def test_openFile(self):
contentYaml = files.openFile(
pyCGM2.TEST_DATA_PATH + "\\LowLevel\\IO\\Hanibal_files\\",
"file.yaml")
contentJson = files.openFile(
pyCGM2.TEST_DATA_PATH + "\\LowLevel\\IO\\Hanibal_files\\",
"file.json")
assert contentYaml != False
assert contentJson != False
files.saveJson(
pyCGM2.TEST_DATA_PATH + "\\LowLevel\\IO\\Hanibal_files\\",
"testJsonOut", contentJson)
def main(args):
DATA_PATH = os.getcwd()+"\\"
# User Settings
if os.path.isfile(DATA_PATH + args.userFile):
userSettings = files.openFile(DATA_PATH,args.userFile)
else:
raise Exception ("user setting file not found")
# internal (expert) Settings
if args.expertFile:
if os.path.isfile(DATA_PATH + args.expertFile):
internalSettings = files.openFile(DATA_PATH,args.expertFile)
else:
raise Exception ("expert setting file not found")
else:
internalSettings = None
# translators
if os.path.isfile(DATA_PATH + "CGM1_1.translators"):
translators = files.openFile(DATA_PATH,"CGM1_1.translators")
else:
translators = None
# --- Manager ----
manager = ModelManager.CGM1_1ConfigManager(userSettings,localInternalSettings=internalSettings,localTranslators=translators)
manager.contruct()
finalSettings = manager.getFinalSettings()
files.prettyDictPrint(finalSettings)
logging.info("=============Calibration=============")
model,finalAcqStatic = cgm1_1.calibrate(DATA_PATH,
manager.staticTrial,
manager.translators,
manager.requiredMp,
manager.optionalMp,
manager.leftFlatFoot,
manager.rightFlatFoot,
manager.markerDiameter,
manager.pointSuffix,
displayCoordinateSystem=True)
btkTools.smartWriter(finalAcqStatic, str(DATA_PATH+finalSettings["Calibration"]["StaticTrial"]))#[:-4]+"-pyCGM2modelled.c3d"))
logging.info("Static Calibration -----> Done")
manager.updateMp(model)
#files.prettyDictPrint(manager.finalSettings)
logging.info("=============Fitting=============")
for trial in manager.dynamicTrials:
mfpa = None if trial["Mfpa"] == "Auto" else trial["Mfpa"]
reconstructFilenameLabelled = trial["File"]
acqGait = cgm1_1.fitting(model,DATA_PATH, reconstructFilenameLabelled,
manager.translators,
manager.markerDiameter,
manager.pointSuffix,
mfpa,
manager.momentProjection,
displayCoordinateSystem=True)
btkTools.smartWriter(acqGait, str(DATA_PATH+reconstructFilenameLabelled))#[:-4]+"-pyCGM2modelled.c3d"))
logging.info("---->dynamic trial (%s) processed" %(reconstructFilenameLabelled))
logging.info("=============Writing of final Settings=============")
i = 0
while os.path.exists("CGM1.1 [%s].completeSettings" % i):
i += 1
filename = "CGM1.1 [" + str(i)+"].completeSettings"
files.saveJson(DATA_PATH, filename, finalSettings)
logging.info("---->complete settings (%s) exported" %(filename))
raw_input("Press return to exit..")
