WcsNDMap

class gammapy.maps.WcsNDMap(geom, data=None, dtype='float32', meta=None, unit='')[source]

Bases: gammapy.maps.WcsMap

Representation of a N+2D map using WCS with two spatial dimensions and N non-spatial dimensions.

This class uses an ND numpy array to store map values. For maps with non-spatial dimensions and variable pixel size it will allocate an array with dimensions commensurate with the largest image plane.

Parameters:

geom : WcsGeom

WCS geometry object.

data : ndarray

Data array. If none then an empty array will be allocated.

dtype : str, optional

Data type, default is float32

meta : OrderedDict

Dictionary to store meta data.

unit : str or Unit

The map unit

Attributes Summary

data Data array (ndarray)
geom Map geometry (MapGeom)
meta Map meta (OrderedDict)
quantity Map data times unit (Quantity)
unit Map unit (Unit)

Methods Summary

coadd(map_in) Add the contents of map_in to this map.
convolve(kernel[, use_fft]) Convolve map with a kernel.
copy(**kwargs) Copy map instance and overwrite given attributes, except for geometry.
create([map_type, npix, binsz, width, proj, …]) Factory method to create an empty WCS map.
crop(crop_width) Crop the spatial dimension of the map by removing a number of pixels from the edge of the map.
cutout(position, width[, mode]) Create a cutout around a given position.
downsample(factor[, preserve_counts]) Downsample the spatial dimension by a given factor.
fill_by_coord(coords[, weights]) Fill pixels at coords with given weights.
fill_by_idx(idx[, weights]) Fill pixels at idx with given weights.
fill_by_pix(pix[, weights]) Fill pixels at pix with given weights.
from_geom(geom[, meta, data, map_type, unit]) Generate an empty map from a MapGeom instance.
from_hdu(hdu[, hdu_bands]) Make a WcsNDMap object from a FITS HDU.
from_hdulist(hdu_list[, hdu, hdu_bands]) Make a WcsMap object from a FITS HDUList.
get_by_coord(coords) Return map values at the given map coordinates.
get_by_idx(idx) Return map values at the given pixel indices.
get_by_pix(pix) Return map values at the given pixel coordinates.
get_image_by_coord(coords) Return spatial map at the given axis coordinates.
get_image_by_idx(idx) Return spatial map at the given axis pixel indices.
get_image_by_pix(pix) Return spatial map at the given axis pixel coordinates
interp_by_coord(coords[, interp, fill_value]) Interpolate map values at the given map coordinates.
interp_by_pix(pix[, interp, fill_value]) Interpolate map values at the given pixel coordinates.
iter_by_coord([buffersize]) Iterate over elements of the map returning a tuple with values and map coordinates.
iter_by_image() Iterate over image planes of the map returning a tuple with the image array and image plane index.
iter_by_pix([buffersize]) Iterate over elements of the map returning a tuple with values and pixel coordinates.
make_hdu([hdu, hdu_bands, sparse, conv]) Make a FITS HDU from this map.
pad(pad_width[, mode, cval, order]) Pad the spatial dimension of the map by extending the edge of the map by the given number of pixels.
plot([ax, fig, add_cbar, stretch]) Plot image on matplotlib WCS axes.
plot_interactive([rc_params]) Plot map with interactive widgets to explore the non spatial axes.
read(filename[, hdu, hdu_bands, map_type]) Read a map from a FITS file.
reproject(geom[, order, mode]) Reproject this map to a different geometry.
set_by_coord(coords, vals) Set pixels at coords with given vals.
set_by_idx(idx, vals) Set pixels at idx with given vals.
set_by_pix(pix, vals) Set pixels at pix with given vals.
slice_by_idx(slices) Slice sub map from map object.
smooth(width[, kernel]) Smooth the image (works on a 2D image and returns a copy).
sum_over_axes() Reduce to a 2D image by summing over non-spatial dimensions.
to_hdulist([hdu, hdu_bands, sparse, conv]) Convert to HDUList.
upsample(factor[, order, preserve_counts]) Upsample the spatial dimension by a given factor.
write(filename[, overwrite]) Write to a FITS file.

Attributes Documentation

data

Data array (ndarray)

geom

Map geometry (MapGeom)

meta

Map meta (OrderedDict)

quantity

Map data times unit (Quantity)

unit

Map unit (Unit)

Methods Documentation

coadd(map_in)

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.
convolve(kernel, use_fft=True, **kwargs)[source]

Convolve map with a kernel.

If the kernel is two dimensional, it is applied to all image planes likewise. If the kernel is higher dimensional it must match the map in the number of dimensions and the corresponding kernel is selected for every image plane.

