# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import numpy as np
from astropy import units as u
from astropy.io.registry import IORegistryError
from astropy.table import Table, vstack
from gammapy.datasets import Datasets
from gammapy.modeling.models import PowerLawSpectralModel
from gammapy.utils.interpolation import interpolate_profile
from gammapy.utils.scripts import make_path
from gammapy.utils.table import table_from_row_data, table_standardise_units_copy
from .core import Estimator
from .flux import FluxEstimator
__all__ = ["FluxPoints", "FluxPointsEstimator"]
log = logging.getLogger(__name__)
REQUIRED_COLUMNS = {
"dnde": ["e_ref", "dnde"],
"e2dnde": ["e_ref", "e2dnde"],
"flux": ["e_min", "e_max", "flux"],
"eflux": ["e_min", "e_max", "eflux"],
# TODO: extend required columns
"likelihood": ["e_min", "e_max", "e_ref", "ref_dnde", "norm",],
}
OPTIONAL_COLUMNS = {
"dnde": ["dnde_err", "dnde_errp", "dnde_errn", "dnde_ul", "is_ul"],
"e2dnde": ["e2dnde_err", "e2dnde_errp", "e2dnde_errn", "e2dnde_ul", "is_ul"],
"flux": ["flux_err", "flux_errp", "flux_errn", "flux_ul", "is_ul"],
"eflux": ["eflux_err", "eflux_errp", "eflux_errn", "eflux_ul", "is_ul"],
"likelihood": ["norm_scan", "stat_scan"],
}
DEFAULT_UNIT = {
"dnde": u.Unit("cm-2 s-1 TeV-1"),
"e2dnde": u.Unit("erg cm-2 s-1"),
"flux": u.Unit("cm-2 s-1"),
"eflux": u.Unit("erg cm-2 s-1"),
}
[docs]class FluxPoints:
"""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 = '$GAMMAPY_DATA/hawc_crab/HAWC19_flux_points.fits'
flux_points = FluxPoints.read(filename)
flux_points.plot()
An instance of `FluxPoints` can also be created by passing an instance of
`astropy.table.Table`, which contains the required columns, such as `'e_ref'`
and `'dnde'`. The corresponding `sed_type` has to be defined in the meta data
of the table::
from astropy import units as u
from astropy.table import Table
from gammapy.estimators import FluxPoints
from gammapy.modeling.models import PowerLawSpectralModel
table = Table()
pwl = PowerLawSpectralModel()
e_ref = np.logspace(0, 2, 7) * u.TeV
table['e_ref'] = e_ref
table['dnde'] = pwl(e_ref)
table.meta['SED_TYPE'] = 'dnde'
flux_points = FluxPoints(table)
flux_points.plot()
If you have flux points in a different data format, the format can be changed
by renaming the table columns and adding meta data::
from astropy import units as u
from astropy.table import Table
from gammapy.estimators import FluxPoints
table = Table.read('$GAMMAPY_DATA/tests/spectrum/flux_points/flux_points_ctb_37b.txt',
format='ascii.csv', delimiter=' ', comment='#')
table.meta['SED_TYPE'] = 'dnde'
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(table)
flux_points.plot()
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``
"""
def __init__(self, table):
self.table = table_standardise_units_copy(table)
# validate that the table is a valid representation
# of the given flux point sed type
self._validate_table(self.table, table.meta["SED_TYPE"])
def __repr__(self):
return f"{self.__class__.__name__}(sed_type={self.sed_type!r}, n_points={len(self.table)})"
@property
def table_formatted(self):
"""Return formatted version of the flux points table. Used for pretty printing"""
table = self.table.copy()
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
[docs] @classmethod
def read(cls, filename, **kwargs):
"""Read flux points.
Parameters
----------
filename : str
Filename
kwargs : dict
Keyword arguments passed to `astropy.table.Table.read`.
