# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function, unicode_literals
from collections import OrderedDict
import numpy as np
from scipy.special import erf
from astropy.io import fits
from astropy.coordinates import Angle
from astropy.units import Quantity
from astropy.table import Table
from ..utils.energy import EnergyBounds, Energy
from ..utils.scripts import make_path
from ..utils.nddata import NDDataArray, BinnedDataAxis
from ..utils.fits import energy_axis_to_ebounds
__all__ = ["EnergyDispersion", "EnergyDispersion2D"]
[docs]class EnergyDispersion(object):
"""Energy dispersion matrix.
Data format specification: :ref:`gadf:ogip-rmf`
Parameters
----------
e_true_lo, e_true_hi : `~astropy.units.Quantity`
True energy axis binning
e_reco_lo, e_reco_hi : `~astropy.units.Quantity`
Reconstruced energy axis binning
data : array_like
2-dim energy dispersion matrix
Examples
--------
Create a Gaussian energy dispersion matrix::
import numpy as np
import astropy.units as u
from gammapy.irf import EnergyDispersion
energy = np.logspace(0, 1, 101) * u.TeV
edisp = EnergyDispersion.from_gauss(
e_true=energy, e_reco=energy,
sigma=0.1, bias=0,
)
Have a quick look:
>>> print(edisp)
>>> edisp.peek()
See Also
--------
EnergyDispersion2D
"""
default_interp_kwargs = dict(bounds_error=False, fill_value=0, method="nearest")
"""Default Interpolation kwargs for `~NDDataArray`. Fill zeros and do not
interpolate"""
def __init__(
self,
e_true_lo,
e_true_hi,
e_reco_lo,
e_reco_hi,
data,
interp_kwargs=None,
meta=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
axes = [
BinnedDataAxis(
e_true_lo, e_true_hi, interpolation_mode="log", name="e_true"
),
BinnedDataAxis(
e_reco_lo, e_reco_hi, interpolation_mode="log", name="e_reco"
),
]
self.data = NDDataArray(axes=axes, data=data, interp_kwargs=interp_kwargs)
self.meta = OrderedDict(meta) if meta else OrderedDict()
def __str__(self):
ss = self.__class__.__name__
ss += "\n{}".format(self.data)
return ss
[docs] def apply(self, data):
"""Apply energy dispersion.
Computes the matrix product of ``data``
(which typically is model flux or counts in true energy bins)
with the energy dispersion matrix.
Parameters
----------
data : array_like
1-dim data array.
Returns
-------
convolved_data : array
1-dim data array after multiplication with the energy dispersion matrix
"""
if len(data) != self.e_true.nbins:
raise ValueError(
"Input size {} does not match true energy axis {}".format(
len(data), self.e_true.nbins
)
)
return np.dot(data, self.data.data)
@property
def e_reco(self):
"""Reconstructed energy axis (`~gammapy.utils.nddata.BinnedDataAxis`)"""
return self.data.axis("e_reco")
@property
def e_true(self):
"""True energy axis (`~gammapy.utils.nddata.BinnedDataAxis`)"""
return self.data.axis("e_true")
@property
def pdf_matrix(self):
"""Energy dispersion PDF matrix (`~numpy.ndarray`).
Rows (first index): True Energy
Columns (second index): Reco Energy
"""
return self.data.data.value
[docs] def pdf_in_safe_range(self, lo_threshold, hi_threshold):
"""PDF matrix with bins outside threshold set to 0.
Parameters
----------
lo_threshold : `~astropy.units.Quantity`
Low reco energy threshold
hi_threshold : `~astropy.units.Quantity`
High reco energy threshold
"""
data = self.pdf_matrix.copy()
idx = np.where(
(self.e_reco.lo < lo_threshold) | (self.e_reco.hi > hi_threshold)
)
data[:, idx] = 0
return data
[docs] @classmethod
def from_gauss(cls, e_true, e_reco, sigma, bias, pdf_threshold=1e-6):
"""Create Gaussian energy dispersion matrix (`EnergyDispersion`).
