class maps2DensMetrics():
def __init__(self,filesIn,filesOut,pdbname,mapfilname1,maptype1,mapfilname2,maptype2,plot):
self.filesIn = filesIn
self.filesOut = filesOut # output directory
self.pdbname = pdbname
self.map1 = {'filename':mapfilname1,'type':maptype1}
self.map2 = {'filename':mapfilname2,'type':maptype2}
self.plot = plot
def maps2atmdensity(self):
self.printTitle()
# write a log file for this eTrack run
logfile = open('{}{}_log.txt'.format(self.filesOut,self.pdbname),'w')
logfile.write('eTrack run log file\n')
logfile.write('Date: '+ str(time.strftime("%d/%m/%Y"))+'\n')
logfile.write('Time: '+ str(time.strftime("%H:%M:%S"))+'\n')
self.readPDBfile()
self.readAtomMap()
self.readDensityMap()
self.reportDensMapInfo()
self.checkMapCompatibility()
self.createVoxelList()
self.plotDensHistPlots()
self.calculateDensMetrics()
if self.plot == True:
self.plotDensScatterPlots()
self.plotPerResidueBoxPlots()
self.pickleAtomList()
def readPDBfile(self):
# read in pdb file info here
logfile = open('{}{}_log.txt'.format(self.filesOut,self.pdbname),'a')
self.startTimer()
print 'Reading in pdb file...'
print 'pdb name: {}{}.pdb'.format(self.filesIn,self.pdbname)
logfile.write('pdb name: {}{}.pdb\n'.format(self.filesOut,self.pdbname))
# next read in the pdb structure file:
# run function to fill PDBarray list with atom objects from structure
self.PDBarray = PDBtoList('{}{}.pdb'.format(self.filesIn,self.pdbname),[])
self.success()
self.stopTimer()
# want to make sure array of structure atoms ordered by atomnumber
# before reading through them
self.PDBarray.sort(key=lambda x: x.atomnum)
# need to get VDW radius for each atom:
for atom in self.PDBarray:
atom.VDW_get()
def readAtomMap(self):
# read in the atom map
logfile = open('{}{}_log.txt'.format(self.filesOut,self.pdbname),'a')
self.startTimer()
self.fillerLine()
print 'Reading in Atom map file...'
print 'Atom map name: {}{}'.format(self.filesIn,self.map1['filename'])
logfile.write('atom map name: {}{}\n'.format(self.filesOut,self.map1['filename']))
self.atmmap,self.atom_indices = readMap(self.filesIn,self.filesOut,self.pdbname,
self.map1['filename'],self.map1['type'],[])
self.success()
self.stopTimer()
# find number of atoms in structure
num_atoms = len(self.PDBarray)
# find atom numbers present in list (repeated atom numbers removed)
seen = set()
seen_add = seen.add
uniq_atms = [x for x in self.atmmap.vxls_val if not (x in seen or seen_add(x))]
# find set of atoms numbers not present (i.e atoms not assigned to voxels)
Atms_notpres = set(range(1,num_atoms+1)) - set(uniq_atms)
print 'Number of atoms not assigned to voxels: %s' %str(len(Atms_notpres))
# append to log file for this eTrack run
logfile.write('Number of atoms not assigned to voxels: %s\n' %str(len(Atms_notpres)))
def readDensityMap(self):
# read in the density map
logfile = open('{}{}_log.txt'.format(self.filesOut,self.pdbname),'a')
self.startTimer()
self.fillerLine()
print 'Reading in Density map file...'
print 'Density map name: {}{}'.format(self.filesIn,self.map2['filename'])
logfile.write('density map name: {}{}\n'.format(self.filesIn,self.map2['filename']))
self.densmap = readMap(self.filesIn,self.filesOut,self.pdbname,
self.map2['filename'],self.map2['type'],
self.atom_indices)
self.success()
self.stopTimer()
def reportDensMapInfo(self):
# print density map summary information to command line
totalNumVxls = np.product(self.atmmap.nxyz.values())
structureNumVxls = len(self.densmap.vxls_val)
totalMean = self.densmap.density['mean']
structureMean = np.mean(self.densmap.vxls_val)
solvNumVxls = totalNumVxls - structureNumVxls
solvMean = (totalNumVxls*totalMean - structureNumVxls*structureMean)/solvNumVxls
print 'For voxels assigned to structure:'
print 'mean structure density : {}'.format(structureMean)
print 'max structure density : {}'.format(max(self.densmap.vxls_val))
print 'min structure density : {}'.format(min(self.densmap.vxls_val))
print 'std structure density : {}'.format(np.std(self.densmap.vxls_val))
print '# voxels included : {}'.format(structureNumVxls)
print 'For voxels assigned to solvent:'
print 'mean solvent-region density : {}'.format(solvMean)
print '# voxels included : {}'.format(solvNumVxls)
def checkMapCompatibility(self):
# check that atom-tagged and density map can be combined successfully
logfile = open('{}{}_log.txt'.format(self.filesOut,self.pdbname),'a')
self.fillerLine()
print 'Checking that maps have same dimensions and sampling properties...'
