# Licensed under a 3-clause BSD style license - see LICENSE.rst
import abc
import copy
import inspect
import json
from collections import OrderedDict
import numpy as np
from astropy import units as u
from astropy.io import fits
import matplotlib.pyplot as plt
from gammapy.utils.random import InverseCDFSampler, get_random_state
from gammapy.utils.scripts import make_path
from gammapy.utils.units import energy_unit_format
from .axes import MapAxis
from .coord import MapCoord
from .geom import pix_tuple_to_idx
from .io import JsonQuantityDecoder
__all__ = ["Map"]
[docs]class Map(abc.ABC):
"""Abstract map class.
This can represent WCS- or HEALPIX-based maps
with 2 spatial dimensions and N non-spatial dimensions.
Parameters
----------
geom : `~gammapy.maps.Geom`
Geometry
data : `~numpy.ndarray` or `~astropy.units.Quantity`
Data array
meta : `dict`
Dictionary to store meta data
unit : str or `~astropy.units.Unit`
Data unit, ignored if data is a Quantity.
"""
tag = "map"
def __init__(self, geom, data, meta=None, unit=""):
self._geom = geom
if isinstance(data, u.Quantity):
self._unit = u.Unit(unit)
self.quantity = data
else:
self.data = data
self._unit = u.Unit(unit)
if meta is None:
self.meta = {}
else:
self.meta = meta
def _init_copy(self, **kwargs):
"""Init map instance by copying missing init arguments from self."""
argnames = inspect.getfullargspec(self.__init__).args
argnames.remove("self")
argnames.remove("dtype")
for arg in argnames:
value = getattr(self, "_" + arg)
kwargs.setdefault(arg, copy.deepcopy(value))
return self.from_geom(**kwargs)
@property
def is_mask(self):
"""Whether map is mask with bool dtype"""
return self.data.dtype == bool
@property
def geom(self):
"""Map geometry (`~gammapy.maps.Geom`)"""
return self._geom
@property
def data(self):
"""Data array (`~numpy.ndarray`)"""
return self._data
@data.setter
def data(self, value):
"""Set data
Parameters
----------
value : array-like
Data array
"""
if np.isscalar(value):
value = value * np.ones(self.geom.data_shape, dtype=type(value))
if isinstance(value, u.Quantity):
raise TypeError("Map data must be a Numpy array. Set unit separately")
if not value.shape == self.geom.data_shape:
value = value.reshape(self.geom.data_shape)
self._data = value
@property
def unit(self):
"""Map unit (`~astropy.units.Unit`)"""
return self._unit
@property
def meta(self):
"""Map meta (`dict`)"""
return self._meta
@meta.setter
def meta(self, val):
self._meta = val
@property
def quantity(self):
"""Map data times unit (`~astropy.units.Quantity`)"""
return u.Quantity(self.data, self.unit, copy=False)
@quantity.setter
def quantity(self, val):
"""Set data and unit
Parameters
----------
value : `~astropy.units.Quantity`
Quantity
"""
val = u.Quantity(val, copy=False)
self.data = val.value
self._unit = val.unit
[docs] def rename_axes(self, names, new_names):
"""Rename the Map axes.
Parameters
----------
names : list or str
Names of the axes.
new_names : list or str
New names of the axes (list must be of same length than `names`).
Returns
-------
geom : `~Map`
Renamed Map.
"""
geom = self.geom.rename_axes(names=names, new_names=new_names)
return self._init_copy(geom=geom)
[docs] @staticmethod
def create(**kwargs):
"""Create an empty map object.
This method accepts generic options listed below, as well as options
for `HpxMap` and `WcsMap` objects. For WCS-specific options, see
`WcsMap.create` and for HPX-specific options, see `HpxMap.create`.
Parameters
----------
frame : str
Coordinate system, either Galactic ("galactic") or Equatorial
("icrs").
map_type : {'wcs', 'wcs-sparse', 'hpx', 'hpx-sparse', 'region'}
Map type. Selects the class that will be used to
instantiate the map.
binsz : float or `~numpy.ndarray`
Pixel size in degrees.
skydir : `~astropy.coordinates.SkyCoord`
Coordinate of map center.
axes : list
List of `~MapAxis` objects for each non-spatial dimension.
If None then the map will be a 2D image.
dtype : str
Data type, default is 'float32'
unit : str or `~astropy.units.Unit`
Data unit.
meta : `dict`
Dictionary to store meta data.
region : `~regions.SkyRegion`
Sky region used for the region map.
Returns
-------
map : `Map`
Empty map object.
"""
from .hpx import HpxMap
from .region import RegionNDMap
from .wcs import WcsMap
map_type = kwargs.setdefault("map_type", "wcs")
if "wcs" in map_type.lower():
return WcsMap.create(**kwargs)
elif "hpx" in map_type.lower():
return HpxMap.create(**kwargs)
elif map_type == "region":
_ = kwargs.pop("map_type")
return RegionNDMap.create(**kwargs)
else:
raise ValueError(f"Unrecognized map type: {map_type!r}")
[docs] @staticmethod
def read(
filename, hdu=None, hdu_bands=None, map_type="auto", format=None, colname=None
):
"""Read a map from a FITS file.
Parameters
----------
filename : str or `~pathlib.Path`
Name of the FITS file.
hdu : str
Name or index of the HDU with the map data.
hdu_bands : str
Name or index of the HDU with the BANDS table. If not
defined this will be inferred from the FITS header of the
map HDU.
map_type : {'wcs', 'wcs-sparse', 'hpx', 'hpx-sparse', 'auto', 'region'}
Map type. Selects the class that will be used to
instantiate the map. The map type should be consistent
with the format of the input file. If map_type is 'auto'
then an appropriate map type will be inferred from the
input file.
colname : str, optional
data column name to be used of healix map.
Returns
-------
map_out : `Map`
Map object
"""
with fits.open(str(make_path(filename)), memmap=False) as hdulist:
return Map.from_hdulist(
hdulist, hdu, hdu_bands, map_type, format=format, colname=colname
)
[docs] @staticmethod
def from_geom(geom, meta=None, data=None, unit="", dtype="float32"):
"""Generate an empty map from a `Geom` instance.
Parameters
----------
geom : `Geom`
Map geometry.
data : `numpy.ndarray`
data array
meta : `dict`
Dictionary to store meta data.
unit : str or `~astropy.units.Unit`
Data unit.
