# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import numpy as np
from astropy import units as u
from astropy.io import fits
from astropy.table import Table
from astropy.utils import classproperty
from gammapy.data import GTI
from gammapy.maps import Map, Maps
from gammapy.modeling.models import (
Models,
PowerLawSpectralModel,
SkyModel,
SpectralModel,
)
from gammapy.utils.scripts import make_path
__all__ = ["FluxMaps"]
log = logging.getLogger(__name__)
DEFAULT_UNIT = {
"dnde": u.Unit("cm-2 s-1 TeV-1"),
"e2dnde": u.Unit("erg cm-2 s-1"),
"flux": u.Unit("cm-2 s-1"),
"eflux": u.Unit("erg cm-2 s-1"),
"norm": u.Unit(""),
}
REQUIRED_MAPS = {
"dnde": ["dnde"],
"e2dnde": ["e2dnde"],
"flux": ["flux"],
"eflux": ["eflux"],
"likelihood": ["norm"],
}
REQUIRED_COLUMNS = {
"dnde": ["e_ref", "dnde"],
"e2dnde": ["e_ref", "e2dnde"],
"flux": ["e_min", "e_max", "flux"],
"eflux": ["e_min", "e_max", "eflux"],
# TODO: extend required columns
"likelihood": [
"e_min",
"e_max",
"e_ref",
"ref_dnde",
"ref_flux",
"ref_eflux",
"norm",
],
}
REQUIRED_QUANTITIES_SCAN = ["stat_scan", "stat"]
OPTIONAL_QUANTITIES = {
"dnde": ["dnde_err", "dnde_errp", "dnde_errn", "dnde_ul"],
"e2dnde": ["e2dnde_err", "e2dnde_errp", "e2dnde_errn", "e2dnde_ul"],
"flux": ["flux_err", "flux_errp", "flux_errn", "flux_ul"],
"eflux": ["eflux_err", "eflux_errp", "eflux_errn", "eflux_ul"],
"likelihood": ["norm_err", "norm_errn", "norm_errp", "norm_ul"],
}
VALID_QUANTITIES = [
"norm",
"norm_err",
"norm_errn",
"norm_errp",
"norm_ul",
"ts",
"sqrt_ts",
"npred",
"npred_excess",
"stat",
"stat_scan",
"stat_null",
"niter",
"is_ul",
"counts",
"success",
]
OPTIONAL_QUANTITIES_COMMON = [
"ts",
"sqrt_ts",
"npred",
"npred_excess",
"stat",
"stat_null",
"niter",
"is_ul",
"counts",
"success",
]
[docs]class FluxMaps:
"""A flux map / points container.
It contains a set of `~gammapy.maps.Map` objects that store the estimated
flux as a function of energy as well as associated quantities (typically
errors, upper limits, delta TS and possibly raw quantities such counts,
excesses etc). It also contains a reference model to convert the flux
values in different formats. Usually, this should be the model used to
produce the flux map.
The associated map geometry can use a `RegionGeom` to store the equivalent
of flux points, or a `WcsGeom`/`HpxGeom` to store an energy dependent flux map.
The container relies internally on the 'Likelihood' SED type defined in
:ref:`gadf:flux-points` and offers convenience properties to convert to
other flux formats, namely: ``dnde``, ``flux``, ``eflux`` or ``e2dnde``.
The conversion is done according to the reference model spectral shape.
Parameters
----------
data : dict of `~gammapy.maps.Map`
the maps dictionary. Expected entries are the following:
* norm : the norm factor
* norm_err : optional, the error on the norm factor.
* norm_errn : optional, the negative error on the norm factor.
* norm_errp : optional, the positive error on the norm factor.
* norm_ul : optional, the upper limit on the norm factor.
* norm_scan : optional, the norm values of the test statistic scan.
* stat_scan : optional, the test statistic scan values.
* ts : optional, the delta TS associated with the flux value.
* sqrt_ts : optional, the square root of the TS, when relevant.
