# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import numpy as np
import astropy.units as u
from astropy.io import fits
from astropy.table import Table
from regions import CircleSkyRegion
import matplotlib.pyplot as plt
from gammapy.data import GTI
from gammapy.irf import EDispKernelMap, EDispMap, PSFKernel, PSFMap
from gammapy.maps import Map, MapAxis
from gammapy.modeling.models import DatasetModels, FoVBackgroundModel
from gammapy.stats import (
CashCountsStatistic,
WStatCountsStatistic,
cash,
cash_sum_cython,
get_wstat_mu_bkg,
wstat,
)
from gammapy.utils.fits import HDULocation, LazyFitsData
from gammapy.utils.random import get_random_state
from gammapy.utils.scripts import make_name, make_path
from gammapy.utils.table import hstack_columns
from .core import Dataset
from .evaluator import MapEvaluator
from .utils import get_axes
__all__ = ["MapDataset", "MapDatasetOnOff", "create_map_dataset_geoms"]
log = logging.getLogger(__name__)
RAD_MAX = 0.66
RAD_AXIS_DEFAULT = MapAxis.from_bounds(
0, RAD_MAX, nbin=66, node_type="edges", name="rad", unit="deg"
)
MIGRA_AXIS_DEFAULT = MapAxis.from_bounds(
0.2, 5, nbin=48, node_type="edges", name="migra"
)
BINSZ_IRF_DEFAULT = 0.2
EVALUATION_MODE = "local"
USE_NPRED_CACHE = True
[docs]def create_map_dataset_geoms(
geom,
energy_axis_true=None,
migra_axis=None,
rad_axis=None,
binsz_irf=None,
):
"""Create map geometries for a `MapDataset`
Parameters
----------
geom : `~gammapy.maps.WcsGeom`
Reference target geometry in reco energy, used for counts and background maps
energy_axis_true : `~gammapy.maps.MapAxis`
True energy axis used for IRF maps
migra_axis : `~gammapy.maps.MapAxis`
If set, this provides the migration axis for the energy dispersion map.
If not set, an EDispKernelMap is produced instead. Default is None
rad_axis : `~gammapy.maps.MapAxis`
Rad axis for the psf map
binsz_irf : float
IRF Map pixel size in degrees.
Returns
-------
geoms : dict
Dict with map geometries.
"""
rad_axis = rad_axis or RAD_AXIS_DEFAULT
if energy_axis_true is not None:
energy_axis_true.assert_name("energy_true")
else:
energy_axis_true = geom.axes["energy"].copy(name="energy_true")
binsz_irf = binsz_irf or BINSZ_IRF_DEFAULT
geom_image = geom.to_image()
geom_exposure = geom_image.to_cube([energy_axis_true])
geom_irf = geom_image.to_binsz(binsz=binsz_irf)
geom_psf = geom_irf.to_cube([rad_axis, energy_axis_true])
if migra_axis:
geom_edisp = geom_irf.to_cube([migra_axis, energy_axis_true])
else:
geom_edisp = geom_irf.to_cube([geom.axes["energy"], energy_axis_true])
return {
"geom": geom,
"geom_exposure": geom_exposure,
"geom_psf": geom_psf,
"geom_edisp": geom_edisp,
}
[docs]class MapDataset(Dataset):
"""
Bundle together binned counts, background, IRFs, models and compute a likelihood.
Uses Cash statistics by default.
Parameters
----------
models : `~gammapy.modeling.models.Models`
Source sky models.
counts : `~gammapy.maps.WcsNDMap` or `~gammapy.utils.fits.HDULocation`
Counts cube
exposure : `~gammapy.maps.WcsNDMap` or `~gammapy.utils.fits.HDULocation`
Exposure cube
background : `~gammapy.maps.WcsNDMap` or `~gammapy.utils.fits.HDULocation`
Background cube
mask_fit : `~gammapy.maps.WcsNDMap` or `~gammapy.utils.fits.HDULocation`
Mask to apply to the likelihood for fitting.
psf : `~gammapy.irf.PSFMap` or `~gammapy.utils.fits.HDULocation`
PSF kernel
edisp : `~gammapy.irf.EDispKernel` or `~gammapy.irf.EDispMap` or `~gammapy.utils.fits.HDULocation`
Energy dispersion kernel
mask_safe : `~gammapy.maps.WcsNDMap` or `~gammapy.utils.fits.HDULocation`
Mask defining the safe data range.
gti : `~gammapy.data.GTI`
GTI of the observation or union of GTI if it is a stacked observation
meta_table : `~astropy.table.Table`
Table listing information on observations used to create the dataset.
One line per observation for stacked datasets.
If an `HDULocation` is passed the map is loaded lazily. This means the
map data is only loaded in memory as the corresponding data attribute
on the MapDataset is accessed. If it was accessed once it is cached for
the next time.
Examples
--------
>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz", name="cta_dataset")
>>> print(dataset)
MapDataset
----------
<BLANKLINE>
Name : cta_dataset
<BLANKLINE>
Total counts : 104317
Total background counts : 91507.70
Total excess counts : 12809.30
<BLANKLINE>
Predicted counts : 91507.69
Predicted background counts : 91507.70
Predicted excess counts : nan
<BLANKLINE>
Exposure min : 6.28e+07 m2 s
Exposure max : 1.90e+10 m2 s
<BLANKLINE>
Number of total bins : 768000
Number of fit bins : 691680
<BLANKLINE>
Fit statistic type : cash
Fit statistic value (-2 log(L)) : nan
<BLANKLINE>
Number of models : 0
Number of parameters : 0
Number of free parameters : 0
See Also
--------
MapDatasetOnOff, SpectrumDataset, FluxPointsDataset
"""
stat_type = "cash"
tag = "MapDataset"
counts = LazyFitsData(cache=True)
exposure = LazyFitsData(cache=True)
edisp = LazyFitsData(cache=True)
background = LazyFitsData(cache=True)
psf = LazyFitsData(cache=True)
mask_fit = LazyFitsData(cache=True)
mask_safe = LazyFitsData(cache=True)
_lazy_data_members = [
"counts",
"exposure",
"edisp",
"psf",
"mask_fit",
"mask_safe",
"background",
]
def __init__(
self,
models=None,
counts=None,
exposure=None,
background=None,
psf=None,
edisp=None,
mask_safe=None,
mask_fit=None,
gti=None,
meta_table=None,
name=None,
):
self._name = make_name(name)
self._evaluators = {}
self.counts = counts
self.exposure = exposure
self.background = background
self._background_cached = None
self._background_parameters_cached = None
self.mask_fit = mask_fit
if psf and not isinstance(psf, (PSFMap, HDULocation)):
raise ValueError(
f"'psf' must be a 'PSFMap' or `HDULocation` object, got {type(psf)}"
)
self.psf = psf
if edisp and not isinstance(edisp, (EDispMap, EDispKernelMap, HDULocation)):
raise ValueError(
f"'edisp' must be a 'EDispMap', `EDispKernelMap` or 'HDULocation' object, got {type(edisp)}"
)
self.edisp = edisp
self.mask_safe = mask_safe
self.gti = gti
self.models = models
self.meta_table = meta_table
# TODO: keep or remove?
@property
def background_model(self):
try:
return self.models[f"{self.name}-bkg"]
except (ValueError, TypeError):
pass
def __str__(self):
str_ = f"{self.__class__.__name__}\n"
str_ += "-" * len(self.__class__.__name__) + "\n"
str_ += "\n"
str_ += "\t{:32}: {{name}} \n\n".format("Name")
str_ += "\t{:32}: {{counts:.0f}} \n".format("Total counts")
str_ += "\t{:32}: {{background:.2f}}\n".format("Total background counts")
str_ += "\t{:32}: {{excess:.2f}}\n\n".format("Total excess counts")
str_ += "\t{:32}: {{npred:.2f}}\n".format("Predicted counts")
str_ += "\t{:32}: {{npred_background:.2f}}\n".format(
"Predicted background counts"
)
str_ += "\t{:32}: {{npred_signal:.2f}}\n\n".format("Predicted excess counts")
str_ += "\t{:32}: {{exposure_min:.2e}}\n".format("Exposure min")
str_ += "\t{:32}: {{exposure_max:.2e}}\n\n".format("Exposure max")
str_ += "\t{:32}: {{n_bins}} \n".format("Number of total bins")
str_ += "\t{:32}: {{n_fit_bins}} \n\n".format("Number of fit bins")
# likelihood section
str_ += "\t{:32}: {{stat_type}}\n".format("Fit statistic type")
str_ += "\t{:32}: {{stat_sum:.2f}}\n\n".format(
"Fit statistic value (-2 log(L))"
)
info = self.info_dict()
str_ = str_.format(**info)
# model section
n_models, n_pars, n_free_pars = 0, 0, 0
if self.models is not None:
n_models = len(self.models)
n_pars = len(self.models.parameters)
n_free_pars = len(self.models.parameters.free_parameters)
str_ += "\t{:32}: {} \n".format("Number of models", n_models)
str_ += "\t{:32}: {}\n".format("Number of parameters", n_pars)
str_ += "\t{:32}: {}\n\n".format("Number of free parameters", n_free_pars)
if self.models is not None:
str_ += "\t" + "\n\t".join(str(self.models).split("\n")[2:])
return str_.expandtabs(tabsize=2)
@property
def geoms(self):
"""Map geometries
Returns
-------
geoms : dict
Dict of map geometries involved in the dataset.
