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]
[ERP_area_baseline["old"], electrodes_read_area[5]] = SPSS_query(file_SPSS_baseline, condition="11")
[ERP_area_baseline["new"], electrodes_read_area[6]] = SPSS_query(file_SPSS_baseline, condition="12")
# Preparing lists of electrodes
if electrodes_read_avg[1] == electrodes_read_avg[2]:
electrodes_avg = electrodes_read_avg[1]
for key in electrodes_read_area.keys():
if electrodes_read_area[key] == electrodes_read_area[1]:
electrodes_area = electrodes_read_area[1]
else:
electrodes = None
print("Electrode mismatch in ERP data files")
break
electrode_indices = select_electrodes(
parameters["hemispheres"], parameters["sites"], parameters["locations"], electrodes_area
)[4]
for key1 in ERP_area.keys():
for key2 in ERP_area[key1].keys():
# NOTE: Copy is done inside perform_scaling
ERP_area_scaled[key1][key2] = perform_scaling(ERP_area[key1][key2], electrode_indices)
# TODO: This should be done as a unit test probably
if test_scaling(ERP_area_scaled[key1][key2], electrode_indices):
print("A problem with scaling has been detected")
if parameters["todo_test_assumptions_data"]:
print("Performing assumption tests on ERP data")
# This is temporary, but works
def test_assumptions(pickHemisphere, pickSite, pickLocation, dataCondition1,
dataCondition2, electrodes, doElectrodesAsFactor = False,
name = ''):
"""This function ... tests assumptions.
Don't start me on how ugly it is, PLEASE FIX!!!
"""
# TODO: Clean up mess inside this function
nSubjects = len(dataCondition1)
[hemispherePicked, sitePicked, locationPicked, electrodesPicked,
electrodeIndices] = select_electrodes(pickHemisphere, pickSite,
pickLocation, electrodes)
# Reading all relevant data into a single vector
dataVector = []
for subject in range(nSubjects):
dataVector = dataVector + list( dataCondition1[subject][electrodeIndices] )
dataVector = dataVector + list( dataCondition2[subject][electrodeIndices] )
# Converting data into an R matrix with rows corresponding to subjects and columns
# corresponding to groups of levels of factors
rDataVector = robjects.FloatVector(dataVector)
robjects.globalenv["dataVector"] = rDataVector
rData = robjects.r['matrix'](rDataVector, nrow = nSubjects, byrow = True)
robjects.globalenv["data"] = rData
# Representing factors as R variables
rHemisphere = robjects.IntVector(2 * hemispherePicked)
rSite = robjects.IntVector(2 * sitePicked)
rLocation = robjects.IntVector(2 * locationPicked)
rElectrodes = robjects.StrVector(2 * electrodesPicked)
# The condition factor as an R variable
rConditions = robjects.r.gl(2, len(electrodesPicked), 2*len(electrodesPicked), labels = ['old', 'new'])
# Creating the variables in the R environment
robjects.globalenv["conditions"] = rConditions
robjects.globalenv["hemispheres"] = rHemisphere
robjects.globalenv["locations"] = rLocation
robjects.globalenv["sites"] = rSite
robjects.globalenv["electrodes"] = rElectrodes
# Creating R factors, currently can't do it via RPy2, need version 2.1
fCondition = robjects.r("condition <- factor(conditions)")
fHemisphere = robjects.r("hemisphere <- factor(hemispheres)")
fLocation = robjects.r("location <- factor(locations)")
fSite = robjects.r("site <- factor(sites)")
fElectrode = robjects.r("electrode <- factor(electrodes)")
