263264' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_template_phase_temporal.rst.txt b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_template_phase_temporal.rst.txt index 59e9ab9ebf5..31ccfb41151 100644 --- a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_template_phase_temporal.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_template_phase_temporal.rst.txt @@ -127,8 +127,8 @@ Here is an example YAML file using the model: scale: utc filename: /home/runner/work/gammapy-docs/gammapy-docs/gammapy-datasets/dev/tests/phasecurve_LSI_DC.fits metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:52.078765' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:40.505214' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_template_temporal.rst.txt b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_template_temporal.rst.txt index 877faada713..8a202b0c729 100644 --- a/docs/dev/_sources/user-guide/model-gallery/temporal/plot_template_temporal.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/temporal/plot_template_temporal.rst.txt @@ -118,8 +118,8 @@ Here is an example YAML file using the model: format: table unit: '' metadata: - creator: Gammapy 1.3.dev1143+ge26e20052 - date: '2024-10-11T08:27:52.318976' + creator: Gammapy 1.3.dev1180+gb03e9b8c7 + date: '2024-10-11T09:59:40.738213' origin: null diff --git a/docs/dev/_sources/user-guide/model-gallery/temporal/sg_execution_times.rst.txt b/docs/dev/_sources/user-guide/model-gallery/temporal/sg_execution_times.rst.txt index 50762e1d214..399c8597390 100644 --- a/docs/dev/_sources/user-guide/model-gallery/temporal/sg_execution_times.rst.txt +++ b/docs/dev/_sources/user-guide/model-gallery/temporal/sg_execution_times.rst.txt @@ -6,7 +6,7 @@ Computation times ================= -**00:01.561** total execution time for 9 files **from user-guide/model-gallery/temporal**: +**00:01.743** total execution time for 9 files **from user-guide/model-gallery/temporal**: .. container:: @@ -33,29 +33,29 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_constant_temporal.py` (``plot_constant_temporal.py``) - - 00:00.579 + - 00:00.752 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_powerlaw_temporal.py` (``plot_powerlaw_temporal.py``) - - 00:00.193 + - 00:00.195 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_template_phase_temporal.py` (``plot_template_phase_temporal.py``) - - 00:00.123 + - 00:00.129 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_template_temporal.py` (``plot_template_temporal.py``) - 00:00.121 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_generalized_gaussian_temporal.py` (``plot_generalized_gaussian_temporal.py``) - - 00:00.114 + - 00:00.111 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_gaussian_temporal.py` (``plot_gaussian_temporal.py``) + * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_sine_temporal.py` (``plot_sine_temporal.py``) - 00:00.110 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_expdecay_temporal.py` (``plot_expdecay_temporal.py``) - - 00:00.110 + * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_gaussian_temporal.py` (``plot_gaussian_temporal.py``) + - 00:00.109 - 0.0 - * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_sine_temporal.py` (``plot_sine_temporal.py``) - - 00:00.108 + * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_expdecay_temporal.py` (``plot_expdecay_temporal.py``) + - 00:00.109 - 0.0 * - :ref:`sphx_glr_user-guide_model-gallery_temporal_plot_linear_temporal.py` (``plot_linear_temporal.py``) - - 00:00.104 + - 00:00.106 - 0.0 diff --git a/docs/dev/_sources/user-guide/package.rst.txt b/docs/dev/_sources/user-guide/package.rst.txt index 242864800ce..d6e0b985cd9 100644 --- a/docs/dev/_sources/user-guide/package.rst.txt +++ b/docs/dev/_sources/user-guide/package.rst.txt @@ -42,7 +42,7 @@ the Gammapy API are explained in more detail in the following. Fig. 1 Data flow and sub-package structure of Gammapy. The folder icons represent the corresponding sub-packages. The direction of the the data flow is illustrated with shaded arrows. The top section - shows the data levels as defined by `CTA`_. + shows the data levels as defined by `CTAO`_. Analysis steps -------------- diff --git a/docs/dev/_sources/user-guide/references.rst.txt b/docs/dev/_sources/user-guide/references.rst.txt index 576f673fabf..cd4f5143e89 100644 --- a/docs/dev/_sources/user-guide/references.rst.txt +++ b/docs/dev/_sources/user-guide/references.rst.txt @@ -91,7 +91,7 @@ Glossary GTI Short for "good time interval": it indicates a continuous time interval of data - acquisition. In CTA, it also represents a time interval in which the IRFs are + acquisition. In the GADF DL3 format, it also represents a time interval in which the IRFs are supposed to be constant. IRF @@ -137,6 +137,10 @@ Glossary object, the type of plot (e.g. :math:`dN/dE`, :math:`E^2\ dN/dE`) is typically adjusted through the `sed_type` quantity. See :ref:`sedtypes` for a list of options. +.. STI + Short for "stable time interval": it indicates a continuous time interval of data + acquisition for which the instrument response files are supposed to be constant. + Stacked Analysis In a stacked analysis individual observations are reduced to datasets which are then stacked to produce a single reduced dataset. The latter is then used diff --git a/docs/dev/_sources/user-guide/utils.rst.txt b/docs/dev/_sources/user-guide/utils.rst.txt index b9733ad3b50..dcb01e7dcdf 100644 --- a/docs/dev/_sources/user-guide/utils.rst.txt +++ b/docs/dev/_sources/user-guide/utils.rst.txt @@ -39,12 +39,12 @@ In Gammapy, `astropy.time.Time` objects are used to represent times: Note that Astropy chose ``format='isot'`` and ``scale='utc'`` as default and in Gammapy these are also the recommended format and time scale. -.. warning:: +.. .. warning:: - Actually what's written here is not true. In CTA it hasn't been decided if +.. Actually what's written here is not true. In CTAO, it hasn't been decided if times will be in ``utc`` or ``tt`` (terrestrial time) format. - Here's a reminder that this needs to be settled / updated: +.. Here's a reminder that this needs to be settled / updated: https://github.com/gammapy/gammapy/issues/284 diff --git a/docs/dev/api/gammapy.data.DataStore.html b/docs/dev/api/gammapy.data.DataStore.html index 96ba819024a..b7304302c76 100644 --- a/docs/dev/api/gammapy.data.DataStore.html +++ b/docs/dev/api/gammapy.data.DataStore.html @@ -989,7 +989,7 @@

DataStoreNumber of observations: 105

-

For further usage example see CTA with Gammapy tutorial.

+

For further usage example see CTAO with Gammapy tutorial.

Attributes Summary

e_refe_mine_maxdndednde_errtssqrt_tsnprednpred_excessstatstat_nullcountssuccess
TeVTeVTeV1 / (cm2 s TeV)1 / (cm2 s TeV)
float64float64float64float64float64float64float64float64[1]float32[1]float64float64float64[1]bool
@@ -1127,8 +1127,6 @@

DataStoreCALDB (example: CALDB = 1dc)

  • IRF (example: IRF = South_z20_50h)

  • -

    This method is useful specifically if you want to load data simulated -with ctobssim.

    Parameters:
    diff --git a/docs/dev/api/gammapy.datasets.MapDatasetMetaData.html b/docs/dev/api/gammapy.datasets.MapDatasetMetaData.html index 4af4ff38ae7..2a59712cd3b 100644 --- a/docs/dev/api/gammapy.datasets.MapDatasetMetaData.html +++ b/docs/dev/api/gammapy.datasets.MapDatasetMetaData.html @@ -955,7 +955,7 @@

    MapDatasetMetaData#

    -class gammapy.datasets.MapDatasetMetaData(*, creation: ~gammapy.utils.metadata.CreatorMetaData | None = CreatorMetaData(creator='Gammapy 1.3.dev1143+ge26e20052', date=<Time object: scale='utc' format='datetime' value=2024-10-11 08:27:31.660345>, origin=None), obs_info: ~gammapy.utils.metadata.ObsInfoMetaData | list[~gammapy.utils.metadata.ObsInfoMetaData] | None = None, pointing: ~gammapy.utils.metadata.PointingInfoMetaData | list[~gammapy.utils.metadata.PointingInfoMetaData] | None = None, event_type: str | list[str] | None = None, optional: dict | None = None)[source]#
    +class gammapy.datasets.MapDatasetMetaData(*, creation: ~gammapy.utils.metadata.CreatorMetaData | None = CreatorMetaData(creator='Gammapy 1.3.dev1180+gb03e9b8c7', date=<Time object: scale='utc' format='datetime' value=2024-10-11 09:59:20.409520>, origin=None), obs_info: ~gammapy.utils.metadata.ObsInfoMetaData | list[~gammapy.utils.metadata.ObsInfoMetaData] | None = None, pointing: ~gammapy.utils.metadata.PointingInfoMetaData | list[~gammapy.utils.metadata.PointingInfoMetaData] | None = None, event_type: str | list[str] | None = None, optional: dict | None = None)[source]#

    Bases: MetaData

    Metadata containing information about the Dataset.

    @@ -1114,7 +1114,7 @@

    MapDatasetMetaData
    -model_fields: ClassVar[Dict[str, FieldInfo]] = {'creation': FieldInfo(annotation=Union[CreatorMetaData, NoneType], required=False, default=CreatorMetaData(creator='Gammapy 1.3.dev1143+ge26e20052', date=<Time object: scale='utc' format='datetime' value=2024-10-11 08:27:31.660345>, origin=None)), 'event_type': FieldInfo(annotation=Union[str, list[str], NoneType], required=False, default=None), 'obs_info': FieldInfo(annotation=Union[ObsInfoMetaData, list[ObsInfoMetaData], NoneType], required=False, default=None), 'optional': FieldInfo(annotation=Union[dict, NoneType], required=False, default=None), 'pointing': FieldInfo(annotation=Union[PointingInfoMetaData, list[PointingInfoMetaData], NoneType], required=False, default=None)}#
    +model_fields: ClassVar[Dict[str, FieldInfo]] = {'creation': FieldInfo(annotation=Union[CreatorMetaData, NoneType], required=False, default=CreatorMetaData(creator='Gammapy 1.3.dev1180+gb03e9b8c7', date=<Time object: scale='utc' format='datetime' value=2024-10-11 09:59:20.409520>, origin=None)), 'event_type': FieldInfo(annotation=Union[str, list[str], NoneType], required=False, default=None), 'obs_info': FieldInfo(annotation=Union[ObsInfoMetaData, list[ObsInfoMetaData], NoneType], required=False, default=None), 'optional': FieldInfo(annotation=Union[dict, NoneType], required=False, default=None), 'pointing': FieldInfo(annotation=Union[PointingInfoMetaData, list[PointingInfoMetaData], NoneType], required=False, default=None)}#

    Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

    This replaces Model.__fields__ from Pydantic V1.

    diff --git a/docs/dev/api/gammapy.estimators.FluxMetaData.html b/docs/dev/api/gammapy.estimators.FluxMetaData.html index 31b16ca759b..1b1fbdfaf76 100644 --- a/docs/dev/api/gammapy.estimators.FluxMetaData.html +++ b/docs/dev/api/gammapy.estimators.FluxMetaData.html @@ -955,7 +955,7 @@

    FluxMetaData#

    -class gammapy.estimators.FluxMetaData(*, sed_type: ~gammapy.estimators.metadata.SEDTYPEEnum | None = None, sed_type_init: ~gammapy.estimators.metadata.SEDTYPEEnum | None = None, n_sigma: float | None = None, n_sigma_ul: float | None = None, sqrt_ts_threshold_ul: float | None = None, n_sigma_sensitivity: float | None = None, target: ~gammapy.utils.metadata.TargetMetaData | None = None, creation: ~gammapy.utils.metadata.CreatorMetaData | None = CreatorMetaData(creator='Gammapy 1.3.dev1143+ge26e20052', date=<Time object: scale='utc' format='datetime' value=2024-10-11 08:27:31.707522>, origin=None), optional: dict | None = None)[source]#
    +class gammapy.estimators.FluxMetaData(*, sed_type: ~gammapy.estimators.metadata.SEDTYPEEnum | None = None, sed_type_init: ~gammapy.estimators.metadata.SEDTYPEEnum | None = None, n_sigma: float | None = None, n_sigma_ul: float | None = None, sqrt_ts_threshold_ul: float | None = None, n_sigma_sensitivity: float | None = None, target: ~gammapy.utils.metadata.TargetMetaData | None = None, creation: ~gammapy.utils.metadata.CreatorMetaData | None = CreatorMetaData(creator='Gammapy 1.3.dev1180+gb03e9b8c7', date=<Time object: scale='utc' format='datetime' value=2024-10-11 09:59:20.457108>, origin=None), optional: dict | None = None)[source]#

    Bases: MetaData

    Metadata containing information about the FluxPoints and FluxMaps.

    @@ -1120,7 +1120,7 @@

    FluxMetaData
    -model_fields: ClassVar[Dict[str, FieldInfo]] = {'creation': FieldInfo(annotation=Union[CreatorMetaData, NoneType], required=False, default=CreatorMetaData(creator='Gammapy 1.3.dev1143+ge26e20052', date=<Time object: scale='utc' format='datetime' value=2024-10-11 08:27:31.707522>, origin=None)), 'n_sigma': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'n_sigma_sensitivity': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'n_sigma_ul': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'optional': FieldInfo(annotation=Union[dict, NoneType], required=False, default=None), 'sed_type': FieldInfo(annotation=Union[SEDTYPEEnum, NoneType], required=False, default=None), 'sed_type_init': FieldInfo(annotation=Union[SEDTYPEEnum, NoneType], required=False, default=None), 'sqrt_ts_threshold_ul': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'target': FieldInfo(annotation=Union[TargetMetaData, NoneType], required=False, default=None)}#
    +model_fields: ClassVar[Dict[str, FieldInfo]] = {'creation': FieldInfo(annotation=Union[CreatorMetaData, NoneType], required=False, default=CreatorMetaData(creator='Gammapy 1.3.dev1180+gb03e9b8c7', date=<Time object: scale='utc' format='datetime' value=2024-10-11 09:59:20.457108>, origin=None)), 'n_sigma': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'n_sigma_sensitivity': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'n_sigma_ul': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'optional': FieldInfo(annotation=Union[dict, NoneType], required=False, default=None), 'sed_type': FieldInfo(annotation=Union[SEDTYPEEnum, NoneType], required=False, default=None), 'sed_type_init': FieldInfo(annotation=Union[SEDTYPEEnum, NoneType], required=False, default=None), 'sqrt_ts_threshold_ul': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'target': FieldInfo(annotation=Union[TargetMetaData, NoneType], required=False, default=None)}#

    Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

    This replaces Model.__fields__ from Pydantic V1.

    diff --git a/docs/dev/api/gammapy.irf.EDispMap.html b/docs/dev/api/gammapy.irf.EDispMap.html index b95d8f6c16e..e96d36e6fd8 100644 --- a/docs/dev/api/gammapy.irf.EDispMap.html +++ b/docs/dev/api/gammapy.irf.EDispMap.html @@ -970,7 +970,7 @@

    EDispMap

    Examples

    -
    diff --git a/docs/dev/api/gammapy.makers.MapDatasetMaker.html b/docs/dev/api/gammapy.makers.MapDatasetMaker.html index 89514043c66..ef901d91fc8 100644 --- a/docs/dev/api/gammapy.makers.MapDatasetMaker.html +++ b/docs/dev/api/gammapy.makers.MapDatasetMaker.html @@ -968,7 +968,7 @@

    MapDatasetMakerint

    Background evaluation oversampling factor in energy.

    background_interp_missing_databool, optional

    Interpolate missing values in background 3d map. -Default is True, have to be set to True for CTA IRF.

    +Default is True, have to be set to True for CTAO IRF.

    background_pad_offsetbool, optional

    Pad one bin in offset for 2d background map. This avoids extrapolation at edges and use the nearest value. diff --git a/docs/dev/api/gammapy.modeling.models.create_cosmic_ray_spectral_model.html b/docs/dev/api/gammapy.modeling.models.create_cosmic_ray_spectral_model.html index 34e2cccbde5..775521cedce 100644 --- a/docs/dev/api/gammapy.modeling.models.create_cosmic_ray_spectral_model.html +++ b/docs/dev/api/gammapy.modeling.models.create_cosmic_ray_spectral_model.html @@ -957,7 +957,7 @@

    create_cosmic_ray_spectral_model gammapy.modeling.models.create_cosmic_ray_spectral_model(particle='proton')[source]#

    Cosmic a cosmic ray spectral model at Earth.