"""
geoms = {}
geoms["geom"] = self._geom
if self.exposure:
geoms["geom_exposure"] = self.exposure.geom
if self.psf:
geoms["geom_psf"] = self.psf.psf_map.geom
if self.edisp:
geoms["geom_edisp"] = self.edisp.edisp_map.geom
return geoms
@property
def models(self):
"""Models set on the dataset (`~gammapy.modeling.models.Models`)."""
return self._models
@property
def excess(self):
"""Observed excess: counts-background"""
return self.counts - self.background
@models.setter
def models(self, models):
"""Models setter"""
self._evaluators = {}
if models is not None:
models = DatasetModels(models)
models = models.select(datasets_names=self.name)
for model in models:
if not isinstance(model, FoVBackgroundModel):
evaluator = MapEvaluator(
model=model,
evaluation_mode=EVALUATION_MODE,
gti=self.gti,
use_cache=USE_NPRED_CACHE,
)
self._evaluators[model.name] = evaluator
self._models = models
@property
def evaluators(self):
"""Model evaluators"""
return self._evaluators
@property
def _geom(self):
"""Main analysis geometry"""
if self.counts is not None:
return self.counts.geom
elif self.background is not None:
return self.background.geom
elif self.mask_safe is not None:
return self.mask_safe.geom
elif self.mask_fit is not None:
return self.mask_fit.geom
else:
raise ValueError(
"Either 'counts', 'background', 'mask_fit'"
" or 'mask_safe' must be defined."
)
@property
def data_shape(self):
"""Shape of the counts or background data (tuple)"""
return self._geom.data_shape
def _energy_range(self, mask_map=None):
"""Compute the energy range maps with or without the fit mask."""
geom = self._geom
energy = geom.axes["energy"].edges
e_i = geom.axes.index_data("energy")
geom = geom.drop("energy")
if mask_map is not None:
mask = mask_map.data
if mask.any():
idx = mask.argmax(e_i)
energy_min = energy.value[idx]
mask_nan = ~mask.any(e_i)
energy_min[mask_nan] = np.nan
mask = np.flip(mask, e_i)
idx = mask.argmax(e_i)
energy_max = energy.value[::-1][idx]
energy_max[mask_nan] = np.nan
else:
energy_min = np.full(geom.data_shape, np.nan)
energy_max = energy_min.copy()
else:
data_shape = geom.data_shape
energy_min = np.full(data_shape, energy.value[0])
energy_max = np.full(data_shape, energy.value[-1])
map_min = Map.from_geom(geom, data=energy_min, unit=energy.unit)
map_max = Map.from_geom(geom, data=energy_max, unit=energy.unit)
return map_min, map_max
@property
def energy_range(self):
"""Energy range maps defined by the full mask (mask_safe and mask_fit)."""
return self._energy_range(self.mask)
@property
def energy_range_safe(self):
"""Energy range maps defined by the mask_safe only."""
return self._energy_range(self.mask_safe)
@property
def energy_range_fit(self):
"""Energy range maps defined by the mask_fit only."""
return self._energy_range(self.mask_fit)
@property
def energy_range_total(self):
"""Largest energy range among all pixels, defined by the full mask (mask_safe and mask_fit)."""
energy_min_map, energy_max_map = self.energy_range
return np.nanmin(energy_min_map.quantity), np.nanmax(energy_max_map.quantity)
[docs] def npred(self):
"""Total predicted source and background counts
Returns
-------
npred : `Map`
Total predicted counts
"""
npred_total = self.npred_signal()
if self.background:
npred_total += self.npred_background()
npred_total.data[npred_total.data < 0.0] = 0
return npred_total
[docs] def npred_background(self):
"""Predicted background counts
The predicted background counts depend on the parameters
of the `FoVBackgroundModel` defined in the dataset.
Returns
-------
npred_background : `Map`
Predicted counts from the background.
"""
background = self.background
if self.background_model and background:
if self._background_parameters_changed:
values = self.background_model.evaluate_geom(geom=self.background.geom)
if self._background_cached is None:
self._background_cached = background * values
else:
self._background_cached.quantity = (
background.quantity * values.value
)
return self._background_cached
else:
return background
return background
def _background_parameters_changed(self):
values = self.background_model.parameters.value
# TODO: possibly allow for a tolerance here?
changed = ~np.all(self._background_parameters_cached == values)
if changed:
self._background_parameters_cached = values
return changed
[docs] def npred_signal(self, model_name=None):
"""Model predicted signal counts.
If a model name is passed, predicted counts from that component are returned.
Else, the total signal counts are returned.
Parameters
----------
model_name: str
Name of SkyModel for which to compute the npred for.
If none, the sum of all components (minus the background model)
is returned
Returns
-------
npred_sig: `gammapy.maps.Map`
Map of the predicted signal counts
"""
npred_total = Map.from_geom(self._geom, dtype=float)
evaluators = self.evaluators
if model_name is not None:
evaluators = {model_name: self.evaluators[model_name]}
for evaluator in evaluators.values():
if evaluator.needs_update:
evaluator.update(
self.exposure,
self.psf,
self.edisp,
self._geom,
self.mask_image,
)
if evaluator.contributes:
npred = evaluator.compute_npred()
npred_total.stack(npred)
return npred_total
[docs] @classmethod
def from_geoms(
cls,
geom,
geom_exposure=None,
geom_psf=None,
geom_edisp=None,
reference_time="2000-01-01",
name=None,
**kwargs,
):
"""
Create a MapDataset object with zero filled maps according to the specified geometries
Parameters
----------
geom : `Geom`
geometry for the counts and background maps
geom_exposure : `Geom`
geometry for the exposure map
geom_psf : `Geom`
geometry for the psf map
geom_edisp : `Geom`
geometry for the energy dispersion kernel map.
If geom_edisp has a migra axis, this will create an EDispMap instead.
reference_time : `~astropy.time.Time`
the reference time to use in GTI definition
name : str
Name of the returned dataset.
Returns
-------
dataset : `MapDataset` or `SpectrumDataset`
A dataset containing zero filled maps
"""
name = make_name(name)
kwargs = kwargs.copy()
kwargs["name"] = name
kwargs["counts"] = Map.from_geom(geom, unit="")
kwargs["background"] = Map.from_geom(geom, unit="")
if geom_exposure:
kwargs["exposure"] = Map.from_geom(geom_exposure, unit="m2 s")
if geom_edisp:
if "energy" in geom_edisp.axes.names:
kwargs["edisp"] = EDispKernelMap.from_geom(geom_edisp)
else:
kwargs["edisp"] = EDispMap.from_geom(geom_edisp)
if geom_psf:
kwargs["psf"] = PSFMap.from_geom(geom_psf)
kwargs.setdefault(
"gti", GTI.create([] * u.s, [] * u.s, reference_time=reference_time)
)
kwargs["mask_safe"] = Map.from_geom(geom, unit="", dtype=bool)
return cls(**kwargs)
[docs] @classmethod
def create(
cls,
geom,
energy_axis_true=None,
migra_axis=None,
rad_axis=None,
binsz_irf=None,
reference_time="2000-01-01",
name=None,
meta_table=None,
**kwargs,
):
"""Create a MapDataset object with zero filled maps.
Parameters
----------
geom : `~gammapy.maps.WcsGeom`
Reference target geometry in reco energy, used for counts and background maps
energy_axis_true : `~gammapy.maps.MapAxis`
True energy axis used for IRF maps
migra_axis : `~gammapy.maps.MapAxis`
If set, this provides the migration axis for the energy dispersion map.
If not set, an EDispKernelMap is produced instead. Default is None
rad_axis : `~gammapy.maps.MapAxis`
Rad axis for the psf map
binsz_irf : float
IRF Map pixel size in degrees.
reference_time : `~astropy.time.Time`
the reference time to use in GTI definition
name : str
Name of the returned dataset.
meta_table : `~astropy.table.Table`
Table listing information on observations used to create the dataset.
One line per observation for stacked datasets.
Returns
-------
empty_maps : `MapDataset`
A MapDataset containing zero filled maps
Examples
--------
>>> from gammapy.datasets import MapDataset
>>> from gammapy.maps import WcsGeom, MapAxis
>>> energy_axis = MapAxis.from_energy_bounds(1.0, 10.0, 4, unit="TeV")
>>> energy_axis_true = MapAxis.from_energy_bounds( 0.5, 20, 10, unit="TeV", name="energy_true")
>>> geom = WcsGeom.create(skydir=(83.633, 22.014), binsz=0.02, width=(2, 2), frame="icrs", proj="CAR", axes=[energy_axis])
>>> empty = MapDataset.create(geom=geom, energy_axis_true=energy_axis_true, name="empty")
"""
geoms = create_map_dataset_geoms(
geom=geom,
energy_axis_true=energy_axis_true,
rad_axis=rad_axis,
migra_axis=migra_axis,
binsz_irf=binsz_irf,
)
kwargs.update(geoms)
return cls.from_geoms(reference_time=reference_time, name=name, **kwargs)
@property
def mask_safe_image(self):
"""Reduced mask safe"""
if self.mask_safe is None:
return None
return self.mask_safe.reduce_over_axes(func=np.logical_or)
@property
def mask_fit_image(self):
"""Reduced mask fit"""
if self.mask_fit is None:
return None
return self.mask_fit.reduce_over_axes(func=np.logical_or)
@property
def mask_image(self):
"""Reduced mask"""
if self.mask is None:
mask = Map.from_geom(self._geom.to_image(), dtype=bool)
mask.data |= True
return mask
return self.mask.reduce_over_axes(func=np.logical_or)
@property
def mask_safe_psf(self):
"""Mask safe for psf maps"""
if self.mask_safe is None or self.psf is None:
return None
geom = self.psf.psf_map.geom.squash("energy_true").squash("rad")
mask_safe_psf = self.mask_safe_image.interp_to_geom(geom.to_image())
return mask_safe_psf.to_cube(geom.axes)
@property
def mask_safe_edisp(self):
"""Mask safe for edisp maps"""
if self.mask_safe is None or self.edisp is None:
return None
if self.mask_safe.geom.is_region:
return self.mask_safe
geom = self.edisp.edisp_map.geom.squash("energy_true")
if "migra" in geom.axes.names:
geom = geom.squash("migra")
mask_safe_edisp = self.mask_safe_image.interp_to_geom(geom.to_image())
return mask_safe_edisp.to_cube(geom.axes)
return self.mask_safe.interp_to_geom(geom)
[docs] def to_masked(self, name=None, nan_to_num=True):
"""Return masked dataset
Parameters
----------
name : str
Name of the masked dataset.
nan_to_num: bool
Non-finite values are replaced by zero if True (default).
