def main():
cvs_file_abs_name_gz = os.path.join(cingDirData, 'PluginCode', 'Whatif',
cvs_file_abs_name + '.gz')
gunzip(cvs_file_abs_name_gz)
reader = csv.reader(open(cvs_file_abs_name, "rb"), quoting=csv.QUOTE_NONE)
valuesBySsAndResType = {}
histJaninBySsAndResType = {}
histJaninBySsAndCombinedResType = {}
# histByCombinedSsAndResType = {}
histJaninCtupleBySsAndResType = {}
valuesByEntrySsAndResType = {}
hrange = (xRange, yRange)
# rowCount = 0
for row in reader:
# rowCount += 1
# 7a3h,A,VAL , 5,H, -62.8, -52.8
# 7a3h,A,VAL , 6,H, -71.2, -33.6
# 7a3h,A,GLU , 7,H, -63.5, -41.6
(entryId, _chainId, resType, _resNum, ssType, chi1, chi2,
_max_bfactor) = row
ssType = to3StateDssp(ssType)[0]
resType = resType.strip()
chi1 = chi1.strip()
chi2 = chi2.strip()
chi1 = floatParse(chi1)
chi2 = floatParse(chi2)
if isNaN(chi1) or isNaN(chi2):
continue
if not inRange(chi1):
nTerror("chi1 not in range for row: %s" % repr(row))
return
if not inRange(chi2):
nTerror("chi2 not in range for row: %s" % repr(row))
return
if not common20AADict.has_key(resType):
nTdebug("Residue not in common 20 for row: %s" % repr(row))
# rowCount -= 1
continue
appendDeepByKeys(valuesBySsAndResType, chi1, ssType, resType, 'chi1')
appendDeepByKeys(valuesByEntrySsAndResType, chi1, entryId, ssType,
resType, 'chi1')
appendDeepByKeys(valuesBySsAndResType, chi2, ssType, resType, 'chi2')
appendDeepByKeys(valuesByEntrySsAndResType, chi2, entryId, ssType,
resType, 'chi2')
# nTdebug('resType,ssType,chi1: %4s %1s %s' % (resType,ssType,floatFormat(chi1, "%6.1f")))
# nTdebug('resType,ssType,chi2: %4s %1s %s' % (resType,ssType,floatFormat(chi2, "%6.1f")))
del (reader) # closes the file handles
os.unlink(cvs_file_abs_name)
for ssType in valuesBySsAndResType.keys():
for resType in valuesBySsAndResType[ssType].keys():
chi1 = valuesBySsAndResType[ssType][resType]['chi1']
chi2 = valuesBySsAndResType[ssType][resType]['chi2']
if chi1 and chi2:
hist2d, _xedges, _yedges = histogram2d(chi2,
chi1,
bins=binCount,
range=hrange)
setDeepByKeys(histJaninBySsAndResType, hist2d, ssType, resType)
cTuple = getEnsembleAverageAndSigmaHis(hist2d)
(c_av, c_sd, hisMin, hisMax) = cTuple
cTuple += tuple([str([ssType, resType])
]) # append the hash keys as a way of id.
nTdebug(
"For ssType %s residue type %s found (av/sd/min/max) %8.0f %8.0f %8.0f %8.0f"
% (ssType, resType, c_av, c_sd, hisMin, hisMax))
if c_sd == None:
nTdebug(
'Failed to get c_sd when testing not all residues are present in smaller sets.'
)
continue
if c_sd == 0.:
nTdebug(
'Got zero c_sd, ignoring histogram. This should only occur in smaller sets. Not setting values.'
)
continue
setDeepByKeys(histJaninCtupleBySsAndResType, cTuple, ssType,
resType)
for ssType in valuesBySsAndResType.keys():
chi1 = []
chi2 = []
for resType in valuesBySsAndResType[ssType].keys():
if resType == 'PRO' or resType == 'GLY':
continue
chi1 += valuesBySsAndResType[ssType][resType]['chi1']
chi2 += valuesBySsAndResType[ssType][resType]['chi2']
if chi1 and chi2:
hist2d, _xedges, _yedges = histogram2d(
chi2, # Note that the x is the chi2 for some stupid reason,
chi1, # otherwise the imagery but also the [row][column] notation is screwed.
