# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Functions to compute TS images."""
import contextlib
import functools
import logging
import warnings
from multiprocessing import Pool
import numpy as np
import scipy.optimize
from astropy.coordinates import Angle
from astropy.utils import lazyproperty
from gammapy.datasets.map import MapEvaluator
from gammapy.maps import Map, Maps
from gammapy.modeling.models import PointSpatialModel, PowerLawSpectralModel, SkyModel
from gammapy.stats import cash_sum_cython, f_cash_root_cython, norm_bounds_cython
from gammapy.utils.array import shape_2N, symmetric_crop_pad_width
from gammapy.utils.pbar import progress_bar
from gammapy.utils.roots import find_roots
from ..core import Estimator
from ..utils import estimate_exposure_reco_energy
from .core import FluxMaps
__all__ = ["TSMapEstimator"]
log = logging.getLogger(__name__)
def _extract_array(array, shape, position):
"""Helper function to extract parts of a larger array.
Simple implementation of an array extract function , because
`~astropy.ndata.utils.extract_array` introduces too much overhead.`
Parameters
----------
array : `~numpy.ndarray`
The array from which to extract.
shape : tuple or int
The shape of the extracted array.
position : tuple of numbers or number
The position of the small array's center with respect to the
large array.
"""
x_width = shape[2] // 2
y_width = shape[1] // 2
y_lo = position[0] - y_width
y_hi = position[0] + y_width + 1
x_lo = position[1] - x_width
x_hi = position[1] + x_width + 1
return array[:, y_lo:y_hi, x_lo:x_hi]
[docs]class TSMapEstimator(Estimator):
r"""Compute TS map from a MapDataset using different optimization methods.
The map is computed fitting by a single parameter norm fit. The fit is
simplified by finding roots of the the derivative of the fit statistics using
various root finding algorithms. The approach is described in Appendix A
in Stewart (2009).
Parameters
----------
model : `~gammapy.modeling.model.SkyModel`
Source model kernel. If set to None,
assume spatail model: point source model, PointSpatialModel.
spectral model: PowerLawSpectral Model of index 2
kernel_width : `~astropy.coordinates.Angle`
Width of the kernel to use: the kernel will be truncated at this size
n_sigma : int
Number of sigma for flux error. Default is 1.
n_sigma_ul : int
Number of sigma for flux upper limits. Default is 2.
downsampling_factor : int
Sample down the input maps to speed up the computation. Only integer
values that are a multiple of 2 are allowed. Note that the kernel is
not sampled down, but must be provided with the downsampled bin size.
threshold : float (None)
If the TS value corresponding to the initial flux estimate is not above
this threshold, the optimizing step is omitted to save computing time.
rtol : float (0.01)
Relative precision of the flux estimate. Used as a stopping criterion for
the norm fit.
selection_optional : list of str
Which maps to compute besides TS, sqrt(TS), flux and symmetric error on flux.
Available options are:
* "all": all the optional steps are executed
* "errn-errp": estimate asymmetric error on flux.
* "ul": estimate upper limits on flux.
Default is None so the optional steps are not executed.
energy_edges : `~astropy.units.Quantity`
Energy edges of the maps bins.
sum_over_energy_groups : bool
Whether to sum over the energy groups or fit the norm on the full energy
cube.
n_jobs : int
Number of processes used in parallel for the computation.
Notes
-----
Negative :math:`TS` values are defined as following:
.. math::
TS = \left \{
\begin{array}{ll}
-TS \text{ if } F < 0 \\
TS \text{ else}
\end{array}
\right.
Where :math:`F` is the fitted flux norm.