Parameters:

kernel : PSFKernel or numpy.ndarray

Convolution kernel.

use_fft : bool

kwargs : dict

Keyword arguments passed to scipy.signal.fftconvolve or scipy.ndimage.convolve.
Returns:

map : WcsNDMap

Convolved map.
copy(**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.
classmethod create(map_type='wcs', npix=None, binsz=0.1, width=None, proj='CAR', coordsys='CEL', refpix=None, axes=None, skydir=None, dtype='float32', conv='gadf', meta=None, unit='')

Factory method to create an empty WCS map.

Parameters:

map_type : {‘wcs’, ‘wcs-sparse’}

Map type. Selects the class that will be used to instantiate the map.

npix : int or tuple or list

Width of the map in pixels. A tuple will be interpreted as parameters for longitude and latitude axes. For maps with non-spatial dimensions, list input can be used to define a different map width in each image plane. This option supersedes width.

width : float or tuple or list

Width of the map in degrees. A tuple will be interpreted as parameters for longitude and latitude axes. For maps with non-spatial dimensions, list input can be used to define a different map width in each image plane.

binsz : float or tuple or list

Map pixel size in degrees. A tuple will be interpreted as parameters for longitude and latitude axes. For maps with non-spatial dimensions, list input can be used to define a different bin size in each image plane.

skydir : tuple or SkyCoord

Sky position of map center. Can be either a SkyCoord object or a tuple of longitude and latitude in deg in the coordinate system of the map.

coordsys : {‘CEL’, ‘GAL’}, optional

Coordinate system, either Galactic (‘GAL’) or Equatorial (‘CEL’).

axes : list

List of non-spatial axes.

proj : string, optional

Any valid WCS projection type. Default is ‘CAR’ (cartesian).

refpix : tuple

Reference pixel of the projection. If None then this will be chosen to be center of the map.

dtype : str, optional

Data type, default is float32

conv : {‘fgst-ccube’,’fgst-template’,’gadf’}, optional

FITS format convention. Default is ‘gadf’.

meta : OrderedDict

Dictionary to store meta data.

unit : str or Unit

The unit of the map
Returns:

map : WcsMap

A WCS map object.
crop(crop_width)[source]

Crop the spatial dimension of the map by removing a number of pixels from the edge 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.
cutout(position, width, mode='trim')[source]

Create a cutout around a given position.

Parameters:

position : SkyCoord

Center position of the cutout region.

width : tuple of Angle

Angular sizes of the region in (lon, lat) in that specific order. If only one value is passed, a square region is extracted.

mode : {‘trim’, ‘partial’, ‘strict’}

Mode option for Cutout2D, for details see Cutout2D.
Returns:

cutout : WcsNDMap

Cutout map
downsample(factor, preserve_counts=True)[source]

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).
Returns:

map : Map

Downsampled map.
fill_by_coord(coords, weights=None)

Fill pixels at coords with given weights.

Parameters:

coords : tuple or 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 : ndarray

Weights vector. Default is weight of one.
fill_by_idx(idx, weights=None)[source]

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 : ndarray

Weights vector. Default is weight of one.
fill_by_pix(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 : ndarray

Weights vector. Default is weight of one.
static from_geom(geom, meta=None, data=None, map_type='auto', unit='')

Generate an empty map from a MapGeom instance.

Parameters:

geom : MapGeom

Map geometry.

data : numpy.ndarray

data array

meta : OrderedDict

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 Unit

Data unit.
Returns:

map_out : Map

Map object
classmethod from_hdu(hdu, hdu_bands=None)[source]

Make a WcsNDMap object from a FITS HDU.

Parameters:

hdu : BinTableHDU or ImageHDU

The map FITS HDU.

hdu_bands : BinTableHDU

The BANDS table HDU.
classmethod from_hdulist(hdu_list, hdu=None, hdu_bands=None)

Make a WcsMap object from a FITS HDUList.

Parameters:

hdu_list : 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.
Returns:

wcs_map : WcsMap

Map object
get_by_coord(coords)

Return map values at the given map coordinates.

Parameters:

coords : tuple or 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 : ndarray

Values of pixels in the map. np.nan used to flag coords outside of map.
get_by_idx(idx)[source]

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 : ndarray

Array of pixel values. np.nan used to flag coordinate outside of map
get_by_pix(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 : ndarray

Array of pixel values. np.nan used to flag coordinates outside of map
get_image_by_coord(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})
get_image_by_idx(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).
Returns:

map_out : Map

Map with spatial dimensions only.