bins=binCount,
range=hrange)
# hist2d = zscaleHist( hist2d, Cav, Csd )
setDeepByKeys(histJaninBySsAndCombinedResType, hist2d, ssType)
# Throws a verbose error message on python 2.6.3 as per issue http://code.google.com/p/cing/issues/detail?id=211
# Using Pickle instead
# dbase = shelve.open( dbase_file_abs_name )
# dbase.close()
if os.path.exists(dbase_file_abs_name):
os.unlink(dbase_file_abs_name)
output = open(dbase_file_abs_name, 'wb')
dbase = {}
dbase['histJaninBySsAndCombinedResType'] = histJaninBySsAndCombinedResType
dbase['histJaninBySsAndResType'] = histJaninBySsAndResType
dbase['histJaninCtupleBySsAndResType'] = histJaninCtupleBySsAndResType
# histJaninCtupleBySsAndResType
cPickle.dump(dbase, output, 2)
output.close()
def main():
cvs_file_abs_name_gz = os.path.join(cingDirData, 'PluginCode', 'Whatif', cvs_file_abs_name + '.gz')
gunzip(cvs_file_abs_name_gz)
reader = csv.reader(open(cvs_file_abs_name, "rb"), quoting=csv.QUOTE_NONE)
valuesBySsAndResType = {}
histJaninBySsAndResType = {}
histJaninBySsAndCombinedResType = {}
# histByCombinedSsAndResType = {}
histJaninCtupleBySsAndResType = {}
valuesByEntrySsAndResType = {}
hrange = (xRange, yRange)
# rowCount = 0
for row in reader:
# rowCount += 1
# 7a3h,A,VAL , 5,H, -62.8, -52.8
# 7a3h,A,VAL , 6,H, -71.2, -33.6
# 7a3h,A,GLU , 7,H, -63.5, -41.6
(entryId, _chainId, resType, _resNum, ssType, chi1, chi2, _max_bfactor) = row
ssType = to3StateDssp(ssType)[0]
resType = resType.strip()
chi1 = chi1.strip()
chi2 = chi2.strip()
chi1 = floatParse(chi1)
chi2 = floatParse(chi2)
if isNaN(chi1) or isNaN(chi2):
continue
if not inRange(chi1):
nTerror("chi1 not in range for row: %s" % repr(row))
return
if not inRange(chi2):
nTerror("chi2 not in range for row: %s" % repr(row))
return
if not common20AADict.has_key(resType):
nTdebug("Residue not in common 20 for row: %s" % repr(row))
# rowCount -= 1
continue
appendDeepByKeys(valuesBySsAndResType, chi1, ssType, resType, 'chi1')
appendDeepByKeys(valuesByEntrySsAndResType, chi1, entryId, ssType, resType, 'chi1')
appendDeepByKeys(valuesBySsAndResType, chi2, ssType, resType, 'chi2')
appendDeepByKeys(valuesByEntrySsAndResType, chi2, entryId, ssType, resType, 'chi2')
# nTdebug('resType,ssType,chi1: %4s %1s %s' % (resType,ssType,floatFormat(chi1, "%6.1f")))
# nTdebug('resType,ssType,chi2: %4s %1s %s' % (resType,ssType,floatFormat(chi2, "%6.1f")))
del(reader) # closes the file handles
os.unlink(cvs_file_abs_name)
for ssType in valuesBySsAndResType.keys():
for resType in valuesBySsAndResType[ssType].keys():
chi1 = valuesBySsAndResType[ssType][resType]['chi1']
chi2 = valuesBySsAndResType[ssType][resType]['chi2']
if chi1 and chi2:
hist2d, _xedges, _yedges = histogram2d(
chi2, chi1,
bins=binCount,
range=hrange)
setDeepByKeys(histJaninBySsAndResType, hist2d, ssType, resType)
cTuple = getEnsembleAverageAndSigmaHis(hist2d)
(c_av, c_sd, hisMin, hisMax) = cTuple
cTuple += tuple([str([ssType, resType])]) # append the hash keys as a way of id.
nTdebug("For ssType %s residue type %s found (av/sd/min/max) %8.0f %8.0f %8.0f %8.0f" % (
ssType, resType, c_av, c_sd, hisMin, hisMax))
if c_sd == None:
nTdebug('Failed to get c_sd when testing not all residues are present in smaller sets.')
continue
if c_sd == 0.:
nTdebug('Got zero c_sd, ignoring histogram. This should only occur in smaller sets. Not setting values.')
continue
setDeepByKeys(histJaninCtupleBySsAndResType, cTuple, ssType, resType)
for ssType in valuesBySsAndResType.keys():
chi1 = []
chi2 = []
for resType in valuesBySsAndResType[ssType].keys():
if resType == 'PRO' or resType == 'GLY':
continue
chi1 += valuesBySsAndResType[ssType][resType]['chi1']
chi2 += valuesBySsAndResType[ssType][resType]['chi2']
if chi1 and chi2:
hist2d, _xedges, _yedges = histogram2d(
chi2, # Note that the x is the chi2 for some stupid reason,
chi1, # otherwise the imagery but also the [row][column] notation is screwed.
bins=binCount,
range=hrange)
# hist2d = zscaleHist( hist2d, Cav, Csd )
setDeepByKeys(histJaninBySsAndCombinedResType, hist2d, ssType)
# Throws a verbose error message on python 2.6.3 as per issue https://github.com/VuisterLab/cing/issues/211
# Using Pickle instead
# dbase = shelve.open( dbase_file_abs_name )
# dbase.close()
if os.path.exists(dbase_file_abs_name):
os.unlink(dbase_file_abs_name)
output = open(dbase_file_abs_name, 'wb')
dbase = {}
dbase[ 'histJaninBySsAndCombinedResType' ] = histJaninBySsAndCombinedResType
dbase[ 'histJaninBySsAndResType' ] = histJaninBySsAndResType
dbase[ 'histJaninCtupleBySsAndResType' ] = histJaninCtupleBySsAndResType
