def runPipeline(self,map_process,post_process,retrieve_PDBmulti,inputfilename):
# the following function reads in the above functions one by one in a scripted
# pipeline. Takes inputs to specify which parts of above pipeline are to be
# included. For each function input specify True if this part is to be performed
# and False otherwise.
self.titleCaption('ETRACK pipeline')
# check whether valid inputs to function
valid = self.checkValidInputs(map_process,post_process,retrieve_PDBmulti,inputfilename)
if valid is False: return
# first need to run function above to read in input file containing info
# on where input files are and where output files should be written
print 'Reading input file: {}'.format(str(inputfilename))
success = self.readInputFile(inputfilename)
if success is False: return
if map_process is True:
self.map_processing()
else:
print 'Map processing task not chosen...'
self.fillerLine()
if post_process is True:
self.post_processing()
# save PDBmulti as pkl file
pklSeries = saveGenericObject(self.combinedAtoms,self.seriesName)
os.system('mv {} {}{}'.format(pklSeries,self.outputDir,pklSeries))
self.pklSeries = pklSeries
else:
print 'Post processing job not chosen...'
self.fillerLine()
if retrieve_PDBmulti is True:
self.PDBmulti_retrieve()
else:
print 'PDBmulti retrieval from pkl file not chosen...'
self.fillerLine()
def runPipeline(self,
map_process = True,
post_process = True,
retrieve = True,
inputFileName = ''):
# the following function reads in the above functions one by one in a scripted
# pipeline. Takes inputs to specify which parts of above pipeline are to be
# included. For each function input specify True if this part is to be performed
# and False otherwise.
self.inputFileName = inputFileName
# check whether valid inputs to function
valid = self.checkValidInputs(map_process,post_process,retrieve)
if not valid:
return
# first need to run function above to read in input file containing info
# on where input files are and where output files should be written
ln = 'Reading input file: {}'.format(self.inputFileName)
self.logFile.writeToLog(str = ln)
success = self.readInputFile()
if not success:
return
success = self.checkInOutDirExist()
if not success:
return
self.setOutputDirs()
if map_process:
self.map_processing()
else:
ln = 'Map processing task not chosen...'
self.logFile.writeToLog(str = ln)
self.fillerLine()
if post_process:
self.post_processing()
# save metric data to pkl file
pklSeries = saveGenericObject(obj = self.combinedAtoms,
fileName = self.seriesName)
move(pklSeries,
'{}{}'.format(self.outputDir,pklSeries))
self.pklSeries = pklSeries
inputfile = open(self.inputFileName,'a')
inputfile.write('\npklDataFile ' + pklSeries)
inputfile.close()
self.feedback(csvOnly = not self.sumFiles)
else:
ln = 'Post processing job not chosen...'
self.logFile.writeToLog(str = ln)
if retrieve:
self.PDBmulti_retrieve()
self.feedback()
self.fillerLine(blank = True)
def post_processing(self):
# group the per-atom density metrics for each dataset together
self.logFile.writeToLog(
str='Combining density metric information for each dataset ' +
'together within the damage series')
txt = 'Input pkl files for post processing chosen from input file:'
for file in self.pklFiles:
txt += '\n\t{}'.format(file.replace(self.outDir, ""))
self.logFile.writeToLog(str=txt)
# next read in the pdb structure file as list of atom objects
initialPDBlist = PDBtoList(pdbFileName=self.get1stDsetPDB())
# retrieve object lists of atoms for each damage set
ln = '\nReading in pkl files for higher dataset structures...'
self.logFile.writeToLog(str=ln)
dList = []
for pkl_filename in self.pklFiles:
ln = 'Damage file number: {}'.format(len(dList)+1)
self.logFile.writeToLog(str=ln)
PDB_ret = retrieve_objectlist(
fileName=pkl_filename, logFile=self.logFile)
# remove pkl file since no longer needed
remove(pkl_filename)
# add new retrieved damage set list to dList
dList.append(PDB_ret)
# create a list of atom objects with attributes as lists varying over
# dose range, only including atoms present in ALL damage datasets
self.logFile.writeToLog(
str='New list of atoms over full dose range calculated...')
combinedAtoms = combinedAtomList(
datasetList=dList, numLigRegDsets=len(dList), doseList=self.doses,
initialPDBList=initialPDBlist, outputDir=self.outputDataDir,
seriesName=self.seriesName, inclFCmetrics=self.inclFCmets)
combinedAtoms.getMultiDoseAtomList(logFile=self.logFile)
# calculate 'average' variant Dloss metrics
combinedAtoms.calcAdditionalMetrics(newMetric='average')
# calculate normalised metrics, if suitable atoms exist
if self.normSet != [[]]:
if combinedAtoms.checkSpecificAtomsExist(self.normSet):
metricsOfInterest = ['loss', 'mean', 'gain', 'Bfactor']
if self.inclFCmets:
metricsOfInterest += ['density_weighted_mean_negOnly',
'density_weighted_loss',
'density_weighted_mean']
for m in metricsOfInterest:
combinedAtoms.calcAdditionalMetrics(
metric=m, newMetric='X-normalised',
normalisationSet=self.normSet)
else:
# if there is a problem finding the set of atoms
error(text='Failed to find the specified set of ' +
'atoms to normalise metrics', log=self.logFile)
# save metric data to pkl file
pklDataFile = saveGenericObject(
obj=combinedAtoms, fileName=self.seriesName)
move(pklDataFile, self.pklFileDir + pklDataFile)
self.pklDataFile = self.pklFileDir + pklDataFile
# append the pklfile name to the input RIDL file
inputfile = open(self.RIDLinputFile, 'r')
pklFound = False
for l in inputfile.readlines():
if l.startswith('pklDataFile'):
pklFound = True
break
inputfile.close()
if not pklFound:
inputfile = open(self.RIDLinputFile, 'a')
inputfile.write('\npklDataFile ' + self.pklDataFile)
inputfile.close()
