# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import numpy as np
import astropy.units as u
from astropy.table import Table
from astropy.time import Time
from gammapy.data import GTI
from gammapy.datasets import Datasets
from gammapy.utils.scripts import make_path
from gammapy.utils.table import table_from_row_data
from .core import Estimator
from .flux_point import FluxPoints, FluxPointsEstimator
__all__ = ["LightCurve", "LightCurveEstimator"]
log = logging.getLogger(__name__)
[docs]class LightCurve:
"""Lightcurve container.
The lightcurve data is stored in ``table``.
For now we only support times stored in MJD format!
TODO: specification of format is work in progress
See https://github.com/open-gamma-ray-astro/gamma-astro-data-formats/pull/61
Usage: :ref:`time`
Parameters
----------
table : `~astropy.table.Table`
Table with lightcurve data
"""
def __init__(self, table):
self.table = table
def __repr__(self):
return f"{self.__class__.__name__}(len={len(self.table)})"
@property
def time_scale(self):
"""Time scale (str).
Taken from table "TIMESYS" header.
Common values: "TT" or "UTC".
Assumed default is "UTC".
"""
return self.table.meta.get("TIMESYS", "utc")
@property
def time_format(self):
"""Time format (str)."""
return "mjd"
# @property
# def time_ref(self):
# """Time reference (`~astropy.time.Time`)."""
# return time_ref_from_dict(self.table.meta)
def _make_time(self, colname):
val = self.table[colname].data
scale = self.time_scale
format = self.time_format
return Time(val, scale=scale, format=format)
@property
def time(self):
"""Time (`~astropy.time.Time`)."""
return self.time_mid
@property
def time_min(self):
"""Time bin start (`~astropy.time.Time`)."""
return self._make_time("time_min")
@property
def time_max(self):
"""Time bin end (`~astropy.time.Time`)."""
return self._make_time("time_max")
@property
def time_mid(self):
"""Time bin center (`~astropy.time.Time`)."""
return self.time_min + 0.5 * self.time_delta
@property
def time_delta(self):
"""Time bin width (`~astropy.time.TimeDelta`)."""
return self.time_max - self.time_min
[docs] @classmethod
def read(cls, filename, **kwargs):
"""Read from file.
Parameters
----------
filename : str
Filename
kwargs : dict
Keyword arguments passed to `astropy.table.Table.read`.
"""
table = Table.read(make_path(filename), **kwargs)
return cls(table=table)
[docs] def write(self, filename, **kwargs):
"""Write to file.
Parameters
----------
filename : str
Filename
kwargs : dict
Keyword arguments passed to `astropy.table.Table.write`.
"""
self.table.write(make_path(filename), **kwargs)
[docs] def plot(
self,
ax=None,
energy_index=None,
time_format="mjd",
flux_unit="cm-2 s-1",
**kwargs,
):
"""Plot flux points.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional.
The `~matplotlib.axes.Axes` object to be drawn on.
If None, uses the current `~matplotlib.axes.Axes`.
energy_index : int
The index of the energy band to use. If set to None, use the first energy index.
Default is None.
time_format : {'mjd', 'iso'}, optional
If 'iso', the x axis will contain Matplotlib dates.
For formatting these dates see: https://matplotlib.org/gallery/ticks_and_spines/date_demo_rrule.html
flux_unit : str, `~astropy.units.Unit`, optional
Unit of the flux axis
kwargs : dict
Keyword arguments passed to :func:`matplotlib.pyplot.errorbar`
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis object
"""
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter
if ax is None:
ax = plt.gca()
x, xerr = self._get_times_and_errors(time_format=time_format)
y, yerr = self._get_fluxes_and_errors(unit=flux_unit)
is_ul, yul = self._get_flux_uls(unit=flux_unit)
if len(y.shape) > 1:
if energy_index is None:
energy_index = 0
y = y[:, energy_index]
if len(yerr) > 1:
yerr = [_[:, energy_index] for _ in yerr]
else:
yerr = yerr[:, energy_index]
is_ul = is_ul[:, energy_index]
yul = yul[:, energy_index]
# length of the ul arrow
ul_arr = (
np.nanmax(np.concatenate((y[~is_ul], yul[is_ul])))
- np.nanmin(np.concatenate((y[~is_ul], yul[is_ul])))
) * 0.1
# join fluxes and upper limits for the plot
y[is_ul] = yul[is_ul]
yerr[0][is_ul] = ul_arr
# set plotting defaults and plot
kwargs.setdefault("marker", "+")
kwargs.setdefault("ls", "None")
ax.errorbar(x=x, y=y, xerr=xerr, yerr=yerr, uplims=is_ul, **kwargs)
ax.set_xlabel("Time ({})".format(time_format.upper()))
ax.set_ylabel("Flux ({:FITS})".format(u.Unit(flux_unit)))
ax.legend()
if time_format == "iso":
ax.xaxis.set_major_formatter(DateFormatter("%Y-%m-%d %H:%M:%S"))
plt.setp(
ax.xaxis.get_majorticklabels(),
rotation=30,
ha="right",
rotation_mode="anchor",
)
return ax
def _get_fluxes_and_errors(self, unit="cm-2 s-1"):
"""Extract fluxes and corresponding errors
Helper function for the plot method.
Parameters
----------
unit : str, `~astropy.units.Unit`, optional
Unit of the returned flux and errors values
Returns
-------
y : `numpy.ndarray`
Flux values
(yn, yp) : tuple of `numpy.ndarray`
Flux error values
"""
y = self.table["flux"].quantity.to(unit)
if all(k in self.table.colnames for k in ["flux_errp", "flux_errn"]):
yp = self.table["flux_errp"].quantity.to(unit)
yn = self.table["flux_errn"].quantity.to(unit)
elif "flux_err" in self.table.colnames:
yp = self.table["flux_err"].quantity.to(unit)
yn = self.table["flux_err"].quantity.to(unit)
else:
yp, yn = np.zeros_like(y), np.zeros_like(y)
return y.value, (yn.value, yp.value)
def _get_flux_uls(self, unit="cm-2 s-1"):
"""Extract flux upper limits
Helper function for the plot method.
