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-rw-r--r--numpy/lib/arraysetops.py24
-rw-r--r--numpy/lib/histograms.py32
-rw-r--r--numpy/lib/nanfunctions.py16
-rw-r--r--numpy/lib/stride_tricks.py2
-rw-r--r--numpy/lib/tests/test__datasource.py4
-rw-r--r--numpy/lib/tests/test_arraysetops.py25
-rw-r--r--numpy/lib/tests/test_histograms.py14
-rw-r--r--numpy/lib/tests/test_index_tricks.py1
-rw-r--r--numpy/lib/tests/test_io.py2
-rw-r--r--numpy/lib/tests/test_stride_tricks.py14
10 files changed, 100 insertions, 34 deletions
diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py
index 5880ea154..d84455a8f 100644
--- a/numpy/lib/arraysetops.py
+++ b/numpy/lib/arraysetops.py
@@ -312,12 +312,12 @@ def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
return_indices : bool
- If True, the indices which correspond to the intersection of the
- two arrays are returned. The first instance of a value is used
- if there are multiple. Default is False.
-
- .. versionadded:: 1.15.0
-
+ If True, the indices which correspond to the intersection of the two
+ arrays are returned. The first instance of a value is used if there are
+ multiple. Default is False.
+
+ .. versionadded:: 1.15.0
+
Returns
-------
intersect1d : ndarray
@@ -326,7 +326,7 @@ def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
The indices of the first occurrences of the common values in `ar1`.
Only provided if `return_indices` is True.
comm2 : ndarray
- The indices of the first occurrences of the common values in `ar2`.
+ The indices of the first occurrences of the common values in `ar2`.
Only provided if `return_indices` is True.
@@ -345,7 +345,7 @@ def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
>>> from functools import reduce
>>> reduce(np.intersect1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2]))
array([3])
-
+
To return the indices of the values common to the input arrays
along with the intersected values:
>>> x = np.array([1, 1, 2, 3, 4])
@@ -355,8 +355,11 @@ def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
(array([0, 2, 4]), array([1, 0, 2]))
>>> xy, x[x_ind], y[y_ind]
(array([1, 2, 4]), array([1, 2, 4]), array([1, 2, 4]))
-
+
"""
+ ar1 = np.asanyarray(ar1)
+ ar2 = np.asanyarray(ar2)
+
if not assume_unique:
if return_indices:
ar1, ind1 = unique(ar1, return_index=True)
@@ -367,7 +370,7 @@ def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
else:
ar1 = ar1.ravel()
ar2 = ar2.ravel()
-
+
aux = np.concatenate((ar1, ar2))
if return_indices:
aux_sort_indices = np.argsort(aux, kind='mergesort')
@@ -389,6 +392,7 @@ def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
else:
return int1d
+
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index 422b356f7..f03f30fb0 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -260,6 +260,32 @@ def _get_outer_edges(a, range):
return first_edge, last_edge
+def _unsigned_subtract(a, b):
+ """
+ Subtract two values where a >= b, and produce an unsigned result
+
+ This is needed when finding the difference between the upper and lower
+ bound of an int16 histogram
+ """
+ # coerce to a single type
+ signed_to_unsigned = {
+ np.byte: np.ubyte,
+ np.short: np.ushort,
+ np.intc: np.uintc,
+ np.int_: np.uint,
+ np.longlong: np.ulonglong
+ }
+ dt = np.result_type(a, b)
+ try:
+ dt = signed_to_unsigned[dt.type]
+ except KeyError:
+ return np.subtract(a, b, dtype=dt)
+ else:
+ # we know the inputs are integers, and we are deliberately casting
+ # signed to unsigned
+ return np.subtract(a, b, casting='unsafe', dtype=dt)
+
+
def _get_bin_edges(a, bins, range, weights):
"""
Computes the bins used internally by `histogram`.
@@ -311,7 +337,7 @@ def _get_bin_edges(a, bins, range, weights):
# Do not call selectors on empty arrays
width = _hist_bin_selectors[bin_name](a)
if width:
- n_equal_bins = int(np.ceil((last_edge - first_edge) / width))
+ n_equal_bins = int(np.ceil(_unsigned_subtract(last_edge, first_edge) / width))
else:
