v1.15.0

==========================
NumPy 1.15.0 Release Notes

NumPy 1.15.0 is a release with an unusual number of cleanups, many deprecations
of old functions, and improvements to many existing functions. Please read the
detailed descriptions below to see if you are affected.

For testing, we have switched to pytest as a replacement for the no longer
maintained nose framework. The old nose based interface remains for downstream
projects who may still be using it.

The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are
linked with OpenBLAS v0.3.0, which should fix some of the linalg problems
reported for NumPy 1.14.

Highlights

• NumPy has switched to pytest for testing.
• A new numpy.printoptions context manager.
• Many improvements to the histogram functions.
• Support for unicode field names in python 2.7.
• Improved support for PyPy.
• Fixes and improvements to numpy.einsum.

New functions

• numpy.gcd and numpy.lcm, to compute the greatest common divisor and least
  common multiple.

• numpy.ma.stack, the numpy.stack array-joining function generalized to
  masked arrays.

• numpy.quantile function, an interface to percentile without factors of
  100

• numpy.nanquantile function, an interface to nanpercentile without
  factors of 100

• numpy.printoptions, a context manager that sets print options temporarily
  for the scope of the with block::

  with np.printoptions(precision=2):
  ... print(np.array([2.0]) / 3)
  [0.67]

• numpy.histogram_bin_edges, a function to get the edges of the bins used by a
  histogram without needing to calculate the histogram.

• C functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
  have been added to deal with compiler optimization changing the order of
  operations. See below for details.

Deprecations

• Aliases of builtin pickle functions are deprecated, in favor of their
  unaliased pickle.<func> names:

  • numpy.loads
  • numpy.core.numeric.load
  • numpy.core.numeric.loads
  • numpy.ma.loads, numpy.ma.dumps
  • numpy.ma.load, numpy.ma.dump - these functions already failed on
    python 3 when called with a string.

• Multidimensional indexing with anything but a tuple is deprecated. This means
  that the index list in ind = [slice(None), 0]; arr[ind] should be changed
  to a tuple, e.g., ind = [slice(None), 0]; arr[tuple(ind)] or
  arr[(slice(None), 0)]. That change is necessary to avoid ambiguity in
  expressions such as arr[[[0, 1], [0, 1]]], currently interpreted as
  arr[array([0, 1]), array([0, 1])], that will be interpreted
  as arr[array([[0, 1], [0, 1]])] in the future.

• Imports from the following sub-modules are deprecated, they will be removed
  at some future date.

  • numpy.testing.utils
  • numpy.testing.decorators
  • numpy.testing.nosetester
  • numpy.testing.noseclasses
  • numpy.core.umath_tests

• Giving a generator to numpy.sum is now deprecated. This was undocumented
  behavior, but worked. Previously, it would calculate the sum of the generator
  expression. In the future, it might return a different result. Use
  np.sum(np.from_iter(generator)) or the built-in Python sum instead.

• Users of the C-API should call PyArrayResolveWriteBackIfCopy or
  PyArray_DiscardWritbackIfCopy on any array with the WRITEBACKIFCOPY
  flag set, before deallocating the array. A deprecation warning will be
  emitted if those calls are not used when needed.

• Users of nditer should use the nditer object as a context manager
  anytime one of the iterator operands is writeable, so that numpy can
  manage writeback semantics, or should call it.close(). A
  RuntimeWarning may be emitted otherwise in these cases.

• The normed argument of np.histogram, deprecated long ago in 1.6.0,
  now emits a DeprecationWarning.

Future Changes

• NumPy 1.16 will drop support for Python 3.4.
• NumPy 1.17 will drop support for Python 2.7.

Compatibility notes

Compiled testing modules renamed and made private

The following compiled modules have been renamed and made private:

• umath_tests -> _umath_tests
• test_rational -> _rational_tests
• multiarray_tests -> _multiarray_tests
• struct_ufunc_test -> _struct_ufunc_tests
• operand_flag_tests -> _operand_flag_tests

The umath_tests module is still available for backwards compatibility, but
will be removed in the future.

The NpzFile returned by np.savez is now a collections.abc.Mapping

This means it behaves like a readonly dictionary, and has a new .values()
method and len() implementation.

For python 3, this means that .iteritems(), .iterkeys() have been
deprecated, and .keys() and .items() now return views and not lists.
This is consistent with how the builtin dict type changed between python 2
and python 3.