References
----------
[Stewart2009]_
"""
tag = "TSMapEstimator"
_available_selection_optional = ["errn-errp", "ul"]
def __init__(
self,
model=None,
kernel_width=None,
downsampling_factor=None,
n_sigma=1,
n_sigma_ul=2,
threshold=None,
rtol=0.01,
selection_optional=None,
energy_edges=None,
sum_over_energy_groups=True,
n_jobs=None,
):
if kernel_width is not None:
kernel_width = Angle(kernel_width)
self.kernel_width = kernel_width
if model is None:
model = SkyModel(
spectral_model=PowerLawSpectralModel(),
spatial_model=PointSpatialModel(),
name="ts-kernel",
)
self.model = model
self.downsampling_factor = downsampling_factor
self.n_sigma = n_sigma
self.n_sigma_ul = n_sigma_ul
self.threshold = threshold
self.rtol = rtol
self.n_jobs = n_jobs
self.sum_over_energy_groups = sum_over_energy_groups
self.selection_optional = selection_optional
self.energy_edges = energy_edges
self._flux_estimator = BrentqFluxEstimator(
rtol=self.rtol,
n_sigma=self.n_sigma,
n_sigma_ul=self.n_sigma_ul,
selection_optional=selection_optional,
ts_threshold=threshold,
)
@property
def selection_all(self):
"""Which quantities are computed"""
selection = [
"ts",
"norm",
"niter",
"norm_err",
"npred",
"npred_excess",
"stat",
"stat_null",
"success",
]
if "errn-errp" in self.selection_optional:
selection += ["norm_errp", "norm_errn"]
if "ul" in self.selection_optional:
selection += ["norm_ul"]
return selection
[docs] def estimate_kernel(self, dataset):
"""Get the convolution kernel for the input dataset.
Convolves the model with the PSFKernel at the center of the dataset.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Input dataset.
Returns
-------
kernel : `Map`
Kernel map
"""
geom = dataset.exposure.geom
if self.kernel_width is not None:
geom = geom.to_odd_npix(max_radius=self.kernel_width / 2)
model = self.model.copy()
model.spatial_model.position = geom.center_skydir
# Creating exposure map with exposure at map center
exposure = Map.from_geom(geom, unit="cm2 s1")
exposure_center = dataset.exposure.to_region_nd_map(geom.center_skydir)
exposure.data[...] = exposure_center.data
# We use global evaluation mode to not modify the geometry
evaluator = MapEvaluator(model=model)
evaluator.update(
exposure=exposure,
psf=dataset.psf,
edisp=dataset.edisp,
geom=dataset.counts.geom,
mask=dataset.mask_image,
)
kernel = evaluator.compute_npred()
kernel.data /= kernel.data.sum()
return kernel
[docs] def estimate_flux_default(self, dataset, kernel=None, exposure=None):
"""Estimate default flux map using a given kernel.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Input dataset.
kernel : `~gammapy.maps.WcsNDMap`
Source model kernel.
exposure : `~gammapy.maps.WcsNDMap`
Exposure map on reconstructed energy.
Returns
-------
flux : `~gammapy.maps.WcsNDMap`
Approximate flux map.
"""
if exposure is None:
exposure = estimate_exposure_reco_energy(dataset, self.model.spectral_model)
if kernel is None:
kernel = self.estimate_kernel(dataset=dataset)
kernel = kernel.data / np.sum(kernel.data ** 2)
with np.errstate(invalid="ignore", divide="ignore"):
flux = (dataset.counts - dataset.npred()) / exposure
flux.data = np.nan_to_num(flux.data)
flux.quantity = flux.quantity.to("1 / (cm2 s)")
flux = flux.convolve(kernel)
return flux.sum_over_axes()
[docs] @staticmethod
def estimate_mask_default(dataset):
"""Compute default mask where to estimate TS values.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Input dataset.
Returns
-------
mask : `WcsNDMap`
Mask map.
