Source code for gammapy.maps.core

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import abc
import copy
import inspect
import json
import numpy as np
from astropy import units as u
from astropy.io import fits
from gammapy.utils.scripts import make_path
from .axes import MapAxis
from .coord import MapCoord
from .geom import pix_tuple_to_idx
from .io import JsonQuantityDecoder

__all__ = ["Map"]


[docs]class Map(abc.ABC): """Abstract map class. This can represent WCS- or HEALPIX-based maps with 2 spatial dimensions and N non-spatial dimensions. Parameters ---------- geom : `~gammapy.maps.Geom` Geometry data : `~numpy.ndarray` or `~astropy.units.Quantity` Data array meta : `dict` Dictionary to store meta data unit : str or `~astropy.units.Unit` Data unit, ignored if data is a Quantity. """ tag = "map" def __init__(self, geom, data, meta=None, unit=""): self._geom = geom if isinstance(data, u.Quantity): self.unit = unit self.quantity = data else: self.data = data self.unit = unit if meta is None: self.meta = {} else: self.meta = meta def _init_copy(self, **kwargs): """Init map instance by copying missing init arguments from self.""" argnames = inspect.getfullargspec(self.__init__).args argnames.remove("self") argnames.remove("dtype") for arg in argnames: value = getattr(self, "_" + arg) kwargs.setdefault(arg, copy.deepcopy(value)) return self.from_geom(**kwargs) @property def is_mask(self): """Whether map is mask with bool dtype""" return self.data.dtype == bool @property def geom(self): """Map geometry (`~gammapy.maps.Geom`)""" return self._geom @property def data(self): """Data array (`~numpy.ndarray`)""" return self._data @data.setter def data(self, value): """Set data Parameters ---------- value : array-like Data array """ if np.isscalar(value): value = value * np.ones(self.geom.data_shape, dtype=type(value)) if isinstance(value, u.Quantity): raise TypeError("Map data must be a Numpy array. Set unit separately") if not value.shape == self.geom.data_shape: value = value.reshape(self.geom.data_shape) self._data = value @property def unit(self): """Map unit (`~astropy.units.Unit`)""" return self._unit @unit.setter def unit(self, val): self._unit = u.Unit(val) @property def meta(self): """Map meta (`dict`)""" return self._meta @meta.setter def meta(self, val): self._meta = val @property def quantity(self): """Map data times unit (`~astropy.units.Quantity`)""" return u.Quantity(self.data, self.unit, copy=False) @quantity.setter def quantity(self, val): """Set data and unit Parameters ---------- value : `~astropy.units.Quantity` Quantity """ val = u.Quantity(val, copy=False) self.data = val.value self.unit = val.unit
[docs] @staticmethod def create(**kwargs): """Create an empty map object. This method accepts generic options listed below, as well as options for `HpxMap` and `WcsMap` objects. For WCS-specific options, see `WcsMap.create` and for HPX-specific options, see `HpxMap.create`. Parameters ---------- frame : str 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. binsz : float or `~numpy.ndarray` Pixel size in degrees. skydir : `~astropy.coordinates.SkyCoord` Coordinate of map center. axes : list List of `~MapAxis` objects for each non-spatial dimension. If None then the map will be a 2D image. dtype : str Data type, default is 'float32' unit : str or `~astropy.units.Unit` Data unit. meta : `dict` Dictionary to store meta data. region : `~regions.SkyRegion` Sky region used for the region map. Returns ------- map : `Map` Empty map object. """ from .hpx import HpxMap from .region import RegionNDMap from .wcs import WcsMap map_type = kwargs.setdefault("map_type", "wcs") if "wcs" in map_type.lower(): return WcsMap.create(**kwargs) elif "hpx" in map_type.lower(): return HpxMap.create(**kwargs) elif map_type == "region": _ = kwargs.pop("map_type") return RegionNDMap.create(**kwargs) else: raise ValueError(f"Unrecognized map type: {map_type!r}")
