{"action":"created","issue":{"url":"https://api.github.com/repos/dgiambra/langevin_dynamics/issues/98","repository_url":"https://api.github.com/repos/dgiambra/langevin_dynamics","labels_url":"https://api.github.com/repos/dgiambra/langevin_dynamics/issues/98/labels{/name}","comments_url":"https://api.github.com/repos/dgiambra/langevin_dynamics/issues/98/comments","events_url":"https://api.github.com/repos/dgiambra/langevin_dynamics/issues/98/events","html_url":"https://github.com/dgiambra/langevin_dynamics/pull/98","id":318652271,"number":98,"title":"Update numpy to 1.14.3","user":{"login":"pyup-bot","id":16239342,"avatar_url":"https://avatars0.githubusercontent.com/u/16239342?v=4","gravatar_id":"","url":"https://api.github.com/users/pyup-bot","html_url":"https://github.com/pyup-bot","followers_url":"https://api.github.com/users/pyup-bot/followers","following_url":"https://api.github.com/users/pyup-bot/following{/other_user}","gists_url":"https://api.github.com/users/pyup-bot/gists{/gist_id}","starred_url":"https://api.github.com/users/pyup-bot/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/pyup-bot/subscriptions","organizations_url":"https://api.github.com/users/pyup-bot/orgs","repos_url":"https://api.github.com/users/pyup-bot/repos","events_url":"https://api.github.com/users/pyup-bot/events{/privacy}","received_events_url":"https://api.github.com/users/pyup-bot/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-04-28T16:58:54Z","updated_at":"2018-04-28T17:03:04Z","closed_at":null,"author_association":"CONTRIBUTOR","pull_request":{"url":"https://api.github.com/repos/dgiambra/langevin_dynamics/pulls/98","html_url":"https://github.com/dgiambra/langevin_dynamics/pull/98","diff_url":"https://github.com/dgiambra/langevin_dynamics/pull/98.diff","patch_url":"https://github.com/dgiambra/langevin_dynamics/pull/98.patch"},"body":"\nThis PR updates [numpy](https://pypi.org/project/numpy) from **1.11.2** to **1.14.3**.\n\n\n\n<details>\n  <summary>Changelog</summary>\n  \n  \n   ### 1.14.2\n   ```\n   ==========================\n\nThis is a bugfix release for some bugs reported following the 1.14.1 release. The major\nproblems dealt with are as follows.\n\n* Residual bugs in the new array printing functionality.\n* Regression resulting in a relocation problem with shared library.\n* Improved PyPy compatibility.\n\nThe Python versions supported in this release are 2.7 and 3.4 - 3.6. The Python\n3.6 wheels available from PIP are built with Python 3.6.2 and should be\ncompatible with all previous versions of Python 3.6. The source releases were\ncythonized with Cython 0.26.1, which is known to **not** support the upcoming\nPython 3.7 release.  People who wish to run Python 3.7 should check out the\nNumPy repo and try building with the, as yet, unreleased master branch of\nCython.\n\nContributors\n============\n\nA total of 4 people contributed to this release.  People with a &quot;+&quot; by their\nnames contributed a patch for the first time.\n\n* Allan Haldane\n* Charles Harris\n* Eric Wieser\n* Pauli Virtanen\n\nPull requests merged\n====================\n\nA total of 5 pull requests were merged for this release.\n\n* `10674 &lt;https://github.com/numpy/numpy/pull/10674&gt;`__: BUG: Further back-compat fix for subclassed array repr\n* `10725 &lt;https://github.com/numpy/numpy/pull/10725&gt;`__: BUG: dragon4 fractional output mode adds too many trailing zeros\n* `10726 &lt;https://github.com/numpy/numpy/pull/10726&gt;`__: BUG: Fix f2py generated code to work on PyPy\n* `10727 &lt;https://github.com/numpy/numpy/pull/10727&gt;`__: BUG: Fix missing NPY_VISIBILITY_HIDDEN on npy_longdouble_to_PyLong\n* `10729 &lt;https://github.com/numpy/numpy/pull/10729&gt;`__: DOC: Create 1.14.2 notes and changelog.\n\n\n=========================\n   ```\n   \n  \n  \n   ### 1.14.1\n   ```\n   ==========================\n\nThis is a bugfix release for some problems reported following the 1.14.0 release. The major\nproblems fixed are the following.\n\n* Problems with the new array printing, particularly the printing of complex\n  values, Please report any additional problems that may turn up.\n* Problems with ``np.einsum`` due to the new ``optimized=True`` default. Some\n  fixes for optimization have been applied and ``optimize=False`` is now the\n  default.\n* The sort order in ``np.unique`` when ``axis=&lt;some-number&gt;`` will now always\n  be lexicographic in the subarray elements. In previous NumPy versions there\n  was an optimization that could result in sorting the subarrays as unsigned\n  byte strings.\n* The change in 1.14.0 that multi-field indexing of structured arrays returns a\n  view instead of a copy has been reverted but remains on track for NumPy 1.15.\n  Affected users should read the 1.14.1 Numpy User Guide section\n  &quot;basics/structured arrays/accessing multiple fields&quot; for advice on how to\n  manage this transition.\n\nThe Python versions supported in this release are 2.7 and 3.4 - 3.6. The Python\n3.6 wheels available from PIP are built with Python 3.6.2 and should be\ncompatible with all previous versions of Python 3.6. The source releases were\ncythonized with Cython 0.26.1, which is known to **not** support the upcoming\nPython 3.7 release.  People who wish to run Python 3.7 should check out the\nNumPy repo and try building with the, as yet, unreleased master branch of\nCython.\n\nContributors\n============\n\nA total of 14 people contributed to this release.  People with a &quot;+&quot; by their\nnames contributed a patch for the first time.\n\n* Allan Haldane\n* Charles Harris\n* Daniel Smith\n* Dennis Weyland +\n* Eric Larson\n* Eric Wieser\n* Jarrod Millman\n* Kenichi Maehashi +\n* Marten van Kerkwijk\n* Mathieu Lamarre\n* Sebastian Berg\n* Simon Conseil\n* Simon Gibbons\n* xoviat\n\nPull requests merged\n====================\n\nA total of 36 pull requests were merged for this release.\n\n* `10339 &lt;https://github.com/numpy/numpy/pull/10339&gt;`__: BUG: restrict the __config__ modifications to win32\n* `10368 &lt;https://github.com/numpy/numpy/pull/10368&gt;`__: MAINT: Adjust type promotion in linalg.norm\n* `10375 &lt;https://github.com/numpy/numpy/pull/10375&gt;`__: BUG: add missing paren and remove quotes from repr of fieldless...\n* `10395 &lt;https://github.com/numpy/numpy/pull/10395&gt;`__: MAINT: Update download URL in setup.py.\n* `10396 &lt;https://github.com/numpy/numpy/pull/10396&gt;`__: BUG: fix einsum issue with unicode input and py2\n* `10397 &lt;https://github.com/numpy/numpy/pull/10397&gt;`__: BUG: fix error message not formatted in einsum\n* `10398 &lt;https://github.com/numpy/numpy/pull/10398&gt;`__: DOC: add documentation about how to handle new array printing\n* `10403 &lt;https://github.com/numpy/numpy/pull/10403&gt;`__: BUG: Set einsum optimize parameter default to `False`.\n* `10424 &lt;https://github.com/numpy/numpy/pull/10424&gt;`__: ENH: Fix repr of np.record objects to match np.void types 10412\n* `10425 &lt;https://github.com/numpy/numpy/pull/10425&gt;`__: MAINT: Update zesty to artful for i386 testing\n* `10431 &lt;https://github.com/numpy/numpy/pull/10431&gt;`__: REL: Add 1.14.1 release notes template\n* `10435 &lt;https://github.com/numpy/numpy/pull/10435&gt;`__: MAINT: Use ValueError for duplicate field names in lookup (backport)\n* `10534 &lt;https://github.com/numpy/numpy/pull/10534&gt;`__: BUG: Provide a better error message for out-of-order fields\n* `10536 &lt;https://github.com/numpy/numpy/pull/10536&gt;`__: BUG: Resize bytes_ columns in genfromtxt (backport of 10401)\n* `10537 &lt;https://github.com/numpy/numpy/pull/10537&gt;`__: BUG: multifield-indexing adds padding bytes: revert for 1.14.1\n* `10539 &lt;https://github.com/numpy/numpy/pull/10539&gt;`__: BUG: fix np.save issue with python 2.7.5\n* `10540 &lt;https://github.com/numpy/numpy/pull/10540&gt;`__: BUG: Add missing DECREF in Py2 int() cast\n* `10541 &lt;https://github.com/numpy/numpy/pull/10541&gt;`__: TST: Add circleci document testing to maintenance/1.14.x\n* `10542 &lt;https://github.com/numpy/numpy/pull/10542&gt;`__: BUG: complex repr has extra spaces, missing + (1.14 backport)\n* `10550 &lt;https://github.com/numpy/numpy/pull/10550&gt;`__: BUG: Set missing exception after malloc\n* `10557 &lt;https://github.com/numpy/numpy/pull/10557&gt;`__: BUG: In numpy.i, clear CARRAY flag if wrapped buffer is not C_CONTIGUOUS.\n* `10558 &lt;https://github.com/numpy/numpy/pull/10558&gt;`__: DEP: Issue FutureWarning when malformed records detected.\n* `10559 &lt;https://github.com/numpy/numpy/pull/10559&gt;`__: BUG: Fix einsum optimize logic for singleton dimensions\n* `10560 &lt;https://github.com/numpy/numpy/pull/10560&gt;`__: BUG: Fix calling ufuncs with a positional output argument.