* success : optional, a boolean tagging the validity of the estimation
reference_model : `~gammapy.modeling.models.SkyModel`, optional
the reference model to use for conversions.
If None, a model consisting of a point source with a power law spectrum of index 2 is assumed.
meta : dict, optional
Dict of metadata.
gti : `~gammapy.data.GTI`, optional
Maps GTI information.
filter_success_nan : boolean, optional
Set fitted norm and error to NaN when the fit has not succeeded.
"""
_expand_slice = (slice(None), np.newaxis, np.newaxis)
def __init__(
self, data, reference_model, meta=None, gti=None, filter_success_nan=True
):
self._data = data
if isinstance(reference_model, SpectralModel):
reference_model = SkyModel(reference_model)
self._reference_model = reference_model
if meta is None:
meta = {}
self.meta = meta
self.gti = gti
self._filter_success_nan = filter_success_nan
@property
def filter_success_nan(self):
return self._filter_success_nan
@filter_success_nan.setter
def filter_success_nan(self, value):
self._filter_success_nan = value
@property
def available_quantities(self):
"""Available quantities"""
return list(self._data.keys())
[docs] @staticmethod
def all_quantities(sed_type):
"""All quantities allowed for a given sed type.
Parameters
----------
sed_type : {"likelihood", "dnde", "e2dnde", "flux", "eflux"}
Sed type.
Returns
-------
list : list of str
All allowed quantities for a given sed type.
"""
quantities = []
quantities += REQUIRED_MAPS[sed_type]
quantities += OPTIONAL_QUANTITIES[sed_type]
quantities += OPTIONAL_QUANTITIES_COMMON
if sed_type == "likelihood":
quantities += REQUIRED_QUANTITIES_SCAN
return quantities
@staticmethod
def _validate_data(data, sed_type, check_scan=False):
"""Check that map input is valid and correspond to one of the SED type."""
try:
keys = data.keys()
required = set(REQUIRED_MAPS[sed_type])
except KeyError:
raise ValueError(f"Unknown SED type: '{sed_type}'")
if check_scan:
required = required.union(REQUIRED_QUANTITIES_SCAN)
if not required.issubset(keys):
missing = required.difference(keys)
raise ValueError(
"Missing data / column for sed type '{}':"
" {}".format(sed_type, missing)
)
# TODO: add support for scan
def _check_quantity(self, quantity):
if quantity not in self.available_quantities:
raise AttributeError(
f"Quantity '{quantity}' is not defined on current flux estimate."
)
@staticmethod
def _guess_sed_type(quantities):
"""Guess SED type from table content."""
valid_sed_types = list(REQUIRED_COLUMNS.keys())
for sed_type in valid_sed_types:
required = set(REQUIRED_COLUMNS[sed_type])
if required.issubset(quantities):
return sed_type
@property
def has_ul(self):
"""Whether the flux estimate has either sqrt(ts) or ts defined"""
return "norm_ul" in self._data
@property
def has_any_ts(self):
"""Whether the flux estimate has either sqrt(ts) or ts defined"""
return any(_ in self._data for _ in ["ts", "sqrt_ts"])
@property
def has_stat_profiles(self):
"""Whether the flux estimate has stat profiles"""
return "stat_scan" in self._data
@property
def has_success(self):
"""Whether the flux estimate has the fit status"""
return "success" in self._data
@property
def n_sigma(self):
"""n sigma UL"""
return self.meta.get("n_sigma", 1)
@property
def n_sigma_ul(self):
"""n sigma UL"""
return self.meta.get("n_sigma_ul")
@property
def sqrt_ts_threshold_ul(self):
"""sqrt(TS) threshold for upper limits"""
return self.meta.get("sqrt_ts_threshold_ul", 2)
@sqrt_ts_threshold_ul.setter
def sqrt_ts_threshold_ul(self, value):
"""sqrt(TS) threshold for upper limits
Parameters
----------
value : int
Threshold value in sqrt(TS) for upper limits
"""
self.meta["sqrt_ts_threshold_ul"] = value
if self.has_any_ts:
self.is_ul = self.sqrt_ts < self.sqrt_ts_threshold_ul
else:
raise ValueError("Either ts or sqrt_ts is required to set the threshold")
@property
def sed_type_init(self):
"""Initial sed type"""
return self.meta.get("sed_type_init")
@property
def geom(self):
"""Reference map geometry (`Geom`)"""
return self.norm.geom
@property
def energy_axis(self):
"""Energy axis (`MapAxis`)"""
return self.geom.axes["energy"]
@classproperty
def reference_model_default(self):
"""Reference model default (`SkyModel`)"""
return SkyModel(PowerLawSpectralModel(index=2))
@property
def reference_model(self):
"""Reference model (`SkyModel`)"""
return self._reference_model
@property
def reference_spectral_model(self):
"""Reference spectral model (`SpectralModel`)"""
return self.reference_model.spectral_model
@property
def energy_ref(self):
"""Reference energy.
