Backport gh-77

CHANGELOG.md

@@ -0,0 +1,12 @@
+# changelog
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [1.3.0] (10/06/2025)
+
+### Changed
+* Used `GIT_DESCRIBE_TAG` and `GIT_DESCRIBE_NUMBER` in `meta.yaml` instead of manual stepping the numbers [gh-75](https://github.com/IntelPython/mkl_random/pull/75)
+* Extended conda build scripts with the use of `WHEELS_OUTPUT_FOLDER` variable to build wheel packages [gh-74](https://github.com/IntelPython/mkl_random/pull/74)
+* Updated `meta.yaml` to have a run dependency on `numpy-base` package [gh-73](https://github.com/IntelPython/mkl_random/pull/73)

conda-recipe-cf/meta.yaml

@@ -1,4 +1,4 @@
-{% set version = "1.3.0dev0" %}
+{% set version = "1.3.0" %}
 {% set buildnumber = 0 %}
 
 package:

docs/source/conf.py

@@ -9,7 +9,7 @@
 project = 'mkl_random'
 copyright = '2017-2025, Intel Corp.'
 author = 'Intel Corp.'
-release = '1.3.0dev0'
+release = '1.3.0'
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

docs/source/how_to.rst

@@ -68,7 +68,7 @@ indistinguishable from independent.
     Initialize the state of the generator using leap-frog method, or raise :code:`ValueError`
     exception if not supported.
 
-    The leap-frog method partitions state tragectory into :code:`nstream` interleaved non-overlapping 
+    The leap-frog method partitions state trajectory into :code:`nstream` interleaved non-overlapping 
     sub-sequences, and argument :code:`k` identifies the subsequence.
 
     The method is supported for :ref:`"mcg31m1" <mcg31m1_brng>`, :ref:`"mcg59" <mcg59_brng>`, and :ref:`"wh" <wh_brng>` 

docs/source/reference/ars5.rst

@@ -23,7 +23,7 @@ initialized with either an integral seed, a list of integral seeds, or automatic
         rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="ars5")
 
         # Use random state instance to generate 1000 random numbers from
-        # Gamma(3, 1) distibution
+        # Gamma(3, 1) distribution
         gsample = rs_vec.gamma(3, 1, size=1000)
 
 When seed is not specified, the generator is initialized using system clock, e.g.:

docs/source/reference/mcg31.rst

@@ -23,7 +23,7 @@ initialized with  either an integral seed, a list of integral seeds, or automati
         rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="MCG31")
 
         # Use random state instance to generate 1000 random numbers from
-        # Gamma(3, 1) distibution
+        # Gamma(3, 1) distribution
         gsample = rs_vec.gamma(3, 1, size=1000)
 
 When seed is not specified, the generator is initialized using system clock, e.g.:

docs/source/reference/mcg59.rst

@@ -23,7 +23,7 @@ initialized with  either an integral seed, a list of integral seeds, or automati
         rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="MCG59")
 
         # Use random state instance to generate 1000 random numbers from
-        # Gamma(3, 1) distibution
+        # Gamma(3, 1) distribution
         gsample = rs_vec.gamma(3, 1, size=1000)
 
 When seed is not specified, the generator is initialized using system clock, e.g.:

docs/source/reference/mrg32k3a.rst

@@ -23,7 +23,7 @@ initialized with  either an integral seed, a list of integral seeds, or automati
         rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="MRG32k3a")
 
         # Use random state instance to generate 1000 random numbers from
-        # Gamma(3, 1) distibution
+        # Gamma(3, 1) distribution
         gsample = rs_vec.gamma(3, 1, size=1000)
 
 When seed is not specified, the generator is initialized using system clock, e.g.:

docs/source/reference/mt2203.rst

@@ -32,7 +32,7 @@ An individual member of the set can be addressed by using a tuple to specify the
         rs = mkl_random.RandomState([1234, 567, 89, 0], brng=("MT2203", 6023))
 
