def bkg_estimate(observations, on_region, exclusion_mask):
"""An example background estimate"""
est = ReflectedRegionsBackgroundEstimator(
observations=observations,
on_region=on_region,
exclusion_mask=exclusion_mask,
min_distance_input="0.2 deg",
)
est.run()
return est.result
def bkg_estimator(observations, exclusion_mask, on_region):
"""Example background estimator for testing."""
maker = ReflectedRegionsBackgroundEstimator(
observations=observations,
on_region=on_region,
exclusion_mask=exclusion_mask,
min_distance_input="0.2 deg",
)
maker.run()
return maker
def _spectrum_extraction(self):
"""Run all steps for the spectrum extraction."""
region = self.settings["datasets"]["geom"]["region"]
log.info("Reducing spectrum datasets.")
on_lon = Angle(region["center"][0])
on_lat = Angle(region["center"][1])
on_center = SkyCoord(on_lon, on_lat, frame=region["frame"])
on_region = CircleSkyRegion(on_center, Angle(region["radius"]))
background_params = {"on_region": on_region}
background = self.settings["datasets"]["background"]
if "exclusion_mask" in background:
map_hdu = {}
filename = background["exclusion_mask"]["filename"]
if "hdu" in background["exclusion_mask"]:
map_hdu = {"hdu": background["exclusion_mask"]["hdu"]}
exclusion_region = Map.read(filename, **map_hdu)
background_params["exclusion_mask"] = exclusion_region
if background["background_estimator"] == "reflected":
self.background_estimator = ReflectedRegionsBackgroundEstimator(
observations=self.observations, **background_params
)
self.background_estimator.run()
else:
# TODO: raise error?
log.info("Background estimation only for reflected regions method.")
extraction_params = {}
if "containment_correction" in self.settings["datasets"]:
extraction_params["containment_correction"] = self.settings["datasets"][
"containment_correction"
]
params = self.settings["datasets"]["geom"]["axes"][0]
e_reco = MapAxis.from_bounds(**params).edges
extraction_params["e_reco"] = e_reco
extraction_params["e_true"] = None
self.extraction = SpectrumExtraction(
observations=self.observations,
bkg_estimate=self.background_estimator.result,
**extraction_params,
)
self.extraction.run()
self.datasets = Datasets(self.extraction.spectrum_observations)
if self.settings["datasets"]["stack-datasets"]:
stacked = self.datasets.stack_reduce()
stacked.name = "stacked"
self.datasets = Datasets([stacked])
def setup_class(cls):
datastore = DataStore.from_dir("$GAMMAPY_DATA/hess-dl3-dr1/")
obs_ids = [23523, 23526]
on_region = CircleSkyRegion(
SkyCoord(83.63 * u.deg, 22.01 * u.deg, frame="icrs"), 0.3 * u.deg)
obs_stats_list = []
for obs_id in obs_ids:
obs = datastore.obs(obs_id)
bkg = ReflectedRegionsBackgroundEstimator(on_region=on_region,
observations=[obs])
bkg.run()
bg_estimate = bkg.result[0]
obs_stats = ObservationStats.from_observation(obs, bg_estimate)
obs_stats_list.append(obs_stats)
cls.obs_summary = ObservationSummary(obs_stats_list)
def test_extract_cta_1dc_data(caplog):
datastore = DataStore.from_dir("$GAMMAPY_DATA/cta-1dc/index/gps/")
obs_ids = [110380, 111140]
observations = datastore.get_observations(obs_ids)
pos = SkyCoord(0.0, 0.0, unit="deg", frame="galactic")
radius = Angle(0.11, "deg")
on_region = CircleSkyRegion(pos, radius)
est = ReflectedRegionsBackgroundEstimator(
observations=observations, on_region=on_region, min_distance_input="0.2 deg"
)
est.run()
# This will test non PSF3D input as well as absence of default thresholds
extract = SpectrumExtraction(
bkg_estimate=est.result, observations=observations, containment_correction=True
)
extract.run()
extract.compute_energy_threshold(method_lo="area_max", area_percent_lo=10)
actual = extract.spectrum_observations[0].energy_range[0]
assert_quantity_allclose(actual, 0.774263 * u.TeV, rtol=1e-3)
class Analysis:
"""Config-driven high-level analysis interface.
It is initialized by default with a set of configuration parameters and values declared in
an internal configuration schema YAML file, though the user can also provide configuration
parameters passed as a nested dictionary at the moment of instantiation. In that case these
parameters will overwrite the default values of those present in the configuration file.
For more info see :ref:`HLI`.
