Initial Update

[pyup-bot]

This is my first visit to this fine repo so I have bundled all updates in a single pull request to make things easier for you to merge.

Close this pull request and delete the branch if you want me to start with single pull requests right away

Here's the executive summary:

Updates

Here's a list of all the updates bundled in this pull request. I've added some links to make it easier for you to find all the information you need.

numpy	1.13.1	»	1.13.1	PyPI | Changelog | Homepage
scipy	0.19.1	»	0.19.1	PyPI | Changelog | Repo | Homepage
astropy	2.0.2	»	2.0.2	PyPI | Changelog | Homepage
matplotlib	2.0.2	»	2.0.2	PyPI | Changelog | Homepage
pandas	0.20.3	»	0.20.3	PyPI | Changelog | Homepage
ephem	3.7.6.0	»	3.7.6.0	PyPI | Homepage
joblib	0.11	»	0.11	PyPI | Changelog | Homepage | Docs
numba	0.35.0	»	0.35.0	PyPI | Changelog | Repo
multiprocess	0.70.5	»	0.70.5	PyPI | Repo
tqdm	4.17.1	»	4.17.1	PyPI | Changelog | Repo
PyAstronomy	0.11.1	»	0.11.1	PyPI | Homepage

Changelogs

numpy -> 1.13.1

1.13.0

==========================

This release supports Python 2.7 and 3.4 - 3.6.

Highlights

• Operations like a + b + c will reuse temporaries on some platforms, resulting in less memory use and faster execution.
• Inplace operations check if inputs overlap outputs and create temporaries to avoid problems.
• New __array_ufunc__ attribute provides improved ability for classes to override default ufunc behavior.
• New np.block function for creating blocked arrays.

New functions

• New np.positive ufunc.
• New np.divmod ufunc provides more efficient divmod.
• New np.isnat ufunc tests for NaT special values.
• New np.heaviside ufunc computes the Heaviside function.
• New np.isin function, improves on in1d.
• New np.block function for creating blocked arrays.
• New PyArray_MapIterArrayCopyIfOverlap added to NumPy C-API.

See below for details.

Deprecations

• Calling np.fix, np.isposinf, and np.isneginf with f(x, y=out) is deprecated - the argument should be passed as f(x, out=out), which matches other ufunc-like interfaces.
• Use of the C-API NPY_CHAR type number deprecated since version 1.7 will now raise deprecation warnings at runtime. Extensions built with older f2py versions need to be recompiled to remove the warning.
• np.ma.argsort, np.ma.minimum.reduce, and np.ma.maximum.reduce should be called with an explicit axis argument when applied to arrays with more than 2 dimensions, as the default value of this argument (None) is inconsistent with the rest of numpy (-1, 0, and 0, respectively).
• np.ma.MaskedArray.mini is deprecated, as it almost duplicates the functionality of np.MaskedArray.min. Exactly equivalent behaviour can be obtained with np.ma.minimum.reduce.
• The single-argument form of np.ma.minimum and np.ma.maximum is deprecated. np.maximum. np.ma.minimum(x) should now be spelt np.ma.minimum.reduce(x), which is consistent with how this would be done with np.minimum.
• Calling ndarray.conjugate on non-numeric dtypes is deprecated (it should match the behavior of np.conjugate, which throws an error).
• Calling expand_dims when the axis keyword does not satisfy -a.ndim - 1 <= axis <= a.ndim, where a is the array being reshaped, is deprecated.

Future Changes

• Assignment between structured arrays with different field names will change in NumPy 1.14. Previously, fields in the dst would be set to the value of the identically-named field in the src. In numpy 1.14 fields will instead be assigned 'by position': The n-th field of the dst will be set to the n-th field of the src array. Note that the FutureWarning raised in NumPy 1.12 incorrectly reported this change as scheduled for NumPy 1.13 rather than NumPy 1.14.

Build System Changes

• numpy.distutils now automatically determines C-file dependencies with GCC compatible compilers.

Compatibility notes

Error type changes

• numpy.hstack() now throws ValueError instead of IndexError when input is empty.
• Functions taking an axis argument, when that argument is out of range, now throw np.AxisError instead of a mixture of IndexError and ValueError. For backwards compatibility, AxisError subclasses both of these.

Tuple object dtypes

Support has been removed for certain obscure dtypes that were unintentionally allowed, of the form (old_dtype, new_dtype), where either of the dtypes is or contains the object dtype. As an exception, dtypes of the form (object, [('name', object)]) are still supported due to evidence of existing use.

DeprecationWarning to error

See Changes section for more detail.

• partition, TypeError when non-integer partition index is used.
• NpyIter_AdvancedNew, ValueError when oa_ndim == 0 and op_axes is NULL
• negative(bool_), TypeError when negative applied to booleans.
• subtract(bool_, bool_), TypeError when subtracting boolean from boolean.
• np.equal, np.not_equal, object identity doesn't override failed comparison.
• np.equal, np.not_equal, object identity doesn't override non-boolean comparison.
• Deprecated boolean indexing behavior dropped. See Changes below for details.
• Deprecated np.alterdot() and np.restoredot() removed.

FutureWarning to changed behavior

See Changes section for more detail.

• numpy.average preserves subclasses
• array == None and array != None do element-wise comparison.
• np.equal, np.not_equal, object identity doesn't override comparison result.

dtypes are now always true

Previously bool(dtype) would fall back to the default python implementation, which checked if len(dtype) > 0. Since dtype objects implement __len__ as the number of record fields, bool of scalar dtypes would evaluate to False, which was unintuitive. Now bool(dtype) == True for all dtypes.

__getslice__ and __setslice__ are no longer needed in ndarray subclasses

When subclassing np.ndarray in Python 2.7, it is no longer necessary to implement __*slice__ on the derived class, as __*item__ will intercept these calls correctly.

Any code that did implement these will work exactly as before. Code that invokesndarray.__getslice__ (e.g. through super(...).__getslice__) will now issue a DeprecationWarning - .__getitem__(slice(start, end)) should be used instead.