Returns
-------
dataset : `MapDataset` or `SpectrumDataset`
Masked dataset
"""
dataset = self.__class__.from_geoms(**self.geoms, name=name)
dataset.stack(self, nan_to_num=nan_to_num)
return dataset
[docs] def stack(self, other, nan_to_num=True):
r"""Stack another dataset in place. The original dataset is modified.
Safe mask is applied to compute the stacked counts data. Counts outside
each dataset safe mask are lost.
The stacking of 2 datasets is implemented as follows. Here, :math:`k`
denotes a bin in reconstructed energy and :math:`j = {1,2}` is the dataset number
The ``mask_safe`` of each dataset is defined as:
.. math::
\epsilon_{jk} =\left\{\begin{array}{cl} 1, &
\mbox{if bin k is inside the thresholds}\\ 0, &
\mbox{otherwise} \end{array}\right.
Then the total ``counts`` and model background ``bkg`` are computed according to:
.. math::
\overline{\mathrm{n_{on}}}_k = \mathrm{n_{on}}_{1k} \cdot \epsilon_{1k} +
\mathrm{n_{on}}_{2k} \cdot \epsilon_{2k}
\overline{bkg}_k = bkg_{1k} \cdot \epsilon_{1k} +
bkg_{2k} \cdot \epsilon_{2k}
The stacked ``safe_mask`` is then:
.. math::
\overline{\epsilon_k} = \epsilon_{1k} OR \epsilon_{2k}
Parameters
----------
other: `~gammapy.datasets.MapDataset` or `~gammapy.datasets.MapDatasetOnOff`
Map dataset to be stacked with this one. If other is an on-off
dataset alpha * counts_off is used as a background model.
nan_to_num: bool
Non-finite values are replaced by zero if True (default).
"""
if self.counts and other.counts:
self.counts.stack(
other.counts, weights=other.mask_safe, nan_to_num=nan_to_num
)
if self.exposure and other.exposure:
self.exposure.stack(
other.exposure, weights=other.mask_safe_image, nan_to_num=nan_to_num
)
# TODO: check whether this can be improved e.g. handling this in GTI
if "livetime" in other.exposure.meta and np.any(other.mask_safe_image):
if "livetime" in self.exposure.meta:
self.exposure.meta["livetime"] += other.exposure.meta["livetime"]
else:
self.exposure.meta["livetime"] = other.exposure.meta[
"livetime"
].copy()
if self.stat_type == "cash":
if self.background and other.background:
background = self.npred_background() * self.mask_safe
background.stack(
other.npred_background(),
weights=other.mask_safe,
nan_to_num=nan_to_num,
)
self.background = background
if self.psf and other.psf:
self.psf.stack(other.psf, weights=other.mask_safe_psf)
if self.edisp and other.edisp:
self.edisp.stack(other.edisp, weights=other.mask_safe_edisp)
if self.mask_safe and other.mask_safe:
self.mask_safe.stack(other.mask_safe)
if self.gti and other.gti:
self.gti.stack(other.gti)
self.gti = self.gti.union()
if self.meta_table and other.meta_table:
self.meta_table = hstack_columns(self.meta_table, other.meta_table)
elif other.meta_table:
self.meta_table = other.meta_table.copy()
[docs] def stat_array(self):
"""Likelihood per bin given the current model parameters"""
return cash(n_on=self.counts.data, mu_on=self.npred().data)
[docs] def residuals(self, method="diff", **kwargs):
"""Compute residuals map.
Parameters
----------
method: {"diff", "diff/model", "diff/sqrt(model)"}
Method used to compute the residuals. Available options are:
- "diff" (default): data - model
- "diff/model": (data - model) / model
- "diff/sqrt(model)": (data - model) / sqrt(model)
**kwargs : dict
Keyword arguments forwarded to `Map.smooth()`
Returns
-------
residuals : `gammapy.maps.Map`
Residual map.
"""
npred, counts = self.npred(), self.counts.copy()
if self.mask:
npred = npred * self.mask
counts = counts * self.mask
if kwargs:
kwargs.setdefault("mode", "constant")
kwargs.setdefault("width", "0.1 deg")
kwargs.setdefault("kernel", "gauss")
with np.errstate(invalid="ignore", divide="ignore"):
npred = npred.smooth(**kwargs)
counts = counts.smooth(**kwargs)
if self.mask:
mask = self.mask.smooth(**kwargs)
npred /= mask
counts /= mask
residuals = self._compute_residuals(counts, npred, method=method)
if self.mask:
residuals.data[~self.mask.data] = np.nan
return residuals
[docs] def plot_residuals_spatial(
self,
ax=None,
method="diff",
smooth_kernel="gauss",
smooth_radius="0.1 deg",
**kwargs,
):
"""Plot spatial residuals.
The normalization used for the residuals computation can be controlled
using the method parameter.
Parameters
----------
ax : `~astropy.visualization.wcsaxes.WCSAxes`
Axes to plot on.
method : {"diff", "diff/model", "diff/sqrt(model)"}
Normalization used to compute the residuals, see `MapDataset.residuals`.
smooth_kernel : {"gauss", "box"}
Kernel shape.
smooth_radius: `~astropy.units.Quantity`, str or float
Smoothing width given as quantity or float. If a float is given, it
is interpreted as smoothing width in pixels.
**kwargs : dict
Keyword arguments passed to `~matplotlib.axes.Axes.imshow`.
Returns
-------
ax : `~astropy.visualization.wcsaxes.WCSAxes`
WCSAxes object.
Examples
--------
>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> kwargs = {"cmap": "RdBu_r", "vmin":-5, "vmax":5, "add_cbar": True}
>>> dataset.plot_residuals_spatial(method="diff/sqrt(model)", **kwargs) # doctest: +SKIP
"""
counts, npred = self.counts.copy(), self.npred()
if counts.geom.is_region:
raise ValueError("Cannot plot spatial residuals for RegionNDMap")
if self.mask is not None:
counts *= self.mask
npred *= self.mask
counts_spatial = counts.sum_over_axes().smooth(
width=smooth_radius, kernel=smooth_kernel
)
npred_spatial = npred.sum_over_axes().smooth(
width=smooth_radius, kernel=smooth_kernel
)
residuals = self._compute_residuals(counts_spatial, npred_spatial, method)
if self.mask_safe is not None:
mask = self.mask_safe.reduce_over_axes(func=np.logical_or, keepdims=True)
residuals.data[~mask.data] = np.nan
kwargs.setdefault("add_cbar", True)
kwargs.setdefault("cmap", "coolwarm")
kwargs.setdefault("vmin", -5)
kwargs.setdefault("vmax", 5)
ax = residuals.plot(ax, **kwargs)
return ax
[docs] def plot_residuals_spectral(self, ax=None, method="diff", region=None, **kwargs):
"""Plot spectral residuals.
The residuals are extracted from the provided region, and the normalization
used for its computation can be controlled using the method parameter.
The error bars are computed using the uncertainty on the excess with a symmetric assumption.
Parameters
----------
ax : `~matplotlib.axes.Axes`
Axes to plot on.
method : {"diff", "diff/sqrt(model)"}
Normalization used to compute the residuals, see `SpectrumDataset.residuals`.
region: `~regions.SkyRegion` (required)
Target sky region.
**kwargs : dict
Keyword arguments passed to `~matplotlib.axes.Axes.errorbar`.
Returns
-------
ax : `~matplotlib.axes.Axes`
Axes object.
Examples
--------
>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> kwargs = {"markerfacecolor": "blue", "markersize":8, "marker":'s', } #plot big blue squares
>>> dataset.plot_residuals_spectral(method="diff/sqrt(model)", **kwargs) # doctest: +SKIP
"""
counts, npred = self.counts.copy(), self.npred()
if self.mask is None:
mask = self.counts.copy()
mask.data = 1
else:
mask = self.mask
counts *= mask
npred *= mask
counts_spec = counts.get_spectrum(region)
npred_spec = npred.get_spectrum(region)
residuals = self._compute_residuals(counts_spec, npred_spec, method)
if self.stat_type == "wstat":
counts_off = (self.counts_off * mask).get_spectrum(region)
with np.errstate(invalid="ignore"):
alpha = (self.background * mask).get_spectrum(region) / counts_off
mu_sig = (self.npred_signal() * mask).get_spectrum(region)
stat = WStatCountsStatistic(
n_on=counts_spec,
n_off=counts_off,
alpha=alpha,
mu_sig=mu_sig,
)
elif self.stat_type == "cash":
stat = CashCountsStatistic(counts_spec.data, npred_spec.data)
excess_error = stat.error
if method == "diff":
yerr = excess_error
elif method == "diff/sqrt(model)":
yerr = excess_error / np.sqrt(npred_spec.data)
else:
raise ValueError(
'Invalid method, choose between "diff" and "diff/sqrt(model)"'
)
kwargs.setdefault("color", kwargs.pop("c", "black"))
ax = residuals.plot(ax, yerr=yerr, **kwargs)
ax.axhline(0, color=kwargs["color"], lw=0.5)
label = self._residuals_labels[method]
ax.set_ylabel(f"Residuals ({label})")
ax.set_yscale("linear")
ymin = 1.05 * np.nanmin(residuals.data - yerr)
ymax = 1.05 * np.nanmax(residuals.data + yerr)
ax.set_ylim(ymin, ymax)
return ax
[docs] def plot_residuals(
self,
ax_spatial=None,
ax_spectral=None,
kwargs_spatial=None,
kwargs_spectral=None,
):
"""Plot spatial and spectral residuals in two panels.
Calls `~MapDataset.plot_residuals_spatial` and `~MapDataset.plot_residuals_spectral`.
