FInal changes for NumPy 2 compat

Author: bashtage

README.md

@@ -120,12 +120,11 @@ the latest commit (unreleased) is available under
 ## Requirements
 Building requires:
 
-* Python (3.6, 3.7, 3.8, 3.9, 3.10)
-* NumPy (1.17+)
-* Cython (0.29+)
-* tempita (0.5+), if not provided by Cython
+* Python (3.9, 3.10, 3.11, 3.12, 3.13)
+* NumPy (1.22.3+, runtime, 2.0.0+, building)
+* Cython (3.0.10+)
 
-Testing requires pytest (6+).
+Testing requires pytest (7+).
 
 **Note:** it might work with other versions but only tested with these
 versions.

doc/source/change-log.rst

@@ -3,15 +3,17 @@
 Change Log
 ----------
 
-.. container:: admonition danger
-
-  .. raw:: html
-
-      <p class="admonition-title"> Deprecated </p>
-
-  ``Generator`` and ``RandomState`` were **REMOVED** in 1.23.
-  You should be using :class:`numpy.random.Generator` or
-  :class:`numpy.random.RandomState` which are maintained. 
+v2.0.0
+======
+- Final compatibility with NumPy 2
+- Minimum NumPy is now 1.22.3.
+- Removed ``"legacy"`` seeding in favor of using :class:`~numpy.random.SeedSequence`.
+- Removed the the vendored copy of ``SeedSequence``.
+- Deprecated using the ``mode`` keyword argument to set the seed mode, since only ``SeedSequences`` are supported.
+- Changed ``ranomgen.common.BitGenerator`` to inherit from ``numpy.random.BitGenerator`` so that
+  numpy will recognize these as ``BitGenerators``.
+- Removed C distribution functions that are available in NumPy (see libnpyrandom)`.
+- General code cleanup and modernization.
 
 v1.26.1
 =======
@@ -54,7 +56,7 @@ v1.20.2
   :class:`~randomgen.mt19937.MT19937`, :class:`~randomgen.philox.Philox`, and
   :class:`~randomgen.sfc.SFC64`. When using this mode, the sequence generated is
   guaranteed to match the sequence produced using the NumPy implementations as long as
-  a :class:`~randomgen.seed_sequence.SeedSequence` or :class:`numpy.random.SeedSequence`
+  a ``randomgen.seed_sequence.SeedSequence`` or :class:`numpy.random.SeedSequence`
   is used with the same initial seed values.
 - Added :func:`~randomgen.generator.ExtendedGenerator.random` with support for
   ``dtype="longdouble"`` to produce extended precision random floats.
@@ -139,7 +141,7 @@ v1.18.0
 =======
 - ``choice`` pulled in upstream performance improvement that
   use a hash set when choosing without replacement and without user-provided probabilities.
-- Added support for :class:`~randomgen.seed_sequence.SeedSequence` (and NumPy's ``SeedSequence``).
+- Added support for ``randomgen.seed_sequence.SeedSequence`` (and NumPy's :class:`~numpy.random.SeedSequence`).
 - Fixed a bug that affected both ``randomgen.generator.Generator.randint``
   in ``Generator`` and ``randint``
   in  ``RandomState`` when ``high=2**32``.  This value is inbounds for

doc/source/evolution.rst

@@ -48,5 +48,5 @@ The main changes are
 * Change ``jump`` which operated in-place to ``jumped`` which returns a new ``BitGenerator``.
 * Rename Basic RNG to bit generator, which impacts the API in multiple places where names
   like ``brng`` and ``basic_rng`` have been replaced by ``bitgen`` or ``bit_generator``.
-* Support for :class:`~randomgen.seed_sequence.SeedSequence` (also support NumPy ``SeedSequence`` instances)
+* Support for ``randomgen.seed_sequence.SeedSequence`` (also support NumPy :class:`~numpy.random.SeedSequence` instances)
 * Removed support for Python 2.7

doc/source/multithreading.rst

@@ -5,7 +5,7 @@ The four core distributions in :class:`~numpy.random.Generator` all allow
 existing arrays to be filled using the ``out`` keyword argument.
 Existing arrays need to be contiguous and well-behaved (writable and aligned).
 Under normal circumstances, arrays created using the common constructors such
-as :meth:`numpy.empty` will satisfy these requirements.
+as :func:`numpy.empty` will satisfy these requirements.
 
 This example makes use of Python 3 :mod:`concurrent.futures` to fill an array
 using multiple threads.  Threads are long-lived so that repeated calls do not

doc/source/new-or-different.rst

@@ -63,96 +63,6 @@ Differences from NumPy (1.17+)
     * :class:`~randomgen.xoshiro256.Xoshiro256`
     * :class:`~randomgen.xoshiro512.Xoshiro512`
 
