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v1.21.0.dev0
December 02, 2020
v1.20.0.rc1 Changes
December 03, 2020
🚀 NumPy 1.20.0 Release Notes

🚀 This NumPy release is the largest so made to date, some 654 PRs
👀 contributed by 182 people have been merged. See the list of highlights
🚀 below for more details. The Python versions supported for this release
👍 are 3.7-3.9, support for Python 3.6 has been dropped. Highlights are
- Annotations for NumPy functions. This work is ongoing and
  👌 improvements can be expected pending feedback from users.
- Wider use of SIMD to increase execution speed of ufuncs. Much work
  has been done in introducing universal functions that will ease use
  of modern features across different hardware platforms. This work is
  ongoing.
- Preliminary work in changing the dtype and casting implementations
  in order to provide an easier path to extending dtypes. This work is
  ongoing but enough has been done to allow experimentation and
  feedback.
- 📚 Extensive documentation improvements comprising some 185 PR merges.
  This work is ongoing and part of the larger project to improve
  NumPy's online presence and usefulness to new users.
- Further cleanups related to removing Python 2.7. This improves code
  🚚 readability and removes technical debt.
- 👍 Preliminary support for the upcoming Cython 3.0.
🆕 New functions

The random.Generator class has a new permuted function.

The new function differs from shuffle and permutation in that the
subarrays indexed by an axis are permuted rather than the axis being
treated as a separate 1-D array for every combination of the other
indexes. For example, it is now possible to permute the rows or columns
of a 2-D array.

(gh-15121)

sliding_window_view provides a sliding window view for numpy arrays

[numpy.lib.stride_tricks.sliding_window_view]{.title-ref} constructs
views on numpy arrays that offer a sliding or moving window access to
the array. This allows for the simple implementation of certain
algorithms, such as running means.

(gh-17394)

[numpy.broadcast_shapes]{.title-ref} is a new user-facing function

[~numpy.broadcast_shapes]{.title-ref} gets the resulting shape from
broadcasting the given shape tuples against each other.
```
>>> np.broadcast_shapes((1, 2), (3, 1))
(3, 2)

>>> np.broadcast_shapes(2, (3, 1))
(3, 2)

>>> np.broadcast_shapes((6, 7), (5, 6, 1), (7,), (5, 1, 7))
(5, 6, 7)
```
(gh-17535)

🗄 Deprecations

🗄 Using the aliases of builtin types like np.int is deprecated

For a long time, np.int has been an alias of the builtin int. This
is repeatedly a cause of confusion for newcomers, and is also simply not
👉 useful.

🗄 These aliases have been deprecated. The table below shows the full list
🗄 of deprecated aliases, along with their exact meaning. Replacing uses of
items in the first column with the contents of the second column will
🗄 work identically and silence the deprecation warning.

In many cases, it may have been intended to use the types from the third
column. Be aware that use of these types may result in subtle but
desirable behavior changes.

🗄 Deprecated name Identical to Possibly intended numpy type

numpy.boolbool [numpy.bool_]{.title-ref}
0️⃣ numpy.intint [numpy.int_]{.title-ref} (default int dtype), [numpy.cint]{.title-ref} (C int)
numpy.floatfloat [numpy.float_]{.title-ref}, [numpy.double]{.title-ref} (equivalent)
numpy.complexcomplex [numpy.complex_]{.title-ref}, [numpy.cdouble]{.title-ref} (equivalent)
numpy.objectobject [numpy.object_]{.title-ref}
numpy.strstr [numpy.str_]{.title-ref}
numpy.longint (long on Python 2) [numpy.int_]{.title-ref} (C long), [numpy.longlong]{.title-ref} (largest integer type)
numpy.unicodestr (unicode on Python 2) [numpy.unicode_]{.title-ref}

🗄 Note that for technical reasons these deprecation warnings will only be
emitted on Python 3.7 and above.

(gh-14882)

🗄 Passing shape=None to functions with a non-optional shape argument is deprecated

🗄 Previously, this was an alias for passing shape=(). This deprecation
is emitted by [PyArray_IntpConverter]{.title-ref} in the C API. If your
👍 API is intended to support passing None, then you should check for
None prior to invoking the converter, so as to be able to distinguish
None and ().

(gh-15886)

Indexing errors will be reported even when index result is empty

In the future, NumPy will raise an IndexError when an integer array
index contains out of bound values even if a non-indexed dimension is of
🗄 length 0. This will now emit a DeprecationWarning. This can happen when
the array is previously empty, or an empty slice is involved:
```
arr1 = np.zeros((5, 0))
arr1[[20]]
arr2 = np.zeros((5, 5))
arr2[[20], :0]
```
Previously the non-empty index [20] was not checked for correctness.
🗄 It will now be checked causing a deprecation warning which will be
turned into an error. This also applies to assignments.

(gh-15900)

🗄 Inexact matches for mode and searchside are deprecated

Inexact and case insensitive matches for mode and searchside were
🗄 valid inputs earlier and will give a DeprecationWarning now. For
🗄 example, below are some example usages which are now deprecated and will
🗄 give a DeprecationWarning:
```
import numpy as np
arr = np.array([[3, 6, 6], [4, 5, 1]])
# mode: inexact match
np.ravel_multi_index(arr, (7, 6), mode="clap") # should be "clip"
# searchside: inexact match
np.searchsorted(arr[0], 4, side='random') # should be "right"
```
(gh-16056)

🗄 Deprecation of [numpy.dual]{.title-ref}

🗄 The module [numpy.dual]{.title-ref} is deprecated. Instead of importing
functions from [numpy.dual]{.title-ref}, the functions should be
imported directly from NumPy or SciPy.

(gh-16156)

🗄 outer and ufunc.outer deprecated for matrix

np.matrix use with [~numpy.outer]{.title-ref} or generic ufunc outer
calls such as numpy.add.outer. Previously, matrix was converted to an
array here. This will not be done in the future requiring a manual
conversion to arrays.

(gh-16232)

💅 Further Numeric Style types Deprecated

💅 The remaining numeric-style type codes Bytes0, Str0, Uint32,
🗄 Uint64, and Datetime64 have been deprecated. The lower-case variants
should be used instead. For bytes and string "S" and "U" are further
alternatives.

(gh-16554)

🗄 The ndincr method of ndindex is deprecated

📚 The documentation has warned against using this function since NumPy
1.8. Use next(it) instead of it.ndincr().

(gh-17233)

Future Changes

Arrays cannot be using subarray dtypes

Array creation and casting using np.array(arr, dtype) and
arr.astype(dtype) will use different logic when dtype is a subarray
dtype such as np.dtype("(2)i,").

For such a dtype the following behaviour is true:
```
res = np.array(arr, dtype)

res.dtype is not dtype
res.dtype is dtype.base
res.shape == arr.shape + dtype.shape
```
But res is filled using the logic:
```
res = np.empty(arr.shape + dtype.shape, dtype=dtype.base)
res[...] = arr
```
which uses incorrect broadcasting (and often leads to an error). In the
future, this will instead cast each element individually, leading to the
same result as:
```
res = np.array(arr, dtype=np.dtype(["f", dtype]))["f"]
```
Which can normally be used to opt-in to the new behaviour.

This change does not affect np.array(list, dtype="(2)i,") unless the
list itself includes at least one array. In particular, the behaviour
is unchanged for a list of tuples.

(gh-17596)

🗄 Expired deprecations
💅 The deprecation of numeric style type-codes np.dtype("Complex64")
(with upper case spelling), is expired. "Complex64" corresponded
to "complex128" and "Complex32" corresponded to "complex64".
🗄 The deprecation of np.sctypeNA and np.typeNA is expired. Both
🚚 have been removed from the public API. Use np.typeDict instead.

(gh-16554)
The 14-year deprecation of np.ctypeslib.ctypes_load_library is
expired. Use ~numpy.ctypeslib.load_library{.interpreted-text
role="func"} instead, which is identical.

(gh-17116)

🚚 Financial functions removed

🚚 In accordance with NEP 32, the financial functions are removed from
🚚 NumPy 1.20. The functions that have been removed are fv, ipmt,
irr, mirr, nper, npv, pmt, ppmt, pv, and rate. These
functions are available in the
numpy_financial library.

(gh-17067)

Compatibility notes

Same kind casting in concatenate with axis=None

When [~numpy.concatenate]{.title-ref} is called with axis=None, the
flattened arrays were cast with unsafe. Any other axis choice uses
🗄 "same kind". That different default has been deprecated and "same
kind" casting will be used instead. The new casting keyword argument
can be used to retain the old behaviour.

(gh-16134)

NumPy Scalars are cast when assigned to arrays

When creating or assigning to arrays, in all relevant cases NumPy
scalars will now be cast identically to NumPy arrays. In particular this
🔄 changes the behaviour in some cases which previously raised an error:
```
np.array([np.float64(np.nan)], dtype=np.int64)
```
will succeed and return an undefined result (usually the smallest
possible integer). This also affects assignments:
```
arr[0] = np.float64(np.nan)
```
At this time, NumPy retains the behaviour for:
```
np.array(np.float64(np.nan), dtype=np.int64)
```
The above changes do not affect Python scalars:
```
np.array([float("NaN")], dtype=np.int64)
```
remains unaffected (np.nan is a Python float, not a NumPy one).
Unlike signed integers, unsigned integers do not retain this special
case, since they always behaved more like casting. The following code
stops raising an error:
```
np.array([np.float64(np.nan)], dtype=np.uint64)
```
To avoid backward compatibility issues, at this time assignment from
👍 datetime64 scalar to strings of too short length remains supported.
This means that np.asarray(np.datetime64("2020-10-10"), dtype="S5")
succeeds now, when it failed before. In the long term this may be
🗄 deprecated or the unsafe cast may be allowed generally to make
assignment of arrays and scalars behave consistently.

Array coercion changes when Strings and other types are mixed

When strings and other types are mixed, such as:
```
np.array(["string", np.float64(3.)], dtype="S")
```
The results will change, which may lead to string dtypes with longer
strings in some cases. In particularly, if dtype="S" is not provided
any numerical value will lead to a string results long enough to hold
all possible numerical values. (e.g. "S32" for floats). Note that you
should always provide dtype="S" when converting non-strings to
strings.

If dtype="S" is provided the results will be largely identical to
before, but NumPy scalars (not a Python float like 1.0), will still
enforce a uniform string length:
```
np.array([np.float64(3.)], dtype="S") # gives "S32"
np.array([3.0], dtype="S") # gives "S3"
```
Previously the first version gave the same result as the second.

Array coercion restructure

Array coercion has been restructured. In general, this should not affect
👉 users. In extremely rare corner cases where array-likes are nested:
```
np.array([array_like1])
```
Things will now be more consistent with:
```
np.array([np.array(array_like1)])
```
This could potentially subtly change output for badly defined
array-likes. We are not aware of any such case where the results were
not clearly incorrect previously.

(gh-16200)

Writing to the result of [numpy.broadcast_arrays]{.title-ref} will export readonly buffers

⚠ In NumPy 1.17 [numpy.broadcast_arrays]{.title-ref} started warning when
⚠ the resulting array was written to. This warning was skipped when the
array was used through the buffer interface (e.g. memoryview(arr)).
The same thing will now occur for the two protocols
__array_interface__, and __array_struct__ returning read-only
⚠ buffers instead of giving a warning.

(gh-16350)

💅 Numeric-style type names have been removed from type dictionaries

🔀 To stay in sync with the deprecation for np.dtype("Complex64") and
💅 other numeric-style (capital case) types. These were removed from
np.sctypeDict and np.typeDict. You should use the lower case
🔖 versions instead. Note that "Complex64" corresponds to "complex128"
💅 and "Complex32" corresponds to "complex64". The numpy style (new)
🔖 versions, denote the full size and not the size of the real/imaginary
part.

(gh-16554)

The operator.concat function now raises TypeError for array arguments

The previous behavior was to fall back to addition and add the two
arrays, which was thought to be unexpected behavior for a concatenation
function.

(gh-16570)

🚚 nickname attribute removed from ABCPolyBase

🚚 An abstract property nickname has been removed from ABCPolyBase as
it was no longer used in the derived convenience classes. This may
affect users who have derived classes from ABCPolyBase and overridden
the methods for representation and display, e.g. __str__, __repr__,
_repr_latex, etc.

(gh-16589)

float->timedelta and uint64->timedelta promotion will raise a TypeError

Float and timedelta promotion consistently raises a TypeError.
np.promote_types("float32", "m8") aligns with
np.promote_types("m8", "float32") now and both raise a TypeError.
Previously, np.promote_types("float32", "m8") returned "m8" which
was considered a bug.

Uint64 and timedelta promotion consistently raises a TypeError.
np.promote_types("uint64", "m8") aligns with
np.promote_types("m8", "uint64") now and both raise a TypeError.
Previously, np.promote_types("uint64", "m8") returned "m8" which was
considered a bug.

(gh-16592)

numpy.genfromtxt now correctly unpacks structured arrays

Previously, [numpy.genfromtxt]{.title-ref} failed to unpack if it was
called with unpack=True and a structured datatype was passed to the
dtype argument (or dtype=None was passed and a structured datatype
was inferred). For example:
```
>>> data = StringIO("21 58.0\n35 72.0")
>>> np.genfromtxt(data, dtype=None, unpack=True)
array([(21, 58.), (35, 72.)], dtype=[('f0', '<i8'), ('f1', '<f8')])
```
Structured arrays will now correctly unpack into a list of arrays, one
for each column:
```
>>> np.genfromtxt(data, dtype=None, unpack=True)
[array([21, 35]), array([58., 72.])]
```
(gh-16650)

0️⃣ mgrid, r_, etc. consistently return correct outputs for non-default precision input

Previously,
np.mgrid[np.float32(0.1):np.float32(0.35):np.float32(0.1),] and
np.r_[0:10:np.complex64(3j)] failed to return meaningful output. This
🐛 bug potentially affects [~numpy.mgrid]{.title-ref},
[~numpy.ogrid]{.title-ref}, [~numpy.r_]{.title-ref}, and
[~numpy.c_]{.title-ref} when an input with dtype other than the
0️⃣ default float64 and complex128 and equivalent Python types were
🛠 used. The methods have been fixed to handle varying precision correctly.

(gh-16815)

Boolean array indices with mismatching shapes now properly give IndexError

Previously, if a boolean array index matched the size of the indexed
array but not the shape, it was incorrectly allowed in some cases. In
other cases, it gave an error, but the error was incorrectly a
ValueError with a message about broadcasting instead of the correct
IndexError.

