# Licensed under a 3-clause BSD style license - see LICENSE.rst
import abc
import copy
import inspect
import json
import numpy as np
from astropy import units as u
from astropy.io import fits
from gammapy.utils.scripts import make_path
from .geom import MapCoord, pix_tuple_to_idx
from .utils import INVALID_VALUE
__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`
Data array
meta : `dict`
Dictionary to store meta data
unit : str or `~astropy.units.Unit`
Data unit
"""
def __init__(self, geom, data, meta=None, unit=""):
self.geom = geom
self.data = data
self.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 geom(self):
"""Map geometry (`~gammapy.maps.Geom`)"""
return self._geom
@geom.setter
def geom(self, val):
self._geom = val
@property
def data(self):
"""Data array (`~numpy.ndarray`)"""
return self._data
@data.setter
def data(self, val):
if val.shape != self.geom.data_shape:
raise ValueError(
f"Shape {val.shape!r} does not match map data shape {self.geom.data_shape!r}"
)
if isinstance(val, u.Quantity):
raise TypeError("Map data must be a Numpy array. Set unit separately")
self._data = val
@property
def unit(self):
"""Map unit (`~astropy.units.Unit`)"""
return self._unit
@unit.setter
def unit(self, val):
self._unit = u.Unit(val)
@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):
val = u.Quantity(val, copy=False)
self.data = val.value
self.unit = val.unit
[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
----------
coordsys : str
Coordinate system, either Galactic ('GAL') or Equatorial
('CEL').
map_type : {'wcs', 'wcs-sparse', 'hpx', 'hpx-sparse'}
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.
Returns
-------
map : `Map`
Empty map object.
"""
from .hpxmap import HpxMap
from .wcsmap 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)
else:
raise ValueError(f"Unrecognized map type: {map_type!r}")
[docs] @staticmethod
def read(filename, hdu=None, hdu_bands=None, map_type="auto"):
"""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'}
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.
Returns
-------
map_out : `Map`
Map object
"""
filename = str(make_path(filename))
with fits.open(filename, memmap=False) as hdulist:
return Map.from_hdulist(hdulist, hdu, hdu_bands, map_type)
[docs] @staticmethod
def from_geom(
geom, meta=None, data=None, map_type="auto", 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.
map_type : {'wcs', 'wcs-sparse', 'hpx', 'hpx-sparse', 'auto'}
Map type. Selects the class that will be used to
instantiate the map. The map type should be consistent
with the geometry. If map_type is 'auto' then an
appropriate map type will be inferred from type of ``geom``.
unit : str or `~astropy.units.Unit`
Data unit.
Returns
-------
map_out : `Map`
Map object
"""
if map_type == "auto":
from .hpx import HpxGeom
from .wcs import WcsGeom
if isinstance(geom, HpxGeom):
map_type = "hpx"
elif isinstance(geom, WcsGeom):
map_type = "wcs"
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"):
"""Create from `astropy.io.fits.HDUList`."""
if map_type == "auto":
map_type = Map._get_map_type(hdulist, hdu)
cls_out = Map._get_map_cls(map_type)
return cls_out.from_hdulist(hdulist, hdu=hdu, hdu_bands=hdu_bands)
@staticmethod
def _get_meta_from_header(header):
"""Load meta data from a FITS header."""
if "META" in header:
return json.loads(header["META"])
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"
else:
return "wcs"
@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 .wcsnd import WcsNDMap
return WcsNDMap
elif map_type == "wcs-sparse":
raise NotImplementedError()
elif map_type == "hpx":
from .hpxnd import HpxNDMap
return HpxNDMap
elif map_type == "hpx-sparse":
from .hpxsparse import HpxSparseMap
return HpxSparseMap
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.
conv : str
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). Supported conventions are 'gadf',
'fgst-ccube', 'fgst-ltcube', 'fgst-bexpcube',
'fgst-template', 'fgst-srcmap', 'fgst-srcmap-sparse',
'galprop', and '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(filename, overwrite=overwrite)
[docs] def iter_by_image(self):
"""Iterate over image planes of the map.
This is a generator yielding ``(data, idx)`` tuples,
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.
The image plane index is in data order, so that the data array can be
indexed directly. See :ref:`mapiter` for further information.
"""
for idx in np.ndindex(self.geom.shape_axes):
yield self.data[idx[::-1]], idx[::-1]
[docs] @abc.abstractmethod
def sum_over_axes(self, keepdims=False):
"""Reduce to a 2D image by summing over non-spatial dimensions."""
pass
[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 reproject(self, geom, order=1, mode="interp"):
"""Reproject this map to a different geometry.
