def test_calculate_bin_indices():
from pyirf.binning import calculate_bin_indices
bins = np.array([0, 1, 2])
values = [0.5, 0.5, 1, 1.1, 1.9, 2, -1, 2.5]
true_idx = np.array([0, 0, 1, 1, 1, 2, -1, 2])
assert np.all(calculate_bin_indices(values, bins) == true_idx)
# test with units
bins *= u.TeV
values *= 1000 * u.GeV
assert np.all(calculate_bin_indices(values, bins) == true_idx)
def energy_bias_resolution(
events,
energy_bins,
energy_type="true",
bias_function=np.median,
resolution_function=inter_quantile_distance,
):
"""
Calculate bias and energy resolution.
Parameters
----------
events: astropy.table.QTable
Astropy Table object containing the reconstructed events information.
energy_bins: numpy.ndarray(dtype=float, ndim=1)
Bin edges in energy.
energy_type: str
Either "true" or "reco" energy.
Default is "true".
bias_function: callable
Function used to calculate the energy bias
resolution_function: callable
Function used to calculate the energy resolution
Returns
-------
result : astropy.table.Table
Table containing the energy bias and resolution
per each bin in true energy.
"""
# create a table to make use of groupby operations
table = Table(events[["true_energy", "reco_energy"]])
table["rel_error"] = (events["reco_energy"] / events["true_energy"]) - 1
table["bin_index"] = calculate_bin_indices(
table[f"{energy_type}_energy"].quantity, energy_bins)
result = Table()
result[f"{energy_type}_energy_low"] = energy_bins[:-1]
result[f"{energy_type}_energy_high"] = energy_bins[1:]
result[f"{energy_type}_energy_center"] = 0.5 * (energy_bins[:-1] +
energy_bins[1:])
result["bias"] = np.nan
result["resolution"] = np.nan
# use groupby operations to calculate the percentile in each bin
by_bin = table.group_by("bin_index")
index = by_bin.groups.keys["bin_index"]
result["bias"][index] = by_bin["rel_error"].groups.aggregate(bias_function)
result["resolution"][index] = by_bin["rel_error"].groups.aggregate(
resolution_function)
return result
def angular_resolution(
events,
energy_bins,
energy_type="true",
):
"""
Calculate the angular resolution.
This implementation corresponds to the 68% containment of the angular
distance distribution.
Parameters
----------
events : astropy.table.QTable
Astropy Table object containing the reconstructed events information.
energy_bins: numpy.ndarray(dtype=float, ndim=1)
Bin edges in energy.
energy_type: str
Either "true" or "reco" energy.
Default is "true".
Returns
-------
result : astropy.table.Table
Table containing the 68% containment of the angular distance
distribution per each reconstructed energy bin.
"""
ONE_SIGMA_QUANTILE = norm.cdf(1) - norm.cdf(-1)
# create a table to make use of groupby operations
table = Table(events[[f"{energy_type}_energy", "theta"]])
table["bin_index"] = calculate_bin_indices(
table[f"{energy_type}_energy"].quantity, energy_bins)
n_bins = len(energy_bins) - 1
mask = (table["bin_index"] >= 0) & (table["bin_index"] < n_bins)
result = Table()
result[f"{energy_type}_energy_low"] = energy_bins[:-1]
result[f"{energy_type}_energy_high"] = energy_bins[1:]
result[f"{energy_type}_energy_center"] = 0.5 * (energy_bins[:-1] +
energy_bins[1:])
result["angular_resolution"] = np.nan * u.deg
# use groupby operations to calculate the percentile in each bin
by_bin = table[mask].group_by("bin_index")
index = by_bin.groups.keys["bin_index"]
result["angular_resolution"][index] = by_bin["theta"].groups.aggregate(
lambda x: np.quantile(x, ONE_SIGMA_QUANTILE))
return result
