HpxNDMap#

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

Bases: HpxMap

HEALPix map with any number of non-spatial dimensions.

This class uses an N+1D numpy array to represent the sequence of HEALPix image planes. Following the convention of WCS-based maps this class uses a column-wise ordering for the data array with the spatial dimension being tied to the last index of the array.

Parameters:

geomHpxGeom: HEALPix geometry object.
datandarray: HEALPix data array. If None, then an empty array will be allocated.
metadict: Dictionary to store metadata.
unitstr or Unit: The map unit.

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.
`convolve`(kernel[, convolution_method])	Convolve map with a WCS kernel.
`convolve_full`(kernel)	Convolve map with a symmetrical WCS kernel.
`convolve_wcs`(kernel, **kwargs)	Convolve map with a WCS kernel.
`copy`(**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`(crop_width)	Crop the spatial dimensions of the map.
`cumsum`(axis_name)	Compute cumulative sum along a given axis.
`cutout`(position, width, args, *kwargs)	Create a cutout around a given position.
`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_hdu`(hdu[, hdu_bands, format, colname])	Make a HpxNDMap object from a FITS HDU.
`from_hdulist`(hdu_list[, hdu, hdu_bands, ...])	Make a HpxMap object from a FITS HDUList.
`from_stack`(maps[, axis, axis_name])	Create Map from a list of images and a non-spatial axis.
`from_wcs_tiles`(wcs_tiles[, nest])	Create HEALPix map from WCS tiles.
`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`([method, ax, normalize, proj, ...])	Quickplot method.
`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.
`plot_mask`([method, ax, proj, oversample, ...])	Plot the mask as a shaded area.
`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.
`smooth`(width[, kernel])	Smooth the map.
`split_by_axis`(axis_name)	Split a Map along an axis into multiple maps.
`stack`(other[, weights, nan_to_num])	Stack cutout into map.
`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_hdu`([hdu, hdu_bands, sparse, format])	Make a FITS HDU with input data.
`to_hdulist`([hdu, hdu_bands, sparse, format])	Convert to `HDUList`.
`to_nside`(nside[, preserve_counts])	Upsample or downsample the map to a given nside.
`to_region_nd_map`(region[, func, weights, method])	Get region ND map in a given region.
`to_swapped`()	Return a new map with the opposite scheme (ring or nested).
`to_unit`(unit)	Convert map to a different unit.
`to_wcs`([sum_bands, normalize, proj, ...])	Make a WCS object and convert HEALPix data into WCS projection.
`to_wcs_tiles`([nside_tiles, margin, method, ...])	Convert HpxNDMap to a list of WCS tiles.
`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)#

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.

convolve(kernel, convolution_method='wcs-tan', **kwargs)[source]#

Convolve map with a WCS kernel.

Project the map into a WCS geometry, convolve with a WCS kernel and project back into the initial HEALPix geometry.

If the kernel is two-dimensional, it is applied to all image planes likewise. If the kernel is higher dimensional it must match the map in the number of dimensions and the corresponding kernel is selected for every image plane.

Parameters:

kernelPSFKernel: Convolution kernel. The pixel size must be upsampled by a factor 2 or bigger with respect to the input map to prevent artifacts in the projection.
convolution_method{“wcs-tan”, “”}: Convolution method. If “wcs-tan”, project on WCS geometry and convolve with WCS kernel. See convolve_wcs. If “”, convolve map with a symmetrical WCS kernel. See convolve_full. Default is “wcs-tan”.
**kwargsdict: Keyword arguments passed to convolve.

Returns:

mapHpxNDMap: Convolved map.

convolve_full(kernel)[source]#

Convolve map with a symmetrical WCS kernel.

Extract the radial profile of the kernel (assuming radial symmetry) and convolve via smoothing. Since no projection is applied, this is suited for full-sky and large maps.

If the kernel is two-dimensional, it is applied to all image planes likewise. If the kernel is higher dimensional it must match the map in the number of dimensions and the corresponding kernel is selected for every image plane.

