# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
from copy import deepcopy
import numpy as np
from scipy import stats
from scipy.interpolate import interp1d
from scipy.optimize import minimize
from astropy.io import fits
from astropy.io.registry import IORegistryError
from astropy.table import Table, vstack
from astropy.time import Time
from astropy.visualization import quantity_support
import matplotlib.pyplot as plt
from gammapy.data import GTI
from gammapy.maps import Map, MapAxis, Maps, RegionNDMap, TimeMapAxis
from gammapy.maps.axes import UNIT_STRING_FORMAT, flat_if_equal
from gammapy.modeling.models import TemplateSpectralModel
from gammapy.modeling.models.spectral import scale_plot_flux
from gammapy.modeling.scipy import stat_profile_ul_scipy
from gammapy.utils.interpolation import interpolate_profile
from gammapy.utils.scripts import make_path
from gammapy.utils.table import table_standardise_units_copy
from gammapy.utils.time import time_ref_to_dict
from ..map.core import DEFAULT_UNIT, FluxMaps
__all__ = ["FluxPoints"]
log = logging.getLogger(__name__)
def squash_fluxpoints(flux_point, axis):
"""Squash a `~FluxPoints` object into one point.
The log-likelihoods profiles in each bin are summed
to compute the resultant quantities. Stat profiles
must be present on the `~FluxPoints` object for
this method to work.
"""
value_scan = flux_point.stat_scan.geom.axes["norm"].center
stat_scan = np.sum(flux_point.stat_scan.data, axis=0).ravel()
f = interp1d(value_scan, stat_scan, kind="quadratic", bounds_error=False)
f = interpolate_profile(value_scan, stat_scan)
minimizer = minimize(
f,
x0=value_scan[int(len(value_scan) / 2)],
bounds=[(value_scan[0], value_scan[-1])],
method="L-BFGS-B",
)
maps = Maps()
geom = flux_point.geom.to_image()
if axis.name != "energy":
geom = geom.to_cube([flux_point.geom.axes["energy"]])
maps["norm"] = Map.from_geom(geom, data=minimizer.x)
maps["norm_err"] = Map.from_geom(
geom, data=np.sqrt(minimizer.hess_inv.todense()).reshape(geom.data_shape)
)
maps["n_dof"] = Map.from_geom(geom, data=flux_point.geom.axes[axis.name].nbin)
if "norm_ul" in flux_point.available_quantities:
delta_ts = flux_point.meta.get("n_sigma_ul", 2) ** 2
ul = stat_profile_ul_scipy(value_scan, stat_scan, delta_ts=delta_ts)
maps["norm_ul"] = Map.from_geom(geom, data=ul.value)
maps["stat"] = Map.from_geom(geom, data=f(minimizer.x))
geom_scan = geom.to_cube([MapAxis.from_nodes(value_scan, name="norm")])
maps["stat_scan"] = Map.from_geom(
geom=geom_scan, data=stat_scan.reshape(geom_scan.data_shape)
)
try:
maps["stat_null"] = Map.from_geom(geom, data=np.sum(flux_point.stat_null.data))
maps["ts"] = maps["stat_null"] - maps["stat"]
except AttributeError:
log.info(
"Stat null info not present on original FluxPoints object. TS not computed"
)
maps["success"] = Map.from_geom(geom=geom, data=minimizer.success, dtype=bool)
combined_fp = FluxPoints.from_maps(
maps=maps,
sed_type=flux_point.sed_type_init,
reference_model=flux_point.reference_model,
gti=flux_point.gti,
meta=flux_point.meta,
)
return combined_fp
[docs]
class FluxPoints(FluxMaps):
"""Flux points container.
The supported formats are described here: :ref:`gadf:flux-points`.
In summary, the following formats and minimum required columns are:
* Format ``dnde``: columns ``e_ref`` and ``dnde``
* Format ``e2dnde``: columns ``e_ref``, ``e2dnde``
* Format ``flux``: columns ``e_min``, ``e_max``, ``flux``
* Format ``eflux``: columns ``e_min``, ``e_max``, ``eflux``
Parameters
----------
table : `~astropy.table.Table`
Table with flux point data.
