# 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
import astropy.units as u
from astropy.io import fits
from astropy.table import Table
from ..utils.nddata import NDDataArray, BinnedDataAxis
from ..utils.energy import EnergyBounds
from ..utils.scripts import make_path
__all__ = ["EffectiveAreaTable", "EffectiveAreaTable2D"]
[docs]class EffectiveAreaTable(object):
"""Effective area table.
TODO: Document
Parameters
-----------
energy_lo : `~astropy.units.Quantity`
Lower bin edges of energy axis
energy_hi : `~astropy.units.Quantity`
Upper bin edges of energy axis
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):
axes = [
BinnedDataAxis(
energy_lo, energy_hi, interpolation_mode="log", name="energy"
)
]
self.data = NDDataArray(axes=axes, data=data)
self.meta = OrderedDict(meta) if meta else OrderedDict()
@property
def energy(self):
return self.data.axis("energy")
[docs] 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.nodes
eff_area = self.data.evaluate(energy=energy)
xerr = (
energy.value - self.energy.lo.value,
self.energy.hi.value - 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(self.energy.unit).value
ax.vlines(ener_val, 0, 1.1 * self.max_area.value, linestyles="dashed")
ax.set_xscale("log")
ax.set_xlabel("Energy [{}]".format(self.energy.unit))
ax.set_ylabel("Effective Area [{}]".format(self.data.data.unit))
return ax
[docs] @classmethod
def from_parametrization(cls, energy, instrument="HESS"):
"""Get parametrized effective area.
Parametrizations of the effective areas of different Cherenkov
telescopes taken from Appendix B of Abramowski et al. (2010), see
http://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 = EnergyBounds(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 = "Unknown instrument: {}\n".format(instrument)
ss += "Valid instruments: HESS, HESS2, CTA"
raise ValueError(ss)
xx = energy.log_centers.to("MeV").value
g1 = pars[instrument][0]
g2 = pars[instrument][1]
g3 = -pars[instrument][2]
value = g1 * xx ** (-g2) * np.exp(g3 / xx)
data = value * u.cm ** 2
return cls(
energy_lo=energy.lower_bounds, energy_hi=energy.upper_bounds, data=data
)
[docs] @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)
[docs] @classmethod
def from_hdulist(cls, hdulist, hdu="SPECRESP"):
"""Create from `~astropy.io.fits.HDUList`."""
return cls.from_table(Table.read(hdulist[hdu]))
[docs] @classmethod
def read(cls, filename, hdu="SPECRESP"):
"""Read from file."""
filename = make_path(filename)
with fits.open(str(filename), memmap=False) as hdulist:
try:
aeff = cls.from_hdulist(hdulist, hdu=hdu)
except KeyError:
msg = 'File {} contains no HDU "{}"'.format(filename, hdu)
msg += "\n Available {}".format([_.name for _ in hdulist])
raise ValueError(msg)
return aeff
[docs] def to_table(self):
"""Convert to `~astropy.table.Table` in ARF format.
Data format specification: :ref:`gadf:ogip-arf`
"""
table = Table()
table.meta = OrderedDict(
[
("EXTNAME", "SPECRESP"),
("hduclass", "OGIP"),
("hduclas1", "RESPONSE"),
("hduclas2", "SPECRESP"),
]
)
table["ENERG_LO"] = self.energy.lo
table["ENERG_HI"] = self.energy.hi
table["SPECRESP"] = self.evaluate_fill_nan()
return table
[docs] def to_hdulist(self, name=None):
"""Convert to `~astropy.io.fits.HDUList`."""
return fits.HDUList(
[fits.PrimaryHDU(), fits.BinTableHDU(self.to_table(), name=name)]
)
[docs] def write(self, filename, **kwargs):
"""Write to file."""
filename = make_path(filename)
self.to_hdulist().writeto(str(filename), **kwargs)
[docs] 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()
[docs] def find_energy(self, aeff, reverse=False):
"""Find energy for given effective area.
