def cleanJob():
jobid = request.form['jobName']
jobDir, jobFn = os.path.split(jobid)
job = jobHandler.jobCpd()
job.load(jobFn)
if job.dirTemp is not None:
# if cpd: ceck in all mains. if only one, clean from main
#1 get list of screenings with cpd
listScreens = jobHandler.jobsWithCpd(job.filename)
# if more than one: stop
if len(listScreens) > 1:
return 'Multiple screening are using this job. It won\'t be removed. <br> <a href="/">Go to the initial page</a>'
elif len(listScreens) == 1:
# Update screening job
screening = jobHandler.jobCpd()
screening.load(listScreens[0])
screening.results = [
x for x in screening.results
if x['JobName'] != os.path.join(jobDir, jobFn)
]
# clean from main job
screening.update()
#clean mol folder and index
job.remove()
else:
for item in job.results:
# check if used by other jobs, in case rmove job cpd.
jobCpd = jobHandler.jobCpd()
jobCpd.load(item['JobName'])
listScreens = jobHandler.jobsWithCpd(jobCpd.filename)
# if more than one: do not remove
if len(listScreens) == 1:
jobCpd.remove()
job.remove()
return '%s, %s REMOVED <br> <a href="/jobs">Go to the list jobs page</a>' % (
jobDir, jobFn)
def getFullTrain(trainModel, jobResults):
datasetInfo = []
listPoses = []
i = 0
for cpd in jobResults:
i += 1
cpdInfo = {}
TSfound = False
for j in range(len(trainModel)):
if cpd['smi'] == trainModel[j]['smi']:
TSfound = True
break
cpdInfo['id'] = i
cpdInfo['smi'] = cpd['smi']
cpdInfo['DGexp'] = float(cpd['DGexp'])
cpdInfo['JobFile'] = cpd['JobName']
cpdInfo['Status'] = cpd['Status']
if TSfound:
cpdInfo['DGcalc'] = float(trainModel[j]['Gcalc'])
wi = ['%.3f' % x for x in trainModel[j]['wi']]
cpdInfo['wi'] = ', '.join(wi)
idposes = ['%02d' % x for x in trainModel[j]['idsims']]
cpdInfo['idSims'] = ', '.join(idposes)
else:
cpdInfo['DGcalc'] = ''
cpdInfo['wi'] = ''
cpdInfo['idSims'] = ''
cpdInfo['CI_decEle'] = ''
cpdInfo['CI_decVdw'] = ''
cpdInfo['CI_Tanimoto'] = ''
cpdInfo['CI_Dene'] = ''
cpdInfo['CI_Yrange'] = ''
datasetInfo.append(cpdInfo)
### Try to extract starting pose files
try:
jobcpd = jobHandler.jobCpd()
jobcpd.load(cpdInfo['JobFile'])
completed, cpdPoses = getPoses(jobcpd.dirTemp)
if completed:
listPoses.append({'cpd': i, 'poses': cpdPoses})
except Exception, e:
logging.debug('Error in loading poses files, %s' % e)
def predictWorkflow(self, molecules, detail, progress):
# output of the fuction should be:
# list of (True, (molPR,molAD,molCI)) for each compound
# output for each compound is a list of:
# (molPR,molAD,molCI)
# molPR: (True, float with predicted y)
# molAD: (True, integer-sum of violations)
# molCI: (True, float- SDEP and violations based value)
launchDir = os.getcwd()
logging.info('WORKING DIRECTORY IS %s' % launchDir)
statEndJob = ['FAILED', 'CANCELLED', 'DONE']
completed = False
modelId = {'modelProt': self.protein, 'modelProtVer': self.version}
# Check existence file input and check number of molecules for preliminary 'Failed' result setting
nmols = 0
