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-rw-r--r--numpy/core/shape_base.py2
-rw-r--r--numpy/distutils/misc_util.py8
-rw-r--r--numpy/lib/info.py6
-rw-r--r--numpy/lib/polynomial.py2
-rw-r--r--numpy/ma/extras.py142
-rw-r--r--numpy/ma/tests/test_extras.py35
6 files changed, 138 insertions, 57 deletions
diff --git a/numpy/core/shape_base.py b/numpy/core/shape_base.py
index 31d7d1c03..2b5110473 100644
--- a/numpy/core/shape_base.py
+++ b/numpy/core/shape_base.py
@@ -146,7 +146,7 @@ def atleast_3d(*arys):
>>> x = np.arange(12.0).reshape(4,3)
>>> np.atleast_3d(x).shape
(4, 3, 1)
- >>> np.atleast_3d(x).base is x
+ >>> np.atleast_3d(x).base is x.base # x is a reshape, so not base itself
True
>>> for arr in np.atleast_3d([1, 2], [[1, 2]], [[[1, 2]]]):
diff --git a/numpy/distutils/misc_util.py b/numpy/distutils/misc_util.py
index 958de1a18..2cd185332 100644
--- a/numpy/distutils/misc_util.py
+++ b/numpy/distutils/misc_util.py
@@ -1512,8 +1512,8 @@ class Configuration(object):
* macros
* include_dirs
* extra_compiler_args
- * extra_f77_compiler_args
- * extra_f90_compiler_args
+ * extra_f77_compile_args
+ * extra_f90_compile_args
* f2py_options
* language
@@ -1565,8 +1565,8 @@ class Configuration(object):
* macros
* include_dirs
* extra_compiler_args
- * extra_f77_compiler_args
- * extra_f90_compiler_args
+ * extra_f77_compile_args
+ * extra_f90_compile_args
* f2py_options
* language
diff --git a/numpy/lib/info.py b/numpy/lib/info.py
index ca1e72397..141df2ace 100644
--- a/numpy/lib/info.py
+++ b/numpy/lib/info.py
@@ -67,9 +67,9 @@ Shape Manipulation
------------------
================ ===================
squeeze Return a with length-one dimensions removed.
-atleast_1d Force arrays to be > 1D
-atleast_2d Force arrays to be > 2D
-atleast_3d Force arrays to be > 3D
+atleast_1d Force arrays to be >= 1D
+atleast_2d Force arrays to be >= 2D
+atleast_3d Force arrays to be >= 3D
vstack Stack arrays vertically (row on row)
hstack Stack arrays horizontally (column on column)
column_stack Stack 1D arrays as columns into 2D array
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py
index d96b8969f..d178ba4b3 100644
--- a/numpy/lib/polynomial.py
+++ b/numpy/lib/polynomial.py
@@ -431,7 +431,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
Returns
-------
- p : ndarray, shape (M,) or (M, K)
+ p : ndarray, shape (deg + 1,) or (deg + 1, K)
Polynomial coefficients, highest power first. If `y` was 2-D, the
coefficients for `k`-th data set are in ``p[:,k]``.
diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py
index cbf7b6cdb..aefdd4e49 100644
--- a/numpy/ma/extras.py
+++ b/numpy/ma/extras.py
@@ -45,9 +45,8 @@ def issequence(seq):
Is seq a sequence (ndarray, list or tuple)?
"""
- if isinstance(seq, (ndarray, tuple, list)):
- return True
- return False
+ return isinstance(seq, (ndarray, tuple, list))
+
def count_masked(arr, axis=None):
"""
@@ -103,6 +102,7 @@ def count_masked(arr, axis=None):
m = getmaskarray(arr)
return m.sum(axis)
+
def masked_all(shape, dtype=float):
"""
Empty masked array with all elements masked.
@@ -154,6 +154,7 @@ def masked_all(shape, dtype=float):
mask=np.ones(shape, make_mask_descr(dtype)))
return a
+
def masked_all_like(arr):
"""
Empty masked array with the properties of an existing array.
