summaryrefslogtreecommitdiff
path: root/numpy
diff options
context:
space:
mode:
Diffstat (limited to 'numpy')
-rw-r--r--numpy/core/src/multiarray/common.c2
-rw-r--r--numpy/core/src/umath/loops.c.src14
-rw-r--r--numpy/core/tests/test_regression.py6
-rw-r--r--numpy/doc/byteswapping.py20
-rw-r--r--numpy/f2py/src/fortranobject.c2
-rw-r--r--numpy/random/mtrand/mtrand.pyx4
6 files changed, 24 insertions, 24 deletions
diff --git a/numpy/core/src/multiarray/common.c b/numpy/core/src/multiarray/common.c
index addb67732..52694d491 100644
--- a/numpy/core/src/multiarray/common.c
+++ b/numpy/core/src/multiarray/common.c
@@ -343,7 +343,7 @@ PyArray_DTypeFromObjectHelper(PyObject *obj, int maxdims,
typestr = PyDict_GetItemString(ip, "typestr");
#if defined(NPY_PY3K)
/* Allow unicode type strings */
- if (PyUnicode_Check(typestr)) {
+ if (typestr && PyUnicode_Check(typestr)) {
tmp = PyUnicode_AsASCIIString(typestr);
typestr = tmp;
}
diff --git a/numpy/core/src/umath/loops.c.src b/numpy/core/src/umath/loops.c.src
index abfd883f7..1017bb94a 100644
--- a/numpy/core/src/umath/loops.c.src
+++ b/numpy/core/src/umath/loops.c.src
@@ -1027,13 +1027,10 @@ NPY_NO_EXPORT void
* #c = u,u,u,ul,ull#
*/
-NPY_NO_EXPORT void
+NPY_NO_EXPORT NPY_GCC_OPT_3 void
@TYPE@_absolute(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(func))
{
- UNARY_LOOP {
- const @type@ in1 = *(@type@ *)ip1;
- *((@type@ *)op1) = in1;
- }
+ UNARY_LOOP_FAST(@type@, @type@, *out = in);
}
NPY_NO_EXPORT NPY_GCC_OPT_3 void
@@ -2231,13 +2228,10 @@ HALF_conjugate(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNU
}
}
-NPY_NO_EXPORT void
+NPY_NO_EXPORT NPY_GCC_OPT_3 void
HALF_absolute(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(func))
{
- UNARY_LOOP {
- const npy_half in1 = *(npy_half *)ip1;
- *((npy_half *)op1) = in1&0x7fffu;
- }
+ UNARY_LOOP_FAST(npy_half, npy_half, *out = in&0x7fffu);
}
NPY_NO_EXPORT void
diff --git a/numpy/core/tests/test_regression.py b/numpy/core/tests/test_regression.py
index 1ba0cda21..4b551d8aa 100644
--- a/numpy/core/tests/test_regression.py
+++ b/numpy/core/tests/test_regression.py
@@ -2448,3 +2448,9 @@ class TestRegression(object):
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
dumped = pickle.dumps(arr, protocol=proto)
assert_equal(pickle.loads(dumped), arr)
+
+ def test_bad_array_interface(self):
+ class T(object):
+ __array_interface__ = {}
+
+ np.array([T()])
diff --git a/numpy/doc/byteswapping.py b/numpy/doc/byteswapping.py
index f9491ed43..7a749c8d5 100644
--- a/numpy/doc/byteswapping.py
+++ b/numpy/doc/byteswapping.py
@@ -31,16 +31,16 @@ Let's say the two integers were in fact 1 and 770. Because 770 = 256 *
3 + 2, the 4 bytes in memory would contain respectively: 0, 1, 3, 2.
