class Background2D:
"""Background 2D.
Data format specification: :ref:`gadf:bkg_2d`
Parameters
----------
energy_lo, energy_hi : `~astropy.units.Quantity`
Energy binning
offset_lo, offset_hi : `~astropy.units.Quantity`
FOV coordinate offset-axis binning
data : `~astropy.units.Quantity`
Background rate (usually: ``s^-1 MeV^-1 sr^-1``)
"""
default_interp_kwargs = dict(bounds_error=False, fill_value=None)
"""Default Interpolation kwargs for `~gammapy.utils.nddata.NDDataArray`. Extrapolate."""
def __init__(
self,
energy_lo,
energy_hi,
offset_lo,
offset_hi,
data,
meta=None,
interp_kwargs=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
e_edges = edges_from_lo_hi(energy_lo, energy_hi)
energy_axis = MapAxis.from_edges(e_edges, interp="log", name="energy")
offset_edges = edges_from_lo_hi(offset_lo, offset_hi)
offset_axis = MapAxis.from_edges(offset_edges,
interp="lin",
name="offset")
self.data = NDDataArray(axes=[energy_axis, offset_axis],
data=data,
interp_kwargs=interp_kwargs)
self.meta = meta or {}
def __str__(self):
ss = self.__class__.__name__
ss += f"\n{self.data}"
return ss
@classmethod
def from_table(cls, table):
"""Read from `~astropy.table.Table`."""
# Spec says key should be "BKG", but there are files around
# (e.g. CTA 1DC) that use "BGD". For now we support both
if "BKG" in table.colnames:
bkg_name = "BKG"
elif "BGD" in table.colnames:
bkg_name = "BGD"
else:
raise ValueError('Invalid column names. Need "BKG" or "BGD".')
# Currently some files (e.g. CTA 1DC) contain unit in the FITS file
# '1/s/MeV/sr', which is invalid ( try: astropy.units.Unit('1/s/MeV/sr')
# This should be corrected.
# For now, we hard-code the unit here:
data_unit = u.Unit("s-1 MeV-1 sr-1")
return cls(
energy_lo=table["ENERG_LO"].quantity[0],
energy_hi=table["ENERG_HI"].quantity[0],
offset_lo=table["THETA_LO"].quantity[0],
offset_hi=table["THETA_HI"].quantity[0],
data=table[bkg_name].data[0] * data_unit,
meta=table.meta,
)
@classmethod
def from_hdulist(cls, hdulist, hdu="BACKGROUND"):
"""Create from `~astropy.io.fits.HDUList`."""
return cls.from_table(Table.read(hdulist[hdu]))
@classmethod
def read(cls, filename, hdu="BACKGROUND"):
"""Read from file."""
with fits.open(make_path(filename), memmap=False) as hdulist:
return cls.from_hdulist(hdulist, hdu=hdu)
def to_table(self):
"""Convert to `~astropy.table.Table`."""
meta = self.meta.copy()
table = Table(meta=meta)
theta = self.data.axis("offset").edges
energy = self.data.axis("energy").edges
table["THETA_LO"] = theta[:-1][np.newaxis]
table["THETA_HI"] = theta[1:][np.newaxis]
table["ENERG_LO"] = energy[:-1][np.newaxis]
table["ENERG_HI"] = energy[1:][np.newaxis]
table["BKG"] = self.data.data[np.newaxis]
return table
def to_fits(self, name="BACKGROUND"):
"""Convert to `~astropy.io.fits.BinTableHDU`."""
return fits.BinTableHDU(self.to_table(), name=name)
def evaluate(self,
fov_lon,
fov_lat,
energy_reco,
method="linear",
**kwargs):
"""Evaluate at a given FOV position and energy.
The fov_lon, fov_lat, energy_reco has to have the same shape
since this is a set of points on which you want to evaluate.
To have the same API than background 3D for the
background evaluation, the offset is ``fov_altaz_lon``.
Parameters
----------
fov_lon, fov_lat : `~astropy.coordinates.Angle`
FOV coordinates expecting in AltAz frame, same shape than energy_reco
energy_reco : `~astropy.units.Quantity`
Reconstructed energy, same dimension than fov_lat and fov_lat
method : str {'linear', 'nearest'}, optional
Interpolation method
kwargs : dict
option for interpolation for `~scipy.interpolate.RegularGridInterpolator`
Returns
-------
array : `~astropy.units.Quantity`
Interpolated values, axis order is the same as for the NDData array
"""
offset = np.sqrt(fov_lon**2 + fov_lat**2)
return self.data.evaluate(offset=offset,
energy=energy_reco,
method=method,
**kwargs)
def evaluate_integrate(self,
fov_lon,
fov_lat,
energy_reco,
method="linear"):
"""Evaluate at given FOV position and energy, by integrating over the energy range.
Parameters
----------
fov_lon, fov_lat : `~astropy.coordinates.Angle`
FOV coordinates expecting in AltAz frame.
energy_reco: `~astropy.units.Quantity`
Reconstructed energy edges.
method : {'linear', 'nearest'}, optional
Interpolation method
Returns
-------
array : `~astropy.units.Quantity`
Returns 2D array with axes offset
"""
data = self.evaluate(fov_lon, fov_lat, energy_reco, method=method)
return trapz_loglog(data, energy_reco, axis=0)
def to_3d(self):
"""Convert to `Background3D`.
Fill in a radially symmetric way.
"""
raise NotImplementedError
def plot(self, ax=None, add_cbar=True, **kwargs):
"""Plot energy offset dependence of the background model.
"""
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
ax = plt.gca() if ax is None else ax
x = self.data.axis("energy").edges.to_value("TeV")
y = self.data.axis("offset").edges.to_value("deg")
z = self.data.data.T.value
kwargs.setdefault("cmap", "GnBu")
kwargs.setdefault("edgecolors", "face")
caxes = ax.pcolormesh(x, y, z, norm=LogNorm(), **kwargs)
ax.set_xscale("log")
ax.set_ylabel(f"Offset (deg)")
ax.set_xlabel(f"Energy (TeV)")
xmin, xmax = x.min(), x.max()
ax.set_xlim(xmin, xmax)
if add_cbar:
label = f"Background rate ({self.data.data.unit})"
ax.figure.colorbar(caxes, ax=ax, label=label)
def peek(self):
from .effective_area import EffectiveAreaTable2D
return EffectiveAreaTable2D.peek(self)
class SensitivityTable(object):
"""Sensitivity table.
The IRF format should be compliant with the one discussed
at http://gamma-astro-data-formats.readthedocs.io/en/latest/irfs/.
Work will be done to fix this.