[docs] @staticmethod def read( filename, hdu=None, hdu_bands=None, map_type="auto", format=None, colname=None ): """Read a map from a FITS file. Parameters ---------- filename : str or `~pathlib.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', '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. colname : str, optional data column name to be used of healix map. Returns ------- map_out : `Map` Map object """ with fits.open(str(make_path(filename)), memmap=False) as hdulist: return Map.from_hdulist( hdulist, hdu, hdu_bands, map_type, format=format, colname=colname )
[docs] @staticmethod def from_geom(geom, meta=None, data=None, 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. unit : str or `~astropy.units.Unit` Data unit. Returns ------- map_out : `Map` Map object """ from .hpx import HpxGeom from .region import RegionGeom from .wcs import WcsGeom if isinstance(geom, HpxGeom): map_type = "hpx" elif isinstance(geom, WcsGeom): map_type = "wcs" elif isinstance(geom, RegionGeom): map_type = "region" else: raise ValueError("Unrecognized geom type.") cls_out = Map._get_map_cls(map_type) return cls_out(geom, data=data, meta=meta, unit=unit, dtype=dtype)
[docs] @staticmethod def from_hdulist( hdulist, hdu=None, hdu_bands=None, map_type="auto", format=None, colname=None ): """Create from `astropy.io.fits.HDUList`. Parameters ---------- hdulist : `~astropy.io.fits.HDUList` HDU list containing HDUs for map data and bands. 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. map_type : {"auto", "wcs", "hpx", "region"} Map type. format : {'gadf', 'fgst-ccube', 'fgst-template'} FITS format convention. colname : str, optional Data column name to be used for the HEALPix map. Returns ------- map_out : `Map` Map object """ if map_type == "auto": map_type = Map._get_map_type(hdulist, hdu) cls_out = Map._get_map_cls(map_type) if map_type == "hpx": return cls_out.from_hdulist( hdulist, hdu=hdu, hdu_bands=hdu_bands, format=format, colname=colname ) else: return cls_out.from_hdulist( hdulist, hdu=hdu, hdu_bands=hdu_bands, format=format )
@staticmethod def _get_meta_from_header(header): """Load meta data from a FITS header.""" if "META" in header: return json.loads(header["META"], cls=JsonQuantityDecoder) else: return {} @staticmethod def _get_map_type(hdu_list, hdu_name): """Infer map type from a FITS HDU. Only read header, never data, to have good performance. """ if hdu_name is None: # Find the header of the first non-empty HDU header = hdu_list[0].header if header["NAXIS"] == 0: header = hdu_list[1].header else: header = hdu_list[hdu_name].header if ("PIXTYPE" in header) and (header["PIXTYPE"] == "HEALPIX"): return "hpx" elif "CTYPE1" in header: return "wcs" else: return "region" @staticmethod def _get_map_cls(map_type): """Get map class for given `map_type` string. This should probably be a registry dict so that users can add supported map types to the `gammapy.maps` I/O (see e.g. the Astropy table format I/O registry), but that's non-trivial to implement without avoiding circular imports. """ if map_type == "wcs": from .wcs import WcsNDMap return WcsNDMap elif map_type == "wcs-sparse": raise NotImplementedError() elif map_type == "hpx": from .hpx import HpxNDMap return HpxNDMap elif map_type == "hpx-sparse": raise NotImplementedError() elif map_type == "region": from .region import RegionNDMap return RegionNDMap else: raise ValueError(f"Unrecognized map type: {map_type!r}")
[docs] def 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. format : str, 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" sparse : bool Sparsify the map by dropping pixels with zero amplitude. This option is only compatible with the 'gadf' format. """ hdulist = self.to_hdulist(**kwargs) hdulist.writeto(str(make_path(filename)), overwrite=overwrite)
[docs] def 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. """ for idx in np.ndindex(self.geom.shape_axes): yield self.data[idx[::-1]], idx[::-1]