def save(self,DATA_PATH,filename):
if self.__normativeData is not None:
files.saveJson(DATA_PATH, filename, self.__normativeData)
else:
raise Exception("[pyCGM2] Normative data is empty")
def save(self, DATA_PATH, pipelineFilename):
files.saveJson(DATA_PATH, pipelineFilename, self.pipSettings)
def main(args):
DATA_PATH = os.getcwd() + "\\"
# User Settings
if os.path.isfile(DATA_PATH + args.userFile):
userSettings = files.openFile(DATA_PATH, args.userFile)
else:
raise Exception("user setting file not found")
# internal (expert) Settings
if args.expertFile:
if os.path.isfile(DATA_PATH + args.expertFile):
internalSettings = files.openFile(DATA_PATH, args.expertFile)
else:
raise Exception("expert setting file not found")
else:
internalSettings = None
# --- Manager ----
manager = EmgManager.EmgConfigManager(
userSettings, localInternalSettings=internalSettings)
manager.contruct()
finalSettings = manager.getFinalSettings()
files.prettyDictPrint(finalSettings)
# reconfiguration of emg settings as lists
EMG_LABELS, EMG_MUSCLES, EMG_CONTEXT, NORMAL_ACTIVITIES = manager.getEmgConfiguration(
)
#----- rectified view -------
rectTrials = manager.temporal_trials
analysis.processEMG(
DATA_PATH,
rectTrials,
EMG_LABELS,
highPassFrequencies=manager.BandpassFrequencies,
envelopFrequency=manager.EnvelopLowpassFrequency,
fileSuffix=None) # high pass then low pass for all c3ds
for trial in rectTrials:
plot.plotTemporalEMG(DATA_PATH,
trial,
EMG_LABELS,
EMG_MUSCLES,
EMG_CONTEXT,
NORMAL_ACTIVITIES,
exportPdf=True,
rectify=manager.rectifyFlag)
#----- Gait normalized envelop -------
envTrials = manager.gaitNormalized_trials
analysis.processEMG(
DATA_PATH,
envTrials,
EMG_LABELS,
highPassFrequencies=manager.BandpassFrequencies,
envelopFrequency=manager.EnvelopLowpassFrequency,
fileSuffix=None) # high pass then low pass for all c3ds
emgAnalysisInstance = analysis.makeEmgAnalysis(DATA_PATH, envTrials,
EMG_LABELS, None, None)
filename = manager.title
if not manager.consistencyFlag:
plot.plotDescriptiveEnvelopEMGpanel(DATA_PATH,
emgAnalysisInstance,
EMG_LABELS,
EMG_MUSCLES,
EMG_CONTEXT,
NORMAL_ACTIVITIES,
normalized=False,
exportPdf=True,
outputName=filename)
else:
plot.plotConsistencyEnvelopEMGpanel(DATA_PATH,
emgAnalysisInstance,
EMG_LABELS,
EMG_MUSCLES,
EMG_CONTEXT,
NORMAL_ACTIVITIES,
normalized=False,
exportPdf=True,
outputName=filename)
logging.info("=============Writing of final Settings=============")
files.saveJson(DATA_PATH, str(filename + "-EMG.completeSettings"),
finalSettings)
logging.info("---->complete settings (%s) exported" %
(str(filename + "-EMG.completeSettings")))
raw_input("Press return to exit..")
def exportJson_Schwartz2008(path, name="Schwartz2008.json"):
"""generate json from schwartz2008 dataset
:param path: path
:type path: string
:param name: filename
:type name: string
:return: json file
:rtype: file
normativeDatasets.exportJson_Schwartz2008(DATA_PATH,name="test.json")
"""
def constructJson(dataset, dataset_label, modalityLabel, data):
dataset[dataset_label].update({modalityLabel: dict()})
for label in data.keys():
mean = data[label]["mean"]
sd = data[label]["sd"]
label = label.replace(".", "")
dataset[dataset_label][modalityLabel].update(
{label: {
'X': None,
'Y': None,
'Z': None
}})
outX = [0, mean[0, 0] - sd[0, 0], mean[0, 0] + sd[0, 0]]