         # Use random state instance to generate 1000 random numbers from
-        # Gamma(3, 1) distibution
+        # Gamma(3, 1) distribution
         gsample = rs_vec.gamma(3, 1, size=1000)
 
 When seed is not specified, the generator is initialized using system clock, e.g.:

docs/source/reference/philox4x32x10.rst

@@ -23,7 +23,7 @@ initialized with  either an integral seed, a list of integral seeds, or automati
         rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="philox4x32x10")
 
         # Use random state instance to generate 1000 random numbers from
-        # Gamma(3, 1) distibution
+        # Gamma(3, 1) distribution
         gsample = rs_vec.gamma(3, 1, size=1000)
 
 When seed is not specified, the generator is initialized using system clock, e.g.:

docs/source/reference/r250.rst

@@ -23,7 +23,7 @@ initialized with  either an integral seed, a list of integral seeds, or automati
         rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="R250")
 
         # Use random state instance to generate 1000 random numbers from
-        # Gamma(3, 1) distibution
+        # Gamma(3, 1) distribution
         gsample = rs_vec.gamma(3, 1, size=1000)
 
 When seed is not specified, the generator is initialized using system clock, e.g.:

docs/source/reference/sfmt19937.rst

@@ -23,7 +23,7 @@ either an integral seed, a list of integral seeds, or automatically.
         rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="SFMT19937")
 
         # Use random state instance to generate 1000 random numbers from
-        # Gamma distibution
+        # Gamma distribution
         gsample = rs_vec.gamma(3, 1, size=1000)
 
 When seed is not specified, the generator is initialized using system clock, e.g.:

docs/source/reference/wichmann_hill.rst

@@ -33,7 +33,7 @@ An individual member of the set can be addressed by using a tuple to specify the
         rs = mkl_random.RandomState([1234, 567, 89, 0], brng=("WH", 200))
 
         # Use random state instance to generate 1000 random numbers from
-        # Gamma(3, 1) distibution
+        # Gamma(3, 1) distribution
         gsample = rs_vec.gamma(3, 1, size=1000)
 
 When seed is not specified, the generator is initialized using system clock, e.g.:

docs/source/tutorials.rst

@@ -115,7 +115,7 @@ The quality of emulation is tested statistically through a battery of test, e.g.
 These tests check if various statistical tests can separate the pseudo-random sequence from a true random one.
 
 Pseudo-random generators usually have an internal state and require its initialization, also sometimes known as seeding.
-States initialization algorithms take user provided _seed_ value, usually an integer or a finite seqeuence of integers, and scramble it 
+States initialization algorithms take user provided _seed_ value, usually an integer or a finite sequence of integers, and scramble it 
 to populate the internal state of the pseudo-random generator. 
 
 The sequence from the pseudo-random generator, unlike from true random generator, is repeatable, provided the internal state can be

mkl_random/_version.py

@@ -1 +1 @@
-__version__ = '1.3.0dev0'
+__version__ = '1.3.0'

mkl_random/mklrand.pyx

@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-# Copyright (c) 2017-2024, Intel Corporation
+# Copyright (c) 2017, Intel Corporation
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions are met:

mkl_random/src/randomkit.cpp

@@ -93,7 +93,7 @@
 const char *irk_strerror[RK_ERR_MAX] =
     {
         "no error",
-        "random device unvavailable"};
+        "random device unavailable"};
 
 /* static functions */
 static unsigned long irk_hash(unsigned long key);

mkl_random/tests/test_random.py

@@ -283,7 +283,7 @@ def test_randint_repeatability(randint):
         res = hashlib.md5(val.view(np.int8)).hexdigest()
         assert tgt[np.dtype(dt).name] == res
 
-    # bools do not depend on endianess
+    # bools do not depend on endianness
     rnd.seed(1234, brng='MT19937')
     val = randint.rfunc(0, 2, size=1000, dtype='bool').view(np.int8)
     res = hashlib.md5(val).hexdigest()