# Width can be zero for some estimators, e.g. FD when
# the IQR of the data is zero.
@@ -703,7 +729,7 @@ def histogram(a, bins=10, range=None, normed=None, weights=None,
n = np.zeros(n_equal_bins, ntype)
# Pre-compute histogram scaling factor
- norm = n_equal_bins / (last_edge - first_edge)
+ norm = n_equal_bins / _unsigned_subtract(last_edge, first_edge)
# We iterate over blocks here for two reasons: the first is that for
# large arrays, it is actually faster (for example for a 10^8 array it
@@ -731,7 +757,7 @@ def histogram(a, bins=10, range=None, normed=None, weights=None,
# Compute the bin indices, and for values that lie exactly on
# last_edge we need to subtract one
- f_indices = (tmp_a - first_edge) * norm
+ f_indices = _unsigned_subtract(tmp_a, first_edge) * norm
indices = f_indices.astype(np.intp)
indices[indices == n_equal_bins] -= 1
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py
index abd2da1a2..8d6b0f139 100644
--- a/numpy/lib/nanfunctions.py
+++ b/numpy/lib/nanfunctions.py
@@ -1178,13 +1178,15 @@ def nanquantile(a, q, axis=None, out=None, overwrite_input=False,
This optional parameter specifies the interpolation method to
use when the desired quantile lies between two data points
``i < j``:
- * linear: ``i + (j - i) * fraction``, where ``fraction``
- is the fractional part of the index surrounded by ``i``
- and ``j``.
- * lower: ``i``.
- * higher: ``j``.
- * nearest: ``i`` or ``j``, whichever is nearest.
- * midpoint: ``(i + j) / 2``.
+
+ * linear: ``i + (j - i) * fraction``, where ``fraction``
+ is the fractional part of the index surrounded by ``i``
+ and ``j``.
+ * lower: ``i``.
+ * higher: ``j``.
+ * nearest: ``i`` or ``j``, whichever is nearest.
+ * midpoint: ``(i + j) / 2``.
+
keepdims : bool, optional
If this is set to True, the axes which are reduced are left in
the result as dimensions with size one. With this option, the
diff --git a/numpy/lib/stride_tricks.py b/numpy/lib/stride_tricks.py
index bc5993802..ca13738c1 100644
--- a/numpy/lib/stride_tricks.py
+++ b/numpy/lib/stride_tricks.py
@@ -242,7 +242,7 @@ def broadcast_arrays(*args, **kwargs):
subok = kwargs.pop('subok', False)
if kwargs:
raise TypeError('broadcast_arrays() got an unexpected keyword '
- 'argument {!r}'.format(kwargs.keys()[0]))
+ 'argument {!r}'.format(list(kwargs.keys())[0]))
args = [np.array(_m, copy=False, subok=subok) for _m in args]
shape = _broadcast_shape(*args)
diff --git a/numpy/lib/tests/test__datasource.py b/numpy/lib/tests/test__datasource.py
index 70fff3bb0..85788941c 100644
--- a/numpy/lib/tests/test__datasource.py
+++ b/numpy/lib/tests/test__datasource.py
@@ -33,14 +33,14 @@ def urlopen_stub(url, data=None):
old_urlopen = None
-def setup():
+def setup_module():
global old_urlopen
old_urlopen = urllib_request.urlopen
urllib_request.urlopen = urlopen_stub
-def teardown():
+def teardown_module():
urllib_request.urlopen = old_urlopen
# A valid website for more robust testing
diff --git a/numpy/lib/tests/test_arraysetops.py b/numpy/lib/tests/test_arraysetops.py
index dace5ade8..c76afb8e5 100644
--- a/numpy/lib/tests/test_arraysetops.py
+++ b/numpy/lib/tests/test_arraysetops.py
@@ -30,19 +30,30 @@ class TestSetOps(object):
ed = np.array([1, 2, 5])
c = intersect1d(a, b)
assert_array_equal(c, ed)
-
assert_array_equal([], intersect1d([], []))
-
+
+ def test_intersect1d_array_like(self):
+ # See gh-11772
+ class Test(object):
+ def __array__(self):
+ return np.arange(3)
+
+ a = Test()
+ res = intersect1d(a, a)
+ assert_array_equal(res, a)
+ res = intersect1d([1, 2, 3], [1, 2, 3])
+ assert_array_equal(res, [1, 2, 3])
+
def test_intersect1d_indices(self):
# unique inputs
- a = np.array([1, 2, 3, 4])
+ a = np.array([1, 2, 3, 4])
b = np.array([2, 1, 4, 6])
c, i1, i2 = intersect1d(a, b, assume_unique=True, return_indices=True)
ee = np.array([1, 2, 4])
assert_array_equal(c, ee)
assert_array_equal(a[i1], ee)
assert_array_equal(b[i2], ee)
-
+
