def gen_simulation(gen_conf, num_sim=1, gen_magnitude_stddev=0):
"""Runs a single simulation of generators of ERP components.
Currently only one epoch for one subject is calculated, maybe this should
change. The generator configuration is specified as input.
"""
# TODO: Remove all the scaling variables!
# Initialisation of the PyBrainSim modelling framework with a homogeneous
# spherical head model with a head radius set to 11.5 cm. Since we are
# modelling only the spatical characteristics of the ERP component, we set
# the temporal sampling frequency to 1, to obtain only one simulated value
# per epoch.
head = Head()
head.setSamplingFrequency(1)
headModel = HeadModelDipoleSphere(head, 11.5)
# Initialisation of the simulated electrode array from predefined electrode
# locations simulating the 10-20 electrode placement system.
# TODO: This isn't elegant and should be changed!
# TODO: This excludes simulations with a line of electrodes, which are
# important for some applications as well, see doOneSimulation.py for
# example implementation.
[electrodes, topoX, topoY, thetaAngles, phiAngles] =\
read_electrode_locations()
for i in range(len(electrodes)):
head.addRegistrationSite([thetaAngles[i], phiAngles[i]])
# TODO: Here we are assuming that we know the generator locations!
# (from the gen_conf variable)
# Adding dipole generators to the head with the configuration parameters
# given in gen_conf.
# TODO: This is not working code
# TODO: It would be really good if this code could fit in less lines!
# TODO: Actually adding the generators to a list is not necessary any more,
# maybe I could just run the constructor?
generators = []
for i in range(len(gen_conf)):
# Combining the generator with the head model
generators.append(
GeneratorNoisy('Gen', head,
position=[gen_conf[i]['depth'],
gen_conf[i]['theta'],
gen_conf[i]['phi'],
gen_conf[i]['orientation'],
gen_conf[i]['orientation_phi']],
mean=gen_conf[i]['magnitude'],
stddev=gen_magnitude_stddev))
# Running the simulation.
# TODO: runSimulation() should already return a numpy.array, now it returns
# a list (checked that)!
simulated_data = array(head.runSimulation(num_sim))
# TODO: I should be returning something different here, perhaps?
# TODO: I need to transpose simulated data to enable it to be used with
# e.g. the topographic maps without transposing, like with the real data...
# but whether this is a good idea in the long run - no idea...
return [simulated_data.transpose(), gen_conf, electrodes]
def initialize_electrode_locations():
"""Reads in electrode locations and transforms them to xyz coordinates, so
that this isn't unnecessarily repeated in the main lead_field calculation
function if not strictly necessary.
"""
# Setting the radius of the head to 11.5 cm
radius = 11.5
# Reading in electrode locations from external file electrodeLocations.elp
[el, el_x, el_y, el_thetas, el_phis] = read_electrode_locations()
# How many electrodes do we have?
n_el = len(el)
# Coordinates of the electrodes (in the frame of reference associated with
# the center of the head)
xyz_el = zeros((n_el, 3))
for i_el in range(n_el):
# Calculating the coordinates of the electrode in the Cartesian coordinates associated with the head
# The X axis points towards the right ear, while the Y axis points towards the front
el_theta = el_thetas[i_el]
el_phi = el_phis[i_el]
xyz_el[i_el, 0] = radius * sin(el_theta) * cos(el_phi)
xyz_el[i_el, 1] = radius * sin(el_theta) * sin(el_phi)
xyz_el[i_el, 2] = radius * cos(el_theta)
return radius, xyz_el
def examine_data(data, electrodes, indices, name):
print("\n\n\n" + name)
print("Data consists of: " + str(data.shape[0]) + " subjects and " + str(data.shape[1]) + " electrodes\n")
# Examine values for single subjects
column_names = [electrodes[index] for index in indices]
column_names.insert(0, "Subject")
column_names.append(" ")
column_names.append(" ")
subjectvalues = PrettyTable(column_names)
for i in range(data.shape[0]):
row = [str(round(data[i, index], 4))[0:8] for index in indices]
row.insert(0, i)
if i != data.shape[0] - 1:
row.append(" ")
row.append(" ")
else:
row.append("MEAN")
row.append("STD DEV")
subjectvalues.add_row(row)
electrode_means = mean(data, 0)
electrode_stddevs = std(data, 0)
electrode_range = []
for i in range(data.shape[1]):
electrode_range.append(max(data[:, i]) - min(data[:, i]))
row = [str(round(electrode_means[index], 4))[0:8] for index in indices]
row.insert(0, "Mean")
row.append(str(round(mean(electrode_means), 4))[0:8])
row.append(str(round(std(electrode_means), 4))[0:8])
subjectvalues.add_row(row)
row = [str(round(electrode_stddevs[index], 4))[0:8] for index in indices]
row.insert(0, "Std Dev")
row.append(str(round(mean(electrode_stddevs), 4))[0:8])
row.append(str(round(std(electrode_stddevs), 4))[0:8])
subjectvalues.add_row(row)
row = [str(round(electrode_range[index], 4))[0:8] for index in indices]
row.insert(0, "Range")
row.append(str(round(mean(electrode_range), 4))[0:8])
row.append(str(round(std(electrode_range), 4))[0:8])
subjectvalues.add_row(row)
subjectvalues.printt(border=False)
# Correlation between electrode mean and standard deviation
figure()
from matplotlib import pyplot
pyplot.subplot(211)
pyplot.plot(electrode_means, electrode_stddevs, ".")