if os.path.isfile(molecules):
with open(molecules, 'r') as infile:
sdf = infile.read()
for line in sdf.splitlines():
if line == '$$$$':
nmols += 1
listPreds = [[False, [(False, 0), (False, 0), (False, 0)]]
for i in range(nmols)]
try:
newSdfFn = molecules
prediction = True
fieldExp = ''
# Submit job
success, results = submitScreen(newSdfFn,
modelId,
prediction,
etoxlie_folder=etoxlie_folder,
fieldExp=str(fieldExp),
jobid=self.jobid)
#if success results is the filename id
if not success:
raise Exception('Submission failed: %s' % results)
# Load job as class
jobFN = results
jobScreen = jobHandler.jobCpd()
jobScreen.load(jobFN)
# Check job status
while jobScreen.status not in statEndJob:
updated, status, results = screenSDF(jobScreen.results,
jobScreen.prediction,
jobScreen.modelProt,
jobScreen.modelProtVer,
jobScreen.experiment)
if updated:
jobScreen.status = status
jobScreen.results = results
jobScreen.update()
sleep(10)
# if it is finished, get the results in the proper format
for i, cpd in enumerate(jobScreen.results):
doneCpd = False
molPR = (False, 0)
molAD = (False, 0)
molCI = (False, 0)
if cpd['Status'] == 'DONE':
molPR = (True, cpd['DGcalc'])
molAD = (True, cpd['CI'])
molCI = (True, cpd['Err'])
doneCpd = True
listPreds[i] = [doneCpd, [molPR, molAD, molCI]]
# Clean single job
cpdFileNm = cpd['JobName']
listScreens = jobHandler.jobsWithCpd(cpdFileNm)
# if more than one: stop
if len(listScreens) == 1:
# Update screening job
cpdJob = jobHandler.jobCpd()
cpdJob.load(cpdFileNm)
#clean mol folder and index
cpdJob.remove()
jobScreen.remove()
completed = True
except Exception, e:
print e
def extractJob(jobId, tmpFolder, modelFolder, outPref=''):
extFile = os.path.splitext(jobId)[1]
if extFile == '.dsr':
dsr = True
else:
dsr = False
jobFn = os.path.join(tmpFolder, jobId)
if not os.path.exists(jobFn):
logging.error('JOB ID %s NOT FOUND!!!' % jobFn)
sys.exit()
job = jobHandler.jobCpd()
job.load(jobFn)
if job.prediction:
typeJob = 'PREDICTION'
else:
typeJob = 'CALIBRATION'
if dsr:
### Write dataset information
datasetInfo = True
if typeJob == 'CALIBRATION':
# LOAD ALL THE INFO ABOUT CALIBRATION JOB
if job.status == 'DONE':
completed, trainModel = loadTrainInfo('trainSet',
job.modelProt,
job.modelProtVer)
if not completed:
datasetInfo = False
logging.error(trainModel)
else:
datasetData, listPoses = getFullTrain(
trainModel, job.results)
else:
# LOAD ALL THE INFO ABOUT PREDICTION JOB
datasetData, listPoses = getFullPred(job.sdf, job.results)
if job.status == 'DONE':
stats = getStats(datasetData, modelFolder, job.modelProt,
job.modelProtVer)
if len(listPoses) > 0:
dirNmPoses = 'Poses_%s' % jobId
extractPoses(listPoses, dirNmPoses)
## WRITE OUT INFORMATION
wb = xlsxwriter.Workbook('Info_%s.xlsx' % jobId)
boldFmt = wb.add_format({'bold': True})
ws = wb.add_worksheet()
ws.write(0, 0, 'Filename', boldFmt)
ws.write(0, 1, job.filename)
ws.write(0, 2, 'Type', boldFmt)