    -

    These are the spectra assumed in this CTA study: +

    These are the spectra assumed in this CTAO study: Table 3 in https://ui.adsabs.harvard.edu/abs/2013APh….43..171B

    The spectrum given is a differential flux dnde in units of cm-2 s-1 TeV-1, as the value integrated over the whole sky. diff --git a/docs/dev/development/dev_howto.html b/docs/dev/development/dev_howto.html index 967ab2495b8..fc375af0d51 100644 --- a/docs/dev/development/dev_howto.html +++ b/docs/dev/development/dev_howto.html @@ -650,9 +650,12 @@

    Coordinate and axis namesVery-high-energy Open Data Format. This format +aims to respect the CTAO data model, to respect the FAIR principles and to follow as much as +possible the IVOA recommendations. In order to handle past, current and old formats, +Gammapy should follow the FAIR4RS principles by proposing a user-friendly interface.

    diff --git a/docs/dev/development/release.html b/docs/dev/development/release.html index cd903e36cc0..f71f8a2f618 100644 --- a/docs/dev/development/release.html +++ b/docs/dev/development/release.html @@ -672,9 +672,8 @@

    Post release
  • https://groups.google.com/forum/#!forum/astropy-dev

  • -
  • https://lists.nasa.gov/mailman/listinfo/open-gamma-ray-astro

  • -
  • CTA DATA list (cta-wp-dm@cta-observatory.org)

  • -
  • hess-analysis

  • +
  • CTAO AS WG list (cta-wg-as [at] cta-observatory [dot] org)

  • +
  • hess-forum list (hess-forum [at] lsw.uni-heidelberg [dot] de)

  • diff --git a/docs/dev/genindex.html b/docs/dev/genindex.html index 8645150dc83..7c0b146ae08 100644 --- a/docs/dev/genindex.html +++ b/docs/dev/genindex.html @@ -11855,6 +11855,8 @@

    S

  • (gammapy.data.ObservationTable method)
  • + +

    -
  • stack_reduce() (gammapy.datasets.Datasets method) -
  • -
  • Stacked Analysis
  • standard_broadcasting (gammapy.astro.population.CaseBattacharya1998 attribute) @@ -13550,10 +13548,6 @@

    T

  • trapz_loglog() (in module gammapy.utils.integrate) -
  • -
  • True Energy -
  • -
  • TS
  • ts (gammapy.estimators.FluxMaps attribute) @@ -14254,8 +14248,6 @@

    W

  • write_rmf() (gammapy.datasets.OGIPDatasetWriter method)
  • write_yaml() (in module gammapy.utils.scripts) -
  • -
  • WStat
  • wstat() (in module gammapy.stats)
  • diff --git a/docs/dev/getting-started/index.html b/docs/dev/getting-started/index.html index 79a09facd15..4280057aed3 100644 --- a/docs/dev/getting-started/index.html +++ b/docs/dev/getting-started/index.html @@ -692,7 +692,7 @@

    Tutorials Overview

    Gammapy can read and access data from multiple gamma-ray instruments. Data from -Imaging Atmospheric Cherenkov Telescopes, such as CTA, H.E.S.S., MAGIC +Imaging Atmospheric Cherenkov Telescopes, such as `CTA`_, H.E.S.S., MAGIC and VERITAS, is typically accessed from the event list data level, called “DL3”. This is most easily done using the DataStore class. In addition data can also be accessed from the level of binned events and pre-reduced instrument response functions, diff --git a/docs/dev/index.html b/docs/dev/index.html index ac694b23bf6..31df70313b9 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -519,7 +519,7 @@

    Gammapy#

    -

    Date: Oct 11, 2024 Version: 1.3.dev1143+ge26e20052

    +

    Date: Oct 11, 2024 Version: 1.3.dev1180+gb03e9b8c7

    Useful links: Web page | Recipes | @@ -527,7 +527,7 @@

    Gammapy#< Acknowledging | Contact

    Gammapy is a community-developed, open-source Python package for gamma-ray -astronomy built on Numpy, Scipy and Astropy. It is the core library for the CTA Science Tools +astronomy built on Numpy, Scipy and Astropy. It is the core library for the CTAO Science Tools but can also be used to analyse data from existing imaging atmospheric Cherenkov telescopes (IACTs), such as H.E.S.S., MAGIC and VERITAS. It also provides some support for Fermi-LAT and HAWC data analysis.

