# WcsNDMap¶

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

HEALPix map with any number of 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
geomWcsGeom

WCS geometry object.

datandarray

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

dtypestr, optional

Data type, default is float32

metadict

Dictionary to store meta data.

unitstr or Unit

The map unit

Attributes Summary

 data Data array (ndarray) geom Map geometry (Geom) meta Map meta (dict) quantity Map data times unit (Quantity) unit Map unit (Unit)

Methods Summary

 apply_edisp(self, edisp) Apply energy dispersion to map. coadd(self, map_in[, weights]) Add the contents of map_in to this map. convolve(self, kernel[, use_fft]) Convolve map with a kernel. copy(self, \*\*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(self, crop_width) Crop the spatial dimensions of the map. cutout(self, position, width[, mode]) Create a cutout around a given position. downsample(self, factor[, preserve_counts, axis]) Downsample the spatial dimension by a given factor. fill_by_coord(self, coords[, weights]) Fill pixels at coords with given weights. fill_by_idx(self, idx[, weights]) Fill pixels at idx with given weights. fill_by_pix(self, pix[, weights]) Fill pixels at pix with given weights. fill_events(self, events) Fill event coordinates (EventList). from_geom(geom[, meta, data, map_type, …]) Generate an empty map from a Geom 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(self, coords) Return map values at the given map coordinates. get_by_idx(self, idx) Return map values at the given pixel indices. get_by_pix(self, pix) Return map values at the given pixel coordinates. get_image_by_coord(self, coords) Return spatial map at the given axis coordinates. get_image_by_idx(self, idx) Return spatial map at the given axis pixel indices. get_image_by_pix(self, pix) Return spatial map at the given axis pixel coordinates get_spectrum(self[, region, func]) Extract spectrum in a given region. interp_by_coord(self, coords[, interp, …]) Interpolate map values at the given map coordinates. interp_by_pix(self, pix[, interp, fill_value]) Interpolate map values at the given pixel coordinates. iter_by_image(self) Iterate over image planes of the map. make_hdu(self[, hdu, hdu_bands, sparse, conv]) Make a FITS HDU from this map. pad(self, pad_width[, mode, cval, order]) Pad the spatial dimensions of the map. plot(self[, ax, fig, add_cbar, stretch]) Plot image on matplotlib WCS axes. plot_interactive(self[, 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. reduce_over_axes(self, func[, keepdims]) Reduce map over all non spatial axes sample_coord(self, n_events[, random_state]) Sample position and energy of events. set_by_coord(self, coords, vals) Set pixels at coords with given vals. set_by_idx(self, idx, vals) Set pixels at idx with given vals. set_by_pix(self, pix, vals) Set pixels at pix with given vals. slice_by_idx(self, slices) Slice sub map from map object. smooth(self, width[, kernel]) Smooth the map. stack(self, other[, weights]) Stack cutout into map. sum_over_axes(self[, keepdims]) To sum map values over all non-spatial axes. to_hdulist(self[, hdu, hdu_bands, sparse, conv]) Convert to HDUList. upsample(self, factor[, order, …]) Upsample the spatial dimension by a given factor. write(self, filename[, overwrite]) Write to a FITS file.

Attributes Documentation

data

Data array (ndarray)

geom

Map geometry (Geom)

meta

Map meta (dict)

quantity

Map data times unit (Quantity)

unit

Map unit (Unit)

Methods Documentation

apply_edisp(self, edisp)[source]

Apply energy dispersion to map. Requires energy axis.

Parameters
edispgammapy.irf.EnergyDispersion

Energy dispersion matrix

Returns
mapWcsNDMap

Map with energy dispersion applied.

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_inMap

Input map.

weights: Map or ~numpy.ndarray

convolve(self, 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

Convolution kernel.

use_fftbool
kwargsdict

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

Returns
mapWcsNDMap

Convolved map.

copy(self, **kwargs)

Copy map instance and overwrite given attributes, except for geometry.

Parameters
**kwargsdict

Keyword arguments to overwrite in the map constructor.

Returns
copyMap

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', 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.

npixint 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.

widthfloat 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.

binszfloat 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.

skydirtuple 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’).

axeslist

List of non-spatial axes.

projstring, optional

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

refpixtuple

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

dtypestr, optional

Data type, default is float32

FITS format convention. Default is ‘gadf’.

metadict

Dictionary to store meta data.

unitstr or Unit

The unit of the map

Returns
mapWcsMap

A WCS map object.

crop(self, crop_width)[source]

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
mapMap

Cropped map.

cutout(self, position, width, mode='trim')[source]

Create a cutout around a given position.