self.startTimer()
# Check that the maps have the same dimensions, grid sampling,..
if (self.atmmap.axis != self.densmap.axis or
self.atmmap.gridsamp != self.densmap.gridsamp or
self.atmmap.start != self.densmap.start or
self.atmmap.nxyz != self.densmap.nxyz or
self.atmmap.type != self.densmap.type):
print 'Incompatible map properties --> terminating script'
logfile.write('Incompatible map properties --> terminating script\n')
sys.exit()
elif self.atmmap.celldims != self.densmap.celldims:
print 'Not exact same map grid dimensions..'
logfile.write('Not exactly same map grid dimensions..')
# now check if grid dims same to a specific dp and consider continuing
stop = True
for i in list(reversed(range(7))):
count = 0
for key in self.atmmap.celldims.keys():
if np.round(self.atmmap.celldims[key],i) == np.round(self.densmap.celldims[key],i):
count += 1
if count == 6:
print 'Map grid dimensions same to {}dp'.format(i)
logfile.write('Map grid dimensions same to {}dp --> continuing with processing anyway'.format(i))
stop = False
break
if stop == True:
print 'Map grid dimensions still not same to 0dp'
logfile.write('Map grid dimensions still not same to 0dp --> terminating script\n')
sys.exit()
else:
self.success()
print 'The atom and density map are of compatible format!'
logfile.write('The atom and density map are of compatible format!\n')
self.stopTimer()
self.fillerLine()
print 'Total number of voxels assigned to atoms: %s' %str(len(self.atmmap.vxls_val))
logfile.write('Total number of voxels assigned to atoms: %s\n' %str(len(self.atmmap.vxls_val)))
logfile.close()
def createVoxelList(self):
# create dictionary of voxels with atom numbers as keys
self.startTimer()
self.fillerLine()
print 'Combining voxel density and atom values...'
vxl_list = {atm:[] for atm in self.atmmap.vxls_val}
for atm,dens in zip(self.atmmap.vxls_val,self.densmap.vxls_val):
vxl_list[atm].append(dens)
self.vxlsPerAtom = vxl_list
# delete atmmap and densmap now to save memory
self.densmap,self.atmmap =[],[]
self.stopTimer()
def plotDensHistPlots(self):
# histogram & kde plots of number of voxels per atom
for plotType in ('histogram','kde'):
plotVxlsPerAtm(self.pdbname,self.filesOut,self.vxlsPerAtom,plotType)
def calculateDensMetrics(self):
# determine density summary metrics per atom, including:
# max, min, mean, median, standard deviation, 90-tile min,
# 90-tile max, 95-tile min, 95-tile max, mode (why not!),
# relative standard deviation (rsd = std/mean)
self.fillerLine()
self.startTimer()
print 'Calculating electron density statistics per atom...'
for atom in self.PDBarray:
atomVxls = self.vxlsPerAtom[atom.atomnum]
if len(atomVxls) != 0:
atom.meandensity = np.mean(atomVxls)
atom.mediandensity = np.median(atomVxls)
atom.mindensity = min(atomVxls)
atom.maxdensity = max(atomVxls)
atom.stddensity = np.std(atomVxls)
atom.min90tile = np.percentile(atomVxls,10)
atom.max90tile = np.percentile(atomVxls,90)
atom.min95tile = np.percentile(atomVxls,5)
atom.max95tile = np.percentile(atomVxls,95)
atom.numvoxels = len(atomVxls)
self.success()
self.stopTimer()
# delete the vxlsPerAtom list now to save memory
del self.vxlsPerAtom
# get additional metrics per atom
for atom in self.PDBarray:
atom.getAdditionalMetrics()
def plotDensScatterPlots(self):
# plot scatter plots for density metrics
self.startTimer()
self.fillerLine()
print 'Plotting scatter plots for electron density statistics...'