The spectral residuals are extracted from the provided region, and the
normalization used for its computation can be controlled using the method
parameter. The region outline is overlaid on the residuals map. If no region is passed,
the residuals are computed for the entire map
Parameters
----------
ax_spatial : `~astropy.visualization.wcsaxes.WCSAxes`
Axes to plot spatial residuals on.
ax_spectral : `~matplotlib.axes.Axes`
Axes to plot spectral residuals on.
kwargs_spatial : dict
Keyword arguments passed to `~MapDataset.plot_residuals_spatial`.
kwargs_spectral : dict
Keyword arguments passed to `~MapDataset.plot_residuals_spectral`.
The region should be passed as a dictionary key
Returns
-------
ax_spatial, ax_spectral : `~astropy.visualization.wcsaxes.WCSAxes`, `~matplotlib.axes.Axes`
Spatial and spectral residuals plots.
Examples
--------
>>> from regions import CircleSkyRegion
>>> from astropy.coordinates import SkyCoord
>>> import astropy.units as u
>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> reg = CircleSkyRegion(SkyCoord(0,0, unit="deg", frame="galactic"), radius=1.0*u.deg)
>>> kwargs_spatial = {"cmap": "RdBu_r", "vmin":-5, "vmax":5, "add_cbar": True}
>>> kwargs_spectral = {"region":reg, "markerfacecolor": "blue", "markersize":8, "marker":'s'}
>>> dataset.plot_residuals(kwargs_spatial=kwargs_spatial, kwargs_spectral=kwargs_spectral) # doctest: +SKIP
"""
ax_spatial, ax_spectral = get_axes(
ax_spatial,
ax_spectral,
12,
4,
[1, 2, 1],
[1, 2, 2],
{"projection": self._geom.to_image().wcs},
)
kwargs_spatial = kwargs_spatial or {}
kwargs_spectral = kwargs_spectral or {}
self.plot_residuals_spatial(ax_spatial, **kwargs_spatial)
self.plot_residuals_spectral(ax_spectral, **kwargs_spectral)
# Overlay spectral extraction region on the spatial residuals
region = kwargs_spectral.get("region")
if region is not None:
pix_region = region.to_pixel(self._geom.to_image().wcs)
pix_region.plot(ax=ax_spatial)
return ax_spatial, ax_spectral
[docs] def stat_sum(self):
"""Total likelihood given the current model parameters."""
counts, npred = self.counts.data.astype(float), self.npred().data
if self.mask is not None:
return cash_sum_cython(counts[self.mask.data], npred[self.mask.data])
else:
return cash_sum_cython(counts.ravel(), npred.ravel())
[docs] def fake(self, random_state="random-seed"):
"""Simulate fake counts for the current model and reduced IRFs.
This method overwrites the counts defined on the dataset object.
Parameters
----------
random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`}
Defines random number generator initialisation.
Passed to `~gammapy.utils.random.get_random_state`.
"""
random_state = get_random_state(random_state)
npred = self.npred()
data = np.nan_to_num(npred.data, copy=True, nan=0.0, posinf=0.0, neginf=0.0)
npred.data = random_state.poisson(data)
self.counts = npred
[docs] def to_hdulist(self):
"""Convert map dataset to list of HDUs.
Returns
-------
hdulist : `~astropy.io.fits.HDUList`
Map dataset list of HDUs.
"""
# TODO: what todo about the model and background model parameters?
exclude_primary = slice(1, None)
hdu_primary = fits.PrimaryHDU()
hdulist = fits.HDUList([hdu_primary])
if self.counts is not None:
hdulist += self.counts.to_hdulist(hdu="counts")[exclude_primary]
if self.exposure is not None:
hdulist += self.exposure.to_hdulist(hdu="exposure")[exclude_primary]
if self.background is not None:
hdulist += self.background.to_hdulist(hdu="background")[exclude_primary]
if self.edisp is not None:
hdulist += self.edisp.to_hdulist()[exclude_primary]
if self.psf is not None:
hdulist += self.psf.to_hdulist()[exclude_primary]
if self.mask_safe is not None:
hdulist += self.mask_safe.to_hdulist(hdu="mask_safe")[exclude_primary]
if self.mask_fit is not None:
hdulist += self.mask_fit.to_hdulist(hdu="mask_fit")[exclude_primary]
if self.gti is not None:
hdulist.append(fits.BinTableHDU(self.gti.table, name="GTI"))
if self.meta_table is not None:
hdulist.append(fits.BinTableHDU(self.meta_table, name="META_TABLE"))
return hdulist
[docs] @classmethod
def from_hdulist(cls, hdulist, name=None, lazy=False, format="gadf"):
"""Create map dataset from list of HDUs.
Parameters
----------
hdulist : `~astropy.io.fits.HDUList`
List of HDUs.
name : str
Name of the new dataset.
format : {"gadf"}
Format the hdulist is given in.
Returns
-------
dataset : `MapDataset`
Map dataset.
"""
name = make_name(name)
kwargs = {"name": name}
if "COUNTS" in hdulist:
kwargs["counts"] = Map.from_hdulist(hdulist, hdu="counts", format=format)
if "EXPOSURE" in hdulist:
exposure = Map.from_hdulist(hdulist, hdu="exposure", format=format)
if exposure.geom.axes[0].name == "energy":
exposure.geom.axes[0].name = "energy_true"
kwargs["exposure"] = exposure
if "BACKGROUND" in hdulist:
kwargs["background"] = Map.from_hdulist(
hdulist, hdu="background", format=format
)
if "EDISP" in hdulist:
edisp_map = Map.from_hdulist(hdulist, hdu="edisp", format=format)
try:
exposure_map = Map.from_hdulist(
hdulist, hdu="edisp_exposure", format=format
)
except KeyError:
exposure_map = None
if edisp_map.geom.axes[0].name == "energy":
kwargs["edisp"] = EDispKernelMap(edisp_map, exposure_map)
else:
kwargs["edisp"] = EDispMap(edisp_map, exposure_map)
if "PSF" in hdulist:
psf_map = Map.from_hdulist(hdulist, hdu="psf", format=format)
try:
exposure_map = Map.from_hdulist(
hdulist, hdu="psf_exposure", format=format
)
except KeyError:
exposure_map = None
kwargs["psf"] = PSFMap(psf_map, exposure_map)
if "MASK_SAFE" in hdulist:
mask_safe = Map.from_hdulist(hdulist, hdu="mask_safe", format=format)
mask_safe.data = mask_safe.data.astype(bool)
kwargs["mask_safe"] = mask_safe
if "MASK_FIT" in hdulist:
mask_fit = Map.from_hdulist(hdulist, hdu="mask_fit", format=format)
mask_fit.data = mask_fit.data.astype(bool)
kwargs["mask_fit"] = mask_fit
if "GTI" in hdulist:
gti = GTI(Table.read(hdulist, hdu="GTI"))
kwargs["gti"] = gti
if "META_TABLE" in hdulist:
meta_table = Table.read(hdulist, hdu="META_TABLE")
kwargs["meta_table"] = meta_table
return cls(**kwargs)
[docs] def write(self, filename, overwrite=False):
"""Write Dataset to file.
A MapDataset is serialised using the GADF format with a WCS geometry.
A SpectrumDataset uses the same format, with a RegionGeom.
Parameters
----------
filename : str
Filename to write to.
overwrite : bool
Overwrite file if it exists.
"""
self.to_hdulist().writeto(str(make_path(filename)), overwrite=overwrite)
@classmethod
def _read_lazy(cls, name, filename, cache, format=format):
kwargs = {"name": name}
try:
kwargs["gti"] = GTI.read(filename)
except KeyError:
pass
path = make_path(filename)
for hdu_name in ["counts", "exposure", "mask_fit", "mask_safe", "background"]:
kwargs[hdu_name] = HDULocation(
hdu_class="map",
file_dir=path.parent,
file_name=path.name,
hdu_name=hdu_name.upper(),
cache=cache,
format=format,
)
kwargs["edisp"] = HDULocation(
hdu_class="edisp_kernel_map",
file_dir=path.parent,
file_name=path.name,
hdu_name="EDISP",
cache=cache,
format=format,
)
kwargs["psf"] = HDULocation(
hdu_class="psf_map",
file_dir=path.parent,
file_name=path.name,
hdu_name="PSF",
cache=cache,
format=format,
)
return cls(**kwargs)
[docs] @classmethod
def read(cls, filename, name=None, lazy=False, cache=True, format="gadf"):
"""Read a dataset from file.
Parameters
----------
filename : str
Filename to read from.
name : str
Name of the new dataset.
lazy : bool
Whether to lazy load data into memory
cache : bool
Whether to cache the data after loading.
format : {"gadf"}
Format of the dataset file.
Returns
-------
dataset : `MapDataset`
Map dataset.
"""
name = make_name(name)
if lazy:
return cls._read_lazy(
name=name, filename=filename, cache=cache, format=format
)
else:
with fits.open(str(make_path(filename)), memmap=False) as hdulist:
return cls.from_hdulist(hdulist, name=name, format=format)
[docs] @classmethod
def from_dict(cls, data, lazy=False, cache=True):
"""Create from dicts and models list generated from YAML serialization."""
filename = make_path(data["filename"])
dataset = cls.read(filename, name=data["name"], lazy=lazy, cache=cache)
return dataset
@property
def _counts_statistic(self):
"""Counts statistics of the dataset."""
return CashCountsStatistic(self.counts, self.background)
[docs] def info_dict(self, in_safe_data_range=True):
"""Info dict with summary statistics, summed over energy
Parameters
----------
in_safe_data_range : bool
Whether to sum only in the safe energy range
Returns
-------
info_dict : dict
Dictionary with summary info.
