def run(self, datasets):
"""Run light curve extraction.
Normalize integral and energy flux between emin and emax.
Parameters
----------
datasets : list of `~gammapy.datasets.SpectrumDataset` or `~gammapy.datasets.MapDataset`
Spectrum or Map datasets.
Returns
-------
lightcurve : `~gammapy.estimators.FluxPoints`
Light curve flux points
"""
datasets = Datasets(datasets)
if self.time_intervals is None:
gti = datasets.gti
else:
gti = GTI.from_time_intervals(self.time_intervals)
gti = gti.union(overlap_ok=False, merge_equal=False)
rows = []
valid_intervals = []
for t_min, t_max in progress_bar(gti.time_intervals,
desc="Time intervals"):
datasets_to_fit = datasets.select_time(time_min=t_min,
time_max=t_max,
atol=self.atol)
if len(datasets_to_fit) == 0:
log.info(
f"No Dataset for the time interval {t_min} to {t_max}. Skipping interval."
)
continue
valid_intervals.append([t_min, t_max])
fp = self.estimate_time_bin_flux(datasets=datasets_to_fit)
for name in ["counts", "npred", "npred_null"]:
fp._data[name] = self.expand_map(fp._data[name],
dataset_names=datasets.names)
rows.append(fp)
if len(rows) == 0:
raise ValueError(
"LightCurveEstimator: No datasets in time intervals")
gti = GTI.from_time_intervals(valid_intervals)
axis = TimeMapAxis.from_gti(gti=gti)
return FluxPoints.from_stack(
maps=rows,
axis=axis,
)
def run(self, datasets):
"""Run light curve extraction.
Normalize integral and energy flux between emin and emax.
Parameters
----------
datasets : list of `~gammapy.datasets.SpectrumDataset` or `~gammapy.datasets.MapDataset`
Spectrum or Map datasets.
Returns
-------
lightcurve : `~gammapy.estimators.LightCurve`
the Light Curve object
"""
datasets = Datasets(datasets)
if self.time_intervals is None:
gti = datasets.gti
else:
gti = GTI.from_time_intervals(self.time_intervals)
gti = gti.union(overlap_ok=False, merge_equal=False)
rows = []
for t_min, t_max in gti.time_intervals:
datasets_to_fit = datasets.select_time(t_min=t_min,
t_max=t_max,
atol=self.atol)
if len(datasets_to_fit) == 0:
log.debug(
f"No Dataset for the time interval {t_min} to {t_max}")
continue
row = {"time_min": t_min.mjd, "time_max": t_max.mjd}
row.update(self.estimate_time_bin_flux(datasets_to_fit))
rows.append(row)
if len(rows) == 0:
raise ValueError(
"LightCurveEstimator: No datasets in time intervals")
table = table_from_row_data(rows=rows, meta={"SED_TYPE": "likelihood"})
model = datasets.models[self.source]
# TODO: cleanup here...
fp = FluxPoints(table,
reference_spectral_model=model.spectral_model.copy())
table_flux = fp.to_table(sed_type="flux")
table_flux.remove_columns(["stat", "ts", "sqrt_ts", "e_min", "e_max"])
return LightCurve(hstack([table, table_flux]))
def run(self, datasets):
"""Run light curve extraction.
Normalize integral and energy flux between emin and emax.
Parameters
----------
datasets : list of `~gammapy.spectrum.SpectrumDataset` or `~gammapy.cube.MapDataset`
Spectrum or Map datasets.
Returns
-------
lightcurve : `~gammapy.time.LightCurve`
the Light Curve object
"""
datasets = Datasets(datasets)
if self.time_intervals is None:
gti = datasets.gti
else:
gti = GTI.from_time_intervals(self.time_intervals)
gti = gti.union(overlap_ok=False, merge_equal=False)
rows = []
for t_min, t_max in gti.time_intervals:
datasets_to_fit = datasets.select_time(t_min=t_min,
t_max=t_max,
atol=self.atol)
if len(datasets_to_fit) == 0:
log.debug(
f"No Dataset for the time interval {t_min} to {t_max}")
continue
row = {"time_min": t_min.mjd, "time_max": t_max.mjd}
data = self.estimate_time_bin_flux(datasets_to_fit)
row.update(data)
row.update(self.estimate_counts(datasets_to_fit))
rows.append(row)
if len(rows) == 0:
raise ValueError(
"LightCurveEstimator: No datasets in time intervals")
table = table_from_row_data(rows=rows, meta={"SED_TYPE": "likelihood"})
table = FluxPoints(table).to_sed_type("flux").table
return LightCurve(table)
def run(self, datasets):
"""Run light curve extraction.
Normalize integral and energy flux between emin and emax.
Parameters
----------
datasets : list of `~gammapy.datasets.SpectrumDataset` or `~gammapy.datasets.MapDataset`
Spectrum or Map datasets.
Returns
-------
lightcurve : `~gammapy.estimators.LightCurve`
the Light Curve object
"""
datasets = Datasets(datasets)
if self.time_intervals is None:
gti = datasets.gti
else:
gti = GTI.from_time_intervals(self.time_intervals)
gti = gti.union(overlap_ok=False, merge_equal=False)
rows = []
for t_min, t_max in progress_bar(gti.time_intervals,
desc="Time intervals"):
datasets_to_fit = datasets.select_time(t_min=t_min,
t_max=t_max,
atol=self.atol)
if len(datasets_to_fit) == 0:
log.debug(
f"No Dataset for the time interval {t_min} to {t_max}")
continue
row = {"time_min": t_min.mjd, "time_max": t_max.mjd}
fp = self.estimate_time_bin_flux(datasets_to_fit)
fp_table = fp.to_table()
for column in fp_table.colnames:
if column == "counts":
data = fp_table[column].quantity.sum(axis=1)
else:
data = fp_table[column].quantity
row[column] = data
fp_table_flux = fp.to_table(sed_type="flux")
for column in fp_table_flux.colnames:
if "flux" in column:
row[column] = fp_table_flux[column].quantity
rows.append(row)
if len(rows) == 0:
raise ValueError(
"LightCurveEstimator: No datasets in time intervals")
table = table_from_row_data(rows=rows, meta={"SED_TYPE": "likelihood"})
# TODO: use FluxPoints here
return LightCurve(table=table)