[docs] def 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 """ if not self.unit.is_equivalent(map_in.unit): raise ValueError("Incompatible units") # TODO: Check whether geometries are aligned and if so sum the # data vectors directly if weights is not None: map_in = map_in * weights idx = map_in.geom.get_idx() coords = map_in.geom.get_coord() vals = u.Quantity(map_in.get_by_idx(idx), map_in.unit) self.fill_by_coord(coords, vals)
[docs] def pad(self, 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_name : str Which axis to downsample. By default spatial axes are padded. mode : {'edge', 'constant', 'interp'} Padding mode. 'edge' pads with the closest edge value. 'constant' pads with a constant value. 'interp' pads with an extrapolated value. cval : float Padding value when mode='consant'. Returns ------- map : `Map` Padded map. """ if axis_name: if np.isscalar(pad_width): pad_width = (pad_width, pad_width) geom = self.geom.pad(pad_width=pad_width, axis_name=axis_name) idx = self.geom.axes.index_data(axis_name) pad_width_np = [(0, 0)] * self.data.ndim pad_width_np[idx] = pad_width kwargs = {} if mode == "constant": kwargs["constant_values"] = cval data = np.pad(self.data, pad_width=pad_width_np, mode=mode, **kwargs) return self.__class__( geom=geom, data=data, unit=self.unit, meta=self.meta.copy() ) return self._pad_spatial(pad_width, mode="constant", cval=cval)
@abc.abstractmethod def _pad_spatial(self, pad_width, mode="constant", cval=0, order=1): pass
[docs] @abc.abstractmethod def 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`. Returns ------- map : `Map` Cropped map. """ pass
[docs] @abc.abstractmethod def downsample(self, factor, preserve_counts=True, axis_name=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_name : str Which axis to downsample. By default spatial axes are downsampled. Returns ------- map : `Map` Downsampled map. """ pass
[docs] @abc.abstractmethod def 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. Returns ------- map : `Map` Upsampled map. """ pass
[docs] def resample_axis(self, axis, weights=None, ufunc=np.add): """Resample map to a new axis binning by grouping over smaller bins and apply ufunc to the bin contents. By default, the map content are summed over the smaller bins. Other numpy ufunc can be used, e.g. np.logical_and, np.logical_or Parameters ---------- axis : `MapAxis` New map axis. weights : `Map` Array to be used as weights. The spatial geometry must be equivalent to `other` and additional axes must be broadcastable. ufunc : `~numpy.ufunc` ufunc to use to resample the axis. Default is numpy.add. Returns ------- map : `Map` Map with resampled axis. """ from .hpx import HpxGeom geom = self.geom.resample_axis(axis) axis_self = self.geom.axes[axis.name] axis_resampled = geom.axes[axis.name] # We don't use MapAxis.coord_to_idx because is does not behave as needed with boundaries coord = axis_resampled.edges.value edges = axis_self.edges.value indices = np.digitize(coord, edges) - 1 idx = self.geom.axes.index_data(axis.name) weights = 1 if weights is None else weights.data if not isinstance(self.geom, HpxGeom): shape = self.geom._shape[:2] else: shape = (self.geom.data_shape[-1],) shape += tuple([ax.nbin if ax != axis else 1 for ax in self.geom.axes]) padded_array = np.append(self.data * weights, np.zeros(shape[::-1]), axis=idx) slices = tuple([slice(0, _) for _ in geom.data_shape]) data = ufunc.reduceat(padded_array, indices=indices, axis=idx)[slices] return self._init_copy(data=data, geom=geom)
[docs] def slice_by_idx( self, slices, ): """Slice sub map from map object. 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. Returns ------- map_out : `Map` Sliced map object. """ geom = self.geom.slice_by_idx(slices) slices = tuple([slices.get(ax.name, slice(None)) for ax in self.geom.axes]) data = self.data[slices[::-1]] return self.__class__(geom=geom, data=data, unit=self.unit, meta=self.meta)
[docs] def 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}. Returns ------- map_out : `Map` 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}) """ if isinstance(coords, tuple): coords = dict(zip(self.geom.axes.names, coords)) idx = self.geom.axes.coord_to_idx(coords) return self.get_image_by_idx(idx)