For example, the following used to incorrectly give
ValueError: operands could not be broadcast together with shapes (2,2) (1,4):
```
np.empty((2, 2))[np.array([[True, False, False, False]])]
```
And the following used to incorrectly return array([], dtype=float64):
```
np.empty((2, 2))[np.array([[False, False, False, False]])]
```
Both now correctly give
IndexError: boolean index did not match indexed array along dimension 0; dimension is 2 but corresponding boolean dimension is 1.

(gh-17010)

Casting errors interrupt Iteration

When iterating while casting values, an error may stop the iteration
earlier than before. In any case, a failed casting operation always
returned undefined, partial results. Those may now be even more
undefined and partial. For users of the NpyIter C-API such cast errors
will now cause the [iternext()]{.title-ref} function to return 0 and
thus abort iteration. Currently, there is no API to detect such an error
directly. It is necessary to check PyErr_Occurred(), which may be
problematic in combination with NpyIter_Reset. These issues always
existed, but new API could be added if required by users.

(gh-17029)

f2py generated code may return unicode instead of byte strings

Some byte strings previously returned by f2py generated code may now be
unicode strings. This results from the ongoing Python2 -> Python3
cleanup.

(gh-17068)

The first element of the __array_interface__ ["data"] tuple must be an integer

This has been the documented interface for many years, but there was
still code that would accept a byte string representation of the pointer
➕ address. That code has been removed, passing the address as a byte
string will now raise an error.

(gh-17241)

poly1d respects the dtype of all-zero argument

Previously, constructing an instance of poly1d with all-zero
coefficients would cast the coefficients to np.float64. This affected
the output dtype of methods which construct poly1d instances
internally, such as np.polymul.

(gh-17577)

The numpy.i file for swig is Python 3 only.

⚡️ Uses of Python 2.7 C-API functions have been updated to Python 3 only.
👉 Users who need the old version should take it from an older version of
NumPy.

(gh-17580)

Void dtype discovery in np.array

In calls using np.array(..., dtype="V"), arr.astype("V"), and
similar a TypeError will now be correctly raised unless all elements
have the identical void length. An example for this is:
```
np.array([b"1", b"12"], dtype="V")
```
Which previously returned an array with dtype "V2" which cannot
represent b"1" faithfully.

(gh-17706)

C API changes

Size of np.ndarray and np.void_ changed

The size of the PyArrayObject and PyVoidScalarObject structures have
🔄 changed. The following header definition has been removed:
```
#define NPY_SIZEOF_PYARRAYOBJECT (sizeof(PyArrayObject_fields))
```
since the size must not be considered a compile time constant: it will
🔄 change for different runtime versions of NumPy.

The most likely relevant use are potential subclasses written in C which
⚡️ will have to be recompiled and should be updated. Please see the
📚 documentation for :cPyArrayObject{.interpreted-text role="type"} for
more details and contact the NumPy developers if you are affected by
this change.

NumPy will attempt to give a graceful error but a program expecting a
🛠 fixed structure size may have undefined behaviour and likely crash.

(gh-16938)

🆕 New Features

where keyword argument for numpy.all and numpy.any functions

The keyword argument where is added and allows to only consider
specified elements or subaxes from an array in the Boolean evaluation of
all and any. This new keyword is available to the functions all
and any both via numpy directly or in the methods of
numpy.ndarray.

Any broadcastable Boolean array or a scalar can be set as where. It
0️⃣ defaults to True to evaluate the functions for all elements in an
array if where is not set by the user. Examples are given in the
📚 documentation of the functions.

where keyword argument for numpy functions mean, std, var

The keyword argument where is added and allows to limit the scope in
the calculation of mean, std and var to only a subset of elements.
It is available both via numpy directly or in the methods of
numpy.ndarray.

Any broadcastable Boolean array or a scalar can be set as where. It
0️⃣ defaults to True to evaluate the functions for all elements in an
array if where is not set by the user. Examples are given in the
📚 documentation of the functions.

(gh-15852)

norm=backward, forward keyword options for numpy.fft functions

The keyword argument option norm=backward is added as an alias for
0️⃣ None and acts as the default option; using it has the direct
transforms unscaled and the inverse transforms scaled by 1/n.

Using the new keyword argument option norm=forward has the direct
transforms scaled by 1/n and the inverse transforms unscaled (i.e.
0️⃣ exactly opposite to the default option norm=backward).

(gh-16476)

NumPy is now typed

Type annotations have been added for large parts of NumPy. There is also
a new [numpy.typing]{.title-ref} module that contains useful types for
end-users. The currently available types are
- ArrayLike: for objects that can be coerced to an array
- DtypeLike: for objects that can be coerced to a dtype
(gh-16515)

numpy.typing is accessible at runtime

The types in numpy.typing can now be imported at runtime. Code like
the following will now work:
```
from numpy.typing import ArrayLike
x: ArrayLike = [1, 2, 3, 4]
```
(gh-16558)

New __f2py_numpy_version__ attribute for f2py generated modules.

Because f2py is released together with NumPy, __f2py_numpy_version__
provides a way to track the version f2py used to generate the module.

(gh-16594)

✅ mypy tests can be run via runtests.py

🔧 Currently running mypy with the NumPy stubs configured requires either:
- Installing NumPy
- ➕ Adding the source directory to MYPYPATH and linking to the
  mypy.ini
Both options are somewhat inconvenient, so add a --mypy option to
✅ runtests that handles setting things up for you. This will also be
👉 useful in the future for any typing codegen since it will ensure the
project is built before type checking.

(gh-17123)

Negation of user defined BLAS/LAPACK detection order

[~numpy.distutils]{.title-ref} allows negation of libraries when
🚚 determining BLAS/LAPACK libraries. This may be used to remove an item
from the library resolution phase, i.e. to disallow NetLIB libraries one
could do:
```
NPY_BLAS_ORDER='^blas' NPY_LAPACK_ORDER='^lapack' python setup.py build
```
That will use any of the accelerated libraries instead.

(gh-17219)

👍 Allow passing optimizations arguments to asv build

It is now possible to pass -j, --cpu-baseline, --cpu-dispatch and
🏗 --disable-optimization flags to ASV build when the --bench-compare
argument is used.

(gh-17284)

👍 The NVIDIA HPC SDK nvfortran compiler is now supported

👌 Support for the nvfortran compiler, a version of pgfortran, has been
➕ added.

(gh-17344)

dtype option for cov and corrcoef

The dtype option is now available for [numpy.cov]{.title-ref} and
[numpy.corrcoef]{.title-ref}. It specifies which data-type the returned
0️⃣ result should have. By default the functions still return a
[numpy.float64]{.title-ref} result.

(gh-17456)

👌 Improvements

Improved string representation for polynomials (__str__)

The string representation (__str__) of all six polynomial types in
⚡️ [numpy.polynomial]{.title-ref} has been updated to give the polynomial
as a mathematical expression instead of an array of coefficients. Two
📦 package-wide formats for the polynomial expressions are available - one
using Unicode characters for superscripts and subscripts, and another
using only ASCII characters.

(gh-15666)

✂ Remove the Accelerate library as a candidate LAPACK library

🚚 Apple no longer supports Accelerate. Remove it.

(gh-15759)

Object arrays containing multi-line objects have a more readable repr

If elements of an object array have a repr containing new lines, then
the wrapped lines will be aligned by column. Notably, this improves the
repr of nested arrays:
```
>>> np.array([np.eye(2), np.eye(3)], dtype=object)
array([array([[1., 0.],
              [0., 1.]]),
       array([[1., 0., 0.],
              [0., 1., 0.],
              [0., 0., 1.]])], dtype=object)
```
(gh-15997)

👍 Concatenate supports providing an output dtype

👌 Support was added to [~numpy.concatenate]{.title-ref} to provide an
output dtype and casting using keyword arguments. The dtype
argument cannot be provided in conjunction with the out one.

(gh-16134)

Thread safe f2py callback functions

Callback functions in f2py are now thread safe.

(gh-16519)

👍 [numpy.core.records.fromfile]{.title-ref} now supports file-like objects

[numpy.rec.fromfile]{.title-ref} can now use file-like objects, for
instance :pyio.BytesIO{.interpreted-text role="class"}

(gh-16675)

👍 RPATH support on AIX added to distutils

This allows SciPy to be built on AIX.

(gh-16710)

👉 Use f90 compiler specified by the command line args

The compiler command selection for Fortran Portland Group Compiler is
🔄 changed in [numpy.distutils.fcompiler]{.title-ref}. This only affects
the linking command. This forces the use of the executable provided by
💻 the command line option (if provided) instead of the pgfortran
💻 executable. If no executable is provided to the command line option it
0️⃣ defaults to the pgf90 executable, wich is an alias for pgfortran
📚 according to the PGI documentation.

(gh-16730)

➕ Add NumPy declarations for Cython 3.0 and later

The pxd declarations for Cython 3.0 were improved to avoid using
🗄 deprecated NumPy C-API features. Extension modules built with Cython
3.0+ that use NumPy can now set the C macro
NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION to avoid C compiler warnings
🗄 about deprecated API usage.

(gh-16986)

👉 Make the window functions exactly symmetric

👉 Make sure the window functions provided by NumPy are symmetric. There
were previously small deviations from symmetry due to numerical
👍 precision that are now avoided by better arrangement of the computation.

(gh-17195)

🐎 Performance improvements and changes

Enable multi-platform SIMD compiler optimizations

A series of improvements for NumPy infrastructure to pave the way to
NEP-38 , that can be summarized as follow:
🆕 New Build Arguments
- --cpu-baseline to specify the minimal set of required
  0️⃣ optimizations, default value is min which provides the minimum
  CPU features that can safely run on a wide range of users
  platforms.
- --cpu-dispatch to specify the dispatched set of additional
  0️⃣ optimizations, default value is max -xop -fma4 which enables
  all CPU features, except for AMD legacy features.
- --disable-optimization to explicitly disable the whole new
  improvements, It also adds a new C compiler #definition
  called NPY_DISABLE_OPTIMIZATION which it can be used as guard
  for any SIMD code.
Advanced CPU dispatcher

A flexible cross-architecture CPU dispatcher built on the top of
👍 Python/Numpy distutils, support all common compilers with a wide
range of CPU features.

The new dispatcher requires a special file extension *.dispatch.c
to mark the dispatch-able C sources. These sources have the
ability to be compiled multiple times so that each compilation
🖨 process represents certain CPU features and provides different

definitions and flags that affect the code paths.
New auto-generated C header
core/src/common/\_cpu\_dispatch.h

This header is generated by the distutils module ccompiler_opt,
and contains all the #definitions and headers of instruction sets,
🔧 that had been configured through command arguments
'--cpu-baseline' and '--cpu-dispatch'.
🆕 New C header core/src/common/npy\_cpu\_dispatch.h

This header contains all utilities that required for the whole CPU
dispatching process, it also can be considered as a bridge linking
the new infrastructure work with NumPy CPU runtime detection.
➕ Add new attributes to NumPy umath module(Python level)
- __cpu_baseline__ a list contains the minimal set of required
  👍 optimizations that supported by the compiler and platform
  according to the specified values to command argument
  '--cpu-baseline'.
- __cpu_dispatch__ a list contains the dispatched set of
  ➕ additional optimizations that supported by the compiler and
  platform according to the specified values to command argument
  '--cpu-dispatch'.
✅ Print the supported CPU features during the run of PytestTester

(gh-13516)

🔄 Changes

🔄 Changed behavior of divmod(1., 0.) and related functions

The changes also assure that different compiler versions have the same
behavior for nan or inf usages in these operations. This was previously
compiler dependent, we now force the invalid and divide by zero flags,
making the results the same across compilers. For example, gcc-5, gcc-8,
or gcc-9 now result in the same behavior. The changes are tabulated
below:

🍎 Operator Old Warning New Warning Old Result New Result Works on MacOS

np.divmod(1.0, 0.0) Invalid Invalid and Dividebyzero nan, nan inf, nan Yes
np.fmod(1.0, 0.0) Invalid Invalid nan nan No? Yes
np.floor_divide(1.0, 0.0) Invalid Dividebyzero nan inf Yes
np.remainder(1.0, 0.0) Invalid Invalid nan nan Yes

: Summary of New Behavior

(gh-16161)

np.linspace on integers now uses floor

When using a int dtype in [numpy.linspace]{.title-ref}, previously
float values would be rounded towards zero. Now
[numpy.floor]{.title-ref} is used instead, which rounds toward -inf.
This changes the results for negative values. For example, the following
would previously give:
```
>>> np.linspace(-3, 1, 8, dtype=int)
array([-3, -2, -1, -1, 0, 0, 0, 1])
```
and now results in:
```
>>> np.linspace(-3, 1, 8, dtype=int)
array([-3, -3, -2, -2, -1, -1, 0, 1])
```
The former result can still be obtained with:
```
>>> np.linspace(-3, 1, 8).astype(int)
array([-3, -2, -1, -1, 0, 0, 0, 1])
```
(gh-16841)