# histJaninCtupleBySsAndResType
cPickle.dump(dbase, output, 2)
output.close()
def main():
cvs_file_abs_name_gz = cvs_file_abs_name + '.gz'
gunzip(cvs_file_abs_name_gz)
reader = csv.reader(open(cvs_file_abs_name, "rb"), quoting=csv.QUOTE_NONE)
valuesBySsAndResType = {}
histRamaBySsAndResType = {}
histRamaBySsAndCombinedResType = {}
# histByCombinedSsAndResType = {}
histRamaCtupleBySsAndResType = {}
valuesByEntrySsAndResType = {}
hrange = (xRange, yRange)
rowCount = 0
for row in reader:
rowCount += 1
# 7a3h,A,VAL , 5,H, -62.8, -52.8
# 7a3h,A,VAL , 6,H, -71.2, -33.6
# 7a3h,A,GLU , 7,H, -63.5, -41.6
(entryId, _chainId, resType, _resNum, ssType, phi, psi,
_max_bfactor) = row
ssType = to3StateDssp(ssType)[0]
resType = resType.strip()
phi = float(phi)
psi = float(psi)
if not (inRange(phi, isRange360=isRange360)
and inRange(psi, isRange360=isRange360)):
nTerror("phi and/or psi not in range for row: %s" % repr(row))
return
if not common20AADict.has_key(resType):
nTdebug("Residue not in common 20 for row: %s" % repr(row))
rowCount -= 1
continue
appendDeepByKeys(valuesBySsAndResType, phi, ssType, resType, 'phi')
appendDeepByKeys(valuesBySsAndResType, psi, ssType, resType, 'psi')
# nTdebug('resType,ssType,phi,psi: %4s %1s %8.3f %8.3f' % (resType,ssType,phi,psi))
appendDeepByKeys(valuesByEntrySsAndResType, phi, entryId, ssType,
resType, 'phi')
appendDeepByKeys(valuesByEntrySsAndResType, psi, entryId, ssType,
resType, 'psi')
del (reader) # closes the file handles
os.unlink(cvs_file_abs_name)
nTdebug('Total number of included residues including PRO/GLY: %d' %
rowCount)
# nTdebug('valuesByEntrySsAndResType:\n%s'%valuesByEntrySsAndResType)
# (cAv, cSd, _Cn) = getRescaling(valuesByEntrySsAndResType)
(cAv, cSd) = (1.0, 1.0)
nTdebug("Overall found av,sd: %r %r" % (cAv, cSd))
for ssType in valuesBySsAndResType.keys():
for resType in valuesBySsAndResType[ssType].keys():
hist2d, _xedges, _yedges = histogram2d(
valuesBySsAndResType[ssType][resType]['psi'],
valuesBySsAndResType[ssType][resType]['phi'],
bins=binCount,
range=hrange)
# hist2d = zscaleHist( hist2d, cAv, cSd )
setDeepByKeys(histRamaBySsAndResType, hist2d, ssType, resType)
# nTdebug('hist2d ssType, resType: %s %s\n%s' % (ssType, resType, hist2d))
cTuple = getEnsembleAverageAndSigmaHis(hist2d)
(c_av, c_sd, hisMin, hisMax) = cTuple
cTuple += tuple([str([ssType, resType])
]) # append the hash keys as a way of id.
nTdebug(
"For ssType %s residue type %s found (av/sd/min/max) %8.0f %8.0f %8.0f %8.0f"
% (ssType, resType, c_av, c_sd, hisMin, hisMax))
# nTdebug("xedges %s" % repr(xedges))
# sys.exit(1)
if c_sd == None:
nTdebug(
'Failed to get c_sd when testing not all residues are present in smaller sets.'
)
continue
if c_sd == 0.:
nTdebug(
'Got zero c_sd, ignoring histogram. This should only occur in smaller sets. Not setting values.'
)
continue
setDeepByKeys(histRamaCtupleBySsAndResType, cTuple, ssType,
resType)
for ssType in valuesBySsAndResType.keys():
phi = []
psi = []
for resType in valuesBySsAndResType[ssType].keys():
if resType == 'PRO' or resType == 'GLY':
continue
phi += valuesBySsAndResType[ssType][resType]['phi']
psi += valuesBySsAndResType[ssType][resType]['psi']
hist2d, _xedges, _yedges = histogram2d(
psi, # Note that the x is the psi for some stupid reason,
phi, # otherwise the imagery but also the [row][column] notation is screwed.
bins=binCount,
range=hrange)
# hist2d = zscaleHist( hist2d, cAv, cSd )
setDeepByKeys(histRamaBySsAndCombinedResType, hist2d, ssType)
phi = []
psi = []
for ssType in valuesBySsAndResType.keys():
for resType in valuesBySsAndResType[ssType].keys():
if resType == 'PRO' or resType == 'GLY':
continue
phi += valuesBySsAndResType[ssType][resType]['phi']
psi += valuesBySsAndResType[ssType][resType]['psi']
nTdebug('Total number of residues without PRO/GLY: %d' % len(psi))
hist2d, _xedges, _yedges = histogram2d(
psi, # Note that the x is the psi for some stupid reason,
phi, # otherwise the imagery but also the [row][column] notation is screwed.
bins=binCount,
range=hrange)
# sumHistCombined = sum( hist2d )
# sumsumHistCombined = sum( sumHistCombined )
nTdebug('hist2d : \n%s' % hist2d)
# nTdebug('sumHistCombined : %s' % repr(sumHistCombined))
# nTdebug('sumsumHistCombined: %.0f' % sumsumHistCombined)
# hist2d = zscaleHist( hist2d, cAv, cSd )
# nTdebug('hist2d scaled : \n%s' % hist2d)
if os.path.exists(dbase_file_abs_name):
os.unlink(dbase_file_abs_name)
# dbase = shelve.open( dbase_file_abs_name )
output = open(dbase_file_abs_name, 'wb')