Calls :func:`gammapy.irf.EnergyDispersion2D.from_gauss`
Parameters
----------
e_true : `~astropy.units.Quantity`, `~gammapy.utils.nddata.BinnedDataAxis`
Bin edges of true energy axis
e_reco : `~astropy.units.Quantity`, `~gammapy.utils.nddata.BinnedDataAxis`
Bin edges of reconstructed energy axis
bias : float or `~numpy.ndarray`
Center of Gaussian energy dispersion, bias
sigma : float or `~numpy.ndarray`
RMS width of Gaussian energy dispersion, resolution
pdf_threshold : float, optional
Zero suppression threshold
"""
migra = np.linspace(1.0 / 3, 3, 200)
# A dummy offset axis (need length 2 for interpolation to work)
offset = Quantity([0, 1, 2], "deg")
edisp = EnergyDispersion2D.from_gauss(
e_true=e_true,
migra=migra,
sigma=sigma,
bias=bias,
offset=offset,
pdf_threshold=pdf_threshold,
)
return edisp.to_energy_dispersion(offset=offset[0], e_reco=e_reco)
[docs] @classmethod
def from_diagonal_response(cls, e_true, e_reco=None):
"""Create energy dispersion from a diagonal response, i.e. perfect energy resolution
This creates the matrix corresponding to a perfect energy response.
It contains ones where the e_true center is inside the e_reco bin.
It is a square diagonal matrix if e_true = e_reco.
This is useful in cases where code always applies an edisp,
but you don't want it to do anything.
Parameters
----------
e_true, e_reco : `~astropy.units.Quantity`
Energy bounds for true and reconstructed energy axis
Examples
--------
If ``e_true`` equals ``e_reco``, you get a diagonal matrix::
e_true = [0.5, 1, 2, 4, 6] * u.TeV
edisp = EnergyDispersion.from_diagonal_response(e_true)
edisp.plot_matrix()
Example with different energy binnings::
e_true = [0.5, 1, 2, 4, 6] * u.TeV
e_reco = [2, 4, 6] * u.TeV
edisp = EnergyDispersion.from_diagonal_response(e_true, e_reco)
edisp.plot_matrix()
"""
if e_reco is None:
e_reco = e_true
e_true_center = 0.5 * (e_true[1:] + e_true[:-1])
etrue_2d, ereco_lo_2d = np.meshgrid(e_true_center, e_reco[:-1])
etrue_2d, ereco_hi_2d = np.meshgrid(e_true_center, e_reco[1:])
data = np.logical_and(etrue_2d >= ereco_lo_2d, etrue_2d < ereco_hi_2d)
data = np.transpose(data).astype("float")
return cls(
e_true_lo=e_true[:-1],
e_true_hi=e_true[1:],
e_reco_lo=e_reco[:-1],
e_reco_hi=e_reco[1:],
data=data,
)
[docs] @classmethod
def from_hdulist(cls, hdulist, hdu1="MATRIX", hdu2="EBOUNDS"):
"""Create `EnergyDispersion` object from `~astropy.io.fits.HDUList`.
Parameters
----------
hdulist : `~astropy.io.fits.HDUList`
HDU list with ``MATRIX`` and ``EBOUNDS`` extensions.
hdu1 : str, optional
HDU containing the energy dispersion matrix, default: MATRIX
hdu2 : str, optional
HDU containing the energy axis information, default, EBOUNDS
"""
matrix_hdu = hdulist[hdu1]
ebounds_hdu = hdulist[hdu2]
data = matrix_hdu.data
header = matrix_hdu.header
pdf_matrix = np.zeros([len(data), header["DETCHANS"]], dtype=np.float64)
for i, l in enumerate(data):
if l.field("N_GRP"):
m_start = 0
for k in range(l.field("N_GRP")):
pdf_matrix[
i,
l.field("F_CHAN")[k] : l.field("F_CHAN")[k]
+ l.field("N_CHAN")[k],
] = l.field("MATRIX")[m_start : m_start + l.field("N_CHAN")[k]]
m_start += l.field("N_CHAN")[k]
e_reco = EnergyBounds.from_ebounds(ebounds_hdu)
e_true = EnergyBounds.from_rmf_matrix(matrix_hdu)
return cls(
e_true_lo=e_true.lower_bounds,
e_true_hi=e_true.upper_bounds,
e_reco_lo=e_reco.lower_bounds,
e_reco_hi=e_reco.upper_bounds,
data=pdf_matrix,
)
[docs] @classmethod
def read(cls, filename, hdu1="MATRIX", hdu2="EBOUNDS"):
"""Read from file.
