# HpxMap¶

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

Base class for HEALPIX map classes.

Parameters: geom : HpxGeom HEALPix geometry object. data : ndarray Data array. meta : dict Dictionary to store meta data. unit : Unit The map unit

Attributes Summary

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

Methods Summary

 coadd(self, map_in[, weights]) Add the contents of map_in to this map. copy(self, \*\*kwargs) Copy map instance and overwrite given attributes, except for geometry. create([nside, binsz, nest, map_type, …]) Factory method to create an empty HEALPix map. crop(self, crop_width) Crop the spatial dimensions of the map. downsample(self, factor[, preserve_counts, axis]) Downsample the spatial dimension by a given factor. fill_by_coord(self, coords[, weights]) Fill pixels at coords with given weights. fill_by_idx(self, idx[, weights]) Fill pixels at idx with given weights. fill_by_pix(self, pix[, weights]) Fill pixels at pix with given weights. from_geom(geom[, meta, data, map_type, …]) Generate an empty map from a Geom instance. from_hdulist(hdu_list[, hdu, hdu_bands]) Make a HpxMap object from a FITS HDUList. get_by_coord(self, coords) Return map values at the given map coordinates. get_by_idx(self, idx) Return map values at the given pixel indices. get_by_pix(self, pix) Return map values at the given pixel coordinates. get_image_by_coord(self, coords) Return spatial map at the given axis coordinates. get_image_by_idx(self, idx) Return spatial map at the given axis pixel indices. get_image_by_pix(self, pix) Return spatial map at the given axis pixel coordinates interp_by_coord(self, coords[, interp, …]) Interpolate map values at the given map coordinates. interp_by_pix(self, pix[, interp, fill_value]) Interpolate map values at the given pixel coordinates. iter_by_image(self) Iterate over image planes of the map. make_hdu(self[, hdu, hdu_bands, sparse, conv]) Make a FITS HDU with input data. pad(self, pad_width[, mode, cval, order]) Pad the spatial dimensions of the map. plot_interactive(self[, rc_params]) Plot map with interactive widgets to explore the non spatial axes. read(filename[, hdu, hdu_bands, map_type]) Read a map from a FITS file. reproject(self, geom[, order, mode]) Reproject this map to a different geometry. set_by_coord(self, coords, vals) Set pixels at coords with given vals. set_by_idx(self, idx, vals) Set pixels at idx with given vals. set_by_pix(self, pix, vals) Set pixels at pix with given vals. slice_by_idx(self, slices) Slice sub map from map object. sum_over_axes(self[, keepdims]) Reduce to a 2D image by summing over non-spatial dimensions. to_hdulist(self[, hdu, hdu_bands, sparse, conv]) Convert to HDUList. to_swapped(self) Return a new map with the opposite scheme (ring or nested). to_ud_graded(self, nside[, preserve_counts]) Upgrade or downgrade the resolution of the map to the chosen nside. to_wcs(self[, sum_bands, normalize, proj, …]) Make a WCS object and convert HEALPIX data into WCS projection. upsample(self, factor[, order, …]) Upsample the spatial dimension by a given factor. write(self, filename[, overwrite]) Write to a FITS file.

Attributes Documentation

data

Data array (ndarray)

geom

Map geometry (Geom)

meta

Map meta (dict)

quantity

Map data times unit (Quantity)

unit

Map unit (Unit)

Methods Documentation

coadd(self, map_in, weights=None)

Add the contents of map_in to this map.

This method can be used to combine maps containing integral quantities (e.g. counts) or differential quantities if the maps have the same binning.

Parameters: map_in : Map Input map. weights: Map or ~numpy.ndarray The weight factors while adding
copy(self, **kwargs)

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

Parameters: **kwargs : dict Keyword arguments to overwrite in the map constructor. copy : Map Copied Map.
classmethod create(nside=None, binsz=None, nest=True, map_type='hpx', coordsys='CEL', data=None, skydir=None, width=None, dtype='float32', region=None, axes=None, meta=None, unit='')[source]

Factory method to create an empty HEALPix map.

Parameters: nside : int or ndarray HEALPix NSIDE parameter. This parameter sets the size of the spatial pixels in the map. binsz : float or ndarray Approximate pixel size in degrees. An NSIDE will be chosen that correponds to a pixel size closest to this value. This option is superseded by nside. nest : bool True for HEALPix “NESTED” indexing scheme, False for “RING” scheme. coordsys : {‘CEL’, ‘GAL’}, optional Coordinate system, either Galactic (‘GAL’) or Equatorial (‘CEL’). skydir : tuple or SkyCoord Sky position of map center. Can be either a SkyCoord object or a tuple of longitude and latitude in deg in the coordinate system of the map. map_type : {‘hpx’, ‘hpx-sparse’} Map type. Selects the class that will be used to instantiate the map. width : float Diameter of the map in degrees. If None then an all-sky geometry will be created. axes : list List of MapAxis objects for each non-spatial dimension. meta : dict Dictionary to store meta data. unit : str or Unit The map unit map : HpxMap A HPX map object.
crop(self, crop_width)

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. map : Map Cropped map.
downsample(self, factor, preserve_counts=True, axis=None)

Downsample the spatial dimension by a given factor.

