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Fix some typos in the new examples
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LSchueler committed Dec 1, 2020
1 parent 3be10eb commit 1a1947a
Showing 2 changed files with 7 additions and 8 deletions.
7 changes: 3 additions & 4 deletions examples/01_random_field/05_mesh_ensemble.py
Original file line number Diff line number Diff line change
@@ -6,10 +6,10 @@
`meshio <https://github.com/nschloe/meshio>`_ and
`ogs5py <https://github.com/GeoStat-Framework/ogs5py>`_ meshes.
When using `meshio`, the generated fields will be stored imediatly in the mesh
container.
When using `meshio`, the generated fields will be stored immediately in the
mesh container.
One has two options to generate a field on a given mesh:
There are two options to generate a field on a given mesh:
- `points="points"` will generate a field on the mesh points
- `points="centroids"` will generate a field on the cell centroids
@@ -57,7 +57,6 @@
# To get an impression we now want to plot the generated fields.
# Luckily, matplotlib supports triangular meshes.


triangulation = tri.Triangulation(points[:, 0], points[:, 1], cells)
# figure setup
cols = 4
8 changes: 4 additions & 4 deletions examples/01_random_field/06_higher_dimensions.py
Original file line number Diff line number Diff line change
@@ -15,7 +15,7 @@
By design the first rotation angles coincide with the lower ones:
- 2D (rotation in x-y plane) -> 3D: first angle describes xy-plane rotation
- 3D (tait-bryan angles) -> 4D: first 3 angles coincide with tait-bryan angles
- 3D (Tait-Bryan angles) -> 4D: first 3 angles coincide with Tait-Bryan angles
By increasing the dimension from `n` to `(n+1)`, `n` angles are added:
@@ -34,7 +34,7 @@
7. ...
The rotation direction in these planes have alternating signs
in order to match tait-bryan in 3D.
in order to match Tait-Bryan in 3D.
Let's have a look at a 4D example, where we naively add a 4th dimension.
"""
@@ -50,7 +50,7 @@
field = srf.structured(pos)

###############################################################################
# In order to "prove" correctnes, we can calculate an empirical variogram
# In order to "prove" correctness, we can calculate an empirical variogram
# of the generated field and fit our model to it.

bin_edges = range(size)
@@ -62,7 +62,7 @@

###############################################################################
# As you can see, the estimated variance and length scale match our input
# quite good.
# quite well.
#
# Let's have a look at the fit and a x-y cross-section of the 4D field:

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