Parameters
----------
filename : `~gammapy.extern.pathlib.Path`, str
File to read
hdu1 : str, optional
HDU containing the energy dispersion matrix, default: MATRIX
hdu2 : str, optional
HDU containing the energy axis information, default, EBOUNDS
"""
filename = make_path(filename)
with fits.open(str(filename), memmap=False) as hdulist:
edisp = cls.from_hdulist(hdulist, hdu1=hdu1, hdu2=hdu2)
return edisp
[docs] def to_hdulist(self, **kwargs):
"""Convert RMF to FITS HDU list format.
Parameters
----------
header : `~astropy.io.fits.Header`
Header to be written in the fits file.
energy_unit : str
Unit in which the energy is written in the HDU list
Returns
-------
hdulist : `~astropy.io.fits.HDUList`
RMF in HDU list format.
Notes
-----
For more info on the RMF FITS file format see:
https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/summary/cal_gen_92_002_summary.html
"""
# Cannot use table_to_fits here due to variable length array
# http://docs.astropy.org/en/v1.0.4/io/fits/usage/unfamiliar.html
table = self.to_table()
name = table.meta.pop("name")
header = fits.Header()
header.update(table.meta)
cols = table.columns
c0 = fits.Column(
name=cols[0].name, format="E", array=cols[0], unit="{}".format(cols[0].unit)
)
c1 = fits.Column(
name=cols[1].name, format="E", array=cols[1], unit="{}".format(cols[1].unit)
)
c2 = fits.Column(name=cols[2].name, format="I", array=cols[2])
c3 = fits.Column(name=cols[3].name, format="PI()", array=cols[3])
c4 = fits.Column(name=cols[4].name, format="PI()", array=cols[4])
c5 = fits.Column(name=cols[5].name, format="PE()", array=cols[5])
hdu = fits.BinTableHDU.from_columns(
[c0, c1, c2, c3, c4, c5], header=header, name=name
)
ebounds = energy_axis_to_ebounds(self.e_reco.bins)
prim_hdu = fits.PrimaryHDU()
return fits.HDUList([prim_hdu, hdu, ebounds])
[docs] def to_table(self):
"""Convert to `~astropy.table.Table`.
The output table is in the OGIP RMF format.
https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/memos/cal_gen_92_002/cal_gen_92_002.html#Tab:1
"""
rows = self.pdf_matrix.shape[0]
n_grp = []
f_chan = np.ndarray(dtype=np.object, shape=rows)
n_chan = np.ndarray(dtype=np.object, shape=rows)
matrix = np.ndarray(dtype=np.object, shape=rows)
# Make RMF type matrix
for i, row in enumerate(self.data.data.value):
pos = np.nonzero(row)[0]
borders = np.where(np.diff(pos) != 1)[0]
# add 1 to borders for correct behaviour of np.split
groups = np.asarray(np.split(pos, borders + 1))
n_grp_temp = groups.shape[0] if groups.size > 0 else 1
n_chan_temp = np.asarray([val.size for val in groups])
try:
f_chan_temp = np.asarray([val[0] for val in groups])
except IndexError:
f_chan_temp = np.zeros(1)
n_grp.append(n_grp_temp)
f_chan[i] = f_chan_temp
n_chan[i] = n_chan_temp
matrix[i] = row[pos]
n_grp = np.asarray(n_grp, dtype=np.int16)
# Get total number of groups and channel subsets
numgrp, numelt = 0, 0
for val, val2 in zip(n_grp, n_chan):
numgrp += np.sum(val)
numelt += np.sum(val2)
table = Table()
table["ENERG_LO"] = self.e_true.lo
table["ENERG_HI"] = self.e_true.hi
table["N_GRP"] = n_grp
table["F_CHAN"] = f_chan
table["N_CHAN"] = n_chan
table["MATRIX"] = matrix
table.meta = OrderedDict(
[
("name", "MATRIX"),
("chantype", "PHA"),
("hduclass", "OGIP"),
("hduclas1", "RESPONSE"),
("hduclas2", "RSP_MATRIX"),
("detchans", self.e_reco.nbins),
("numgrp", numgrp),
("numelt", numelt),
("tlmin4", 0),
]
)
return table
[docs] def write(self, filename, **kwargs):
"""Write to file."""
filename = make_path(filename)
self.to_hdulist().writeto(str(filename), **kwargs)
[docs] def get_resolution(self, e_true):
"""Get energy resolution for a given true energy.
