# Licensed under a 3-clause BSD style license - see LICENSE.rst import numpy as np import warnings from matplotlib.patches import Polygon from astropy import units as u from astropy.coordinates import SkyCoord from astropy.coordinates.representation import UnitSphericalRepresentation, SphericalRepresentation from astropy.coordinates.matrix_utilities import rotation_matrix, matrix_product from astropy.utils.exceptions import AstropyUserWarning __all__ = ['Quadrangle', 'SphericalCircle'] # Monkey-patch the docs to fix CapStyle and JoinStyle subs. # TODO! delete when upstream fix matplotlib/matplotlib#19839 Polygon.__init__.__doc__ = Polygon.__init__.__doc__.replace( "`.CapStyle`", "``matplotlib._enums.CapStyle``") Polygon.__init__.__doc__ = Polygon.__init__.__doc__.replace( "`.JoinStyle`", "``matplotlib._enums.JoinStyle``") Polygon.set_capstyle.__doc__ = Polygon.set_capstyle.__doc__.replace( "`.CapStyle`", "``matplotlib._enums.CapStyle``") Polygon.set_joinstyle.__doc__ = Polygon.set_joinstyle.__doc__.replace( "`.JoinStyle`", "``matplotlib._enums.JoinStyle``") def _rotate_polygon(lon, lat, lon0, lat0): """ Given a polygon with vertices defined by (lon, lat), rotate the polygon such that the North pole of the spherical coordinates is now at (lon0, lat0). Therefore, to end up with a polygon centered on (lon0, lat0), the polygon should initially be drawn around the North pole. """ # Create a representation object polygon = UnitSphericalRepresentation(lon=lon, lat=lat) # Determine rotation matrix to make it so that the circle is centered # on the correct longitude/latitude. m1 = rotation_matrix(-(0.5 * np.pi * u.radian - lat0), axis='y') m2 = rotation_matrix(-lon0, axis='z') transform_matrix = matrix_product(m2, m1) # Apply 3D rotation polygon = polygon.to_cartesian() polygon = polygon.transform(transform_matrix) polygon = UnitSphericalRepresentation.from_cartesian(polygon) return polygon.lon, polygon.lat class SphericalCircle(Polygon): """ Create a patch representing a spherical circle - that is, a circle that is formed of all the points that are within a certain angle of the central coordinates on a sphere. Here we assume that latitude goes from -90 to +90 This class is needed in cases where the user wants to add a circular patch to a celestial image, since otherwise the circle will be distorted, because a fixed interval in longitude corresponds to a different angle on the sky depending on the latitude. Parameters ---------- center : tuple or `~astropy.units.Quantity` ['angle'] This can be either a tuple of two `~astropy.units.Quantity` objects, or a single `~astropy.units.Quantity` array with two elements or a `~astropy.coordinates.SkyCoord` object. radius : `~astropy.units.Quantity` ['angle'] The radius of the circle resolution : int, optional The number of points that make up the circle - increase this to get a smoother circle. vertex_unit : `~astropy.units.Unit` The units in which the resulting polygon should be defined - this should match the unit that the transformation (e.g. the WCS transformation) expects as input. Notes ----- Additional keyword arguments are passed to `~matplotlib.patches.Polygon` """ def __init__(self, center, radius, resolution=100, vertex_unit=u.degree, **kwargs): # Extract longitude/latitude, either from a SkyCoord object, or # from a tuple of two quantities or a single 2-element Quantity. # The SkyCoord is converted to SphericalRepresentation, if not already. if isinstance(center, SkyCoord): rep_type = center.representation_type if not issubclass(rep_type, (SphericalRepresentation, UnitSphericalRepresentation)): warnings.warn(f'Received `center` of representation type {rep_type} ' 'will be converted to SphericalRepresentation ', AstropyUserWarning) longitude, latitude = center.spherical.lon, center.spherical.lat else: longitude, latitude = center # Start off by generating the circle around the North pole lon = np.linspace(0., 2 * np.pi, resolution + 1)[:-1] * u.radian lat = np.repeat(0.5 * np.pi - radius.to_value(u.radian), resolution) * u.radian lon, lat = _rotate_polygon(lon, lat, longitude, latitude) # Extract new longitude/latitude in the requested units lon = lon.to_value(vertex_unit) lat = lat.to_value(vertex_unit) # Create polygon vertices vertices = np.array([lon, lat]).transpose() super().__init__(vertices, **kwargs) class Quadrangle(Polygon): """ Create a patch representing a latitude-longitude quadrangle. The edges of the quadrangle lie on two lines of constant longitude and two lines of constant latitude (or the equivalent component names in the coordinate frame of interest, such as right ascension and declination). Note that lines of constant latitude are not great circles. Unlike `matplotlib.patches.Rectangle`, the edges of this patch will render as curved lines if appropriate for the WCS transformation. Parameters ---------- anchor : tuple or `~astropy.units.Quantity` ['angle'] This can be either a tuple of two `~astropy.units.Quantity` objects, or a single `~astropy.units.Quantity` array with two elements. width : `~astropy.units.Quantity` ['angle'] The width of the quadrangle in longitude (or, e.g., right ascension) height : `~astropy.units.Quantity` ['angle'] The height of the quadrangle in latitude (or, e.g., declination) resolution : int, optional The number of points that make up each side of the quadrangle - increase this to get a smoother quadrangle. vertex_unit : `~astropy.units.Unit` ['angle'] The units in which the resulting polygon should be defined - this should match the unit that the transformation (e.g. the WCS transformation) expects as input. Notes ----- Additional keyword arguments are passed to `~matplotlib.patches.Polygon` """ def __init__(self, anchor, width, height, resolution=100, vertex_unit=u.degree, **kwargs): # Extract longitude/latitude, either from a tuple of two quantities, or # a single 2-element Quantity. longitude, latitude = u.Quantity(anchor).to_value(vertex_unit) # Convert the quadrangle dimensions to the appropriate units width = width.to_value(vertex_unit) height = height.to_value(vertex_unit) # Create progressions in longitude and latitude lon_seq = longitude + np.linspace(0, width, resolution + 1) lat_seq = latitude + np.linspace(0, height, resolution + 1) # Trace the path of the quadrangle lon = np.concatenate([lon_seq[:-1], np.repeat(lon_seq[-1], resolution), np.flip(lon_seq[1:]), np.repeat(lon_seq[0], resolution)]) lat = np.concatenate([np.repeat(lat_seq[0], resolution), lat_seq[:-1], np.repeat(lat_seq[-1], resolution), np.flip(lat_seq[1:])]) # Create polygon vertices vertices = np.array([lon, lat]).transpose() super().__init__(vertices, **kwargs)