Changeset 326

Timestamp:

11/12/08 19:49:46 (2 months ago)

Author:

LaurentPerrinet

Message:

wiki:examples : reflect changes in the wiki:parameters API / polishing figures to give usable examples

Files:

• trunk/examples/parameter_search/parameter_search_example.py (modified) (1 diff)
• trunk/examples/retina/benchmark_linear.py (modified) (3 diffs, 1 prop)
• trunk/examples/retina/benchmark_noise.py (modified) (2 diffs, 1 prop)
• trunk/examples/retina/benchmark_retina.py (modified) (10 diffs, 1 prop)
• trunk/examples/retina/make_all.py (modified) (1 prop)
• trunk/examples/retina/results/fig-benchmark_linear.png (modified) (previous)
• trunk/examples/retina/results/fig-benchmark_noise.png (modified) (previous)
• trunk/examples/retina/retina.py (modified) (4 diffs, 1 prop)
• trunk/examples/retina/test_parallel.py (modified) (1 prop)
• trunk/examples/single_neuron/CRF_neuron_vs_signal.py (modified) (2 diffs, 1 prop)
• trunk/examples/single_neuron/playing_with_simple_single_neuron.py (modified) (1 prop)
• trunk/examples/single_neuron/simple_single_neuron.py (modified) (1 prop)

trunk/examples/parameter_search/parameter_search_example.py

@@ -49 +49 @@
-    """ 
-    #set up the network
-uron
+st2
-    sim.setup(dt = 0.01,  min_delay = 1.,  max_delay = 1.,  debug = False,  
-              quit_on_end = False)

trunk/examples/retina/benchmark_linear.py

@@ -19 +19 @@
-
-N, N_exp = 1000, 6
-p = ParameterSet({})
-t_smooth = 100. # width (in ms) of the integration window
-from retina import *
-retina = Retina(N)
+retina.params['amplitude'] = 1
+snr  = retina.params['snr']* numpy.linspace(0.1,2.0,N_exp)
+p =  ParameterSpace({'snr' : ParameterRange(list(snr))})
+
-
-name = sys.argv[0].split('.')[0] # name of the current script withpout the '.py' part
@@ -33 +36 @@
-
-except:
-
-
+
-    # calculates the dimension of the parameter space
-    results_dim, results_label = p.parameter_space_dimension_labels()
@@ -78 +80 @@
-
-"""
-
-
-
-
-
+
+
+
+
-
+pylab.subplot(211)
-for i_exp in range(N_exp):
-
-
-
-
-
+
+
-pylab.xticks( numpy.round(numpy.linspace(0, retina.params.simtime, 5),0) )
-
+
+
+
+
+
+
+
+
+
-pylab.xlabel('time (ms)')
-'tight'
+[0, retina.params.simtime, 0.0, fmax ]
-
-#pylab.legend(
+pylab.legend(loc='upper right'
-
-#pylab.subplot(122)
-#pylab.title('time course OFF')
-#pylab.xticks( numpy.linspace(0, simtime, 5) )
-#pylab.ylabel('firing frequency (Hz)')
-#pylab.axis('tight')
-#pylab.xlabel('time (ms)')
-#pylab.legend()
-
-
-
+
+
+
+
+
+
-
-#TODO: make a plot showing it's the right point process with a histogram

trunk/examples/retina/benchmark_noise.py

@@ -25 +25 @@
-import progressbar # see http://projects.scipy.org/pipermail/scipy-dev/2008-January/008200.html
-
-
-
-.noise_std = ParameterRange(list(10.**(numpy.linspace(-.50,1.,N_exp_noise)))
+0
+
+ =  ParameterSpace({'noise_std' : ParameterRange(list(10.**(numpy.linspace(-.50,1.,N_exp_noise))))}
-
-import retina as model
-
-
-retina = model.Retina(N) 
-retina.params['snr'] = 0 # no input
@@ -87 +85 @@
-pylab.xlabel('Noise amplitude')
-
-pylab.savefig('results/fig-' + name + '.pdf')
-pylab.savefig('results/fig-' + name + '.png', dpi = 300)
-
+if 0:
+    pylab.show()
+else:
+    pylab.savefig('results/fig-' + name + '.pdf')
+    pylab.savefig('results/fig-' + name + '.png', dpi = 300)
+
+
+#TODO: make a plot showing that spontaneous activity is a point process with a known histogram

