def main(argv):
statistics = StatisticsClass()
if sys.argv[1] == '-h':
print('How to run file: main.py -i <inputfile> <algorithm>')
elif sys.argv[1] == '-i':
coordX = []
coordY = []
parsedFile, statistics.ImportAndConvertFileStatistic = createObjectsFromFile(sys.argv[2])
measurementFixations = []
if (sys.argv[4] == '-d'):
statistics.ImportDataToDatabase = initialize_db(parsedFile)
print('Converting file time: %s' % statistics.ImportAndConvertFileStatistic)
parsedMeasurements = []
if sys.argv[4] == '-f':
parsedMeasurements = parsedFile
elif sys.argv[4] == '-d':
parsedMeasurements, statistics.ImportAndConvertDatabaseStatistic = getFromDatabase()
else:
parsedMeasurements = parsedFile
if sys.argv[3] == 'I-DT':
print('Starting measurement using I-DT algorithm')
for e, measurement in enumerate(parsedMeasurements):
if measurement.Type == 'SS':
plt.plot(measurement.CoordX, measurement.CoordY, 'ko', markersize=10, label='Eye-tracker points' if e == 0 else "")
coordX, coordY, statistics.AlgorithmRunTimeStatistic, statistics.NumberOfFixationsCount, fixations, statistics.SaccadeCount, newPointList, summaryList = idt.calculateIdtAlgorithm(parsedMeasurements)
measurementFixations.append(fixations)
plt.plot(coordX, coordY, 'wo', markersize=5, markeredgecolor='r', label='Calculated fixations')
print('Ending measurement using I-DT algorithm')
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode="expand", borderaxespad=0.)
elif sys.argv[3] == 'I-VT':
print('Starting measurement using I-VT algorithm')
for e, measurement in enumerate(parsedMeasurements):
if measurement.Type == 'SS':
plt.plot(measurement.CoordX, measurement.CoordY, 'ko', markersize=10, label='Eye-tracker points' if e == 0 else "")
plt.plot(measurement.CoordX, measurement.CoordY,'ko', color='green', markersize=6)
coordX, coordY, statistics.AlgorithmRunTimeStatistic, statistics.NumberOfFixationsCount, fixations, statistics.SaccadeCount, newPointList, summaryList = ivt.calculateIvtAlgorithm(parsedMeasurements)
measurementFixations.append(newPointList)
#plt.plot(coordX, coordY, 'wo', markersize=5, markeredgecolor='r', label='Calculated fixations')
print('Ending measurement using I-VT algorithm')
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode="expand", borderaxespad=0.)
elif sys.argv[3] == 'ML':
print('Starting measurement using Machine Learning algorithm')
for i, item in enumerate(parsedMeasurements):
if item.Type == 'SS':
plt.plot(item.CoordX, item.CoordY, 'ko', markersize=10, label='Eye-tracker points' if i == 0 else "")
fixations = ivt.prepareDataIvt(parsedMeasurements)
points = ml.calculateMlHelper(fixations)
coordX, coordY, fixationsForPoint, timealgorithm, ite, fixations, saccades, newPointList, summaryList = ml.calculateML(points)
plt.plot(coordX, coordY, 'wo', markersize=5, markeredgecolor='r', label='Calculated fixations')
statistics.NumberOfFixationsCount = fixationsForPoint
statistics.AlgorithmRunTimeStatistic = timealgorithm
statistics.MLPrecision = ite
statistics.SaccadeCount = len(saccades)
measurementFixations.append(newPointList)
print('Ending measurement using Machine Learning algorithm')
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode="expand", borderaxespad=0.)
