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-rw-r--r--numpy/lib/function_base.py146
-rw-r--r--numpy/lib/index_tricks.py2
-rw-r--r--numpy/lib/npyio.py39
-rw-r--r--numpy/lib/shape_base.py6
-rw-r--r--numpy/lib/stride_tricks.py4
-rw-r--r--numpy/lib/tests/test_function_base.py77
-rw-r--r--numpy/lib/tests/test_recfunctions.py30
-rw-r--r--numpy/lib/tests/test_stride_tricks.py5
-rw-r--r--numpy/lib/utils.py1
9 files changed, 239 insertions, 71 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index d22e8c047..3826715e1 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -18,7 +18,7 @@ from numpy.core.umath import (
mod, exp, log10
)
from numpy.core.fromnumeric import (
- ravel, nonzero, sort, partition, mean
+ ravel, nonzero, sort, partition, mean, any, sum
)
from numpy.core.numerictypes import typecodes, number
from numpy.lib.twodim_base import diag
@@ -776,6 +776,7 @@ def select(condlist, choicelist, default=0):
# Now that the dtype is known, handle the deprecated select([], []) case
if len(condlist) == 0:
+ # 2014-02-24, 1.9
warnings.warn("select with an empty condition list is not possible"
"and will be deprecated",
DeprecationWarning)
@@ -809,6 +810,7 @@ def select(condlist, choicelist, default=0):
'invalid entry in choicelist: should be boolean ndarray')
if deprecated_ints:
+ # 2014-02-24, 1.9
msg = "select condlists containing integer ndarrays is deprecated " \
"and will be removed in the future. Use `.astype(bool)` to " \
"convert to bools."
@@ -1829,9 +1831,9 @@ class vectorize(object):
return _res
-def cov(m, y=None, rowvar=1, bias=0, ddof=None):
+def cov(m, y=None, rowvar=1, bias=0, ddof=None, fweights=None, aweights=None):
"""
- Estimate a covariance matrix, given data.
+ Estimate a covariance matrix, given data and weights.
Covariance indicates the level to which two variables vary together.
If we examine N-dimensional samples, :math:`X = [x_1, x_2, ... x_N]^T`,
@@ -1839,6 +1841,8 @@ def cov(m, y=None, rowvar=1, bias=0, ddof=None):
:math:`x_i` and :math:`x_j`. The element :math:`C_{ii}` is the variance
of :math:`x_i`.
+ See the notes for an outline of the algorithm.
+
Parameters
----------
m : array_like
@@ -1846,23 +1850,35 @@ def cov(m, y=None, rowvar=1, bias=0, ddof=None):
Each row of `m` represents a variable, and each column a single
observation of all those variables. Also see `rowvar` below.
y : array_like, optional
- An additional set of variables and observations. `y` has the same
- form as that of `m`.
+ An additional set of variables and observations. `y` has the same form
+ as that of `m`.
rowvar : int, optional
If `rowvar` is non-zero (default), then each row represents a
variable, with observations in the columns. Otherwise, the relationship
is transposed: each column represents a variable, while the rows
contain observations.
bias : int, optional
- Default normalization is by ``(N - 1)``, where ``N`` is the number of
- observations given (unbiased estimate). If `bias` is 1, then
- normalization is by ``N``. These values can be overridden by using
- the keyword ``ddof`` in numpy versions >= 1.5.
+ Default normalization is by ``(N - 1)``, where ``N`` corresponds to the
+ number of observations given (unbiased estimate). If `bias` is 1, then
+ normalization is by ``N``. These values can be overridden by using the
+ keyword ``ddof`` in numpy versions >= 1.5.
ddof : int, optional
.. versionadded:: 1.5
- If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is
- the number of observations; this overrides the value implied by
- ``bias``. The default value is ``None``.
+ If not ``None`` the default value implied by `bias` is overridden.
+ Note that ``ddof=1`` will return the unbiased estimate, even if both
+ `fweights` and `aweights` are specified, and ``ddof=0`` will return
+ the simple average. See the notes for the details. The default value
+ is ``None``.
+ fweights : array_like, int, optional
+ .. versionadded:: 1.10
+ 1-D array of integer freguency weights; the number of times each
+ observation vector should be repeated.