[docs] def 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. See Also -------- get_image_by_coord, get_image_by_idx Returns ------- map_out : `Map` Map with spatial dimensions only. """ idx = self.geom.pix_to_idx(pix) return self.get_image_by_idx(idx)
[docs] def 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). See Also -------- get_image_by_coord, get_image_by_pix Returns ------- map_out : `Map` Map with spatial dimensions only. """ if len(idx) != len(self.geom.axes): raise ValueError("Tuple length must equal number of non-spatial dimensions") # Only support scalar indices per axis idx = tuple([int(_) for _ in idx]) geom = self.geom.to_image() data = self.data[idx[::-1]] return self.__class__(geom=geom, data=data, unit=self.unit, meta=self.meta)
[docs] def get_by_coord(self, coords, fill_value=np.nan): """Return map values at the given map coordinates. Parameters ---------- coords : tuple or `~gammapy.maps.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_value : float Value which is returned if the position is outside of the projection footprint Returns ------- vals : `~numpy.ndarray` Values of pixels in the map. np.nan used to flag coords outside of map. """ coords = MapCoord.create( coords, frame=self.geom.frame, axis_names=self.geom.axes.names ) pix = self.geom.coord_to_pix(coords) vals = self.get_by_pix(pix, fill_value=fill_value) return vals
[docs] def get_by_pix(self, pix, fill_value=np.nan): """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. fill_value : float Value which is returned if the position is outside of the projection footprint Returns ------- vals : `~numpy.ndarray` Array of pixel values. np.nan used to flag coordinates outside of map """ # FIXME: Support local indexing here? # FIXME: Support slicing? pix = np.broadcast_arrays(*pix) idx = self.geom.pix_to_idx(pix) vals = self.get_by_idx(idx) mask = self.geom.contains_pix(pix) if not mask.all(): vals = vals.astype(type(fill_value)) vals[~mask] = fill_value return vals
[docs] @abc.abstractmethod def 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. Returns ------- vals : `~numpy.ndarray` Array of pixel values. np.nan used to flag coordinate outside of map """ pass
[docs] @abc.abstractmethod def interp_by_coord(self, coords, method="nearest", fill_value=None): """Interpolate map values at the given map coordinates. Parameters ---------- coords : tuple or `~gammapy.maps.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 : {"nearest", "linear"} 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. 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. Returns ------- vals : `~numpy.ndarray` Interpolated pixel values. """ pass
[docs] @abc.abstractmethod def interp_by_pix(self, pix, method="nearest", 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. method : {"nearest", "linear"} 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. 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. Returns ------- vals : `~numpy.ndarray` Interpolated pixel values. """ pass
[docs] def interp_to_geom(self, geom, preserve_counts=False, fill_value=0, **kwargs): """Interpolate map to input geometry. Parameters ---------- geom : `~gammapy.maps.Geom` Target Map geometry 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) **kwargs : dict Keyword arguments passed to `Map.interp_by_coord` Returns ------- interp_map : `Map` Interpolated Map """ coords = geom.get_coord() map_copy = self.copy() if preserve_counts: if geom.ndim > 2 and geom.axes[0] != self.geom.axes[0]: raise ValueError( f"Energy axis do not match: expected {self.geom.axes[0]}, but got {geom.axes[0]}." ) map_copy.data /= map_copy.geom.solid_angle().to_value("deg2") if map_copy.is_mask: # TODO: check this NaN handling is needed data = map_copy.get_by_coord(coords) data = np.nan_to_num(data, nan=fill_value).astype(bool) else: data = map_copy.interp_by_coord(coords, **kwargs) if preserve_counts: data *= geom.solid_angle().to_value("deg2") return Map.from_geom(geom, data=data, unit=self.unit)