Parameters
-----------
energy_lo, energy_hi : `~astropy.units.Quantity`, `~gammapy.utils.nddata.BinnedDataAxis`
Bin edges of energy axis
data : `~astropy.units.Quantity`
Sensitivity
"""
def __init__(self, energy_lo, energy_hi, data):
axes = [
BinnedDataAxis(energy_lo,
energy_hi,
interpolation_mode='log',
name='energy'),
]
self.data = NDDataArray(axes=axes, data=data)
@property
def energy(self):
return self.data.axis('energy')
@classmethod
def from_table(cls, table):
energy_lo = table['ENERG_LO'].quantity
energy_hi = table['ENERG_HI'].quantity
data = table['SENSITIVITY'].quantity
return cls(energy_lo=energy_lo, energy_hi=energy_hi, data=data)
@classmethod
def from_hdulist(cls, hdulist, hdu='SENSITIVITY'):
fits_table = hdulist[hdu]
table = Table.read(fits_table)
return cls.from_table(table)
@classmethod
def read(cls, filename, hdu='SENSITVITY'):
filename = make_path(filename)
with fits.open(str(filename), memmap=False) as hdulist:
return cls.from_hdulist(hdulist, hdu=hdu)
def plot(self, ax=None, energy=None, **kwargs):
"""Plot sensitivity.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis
energy : `~astropy.units.Quantity`
Energy nodes
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis
"""
import matplotlib.pyplot as plt
ax = plt.gca() if ax is None else ax
energy = energy or self.energy.nodes
values = self.data.evaluate(energy=energy)
xerr = (
energy.value - self.energy.lo.value,
self.energy.hi.value - energy.value,
)
ax.errorbar(energy.value, values.value, xerr=xerr, fmt='o', **kwargs)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel('Reco Energy [{}]'.format(self.energy.unit))
ax.set_ylabel('Sensitivity [{}]'.format(self.data.data.unit))
return ax
class Background3D:
"""Background 3D.
Data format specification: :ref:`gadf:bkg_3d`
Parameters
----------
energy_lo, energy_hi : `~astropy.units.Quantity`
Energy binning
fov_lon_lo, fov_lon_hi : `~astropy.units.Quantity`
FOV coordinate X-axis binning.
fov_lat_lo, fov_lat_hi : `~astropy.units.Quantity`
FOV coordinate Y-axis binning.
data : `~astropy.units.Quantity`
Background rate (usually: ``s^-1 MeV^-1 sr^-1``)
Examples
--------
Here's an example you can use to learn about this class:
>>> from gammapy.irf import Background3D
>>> filename = '$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits'
>>> bkg_3d = Background3D.read(filename, hdu='BACKGROUND')
>>> print(bkg_3d)
Background3D
NDDataArray summary info
energy : size = 21, min = 0.016 TeV, max = 158.489 TeV
fov_lon : size = 36, min = -5.833 deg, max = 5.833 deg
fov_lat : size = 36, min = -5.833 deg, max = 5.833 deg
Data : size = 27216, min = 0.000 1 / (MeV s sr), max = 0.421 1 / (MeV s sr)
"""
default_interp_kwargs = dict(bounds_error=False,
fill_value=None,
values_scale="log")
"""Default Interpolation kwargs for `~gammapy.utils.nddata.NDDataArray`. Extrapolate."""
def __init__(
self,
energy_lo,
energy_hi,
fov_lon_lo,
fov_lon_hi,
fov_lat_lo,
fov_lat_hi,
data,
meta=None,
interp_kwargs=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
e_edges = edges_from_lo_hi(energy_lo, energy_hi)
energy_axis = MapAxis.from_edges(e_edges, interp="log", name="energy")
fov_lon_edges = edges_from_lo_hi(fov_lon_lo, fov_lon_hi)
fov_lon_axis = MapAxis.from_edges(fov_lon_edges,
interp="lin",
name="fov_lon")
fov_lat_edges = edges_from_lo_hi(fov_lat_lo, fov_lat_hi)
fov_lat_axis = MapAxis.from_edges(fov_lat_edges,
interp="lin",
name="fov_lat")
self.data = NDDataArray(
axes=[energy_axis, fov_lon_axis, fov_lat_axis],
data=data,
interp_kwargs=interp_kwargs,
)
self.meta = meta or {}
def __str__(self):
ss = self.__class__.__name__
ss += f"\n{self.data}"
return ss
@classmethod
def from_table(cls, table):
"""Read from `~astropy.table.Table`."""
# Spec says key should be "BKG", but there are files around
# (e.g. CTA 1DC) that use "BGD". For now we support both
if "BKG" in table.colnames:
bkg_name = "BKG"
elif "BGD" in table.colnames:
bkg_name = "BGD"
else:
raise ValueError('Invalid column names. Need "BKG" or "BGD".')
# Currently some files (e.g. CTA 1DC) contain unit in the FITS file
# '1/s/MeV/sr', which is invalid ( try: astropy.units.Unit('1/s/MeV/sr')
# This should be corrected.
# For now, we hard-code the unit here:
data_unit = u.Unit("s-1 MeV-1 sr-1")
return cls(
energy_lo=table["ENERG_LO"].quantity[0],
energy_hi=table["ENERG_HI"].quantity[0],
fov_lon_lo=table["DETX_LO"].quantity[0],
fov_lon_hi=table["DETX_HI"].quantity[0],
fov_lat_lo=table["DETY_LO"].quantity[0],
fov_lat_hi=table["DETY_HI"].quantity[0],
data=table[bkg_name].data[0] * data_unit,
meta=table.meta,
)
@classmethod
def from_hdulist(cls, hdulist, hdu="BACKGROUND"):
"""Create from `~astropy.io.fits.HDUList`."""
return cls.from_table(Table.read(hdulist[hdu]))
@classmethod
def read(cls, filename, hdu="BACKGROUND"):
"""Read from file."""
with fits.open(make_path(filename), memmap=False) as hdulist:
return cls.from_hdulist(hdulist, hdu=hdu)
def to_table(self):
"""Convert to `~astropy.table.Table`."""
meta = self.meta.copy()
detx = self.data.axis("fov_lon").edges
dety = self.data.axis("fov_lat").edges
energy = self.data.axis("energy").edges
table = Table(meta=meta)
table["DETX_LO"] = detx[:-1][np.newaxis]
table["DETX_HI"] = detx[1:][np.newaxis]
table["DETY_LO"] = dety[:-1][np.newaxis]
table["DETY_HI"] = dety[1:][np.newaxis]
table["ENERG_LO"] = energy[:-1][np.newaxis]
table["ENERG_HI"] = energy[1:][np.newaxis]
table["BKG"] = self.data.data[np.newaxis]
return table
def to_fits(self, name="BACKGROUND"):
"""Convert to `~astropy.io.fits.BinTableHDU`."""
return fits.BinTableHDU(self.to_table(), name=name)
def evaluate(self,
fov_lon,
fov_lat,
energy_reco,
method="linear",
**kwargs):
"""Evaluate at given FOV position and energy.
Parameters
----------
fov_lon, fov_lat : `~astropy.coordinates.Angle`
FOV coordinates expecting in AltAz frame.
energy_reco : `~astropy.units.Quantity`
energy on which you want to interpolate. Same dimension than fov_lat and fov_lat
method : str {'linear', 'nearest'}, optional
Interpolation method
kwargs : dict
option for interpolation for `~scipy.interpolate.RegularGridInterpolator`
Returns
-------
array : `~astropy.units.Quantity`
Interpolated values, axis order is the same as for the NDData array
"""
values = self.data.evaluate(
fov_lon=fov_lon,
fov_lat=fov_lat,
energy=energy_reco,
method=method,
**kwargs,
)
return values
def evaluate_integrate(self,
fov_lon,
fov_lat,
energy_reco,
method="linear",
**kwargs):
"""Integrate in a given energy band.
Parameters
----------
fov_lon, fov_lat : `~astropy.coordinates.Angle`
FOV coordinates expecting in AltAz frame.
energy_reco: `~astropy.units.Quantity`
Reconstructed energy edges.
method : {'linear', 'nearest'}, optional
Interpolation method
Returns
-------
array : `~astropy.units.Quantity`
Returns 2D array with axes offset
"""
data = self.evaluate(fov_lon, fov_lat, energy_reco, method=method)
return trapz_loglog(data, energy_reco, axis=0)
def to_2d(self):
"""Convert to `Background2D`.
