# Licensed under a 3-clause BSD style license - see LICENSE.rst
import abc
import copy
import html
import inspect
import json
from collections import OrderedDict
from itertools import repeat
import numpy as np
from astropy import units as u
from astropy.io import fits
import matplotlib.pyplot as plt
import gammapy.utils.parallel as parallel
from gammapy.utils.random import InverseCDFSampler, get_random_state
from gammapy.utils.scripts import make_path
from gammapy.utils.types import JsonQuantityDecoder
from gammapy.utils.units import energy_unit_format
from .axes import MapAxis
from .coord import MapCoord
from .geom import pix_tuple_to_idx
__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`, optional
Dictionary to store metadata. Default is None.
unit : str or `~astropy.units.Unit`, optional
Data unit, ignored if data is a Quantity. Default is ''.
"""
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 _repr_html_(self):
try:
return self.to_html()
except AttributeError:
return f"<pre>{html.escape(str(self))}</pre>"
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)
if arg not in kwargs:
kwargs[arg] = copy.deepcopy(value)
return self.from_geom(**kwargs)
@property
def is_mask(self):
"""Whether map is a mask with boolean data type."""
return self.data.dtype == bool
@property
def geom(self):
"""Map geometry as a `~gammapy.maps.Geom` object."""
return self._geom
@property
def data(self):
"""Data array as a `~numpy.ndarray` object."""
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 as an `~astropy.units.Unit` object."""
return self._unit
@property
def meta(self):
"""Map metadata as a `dict`."""
return self._meta
@meta.setter
def meta(self, val):
self._meta = val
@property
def quantity(self):
"""Map data as a `~astropy.units.Quantity` object."""
return u.Quantity(self.data, self.unit, copy=False)
@quantity.setter
def quantity(self, val):
"""Set data and unit.
Parameters
----------
val : `~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 : str or list of str
Names of the axes.
new_names : str or list of str
New names of the axes. The list must be of the same length as `names`).
Returns
-------
geom : `~Map`
Map with renamed axes.
"""
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`; for HPX-specific options, see `HpxMap.create`.
Parameters
----------
frame : {"icrs", "galactic"}
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. Default is 'wcs'.
binsz : float or `~numpy.ndarray`
Pixel size in degrees.
skydir : `~astropy.coordinates.SkyCoord`
Coordinate of the 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 metadata.
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,
checksum=False,
):
"""Read a map from a FITS file.
Parameters
----------
filename : str or `~pathlib.Path`
Name of the FITS file.
hdu : str, optional
Name or index of the HDU with the map data. Default is None.
hdu_bands : str, optional
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. Default is None.
map_type : {'auto', 'wcs', 'wcs-sparse', 'hpx', 'hpx-sparse', '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. Default is 'auto'.
colname : str, optional
data column name to be used for HEALPix map.
checksum : bool
If True checks both DATASUM and CHECKSUM cards in the file headers. Default is False.
Returns
-------
map_out : `Map`
Map object.
"""
with fits.open(
str(make_path(filename)), memmap=False, checksum=checksum
) 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.
meta : `dict`, optional
Dictionary to store metadata. Default is None.
data : `numpy.ndarray`, optional
Data array. Default is None.
unit : str or `~astropy.units.Unit`
Data unit.
dtype : str, optional
Data type. Default is 'float32'.
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 a `astropy.io.fits.HDUList` object.
Parameters
----------
hdulist : `~astropy.io.fits.HDUList`
HDU list containing HDUs for map data and bands.
hdu : str, optional
Name or index of the HDU with the map data. Default is None.
hdu_bands : str, optional
Name or index of the HDU with the BANDS table. Default is None.
map_type : {"auto", "wcs", "hpx", "region"}
Map type. Default is "auto".
format : {'gadf', 'fgst-ccube', 'fgst-template'}
FITS format convention. Default is None.
colname : str, optional
Data column name to be used for HEALPix map. Default is None.