\n* `10561 &lt;https://github.com/numpy/numpy/pull/10561&gt;`__: BUG: Fix various Big-Endian test failures (ppc64)\n* `10562 &lt;https://github.com/numpy/numpy/pull/10562&gt;`__: BUG: Make dtype.descr error for out-of-order fields.\n* `10563 &lt;https://github.com/numpy/numpy/pull/10563&gt;`__: BUG: arrays not being flattened in `union1d`\n* `10607 &lt;https://github.com/numpy/numpy/pull/10607&gt;`__: MAINT: Update sphinxext submodule hash.\n* `10608 &lt;https://github.com/numpy/numpy/pull/10608&gt;`__: BUG: Revert sort optimization in np.unique.\n* `10609 &lt;https://github.com/numpy/numpy/pull/10609&gt;`__: BUG: infinite recursion in str of 0d subclasses\n* `10610 &lt;https://github.com/numpy/numpy/pull/10610&gt;`__: BUG: Align type definition with generated lapack\n* `10612 &lt;https://github.com/numpy/numpy/pull/10612&gt;`__: BUG/ENH: Improve output for structured non-void types\n* `10622 &lt;https://github.com/numpy/numpy/pull/10622&gt;`__: BUG: deallocate recursive closure in arrayprint.py (1.14 backport)\n* `10624 &lt;https://github.com/numpy/numpy/pull/10624&gt;`__: BUG: Correctly identify comma separated dtype strings\n* `10629 &lt;https://github.com/numpy/numpy/pull/10629&gt;`__: BUG: deallocate recursive closure in arrayprint.py (backport...\n* `10630 &lt;https://github.com/numpy/numpy/pull/10630&gt;`__: REL: Prepare for 1.14.1 release.\n\n\n==========================\n   ```\n   \n  \n  \n   ### 1.14.0\n   ```\n   ==========================\n\nNumpy 1.14.0 is the result of seven months of work and contains a large number\nof bug fixes and new features, along with several changes with potential\ncompatibility issues. The major change that users will notice are the\nstylistic changes in the way numpy arrays and scalars are printed, a change\nthat will affect doctests. See below for details on how to preserve the\nold style printing when needed.\n\nA major decision affecting future development concerns the schedule for\ndropping Python 2.7 support in the runup to 2020. The decision has been made to\nsupport 2.7 for all releases made in 2018, with the last release being\ndesignated a long term release with support for bug fixes extending through\n2019. In 2019 support for 2.7 will be dropped in all new releases. More details\ncan be found in the relevant NEP_.\n\nThis release supports Python 2.7 and 3.4 - 3.6.\n\n.. _NEP: https://github.com/numpy/numpy/blob/master/doc/neps/dropping-python2.7-proposal.rst\n\n\nHighlights\n==========\n\n* The `np.einsum` function uses BLAS when possible\n\n* ``genfromtxt``, ``loadtxt``, ``fromregex`` and ``savetxt`` can now handle\n  files with arbitrary Python supported encoding.\n\n* Major improvements to printing of NumPy arrays and scalars.\n\n\nNew functions\n=============\n\n* ``parametrize``: decorator added to numpy.testing\n\n* ``chebinterpolate``: Interpolate function at Chebyshev points.\n\n* ``format_float_positional`` and ``format_float_scientific`` : format\n  floating-point scalars unambiguously with control of rounding and padding.\n\n* ``PyArray_ResolveWritebackIfCopy`` and ``PyArray_SetWritebackIfCopyBase``,\n  new C-API functions useful in achieving PyPy compatibity.\n\n\nDeprecations\n============\n\n* Using ``np.bool_`` objects in place of integers is deprecated.  Previously\n  ``operator.index(np.bool_)`` was legal and allowed constructs such as\n  ``[1, 2, 3][np.True_]``. That was misleading, as it behaved differently from\n  ``np.array([1, 2, 3])[np.True_]``.\n\n* Truth testing of an empty array is deprecated. To check if an array is not\n  empty, use ``array.size &gt; 0``.\n\n* Calling ``np.bincount`` with ``minlength=None`` is deprecated.\n  ``minlength=0`` should be used instead.\n\n* Calling ``np.fromstring`` with the default value of the ``sep`` argument is\n  deprecated.  When that argument is not provided, a broken version of\n  ``np.frombuffer`` is used that silently accepts unicode strings and -- after\n  encoding them as either utf-8 (python 3) or the default encoding\n  (python 2) -- treats them as binary data. If reading binary data is\n  desired, ``np.frombuffer`` should be used directly.\n\n* The ``style`` option of array2string is deprecated in non-legacy printing mode.\n\n* ``PyArray_SetUpdateIfCopyBase`` has been deprecated. For NumPy versions &gt;= 1.14\n  use ``PyArray_SetWritebackIfCopyBase`` instead, see `C API changes` below for\n  more details.\n\n\n\n* The use of ``UPDATEIFCOPY`` arrays is deprecated, see  `C API changes` below\n  for details.  We will not be dropping support for those arrays, but they are\n  not compatible with PyPy.\n\n\nFuture Changes\n==============\n\n* ``np.issubdtype`` will stop downcasting dtype-like arguments.\n  It might be expected that ``issubdtype(np.float32, &#39;float64&#39;)`` and\n  ``issubdtype(np.float32, np.float64)`` mean the same thing - however, there\n  was an undocumented special case that translated the former into\n  ``issubdtype(np.float32, np.floating)``, giving the surprising result of True.\n\n  This translation now gives a warning that explains what translation is\n  occurring.  In the future, the translation will be disabled, and the first\n  example will be made equivalent to the second.\n\n* ``np.linalg.lstsq`` default for ``rcond`` will be changed.  The ``rcond``\n  parameter to ``np.linalg.lstsq`` will change its default to machine precision\n  times the largest of the input array dimensions. A FutureWarning is issued\n  when ``rcond`` is not passed explicitly.\n\n* ``a.flat.__array__()`` will return a writeable copy of ``a`` when ``a`` is\n  non-contiguous.  Previously it returned an UPDATEIFCOPY array when ``a`` was\n  writeable. Currently it returns a non-writeable copy. See gh-7054 for a\n  discussion of the issue.\n\n* Unstructured void array&#39;s ``.item`` method will return a bytes object. In the\n  future, calling ``.item()`` on arrays or scalars of ``np.void`` datatype will\n  return a ``bytes`` object instead of a buffer or int array, the same as\n  returned by ``bytes(void_scalar)``. This may affect code which assumed the\n  return value was mutable, which will no longer be the case. A\n  ``FutureWarning`` is now issued when this would occur.\n\n\nCompatibility notes\n===================\n\nThe mask of a masked array view is also a view rather than a copy\n-----------------------------------------------------------------\nThere was a FutureWarning about this change in NumPy 1.11.x. In short, it is\nnow the case that, when changing a view of a masked array, changes to the mask\nare propagated to the original. That was not previously the case. This change\naffects slices in particular. Note that this does not yet work properly if the\nmask of the original array is ``nomask`` and the mask of the view is changed.\nSee gh-5580 for an extended discussion. The original behavior of having a copy\nof the mask can be obtained by calling the ``unshare_mask`` method of the view.\n\n``np.ma.masked`` is no longer writeable\n---------------------------------------\nAttempts to mutate the ``masked`` constant now error, as the underlying arrays\nare marked readonly. In the past, it was possible to get away with::\n\n     emulating a function that sometimes returns np.ma.masked\n    val = random.choice([np.ma.masked, 10])\n    var_arr = np.asarray(val)\n    val_arr += 1   now errors, previously changed np.ma.masked.data\n\n``np.ma`` functions producing ``fill_value``s have changed\n----------------------------------------------------------\nPreviously, ``np.ma.default_fill_value`` would return a 0d array, but\n``np.ma.minimum_fill_value`` and ``np.ma.maximum_fill_value`` would return a\ntuple of the fields. Instead, all three methods return a structured ``np.void``\nobject, which is what you would already find in the ``.fill_value`` attribute.\n\nAdditionally, the dtype guessing now matches that of ``np.array`` - so when\npassing a python scalar ``x``, ``maximum_fill_value(x)`` is always the same as\n``maximum_fill_value(np.array(x))``. Previously ``x = long(1)`` on Python 2\nviolated this assumption.\n\n``a.flat.__array__()`` returns non-writeable arrays when ``a`` is non-contiguous\n--------------------------------------------------------------------------------\nThe intent is that the UPDATEIFCOPY array previously returned when ``a`` was\nnon-contiguous will be replaced by a writeable copy in the future. This\ntemporary measure is aimed to notify folks who expect the underlying array be\nmodified in this situation that that will no longer be the case. The most\nlikely places for this to be noticed is when expressions of the form\n``np.asarray(a.flat)`` are used, or when ``a.flat`` is passed as the out\nparameter to a ufunc.