# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Functions to compute test statistic images."""
import warnings
from itertools import repeat
import numpy as np
import scipy.optimize
from scipy.interpolate import InterpolatedUnivariateSpline
from astropy.coordinates import Angle
from astropy.utils import lazyproperty
import gammapy.utils.parallel as parallel
from gammapy.datasets import Datasets
from gammapy.datasets.map import MapEvaluator
from gammapy.datasets.utils import get_nearest_valid_exposure_position
from gammapy.maps import Map, MapAxis, Maps
from gammapy.modeling.models import PointSpatialModel, PowerLawSpectralModel, SkyModel
from gammapy.stats import cash, 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 (
_generate_scan_values,
_get_default_norm,
_get_norm_scan_values,
estimate_exposure_reco_energy,
)
from .core import FluxMaps
__all__ = ["TSMapEstimator"]
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, parallel.ParallelMixin):
r"""Compute test statistic 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 derivative of the fit statistics using
various root finding algorithms. The approach is described in Appendix A
in Stewart (2009).
Parameters
----------
model : `~gammapy.modeling.models.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, optional
If the test statistic value corresponding to the initial flux estimate is not above
this threshold, the optimizing step is omitted to save computing time. Default is None.
rtol : float
Relative precision of the flux estimate. Used as a stopping criterion for
the norm fit. Default is 0.01.
selection_optional : list of str, optional
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.
* "stat_scan": estimate likelihood profile
Default is None so the optional steps are not executed.
energy_edges : list of `~astropy.units.Quantity`, optional
Edges of the target maps energy bins. The resulting bin edges won't be exactly equal to the input ones,
but rather the closest values to the energy axis edges of the parent dataset.
Default is None: apply the estimator in each energy bin of the parent dataset.
For further explanation see :ref:`estimators`.
sum_over_energy_groups : bool
Whether to sum over the energy groups or fit the norm on the full energy
cube.
norm : `~gammapy.modeling.Parameter` or dict
Norm parameter used for the likelihood profile computation on a fixed norm range.
Only used for "stat_scan" in `selection_optional`.
Default is None and a new parameter is created automatically,
with value=1, name="norm", scan_min=-100, scan_max=100,
and values sampled such as we can probe a 0.1% relative error on the norm.
If a dict is given the entries should be a subset of
`~gammapy.modeling.Parameter` arguments.
n_jobs : int
Number of processes used in parallel for the computation. Default is one,
unless `~gammapy.utils.parallel.N_JOBS_DEFAULT` was modified. The number
of jobs limited to the number of physical CPUs.
parallel_backend : {"multiprocessing", "ray"}
Which backend to use for multiprocessing. Defaults to `~gammapy.utils.parallel.BACKEND_DEFAULT`.
max_niter : int
Maximal number of iterations used by the root finding algorithm.
Default is 100.
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.
Examples
--------
>>> import astropy.units as u
>>> from gammapy.estimators import TSMapEstimator
>>> from gammapy.datasets import MapDataset
>>> from gammapy.modeling.models import (SkyModel, PowerLawSpectralModel,PointSpatialModel)
>>> spatial_model = PointSpatialModel()
>>> spectral_model = PowerLawSpectralModel(amplitude="1e-22 cm-2 s-1 keV-1", index=2)
>>> model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)
>>> dataset = MapDataset.read("$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc.fits.gz")
>>> estimator = TSMapEstimator(
... model, kernel_width="1 deg", energy_edges=[10, 100] * u.GeV, downsampling_factor=4
... )
>>> maps = estimator.run(dataset)
>>> print(maps)
FluxMaps
--------
<BLANKLINE>
geom : WcsGeom
axes : ['lon', 'lat', 'energy']
shape : (np.int64(400), np.int64(200), 1)
quantities : ['ts', 'norm', 'niter', 'norm_err', 'npred', 'npred_excess', 'stat', 'stat_null', 'success']
ref. model : pl
n_sigma : 1
n_sigma_ul : 2
sqrt_ts_threshold_ul : 2
sed type init : likelihood
References
----------
[Stewart2009]_
"""
tag = "TSMapEstimator"
_available_selection_optional = ["errn-errp", "ul", "stat_scan"]
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,
parallel_backend=None,
norm=None,
max_niter=100,
):
if kernel_width is not None:
kernel_width = Angle(kernel_width)
self.kernel_width = kernel_width
self.norm = _get_default_norm(norm, scan_values=_generate_scan_values())
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.parallel_backend = parallel_backend
self.sum_over_energy_groups = sum_over_energy_groups
self.max_niter = max_niter
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,
norm=self.norm,
max_niter=self.max_niter,
)
@property
def selection_all(self):
"""Which quantities are computed."""
selection = [
"ts",
"norm",
"niter",
"norm_err",
"npred",
"npred_excess",
"stat",
"stat_null",
"success",
]
if "stat_scan" in self.selection_optional:
selection += [
"dnde_scan_values",
"stat_scan",
"norm_errp",
"norm_errn",
"norm_ul",
]
else:
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 IRFs at the center of the dataset,
or at the nearest position with non-zero exposure.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Input dataset.
