@charris charris released this Jan 7, 2018 · 1938 commits to master since this release

Assets 6

NumPy 1.14.0 Release Notes

Numpy 1.14.0 is the result of seven months of work and contains a large number
of bug fixes and new features, along with several changes with potential
compatibility issues. The major change that users will notice are the
stylistic changes in the way numpy arrays and scalars are printed, a change
that will affect doctests. See below for details on how to preserve the
old style printing when needed.

A major decision affecting future development concerns the schedule for
dropping Python 2.7 support in the runup to 2020. The decision has been made to
support 2.7 for all releases made in 2018, with the last release being
designated a long term release with support for bug fixes extending through
2019. In 2019 support for 2.7 will be dropped in all new releases. More details
can be found in the relevant NEP_.

This release supports Python 2.7 and 3.4 - 3.6.

.. _NEP: https://github.com/numpy/numpy/blob/master/doc/neps/dropping-python2.7-proposal.rst


  • The np.einsum function uses BLAS when possible

  • genfromtxt, loadtxt, fromregex and savetxt can now handle
    files with arbitrary Python supported encoding.

  • Major improvements to printing of NumPy arrays and scalars.

New functions

  • parametrize: decorator added to numpy.testing

  • chebinterpolate: Interpolate function at Chebyshev points.

  • format_float_positional and format_float_scientific : format
    floating-point scalars unambiguously with control of rounding and padding.

  • PyArray_ResolveWritebackIfCopy and PyArray_SetWritebackIfCopyBase,
    new C-API functions useful in achieving PyPy compatibity.


  • Using np.bool_ objects in place of integers is deprecated. Previously
    operator.index(np.bool_) was legal and allowed constructs such as
    [1, 2, 3][np.True_]. That was misleading, as it behaved differently from
    np.array([1, 2, 3])[np.True_].

  • Truth testing of an empty array is deprecated. To check if an array is not
    empty, use array.size > 0.

  • Calling np.bincount with minlength=None is deprecated.
    minlength=0 should be used instead.

  • Calling np.fromstring with the default value of the sep argument is
    deprecated. When that argument is not provided, a broken version of
    np.frombuffer is used that silently accepts unicode strings and -- after
    encoding them as either utf-8 (python 3) or the default encoding
    (python 2) -- treats them as binary data. If reading binary data is
    desired, np.frombuffer should be used directly.

  • The style option of array2string is deprecated in non-legacy printing mode.

  • PyArray_SetUpdateIfCopyBase has been deprecated. For NumPy versions >= 1.14
    use PyArray_SetWritebackIfCopyBase instead, see C API changes below for
    more details.

  • The use of UPDATEIFCOPY arrays is deprecated, see C API changes below
    for details. We will not be dropping support for those arrays, but they are
    not compatible with PyPy.

Future Changes

  • np.issubdtype will stop downcasting dtype-like arguments.
    It might be expected that issubdtype(np.float32, 'float64') and
    issubdtype(np.float32, np.float64) mean the same thing - however, there
    was an undocumented special case that translated the former into
    issubdtype(np.float32, np.floating), giving the surprising result of True.

    This translation now gives a warning that explains what translation is
    occurring. In the future, the translation will be disabled, and the first
    example will be made equivalent to the second.

  • np.linalg.lstsq default for rcond will be changed. The rcond
    parameter to np.linalg.lstsq will change its default to machine precision
    times the largest of the input array dimensions. A FutureWarning is issued
    when rcond is not passed explicitly.

  • a.flat.__array__() will return a writeable copy of a when a is
    non-contiguous. Previously it returned an UPDATEIFCOPY array when a was
    writeable. Currently it returns a non-writeable copy. See gh-7054 for a
    discussion of the issue.

  • Unstructured void array's .item method will return a bytes object. In the
    future, calling .item() on arrays or scalars of np.void datatype will
    return a bytes object instead of a buffer or int array, the same as
    returned by bytes(void_scalar). This may affect code which assumed the
    return value was mutable, which will no longer be the case. A
    FutureWarning is now issued when this would occur.

