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diff --git a/docs/dev/.doctrees/api/gammapy.datasets.MapDatasetMetaData.doctree b/docs/dev/.doctrees/api/gammapy.datasets.MapDatasetMetaData.doctree
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diff --git a/docs/dev/.doctrees/tutorials/analysis-1d/sed_fitting.doctree b/docs/dev/.doctrees/tutorials/analysis-1d/sed_fitting.doctree
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diff --git a/docs/dev/_downloads/00d3ac5b41ed4f4efa15d4f26129de27/event_sampling_nrg_depend_models.zip b/docs/dev/_downloads/00d3ac5b41ed4f4efa15d4f26129de27/event_sampling_nrg_depend_models.zip
diff --git a/docs/dev/_downloads/00d8cba1967017b728f6c0777b24e13f/plot_exp_cutoff_powerlaw.zip b/docs/dev/_downloads/00d8cba1967017b728f6c0777b24e13f/plot_exp_cutoff_powerlaw.zip
diff --git a/docs/dev/_downloads/00e16d4c09559d67d10cd2e55c12eff8/plot_template_spectral.zip b/docs/dev/_downloads/00e16d4c09559d67d10cd2e55c12eff8/plot_template_spectral.zip
diff --git a/docs/dev/_downloads/041be2d7422101460b6f928b074608da/light_curve_flare.zip b/docs/dev/_downloads/041be2d7422101460b6f928b074608da/light_curve_flare.zip
diff --git a/docs/dev/_downloads/04f3ecb20bae1e7983af36784e65887d/plot_super_exp_cutoff_powerlaw_3fgl.zip b/docs/dev/_downloads/04f3ecb20bae1e7983af36784e65887d/plot_super_exp_cutoff_powerlaw_3fgl.zip
diff --git a/docs/dev/_downloads/0595e7d3ba49081161c8f9931701216b/sed_fitting.zip b/docs/dev/_downloads/0595e7d3ba49081161c8f9931701216b/sed_fitting.zip
diff --git a/docs/dev/_downloads/062bafee8fcb97ea6e8ccd016a8baf1e/plot_bkg_3d.pdf b/docs/dev/_downloads/062bafee8fcb97ea6e8ccd016a8baf1e/plot_bkg_3d.pdf
diff --git a/docs/dev/_downloads/068f4a465f9ece97104fde9df27a1c7b/model-gallery_jupyter.zip b/docs/dev/_downloads/068f4a465f9ece97104fde9df27a1c7b/model-gallery_jupyter.zip
diff --git a/docs/dev/_downloads/06ba2363ac6d8e23d2098c691bbc6e58/priors.zip b/docs/dev/_downloads/06ba2363ac6d8e23d2098c691bbc6e58/priors.zip
diff --git a/docs/dev/_downloads/088ccc2817ab643e12f4e39275459a14/simulate_3d.zip b/docs/dev/_downloads/088ccc2817ab643e12f4e39275459a14/simulate_3d.zip
diff --git a/docs/dev/_downloads/0c4afa10f7306271310df66616417cf8/spectral_analysis.zip b/docs/dev/_downloads/0c4afa10f7306271310df66616417cf8/spectral_analysis.zip
diff --git a/docs/dev/_downloads/0e38841659c96968a365d83de7882cd1/variability_estimation.py b/docs/dev/_downloads/0e38841659c96968a365d83de7882cd1/variability_estimation.py
@@ -58,6 +58,7 @@
 )
 
 plt.figure(figsize=(8, 6))
+plt.subplots_adjust(bottom=0.2, left=0.2)
 lc_1d.plot(marker="o")
 plt.show()
 
@@ -116,7 +117,8 @@
 print(relative_variability_significance)
 
 ######################################################################
-# The variability amplitude as presented in `Heidt & Wagner, 1996 <https://ui.adsabs.harvard.edu/abs/1996A%26A...305...42H/abstract>`__ is:
+# The variability amplitude as presented in
+# `Heidt & Wagner, 1996 <https://ui.adsabs.harvard.edu/abs/1996A%26A...305...42H/abstract>`__ is:
 
 variability_amplitude = np.sqrt((f_max - f_min) ** 2 - 2 * f_mean_err**2)
 
@@ -143,9 +145,9 @@
 #  Fractional excess variance, point-to-point fractional variance and doubling/halving time
 # -----------------------------------------------------------------------------------------
 # The fractional excess variance, as presented by
-# `Vaughan et al., 2003 <https://ui.adsabs.harvard.edu/abs/2003MNRAS.345.1271V/abstract>`__, is a simple but effective
-# method to assess the significance of a time variability feature in an object, for example an AGN flare.
-# It is important to note that it requires Gaussian errors to be applicable.
+# `Vaughan et al., 2003 <https://ui.adsabs.harvard.edu/abs/2003MNRAS.345.1271V/abstract>`__, is
+# a simple but effective method to assess the significance of a time variability feature in an object,
+# for example an AGN flare. It is important to note that it requires Gaussian errors to be applicable.
 # The excess variance computation is implemented in `~gammapy.estimators.utils`.
 
 fvar_table = compute_lightcurve_fvar(lc_1d)
@@ -176,7 +178,8 @@
 # (`Scargle et al., 2013 <https://ui.adsabs.harvard.edu/abs/2013ApJ...764..167S/abstract>`__).
 # A good and simple-to-use implementation of the algorithm is found in
 # `astropy.stats.bayesian_blocks`.
-# This implementation uses Gaussian statistics, as opposed to the `first introductory paper <https://iopscience.iop.org/article/10.1086/306064>`__
+# This implementation uses Gaussian statistics, as opposed to the
+# `first introductory paper <https://iopscience.iop.org/article/10.1086/306064>`__
 # which is based on Poissonian statistics.
 #
 # By passing the flux and error on the flux as ``measures`` to the method we can obtain the list of optimal bin edges
@@ -190,27 +193,36 @@
 
 ######################################################################
 # The result giving a significance estimation for variability in the lightcurve is the number of *change points*,
-# i.e. the number of internal bin edges: if at least one change point is identified by the algorithm, there is significant variability.
+# i.e. the number of internal bin edges: if at least one change point is identified by the algorithm,
+# there is significant variability.
 
 ncp = len(bayesian_edges) - 2
 print(ncp)
 
 ######################################################################
-# We can rebin the lightcurve to compute the one expected with bayesian edges
-# First, we adjust the first and last bins of the bayesian_edges to coincide
+# We can rebin the lightcurve to compute the one expected with bayesian edges.
+# First, we adjust the first and last bins of the ``bayesian_edges`` to coincide
 # with the original light curve start and end points.
 
