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Expand Up @@ -317,7 +317,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute Flux Points\n\nTo round up our analysis we can compute flux points by fitting the norm\nof the global model in energy bands.\nWe can utilise the `~gammapy.estimators.utils.resample_energy_edges`\nfor defining the energy bins in which the minimum number of `sqrt_ts` is 2.\nTo do so we first stack the individual datasets, only for obtaining the energies:\n\n\n"
"## Compute Flux Points\n\nTo round up our analysis we can compute flux points by fitting the norm\nof the global model in energy bands.\nWe create an instance of the\n`~gammapy.estimators.FluxPointsEstimator`, by passing the dataset and\nthe energy binning:\n\n\n"
]
},
{
Expand All @@ -328,25 +328,7 @@
},
"outputs": [],
"source": [
"dataset_stacked = Datasets(datasets).stack_reduce()\nenergy_edges = resample_energy_edges(dataset_stacked, conditions={\"sqrt_ts_min\": 2})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we create an instance of the\n`~gammapy.estimators.FluxPointsEstimator`, by passing the dataset and\nthe energy binning:\n\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fpe = FluxPointsEstimator(\n energy_edges=energy_edges, source=\"crab\", selection_optional=\"all\"\n)\nflux_points = fpe.run(datasets=datasets)"
"fpe = FluxPointsEstimator(\n energy_edges=energy_axis.edges, source=\"crab\", selection_optional=\"all\"\n)\nflux_points = fpe.run(datasets=datasets)"
]
},
{
Expand Down Expand Up @@ -382,7 +364,7 @@
},
"outputs": [],
"source": [
"fig, ax = plt.subplots()\nflux_points.plot(ax=ax, sed_type=\"e2dnde\", color=\"darkorange\")\nflux_points.plot_ts_profiles(ax=ax, sed_type=\"e2dnde\")\nplt.show()"
"fig, ax = plt.subplots()\nflux_points.plot(ax=ax, sed_type=\"e2dnde\", color=\"darkorange\")\nflux_points.plot_ts_profiles(ax=ax, sed_type=\"e2dnde\")\nax.set_xlim(0.6, 40)\nplt.show()"
]
},
{
Expand All @@ -407,7 +389,7 @@
},
"outputs": [],
"source": [
"flux_points_dataset = FluxPointsDataset(data=flux_points, models=model_best_joint)\nflux_points_dataset.plot_fit()\nplt.show()"
"flux_points_dataset = FluxPointsDataset(\n data=flux_points, models=model_best_joint.copy()\n)\nax, _ = flux_points_dataset.plot_fit()\nax.set_xlim(0.6, 40)\nplt.show()"
]
},
{
Expand Down Expand Up @@ -482,6 +464,35 @@
"fig, ax = plt.subplots()\n\nplot_kwargs = {\n \"energy_bounds\": [0.1, 30] * u.TeV,\n \"sed_type\": \"e2dnde\",\n \"yunits\": u.Unit(\"erg cm-2 s-1\"),\n \"ax\": ax,\n}\n\n# plot stacked model\nmodel_best_stacked.spectral_model.plot(**plot_kwargs, label=\"Stacked analysis result\")\nmodel_best_stacked.spectral_model.plot_error(facecolor=\"blue\", alpha=0.3, **plot_kwargs)\n\n# plot joint model\nmodel_best_joint.spectral_model.plot(\n **plot_kwargs, label=\"Joint analysis result\", ls=\"--\"\n)\nmodel_best_joint.spectral_model.plot_error(facecolor=\"orange\", alpha=0.3, **plot_kwargs)\n\ncreate_crab_spectral_model(\"hess_ecpl\").plot(\n **plot_kwargs,\n label=\"Crab reference\",\n)\nax.legend()\nplt.show()\n\n# sphinx_gallery_thumbnail_number = 5"
]
},
{
"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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"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\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": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down
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Expand Up @@ -380,23 +380,13 @@
#
# To round up our analysis we can compute flux points by fitting the norm
# of the global model in energy bands.
# We can utilise the `~gammapy.estimators.utils.resample_energy_edges`
# for defining the energy bins in which the minimum number of `sqrt_ts` is 2.
# To do so we first stack the individual datasets, only for obtaining the energies:
#

dataset_stacked = Datasets(datasets).stack_reduce()
energy_edges = resample_energy_edges(dataset_stacked, conditions={"sqrt_ts_min": 2})