\n\n``np.tensordot`` now returns zero array when contracting over 0-length dimension\n--------------------------------------------------------------------------------\nPreviously ``np.tensordot`` raised a ValueError when contracting over 0-length\ndimension. Now it returns a zero array, which is consistent with the behaviour\nof ``np.dot`` and ``np.einsum``.\n\n``numpy.testing`` reorganized\n-----------------------------\nThis is not expected to cause problems, but possibly something has been left\nout. If you experience an unexpected import problem using ``numpy.testing``\nlet us know.\n\n``np.asfarray`` no longer accepts non-dtypes through the ``dtype`` argument\n---------------------------------------------------------------------------\nThis previously would accept ``dtype=some_array``, with the implied semantics\nof ``dtype=some_array.dtype``. This was undocumented, unique across the numpy\nfunctions, and if used would likely correspond to a typo.\n\n1D ``np.linalg.norm`` preserves float input types, even for arbitrary orders\n----------------------------------------------------------------------------\nPreviously, this would promote to ``float64`` when arbitrary orders were\npassed, despite not doing so under the simple cases::\n\n    &gt;&gt;&gt; f32 = np.float32([[1, 2]])\n    &gt;&gt;&gt; np.linalg.norm(f32, 2.0, axis=-1).dtype\n    dtype(&#39;float32&#39;)\n    &gt;&gt;&gt; np.linalg.norm(f32, 2.0001, axis=-1).dtype\n    dtype(&#39;float64&#39;)   numpy 1.13\n    dtype(&#39;float32&#39;)   numpy 1.14\n\nThis change affects only ``float32`` and ``float16`` arrays.\n\n``count_nonzero(arr, axis=())`` now counts over no axes, not all axes\n---------------------------------------------------------------------\nElsewhere, ``axis==()`` is always understood as &quot;no axes&quot;, but\n`count_nonzero` had a special case to treat this as &quot;all axes&quot;. This was\ninconsistent and surprising. The correct way to count over all axes has always\nbeen to pass ``axis == None``.\n\n``__init__.py`` files added to test directories\n-----------------------------------------------\nThis is for pytest compatibility in the case of duplicate test file names in\nthe different directories. As a result, ``run_module_suite`` no longer works,\ni.e., ``python &lt;path-to-test-file&gt;`` results in an error.\n\n``.astype(bool)`` on unstructured void arrays now calls ``bool`` on each element\n--------------------------------------------------------------------------------\nOn Python 2, ``void_array.astype(bool)`` would always return an array of\n``True``, unless the dtype is ``V0``. On Python 3, this operation would usually\ncrash. Going forwards, `astype` matches the behavior of ``bool(np.void)``,\nconsidering a buffer of all zeros as false, and anything else as true.\nChecks for ``V0`` can still be done with ``arr.dtype.itemsize == 0``.\n\n``MaskedArray.squeeze`` never returns ``np.ma.masked``\n------------------------------------------------------\n``np.squeeze`` is documented as returning a view, but the masked variant would\nsometimes return ``masked``, which is not a view. This has been fixed, so that\nthe result is always a view on the original masked array.\nThis breaks any code that used ``masked_arr.squeeze() is np.ma.masked``, but\nfixes code that writes to the result of `.squeeze()`.\n\nRenamed first parameter of ``can_cast`` from ``from`` to ``from_``\n------------------------------------------------------------------\nThe previous parameter name ``from`` is a reserved keyword in Python, which made\nit difficult to pass the argument by name. This has been fixed by renaming\nthe parameter to ``from_``.\n\n``isnat`` raises ``TypeError`` when passed wrong type\n------------------------------------------------------\nThe ufunc ``isnat`` used to raise a ``ValueError`` when it was not passed\nvariables of type ``datetime`` or ``timedelta``. This has been changed to\nraising a ``TypeError``.\n\n``dtype.__getitem__`` raises ``TypeError`` when passed wrong type\n-----------------------------------------------------------------\nWhen indexed with a float, the dtype object used to raise ``ValueError``.\n\nUser-defined types now need to implement ``__str__`` and ``__repr__``\n---------------------------------------------------------------------\nPreviously, user-defined types could fall back to a default implementation of\n``__str__`` and ``__repr__`` implemented in numpy, but this has now been\nremoved. Now user-defined types will fall back to the python default\n``object.__str__`` and ``object.__repr__``.\n\nMany changes to array printing, disableable with the new &quot;legacy&quot; printing mode\n-------------------------------------------------------------------------------\nThe ``str`` and ``repr`` of ndarrays and numpy scalars have been changed in\na variety of ways. These changes are likely to break downstream user&#39;s\ndoctests.\n\nThese new behaviors can be disabled to mostly reproduce numpy 1.13 behavior by\nenabling the new 1.13 &quot;legacy&quot; printing mode. This is enabled by calling\n``np.set_printoptions(legacy=&quot;1.13&quot;)``, or using the new ``legacy`` argument to\n``np.array2string``, as ``np.array2string(arr, legacy=&#39;1.13&#39;)``.\n\nIn summary, the major changes are:\n\n* For floating-point types:\n\n  * The ``repr`` of float arrays often omits a space previously printed\n    in the sign position. See the new ``sign`` option to ``np.set_printoptions``.\n  * Floating-point arrays and scalars use a new algorithm for decimal\n    representations, giving the shortest unique representation. This will\n    usually shorten ``float16`` fractional output, and sometimes ``float32`` and\n    ``float128`` output. ``float64`` should be unaffected.  See the new\n    ``floatmode`` option to ``np.set_printoptions``.\n  * Float arrays printed in scientific notation no longer use fixed-precision,\n    and now instead show the shortest unique representation.\n  * The ``str`` of floating-point scalars is no longer truncated in python2.\n\n* For other data types:\n\n  * Non-finite complex scalars print like ``nanj`` instead of ``nan*j``.\n  * ``NaT`` values in datetime arrays are now properly aligned.\n  * Arrays and scalars of ``np.void`` datatype are now printed using hex\n    notation.\n\n* For line-wrapping:\n\n  * The &quot;dtype&quot; part of ndarray reprs will now be printed on the next line\n    if there isn&#39;t space on the last line of array output.\n  * The ``linewidth`` format option is now always respected.\n    The `repr` or `str` of an array will never exceed this, unless a single\n    element is too wide.\n  * The last line of an array string will never have more elements than earlier\n    lines.\n  * An extra space is no longer inserted on the first line if the elements are\n    too wide.\n\n* For summarization (the use of ``...`` to shorten long arrays):\n\n  * A trailing comma is no longer inserted for ``str``.\n    Previously, ``str(np.arange(1001))`` gave\n    ``&#39;[   0    1    2 ...,  998  999 1000]&#39;``, which has an extra comma.\n  * For arrays of 2-D and beyond, when ``...`` is printed on its own line in\n    order to summarize any but the last axis, newlines are now appended to that\n    line to match its leading newlines and a trailing space character is\n    removed.\n\n* ``MaskedArray`` arrays now separate printed elements with commas, always\n  print the dtype, and correctly wrap the elements of long arrays to multiple\n  lines. If there is more than 1 dimension, the array attributes are now\n  printed in a new &quot;left-justified&quot; printing style.\n* ``recarray`` arrays no longer print a trailing space before their dtype, and\n  wrap to the right number of columns.\n* 0d arrays no longer have their own idiosyncratic implementations of ``str``\n  and ``repr``. The ``style`` argument to ``np.array2string`` is deprecated.\n* Arrays of ``bool`` datatype will omit the datatype in the ``repr``.\n* User-defined ``dtypes`` (subclasses of ``np.generic``) now need to\n  implement ``__str__`` and ``__repr__``.\n\nSome of these changes are described in more detail below. If you need to retain\nthe previous behavior for doctests or other reasons, you may want to do\nsomething like::\n\n     FIXME: We need the str/repr formatting used in Numpy &lt; 1.14.\n    try:\n        np.set_printoptions(legacy=&#39;1.13&#39;)\n    except TypeError:\n        pass\n\n\nC API changes\n=============\n\nPyPy compatible alternative to ``UPDATEIFCOPY`` arrays\n------------------------------------------------------\n``UPDATEIFCOPY`` arrays are contiguous copies of existing arrays, possibly with\ndifferent dimensions, whose contents are copied back to the original array when\ntheir refcount goes to zero and they are deallocated. Because PyPy does not use\nrefcounts, they do not function correctly with PyPy. NumPy is in the process of\neliminating their use internally and two new C-API functions,\n\n* ``PyArray_SetWritebackIfCopyBase``\n* ``PyArray_ResolveWritebackIfCopy``,\n\nhave been added together with a complimentary flag,\n``NPY_ARRAY_WRITEBACKIFCOPY``. Using the new functionality also requires that\nsome flags be changed when new arrays are created, to wit:\n``NPY_ARRAY_INOUT_ARRAY`` should be replaced by ``NPY_ARRAY_INOUT_ARRAY2`` and\n``NPY_ARRAY_INOUT_FARRAY`` should be replaced by ``NPY_ARRAY_INOUT_FARRAY2``.