"""
geom = dataset.counts.geom.to_image()
mask = np.ones(geom.data_shape, dtype=bool)
if dataset.mask is not None:
mask &= dataset.mask.reduce_over_axes(func=np.logical_or, keepdims=False)
# in some image there are pixels, which have exposure, but zero
# background, which doesn't make sense and causes the TS computation
# to fail, this is a temporary fix
background = dataset.npred().sum_over_axes(keepdims=False)
mask[background.data == 0] = False
return Map.from_geom(data=mask, geom=geom)
[docs] def estimate_pad_width(self, dataset, kernel=None):
"""Estimate pad width of the dataset
Parameters
----------
dataset : `MapDataset`
Input MapDataset.
kernel : `WcsNDMap`
Source model kernel.
Returns
-------
pad_width : tuple
Padding width
"""
if kernel is None:
kernel = self.estimate_kernel(dataset=dataset)
geom = dataset.counts.geom.to_image()
geom_kernel = kernel.geom.to_image()
pad_width = np.array(geom_kernel.data_shape) // 2
if self.downsampling_factor and self.downsampling_factor > 1:
shape = tuple(np.array(geom.data_shape) + 2 * pad_width)
pad_width = symmetric_crop_pad_width(geom.data_shape, shape_2N(shape))[0]
return tuple(pad_width)
[docs] def estimate_flux_map(self, dataset):
"""Estimate flux and ts maps for single dataset
Parameters
----------
dataset : `MapDataset`
Map dataset
"""
maps = self.estimate_fit_input_maps(dataset=dataset)
wrap = functools.partial(
_ts_value,
counts=maps["counts"].data.astype(float),
exposure=maps["exposure"].data.astype(float),
background=maps["background"].data.astype(float),
kernel=maps["kernel"].data,
norm=maps["norm"].data,
flux_estimator=self._flux_estimator,
)
x, y = np.where(np.squeeze(maps["mask"].data))
positions = list(zip(x, y))
if self.n_jobs is None:
results = list(map(wrap, positions))
else:
with contextlib.closing(Pool(processes=self.n_jobs)) as pool:
log.info("Using {} jobs to compute TS map.".format(self.n_jobs))
results = pool.map(wrap, positions)
pool.join()
result = {}
j, i = zip(*positions)
geom = maps["counts"].geom.squash(axis_name="energy")
for name in self.selection_all:
m = Map.from_geom(geom=geom, data=np.nan, unit="")
m.data[0, j, i] = [_[name] for _ in results]
result[name] = m
return result
[docs] def run(self, dataset):
"""
Run TS map estimation.
Requires a MapDataset with counts, exposure and background_model
properly set to run.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Input MapDataset.
Returns
-------
maps : dict
Dictionary containing result maps. Keys are:
* ts : delta TS map
* sqrt_ts : sqrt(delta TS), or significance map
* flux : flux map
* flux_err : symmetric error map
* flux_ul : upper limit map
"""
dataset_models = dataset.models
pad_width = self.estimate_pad_width(dataset=dataset)
dataset = dataset.pad(pad_width, name=dataset.name)
dataset = dataset.downsample(self.downsampling_factor, name=dataset.name)
energy_axis = self._get_energy_axis(dataset=dataset)
results = []
for energy_min, energy_max in progress_bar(
energy_axis.iter_by_edges, desc="Energy bins"
):
sliced_dataset = dataset.slice_by_energy(
energy_min=energy_min, energy_max=energy_max, name=dataset.name
)
if self.sum_over_energy_groups:
sliced_dataset = sliced_dataset.to_image(name=dataset.name)
sliced_dataset.models = dataset_models
result = self.estimate_flux_map(sliced_dataset)
results.append(result)
maps = Maps()
for name in self.selection_all:
m = Map.from_stack(maps=[_[name] for _ in results], axis_name="energy")
order = 0 if name in ["niter", "success"] else 1
m = m.upsample(
factor=self.downsampling_factor, preserve_counts=False, order=order
)
maps[name] = m.crop(crop_width=pad_width)
maps["success"].data = maps["success"].data.astype(bool)
meta = {"n_sigma": self.n_sigma, "n_sigma_ul": self.n_sigma_ul}
return FluxMaps(
data=maps,
reference_model=self.model,
gti=dataset.gti,
meta=meta,
)
