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)
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:`analysis`.
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.datastore = None
self.observations = None
self.datasets = 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():
self.datastore = DataStore().from_file(datastore_path)
elif datastore_path.is_dir():
self.datastore = DataStore().from_dir(datastore_path)
else:
raise FileNotFoundError(f"Datastore {datastore_path} not found.")
ids = []
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 = self.datastore.obs_table.select_observations(
selection)
if selection["type"] == "par_value":
mask = (self.datastore.obs_table[criteria["variable"]] ==
criteria["value_param"])
selected_obs = self.datastore.obs_table[mask]
if selection["type"] == "ids":
obs_list = self.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 = self.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"]:
exclude = selected_obs["OBS_ID"].tolist()
selection = np.isin(ids, exclude)
ids = list(np.array(ids)[~selection])
else:
ids.extend(selected_obs["OBS_ID"].tolist())
self.observations = self.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:
if isinstance(dataset, MapDataset):
dataset.model = self.model
else:
if len(self.model) > 1:
raise ValueError("Cannot fit multiple spectral models")
dataset.model = self.model[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:
# TODO: fit_range handled in jsonschema validation class
if "fit" in self.settings and "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."""
geom_params = copy.deepcopy(params)
axes = []
for axis_params in params.get("axes", []):
ax = MapAxis.from_bounds(**axis_params)
axes.append(ax)
geom_params["axes"] = axes
if "skydir" in geom_params:
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"])
geom_irf = dict(energy_axis_true=None, binsz_irf=None, margin_irf=None)
if "energy-axis-true" in self.settings["datasets"]:
axis_params = self.settings["datasets"]["energy-axis-true"]
geom_irf["energy_axis_true"] = MapAxis.from_bounds(**axis_params)
geom_irf["binsz_irf"] = self.settings["datasets"].get("binsz", None)
geom_irf["margin_irf"] = self.settings["datasets"].get("margin", None)
offset_max = Angle(self.settings["datasets"]["offset-max"])
log.info("Creating datasets.")
maker = MapDatasetMaker(geom=geom, offset_max=offset_max, **geom_irf)
if self.settings["datasets"]["stack-datasets"]:
stacked = MapDataset.create(geom=geom, name="stacked", **geom_irf)
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"]))
maker_config = {}
if "containment_correction" in self.settings["datasets"]:
maker_config["containment_correction"] = self.settings["datasets"][
"containment_correction"]
params = self.settings["datasets"]["geom"]["axes"][0]
e_reco = MapAxis.from_bounds(**params).edges
maker_config["e_reco"] = e_reco
# TODO: remove hard-coded e_true and make it configurable
maker_config["e_true"] = np.logspace(-2, 2.5, 109) * u.TeV
maker_config["region"] = on_region
dataset_maker = SpectrumDatasetMaker(**maker_config)
bkg_maker_config = {}
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)
bkg_maker_config["exclusion_mask"] = exclusion_region
if background["background_estimator"] == "reflected":
reflected_bkg_maker = ReflectedRegionsBackgroundMaker(
**bkg_maker_config)
else:
# TODO: raise error?
log.info(
"Background estimation only for reflected regions method.")
safe_mask_maker = SafeMaskMaker(methods=["aeff-default", "aeff-max"])
datasets = []
for obs in self.observations:
selection = ["counts", "aeff", "edisp"]
dataset = dataset_maker.run(obs, selection=selection)
dataset = reflected_bkg_maker.run(dataset, obs)
dataset = safe_mask_maker.run(dataset, obs)
datasets.append(dataset)
self.datasets = Datasets(datasets)
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 len(self.datasets) != 0:
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