#219-fix typos in docstrings

Completed. Fully validated.

Author: schmouk

Python3.10/PyRandLib/listindexstate.py

@@ -30,8 +30,8 @@ class ListIndexState( BaseRandom ):
     """The base class for all LFib PRNG based on 64-bits numbers.
         
         Definition of the class of the internal state data for many  pseudo
-        random  generators: those that embed a list a integers and maybe an
-        index related to this list..
+        random generators: those that embed a list of integers and maybe an
+        index related to this list.
 
         This module is part of library PyRandLib.
         
@@ -47,14 +47,13 @@ def __init__(self, _initRandClass, _stateSize: int, _seedState: SeedStateType =
         initialization of the internal state list of integers.
         _stateSize is the size of the internal state list of integers.
         _seedState is either a valid state, an integer,  a float or None.
-        About  valid  state:  this  is  a  tuple  containing  a  list  of  
+        About  valid  state:   this  is  a  tuple  containing  a  list  of  
         self._STATE_SIZE integers and  an index in this list (index  value 
         being  then  in range (0,self._STATE_SIZE)).  Should _seedState be 
-        a sole integer or float then it  is  used  as  initial  seed  for 
+        a sole integer or float then  it  is  used  as  initial  seed  for 
         the  random  filling  of  the  internal  list  of self._STATE_SIZE  
-        integers.  Should _seedState be anything else  (e.g.  None)  then  
-        the  shuffling of the local current time value is used as such an 
-        initial seed.
+        integers.  Should _seedState be None)  then  the  shuffling of the
+        local current time value is used as such an initial seed.
         """
         self._initRandClass = _initRandClass
         self._STATE_SIZE = _stateSize
@@ -66,11 +65,11 @@ def __init__(self, _initRandClass, _stateSize: int, _seedState: SeedStateType =
 
     #-------------------------------------------------------------------------
     def getstate(self) -> StateType:
-        """Returns an object capturing the current internal state of the generator.
+        """Returns a tuple capturing the current internal state of the generator.
         
-        This  object can be passed to setstate() to restore the state.  It is a
-        tuple containing a list of self._STATE_SIZE integers and an 
-        index in this list (index value being then in range(0,self._STATE_SIZE).
+        This object can be passed to setstate() to restore the state. It is a
+        tuple  containing a list of self._STATE_SIZE integers and an index in 
+        this list (index value being then in range(0,self._STATE_SIZE).
         """
         return (self._state, self._index)  # type: ignore
 
@@ -152,9 +151,9 @@ def _initstate(self, _initialSeed: Numerical = None, /) -> None:  # type: ignore
         """Inits the internal list of values.
         
         Inits the internal list of values according to some initial
-        seed  that  has  to be an integer or a float ranging within
-        [0.0, 1.0).  Should it be None or anything  else  then  the
-        current local time value is used as initial seed value.
+        seed  that  has to be an integer, or a float ranging within
+        [0.0, 1.0).  Should it be None then the current local  time 
+        value is used as the initial seed value.
         """
         initRand = self._initRandClass( _initialSeed )
         self._state = [ initRand() for _ in range(self._STATE_SIZE) ]        

Python3.10/PyRandLib/melg19937.py

@@ -46,7 +46,7 @@ class Melg19937( BaseMELG ):
     Notice: the implementation of this version of the MELG algorithm in  PyRandLib  is 
     not as optimized as it is in C code provided by MELG authors. It is rather derived
     from the formal description and related tables provided in paper  referenced  [11]
-    in  file  README.md,  to be able to easier validate the Python code here.
+    in file README.md, to be able to easier validate the Python code here.
 
     Notice also:  in the original paper [11],  in the description of  Algorithm 1,  an 
     error  (typo)  appears at the initialization of 'x'.  An bit-xor operation appears 
@@ -56,6 +56,7 @@ class Melg19937( BaseMELG ):
     See Melg607 for a large period MELG-Generator (2^607, i.e. 5.31e+182)  with medium
     computation  time  and  the  equivalent  of  21  32-bits  integers  memory  little 
     consumption.
+    
     See Melg44497 for a very large period (2^44,497, i.e. 8.55e+13,395)  with  similar 
     computation  time  but  use of even more memory space (equivalent of 1,393 32-bits
     integers). This is the longest period version proposed in paper [11].

Python3.10/PyRandLib/melg44497.py

@@ -46,7 +46,7 @@ class Melg44497( BaseMELG ):
     Notice: the implementation of this version of the MELG algorithm in  PyRandLib  is 
     not as optimized as it is in C code provided by MELG authors. It is rather derived
     from the formal description and related tables provided in paper  referenced  [11]
-    in  file  README.md,  to be able to easier validate the Python code here.
+    in file README.md, to be able to easier validate the Python code here.
 
     Notice also:  in the original paper [11],  in the description of  Algorithm 1,  an 
     error  (typo)  appears at the initialization of 'x'.  An bit-xor operation appears 
@@ -56,6 +56,7 @@ class Melg44497( BaseMELG ):
     See Melg607 for a large period MELG-Generator (2^607, i.e. 5.31e+182)  with medium
     computation  time  and  the  equivalent  of  21  32-bits  integers  memory  little 
     consumption.
+    
     See Melg19937 for an even larger period MELG-Generator (2^19937, i.e. 4.32e+6001),
     same computation time and equivalent of 625 integers memory consumption.
     