[docs] def fill_events(self, events): """Fill event coordinates (`~gammapy.data.EventList`).""" self.fill_by_coord(events.map_coord(self.geom))
[docs] def fill_by_coord(self, coords, weights=None): """Fill pixels at ``coords`` with given ``weights``. Parameters ---------- coords : tuple or `~gammapy.maps.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 : `~numpy.ndarray` Weights vector. Default is weight of one. """ idx = self.geom.coord_to_idx(coords) self.fill_by_idx(idx, weights)
[docs] def 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 : `~numpy.ndarray` Weights vector. Default is weight of one. """ idx = pix_tuple_to_idx(pix) return self.fill_by_idx(idx, weights=weights)
[docs] @abc.abstractmethod def 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 : `~numpy.ndarray` Weights vector. Default is weight of one. """ pass
[docs] def set_by_coord(self, coords, vals): """Set pixels at ``coords`` with given ``vals``. Parameters ---------- coords : tuple or `~gammapy.maps.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 : `~numpy.ndarray` Values vector. """ idx = self.geom.coord_to_pix(coords) self.set_by_pix(idx, vals)
[docs] def 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 : `~numpy.ndarray` Values vector. """ idx = pix_tuple_to_idx(pix) return self.set_by_idx(idx, vals)
[docs] @abc.abstractmethod def 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 : `~numpy.ndarray` Values vector. """ pass
[docs] def plot_grid(self, figsize=None, ncols=3, **kwargs): """Plot map as a grid of subplots for non-spatial axes Parameters ---------- figsize : tuple of int Figsize to plot on ncols : int Number of columns to plot **kwargs : dict Keyword arguments passed to `Map.plot`. Returns ------- axes : `~numpy.ndarray` of `~matplotlib.pyplot.Axes` Axes grid """ import matplotlib.pyplot as plt if len(self.geom.axes) > 1: raise ValueError("Grid plotting is only supported for one non spatial axis") axis = self.geom.axes[0] cols = min(ncols, axis.nbin) rows = 1 + (axis.nbin - 1) // cols if figsize is None: width = 12 figsize = (width, width * rows / cols) if self.geom.is_hpx: wcs = self.geom.to_wcs_geom().wcs else: wcs = self.geom.wcs fig, axes = plt.subplots( ncols=cols, nrows=rows, subplot_kw={"projection": wcs}, figsize=figsize, gridspec_kw={"hspace": 0.1, "wspace": 0.1}, ) for idx in range(cols * rows): ax = axes.flat[idx] try: image = self.get_image_by_idx((idx,)) except IndexError: ax.set_visible(False) continue if image.geom.is_hpx: image_wcs = image.to_wcs( normalize=False, proj="AIT", oversample=2, ) else: image_wcs = image image_wcs.plot(ax=ax, **kwargs) if axis.node_type == "center": info = f"{axis.center[idx]:.1f}" else: info = f"{axis.edges[idx]:.1f} - {axis.edges[idx + 1]:.1f} " ax.set_title(f"{axis.name.capitalize()} " + info) lon, lat = ax.coords[0], ax.coords[1] lon.set_ticks_position("b") lat.set_ticks_position("l") row, col = np.unravel_index(idx, shape=(rows, cols)) if col > 0: lat.set_ticklabel_visible(False) lat.set_axislabel("") if row < (rows - 1): lon.set_ticklabel_visible(False) lon.set_axislabel("") return axes
[docs] def 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 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) """ import matplotlib as mpl import matplotlib.pyplot as plt from ipywidgets import RadioButtons, SelectionSlider from ipywidgets.widgets.interaction import fixed, interact if self.geom.is_image: raise TypeError("Use .plot() for 2D Maps") kwargs.setdefault("interpolation", "nearest") kwargs.setdefault("origin", "lower") kwargs.setdefault("cmap", "afmhot") rc_params = rc_params or {} stretch = kwargs.pop("stretch", "sqrt") interact_kwargs = {} for axis in self.geom.axes: options = axis.as_plot_labels interact_kwargs[axis.name] = SelectionSlider( options=options, description=f"Select {axis.name}:", continuous_update=False, style={"description_width": "initial"}, layout={"width": "50%"}, ) interact_kwargs[axis.name + "_options"] = fixed(options) interact_kwargs["stretch"] = RadioButtons( options=["linear", "sqrt", "log"], value=stretch, description="Select stretch:", style={"description_width": "initial"}, ) @interact(**interact_kwargs) def _plot_interactive(**ikwargs): idx = [ ikwargs[ax.name + "_options"].index(ikwargs[ax.name]) for ax in self.geom.axes ] img = self.get_image_by_idx(idx) stretch = ikwargs["stretch"] with mpl.rc_context(rc=rc_params): img.plot(stretch=stretch, **kwargs) plt.show()
