class Gait(FeatureExtractorAbstract):
def __init__(self):
self.dc = DistanceCalc()
def getCSVheader(self):
return ["gaitPeriodX", "gaitErrorX", "gaitPeriodY", "gaitErrorY", "gaitPeriodZ", "gaitErrorZ"]
def extract(self, experiment, type, indiv):
filepath = experiment[2] + os.path.sep + PathConfig.traceFolderNormal + os.path.sep + indiv[0] + ".trace"
if not os.path.isfile(filepath):
filepath = experiment[2] + os.path.sep + PathConfig.traceFoldersAlt[type] + os.path.sep + indiv[
0] + ".trace"
if not os.path.isfile(filepath):
return ['NA'] * 3
with open(filepath) as fh:
xs = []
ys = []
zs = []
for line in fh:
linesplit = line.split("\t")
if not self.dc.isValidLine(linesplit):
linesplit = line.split(" ")
if not self.dc.isValidLine(linesplit):
continue
xs.append(linesplit[-3])
ys.append(linesplit[-2])
zs.append(linesplit[-1])
xs = map(float,xs)
ys = map(float,ys)
zs = map(float,zs)
xPeriod, xError = self._getPeriod(xs)
yPeriod, yError = self._getPeriod(ys)
zPeriod, zError = self._getPeriod(zs)
return xPeriod, xError, yPeriod, yError, zPeriod, zError
@staticmethod
def _getPeriod(signal):
if len(signal) == 0:
return 'NA'
signal = np.array(signal)
fft = np.fft.rfft(signal).real
fft = fft[:len(fft) / 2 + 1]
fft[1:] = fft[1:] / (len(signal)/2)
fft[0] = fft[0]/len(signal)
period = np.argmax(fft[1:]) + 1
period_value = fft[1:].max()
linspace = np.linspace(0,len(signal), len(signal))
mse = np.average(signal - (period_value * np.sin(period*linspace+np.average(signal)))**2)
return period, mse
def extract(self, experiment, type, indiv):
errorReturnVal = ['NA'] * 2
filepath = experiment[2] + os.path.sep + PathConfig.traceFolderNormal + os.path.sep + indiv[0] + ".trace"
dc = DistanceCalc()
if not os.path.isfile(filepath):
filepath = experiment[2] + os.path.sep + PathConfig.traceFoldersAlt[type] + os.path.sep + indiv[
0] + ".trace"
if not os.path.isfile(filepath):
return errorReturnVal
with open(filepath, 'r') as inputFile:
traceLines = []
lines = []
for line in inputFile:
lines.append(line)
if len(lines) == 110:
break
if len(lines) < 20:
return errorReturnVal
for line in lines[-10:]:
lineSplit = line.split("\t")
if not dc.isValidLine(lineSplit):
lineSplit = line.split(" ")
if not dc.isValidLine(lineSplit):
continue
traceLines.append(
(float(lineSplit[2]), float(lineSplit[2]))
)
if len(traceLines) < 6:
return errorReturnVal
self._setPoints(traceLines[0], traceLines[3], traceLines[5])
self._getCenter()
self._getRadius()
self._getCircumference()
dist = dc.distanceStep(filepath, "euclidean")
percentCircle = dist / self.circumference
return [self.radius, percentCircle]
def extract(self, experiment, type, indiv):
filepath = experiment[2] + os.path.sep + PathConfig.traceFolderNormal + os.path.sep + indiv[0] + ".trace"
dc = DistanceCalc()
if not os.path.isfile(filepath):
filepath = (
experiment[2] + os.path.sep + PathConfig.traceFoldersAlt[type] + os.path.sep + indiv[0] + ".trace"
)
if not os.path.isfile(filepath):
return ["NA"]
with open(filepath, "r") as inputFile:
monotonyUp = 0
monotonyDown = 0
monotonyLeft = 0
monotonyRight = 0
firstLine = True
xy = (0.0, 0.0)
for line in inputFile:
lineSplit = line.split("\t")
if not dc.isValidLine(lineSplit):
lineSplit = line.split(" ")
if not dc.isValidLine(lineSplit):
continue
if firstLine:
firstLine = False
xy = (lineSplit[2], lineSplit[3])
else:
xyNew = (lineSplit[2], lineSplit[3])
if xyNew[1] > xy[1]:
monotonyUp += 1
if xyNew[1] < xy[1]:
monotonyDown += 1
if xyNew[0] > xy[0]:
monotonyRight += 1
if xyNew[0] < xy[0]:
monotonyLeft += 1
xy = xyNew
return [monotonyRight - monotonyLeft, monotonyUp - monotonyDown]
