# 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]) pylab.yticks(numpy.arange(0.5, dims[0] + 0.5, 1.0), [str(i) for i in p.c._values]) pylab.xlim(0, dims[1]) pylab.ylim(dims[0], 0) pylab.xlabel('jitter (ms)') pylab.ylabel('correlation') ax = pylab.colorbar() ax.set_label('correlation')
# 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]) pylab.yticks(numpy.arange(0.5,dims[0]+0.5,1.0),[str(i) for i in p.c._values]) pylab.xlim(0,dims[1]) pylab.ylim(dims[0],0) pylab.xlabel('jitter (ms)') pylab.ylabel('correlation') ax = pylab.colorbar() ax.set_label('correlation') pylab.draw()