for i in range(1, mean.shape[0]):
values = [i * 2, mean[i, 0] - sd[i, 0], mean[i, 0] + sd[i, 0]]
outX.append(values)
outY = [0, mean[0, 1] - sd[0, 1], mean[0, 1] + sd[0, 1]]
for i in range(1, mean.shape[0]):
values = [i * 2, mean[i, 1] - sd[i, 1], mean[i, 1] + sd[i, 1]]
outY.append(values)
outZ = [0, mean[0, 2] - sd[0, 2], mean[0, 2] + sd[0, 2]]
for i in range(1, mean.shape[0]):
values = [i * 2, mean[i, 2] - sd[i, 2], mean[i, 2] + sd[i, 2]]
outZ.append(values)
dataset[dataset_label][modalityLabel][label]["X"] = outX
dataset[dataset_label][modalityLabel][label]["Y"] = outY
dataset[dataset_label][modalityLabel][label]["Z"] = outZ
dataset = {"Schwartz2008": dict()}
nds = Schwartz2008("VerySlow")
nds.constructNormativeData()
constructJson(dataset, "Schwartz2008", "VerySlow", nds.data)
nds = Schwartz2008("Slow")
nds.constructNormativeData()
constructJson(dataset, "Schwartz2008", "Slow", nds.data)
nds = Schwartz2008("Free")
nds.constructNormativeData()
constructJson(dataset, "Schwartz2008", "Free", nds.data)
nds = Schwartz2008("Fast")
nds.constructNormativeData()
constructJson(dataset, "Schwartz2008", "Fast", nds.data)
nds = Schwartz2008("VeryFast")
nds.constructNormativeData()
constructJson(dataset, "Schwartz2008", "VeryFast", nds.data)
files.saveJson(path, name, dataset)
def export(self,outputName, path=None):
out=OrderedDict()
if self.analysis.stpStats != {}:
processedKeys=list()
for keys in self.analysis.stpStats.keys():
if keys not in processedKeys:
processedKeys.append(keys)
else:
raise Exception ( "[pyCGM2] - duplicated keys[ %s - %s] detected" %(keys[0],keys[1]))
if keys[0] not in out.keys():
out[keys[0]]=dict()
out[keys[0]][keys[1]]=dict()
out[keys[0]][keys[1]]["values"]=self.analysis.stpStats[keys]["values"].tolist()
else:
out[keys[0]][keys[1]]=dict()
out[keys[0]][keys[1]]["values"]=self.analysis.stpStats[keys]["values"].tolist()
if self.analysis.kinematicStats.data != {}:
processedKeys=list()
for keys in self.analysis.kinematicStats.data.keys():
if not np.all( self.analysis.kinematicStats.data[keys]["mean"]==0):
if keys not in processedKeys:
processedKeys.append(keys)
else:
raise Exception ( "[pyCGM2] - duplicated keys[ %s - %s] detected" %(keys[0],keys[1]))
if keys[0] not in out.keys():
out[keys[0]]=dict()
out[keys[0]][keys[1]]=dict()
out[keys[0]][keys[1]]["values"]= {"X":[],"Y":[],"Z":[]}
else:
out[keys[0]][keys[1]]=dict()
out[keys[0]][keys[1]]["values"]= {"X":[],"Y":[],"Z":[]}
li_X = list()
for cycle in self.analysis.kinematicStats.data[keys]["values"]:
li_X.append(cycle[:,0].tolist())
li_Y = list()
for cycle in self.analysis.kinematicStats.data[keys]["values"]:
li_Y.append(cycle[:,1].tolist())
li_Z = list()
for cycle in self.analysis.kinematicStats.data[keys]["values"]:
li_Z.append(cycle[:,2].tolist())
out[keys[0]][keys[1]]["values"]["X"] = li_X
out[keys[0]][keys[1]]["values"]["Y"] = li_Y
out[keys[0]][keys[1]]["values"]["Z"] = li_Z
if self.analysis.kineticStats.data != {}:
processedKeys=list()
for keys in self.analysis.kineticStats.data.keys():
if not np.all( self.analysis.kineticStats.data[keys]["mean"]==0):
if keys not in processedKeys:
processedKeys.append(keys)
else:
raise Exception ( "[pyCGM2] - duplicated keys[ %s - %s] detected" %(keys[0],keys[1]))
if keys[0] not in out.keys():
out[keys[0]]=dict()