-# create a new axis
+######################################################################
+# Create a new axis:
+
 axis_original = lc_1d.geom.axes["time"]
 bayesian_edges[0] = axis_original.time_edges[0].value
 bayesian_edges[-1] = axis_original.time_edges[-1].value
 edges = Time(bayesian_edges, format="mjd", scale=axis_original.reference_time.scale)
 axis_new = TimeMapAxis.from_time_edges(edges[:-1], edges[1:])
 
-# rebin the lightcurve
+######################################################################
+# Rebin the lightcurve:
+
 resample = lc_1d.resample_axis(axis_new)
 
-# plot the new lightcurve on top of the old one
+######################################################################
+# Plot the new lightcurve on top of the old one:
+
+plt.figure(figsize=(8, 6))
+plt.subplots_adjust(bottom=0.2, left=0.2)
 ax = lc_1d.plot(label="original")
 resample.plot(ax=ax, marker="s", label="rebinned")
 plt.legend()
diff --git a/docs/dev/_downloads/0ee9d6f1024a6949ed84ed2823f76884/plot_constant_temporal.zip b/docs/dev/_downloads/0ee9d6f1024a6949ed84ed2823f76884/plot_constant_temporal.zip
diff --git a/docs/dev/_downloads/10f1f97c8b6423f5cd3c3dd929ba1c7d/plot_stack.pdf b/docs/dev/_downloads/10f1f97c8b6423f5cd3c3dd929ba1c7d/plot_stack.pdf
diff --git a/docs/dev/_downloads/1370bd9099a95d65461f2a665bb9decd/catalog.zip b/docs/dev/_downloads/1370bd9099a95d65461f2a665bb9decd/catalog.zip
diff --git a/docs/dev/_downloads/16d6399e308f3f4937d0512c9c9dd023/plot_psf.pdf b/docs/dev/_downloads/16d6399e308f3f4937d0512c9c9dd023/plot_psf.pdf
diff --git a/docs/dev/_downloads/173b2147fa2a0402fd092316ab70a935/plot_smooth_broken_powerlaw.zip b/docs/dev/_downloads/173b2147fa2a0402fd092316ab70a935/plot_smooth_broken_powerlaw.zip
diff --git a/docs/dev/_downloads/191e39c4584da5619df61ef94dbf1770/plot_spiral_arms.hires.png b/docs/dev/_downloads/191e39c4584da5619df61ef94dbf1770/plot_spiral_arms.hires.png
diff --git a/docs/dev/_downloads/1d3f6beaf08c01b5183c9c9291d0b28a/plot_piecewise_norm_spectral.zip b/docs/dev/_downloads/1d3f6beaf08c01b5183c9c9291d0b28a/plot_piecewise_norm_spectral.zip
diff --git a/docs/dev/_downloads/21c6356dca773bb211972068bdd8edac/irfs.zip b/docs/dev/_downloads/21c6356dca773bb211972068bdd8edac/irfs.zip
diff --git a/docs/dev/_downloads/23a95fa742150b45b73b7f187c1cc8a8/plot_broken_powerlaw.zip b/docs/dev/_downloads/23a95fa742150b45b73b7f187c1cc8a8/plot_broken_powerlaw.zip
diff --git a/docs/dev/_downloads/25f9c102794e0966d636f8ca130ecabc/extended_source_spectral_analysis.zip b/docs/dev/_downloads/25f9c102794e0966d636f8ca130ecabc/extended_source_spectral_analysis.zip
diff --git a/docs/dev/_downloads/26224d3995e07325df7f0b64d1681fb9/regionmap-1.pdf b/docs/dev/_downloads/26224d3995e07325df7f0b64d1681fb9/regionmap-1.pdf
diff --git a/docs/dev/_downloads/277e8d586c77a3ac9f6ad21ecdfc3485/detect.zip b/docs/dev/_downloads/277e8d586c77a3ac9f6ad21ecdfc3485/detect.zip
diff --git a/docs/dev/_downloads/296ebb35e9dd7deb47d7997d9fed9214/event_sampling.zip b/docs/dev/_downloads/296ebb35e9dd7deb47d7997d9fed9214/event_sampling.zip
diff --git a/docs/dev/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip b/docs/dev/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip
diff --git a/docs/dev/_downloads/33bb560bd3c93b9dd08e3984c7eb1b58/plot_disk.zip b/docs/dev/_downloads/33bb560bd3c93b9dd08e3984c7eb1b58/plot_disk.zip
diff --git a/docs/dev/_downloads/341317315e7e4253fe9cdaa5bbc2c6e2/plot_constant.zip b/docs/dev/_downloads/341317315e7e4253fe9cdaa5bbc2c6e2/plot_constant.zip
diff --git a/docs/dev/_downloads/346fe536153ddf4e1f253ab559e610d4/survey_map.zip b/docs/dev/_downloads/346fe536153ddf4e1f253ab559e610d4/survey_map.zip
diff --git a/docs/dev/_downloads/3887251ea091cf5a495cbd0984520eec/hawc.zip b/docs/dev/_downloads/3887251ea091cf5a495cbd0984520eec/hawc.zip
diff --git a/docs/dev/_downloads/388f0fd944eebe1e0103655d9014c555/plot_gauss.zip b/docs/dev/_downloads/388f0fd944eebe1e0103655d9014c555/plot_gauss.zip
diff --git a/docs/dev/_downloads/39983d17a2a11f56c5f5cc42710364f2/analysis_1.zip b/docs/dev/_downloads/39983d17a2a11f56c5f5cc42710364f2/analysis_1.zip
diff --git a/docs/dev/_downloads/3b10ba4ac6696d908bde0bbc7a89c9d7/plot_powerlaw_temporal.zip b/docs/dev/_downloads/3b10ba4ac6696d908bde0bbc7a89c9d7/plot_powerlaw_temporal.zip
diff --git a/docs/dev/_downloads/3edea76cb3aefe6dfce230edb07b24e2/models.zip b/docs/dev/_downloads/3edea76cb3aefe6dfce230edb07b24e2/models.zip
diff --git a/docs/dev/_downloads/408398be7d2e8fce69b0c175dfb7db6c/hess.zip b/docs/dev/_downloads/408398be7d2e8fce69b0c175dfb7db6c/hess.zip
diff --git a/docs/dev/_downloads/42884adaf5dec1438e6da837d7acfad0/cta.zip b/docs/dev/_downloads/42884adaf5dec1438e6da837d7acfad0/cta.zip