"""
filename = make_path(filename)
try:
table = Table.read(filename, **kwargs)
except IORegistryError:
kwargs.setdefault("format", "ascii.ecsv")
table = Table.read(filename, **kwargs)
if "SED_TYPE" not in table.meta.keys():
sed_type = cls._guess_sed_type(table)
table.meta["SED_TYPE"] = sed_type
# 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 cls(table=table)
[docs] def write(self, filename, **kwargs):
"""Write flux points.
Parameters
----------
filename : str
Filename
kwargs : dict
Keyword arguments passed to `astropy.table.Table.write`.
"""
filename = make_path(filename)
try:
self.table.write(filename, **kwargs)
except IORegistryError:
kwargs.setdefault("format", "ascii.ecsv")
self.table.write(filename, **kwargs)
[docs] @classmethod
def stack(cls, flux_points):
"""Create flux points by stacking list of flux points.
The first `FluxPoints` object in the list is taken as a reference to infer
column names and units for the stacked object.
Parameters
----------
flux_points : list of `FluxPoints`
List of flux points to stack.
Returns
-------
flux_points : `FluxPoints`
Flux points without upper limit points.
"""
reference = flux_points[0].table
tables = []
for _ in flux_points:
table = _.table
for colname in reference.colnames:
column = reference[colname]
if column.unit:
table[colname] = table[colname].quantity.to(column.unit)
tables.append(table[reference.colnames])
table_stacked = vstack(tables)
table_stacked.meta["SED_TYPE"] = reference.meta["SED_TYPE"]
return cls(table_stacked)
[docs] def drop_ul(self):
"""Drop upper limit flux points.
Returns
-------
flux_points : `FluxPoints`
Flux points with upper limit points removed.
Examples
--------
>>> from gammapy.estimators import FluxPoints
>>> filename = '$GAMMAPY_DATA/tests/spectrum/flux_points/flux_points.fits'
>>> flux_points = FluxPoints.read(filename)
>>> print(flux_points)
FluxPoints(sed_type="flux", n_points=24)
>>> print(flux_points.drop_ul())
FluxPoints(sed_type="flux", n_points=19)
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``
"""
table_drop_ul = self.table[~self.is_ul]
return self.__class__(table_drop_ul)
def _flux_to_dnde(self, energy_ref, table, model, pwl_approx):
if model is None:
model = PowerLawSpectralModel()
energy_min, energy_max = self.energy_min, self.energy_max
flux = table["flux"].quantity
dnde = self._dnde_from_flux(
flux, model, energy_ref, energy_min, energy_max, pwl_approx
)
# Add to result table
table["e_ref"] = energy_ref
table["dnde"] = dnde
if "flux_err" in table.colnames:
table["dnde_err"] = dnde * table["flux_err"].quantity / flux
if "flux_errn" in table.colnames:
table["dnde_errn"] = dnde * table["flux_errn"].quantity / flux
table["dnde_errp"] = dnde * table["flux_errp"].quantity / flux
if "flux_ul" in table.colnames:
flux_ul = table["flux_ul"].quantity
dnde_ul = self._dnde_from_flux(
flux_ul, model, energy_ref, energy_min, energy_max, pwl_approx
)
table["dnde_ul"] = dnde_ul
return table
@staticmethod
def _dnde_to_e2dnde(energy_ref, table):
for suffix in ["", "_ul", "_err", "_errp", "_errn"]:
try:
data = table["dnde" + suffix].quantity
table["e2dnde" + suffix] = (energy_ref ** 2 * data).to(
DEFAULT_UNIT["e2dnde"]
)
except KeyError:
continue
return table
@staticmethod
def _e2dnde_to_dnde(energy_ref, table):
for suffix in ["", "_ul", "_err", "_errp", "_errn"]:
try:
data = table["e2dnde" + suffix].quantity
table["dnde" + suffix] = (data / energy_ref ** 2).to(
DEFAULT_UNIT["dnde"]
)
except KeyError:
continue
return table
[docs] def to_sed_type(self, sed_type, method="log_center", model=None, pwl_approx=False):
"""Convert to a different SED type (return new `FluxPoints`).
See: https://ui.adsabs.harvard.edu/abs/1995NIMPA.355..541L for details
on the `'lafferty'` method.
