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, optional: Dictionary to store metadata. Default is None.
unitstr or Unit, optional: Data unit, ignored if data is a Quantity. Default is ‘’.

Attributes Summary

`data`	Data array as a `ndarray` object.
`geom`	Map geometry as a `Geom` object.
`is_mask`	Whether map is a mask with boolean data type.
`meta`	Map metadata as a `dict`.
`quantity`	Map data as a `Quantity` object.
`tag`
`unit`	Map unit as an `Unit` object.

Methods Summary

`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.
`dot`(other)	Apply dot product with the input map.
`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[, weights])	Fill the map from an `EventList` object.
`from_geom`(geom[, meta, data, unit, dtype])	Generate an empty map from a `Geom` instance.
`from_hdulist`(hdulist[, hdu, hdu_bands, ...])	Create from a `astropy.io.fits.HDUList` object.
`from_stack`(maps[, axis, axis_name])	Create Map from a 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.
`iter_by_image_index`()	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.
`reorder_axes`(axes_names)	Return a new map re-ordering the non-spatial axes.
`reproject_by_image`(geom[, preserve_counts, ...])	Reproject each image of a ND map to input 2d geometry.
`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 function `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])	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 a 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 as a ndarray object.

geom#: Map geometry as a Geom object.

is_mask#: Whether map is a mask with boolean data type.

meta#: Map metadata as a dict.

quantity#: Map data as a Quantity object.

tag = 'map'#

unit#: Map unit as an Unit object.

Methods Documentation

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: Map to add.
weights: `Map` or `~numpy.ndarray`: The weight factors while adding. Default is None.

copy(**kwargs)[source]#

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

Parameters

**kwargsdict, optional: 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; for HPX-specific options, see HpxMap.create.

Parameters

frame{“icrs”, “galactic”}: 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. Default is ‘wcs’.
binszfloat or ndarray: Pixel size in degrees.
skydirSkyCoord: Coordinate of the 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 metadata.
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 sum.

Returns

cumsumMap: Map with cumulative sum.

dot(other)[source]#

Apply dot product with the input map.

The input Map has to share a single MapAxis with the current Map. Because it has no spatial dimension, it must be a RegionNDMap.

Parameters

otherRegionNDMap: Map to apply the dot product to. It must share a unique non-spatial MapAxis with the current Map.

Returns

mapMap: Map with dot product applied.

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

Downsample the spatial dimension by a given factor.

Parameters

factorint: Downsampling factor.
preserve_countsbool, optional: Preserve the integral over each bin. This should be set to True if the map is an integral quantity (e.g. counts) and False if the map is a differential quantity (e.g. intensity). Default is True.
axis_namestr, optional: Which axis to downsample. By default, spatial axes are downsampled. Default is None.

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, optional: Weights vector. If None, weights are set to 1. Default is None.

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, optional: Weights vector. If None, weights are set to 1. Default is None.

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, optional: Weights vector. If None, weights are set to 1. Default is None.

fill_events(events, weights=None)[source]#

Fill the map from an EventList object.

Parameters

eventsEventList: Events to fill in the map with.
weightsndarray, optional: Weights vector. The weights vector must be of the same length as the events column length. If None, weights are set to 1. Default is None.

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

Generate an empty map from a Geom instance.

Parameters

geomGeom: Map geometry.
metadict, optional: Dictionary to store metadata. Default is None.
datanumpy.ndarray, optional: Data array. Default is None.
unitstr or Unit: Data unit.
dtypestr, optional: Data type. Default is ‘float32’.

Returns

map_outMap: Map object.

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

Create from a astropy.io.fits.HDUList object.

Parameters

hdulistHDUList: HDU list containing HDUs for map data and bands.
hdustr, optional: Name or index of the HDU with the map data. Default is None.
hdu_bandsstr, optional: Name or index of the HDU with the BANDS table. Default is None.
map_type{“auto”, “wcs”, “hpx”, “region”}: Map type. Default is “auto”.
format{‘gadf’, ‘fgst-ccube’, ‘fgst-template’}: FITS format convention. Default is None.
colnamestr, optional: Data column name to be used for HEALPix map. Default is None.

Returns

map_outMap: Map object.

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

Create Map from a 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, optional: If a MapAxis is provided the maps are stacked along the last data axis and the new axis is introduced. Default is None.
axis_namestr, optional: 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 the projection footprint. Default is numpy.nan.

Returns

valsndarray: Values of pixels in the map. numpy.nan is used to flag coordinates outside the 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. numpy.nan is used to flag coordinates outside the 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 the projection footprint. Default is numpy.nan.

Returns

valsndarray: Array of pixel values. numpy.nan is used to flag coordinates outside the 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. Dictionary 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`, optional: Region to extract the spectrum from. Pixel or sky regions are accepted. Default is None.
funcnumpy.func, optional: Function to reduce the data. Default is nansum. For a boolean Map, use numpy.any or numpy.all. Default is numpy.nansum.
weightsWcsNDMap, optional: Array to be used as weights. The geometry must be equivalent. Default is None.

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, optional: Keyword arguments passed to Map.interp_by_coord.

Returns

arrayQuantity: 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. Default is “linear”.
fill_valuefloat, optional: The value to use for points outside the interpolation domain. If None, values outside the domain are extrapolated. Default is None.

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. Default is “linear”.
fill_valuefloat, optional: The value to use for points outside the interpolation domain. If None, values outside the domain are extrapolated. Default is None.