    diff --git a/docs/dev/notebooks/dev/tutorials/analysis-1d/cta_sensitivity.ipynb b/docs/dev/notebooks/dev/tutorials/analysis-1d/cta_sensitivity.ipynb index b7f7ad6b855..ea5b5238392 100644 --- a/docs/dev/notebooks/dev/tutorials/analysis-1d/cta_sensitivity.ipynb +++ b/docs/dev/notebooks/dev/tutorials/analysis-1d/cta_sensitivity.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# Point source sensitivity\n\nEstimate the CTA sensitivity for a point-like IRF at a fixed zenith angle and fixed offset.\n\n## Introduction\n\nThis notebook explains how to estimate the CTA sensitivity for a\npoint-like IRF at a fixed zenith angle and fixed offset, using the full\ncontainment IRFs distributed for the CTA 1DC. The significance is\ncomputed for a 1D analysis (ON-OFF regions) with the Li&Ma formula.\n\nWe use here an approximate approach with an energy dependent integration\nradius to take into account the variation of the PSF. We will first\ndetermine the 1D IRFs including a containment correction.\n\nWe will be using the following Gammapy class:\n\n- `~gammapy.estimators.SensitivityEstimator`\n" + "\n# Point source sensitivity\n\nEstimate the CTAO sensitivity for a point-like IRF at a fixed zenith angle and fixed offset.\n\n## Introduction\n\nThis notebook explains how to estimate the CTAO sensitivity for a\npoint-like IRF at a fixed zenith angle and fixed offset, using the full\ncontainment IRFs distributed for the CTA 1DC. The significance is\ncomputed for a 1D analysis (ON-OFF regions) with the Li&Ma formula.\n\nWe use here an approximate approach with an energy dependent integration\nradius to take into account the variation of the PSF. We will first\ndetermine the 1D IRFs including a containment correction.\n\nWe will be using the following Gammapy class:\n\n- `~gammapy.estimators.SensitivityEstimator`\n" ] }, { @@ -76,7 +76,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Load IRFs and prepare dataset\n\nWe extract the 1D IRFs from the full 3D IRFs provided by CTA.\n\n\n" + "## Load IRFs and prepare dataset\n\nWe extract the 1D IRFs from the full 3D IRFs provided by CTAO.\n\n\n" ] }, { @@ -166,7 +166,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Compute sensitivity\n\nWe impose a minimal number of expected signal counts of 10 per bin and a\nminimal significance of 5 per bin. The excess must also be larger than 5% of the background.\n\nWe assume an alpha of 0.2 (ratio between ON and OFF area). We then run the sensitivity estimator.\n\nThese are the conditions imposed in standard CTA sensitivity computations.\n\n" + "## Compute sensitivity\n\nWe impose a minimal number of expected signal counts of 10 per bin and a\nminimal significance of 5 per bin. The excess must also be larger than 5% of the background.\n\nWe assume an alpha of 0.2 (ratio between ON and OFF area). We then run the sensitivity estimator.\n\nThese are the conditions imposed in standard CTAO sensitivity computations.\n\n" ] }, { diff --git a/docs/dev/notebooks/dev/tutorials/analysis-1d/spectrum_simulation.ipynb b/docs/dev/notebooks/dev/tutorials/analysis-1d/spectrum_simulation.ipynb index 4da8f7e82a1..328b3d29f71 100644 --- a/docs/dev/notebooks/dev/tutorials/analysis-1d/spectrum_simulation.ipynb +++ b/docs/dev/notebooks/dev/tutorials/analysis-1d/spectrum_simulation.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# 1D spectrum simulation\n\nSimulate a number of spectral on-off observations of a source with a power-law spectral\nmodel using the CTA 1DC response and fit them with the assumed spectral model.\n\n## Prerequisites\n\n- Knowledge of spectral extraction and datasets used in gammapy, see\n for instance the :doc:`spectral analysis\n tutorial `\n\n## Context\n\nTo simulate a specific observation, it is not always necessary to\nsimulate the full photon list. For many uses cases, simulating directly\na reduced binned dataset is enough: the IRFs reduced in the correct\ngeometry are combined with a source model to predict an actual number of\ncounts per bin. The latter is then used to simulate a reduced dataset\nusing Poisson probability distribution.\n\nThis can be done to check the feasibility of a measurement, to test\nwhether fitted parameters really provide a good fit to the data etc.\n\nHere we will see how to perform a 1D spectral simulation of a CTA\nobservation, in particular, we will generate OFF observations following\nthe template background stored in the CTA IRFs.\n\n**Objective: simulate a number of spectral ON-OFF observations of a\nsource with a power-law spectral model with CTA using the CTA 1DC\nresponse, fit them with the assumed spectral model and check that the\ndistribution of fitted parameters is consistent with the input values.**\n\n## Proposed approach\n\nWe will use the following classes and functions:\n\n- `~gammapy.datasets.SpectrumDatasetOnOff`\n- `~gammapy.datasets.SpectrumDataset`\n- `~gammapy.irf.load_irf_dict_from_file`\n- `~gammapy.modeling.models.PowerLawSpectralModel`\n" + "\n# 1D spectrum simulation\n\nSimulate a number of spectral on-off observations of a source with a power-law spectral\nmodel using the CTA 1DC response and fit them with the assumed spectral model.\n\n## Prerequisites\n\n- Knowledge of spectral extraction and datasets used in gammapy, see\n for instance the :doc:`spectral analysis\n tutorial `\n\n## Context\n\nTo simulate a specific observation, it is not always necessary to\nsimulate the full photon list. For many uses cases, simulating directly\na reduced binned dataset is enough: the IRFs reduced in the correct\ngeometry are combined with a source model to predict an actual number of\ncounts per bin. The latter is then used to simulate a reduced dataset\nusing Poisson probability distribution.\n\nThis can be done to check the feasibility of a measurement, to test\nwhether fitted parameters really provide a good fit to the data etc.\n\nHere we will see how to perform a 1D spectral simulation of a CTAO\nobservation, in particular, we will generate OFF observations following\nthe template background stored in the CTAO IRFs.\n\n**Objective: simulate a number of spectral ON-OFF observations of a\nsource with a power-law spectral model with CTAO using the CTA 1DC\nresponse, fit them with the assumed spectral model and check that the\ndistribution of fitted parameters is consistent with the input values.**\n\n## Proposed approach\n\nWe will use the following classes and functions:\n\n- `~gammapy.datasets.SpectrumDatasetOnOff`\n- `~gammapy.datasets.SpectrumDataset`\n- `~gammapy.irf.load_irf_dict_from_file`\n- `~gammapy.modeling.models.PowerLawSpectralModel`\n" ] }, { diff --git a/docs/dev/notebooks/dev/tutorials/analysis-2d/modeling_2D.ipynb b/docs/dev/notebooks/dev/tutorials/analysis-2d/modeling_2D.ipynb index 05af45c5b88..743fe2ab918 100644 --- a/docs/dev/notebooks/dev/tutorials/analysis-2d/modeling_2D.ipynb +++ b/docs/dev/notebooks/dev/tutorials/analysis-2d/modeling_2D.ipynb @@ -58,7 +58,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Creating the config file\n\nNow, we create a config file for out analysis. You may load this from\ndisc if you have a pre-defined config file.\n\nHere, we use 3 simulated CTA runs of the galactic center.\n\n\n" + "## Creating the config file\n\nNow, we create a config file for out analysis. You may load this from\ndisc if you have a pre-defined config file.\n\nHere, we use 3 simulated CTAO runs of the galactic center.\n\n\n" ] }, { diff --git a/docs/dev/notebooks/dev/tutorials/analysis-3d/cta_data_analysis.ipynb b/docs/dev/notebooks/dev/tutorials/analysis-3d/cta_data_analysis.ipynb index 8042d807932..d09a50d0ab8 100644 --- a/docs/dev/notebooks/dev/tutorials/analysis-3d/cta_data_analysis.ipynb +++ b/docs/dev/notebooks/dev/tutorials/analysis-3d/cta_data_analysis.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# Basic image exploration and fitting\n\nDetect sources, produce a sky image and a spectrum using CTA-1DC data.\n\n## Introduction\n\n**This notebook shows an example how to make a sky image and spectrum\nfor simulated CTA data with Gammapy.**\n\nThe dataset we will use is three observation runs on the Galactic\nCenter. This is a tiny (and thus quick to process and play with and\nlearn) subset of the simulated CTA dataset that was produced for the\nfirst data challenge in August 2017.\n" + "\n# Basic image exploration and fitting\n\nDetect sources, produce a sky image and a spectrum using CTA-1DC data.\n\n## Introduction\n\n**This notebook shows an example how to make a sky image and spectrum\nfor simulated CTAO data with Gammapy.**\n\nThe dataset we will use is three observation runs on the Galactic\nCenter. This is a tiny (and thus quick to process and play with and\nlearn) subset of the simulated CTAO dataset that was produced for the\nfirst data challenge in August 2017.\n" ] }, { @@ -371,7 +371,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## What next?\n\n- This notebook showed an example of a first CTA analysis with Gammapy,\n using simulated 1DC data.\n- Let us know if you have any questions or issues!\n\n\n" + "## What next?\n\n- This notebook showed an example of a first CTAO analysis with Gammapy,\n using simulated 1DC data.\n- Let us know if you have any questions or issues!\n\n\n" ] } ], diff --git a/docs/dev/notebooks/dev/tutorials/analysis-3d/event_sampling_nrg_depend_models.ipynb b/docs/dev/notebooks/dev/tutorials/analysis-3d/event_sampling_nrg_depend_models.ipynb index c0c7066aeac..3baa260ad36 100644 --- a/docs/dev/notebooks/dev/tutorials/analysis-3d/event_sampling_nrg_depend_models.ipynb +++ b/docs/dev/notebooks/dev/tutorials/analysis-3d/event_sampling_nrg_depend_models.ipynb @@ -191,7 +191,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Define an observation and make a dataset\n\nIn the following, we define an observation of 1 hr with CTA in the\nalpha-configuration for the south array, and we also create a dataset\nto be passed to the event sampler. The full `SkyModel` created above\nis passed to the dataset.\n\n\n" + "### Define an observation and make a dataset\n\nIn the following, we define an observation of 1 hr with CTAO in the\nalpha-configuration for the south array, and we also create a dataset\nto be passed to the event sampler. The full `SkyModel` created above\nis passed to the dataset.\n\n\n" ] }, { diff --git a/docs/dev/notebooks/dev/tutorials/analysis-time/pulsar_analysis.ipynb b/docs/dev/notebooks/dev/tutorials/analysis-time/pulsar_analysis.ipynb index d1f7e9c188f..5301b900b99 100644 --- a/docs/dev/notebooks/dev/tutorials/analysis-time/pulsar_analysis.ipynb +++ b/docs/dev/notebooks/dev/tutorials/analysis-time/pulsar_analysis.ipynb @@ -263,7 +263,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that here we are lacking statistic because we only use one run of CTA.\n\n## Phase-resolved spectrum\n\nWe can also make a phase-resolved spectrum.\nIn order to do that, we are going to use the `~gammapy.makers.PhaseBackgroundMaker` to create a\n`~gammapy.datasets.SpectrumDatasetOnOff` with the ON and OFF taken in the phase space.\nNote that this maker take the ON and OFF in the same spatial region.\n\nHere to create the `~gammapy.datasets.SpectrumDatasetOnOff`, we are going to redo the whole data reduction.\nHowever, note that one can use the `~gammapy.datasets.MapDatasetOnOff.to_spectrum_dataset()` method\n(with the `containment_correction` parameter set to True) if such a `~gammapy.datasets.MapDatasetOnOff`\nhas been created as shown above.\n\n" + "Note that here we are lacking statistic because we only use one run of CTAO.\n\n## Phase-resolved spectrum\n\nWe can also make a phase-resolved spectrum.\nIn order to do that, we are going to use the `~gammapy.makers.PhaseBackgroundMaker` to create a\n`~gammapy.datasets.SpectrumDatasetOnOff` with the ON and OFF taken in the phase space.\nNote that this maker take the ON and OFF in the same spatial region.\n\nHere to create the `~gammapy.datasets.SpectrumDatasetOnOff`, we are going to redo the whole data reduction.\nHowever, note that one can use the `~gammapy.datasets.MapDatasetOnOff.to_spectrum_dataset()` method\n(with the `containment_correction` parameter set to True) if such a `~gammapy.datasets.MapDatasetOnOff`\nhas been created as shown above.\n\n" ] }, { diff --git a/docs/dev/notebooks/dev/tutorials/data/cta.ipynb b/docs/dev/notebooks/dev/tutorials/data/cta.ipynb index a9226c29ff8..5a852e36c47 100644 --- a/docs/dev/notebooks/dev/tutorials/data/cta.ipynb +++ b/docs/dev/notebooks/dev/tutorials/data/cta.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# CTA with Gammapy\n\nAccess and inspect CTA data and instrument response functions (IRFs) using Gammapy.\n\n## Introduction\n\nThe [Cherenkov Telescope Array\n(CTA)](https://www.cta-observatory.org/)_ is the next generation\nground-based observatory for gamma-ray astronomy. Gammapy is the core\nlibrary for the Cherenkov Telescope Array (CTA) science tools\n([2017ICRC\u202635..766D](https://ui.adsabs.harvard.edu/abs/2017ICRC...35..766D)_\nand [CTAO Press\nRelease](https://www.cta-observatory.org/ctao-adopts-the-gammapy-software-package-for-science-analysis/)_).\n\nCTA will start taking data in the coming years. For now, to learn how to\nanalyse CTA data and to use Gammapy, if you are a member of the CTA\nconsortium, you can use the simulated dataset from the CTA first data\nchallenge which ran in 2017 and 2018.\n\n- https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki (CTA\n internal)\n\nGammapy fully supports the FITS data formats (events, IRFs) used in CTA\n1DC. The XML sky model format is not supported, but are also not needed\nto analyse the data, you have to specify your model via the Gammapy YAML\nmodel format, or Python code, as shown below.\n\nYou can use Gammapy to simulate CTA data and evaluate CTA performance\nusing the CTA response files available here:\n\n- https://www.cta-observatory.org/science/cta-performance/\n\nThe current FITS format `CTA-Performance-prod3b-v2-FITS.tar` is fully\nsupported by Gammapy, as shown below.\n\n## Tutorial overview\n\nThis notebook shows how to access CTA data and instrument response\nfunctions (IRFs) using Gammapy, and gives some examples how to quick\nlook the content of CTA files, especially to see the shape of CTA IRFs.\n\nAt the end of the notebooks, we give several links to other tutorial\nnotebooks that show how to simulate CTA data and how to evaluate CTA\nobservability and sensitivity, or how to analyse CTA data.\n\nNote that the FITS data and IRF format currently used by CTA is the one\ndocumented at https://gamma-astro-data-formats.readthedocs.io/, and is\nalso used by H.E.S.S. and other imaging atmospheric Cherenkov telescopes\n(IACTs). So if you see other Gammapy tutorials using e.g. H.E.S.S.\nexample data, know that they also apply to CTA, all you have to do is to\nchange the loaded data or IRFs to CTA.\n\n## Setup\n" + "\n# CTAO with Gammapy\n\nAccess and inspect CTAO data and instrument response functions (IRFs) using Gammapy.\n\n## Introduction\n\nThe [Cherenkov Telescope Array Observatory (CTAO)](https://www.ctao.org/)_ is the next generation\nground-based observatory for gamma-ray astronomy. Gammapy is the core\nlibrary for the Cherenkov Telescope Array Observatory (CTAO) science tools\n([2017ICRC\u202635..766D](https://ui.adsabs.harvard.edu/abs/2017ICRC...35..766D)_\nand [CTAO Press\nRelease](https://www.ctao.org/news/ctao-adopts-the-gammapy-software-package-for-science-analysis/)_).