"""
info = {}
info["name"] = self.name
if self.mask_safe and in_safe_data_range:
mask = self.mask_safe.data.astype(bool)
else:
mask = slice(None)
counts = 0
background, excess, sqrt_ts = np.nan, np.nan, np.nan
if self.counts:
summed_stat = self._counts_statistic[mask].sum()
counts = summed_stat.n_on
if self.background:
background = summed_stat.n_bkg
excess = summed_stat.n_sig
sqrt_ts = summed_stat.sqrt_ts
info["counts"] = int(counts)
info["excess"] = float(excess)
info["sqrt_ts"] = sqrt_ts
info["background"] = float(background)
npred = np.nan
if self.models or not np.isnan(background):
npred = self.npred().data[mask].sum()
info["npred"] = float(npred)
npred_background = np.nan
if self.background:
npred_background = self.npred_background().data[mask].sum()
info["npred_background"] = float(npred_background)
npred_signal = np.nan
if self.models:
npred_signal = self.npred_signal().data[mask].sum()
info["npred_signal"] = float(npred_signal)
exposure_min = np.nan * u.Unit("cm s")
exposure_max = np.nan * u.Unit("cm s")
livetime = np.nan * u.s
if self.exposure is not None:
mask_exposure = self.exposure.data > 0
if self.mask_safe is not None:
mask_spatial = self.mask_safe.reduce_over_axes(func=np.logical_or).data
mask_exposure = mask_exposure & mask_spatial[np.newaxis, :, :]
if not mask_exposure.any():
mask_exposure = slice(None)
exposure_min = np.min(self.exposure.quantity[mask_exposure])
exposure_max = np.max(self.exposure.quantity[mask_exposure])
livetime = self.exposure.meta.get("livetime", np.nan * u.s).copy()
info["exposure_min"] = exposure_min.item()
info["exposure_max"] = exposure_max.item()
info["livetime"] = livetime
ontime = u.Quantity(np.nan, "s")
if self.gti:
ontime = self.gti.time_sum
info["ontime"] = ontime
info["counts_rate"] = info["counts"] / info["livetime"]
info["background_rate"] = info["background"] / info["livetime"]
info["excess_rate"] = info["excess"] / info["livetime"]
# data section
n_bins = 0
if self.counts is not None:
n_bins = self.counts.data.size
info["n_bins"] = int(n_bins)
n_fit_bins = 0
if self.mask is not None:
n_fit_bins = np.sum(self.mask.data)
info["n_fit_bins"] = int(n_fit_bins)
info["stat_type"] = self.stat_type
stat_sum = np.nan
if self.counts is not None and self.models is not None:
stat_sum = self.stat_sum()
info["stat_sum"] = float(stat_sum)
return info
[docs] def to_spectrum_dataset(self, on_region, containment_correction=False, name=None):
"""Return a ~gammapy.datasets.SpectrumDataset from on_region.
Counts and background are summed in the on_region. Exposure is taken
from the average exposure.
The energy dispersion kernel is obtained at the on_region center.
Only regions with centers are supported.
The model is not exported to the ~gammapy.datasets.SpectrumDataset.
It must be set after the dataset extraction.
Parameters
----------
on_region : `~regions.SkyRegion`
the input ON region on which to extract the spectrum
containment_correction : bool
Apply containment correction for point sources and circular on regions
name : str
Name of the new dataset.
Returns
-------
dataset : `~gammapy.datasets.SpectrumDataset`
the resulting reduced dataset
"""
from .spectrum import SpectrumDataset
dataset = self.to_region_map_dataset(region=on_region, name=name)
if containment_correction:
if not isinstance(on_region, CircleSkyRegion):
raise TypeError(
"Containment correction is only supported for" " `CircleSkyRegion`."
)
elif self.psf is None or isinstance(self.psf, PSFKernel):
raise ValueError("No PSFMap set. Containment correction impossible")
else:
geom = dataset.exposure.geom
energy_true = geom.axes["energy_true"].center
containment = self.psf.containment(
position=on_region.center,
energy_true=energy_true,
rad=on_region.radius,
)
dataset.exposure.quantity *= containment.reshape(geom.data_shape)
kwargs = {"name": name}
for key in [
"counts",
"edisp",
"mask_safe",
"mask_fit",
"exposure",
"gti",
"meta_table",
]:
kwargs[key] = getattr(dataset, key)
if self.stat_type == "cash":
kwargs["background"] = dataset.background
return SpectrumDataset(**kwargs)
[docs] def to_region_map_dataset(self, region, name=None):
"""Integrate the map dataset in a given region.
Counts and background of the dataset are integrated in the given region,
taking the safe mask into accounts. The exposure is averaged in the
region again taking the safe mask into account. The PSF and energy
dispersion kernel are taken at the center of the region.
Parameters
----------
region : `~regions.SkyRegion`
Region from which to extract the spectrum
name : str
Name of the new dataset.
Returns
-------
dataset : `~gammapy.datasets.MapDataset`
the resulting reduced dataset
"""
name = make_name(name)
kwargs = {"gti": self.gti, "name": name, "meta_table": self.meta_table}
if self.mask_safe:
kwargs["mask_safe"] = self.mask_safe.to_region_nd_map(region, func=np.any)
if self.mask_fit:
kwargs["mask_fit"] = self.mask_fit.to_region_nd_map(region, func=np.any)
if self.counts:
kwargs["counts"] = self.counts.to_region_nd_map(
region, np.sum, weights=self.mask_safe
)
if self.stat_type == "cash" and self.background:
kwargs["background"] = self.background.to_region_nd_map(
region, func=np.sum, weights=self.mask_safe
)
if self.exposure:
kwargs["exposure"] = self.exposure.to_region_nd_map(region, func=np.mean)
region = region.center if region else None
# TODO: Compute average psf in region
if self.psf:
kwargs["psf"] = self.psf.to_region_nd_map(region)
# TODO: Compute average edisp in region
if self.edisp is not None:
kwargs["edisp"] = self.edisp.to_region_nd_map(region)
return self.__class__(**kwargs)
[docs] def cutout(self, position, width, mode="trim", name=None):
"""Cutout map dataset.
Parameters
----------
position : `~astropy.coordinates.SkyCoord`
Center position of the cutout region.
width : tuple of `~astropy.coordinates.Angle`
Angular sizes of the region in (lon, lat) in that specific order.
If only one value is passed, a square region is extracted.
mode : {'trim', 'partial', 'strict'}
Mode option for Cutout2D, for details see `~astropy.nddata.utils.Cutout2D`.
name : str
Name of the new dataset.
Returns
-------
cutout : `MapDataset`
Cutout map dataset.
"""
name = make_name(name)
kwargs = {"gti": self.gti, "name": name, "meta_table": self.meta_table}
cutout_kwargs = {"position": position, "width": width, "mode": mode}
if self.counts is not None:
kwargs["counts"] = self.counts.cutout(**cutout_kwargs)
if self.exposure is not None:
kwargs["exposure"] = self.exposure.cutout(**cutout_kwargs)
if self.background is not None and self.stat_type == "cash":
kwargs["background"] = self.background.cutout(**cutout_kwargs)
if self.edisp is not None:
kwargs["edisp"] = self.edisp.cutout(**cutout_kwargs)
if self.psf is not None:
kwargs["psf"] = self.psf.cutout(**cutout_kwargs)
if self.mask_safe is not None:
kwargs["mask_safe"] = self.mask_safe.cutout(**cutout_kwargs)
if self.mask_fit is not None:
kwargs["mask_fit"] = self.mask_fit.cutout(**cutout_kwargs)
return self.__class__(**kwargs)
[docs] def downsample(self, factor, axis_name=None, name=None):
"""Downsample map dataset.
The PSFMap and EDispKernelMap are not downsampled, except if
a corresponding axis is given.
Parameters
----------
factor : int
Downsampling factor.
axis_name : str
Which non-spatial axis to downsample. By default only spatial axes are downsampled.
name : str
Name of the downsampled dataset.
Returns
-------
dataset : `MapDataset` or `SpectrumDataset`
Downsampled map dataset.
"""
name = make_name(name)
kwargs = {"gti": self.gti, "name": name, "meta_table": self.meta_table}
if self.counts is not None:
kwargs["counts"] = self.counts.downsample(
factor=factor,
preserve_counts=True,
axis_name=axis_name,
weights=self.mask_safe,
)
if self.exposure is not None:
if axis_name is None:
kwargs["exposure"] = self.exposure.downsample(
factor=factor, preserve_counts=False, axis_name=None
)
else:
kwargs["exposure"] = self.exposure.copy()
if self.background is not None and self.stat_type == "cash":
kwargs["background"] = self.background.downsample(
factor=factor, axis_name=axis_name, weights=self.mask_safe
)
if self.edisp is not None:
if axis_name is not None:
kwargs["edisp"] = self.edisp.downsample(
factor=factor, axis_name=axis_name, weights=self.mask_safe_edisp
)
else:
kwargs["edisp"] = self.edisp.copy()
if self.psf is not None:
kwargs["psf"] = self.psf.copy()
if self.mask_safe is not None:
kwargs["mask_safe"] = self.mask_safe.downsample(
factor=factor, preserve_counts=False, axis_name=axis_name
)
if self.mask_fit is not None:
kwargs["mask_fit"] = self.mask_fit.downsample(
factor=factor, preserve_counts=False, axis_name=axis_name
)
return self.__class__(**kwargs)
[docs] def pad(self, pad_width, mode="constant", name=None):
"""Pad the spatial dimensions of the dataset.
The padding only applies to counts, masks, background and exposure.
Counts, background and masks are padded with zeros, exposure is padded with edge value.
Parameters
----------
pad_width : {sequence, array_like, int}
Number of pixels padded to the edges of each axis.
name : str
Name of the padded dataset.
Returns
-------
dataset : `MapDataset`
Padded map dataset.
"""
name = make_name(name)
kwargs = {"gti": self.gti, "name": name, "meta_table": self.meta_table}
if self.counts is not None:
kwargs["counts"] = self.counts.pad(pad_width=pad_width, mode=mode)
if self.exposure is not None:
kwargs["exposure"] = self.exposure.pad(pad_width=pad_width, mode=mode)
if self.background is not None:
kwargs["background"] = self.background.pad(pad_width=pad_width, mode=mode)
if self.edisp is not None:
kwargs["edisp"] = self.edisp.copy()
if self.psf is not None:
kwargs["psf"] = self.psf.copy()
if self.mask_safe is not None:
kwargs["mask_safe"] = self.mask_safe.pad(pad_width=pad_width, mode=mode)
if self.mask_fit is not None:
kwargs["mask_fit"] = self.mask_fit.pad(pad_width=pad_width, mode=mode)
return self.__class__(**kwargs)
[docs] def slice_by_idx(self, slices, name=None):
"""Slice sub dataset.
