Map

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

Bases: abc.ABC

Abstract map class.

This can represent WCS- or HEALPIX-based maps with 2 spatial dimensions and N non-spatial dimensions.

Parameters
geomGeom

Geometry

datandarray

Data array

metadict

Dictionary to store meta data

unitstr or Unit

Data unit

Attributes Summary

data

Data array (ndarray)

geom

Map geometry (Geom)

meta

Map meta (dict)

quantity

Map data times unit (Quantity)

tag

unit

Map unit (Unit)

Methods Summary

apply_edisp(edisp)

Apply energy dispersion to map.

coadd(map_in[, weights])

Add the contents of map_in to this map.

copy(**kwargs)

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

create(**kwargs)

Create an empty map object.

crop(crop_width)

Crop the spatial dimensions of the map.

downsample(factor[, preserve_counts, axis])

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.

fill_events(events)

Fill event coordinates (EventList).

from_geom(geom[, meta, data, map_type, …])

Generate an empty map from a Geom instance.

from_hdulist(hdulist[, hdu, hdu_bands, map_type])

Create from astropy.io.fits.HDUList.

from_images(images[, axis])

Create Map from list of images and a non-spatial axis.

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.

interp_to_geom(geom, **kwargs)

Interpolate map to input geometry.

iter_by_image()

Iterate over image planes of the map.

pad(pad_width[, mode, cval, order])

Pad the spatial dimensions of the map.

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.

reduce(axis[, func, keepdims, weights])

Reduce map over a single non-spatial axis

reduce_over_axes([func, keepdims, axes, weights])

Reduce map over non-spatial axes

resample_axis(axis[, weights])

Resample map to a new axis binning by grouping and summing smaller bins.

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.

sum_over_axes([axes, keepdims, weights])

To sum map values over all non-spatial axes.

upsample(factor[, order, preserve_counts, axis])

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 (Geom)

meta

Map meta (dict)

quantity

Map data times unit (Quantity)

tag = 'map'
unit

Map unit (Unit)

Methods Documentation

apply_edisp(edisp)[source]

Apply energy dispersion to map. Requires energy axis.

Parameters
edispgammapy.irf.EDispKernel

Energy dispersion matrix

Returns
mapWcsNDMap

Map with energy dispersion applied.

coadd(map_in, weights=None)[source]

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`

The weight factors while adding

copy(**kwargs)[source]

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

Parameters
**kwargsdict

Keyword arguments to overwrite in the map constructor.

Returns
copyMap

Copied Map.

static create(**kwargs)[source]

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
framestr

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.

binszfloat or ndarray

Pixel size in degrees.

skydirSkyCoord

Coordinate of map center.

axeslist

List of MapAxis objects for each non-spatial dimension. If None then the map will be a 2D image.

dtypestr

Data type, default is ‘float32’

unitstr or Unit

Data unit.

metadict

Dictionary to store meta data.

regionSkyRegion

Sky region used for the region map.

Returns
mapMap

Empty map object.

abstract crop(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.

abstract downsample(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(coords, weights=None)[source]

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.

abstract fill_by_idx(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(pix, weights=None)[source]

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(events)[source]

Fill event coordinates (EventList).

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

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

static from_hdulist(hdulist, hdu=None, hdu_bands=None, map_type='auto')[source]

Create from astropy.io.fits.HDUList.

classmethod from_images(images, axis=None)[source]

Create Map from list of images and a non-spatial axis.

If the images have a non-spatial axis of length 1 a new axes is generated from by merging the individual axes. The image geometries must be aligned.

Parameters
imageslist of Map objects

Images

axisMapAxis

Map axis

Returns
mapMap

Map with additional non-spatial axis.

get_by_coord(coords)[source]

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.

abstract get_by_idx(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(pix)[source]

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(coords)[source]

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(idx)[source]

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(pix)[source]

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.

abstract interp_by_coord(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.

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

Interpolate map values at the given pixel coordinates.

Parameters
pixtuple

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_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_to_geom(geom, **kwargs)[source]

Interpolate map to input geometry.

Parameters
geomGeom

Target Map geometry

**kwargsdict

Keyword arguments passed to Map.interp_by_coord

Returns
interp_mapMap

Interpolated Map

iter_by_image()[source]

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.

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

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.

cvalfloat

Padding value when mode=’consant’.

orderint

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

Returns
mapMap

Padded map.

plot_interactive(rc_params=None, **kwargs)[source]

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

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

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(axis, func=<ufunc 'add'>, keepdims=False, weights=None)[source]

Reduce map over a single non-spatial axis

Parameters
axis: str

The name of the axis to reduce over

funcufunc

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

weightsMap

Weights to be applied.

Returns
map_outMap

Map with the given non-spatial axes reduced

reduce_over_axes(func=<ufunc 'add'>, keepdims=False, axes=None, weights=None)[source]

Reduce map over non-spatial axes

Parameters
funcufunc

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

axes: list

Names of MapAxis to reduce over If None, all will reduced

weightsMap

Weights to be applied.

Returns
map_outMap

Map with non-spatial axes reduced

resample_axis(axis, weights=None)[source]

Resample map to a new axis binning by grouping and summing smaller bins.

Parameters
axisMapAxis

New map axis.

weightsMap

Array to be used as weights. The spatial geometry must be equivalent to other and additional axes must be broadcastable.

Returns
mapMap

Map with resampled axis.

set_by_coord(coords, vals)[source]

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.

abstract set_by_idx(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(pix, vals)[source]

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(slices)[source]

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.

sum_over_axes(axes=None, keepdims=True, weights=None)[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

axes: list

Names of MapAxis to reduce over If None, all will summed over

weightsMap

Weights to be applied. The Map should have the same geometry.

Returns
map_outMap

Map with non-spatial axes summed over

abstract upsample(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(filename, overwrite=False, **kwargs)[source]

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.