\n\nCTAO will start taking data in the coming years. For now, to learn how to\nanalyse CTAO data and to use Gammapy, if you are a member of the CTAO\nconsortium, you can use the simulated dataset from:\n\n- the CTA first data challenge which ran in 2017 and 2018 (https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki),\n- the CTAO Science Data Challenge of 2024 (https://ctaoobservatory.sharepoint.com/:f:/r/sites/share-open-data/Shared%20Documents/Reference%20datasets/Internal%20Science%20Data%20Challenge?csf=1&web=1&e=gNuFzI)\n\nGammapy fully supports the FITS data formats (events, IRFs) used in CTA\n1DC and SDC. The XML sky model format is not supported, but are also not needed\nto analyse the data, you have to specify your model via the Gammapy YAML\nmodel format, or Python code, as shown below.\n\nYou can also use Gammapy to simulate CTAO data and evaluate CTAO performance\nusing the CTAO response files. Two sets of responses are available for different\narray layouts:\n\n- the Omega configuration (prod3b, 2016): https://zenodo.org/records/5163273,\n- the Alpha configuration (prod5, 2021): https://zenodo.org/records/5499840.\n\nThey are all fully supported by Gammapy.\n\n\n## Tutorial overview\n\nThis notebook shows how to access CTAO data and instrument response\nfunctions (IRFs) using Gammapy, and gives some examples how to quick\nlook the content of CTAO files, especially to see the shape of CTAO IRFs.\n\nAt the end of the notebooks, we give several links to other tutorial\nnotebooks that show how to simulate CTAO data and how to evaluate CTAO\nobservability and sensitivity, or how to analyse CTAO data.\n\nNote that the FITS data and IRF format currently used by CTAO is the one\ndocumented at https://gamma-astro-data-formats.readthedocs.io/, and is\nalso used by H.E.S.S. and other Imaging Atmospheric Cherenkov Telescopes\n(IACTs). So if you see other Gammapy tutorials using e.g. H.E.S.S.\nexample data, know that they also apply to CTAO, all you have to do is to\nchange the loaded data or IRFs to CTAO.\n\n## Setup\n" ] }, { @@ -40,7 +40,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## CTA 1DC\n\nThe CTA first data challenge (1DC) ran in 2017 and 2018. It is described\nin detail\n[here](https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki)_\nand a description of the data and how to download it is\n[here](https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki#Data-access)_.\n\nYou should download `caldb.tar.gz` (1.2 MB), `models.tar.gz` (0.9\nGB), `index.tar.gz` (0.5 MB), as well as optionally the simulated\nsurvey data you are interested in: Galactic plane survey `gps.tar.gz`\n(8.3 GB), Galactic center `gc.tar.gz` (4.4 MB), Extragalactic survey\n`egal.tar.gz` (2.5 GB), AGN monitoring `agn.wobble.tar.gz` (4.7 GB).\nAfter download, follow the instructions how to `untar` the files, and\nset a `CTADATA` environment variable to point to the data.\n\nFor convenience, since the 1DC data files are large, and not publicly\navailable to anyone, we have taken a tiny subset of the CTA 1DC data,\nfour observations with the southern array from the GPS survey, pointing\nnear the Galactic center, and included them at `$GAMMAPY_DATA/cta-1dc`\nwhich you get via `gammapy download datasets`.\n\n### Files\n\nNext we will show a quick overview of the files and how to load them,\nand some quick look plots showing the shape of the CTA IRFs. How to do\nCTA simulations and analyses is shown in other tutorials, see links at\nthe end of this notebook.\n\n\n" + "## CTA 1DC\n\nThe CTA first data challenge (1DC) ran in 2017 and 2018. It is described\nin detail\n[here](https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki)_\nand a description of the data and how to download it is\n[here](https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki#Data-access)_.\n\nYou should download `caldb.tar.gz` (1.2 MB), `models.tar.gz` (0.9\nGB), `index.tar.gz` (0.5 MB), as well as optionally the simulated\nsurvey data you are interested in: Galactic plane survey `gps.tar.gz`\n(8.3 GB), Galactic center `gc.tar.gz` (4.4 MB), Extragalactic survey\n`egal.tar.gz` (2.5 GB), AGN monitoring `agn.wobble.tar.gz` (4.7 GB).\nAfter download, follow the instructions how to `untar` the files, and\nset a `CTADATA` environment variable to point to the data.\n\n**For convenience**, since the 1DC data files are large and not publicly\navailable to anyone, we have taken a tiny subset of the CTA 1DC data,\nfour observations with the southern array from the GPS survey, pointing\nnear the Galactic center, and **included them at `$GAMMAPY_DATA/cta-1dc`**\nwhich you get via `gammapy download datasets`.\n\n### Files\n\nNext we will show a quick overview of the files and how to load them,\nand some quick look plots showing the shape of the CTAO IRFs. How to do\nCTAO simulations and analyses is shown in other tutorials, see links at\nthe end of this notebook.\n\n\n" ] }, { @@ -112,7 +112,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Events\n\nWe can load events data via the data store and observation, or\nequivalently via the `~gammapy.data.EventList` class by specifying the\nEVENTS filename.\n\nThe quick-look `events.peek()` plot below shows that CTA has a field\nof view of a few degrees, and two energy thresholds, one significantly\nbelow 100 GeV where the CTA large-size telescopes (LSTs) detect events,\nand a second one near 100 GeV where the mid-sized telescopes (MSTs)\nstart to detect events.\n\nNote that most events are \u201chadronic background\u201d due to cosmic ray\nshowers in the atmosphere that pass the gamma-hadron selection cuts for\nthis analysis configuration. Since this is simulated data, column\n`MC_ID` is available that gives an emission component identifier code,\nand the EVENTS header in `events.table.meta` can be used to look up\nwhich `MC_ID` corresponds to which emission component.\n\nEvents can be accessed from the observation object like:\n\n" + "## Events\n\nWe can load events data via the data store and observation, or\nequivalently via the `~gammapy.data.EventList` class by specifying the\nEVENTS filename.\n\nThe quick-look `events.peek()` plot below shows that CTAO has a field\nof view of a few degrees, and two energy thresholds, one significantly\nbelow 100 GeV where the CTAO large-size telescopes (LSTs) detect events,\nand a second one near 100 GeV where the mid-sized telescopes (MSTs)\nstart to detect events.\n\nNote that most events are \u201chadronic background\u201d due to cosmic ray\nshowers in the atmosphere that pass the gamma-hadron selection cuts for\nthis analysis configuration. Since this is simulated data, column\n`MC_ID` is available that gives an emission component identifier code,\nand the EVENTS header in `events.table.meta` can be used to look up\nwhich `MC_ID` corresponds to which emission component.\n\nEvents can be accessed from the observation object like:\n\n" ] }, { @@ -184,7 +184,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## IRFs\n\nThe CTA instrument response functions (IRFs) are given as FITS files in\nthe `caldb` folder, the following IRFs are available:\n\n- effective area\n- energy dispersion\n- point spread function\n- background\n\nNotes:\n\n- The IRFs contain the energy and offset dependence of the CTA response\n- CTA 1DC was based on an early version of the CTA FITS responses\n produced in 2017, improvements have been made since.\n- The point spread function was approximated by a Gaussian shape\n- The background is from hadronic and electron air shower events that\n pass CTA selection cuts. It was given as a function of field of view\n coordinates, although it is radially symmetric.\n- The energy dispersion in CTA 1DC is noisy at low energy, leading to\n unreliable spectral points for some analyses.\n- The CTA 1DC response files have the first node at field of view\n offset 0.5 deg, so to get the on-axis response at offset 0 deg,\n Gammapy has to extrapolate. Furthermore, because diffuse gamma-rays\n in the FOV were used to derive the IRFs, and the solid angle at small\n FOV offset circles is small, the IRFs at the center of the FOV are\n somewhat noisy. This leads to unstable analysis and simulation issues\n when using the DC1 IRFs for some analyses.\n\n\n" + "## IRFs\n\nThe CTAO instrument response functions (IRFs) are given as FITS files in\nthe `caldb` folder, the following IRFs are available:\n\n- effective area\n- energy dispersion\n- point spread function\n- background\n\nNotes:\n\n- The IRFs contain the energy and offset dependence of the CTAO response\n- CTA 1DC was based on an early version of the CTAO FITS responses\n produced in 2017, improvements have been made since.\n- The point spread function was approximated by a Gaussian shape\n- The background is from hadronic and electron air shower events that\n pass CTAO selection cuts. It was given as a function of field of view\n coordinates, although it is radially symmetric.\n- The energy dispersion in CTA 1DC is noisy at low energy, leading to\n unreliable spectral points for some analyses.\n- The CTA 1DC response files have the first node at field of view\n offset 0.5 deg, so to get the on-axis response at offset 0 deg,\n Gammapy has to extrapolate. Furthermore, because diffuse gamma-rays\n in the FOV were used to derive the IRFs, and the solid angle at small\n FOV offset circles is small, the IRFs at the center of the FOV are\n somewhat noisy. This leads to unstable analysis and simulation issues\n when using the DC1 IRFs for some analyses.\n\n\n" ] }, { @@ -328,7 +328,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## CTA performance files\n\nCTA 1DC is useful to learn how to analyse CTA data. But to do\nsimulations and studies for CTA now, you should get the most recent CTA\nIRFs in FITS format from\nhttps://www.cta-observatory.org/science/cta-performance/\n\nIf you want to run the download and examples in the next code cells,\nremove the # to uncomment.\n\n\n" + "## Latest CTAO performance files\n\nCTA 1DC is useful to learn how to analyse CTAO data. But to do\nsimulations and studies for CTAO now, you should get the most recent CTAO\nIRFs in FITS format from https://www.ctao.org/for-scientists/performance/.\n\nIf you want to use other response files, the following code cells (remove the # to uncomment)\nexplain how to proceed. This example is made with the Alpha configuration (Prod5).\n\n\n" ] }, { @@ -339,14 +339,14 @@ }, "outputs": [], "source": [ - "# !curl -O https://www.cta-observatory.org/wp-content/uploads/2019/04/CTA-Performance-prod3b-v2-FITS.tar.gz\n\n# !tar xf CTA-Performance-prod3b-v2-FITS.tar.gz\n\n# !ls caldb/data/cta/prod3b-v2/bcf\n\n# irfs1 = load_irf_dict_from_file(\"caldb/data/cta/prod3b-v2/bcf/South_z20_50h/irf_file.fits\")\n# irfs1[\"aeff\"].plot_energy_dependence()\n\n# irfs2 = load_irf_dict_from_file(\"caldb/data/cta/prod3b-v2/bcf/South_z40_50h/irf_file.fits\")\n# irfs2[\"aeff\"].plot_energy_dependence()" + "# !curl -o cta-prod5-zenodo-fitsonly-v0.1.zip https://zenodo.org/records/5499840/files/cta-prod5-zenodo-fitsonly-v0.1.zip\n# !unzip cta-prod5-zenodo-fitsonly-v0.1.zip\n# !ls fits/\n\n# !tar xf fits/CTA-Performance-prod5-v0.1-South-40deg.FITS.tar.gz -C fits/.\n# !ls fits/*.fits.gz\n\n# irfs1 = load_irf_dict_from_file(\"fits/Prod5-South-40deg-SouthAz-14MSTs37SSTs.180000s-v0.1.fits.gz\")\n# irfs1[\"aeff\"].plot_energy_dependence()\n\n# irfs2 = load_irf_dict_from_file(\"fits/Prod5-South-40deg-SouthAz-14MSTs37SSTs.1800s-v0.1.fits.gz\")\n# irfs2[\"aeff\"].plot_energy_dependence()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercises\n\n- Load the EVENTS file for `obs_id=111159` as a\n `~gammapy.data.EventList` object.\n- Use `~gammapy.data.EventList.table` to find the energy, sky coordinate and time of\n the highest-energy event.\n- Use `~gammapy.data.EventList.pointing_radec` to find the pointing position of this\n observation, and use `astropy.coordinates.SkyCoord` methods to find\n the field of view offset of the highest-energy event.\n- What is the effective area and PSF 68% containment radius of CTA at 1\n TeV for the `South_z20_50h` configuration used for the CTA 1DC\n simulation?\n- Get the latest CTA FITS performance files from\n https://www.cta-observatory.org/science/cta-performance/ and run the\n code example above. Make an effective area ratio plot of 40 deg\n zenith versus 20 deg zenith for the `South_z40_50h` and\n `South_z20_50h` configurations.\n\n\n" + "## Exercises\n\n- Load the EVENTS file for `obs_id=111159` as a\n `~gammapy.data.EventList` object.\n- Use `~gammapy.data.EventList.table` to find the energy, sky coordinate and time of\n the highest-energy event.\n- Use `~gammapy.data.EventList.pointing_radec` to find the pointing position of this\n observation, and use `astropy.coordinates.SkyCoord` methods to find\n the field of view offset of the highest-energy event.\n- What is the effective area and PSF 68% containment radius of CTAO at 1\n TeV for the `South_z20_50h` configuration used for the CTA 1DC\n simulation?\n- Get the latest CTAO FITS performance files from\n https://www.ctao.org/for-scientists/performance/ and run the\n code example above. Make an effective area ratio plot of 40 deg\n zenith versus 20 deg zenith for the `South_z40_50h` and\n `South_z20_50h` configurations.\n\n\n" ] }, { @@ -364,7 +364,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Next steps\n\n- Learn how to analyse data with\n :doc:`/tutorials/starting/analysis_1` and\n :doc:`/tutorials/starting/analysis_2` or any other\n Gammapy analysis tutorial.\n- Learn how to evaluate CTA observability and sensitivity with\n :doc:`/tutorials/analysis-3d/simulate_3d`,\n :doc:`/tutorials/analysis-1d/spectrum_simulation`\n or :doc:`/tutorials/analysis-1d/cta_sensitivity`.\n\n\n" + "## Next steps\n\n- Learn how to analyse data with\n :doc:`/tutorials/starting/analysis_1` and\n :doc:`/tutorials/starting/analysis_2` or any other\n Gammapy analysis tutorial.\n- Learn how to evaluate CTAO observability and sensitivity with\n :doc:`/tutorials/analysis-3d/simulate_3d`,\n :doc:`/tutorials/analysis-1d/spectrum_simulation`\n or :doc:`/tutorials/analysis-1d/cta_sensitivity`.\n\n\n" ] } ], diff --git a/docs/dev/notebooks/dev/tutorials/data/hawc.ipynb b/docs/dev/notebooks/dev/tutorials/data/hawc.ipynb index 8e6c523547c..fcbb4b7d097 100644 --- a/docs/dev/notebooks/dev/tutorials/data/hawc.ipynb +++ b/docs/dev/notebooks/dev/tutorials/data/hawc.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# HAWC with Gammapy\nExplore HAWC event lists and IRFs and perform the data reduction steps.\n\n[HAWC](https://www.hawc-observatory.org/)_ is an array of\nwater Cherenkov detectors located in Mexico that detects gamma-rays\nin the range between hundreds of GeV and hundreds of TeV.\nGammapy recently added support of HAWC high level data analysis,\nafter export to the current [open data level 3\nformat](https://gamma-astro-data-formats.readthedocs.io/)_.\n\nThe HAWC data is largely private. However, in 2022, a small\nsub-set of HAWC Pass4 event lists from the Crab nebula region\nwas publicly [released](https://data.hawc-observatory.org/datasets/crab_events_pass4/index.php)_.\nThis dataset is 1 GB in size, so only a subset will be used here as an example.\n\nThis notebook is a quick introduction to HAWC data analysis with Gammapy.\nIt briefly describes the HAWC data and how to access it, and then uses a\nsubset of it to produce a MapDataset, to show how the data reduction is performed.