Source code for astropy.visualization.wcsaxes.coordinates_map

# Licensed under a 3-clause BSD style license - see LICENSE.rst

from .coordinate_helpers import CoordinateHelper
from .transforms import WCSPixel2WorldTransform
from .utils import coord_type_from_ctype
from .frame import RectangularFrame
from .coordinate_range import find_coordinate_range

[docs]class CoordinatesMap: """ A container for coordinate helpers that represents a coordinate system. This object can be used to access coordinate helpers by index (like a list) or by name (like a dictionary). Parameters ---------- axes : :class:`~astropy.visualization.wcsaxes.WCSAxes` The axes the coordinate map belongs to. wcs : :class:`~astropy.wcs.WCS`, optional The WCS for the data. If this is specified, ``transform`` cannot be specified. transform : `~matplotlib.transforms.Transform`, optional The transform for the data. If this is specified, ``wcs`` cannot be specified. coord_meta : dict, optional A dictionary providing additional metadata when ``transform`` is specified. This should include the keys ``type``, ``wrap``, and ``unit``. Each of these should be a list with as many items as the dimension of the WCS. The ``type`` entries should be one of ``longitude``, ``latitude``, or ``scalar``, the ``wrap`` entries should give, for the longitude, the angle at which the coordinate wraps (and `None` otherwise), and the ``unit`` should give the unit of the coordinates as :class:`~astropy.units.Unit` instances. This can optionally also include a ``format_unit`` entry giving the units to use for the tick labels (if not specified, this defaults to ``unit``). slice : tuple, optional For WCS transformations with more than two dimensions, we need to choose which dimensions are being shown in the 2D image. The slice should contain one ``x`` entry, one ``y`` entry, and the rest of the values should be integers indicating the slice through the data. The order of the items in the slice should be the same as the order of the dimensions in the :class:`~astropy.wcs.WCS`, and the opposite of the order of the dimensions in Numpy. For example, ``(50, 'x', 'y')`` means that the first WCS dimension (last Numpy dimension) will be sliced at an index of 50, the second WCS and Numpy dimension will be shown on the x axis, and the final WCS dimension (first Numpy dimension) will be shown on the y-axis (and therefore the data will be plotted using ``data[:, :, 50].transpose()``) frame_class : type, optional The class for the frame, which should be a subclass of :class:`~astropy.visualization.wcsaxes.frame.BaseFrame`. The default is to use a :class:`~astropy.visualization.wcsaxes.frame.RectangularFrame` previous_frame_path : `~matplotlib.path.Path`, optional When changing the WCS of the axes, the frame instance will change but we might want to keep re-using the same underlying matplotlib `~matplotlib.path.Path` - in that case, this can be passed to this keyword argument. """ def __init__(self, axes, wcs=None, transform=None, coord_meta=None, slice=None, frame_class=RectangularFrame, previous_frame_path=None): # Keep track of parent axes and WCS self._axes = axes if wcs is None: if transform is None: raise ValueError("Either `wcs` or `transform` are required") if coord_meta is None: raise ValueError("`coord_meta` is required when " "`transform` is passed") self._transform = transform naxis = 2 else: if transform is not None: raise ValueError("Cannot specify both `wcs` and `transform`") if coord_meta is not None: raise ValueError("Cannot pass `coord_meta` if passing `wcs`") self._transform = WCSPixel2WorldTransform(wcs, slice=slice) naxis = wcs.wcs.naxis self.frame = frame_class(axes, self._transform, path=previous_frame_path) # Set up coordinates self._coords = [] self._aliases = {} for coord_index in range(naxis): # Extract coordinate metadata from WCS object or transform if wcs is not None: coord_unit = wcs.wcs.cunit[coord_index] coord_type, format_unit, coord_wrap = coord_type_from_ctype(wcs.wcs.ctype[coord_index]) name = wcs.wcs.ctype[coord_index][:4].replace('-', '') else: try: coord_type = coord_meta['type'][coord_index] coord_wrap = coord_meta['wrap'][coord_index] coord_unit = coord_meta['unit'][coord_index] name = coord_meta['name'][coord_index] if 'format_unit' in coord_meta: format_unit = coord_meta['format_unit'][coord_index] else: format_unit = None except IndexError: raise ValueError("coord_meta items should have a length of {0}".format(len(wcs.wcs.naxis))) self._coords.append(CoordinateHelper(parent_axes=axes, parent_map=self, transform=self._transform, coord_index=coord_index, coord_type=coord_type, coord_wrap=coord_wrap, coord_unit=coord_unit, format_unit=format_unit, frame=self.frame)) # Set up aliases for coordinates self._aliases[name.lower()] = coord_index def __getitem__(self, item): if isinstance(item, str): return self._coords[self._aliases[item.lower()]] else: return self._coords[item] def __contains__(self, item): if isinstance(item, str): return item.lower() in self._aliases else: return 0 <= item < len(self._coords)
[docs] def set_visible(self, visibility): raise NotImplementedError()
def __iter__(self): for coord in self._coords: yield coord
[docs] def grid(self, draw_grid=True, grid_type=None, **kwargs): """ Plot gridlines for both coordinates. Standard matplotlib appearance options (color, alpha, etc.) can be passed as keyword arguments. Parameters ---------- draw_grid : bool Whether to show the gridlines grid_type : { 'lines' | 'contours' } Whether to plot the contours by determining the grid lines in world coordinates and then plotting them in world coordinates (``'lines'``) or by determining the world coordinates at many positions in the image and then drawing contours (``'contours'``). The first is recommended for 2-d images, while for 3-d (or higher dimensional) cubes, the ``'contours'`` option is recommended. By default, 'lines' is used if the transform has an inverse, otherwise 'contours' is used. """ for coord in self: coord.grid(draw_grid=draw_grid, grid_type=grid_type, **kwargs)
[docs] def get_coord_range(self): xmin, xmax = self._axes.get_xlim() ymin, ymax = self._axes.get_ylim() return find_coordinate_range(self._transform, [xmin, xmax, ymin, ymax], [coord.coord_type for coord in self], [coord.coord_unit for coord in self])