Checksums

MD5
```
c182567139ec82a140d5fbf363ed1697 numpy-1.20.0rc1-cp37-cp37m-macosx_10_9_x86_64.whl
06de963440e3dcf0dfd619a2b4936e59 numpy-1.20.0rc1-cp37-cp37m-manylinux1_i686.whl
436a34182e234dce16bc597929a61313 numpy-1.20.0rc1-cp37-cp37m-manylinux1_x86_64.whl
73aa4c274c2aee36f5f4d1c58b74a4a8 numpy-1.20.0rc1-cp37-cp37m-manylinux2010_i686.whl
cc0bbd29cca3f80dbb09a3177df6b677 numpy-1.20.0rc1-cp37-cp37m-manylinux2010_x86_64.whl
be993784ee6c9e9e95f949071f30a853 numpy-1.20.0rc1-cp37-cp37m-manylinux2014_aarch64.whl
971e01facbe869f95dda518ecc0b4c09 numpy-1.20.0rc1-cp37-cp37m-win32.whl
951e744fb554af874a0ba4bdbeedc882 numpy-1.20.0rc1-cp37-cp37m-win_amd64.whl
197de0d040463b4d6026e83284f272ac numpy-1.20.0rc1-cp38-cp38-macosx_10_9_x86_64.whl
27f146dbee25d7058def106f5c15eca0 numpy-1.20.0rc1-cp38-cp38-manylinux1_i686.whl
b0f5ec0b31566270f8546a79d73e3424 numpy-1.20.0rc1-cp38-cp38-manylinux1_x86_64.whl
2e9c06be7d826451b9227b11b3e6cd69 numpy-1.20.0rc1-cp38-cp38-manylinux2010_i686.whl
f9fb7537b1e8197824f47650e883c63d numpy-1.20.0rc1-cp38-cp38-manylinux2010_x86_64.whl
83ad71e9a7a46947e2fe203e3f822ad3 numpy-1.20.0rc1-cp38-cp38-manylinux2014_aarch64.whl
318da96660e8c8ce5bac22e851969d15 numpy-1.20.0rc1-cp38-cp38-win32.whl
051419fe996b984eced3a6e28320a45a numpy-1.20.0rc1-cp38-cp38-win_amd64.whl
bca434dd07cd58b2436e592efc72c10b numpy-1.20.0rc1-cp39-cp39-macosx_10_9_x86_64.whl
91beb7602bc10db4a912b9de8d082efe numpy-1.20.0rc1-cp39-cp39-manylinux2010_i686.whl
d213368ea0f28697597041ab175242f1 numpy-1.20.0rc1-cp39-cp39-manylinux2010_x86_64.whl
c1f3936e707014e1683eaeae30b71649 numpy-1.20.0rc1-cp39-cp39-manylinux2014_aarch64.whl
d4881ad35eb820a5e3a27bb47adcbb2b numpy-1.20.0rc1-cp39-cp39-win32.whl
ae4a01a84de51a0957452611dbad1199 numpy-1.20.0rc1-cp39-cp39-win_amd64.whl
c3d85a1bada3081b917ee9498ec4fb08 numpy-1.20.0rc1-pp37-pypy37_pp73-manylinux2010_x86_64.whl
94bb7d8f42e03c0c1cd37c230fcdfc14 numpy-1.20.0rc1.tar.gz
1ed93be9e6bfb1de153af93d20c4e443 numpy-1.20.0rc1.zip
```
SHA256
```
01e9029472857f8dd9868e1f83f3ff9df0b477e9e7554bc4455eb5293b8ae335 numpy-1.20.0rc1-cp37-cp37m-macosx_10_9_x86_64.whl
c6545bc46b3e4accaff4c542b4b4b95993ef0e4d74bb86fe19189b630740f76e numpy-1.20.0rc1-cp37-cp37m-manylinux1_i686.whl
b028d1104eb8f7c5f0bc5bdc4c35768efc755b459f3a67b38a79eb9b86e354c2 numpy-1.20.0rc1-cp37-cp37m-manylinux1_x86_64.whl
4789ad6cc531c9a24ce8cff59ac3c669e307599fba2bd40c2bc700d5b3013105 numpy-1.20.0rc1-cp37-cp37m-manylinux2010_i686.whl
e9b0138142f72a3c143f262fd435b331e9807a2eb1c0e2ff6904f2cdc9b9b1a5 numpy-1.20.0rc1-cp37-cp37m-manylinux2010_x86_64.whl
a2a4d00b119c71ba83fc1dd0f4dc71e2dd0fd61acd5cbd40541da4f9172427d3 numpy-1.20.0rc1-cp37-cp37m-manylinux2014_aarch64.whl
dcee4823291188e213d681b05a2749ff36a87d4933a91b9654fcf0e2bf02ce4a numpy-1.20.0rc1-cp37-cp37m-win32.whl
b1cf3925dda0920ee469c95260d2313f1f4a8d6381d42cfdd607e6fd991c4256 numpy-1.20.0rc1-cp37-cp37m-win_amd64.whl
10baa94959bcbea0070e82e7ed3db9090fbaf267811a74701bdc0a0697dfe2ae numpy-1.20.0rc1-cp38-cp38-macosx_10_9_x86_64.whl
7f7f08cc1a3415a61382a2c60bd71d5bd3efa33ee90ebc14ce4754dfa0a138c9 numpy-1.20.0rc1-cp38-cp38-manylinux1_i686.whl
1a544c8d7928c85fcfb3af1649aad7d60376e89f9bc5be89f6da6d5eff1ed107 numpy-1.20.0rc1-cp38-cp38-manylinux1_x86_64.whl
c9a21184a9b40793bc17a1456c3713a98c9466af2d1f849354cef0a756e2f7c6 numpy-1.20.0rc1-cp38-cp38-manylinux2010_i686.whl
09fcbaef175786b99287039ada5dcf2c9131b65ceab1807fa9a61e5b062091a8 numpy-1.20.0rc1-cp38-cp38-manylinux2010_x86_64.whl
ce8c2a2fbfdaf14fd7ed85e9a10bea9247f9f884bf504ed773aee8c0adcee220 numpy-1.20.0rc1-cp38-cp38-manylinux2014_aarch64.whl
dd298a8efe8c62acb94797932d39ad3b74d1c4c1f496fa259b28a677e8f4793e numpy-1.20.0rc1-cp38-cp38-win32.whl
55dcf4a830d1198a72b6afb72cb02879c3abb95024c250402851de66db94a30d numpy-1.20.0rc1-cp38-cp38-win_amd64.whl
76cda96f70435bf75cac28dd081bb9e47e8eaf2badd1d9ad5ab19723d74e5921 numpy-1.20.0rc1-cp39-cp39-macosx_10_9_x86_64.whl
ed955ab39527f7500b31dae6075b00f284a0c2eb23e7fd1a5a40a5066cab309f numpy-1.20.0rc1-cp39-cp39-manylinux2010_i686.whl
b38a2caf64429bab2c06b87eea0d2b24f736894f805f3d7210ecafb5573cb0de numpy-1.20.0rc1-cp39-cp39-manylinux2010_x86_64.whl
14aa454e438290d1ce464c80530157dc1ece86e34dd79a8077ca0e05ebab3665 numpy-1.20.0rc1-cp39-cp39-manylinux2014_aarch64.whl
3c27251c07f8e3cd727cab8cc275c1294be006ed725496090a6081f6d8d1d811 numpy-1.20.0rc1-cp39-cp39-win32.whl
4925c540f1bde557987c2d0b258b9c57cb6da020957ffa4ac355f13d6819b121 numpy-1.20.0rc1-cp39-cp39-win_amd64.whl
e9a5652afbe2128cb1734546608a0a0d5dbd21160738a40521464d7cb4cc22b5 numpy-1.20.0rc1-pp37-pypy37_pp73-manylinux2010_x86_64.whl
b4993844022e98fe363467ce42404ffd6f975239d294b6e4ea44a6e797891fac numpy-1.20.0rc1.tar.gz
98f4e754f1c3db7ca53e53b1ef6474703b167af75f3784f99b1fe4dd936ea77f numpy-1.20.0rc1.zip
```

v1.19.4 Changes

November 02, 2020

🚀 NumPy 1.19.4 Release Notes

🚀 NumPy 1.19.4 is a quick release to revert the OpenBLAS library version.
It was hoped that the 0.3.12 OpenBLAS version used in 1.19.3 would work
🐳 around the Microsoft fmod bug, but problems in some docker environments
turned up. Instead, 1.19.4 will use the older library and run a sanity
check on import, raising an error if the problem is detected. Microsoft
⬆️ is aware of the problem and has promised a fix, users should upgrade
when it becomes available.

🚀 This release supports Python 3.6-3.9

Contributors

🚀 A total of 1 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

Charles Harris

🔀 Pull requests merged

🚀 A total of 2 pull requests were merged for this release.

🏁 #17679: MAINT: Add check for Windows 10 version 2004 bug.
⏪ #17680: REV: Revert OpenBLAS to 1.19.2 version for 1.19.4

Checksums

MD5

09b6f7f17ca61f0f3b943d4107ea6a6c numpy-1.19.4-cp36-cp36m-macosx_10_9_x86_64.whl
bfb801672e0d9916407352f7158b5584 numpy-1.19.4-cp36-cp36m-manylinux1_i686.whl
2469be359c8c383509eaded8e758488a numpy-1.19.4-cp36-cp36m-manylinux1_x86_64.whl
4af398903b0957ad3a40ec17631879ed numpy-1.19.4-cp36-cp36m-manylinux2010_i686.whl
bb3f911ba616d36a2daff5b8e1402b1b numpy-1.19.4-cp36-cp36m-manylinux2010_x86_64.whl
3b754c1135f7aa3e6a7c1f46af6a84c9 numpy-1.19.4-cp36-cp36m-manylinux2014_aarch64.whl
9db8749b90405780614f126c77eef3bb numpy-1.19.4-cp36-cp36m-win32.whl
25bc59391b8b4f06eb28e74e97afc488 numpy-1.19.4-cp36-cp36m-win_amd64.whl
355d7f49b9e442f9e73580e64c8bf2c2 numpy-1.19.4-cp37-cp37m-macosx_10_9_x86_64.whl
3c1ce8ca6f6f11ea9d49859b2ffb70cf numpy-1.19.4-cp37-cp37m-manylinux1_i686.whl
5524143ee95cc7e3400dbbff709de7cd numpy-1.19.4-cp37-cp37m-manylinux1_x86_64.whl
c40206040b8ddb62309cbef1cdf0fa82 numpy-1.19.4-cp37-cp37m-manylinux2010_i686.whl
552839ea3bc2dfc98611254f8188feb8 numpy-1.19.4-cp37-cp37m-manylinux2010_x86_64.whl
2e5c50e57cff5085ffb32185591e49ed numpy-1.19.4-cp37-cp37m-manylinux2014_aarch64.whl
ce6c1cd93d5fc56d0de608b84cc14a7e numpy-1.19.4-cp37-cp37m-win32.whl
a73acaea97da74db366372b3d70219a7 numpy-1.19.4-cp37-cp37m-win_amd64.whl
2f52c91231b2b3c54535dee98a5ad0a3 numpy-1.19.4-cp38-cp38-macosx_10_9_x86_64.whl
e619d04f2ac42a9feb0efcc1d9901d94 numpy-1.19.4-cp38-cp38-manylinux1_i686.whl
01c2f102e73b2569cf3ebe5eab112c4e numpy-1.19.4-cp38-cp38-manylinux1_x86_64.whl
6a66109907b356ddd67f1e282e1879e6 numpy-1.19.4-cp38-cp38-manylinux2010_i686.whl
79354b01e11789bb5d12c9edc754297b numpy-1.19.4-cp38-cp38-manylinux2010_x86_64.whl
4f1b335dfe5c7fcf5c8c89983cef9f0b numpy-1.19.4-cp38-cp38-manylinux2014_aarch64.whl
949a5f9e9a75b9cbb3c74e4bf4eb0683 numpy-1.19.4-cp38-cp38-win32.whl
27eb1b83f3cac67fb26c7fe9a25b0635 numpy-1.19.4-cp38-cp38-win_amd64.whl
ae1e4a06e721e83b530860835c708690 numpy-1.19.4-cp39-cp39-macosx_10_9_x86_64.whl
d263c7d04c46d5ecca3b32ad11925bad numpy-1.19.4-cp39-cp39-manylinux1_i686.whl
132e95910d76b045caf1883146ec34a6 numpy-1.19.4-cp39-cp39-manylinux1_x86_64.whl
4d4e5f147fe6fdedbdde4df9eaf2a4b1 numpy-1.19.4-cp39-cp39-manylinux2010_i686.whl
5ac2071e995ff4fc066741b1edcc159c numpy-1.19.4-cp39-cp39-manylinux2010_x86_64.whl
5d678c6cc45ee3ee976e8b3b2ebe9c13 numpy-1.19.4-cp39-cp39-manylinux2014_aarch64.whl
7bc02e21133a1b82994c81c7521156a8 numpy-1.19.4-cp39-cp39-win32.whl
55c735347e8fb2ce3674243b38b3cee3 numpy-1.19.4-cp39-cp39-win_amd64.whl
673234a8dc2d3d3912c24c64aef6263e numpy-1.19.4-pp36-pypy36_pp73-manylinux2010_x86_64.whl
a25e91ea62ffd37ccf8e0d917484962c numpy-1.19.4.tar.gz
d40f6fcf611ab40eed4ff90606e05307 numpy-1.19.4.zip