Attributes
----------
table : `~astropy.table.Table`
Table with flux point data.
Examples
--------
The `FluxPoints` object is most easily created by reading a file with
flux points given in one of the formats documented above::
>>> from gammapy.estimators import FluxPoints
>>> filename = '$GAMMAPY_DATA/hawc_crab/HAWC19_flux_points.fits'
>>> flux_points = FluxPoints.read(filename)
>>> flux_points.plot() #doctest: +SKIP
An instance of `FluxPoints` can also be created by passing an instance of
`astropy.table.Table`, which contains the required columns, such as `'e_ref'`
and `'dnde'`. The corresponding `sed_type` has to be defined in the meta data
of the table::
>>> import numpy as np
>>> from astropy import units as u
>>> from astropy.table import Table
>>> from gammapy.estimators import FluxPoints
>>> from gammapy.modeling.models import PowerLawSpectralModel
>>> table = Table()
>>> pwl = PowerLawSpectralModel()
>>> e_ref = np.geomspace(1, 100, 7) * u.TeV
>>> table["e_ref"] = e_ref
>>> table["dnde"] = pwl(e_ref)
>>> table["dnde_err"] = pwl.evaluate_error(e_ref)[0]
>>> table.meta["SED_TYPE"] = "dnde"
>>> flux_points = FluxPoints.from_table(table)
>>> flux_points.plot(sed_type="flux") #doctest: +SKIP
If you have flux points in a different data format, the format can be changed
by renaming the table columns and adding meta data::
>>> from astropy import units as u
>>> from astropy.table import Table
>>> from gammapy.estimators import FluxPoints
>>> from gammapy.utils.scripts import make_path
>>> filename = make_path('$GAMMAPY_DATA/tests/spectrum/flux_points/flux_points_ctb_37b.txt')
>>> table = Table.read(filename ,format='ascii.csv', delimiter=' ', comment='#')
>>> table.rename_column('Differential_Flux', 'dnde')
>>> table['dnde'].unit = 'cm-2 s-1 TeV-1'
>>> table.rename_column('lower_error', 'dnde_errn')
>>> table['dnde_errn'].unit = 'cm-2 s-1 TeV-1'
>>> table.rename_column('upper_error', 'dnde_errp')
>>> table['dnde_errp'].unit = 'cm-2 s-1 TeV-1'
>>> table.rename_column('E', 'e_ref')
>>> table['e_ref'].unit = 'TeV'
>>> flux_points = FluxPoints.from_table(table, sed_type="dnde")
>>> flux_points.plot(sed_type="e2dnde") #doctest: +SKIP
Note: In order to reproduce the example you need the tests datasets folder.
You may download it with the command
``gammapy download datasets --tests --out $GAMMAPY_DATA``
"""
[docs]
@classmethod
def read(
cls,
filename,
sed_type=None,
format=None,
reference_model=None,
checksum=False,
table_format="ascii.ecsv",
**kwargs,
):
"""Read precomputed flux points.
Parameters
----------
filename : str
Filename.
sed_type : {"dnde", "flux", "eflux", "e2dnde", "likelihood"}
SED type.
format : {"gadf-sed", "lightcurve", "profile"}, optional
Format string. If None, the format is extracted from the input.
Default is None.
reference_model : `SpectralModel`
Reference spectral model.
checksum : bool
If True checks both DATASUM and CHECKSUM cards in the file headers. Default is False.
table_format : str
Format string for the ~astropy.Table object. Default is "ascii.ecsv"
**kwargs : dict, optional
Keyword arguments passed to `astropy.table.Table.read`.
Returns
-------
flux_points : `FluxPoints`
Flux points.
"""
filename = make_path(filename)
gti = None
try:
table = Table.read(filename, format=table_format, **kwargs)
except (IORegistryError, UnicodeDecodeError):
with fits.open(filename, checksum=checksum) as hdulist:
if "FLUXPOINTS" in hdulist:
fp = hdulist["FLUXPOINTS"]
else:
fp = hdulist[""] # to handle older files
table = Table.read(fp)
if "GTI" in hdulist:
gti = GTI.from_table_hdu(hdulist["GTI"])
return cls.from_table(
table=table,
sed_type=sed_type,
reference_model=reference_model,
format=format,
gti=gti,
)
[docs]
def write(
self, filename, sed_type=None, format=None, overwrite=False, checksum=False
):
"""Write flux points.