This takes the values at Y = 0 and X >= 0.
"""
idx_lon = self.data.axis("fov_lon").coord_to_idx(0 * u.deg)[0]
idx_lat = self.data.axis("fov_lat").coord_to_idx(0 * u.deg)[0]
data = self.data.data[:, idx_lon:, idx_lat].copy()
energy = self.data.axis("energy").edges
offset = self.data.axis("fov_lon").edges[idx_lon:]
return Background2D(
energy_lo=energy[:-1],
energy_hi=energy[1:],
offset_lo=offset[:-1],
offset_hi=offset[1:],
data=data,
)
class EffectiveAreaTable2D:
"""2D effective area table.
Data format specification: :ref:`gadf:aeff_2d`
Parameters
----------
energy_lo, energy_hi : `~astropy.units.Quantity`
Energy binning
offset_lo, offset_hi : `~astropy.units.Quantity`
Field of view offset angle.
data : `~astropy.units.Quantity`
Effective area
Examples
--------
Here's an example you can use to learn about this class:
>>> from gammapy.irf import EffectiveAreaTable2D
>>> filename = '$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits'
>>> aeff = EffectiveAreaTable2D.read(filename, hdu='EFFECTIVE AREA')
>>> print(aeff)
EffectiveAreaTable2D
NDDataArray summary info
energy : size = 42, min = 0.014 TeV, max = 177.828 TeV
offset : size = 6, min = 0.500 deg, max = 5.500 deg
Data : size = 252, min = 0.000 m2, max = 5371581.000 m2
Here's another one, created from scratch, without reading a file:
>>> from gammapy.irf import EffectiveAreaTable2D
>>> import astropy.units as u
>>> import numpy as np
>>> energy = np.logspace(0,1,11) * u.TeV
>>> offset = np.linspace(0,1,4) * u.deg
>>> data = np.ones(shape=(10,3)) * u.cm * u.cm
>>> aeff = EffectiveAreaTable2D(energy_lo=energy[:-1], energy_hi=energy[1:], offset_lo=offset[:-1],
>>> offset_hi=offset[1:], data= data)
>>> print(aeff)
Data array summary info
energy : size = 11, min = 1.000 TeV, max = 10.000 TeV
offset : size = 4, min = 0.000 deg, max = 1.000 deg
Data : size = 30, min = 1.000 cm2, max = 1.000 cm2
"""
tag = "aeff_2d"
default_interp_kwargs = dict(bounds_error=False, fill_value=None)
"""Default Interpolation kwargs for `~NDDataArray`. Extrapolate."""
def __init__(
self,
energy_lo,
energy_hi,
offset_lo,
offset_hi,
data,
meta=None,
interp_kwargs=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
e_edges = edges_from_lo_hi(energy_lo, energy_hi)
energy_axis = MapAxis.from_edges(e_edges,
interp="log",
name="energy_true")
# TODO: for some reason the H.E.S.S. DL3 files contain the same values for offset_hi and offset_lo
if np.allclose(offset_lo.to_value("deg"), offset_hi.to_value("deg")):
offset_axis = MapAxis.from_nodes(offset_lo,
interp="lin",
name="offset")
else:
offset_edges = edges_from_lo_hi(offset_lo, offset_hi)
offset_axis = MapAxis.from_edges(offset_edges,
interp="lin",
name="offset")
self.data = NDDataArray(axes=[energy_axis, offset_axis],
data=data,
interp_kwargs=interp_kwargs)
self.meta = meta or {}
def __str__(self):
ss = self.__class__.__name__
ss += f"\n{self.data}"
return ss
@property
def low_threshold(self):
"""Low energy threshold"""
return self.meta["LO_THRES"] * u.TeV
@property
def high_threshold(self):
"""High energy threshold"""
return self.meta["HI_THRES"] * u.TeV
@classmethod
def from_table(cls, table):
"""Read from `~astropy.table.Table`."""
return cls(
energy_lo=table["ENERG_LO"].quantity[0],
energy_hi=table["ENERG_HI"].quantity[0],
offset_lo=table["THETA_LO"].quantity[0],
offset_hi=table["THETA_HI"].quantity[0],
data=table["EFFAREA"].quantity[0].transpose(),
meta=table.meta,
)
@classmethod
def from_hdulist(cls, hdulist, hdu="EFFECTIVE AREA"):
"""Create from `~astropy.io.fits.HDUList`."""
return cls.from_table(Table.read(hdulist[hdu]))
@classmethod
def read(cls, filename, hdu="EFFECTIVE AREA"):
"""Read from file."""
with fits.open(str(make_path(filename)), memmap=False) as hdulist:
return cls.from_hdulist(hdulist, hdu=hdu)
def to_effective_area_table(self, offset, energy=None):
"""Evaluate at a given offset and return `~gammapy.irf.EffectiveAreaTable`.
Parameters
----------
offset : `~astropy.coordinates.Angle`
Offset
energy : `~astropy.units.Quantity`
Energy axis bin edges
"""
if energy is None:
energy = self.data.axis("energy_true").edges
area = self.data.evaluate(offset=offset,
energy_true=MapAxis.from_edges(
energy, interp="log").center)
return EffectiveAreaTable(energy_lo=energy[:-1],
energy_hi=energy[1:],
data=area)
def plot_energy_dependence(self,
ax=None,
offset=None,
energy=None,
**kwargs):
"""Plot effective area versus energy for a given offset.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis
offset : `~astropy.coordinates.Angle`
Offset
energy : `~astropy.units.Quantity`
Energy axis
kwargs : dict
Forwarded tp plt.plot()
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis
"""
import matplotlib.pyplot as plt
ax = plt.gca() if ax is None else ax
if offset is None:
off_min, off_max = self.data.axis("offset").center[[0, -1]]
offset = np.linspace(off_min.value, off_max.value,
4) * off_min.unit
if energy is None:
energy = self.data.axis("energy_true").center
for off in offset:
area = self.data.evaluate(offset=off, energy_true=energy)
kwargs.setdefault("label", f"offset = {off:.1f}")
ax.plot(energy, area.value, **kwargs)
ax.set_xscale("log")
ax.set_xlabel(f"Energy [{energy.unit}]")
ax.set_ylabel(f"Effective Area [{self.data.data.unit}]")
ax.set_xlim(min(energy.value), max(energy.value))
return ax
def plot_offset_dependence(self,
ax=None,
offset=None,
energy=None,
**kwargs):
"""Plot effective area versus offset for a given energy.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis
offset : `~astropy.coordinates.Angle`
Offset axis
energy : `~astropy.units.Quantity`
Energy
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis
"""
import matplotlib.pyplot as plt
ax = plt.gca() if ax is None else ax
if energy is None:
e_min, e_max = np.log10(
self.data.axis("energy_true").center.value[[0, -1]])
energy = np.logspace(e_min, e_max,
4) * self.data.axis("energy_true").unit
if offset is None:
offset = self.data.axis("offset").center
for ee in energy:
area = self.data.evaluate(offset=offset, energy_true=ee)
area /= np.nanmax(area)
if np.isnan(area).all():
continue
label = f"energy = {ee:.1f}"
ax.plot(offset, area, label=label, **kwargs)
ax.set_ylim(0, 1.1)
ax.set_xlabel(f"Offset ({self.data.axis('offset').unit})")
ax.set_ylabel("Relative Effective Area")
ax.legend(loc="best")
return ax
def plot(self, ax=None, add_cbar=True, **kwargs):
"""Plot effective area image."""