diff --git a/docs/dev/_downloads/432ee3a608125ae95df2e8d950f07716/dl3-1.pdf b/docs/dev/_downloads/432ee3a608125ae95df2e8d950f07716/dl3-1.pdf
diff --git a/docs/dev/_downloads/440149f3931d426b0d9c554d4bb23c5b/light_curve_simulation.zip b/docs/dev/_downloads/440149f3931d426b0d9c554d4bb23c5b/light_curve_simulation.zip
diff --git a/docs/dev/_downloads/440e264b73eac3e4291763a82299dcb1/model_management.zip b/docs/dev/_downloads/440e264b73eac3e4291763a82299dcb1/model_management.zip
diff --git a/docs/dev/_downloads/471a23ea48282aca3ed18c0dab2c428f/plot_shell2.zip b/docs/dev/_downloads/471a23ea48282aca3ed18c0dab2c428f/plot_shell2.zip
diff --git a/docs/dev/_downloads/4722787c7fa105f0833ba203665f472d/maps.zip b/docs/dev/_downloads/4722787c7fa105f0833ba203665f472d/maps.zip
diff --git a/docs/dev/_downloads/4c5e33d8438fd5229f2e9205d79c47b5/create_region.pdf b/docs/dev/_downloads/4c5e33d8438fd5229f2e9205d79c47b5/create_region.pdf
diff --git a/docs/dev/_downloads/4ea9a264083bf99663b11b14fd6b5368/plot_constant_spectral.zip b/docs/dev/_downloads/4ea9a264083bf99663b11b14fd6b5368/plot_constant_spectral.zip
diff --git a/...ev/_downloads/4f3c47ff11347e8788ce002c9c32adc3/plot_exp_cutoff_powerlaw_norm_spectral.zip b/...ev/_downloads/4f3c47ff11347e8788ce002c9c32adc3/plot_exp_cutoff_powerlaw_norm_spectral.zip
diff --git a/docs/dev/_downloads/5195feb3ff54e1188c53522657e57ed1/variability_estimation.zip b/docs/dev/_downloads/5195feb3ff54e1188c53522657e57ed1/variability_estimation.zip
diff --git a/docs/dev/_downloads/51d6cfa441dab374164568c0a25aafdf/plot_snr_radius_evolution.pdf b/docs/dev/_downloads/51d6cfa441dab374164568c0a25aafdf/plot_snr_radius_evolution.pdf
diff --git a/docs/dev/_downloads/5375f8e9e54dd5a68d37e8a2fc4246b9/plot_spiral_arms.png b/docs/dev/_downloads/5375f8e9e54dd5a68d37e8a2fc4246b9/plot_spiral_arms.png
diff --git a/docs/dev/_downloads/5d70733460edff98657b0fd5d0eb69e4/modeling_2D.zip b/docs/dev/_downloads/5d70733460edff98657b0fd5d0eb69e4/modeling_2D.zip
diff --git a/docs/dev/_downloads/5f7885af4d03116bd8091cb6252eae48/analysis_3d.zip b/docs/dev/_downloads/5f7885af4d03116bd8091cb6252eae48/analysis_3d.zip
diff --git a/docs/dev/_downloads/60399e3ce9aa0bfe3b7ca8f286fad1ce/plot_powerlaw_norm_spectral.zip b/docs/dev/_downloads/60399e3ce9aa0bfe3b7ca8f286fad1ce/plot_powerlaw_norm_spectral.zip
diff --git a/docs/dev/_downloads/66ad9a060e067da15dee5cf2536ae998/spectral_analysis_rad_max.zip b/docs/dev/_downloads/66ad9a060e067da15dee5cf2536ae998/spectral_analysis_rad_max.zip
diff --git a/docs/dev/_downloads/682e4483582d9fdb778dbd09a795d104/plot_compound.zip b/docs/dev/_downloads/682e4483582d9fdb778dbd09a795d104/plot_compound.zip
diff --git a/docs/dev/_downloads/692bf70e2aa9caeb987bbb35050f71d2/astro_dark_matter.zip b/docs/dev/_downloads/692bf70e2aa9caeb987bbb35050f71d2/astro_dark_matter.zip
diff --git a/docs/dev/_downloads/6e47fca124ba94afacd2e1f76cf61081/plot_template_phase_temporal.zip b/docs/dev/_downloads/6e47fca124ba94afacd2e1f76cf61081/plot_template_phase_temporal.zip
diff --git a/docs/dev/_downloads/70db13717c83b8e636f99ad0018eeb90/plot_template.zip b/docs/dev/_downloads/70db13717c83b8e636f99ad0018eeb90/plot_template.zip
diff --git a/docs/dev/_downloads/70f4f45e10d92d7ae2e6576d43cb98a7/variability_estimation.ipynb b/docs/dev/_downloads/70f4f45e10d92d7ae2e6576d43cb98a7/variability_estimation.ipynb
@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "lc_1d = FluxPoints.read(\n    \"$GAMMAPY_DATA/estimators/pks2155_hess_lc/pks2155_hess_lc.fits\", format=\"lightcurve\"\n)\n\nplt.figure(figsize=(8, 6))\nlc_1d.plot(marker=\"o\")\nplt.show()"
+        "lc_1d = FluxPoints.read(\n    \"$GAMMAPY_DATA/estimators/pks2155_hess_lc/pks2155_hess_lc.fits\", format=\"lightcurve\"\n)\n\nplt.figure(figsize=(8, 6))\nplt.subplots_adjust(bottom=0.2, left=0.2)\nlc_1d.plot(marker=\"o\")\nplt.show()"
       ]
     },
     {
@@ -83,7 +83,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The variability amplitude as presented in [Heidt & Wagner, 1996](https://ui.adsabs.harvard.edu/abs/1996A%26A...305...42H/abstract)_ is:\n\n"
+        "The variability amplitude as presented in\n[Heidt & Wagner, 1996](https://ui.adsabs.harvard.edu/abs/1996A%26A...305...42H/abstract)_ is:\n\n"
       ]
     },
     {
@@ -101,7 +101,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Fractional excess variance, point-to-point fractional variance and doubling/halving time\nThe fractional excess variance, as presented by\n[Vaughan et al., 2003](https://ui.adsabs.harvard.edu/abs/2003MNRAS.345.1271V/abstract)_, is a simple but effective\nmethod to assess the significance of a time variability feature in an object, for example an AGN flare.\nIt is important to note that it requires Gaussian errors to be applicable.\nThe excess variance computation is implemented in `~gammapy.estimators.utils`.\n\n"
