Initial Update - Githubissues

This PR sets up pyup.io on this repo and updates all dependencies at once, in a single branch.

Subsequent pull requests will update one dependency at a time, each in their own branch. If you want to start with that right away, simply close this PR.

Update numpy from 1.15.4 to 1.16.0.

Changelog

### 1.16.0 ``` ========================== This NumPy release is the last one to support Python 2.7. It will be maintained as a long term release with bug fixes only through 2020. To that end, the planned code reorganization detailed in `NEP 15`_ has been made in order to facilitate backporting fixes from future releases, which will now have the same code organization. Support for Python 3.4 been dropped in this release, the supported Python versions are 2.7 and 3.5-3.7. The wheels are linked with OpenBLAS v0.3.0 . Highlights ========== New functions ============= * New functions in the `numpy.lib.recfunctions` module to ease the structured assignment changes: `assign_fields_by_name`, `structured_to_unstructured`, `unstructured_to_structured`, `apply_along_fields`, and `require_fields`. See the user guide at <https://docs.scipy.org/doc/numpy/user/basics.rec.html> for more info. Deprecations ============ `typeNA` and `sctypeNA` have been deprecated -------------------------------------------- The type dictionaries `numpy.core.typeNA` and `numpy.core.sctypeNA` were buggy and not documented. They will be removed in the 1.18 release. Use `numpy.sctypeDict` instead. ``np.PackageLoader`` and ``np.pkgload`` have been removed --------------------------------------------------------- These were deprecated in 1.10, had no tests, and seem to no longer work in 1.15 anyway. `numpy.asscalar` has been deprecated ------------------------------------ It is an alias to the more powerful `numpy.ndarray.item`, not tested, and fails for scalars. `np.set_array_ops` and `np.get_array_ops` have been deprecated -------------------------------------------------------------- As part of `NEP 15`, they have been deprecated along with the C-API functions :c:func:`PyArray_SetNumericOps` and :c:func:`PyArray_GetNumericOps`. Users who wish to override the inner loop functions in built-in ufuncs should use :c:func:`PyUFunc_ReplaceLoopBySignature`. Future Changes ============== * NumPy 1.17 will drop support for Python 2.7. Expired deprecations ==================== * NaT comparisons now return ``False`` without a warning, finishing a deprecation cycle begun in NumPy 1.11. * ``np.lib.function_base.unique`` was removed, finishing a deprecation cycle begun in NumPy 1.4. Use `numpy.unique` instead. * multi-field indexing now returns views instead of copies, finishing a deprecation cycle begun in NumPy 1.7. The change was previously attempted in NumPy 1.14 but reverted until now. Compatibility notes =================== f2py script on Windows ---------------------- On Windows, the installed script for running f2py is now an ``.exe`` file rather than a ``*.py`` file and should be run from the command line as ``f2py`` whenever the ``Scripts`` directory is in the path. Folks needing compatibility with earler versions of Numpy should run ``f2py`` as a module: ``python -m numpy.f2py [...]``. NaT comparisons --------------- Consistent with the behavior of NaN, all comparisons other than inequality checks with datetime64 or timedelta64 NaT ("not-a-time") values now always return ``False``, and inequality checks with NaT now always return ``True``. This includes comparisons beteween NaT values. For compatibility with the old behavior, use ``np.isnat`` to explicitly check for NaT or convert datetime64/timedelta64 arrays with ``.astype(np.int64)`` before making comparisons. complex64/128 alignment has changed ----------------------------------- The memory alignment of complex types is now the same as a C-struct composed of two floating point values, while before it was equal to the size of the type. For many users (for instance on x64/unix/gcc) this means that complex64 is now 4-byte aligned instead of 8-byte aligned. An important consequence is that aligned structured dtypes may now have a different size. For instance, ``np.dtype('c8,u1', align=True)`` used to have an itemsize of 16 (on x64/gcc) but now it is 12. More in detail, the complex64 type now has the same alignment as a C-struct ``struct {float r, i;}``, according to the compiler used to compile numpy, and similarly for the complex128 and complex256 types. nd_grid __len__ removal ----------------------- ``len(np.mgrid)`` and ``len(np.ogrid)`` are now considered nonsensical and raise a ``TypeError``. ``np.unravel_index`` now accepts ``shape`` keyword argument ----------------------------------------------------------- Previously, only the ``dims`` keyword argument was accepted for specification of the shape of the array to be used for unraveling. ``dims`` remains supported, but is now deprecated. multi-field views return a view instead of a copy ------------------------------------------------- Indexing a structured array with multiple fields, e.g., ``arr[['f1', 'f3']]``, returns a view into the original array instead of a copy. The returned view will often have extra padding bytes corresponding to intervening fields in the original array, unlike before, which will affect code such as ``arr[['f1', 'f3']].view('float64')``. This change has been planned since numpy 1.7 and such operations have emitted ``FutureWarnings`` since then and more since 1.12. To help users update their code to account for these changes, a number of functions have been added to the ``numpy.lib.recfunctions`` module which safely allow such operations. For instance, the code above can be replaced with ``structured_to_unstructured(arr[['f1', 'f3']], dtype='float64')``. See the "accessing multiple fields" section of the `user guide <https://docs.scipy.org/doc/numpy/user/basics.rec.html>`__. C API changes ============= The :c:data:`NPY_API_VERSION` was incremented to 0x0000D, due to the addition of: * :c:member:`PyUFuncObject.core_dim_flags` * :c:member:`PyUFuncObject.core_dim_sizes` * :c:member:`PyUFuncObject.identity_value` * :c:function:`PyUFunc_FromFuncAndDataAndSignatureAndIdentity` New Features ============ Integrated squared error (ISE) estimator added to ``histogram`` --------------------------------------------------------------- This method (``bins='stone'``) for optimizing the bin number is a generalization of the Scott's rule. The Scott's rule assumes the distribution is approximately Normal, while the ISE is a nonparametric method based on cross-validation. https://en.wikipedia.org/wiki/HistogramMinimizing_cross-validation_estimated_squared_error ``max_rows`` keyword added for ``np.loadtxt`` --------------------------------------------- New keyword ``max_rows`` in `numpy.loadtxt` sets the maximum rows of the content to be read after ``skiprows``, as in `numpy.genfromtxt`. modulus operator support added for ``np.timedelta64`` operands -------------------------------------------------------------- The modulus (remainder) operator is now supported for two operands of type ``np.timedelta64``. The operands may have different units and the return value will match the type of the operands. Improvements ============ no-copy pickling of numpy arrays -------------------------------- Up to protocol 4, numpy array pickling created 2 spurious copies of the data being serlialized. With pickle protocol 5, and the ``PickleBuffer`` API, a large variety of numpy arrays can now be serialized without any copy using out-of-band buffers, and with one less copy using in-band buffers. This results, for large arrays, in an up to 66% drop in peak memory usage. build shell independence ------------------------ NumPy builds should no longer interact with the host machine shell directly. ``exec_command`` has been replaced with ``subprocess.check_output`` where appropriate. `np.polynomial.Polynomial` classes render in LaTeX in Jupyter notebooks ----------------------------------------------------------------------- When used in a front-end that supports it, `Polynomial` instances are now rendered through LaTeX. The current format is experimental, and is subject to change. ``randint`` and ``choice`` now work on empty distributions ---------------------------------------------------------- Even when no elements needed to be drawn, ``np.random.randint`` and ``np.random.choice`` raised an error when the arguments described an empty distribution. This has been fixed so that e.g. ``np.random.choice([], 0) == np.array([], dtype=float64)``. ``linalg.lstsq`` and ``linalg.qr`` now work with empty matrices --------------------------------------------------------------- Previously, a ``LinAlgError`` would be raised when an empty matrix/empty matrices (with zero rows and/or columns) is/are passed in. Now outputs of appropriate shapes are returned. ``np.diff`` Added kwargs prepend and append ------------------------------------------- Add kwargs prepend and append, allowing for values to be inserted on either end of the differences. Similar to options for ediff1d. Allows for the inverse of cumsum easily via prepend=0 ARM support updated ------------------- Support for