\n\nThe HAWC data release contains events where energy is estimated using\ntwo different algorithms, referred to as \"NN\" and \"GP\" (see this [paper](https://iopscience.iop.org/article/10.3847/1538-4357/ab2f7d)_\nfor a detailed description). These two event classes are not independent, meaning that\nevents are repeated between the NN and GP datasets. So these data should never\nbe analyzed jointly, and one of the two estimators needs to be chosen before\nproceeding.\n\nOnce the data has been reduced to a MapDataset, there are no differences\nin the way that HAWC data is handled with respect to data from any other\nobservatory, such as H.E.S.S. or CTA.\n\n\n## HAWC data access and reduction\n\nThis is how to access data and IRFs from the HAWC Crab event data release.\n" + "\n# HAWC with Gammapy\nExplore HAWC event lists and IRFs and perform the data reduction steps.\n\n[HAWC](https://www.hawc-observatory.org/)_ is an array of\nwater Cherenkov detectors located in Mexico that detects gamma-rays\nin the range between hundreds of GeV and hundreds of TeV.\nGammapy recently added support of HAWC high level data analysis,\nafter export to the current [open data level 3\nformat](https://gamma-astro-data-formats.readthedocs.io/)_.\n\nThe HAWC data is largely private. However, in 2022, a small\nsub-set of HAWC Pass4 event lists from the Crab nebula region\nwas publicly [released](https://data.hawc-observatory.org/datasets/crab_events_pass4/index.php)_.\nThis dataset is 1 GB in size, so only a subset will be used here as an example.\n\nThis notebook is a quick introduction to HAWC data analysis with Gammapy.\nIt briefly describes the HAWC data and how to access it, and then uses a\nsubset of it to produce a MapDataset, to show how the data reduction is performed.\n\nThe HAWC data release contains events where energy is estimated using\ntwo different algorithms, referred to as \"NN\" and \"GP\" (see this [paper](https://iopscience.iop.org/article/10.3847/1538-4357/ab2f7d)_\nfor a detailed description). These two event classes are not independent, meaning that\nevents are repeated between the NN and GP datasets. So these data should never\nbe analyzed jointly, and one of the two estimators needs to be chosen before\nproceeding.\n\nOnce the data has been reduced to a MapDataset, there are no differences\nin the way that HAWC data is handled with respect to data from any other\nobservatory, such as H.E.S.S. or CTAO.\n\n\n## HAWC data access and reduction\n\nThis is how to access data and IRFs from the HAWC Crab event data release.\n" ] }, { diff --git a/docs/dev/notebooks/dev/tutorials/data/hess.ipynb b/docs/dev/notebooks/dev/tutorials/data/hess.ipynb index 824bf89972d..8344b482a1f 100644 --- a/docs/dev/notebooks/dev/tutorials/data/hess.ipynb +++ b/docs/dev/notebooks/dev/tutorials/data/hess.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# H.E.S.S. with Gammapy\nExplore H.E.S.S. event lists and IRFs.\n\n[H.E.S.S.](https://www.mpi-hd.mpg.de/hfm/HESS/)_ is an array of\ngamma-ray telescopes located in Namibia. Gammapy is regularly used and\nfully supports H.E.S.S. high level data analysis, after export to the\ncurrent [open data level 3\nformat](https://gamma-astro-data-formats.readthedocs.io/)_.\n\nThe H.E.S.S. data is private, and H.E.S.S. analysis is mostly documented\nand discussed at https://hess-confluence.desy.de/ and in\nH.E.S.S.-internal communication channels. However, in 2018, a small\nsub-set of archival H.E.S.S. data was publicly released, called the\n[H.E.S.S. DL3\nDR1](https://www.mpi-hd.mpg.de/hfm/HESS/pages/dl3-dr1/)_, the data\nlevel 3, data release number 1. This dataset is 50 MB in size and is\nused in many Gammapy analysis tutorials, and can be downloaded via\n[gammapy\ndownload](https://docs.gammapy.org/dev/getting-started/index.html#quickstart-setup)_.\n\nThis notebook is a quick introduction to this specific DR1 release. It\nbriefly describes H.E.S.S. data and instrument responses and show a\nsimple exploration of the data with the creation of theta-squared plot.\n\nH.E.S.S. members can find details on the DL3 FITS production on this\n[Confluence\npage](https://hess-confluence.desy.de/confluence/display/HESS/HESS+FITS+data)_\nand access more detailed tutorials in this\n[repository](https://bitbucket.org/hess_software/hess-open-source-tools/src/master/)_\n\n## DL3 DR1\n\nThis is how to access data and IRFs from the H.E.S.S. data level 3, data\nrelease 1.\n" + "\n# H.E.S.S. with Gammapy\nExplore H.E.S.S. event lists and IRFs.\n\n[H.E.S.S.](https://www.mpi-hd.mpg.de/hfm/HESS/)_ is an array of\ngamma-ray telescopes located in Namibia. Gammapy is regularly used and\nfully supports H.E.S.S. high level data analysis, after export to the\ncurrent [open data level 3\nformat](https://gamma-astro-data-formats.readthedocs.io/)_.\n\nThe H.E.S.S. data is private, and H.E.S.S. analysis is mostly documented\nand discussed in the internal Wiki pages and in\nH.E.S.S.-internal communication channels. However, in 2018, a small\nsub-set of archival H.E.S.S. data was publicly released, called the\n[H.E.S.S. DL3\nDR1](https://www.mpi-hd.mpg.de/hfm/HESS/pages/dl3-dr1/)_, the data\nlevel 3, data release number 1. This dataset is 50 MB in size and is\nused in many Gammapy analysis tutorials, and can be downloaded via\n[gammapy\ndownload](https://docs.gammapy.org/dev/getting-started/index.html#quickstart-setup)_.\n\nThis notebook is a quick introduction to this specific DR1 release. It\nbriefly describes H.E.S.S. data and instrument responses and show a\nsimple exploration of the data with the creation of theta-squared plot.\n\nH.E.S.S. members can find details on the DL3 FITS production on this\n[Confluence\npage](https://cchesswiki.in2p3.fr/en/hess/working_groups/analysis_and_reconstruction_working_group/ar_active_tasks/hess_fits_data)_\nand access more detailed tutorials in the BitBucket `hess-open-tools` repository.\n\n## DL3 DR1\n\nThis is how to access data and IRFs from the H.E.S.S. data level 3, data\nrelease 1.\n" ] }, { @@ -216,7 +216,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Next steps\n\nNow you know how to access and work with H.E.S.S. data. All other\ntutorials and documentation apply to H.E.S.S. and CTA or any other IACT\nthat provides DL3 data and IRFs in the standard format.\n\n\n" + "## Next steps\n\nNow you know how to access and work with H.E.S.S. data. All other\ntutorials and documentation apply to H.E.S.S. and CTAO or any other IACT\nthat provides DL3 data and IRFs in the standard format.\n\n\n" ] } ], diff --git a/docs/dev/objects.inv b/docs/dev/objects.inv index 48fbaea655e..fd10ac21eb7 100644 Binary files a/docs/dev/objects.inv and b/docs/dev/objects.inv differ diff --git a/docs/dev/plot_directive/api/gammapy-irf-EnergyDependentMultiGaussPSF-1.pdf b/docs/dev/plot_directive/api/gammapy-irf-EnergyDependentMultiGaussPSF-1.pdf index f620a71a2ef..8027d45c979 100644 Binary files a/docs/dev/plot_directive/api/gammapy-irf-EnergyDependentMultiGaussPSF-1.pdf and b/docs/dev/plot_directive/api/gammapy-irf-EnergyDependentMultiGaussPSF-1.pdf differ diff --git a/docs/dev/plot_directive/user-guide/astro/population/plot_radial_distributions.pdf b/docs/dev/plot_directive/user-guide/astro/population/plot_radial_distributions.pdf index 249febb66de..dabba04d3e6 100644 Binary files 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53, 54, 134, 288, 385, 439, 466, 570], "charg": [400, 547], "chatti": [374, 458], "cheat": 409, "check": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 82, 83, 86, 87, 88, 91, 102, 105, 106, 109, 110, 122, 124, 146, 147, 148, 154, 159, 162, 187, 188, 189, 190, 191, 192, 193, 194, 197, 198, 199, 200, 201, 202, 211, 213, 220, 242, 258, 297, 348, 350, 377, 390, 396, 400, 403, 404, 406, 407, 408, 411, 420, 425, 433, 446, 464, 465, 477, 491, 499, 511, 515, 524, 568, 571, 572], "check_limit": [210, 211, 212, 213], "check_random_st": 374, "check_tutorials_setup": [443, 445, 448, 449, 450, 451, 452, 453, 454, 456, 457, 458, 459, 460, 461, 462, 467, 468, 469, 470, 472, 473, 474, 476, 478, 479, 480, 481, 482, 484, 486, 487, 488, 489, 495, 496, 497], "checker": [431, 434], "checklist": 404, "checkout": [374, 377, 416, 456, 460, 482], "checksum": [83, 86, 88, 100, 101, 102, 105, 106, 107, 109, 110, 122, 124, 146, 147, 148, 154, 159, 162, 189, 190, 192, 196, 198, 201, 202, 208, 220, 242, 337, 340, 343, 440, 457, 487], "chen": 434, "cherenkov": [149, 387, 406, 411, 445, 448, 454, 467, 478, 486, 488, 520, 521, 570], "cherri": [374, 381, 388, 399], "chi": [287, 389, 572, 573, 574], "chi2": [101, 140, 386, 416, 448, 459, 469, 476, 507, 573], "chi2assym": 419, "child": 384, "children": [456, 464, 479, 495, 496], "chime": 377, "chisq": 287, "chisquar": 287, "chmod": 571, "choic": [133, 387, 391, 393, 400, 448, 449, 451, 453, 476, 488, 495, 512, 568, 571], "choos": [45, 77, 187, 188, 191, 193, 194, 197, 200, 271, 315, 374, 386, 388, 390, 394, 406, 408, 425, 446, 448, 452, 456, 458, 469, 474, 476, 478, 479, 481, 482, 483, 487, 496, 511, 571], "chose": [460, 487, 488, 573, 575], "chosen": [26, 146, 159, 162, 188, 189, 190, 198, 201, 202, 220, 229, 242, 374, 377, 389, 390, 393, 449, 468, 478, 479, 483, 484, 488, 495, 509, 519, 520, 524], "christoph": [379, 380, 381, 382, 383, 384, 385, 386, 388, 390, 391, 392, 393, 394, 413, 414, 415, 416, 417, 418, 419, 420, 426, 429, 430, 431, 432, 433, 434, 435], "chrome": [87, 91], "chromei": 427, "chunk": [148, 159, 162], "chunk_siz": [148, 159, 162], "ci": [374, 377, 381, 400, 404, 419, 425, 427, 430, 436], "circl": [83, 177, 371, 375, 388, 427, 448, 458, 469, 472, 478, 480, 483, 486, 496, 497, 508, 518, 520, 521, 524], "circle_area": 375, "circleannulusskyregion": [255, 256, 520], "circledast": 507, "circlepixelregion": 388, "circleskyregion": [102, 105, 109, 110, 171, 172, 176, 200, 221, 375, 388, 443, 445, 448, 450, 451, 454, 456, 458, 460, 461, 465, 468, 472, 474, 478, 480, 481, 496, 518, 520, 521, 524], "circular": [91, 102, 105, 176, 188, 190, 200, 385, 393, 395, 445, 450, 474, 483, 520, 524], "circumv": [19, 20, 26, 214, 215, 218, 222, 224, 225, 226, 230, 236, 237, 239, 243, 245, 247, 248, 249, 254, 259, 261, 262, 263, 264, 267, 271], "cirelli": [26, 499], "citat": 403, "cite": [377, 400], "cl": [16, 84, 90, 102, 104, 109, 123, 401], "clabel": 476, "clang": 410, "clarif": 379, "clarifi": [398, 439], "class": [6, 15, 16, 17, 18, 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497, 571], "clip": [19, 20, 26, 187, 188, 191, 193, 194, 197, 199, 200, 214, 215, 218, 222, 224, 225, 226, 230, 236, 237, 239, 243, 245, 247, 248, 249, 254, 259, 261, 262, 263, 264, 267, 270, 271, 313, 418, 419, 452], "clockwis": [221, 229, 232, 528], "clone": [377, 407, 434], "close": [143, 144, 145, 146, 149, 150, 151, 153, 156, 158, 160, 161, 188, 189, 190, 191, 192, 193, 194, 197, 198, 199, 200, 201, 202, 270, 349, 350, 351, 383, 388, 403, 428, 439, 440, 476, 556], "closer": 497, "closest": [117, 121, 125, 129, 132, 188, 189, 190, 192, 198, 201, 202, 475, 480, 509], "cloud": [404, 491], "cluster": [6, 96, 299, 300, 387, 439, 440, 442, 485, 491, 508, 511], "clutter": [456, 511], "cm": [19, 20, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 45, 51, 52, 53, 54, 67, 101, 124, 130, 132, 214, 218, 224, 226, 230, 237, 240, 247, 249, 258, 259, 262, 263, 264, 273, 359, 385, 397, 444, 445, 446, 448, 449, 450, 451, 452, 453, 456, 457, 458, 459, 460, 461, 464, 469, 470, 472, 473, 475, 476, 482, 484, 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371, 372, 374, 387, 389, 398, 436, 439, 440, 443, 460, 461, 470, 473, 479, 482, 486, 508, 511], "column_nam": 477, "column_prefix": 194, "columnclass": [87, 91], "columnspac": 371, "com": [134, 140, 297, 328, 329, 341, 377, 394, 403, 404, 407], "combin": [99, 101, 102, 105, 109, 110, 141, 175, 183, 189, 190, 192, 197, 198, 201, 202, 215, 258, 374, 380, 384, 385, 386, 387, 388, 389, 394, 397, 406, 418, 433, 439, 446, 451, 456, 457, 464, 469, 474, 476, 477, 478, 479, 480, 482, 488, 496, 497, 499, 501, 511, 518, 523, 539, 569, 570], "combined_ev": 508, "combining_and_defin": [443, 451, 453, 456, 458, 464, 469, 486], "come": [69, 377, 380, 381, 382, 383, 384, 388, 389, 390, 391, 393, 394, 399, 401, 403, 404, 421, 422, 425, 434, 435, 439, 448, 456, 464, 482, 486, 495, 571], "comma": [71, 72, 74, 75, 76, 78, 79, 80, 473], "command": [6, 13, 94, 101, 124, 150, 156, 158, 160, 373, 375, 377, 382, 383, 385, 390, 391, 404, 405, 406, 407, 408, 409, 415, 419, 430, 433, 434, 436, 460, 510, 569], 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194, 197, 198, 200, 201, 202, 331, 430, 501], "compat": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 87, 91, 189, 190, 192, 201, 202, 228, 379, 381, 385, 388, 397, 399, 401, 402, 410, 413, 415, 417, 434, 440, 479, 523], "compil": [377, 426], "complain": 377, "complaint": 392, "complement": 394, "complementari": [101, 448], "complet": [13, 16, 84, 90, 104, 123, 331, 374, 380, 384, 385, 386, 387, 388, 391, 392, 394, 398, 399, 400, 407, 412, 414, 415, 416, 417, 418, 419, 420, 421, 422, 425, 427, 430, 431, 432, 433, 434, 435, 456, 461, 476, 483, 491, 511, 516], "complex": [374, 375, 380, 384, 388, 389, 390, 396, 398, 410, 445, 448, 458, 461, 466, 469, 474, 479, 480, 482, 483, 487, 520], "complianc": [400, 436], "compliant": [189, 190, 192, 201, 202, 440, 570], "complic": [388, 524], "compon": [15, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 67, 80, 81, 88, 102, 103, 105, 108, 109, 110, 129, 134, 161, 163, 197, 220, 242, 243, 258, 273, 385, 386, 387, 390, 396, 397, 415, 419, 421, 425, 433, 434, 444, 449, 450, 451, 453, 456, 457, 458, 459, 460, 461, 464, 465, 467, 468, 469, 473, 474, 475, 481, 482, 486, 487, 495, 496, 499, 507, 509, 511, 524, 527, 528, 529, 530, 531, 532, 533, 534, 535, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 558, 559, 560, 561, 562, 563, 564, 565, 566, 568, 570], "component_1": 385, "component_2": 385, "component_class": 473, "components_model": 80, "components_statu": 67, "compos": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 380, 400], "composit": [57, 70, 80, 312, 400, 432], "compound": [197, 204, 215, 332, 336, 388, 397, 442, 444, 462, 525, 557], "compoundmodel": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 427], "compoundpixelregion": 336, "compoundskymodel": 385, "compoundskyregion": [204, 332, 336], "compoundsourcemodel": 385, "compoundspectralmodel": [253, 397, 439, 444, 482, 487, 537, 539, 543, 546, 548, 551, 556], "compress": 382, "compris": 488, "compromis": [374, 379, 387, 484], "compton": [243, 421, 547], "compulsori": 482, "comput": [16, 19, 20, 23, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 83, 84, 86, 88, 90, 94, 96, 100, 101, 102, 104, 105, 109, 110, 117, 119, 121, 122, 123, 124, 125, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 138, 140, 143, 144, 145, 146, 147, 148, 149, 150, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 164, 175, 176, 178, 181, 182, 183, 184, 186, 188, 189, 190, 192, 194, 197, 198, 199, 200, 201, 202, 203, 207, 214, 215, 218, 220, 222, 224, 225, 226, 230, 234, 236, 237, 239, 242, 243, 245, 247, 248, 249, 254, 259, 261, 262, 263, 264, 267, 269, 271, 273, 274, 280, 281, 282, 284, 287, 288, 289, 290, 292, 296, 297, 300, 304, 305, 308, 374, 383, 384, 385, 386, 387, 388, 389, 392, 394, 395, 396, 397, 398, 402, 404, 406, 407, 411, 413, 415, 416, 417, 418, 421, 422, 424, 425, 426, 428, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 445, 450, 452, 456, 457, 461, 462, 466, 467, 468, 470, 472, 474, 475, 478, 481, 482, 483, 484, 487, 488, 489, 495, 497, 499, 507, 509, 511, 516, 520, 547, 569, 570, 571, 572, 573, 574, 575], "computation": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 207, 387, 388, 507], "compute_chisq": 