The resolution is given as a percentage of the true energy
Parameters
----------
e_true : `~astropy.units.Quantity`
True energy
"""
var = self._get_variance(e_true)
idx_true = self.e_true.find_node(e_true)
e_true_real = self.e_true.nodes[idx_true]
return np.sqrt(var) / e_true_real
[docs] def get_bias(self, e_true):
r"""Get reconstruction bias for a given true energy.
Bias is defined as
.. math::
\frac{E_{reco}-E_{true}}{E_{true}}
Parameters
----------
e_true : `~astropy.units.Quantity`
True energy
"""
e_reco = self.get_mean(e_true)
idx_true = self.e_true.find_node(e_true)
e_true_real = self.e_true.nodes[idx_true]
bias = (e_reco - e_true_real) / e_true_real
return bias
[docs] def get_mean(self, e_true):
"""Get mean reconstructed energy for a given true energy."""
# find pdf for true energies
idx = self.e_true.find_node(e_true)
pdf = self.data.data[idx]
# compute sum along reconstructed energy
# axis to determine the mean
norm = np.sum(pdf, axis=-1)
temp = np.sum(pdf * self.e_reco.nodes, axis=-1)
return temp / norm
def _get_variance(self, e_true):
"""Get variance of log reconstructed energy."""
# evaluate the pdf at given true energies
idx = self.e_true.find_node(e_true)
pdf = self.data.data[idx]
# compute mean
mean = self.get_mean(e_true)
# create array of reconstructed-energy nodes
# for each given true energy value
# (first axis is reconstructed energy)
erec = self.e_reco.nodes
erec = np.repeat(erec, max(np.sum(mean.shape), 1)).reshape(
erec.shape + mean.shape
)
# compute deviation from mean
# (and move reconstructed energy axis to last axis)
temp_ = (erec - mean) ** 2
temp = np.rollaxis(temp_, 1)
# compute sum along reconstructed energy
# axis to determine the variance
norm = np.sum(pdf, axis=-1)
var = np.sum(temp * pdf, axis=-1)
return var / norm
[docs] def to_sherpa(self, name):
"""Convert to `sherpa.astro.data.DataRMF`.
Parameters
----------
name : str
Instance name
"""
from sherpa.astro.data import DataRMF
from sherpa.utils import SherpaUInt, SherpaFloat
# Need to modify RMF data
# see https://github.com/sherpa/sherpa/blob/master/sherpa/astro/io/pyfits_backend.py#L727
table = self.to_table()
n_grp = table["N_GRP"].data.astype(SherpaUInt)
f_chan = table["F_CHAN"].data
n_chan = table["N_CHAN"].data
matrix = table["MATRIX"].data
good = n_grp > 0
matrix = matrix[good]
matrix = np.concatenate([row for row in matrix])
matrix = matrix.astype(SherpaFloat)
good = n_grp > 0
f_chan = f_chan[good]
f_chan = np.concatenate([row for row in f_chan]).astype(SherpaUInt)
n_chan = n_chan[good]
n_chan = np.concatenate([row for row in n_chan]).astype(SherpaUInt)
return DataRMF(
name=name,
energ_lo=table["ENERG_LO"].quantity.to_value("keV").astype(SherpaFloat),
energ_hi=table["ENERG_HI"].quantity.to_value("keV").astype(SherpaFloat),
matrix=matrix,
n_grp=n_grp,
n_chan=n_chan,
f_chan=f_chan,
detchans=self.e_reco.nbins,
e_min=self.e_reco.lo.to_value("keV"),
e_max=self.e_reco.hi.to_value("keV"),
offset=0,
)
[docs] def plot_matrix(self, ax=None, show_energy=None, add_cbar=False, **kwargs):
"""Plot PDF matrix.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis
show_energy : `~astropy.units.Quantity`, optional
Show energy, e.g. threshold, as vertical line
add_cbar : bool
Add a colorbar to the plot.