else:
print('INCORRECT ALGORITHM')
print('Number of fixations: %s, Algorithm runtime: %s s, Saccades %s' % (statistics.NumberOfFixationsCount, statistics.AlgorithmRunTimeStatistic, statistics.SaccadeCount))
fig1 = plt.gcf()
plt.show()
plt.draw()
fig1.savefig('result/' + sys.argv[2] + sys.argv[3] + '.png', dpi=100)
createExitFile(sys.argv[2], statistics, sys.argv[3])
createExitFixationFile(sys.argv[2], measurementFixations, sys.argv[3])
createSummaryFile(sys.argv[2], summaryList, sys.argv[3])
elif sys.argv[1] == '-a':
print('Available algorithms: "I-DT", "I-VT", "ML"')
else:
print('How to run file: main.py -i <inputfile> <algorithm>')
def calculateMetric(metric_name, param_vals): if metric_name == 'count': if len(param_vals)!= 1: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be count(data)' raise Exception() return red.count(*param_vals) elif metric_name == 'pdf': if len(param_vals)!= 3: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be pdf(data, bin_values, continuous_bins)' raise Exception() return PDF.single(*param_vals) elif metric_name == 'deft': if len(param_vals) < 2 or len(param_vals) > 3: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be deft(data, g, alpha)' raise Exception() return deft.deft(*param_vals) elif metric_name == 'pdf_joint': if len(param_vals)!= 6: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be pdf_joint(dataA, bin_valuesA, continuous_binsA, dataB, bin_valuesB, continuous_binsB)' raise Exception() return PDF.joint(*param_vals) elif metric_name == 'mutual_information': if len(param_vals) < 3 or len(param_vals) > 4: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be mutual_information(pdfA, pdfB, joint_pdf, logbase="log2")' raise Exception() return MI.calculate(*param_vals) elif metric_name == 'shannon': if len(param_vals) < 1 or len(param_vals) > 2: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be shannon(pdf, logbase="log2")' raise Exception() return shannon.calculate(*param_vals) elif metric_name == 'kullback-leibler': if len(param_vals) < 2 or len(param_vals) > 3: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be kullback-leibler(pdf_p, pdf_q, logbase="log2")' raise Exception() return kullback.calculate(*param_vals) elif metric_name == 'fisher': if len(param_vals) < 2 or len(param_vals) > 3: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be fisher(pdf, eps, logbase="log2")' raise Exception() return fis.calculate(*param_vals) elif metric_name == 'hellinger-distance': if len(param_vals) != 2: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be hellinger-distance(pdf_p, pdf_q)' raise Exception() return hellinger.calculate(*param_vals) elif metric_name == 'surprise': if len(param_vals)!= 1: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be surprise(prob)' raise Exception() return surprise.calculate(*param_vals) elif metric_name == 'idt': if len(param_vals) < 6 or len(param_vals) > 7: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be idt(initial, time_series, epsilon, dt, bin_values, continuous_bins, logbase="log2")' raise Exception() return IDT.system(*param_vals) elif metric_name == 'idt_individual': if len(param_vals) < 8 or len(param_vals) > 9: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be idt_individual(initial, time_series, dt, bin_values, continuous_bins, sample_state_0, sample_state_t, sample_time, logbase="log2")' raise Exception() return IDT.individual(*param_vals) elif metric_name == 'information_integration': if len(param_vals) < 9 or len(param_vals) > 10: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be information_integration(initial, group, dt, bin_values, continuous_bins, sample_N1, sample_N2, sample_G, sample_t, logbase="log2")' raise Exception() return II.calculate(*param_vals) elif metric_name == 'multi_information': if len(param_vals) < 6 or len(param_vals) > 7: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be multi_information(data, bin_values, continuous_bins, sample_var, sample_elems, sample_pop, logbase="log2")' raise Exception() return multi.calculate(*param_vals) elif metric_name == 'swap_axes': if len(param_vals)!= 3: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be swap_axes(data, axis0, axis1)' raise Exception() return np.swapaxes(*param_vals) elif metric_name == 'add_dimension': if len(param_vals)!= 2: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be add_dimension(data, dimNumber)' raise Exception() return np.expand_dims(*param_vals) elif metric_name == 'join_dimensions': if len(param_vals)!= 3: print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be join_dimensions(data, dimNumberA, dimNumberB)' raise Exception() return red.join(*param_vals) else : # Try to get a numpy function try : func = getattr(np, metric_name) return func(*param_vals) except: print 'ERROR:Metric ', metric_name, ' does not exist' raise Exception()
def calculateMetric(metric_name, param_vals):
'''
Calculates a metric.
