308 lines
13 KiB
Python
308 lines
13 KiB
Python
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"""
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Mesh refinement for triangular grids.
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"""
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import numpy as np
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from matplotlib import cbook
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from matplotlib.tri.triangulation import Triangulation
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import matplotlib.tri.triinterpolate
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class TriRefiner:
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"""
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Abstract base class for classes implementing mesh refinement.
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A TriRefiner encapsulates a Triangulation object and provides tools for
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mesh refinement and interpolation.
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Derived classes must implement:
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- ``refine_triangulation(return_tri_index=False, **kwargs)`` , where
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the optional keyword arguments *kwargs* are defined in each
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TriRefiner concrete implementation, and which returns:
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- a refined triangulation,
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- optionally (depending on *return_tri_index*), for each
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point of the refined triangulation: the index of
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the initial triangulation triangle to which it belongs.
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- ``refine_field(z, triinterpolator=None, **kwargs)``, where:
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- *z* array of field values (to refine) defined at the base
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triangulation nodes,
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- *triinterpolator* is an optional `~matplotlib.tri.TriInterpolator`,
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- the other optional keyword arguments *kwargs* are defined in
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each TriRefiner concrete implementation;
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and which returns (as a tuple) a refined triangular mesh and the
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interpolated values of the field at the refined triangulation nodes.
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"""
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def __init__(self, triangulation):
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cbook._check_isinstance(Triangulation, triangulation=triangulation)
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self._triangulation = triangulation
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class UniformTriRefiner(TriRefiner):
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"""
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Uniform mesh refinement by recursive subdivisions.
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Parameters
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----------
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triangulation : `~matplotlib.tri.Triangulation`
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The encapsulated triangulation (to be refined)
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"""
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# See Also
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# --------
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# :class:`~matplotlib.tri.CubicTriInterpolator` and
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# :class:`~matplotlib.tri.TriAnalyzer`.
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# """
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def __init__(self, triangulation):
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TriRefiner.__init__(self, triangulation)
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def refine_triangulation(self, return_tri_index=False, subdiv=3):
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"""
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Compute an uniformly refined triangulation *refi_triangulation* of
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the encapsulated :attr:`triangulation`.
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This function refines the encapsulated triangulation by splitting each
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father triangle into 4 child sub-triangles built on the edges midside
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nodes, recursing *subdiv* times. In the end, each triangle is hence
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divided into ``4**subdiv`` child triangles.
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Parameters
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----------
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return_tri_index : bool, default: False
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Whether an index table indicating the father triangle index of each
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point is returned.
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subdiv : int, default: 3
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Recursion level for the subdivision.
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Each triangle is divided into ``4**subdiv`` child triangles;
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hence, the default results in 64 refined subtriangles for each
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triangle of the initial triangulation.
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Returns
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-------
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refi_triangulation : `~matplotlib.tri.Triangulation`
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The refined triangulation.
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found_index : int array
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Index of the initial triangulation containing triangle, for each
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point of *refi_triangulation*.
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Returned only if *return_tri_index* is set to True.
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"""
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refi_triangulation = self._triangulation
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ntri = refi_triangulation.triangles.shape[0]
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# Computes the triangulation ancestors numbers in the reference
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# triangulation.
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ancestors = np.arange(ntri, dtype=np.int32)
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for _ in range(subdiv):
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refi_triangulation, ancestors = self._refine_triangulation_once(
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refi_triangulation, ancestors)
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refi_npts = refi_triangulation.x.shape[0]
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refi_triangles = refi_triangulation.triangles
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# Now we compute found_index table if needed
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if return_tri_index:
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# We have to initialize found_index with -1 because some nodes
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# may very well belong to no triangle at all, e.g., in case of
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# Delaunay Triangulation with DuplicatePointWarning.
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found_index = np.full(refi_npts, -1, dtype=np.int32)
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tri_mask = self._triangulation.mask
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if tri_mask is None:
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found_index[refi_triangles] = np.repeat(ancestors,
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3).reshape(-1, 3)
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else:
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# There is a subtlety here: we want to avoid whenever possible
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# that refined points container is a masked triangle (which
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# would result in artifacts in plots).