Indexing MaskedArrays/Constants with ... (ellipsis) now returns MaskedArray

This behavior mirrors that of np.ndarray, and accounts for nested arrays in MaskedArrays of object dtype, and ellipsis combined with other forms of indexing.

C API changes

GUfuncs on empty arrays and NpyIter axis removal

It is now allowed to remove a zero-sized axis from NpyIter. Which may mean that code removing axes from NpyIter has to add an additional check when accessing the removed dimensions later on.

The largest followup change is that gufuncs are now allowed to have zero-sized inner dimensions. This means that a gufunc now has to anticipate an empty inner dimension, while this was never possible and an error raised instead.

For most gufuncs no change should be necessary. However, it is now possible for gufuncs with a signature such as (..., N, M) -> (..., M) to return a valid result if N=0 without further wrapping code.

PyArray_MapIterArrayCopyIfOverlap added to NumPy C-API

Similar to PyArray_MapIterArray but with an additional copy_if_overlap argument. If copy_if_overlap != 0, checks if input has memory overlap with any of the other arrays and make copies as appropriate to avoid problems if the input is modified during the iteration. See the documentation for more complete documentation.

New Features

__array_ufunc__ added

This is the renamed and redesigned __numpy_ufunc__. Any class, ndarray subclass or not, can define this method or set it to None in order to override the behavior of NumPy's ufuncs. This works quite similarly to Python's __mul__ and other binary operation routines. See the documentation for a more detailed description of the implementation and behavior of this new option. The API is provisional, we do not yet guarantee backward compatibility as modifications may be made pending feedback. See the NEP and documentation for more details.

.. _NEP: https://github.com/numpy/numpy/blob/master/doc/neps/ufunc-overrides.rst .. _documentation: https://github.com/charris/numpy/blob/master/doc/source/reference/arrays.classes.rst

New positive ufunc

This ufunc corresponds to unary +, but unlike + on an ndarray it will raise an error if array values do not support numeric operations.

New divmod ufunc

This ufunc corresponds to the Python builtin divmod, and is used to implement divmod when called on numpy arrays. np.divmod(x, y) calculates a result equivalent to (np.floor_divide(x, y), np.remainder(x, y)) but is approximately twice as fast as calling the functions separately.

np.isnat ufunc tests for NaT special datetime and timedelta values

The new ufunc np.isnat finds the positions of special NaT values within datetime and timedelta arrays. This is analogous to np.isnan.

np.heaviside ufunc computes the Heaviside function

The new function np.heaviside(x, h0) (a ufunc) computes the Heaviside function:

.. code::

                  { 0   if x < 0,

heaviside(x, h0) = { h0 if x == 0, { 1 if x > 0.

np.block function for creating blocked arrays

Add a new block function to the current stacking functions vstack, hstack, and stack. This allows concatenation across multiple axes simultaneously, with a similar syntax to array creation, but where elements can themselves be arrays. For instance::

>>> A = np.eye(2) 2 >>> B = np.eye(3) 3 >>> np.block([ ... [A, np.zeros((2, 3))], ... [np.ones((3, 2)), B ] ... ]) array([[ 2., 0., 0., 0., 0.], [ 0., 2., 0., 0., 0.], [ 1., 1., 3., 0., 0.], [ 1., 1., 0., 3., 0.], [ 1., 1., 0., 0., 3.]])

While primarily useful for block matrices, this works for arbitrary dimensions of arrays.

It is similar to Matlab's square bracket notation for creating block matrices.

isin function, improving on in1d

The new function isin tests whether each element of an N-dimensonal array is present anywhere within a second array. It is an enhancement of in1d that preserves the shape of the first array.

Temporary elision

On platforms providing the backtrace function NumPy will try to avoid creating temporaries in expression involving basic numeric types. For example d = a + b + c is transformed to d = a + b; d += c which can improve performance for large arrays as less memory bandwidth is required to perform the operation.

axes argument for unique

In an N-dimensional array, the user can now choose the axis along which to look for duplicate N-1-dimensional elements using numpy.unique. The original behaviour is recovered if axis=None (default).

np.gradient now supports unevenly spaced data

Users can now specify a not-constant spacing for data. In particular np.gradient can now take:

1. A single scalar to specify a sample distance for all dimensions.
2. N scalars to specify a constant sample distance for each dimension. i.e. dx, dy, dz, ...
3. N arrays to specify the coordinates of the values along each dimension of F. The length of the array must match the size of the corresponding dimension
4. Any combination of N scalars/arrays with the meaning of 2. and 3.

This means that, e.g., it is now possible to do the following::

>>> f = np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float) >>> dx = 2. >>> y = [1., 1.5, 3.5] >>> np.gradient(f, dx, y) [array([[ 1. , 1. , -0.5], [ 1. , 1. , -0.5]]), array([[ 2. , 2. , 2. ], [ 2. , 1.7, 0.5]])]

Support for returning arrays of arbitrary dimensions in apply_along_axis

Previously, only scalars or 1D arrays could be returned by the function passed to apply_along_axis. Now, it can return an array of any dimensionality (including 0D), and the shape of this array replaces the axis of the array being iterated over.

.ndim property added to dtype to complement .shape

For consistency with ndarray and broadcast, d.ndim is a shorthand for len(d.shape).

Support for tracemalloc in Python 3.6

NumPy now supports memory tracing with tracemalloc_ module of Python 3.6 or newer. Memory allocations from NumPy are placed into the domain defined by numpy.lib.tracemalloc_domain. Note that NumPy allocation will not show up in tracemalloc_ of earlier Python versions.

.. _tracemalloc: https://docs.python.org/3/library/tracemalloc.html

NumPy may be built with relaxed stride checking debugging

Setting NPY_RELAXED_STRIDES_DEBUG=1 in the environment when relaxed stride checking is enabled will cause NumPy to be compiled with the affected strides set to the maximum value of npy_intp in order to help detect invalid usage of the strides in downstream projects. When enabled, invalid usage often results in an error being raised, but the exact type of error depends on the details of the code. TypeError and OverflowError have been observed in the wild.

