{"action":"opened","number":10,"pull_request":{"url":"https://api.github.com/repos/jason-neal/nod_combination/pulls/10","id":166569426,"html_url":"https://github.com/jason-neal/nod_combination/pull/10","diff_url":"https://github.com/jason-neal/nod_combination/pull/10.diff","patch_url":"https://github.com/jason-neal/nod_combination/pull/10.patch","issue_url":"https://api.github.com/repos/jason-neal/nod_combination/issues/10","number":10,"state":"open","locked":false,"title":"Scheduled monthly dependency update for February","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},"body":"\n\n\n## Updates\nHere'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.\n<table align=\"center\">\n\n<tr>\n<td><b>astropy</b></td>\n<td align=\"center\">2.0.2</td>\n<td align=\"center\">&raquo;</td>\n<td align=\"center\">2.0.3</td>\n<td>\n     <a href=\"https://pypi.python.org/pypi/astropy\">PyPI</a> | <a href=\"https://pyup.io/changelogs/astropy/\">Changelog</a> | <a href=\"http://astropy.org\">Homepage</a> \n\n</td>\n\n<tr>\n<td><b>matplotlib</b></td>\n<td align=\"center\">2.0.2</td>\n<td align=\"center\">&raquo;</td>\n<td align=\"center\">2.1.2</td>\n<td>\n     <a href=\"https://pypi.python.org/pypi/matplotlib\">PyPI</a> | <a href=\"https://pyup.io/changelogs/matplotlib/\">Changelog</a> | <a href=\"http://matplotlib.org\">Homepage</a> \n\n</td>\n\n<tr>\n<td><b>numpy</b></td>\n<td align=\"center\">1.13.3</td>\n<td align=\"center\">&raquo;</td>\n<td align=\"center\">1.14.0</td>\n<td>\n     <a href=\"https://pypi.python.org/pypi/numpy\">PyPI</a> | <a href=\"https://pyup.io/changelogs/numpy/\">Changelog</a> | <a href=\"http://www.numpy.org\">Homepage</a> \n\n</td>\n\n<tr>\n<td><b>tqdm</b></td>\n<td align=\"center\">4.17.1</td>\n<td align=\"center\">&raquo;</td>\n<td align=\"center\">4.19.5</td>\n<td>\n     <a href=\"https://pypi.python.org/pypi/tqdm\">PyPI</a> | <a href=\"https://pyup.io/changelogs/tqdm/\">Changelog</a> | <a href=\"https://github.com/tqdm/tqdm/issues\">Repo</a> \n\n</td>\n\n</tr>\n</table>\n\n\n\n## Changelogs\n\n\n### astropy 2.0.2 -> 2.0.3\n\n>### 2.0.3\n\n>==================\n\n>Bug Fixes\n>---------\n\n>astropy.coordinates\n>^^^^^^^^^^^^^^^^^^^\n\n>- Ecliptic frame classes now support attributes ``v_x``, ``v_y``, ``v_z`` when\n>  using with a Cartesian representation. [6569]\n\n>- Added a nicer error message when accidentally calling ``frame.representation``\n>  instead of ``frame.data`` in the context of methods that use ``._apply()``.\n>  [6561]\n\n>- Creating a new ``SkyCoord`` from a list of multiple ``SkyCoord`` objects now\n>  yield the correct type of frame, and works at all for non-equatorial frames.\n>  [6612]\n\n>- Improved accuracy of velocity calculation in ``EarthLocation.get_gcrs_posvel``.\n>  [6699]\n\n>- Improved accuracy of radial velocity corrections in\n>  ``SkyCoord.radial_velocity_correction```. [6861]\n\n>- The precision of ecliptic frames is now much better, after removing the\n>  nutation from the rotation and fixing the computation of the position of the\n>  Sun. [6508]\n\n>astropy.extern\n>^^^^^^^^^^^^^^\n\n>- Version 0.2.1 of ``pytest-astropy`` is included as an external package.\n>  [6918]\n\n>astropy.io.fits\n>^^^^^^^^^^^^^^^\n\n>- Fix writing the result of ``fitsdiff`` to file with ``--output-file``. [6621]\n\n>- Fix a minor bug where ``FITS_rec`` instances can not be indexed with tuples\n>  and other sequences that end up with a scalar. [6955, 6966]\n\n>astropy.io.misc\n>^^^^^^^^^^^^^^^\n\n>- Fix ``ImportError`` when ``hdf5`` is imported first in a fresh Python\n>  interpreter in Python 3. [6604, 6610]\n\n>astropy.nddata\n>^^^^^^^^^^^^^^\n\n>- Suppress errors during WCS creation in CCDData.read(). [6500]\n\n>- Fixed a problem with ``CCDData.read`` when the extension wasn&#39;t given and the\n>  primary HDU contained no ``data`` but another HDU did. In that case the header\n>  were not correctly combined. [6489]\n\n>astropy.stats\n>^^^^^^^^^^^^^\n\n>- Fixed an issue where the biweight statistics functions would\n>  sometimes cause runtime underflow/overflow errors for float32 input\n>  arrays. [6905]\n\n>astropy.table\n>^^^^^^^^^^^^^\n\n>- Fixed