SHA256

e9b30d4bd69498fc0c3fe9db5f62fffbb06b8eb9321f92cc970f2969be5e3949 numpy-1.19.4-cp36-cp36m-macosx_10_9_x86_64.whl
fedbd128668ead37f33917820b704784aff695e0019309ad446a6d0b065b57e4 numpy-1.19.4-cp36-cp36m-manylinux1_i686.whl
8ece138c3a16db8c1ad38f52eb32be6086cc72f403150a79336eb2045723a1ad numpy-1.19.4-cp36-cp36m-manylinux1_x86_64.whl
64324f64f90a9e4ef732be0928be853eee378fd6a01be21a0a8469c4f2682c83 numpy-1.19.4-cp36-cp36m-manylinux2010_i686.whl
ad6f2ff5b1989a4899bf89800a671d71b1612e5ff40866d1f4d8bcf48d4e5764 numpy-1.19.4-cp36-cp36m-manylinux2010_x86_64.whl
d6c7bb82883680e168b55b49c70af29b84b84abb161cbac2800e8fcb6f2109b6 numpy-1.19.4-cp36-cp36m-manylinux2014_aarch64.whl
13d166f77d6dc02c0a73c1101dd87fdf01339febec1030bd810dcd53fff3b0f1 numpy-1.19.4-cp36-cp36m-win32.whl
448ebb1b3bf64c0267d6b09a7cba26b5ae61b6d2dbabff7c91b660c7eccf2bdb numpy-1.19.4-cp36-cp36m-win_amd64.whl
27d3f3b9e3406579a8af3a9f262f5339005dd25e0ecf3cf1559ff8a49ed5cbf2 numpy-1.19.4-cp37-cp37m-macosx_10_9_x86_64.whl
16c1b388cc31a9baa06d91a19366fb99ddbe1c7b205293ed072211ee5bac1ed2 numpy-1.19.4-cp37-cp37m-manylinux1_i686.whl
e5b6ed0f0b42317050c88022349d994fe72bfe35f5908617512cd8c8ef9da2a9 numpy-1.19.4-cp37-cp37m-manylinux1_x86_64.whl
18bed2bcb39e3f758296584337966e68d2d5ba6aab7e038688ad53c8f889f757 numpy-1.19.4-cp37-cp37m-manylinux2010_i686.whl
fe45becb4c2f72a0907c1d0246ea6449fe7a9e2293bb0e11c4e9a32bb0930a15 numpy-1.19.4-cp37-cp37m-manylinux2010_x86_64.whl
6d7593a705d662be5bfe24111af14763016765f43cb6923ed86223f965f52387 numpy-1.19.4-cp37-cp37m-manylinux2014_aarch64.whl
6ae6c680f3ebf1cf7ad1d7748868b39d9f900836df774c453c11c5440bc15b36 numpy-1.19.4-cp37-cp37m-win32.whl
9eeb7d1d04b117ac0d38719915ae169aa6b61fca227b0b7d198d43728f0c879c numpy-1.19.4-cp37-cp37m-win_amd64.whl
cb1017eec5257e9ac6209ac172058c430e834d5d2bc21961dceeb79d111e5909 numpy-1.19.4-cp38-cp38-macosx_10_9_x86_64.whl
edb01671b3caae1ca00881686003d16c2209e07b7ef8b7639f1867852b948f7c numpy-1.19.4-cp38-cp38-manylinux1_i686.whl
f29454410db6ef8126c83bd3c968d143304633d45dc57b51252afbd79d700893 numpy-1.19.4-cp38-cp38-manylinux1_x86_64.whl
ec149b90019852266fec2341ce1db513b843e496d5a8e8cdb5ced1923a92faab numpy-1.19.4-cp38-cp38-manylinux2010_i686.whl
1aeef46a13e51931c0b1cf8ae1168b4a55ecd282e6688fdb0a948cc5a1d5afb9 numpy-1.19.4-cp38-cp38-manylinux2010_x86_64.whl
08308c38e44cc926bdfce99498b21eec1f848d24c302519e64203a8da99a97db numpy-1.19.4-cp38-cp38-manylinux2014_aarch64.whl
5734bdc0342aba9dfc6f04920988140fb41234db42381cf7ccba64169f9fe7ac numpy-1.19.4-cp38-cp38-win32.whl
09c12096d843b90eafd01ea1b3307e78ddd47a55855ad402b157b6c4862197ce numpy-1.19.4-cp38-cp38-win_amd64.whl
e452dc66e08a4ce642a961f134814258a082832c78c90351b75c41ad16f79f63 numpy-1.19.4-cp39-cp39-macosx_10_9_x86_64.whl
a5d897c14513590a85774180be713f692df6fa8ecf6483e561a6d47309566f37 numpy-1.19.4-cp39-cp39-manylinux1_i686.whl
a09f98011236a419ee3f49cedc9ef27d7a1651df07810ae430a6b06576e0b414 numpy-1.19.4-cp39-cp39-manylinux1_x86_64.whl
50e86c076611212ca62e5a59f518edafe0c0730f7d9195fec718da1a5c2bb1fc numpy-1.19.4-cp39-cp39-manylinux2010_i686.whl
f0d3929fe88ee1c155129ecd82f981b8856c5d97bcb0d5f23e9b4242e79d1de3 numpy-1.19.4-cp39-cp39-manylinux2010_x86_64.whl
c42c4b73121caf0ed6cd795512c9c09c52a7287b04d105d112068c1736d7c753 numpy-1.19.4-cp39-cp39-manylinux2014_aarch64.whl
8cac8790a6b1ddf88640a9267ee67b1aee7a57dfa2d2dd33999d080bc8ee3a0f numpy-1.19.4-cp39-cp39-win32.whl
4377e10b874e653fe96985c05feed2225c912e328c8a26541f7fc600fb9c637b numpy-1.19.4-cp39-cp39-win_amd64.whl
2a2740aa9733d2e5b2dfb33639d98a64c3b0f24765fed86b0fd2aec07f6a0a08 numpy-1.19.4-pp36-pypy36_pp73-manylinux2010_x86_64.whl
fe836a685d6838dbb3f603caef01183ea98e88febf4ce956a2ea484a75378413 numpy-1.19.4.tar.gz
141ec3a3300ab89c7f2b0775289954d193cc8edb621ea05f99db9cb181530512 numpy-1.19.4.zip

v1.19.3 Changes

October 29, 2020

🚀 NumPy 1.19.3 Release Notes

🚀 NumPy 1.19.3 is a small maintenace release with two major improvements:

👍 Python 3.9 binary wheels on all supported platforms.
🏁 OpenBLAS fixes for Windows 10 version 2004 fmod bug.

🚀 This release supports Python 3.6-3.9 and is linked with OpenBLAS 3.7 to
🏁 avoid some of the fmod problems on Windows 10 version 2004. Microsoft is
⬆️ aware of the problem and users should upgrade when the fix becomes
available, the fix here is limited in scope.

Contributors

🚀 A total of 8 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

Charles Harris
Chris Brown +
Daniel Vanzo +
E. Madison Bray +
Hugo van Kemenade +
Ralf Gommers
Sebastian Berg
@danbeibei +

🔀 Pull requests merged

🚀 A total of 10 pull requests were merged for this release.

🏗 #17298: BLD: set upper versions for build dependencies
🗄 #17336: BUG: Set deprecated fields to null in PyArray_InitArrFuncs
👍 #17446: ENH: Warn on unsupported Python 3.10+
⚡️ #17450: MAINT: Update test_requirements.txt.
👍 #17522: ENH: Support for the NVIDIA HPC SDK nvfortran compiler
↪ #17568: BUG: Cygwin Workaround for #14787 on affected platforms
#17647: BUG: Fix memory leak of buffer-info cache due to relaxed strides
👍 #17652: MAINT: Backport openblas_support from master.
🏁 #17653: TST: Add Python 3.9 to the CI testing on Windows, Mac.
✅ #17660: TST: Simplify source path names in test_extending.

Checksums

MD5

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SHA256

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v1.19.2 Changes

September 10, 2020

🚀 NumPy 1.19.2 Release Notes

🛠 NumPy 1.19.2 fixes several bugs, prepares for the upcoming Cython 3.x
🚀 release. and pins setuptools to keep distutils working while upstream
✅ modifications are ongoing. The aarch64 wheels are built with the latest
🚀 manylinux2014 release that fixes the problem of differing page sizes
🐧 used by different linux distros.

🚀 This release supports Python 3.6-3.8. Cython >= 0.29.21 needs to be
🏗 used when building with Python 3.9 for testing purposes.

🏁 There is a known problem with Windows 10 version=2004 and OpenBLAS svd
🏁 that we are trying to debug. If you are running that Windows version you
should use a NumPy version that links to the MKL library, earlier
🏁 Windows versions are fine.

👌 Improvements

➕ Add NumPy declarations for Cython 3.0 and later

The pxd declarations for Cython 3.0 were improved to avoid using
🗄 deprecated NumPy C-API features. Extension modules built with Cython
3.0+ that use NumPy can now set the C macro
NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION to avoid C compiler warnings
🗄 about deprecated API usage.

Contributors

🚀 A total of 8 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

Charles Harris
Matti Picus
Pauli Virtanen
Philippe Ombredanne +
Sebastian Berg
Stefan Behnel +
Stephan Loyd +
Zac Hatfield-Dodds

🔀 Pull requests merged

🚀 A total of 9 pull requests were merged for this release.

#16959: TST: Change aarch64 to arm64 in travis.yml.
🔧 #16998: MAINT: Configure hypothesis in np.test() for determinism,...
📌 #17000: BLD: pin setuptools < 49.2.0
#17015: ENH: Add NumPy declarations to be used by Cython 3.0+
🚚 #17125: BUG: Remove non-threadsafe sigint handling from fft calculation
#17243: BUG: core: fix ilp64 blas dot/vdot/... for strides > int32 max
#17244: DOC: Use SPDX license expressions with correct license
#17245: DOC: Fix the link to the quick-start in the old API functions
#17272: BUG: fix pickling of arrays larger than 2GiB

Checksums

MD5

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SHA256

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v1.19.1 Changes

July 21, 2020

🚀 NumPy 1.19.1 Release Notes

🚀 NumPy 1.19.1 fixes several bugs found in the 1.19.0 release, replaces
🚀 several functions deprecated in the upcoming Python-3.9 release, has
👌 improved support for AIX, and has a number of development related
⚡️ updates to keep CI working with recent upstream changes.

🚀 This release supports Python 3.6-3.8. Cython >= 0.29.21 needs to be
🏗 used when building with Python 3.9 for testing purposes.

Contributors

🚀 A total of 15 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

Abhinav Reddy +
Anirudh Subramanian
Antonio Larrosa +
Charles Harris
Chunlin Fang
Eric Wieser
Etienne Guesnet +
Kevin Sheppard
Matti Picus
Raghuveer Devulapalli
Roman Yurchak
Ross Barnowski
Sayed Adel
Sebastian Berg
Tyler Reddy

🔀 Pull requests merged

🚀 A total of 25 pull requests were merged for this release.

#16649: MAINT, CI: disable Shippable cache
#16652: MAINT: Replace PyUString_GET_SIZE with PyUnicode_GetLength.
📄 #16654: REL: Fix outdated docs link
👻 #16656: BUG: raise IEEE exception on AIX
#16672: BUG: Fix bug in AVX complex absolute while processing array of...
#16693: TST: Add extra debugging information to CPU features detection
#16703: BLD: Add CPU entry for Emscripten / WebAssembly
✅ #16705: TST: Disable Python 3.9-dev testing.
#16714: MAINT: Disable use_hugepages in case of ValueError
#16724: BUG: Fix PyArray_SearchSorted signature.
🗄 #16768: MAINT: Fixes for deprecated functions in scalartypes.c.src
🚚 #16772: MAINT: Remove unneeded call to PyUnicode_READY
🗄 #16776: MAINT: Fix deprecated functions in scalarapi.c
👍 #16779: BLD, ENH: Add RPATH support for AIX
0️⃣ #16780: BUG: Fix default fallback in genfromtxt
#16784: BUG: Added missing return after raising error in methods.c
⚡️ #16795: BLD: update cython to 0.29.21
⚠ #16832: MAINT: setuptools 49.2.0 emits a warning, avoid it
#16872: BUG: Validate output size in bin- and multinomial
📌 #16875: BLD, MAINT: Pin setuptools
✅ #16904: DOC: Reconstruct Testing Guideline.
#16905: TST, BUG: Re-raise MemoryError exception in test_large_zip's...
#16906: BUG, DOC: Fix bad MPL kwarg.
#16916: BUG: Fix string/bytes to complex assignment
🚀 #16922: REL: Prepare for NumPy 1.19.1 release

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v1.19.0 Changes
June 20, 2020
🚀 NumPy 1.19.0 Release Notes

🚀 This NumPy release is marked by the removal of much technical debt:
👌 support for Python 2 has been removed, many deprecations have been
📚 expired, and documentation has been improved. The polishing of the
🛠 random module continues apace with bug fixes and better usability from
Cython.

🚀 The Python versions supported for this release are 3.6-3.8. Downstream
👍 developers should use Cython >= 0.29.16 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid problems on the Skylake architecture.

Highlights
Code compatibility with Python versions < 3.6 (including Python 2)
was dropped from both the python and C code. The shims in
📦 numpy.compat will remain to support third-party packages, but they
may be deprecated in a future release. Note that 1.19.x will not
compile with earlier versions of Python due to the use of f-strings.

(gh-15233)

🗄 Expired deprecations

numpy.insert and numpy.delete can no longer be passed an axis on 0d arrays

🗄 This concludes a deprecation from 1.9, where when an axis argument was
passed to a call to ~numpy.insert and ~numpy.delete on a 0d array,
the axis and obj argument and indices would be completely ignored.
In these cases, insert(arr, "nonsense", 42, axis=0) would actually
overwrite the entire array, while delete(arr, "nonsense", axis=0)
would be arr.copy()

Now passing axis on a 0d array raises ~numpy.AxisError.

(gh-15802)

numpy.delete no longer ignores out-of-bounds indices

🗄 This concludes deprecations from 1.8 and 1.9, where np.delete would
ignore both negative and out-of-bounds items in a sequence of indices.
This was at odds with its behavior when passed a single index.

Now out-of-bounds items throw IndexError, and negative items index
from the end.

(gh-15804)

numpy.insert and numpy.delete no longer accept non-integral indices

🗄 This concludes a deprecation from 1.9, where sequences of non-integers
indices were allowed and cast to integers. Now passing sequences of
non-integral indices raises IndexError, just like it does when passing
a single non-integral scalar.

(gh-15805)

numpy.delete no longer casts boolean indices to integers

🗄 This concludes a deprecation from 1.8, where np.delete would cast
boolean arrays and scalars passed as an index argument into integer
indices. The behavior now is to treat boolean arrays as a mask, and to
raise an error on boolean scalars.

(gh-15815)

Compatibility notes

🔄 Changed random variate stream from numpy.random.Generator.dirichlet

A bug in the generation of random variates for the Dirichlet
🛠 distribution with small 'alpha' values was fixed by using a different
algorithm when max(alpha) < 0.1. Because of the change, the stream of
variates generated by dirichlet in this case will be different from
🚀 previous releases.

(gh-14924)

Scalar promotion in PyArray_ConvertToCommonType

The promotion of mixed scalars and arrays in
PyArray_ConvertToCommonType has been changed to adhere to those used
by np.result_type. This means that input such as
(1000, np.array([1], dtype=np.uint8))) will now return uint16
dtypes. In most cases the behaviour is unchanged. Note that the use of
🛠 this C-API function is generally discouraged. This also fixes
np.choose to behave the same way as the rest of NumPy in this respect.

(gh-14933)

🗄 Fasttake and fastputmask slots are deprecated and NULL'ed

The fasttake and fastputmask slots are now never used and must always be
set to NULL. This will result in no change in behaviour. However, if a
🗄 user dtype should set one of these a DeprecationWarning will be given.

(gh-14942)

np.ediff1d casting behaviour with to_end and to_begin

np.ediff1d now uses the "same_kind" casting rule for its additional
to_end and to_begin arguments. This ensures type safety except when
the input array has a smaller integer type than to_begin or to_end.
In rare cases, the behaviour will be more strict than it was previously
in 1.16 and 1.17. This is necessary to solve issues with floating point
NaN.

(gh-14981)

Converting of empty array-like objects to NumPy arrays

Objects with len(obj) == 0 which implement an "array-like"
interface, meaning an object implementing obj. __array__ (),
obj. __array_interface__, obj. __array_struct__, or the python buffer
interface and which are also sequences (i.e. Pandas objects) will now
always retain there shape correctly when converted to an array. If such
an object has a shape of (0, 1) previously, it could be converted into
an array of shape (0,) (losing all dimensions after the first 0).