432, "compute_differential_jfactor": 23, "compute_errn": [282, 284, 436, 573], "compute_errp": [282, 284, 573], "compute_flux_doubl": 134, "compute_fpp": 135, "compute_fvar": [136, 289], "compute_jfactor": [23, 472], "compute_lightcurve_doublingtim": 466, "compute_lightcurve_fpp": 466, "compute_lightcurve_fvar": 466, "compute_lima_map": 421, "compute_npr": 387, "compute_upper_limit": [282, 284], "computedfieldinfo": [16, 84, 90, 104, 123], "con": [374, 390], "concaten": [83, 86, 92, 191, 194, 386, 390, 396, 439, 479], "concentr": 333, "concept": [16, 84, 90, 104, 123, 394, 395, 401, 411, 472, 479, 512], "conception": [387, 396], "concern": [377, 379, 388, 390, 391, 393, 395, 397, 400, 425, 435, 488, 573], "conclud": 383, "concret": [16, 84, 90, 104, 123, 391, 394], "conda": [373, 377, 381, 382, 391, 394, 399, 403, 404, 406, 407, 408, 409, 410, 415, 417, 433, 434, 571], "condit": [52, 53, 142, 194, 211, 213, 220, 242, 398, 402, 422, 443, 448, 451, 461, 473, 480, 481, 483, 488, 507, 516], "conduct": [436, 469], "cone": [91, 388, 449, 483, 495, 496, 511], "conesearch": 390, "conf": [87, 91, 374, 375, 404, 416, 431], "conf_1d": 467, "conf_3d": 467, "conf_max": [210, 212], "conf_min": [210, 212], "confid": [121, 207, 210, 212, 282, 284, 385, 392, 416, 421, 435, 570, 573], "confidence_opt": [207, 475], "config": [15, 16, 84, 90, 104, 123, 373, 375, 380, 382, 383, 390, 391, 394, 401, 406, 408, 418, 419, 420, 425, 433, 449, 456, 495, 568], "config_dict": 495, "config_joint": 456, "config_paramet": [117, 119, 120, 121, 125, 126, 128, 129, 130, 131, 132], "config_stack": 456, "config_str": 16, "configdict": [16, 84, 90, 104, 123], "configpars": 431, "configur": [11, 15, 16, 46, 84, 87, 90, 91, 101, 104, 109, 110, 117, 119, 120, 121, 123, 125, 126, 128, 129, 130, 131, 132, 321, 374, 375, 377, 383, 385, 389, 390, 394, 395, 401, 404, 410, 418, 420, 421, 425, 427, 436, 439, 440, 448, 450, 452, 458, 460, 461, 465, 486, 491, 510, 516, 570, 571], "confirm": 377, "conflict": [87, 91, 374, 377, 400], "confluenc": 489, "conform": [16, 84, 90, 104, 123, 479], "confus": [374, 375, 380, 393], "connect": [384, 448, 462, 464, 480, 496, 568], "consant": [189, 190, 192, 198, 201, 202], "consciou": 379, "consecut": [172, 331, 374, 399], "consensu": [379, 400], "consent": 374, "consequ": [400, 461, 524], "conserv": [507, 532], "consid": [19, 20, 59, 96, 169, 176, 179, 180, 182, 183, 190, 202, 210, 214, 217, 218, 221, 222, 224, 225, 226, 227, 229, 230, 231, 232, 233, 234, 235, 236, 237, 239, 243, 246, 247, 248, 249, 250, 254, 255, 256, 257, 259, 262, 263, 264, 269, 270, 280, 370, 379, 380, 385, 386, 388, 389, 390, 391, 392, 393, 394, 396, 400, 402, 445, 448, 450, 456, 460, 461, 466, 477, 480, 508, 516, 574], "consider": [390, 415, 425, 433, 456], "consist": [74, 91, 95, 122, 124, 147, 148, 159, 162, 189, 190, 192, 201, 202, 229, 232, 374, 384, 385, 387, 388, 389, 394, 401, 418, 419, 420, 421, 422, 429, 433, 434, 436, 438, 439, 440, 449, 450, 451, 452, 459, 464, 469, 476, 478, 479, 482, 484, 495, 545, 547, 572], "consol": [374, 571], "consortium": [401, 486], "const": [216, 217, 218, 219, 397, 461, 482, 540, 558], "const3d": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40], "const_norm": 397, "constant": [50, 51, 52, 53, 102, 109, 117, 149, 189, 190, 192, 198, 200, 201, 202, 216, 217, 218, 219, 221, 233, 235, 330, 387, 388, 393, 433, 442, 467, 487, 504, 507, 525, 536, 557, 567, 570, 574], "constant_model": 558, "constantmodel": 435, "constantnormspectralmodel": 397, "constantspatialmodel": [252, 397, 482, 487, 527], "constantspectralmodel": [253, 397, 461, 482, 540], "constanttemporalmodel": [265, 397, 418, 482, 558], "constitu": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40], "constitut": 474, "constrain": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 400, 444, 453, 457, 478, 484, 518], "constraint": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 511], "construct": [16, 84, 90, 104, 123, 194, 236, 237, 339, 524, 570], "constructor": [187, 188, 189, 190, 192, 194, 197, 198, 199, 200, 201, 202, 439, 496, 522], "consult": 409, "consum": [476, 511], "contact": [376, 381, 383, 400, 409, 411, 414, 415, 416, 430, 433], "contain": [13, 16, 19, 20, 26, 57, 59, 63, 67, 69, 82, 83, 84, 86, 87, 88, 90, 91, 92, 95, 96, 100, 101, 102, 104, 105, 108, 109, 110, 112, 122, 123, 124, 132, 133, 138, 140, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 169, 171, 174, 176, 181, 184, 186, 187, 188, 189, 190, 192, 193, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 207, 211, 214, 215, 216, 217, 218, 219, 220, 221, 222, 224, 225, 226, 227, 229, 230, 231, 232, 233, 234, 235, 236, 237, 239, 240, 242, 243, 244, 245, 246, 247, 248, 249, 250, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 266, 267, 269, 270, 271, 272, 288, 299, 300, 367, 369, 371, 372, 374, 377, 378, 380, 382, 383, 384, 387, 388, 389, 390, 391, 393, 394, 395, 396, 398, 400, 403, 404, 411, 415, 419, 420, 421, 422, 424, 425, 427, 428, 431, 432, 433, 434, 435, 439, 440, 443, 446, 448, 449, 450, 453, 456, 457, 460, 461, 464, 467, 468, 470, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 486, 487, 488, 489, 495, 496, 497, 499, 500, 502, 507, 508, 509, 511, 515, 519, 520, 523, 524, 525, 568, 570, 572, 573, 574], "containment_correct": [16, 102, 105, 176, 443, 448, 449, 450, 453, 454, 456, 458, 465, 467, 468, 470, 474, 478, 495], "containment_fract": 96, "containment_radiu": [150, 156, 158, 159, 160, 162, 443, 474, 477, 480], "containment_radius_map": [159, 162], "contains_pix": [187, 188, 197, 200], "contains_wcs_pix": 197, "contamin": [67, 454, 473, 480, 520], "content": [84, 90, 104, 123, 124, 189, 190, 192, 198, 201, 202, 337, 340, 374, 377, 379, 383, 389, 394, 401, 404, 419, 428, 433, 445, 479, 480, 486, 488, 508, 511, 522, 523], "content_typ": [16, 84, 90, 104, 123], "context": [16, 84, 87, 90, 91, 104, 123, 211, 213, 220, 242, 321, 352, 374, 389, 400, 404, 440, 479, 501], "contigu": [194, 199, 425], "continu": [194, 381, 383, 384, 387, 388, 390, 391, 393, 394, 399, 410, 413, 414, 417, 425, 430, 431, 432, 434, 448, 461, 465, 480, 523, 570], "continuous_upd": [456, 464, 479, 495, 496], "contour": [204, 207, 366, 383, 435], "contour_": 476, "contourf": 202, "contract": 400, "contrari": [375, 382], "contrast": [394, 570], "contrera": 435, "contribut": [100, 114, 116, 220, 228, 242, 258, 370, 374, 376, 383, 388, 396, 398, 404, 411, 414, 425, 427, 429, 478, 512, 547, 568, 571, 572], "contributes_to_stat": 100, "contributor": [374, 377, 394, 404, 412, 423, 424], "control": [83, 87, 91, 102, 105, 109, 110, 374, 387, 405, 407, 443, 472, 479, 484, 511, 573], "controversi": [374, 379, 380, 394], "conveni": [13, 122, 373, 374, 377, 384, 388, 396, 398, 405, 415, 417, 422, 425, 427, 432, 458, 465, 472, 473, 474, 479, 480, 481, 482, 486, 488, 497, 506, 510, 569], "convent": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 188, 189, 190, 192, 200, 201, 202, 396, 398, 417, 418, 421, 422, 436, 474, 478, 479, 482, 507, 509, 516, 523, 573], "converg": [402, 416, 437, 448, 450, 456, 465, 476, 507, 511, 518, 528], "convers": [84, 87, 90, 91, 104, 122, 123, 124, 379, 398, 401, 406, 408, 425, 438, 439, 440, 443, 524], "conversion_typ": [487, 497], "convert": [16, 19, 23, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 50, 83, 85, 86, 87, 91, 99, 101, 102, 105, 107, 109, 110, 122, 124, 127, 143, 144, 145, 146, 147, 148, 149, 150, 151, 153, 154, 156, 158, 159, 160, 161, 162, 172, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 206, 209, 210, 211, 212, 213, 220, 234, 242, 280, 298, 301, 302, 306, 319, 335, 355, 357, 358, 375, 380, 388, 389, 398, 401, 404, 426, 438, 439, 440, 443, 452, 454, 459, 465, 468, 469, 474, 475, 476, 480, 481, 486, 497, 509, 515, 520, 523, 573, 575], "convert_bytestring_to_unicod": [87, 91], "convert_unicode_to_bytestr": [87, 91], "convinc": 497, "convolut": [117, 121, 127, 132, 157, 190, 202, 373, 386, 392, 413, 420, 422, 431, 433, 434, 440, 454, 479, 507, 524], "convolution_method": 190, "convolv": [105, 111, 115, 132, 157, 190, 202, 385, 413, 422, 425, 434, 451, 452, 454, 479, 517, 547], "convolve_ful": 190, "convolve_wc": 190, "cookiecutt": 390, "coolwarm": [456, 487, 488, 496], "coord": [13, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 83, 187, 188, 189, 190, 191, 192, 193, 194, 195, 197, 198, 199, 200, 201, 202, 244, 288, 385, 418, 461, 479, 480, 520, 523, 524, 531], "coord_to_idx": [187, 188, 191, 193, 194, 197, 199, 200, 479], "coord_to_pix": [187, 188, 191, 193, 194, 197, 199, 200, 479], "coordiant": 477, "coordin": [6, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 41, 46, 47, 83, 85, 87, 88, 89, 90, 91, 94, 95, 96, 102, 103, 105, 108, 109, 110, 122, 124, 126, 127, 128, 134, 138, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 156, 158, 159, 160, 161, 162, 172, 182, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 197, 198, 199, 200, 201, 202, 220, 221, 229, 232, 234, 242, 244, 246, 255, 256, 258, 270, 288, 292, 297, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 313, 323, 329, 333, 334, 350, 375, 379, 380, 381, 383, 387, 388, 393, 394, 397, 399, 400, 413, 415, 418, 420, 431, 433, 434, 436, 443, 445, 448, 450, 451, 454, 458, 459, 460, 461, 462, 464, 465, 467, 468, 469, 470, 472, 474, 477, 478, 479, 480, 481, 482, 483, 486, 487, 488, 489, 496, 497, 507, 515, 520, 522, 523, 528, 529, 532, 569, 570, 575], "coordsi": 420, "copi": [11, 16, 19, 20, 26, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 82, 83, 84, 86, 87, 88, 89, 90, 91, 92, 99, 100, 101, 102, 104, 105, 108, 109, 110, 117, 119, 120, 121, 122, 123, 124, 125, 126, 128, 129, 130, 131, 132, 147, 148, 151, 154, 159, 162, 187, 188, 189, 190, 191, 192, 193, 194, 195, 197, 198, 199, 200, 201, 202, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 239, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 266, 267, 268, 269, 270, 271, 272, 301, 346, 374, 379, 382, 384, 388, 390, 396, 403, 404, 410, 425, 427, 434, 435, 436, 439, 444, 445, 448, 450, 451, 456, 458, 464, 465, 468, 469, 473, 476, 478, 482, 484, 487, 511, 520, 524, 535, 571], "copy_data": [87, 91, 220, 228, 242, 258, 266, 268, 270], "copy_indic": [87, 91], "copy_ob": [82, 511], "copy_on_getitem": [87, 91], "copyright": 410, "core": [16, 53, 84, 90, 104, 123, 155, 373, 374, 381, 387, 388, 395, 399, 400, 411, 414, 431, 435, 439, 443, 450, 451, 453, 456, 458, 460, 462, 464, 465, 469, 481, 486, 499, 509, 571], "corner": [388, 391, 443, 445, 448, 449, 450, 451, 452, 453, 454, 456, 457, 458, 459, 460, 461, 462, 467, 468, 469, 470, 472, 473, 474, 476, 478, 479, 480, 481, 482, 484, 486, 487, 488, 489, 495, 496, 497], "corr_coord": [159, 162], "correct": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 67, 83, 88, 102, 103, 105, 148, 159, 162, 176, 181, 182, 195, 228, 244, 245, 270, 374, 386, 387, 399, 400, 401, 403, 417, 420, 422, 423, 424, 425, 428, 431, 432, 434, 436, 437, 438, 439, 440, 443, 444, 448, 449, 450, 451, 460, 464, 467, 473, 474, 476, 478, 480, 481, 482, 484, 488, 495, 497, 508, 509, 518, 531, 543, 546, 548, 551, 556], "correctli": [19, 20, 26, 77, 147, 148, 154, 159, 162, 214, 215, 218, 222, 224, 225, 226, 230, 236, 237, 239, 243, 245, 247, 248, 249, 254, 259, 261, 262, 263, 264, 267, 271, 375, 377, 400, 401, 415, 417, 422, 427, 428, 437, 477, 478, 479, 480, 481, 484, 523, 529], "correl": [19, 20, 26, 121, 127, 133, 205, 214, 215, 218, 222, 224, 225, 226, 230, 236, 237, 239, 243, 245, 247, 254, 259, 261, 262, 263, 264, 267, 271, 292, 392, 397, 398, 425, 435, 459, 462, 476, 480, 482, 484, 495, 509, 568], "correlate_off": 121, "correlation_radiu": [16, 121, 138, 139, 449, 453, 454, 456, 470, 475, 488, 495, 509], "correspond": [16, 19, 20, 26, 28, 29, 30, 31, 32, 33, 35, 36, 37, 39, 40, 45, 84, 87, 89, 90, 91, 100, 102, 104, 105, 109, 110, 122, 123, 124, 125, 126, 127, 129, 130, 132, 146, 147, 148, 154, 157, 159, 161, 162, 163, 187, 188, 189, 190, 192, 193, 194, 196, 197, 198, 199, 200, 201, 202, 204, 214, 215, 218, 222, 224, 225, 226, 229, 230, 236, 237, 239, 243, 245, 247, 248, 249, 254, 259, 261, 262, 263, 264, 267, 269, 271, 299, 367, 384, 385, 386, 387, 394, 395, 398, 400, 402, 419, 422, 445, 446, 448, 449, 450, 456, 460, 461, 467, 473, 476, 478, 479, 482, 483, 484, 486, 487, 488, 495, 497, 507, 508, 509, 511, 523, 524, 534, 537, 569, 570, 575], "cosimo": [422, 425, 427, 433, 434, 436, 439], "cosmic": [52, 222, 273, 392, 486], "cost": 570, "coto": 433, "could": [16, 84, 90, 104, 123, 375, 377, 379, 380, 383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 398, 400, 401, 404, 445, 448, 456, 458, 473, 475, 484, 486, 487, 495, 510], "count": [16, 83, 92, 100, 102, 105, 106, 107, 108, 109, 110, 113, 117, 121, 122, 124, 125, 126, 128, 129, 130, 131, 132, 137, 164, 166, 169, 170, 171, 176, 178, 182, 186, 189, 190, 192, 193, 194, 198, 201, 202, 211, 213, 220, 242, 282, 284, 285, 286, 291, 293, 296, 298, 323, 359, 361, 370, 372, 380, 382, 385, 386, 387, 389, 390, 394, 398, 416, 417, 420, 422, 425, 427, 428, 434, 435, 439, 440, 443, 444, 445, 448, 449, 450, 451, 452, 453, 454, 456, 458, 460, 462, 464, 465, 467, 468, 469, 470, 474, 476, 478, 479, 480, 484, 488, 493, 495, 496, 497, 509, 511, 515, 518, 519, 520, 521, 524, 570, 571, 572, 574], "count_bkg": 470, "count_statist": 573, "counter": [221, 229, 232, 528], "counts_3d": 479, "counts_imag": [462, 474], "counts_min": 142, "counts_off": [102, 105, 106, 109, 110, 164, 170, 171, 174, 372, 386, 427, 438, 439, 444, 449, 450, 451, 454, 458, 470, 474, 507, 573], "counts_on": 416, "counts_rat": 474, "countspredictor": 385, "countsspectrum": [386, 389, 416, 417, 419, 421, 430], "countsstatist": [282, 284, 398, 421, 422, 425, 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200], "c_n": [187, 188, 197, 200], "cach": [87, 88, 91, 100, 102, 105, 109, 110, 268, 309, 310, 383, 384, 386, 393, 396, 404, 418, 422, 425, 440], "cahil": 427, "cal_gen_92_002": [146, 194], "cal_gen_92_002_summari": 146, "calcul": [13, 15, 19, 20, 26, 96, 105, 110, 133, 134, 135, 136, 175, 214, 215, 218, 222, 224, 225, 226, 230, 236, 237, 239, 243, 245, 247, 254, 259, 261, 262, 263, 264, 267, 270, 271, 287, 289, 290, 298, 300, 323, 356, 364, 365, 384, 387, 415, 421, 425, 426, 427, 433, 438, 440, 443, 466, 470, 472, 475, 484, 495, 496, 499, 511, 570, 574], "caldb": [82, 144, 146, 148, 149, 150, 159, 194, 427, 443, 451, 460, 461, 464, 469, 477, 486], "calendar": [399, 403], "calib_vers": [487, 497], "call": [16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 83, 84, 86, 87, 90, 91, 101, 102, 104, 105, 109, 110, 123, 133, 134, 135, 136, 143, 144, 145, 146, 149, 150, 151, 153, 156, 158, 160, 161, 171, 187, 188, 197, 200, 207, 214, 215, 216, 217, 218, 221, 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521, 570], "cherri": [374, 381, 388, 399], "chi": [287, 389, 572, 573, 574], "chi2": [101, 140, 386, 416, 448, 459, 469, 476, 507, 573], "chi2assym": 419, "child": 384, "children": [456, 464, 479, 495, 496], "chime": 377, "chisq": 287, "chisquar": 287, "chmod": 571, "choic": [133, 387, 391, 393, 400, 448, 449, 451, 453, 476, 488, 495, 512, 568, 571], "choos": [45, 77, 187, 188, 191, 193, 194, 197, 200, 271, 315, 386, 388, 390, 394, 406, 408, 425, 446, 448, 452, 456, 458, 469, 474, 476, 478, 479, 481, 482, 483, 487, 496, 511, 571], "chose": [460, 487, 488, 573, 575], "chosen": [26, 146, 159, 162, 188, 189, 190, 198, 201, 202, 220, 229, 242, 374, 377, 389, 390, 393, 449, 468, 478, 479, 483, 484, 488, 495, 509, 519, 520, 524], "christoph": [379, 380, 381, 382, 383, 384, 385, 386, 388, 390, 391, 392, 393, 394, 413, 414, 415, 416, 417, 