The bytes I have loaded from the file would have these contents:
->>> big_end_str = chr(0) + chr(1) + chr(3) + chr(2)
->>> big_end_str
-'\\x00\\x01\\x03\\x02'
+>>> big_end_buffer = bytearray([0,1,3,2])
+>>> big_end_buffer
+bytearray(b'\\x00\\x01\\x03\\x02')
We might want to use an ``ndarray`` to access these integers. In that
case, we can create an array around this memory, and tell numpy that
there are two integers, and that they are 16 bit and big-endian:
>>> import numpy as np
->>> big_end_arr = np.ndarray(shape=(2,),dtype='>i2', buffer=big_end_str)
+>>> big_end_arr = np.ndarray(shape=(2,),dtype='>i2', buffer=big_end_buffer)
>>> big_end_arr[0]
1
>>> big_end_arr[1]
@@ -53,7 +53,7 @@ integer, the dtype string would be ``<u4``.
In fact, why don't we try that?
->>> little_end_u4 = np.ndarray(shape=(1,),dtype='<u4', buffer=big_end_str)
+>>> little_end_u4 = np.ndarray(shape=(1,),dtype='<u4', buffer=big_end_buffer)
>>> little_end_u4[0] == 1 * 256**1 + 3 * 256**2 + 2 * 256**3
True
@@ -97,7 +97,7 @@ Data and dtype endianness don't match, change dtype to match data
We make something where they don't match:
->>> wrong_end_dtype_arr = np.ndarray(shape=(2,),dtype='<i2', buffer=big_end_str)
+>>> wrong_end_dtype_arr = np.ndarray(shape=(2,),dtype='<i2', buffer=big_end_buffer)
>>> wrong_end_dtype_arr[0]
256
@@ -110,7 +110,7 @@ the correct endianness:
Note the array has not changed in memory:
->>> fixed_end_dtype_arr.tobytes() == big_end_str
+>>> fixed_end_dtype_arr.tobytes() == big_end_buffer
True
Data and type endianness don't match, change data to match dtype
@@ -126,7 +126,7 @@ that needs a certain byte ordering.
Now the array *has* changed in memory:
->>> fixed_end_mem_arr.tobytes() == big_end_str
+>>> fixed_end_mem_arr.tobytes() == big_end_buffer
False
Data and dtype endianness match, swap data and dtype
@@ -140,7 +140,7 @@ the previous operations:
>>> swapped_end_arr = big_end_arr.byteswap().newbyteorder()
>>> swapped_end_arr[0]
1
->>> swapped_end_arr.tobytes() == big_end_str
+>>> swapped_end_arr.tobytes() == big_end_buffer
False
An easier way of casting the data to a specific dtype and byte ordering
@@ -149,7 +149,7 @@ can be achieved with the ndarray astype method:
>>> swapped_end_arr = big_end_arr.astype('<i2')
>>> swapped_end_arr[0]
1
->>> swapped_end_arr.tobytes() == big_end_str
+>>> swapped_end_arr.tobytes() == big_end_buffer
False
"""
diff --git a/numpy/f2py/src/fortranobject.c b/numpy/f2py/src/fortranobject.c
index 78b06f066..4a981bf55 100644
--- a/numpy/f2py/src/fortranobject.c
+++ b/numpy/f2py/src/fortranobject.c
@@ -135,7 +135,7 @@ format_def(char *buf, Py_ssize_t size, FortranDataDef def)
if (def.data == NULL) {
static const char notalloc[] = ", not allocated";
- if (size < sizeof(notalloc)) {
+ if ((size_t) size < sizeof(notalloc)) {
return -1;
}
memcpy(p, notalloc, sizeof(notalloc));
diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx
index 24826d52d..c97c166aa 100644
--- a/numpy/random/mtrand/mtrand.pyx
+++ b/numpy/random/mtrand/mtrand.pyx
@@ -3779,8 +3779,8 @@ cdef class RandomState:
product p*n <=5, where p = population proportion estimate, and n =
number of samples, in which case the binomial distribution is used
instead. For example, a sample of 15 people shows 4 who are left
- handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,
- so the binomial distribution should be used in this case.
+ handed, and 11 who are right handed. Then p = 4/15 = 27%. Since
+ 0.27*15 = 4, the binomial distribution should be used in this case.
References
----------
View Lorry Controller Status