\nArrays created with these new flags will then have the ``WRITEBACKIFCOPY``\nsemantics.\n\nIf PyPy compatibility is not a concern, these new functions can be ignored,\nalthough there will be a ``DeprecationWarning``. If you do wish to pursue PyPy\ncompatibility, more information on these functions and their use may be found\nin the c-api_ documentation and the example in how-to-extend_.\n\n.. _c-api: https://github.com/numpy/numpy/blob/master/doc/source/reference/c-api.array.rst\n.. _how-to-extend: https://github.com/numpy/numpy/blob/master/doc/source/user/c-info.how-to-extend.rst\n\n\nNew Features\n============\n\nEncoding argument for text IO functions\n---------------------------------------\n``genfromtxt``, ``loadtxt``, ``fromregex`` and ``savetxt`` can now handle files\nwith arbitrary encoding supported by Python via the encoding argument.\nFor backward compatibility the argument defaults to the special ``bytes`` value\nwhich continues to treat text as raw byte values and continues to pass latin1\nencoded bytes to custom converters.\nUsing any other value (including ``None`` for system default) will switch the\nfunctions to real text IO so one receives unicode strings instead of bytes in\nthe resulting arrays.\n\nExternal ``nose`` plugins are usable by ``numpy.testing.Tester``\n----------------------------------------------------------------\n``numpy.testing.Tester`` is now aware of ``nose`` plugins that are outside the\n``nose`` built-in ones.  This allows using, for example, ``nose-timer`` like\nso:  ``np.test(extra_argv=[&#39;--with-timer&#39;, &#39;--timer-top-n&#39;, &#39;20&#39;])`` to\nobtain the runtime of the 20 slowest tests.  An extra keyword ``timer`` was\nalso added to ``Tester.test``, so ``np.test(timer=20)`` will also report the 20\nslowest tests.\n\n``parametrize`` decorator added to ``numpy.testing``\n----------------------------------------------------\nA basic ``parametrize`` decorator is now available in ``numpy.testing``. It is\nintended to allow rewriting yield based tests that have been deprecated in\npytest so as to facilitate the transition to pytest in the future. The nose\ntesting framework has not been supported for several years and looks like\nabandonware.\n\nThe new ``parametrize`` decorator does not have the full functionality of the\none in pytest. It doesn&#39;t work for classes, doesn&#39;t support nesting, and does\nnot substitute variable names. Even so, it should be adequate to rewrite the\nNumPy tests.\n\n``chebinterpolate`` function added to ``numpy.polynomial.chebyshev``\n--------------------------------------------------------------------\nThe new ``chebinterpolate`` function interpolates a given function at the\nChebyshev points of the first kind. A new ``Chebyshev.interpolate`` class\nmethod adds support for interpolation over arbitrary intervals using the scaled\nand shifted Chebyshev points of the first kind.\n\nSupport for reading lzma compressed text files in Python 3\n----------------------------------------------------------\nWith Python versions containing the ``lzma`` module the text IO functions can\nnow transparently read from files with ``xz`` or ``lzma`` extension.\n\n``sign`` option added to ``np.setprintoptions`` and ``np.array2string``\n-----------------------------------------------------------------------\nThis option controls printing of the sign of floating-point types, and may be\none of the characters &#39;-&#39;, &#39;+&#39; or &#39; &#39;. With &#39;+&#39; numpy always prints the sign of\npositive values, with &#39; &#39; it always prints a space (whitespace character) in\nthe sign position of positive values, and with &#39;-&#39; it will omit the sign\ncharacter for positive values. The new default is &#39;-&#39;.\n\nThis new default changes the float output relative to numpy 1.13. The old\nbehavior can be obtained in 1.13 &quot;legacy&quot; printing mode, see compatibility\nnotes above.\n\n``hermitian`` option added to``np.linalg.matrix_rank``\n------------------------------------------------------\nThe new ``hermitian`` option allows choosing between standard SVD based matrix\nrank calculation and the more efficient eigenvalue based method for\nsymmetric/hermitian matrices.\n\n``threshold`` and ``edgeitems`` options added to ``np.array2string``\n--------------------------------------------------------------------\nThese options could previously be controlled using ``np.set_printoptions``, but\nnow can be changed on a per-call basis as arguments to ``np.array2string``.\n\n``concatenate`` and ``stack`` gained an ``out`` argument\n--------------------------------------------------------\nA preallocated buffer of the desired dtype can now be used for the output of\nthese functions.\n\nSupport for PGI flang compiler on Windows\n-----------------------------------------\nThe PGI flang compiler is a Fortran front end for LLVM released by NVIDIA under\nthe Apache 2 license. It can be invoked by ::\n\n    python setup.py config --compiler=clang --fcompiler=flang install\n\nThere is little experience with this new compiler, so any feedback from people\nusing it will be appreciated.\n\n\nImprovements\n============\n\nNumerator degrees of freedom in ``random.noncentral_f`` need only be positive.\n------------------------------------------------------------------------------\nPrior to NumPy 1.14.0, the numerator degrees of freedom needed to be &gt; 1, but\nthe distribution is valid for values &gt; 0, which is the new requirement.\n\nThe GIL is released for all ``np.einsum`` variations\n----------------------------------------------------\nSome specific loop structures which have an accelerated loop version\ndid not release the GIL prior to NumPy 1.14.0.  This oversight has been\nfixed.\n\nThe `np.einsum` function will use BLAS when possible and optimize by default\n----------------------------------------------------------------------------\nThe ``np.einsum`` function will now call ``np.tensordot`` when appropriate.\nBecause ``np.tensordot`` uses BLAS when possible, that will speed up execution.\nBy default, ``np.einsum`` will also attempt optimization as the overhead is\nsmall relative to the potential improvement in speed.\n\n``f2py`` now handles arrays of dimension 0\n------------------------------------------\n``f2py`` now allows for the allocation of arrays of dimension 0. This allows\nfor more consistent handling of corner cases downstream.\n\n``numpy.distutils`` supports using MSVC and mingw64-gfortran together\n---------------------------------------------------------------------\nNumpy distutils now supports using Mingw64 gfortran and MSVC compilers\ntogether. This enables the production of Python extension modules on Windows\ncontaining Fortran code while retaining compatibility with the\nbinaries distributed by Python.org. Not all use cases are supported,\nbut most common ways to wrap Fortran for Python are functional.\n\nCompilation in this mode is usually enabled automatically, and can be\nselected via the ``--fcompiler`` and ``--compiler`` options to\n``setup.py``. Moreover, linking Fortran codes to static OpenBLAS is\nsupported; by default a gfortran compatible static archive\n``openblas.a`` is looked for.\n\n``np.linalg.pinv`` now works on stacked matrices\n------------------------------------------------\nPreviously it was limited to a single 2d array.\n\n``numpy.save`` aligns data to 64 bytes instead of 16\n----------------------------------------------------\nSaving NumPy arrays in the ``npy`` format with ``numpy.save`` inserts\npadding before the array data to align it at 64 bytes.  Previously\nthis was only 16 bytes (and sometimes less due to a bug in the code\nfor version 2).  Now the alignment is 64 bytes, which matches the\nwidest SIMD instruction set commonly available, and is also the most\ncommon cache line size.  This makes ``npy`` files easier to use in\nprograms which open them with ``mmap``, especially on Linux where an\n``mmap`` offset must be a multiple of the page size.\n\nNPZ files now can be written without using temporary files\n----------------------------------------------------------\nIn Python 3.6+ ``numpy.savez`` and ``numpy.savez_compressed`` now write\ndirectly to a ZIP file, without creating intermediate temporary files.