Returns
-------
map_out : `Map`
Map object
"""
from .hpx import HpxGeom
from .region import RegionGeom
from .wcs import WcsGeom
if isinstance(geom, HpxGeom):
map_type = "hpx"
elif isinstance(geom, WcsGeom):
map_type = "wcs"
elif isinstance(geom, RegionGeom):
map_type = "region"
else:
raise ValueError("Unrecognized geom type.")
cls_out = Map._get_map_cls(map_type)
return cls_out(geom, data=data, meta=meta, unit=unit, dtype=dtype)
[docs] @staticmethod
def from_hdulist(
hdulist, hdu=None, hdu_bands=None, map_type="auto", format=None, colname=None
):
"""Create from `astropy.io.fits.HDUList`.
Parameters
----------
hdulist : `~astropy.io.fits.HDUList`
HDU list containing HDUs for map data and bands.
hdu : str
Name or index of the HDU with the map data.
hdu_bands : str
Name or index of the HDU with the BANDS table.
map_type : {"auto", "wcs", "hpx", "region"}
Map type.
format : {'gadf', 'fgst-ccube', 'fgst-template'}
FITS format convention.
colname : str, optional
Data column name to be used for the HEALPix map.
Returns
-------
map_out : `Map`
Map object
"""
if map_type == "auto":
map_type = Map._get_map_type(hdulist, hdu)
cls_out = Map._get_map_cls(map_type)
if map_type == "hpx":
return cls_out.from_hdulist(
hdulist, hdu=hdu, hdu_bands=hdu_bands, format=format, colname=colname
)
else:
return cls_out.from_hdulist(
hdulist, hdu=hdu, hdu_bands=hdu_bands, format=format
)
@staticmethod
def _get_meta_from_header(header):
"""Load meta data from a FITS header."""
if "META" in header:
return json.loads(header["META"], cls=JsonQuantityDecoder)
else:
return {}
@staticmethod
def _get_map_type(hdu_list, hdu_name):
"""Infer map type from a FITS HDU.
Only read header, never data, to have good performance.
"""
if hdu_name is None:
# Find the header of the first non-empty HDU
header = hdu_list[0].header
if header["NAXIS"] == 0:
header = hdu_list[1].header
else:
header = hdu_list[hdu_name].header
if ("PIXTYPE" in header) and (header["PIXTYPE"] == "HEALPIX"):
return "hpx"
elif "CTYPE1" in header:
return "wcs"
else:
return "region"
@staticmethod
def _get_map_cls(map_type):
"""Get map class for given `map_type` string.
This should probably be a registry dict so that users
can add supported map types to the `gammapy.maps` I/O
(see e.g. the Astropy table format I/O registry),
but that's non-trivial to implement without avoiding circular imports.
"""
if map_type == "wcs":
from .wcs import WcsNDMap
return WcsNDMap
elif map_type == "wcs-sparse":
raise NotImplementedError()
elif map_type == "hpx":
from .hpx import HpxNDMap
return HpxNDMap
elif map_type == "hpx-sparse":
raise NotImplementedError()
elif map_type == "region":
from .region import RegionNDMap
return RegionNDMap
else:
raise ValueError(f"Unrecognized map type: {map_type!r}")
[docs] def write(self, filename, overwrite=False, **kwargs):
"""Write to a FITS file.
Parameters
----------
filename : str
Output file name.
overwrite : bool
Overwrite existing file?
hdu : str
Set the name of the image extension. By default this will
be set to SKYMAP (for BINTABLE HDU) or PRIMARY (for IMAGE
HDU).
hdu_bands : str
Set the name of the bands table extension. By default this will
be set to BANDS.
format : str, optional
FITS format convention. By default files will be written
to the gamma-astro-data-formats (GADF) format. This
option can be used to write files that are compliant with
format conventions required by specific software (e.g. the
Fermi Science Tools). The following formats are supported:
- "gadf" (default)
- "fgst-ccube"
- "fgst-ltcube"
- "fgst-bexpcube"
- "fgst-srcmap"
- "fgst-template"
- "fgst-srcmap-sparse"
- "galprop"
- "galprop2"
sparse : bool
Sparsify the map by dropping pixels with zero amplitude.
This option is only compatible with the 'gadf' format.
"""
hdulist = self.to_hdulist(**kwargs)
hdulist.writeto(str(make_path(filename)), overwrite=overwrite)
[docs] def iter_by_axis(self, axis_name, keepdims=False):
""" "Iterate over a given axis
Yields
------
map : `Map`
Map iteration.
See also
--------
iter_by_image : iterate by image returning a map
"""
axis = self.geom.axes[axis_name]
for idx in range(axis.nbin):
idx_axis = slice(idx, idx + 1) if keepdims else idx
slices = {axis_name: idx_axis}
yield self.slice_by_idx(slices=slices)
[docs] def iter_by_image(self, keepdims=False):
"""Iterate over image planes of a map.
Parameters
----------
keepdims : bool
Keep dimensions.
Yields
------
map : `Map`
Map iteration.
See also
--------
iter_by_image_data : iterate by image returning data and index
"""
for idx in np.ndindex(self.geom.shape_axes):
if keepdims:
names = self.geom.axes.names
slices = {name: slice(_, _ + 1) for name, _ in zip(names, idx)}
yield self.slice_by_idx(slices=slices)
else:
yield self.get_image_by_idx(idx=idx)
[docs] def iter_by_image_data(self):
"""Iterate over image planes of the map.
The image plane index is in data order, so that the data array can be
indexed directly.
Yields
------
(data, idx) : tuple
Where ``data`` is a `numpy.ndarray` view of the image plane data,
and ``idx`` is a tuple of int, the index of the image plane.
See also
--------
iter_by_image : iterate by image returning a map
"""
for idx in np.ndindex(self.geom.shape_axes):
yield self.data[idx[::-1]], idx[::-1]
[docs] def coadd(self, map_in, weights=None):
"""Add the contents of ``map_in`` to this map.
This method can be used to combine maps containing integral quantities (e.g. counts)
or differential quantities if the maps have the same binning.
Parameters
----------
map_in : `Map`
Input map.
weights: `Map` or `~numpy.ndarray`
The weight factors while adding
"""
if not self.unit.is_equivalent(map_in.unit):
raise ValueError("Incompatible units")
# TODO: Check whether geometries are aligned and if so sum the
# data vectors directly
if weights is not None:
map_in = map_in * weights
idx = map_in.geom.get_idx()
coords = map_in.geom.get_coord()
vals = u.Quantity(map_in.get_by_idx(idx), map_in.unit)
self.fill_by_coord(coords, vals)
[docs] def pad(self, pad_width, axis_name=None, mode="constant", cval=0, method="linear"):
"""Pad the spatial dimensions of the map.
Parameters
----------
pad_width : {sequence, array_like, int}
Number of pixels padded to the edges of each axis.
axis_name : str
Which axis to downsample. By default spatial axes are padded.
mode : {'edge', 'constant', 'interp'}
Padding mode. 'edge' pads with the closest edge value.