Under certain conditions, nditer must be used in a context manager

When using an numpy.nditer with the "writeonly" or "readwrite" flags, there
are some circumstances where nditer doesn't actually give you a view of the
writable array. Instead, it gives you a copy, and if you make changes to the
copy, nditer later writes those changes back into your actual array. Currently,
this writeback occurs when the array objects are garbage collected, which makes
this API error-prone on CPython and entirely broken on PyPy. Therefore,
nditer should now be used as a context manager whenever it is used
with writeable arrays, e.g., with np.nditer(...) as it: .... You may also
explicitly call it.close() for cases where a context manager is unusable,
for instance in generator expressions.

Numpy has switched to using pytest instead of nose for testing

The last nose release was 1.3.7 in June, 2015, and development of that tool has
ended, consequently NumPy has now switched to using pytest. The old decorators
and nose tools that were previously used by some downstream projects remain
available, but will not be maintained. The standard testing utilities,
assert_almost_equal and such, are not be affected by this change except for
the nose specific functions import_nose and raises. Those functions are
not used in numpy, but are kept for downstream compatibility.

Numpy no longer monkey-patches ctypes with __array_interface__

Previously numpy added __array_interface__ attributes to all the integer
types from ctypes.

np.ma.notmasked_contiguous and np.ma.flatnotmasked_contiguous always return lists

This is the documented behavior, but previously the result could be any of
slice, None, or list.

All downstream users seem to check for the None result from
flatnotmasked_contiguous and replace it with []. Those callers will
continue to work as before.

np.squeeze restores old behavior of objects that cannot handle an axis argument

Prior to version 1.7.0, numpy.squeeze did not have an axis argument and
all empty axes were removed by default. The incorporation of an axis
argument made it possible to selectively squeeze single or multiple empty axes,
but the old API expectation was not respected because axes could still be
selectively removed (silent success) from an object expecting all empty axes to
be removed. That silent, selective removal of empty axes for objects expecting
the old behavior has been fixed and the old behavior restored.

unstructured void array's .item method now returns a bytes object

.item now returns a bytes object instead of a buffer or byte array.
This may affect code which assumed the return value was mutable, which is no
longer the case.

copy.copy and copy.deepcopy no longer turn masked into an array

Since np.ma.masked is a readonly scalar, copying should be a no-op. These
functions now behave consistently with np.copy().

Multifield Indexing of Structured Arrays will still return a copy

The change that multi-field indexing of structured arrays returns a view
instead of a copy is pushed back to 1.16. A new method
numpy.lib.recfunctions.repack_fields has been introduced to help mitigate
the effects of this change, which can be used to write code compatible with
both numpy 1.15 and 1.16. For more information on how to update code to account
for this future change see the "accessing multiple fields" section of the
user guide <https://docs.scipy.org/doc/numpy/user/basics.rec.html>__.

C API changes

New functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier

Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
have been added and should be used in place of the npy_get_floatstatusand
npy_clear_status functions. Optimizing compilers like GCC 8.1 and Clang
were rearranging the order of operations when the previous functions were used
in the ufunc SIMD functions, resulting in the floatstatus flags being checked
before the operation whose status we wanted to check was run. See #10339 <https://github.com/numpy/numpy/issues/10370>__.

Changes to PyArray_GetDTypeTransferFunction

PyArray_GetDTypeTransferFunction now defaults to using user-defined
copyswapn / copyswap for user-defined dtypes. If this causes a
significant performance hit, consider implementing copyswapn to reflect the
implementation of PyArray_GetStridedCopyFn. See #10898 <https://github.com/numpy/numpy/pull/10898>__.

• Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
  have been added and should be used in place of the npy_get_floatstatusand
  npy_clear_status functions. Optimizing compilers like GCC 8.1 and Clang
  were rearranging the order of operations when the previous functions were
  used in the ufunc SIMD functions, resulting in the floatstatus flags being '
  checked before the operation whose status we wanted to check was run.
  See #10339 <https://github.com/numpy/numpy/issues/10370>__.

New Features

np.gcd and np.lcm ufuncs added for integer and objects types

These compute the greatest common divisor, and lowest common multiple,
respectively. These work on all the numpy integer types, as well as the
builtin arbitrary-precision Decimal and long types.

Support for cross-platform builds for iOS

The build system has been modified to add support for the
_PYTHON_HOST_PLATFORM environment variable, used by distutils when
compiling on one platform for another platform. This makes it possible to
compile NumPy for iOS targets.

