# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function, unicode_literals
from copy import deepcopy
import logging
import numpy as np
from ..stats import significance, significance_on_off
__all__ = ["compute_lima_image", "compute_lima_on_off_image"]
log = logging.getLogger(__name__)
[docs]def compute_lima_image(counts, background, kernel):
"""Compute Li & Ma significance and flux images for known background.
Parameters
----------
counts : `~gammapy.maps.WcsNDMap`
Counts image
background : `~gammapy.maps.WcsNDMap`
Background image
kernel : `astropy.convolution.Kernel2D`
Convolution kernel
Returns
-------
images : dict
Dictionary containing result maps
Keys are: significance, counts, background and excess
See Also
--------
gammapy.stats.significance
"""
# Kernel is modified later make a copy here
kernel = deepcopy(kernel)
kernel.normalize("peak")
counts_conv = counts.convolve(kernel.array).data
background_conv = background.convolve(kernel.array).data
excess_conv = counts_conv - background_conv
significance_conv = significance(counts_conv, background_conv, method="lima")
return {
"significance": counts.copy(data=significance_conv),
"counts": counts.copy(data=counts_conv),
"background": counts.copy(data=background_conv),
"excess": counts.copy(data=excess_conv),
}
[docs]def compute_lima_on_off_image(n_on, n_off, a_on, a_off, kernel):
"""Compute Li & Ma significance and flux images for on-off observations.
Parameters
----------
n_on : `~gammapy.maps.WcsNDMap`
Counts image
n_off : `~gammapy.maps.WcsNDMap`
Off counts image
a_on : `~gammapy.maps.WcsNDMap`
Relative background efficiency in the on region
a_off : `~gammapy.maps.WcsNDMap`
Relative background efficiency in the off region
kernel : `astropy.convolution.Kernel2D`
Convolution kernel
Returns
-------
images : dict
Dictionary containing result maps
Keys are: significance, n_on, background, excess, alpha
See also
--------
gammapy.stats.significance_on_off
"""
# Kernel is modified later make a copy here
kernel = deepcopy(kernel)
kernel.normalize("peak")
n_on_conv = n_on.convolve(kernel.array).data
a_on_conv = a_on.convolve(kernel.array).data
alpha_conv = a_on_conv / a_off.data
significance_conv = significance_on_off(
n_on_conv, n_off.data, alpha_conv, method="lima"
)
with np.errstate(invalid="ignore"):
background_conv = alpha_conv * n_off.data
excess_conv = n_on_conv - background_conv
return {
"significance": n_on.copy(data=significance_conv),
"n_on": n_on.copy(data=n_on_conv),
"background": n_on.copy(data=background_conv),
"excess": n_on.copy(data=excess_conv),
"alpha": n_on.copy(data=alpha_conv),
}