# TODO: merge with MapDataset?
class SimpleMapDataset:
"""Simple map dataset
Parameters
----------
counts : `~numpy.ndarray`
Counts array
background : `~numpy.ndarray`
Background array
model : `~numpy.ndarray`
Kernel array
"""
def __init__(self, model, counts, background, norm_guess):
self.model = model
self.counts = counts
self.background = background
self.norm_guess = norm_guess
@lazyproperty
def norm_bounds(self):
"""Bounds for x"""
return norm_bounds_cython(
self.counts.ravel(), self.background.ravel(), self.model.ravel()
)
def npred(self, norm):
"""Predicted number of counts"""
return self.background + norm * self.model
def stat_sum(self, norm):
"""Stat sum"""
return cash_sum_cython(self.counts.ravel(), self.npred(norm).ravel())
def stat_derivative(self, norm):
"""Stat derivative"""
return f_cash_root_cython(
norm, self.counts.ravel(), self.background.ravel(), self.model.ravel()
)
def stat_2nd_derivative(self, norm):
"""Stat 2nd derivative"""
term_top = self.model ** 2 * self.counts
term_bottom = (self.background + norm * self.model) ** 2
mask = term_bottom == 0
return (term_top / term_bottom)[~mask].sum()
@classmethod
def from_arrays(cls, counts, background, exposure, norm, position, kernel):
""""""
counts_cutout = _extract_array(counts, kernel.shape, position)
background_cutout = _extract_array(background, kernel.shape, position)
exposure_cutout = _extract_array(exposure, kernel.shape, position)
norm_guess = norm[0, position[0], position[1]]
return cls(
counts=counts_cutout,
background=background_cutout,
model=kernel * exposure_cutout,
norm_guess=norm_guess,
)
# TODO: merge with `FluxEstimator`?
class BrentqFluxEstimator(Estimator):
"""Single parameter flux estimator"""
_available_selection_optional = ["errn-errp", "ul"]
tag = "BrentqFluxEstimator"
def __init__(
self,
rtol,
n_sigma,
n_sigma_ul,
selection_optional=None,
max_niter=20,
ts_threshold=None,
):
self.rtol = rtol
self.n_sigma = n_sigma
self.n_sigma_ul = n_sigma_ul
self.selection_optional = selection_optional
self.max_niter = max_niter
self.ts_threshold = ts_threshold
def estimate_best_fit(self, dataset):
"""Estimate best fit norm parameter
Parameters
----------
dataset : `SimpleMapDataset`
Simple map dataset
Returns
-------
result : dict
Result dict including 'norm' and 'norm_err'
"""
# Compute norm bounds and assert counts > 0
norm_min, norm_max, norm_min_total = dataset.norm_bounds
if not dataset.counts.sum() > 0:
norm, niter, success = norm_min_total, 0, True
else:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
try:
# here we do not use avoid find_roots for performance
result_fit = scipy.optimize.brentq(
f=dataset.stat_derivative,
a=norm_min,
b=norm_max,
maxiter=self.max_niter,
full_output=True,
rtol=self.rtol,
)
norm = max(result_fit[0], norm_min_total)
niter = result_fit[1].iterations
success = result_fit[1].converged
except (RuntimeError, ValueError):
norm, niter, success = norm_min_total, self.max_niter, False
with np.errstate(invalid="ignore", divide="ignore"):
norm_err = np.sqrt(1 / dataset.stat_2nd_derivative(norm)) * self.n_sigma
stat = dataset.stat_sum(norm=norm)
stat_null = dataset.stat_sum(norm=0)
return {
"norm": norm,
"norm_err": norm_err,
"niter": niter,
"ts": stat_null - stat,
"stat": stat,
"stat_null": stat_null,
"success": success,
}
def _confidence(self, dataset, n_sigma, result, positive):
stat_best = result["stat"]
norm = result["norm"]
norm_err = result["norm_err"]
def ts_diff(x):
return (stat_best + n_sigma ** 2) - dataset.stat_sum(x)
if positive:
min_norm = norm
max_norm = norm + 1e2 * norm_err
factor = 1
else:
min_norm = norm - 1e2 * norm_err
max_norm = norm
factor = -1
with warnings.catch_warnings():
warnings.simplefilter("ignore")
roots, res = find_roots(
ts_diff,
[min_norm],
[max_norm],
nbin=1,
maxiter=self.max_niter,
rtol=self.rtol,
)
# Where the root finding fails NaN is set as norm
return (roots[0] - norm) * factor
def estimate_ul(self, dataset, result):
"""Compute upper limit using likelihood profile method.