This only enables you to compile NumPy for one specific platform at a time.
Creating a full iOS-compatible NumPy package requires building for the 5
architectures supported by iOS (i386, x86_64, armv7, armv7s and arm64), and
combining these 5 compiled builds products into a single "fat" binary.

return_indices keyword added for np.intersect1d

New keyword return_indices returns the indices of the two input arrays
that correspond to the common elements.

np.quantile and np.nanquantile

Like np.percentile and np.nanpercentile, but takes quantiles in [0, 1]
rather than percentiles in [0, 100]. np.percentile is now a thin wrapper
around np.quantile with the extra step of dividing by 100.

Build system

Added experimental support for the 64-bit RISC-V architecture.

Improvements

np.einsum updates

Syncs einsum path optimization tech between numpy and opt_einsum. In
particular, the greedy path has received many enhancements by @jcmgray. A
full list of issues fixed are:

• Arbitrary memory can be passed into the greedy path. Fixes gh-11210.
• The greedy path has been updated to contain more dynamic programming ideas
  preventing a large number of duplicate (and expensive) calls that figure out
  the actual pair contraction that takes place. Now takes a few seconds on
  several hundred input tensors. Useful for matrix product state theories.
• Reworks the broadcasting dot error catching found in gh-11218 gh-10352 to be
  a bit earlier in the process.
• Enhances the can_dot functionality that previous missed an edge case (part
  of gh-11308).

np.ufunc.reduce and related functions now accept an initial value

np.ufunc.reduce, np.sum, np.prod, np.min and np.max all
now accept an initial keyword argument that specifies the value to start
the reduction with.

np.flip can operate over multiple axes

np.flip now accepts None, or tuples of int, in its axis argument. If
axis is None, it will flip over all the axes.

histogram and histogramdd functions have moved to np.lib.histograms

These were originally found in np.lib.function_base. They are still
available under their un-scoped np.histogram(dd) names, and
to maintain compatibility, aliased at np.lib.function_base.histogram(dd).

Code that does from np.lib.function_base import * will need to be updated
with the new location, and should consider not using import * in future.

histogram will accept NaN values when explicit bins are given

Previously it would fail when trying to compute a finite range for the data.
Since the range is ignored anyway when the bins are given explicitly, this error
was needless.

Note that calling histogram on NaN values continues to raise the
RuntimeWarning s typical of working with nan values, which can be silenced
as usual with errstate.

histogram works on datetime types, when explicit bin edges are given

Dates, times, and timedeltas can now be histogrammed. The bin edges must be
passed explicitly, and are not yet computed automatically.

histogram "auto" estimator handles limited variance better

No longer does an IQR of 0 result in n_bins=1, rather the number of bins
chosen is related to the data size in this situation.

The edges retuned by `histogramandhistogramdd`` now match the data float type

When passed np.float16, np.float32, or np.longdouble data, the
returned edges are now of the same dtype. Previously, histogram would only
return the same type if explicit bins were given, and histogram would
produce float64 bins no matter what the inputs.

histogramdd allows explicit ranges to be given in a subset of axes

The range argument of numpy.histogramdd can now contain None values to
indicate that the range for the corresponding axis should be computed from the
data. Previously, this could not be specified on a per-axis basis.

The normed arguments of histogramdd and histogram2d have been renamed

These arguments are now called density, which is consistent with
histogram. The old argument continues to work, but the new name should be
preferred.

np.r_ works with 0d arrays, and np.ma.mr_ works with np.ma.masked

0d arrays passed to the r_ and mr_ concatenation helpers are now treated as
though they are arrays of length 1. Previously, passing these was an error.
As a result, numpy.ma.mr_ now works correctly on the masked constant.

np.ptp accepts a keepdims argument, and extended axis tuples

np.ptp (peak-to-peak) can now work over multiple axes, just like np.max
and np.min.

MaskedArray.astype now is identical to ndarray.astype

This means it takes all the same arguments, making more code written for
ndarray work for masked array too.

Enable AVX2/AVX512 at compile time

Change to simd.inc.src to allow use of AVX2 or AVX512 at compile time. Previously
compilation for avx2 (or 512) with -march=native would still use the SSE
code for the simd functions even when the rest of the code got AVX2.

nan_to_num always returns scalars when receiving scalar or 0d inputs

Previously an array was returned for integer scalar inputs, which is
inconsistent with the behavior for float inputs, and that of ufuncs in general.
For all types of scalar or 0d input, the result is now a scalar.

np.flatnonzero works on numpy-convertible types

np.flatnonzero now uses np.ravel(a) instead of a.ravel(), so it
works for lists, tuples, etc.

np.interp returns numpy scalars rather than builtin scalars

Previously np.interp(0.5, [0, 1], [10, 20]) would return a float, but
now it returns a np.float64 object, which more closely matches the behavior
of other functions.