Parameters
----------
unit : str, `~astropy.units.Unit`, optional
Unit of the returned flux upper limit values
Returns
-------
is_ul : `numpy.ndarray`
Is flux point is an upper limit? (boolean array)
yul : `numpy.ndarray`
Flux upper limit values
"""
try:
is_ul = self.table["is_ul"].data.astype("bool")
except KeyError:
is_ul = np.zeros_like(self.table["flux"]).data.astype("bool")
if is_ul.any():
yul = self.table["flux_ul"].quantity.to(unit)
else:
yul = np.zeros_like(self.table["flux"]).quantity
yul[:] = np.nan
return is_ul, yul.value
def _get_times_and_errors(self, time_format="mjd"):
"""Extract times and corresponding errors
Helper function for the plot method.
Parameters
----------
time_format : {'mjd', 'iso'}, optional
Time format of the times. If 'iso', times and errors will be
returned as `~datetime.datetime` and `~datetime.timedelta` objects
Returns
-------
x : `~numpy.ndarray` or of `~datetime.datetime`
Time values or `~datetime.datetime` instances if 'iso' is chosen
as time format
(xn, xp) : tuple of `numpy.ndarray` of `~datetime.timedelta`
Tuple of time error values or `~datetime.timedelta` instances if
'iso' is chosen as time format
"""
x = self.time
try:
xn, xp = x - self.time_min, self.time_max - x
except KeyError:
xn, xp = x - x, x - x
if time_format == "iso":
x = x.datetime
xn = xn.to_datetime()
xp = xp.to_datetime()
elif time_format == "mjd":
x = x.mjd
xn = xn.to("day").value
xp = xp.to("day").value
else:
raise ValueError(f"Invalid time_format: {time_format}")
return x, (xn, xp)
[docs]class LightCurveEstimator(Estimator):
"""Estimate light curve.
The estimator will fit the source model component to datasets in each of the
provided time intervals.
If no time intervals are provided, the estimator will use the time intervals
defined by the datasets GTIs.
To be included in the estimation, the dataset must have their GTI fully
overlapping a time interval.
Parameters
----------
time_intervals : list of `astropy.time.Time`
Start and stop time for each interval to compute the LC
source : str
For which source in the model to compute the flux points. Default is 0
energy_edges : `~astropy.units.Quantity`
Energy edges of the light curve.
atol : `~astropy.units.Quantity`
Tolerance value for time comparison with different scale. Default 1e-6 sec.
norm_min : float
Minimum value for the norm used for the fit statistic profile evaluation.
norm_max : float
Maximum value for the norm used for the fit statistic profile evaluation.
norm_n_values : int
Number of norm values used for the fit statistic profile.
norm_values : `numpy.ndarray`
Array of norm values to be used for the fit statistic profile.
n_sigma : int
Number of sigma to use for asymmetric error computation. Default is 1.
n_sigma_ul : int
Number of sigma to use for upper limit computation. Default is 2.
reoptimize : bool
reoptimize other parameters during fit statistic scan?
selection_optional : list of str
Which steps to execute. Available options are:
* "errn-errp": estimate asymmetric errors.
* "ul": estimate upper limits.
* "scan": estimate fit statistic profiles.
By default all steps are executed.
"""
tag = "LightCurveEstimator"
_available_selection_optional = ["errn-errp", "ul", "scan"]
def __init__(
self,
time_intervals=None,
source=0,
energy_edges=None,
atol="1e-6 s",
norm_min=0.2,
norm_max=5,
norm_n_values=11,
norm_values=None,
n_sigma=1,
n_sigma_ul=2,
reoptimize=False,
selection_optional="all",
):
self.source = source
self.time_intervals = time_intervals
self.atol = u.Quantity(atol)
self.energy_edges = energy_edges
self.norm_min = norm_min
self.norm_max = norm_max
self.norm_n_values = norm_n_values
self.norm_values = norm_values
self.n_sigma = n_sigma
self.n_sigma_ul = n_sigma_ul
self.reoptimize = reoptimize
self.selection_optional = selection_optional
[docs] 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"})
table = FluxPoints(table).to_sed_type("flux").table
return LightCurve(table)
[docs] def estimate_time_bin_flux(self, datasets):
"""Estimate flux point for a single energy group.
Parameters
----------
datasets : `~gammapy.modeling.Datasets`
the list of dataset object
Returns
-------
result : dict
Dict with results for the flux point.
"""
if self.energy_edges is None:
energy_min, energy_max = datasets.energy_ranges
energy_edges = energy_min.min(), energy_max.max()
else:
energy_edges = self.energy_edges
fe = FluxPointsEstimator(
source=self.source,
energy_edges=energy_edges,
norm_min=self.norm_min,
norm_max=self.norm_max,
norm_n_values=self.norm_n_values,
norm_values=self.norm_values,
n_sigma=self.n_sigma,
n_sigma_ul=self.n_sigma_ul,
reoptimize=self.reoptimize,
selection_optional=self.selection_optional,
)
fp = fe.run(datasets)
# TODO: remove once FluxPointsEstimator returns object with all energies in one row
result = {}
for colname in fp.table.colnames:
if colname is not "counts":
result[colname] = fp.table[colname].quantity
else:
result[colname] = np.atleast_1d(fp.table[colname].quantity.sum(axis=1))
# return fp.to_sed_type("flux")#
return result