import matplotlib.pyplot as plt
ax = plt.gca() if ax is None else ax
energy = self.data.axis("energy_true").edges
offset = self.data.axis("offset").edges
aeff = self.data.evaluate(offset=offset,
energy_true=energy[:, np.newaxis])
vmin, vmax = np.nanmin(aeff.value), np.nanmax(aeff.value)
kwargs.setdefault("cmap", "GnBu")
kwargs.setdefault("edgecolors", "face")
kwargs.setdefault("vmin", vmin)
kwargs.setdefault("vmax", vmax)
caxes = ax.pcolormesh(energy.value, offset.value, aeff.value.T,
**kwargs)
ax.set_xscale("log")
ax.set_ylabel(f"Offset ({offset.unit})")
ax.set_xlabel(f"Energy ({energy.unit})")
xmin, xmax = energy.value.min(), energy.value.max()
ax.set_xlim(xmin, xmax)
if add_cbar:
label = f"Effective Area ({aeff.unit})"
ax.figure.colorbar(caxes, ax=ax, label=label)
return ax
def peek(self, figsize=(15, 5)):
"""Quick-look summary plots."""
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=figsize)
self.plot(ax=axes[2])
self.plot_energy_dependence(ax=axes[0])
self.plot_offset_dependence(ax=axes[1])
plt.tight_layout()
def to_table(self):
"""Convert to `~astropy.table.Table`."""
meta = self.meta.copy()
energy = self.data.axis("energy_true").edges
theta = self.data.axis("offset").edges
table = Table(meta=meta)
table["ENERG_LO"] = energy[:-1][np.newaxis]
table["ENERG_HI"] = energy[1:][np.newaxis]
table["THETA_LO"] = theta[:-1][np.newaxis]
table["THETA_HI"] = theta[1:][np.newaxis]
table["EFFAREA"] = self.data.data.T[np.newaxis]
return table
def to_fits(self, name="EFFECTIVE AREA"):
"""Convert to `~astropy.io.fits.BinTableHDU`."""
return fits.BinTableHDU(self.to_table(), name=name)
import astropy.units as u
# ## 1D example
#
# Let's start with a simple example. A one dimensional array storing an exposure in ``cm-2 s-1`` as a function of energy. The energy axis is log spaced and thus also the interpolation shall take place in log.
# In[ ]:
energies = Energy.equal_log_spacing(10, 100, 10, unit=u.TeV)
x_axis = DataAxis(energies, name="energy", interpolation_mode="log")
data = np.arange(20, 0, -2) / u.cm ** 2 / u.s
nddata = NDDataArray(axes=[x_axis], data=data)
print(nddata)
print(nddata.axis("energy"))
# In[ ]:
eval_energies = np.linspace(2, 6, 20) * 1e4 * u.GeV
eval_exposure = nddata.evaluate(energy=eval_energies, method="linear")
plt.plot(
nddata.axis("energy").nodes.value,
nddata.data.value,
".",
label="Interpolation nodes",
)
print(nddata.axis("energy").nodes)
class EffectiveAreaTable:
"""Effective area table.
TODO: Document
Parameters
----------
energy_lo, energy_hi : `~astropy.units.Quantity`
Energy axis bin edges
data : `~astropy.units.Quantity`
Effective area
Examples
--------
Plot parametrized effective area for HESS, HESS2 and CTA.
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
import astropy.units as u
from gammapy.irf import EffectiveAreaTable
energy = np.logspace(-3, 3, 100) * u.TeV
for instrument in ['HESS', 'HESS2', 'CTA']:
aeff = EffectiveAreaTable.from_parametrization(energy, instrument)
ax = aeff.plot(label=instrument)
ax.set_yscale('log')
ax.set_xlim([1e-3, 1e3])
ax.set_ylim([1e3, 1e12])
plt.legend(loc='best')
plt.show()
Find energy where the effective area is at 10% of its maximum value
>>> import numpy as np
>>> import astropy.units as u
>>> from gammapy.irf import EffectiveAreaTable
>>> energy = np.logspace(-1, 2) * u.TeV
>>> aeff_max = aeff.max_area
>>> print(aeff_max).to('m2')
156909.413371 m2
>>> energy_threshold = aeff.find_energy(0.1 * aeff_max)
>>> print(energy_threshold)
0.185368478744 TeV
"""
def __init__(self, energy_lo, energy_hi, data, meta=None):
e_edges = edges_from_lo_hi(energy_lo, energy_hi)
energy_axis = MapAxis.from_edges(e_edges,
interp="log",
name="energy_true")
interp_kwargs = {"extrapolate": False, "bounds_error": False}
self.data = NDDataArray(axes=[energy_axis],
data=data,
interp_kwargs=interp_kwargs)
self.meta = meta or {}
@property
def energy(self):
return self.data.axis("energy_true")
def plot(self, ax=None, energy=None, show_energy=None, **kwargs):
"""Plot effective area.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis
energy : `~astropy.units.Quantity`
Energy nodes
show_energy : `~astropy.units.Quantity`, optional
Show energy, e.g. threshold, as vertical line
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis
"""
import matplotlib.pyplot as plt
ax = plt.gca() if ax is None else ax
kwargs.setdefault("lw", 2)
if energy is None:
energy = self.energy.center
eff_area = self.data.evaluate(energy_true=energy)
xerr = (
(energy - self.energy.edges[:-1]).value,
(self.energy.edges[1:] - energy).value,
)
ax.errorbar(energy.value, eff_area.value, xerr=xerr, **kwargs)
if show_energy is not None:
ener_val = u.Quantity(show_energy).to_value(self.energy.unit)
ax.vlines(ener_val,
0,
1.1 * self.max_area.value,
linestyles="dashed")
ax.set_xscale("log")
ax.set_xlabel(f"Energy [{self.energy.unit}]")
ax.set_ylabel(f"Effective Area [{self.data.data.unit}]")
return ax
@classmethod
def from_parametrization(cls, energy, instrument="HESS"):
r"""Create parametrized effective area.
Parametrizations of the effective areas of different Cherenkov
telescopes taken from Appendix B of Abramowski et al. (2010), see
https://ui.adsabs.harvard.edu/abs/2010MNRAS.402.1342A .
.. math::
A_{eff}(E) = g_1 \left(\frac{E}{\mathrm{MeV}}\right)^{-g_2}\exp{\left(-\frac{g_3}{E}\right)}
Parameters
----------
energy : `~astropy.units.Quantity`
Energy binning, analytic function is evaluated at log centers
instrument : {'HESS', 'HESS2', 'CTA'}
Instrument name
"""
energy = u.Quantity(energy)
# Put the parameters g in a dictionary.
# Units: g1 (cm^2), g2 (), g3 (MeV)
# Note that whereas in the paper the parameter index is 1-based,
# here it is 0-based
pars = {
"HESS": [6.85e9, 0.0891, 5e5],
"HESS2": [2.05e9, 0.0891, 1e5],
"CTA": [1.71e11, 0.0891, 1e5],
}
if instrument not in pars.keys():
ss = f"Unknown instrument: {instrument}\n"
ss += "Valid instruments: HESS, HESS2, CTA"
raise ValueError(ss)
xx = MapAxis.from_edges(energy, interp="log").center.to_value("MeV")
g1 = pars[instrument][0]
g2 = pars[instrument][1]
g3 = -pars[instrument][2]
value = g1 * xx**(-g2) * np.exp(g3 / xx)
data = u.Quantity(value, "cm2", copy=False)
return cls(energy_lo=energy[:-1], energy_hi=energy[1:], data=data)
@classmethod
def from_constant(cls, energy, value):
"""Create constant value effective area.