ws.write(0, 3, typeJob)
ws.write(0, 4, 'Status', boldFmt)
ws.write(0, 5, job.status)
ws.write(1, 0, 'Model Id', boldFmt)
ws.write(1, 1, job.modelProt)
ws.write(1, 2, 'Model Version', boldFmt)
ws.write(1, 3, job.modelProtVer)
ws.write(2, 0, 'Data Creation', boldFmt)
ws.write(2, 1,
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(job.dateSub)))
if datasetInfo:
ws.write(4, 0, 'STATISTICS', boldFmt)
ws.write(5, 0, 'n cpds', boldFmt)
ws.write(5, 1, 'SDEP', boldFmt)
ws.write(5, 2, 'SSreg', boldFmt)
ws.write(5, 3, 'SSy', boldFmt)
ws.write(5, 4, 'q2', boldFmt)
ws.write(5, 5, 'SSy noInt', boldFmt)
ws.write(5, 6, 'q2 noInt', boldFmt)
####
if job.status == 'DONE':
ws.write(6, 0, stats['ncpd'])
ws.write(6, 1, stats['RMSE'])
ws.write(6, 2, stats['ssreg'])
ws.write(6, 3, stats['ssY'])
ws.write(6, 4, stats['r2'])
ws.write(6, 5, stats['ssYnoint'])
ws.write(6, 6, stats['r2noint'])
ws.write(8, 0, 'LIST COMPOUNDS', boldFmt)
### stats['ssYnoint']
ws.write(9, 0, 'n.', boldFmt)
ws.write(9, 1, 'smi', boldFmt)
ws.write(9, 2, 'experimental', boldFmt)
ws.write(9, 3, 'calculated', boldFmt)
ws.write(9, 4, 'file', boldFmt)
ws.write(9, 5, 'status', boldFmt)
ws.write(9, 6, 'simulations ID', boldFmt)
ws.write(9, 7, 'simulations Wi', boldFmt)
ws.write(9, 8, 'CI:', boldFmt)
ws.write(9, 9, 'Y range', boldFmt)
ws.write(9, 10, 'Tanimoto', boldFmt)
ws.write(9, 11, 'Delta Ene', boldFmt)
ws.write(9, 12, 'decomp Coul', boldFmt)
ws.write(9, 13, 'decomp L-J', boldFmt)
row0 = 10
row = 10
for cpd in datasetData:
ws.write(row, 0, cpd['id'])
ws.write(row, 1, cpd['smi'])
ws.write(row, 2, cpd['DGexp'])
ws.write(row, 3, cpd['DGcalc'])
ws.write(row, 4, cpd['JobFile'])
ws.write(row, 5, cpd['Status'])
ws.write(row, 6, cpd['idSims'])
ws.write(row, 7, cpd['wi'])
ws.write(
row, 8, cpd['CI_Yrange'] + cpd['CI_Tanimoto'] +
cpd['CI_Dene'] + cpd['CI_decEle'] + cpd['CI_decVdw'])
ws.write(row, 9, cpd['CI_Yrange'])
ws.write(row, 10, cpd['CI_Tanimoto'])
ws.write(row, 11, cpd['CI_Dene'])
ws.write(row, 12, cpd['CI_decEle'])
ws.write(row, 13, cpd['CI_decVdw'])
row += 1
wb.close()
sdfOutFn = 'Input_%s.sdf' % jobId
with open(sdfOutFn, 'w') as sdfOut:
sdfOut.write(job.sdf)
def getFullPred(strSdf, results):
#for cpd: {'Status', 'JobFile', 'DGexp', 'DGcalc', 'wi', 'smi', 'idSims', 'id'}
## Try to get DGexp from sdf
sdfMols = []
molsdf = ''
for line in strSdf.splitlines():
molsdf = '%s%s\n' % (molsdf, line)
if line == '$$$$':
sdfMols.append(molsdf)
molsdf = ''
pbDS = []
listkeys = []
for nm, mol in enumerate(sdfMols):
pbMol = pb.readstring('sdf', str(mol))
pbDS.append(pbMol)
listkeys = listkeys + pbMol.data.keys()
lenDS = len(pbDS)
listkeys = [x for x in set(listkeys) if listkeys.count(x) == lenDS]
print '\n\n ATTRIBUTES FOUND IN THE SUBMITTED SDF FILE:\n\n'
for i, item in enumerate(listkeys):
print('%d.\t\t\t%s' % (i + 1, item))
while True:
keyId = raw_input(
'\nSelect the field with experimental DGbind [0 is not present]?')