Parameters
----------
dataset : `SimpleMapDataset`
Simple map dataset
Returns
-------
result : dict
Result dict including 'norm_ul'
"""
flux_ul = result["norm"] + self._confidence(
dataset=dataset, n_sigma=self.n_sigma_ul, result=result, positive=True
)
return {"norm_ul": flux_ul}
def estimate_errn_errp(self, dataset, result):
"""Compute norm errors using likelihood profile method.
Parameters
----------
dataset : `SimpleMapDataset`
Simple map dataset
Returns
-------
result : dict
Result dict including 'norm_errp' and 'norm_errn'
"""
flux_errn = self._confidence(
dataset=dataset, result=result, n_sigma=self.n_sigma, positive=False
)
flux_errp = self._confidence(
dataset=dataset, result=result, n_sigma=self.n_sigma, positive=True
)
return {"norm_errn": flux_errn, "norm_errp": flux_errp}
def estimate_default(self, dataset):
"""Estimate default norm
Parameters
----------
dataset : `SimpleMapDataset`
Simple map dataset
Returns
-------
result : dict
Result dict including 'norm', 'norm_err' and "niter"
"""
norm = dataset.norm_guess
with np.errstate(invalid="ignore", divide="ignore"):
norm_err = np.sqrt(1 / dataset.stat_2nd_derivative(norm)) * self.n_sigma
stat = dataset.stat_sum(norm=norm)
stat_null = dataset.stat_sum(norm=0)
return {
"norm": norm,
"norm_err": norm_err,
"niter": 0,
"ts": stat_null - stat,
"stat": stat,
"stat_null": stat_null,
"success": True,
}
def run(self, dataset):
"""Run flux estimator
Parameters
----------
dataset : `SimpleMapDataset`
Simple map dataset
Returns
-------
result : dict
Result dict
"""
if self.ts_threshold is not None:
result = self.estimate_default(dataset)
if result["ts"] > self.ts_threshold:
result = self.estimate_best_fit(dataset)
else:
result = self.estimate_best_fit(dataset)
norm = result["norm"]
result["npred"] = dataset.npred(norm=norm).sum()
result["npred_excess"] = result["npred"] - dataset.npred(norm=0).sum()
if "ul" in self.selection_optional:
result.update(self.estimate_ul(dataset, result))
if "errn-errp" in self.selection_optional:
result.update(self.estimate_errn_errp(dataset, result))
return result
def _ts_value(position, counts, exposure, background, kernel, norm, flux_estimator):
"""Compute TS value at a given pixel position.
Uses approach described in Stewart (2009).
Parameters
----------
position : tuple (i, j)
Pixel position.
counts : `~numpy.ndarray`
Counts image
background : `~numpy.ndarray`
Background image
exposure : `~numpy.ndarray`
Exposure image
kernel : `astropy.convolution.Kernel2D`
Source model kernel
norm : `~numpy.ndarray`
Norm image. The flux value at the given pixel position is used as
starting value for the minimization.
Returns
-------
TS : float
TS value at the given pixel position.
"""
dataset = SimpleMapDataset.from_arrays(
counts=counts,
background=background,
exposure=exposure,
kernel=kernel,
position=position,
norm=norm,
)
return flux_estimator.run(dataset)