def test_descriptivePlot_OneModelOutputPlot(self):
"""
Plot only one Model output
"""
# ----DATA-----
DATA_PATH = pyCGM2.TEST_DATA_PATH + "GaitData\CGM1-NormalGaitData-Events\Hånnibøl Lecter\\"
modelledFilenames = ["gait Trial 01.c3d", "gait Trial 02.c3d"]
#---- Analysis
#--------------------------------------------------------------------------
modelInfo = None
subjectInfo = None
experimentalInfo = None
analysisInstance = analysis.makeAnalysis(DATA_PATH, modelledFilenames)
fig = plt.figure()
ax = plt.gca()
plot.gaitDescriptivePlot(ax,
analysisInstance.kinematicStats,
"LPelvisAngles",
"Left",
0,
color="blue",
title="",
xlabel="",
ylabel="",
ylim=None,
customLimits=None)
def test_gaitDescriptivePlot(self):
DATA_PATH, analysisInstance = dataTest1()
fig = plt.figure()
ax = plt.gca()
reportPlot.gaitDescriptivePlot(ax,
analysisInstance.kinematicStats,
"LPelvisAngles",
"Left",
0,
color="blue",
title="",
xlabel="",
ylabel="",
ylim=None,
customLimits=None)
if SHOW: plt.show()
return fig
def descriptivePlot_OneModelOutputPlot(cls):
"""
Plot only one Model output
"""
# ----DATA-----
DATA_PATH = pyCGM2.TEST_DATA_PATH + "operations\\plot\\gaitPlot\\"
modelledFilenames = ["gait Trial 03 - viconName.c3d"]
#---- c3d manager
#--------------------------------------------------------------------------
c3dmanagerProcedure = c3dManager.UniqueC3dSetProcedure(
DATA_PATH, modelledFilenames)
cmf = c3dManager.C3dManagerFilter(c3dmanagerProcedure)
cmf.enableEmg(False)
trialManager = cmf.generate()
#---- Analysis
#--------------------------------------------------------------------------
modelInfo = None
subjectInfo = None
experimentalInfo = None
analysis = gaitSmartFunctions.make_analysis(
trialManager, cgm.CGM1LowerLimbs.ANALYSIS_KINEMATIC_LABELS_DICT,
cgm.CGM1LowerLimbs.ANALYSIS_KINETIC_LABELS_DICT, modelInfo,
subjectInfo, experimentalInfo)
fig = plt.figure()
ax = plt.gca()
plot.gaitDescriptivePlot(ax,
analysis.kinematicStats,
"LPelvisAngles",
"Left",
0,
color="blue",
title="",
xlabel="",
ylabel="",
ylim=None,
customLimits=None)
plt.show()
def descriptivePlot_OneModelOutputPlot(cls):
"""
Plot only one Model output
"""
# ----DATA-----
DATA_PATH = pyCGM2.TEST_DATA_PATH + "operations\\plot\\gaitPlot\\"
modelledFilenames = ["gait Trial 03 - viconName.c3d"]
#---- c3d manager
#--------------------------------------------------------------------------
c3dmanagerProcedure = c3dManager.UniqueC3dSetProcedure(
DATA_PATH, modelledFilenames)
cmf = c3dManager.C3dManagerFilter(c3dmanagerProcedure)
cmf.enableEmg(False)
trialManager = cmf.generate()
#---- Analysis
#--------------------------------------------------------------------------
modelInfo = None
subjectInfo = None
experimentalInfo = None
analysisInstance = analysis.makeAnalysis("Gait", "CGM1.0", DATA_PATH,
modelledFilenames, None, None,
None)
fig = plt.figure()
ax = plt.gca()
plot.gaitDescriptivePlot(ax,
analysisInstance.kinematicStats,
"LPelvisAngles",
"Left",
0,
color="blue",
title="",
xlabel="",
ylabel="",
ylim=None,
customLimits=None)
plt.show()
def test_envelopPlotSingleEmg(self):
# ----DATA-----
DATA_PATH = pyCGM2.TEST_DATA_PATH+"GaitData\\EMG\\Hånnibøl Lecter-nerve block\\"
gaitTrial = "PRE-gait trial 01.c3d"
restTrial = "PRE-repos.c3d"
DATA_PATH_OUT = pyCGM2.TEST_DATA_PATH_OUT+"GaitData\\EMG\\Hånnibøl Lecter-nerve block\\"
files.createDir(DATA_PATH_OUT)
EMG_LABELS=['EMG1','EMG2']
acq = btkTools.smartReader(DATA_PATH +gaitTrial)
bf = emgFilters.BasicEmgProcessingFilter(acq,EMG_LABELS)
bf.setHighPassFrequencies(20.0,200.0)
bf.run()
envf = emgFilters.EmgEnvelopProcessingFilter(acq,EMG_LABELS)
envf.setCutoffFrequency(180.0)
envf.run()
btkTools.smartWriter(acq,DATA_PATH_OUT+"test_envelopPlotSingleEmg.c3d")
modelledFilenames = ["test_envelopPlotSingleEmg.c3d"]
c3dmanagerProcedure = c3dManager.UniqueC3dSetProcedure(DATA_PATH_OUT,modelledFilenames)
cmf = c3dManager.C3dManagerFilter(c3dmanagerProcedure)
cmf.enableSpatioTemporal(False)
cmf.enableKinematic(False)
cmf.enableKinetic(False)
cmf.enableEmg(True)
