Changeset 482 for trunk

Timestamp:

02/24/11 00:42:40 (15 months ago)

Author:

mpereira

bzr:base-revision:

pereira@dantur-20110216181753-oju9on3c6n026j12

bzr:committer:

Michael Pereira michael.fsp@gmail.com

bzr:file-ids:

src/analysis.py 106@400bb3d0-8d2e-0410-a2c0-f692ac833539:trunk%2Fsrc%2Fanalysis.py

bzr:mapping-version:

v4

bzr:repository-uuid:

400bb3d0-8d2e-0410-a2c0-f692ac833539

bzr:revision-id:

michael.fsp@gmail.com-20110223232612-ddlqcvleyq48wqnn

bzr:revno:

426

bzr:revprop:branch-nick:

NeuroTools_branch

bzr:root:

trunk

bzr:timestamp:

2011-02-24 00:26:12.588999987 +0100

bzr:user-agent:

bzr2.2.4+bzr-svn1.0.3

svn:original-date:

2011-02-23T23:26:12.589000Z

Message:

analysis.crosscorrelate now handles empty correlograms more gracefuly

Files:

• trunk/src/analysis.py (modified) (5 diffs)

trunk/src/analysis.py

@@ -174 +174 @@
             sua.append(x.ravel())
         else:
-            raise TypeError("sua1 and sua2 must be either instances of the \
-                            SpikeTrain class or column/row vectors")
+            raise TypeError("sua1 and sua2 must be either instances of the" \
+                            "SpikeTrain class or column/row vectors")
     sua1 = sua[0]
     sua2 = sua[1]
@@ -194 +194 @@
         sua2_ = numpy.array([])
         for ni in xrange(1,n_pred+1):
-            idx = numpy.random.permutation(isi.size-1)
-            
+            idx = numpy.random.permutation(isi.size-1)            
             sua2_ = numpy.append(sua2_, numpy.add(numpy.insert(
                 (numpy.cumsum(isi[idx])), 0, 0), sua2.min() + (
@@ -210 +209 @@
             int_ = numpy.append(int_, sua1[k] - sua2_[numpy.nonzero(
                 (sua2_ > sua1[k] - lag) & (sua2_ < sua1[k] + lag))])
-    
     if reverse is True:
         int = -int
@@ -217 +215 @@
     norm = numpy.sqrt(sua1.size * sua2.size)
     
-    # Plot the results if display=True
+    # Plot the results if display=True   
     subplot = get_display(display)
     if not subplot or not HAVE_PYLAB:
@@ -223 +221 @@
     else:
         # Plot the cross-correlation
-        counts, bin_edges = numpy.histogram(int, **kwargs)
-        counts = counts / norm
-        xlabel = "Time"
-        ylabel = "Cross-correlation coefficient"
-        #NOTE: the x axis corresponds to the upper edge of each bin
-        subplot.plot(bin_edges[1:], counts, label='cross-correlation')
-        if predictor is None:
-            set_labels(subplot, xlabel, ylabel)
-            pylab.draw()
-        elif predictor is 'shuffle':            
-            # Plot the predictor
-            norm_ = norm * n_pred
-            counts_, bin_edges_ = numpy.histogram(int_, **kwargs)
-            counts_ = counts_ / norm_
-            subplot.plot(bin_edges_[1:], counts_, label='predictor')
-            subplot.legend()
-            pylab.draw()
-
+        try:
+            counts, bin_edges = numpy.histogram(int, **kwargs)
+            counts = counts / norm
+            xlabel = "Time"
+            ylabel = "Cross-correlation coefficient"
+            #NOTE: the x axis corresponds to the upper edge of each bin
+            subplot.plot(bin_edges[1:], counts, label='cross-correlation')
+            if predictor is None:
+                set_labels(subplot, xlabel, ylabel)
+                pylab.draw()
+            elif predictor is 'shuffle':            
+                # Plot the predictor
+                norm_ = norm * n_pred
+                counts_, bin_edges_ = numpy.histogram(int_, **kwargs)
+                counts_ = counts_ / norm_
+                subplot.plot(bin_edges_[1:], counts_, label='predictor')
+                subplot.legend()
+                pylab.draw()
+        except ValueError:
+            print "There are no correlated events within the selected lag"\
+            " window of %s" % lag
 def _dict_max(D):
     """