Parameters
----------
filename : str
Filename.
sed_type : {"dnde", "flux", "eflux", "e2dnde", "likelihood"}, optional
SED type. Default is None.
format : {"gadf-sed", "lightcurve", "binned-time-series", "profile"}, optional
Format specification. The following formats are supported:
* "gadf-sed": format for SED flux points see :ref:`gadf:flux-points`
for details
* "lightcurve": Gammapy internal format to store energy dependent
lightcurves. Basically a generalisation of the "gadf" format, but
currently there is no detailed documentation available.
* "binned-time-series": table format support by Astropy's
`~astropy.timeseries.BinnedTimeSeries`.
* "profile": Gammapy internal format to store energy dependent
flux profiles. Basically a generalisation of the "gadf" format, but
currently there is no detailed documentation available.
If None, the format will be guessed by looking at the axes that are present in the object.
Default is None.
overwrite : bool, optional
Overwrite existing file. Default is False.
checksum : bool, optional
When True adds both DATASUM and CHECKSUM cards to the headers written to the file.
Default is False.
"""
filename = make_path(filename)
if sed_type is None:
sed_type = self.sed_type_init
table = self.to_table(sed_type=sed_type, format=format)
if ".fits" not in filename.suffixes:
table.write(filename, overwrite=overwrite)
return
primary_hdu = fits.PrimaryHDU()
hdu_evt = fits.BinTableHDU(table, name="FLUXPOINTS")
hdu_all = fits.HDUList([primary_hdu, hdu_evt])
if self.gti:
hdu_all.append(self.gti.to_table_hdu())
hdu_all.writeto(filename, overwrite=overwrite, checksum=checksum)
@staticmethod
def _convert_loglike_columns(table):
# TODO: check sign and factor 2 here
# https://github.com/gammapy/gammapy/pull/2546#issuecomment-554274318
# The idea below is to support the format here:
# https://gamma-astro-data-formats.readthedocs.io/en/latest/spectra/flux_points/index.html#likelihood-columns
# but internally to go to the uniform "stat"
if "loglike" in table.colnames and "stat" not in table.colnames:
table["stat"] = 2 * table["loglike"]
if "loglike_null" in table.colnames and "stat_null" not in table.colnames:
table["stat_null"] = 2 * table["loglike_null"]
if "dloglike_scan" in table.colnames and "stat_scan" not in table.colnames:
table["stat_scan"] = 2 * table["dloglike_scan"]
return table
@staticmethod
def _table_guess_format(table):
"""Format of the table to be transformed to FluxPoints."""
names = table.colnames
if "time_min" in names:
return "lightcurve"
elif "x_min" in names:
return "profile"
else:
return "gadf-sed"
[docs]
@classmethod
def from_table(
cls, table, sed_type=None, format=None, reference_model=None, gti=None
):
"""Create flux points from a table. The table column names must be consistent with the sed_type.
Parameters
----------
table : `~astropy.table.Table`
Table.
sed_type : {"dnde", "flux", "eflux", "e2dnde", "likelihood"}, optional
SED type. Default is None.
format : {"gadf-sed", "lightcurve", "profile"}, optional
Table format. If None, it is extracted from the table column content. Default is None.
reference_model : `SpectralModel`, optional
Reference spectral model. Default is None.
gti : `GTI`, optional
Good time intervals. Default is None.
Returns
-------
flux_points : `FluxPoints`
Flux points.