Parameters:

kernelPSFKernel: Convolution kernel. The pixel size must be upsampled by a factor 2 or bigger with respect to the input map to prevent artifacts in the projection.

Returns:

mapHpxNDMap: Convolved map.

convolve_wcs(kernel, **kwargs)[source]#

Convolve map with a WCS kernel.

Project the map into a WCS geometry, convolve with a WCS kernel and project back into the initial HEALPix geometry.

If the kernel is two-dimensional, it is applied to all image planes likewise. If the kernel is higher dimensional should either match the map in the number of dimensions or the map must be an image (no non-spatial axes). In that case, the corresponding kernel is selected and applied to every image plane or to the single input image respectively.

Parameters:

kernelPSFKernel: Convolution kernel. The pixel size must be upsampled by a factor 2 or bigger with respect to the input map to prevent artifacts in the projection.
**kwargsdict: Keyword arguments passed to convolve.

Returns:

mapHpxNDMap: Convolved map.

copy(**kwargs)#

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.

classmethod create(nside=None, binsz=None, nest=True, map_type='hpx', frame='icrs', data=None, skydir=None, width=None, dtype='float32', region=None, axes=None, meta=None, unit='')#

Factory method to create an empty HEALPix map.

Parameters:

nsideint or ndarray, optional: HEALPix NSIDE parameter. This parameter sets the size of the spatial pixels in the map. Default is None.
binszfloat or ndarray, optional: Approximate pixel size in degrees. An NSIDE will be chosen that corresponds to a pixel size closest to this value. This option is superseded by nside. Default is None.
nestbool, optional: Indexing scheme. If True, “NESTED” scheme. If False, “RING” scheme. Default is True.
map_type{‘hpx’, ‘hpx-sparse’}, optional: Map type. Selects the class that will be used to instantiate the map. Default is “hpx”.
frame{“icrs”, “galactic”}: Coordinate system, either Galactic (“galactic”) or Equatorial (“icrs”). Default is “icrs”.
datandarray, optional: Data array. Default is None.
skydirtuple or SkyCoord, optional: 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. Default is None.
widthfloat, optional: Diameter of the map in degrees. If None then an all-sky geometry will be created. Default is None.
dtypestr, optional: Data type. Default is “float32”.
regionstr, optional: HEALPix region string. Default is None.
axeslist, optional: List of MapAxis objects for each non-spatial dimension. Default is None.
metadict, optional: Dictionary to store the metadata. Default is None.
unitstr or Unit, optional: The map unit. Default is “”.

Returns:

mapHpxMap: A HEALPix map object.

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)#

Compute cumulative sum along a given axis.

Parameters:

axis_namestr: Along which axis to sum.

Returns:

cumsumMap: Map with cumulative sum.

cutout(position, width, *args, **kwargs)[source]#

Create a cutout around a given position.

Parameters:

positionSkyCoord: Center position of the cutout region.
widthAngle or Quantity: Diameter of the circular cutout region.

Returns:

cutoutHpxNDMap: Cutout map.

dot(other)#

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.

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)#

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.

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)#

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)#

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')#

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.

classmethod from_hdu(hdu, hdu_bands=None, format=None, colname=None)[source]#

Make a HpxNDMap object from a FITS HDU.

Parameters:

hduBinTableHDU

The FITS HDU.

hdu_bandsBinTableHDU, optional

The BANDS table HDU. Default is None.

formatstr, optional

FITS convention. Default is None. If None the format is guessed. The following formats are supported:

“gadf”

“fgst-ccube”

“fgst-ltcube”

“fgst-bexpcube”

“fgst-srcmap”

“fgst-template”

“fgst-srcmap-sparse”

“galprop”

“galprop2”

colnamestr, optional

Data column name to be used for the HEALPix map. Default is None.

Returns:

mapHpxMap: HEALPix map.

classmethod from_hdulist(hdu_list, hdu=None, hdu_bands=None, format=None, colname=None)#

Make a HpxMap object from a FITS HDUList.

