def appendAnalogs(self):
for nexusAnalogDevice in self.m_nexusAnalogDevices:
channels = nexusAnalogDevice.getChannels()
for channel in channels:
start = self.m_firstFrame - 1
end = self.m_lastFrame - 1
data = channel.getValues()[
(start) * self.m_numberAnalogSamplePerFrame:(end + 1) *
self.m_numberAnalogSamplePerFrame]
if self.m_firstFrame == 1:
time_init = 0.0
else:
time_init = self.m_firstFrame / self.m_analogFrameRate
ts = ma.TimeSequence(utils.str(channel.getLabel()), 1,
data.shape[0], self.m_analogFrameRate,
time_init, ma.TimeSequence.Type_Analog,
"V", 1.0, 0.0, [-10.0, 10.0],
self.m_trial.timeSequences())
ts.setData(data.reshape((data.shape[0], 1)))
ts.setDescription(channel.getDescription())
def appendMarkers(self):
markersLoaded = NEXUS.GetMarkerNames(self.m_subject)
markers = []
for i in range(0, len(markersLoaded)):
data = NEXUS.GetTrajectory(self.m_subject, markersLoaded[i])
if data != ([], [], [], []):
markers.append(markersLoaded[i])
for marker in markers:
rawDataX, rawDataY, rawDataZ, E = NEXUS.GetTrajectory(
self.m_subject, marker)
E = np.asarray(E).astype("float") - 1
values = np.array([
np.asarray(rawDataX),
np.asarray(rawDataY),
np.asarray(rawDataZ)
]).T
start = self.m_firstFrame - self.m_trialFirstFrame
end = self.m_lastFrame - self.m_trialFirstFrame
#if marker == "LTHI": import ipdb; ipdb.set_trace()
values_cut = values[start:end + 1, :]
E_cut = E[start:end + 1]
data = np.zeros((values_cut.shape[0], 4))
data[:, 0:3] = values_cut
data[:, 3] = E_cut
if self.m_firstFrame == 1:
time_init = 0.0
else:
time_init = self.m_firstFrame / self.m_framerate
ts = ma.TimeSequence(str(marker), 4, data.shape[0],
self.m_framerate, time_init,
ma.TimeSequence.Type_Marker, "mm",
self.m_trial.timeSequences())
ts.setData(data)
def appendModelOutputs(self):
modelOutputNames = NEXUS.GetModelOutputNames(self.m_subject)
if modelOutputNames != []:
for modelOutputName in modelOutputNames:
data, E = NEXUS.GetModelOutput(self.m_subject, modelOutputName)
type = NEXUS.GetModelOutputDetails(self.m_subject,
modelOutputName)[0]
E = np.asarray(E).astype("float") - 1
values = np.array([
np.asarray(data[0]),
np.asarray(data[1]),
np.asarray(data[2])
]).T
start = self.m_firstFrame - self.m_trialFirstFrame
end = self.m_lastFrame - self.m_trialFirstFrame
values_cut = values[start:end + 1, :]
E_cut = E[start:end + 1]
data = np.zeros((values_cut.shape[0], 4))
data[:, 0:3] = values_cut
data[:, 3] = E_cut
if self.m_firstFrame == 1:
time_init = 0.0
else:
time_init = self.m_firstFrame / self.m_framerate
if type == "Angles":
ts = ma.TimeSequence(utils.str(modelOutputName), 4,
values_cut.shape[0], self.m_framerate,
time_init, ma.TimeSequence.Type_Angle,
"Deg", self.m_trial.timeSequences())
ts.setData(data)
elif type == "Forces":
ts = ma.TimeSequence(utils.str(modelOutputName), 4,
values_cut.shape[0], self.m_framerate,
time_init,
ma.TimeSequence.Type_Force, "N.Kg-1",
self.m_trial.timeSequences())
ts.setData(data)
elif type == "Moments":
ts = ma.TimeSequence(utils.str(modelOutputName), 4,
values_cut.shape[0], self.m_framerate,
time_init,
ma.TimeSequence.Type_Moment,
"Nmm.Kg-1",
self.m_trial.timeSequences())
