[Pyphant-devel] SF.net SVN: pyphant:[649] trunk/src/workers/OSC/OSC/EstimateParameter.py

[<zk...@us...>]

Revision: 649
          http://pyphant.svn.sourceforge.net/pyphant/?rev=649&view=rev
Author:   zklaus
Date:     2009-07-17 13:44:14 +0000 (Fri, 17 Jul 2009)

Log Message:
-----------
Merge branch 'master' into svn-trunk

* master:
  Enh: Take steepness of model into account.

Modified Paths:
--------------
    trunk/src/workers/OSC/OSC/EstimateParameter.py

Modified: trunk/src/workers/OSC/OSC/EstimateParameter.py
===================================================================
--- trunk/src/workers/OSC/OSC/EstimateParameter.py	2009-06-24 13:56:20 UTC (rev 648)
+++ trunk/src/workers/OSC/OSC/EstimateParameter.py	2009-07-17 13:44:14 UTC (rev 649)
@@ -56,18 +56,25 @@
     _params = [("extentX", u"Extension of x-axis [%%]", 10, None),
                ("extentY", u"Extension of y-axis [%%]", 10, None)]
 
-    def calculateThickness(self, row, model,error=None):
+    def calculateThickness(self, row, model, error=None):
+        """
+        Given a vector of minima (row) and a 2 dimensional model,
+        this estimates the corresponding parameter.
+        """
         if len(row)==0:
             return numpy.nan
         data = model.data.transpose()
-        def calc(row, col,error):
+        data = data[:,:-1]*(numpy.diff(data)**2)
+        def calc(row, col, error):
             if error:
-                return sum([col[numpy.argmin(((model.dimensions[0].data-c)/e)**2)]
-                            for c,e in zip(row,error)])
+                return sum([
+                        col[numpy.argmin(((model.dimensions[0].data-c)/e)**2)]
+                        for c,e in zip(row, error)
+                        ])
             else:
                 return sum([col[numpy.argmin((model.dimensions[0].data-c)**2)]
                             for c in row])
-        i = numpy.argmin(numpy.array([calc(row, col,error) for col in data]))
+        i = numpy.argmin(numpy.array([calc(row, col, error) for col in data]))
         return model.dimensions[1].data[i]
 
     @Worker.plug(Connectors.TYPE_IMAGE)
@@ -84,17 +91,21 @@
         subscriber %= acc
         for row in minima:
             if error:
-                filteredError = filter(lambda c: not numpy.isnan(c), error.next())
+                filteredError = filter(
+                    lambda c: not numpy.isnan(c), error.next())
             else:
                 filteredError = None
-            parameter.append(self.calculateThickness( filter(lambda c: not numpy.isnan(c), row),
-                                                      model,
-                                                      filteredError))
+            parameter.append(self.calculateThickness(
+                    filter(lambda c: not numpy.isnan(c), row),
+                    model,
+                    filteredError
+                    ))
             acc += inc
             subscriber %= acc
-        result = DataContainer.FieldContainer(numpy.array(parameter),
-                                              longname = model.dimensions[-1].longname,
-                                              shortname = model.dimensions[-1].shortname,
-                                              unit = model.dimensions[-1].unit)
+        result = DataContainer.FieldContainer(
+            numpy.array(parameter),
+            longname = model.dimensions[-1].longname,
+            shortname = model.dimensions[-1].shortname,
+            unit = model.dimensions[-1].unit)
         result.seal()
         return result


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