Changeset 437 for trunk

Timestamp:

10/01/09 17:29:53 (3 years ago)

Author:

emuller

Message:

Doc string for inh_gamma_generator, and new test

Location:

trunk

Files:

• src/stgen.py (modified) (1 diff)
• test/test_stgen.py (modified) (3 diffs)

trunk/src/stgen.py

@@ -396 +396 @@
       
     def inh_gamma_generator(self, a, b, t, t_stop, array=False):
+        """
+        Returns a SpikeList whose spikes are a realization of an inhomogeneous gamma process 
+        (dynamic rate). The implementation uses the thinning method, as presented in the 
+        references.
+
+        Inputs:
+            a,b    - arrays of the parameters of the gamma PDF where a[i] and b[i] 
+                     will be active on interval [t[i],t[i+1]]
+            t      - an array specifying the time bins (in milliseconds) at which to 
+                     specify the rate
+            t_stop - length of time to simulate process (in ms)
+            array  - if True, a numpy array of sorted spikes is returned,
+                     rather than a SpikeList object.
+
+        Note: 
+            t_start=t[0]
+            a is a dimensionless quantity > 0, but typically on the order of 2-10. 
+            a = 1 results in a poisson process.
+            b is assumed to be in units of 1/Hz (seconds).
+
+        References:
+
+        Eilif Muller, Lars Buesing, Johannes Schemmel, and Karlheinz Meier 
+        Spike-Frequency Adapting Neural Ensembles: Beyond Mean Adaptation and Renewal Theories
+        Neural Comput. 2007 19: 2958-3010.
+        
+        Devroye, L. (1986). Non-uniform random variate generation. New York: Springer-Verlag.
+
+        Examples:
+            See source:trunk/examples/stgen/inh_gamma_psth.py
+
+        See also:
+            inh_poisson_generator, gamma_hazard
+        """
+
         if not self.rpy_checked:
             self.have_rpy = check_dependency('rpy')

trunk/test/test_stgen.py

@@ -187 +187 @@
 
 
+    def testInhGammaBasic(self):
+
+        from numpy import array
+        import NeuroTools.signals
+
+        stg = stgen.StGen()
+
+        st = stg.inh_gamma_generator(array([3.0,3.0]),array([1.0/100.0/3.0,1.0/200.0/3.0]),array([500.0,1500.0]),2500.0,array=True)
+
+        assert(type(st)==numpy.ndarray)
+
+        st = stg.inh_gamma_generator(array([3.0,3.0]),array([1.0/100.0/3.0,1.0/200.0/3.0]),array([500.0,1500.0]),2500.0,array=False)
+
+        assert(type(st)==NeuroTools.signals.spikes.SpikeTrain)
+
+
+
     def testStatsInhGamma(self):
 
@@ -192 +209 @@
 
         from numpy import array
+        import NeuroTools.signals
 
         stg = stgen.StGen()
@@ -198 +216 @@
 
         st = stg.inh_gamma_generator(array([3.0,3.0]),array([1.0/100.0/3.0,1.0/200.0/3.0]),array([500.0,1500.0]),2500.0,array=True)
+
+        assert(type(st)==numpy.ndarray)