Defined by `energy_ref` column in `FluxPoints.table` or computed as log
center, if `energy_min` and `energy_max` columns are present in `FluxEstimate.data`.
Returns
-------
energy_ref : `~astropy.units.Quantity`
Reference energy.
"""
return self.energy_axis.center
@property
def energy_min(self):
"""Energy min
Returns
-------
energy_min : `~astropy.units.Quantity`
Lower bound of energy bin.
"""
return self.energy_axis.edges[:-1]
@property
def energy_max(self):
"""Energy max
Returns
-------
energy_max : `~astropy.units.Quantity`
Upper bound of energy bin.
"""
return self.energy_axis.edges[1:]
# TODO: keep or remove?
@property
def niter(self):
"""Number of iterations of fit"""
self._check_quantity("niter")
return self._data["niter"]
@property
def success(self):
"""Fit success flag"""
self._check_quantity("success")
return self._data["success"]
@property
def is_ul(self):
"""Whether data is an upper limit"""
# TODO: make this a well defined behaviour
is_ul = self.norm.copy(data=False)
if "is_ul" in self._data:
is_ul = self._data["is_ul"]
elif self.has_any_ts and self.has_ul:
is_ul.data = self.sqrt_ts.data < self.sqrt_ts_threshold_ul
elif self.has_ul:
is_ul.data = np.isfinite(self.norm_ul)
else:
is_ul.data = np.isnan(self.norm)
return is_ul
@is_ul.setter
def is_ul(self, value):
"""Whether data is an upper limit
Parameters
----------
value : `~Map`
Boolean map.
"""
if not isinstance(value, Map):
value = self.norm.copy(data=value)
self._data["is_ul"] = value
@property
def counts(self):
"""Predicted counts null hypothesis"""
self._check_quantity("counts")
return self._data["counts"]
@property
def npred(self):
"""Predicted counts from best fit hypothesis"""
self._check_quantity("npred")
return self._data["npred"]
@property
def npred_background(self):
"""Predicted background counts from best fit hypothesis"""
self._check_quantity("npred")
self._check_quantity("npred_excess")
return self.npred - self.npred_excess
@property
def npred_excess(self):
"""Predicted excess count rom best fit hypothesis"""
self._check_quantity("npred_excess")
return self._data["npred_excess"]
def _expand_dims(self, data):
# TODO: instead make map support broadcasting
axes = self.npred.geom.axes
# here we need to rely on broadcasting
if "dataset" in axes.names:
idx = axes.index_data("dataset")
data = np.expand_dims(data, axis=idx)
return data
@property
def npred_excess_ref(self):
"""Predicted excess reference counts"""
return self.npred_excess / self._expand_dims(self.norm.data)
@property
def npred_excess_err(self):
"""Predicted excess counts error"""
return self.npred_excess_ref * self._expand_dims(self.norm_err.data)
@property
def npred_excess_errp(self):
"""Predicted excess counts positive error"""
return self.npred_excess_ref * self._expand_dims(self.norm_errp.data)
@property
def npred_excess_errn(self):
"""Predicted excess counts negative error"""
return self.npred_excess_ref * self._expand_dims(self.norm_errn.data)
@property
def npred_excess_ul(self):
"""Predicted excess counts upper limits"""
return self.npred_excess_ref * self._expand_dims(self.norm_ul.data)
@property
def stat_scan(self):
"""Fit statistic scan value"""
self._check_quantity("stat_scan")
return self._data["stat_scan"]
@property
def stat(self):
"""Fit statistic value"""
self._check_quantity("stat")
return self._data["stat"]
@property
def stat_null(self):
"""Fit statistic value for the null hypothesis"""
self._check_quantity("stat_null")
return self._data["stat_null"]
@property
def ts(self):
"""ts map (`Map`)"""
self._check_quantity("ts")
return self._data["ts"]
@property
def ts_scan(self):
"""ts scan (`Map`)"""
return self.stat_scan - np.expand_dims(self.stat.data, 2)
