Changeset 186

Timestamp:

07/25/08 14:55:23 (4 months ago)

Author:

apdavison

Message:

In signals module:

• renamed SpikeTrain.rescale() to SpikeTrain.relative_times(). This seems clearer to me since 'rescale' implies multiplication whereas we only do a subtraction.
  
• added SpikeTrain.merge(), which adds the spike times from another spike train to this one.
  
• fixed a bug in iterating over a SpikeList object.
  
• added SpikeList.f1f0_ratios(). This might be a bit too vision-specific, but since it is not uncommon to have oscillatory patterns in spike trains I think it should be here.
  

In plotting module: minor improvement to spacing between subplots, which is now proportional to the size of the subplots and may be different for the vertical and horizontal layout.

Files:

• trunk/src/plotting.py (modified) (1 diff)
• trunk/src/signals.py (modified) (7 diffs)

trunk/src/plotting.py

@@ -87 +87 @@
-        bottommargin = 0.1
-        leftmargin=0.1
-
-
-
+
+
+
+
-        assert panelheight > 0
-        
-
-
+v_
+h_
-        bottomlist.reverse()
-        for j in range(ncolumns):

trunk/src/signals.py

@@ -11 +11 @@
-    print "Warning: pylab not present"
-from NeuroTools import analysis
-
+
-
-class SpikeTrain(object):
@@ -259 +259 @@
-        return hist
-
-
-
+
+
+
+
-        if self.t_start != 0:
-            self.spike_times -= self.t_start
@@ -339 +341 @@
-        return F1/F0
-    
+    def merge(self, spiketrain, relative_times=False):
+        """
+        Add the spike times from `spiketrain` to this `SpikeTrain`s spike list.
+        """
+        if relative_times:
+            self.relative_times()
+            spiketrain.relative_times()
+        self.spike_times = numpy.concatenate((self.spike_times, spiketrain.spike_times))
+        self.spike_times.sort()
+        self.t_start = min(self.t_start, spiketrain.t_start)
+        self.t_stop = max(self.t_stop, spiketrain.t_stop)
+        
-
-class SpikeList(object):
@@ -430 +444 @@
-        self.__calc_startstop()
-
+    def __iter__(self):
+        return self.spiketrains.itervalues()
+
-    def __len__(self):
-        return len(self.spiketrains)
@@ -468 +485 @@
-                self.append(id, sl.spiketrains[id])
-
+    def merge(self, spikelist, relative_times=False):
+        """
+        For each `id` in `spikelist` that matches an `id` in this `SpikeList`,
+        merge the two `SpikeTrains` and save the result in this `SpikeList`.
+        
+        `SpikeTrain`s with `id`s not in this `SpikeList` are appended to it.
+        """
+        for id, spiketrain in spikelist.spiketrains.items():
+            if id in self.id_list:
+                self.spiketrains[id].merge(spiketrain, relative_times)
+            else:
+                if relative_times:
+                    spiketrain.relative_times()
+                self.append(spiketrain)
+                
-    
-    def idsubSpikeList(self, id_list):
@@ -618 +650 @@
-        """
-        nbins = numpy.ceil((self.t_stop-self.t_start)/time_bin)
-firing_rate
-print "nbins = %d" % nbins
+spike_hist
+logging.debug("nbins = %d" % nbins)
-        for idx,id in enumerate(self.id_list):
-
-
-
-
-
+
+
-            pylab.figure()
-firing_rate
+spike_hist
-            pylab.ylabel("Mean Number of Spikes per bin", size="x-large")
-            pylab.xlabel("Time (ms)", size="x-large")
+        return spike_hist
-
-    def firing_rate(self, time_bin, display=False):
@@ -894 +924 @@
-        return cor_coef_mean, cor_coef_std
-
+    def f1f0_ratios(self, time_bin, f_stim):
+        """
+        Returns the F1/F0 amplitude ratios for the spike trains contained in the
+        spike list, where the input stimulus frequency is f_stim.
+        """
+        f1f0_dict = {}
+        for id, spiketrain in self.spiketrains.items():
+            f1f0_dict[id] = spiketrain.f1f0_ratio(time_bin, f_stim)
+        return f1f0_dict
+
-        # methods for different output formats
-    def as_ids_times(self, relative=False, quantized=False):