The slicing only applies to the maps that define the corresponding axes.
Parameters
----------
slices : dict
Dict of axes names and integers or `slice` object pairs. Contains one
element for each non-spatial dimension. For integer indexing the
corresponding axes is dropped from the map. Axes not specified in the
dict are kept unchanged.
name : str
Name of the sliced dataset.
Returns
-------
dataset : `MapDataset` or `SpectrumDataset`
Sliced dataset
Examples
--------
>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> slices = {"energy": slice(0, 3)} #to get the first 3 energy slices
>>> sliced = dataset.slice_by_idx(slices)
>>> print(sliced.geoms["geom"])
WcsGeom
axes : ['lon', 'lat', 'energy']
shape : (320, 240, 3)
ndim : 3
frame : galactic
projection : CAR
center : 0.0 deg, 0.0 deg
width : 8.0 deg x 6.0 deg
wcs ref : 0.0 deg, 0.0 deg
"""
name = make_name(name)
kwargs = {"gti": self.gti, "name": name, "meta_table": self.meta_table}
if self.counts is not None:
kwargs["counts"] = self.counts.slice_by_idx(slices=slices)
if self.exposure is not None:
kwargs["exposure"] = self.exposure.slice_by_idx(slices=slices)
if self.background is not None and self.stat_type == "cash":
kwargs["background"] = self.background.slice_by_idx(slices=slices)
if self.edisp is not None:
kwargs["edisp"] = self.edisp.slice_by_idx(slices=slices)
if self.psf is not None:
kwargs["psf"] = self.psf.slice_by_idx(slices=slices)
if self.mask_safe is not None:
kwargs["mask_safe"] = self.mask_safe.slice_by_idx(slices=slices)
if self.mask_fit is not None:
kwargs["mask_fit"] = self.mask_fit.slice_by_idx(slices=slices)
return self.__class__(**kwargs)
[docs] def slice_by_energy(self, energy_min=None, energy_max=None, name=None):
"""Select and slice datasets in energy range
Parameters
----------
energy_min, energy_max : `~astropy.units.Quantity`
Energy bounds to compute the flux point for.
name : str
Name of the sliced dataset.
Returns
-------
dataset : `MapDataset`
Sliced Dataset
Examples
--------
>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> sliced = dataset.slice_by_energy(energy_min="1 TeV", energy_max="5 TeV")
>>> sliced.data_shape
(3, 240, 320)
"""
name = make_name(name)
energy_axis = self._geom.axes["energy"]
if energy_min is None:
energy_min = energy_axis.bounds[0]
if energy_max is None:
energy_max = energy_axis.bounds[1]
energy_min, energy_max = u.Quantity(energy_min), u.Quantity(energy_max)
group = energy_axis.group_table(edges=[energy_min, energy_max])
is_normal = group["bin_type"] == "normal "
group = group[is_normal]
slices = {
"energy": slice(int(group["idx_min"][0]), int(group["idx_max"][0]) + 1)
}
return self.slice_by_idx(slices, name=name)
[docs] def reset_data_cache(self):
"""Reset data cache to free memory space"""
for name in self._lazy_data_members:
if self.__dict__.pop(name, False):
log.info(f"Clearing {name} cache for dataset {self.name}")
[docs] def resample_energy_axis(self, energy_axis, name=None):
"""Resample MapDataset over new reco energy axis.
Counts are summed taking into account safe mask.
Parameters
----------
energy_axis : `~gammapy.maps.MapAxis`
New reconstructed energy axis.
name: str
Name of the new dataset.
Returns
-------
dataset: `MapDataset` or `SpectrumDataset`
Resampled dataset.
"""
name = make_name(name)
kwargs = {"gti": self.gti, "name": name, "meta_table": self.meta_table}
if self.exposure:
kwargs["exposure"] = self.exposure
if self.psf:
kwargs["psf"] = self.psf
if self.mask_safe is not None:
kwargs["mask_safe"] = self.mask_safe.resample_axis(
axis=energy_axis, ufunc=np.logical_or
)
if self.mask_fit is not None:
kwargs["mask_fit"] = self.mask_fit.resample_axis(
axis=energy_axis, ufunc=np.logical_or
)
if self.counts is not None:
kwargs["counts"] = self.counts.resample_axis(
axis=energy_axis, weights=self.mask_safe
)
if self.background is not None and self.stat_type == "cash":
kwargs["background"] = self.background.resample_axis(
axis=energy_axis, weights=self.mask_safe
)
# Mask_safe or mask_irf??
if isinstance(self.edisp, EDispKernelMap):
kwargs["edisp"] = self.edisp.resample_energy_axis(
energy_axis=energy_axis, weights=self.mask_safe_edisp
)
else: # None or EDispMap
kwargs["edisp"] = self.edisp
return self.__class__(**kwargs)
[docs] def to_image(self, name=None):
"""Create images by summing over the reconstructed energy axis.
Parameters
----------
name : str
Name of the new dataset.
Returns
-------
dataset : `MapDataset` or `SpectrumDataset`
Dataset integrated over non-spatial axes.
"""
energy_axis = self._geom.axes["energy"].squash()
return self.resample_energy_axis(energy_axis=energy_axis, name=name)
[docs] def peek(self, figsize=(12, 10)):
"""Quick-look summary plots.
Parameters
----------
figsize : tuple
Size of the figure.
"""
def plot_mask(ax, mask, **kwargs):
if mask is not None:
mask.plot_mask(ax=ax, **kwargs)
fig, axes = plt.subplots(
ncols=2,
nrows=2,
subplot_kw={"projection": self._geom.wcs},
figsize=figsize,
gridspec_kw={"hspace": 0.1, "wspace": 0.1},
)
axes = axes.flat
axes[0].set_title("Counts")
self.counts.sum_over_axes().plot(ax=axes[0], add_cbar=True)
plot_mask(ax=axes[0], mask=self.mask_fit_image, alpha=0.2)
plot_mask(ax=axes[0], mask=self.mask_safe_image, hatches=["///"], colors="w")
axes[1].set_title("Excess counts")
self.excess.sum_over_axes().plot(ax=axes[1], add_cbar=True)
plot_mask(ax=axes[1], mask=self.mask_fit_image, alpha=0.2)
plot_mask(ax=axes[1], mask=self.mask_safe_image, hatches=["///"], colors="w")
axes[2].set_title("Exposure")
self.exposure.sum_over_axes().plot(ax=axes[2], add_cbar=True)
plot_mask(ax=axes[2], mask=self.mask_safe_image, hatches=["///"], colors="w")
axes[3].set_title("Background")
self.background.sum_over_axes().plot(ax=axes[3], add_cbar=True)
plot_mask(ax=axes[3], mask=self.mask_fit_image, alpha=0.2)
plot_mask(ax=axes[3], mask=self.mask_safe_image, hatches=["///"], colors="w")
[docs]class MapDatasetOnOff(MapDataset):
"""Map dataset for on-off likelihood fitting. Uses wstat statistics.
Parameters
----------
models : `~gammapy.modeling.models.Models`
Source sky models.
counts : `~gammapy.maps.WcsNDMap`
Counts cube
counts_off : `~gammapy.maps.WcsNDMap`
Ring-convolved counts cube
acceptance : `~gammapy.maps.WcsNDMap`
Acceptance from the IRFs
acceptance_off : `~gammapy.maps.WcsNDMap`
Acceptance off
exposure : `~gammapy.maps.WcsNDMap`
Exposure cube
mask_fit : `~gammapy.maps.WcsNDMap`
Mask to apply to the likelihood for fitting.
psf : `~gammapy.irf.PSFKernel`
PSF kernel
edisp : `~gammapy.irf.EDispKernel`
Energy dispersion
mask_safe : `~gammapy.maps.WcsNDMap`
Mask defining the safe data range.
gti : `~gammapy.data.GTI`
GTI of the observation or union of GTI if it is a stacked observation
meta_table : `~astropy.table.Table`
Table listing information on observations used to create the dataset.
One line per observation for stacked datasets.
name : str
Name of the dataset.
See Also
--------
MapDataset, SpectrumDataset, FluxPointsDataset
"""
stat_type = "wstat"
tag = "MapDatasetOnOff"
def __init__(
self,
models=None,
counts=None,
counts_off=None,
acceptance=None,
acceptance_off=None,
exposure=None,
mask_fit=None,
psf=None,
edisp=None,
name=None,
mask_safe=None,
gti=None,
meta_table=None,
):
self._name = make_name(name)
self._evaluators = {}
self.counts = counts
self.counts_off = counts_off
self.exposure = exposure
self.acceptance = acceptance
self.acceptance_off = acceptance_off
self.gti = gti
self.mask_fit = mask_fit
self.psf = psf
self.edisp = edisp
self.models = models
self.mask_safe = mask_safe
self.meta_table = meta_table
def __str__(self):
str_ = super().__str__()
counts_off = np.nan
if self.counts_off is not None:
counts_off = np.sum(self.counts_off.data)
str_ += "\t{:32}: {:.0f} \n".format("Total counts_off", counts_off)
acceptance = np.nan
if self.acceptance is not None:
acceptance = np.sum(self.acceptance.data)
str_ += "\t{:32}: {:.0f} \n".format("Acceptance", acceptance)
acceptance_off = np.nan
if self.acceptance_off is not None:
acceptance_off = np.sum(self.acceptance_off.data)
str_ += "\t{:32}: {:.0f} \n".format("Acceptance off", acceptance_off)
return str_.expandtabs(tabsize=2)
@property
def _geom(self):
"""Main analysis geometry"""
if self.counts is not None:
return self.counts.geom
elif self.counts_off is not None:
return self.counts_off.geom
elif self.acceptance is not None:
return self.acceptance.geom
elif self.acceptance_off is not None:
return self.acceptance_off.geom
else:
raise ValueError(
"Either 'counts', 'counts_off', 'acceptance' or 'acceptance_of' must be defined."
