forked from 170010011/fr
221 lines
8.1 KiB
Python
221 lines
8.1 KiB
Python
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import numpy as np
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class Triangulation:
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"""
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An unstructured triangular grid consisting of npoints points and
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ntri triangles. The triangles can either be specified by the user
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or automatically generated using a Delaunay triangulation.
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Parameters
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----------
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x, y : array-like of shape (npoints)
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Coordinates of grid points.
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triangles : int array-like of shape (ntri, 3), optional
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For each triangle, the indices of the three points that make
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up the triangle, ordered in an anticlockwise manner. If not
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specified, the Delaunay triangulation is calculated.
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mask : bool array-like of shape (ntri), optional
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Which triangles are masked out.
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Attributes
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----------
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edges : int array of shape (nedges, 2)
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See `~.Triangulation.edges`
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neighbors : int array of shape (ntri, 3)
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See `~.Triangulation.neighbors`
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mask : bool array of shape (ntri, 3)
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Masked out triangles.
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is_delaunay : bool
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Whether the Triangulation is a calculated Delaunay
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triangulation (where *triangles* was not specified) or not.
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Notes
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-----
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For a Triangulation to be valid it must not have duplicate points,
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triangles formed from colinear points, or overlapping triangles.
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"""
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def __init__(self, x, y, triangles=None, mask=None):
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from matplotlib import _qhull
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self.x = np.asarray(x, dtype=np.float64)
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self.y = np.asarray(y, dtype=np.float64)
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if self.x.shape != self.y.shape or self.x.ndim != 1:
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raise ValueError("x and y must be equal-length 1-D arrays")
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self.mask = None
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self._edges = None
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self._neighbors = None
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self.is_delaunay = False
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if triangles is None:
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# No triangulation specified, so use matplotlib._qhull to obtain
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# Delaunay triangulation.
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self.triangles, self._neighbors = _qhull.delaunay(x, y)
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self.is_delaunay = True
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else:
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# Triangulation specified. Copy, since we may correct triangle
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# orientation.
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self.triangles = np.array(triangles, dtype=np.int32, order='C')
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if self.triangles.ndim != 2 or self.triangles.shape[1] != 3:
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raise ValueError('triangles must be a (?, 3) array')
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if self.triangles.max() >= len(self.x):
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raise ValueError('triangles max element is out of bounds')
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if self.triangles.min() < 0:
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raise ValueError('triangles min element is out of bounds')
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if mask is not None:
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self.mask = np.asarray(mask, dtype=bool)
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if self.mask.shape != (self.triangles.shape[0],):
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raise ValueError('mask array must have same length as '
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'triangles array')
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# Underlying C++ object is not created until first needed.
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self._cpp_triangulation = None
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# Default TriFinder not created until needed.
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self._trifinder = None
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def calculate_plane_coefficients(self, z):
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"""
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Calculate plane equation coefficients for all unmasked triangles from
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the point (x, y) coordinates and specified z-array of shape (npoints).
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The returned array has shape (npoints, 3) and allows z-value at (x, y)
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position in triangle tri to be calculated using
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``z = array[tri, 0] * x + array[tri, 1] * y + array[tri, 2]``.
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"""
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return self.get_cpp_triangulation().calculate_plane_coefficients(z)
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@property
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def edges(self):
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"""
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Return integer array of shape (nedges, 2) containing all edges of
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non-masked triangles.
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Each row defines an edge by it's start point index and end point
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index. Each edge appears only once, i.e. for an edge between points
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*i* and *j*, there will only be either *(i, j)* or *(j, i)*.
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"""
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if self._edges is None:
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self._edges = self.get_cpp_triangulation().get_edges()
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return self._edges
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def get_cpp_triangulation(self):
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"""
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Return the underlying C++ Triangulation object, creating it
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if necessary.
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"""
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from matplotlib import _tri
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if self._cpp_triangulation is None:
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self._cpp_triangulation = _tri.Triangulation(
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self.x, self.y, self.triangles, self.mask, self._edges,
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self._neighbors, not self.is_delaunay)
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return self._cpp_triangulation
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def get_masked_triangles(self):
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"""
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Return an array of triangles that are not masked.
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"""
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if self.mask is not None:
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return self.triangles[~self.mask]
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else:
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return self.triangles
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@staticmethod
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def get_from_args_and_kwargs(*args, **kwargs):
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"""
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Return a Triangulation object from the args and kwargs, and
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the remaining args and kwargs with the consumed values removed.
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There are two alternatives: either the first argument is a
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Triangulation object, in which case it is returned, or the args
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and kwargs are sufficient to create a new Triangulation to
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return. In the latter case, see Triangulation.__init__ for
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the possible args and kwargs.
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"""
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if isinstance(args[0], Triangulation):
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triangulation, *args = args
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else:
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x, y, *args = args
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# Check triangles in kwargs then args.
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triangles = kwargs.pop('triangles', None)
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from_args = False
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if triangles is None and args:
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triangles = args[0]
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from_args = True
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if triangles is not None:
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try:
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triangles = np.asarray(triangles, dtype=np.int32)
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except ValueError:
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triangles = None
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if triangles is not None and (triangles.ndim != 2 or
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triangles.shape[1] != 3):
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triangles = None
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if triangles is not None and from_args:
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args = args[1:] # Consumed first item in args.
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# Check for mask in kwargs.
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mask = kwargs.pop('mask', None)
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triangulation = Triangulation(x, y, triangles, mask)
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return triangulation, args, kwargs
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def get_trifinder(self):
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"""
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Return the default `matplotlib.tri.TriFinder` of this
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triangulation, creating it if necessary. This allows the same
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TriFinder object to be easily shared.
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"""
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if self._trifinder is None:
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# Default TriFinder class.
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from matplotlib.tri.trifinder import TrapezoidMapTriFinder
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self._trifinder = TrapezoidMapTriFinder(self)
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return self._trifinder
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@property
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def neighbors(self):
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"""
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Return integer array of shape (ntri, 3) containing neighbor triangles.
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For each triangle, the indices of the three triangles that
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share the same edges, or -1 if there is no such neighboring
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triangle. ``neighbors[i, j]`` is the triangle that is the neighbor
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to the edge from point index ``triangles[i, j]`` to point index
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``triangles[i, (j+1)%3]``.
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"""
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if self._neighbors is None:
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self._neighbors = self.get_cpp_triangulation().get_neighbors()
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return self._neighbors
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def set_mask(self, mask):
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"""
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Set or clear the mask array.
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Parameters
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----------
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mask : None or bool array of length ntri
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"""
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if mask is None:
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self.mask = None
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else:
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self.mask = np.asarray(mask, dtype=bool)
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if self.mask.shape != (self.triangles.shape[0],):
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raise ValueError('mask array must have same length as '
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'triangles array')
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# Set mask in C++ Triangulation.
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if self._cpp_triangulation is not None:
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self._cpp_triangulation.set_mask(self.mask)
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# Clear derived fields so they are recalculated when needed.
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self._edges = None
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self._neighbors = None
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# Recalculate TriFinder if it exists.
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if self._trifinder is not None:
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self._trifinder._initialize()
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