Returns
-------
kernel : `~gammapy.maps.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 the mean non-null exposure
exposure = Map.from_geom(geom, unit=dataset.exposure.unit)
position = get_nearest_valid_exposure_position(
dataset.exposure, geom.center_skydir
)
exposure_position = dataset.exposure.to_region_nd_map(position)
if not np.any(exposure_position.data):
raise ValueError(
"No valid exposure. Impossible to compute kernel for TS Map."
)
exposure.data[...] = exposure_position.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)
if dataset.mask_safe:
flux *= dataset.mask_safe
return flux.sum_over_axes()
[docs]
@staticmethod
def estimate_mask_default(dataset):
"""Compute default mask where to estimate test statistic 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
npred = dataset.npred()
if dataset.mask_safe:
npred *= dataset.mask_safe
background = 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, datasets):
"""Estimate flux and test statistic maps for single dataset.
Parameters
----------
dataset : `~gammapy.datasets.Datasets` or `~gammapy.datasets.MapDataset`
Map dataset or Datasets (list of MapDataset with the same spatial geometry).
"""
maps = [self.estimate_fit_input_maps(dataset=d) for d in datasets]
mask = np.sum([_["mask"].data for _ in maps], axis=0).astype(bool)
if not np.any(mask):
raise ValueError(
"""No valid positions found.
Check that the dataset background is defined and not only zeros,
or that the mask_safe is not all False."
"""
)
x, y = np.where(np.squeeze(mask))
positions = list(zip(x, y))
inputs = zip(
positions,
repeat([_["counts"].data.astype(float) for _ in maps]),
repeat([_["exposure"].data.astype(float) for _ in maps]),
repeat([_["background"].data.astype(float) for _ in maps]),
repeat([_["kernel"].data for _ in maps]),
repeat([_["norm"].data for _ in maps]),
repeat([_["mask_safe"] for _ in maps]),
repeat(self._flux_estimator),
)
results = parallel.run_multiprocessing(
_ts_value,
inputs,
backend=self.parallel_backend,
pool_kwargs=dict(processes=self.n_jobs),
task_name="TS map",
)
result = {}
j, i = zip(*positions)
geom = maps[0]["counts"].geom.squash(axis_name="energy")
energy_axis = geom.axes["energy"]
dnde_ref = self.model.spectral_model(energy_axis.center)
for name in self.selection_all:
if name in ["dnde_scan_values", "stat_scan"]:
norm_bin_axis = MapAxis(
range(len(results[0]["dnde_scan_values"])),
interp="lin",
node_type="center",
name="dnde_bin",
)
axes = geom.axes + [norm_bin_axis]
geom_scan = geom.to_image().to_cube(axes)
if name == "dnde_scan_values":
unit = dnde_ref.unit
factor = dnde_ref.value
else:
unit = ""
factor = 1
m = Map.from_geom(geom_scan, data=np.nan, unit=unit)
m.data[:, 0, j, i] = np.array([_[name] for _ in results]).T * factor
else:
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, datasets):
"""
Run test statistic map estimation.
Requires a MapDataset with counts, exposure and background_model
properly set to run.
Notes
-----
The progress bar can be displayed for this function.
Parameters
----------
dataset : `~gammapy.datasets.Datasets` or `~gammapy.datasets.MapDataset`
Map dataset or Datasets (list of MapDataset with the same spatial geometry).
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.
"""
datasets = Datasets(datasets)
geom_ref = datasets[0].counts.geom
for dataset in datasets:
if dataset.stat_type != "cash":
raise TypeError(
f"{type(dataset)} is not a valid type for {self.__class__}"
)
if dataset.counts.geom.to_image() != geom_ref.to_image():
raise TypeError("Datasets geometries must match")
datasets_models = datasets.models
pad_width = (0, 0)
for dataset in datasets:
pad_width_dataset = self.estimate_pad_width(dataset=dataset)
pad_width = tuple(np.maximum(pad_width, pad_width_dataset))
datasets_padded = Datasets()
for dataset in datasets:
dataset = dataset.pad(pad_width, name=dataset.name)
dataset = dataset.downsample(self.downsampling_factor, name=dataset.name)
datasets_padded.append(dataset)
datasets = datasets_padded
energy_axis = self._get_energy_axis(dataset=datasets[0])
results = []
for energy_min, energy_max in progress_bar(
energy_axis.iter_by_edges, desc="Energy bins"
):
datasets_sliced = datasets.slice_by_energy(
energy_min=energy_min, energy_max=energy_max
)
if datasets_models is not None:
models_sliced = datasets_models._slice_by_energy(
energy_min=energy_min,
energy_max=energy_max,
sum_over_energy_groups=self.sum_over_energy_groups,
)
datasets_sliced.models = models_sliced
result = self.estimate_flux_map(datasets_sliced)
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, self.background, self.model)
def npred(self, norm):
"""Predicted number of counts."""
return self.background + norm * self.model
def stat_sum(self, norm):
"""Statistics sum."""
return cash_sum_cython(self.counts, self.npred(norm))
def stat_derivative(self, norm):
"""Statistics derivative."""
