Changeset 485 for trunk

Timestamp:

06/08/11 16:44:37 (12 months ago)

Author:

pierre

Message:

Add a binary flag to the spike_histogram function, and minor changes.

Location:

trunk/src/signals

Files:

• analogs.py (modified) (3 diffs)
• spikes.py (modified) (12 diffs)

trunk/src/signals/analogs.py

@@ -398 +398 @@
 
     Inputs:
-        signals - a list of the AnalogSignals objects
+        signals - a list of tuples (id, value) with all the values sorted in time of the analog signals
         id_list - the list of the ids of all recorded cells (needed for silent cells)
         dt      - if dt is specified, time values should be floats
@@ -423 +423 @@
         
         signals = numpy.array(signals)
-        
         for id in id_list:
             signal = numpy.transpose(signals[signals[:,0] == id, 1:])[0]
@@ -821 +820 @@
                 if v_thresh is not None:
                     to_be_plot = pylab.where(to_be_plot>=v_thresh-0.02, v_thresh+0.5, to_be_plot)
+                if len(time_axis) > len(to_be_plot):
+                    time_axis = time_axis[:-1]
+                if len(to_be_plot) > len(time_axis):
+                    to_be_plot = to_be_plot[:-1]
                 subplot.plot(time_axis, to_be_plot, **kwargs)
                 subplot.hold(1)

trunk/src/signals/spikes.py

@@ -148 +148 @@
 
     def __del__(self):
-        pass
+        del self.spike_times
 
     def __len__(self):
@@ -534 +534 @@
         
 
-    def time_histogram(self, time_bin=10, normalized=True):
+    def time_histogram(self, time_bin=10, normalized=True, binary=False):
         """
         Bin the spikes with the specified bin width. The first and last bins
@@ -544 +544 @@
                          in spikes/second, otherwise otherwise it's the number of spikes 
                          per bin.
+            binary     - if True, a binary matrix of 0/1 is returned
         
         Examples:
@@ -566 +567 @@
         if normalized: # what about normalization if time_bin is a sequence?
             hist *= 1000.0/float(time_bin)
+        if binary:
+            hist = hist.astype(bool).astype(int)
         return hist
 
@@ -867 +870 @@
 
     def __del__(self):
-        pass
+        for id in self.id_list:
+            del self.spiketrains[id]
 
     @property
@@ -1071 +1075 @@
                     spiketrain.relative_times()
                 self.append(id, spiketrain)
-                
+        self.__calc_startstop()    
     
     def complete(self, id_list):
@@ -1617 +1621 @@
 
 
-    def spike_histogram(self, time_bin, normalized=False, display=False, kwargs={}):
+    def spike_histogram(self, time_bin, normalized=False, binary=False, display=False, kwargs={}):
         """
         Generate an array with all the spike_histograms of all the SpikeTrains
@@ -1628 +1632 @@
             display    - if True, a new figure is created. Could also be a subplot. The averaged
                          spike_histogram over the whole population is then plotted
+            binary     - if True, a binary matrix of 0/1 is returned
             kwargs     - dictionary contening extra parameters that will be sent to the plot 
                          function
@@ -1639 +1644 @@
         if newnum:
             M -= 1
-        spike_hist = numpy.zeros((N, M), numpy.float32)
+        if binary:
+            spike_hist = numpy.zeros((N, M), numpy.int)
+        else:
+            spike_hist = numpy.zeros((N, M), numpy.float32)
         subplot    = get_display(display)
+
         for idx,id in enumerate(self.id_list):
-            spike_hist[idx,:] = self.spiketrains[id].time_histogram(time_bin, normalized)
+            if newnum:
+                if not hist_new:
+                    hist, edges = numpy.histogram(self.spiketrains[id].spike_times, nbins)
+                else:
+                    hist, edges = numpy.histogram(self.spiketrains[id].spike_times, nbins, new=newnum)
+            else:
+                hist, edges = numpy.histogram(self.spike_times, bins)
+            hist = hist.astype(float)
+            if normalized: # what about normalization if time_bin is a sequence?
+                hist *= 1000.0/float(time_bin)
+            if binary:
+                hist = hist.astype(bool)
+            spike_hist[idx,:] = hist
         if not subplot or not HAVE_PYLAB:
             return spike_hist
@@ -1659 +1680 @@
             
 
-    def firing_rate(self, time_bin, display=False, average=False, kwargs={}):
+    def firing_rate(self, time_bin, display=False, average=False, binary=False, kwargs={}):
         """
         Generate an array with all the instantaneous firing rates along time (in Hz) 
@@ -1670 +1691 @@
             display    - if True, a new figure is created. Could also be a subplot. The averaged
                          spike_histogram over the whole population is then plotted
+            binary     - If True, a binary matrix with 0/1 is returned. 
             kwargs     - dictionary contening extra parameters that will be sent to the plot 
                          function
@@ -1676 +1698 @@
             spike_histogram, time_axis
         """
-        result = self.spike_histogram(time_bin, normalized=True, display=display, kwargs=kwargs)
+        result = self.spike_histogram(time_bin, normalized=True, binary=False, display=display, kwargs=kwargs)
         if average:
             return numpy.mean(result, axis=0)