See also

get_image_by_coord, get_image_by_pix

get_image_by_pix(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.
Returns:

map_out : Map

Map with spatial dimensions only.

See also

get_image_by_coord, get_image_by_idx

interp_by_coord(coords, interp=None, fill_value=None)[source]

Interpolate map values at the given map coordinates.

Parameters:

coords : tuple or 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 : ndarray

Interpolated pixel values.
interp_by_pix(pix, interp=None, fill_value=None)[source]

Interpolate map values at the given pixel coordinates.

iter_by_coord(buffersize=1)

Iterate over elements of the map returning a tuple with values and map coordinates.

Parameters:

buffersize : int

Set the size of the buffer. The map will be returned in chunks of the given size.
Returns:

val : ndarray

Map values.

coords : tuple

Tuple of map coordinates.
iter_by_image()

Iterate over image planes of the map returning a tuple with the image array and image plane index.

The image plane index is in data order, so that the data array can be indexed directly. See Iterating on a Map for further information.

Returns:

val : ndarray

Array of image plane values.

idx : tuple

Index of image plane.
iter_by_pix(buffersize=1)

Iterate over elements of the map returning a tuple with values and pixel coordinates.

Parameters:

buffersize : int

Set the size of the buffer. The map will be returned in chunks of the given size.
Returns:

val : ndarray

Map values.

pix : tuple

Tuple of pixel coordinates.
make_hdu(hdu='SKYMAP', hdu_bands=None, sparse=False, conv=None)

Make a FITS HDU from this map.

Parameters:

hdu : str

The HDU extension name.

hdu_bands : str

The HDU extension name for BANDS table.

sparse : bool

Set INDXSCHM to SPARSE and sparsify the map by only writing pixels with non-zero amplitude.
Returns:

hdu : BinTableHDU or ImageHDU

HDU containing the map data.
pad(pad_width, mode='constant', cval=0, order=1)[source]

Pad the spatial dimension of the map by extending the edge of the map by the given number of pixels.

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.
plot(ax=None, fig=None, add_cbar=False, stretch='linear', **kwargs)[source]

Plot image on matplotlib WCS axes.

Parameters:

ax : WCSAxes, optional

WCS axis object to plot on.

fig : Figure

Figure object.

add_cbar : bool

Add color bar?

stretch : str

**kwargs : dict

Keyword arguments passed to imshow.
Returns:

fig : Figure

Figure object.

ax : WCSAxes

WCS axis object

cbar : Colorbar or None

Colorbar object.
plot_interactive(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_EXTRA/datasets/vela_region/gll_iem_v05_rev1_cutout.fits")
m.plot_interactive(cmap='gnuplot2')

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)
static read(filename, hdu=None, hdu_bands=None, map_type='auto')

Read a map from a FITS file.

Parameters:

filename : str or 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
reproject(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 : MapGeom

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.
set_by_coord(coords, vals)

Set pixels at coords with given vals.

Parameters:

coords : tuple or 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 : ndarray

Values vector.
set_by_idx(idx, vals)[source]

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 : ndarray

Values vector.
set_by_pix(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 : ndarray

Values vector.
slice_by_idx(slices)

Slice sub map from map object.

For usage examples, see Indexing and Slicing.

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.
smooth(width, kernel='gauss', **kwargs)[source]

Smooth the image (works on a 2D image and returns a copy).

Parameters:

width : Quantity or float

Smoothing width given as quantity or float. If a float is given it interpreted as smoothing width in pixels. If an (angular) quantity is given it converted to pixels using geom.wcs.wcs.cdelt. It corresponds to the standard deviation in case of a Gaussian kernel, the radius in case of a disk kernel, and the side length in case of a box kernel.

kernel : {‘gauss’, ‘disk’, ‘box’}

Kernel shape

kwargs : dict

Keyword arguments passed to uniform_filter (‘box’), gaussian_filter (‘gauss’) or convolve (‘disk’).
Returns:

image : WcsNDMap

Smoothed image (a copy, the original object is unchanged).
sum_over_axes()[source]

Reduce to a 2D image by summing over non-spatial dimensions.

to_hdulist(hdu=None, hdu_bands=None, sparse=False, conv=None)

Convert to HDUList.

Parameters:

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.

sparse : bool

Sparsify the map by only writing pixels with non-zero amplitude.

conv : {‘fgst-ccube’,’fgst-template’,’gadf’,None}, optional

FITS format convention. If None this will be set to the default convention of the map.
Returns:

hdu_list : HDUList
upsample(factor, order=0, preserve_counts=True)[source]

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).
Returns:

map : Map

Upsampled map.
write(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.