a problem when printing a table when a column is deleted and\n>  garbage-collected, and the format function caching mechanism happens\n>  to re-use the same cache key. [6714]\n\n>- Fixed a problem when comparing a unicode masked column (on left side) to\n>  a bytes masked column (on right side). [6899]\n\n>- Fixed a problem in comparing masked columns in bytes and unicode when the\n>  unicode had masked entries. [6899]\n\n>astropy.tests\n>^^^^^^^^^^^^^\n\n>- Fixed a bug that causes tests for rst files to not be run on certain\n>  platforms. [6555, 6608]\n\n>- Fixed a bug that caused the doctestplus plugin to not work nicely with the\n>  hypothesis package. [6605, 6609]\n\n>- Fixed a bug that meant that the data.astropy.org mirror could not be used when\n>  using --remote-data=astropy. [6724]\n\n>- Support compatibility with new ``pytest-astropy`` plugins. [6918]\n\n>- When testing, astropy (or the package being tested) is now installed to\n>  a temporary directory instead of copying the build. This allows\n>  entry points to work correctly. [6890]\n\n>astropy.time\n>^^^^^^^^^^^^\n\n>- Initialization of Time instances now is consistent for all formats to\n>  ensure that ``-0.5 &lt;= jd2 &lt; 0.5``. [6653]\n\n>astropy.units\n>^^^^^^^^^^^^^\n\n>- Ensure that ``Quantity`` slices can be set with objects that have a ``unit``\n>  attribute (such as ``Column``). [6123]\n\n>astropy.utils\n>^^^^^^^^^^^^^\n\n>- ``download_files_in_parallel`` now respects the given ``timeout`` value.\n>  [6658]\n\n>- Fixed bugs in remote data handling and also in IERS unit test related to path\n>  URL, and URI normalization on Windows. [6651]\n\n>- Fixed a bug that caused ``get_pkg_data_fileobj`` to not work correctly when\n>  used with non-local data from inside packages. [6724]\n\n>- Make sure ``get_pkg_data_fileobj`` fails if the URL can not be read, and\n>  correctly falls back on the mirror if necessary. [6767]\n\n>- Fix the ``finddiff`` option in ``find_current_module`` to properly deal\n>  with submodules. [6767]\n\n>- Fixed ``pyreadline`` import in ``utils.console.isatty`` for older IPython\n>  versions on Windows. [6800]\n\n>astropy.visualization\n>^^^^^^^^^^^^^^^^^^^^^\n\n>- Fixed the vertical orientation of the ``fits2bitmap`` output bitmap\n>  image to match that of the FITS image. [6844, 6969]\n\n>- Added a workaround for a bug in matplotlib so that the ``fits2bitmap``\n>  script generates the correct output file type. [6969]\n\n\n>Other Changes and Additions\n>---------------------------\n\n>- No longer require LaTeX to build the documentation locally and\n>  use mathjax instead. [6701]\n\n>- Fixed broken links in the documentation. [6745]\n\n>- Ensured that all tests use the Astropy data mirror if needed. [6767]\n\n\n\n\n\n\n\n\n### matplotlib 2.0.2 -> 2.1.2\n\n>### 2.1.2\n\n\n\n\n\n>### 2.1.1\n\n\n\n\n\n>### 2.1.0\n\n>This is the second minor release in the Matplotlib 2.x series and the first\n>release with major new features since 1.5.\n\n>This release contains approximately 2 years worth of work by 275 contributors\n>across over 950 pull requests.  Highlights from this release include:\n\n> - support for string categorical values\n> - export of animations to interactive javascript widgets\n> - major overhaul of polar plots\n> - reproducible output for ps/eps, pdf, and svg backends\n> - performance improvements in drawing lines and images\n> - GUIs show a busy cursor while rendering the plot\n\n\n>along with many other enhancements and bug fixes.\n\n\n\n>### 2.1.0rc1\n\n\n\n\n\n\n\n\n\n### numpy 1.13.3 -> 1.14.0\n\n>### 1.14.0\n\n>==========================\n\n>Numpy 1.14.0 is the result of seven months of work and contains a large number\n>of bug fixes and new features, along with several changes with potential\n>compatibility issues. The major change that users will notice are the\n>stylistic changes in the way numpy arrays and scalars are printed, a change\n>that will affect doctests. See below for details on how to preserve the\n>old style printing when needed.