+        "## Fractional excess variance, point-to-point fractional variance and doubling/halving time\nThe fractional excess variance, as presented by\n[Vaughan et al., 2003](https://ui.adsabs.harvard.edu/abs/2003MNRAS.345.1271V/abstract)_, is\na simple but effective method to assess the significance of a time variability feature in an object,\nfor example an AGN flare. It is important to note that it requires Gaussian errors to be applicable.\nThe excess variance computation is implemented in `~gammapy.estimators.utils`.\n\n"
       ]
     },
     {
@@ -155,7 +155,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Bayesian blocks\nThe presence of temporal variability in a lightcurve can be assessed by using bayesian blocks\n([Scargle et al., 2013](https://ui.adsabs.harvard.edu/abs/2013ApJ...764..167S/abstract)_).\nA good and simple-to-use implementation of the algorithm is found in\n`astropy.stats.bayesian_blocks`.\nThis implementation uses Gaussian statistics, as opposed to the [first introductory paper](https://iopscience.iop.org/article/10.1086/306064)_\nwhich is based on Poissonian statistics.\n\nBy passing the flux and error on the flux as ``measures`` to the method we can obtain the list of optimal bin edges\ndefined by the bayesian blocks algorithm.\n\n"
+        "## Bayesian blocks\nThe presence of temporal variability in a lightcurve can be assessed by using bayesian blocks\n([Scargle et al., 2013](https://ui.adsabs.harvard.edu/abs/2013ApJ...764..167S/abstract)_).\nA good and simple-to-use implementation of the algorithm is found in\n`astropy.stats.bayesian_blocks`.\nThis implementation uses Gaussian statistics, as opposed to the\n[first introductory paper](https://iopscience.iop.org/article/10.1086/306064)_\nwhich is based on Poissonian statistics.\n\nBy passing the flux and error on the flux as ``measures`` to the method we can obtain the list of optimal bin edges\ndefined by the bayesian blocks algorithm.\n\n"
       ]
     },
     {
@@ -173,7 +173,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The result giving a significance estimation for variability in the lightcurve is the number of *change points*,\ni.e. the number of internal bin edges: if at least one change point is identified by the algorithm, there is significant variability.\n\n"
+        "The result giving a significance estimation for variability in the lightcurve is the number of *change points*,\ni.e. the number of internal bin edges: if at least one change point is identified by the algorithm,\nthere is significant variability.\n\n"
       ]
     },
     {
@@ -191,7 +191,50 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We can rebin the lightcurve to compute the one expected with bayesian edges\nFirst, we adjust the first and last bins of the bayesian_edges to coincide\nwith the original light curve start and end points.\n\n"
+        "We can rebin the lightcurve to compute the one expected with bayesian edges.\nFirst, we adjust the first and last bins of the ``bayesian_edges`` to coincide\nwith the original light curve start and end points.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create a new axis:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "axis_original = lc_1d.geom.axes[\"time\"]\nbayesian_edges[0] = axis_original.time_edges[0].value\nbayesian_edges[-1] = axis_original.time_edges[-1].value\nedges = Time(bayesian_edges, format=\"mjd\", scale=axis_original.reference_time.scale)\naxis_new = TimeMapAxis.from_time_edges(edges[:-1], edges[1:])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Rebin the lightcurve:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "resample = lc_1d.resample_axis(axis_new)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot the new lightcurve on top of the old one:\n\n"
       ]
     },
     {
@@ -202,7 +245,7 @@
       },
       "outputs": [],
       "source": [
-        "# create a new axis\naxis_original = lc_1d.geom.axes[\"time\"]\nbayesian_edges[0] = axis_original.time_edges[0].value\nbayesian_edges[-1] = axis_original.time_edges[-1].value\nedges = Time(bayesian_edges, format=\"mjd\", scale=axis_original.reference_time.scale)\naxis_new = TimeMapAxis.from_time_edges(edges[:-1], edges[1:])\n\n# rebin the lightcurve\nresample = lc_1d.resample_axis(axis_new)\n\n# plot the new lightcurve on top of the old one\nax = lc_1d.plot(label=\"original\")\nresample.plot(ax=ax, marker=\"s\", label=\"rebinned\")\nplt.legend()"
+        "plt.figure(figsize=(8, 6))\nplt.subplots_adjust(bottom=0.2, left=0.2)\nax = lc_1d.plot(label=\"original\")\nresample.plot(ax=ax, marker=\"s\", label=\"rebinned\")\nplt.legend()"
       ]
     }
   ],