Parameters
----------
sed_type : {'dnde'}
SED type to convert to.
model : `~gammapy.modeling.models.SpectralModel`
Spectral model assumption. Note that the value of the amplitude parameter
does not matter. Still it is recommended to use something with the right
scale and units. E.g. `amplitude = 1e-12 * u.Unit('cm-2 s-1 TeV-1')`
method : {'lafferty', 'log_center', 'table'}
Flux points `energy_ref` estimation method:
* `'laferty'` Lafferty & Wyatt model-based energy_ref
* `'log_center'` log bin center energy_ref
* `'table'` using column 'energy_ref' from input flux_points
pwl_approx : bool
Use local power law appoximation at energy_ref to compute differential flux
from the integral flux. This method is used by the Fermi-LAT catalogs.
Returns
-------
flux_points : `FluxPoints`
Flux points including differential quantity columns `dnde`
and `dnde_err` (optional), `dnde_ul` (optional).
Examples
--------
>>> from gammapy.estimators import FluxPoints
>>> from gammapy.modeling.models import PowerLawSpectralModel
>>> filename = '$GAMMAPY_DATA/tests/spectrum/flux_points/flux_points.fits'
>>> flux_points = FluxPoints.read(filename)
>>> model = PowerLawSpectralModel(index=2.2)
>>> flux_points_dnde = flux_points.to_sed_type('dnde', model=model)
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``
"""
# TODO: implement other directions.
table = self.table.copy()
if self.sed_type == "flux" and sed_type == "dnde":
# Compute energy_ref
if method == "table":
energy_ref = table["e_ref"].quantity
elif method == "log_center":
energy_ref = np.sqrt(self.energy_min * self.energy_max)
elif method == "lafferty":
# set energy_ref that it represents the mean dnde in the given energy bin
energy_ref = self._energy_ref_lafferty(
model, self.energy_min, self.energy_max
)
else:
raise ValueError(f"Invalid method: {method}")
table = self._flux_to_dnde(energy_ref, table, model, pwl_approx)
elif self.sed_type == "dnde" and sed_type == "e2dnde":
table = self._dnde_to_e2dnde(self.energy_ref, table)
elif self.sed_type == "e2dnde" and sed_type == "dnde":
table = self._e2dnde_to_dnde(self.energy_ref, table)
elif self.sed_type == "likelihood" and sed_type in ["dnde", "flux", "eflux"]:
for suffix in ["", "_ul", "_err", "_errp", "_errn"]:
try:
table[sed_type + suffix] = (
table["ref_" + sed_type] * table["norm" + suffix]
)
except KeyError:
continue
elif self.sed_type == sed_type:
# do nothing if the sed type is the same
pass
else:
raise NotImplementedError
table.meta["SED_TYPE"] = sed_type
return FluxPoints(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)
@staticmethod
def _dnde_from_flux(flux, model, energy_ref, energy_min, energy_max, pwl_approx):
"""Helper for `to_sed_type`.
Compute dnde under the assumption that flux equals expected
flux from model.
"""
dnde_model = model(energy_ref)
if pwl_approx:
index = model.spectral_index(energy_ref)
flux_model = PowerLawSpectralModel.evaluate_integral(
energy_min=energy_min,
energy_max=energy_max,
index=index,
reference=energy_ref,
amplitude=dnde_model,
)
else:
flux_model = model.integral(energy_min, energy_max)
return dnde_model * (flux / flux_model)
@property
def sed_type(self):
"""SED type (str).
