Changeset 978 in lmdz_wrf for trunk/tools

Timestamp:

Aug 8, 2016, 5:13:30 PM (8 years ago)

Author:

lfita

Message:

Adding `std_stats2Val': two variables standard Statistics class

File:

• trunk/tools/generic_tools.py (modified) (5 diffs)

trunk/tools/generic_tools.py

@@ -2166 +2166 @@
     return
 
+class std_stats2Val(object):
+  """two variables standard Statistics class providing:
+  vals1 = variable 1
+  vals2 = variable 2
+  self.bias=mean(var1-var2) 
+  self.mae=mean(abs(var1-var2)) 
+  self.rmse=sqrt((var1-var2)**2)
+  self.explvar = stddev1/stddev2
+  self.correlation (and p-value) 
+  """
+
+  def __init__(self, vals1, vals2):
+    import numpy as np
+    from scipy import stats as sts
+
+    fname = 'std_stats2Val'
+
+    if vals1 is None:
+      self.bias = None
+      self.mae = None
+      self.rmse = None
+      self.explvar = None
+      self.corr = None
+    else:
+      values1 = vals1.flatten() 
+      values2 = vals2.flatten() 
+
+      Nvals1 = len(values1)
+      Nvals2 = len(values2)
+
+
+      if not len(values1) == len(values2):
+        print errormsg
+        print '    ' + fname + ': lengths of variables differ!! Lvar1: ', Nvals1,    \
+          ' Lvar2: ', Nvals2,' statistics between them can not be computed!'
+        quit(-1)
+
+      self.bias = 0.
+      self.mae = 0.
+      self.rmse = 0.
+      self.explvar = 0.
+      self.corr = np.zeros(2, dtype=np.float)
+
+      for inum in range(Nvals1):
+          Sval = values1[inum] - values2[inum]
+          self.mae = self.mae + abs(Sval)
+          self.rmse = self.rmse + Sval**2
+
+      self.bias = np.mean(vals1) - np.mean(vals2)
+      self.mae = self.mae/Nvals1
+      self.rmse = np.sqrt(self.rmse/Nvals1)
+      self.explvar = np.var(vals1)/np.var(vals2)
+
+      self.corr[0], self.corr[1] = sts.pearsonr(values1, values2)
+
+    return
+
 class stats2Val(object):
   """two variables Statistics class providing:
@@ -2213 +2270 @@
       self.mean2v1Pv2 = None
       self.stdv1Pv2 = None
+      self.bias = None
       self.mae = None
       self.rmse = None
@@ -2251 +2309 @@
       self.mean2v1Pv2=0.
       self.stdv1Pv2=0.
+      self.bias = 0.
       self.mae = 0.
       self.rmse = 0.
-      self.corr = np.array([0., 0.])
+      self.corr = np.zeros(2, dtype=np.float)
       self.linRegress = np.zeros(5, float)
       self.polRegress = np.zeros(power+1, float)
@@ -2284 +2343 @@
             Dval = values1[inum] / values2[inum]
 
+          self.bias = self.bias + Sval
           self.mae = self.mae + abs(Sval)
           self.rmse = self.rmse + Sval**2
@@ -2335 +2395 @@
       self.stdv1Pv2 = np.sqrt(self.mean2v1Pv2-self.meanv1Pv2*self.meanv1Pv2)
 
+      self.bias = self.bias/self.Nokvalues12
       self.mae = self.mae/self.Nokvalues12
       self.rmse = np.sqrt(self.rmse/self.Nokvalues12)
 
-      self.corr = sts.pearsonr(values1, values2)
-
+      self.corr[0], self.corr[1] = sts.pearsonr(values1, values2)
       self.linRegress[0], self.linRegress[1], self.linRegress[2], self.linRegress[3], self.linRegress[4] = sts.linregress(values1, values2)