It was previously the case that this option was disabled for releases and enabled in master and changing between the two required editing the code. It is now disabled by default but can be enabled for test builds.

Improvements

Ufunc behavior for overlapping inputs

Operations where ufunc input and output operands have memory overlap produced undefined results in previous NumPy versions, due to data dependency issues. In NumPy 1.13.0, results from such operations are now defined to be the same as for equivalent operations where there is no memory overlap.

Operations affected now make temporary copies, as needed to eliminate data dependency. As detecting these cases is computationally expensive, a heuristic is used, which may in rare cases result to needless temporary copies. For operations where the data dependency is simple enough for the heuristic to analyze, temporary copies will not be made even if the arrays overlap, if it can be deduced copies are not necessary. As an example,np.add(a, b, out=a) will not involve copies.

To illustrate a previously undefined operation::

>>> x = np.arange(16).astype(float) >>> np.add(x[1:], x[:-1], out=x[1:])

In NumPy 1.13.0 the last line is guaranteed to be equivalent to::

>>> np.add(x[1:].copy(), x[:-1].copy(), out=x[1:])

A similar operation with simple non-problematic data dependence is::

>>> x = np.arange(16).astype(float) >>> np.add(x[1:], x[:-1], out=x[:-1])

It will continue to produce the same results as in previous NumPy versions, and will not involve unnecessary temporary copies.

The change applies also to in-place binary operations, for example::

>>> x = np.random.rand(500, 500) >>> x += x.T

This statement is now guaranteed to be equivalent to x[...] = x + x.T, whereas in previous NumPy versions the results were undefined.

Partial support for 64-bit f2py extensions with MinGW

Extensions that incorporate Fortran libraries can now be built using the free MinGW toolset, also under Python 3.5. This works best for extensions that only do calculations and uses the runtime modestly (reading and writing from files, for instance). Note that this does not remove the need for Mingwpy; if you make extensive use of the runtime, you will most likely run into issues. Instead, it should be regarded as a band-aid until Mingwpy is fully functional.

Extensions can also be compiled using the MinGW toolset using the runtime library from the (moveable) WinPython 3.4 distribution, which can be useful for programs with a PySide1/Qt4 front-end.

.. _MinGW: https://sf.net/projects/mingw-w64/files/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/6.2.0/threads-win32/seh/

.. _issues: https://mingwpy.github.io/issues.html

Performance improvements for packbits and unpackbits

The functions numpy.packbits with boolean input and numpy.unpackbits have been optimized to be a significantly faster for contiguous data.

Fix for PPC long double floating point information

In previous versions of NumPy, the finfo function returned invalid information about the double double_ format of the longdouble float type on Power PC (PPC). The invalid values resulted from the failure of the NumPy algorithm to deal with the variable number of digits in the significand that are a feature of PPC long doubles. This release by-passes the failing algorithm by using heuristics to detect the presence of the PPC double double format. A side-effect of using these heuristics is that the finfo function is faster than previous releases.

.. _PPC long doubles: https://www.ibm.com/support/knowledgecenter/en/ssw_aix_71/com.ibm.aix.genprogc/128bit_long_double_floating-point_datatype.htm

.. _double double: https://en.wikipedia.org/wiki/Quadruple-precision_floating-point_formatDouble-double_arithmetic

Better default repr for ndarray subclasses

Subclasses of ndarray with no repr specialization now correctly indent their data and type lines.

More reliable comparisons of masked arrays

Comparisons of masked arrays were buggy for masked scalars and failed for structured arrays with dimension higher than one. Both problems are now solved. In the process, it was ensured that in getting the result for a structured array, masked fields are properly ignored, i.e., the result is equal if all fields that are non-masked in both are equal, thus making the behaviour identical to what one gets by comparing an unstructured masked array and then doing .all() over some axis.

np.matrix with booleans elements can now be created using the string syntax

np.matrix failed whenever one attempts to use it with booleans, e.g., np.matrix('True'). Now, this works as expected.

More linalg operations now accept empty vectors and matrices

All of the following functions in np.linalg now work when given input arrays with a 0 in the last two dimensions: det, slogdet, pinv, eigvals, eigvalsh, eig, eigh.

Bundled version of LAPACK is now 3.2.2

NumPy comes bundled with a minimal implementation of lapack for systems without a lapack library installed, under the name of lapack_lite. This has been upgraded from LAPACK 3.0.0 (June 30, 1999) to LAPACK 3.2.2 (June 30, 2010). See the LAPACK changelogs_ for details on the all the changes this entails.

While no new features are exposed through numpy, this fixes some bugs regarding "workspace" sizes, and in some places may use faster algorithms.

.. _LAPACK changelogs: http://www.netlib.org/lapack/release_notes.html_4_history_of_lapack_releases

reduce of np.hypot.reduce and np.logical_xor allowed in more cases

This now works on empty arrays, returning 0, and can reduce over multiple axes. Previously, a ValueError was thrown in these cases.

Better repr of object arrays

Object arrays that contain themselves no longer cause a recursion error.

Object arrays that contain list objects are now printed in a way that makes clear the difference between a 2d object array, and a 1d object array of lists.

Changes

argsort on masked arrays takes the same default arguments as sort

By default, argsort now places the masked values at the end of the sorted array, in the same way that sort already did. Additionally, the end_with argument is added to argsort, for consistency with sort. Note that this argument is not added at the end, so breaks any code that passed fill_value as a positional argument.

average now preserves subclasses

For ndarray subclasses, numpy.average will now return an instance of the subclass, matching the behavior of most other NumPy functions such as mean. As a consequence, also calls that returned a scalar may now return a subclass array scalar.

array == None and array != None do element-wise comparison

Previously these operations returned scalars False and True respectively.

np.equal, np.not_equal for object arrays ignores object identity

Previously, these functions always treated identical objects as equal. This had the effect of overriding comparison failures, comparison of objects that did not return booleans, such as np.arrays, and comparison of objects where the results differed from object identity, such as NaNs.

