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Improved qhull triangulations with large x,y offset #8873

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Merged
merged 4 commits into from
Aug 25, 2017
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Improved qhull triangulations with large x,y offset #8873

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be920ad
Improved qhull triangulations with large x,y offset
ianthomas23 Jul 14, 2017
8a5abdc
Increased test tolerance
ianthomas23 Jul 21, 2017
136bbb2
Increased another test tolerance
ianthomas23 Jul 21, 2017
d9ea551
Improved api change comment
ianthomas23 Aug 23, 2017
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10 changes: 10 additions & 0 deletions doc/api/api_changes/2017-07-14-IT_qhull_large_offset.rst
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Original file line number Diff line number Diff line change
@@ -0,0 +1,10 @@
Improved Delaunay triangulations with large offsets
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Could you add a mention of the Matplotlib methods that are affected by this change? Users might not be aware of what a Delaunay triangulation is or what Matplotlib is doing under the hood, but will be able to identify with a specific Matplotlib method that they use.

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Ping @ianthomas23.

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@dstansby @QuLogic My apologies, I thought I'd responded to this ages ago but evidently I didn't. I have amended the API change text now.

```````````````````````````````````````````````````

Delaunay triangulations now deal with large x,y offsets in a better
way. This can cause minor changes to any triangulations calculated
using Matplotlib, i.e. any use of `matplotlib.tri.Triangulation` that
requests that a Delaunay triangulation is calculated, which includes
`matplotlib.pyplot.tricontour`, `matplotlib.pyplot.tricontourf`,
`matplotlib.pyplot.tripcolor`, `matplotlib.pyplot.triplot`,
`mlab.griddata` and `mpl_toolkits.mplot3d.plot_trisurf`.
Binary file modified lib/matplotlib/tests/baseline_images/test_triangulation/tri_smooth_contouring.png
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Binary file modified lib/matplotlib/tests/baseline_images/test_triangulation/tri_smooth_gradient.png
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16 changes: 13 additions & 3 deletions lib/matplotlib/tests/test_triangulation.py
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Expand Up @@ -745,7 +745,7 @@ def z(x, y):


@image_comparison(baseline_images=['tri_smooth_contouring'],
extensions=['png'], remove_text=True)
extensions=['png'], remove_text=True, tol=0.07)
def test_tri_smooth_contouring():
# Image comparison based on example tricontour_smooth_user.
n_angles = 20
Expand Down Expand Up @@ -786,8 +786,7 @@ def z(x, y):


@image_comparison(baseline_images=['tri_smooth_gradient'],
extensions=['png'], remove_text=True,
tol=0.03 if on_win else 0)
extensions=['png'], remove_text=True, tol=0.035)
def test_tri_smooth_gradient():
# Image comparison based on example trigradient_demo.

Expand Down Expand Up @@ -1123,3 +1122,14 @@ def test_internal_cpp_api():
with pytest.raises(ValueError) as excinfo:
trifinder.find_many([0], [0, 1])
excinfo.match(r'x and y must be array_like with same shape')


def test_qhull_large_offset():
# github issue 8682.
x = np.asarray([0, 1, 0, 1, 0.5])
y = np.asarray([0, 0, 1, 1, 0.5])

offset = 1e10
triang = mtri.Triangulation(x, y)
triang_offset = mtri.Triangulation(x + offset, y + offset)
assert len(triang.triangles) == len(triang_offset.triangles)
14 changes: 12 additions & 2 deletions src/qhull_wrap.c
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Expand Up @@ -108,6 +108,8 @@ delaunay_impl(int npoints, const double* x, const double* y,
PyArrayObject* neighbors = NULL;
int* triangles_ptr;
int* neighbors_ptr;
double x_mean = 0.0;
double y_mean = 0.0;

QHULL_LIB_CHECK

Expand All @@ -119,10 +121,18 @@ delaunay_impl(int npoints, const double* x, const double* y,
goto error_before_qhull;
}

/* Determine mean x, y coordinates. */
for (i = 0; i < npoints; ++i) {
x_mean += x[i];
y_mean += y[i];
}
x_mean /= npoints;
y_mean /= npoints;

/* Prepare points array to pass to qhull. */
for (i = 0; i < npoints; ++i) {
points[2*i ] = x[i];
points[2*i+1] = y[i];
points[2*i ] = x[i] - x_mean;
points[2*i+1] = y[i] - y_mean;
}

/* qhull expects a FILE* to write errors to. */
Expand Down