Merge pull request #281 from zoj613/doc_typo

DOC: fix some typo and grammatical errors in docs

Author: bashtage

doc/source/bit_generators/index.rst

@@ -4,7 +4,7 @@ Bit Generators
 The random values produced by :class:`numpy.random.Generator`
 (and also  :class:`~randomgen.generator.Generator`)
 are produced by a bit generator.  These bit generators do not directly provide
-random numbers and only contains methods used for seeding, getting or
+random numbers and only contain methods used for seeding, getting or
 setting the state, jumping or advancing the state, and for accessing
 low-level wrappers for consumption by code that can efficiently
 access the functions provided, e.g., `numba <https://numba.pydata.org>`_.

doc/source/custom-bit-generators.ipynb

@@ -19,7 +19,7 @@
     "bit generators in pure Python or, if performance is an issue, using Cython or by\n",
     "accessing functions in a compiled library (e.g., a DLL).\n",
     "\n",
-    "Here we look examine the steps needed to write a pure Python bit\n",
+    "Here we examine the steps needed to write a pure Python bit\n",
     "generator and higher-performance generator using Cython."
    ]
   },
@@ -33,7 +33,7 @@
    "source": [
     "## Using Python\n",
     "\n",
-    "The example here beings by writing a class that implements the PCG64\n",
+    "The example here begins by writing a class that implements the PCG64\n",
     "bit generator using the XSL-RR output transformation. While this is not\n",
     "a complete implementation (it does not support `advance` or `seed`),\n",
     "it is simple. The key to understanding PCG is that the underlying state\n",
@@ -253,7 +253,7 @@
     "wire up this function by setting `state_setter` and `state_getter` in `UserBitGenerator`.\n",
     "These both take callable functions.\n",
     "\n",
-    "This time the `state_getter` and `state_setter` are used to that the state can be read\n",
+    "This time the `state_getter` and `state_setter` are used so that the state can be read\n",
     "and set through the bit generator."
    ]
   },
@@ -842,4 +842,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

doc/source/future.rst

@@ -11,7 +11,7 @@ The future plans for randomgen are:
 * Remove :class:`~randomgen.generator.Generator` and :class:`~randomgen.mtrand.RandomState`. These
   duplicate NumPy and will diverge over time.  The versions in NumPy are authoritative. These
   have been deprecated as of version 1.19 and will be removed in 1.21.
-* The novel methods of :class:`~randomgen.generator.Generator` in a
+* Put the novel methods of :class:`~randomgen.generator.Generator` in a
   :class:`~randomgen.generator.ExtendedGenerator`. :class:`~randomgen.generator.ExtendedGenerator`
   will be maintained, although it is possible that some of the methods may
   migrate to NumPy.

doc/source/index.rst

@@ -105,7 +105,7 @@ generators, 'in addition' to the standard PRNG in NumPy.  The included PRNGs are
   :meth:`~randomgen.xoroshiro128.Xoroshiro128.jumped` for details.
   More information about this PRNG is available at the
   `xorshift, xoroshiro and xoshiro authors' page`_.
-* XorShift1024*φ - Fast fast generator based on the XSadd
+* XorShift1024*φ - Fast generator based on the XSadd
   generator. Supports ``jump`` and so can be used in
   parallel applications. See the documentation for
   :meth:`~randomgen.xorshift1024.Xorshift1024.jumped` for details. More information

doc/source/multithreading.rst

@@ -94,7 +94,7 @@ The single threaded call directly uses the PRNG.
 
     66.5 ms ± 171 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
 
-The gains are substantial and the scaling is reasonable even for large that
+The gains are substantial and the scaling is reasonable even for arrays that
 are only moderately large.  The gains are even larger when compared to a call
 that does not use an existing array due to array creation overhead.
 

doc/source/parallel.rst

@@ -43,7 +43,7 @@ Distinct Keys in Cryptographic Generators
 The cryptographic pseudo-random number generators (PRNGs) support using distinct
 keys to produce distinct sequence.
 Generators that support this form of parallelization include :class:`~randomgen.aes.AESCounter`,
-:class:`~randomgen.chacha.ChaCha`,,:class:`~randomgen.hc128.HC128`
+:class:`~randomgen.chacha.ChaCha`, :class:`~randomgen.hc128.HC128`,
 :class:`~randomgen.threefry.ThreeFry`, :class:`~randomgen.philox.Philox`, and
 :class:`~randomgen.speck128.SPECK128`.
 
@@ -81,7 +81,7 @@ the core PRNG. This occurs for two reasons:
 
 * The random values are simulated using a rejection-based method
   and so, on average, more than one value from the underlying
-  PRNG is required to generate an single draw.
+  PRNG is required to generate a single draw.
 * The number of bits required to generate a simulated value
   differs from the number of bits generated by the underlying
   PRNG.  For example, two 16-bit integer values can be simulated