diff options
Diffstat (limited to 'numpy/matrixlib')
| -rw-r--r-- | numpy/matrixlib/defmatrix.py | 40 | ||||
| -rw-r--r-- | numpy/matrixlib/tests/test_defmatrix.py | 47 |
2 files changed, 76 insertions, 11 deletions
diff --git a/numpy/matrixlib/defmatrix.py b/numpy/matrixlib/defmatrix.py index 2d785e213..51903e258 100644 --- a/numpy/matrixlib/defmatrix.py +++ b/numpy/matrixlib/defmatrix.py @@ -927,24 +927,42 @@ class matrix(N.ndarray): return self.__array__().ravel() - def ravel(self): + def ravel(self, order='C'): """ - Return a flattened array. + Return a flattened matrix. - Refer to `numpy.ravel` for full documentation. + Refer to `numpy.ravel` for more documentation. - See Also - -------- - numpy.ravel : equivalent function + Parameters + ---------- + order : {'C', 'F', 'A', 'K'}, optional + The elements of `m` are read using this index order. 'C' means to + index the elements in C-like order, with the last axis index + changing fastest, back to the first axis index changing slowest. + 'F' means to index the elements in Fortran-like index order, with + the first index changing fastest, and the last index changing + slowest. Note that the 'C' and 'F' options take no account of the + memory layout of the underlying array, and only refer to the order + of axis indexing. 'A' means to read the elements in Fortran-like + index order if `m` is Fortran *contiguous* in memory, C-like order + otherwise. 'K' means to read the elements in the order they occur + in memory, except for reversing the data when strides are negative. + By default, 'C' index order is used. - ndarray.flat : a flat iterator on the array. + Returns + ------- + ret : matrix + A copy of the matrix, flattened to a `(1, N)` matrix where `N` + is the number of elements in the original matrix. - Notes - ----- - This returns a matrix. + See Also + -------- + matrix.flatten : returns a similar output matrix but always a copy + matrix.flat : a flat iterator on the array. + numpy.ravel : related function which returns an ndarray """ - return core.multiarray.ndarray.ravel(self) + return core.multiarray.ndarray.ravel(self, order=order) def getT(self): diff --git a/numpy/matrixlib/tests/test_defmatrix.py b/numpy/matrixlib/tests/test_defmatrix.py index a06a564aa..d2a89bd51 100644 --- a/numpy/matrixlib/tests/test_defmatrix.py +++ b/numpy/matrixlib/tests/test_defmatrix.py @@ -386,6 +386,7 @@ class TestNewScalarIndexing(TestCase): assert_array_equal(x[:, [1, 0]], x[:, ::-1]) assert_array_equal(x[[2, 1, 0],:], x[::-1,:]) + class TestPower(TestCase): def test_returntype(self): a = array([[0, 1], [0, 0]]) @@ -396,5 +397,51 @@ class TestPower(TestCase): def test_list(self): assert_array_equal(matrix_power([[0, 1], [0, 0]], 2), [[0, 0], [0, 0]]) + +class TestShape(TestCase): + def setUp(self): + self.a = array([[1], [2]]) + self.m = matrix([[1], [2]]) + + def test_shape(self): + assert_equal(self.a.shape, (2, 1)) + assert_equal(self.m.shape, (2, 1)) + + def test_numpy_ravel(self): + assert_equal(np.ravel(self.a).shape, (2,)) + assert_equal(np.ravel(self.m).shape, (2,)) + + def test_member_ravel(self): + assert_equal(self.a.ravel().shape, (2,)) + assert_equal(self.m.ravel().shape, (1, 2)) + + def test_member_flatten(self): + assert_equal(self.a.flatten().shape, (2,)) + assert_equal(self.m.flatten().shape, (1, 2)) + + def test_numpy_ravel_order(self): + for t in array, matrix: + x = t([[1, 2, 3], [4, 5, 6]]) + assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6]) + assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6]) + assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6]) + assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6]) + + def test_matrix_ravel_order(self): + x = matrix([[1, 2, 3], [4, 5, 6]]) + assert_equal(x.ravel(), [[1, 2, 3, 4, 5, 6]]) + assert_equal(x.ravel(order='F'), [[1, 4, 2, 5, 3, 6]]) + assert_equal(x.T.ravel(), [[1, 4, 2, 5, 3, 6]]) + assert_equal(x.T.ravel(order='A'), [[1, 2, 3, 4, 5, 6]]) + + def test_array_memory_sharing(self): + assert_(np.may_share_memory(self.a, self.a.ravel())) + assert_(not np.may_share_memory(self.a, self.a.flatten())) + + def test_matrix_memory_sharing(self): + assert_(np.may_share_memory(self.m, self.m.ravel())) + assert_(not np.may_share_memory(self.m, self.m.flatten())) + + if __name__ == "__main__": run_module_suite() |