# TODO: always derive sqrt(TS) from TS?
@property
def sqrt_ts(self):
r"""sqrt(TS) as defined by:
.. math::
\sqrt{TS} = \left \{
\begin{array}{ll}
-\sqrt{TS} & : \text{if} \ norm < 0 \\
\sqrt{TS} & : \text{else}
\end{array}
\right.
Returns
-------
sqrt_ts : `Map`
sqrt(TS) map
"""
if "sqrt_ts" in self._data:
return self._data["sqrt_ts"]
else:
with np.errstate(invalid="ignore", divide="ignore"):
ts = np.clip(self.ts.data, 0, None)
data = np.where(self.norm > 0, np.sqrt(ts), -np.sqrt(ts))
return Map.from_geom(geom=self.geom, data=data)
@property
def norm(self):
"""Norm values"""
return self._filter_convergence_failure(self._data["norm"])
@property
def norm_err(self):
"""Norm error"""
self._check_quantity("norm_err")
return self._filter_convergence_failure(self._data["norm_err"])
@property
def norm_errn(self):
"""Negative norm error"""
self._check_quantity("norm_errn")
return self._data["norm_errn"]
@property
def norm_errp(self):
"""Positive norm error"""
self._check_quantity("norm_errp")
return self._data["norm_errp"]
@property
def norm_ul(self):
"""Norm upper limit"""
self._check_quantity("norm_ul")
return self._data["norm_ul"]
@property
def dnde_ref(self):
"""Reference differential flux"""
result = self.reference_spectral_model(self.energy_axis.center)
return result[self._expand_slice]
@property
def e2dnde_ref(self):
"""Reference differential flux * energy ** 2"""
energy = self.energy_axis.center
result = self.reference_spectral_model(energy) * energy ** 2
return result[self._expand_slice]
@property
def flux_ref(self):
"""Reference integral flux"""
energy_min = self.energy_axis.edges[:-1]
energy_max = self.energy_axis.edges[1:]
result = self.reference_spectral_model.integral(energy_min, energy_max)
return result[self._expand_slice]
@property
def eflux_ref(self):
"""Reference energy flux"""
energy_min = self.energy_axis.edges[:-1]
energy_max = self.energy_axis.edges[1:]
result = self.reference_spectral_model.energy_flux(energy_min, energy_max)
return result[self._expand_slice]
@property
def dnde(self):
"""Return differential flux (dnde) SED values."""
return self.norm * self.dnde_ref
@property
def dnde_err(self):
"""Return differential flux (dnde) SED errors."""
return self.norm_err * self.dnde_ref
@property
def dnde_errn(self):
"""Return differential flux (dnde) SED negative errors."""
return self.norm_errn * self.dnde_ref
@property
def dnde_errp(self):
"""Return differential flux (dnde) SED positive errors."""
return self.norm_errp * self.dnde_ref
@property
def dnde_ul(self):
"""Return differential flux (dnde) SED upper limit."""