# dbase = {'bar':'milky'}
dbase = {}
# Pickle the list using the highest protocol available.
dbase['histRamaCombined'] = hist2d
dbase['histRamaBySsAndCombinedResType'] = histRamaBySsAndCombinedResType
dbase['histRamaBySsAndResType'] = histRamaBySsAndResType
dbase['histRamaCtupleBySsAndResType'] = histRamaCtupleBySsAndResType
# pickle.dump(dbase, output, -1)
# pickle.dump(dbase, output)
cPickle.dump(
dbase, output,
2) # Was -1 for the most recent version but this caused an issue 239
# NB 2 is the highest listed protocol too but behind the scenes cPickle will probably write something higher still.
# If the protocol parameter is omitted, protocol 0 is used.
# If protocol is specified as a negative value or HIGHEST_PROTOCOL, the highest protocol version will be used.
output.close()
def main():
cvs_file_abs_name_gz = cvs_file_abs_name + '.gz'
gunzip(cvs_file_abs_name_gz)
reader = csv.reader(open(cvs_file_abs_name, "rb"), quoting=csv.QUOTE_NONE)
valuesBySsAndResType = {}
histRamaBySsAndResType = {}
histRamaBySsAndCombinedResType = {}
# histByCombinedSsAndResType = {}
histRamaCtupleBySsAndResType = {}
valuesByEntrySsAndResType = {}
hrange = (xRange, yRange)
rowCount = 0
for row in reader:
rowCount += 1
# 7a3h,A,VAL , 5,H, -62.8, -52.8
# 7a3h,A,VAL , 6,H, -71.2, -33.6
# 7a3h,A,GLU , 7,H, -63.5, -41.6
(entryId, _chainId, resType, _resNum, ssType, phi, psi, _max_bfactor) = row
ssType = to3StateDssp(ssType)[0]
resType = resType.strip()
phi = float(phi)
psi = float(psi)
if not (inRange(phi, isRange360=isRange360) and inRange(psi, isRange360=isRange360)):
nTerror("phi and/or psi not in range for row: %s" % repr(row))
return
if not common20AADict.has_key(resType):
nTdebug("Residue not in common 20 for row: %s" % repr(row))
rowCount -= 1
continue
appendDeepByKeys(valuesBySsAndResType, phi, ssType, resType, 'phi')
appendDeepByKeys(valuesBySsAndResType, psi, ssType, resType, 'psi')
# nTdebug('resType,ssType,phi,psi: %4s %1s %8.3f %8.3f' % (resType,ssType,phi,psi))
appendDeepByKeys(valuesByEntrySsAndResType, phi, entryId, ssType, resType, 'phi')
appendDeepByKeys(valuesByEntrySsAndResType, psi, entryId, ssType, resType, 'psi')
del(reader) # closes the file handles
os.unlink(cvs_file_abs_name)
nTdebug('Total number of included residues including PRO/GLY: %d' % rowCount)
# nTdebug('valuesByEntrySsAndResType:\n%s'%valuesByEntrySsAndResType)
# (cAv, cSd, _Cn) = getRescaling(valuesByEntrySsAndResType)
(cAv, cSd) = (1.0, 1.0)
nTdebug("Overall found av,sd: %r %r" % (cAv, cSd))
for ssType in valuesBySsAndResType.keys():
for resType in valuesBySsAndResType[ssType].keys():
hist2d, _xedges, _yedges = histogram2d(
valuesBySsAndResType[ssType][resType]['psi'],
valuesBySsAndResType[ssType][resType]['phi'],
bins=binCount,
range=hrange)
# hist2d = zscaleHist( hist2d, cAv, cSd )
setDeepByKeys(histRamaBySsAndResType, hist2d, ssType, resType)
# nTdebug('hist2d ssType, resType: %s %s\n%s' % (ssType, resType, hist2d))
cTuple = getEnsembleAverageAndSigmaHis(hist2d)
(c_av, c_sd, hisMin, hisMax) = cTuple
cTuple += tuple([str([ssType, resType])]) # append the hash keys as a way of id.
nTdebug("For ssType %s residue type %s found (av/sd/min/max) %8.0f %8.0f %8.0f %8.0f" % (
ssType, resType, c_av, c_sd, hisMin, hisMax))
# nTdebug("xedges %s" % repr(xedges))
# sys.exit(1)
if c_sd == None:
nTdebug('Failed to get c_sd when testing not all residues are present in smaller sets.')
continue
if c_sd == 0.:
nTdebug('Got zero c_sd, ignoring histogram. This should only occur in smaller sets. Not setting values.')
continue
setDeepByKeys(histRamaCtupleBySsAndResType, cTuple, ssType, resType)
for ssType in valuesBySsAndResType.keys():
phi = []
psi = []
for resType in valuesBySsAndResType[ssType].keys():
if resType == 'PRO' or resType == 'GLY':
continue
phi += valuesBySsAndResType[ssType][resType]['phi']
psi += valuesBySsAndResType[ssType][resType]['psi']
hist2d, _xedges, _yedges = histogram2d(
psi, # Note that the x is the psi for some stupid reason,
phi, # otherwise the imagery but also the [row][column] notation is screwed.
bins=binCount,
range=hrange)
# hist2d = zscaleHist( hist2d, cAv, cSd )
setDeepByKeys(histRamaBySsAndCombinedResType, hist2d, ssType)
phi = []
psi = []
for ssType in valuesBySsAndResType.keys():
for resType in valuesBySsAndResType[ssType].keys():
if resType == 'PRO' or resType == 'GLY':
continue
phi += valuesBySsAndResType[ssType][resType]['phi']
psi += valuesBySsAndResType[ssType][resType]['psi']
nTdebug('Total number of residues without PRO/GLY: %d' % len(psi))
hist2d, _xedges, _yedges = histogram2d(
psi, # Note that the x is the psi for some stupid reason,
phi, # otherwise the imagery but also the [row][column] notation is screwed.
bins=binCount,
range=hrange)
# sumHistCombined = sum( hist2d )
# sumsumHistCombined = sum( sumHistCombined )
nTdebug('hist2d : \n%s' % hist2d)
# nTdebug('sumHistCombined : %s' % repr(sumHistCombined))
# nTdebug('sumsumHistCombined: %.0f' % sumsumHistCombined)
# hist2d = zscaleHist( hist2d, cAv, cSd )
# nTdebug('hist2d scaled : \n%s' % hist2d)
if os.path.exists(dbase_file_abs_name):
os.unlink(dbase_file_abs_name)
# dbase = shelve.open( dbase_file_abs_name )
output = open(dbase_file_abs_name, 'wb')
# dbase = {'bar':'milky'}
dbase = {}
# Pickle the list using the highest protocol available.
dbase[ 'histRamaCombined' ] = hist2d
dbase[ 'histRamaBySsAndCombinedResType' ] = histRamaBySsAndCombinedResType
dbase[ 'histRamaBySsAndResType' ] = histRamaBySsAndResType
dbase[ 'histRamaCtupleBySsAndResType' ] = histRamaCtupleBySsAndResType
# pickle.dump(dbase, output, -1)
# pickle.dump(dbase, output)
cPickle.dump(dbase, output, 2) # Was -1 for the most recent version but this caused an issue 239
# NB 2 is the highest listed protocol too but behind the scenes cPickle will probably write something higher still.
# If the protocol parameter is omitted, protocol 0 is used.
# If protocol is specified as a negative value or HIGHEST_PROTOCOL, the highest protocol version will be used.
output.close()
def main():
'See above.'