418, 419, 420, 426, 429, 430, 431, 432, 433, 434, 435], "chrome": [87, 91], "chromei": 427, "chunk": [148, 159, 162], "chunk_siz": [148, 159, 162], "ci": 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231, 232, 233, 234, 235, 236, 237, 239, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 266, 267, 268, 269, 270, 271, 272, 401], "classproperti": 425, "classvar": [16, 84, 90, 104, 123], "claudio": [438, 440], "claus": [374, 400], "clean": [374, 375, 377, 382, 383, 388, 390, 392, 413, 415, 416, 417, 418, 419, 420, 421, 422, 425, 432, 433, 434, 435], "cleaner": 436, "cleanup": [381, 393, 404, 413, 418, 419, 424, 425, 426, 430, 431, 433], "clear": [92, 100, 196, 242, 374, 379, 381, 385, 388, 389, 393, 394, 395, 433, 459, 575], "clearer": 418, "clearli": [134, 374, 379, 380, 388, 391, 399], "cli": [11, 390, 435, 569], "click": [373, 390, 391, 410, 418, 433, 443, 445, 448, 449, 450, 451, 452, 453, 454, 456, 457, 458, 459, 460, 461, 462, 467, 468, 469, 470, 472, 473, 474, 476, 478, 479, 480, 481, 482, 484, 486, 487, 488, 489, 495, 496, 497, 571], "clip": [19, 20, 26, 187, 188, 191, 193, 194, 197, 199, 200, 214, 215, 218, 222, 224, 225, 226, 230, 236, 237, 239, 243, 245, 247, 248, 249, 254, 259, 261, 262, 263, 264, 267, 270, 271, 313, 418, 419, 452], "clockwis": [221, 229, 232, 528], "clone": [377, 407, 434], "close": [143, 144, 145, 146, 149, 150, 151, 153, 156, 158, 160, 161, 188, 189, 190, 191, 192, 193, 194, 197, 198, 199, 200, 201, 202, 270, 349, 350, 351, 383, 388, 403, 428, 439, 440, 476, 556], "closer": 497, "closest": [117, 121, 125, 129, 132, 188, 189, 190, 192, 198, 201, 202, 475, 480, 509], "cloud": [404, 491], "cluster": [6, 96, 299, 300, 387, 439, 440, 442, 485, 491, 508, 511], "clutter": [456, 511], "cm": [19, 20, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 45, 51, 52, 53, 54, 67, 101, 124, 130, 132, 214, 218, 224, 226, 230, 237, 240, 247, 249, 258, 259, 262, 263, 264, 273, 359, 385, 397, 444, 445, 446, 448, 449, 450, 451, 452, 453, 456, 457, 458, 459, 460, 461, 464, 469, 470, 472, 473, 475, 476, 482, 484, 486, 487, 495, 497, 504, 505, 509, 511, 527, 528, 529, 530, 532, 533, 534, 535, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 558, 559, 560, 561, 562, 563, 564, 565, 566, 570], "cm2": [51, 54, 67, 107, 124, 139, 149, 202, 274, 359, 443, 444, 446, 448, 449, 451, 452, 456, 457, 458, 460, 461, 464, 465, 467, 468, 469, 470, 472, 473, 474, 475, 479, 481, 482, 484, 487, 495, 496, 497, 509, 524, 540, 543, 546, 551, 556], "cm3": [17, 18, 19, 21, 22, 24, 25, 27, 52, 472], "cm5": 472, "cmap": [102, 105, 109, 110, 364, 365, 450, 456, 472, 479, 481, 487, 488, 495, 496, 520], "cmb": 547, "co": [53, 195, 399, 400, 443, 528, 529, 530], "coadd": [189, 190, 192, 198, 201, 202], "coars": 474, "coarser": [194, 199, 474], "codaci": 404, "codata": 52, "code": [19, 20, 26, 82, 83, 87, 91, 146, 214, 215, 218, 222, 224, 225, 226, 230, 236, 237, 239, 243, 245, 247, 248, 249, 254, 259, 261, 262, 263, 264, 267, 271, 369, 377, 378, 379, 381, 382, 384, 387, 388, 389, 390, 391, 392, 393, 394, 395, 397, 398, 399, 400, 401, 407, 410, 413, 416, 417, 419, 420, 421, 422, 425, 427, 430, 431, 432, 433, 434, 436, 439, 443, 444, 445, 446, 448, 449, 450, 451, 452, 453, 454, 456, 457, 458, 459, 460, 461, 462, 464, 465, 466, 467, 468, 469, 470, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 486, 487, 488, 489, 491, 493, 495, 496, 497, 499, 511, 518, 525, 527, 528, 529, 530, 531, 532, 533, 534, 535, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 558, 559, 560, 561, 562, 563, 564, 565, 566, 568, 571], "codebas": [375, 391, 413, 432, 434], "codemeta": 427, "codespel": 425, "codestyl": 377, "coeffici": [127, 235, 239, 537], "coerce_numbers_to_str": 104, "coerce_unit": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40], "coincid": 466, "col": [87, 91, 425], "col0": [87, 91], "col1": [87, 91], "col2": [87, 91], "col_b": [87, 91], "col_c": [87, 91], "col_d": [87, 91], "col_fit_deriv": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40], "col_label": 368, "collabor": [77, 364, 365, 379, 381, 383, 387, 427, 459, 487, 570], "colleagu": [377, 571], "collect": [196, 242, 374, 376, 401, 404, 432, 433, 449, 474, 481, 499, 508, 512, 516, 570, 575], "collis": 50, "colnam": [87, 91, 189, 190, 192, 198, 201, 202, 487, 497], "color": [101, 109, 110, 143, 144, 202, 363, 364, 365, 368, 371, 375, 443, 444, 446, 448, 449, 450, 451, 452, 454, 456, 458, 459, 462, 467, 470, 472, 475, 476, 478, 481, 483, 484, 511, 520, 524, 528, 529, 547, 556], "colorbar": [124, 143, 144, 146, 149, 150, 151, 156, 157, 158, 160, 202, 362, 368, 465, 476, 479], "colorblind": 511, "colorblind10": 511, "colormap": [363, 364, 365, 413, 426, 497], "colormap_hess": [395, 576], "colormap_milagro": 576, "colour": 436, "column": [41, 43, 44, 46, 47, 56, 83, 87, 91, 103, 108, 122, 124, 127, 138, 143, 144, 146, 170, 189, 190, 191, 192, 193, 194, 198, 199, 201, 202, 269, 290, 299, 344, 345, 346, 347, 367, 368, 371, 372, 374, 387, 389, 398, 436, 439, 440, 443, 460, 461, 470, 473, 479, 482, 486, 508, 511], "column_nam": 477, "column_prefix": 194, "columnclass": [87, 91], "columnspac": 371, "com": [134, 140, 297, 328, 329, 341, 377, 394, 403, 404, 407, 486], "combin": [99, 101, 102, 105, 109, 110, 141, 175, 183, 189, 190, 192, 197, 198, 201, 202, 215, 258, 374, 380, 384, 385, 386, 387, 388, 389, 394, 397, 406, 418, 433, 439, 446, 451, 456, 457, 464, 469, 474, 476, 477, 478, 479, 480, 482, 488, 496, 497, 499, 501, 511, 518, 523, 539, 569, 570], "combined_ev": 508, "combining_and_defin": [443, 451, 453, 456, 458, 464, 469, 486], "come": [69, 377, 380, 381, 382, 383, 384, 388, 389, 390, 391, 393, 394, 399, 401, 403, 404, 421, 422, 425, 434, 435, 439, 448, 456, 464, 482, 486, 495, 571], "comma": [71, 72, 74, 75, 76, 78, 79, 80, 473], "command": [6, 13, 94, 101, 124, 150, 156, 158, 160, 373, 375, 377, 382, 383, 385, 390, 391, 404, 405, 406, 407, 408, 409, 415, 419, 430, 433, 434, 436, 460, 510, 569], "commensur": 202, "comment": [124, 374, 375, 379, 383, 387, 388, 390, 391, 393, 395, 397, 399, 400], "commit": [374, 377, 382, 388, 399, 400, 412, 413, 436], "committe": [379, 381, 399, 400], "commod": 400, "common": [95, 373, 374, 375, 377, 379, 380, 388, 390, 394, 395, 397, 398, 400, 407, 409, 410, 425, 427, 449, 450, 462, 473, 477, 480, 482, 495, 496, 499, 506, 509, 510, 524, 529, 571], "common_irf_head": 477, "commonli": [29, 83, 388, 392, 434, 462, 499, 568, 569, 571, 573, 576], "commun": [374, 390, 396, 400, 403, 405, 411, 489], "comp": 473, "compact": [16, 84, 87, 90, 91, 104, 123], "compar": [87, 91, 125, 126, 129, 130, 143, 144, 145, 146, 149, 150, 151, 153, 156, 158, 160, 161, 188, 189, 190, 192, 197, 198, 200, 201, 202, 207, 280, 374, 388, 391, 394, 422, 438, 439, 443, 444, 445, 448, 452, 456, 459, 462, 464, 465, 468, 473, 476, 478, 484, 487, 495, 506, 570, 572], "comparison": [86, 87, 91, 100, 129, 143, 144, 145, 146, 149, 150, 151, 153, 156, 158, 160, 161, 188, 189, 190, 192, 194, 197, 198, 200, 201, 202, 331, 430, 501], "compat": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 87, 91, 189, 190, 192, 201, 202, 228, 379, 381, 385, 388, 397, 399, 401, 402, 410, 413, 415, 417, 434, 440, 479, 523], "compil": [377, 426], "complain": 377, "complaint": 392, "complement": 394, "complementari": [101, 448], "complet": [13, 16, 84, 90, 104, 123, 331, 374, 380, 384, 385, 386, 387, 388, 391, 392, 394, 398, 399, 400, 407, 412, 414, 415, 416, 417, 418, 419, 420, 421, 422, 425, 427, 430, 431, 432, 433, 434, 435, 456, 461, 476, 483, 491, 511, 516], "complex": [374, 375, 380, 384, 388, 389, 390, 396, 398, 410, 445, 448, 458, 461, 466, 469, 474, 479, 480, 482, 483, 487, 520], "complianc": [400, 436], "compliant": [189, 190, 192, 201, 202, 440, 570], "complic": [388, 524], "compon": [15, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 67, 80, 81, 88, 102, 103, 105, 108, 109, 110, 129, 134, 161, 163, 197, 220, 242, 243, 258, 273, 385, 386, 387, 390, 396, 397, 415, 419, 421, 425, 433, 434, 444, 449, 450, 451, 453, 456, 457, 458, 459, 460, 461, 464, 465, 467, 468, 469, 473, 474, 475, 481, 482, 486, 487, 495, 496, 499, 507, 509, 511, 524, 527, 528, 529, 530, 531, 532, 533, 534, 535, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 558, 559, 560, 561, 562, 563, 564, 565, 566, 568, 570], "component_1": 385, "component_2": 385, "component_class": 473, "components_model": 80, "components_statu": 67, "compos": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 380, 400], "composit": [57, 70, 80, 312, 400, 432], "compound": [197, 204, 215, 332, 336, 388, 397, 442, 444, 462, 525, 557], "compoundmodel": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 427], "compoundpixelregion": 336, "compoundskymodel": 385, "compoundskyregion": [204, 332, 336], "compoundsourcemodel": 385, "compoundspectralmodel": [253, 397, 439, 444, 482, 487, 537, 539, 543, 546, 548, 551, 556], "compress": 382, "compris": 488, "compromis": [374, 379, 387, 484], "compton": [243, 421, 547], "compulsori": 482, "comput": [16, 19, 20, 23, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 83, 84, 86, 88, 90, 94, 96, 100, 101, 102, 104, 105, 109, 110, 117, 119, 121, 122, 123, 124, 125, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 138, 140, 143, 144, 145, 146, 147, 148, 149, 150, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 164, 175, 176, 178, 181, 182, 183, 184, 186, 188, 189, 190, 192, 194, 197, 198, 199, 200, 201, 202, 203, 207, 214, 215, 218, 220, 222, 224, 225, 226, 230, 234, 236, 237, 239, 242, 243, 245, 247, 248, 249, 254, 259, 261, 262, 263, 264, 267, 269, 271, 273, 274, 280, 281, 282, 284, 287, 288, 289, 290, 292, 296, 297, 300, 304, 305, 308, 374, 383, 384, 385, 386, 387, 388, 389, 392, 394, 395, 396, 397, 398, 402, 404, 406, 407, 411, 413, 415, 416, 417, 418, 421, 422, 424, 425, 426, 428, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 445, 450, 452, 456, 457, 461, 462, 466, 467, 468, 470, 472, 474, 475, 478, 481, 482, 483, 484, 487, 488, 489, 495, 497, 499, 507, 509, 511, 516, 520, 547, 569, 570, 571, 572, 573, 574, 575], "computation": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 207, 387, 388, 507], "compute_chisq": 432, "compute_differential_jfactor": 23, "compute_errn": [282, 284, 436, 573], "compute_errp": [282, 284, 573], "compute_flux_doubl": 134, "compute_fpp": 135, "compute_fvar": [136, 289], "compute_jfactor": [23, 472], "compute_lightcurve_doublingtim": 466, "compute_lightcurve_fpp": 466, "compute_lightcurve_fvar": 466, "compute_lima_map": 421, "compute_npr": 387, "compute_upper_limit": [282, 284], "computedfieldinfo": [16, 84, 90, 104, 123], "con": [374, 390], "concaten": [83, 86, 92, 191, 194, 386, 390, 396, 439, 479], "concentr": 333, "concept": [16, 84, 90, 104, 123, 394, 395, 401, 411, 472, 479, 512], "conception": [387, 396], "concern": [377, 379, 388, 390, 391, 393, 395, 397, 400, 425, 435, 488, 573], "conclud": 383, "concret": [16, 84, 90, 104, 123, 391, 394], "conda": [373, 377, 381, 382, 391, 394, 399, 403, 404, 406, 407, 408, 409, 410, 415, 417, 433, 434, 571], "condit": [52, 53, 142, 194, 211, 213, 220, 242, 398, 402, 422, 443, 448, 451, 461, 473, 480, 481, 483, 488, 507, 516], "conduct": [436, 469], "cone": [91, 388, 449, 483, 495, 496, 511], "conesearch": 390, "conf": [87, 91, 374, 375, 404, 416, 431], "conf_1d": 467, "conf_3d": 467, "conf_max": [210, 212], "conf_min": [210, 212], "confid": [121, 207, 210, 212, 282, 284, 385, 392, 416, 421, 435, 570, 573], "confidence_opt": [207, 475], "config": [15, 16, 84, 90, 104, 123, 373, 375, 380, 382, 383, 390, 391, 394, 401, 406, 408, 418, 419, 420, 425, 433, 449, 456, 495, 568], "config_dict": 495, "config_joint": 456, "config_paramet": [117, 119, 120, 121, 125, 126, 128, 129, 130, 131, 132], "config_stack": 456, "config_str": 16, "configdict": [16, 84, 90, 104, 123], "configpars": 431, "configur": [11, 15, 16, 46, 84, 87, 90, 91, 101, 104, 109, 110, 117, 119, 120, 121, 123, 125, 126, 128, 129, 130, 131, 132, 321, 374, 375, 377, 383, 385, 389, 390, 394, 395, 401, 404, 410, 418, 420, 421, 425, 427, 436, 439, 440, 448, 450, 452, 458, 460, 461, 465, 486, 491, 510, 516, 570, 571], "confirm": 377, "conflict": [87, 91, 374, 377, 400], "confluenc": 489, "conform": [16, 84, 90, 104, 123, 479], "confus": [374, 375, 380, 393], "connect": [384, 448, 462, 464, 480, 496, 568], "consant": [189, 190, 192, 198, 201, 202], "consciou": 379, "consecut": [172, 331, 374, 399], "consensu": [379, 400], "consent": 374, "consequ": [400, 461, 524], "conserv": [507, 532], "consid": [19, 20, 59, 96, 169, 176, 179, 180, 182, 183, 190, 202, 210, 214, 217, 218, 221, 222, 224, 225, 226, 227, 229, 230, 231, 232, 233, 234, 235, 236, 237, 239, 243, 246, 247, 248, 249, 250, 254, 255, 256, 257, 259, 262, 263, 264, 269, 270, 280, 370, 379, 380, 385, 386, 388, 389, 390, 391, 392, 393, 394, 396, 400, 402, 445, 448, 450, 456, 460, 461, 466, 477, 480, 508, 516, 574], "consider": [390, 415, 425, 433, 456], "consist": [74, 91, 95, 122, 124, 147, 148, 159, 162, 189, 190, 192, 201, 202, 229, 232, 374, 384, 385, 387, 388, 389, 394, 401, 418, 419, 420, 421, 422, 429, 433, 434, 436, 438, 439, 440, 449, 450, 451, 452, 459, 464, 469, 476, 478, 479, 482, 484, 495, 545, 547, 572], "consol": [374, 571], "consortium": [401, 486], "const": [216, 217, 218, 219, 397, 461, 482, 540, 558], "const3d": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40], "const_norm": 397, "constant": [50, 51, 52, 53, 102, 109, 117, 149, 189, 190, 192, 198, 200, 201, 202, 216, 217, 218, 219, 221, 233, 235, 330, 387, 388, 393, 433, 442, 467, 487, 504, 507, 525, 536, 557, 567, 570, 574], "constant_model": 558, "constantmodel": 435, "constantnormspectralmodel": 397, "constantspatialmodel": [252, 397, 482, 487, 527], "constantspectralmodel": [253, 397, 461, 482, 540], "constanttemporalmodel": [265, 397, 418, 482, 558], "constitu": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40], "constitut": 474, "constrain": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 400, 444, 453, 457, 478, 484, 518], "constraint": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 511], "construct": [16, 84, 90, 104, 123, 194, 236, 237, 339, 524, 570], "constructor": [187, 188, 189, 190, 192, 194, 197, 198, 199, 200, 201, 202, 439, 496, 522], "consult": 409, "consum": [476, 511], "contact": [376, 381, 383, 400, 409, 411, 414, 415, 416, 430, 433], "contain": [13, 16, 19, 20, 26, 57, 59, 63, 67, 69, 82, 83, 84, 86, 87, 88, 90, 91, 92, 95, 96, 100, 101, 102, 104, 105, 108, 109, 110, 112, 122, 123, 124, 132, 133, 138, 140, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 169, 171, 174, 176, 181, 184, 186, 187, 188, 189, 190, 192, 193, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 207, 211, 214, 215, 216, 217, 218, 219, 220, 221, 222, 224, 225, 226, 227, 229, 230, 231, 232, 233, 234, 235, 236, 237, 239, 240, 242, 243, 244, 245, 246, 247, 248, 249, 250, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 266, 267, 269, 270, 271, 272, 288, 299, 300, 367, 369, 371, 372, 374, 377, 378, 380, 382, 383, 384, 387, 388, 389, 390, 391, 393, 394, 395, 396, 398, 400, 403, 404, 411, 415, 419, 420, 421, 422, 424, 425, 427, 428, 431, 432, 433, 434, 435, 439, 440, 443, 446, 448, 449, 450, 453, 456, 457, 460, 461, 464, 467, 468, 470, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 486, 487, 488, 489, 495, 496, 497, 499, 500, 502, 507, 508, 509, 511, 515, 519, 520, 523, 524, 525, 568, 570, 572, 573, 574], "containment_correct": [16, 102, 105, 176, 443, 448, 449, 450, 453, 454, 456, 458, 465, 467, 468, 470, 474, 478, 495], "containment_fract": 