try:
temporal_ON = results['temporal_ON']
temporal_OFF = results['temporal_OFF']
lower_edges = results['lower_edges']
params = results['params']
#if (params == retina.params): raise('Parameters have changed')
except:
from retina import *
retina = Retina(N)
retina.params['amplitude'] = numpy.ones(retina.params['amplitude'].shape)
# calculates the dimension of the parameter space
results_dim, results_label = p.parameter_space_dimension_labels()
# 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)
from NeuroTools.sandbox import make_name
# creating a ParameterSpace
p = ParameterSpace({})
# adding fixed parameters
p.nu = 20. # rate [Hz]
p.duration = 1000.
# adding ParameterRanges
p.c = ParameterRange([0.0,0.01,0.1,0.5])
p.jitter = ParameterRange([0.0,1.0,5.0,])
# calculation of the ParameterSpace dimension and the labels of the parameters
# containing a range
dims, labels = p.parameter_space_dimension_labels()
print "dimensions: ", dims
print ' labels: ', labels
def calc_cc(p):
"""
Generate correlated spike trains from the ParameterSet.
Parameter:
p - ParameterSet containing parameters nu (rate), c (correlation),
duration (in ms), jitter (in ms).
Returns: (cc, time_axis_cc, corrcoef)
cc - correlation coefficient
time_axis_cc - time axis for cross-correlation (for plotting)
corrcoef - correlation coefficient between the two SpikeTrains
# adding fixed parameters
p.nu = 20. # rate [Hz]
p.duration = 1000.
# adding ParameterRanges
p.c = ParameterRange([0.0, 0.01, 0.1, 0.5])
p.jitter = ParameterRange([
0.0,
1.0,
5.0,
])
# calculation of the ParameterSpace dimension and the labels of the parameters
# containing a range
dims, labels = p.parameter_space_dimension_labels()
print "dimensions: ", dims
print ' labels: ', labels
def calc_cc(p):
"""
Generate correlated spike trains from the ParameterSet.
Parameter:
p - ParameterSet containing parameters nu (rate), c (correlation),
duration (in ms), jitter (in ms).
Returns: (cc, time_axis_cc, corrcoef)
cc - correlation coefficient
time_axis_cc - time axis for cross-correlation (for plotting)
results = shelve.open('results/mat-' + name)
try:
temporal_ON = results['temporal_ON']
temporal_OFF = results['temporal_OFF']
lower_edges = results['lower_edges']
params = results['params']
#if (params == retina.params): raise('Parameters have changed')
except:
from retina import *
retina = Retina(N)
retina.params['amplitude'] = numpy.ones(retina.params['amplitude'].shape)
# calculates the dimension of the parameter space
results_dim, results_label = p.parameter_space_dimension_labels()
# 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)