Input:
metric_name metric name
param_vals metric parameters
Returns:
result of the metric
'''
if metric_name == 'count':
if len(param_vals)!= 1:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be count(data)'
raise Exception()
return red.count(*param_vals)
elif metric_name == 'pdf':
if len(param_vals)!= 3:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be pdf(data, bin_values, continuous_bins)'
raise Exception()
return PDF.single(*param_vals)
elif metric_name == 'deft':
if len(param_vals) < 4 or len(param_vals) > 5:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be deft(data, g, minLimit, maxLimit, alpha=2)'
raise Exception()
return deft.deft(*param_vals)
elif metric_name == 'deft_joint':
if len(param_vals) < 7 or len(param_vals) > 8:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be deft_joint(dataA, dataB, g, minLimitA, maxLimitA, minLimitB, maxLimitB, alpha=2)'
raise Exception()
return deft.deft(*param_vals)
elif metric_name == 'pdf_joint':
if len(param_vals)!= 6:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be pdf_joint(dataA, bin_valuesA, continuous_binsA, dataB, bin_valuesB, continuous_binsB)'
raise Exception()
return PDF.joint(*param_vals)
elif metric_name == 'mutual_information':
if len(param_vals) < 3 or len(param_vals) > 4:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be mutual_information(pdfA, pdfB, joint_pdf, logbase="log2")'
raise Exception()
return MI.calculate(*param_vals)
elif metric_name == 'shannon':
if len(param_vals) < 1 or len(param_vals) > 2:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be shannon(pdf, logbase="log2")'
raise Exception()
return shannon.calculate(*param_vals)
elif metric_name == 'kullback-leibler':
if len(param_vals) < 2 or len(param_vals) > 3:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be kullback-leibler(pdf_p, pdf_q, logbase="log2")'
raise Exception()
return kullback.calculate(*param_vals)
elif metric_name == 'fisher':
if len(param_vals) < 2 or len(param_vals) > 3:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be fisher(pdf, eps, logbase="log2")'
raise Exception()
return fis.calculate(*param_vals)
elif metric_name == 'hellinger-distance':
if len(param_vals) != 2:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be hellinger-distance(pdf_p, pdf_q)'
raise Exception()
return hellinger.calculate(*param_vals)
elif metric_name == 'surprise':
if len(param_vals)!= 1:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be surprise(prob)'
raise Exception()
return surprise.calculate(*param_vals)
elif metric_name == 'idt':
if len(param_vals) < 6 or len(param_vals) > 7:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be idt(initial, time_series, epsilon, dt, bin_values, continuous_bins, logbase="log2")'
raise Exception()
return IDT.system(*param_vals)
elif metric_name == 'idt_individual':
if len(param_vals) < 8 or len(param_vals) > 9:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be idt_individual(initial, time_series, dt, bin_values, continuous_bins, sample_state_0, sample_state_t, sample_time, logbase="log2")'
raise Exception()
return IDT.individual(*param_vals)
elif metric_name == 'information_integration':
if len(param_vals) < 9 or len(param_vals) > 10:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be information_integration(initial, group, dt, bin_values, continuous_bins, sample_N1, sample_N2, sample_G, sample_t, logbase="log2")'
raise Exception()
return II.calculate(*param_vals)
elif metric_name == 'multi_information':
if len(param_vals) < 6 or len(param_vals) > 7:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be multi_information(data, bin_values, continuous_bins, sample_var, sample_elems, sample_pop, logbase="log2")'
raise Exception()
return multi.calculate(*param_vals)
elif metric_name == 'early_warning_difference':
if len(param_vals) < 4 or len(param_vals) > 5:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be early_warning_difference(time_series_ref, time_series_comp, change_values, warning_values, histogram_limit=50)'
raise Exception()
return ew.early_warning_difference(*param_vals)
elif metric_name == 'early_warning_flips':
if len(param_vals) != 2:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be early_warning_flips(time_series, change_values)'
raise Exception()
return ew.early_warning_flips(*param_vals)
elif metric_name == 'add_dimension':
if len(param_vals)!= 2:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be add_dimension(data, dimNumber)'
raise Exception()
return np.expand_dims(*param_vals)
elif metric_name == 'join_dimensions':
if len(param_vals)!= 3:
print 'ERROR:Error in ', metric_name, ', number of parameters incorrect. It must be join_dimensions(data, dimNumberA, dimNumberB)'
raise Exception()
return red.join(*param_vals)
else :
# Try to get a numpy function
try :
func = getattr(np, metric_name)
return func(*param_vals)
except:
print 'ERROR:Metric ', metric_name, ' does not exist'
raise Exception()
