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 or Quantity: Data array
metadict: Dictionary to store meta data
unitstr or Unit: Data unit, ignored if data is a Quantity.

Attributes Summary

`data`	Data array (`ndarray`)
`geom`	Map geometry (`Geom`)
`is_mask`	Whether map is mask with bool dtype
`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.
`cumsum`(axis_name)	Compute cumulative sum along a given axis
`downsample`(factor[, preserve_counts, axis_name])	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, unit, dtype])	Generate an empty map from a `Geom` instance.
`from_hdulist`(hdulist[, hdu, hdu_bands, ...])	Create from `astropy.io.fits.HDUList`.
`from_stack`(maps[, axis, axis_name])	Create Map from list of images and a non-spatial axis.
`get_by_coord`(coords[, fill_value])	Return map values at the given map coordinates.
`get_by_idx`(idx)	Return map values at the given pixel indices.
`get_by_pix`(pix[, fill_value])	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
`get_spectrum`([region, func, weights])	Extract spectrum in a given region.
`integral`(axis_name, coords, **kwargs)	Compute integral along a given axis
`interp_by_coord`(coords[, method, fill_value])	Interpolate map values at the given map coordinates.
`interp_by_pix`(pix[, method, fill_value])	Interpolate map values at the given pixel coordinates.
`interp_to_geom`(geom[, preserve_counts, ...])	Interpolate map to input geometry.
`is_allclose`(other[, rtol_axes, atol_axes])	Compare two Maps for close equivalency
`iter_by_axis`(axis_name[, keepdims])	"Iterate over a given axis
`iter_by_image`([keepdims])	Iterate over image planes of a map.
`iter_by_image_data`()	Iterate over image planes of the map.
`mask_nearest_position`(position)	Given a sky coordinate return nearest valid position in the mask
`normalize`([axis_name])	Normalise data in place along a given axis.
`pad`(pad_width[, axis_name, mode, cval, method])	Pad the spatial dimensions of the map.
`plot_grid`([figsize, ncols])	Plot map as a grid of subplots for non-spatial 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.
`reduce`(axis_name[, func, keepdims, weights])	Reduce map over a single non-spatial axis
`reduce_over_axes`([func, keepdims, ...])	Reduce map over non-spatial axes
`rename_axes`(names, new_names)	Rename the Map axes.
`reproject_to_geom`(geom[, preserve_counts, ...])	Reproject map to input geometry.
`resample`(geom[, weights, preserve_counts])	Resample pixels to `geom` with given `weights`.
`resample_axis`(axis[, weights, ufunc])	Resample map to a new axis by grouping and reducing smaller bins by a given ufunc
`sample_coord`(n_events[, random_state])	Sample position and energy of events.
`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.
`split_by_axis`(axis_name)	Split a Map along an axis into multiple maps.
`sum_over_axes`([axes_names, keepdims, weights])	To sum map values over all non-spatial axes.
`to_cube`(axes)	Append non-spatial axes to create a higher-dimensional Map.
`to_unit`(unit)	Convert map to different unit
`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 (Geom)

is_mask#: Whether map is mask with bool dtype

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.

cumsum(axis_name)[source]#

Compute cumulative sum along a given axis

Parameters

axis_namestr: Along which axis to integrate.

Returns

cumsumMap: Map with cumulative sum

abstract downsample(factor, preserve_counts=True, axis_name=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).
axis_namestr: 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, 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.
unitstr or Unit: Data unit.

Returns

map_outMap: Map object

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

Create from astropy.io.fits.HDUList.

Parameters

hdulistHDUList: 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.
map_type{“auto”, “wcs”, “hpx”, “region”}: Map type.
format{‘gadf’, ‘fgst-ccube’, ‘fgst-template’}: FITS format convention.
colnamestr, optional: Data column name to be used for the HEALPix map.

Returns

map_outMap: Map object

classmethod from_stack(maps, axis=None, axis_name=None)[source]#

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

The image geometries must be aligned, except for the axis that is stacked.

Parameters

mapslist of Map objects: List of maps
axisMapAxis: If a MapAxis is provided the maps are stacked along the last data axis and the new axis is introduced.
axis_namestr: If an axis name is as string the given the maps are stacked along the given axis name.

Returns

mapMap: Map with additional non-spatial axis.

get_by_coord(coords, fill_value=nan)[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.
fill_valuefloat: Value which is returned if the position is outside of the projection footprint

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, fill_value=nan)[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.
fill_valuefloat: Value which is returned if the position is outside of the projection footprint

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.

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)[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.

See also

get_image_by_coord, get_image_by_pix

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.

See also

get_image_by_coord, get_image_by_idx

get_spectrum(region=None, func=<function nansum>, weights=None)[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.func: Function to reduce the data. Default is np.nansum. For a boolean Map, use np.any or np.all.
weightsWcsNDMap: Array to be used as weights. The geometry must be equivalent.

Returns

spectrumRegionNDMap: Spectrum in the given region.

integral(axis_name, coords, **kwargs)[source]#

Compute integral along a given axis

This method uses interpolation of the cumulative sum.

Parameters

axis_namestr: Along which axis to integrate.
coordsdict or MapCoord: Map coordinates
**kwargsdict: Coordinates at which to evaluate the IRF

Returns

arrayQuantity: Returns 2D array with axes offset

abstract interp_by_coord(coords, method='linear', 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.
method{“linear”, “nearest”}: Method to interpolate data values. By default linear interpolation is performed.
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, method='linear', 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.
method{“linear”, “nearest”}: Method to interpolate data values. By default linear interpolation is performed.
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, preserve_counts=False, fill_value=0, **kwargs)[source]#

Interpolate map to input geometry.