return self.norm_ul * self.dnde_ref
@property
def e2dnde(self):
"""Return differential energy flux (e2dnde) SED values."""
return self.norm * self.e2dnde_ref
@property
def e2dnde_err(self):
"""Return differential energy flux (e2dnde) SED errors."""
return self.norm_err * self.e2dnde_ref
@property
def e2dnde_errn(self):
"""Return differential energy flux (e2dnde) SED negative errors."""
return self.norm_errn * self.e2dnde_ref
@property
def e2dnde_errp(self):
"""Return differential energy flux (e2dnde) SED positive errors."""
return self.norm_errp * self.e2dnde_ref
@property
def e2dnde_ul(self):
"""Return differential energy flux (e2dnde) SED upper limit."""
return self.norm_ul * self.e2dnde_ref
@property
def flux(self):
"""Return integral flux (flux) SED values."""
return self.norm * self.flux_ref
@property
def flux_err(self):
"""Return integral flux (flux) SED values."""
return self.norm_err * self.flux_ref
@property
def flux_errn(self):
"""Return integral flux (flux) SED negative errors."""
return self.norm_errn * self.flux_ref
@property
def flux_errp(self):
"""Return integral flux (flux) SED positive errors."""
return self.norm_errp * self.flux_ref
@property
def flux_ul(self):
"""Return integral flux (flux) SED upper limits."""
return self.norm_ul * self.flux_ref
@property
def eflux(self):
"""Return energy flux (eflux) SED values."""
return self.norm * self.eflux_ref
@property
def eflux_err(self):
"""Return energy flux (eflux) SED errors."""
return self.norm_err * self.eflux_ref
@property
def eflux_errn(self):
"""Return energy flux (eflux) SED negative errors."""
return self.norm_errn * self.eflux_ref
@property
def eflux_errp(self):
"""Return energy flux (eflux) SED positive errors."""
return self.norm_errp * self.eflux_ref
@property
def eflux_ul(self):
"""Return energy flux (eflux) SED upper limits."""
return self.norm_ul * self.eflux_ref
def _filter_convergence_failure(self, some_map):
"""Put NaN where pixels did not converge."""
if not self._filter_success_nan:
return some_map
if not self.has_success:
return some_map
if self.success.data.shape == some_map.data.shape:
new_map = some_map.copy()
new_map.data[~self.success.data] = np.nan
else:
mask_nan = np.ones(self.success.data.shape)
mask_nan[~self.success.data] = np.nan
new_map = some_map * np.expand_dims(mask_nan, 2)
new_map.data = new_map.data.astype(some_map.data.dtype)
return new_map
[docs] def get_flux_points(self, position=None):
"""Extract flux point at a given position.
Parameters
----------
position : `~astropy.coordinates.SkyCoord`
Position where the flux points are extracted.
Returns
-------
flux_points : `~gammapy.estimators.FluxPoints`
Flux points object
"""
from gammapy.estimators import FluxPoints
if position is None:
position = self.geom.center_skydir
data = {}
for name in self._data:
m = getattr(self, name)
if m.data.dtype is np.dtype(bool):
data[name] = m.to_region_nd_map(
region=position, method="nearest", func=np.any
)
else:
data[name] = m.to_region_nd_map(region=position, method="nearest")
return FluxPoints(
data,
reference_model=self.reference_model,
meta=self.meta.copy(),
gti=self.gti,
)
[docs] def to_maps(self, sed_type="likelihood"):
"""Return maps in a given SED type.
Parameters
----------
sed_type : {"likelihood", "dnde", "e2dnde", "flux", "eflux"}
sed type to convert to. Default is `Likelihood`
Returns
-------
maps : `Maps`
Maps object containing the requested maps.
"""
maps = Maps()
for quantity in self.all_quantities(sed_type=sed_type):
m = getattr(self, quantity, None)
if m is not None:
maps[quantity] = m
return maps
[docs] @classmethod
def from_stack(cls, maps, axis, meta=None):
"""Create flux points by stacking list of flux points.