Compatibility notes

The mask of a masked array view is also a view rather than a copy

There was a FutureWarning about this change in NumPy 1.11.x. In short, it is
now the case that, when changing a view of a masked array, changes to the mask
are propagated to the original. That was not previously the case. This change
affects slices in particular. Note that this does not yet work properly if the
mask of the original array is nomask and the mask of the view is changed.
See gh-5580 for an extended discussion. The original behavior of having a copy
of the mask can be obtained by calling the unshare_mask method of the view.

np.ma.masked is no longer writeable

Attempts to mutate the masked constant now error, as the underlying arrays
are marked readonly. In the past, it was possible to get away with::

# emulating a function that sometimes returns np.ma.masked
val = random.choice([np.ma.masked, 10])
var_arr = np.asarray(val)
val_arr += 1  # now errors, previously changed np.ma.masked.data

np.ma functions producing fill_values have changed

Previously, np.ma.default_fill_value would return a 0d array, but
np.ma.minimum_fill_value and np.ma.maximum_fill_value would return a
tuple of the fields. Instead, all three methods return a structured np.void
object, which is what you would already find in the .fill_value attribute.

Additionally, the dtype guessing now matches that of np.array - so when
passing a python scalar x, maximum_fill_value(x) is always the same as
maximum_fill_value(np.array(x)). Previously x = long(1) on Python 2
violated this assumption.

a.flat.__array__() returns non-writeable arrays when a is non-contiguous

The intent is that the UPDATEIFCOPY array previously returned when a was
non-contiguous will be replaced by a writeable copy in the future. This
temporary measure is aimed to notify folks who expect the underlying array be
modified in this situation that that will no longer be the case. The most
likely places for this to be noticed is when expressions of the form
np.asarray(a.flat) are used, or when a.flat is passed as the out
parameter to a ufunc.

np.tensordot now returns zero array when contracting over 0-length dimension

Previously np.tensordot raised a ValueError when contracting over 0-length
dimension. Now it returns a zero array, which is consistent with the behaviour
of np.dot and np.einsum.

numpy.testing reorganized

This is not expected to cause problems, but possibly something has been left
out. If you experience an unexpected import problem using numpy.testing
let us know.

np.asfarray no longer accepts non-dtypes through the dtype argument

This previously would accept dtype=some_array, with the implied semantics
of dtype=some_array.dtype. This was undocumented, unique across the numpy
functions, and if used would likely correspond to a typo.

1D np.linalg.norm preserves float input types, even for arbitrary orders

Previously, this would promote to float64 when arbitrary orders were
passed, despite not doing so under the simple cases::

>>> f32 = np.float32([1, 2])
>>> np.linalg.norm(f32, 2.0).dtype
>>> np.linalg.norm(f32, 2.0001).dtype
dtype('float64')  # numpy 1.13
dtype('float32')  # numpy 1.14

This change affects only float32 and float16 arrays.

count_nonzero(arr, axis=()) now counts over no axes, not all axes

Elsewhere, axis==() is always understood as "no axes", but
count_nonzero had a special case to treat this as "all axes". This was
inconsistent and surprising. The correct way to count over all axes has always
been to pass axis == None.

__init__.py files added to test directories

This is for pytest compatibility in the case of duplicate test file names in
the different directories. As a result, run_module_suite no longer works,
i.e., python <path-to-test-file> results in an error.

.astype(bool) on unstructured void arrays now calls bool on each element

On Python 2, void_array.astype(bool) would always return an array of
True, unless the dtype is V0. On Python 3, this operation would usually
crash. Going forwards, astype matches the behavior of bool(np.void),
considering a buffer of all zeros as false, and anything else as true.
Checks for V0 can still be done with arr.dtype.itemsize == 0.

MaskedArray.squeeze never returns np.ma.masked

np.squeeze is documented as returning a view, but the masked variant would
sometimes return masked, which is not a view. This has been fixed, so that
the result is always a view on the original masked array.
This breaks any code that used masked_arr.squeeze() is np.ma.masked, but
fixes code that writes to the result of .squeeze().

Renamed first parameter of can_cast from from to from_

The previous parameter name from is a reserved keyword in Python, which made
it difficult to pass the argument by name. This has been fixed by renaming
the parameter to from_.

isnat raises TypeError when passed wrong type

The ufunc isnat used to raise a ValueError when it was not passed
variables of type datetime or timedelta. This has been changed to
raising a TypeError.

dtype.__getitem__ raises TypeError when passed wrong type

When indexed with a float, the dtype object used to raise ValueError.

User-defined types now need to implement __str__ and __repr__

Previously, user-defined types could fall back to a default implementation of
__str__ and __repr__ implemented in numpy, but this has now been
removed. Now user-defined types will fall back to the python default
object.__str__ and object.__repr__.

