def print_model(self, topographies=False):
print('ERP Model Parameters')
print('====================')
print('Number of generators: ' + str(self.n_gen) + '\n')
print('Variability')
print('-----------')
if self.sigma_e == 0:
print('* No electrode variance.')
else:
print('* Electrode variance: ' + str(self.sigma_e))
print('* Generator covariance matrix:')
print(self.cov_gen)
#if self.n_gen > 1 and isinstance(self.sigma_g, list):
# print('* Individual generator variance.')
#elif self.n_gen > 1:
# print('* Generator variance: ' + str(self.sigma_g))
#if self.n_gen > 1 and isinstance(self.sigma_c, list):
# print('* Individual generator covariance.')
#elif self.n_gen > 1:
# print('* Generator variance: ' + str(self.sigma_g))
for gen in range(self.n_gen):
print('Generator ' + str(gen+1))
print('-------------------')
print('LOCATION, Depth: ' + str(self.gen_conf[gen]['depth']))
print('LOCATION, Theta: ' + str(self.gen_conf[gen]['theta']))
print('LOCATION, Phi: ' + str(self.gen_conf[gen]['phi']))
print('ORIENTATION: ' + str(self.gen_conf[gen]['orientation']))
print('ORIENTATION, Phi: ' + str(self.gen_conf[gen]['orientation_phi']))
print('MAGNITUDE: ' + str(self.gen_conf[gen]['magnitude']))
if topographies:
pyplot.figure()
plot_topographic_map((self.gen_conf[gen]['magnitude'] *
self.lf.calculate(
[self.gen_conf[gen]]))[:,0])
def plot_model(self, to_plot):
for one_plot in to_plot:
if one_plot == "mean":
plot_topographic_map(self.mean)
pyplot.title("Topographic map of mean")
# Individual generator means
pyplot.figure()
for i in range(self.n_gen):
pyplot.subplot(1, self.n_gen, i)
plot_topographic_map(self.mean)
pyplot.figtext(0.5, 0.75, "Topographic maps of means of each generator", ha="center")
if one_plot == "variance":
pyplot.figure()
plot_topographic_map(self.cov.diagonal())
pyplot.title("Topographic map of variance")
# Individual generator variances
pyplot.figure()
for i in range(self.n_gen):
pyplot.subplot(1, self.n_gen, i)
single_cov = dot(transpose(self.lead_field[:, i][newaxis]), self.lead_field[:, i][newaxis])
if self.variability_generators == "constant":
single_cov = self.sigma_g * single_cov
elif self.variability_generators == "individual":
single_cov = self.sigma_g[i] * single_cov
plot_topographic_map(single_cov.diagonal())
pyplot.figtext(0.5, 0.75, "Topographic maps of variance of each generator", ha="center")
if one_plot == "covariance matrix":
pyplot.figure()
plot_covariance_matrix(self.data, self.cov)
pyplot.title("Covariance matrix")
def plot_variability_visualization(data, to_plot='all'):
if to_plot == 'all':
to_plot = ['topography', 'variance topography', 'covariance matrix',
'variogram', 'unnormalized variogram', 'mean and variance']
for next_plot in to_plot:
pyplot.figure()
if next_plot == 'topography':
topographicmap.plot_topographic_map(mean(data,0))
pyplot.title('Mean')
elif next_plot == 'variance topography':
topographicmap.plot_topographic_map(var(data,0))
pyplot.title('Variance')
elif next_plot == 'covariance matrix':
plot_covariance_matrix(data)
elif next_plot == 'variogram':
plot_variogram(data, norm='normalized')
elif next_plot == 'unnormalized variogram':
plot_variogram(data, norm='unnormalized')
elif next_plot == 'mean and variance':
plot_mean_and_variance(data)
def print_model(self, topographies=False):
print ("ERP Model Parameters")
print ("====================")
print ("Number of generators: " + str(self.n_gen) + "\n")
print ("Variability")
print ("-----------")
print ("* Electrodes (self.sigma_e)")
print self.sigma_e
print ("* Generators (self.cov_gen)")
print self.cov_gen
print ("* Final covariance matrix (self.cov)")
print self.cov
for gen in range(self.n_gen):
print ("\nGenerator " + str(gen + 1))
print ("-------------------")
print ("LOCATION, Depth: " + str(self.gen_conf[gen]["depth"]))
print ("LOCATION, Theta: " + str(self.gen_conf[gen]["theta"]))
print ("LOCATION, Phi: " + str(self.gen_conf[gen]["phi"]))
print ("ORIENTATION: " + str(self.gen_conf[gen]["orientation"]))
print ("ORIENTATION, Phi: " + str(self.gen_conf[gen]["orientation_phi"]))
print ("MAGNITUDE: " + str(self.gen_conf[gen]["magnitude"]))
if topographies:
pyplot.figure()
plot_topographic_map((self.gen_conf[gen]["magnitude"] * self.lf.calculate([self.gen_conf[gen]]))[:, 0])
if parameters["todo_plot_grand_averages"]:
print("Drawing grand averages for all imported data")
figure()
plot_ERP(ERP_avg["old"][0])
if parameters["todo_plot_simulated_noise"]:
print("Running simulation to show topographic properties of noise")
gen_conf = random_generator_configuration()
[simulated_data, gen_conf, electrodes] = gen_simulation(
gen_conf,
num_sim=parameters["number_of_subjects"],
gen_magnitude_stddev=parameters["generator_magnitude_stddev"],
)
figure()
plot_topographic_map(mean(simulated_data, 0))
# savefig('Results/' + timestamp + '/simulated_noise')
savefig("Results/" + label + "/simulated_noise")
# Here we will be running the scaling experiments
# se <-- holds all simulation experiments, for all parameter combinations
se = {}
# All simulation experiment parameters we will be looping through
experiments = [1, 2, 3, 4]
configurations = ["random", "uandk"]
variabilities = ["scaling", "gen_amplitude", "gen_location"]
noise_types = ["normal_only", "constant_and_normal", "topographic_and_normal"]
references = ["none", "left_mastoid", "average_mastoid", "average"]
factors = ["hsl", "electrodes"]