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis
"""
import matplotlib.pyplot as plt
from matplotlib.colors import PowerNorm
kwargs.setdefault("cmap", "GnBu")
norm = PowerNorm(gamma=0.5)
kwargs.setdefault("norm", norm)
ax = plt.gca() if ax is None else ax
e_true = self.e_true.bins
e_reco = self.e_reco.bins
x = e_true.value
y = e_reco.value
z = self.pdf_matrix
caxes = ax.pcolormesh(x, y, z.T, **kwargs)
if show_energy is not None:
ener_val = show_energy.to_value(self.reco_energy.unit)
ax.hlines(ener_val, 0, 200200, linestyles="dashed")
if add_cbar:
label = "Probability density (A.U.)"
cbar = ax.figure.colorbar(caxes, ax=ax, label=label)
ax.set_xlabel(r"$E_\mathrm{{True}}$ [{unit}]".format(unit=e_true.unit))
ax.set_ylabel(r"$E_\mathrm{{Reco}}$ [{unit}]".format(unit=e_reco.unit))
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_xlim(x.min(), x.max())
ax.set_ylim(y.min(), y.max())
return ax
[docs] def plot_bias(self, ax=None, **kwargs):
"""Plot reconstruction bias.
See `~gammapy.irf.EnergyDispersion.get_bias` method.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis
"""
import matplotlib.pyplot as plt
ax = plt.gca() if ax is None else ax
x = self.e_true.nodes.to_value("TeV")
y = self.get_bias(self.e_true.nodes)
ax.plot(x, y, **kwargs)
ax.set_xlabel(r"$E_\mathrm{{True}}$ [TeV]")
ax.set_ylabel(r"($E_\mathrm{{True}} - E_\mathrm{{Reco}} / E_\mathrm{{True}}$)")
ax.set_xscale("log")
return ax
[docs] def peek(self, figsize=(15, 5)):
"""Quick-look summary plot."""
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=figsize)
self.plot_bias(ax=axes[0])
self.plot_matrix(ax=axes[1])
plt.tight_layout()
[docs]class EnergyDispersion2D(object):
"""Offset-dependent energy dispersion matrix.
Data format specification: :ref:`gadf:edisp_2d`
Parameters
----------
e_true_lo, e_true_hi : `~astropy.units.Quantity`
True energy axis binning
migra_lo, migra_hi : `~numpy.ndarray`
Energy migration axis binning
offset_lo, offset_hi : `~astropy.coordinates.Angle`
Field of view offset axis binning
data : `~numpy.ndarray`
Energy dispersion probability density
Examples
--------
Read energy dispersion IRF from disk:
>>> from gammapy.irf import EnergyDispersion2D
>>> from gammapy.utils.energy import EnergyBounds
>>> filename = '$GAMMAPY_DATA/tests/irf/hess/pa/hess_edisp_2d_023523.fits.gz'
>>> edisp2d = EnergyDispersion2D.read(filename, hdu='ENERGY DISPERSION')
>>> print(edisp2d)
EnergyDispersion2D
NDDataArray summary info
e_true : size = 15, min = 0.125 TeV, max = 80.000 TeV
migra : size = 100, min = 0.051, max = 10.051
offset : size = 6, min = 0.125 deg, max = 2.500 deg
Data : size = 9000, min = 0.000, max = 3.405
Create energy dispersion matrix (`~gammapy.irf.EnergyDispersion`)
for a given field of view offset and energy binning:
>>> energy = EnergyBounds.equal_log_spacing(0.1,20,60, 'TeV')
>>> edisp = edisp2d.to_energy_dispersion(offset='1.2 deg', e_reco=energy, e_true=energy)
>>> print(edisp)
EnergyDispersion
NDDataArray summary info
e_true : size = 60, min = 0.105 TeV, max = 19.136 TeV
e_reco : size = 60, min = 0.105 TeV, max = 19.136 TeV
Data : size = 3600, min = 0.000, max = 0.266
See Also
--------
EnergyDispersion
"""
default_interp_kwargs = dict(bounds_error=False, fill_value=None)
"""Default Interpolation kwargs for `~gammapy.utils.nddata.NDDataArray`. Extrapolate."""
