Changeset 406 for trunk

Timestamp:

06/25/09 18:04:50 (3 years ago)

Author:

pierre

Message:

Add a sort_by() method to allow the sorting of cells in a SpikeList acording to a user defined criteria. Minors modifications in addition ;-)

Files:

• trunk/src/signals/spikes.py (modified) (10 diffs)

trunk/src/signals/spikes.py

@@ -617 +617 @@
             events  - Can be a SpikeTrain object (and events will be the spikes) or just a list 
                       of times
+            time_bin- The time bin (in ms) used to gather the spike for the psth
             t_min   - Time (>0) to average the signal before an event, in ms (default 0)
             t_max   - Time (>0) to average the signal after an event, in ms  (default 100)
@@ -876 +877 @@
     #def __setslice__(self, i, j):
 
+
+
     def __setitem__(self, id, spktrain):
         assert isinstance(spktrain, SpikeTrain), "A SpikeList object can only contain SpikeTrain objects"
@@ -1182 +1185 @@
     
     
-    def select_ids(self, criteria=None):
+    def select_ids(self, criteria):
         """
         Return the list of all the cells in the SpikeList that will match the criteria
@@ -1203 +1206 @@
                 selected_ids.append(id)
         return selected_ids
+
+    def sort_by(self, criteria, descending=False):
+        """
+        Return an array with all the ids of the cells in the SpikeList, 
+        sorted according to a particular criteria.
+        
+        Inputs:
+            criteria   - the criteria used to sort the cells. It should be a string
+                         that can be evaluated on a SpikeTrain object, where the 
+                         SpikeTrain should be named ``cell''.
+            descending - if True, then the cells are sorted from max to min. 
+            
+        Examples:
+            >> spk.sort_by('cell.mean_rate()')
+            >> spk.sort_by('cell.cv_isi()', descending=True)
+            >> spk.sort_by('cell.distance_victorpurpura(target, 0.05)')
+        """
+        criterias = numpy.zeros(len(self), float)
+        for count, id in enumerate(self.id_list()):
+            cell  = self.spiketrains[id]
+            criterias[count] = eval(criteria)
+        result = self.id_list()[numpy.argsort(criterias)]
+        if descending:
+            return result[numpy.arange(len(result)-1, -1, -1)]
+        else:
+            return result
 
     
@@ -1559 +1588 @@
             if newnum:
                 axis = axis[:len(axis)-1]
-            subplot.plot(axis,numpy.sum(spike_hist, axis=0)/N,**kwargs)
+            subplot.plot(axis,numpy.mean(spike_hist, axis=0),**kwargs)
             pylab.draw()
             
 
-    def firing_rate(self, time_bin, display=False, kwargs={}):
+    def firing_rate(self, time_bin, display=False, average=False, kwargs={}):
         """
         Generate an array with all the instantaneous firing rates along time (in Hz) 
-        of all the SpikeTrains objects within the SpikeList.
+        of all the SpikeTrains objects within the SpikeList. If average is True, it gives the
+        average firing rate over the whole SpikeList
         
         Inputs:
             time_bin   - the time bin used to gather the data
+            average    - If True, return a single vector of the average firing rate over the whole SpikeList
             display    - if True, a new figure is created. Could also be a subplot. The averaged
                          spike_histogram over the whole population is then plotted
@@ -1578 +1609 @@
             spike_histogram, time_axis
         """
-        return self.spike_histogram(time_bin, normalized=True, display=display, kwargs=kwargs)
-
+        result = self.spike_histogram(time_bin, normalized=True, display=display, kwargs=kwargs)
+        if average:
+            return numpy.mean(result, axis=0)
+        else:
+            return result
 
     def fano_factor(self, time_bin):
@@ -1596 +1630 @@
         """
         firing_rate = self.spike_histogram(time_bin)
-        firing_rate = numpy.sum(firing_rate,axis=0)
+        firing_rate = numpy.mean(firing_rate, axis=0)
         fano        = numpy.var(firing_rate)/numpy.mean(firing_rate)
         return fano
@@ -1973 +2007 @@
         """
         firing_rate = self.firing_rate(time_bin)
-        return numpy.var(numpy.sum(firing_rate, axis=0)/len(self))
+        return numpy.var(numpy.mean(firing_rate, axis=0))
 
     def mean_rate_covariance(self, spikelist, time_bin):
@@ -1991 +2025 @@
         if not spikelist.time_parameters() == self.time_parameters():
             raise Exception("Error, both SpikeLists should share common t_start, t_stop")
-        frate_1 = self.firing_rate(time_bin)
-        frate_1 = numpy.sum(frate_1, axis=0)/len(self)
-        frate_2 = spikelist.firing_rate(time_bin)
-        frate_2 = numpy.sum(frate_2, axis=0)/len(spikelist)
-        N = len(frate_1)
+        frate_1 = self.firing_rate(time_bin, average=True)
+        frate_2 = spikelist.firing_rate(time_bin, average=True)
+        N       = len(frate_1)
         cov = numpy.sum(frate_1*frate_2)/N-numpy.sum(frate_1)*numpy.sum(frate_2)/(N*N)
         return cov
@@ -2062 +2094 @@
             average - If True, return a single vector of the averaged waveform. If False, 
                       return an array of all the waveforms.
+            time_bin- The time bin (in ms) used to gather the spike for the psth
             t_min   - Time (>0) to average the signal before an event, in ms (default 0)
             t_max   - Time (>0) to average the signal after an event, in ms  (default 100)