)
@property
def alpha(self):
"""Exposure ratio between signal and background regions
See :ref:`wstat`
Returns
-------
alpha : `Map`
Alpha map
"""
with np.errstate(invalid="ignore", divide="ignore"):
alpha = self.acceptance / self.acceptance_off
alpha.data = np.nan_to_num(alpha.data)
return alpha
[docs] def npred_background(self):
"""Predicted background counts (mu_bkg) estimated from the marginalized likelihood estimate.
See :ref:`wstat`
Returns
-------
npred_background : `Map`
Predicted background counts
"""
mu_bkg = self.alpha.data * get_wstat_mu_bkg(
n_on=self.counts.data,
n_off=self.counts_off.data,
alpha=self.alpha.data,
mu_sig=self.npred_signal().data,
)
mu_bkg = np.nan_to_num(mu_bkg)
return Map.from_geom(geom=self._geom, data=mu_bkg)
[docs] def npred_off(self):
"""Predicted counts in the off region; mu_bkg/alpha
See :ref:`wstat`
Returns
-------
npred_off : `Map`
Predicted off counts
"""
return self.npred_background() / self.alpha
@property
def background(self):
"""Computed as alpha * n_off
See :ref:`wstat`
Returns
-------
background : `Map`
Background map
"""
if self.counts_off is None:
return None
return self.alpha * self.counts_off
[docs] def stat_array(self):
"""Likelihood per bin given the current model parameters"""
mu_sig = self.npred_signal().data
on_stat_ = wstat(
n_on=self.counts.data,
n_off=self.counts_off.data,
alpha=list(self.alpha.data),
mu_sig=mu_sig,
)
return np.nan_to_num(on_stat_)
@property
def _counts_statistic(self):
"""Counts statistics of the dataset."""
return WStatCountsStatistic(self.counts, self.counts_off, self.alpha)
[docs] @classmethod
def from_geoms(
cls,
geom,
geom_exposure,
geom_psf=None,
geom_edisp=None,
reference_time="2000-01-01",
name=None,
**kwargs,
):
"""Create a MapDatasetOnOff object switch zero filled maps according to the specified geometries
Parameters
----------
geom : `gammapy.maps.WcsGeom`
geometry for the counts, counts_off, acceptance and acceptance_off maps
geom_exposure : `gammapy.maps.WcsGeom`
geometry for the exposure map
geom_psf : `gammapy.maps.WcsGeom`
geometry for the psf map
geom_edisp : `gammapy.maps.WcsGeom`
geometry for the energy dispersion kernel map.
If geom_edisp has a migra axis, this will create an EDispMap instead.
reference_time : `~astropy.time.Time`
the reference time to use in GTI definition
name : str
Name of the returned dataset.
Returns
-------
empty_maps : `MapDatasetOnOff`
A MapDatasetOnOff containing zero filled maps
"""
# TODO: it seems the super() pattern does not work here?
dataset = MapDataset.from_geoms(
geom=geom,
geom_exposure=geom_exposure,
geom_psf=geom_psf,
geom_edisp=geom_edisp,
name=name,
reference_time=reference_time,
**kwargs,
)
off_maps = {}
for key in ["counts_off", "acceptance", "acceptance_off"]:
off_maps[key] = Map.from_geom(geom, unit="")
return cls.from_map_dataset(dataset, name=name, **off_maps)
[docs] @classmethod
def from_map_dataset(
cls, dataset, acceptance, acceptance_off, counts_off=None, name=None
):
"""Create on off dataset from a map dataset.
Parameters
----------
dataset : `MapDataset`
Spectrum dataset defining counts, edisp, aeff, livetime etc.
acceptance : `Map`
Relative background efficiency in the on region.
acceptance_off : `Map`
Relative background efficiency in the off region.
counts_off : `Map`
Off counts map . If the dataset provides a background model,
and no off counts are defined. The off counts are deferred from
counts_off / alpha.
name : str
Name of the returned dataset.
Returns
-------
dataset : `MapDatasetOnOff`
Map dataset on off.
"""
if counts_off is None and dataset.background is not None:
alpha = acceptance / acceptance_off
counts_off = dataset.npred_background() / alpha
if np.isscalar(acceptance):
acceptance = Map.from_geom(dataset._geom, data=acceptance)
if np.isscalar(acceptance_off):
acceptance_off = Map.from_geom(dataset._geom, data=acceptance_off)
return cls(
models=dataset.models,
counts=dataset.counts,
exposure=dataset.exposure,
counts_off=counts_off,
edisp=dataset.edisp,
psf=dataset.psf,
mask_safe=dataset.mask_safe,
mask_fit=dataset.mask_fit,
acceptance=acceptance,
acceptance_off=acceptance_off,
gti=dataset.gti,
name=name,
meta_table=dataset.meta_table,
)
[docs] def to_map_dataset(self, name=None):
"""Convert a MapDatasetOnOff to MapDataset
The background model template is taken as alpha * counts_off
Parameters
----------
name: str
Name of the new dataset
Returns
-------
dataset: `MapDataset`
Map dataset with cash statistics
"""
name = make_name(name)
return MapDataset(
counts=self.counts,
exposure=self.exposure,
psf=self.psf,
edisp=self.edisp,
name=name,
gti=self.gti,
mask_fit=self.mask_fit,
mask_safe=self.mask_safe,
background=self.counts_off * self.alpha,
meta_table=self.meta_table,
)
@property
def _is_stackable(self):
"""Check if the Dataset contains enough information to be stacked"""
incomplete = (
self.acceptance_off is None
or self.acceptance is None
or self.counts_off is None
)
unmasked = np.any(self.mask_safe.data)
if incomplete and unmasked:
return False
else:
return True
[docs] def stack(self, other, nan_to_num=True):
r"""Stack another dataset in place.
The ``acceptance`` of the stacked dataset is normalized to 1,
and the stacked ``acceptance_off`` is scaled so that:
.. math::
\alpha_\text{stacked} =
\frac{1}{a_\text{off}} =
\frac{\alpha_1\text{OFF}_1 + \alpha_2\text{OFF}_2}{\text{OFF}_1 + OFF_2}
Parameters
----------
other : `MapDatasetOnOff`
Other dataset
nan_to_num: bool
Non-finite values are replaced by zero if True (default).
"""
if not isinstance(other, MapDatasetOnOff):
raise TypeError("Incompatible types for MapDatasetOnOff stacking")
if not self._is_stackable or not other._is_stackable:
raise ValueError("Cannot stack incomplete MapDatsetOnOff.")
geom = self.counts.geom
total_off = Map.from_geom(geom)
total_alpha = Map.from_geom(geom)
if self.counts_off:
total_off.stack(
self.counts_off, weights=self.mask_safe, nan_to_num=nan_to_num
)
total_alpha.stack(
self.alpha * self.counts_off,
weights=self.mask_safe,
nan_to_num=nan_to_num,
)
if other.counts_off:
total_off.stack(
other.counts_off, weights=other.mask_safe, nan_to_num=nan_to_num
)
total_alpha.stack(
other.alpha * other.counts_off,
weights=other.mask_safe,
nan_to_num=nan_to_num,
)
with np.errstate(divide="ignore", invalid="ignore"):
acceptance_off = total_off / total_alpha
average_alpha = total_alpha.data.sum() / total_off.data.sum()
# For the bins where the stacked OFF counts equal 0, the alpha value is performed by weighting on the total
# OFF counts of each run
is_zero = total_off.data == 0
acceptance_off.data[is_zero] = 1 / average_alpha
self.acceptance.data[...] = 1
self.acceptance_off = acceptance_off
self.counts_off = total_off
super().stack(other, nan_to_num=nan_to_num)
[docs] def stat_sum(self):
"""Total likelihood given the current model parameters."""
return Dataset.stat_sum(self)
[docs] def fake(self, npred_background, random_state="random-seed"):
"""Simulate fake counts (on and off) for the current model and reduced IRFs.
This method overwrites the counts defined on the dataset object.
Parameters
----------
random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`}
Defines random number generator initialisation.
Passed to `~gammapy.utils.random.get_random_state`.
"""
random_state = get_random_state(random_state)
npred = self.npred_signal()
data = np.nan_to_num(npred.data, copy=True, nan=0.0, posinf=0.0, neginf=0.0)
npred.data = random_state.poisson(data)
npred_bkg = random_state.poisson(npred_background.data)
self.counts = npred + npred_bkg
npred_off = npred_background / self.alpha
data_off = np.nan_to_num(
npred_off.data, copy=True, nan=0.0, posinf=0.0, neginf=0.0
)
npred_off.data = random_state.poisson(data_off)
self.counts_off = npred_off
[docs] def to_hdulist(self):
"""Convert map dataset to list of HDUs.
Returns
-------
hdulist : `~astropy.io.fits.HDUList`
Map dataset list of HDUs.
"""
hdulist = super().to_hdulist()
exclude_primary = slice(1, None)
del hdulist["BACKGROUND"]
del hdulist["BACKGROUND_BANDS"]
if self.counts_off is not None:
hdulist += self.counts_off.to_hdulist(hdu="counts_off")[exclude_primary]
if self.acceptance is not None:
hdulist += self.acceptance.to_hdulist(hdu="acceptance")[exclude_primary]
if self.acceptance_off is not None:
hdulist += self.acceptance_off.to_hdulist(hdu="acceptance_off")[
exclude_primary
]
return hdulist
@classmethod
def _read_lazy(cls, filename, name=None, cache=True, format="gadf"):
raise NotImplementedError(
f"Lazy loading is not implemented for {cls}, please use option lazy=False."
)
[docs] @classmethod
def from_hdulist(cls, hdulist, name=None, format="gadf"):
"""Create map dataset from list of HDUs.