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 metadata 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 a 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, optional
Overwrite existing file. Default is False.
hdu : str, optional
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, optional
Set the name of the bands table extension. Default is '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, optional
Sparsify the map by dropping pixels with zero amplitude.
This option is only compatible with the 'gadf' format.
checksum : bool, optional
When True adds both DATASUM and CHECKSUM cards to the headers written to the file.
Default is False.
"""
checksum = kwargs.pop("checksum", False)
hdulist = self.to_hdulist(**kwargs)
hdulist.writeto(make_path(filename), overwrite=overwrite, checksum=checksum)
[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, optional
Keep dimensions. Default is False.
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 iter_by_image_index(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
------
idx : tuple
``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 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`
Map to add.
weights: `Map` or `~numpy.ndarray`
The weight factors while adding. Default is None.
"""
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, optional
Which axis to downsample. By default, spatial axes are padded. Default is None.
mode : {'constant', 'edge', 'interp'}
Padding mode. 'edge' pads with the closest edge value.
'constant' pads with a constant value. 'interp' pads with
an extrapolated value. Default is 'constant'.
cval : float, optional
Padding value when ``mode='consant'``. Default is 0.
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, optional
Preserve the integral over each bin. This should be set to True
if the map is an integral quantity (e.g. counts) and False if
the map is a differential quantity (e.g. intensity). Default is True.
axis_name : str, optional
Which axis to downsample. By default, spatial axes are downsampled. Default is None.
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, optional
Order of the interpolation used for upsampling. Default is 0.
preserve_counts : bool, optional
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). Default is True.
axis_name : str, optional
Which axis to upsample. By default, spatial axes are upsampled. Default is None.
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`, optional
Weights vector. It must have same shape as the data of the map.
If set to None, weights will be set to 1. Default is None.
preserve_counts : bool, optional
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). Default is True.
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`, optional
Weights vector. If set to None, weights will be set to 1. Default is None.
preserve_counts : bool, optional
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). Default is False.
"""
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 function `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`, optional
Array to be used as weights. The spatial geometry must be equivalent
to `other` and additional axes must be broadcastable. If set to None, weights will be set to 1.
Default is None.
ufunc : `~numpy.ufunc`
Universal function 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
Dictionary 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
dictionary 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. Dictionary 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(_.item()) for _ in np.array(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 the projection
footprint. Default is `numpy.nan`.
Returns
-------
vals : `~numpy.ndarray`
Values of pixels in the map. `numpy.nan` is used to flag coordinates
outside the 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 the projection
footprint. Default is `numpy.nan`.
Returns
-------
vals : `~numpy.ndarray`
Array of pixel values. `numpy.nan` is used to flag coordinates
outside the 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. `numpy.nan` is used to flag coordinates outside the 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. Default is "linear".
fill_value : float, optional
The value to use for points outside the interpolation domain.
If None, values outside the domain are extrapolated. Default is None.
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. Default is "linear".
fill_value : float, optional
The value to use for points outside the interpolation domain.
If None, values outside the domain are extrapolated. Default is None.
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, optional
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). Default is False.
fill_value : float, optional
The value to use for points outside the interpolation domain.
Default is 0.
**kwargs : dict, optional
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 axes do not match, expected: \n {self.geom.axes[0]},"
f" but got: \n {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, optional
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). Default is False.
precision_factor : int, optional
Minimal factor between the bin size of the output map and the oversampled base map.
Used only for the oversampling method. Default is 10.
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 reproject_by_image(
self,
geom,
preserve_counts=False,
precision_factor=10,
):
"""Reproject each image of a ND map to input 2d geometry.
For large maps this method is faster than `reproject_to_geom`.
Parameters
----------
geom : `~gammapy.maps.Geom`
Target slice geometry in 2D.
preserve_counts : bool, optional
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). Default is False.
precision_factor : int, optional
Minimal factor between the bin size of the output map and the oversampled base map.
