move random_generator for his own file

pymath/stat/dataset.py

@@ -8,8 +8,8 @@
 #
 
 from math import sqrt, ceil
-from random import randint, uniform, gauss
 from .number_tools import number_factory
+from .random_generator import random_generator
 
 class Dataset(list):
     """ A dataset (a list) with statistics and latex rendering methods
@@ -29,11 +29,11 @@ class Dataset(list):
 
     @classmethod
     def random(cls, length, data_name = "Valeurs", \
-            distrib = gauss, rd_args = (0,1),  \
+            distrib = "gauss", rd_args = (0,1),  \
             nbr_format = lambda x:round(x,2), \
             v_min = None, v_max = None, \
             exact_mean = None):
-        """ Create a random Dataset. 
+        """ Generate a random list of value
 
         :param length: length of the dataset
         :param distrib: Distribution of the data set. It can be a function or string from ["randint", "uniform", "gauss"]
@@ -42,53 +42,12 @@ class Dataset(list):
         :param v_min: minimum accepted value
         :param v_max: maximum accepted value
         :param exact_mean: if set, the last generated number will be create in order that the computed mean is exacly equal to "exact_mean"
-
-        : Exemple:
-        >>> Dataset.random(10)
-        >>> Dataset.random(10, distrib = uniform, rd_args = (5, 10))
-        >>> Dataset.random(10, distrib = "uniform", rd_args = (5, 10))
-        >>> Dataset.random(10, v_min = 0)
-        >>> Dataset.random(10, exact_mean = 0)
-        >>> Dataset.random(10, distrib = gauss, rd_args = (50,20), nbr_format = int)
-
         """
-        # if exact_mean is set, we create automaticaly only length-1 value
-        if exact_mean != None:
-            length = length - 1
-
-        # build function to test created values
-        if v_min == None:
-            v1 = lambda x: True
-        else:
-            v1 = lambda x: x >= v_min
-        if v_max == None:
-            v2 = lambda x: True
-        else:
-            v2 = lambda x: x <= v_max
-        validate = lambda x : v1(x) and v2(x)
-
-        # get distrib function
-        distribs = {"gauss": gauss, "uniform": uniform, "randint":randint}
-        try:
-            distrib(*rd_args)
-        except TypeError:
-            distrib = distribs[distrib]
-
-        # building values
-        data = []
-        for _ in range(length):
-            valid = False
-            while not valid:
-                v = nbr_format(distrib(*rd_args))
-                valid = validate(v)
-            data.append(v)
-
-        # Build last value
-        if exact_mean != None:
-            last_v = nbr_format((length+1) * exact_mean - sum(data))
-            if not validate(last_v):
-                raise ValueError("Can't build the last value. Conflict between v_min/v_max and exact_mean")
-            data.append(last_v)
+        data = random_generator(length,\
+            distrib, rd_args,  \
+            nbr_format, \
+            v_min, v_max, \
+            exact_mean)
 
         return cls(data, data_name = data_name)
     

pymath/stat/random_generator.py

@@ -0,0 +1,75 @@
+#/usr/bin/env python
+# -*- coding:Utf-8 -*-
+
+from random import randint, uniform, gauss
+
+
+def random_generator(length,\
+        distrib = gauss, rd_args = (0,1),  \
+        nbr_format = lambda x:round(x,2), \
+        v_min = None, v_max = None, \
+        exact_mean = None):
+    """ Generate a random list of value
+
+    :param length: length of the dataset
+    :param distrib: Distribution of the data set. It can be a function or string from ["randint", "uniform", "gauss"]
+    :param rd_args: arguments to pass to distrib
+    :param nbr_format: function which format value
+    :param v_min: minimum accepted value
+    :param v_max: maximum accepted value
+    :param exact_mean: if set, the last generated number will be create in order that the computed mean is exacly equal to "exact_mean"
+
+    : Exemple:
+    >>> random_generator(10)
+    >>> random_generator(10, distrib = uniform, rd_args = (5, 10))
+    >>> random_generator(10, distrib = "uniform", rd_args = (5, 10))
+    >>> random_generator(10, v_min = 0)
+    >>> random_generator(10, exact_mean = 0)
+    >>> random_generator(10, distrib = gauss, rd_args = (50,20), nbr_format = int)
+
+    """
+    # if exact_mean is set, we create automaticaly only length-1 value
+    if exact_mean != None:
+        length = length - 1
+
+    # build function to test created values
+    if v_min == None:
+        v1 = lambda x: True
+    else:
+        v1 = lambda x: x >= v_min
+    if v_max == None:
+        v2 = lambda x: True
+    else:
+        v2 = lambda x: x <= v_max
+    validate = lambda x : v1(x) and v2(x)
+
+    # get distrib function
+    distribs = {"gauss": gauss, "uniform": uniform, "randint":randint}
+    try:
+        distrib(*rd_args)
+    except TypeError:
+        distrib = distribs[distrib]
+
+    # building values
+    data = []
+    for _ in range(length):
+        valid = False
+        while not valid:
+            v = nbr_format(distrib(*rd_args))
+            valid = validate(v)
+        data.append(v)
+
+    # Build last value
+    if exact_mean != None:
+        last_v = nbr_format((length+1) * exact_mean - sum(data))
+        if not validate(last_v):
+            raise ValueError("Can't build the last value. Conflict between v_min/v_max and exact_mean")
+        data.append(last_v)
+
+    return data
+
+# -----------------------------
+# Reglages pour 'vim'
+# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
+# cursor: 16 del 
+