Parameters
----------
config : dict or `AnalysisConfig`
Configuration options following `AnalysisConfig` schema
"""
def __init__(self, config=None):
if isinstance(config, dict):
self._config = AnalysisConfig(config)
elif isinstance(config, AnalysisConfig):
self._config = config
else:
raise ValueError("Dict or `AnalysiConfig` object required.")
self._set_logging()
self.observations = None
self.background_estimator = None
self.datasets = None
self.extraction = None
self.model = None
self.fit = None
self.fit_result = None
self.flux_points = None
@property
def config(self):
"""Analysis configuration (`AnalysisConfig`)"""
return self._config
@property
def settings(self):
"""Configuration settings for the analysis session."""
return self.config.settings
def get_observations(self):
"""Fetch observations from the data store according to criteria defined in the configuration."""
self.config.validate()
log.info("Fetching observations.")
datastore_path = make_path(self.settings["observations"]["datastore"])
if datastore_path.is_file():
datastore = DataStore().from_file(datastore_path)
elif datastore_path.is_dir():
datastore = DataStore().from_dir(datastore_path)
else:
raise FileNotFoundError(f"Datastore {datastore_path} not found.")
ids = set()
selection = dict()
for criteria in self.settings["observations"]["filters"]:
selected_obs = ObservationTable()
# TODO: Reduce significantly the code.
# This block would be handled by datastore.obs_table.select_observations
selection["type"] = criteria["filter_type"]
for key, val in criteria.items():
if key in ["lon", "lat", "radius", "border"]:
val = Angle(val)
selection[key] = val
if selection["type"] == "angle_box":
selection["type"] = "par_box"
selection["value_range"] = Angle(criteria["value_range"])
if selection["type"] == "sky_circle" or selection["type"].endswith("_box"):
selected_obs = datastore.obs_table.select_observations(selection)
if selection["type"] == "par_value":
mask = (
datastore.obs_table[criteria["variable"]] == criteria["value_param"]
)
selected_obs = datastore.obs_table[mask]
if selection["type"] == "ids":
obs_list = datastore.get_observations(criteria["obs_ids"])
selected_obs["OBS_ID"] = [obs.obs_id for obs in obs_list.list]
if selection["type"] == "all":
obs_list = datastore.get_observations()
selected_obs["OBS_ID"] = [obs.obs_id for obs in obs_list.list]
if len(selected_obs):
if "exclude" in criteria and criteria["exclude"]:
ids.difference_update(selected_obs["OBS_ID"].tolist())
else:
ids.update(selected_obs["OBS_ID"].tolist())
self.observations = datastore.get_observations(ids, skip_missing=True)
for obs in self.observations.list:
log.info(obs)
def get_datasets(self):
"""Produce reduced datasets."""
if not self._validate_reduction_settings():
return False
if self.settings["datasets"]["dataset-type"] == "SpectrumDatasetOnOff":
self._spectrum_extraction()
elif self.settings["datasets"]["dataset-type"] == "MapDataset":
self._map_making()
else:
# TODO raise error?
log.info("Data reduction method not available.")
return False
def set_model(self, model=None, filename=""):
"""Read the model from dict or filename and attach it to datasets.
Parameters
----------
model: dict or string
Dictionary or string in YAML format with the serialized model.
filename : string
Name of the model YAML file describing the model.
"""
if not self._validate_set_model():
return False
log.info(f"Reading model.")
if isinstance(model, str):
model = yaml.safe_load(model)
if model:
self.model = SkyModels(dict_to_models(model))
elif filename:
filepath = make_path(filename)
self.model = SkyModels.from_yaml(filepath)
else:
return False
# TODO: Deal with multiple components
for dataset in self.datasets.datasets:
if isinstance(dataset, MapDataset):
dataset.model = self.model
else:
if len(self.model.skymodels) > 1:
raise ValueError(
"Can only fit a single spectral model at one time."
)
dataset.model = self.model.skymodels[0].spectral_model
log.info(self.model)
def run_fit(self, optimize_opts=None):
"""Fitting reduced datasets to model."""
if not self._validate_fitting_settings():
return False
for ds in self.datasets.datasets:
# TODO: fit_range handled in jsonschema validation class
if "fit_range" in self.settings["fit"]:
e_min = u.Quantity(self.settings["fit"]["fit_range"]["min"])
e_max = u.Quantity(self.settings["fit"]["fit_range"]["max"])
if isinstance(ds, MapDataset):
ds.mask_fit = ds.counts.geom.energy_mask(e_min, e_max)
else:
ds.mask_fit = ds.counts.energy_mask(e_min, e_max)
log.info("Fitting reduced datasets.")
self.fit = Fit(self.datasets)
self.fit_result = self.fit.run(optimize_opts=optimize_opts)
log.info(self.fit_result)
def get_flux_points(self, source="source"):
"""Calculate flux points for a specific model component.