ts.setData(data)
elif type == "Powers":
ts = ma.TimeSequence(utils.str(modelOutputName), 4,
values_cut.shape[0], self.m_framerate,
time_init, ma.TimeSequence.Type_Power,
"Watt.Kg-1",
self.m_trial.timeSequences())
ts.setData(data)
elif type == "Modeled Markers":
ts = ma.TimeSequence(utils.str(modelOutputName), 4,
values_cut.shape[0], self.m_framerate,
time_init,
ma.TimeSequence.Type_Marker, "mm",
self.m_trial.timeSequences())
ts.setData(data)
else:
logging.warning(
"[pyCGM2] : Model Output (%s) from Nexus not added to the openma trial"
% (modelOutputName))
def convertBtkAcquisition(acq, returnType="Trial"):
root = ma.Node('root')
trial = ma.Trial("Trial", root)
framerate = acq.GetPointFrequency()
firstFrame = acq.GetFirstFrame()
numberAnalogSamplePerFrame = acq.GetNumberAnalogSamplePerFrame()
analogFramerate = acq.GetAnalogFrequency()
if firstFrame == 1:
time_init = 0.0
else:
time_init = firstFrame / framerate
for it in btk.Iterate(acq.GetPoints()):
label = it.GetLabel()
values = it.GetValues()
residuals = it.GetResiduals()
desc = it.GetDescription()
data = np.zeros((values.shape[0], 4))
data[:, 0:3] = values
data[:, 3] = residuals[:, 0]
if it.GetType() == btk.btkPoint.Marker:
ts = ma.TimeSequence(str(label), 4, data.shape[0], framerate,
time_init, ma.TimeSequence.Type_Marker, "mm",
trial.timeSequences())
elif it.GetType() == btk.btkPoint.Angle:
ts = ma.TimeSequence(str(label), 4, data.shape[0], framerate,
time_init, ma.TimeSequence.Type_Angle, "Deg",
trial.timeSequences())
elif it.GetType() == btk.btkPoint.Force:
ts = ma.TimeSequence(str(label), 4, data.shape[0], framerate,
time_init, ma.TimeSequence.Type_Force,
"N.Kg-1", trial.timeSequences())
elif it.GetType() == btk.btkPoint.Moment:
ts = ma.TimeSequence(str(label), 4, data.shape[0], framerate,
time_init, ma.TimeSequence.Type_Moment,
"Nmm.Kg-1", trial.timeSequences())
elif it.GetType() == btk.btkPoint.Power:
ts = ma.TimeSequence(str(label), 4, data.shape[0], framerate,
time_init, ma.TimeSequence.Type_Power,
"Watt.Kg-1", trial.timeSequences())
else:
logging.warning(
"[pyCGM2] point [%s] not copied into openma trial" % (label))
ts.setData(data)
ts.setDescription(desc)
for it in btk.Iterate(acq.GetAnalogs()):
label = it.GetLabel()
values = it.GetValues()
desc = it.GetDescription()
data = values
ts = ma.TimeSequence(str(label), 1, data.shape[0], analogFramerate,
time_init, ma.TimeSequence.Type_Analog, "V", 1.0,
0.0, [-10.0, 10.0], trial.timeSequences())
ts.setData(data)
ts.setDescription(desc)
for it in btk.Iterate(acq.GetEvents()):
label = it.GetLabel()
time = it.GetTime()
context = it.GetContext()
subject = it.GetSubject()
ev = ma.Event(label, time, context, str(subject), trial.events())
sortedEvents(trial)
if returnType == "Trial":
return trial
else:
return root
def export(self, outputName, path=None):
root = ma.Node('root')
trial = ma.Trial("AnalysisC3d", root)
# metadata
#-------------
# subject infos
if self.analysis.subjectInfo is not None:
subjInfo = self.analysis.subjectInfo
for item in subjInfo.items():
trial.setProperty("SUBJECT_INFO:" + str(item[0]), item[1])
# model infos
if self.analysis.modelInfo is not None:
modelInfo = self.analysis.modelInfo
for item in modelInfo.items():
trial.setProperty("MODEL_INFO:" + str(item[0]), item[1])
# model infos
if self.analysis.experimentalInfo is not None:
experimentalConditionInfo = self.analysis.experimentalInfo
for item in experimentalConditionInfo.items():
trial.setProperty("EXPERIMENTAL_INFO:" + str(item[0]), item[1])
#trial.setProperty('MY_GROUP:MY_PARAMETER',10.0)
# kinematic cycles
#------------------
# metadata
# for key in self.analysis.kinematicStats.data.keys():
# if key[1]=="Left":
# n_left_cycle = len(self.analysis.kinematicStats.data[key[0],key[1]]["values"])
# trial.setProperty('PROCESSING:LeftKinematicCycleNumber',n_left_cycle)
# break
#
# for key in self.analysis.kinematicStats.data.keys():
# if key[1]=="Right":
# n_right_cycle = len(self.analysis.kinematicStats.data[key[0],key[1]]["values"])
# trial.setProperty('PROCESSING:RightKinematicCycleNumber',n_right_cycle)
# break
# cycles
for key in self.analysis.kinematicStats.data.keys():
label = key[0]
context = key[1]
if not np.all(
self.analysis.kinematicStats.data[label,
context]["mean"] == 0):
cycle = 0
values = np.zeros((101, 4))
values2 = np.zeros((101, 1))
for val in self.analysis.kinematicStats.data[
label, context]["values"]:
angle = ma.TimeSequence(
str(label + "." + context + "." + str(cycle)), 4, 101,
1.0, 0.0, ma.TimeSequence.Type_Angle, "deg",
trial.timeSequences())
values[:, 0:3] = val
angle.setData(values)
cycle += 1
# kinetic cycles
#------------------
# # metadata
# for key in self.analysis.kineticStats.data.keys():
# if key[1]=="Left":
# n_left_cycle = len(self.analysis.kineticStats.data[key[0],key[1]]["values"])
# trial.setProperty('PROCESSING:LeftKineticCycleNumber',n_left_cycle)
# break
#
# for key in self.analysis.kineticStats.data.keys():
# if key[1]=="Right":
# n_right_cycle = len(self.analysis.kineticStats.data[key[0],key[1]]["values"])
# trial.setProperty('PROCESSING:RightKineticCycleNumber',n_right_cycle)
# break
# cycles
for key in self.analysis.kineticStats.data.keys():
label = key[0]
context = key[1]
if not np.all(
self.analysis.kineticStats.data[label,
context]["mean"] == 0):
cycle = 0
values = np.zeros((101, 4))
for val in self.analysis.kineticStats.data[label,
context]["values"]:
moment = ma.TimeSequence(
str(label + "." + context + "." + str(cycle)), 4, 101,
1.0, 0.0, ma.TimeSequence.Type_Moment, "N.mm",
trial.timeSequences())
values[:, 0:3] = val
moment.setData(values)
cycle += 1
# for key in self.analysis.emgStats.data.keys():
# label = key[0]
# context = key[1]
# if not np.all( self.analysis.emgStats.data[label,context]["mean"]==0):
# cycle = 0
# for val in self.analysis.emgStats.data[label,context]["values"]:
# analog = ma.TimeSequence(str(label+"."+context+"."+str(cycle)),1,101,1.0,0.0,ma.TimeSequence.Type_Analog,"V", 1.0,0.0,[-10.0,10.0], trial.timeSequences())
# analog.setData(val)
# cycle+=1
try:
if path == None:
ma.io.write(root, str(outputName + ".c3d"))
else:
ma.io.write(root, str(path + outputName + ".c3d"))
logging.info("Analysis c3d [%s.c3d] Exported" %
(str(outputName + ".c3d")))
except:
raise Exception("[pyCGM2] : analysis c3d doesn t export")