try:
keyId = int(keyId)
if keyId >= 0:
if keyId <= len(listkeys):
break
raise exception
except:
print '\t Invalid choice %s' % str(keyId)
if keyId > 0:
expList = [{
'smi': x.write('smi').split()[0],
'DGexp': x.data[listkeys[keyId - 1]]
} for x in pbDS]
else:
expList = [{
'smi': x.write('smi').split()[0],
'DGexp': 0
} for x in pbDS]
##### EXPERIMENTS LOADED, BEGIN TO RESUME DATA
datasetInfo = []
listPoses = []
i = 0
for cpd in results:
i += 1
cpdInfo = {}
TSfound = False
for j in range(len(expList)):
if cpd['smi'] == expList[j]['smi']:
TSfound = True
break
cpdInfo['id'] = i
cpdInfo['smi'] = cpd['smi']
try:
if not TSfound:
raise exception
cpdInfo['DGexp'] = float(expList[j]['DGexp'])
except:
cpdInfo['DGexp'] = ''
cpdInfo['JobFile'] = cpd['JobName']
cpdInfo['Status'] = cpd['Status']
try:
cpdInfo['DGcalc'] = float(cpd['DGcalc'])
except:
cpdInfo['DGcalc'] = ''
try:
wi = ['%.3f' % x for x in cpd['wi']]
cpdInfo['wi'] = ', '.join(wi)
except:
cpdInfo['wi'] = ''
try:
idposes = ['%02d' % x for x in cpd['idpose']]
cpdInfo['idSims'] = ', '.join(idposes)
except:
cpdInfo['idSims'] = ''
try:
cpdInfo['CI_decEle'] = cpd['CI_analysis']['decEle']
cpdInfo['CI_decVdw'] = cpd['CI_analysis']['decVdw']
cpdInfo['CI_Tanimoto'] = cpd['CI_analysis']['Tanimoto']
cpdInfo['CI_Dene'] = cpd['CI_analysis']['Dene']
cpdInfo['CI_Yrange'] = cpd['CI_analysis']['Yrange']
except:
cpdInfo['CI_decEle'] = ''
cpdInfo['CI_decVdw'] = ''
cpdInfo['CI_Tanimoto'] = ''
cpdInfo['CI_Dene'] = ''
cpdInfo['CI_Yrange'] = ''
datasetInfo.append(cpdInfo)
### Try to extract starting pose files
try:
jobcpd = jobHandler.jobCpd()
jobcpd.load(cpdInfo['JobFile'])
completed, cpdPoses = getPoses(jobcpd.dirTemp)
if completed:
listPoses.append({'cpd': i, 'poses': cpdPoses})
except Exception, e:
logging.debug('Error in loading poses files, %s' % e)
def submitScreen(sdfFn,
modelId,
prediction,
etoxlie_folder='.',
fieldExp='Activity',
misc=None,
jobid=None):
# sdfFn: filename sdf
# modelId: {'modelProt': 1A2; 'modelProtVer':1}
# prediction: True or False
# in case of prediction=False: misc={'isGamma':True|False,'fixBeta':True|False}
# 1. check sdf
status = 'SUBMITTED'
if os.path.exists(sdfFn):
with open(sdfFn, 'r') as infile:
sdf = infile.read()
else:
results = "File not found: %s" % sdfFn
status = 'FAILED'
sdf = ''
# 2 Checking consistency of work and models
if status != 'FAILED':
# A. check other runs, in case start
listJobs = jobHandler.collectJobs(cpd=False, sort=True)
for job in listJobs:
if job.status != 'FAILED':
if sdf == job.sdf:
if modelId['modelProt'] == job.modelProt:
if modelId['modelProtVer'] == job.modelProtVer:
if prediction == job.prediction:
if not prediction:
if misc == job.experiment:
status = 'FAILED'
results = 'SDF screen is already in process (jobID: %s)' % job.filename
if status != 'FAILED':
# B. check model in case quit
success, results = loadModel(os.path.join(modelDir,
modelId['modelProt']),
prediction=prediction,
verModel=modelId['modelProtVer'])
if success:
if prediction:
model, params = results
else:
model = results
else:
results = results
status = 'FAILED'
# 3 Create job
if status != 'FAILED':
jobSDR = jobHandler.jobCpd()
jobSDR.create(sdf,
modelId['modelProt'],
modelId['modelProtVer'],
prediction=prediction,
jobid=jobid)
jobSDR.sdf = sdf
jobSDR.experiment = misc
# 4 Now submit jobs (if already submitted, link to the existing
if status != 'FAILED':
success, results = submitCPD(sdf, modelId['modelProt'],
modelId['modelProtVer'], prediction,
fieldExp)
if success:
jobSDR.results = results
else:
jobSDR.status = 'FAILED'
jobSDR.results = results
jobSDR.update()
if status == 'FAILED':
return (False, results)
else:
return (True, jobSDR.filename)