Additionally, the special case of np.interp(object_array_0d, ...) is no
longer supported, as np.interp(object_array_nd) was never supported anyway.

As a result of this change, the period argument can now be used on 0d
arrays.

Allow dtype field names to be unicode in Python 2

Previously np.dtype([(u'name', float)]) would raise a TypeError in
Python 2, as only bytestrings were allowed in field names. Now any unicode
string field names will be encoded with the ascii codec, raising a
UnicodeEncodeError upon failure.

This change makes it easier to write Python 2/3 compatible code using
from __future__ import unicode_literals, which previously would cause
string literal field names to raise a TypeError in Python 2.

Comparison ufuncs accept dtype=object, overriding the default bool

This allows object arrays of symbolic types, which override == and other
operators to return expressions, to be compared elementwise with
np.equal(a, b, dtype=object).

sort functions accept kind='stable'

Up until now, to perform a stable sort on the data, the user must do:

>>> np.sort([5, 2, 6, 2, 1], kind='mergesort')
[1, 2, 2, 5, 6]

because merge sort is the only stable sorting algorithm available in
NumPy. However, having kind='mergesort' does not make it explicit that
the user wants to perform a stable sort thus harming the readability.

This change allows the user to specify kind='stable' thus clarifying
the intent.

Do not make temporary copies for in-place accumulation

When ufuncs perform accumulation they no longer make temporary copies because
of the overlap between input an output, that is, the next element accumulated
is added before the accumulated result is stored in its place, hence the
overlap is safe. Avoiding the copy results in faster execution.

linalg.matrix_power can now handle stacks of matrices

Like other functions in linalg, matrix_power can now deal with arrays
of dimension larger than 2, which are treated as stacks of matrices. As part
of the change, to further improve consistency, the name of the first argument
has been changed to a (from M), and the exceptions for non-square
matrices have been changed to LinAlgError (from ValueError).

Increased performance in random.permutation for multidimensional arrays

permutation uses the fast path in random.shuffle for all input
array dimensions. Previously the fast path was only used for 1-d arrays.

Generalized ufuncs now accept axes, axis and keepdims arguments

One can control over which axes a generalized ufunc operates by passing in an
axes argument, a list of tuples with indices of particular axes. For
instance, for a signature of (i,j),(j,k)->(i,k) appropriate for matrix
multiplication, the base elements are two-dimensional matrices and these are
taken to be stored in the two last axes of each argument. The corresponding
axes keyword would be [(-2, -1), (-2, -1), (-2, -1)]. If one wanted to
use leading dimensions instead, one would pass in [(0, 1), (0, 1), (0, 1)].

For simplicity, for generalized ufuncs that operate on 1-dimensional arrays
(vectors), a single integer is accepted instead of a single-element tuple, and
for generalized ufuncs for which all outputs are scalars, the (empty) output
tuples can be omitted. Hence, for a signature of (i),(i)->() appropriate
for an inner product, one could pass in axes=[0, 0] to indicate that the
vectors are stored in the first dimensions of the two inputs arguments.

As a short-cut for generalized ufuncs that are similar to reductions, i.e.,
that act on a single, shared core dimension such as the inner product example
above, one can pass an axis argument. This is equivalent to passing in
axes with identical entries for all arguments with that core dimension
(e.g., for the example above, axes=[(axis,), (axis,)]).

Furthermore, like for reductions, for generalized ufuncs that have inputs that
all have the same number of core dimensions and outputs with no core dimension,
one can pass in keepdims to leave a dimension with size 1 in the outputs,
thus allowing proper broadcasting against the original inputs. The location of
the extra dimension can be controlled with axes. For instance, for the
inner-product example, keepdims=True, axes=[-2, -2, -2] would act on the
inner-product example, keepdims=True, axis=-2 would act on the
one-but-last dimension of the input arguments, and leave a size 1 dimension in
that place in the output.

float128 values now print correctly on ppc systems

Previously printing float128 values was buggy on ppc, since the special
double-double floating-point-format on these systems was not accounted for.
float128s now print with correct rounding and uniqueness.

