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From jo...@apache.org
Subject [4/6] climate git commit: CLIMATE-542: Make variable names more meaningful
Date Thu, 06 Nov 2014 20:51:13 GMT
CLIMATE-542: Make variable names more meaningful


Project: http://git-wip-us.apache.org/repos/asf/climate/repo
Commit: http://git-wip-us.apache.org/repos/asf/climate/commit/7363794b
Tree: http://git-wip-us.apache.org/repos/asf/climate/tree/7363794b
Diff: http://git-wip-us.apache.org/repos/asf/climate/diff/7363794b

Branch: refs/heads/master
Commit: 7363794bf0e3567a9e0627551e8885a00a85430a
Parents: 00bffd7
Author: rlaidlaw <rlaidlaw.open@gmail.com>
Authored: Thu Nov 6 12:04:37 2014 -0800
Committer: rlaidlaw <rlaidlaw.open@gmail.com>
Committed: Thu Nov 6 12:04:37 2014 -0800

----------------------------------------------------------------------
 ocw/metrics.py | 24 +++++++++++++-----------
 1 file changed, 13 insertions(+), 11 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/climate/blob/7363794b/ocw/metrics.py
----------------------------------------------------------------------
diff --git a/ocw/metrics.py b/ocw/metrics.py
index 1504404..1ef5f87 100644
--- a/ocw/metrics.py
+++ b/ocw/metrics.py
@@ -171,17 +171,19 @@ class TemporalCorrelation(BinaryMetric):
             array of confidence levels associated with the temporal correlation
             coefficients
         '''
-        nt, ny, nx = reference_dataset.values.shape
-        tc = numpy.zeros([ny, nx])
-        cl = numpy.zeros([ny, nx])
-        for iy in numpy.arange(ny):
-            for ix in numpy.arange(nx):
-                tc[iy, ix], cl[iy, ix] = stats.pearsonr(
-                                           reference_dataset.values[:, iy, ix],
-                                           target_dataset.values[:, iy, ix]
-                                         )
-                cl[iy, ix] = 1 - cl[iy, ix]
-        return tc, cl 
+        nTimes, nLats, nLons = reference_dataset.values.shape
+        coefficients = numpy.zeros([nLats, nLons])
+        levels = numpy.zeros([nLats, nLons])
+        for iLats in numpy.arange(nLats):
+            for iLons in numpy.arange(nLons):
+                coefficients[iLats, iLons], levels[iLats, iLons] = (
+                    stats.pearsonr(
+                        reference_dataset.values[:, iLats, iLons],
+                        target_dataset.values[:, iLats, iLons]
+                    )
+                )
+                levels[iLats, iLons] = 1 - levels[iLats, iLons]
+        return coefficients, levels 
 
 
 class TemporalMeanBias(BinaryMetric):


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