@@ -221,6 +222,9 @@ class _fromnxfunction:
as the wrapped NumPy function. The docstring of `newfunc` is adapted from
the wrapped function as well, see `getdoc`.
+ This class should not be used directly. Instead, one of its extensions that
+ provides support for a specific type of input should be used.
+
Parameters
----------
funcname : str
@@ -261,48 +265,100 @@ class _fromnxfunction:
return
def __call__(self, *args, **params):
+ pass
+
+
+class _fromnxfunction_single(_fromnxfunction):
+ """
+ A version of `_fromnxfunction` that is called with a single array
+ argument followed by auxiliary args that are passed verbatim for
+ both the data and mask calls.
+ """
+ def __call__(self, x, *args, **params):
func = getattr(np, self.__name__)
- if len(args) == 1:
- x = args[0]
- if isinstance(x, ndarray):
- _d = func(x.__array__(), **params)
- _m = func(getmaskarray(x), **params)
- return masked_array(_d, mask=_m)
- elif isinstance(x, tuple) or isinstance(x, list):
- _d = func(tuple([np.asarray(a) for a in x]), **params)
- _m = func(tuple([getmaskarray(a) for a in x]), **params)
- return masked_array(_d, mask=_m)
- else:
- _d = func(np.asarray(x), **params)
- _m = func(getmaskarray(x), **params)
- return masked_array(_d, mask=_m)
+ if isinstance(x, ndarray):
+ _d = func(x.__array__(), *args, **params)
+ _m = func(getmaskarray(x), *args, **params)
+ return masked_array(_d, mask=_m)
else:
- arrays = []
- args = list(args)
- while len(args) > 0 and issequence(args[0]):
- arrays.append(args.pop(0))
- res = []
- for x in arrays:
- _d = func(np.asarray(x), *args, **params)
- _m = func(getmaskarray(x), *args, **params)
- res.append(masked_array(_d, mask=_m))
- return res
-
-atleast_1d = _fromnxfunction('atleast_1d')
-atleast_2d = _fromnxfunction('atleast_2d')
-atleast_3d = _fromnxfunction('atleast_3d')
-#atleast_1d = np.atleast_1d
-#atleast_2d = np.atleast_2d
-#atleast_3d = np.atleast_3d
-
-vstack = row_stack = _fromnxfunction('vstack')
-hstack = _fromnxfunction('hstack')
-column_stack = _fromnxfunction('column_stack')
-dstack = _fromnxfunction('dstack')
-
-hsplit = _fromnxfunction('hsplit')
-
-diagflat = _fromnxfunction('diagflat')
+ _d = func(np.asarray(x), *args, **params)
+ _m = func(getmaskarray(x), *args, **params)
+ return masked_array(_d, mask=_m)
+
+
+class _fromnxfunction_seq(_fromnxfunction):
+ """
+ A version of `_fromnxfunction` that is called with a single sequence
+ of arrays followed by auxiliary args that are passed verbatim for
+ both the data and mask calls.
+ """
+ def __call__(self, x, *args, **params):
+ func = getattr(np, self.__name__)
+ _d = func(tuple([np.asarray(a) for a in x]), *args, **params)
+ _m = func(tuple([getmaskarray(a) for a in x]), *args, **params)
+ return masked_array(_d, mask=_m)
+
+
+class _fromnxfunction_args(_fromnxfunction):
+ """
+ A version of `_fromnxfunction` that is called with multiple array
+ arguments. The first non-array-like input marks the beginning of the
+ arguments that are passed verbatim for both the data and mask calls.
+ Array arguments are processed independently and the results are
+ returned in a list. If only one array is found, the return value is
+ just the processed array instead of a list.
+ """
+ def __call__(self, *args, **params):
+ func = getattr(np, self.__name__)
+ arrays = []
+ args = list(args)
+ while len(args) > 0 and issequence(args[0]):
+ arrays.append(args.pop(0))
+ res = []
+ for x in arrays:
+ _d = func(np.asarray(x), *args, **params)
+ _m = func(getmaskarray(x), *args, **params)
+ res.append(masked_array(_d, mask=_m))
+ if len(arrays) == 1:
+ return res[0]
+ return res
+
+
+class _fromnxfunction_allargs(_fromnxfunction):
+ """
+ A version of `_fromnxfunction` that is called with multiple array
+ arguments. Similar to `_fromnxfunction_args` except that all args
+ are converted to arrays even if they are not so already. This makes
+ it possible to process scalars as 1-D arrays. Only keyword arguments
+ are passed through verbatim for the data and mask calls. Arrays
+ arguments are processed independently and the results are returned
+ in a list. If only one arg is present, the return value is just the
+ processed array instead of a list.