diff --git a/docs/dev/_downloads/73e595818811c6987149d60742eef584/make_reflected_regions.pdf b/docs/dev/_downloads/73e595818811c6987149d60742eef584/make_reflected_regions.pdf
diff --git a/...dev/_downloads/73e7de11148f573eeb51f279652f7b9b/make_rectangular_reflected_background.pdf b/...dev/_downloads/73e7de11148f573eeb51f279652f7b9b/make_rectangular_reflected_background.pdf
diff --git a/docs/dev/_downloads/74468ce448910c5a873be0e51cee13fb/plot_pwn_evolution.pdf b/docs/dev/_downloads/74468ce448910c5a873be0e51cee13fb/plot_pwn_evolution.pdf
diff --git a/docs/dev/_downloads/760eb275a0d8a9d11ea857eb956132f2/plot_naima.zip b/docs/dev/_downloads/760eb275a0d8a9d11ea857eb956132f2/plot_naima.zip
diff --git a/docs/dev/_downloads/7757318506e3a6721d12df18db780f37/plot_sine_temporal.zip b/docs/dev/_downloads/7757318506e3a6721d12df18db780f37/plot_sine_temporal.zip
diff --git a/docs/dev/_downloads/7f0f5ef8ba9bdfd9da1248f73ad010fe/overview.zip b/docs/dev/_downloads/7f0f5ef8ba9bdfd9da1248f73ad010fe/overview.zip
diff --git a/docs/dev/_downloads/800707065c222d9fe3c88a38845901ae/regionmap-2.pdf b/docs/dev/_downloads/800707065c222d9fe3c88a38845901ae/regionmap-2.pdf
diff --git a/docs/dev/_downloads/83da3445f53cc2258537f3ea6b2b0970/regionmap-5.pdf b/docs/dev/_downloads/83da3445f53cc2258537f3ea6b2b0970/regionmap-5.pdf
diff --git a/docs/dev/_downloads/84424d59ec624940199b69d87a792d50/index-1.pdf b/docs/dev/_downloads/84424d59ec624940199b69d87a792d50/index-1.pdf
diff --git a/docs/dev/_downloads/8755395ab1ca43f7e8444bdd5355f7fb/cta_sensitivity.zip b/docs/dev/_downloads/8755395ab1ca43f7e8444bdd5355f7fb/cta_sensitivity.zip
diff --git a/docs/dev/_downloads/879fb38cf085340a15de2c5c775d1fa2/pulsar_analysis.zip b/docs/dev/_downloads/879fb38cf085340a15de2c5c775d1fa2/pulsar_analysis.zip
diff --git a/...downloads/8a9ae62d4481bef2294fabff4afc70cf/gammapy-irf-EnergyDependentMultiGaussPSF-1.pdf b/...downloads/8a9ae62d4481bef2294fabff4afc70cf/gammapy-irf-EnergyDependentMultiGaussPSF-1.pdf
diff --git a/docs/dev/_downloads/8d795d04d6c1419b268a0a4676041f36/model-gallery_python.zip b/docs/dev/_downloads/8d795d04d6c1419b268a0a4676041f36/model-gallery_python.zip
diff --git a/docs/dev/_downloads/8f7cba6e147ac68586bc14a007ba4cc5/plot_spiral_arms.pdf b/docs/dev/_downloads/8f7cba6e147ac68586bc14a007ba4cc5/plot_spiral_arms.pdf
diff --git a/docs/dev/_downloads/90e01209a5243d5c972882c04584c8e3/plot_aeff_param.pdf b/docs/dev/_downloads/90e01209a5243d5c972882c04584c8e3/plot_aeff_param.pdf
diff --git a/docs/dev/_downloads/920df746e67257657f5d44e868b95ff9/fitting.zip b/docs/dev/_downloads/920df746e67257657f5d44e868b95ff9/fitting.zip
diff --git a/docs/dev/_downloads/922821654c8ba9bfa3b550cbf4e44e37/estimators.zip b/docs/dev/_downloads/922821654c8ba9bfa3b550cbf4e44e37/estimators.zip
diff --git a/docs/dev/_downloads/947d48cd2a213f701fd804045a8e07ba/plot_template_temporal.zip b/docs/dev/_downloads/947d48cd2a213f701fd804045a8e07ba/plot_template_temporal.zip
diff --git a/docs/dev/_downloads/97689216e51c6b88714828e3017abcd6/plot_expdecay_temporal.zip b/docs/dev/_downloads/97689216e51c6b88714828e3017abcd6/plot_expdecay_temporal.zip
diff --git a/docs/dev/_downloads/9784111b1a9a5c8f07b727039a6e8b82/plot_radial_distributions.pdf b/docs/dev/_downloads/9784111b1a9a5c8f07b727039a6e8b82/plot_radial_distributions.pdf
diff --git a/docs/dev/_downloads/9b87dc4ad92a01355cc5cdce7c2881d3/ring_background.zip b/docs/dev/_downloads/9b87dc4ad92a01355cc5cdce7c2881d3/ring_background.zip
diff --git a/docs/dev/_downloads/a28867a6c5c2c6d9e3f31c1c79412c84/plot_gen_gauss.zip b/docs/dev/_downloads/a28867a6c5c2c6d9e3f31c1c79412c84/plot_gen_gauss.zip
diff --git a/docs/dev/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip b/docs/dev/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip
diff --git a/docs/dev/_downloads/a5b2ee0329e727a4750cc6807854a799/plot_snr_brightness_evolution.pdf b/docs/dev/_downloads/a5b2ee0329e727a4750cc6807854a799/plot_snr_brightness_evolution.pdf
diff --git a/docs/dev/_downloads/a60ac3367f6b8b4daf62f7767737c935/regionmap-3.pdf b/docs/dev/_downloads/a60ac3367f6b8b4daf62f7767737c935/regionmap-3.pdf
diff --git a/docs/dev/_downloads/a647e703f1c14b3b2be137109a402268/plot_wstat_significance.pdf b/docs/dev/_downloads/a647e703f1c14b3b2be137109a402268/plot_wstat_significance.pdf
diff --git a/docs/dev/_downloads/a6ff1b10c982034bb913f63627b76412/plot_point.zip b/docs/dev/_downloads/a6ff1b10c982034bb913f63627b76412/plot_point.zip
diff --git a/docs/dev/_downloads/a7638b2c3d4b20bfbdde841a8af3fc11/catalog.py b/docs/dev/_downloads/a7638b2c3d4b20bfbdde841a8af3fc11/catalog.py
@@ -61,6 +61,7 @@
 # Check setup
 # -----------
 from gammapy.utils.check import check_tutorials_setup
+
 check_tutorials_setup()
 