'constant' pads with a constant value. 'interp' pads with
an extrapolated value.
cval : float
Padding value when mode='consant'.
Returns
-------
map : `Map`
Padded map.
"""
if axis_name:
if np.isscalar(pad_width):
pad_width = (pad_width, pad_width)
geom = self.geom.pad(pad_width=pad_width, axis_name=axis_name)
idx = self.geom.axes.index_data(axis_name)
pad_width_np = [(0, 0)] * self.data.ndim
pad_width_np[idx] = pad_width
kwargs = {}
if mode == "constant":
kwargs["constant_values"] = cval
data = np.pad(self.data, pad_width=pad_width_np, mode=mode, **kwargs)
return self.__class__(
geom=geom, data=data, unit=self.unit, meta=self.meta.copy()
)
return self._pad_spatial(pad_width, mode="constant", cval=cval)
@abc.abstractmethod
def _pad_spatial(self, pad_width, mode="constant", cval=0, order=1):
pass
[docs] @abc.abstractmethod
def crop(self, crop_width):
"""Crop the spatial dimensions of the map.
Parameters
----------
crop_width : {sequence, array_like, int}
Number of pixels cropped from the edges of each axis.
Defined analogously to ``pad_with`` from `numpy.pad`.
Returns
-------
map : `Map`
Cropped map.
"""
pass
[docs] @abc.abstractmethod
def downsample(self, factor, preserve_counts=True, axis_name=None):
"""Downsample the spatial dimension by a given factor.
Parameters
----------
factor : int
Downsampling factor.
preserve_counts : bool
Preserve the integral over each bin. This should be true
if the map is an integral quantity (e.g. counts) and false if
the map is a differential quantity (e.g. intensity).
axis_name : str
Which axis to downsample. By default spatial axes are downsampled.
Returns
-------
map : `Map`
Downsampled map.
"""
pass
[docs] @abc.abstractmethod
def upsample(self, factor, order=0, preserve_counts=True, axis_name=None):
"""Upsample the spatial dimension by a given factor.
Parameters
----------
factor : int
Upsampling factor.
order : int
Order of the interpolation used for upsampling.
preserve_counts : bool
Preserve the integral over each bin. This should be true
if the map is an integral quantity (e.g. counts) and false if
the map is a differential quantity (e.g. intensity).
axis_name : str
Which axis to upsample. By default spatial axes are upsampled.
Returns
-------
map : `Map`
Upsampled map.
"""
pass
[docs] def resample(self, geom, weights=None, preserve_counts=True):
"""Resample pixels to ``geom`` with given ``weights``.
Parameters
----------
geom : `~gammapy.maps.Geom`
Target Map geometry
weights : `~numpy.ndarray`
Weights vector. Default is weight of one. Must have same shape as
the data of the map.
preserve_counts : bool
Preserve the integral over each bin. This should be true
if the map is an integral quantity (e.g. counts) and false if
the map is a differential quantity (e.g. intensity)
Returns
-------
resampled_map : `Map`
Resampled map
"""
coords = self.geom.get_coord()
idx = geom.coord_to_idx(coords)
weights = 1 if weights is None else weights
resampled = self._init_copy(data=None, geom=geom)
resampled._resample_by_idx(
idx, weights=self.data * weights, preserve_counts=preserve_counts
)
return resampled
@abc.abstractmethod
def _resample_by_idx(self, idx, weights=None, preserve_counts=False):
"""Resample pixels at ``idx`` with given ``weights``.
Parameters
----------
idx : tuple
Tuple of pixel index arrays for each dimension of the map.
Tuple should be ordered as (I_lon, I_lat, I_0, ..., I_n)
for WCS maps and (I_hpx, I_0, ..., I_n) for HEALPix maps.
weights : `~numpy.ndarray`
Weights vector. Default is weight of one.
preserve_counts : bool
Preserve the integral over each bin. This should be true
if the map is an integral quantity (e.g. counts) and false if
the map is a differential quantity (e.g. intensity)
"""
pass
[docs] def resample_axis(self, axis, weights=None, ufunc=np.add):
"""Resample map to a new axis by grouping and reducing smaller bins by a given ufunc
By default, the map content are summed over the smaller bins. Other numpy ufunc can be
used, e.g. `numpy.logical_and` or `numpy.logical_or`.
Parameters
----------
axis : `MapAxis`
New map axis.
weights : `Map`
Array to be used as weights. The spatial geometry must be equivalent
to `other` and additional axes must be broadcastable.
ufunc : `~numpy.ufunc`
ufunc to use to resample the axis. Default is numpy.add.
Returns
-------
map : `Map`
Map with resampled axis.
"""
from .hpx import HpxGeom
geom = self.geom.resample_axis(axis)
axis_self = self.geom.axes[axis.name]
axis_resampled = geom.axes[axis.name]
# We don't use MapAxis.coord_to_idx because is does not behave as needed with boundaries
coord = axis_resampled.edges.value
edges = axis_self.edges.value
indices = np.digitize(coord, edges) - 1
idx = self.geom.axes.index_data(axis.name)
weights = 1 if weights is None else weights.data
if not isinstance(self.geom, HpxGeom):
shape = self.geom._shape[:2]
else:
shape = (self.geom.data_shape[-1],)
shape += tuple([ax.nbin if ax != axis else 1 for ax in self.geom.axes])
padded_array = np.append(self.data * weights, np.zeros(shape[::-1]), axis=idx)
slices = tuple([slice(0, _) for _ in geom.data_shape])
data = ufunc.reduceat(padded_array, indices=indices, axis=idx)[slices]
return self._init_copy(data=data, geom=geom)
[docs] def slice_by_idx(
self,
slices,
):
"""Slice sub map from map object.
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
-------
map_out : `Map`
Sliced map object.
"""
geom = self.geom.slice_by_idx(slices)
slices = tuple([slices.get(ax.name, slice(None)) for ax in self.geom.axes])
data = self.data[slices[::-1]]
return self.__class__(geom=geom, data=data, unit=self.unit, meta=self.meta)
[docs] def get_image_by_coord(self, coords):
"""Return spatial map at the given axis coordinates.
Parameters
----------
coords : tuple or dict
Tuple should be ordered as (x_0, ..., x_n) where x_i are coordinates
for non-spatial dimensions of the map. Dict should specify the axis
names of the non-spatial axes such as {'axes0': x_0, ..., 'axesn': x_n}.
Returns
-------
map_out : `Map`
Map with spatial dimensions only.