-.. container:: admonition danger
-
-  .. raw:: html
-
-      <p class="admonition-title"> Deprecated </p>
-
-  ``Generator`` is **deprecated**. You should be using
-  :class:`numpy.random.Generator`.
-
-* randomgen's ``Generator`` continues to expose legacy
-  methods ``random_sample``, ``randint``, ``random_integers``, ``rand``, ``randn``,
-  and ``tomaxint``. **Note**: These should not be used, and their modern replacements are preferred:
-
-  * ``random_sample``, ``rand` → ``random``
-  * ``random_integers``, ``randint`` → ``integers``
-  * ``randn`` → ``standard_normal``
-  * ``tomaxint`` → ``integers`` with ``dtype`` set to ``int``
-
-* randomgen's bit generators remain seedable and the convenience function
-  ``seed` is exposed as part of``Generator``. Additionally, the convenience
-  property ``state`` is available to get or set the state of the underlying bit generator.
-
-* :func:`numpy.random.Generator.multivariate_hypergeometric` was added after
-  ``Generator`` was merged into NumPy and will not be ported over.
-
-* :func:`numpy.random.Generator.shuffle` and :func:`numpy.random.Generator.permutation`
-  support ``axis`` keyword to operator along an axis other than 0.
-
-* ``integers`` supports the keyword argument ``use_masked`` to switch between masked
-  generation of bounded integers and Lemire's superior method.
-
-Differences from NumPy before 1.17
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-* The normal, exponential and gamma generators use 256-step Ziggurat
-  methods which are 2-10 times faster than NumPy's default implementation in
-  ``standard_normal``, ``standard_exponential`` or ``standard_gamma``.
-
-* The Box-Muller used to produce NumPy's normals is no longer available.
-* All bit generators functions to produce doubles, uint64s and
-  uint32s via CTypes (:meth:`~randomgen.xoroshiro128.Xoroshiro128.ctypes`)
-  and CFFI (:meth:`~randomgen.xoroshiro128.Xoroshiro128.cffi`).  This allows
-  the bit generators to be used in numba or in other low-level applications
-* The bit generators can be used in downstream projects via Cython.
-* Optional ``dtype`` argument that accepts ``np.float32`` or ``np.float64``
-  to produce either single or double prevision uniform random variables for
-  select core distributions
-
-  * Uniforms (``random`` and ``rand``)
-  * Normals (``standard_normal`` and ``randn``)
-  * Standard Gammas (``standard_gamma``)
-  * Standard Exponentials (``standard_exponential``)
-
-* Optional ``out`` argument that allows existing arrays to be filled for
-  select core distributions
-
-  * Uniforms (``random``)
-  * Normals (``standard_normal``)
-  * Standard Gammas (``standard_gamma``)
-  * Standard Exponentials (``standard_exponential``)
-
-  This allows multithreading to fill large arrays in chunks using suitable
-  PRNGs in parallel.
-
-
-* ``integers`` supports broadcasting inputs.
-
-* ``integers`` supports drawing from open (default, ``[low, high)``) or closed
-  (``[low, high]``) intervals using the keyword argument
-  ``endpoint``. Closed intervals are simpler to use when the
-  distribution may include the maximum value of a given integer type.
-
-
-* The closed interval is particularly helpful when using arrays since
-  it avoids object-dtype arrays when sampling from the full range.
-
-
-* Support for Lemire’s method of generating uniform integers on an
-  arbitrary interval by setting ``use_masked=True`` in
-  (``integers``).
-
-
-* ``multinomial`` supports multidimensional values of ``n``
-
-
-* ``choice``  is much faster when sampling small amounts from large arrays
-
-
-* ``choice`` supports the ``axis`` keyword to work with multidimensional arrays.
-
-
 * For changes since the previous release, see the :ref:`change-log`
 
 .. _the demonstration notebook: custom-bit-generators.ipynb

doc/source/seed_sequence.rst

@@ -1,31 +1,12 @@
 Seed Sequences
 --------------
 
-.. module:: randomgen._seed_sequence
-
-.. currentmodule:: randomgen._seed_sequence
-
 .. note::
 
-  randomgen imports ``SeedSequece`` from NumPy if available, and only falls back to a vendored
-  copy if not.  The correct import location is ``randomgen.seed_sequence`` which handles to
-  selection of the correct ``SeedSequence``.
-
-.. autoclass:: SeedSequence
-
-Using a SeedSequence
-====================
-
-.. autosummary::
-   :toctree: generated/
-
-   ~SeedSequence.generate_state
-   ~SeedSequence.spawn
-
-State
-=====
+  randomgen imports :class:`~numpy.random.SeedSequence` from NumPy. As of version 2.0.0, randomgen does not provide
+  a vendored copy of ``SeedSequence``.
 
-.. autosummary::
-   :toctree: generated/
+.. deprecated:: 2.0.0
 
-   ~SeedSequence.state
+   ``SeedSequence`` is now available in NumPy. Use :class:`~numpy.random.SeedSequence`
+   instead. Importing ``SeedSequence`` from randomgen will be removed in a future version.

randomgen/aes.pyx

@@ -17,7 +17,7 @@ cdef double aes_double(void* st) noexcept nogil:
 
 cdef class AESCounter(BitGenerator):
     """
-    AESCounter(seed=None, *, counter=None, key=None, mode=<deprecated>)
+    AESCounter(seed=None, *, counter=None, key=None, mode="sequence")
 
     Container for the AES Counter pseudo-random number generator.
 

randomgen/chacha.pyx

@@ -18,7 +18,7 @@ cdef double chacha_double(void* st) noexcept nogil:
 
 cdef class ChaCha(BitGenerator):
     """
-    ChaCha(seed=None, *, counter=None, key=None, rounds=20, mode=<deprecated>)
+    ChaCha(seed=None, *, counter=None, key=None, rounds=20, mode="sequence")
 
     Container for the ChaCha family of Counter pseudo-random number generators
 

randomgen/dsfmt.pyx

@@ -27,7 +27,7 @@ cdef uint64_t dsfmt_raw(void *st) noexcept nogil:
 
 cdef class DSFMT(BitGenerator):
     """
-    DSFMT(seed=None, *, mode=<deprecated>)
+    DSFMT(seed=None, *, mode="sequence")
 
     Container for the SIMD-based Mersenne Twister pseudo RNG.
 

randomgen/generator.pyx

@@ -255,7 +255,7 @@ cdef class ExtendedGenerator:
 
         Returns
         -------
-        out : integer or ndarray
+        out : int or ndarray
             Drawn samples.
 
         Notes