Many changes to array printing, disableable with the new "legacy" printing mode

The str and repr of ndarrays and numpy scalars have been changed in
a variety of ways. These changes are likely to break downstream user's

These new behaviors can be disabled to mostly reproduce numpy 1.13 behavior by
enabling the new 1.13 "legacy" printing mode. This is enabled by calling
np.set_printoptions(legacy="1.13"), or using the new legacy argument to
np.array2string, as np.array2string(arr, legacy='1.13').

In summary, the major changes are:

  • For floating-point types:

    • The repr of float arrays often omits a space previously printed
      in the sign position. See the new sign option to np.set_printoptions.
    • Floating-point arrays and scalars use a new algorithm for decimal
      representations, giving the shortest unique representation. This will
      usually shorten float16 fractional output, and sometimes float32 and
      float128 output. float64 should be unaffected. See the new
      floatmode option to np.set_printoptions.
    • Float arrays printed in scientific notation no longer use fixed-precision,
      and now instead show the shortest unique representation.
    • The str of floating-point scalars is no longer truncated in python2.
  • For other data types:

    • Non-finite complex scalars print like nanj instead of nan*j.
    • NaT values in datetime arrays are now properly aligned.
    • Arrays and scalars of np.void datatype are now printed using hex
  • For line-wrapping:

    • The "dtype" part of ndarray reprs will now be printed on the next line
      if there isn't space on the last line of array output.
    • The linewidth format option is now always respected.
      The repr or str of an array will never exceed this, unless a single
      element is too wide.
    • The last line of an array string will never have more elements than earlier
    • An extra space is no longer inserted on the first line if the elements are
      too wide.
  • For summarization (the use of ... to shorten long arrays):

    • A trailing comma is no longer inserted for str.
      Previously, str(np.arange(1001)) gave
      '[ 0 1 2 ..., 998 999 1000]', which has an extra comma.
    • For arrays of 2-D and beyond, when ... is printed on its own line in
      order to summarize any but the last axis, newlines are now appended to that
      line to match its leading newlines and a trailing space character is
  • MaskedArray arrays now separate printed elements with commas, always
    print the dtype, and correctly wrap the elements of long arrays to multiple
    lines. If there is more than 1 dimension, the array attributes are now
    printed in a new "left-justified" printing style.

  • recarray arrays no longer print a trailing space before their dtype, and
    wrap to the right number of columns.

  • 0d arrays no longer have their own idiosyncratic implementations of str
    and repr. The style argument to np.array2string is deprecated.

  • Arrays of bool datatype will omit the datatype in the repr.

  • User-defined dtypes (subclasses of np.generic) now need to
    implement __str__ and __repr__.

Some of these changes are described in more detail below. If you need to retain
the previous behavior for doctests or other reasons, you may want to do
something like::

# FIXME: We need the str/repr formatting used in Numpy < 1.14.
except TypeError:

C API changes

PyPy compatible alternative to UPDATEIFCOPY arrays

UPDATEIFCOPY arrays are contiguous copies of existing arrays, possibly with
different dimensions, whose contents are copied back to the original array when
their refcount goes to zero and they are deallocated. Because PyPy does not use
refcounts, they do not function correctly with PyPy. NumPy is in the process of
eliminating their use internally and two new C-API functions,

  • PyArray_SetWritebackIfCopyBase
  • PyArray_ResolveWritebackIfCopy,

have been added together with a complimentary flag,
NPY_ARRAY_WRITEBACKIFCOPY. Using the new functionality also requires that
some flags be changed when new arrays are created, to wit:
Arrays created with these new flags will then have the WRITEBACKIFCOPY

If PyPy compatibility is not a concern, these new functions can be ignored,
although there will be a DeprecationWarning. If you do wish to pursue PyPy
compatibility, more information on these functions and their use may be found
in the c-api_ documentation and the example in how-to-extend_.

.. _c-api: https://github.com/numpy/numpy/blob/master/doc/source/reference/c-api.array.rst
.. _how-to-extend: https://github.com/numpy/numpy/blob/master/doc/source/user/c-info.how-to-extend.rst

New Features

Encoding argument for text IO functions

genfromtxt, loadtxt, fromregex and savetxt can now handle files
with arbitrary encoding supported by Python via the encoding argument.
For backward compatibility the argument defaults to the special bytes value
which continues to treat text as raw byte values and continues to pass latin1
encoded bytes to custom converters.
Using any other value (including None for system default) will switch the
functions to real text IO so one receives unicode strings instead of bytes in
the resulting arrays.

