# Message for terminal
###############################################################################
print('\n')
print('**********************************************************************')
print('* Processing ' + str(len(files)) + ' experiments [' +
str(datetime.datetime.now()) + ']')
print('**********************************************************************')
for (i, file) in enumerate(filenames):
start = time.time()
# Load files
dataA = monet.loadAndHashFactorialCSV(path + folderA + '/' + file + '.csv',
floatMultiplier=coverageRescale)
dataB = monet.loadAndHashFactorialCSV(path + folderB + '/' + file + '.csv',
floatMultiplier=coverageRescale)
# Claculate the hashed differences
differencesHash = monet.calculateFactorialHashError(
dataA, dataB, monet.sampleDifference)
deHashed = monet.deHashFactorial(differencesHash)
# Save output
np.savetxt(path + file + ".csv", deHashed, fmt='%2.6f', delimiter=",")
# Terminal Output
end = time.time()
print('* {0}) {1} [{2:.2f} min]'.format(i + 1, file, (end - start) / 60))
###############################################################################
# Message for terminal
###############################################################################
print('**********************************************************************')
print('* Finished ' + str(len(files)) + ' experiments [' +
str(datetime.datetime.now()) + ']')
print('**********************************************************************')
if (sensitivitySelector == 2):
# 010 adult life increase
# 020 adult life decrease
title = "Adult Mortality"
testFileA = "TSA_010.csv"
testFileB = "TSA_020.csv"
if (sensitivitySelector == 3):
# 100 fitness cost 10% reduction
# 200 fitness cost 20% reduction
title = "Fitness Cost"
testFileA = "TSA_100.csv"
testFileB = "TSA_200.csv"
centralData = monet.loadAndHashFactorialCSV(path + centralFile)
probeDataA = monet.loadAndHashFactorialCSV(path + testFileA)
probeDataB = monet.loadAndHashFactorialCSV(path + testFileB)
differencesHashA = monet.calculateFactorialHashError(probeDataA, centralData,
monet.sampleDifference)
differencesHashB = monet.calculateFactorialHashError(probeDataB, centralData,
monet.sampleDifference)
errorsA = differencesHashA.values()
errorsB = differencesHashB.values()
deHashedA = monet.deHashFactorial(differencesHashA)
deHashedB = monet.deHashFactorial(differencesHashB)
np.savetxt(path + "deHashedA.csv", deHashedA, fmt='%2.6f', delimiter=",")
np.savetxt(path + "deHashedB.csv", deHashedB, fmt='%2.6f', delimiter=",")
###############################################################################
# Plotting the results
###############################################################################
binsDict = dict(start=0, end=1, size=0.05)
cData = monet.loadAndHashFactorialCSV(
pathCF+cfFile+'.csv', floatMultiplier=cScale
)
# Filter the SA files for matching IDs
expCoreID = ["_".join(i.split('_')[0:-1]) for i in saFiles]
matchingIndices = [i == cfFile for i in expCoreID]
matchingFiles = list(compress(saFiles, matchingIndices))
# Go through the SA files doing the differences
for (j, pFile) in enumerate(matchingFiles):
# Load probe file
pData = monet.loadAndHashFactorialCSV(
pathSA+pFile+'.csv', floatMultiplier=cScale
)
# Calculate differences between files
diffHash = monet.calculateFactorialHashError(
pData, cData, monet.sampleDifference
)
deHashed = monet.deHashFactorial(diffHash)
# Export results
np.savetxt(
pathSA + 'sensitivity/' + pFile + "_SA.csv",
deHashed, fmt='%2.6f', delimiter=","
)
end = time.time()
count = count + 1
print('* {0}) {1} [{2:.2f} min]'.format(count, pFile, (end-start)/60))
###############################################################################
# Message for terminal
###############################################################################
print('**********************************************************************')
print('* Finished ' + str(len(saFiles)) + ' experiments [' + str(datetime.datetime.now()) + ']')