######################################################################
# Now we create an instance of the
# We create an instance of the
# `~gammapy.estimators.FluxPointsEstimator`, by passing the dataset and
# the energy binning:
#

fpe = FluxPointsEstimator(
energy_edges=energy_edges, source="crab", selection_optional="all"
energy_edges=energy_axis.edges, source="crab", selection_optional="all"
)
flux_points = fpe.run(datasets=datasets)

Expand All @@ -416,6 +406,7 @@
fig, ax = plt.subplots()
flux_points.plot(ax=ax, sed_type="e2dnde", color="darkorange")
flux_points.plot_ts_profiles(ax=ax, sed_type="e2dnde")
ax.set_xlim(0.6, 40)
plt.show()

######################################################################
Expand All @@ -430,8 +421,11 @@
# quickly made like this:
#

flux_points_dataset = FluxPointsDataset(data=flux_points, models=model_best_joint)
flux_points_dataset.plot_fit()
flux_points_dataset = FluxPointsDataset(
data=flux_points, models=model_best_joint.copy()
)
ax, _ = flux_points_dataset.plot_fit()
ax.set_xlim(0.6, 40)
plt.show()


Expand Down Expand Up @@ -503,6 +497,82 @@

# sphinx_gallery_thumbnail_number = 5

######################################################################
# 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,
# 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
# which allows to take into accounts upper limit and asymetrics error.
#
# In the example below we can see that the `profile` case matches exactly the result
# from the joint analysis of the ON/OFF datasets using `wstat` (as labelled).


def plot_stat(fp_dataset):
fig, ax = plt.subplots()

plot_kwargs = {
"energy_bounds": [0.1, 30] * u.TeV,
"sed_type": "e2dnde",
"ax": ax,
}

fp_dataset.data.plot(energy_power=2, ax=ax)
model_best_joint.spectral_model.plot(
color="b", lw=0.5, **plot_kwargs, label="wstat"
)

stat_types = ["chi2", "profile", "distrib"]
colors = ["red", "g", "c"]
lss = ["--", ":", "--"]

for ks, stat in enumerate(stat_types):

fp_dataset.stat_type = stat

fit = Fit()
fit.run([fp_dataset])

fp_dataset.models[0].spectral_model.plot(
color=colors[ks], ls=lss[ks], **plot_kwargs, label=stat
)
fp_dataset.models[0].spectral_model.plot_error(
facecolor=colors[ks], **plot_kwargs
)
plt.legend()


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.
# In that case all the statistics definitions give equivalent results.
#

energy_edges = resample_energy_edges(dataset_stacked, conditions={"sqrt_ts_min": 2})

fpe_no_ul = FluxPointsEstimator(
energy_edges=energy_edges, source="crab", selection_optional="all"
)
flux_points_no_ul = fpe_no_ul.run(datasets=datasets)
flux_points_dataset_no_ul = FluxPointsDataset(
data=flux_points_no_ul,
models=model_best_joint.copy(),
)

plot_stat(flux_points_dataset_no_ul)

######################################################################
# Exercises
# ---------
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2 changes: 1 addition & 1 deletion docs/dev/_modules/gammapy/analysis/config.html
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<meta name="viewport" content="width=device-width, initial-scale=1"/>
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<meta name="docbuild:last-update" content="16 May 2024"/>
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2 changes: 1 addition & 1 deletion docs/dev/_modules/gammapy/catalog/hess.html
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2 changes: 1 addition & 1 deletion docs/dev/_modules/gammapy/catalog/lhaaso.html
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2 changes: 1 addition & 1 deletion docs/dev/_modules/gammapy/data/data_store.html
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