External nose plugins are usable by numpy.testing.Tester

numpy.testing.Tester is now aware of nose plugins that are outside the
nose built-in ones. This allows using, for example, nose-timer like
so: np.test(extra_argv=['--with-timer', '--timer-top-n', '20']) to
obtain the runtime of the 20 slowest tests. An extra keyword timer was
also added to Tester.test, so np.test(timer=20) will also report the 20
slowest tests.

parametrize decorator added to numpy.testing

A basic parametrize decorator is now available in numpy.testing. It is
intended to allow rewriting yield based tests that have been deprecated in
pytest so as to facilitate the transition to pytest in the future. The nose
testing framework has not been supported for several years and looks like

The new parametrize decorator does not have the full functionality of the
one in pytest. It doesn't work for classes, doesn't support nesting, and does
not substitute variable names. Even so, it should be adequate to rewrite the
NumPy tests.

chebinterpolate function added to numpy.polynomial.chebyshev

The new chebinterpolate function interpolates a given function at the
Chebyshev points of the first kind. A new Chebyshev.interpolate class
method adds support for interpolation over arbitrary intervals using the scaled
and shifted Chebyshev points of the first kind.

Support for reading lzma compressed text files in Python 3

With Python versions containing the lzma module the text IO functions can
now transparently read from files with xz or lzma extension.

sign option added to np.setprintoptions and np.array2string

This option controls printing of the sign of floating-point types, and may be
one of the characters '-', '+' or ' '. With '+' numpy always prints the sign of
positive values, with ' ' it always prints a space (whitespace character) in
the sign position of positive values, and with '-' it will omit the sign
character for positive values. The new default is '-'.

This new default changes the float output relative to numpy 1.13. The old
behavior can be obtained in 1.13 "legacy" printing mode, see compatibility
notes above.

hermitian option added tonp.linalg.matrix_rank

The new hermitian option allows choosing between standard SVD based matrix
rank calculation and the more efficient eigenvalue based method for
symmetric/hermitian matrices.

threshold and edgeitems options added to np.array2string

These options could previously be controlled using np.set_printoptions, but
now can be changed on a per-call basis as arguments to np.array2string.

concatenate and stack gained an out argument

A preallocated buffer of the desired dtype can now be used for the output of
these functions.

Support for PGI flang compiler on Windows

The PGI flang compiler is a Fortran front end for LLVM released by NVIDIA under
the Apache 2 license. It can be invoked by ::

python setup.py config --compiler=clang --fcompiler=flang install

There is little experience with this new compiler, so any feedback from people
using it will be appreciated.


Numerator degrees of freedom in random.noncentral_f need only be positive.

Prior to NumPy 1.14.0, the numerator degrees of freedom needed to be > 1, but
the distribution is valid for values > 0, which is the new requirement.

The GIL is released for all np.einsum variations

Some specific loop structures which have an accelerated loop version
did not release the GIL prior to NumPy 1.14.0. This oversight has been

The np.einsum function will use BLAS when possible and optimize by default

The np.einsum function will now call np.tensordot when appropriate.
Because np.tensordot uses BLAS when possible, that will speed up execution.
By default, np.einsum will also attempt optimization as the overhead is
small relative to the potential improvement in speed.

f2py now handles arrays of dimension 0

f2py now allows for the allocation of arrays of dimension 0. This allows
for more consistent handling of corner cases downstream.

numpy.distutils supports using MSVC and mingw64-gfortran together

Numpy distutils now supports using Mingw64 gfortran and MSVC compilers
together. This enables the production of Python extension modules on Windows
containing Fortran code while retaining compatibility with the
binaries distributed by Python.org. Not all use cases are supported,
but most common ways to wrap Fortran for Python are functional.

Compilation in this mode is usually enabled automatically, and can be
selected via the --fcompiler and --compiler options to
setup.py. Moreover, linking Fortran codes to static OpenBLAS is
supported; by default a gfortran compatible static archive
openblas.a is looked for.

np.linalg.pinv now works on stacked matrices

Previously it was limited to a single 2d array.

numpy.save aligns data to 64 bytes instead of 16

Saving NumPy arrays in the npy format with numpy.save inserts
padding before the array data to align it at 64 bytes. Previously
this was only 16 bytes (and sometimes less due to a bug in the code
for version 2). Now the alignment is 64 bytes, which matches the
widest SIMD instruction set commonly available, and is also the most
common cache line size. This makes npy files easier to use in
programs which open them with mmap, especially on Linux where an
mmap offset must be a multiple of the page size.