Parameters
positionSkyCoord

Center position of the cutout region.

widthtuple 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
cutoutWcsNDMap

Cutout map

downsample(self, factor, preserve_counts=True, axis=None)[source]

Downsample the spatial dimension by a given factor.

Parameters
factorint

Downsampling factor.

preserve_countsbool

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).

axisstr

Which axis to downsample. By default spatial axes are downsampled.

Returns
mapMap

Downsampled map.

fill_by_coord(self, coords, weights=None)

Fill pixels at coords with given weights.

Parameters
coordstuple 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.

weightsndarray

Weights vector. Default is weight of one.

fill_by_idx(self, idx, weights=None)[source]

Fill pixels at idx with given weights.

Parameters
idxtuple

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.

weightsndarray

Weights vector. Default is weight of one.

fill_by_pix(self, pix, weights=None)

Fill pixels at pix with given weights.

Parameters
pixtuple

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.

weightsndarray

Weights vector. Default is weight of one.

fill_events(self, events)

Fill event coordinates (EventList).

static from_geom(geom, meta=None, data=None, map_type='auto', unit='', dtype='float32')

Generate an empty map from a Geom instance.

Parameters
geomGeom

Map geometry.

datanumpy.ndarray

data array

metadict

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.

unitstr or Unit

Data unit.

Returns
map_outMap

Map object

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

Make a WcsNDMap object from a FITS HDU.

Parameters
hdu

The map FITS HDU.

hdu_bandsBinTableHDU

The BANDS table HDU.

classmethod from_hdulist(hdu_list, hdu=None, hdu_bands=None)

Make a WcsMap object from a FITS HDUList.

Parameters
hdu_listHDUList

HDU list containing HDUs for map data and bands.

hdustr

Name or index of the HDU with the map data.

hdu_bandsstr

Name or index of the HDU with the BANDS table.

Returns
wcs_mapWcsMap

Map object

get_by_coord(self, coords)

Return map values at the given map coordinates.

Parameters
coordstuple 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
valsndarray

Values of pixels in the map. np.nan used to flag coords outside of map.

get_by_idx(self, idx)[source]

Return map values at the given pixel indices.

Parameters
idxtuple

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
valsndarray

Array of pixel values. np.nan used to flag coordinate outside of map

get_by_pix(self, pix)

Return map values at the given pixel coordinates.

Parameters
pixtuple

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
valsndarray

Array of pixel values. np.nan used to flag coordinates outside of map

get_image_by_coord(self, coords)

Return spatial map at the given axis coordinates.

Parameters
coordstuple 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_outMap

Map with spatial dimensions only.

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(self, idx)

Return spatial map at the given axis pixel indices.

Parameters
idxtuple

Tuple of scalar indices for each non spatial dimension of the map. Tuple should be ordered as (I_0, …, I_n).

Returns
map_outMap

Map with spatial dimensions only.

get_image_by_pix(self, pix)

Return spatial map at the given axis pixel coordinates

Parameters
pixtuple

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_outMap

Map with spatial dimensions only.

get_spectrum(self, region=None, func=<function nansum at 0x116a72730>)[source]

Extract spectrum in a given region.

The spectrum can be computed by summing (or, more generally, applying func) along the spatial axes in each energy bin. This occurs only inside the region, which by default is assumed to be the whole spatial extension of the map.

Parameters
region: ~regions.Region

Region (pixel or sky regions accepted).

funcnumpy.ufunc

Function to reduce the data.

Returns
spectrumCountsSpectrum

Spectrum in the given region.

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

Interpolate map values at the given map coordinates.

Parameters
coordstuple 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_valueNone or float value

The value to use for points outside of the interpolation domain. If None, values outside the domain are extrapolated.

Returns
valsndarray

Interpolated pixel values.

interp_by_pix(self, pix, interp=None, fill_value=None)[source]

Interpolate map values at the given pixel coordinates.

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 Iterating by image for further information.

make_hdu(self, hdu='SKYMAP', hdu_bands=None, sparse=False, conv=None)

Make a FITS HDU from this map.