Boolean indexing changes

• Boolean array-likes (such as lists of python bools) are always treated as boolean indexes.

• Boolean scalars (including python True) are legal boolean indexes and never treated as integers.

• Boolean indexes must match the dimension of the axis that they index.

• Boolean indexes used on the lhs of an assignment must match the dimensions of the rhs.

• Boolean indexing into scalar arrays return a new 1-d array. This means that array(1)[array(True)] gives array([1]) and not the original array.

np.random.multivariate_normal behavior with bad covariance matrix

It is now possible to adjust the behavior the function will have when dealing with the covariance matrix by using two new keyword arguments:

• tol can be used to specify a tolerance to use when checking that the covariance matrix is positive semidefinite.

• check_valid can be used to configure what the function will do in the presence of a matrix that is not positive semidefinite. Valid options are ignore, warn and raise. The default value, warn keeps the the behavior used on previous releases.

assert_array_less compares np.inf and -np.inf now

Previously, np.testing.assert_array_less ignored all infinite values. This is not the expected behavior both according to documentation and intuitively. Now, -inf < x < inf is considered True for any real number x and all other cases fail.

assert_array_ and masked arrays assert_equal hide less warnings

Some warnings that were previously hidden by the assert_array_ functions are not hidden anymore. In most cases the warnings should be correct and, should they occur, will require changes to the tests using these functions. For the masked array assert_equal version, warnings may occur when comparing NaT. The function presently does not handle NaT or NaN specifically and it may be best to avoid it at this time should a warning show up due to this change.

offset attribute value in memmap objects

The offset attribute in a memmap object is now set to the offset into the file. This is a behaviour change only for offsets greater than mmap.ALLOCATIONGRANULARITY.

np.real and np.imag return scalars for scalar inputs

Previously, np.real and np.imag used to return array objects when provided a scalar input, which was inconsistent with other functions like np.angle and np.conj.

The polynomial convenience classes cannot be passed to ufuncs

The ABCPolyBase class, from which the convenience classes are derived, sets __array_ufun__ = None in order of opt out of ufuncs. If a polynomial convenience class instance is passed as an argument to a ufunc, a TypeError will now be raised.

Output arguments to ufuncs can be tuples also for ufunc methods

For calls to ufuncs, it was already possible, and recommended, to use an out argument with a tuple for ufuncs with multiple outputs. This has now been extended to output arguments in the reduce, accumulate, and reduceat methods. This is mostly for compatibility with __array_ufunc; there are no ufuncs yet that have more than one output.

==========================

1.12.1

==========================

NumPy 1.12.1 supports Python 2.7 and 3.4 - 3.6 and fixes bugs and regressions found in NumPy 1.12.0. In particular, the regression in f2py constant parsing is fixed. Wheels for Linux, Windows, and OSX can be found on pypi,

Bugs Fixed

• BUG: Fix wrong future nat warning and equiv type logic error...
• BUG: Fix wrong masked median for some special cases
• DOC: Place np.average in inline code
• TST: Work around isfinite inconsistency on i386
• BUG: Guard against replacing constants without '_' spec in f2py.
• BUG: Fix mean for float 16 non-array inputs for 1.12
• BUG: Fix calling python api with error set and minor leaks for...
• BUG: Make iscomplexobj compatible with custom dtypes again
• BUG: Fix undefined behaviour induced by bad __array_wrap__
• BUG: Fix MaskedArray.setitem
• BUG: PPC64el machines are POWER for Fortran in f2py
• BUG: Look up methods on MaskedArray in _frommethod
• BUG: Remove extra digit in binary_repr at limit
• BUG: Fix deepcopy regression for empty arrays.
• BUG: Fix ma.median for empty ndarrays

==========================

1.12.0

==========================

This release supports Python 2.7 and 3.4 - 3.6.

Highlights

The NumPy 1.12.0 release contains a large number of fixes and improvements, but few that stand out above all others. That makes picking out the highlights somewhat arbitrary but the following may be of particular interest or indicate areas likely to have future consequences.

• Order of operations in np.einsum can now be optimized for large speed improvements.
• New signature argument to np.vectorize for vectorizing with core dimensions.
• The keepdims argument was added to many functions.
• New context manager for testing warnings
• Support for BLIS in numpy.distutils
• Much improved support for PyPy (not yet finished)

Dropped Support

• Support for Python 2.6, 3.2, and 3.3 has been dropped.

Added Support

• Support for PyPy 2.7 v5.6.0 has been added. While not complete (nditer updateifcopy is not supported yet), this is a milestone for PyPy's C-API compatibility layer.

Build System Changes

• Library order is preserved, instead of being reordered to match that of the directories.

Deprecations

Assignment of ndarray object's data attribute

Assigning the 'data' attribute is an inherently unsafe operation as pointed out in gh-7083. Such a capability will be removed in the future.

Unsafe int casting of the num attribute in linspace

np.linspace now raises DeprecationWarning when num cannot be safely interpreted as an integer.

Insufficient bit width parameter to binary_repr

If a 'width' parameter is passed into binary_repr that is insufficient to represent the number in base 2 (positive) or 2's complement (negative) form, the function used to silently ignore the parameter and return a representation using the minimal number of bits needed for the form in question. Such behavior is now considered unsafe from a user perspective and will raise an error in the future.

Future Changes

• In 1.13 NAT will always compare False except for NAT != NAT, which will be True. In short, NAT will behave like NaN
• In 1.13 np.average will preserve subclasses, to match the behavior of most other numpy functions such as np.mean. In particular, this means calls which returned a scalar may return a 0-d subclass object instead.