# non-unique inputs
a = np.array([1, 2, 2, 3, 4, 3, 2])
b = np.array([1, 8, 4, 2, 2, 3, 2, 3])
@@ -51,7 +62,7 @@ class TestSetOps(object):
assert_array_equal(c, ef)
assert_array_equal(a[i1], ef)
assert_array_equal(b[i2], ef)
-
+
# non1d, unique inputs
a = np.array([[2, 4, 5, 6], [7, 8, 1, 15]])
b = np.array([[3, 2, 7, 6], [10, 12, 8, 9]])
@@ -61,7 +72,7 @@ class TestSetOps(object):
ea = np.array([2, 6, 7, 8])
assert_array_equal(ea, a[ui1])
assert_array_equal(ea, b[ui2])
-
+
# non1d, not assumed to be uniqueinputs
a = np.array([[2, 4, 5, 6, 6], [4, 7, 8, 7, 2]])
b = np.array([[3, 2, 7, 7], [10, 12, 8, 7]])
@@ -71,7 +82,7 @@ class TestSetOps(object):
ea = np.array([2, 7, 8])
assert_array_equal(ea, a[ui1])
assert_array_equal(ea, b[ui2])
-
+
def test_setxor1d(self):
a = np.array([5, 7, 1, 2])
b = np.array([2, 4, 3, 1, 5])
diff --git a/numpy/lib/tests/test_histograms.py b/numpy/lib/tests/test_histograms.py
index f136b5c81..561f5f938 100644
--- a/numpy/lib/tests/test_histograms.py
+++ b/numpy/lib/tests/test_histograms.py
@@ -310,6 +310,20 @@ class TestHistogram(object):
assert_equal(d_edge.dtype, dates.dtype)
assert_equal(t_edge.dtype, td)
+ def do_signed_overflow_bounds(self, dtype):
+ exponent = 8 * np.dtype(dtype).itemsize - 1
+ arr = np.array([-2**exponent + 4, 2**exponent - 4], dtype=dtype)
+ hist, e = histogram(arr, bins=2)
+ assert_equal(e, [-2**exponent + 4, 0, 2**exponent - 4])
+ assert_equal(hist, [1, 1])
+
+ def test_signed_overflow_bounds(self):
+ self.do_signed_overflow_bounds(np.byte)
+ self.do_signed_overflow_bounds(np.short)
+ self.do_signed_overflow_bounds(np.intc)
+ self.do_signed_overflow_bounds(np.int_)
+ self.do_signed_overflow_bounds(np.longlong)
+
def do_precision_lower_bound(self, float_small, float_large):
eps = np.finfo(float_large).eps
diff --git a/numpy/lib/tests/test_index_tricks.py b/numpy/lib/tests/test_index_tricks.py
index 315251daa..7e9c026e4 100644
--- a/numpy/lib/tests/test_index_tricks.py
+++ b/numpy/lib/tests/test_index_tricks.py
@@ -113,7 +113,6 @@ class TestRavelUnravelIndex(object):
assert_(x.flags.writeable)
assert_(y.flags.writeable)
-
def test_0d(self):
# gh-580
x = np.unravel_index(0, ())
diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py
index f58c9e33d..1f3664d92 100644
--- a/numpy/lib/tests/test_io.py
+++ b/numpy/lib/tests/test_io.py
@@ -348,7 +348,6 @@ class TestSaveTxt(object):
assert_raises(ValueError, np.savetxt, c, np.array(1))
assert_raises(ValueError, np.savetxt, c, np.array([[[1], [2]]]))
-
def test_record(self):
a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
c = BytesIO()
@@ -2025,7 +2024,6 @@ M 33 21.99
assert_equal(test['f0'], 0)
assert_equal(test['f1'], "testNonethe" + utf8.decode("UTF-8"))
-
def test_utf8_file_nodtype_unicode(self):
# bytes encoding with non-latin1 -> unicode upcast
utf8 = u'\u03d6'
diff --git a/numpy/lib/tests/test_stride_tricks.py b/numpy/lib/tests/test_stride_tricks.py
index 3c2ca8b87..b2bd7da3e 100644
--- a/numpy/lib/tests/test_stride_tricks.py
+++ b/numpy/lib/tests/test_stride_tricks.py
@@ -3,7 +3,8 @@ from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.core._rational_tests import rational
from numpy.testing import (
- assert_equal, assert_array_equal, assert_raises, assert_
+ assert_equal, assert_array_equal, assert_raises, assert_,
+ assert_raises_regex
)
from numpy.lib.stride_tricks import (
as_strided, broadcast_arrays, _broadcast_shape, broadcast_to
@@ -57,6 +58,17 @@ def test_same():
assert_array_equal(x, bx)
assert_array_equal(y, by)
+def test_broadcast_kwargs():
+ # ensure that a TypeError is appropriately raised when
+ # np.broadcast_arrays() is called with any keyword
+ # argument other than 'subok'
+ x = np.arange(10)
+ y = np.arange(10)
+
+ with assert_raises_regex(TypeError,
+ r'broadcast_arrays\(\) got an unexpected keyword*'):
+ broadcast_arrays(x, y, dtype='float64')
+
def test_one_off():
x = np.array([[1, 2, 3]])
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