+ aweights : array_like, optional
+ .. versionadded:: 1.10
+ 1-D array of observation vector weights. These relative weights are
+ typically large for observations considered "important" and smaller for
+ observations considered less "important". If ``ddof=0`` the array of
+ weights can be used to assign probabilities to observation vectors.
Returns
-------
@@ -1873,6 +1889,22 @@ def cov(m, y=None, rowvar=1, bias=0, ddof=None):
--------
corrcoef : Normalized covariance matrix
+ Notes
+ -----
+ Assume that the observations are in the columns of the observation
+ array `m` and let ``f = fweights`` and ``a = aweights`` for brevity. The
+ steps to compute the weighted covariance are as follows::
+
+ >>> w = f * a
+ >>> v1 = np.sum(w)
+ >>> v2 = np.sum(w * a)
+ >>> m -= np.sum(m * w, axis=1, keepdims=True) / v1
+ >>> cov = np.dot(m * w, m.T) * v1 / (v1**2 - ddof * v2)
+
+ Note that when ``a == 1``, the normalization factor
+ ``v1 / (v1**2 - ddof * v2)`` goes over to ``1 / (np.sum(f) - ddof)``
+ as it should.
+
Examples
--------
Consider two variables, :math:`x_0` and :math:`x_1`, which
@@ -1921,36 +1953,78 @@ def cov(m, y=None, rowvar=1, bias=0, ddof=None):
y = np.asarray(y)
dtype = np.result_type(m, y, np.float64)
X = array(m, ndmin=2, dtype=dtype)
+ if rowvar == 0 and X.shape[0] != 1:
+ X = X.T
+ if X.shape[0] == 0:
+ return np.array([]).reshape(0, 0)
+ if y is not None:
+ y = array(y, copy=False, ndmin=2, dtype=dtype)
+ if rowvar == 0 and y.shape[0] != 1:
+ y = y.T
+ X = np.vstack((X, y))
- if X.shape[0] == 1:
- rowvar = 1
- if rowvar:
- N = X.shape[1]
- axis = 0
- else:
- N = X.shape[0]
- axis = 1
-
- # check ddof
if ddof is None:
if bias == 0:
ddof = 1
else:
ddof = 0
- fact = float(N - ddof)
+
+ # Get the product of frequencies and weights
+ w = None
+ if fweights is not None:
+ fweights = np.asarray(fweights, dtype=np.float)
+ if not np.all(fweights == np.around(fweights)):
+ raise TypeError(
+ "fweights must be integer")
+ if fweights.ndim > 1:
+ raise RuntimeError(
+ "cannot handle multidimensional fweights")
+ if fweights.shape[0] != X.shape[1]:
+ raise RuntimeError(
+ "incompatible numbers of samples and fweights")
+ if any(fweights < 0):
+ raise ValueError(
+ "fweights cannot be negative")
+ w = fweights
+ if aweights is not None:
+ aweights = np.asarray(aweights, dtype=np.float)
+ if aweights.ndim > 1:
+ raise RuntimeError(
+ "cannot handle multidimensional aweights")
+ if aweights.shape[0] != X.shape[1]:
+ raise RuntimeError(
+ "incompatible numbers of samples and aweights")
+ if any(aweights < 0):
+ raise ValueError(
+ "aweights cannot be negative")
+ if w is None:
+ w = aweights
+ else:
+ w *= aweights
+
+ avg, w_sum = average(X, axis=1, weights=w, returned=True)
+ w_sum = w_sum[0]
+
+ # Determine the normalization
+ if w is None:
+ fact = float(X.shape[1] - ddof)
+ elif ddof == 0:
+ fact = w_sum
+ elif aweights is None:
+ fact = w_sum - ddof
+ else:
+ fact = w_sum - ddof*sum(w*aweights)/w_sum
+
if fact <= 0:
warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning)
fact = 0.0
- if y is not None:
- y = array(y, copy=False, ndmin=2, dtype=dtype)
- X = concatenate((X, y), axis)
-
- X -= X.mean(axis=1-axis, keepdims=True)
- if not rowvar:
- return (dot(X.T, X.conj()) / fact).squeeze()
+ X -= avg[:, None]
+ if w is None:
+ X_T = X.T
else:
- return (dot(X, X.T.conj()) / fact).squeeze()
+ X_T = (X*w).T
+ return (dot(X, X_T.conj())/fact).squeeze()
def corrcoef(x, y=None, rowvar=1, bias=np._NoValue, ddof=np._NoValue):
@@ -2003,6 +2077,7 @@ def corrcoef(x, y=None, rowvar=1, bias=np._NoValue, ddof=np._NoValue):
safely ignored in this and previous versions of numpy.
