Source code for gammapy.maps.base

# 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 .geom import MapCoord, pix_tuple_to_idx
from .utils import INVALID_VALUE

__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` Data array meta : `dict` Dictionary to store meta data unit : str or `~astropy.units.Unit` Data unit """ def __init__(self, geom, data, meta=None, unit=""): self._geom = geom 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 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, val): if val.shape != self.geom.data_shape: raise ValueError( f"Shape {val.shape!r} does not match map data shape {self.geom.data_shape!r}" ) if isinstance(val, u.Quantity): raise TypeError("Map data must be a Numpy array. Set unit separately") self._data = val @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): 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 ---------- coordsys : str Coordinate system, either Galactic ('GAL') or Equatorial ('CEL'). map_type : {'wcs', 'wcs-sparse', 'hpx', 'hpx-sparse'} 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. Returns ------- map : `Map` Empty map object. """ from .hpxmap import HpxMap from .wcsmap 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) else: raise ValueError(f"Unrecognized map type: {map_type!r}")
[docs] @staticmethod def read(filename, hdu=None, hdu_bands=None, map_type="auto"): """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'} 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. Returns ------- map_out : `Map` Map object """ with fits.open(make_path(filename), memmap=False) as hdulist: return Map.from_hdulist(hdulist, hdu, hdu_bands, map_type)
[docs] @staticmethod def 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 `~astropy.units.Unit` Data unit. Returns ------- map_out : `Map` Map object """ if map_type == "auto": from .hpx import HpxGeom from .wcs import WcsGeom if isinstance(geom, HpxGeom): map_type = "hpx" elif isinstance(geom, WcsGeom): map_type = "wcs" 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"): """Create from `astropy.io.fits.HDUList`.""" if map_type == "auto": map_type = Map._get_map_type(hdulist, hdu) cls_out = Map._get_map_cls(map_type) return cls_out.from_hdulist(hdulist, hdu=hdu, hdu_bands=hdu_bands)
@staticmethod def _get_meta_from_header(header): """Load meta data from a FITS header.""" if "META" in header: return json.loads(header["META"]) 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" else: return "wcs" @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 .wcsnd import WcsNDMap return WcsNDMap elif map_type == "wcs-sparse": raise NotImplementedError() elif map_type == "hpx": from .hpxnd import HpxNDMap return HpxNDMap elif map_type == "hpx-sparse": from .hpxsparse import HpxSparseMap return HpxSparseMap 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. 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. """ hdulist = self.to_hdulist(**kwargs) hdulist.writeto(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. See :ref:`mapiter` for further information. """ for idx in np.ndindex(self.geom.shape_axes): yield self.data[idx[::-1]], idx[::-1]
[docs] @abc.abstractmethod def sum_over_axes(self, keepdims=False): """Reduce to a 2D image by summing over non-spatial dimensions.""" pass
[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 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). Returns ------- map : `Map` Reprojected map. """ if geom.is_image: axes = [ax.copy() for ax in self.geom.axes] geom = geom.copy(axes=axes) else: axes_eq = geom.ndim == self.geom.ndim axes_eq &= np.all( [ax0 == ax1 for ax0, ax1 in zip(geom.axes, self.geom.axes)] ) if not axes_eq: raise ValueError( "Map and target geometry non-spatial axes must match." "Use interp_by_coord to interpolate in non-spatial axes." ) if geom.is_hpx: return self._reproject_to_hpx(geom, mode=mode, order=order) else: return self._reproject_to_wcs(geom, mode=mode, order=order)
[docs] @abc.abstractmethod def 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). Returns ------- map : `Map` Padded map. """ 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=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. 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 slice_by_idx(self, slices): """Slice sub map from map object. For usage examples, see :ref:`mapslicing`. 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): axes_names = [_.name for _ in self.geom.axes] coords = dict(zip(axes_names, coords)) idx = [] for ax in self.geom.axes: value = coords[ax.name] idx.append(ax.coord_to_idx(value)) 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): """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. Returns ------- vals : `~numpy.ndarray` Values of pixels in the map. np.nan used to flag coords outside of map. """ coords = MapCoord.create(coords, coordsys=self.geom.coordsys) pix = self.geom.coord_to_pix(coords) vals = self.get_by_pix(pix) return vals
[docs] def 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. 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(): invalid = INVALID_VALUE[self.data.dtype] vals = vals.astype(type(invalid)) vals[~mask] = invalid 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, interp=None, 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. 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. Returns ------- vals : `~numpy.ndarray` Interpolated pixel values. """ pass
[docs] @abc.abstractmethod def 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. Returns ------- vals : `~numpy.ndarray` Interpolated pixel values. """ pass
[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_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.widgets.interaction import interact, fixed from ipywidgets import SelectionSlider, RadioButtons 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: if axis.node_type == "edges": options = [ f"{val_min:.2e} - {val_max:.2e} {axis.unit}" for val_min, val_max in zip(axis.edges[:-1], axis.edges[1:]) ] else: options = [f"{val:.2e} {axis.unit}" for val in axis.center] 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): fig, ax, cbar = 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: raise ValueError("Can't copy and change geometry of the map.") return self._init_copy(**kwargs)
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 arithmetics on maps after checking geometry consistency.""" if isinstance(other, Map): if self.geom == other.geom: q = other.quantity else: raise ValueError("Map Arithmetics: 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 __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 __array__(self): return self.data