Warning to ppc users: You should upgrade glibc if it is version <=2.23,
especially if using float128. On ppc, glibc's malloc in these version often
misaligns allocated memory which can crash numpy when using float128 values.

New np.take_along_axis and np.put_along_axis functions

When used on multidimensional arrays, argsort, argmin, argmax, and
argpartition return arrays that are difficult to use as indices.
take_along_axis provides an easy way to use these indices to lookup values
within an array, so that::

np.take_along_axis(a, np.argsort(a, axis=axis), axis=axis)

is the same as::

np.sort(a, axis=axis)

np.put_along_axis acts as the dual operation for writing to these indices
within an array.

Checksums

MD5

4957a50c1125fdecb4cb51829f5feba1  numpy-1.15.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
d5ffa73c6a3eeba8cfcab283e7db3c2f  numpy-1.15.0-cp27-cp27m-manylinux1_i686.whl
a6f7aa33d4d1598dc33831a4bb36570d  numpy-1.15.0-cp27-cp27m-manylinux1_x86_64.whl
cbdd2291782deb29f41c9b7d121264e0  numpy-1.15.0-cp27-cp27mu-manylinux1_i686.whl
0bd79da73435161850099bfcacc75fae  numpy-1.15.0-cp27-cp27mu-manylinux1_x86_64.whl
73f930c046ac09e518d0b4cf2f8ff642  numpy-1.15.0-cp27-none-win32.whl
7ba5b463728a792dced42fd6259e511f  numpy-1.15.0-cp27-none-win_amd64.whl
badfc9f713510d59f478037c88b3d963  numpy-1.15.0-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
662f2536cac7b841f86e9b7488e52371  numpy-1.15.0-cp34-cp34m-manylinux1_i686.whl
346d9239f7f12bb7042f8bc847928dc1  numpy-1.15.0-cp34-cp34m-manylinux1_x86_64.whl
fd03012584359cd05cee08408df5897d  numpy-1.15.0-cp34-none-win32.whl
1032db03cefd82e87f72f2b04b15b7ae  numpy-1.15.0-cp34-none-win_amd64.whl
cc463ee62af94c8410fdf95ce9933c3c  numpy-1.15.0-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
77655199a4e18719dd5a0b348c44fc92  numpy-1.15.0-cp35-cp35m-manylinux1_i686.whl
d76c54272549cf3a2165d40d3fea5e30  numpy-1.15.0-cp35-cp35m-manylinux1_x86_64.whl
956c6f7c216b677b27628a97150cd069  numpy-1.15.0-cp35-none-win32.whl
2ab8080576932775167a6f9c772b91e4  numpy-1.15.0-cp35-none-win_amd64.whl
1a01c8d089d488565acc2836d03a7482  numpy-1.15.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
5606fa1c1e13e789b802102699d613e2  numpy-1.15.0-cp36-cp36m-manylinux1_i686.whl
5635343a70f7cdd17f372966db1526d3  numpy-1.15.0-cp36-cp36m-manylinux1_x86_64.whl
166e901c1a86da5ffb8c6d3090ed917e  numpy-1.15.0-cp36-none-win32.whl
6423497ad5a610c1deed606ce44893bd  numpy-1.15.0-cp36-none-win_amd64.whl
e232fbba29585812bf7fa547f671b768  numpy-1.15.0-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
b2fc4551651fae84eb01b8a37f2e1e69  numpy-1.15.0-cp37-cp37m-manylinux1_i686.whl
36ed60bef7c5cb252b9d0e8dc5029e08  numpy-1.15.0-cp37-cp37m-manylinux1_x86_64.whl
4482a89fa4540c8bbf76028621931266  numpy-1.15.0-cp37-none-win32.whl
cfef18ee246468752f1686147c70bd0a  numpy-1.15.0-cp37-none-win_amd64.whl
5cf4daff88042326334266f80ad38884  numpy-1.15.0.tar.gz
20e13185089011116a98e11c9bf8aa07  numpy-1.15.0.zip