\n\nBetter support for empty structured and string types\n----------------------------------------------------\nStructured types can contain zero fields, and string dtypes can contain zero\ncharacters. Zero-length strings still cannot be created directly, and must be\nconstructed through structured dtypes::\n\n    str0 = np.empty(10, np.dtype([(&#39;v&#39;, str, N)]))[&#39;v&#39;]\n    void0 = np.empty(10, np.void)\n\nIt was always possible to work with these, but the following operations are\nnow supported for these arrays:\n\n * `arr.sort()`\n * `arr.view(bytes)`\n * `arr.resize(...)`\n * `pickle.dumps(arr)`\n\nSupport for ``decimal.Decimal`` in ``np.lib.financial``\n-------------------------------------------------------\nUnless otherwise stated all functions within the ``financial`` package now\nsupport using the ``decimal.Decimal`` built-in type.\n\nFloat printing now uses &quot;dragon4&quot; algorithm for shortest decimal representation\n-------------------------------------------------------------------------------\nThe ``str`` and ``repr`` of floating-point values (16, 32, 64 and 128 bit) are\nnow printed to give the shortest decimal representation which uniquely\nidentifies the value from others of the same type. Previously this was only\ntrue for ``float64`` values. The remaining float types will now often be shorter\nthan in numpy 1.13. Arrays printed in scientific notation now also use the\nshortest scientific representation, instead of fixed precision as before.\n\n Additionally, the `str` of float scalars scalars will no longer be truncated\n in python2, unlike python2 `float`s.  `np.double` scalars now have a ``str``\n and ``repr`` identical to that of a python3 float.\n\nNew functions ``np.format_float_scientific`` and ``np.format_float_positional``\nare provided to generate these decimal representations.\n\nA new option ``floatmode`` has been added to ``np.set_printoptions`` and\n``np.array2string``, which gives control over uniqueness and rounding of\nprinted elements in an array. The new default is ``floatmode=&#39;maxprec&#39;`` with\n``precision=8``, which will print at most 8 fractional digits, or fewer if an\nelement can be uniquely represented with fewer. A useful new mode is\n``floatmode=&quot;unique&quot;``, which will output enough digits to specify the array\nelements uniquely.\n\nNumpy complex-floating-scalars with values like ``inf*j`` or ``nan*j`` now\nprint as ``infj`` and ``nanj``, like the pure-python ``complex`` type.\n\nThe ``FloatFormat`` and ``LongFloatFormat`` classes are deprecated and should\nboth be replaced by ``FloatingFormat``. Similarly ``ComplexFormat`` and\n``LongComplexFormat`` should be replaced by ``ComplexFloatingFormat``.\n\n``void`` datatype elements are now printed in hex notation\n----------------------------------------------------------\nA hex representation compatible with the python ``bytes`` type is now printed\nfor unstructured ``np.void`` elements, e.g., ``V4`` datatype. Previously, in\npython2 the raw void data of the element was printed to stdout, or in python3\nthe integer byte values were shown.\n\nprinting style for ``void`` datatypes is now independently customizable\n-----------------------------------------------------------------------\nThe printing style of ``np.void`` arrays is now independently customizable\nusing the ``formatter`` argument to ``np.set_printoptions``, using the\n``&#39;void&#39;`` key, instead of the catch-all ``numpystr`` key as before.\n\nReduced memory usage of ``np.loadtxt``\n--------------------------------------\n``np.loadtxt`` now reads files in chunks instead of all at once which decreases\nits memory usage significantly for large files.\n\n\nChanges\n=======\n\nMultiple-field indexing/assignment of structured arrays\n-------------------------------------------------------\nThe indexing and assignment of structured arrays with multiple fields has\nchanged in a number of ways, as warned about in previous releases.\n\nFirst, indexing a structured array with multiple fields, e.g.,\n``arr[[&#39;f1&#39;, &#39;f3&#39;]]``, returns a view into the original array instead of a\ncopy. The returned view will have extra padding bytes corresponding to\nintervening fields in the original array, unlike the copy in 1.13, which will\naffect code such as ``arr[[&#39;f1&#39;, &#39;f3&#39;]].view(newdtype)``.\n\nSecond, assignment between structured arrays will now occur &quot;by position&quot;\ninstead of &quot;by field name&quot;. The Nth field of the destination will be set to the\nNth field of the source regardless of field name, unlike in numpy versions 1.6\nto 1.13 in which fields in the destination array were set to the\nidentically-named field in the source array or to 0 if the source did not have\na field.\n\nCorrespondingly, the order of fields in a structured dtypes now matters when\ncomputing dtype equality. For example, with the dtypes ::\n\n    x = dtype({&#39;names&#39;: [&#39;A&#39;, &#39;B&#39;], &#39;formats&#39;: [&#39;i4&#39;, &#39;f4&#39;], &#39;offsets&#39;: [0, 4]})\n    y = dtype({&#39;names&#39;: [&#39;B&#39;, &#39;A&#39;], &#39;formats&#39;: [&#39;f4&#39;, &#39;i4&#39;], &#39;offsets&#39;: [4, 0]})\n\nthe expression ``x == y`` will now return ``False``, unlike before.\nThis makes dictionary based dtype specifications like\n``dtype({&#39;a&#39;: (&#39;i4&#39;, 0), &#39;b&#39;: (&#39;f4&#39;, 4)})`` dangerous in python &lt; 3.6\nsince dict key order is not preserved in those versions.\n\nAssignment from a structured array to a boolean array now raises a ValueError,\nunlike in 1.13, where it always set the destination elements to ``True``.\n\nAssignment from structured array with more than one field to a non-structured\narray now raises a ValueError. In 1.13 this copied just the first field of the\nsource to the destination.\n\nUsing field &quot;titles&quot; in multiple-field indexing is now disallowed, as is\nrepeating a field name in a multiple-field index.\n\nThe documentation for structured arrays in the user guide has been\nsignificantly updated to reflect these changes.\n\nInteger and Void scalars are now unaffected by ``np.set_string_function``\n-------------------------------------------------------------------------\nPreviously, unlike most other numpy scalars, the ``str`` and ``repr`` of\ninteger and void scalars could be controlled by ``np.set_string_function``.\nThis is no longer possible.\n\n0d array printing changed, ``style`` arg of array2string deprecated\n-------------------------------------------------------------------\nPreviously the ``str`` and ``repr`` of 0d arrays had idiosyncratic\nimplementations which returned ``str(a.item())`` and ``&#39;array(&#39; +\nrepr(a.item()) + &#39;)&#39;`` respectively for 0d array ``a``, unlike both numpy\nscalars and higher dimension ndarrays.\n\nNow, the ``str`` of a 0d array acts like a numpy scalar using ``str(a[()])``\nand the ``repr`` acts like higher dimension arrays using ``formatter(a[()])``,\nwhere  ``formatter``  can be specified using ``np.set_printoptions``. The\n``style`` argument of ``np.array2string`` is deprecated.\n\nThis new behavior is disabled in 1.13 legacy printing mode, see compatibility\nnotes above.\n\nSeeding ``RandomState`` using an array requires a 1-d array\n-----------------------------------------------------------\n``RandomState`` previously would accept empty arrays or arrays with 2 or more\ndimensions, which resulted in either a failure to seed (empty arrays) or for\nsome of the passed values to be ignored when setting the seed.\n\n``MaskedArray`` objects show a more useful ``repr``\n---------------------------------------------------\nThe ``repr`` of a ``MaskedArray`` is now closer to the python code that would\nproduce it, with arrays now being shown with commas and dtypes. Like the other\nformatting changes, this can be disabled with the 1.13 legacy printing mode in\norder to help transition doctests.\n\nThe ``repr`` of ``np.polynomial`` classes is more explicit\n----------------------------------------------------------\nIt now shows the domain and window parameters as keyword arguments to make\nthem more clear::\n\n    &gt;&gt;&gt; np.polynomial.Polynomial(range(4))\n    Polynomial([0.,  1.,  2.,  3.], domain=[-1,  1], window=[-1,  1])\n\n\n==========================\n   ```\n   \n  \n  \n   ### 1.13.3\n   ```\n   ==========================\n\nThis is a bugfix release for some problems found since 1.13.1. The most\nimportant fixes are for CVE-2017-12852 and temporary elision. Users of earlier\nversions of 1.13 should upgrade.\n\nThe Python versions supported are 2.7 and 3.4 - 3.6. The Python 3.6 wheels\navailable from PIP are built with Python 3.6.2 and should be compatible with\nall previous versions of Python 3.6. It was cythonized with Cython 0.26.1,\nwhich should be free of the bugs found in 0.27 while also being compatible with\nPython 3.7-dev. The Windows wheels were built with OpenBlas instead ATLAS,\nwhich should improve the performance of the linear algebra functions.