Dec 9, 2014 · Nov 29, 2014 · Nov 29, 2014 · Dec 4, 2014 · Dec 4, 2014 · Dec 4, 2014
diff --git a/lib/matplotlib/tests/test_transforms.py b/lib/matplotlib/tests/test_transforms.py
    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)
diff --git a/lib/matplotlib/transforms.py b/lib/matplotlib/transforms.py

        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):
        """

        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)


        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

    get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__

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

    transform_affine = transform
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)
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 DownExpand Up		@@ -1944,7 +1975,7 @@ def get_matrix(self):
		get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__

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

		transform_affine = transform
Expand Down