Multiple-field manipulation of structured arrays

In 1.13 the behavior of structured arrays involving multiple fields will change in two ways:

First, indexing a structured array with multiple fields (eg, arr[['f1', 'f3']]) will return a view into the original array in 1.13, instead of a copy. Note the returned view will have extra padding bytes corresponding to intervening fields in the original array, unlike the copy in 1.12, which will affect code such as arr[['f1', 'f3']].view(newdtype).

Second, for numpy versions 1.6 to 1.12 assignment between structured arrays occurs "by field name": Fields in the destination array are set to the identically-named field in the source array or to 0 if the source does not have a field::

>>> a = np.array([(1,2),(3,4)], dtype=[('x', 'i4'), ('y', 'i4')]) >>> b = np.ones(2, dtype=[('z', 'i4'), ('y', 'i4'), ('x', 'i4')]) >>> b[:] = a >>> b array([(0, 2, 1), (0, 4, 3)], dtype=[('z', '<i4'), ('y', '<i4'), ('x', '<i4')])

In 1.13 assignment will instead occur "by position": The Nth field of the destination will be set to the Nth field of the source regardless of field name. The old behavior can be obtained by using indexing to reorder the fields before assignment, e.g., b[['x', 'y']] = a[['y', 'x']].

Compatibility notes

DeprecationWarning to error

• Indexing with floats raises IndexError, e.g., a[0, 0.0].
• Indexing with non-integer array_like raises IndexError, e.g., a['1', '2']
• Indexing with multiple ellipsis raises IndexError, e.g., a[..., ...].
• Non-integers used as index values raise TypeError, e.g., in reshape, take, and specifying reduce axis.

FutureWarning to changed behavior

• np.full now returns an array of the fill-value's dtype if no dtype is given, instead of defaulting to float.
• np.average will emit a warning if the argument is a subclass of ndarray, as the subclass will be preserved starting in 1.13. (see Future Changes)

power and ** raise errors for integer to negative integer powers

The previous behavior depended on whether numpy scalar integers or numpy integer arrays were involved.

For arrays

• Zero to negative integer powers returned least integral value.
• Both 1, -1 to negative integer powers returned correct values.
• The remaining integers returned zero when raised to negative integer powers.

For scalars

• Zero to negative integer powers returned least integral value.
• Both 1, -1 to negative integer powers returned correct values.
• The remaining integers sometimes returned zero, sometimes the correct float depending on the integer type combination.

All of these cases now raise a ValueError except for those integer combinations whose common type is float, for instance uint64 and int8. It was felt that a simple rule was the best way to go rather than have special exceptions for the integer units. If you need negative powers, use an inexact type.

Relaxed stride checking is the default

This will have some impact on code that assumed that F_CONTIGUOUS and C_CONTIGUOUS were mutually exclusive and could be set to determine the default order for arrays that are now both.

The np.percentile 'midpoint' interpolation method fixed for exact indices

The 'midpoint' interpolator now gives the same result as 'lower' and 'higher' when the two coincide. Previous behavior of 'lower' + 0.5 is fixed.

keepdims kwarg is passed through to user-class methods

numpy functions that take a keepdims kwarg now pass the value through to the corresponding methods on ndarray sub-classes. Previously the keepdims keyword would be silently dropped. These functions now have the following behavior:

1. If user does not provide keepdims, no keyword is passed to the underlying method.
2. Any user-provided value of keepdims is passed through as a keyword argument to the method.

This will raise in the case where the method does not support a keepdims kwarg and the user explicitly passes in keepdims.

The following functions are changed: sum, product, sometrue, alltrue, any, all, amax, amin, prod, mean, std, var, nanmin, nanmax, nansum, nanprod, nanmean, nanmedian, nanvar, nanstd

bitwise_and identity changed

The previous identity was 1, it is now -1. See entry in Improvements for more explanation.

ma.median warns and returns nan when unmasked invalid values are encountered

Similar to unmasked median the masked median ma.median now emits a Runtime warning and returns NaN in slices where an unmasked NaN is present.

Greater consistancy in assert_almost_equal

The precision check for scalars has been changed to match that for arrays. It is now::

abs(actual - desired) < 1.5 * 10**(-decimal)

Note that this is looser than previously documented, but agrees with the previous implementation used in assert_array_almost_equal. Due to the change in implementation some very delicate tests may fail that did not fail before.

NoseTester behaviour of warnings during testing

When raise_warnings="develop" is given, all uncaught warnings will now be considered a test failure. Previously only selected ones were raised. Warnings which are not caught or raised (mostly when in release mode) will be shown once during the test cycle similar to the default python settings.

assert_warns and deprecated decorator more specific

The assert_warns function and context manager are now more specific to the given warning category. This increased specificity leads to them being handled according to the outer warning settings. This means that no warning may be raised in cases where a wrong category warning is given and ignored outside the context. Alternatively the increased specificity may mean that warnings that were incorrectly ignored will now be shown or raised. See also the new suppress_warnings context manager. The same is true for the deprecated decorator.

C API

No changes.

New Features

Writeable keyword argument for as_strided

np.lib.stride_tricks.as_strided now has a writeable keyword argument. It can be set to False when no write operation to the returned array is expected to avoid accidental unpredictable writes.

axes keyword argument for rot90

The axes keyword argument in rot90 determines the plane in which the array is rotated. It defaults to axes=(0,1) as in the original function.

Generalized flip

flipud and fliplr reverse the elements of an array along axis=0 and axis=1 respectively. The newly added flip function reverses the elements of an array along any given axis.

• np.count_nonzero now has an axis parameter, allowing non-zero counts to be generated on more than just a flattened array object.

BLIS support in numpy.distutils

Building against the BLAS implementation provided by the BLIS library is now supported. See the [blis] section in site.cfg.example (in the root of the numpy repo or source distribution).

Hook in numpy/__init__.py to run distribution-specific checks

Binary distributions of numpy may need to run specific hardware checks or load specific libraries during numpy initialization. For example, if we are distributing numpy with a BLAS library that requires SSE2 instructions, we would like to check the machine on which numpy is running does have SSE2 in order to give an informative error.

