v1.14.0
==========================
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
Highlights
-
The
np.einsum
function uses BLAS when possible -
genfromtxt
,loadtxt
,fromregex
andsavetxt
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
andformat_float_scientific
: format
floating-point scalars unambiguously with control of rounding and padding. -
PyArray_ResolveWritebackIfCopy
andPyArray_SetWritebackIfCopyBase
,
new C-API functions useful in achieving PyPy compatibity.
Deprecations
-
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, usearray.size > 0
. -
Calling
np.bincount
withminlength=None
is deprecated.
minlength=0
should be used instead. -
Calling
np.fromstring
with the default value of thesep
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
usePyArray_SetWritebackIfCopyBase
instead, seeC API changes
below for
more details. -
The use of
UPDATEIFCOPY
arrays is deprecated, seeC 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 thatissubdtype(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 forrcond
will be changed. Thercond
parameter tonp.linalg.lstsq
will change its default to machine precision
times the largest of the input array dimensions. A FutureWarning is issued
whenrcond
is not passed explicitly. -
a.flat.__array__()
will return a writeable copy ofa
whena
is
non-contiguous. Previously it returned an UPDATEIFCOPY array whena
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 ofnp.void
datatype will
return abytes
object instead of a buffer or int array, the same as
returned bybytes(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_value
s 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
dtype('float32')
>>> 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
doctests.
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 newsign
option tonp.set_printoptions
. - Floating-point arrays and scalars use a new algorithm for decimal
representations, giving the shortest unique representation. This will
usually shortenfloat16
fractional output, and sometimesfloat32
and
float128
output.float64
should be unaffected. See the new
floatmode
option tonp.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.
- The
-
For other data types:
- Non-finite complex scalars print like
nanj
instead ofnan*j
. NaT
values in datetime arrays are now properly aligned.- Arrays and scalars of
np.void
datatype are now printed using hex
notation.
- Non-finite complex scalars print like
-
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.
Therepr
orstr
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
lines. - An extra space is no longer inserted on the first line if the elements are
too wide.
- The "dtype" part of ndarray reprs will now be printed on the next line
-
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
removed.
- A trailing comma is no longer inserted for
-
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
andrepr
. Thestyle
argument tonp.array2string
is deprecated. -
Arrays of
bool
datatype will omit the datatype in therepr
. -
User-defined
dtypes
(subclasses ofnp.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.
try:
np.set_printoptions(legacy='1.13')
except TypeError:
pass
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:
NPY_ARRAY_INOUT_ARRAY
should be replaced by NPY_ARRAY_INOUT_ARRAY2
and
NPY_ARRAY_INOUT_FARRAY
should be replaced by NPY_ARRAY_INOUT_FARRAY2
.
Arrays created with these new flags will then have the WRITEBACKIFCOPY
semantics.
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
abandonware.
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.
Improvements
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
fixed.
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 float
s. 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.
Changes
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])
Checksums
MD5
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
SHA256
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