"""
if bias is not np._NoValue or ddof is not np._NoValue:
+ # 2015-03-15, 1.10
warnings.warn('bias and ddof have no affect and are deprecated',
DeprecationWarning)
c = cov(x, y, rowvar)
@@ -2248,7 +2323,7 @@ def hanning(M):
.. math:: w(n) = 0.5 - 0.5cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
\\qquad 0 \\leq n \\leq M-1
- The Hanning was named for Julius van Hann, an Austrian meteorologist.
+ The Hanning was named for Julius von Hann, an Austrian meteorologist.
It is also known as the Cosine Bell. Some authors prefer that it be
called a Hann window, to help avoid confusion with the very similar
Hamming window.
@@ -3554,6 +3629,7 @@ def delete(arr, obj, axis=None):
ndim = arr.ndim
axis = ndim - 1
if ndim == 0:
+ # 2013-09-24, 1.9
warnings.warn(
"in the future the special handling of scalars will be removed "
"from delete and raise an error", DeprecationWarning)
@@ -3648,6 +3724,7 @@ def delete(arr, obj, axis=None):
if not np.can_cast(obj, intp, 'same_kind'):
# obj.size = 1 special case always failed and would just
# give superfluous warnings.
+ # 2013-09-24, 1.9
warnings.warn(
"using a non-integer array as obj in delete will result in an "
"error in the future", DeprecationWarning)
@@ -3657,6 +3734,7 @@ def delete(arr, obj, axis=None):
# Test if there are out of bound indices, this is deprecated
inside_bounds = (obj < N) & (obj >= -N)
if not inside_bounds.all():
+ # 2013-09-24, 1.9
warnings.warn(
"in the future out of bounds indices will raise an error "
"instead of being ignored by `numpy.delete`.",
@@ -3715,7 +3793,7 @@ def insert(arr, obj, values, axis=None):
See Also
--------
append : Append elements at the end of an array.
- concatenate : Join a sequence of arrays together.
+ concatenate : Join a sequence of arrays along an existing axis.
delete : Delete elements from an array.
Notes
@@ -3789,6 +3867,7 @@ def insert(arr, obj, values, axis=None):
if (axis < 0):
axis += ndim
if (ndim == 0):
+ # 2013-09-24, 1.9
warnings.warn(
"in the future the special handling of scalars will be removed "
"from insert and raise an error", DeprecationWarning)
@@ -3860,6 +3939,7 @@ def insert(arr, obj, values, axis=None):
indices = indices.astype(intp)
if not np.can_cast(indices, intp, 'same_kind'):
+ # 2013-09-24, 1.9
warnings.warn(
"using a non-integer array as obj in insert will result in an "
"error in the future", DeprecationWarning)
diff --git a/numpy/lib/index_tricks.py b/numpy/lib/index_tricks.py
index 752407f18..030261dee 100644
--- a/numpy/lib/index_tricks.py
+++ b/numpy/lib/index_tricks.py
@@ -404,7 +404,7 @@ class RClass(AxisConcatenator):
See Also
--------
- concatenate : Join a sequence of arrays together.
+ concatenate : Join a sequence of arrays along an existing axis.
c_ : Translates slice objects to concatenation along the second axis.