[docs] def copy(self, **kwargs): """Copy map instance and overwrite given attributes, except for geometry. Parameters ---------- **kwargs : dict Keyword arguments to overwrite in the map constructor. Returns ------- copy : `Map` Copied Map. """ if "geom" in kwargs: geom = kwargs["geom"] if not geom.data_shape == self.geom.data_shape: raise ValueError( "Can't copy and change data size of the map. " f" Current shape {self.geom.data_shape}," f" requested shape {geom.data_shape}" ) return self._init_copy(**kwargs)
[docs] def apply_edisp(self, edisp): """Apply energy dispersion to map. Requires energy axis. Parameters ---------- edisp : `gammapy.irf.EDispKernel` Energy dispersion matrix Returns ------- map : `WcsNDMap` Map with energy dispersion applied. """ # TODO: either use sparse matrix mutiplication or something like edisp.is_diagonal if edisp is not None: loc = self.geom.axes.index("energy_true") data = np.rollaxis(self.data, loc, len(self.data.shape)) data = np.dot(data, edisp.pdf_matrix) data = np.rollaxis(data, -1, loc) energy_axis = edisp.axes["energy"].copy(name="energy") else: data = self.data energy_axis = self.geom.axes["energy_true"].copy(name="energy") geom = self.geom.to_image().to_cube(axes=[energy_axis]) return self._init_copy(geom=geom, data=data)
[docs] def mask_nearest_position(self, position): """Given a sky coordinate return nearest valid position in the mask If the mask contains additional axes, the mask is reduced over those. Parameters ---------- position : `SkyCoord` Test position Returns ------- position : `SkyCoord` Nearest position in the mask """ if not self.geom.is_image: raise ValueError("Method only supported for 2D images") coords = self.geom.to_image().get_coord().skycoord separation = coords.separation(position) separation[~self.data] = np.inf idx = np.argmin(separation) return coords.flatten()[idx]
[docs] def sum_over_axes(self, axes_names=None, keepdims=True, weights=None): """To sum map values over all non-spatial axes. Parameters ---------- keepdims : bool, optional If this is set to true, the axes which are summed over are left in the map with a single bin axes_names: list of str Names of MapAxis to reduce over. If None, all will summed over weights : `Map` Weights to be applied. The Map should have the same geometry. Returns ------- map_out : `~Map` Map with non-spatial axes summed over """ return self.reduce_over_axes( func=np.add, axes_names=axes_names, keepdims=keepdims, weights=weights )
[docs] def reduce_over_axes( self, func=np.add, keepdims=False, axes_names=None, weights=None ): """Reduce map over non-spatial axes Parameters ---------- func : `~numpy.ufunc` Function to use for reducing the data. keepdims : bool, optional If this is set to true, the axes which are summed over are left in the map with a single bin axes_names: list Names of MapAxis to reduce over If None, all will reduced weights : `Map` Weights to be applied. Returns ------- map_out : `~Map` Map with non-spatial axes reduced """ if axes_names is None: axes_names = self.geom.axes.names map_out = self.copy() for axis_name in axes_names: map_out = map_out.reduce( axis_name, func=func, keepdims=keepdims, weights=weights ) return map_out