def __init__(
self,
e_true_lo,
e_true_hi,
migra_lo,
migra_hi,
offset_lo,
offset_hi,
data,
interp_kwargs=None,
meta=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
axes = [
BinnedDataAxis(
e_true_lo, e_true_hi, interpolation_mode="log", name="e_true"
),
BinnedDataAxis(
migra_lo, migra_hi, interpolation_mode="linear", name="migra"
),
BinnedDataAxis(
offset_lo, offset_hi, interpolation_mode="linear", name="offset"
),
]
self.data = NDDataArray(axes=axes, data=data, interp_kwargs=interp_kwargs)
self.meta = OrderedDict(meta) if meta else OrderedDict()
def __str__(self):
ss = self.__class__.__name__
ss += "\n{}".format(self.data)
return ss
[docs] @classmethod
def from_gauss(cls, e_true, migra, bias, sigma, offset, pdf_threshold=1e-6):
"""Create Gaussian energy dispersion matrix (`EnergyDispersion2D`).
The output matrix will be Gaussian in (e_true / e_reco).
The ``bias`` and ``sigma`` should be either floats or arrays of same dimension than
``e_true``. ``bias`` refers to the mean value of the ``migra``
distribution minus one, i.e. ``bias=0`` means no bias.
Note that, the output matrix is flat in offset.
Parameters
----------
e_true : `~astropy.units.Quantity`, `~gammapy.utils.nddata.BinnedDataAxis`
Bin edges of true energy axis
migra : `~astropy.units.Quantity`, `~gammapy.utils.nddata.BinnedDataAxis`
Bin edges of migra axis
bias : float or `~numpy.ndarray`
Center of Gaussian energy dispersion, bias
sigma : float or `~numpy.ndarray`
RMS width of Gaussian energy dispersion, resolution
offset : `~astropy.units.Quantity`, `~gammapy.utils.nddata.BinnedDataAxis`
Bin edges of offset
pdf_threshold : float, optional
Zero suppression threshold
"""
e_true = EnergyBounds(e_true)
# erf does not work with Quantities
true = e_true.log_centers.to_value("TeV")
true2d, migra2d = np.meshgrid(true, migra)
migra2d_lo = migra2d[:-1, :]
migra2d_hi = migra2d[1:, :]
# Analytical formula for integral of Gaussian
s = np.sqrt(2) * sigma
t1 = (migra2d_hi - 1 - bias) / s
t2 = (migra2d_lo - 1 - bias) / s
pdf = (erf(t1) - erf(t2)) / 2
pdf_array = pdf.T[:, :, np.newaxis] * np.ones(len(offset) - 1)
pdf_array = np.where(pdf_array > pdf_threshold, pdf_array, 0)
return cls(
e_true[:-1],
e_true[1:],
migra[:-1],
migra[1:],
offset[:-1],
offset[1:],
pdf_array,
)
[docs] @classmethod
def from_table(cls, table):
"""Create from `~astropy.table.Table`."""
if "ENERG_LO" in table.colnames:
e_lo = table["ENERG_LO"].quantity[0]
e_hi = table["ENERG_HI"].quantity[0]
elif "ETRUE_LO" in table.colnames:
e_lo = table["ETRUE_LO"].quantity[0]
e_hi = table["ETRUE_HI"].quantity[0]
else:
raise ValueError(
'Invalid column names. Need "ENERG_LO/ENERG_HI" or "ETRUE_LO/ETRUE_HI"'
)
o_lo = table["THETA_LO"].quantity[0]
o_hi = table["THETA_HI"].quantity[0]
m_lo = table["MIGRA_LO"].quantity[0]
m_hi = table["MIGRA_HI"].quantity[0]
# TODO Why does this need to be transposed?
matrix = table["MATRIX"].quantity[0].transpose()
return cls(
e_true_lo=e_lo,
e_true_hi=e_hi,
offset_lo=o_lo,
offset_hi=o_hi,
migra_lo=m_lo,
migra_hi=m_hi,
data=matrix,
)
[docs] @classmethod
def from_hdulist(cls, hdulist, hdu="edisp_2d"):
"""Create from `~astropy.io.fits.HDUList`."""
return cls.from_table(Table.read(hdulist[hdu]))
[docs] @classmethod
def read(cls, filename, hdu="edisp_2d"):
"""Read from FITS file.
