diff options
| -rw-r--r-- | doc/release/1.10.0-notes.rst | 13 | ||||
| -rw-r--r-- | numpy/core/arrayprint.py | 3 | ||||
| -rw-r--r-- | numpy/core/fromnumeric.py | 11 | ||||
| -rw-r--r-- | numpy/ma/core.py | 22 | ||||
| -rw-r--r-- | numpy/ma/tests/test_core.py | 3 | ||||
| -rw-r--r-- | numpy/matrixlib/tests/test_defmatrix.py | 18 |
6 files changed, 51 insertions, 19 deletions
diff --git a/doc/release/1.10.0-notes.rst b/doc/release/1.10.0-notes.rst index be5e475e6..e1156466a 100644 --- a/doc/release/1.10.0-notes.rst +++ b/doc/release/1.10.0-notes.rst @@ -39,11 +39,14 @@ relaxed stride checking ~~~~~~~~~~~~~~~~~~~~~~~ NPY_RELAXED_STRIDE_CHECKING is now true by default. -diag and diagonal -~~~~~~~~~~~~~~~~~ -Both diag and diagonal now preserve subtypes. This will change the -dimensions of the returned diagonal in the case of matrices and that -may lead to problems in using the result. +*np.ravel*, *np.diagonal* and *np.diag* now preserve subtypes +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +There was inconsistent behavior between *x.ravel()* and *np.ravel(x)*, as well +as between *x.diagonal()* and *np.diagonal(x)*. For example, if *x* was a +matrix, then *x.ravel()* returned a matrix, while *np.ravel(x)* returned an +ndarray. In the case of matrices, the returned value will have different +dimensions and that may lead to problems in using the result. + New Features ============ diff --git a/numpy/core/arrayprint.py b/numpy/core/arrayprint.py index db491e6f5..125d57672 100644 --- a/numpy/core/arrayprint.py +++ b/numpy/core/arrayprint.py @@ -21,6 +21,7 @@ from . import numerictypes as _nt from .umath import maximum, minimum, absolute, not_equal, isnan, isinf from .multiarray import format_longfloat, datetime_as_string, datetime_data from .fromnumeric import ravel +from .numeric import asarray if sys.version_info[0] >= 3: _MAXINT = sys.maxsize @@ -250,7 +251,7 @@ def _array2string(a, max_line_width, precision, suppress_small, separator=' ', data = _leading_trailing(a) else: summary_insert = "" - data = ravel(a) + data = ravel(asarray(a)) formatdict = {'bool' : _boolFormatter, 'int' : IntegerFormat(data), diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index 321deb014..2dc270a28 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -1340,6 +1340,10 @@ def ravel(a, order='C'): A 1-D array, containing the elements of the input, is returned. A copy is made only if needed. + As of NumPy 1.10, the returned array will have the same type as the input + array. (for example, a masked array will be returned for a masked array + input) + Parameters ---------- a : array_like @@ -1361,8 +1365,9 @@ def ravel(a, order='C'): Returns ------- - 1d_array : ndarray - Output of the same dtype as `a`, and of shape ``(a.size,)``. + y : array_like + Array of the same type as `a`, and of shape ``(a.size,)`` + or ``(1, a.size)`` for matrices. See Also -------- @@ -1420,7 +1425,7 @@ def ravel(a, order='C'): array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) """ - return asarray(a).ravel(order) + return asanyarray(a).ravel(order) def nonzero(a): diff --git a/numpy/ma/core.py b/numpy/ma/core.py index 9ca9136dd..bbaaaa3f1 100644 --- a/numpy/ma/core.py +++ b/numpy/ma/core.py @@ -4041,10 +4041,26 @@ class MaskedArray(ndarray): #............................................ flatten = _arraymethod('flatten') # - def ravel(self): + def ravel(self, order='C'): """ Returns a 1D version of self, as a view. + Parameters + ---------- + order : {'C', 'F', 'A', 'K'}, optional + The elements of `a` 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. + Returns ------- MaskedArray @@ -4062,10 +4078,10 @@ class MaskedArray(ndarray): [1 -- 3 -- 5 -- 7 -- 9] """ - r = ndarray.ravel(self._data).view(type(self)) + r = ndarray.ravel(self._data, order=order).view(type(self)) r._update_from(self) if self._mask is not nomask: - r._mask = ndarray.ravel(self._mask).reshape(r.shape) + r._mask = ndarray.ravel(self._mask, order=order).reshape(r.shape) else: r._mask = nomask return r diff --git a/numpy/ma/tests/test_core.py b/numpy/ma/tests/test_core.py index 4ac3465aa..a2ecccb40 100644 --- a/numpy/ma/tests/test_core.py +++ b/numpy/ma/tests/test_core.py @@ -2325,6 +2325,9 @@ class TestMaskedArrayMethods(TestCase): assert_equal(ar._mask, [0, 0, 0, 0]) assert_equal(ar._data, [1, 2, 3, 4]) assert_equal(ar.fill_value, -99) + # Test index ordering + assert_equal(a.ravel(order='C'), [1, 2, 3, 4]) + assert_equal(a.ravel(order='F'), [1, 3, 2, 4]) def test_reshape(self): # Tests reshape diff --git a/numpy/matrixlib/tests/test_defmatrix.py b/numpy/matrixlib/tests/test_defmatrix.py index d2a89bd51..93843c55c 100644 --- a/numpy/matrixlib/tests/test_defmatrix.py +++ b/numpy/matrixlib/tests/test_defmatrix.py @@ -409,7 +409,7 @@ class TestShape(TestCase): def test_numpy_ravel(self): assert_equal(np.ravel(self.a).shape, (2,)) - assert_equal(np.ravel(self.m).shape, (2,)) + assert_equal(np.ravel(self.m).shape, (1, 2)) def test_member_ravel(self): assert_equal(self.a.ravel().shape, (2,)) @@ -420,12 +420,16 @@ class TestShape(TestCase): 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]) + x = array([[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]) + x = matrix([[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]]) |