cvs_file_abs_name_gz = os.path.join(cingDirData, 'PluginCode', 'Whatif', cvs_file_abs_name + '.gz')
gunzip(cvs_file_abs_name_gz)
reader = csv.reader(open(cvs_file_abs_name, "rb"), quoting=csv.QUOTE_NONE)
valueBySs0AndResTypes = {} # keys are SSi, RTi, RTi-1
valueBySs1AndResTypes = {} # keys are SSi-1, RTi, RTi-1
valueByResTypes = {}
valueBySs0 = {} # keys are SSi
valueBySs1 = {} # keys are SSi-1
histd1CtupleBySsAndResTypes = {}
value = [] # NB is an array without being keyed.
histd1BySs0AndResTypes = {} # keys are SSi, RTi, RTi-1
histd1BySs1AndResTypes = {} # keys are SSi-1, RTi, RTi-1
histd1ByResTypes = {}
histd1BySs0 = {}
histd1BySs1 = {}
linesByEntry = {}
lineCount = 0
for row in reader:
lineCount += 1
if lineCount > lineCountMax:
break
entryId = row[0]
if not linesByEntry.has_key(entryId):
linesByEntry[ entryId ] = []
linesByEntry[ entryId ].append( row )
skippedResTypes = []
entryIdList = linesByEntry.keys()
entryIdList.sort()
# Do some pre filtering.
for entryId2 in entryIdList:
lineList = linesByEntry[ entryId2 ]
for idx,line in enumerate(lineList):
line.append(idx)
lineListSorted = NTsort(lineList,BFACTOR_COLUMN,inplace=False)
# Now throw away the worst 10 % of residues.
n = len(lineListSorted)
bad_count = int(round((n * DEFAULT_BFACTOR_PERCENTAGE_FILTER) / 100.))
to_remove_count = n-bad_count
# nTmessage("Removing at least %d from %d residues" % (bad_count,n))
badIdxList = [lineItem[IDX_COLUMN] for lineItem in lineListSorted[to_remove_count:n]]
iList = range(n)
iList.reverse()
for i in iList:
lineItem = lineList[i]
max_bfactor = float(lineItem[BFACTOR_COLUMN])
if max_bfactor > DEFAULT_MAX_BFACTOR:
# nTdebug('Skipping because max bfactor in dihedral %.3f is above %.3f %s' % (max_bfactor, DEFAULT_MAX_BFACTOR, lineItem))
del lineList[i] # TODO: check if indexing is still right or we shoot in the foot.
continue
if i in badIdxList:
# nTdebug('Skipping because bfactor worst %.3f %s' % (max_bfactor, lineItem))
del lineList[i]
continue
removed_count = n - len(lineList)
# nTdebug("Reduced list by %d" % removed_count)
if removed_count < bad_count:
nTwarning("Failed to remove at least %d residues" % bad_count)
for entryId2 in entryIdList:
prevChainId = None
prevResType = None
prevResNum = None
prevSsType = None
for _r, row in enumerate(linesByEntry[ entryId2 ]):
#1zzk,A,GLN , 17,E, 205.2, 193.6
#1zzk,A,VAL , 18,E, 193.6, 223.2
#1zzk,A,THR , 19,E, 223.2, 190.1
(entryId, chainId, resType, resNum, ssType, d1, _d2, _max_bfactor, _idx) = row
resNum = int(resNum)
ssType = to3StateDssp(ssType)[0]
resType = resType.strip()
db = NTdb.getResidueDefByName( resType )
if not db:
nTerror("resType not in db: %s" % resType)
return
resType = db.nameDict['IUPAC']
d1 = d1.strip()
d1 = floatParse(d1)
if isNaN(d1):
# nTdebug("d1 %s is a NaN on row: %s" % (d1,row))
continue
if not inRange(d1):
nTerror("d1 not in range for row: %s" % str(row))
return
if not (resType in common20AAList):
# nTmessage("Skipping uncommon residue: %s" % resType)
if not ( resType in skippedResTypes):
skippedResTypes.append( resType )
continue
if isSibling(chainId, resNum, prevChainId, prevResNum):
appendDeepByKeys(valueBySs0AndResTypes, d1, ssType, resType, prevResType)
appendDeepByKeys(valueBySs1AndResTypes, d1, prevSsType, resType, prevResType)
appendDeepByKeys(valueByResTypes, d1, resType, prevResType)
appendDeepByKeys(valueBySs0, d1, ssType)
appendDeepByKeys(valueBySs1, d1, prevSsType)
value.append( d1 )
prevResType = resType
prevResNum = resNum
prevChainId = chainId
prevSsType = ssType
os.unlink(cvs_file_abs_name)
nTmessage("Skipped skippedResTypes: %r" % skippedResTypes )
nTmessage("Got count of values: %r" % len(value) )
