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Use a FILL_VALUE=-1 everywhere #299

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Sep 6, 2024
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Use a FILL_VALUE=-1 everywhere #299

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333419b
Use a FILL_VALUE=-1 everywhere.
Huite Sep 5, 2024
a1c0a7f
Update voronoi dev example
Huite Sep 5, 2024
15bb386
Update changelog
Huite Sep 5, 2024
8e0fed4
Mention to_netcdf in changelog too
Huite Sep 6, 2024
125a0c8
Address review comments
Huite Sep 6, 2024
ee87753
Only replace the fill_value in the returned connectivity if it's diff…
Huite Sep 6, 2024
81fa3a6
Add a test to assert that fill_value is set back
Huite Sep 6, 2024
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21 changes: 21 additions & 0 deletions docs/changelog.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,27 @@ All notable changes to this project will be documented in this file.
The format is based on `Keep a Changelog`_, and this project adheres to
`Semantic Versioning`_.

Unreleased
----------

Fixed
~~~~~

- Release 0.12.0 changed the return type of the face node connectivity of
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Nitpick: This should be one bullet in the list right? The first paragraph is context

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Yeah, I'd started off by just fixing the fill value mismatch, but ended up getting rid of all of them. I've changed it to a single bullet.

:attr:`xugrid.Ugrid2d.voronoi_topology` from a `scipy.sparse.coo_matrix` to
an ordinary `np.array` of integers (and similarly for internal voronoi
tesselations); this dense array had fill (hard-coded) values of -1,
potentially differing from the grid's fill value. This lead to a number of
errors for methods relying on voronoi tesselations (such as contour plots)
if the fill value of the grid was not -1. Internally, a ``FILL_VALUE = -1``
is now used everywhere in connectivity arrays, and fill values are no longer
passed for internal methods; a value of -1 is always assumed. When converting
the grid (back) to a dataset with :meth:`xugrid.Ugrid1d.to_dataset` or
:meth:`xugrid.Ugrid2d.to_dataset`, the fill value is set back to its original
value; the fill value is also set when calling
:meth:`xugrid.UgridDataArrayAccessor.to_netcdf` or
:meth:`xugrid.UgridDatasetAccessor.to_netcdf`.

[0.12.0] 2024-09-03
-------------------

Expand Down
70 changes: 29 additions & 41 deletions examples-dev/voronoi.py
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Expand Up @@ -19,11 +19,14 @@
modules, should you not want to rely on more complex dependencies such as
``xugrid`` and ``xarray``.
"""
# %%
import matplotlib.pyplot as plt
import matplotlib.tri as mtri
import numpy as np
from matplotlib.collections import LineCollection, PolyCollection

from xugrid.constants import FILL_VALUE # equals -1

# %%
# From xugrid, we need only import the ``connectivity`` and ``voronoi``
# modules. The functions in these modules depend only on ``numpy`` and
Expand Down Expand Up @@ -63,12 +66,12 @@ def generate_disk(partitions: int, depth: int):
return np.column_stack((x, y)), triang.triangles


def edge_plot(vertices, edge_nodes, ax, fill_value=-1, **kwargs):
def edge_plot(vertices, edge_nodes, ax, **kwargs):
n_edge = len(edge_nodes)
edge_coords = np.empty((n_edge, 2, 2), dtype=float)
node_0 = edge_nodes[:, 0]
node_1 = edge_nodes[:, 1]
valid = (node_0 != fill_value) & (node_1 != fill_value)
valid = (node_0 != FILL_VALUE) & (node_1 != FILL_VALUE)
node_0 = node_0[valid]
node_1 = node_1[valid]
edge_coords[:, 0, 0] = vertices[node_0, 0]
Expand All @@ -81,10 +84,10 @@ def edge_plot(vertices, edge_nodes, ax, fill_value=-1, **kwargs):
return primitive


def face_plot(vertices, face_nodes, ax, fill_value=-1, **kwargs):
def face_plot(vertices, face_nodes, ax, **kwargs):
vertices = vertices[face_nodes]