"""
table = table_standardise_units_copy(table)
if format is None:
format = cls._table_guess_format(table)
log.info("Inferred format: " + format)
if sed_type is None:
sed_type = table.meta.get("SED_TYPE", None)
if sed_type is None:
sed_type = cls._guess_sed_type(table.colnames)
if sed_type is None:
raise ValueError("Specifying the SED type is required")
if sed_type == "likelihood":
table = cls._convert_loglike_columns(table)
if reference_model is None:
reference_model = TemplateSpectralModel(
energy=flat_if_equal(table["e_ref"].quantity),
values=flat_if_equal(table["ref_dnde"].quantity),
)
maps = Maps()
table.meta.setdefault("SED_TYPE", sed_type)
for name in cls.all_quantities(sed_type=sed_type):
if name in table.colnames:
maps[name] = RegionNDMap.from_table(
table=table, colname=name, format=format
)
meta = cls._get_meta_gadf(table)
return cls.from_maps(
maps=maps,
reference_model=reference_model,
meta=meta,
sed_type=sed_type,
gti=gti,
)
@staticmethod
def _get_meta_gadf(table):
meta = {}
meta.update(table.meta)
conf_ul = table.meta.get("UL_CONF")
if conf_ul:
n_sigma_ul = np.round(stats.norm.isf(0.5 * (1 - conf_ul)), 1)
meta["n_sigma_ul"] = n_sigma_ul
meta["sed_type_init"] = table.meta.get("SED_TYPE")
return meta
@staticmethod
def _format_table(table):
"""Format table."""
for column in table.colnames:
if column.startswith(("dnde", "eflux", "flux", "e2dnde", "ref")):
table[column].format = ".3e"
elif column.startswith(
("e_min", "e_max", "e_ref", "sqrt_ts", "norm", "ts", "stat")
):
table[column].format = ".3f"
return table
def _guess_format(self):
"""Format of the FluxPoints object."""
names = self.geom.axes.names
if "time" in names:
return "lightcurve"
elif "projected-distance" in names:
return "profile"
else:
return "gadf-sed"
[docs]
def to_table(self, sed_type=None, format=None, formatted=False):
"""Create table for a given SED type.
Parameters
----------
sed_type : {"likelihood", "dnde", "e2dnde", "flux", "eflux"}
SED type to convert to. Default is `likelihood`.
format : {"gadf-sed", "lightcurve", "binned-time-series", "profile"}, optional
Format specification. The following formats are supported:
* "gadf-sed": format for SED flux points see :ref:`gadf:flux-points`
for details
* "lightcurve": Gammapy internal format to store energy dependent
lightcurves. Basically a generalisation of the "gadf" format, but
currently there is no detailed documentation available.
* "binned-time-series": table format support by Astropy's
`~astropy.timeseries.BinnedTimeSeries`.
* "profile": Gammapy internal format to store energy dependent
flux profiles. Basically a generalisation of the "gadf" format, but
currently there is no detailed documentation available.
If None, the format will be guessed by looking at the axes that are present in the object.
Default is None.
formatted : bool
Formatted version with column formats applied. Numerical columns are
formatted to .3f and .3e respectively. Default is False.
Returns
-------
table : `~astropy.table.Table`
Flux points table.
Examples
--------
This is how to read and plot example flux points:
>>> from gammapy.estimators import FluxPoints
>>> fp = FluxPoints.read("$GAMMAPY_DATA/hawc_crab/HAWC19_flux_points.fits")
>>> table = fp.to_table(sed_type="flux", formatted=True)
>>> print(table[:2])
e_ref e_min e_max flux flux_err flux_ul ts sqrt_ts is_ul
TeV TeV TeV 1 / (s cm2) 1 / (s cm2) 1 / (s cm2)
----- ----- ----- ----------- ----------- ----------- -------- ------- -----
1.334 1.000 1.780 1.423e-11 3.135e-13 nan 2734.000 52.288 False
2.372 1.780 3.160 5.780e-12 1.082e-13 nan 4112.000 64.125 False
"""
if sed_type is None:
sed_type = self.sed_type_init
if format is None:
format = self._guess_format()
log.info("Inferred format: " + format)
if format == "gadf-sed":