See Also
--------
get_image_by_idx, get_image_by_pix
Examples
--------
::
import numpy as np
from gammapy.maps import Map, MapAxis
from astropy.coordinates import SkyCoord
from astropy import units as u
# Define map axes
energy_axis = MapAxis.from_edges(
np.logspace(-1., 1., 4), unit='TeV', name='energy',
)
time_axis = MapAxis.from_edges(
np.linspace(0., 10, 20), unit='h', name='time',
)
# Define map center
skydir = SkyCoord(0, 0, frame='galactic', unit='deg')
# Create map
m_wcs = Map.create(
map_type='wcs',
binsz=0.02,
skydir=skydir,
width=10.0,
axes=[energy_axis, time_axis],
)
# Get image by coord tuple
image = m_wcs.get_image_by_coord(('500 GeV', '1 h'))
# Get image by coord dict with strings
image = m_wcs.get_image_by_coord({'energy': '500 GeV', 'time': '1 h'})
# Get image by coord dict with quantities
image = m_wcs.get_image_by_coord({'energy': 0.5 * u.TeV, 'time': 1 * u.h})
"""
if isinstance(coords, tuple):
coords = dict(zip(self.geom.axes.names, coords))
idx = self.geom.axes.coord_to_idx(coords)
return self.get_image_by_idx(idx)
[docs] def get_image_by_pix(self, pix):
"""Return spatial map at the given axis pixel coordinates
Parameters
----------
pix : tuple
Tuple of scalar pixel coordinates for each non-spatial dimension of
the map. Tuple should be ordered as (I_0, ..., I_n). Pixel coordinates
can be either float or integer type.
See Also
--------
get_image_by_coord, get_image_by_idx
Returns
-------
map_out : `Map`
Map with spatial dimensions only.
"""
idx = self.geom.pix_to_idx(pix)
return self.get_image_by_idx(idx)
[docs] def get_image_by_idx(self, idx):
"""Return spatial map at the given axis pixel indices.
Parameters
----------
idx : tuple
Tuple of scalar indices for each non spatial dimension of the map.
Tuple should be ordered as (I_0, ..., I_n).
See Also
--------
get_image_by_coord, get_image_by_pix
Returns
-------
map_out : `Map`
Map with spatial dimensions only.
"""
if len(idx) != len(self.geom.axes):
raise ValueError("Tuple length must equal number of non-spatial dimensions")
# Only support scalar indices per axis
idx = tuple([int(_) for _ in idx])
geom = self.geom.to_image()
data = self.data[idx[::-1]]
return self.__class__(geom=geom, data=data, unit=self.unit, meta=self.meta)
[docs] def get_by_coord(self, coords, fill_value=np.nan):
"""Return map values at the given map coordinates.
Parameters
----------
coords : tuple or `~gammapy.maps.MapCoord`
Coordinate arrays for each dimension of the map. Tuple
should be ordered as (lon, lat, x_0, ..., x_n) where x_i
are coordinates for non-spatial dimensions of the map.
fill_value : float
Value which is returned if the position is outside of the projection
footprint
Returns
-------
vals : `~numpy.ndarray`
Values of pixels in the map. np.nan used to flag coords
outside of map.
"""
pix = self.geom.coord_to_pix(coords=coords)
vals = self.get_by_pix(pix, fill_value=fill_value)
return vals
[docs] def get_by_pix(self, pix, fill_value=np.nan):
"""Return map values at the given pixel coordinates.
Parameters
----------
pix : tuple
Tuple of pixel index arrays for each dimension of the map.
Tuple should be ordered as (I_lon, I_lat, I_0, ..., I_n)
for WCS maps and (I_hpx, I_0, ..., I_n) for HEALPix maps.
Pixel indices can be either float or integer type.
fill_value : float
Value which is returned if the position is outside of the projection
footprint
Returns
-------
vals : `~numpy.ndarray`
Array of pixel values. np.nan used to flag coordinates
outside of map
"""
# FIXME: Support local indexing here?
# FIXME: Support slicing?
pix = np.broadcast_arrays(*pix)
idx = self.geom.pix_to_idx(pix)
vals = self.get_by_idx(idx)
mask = self.geom.contains_pix(pix)
if not mask.all():
vals = vals.astype(type(fill_value))
vals[~mask] = fill_value
return vals
[docs] @abc.abstractmethod
def get_by_idx(self, idx):
"""Return map values at the given pixel indices.
Parameters
----------
idx : tuple
Tuple of pixel index arrays for each dimension of the map.
Tuple should be ordered as (I_lon, I_lat, I_0, ..., I_n)
for WCS maps and (I_hpx, I_0, ..., I_n) for HEALPix maps.
Returns
-------
vals : `~numpy.ndarray`
Array of pixel values.
np.nan used to flag coordinate outside of map
"""
pass
[docs] @abc.abstractmethod
def interp_by_coord(self, coords, method="linear", fill_value=None):
"""Interpolate map values at the given map coordinates.
Parameters
----------
coords : tuple or `~gammapy.maps.MapCoord`
Coordinate arrays for each dimension of the map. Tuple
should be ordered as (lon, lat, x_0, ..., x_n) where x_i
are coordinates for non-spatial dimensions of the map.
method : {"linear", "nearest"}
Method to interpolate data values. By default linear
interpolation is performed.
fill_value : None or float value
The value to use for points outside of the interpolation domain.
If None, values outside the domain are extrapolated.
Returns
-------
vals : `~numpy.ndarray`
Interpolated pixel values.
"""
pass
[docs] @abc.abstractmethod
def interp_by_pix(self, pix, method="linear", fill_value=None):
"""Interpolate map values at the given pixel coordinates.
Parameters
----------
pix : tuple
Tuple of pixel coordinate arrays for each dimension of the
map. Tuple should be ordered as (p_lon, p_lat, p_0, ...,
p_n) where p_i are pixel coordinates for non-spatial
dimensions of the map.
method : {"linear", "nearest"}
Method to interpolate data values. By default linear
interpolation is performed.
fill_value : None or float value
The value to use for points outside of the interpolation domain.
If None, values outside the domain are extrapolated.
Returns
-------
vals : `~numpy.ndarray`
Interpolated pixel values.
"""
pass
[docs] def interp_to_geom(self, geom, preserve_counts=False, fill_value=0, **kwargs):
"""Interpolate map to input geometry.
Parameters
----------
geom : `~gammapy.maps.Geom`
Target Map geometry
preserve_counts : bool
Preserve the integral over each bin. This should be true
if the map is an integral quantity (e.g. counts) and false if
the map is a differential quantity (e.g. intensity)
**kwargs : dict
Keyword arguments passed to `Map.interp_by_coord`
Returns
-------
interp_map : `Map`
Interpolated Map
"""
coords = geom.get_coord()
map_copy = self.copy()
if preserve_counts:
if geom.ndim > 2 and geom.axes[0] != self.geom.axes[0]:
raise ValueError(
f"Energy axis do not match: expected {self.geom.axes[0]},"
" but got {geom.axes[0]}."