 
@@ -98,7 +99,6 @@
 #
 
 
-
 catalog = SourceCatalog4FGL()
 print("Number of sources :", len(catalog.table))
 
@@ -397,6 +397,8 @@
 display(lightcurve.to_table(format="lightcurve", sed_type="flux"))
 
 # %%
+plt.figure(figsize=(8, 6))
+plt.subplots_adjust(bottom=0.2, left=0.2)
 lightcurve.plot()
 plt.show()
 

diff --git a/docs/dev/_downloads/a9d5014c13b41c72b29e8ab09728a51b/plot_logparabola_norm_spectral.zip b/docs/dev/_downloads/a9d5014c13b41c72b29e8ab09728a51b/plot_logparabola_norm_spectral.zip
diff --git a/docs/dev/_downloads/ac30ac3c375ddb5b0d1e27c90650e55b/spectral_analysis.ipynb b/docs/dev/_downloads/ac30ac3c375ddb5b0d1e27c90650e55b/spectral_analysis.ipynb
@@ -468,7 +468,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## A note on statistics\n\nDifferent statistic are available for the FluxPointDataset :\n* chi2 : estimate from chi2 statistics.\n* profile : estimate from interpolation of the likelihood profile.\n* distrib : estimate from probability distributions,\n            assuming that flux points correspond to asymmetric gaussians\n            and upper limits complementary error functions.\nDefault is `chi2`, in that case upper limits are ignored and the mean of asymetrics error is used.\nSo it is recommended to use `profile` if `stat_scan` is available on flux points.\nThe `distrib` case provides an approximation if the `profile` is not available\nwhich allows to take into accounts upper limit and asymetrics error.\n\nIn the example below we can see that the `profile` case matches exactly the result\nfrom the joint analysis of the ON/OFF datasets using `wstat` (as labelled).\n\n"
+        "## A note on statistics\n\nDifferent statistic are available for the `~gammapy.datasets.FluxPointsDataset` :\n\n- chi2 : estimate from chi2 statistics.\n- profile : estimate from interpolation of the likelihood profile.\n- distrib : estimate from probability distributions,\n            assuming that flux points correspond to asymmetric gaussians\n            and upper limits complementary error functions.\n\nDefault is `chi2`, in that case upper limits are ignored and the mean of asymetrics error is used.\nSo it is recommended to use `profile` if `stat_scan` is available on flux points.\nThe `distrib` case provides an approximation if the `profile` is not available\nwhich allows to take into accounts upper limit and asymetrics error.\n\nIn the example below we can see that the `profile` case matches exactly the result\nfrom the joint analysis of the ON/OFF datasets using `wstat` (as labelled).\n\n"
       ]
     },
     {
@@ -482,6 +482,13 @@
         "def plot_stat(fp_dataset):\n    fig, ax = plt.subplots()\n\n    plot_kwargs = {\n        \"energy_bounds\": [0.1, 30] * u.TeV,\n        \"sed_type\": \"e2dnde\",\n        \"ax\": ax,\n    }\n\n    fp_dataset.data.plot(energy_power=2, ax=ax)\n    model_best_joint.spectral_model.plot(\n        color=\"b\", lw=0.5, **plot_kwargs, label=\"wstat\"\n    )\n\n    stat_types = [\"chi2\", \"profile\", \"distrib\"]\n    colors = [\"red\", \"g\", \"c\"]\n    lss = [\"--\", \":\", \"--\"]\n\n    for ks, stat in enumerate(stat_types):\n        fp_dataset.stat_type = stat\n\n        fit = Fit()\n        fit.run([fp_dataset])\n\n        fp_dataset.models[0].spectral_model.plot(\n            color=colors[ks], ls=lss[ks], **plot_kwargs, label=stat\n        )\n        fp_dataset.models[0].spectral_model.plot_error(\n            facecolor=colors[ks], **plot_kwargs\n        )\n        plt.legend()\n\n\nplot_stat(flux_points_dataset)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In order to avoid discrepancies due to the treatment of upper limits\nwe can utilise the `~gammapy.estimators.utils.resample_energy_edges`\nfor defining energy bins in which the minimum number of `sqrt_ts` is 2.\nIn that case all the statistics definitions give equivalent results.\n\n\n"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -490,7 +497,7 @@
       },
       "outputs": [],
       "source": [
-        "# In order to avoid discrepancies due to the treatment of upper limits\n# we can utilise the `~gammapy.estimators.utils.resample_energy_edges`\n# for defining energy bins in which the minimum number of `sqrt_ts` is 2.\n# In that case all the statistics definitions give equivalent results.\n#\n\nenergy_edges = resample_energy_edges(dataset_stacked, conditions={\"sqrt_ts_min\": 2})\n\nfpe_no_ul = FluxPointsEstimator(\n    energy_edges=energy_edges, source=\"crab\", selection_optional=\"all\"\n)\nflux_points_no_ul = fpe_no_ul.run(datasets=datasets)\nflux_points_dataset_no_ul = FluxPointsDataset(\n    data=flux_points_no_ul,\n    models=model_best_joint.copy(),\n)\n\nplot_stat(flux_points_dataset_no_ul)"
+        "energy_edges = resample_energy_edges(dataset_stacked, conditions={\"sqrt_ts_min\": 2})\n\nfpe_no_ul = FluxPointsEstimator(\n    energy_edges=energy_edges, source=\"crab\", selection_optional=\"all\"\n)\nflux_points_no_ul = fpe_no_ul.run(datasets=datasets)\nflux_points_dataset_no_ul = FluxPointsDataset(\n    data=flux_points_no_ul,\n    models=model_best_joint.copy(),\n)\n\nplot_stat(flux_points_dataset_no_ul)"
       ]
     },
     {

diff --git a/docs/dev/_downloads/ac76b976c8eed3f5e3889db8bc14cf8e/makers.zip b/docs/dev/_downloads/ac76b976c8eed3f5e3889db8bc14cf8e/makers.zip
diff --git a/docs/dev/_downloads/ad89d541901d6f48959e38416a90de29/catalog.ipynb b/docs/dev/_downloads/ad89d541901d6f48959e38416a90de29/catalog.ipynb
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "from gammapy.utils.check import check_tutorials_setup\ncheck_tutorials_setup()"
+        "from gammapy.utils.check import check_tutorials_setup\n\ncheck_tutorials_setup()"
       ]
     },
     {
@@ -591,7 +591,7 @@
       },
       "outputs": [],
       "source": [
-        "lightcurve.plot()\nplt.show()"
+        "plt.figure(figsize=(8, 6))\nplt.subplots_adjust(bottom=0.2, left=0.2)\nlightcurve.plot()\nplt.show()"
       ]
     },
     {