# Replace fill value; PolyCollection ignores NaN.
vertices[face_nodes == fill_value] = np.nan
vertices[face_nodes == FILL_VALUE] = np.nan
collection = PolyCollection(vertices, **kwargs)
primitive = ax.add_collection(collection)
ax.autoscale()
Expand All @@ -107,12 +110,12 @@ def comparison_plot(
sharex=True,
)

edges0, _ = connectivity.edge_connectivity(faces0, -1)
edges0, _ = connectivity.edge_connectivity(faces0)
edge_plot(vertices0, edges0, ax0, colors="black")
ax0.scatter(*centroids0.T, color="red")
ax0.scatter(*vertices0.T, color="black")

edges1, _ = connectivity.edge_connectivity(faces1, -1)
edges1, _ = connectivity.edge_connectivity(faces1)
edge_plot(vertices0, edges0, ax1, colors="black")
edge_plot(vertices1, edges1, ax1, colors="red")

Expand All @@ -126,13 +129,11 @@ def comparison_plot(
# %%
# Let's start by generating a simple unstructured mesh and use only its
# centroids to generate a voronoi tesselation.
#
# Note: ``-1`` functions as the fill value in this example.

vertices, faces = generate_disk(5, 2)
centroids = vertices[faces].mean(axis=1)

node_face_connectivity = connectivity.invert_dense_to_sparse(faces, -1)
node_face_connectivity = connectivity.invert_dense_to_sparse(faces)
voronoi_vertices, voronoi_faces, face_index, _ = voronoi.voronoi_topology(
node_face_connectivity,
vertices,
Expand All @@ -158,17 +159,14 @@ def comparison_plot(
# The ``voronoi_topology`` is capable of preserving the exterior exactly, but
# this requires more topological information.

edge_node_connectivity, face_edge_connectivity = connectivity.edge_connectivity(
faces, -1
)
edge_face_connectivity = connectivity.invert_dense(face_edge_connectivity, -1)
edge_node_connectivity, face_edge_connectivity = connectivity.edge_connectivity(faces)
edge_face_connectivity = connectivity.invert_dense(face_edge_connectivity)
voronoi_vertices, voronoi_faces, face_index, _ = voronoi.voronoi_topology(
node_face_connectivity,
vertices,
centroids,
edge_face_connectivity=edge_face_connectivity,
edge_node_connectivity=edge_node_connectivity,
fill_value=-1,
add_exterior=True,
add_vertices=True,
)
Expand All @@ -190,18 +188,15 @@ def comparison_plot(
faces = connectivity.renumber(new)
centroids = vertices[faces].mean(axis=1)

node_face_connectivity = connectivity.invert_dense_to_sparse(faces, -1)
edge_node_connectivity, face_edge_connectivity = connectivity.edge_connectivity(
faces, -1
)
edge_face_connectivity = connectivity.invert_dense(face_edge_connectivity, -1)
node_face_connectivity = connectivity.invert_dense_to_sparse(faces)
edge_node_connectivity, face_edge_connectivity = connectivity.edge_connectivity(faces)
edge_face_connectivity = connectivity.invert_dense(face_edge_connectivity)
voronoi_vertices, voronoi_faces, face_index, _ = voronoi.voronoi_topology(
node_face_connectivity,
vertices,
centroids,
edge_face_connectivity=edge_face_connectivity,
edge_node_connectivity=edge_node_connectivity,
fill_value=-1,
add_exterior=True,
add_vertices=True,
)