SHA256

a17a8fd5df4fec5b56b4d11c9ba8b9ebfb883c90ec361628d07be00aaa4f009a  numpy-1.15.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
c3ac359ace241707e5a48fe2922e566ac666aacacf4f8031f2994ac429c31344  numpy-1.15.0-cp27-cp27m-manylinux1_i686.whl
e2317cf091c2e7f0dacdc2e72c693cc34403ca1f8e3807622d0bb653dc978616  numpy-1.15.0-cp27-cp27m-manylinux1_x86_64.whl
64c6acf5175745fd1b7b7e17c74fdbfb7191af3b378bc54f44560279f41238d3  numpy-1.15.0-cp27-cp27mu-manylinux1_i686.whl
924f37e66db78464b4b85ed4b6d2e5cda0c0416e657cac7ccbef14b9fa2c40b5  numpy-1.15.0-cp27-cp27mu-manylinux1_x86_64.whl
674ea7917f0657ddb6976bd102ac341bc493d072c32a59b98e5b8c6eaa2d5ec0  numpy-1.15.0-cp27-none-win32.whl
ae3864816287d0e86ead580b69921daec568fe680857f07ee2a87bf7fd77ce24  numpy-1.15.0-cp27-none-win_amd64.whl
78c35dc7ad184aebf3714dbf43f054714c6e430e14b9c06c49a864fb9e262030  numpy-1.15.0-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
c7c660cc0209fdf29a4e50146ca9ac9d8664acaded6b6ae2f5d0ae2e91a0f0cd  numpy-1.15.0-cp34-cp34m-manylinux1_i686.whl
3fbccb399fe9095b1c1d7b41e7c7867db8aa0d2347fc44c87a7a180cedda112b  numpy-1.15.0-cp34-cp34m-manylinux1_x86_64.whl
aaa519335a71f87217ca8a680c3b66b61960e148407bdf5c209c42f50fe30f49  numpy-1.15.0-cp34-none-win32.whl
62cb836506f40ce2529bfba9d09edc4b2687dd18c56cf4457e51c3e7145402fd  numpy-1.15.0-cp34-none-win_amd64.whl
55daf757e5f69aa75b4477cf4511bf1f96325c730e4ad32d954ccb593acd2585  numpy-1.15.0-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
b5f8c15cb9173f6cdf0f994955e58d1265331029ae26296232379461a297e5f2  numpy-1.15.0-cp35-cp35m-manylinux1_i686.whl
24f3bb9a5f6c3936a8ccd4ddfc1210d9511f4aeb879a12efd2e80bec647b8695  numpy-1.15.0-cp35-cp35m-manylinux1_x86_64.whl
34033b581bc01b1135ca2e3e93a94daea7c739f21a97a75cca93e29d9f0c8e71  numpy-1.15.0-cp35-none-win32.whl
f5a758252502b466b9c2b201ea397dae5a914336c987f3a76c3741a82d43c96e  numpy-1.15.0-cp35-none-win_amd64.whl
14fb76bde161c87dcec52d91c78f65aa8a23aa2e1530a71f412dabe03927d917  numpy-1.15.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
816645178f2180be257a576b735d3ae245b1982280b97ae819550ce8bcdf2b6b  numpy-1.15.0-cp36-cp36m-manylinux1_i686.whl
f2a778dd9bb3e4590dbe3bbac28e7c7134280c4ec97e3bf8678170ee58c67b21  numpy-1.15.0-cp36-cp36m-manylinux1_x86_64.whl
7f17efe9605444fcbfd990ba9b03371552d65a3c259fc2d258c24fb95afdd728  numpy-1.15.0-cp36-none-win32.whl
73a816e441dace289302e04a7a34ec4772ed234ab6885c968e3ca2fc2d06fe2d  numpy-1.15.0-cp36-none-win_amd64.whl
21041014b7529237994a6b578701c585703fbb3b1bea356cdb12a5ea7804241c  numpy-1.15.0-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
d690a2ff49f6c3bc35336693c9924fe5916be3cc0503fe1ea6c7e2bf951409ee  numpy-1.15.0-cp37-cp37m-manylinux1_i686.whl
50718eea8e77a1bedcc85befd22c8dbf5a24c9d2c0c1e36bbb8d7a38da847eb3  numpy-1.15.0-cp37-cp37m-manylinux1_x86_64.whl
fb4c33a404d9eff49a0cdc8ead0af6453f62f19e071b60d283f9dc05581e4134  numpy-1.15.0-cp37-none-win32.whl
61efc65f325770bbe787f34e00607bc124f08e6c25fdf04723848585e81560dc  numpy-1.15.0-cp37-none-win_amd64.whl
259934a941663e93fdd5d28ce3f6aa2a81ce7dda85c395dd07b1f1edff2e0236  numpy-1.15.0.tar.gz
f28e73cf18d37a413f7d5de35d024e6b98f14566a10d82100f9dc491a7d449f9  numpy-1.15.0.zip