Used only for the oversampling method. Default is 10.
Returns
-------
output_map : `Map`
Reprojected Map.
"""
if not geom.is_image:
raise TypeError("This method is only valid for 2d geom")
output_map = Map.from_geom(geom.to_cube(self.geom.axes))
maps = parallel.run_multiprocessing(
self._reproject_image,
zip(
self.iter_by_image(),
repeat(geom),
repeat(preserve_counts),
repeat(precision_factor),
),
task_name="Reprojection",
)
for idx in np.ndindex(self.geom.shape_axes):
output_map.data[idx[0]] = maps[idx[0]].data
return output_map
@staticmethod
def _reproject_image(image, geom, preserve_counts, precision_factor):
return image.reproject_to_geom(
geom, precision_factor=precision_factor, preserve_counts=preserve_counts
)
[docs]
def fill_events(self, events, weights=None):
"""Fill the map from an `~gammapy.data.EventList` object.
Parameters
----------
events : `~gammapy.data.EventList`
Events to fill in the map with.
weights : `~numpy.ndarray`, optional
Weights vector. The weights vector must be of the same length
as the events column length. If None, weights are set to 1. Default is None.
"""
self.fill_by_coord(events.map_coord(self.geom), weights=weights)
[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`, optional
Weights vector. If None, weights are set to 1. Default is None.
"""
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`, optional
Weights vector. If None, weights are set to 1. Default is None.
"""
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`, optional
Weights vector. If None, weights are set to 1. Default is None.
"""
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, optional
Figsize to plot on. Default is None.
ncols : int, optional
Number of columns to plot. Default is 3.
**kwargs : dict, optional
Keyword arguments passed to `WcsNDMap.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, optional
Passed to ``matplotlib.rc_context(rc=rc_params)`` to style the plot. Default is None.
**kwargs : dict, optional
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, optional
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 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 axes.
Parameters
----------
position : `~astropy.coordinates.SkyCoord`
Test position.
Returns
-------
position : `~astropy.coordinates.SkyCoord`
The 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):
"""Sum map values over all non-spatial axes.
Parameters
----------
axes_names: list of str
Names of the axis to reduce over. If None, all non-spatial axis will be summed over. Default is None.
keepdims : bool, optional
If this is set to true, the axes which are summed over are left in
the map with a single bin. Default is True.
weights : `Map`, optional
Weights to be applied. The map should have the same geometry as this one. Default is None.
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`, optional
Function to use for reducing the data. Default is `numpy.add`.
keepdims : bool, optional
If this is set to true, the axes which are summed over are left in
the map with a single bin. Default is False.
axes_names: list, optional
Names of axis to reduce over. If None, all non-spatial axis will be reduced.
weights : `Map`, optional
Weights to be applied. The map should have the same geometry as this one. Default is None.
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`, optional
Function to use for reducing the data. Default is `numpy.add`.
keepdims : bool, optional
If this is set to true, the axes which are summed over are left in
the map with a single bin. Default is False.
weights : `Map`
Weights to be applied. The map should have the same geometry as this one. Default is None.
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 sum.
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, optional
Keyword arguments passed to `Map.interp_by_coord`.
Returns
-------
array : `~astropy.units.Quantity`
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, optional
Along which axis to normalise.
"""
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 a 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`, optional
If a `MapAxis` is provided the maps are stacked along the last data
axis and the new axis is introduced. Default is None.
axis_name : str, optional
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
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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 this
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`, optional
Region to extract the spectrum from. Pixel or sky regions are accepted. Default is None.
func : `numpy.func`, optional
Function to reduce the data. Default is `~numpy.nansum`.
For a boolean Map, use `numpy.any` or `numpy.all`. Default is `numpy.nansum`.
weights : `WcsNDMap`, optional
Array to be used as weights. The geometry must be equivalent. Default is None.
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)
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def to_unit(self, unit):
"""Convert map to a different unit.
