Source code for gammapy.irf.edisp.core

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
import scipy.special
from astropy import units as u
from astropy.coordinates import Angle, SkyCoord
from astropy.visualization import quantity_support
from gammapy.maps import MapAxes, MapAxis, RegionGeom
from ..core import IRF

__all__ = ["EnergyDispersion2D"]

[docs]class EnergyDispersion2D(IRF): """Offset-dependent energy dispersion matrix. Data format specification: :ref:`gadf:edisp_2d` Parameters ---------- energy_axis_true : `MapAxis` True energy axis migra_axis : `MapAxis` Energy migration axis offset_axis : `MapAxis` Field of view offset axis 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 =, 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_edisp_kernel(offset='1.2 deg', energy=energy, energy_true=energy) See Also -------- EnergyDispersion """ tag = "edisp_2d" required_axes = ["energy_true", "migra", "offset"] def _mask_out_bounds(self, invalid): return ( invalid[self.axes.index("energy_true")] & invalid[self.axes.index("migra")] ) | invalid[self.axes.index("offset")]
[docs] @classmethod def from_gauss( cls, energy_axis_true, migra_axis, offset_axis, bias, sigma, pdf_threshold=1e-6 ): """Create Gaussian energy dispersion matrix (`EnergyDispersion2D`). The output matrix will be Gaussian in (energy_true / energy). The ``bias`` and ``sigma`` should be either floats or arrays of same dimension than ``energy_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 ---------- energy_axis_true : `MapAxis` True energy axis migra_axis : `~astropy.units.Quantity` Migra axis offset_axis : `~astropy.units.Quantity` Bin edges of offset 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 """ axes = MapAxes([energy_axis_true, migra_axis, offset_axis]) coords = axes.get_coord(mode="edges", axis_name="migra") migra_min = coords["migra"][:, :-1, :] migra_max = coords["migra"][:, 1:, :] # Analytical formula for integral of Gaussian s = np.sqrt(2) * sigma t1 = (migra_max - 1 - bias) / s t2 = (migra_min - 1 - bias) / s pdf = (scipy.special.erf(t1) - scipy.special.erf(t2)) / 2 pdf = pdf / (migra_max - migra_min) # no offset dependence data = pdf * np.ones(axes.shape) data[data < pdf_threshold] = 0 return cls( axes=axes, data=data.value, )
[docs] def to_edisp_kernel(self, offset, energy_true=None, energy=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 energy_true : `~astropy.units.Quantity`, None True energy axis energy : `~astropy.units.Quantity` Reconstructed energy axis Returns ------- edisp : `~gammapy.irf.EDispKernel` Energy dispersion matrix """ from gammapy.makers.utils import make_edisp_kernel_map offset = Angle(offset) # TODO: expect directly MapAxis here? if energy is None: energy_axis = self.axes["energy_true"].copy(name="energy") else: energy_axis = MapAxis.from_energy_edges(energy) if energy_true is None: energy_axis_true = self.axes["energy_true"] else: energy_axis_true = MapAxis.from_energy_edges( energy_true, name="energy_true", ) pointing = SkyCoord("0d", "0d") center = pointing.directional_offset_by( position_angle=0 * u.deg, separation=offset ) geom = RegionGeom.create(region=center, axes=[energy_axis, energy_axis_true]) edisp = make_edisp_kernel_map(geom=geom, edisp=self, pointing=pointing) return edisp.get_edisp_kernel()
[docs] def normalize(self): """Normalise energy dispersion""" super().normalize(axis_name="migra")
[docs] def plot_migration(self, ax=None, offset=None, energy_true=None, **kwargs): """Plot energy dispersion for given offset and true energy. Parameters ---------- ax : `~matplotlib.axes.Axes`, optional Axis offset : `~astropy.coordinates.Angle`, optional Offset energy_true : `~astropy.units.Quantity`, optional True energy **kwargs : dict Keyword arguments forwarded to `~matplotlib.pyplot.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: offset = Angle([1], "deg") else: offset = np.atleast_1d(Angle(offset)) if energy_true is None: energy_true = u.Quantity([0.1, 1, 10], "TeV") else: energy_true = np.atleast_1d(u.Quantity(energy_true)) migra = self.axes["migra"] with quantity_support(): for ener in energy_true: for off in offset: disp = self.evaluate( offset=off, energy_true=ener, ) label = f"offset = {off:.1f}\nenergy = {ener:.1f}" ax.plot(, disp, label=label, **kwargs) migra.format_plot_xaxis(ax=ax) 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 """ import matplotlib.pyplot as plt from matplotlib.colors import PowerNorm 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") energy_true = self.axes["energy_true"] migra = self.axes["migra"] z = self.evaluate( offset=offset,, -1, 1),, 1, -1), ).value[0] with quantity_support(): caxes = ax.pcolormesh(energy_true.edges, migra.edges, z.T, **kwargs) energy_true.format_plot_xaxis(ax=ax) migra.format_plot_yaxis(ax=ax) if add_cbar: label = "Probability density (A.U.)" ax.figure.colorbar(caxes, ax=ax, label=label) 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_edisp_kernel(offset="1 deg") edisp.plot_matrix(ax=axes[2]) plt.tight_layout()