NPZ files now can be written without using temporary files

In Python 3.6+ numpy.savez and numpy.savez_compressed now write
directly to a ZIP file, without creating intermediate temporary files.

Better support for empty structured and string types

Structured types can contain zero fields, and string dtypes can contain zero
characters. Zero-length strings still cannot be created directly, and must be
constructed through structured dtypes::

str0 = np.empty(10, np.dtype([('v', str, N)]))['v']
void0 = np.empty(10, np.void)

It was always possible to work with these, but the following operations are
now supported for these arrays:

  • arr.sort()
  • arr.view(bytes)
  • arr.resize(...)
  • pickle.dumps(arr)

Support for decimal.Decimal in np.lib.financial

Unless otherwise stated all functions within the financial package now
support using the decimal.Decimal built-in type.

Float printing now uses "dragon4" algorithm for shortest decimal representation

The str and repr of floating-point values (16, 32, 64 and 128 bit) are
now printed to give the shortest decimal representation which uniquely
identifies the value from others of the same type. Previously this was only
true for float64 values. The remaining float types will now often be shorter
than in numpy 1.13. Arrays printed in scientific notation now also use the
shortest scientific representation, instead of fixed precision as before.

Additionally, the str of float scalars scalars will no longer be truncated
in python2, unlike python2 floats. np.double scalars now have a str
and repr identical to that of a python3 float.

New functions np.format_float_scientific and np.format_float_positional
are provided to generate these decimal representations.

A new option floatmode has been added to np.set_printoptions and
np.array2string, which gives control over uniqueness and rounding of
printed elements in an array. The new default is floatmode='maxprec' with
precision=8, which will print at most 8 fractional digits, or fewer if an
element can be uniquely represented with fewer. A useful new mode is
floatmode="unique", which will output enough digits to specify the array
elements uniquely.

Numpy complex-floating-scalars with values like inf*j or nan*j now
print as infj and nanj, like the pure-python complex type.

The FloatFormat and LongFloatFormat classes are deprecated and should
both be replaced by FloatingFormat. Similarly ComplexFormat and
LongComplexFormat should be replaced by ComplexFloatingFormat.

void datatype elements are now printed in hex notation

A hex representation compatible with the python bytes type is now printed
for unstructured np.void elements, e.g., V4 datatype. Previously, in
python2 the raw void data of the element was printed to stdout, or in python3
the integer byte values were shown.

printing style for void datatypes is now independently customizable

The printing style of np.void arrays is now independently customizable
using the formatter argument to np.set_printoptions, using the
'void' key, instead of the catch-all numpystr key as before.

Reduced memory usage of np.loadtxt

np.loadtxt now reads files in chunks instead of all at once which decreases
its memory usage significantly for large files.


Multiple-field indexing/assignment of structured arrays

The indexing and assignment of structured arrays with multiple fields has
changed in a number of ways, as warned about in previous releases.

First, indexing a structured array with multiple fields, e.g.,
arr[['f1', 'f3']], returns a view into the original array instead of a
copy. The returned view will have extra padding bytes corresponding to
intervening fields in the original array, unlike the copy in 1.13, which will
affect code such as arr[['f1', 'f3']].view(newdtype).

Second, assignment between structured arrays will now occur "by position"
instead of "by field name". The Nth field of the destination will be set to the
Nth field of the source regardless of field name, unlike in numpy versions 1.6
to 1.13 in which fields in the destination array were set to the
identically-named field in the source array or to 0 if the source did not have
a field.

Correspondingly, the order of fields in a structured dtypes now matters when
computing dtype equality. For example, with the dtypes ::

x = dtype({'names': ['A', 'B'], 'formats': ['i4', 'f4'], 'offsets': [0, 4]})
y = dtype({'names': ['B', 'A'], 'formats': ['f4', 'i4'], 'offsets': [4, 0]})

the expression x == y will now return False, unlike before.
This makes dictionary based dtype specifications like
dtype({'a': ('i4', 0), 'b': ('f4', 4)}) dangerous in python < 3.6
since dict key order is not preserved in those versions.

