From e1cb61a1a25e37ae5a450ff1f99ae63e1aa714f5 Mon Sep 17 00:00:00 2001
From: Benjamin Bertrand <lafrite@poneyworld.net>
Date: Sat, 9 Jan 2016 18:40:02 +0300
Subject: [PATCH] decorator to control returned values

---
 pymath/stat/dataset.py         | 11 +++++++++--
 pymath/stat/number_tools.py    | 27 +++++++++++++++++++++++++++
 pymath/stat/weightedDataset.py | 20 ++++++++++++++------
 3 files changed, 50 insertions(+), 8 deletions(-)
 create mode 100644 pymath/stat/number_tools.py

diff --git a/pymath/stat/dataset.py b/pymath/stat/dataset.py
index ddb2f31..b0932c8 100644
--- a/pymath/stat/dataset.py
+++ b/pymath/stat/dataset.py
@@ -9,6 +9,7 @@
 
 from math import sqrt, ceil
 from random import randint, uniform, gauss
+from .number_tools import number_factory
 
 class Dataset(list):
     """ A dataset (a list) with statistics and latex rendering methods
@@ -19,11 +20,11 @@ class Dataset(list):
     >>> s.mean()
     49.5
     >>> s.deviation()
-    83325.0
+    83325
     >>> s.variance()
     833.25
     >>> s.sd()
-    28.86607004772212
+    28.87
     """
 
     @classmethod
@@ -118,20 +119,25 @@ class Dataset(list):
     def effectif_total(self):
         return len(self)
 
+    @number_factory
     def sum(self):
         return sum(self)
     
+    @number_factory
     def mean(self):
         return self.sum()/self.effectif_total() 
 
+    @number_factory
     def deviation(self):
         """ Compute the deviation (not normalized) """
         mean = self.mean()
         return sum([(x - mean)**2 for x in self])
 
+    @number_factory
     def variance(self):
         return self.deviation()/self.effectif_total()
     
+    @number_factory
     def sd(self):
         """ Compute the standard deviation """
         return sqrt(self.variance())
@@ -148,6 +154,7 @@ class Dataset(list):
         """
         return (min(self) , self.quartile(1) , self.quartile(2) , self.quartile(3), max(self))
 
+    @number_factory
     def quartile(self, quartile = 1):
         """
         Calcul un quartile de la série.
diff --git a/pymath/stat/number_tools.py b/pymath/stat/number_tools.py
new file mode 100644
index 0000000..04a597b
--- /dev/null
+++ b/pymath/stat/number_tools.py
@@ -0,0 +1,27 @@
+#/usr/bin/env python
+# -*- coding:Utf-8 -*-
+
+from functools import wraps
+
+
+def number_factory(fun):
+    """ Decorator which format returned value """
+    @wraps(fun)
+    def wrapper(*args, **kwargs):
+        ans = fun(*args, **kwargs)
+        try:
+            if ans.is_integer():
+                return int(ans)
+            else:
+                return round(ans, 2)
+        except AttributeError:
+            return ans
+    return wrapper
+
+
+
+# -----------------------------
+# Reglages pour 'vim'
+# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
+# cursor: 16 del 
+
diff --git a/pymath/stat/weightedDataset.py b/pymath/stat/weightedDataset.py
index 3c0131c..63e4cff 100644
--- a/pymath/stat/weightedDataset.py
+++ b/pymath/stat/weightedDataset.py
@@ -11,6 +11,7 @@ from math import sqrt, ceil
 from collections import Counter
 from .dataset import Dataset
 from ..calculus.generic import flatten_list
+from .number_tools import number_factory
 
 
 class WeightedDataset(dict):
@@ -24,13 +25,13 @@ class WeightedDataset(dict):
     >>> w.sum()
     120
     >>> w.mean()
-    2.608695652173913
+    2.61
     >>> w.deviation()
-    56.95652173913044
+    56.96
     >>> w.variance()
-    1.2381852551984878
+    1.24
     >>> w.sd()
-    1.1127377297451937
+    1.11
     
     """
     
@@ -57,27 +58,33 @@ class WeightedDataset(dict):
         except KeyError:
             self[data] = weight
 
+    @number_factory
     def total_weight(self):
         return sum(self.values())
 
     def effectif_total(self):
         return self.total_weight()
 
+    @number_factory
     def sum(self):
         """ Not really a sum but the sum of the product of key and values """
         return sum([k*v for (k,v) in self.items()])
 
+    @number_factory
     def mean(self):
         return self.sum()/self.effectif_total() 
 
+    @number_factory
     def deviation(self):
         """ Compute the deviation (not normalized) """
         mean = self.mean()
         return sum([v*(k - mean)**2 for (k,v) in self.items()])
 
+    @number_factory
     def variance(self):
         return self.deviation()/self.effectif_total()
     
+    @number_factory
     def sd(self):
         """ Compute the standard deviation """
         return sqrt(self.variance())
@@ -92,7 +99,7 @@ class WeightedDataset(dict):
 
         >>> w = WeightedDataset(flatten_list([i*[i] for i in range(5)]))
         >>> w.quartiles()
-        (1, 2, 3.0, 4, 4)
+        (1, 2, 3, 4, 4)
         >>> w = WeightedDataset(flatten_list([i*[i] for i in range(6)]))
         >>> w.quartiles()
         (1, 3, 4, 5, 5)
@@ -100,6 +107,7 @@ class WeightedDataset(dict):
         """
         return (min(self.keys()) , self.quartile(1) , self.quartile(2) , self.quartile(3), max(self.keys()))
 
+    @number_factory
     def quartile(self, quartile = 1):
         """
         Calcul un quartile de la série.
@@ -114,7 +122,7 @@ class WeightedDataset(dict):
         >>> w.quartile(1)
         2
         >>> w.quartile(2)
-        3.0
+        3
         >>> w.quartile(3)
         4
         >>> w = WeightedDataset(flatten_list([i*[i] for i in range(6)]))