(gh-14995)

✂ Removed multiarray.int_asbuffer

As part of the continued removal of Python 2 compatibility,
🚚 multiarray.int_asbuffer was removed. On Python 3, it threw a
NotImplementedError and was unused internally. It is expected that
there are no downstream use cases for this method with Python 3.

(gh-15229)

🚚 numpy.distutils.compat has been removed

This module contained only the function get_exception(), which was
👉 used as:
```
try:
    ...
except Exception:
    e = get_exception()
```
Its purpose was to handle the change in syntax introduced in Python 2.6,
👻 from except Exception, e: to except Exception as e:, meaning it was
👍 only necessary for codebases supporting Python 2.5 and older.

(gh-15255)

issubdtype no longer interprets float as np.floating

⚠ numpy.issubdtype had a FutureWarning since NumPy 1.14 which has
expired now. This means that certain input where the second argument was
neither a datatype nor a NumPy scalar type (such as a string or a python
type like int or float) will now be consistent with passing in
np.dtype(arg2).type. This makes the result consistent with
expectations and leads to a false result in some cases which previously
returned true.

(gh-15773)

🔄 Change output of round on scalars to be consistent with Python

Output of the __round__ dunder method and consequently the Python
built-in round has been changed to be a Python int to be consistent
with calling it on Python float objects when called with no arguments.
Previously, it would return a scalar of the np.dtype that was passed
in.

(gh-15840)

The numpy.ndarray constructor no longer interprets strides=() as strides=None

The former has changed to have the expected meaning of setting
numpy.ndarray.strides to (), while the latter continues to result in
strides being chosen automatically.

(gh-15882)

C-Level string to datetime casts changed

🛠 The C-level casts from strings were simplified. This changed also fixes
string to datetime and timedelta casts to behave correctly (i.e. like
Python casts using string_arr.astype("M8") while previously the cast
would behave like string_arr.astype(np.int_).astype("M8"). This only
affects code using low-level C-API to do manual casts (not full array
casts) of single scalar values or using e.g. PyArray_GetCastFunc, and
should thus not affect the vast majority of users.

(gh-16068)

👀 SeedSequence with small seeds no longer conflicts with spawning

👀 Small seeds (less than 2**96) were previously implicitly 0-padded out
to 128 bits, the size of the internal entropy pool. When spawned, the
spawn key was concatenated before the 0-padding. Since the first spawn
👀 key is (0,), small seeds before the spawn created the same states as
👀 the first spawned SeedSequence. Now, the seed is explicitly 0-padded
out to the internal pool size before concatenating the spawn key.
👀 Spawned SeedSequences will produce different results than in the
🚀 previous release. Unspawned SeedSequences will still produce the same
results.

(gh-16551)

🗄 Deprecations

🗄 Deprecate automatic dtype=object for ragged input

🗄 Calling np.array([[1, [1, 2, 3]]) will issue a DeprecationWarning as
per NEP 34. Users should
⚠ explicitly use dtype=object to avoid the warning.

(gh-15119)

🗄 Passing shape=0 to factory functions in numpy.rec is deprecated

0 is treated as a special case and is aliased to None in the
functions:
- numpy.core.records.fromarrays
- numpy.core.records.fromrecords
- numpy.core.records.fromstring
- numpy.core.records.fromfile
In future, 0 will not be special cased, and will be treated as an
array length like any other integer.

(gh-15217)

🗄 Deprecation of probably unused C-API functions

The following C-API functions are probably unused and have been
🗄 deprecated:
- PyArray_GetArrayParamsFromObject
- PyUFunc_GenericFunction
- PyUFunc_SetUsesArraysAsData
In most cases PyArray_GetArrayParamsFromObject should be replaced by
converting to an array, while PyUFunc_GenericFunction can be replaced
📚 with PyObject_Call (see documentation for details).

(gh-15427)

🗄 Converting certain types to dtypes is Deprecated

The super classes of scalar types, such as np.integer, np.generic,
🗄 or np.inexact will now give a deprecation warning when converted to a
dtype (or used in a dtype keyword argument). The reason for this is that
np.integer is converted to np.int_, while it would be expected to
represent any integer (e.g. also int8, int16, etc. For example,
dtype=np.floating is currently identical to dtype=np.float64, even
though also np.float32 is a subclass of np.floating.

(gh-15534)

🗄 Deprecation of round for np.complexfloating scalars

Output of the __round__ dunder method and consequently the Python
🗄 built-in round has been deprecated on complex scalars. This does not
affect np.round.

(gh-15840)

🗄 numpy.ndarray.tostring() is deprecated in favor of tobytes()

🚀 ~numpy.ndarray.tobytes has existed since the 1.9 release, but until
🚀 this release ~numpy.ndarray.tostring emitted no warning. The change to
⚠ emit a warning brings NumPy in line with the builtin array.array
methods of the same name.

(gh-15867)

C API changes

👍 Better support for const dimensions in API functions

The following functions now accept a constant array of npy_intp:
- PyArray_BroadcastToShape
- PyArray_IntTupleFromIntp
- PyArray_OverflowMultiplyList
Previously the caller would have to cast away the const-ness to call
these functions.

(gh-15251)

Const qualify UFunc inner loops

UFuncGenericFunction now expects pointers to const dimension and
strides as arguments. This means inner loops may no longer modify
either dimension or strides. This change leads to an
⚠ incompatible-pointer-types warning forcing users to either ignore the
⚠ compiler warnings or to const qualify their own loop signatures.

(gh-15355)

🆕 New Features

numpy.frompyfunc now accepts an identity argument

This allows the `numpy.ufunc.identity{.interpreted-text
role="attr"}[ attribute to be set on the resulting ufunc, meaning it can
be used for empty and multi-dimensional calls to
:meth:]{.title-ref}[numpy.ufunc.reduce]{.title-ref}`.

(gh-8255)

👍 np.str_ scalars now support the buffer protocol

np.str_ arrays are always stored as UCS4, so the corresponding scalars
now expose this through the buffer interface, meaning
✅ memoryview(np.str_('test')) now works.

(gh-15385)

subok option for numpy.copy

A new kwarg, subok, was added to numpy.copy to allow users to toggle
the behavior of numpy.copy with respect to array subclasses. The
0️⃣ default value is False which is consistent with the behavior of
numpy.copy for previous numpy versions. To create a copy that
preserves an array subclass with numpy.copy, call
👍 np.copy(arr, subok=True). This addition better documents that the
0️⃣ default behavior of numpy.copy differs from the numpy.ndarray.copy
0️⃣ method which respects array subclasses by default.

(gh-15685)

numpy.linalg.multi_dot now accepts an out argument

out can be used to avoid creating unnecessary copies of the final
product computed by numpy.linalg.multidot.

(gh-15715)

keepdims parameter for numpy.count_nonzero

The parameter keepdims was added to numpy.count_nonzero. The
parameter has the same meaning as it does in reduction functions such as
numpy.sum or numpy.mean.

(gh-15870)

equal_nan parameter for numpy.array_equal

The keyword argument equal_nan was added to numpy.array_equal.
equal_nan is a boolean value that toggles whether or not nan values
0️⃣ are considered equal in comparison (default is False). This matches
API used in related functions such as numpy.isclose and
numpy.allclose.

(gh-16128)

👌 Improvements

👌 Improve detection of CPU features

Replace npy_cpu_supports which was a gcc specific mechanism to test
support of AVX with more general functions npy_cpu_init and
npy_cpu_have, and expose the results via a NPY_CPU_HAVE c-macro as
well as a python-level __cpu_features__ dictionary.

(gh-13421)

👉 Use 64-bit integer size on 64-bit platforms in fallback lapack_lite

👉 Use 64-bit integer size on 64-bit platforms in the fallback LAPACK
library, which is used when the system has no LAPACK installed, allowing
it to deal with linear algebra for large arrays.

(gh-15218)

👉 Use AVX512 intrinsic to implement np.exp when input is np.float64

👉 Use AVX512 intrinsic to implement np.exp when input is np.float64,
🐎 which can improve the performance of np.exp with np.float64 input
5-7x faster than before. The _multiarray_umath.so module has grown
🐧 about 63 KB on linux64.

(gh-15648)

Ability to disable madvise hugepages

🐧 On Linux NumPy has previously added support for madavise hugepages which
🐎 can improve performance for very large arrays. Unfortunately, on older
0️⃣ Kernel versions this led to peformance regressions, thus by default the
👌 support has been disabled on kernels before version 4.6. To override the
0️⃣ default, you can use the environment variable:
```
NUMPY_MADVISE_HUGEPAGE=0
```
👍 or set it to 1 to force enabling support. Note that this only makes a
difference if the operating system is set up to use madvise transparent
hugepage.

(gh-15769)

numpy.einsum accepts NumPy int64 type in subscript list

There is no longer a type error thrown when numpy.einsum is passed a
NumPy int64 array as its subscript list.

(gh-16080)

np.logaddexp2.identity changed to -inf

The ufunc ~numpy.logaddexp2 now has an identity of -inf, allowing it
to be called on empty sequences. This matches the identity of
~numpy.logaddexp.

(gh-16102)

🔄 Changes

Remove handling of extra argument to __array__

✅ A code path and test have been in the code since NumPy 0.4 for a
two-argument variant of __array__ (dtype=None, context=None). It was
activated when calling ufunc(op) or ufunc.reduce(op) if
op. __array__ existed. However that variant is not documented, and it
🚚 is not clear what the intention was for its use. It has been removed.

(gh-15118)

numpy.random._bit_generator moved to numpy.random.bit_generator

In order to expose numpy.random.BitGenerator and
👀 numpy.random.SeedSequence to Cython, the _bitgenerator module is now
public as numpy.random.bit_generator

Cython access to the random distributions is provided via a pxd file

c_distributions.pxd provides access to the c functions behind many of
the random distributions from Cython, making it convenient to use and
extend them.

(gh-15463)

🛠 Fixed eigh and cholesky methods in numpy.random.multivariate_normal

Previously, when passing method='eigh' or method='cholesky',
numpy.random.multivariate_normal produced samples from the wrong
🛠 distribution. This is now fixed.

(gh-15872)

🛠 Fixed the jumping implementation in MT19937.jumped

This fix changes the stream produced from jumped MT19937 generators. It
does not affect the stream produced using RandomState or MT19937
👀 that are directly seeded.

🌐 The translation of the jumping code for the MT19937 contained a reversed
loop ordering. MT19937.jumped matches the Makoto Matsumoto's original
implementation of the Horner and Sliding Window jump methods.

(gh-16153)

