RegionNDMap#

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

Bases: gammapy.maps.core.Map

N-dimensional region map. A RegionNDMap owns a RegionGeom instance as well as a data array containing the values associated to that region in the sky along the non-spatial axis, usually an energy axis. The spatial dimensions of a RegionNDMap are reduced to a single spatial bin with an arbitrary shape, and any extra dimensions are described by an arbitrary number of non-spatial axes.

Parameters

geomRegionGeom: Region 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`)
`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`(region[, axes, dtype, meta, unit, ...])	Create an empty region map object.
`crop`()	Crop the spatial dimensions of the map.
`cumsum`(axis_name)	Compute cumulative sum along a given axis
`cutout`(args, *kwargs)	Return self
`downsample`(factor[, preserve_counts, ...])	Downsample the non-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[, format, ogip_column, hdu])	Create from `HDUList`.
`from_stack`(maps[, axis, axis_name])	Create Map from list of images and a non-spatial axis.
`from_table`(table[, format, colname])	Create region map from table
`get_by_coord`(coords[, fill_value])	Return map values at the given map coordinates.
`get_by_idx`(idxs)	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`(args, *kwargs)	Return self
`integral`(axis_name, coords, **kwargs)	Compute integral along a given axis
`interp_by_coord`(coords, **kwargs)	Interpolate map values at the given map coordinates.
`interp_by_pix`(pix, **kwargs)	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_axis_data`(axis_name)	Iterate data by 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`([ax, axis_name])	Plot the data contained in region map along the non-spatial axis.
`plot_grid`([figsize, ncols])	Plot map as a grid of subplots for non-spatial axes
`plot_hist`([ax])	Plot as histogram.
`plot_interactive`()	Plot map with interactive widgets to explore the non spatial axes.
`plot_mask`([ax])	Plot the mask as a shaded area in a xmin-xmax range
`plot_region`([ax])	Plot region
`read`(filename[, format, ogip_column, hdu])	Read from 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
`resample`(geom[, weights, preserve_counts])	Resample pixels to `geom` with given `weights`.
`resample_axis`(axis[, weights, ufunc])	Resample map to a new axis binning by grouping over smaller bins and apply ufunc to the bin contents.
`set_by_coord`(coords, vals)	Set pixels at `coords` with given `vals`.
`set_by_idx`(idx, value)	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.
`stack`(other[, weights, nan_to_num])	Stack other region map into map.
`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_hdulist`([format, hdu, hdu_bands, hdu_region])	Convert to `HDUList`.
`to_region_nd_map`(args, *kwargs)
`to_table`([format])	Convert to `Table`.
`to_unit`(unit)	Convert map to different unit
`upsample`(factor[, order, preserve_counts, ...])	Upsample the non-spatial dimension by a given factor.
`write`(filename[, overwrite, format, hdu])	Write map to 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)#

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)#

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)#

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(region, axes=None, dtype='float32', meta=None, unit='', wcs=None, binsz_wcs='0.1deg', data=None)[source]#

Create an empty region map object.

Parameters

regionstr or SkyRegion: Region specification
axeslist of MapAxis: Non spatial axes.
dtypestr: Data type, default is ‘float32’
unitstr or Unit: Data unit.
metadict: Dictionary to store meta data.
wcsWCS: WCS projection to use for local projections of the region
binsz_wcs: `~astropy.units.Quantity` ot str: Bin size used for the default WCS, if wcs=None.
datandarray: Data array

Returns

mapRegionNDMap: Region map

crop()[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)#

Compute cumulative sum along a given axis

Parameters

axis_namestr: Along which axis to integrate.

Returns

cumsumMap: Map with cumulative sum

cutout(*args, **kwargs)[source]#: Return self

downsample(factor, preserve_counts=True, axis_name=None, weights=None)[source]#

Downsample the non-spatial dimension by a given factor.

By default the first axes is downsampled.

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. Default is “energy”.
weightsRegionNDMap: Contains the weights to apply to the axis to reduce. Default is just weighs of one.

Returns

mapRegionNDMap: Downsampled region map.

fill_by_coord(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(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)#

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)#: Fill event coordinates (EventList).

static from_geom(geom, meta=None, data=None, 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.
unitstr or Unit: Data unit.

Returns

map_outMap: Map object

classmethod from_hdulist(hdulist, format='gadf', ogip_column=None, hdu=None, **kwargs)[source]#

Create from HDUList.

Parameters

hdulistHDUList: HDU list.
format{“gadf”, “ogip”, “ogip-arf”}: Format specification
ogip_column{“COUNTS”, “QUALITY”, “BACKSCAL”}: If format ‘ogip’ is chosen which table hdu column to read.
hdustr: Name or index of the HDU with the map data.

Returns

region_nd_mapRegionNDMap: Region map.

classmethod from_stack(maps, axis=None, axis_name=None)#

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.

classmethod from_table(table, format='', colname=None)[source]#

Create region map from table

Parameters

tableTable: Table with input data
format{“gadf-sed”, “lightcurve”, “profile”}: Format to use
colnamestr: Column name to take the data from.