+ """
+ def __call__(self, *args, **params):
+ func = getattr(np, self.__name__)
+ res = []
+ for x in args:
+ _d = func(np.asarray(x), **params)
+ _m = func(getmaskarray(x), **params)
+ res.append(masked_array(_d, mask=_m))
+ if len(args) == 1:
+ return res[0]
+ return res
+
+
+atleast_1d = _fromnxfunction_allargs('atleast_1d')
+atleast_2d = _fromnxfunction_allargs('atleast_2d')
+atleast_3d = _fromnxfunction_allargs('atleast_3d')
+
+vstack = row_stack = _fromnxfunction_seq('vstack')
+hstack = _fromnxfunction_seq('hstack')
+column_stack = _fromnxfunction_seq('column_stack')
+dstack = _fromnxfunction_seq('dstack')
+
+hsplit = _fromnxfunction_single('hsplit')
+
+diagflat = _fromnxfunction_single('diagflat')
#####--------------------------------------------------------------------------
diff --git a/numpy/ma/tests/test_extras.py b/numpy/ma/tests/test_extras.py
index bb59aad96..33c4b1922 100644
--- a/numpy/ma/tests/test_extras.py
+++ b/numpy/ma/tests/test_extras.py
@@ -1202,7 +1202,7 @@ class TestArraySetOps(TestCase):
class TestShapeBase(TestCase):
- def test_atleast2d(self):
+ def test_atleast_2d(self):
# Test atleast_2d
a = masked_array([0, 1, 2], mask=[0, 1, 0])
b = atleast_2d(a)
@@ -1210,21 +1210,46 @@ class TestShapeBase(TestCase):
assert_equal(b.mask.shape, b.data.shape)
assert_equal(a.shape, (3,))
assert_equal(a.mask.shape, a.data.shape)
+ assert_equal(b.mask.shape, b.data.shape)
def test_shape_scalar(self):
# the atleast and diagflat function should work with scalars
# GitHub issue #3367
+ # Additionally, the atleast functions should accept multiple scalars
+ # correctly
b = atleast_1d(1.0)
- assert_equal(b.shape, (1, ))
- assert_equal(b.mask.shape, b.data.shape)
+ assert_equal(b.shape, (1,))
+ assert_equal(b.mask.shape, b.shape)
+ assert_equal(b.data.shape, b.shape)
+
+ b = atleast_1d(1.0, 2.0)
+ for a in b:
+ assert_equal(a.shape, (1,))
+ assert_equal(a.mask.shape, a.shape)
+ assert_equal(a.data.shape, a.shape)
b = atleast_2d(1.0)
assert_equal(b.shape, (1, 1))
- assert_equal(b.mask.shape, b.data.shape)
+ assert_equal(b.mask.shape, b.shape)
+ assert_equal(b.data.shape, b.shape)
+
+ b = atleast_2d(1.0, 2.0)
+ for a in b:
+ assert_equal(a.shape, (1, 1))
+ assert_equal(a.mask.shape, a.shape)
+ assert_equal(a.data.shape, a.shape)
b = atleast_3d(1.0)
assert_equal(b.shape, (1, 1, 1))
- assert_equal(b.mask.shape, b.data.shape)
+ assert_equal(b.mask.shape, b.shape)
+ assert_equal(b.data.shape, b.shape)
+
+ b = atleast_3d(1.0, 2.0)
+ for a in b:
+ assert_equal(a.shape, (1, 1, 1))
+ assert_equal(a.mask.shape, a.shape)
+ assert_equal(a.data.shape, a.shape)
+
b = diagflat(1.0)
assert_equal(b.shape, (1, 1))
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