# TODO: what to do with GTI info?
if not self.geom.axes.names == ["energy"]:
raise ValueError(
"Only flux points with a single energy axis "
"can be converted to 'gadf-sed'"
)
idx = (Ellipsis, 0, 0)
table = self.energy_axis.to_table(format="gadf-sed")
table.meta["SED_TYPE"] = sed_type
if not self.is_convertible_to_flux_sed_type:
table.remove_columns(["e_min", "e_max"])
if self.n_sigma_ul:
table.meta["UL_CONF"] = np.round(
1 - 2 * stats.norm.sf(self.n_sigma_ul), 7
)
if sed_type == "likelihood":
table["ref_dnde"] = self.dnde_ref[idx]
table["ref_flux"] = self.flux_ref[idx]
table["ref_eflux"] = self.eflux_ref[idx]
for quantity in self.all_quantities(sed_type=sed_type):
data = getattr(self, quantity, None)
if data:
table[quantity] = data.quantity[idx]
if self.has_stat_profiles:
norm_axis = self.stat_scan.geom.axes["norm"]
table["norm_scan"] = norm_axis.center.reshape((1, -1))
table["stat"] = self.stat.data[idx]
table["stat_scan"] = self.stat_scan.data[idx]
table["is_ul"] = self.is_ul.data[idx]
if not self.has_ul:
table.remove_columns("is_ul")
elif format == "lightcurve":
time_axis = self.geom.axes["time"]
tables = []
for idx, (time_min, time_max) in enumerate(time_axis.iter_by_edges):
table_flat = Table()
table_flat["time_min"] = [time_min.mjd]
table_flat["time_max"] = [time_max.mjd]
fp = self.slice_by_idx(slices={"time": idx})
table = fp.to_table(sed_type=sed_type, format="gadf-sed")
for column in table.columns:
table_flat[column] = table[column][np.newaxis]
tables.append(table_flat)
table = vstack(tables)
# serialize with reference time set to mjd=0.0
ref_time = Time(0.0, format="mjd", scale=time_axis.reference_time.scale)
table.meta.update(time_ref_to_dict(ref_time, scale=ref_time.scale))
table.meta["TIMEUNIT"] = "d"
elif format == "binned-time-series":
message = (
"Format 'binned-time-series' support a single time axis "
f"only. Got {self.geom.axes.names}"
)
if not self.geom.axes.is_unidimensional:
raise ValueError(message)
axis = self.geom.axes.primary_axis
if not isinstance(axis, TimeMapAxis):
raise ValueError(message)
table = Table()
table["time_bin_start"] = axis.time_min
table["time_bin_size"] = axis.time_delta
for quantity in self.all_quantities(sed_type=sed_type):
data = getattr(self, quantity, None)
if data:
table[quantity] = data.quantity.squeeze()
elif format == "profile":
x_axis = self.geom.axes["projected-distance"]
tables = []
for idx, (x_min, x_max) in enumerate(x_axis.iter_by_edges):
table_flat = Table()
table_flat["x_min"] = [x_min]
table_flat["x_max"] = [x_max]
table_flat["x_ref"] = [(x_max + x_min) / 2]
fp = self.slice_by_idx(slices={"projected-distance": idx})
table = fp.to_table(sed_type=sed_type, format="gadf-sed")
for column in table.columns:
table_flat[column] = table[column][np.newaxis]
tables.append(table_flat)
table = vstack(tables)
else:
raise ValueError(f"Not a supported format {format}")
if formatted:
table = self._format_table(table=table)
return table
@staticmethod
def _energy_ref_lafferty(model, energy_min, energy_max):
"""Helper for `to_sed_type`.
Compute energy_ref that the value at energy_ref corresponds
to the mean value between energy_min and energy_max.
"""
flux = model.integral(energy_min, energy_max)
dnde_mean = flux / (energy_max - energy_min)
return model.inverse(dnde_mean)
def _plot_get_flux_err(self, sed_type=None):
"""Compute flux error for given SED type."""
y_errn, y_errp = None, None
if "norm_err" in self.available_quantities:
# symmetric error
y_errn = getattr(self, sed_type + "_err")
y_errp = y_errn.copy()
if "norm_errp" in self.available_quantities:
y_errn = getattr(self, sed_type + "_errn")
y_errp = getattr(self, sed_type + "_errp")
return y_errn, y_errp
[docs]
def plot(self, ax=None, sed_type=None, energy_power=0, time_format="iso", **kwargs):
"""Plot flux points.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis object to plot on. Default is None.
sed_type : {"dnde", "flux", "eflux", "e2dnde"}, optional
SED type. Default is None.
energy_power : float, optional
Power of energy to multiply flux axis with. Default is 0.
time_format : {"iso", "mjd"}
Used time format is a time axis is present. Default is "iso".