Checksums

MD5
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3f939fa2f3b2f881862f7e02a0116970 numpy-1.19.0-cp37-cp37m-macosx_10_9_x86_64.whl
012026c54f196b8f342e4b49cb4b9294 numpy-1.19.0-cp37-cp37m-manylinux1_i686.whl
27227fdd6329f098fc9a85e9d40b1916 numpy-1.19.0-cp37-cp37m-manylinux1_x86_64.whl
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27af6195869cd518f5d2a71885f21806 numpy-1.19.0-cp37-cp37m-manylinux2010_x86_64.whl
62dbe6623e9aebd2bb1aef6d1e0f815d numpy-1.19.0-cp37-cp37m-manylinux2014_aarch64.whl
760e6b5681eea93cf6c85bcd1a739068 numpy-1.19.0-cp37-cp37m-win32.whl
d75a6104a6cce3c669e2363470d567bc numpy-1.19.0-cp37-cp37m-win_amd64.whl
09f870d54906d964bd0f93b22695f9ae numpy-1.19.0-cp38-cp38-macosx_10_9_x86_64.whl
ea9f4248d9ba0c647e07427cb542c2bf numpy-1.19.0-cp38-cp38-manylinux1_i686.whl
11b7a5b055bb1417c8935d267b7d88de numpy-1.19.0-cp38-cp38-manylinux1_x86_64.whl
6f6dec62163fa21259b7157516cc9e84 numpy-1.19.0-cp38-cp38-manylinux2010_i686.whl
ca83ee74cbdac0ffe3ec2c8c79294d67 numpy-1.19.0-cp38-cp38-manylinux2010_x86_64.whl
560567c2b3017ed146c3d08b0a58cadb numpy-1.19.0-cp38-cp38-manylinux2014_aarch64.whl
d160b64e914c5f2e4807943c83dae54a numpy-1.19.0-cp38-cp38-win32.whl
4e563e6434af5b90f1f99d9b916b2525 numpy-1.19.0-cp38-cp38-win_amd64.whl
a26c769ffe249f02cb73e6fbec7ff9ca numpy-1.19.0-pp36-pypy36_pp73-manylinux2010_x86_64.whl
d59aadf47354bd10c7b9996032ba4da0 numpy-1.19.0.tar.gz
3f5ce88a859302f0a1aceb5f75b563fc numpy-1.19.0.zip
```
SHA256
```
63d971bb211ad3ca37b2adecdd5365f40f3b741a455beecba70fd0dde8b2a4cb numpy-1.19.0-cp36-cp36m-macosx_10_9_x86_64.whl
b6aaeadf1e4866ca0fdf7bb4eed25e521ae21a7947c59f78154b24fc7abbe1dd numpy-1.19.0-cp36-cp36m-manylinux1_i686.whl
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df1889701e2dfd8ba4dc9b1a010f0a60950077fb5242bb92c8b5c7f1a6f2668a numpy-1.19.0-cp38-cp38-manylinux1_x86_64.whl
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26f509450db547e4dfa3ec739419b31edad646d21fb8d0ed0734188b35ff6b27 numpy-1.19.0-cp38-cp38-manylinux2010_x86_64.whl
7b57f26e5e6ee2f14f960db46bd58ffdca25ca06dd997729b1b179fddd35f5a3 numpy-1.19.0-cp38-cp38-manylinux2014_aarch64.whl
a8705c5073fe3fcc297fb8e0b31aa794e05af6a329e81b7ca4ffecab7f2b95ef numpy-1.19.0-cp38-cp38-win32.whl
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153cf8b0176e57a611931981acfe093d2f7fef623b48f91176efa199798a6b90 numpy-1.19.0.tar.gz
76766cc80d6128750075378d3bb7812cf146415bd29b588616f72c943c00d598 numpy-1.19.0.zip
```
v1.19.0.rc2 Changes
May 31, 2020
🚀 NumPy 1.19.0 Release Notes

🚀 This NumPy release is marked by the removal of much technical debt:
👌 support for Python 2 has been removed, many deprecations have been
📚 expired, and documentation has been improved. The polishing of the
🛠 random module continues apace with bug fixes and better usability from
Cython.

🚀 The Python versions supported for this release are 3.6-3.8. Downstream
👍 developers should use Cython >= 0.29.16 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid problems on the Skylake architecture.

Highlights
Code compatibility with Python versions < 3.6 (including Python 2)
was dropped from both the python and C code. The shims in
📦 numpy.compat will remain to support third-party packages, but they
may be deprecated in a future release. Note that 1.19.x will not
compile with earlier versions of Python due to the use of f-strings.

(gh-15233)

🗄 Expired deprecations

numpy.insert and numpy.delete can no longer be passed an axis on 0d arrays

🗄 This concludes a deprecation from 1.9, where when an axis argument was
passed to a call to ~numpy.insert and ~numpy.delete on a 0d array,
the axis and obj argument and indices would be completely ignored.
In these cases, insert(arr, "nonsense", 42, axis=0) would actually
overwrite the entire array, while delete(arr, "nonsense", axis=0)
would be arr.copy()

Now passing axis on a 0d array raises ~numpy.AxisError.

(gh-15802)

numpy.delete no longer ignores out-of-bounds indices

🗄 This concludes deprecations from 1.8 and 1.9, where np.delete would
ignore both negative and out-of-bounds items in a sequence of indices.
This was at odds with its behavior when passed a single index.

Now out-of-bounds items throw IndexError, and negative items index
from the end.

(gh-15804)

numpy.insert and numpy.delete no longer accept non-integral indices

🗄 This concludes a deprecation from 1.9, where sequences of non-integers
indices were allowed and cast to integers. Now passing sequences of
non-integral indices raises IndexError, just like it does when passing
a single non-integral scalar.

(gh-15805)

numpy.delete no longer casts boolean indices to integers

🗄 This concludes a deprecation from 1.8, where np.delete would cast
boolean arrays and scalars passed as an index argument into integer
indices. The behavior now is to treat boolean arrays as a mask, and to
raise an error on boolean scalars.

(gh-15815)

Compatibility notes

🔄 Changed random variate stream from numpy.random.Generator.dirichlet

A bug in the generation of random variates for the Dirichlet
🛠 distribution with small 'alpha' values was fixed by using a different
algorithm when max(alpha) < 0.1. Because of the change, the stream of
variates generated by dirichlet in this case will be different from
🚀 previous releases.

(gh-14924)

Scalar promotion in PyArray_ConvertToCommonType

The promotion of mixed scalars and arrays in
PyArray_ConvertToCommonType has been changed to adhere to those used
by np.result_type. This means that input such as
(1000, np.array([1], dtype=np.uint8))) will now return uint16
dtypes. In most cases the behaviour is unchanged. Note that the use of
🛠 this C-API function is generally discouraged. This also fixes
np.choose to behave the same way as the rest of NumPy in this respect.

(gh-14933)

🗄 Fasttake and fastputmask slots are deprecated and NULL'ed

The fasttake and fastputmask slots are now never used and must always be
set to NULL. This will result in no change in behaviour. However, if a
🗄 user dtype should set one of these a DeprecationWarning will be given.

(gh-14942)

np.ediff1d casting behaviour with to_end and to_begin

np.ediff1d now uses the "same_kind" casting rule for its additional
to_end and to_begin arguments. This ensures type safety except when
the input array has a smaller integer type than to_begin or to_end.
In rare cases, the behaviour will be more strict than it was previously
in 1.16 and 1.17. This is necessary to solve issues with floating point
NaN.

(gh-14981)

Converting of empty array-like objects to NumPy arrays

Objects with len(obj) == 0 which implement an "array-like"
interface, meaning an object implementing obj. __array__ (),
obj. __array_interface__, obj. __array_struct__, or the python buffer
interface and which are also sequences (i.e. Pandas objects) will now
always retain there shape correctly when converted to an array. If such
an object has a shape of (0, 1) previously, it could be converted into
an array of shape (0,) (losing all dimensions after the first 0).

(gh-14995)

✂ Removed multiarray.int_asbuffer

As part of the continued removal of Python 2 compatibility,
🚚 multiarray.int_asbuffer was removed. On Python 3, it threw a
NotImplementedError and was unused internally. It is expected that
there are no downstream use cases for this method with Python 3.

(gh-15229)

🚚 numpy.distutils.compat has been removed

This module contained only the function get_exception(), which was
👉 used as:
```
try:
    ...
except Exception:
    e = get_exception()
```
Its purpose was to handle the change in syntax introduced in Python 2.6,
👻 from except Exception, e: to except Exception as e:, meaning it was
👍 only necessary for codebases supporting Python 2.5 and older.

(gh-15255)

issubdtype no longer interprets float as np.floating

⚠ numpy.issubdtype had a FutureWarning since NumPy 1.14 which has
expired now. This means that certain input where the second argument was
neither a datatype nor a NumPy scalar type (such as a string or a python
type like int or float) will now be consistent with passing in
np.dtype(arg2).type. This makes the result consistent with
expectations and leads to a false result in some cases which previously
returned true.

(gh-15773)

🔄 Change output of round on scalars to be consistent with Python

Output of the __round__ dunder method and consequently the Python
built-in round has been changed to be a Python int to be consistent
with calling it on Python float objects when called with no arguments.
Previously, it would return a scalar of the np.dtype that was passed
in.

(gh-15840)

The numpy.ndarray constructor no longer interprets strides=() as strides=None

The former has changed to have the expected meaning of setting
numpy.ndarray.strides to (), while the latter continues to result in
strides being chosen automatically.

(gh-15882)

C-Level string to datetime casts changed

🛠 The C-level casts from strings were simplified. This changed also fixes
string to datetime and timedelta casts to behave correctly (i.e. like
Python casts using string_arr.astype("M8") while previously the cast
would behave like string_arr.astype(np.int_).astype("M8"). This only
affects code using low-level C-API to do manual casts (not full array
casts) of single scalar values or using e.g. PyArray_GetCastFunc, and
should thus not affect the vast majority of users.

(gh-16068)

🗄 Deprecations

🗄 Deprecate automatic dtype=object for ragged input

🗄 Calling np.array([[1, [1, 2, 3]]) will issue a DeprecationWarning as
per NEP 34. Users should
⚠ explicitly use dtype=object to avoid the warning.

(gh-15119)

🗄 Passing shape=0 to factory functions in numpy.rec is deprecated

0 is treated as a special case and is aliased to None in the
functions:
- numpy.core.records.fromarrays
- numpy.core.records.fromrecords
- numpy.core.records.fromstring
- numpy.core.records.fromfile
In future, 0 will not be special cased, and will be treated as an
array length like any other integer.

(gh-15217)

🗄 Deprecation of probably unused C-API functions

The following C-API functions are probably unused and have been
🗄 deprecated:
- PyArray_GetArrayParamsFromObject
- PyUFunc_GenericFunction
- PyUFunc_SetUsesArraysAsData
In most cases PyArray_GetArrayParamsFromObject should be replaced by
converting to an array, while PyUFunc_GenericFunction can be replaced
📚 with PyObject_Call (see documentation for details).

(gh-15427)

🗄 Converting certain types to dtypes is Deprecated

The super classes of scalar types, such as np.integer, np.generic,
🗄 or np.inexact will now give a deprecation warning when converted to a
dtype (or used in a dtype keyword argument). The reason for this is that
np.integer is converted to np.int_, while it would be expected to
represent any integer (e.g. also int8, int16, etc. For example,
dtype=np.floating is currently identical to dtype=np.float64, even
though also np.float32 is a subclass of np.floating.

(gh-15534)

🗄 Deprecation of round for np.complexfloating scalars

Output of the __round__ dunder method and consequently the Python
🗄 built-in round has been deprecated on complex scalars. This does not
affect np.round.

(gh-15840)

🗄 numpy.ndarray.tostring() is deprecated in favor of tobytes()

🚀 ~numpy.ndarray.tobytes has existed since the 1.9 release, but until
🚀 this release ~numpy.ndarray.tostring emitted no warning. The change to
⚠ emit a warning brings NumPy in line with the builtin array.array
methods of the same name.

(gh-15867)

C API changes

👍 Better support for const dimensions in API functions

The following functions now accept a constant array of npy_intp:
- PyArray_BroadcastToShape
- PyArray_IntTupleFromIntp
- PyArray_OverflowMultiplyList
Previously the caller would have to cast away the const-ness to call
these functions.

(gh-15251)

Const qualify UFunc inner loops

UFuncGenericFunction now expects pointers to const dimension and
strides as arguments. This means inner loops may no longer modify
either dimension or strides. This change leads to an
⚠ incompatible-pointer-types warning forcing users to either ignore the
⚠ compiler warnings or to const qualify their own loop signatures.

(gh-15355)

🆕 New Features

numpy.frompyfunc now accepts an identity argument

This allows the `numpy.ufunc.identity{.interpreted-text
role="attr"}[ attribute to be set on the resulting ufunc, meaning it can
be used for empty and multi-dimensional calls to
:meth:]{.title-ref}[numpy.ufunc.reduce]{.title-ref}`.

(gh-8255)

👍 np.str_ scalars now support the buffer protocol

np.str_ arrays are always stored as UCS4, so the corresponding scalars
now expose this through the buffer interface, meaning
✅ memoryview(np.str_('test')) now works.

(gh-15385)

subok option for numpy.copy

A new kwarg, subok, was added to numpy.copy to allow users to toggle
the behavior of numpy.copy with respect to array subclasses. The
0️⃣ default value is False which is consistent with the behavior of
numpy.copy for previous numpy versions. To create a copy that
preserves an array subclass with numpy.copy, call
👍 np.copy(arr, subok=True). This addition better documents that the
0️⃣ default behavior of numpy.copy differs from the numpy.ndarray.copy
0️⃣ method which respects array subclasses by default.

(gh-15685)

numpy.linalg.multi_dot now accepts an out argument

out can be used to avoid creating unnecessary copies of the final
product computed by numpy.linalg.multidot.

(gh-15715)

keepdims parameter for numpy.count_nonzero

The parameter keepdims was added to numpy.count_nonzero. The
parameter has the same meaning as it does in reduction functions such as
numpy.sum or numpy.mean.

(gh-15870)

equal_nan parameter for numpy.array_equal

The keyword argument equal_nan was added to numpy.array_equal.
equal_nan is a boolean value that toggles whether or not nan values
0️⃣ are considered equal in comparison (default is False). This matches
API used in related functions such as numpy.isclose and
numpy.allclose.

(gh-16128)

👌 Improvements

👌 Improve detection of CPU features

Replace npy_cpu_supports which was a gcc specific mechanism to test
support of AVX with more general functions npy_cpu_init and
npy_cpu_have, and expose the results via a NPY_CPU_HAVE c-macro as
well as a python-level __cpu_features__ dictionary.

(gh-13421)

👉 Use 64-bit integer size on 64-bit platforms in fallback lapack_lite

👉 Use 64-bit integer size on 64-bit platforms in the fallback LAPACK
library, which is used when the system has no LAPACK installed, allowing
it to deal with linear algebra for large arrays.

(gh-15218)

👉 Use AVX512 intrinsic to implement np.exp when input is np.float64

👉 Use AVX512 intrinsic to implement np.exp when input is np.float64,
🐎 which can improve the performance of np.exp with np.float64 input
5-7x faster than before. The _multiarray_umath.so module has grown
🐧 about 63 KB on linux64.

(gh-15648)

Ability to disable madvise hugepages

🐧 On Linux NumPy has previously added support for madavise hugepages which
🐎 can improve performance for very large arrays. Unfortunately, on older
0️⃣ Kernel versions this led to peformance regressions, thus by default the
👌 support has been disabled on kernels before version 4.6. To override the
0️⃣ default, you can use the environment variable:
```
NUMPY_MADVISE_HUGEPAGE=0
```
👍 or set it to 1 to force enabling support. Note that this only makes a
difference if the operating system is set up to use madvise transparent
hugepage.

(gh-15769)

numpy.einsum accepts NumPy int64 type in subscript list

There is no longer a type error thrown when numpy.einsum is passed a
NumPy int64 array as its subscript list.

(gh-16080)

np.logaddexp2.identity changed to -inf

The ufunc ~numpy.logaddexp2 now has an identity of -inf, allowing it
to be called on empty sequences. This matches the identity of
~numpy.logaddexp.

(gh-16102)

🔄 Changes

Remove handling of extra argument to __array__

✅ A code path and test have been in the code since NumPy 0.4 for a
two-argument variant of __array__ (dtype=None, context=None). It was
activated when calling ufunc(op) or ufunc.reduce(op) if
op. __array__ existed. However that variant is not documented, and it
🚚 is not clear what the intention was for its use. It has been removed.

(gh-15118)

numpy.random._bit_generator moved to numpy.random.bit_generator

In order to expose numpy.random.BitGenerator and
👀 numpy.random.SeedSequence to Cython, the _bitgenerator module is now
public as numpy.random.bit_generator

Cython access to the random distributions is provided via a pxd file

c_distributions.pxd provides access to the c functions behind many of
the random distributions from Cython, making it convenient to use and
extend them.

(gh-15463)

🛠 Fixed eigh and cholesky methods in numpy.random.multivariate_normal

Previously, when passing method='eigh' or method='cholesky',
numpy.random.multivariate_normal produced samples from the wrong
🛠 distribution. This is now fixed.

(gh-15872)

🛠 Fixed the jumping implementation in MT19937.jumped

This fix changes the stream produced from jumped MT19937 generators. It
does not affect the stream produced using RandomState or MT19937
👀 that are directly seeded.

🌐 The translation of the jumping code for the MT19937 contained a reversed
loop ordering. MT19937.jumped matches the Makoto Matsumoto's original
implementation of the Horner and Sliding Window jump methods.

(gh-16153)

Checksums

MD5
```
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be31ca0f2dbef9450dfd737fdf9ea297 numpy-1.19.0rc2-cp38-cp38-manylinux2010_i686.whl
d7a19658651a4290c289b7736a3d0471 numpy-1.19.0rc2-cp38-cp38-manylinux2010_x86_64.whl
118e4b75b557b29ea284cb1f97ab76ee numpy-1.19.0rc2-cp38-cp38-manylinux2014_aarch64.whl
8b1325b84fa1dea25137595bcc3fba4d numpy-1.19.0rc2-cp38-cp38-win32.whl
022defe479409b7b5604c38df63ba330 numpy-1.19.0rc2-cp38-cp38-win_amd64.whl
1ba5b606b95e0fee55ec99da3b15fae5 numpy-1.19.0rc2-pp36-pypy36_pp73-manylinux2010_x86_64.whl
b24c70ce8bed9e03ef08730127f4d30b numpy-1.19.0rc2.tar.gz
93e7419f48a0faa2ba91531df8f2f2a6 numpy-1.19.0rc2.zip
```
SHA256
```
6068db7fc6e34aed8a2d4ea4041fbeff3485a05452524d307c70da708ea40d63 numpy-1.19.0rc2-cp36-cp36m-macosx_10_9_x86_64.whl
e1c4e32318501ec8e8fa3dead802dd1b913dcf8eddeb2b0370f35b58c71d6018 numpy-1.19.0rc2-cp36-cp36m-manylinux1_i686.whl
01e17a9c1fdc7b97c75ad926f816694397be76251222a6f6cb50bbe3218cf3e5 numpy-1.19.0rc2-cp36-cp36m-manylinux1_x86_64.whl
9e8bf8bb69ef268eaab6483b354039aabb737c3aaab4ad526e4ad7c95a87bd3c numpy-1.19.0rc2-cp36-cp36m-manylinux2010_i686.whl
c49cc2b4e1b40bd836b2077d1cfee738577d2a411268eccace4f01dc22ef90ed numpy-1.19.0rc2-cp36-cp36m-manylinux2010_x86_64.whl
3eb013e193de97ec196441f6bdf9a1bea84dfbfb2421d5cccfdbba3aa2d60ec0 numpy-1.19.0rc2-cp36-cp36m-manylinux2014_aarch64.whl
