data1 = ERP_area["old"]["300-500"]
data2 = ERP_area["new"]["300-500"]
for factor in ["hsl", "electrodes"]:
if factor == "hsl":
doElectrodesAsFactor = False
elif factor == "electrodes":
doElectrodesAsFactor = True
if parameters["todo_anova_" + factor]:
[anova_results, electrodeIndices] = run_anova(
parameters["hemispheres"],
parameters["sites"],
parameters["locations"],
data1,
data2,
electrodes_area,
doElectrodesAsFactor,
printResults=True,
name="testu",
)
if parameters["todo_plot_data_topographies"]:
print("Drawing topographic maps for all imported data")
map_array = [
[
ERP_area["old"]["300-500"],
ERP_area["new"]["300-500"],
ERP_area["old"]["300-500"] - ERP_area["new"]["300-500"],
def run(self, run_generators=True, run_noise=True, run_reference=True):
smallest_p = create_scaled_data_structure([])
sig_p_count = create_scaled_data_structure(0)
sig_p_count_interaction = create_scaled_data_structure(0)
for simulation_number in range(self.number_of_simulations):
if run_generators:
if simulation_number == 0 or self.configuration == 'random':
gen_conf_1 = self.condition_1_gen_conf()
gen_conf_2 = self.condition_2_gen_conf(gen_conf_1)
if self.configuration == 'uandk':
gen_conf_1 = [{'depth': 7.0,\
'magnitude': -1050.0,\
'orientation': 0.0,\
'orientation_phi': 0.0,\
'phi': -0.65,\
'theta': 0.6}]
#print('Ran from here')
[gen_conf_1, magnitude_stddev] =\
scale_generator_configuration(gen_conf_1, 3.5, 2)
#print('STOP')
#print('HUUUUUGE FAILLLL!')
#print magnitude_stddev
gen_conf_2 = self.condition_2_gen_conf(gen_conf_1)
if simulation_number == 0 or self.configuration == 'random' or \
self.generator_magnitude_stddev != 0:
[simulated_gen_1, gen_conf_1, simulated_gen_2, gen_conf_2,
electrodes] = self.simulate_sources(gen_conf_1, gen_conf_2)
self.electrodes = electrodes
else:
simulated_gen_1 = deepcopy(self.simulated_gen_1[simulation_number])
simulated_gen_2 = deepcopy(self.simulated_gen_2[simulation_number])
gen_conf_1 = deepcopy(self.gen_conf_1[simulation_number])
gen_conf_2 = deepcopy(self.gen_conf_2[simulation_number])
simulated_data_1 = deepcopy(simulated_gen_1)
simulated_data_2 = deepcopy(simulated_gen_2)
if run_noise:
[normal_noise_1, topographic_noise_1,
normal_noise_2, topographic_noise_2, constant_noise] =\
self.simulate_noise(simulated_gen_1.shape)
else:
normal_noise_1 = deepcopy(self.normal_noise_1[simulation_number])
normal_noise_2 = deepcopy(self.normal_noise_2[simulation_number])
topographic_noise_1 = deepcopy(self.topographic_noise_1[simulation_number])
topographic_noise_2 = deepcopy(self.topographic_noise_2[simulation_number])
constant_noise = deepcopy(self.constant_noise[simulation_number])
if normal_noise_1 != None and normal_noise_2 != None:
simulated_data_1 += normal_noise_1
simulated_data_2 += normal_noise_2
if constant_noise != None:
simulated_data_1 += constant_noise
simulated_data_2 += constant_noise
if topographic_noise_1 != None and topographic_noise_2 != None:
simulated_data_1 += topographic_noise_1
simulated_data_2 += topographic_noise_2
if run_reference and self.reference != 'none':
[simulated_data_1, simulated_data_2] =\
self.rereference(simulated_data_1, simulated_data_2,
self.electrodes)
electrode_indices = select_electrodes(self.hemispheres,
self.sites,
self.locations,
self.electrodes)[4]
simulated_data_1_scaled = perform_scaling(simulated_data_1,
electrode_indices)
simulated_data_2_scaled = perform_scaling(simulated_data_2,
electrode_indices)
self.electrode_indices = electrode_indices
anova_results_scaled = \
create_scaled_data_structure(None)
[anova_results, electrode_indices] = \
run_anova(self.hemispheres, self.sites,
self.locations, simulated_data_1,
simulated_data_2, self.electrodes,
self.doElectrodesAsFactor, printResults=False,
name='testu_sim')
p = self.find_smallest_interaction_p(anova_results)
smallest_p['Unrescaled'].append(p)
if p < 0.05:
found_interaction_on_unrescaled = True
sig_p_count['Unrescaled'] += 1
else:
found_interaction_on_unrescaled = False
for key1 in anova_results_scaled.keys():
if key1 == 'Unrescaled':
break
for key2 in anova_results_scaled[key1].keys():
for key3 in anova_results_scaled[key1][key2].keys():
anova_results_scaled[key1][key2][key3] = \
run_anova(self.hemispheres, self.sites,
self.locations,
simulated_data_1_scaled[key1][key2][key3],
simulated_data_2_scaled[key1][key2][key3],
self.electrodes, self.doElectrodesAsFactor,
printResults=False,
name='testu_sim_scaled')[0]
p = self.find_smallest_interaction_p\
(anova_results_scaled[key1][key2][key3])
smallest_p[key1][key2][key3].append(p)
if p <0.05:
sig_p_count[key1][key2][key3] += 1
if found_interaction_on_unrescaled:
sig_p_count_interaction[key1][key2][key3] += 1
if self.collect:
if len(self.gen_conf_1) == self.number_of_simulations:
self.gen_conf_1[simulation_number] = gen_conf_1
self.gen_conf_2[simulation_number] = gen_conf_2
self.simulated_gen_1[simulation_number] = simulated_gen_1
self.simulated_gen_2[simulation_number] = simulated_gen_2
self.normal_noise_1[simulation_number] = normal_noise_1
self.normal_noise_2[simulation_number] = normal_noise_2
self.constant_noise[simulation_number] = constant_noise
self.topographic_noise_1[simulation_number] = topographic_noise_1
self.topographic_noise_2[simulation_number] = topographic_noise_2
self.simulated_data_1[simulation_number] = simulated_data_1
self.simulated_data_2[simulation_number] = simulated_data_2
if self.electrodes == None:
self.electrodes = electrodes
self.sig_p_count[simulation_number] = sig_p_count
self.sig_p_count_interaction[simulation_number] = sig_p_count_interaction
if not (len(self.gen_conf_1) == self.number_of_simulations):
self.gen_conf_1.append(gen_conf_1)
self.gen_conf_2.append(gen_conf_2)
self.simulated_gen_1.append(simulated_gen_1)
self.simulated_gen_2.append(simulated_gen_2)
self.normal_noise_1.append(normal_noise_1)
self.normal_noise_2.append(normal_noise_2)
self.constant_noise.append(constant_noise)
self.topographic_noise_1.append(topographic_noise_1)
self.topographic_noise_2.append(topographic_noise_2)
self.simulated_data_1.append(simulated_data_1)
self.simulated_data_2.append(simulated_data_2)
if self.electrodes == None:
self.electrodes = electrodes
self.sig_p_count.append(sig_p_count)
self.sig_p_count_interaction.append(sig_p_count_interaction)
self.print_stats(sig_p_count, sig_p_count_interaction)
return [simulated_data_1, simulated_data_2, simulated_data_1_scaled,
simulated_data_2_scaled, gen_conf_1, gen_conf_2, smallest_p,
sig_p_count, topographic_noise_1, topographic_noise_2,
constant_noise]