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# So we impose the numbering from masked ancestors first,
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# then overwrite it with unmasked ancestor numbers.
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ancestor_mask = tri_mask[ancestors]
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found_index[refi_triangles[ancestor_mask, :]
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] = np.repeat(ancestors[ancestor_mask],
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3).reshape(-1, 3)
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found_index[refi_triangles[~ancestor_mask, :]
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] = np.repeat(ancestors[~ancestor_mask],
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3).reshape(-1, 3)
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return refi_triangulation, found_index
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else:
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return refi_triangulation
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def refine_field(self, z, triinterpolator=None, subdiv=3):
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"""
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Refine a field defined on the encapsulated triangulation.
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Parameters
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----------
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z : 1d-array-like of length ``n_points``
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Values of the field to refine, defined at the nodes of the
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encapsulated triangulation. (``n_points`` is the number of points
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in the initial triangulation)
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triinterpolator : `~matplotlib.tri.TriInterpolator`, optional
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Interpolator used for field interpolation. If not specified,
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a `~matplotlib.tri.CubicTriInterpolator` will be used.
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subdiv : int, default: 3
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Recursion level for the subdivision.
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Each triangle is divided into ``4**subdiv`` child triangles.
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Returns
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-------
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refi_tri : `~matplotlib.tri.Triangulation`
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The returned refined triangulation.
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refi_z : 1d array of length: *refi_tri* node count.
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The returned interpolated field (at *refi_tri* nodes).
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"""
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if triinterpolator is None:
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interp = matplotlib.tri.CubicTriInterpolator(
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self._triangulation, z)
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else:
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cbook._check_isinstance(matplotlib.tri.TriInterpolator,
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triinterpolator=triinterpolator)
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interp = triinterpolator
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refi_tri, found_index = self.refine_triangulation(
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subdiv=subdiv, return_tri_index=True)
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refi_z = interp._interpolate_multikeys(
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refi_tri.x, refi_tri.y, tri_index=found_index)[0]
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return refi_tri, refi_z
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@staticmethod
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def _refine_triangulation_once(triangulation, ancestors=None):
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"""
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Refine a `.Triangulation` by splitting each triangle into 4
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child-masked_triangles built on the edges midside nodes.
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Masked triangles, if present, are also split, but their children
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returned masked.
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If *ancestors* is not provided, returns only a new triangulation:
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child_triangulation.
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If the array-like key table *ancestor* is given, it shall be of shape
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(ntri,) where ntri is the number of *triangulation* masked_triangles.
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In this case, the function returns
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(child_triangulation, child_ancestors)
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child_ancestors is defined so that the 4 child masked_triangles share
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the same index as their father: child_ancestors.shape = (4 * ntri,).
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"""
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x = triangulation.x
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y = triangulation.y
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# According to tri.triangulation doc:
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# neighbors[i, j] is the triangle that is the neighbor
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# to the edge from point index masked_triangles[i, j] to point
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# index masked_triangles[i, (j+1)%3].
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neighbors = triangulation.neighbors
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triangles = triangulation.triangles
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npts = np.shape(x)[0]
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ntri = np.shape(triangles)[0]
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if ancestors is not None:
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ancestors = np.asarray(ancestors)
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if np.shape(ancestors) != (ntri,):
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raise ValueError(
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"Incompatible shapes provide for triangulation"
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".masked_triangles and ancestors: {0} and {1}".format(
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np.shape(triangles), np.shape(ancestors)))
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# Initiating tables refi_x and refi_y of the refined triangulation
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# points
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# hint: each apex is shared by 2 masked_triangles except the borders.
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borders = np.sum(neighbors == -1)
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added_pts = (3*ntri + borders) // 2
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refi_npts = npts + added_pts
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refi_x = np.zeros(refi_npts)
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refi_y = np.zeros(refi_npts)
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# First part of refi_x, refi_y is just the initial points
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refi_x[:npts] = x
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refi_y[:npts] = y
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# Second part contains the edge midside nodes.
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# Each edge belongs to 1 triangle (if border edge) or is shared by 2
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# masked_triangles (interior edge).