# fill FOUR types of hist.
# TODO: filter differently for pro/gly
keyListSorted1 = valueBySs0AndResTypes.keys()
keyListSorted1.sort()
for isI in (True, False):
if isI:
valueBySs = valueBySs0
valueBySsAndResTypes = valueBySs0AndResTypes
histd1BySs = histd1BySs0
histd1BySsAndResTypes = histd1BySs0AndResTypes
else:
valueBySs = valueBySs1
valueBySsAndResTypes = valueBySs1AndResTypes
histd1BySs = histd1BySs1
histd1BySsAndResTypes = histd1BySs1AndResTypes
for ssType in keyListSorted1:
# keyListSorted1b = deepcopy(keyListSorted1)
# for ssTypePrev in keyListSorted1b:
d1List = valueBySs[ssType]
if not d1List:
nTerror("Expected d1List from valueBySs[%s]" % (ssType))
continue
hist1d, _bins, _patches = hist(d1List, bins=binCount, range=xRange)
nTmessage("Count %6d in valueBySs[%s]" % (sum(hist1d), ssType))
setDeepByKeys(histd1BySs, hist1d, ssType)
keyListSorted2 = valueBySsAndResTypes[ssType].keys()
keyListSorted2.sort()
for resType in keyListSorted2:
# nTmessage("Working on valueBySsAndResTypes for [%s][%s]" % (ssType, resType)) # nice for balancing output verbosity.
keyListSorted3 = valueBySsAndResTypes[ssType][resType].keys()
keyListSorted3.sort()
for prevResType in keyListSorted3:
# nTmessage("Working on valueBySsAndResTypes[%s][%s][%s]" % (ssType, resType, prevResType))
d1List = valueBySsAndResTypes[ssType][resType][prevResType]
if not d1List:
nTerror("Expected d1List from valueBySsAndResTypes[%s][%s][%s]" % (ssType, resType, prevResType))
continue
hist1d, _bins, _patches = hist(d1List, bins=binCount, range=xRange)
# nTmessage("Count %6d in valueBySsAndResTypes[%s][%s][%s]" % (sum(hist1d), ssType, resType, prevResType))
setDeepByKeys(histd1BySsAndResTypes, hist1d, ssType, resType, prevResType)
# Now that they are all in we can redo this.
# Delete the reference -not- the object.
valueBySs = None
valueBySsAndResTypes = None
histd1BySs = None
histd1BySsAndResTypes = None
for ssType in keyListSorted1:
for resType in keyListSorted2:
# nTmessage("Working on valueBySsAndResTypes for [%s][%s]" % (ssType, resType)) # nice for balancing output verbosity.
keyListSorted3 = valueBySs0AndResTypes[ssType][resType].keys()
keyListSorted3.sort()
for resTypePrev in keyListSorted3:
keyListSorted4 = keyListSorted3[:] # take a copy
for resTypeNext in keyListSorted4:
hist1 = getDeepByKeys(histd1BySs0AndResTypes, ssType, resType, resTypePrev) # x-axis
# This was bug! It needs to be hashed on the ssType of resType -not- on resTypeNext
hist2 = getDeepByKeys(histd1BySs1AndResTypes, ssType, resTypeNext, resType)
if hist1 == None:
nTdebug('skipping for hist1 is empty for [%s] [%s] [%s]' % (ssType, resTypePrev, resType))
continue
if hist2 == None:
nTdebug('skipping for hist2 is empty for [%s] [%s] [%s]' % (ssType, resType, resTypeNext))
continue
m1 = mat(hist1,dtype='float')
m2 = mat(hist2,dtype='float')
m2 = m2.transpose() # pylint: disable=E1101
hist2d = multiply(m1,m2)
cTuple = getEnsembleAverageAndSigmaHis( hist2d )
(_c_av, c_sd, _hisMin, _hisMax) = cTuple #@UnusedVariable
cTuple += tuple([str([ssType, resType, resTypePrev, resTypeNext])]) # append the hash keys as a way of id.
# nTdebug("For ssType %s residue types %s %s %s found (av/sd/min/max) %8.0f %8.0f %8.0f %8.0f" % (
# ssType, resType, resTypePrev, resTypeNext, c_av, c_sd, hisMin, hisMax))
if c_sd == None:
nTdebug('Failed to get c_sd when testing not all residues are present in smaller sets.')
continue
if c_sd == 0.:
nTdebug('Got zero c_sd, ignoring histogram. This should only occur in smaller sets. Not setting values.')
continue
setDeepByKeys( histd1CtupleBySsAndResTypes, cTuple, ssType, resType, resTypePrev, resTypeNext)
# end for isI
keyListSorted1 = valueByResTypes.keys()
keyListSorted1.sort()
for resType in keyListSorted1:
keyListSorted2 = valueByResTypes[resType].keys()
keyListSorted2.sort()
for prevResType in keyListSorted2:
d1List = valueByResTypes[resType][prevResType]
if not d1List:
nTerror("Expected d1List from valueByResTypes[%s][%s]" % (resType, prevResType))
continue
hist1d, _bins, _patches = hist(d1List, bins=binCount, range=xRange)
# nTmessage("Count %6d in valueByResTypes[%s][%s]" % (sum(hist1d), resType, prevResType))
setDeepByKeys(histd1ByResTypes, hist1d, resType, prevResType)
histd1, _bins, _patches = hist(value, bins=binCount, range=xRange)
nTmessage("Count %6d in value" % sum(histd1))
# setDeepByKeys(histd1, hist1d, resType, prevResType)
if os.path.exists(dbase_file_abs_name):
os.unlink(dbase_file_abs_name)
output = open(dbase_file_abs_name, 'wb')
dbase = {}
dbase[ 'histd1BySs0AndResTypes' ] = histd1BySs0AndResTypes # 92 kb uncompressed in the case of ~1000 lines only
dbase[ 'histd1BySs1AndResTypes' ] = histd1BySs1AndResTypes
dbase[ 'histd1CtupleBySsAndResTypes' ] = histd1CtupleBySsAndResTypes
dbase[ 'histd1ByResTypes' ] = histd1ByResTypes # 56 kb
dbase[ 'histd1BySs0' ] = histd1BySs0 # 4 kb
dbase[ 'histd1BySs1' ] = histd1BySs1
dbase[ 'histd1' ] = histd1 # 4 kb
cPickle.dump(dbase, output, 2)
output.close()
def main():
'See above.'