One of: {'dnde', 'e2dnde', 'flux', 'eflux'}
"""
return self.table.meta["SED_TYPE"]
@staticmethod
def _guess_sed_type(table):
"""Guess SED type from table content."""
valid_sed_types = list(REQUIRED_COLUMNS.keys())
for sed_type in valid_sed_types:
required = set(REQUIRED_COLUMNS[sed_type])
if required.issubset(table.colnames):
return sed_type
@staticmethod
def _guess_sed_type_from_unit(unit):
"""Guess SED type from unit."""
for sed_type, default_unit in DEFAULT_UNIT.items():
if unit.is_equivalent(default_unit):
return sed_type
@staticmethod
def _validate_table(table, sed_type):
"""Validate input table."""
required = set(REQUIRED_COLUMNS[sed_type])
if not required.issubset(table.colnames):
missing = required.difference(table.colnames)
raise ValueError(
"Missing columns for sed type '{}':" " {}".format(sed_type, missing)
)
@staticmethod
def _get_y_energy_unit(y_unit):
"""Get energy part of the given y unit."""
try:
return [_ for _ in y_unit.bases if _.physical_type == "energy"][0]
except IndexError:
return u.Unit("TeV")
def _plot_get_energy_err(self):
"""Compute energy error for given sed type"""
try:
energy_min = self.energy_min
energy_max = self.energy_max
energy_ref = self.energy_ref
x_err = ((energy_ref - energy_min), (energy_max - energy_ref))
except KeyError:
x_err = None
return x_err
def _plot_get_flux_err(self, sed_type=None):
"""Compute flux error for given sed type"""
try:
# asymmetric error
y_errn = self.table[sed_type + "_errn"].quantity
y_errp = self.table[sed_type + "_errp"].quantity
y_err = (y_errn, y_errp)
except KeyError:
try:
# symmetric error
y_err = self.table[sed_type + "_err"].quantity
y_err = (y_err, y_err)
except KeyError:
# no error at all
y_err = None
return y_err
@property
def is_ul(self):
try:
return self.table["is_ul"].data.astype("bool")
except KeyError:
return np.isnan(self.table[self.sed_type])
@property
def energy_ref(self):
"""Reference energy.
Defined by `energy_ref` column in `FluxPoints.table` or computed as log
center, if `energy_min` and `energy_max` columns are present in `FluxPoints.table`.
Returns
-------
energy_ref : `~astropy.units.Quantity`
Reference energy.
"""
try:
return self.table["e_ref"].quantity
except KeyError:
return np.sqrt(self.energy_min * self.energy_max)
@property
def energy_edges(self):
"""Edges of the energy bin.
Returns
-------
energy_edges : `~astropy.units.Quantity`
Energy edges.
"""
energy_edges = list(self.energy_min)
energy_edges += [self.energy_max[-1]]
return u.Quantity(energy_edges, self.energy_min.unit, copy=False)
@property
def energy_min(self):
"""Lower bound of energy bin.
Defined by `energy_min` column in `FluxPoints.table`.
Returns
-------
energy_min : `~astropy.units.Quantity`
Lower bound of energy bin.
"""
return self.table["e_min"].quantity
@property
def energy_max(self):
"""Upper bound of energy bin.
Defined by ``energy_max`` column in ``table``.
Returns
-------
energy_max : `~astropy.units.Quantity`
Upper bound of energy bin.
"""
return self.table["e_max"].quantity
[docs] def plot(
self, ax=None, energy_unit="TeV", flux_unit=None, energy_power=0, **kwargs
):
"""Plot flux points.
Parameters
----------
ax : `~matplotlib.axes.Axes`
Axis object to plot on.