Parameters: factor : int Downsampling factor. preserve_counts : bool 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 : str Which axis to downsample. By default spatial axes are downsampled. map : Map Downsampled map.
fill_by_coord(self, coords, weights=None)

Fill pixels at coords with given weights.

Parameters: coords : tuple 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. weights : ndarray Weights vector. Default is weight of one.
fill_by_idx(self, idx, weights=None)

Fill pixels at idx with given weights.

Parameters: idx : tuple 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. weights : ndarray Weights vector. Default is weight of one.
fill_by_pix(self, pix, weights=None)

Fill pixels at pix with given weights.

Parameters: pix : tuple 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. weights : ndarray Weights vector. Default is weight of one.
static from_geom(geom, meta=None, data=None, map_type='auto', unit='', dtype='float32')

Generate an empty map from a Geom instance.

Parameters: geom : Geom Map geometry. data : numpy.ndarray data array meta : dict 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. unit : str or Unit Data unit. map_out : Map Map object
classmethod from_hdulist(hdu_list, hdu=None, hdu_bands=None)[source]

Make a HpxMap object from a FITS HDUList.

Parameters: hdu_list : HDUList HDU list containing HDUs for map data and bands. hdu : str Name or index of the HDU with the map data. If None then the method will try to load map data from the first BinTableHDU in the file. hdu_bands : str Name or index of the HDU with the BANDS table. hpx_map : HpxMap Map object
get_by_coord(self, coords)

Return map values at the given map coordinates.

Parameters: coords : tuple 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. vals : ndarray Values of pixels in the map. np.nan used to flag coords outside of map.
get_by_idx(self, idx)

Return map values at the given pixel indices.

Parameters: idx : tuple 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. vals : ndarray Array of pixel values. np.nan used to flag coordinate outside of map
get_by_pix(self, pix)

Return map values at the given pixel coordinates.

Parameters: pix : tuple 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. vals : ndarray Array of pixel values. np.nan used to flag coordinates outside of map
get_image_by_coord(self, coords)

Return spatial map at the given axis coordinates.

Parameters: coords : tuple 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}. map_out : Map Map with spatial dimensions only.

Examples

import numpy as np
from gammapy.maps import Map, MapAxis
from astropy.coordinates import SkyCoord
from astropy import units as u

# Define map axes
energy_axis = MapAxis.from_edges(
np.logspace(-1., 1., 4), unit='TeV', name='energy',
)

time_axis = MapAxis.from_edges(
np.linspace(0., 10, 20), unit='h', name='time',
)

# Define map center
skydir = SkyCoord(0, 0, frame='galactic', unit='deg')

# Create map
m_wcs = Map.create(
map_type='wcs',
binsz=0.02,
skydir=skydir,
width=10.0,
axes=[energy_axis, time_axis],
)

# Get image by coord tuple
image = m_wcs.get_image_by_coord(('500 GeV', '1 h'))

# Get image by coord dict with strings
image = m_wcs.get_image_by_coord({'energy': '500 GeV', 'time': '1 h'})

# Get image by coord dict with quantities
image = m_wcs.get_image_by_coord({'energy': 0.5 * u.TeV, 'time': 1 * u.h})

get_image_by_idx(self, idx)

Return spatial map at the given axis pixel indices.

Parameters: idx : tuple Tuple of scalar indices for each non spatial dimension of the map. Tuple should be ordered as (I_0, …, I_n). map_out : Map Map with spatial dimensions only.
get_image_by_pix(self, pix)

Return spatial map at the given axis pixel coordinates

Parameters: pix : tuple 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. map_out : Map Map with spatial dimensions only.
interp_by_coord(self, coords, interp=None, fill_value=None)

Interpolate map values at the given map coordinates.

Parameters: coords : tuple 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_value : None or float value The value to use for points outside of the interpolation domain. If None, values outside the domain are extrapolated. vals : ndarray Interpolated pixel values.
interp_by_pix(self, pix, interp=None, fill_value=None)

Interpolate map values at the given pixel coordinates.

Parameters: pix : tuple 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_value : None or float value The value to use for points outside of the interpolation domain. If None, values outside the domain are extrapolated. vals : ndarray Interpolated pixel values.
iter_by_image(self)

Iterate over image planes of the map.

This is a generator yielding (data, idx) tuples, where data is a numpy.ndarray view of the image plane data, and idx is a tuple of int, the index of the image plane.

The image plane index is in data order, so that the data array can be indexed directly. See Iterating by image for further information.

make_hdu(self, hdu=None, hdu_bands=None, sparse=False, conv=None)[source]

Make a FITS HDU with input data.