Parameters
----------
unit : str or `~astropy.unit.Unit`
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, optional
Relative tolerance for the axes' comparison. Default is 1e-3.
atol_axes : float, optional
Absolute tolerance for the axes' comparison. Default is 1e-6.
**kwargs : dict, optional
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.geom.axes.is_allclose(
other.geom.axes, rtol=rtol_axes, atol=atol_axes
)
data_eq = np.allclose(self.quantity, other.quantity, **kwargs)
return axes_eq and data_eq
def __str__(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`. Default is 0.
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)
[docs]
def reorder_axes(self, axes_names):
"""Return a new map re-ordering the non-spatial axes.
Parameters
----------
axes_names : list of str
The list of axes names in the required order.
Returns
-------
map : `~gammapy.maps.Map`
The map with axes re-ordered.
"""
old_axes = self.geom.axes
if not set(old_axes.names) == set(axes_names):
raise ValueError(f"{old_axes.names} is not compatible with {axes_names}")
new_axes = [old_axes[_] for _ in axes_names]
new_geom = self.geom.to_image().to_cube(new_axes)
old_indices = [old_axes.index_data(ax) for ax in axes_names]
new_indices = [new_geom.axes.index_data(ax) for ax in axes_names]
data = np.moveaxis(self.data, old_indices, new_indices)
return Map.from_geom(new_geom, data=data)
[docs]
def dot(self, other):
"""Apply dot product with the input map.
The input Map has to share a single MapAxis with the current Map.
Because it has no spatial dimension, it must be a `~gammapy.maps.RegionNDMap`.
Parameters
----------
other : `~gammapy.maps.RegionNDMap`
Map to apply the dot product to.
It must share a unique non-spatial MapAxis with the current Map.
Returns
-------
map : `~gammapy.maps.Map`
Map with dot product applied.
"""
from .region import RegionNDMap
if not isinstance(other, RegionNDMap):
raise TypeError(
f"Dot product can be applied to a RegionNDMap. Got {type(other)} instead."
)
common_names = list(
set(other.geom.axes.names).intersection(self.geom.axes.names)
)
if len(common_names) == 0:
raise ValueError(
"Map geometries have no axis in common. Cannot apply dot product."
)
elif len(common_names) > 1:
raise ValueError(
f"Map geometries have more than one axis in common: {common_names}."
"Cannot apply dot product."
)
axis_name = common_names[0]
if self.geom.axes[axis_name] != other.geom.axes[axis_name]:
raise ValueError(
f"Axes {axis_name} are not equal. Cannot apply dot product."
)
loc = self.geom.axes.index_data(axis_name)
other_loc = other.geom.axes.index_data(axis_name)
# move axes because numpy dot product is performed on last axis of a and second-to-last axis of b
data = np.moveaxis(self.data, loc, -1)
if len(other.geom.axes) > 1:
other_data = np.moveaxis(other.data[..., 0, 0], other_loc, -2)
else:
other_data = other.data[..., 0, 0]
data = np.dot(data, other_data)
# prepare new axes with expected shape (i.e. common axis replaced by other's axes)
index = self.geom.axes.index(axis_name)
axes1 = self.geom.axes.drop(axis_name)
inserted_axes = other.geom.axes.drop(axis_name)
new_axes = axes1[:index] + inserted_axes + axes1[index:]
# reorder axes to get the expected shape
remaining_axes = np.arange(len(inserted_axes))
old_axes_pos = -1 - remaining_axes
new_axes_pos = loc + remaining_axes[::-1]
data = np.moveaxis(data, old_axes_pos, new_axes_pos)
geom = self.geom.to_image().to_cube(new_axes)
return self._init_copy(geom=geom, data=data)
def __matmul__(self, other):
"""Apply dot product with the input map.
The input Map has to share a single MapAxis with the current Map.
Because it has no spatial dimension, it must be a `~gammapy.maps.RegionNDMap`.
"""
return self.dot(other)