pyplot.xlabel("Electrode mean")
pyplot.ylabel("Electrode standard deviation")
pyplot.title(name)
[coefficient, pvalue] = pearsonr(electrode_means, electrode_stddevs)
print(
"\nCorrelation between mean and standard deviation, coefficient: "
+ str(coefficient)
+ ", significance value: "
+ str(pvalue)
)
# Correlation (across subjects) between electrodes depending on distance
# between the electrodes
pyplot.subplot(212)
electrode_correlations = []
electrode_distances = []
[electrodes_file, pos_x, pos_y, pos_theta, pos_phi] = read_electrode_locations()
for electrode1 in range(data.shape[1]):
for electrode2 in range(data.shape[1]):
# if electrode1 <= electrode2:
# break
[coefficient, pvalue] = pearsonr(data[:, electrode1], data[:, electrode2])
electrode_correlations.append(coefficient)
distance = sqrt(
square(pos_x[electrode1] - pos_x[electrode2]) + square(pos_y[electrode1] - pos_y[electrode2])
)
electrode_distances.append(distance)
pyplot.plot(electrode_distances, electrode_correlations, ".")
pyplot.xlabel("Distance between two electrodes")
pyplot.ylabel("Correlation between two\nelectrodes across subjects")
def plot_variogram(data, cov_data, data_bg=None, cov_data_bg=None,
norm='normalized', binned=False, color='b'):
# Correlation (across subjects) between electrodes depending on distance
# between the electrodes
electrode_correlations = []
electrode_correlations_bg = []
electrode_distances = []
[electrodes_file, pos_x, pos_y, pos_theta, pos_phi] = \
read_electrode_locations()
if data_bg != None:
color = 'r'
for electrode1 in range(data.shape[1]):
for electrode2 in range(data.shape[1]):
#if electrode1 <= electrode2:
# break
if norm == 'normalized':
[coefficient, pvalue] = pearsonr(data[:,electrode1], data[:,electrode2])
if data_bg != None:
[coefficient_bg, pvalue_bg] = pearsonr(data_bg[:,electrode1], data_bg[:,electrode2])
elif norm == 'unnormalized':
coefficient = cov_data[electrode1][electrode2]
if cov_data_bg != None:
coefficient_bg = cov_data_bg[electrode1][electrode2]
else:
print norm + ' not recognized!!!'
electrode_correlations.append(coefficient)
if data_bg != None:
electrode_correlations_bg.append(coefficient_bg)
distance = sqrt(square(pos_x[electrode1] - pos_x[electrode2])
+ square(pos_y[electrode1] - pos_y[electrode2]))
electrode_distances.append(distance)
if binned is False:
if data_bg != None:
pyplot.plot(electrode_distances, electrode_correlations_bg, 'b.')
pyplot.plot(electrode_distances, electrode_correlations, color+'.')
elif binned is True:
(numbers,bins) = histogram(electrode_distances,20)
corr_means = []
corr_sems = []
corr_means_bg = []
corr_sems_bg = []
dists = []
for i in range(len(bins[:-1])):
corr_bin = []
corr_bin_bg = []
for j in range(len(electrode_correlations)):
if electrode_distances[j] >= bins[i] and electrode_distances[j] < bins[i+1]:
corr_bin.append(electrode_correlations[j])
if data_bg != None:
corr_bin_bg.append(electrode_correlations_bg[j])
corr_means.append(mean(corr_bin))
#corr_means.append(median(corr_bin))
corr_sems.append(2*sem(corr_bin))
#corr_sems.append(std(corr_bin))
dists.append((bins[i+1] - bins[i])/2.0 + bins[i])
if data_bg != None:
corr_means_bg.append(mean(corr_bin_bg))
#corr_means_bg.append(median(corr_bin_bg))
corr_sems_bg.append(2*sem(corr_bin_bg))
#corr_sems_bg.append(std(corr_bin_bg))
if data_bg != None:
pyplot.errorbar(dists, corr_means_bg, yerr=corr_sems_bg,fmt='bo')
pyplot.errorbar(dists, corr_means, yerr=corr_sems,fmt=color + 'o')
handle = pyplot.gca()
pyplot.xlabel('Distance between two electrodes')
if norm == 'normalized':
pyplot.ylabel('Pearson\'s R Correlation between\ntwo electrodes across subjects')
pyplot.ylim(-1.1,1.1)
elif norm == 'unnormalized':
pyplot.ylabel('Covariance between two\nelectrodes across subjects')
return handle