diff --git a/docs/dev/_downloads/b3336d8a1b4e8487945a83d57e35ce6b/plot_exp_cutoff_powerlaw_3fgl.zip b/docs/dev/_downloads/b3336d8a1b4e8487945a83d57e35ce6b/plot_exp_cutoff_powerlaw_3fgl.zip
diff --git a/docs/dev/_downloads/ba616a9da5fd4ea6727cee926df2b69e/ebl.zip b/docs/dev/_downloads/ba616a9da5fd4ea6727cee926df2b69e/ebl.zip
diff --git a/docs/dev/_downloads/bc7f4b2c8d9d79b35ecf0b38127aca8c/energy_dependent_estimation.zip b/docs/dev/_downloads/bc7f4b2c8d9d79b35ecf0b38127aca8c/energy_dependent_estimation.zip
diff --git a/docs/dev/_downloads/be73f34ea8068af3a10d3559a704a86c/plot_piecewise_norm_spatial.zip b/docs/dev/_downloads/be73f34ea8068af3a10d3559a704a86c/plot_piecewise_norm_spatial.zip
diff --git a/...v/_downloads/bf0a16a362522ed59ca5852f58d8d53c/plot_super_exp_cutoff_powerlaw_4fgl_dr1.zip b/...v/_downloads/bf0a16a362522ed59ca5852f58d8d53c/plot_super_exp_cutoff_powerlaw_4fgl_dr1.zip
diff --git a/docs/dev/_downloads/bfe6aa14bdebe4f65370b05f400755e5/plot_cash_significance.pdf b/docs/dev/_downloads/bfe6aa14bdebe4f65370b05f400755e5/plot_cash_significance.pdf
diff --git a/docs/dev/_downloads/c1da6aedff551cba26aafdf962f74127/plot_shell.zip b/docs/dev/_downloads/c1da6aedff551cba26aafdf962f74127/plot_shell.zip
diff --git a/docs/dev/_downloads/c2775618fc6e3f5a7b28e723a9132470/plot_aeff.pdf b/docs/dev/_downloads/c2775618fc6e3f5a7b28e723a9132470/plot_aeff.pdf
diff --git a/docs/dev/_downloads/c2b61900ea448b3f1bbacb5848c7ae6e/plot_pulsar_spindown.pdf b/docs/dev/_downloads/c2b61900ea448b3f1bbacb5848c7ae6e/plot_pulsar_spindown.pdf
diff --git a/docs/dev/_downloads/c3e24f907cf5ba5950daf05c578e630c/plot_powerlaw.zip b/docs/dev/_downloads/c3e24f907cf5ba5950daf05c578e630c/plot_powerlaw.zip
diff --git a/docs/dev/_downloads/c6bad13b7bd034ce07497e4d5025d5d2/edisp-1.pdf b/docs/dev/_downloads/c6bad13b7bd034ce07497e4d5025d5d2/edisp-1.pdf
diff --git a/docs/dev/_downloads/c8b2c52022beac75de2a3a8b628986e3/plot_absorbed.zip b/docs/dev/_downloads/c8b2c52022beac75de2a3a8b628986e3/plot_absorbed.zip
diff --git a/docs/dev/_downloads/c8dacdfbdf94c97cce7a5ee9555295b5/plot_cash_errors.pdf b/docs/dev/_downloads/c8dacdfbdf94c97cce7a5ee9555295b5/plot_cash_errors.pdf
diff --git a/docs/dev/_downloads/cbf21326239e0fa794d51e4515e770f5/spectral_analysis_hli.zip b/docs/dev/_downloads/cbf21326239e0fa794d51e4515e770f5/spectral_analysis_hli.zip
diff --git a/docs/dev/_downloads/cf72688370a27db6e3e5a36401e825da/plot_powerlaw2.zip b/docs/dev/_downloads/cf72688370a27db6e3e5a36401e825da/plot_powerlaw2.zip
diff --git a/docs/dev/_downloads/d0da679c6d748017e74a51a72190ec8f/spectrum_simulation.zip b/docs/dev/_downloads/d0da679c6d748017e74a51a72190ec8f/spectrum_simulation.zip
diff --git a/docs/dev/_downloads/d3f2b47ffbc735c23e8ffc373b698607/light_curve.zip b/docs/dev/_downloads/d3f2b47ffbc735c23e8ffc373b698607/light_curve.zip
diff --git a/docs/dev/_downloads/d46f4cc1fb54f9148150a46cabce732a/plot_spiral_arm_models.pdf b/docs/dev/_downloads/d46f4cc1fb54f9148150a46cabce732a/plot_spiral_arm_models.pdf
diff --git a/docs/dev/_downloads/d47b4b3bccd5fc1699bbc9406a3c4035/plot_edisp.pdf b/docs/dev/_downloads/d47b4b3bccd5fc1699bbc9406a3c4035/plot_edisp.pdf
diff --git a/docs/dev/_downloads/d84a1f60bef1b2a4d70bc60ed632228b/plot_generalized_gaussian_temporal.zip b/docs/dev/_downloads/d84a1f60bef1b2a4d70bc60ed632228b/plot_generalized_gaussian_temporal.zip
diff --git a/docs/dev/_downloads/db49dbe8386e235e780f90506f4c13d4/plot_gaussian_temporal.zip b/docs/dev/_downloads/db49dbe8386e235e780f90506f4c13d4/plot_gaussian_temporal.zip
diff --git a/docs/dev/_downloads/dc84ab4fb2154102a7b86605311ec685/plot_linear_temporal.zip b/docs/dev/_downloads/dc84ab4fb2154102a7b86605311ec685/plot_linear_temporal.zip
diff --git a/docs/dev/_downloads/dc889dafa0f60a201223634e8d485a12/mask_maps.zip b/docs/dev/_downloads/dc889dafa0f60a201223634e8d485a12/mask_maps.zip
diff --git a/docs/dev/_downloads/ddf4900ca46e50deae170060d7a15571/flux_profiles.zip b/docs/dev/_downloads/ddf4900ca46e50deae170060d7a15571/flux_profiles.zip
diff --git a/docs/dev/_downloads/df937214d98218a68007eccea50ab49d/plot_logparabola.zip b/docs/dev/_downloads/df937214d98218a68007eccea50ab49d/plot_logparabola.zip
diff --git a/docs/dev/_downloads/e0a543cc50809937467ce947bbbfc283/spectral_analysis.py b/docs/dev/_downloads/e0a543cc50809937467ce947bbbfc283/spectral_analysis.py
@@ -499,12 +499,14 @@
 # A note on statistics
 # --------------------
 #
-# Different statistic are available for the FluxPointDataset :
-# * chi2 : estimate from chi2 statistics.
-# * profile : estimate from interpolation of the likelihood profile.
-# * distrib : estimate from probability distributions,
+# Different statistic are available for the `~gammapy.datasets.FluxPointsDataset` :
+#
+# - chi2 : estimate from chi2 statistics.
+# - profile : estimate from interpolation of the likelihood profile.
+# - distrib : estimate from probability distributions,
 #             assuming that flux points correspond to asymmetric gaussians
 #             and upper limits complementary error functions.
+#
 # Default is `chi2`, in that case upper limits are ignored and the mean of asymetrics error is used.
 # So it is recommended to use `profile` if `stat_scan` is available on flux points.
 # The `distrib` case provides an approximation if the `profile` is not available
@@ -550,7 +552,7 @@ def plot_stat(fp_dataset):
 plot_stat(flux_points_dataset)
 
 ######################################################################
-
+#
 # In order to avoid discrepancies due to the treatment of upper limits
 # we can utilise the `~gammapy.estimators.utils.resample_energy_edges`
 # for defining energy bins in which the minimum number of `sqrt_ts` is 2.