[docs] def reduce(self, axis_name, func=np.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 func : `~numpy.ufunc` Function to use for reducing the data. keepdims : bool, optional If this is set to true, the axes which are summed over are left in the map with a single bin weights : `Map` Weights to be applied. Returns ------- map_out : `~Map` Map with the given non-spatial axes reduced """ if keepdims: geom = self.geom.squash(axis_name=axis_name) else: geom = self.geom.drop(axis_name=axis_name) idx = self.geom.axes.index_data(axis_name) data = self.data if weights is not None: data = data * weights data = func.reduce(data, axis=idx, keepdims=keepdims, where=~np.isnan(data)) return self._init_copy(geom=geom, data=data)
[docs] def cumsum(self, axis_name): """Compute cumulative sum along a given axis Parameters ---------- axis_name : str Along which axis to integrate. Returns ------- cumsum : `Map` Map with cumulative sum """ axis = self.geom.axes[axis_name] axis_idx = self.geom.axes.index_data(axis_name) # TODO: the broadcasting should be done by axis.center, axis.bin_width etc. shape = [1] * len(self.geom.data_shape) shape[axis_idx] = -1 values = self.quantity * axis.bin_width.reshape(shape) if axis_name == "rad": # take Jacobian into account values = 2 * np.pi * axis.center.reshape(shape) * values data = np.insert(values.cumsum(axis=axis_idx), 0, 0, axis=axis_idx) axis_shifted = MapAxis.from_nodes( axis.edges, name=axis.name, interp=axis.interp ) axes = self.geom.axes.replace(axis_shifted) geom = self.geom.to_image().to_cube(axes) return self.__class__(geom=geom, data=data.value, unit=data.unit)
[docs] def integral(self, axis_name, coords, **kwargs): """Compute integral along a given axis This method uses interpolation of the cumulative sum. Parameters ---------- axis_name : str Along which axis to integrate. coords : dict or `MapCoord` Map coordinates **kwargs : dict Coordinates at which to evaluate the IRF Returns ------- array : `~astropy.units.Quantity` Returns 2D array with axes offset """ cumsum = self.cumsum(axis_name=axis_name) cumsum = cumsum.pad(pad_width=1, axis_name=axis_name, mode="edge") return u.Quantity( cumsum.interp_by_coord(coords, **kwargs), cumsum.unit, copy=False )
[docs] def normalize(self, axis_name=None): """Normalise data in place along a given axis. Parameters ---------- axis_name : str Along which axis to normalize. """ cumsum = self.cumsum(axis_name=axis_name).quantity with np.errstate(invalid="ignore", divide="ignore"): axis = self.geom.axes.index_data(axis_name=axis_name) normed = self.quantity / cumsum.max(axis=axis, keepdims=True) self.quantity = np.nan_to_num(normed)
[docs] @classmethod def from_stack(cls, maps, axis=None, axis_name=None): """Create Map from list of images and a non-spatial axis. The image geometries must be aligned, except for the axis that is stacked. Parameters ---------- maps : list of `Map` objects List of maps axis : `MapAxis` If a `MapAxis` is provided the maps are stacked along the last data axis and the new axis is introduced. axis_name : str If an axis name is as string the given the maps are stacked along the given axis name. Returns ------- map : `Map` Map with additional non-spatial axis. """ geom = maps[0].geom if axis_name is None and axis is None: axis_name = geom.axes.names[-1] if axis_name: axis = MapAxis.from_stack(axes=[m.geom.axes[axis_name] for m in maps]) geom = geom.drop(axis_name=axis_name) data = [] for m in maps: if axis_name: m_geom = m.geom.drop(axis_name=axis_name) else: m_geom = m.geom if not m_geom == geom: raise ValueError(f"Image geometries not aligned: {m.geom} and {geom}") data.append(m.quantity.to_value(maps[0].unit)) return cls.from_geom( data=np.stack(data), geom=geom.to_cube(axes=[axis]), unit=maps[0].unit )
[docs] def to_cube(self, axes): """Append non-spatial axes to create a higher-dimensional Map. This will result in a Map with a new geometry with N+M dimensions where N is the number of current dimensions and M is the number of axes in the list. The data is reshaped onto the new geometry Parameters ---------- axes : list Axes that will be appended to this Map. The axes should have only one bin Returns ------- map : `~gammapy.maps.WcsNDMap` new map """ for ax in axes: if ax.nbin > 1: raise ValueError(ax.name, "should have only one bin") geom = self.geom.to_cube(axes) data = self.data.reshape((1,) * len(axes) + self.data.shape) return self.from_geom(data=data, geom=geom, unit=self.unit)