energy_unit : str, `~astropy.units.Unit`, optional
Unit of the energy axis
flux_unit : str, `~astropy.units.Unit`, optional
Unit of the flux axis
energy_power : int
Power of energy to multiply y axis with
kwargs : dict
Keyword arguments passed to :func:`matplotlib.pyplot.errorbar`
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis object
"""
import matplotlib.pyplot as plt
if ax is None:
ax = plt.gca()
sed_type = self.sed_type
y_unit = u.Unit(flux_unit or DEFAULT_UNIT[sed_type])
y = self.table[sed_type].quantity.to(y_unit)
x = self.energy_ref.to(energy_unit)
# get errors and ul
is_ul = self.is_ul
x_err_all = self._plot_get_energy_err()
y_err_all = self._plot_get_flux_err(sed_type)
# handle energy power
energy_unit_y = self._get_y_energy_unit(y_unit)
y_unit = y.unit * energy_unit_y ** energy_power
y = (y * np.power(x, energy_power)).to(y_unit)
y_err, x_err = None, None
if y_err_all:
y_errn = (y_err_all[0] * np.power(x, energy_power)).to(y_unit)
y_errp = (y_err_all[1] * np.power(x, energy_power)).to(y_unit)
y_err = (y_errn[~is_ul].to_value(y_unit), y_errp[~is_ul].to_value(y_unit))
if x_err_all:
x_errn, x_errp = x_err_all
x_err = (
x_errn[~is_ul].to_value(energy_unit),
x_errp[~is_ul].to_value(energy_unit),
)
# set flux points plotting defaults
kwargs.setdefault("marker", "+")
kwargs.setdefault("ls", "None")
print(x_err)
ebar = ax.errorbar(
x[~is_ul].value, y[~is_ul].value, yerr=y_err, xerr=x_err, **kwargs
)
if is_ul.any():
if x_err_all:
x_errn, x_errp = x_err_all
x_err = (
x_errn[is_ul].to_value(energy_unit),
x_errp[is_ul].to_value(energy_unit),
)
y_ul = self.table[sed_type + "_ul"].quantity
y_ul = (y_ul * np.power(x, energy_power)).to(y_unit)
y_err = (0.5 * y_ul[is_ul].value, np.zeros_like(y_ul[is_ul].value))
kwargs.setdefault("color", ebar[0].get_color())
# pop label keyword to avoid that it appears twice in the legend
kwargs.pop("label", None)
ax.errorbar(
x[is_ul].value,
y_ul[is_ul].value,
xerr=x_err,
yerr=y_err,
uplims=True,
**kwargs,
)
ax.set_xscale("log", nonposx="clip")
ax.set_yscale("log", nonposy="clip")
ax.set_xlabel(f"Energy ({energy_unit})")
ax.set_ylabel(f"{self.sed_type} ({y_unit})")
return ax
[docs] def plot_ts_profiles(
self,
ax=None,
energy_unit="TeV",
add_cbar=True,
y_values=None,
y_unit=None,
**kwargs,
):
"""Plot fit statistic SED profiles as a density plot.
Parameters
----------
ax : `~matplotlib.axes.Axes`
Axis object to plot on.
energy_unit : str, `~astropy.units.Unit`, optional
Unit of the energy axis
y_values : `astropy.units.Quantity`
Array of y-values to use for the fit statistic profile evaluation.
y_unit : str or `astropy.units.Unit`
Unit to use for the y-axis.
add_cbar : bool
Whether to add a colorbar to the plot.
kwargs : dict
Keyword arguments passed to :func:`matplotlib.pyplot.pcolormesh`
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis object
"""
import matplotlib.pyplot as plt
if ax is None:
ax = plt.gca()
self._validate_table(self.table, "likelihood")
y_unit = u.Unit(y_unit or DEFAULT_UNIT[self.sed_type])
if y_values is None:
ref_values = self.table["ref_" + self.sed_type].quantity
y_values = np.logspace(
np.log10(0.2 * ref_values.value.min()),
np.log10(5 * ref_values.value.max()),
500,
)
y_values = u.Quantity(y_values, y_unit, copy=False)
x = self.energy_edges.to(energy_unit)
# Compute fit statistic "image" one energy bin at a time
# by interpolating e2dnde at the log bin centers
z = np.empty((len(self.table), len(y_values)))
for idx, row in enumerate(self.table):
y_ref = self.table["ref_" + self.sed_type].quantity[idx]
norm = (y_values / y_ref).to_value("")
norm_scan = row["norm_scan"]
ts_scan = row["stat_scan"] - row["stat"]
interp = interpolate_profile(norm_scan, ts_scan)
z[idx] = interp((norm,))
kwargs.setdefault("vmax", 0)
kwargs.setdefault("vmin", -4)
kwargs.setdefault("zorder", 0)
kwargs.setdefault("cmap", "Blues")
kwargs.setdefault("linewidths", 0)
# clipped values are set to NaN so that they appear white on the plot
z[-z < kwargs["vmin"]] = np.nan
caxes = ax.pcolormesh(x.value, y_values.value, -z.T, **kwargs)
ax.set_xscale("log", nonposx="clip")
ax.set_yscale("log", nonposy="clip")
ax.set_xlabel(f"Energy ({energy_unit})")
ax.set_ylabel(f"{self.sed_type} ({y_values.unit})")
if add_cbar:
label = "fit statistic difference"
ax.figure.colorbar(caxes, ax=ax, label=label)
return ax
[docs]class FluxPointsEstimator(Estimator):
"""Flux points estimator.