Examples
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py
index ec89397a0..078c6d7ca 100644
--- a/numpy/lib/npyio.py
+++ b/numpy/lib/npyio.py
@@ -1301,13 +1301,12 @@ def fromregex(file, regexp, dtype):
def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
- skiprows=0, skip_header=0, skip_footer=0, converters=None,
- missing='', missing_values=None, filling_values=None,
- usecols=None, names=None,
- excludelist=None, deletechars=None, replace_space='_',
- autostrip=False, case_sensitive=True, defaultfmt="f%i",
- unpack=None, usemask=False, loose=True, invalid_raise=True,
- max_rows=None):
+ skip_header=0, skip_footer=0, converters=None,
+ missing_values=None, filling_values=None, usecols=None,
+ names=None, excludelist=None, deletechars=None,
+ replace_space='_', autostrip=False, case_sensitive=True,
+ defaultfmt="f%i", unpack=None, usemask=False, loose=True,
+ invalid_raise=True, max_rows=None):
"""
Load data from a text file, with missing values handled as specified.
@@ -1332,8 +1331,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
whitespaces act as delimiter. An integer or sequence of integers
can also be provided as width(s) of each field.
skiprows : int, optional
- `skiprows` was deprecated in numpy 1.5, and will be removed in
- numpy 2.0. Please use `skip_header` instead.
+ `skiprows` was removed in numpy 1.10. Please use `skip_header` instead.
skip_header : int, optional
The number of lines to skip at the beginning of the file.
skip_footer : int, optional
@@ -1343,8 +1341,8 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
The converters can also be used to provide a default value
for missing data: ``converters = {3: lambda s: float(s or 0)}``.
missing : variable, optional
- `missing` was deprecated in numpy 1.5, and will be removed in
- numpy 2.0. Please use `missing_values` instead.
+ `missing` was removed in numpy 1.10. Please use `missing_values`
+ instead.
missing_values : variable, optional
The set of strings corresponding to missing data.
filling_values : variable, optional
@@ -1475,8 +1473,6 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
comments = asbytes(comments)
if isinstance(delimiter, unicode):
delimiter = asbytes(delimiter)
- if isinstance(missing, unicode):
- missing = asbytes(missing)
if isinstance(missing_values, (unicode, list, tuple)):
missing_values = asbytes_nested(missing_values)
@@ -1513,13 +1509,6 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
case_sensitive=case_sensitive,
replace_space=replace_space)
- # Get the first valid lines after the first skiprows ones ..
- if skiprows:
- warnings.warn(
- "The use of `skiprows` is deprecated, it will be removed in "
- "numpy 2.0.\nPlease use `skip_header` instead.",
- DeprecationWarning)
- skip_header = skiprows
# Skip the first `skip_header` rows
for i in range(skip_header):
next(fhd)
@@ -1648,16 +1637,6 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
for entry in missing_values:
entry.extend([str(user_missing_values)])
- # Process the deprecated `missing`
- if missing != asbytes(''):
- warnings.warn(
- "The use of `missing` is deprecated, it will be removed in "
- "Numpy 2.0.\nPlease use `missing_values` instead.",
- DeprecationWarning)
- values = [str(_) for _ in missing.split(asbytes(","))]
- for entry in missing_values:
- entry.extend(values)
-
# Process the filling_values ...............................
# Rename the input for convenience
user_filling_values = filling_values
diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py
index 011434dda..280765df8 100644
--- a/numpy/lib/shape_base.py
+++ b/numpy/lib/shape_base.py
@@ -338,9 +338,10 @@ def dstack(tup):
See Also
--------
+ stack : Join a sequence of arrays along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
- concatenate : Join arrays.
+ concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.
Notes
@@ -477,7 +478,8 @@ def split(ary,indices_or_sections,axis=0):
hsplit : Split array into multiple sub-arrays horizontally (column-wise).
vsplit : Split array into multiple sub-arrays vertically (row wise).
dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).
- concatenate : Join arrays together.
+ concatenate : Join a sequence of arrays along an existing axis.