trialManager = cmf.generate()
#---- GAIT CYCLES FILTER
#--------------------------------------------------------------------------
cycleBuilder = cycle.GaitCyclesBuilder(spatioTemporalTrials=trialManager.spatioTemporal["Trials"],
kinematicTrials = trialManager.kinematic["Trials"],
kineticTrials = trialManager.kinetic["Trials"],
emgTrials=trialManager.emg["Trials"])
cyclefilter = cycle.CyclesFilter()
cyclefilter.setBuilder(cycleBuilder)
cycles = cyclefilter.build()
modelInfo=None
subjectInfo=None
experimentalInfo=None
analysisBuilder = analysis.GaitAnalysisBuilder(cycles,
kinematicLabelsDict = None,
kineticLabelsDict = None,
emgLabelList = ['EMG1_Rectify_Env','EMG2_Rectify_Env'],
subjectInfos=subjectInfo,
modelInfos=modelInfo,
experimentalInfos=experimentalInfo)
analysisFilter = analysis.AnalysisFilter()
analysisFilter.setBuilder(analysisBuilder)
analysisFilter.setInfo(model = modelInfo)
analysisFilter.build()
analysisInstance = analysisFilter.analysis
envnf = emgFilters.EmgNormalisationProcessingFilter(analysisInstance,"EMG1","Left")
envnf.setMaxMethod(enums.EmgAmplitudeNormalization.MeanMax)
envnf.run()
fig = plt.figure()
ax = plt.gca()
plot.gaitDescriptivePlot(ax,analysisInstance.emgStats,
"EMG1_Rectify_Env","Left",0,
color=None,
title="title", xlabel=None, ylabel=None,ylim=None,legendLabel=None,
customLimits=None)
footOff = analysisInstance.emgStats.pst['stancePhase', "Left"]["mean"]
plot.addNormalActivationLayer(ax,"RECFEM", footOff)
def compareSelectedEmgEvelops(analyses,
legends,
emgChannels,
contexts,
normalized=False,
plotType="Descriptive",
type="Gait",
show=True,
title=None):
"""
compareSelectedEmgEvelops : compare selected emg envelops from pyCGM2 analysis instances
:param analysis [pyCGM2.Processing.analysis.Analysis list]: list of pyCGM2 analysis instances
:param legends [string list]: legend of each analysis instance
:param emgChannels [string list]: label of your emg channels
:param contexts [string list]: context associated with your emg channels
**optional**
:param normalized [bool]: display normalized amplitude envelop (false [defaut])
:param plotType [string]: trace type ( Descriptive [default] or Consistency)
:param type [string]: display events (Gait [defaut] or None)
:param show [bool]: enable matplotlib show function
:param title [string]: change default title of the plot panel
:example:
>>> plot.compareSelectedEmgEvelops([emgAnalysisPre,emgAnalysisPost],["Pre","Post"],["EMG1","EMG1"],["Left","Left"],normalized=False)
"""
fig = plt.figure()
ax = plt.gca()
colormap_i_left = [
plt.cm.Reds(k) for k in np.linspace(0.2, 1, len(analyses))
]
colormap_i_right = [
plt.cm.Blues(k) for k in np.linspace(0.2, 1, len(analyses))
]
i = 0
for analysis in analyses:
label = emgChannels[
i] + "_Rectify_Env" if not normalized else emgChannels[
i] + "_Rectify_Env_Norm"
title = "EMG Envelop Comparison" if not normalized else "Normalized EMG Envelop Comparison"
if contexts[i] == "Left":
color = colormap_i_left[i]
elif contexts[i] == "Right":
color = colormap_i_right[i]
if plotType == "Descriptive":
if type == "Gait":
plot.gaitDescriptivePlot(ax,
analysis.emgStats,
label,
contexts[i],
0,
color=color,
title=title,
xlabel="Gait Cycle",
ylabel="emg",
ylim=None,
customLimits=None,
legendLabel=legends[i])
else:
plot.descriptivePlot(ax,
analysis.emgStats,
label,
contexts[i],
0,
color=None,
title=title,
xlabel="Gait Cycle",
ylabel="emg",
ylim=None,
customLimits=None,
legendLabel=legends[i])
elif plotType == "Consistency":
if type == "Gait":
plot.gaitConsistencyPlot(ax,
analysis.emgStats,
label,
contexts[i],
0,
color=color,
title=title,
xlabel="Gait Cycle",
ylabel="emg",
ylim=None,
customLimits=None,
legendLabel=legends[i])
else:
plot.consistencyPlot(ax,
analysis.emgStats,
label,
contexts[i],
0,
color=None,
title=title,
xlabel="Gait Cycle",
ylabel="emg",
ylim=None,
customLimits=None,
legendLabel=legends[i])
else:
raise Exception("[pyCGM2]: plot type does not recongnized")
i += 1
ax.legend(fontsize=6)
if show: plt.show()