307da8faeb1e84bbee082004c06aa41510e52321025d4a54a16ca48f8329ca4f numpy-1.19.0rc2-cp36-cp36m-win32.whl
7c716527392f34c217f18672aac79e88f4747e2717bd0c0c99755b197a5f5197 numpy-1.19.0rc2-cp36-cp36m-win_amd64.whl
c2f32979427df01cda8834af714dfacd06dce92f6c9275482a2e2932c67e67a1 numpy-1.19.0rc2-cp37-cp37m-macosx_10_9_x86_64.whl
d9b07673ac07cd02b1ba4d7eb920cd762d1559cc40af63d8e2b16774fdc3aa36 numpy-1.19.0rc2-cp37-cp37m-manylinux1_i686.whl
5e5b36b986a28d6651f6c8ebed084290e30833f50a7e0fe04f916b59d5113863 numpy-1.19.0rc2-cp37-cp37m-manylinux1_x86_64.whl
707be2715ca33f98335fdc84e3a79de4d85c7dd6b24aff6a57e45bf393205eb5 numpy-1.19.0rc2-cp37-cp37m-manylinux2010_i686.whl
cf6a8eb39bd191584de2f47dcc40155ffc902a32cff2a985ac58d93c035b306a numpy-1.19.0rc2-cp37-cp37m-manylinux2010_x86_64.whl
ec6c41348e05e2bee6b34cedb5bb38f7e53dee7e0791a4a63e1425dbee5ef326 numpy-1.19.0rc2-cp37-cp37m-manylinux2014_aarch64.whl
45c0a742198566b46479231cb4f189f69c4fd8fe1331f1217f9c58496fe52fc2 numpy-1.19.0rc2-cp37-cp37m-win32.whl
32073a47eeb37172f23f4f432efb2068c6b13b04d3eb4f0558056430ee3f32c5 numpy-1.19.0rc2-cp37-cp37m-win_amd64.whl
7526a8dbc68d730785a57ec18541b194d4ac7402843addb0d706174668f5be16 numpy-1.19.0rc2-cp38-cp38-macosx_10_9_x86_64.whl
b82511ae4d8e3dbf727c91bf6c761f882750428e888e0c1795b57f3c4b8cfc1e numpy-1.19.0rc2-cp38-cp38-manylinux1_i686.whl
159741a29c33b5e2829e4fcdcd712c35651f1b7571672002453f27fe438459d4 numpy-1.19.0rc2-cp38-cp38-manylinux1_x86_64.whl
e9ad332f8ff6f53dba38f39f3832a2f9fd4627039bc3a2baddb699fdf445adb1 numpy-1.19.0rc2-cp38-cp38-manylinux2010_i686.whl
93bb0c1f9c69e5ce97e8d6b45c472a050bfa1e433c4c70c4568718c60cc7c306 numpy-1.19.0rc2-cp38-cp38-manylinux2010_x86_64.whl
53564bfd09dda34cd74d11cbc1aad88b7fd2ad8b1d6eae6b4274ac789f30d6c0 numpy-1.19.0rc2-cp38-cp38-manylinux2014_aarch64.whl
dd21db931bdeb5d6ecffe36673bbaee4510f7e79b9afdbbdc2bf9c157ec8734c numpy-1.19.0rc2-cp38-cp38-win32.whl
612878ef8025af60c9d43556e45d93fa07d2e6a960e252a475575d3018e361cc numpy-1.19.0rc2-cp38-cp38-win_amd64.whl
e20452ad415c56cec51f52080adb4eccc4891ee86cf6b194e2434d09d42a183d numpy-1.19.0rc2-pp36-pypy36_pp73-manylinux2010_x86_64.whl
39814c52f65c89385028da97da574d5e2a74de5c52d6273cae755982c91597bc numpy-1.19.0rc2.tar.gz
a233044f7100e9f2100a4fc0f82021c827f7a82624b649059c5dd92cec4cee17 numpy-1.19.0rc2.zip
```
v1.19.0.rc1 Changes
May 18, 2020
🚀 NumPy 1.19.0 Release Notes

🚀 This NumPy release is marked by the removal of much technical debt:
👌 support for Python 2 has been removed, many deprecations have been
📚 expired, and documentation has been improved. The polishing of the
🛠 random module continues apace with bug fixes and better usability from
Cython.

🚀 The Python versions supported for this release are 3.6-3.8. Downstream
👍 developers should use Cython >= 0.29.16 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid problems on the Skylake architecture.

Highlights
Code compatibility with Python versions < 3.5 (including Python 2)
was dropped from both the python and C code. The shims in
📦 numpy.compat will remain to support third-party packages, but they
🚀 may be deprecated in a future release.

(gh-15233)

🗄 Expired deprecations

numpy.insert and numpy.delete can no longer be passed an axis on 0d arrays

🗄 This concludes a deprecation from 1.9, where when an axis argument was
passed to a call to ~numpy.insert and ~numpy.delete on a 0d array,
the axis and obj argument and indices would be completely ignored.
In these cases, insert(arr, "nonsense", 42, axis=0) would actually
overwrite the entire array, while delete(arr, "nonsense", axis=0)
would be arr.copy()

Now passing axis on a 0d array raises ~numpy.AxisError.

(gh-15802)

numpy.delete no longer ignores out-of-bounds indices

🗄 This concludes deprecations from 1.8 and 1.9, where np.delete would
ignore both negative and out-of-bounds items in a sequence of indices.
This was at odds with its behavior when passed a single index.

Now out-of-bounds items throw IndexError, and negative items index
from the end.

(gh-15804)

numpy.insert and numpy.delete no longer accept non-integral indices

🗄 This concludes a deprecation from 1.9, where sequences of non-integers
indices were allowed and cast to integers. Now passing sequences of
non-integral indices raises IndexError, just like it does when passing
a single non-integral scalar.

(gh-15805)

numpy.delete no longer casts boolean indices to integers

🗄 This concludes a deprecation from 1.8, where np.delete would cast
boolean arrays and scalars passed as an index argument into integer
indices. The behavior now is to treat boolean arrays as a mask, and to
raise an error on boolean scalars.

(gh-15815)

Compatibility notes

🔄 Changed random variate stream from numpy.random.Generator.dirichlet

A bug in the generation of random variates for the Dirichlet
🛠 distribution with small 'alpha' values was fixed by using a different
algorithm when max(alpha) < 0.1. Because of the change, the stream of
variates generated by dirichlet in this case will be different from
🚀 previous releases.

(gh-14924)

Scalar promotion in PyArray_ConvertToCommonType

The promotion of mixed scalars and arrays in
PyArray_ConvertToCommonType has been changed to adhere to those used
by np.result_type. This means that input such as
(1000, np.array([1], dtype=np.uint8))) will now return uint16
dtypes. In most cases the behaviour is unchanged. Note that the use of
🛠 this C-API function is generally discouraged. This also fixes
np.choose to behave the same way as the rest of NumPy in this respect.

(gh-14933)

🗄 Fasttake and fastputmask slots are deprecated and NULL'ed

The fasttake and fastputmask slots are now never used and must always be
set to NULL. This will result in no change in behaviour. However, if a
🗄 user dtype should set one of these a DeprecationWarning will be given.

(gh-14942)

np.ediff1d casting behaviour with to_end and to_begin

np.ediff1d now uses the "same_kind" casting rule for its additional
to_end and to_begin arguments. This ensures type safety except when
the input array has a smaller integer type than to_begin or to_end.
In rare cases, the behaviour will be more strict than it was previously
in 1.16 and 1.17. This is necessary to solve issues with floating point
NaN.

(gh-14981)

Converting of empty array-like objects to NumPy arrays

Objects with len(obj) == 0 which implement an "array-like"
interface, meaning an object implementing obj. __array__ (),
obj. __array_interface__, obj. __array_struct__, or the python buffer
interface and which are also sequences (i.e. Pandas objects) will now
always retain there shape correctly when converted to an array. If such
an object has a shape of (0, 1) previously, it could be converted into
an array of shape (0,) (losing all dimensions after the first 0).

(gh-14995)

✂ Removed multiarray.int_asbuffer

As part of the continued removal of Python 2 compatibility,
🚚 multiarray.int_asbuffer was removed. On Python 3, it threw a
NotImplementedError and was unused internally. It is expected that
there are no downstream use cases for this method with Python 3.

(gh-15229)

🚚 numpy.distutils.compat has been removed

This module contained only the function get_exception(), which was
👉 used as:
```
try:
    ...
except Exception:
    e = get_exception()
```
Its purpose was to handle the change in syntax introduced in Python 2.6,
👻 from except Exception, e: to except Exception as e:, meaning it was
👍 only necessary for codebases supporting Python 2.5 and older.

(gh-15255)

issubdtype no longer interprets float as np.floating

⚠ numpy.issubdtype had a FutureWarning since NumPy 1.14 which has
expired now. This means that certain input where the second argument was
neither a datatype nor a NumPy scalar type (such as a string or a python
type like int or float) will now be consistent with passing in
np.dtype(arg2).type. This makes the result consistent with
expectations and leads to a false result in some cases which previously
returned true.

(gh-15773)

🔄 Change output of round on scalars to be consistent with Python

Output of the __round__ dunder method and consequently the Python
built-in round has been changed to be a Python int to be consistent
with calling it on Python float objects when called with no arguments.
Previously, it would return a scalar of the np.dtype that was passed
in.

(gh-15840)

The numpy.ndarray constructor no longer interprets strides=() as strides=None

The former has changed to have the expected meaning of setting
numpy.ndarray.strides to (), while the latter continues to result in
strides being chosen automatically.

(gh-15882)

C-Level string to datetime casts changed

🛠 The C-level casts from strings were simplified. This changed also fixes
string to datetime and timedelta casts to behave correctly (i.e. like
Python casts using string_arr.astype("M8") while previously the cast
would behave like string_arr.astype(np.int_).astype("M8"). This only
affects code using low-level C-API to do manual casts (not full array
casts) of single scalar values or using e.g. PyArray_GetCastFunc, and
should thus not affect the vast majority of users.

(gh-16068)

🗄 Deprecations

🗄 Deprecate automatic dtype=object for ragged input

🗄 Calling np.array([[1, [1, 2, 3]]) will issue a DeprecationWarning as
per NEP 34. Users should
⚠ explicitly use dtype=object to avoid the warning.

(gh-15119)

🗄 Passing shape=0 to factory functions in numpy.rec is deprecated

0 is treated as a special case and is aliased to None in the
functions:
- numpy.core.records.fromarrays
- numpy.core.records.fromrecords
- numpy.core.records.fromstring
- numpy.core.records.fromfile
In future, 0 will not be special cased, and will be treated as an
array length like any other integer.

(gh-15217)

🗄 Deprecation of probably unused C-API functions

The following C-API functions are probably unused and have been
🗄 deprecated:
- PyArray_GetArrayParamsFromObject
- PyUFunc_GenericFunction
- PyUFunc_SetUsesArraysAsData
In most cases PyArray_GetArrayParamsFromObject should be replaced by
converting to an array, while PyUFunc_GenericFunction can be replaced
📚 with PyObject_Call (see documentation for details).

(gh-15427)

🗄 Converting certain types to dtypes is Deprecated

The super classes of scalar types, such as np.integer, np.generic,
🗄 or np.inexact will now give a deprecation warning when converted to a
dtype (or used in a dtype keyword argument). The reason for this is that
np.integer is converted to np.int_, while it would be expected to
represent any integer (e.g. also int8, int16, etc. For example,
dtype=np.floating is currently identical to dtype=np.float64, even
though also np.float32 is a subclass of np.floating.

(gh-15534)

🗄 Deprecation of round for np.complexfloating scalars

Output of the __round__ dunder method and consequently the Python
🗄 built-in round has been deprecated on complex scalars. This does not
affect np.round.

(gh-15840)

🗄 numpy.ndarray.tostring() is deprecated in favor of tobytes()

🚀 ~numpy.ndarray.tobytes has existed since the 1.9 release, but until
🚀 this release ~numpy.ndarray.tostring emitted no warning. The change to
⚠ emit a warning brings NumPy in line with the builtin array.array
methods of the same name.

(gh-15867)

C API changes

👍 Better support for const dimensions in API functions

The following functions now accept a constant array of npy_intp:
- PyArray_BroadcastToShape
- PyArray_IntTupleFromIntp
- PyArray_OverflowMultiplyList
Previously the caller would have to cast away the const-ness to call
these functions.

(gh-15251)

Const qualify UFunc inner loops

UFuncGenericFunction now expects pointers to const dimension and
strides as arguments. This means inner loops may no longer modify
either dimension or strides. This change leads to an
⚠ incompatible-pointer-types warning forcing users to either ignore the
⚠ compiler warnings or to const qualify their own loop signatures.

(gh-15355)

🆕 New Features

numpy.frompyfunc now accepts an identity argument

This allows the `numpy.ufunc.identity{.interpreted-text
role="attr"}[ attribute to be set on the resulting ufunc, meaning it can
be used for empty and multi-dimensional calls to
:meth:]{.title-ref}[numpy.ufunc.reduce]{.title-ref}`.

(gh-8255)

👍 np.str_ scalars now support the buffer protocol

np.str_ arrays are always stored as UCS4, so the corresponding scalars
now expose this through the buffer interface, meaning
✅ memoryview(np.str_('test')) now works.

(gh-15385)

subok option for numpy.copy

A new kwarg, subok, was added to numpy.copy to allow users to toggle
the behavior of numpy.copy with respect to array subclasses. The
0️⃣ default value is False which is consistent with the behavior of
numpy.copy for previous numpy versions. To create a copy that
preserves an array subclass with numpy.copy, call
👍 np.copy(arr, subok=True). This addition better documents that the
0️⃣ default behavior of numpy.copy differs from the numpy.ndarray.copy
0️⃣ method which respects array subclasses by default.

(gh-15685)

numpy.linalg.multi_dot now accepts an out argument

out can be used to avoid creating unnecessary copies of the final
product computed by numpy.linalg.multidot.

(gh-15715)

keepdims parameter for numpy.count_nonzero

The parameter keepdims was added to numpy.count_nonzero. The
parameter has the same meaning as it does in reduction functions such as
numpy.sum or numpy.mean.

(gh-15870)

equal_nan parameter for numpy.array_equal

The keyword argument equal_nan was added to numpy.array_equal.
equal_nan is a boolean value that toggles whether or not nan values
0️⃣ are considered equal in comparison (default is False). This matches
API used in related functions such as numpy.isclose and
numpy.allclose.

(gh-16128)

👌 Improvements

👌 Improve detection of CPU features

Replace npy_cpu_supports which was a gcc specific mechanism to test
support of AVX with more general functions npy_cpu_init and
npy_cpu_have, and expose the results via a NPY_CPU_HAVE c-macro as
well as a python-level __cpu_features__ dictionary.

(gh-13421)

👉 Use 64-bit integer size on 64-bit platforms in fallback lapack_lite

👉 Use 64-bit integer size on 64-bit platforms in the fallback LAPACK
library, which is used when the system has no LAPACK installed, allowing
it to deal with linear algebra for large arrays.

(gh-15218)

👉 Use AVX512 intrinsic to implement np.exp when input is np.float64

👉 Use AVX512 intrinsic to implement np.exp when input is np.float64,
🐎 which can improve the performance of np.exp with np.float64 input
5-7x faster than before. The _multiarray_umath.so module has grown
🐧 about 63 KB on linux64.

(gh-15648)

Ability to disable madvise hugepages

🐧 On Linux NumPy has previously added support for madavise hugepages which
🐎 can improve performance for very large arrays. Unfortunately, on older
0️⃣ Kernel versions this led to peformance regressions, thus by default the
👌 support has been disabled on kernels before version 4.6. To override the
0️⃣ default, you can use the environment variable:
```
NUMPY_MADVISE_HUGEPAGE=0
```
👍 or set it to 1 to force enabling support. Note that this only makes a
difference if the operating system is set up to use madvise transparent
hugepage.

(gh-15769)

numpy.einsum accepts NumPy int64 type in subscript list

There is no longer a type error thrown when numpy.einsum is passed a
NumPy int64 array as its subscript list.

(gh-16080)

np.logaddexp2.identity changed to -inf

The ufunc ~numpy.logaddexp2 now has an identity of -inf, allowing it
to be called on empty sequences. This matches the identity of
~numpy.logaddexp.

(gh-16102)

🔄 Changes

Remove handling of extra argument to __array__

✅ A code path and test have been in the code since NumPy 0.4 for a
two-argument variant of __array__ (dtype=None, context=None). It was
activated when calling ufunc(op) or ufunc.reduce(op) if
op. __array__ existed. However that variant is not documented, and it
🚚 is not clear what the intention was for its use. It has been removed.

(gh-15118)

numpy.random._bit_generator moved to numpy.random.bit_generator

In order to expose numpy.random.BitGenerator and
👀 numpy.random.SeedSequence to Cython, the _bitgenerator module is now
public as numpy.random.bit_generator

Cython access to the random distributions is provided via a pxd file

c_distributions.pxd provides access to the c functions behind many of
the random distributions from Cython, making it convenient to use and
extend them.

(gh-15463)

🛠 Fixed eigh and cholesky methods in numpy.random.multivariate_normal

Previously, when passing method='eigh' or method='cholesky',
numpy.random.multivariate_normal produced samples from the wrong
🛠 distribution. This is now fixed.

(gh-15872)

🛠 Fixed the jumping implementation in MT19937.jumped

This fix changes the stream produced from jumped MT19937 generators. It
does not affect the stream produced using RandomState or MT19937
👀 that are directly seeded.

🌐 The translation of the jumping code for the MT19937 contained a reversed
loop ordering. MT19937.jumped matches the Makoto Matsumoto's original
implementation of the Horner and Sliding Window jump methods.

(gh-16153)

Checksums

MD5
```
dac784fdc5f86f6b4daabb8a3edb59ef numpy-1.19.0rc1-cp36-cp36m-macosx_10_9_x86_64.whl
40df9787a18b84fbb7ab06de9e557abd numpy-1.19.0rc1-cp36-cp36m-manylinux1_i686.whl
f54d2c826b31469e1bddf271e8ebbbfa numpy-1.19.0rc1-cp36-cp36m-manylinux1_x86_64.whl
11da401f740d8fed948ab84f92b52490 numpy-1.19.0rc1-cp36-cp36m-manylinux2010_i686.whl
3a49ddd9cc062896b352d9914213d6c8 numpy-1.19.0rc1-cp36-cp36m-manylinux2010_x86_64.whl
426c0c9859f7384781be7fbfa1fbca28 numpy-1.19.0rc1-cp36-cp36m-manylinux2014_aarch64.whl
5e7c8d718b3e867a8c063bd18addde3f numpy-1.19.0rc1-cp36-cp36m-win32.whl
5dfdf8b5dea6ac218a93038dc1eb5b8b numpy-1.19.0rc1-cp36-cp36m-win_amd64.whl
10575aa075e31e1f190a802e8c784bd5 numpy-1.19.0rc1-cp37-cp37m-macosx_10_9_x86_64.whl
1cf8fa399117e8d95ef9ca8847451362 numpy-1.19.0rc1-cp37-cp37m-manylinux1_i686.whl
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3798e81e08e02c9683b03245ef181fe0 numpy-1.19.0rc1-cp37-cp37m-manylinux2010_i686.whl
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17c1867c83b2f07621d064145a255fc1 numpy-1.19.0rc1-cp38-cp38-win32.whl
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704bc03983673b18017e6b461d55a8c8 numpy-1.19.0rc1-pp36-pypy36_pp73-manylinux2010_x86_64.whl
fd6765ceb2074658eb40641de9ad596f numpy-1.19.0rc1.tar.gz
cc786052918361cb08f885e3e8c257fe numpy-1.19.0rc1.zip
```
SHA256
```
361c84cdf8e10a27d1ce7bb0404284eed2f704fb10ebbdb714fe5a51ef4f2765 numpy-1.19.0rc1-cp36-cp36m-macosx_10_9_x86_64.whl
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```