)
map_copy.data /= map_copy.geom.solid_angle().to_value("deg2")
if map_copy.is_mask:
# TODO: check this NaN handling is needed
data = map_copy.get_by_coord(coords)
data = np.nan_to_num(data, nan=fill_value).astype(bool)
else:
data = map_copy.interp_by_coord(coords, fill_value=fill_value, **kwargs)
if preserve_counts:
data *= geom.solid_angle().to_value("deg2")
return Map.from_geom(geom, data=data, unit=self.unit)
[docs] def reproject_to_geom(self, geom, preserve_counts=False, precision_factor=10):
"""Reproject map to input geometry.
Parameters
----------
geom : `~gammapy.maps.Geom`
Target Map geometry
preserve_counts : bool
Preserve the integral over each bin. This should be true
if the map is an integral quantity (e.g. counts) and false if
the map is a differential quantity (e.g. intensity)
precision_factor : int
Minimal factor between the bin size of the output map and the oversampled base map.
Used only for the oversampling method.
Returns
-------
output_map : `Map`
Reprojected Map
"""
from .hpx import HpxGeom
from .region import RegionGeom
axes = [ax.copy() for ax in self.geom.axes]
geom3d = geom.copy(axes=axes)
if not geom.is_image:
if geom.axes.names != geom3d.axes.names:
raise ValueError("Axis names and order should be the same.")
if geom.axes != geom3d.axes and (
isinstance(geom3d, HpxGeom) or isinstance(self.geom, HpxGeom)
):
raise TypeError(
"Reprojection to 3d geom with non-identical axes is not supported for HpxGeom. "
"Reproject to 2d geom first and then use inter_to_geom method."
)
if isinstance(geom3d, RegionGeom):
base_factor = (
geom3d.to_wcs_geom().pixel_scales.min() / self.geom.pixel_scales.min()
)
elif isinstance(self.geom, RegionGeom):
base_factor = (
geom3d.pixel_scales.min() / self.geom.to_wcs_geom().pixel_scales.min()
)
else:
base_factor = geom3d.pixel_scales.min() / self.geom.pixel_scales.min()
if base_factor >= precision_factor:
input_map = self
else:
factor = precision_factor / base_factor
if isinstance(self.geom, HpxGeom):
factor = int(2 ** np.ceil(np.log(factor) / np.log(2)))
else:
factor = int(np.ceil(factor))
input_map = self.upsample(factor=factor, preserve_counts=preserve_counts)
output_map = input_map.resample(geom3d, preserve_counts=preserve_counts)
if not geom.is_image and geom.axes != geom3d.axes:
for base_ax, target_ax in zip(geom3d.axes, geom.axes):
base_factor = base_ax.bin_width.min() / target_ax.bin_width.min()
if not base_factor >= precision_factor:
factor = precision_factor / base_factor
factor = int(np.ceil(factor))
output_map = output_map.upsample(
factor=factor,
preserve_counts=preserve_counts,
axis_name=base_ax.name,
)
output_map = output_map.resample(geom, preserve_counts=preserve_counts)
return output_map
[docs] def fill_events(self, events):
"""Fill event coordinates (`~gammapy.data.EventList`)."""
self.fill_by_coord(events.map_coord(self.geom))
[docs] def fill_by_coord(self, coords, weights=None):
"""Fill pixels at ``coords`` with given ``weights``.
Parameters
----------
coords : tuple or `~gammapy.maps.MapCoord`
Coordinate arrays for each dimension of the map. Tuple
should be ordered as (lon, lat, x_0, ..., x_n) where x_i
are coordinates for non-spatial dimensions of the map.
weights : `~numpy.ndarray`
Weights vector. Default is weight of one.
"""
idx = self.geom.coord_to_idx(coords)
self.fill_by_idx(idx, weights=weights)
[docs] def fill_by_pix(self, pix, weights=None):
"""Fill pixels at ``pix`` with given ``weights``.
Parameters
----------
pix : tuple
Tuple of pixel index arrays for each dimension of the map.
Tuple should be ordered as (I_lon, I_lat, I_0, ..., I_n)
for WCS maps and (I_hpx, I_0, ..., I_n) for HEALPix maps.
Pixel indices can be either float or integer type. Float
indices will be rounded to the nearest integer.
weights : `~numpy.ndarray`
Weights vector. Default is weight of one.
"""
idx = pix_tuple_to_idx(pix)
return self.fill_by_idx(idx, weights=weights)
[docs] @abc.abstractmethod
def fill_by_idx(self, idx, weights=None):
"""Fill pixels at ``idx`` with given ``weights``.
Parameters
----------
idx : tuple
Tuple of pixel index arrays for each dimension of the map.
Tuple should be ordered as (I_lon, I_lat, I_0, ..., I_n)
for WCS maps and (I_hpx, I_0, ..., I_n) for HEALPix maps.
weights : `~numpy.ndarray`
Weights vector. Default is weight of one.
"""
pass
[docs] def set_by_coord(self, coords, vals):
"""Set pixels at ``coords`` with given ``vals``.
Parameters
----------
coords : tuple or `~gammapy.maps.MapCoord`
Coordinate arrays for each dimension of the map. Tuple
should be ordered as (lon, lat, x_0, ..., x_n) where x_i
are coordinates for non-spatial dimensions of the map.
vals : `~numpy.ndarray`
Values vector.
"""
idx = self.geom.coord_to_pix(coords)
self.set_by_pix(idx, vals)
[docs] def set_by_pix(self, pix, vals):
"""Set pixels at ``pix`` with given ``vals``.
Parameters
----------
pix : tuple
Tuple of pixel index arrays for each dimension of the map.
Tuple should be ordered as (I_lon, I_lat, I_0, ..., I_n)
for WCS maps and (I_hpx, I_0, ..., I_n) for HEALPix maps.
Pixel indices can be either float or integer type. Float
indices will be rounded to the nearest integer.
vals : `~numpy.ndarray`
Values vector.
"""
idx = pix_tuple_to_idx(pix)
return self.set_by_idx(idx, vals)
[docs] @abc.abstractmethod
def set_by_idx(self, idx, vals):
"""Set pixels at ``idx`` with given ``vals``.
Parameters
----------
idx : tuple
Tuple of pixel index arrays for each dimension of the map.
Tuple should be ordered as (I_lon, I_lat, I_0, ..., I_n)
for WCS maps and (I_hpx, I_0, ..., I_n) for HEALPix maps.
vals : `~numpy.ndarray`
Values vector.
"""
pass
[docs] def plot_grid(self, figsize=None, ncols=3, **kwargs):
"""Plot map as a grid of subplots for non-spatial axes
Parameters
----------
figsize : tuple of int
Figsize to plot on
ncols : int
Number of columns to plot
**kwargs : dict
Keyword arguments passed to `Map.plot`.
Returns
-------
axes : `~numpy.ndarray` of `~matplotlib.pyplot.Axes`
Axes grid
"""
if len(self.geom.axes) > 1:
raise ValueError("Grid plotting is only supported for one non spatial axis")
axis = self.geom.axes[0]
cols = min(ncols, axis.nbin)
rows = 1 + (axis.nbin - 1) // cols
if figsize is None:
width = 12
figsize = (width, width * rows / cols)
if self.geom.is_hpx:
wcs = self.geom.to_wcs_geom().wcs
else:
wcs = self.geom.wcs
fig, axes = plt.subplots(
ncols=cols,
nrows=rows,
subplot_kw={"projection": wcs},
figsize=figsize,
gridspec_kw={"hspace": 0.1, "wspace": 0.1},
)
for idx in range(cols * rows):
ax = axes.flat[idx]
try:
image = self.get_image_by_idx((idx,))
except IndexError:
ax.set_visible(False)
continue
if image.geom.is_hpx:
image_wcs = image.to_wcs(
normalize=False,
proj="AIT",
oversample=2,
)
else:
image_wcs = image
image_wcs.plot(ax=ax, **kwargs)
if axis.node_type == "center":
if axis.name == "energy" or axis.name == "energy_true":
info = energy_unit_format(axis.center[idx])
else:
info = f"{axis.center[idx]:.1f}"
elif axis.node_type == "label":
info = f"{axis.center[idx]}"
else:
if axis.name == "energy" or axis.name == "energy_true":
info = (
f"{energy_unit_format(axis.edges[idx])} - "
f"{energy_unit_format(axis.edges[idx+1])}"
)
else:
info = f"{axis.edges_min[idx]:.1f} - {axis.edges_max[idx]:.1f} "
ax.set_title(f"{axis.name.capitalize()} " + info)
lon, lat = ax.coords[0], ax.coords[1]
lon.set_ticks_position("b")
lat.set_ticks_position("l")
row, col = np.unravel_index(idx, shape=(rows, cols))
if col > 0:
lat.set_ticklabel_visible(False)
lat.set_axislabel("")
if row < (rows - 1):
lon.set_ticklabel_visible(False)
lon.set_axislabel("")
return axes
[docs] def plot_interactive(self, rc_params=None, **kwargs):
"""
Plot map with interactive widgets to explore the non spatial axes.
Parameters
----------
rc_params : dict
Passed to ``matplotlib.rc_context(rc=rc_params)`` to style the plot.
**kwargs : dict
Keyword arguments passed to `WcsNDMap.plot`.
Examples
--------
You can try this out e.g. using a Fermi-LAT diffuse model cube with an energy axis::
from gammapy.maps import Map
m = Map.read("$GAMMAPY_DATA/fermi_3fhl/gll_iem_v06_cutout.fits")
m.plot_interactive(add_cbar=True, stretch="sqrt")
If you would like to adjust the figure size you can use the ``rc_params`` argument::
rc_params = {'figure.figsize': (12, 6), 'font.size': 12}
m.plot_interactive(rc_params=rc_params)
"""
import matplotlib as mpl
from ipywidgets import RadioButtons, SelectionSlider
from ipywidgets.widgets.interaction import fixed, interact
if self.geom.is_image:
raise TypeError("Use .plot() for 2D Maps")
kwargs.setdefault("interpolation", "nearest")
kwargs.setdefault("origin", "lower")
kwargs.setdefault("cmap", "afmhot")
rc_params = rc_params or {}
stretch = kwargs.pop("stretch", "sqrt")
interact_kwargs = {}
for axis in self.geom.axes:
if axis.node_type == "center":
if axis.name == "energy" or axis.name == "energy_true":
options = energy_unit_format(axis.center)
else:
options = axis.as_plot_labels
elif axis.name == "energy" or axis.name == "energy_true":
E = energy_unit_format(axis.edges)
options = [f"{E[i]} - {E[i+1]}" for i in range(len(E) - 1)]
else:
options = axis.as_plot_labels
interact_kwargs[axis.name] = SelectionSlider(
options=options,
description=f"Select {axis.name}:",
continuous_update=False,
style={"description_width": "initial"},
layout={"width": "50%"},
)
interact_kwargs[axis.name + "_options"] = fixed(options)
interact_kwargs["stretch"] = RadioButtons(
options=["linear", "sqrt", "log"],
value=stretch,
description="Select stretch:",
style={"description_width": "initial"},
)
@interact(**interact_kwargs)
def _plot_interactive(**ikwargs):
idx = [
ikwargs[ax.name + "_options"].index(ikwargs[ax.name])
for ax in self.geom.axes
]
img = self.get_image_by_idx(idx)
stretch = ikwargs["stretch"]
with mpl.rc_context(rc=rc_params):
img.plot(stretch=stretch, **kwargs)
plt.show()
[docs] def copy(self, **kwargs):
"""Copy map instance and overwrite given attributes, except for geometry.
Parameters
----------
**kwargs : dict
Keyword arguments to overwrite in the map constructor.
Returns
-------
copy : `Map`
Copied Map.
"""
if "geom" in kwargs:
geom = kwargs["geom"]
if not geom.data_shape == self.geom.data_shape:
raise ValueError(
"Can't copy and change data size of the map. "
f" Current shape {self.geom.data_shape},"
f" requested shape {geom.data_shape}"
)
return self._init_copy(**kwargs)
[docs] def apply_edisp(self, edisp):
"""Apply energy dispersion to map. Requires energy axis.
Parameters
----------
edisp : `gammapy.irf.EDispKernel`
Energy dispersion matrix
Returns
-------
map : `WcsNDMap`
Map with energy dispersion applied.
"""
# TODO: either use sparse matrix mutiplication or something like edisp.is_diagonal
if edisp is not None:
loc = self.geom.axes.index("energy_true")
data = np.rollaxis(self.data, loc, len(self.data.shape))
data = np.dot(data, edisp.pdf_matrix)
data = np.rollaxis(data, -1, loc)
energy_axis = edisp.axes["energy"].copy(name="energy")
else:
data = self.data
energy_axis = self.geom.axes["energy_true"].copy(name="energy")
geom = self.geom.to_image().to_cube(axes=[energy_axis])
return self._init_copy(geom=geom, data=data)
[docs] def mask_nearest_position(self, position):
"""Given a sky coordinate return nearest valid position in the mask
If the mask contains additional axes, the mask is reduced over those.