Parameters
----------
energy : `~astropy.units.Quantity`
Energy binning, analytic function is evaluated at log centers
value : `~astropy.units.Quantity`
Effective area
"""
data = np.ones((len(energy) - 1)) * u.Quantity(value)
return cls(energy_lo=energy[:-1], energy_hi=energy[1:], data=data)
@classmethod
def from_table(cls, table):
"""Create from `~astropy.table.Table` in ARF format.
Data format specification: :ref:`gadf:ogip-arf`
"""
energy_lo = table["ENERG_LO"].quantity
energy_hi = table["ENERG_HI"].quantity
data = table["SPECRESP"].quantity
return cls(energy_lo=energy_lo, energy_hi=energy_hi, data=data)
@classmethod
def from_hdulist(cls, hdulist, hdu="SPECRESP"):
"""Create from `~astropy.io.fits.HDUList`."""
return cls.from_table(Table.read(hdulist[hdu]))
@classmethod
def read(cls, filename, hdu="SPECRESP"):
"""Read from file."""
filename = str(make_path(filename))
with fits.open(filename, memmap=False) as hdulist:
try:
return cls.from_hdulist(hdulist, hdu=hdu)
except KeyError:
raise ValueError(f"File {filename} contains no HDU {hdu!r}\n"
f"Available: {[_.name for _ in hdulist]}")
def to_table(self):
"""Convert to `~astropy.table.Table` in ARF format.
Data format specification: :ref:`gadf:ogip-arf`
"""
table = Table()
table.meta = {
"EXTNAME": "SPECRESP",
"hduclass": "OGIP",
"hduclas1": "RESPONSE",
"hduclas2": "SPECRESP",
}
energy = self.energy.edges
table["ENERG_LO"] = energy[:-1]
table["ENERG_HI"] = energy[1:]
table["SPECRESP"] = self.evaluate_fill_nan()
return table
def to_region_map(self, region=None):
""""""
axis = self.data.axis("energy_true")
geom = RegionGeom(region=region, axes=[axis])
return RegionNDMap.from_geom(geom=geom,
data=self.data.data.value,
unit=self.data.data.unit)
def to_hdulist(self, name=None, use_sherpa=False):
"""Convert to `~astropy.io.fits.HDUList`."""
table = self.to_table()
if use_sherpa:
table["ENERG_HI"] = table["ENERG_HI"].quantity.to("keV")
table["ENERG_LO"] = table["ENERG_LO"].quantity.to("keV")
table["SPECRESP"] = table["SPECRESP"].quantity.to("cm2")
return fits.HDUList(
[fits.PrimaryHDU(),
fits.BinTableHDU(table, name=name)])
def write(self, filename, use_sherpa=False, **kwargs):
"""Write to file."""
filename = str(make_path(filename))
self.to_hdulist(use_sherpa=use_sherpa).writeto(filename, **kwargs)
def evaluate_fill_nan(self, **kwargs):
"""Modified evaluate function.
Calls :func:`gammapy.utils.nddata.NDDataArray.evaluate` and replaces
possible nan values. Below the finite range the effective area is set
to zero and above to value of the last valid note. This is needed since
other codes, e.g. sherpa, don't like nan values in FITS files. Make
sure that the replacement happens outside of the energy range, where
the `~gammapy.irf.EffectiveAreaTable` is used.
"""
retval = self.data.evaluate(**kwargs)
idx = np.where(np.isfinite(retval))[0]
retval[np.arange(idx[0])] = 0
retval[np.arange(idx[-1], len(retval))] = retval[idx[-1]]
return retval
@property
def max_area(self):
"""Maximum effective area."""
cleaned_data = self.data.data[np.where(~np.isnan(self.data.data))]
return cleaned_data.max()
def find_energy(self, aeff, emin=None, emax=None):
"""Find energy for a given effective area.
In case the solution is not unique, provide the `emin` or `emax` arguments
to limit the solution to the given range. By default the peak energy of the
effective area is chosen as `emax`.
Parameters
----------
aeff : `~astropy.units.Quantity`
Effective area value
emin : `~astropy.units.Quantity`
Lower bracket value in case solution is not unique.
emax : `~astropy.units.Quantity`
Upper bracket value in case solution is not unique.
Returns
-------
energy : `~astropy.units.Quantity`
Energy corresponding to the given aeff.
"""
from gammapy.modeling.models import TemplateSpectralModel
energy = self.energy.center
if emin is None:
emin = energy[0]
if emax is None:
# use the peak effective area as a default for the energy maximum
emax = energy[np.argmax(self.data.data)]
aeff_spectrum = TemplateSpectralModel(energy, self.data.data)
return aeff_spectrum.inverse(aeff, emin=emin, emax=emax)
class EnergyDispersion2D:
"""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.maps import MapAxis
>>> from gammapy.irf import EnergyDispersion2D
>>> filename = '$GAMMAPY_DATA/hess-dl3-dr1/data/hess_dl3_dr1_obs_id_020136.fits.gz'
>>> edisp2d = EnergyDispersion2D.read(filename, hdu="EDISP")
Create energy dispersion matrix (`~gammapy.irf.EnergyDispersion`)
for a given field of view offset and energy binning:
>>> energy = MapAxis.from_bounds(0.1, 20, nbin=60, unit="TeV", interp="log").edges
>>> edisp = edisp2d.to_energy_dispersion(offset='1.2 deg', e_reco=energy, e_true=energy)
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
e_true_edges = edges_from_lo_hi(e_true_lo, e_true_hi)
e_true_axis = MapAxis.from_edges(e_true_edges,
interp="log",
name="e_true")
migra_edges = edges_from_lo_hi(migra_lo, migra_hi)
migra_axis = MapAxis.from_edges(migra_edges,
interp="log",
name="migra",
unit="")
# TODO: for some reason the H.E.S.S. DL3 files contain the same values for offset_hi and offset_lo
if np.allclose(offset_lo.to_value("deg"), offset_hi.to_value("deg")):
offset_axis = MapAxis.from_nodes(offset_lo,
interp="lin",
name="offset")
else:
offset_edges = edges_from_lo_hi(offset_lo, offset_hi)
offset_axis = MapAxis.from_edges(offset_edges,
interp="lin",
name="offset")
axes = [e_true_axis, migra_axis, offset_axis]
self.data = NDDataArray(axes=axes,
data=data,
interp_kwargs=interp_kwargs)
self.meta = meta or {}
def __str__(self):
ss = self.__class__.__name__
ss += f"\n{self.data}"
return ss
@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`
Bin edges of true energy axis
migra : `~astropy.units.Quantity`
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`
Bin edges of offset
pdf_threshold : float, optional
Zero suppression threshold
"""
e_true = Quantity(e_true)
# erf does not work with Quantities
true = MapAxis.from_edges(e_true, interp="log").center.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 = (scipy.special.erf(t1) - scipy.special.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,
)
@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,
)
@classmethod
def from_hdulist(cls, hdulist, hdu="edisp_2d"):
"""Create from `~astropy.io.fits.HDUList`."""
return cls.from_table(Table.read(hdulist[hdu]))
@classmethod
def read(cls, filename, hdu="edisp_2d"):
"""Read from FITS file.