# Now we need to choose which factors will be included in the Anova.
# We exclude all those that have only one level
allFactors = ''
if doElectrodesAsFactor == True:
allFactors = ',electrode'
else:
if len(pickHemisphere)>1: allFactors += ',hemisphere'
if len(pickLocation)>1: allFactors += ',location'
if len(pickSite)>1 and not pickSite.count(0): allFactors += ',site'
rFactors = robjects.r('allFactors <- data.frame(condition' + allFactors + ')')
robjects.r("options(contrasts=c(\"contr.sum\", \"contr.poly\"))")
anovaModel = robjects.r("model <- lm(data ~ 1)")
robjects.r("library(car)")
# Testing ANOVA assumptions for each electrode
# Electrode factors for levene's test
electrodesPicked1 = []
electrodesPicked2 = []
for electrode in electrodesPicked:
electrodesPicked1.append('Old ' + electrode)
electrodesPicked2.append('New ' + electrode)
electrodesPickedLevene = electrodesPicked1 + electrodesPicked2
rElectrodesLevene = robjects.StrVector((len(rDataVector)/len(electrodesPickedLevene)) * electrodesPickedLevene)
robjects.globalenv["electrodesLevene"] = rElectrodesLevene
fElectrodeLevene = robjects.r("electrodeLevene <- factor(electrodesLevene)")
# NORMALITY ASSUMPTION
print('\n#\n# Testing ANOVA assumptions\n#')
# Preparing to plot normal q-q plots
dev_off = robjects.r('dev.off')
nRows = len(pickLocation)
if pickSite.count(0):
nColumns = len(pickHemisphere)
else:
nColumns = len(pickHemisphere) * len(pickLocation)
# Looping through all electrodes to do plots and the Shapiro-Wilk test
print('\n# Shapiro-Wilk test for normality of noise at each electrode')
for i in [1,2]:
robjects.r.pdf('qqPlots' + name + str(i) + '.pdf')
robjects.r('par(mfrow=c('+str(nRows)+','+str(nColumns) + '), pty="s", mar=c(2.5,2.5,1.5,0), mgp=c(1.5,0.1,0),tck=0.03,cex=0.8)')
for j in range(len(electrodesPicked)):
# Shapiro-Wilk test
shapiro_test = robjects.r('shapiro.test')
sw = shapiro_test(rData.rx(True,(i-1)*len(electrodesPicked)+j+1))
print('Shapiro-Wilk test for electrode ' + electrodesPicked[j] + ' in condition ' + str(i) + ', p = '+str(sw[1][0]))
# Plotting q-q plots
ylimits=robjects.FloatVector([-10,10])
if j == len(electrodesPicked)-1:
robjects.r.qqnorm(rData.rx(True,(i-1)*len(electrodesPicked)+j+1),main=electrodesPicked[j].strip(),ylim=ylimits,col='blue', ylab='Scalp potential [uV]', xlab='Standard normal quantiles')
robjects.r.qqline(rData.rx(True,(i-1)*len(electrodesPicked)+j+1), col='green4')
else:
#labels=False to turn off labels with numbers
robjects.r.qqnorm(rData.rx(True,(i-1)*len(electrodesPicked)+j+1), ann=False, ylim=ylimits, tck=0.03, col='blue')
robjects.r.qqline(rData.rx(True,(i-1)*len(electrodesPicked)+j+1), ann=False, ylim=ylimits, tck=0.03, col='green4')
robjects.r.title(electrodesPicked[j].strip())
dev_off()
# HOMOGENEITY OF VARIANCE ASSUMPTION
# Levene's test
print('\n# Levene\'s test for homogeneity of variance between electrodes in all conditions')
levene_test = robjects.r('levene.test')
lt = levene_test(rDataVector,fElectrodeLevene)
print lt
# Plot
robjects.r.pdf('variancePlot' + name + '.pdf')
robjects.r('par(mar=c(5,4,1,1), mgp=c(2,1,0),tck=0.01,cex=1, las=2)')
robjects.r('boxplot(dataVector ~ electrodeLevene, range=0, col="green3", ylab=expression(paste("Scalp potential [",u,"V]")) )')
dev_off()
# Magnitude measurements
print('\n#\n# Measuring the magnitude range of the data \n#')
print('\n# Max - min magnitude for condition 1:')
print(max(mean(dataCondition1,0)) - min(mean(dataCondition1,0)))
print('\n# Max - min magnitude for condition 2:')
print(max(mean(dataCondition2,0)) - min(mean(dataCondition2,0)))
print('\n# Difference between maximal values in the two conditions:')
print(max(mean(dataCondition1,0)) - max(mean(dataCondition2,0)))
print('\n# The mean standard deviation for all electrodes for both conditions is: \n' + str(mean([mean(std(dataCondition1,0)), mean(std(dataCondition2,0))])))
print('\n# Max - min magnitude for difference between conditions:')
print(max(mean(dataCondition1-dataCondition2,0)) - min(mean(dataCondition1-dataCondition2,0)))
print('\n# The mean standard deviation for all electrodes for difference between conditions is: \n' + str(mean(std(dataCondition1-dataCondition2,0))))