Parameters: hdu : str The HDU extension name. hdu_bands : str The HDU extension name for BANDS table. sparse : bool Set INDXSCHM to SPARSE and sparsify the map by only writing pixels with non-zero amplitude. conv : {‘fgst-ccube’, ‘fgst-template’, ‘gadf’, None}, optional FITS format convention. If None this will be set to the default convention of the map. hdu_out : Output HDU containing map data.
pad(self, pad_width, mode='constant', cval=0, order=1)

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. cval : float Padding value when mode=’consant’. order : int Order of interpolation when mode=’constant’ (0 = nearest-neighbor, 1 = linear, 2 = quadratic, 3 = cubic). map : Map Padded map.
plot_interactive(self, rc_params=None, **kwargs)

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

Parameters: rc_params : dict Passed to matplotlib.rc_context(rc=rc_params) to style the plot. **kwargs : dict Keyword arguments passed to WcsNDMap.plot.

Examples

You can try this out e.g. using a Fermi-LAT diffuse model cube with an energy axis:

from gammapy.maps import Map



If you would like to adjust the figure size you can use the rc_params argument:

rc_params = {'figure.figsize': (12, 6), 'font.size': 12}
m.plot_interactive(rc_params=rc_params)

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

Read a map from a FITS file.

Parameters: filename : str or Path Name of the FITS file. hdu : str Name or index of the HDU with the map data. hdu_bands : str 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. map_out : Map Map object
reproject(self, geom, order=1, mode='interp')

Reproject this map to a different geometry.

Only spatial axes are reprojected, if you would like to reproject non-spatial axes consider using Map.interp_by_coord() instead.

Parameters: geom : Geom Geometry of projection. mode : {‘interp’, ‘exact’} Method for reprojection. ‘interp’ method interpolates at pixel centers. ‘exact’ method integrates over intersection of pixels. order : int or str Order of interpolating polynomial (0 = nearest-neighbor, 1 = linear, 2 = quadratic, 3 = cubic). map : Map Reprojected map.
set_by_coord(self, coords, vals)

Set pixels at coords with given vals.

Parameters: coords : tuple 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. vals : ndarray Values vector.
set_by_idx(self, idx, vals)

Set pixels at idx with given vals.

Parameters: idx : tuple 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. vals : ndarray Values vector.
set_by_pix(self, pix, vals)

Set pixels at pix with given vals.

Parameters: pix : tuple 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. vals : ndarray Values vector.
slice_by_idx(self, slices)

Slice sub map from map object.

For usage examples, see Indexing and Slicing.

Parameters: slices : dict 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. map_out : Map Sliced map object.
sum_over_axes(self, keepdims=False)

Reduce to a 2D image by summing over non-spatial dimensions.

to_hdulist(self, hdu='SKYMAP', hdu_bands=None, sparse=False, conv='gadf')[source]

Convert to HDUList.

Parameters: hdu : str The HDU extension name. hdu_bands : str The HDU extension name for BANDS table. sparse : bool Set INDXSCHM to SPARSE and sparsify the map by only writing pixels with non-zero amplitude. conv : {‘fgst-ccube’,’fgst-template’,’gadf’,None}, optional FITS format convention. If None this will be set to the default convention of the map. hdu_list : HDUList
to_swapped(self)[source]

Return a new map with the opposite scheme (ring or nested).

Returns: map : HpxMap Map object.
to_ud_graded(self, nside, preserve_counts=False)[source]

Upgrade or downgrade the resolution of the map to the chosen nside.

Parameters: nside : int NSIDE parameter of the new map. preserve_counts : bool Choose whether to preserve counts (total amplitude) or intensity (amplitude per unit solid angle). map : HpxMap Map object.
to_wcs(self, sum_bands=False, normalize=True, proj='AIT', oversample=2, width_pix=None, hpx2wcs=None)[source]

Make a WCS object and convert HEALPIX data into WCS projection.

Parameters: sum_bands : bool Sum over non-spatial axes before reprojecting. If False then the WCS map will have the same dimensionality as the HEALPix one. normalize : bool Preserve integral by splitting HEALPIX values between bins? proj : str WCS-projection oversample : float Oversampling factor for WCS map. This will be the approximate ratio of the width of a HPX pixel to a WCS pixel. If this parameter is None then the width will be set from width_pix. width_pix : int Width of the WCS geometry in pixels. The pixel size will be set to the number of pixels satisfying oversample or width_pix whichever is smaller. If this parameter is None then the width will be set from oversample. hpx2wcs : HpxToWcsMapping Set the HPX to WCS mapping object that will be used to generate the WCS map. If none then a new mapping will be generated based on proj and oversample arguments. map_out : WcsMap WCS map object.
upsample(self, factor, order=0, preserve_counts=True, axis=None)

Upsample the spatial dimension by a given factor.

Parameters: factor : int Upsampling factor. order : int Order of the interpolation used for upsampling. preserve_counts : bool 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 : str Which axis to upsample. By default spatial axes are upsampled. map : Map Upsampled map.
write(self, filename, overwrite=False, **kwargs)

Write to a FITS file.

Parameters: filename : str Output file name. overwrite : bool Overwrite existing file? hdu : str Set the name of the image extension. By default this will be set to SKYMAP (for BINTABLE HDU) or PRIMARY (for IMAGE HDU). hdu_bands : str Set the name of the bands table extension. By default this will be set to BANDS. conv : str 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’. sparse : bool Sparsify the map by dropping pixels with zero amplitude. This option is only compatible with the ‘gadf’ format.