Changeset 362

Timestamp:

12/08/08 18:35:59 (3 years ago)

Author:

LaurentPerrinet

Message:

Small fixes on example/single _neuron and sfn2008 / corrected ylabel in isi plot

Location:

trunk

Files:

• examples/sfn2008/sfn_example_simulated_data.py (modified) (1 diff)
• examples/sfn2008/sfn_example_stgen.py (modified) (2 diffs)
• examples/single_neuron/CRF_neuron_vs_signal.py (modified) (1 diff)
• examples/single_neuron/SpikeTrain2Play.wav (modified) (previous)
• examples/single_neuron/results/fig-CRF_neuron_vs_signal.png (modified) (previous)
• src/__init__.py (modified) (2 diffs)
• src/signals.py (modified) (8 diffs)

trunk/examples/sfn2008/sfn_example_simulated_data.py

@@ -28 +28 @@
 
 # isi distribution
-hs = s.isi_hist()
-pylab.figure()
-pylab.plot(hs[1],hs[0])
+hs = s.isi_hist(bins=20, display=True)
 
 

trunk/examples/sfn2008/sfn_example_stgen.py

@@ -29 +29 @@
 print "Spiketrain 1:"
 print "mean rate: %f" % st1.mean_rate()
-print "coefficient of variance: %f" % st1.cv_isi()
+print "coefficient of variation: %f" % st1.cv_isi()
 print "fano factor: %f" % st1.fano_factor_isi()
 
@@ -35 +35 @@
 print "Spiketrain 2:"
 print "mean rate: %f" % st2.mean_rate()
-print "coefficient of variance: %f" % st2.cv_isi()
+print "coefficient of variation: %f" % st2.cv_isi()
 print "fano factor: %f" % st2.fano_factor_isi()
 

trunk/examples/single_neuron/CRF_neuron_vs_signal.py

@@ -27 +27 @@
 
 
-N_exp_snr = 25
+N_exp_snr = 20
 N_exp_noise = 9
 

trunk/src/__init__.py

@@ -3 +3 @@
 ==========
 
-NeuroTools is not a neural simulator, but rather a collection of tools
+NeuroTools is not a neural simulator, but a collection of tools
 to support all tasks associated with a neural simulation project which
 are not handled by the simulation engine.
@@ -42 +42 @@
 #########################################################
 
-# The nice thing would be to gathered every non standard
+# The nice thing would be to gather every non standard
 # dependency here, in order to centralize the warning
 # messages and the check

trunk/src/signals.py

@@ -316 +316 @@
         can use the average irregularity across all neurons.
         
+        http://en.wikipedia.org/wiki/Coefficient_of_variation
+        
         See also
             isi, cv_kl
@@ -351 +353 @@
         """
         isi = self.isi() / 1000.
-        if len(isi) == 0:
+        if len(isi) < 2:
             logging.debug("Warning, a CV can't be computed because there are not enough spikes")
             return numpy.nan
@@ -357 +359 @@
             if newnum:
                 proba_isi, xaxis = numpy.histogram(isi, bins=bins, normed=True, new=True)
-                #xaxis = xaxis[:len(xaxis)-1]
+                xaxis = xaxis[:-1]
             else:
                 proba_isi, xaxis = numpy.histogram(isi, bins=bins, normed=True)
@@ -374 +376 @@
         
         The Fano Factor is defined as the variance of the isi divided by the mean of the isi
+        
+        http://en.wikipedia.org/wiki/Fano_factor
         
         See also
@@ -1169 +1173 @@
         if newnum:
             values, xaxis = numpy.histogram(isis, bins=bins, normed=True, new=newnum)
-            xaxis = xaxis[:len(xaxis)-1]
+            xaxis = xaxis[:-1]
         else:
             values, xaxis = numpy.histogram(isis, bins=bins, normed=True)
@@ -1177 +1181 @@
         else:
             xlabel = "Inter Spike Interval (ms)"
-            ylabel = "% of Neurons"
+            ylabel = "Probability"
             set_labels(subplot, xlabel, ylabel)
             subplot.plot(xaxis, values, **kwargs)
+            subplot.yticks([]) # arbitrary units
             pylab.draw()
 
@@ -1263 +1268 @@
         if newnum:
             values, xaxis = numpy.histogram(cvs, bins=bins, normed=True, new=newnum)
-            xaxis = xaxis[:len(xaxis)-1]
+            xaxis = xaxis[:-1]
         else:
             values, xaxis = numpy.histogram(cvs, bins=bins, normed=True)
@@ -1412 +1417 @@
             if newnum:
                 values, xaxis = numpy.histogram(rates, nbins, normed=True, new=newnum)
-                xaxis = xaxis[:len(xaxis)-1]
+                xaxis = xaxis[:-1]
             else:
                 values, xaxis = numpy.histogram(rates, nbins, normed=True)