**kwargs : dict, optional
Keyword arguments passed to `~RegionNDMap.plot`.
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis object.
"""
if sed_type is None:
sed_type = self.sed_type_plot_default
if not self.norm.geom.is_region:
raise ValueError("Plotting only supported for region based flux points")
if ax is None:
ax = plt.gca()
flux_unit = DEFAULT_UNIT[sed_type]
flux = getattr(self, sed_type)
# get errors and ul
y_errn, y_errp = self._plot_get_flux_err(sed_type=sed_type)
is_ul = self.is_ul.data
if self.has_ul and y_errn and is_ul.any():
flux_ul = getattr(self, sed_type + "_ul").quantity
y_errn.data[is_ul] = np.clip(
0.5 * flux_ul[is_ul].to_value(y_errn.unit), 0, np.inf
)
y_errp.data[is_ul] = 0
flux.data[is_ul] = flux_ul[is_ul].to_value(flux.unit)
kwargs.setdefault("uplims", is_ul)
# set flux points plotting defaults
if y_errp and y_errn:
y_errp = np.clip(
scale_plot_flux(y_errp, energy_power=energy_power).quantity, 0, np.inf
)
y_errn = np.clip(
scale_plot_flux(y_errn, energy_power=energy_power).quantity, 0, np.inf
)
kwargs.setdefault("yerr", (y_errn, y_errp))
else:
kwargs.setdefault("yerr", None)
flux = scale_plot_flux(flux=flux.to_unit(flux_unit), energy_power=energy_power)
if "time" in flux.geom.axes_names:
flux.geom.axes["time"].time_format = time_format
ax = flux.plot(ax=ax, **kwargs)
else:
ax = flux.plot(ax=ax, **kwargs)
ax.set_xlabel(f"Energy [{ax.xaxis.units.to_string(UNIT_STRING_FORMAT)}]")
ax.set_xscale("log", nonpositive="clip")
self._plot_format_yax(ax=ax, energy_power=energy_power, sed_type=sed_type)
if len(flux.geom.axes) > 1:
ax.legend()
return ax
@staticmethod
def _plot_format_yax(ax, energy_power, sed_type):
if energy_power > 0:
ax.set_ylabel(
f"e{energy_power} * {sed_type} [{ax.yaxis.units.to_string(UNIT_STRING_FORMAT)}]"
)
else:
ax.set_ylabel(
f"{sed_type} [{ax.yaxis.units.to_string(UNIT_STRING_FORMAT)}]"
)
ax.set_yscale("log", nonpositive="clip")
[docs]
def plot_ts_profiles(
self,
ax=None,
sed_type=None,
add_cbar=True,
**kwargs,
):
"""Plot fit statistic SED profiles as a density plot.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis object to plot on. Default is None.
sed_type : {"dnde", "flux", "eflux", "e2dnde"}, optional
SED type. Default is None.
add_cbar : bool, optional
Whether to add a colorbar to the plot. Default is True.
**kwargs : dict, optional
Keyword arguments passed to `~matplotlib.pyplot.pcolormesh`.
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis object.