cvs_file_abs_name_gz = os.path.join(cingDirData, 'PluginCode', 'Whatif',
cvs_file_abs_name + '.gz')
gunzip(cvs_file_abs_name_gz)
reader = csv.reader(open(cvs_file_abs_name, "rb"), quoting=csv.QUOTE_NONE)
valueBySs0AndResTypes = {} # keys are SSi, RTi, RTi-1
valueBySs1AndResTypes = {} # keys are SSi-1, RTi, RTi-1
valueByResTypes = {}
valueBySs0 = {} # keys are SSi
valueBySs1 = {} # keys are SSi-1
histd1CtupleBySsAndResTypes = {}
value = [] # NB is an array without being keyed.
histd1BySs0AndResTypes = {} # keys are SSi, RTi, RTi-1
histd1BySs1AndResTypes = {} # keys are SSi-1, RTi, RTi-1
histd1ByResTypes = {}
histd1BySs0 = {}
histd1BySs1 = {}
linesByEntry = {}
lineCount = 0
for row in reader:
lineCount += 1
if lineCount > lineCountMax:
break
entryId = row[0]
if not linesByEntry.has_key(entryId):
linesByEntry[entryId] = []
linesByEntry[entryId].append(row)
skippedResTypes = []
entryIdList = linesByEntry.keys()
entryIdList.sort()
# Do some pre filtering.
for entryId2 in entryIdList:
lineList = linesByEntry[entryId2]
for idx, line in enumerate(lineList):
line.append(idx)
lineListSorted = NTsort(lineList, BFACTOR_COLUMN, inplace=False)
# Now throw away the worst 10 % of residues.
n = len(lineListSorted)
bad_count = int(round((n * DEFAULT_BFACTOR_PERCENTAGE_FILTER) / 100.))
to_remove_count = n - bad_count
# nTmessage("Removing at least %d from %d residues" % (bad_count,n))
badIdxList = [
lineItem[IDX_COLUMN]
for lineItem in lineListSorted[to_remove_count:n]
]
iList = range(n)
iList.reverse()
for i in iList:
lineItem = lineList[i]
max_bfactor = float(lineItem[BFACTOR_COLUMN])
if max_bfactor > DEFAULT_MAX_BFACTOR:
# nTdebug('Skipping because max bfactor in dihedral %.3f is above %.3f %s' % (max_bfactor, DEFAULT_MAX_BFACTOR, lineItem))
del lineList[
i] # TODO: check if indexing is still right or we shoot in the foot.
continue
if i in badIdxList:
# nTdebug('Skipping because bfactor worst %.3f %s' % (max_bfactor, lineItem))
del lineList[i]
continue
removed_count = n - len(lineList)
# nTdebug("Reduced list by %d" % removed_count)
if removed_count < bad_count:
nTwarning("Failed to remove at least %d residues" % bad_count)
for entryId2 in entryIdList:
prevChainId = None
prevResType = None
prevResNum = None
prevSsType = None
for _r, row in enumerate(linesByEntry[entryId2]):
#1zzk,A,GLN , 17,E, 205.2, 193.6
#1zzk,A,VAL , 18,E, 193.6, 223.2
#1zzk,A,THR , 19,E, 223.2, 190.1
(entryId, chainId, resType, resNum, ssType, d1, _d2, _max_bfactor,
_idx) = row
resNum = int(resNum)
ssType = to3StateDssp(ssType)[0]
resType = resType.strip()
db = NTdb.getResidueDefByName(resType)
if not db:
nTerror("resType not in db: %s" % resType)
return
resType = db.nameDict['IUPAC']
d1 = d1.strip()
d1 = floatParse(d1)
if isNaN(d1):
# nTdebug("d1 %s is a NaN on row: %s" % (d1,row))
continue
if not inRange(d1):
nTerror("d1 not in range for row: %s" % str(row))
return
if not (resType in common20AAList):
# nTmessage("Skipping uncommon residue: %s" % resType)
if not (resType in skippedResTypes):
skippedResTypes.append(resType)
continue
if isSibling(chainId, resNum, prevChainId, prevResNum):
appendDeepByKeys(valueBySs0AndResTypes, d1, ssType, resType,
prevResType)
appendDeepByKeys(valueBySs1AndResTypes, d1, prevSsType,
resType, prevResType)
appendDeepByKeys(valueByResTypes, d1, resType, prevResType)
appendDeepByKeys(valueBySs0, d1, ssType)
appendDeepByKeys(valueBySs1, d1, prevSsType)
value.append(d1)
prevResType = resType
prevResNum = resNum
prevChainId = chainId
prevSsType = ssType
os.unlink(cvs_file_abs_name)
nTmessage("Skipped skippedResTypes: %r" % skippedResTypes)
nTmessage("Got count of values: %r" % len(value))