Parameters
hdustr

The HDU extension name.

hdu_bandsstr

The HDU extension name for BANDS table.

sparsebool

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

Returns
hdu

HDU containing the map data.

pad(self, pad_width, mode='constant', cval=0, order=1)[source]

Pad the spatial dimensions of the map.

Parameters

Number of pixels padded to the edges of each axis.

mode{‘edge’, ‘constant’, ‘interp’}

cvalfloat

orderint

Order of interpolation when mode=’constant’ (0 = nearest-neighbor, 1 = linear, 2 = quadratic, 3 = cubic).

Returns
mapMap

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

Plot image on matplotlib WCS axes.

Parameters
axWCSAxes, optional

WCS axis object to plot on.

figFigure

Figure object.

stretchstr
**kwargsdict

Keyword arguments passed to imshow.

Returns
figFigure

Figure object.

axWCSAxes

WCS axis object

cbarColorbar or None

Colorbar object.

plot_interactive(self, rc_params=None, **kwargs)

Plot map with interactive widgets to explore the non spatial axes.

Parameters
rc_paramsdict

Passed to matplotlib.rc_context(rc=rc_params) to style the plot.

**kwargsdict

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



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
filenamestr or Path

Name of the FITS file.

hdustr

Name or index of the HDU with the map data.

hdu_bandsstr

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_outMap

Map object

reduce_over_axes(self, func, keepdims=False)[source]

Reduce map over all non spatial axes

Parameters
func~numpy.ufunc

Function to use for reducing the data.

keepdimsbool, optional

If this is set to true, the axes which are summed over are left in the map with a single bin

Returns
map_outWcsNDMap

Map with non-spatial axes reduced

sample_coord(self, n_events, random_state=0)[source]

Sample position and energy of events.

Parameters
n_eventsint

Number of events to sample.

random_state{int, ‘random-seed’, ‘global-rng’, RandomState}

Defines random number generator initialisation. Passed to get_random_state.

Returns
coordsMapCoord object.

Sequence of coordinates and energies of the sampled events.

set_by_coord(self, coords, vals)

Set pixels at coords with given vals.

Parameters
coordstuple 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.

valsndarray

Values vector.

set_by_idx(self, idx, vals)[source]

Set pixels at idx with given vals.

Parameters
idxtuple

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.

valsndarray

Values vector.

set_by_pix(self, pix, vals)

Set pixels at pix with given vals.

Parameters
pixtuple

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.

valsndarray

Values vector.

slice_by_idx(self, slices)

Slice sub map from map object.

For usage examples, see Indexing and Slicing.

Parameters
slicesdict

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_outMap

Sliced map object.

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

Smooth the map.

Iterates over 2D image planes, processing one at a time.

Parameters
widthQuantity, str 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

kwargsdict

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

Returns
imageWcsNDMap

Smoothed image (a copy, the original object is unchanged).

stack(self, other, weights=None)[source]

Stack cutout into map.

Parameters
otherWcsNDMap

Other map to stack

weightsWcsNDMap

Array to be used as weights.

sum_over_axes(self, keepdims=False)[source]

To sum map values over all non-spatial axes.

Parameters
keepdimsbool, optional

If this is set to true, the axes which are summed over are left in the map with a single bin

Returns
map_outWcsNDMap

Map with non-spatial axes summed over

to_hdulist(self, hdu=None, hdu_bands=None, sparse=False, conv='gadf')

Convert to HDUList.

Parameters
hdustr

Name or index of the HDU with the map data.

hdu_bandsstr

Name or index of the HDU with the BANDS table.

sparsebool

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

FITS format convention.

Returns
hdu_listHDUList
upsample(self, factor, order=0, preserve_counts=True, axis=None)[source]

Upsample the spatial dimension by a given factor.

Parameters
factorint

Upsampling factor.

orderint

Order of the interpolation used for upsampling.

preserve_countsbool

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).

axisstr

Which axis to upsample. By default spatial axes are upsampled.

Returns
mapMap

Upsampled map.

write(self, filename, overwrite=False, **kwargs)

Write to a FITS file.

Parameters
filenamestr

Output file name.

overwritebool

Overwrite existing file?

hdustr

Set the name of the image extension. By default this will be set to SKYMAP (for BINTABLE HDU) or PRIMARY (for IMAGE HDU).

hdu_bandsstr

Set the name of the bands table extension. By default this will be set to BANDS.

convstr

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’.

sparsebool

Sparsify the map by dropping pixels with zero amplitude. This option is only compatible with the ‘gadf’ format.