\n\nThe NumPy 1.13.3 release is a re-release of 1.13.2, which suffered from a\nbug in Cython 0.27.0.\n\nContributors\n============\n\nA total of 12 people contributed to this release.  People with a &quot;+&quot; by their\nnames contributed a patch for the first time.\n\n* Allan Haldane\n* Brandon Carter\n* Charles Harris\n* Eric Wieser\n* Iryna Shcherbina +\n* James Bourbeau +\n* Jonathan Helmus\n* Julian Taylor\n* Matti Picus\n* Michael Lamparski +\n* Michael Seifert\n* Ralf Gommers\n\nPull requests merged\n====================\n\nA total of 22 pull requests were merged for this release.\n\n* 9390 BUG: Return the poly1d coefficients array directly\n* 9555 BUG: Fix regression in 1.13.x in distutils.mingw32ccompiler.\n* 9556 BUG: Fix true_divide when dtype=np.float64 specified.\n* 9557 DOC: Fix some rst markup in numpy/doc/basics.py.\n* 9558 BLD: Remove -xhost flag from IntelFCompiler.\n* 9559 DOC: Removes broken docstring example (source code, png, pdf)...\n* 9580 BUG: Add hypot and cabs functions to WIN32 blacklist.\n* 9732 BUG: Make scalar function elision check if temp is writeable.\n* 9736 BUG: Various fixes to np.gradient\n* 9742 BUG: Fix np.pad for CVE-2017-12852\n* 9744 BUG: Check for exception in sort functions, add tests\n* 9745 DOC: Add whitespace after &quot;versionadded::&quot; directive so it actually...\n* 9746 BUG: Memory leak in np.dot of size 0\n* 9747 BUG: Adjust gfortran version search regex\n* 9757 BUG: Cython 0.27 breaks NumPy on Python 3.\n* 9764 BUG: Ensure `_npy_scaled_cexp{,f,l}` is defined when needed.\n* 9765 BUG: PyArray_CountNonzero does not check for exceptions\n* 9766 BUG: Fixes histogram monotonicity check for unsigned bin values\n* 9767 BUG: Ensure consistent result dtype of count_nonzero\n* 9771 BUG: MAINT: Fix mtrand for Cython 0.27.\n* 9772 DOC: Create the 1.13.2 release notes.\n* 9794 DOC: Create 1.13.3 release notes.\n\n\n=========================\n   ```\n   \n  \n  \n   ### 1.13.1\n   ```\n   ==========================\n\nThis is a bugfix release for problems found in 1.13.0. The major changes are\nfixes for the new memory overlap detection and temporary elision as well as\nreversion of the removal of the boolean binary ``-`` operator. Users of 1.13.0\nshould upgrade.\n\nThr Python versions supported are 2.7 and 3.4 - 3.6. Note that the Python 3.6\nwheels available from PIP are built against 3.6.1, hence will not work when\nused with 3.6.0 due to Python bug 29943_. NumPy 1.13.2 will be released shortly\nafter Python 3.6.2 is out to fix that problem. If you are using 3.6.0 the\nworkaround is to upgrade to 3.6.1 or use an earlier Python version.\n\n.. _29943: https://bugs.python.org/issue29943\n\n\nPull requests merged\n====================\nA total of 19 pull requests were merged for this release.\n\n* 9240 DOC: BLD: fix lots of Sphinx warnings/errors.\n* 9255 Revert &quot;DEP: Raise TypeError for subtract(bool_, bool_).&quot;\n* 9261 BUG: don&#39;t elide into readonly and updateifcopy temporaries for...\n* 9262 BUG: fix missing keyword rename for common block in numpy.f2py\n* 9263 BUG: handle resize of 0d array\n* 9267 DOC: update f2py front page and some doc build metadata.\n* 9299 BUG: Fix Intel compilation on Unix.\n* 9317 BUG: fix wrong ndim used in empty where check\n* 9319 BUG: Make extensions compilable with MinGW on Py2.7\n* 9339 BUG: Prevent crash if ufunc doc string is null\n* 9340 BUG: umath: un-break ufunc where= when no out= is given\n* 9371 DOC: Add isnat/positive ufunc to documentation\n* 9372 BUG: Fix error in fromstring function from numpy.core.records...\n* 9373 BUG: &#39;)&#39; is printed at the end pointer of the buffer in numpy.f2py.\n* 9374 DOC: Create NumPy 1.13.1 release notes.\n* 9376 BUG: Prevent hang traversing ufunc userloop linked list\n* 9377 DOC: Use x1 and x2 in the heaviside docstring.\n* 9378 DOC: Add $PARAMS to the isnat docstring\n* 9379 DOC: Update the 1.13.1 release notes\n\n\nContributors\n============\nA total of 12 people contributed to this release.  People with a &quot;+&quot; by their\nnames contributed a patch for the first time.\n\n* Andras Deak +\n* Bob Eldering +\n* Charles Harris\n* Daniel Hrisca +\n* Eric Wieser\n* Joshua Leahy +\n* Julian Taylor\n* Michael Seifert\n* Pauli Virtanen\n* Ralf Gommers\n* Roland Kaufmann\n* Warren Weckesser\n\n\n=========================\n   ```\n   \n  \n  \n   ### 1.13.0\n   ```\n   ==========================\n\nThis release supports Python 2.7 and 3.4 - 3.6.\n\n\nHighlights\n==========\n\n * Operations like ``a + b + c`` will reuse temporaries on some platforms,\n   resulting in less memory use and faster execution.\n * Inplace operations check if inputs overlap outputs and create temporaries\n   to avoid problems.\n * New ``__array_ufunc__`` attribute provides improved ability for classes to\n   override default ufunc behavior.\n * New ``np.block`` function for creating blocked arrays.\n\n\nNew functions\n=============\n\n* New ``np.positive`` ufunc.\n* New ``np.divmod`` ufunc provides more efficient divmod.\n* New ``np.isnat`` ufunc tests for NaT special values.\n* New ``np.heaviside`` ufunc computes the Heaviside function.\n* New ``np.isin`` function, improves on ``in1d``.\n* New ``np.block`` function for creating blocked arrays.\n* New ``PyArray_MapIterArrayCopyIfOverlap`` added to NumPy C-API.\n\nSee below for details.\n\n\nDeprecations\n============\n\n* Calling ``np.fix``, ``np.isposinf``, and ``np.isneginf`` with ``f(x, y=out)``\n  is deprecated - the argument should be passed as ``f(x, out=out)``, which\n  matches other ufunc-like interfaces.\n* Use of the C-API ``NPY_CHAR`` type number deprecated since version 1.7 will\n  now raise deprecation warnings at runtime. Extensions built with older f2py\n  versions need to be recompiled to remove the warning.\n* ``np.ma.argsort``, ``np.ma.minimum.reduce``, and ``np.ma.maximum.reduce``\n  should be called with an explicit `axis` argument when applied to arrays with\n  more than 2 dimensions, as the default value of this argument (``None``) is\n  inconsistent with the rest of numpy (``-1``, ``0``, and ``0``, respectively).\n* ``np.ma.MaskedArray.mini`` is deprecated, as it almost duplicates the\n  functionality of ``np.MaskedArray.min``. Exactly equivalent behaviour\n  can be obtained with ``np.ma.minimum.reduce``.\n* The single-argument form of ``np.ma.minimum`` and ``np.ma.maximum`` is\n  deprecated. ``np.maximum``. ``np.ma.minimum(x)`` should now be spelt\n  ``np.ma.minimum.reduce(x)``, which is consistent with how this would be done\n  with ``np.minimum``.\n* Calling ``ndarray.conjugate`` on non-numeric dtypes is deprecated (it\n  should match the behavior of ``np.conjugate``, which throws an error).\n* Calling ``expand_dims`` when the ``axis`` keyword does not satisfy\n  ``-a.ndim - 1 &lt;= axis &lt;= a.ndim``, where ``a`` is the array being reshaped,\n  is deprecated.\n\n\nFuture Changes\n==============\n\n* Assignment between structured arrays with different field names will change\n  in NumPy 1.14. Previously, fields in the dst would be set to the value of the\n  identically-named field in the src. In numpy 1.14 fields will instead be\n  assigned &#39;by position&#39;: The n-th field of the dst will be set to the n-th\n  field of the src array. Note that the ``FutureWarning`` raised in NumPy 1.12\n  incorrectly reported this change as scheduled for NumPy 1.13 rather than\n  NumPy 1.14.\n\n\nBuild System Changes\n====================\n\n* ``numpy.distutils`` now automatically determines C-file dependencies with\n  GCC compatible compilers.\n\n\nCompatibility notes\n===================\n\nError type changes\n------------------\n\n* ``numpy.hstack()`` now throws ``ValueError`` instead of ``IndexError`` when\n  input is empty.\n* Functions taking an axis argument, when that argument is out of range, now\n  throw ``np.AxisError`` instead of a mixture of ``IndexError`` and\n  ``ValueError``. For backwards compatibility, ``AxisError`` subclasses both of\n  these.\n\nTuple object dtypes\n-------------------\n\nSupport has been removed for certain obscure dtypes that were unintentionally\nallowed, of the form ``(old_dtype, new_dtype)``, where either of the dtypes\nis or contains the ``object`` dtype. As an exception, dtypes of the form\n``(object, [(&#39;name&#39;, object)])`` are still supported due to evidence of\nexisting use.\n\nDeprecationWarning to error\n---------------------------\nSee Changes section for more detail.\n\n* ``partition``, TypeError when non-integer partition index is used.