Parameters
----------
source : string
Name of the model component where to calculate the flux points.
"""
if not self._validate_fp_settings():
return False
# TODO: add "source" to config
log.info("Calculating flux points.")
axis_params = self.settings["flux-points"]["fp_binning"]
e_edges = MapAxis.from_bounds(**axis_params).edges
flux_point_estimator = FluxPointsEstimator(
e_edges=e_edges, datasets=self.datasets, source=source
)
fp = flux_point_estimator.run()
fp.table["is_ul"] = fp.table["ts"] < 4
model = self.model[source].spectral_model.copy()
self.flux_points = FluxPointsDataset(data=fp, model=model)
cols = ["e_ref", "ref_flux", "dnde", "dnde_ul", "dnde_err", "is_ul"]
log.info("\n{}".format(self.flux_points.data.table[cols]))
@staticmethod
def _create_geometry(params):
"""Create the geometry."""
# TODO: handled in jsonschema validation class
geom_params = copy.deepcopy(params)
axes = []
for axis_params in geom_params.get("axes", []):
ax = MapAxis.from_bounds(**axis_params)
axes.append(ax)
geom_params["axes"] = axes
geom_params["skydir"] = tuple(geom_params["skydir"])
return WcsGeom.create(**geom_params)
def _map_making(self):
"""Make maps and datasets for 3d analysis."""
log.info("Creating geometry.")
geom = self._create_geometry(self.settings["datasets"]["geom"])
if "geom-irf" in self.settings["datasets"]:
geom_irf = self._create_geometry(self.settings["datasets"]["geom-irf"])
else:
geom_irf = geom.to_binsz(binsz=BINSZ_IRF)
offset_max = Angle(self.settings["datasets"]["offset-max"])
stack_datasets = self.settings["datasets"]["stack-datasets"]
log.info("Creating datasets.")
maker = MapDatasetMaker(
geom=geom,
geom_true=geom_irf,
offset_max=offset_max,
)
if stack_datasets:
stacked = MapDataset.create(geom=geom, geom_irf=geom_irf, name="stacked")
for obs in self.observations:
dataset = maker.run(obs)
stacked.stack(dataset)
self._extract_irf_kernels(stacked)
datasets = [stacked]
else:
datasets = []
for obs in self.observations:
dataset = maker.run(obs)
self._extract_irf_kernels(dataset)
datasets.append(dataset)
self.datasets = Datasets(datasets)
def _extract_irf_kernels(self, dataset):
# TODO: remove hard-coded default value
max_radius = self.settings["datasets"].get("psf-kernel-radius", "0.6 deg")
# TODO: handle IRF maps in fit
geom = dataset.counts.geom
geom_irf = dataset.exposure.geom
position = geom.center_skydir
geom_psf = geom.to_image().to_cube(geom_irf.axes)
dataset.psf = dataset.psf.get_psf_kernel(
position=position, geom=geom_psf, max_radius=max_radius
)
e_reco = geom.get_axis_by_name("energy").edges
dataset.edisp = dataset.edisp.get_energy_dispersion(
position=position, e_reco=e_reco
)
def _set_logging(self):
"""Set logging parameters for API."""
logging.basicConfig(**self.settings["general"]["logging"])
log.info(
"Setting logging config: {!r}".format(self.settings["general"]["logging"])
)
def _spectrum_extraction(self):
"""Run all steps for the spectrum extraction."""
region = self.settings["datasets"]["geom"]["region"]
log.info("Reducing spectrum datasets.")
on_lon = Angle(region["center"][0])
on_lat = Angle(region["center"][1])
on_center = SkyCoord(on_lon, on_lat, frame=region["frame"])
on_region = CircleSkyRegion(on_center, Angle(region["radius"]))
background_params = {"on_region": on_region}
background = self.settings["datasets"]["background"]
if "exclusion_mask" in background:
map_hdu = {}
filename = background["exclusion_mask"]["filename"]
if "hdu" in background["exclusion_mask"]:
map_hdu = {"hdu": background["exclusion_mask"]["hdu"]}
exclusion_region = Map.read(filename, **map_hdu)
background_params["exclusion_mask"] = exclusion_region
if background["background_estimator"] == "reflected":
self.background_estimator = ReflectedRegionsBackgroundEstimator(
observations=self.observations, **background_params
)
self.background_estimator.run()
else:
# TODO: raise error?
log.info("Background estimation only for reflected regions method.")
