Merge pull request #97 from nschloe/even-more-tests

Even more tests

.flake8

@@ -1,5 +1,5 @@
 [flake8]
-ignore = E203, E266, E501, W503
+ignore = E203, E266, E501, W503, C901
 max-line-length = 80
 max-complexity = 18
 select = B,C,E,F,W,T4,B9

.github/workflows/ci.yml

@@ -49,10 +49,6 @@ jobs:
     - uses: actions/checkout@v2
       with:
         lfs: true
-    - name: Install system requirements
-      run: |
-        sudo apt-get update
-        sudo apt-get install -y python3-vtk7
     - name: Test with tox
       run: |
         pip install tox

README.md

@@ -24,6 +24,7 @@ meshplex is used in [optimesh](https://github.com/nschloe/optimesh) and
 
 ### Quickstart
 
+For triangular and tetrahedral meshes, meshplex can compute the following data:
 ```python
 import numpy
 import meshplex
@@ -62,9 +63,15 @@ mesh.flip_until_delaunay()
 # show the mesh
 mesh.show()
 ```
+For triangular meshes, meshplex also has some mesh manipulation routines:
+<!--exdown-skip-->
+```python
+mesh.flip_until_delaunay()  # flips edges until the mesh is Delaunay
+mesh.remove_cells([0, 2, ...])  # removes some cells
+```
 
-meshplex works much the same way with tetrahedral meshes. For a documentation of all
-classes and functions, see [readthedocs](https://meshplex.readthedocs.io/).
+For a documentation of all classes and functions, see
+[readthedocs](https://meshplex.readthedocs.io/).
 
 (For mesh creation, check out
 [this list](https://github.com/nschloe/awesome-scientific-computing#meshing)).

meshplex/helpers.py

@@ -147,11 +147,11 @@ def compute_triangle_circumcenters(X, ei_dot_ei, ei_dot_ej):
     # coordinates are (see
     # <https://en.wikipedia.org/wiki/Trilinear_coordinates#Between_Cartesian_and_trilinear_coordinates>)
     #
-    #     C = sum_i ||e_i|| * cos(alpha_i)/beta * P_i
+    #     C = sum_i ||e_i|| * cos(alpha_i) / beta * P_i
     #
     # with
     #
-    #     beta = sum ||e_i||*cos(alpha_i)
+    #     beta = sum ||e_i|| * cos(alpha_i)
     #
     # Incidentally, the cosines are
     #
@@ -163,20 +163,22 @@ def compute_triangle_circumcenters(X, ei_dot_ei, ei_dot_ej):
     #      + ... P1
     #      + ... P2.
     #
+    alpha = ei_dot_ei * ei_dot_ej
+    alpha_sum = alpha[0] + alpha[1] + alpha[2]
+    beta = alpha / alpha_sum[None]
+    a = X * beta[..., None]
+    cc = a[0] + a[1] + a[2]
+
     # An even nicer formula is given on
     # <https://en.wikipedia.org/wiki/Circumscribed_circle#Barycentric_coordinates>: The
     # barycentric coordinates of the circumcenter are
     #
     #   a^2 (b^2 + c^2 - a^2) : b^2 (c^2 + a^2 - b^2) : c^2 (a^2 + b^2 - c^2).
     #
     # This is only using the squared edge lengths, too!
+    # TODO make this happen, perhaps with something like
+    #    ei_dot_ei * (numpy.sum(ei_dot_ei, axis=0) - 2 * ei_dot_ei)
     #
-    alpha = ei_dot_ei * ei_dot_ej
-    alpha_sum = alpha[0] + alpha[1] + alpha[2]
-    beta = alpha / alpha_sum[None]
-    a = X * beta[..., None]
-    cc = a[0] + a[1] + a[2]
-
     # alpha = numpy.array([
     #     ei_dot_ei[0] * (ei_dot_ei[1] + ei_dot_ei[2] - ei_dot_ei[0]),
     #     ei_dot_ei[1] * (ei_dot_ei[2] + ei_dot_ei[0] - ei_dot_ei[1]),