A linear interpolation is performed between the two nodes closest to
the desired effective area value. By default, the first match is
returned (use `reverse` to search starting from the end of the array)
TODO: Move to `~gammapy.utils.nddata.NDDataArray`
Parameters
----------
aeff : `~astropy.units.Quantity`
Effective area value
reverse : bool
Reverse the direction, i.e. search starting from the end of the array
Returns
-------
energy : `~astropy.units.Quantity`
Energy corresponding to aeff
"""
valid = np.where(self.data.data > aeff)[0]
idx = valid[0]
if reverse:
idx = valid[-1]
if not reverse:
# Return lower edge if first bin is selected
if idx == 0:
energy = self.energy.lo[idx].value
# Perform linear interpolation otherwise
else:
energy = np.interp(
aeff.value,
(self.data.data[[idx - 1, idx]].value),
(self.energy.nodes[[idx - 1, idx]].value),
)
else:
# Return upper edge if last bin is selected
if idx == self.data.data.size - 1:
energy = self.energy.hi[idx].value
# Perform linear interpolation otherwise
else:
energy = np.interp(
aeff.value,
(self.data.data[[idx, idx + 1]].value),
(self.energy.nodes[[idx, idx + 1]].value),
)
return energy * self.energy.unit
[docs] def to_sherpa(self, name):
"""Convert to `~sherpa.astro.data.DataARF`
Parameters
----------
name : str
Instance name
"""
from sherpa.astro.data import DataARF
table = self.to_table()
return DataARF(
name=name,
energ_lo=table["ENERG_LO"].quantity.to("keV").value,
energ_hi=table["ENERG_HI"].quantity.to("keV").value,
specresp=table["SPECRESP"].quantity.to("cm2").value,
)
[docs]class EffectiveAreaTable2D(object):
"""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
"""
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
axes = [
BinnedDataAxis(
energy_lo, energy_hi, interpolation_mode="log", name="energy"
),
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
@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
[docs] @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,
)
[docs] @classmethod
def from_hdulist(cls, hdulist, hdu="EFFECTIVE AREA"):
"""Create from `~astropy.io.fits.HDUList`."""
return cls.from_table(Table.read(hdulist[hdu]))
[docs] @classmethod
def read(cls, filename, hdu="EFFECTIVE AREA"):
"""Read from file."""
filename = make_path(filename)
with fits.open(str(filename), memmap=False) as hdulist:
aeff = cls.from_hdulist(hdulist, hdu=hdu)
return aeff
[docs] 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").bins
energy = EnergyBounds(energy)
area = self.data.evaluate(offset=offset, energy=energy.log_centers)
return EffectiveAreaTable(
energy_lo=energy.lower_bounds, energy_hi=energy.upper_bounds, data=area
)
[docs] 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").nodes[[0, -1]].value
offset = np.linspace(off_min, off_max, 4) * self.data.axis("offset").unit
if energy is None:
energy = self.data.axis("energy").nodes
for off in offset:
area = self.data.evaluate(offset=off, energy=energy)
label = "offset = {:.1f}".format(off)
ax.plot(energy, area.value, label=label, **kwargs)
ax.set_xscale("log")
ax.set_xlabel("Energy [{}]".format(self.data.axis("energy").unit))
ax.set_ylabel("Effective Area [{}]".format(self.data.data.unit))
ax.set_xlim(min(energy.value), max(energy.value))
ax.legend(loc="upper left")
return ax
[docs] 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 : `~gammapy.utils.energy.Energy`
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").nodes[[0, -1]].value)
energy = np.logspace(e_min, e_max, 4) * self.data.axis("energy").unit
if offset is None:
off_lo, off_hi = self.data.axis("offset").nodes[[0, -1]].to("deg").value
offset = np.linspace(off_lo, off_hi, 100) * u.deg
for ee in energy:
area = self.data.evaluate(offset=offset, energy=ee)
area /= np.nanmax(area)
if np.isnan(area).all():
continue
label = "energy = {:.1f}".format(ee)
ax.plot(offset, area, label=label, **kwargs)
ax.set_ylim(0, 1.1)
ax.set_xlabel("Offset ({})".format(self.data.axis("offset").unit))
ax.set_ylabel("Relative Effective Area")
ax.legend(loc="best")
return ax
[docs] 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
offset = self.data.axis("offset").bins
energy = self.data.axis("energy").bins
aeff = self.data.evaluate(offset=offset, energy=energy)
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("Offset ({})".format(offset.unit))
ax.set_xlabel("Energy ({})".format(energy.unit))
xmin, xmax = energy.value.min(), energy.value.max()
ax.set_xlim(xmin, xmax)
if add_cbar:
label = "Effective Area ({unit})".format(unit=aeff.unit)
ax.figure.colorbar(caxes, ax=ax, label=label)
return ax
[docs] 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()
[docs] def to_table(self):
"""Convert to `~astropy.table.Table`."""
meta = self.meta.copy()
table = Table(meta=meta)
table["ENERG_LO"] = self.data.axis("energy").lo[np.newaxis]
table["ENERG_HI"] = self.data.axis("energy").hi[np.newaxis]
table["THETA_LO"] = self.data.axis("offset").lo[np.newaxis]
table["THETA_HI"] = self.data.axis("offset").hi[np.newaxis]
table["EFFAREA"] = self.data.data.T[np.newaxis]
return table
[docs] def to_fits(self, name="EFFECTIVE AREA"):
"""Convert to `~astropy.io.fits.BinTable`."""
return fits.BinTableHDU(self.to_table(), name=name)