def submitCPD(strSdf,
modelProt,
modelProtVer,
prediction,
fieldExp='',
jobid=None):
# From a sdf submitted as string, lunch or recover jobs
results = []
logging.debug('Start submitting compounds simulations')
try:
listCpds = jobHandler.collectJobs(etoxlie_folder, cpd=True, sort=True)
#create list of pybel molecules
#Workaround to read multiple molecules from string (not default in pybel)
'''OBMol=ob.OBMol()
conv = ob.OBConversion()
conv.SetInAndOutFormats("sdf", "sdf")
conv.ReadString(OBMol, strSdf)
sdfMols=[]
sdfMols.append(pb.Molecule(ob.OBMol(OBMol)))
while conv.Read(OBMol):
sdfMols.append(pb.Molecule(ob.OBMol(OBMol)))'''
sdfMols = []
molsdf = ''
for line in strSdf.splitlines():
molsdf = '%s%s\n' % (molsdf, line)
if line == '$$$$':
sdfMols.append(molsdf)
molsdf = ''
for nm, mol in enumerate(sdfMols):
pbMol = pb.readstring('sdf', mol)
molDict = {}
if not prediction:
dg = pbMol.data[fieldExp]
else:
dg = None
sdf = mol
smi = pbMol.write('smi').split()[0]
for runningCpd in listCpds:
## Check if already submitted = same sdf and protein model
if sdf == runningCpd.sdf and modelProt == runningCpd.modelProt:
molDict['JobName'] = runningCpd.filename
molDict['Status'] = 'SUBMITTED' #runningCpd.status
#molDict['Results']=runningCpd.results
logging.debug(
"job for molecule %d:%s already processed id %s" %
(nm, smi, runningCpd.filename))
if not bool(molDict):
newJob = jobHandler.jobCpd()
newJob.create(sdf, modelProt, modelProtVer, jobid=jobid)
molDict['JobName'] = newJob.filename
molDict['Status'] = newJob.status
logging.debug("job for molecule %d:%s started with id %s" %
(nm, smi, newJob.filename))
molDict['smi'] = smi
molDict['DGexp'] = dg
results.append(molDict)
except Exception, e:
return (False, e)
def getEnergies(listCpds):
'''From list of compounds of the job, get:
ene,dec,Y,fp
ene is list of simulations. for simulation [ncpd, index, vdw, ele]
decvdw and decene are list of compounds. for each compounds list of simulations. for simulation [ncpd, index, vdwres1, vdwres2, ...]
'''
logging.debug("Gathering energies for the list of compounds")
ene = []
decVdw = []
decEle = []
Y = []
smi = []
idcpd = []
jobCpd = jobHandler.jobCpd()
for ncpd, cpd in enumerate(listCpds):
# Exclude FAILED jobs
if cpd['Status'] == 'DONE':
logging.debug("Loading data for compound: %s" % cpd['JobName'])
# 1. Get Y
if cpd['DGexp'] is not None:
Y.append(float(cpd['DGexp']))
else:
Y.append(None)
# 2. Get fingerprint
smi.append(cpd['smi'])
# 3 Load energies from job
jobCpd.load(os.path.join(modelDir, cpd['JobName']))
energies = jobCpd.results
energies = sorted(jobCpd.results,
key=lambda k: k['index'],
reverse=False)
# GET ENERGIES
listContr = ['vdw', 'ele']
# 4. Get interaction energies
ene0 = {}
for contribute in listContr:
ene0[contribute] = [
x[contribute] for x in energies
if ('%03d' % x['index'])[0] == '0'
]
if len(ene0[contribute]) > 1:
ene0[contribute] = np.mean(ene0[contribute])
indeces = list(
set([('%03d' % x['index'])[:-1] for x in energies
if ('%03d' % x['index'])[0] != '0']))
for idx in indeces:
enepose = [[
sim[contribute] - ene0[contribute]
for contribute in listContr
] for sim in energies if ('%03d' % sim['index'])[:-1] == idx]
if len(enepose) > 1:
enepose = np.mean(enepose, axis=0)
elif len(enepose) == 1:
enepose = enepose[0]
ene.append([ncpd, int(idx)] + list(enepose))
# 5. Get decomposed interaction energies
deceneDict = {}
for contribute in listContr:
contribute = 'dec_%s' % contribute
deceneDict[contribute] = []
for idx in indeces:
decpose = {(int(k) if k != 'rest' else str(k)): []
for k in sim[contribute] for sim in energies
if ('%03d' % sim['index'])[:-1] == idx}
for sim in energies:
if ('%03d' % sim['index'])[:-1] == idx:
for k, v in sim[contribute].items():
try:
k = int(k)
except Exception, e:
k = str(k)
decpose[k].append(v)
decpose = {k: np.mean(v) for k, v in decpose.items()}
deceneDict[contribute].append(
[ncpd, int(idx)] +
[decpose[x] for x in sorted(decpose)])
#if ('%03d'%sim['index'])[0] != '0' :
# print ('%03d'%x['index'])[0]
# simdec={( int(k) if k != 'rest' else str(k)):float(v) for k,v in sim[contribute].items()}
# print simdec
# deceneDict[contribute].append( [ncpd,sim['index']]+[simdec[x] for x in sorted(simdec)] )
decVdw.append(deceneDict['dec_vdw'])
decEle.append(deceneDict['dec_ele'])
idcpd.append(ncpd)
def screenSDF(listCpds, prediction, modelProt, modelProtVer, misc):
# Check all the processes of a sdf screening, update status job and status single cpds runs in results
updated = False
status = 'SUBMITTED'
try:
completed = True
for nc, cpd in enumerate(listCpds):
# Check status single cpd (only if not already finished)
if cpd['Status'] not in statEndJob:
completed = False
jobCpd = jobHandler.jobCpd()
jobCpd.load(os.path.join(modelDir, cpd['JobName']))
#Update progress
if cpd['Status'] != jobCpd.status:
updated = True
listCpds[nc]['Status'] = jobCpd.status
listCpds[nc]['Results'] = jobCpd.results
if jobCpd.status == 'DONE':
if prediction:
### PREDICT COMPOUND AND UPDATE RESULTS WITH PREDICTION
#here function for pred and AD. Results is a list of dictionaries containing energies for each simulation
data = getEnergies([listCpds[nc]])
success, results = loadModel(os.path.join(
modelDir, modelProt),
prediction=True,
verModel=modelProtVer)
if not success:
print results
listCpds[nc]['Status'] = 'FAILED'
#raise Exception, results
continue
else:
modelLIE, params = results
try:
Gcalc, idposes, wi, sdep, sspred = predictLie(
data, params['LIE'])
listCpds[nc]
listCpds[nc]['DGexp'] = None
listCpds[nc]['DGcalc'] = Gcalc[0]
listCpds[nc]['idpose'] = idposes[0]
listCpds[nc]['wi'] = wi[0]
CI = predictAD(data, {
'DGcalc': Gcalc,
'idposes': idposes,
'wi': wi
}, params['AD'])
listCpds[nc]['CI_analysis'] = CI[0]
listCpds[nc]['CI'] = sum(CI[0].values())
listCpds[nc]['Err'] = predictError(
sum(CI[0].values()), params['LIE']['sdep'])
logging.info('RESULTS:')
for item in listCpds[nc]:
logging.info("{0} {1}".format(
item, listCpds[nc][item]))
logging.info('END RESULTS')
except Exception, e:
logging.error(
'Error in computing energy for job %s: %s'
% (cpd['JobName'], e))
listCpds[nc]['Status'] = 'FAILED'
continue
if completed:
logging.info('Job completed')
if not prediction:
# check type of calibration for backward compatibility:
if misc == None:
misc = {'isGamma': False, 'fixBeta': False}
# Here collect energies
print 'GET ENERGIES'
data = getEnergies(
listCpds
) # results is a dictionary with {ids, Y, ene, smi, decVdw, decEle}
logging.info(
'Starting calibration for protein %s, version %s' %
(modelProt, modelProtVer))
# Here create model
print 'CALIBRATE LIE'
LIEmodel = calibrateLie(data,
gamma=misc['isGamma'],
fixBeta=misc['fixBeta'])
print 'CALIBRATE AD'
ADmodel = calibrateAD(data, LIEmodel)
modelId = {
'modelProt': modelProt,
'modelProtVer': '%04d' % modelProtVer
}
print 'SAVE MODEL'
success, out = saveModel(modelDir, modelId, LIEmodel, ADmodel)
if not success:
status = 'FAILED'
raise Exception, out
logging.info('New model saved on file %s' % out)
deltaId = 0
for n, id in enumerate(data['ids']):
if listCpds[id + deltaId]['Status'] == 'DONE':
listCpds[id + deltaId]['DGcalc'] = LIEmodel['Gcalc'][n]
listCpds[id + deltaId]['idpose'] = LIEmodel['idx'][n]
listCpds[id + deltaId]['wi'] = LIEmodel['wi'][n]
else:
deltaId += 1
status = 'DONE'
updated = True
results = listCpds