out[keys[0]][keys[1]]=dict()
out[keys[0]][keys[1]]["values"]= {"X":[],"Y":[],"Z":[]}
else:
out[keys[0]][keys[1]]=dict()
out[keys[0]][keys[1]]["values"]= {"X":[],"Y":[],"Z":[]}
li_X = list()
for cycle in self.analysis.kineticStats.data[keys]["values"]:
li_X.append(cycle[:,0].tolist())
li_Y = list()
for cycle in self.analysis.kineticStats.data[keys]["values"]:
li_Y.append(cycle[:,1].tolist())
li_Z = list()
for cycle in self.analysis.kineticStats.data[keys]["values"]:
li_Z.append(cycle[:,2].tolist())
out[keys[0]][keys[1]]["values"]["X"] = li_X
out[keys[0]][keys[1]]["values"]["Y"] = li_Y
out[keys[0]][keys[1]]["values"]["Z"] = li_Z
if self.analysis.emgStats.data != {}:
processedKeys=list()
for keys in self.analysis.emgStats.data.keys():
if not np.all( self.analysis.emgStats.data[keys]["mean"]==0):
if keys not in processedKeys:
processedKeys.append(keys)
else:
raise Exception ( "[pyCGM2] - duplicated keys[ %s - %s] detected" %(keys[0],keys[1]))
if keys[0] not in out.keys():
out[keys[0]]=dict()
out[keys[0]][keys[1]]=dict()
out[keys[0]][keys[1]]["values"]=[]
else:
out[keys[0]][keys[1]]=dict()
out[keys[0]][keys[1]]["values"]=[]
li = list()
for cycle in self.analysis.emgStats.data[keys]["values"]:
li.append(cycle[:,0].tolist())
out[keys[0]][keys[1]]["values"] = li
files.saveJson(path,outputName,out)
return out
def export(self, outputName, path=None):
out = dict()
if self.analysis.kinematicStats.data != {}:
for keys in self.analysis.kinematicStats.data.keys():
if not np.all(
self.analysis.kinematicStats.data[keys]["mean"] == 0):
out[keys[0]] = dict()
out[keys[0]][keys[1]] = dict()
out[keys[0]][keys[1]]["values"] = {
"X": [],
"Y": [],
"Z": []
}
li_X = list()
for cycle in self.analysis.kinematicStats.data[keys][
"values"]:
li_X.append(cycle[:, 0].tolist())
li_Y = list()
for cycle in self.analysis.kinematicStats.data[keys][
"values"]:
li_Y.append(cycle[:, 1].tolist())
li_Z = list()
for cycle in self.analysis.kinematicStats.data[keys][
"values"]:
li_Z.append(cycle[:, 2].tolist())
out[keys[0]][keys[1]]["values"]["X"] = li_X
out[keys[0]][keys[1]]["values"]["Y"] = li_Y
out[keys[0]][keys[1]]["values"]["Z"] = li_Z
if self.analysis.kineticStats.data != {}:
for keys in self.analysis.kineticStats.data.keys():
if not np.all(
self.analysis.kineticStats.data[keys]["mean"] == 0):
out[keys[0]] = dict()
out[keys[0]][keys[1]] = dict()
out[keys[0]][keys[1]]["values"] = {
"X": [],
"Y": [],
"Z": []
}
li_X = list()
for cycle in self.analysis.kineticStats.data[keys][
"values"]:
li_X.append(cycle[:, 0].tolist())
li_Y = list()
for cycle in self.analysis.kineticStats.data[keys][
"values"]:
li_Y.append(cycle[:, 1].tolist())
li_Z = list()
for cycle in self.analysis.kineticStats.data[keys][
"values"]:
li_Z.append(cycle[:, 2].tolist())
out[keys[0]][keys[1]]["values"]["X"] = li_X
out[keys[0]][keys[1]]["values"]["Y"] = li_Y
out[keys[0]][keys[1]]["values"]["Z"] = li_Z
if self.analysis.emgStats.data != {}:
for keys in self.analysis.emgStats.data.keys():
if not np.all(self.analysis.emgStats.data[keys]["mean"] == 0):
out[keys[0]] = dict()
out[keys[0]][keys[1]] = dict()
out[keys[0]][keys[1]]["values"] = []
li = list()
for cycle in self.analysis.emgStats.data[keys]["values"]:
li.append(cycle[:, 0].tolist())
out[keys[0]][keys[1]]["values"] = li
files.saveJson(path, outputName, out)