Returns

region_mapRegionNDMap: Region map

get_by_coord(coords, fill_value=nan)#

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.

get_by_idx(idxs)[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)#

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)#

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)#

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)#

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(*args, **kwargs)[source]#: Return self

integral(axis_name, coords, **kwargs)#

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

interp_by_coord(coords, **kwargs)[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.

interp_by_pix(pix, **kwargs)[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)#

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)#

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)#

“Iterate over a given axis

Yields

mapMap: Map iteration.

See also

iter_by_image: iterate by image returning a map

iter_by_axis_data(axis_name)[source]#

Iterate data by axis

Parameters

axis_namestr: Axis name

Returns

idx, datatuple, Quantity: Data and index

iter_by_image(keepdims=False)#

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()#

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)#

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)#

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')#

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(ax=None, axis_name=None, **kwargs)[source]#

Plot the data contained in region map along the non-spatial axis.

Parameters

axAxis: Axis used for plotting
axis_namestr: Which axis to plot on the x axis. Extra axes will be plotted as additional lines.
**kwargsdict: Keyword arguments passed to errorbar

Returns

axAxis: Axis used for plotting

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

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_hist(ax=None, **kwargs)[source]#

Plot as histogram.

kwargs are forwarded to hist

Parameters

axaxis (optional): Axis instance to be used for the plot
**kwargsdict: Keyword arguments passed to hist

Returns

axAxis: Axis used for plotting

plot_interactive()[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)

plot_mask(ax=None, **kwargs)[source]#

Plot the mask as a shaded area in a xmin-xmax range

Parameters

axaxis: Axis instance to be used for the plot.
**kwargsdict: Keyword arguments passed to axvspan

Returns

axAxis: Axis used for plotting

plot_region(ax=None, **kwargs)[source]#

Plot region

Parameters

axWCSAxes: Axes to plot on. If no axes are given, the region is shown using the minimal equivalent WCS geometry.
**kwargsdict: Keyword arguments forwarded to as_artist

classmethod read(filename, format='gadf', ogip_column=None, hdu=None)[source]#

Read from file.

Parameters

filenamepathlib.Path or str: Filename.
format{“gadf”, “ogip”, “ogip-arf”}: Which format to use.
ogip_column{None, “COUNTS”, “QUALITY”, “BACKSCAL”}: If format ‘ogip’ is chosen which table hdu column to read.
hdustr: Name or index of the HDU with the map data.

Returns

region_mapRegionNDMap: Region nd map

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

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)#

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

resample(geom, weights=None, preserve_counts=True)#

Resample pixels to geom with given weights.

Parameters

geomGeom: Target Map geometry
weightsndarray: Weights vector. Default is weight of one.
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)

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

Resample map to a new axis binning by grouping over smaller bins and apply ufunc to the bin contents.

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

set_by_coord(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(idx, value)[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)#

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)#

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)#

Split a Map along an axis into multiple maps.

Parameters

axis_namestr: Name of the axis to split

Returns

mapslist: A list of Map

stack(other, weights=None, nan_to_num=True)[source]#

Stack other region map into map.

Parameters

otherRegionNDMap: Other map to stack
weightsRegionNDMap: Array to be used as weights. The spatial geometry must be equivalent to other and additional axes must be broadcastable.
nan_to_num: bool: Non-finite values are replaced by zero if True (default).

sum_over_axes(axes_names=None, keepdims=True, weights=None)#

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)#

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_hdulist(format='gadf', hdu='SKYMAP', hdu_bands=None, hdu_region=None)[source]#

Convert to HDUList.

Parameters

format{“gadf”, “ogip”, “ogip-sherpa”, “ogip-arf”, “ogip-arf-sherpa”}: Format specification
hdustr: Name of the HDU with the map data, used for “gadf” format.
hdu_bandsstr: Name or index of the HDU with the BANDS table, used for “gadf” format.
hdu_regionstr: Name or index of the HDU with the region table.

Returns

hdulistHDUList: HDU list

to_region_nd_map(*args, **kwargs)[source]#

to_table(format='gadf')[source]#

Convert to Table.

Data format specification: PHA

Parameters

format{“gadf”, “ogip”, “ogip-arf”, “ogip-arf-sherpa”}: Format specification

Returns

tableTable: Table

to_unit(unit)#

Convert map to different unit

Parameters

unitUnit or str: New unit

Returns

mapMap: Map with new unit and converted data

upsample(factor, order=0, preserve_counts=True, axis_name=None)[source]#

Upsample the non-spatial dimension by a given factor.

By default the first axes is upsampled.

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 RegionNDMap is an integral quantity (e.g. counts) and false if the RegionNDMap is a differential quantity (e.g. intensity).
axis_namestr: Which axis to upsample. Default is “energy”.

Returns

mapRegionNDMap: Upsampled region map.

write(filename, overwrite=False, format='gadf', hdu='SKYMAP')[source]#

Write map to file

Parameters

filenamepathlib.Path or str: Filename.
format{“gadf”, “ogip”, “ogip-sherpa”, “ogip-arf”, “ogip-arf-sherpa”}: Which format to use.
overwritebool: Overwrite existing files?