* Added class wpylib.math.stats.avg.weight_stats for weighted statistical

analysis.

math/stats/avg.py

@@ -1,6 +1,9 @@
 # $Id: avg.py,v 1.1 2010-10-07 15:56:55 wirawan Exp $
 # Create: 20090112
 
+# TODO: Use less roundoff-error algorithm found in:
+# http://en.wikipedia.org/wiki/Standard_deviation#Weighted_calculation
+
 import math
 
 class avg(object):
@@ -27,3 +30,48 @@ class avg(object):
 # Operator overloading:
 avg.__iadd__ = avg.add
 avg.__call__ = avg.mean
+
+
+class weighted_stats(object):
+  def __init__(self, a=None, weights=None):
+    if a != None:
+      get_sample_stats(a, weights, result=self)
+
+
+def get_sample_stats(a, weights=None, result=None):
+  """Applies basic statistics (average, variance, standard deviation)
+  for a given sample, optionally with a weight.
+  """
+  from numpy import asarray, count_nonzero, product, sqrt, sum, nan
+  a = asarray(a)
+  if result == None:
+    r = weighted_stats()
+  else:
+    r = result
+  if weights == None:
+    r.s0 = product(a.shape)
+    r.s1 = sum(a)
+    r.s2 = sum(a**2)
+    r.N = r.s0
+    r.N_nz = r.s0
+  else:
+    weights = asarray(weights)
+    r.s0 = sum(weights)
+    r.s1 = sum(weights * a)
+    r.s2 = sum(weights * a**2)
+    r.N_nz = count_nonzero(weights)
+    r.N = product(a.shape)
+  # Ref: http://en.wikipedia.org/wiki/Standard_deviation#Weighted_calculation
+  # TODO: Use less roundoff-error algorithm found therein.
+  r.avg = r.s1 / r.s0
+  r.var_pop = (r.s0 * r.s2 - r.s1**2) / (r.s0**2)
+  r.std_pop = sqrt(r.var_pop)
+  if r.N_nz > 1:
+    r.var_samp = (r.N_nz / (r.N_nz - 1.0)) * r.var_pop
+    r.std_samp = sqrt(r.var_samp)
+  else:
+    r.var_samp = nan
+    r.std_samp = nan
+
+  return r
+