Parameters
----------
filename : str
File name
"""
with fits.open(make_path(filename), memmap=False) as hdulist:
return cls.from_hdulist(hdulist, hdu)
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 : `~astropy.units.Quantity`, None
True energy axis
e_reco : `~astropy.units.Quantity`
Reconstructed energy axis
Returns
-------
edisp : `~gammapy.irf.EnergyDispersion`
Energy dispersion matrix
"""
offset = Angle(offset)
e_true = self.data.axis("e_true").edges if e_true is None else e_true
e_reco = self.data.axis("e_true").edges if e_reco is None else e_reco
data = []
for energy in MapAxis.from_edges(e_true, interp="log").center:
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,
)
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 : `~astropy.units.Quantity`
True energy
e_reco : `~astropy.units.Quantity`, None
Reconstructed energy axis
offset : `~astropy.coordinates.Angle`
Offset
migra_step : float
Integration step in migration
Returns
-------
rv : `~numpy.ndarray`
Redistribution vector
"""
e_true = Quantity(e_true)
if e_reco is None:
# Default: e_reco nodes = migra nodes * e_true nodes
e_reco = self.data.axis("migra").edges * e_true
else:
# Translate given e_reco binning to migra at bin center
e_reco = Quantity(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").edges[0]
mrec_max = self.data.axis("migra").edges[-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
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 : `~astropy.units.Quantity`, optional
True energy
migra : `~numpy.ndarray`, 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 = Quantity([0.1, 1, 10], "TeV")
else:
e_true = np.atleast_1d(Quantity(e_true))
migra = self.data.axis("migra").center 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 = f"offset = {off:.1f}\nenergy = {ener:.1f}"
ax.plot(migra, disp, label=label, **kwargs)
ax.set_xlabel(r"$E_\mathrm{{Reco}} / E_\mathrm{{True}}$")
ax.set_ylabel("Probability density")
ax.legend(loc="upper left")
return ax
def plot_bias(self, ax=None, offset=None, add_cbar=False, **kwargs):
"""Plot migration as a function of true energy for a given offset.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis
offset : `~astropy.coordinates.Angle`, optional
Offset
add_cbar : bool
Add a colorbar to the plot.
kwargs : dict
Keyword arguments passed to `~matplotlib.pyplot.pcolormesh`.
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis
"""
from matplotlib.colors import PowerNorm
import matplotlib.pyplot as plt
kwargs.setdefault("cmap", "GnBu")
kwargs.setdefault("norm", PowerNorm(gamma=0.5))
ax = plt.gca() if ax is None else ax
if offset is None:
offset = Angle(1, "deg")
e_true = self.data.axis("e_true").edges
migra = self.data.axis("migra").edges
x = e_true.value
y = migra.value
z = self.data.evaluate(
offset=offset,
e_true=e_true.reshape(1, -1, 1),
migra=migra.reshape(1, 1, -1),
).value[0]
caxes = ax.pcolormesh(x, y, z.T, **kwargs)
if add_cbar:
label = "Probability density (A.U.)"
ax.figure.colorbar(caxes, ax=ax, label=label)
ax.set_xlabel(fr"$E_\mathrm{{True}}$ [{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
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()
def to_table(self):
"""Convert to `~astropy.table.Table`."""
meta = self.meta.copy()
energy = self.data.axis("e_true").edges
migra = self.data.axis("migra").edges
theta = self.data.axis("offset").edges
table = Table(meta=meta)
table["ENERG_LO"] = energy[:-1][np.newaxis]
table["ENERG_HI"] = energy[1:][np.newaxis]
table["MIGRA_LO"] = migra[:-1][np.newaxis]
table["MIGRA_HI"] = migra[1:][np.newaxis]
table["THETA_LO"] = theta[:-1][np.newaxis]
table["THETA_HI"] = theta[1:][np.newaxis]
table["MATRIX"] = self.data.data.T[np.newaxis]
return table
def to_fits(self, name="ENERGY DISPERSION"):
"""Convert to `~astropy.io.fits.BinTable`."""
return fits.BinTableHDU(self.to_table(), name=name)
class EnergyDispersion:
"""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
e_true_edges = edges_from_lo_hi(e_true_lo, e_true_hi)
e_true_axis = MapAxis.from_edges(e_true_edges,
interp="log",
name="e_true")
e_reco_edges = edges_from_lo_hi(e_reco_lo, e_reco_hi)
e_reco_axis = MapAxis.from_edges(e_reco_edges,
interp="log",
name="e_reco")
self.data = NDDataArray(axes=[e_true_axis, e_reco_axis],
data=data,
interp_kwargs=interp_kwargs)
self.meta = meta or {}
def __str__(self):
ss = self.__class__.__name__
ss += f"\n{self.data}"
return ss
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.nbin:
raise ValueError(
f"Input size {len(data)} does not match true energy axis {self.e_true.nbin}"
)
return np.dot(data, self.data.data)
@property
def e_reco(self):
"""Reconstructed energy axis (`~gammapy.maps.MapAxis`)"""
return self.data.axis("e_reco")
@property
def e_true(self):
"""True energy axis (`~gammapy.maps.MapAxis`)"""
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
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()
energy = self.e_reco.edges
if lo_threshold is None and hi_threshold is None:
idx = slice(None)
else:
idx = (energy[:-1] < lo_threshold) | (energy[1:] > hi_threshold)
data[:, idx] = 0
return data
@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`
Bin edges of true energy axis
e_reco : `~astropy.units.Quantity`
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)
@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,
)
@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]
unit = ebounds_hdu.header.get("TUNIT2")
e_reco_lo = Quantity(ebounds_hdu.data["E_MIN"], unit=unit)
e_reco_hi = Quantity(ebounds_hdu.data["E_MAX"], unit=unit)
unit = matrix_hdu.header.get("TUNIT1")
e_true_lo = Quantity(matrix_hdu.data["ENERG_LO"], unit=unit)
e_true_hi = Quantity(matrix_hdu.data["ENERG_HI"], unit=unit)
return cls(
e_true_lo=e_true_lo,
e_true_hi=e_true_hi,
e_reco_lo=e_reco_lo,
e_reco_hi=e_reco_hi,
data=pdf_matrix,
)
@classmethod
def read(cls, filename, hdu1="MATRIX", hdu2="EBOUNDS"):
"""Read from file.