\n\n>A major decision affecting future development concerns the schedule for\n>dropping Python 2.7 support in the runup to 2020. The decision has been made to\n>support 2.7 for all releases made in 2018, with the last release being\n>designated a long term release with support for bug fixes extending through\n>2019. In 2019 support for 2.7 will be dropped in all new releases. More details\n>can be found in the relevant NEP_.\n\n>This 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\n>Highlights\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\n>New 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\n>Deprecations\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\n>Future 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\n>Compatibility notes\n>===================\n\n>The mask of a masked array view is also a view rather than a copy\n>-----------------------------------------------------------------\n>There was a FutureWarning about this change in NumPy 1.11.x. In short, it is\n>now the case that, when changing a view of a masked array, changes to the mask\n>are propagated to the original. That was not previously the case. This change\n>affects slices in particular. Note that this does not yet work properly if the\n>mask of the original array is ``nomask`` and the mask of the view is changed.\n>See gh-5580 for an extended discussion. The original behavior of having a copy\n>of the mask can be obtained by calling the ``unshare_mask`` method of the view.\n\n>``np.ma.masked`` is no longer writeable\n>---------------------------------------\n>Attempts to mutate the ``masked`` constant now error, as the underlying arrays\n>are 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>----------------------------------------------------------\n>Previously, ``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\n>tuple of the fields. Instead, all three methods return a structured ``np.void``\n>object, which is what you would already find in the ``.fill_value`` attribute.\n\n>Additionally, the dtype guessing now matches that of ``np.array`` - so when\n>passing 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\n>violated this assumption.\n\n>``a.flat.__array__()`` returns non-writeable arrays when ``a`` is non-contiguous\n>--------------------------------------------------------------------------------\n>The intent is that the UPDATEIFCOPY array previously returned when ``a`` was\n>non-contiguous will be replaced by a writeable copy in the future. This\n>temporary measure is aimed to notify folks who expect the underlying array be\n>modified in this situation that that will no longer be the case. The most\n>likely 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\n>parameter to a ufunc.\n\n>``np.tensordot`` now returns zero array when contracting over 0-length dimension\n>--------------------------------------------------------------------------------\n>Previously ``np.tensordot`` raised a ValueError when contracting over 0-length\n>dimension. Now it returns a zero array, which is consistent with the behaviour\n>of ``np.dot`` and ``np.einsum``.\n\n>``numpy.testing`` reorganized\n>-----------------------------\n>This is not expected to cause problems, but possibly something has been left\n>out. If you experience an unexpected import problem using ``numpy.testing``\n>let us know.\n\n>``np.asfarray`` no longer accepts non-dtypes through the ``dtype`` argument\n>---------------------------------------------------------------------------\n>This previously would accept ``dtype=some_array``, with the implied semantics\n>of ``dtype=some_array.dtype``. This was undocumented, unique across the numpy\n>functions, and if used would likely correspond to a typo.\n\n>1D ``np.linalg.norm`` preserves float input types, even for arbitrary orders\n>----------------------------------------------------------------------------\n>Previously, this would promote to ``float64`` when arbitrary orders were\n>passed, 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).dtype\n>    dtype(&#39;float32&#39;)\n>    &gt;&gt;&gt; np.linalg.norm(f32, 2.0001).dtype\n>    dtype(&#39;float64&#39;)   numpy 1.13\n>    dtype(&#39;float32&#39;)   numpy 1.14\n\n>This change affects only ``float32`` and ``float16`` arrays.