Add a hook in numpy/__init__.py to import a numpy/_distributor_init.py file that will remain empty (bar a docstring) in the standard numpy source, but that can be overwritten by people making binary distributions of numpy.

New nanfunctions nancumsum and nancumprod added

Nan-functions nancumsum and nancumprod have been added to compute cumsum and cumprod by ignoring nans.

np.interp can now interpolate complex values

np.lib.interp(x, xp, fp) now allows the interpolated array fp to be complex and will interpolate at complex128 precision.

New polynomial evaluation function polyvalfromroots added

The new function polyvalfromroots evaluates a polynomial at given points from the roots of the polynomial. This is useful for higher order polynomials, where expansion into polynomial coefficients is inaccurate at machine precision.

New array creation function geomspace added

The new function geomspace generates a geometric sequence. It is similar to logspace, but with start and stop specified directly: geomspace(start, stop) behaves the same as logspace(log10(start), log10(stop)).

New context manager for testing warnings

A new context manager suppress_warnings has been added to the testing utils. This context manager is designed to help reliably test warnings. Specifically to reliably filter/ignore warnings. Ignoring warnings by using an "ignore" filter in Python versions before 3.4.x can quickly result in these (or similar) warnings not being tested reliably.

The context manager allows to filter (as well as record) warnings similar to the catch_warnings context, but allows for easier specificity. Also printing warnings that have not been filtered or nesting the context manager will work as expected. Additionally, it is possible to use the context manager as a decorator which can be useful when multiple tests give need to hide the same warning.

New masked array functions ma.convolve and ma.correlate added

These functions wrapped the non-masked versions, but propagate through masked values. There are two different propagation modes. The default causes masked values to contaminate the result with masks, but the other mode only outputs masks if there is no alternative.

New float_power ufunc

The new float_power ufunc is like the power function except all computation is done in a minimum precision of float64. There was a long discussion on the numpy mailing list of how to treat integers to negative integer powers and a popular proposal was that the __pow__ operator should always return results of at least float64 precision. The float_power function implements that option. Note that it does not support object arrays.

np.loadtxt now supports a single integer as usecol argument

Instead of using usecol=(n,) to read the nth column of a file it is now allowed to use usecol=n. Also the error message is more user friendly when a non-integer is passed as a column index.

Improved automated bin estimators for histogram

Added 'doane' and 'sqrt' estimators to histogram via the bins argument. Added support for range-restricted histograms with automated bin estimation.

np.roll can now roll multiple axes at the same time

The shift and axis arguments to roll are now broadcast against each other, and each specified axis is shifted accordingly.

The __complex__ method has been implemented for the ndarrays

Calling complex() on a size 1 array will now cast to a python complex.

pathlib.Path objects now supported

The standard np.load, np.save, np.loadtxt, np.savez, and similar functions can now take pathlib.Path objects as an argument instead of a filename or open file object.

New bits attribute for np.finfo

This makes np.finfo consistent with np.iinfo which already has that attribute.

New signature argument to np.vectorize

This argument allows for vectorizing user defined functions with core dimensions, in the style of NumPy's :ref:generalized universal functions<c-api.generalized-ufuncs>. This allows for vectorizing a much broader class of functions. For example, an arbitrary distance metric that combines two vectors to produce a scalar could be vectorized with signature='(n),(n)->()'. See np.vectorize for full details.

Emit py3kwarnings for division of integer arrays

To help people migrate their code bases from Python 2 to Python 3, the python interpreter has a handy option -3, which issues warnings at runtime. One of its warnings is for integer division::

$ python -3 -c "2/3"

-c:1: DeprecationWarning: classic int division

In Python 3, the new integer division semantics also apply to numpy arrays. With this version, numpy will emit a similar warning::

$ python -3 -c "import numpy as np; np.array(2)/np.array(3)"

-c:1: DeprecationWarning: numpy: classic int division

numpy.sctypes now includes bytes on Python3 too

Previously, it included str (bytes) and unicode on Python2, but only str (unicode) on Python3.

Improvements

bitwise_and identity changed

The previous identity was 1 with the result that all bits except the LSB were masked out when the reduce method was used. The new identity is -1, which should work properly on twos complement machines as all bits will be set to one.

Generalized Ufuncs will now unlock the GIL

Generalized Ufuncs, including most of the linalg module, will now unlock the Python global interpreter lock.

Caches in np.fft are now bounded in total size and item count

The caches in np.fft that speed up successive FFTs of the same length can no longer grow without bounds. They have been replaced with LRU (least recently used) caches that automatically evict no longer needed items if either the memory size or item count limit has been reached.

Improved handling of zero-width string/unicode dtypes

Fixed several interfaces that explicitly disallowed arrays with zero-width string dtypes (i.e. dtype('S0') or dtype('U0'), and fixed several bugs where such dtypes were not handled properly. In particular, changed ndarray.__new__ to not implicitly convert dtype('S0') to dtype('S1') (and likewise for unicode) when creating new arrays.

Integer ufuncs vectorized with AVX2

If the cpu supports it at runtime the basic integer ufuncs now use AVX2 instructions. This feature is currently only available when compiled with GCC.

Order of operations optimization in np.einsum

np.einsum now supports the optimize argument which will optimize the order of contraction. For example, np.einsum would complete the chain dot example np.einsum(‘ij,jk,kl->il’, a, b, c) in a single pass which would scale like N^4; however, when optimize=True np.einsum will create an intermediate array to reduce this scaling to N^3 or effectively np.dot(a, b).dot(c). Usage of intermediate tensors to reduce scaling has been applied to the general einsum summation notation. See np.einsum_path for more details.

quicksort has been changed to an introsort

The quicksort kind of np.sort and np.argsort is now an introsort which is regular quicksort but changing to a heapsort when not enough progress is made. This retains the good quicksort performance while changing the worst case runtime from O(N^2) to O(N*log(N)).

ediff1d improved performance and subclass handling

The ediff1d function uses an array instead on a flat iterator for the subtraction. When to_begin or to_end is not None, the subtraction is performed in place to eliminate a copy operation. A side effect is that certain subclasses are handled better, namely astropy.Quantity, since the complete array is created, wrapped, and then begin and end values are set, instead of using concatenate.

