Changeset 180

Timestamp:

07/23/08 19:01:56 (4 months ago)

Author:

apdavison

Message:

Minor improvements to spikes.py and analysis.py

Files:

• trunk/src/analysis.py (modified) (2 diffs)
• trunk/src/spikes.py (modified) (12 diffs)

trunk/src/analysis.py

@@ -85 +85 @@
-    os.remove(cfilename)
-
+def _dict_max(D):
+    """
+    For a dict containing numerical values, contain the key for the
+    highest value. If there is more than one item with the same highest
+    value, return one of them (arbitrary - depends on the order produced
+    by the iterator).
+    """
+    max_val = max(D.values())
+    for k in D:
+        if D[k] == max_val:
+            return k
-
-class TuningCurve(object):
@@ -122 +133 @@
-            stderr[k] = arr.std()*n/(n-1)/numpy.sqrt(n)
-        return mean, stderr
+
+    def max(self):
+        """Return the key of the max value and the max value."""
+        k = _dict_max(self._tuning_curves)
+        return k, self._tuning_curves[k]

trunk/src/spikes.py

@@ -317 +317 @@
-    ## Constructor and key methods to manipulate the SpikeList objects   ##
-    #######################################################################
-
+, label=''
-        """
-        `spikes` is a list/tuple of (id,t) tuples (id in id_list)
@@ -329 +329 @@
-        """
-        self.id_list = id_list
-        self.N = len(id_list)
-        self.t_start = t_start
-        self.t_stop = t_stop
-        self.dt = dt
+        self.label = label
-        # transform spikes in a spike array
-        self.spiketrains = {}
@@ -346 +346 @@
-        if len(self) > 0 and (self.t_start is None or self.t_stop is None):
-            self.__calc_startstop()
+
+    def N(self):
+        return len(self.id_list)
-
-    def __calc_startstop(self):
@@ -451 +454 @@
-        else:
-            ISI = numpy.array([])
-
+()
-                ISI = numpy.concatenate((ISI,isis[idx]))
-            values, xaxis = numpy.histogram(ISI, nbins, normed=1)
@@ -533 +536 @@
-    # If display is True, then it plots the distribution of the rates
-    def rate_distribution(self, nbins=25, normalize=True, display=False):
-
+()
-        #for idx,id in enumerate(self.id_list):
-        #    rates[idx] = self.spiketrains[id].mean_firing_rate()
@@ -550 +553 @@
-    def spike_histogram(self, time_bin, normalized=False, display=False):
-        nbins = numpy.ceil((self.t_stop-self.t_start)/time_bin)
-
-
+()
+
-        for idx,id in enumerate(self.id_list):
+            print idx, id
-            firing_rate[idx,:] = self.spiketrains[id].time_histogram(time_bin,normalized)
-        if not display:
@@ -558 +562 @@
-        else:
-            pylab.figure()
-
+()
-            pylab.ylabel("Mean Number of Spikes per bin", size="x-large")
-            pylab.xlabel("Time (ms)", size="x-large")
@@ -677 +681 @@
-    def mean_rate_variance(self, time_bin):
-        firing_rate = self.firing_rate(time_bin)
-
+()
-
-    # Function to extract the covariance of the firing rate along time,
@@ -688 +692 @@
-            raise Exception("Error, both SpikeLists should share common t_start, t_stop and dt")
-        frate_1 = self.firing_rate(time_bin)
-
+()
-        frate_2 = SpkList.firing_rate(time_bin)
-
+()
-        N = len(frate_1)
-        cov = sum(frate_1*frate_2)/N-sum(frate_1)*sum(frate_2)/(N*N)
-        return cov
-
-
-
-
-
-
-
+
-    def raster_plot(self, id_list=None, t_start=None, t_stop=None, colors='b', subplot=None, size=1):
+        """
+        Generate a raster plot of a certain number of cells in the
+        SpikeList object. If id_list is an integer, then N ids will be randomly choosen
+        in id_list. If this is a list, those id will be selected.
+        Raster is made between t_start and t_stop (region of interest, not the global ones)
+        and colors can be a list of color (each for one cells) or a single string
+        to apply the same color to all the raster plots.
+        """
-        if id_list == None: 
-            id_list = self.id_list
@@ -723 +730 @@
-            ids = ids[idx]
-            if len(spike_times) > 0:
+                print "Plotting %d points for %s" % (len(spike_times), self.label)
-                subplot.scatter(spike_times, ids, s=size, c=colors)
-        elif len(colors) != len(id_list):
@@ -874 +882 @@
-        """
-
-
+()
-        for spike in spikes:
-            spike_array[spike[0]].append(spike[1])
@@ -954 +962 @@
-        id_list = range(id_list)
-    spikes = readFile(filename)
-
+
+
-
-