Parameters:

hdu_listHDUList

HDU list containing HDUs for map data and bands.

hdustr, optional

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. Default is None.

hdu_bandsstr, optional

Name or index of the HDU with the BANDS table. Default is None.

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”

Returns:

hpx_mapHpxMap: Map object.

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

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.

classmethod from_wcs_tiles(wcs_tiles, nest=True)[source]#

Create HEALPix map from WCS tiles.

Parameters:

wcs_tileslist of WcsNDMap: WCS map tiles.
nestbool, optional: Indexing scheme. If True, “NESTED” scheme. If False, “RING” scheme. Default is True.

Returns:

hpx_mapHpxNDMap: HEALPix 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 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.

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)#

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)#

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)#

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(region=None, func=<function nansum>, weights=None)#

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)#

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.

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.

interp_by_pix(pix, method=None, fill_value=None)[source]#: Interpolate map values at the given pixel coordinates.

interp_to_geom(geom, preserve_counts=False, fill_value=0, **kwargs)#

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. If None, values outside the domain are extrapolated. 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)#

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)#

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)#

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()#

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()#

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)#

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)#

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')#

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(method='raster', ax=None, normalize=False, proj='AIT', oversample=2, width_pix=1000, **kwargs)[source]#

Quickplot method.

This will generate a visualization of the map by converting to a rasterized WCS image (method=’raster’) or drawing polygons for each pixel (method=’poly’).

Parameters:

method{‘raster’,’poly’}: Method for mapping HEALPix pixels to a two-dimensional image. Can be set to ‘raster’ (rasterization to cartesian image plane) or ‘poly’ (explicit polygons for each pixel). WARNING: The ‘poly’ method is much slower than ‘raster’ and only suitable for maps with less than ~10k pixels. Default is “raster”.
projstring, optional: Any valid WCS projection type. Default is “AIT”.
oversamplefloat, optional: 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. Default is 2.
width_pixint, optional: 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. Default is 1000.
**kwargsdict: Keyword arguments passed to imshow.

Returns:

axWCSAxes: WCS axes object.

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

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)#

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)

plot_mask(method='raster', ax=None, proj='AIT', oversample=2, width_pix=1000, **kwargs)[source]#

Plot the mask as a shaded area.

Parameters:

method{‘raster’,’poly’}: Method for mapping HEALPix pixels to a two-dimensional image. Can be set to ‘raster’ (rasterization to cartesian image plane) or ‘poly’ (explicit polygons for each pixel). WARNING: The ‘poly’ method is much slower than ‘raster’ and only suitable for maps with less than ~10k pixels. Default is “raster”.
projstring, optional: Any valid WCS projection type. Default is “AIT”.
oversamplefloat, optional: 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. Default is 2.
width_pixint, optional: 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. Default is 1000.
**kwargsdict: Keyword arguments passed to imshow.

Returns:

axWCSAxes: WCS axis object.

static read(filename, hdu=None, hdu_bands=None, map_type='auto', format=None, colname=None, checksum=False)#

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)#

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)#

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)#

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)#

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)#

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)#

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)#

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'>)#

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)#

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, 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)#

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: 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.

Examples

>>> from gammapy.maps import Map
>>> m = Map.read("$GAMMAPY_DATA/fermi_3fhl/gll_iem_v06_cutout.fits")
>>> slices = {"energy": slice(0, 5)}
>>> sliced = m.slice_by_idx(slices)

smooth(width, kernel='gauss')[source]#

Smooth the map.

Iterates over 2D image planes, processing one at a time.

Parameters:

widthQuantity, str or float: Smoothing width given as quantity or float. If a float is given it is interpreted as smoothing width in pixels. If an (angular) quantity is given it is converted to pixels using nside2resol. It corresponds to the standard deviation in case of a Gaussian kernel, and the radius in case of a disk kernel.
kernel{‘gauss’, ‘disk’}, optional: Kernel shape. Default is “gauss”.

Returns:

imageHpxNDMap: Smoothed image (a copy, the original object is unchanged).

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 cutout into map.