The first `FluxPoints` object in the list is taken as a reference to infer
column names and units for the stacked object.
Parameters
----------
maps : list of `FluxMaps`
List of maps to stack.
axis : `MapAxis`
New axis to create
Returns
-------
flux_maps : `FluxMaps`
Stacked flux maps along axis.
"""
reference = maps[0]
data = {}
for quantity in reference.available_quantities:
data[quantity] = Map.from_stack(
[_._data[quantity] for _ in maps], axis=axis
)
if meta is None:
meta = reference.meta.copy()
gtis = [_.gti for _ in maps if _.gti]
if gtis:
gti = GTI.from_stack(gtis)
else:
gti = None
return cls(
data=data, reference_model=reference.reference_model, meta=meta, gti=gti
)
[docs] @classmethod
def from_maps(cls, maps, sed_type=None, reference_model=None, gti=None, meta=None):
"""Create FluxMaps from a dictionary of maps.
Parameters
----------
maps : `Maps`
Maps object containing the input maps.
sed_type : str
SED type of the input maps. Default is `Likelihood`
reference_model : `~gammapy.modeling.models.SkyModel`, optional
Reference model to use for conversions. Default in None.
If None, a model consisting of a point source with a power
law spectrum of index 2 is assumed.
gti : `~gammapy.data.GTI`
Maps GTI information. Default is None.
meta : `dict`
Meta dict.
Returns
-------
flux_maps : `~gammapy.estimators.FluxMaps`
Flux maps object.
"""
if sed_type is None:
sed_type = cls._guess_sed_type(maps.keys())
if sed_type is None:
raise ValueError("Specifying the sed type is required")
cls._validate_data(data=maps, sed_type=sed_type)
if sed_type == "likelihood":
return cls(data=maps, reference_model=reference_model, gti=gti, meta=meta)
if reference_model is None:
log.warning(
"No reference model set for FluxMaps. Assuming point source with E^-2 spectrum."
)
reference_model = cls.reference_model_default
elif isinstance(reference_model, SpectralModel):
reference_model = SkyModel(reference_model)
map_ref = maps[sed_type]
energy_axis = map_ref.geom.axes["energy"]
with np.errstate(invalid="ignore", divide="ignore"):
fluxes = reference_model.spectral_model.reference_fluxes(
energy_axis=energy_axis
)
# TODO: handle reshaping in MapAxis
factor = fluxes[f"ref_{sed_type}"].to(map_ref.unit)[cls._expand_slice]
data = {}
data["norm"] = map_ref / factor
for key in OPTIONAL_QUANTITIES[sed_type]:
if key in maps:
norm_type = key.replace(sed_type, "norm")
data[norm_type] = maps[key] / factor
# We add the remaining maps
for key in OPTIONAL_QUANTITIES_COMMON:
if key in maps:
data[key] = maps[key]
return cls(data=data, reference_model=reference_model, gti=gti, meta=meta)
[docs] def to_hdulist(self, sed_type="likelihood", hdu_bands=None):
"""Convert flux map to list of HDUs.
For now, one cannot export the reference model.
Parameters
----------
sed_type : str
sed type to convert to. Default is `Likelihood`
hdu_bands : str
Name of the HDU with the BANDS table. Default is 'BANDS'
If set to None, each map will have its own hdu_band
Returns
-------
hdulist : `~astropy.io.fits.HDUList`
Map dataset list of HDUs.
"""
exclude_primary = slice(1, None)
hdu_primary = fits.PrimaryHDU()
hdu_primary.header["SED_TYPE"] = sed_type
hdulist = fits.HDUList([hdu_primary])
maps = self.to_maps(sed_type=sed_type)
hdulist.extend(maps.to_hdulist(hdu_bands=hdu_bands)[exclude_primary])
if self.gti:
hdu = fits.BinTableHDU(self.gti.table, name="GTI")
hdulist.append(hdu)
return hdulist
[docs] @classmethod
def from_hdulist(cls, hdulist, hdu_bands=None, sed_type=None):
"""Create flux map dataset from list of HDUs.