Parameters
----------
hdulist : `~astropy.io.fits.HDUList`
List of HDUs.
name : str
Name of the new dataset.
format : {"gadf"}
Format the hdulist is given in.
Returns
-------
dataset : `MapDatasetOnOff`
Map dataset.
"""
kwargs = {}
kwargs["name"] = name
if "COUNTS" in hdulist:
kwargs["counts"] = Map.from_hdulist(hdulist, hdu="counts", format=format)
if "COUNTS_OFF" in hdulist:
kwargs["counts_off"] = Map.from_hdulist(
hdulist, hdu="counts_off", format=format
)
if "ACCEPTANCE" in hdulist:
kwargs["acceptance"] = Map.from_hdulist(
hdulist, hdu="acceptance", format=format
)
if "ACCEPTANCE_OFF" in hdulist:
kwargs["acceptance_off"] = Map.from_hdulist(
hdulist, hdu="acceptance_off", format=format
)
if "EXPOSURE" in hdulist:
kwargs["exposure"] = Map.from_hdulist(
hdulist, hdu="exposure", format=format
)
if "EDISP" in hdulist:
edisp_map = Map.from_hdulist(hdulist, hdu="edisp", format=format)
try:
exposure_map = Map.from_hdulist(
hdulist, hdu="edisp_exposure", format=format
)
except KeyError:
exposure_map = None
if edisp_map.geom.axes[0].name == "energy":
kwargs["edisp"] = EDispKernelMap(edisp_map, exposure_map)
else:
kwargs["edisp"] = EDispMap(edisp_map, exposure_map)
if "PSF" in hdulist:
psf_map = Map.from_hdulist(hdulist, hdu="psf", format=format)
try:
exposure_map = Map.from_hdulist(
hdulist, hdu="psf_exposure", format=format
)
except KeyError:
exposure_map = None
kwargs["psf"] = PSFMap(psf_map, exposure_map)
if "MASK_SAFE" in hdulist:
mask_safe = Map.from_hdulist(hdulist, hdu="mask_safe", format=format)
kwargs["mask_safe"] = mask_safe
if "MASK_FIT" in hdulist:
mask_fit = Map.from_hdulist(hdulist, hdu="mask_fit", format=format)
kwargs["mask_fit"] = mask_fit
if "GTI" in hdulist:
gti = GTI(Table.read(hdulist, hdu="GTI"))
kwargs["gti"] = gti
if "META_TABLE" in hdulist:
meta_table = Table.read(hdulist, hdu="META_TABLE")
kwargs["meta_table"] = meta_table
return cls(**kwargs)
[docs] def info_dict(self, in_safe_data_range=True):
"""Basic info dict with summary statistics
If a region is passed, then a spectrum dataset is
extracted, and the corresponding info returned.
Parameters
----------
in_safe_data_range : bool
Whether to sum only in the safe energy range
Returns
-------
info_dict : dict
Dictionary with summary info.
"""
# TODO: remove code duplication with SpectrumDatasetOnOff
info = super().info_dict(in_safe_data_range)
if self.mask_safe and in_safe_data_range:
mask = self.mask_safe.data.astype(bool)
else:
mask = slice(None)
summed_stat = self._counts_statistic[mask].sum()
counts_off = 0
if self.counts_off is not None:
counts_off = summed_stat.n_off
info["counts_off"] = int(counts_off)
acceptance = 1
if self.acceptance:
acceptance = self.acceptance.data[mask].sum()
info["acceptance"] = float(acceptance)
acceptance_off = np.nan
alpha = np.nan
if self.acceptance_off:
alpha = summed_stat.alpha
acceptance_off = acceptance / alpha
info["acceptance_off"] = float(acceptance_off)
info["alpha"] = float(alpha)
info["stat_sum"] = self.stat_sum()
return info
[docs] def to_spectrum_dataset(self, on_region, containment_correction=False, name=None):
"""Return a ~gammapy.datasets.SpectrumDatasetOnOff from on_region.
Counts and OFF counts are summed in the on_region.
Acceptance is the average of all acceptances while acceptance OFF
is taken such that number of excess is preserved in the on_region.
Effective area is taken from the average exposure.
The energy dispersion kernel is obtained at the on_region center.
Only regions with centers are supported.
The models are not exported to the ~gammapy.dataset.SpectrumDatasetOnOff.
It must be set after the dataset extraction.
Parameters
----------
on_region : `~regions.SkyRegion`
the input ON region on which to extract the spectrum
containment_correction : bool
Apply containment correction for point sources and circular on regions
name : str
Name of the new dataset.
Returns
-------
dataset : `~gammapy.datasets.SpectrumDatasetOnOff`
the resulting reduced dataset
"""
from .spectrum import SpectrumDatasetOnOff
dataset = super().to_spectrum_dataset(
on_region=on_region,
containment_correction=containment_correction,
name=name,
)
kwargs = {"name": name}
if self.counts_off is not None:
kwargs["counts_off"] = self.counts_off.get_spectrum(
on_region, np.sum, weights=self.mask_safe
)
if self.acceptance is not None:
kwargs["acceptance"] = self.acceptance.get_spectrum(
on_region, np.mean, weights=self.mask_safe
)
norm = self.background.get_spectrum(
on_region, np.sum, weights=self.mask_safe
)
acceptance_off = kwargs["acceptance"] * kwargs["counts_off"] / norm
np.nan_to_num(acceptance_off.data, copy=False)
kwargs["acceptance_off"] = acceptance_off
return SpectrumDatasetOnOff.from_spectrum_dataset(dataset=dataset, **kwargs)
[docs] def cutout(self, position, width, mode="trim", name=None):
"""Cutout map dataset.
Parameters
----------
position : `~astropy.coordinates.SkyCoord`
Center position of the cutout region.
width : tuple of `~astropy.coordinates.Angle`
Angular sizes of the region in (lon, lat) in that specific order.
If only one value is passed, a square region is extracted.
mode : {'trim', 'partial', 'strict'}
Mode option for Cutout2D, for details see `~astropy.nddata.utils.Cutout2D`.
name : str
Name of the new dataset.
Returns
-------
cutout : `MapDatasetOnOff`
Cutout map dataset.
"""
cutout_kwargs = {
"position": position,
"width": width,
"mode": mode,
"name": name,
}
cutout_dataset = super().cutout(**cutout_kwargs)
del cutout_kwargs["name"]
if self.counts_off is not None:
cutout_dataset.counts_off = self.counts_off.cutout(**cutout_kwargs)
if self.acceptance is not None:
cutout_dataset.acceptance = self.acceptance.cutout(**cutout_kwargs)
if self.acceptance_off is not None:
cutout_dataset.acceptance_off = self.acceptance_off.cutout(**cutout_kwargs)
return cutout_dataset
[docs] def downsample(self, factor, axis_name=None, name=None):
"""Downsample map dataset.
The PSFMap and EDispKernelMap are not downsampled, except if
a corresponding axis is given.
Parameters
----------
factor : int
Downsampling factor.
axis_name : str
Which non-spatial axis to downsample. By default only spatial axes are downsampled.
name : str
Name of the downsampled dataset.
Returns
-------
dataset : `MapDatasetOnOff`
Downsampled map dataset.
"""
dataset = super().downsample(factor, axis_name, name)
counts_off = None
if self.counts_off is not None:
counts_off = self.counts_off.downsample(
factor=factor,
preserve_counts=True,
axis_name=axis_name,
weights=self.mask_safe,
)
acceptance, acceptance_off = None, None
if self.acceptance_off is not None:
acceptance = self.acceptance.downsample(
factor=factor, preserve_counts=False, axis_name=axis_name
)
factor = self.background.downsample(
factor=factor,
preserve_counts=True,
axis_name=axis_name,
weights=self.mask_safe,
)
acceptance_off = acceptance * counts_off / factor
return self.__class__.from_map_dataset(
dataset,
acceptance=acceptance,
acceptance_off=acceptance_off,
counts_off=counts_off,
)
[docs] def pad(self):
raise NotImplementedError
[docs] def slice_by_idx(self, slices, name=None):
"""Slice sub dataset.
The slicing only applies to the maps that define the corresponding axes.
Parameters
----------
slices : dict
Dict of axes names and integers or `slice` object pairs. Contains one
element for each non-spatial dimension. For integer indexing the
corresponding axes is dropped from the map. Axes not specified in the
dict are kept unchanged.
name : str
Name of the sliced dataset.
Returns
-------
map_out : `Map`
Sliced map object.
"""
kwargs = {"name": name}
dataset = super().slice_by_idx(slices, name)
if self.counts_off is not None:
kwargs["counts_off"] = self.counts_off.slice_by_idx(slices=slices)
if self.acceptance is not None:
kwargs["acceptance"] = self.acceptance.slice_by_idx(slices=slices)
if self.acceptance_off is not None:
kwargs["acceptance_off"] = self.acceptance_off.slice_by_idx(slices=slices)
return self.from_map_dataset(dataset, **kwargs)
[docs] def resample_energy_axis(self, energy_axis, name=None):
"""Resample MapDatasetOnOff over reconstructed energy edges.
Counts are summed taking into account safe mask.
Parameters
----------
energy_axis : `~gammapy.maps.MapAxis`
New reco energy axis.
name: str
Name of the new dataset.
Returns
-------
dataset: `SpectrumDataset`
Resampled spectrum dataset .
"""
dataset = super().resample_energy_axis(energy_axis, name)
counts_off = None
if self.counts_off is not None:
counts_off = self.counts_off
counts_off = counts_off.resample_axis(
axis=energy_axis, weights=self.mask_safe
)
acceptance = 1
acceptance_off = None
if self.acceptance is not None:
acceptance = self.acceptance
acceptance = acceptance.resample_axis(
axis=energy_axis, weights=self.mask_safe
)
norm_factor = self.background.resample_axis(
axis=energy_axis, weights=self.mask_safe
)
acceptance_off = acceptance * counts_off / norm_factor
return self.__class__.from_map_dataset(
dataset,
acceptance=acceptance,
acceptance_off=acceptance_off,
counts_off=counts_off,
name=name,
)