Estimates flux points for a given list of datasets, energies and spectral model.
To estimate the flux point the amplitude of the reference spectral model is
fitted within the energy range defined by the energy group. This is done for
each group independently. The amplitude is re-normalized using the "norm" parameter,
which specifies the deviation of the flux from the reference model in this
energy group. See https://gamma-astro-data-formats.readthedocs.io/en/latest/spectra/binned_likelihoods/index.html
for details.
The method is also described in the Fermi-LAT catalog paper
https://ui.adsabs.harvard.edu/#abs/2015ApJS..218...23A
or the HESS Galactic Plane Survey paper
https://ui.adsabs.harvard.edu/#abs/2018A%26A...612A...1H
Parameters
----------
energy_edges : `~astropy.units.Quantity`
Energy edges of the flux point bins.
source : str or int
For which source in the model to compute the flux points.
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
Re-optimize other free model parameters.
selection_optional : list of str
Which additional quantities to estimate. Available options are:
* "errn-errp": estimate asymmetric errors on flux.
* "ul": estimate upper limits.
* "norm-scan": estimate fit statistic profiles.
By default all steps are executed.
"""
tag = "FluxPointsEstimator"
_available_selection_optional = ["errn-errp", "ul", "scan"]
def __init__(
self,
energy_edges=[1, 10] * u.TeV,
source=0,
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.energy_edges = energy_edges
self.source = source
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
def _flux_estimator(self, energy_min, energy_max):
return FluxEstimator(
source=self.source,
energy_min=energy_min,
energy_max=energy_max,
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,
)
[docs] def run(self, datasets):
"""Run the flux point estimator for all energy groups.
Parameters
----------
datasets : list of `~gammapy.datasets.Dataset`
Datasets
Returns
-------
flux_points : `FluxPoints`
Estimated flux points.
"""
datasets = Datasets(datasets).copy()
rows = []
for energy_min, energy_max in zip(
self.energy_edges[:-1], self.energy_edges[1:]
):
row = self.estimate_flux_point(
datasets, energy_min=energy_min, energy_max=energy_max
)
rows.append(row)
table = table_from_row_data(rows=rows, meta={"SED_TYPE": "likelihood"})
# TODO: this should be changed once likelihood is fully supported
return FluxPoints(table).to_sed_type("dnde")
[docs] def estimate_flux_point(self, datasets, energy_min, energy_max):
"""Estimate flux point for a single energy group.
Parameters
----------
datasets : Datasets
Datasets
energy_min, energy_max : `~astropy.units.Quantity`
Energy bounds to compute the flux point for.
Returns
-------
result : dict
Dict with results for the flux point.
"""
result = self.estimate_counts(
datasets, energy_min=energy_min, energy_max=energy_max
)
fe = self._flux_estimator(energy_min=energy_min, energy_max=energy_max)
result.update(fe.run(datasets=datasets))
return result
[docs] @staticmethod
def estimate_counts(datasets, energy_min, energy_max):
"""Estimate counts for the flux point.
Parameters
----------
datasets : Datasets
Datasets
energy_min, energy_max : `~astropy.units.Quantity`
Energy bounds to compute the flux point for.
Returns
-------
result : dict
Dict with an array with one entry per dataset with counts for the flux point.
"""
counts = []
for dataset in datasets:
energy_mask = dataset.counts.geom.energy_mask(
energy_min=energy_min, energy_max=energy_max, round_to_edge=True
)
mask = dataset.mask & energy_mask
counts.append(dataset.counts.data[mask].sum())
return {"counts": np.array(counts, dtype=int)}