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# We first build 2 * ntri arrays of edge starting nodes (edge_elems,
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# edge_apexes); we then extract only the masters to avoid overlaps.
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# The so-called 'master' is the triangle with biggest index
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# The 'slave' is the triangle with lower index
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# (can be -1 if border edge)
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# For slave and master we will identify the apex pointing to the edge
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# start
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edge_elems = np.tile(np.arange(ntri, dtype=np.int32), 3)
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edge_apexes = np.repeat(np.arange(3, dtype=np.int32), ntri)
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edge_neighbors = neighbors[edge_elems, edge_apexes]
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mask_masters = (edge_elems > edge_neighbors)
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# Identifying the "masters" and adding to refi_x, refi_y vec
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masters = edge_elems[mask_masters]
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apex_masters = edge_apexes[mask_masters]
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x_add = (x[triangles[masters, apex_masters]] +
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x[triangles[masters, (apex_masters+1) % 3]]) * 0.5
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y_add = (y[triangles[masters, apex_masters]] +
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y[triangles[masters, (apex_masters+1) % 3]]) * 0.5
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refi_x[npts:] = x_add
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refi_y[npts:] = y_add
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# Building the new masked_triangles; each old masked_triangles hosts
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# 4 new masked_triangles
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# there are 6 pts to identify per 'old' triangle, 3 new_pt_corner and
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# 3 new_pt_midside
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new_pt_corner = triangles
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# What is the index in refi_x, refi_y of point at middle of apex iapex
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# of elem ielem ?
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# If ielem is the apex master: simple count, given the way refi_x was
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# built.
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# If ielem is the apex slave: yet we do not know; but we will soon
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# using the neighbors table.
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new_pt_midside = np.empty([ntri, 3], dtype=np.int32)
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cum_sum = npts
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for imid in range(3):
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mask_st_loc = (imid == apex_masters)
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n_masters_loc = np.sum(mask_st_loc)
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elem_masters_loc = masters[mask_st_loc]
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new_pt_midside[:, imid][elem_masters_loc] = np.arange(
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n_masters_loc, dtype=np.int32) + cum_sum
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cum_sum += n_masters_loc
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# Now dealing with slave elems.
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# for each slave element we identify the master and then the inode
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# once slave_masters is identified, slave_masters_apex is such that:
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# neighbors[slaves_masters, slave_masters_apex] == slaves
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mask_slaves = np.logical_not(mask_masters)
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slaves = edge_elems[mask_slaves]
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slaves_masters = edge_neighbors[mask_slaves]
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diff_table = np.abs(neighbors[slaves_masters, :] -
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np.outer(slaves, np.ones(3, dtype=np.int32)))
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slave_masters_apex = np.argmin(diff_table, axis=1)
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slaves_apex = edge_apexes[mask_slaves]
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new_pt_midside[slaves, slaves_apex] = new_pt_midside[
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slaves_masters, slave_masters_apex]
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# Builds the 4 child masked_triangles
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child_triangles = np.empty([ntri*4, 3], dtype=np.int32)
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child_triangles[0::4, :] = np.vstack([
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new_pt_corner[:, 0], new_pt_midside[:, 0],
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new_pt_midside[:, 2]]).T
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child_triangles[1::4, :] = np.vstack([
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new_pt_corner[:, 1], new_pt_midside[:, 1],
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new_pt_midside[:, 0]]).T
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child_triangles[2::4, :] = np.vstack([
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new_pt_corner[:, 2], new_pt_midside[:, 2],
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new_pt_midside[:, 1]]).T
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child_triangles[3::4, :] = np.vstack([
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new_pt_midside[:, 0], new_pt_midside[:, 1],
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new_pt_midside[:, 2]]).T
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child_triangulation = Triangulation(refi_x, refi_y, child_triangles)
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# Builds the child mask
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if triangulation.mask is not None:
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child_triangulation.set_mask(np.repeat(triangulation.mask, 4))
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if ancestors is None:
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return child_triangulation
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else:
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return child_triangulation, np.repeat(ancestors, 4)
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