def data_reduction(instrument):
log.info(f"data_reduction: {instrument}")
config = AnalysisConfig.read(f"config.yaml")
config.observations.datastore = f"$JOINT_CRAB/data/{instrument}"
config.datasets.stack = instrument_opts[instrument]['stack']
config.datasets.containment_correction = instrument_opts[instrument][
'containment']
config.datasets.on_region.radius = instrument_opts[instrument]['on_radius']
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
# TODO remove when safe mask can be set on config
if instrument is 'fact':
from gammapy.datasets import SpectrumDatasetOnOff
stacked = SpectrumDatasetOnOff.create(
e_reco=analysis.datasets[0]._energy_axis.edges,
e_true=analysis.datasets[0]._energy_axis.edges,
region=None)
for ds in analysis.datasets:
ds.mask_safe[:] = True
stacked.stack(ds)
analysis.datasets = Datasets([stacked])
analysis.datasets.write(f"reduced_{instrument}", overwrite=True)
def run_analysis(method, target_dict, debug):
"""If the method is "1d", runs joint spectral analysis for the selected target. If
instead it is "3d", runs stacked 3D analysis."""
tag = target_dict["tag"]
log.info(f"Running {method} analysis, {tag}")
path_res = Path(tag + "/results/")
log.info("Reading config")
txt = Path(f"config_{method}.yaml").read_text()
txt = txt.format_map(target_dict)
config = AnalysisConfig.from_yaml(txt)
if debug:
config.observations.obs_ids = [target_dict["debug_run"]]
config.flux_points.energy.nbins = 1
if method == "3d":
config.datasets.geom.axes.energy_true.nbins = 10
analysis = Analysis(config)
log.info("Running observations selection")
analysis.get_observations()
log.info(f"Running data reduction")
analysis.get_datasets()
# TODO: This is a workaround. We should somehow apply the safe mask (run by run) from the HLI
from gammapy.cube import SafeMaskMaker
datasets = []
maker_safe_mask = SafeMaskMaker(methods=["edisp-bias", "bkg-peak"],
bias_percent=10)
for dataset in analysis.datasets:
dataset = maker_safe_mask.run(dataset)
datasets.append(dataset)
analysis.datasets = datasets
log.info(f"Setting the model")
txt = Path("model_config.yaml").read_text()
txt = txt.format_map(target_dict)
log.info(txt)
analysis.set_models(txt)
if method == "3d" and target_dict["spatial_model"] == "DiskSpatialModel":
analysis.models[0].spatial_model.e.frozen = False
analysis.models[0].spatial_model.phi.frozen = False
analysis.models[0].spatial_model.r_0.value = 0.3
log.info(f"Running fit ...")
analysis.run_fit()
# TODO: This is a workaround. Set covariance automatically
results = analysis.fit_result
names = ["spectral_model", "spatial_model"]
for name in names:
if name == "spatial_model" and method == "1d":
continue
model = getattr(analysis.models[0], name)
model.parameters.covariance = results.parameters.get_subcovariance(
model.parameters.names)
log.info(f"Writing {path_res}")
write_fit_summary(analysis.models[0].parameters,
str(path_res / f"results-summary-fit-{method}.yaml"))
log.info(f"Running flux points estimation")
# TODO: For the 3D analysis, re-optimize the background norm in each energy
# bin. For now, this is not possible from the HLI.
analysis.get_flux_points(source=tag)
flux_points = analysis.flux_points.data
flux_points.table["is_ul"] = flux_points.table["ts"] < 4
keys = [
"e_ref",
"e_min",
"e_max",
"dnde",
"dnde_errp",
"dnde_errn",
"is_ul",
"dnde_ul",
]
log.info(f"Writing {path_res}")
flux_points.table_formatted[keys].write(path_res /
f"flux-points-{method}.ecsv",
format="ascii.ecsv")