# fill FOUR types of hist.
# TODO: filter differently for pro/gly
keyListSorted1 = valueBySs0AndResTypes.keys()
keyListSorted1.sort()
for isI in (True, False):
if isI:
valueBySs = valueBySs0
valueBySsAndResTypes = valueBySs0AndResTypes
histd1BySs = histd1BySs0
histd1BySsAndResTypes = histd1BySs0AndResTypes
else:
valueBySs = valueBySs1
valueBySsAndResTypes = valueBySs1AndResTypes
histd1BySs = histd1BySs1
histd1BySsAndResTypes = histd1BySs1AndResTypes
for ssType in keyListSorted1:
# keyListSorted1b = deepcopy(keyListSorted1)
# for ssTypePrev in keyListSorted1b:
d1List = valueBySs[ssType]
if not d1List:
nTerror("Expected d1List from valueBySs[%s]" % (ssType))
continue
hist1d, _bins, _patches = hist(d1List, bins=binCount, range=xRange)
nTmessage("Count %6d in valueBySs[%s]" % (sum(hist1d), ssType))
setDeepByKeys(histd1BySs, hist1d, ssType)
keyListSorted2 = valueBySsAndResTypes[ssType].keys()
keyListSorted2.sort()
for resType in keyListSorted2:
# nTmessage("Working on valueBySsAndResTypes for [%s][%s]" % (ssType, resType)) # nice for balancing output verbosity.
keyListSorted3 = valueBySsAndResTypes[ssType][resType].keys()
keyListSorted3.sort()
for prevResType in keyListSorted3:
# nTmessage("Working on valueBySsAndResTypes[%s][%s][%s]" % (ssType, resType, prevResType))
d1List = valueBySsAndResTypes[ssType][resType][prevResType]
if not d1List:
nTerror(
"Expected d1List from valueBySsAndResTypes[%s][%s][%s]"
% (ssType, resType, prevResType))
continue
hist1d, _bins, _patches = hist(d1List,
bins=binCount,
range=xRange)
# nTmessage("Count %6d in valueBySsAndResTypes[%s][%s][%s]" % (sum(hist1d), ssType, resType, prevResType))
setDeepByKeys(histd1BySsAndResTypes, hist1d, ssType,
resType, prevResType)
# Now that they are all in we can redo this.
# Delete the reference -not- the object.
valueBySs = None
valueBySsAndResTypes = None
histd1BySs = None
histd1BySsAndResTypes = None
for ssType in keyListSorted1:
for resType in keyListSorted2:
# nTmessage("Working on valueBySsAndResTypes for [%s][%s]" % (ssType, resType)) # nice for balancing output verbosity.
keyListSorted3 = valueBySs0AndResTypes[ssType][resType].keys()
keyListSorted3.sort()
for resTypePrev in keyListSorted3:
keyListSorted4 = keyListSorted3[:] # take a copy
for resTypeNext in keyListSorted4:
hist1 = getDeepByKeys(histd1BySs0AndResTypes, ssType,
resType, resTypePrev) # x-axis
# This was bug! It needs to be hashed on the ssType of resType -not- on resTypeNext
hist2 = getDeepByKeys(histd1BySs1AndResTypes, ssType,
resTypeNext, resType)
if hist1 == None:
nTdebug(
'skipping for hist1 is empty for [%s] [%s] [%s]' %
(ssType, resTypePrev, resType))
continue
if hist2 == None:
nTdebug(
'skipping for hist2 is empty for [%s] [%s] [%s]' %
(ssType, resType, resTypeNext))
continue
m1 = mat(hist1, dtype='float')
m2 = mat(hist2, dtype='float')
m2 = m2.transpose() # pylint: disable=E1101
hist2d = multiply(m1, m2)
cTuple = getEnsembleAverageAndSigmaHis(hist2d)
(_c_av, c_sd, _hisMin, _hisMax) = cTuple #@UnusedVariable
cTuple += tuple([
str([ssType, resType, resTypePrev, resTypeNext])
]) # append the hash keys as a way of id.
# nTdebug("For ssType %s residue types %s %s %s found (av/sd/min/max) %8.0f %8.0f %8.0f %8.0f" % (
# ssType, resType, resTypePrev, resTypeNext, c_av, c_sd, hisMin, hisMax))
if c_sd == None:
nTdebug(
'Failed to get c_sd when testing not all residues are present in smaller sets.'
)
continue
if c_sd == 0.:
nTdebug(
'Got zero c_sd, ignoring histogram. This should only occur in smaller sets. Not setting values.'
)
continue
setDeepByKeys(histd1CtupleBySsAndResTypes, cTuple, ssType,
resType, resTypePrev, resTypeNext)
# end for isI
keyListSorted1 = valueByResTypes.keys()
keyListSorted1.sort()
for resType in keyListSorted1:
keyListSorted2 = valueByResTypes[resType].keys()
keyListSorted2.sort()
for prevResType in keyListSorted2:
d1List = valueByResTypes[resType][prevResType]
if not d1List:
nTerror("Expected d1List from valueByResTypes[%s][%s]" %
(resType, prevResType))
continue
hist1d, _bins, _patches = hist(d1List, bins=binCount, range=xRange)
# nTmessage("Count %6d in valueByResTypes[%s][%s]" % (sum(hist1d), resType, prevResType))
setDeepByKeys(histd1ByResTypes, hist1d, resType, prevResType)
histd1, _bins, _patches = hist(value, bins=binCount, range=xRange)
nTmessage("Count %6d in value" % sum(histd1))
# setDeepByKeys(histd1, hist1d, resType, prevResType)
if os.path.exists(dbase_file_abs_name):
os.unlink(dbase_file_abs_name)
output = open(dbase_file_abs_name, 'wb')
dbase = {}
dbase[
'histd1BySs0AndResTypes'] = histd1BySs0AndResTypes # 92 kb uncompressed in the case of ~1000 lines only
dbase['histd1BySs1AndResTypes'] = histd1BySs1AndResTypes
dbase['histd1CtupleBySsAndResTypes'] = histd1CtupleBySsAndResTypes
dbase['histd1ByResTypes'] = histd1ByResTypes # 56 kb
dbase['histd1BySs0'] = histd1BySs0 # 4 kb
dbase['histd1BySs1'] = histd1BySs1
dbase['histd1'] = histd1 # 4 kb
cPickle.dump(dbase, output, 2)
output.close()