"""
if ax is None:
ax = plt.gca()
if sed_type is None:
sed_type = self.sed_type_plot_default
if not self.norm.geom.is_region:
raise ValueError("Plotting only supported for region based flux points")
if not self.geom.axes.is_unidimensional:
raise ValueError(
"Profile plotting is only supported for unidimensional maps"
)
axis = self.geom.axes.primary_axis
if isinstance(axis, TimeMapAxis) and not axis.is_contiguous:
axis = axis.to_contiguous()
if ax.yaxis.units is None:
yunits = DEFAULT_UNIT[sed_type]
else:
yunits = ax.yaxis.units
ax.yaxis.set_units(yunits)
flux_ref = getattr(self, sed_type + "_ref").to(yunits)
ts = self.ts_scan
norm_min, norm_max = ts.geom.axes["norm"].bounds.to_value("")
flux = MapAxis.from_bounds(
norm_min * flux_ref.value.min(),
norm_max * flux_ref.value.max(),
nbin=500,
interp=axis.interp,
unit=flux_ref.unit,
)
norm = flux.center / flux_ref.reshape((-1, 1))
coords = ts.geom.get_coord()
coords["norm"] = norm
coords[axis.name] = axis.center.reshape((-1, 1))
z = ts.interp_by_coord(coords, values_scale="stat-profile")
kwargs.setdefault("vmax", 0)
kwargs.setdefault("vmin", -4)
kwargs.setdefault("zorder", 0)
kwargs.setdefault("cmap", "Blues")
kwargs.setdefault("linewidths", 0)
kwargs.setdefault("shading", "auto")
# clipped values are set to NaN so that they appear white on the plot
z[-z < kwargs["vmin"]] = np.nan
with quantity_support():
caxes = ax.pcolormesh(axis.as_plot_edges, flux.edges, -z.T, **kwargs)
axis.format_plot_xaxis(ax=ax)
ax.set_ylabel(f"{sed_type} [{ax.yaxis.units.to_string(UNIT_STRING_FORMAT)}]")
ax.set_yscale("log")
if add_cbar:
label = "Fit statistic difference"
ax.figure.colorbar(caxes, ax=ax, label=label)
return ax
[docs]
def recompute_ul(self, n_sigma_ul=2, **kwargs):
"""Recompute upper limits corresponding to the given value.
The pre-computed statistic profiles must exist for the re-computation.
Parameters
----------
n_sigma_ul : int
Number of sigma to use for upper limit computation. Default is 2.
**kwargs : dict, optional
Keyword arguments passed to `~scipy.optimize.brentq`.
Returns
-------
flux_points : `~gammapy.estimators.FluxPoints`
A new FluxPoints object with modified upper limits.
Examples
--------
>>> from gammapy.estimators import FluxPoints
>>> filename = '$GAMMAPY_DATA/tests/spectrum/flux_points/binlike.fits'
>>> flux_points = FluxPoints.read(filename)
>>> flux_points_recomputed = flux_points.recompute_ul(n_sigma_ul=3)
>>> print(flux_points.meta["n_sigma_ul"], flux_points.flux_ul.data[0])
2.0 [[3.95451985e-09]]
>>> print(flux_points_recomputed.meta["n_sigma_ul"], flux_points_recomputed.flux_ul.data[0])
3 [[6.22245374e-09]]
"""
if not self.has_stat_profiles:
raise ValueError(
"Stat profiles not present. Upper limit computation is not possible"
)
delta_ts = n_sigma_ul**2
flux_points = deepcopy(self)
value_scan = self.stat_scan.geom.axes["norm"].center
shape_axes = self.stat_scan.geom._shape[slice(3, None)][::-1]
for idx in np.ndindex(shape_axes):
stat_scan = np.abs(
self.stat_scan.data[idx].squeeze() - self.stat.data[idx].squeeze()
)
flux_points.norm_ul.data[idx] = stat_profile_ul_scipy(
value_scan, stat_scan, delta_ts=delta_ts, **kwargs
)
flux_points.meta["n_sigma_ul"] = n_sigma_ul
return flux_points
[docs]
def resample_axis(self, axis_new):
"""Rebin the flux point object along the new axis.
The log-likelihoods profiles in each bin are summed
to compute the resultant quantities.
Stat profiles must be present on the `~gammapy.estimators.FluxPoints` object for
this method to work.
For now, works only for flat `~gammapy.estimators.FluxPoints`.
Parameters
----------
axis_new : `MapAxis` or `TimeMapAxis`
The new axis to resample along
Returns
-------
flux_points : `~gammapy.estimators.FluxPoints`
A new FluxPoints object with modified axis.
"""
if not self.has_stat_profiles:
raise ValueError("Stat profiles not present, rebinning is not possible")
fluxpoints = []
for edge_min, edge_max in zip(axis_new.edges_min, axis_new.edges_max):
if isinstance(axis_new, TimeMapAxis):
edge_min = edge_min + axis_new.reference_time
edge_max = edge_max + axis_new.reference_time
fp = self.slice_by_coord({axis_new.name: slice(edge_min, edge_max)})
fp_new = squash_fluxpoints(fp, axis_new)
fluxpoints.append(fp_new)
return self.__class__.from_stack(fluxpoints, axis=axis_new)