Parameters
----------
position : `~astropy.coordinates.SkyCoord`
Test position
Returns
-------
position : `~astropy.coordinates.SkyCoord`
Nearest position in the mask
"""
if not self.geom.is_image:
raise ValueError("Method only supported for 2D images")
coords = self.geom.to_image().get_coord().skycoord
separation = coords.separation(position)
separation[~self.data] = np.inf
idx = np.argmin(separation)
return coords.flatten()[idx]
[docs] def sum_over_axes(self, axes_names=None, keepdims=True, weights=None):
"""To sum map values over all non-spatial axes.
Parameters
----------
keepdims : bool, optional
If this is set to true, the axes which are summed over are left in
the map with a single bin
axes_names: list of str
Names of MapAxis to reduce over. If None, all will summed over
weights : `Map`
Weights to be applied. The Map should have the same geometry.
Returns
-------
map_out : `~Map`
Map with non-spatial axes summed over
"""
return self.reduce_over_axes(
func=np.add, axes_names=axes_names, keepdims=keepdims, weights=weights
)
[docs] def reduce_over_axes(
self, func=np.add, keepdims=False, axes_names=None, weights=None
):
"""Reduce map over non-spatial axes
Parameters
----------
func : `~numpy.ufunc`
Function to use for reducing the data.
keepdims : bool, optional
If this is set to true, the axes which are summed over are left in
the map with a single bin
axes_names: list
Names of MapAxis to reduce over
If None, all will reduced
weights : `Map`
Weights to be applied.
Returns
-------
map_out : `~Map`
Map with non-spatial axes reduced
"""
if axes_names is None:
axes_names = self.geom.axes.names
map_out = self.copy()
for axis_name in axes_names:
map_out = map_out.reduce(
axis_name, func=func, keepdims=keepdims, weights=weights
)
return map_out
[docs] def reduce(self, axis_name, func=np.add, keepdims=False, weights=None):
"""Reduce map over a single non-spatial axis
Parameters
----------
axis_name: str
The name of the axis to reduce over
func : `~numpy.ufunc`
Function to use for reducing the data.
keepdims : bool, optional
If this is set to true, the axes which are summed over are left in
the map with a single bin
weights : `Map`
Weights to be applied.
Returns
-------
map_out : `~Map`
Map with the given non-spatial axes reduced
"""
if keepdims:
geom = self.geom.squash(axis_name=axis_name)
else:
geom = self.geom.drop(axis_name=axis_name)
idx = self.geom.axes.index_data(axis_name)
data = self.data
if weights is not None:
data = data * weights
data = func.reduce(data, axis=idx, keepdims=keepdims, where=~np.isnan(data))
return self._init_copy(geom=geom, data=data)
[docs] def cumsum(self, axis_name):
"""Compute cumulative sum along a given axis
Parameters
----------
axis_name : str
Along which axis to integrate.
Returns
-------
cumsum : `Map`
Map with cumulative sum
"""
axis = self.geom.axes[axis_name]
axis_idx = self.geom.axes.index_data(axis_name)
# TODO: the broadcasting should be done by axis.center, axis.bin_width etc.
shape = [1] * len(self.geom.data_shape)
shape[axis_idx] = -1
values = self.quantity * axis.bin_width.reshape(shape)
if axis_name == "rad":
# take Jacobian into account
values = 2 * np.pi * axis.center.reshape(shape) * values
data = np.insert(values.cumsum(axis=axis_idx), 0, 0, axis=axis_idx)
axis_shifted = MapAxis.from_nodes(
axis.edges, name=axis.name, interp=axis.interp
)
axes = self.geom.axes.replace(axis_shifted)
geom = self.geom.to_image().to_cube(axes)
return self.__class__(geom=geom, data=data.value, unit=data.unit)
[docs] def integral(self, axis_name, coords, **kwargs):
"""Compute integral along a given axis
This method uses interpolation of the cumulative sum.
Parameters
----------
axis_name : str
Along which axis to integrate.
coords : dict or `MapCoord`
Map coordinates
**kwargs : dict
Coordinates at which to evaluate the IRF
Returns
-------
array : `~astropy.units.Quantity`
Returns 2D array with axes offset
"""
cumsum = self.cumsum(axis_name=axis_name)
cumsum = cumsum.pad(pad_width=1, axis_name=axis_name, mode="edge")
return u.Quantity(
cumsum.interp_by_coord(coords, **kwargs), cumsum.unit, copy=False
)
[docs] def normalize(self, axis_name=None):
"""Normalise data in place along a given axis.
Parameters
----------
axis_name : str
Along which axis to normalize.
"""
cumsum = self.cumsum(axis_name=axis_name).quantity
with np.errstate(invalid="ignore", divide="ignore"):
axis = self.geom.axes.index_data(axis_name=axis_name)
normed = self.quantity / cumsum.max(axis=axis, keepdims=True)
self.quantity = np.nan_to_num(normed)
[docs] @classmethod
def from_stack(cls, maps, axis=None, axis_name=None):
"""Create Map from list of images and a non-spatial axis.
The image geometries must be aligned, except for the axis that is stacked.
Parameters
----------
maps : list of `Map` objects
List of maps
axis : `MapAxis`
If a `MapAxis` is provided the maps are stacked along the last data
axis and the new axis is introduced.
axis_name : str
If an axis name is as string the given the maps are stacked along
the given axis name.
Returns
-------
map : `Map`
Map with additional non-spatial axis.
"""
geom = maps[0].geom
if axis_name is None and axis is None:
axis_name = geom.axes.names[-1]
if axis_name:
axis = MapAxis.from_stack(axes=[m.geom.axes[axis_name] for m in maps])
geom = geom.drop(axis_name=axis_name)
data = []
for m in maps:
if axis_name:
m_geom = m.geom.drop(axis_name=axis_name)
else:
m_geom = m.geom
if not m_geom == geom:
raise ValueError(f"Image geometries not aligned: {m.geom} and {geom}")
data.append(m.quantity.to_value(maps[0].unit))
return cls.from_geom(
data=np.stack(data), geom=geom.to_cube(axes=[axis]), unit=maps[0].unit
)
[docs] def split_by_axis(self, axis_name):
"""Split a Map along an axis into multiple maps.
Parameters
----------
axis_name : str
Name of the axis to split
Returns
-------
maps : list
A list of `~gammapy.maps.Map`
"""
maps = []
axis = self.geom.axes[axis_name]
for idx in range(axis.nbin):
m = self.slice_by_idx({axis_name: idx})
maps.append(m)
return maps
[docs] def to_cube(self, axes):
"""Append non-spatial axes to create a higher-dimensional Map.
