Changeset 212

Timestamp:

10/03/08 16:21:08 (3 months ago)

Author:

pierre

Message:

Fix some bugs, add a select_ids method() to the SpikeList object to allow the selection of cells matching only a particular criteria. Need to test all the methods that compute averaged values on pairs of cells, like pairwise_cc, pairwise_pearson_corrcoeff, ....

Files:

• branches/cleanup/src/signals.py (modified) (10 diffs)

branches/cleanup/src/signals.py

@@ -904 +904 @@
-            self.spiketrains[id].add_offset(offset)
-    
+    def first_spike_time(self):
+        """
+        Get the time of the first real spike in the SpikeList
+        """
+        first_spike = self.t_stop
+        is_empty    = True
+        for id in self.id_list():
+            if len(self.spiketrains[id]) > 0:
+                is_empty = False
+                if self.spiketrains[id].spike_times[0] < first_spike:
+                    first_spike = self.spiketrains[id].spike_times[0]
+        if is_empty:
+            raise Exception("No spikes can be found in the SpikeList object !")
+        else:
+            return first_spike
+    
+    def last_spike_time(self):
+        """
+        Get the time of the last real spike in the SpikeList
+        """
+        last_spike = self.t_start
+        is_empty    = True
+        for id in self.id_list():
+            if len(self.spiketrains[id]) > 0:
+                is_empty = False
+                if self.spiketrains[id].spike_times[-1] > last_spike:
+                    last_spike = self.spiketrains[id].spike_times[-1]
+        if is_empty:
+            raise Exception("No spikes can be found in the SpikeList object !")
+        else:
+            return last_spike
+    
+    
+    def select_ids(self, criteria=None):
+        """
+        Return the list of all the cells in the SpikeList that will match the criteria
+        expressed with the following syntax. 
+        
+        Inputs : 
+            criteria - a string that can be evaluated on a SpikeTrain object, where the 
+                       SpikeTrain should be named ``cell''.
+        
+        Exemples:
+            >>> spklist.select_ids("cell.mean_rate() > 0") (all the active cells)
+            >>> spklist.select_ids("cell.mean_rate() == 0") (all the silent cells)
+            >>> spklist.select_ids("len(cell.spiketimes) > 10")
+            >>> spklist.select_ids("mean(cell.isi()) < 1")
+        """
+        selected_ids = []
+        for id in self.id_list():
+            cell = self.spiketrains[id]
+            if eval(criteria):
+                selected_ids.append(id)
+        return selected_ids
+
+    
-    def save(self, filename, method="text"):
-        """
@@ -986 +1042 @@
-            if len(isi) > 1:
-                cvs_isi.append(numpy.std(isi)/numpy.mean(isi))
-
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@@ -1309 +1368 @@
-        """
-        subplot = self.__getdisplay__(display)
+        
+        if spklist == None:
+            spklist = self
-        
-        if type(pairs) == int:
@@ -1454 +1516 @@
-
-        """
+        if spklist == None:
+            spklist = self
-        
-        if type(id_list) == int:
@@ -1484 +1548 @@
-                       if None, this is the one calling the function
-        """
+        if spklist == None:
+            spklist = self
+        
-        if type(id_list) == int:
-            spk1 = self.id_slice(id_list)
@@ -1511 +1578 @@
-        """
-        firing_rate = self.firing_rate(time_bin)
-self.N
+len(self)
-
-    def mean_rate_covariance(self, spikelist, time_bin):
@@ -1530 +1597 @@
-            raise Exception("Error, both SpikeLists should share common t_start, t_stop and dt")
-        frate_1 = self.firing_rate(time_bin)
-self.N
+len(self)
-        frate_2 = spikelist.firing_rate(time_bin)
-spikelist.N
+len(spikelist)
-        N = len(frate_1)
-        cov = numpy.sum(frate_1*frate_2)/N-numpy.sum(frate_1)*numpy.sum(frate_2)/(N*N)
@@ -1583 +1650 @@
-            min_id = numpy.min(spk.id_list)
-            max_id = numpy.max(spk.id_list)
-(t_stop - t_start)
+t_stop - t_start
-            subplot.axis([t_start-0.05*length, t_stop+0.05*length, min_id-2, max_id+2])
-            pylab.draw()
@@ -1674 +1741 @@
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-            # correlation
-            # TODO: normalize the cor, look in 1.6 in Kumar et al.
-            # bruederle: the function corrcoeff actually implements the definition in Kumar 1.6
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-            # bruederle: the expression 'cov' is already, per definition, the pearson correlation coefficient 
@@ -1720 +1797 @@
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-        cor_coef_mean = cor.mean()
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-    ####################################################################