ARM CPUs has been updated to accommodate 32 and 64 bit targets, and also big and little endian byte ordering. AARCH32 memory alignment issues have been addressed. Appending to build flags ------------------------ `numpy.distutils` has always overridden rather than appended to `LDFLAGS` and other similar such environment variables for compiling Fortran extensions. Now, if the `NPY_DISTUTILS_APPEND_FLAGS` environment variable is set to 1, the behavior will be appending. This applied to: `LDFLAGS`, `F77FLAGS`, `F90FLAGS`, `FREEFLAGS`, `FOPT`, `FDEBUG`, and `FFLAGS`. See gh-11525 for more details. Generalized ufunc signatures now allow fixed-size dimensions ------------------------------------------------------------ By using a numerical value in the signature of a generalized ufunc, one can indicate that the given function requires input or output to have dimensions with the given size. E.g., the signature of a function that converts a polar angle to a two-dimensional cartesian unit vector would be ``()->(2)``; that for one that converts two spherical angles to a three-dimensional unit vector would be ``(),()->(3)``; and that for the cross product of two three-dimensional vectors would be ``(3),(3)->(3)``. Note that to the elementary function these dimensions are not treated any differently from variable ones indicated with a name starting with a letter; the loop still is passed the corresponding size, but it can now count on that size being equal to the fixed one given in the signature. Generalized ufunc signatures now allow flexible dimensions ---------------------------------------------------------- Some functions, in particular numpy's implementation of ` as ``matmul``, are very similar to generalized ufuncs in that they operate over core dimensions, but one could not present them as such because they were able to deal with inputs in which a dimension is missing. To support this, it is now allowed to postfix a dimension name with a question mark to indicate that the dimension does not necessarily have to be present. With this addition, the signature for ``matmul`` can be expressed as ``(m?,n),(n,p?)->(m?,p?)``. This indicates that if, e.g., the second operand has only one dimension, for the purposes of the elementary function it will be treated as if that input has core shape ``(n, 1)``, and the output has the corresponding core shape of ``(m, 1)``. The actual output array, however, has the flexible dimension removed, i.e., it will have shape ``(..., m)``. Similarly, if both arguments have only a single dimension, the inputs will be presented as having shapes ``(1, n)`` and ``(n, 1)`` to the elementary function, and the output as ``(1, 1)``, while the actual output array returned will have shape ``()``. In this way, the signature allows one to use a single elementary function for four related but different signatures, ``(m,n),(n,p)->(m,p)``, ``(n),(n,p)->(p)``, ``(m,n),(n)->(m)`` and ``(n),(n)->()``. ``np.clip`` and the ``clip`` method check for memory overlap ------------------------------------------------------------ The ``out`` argument to these functions is now always tested for memory overlap to avoid corrupted results when memory overlap occurs. New value ``unscaled`` for option ``cov`` in ``np.polyfit'' ----------------------------------------------------------- A further possible value has been added to the ``cov`` parameter of the ``np.polyfit`` function. With ``cov='unscaled'`` the scaling of the covariance matrix is disabled completely (similar to setting ``absolute_sigma=True'' in ``scipy.optimize.curve_fit``). This would be useful in occasions, where the weights are given by 1/sigma with sigma being the (known) standard errors of (Gaussian distributed) data points, in which case the unscaled matrix is already a correct estimate for the covariance matrix. Detailed docstrings for scalar numeric types -------------------------------------------- The ``help`` function, when applied to numeric types such as `np.intc`, `np.int_`, and `np.longlong`, now lists all of the aliased names for that type, distinguishing between platform -dependent and -independent aliases. ``__module__`` attribute now points to public modules ----------------------------------------------------- The ``__module__`` attribute on most NumPy functions has been updated to refer to the preferred public module from which to access a function, rather than the module in which the function happens to be defined. This produces more informative displays for functions in tools such as IPython, e.g., instead of ``<function 'numpy.core.fromnumeric.sum'>`` you now see ``<function 'numpy.sum'>``. Large allocations marked as suitable for transparent hugepages -------------------------------------------------------------- On systems that support transparent hugepages over the madvise system call numpy now marks that large memory allocations can be backed by hugepages which reduces page fault overhead and can in some fault heavy cases improve performance significantly. On Linux for huge pages to be used the setting `/sys/kernel/mm/transparent_hugepage/enabled` must be at least `madvise`. Systems which already have it set to `always` will not see much difference as the kernel will automatically use huge pages where appropriate. Users of very old Linux kernels (~3.x and older) should make sure that `/sys/kernel/mm/transparent_hugepage/defrag` is not set to `always` to avoid performance problems due concurrency issues in the memory defragmentation. Alpine Linux (and other musl c library distros) support ------------------------------------------------------- We now default to use `fenv.h` for floating point status error reporting. Previously we had a broken default that sometimes would not report underflow, overflow, and invalid floating point operations. Now we can support non-glibc distrubutions like Alpine Linux as long as they ship `fenv.h`. Speedup ``np.block`` for large arrays ------------------------------------- Large arrays (greater than ``512 * 512``) now use a blocking algorithm based on copying the data directly into the appropriate slice of the resulting array. This results in significant speedups for these large arrays, particularly for arrays being blocked along more than 2 dimensions. ``arr.ctypes.data_as(...)`` holds a reference to arr ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Previously the caller was responsible for keeping the array alive for the lifetime of the pointer. Speedup ``np.take`` for read-only arrays ---------------------------------------- The implementation of ``np.take`` no longer makes an unnecessary copy of the source array when its ``writeable`` flag is set to ``False``. Support path-like objects for more functions -------------------------------------------- The ``np.core.records.fromfile`` function now supports ``pathlib.Path`` and other path-like objects in addition to a file object. Furthermore, the ``np.load`` function now also supports path-like objects when using memory mapping (``mmap_mode`` keyword argument). Better behaviour of ufunc identities during reductions ------------------------------------------------------ Universal functions have an ``.identity`` which is used when ``.reduce`` is called on an empty axis. As of this release, the logical binary ufuncs, `logical_and`, `logical_or`, and `logical_xor`, now have ``identity``s of type `bool`, where previously they were of type `int`. This restores the 1.14 behavior of getting ``bool``s when reducing empty object arrays with these ufuncs, while also keeping the 1.15 behavior of getting ``int``s when reducing empty object arrays with arithmetic ufuncs like ``add`` and ``multiply``. Additionally, `logaddexp` now has an identity of ``-inf``, allowing it to be called on empty sequences, where previously it could not be. This is possible thanks to the new :c:function:`PyUFunc_FromFuncAndDataAndSignatureAndIdentity`, which allows arbitrary values to be used as identities now. Improved conversion from ctypes objects --------------------------------------- Numpy has always supported taking a value or type from ``ctypes`` and converting it into an array or dtype, but only behaved correctly for simpler types. As of this release, this caveat is lifted - now: * The ``_pack_`` attribute of ``ctypes.Structure``, used to emulate C's ``__attribute__((packed))``, is respected. * Endianness of all ctypes objects is preserved * ``ctypes.Union`` is supported * Unrepresentable constructs raise exceptions, rather than producing dangerously incorrect results: * Bitfields are no longer interpreted as sub-arrays * Pointers are no longer replaced with the type that they point to A new ``ndpointer.contents`` member ----------------------------------- This matches the ``.contents`` member of normal ctypes arrays, and can be used to construct an ``np.array`` around the pointers contents. This replaces ``np.array(some_nd_pointer)``, which stopped working in 1.15. As a side effect of this change, ``ndpointer`` now supports dtypes with overlapping fields and padding. ``matmul`` is now a ``ufunc`` ----------------------------- `numpy.matmul` is now a ufunc which means that both the function and the ``__matmul__`` operator can now be overridden by ``__array_ufunc__``. Its implementation has also changed, ensuring it uses the same BLAS routines as `numpy.dot`, ensuring its performance is similar for large matrices. Start and stop arrays for ``linspace``, ``logspace`` and ``geomspace`` ---------------------------------------------------------------------- These functions used to be limited to scalar stop and start values, but can now take arrays, which will be properly broadcast and result in an output which has one axis prepended. This can be used, e.g., to obtain linearly interpolated points between sets of points. Changes ======= Comparison ufuncs will now error rather than return NotImplemented ------------------------------------------------------------------ Previously, comparison ufuncs such as ``np.equal`` would return `NotImplemented` if their arguments had structured dtypes, to help comparison operators such as ``__eq__`` deal with those. This is no longer needed, as the relevant logic has moved to the comparison operators proper (which thus do continue to return `NotImplemented` as needed). Hence, like all other ufuncs, the comparison ufuncs will now error on structured dtypes. Positive will now raise a deprecation warning for non-numerical arrays ---------------------------------------------------------------------- Previously, ``+array`` unconditionally returned a copy. Now, it will raise a ``DeprecationWarning`` if the array is not numerical (i.e., if ``np.positive(array)`` raises a ``TypeError``. For ``ndarray`` subclasses that override the default ``__array_ufunc__`` implementation, the ``TypeError`` is passed on. ``NDArrayOperatorsMixin`` now implements matrix multiplication -------------------------------------------------------------- Previously, ``np.lib.mixins.NDArrayOperatorsMixin`` did not implement the special methods for Python's matrix multiplication operator (`). This has changed now that ``matmul`` is a ufunc and can be overriden using ``__array_ufunc__``. The scaling of the covariance matrix in ``np.polyfit`` is different ------------------------------------------------------------------- So far, ``np.polyfit`` used a non-standard factor in the scaling of the the covariance matrix. Namely, rather than using the standard chisq/(M-N), it scales it with chisq/(M-N-2) where M is the number of data points and N is the number of parameters. This scaling is inconsistent with other fitting programs such as e.g. ``scipy.optimize.curve_fit`` and was changed to chisq/(M-N). ``maximum`` and ``minimum`` no longer emit warnings --------------------------------------------------- As part of code introduced in 1.10, ``float32`` and ``float64`` set invalid float status when a Nan is encountered in `numpy.maximum` and `numpy.minimum`, when using SSE2 semantics. This caused a `RuntimeWarning` to sometimes be emitted. In 1.15 we fixed the inconsistencies which caused the warnings to become more conspicuous. Now no warnings will be emitted. Umath and multiarray c-extension modules merged into a single module -------------------------------------------------------------------- The two modules were merged, according to the first step in `NEP 15`_. Previously `np.core.umath` and `np.core.multiarray` were the c-extension modules, they are now python wrappers to the single `np.core/_multiarray_math` c-extension module. .. _`NEP 15` : http://www.numpy.org/neps/nep-0015-merge-multiarray-umath.html ``getfield`` validity checks extended ---------------------------------------- `numpy.ndarray.getfield` now checks the dtype and offset arguments to prevent accessing invalid memory locations. NumPy functions now support overrides with ``__array_function__`` ----------------------------------------------------------------- It is now possible to override the implementation of almost all NumPy functions on non-NumPy arrays by defining a ``__array_function__`` method, as described in `NEP 18`_. The sole exception are functions for explicitly casting to NumPy arrays such as ``np.array``. As noted in the NEP, this feature remains experimental and the details of how to implement such overrides may change in the future. .. _`NEP 15` : http://www.numpy.org/neps/nep-0015-merge-multiarray-umath.html .. _`NEP 18` : http://www.numpy.org/neps/nep-0018-array-function-protocol.html Arrays based off readonly buffers cannot be set ``writeable`` ------------------------------------------------------------- We now disallow setting the ``writeable`` flag True on arrays created from ``fromstring(readonly-buffer)``. ========================= ```

Links

- PyPI: https://pypi.org/project/numpy - Changelog: https://pyup.io/changelogs/numpy/ - Homepage: https://www.numpy.org

Update pyparsing from 2.3.0 to 2.3.1.

Changelog

### 2.3.1 ``` ----------------------------- - POSSIBLE API CHANGE: this release fixes a bug when results names were attached to a MatchFirst or Or object containing an And object. Previously, a results name on an And object within an enclosing MatchFirst or Or could return just the first token in the And. Now, all the tokens matched by the And are correctly returned. This may result in subtle changes in the tokens returned if you have this condition in your pyparsing scripts. - New staticmethod ParseException.explain() to help diagnose parse exceptions by showing the failing input line and the trace of ParserElements in the parser leading up to the exception. explain() returns a multiline string listing each element by name. (This is still an experimental method, and the method signature and format of the returned string may evolve over the next few releases.) Example: define a parser to parse an integer followed by an alphabetic word expr = pp.Word(pp.nums).setName("int") + pp.Word(pp.alphas).setName("word") try: parse a string with a numeric second value instead of alpha expr.parseString("123 355") except pp.ParseException as pe: print_(pp.ParseException.explain(pe)) Prints: 123 355 ^ ParseException: Expected word (at char 4), (line:1, col:5) __main__.ExplainExceptionTest pyparsing.And - {int word} pyparsing.Word - word explain() will accept any exception type and will list the function names and parse expressions in the stack trace. This is especially useful when an exception is raised in a parse action. Note: explain() is only supported under Python 3. - Fix bug in dictOf which could match an empty sequence, making it infinitely loop if wrapped in a OneOrMore. - Added unicode sets to pyparsing_unicode for Latin-A and Latin-B ranges. - Added ability to define custom unicode sets as combinations of other sets using multiple inheritance. class Turkish_set(pp.pyparsing_unicode.Latin1, pp.pyparsing_unicode.LatinA): pass turkish_word = pp.Word(Turkish_set.alphas) - Updated state machine import examples, with state machine demos for: . traffic light . library book checkin/checkout . document review/approval In the traffic light example, you can use the custom 'statemachine' keyword to define the states for a traffic light, and have the state classes auto-generated for you: statemachine TrafficLightState: Red -> Green Green -> Yellow Yellow -> Red Similar for state machines with named transitions, like the library book state example: statemachine LibraryBookState: New -(shelve)-> Available Available -(reserve)-> OnHold OnHold -(release)-> Available Available -(checkout)-> CheckedOut CheckedOut -(checkin)-> Available Once the classes are defined, then additional Python code can reference those classes to add class attributes, instance methods, etc. See the examples in examples/statemachine - Added an example parser for the decaf language. This language is used in CS compiler classes in many colleges and universities. - Fixup of docstrings to Sphinx format, inclusion of test files in the source package, and convert markdown to rst throughout the distribution, great job by Matěj Cepl! - Expanded the whitespace characters recognized by the White class to include all unicode defined spaces. Suggested in Issue 51 by rtkjbillo. - Added optional postParse argument to ParserElement.runTests() to add a custom callback to be called for test strings that parse successfully. Useful for running tests that do additional validation or processing on the parsed results. See updated chemicalFormulas.py example. - Removed distutils fallback in setup.py. If installing the package fails, please update to the latest version of setuptools. Plus overall project code cleanup (CRLFs, whitespace, imports, etc.), thanks Jon Dufresne! - Fix bug in CaselessKeyword, to make its behavior consistent with Keyword(caseless=True). Fixes Issue 65 reported by telesphore. ```

Links

- PyPI: https://pypi.org/project/pyparsing - Changelog: https://pyup.io/changelogs/pyparsing/ - Repo: https://github.com/pyparsing/pyparsing/ - Docs: https://pythonhosted.org/pyparsing/

Update typed-ast from 1.1.1 to 1.2.0.

The bot wasn't able to find a changelog for this release. Got an idea?

Links

- PyPI: https://pypi.org/project/typed-ast - Repo: https://github.com/python/typed_ast

djbrown / hbscorez

Initial Update #8

Update numpy from 1.15.4 to 1.16.0.

Update pyparsing from 2.3.0 to 2.3.1.

Update typed-ast from 1.1.1 to 1.2.0.

Codecov Report