\n\n>``count_nonzero(arr, axis=())`` now counts over no axes, not all axes\n>---------------------------------------------------------------------\n>Elsewhere, ``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\n>inconsistent and surprising. The correct way to count over all axes has always\n>been to pass ``axis == None``.\n\n>``__init__.py`` files added to test directories\n>-----------------------------------------------\n>This is for pytest compatibility in the case of duplicate test file names in\n>the different directories. As a result, ``run_module_suite`` no longer works,\n>i.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>--------------------------------------------------------------------------------\n>On 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\n>crash. Going forwards, `astype` matches the behavior of ``bool(np.void)``,\n>considering a buffer of all zeros as false, and anything else as true.\n>Checks 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\n>sometimes return ``masked``, which is not a view. This has been fixed, so that\n>the result is always a view on the original masked array.\n>This breaks any code that used ``masked_arr.squeeze() is np.ma.masked``, but\n>fixes code that writes to the result of `.squeeze()`.\n\n>Renamed first parameter of ``can_cast`` from ``from`` to ``from_``\n>------------------------------------------------------------------\n>The previous parameter name ``from`` is a reserved keyword in Python, which made\n>it difficult to pass the argument by name. This has been fixed by renaming\n>the parameter to ``from_``.\n\n>``isnat`` raises ``TypeError`` when passed wrong type\n>------------------------------------------------------\n>The ufunc ``isnat`` used to raise a ``ValueError`` when it was not passed\n>variables of type ``datetime`` or ``timedelta``. This has been changed to\n>raising a ``TypeError``.\n\n>``dtype.__getitem__`` raises ``TypeError`` when passed wrong type\n>-----------------------------------------------------------------\n>When indexed with a float, the dtype object used to raise ``ValueError``.\n\n>User-defined types now need to implement ``__str__`` and ``__repr__``\n>---------------------------------------------------------------------\n>Previously, user-defined types could fall back to a default implementation of\n>``__str__`` and ``__repr__`` implemented in numpy, but this has now been\n>removed. Now user-defined types will fall back to the python default\n>``object.__str__`` and ``object.__repr__``.\n\n>Many changes to array printing, disableable with the new &quot;legacy&quot; printing mode\n>-------------------------------------------------------------------------------\n>The ``str`` and ``repr`` of ndarrays and numpy scalars have been changed in\n>a variety of ways. These changes are likely to break downstream user&#39;s\n>doctests.\n\n>These new behaviors can be disabled to mostly reproduce numpy 1.13 behavior by\n>enabling 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\n>In 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\n>You may want to do something like::\n\n>     FIXME: Set numpy array str/repr to legacy behaviour on numpy &gt; 1.13\n>    try:\n>        np.set_printoptions(legacy=&#39;1.13&#39;)\n>    except TypeError:\n>        pass\n\n>after ::\n\n>    import numpy as np\n\n>Some of these changes are described in more detail below.\n\n\n>C API changes\n>=============\n\n>PyPy compatible alternative to ``UPDATEIFCOPY`` arrays\n>------------------------------------------------------\n>``UPDATEIFCOPY`` arrays are contiguous copies of existing arrays, possibly with\n>different dimensions, whose contents are copied back to the original array when\n>their refcount goes to zero and they are deallocated. Because PyPy does not use\n>refcounts, they do not function correctly with PyPy. NumPy is in the process of\n>eliminating their use internally and two new C-API functions,\n\n>* ``PyArray_SetWritebackIfCopyBase``\n>* ``PyArray_ResolveWritebackIfCopy``,\n\n>have been added together with a complimentary flag,\n>``NPY_ARRAY_WRITEBACKIFCOPY``. Using the new functionality also requires that\n>some 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``.\n>Arrays created with these new flags will then have the ``WRITEBACKIFCOPY``\n>semantics.\n\n>If PyPy compatibility is not a concern, these new functions can be ignored,\n>although there will be a ``DeprecationWarning``. If you do wish to pursue PyPy\n>compatibility, more information on these functions and their use may be found\n>in 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\n>New Features\n>============\n\n>Encoding argument for text IO functions\n>---------------------------------------\n>``genfromtxt``, ``loadtxt``, ``fromregex`` and ``savetxt`` can now handle files\n>with arbitrary encoding supported by Python via the encoding argument.\n>For backward compatibility the argument defaults to the special ``bytes`` value\n>which continues to treat text as raw byte values and continues to pass latin1\n>encoded bytes to custom converters.\n>Using any other value (including ``None`` for system default) will switch the\n>functions to real text IO so one receives unicode strings instead of bytes in\n>the resulting arrays.\n\n>External ``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\n>so:  ``np.test(extra_argv=[&#39;--with-timer&#39;, &#39;--timer-top-n&#39;, &#39;20&#39;])`` to\n>obtain the runtime of the 20 slowest tests.  An extra keyword ``timer`` was\n>also added to ``Tester.test``, so ``np.test(timer=20)`` will also report the 20\n>slowest tests.\n\n>``parametrize`` decorator added to ``numpy.testing``\n>----------------------------------------------------\n>A basic ``parametrize`` decorator is now available in ``numpy.testing``. It is\n>intended to allow rewriting yield based tests that have been deprecated in\n>pytest so as to facilitate the transition to pytest in the future. The nose\n>testing framework has not been supported for several years and looks like\n>abandonware.\n\n>The new ``parametrize`` decorator does not have the full functionality of the\n>one in pytest. It doesn&#39;t work for classes, doesn&#39;t support nesting, and does\n>not substitute variable names. Even so, it should be adequate to rewrite the\n>NumPy tests.\n\n>``chebinterpolate`` function added to ``numpy.polynomial.chebyshev``\n>--------------------------------------------------------------------\n>The new ``chebinterpolate`` function interpolates a given function at the\n>Chebyshev points of the first kind. A new ``Chebyshev.interpolate`` class\n>method adds support for interpolation over arbitrary intervals using the scaled\n>and shifted Chebyshev points of the first kind.\n\n>Support for reading lzma compressed text files in Python 3\n>----------------------------------------------------------\n>With Python versions containing the ``lzma`` module the text IO functions can\n>now transparently read from files with ``xz`` or ``lzma`` extension.\n\n>``sign`` option added to ``np.setprintoptions`` and ``np.array2string``\n>-----------------------------------------------------------------------\n>This option controls printing of the sign of floating-point types, and may be\n>one of the characters &#39;-&#39;, &#39;+&#39; or &#39; &#39;. With &#39;+&#39; numpy always prints the sign of\n>positive values, with &#39; &#39; it always prints a space (whitespace character) in\n>the sign position of positive values, and with &#39;-&#39; it will omit the sign\n>character for positive values. The new default is &#39;-&#39;.\n\n>This new default changes the float output relative to numpy 1.13. The old\n>behavior can be obtained in 1.13 &quot;legacy&quot; printing mode, see compatibility\n>notes above.\n\n>``hermitian`` option added to``np.linalg.matrix_rank``\n>------------------------------------------------------\n>The new ``hermitian`` option allows choosing between standard SVD based matrix\n>rank calculation and the more efficient eigenvalue based method for\n>symmetric/hermitian matrices.\n\n>``threshold`` and ``edgeitems`` options added to ``np.array2string``\n>--------------------------------------------------------------------\n>These options could previously be controlled using ``np.set_printoptions``, but\n>now can be changed on a per-call basis as arguments to ``np.array2string``.\n\n>``concatenate`` and ``stack`` gained an ``out`` argument\n>--------------------------------------------------------\n>A preallocated buffer of the desired dtype can now be used for the output of\n>these functions.\n\n>Support for PGI flang compiler on Windows\n>-----------------------------------------\n>The PGI flang compiler is a Fortran front end for LLVM released by NVIDIA under\n>the Apache 2 license. It can be invoked by ::\n\n>    python setup.py config --compiler=clang --fcompiler=flang install\n\n>There is little experience with this new compiler, so any feedback from people\n>using it will be appreciated.\n\n\n>Improvements\n>============\n\n>Numerator degrees of freedom in ``random.noncentral_f`` need only be positive.\n>------------------------------------------------------------------------------\n>Prior to NumPy 1.14.0, the numerator degrees of freedom needed to be &gt; 1, but\n>the distribution is valid for values &gt; 0, which is the new requirement.\n\n>The GIL is released for all ``np.einsum`` variations\n>----------------------------------------------------\n>Some specific loop structures which have an accelerated loop version\n>did not release the GIL prior to NumPy 1.14.0.  This oversight has been\n>fixed.\n\n>The `np.einsum` function will use BLAS when possible and optimize by default\n>----------------------------------------------------------------------------\n>The ``np.einsum`` function will now call ``np.tensordot`` when appropriate.\n>Because ``np.tensordot`` uses BLAS when possible, that will speed up execution.\n>By default, ``np.einsum`` will also attempt optimization as the overhead is\n>small 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\n>for more consistent handling of corner cases downstream.\n\n>``numpy.distutils`` supports using MSVC and mingw64-gfortran together\n>---------------------------------------------------------------------\n>Numpy distutils now supports using Mingw64 gfortran and MSVC compilers\n>together. This enables the production of Python extension modules on Windows\n>containing Fortran code while retaining compatibility with the\n>binaries distributed by Python.org. Not all use cases are supported,\n>but most common ways to wrap Fortran for Python are functional.\n\n>Compilation in this mode is usually enabled automatically, and can be\n>selected via the ``--fcompiler`` and ``--compiler`` options to\n>``setup.py``. Moreover, linking Fortran codes to static OpenBLAS is\n>supported; by default a gfortran compatible static archive\n>``openblas.a`` is looked for.\n\n>``np.linalg.pinv`` now works on stacked matrices\n>------------------------------------------------\n>Previously it was limited to a single 2d array.\n\n>``numpy.save`` aligns data to 64 bytes instead of 16\n>----------------------------------------------------\n>Saving NumPy arrays in the ``npy`` format with ``numpy.save`` inserts\n>padding before the array data to align it at 64 bytes.  Previously\n>this was only 16 bytes (and sometimes less due to a bug in the code\n>for version 2).  Now the alignment is 64 bytes, which matches the\n>widest SIMD instruction set commonly available, and is also the most\n>common cache line size.  This makes ``npy`` files easier to use in\n>programs which open them with ``mmap``, especially on Linux where an\n>``mmap`` offset must be a multiple of the page size.\n\n>NPZ files now can be written without using temporary files\n>----------------------------------------------------------\n>In Python 3.6+ ``numpy.savez`` and ``numpy.savez_compressed`` now write\n>directly to a ZIP file, without creating intermediate temporary files.\n\n>Better support for empty structured and string types\n>----------------------------------------------------\n>Structured types can contain zero fields, and string dtypes can contain zero\n>characters. Zero-length strings still cannot be created directly, and must be\n>constructed 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\n>It was always possible to work with these, but the following operations are\n>now supported for these arrays:\n\n> * `arr.sort()`\n> * `arr.view(bytes)`\n> * `arr.resize(...)`\n> * `pickle.dumps(arr)`\n\n>Support for ``decimal.Decimal`` in ``np.lib.financial``\n>-------------------------------------------------------\n>Unless otherwise stated all functions within the ``financial`` package now\n>support using the ``decimal.Decimal`` built-in type.\n\n>Float printing now uses &quot;dragon4&quot; algorithm for shortest decimal representation\n>-------------------------------------------------------------------------------\n>The ``str`` and ``repr`` of floating-point values (16, 32, 64 and 128 bit) are\n>now printed to give the shortest decimal representation which uniquely\n>identifies the value from others of the same type. Previously this was only\n>true for ``float64`` values. The remaining float types will now often be shorter\n>than in numpy 1.13. Arrays printed in scientific notation now also use the\n>shortest 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\n>New functions ``np.format_float_scientific`` and ``np.format_float_positional``\n>are provided to generate these decimal representations.\n\n>A new option ``floatmode`` has been added to ``np.set_printoptions`` and\n>``np.array2string``, which gives control over uniqueness and rounding of\n>printed 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\n>element 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\n>elements uniquely.\n\n>Numpy complex-floating-scalars with values like ``inf*j`` or ``nan*j`` now\n>print as ``infj`` and ``nanj``, like the pure-python ``complex`` type.\n\n>The ``FloatFormat`` and ``LongFloatFormat`` classes are deprecated and should\n>both 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>----------------------------------------------------------\n>A hex representation compatible with the python ``bytes`` type is now printed\n>for unstructured ``np.void`` elements, e.g., ``V4`` datatype. Previously, in\n>python2 the raw void data of the element was printed to stdout, or in python3\n>the integer byte values were shown.\n\n>printing style for ``void`` datatypes is now independently customizable\n>-----------------------------------------------------------------------\n>The printing style of ``np.void`` arrays is now independently customizable\n>using the ``formatter`` argument to ``np.set_printoptions``, using the\n>``&#39;void&#39;`` key, instead of the catch-all ``numpystr`` key as before.\n\n>Reduced memory usage of ``np.loadtxt``\n>--------------------------------------\n>``np.loadtxt`` now reads files in chunks instead of all at once which decreases\n>its memory usage significantly for large files.\n\n\n>Changes\n>=======\n\n>Multiple-field indexing/assignment of structured arrays\n>-------------------------------------------------------\n>The indexing and assignment of structured arrays with multiple fields has\n>changed in a number of ways, as warned about in previous releases.\n\n>First, 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\n>copy. The returned view will have extra padding bytes corresponding to\n>intervening fields in the original array, unlike the copy in 1.13, which will\n>affect code such as ``arr[[&#39;f1&#39;, &#39;f3&#39;]].view(newdtype)``.\n\n>Second, assignment between structured arrays will now occur &quot;by position&quot;\n>instead of &quot;by field name&quot;. The Nth field of the destination will be set to the\n>Nth field of the source regardless of field name, unlike in numpy versions 1.6\n>to 1.13 in which fields in the destination array were set to the\n>identically-named field in the source array or to 0 if the source did not have\n>a field.\n\n>Correspondingly, the order of fields in a structured dtypes now matters when\n>computing 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\n>the expression ``x == y`` will now return ``False``, unlike before.\n>This 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\n>since dict key order is not preserved in those versions.