Parameters:

otherHpxNDMap: Other map to stack.
weightsHpxNDMap, optional: Array to be used as weights. The spatial geometry must be equivalent to other and additional axes must be broadcastable. Default is None.
nan_to_num: bool, optional: Non-finite values are replaced by zero if True. Default is True.

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

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)#

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_hdu(hdu=None, hdu_bands=None, sparse=False, format=None)#

Make a FITS HDU with input data.

Parameters:

hdustr, optional: The HDU extension name. Default is None.
hdu_bandsstr, optional: The HDU extension name for BANDS table. Default is None.
sparsebool, optional: Set INDXSCHM to SPARSE and sparsify the map by only writing pixels with non-zero amplitude. Default is False.
format{None, ‘fgst-ccube’, ‘fgst-template’, ‘gadf’}: FITS format convention. If None this will be set to the default convention of the map. Default is None.

Returns:

hdu_outBinTableHDU or ImageHDU: Output HDU containing map data.

to_hdulist(hdu='SKYMAP', hdu_bands=None, sparse=False, format='gadf')#

Convert to HDUList.

Parameters:

hdustr, optional

The HDU extension name. Default is “SKYMAP”.

hdu_bandsstr, optional

The HDU extension name for BANDS table. Default is None.

sparsebool, optional

Set INDXSCHM to SPARSE and sparsify the map by only writing pixels with non-zero amplitude. Default is False.

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”

Returns:

hdu_listHDUList: The FITS HDUList.

to_nside(nside, preserve_counts=True)[source]#

Upsample or downsample the map to a given nside.

Parameters:

nsideint: HEALPix NSIDE parameter.
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:

geomHpxNDMap: HEALPix map with new NSIDE.

to_region_nd_map(region, func=<function nansum>, weights=None, method='nearest')[source]#

Get region ND map in a given region.

By default, the whole map region is considered.

Parameters:

region: `~regions.Region` or `~astropy.coordinates.SkyCoord`: Region.
funcnumpy.func, optional: Function to reduce the data. Default is np.nansum. For boolean Map, use np.any or np.all.
weightsWcsNDMap, optional: Array to be used as weights. The geometry must be equivalent. Default is None.
method{“nearest”, “linear”}: How to interpolate if a position is given. Default is “neraest”.

Returns:

spectrumRegionNDMap: Spectrum in the given region.

to_swapped()[source]#

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

Returns:

mapHpxMap: Map object.

to_unit(unit)#

Convert map to a different unit.

Parameters:

unitstr or Unit: New unit.

Returns:

mapMap: Map with new unit and converted data.

to_wcs(sum_bands=False, normalize=True, proj='AIT', oversample=2, width_pix=None, hpx2wcs=None, fill_nan=True)[source]#

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

Parameters:

sum_bandsbool, optional: Sum over non-spatial axes before reprojecting. If False then the WCS map will have the same dimensionality as the HEALPix one. Default is False.
normalizebool, optional: Preserve integral by splitting HEALPix values between bins. Default is True.
projstr, optional: WCS-projection. Default is “AIT”.
oversamplefloat, optional: Oversampling factor for WCS map. This will be the approximate ratio of the width of a HEALPix pixel to a WCS pixel. If this parameter is None then the width will be set from width_pix. Default is 2.
width_pixint, optional: 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. Default is None.
hpx2wcsHpxToWcsMapping, optional: Set the HEALPix 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. Default is None.

Returns:

map_outWcsMap: WCS map object.

to_wcs_tiles(nside_tiles=4, margin='0 deg', method='nearest', oversampling_factor=1)[source]#

Convert HpxNDMap to a list of WCS tiles.

Parameters:

nside_tilesint, optional: HEALPix NSIDE parameter for super pixel tiles. Default is 4.
marginAngle, optional: Width margin of the WCS tile. Default is “0 deg”.
method{‘nearest’, ‘linear’}: Interpolation method. Default is “nearest”.
oversampling_factorint, optional: Oversampling factor. Default is 1.

Returns:

wcs_tileslist of WcsNDMap: WCS tiles.

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)#

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.