96, "containment_radiu": [150, 156, 158, 159, 160, 162, 443, 474, 477, 480], "containment_radius_map": [159, 162], "contains_pix": [187, 188, 197, 200], "contains_wcs_pix": 197, "contamin": [67, 454, 473, 480, 520], "content": [84, 90, 104, 123, 124, 189, 190, 192, 198, 201, 202, 337, 340, 374, 377, 379, 383, 389, 394, 401, 404, 419, 428, 433, 445, 479, 480, 486, 488, 508, 511, 522, 523], "content_typ": [16, 84, 90, 104, 123], "context": [16, 84, 87, 90, 91, 104, 123, 211, 213, 220, 242, 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[26, 472], "era": 222, "ereco": [146, 477], "erf": [230, 528], "erg": [52, 53, 67, 443, 448, 473, 497, 504, 511], "erlangen": 393, "erod": 202, "eros": [200, 202], "err": 476, "errat": 374, "errn": [121, 125, 129, 130, 132, 207, 436, 573], "errn_2sigma": 573, "error": [11, 16, 19, 20, 26, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 67, 68, 79, 84, 86, 90, 101, 102, 104, 105, 109, 110, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 134, 135, 136, 165, 191, 194, 199, 210, 212, 214, 215, 216, 217, 218, 221, 222, 224, 225, 226, 227, 229, 230, 231, 232, 233, 234, 235, 236, 237, 239, 243, 244, 245, 246, 247, 248, 249, 250, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 266, 267, 269, 270, 271, 282, 284, 288, 289, 290, 292, 297, 352, 374, 375, 388, 389, 390, 392, 397, 398, 401, 402, 415, 419, 421, 423, 425, 426, 428, 432, 433, 436, 438, 439, 440, 444, 445, 446, 448, 449, 451, 452, 453, 456, 457, 458, 459, 461, 464, 465, 466, 467, 469, 470, 473, 475, 482, 483, 484, 486, 495, 496, 497, 499, 507, 509, 511, 529, 547, 556, 568, 571], "error_callback": 165, "errorbar": [83, 101, 102, 105, 109, 110, 194, 198, 219, 227, 231, 233, 234, 235, 250, 257, 269, 272, 443, 465, 484], "errp": [121, 125, 129, 130, 132, 207, 436, 573], "errp_2sigma": 573, "escap": [374, 400], "especi": [87, 91, 134, 377, 381, 384, 388, 394, 395, 404, 425, 431, 445, 458, 468, 473, 479, 484, 486, 487], "essenti": [384, 386, 389, 419, 420, 421, 462, 479], "establish": [374, 387], "estim": [6, 19, 20, 26, 50, 72, 101, 105, 110, 112, 117, 118, 119, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 164, 170, 171, 172, 178, 187, 188, 197, 200, 202, 207, 210, 214, 215, 217, 218, 221, 222, 224, 225, 226, 227, 229, 230, 231, 232, 233, 234, 235, 236, 237, 239, 243, 244, 245, 246, 247, 248, 249, 250, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 266, 267, 269, 270, 271, 280, 287, 290, 295, 321, 367, 378, 380, 383, 387, 388, 389, 390, 393, 396, 402, 406, 415, 416, 419, 420, 421, 422, 425, 426, 427, 431, 432, 433, 434, 436, 439, 440, 442, 443, 444, 448, 450, 454, 456, 457, 458, 461, 463, 468, 469, 470, 471, 474, 476, 480, 484, 485, 488, 491, 495, 496, 507, 508, 511, 521, 523, 568, 569, 570, 572, 574], "estimate_best_fit": [125, 126, 129, 130], "estimate_count": [125, 126, 129, 130], "estimate_energy_depend": 119, "estimate_errn_errp": [125, 126, 129, 130], "estimate_excess_map": 121, "estimate_exposure_reco_energi": 121, "estimate_fit_input_map": 132, "estimate_flux_default": 132, "estimate_flux_map": 132, "estimate_flux_point": [125, 126, 129], "estimate_kernel": [121, 132], "estimate_map": 117, "estimate_mask_default": [121, 132], "estimate_min_e2dnd": 131, "estimate_min_excess": 131, "estimate_npr": [125, 126, 129, 130], "estimate_npred_excess": [125, 126, 129], "estimate_pad_width": 132, "estimate_scan": [125, 126, 129, 130], "estimate_t": [125, 126, 129, 130], "estimate_time_bin_flux": 129, "estimate_ul": [125, 126, 129, 130], "estimator_result": [140, 470], "et": [26, 33, 59, 101, 133, 134, 149, 222, 289, 290, 292, 297, 384, 452, 459, 466, 499, 570], "eta": [232, 233, 255, 530, 534, 561], "eta_": 50, "eta_b": 50, "eta_rang": [530, 534], "etc": [105, 110, 122, 374, 379, 380, 384, 385, 386, 389, 390, 394, 395, 398, 400, 401, 422, 449, 451, 456, 464, 465, 467, 469, 470, 474, 479, 482, 484, 487, 495, 507, 520], "etru": 477, "eu": 394, "euclidean": 299, "europ": 400, "ev": [475, 477, 511, 547], "ev2": 477, "evalu": [17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 68, 87, 91, 102, 105, 108, 109, 110, 143, 144, 145, 146, 149, 150, 151, 153, 156, 158, 160, 161, 169, 176, 182, 197, 208, 209, 214, 215, 216, 217, 218, 219, 220, 221, 222, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 239, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 266, 267, 268, 269, 270, 271, 272, 314, 374, 379, 383, 389, 392, 393, 396, 397, 398, 402, 414, 415, 416, 418, 421, 425, 430, 431, 433, 434, 435, 436, 437, 438, 439, 440, 460, 461, 470, 477, 482, 484, 486, 487, 507, 529, 530, 537, 570, 573], "evaluate_contain": [150, 158, 160, 427], "evaluate_direct": [150, 158, 160], "evaluate_energy_flux": [248, 249], "evaluate_error": [19, 20, 26, 124, 214, 215, 218, 222, 224, 225, 226, 230, 236, 237, 239, 243, 245, 247, 248, 249, 254, 259, 261, 262, 263, 264, 267, 271, 392], "evaluate_geom": [216, 217, 221, 228, 229, 232, 244, 246, 255, 256, 258, 260, 266, 270, 440, 482, 530], "evaluate_integr": [247, 248, 249], "evaluate_paramet": [150, 158, 160], "evaluation_bin_size_min": [216, 217, 221, 229, 232, 244, 246, 255, 256, 258, 260, 266, 270], "evaluation_radiu": [216, 217, 221, 229, 232, 244, 246, 255, 256, 258, 260, 266, 268, 270, 385, 386, 397, 482], "evaluation_region": [216, 217, 221, 229, 232, 244, 246, 255, 256, 258, 260, 266, 270], "even": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 87, 91, 200, 211, 213, 374, 377, 379, 381, 382, 388, 391, 392, 400, 401, 417, 467, 470, 475, 476, 478, 479, 480, 481, 482, 495, 507, 509, 533, 534, 571], "event": [82, 83, 84, 85, 86, 87, 88, 89, 90, 93, 103, 104, 108, 113, 121, 131, 148, 159, 162, 163, 170, 178, 189, 190, 192, 198, 201, 202, 219, 227, 231, 233, 234, 235, 250, 257, 269, 272, 331, 374, 375, 378, 380, 384, 386, 388, 389, 390, 401, 406, 410, 417, 418, 419, 421, 425, 426, 427, 432, 433, 434, 435, 436, 439, 440, 442, 445, 448, 449, 450, 454, 456, 457, 463, 469, 478, 488, 491, 493, 495, 496, 507, 511, 519, 520, 521, 569, 570, 573], "event_3fhl": 497, "event_class": [84, 487, 497], "event_det_coord": [103, 108], "event_fil": 88, "event_filt": 89, "event_filter_typ": 89, "event_id": [387, 486, 487, 497, 508], "event_list": [83, 374, 375, 387], "event_list_meta": [103, 108, 420], "event_sampl": [442, 460, 463], "event_sampling_nrg_depend_model": [442, 461, 463], "event_typ": [104, 487, 497], "eventlist": [84, 88, 103, 108, 178, 189, 190, 192, 198, 201, 202, 374, 375, 380, 387, 388, 389, 401, 410, 415, 422, 427, 429, 430, 431, 433, 434, 460, 461, 470, 479, 486, 487, 497, 511, 520], "eventlistbas": 89, "eventlistdataset": 434, "eventlistlat": 508, "eventlistmetadata": [83, 401], "events2": 83, "events_": 460, "events_0001": 460, "events_1": 508, "events_2": 508, "events_3fhl": 497, "events_bkg": 387, "events_gc_3fhl": 497, "events_list": 387, "events_path": [82, 460], "events_src": 387, "events_tot": 387, "events_vela": 470, "eventu": [374, 379, 390, 393, 394, 410, 482], "ever": 574, "everi": [188, 190, 202, 270, 374, 377, 382, 383, 384, 386, 390, 399, 401, 404, 445, 448, 479, 496, 571], "everyon": 379, "everyth": [374, 392, 393, 511], "everywher": [374, 388, 393, 394, 414], "evid": [134, 473], "evolut": [50, 52, 53, 222, 380, 394, 442, 463, 491, 504, 505, 507, 508, 516, 519, 523, 525], "evolv": [28, 29, 30, 35, 36, 39, 40, 50, 52, 53, 222, 391, 394, 401, 461, 477], "evt_sampl": 460, "ex": [11, 479, 481, 482], "exact": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 91, 392, 479, 487], "exactli": [117, 121, 125, 129, 132, 194, 369, 448, 450, 468, 470, 480, 484, 487, 507, 524], "exagger": 365, "examin": [400, 515], "exampl": [11, 19, 20, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 66, 67, 80, 82, 83, 85, 86, 87, 88, 89, 91, 94, 96, 99, 101, 102, 105, 109, 110, 111, 114, 115, 116, 117, 119, 121, 122, 124, 126, 128, 129, 130, 131, 132, 134, 138, 139, 141, 142, 144, 146, 148, 149, 150, 151, 157, 159, 162, 169, 172, 174, 187, 188, 189, 190, 191, 192, 193, 194, 195, 197, 198, 199, 200, 201, 202, 207, 211, 213, 234, 240, 271, 274, 280, 283, 299, 300, 321, 325, 331, 341, 353, 354, 359, 362, 364, 365, 367, 369, 371, 374, 375, 377, 379, 382, 384, 385, 386, 387, 388, 389, 390, 391, 392, 394, 397, 400, 401, 402, 406, 407, 408, 410, 414, 416, 419, 422, 425, 426, 427, 430, 432, 433, 434, 440, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 501, 502, 511, 513, 514, 515, 517, 522, 524, 525, 526, 536, 557, 565, 566, 567, 571, 573, 575, 576], "example_cub": 479, "example_cube_fgst": 479, "exce": 112, "except": [67, 69, 80, 87, 91, 102, 105, 109, 110, 165, 189, 190, 192, 198, 201, 202, 301, 371, 374, 385, 389, 393, 477, 481, 524], "excerpt": 489, "excess": [15, 67, 72, 102, 105, 109, 110, 118, 121, 122, 124, 125, 126, 129, 131, 135, 136, 140, 169, 282, 284, 289, 290, 372, 386, 389, 421, 422, 428, 433, 440, 443, 444, 448, 449, 451, 452, 453, 456, 458, 462, 464, 469, 470, 473, 474, 475, 478, 480, 481, 488, 495, 496, 509, 511], "excess_estim": 488, "excess_estimator_integr": 488, "excess_integr": 488, "excess_map": [16, 449, 453, 454, 456, 495], "excess_map_estim": 470, "excess_matching_signific": [424, 434], "excess_min": 142, "excess_r": 474, "excessestim": 398, "excessmapconfig": 16, "excessmapestim": [118, 138, 139, 140, 398, 421, 422, 423, 425, 428, 438, 440, 454, 456, 470, 475, 480, 488, 509], "excesspointsestim": 398, "excessprofileestim": [398, 422], "exchang": 388, "exclud": [16, 84, 90, 101, 102, 104, 105, 109, 110, 123, 128, 170, 172, 211, 213, 220, 242, 380, 437, 478, 480, 520, 521], "exclude_default": [16, 84, 90, 104, 123], "exclude_non": [16, 84, 90, 104, 123], "exclude_unset": [16, 84, 90, 104, 123], "exclus": [16, 83, 164, 166, 171, 172, 173, 174, 177, 188, 200, 380, 388, 390, 421, 423, 445, 453, 456, 478, 518, 520], "exclusion_mask": [164, 166, 171, 172, 173, 174, 177, 448, 454, 458, 478, 480, 496, 518, 520, 521], "exclusion_mask_2d": 478, "exclusion_mask_crab": [449, 495], "exclusion_radiu": 480, "exclusion_region": 448, "exclusionmask": 431, "execut": [11, 121, 125, 129, 130, 132, 374, 375, 377, 379, 381, 382, 383, 389, 390, 394, 404, 405, 406, 407, 408, 409, 410, 419, 427, 433, 442, 447, 455, 463, 471, 478, 479, 485, 487, 490, 491, 492, 494, 497, 498, 526, 536, 557, 567, 569], "exercis": [377, 390, 394, 400], "exhibit": 459, "exist": [11, 15, 16, 26, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 87, 88, 91, 99, 100, 101, 102, 105, 107, 109, 110, 122, 124, 147, 148, 154, 159, 162, 174, 187, 188, 189, 190, 192, 193, 196, 197, 198, 200, 201, 202, 208, 220, 234, 242, 266, 267, 268, 270, 343, 358, 374, 377, 379, 382, 383, 385, 386, 387, 388, 390, 391, 393, 394, 395, 397, 398, 400, 401, 406, 407, 410, 411, 414, 418, 419, 433, 434, 436, 439, 452, 454, 474, 479, 480, 482, 483, 487, 508, 523, 524, 572], "exist_ok": [448, 449, 456, 457, 460, 495, 496], "exit": [11, 15, 87, 91, 211, 213, 220, 242, 415, 535, 571], "exot": 391, "exp": [21, 28, 29, 30, 31, 32, 35, 36, 39, 40, 149, 227, 230, 237, 248, 249, 262, 375, 397, 469, 482, 529, 530, 537, 541, 542, 543, 544, 553, 554, 555, 559, 560, 561, 574], "expand": [129, 339, 384, 425], "expand_map": 129, "expans": [50, 52, 53], "expcutoffpowerlaw": 543, "expcutoffpowerlaw3fglspectralmodel": [253, 397, 446, 482, 542], "expcutoffpowerlawnormspectralmodel": [226, 253, 440, 482, 543], "expcutoffpowerlawspectralmodel": [19, 20, 26, 80, 214, 215, 218, 222, 224, 225, 230, 236, 237, 239, 243, 245, 247, 248, 249, 253, 254, 259, 261, 262, 263, 264, 267, 271, 397, 446, 448, 456, 482, 541, 547], "expdecai": [227, 442, 525, 567], "expdecay_model": [460, 559], "expdecaytemporalmodel": [265, 397, 421, 425, 460, 469, 482, 559], "expect": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 105, 108, 110, 122, 174, 179, 180, 185, 200, 282, 283, 284, 285, 287, 291, 298, 331, 374, 379, 387, 389, 390, 391, 394, 399, 401, 443, 445, 450, 451, 452, 454, 460, 461, 466, 467, 472, 476, 479, 480, 484, 487, 489, 507, 509, 572, 573, 574], "expens": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 448, 461], "experi": [374, 377, 379, 383, 385, 389, 402, 404, 433, 499, 570, 575], "experienc": [383, 409], "experiment": [28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 390, 571], "expert": 394, "expertis": 391, "expfactor": [263, 264, 554, 555], "expir": 387, "explain": [87, 91, 374, 377, 379, 388, 391, 393, 394, 400, 404, 406, 408, 419, 425, 443, 448, 450, 472, 476, 480, 482, 486, 511, 569, 571], "explan": [117, 121, 125, 129, 132, 379, 411, 466, 575], "explicit": [87, 91, 188, 190, 374, 375, 390, 392, 398, 523, 575], "explicitli": [16, 19, 20, 26, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 84, 90, 104, 123, 143, 144, 145, 146, 149, 150, 151, 153, 156, 158, 160, 161, 214, 215, 218, 222, 224, 225, 226, 230, 236, 237, 239, 243, 245, 247, 248, 249, 254, 259, 261, 262, 263, 264, 267, 271, 301, 384, 396, 405, 415, 461, 481, 509, 523, 575], "explor": [189, 190, 192, 198, 201, 202, 382, 390, 410, 442, 448, 453, 456, 459, 462, 463, 465, 466, 467, 468, 473, 474, 481, 488, 489, 495, 497, 507, 508, 509, 511, 519, 520, 523, 568], "explos": 52, "expomap": 387, "expon": [397, 482], "exponenti": [28, 30, 36, 39, 40, 77, 224, 225, 226, 227, 262, 263, 264, 375, 385, 419, 442, 456, 460, 469, 525, 557], "exponentialcutoffpowerlaw": 547, "export": [84, 87, 90, 91, 102, 104, 105, 122, 123, 124, 377, 398, 401, 406, 408, 419, 429, 436, 437, 440, 449, 482, 488, 489, 495], "expos": [6, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 383, 388, 389, 390, 393, 394, 438, 440, 479, 491, 509, 510, 569], "exposur": [16, 83, 88, 102, 105, 106, 108, 109, 110, 121, 132, 137, 147, 148, 154, 157, 159, 162, 164, 169, 176, 179, 180, 181, 183, 184, 185, 258, 293, 296, 298, 380, 383, 384, 385, 386, 387, 398, 417, 422, 430, 432, 433, 434, 436, 443, 444, 445, 448, 449, 450, 451, 452, 453, 454, 456, 458, 460, 461, 464, 465, 468, 469, 474, 478, 479, 480, 488, 495, 496, 507, 516, 519, 524, 573], "exposure_geom": 148, "exposure_hdu": [147, 148, 154, 159, 162], "exposure_hdu_band": [147, 148, 154, 159, 162], "exposure_hpx": 487, "exposure_map": [147, 148, 154, 159, 162, 179, 180, 185, 477], "exposure_max": 474, "exposure_min": 474, "exposure_tim": 387, "express": [121, 134, 282, 284, 385, 397, 398, 440, 454, 476, 477, 482, 484, 507, 523, 530, 572, 573, 574], "exptest": 431, "extend": [15, 19, 20, 26, 69, 72, 87, 91, 92, 100, 164, 191, 194, 202, 214, 215, 218, 222, 224, 225, 226, 230, 236, 237, 239, 242, 243, 245, 247, 248, 249, 254, 259, 261, 262, 263, 264, 267, 271, 283, 381, 385, 386, 387, 388, 394, 396, 397, 398, 401, 406, 418, 420, 425, 442, 447, 448, 452, 461, 467, 473, 479, 480, 481, 482, 487, 491, 507, 508, 519, 520, 568], "extended_archive_v18": 535, "extended_source_spectral_analysi": [442, 445, 447], "extens": [41, 146, 189, 190, 192, 194, 201, 202, 216, 217, 220, 221, 229, 232, 242, 244, 246, 255, 256, 260, 266, 270, 376, 379, 380, 381, 382, 383, 385, 387, 389, 390, 391, 392, 393, 394, 404, 428, 459, 460, 461, 482, 571], "extent": [200, 220, 242, 388, 452, 482], "extern": [113, 375, 400, 426, 430, 431, 434, 486, 511], "extnam": 477, "extra": [16, 28, 29, 30, 31, 32, 35, 36, 37, 39, 40, 47, 83, 84, 90, 101, 104, 123, 187, 188, 193, 197, 198, 200, 206, 207, 209, 266, 267, 299, 373, 375, 381, 382, 388, 390, 391, 394, 400, 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    Computation times#

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    09:53.580 total execution time for 84 files from all galleries:

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    10:02.876 total execution time for 84 files from all galleries: