def getSesh(subjVal, eTypeVal):
seshList = hf.sessionPerDRG(subjVal, eTypeVal)
seshDrop = []
for iDRG in sorted(seshList.keys()):
for iSesh in sorted(seshList[iDRG]):
seshDrop.append({
'label': 'session %d (%s)' % (iSesh, iDRG),
'value': iSesh
})
return seshDrop
subjectList = hf.getSubjects(eType)
allDRG = hf.allDRG
meanAmpDict = dict.fromkeys(subjectList)
for subject in subjectList:
meanAmpDict[subject] = {}
for DRG in allDRG:
meanAmpDict[subject][DRG] = {}
for cuff in allCuffs:
meanAmpDict[subject][DRG][cuff] = []
for subject in subjectList:
seshDict = {}
# returns all sessions per DRG per subject
res2 = hf.sessionPerDRG(
subject) # add arguemnt for penetrating vs epineural
res3 = hf.sessionPerElectrodeType(subject)
# iterate over each DRG
for iDRG in res2['result']:
if iDRG['DRG'] in allDRG:
allEtypeSesh = res3[eType]
targetSesh = sorted(
[value for value in allEtypeSesh if value in iDRG['session']])
# iterate over each session
for sesh in targetSesh:
threshDict = hf.thresholdPerCuff(subject, sesh, allCuffs,
False)
allActiveChans = sorted(threshDict.keys())
numActiveChans = len(allActiveChans)
normFactor = []
subjectRow = 0
# fig_numCh = tools.make_subplots(rows=len(subjectList), cols=len(allDRG), subplot_titles=allDRG*len(allDRG))
# fig_meanA = tools.make_subplots(rows=len(subjectList), cols=len(allDRG), subplot_titles=allDRG*len(allDRG))
# fig_numChan = tools.make_subplots(rows=1, cols=len(allDRG), subplot_titles=allDRG)
fig_meanAmp = tools.make_subplots(rows=len(allDRG), cols=1, subplot_titles=allDRG)
# for subject in subjectList:
for iDRG in allDRG:
for subject in subjectList:
subjectRow = subjectList.index(subject) + 1
# returns all sessions per iDRG per subject
res2 = hf.sessionPerDRG(subject, eType)
# iterate over each iDRG
# for iDRG in res2.keys():
subjectNumChanDict = dict.fromkeys(targetNerveLabels, 0)
subjectMeanAmpDict = {k: [] for k in targetNerveLabels}
subjectCol = allDRG.index(iDRG) + 1
if iDRG in res2.keys():
targetSesh = res2[iDRG] #sorted([value for value in allEtypeSesh if value in res2[iDRG]])
for sesh in targetSesh:
threshDict = hf.thresholdPerCuff(subject, sesh, ignoreCuffs, collapseCuffs,stimUnits=stimunit)
allActiveChans = sorted(threshDict.keys())
colorList = hf.colorOrder
subjectList = hf.getSubjects(eType)
targetNerveLabels = hf.allCuffs_mdf.keys()
coactivationDF = pd.DataFrame(0,
columns=targetNerveLabels,
index=targetNerveLabels)
coactivationDict_perDRG = {}
for iDRG in hf.allDRG:
coactivationDF_perDRG = pd.DataFrame(0,
columns=targetNerveLabels,
index=targetNerveLabels)
for subject in subjectList:
seshPerDRG = hf.sessionPerDRG(
subject, eType) # add argument for penetrating vs epineural
if iDRG in seshPerDRG.keys():
# iterate over each session
for iSesh in seshPerDRG[iDRG]:
threshDict = hf.thresholdPerCuff(subject, iSesh, ignoreCuffs,
collapseCuffs)
threshChans = sorted(threshDict.keys())
discardChans = hf.getSingleAmplitudeChannels(subject, iSesh)
allStimChans = [
chans for chans in threshChans if chans not in discardChans
]
binaryRes = hf.convertCurrentToCharge(
hf.binarySearchResolutionbySession[subject][iSesh],
subject, iSesh)
from helperFcns import plt
sns.set(style="white", color_codes=True)
from itertools import chain
import scipy
eType = 'penetrating'
stimUnits = 'charge'
# f, violinAx = plt.subplots(1, 2, figsize=(14, 5))
cvList = []
i = 0
for eType in ['penetrating', 'epineural']:
subjectList = hf.getSubjects(eType)
for nerve in ['Femoral', 'Sciatic']:
for iSub in subjectList:
seshPerDRG = hf.sessionPerDRG(iSub, eType)
allSessions = sum(seshPerDRG.values(), [])
for iSesh in allSessions:
res = hf.db.command({
'aggregate':
hf.collection,
'pipeline': [{
'$match': {
"mdf_metadata.subject": iSub,
"mdf_metadata.session": iSesh,
"mdf_metadata.location": nerve,
"mdf_def.mdf_type": 'CV',
"mdf_metadata.is_sig_manual": {
"$in": [1, None]
},