Parameters
----------
filename : str
File name
"""
filename = make_path(filename)
with fits.open(str(filename), memmap=False) as hdulist:
edisp = cls.from_hdulist(hdulist, hdu)
return edisp
[docs] def to_energy_dispersion(self, offset, e_true=None, e_reco=None):
"""Detector response R(Delta E_reco, Delta E_true)
Probability to reconstruct an energy in a given true energy band
in a given reconstructed energy band
Parameters
----------
offset : `~astropy.coordinates.Angle`
Offset
e_true : `~gammapy.utils.energy.EnergyBounds`, None
True energy axis
e_reco : `~gammapy.utils.energy.EnergyBounds`
Reconstructed energy axis
Returns
-------
edisp : `~gammapy.irf.EnergyDispersion`
Energy dispersion matrix
"""
offset = Angle(offset)
e_true = self.data.axis("e_true").bins if e_true is None else e_true
e_reco = self.data.axis("e_true").bins if e_reco is None else e_reco
e_true = EnergyBounds(e_true)
e_reco = EnergyBounds(e_reco)
data = []
for energy in e_true.log_centers:
vec = self.get_response(offset=offset, e_true=energy, e_reco=e_reco)
data.append(vec)
data = np.asarray(data)
e_lo, e_hi = e_true[:-1], e_true[1:]
ereco_lo, ereco_hi = (e_reco[:-1], e_reco[1:])
return EnergyDispersion(
e_true_lo=e_lo,
e_true_hi=e_hi,
e_reco_lo=ereco_lo,
e_reco_hi=ereco_hi,
data=data,
)
[docs] def get_response(self, offset, e_true, e_reco=None, migra_step=5e-3):
"""Detector response R(Delta E_reco, E_true)
Probability to reconstruct a given true energy in a given reconstructed
energy band. In each reco bin, you integrate with a riemann sum over
the default migra bin of your analysis.
Parameters
----------
e_true : `~gammapy.utils.energy.Energy`
True energy
e_reco : `~gammapy.utils.energy.EnergyBounds`, None
Reconstructed energy axis
offset : `~astropy.coordinates.Angle`
Offset
migra_step : float
Integration step in migration
Returns
-------
rv : `~numpy.ndarray`
Redistribution vector
"""
e_true = Energy(e_true)
if e_reco is None:
# Default: e_reco nodes = migra nodes * e_true nodes
migra_axis = self.data.axis("migra")
e_reco = EnergyBounds.from_lower_and_upper_bounds(
migra_axis.lo * e_true, migra_axis.hi * e_true
)
else:
# Translate given e_reco binning to migra at bin center
e_reco = EnergyBounds(e_reco)
# migration value of e_reco bounds
migra_e_reco = e_reco / e_true
# Define a vector of migration with mig_step step
mrec_min = self.data.axis("migra").lo[0]
mrec_max = self.data.axis("migra").hi[-1]
mig_array = np.arange(mrec_min, mrec_max, migra_step)
# Compute energy dispersion probability dP/dm for each element of migration array
vals = self.data.evaluate(offset=offset, e_true=e_true, migra=mig_array)
# Compute normalized cumulative sum to prepare integration
with np.errstate(invalid="ignore"):
tmp = np.nan_to_num(np.cumsum(vals) / np.sum(vals))
# Determine positions (bin indices) of e_reco bounds in migration array
pos_mig = np.digitize(migra_e_reco, mig_array) - 1
# We ensure that no negative values are found
pos_mig = np.maximum(pos_mig, 0)
# We compute the difference between 2 successive bounds in e_reco
# to get integral over reco energy bin
integral = np.diff(tmp[pos_mig])
return integral
[docs] def plot_migration(self, ax=None, offset=None, e_true=None, migra=None, **kwargs):