+ stack : Join a sequence of arrays along a new axis.
hstack : Stack arrays in sequence horizontally (column wise).
vstack : Stack arrays in sequence vertically (row wise).
dstack : Stack arrays in sequence depth wise (along third dimension).
diff --git a/numpy/lib/stride_tricks.py b/numpy/lib/stride_tricks.py
index c05d56e2f..416776ff4 100644
--- a/numpy/lib/stride_tricks.py
+++ b/numpy/lib/stride_tricks.py
@@ -126,6 +126,10 @@ def _broadcast_shape(*args):
b = np.broadcast(*args[:32])
# unfortunately, it cannot handle 32 or more arguments directly
for pos in range(32, len(args), 31):
+ # ironically, np.broadcast does not properly handle np.broadcast
+ # objects (it treats them as scalars)
+ # use broadcasting to avoid allocating the full array
+ b = broadcast_to(0, b.shape)
b = np.broadcast(b, *args[pos:(pos + 31)])
return b.shape
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index 12f9d414b..ad71fd3fa 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -1391,9 +1391,20 @@ class TestCorrCoef(TestCase):
class TestCov(TestCase):
+ x1 = np.array([[0, 2], [1, 1], [2, 0]]).T
+ res1 = np.array([[1., -1.], [-1., 1.]])
+ x2 = np.array([0.0, 1.0, 2.0], ndmin=2)
+ frequencies = np.array([1, 4, 1])
+ x2_repeats = np.array([[0.0], [1.0], [1.0], [1.0], [1.0], [2.0]]).T
+ res2 = np.array([[0.4, -0.4], [-0.4, 0.4]])
+ unit_frequencies = np.ones(3, dtype=np.integer)
+ weights = np.array([1.0, 4.0, 1.0])
+ res3 = np.array([[2./3., -2./3.], [-2./3., 2./3.]])
+ unit_weights = np.ones(3)
+ x3 = np.array([0.3942, 0.5969, 0.7730, 0.9918, 0.7964])
+
def test_basic(self):
- x = np.array([[0, 2], [1, 1], [2, 0]]).T
- assert_allclose(cov(x), np.array([[1., -1.], [-1., 1.]]))
+ assert_allclose(cov(self.x1), self.res1)
def test_complex(self):
x = np.array([[1, 2, 3], [1j, 2j, 3j]])
@@ -1414,11 +1425,67 @@ class TestCov(TestCase):
np.array([[np.nan, np.nan], [np.nan, np.nan]]))
def test_wrong_ddof(self):
- x = np.array([[0, 2], [1, 1], [2, 0]]).T
with warnings.catch_warnings(record=True):
warnings.simplefilter('always', RuntimeWarning)
- assert_array_equal(cov(x, ddof=5),
- np.array([[np.inf, -np.inf], [-np.inf, np.inf]]))
+ assert_array_equal(cov(self.x1, ddof=5),
+ np.array([[np.inf, -np.inf],
+ [-np.inf, np.inf]]))
+
+ def test_1D_rowvar(self):
+ assert_allclose(cov(self.x3), cov(self.x3, rowvar=0))
+ y = np.array([0.0780, 0.3107, 0.2111, 0.0334, 0.8501])
+ assert_allclose(cov(self.x3, y), cov(self.x3, y, rowvar=0))
+
+ def test_1D_variance(self):
+ assert_allclose(cov(self.x3, ddof=1), np.var(self.x3, ddof=1))
+
+ def test_fweights(self):
+ assert_allclose(cov(self.x2, fweights=self.frequencies),
+ cov(self.x2_repeats))
+ assert_allclose(cov(self.x1, fweights=self.frequencies),
+ self.res2)
+ assert_allclose(cov(self.x1, fweights=self.unit_frequencies),
+ self.res1)
+ nonint = self.frequencies + 0.5
+ assert_raises(TypeError, cov, self.x1, fweights=nonint)
+ f = np.ones((2, 3), dtype=np.integer)
+ assert_raises(RuntimeError, cov, self.x1, fweights=f)
+ f = np.ones(2, dtype=np.integer)
+ assert_raises(RuntimeError, cov, self.x1, fweights=f)
+ f = -1*np.ones(3, dtype=np.integer)
+ assert_raises(ValueError, cov, self.x1, fweights=f)
+
+ def test_aweights(self):
+ assert_allclose(cov(self.x1, aweights=self.weights), self.res3)
+ assert_allclose(cov(self.x1, aweights=3.0*self.weights),
+ cov(self.x1, aweights=self.weights))
+ assert_allclose(cov(self.x1, aweights=self.unit_weights), self.res1)