Assignment from a structured array to a boolean array now raises a ValueError,
unlike in 1.13, where it always set the destination elements to True.

Assignment from structured array with more than one field to a non-structured
array now raises a ValueError. In 1.13 this copied just the first field of the
source to the destination.

Using field "titles" in multiple-field indexing is now disallowed, as is
repeating a field name in a multiple-field index.

The documentation for structured arrays in the user guide has been
significantly updated to reflect these changes.

Integer and Void scalars are now unaffected by np.set_string_function

Previously, unlike most other numpy scalars, the str and repr of
integer and void scalars could be controlled by np.set_string_function.
This is no longer possible.

0d array printing changed, style arg of array2string deprecated

Previously the str and repr of 0d arrays had idiosyncratic
implementations which returned str(a.item()) and 'array(' + repr(a.item()) + ')' respectively for 0d array a, unlike both numpy
scalars and higher dimension ndarrays.

Now, the str of a 0d array acts like a numpy scalar using str(a[()])
and the repr acts like higher dimension arrays using formatter(a[()]),
where formatter can be specified using np.set_printoptions. The
style argument of np.array2string is deprecated.

This new behavior is disabled in 1.13 legacy printing mode, see compatibility
notes above.

Seeding RandomState using an array requires a 1-d array

RandomState previously would accept empty arrays or arrays with 2 or more
dimensions, which resulted in either a failure to seed (empty arrays) or for
some of the passed values to be ignored when setting the seed.

MaskedArray objects show a more useful repr

The repr of a MaskedArray is now closer to the python code that would
produce it, with arrays now being shown with commas and dtypes. Like the other
formatting changes, this can be disabled with the 1.13 legacy printing mode in
order to help transition doctests.

The repr of np.polynomial classes is more explicit

It now shows the domain and window parameters as keyword arguments to make
them more clear::

>>> np.polynomial.Polynomial(range(4))
Polynomial([0.,  1.,  2.,  3.], domain=[-1,  1], window=[-1,  1])



dddfd1effddd4b73120bfa0f31a27f30  numpy-1.14.0-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
ab7e3518c7fe2a0d8e3c1e90c32ab57b  numpy-1.14.0-cp27-cp27m-manylinux1_i686.whl
8ba61f88afea560cc93ffddc9ea717d8  numpy-1.14.0-cp27-cp27m-manylinux1_x86_64.whl
edea7b57d4d924173c9c6d8125affe4e  numpy-1.14.0-cp27-cp27mu-manylinux1_i686.whl
bb048ccc49572e5e661ec7ead183272d  numpy-1.14.0-cp27-cp27mu-manylinux1_x86_64.whl
23248896d89fd09ef06aecbdc1e74eb7  numpy-1.14.0-cp27-none-win32.whl
09bef789f8d9352cafe98a6773c53f3a  numpy-1.14.0-cp27-none-win_amd64.whl
8bc2cc282df9597fe4c5fda72d0ff851  numpy-1.14.0-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
c9ce8e4de0293585ad8455a829cdbdff  numpy-1.14.0-cp34-cp34m-manylinux1_i686.whl
8179996e99adfbe7c66c5a9f7ad37139  numpy-1.14.0-cp34-cp34m-manylinux1_x86_64.whl
bf839d99514ecc03da1a2dc42808c1c7  numpy-1.14.0-cp34-none-win32.whl
80ead56627e40a00d0832793f5798ce8  numpy-1.14.0-cp34-none-win_amd64.whl
1a58fbaea199e425b71b3bfb1276d957  numpy-1.14.0-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
00cbdb6d9aa1eca97ecdb73a2705730b  numpy-1.14.0-cp35-cp35m-manylinux1_i686.whl
47de646ff0d4591431030ee93412f9f3  numpy-1.14.0-cp35-cp35m-manylinux1_x86_64.whl
f42aab8c9d6a21f60b051d6ea0b33425  numpy-1.14.0-cp35-none-win32.whl
b6d917bb34760e0f659c671efc08c602  numpy-1.14.0-cp35-none-win_amd64.whl
5a6456f4471b2f7d03fec5759e929544  numpy-1.14.0-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
3c72836ee01e7b88d7cebee0a1c94c6e  numpy-1.14.0-cp36-cp36m-manylinux1_i686.whl
34f763b99cc39ca3224c158ec82e2b39  numpy-1.14.0-cp36-cp36m-manylinux1_x86_64.whl
3bee8e2e4414a9df909d6510bd803aa1  numpy-1.14.0-cp36-none-win32.whl
f3db47f66b406f2803b681051f452f6e  numpy-1.14.0-cp36-none-win_amd64.whl
c573e2d2f26a5786d5198a660f87d8e4  numpy-1.14.0.tar.gz
c12d4bf380ac925fcdc8a59ada6c3298  numpy-1.14.0.zip