trunk/examples/retina/benchmark_retina.py

@@ -28 +28 @@
-
-N, N_snr, N_seeds = 1000, 5, 10
-
+
+
+
-t_smooth = 100. # ms.  integration time to show fiber activity
-
-
-
-
-
+
+
+
+
-
-name = sys.argv[0].split('.')[0] # name of the current script withpout the '.py' part
-
-#### MAKING THE SIMULATIONS ##########
-results = shelve.open('results/mat-' + name)
@@ -43 +44 @@
-    DATA = results['DATA']
-except:
-
+
-    # calculates the dimension of the parameter space
-    results_dim, results_label = p.parameter_space_dimension_labels()
@@ -55 +56 @@
-        params.update(experiment) # updates what changed in the dictionary
-        # simulate the experiment and get its data
-
+)#
-        # store it
-        DATA.append(data)#
@@ -61 +62 @@
-
-    results['DATA'] = DATA
-
+
-    pbar.finish()
-
-results.close()
-
-############## PRE-PROCESSING ###########################
-results = shelve.open('results/mat-' + name)
-try:
-
+uncomment 
-    lower_edges = results['lower_edges']
-    temporal_ON = results['temporal_ON']
@@ -77 +75 @@
-
-except:
-
+
-    lower_edges = DATA[0]['out_ON_DATA'].time_axis(t_smooth)
-    N_smooth = len(lower_edges)
-    params = retina.params
-
+
-    #N_snr = len(p.snr)
-    temporal_ON, temporal_OFF = numpy.zeros((N_smooth,N_snr)), numpy.zeros((N_smooth,N_snr))
-    map_spatial_ON, map_spatial_OFF  = numpy.zeros((N,N_snr)), numpy.zeros((N,N_snr))
-
+
-    #
-    N_ret, simtime = params['N_ret'], params['simtime']
@@ -95 +93 @@
-    id_center = [int(k) for k in numpy.where( r2 < N_ret**2)]
-
-    
+
-    for i_exp, experiment in enumerate(p.iter_inner()):
-        # calculating the index in the parameter space
@@ -104 +102 @@
-        map_spatial_ON[:,index[1]] += DATA[i_exp]['out_ON_DATA'].mean_rates(t_start=simtime/4.,t_stop=3*simtime/4.)/N_seeds
-        map_spatial_OFF[:,index[1]] += DATA[i_exp]['out_OFF_DATA'].mean_rates(t_start=simtime/4.,t_stop=3*simtime/4.)/N_seeds
-
+
-    results['temporal_ON'] = temporal_ON
-    results['map_spatial_OFF'] = map_spatial_OFF
@@ -110 +108 @@
-    results['map_spatial_ON'] = map_spatial_ON
-
+results.close()
-
-############# MAKING FIGURE ############################
@@ -118 +117 @@
-pylab.ioff() #pylab.ion() #
-
- 
+
-
-Prints to a figure the mean firing rate
@@ -164 +163 @@
-pylab.savefig('results/fig-' + name + '.pdf')
-pylab.savefig('results/fig-' + name + '.png', dpi = 300)
-

trunk/examples/retina/retina.py

@@ -179 +179 @@
-        phr_ON = sim.Population((N,),'dc_generator')
-        phr_OFF  = sim.Population((N,),'dc_generator')
+
+
-        for factor, phr in [(-params['snr'],phr_OFF),(params['snr'],phr_ON)]:
-            phr.tset('amplitude', params['amplitude'] * factor )
@@ -251 +253 @@
-            print(params['description'])
-            print "Number of Neurons  : ", N
-#print "Output rate  (ON) : ", out_ON_DATA.meanRate(), "spikes
-#print "Output rate (OFF)   : ", out_OFF_DATA.meanSpikeCount(), "spikes
+print "Output rate  (ON) : ", out_ON_DATA.mean_rate(), "Hz
+print "Output rate (OFF)   : ", out_OFF_DATA.mean_rate(), "Hz
-            print("Build time             : %g s" % buildCPUTime) 
-            print("Simulation time        : %g s" % simCPUTime)
@@ -262 +264 @@
-if __name__ == '__main__':
-
-0
+
-    out = ret.run(ret.params)
-    # plotting
@@ -268 +270 @@
-    fig = pylab.figure(1)
-    z = pylab.subplot(121)
-id_list=range(20), kwargs={'color':'b
+display=z, id_list=range(20), kwargs={'color':'r
-    z = pylab.subplot(122)
-
-
-
+
+
+
+
+
+
+

trunk/examples/single_neuron/CRF_neuron_vs_signal.py

@@ -27 +27 @@
-
-
-p = ParameterSet({})
-N_exp_noise = 10
-N_exp_snr = 25
+N_exp_noise = 9
-
-ps =  ParameterSpace({
@@ -69 +68 @@
-
-#numpy.array(p.noise_std._values),numpy.array(p.snr._values),
-
+
+
+
-#pylab.yticks(p.noise_std._values[:2:])
-pylab.ylabel('Firing Rate (Hz/neuron)')
-#pylab.xticks(p.snr._values[:2:])
-pylab.xlabel('Signal')
-
-
-
-
+
+
+
+
+
+
+