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Fix log transforms (fixes #3809) [back port to 1.4.x] #3863

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Merged
merged 7 commits into from
Dec 9, 2014
Merged

Fix log transforms (fixes #3809) [back port to 1.4.x] #3863

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1c5ee87
Ensure that IdentityTransform always returns an array.
Nov 29, 2014
b5685f4
Ensure that 'points' is an array to avoid errors when indexing (compl…
Nov 29, 2014
9dcc1fa
Even with the fix for single-point transforms (77ccc8230) it's better…
maxalbert Dec 4, 2014
49d2cf1
Add test for log transform.
maxalbert Dec 4, 2014
0fa00ef
Merge upstream master.
maxalbert Dec 4, 2014
7d35c5e
Since most transformation code assumes 2d input, convert the values t…
maxalbert Dec 4, 2014
0b43f83
Support transforming scalars as some routines need this.
maxalbert Dec 4, 2014
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10 changes: 10 additions & 0 deletions lib/matplotlib/tests/test_transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -492,6 +492,16 @@ def test_transform_single_point():
r = t.transform_affine((1, 1))
assert r.shape == (2,)


@cleanup
def test_log_transform():
# Tests that the last line runs without exception (previously the
# transform would fail if one of the axes was logarithmic).
fig, ax = plt.subplots()
ax.set_yscale('log')
ax.transData.transform((1,1))


if __name__=='__main__':
import nose
nose.runmodule(argv=['-s','--with-doctest'], exit=False)
37 changes: 34 additions & 3 deletions lib/matplotlib/transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1285,8 +1285,33 @@ def transform(self, values):

Accepts a numpy array of shape (N x :attr:`input_dims`) and
returns a numpy array of shape (N x :attr:`output_dims`).
"""
return self.transform_affine(self.transform_non_affine(values))

Alternatively, accepts a numpy array of length :attr:`input_dims`
and returns a numpy array of length :attr:`output_dims`.
"""
# Ensure that values is a 2d array (but remember whether
# we started with a 1d or 2d array).
values = np.asanyarray(values)
ndim = values.ndim
values = values.reshape((-1, self.input_dims))

# Transform the values
res = self.transform_affine(self.transform_non_affine(values))

# Convert the result back to the shape of the input values.
if ndim == 0:
assert not np.ma.is_masked(res) # just to be on the safe side
return res[0, 0]
if ndim == 1:
return res.reshape(-1)
elif ndim == 2:
return res
else:
raise ValueError(
"Input values must have shape (N x {dims}) "
"or ({dims}).".format(dims=self.input_dims))

return res

def transform_affine(self, values):
"""
Expand All @@ -1302,6 +1327,9 @@ def transform_affine(self, values):

Accepts a numpy array of shape (N x :attr:`input_dims`) and
returns a numpy array of shape (N x :attr:`output_dims`).

Alternatively, accepts a numpy array of length :attr:`input_dims`
and returns a numpy array of length :attr:`output_dims`.
"""
return self.get_affine().transform(values)

Expand All @@ -1318,6 +1346,9 @@ def transform_non_affine(self, values):

Accepts a numpy array of shape (N x :attr:`input_dims`) and
returns a numpy array of shape (N x :attr:`output_dims`).

Alternatively, accepts a numpy array of length :attr:`input_dims`
and returns a numpy array of length :attr:`output_dims`.
"""
return values

Expand Down Expand Up @@ -1944,7 +1975,7 @@ def get_matrix(self):
get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__

def transform(self, points):
return points
return np.asanyarray(points)
transform.__doc__ = Affine2DBase.transform.__doc__

transform_affine = transform
Expand Down