Improved precision of ndarray.mean for float16 arrays

The computation of the mean of float16 arrays is now carried out in float32 for improved precision. This should be useful in packages such as Theano where the precision of float16 is adequate and its smaller footprint is desirable.

Changes

All array-like methods are now called with keyword arguments in fromnumeric.py

Internally, many array-like methods in fromnumeric.py were being called with positional arguments instead of keyword arguments as their external signatures were doing. This caused a complication in the downstream 'pandas' library that encountered an issue with 'numpy' compatibility. Now, all array-like methods in this module are called with keyword arguments instead.

Operations on np.memmap objects return numpy arrays in most cases

Previously operations on a memmap object would misleadingly return a memmap instance even if the result was actually not memmapped. For example, arr + 1 or arr + arr would return memmap instances, although no memory from the output array is memmapped. Version 1.12 returns ordinary numpy arrays from these operations.

Also, reduction of a memmap (e.g. .sum(axis=None) now returns a numpy scalar instead of a 0d memmap.

stacklevel of warnings increased

The stacklevel for python based warnings was increased so that most warnings will report the offending line of the user code instead of the line the warning itself is given. Passing of stacklevel is now tested to ensure that new warnings will receive the stacklevel argument.

This causes warnings with the "default" or "module" filter to be shown once for every offending user code line or user module instead of only once. On python versions before 3.4, this can cause warnings to appear that were falsely ignored before, which may be surprising especially in test suits.

=========================

1.11.3

==========================

Numpy 1.11.3 fixes a bug that leads to file corruption when very large files opened in append mode are used in ndarray.tofile. It supports Python versions 2.6 - 2.7 and 3.2 - 3.5. Wheels for Linux, Windows, and OS X can be found on PyPI.

Contributors to maintenance/1.11.3

A total of 2 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• Charles Harris
• Pavel Potocek +

Pull Requests Merged

• 8341 <https://github.com/numpy/numpy/pull/8341>__: BUG: Fix ndarray.tofile large file corruption in append mode.
• 8346 <https://github.com/numpy/numpy/pull/8346>__: TST: Fix tests in PR 8341 for NumPy 1.11.x

==========================

1.11.2

==========================

Numpy 1.11.2 supports Python 2.6 - 2.7 and 3.2 - 3.5. It fixes bugs and regressions found in Numpy 1.11.1 and includes several build related improvements. Wheels for Linux, Windows, and OS X can be found on PyPI.

Pull Requests Merged

Fixes overridden by later merges and release notes updates are omitted.

• 7736 BUG: Many functions silently drop 'keepdims' kwarg.
• 7738 ENH: Add extra kwargs and update doc of many MA methods.
• 7778 DOC: Update Numpy 1.11.1 release notes.
• 7793 BUG: MaskedArray.count treats negative axes incorrectly.
• 7816 BUG: Fix array too big error for wide dtypes.
• 7821 BUG: Make sure npy_mul_withoverflow<type> detects overflow.
• 7824 MAINT: Allocate fewer bytes for empty arrays.
• 7847 MAINT,DOC: Fix some imp module uses and update f2py.compile docstring.
• 7849 MAINT: Fix remaining uses of deprecated Python imp module.
• 7851 BLD: Fix ATLAS version detection.
• 7896 BUG: Construct ma.array from np.array which contains padding.
• 7904 BUG: Fix float16 type not being called due to wrong ordering.
• 7917 BUG: Production install of numpy should not require nose.
• 7919 BLD: Fixed MKL detection for recent versions of this library.
• 7920 BUG: Fix for issue 7835 (ma.median of 1d).
• 7932 BUG: Monkey-patch _msvccompile.gen_lib_option like other compilers.
• 7939 BUG: Check for HAVE_LDOUBLE_DOUBLE_DOUBLE_LE in npy_math_complex.
• 7953 BUG: Guard against buggy comparisons in generic quicksort.
• 7954 BUG: Use keyword arguments to initialize Extension base class.
• 7955 BUG: Make sure numpy globals keep identity after reload.
• 7972 BUG: MSVCCompiler grows 'lib' & 'include' env strings exponentially.
• 8005 BLD: Remove __NUMPY_SETUP__ from builtins at end of setup.py.
• 8010 MAINT: Remove leftover imp module imports.
• 8020 BUG: Fix return of np.ma.count if keepdims is True and axis is None.
• 8024 BUG: Fix numpy.ma.median.
• 8031 BUG: Fix np.ma.median with only one non-masked value.
• 8044 BUG: Fix bug in NpyIter buffering with discontinuous arrays.

=========================

1.11.1

==========================

Numpy 1.11.1 supports Python 2.6 - 2.7 and 3.2 - 3.5. It fixes bugs and regressions found in Numpy 1.11.0 and includes several build related improvements. Wheels for Linux, Windows, and OSX can be found on pypi.

