# creates results array with size of parameter space dimension
data = retina.run(retina.params,verbose=False)
lower_edges = data['out_ON_DATA'].time_axis(t_smooth)
N_smooth = len(lower_edges)
temporal_ON, temporal_OFF = [],[]
import progressbar # see http://projects.scipy.org/pipermail/scipy-dev/2008-January/008200.html
pbar=progressbar.ProgressBar(widgets=[name, " ", progressbar.Percentage(), ' ',
progressbar.Bar(), ' ', progressbar.ETA()], maxval=N_exp)
for i_exp,experiment in enumerate(p.iter_inner()):
params = retina.params
params.update(experiment) # updates what changed in the dictionary
# simulate the experiment and get its data
data = retina.run(params,verbose=False)
# calculating the index in the parameter space
index = p.parameter_space_index(experiment)
# put the data at the right position in the results array
temporal_ON.append(sum(data['out_ON_DATA'].firing_rate(t_smooth))/N)#
temporal_OFF.append(sum(data['out_OFF_DATA'].firing_rate(t_smooth))/N)#
pbar.update(i_exp)
results['lower_edges'] = lower_edges
results['temporal_ON'] = temporal_ON
results['temporal_OFF'] = temporal_OFF
results['params'] = retina.params
pbar.finish()
results.close()
st2.time_histogram(time_bin=1.),
mode='same')
corrcoef = numpy.corrcoef(st1.time_histogram(time_bin=1.0),
st2.time_histogram(time_bin=1.))
time_axis_cc = numpy.linspace(-cc.shape[0] / 2., cc.shape[0] / 2.,
cc.shape[0])
return cc, time_axis_cc, corrcoef[0][1]
# creating a results array, with the dimensions of the ParameterSpace
corrcoef_results = numpy.empty(dims)
# scanning the ParameterSpace
for experiment in p.iter_inner():
# calculation of the index in the space
index = p.parameter_space_index(experiment)
# perfomring the experiment
cc, time_axis_cc, corrcoef = calc_cc(experiment)
corrcoef_results[index] = corrcoef
# plotting the cc's
subplot_index = (dims[1] * index[0]) + index[1]
pylab.subplot(dims[0], dims[1], subplot_index + 1)
pylab.plot(time_axis_cc, cc)
pylab.title(make_name(experiment, p.range_keys()))
pylab.xlim(-30, 30.)
pylab.ylim(0, 10.)
# plot the results
pylab.matshow(corrcoef_results)
pylab.xticks(numpy.arange(0.5, dims[1] + 0.5, 1.0),
[str(i) for i in p.jitter._values])