\n* ``NpyIter_AdvancedNew``, ValueError when ``oa_ndim == 0`` and ``op_axes`` is NULL\n* ``negative(bool_)``, TypeError when negative applied to booleans.\n* ``subtract(bool_, bool_)``, TypeError when subtracting boolean from boolean.\n* ``np.equal, np.not_equal``, object identity doesn&#39;t override failed comparison.\n* ``np.equal, np.not_equal``, object identity doesn&#39;t override non-boolean comparison.\n* Deprecated boolean indexing behavior dropped. See Changes below for details.\n* Deprecated ``np.alterdot()`` and ``np.restoredot()`` removed.\n\nFutureWarning to changed behavior\n---------------------------------\nSee Changes section for more detail.\n\n* ``numpy.average`` preserves subclasses\n* ``array == None`` and ``array != None`` do element-wise comparison.\n* ``np.equal, np.not_equal``, object identity doesn&#39;t override comparison result.\n\ndtypes are now always true\n--------------------------\n\nPreviously ``bool(dtype)`` would fall back to the default python\nimplementation, which checked if ``len(dtype) &gt; 0``. Since ``dtype`` objects\nimplement ``__len__`` as the number of record fields, ``bool`` of scalar dtypes\nwould evaluate to ``False``, which was unintuitive. Now ``bool(dtype) == True``\nfor all dtypes.\n\n``__getslice__`` and ``__setslice__`` are no longer needed in ``ndarray`` subclasses\n------------------------------------------------------------------------------------\nWhen subclassing np.ndarray in Python 2.7, it is no longer _necessary_ to\nimplement ``__*slice__`` on the derived class, as ``__*item__`` will intercept\nthese calls correctly.\n\nAny code that did implement these will work exactly as before. Code that\ninvokes``ndarray.__getslice__`` (e.g. through ``super(...).__getslice__``) will\nnow issue a DeprecationWarning - ``.__getitem__(slice(start, end))`` should be\nused instead.\n\nIndexing MaskedArrays/Constants with ``...`` (ellipsis) now returns MaskedArray\n-------------------------------------------------------------------------------\nThis behavior mirrors that of np.ndarray, and accounts for nested arrays in\nMaskedArrays of object dtype, and ellipsis combined with other forms of\nindexing.\n\nC API changes\n=============\n\nGUfuncs on empty arrays and NpyIter axis removal\n------------------------------------------------\nIt is now allowed to remove a zero-sized axis from NpyIter. Which may mean\nthat code removing axes from NpyIter has to add an additional check when\naccessing the removed dimensions later on.\n\nThe largest followup change is that gufuncs are now allowed to have zero-sized\ninner dimensions. This means that a gufunc now has to anticipate an empty inner\ndimension, while this was never possible and an error raised instead.\n\nFor most gufuncs no change should be necessary. However, it is now possible\nfor gufuncs with a signature such as ``(..., N, M) -&gt; (..., M)`` to return\na valid result if ``N=0`` without further wrapping code.\n\n``PyArray_MapIterArrayCopyIfOverlap`` added to NumPy C-API\n----------------------------------------------------------\nSimilar to ``PyArray_MapIterArray`` but with an additional ``copy_if_overlap``\nargument. If ``copy_if_overlap != 0``,  checks if input has memory overlap with\nany of the other arrays and make copies as appropriate to avoid problems if the\ninput is modified during the iteration. See the documentation for more complete\ndocumentation.\n\n\nNew Features\n============\n\n``__array_ufunc__`` added\n-------------------------\nThis is the renamed and redesigned ``__numpy_ufunc__``. Any class, ndarray\nsubclass or not, can define this method or set it to ``None`` in order to\noverride the behavior of NumPy&#39;s ufuncs. This works quite similarly to Python&#39;s\n``__mul__`` and other binary operation routines. See the documentation for a\nmore detailed description of the implementation and behavior of this new\noption. The API is provisional, we do not yet guarantee backward compatibility\nas modifications may be made pending feedback. See the NEP_ and\ndocumentation_ for more details.\n\n.. _NEP: https://github.com/numpy/numpy/blob/master/doc/neps/ufunc-overrides.rst\n.. _documentation: https://github.com/charris/numpy/blob/master/doc/source/reference/arrays.classes.rst\n\nNew ``positive`` ufunc\n----------------------\nThis ufunc corresponds to unary `+`, but unlike `+` on an ndarray it will raise\nan error if array values do not support numeric operations.\n\nNew ``divmod`` ufunc\n--------------------\nThis ufunc corresponds to the Python builtin `divmod`, and is used to implement\n`divmod` when called on numpy arrays. ``np.divmod(x, y)`` calculates a result\nequivalent to ``(np.floor_divide(x, y), np.remainder(x, y))`` but is\napproximately twice as fast as calling the functions separately.\n\n``np.isnat`` ufunc tests for NaT special datetime and timedelta values\n----------------------------------------------------------------------\nThe new ufunc ``np.isnat`` finds the positions of special NaT values\nwithin datetime and timedelta arrays. This is analogous to ``np.isnan``.\n\n``np.heaviside`` ufunc computes the Heaviside function\n------------------------------------------------------\nThe new function ``np.heaviside(x, h0)`` (a ufunc) computes the Heaviside\nfunction:\n\n.. code::\n\n                       { 0   if x &lt; 0,\n    heaviside(x, h0) = { h0  if x == 0,\n                       { 1   if x &gt; 0.\n\n``np.block`` function for creating blocked arrays\n-------------------------------------------------\nAdd a new ``block`` function to the current stacking functions ``vstack``,\n``hstack``, and ``stack``. This allows concatenation across multiple axes\nsimultaneously, with a similar syntax to array creation, but where elements\ncan themselves be arrays. For instance::\n\n    &gt;&gt;&gt; A = np.eye(2) * 2\n    &gt;&gt;&gt; B = np.eye(3) * 3\n    &gt;&gt;&gt; np.block([\n    ...     [A,               np.zeros((2, 3))],\n    ...     [np.ones((3, 2)), B               ]\n    ... ])\n    array([[ 2.,  0.,  0.,  0.,  0.],\n           [ 0.,  2.,  0.,  0.,  0.],\n           [ 1.,  1.,  3.,  0.,  0.],\n           [ 1.,  1.,  0.,  3.,  0.],\n           [ 1.,  1.,  0.,  0.,  3.]])\n\nWhile primarily useful for block matrices, this works for arbitrary dimensions\nof arrays.\n\nIt is similar to Matlab&#39;s square bracket notation for creating block matrices.\n\n``isin`` function, improving on ``in1d``\n----------------------------------------\nThe new function ``isin`` tests whether each element of an N-dimensonal\narray is present anywhere within a second array. It is an enhancement\nof ``in1d`` that preserves the shape of the first array.\n\nTemporary elision\n-----------------\nOn platforms providing the ``backtrace`` function NumPy will try to avoid\ncreating temporaries in expression involving basic numeric types.\nFor example ``d = a + b + c`` is transformed to ``d = a + b; d += c`` which can\nimprove performance for large arrays as less memory bandwidth is required to\nperform the operation.\n\n``axes`` argument for ``unique``\n--------------------------------\nIn an N-dimensional array, the user can now choose the axis along which to look\nfor duplicate N-1-dimensional elements using ``numpy.unique``. The original\nbehaviour is recovered if ``axis=None`` (default).\n\n``np.gradient`` now supports unevenly spaced data\n-------------------------------------------------\nUsers can now specify a not-constant spacing for data.\nIn particular ``np.gradient`` can now take:\n\n1. A single scalar to specify a sample distance for all dimensions.\n2. N scalars to specify a constant sample distance for each dimension.\n   i.e. ``dx``, ``dy``, ``dz``, ...\n3. N arrays to specify the coordinates of the values along each dimension of F.\n   The length of the array must match the size of the corresponding dimension\n4. Any combination of N scalars/arrays with the meaning of 2. and 3.\n\nThis means that, e.g., it is now possible to do the following::\n\n    &gt;&gt;&gt; f = np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float)\n    &gt;&gt;&gt; dx = 2.\n    &gt;&gt;&gt; y = [1., 1.5, 3.5]\n    &gt;&gt;&gt; np.gradient(f, dx, y)\n    [array([[ 1. ,  1. , -0.5], [ 1. ,  1. , -0.5]]),\n     array([[ 2. ,  2. ,  2. ], [ 2. ,  1.7,  0.5]])]\n\nSupport for returning arrays of arbitrary dimensions in ``apply_along_axis``\n----------------------------------------------------------------------------\nPreviously, only scalars or 1D arrays could be returned by the function passed\nto ``apply_along_axis``. Now, it can return an array of any dimensionality\n(including 0D), and the shape of this array replaces the axis of the array\nbeing iterated over.\n\n``.ndim`` property added to ``dtype`` to complement ``.shape``\n--------------------------------------------------------------\nFor consistency with ``ndarray`` and ``broadcast``, ``d.ndim`` is a shorthand\nfor ``len(d.shape)``.