428cd3c0b197cf857671353d8c85833193921af9fafcc169a1f29c7185833d50  numpy-1.14.0-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
a476e437d73e5754aa66e1e75840d0163119c3911b7361f4cd06985212a3c3fb  numpy-1.14.0-cp27-cp27m-manylinux1_i686.whl
289ff717138cd9aa133adcbd3c3e284458b9c8230db4d42b39083a3407370317  numpy-1.14.0-cp27-cp27m-manylinux1_x86_64.whl
c5eccb4bf96dbb2436c61bb3c2658139e779679b6ae0d04c5e268e6608b58053  numpy-1.14.0-cp27-cp27mu-manylinux1_i686.whl
75471acf298d455b035226cc609a92aee42c4bb6aa71def85f77fa2c2b646b61  numpy-1.14.0-cp27-cp27mu-manylinux1_x86_64.whl
5c54fb98ecf42da59ed93736d1c071842482b18657eb16ba6e466bd873e1b923  numpy-1.14.0-cp27-none-win32.whl
9ddf384ac3aacb72e122a8207775cc29727cbd9c531ee1a4b95754f24f42f7f3  numpy-1.14.0-cp27-none-win_amd64.whl
781d3197da49c421a07f250750de70a52c42af08ca02a2f7bdb571c0625ae7eb  numpy-1.14.0-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
93b26d6c06a22e64d56aaca32aaaffd27a4143db0ac2f21a048f0b571f2bfc55  numpy-1.14.0-cp34-cp34m-manylinux1_i686.whl
b2547f57d05ba59df4289493254f29f4c9082d255f1f97b7e286f40f453e33a1  numpy-1.14.0-cp34-cp34m-manylinux1_x86_64.whl
eef6af1c752eef538a96018ef9bdf8e37bbf28aab50a1436501a4aa47a6467df  numpy-1.14.0-cp34-none-win32.whl
ff8a4b2c3ac831964f529a2da506c28d002562b230261ae5c16885f5f53d2e75  numpy-1.14.0-cp34-none-win_amd64.whl
194074058c22a4066e1b6a4ea432486ee468d24ab16f13630c1030409e6b8666  numpy-1.14.0-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
4e13f1a848fde960dea33702770265837c72b796a6a3eaac7528cfe75ddefadd  numpy-1.14.0-cp35-cp35m-manylinux1_i686.whl
91101216d72749df63968d86611b549438fb18af2c63849c01f9a897516133c7  numpy-1.14.0-cp35-cp35m-manylinux1_x86_64.whl
97507349abb7d1f6b76b877258defe8720833881dc7e7fd052bac90c88587387  numpy-1.14.0-cp35-none-win32.whl
1479b46b6040b5c689831496354c8859c456b152d37315673a0c18720b41223b  numpy-1.14.0-cp35-none-win_amd64.whl
98b1ac79c160e36093d7914244e40ee1e7164223e795aa2c71dcce367554e646  numpy-1.14.0-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
24bbec9a199f938eab75de8390f410969bc33c218e5430fa1ae9401b00865255  numpy-1.14.0-cp36-cp36m-manylinux1_i686.whl
7880f412543e96548374a4bb1d75e4cdb8cad80f3a101ed0f8d0e0428f719c1c  numpy-1.14.0-cp36-cp36m-manylinux1_x86_64.whl
6112f152b76a28c450bbf665da11757078a724a90330112f5b7ea2d6b6cefd67  numpy-1.14.0-cp36-none-win32.whl
7c5276763646480143d5f3a6c2acb2885460c765051a1baf4d5070f63d05010f  numpy-1.14.0-cp36-none-win_amd64.whl
c45d99134bb07600e916537c51c22e87f9e0c44b05d71018ab4907e195f007ce  numpy-1.14.0.tar.gz
3de643935b212307b420248018323a44ec51987a336d1d747c1322afc3c099fb  numpy-1.14.0.zip