"""Plot energy dispersion for given offset and true energy.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis
offset : `~astropy.coordinates.Angle`, optional
Offset
e_true : `~gammapy.utils.energy.Energy`, optional
True energy
migra : `~numpy.array`, list, optional
Migration nodes
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis
"""
import matplotlib.pyplot as plt
ax = plt.gca() if ax is None else ax
if offset is None:
offset = Angle([1], "deg")
else:
offset = np.atleast_1d(Angle(offset))
if e_true is None:
e_true = Energy([0.1, 1, 10], "TeV")
else:
e_true = np.atleast_1d(Energy(e_true))
migra = self.data.axis("migra").nodes if migra is None else migra
for ener in e_true:
for off in offset:
disp = self.data.evaluate(offset=off, e_true=ener, migra=migra)
label = "offset = {0:.1f}\nenergy = {1:.1f}".format(off, ener)
ax.plot(migra, disp, label=label, **kwargs)
ax.set_xlabel(r"$E_\mathrm{{Reco}} / E_\mathrm{{True}}$")
ax.set_ylabel("Probability density")
ax.legend(loc="upper left")
return ax
[docs] def plot_bias(self, ax=None, offset=None, add_cbar=False, **kwargs):
"""Plot migration as a function of true energy for a given offset.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis
offset : `~astropy.coordinates.Angle`, optional
Offset
add_cbar : bool
Add a colorbar to the plot.
kwargs : dict
Keyword arguments passed to `~matplotlib.pyplot.pcolormesh`.
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis
"""
from matplotlib.colors import PowerNorm
import matplotlib.pyplot as plt
kwargs.setdefault("cmap", "GnBu")
kwargs.setdefault("norm", PowerNorm(gamma=0.5))
ax = plt.gca() if ax is None else ax
if offset is None:
offset = Angle(1, "deg")
e_true = self.data.axis("e_true").bins
migra = self.data.axis("migra").bins
x = e_true.value
y = migra.value
z = self.data.evaluate(
offset=offset,
e_true=e_true.reshape(1, -1, 1),
migra=migra.reshape(1, 1, -1),
).value[0]
caxes = ax.pcolormesh(x, y, z.T, **kwargs)
if add_cbar:
label = "Probability density (A.U.)"
ax.figure.colorbar(caxes, ax=ax, label=label)
ax.set_xlabel(r"$E_\mathrm{{True}}$ [{unit}]".format(unit=e_true.unit))
ax.set_ylabel(r"$E_\mathrm{{Reco}} / E_\mathrm{{True}}$")
ax.set_xlim(x.min(), x.max())
ax.set_ylim(y.min(), y.max())
ax.set_xscale("log")
return ax
[docs] def peek(self, figsize=(15, 5)):
"""Quick-look summary plots.
Parameters
----------
figsize : (float, float)
Size of the resulting plot
"""
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=figsize)
self.plot_bias(ax=axes[0])
self.plot_migration(ax=axes[1])
edisp = self.to_energy_dispersion(offset="1 deg")
edisp.plot_matrix(ax=axes[2])
plt.tight_layout()
[docs] def to_table(self):
"""Convert to `~astropy.table.Table`."""
meta = self.meta.copy()
table = Table(meta=meta)
table["ENERG_LO"] = self.data.axis("e_true").lo[np.newaxis]
table["ENERG_HI"] = self.data.axis("e_true").hi[np.newaxis]
table["MIGRA_LO"] = self.data.axis("migra").hi[np.newaxis]
table["MIGRA_HI"] = self.data.axis("migra").hi[np.newaxis]
table["THETA_LO"] = self.data.axis("offset").lo[np.newaxis]
table["THETA_HI"] = self.data.axis("offset").hi[np.newaxis]
table["MATRIX"] = self.data.data.T[np.newaxis]
return table
[docs] def to_fits(self, name="ENERGY DISPERSION"):
"""Convert to `~astropy.io.fits.BinTable`."""
return fits.BinTableHDU(self.to_table(), name=name)