+ w = np.ones((2, 3))
+ assert_raises(RuntimeError, cov, self.x1, aweights=w)
+ w = np.ones(2)
+ assert_raises(RuntimeError, cov, self.x1, aweights=w)
+ w = -1.0*np.ones(3)
+ assert_raises(ValueError, cov, self.x1, aweights=w)
+
+ def test_unit_fweights_and_aweights(self):
+ assert_allclose(cov(self.x2, fweights=self.frequencies,
+ aweights=self.unit_weights),
+ cov(self.x2_repeats))
+ assert_allclose(cov(self.x1, fweights=self.frequencies,
+ aweights=self.unit_weights),
+ self.res2)
+ assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
+ aweights=self.unit_weights),
+ self.res1)
+ assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
+ aweights=self.weights),
+ self.res3)
+ assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
+ aweights=3.0*self.weights),
+ cov(self.x1, aweights=self.weights))
+ assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
+ aweights=self.unit_weights),
+ self.res1)
class Test_I0(TestCase):
diff --git a/numpy/lib/tests/test_recfunctions.py b/numpy/lib/tests/test_recfunctions.py
index 51a2077eb..13e75cbd0 100644
--- a/numpy/lib/tests/test_recfunctions.py
+++ b/numpy/lib/tests/test_recfunctions.py
@@ -700,6 +700,36 @@ class TestJoinBy2(TestCase):
assert_equal(test.dtype, control.dtype)
assert_equal(test, control)
+class TestAppendFieldsObj(TestCase):
+ """
+ Test append_fields with arrays containing objects
+ """
+ # https://github.com/numpy/numpy/issues/2346
+
+ def setUp(self):
+ from datetime import date
+ self.data = dict(obj=date(2000, 1, 1))
+
+ def test_append_to_objects(self):
+ "Test append_fields when the base array contains objects"
+ obj = self.data['obj']
+ x = np.array([(obj, 1.), (obj, 2.)],
+ dtype=[('A', object), ('B', float)])
+ y = np.array([10, 20], dtype=int)
+ test = append_fields(x, 'C', data=y, usemask=False)
+ control = np.array([(obj, 1.0, 10), (obj, 2.0, 20)],
+ dtype=[('A', object), ('B', float), ('C', int)])
+ assert_equal(test, control)
+
+ def test_append_with_objects(self):
+ "Test append_fields when the appended data contains objects"
+ obj = self.data['obj']
+ x = np.array([(10, 1.), (20, 2.)], dtype=[('A', int), ('B', float)])
+ y = np.array([obj, obj], dtype=object)
+ test = append_fields(x, 'C', data=y, dtypes=object, usemask=False)
+ control = np.array([(10, 1.0, obj), (20, 2.0, obj)],
+ dtype=[('A', int), ('B', float), ('C', object)])
+ assert_equal(test, control)
if __name__ == '__main__':
run_module_suite()
diff --git a/numpy/lib/tests/test_stride_tricks.py b/numpy/lib/tests/test_stride_tricks.py
index e079e0bf4..44112c970 100644
--- a/numpy/lib/tests/test_stride_tricks.py
+++ b/numpy/lib/tests/test_stride_tricks.py
@@ -273,6 +273,11 @@ def test_broadcast_shape():
assert_equal(_broadcast_shape(*([np.ones((1, 2))] * 32)), (1, 2))
assert_equal(_broadcast_shape(*([np.ones((1, 2))] * 100)), (1, 2))
+ # regression tests for gh-5862
+ assert_equal(_broadcast_shape(*([np.ones(2)] * 32 + [1])), (2,))
+ bad_args = [np.ones(2)] * 32 + [np.ones(3)] * 32
+ assert_raises(ValueError, lambda: _broadcast_shape(*bad_args))
+
def test_as_strided():
a = np.array([None])
diff --git a/numpy/lib/utils.py b/numpy/lib/utils.py
index 519d0e9b9..5a92cc316 100644
--- a/numpy/lib/utils.py
+++ b/numpy/lib/utils.py
@@ -1011,6 +1011,7 @@ class SafeEval(object):
"""
def __init__(self):
+ # 2014-10-15, 1.10
warnings.warn("SafeEval is deprecated in 1.10 and will be removed.",
DeprecationWarning)
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