return f_cash_root_cython(norm, self.counts, self.background, self.model)
def stat_2nd_derivative(self, norm):
"""Statistics 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, mask_safe
):
""""""
if mask_safe:
# compute mask_safe weighted kernel for the sum_over_axes case
mask_safe = _extract_array(mask_safe.data, kernel.shape, position)
kernel = (kernel * mask_safe).sum(axis=0, keepdims=True)
with np.errstate(invalid="ignore", divide="ignore"):
kernel /= mask_safe.sum(axis=0, keepdims=True)
kernel[~np.isfinite(kernel)] = 0
counts_cutout = _extract_array(counts, kernel.shape, position)
background_cutout = _extract_array(background, kernel.shape, position)
exposure_cutout = _extract_array(exposure, kernel.shape, position)
model = kernel * exposure_cutout
norm_guess = norm[0, position[0], position[1]]
mask_invalid = (counts_cutout == 0) & (background_cutout == 0) & (model == 0)
return cls(
counts=counts_cutout[~mask_invalid],
background=background_cutout[~mask_invalid],
model=model[~mask_invalid],
norm_guess=norm_guess,
)
# TODO: merge with `FluxEstimator`?
class BrentqFluxEstimator(Estimator):
"""Single parameter flux estimator."""
_available_selection_optional = ["errn-errp", "ul", "stat_scan"]
tag = "BrentqFluxEstimator"
def __init__(
self,
rtol,
n_sigma,
n_sigma_ul,
selection_optional=None,
max_niter=100,
ts_threshold=None,
norm=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
self.norm = norm
def estimate_best_fit(self, dataset):
"""Estimate best fit norm parameter.
Parameters
----------
dataset : `SimpleMapDataset`
Simple map dataset.
Returns
-------
result : dict
Result dictionary 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 dictionary 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 dictionary 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_scan(self, dataset, result):
"""Compute likelihood profile.
Parameters
----------
dataset : `SimpleMapDataset`
Simple map dataset.
Returns
-------
result : dict
Result dictionary including 'stat_scan'.
"""
sparse_norms = _get_norm_scan_values(self.norm, result)
scale = sparse_norms[None, :]
model = dataset.model.ravel()[:, None]
background = dataset.background.ravel()[:, None]
counts = dataset.counts.ravel()[:, None]
stat_scan = cash(counts, model * scale + background)
stat_scan_local = stat_scan.sum(axis=0) - result["stat_null"]
spline = InterpolatedUnivariateSpline(
sparse_norms, stat_scan_local, k=1, ext="raise", check_finite=True
)
norms = np.unique(np.concatenate((sparse_norms, self.norm.scan_values)))
stat_scan = spline(norms)
ts = -stat_scan.min()
ind = stat_scan.argmin()
norm = norms[ind]
maskp = norms > norm
stat_diff = stat_scan - stat_scan.min()
ind = np.abs(stat_diff - self.n_sigma**2)[~maskp].argmin()
norm_errn = norm - norms[~maskp][ind]
ind = np.abs(stat_diff - self.n_sigma**2)[maskp].argmin()
norm_errp = norms[maskp][ind] - norm
ind = np.abs(stat_diff - self.n_sigma_ul**2)[maskp].argmin()
norm_ul = norms[maskp][ind]
norm_err = (norm_errn + norm_errp) / 2
return dict(
ts=ts,
norm=norm,
norm_err=norm_err,
norm_errn=norm_errn,
norm_errp=norm_errp,
norm_ul=norm_ul,
stat_scan=stat_scan_local,
dnde_scan_values=sparse_norms,
)
def estimate_default(self, dataset):
"""Estimate default norm.
Parameters
----------
dataset : `SimpleMapDataset`
Simple map dataset.
Returns
-------
result : dict
Result dictionary 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 dictionary.
"""
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)
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))
if "stat_scan" in self.selection_optional:
result.update(self.estimate_scan(dataset, result))
norm = result["norm"]
result["npred"] = dataset.npred(norm=norm).sum()
result["npred_excess"] = result["npred"] - dataset.npred(norm=0).sum()
result["stat"] = dataset.stat_sum(norm=norm)
return result
def _ts_value(
position, counts, exposure, background, kernel, norm, mask_safe, flux_estimator
):
"""Compute test statistic 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
Test statistic value at the given pixel position.
"""
datasets = []
nd = len(counts)
for idx in range(nd):
datasets.append(
SimpleMapDataset.from_arrays(
counts=counts[idx],
background=background[idx],
exposure=exposure[idx],
norm=norm[idx],
position=position,
kernel=kernel[idx],
mask_safe=mask_safe[idx],
)
)
norm_guess = np.array([d.norm_guess for d in datasets])
mask_valid = np.isfinite(norm_guess)
if np.any(mask_valid):
norm_guess = np.mean(norm_guess[mask_valid])
else:
norm_guess = 1.0
dataset = SimpleMapDataset(
counts=np.concatenate([d.counts for d in datasets]),
background=np.concatenate([d.background for d in datasets]),
model=np.concatenate([d.model for d in datasets]),
norm_guess=norm_guess,
)
return flux_estimator.run(dataset)