diff --git a/docs/dev/_downloads/e1ae92381427a3e34d582ae0a377cb19/time_resolved_spectroscopy.zip b/docs/dev/_downloads/e1ae92381427a3e34d582ae0a377cb19/time_resolved_spectroscopy.zip
diff --git a/docs/dev/_downloads/e62e3d07400cc367a5dd87acd8e51755/analysis_mwl.zip b/docs/dev/_downloads/e62e3d07400cc367a5dd87acd8e51755/analysis_mwl.zip
diff --git a/docs/dev/_downloads/e70cbbbc7e5076450d85dba47e48c39d/cta_data_analysis.zip b/docs/dev/_downloads/e70cbbbc7e5076450d85dba47e48c39d/cta_data_analysis.zip
diff --git a/docs/dev/_downloads/e8a68734048b7e0ceca7060951d52b8d/regionmap-4.pdf b/docs/dev/_downloads/e8a68734048b7e0ceca7060951d52b8d/regionmap-4.pdf
diff --git a/docs/dev/_downloads/e91c6a55acd94e0e8b0019a37008ed49/plot_wstat_errors.pdf b/docs/dev/_downloads/e91c6a55acd94e0e8b0019a37008ed49/plot_wstat_errors.pdf
diff --git a/docs/dev/_downloads/e9a1b16d92240aed069574aa6cac746b/datasets.zip b/docs/dev/_downloads/e9a1b16d92240aed069574aa6cac746b/datasets.zip
diff --git a/docs/dev/_downloads/ebd45185ed3e93103504e3303364e3ef/plot_velocity_distributions.pdf b/docs/dev/_downloads/ebd45185ed3e93103504e3303364e3ef/plot_velocity_distributions.pdf
diff --git a/docs/dev/_downloads/f48159f5f5e15c2ab4d4c84f71fb7688/plot_edisp_kernel_param.pdf b/docs/dev/_downloads/f48159f5f5e15c2ab4d4c84f71fb7688/plot_edisp_kernel_param.pdf
diff --git a/docs/dev/_downloads/f49aa1b6b6eeb66ac89f34f562b16f06/observation_clustering.zip b/docs/dev/_downloads/f49aa1b6b6eeb66ac89f34f562b16f06/observation_clustering.zip
diff --git a/docs/dev/_downloads/f5a9665c6b3d034a7c33a9ef624a6185/plot_edisp_kernel.pdf b/docs/dev/_downloads/f5a9665c6b3d034a7c33a9ef624a6185/plot_edisp_kernel.pdf
diff --git a/docs/dev/_downloads/f859fe731238e2fbe18ae0bd172f1eb8/fermi_lat.zip b/docs/dev/_downloads/f859fe731238e2fbe18ae0bd172f1eb8/fermi_lat.zip
diff --git a/docs/dev/_downloads/f9c68c424e775be9a0733268e0a48a8e/plot_gauss_spectral.zip b/docs/dev/_downloads/f9c68c424e775be9a0733268e0a48a8e/plot_gauss_spectral.zip
diff --git a/docs/dev/_downloads/fc59bdccd0cfc40136461f8aea2aa6fe/colormap_example.pdf b/docs/dev/_downloads/fc59bdccd0cfc40136461f8aea2aa6fe/colormap_example.pdf
diff --git a/docs/dev/_downloads/fcaf31b314fb2e9a9a35ee9755b2f688/plot_super_exp_cutoff_powerlaw_4fgl.zip b/docs/dev/_downloads/fcaf31b314fb2e9a9a35ee9755b2f688/plot_super_exp_cutoff_powerlaw_4fgl.zip
diff --git a/docs/dev/_downloads/ff119672161c3dc35004bc7de3874a4d/analysis_2.zip b/docs/dev/_downloads/ff119672161c3dc35004bc7de3874a4d/analysis_2.zip
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diff --git a/docs/dev/_images/sphx_glr_plot_constant_temporal_001.png b/docs/dev/_images/sphx_glr_plot_constant_temporal_001.png
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diff --git a/docs/dev/_images/sphx_glr_plot_linear_temporal_thumb.png b/docs/dev/_images/sphx_glr_plot_linear_temporal_thumb.png
diff --git a/docs/dev/_images/sphx_glr_plot_powerlaw_temporal_001.png b/docs/dev/_images/sphx_glr_plot_powerlaw_temporal_001.png
diff --git a/docs/dev/_images/sphx_glr_plot_powerlaw_temporal_thumb.png b/docs/dev/_images/sphx_glr_plot_powerlaw_temporal_thumb.png
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diff --git a/docs/dev/_images/sphx_glr_priors_002.png b/docs/dev/_images/sphx_glr_priors_002.png
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diff --git a/docs/dev/_images/sphx_glr_simulate_3d_001.png b/docs/dev/_images/sphx_glr_simulate_3d_001.png
diff --git a/docs/dev/_images/sphx_glr_simulate_3d_002.png b/docs/dev/_images/sphx_glr_simulate_3d_002.png
diff --git a/docs/dev/_images/sphx_glr_simulate_3d_thumb.png b/docs/dev/_images/sphx_glr_simulate_3d_thumb.png
diff --git a/docs/dev/_images/sphx_glr_spectral_analysis_008.png b/docs/dev/_images/sphx_glr_spectral_analysis_008.png
diff --git a/docs/dev/_images/sphx_glr_spectral_analysis_rad_max_005.png b/docs/dev/_images/sphx_glr_spectral_analysis_rad_max_005.png
diff --git a/docs/dev/_images/sphx_glr_variability_estimation_001.png b/docs/dev/_images/sphx_glr_variability_estimation_001.png
diff --git a/docs/dev/_images/sphx_glr_variability_estimation_002.png b/docs/dev/_images/sphx_glr_variability_estimation_002.png
diff --git a/docs/dev/_images/sphx_glr_variability_estimation_thumb.png b/docs/dev/_images/sphx_glr_variability_estimation_thumb.png
diff --git a/docs/dev/_modules/gammapy/analysis/config.html b/docs/dev/_modules/gammapy/analysis/config.html
@@ -67,7 +67,7 @@
     <link rel="search" title="Search" href="../../../search.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
-  <meta name="docsearch:version" content="1.3.dev1388+g7690890e7" />
+  <meta name="docsearch:version" content="1.3.dev1400+g0e41da044" />
     <meta name="docbuild:last-update" content="12 Nov 2024"/>
   </head>
 

diff --git a/docs/dev/_modules/gammapy/analysis/core.html b/docs/dev/_modules/gammapy/analysis/core.html
@@ -67,7 +67,7 @@
     <link rel="search" title="Search" href="../../../search.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
-  <meta name="docsearch:version" content="1.3.dev1388+g7690890e7" />
+  <meta name="docsearch:version" content="1.3.dev1400+g0e41da044" />
     <meta name="docbuild:last-update" content="12 Nov 2024"/>
   </head>
 

diff --git a/docs/dev/_modules/gammapy/astro/darkmatter/profiles.html b/docs/dev/_modules/gammapy/astro/darkmatter/profiles.html
@@ -67,7 +67,7 @@
     <link rel="search" title="Search" href="../../../../search.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
-  <meta name="docsearch:version" content="1.3.dev1388+g7690890e7" />
+  <meta name="docsearch:version" content="1.3.dev1400+g0e41da044" />
     <meta name="docbuild:last-update" content="12 Nov 2024"/>
   </head>
 

diff --git a/docs/dev/_modules/gammapy/astro/darkmatter/spectra.html b/docs/dev/_modules/gammapy/astro/darkmatter/spectra.html
@@ -67,7 +67,7 @@
     <link rel="search" title="Search" href="../../../../search.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
-  <meta name="docsearch:version" content="1.3.dev1388+g7690890e7" />
+  <meta name="docsearch:version" content="1.3.dev1400+g0e41da044" />
     <meta name="docbuild:last-update" content="12 Nov 2024"/>
   </head>
 

diff --git a/docs/dev/_modules/gammapy/astro/darkmatter/utils.html b/docs/dev/_modules/gammapy/astro/darkmatter/utils.html
@@ -67,7 +67,7 @@
     <link rel="search" title="Search" href="../../../../search.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
-  <meta name="docsearch:version" content="1.3.dev1388+g7690890e7" />
+  <meta name="docsearch:version" content="1.3.dev1400+g0e41da044" />
     <meta name="docbuild:last-update" content="12 Nov 2024"/>
   </head>