Parameters
----------
filename : `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
"""
with fits.open(make_path(filename), memmap=False) as hdulist:
return cls.from_hdulist(hdulist, hdu1=hdu1, hdu2=hdu2)
def to_hdulist(self, use_sherpa=False, **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)
if use_sherpa:
table["ENERG_HI"] = table["ENERG_HI"].quantity.to("keV")
table["ENERG_LO"] = table["ENERG_LO"].quantity.to("keV")
cols = table.columns
c0 = fits.Column(name=cols[0].name,
format="E",
array=cols[0],
unit=str(cols[0].unit))
c1 = fits.Column(name=cols[1].name,
format="E",
array=cols[1],
unit=str(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)
energy = self.e_reco.edges
if use_sherpa:
energy = energy.to("keV")
ebounds = energy_axis_to_ebounds(energy)
prim_hdu = fits.PrimaryHDU()
return fits.HDUList([prim_hdu, hdu, ebounds])
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()
energy = self.e_true.edges
table["ENERG_LO"] = energy[:-1]
table["ENERG_HI"] = energy[1:]
table["N_GRP"] = n_grp
table["F_CHAN"] = f_chan
table["N_CHAN"] = n_chan
table["MATRIX"] = matrix
table.meta = {
"name": "MATRIX",
"chantype": "PHA",
"hduclass": "OGIP",
"hduclas1": "RESPONSE",
"hduclas2": "RSP_MATRIX",
"detchans": self.e_reco.nbin,
"numgrp": numgrp,
"numelt": numelt,
"tlmin4": 0,
}
return table
def write(self, filename, use_sherpa=False, **kwargs):
"""Write to file."""
filename = make_path(filename)
self.to_hdulist(use_sherpa=use_sherpa).writeto(filename, **kwargs)
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.coord_to_idx(e_true)
e_true_real = self.e_true.center[idx_true]
return np.sqrt(var) / e_true_real
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.coord_to_idx(e_true)
e_true_real = self.e_true.center[idx_true]
bias = (e_reco - e_true_real) / e_true_real
return bias
def get_bias_energy(self, bias, emin=None, emax=None):
"""Find energy corresponding to a given bias.
In case the solution is not unique, provide the ``emin`` or ``emax`` arguments
to limit the solution to the given range. By default the peak energy of the
bias is chosen as ``emin``.
Parameters
----------
bias : float
Bias value.
emin : `~astropy.units.Quantity`
Lower bracket value in case solution is not unique.
emax : `~astropy.units.Quantity`
Upper bracket value in case solution is not unique.
Returns
-------
bias_energy : `~astropy.units.Quantity`
Reconstructed energy corresponding to the given bias.
"""
from gammapy.modeling.models import TemplateSpectralModel
e_true = self.e_true.center
values = self.get_bias(e_true)
if emin is None:
# use the peak bias energy as default minimum
emin = e_true[np.nanargmax(values)]
if emax is None:
emax = e_true[-1]
bias_spectrum = TemplateSpectralModel(e_true, values)
e_true_bias = bias_spectrum.inverse(Quantity(bias),
emin=emin,
emax=emax)
# return reconstructed energy
return e_true_bias * (1 + bias)
def get_mean(self, e_true):
"""Get mean reconstructed energy for a given true energy."""
# find pdf for true energies
idx = self.e_true.coord_to_idx(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.center, axis=-1)
with np.errstate(invalid="ignore"):
# corm can be zero
mean = np.nan_to_num(temp / norm)
return mean
def _get_variance(self, e_true):
"""Get variance of log reconstructed energy."""
# evaluate the pdf at given true energies
idx = self.e_true.coord_to_idx(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.center
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
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.edges
e_reco = self.e_reco.edges
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(fr"$E_\mathrm{{True}}$ [{e_true.unit}]")
ax.set_ylabel(fr"$E_\mathrm{{Reco}}$ [{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
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.center.to_value("TeV")
y = self.get_bias(self.e_true.center)
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
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()
class Background2D:
"""Background 2D.
Data format specification: :ref:`gadf:bkg_2d`
Parameters
----------
energy_lo, energy_hi : `~astropy.units.Quantity`
Energy binning
offset_lo, offset_hi : `~astropy.units.Quantity`
FOV coordinate offset-axis binning
data : `~astropy.units.Quantity`
Background rate (usually: ``s^-1 MeV^-1 sr^-1``)
"""
default_interp_kwargs = dict(bounds_error=False, fill_value=None)
"""Default Interpolation kwargs for `~gammapy.utils.nddata.NDDataArray`. Extrapolate."""
def __init__(
self,
energy_lo,
energy_hi,
offset_lo,
offset_hi,
data,
meta=None,
interp_kwargs=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
e_edges = edges_from_lo_hi(energy_lo, energy_hi)
energy_axis = MapAxis.from_edges(e_edges, interp="log", name="energy")
offset_edges = edges_from_lo_hi(offset_lo, offset_hi)
offset_axis = MapAxis.from_edges(offset_edges,
interp="lin",
name="offset")
self.data = NDDataArray(axes=[energy_axis, offset_axis],
data=data,
interp_kwargs=interp_kwargs)
self.meta = meta or {}
def __str__(self):
ss = self.__class__.__name__
ss += f"\n{self.data}"
return ss
@classmethod
def from_table(cls, table):
"""Read from `~astropy.table.Table`."""
# Spec says key should be "BKG", but there are files around
# (e.g. CTA 1DC) that use "BGD". For now we support both
if "BKG" in table.colnames:
bkg_name = "BKG"
elif "BGD" in table.colnames:
bkg_name = "BGD"
else:
raise ValueError('Invalid column names. Need "BKG" or "BGD".')
data_unit = u.Unit(table[bkg_name].unit, parse_strict="silent")
if isinstance(data_unit, u.UnrecognizedUnit):
data_unit = u.Unit("s-1 MeV-1 sr-1")
log.warning(
"Invalid unit found in background table! Assuming (s-1 MeV-1 sr-1)"
)
return cls(
energy_lo=table["ENERG_LO"].quantity[0],
energy_hi=table["ENERG_HI"].quantity[0],
offset_lo=table["THETA_LO"].quantity[0],
offset_hi=table["THETA_HI"].quantity[0],
data=table[bkg_name].data[0] * data_unit,
meta=table.meta,
)
@classmethod
def from_hdulist(cls, hdulist, hdu="BACKGROUND"):
"""Create from `~astropy.io.fits.HDUList`."""
return cls.from_table(Table.read(hdulist[hdu]))
@classmethod
def read(cls, filename, hdu="BACKGROUND"):
"""Read from file."""
with fits.open(make_path(filename), memmap=False) as hdulist:
return cls.from_hdulist(hdulist, hdu=hdu)
def to_table(self):
"""Convert to `~astropy.table.Table`."""
meta = self.meta.copy()
table = Table(meta=meta)
theta = self.data.axis("offset").edges
energy = self.data.axis("energy").edges
table["THETA_LO"] = theta[:-1][np.newaxis]
table["THETA_HI"] = theta[1:][np.newaxis]
table["ENERG_LO"] = energy[:-1][np.newaxis]
table["ENERG_HI"] = energy[1:][np.newaxis]
table["BKG"] = self.data.data[np.newaxis]
return table
def to_fits(self, name="BACKGROUND"):
"""Convert to `~astropy.io.fits.BinTableHDU`."""
return fits.BinTableHDU(self.to_table(), name=name)
def evaluate(self,
fov_lon,
fov_lat,
energy_reco,
method="linear",
**kwargs):
"""Evaluate at a given FOV position and energy.
The fov_lon, fov_lat, energy_reco has to have the same shape
since this is a set of points on which you want to evaluate.
To have the same API than background 3D for the
background evaluation, the offset is ``fov_altaz_lon``.
Parameters
----------
fov_lon, fov_lat : `~astropy.coordinates.Angle`
FOV coordinates expecting in AltAz frame, same shape than energy_reco
energy_reco : `~astropy.units.Quantity`
Reconstructed energy, same dimension than fov_lat and fov_lat
method : str {'linear', 'nearest'}, optional
Interpolation method
kwargs : dict
option for interpolation for `~scipy.interpolate.RegularGridInterpolator`
Returns
-------
array : `~astropy.units.Quantity`
Interpolated values, axis order is the same as for the NDData array
"""
offset = np.sqrt(fov_lon**2 + fov_lat**2)
return self.data.evaluate(offset=offset,
energy=energy_reco,
method=method,
**kwargs)
def evaluate_integrate(self,
fov_lon,
fov_lat,
energy_reco,
method="linear"):
"""Evaluate at given FOV position and energy, by integrating over the energy range.