Python3.10/PyRandLib/melg607.py

@@ -46,7 +46,7 @@ class Melg607( BaseMELG ):
     Notice: the implementation of this version of the MELG algorithm in  PyRandLib  is 
     not as optimized as it is in C code provided by MELG authors. It is rather derived
     from the formal description and related tables provided in paper  referenced  [11]
-    in  file  README.md,  to be able to easier validate the Python code here.
+    in file README.md, to be able to easier validate the Python code here.
 
     Notice also:  in the original paper [11],  in the description of  Algorithm 1,  an 
     error  (typo)  appears at the initialization of 'x'.  An bit-xor operation appears 
@@ -55,6 +55,7 @@ class Melg607( BaseMELG ):
        
     See Melg19937 for an even larger period MELG-Generator (2^19937, i.e. 4.32e+6001),
     same computation time and equivalent of 625 integers memory consumption.
+    
     See Melg44497 for a very large period (2^44,497, i.e. 8.55e+13,395)  with  similar 
     computation  time  but  use of even more memory space (equivalent of 1,393 32-bits
     integers). This is the longest period version proposed in paper [11].

Python3.10/PyRandLib/mrg1457.py

@@ -35,6 +35,8 @@ class Mrg1457( BaseMRG ):
     Generator with long period (3.98e+438).
 
     This module is part of library PyRandLib.
+    
+    Copyright (c) 2016-2025 Philippe Schmouker
 
     Multiple Recursive Generators (MRGs) uses  recurrence  to  evaluate  pseudo-random
     numbers suites. Recurrence is of the form:
@@ -60,6 +62,7 @@ class Mrg1457( BaseMRG ):
 
     See Mrg287 for  a  short  period  MR-Generator  (2^287,  i.e. 2.49e+86)  with  low
     computation time but 256 integers memory consumption.
+
     See Mrg49507 for  a  far  longer  period  (2^49507,  i.e. 1.2e+14903)  with  lower 
     computation  time  too  (31-bits  modulus)  but  use  of  more  memory space (1597 
     integers).
@@ -69,6 +72,7 @@ class Mrg1457( BaseMRG ):
       random(), seed(), getstate(), and setstate().
       
     Furthermore this class is callable:
+      rand = Mrg1457()
       print( rand() )     # prints a pseudo-random value within [0.0, 1.0)
       print( rand(a) )    # prints a pseudo-random value within [0, a) or [0.0, a) depending on the type of a
       print( rand(a, n) ) # prints a list of n pseudo-random values each within [0, a)

Python3.10/PyRandLib/mrg287.py

@@ -77,6 +77,7 @@ class Mrg287( BaseMRG ):
     See Mrg1457 for a longer period MR-Generator  (2^1457,  i.e. 4.0e+438)  and longer
     computation  time  (2^31-1 modulus calculations) but less memory space consumption 
     (47 integers).
+    
     See Mrg49507 for  a  far  longer  period  (2^49507,  i.e. 1.2e+14903)  with  lower 
     computation  time  too  (31-bits  modulus)  but  use  of  more memory space (1_597 
     integers).

Python3.10/PyRandLib/mrg49507.py

@@ -35,6 +35,8 @@ class Mrg49507( BaseMRG ):
     Generator with a very long period (1.17e+14_903).
 
     This module is part of library PyRandLib.
+    
+    Copyright (c) 2016-2025 Philippe Schmouker
 
     Multiple Recursive Generators (MRGs) uses  recurrence  to  evaluate  pseudo-random
     numbers suites. Recurrence is of the form:
@@ -60,6 +62,7 @@ class Mrg49507( BaseMRG ):
 
     See Mrg287 fo r a  short  period  MR-Generator  (2^287,  i.e. 2.49e+86)  with  low
     computation time but 256 integers memory consumption.
+    
     See Mrg457 for a longer period  MR-Generator  (2^1457,  i.e. 4.0e+438)  and longer 
     computation  time (2^31-1 modulus calculations) but less memory space  consumption 
     (47 integers).

Python3.10/PyRandLib/pcg1024_32.py

@@ -47,7 +47,7 @@ class Pcg1024_32( Pcg64_32 ):
  
        x(i) = (a * x(i-1) + c) mod m
 
-    PCGs associate to this recurrence a permutation of a subpart o f the  bits 
+    PCGs associate to this recurrence a permutation of a subpart of  the  bits 
     of  the internal state of the PRNG.  The output of PCGs is this permutated
     subpart of its internal state,  leading to a very large enhancement of the
     randomness of these algorithms compared with the LCGs one.

Python3.10/PyRandLib/pcg128_64.py

@@ -45,7 +45,7 @@ class Pcg128_64( BasePCG ):
  
        x(i) = (a * x(i-1) + c) mod m
 
-    PCGs associate to this recurrence a permutation of a subpart o f the  bits 
+    PCGs associate to this recurrence a permutation of a subpart of  the  bits 
     of  the internal state of the PRNG.  The output of PCGs is this permutated
     subpart of its internal state,  leading to a very large enhancement of the
     randomness of these algorithms compared with the LCGs one.

Python3.10/PyRandLib/pcg64_32.py

@@ -43,7 +43,7 @@ class Pcg64_32( BasePCG ):
  
        x(i) = (a * x(i-1) + c) mod m
 
-    PCGs associate to this recurrence a permutation of a subpart o f the  bits 
+    PCGs associate to this recurrence a permutation of a subpart of  the  bits 
     of  the internal state of the PRNG.  The output of PCGs is this permutated
     subpart of its internal state,  leading to a very large enhancement of the
     randomness of these algorithms compared with the LCGs one.