v1.18.5 Changes

June 04, 2020

🚀 NumPy 1.18.5 Release Notes

🚀 This is a short release to allow pickle protocol=5 to be used in
Python3.5. It is motivated by the recent backport of pickle5 to
Python3.5.

🚀 The Python versions supported in this release are 3.5-3.8. Downstream
👍 developers should use Cython >= 0.29.15 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Contributors

🚀 A total of 3 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

Charles Harris
Matti Picus
Siyuan Zhuang +

🔀 Pull requests merged

🚀 A total of 2 pull requests were merged for this release.

👍 #16439: ENH: enable pickle protocol 5 support for python3.5
🏁 #16441: BUG: relpath fails for different drives on windows

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NumPy changelog

The fundamental package for scientific computing with Python.

Changelog History Page 1

v1.21.0.dev0

v1.20.0.rc1 Changes

🚀 NumPy 1.20.0 Release Notes

🆕 New functions

The random.Generator class has a new permuted function.

sliding_window_view provides a sliding window view for numpy arrays

[numpy.broadcast_shapes]{.title-ref} is a new user-facing function

🗄 Deprecations

🗄 Using the aliases of builtin types like np.int is deprecated

🗄 Passing shape=None to functions with a non-optional shape argument is deprecated

Indexing errors will be reported even when index result is empty

🗄 Inexact matches for mode and searchside are deprecated

🗄 Deprecation of [numpy.dual]{.title-ref}

🗄 outer and ufunc.outer deprecated for matrix

💅 Further Numeric Style types Deprecated

🗄 The ndincr method of ndindex is deprecated

Future Changes

Arrays cannot be using subarray dtypes

🗄 Expired deprecations

🚚 Financial functions removed

Compatibility notes

Same kind casting in concatenate with axis=None

NumPy Scalars are cast when assigned to arrays

Array coercion changes when Strings and other types are mixed

Array coercion restructure

Writing to the result of [numpy.broadcast_arrays]{.title-ref} will export readonly buffers

💅 Numeric-style type names have been removed from type dictionaries

The operator.concat function now raises TypeError for array arguments

🚚 nickname attribute removed from ABCPolyBase

float->timedelta and uint64->timedelta promotion will raise a TypeError

numpy.genfromtxt now correctly unpacks structured arrays

0️⃣ mgrid, r_, etc. consistently return correct outputs for non-default precision input

Boolean array indices with mismatching shapes now properly give IndexError

Casting errors interrupt Iteration

f2py generated code may return unicode instead of byte strings

The first element of the __array_interface__ ["data"] tuple must be an integer

poly1d respects the dtype of all-zero argument

The numpy.i file for swig is Python 3 only.

Void dtype discovery in np.array

C API changes

Size of np.ndarray and np.void_ changed

🆕 New Features

where keyword argument for numpy.all and numpy.any functions

where keyword argument for numpy functions mean, std, var

norm=backward, forward keyword options for numpy.fft functions

NumPy is now typed

numpy.typing is accessible at runtime

New __f2py_numpy_version__ attribute for f2py generated modules.

✅ mypy tests can be run via runtests.py

Negation of user defined BLAS/LAPACK detection order

👍 Allow passing optimizations arguments to asv build

👍 The NVIDIA HPC SDK nvfortran compiler is now supported

dtype option for cov and corrcoef

👌 Improvements

Improved string representation for polynomials (__str__)

✂ Remove the Accelerate library as a candidate LAPACK library

Object arrays containing multi-line objects have a more readable repr

👍 Concatenate supports providing an output dtype

Thread safe f2py callback functions

👍 [numpy.core.records.fromfile]{.title-ref} now supports file-like objects

👍 RPATH support on AIX added to distutils

👉 Use f90 compiler specified by the command line args

➕ Add NumPy declarations for Cython 3.0 and later

👉 Make the window functions exactly symmetric

🐎 Performance improvements and changes

Enable multi-platform SIMD compiler optimizations

definitions and flags that affect the code paths.

🔄 Changes

🔄 Changed behavior of divmod(1., 0.) and related functions

np.linspace on integers now uses floor

Checksums

MD5

SHA256

v1.19.4 Changes

🚀 NumPy 1.19.4 Release Notes

Contributors

🔀 Pull requests merged

Changelog History

Page 1

The random.Generator class has a new `permuted` function.

`sliding_window_view` provides a sliding window view for numpy arrays

🗄 Using the aliases of builtin types like `np.int` is deprecated

🗄 Passing `shape=None` to functions with a non-optional shape argument is deprecated

🗄 Inexact matches for `mode` and `searchside` are deprecated

🗄 `outer` and `ufunc.outer` deprecated for matrix

🗄 The `ndincr` method of `ndindex` is deprecated

Same kind casting in concatenate with `axis=None`

The `operator.concat` function now raises TypeError for array arguments

🚚 `nickname` attribute removed from ABCPolyBase

`float->timedelta` and `uint64->timedelta` promotion will raise a TypeError

`numpy.genfromtxt` now correctly unpacks structured arrays

0️⃣ `mgrid`, `r_`, etc. consistently return correct outputs for non-default precision input

Boolean array indices with mismatching shapes now properly give `IndexError`

The first element of the `__array_interface__ ["data"]` tuple must be an integer

Void dtype discovery in `np.array`

Size of `np.ndarray` and `np.void_` changed

`where` keyword argument for `numpy.all` and `numpy.any` functions

`where` keyword argument for `numpy` functions `mean`, `std`, `var`

`norm=backward`, `forward` keyword options for `numpy.fft` functions

`numpy.typing` is accessible at runtime

New `__f2py_numpy_version__` attribute for f2py generated modules.

✅ `mypy` tests can be run via runtests.py

`dtype` option for `cov` and `corrcoef`

Improved string representation for polynomials (`str`)

Object arrays containing multi-line objects have a more readable `repr`

🔄 Changed behavior of `divmod(1., 0.)` and related functions

`np.linspace` on integers now uses floor

`numpy.insert` and `numpy.delete` can no longer be passed an axis on 0d arrays

`numpy.delete` no longer ignores out-of-bounds indices

`numpy.insert` and `numpy.delete` no longer accept non-integral indices

`numpy.delete` no longer casts boolean indices to integers

🔄 Changed random variate stream from `numpy.random.Generator.dirichlet`

Scalar promotion in `PyArray_ConvertToCommonType`

`np.ediff1d` casting behaviour with `to_end` and `to_begin`

✂ Removed `multiarray.int_asbuffer`

🚚 `numpy.distutils.compat` has been removed

`issubdtype` no longer interprets `float` as `np.floating`

🔄 Change output of `round` on scalars to be consistent with Python

The `numpy.ndarray` constructor no longer interprets `strides=()` as `strides=None`

👀 `SeedSequence` with small seeds no longer conflicts with spawning

🗄 Deprecate automatic `dtype=object` for ragged input

🗄 Passing `shape=0` to factory functions in `numpy.rec` is deprecated

🗄 Deprecation of `round` for `np.complexfloating` scalars

🗄 `numpy.ndarray.tostring()` is deprecated in favor of `tobytes()`

👍 Better support for `const` dimensions in API functions

`numpy.frompyfunc` now accepts an identity argument

👍 `np.str_` scalars now support the buffer protocol

`subok` option for `numpy.copy`

`numpy.linalg.multi_dot` now accepts an `out` argument

`keepdims` parameter for `numpy.count_nonzero`

`equal_nan` parameter for `numpy.array_equal`

👉 Use AVX512 intrinsic to implement `np.exp` when input is `np.float64`

`numpy.einsum` accepts NumPy `int64` type in subscript list