Merge branch 'master' into pathlib_support

numpy · mattip · Jun 4, 2019 · Feb 1, 2019 · Feb 4, 2019 · Feb 5, 2019
commit 9a968f3e41f9f187251c489fa108e68d1ae99884
diff --git a/doc/release/1.17.0-notes.rst b/doc/release/1.17.0-notes.rst
@@ -318,6 +318,49 @@ Pathlib support for ``np.fromfile``, ``ndarray.tofile`` and ``ndarray.dump``
 ``np.fromfile``, ``np.ndarray.tofile`` and ``np.ndarray.dump`` now support
 the `pathlib.Path` type for the ``file``/``fid`` parameter.
 
+Specialized ``np.isnan``, ``np.isinf``, and ``np.isfinite`` ufuncs for bool and int types
+-----------------------------------------------------------------------------------------
+The boolean and integer types are incapable of storing ``np.nan`` and
+``np.inf`` values, which allows us to provide specialized ufuncs that are up to
+250x faster than the current approach.
+
+``np.isfinite`` supports ``datetime64`` and ``timedelta64`` types
+-----------------------------------------------------------------
+Previously, `np.isfinite` used to raise a ``TypeError`` on being used on these
+two types.
+
+New keywords added to ``np.nan_to_num``
+---------------------------------------
+``np.nan_to_num`` now accepts keywords ``nan``, ``posinf`` and ``neginf``
+allowing the user to define the value to replace the ``nan``, positive and
+negative ``np.inf`` values respectively.
+
+MemoryErrors caused by allocated overly large arrays are more descriptive
+-------------------------------------------------------------------------
+Often the cause of a MemoryError is incorrect broadcasting, which results in a
+very large and incorrect shape. The message of the error now includes this
+shape to help diagnose the cause of failure.
+
+`floor`, `ceil`, and `trunc` now respect builtin magic methods
+--------------------------------------------------------------
+These ufuncs now call the ``__floor__``, ``__ceil__``, and ``__trunc__``
+methods when called on object arrays, making them compatible with
+`decimal.Decimal` and `fractions.Fraction` objects.
+
+`quantile` now works on `fraction.Fraction` and `decimal.Decimal` objects
+-------------------------------------------------------------------------
+In general, this handles object arrays more gracefully, and avoids floating-
+point operations if exact arithmetic types are used.
+
+Support of object arrays in ``np.matmul``
+-----------------------------------------
+It is now possible to use ``np.matmul`` (or the ``@`` operator) with object arrays.
+For instance, it is now possible to do::
+
+    from fractions import Fraction
+    a = np.array([[Fraction(1, 2), Fraction(1, 3)], [Fraction(1, 3), Fraction(1, 2)]])
+    b = a @ a
+
 
 Changes
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