def splitDataAboveBelowMean(self, npIn, sensorsIn, returnType, n_tests = -1):
above = []
below = []
restSensors = []
extSensors = []
mean = 0
if returnType == 'm':
mean = np.mean(npIn)
above = npIn[npIn > mean]
below = npIn[npIn < mean]
else: #it's for points
mean = Point.averagePoints(npIn).sqrMagnitude()
for i, p in enumerate(npIn):
if p.sqrMagnitude() > mean:
above.append(p)
extSensors.append(sensorsIn[i])
else:
below.append(p)
restSensors.append(sensorsIn[i])
# above = [p for p in npIn if p.sqrMagnitude() > mean]
# below = [p for p in npIn if p.sqrMagnitude() < mean]
if n_tests >= 1:
print("n_tests is used!")
above, below, restSensors, extSensors = above[:n_tests], below[:n_tests], restSensors[:n_tests], extSensors[:n_tests]
return np.array(above), np.array(below), Point.averagePoints(np.append(above,below)), np.array(extSensors), np.array(restSensors)
def generateFeatures(self, byValue, threshold):
# Returns numpy arrays where possible
self.plateaus = self.lookAheadForPlateau(by = byValue, varianceThreshold = threshold)
self.plateauPoints = self.averagePlateauSections(self.plateaus, 'p')
self.plateauSensorValues = self.extractData6Values(self.plateaus)
self.plateauSensorAverages = self.avgSensorValues(self.plateauSensorValues)
self.extensionPoints, self.restPoints, self.meanPoint, self.extensionSensors, self.restSensors = self.splitDataAboveBelowMean(self.plateauPoints, self.plateauSensorAverages, returnType = 'p')
self.meanRestPoint = Point.averagePoints(self.restPoints)
self.trimLists()
self.vectorsBetween = [self.extensionPoints[i] - self.restPoints[i] for i, val in enumerate(self.restPoints)]
self.anglesBetween = [Point.angleBetween(self.extensionPoints[i] ,self.restPoints[i] ) for i, val in enumerate(self.restPoints)]
def averagePlateauSections(self, plateaus, returnType = 'm'):
'''
array to hold each flat part of the palteaus
return array
loop through through plateaus array
if i is 0,
then we save the mean of avgFlat,
then clear avgFlat and move on
else we save the length of the point to avgFlat
then average any flat parts and append to return array
return array
'''
avgFlat, returnArray = [],[]
for i, num in enumerate(plateaus):
if num == 0:
if len(avgFlat) != 0: #we've reached the end of a plateau
if returnType == 'm':
returnArray.append(np.mean(avgFlat))
else:
returnArray.append(Point.averagePoints(avgFlat))
avgFlat.clear()
continue
if returnType == 'm':
avgFlat.append(self.copPoints[i].magnitude())
else:
avgFlat.append(self.copPoints[i])
# Consider the code reaching the end of the list with items not yet averaged
if len(avgFlat) != 0:
if returnType == 'm':
returnArray.append(np.mean(avgFlat))
else:
returnArray.append(Point.averagePoints(avgFlat))
return np.array(returnArray)