[docs] def get_spectrum(self, region=None, func=np.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` Region (pixel or sky regions accepted). func : numpy.func Function to reduce the data. Default is np.nansum. For a boolean Map, use np.any or np.all. weights : `WcsNDMap` Array to be used as weights. The geometry must be equivalent. Returns ------- spectrum : `~gammapy.maps.RegionNDMap` Spectrum in the given region. """ if not self.geom.has_energy_axis: raise ValueError("Energy axis required") return self.to_region_nd_map(region=region, func=func, weights=weights)
[docs] def to_unit(self, unit): """Convert map to different unit Parameters ---------- unit : `~astropy.unit.Unit` or str New unit Returns ------- map : `Map` Map with new unit and converted data """ data = self.quantity.to_value(unit) return self.from_geom(self.geom, data=data, unit=unit)
def __repr__(self): geom = self.geom.__class__.__name__ axes = ["skycoord"] if self.geom.is_hpx else ["lon", "lat"] axes = axes + [_.name for _ in self.geom.axes] return ( f"{self.__class__.__name__}\n\n" f"\tgeom : {geom} \n " f"\taxes : {axes}\n" f"\tshape : {self.geom.data_shape[::-1]}\n" f"\tndim : {self.geom.ndim}\n" f"\tunit : {self.unit}\n" f"\tdtype : {self.data.dtype}\n" ) def _arithmetics(self, operator, other, copy): """Perform arithmetic on maps after checking geometry consistency.""" if isinstance(other, Map): if self.geom == other.geom: q = other.quantity else: raise ValueError("Map Arithmetic: Inconsistent geometries.") else: q = u.Quantity(other, copy=False) out = self.copy() if copy else self out.quantity = operator(out.quantity, q) return out def _boolean_arithmetics(self, operator, other, copy): """Perform arithmetic on maps after checking geometry consistency.""" if operator == np.logical_not: out = self.copy() out.data = operator(out.data) return out if isinstance(other, Map): if self.geom == other.geom: other = other.data else: raise ValueError("Map Arithmetic: Inconsistent geometries.") out = self.copy() if copy else self out.data = operator(out.data, other) return out def __add__(self, other): return self._arithmetics(np.add, other, copy=True) def __iadd__(self, other): return self._arithmetics(np.add, other, copy=False) def __sub__(self, other): return self._arithmetics(np.subtract, other, copy=True) def __isub__(self, other): return self._arithmetics(np.subtract, other, copy=False) def __mul__(self, other): return self._arithmetics(np.multiply, other, copy=True) def __imul__(self, other): return self._arithmetics(np.multiply, other, copy=False) def __truediv__(self, other): return self._arithmetics(np.true_divide, other, copy=True) def __itruediv__(self, other): return self._arithmetics(np.true_divide, other, copy=False) def __le__(self, other): return self._arithmetics(np.less_equal, other, copy=True) def __lt__(self, other): return self._arithmetics(np.less, other, copy=True) def __ge__(self, other): return self._arithmetics(np.greater_equal, other, copy=True) def __gt__(self, other): return self._arithmetics(np.greater, other, copy=True) def __eq__(self, other): return self._arithmetics(np.equal, other, copy=True) def __ne__(self, other): return self._arithmetics(np.not_equal, other, copy=True) def __and__(self, other): return self._boolean_arithmetics(np.logical_and, other, copy=True) def __or__(self, other): return self._boolean_arithmetics(np.logical_or, other, copy=True) def __invert__(self): return self._boolean_arithmetics(np.logical_not, None, copy=True) def __xor__(self, other): return self._boolean_arithmetics(np.logical_xor, other, copy=True) def __iand__(self, other): return self._boolean_arithmetics(np.logical_and, other, copy=False) def __ior__(self, other): return self._boolean_arithmetics(np.logical_or, other, copy=False) def __ixor__(self, other): return self._boolean_arithmetics(np.logical_xor, other, copy=False) def __array__(self): return self.data