Only spatial axes are reprojected, if you would like to reproject
non-spatial axes consider using `Map.interp_by_coord()` instead.
Parameters
----------
geom : `Geom`
Geometry of projection.
mode : {'interp', 'exact'}
Method for reprojection. 'interp' method interpolates at pixel
centers. 'exact' method integrates over intersection of pixels.
order : int or str
Order of interpolating polynomial (0 = nearest-neighbor, 1 =
linear, 2 = quadratic, 3 = cubic).
Returns
-------
map : `Map`
Reprojected map.
"""
if geom.is_image:
axes = [ax.copy() for ax in self.geom.axes]
geom = geom.copy(axes=axes)
else:
axes_eq = geom.ndim == self.geom.ndim
axes_eq &= np.all(
[ax0 == ax1 for ax0, ax1 in zip(geom.axes, self.geom.axes)]
)
if not axes_eq:
raise ValueError(
"Map and target geometry non-spatial axes must match."
"Use interp_by_coord to interpolate in non-spatial axes."
)
if geom.is_hpx:
return self._reproject_to_hpx(geom, mode=mode, order=order)
else:
return self._reproject_to_wcs(geom, mode=mode, order=order)
[docs] @abc.abstractmethod
def pad(self, pad_width, mode="constant", cval=0, order=1):
"""Pad the spatial dimensions of the map.
Parameters
----------
pad_width : {sequence, array_like, int}
Number of pixels padded to the edges of each axis.
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'.
order : int
Order of interpolation when mode='constant' (0 =
nearest-neighbor, 1 = linear, 2 = quadratic, 3 = cubic).
Returns
-------
map : `Map`
Padded map.
"""
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=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 : 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=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 : str
Which axis to upsample. By default spatial axes are upsampled.
Returns
-------
map : `Map`
Upsampled map.
"""
pass
[docs] def slice_by_idx(self, slices):
"""Slice sub map from map object.
For usage examples, see :ref:`mapslicing`.
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):
axes_names = [_.name for _ in self.geom.axes]
coords = dict(zip(axes_names, coords))
idx = []
for ax in self.geom.axes:
value = coords[ax.name]
idx.append(ax.coord_to_idx(value))
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):
"""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.
Returns
-------
vals : `~numpy.ndarray`
Values of pixels in the map. np.nan used to flag coords
outside of map.
"""
coords = MapCoord.create(coords, coordsys=self.geom.coordsys)
pix = self.geom.coord_to_pix(coords)
vals = self.get_by_pix(pix)
return vals
[docs] def get_by_pix(self, pix):
"""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.
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():
invalid = INVALID_VALUE[self.data.dtype]
vals = vals.astype(type(invalid))
vals[~mask] = invalid
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, interp=None, 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.
interp : {None, 'nearest', 'linear', 'cubic', 0, 1, 2, 3}
Method to interpolate data values. By default no
interpolation is performed and the return value will be
the amplitude of the pixel encompassing the given
coordinate. Integer values can be used in lieu of strings
to choose the interpolation method of the given order
(0='nearest', 1='linear', 2='quadratic', 3='cubic'). Note
that only 'nearest' and 'linear' methods are supported for
all map types.
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, interp=None, 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.
interp : {None, 'nearest', 'linear', 'cubic', 0, 1, 2, 3}
Method to interpolate data values. By default no
interpolation is performed and the return value will be
the amplitude of the pixel encompassing the given
coordinate. Integer values can be used in lieu of strings
to choose the interpolation method of the given order
(0='nearest', 1='linear', 2='quadratic', 3='cubic'). Note
that only 'nearest' and 'linear' methods are supported for
all map types.
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 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)
[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_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
import matplotlib.pyplot as plt
from ipywidgets.widgets.interaction import interact, fixed
from ipywidgets import SelectionSlider, RadioButtons
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 == "edges":
options = [
f"{val_min:.2e} - {val_max:.2e} {axis.unit}"
for val_min, val_max in zip(axis.edges[:-1], axis.edges[1:])
]
else:
options = [f"{val:.2e} {axis.unit}" for val in axis.center]
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):
fig, ax, cbar = 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:
raise ValueError("Can't copy and change geometry of the map.")
return self._init_copy(**kwargs)
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 arithmetics on maps after checking geometry consistency."""
if isinstance(other, Map):
if self.geom == other.geom:
q = other.quantity
else:
raise ValueError("Map Arithmetics: 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 __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 __array__(self):
return self.data