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27 changes: 25 additions & 2 deletions lib/matplotlib/tests/test_delaunay.py
Original file line number Diff line number Diff line change
Expand Up @@ -159,7 +159,7 @@ def interpolator(self, func):
z = func(self.x, self.y)
return self.tri.nn_extrapolator(z, bbox=self.xrange+self.yrange)

def make_all_testfuncs(allfuncs=allfuncs):
def make_all_2d_testfuncs(allfuncs=allfuncs):
def make_test(func):
filenames = [
'%s-%s' % (func.func_name, x) for x in
Expand All @@ -186,4 +186,27 @@ def reference_test():
for func in allfuncs:
globals()['test_%s' % func.func_name] = make_test(func)

make_all_testfuncs()
make_all_2d_testfuncs()

# 1d and 0d grid tests

ref_interpolator = Triangulation([0,10,10,0],
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Rather than using the module level namespace, it might be worth considering constructing a class which has "test_" methods.

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When adding tests to an existing module, I should try staying close to existing style.
make_all_testfuncs() was there (I just renamed it), and since it used module functions rather than class methods, so did I
( and that would have been my default choice anyways in this case).

[0,0,10,10]).linear_interpolator([1,10,5,2.0])

def equal_arrays(a1,a2, tolerance=1e-10):
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Were you aware of numpy.testing? In particular the numpy.testing.assert_array_almost_equal function.

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Nope, but always happy to learn.
help(assert_array_almost_equal) referred me to np.testing.assert_allclose, which in turn referenced np.allclose(). A useful np shortcut I was not aware of. Thanks 👍
(p.s. It seems that generalizations added by assert_allclose, like handling inf/nan values, are redundant in this case, and probably not worth the complication of extra import and runtime, so I use plain allclose() ).

return np.all(np.absolute(a1 - a2) < tolerance)

def test_1d_grid():
res = ref_interpolator[3:6:2j,1:1:1j]
assert equal_arrays(res, [[1.6],[1.9]])

def test_0d_grid():
res = ref_interpolator[3:3:1j,1:1:1j]
assert equal_arrays(res, [[1.6]])

@image_comparison(baseline_images=['delaunay-1d-interp'], extensions=['png'])
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I think we should probably remove the fonts from this plot. (see other examples in test_axes)

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I thought the same myself, but wanted to get feedback, since these "freetype_version=" arguments seem to appear everywhere. Indeed, test_axes has a couple of tick-less plots. Point taken.

def test_1d_plots():
x_range = slice(0.25,9.75,20j)
x = np.mgrid[x_range]
for y in xrange(2,10,2):
plt.plot(x, ref_interpolator[x_range,y:y:1j])