Expand All @@ -217,18 +212,15 @@ def comparison_plot(
# of the original mesh altogether. We still add an orthogonal projection of
# every centroid to exterior edges.

node_face_connectivity = connectivity.invert_dense_to_sparse(faces, -1)
edge_node_connectivity, face_edge_connectivity = connectivity.edge_connectivity(
faces, -1
)
edge_face_connectivity = connectivity.invert_dense(face_edge_connectivity, -1)
node_face_connectivity = connectivity.invert_dense_to_sparse(faces)
edge_node_connectivity, face_edge_connectivity = connectivity.edge_connectivity(faces)
edge_face_connectivity = connectivity.invert_dense(face_edge_connectivity)
voronoi_vertices, voronoi_faces, face_index, _ = voronoi.voronoi_topology(
node_face_connectivity,
vertices,
centroids,
edge_face_connectivity=edge_face_connectivity,
edge_node_connectivity=edge_node_connectivity,
fill_value=-1,
add_exterior=True,
add_vertices=False,
)
Expand All @@ -240,18 +232,15 @@ def comparison_plot(
# exactly. Alternatively, we can choose to skip the exterior vertex if it
# creates a concave face:

node_face_connectivity = connectivity.invert_dense_to_sparse(faces, -1)
edge_node_connectivity, face_edge_connectivity = connectivity.edge_connectivity(
faces, -1
)
edge_face_connectivity = connectivity.invert_dense(face_edge_connectivity, -1)
node_face_connectivity = connectivity.invert_dense_to_sparse(faces)
edge_node_connectivity, face_edge_connectivity = connectivity.edge_connectivity(faces)
edge_face_connectivity = connectivity.invert_dense(face_edge_connectivity)
voronoi_vertices, voronoi_faces, face_index, _ = voronoi.voronoi_topology(
node_face_connectivity,
vertices,
centroids,
edge_face_connectivity=edge_face_connectivity,
edge_node_connectivity=edge_node_connectivity,
fill_value=-1,
add_exterior=True,
add_vertices=True,
skip_concave=True,
Expand All @@ -267,44 +256,41 @@ def comparison_plot(
vertices,
centroids,
)
edges0, _ = connectivity.edge_connectivity(faces0, -1)
edges0, _ = connectivity.edge_connectivity(faces0)

nodes1, faces1, face_index1, _ = voronoi.voronoi_topology(
node_face_connectivity,
vertices,
centroids,
edge_face_connectivity=edge_face_connectivity,
edge_node_connectivity=edge_node_connectivity,
fill_value=-1,
add_exterior=True,
add_vertices=False,
)
edges1, _ = connectivity.edge_connectivity(faces1, -1)
edges1, _ = connectivity.edge_connectivity(faces1)

nodes2, faces2, _, _ = voronoi.voronoi_topology(
node_face_connectivity,
vertices,
centroids,
edge_face_connectivity=edge_face_connectivity,
edge_node_connectivity=edge_node_connectivity,
fill_value=-1,
add_exterior=True,
add_vertices=True,
)
edges2, _ = connectivity.edge_connectivity(faces2, -1)
edges2, _ = connectivity.edge_connectivity(faces2)

nodes3, faces3, face_index3, node_map3 = voronoi.voronoi_topology(
node_face_connectivity,
vertices,
centroids,
edge_face_connectivity=edge_face_connectivity,
edge_node_connectivity=edge_node_connectivity,
fill_value=-1,
add_exterior=True,
add_vertices=True,
skip_concave=True,
)
edges3, _ = connectivity.edge_connectivity(faces3, -1)
edges3, _ = connectivity.edge_connectivity(faces3)

fig, axes = plt.subplots(
nrows=1,
Expand Down Expand Up @@ -342,10 +328,10 @@ def comparison_plot(
# fourth option, since it includes some vertices of the original mesh, which
# are connected to multiple faces.

triangles0, face_triangles0 = connectivity.triangulate(faces0, -1)
triangles0, face_triangles0 = connectivity.triangulate(faces0)
triangulation0 = mtri.Triangulation(nodes0[:, 0], nodes0[:, 1], triangles0)

triangles1, face_triangles1 = connectivity.triangulate(faces1, -1)
triangles1, face_triangles1 = connectivity.triangulate(faces1)
triangulation1 = mtri.Triangulation(nodes1[:, 0], nodes1[:, 1], triangles1)


Expand Down Expand Up @@ -422,3 +408,5 @@ def comparison_plot(

ax0.set_xlim(-1.5, 1.5)
ax0.set_ylim(-1.5, 1.5)

# %%
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