\n\nSupport for tracemalloc in Python 3.6\n-------------------------------------\nNumPy now supports memory tracing with tracemalloc_ module of Python 3.6 or\nnewer. Memory allocations from NumPy are placed into the domain defined by\n``numpy.lib.tracemalloc_domain``.\nNote that NumPy allocation will not show up in tracemalloc_ of earlier Python\nversions.\n\n.. _tracemalloc: https://docs.python.org/3/library/tracemalloc.html\n\nNumPy may be built with relaxed stride checking debugging\n---------------------------------------------------------\nSetting NPY_RELAXED_STRIDES_DEBUG=1 in the environment when relaxed stride\nchecking is enabled will cause NumPy to be compiled with the affected strides\nset to the maximum value of npy_intp in order to help detect invalid usage of\nthe strides in downstream projects. When enabled, invalid usage often results\nin an error being raised, but the exact type of error depends on the details of\nthe code. TypeError and OverflowError have been observed in the wild.\n\nIt was previously the case that this option was disabled for releases and\nenabled in master and changing between the two required editing the code. It is\nnow disabled by default but can be enabled for test builds.\n\n\nImprovements\n============\n\nUfunc behavior for overlapping inputs\n-------------------------------------\n\nOperations where ufunc input and output operands have memory overlap\nproduced undefined results in previous NumPy versions, due to data\ndependency issues. In NumPy 1.13.0, results from such operations are\nnow defined to be the same as for equivalent operations where there is\nno memory overlap.\n\nOperations affected now make temporary copies, as needed to eliminate\ndata dependency. As detecting these cases is computationally\nexpensive, a heuristic is used, which may in rare cases result to\nneedless temporary copies.  For operations where the data dependency\nis simple enough for the heuristic to analyze, temporary copies will\nnot be made even if the arrays overlap, if it can be deduced copies\nare not necessary.  As an example,``np.add(a, b, out=a)`` will not\ninvolve copies.\n\nTo illustrate a previously undefined operation::\n\n    &gt;&gt;&gt; x = np.arange(16).astype(float)\n    &gt;&gt;&gt; np.add(x[1:], x[:-1], out=x[1:])\n\nIn NumPy 1.13.0 the last line is guaranteed to be equivalent to::\n\n    &gt;&gt;&gt; np.add(x[1:].copy(), x[:-1].copy(), out=x[1:])\n\nA similar operation with simple non-problematic data dependence is::\n\n    &gt;&gt;&gt; x = np.arange(16).astype(float)\n    &gt;&gt;&gt; np.add(x[1:], x[:-1], out=x[:-1])\n\nIt will continue to produce the same results as in previous NumPy\nversions, and will not involve unnecessary temporary copies.\n\nThe change applies also to in-place binary operations, for example::\n\n    &gt;&gt;&gt; x = np.random.rand(500, 500)\n    &gt;&gt;&gt; x += x.T\n\nThis statement is now guaranteed to be equivalent to ``x[...] = x + x.T``,\nwhereas in previous NumPy versions the results were undefined.\n\nPartial support for 64-bit f2py extensions with MinGW\n-----------------------------------------------------\nExtensions that incorporate Fortran libraries can now be built using the free\nMinGW_ toolset, also under Python 3.5. This works best for extensions that only\ndo calculations and uses the runtime modestly (reading and writing from files,\nfor instance). Note that this does not remove the need for Mingwpy; if you make\nextensive use of the runtime, you will most likely run into issues_. Instead,\nit should be regarded as a band-aid until Mingwpy is fully functional.\n\nExtensions can also be compiled using the MinGW toolset using the runtime\nlibrary from the (moveable) WinPython 3.4 distribution, which can be useful for\nprograms with a PySide1/Qt4 front-end.\n\n.. _MinGW: https://sf.net/projects/mingw-w64/files/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/6.2.0/threads-win32/seh/\n\n.. _issues: https://mingwpy.github.io/issues.html\n\nPerformance improvements for ``packbits`` and ``unpackbits``\n------------------------------------------------------------\nThe functions ``numpy.packbits`` with boolean input and ``numpy.unpackbits`` have\nbeen optimized to be a significantly faster for contiguous data.\n\nFix for PPC long double floating point information\n--------------------------------------------------\nIn previous versions of NumPy, the ``finfo`` function returned invalid\ninformation about the `double double`_ format of the ``longdouble`` float type\non Power PC (PPC).  The invalid values resulted from the failure of the NumPy\nalgorithm to deal with the variable number of digits in the significand\nthat are a feature of `PPC long doubles`.  This release by-passes the failing\nalgorithm by using heuristics to detect the presence of the PPC double double\nformat.  A side-effect of using these heuristics is that the ``finfo``\nfunction is faster than previous releases.\n\n.. _PPC long doubles: https://www.ibm.com/support/knowledgecenter/en/ssw_aix_71/com.ibm.aix.genprogc/128bit_long_double_floating-point_datatype.htm\n\n.. _double double: https://en.wikipedia.org/wiki/Quadruple-precision_floating-point_formatDouble-double_arithmetic\n\nBetter default repr for ``ndarray`` subclasses\n----------------------------------------------\nSubclasses of ndarray with no ``repr`` specialization now correctly indent\ntheir data and type lines.\n\nMore reliable comparisons of masked arrays\n------------------------------------------\nComparisons of masked arrays were buggy for masked scalars and failed for\nstructured arrays with dimension higher than one. Both problems are now\nsolved. In the process, it was ensured that in getting the result for a\nstructured array, masked fields are properly ignored, i.e., the result is equal\nif all fields that are non-masked in both are equal, thus making the behaviour\nidentical to what one gets by comparing an unstructured masked array and then\ndoing ``.all()`` over some axis.\n\nnp.matrix with booleans elements can now be created using the string syntax\n---------------------------------------------------------------------------\n``np.matrix`` failed whenever one attempts to use it with booleans, e.g.,\n``np.matrix(&#39;True&#39;)``. Now, this works as expected.\n\nMore ``linalg`` operations now accept empty vectors and matrices\n----------------------------------------------------------------\nAll of the following functions in ``np.linalg`` now work when given input\narrays with a 0 in the last two dimensions: ``det``, ``slogdet``, ``pinv``,\n``eigvals``, ``eigvalsh``, ``eig``, ``eigh``.\n\nBundled version of LAPACK is now 3.2.2\n--------------------------------------\nNumPy comes bundled with a minimal implementation of lapack for systems without\na lapack library installed, under the name of ``lapack_lite``. This has been\nupgraded from LAPACK 3.0.0 (June 30, 1999) to LAPACK 3.2.2 (June 30, 2010). See\nthe `LAPACK changelogs`_ for details on the all the changes this entails.\n\nWhile no new features are exposed through ``"},"comment":{"url":"https://api.github.com/repos/dgiambra/langevin_dynamics/issues/comments/385190872","html_url":"https://github.com/dgiambra/langevin_dynamics/pull/98#issuecomment-385190872","issue_url":"https://api.github.com/repos/dgiambra/langevin_dynamics/issues/98","id":385190872,"user":{"login":"coveralls","id":2354108,"avatar_url":"https://avatars1.githubusercontent.com/u/2354108?v=4","gravatar_id":"","url":"https://api.github.com/users/coveralls","html_url":"https://github.com/coveralls","followers_url":"https://api.github.com/users/coveralls/followers","following_url":"https://api.github.com/users/coveralls/following{/other_user}","gists_url":"https://api.github.com/users/coveralls/gists{/gist_id}","starred_url":"https://api.github.com/users/coveralls/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/coveralls/subscriptions","organizations_url":"https://api.github.com/users/coveralls/orgs","repos_url":"https://api.github.com/users/coveralls/repos","events_url":"https://api.github.com/users/coveralls/events{/privacy}","received_events_url":"https://api.github.com/users/coveralls/received_events","type":"User","site_admin":false},"created_at":"2018-04-28T17:03:03Z","updated_at":"2018-04-28T17:03:03Z","author_association":"NONE","body":"\n[![Coverage Status](https://coveralls.io/builds/16744247/badge)](https://coveralls.io/builds/16744247)\n\nCoverage remained the same at 100.0% when pulling **b5844b33c67644f4b0f11b996fa324620bd52917 on pyup-update-numpy-1.11.2-to-1.14.3** into **40fd7024e09beccd6325eaf44bab58a430f06099 on master**.\n"}}
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