class PanTompkinsTest(unittest.TestCase):
def setUp(self):
refDir = os.path.dirname(os.getcwd())
refNumStr = '101'
refInFileDir = os.path.join(refDir, refNumStr, 'Input.csv')
refOutFileDir = os.path.join(refDir, refNumStr, 'PanTompkinsOutput.csv')
resOutFileDir = os.path.join(refDir, refNumStr, 'PanTompkinsResultsPython.csv')
self.samplingFreq = 360.0
self.refInFile = open(refInFileDir, 'r')
self.refOutFile = open(refOutFileDir, 'r')
# self.refAdditionalFile = open(refAdditionalFileDir, 'r')
self.resultFile = open(resOutFileDir, 'w')
self.refInVector = []
self.refOutVector = []
self.resultVector = []
self.refIntSignalVector = []
self.refMarksVector = []
def tearDown(self):
self.refInFile.close()
self.refOutFile.close()
# self.refAdditionalFile.close()
self.resultFile.close()
def loadReferenceData(self):
refInVectorStr = self.refInFile.readline().split(',')
del refInVectorStr[-1]
for item in refInVectorStr:
val = float(item)
self.refInVector.append(val)
refOutVectorStr = self.refOutFile.readline().split(',')
del refOutVectorStr[-1]
for item in refOutVectorStr:
val = int(float(item))
self.refOutVector.append(val)
def runAlgorithm(self):
self.alg = PanTompkins(self.refInVector, samplingFrequency=self.samplingFreq)
self.resultVector = self.alg.process()
for val in self.resultVector:
self.resultFile.write('%d\n' % val)
def comapareOtherValues(self):
# 1st line
intSignalStr = self.refAdditionalFile.readline().split(',')
del intSignalStr[-1]
for item in intSignalStr:
self.refIntSignalVector.append(float(item))
for ref, res in zip(self.refIntSignalVector[28:], self.alg.nSignal):
diff = ref - res
if diff > 0.0000001:
print diff
# 2nd line
marksStr = self.refAdditionalFile.readline().split(',')
del marksStr[-1]
for item in marksStr:
self.refMarksVector.append(int(float(item)))
for ref, res in zip(self.refMarksVector, self.alg.fiducialMarks):
self.assertEqual(ref, res)
pass
def isResultIdentical(self):
result = True
for ref, res in zip(self.refOutVector, self.resultVector):
print '{} {}'.format(ref, res)
if ref != res:
print 'reference ({}) and result ({}) are not equal'.format(ref, res)
result = False
return result
def testAlgorithm(self):
self.loadReferenceData()
self.runAlgorithm()
# self.comapareOtherValues()
self.assertTrue(self.isResultIdentical(), 'Reference vector and result vector are not identical')
def runAlgorithm(self):
self.alg = PanTompkins(self.refInVector, samplingFrequency=self.samplingFreq)
self.resultVector = self.alg.process()
for val in self.resultVector:
self.resultFile.write('%d\n' % val)
import os
from PanTompkins import PanTompkins
refDir = os.path.dirname(os.getcwd())
refNumStr = '102'
refInFileDir = os.path.join(refDir, refNumStr, 'Input.csv')
resOutFileDir = os.path.join(refDir, refNumStr, 'PanTompkinsResultsPython.csv')
refInFile = open(refInFileDir, 'r')
resultFile = open(resOutFileDir, 'w')
refInVector = []
refInVectorStr = refInFile.readline().split(',')
del refInVectorStr[-1]
for item in refInVectorStr:
val = float(item)
refInVector.append(val)
alg = PanTompkins(refInVector,
samplingFrequency=360)
resultVector = alg.process()
for val in resultVector or []:
resultFile.write('%d\n' % val)
refInFile.close()
resultFile.close()
class PanTompkinsTest(unittest.TestCase):
def setUp(self):
refDir = os.path.dirname(os.getcwd())
refNumStr = '101'
refInFileDir = os.path.join(refDir, refNumStr, 'Input.csv')
refOutFileDir = os.path.join(refDir, refNumStr,
'PanTompkinsOutput.csv')
resOutFileDir = os.path.join(refDir, refNumStr,
'PanTompkinsResultsPython.csv')
self.samplingFreq = 360.0
self.refInFile = open(refInFileDir, 'r')
self.refOutFile = open(refOutFileDir, 'r')
# self.refAdditionalFile = open(refAdditionalFileDir, 'r')
self.resultFile = open(resOutFileDir, 'w')
self.refInVector = []
self.refOutVector = []
self.resultVector = []
self.refIntSignalVector = []
self.refMarksVector = []
def tearDown(self):
self.refInFile.close()
self.refOutFile.close()
# self.refAdditionalFile.close()
self.resultFile.close()
def loadReferenceData(self):
refInVectorStr = self.refInFile.readline().split(',')
del refInVectorStr[-1]
for item in refInVectorStr:
val = float(item)
self.refInVector.append(val)
refOutVectorStr = self.refOutFile.readline().split(',')
del refOutVectorStr[-1]
for item in refOutVectorStr:
val = int(float(item))
self.refOutVector.append(val)
def runAlgorithm(self):
self.alg = PanTompkins(self.refInVector,
samplingFrequency=self.samplingFreq)
self.resultVector = self.alg.process()
for val in self.resultVector:
self.resultFile.write('%d\n' % val)
def comapareOtherValues(self):
# 1st line
intSignalStr = self.refAdditionalFile.readline().split(',')
del intSignalStr[-1]
for item in intSignalStr:
self.refIntSignalVector.append(float(item))
for ref, res in zip(self.refIntSignalVector[28:], self.alg.nSignal):
diff = ref - res
if diff > 0.0000001:
print diff
# 2nd line
marksStr = self.refAdditionalFile.readline().split(',')
del marksStr[-1]
for item in marksStr:
self.refMarksVector.append(int(float(item)))
for ref, res in zip(self.refMarksVector, self.alg.fiducialMarks):
self.assertEqual(ref, res)
pass
def isResultIdentical(self):
result = True
for ref, res in zip(self.refOutVector, self.resultVector):
print '{} {}'.format(ref, res)
if ref != res:
print 'reference ({}) and result ({}) are not equal'.format(
ref, res)
result = False
return result
def testAlgorithm(self):
self.loadReferenceData()
self.runAlgorithm()
# self.comapareOtherValues()
self.assertTrue(
self.isResultIdentical(),
'Reference vector and result vector are not identical')
def runAlgorithm(self):
self.alg = PanTompkins(self.refInVector,
samplingFrequency=self.samplingFreq)
self.resultVector = self.alg.process()
for val in self.resultVector:
self.resultFile.write('%d\n' % val)
import os
from PanTompkins import PanTompkins
refDir = os.path.dirname(os.getcwd())
refNumStr = '102'
refInFileDir = os.path.join(refDir, refNumStr, 'Input.csv')
resOutFileDir = os.path.join(refDir, refNumStr, 'PanTompkinsResultsPython.csv')
refInFile = open(refInFileDir, 'r')
resultFile = open(resOutFileDir, 'w')
refInVector = []
refInVectorStr = refInFile.readline().split(',')
del refInVectorStr[-1]
for item in refInVectorStr:
val = float(item)
refInVector.append(val)
alg = PanTompkins(refInVector, samplingFrequency=360)
resultVector = alg.process()
for val in resultVector or []:
resultFile.write('%d\n' % val)
refInFile.close()
resultFile.close()