Fixes Merged

• 7506 BUG: Make sure numpy imports on python 2.6 when nose is unavailable.
• 7530 BUG: Floating exception with invalid axis in np.lexsort.
• 7535 BUG: Extend glibc complex trig functions blacklist to glibc < 2.18.
• 7551 BUG: Allow graceful recovery for no compiler.
• 7558 BUG: Constant padding expected wrong type in constant_values.
• 7578 BUG: Fix OverflowError in Python 3.x. in swig interface.
• 7590 BLD: Fix configparser.InterpolationSyntaxError.
• 7597 BUG: Make np.ma.take work on scalars.
• 7608 BUG: linalg.norm(): Don't convert object arrays to float.
• 7638 BLD: Correct C compiler customization in system_info.py.
• 7654 BUG: ma.median of 1d array should return a scalar.
• 7656 BLD: Remove hardcoded Intel compiler flag -xSSE4.2.
• 7660 BUG: Temporary fix for str(mvoid) for object field types.
• 7665 BUG: Fix incorrect printing of 1D masked arrays.
• 7670 BUG: Correct initial index estimate in histogram.
• 7671 BUG: Boolean assignment no GIL release when transfer needs API.
• 7676 BUG: Fix handling of right edge of final histogram bin.
• 7680 BUG: Fix np.clip bug NaN handling for Visual Studio 2015.
• 7724 BUG: Fix segfaults in np.random.shuffle.
• 7731 MAINT: Change mkl_info.dir_env_var from MKL to MKLROOT.
• 7737 BUG: Fix issue on OS X with Python 3.x, npymath.ini not installed.

=========================

1.11.0

==========================

This release supports Python 2.6 - 2.7 and 3.2 - 3.5 and contains a number of enhancements and improvements. Note also the build system changes listed below as they may have subtle effects.

No Windows (TM) binaries are provided for this release due to a broken toolchain. One of the providers of Python packages for Windows (TM) is your best bet.

Highlights

Details of these improvements can be found below.

• The datetime64 type is now timezone naive.
• A dtype parameter has been added to randint.
• Improved detection of two arrays possibly sharing memory.
• Automatic bin size estimation for np.histogram.
• Speed optimization of A A.T and dot(A, A.T).
• New function np.moveaxis for reordering array axes.

Build System Changes

• Numpy now uses setuptools for its builds instead of plain distutils. This fixes usage of install_requires='numpy' in the setup.py files of projects that depend on Numpy (see gh-6551). It potentially affects the way that build/install methods for Numpy itself behave though. Please report any unexpected behavior on the Numpy issue tracker.
• Bento build support and related files have been removed.
• Single file build support and related files have been removed.

Future Changes

The following changes are scheduled for Numpy 1.12.0.

• Support for Python 2.6, 3.2, and 3.3 will be dropped.
• Relaxed stride checking will become the default. See the 1.8.0 release notes for a more extended discussion of what this change implies.
• The behavior of the datetime64 "not a time" (NaT) value will be changed to match that of floating point "not a number" (NaN) values: all comparisons involving NaT will return False, except for NaT != NaT which will return True.
• Indexing with floats will raise IndexError, e.g., a[0, 0.0].
• Indexing with non-integer array_like will raise IndexError, e.g., a['1', '2']
• Indexing with multiple ellipsis will raise IndexError, e.g., a[..., ...].
• Non-integers used as index values will raise TypeError, e.g., in reshape, take, and specifying reduce axis.

In a future release the following changes will be made.

• The rand function exposed in numpy.testing will be removed. That function is left over from early Numpy and was implemented using the Python random module. The random number generators from numpy.random should be used instead.
• The ndarray.view method will only allow c_contiguous arrays to be viewed using a dtype of different size causing the last dimension to change. That differs from the current behavior where arrays that are f_contiguous but not c_contiguous can be viewed as a dtype type of different size causing the first dimension to change.
• Slicing a MaskedArray will return views of both data and mask. Currently the mask is copy-on-write and changes to the mask in the slice do not propagate to the original mask. See the FutureWarnings section below for details.

Compatibility notes

datetime64 changes

In prior versions of NumPy the experimental datetime64 type always stored times in UTC. By default, creating a datetime64 object from a string or printing it would convert from or to local time::

old behavior

>>>> np.datetime64('2000-01-01T00:00:00') numpy.datetime64('2000-01-01T00:00:00-0800') note the timezone offset -08:00

A consensus of datetime64 users agreed that this behavior is undesirable and at odds with how datetime64 is usually used (e.g., by pandas <http://pandas.pydata.org>__). For most use cases, a timezone naive datetime type is preferred, similar to the datetime.datetime type in the Python standard library. Accordingly, datetime64 no longer assumes that input is in local time, nor does it print local times::

>>>> np.datetime64('2000-01-01T00:00:00') numpy.datetime64('2000-01-01T00:00:00')

For backwards compatibility, datetime64 still parses timezone offsets, which it handles by converting to UTC. However, the resulting datetime is timezone naive::

>>> np.datetime64('2000-01-01T00:00:00-08') DeprecationWarning: parsing timezone aware datetimes is deprecated; this will raise an error in the future numpy.datetime64('2000-01-01T08:00:00')

As a corollary to this change, we no longer prohibit casting between datetimes with date units and datetimes with time units. With timezone naive datetimes, the rule for casting from dates to times is no longer ambiguous.

linalg.norm return type changes

The return type of the linalg.norm function is now floating point without exception. Some of the norm types previously returned integers.

polynomial fit changes

The various fit functions in the numpy polynomial package no longer accept non-integers for degree specification.

np.dot now raises TypeError instead of ValueError

This behaviour mimics that of other functions such as np.inner. If the two arguments cannot be cast to a common type, it could have raised a TypeError or ValueError depending on their order. Now, np.dot will now always raise a TypeError.

FutureWarning to changed behavior

• In np.lib.split an empty array in the result always had dimension (0,) no matter the dimensions of the array being split. This has been changed so that the dimensions will be preserved. A FutureWarning for this change has been in place since Numpy 1.9 but, due to a bug, sometimes no warning was raised and the dimensions were already preserved.

% and // operators

These operators are implemented with the remainder and floor_divide functions respectively. Those functions are now based around fmod and are computed together so as to be compatible with each other and with the Python versions for float types. The results should be marginally more accurate or outright bug fixes compared to the previous results, but they may differ significantly in cases where roundoff makes a

[coveralls]

Coverage remained the same at 0.0% when pulling b7e557dd184635e3979b91cbc982744775ec9fd7 on pyup-initial-update into b1feaa4e573f7aad851bf629f1f86f0dc2de5f8e on master.