Source code for gammapy.irf.energy_dispersion

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
import scipy.special
from scipy.interpolate import interp1d
from astropy.coordinates import Angle
from astropy.io import fits
from astropy.table import Table
from astropy.units import Quantity
from gammapy.maps import MapAxis
from gammapy.utils.nddata import NDDataArray
from gammapy.utils.scripts import make_path
from .edisp_kernel import EDispKernel

__all__ = ["EnergyDispersion2D"]


[docs]class EnergyDispersion2D: """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 = 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_edisp_kernel(offset='1.2 deg', e_reco=energy, energy_true=energy) See Also -------- EnergyDispersion """ tag = "edisp_2d" default_interp_kwargs = dict(bounds_error=False, fill_value=None) """Default Interpolation kwargs for `~gammapy.utils.nddata.NDDataArray`. Extrapolate.""" def __init__( self, energy_axis_true, migra_axis, offset_axis, data, interp_kwargs=None, meta=None, ): if interp_kwargs is None: interp_kwargs = self.default_interp_kwargs axes = [energy_axis_true, 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
[docs] @classmethod def from_gauss(cls, energy_true, migra, bias, sigma, offset, 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_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 """ energy_true = Quantity(energy_true) # erf does not work with Quantities energy_axis_true = MapAxis.from_energy_edges( energy_true, interp="log", name="energy_true" ) true2d, migra2d = np.meshgrid(energy_axis_true.center, 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 data = pdf.T[:, :, np.newaxis] * np.ones(len(offset) - 1) data[data < pdf_threshold] = 0 offset_axis = MapAxis.from_edges(offset, name="offset") migra_axis = MapAxis.from_edges(migra, name="migra") return cls( energy_axis_true=energy_axis_true, migra_axis=migra_axis, offset_axis=offset_axis, data=data, )
[docs] @classmethod def from_table(cls, table): """Create from `~astropy.table.Table`.""" # TODO: move this to MapAxis.from_table() if "ENERG_LO" in table.colnames: energy_axis_true = MapAxis.from_table( table, column_prefix="ENERG", format="gadf-dl3" ) elif "ETRUE_LO" in table.colnames: energy_axis_true = MapAxis.from_table( table, column_prefix="ETRUE", format="gadf-dl3" ) else: raise ValueError( 'Invalid column names. Need "ENERG_LO/ENERG_HI" or "ETRUE_LO/ETRUE_HI"' ) offset_axis = MapAxis.from_table( table, column_prefix="THETA", format="gadf-dl3" ) migra_axis = MapAxis.from_table(table, column_prefix="MIGRA", format="gadf-dl3") matrix = table["MATRIX"].quantity[0].transpose() return cls( energy_axis_true=energy_axis_true, offset_axis=offset_axis, migra_axis=migra_axis, 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 """ with fits.open(str(make_path(filename)), memmap=False) as hdulist: return cls.from_hdulist(hdulist, hdu)
[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 """ offset = Angle(offset) # TODO: expect directly MapAxis here? if energy is None: energy_axis = self.data.axes["energy_true"].copy(name="energy") else: energy_axis = MapAxis.from_energy_edges(energy) if energy_true is None: energy_axis_true = self.data.axes["energy_true"] else: energy_axis_true = MapAxis.from_energy_edges( energy_true, name="energy_true" ) data = [] for value in energy_axis_true.center: vec = self.get_response( offset=offset, energy_true=value, energy=energy_axis.edges ) data.append(vec) return EDispKernel( energy_axis=energy_axis, energy_axis_true=energy_axis_true, data=np.asarray(data), )
[docs] def get_response(self, offset, energy_true, energy=None): """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 ---------- energy_true : `~astropy.units.Quantity` True energy energy : `~astropy.units.Quantity`, None Reconstructed energy axis offset : `~astropy.coordinates.Angle` Offset Returns ------- rv : `~numpy.ndarray` Redistribution vector """ energy_true = Quantity(energy_true) migra_axis = self.data.axes["migra"] if energy is None: # Default: energy nodes = migra nodes * energy_true nodes energy = migra_axis.edges * energy_true else: # Translate given energy binning to migra at bin center energy = Quantity(energy) # migration value of energy bounds migra = energy / energy_true values = self.data.evaluate( offset=offset, energy_true=energy_true, migra=migra_axis.center ) cumsum = np.insert(values, 0, 0).cumsum() with np.errstate(invalid="ignore"): cumsum = np.nan_to_num(cumsum / cumsum[-1]) f = interp1d( migra_axis.edges.value, cumsum, kind="linear", bounds_error=False, fill_value=(0, 1), ) # We compute the difference between 2 successive bounds in energy # to get integral over reco energy bin integral = np.diff(np.clip(f(migra), a_min=0, a_max=1)) return integral
[docs] def plot_migration( self, ax=None, offset=None, energy_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 energy_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 energy_true is None: energy_true = Quantity([0.1, 1, 10], "TeV") else: energy_true = np.atleast_1d(Quantity(energy_true)) migra = self.data.axes["migra"].center if migra is None else migra for ener in energy_true: for off in offset: disp = self.data.evaluate(offset=off, energy_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}}$") ax.set_ylabel("Probability density") ax.legend(loc="upper left") return ax
[docs] def plot_bias(self, ax=None, offset=None, add_cbar=False, **kwargs): """Plot migration as a function of true energy for a given offset. Parameters ---------- ax : `~matplotlib.axes.Axes`, optional Axis offset : `~astropy.coordinates.Angle`, optional Offset add_cbar : bool Add a colorbar to the plot. kwargs : dict Keyword arguments passed to `~matplotlib.pyplot.pcolormesh`. Returns ------- ax : `~matplotlib.axes.Axes` Axis """ from matplotlib.colors import PowerNorm import matplotlib.pyplot as plt kwargs.setdefault("cmap", "GnBu") kwargs.setdefault("norm", PowerNorm(gamma=0.5)) ax = plt.gca() if ax is None else ax if offset is None: offset = Angle(1, "deg") energy_true = self.data.axes["energy_true"] migra = self.data.axes["migra"] x = energy_true.edges.value y = migra.edges.value z = self.data.evaluate( offset=offset, energy_true=energy_true.center.reshape(1, -1, 1), migra=migra.center.reshape(1, 1, -1), ).value[0] caxes = ax.pcolormesh(x, y, z.T, **kwargs) if add_cbar: label = "Probability density (A.U.)" ax.figure.colorbar(caxes, ax=ax, label=label) ax.set_xlabel(fr"$E_\mathrm{{True}}$ [{energy_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_edisp_kernel(offset="1 deg") edisp.plot_matrix(ax=axes[2]) plt.tight_layout()
[docs] def to_table(self): """Convert to `~astropy.table.Table`.""" table = self.data.axes.to_table(format="gadf-dl3") table.meta = self.meta.copy() table["MATRIX"] = self.data.data.T[np.newaxis] return table
[docs] def to_table_hdu(self, name="ENERGY DISPERSION"): """Convert to `~astropy.io.fits.BinTable`.""" return fits.BinTableHDU(self.to_table(), name=name)