Parameters
----------
fov_lon, fov_lat : `~astropy.coordinates.Angle`
FOV coordinates expecting in AltAz frame.
energy_reco: `~astropy.units.Quantity`
Reconstructed energy edges.
method : {'linear', 'nearest'}, optional
Interpolation method
Returns
-------
array : `~astropy.units.Quantity`
Returns 2D array with axes offset
"""
data = self.evaluate(fov_lon, fov_lat, energy_reco, method=method)
return trapz_loglog(data, energy_reco, axis=0)
def to_3d(self):
"""Convert to `Background3D`.
Fill in a radially symmetric way.
"""
raise NotImplementedError
def plot(self, ax=None, add_cbar=True, **kwargs):
"""Plot energy offset dependence of the background model.
"""
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
ax = plt.gca() if ax is None else ax
x = self.data.axis("energy").edges.to_value("TeV")
y = self.data.axis("offset").edges.to_value("deg")
z = self.data.data.T.value
kwargs.setdefault("cmap", "GnBu")
kwargs.setdefault("edgecolors", "face")
caxes = ax.pcolormesh(x, y, z, norm=LogNorm(), **kwargs)
ax.set_xscale("log")
ax.set_ylabel(f"Offset (deg)")
ax.set_xlabel(f"Energy (TeV)")
xmin, xmax = x.min(), x.max()
ax.set_xlim(xmin, xmax)
if add_cbar:
label = f"Background rate ({self.data.data.unit})"
ax.figure.colorbar(caxes, ax=ax, label=label)
def plot_offset_dependence(self,
ax=None,
offset=None,
energy=None,
**kwargs):
"""Plot background rate versus offset for a given energy.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis
offset : `~astropy.coordinates.Angle`
Offset axis
energy : `~astropy.units.Quantity`
Energy
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis
"""
import matplotlib.pyplot as plt
ax = plt.gca() if ax is None else ax
if energy is None:
e_min, e_max = np.log10(
self.data.axis("energy").center.value[[0, -1]])
energy = np.logspace(e_min, e_max,
4) * self.data.axis("energy").unit
if offset is None:
offset = self.data.axis("offset").center
for ee in energy:
bkg = self.data.evaluate(offset=offset, energy=ee)
if np.isnan(bkg).all():
continue
label = f"energy = {ee:.1f}"
ax.plot(offset, bkg.value, label=label, **kwargs)
ax.set_xlabel(f"Offset ({self.data.axis('offset').unit})")
ax.set_ylabel(f"Background rate ({self.data.data.unit})")
ax.set_yscale("log")
ax.legend(loc="upper right")
return ax
def plot_energy_dependence(self,
ax=None,
offset=None,
energy=None,
**kwargs):
"""Plot background rate versus energy for a given offset.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis
offset : `~astropy.coordinates.Angle`
Offset
energy : `~astropy.units.Quantity`
Energy axis
kwargs : dict
Forwarded tp plt.plot()
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis
"""
import matplotlib.pyplot as plt
ax = plt.gca() if ax is None else ax
if offset is None:
off_min, off_max = self.data.axis("offset").center.value[[0, -1]]
offset = np.linspace(off_min, off_max,
4) * self.data.axis("offset").unit
if energy is None:
energy = self.data.axis("energy").center
for off in offset:
bkg = self.data.evaluate(offset=off, energy=energy)
label = f"offset = {off:.1f}"
ax.plot(energy, bkg.value, label=label, **kwargs)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_xlabel(f"Energy [{energy.unit}]")
ax.set_ylabel(f"Background rate ({self.data.data.unit})")
ax.set_xlim(min(energy.value), max(energy.value))
ax.legend(loc="best")
return ax
def plot_spectrum(self, ax=None, **kwargs):
"""Plot angle integrated background rate versus energy.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis
kwargs : dict
Forwarded tp plt.plot()
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis
"""
import matplotlib.pyplot as plt
ax = plt.gca() if ax is None else ax
offset = self.data.axis("offset").edges
energy = self.data.axis("energy").center
bkg = []
for ee in energy:
data = self.data.evaluate(offset=offset, energy=ee)
val = np.nansum(trapz_loglog(data, offset, axis=0))
bkg.append(val.value)
ax.plot(energy, bkg, label="integrated spectrum", **kwargs)
unit = self.data.data.unit * offset.unit * offset.unit
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_xlabel(f"Energy [{energy.unit}]")
ax.set_ylabel(f"Background rate ({unit})")
ax.set_xlim(min(energy.value), max(energy.value))
ax.legend(loc="best")
return ax
def peek(self, figsize=(10, 8)):
"""Quick-look summary plots."""
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=figsize)
self.plot(ax=axes[1][1])
self.plot_offset_dependence(ax=axes[0][0])
self.plot_energy_dependence(ax=axes[1][0])
self.plot_spectrum(ax=axes[0][1])
plt.tight_layout()
class SensitivityTable(object):
"""Sensitivity table.
The IRF format should be compliant with the one discussed
at http://gamma-astro-data-formats.readthedocs.io/en/latest/irfs/.
Work will be done to fix this.
Parameters
-----------
energy_lo, energy_hi : `~astropy.units.Quantity`, `~gammapy.utils.nddata.BinnedDataAxis`
Bin edges of energy axis
data : `~astropy.units.Quantity`
Sensitivity
"""
def __init__(self, energy_lo, energy_hi, data):
axes = [
BinnedDataAxis(energy_lo, energy_hi, interpolation_mode='log', name='energy'),
]
self.data = NDDataArray(axes=axes, data=data)
@property
def energy(self):
return self.data.axis('energy')
@classmethod
def from_table(cls, table):
energy_lo = table['ENERG_LO'].quantity
energy_hi = table['ENERG_HI'].quantity
data = table['SENSITIVITY'].quantity
return cls(energy_lo=energy_lo, energy_hi=energy_hi, data=data)
@classmethod
def from_hdulist(cls, hdulist, hdu='SENSITIVITY'):
fits_table = hdulist[hdu]
table = Table.read(fits_table)
return cls.from_table(table)
@classmethod
def read(cls, filename, hdu='SENSITVITY'):
filename = make_path(filename)
with fits.open(str(filename), memmap=False) as hdulist:
return cls.from_hdulist(hdulist, hdu=hdu)
def plot(self, ax=None, energy=None, **kwargs):
"""Plot sensitivity.
Parameters
----------
ax : `~matplotlib.axes.Axes`, optional
Axis
energy : `~astropy.units.Quantity`
Energy nodes
Returns
-------
ax : `~matplotlib.axes.Axes`
Axis
"""
import matplotlib.pyplot as plt
ax = plt.gca() if ax is None else ax
energy = energy or self.energy.nodes
values = self.data.evaluate(energy=energy)
xerr = (
energy.value - self.energy.lo.value,
self.energy.hi.value - energy.value,
)
ax.errorbar(energy.value, values.value, xerr=xerr, fmt='o', **kwargs)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel('Reco Energy [{}]'.format(self.energy.unit))
ax.set_ylabel('Sensitivity [{}]'.format(self.data.data.unit))
return ax