Parameters
----------
hdulist : `~astropy.io.fits.HDUList`
List of HDUs.
hdu_bands : str
Name of the HDU with the BANDS table. Default is 'BANDS'
If set to None, each map should have its own hdu_band
sed_type : {"dnde", "flux", "e2dnde", "eflux", "likelihood"}
Sed type
Returns
-------
flux_maps : `~gammapy.estimators.FluxMaps`
Flux maps object.
"""
maps = Maps.from_hdulist(hdulist=hdulist, hdu_bands=hdu_bands)
if sed_type is None:
sed_type = hdulist[0].header.get("SED_TYPE", None)
filename = hdulist[0].header.get("MODEL", None)
if filename:
reference_model = Models.read(filename)[0]
else:
reference_model = None
if "GTI" in hdulist:
gti = GTI(Table.read(hdulist["GTI"]))
else:
gti = None
return cls.from_maps(
maps=maps, sed_type=sed_type, reference_model=reference_model, gti=gti
)
[docs] def write(
self, filename, filename_model=None, overwrite=False, sed_type="likelihood"
):
"""Write flux map to file.
Parameters
----------
filename : str
Filename to write to.
filename_model : str
Filename of the model (yaml format).
If None, keep string before '.' and add '_model.yaml' suffix
overwrite : bool
Overwrite file if it exists.
sed_type : str
sed type to convert to. Default is `likelihood`
"""
filename = make_path(filename)
if filename_model is None:
name_string = filename.as_posix()
for suffix in filename.suffixes:
name_string.replace(suffix, "")
filename_model = name_string + "_model.yaml"
filename_model = make_path(filename_model)
hdulist = self.to_hdulist(sed_type)
models = Models(self.reference_model)
models.write(filename_model, overwrite=overwrite, write_covariance=False)
hdulist[0].header["MODEL"] = filename_model.as_posix()
hdulist.writeto(filename, overwrite=overwrite)
[docs] @classmethod
def read(cls, filename):
"""Read map dataset from file.
Parameters
----------
filename : str
Filename to read from.
Returns
-------
flux_maps : `~gammapy.estimators.FluxMaps`
Flux maps object.
"""
with fits.open(str(make_path(filename)), memmap=False) as hdulist:
return cls.from_hdulist(hdulist)
[docs] def slice_by_idx(self, slices):
"""Slice flux maps by idx
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.
Returns
-------
flux_maps : `FluxMaps`
Sliced flux maps object.
"""
data = {}
for key, item in self._data.items():
data[key] = item.slice_by_idx(slices)
return self.__class__(
data=data,
reference_model=self.reference_model,
meta=self.meta.copy(),
gti=self.gti,
)
# TODO: should we allow this?
def __getitem__(self, item):
return getattr(self, item)
def __str__(self):
str_ = f"{self.__class__.__name__}\n"
str_ += "-" * len(self.__class__.__name__)
str_ += "\n\n"
str_ += "\t" + f"geom : {self.geom.__class__.__name__}\n"
str_ += "\t" + f"axes : {self.geom.axes_names}\n"
str_ += "\t" + f"shape : {self.geom.data_shape[::-1]}\n"
str_ += "\t" + f"quantities : {list(self.available_quantities)}\n"
str_ += (
"\t" + f"ref. model : {self.reference_spectral_model.tag[-1]}\n"
)
str_ += "\t" + f"n_sigma : {self.n_sigma}\n"
str_ += "\t" + f"n_sigma_ul : {self.n_sigma_ul}\n"
str_ += "\t" + f"sqrt_ts_threshold_ul : {self.sqrt_ts_threshold_ul}\n"
str_ += "\t" + f"sed type init : {self.sed_type_init}\n"
return str_.expandtabs(tabsize=2)