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, optional: 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). Default is False.
fill_valuefloat, optional: The value to use for points outside the interpolation domain. Default is 0.
**kwargsdict, optional: 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, optional: Relative tolerance for the axes’ comparison. Default is 1e-3.
atol_axesfloat, optional: Absolute tolerance for the axes’ comparison. Default is 1e-6.
**kwargsdict, optional: Keywords passed to 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, optional: Keep dimensions. Default is False.

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.

iter_by_image_index()[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

idxtuple: 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 axes.

Parameters

positionSkyCoord: Test position.

Returns

positionSkyCoord: The nearest position in the mask.

normalize(axis_name=None)[source]#

Normalise data in place along a given axis.

Parameters

axis_namestr, optional: Along which axis to normalise.

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, optional: Which axis to downsample. By default, spatial axes are padded. Default is None.
mode{‘constant’, ‘edge’, ‘interp’}: Padding mode. ‘edge’ pads with the closest edge value. ‘constant’ pads with a constant value. ‘interp’ pads with an extrapolated value. Default is ‘constant’.
cvalfloat, optional: Padding value when `mode`=’consant’. Default is 0.

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, optional: Figsize to plot on. Default is None.
ncolsint, optional: Number of columns to plot. Default is 3.
**kwargsdict, optional: Keyword arguments passed to WcsNDMap.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, optional: Passed to matplotlib.rc_context(rc=rc_params) to style the plot. Default is None.
**kwargsdict, optional: 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, checksum=False)[source]#

Read a map from a FITS file.

Parameters

filenamestr or Path: Name of the FITS file.
hdustr, optional: Name or index of the HDU with the map data. Default is None.
hdu_bandsstr, optional: 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. Default is None.
map_type{‘auto’, ‘wcs’, ‘wcs-sparse’, ‘hpx’, ‘hpx-sparse’, ‘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. Default is ‘auto’.
colnamestr, optional: data column name to be used for HEALPix map.
checksumbool: If True checks both DATASUM and CHECKSUM cards in the file headers. Default is False.

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, optional: Function to use for reducing the data. Default is numpy.add.
keepdimsbool, optional: If this is set to true, the axes which are summed over are left in the map with a single bin. Default is False.
weightsMap: Weights to be applied. The map should have the same geometry as this one. Default is None.

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, optional: Function to use for reducing the data. Default is numpy.add.
keepdimsbool, optional: If this is set to true, the axes which are summed over are left in the map with a single bin. Default is False.
axes_names: list, optional: Names of axis to reduce over. If None, all non-spatial axis will be reduced.
weightsMap, optional: Weights to be applied. The map should have the same geometry as this one. Default is None.

Returns

map_outMap: Map with non-spatial axes reduced.

rename_axes(names, new_names)[source]#

Rename the Map axes.

Parameters

namesstr or list of str: Names of the axes.
new_namesstr or list of str: New names of the axes. The list must be of the same length as names).

Returns

geomMap: Map with renamed axes.

reorder_axes(axes_names)[source]#

Return a new map re-ordering the non-spatial axes.

Parameters

axes_nameslist of str: The list of axes names in the required order.

Returns

mapMap: The map with axes re-ordered.

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

Reproject each image of a ND map to input 2d geometry.

For large maps this method is faster than reproject_to_geom.

Parameters

geomGeom: Target slice geometry in 2D.
preserve_countsbool, optional: 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). Default is False.
precision_factorint, optional: Minimal factor between the bin size of the output map and the oversampled base map. Used only for the oversampling method. Default is 10.

Returns

output_mapMap: Reprojected Map.

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

Reproject map to input geometry.

Parameters

geomGeom: Target Map geometry.
preserve_countsbool, optional: 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). Default is False.
precision_factorint, optional: Minimal factor between the bin size of the output map and the oversampled base map. Used only for the oversampling method. Default is 10.

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, optional: Weights vector. It must have same shape as the data of the map. If set to None, weights will be set to 1. Default is None.
preserve_countsbool, optional: 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). Default is True.

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 function 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, optional: Array to be used as weights. The spatial geometry must be equivalent to other and additional axes must be broadcastable. If set to None, weights will be set to 1. Default is None.
ufuncufunc: Universal function 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. Default is 0.

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: Dictionary 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 dictionary 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]#

Sum map values over all non-spatial axes.

Parameters

axes_names: list of str: Names of the axis to reduce over. If None, all non-spatial axis will be summed over. Default is None.
keepdimsbool, optional: If this is set to true, the axes which are summed over are left in the map with a single bin. Default is True.
weightsMap, optional: Weights to be applied. The map should have the same geometry as this one. Default is None.

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 this 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 a different unit.

Parameters

unitstr or Unit: 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, optional: Order of the interpolation used for upsampling. Default is 0.
preserve_countsbool, optional: 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). Default is True.
axis_namestr, optional: Which axis to upsample. By default, spatial axes are upsampled. Default is None.

Returns

mapMap: Upsampled map.

write(filename, overwrite=False, **kwargs)[source]#

Write to a FITS file.

Parameters

filenamestr

Output file name.

overwritebool, optional

Overwrite existing file. Default is False.

hdustr, optional

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, optional

Set the name of the bands table extension. Default is ‘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, optional

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

checksumbool, optional

When True adds both DATASUM and CHECKSUM cards to the headers written to the file. Default is False.