extraction_params = {}
if "containment_correction" in self.settings["datasets"]:
extraction_params["containment_correction"] = self.settings["datasets"][
"containment_correction"
]
params = self.settings["datasets"]["geom"]["axes"][0]
e_reco = MapAxis.from_bounds(**params).edges
extraction_params["e_reco"] = e_reco
extraction_params["e_true"] = None
self.extraction = SpectrumExtraction(
observations=self.observations,
bkg_estimate=self.background_estimator.result,
**extraction_params,
)
self.extraction.run()
self.datasets = Datasets(self.extraction.spectrum_observations)
if self.settings["datasets"]["stack-datasets"]:
stacked = self.datasets.stack_reduce()
stacked.name = "stacked"
self.datasets = Datasets([stacked])
def _validate_reduction_settings(self):
"""Validate settings before proceeding to data reduction."""
if self.observations and len(self.observations):
self.config.validate()
return True
else:
log.info("No observations selected.")
log.info("Data reduction cannot be done.")
return False
def _validate_set_model(self):
if self.datasets and self.datasets.datasets:
self.config.validate()
return True
else:
log.info("No datasets reduced.")
return False
def _validate_fitting_settings(self):
"""Validate settings before proceeding to fit 1D."""
if not self.model:
log.info("No model fetched for datasets.")
log.info("Fit cannot be done.")
return False
else:
return True
def _validate_fp_settings(self):
"""Validate settings before proceeding to flux points estimation."""
valid = True
if self.fit:
self.config.validate()
else:
log.info("No results available from fit.")
valid = False
if "flux-points" not in self.settings:
log.info("No values declared for the energy bins.")
valid = False
elif "fp_binning" not in self.settings["flux-points"]:
log.info("No values declared for the energy bins.")
valid = False
if not valid:
log.info("Flux points calculation cannot be done.")
return valid
data_store = DataStore.from_dir("$GAMMAPY_DATA/hess-dl3-dr1/")
mask = data_store.obs_table["TARGET_NAME"] == "Crab"
obs_ids = data_store.obs_table["OBS_ID"][mask].data
observations = data_store.get_observations(obs_ids)
crab_position = SkyCoord(83.63, 22.01, unit="deg", frame="icrs")
# The ON region center is defined in the icrs frame. The angle is defined w.r.t. to its axis.
on_region = RectangleSkyRegion(
center=crab_position, width=0.5 * u.deg, height=0.4 * u.deg, angle=0 * u.deg
)
background_estimator = ReflectedRegionsBackgroundEstimator(
observations=observations, on_region=on_region, min_distance=0.1 * u.rad
)
background_estimator.run()
# Let's inspect the data extracted in the first observation
print(background_estimator.result[0])
background_estimator.plot()
# Now we change the ON region, and use a center defined in the galactic frame
on_region_galactic = RectangleSkyRegion(
center=crab_position.galactic,
width=0.5 * u.deg,
height=0.4 * u.deg,
angle=0 * u.deg,
proj="TAN",
coordsys="GAL")
mask = exclusion_mask.geom.region_mask([exclusion_region], inside=False)
exclusion_mask.data = mask
exclusion_mask.plot()
# ## Estimate background
#
# Next we will manually perform a background estimate by placing [reflected regions](https://docs.gammapy.org/dev/spectrum/reflected.html) around the pointing position and looking at the source statistics. This will result in a [gammapy.spectrum.BackgroundEstimate](https://docs.gammapy.org/dev/api/gammapy.spectrum.BackgroundEstimate.html) that serves as input for other classes in gammapy.
# In[ ]:
background_estimator = ReflectedRegionsBackgroundEstimator(
observations=observations,
on_region=on_region,
exclusion_mask=exclusion_mask,
)
background_estimator.run()
# In[ ]:
plt.figure(figsize=(8, 8))
background_estimator.plot(add_legend=True)
# ## Source statistic
#
# Next we're going to look at the overall source statistics in our signal region. For more info about what debug plots you can create check out the [ObservationSummary](https://docs.gammapy.org/dev/api/gammapy.data.ObservationSummary.html) class.
# In[ ]:
#
# We need to define the ON extraction region. We will keep the same reco and true energy axes as in 3D.
# In[ ]:
# Target definition
on_region_radius = Angle("0.11 deg")
on_region = CircleSkyRegion(center=target_position, radius=on_region_radius)
# ### Extracting the background
#
# We perform here an ON - OFF measurement with reflected regions. We perform first the background extraction.
# In[ ]:
bkg_estimator = ReflectedRegionsBackgroundEstimator(on_region=on_region,
observations=crab_obs)
bkg_estimator.run()
# ### Creation of the datasets
#
# We now apply spectral extraction to create the datasets.
#
# NB: we are using here time intervals defined by the observations start and stop times. The standard observation based spectral extraction is therefore defined in the right time bins.
#
# A proper time resolved spectral extraction will be included in a coming gammapy release.
# In[ ]:
# Note that we are not performing the extraction in time bins
extraction = SpectrumExtraction(
observations=crab_obs,