\n\n>Assignment from a structured array to a boolean array now raises a ValueError,\n>unlike in 1.13, where it always set the destination elements to ``True``.\n\n>Assignment from structured array with more than one field to a non-structured\n>array now raises a ValueError. In 1.13 this copied just the first field of the\n>source to the destination.\n\n>Using field &quot;titles&quot; in multiple-field indexing is now disallowed, as is\n>repeating a field name in a multiple-field index.\n\n>The documentation for structured arrays in the user guide has been\n>significantly updated to reflect these changes.\n\n>Integer and Void scalars are now unaffected by ``np.set_string_function``\n>-------------------------------------------------------------------------\n>Previously, unlike most other numpy scalars, the ``str`` and ``repr`` of\n>integer and void scalars could be controlled by ``np.set_string_function``.\n>This is no longer possible.\n\n>0d array printing changed, ``style`` arg of array2string deprecated\n>-------------------------------------------------------------------\n>Previously the ``str`` and ``repr`` of 0d arrays had idiosyncratic\n>implementations which returned ``str(a.item())`` and ``&#39;array(&#39; +\n>repr(a.item()) + &#39;)&#39;`` respectively for 0d array ``a``, unlike both numpy\n>scalars and higher dimension ndarrays.\n\n>Now, the ``str`` of a 0d array acts like a numpy scalar using ``str(a[()])``\n>and the ``repr`` acts like higher dimension arrays using ``formatter(a[()])``,\n>where  ``formatter``  can be specified using ``np.set_printoptions``. The\n>``style`` argument of ``np.array2string`` is deprecated.\n\n>This new behavior is disabled in 1.13 legacy printing mode, see compatibility\n>notes above.\n\n>Seeding ``RandomState`` using an array requires a 1-d array\n>-----------------------------------------------------------\n>``RandomState`` previously would accept empty arrays or arrays with 2 or more\n>dimensions, which resulted in either a failure to seed (empty arrays) or for\n>some of the passed values to be ignored when setting the seed.\n\n>``MaskedArray`` objects show a more useful ``repr``\n>---------------------------------------------------\n>The ``repr`` of a ``MaskedArray`` is now closer to the python code that would\n>produce it, with arrays now being shown with commas and dtypes. Like the other\n>formatting changes, this can be disabled with the 1.13 legacy printing mode in\n>order to help transition doctests.\n\n>The ``repr`` of ``np.polynomial`` classes is more explicit\n>----------------------------------------------------------\n>It now shows the domain and window parameters as keyword arguments to make\n>them 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\n\n### tqdm 4.17.1 -> 4.19.5\n\n>### 4.19.5\n\n>- multiprocess/lock fixes (457)\n>- `set_description` in notebook (345 -&gt; 475)\n>- minor tidy (476)\n>- documentation updates\n\n\n\n>### 4.19.4\n\n>- fix `Lock:NotImplementedError` on certain systems (466 -&gt; 468)\n>- use recursive locks (469 -&gt; 468)\n>    - fix deadlocks\n>- tidy (448)\n>- `flush()` on `moveto()` (398 -&gt; 399, 420, 467)\n>- update tests and benchmarks\n\n\n\n>### 4.19.2\n\n>- `flush()` on `refresh()` (459 from 317)\n>- status printer updates (331)\n>    + use `sp()` in `refresh()`\n>    + remove redundant `clear():nomove=False`\n>- misc minor documentation updates\n>- unit tests\n\n\n\n>### 4.19.1\n\n>- `rate_(no)inv(_fmt)` (72, b228bc3)\n>- `__repr__()` tidy (389)\n>- fix `write()` before initialisation `AttributeError: _lock` (457)\n>- `man` pages\n>- documentation updates\n\n\n\n>### 4.18.0\n\n>- Thread safety! (285 -&gt; 291 -&gt; 329, 407, 417)\n>- Ease redirection of `sys.stdout`/`stderr` (422)\n>- Minor internal stream bugfix (439)\n>- `AttributeError` fixes (323, 324, 418)\n\n>Related to:\n\n>- Misc bugs (334)\n>- `concurrent.futures` (97)\n>- Multi-`tqdm` (143)\n>- `flush()` and `refresh()` (331)\n>- Newline on `refresh()` (361)\n>- Nested trees (384)\n>- Manually positioned nested bars clearing (385)\n\n\n\n\n\n\n\n\n\n\nThat's it for now!\n\nHappy merging! 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