def lightcurve(self):
"""Lightcurve (`~gammapy.time.LightCurve`)."""
flux = self.data["Flux_History"]
# Flux error is given as asymmetric high/low
flux_errn = -self.data["Unc_Flux_History"][:, 0]
flux_errp = self.data["Unc_Flux_History"][:, 1]
# Really the time binning is stored in a separate HDU in the FITS
# catalog file called `Hist_Start`, with a single column `Hist_Start`
# giving the time binning in MET (mission elapsed time)
# This is not available here for now.
# TODO: read that info in `SourceCatalog3FGL` and pass it down to the
# `SourceCatalogObject3FGL` object somehow.
# For now, we just hard-code the start and stop time and assume
# equally-spaced time intervals. This is roughly correct,
# for plotting the difference doesn't matter, only for analysis
time_start = Time("2008-08-02T00:33:19")
time_end = Time("2012-07-31T22:45:47")
n_points = len(flux)
time_step = (time_end - time_start) / n_points
time_bounds = time_start + np.arange(n_points + 1) * time_step
table = Table([
Column(time_bounds[:-1].utc.mjd, "time_min"),
Column(time_bounds[1:].utc.mjd, "time_max"),
Column(flux, "flux"),
Column(flux_errp, "flux_errp"),
Column(flux_errn, "flux_errn"),
])
return LightCurve(table)
def test_lightcurve_read_write(tmp_path, lc, format):
lc.write(tmp_path / "tmp", format=format)
lc = LightCurve.read(tmp_path / "tmp", format=format)
# Check if time-related info round-trips
time = lc.time
assert time.scale == "utc"
assert time.format == "mjd"
assert_allclose(time.mjd, [55198, 55202.5])
def lightcurve(self, interval="1-year"):
"""Lightcurve (`~gammapy.estimators.LightCurve`).
Parameters
----------
interval : {'1-year', '2-month'}
Time interval of the lightcurve. Default is '1-year'.
Note that '2-month' is not available for all catalogue version.
"""
if interval == "1-year":
tag = "Flux_History"
elif interval == "2-month":
tag = "Flux2_History"
if tag not in self.data:
raise ValueError(
"Only '1-year' interval is available for this catalogue version"
)
else:
raise ValueError("Time intervals available are '1-year' or '2-month'")
flux = self.data[tag]
# Flux error is given as asymmetric high/low
flux_errn = -self.data[f"Unc_{tag}"][:, 0]
flux_errp = self.data[f"Unc_{tag}"][:, 1]
# Really the time binning is stored in a separate HDU in the FITS
# catalog file called `Hist_Start`, with a single column `Hist_Start`
# giving the time binning in MET (mission elapsed time)
# This is not available here for now.
# TODO: read that info in `SourceCatalog3FGL` and pass it down to the
# `SourceCatalogObject3FGL` object somehow.
# For now, we just hard-code the start and stop time and assume
# equally-spaced time intervals. This is roughly correct,
# for plotting the difference doesn't matter, only for analysis
time_start = Time("2008-08-04T15:43:36.0000")
n_points = len(flux)
if n_points in [8, 48]:
# 8 = 1/years * 8 years
# 48 = (12 month/year / 2month) * 8 years
time_end = Time("2016-08-02T05:44:11.9999")
else:
time_end = Time("2018-08-02T05:44:11.9999")
time_step = (time_end - time_start) / n_points
time_bounds = time_start + np.arange(n_points + 1) * time_step
table = Table(
[
Column(time_bounds[:-1].utc.mjd, "time_min"),
Column(time_bounds[1:].utc.mjd, "time_max"),
Column(flux, "flux"),
Column(flux_errp, "flux_errp"),
Column(flux_errn, "flux_errn"),
]
)
return LightCurve(table)
def lc():
meta = dict(TIMESYS="utc")
table = Table(
meta=meta,
data=[
Column(Time(["2010-01-01", "2010-01-03"]).mjd, "time_min"),
Column(Time(["2010-01-03", "2010-01-10"]).mjd, "time_max"),
Column([1e-11, 3e-11], "flux", unit="cm-2 s-1"),
Column([0.1e-11, 0.3e-11], "flux_err", unit="cm-2 s-1"),
Column([np.nan, 3.6e-11], "flux_ul", unit="cm-2 s-1"),
Column([False, True], "is_ul"),
],
)
return LightCurve(table=table)
def lc_2d():
meta = dict(TIMESYS="utc")
table = Table(
meta=meta,
data=[
Column(Time(["2010-01-01", "2010-01-03"]).mjd, "time_min"),
Column(Time(["2010-01-03", "2010-01-10"]).mjd, "time_max"),
Column([[1.0, 2.0], [1.0, 2.0]], "e_min", unit="TeV"),
Column([[2.0, 4.0], [2.0, 4.0]], "e_max", unit="TeV"),
Column([[1e-11, 1e-12], [3e-11, 3e-12]], "flux", unit="cm-2 s-1"),
Column([[0.1e-11, 1e-13], [0.3e-11, 3e-13]],
"flux_err",
unit="cm-2 s-1"),
Column([[np.nan, np.nan], [3.6e-11, 3.6e-12]],
"flux_ul",
unit="cm-2 s-1"),
Column([[False, False], [True, True]], "is_ul"),
],
)
return LightCurve(table=table)