Parameters

geomGeom: Target Map geometry
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)
**kwargsdict: Keyword arguments passed to Map.interp_by_coord

Returns

interp_mapMap: Interpolated Map

is_allclose(other, rtol_axes=0.001, atol_axes=1e-06, **kwargs)[source]#

Compare two Maps for close equivalency

Parameters

othergammapy.maps.Map: The Map to compare against
rtol_axesfloat: Relative tolerance for the axes comparison.
atol_axesfloat: Relative tolerance for the axes comparison.
**kwargsdict: keywords passed to numpy.allclose

Returns

is_allclosebool: Whether the Map is all close.

iter_by_axis(axis_name, keepdims=False)[source]#

“Iterate over a given axis

Yields

mapMap: Map iteration.

See also

iter_by_image: iterate by image returning a map

iter_by_image(keepdims=False)[source]#

Iterate over image planes of a map.

Parameters

keepdimsbool: Keep dimensions.

Yields

mapMap: Map iteration.

See also

iter_by_image_data: iterate by image returning data and index

iter_by_image_data()[source]#

Iterate over image planes of the map.

The image plane index is in data order, so that the data array can be indexed directly.

Yields

(data, idx)tuple: Where data is a numpy.ndarray view of the image plane data, and idx is a tuple of int, the index of the image plane.

See also

iter_by_image: iterate by image returning a map

mask_nearest_position(position)[source]#

Given a sky coordinate return nearest valid position in the mask

If the mask contains additional axes, the mask is reduced over those.

Parameters

positionSkyCoord: Test position

Returns

positionSkyCoord: Nearest position in the mask

normalize(axis_name=None)[source]#

Normalise data in place along a given axis.

Parameters

axis_namestr: Along which axis to normalize.

pad(pad_width, axis_name=None, mode='constant', cval=0, method='linear')[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.
axis_namestr: Which axis to downsample. By default spatial axes are padded.
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’.

Returns

mapMap: Padded map.

plot_grid(figsize=None, ncols=3, **kwargs)[source]#

Plot map as a grid of subplots for non-spatial axes

Parameters

figsizetuple of int: Figsize to plot on
ncolsint: Number of columns to plot
**kwargsdict: Keyword arguments passed to Map.plot.

Returns

axesndarray of Axes: Axes grid

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', format=None, colname=None)[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’, ‘region’}: Map type. Selects the class that will be used to instantiate the map. The map type should be consistent with the format of the input file. If map_type is ‘auto’ then an appropriate map type will be inferred from the input file.
colnamestr, optional: data column name to be used of healix map.

Returns

map_outMap: Map object

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

Reduce map over a single non-spatial axis

Parameters

axis_name: 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_names=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_names: 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

rename_axes(names, new_names)[source]#

Rename the Map axes.

Parameters

nameslist or str: Names of the axes.
new_nameslist or str: New names of the axes (list must be of same length than names).

Returns

geomMap: Renamed Map.

reproject_to_geom(geom, preserve_counts=False, precision_factor=10)[source]#

Reproject map to input geometry.

Parameters

geomGeom: Target Map geometry
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)
precision_factorint: Minimal factor between the bin size of the output map and the oversampled base map. Used only for the oversampling method.

Returns

output_mapMap: Reprojected Map

resample(geom, weights=None, preserve_counts=True)[source]#

Resample pixels to geom with given weights.

Parameters

geomGeom: Target Map geometry
weightsndarray: Weights vector. Default is weight of one. Must have same shape as the data of the map.
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)

Returns

resampled_mapMap: Resampled map

resample_axis(axis, weights=None, ufunc=<ufunc 'add'>)[source]#

Resample map to a new axis by grouping and reducing smaller bins by a given ufunc

By default, the map content are summed over the smaller bins. Other numpy ufunc can be used, e.g. numpy.logical_and or numpy.logical_or.

Parameters

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.
ufuncufunc: ufunc to use to resample the axis. Default is numpy.add.

Returns

mapMap: Map with resampled axis.

sample_coord(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(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.

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.

split_by_axis(axis_name)[source]#

Split a Map along an axis into multiple maps.

Parameters

axis_namestr: Name of the axis to split

Returns

mapslist: A list of Map

sum_over_axes(axes_names=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_names: list of str: 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

to_cube(axes)[source]#

Append non-spatial axes to create a higher-dimensional Map.

This will result in a Map with a new geometry with N+M dimensions where N is the number of current dimensions and M is the number of axes in the list. The data is reshaped onto the new geometry

Parameters

axeslist: Axes that will be appended to this Map. The axes should have only one bin

Returns

mapWcsNDMap: new map

to_unit(unit)[source]#

Convert map to different unit

Parameters

unitUnit or str: New unit

Returns

mapMap: Map with new unit and converted data

abstract upsample(factor, order=0, preserve_counts=True, axis_name=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).
axis_namestr: 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.

formatstr, optional

FITS format convention. By default files will be written to the gamma-astro-data-formats (GADF) format. This option can be used to write files that are compliant with format conventions required by specific software (e.g. the Fermi Science Tools). The following formats are supported:

“gadf” (default)

“fgst-ccube”

“fgst-ltcube”

“fgst-bexpcube”

“fgst-srcmap”

“fgst-template”

“fgst-srcmap-sparse”

“galprop”

“galprop2”

sparsebool

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