This will result in a Map with a new geometry with
N+M dimensions where N is the number of current dimensions and
M is the number of axes in the list. The data is reshaped onto the
new geometry
Parameters
----------
axes : list
Axes that will be appended to this Map.
The axes should have only one bin
Returns
-------
map : `~gammapy.maps.WcsNDMap`
new map
"""
for ax in axes:
if ax.nbin > 1:
raise ValueError(ax.name, "should have only one bin")
geom = self.geom.to_cube(axes)
data = self.data.reshape((1,) * len(axes) + self.data.shape)
return self.from_geom(data=data, geom=geom, unit=self.unit)
[docs] def get_spectrum(self, region=None, func=np.nansum, weights=None):
"""Extract spectrum in a given region.
The spectrum can be computed by summing (or, more generally, applying ``func``)
along the spatial axes in each energy bin. This occurs only inside the ``region``,
which by default is assumed to be the whole spatial extension of the map.
Parameters
----------
region: `~regions.Region`
Region (pixel or sky regions accepted).
func : numpy.func
Function to reduce the data. Default is np.nansum.
For a boolean Map, use np.any or np.all.
weights : `WcsNDMap`
Array to be used as weights. The geometry must be equivalent.
Returns
-------
spectrum : `~gammapy.maps.RegionNDMap`
Spectrum in the given region.
"""
if not self.geom.has_energy_axis:
raise ValueError("Energy axis required")
return self.to_region_nd_map(region=region, func=func, weights=weights)
[docs] def to_unit(self, unit):
"""Convert map to different unit
Parameters
----------
unit : `~astropy.unit.Unit` or str
New unit
Returns
-------
map : `Map`
Map with new unit and converted data
"""
data = self.quantity.to_value(unit)
return self.from_geom(self.geom, data=data, unit=unit)
[docs] def is_allclose(self, other, rtol_axes=1e-3, atol_axes=1e-6, **kwargs):
"""Compare two Maps for close equivalency
Parameters
----------
other : `gammapy.maps.Map`
The Map to compare against
rtol_axes : float
Relative tolerance for the axes comparison.
atol_axes : float
Relative tolerance for the axes comparison.
**kwargs : dict
keywords passed to `numpy.allclose`
Returns
-------
is_allclose : bool
Whether the Map is all close.
"""
if not isinstance(other, self.__class__):
return TypeError(f"Cannot compare {type(self)} and {type(other)}")
if self.data.shape != other.data.shape:
return False
axes_eq = self.axes.is_allclose(other.axes, rtol=rtol_axes, atol=atol_axes)
data_eq = np.allclose(self.quantity, other.quantity, **kwargs)
return axes_eq and data_eq
def __repr__(self):
geom = self.geom.__class__.__name__
axes = ["skycoord"] if self.geom.is_hpx else ["lon", "lat"]
axes = axes + [_.name for _ in self.geom.axes]
return (
f"{self.__class__.__name__}\n\n"
f"\tgeom : {geom} \n "
f"\taxes : {axes}\n"
f"\tshape : {self.geom.data_shape[::-1]}\n"
f"\tndim : {self.geom.ndim}\n"
f"\tunit : {self.unit}\n"
f"\tdtype : {self.data.dtype}\n"
)
def _arithmetics(self, operator, other, copy):
"""Perform arithmetic on maps after checking geometry consistency."""
if isinstance(other, Map):
if self.geom == other.geom:
q = other.quantity
else:
raise ValueError("Map Arithmetic: Inconsistent geometries.")
else:
q = u.Quantity(other, copy=False)
out = self.copy() if copy else self
out.quantity = operator(out.quantity, q)
return out
def _boolean_arithmetics(self, operator, other, copy):
"""Perform arithmetic on maps after checking geometry consistency."""
if operator == np.logical_not:
out = self.copy()
out.data = operator(out.data)
return out
if isinstance(other, Map):
if self.geom == other.geom:
other = other.data
else:
raise ValueError("Map Arithmetic: Inconsistent geometries.")
out = self.copy() if copy else self
out.data = operator(out.data, other)
return out
def __add__(self, other):
return self._arithmetics(np.add, other, copy=True)
def __iadd__(self, other):
return self._arithmetics(np.add, other, copy=False)
def __sub__(self, other):
return self._arithmetics(np.subtract, other, copy=True)
def __isub__(self, other):
return self._arithmetics(np.subtract, other, copy=False)
def __mul__(self, other):
return self._arithmetics(np.multiply, other, copy=True)
def __imul__(self, other):
return self._arithmetics(np.multiply, other, copy=False)
def __truediv__(self, other):
return self._arithmetics(np.true_divide, other, copy=True)
def __itruediv__(self, other):
return self._arithmetics(np.true_divide, other, copy=False)
def __le__(self, other):
return self._arithmetics(np.less_equal, other, copy=True)
def __lt__(self, other):
return self._arithmetics(np.less, other, copy=True)
def __ge__(self, other):
return self._arithmetics(np.greater_equal, other, copy=True)
def __gt__(self, other):
return self._arithmetics(np.greater, other, copy=True)
def __eq__(self, other):
return self._arithmetics(np.equal, other, copy=True)
def __ne__(self, other):
return self._arithmetics(np.not_equal, other, copy=True)
def __and__(self, other):
return self._boolean_arithmetics(np.logical_and, other, copy=True)
def __or__(self, other):
return self._boolean_arithmetics(np.logical_or, other, copy=True)
def __invert__(self):
return self._boolean_arithmetics(np.logical_not, None, copy=True)
def __xor__(self, other):
return self._boolean_arithmetics(np.logical_xor, other, copy=True)
def __iand__(self, other):
return self._boolean_arithmetics(np.logical_and, other, copy=False)
def __ior__(self, other):
return self._boolean_arithmetics(np.logical_or, other, copy=False)
def __ixor__(self, other):
return self._boolean_arithmetics(np.logical_xor, other, copy=False)
def __array__(self):
return self.data
[docs] def sample_coord(self, n_events, random_state=0):
"""Sample position and energy of events.
Parameters
----------
n_events : int
Number of events to sample.
random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`}
Defines random number generator initialisation.
Passed to `~gammapy.utils.random.get_random_state`.
Returns
-------
coords : `~gammapy.maps.MapCoord` object.
Sequence of coordinates and energies of the sampled events.
"""
random_state = get_random_state(random_state)
sampler = InverseCDFSampler(pdf=self.data, random_state=random_state)
coords_pix = sampler.sample(n_events)
coords = self.geom.pix_to_coord(coords_pix[::-1])
# TODO: pix_to_coord should return a MapCoord object
cdict = OrderedDict(zip(self.geom.axes_names, coords))
return MapCoord.create(cdict, frame=self.geom.frame)