plotVars = (['mean','max'],['mean','median'],['mean','min'],['min','max'],
['mean','std'],['std','rsd'],['min','min90tile'],['max','max90tile'],
['min90tile','min95tile'],['max90tile','max95tile'],
['std','range'],['mean','range'])
for pVars in plotVars:
edens_scatter(self.filesOut,pVars,self.PDBarray,self.pdbname)
def plotPerResidueBoxPlots(self):
# perform residue analysis for datatset, outputting boxplots for each atom specific
# to each residue, and also a combined boxplot across all residues in structures.
for densMet in ('mean','min','max'):
residueArray = densper_resatom_NOresidueclass(self.filesOut,self.PDBarray,'y',densMet,self.pdbname)
minresnum = 0
sideormain = ['sidechain','mainchain']
densper_res(self.filesOut,residueArray,minresnum,sideormain,'min',self.pdbname)
# remove residueArray now to save memory
residueArray = []
self.stopTimer()
def pickleAtomList(self):
self.pklFileName = save_objectlist(self.PDBarray,self.pdbname)
def startTimer(self):
self.timeStart = time.time()
def stopTimer(self):
elapsedTime = time.time() - self.timeStart
print 'section time: {}'.format(elapsedTime)
sys.stdout.flush()
def success(self):
print '---> success'
def fillerLine(self):
print '\n------------------------------------------------'
def printTitle(self):
print '\n================================================'
print '------------------------------------------------'
print '||| eTrack run |||'
print '------------------------------------------------'
print '================================================\n'
class maps2DensMetrics(object):
# assign values within a density map to specific atoms, using
# the an atom-tagged map to determine which regions of space
# are to be assigned to each atom
def __init__(self,
filesIn='', filesOut='', pdbName='', atomTagMap='',
densityMap='', FCmap='', plotScatter=False, plotHist=False,
logFile='./untitled.log', calcFCmap=True,
doXYZanalysis=False):
# the input directory
self.filesIn = filesIn
# the output directory
self.filesOut = filesOut
# the pdb file name
self.pdbName = pdbName
# atom-tagged map name
self.atomMapIn = atomTagMap
# density map name (typically Fo-Fo)
self.densMapIn = densityMap
# FC map name
self.FCmapIn = FCmap
# (bool) plot scatter plots or not
self.plotScatter = plotScatter
# (bool) plot histogram plots or not
self.plotHist = plotHist
# log file name
self.log = logFile
# whether FC map should be generated
self.calcFCmap = calcFCmap
# whether to do analysis based on xyz of each voxel
self.doXYZanalysis = doXYZanalysis
def maps2atmdensity(self,
mapsAlreadyRead=False):
# the map run method for this class. Will read in an atom-tagged map
# and density map and assign density values for each individual atom
# (as specified within the atom-tagged map). From these summary metrics
# describing the density map behaviour in the vicinity of each refined
# atom can be calculated
if not mapsAlreadyRead:
self.readPDBfile()
self.readAtomMap()
self.readDensityMap()
self.reportDensMapInfo()
self.checkMapCompatibility()
if self.calcFCmap and not mapsAlreadyRead:
self.readFCMap()
self.reportDensMapInfo(mapType='calc')
self.createVoxelList()
if self.plotHist:
self.plotDensHistPlots()
self.calcDensMetrics(showProgress=False)
if self.plotScatter:
self.plotDensScatterPlots()
def readPDBfile(self):
# read in pdb file info here. A list of atom objects
# is created, to which density metric information
# will be added as additional attributes in the
# methods included below
self.printStepNumber()
self.startTimer()
self.lgwrite(ln='Reading pdb file: {}'.format(self.pdbName))
# read in the pdb file to fill list of atom objects
self.PDBarray = PDBtoList('{}{}'.format(
self.filesIn, self.pdbName))
self.stopTimer()
# make sure array of atoms ordered by atom number
self.PDBarray.sort(key=lambda x: x.atomnum)
def readAtomMap(self):
# read in the atom-tagged map
self.printStepNumber()
self.startTimer()
self.lgwrite(ln='Reading atom-tagged map file...\n' +
'Atom map name: {}'.format(self.atomMapIn))
self.atmmap, self.atomIndices = readMap(
dirIn=self.filesIn, dirOut=self.filesOut, mapName=self.atomMapIn,
mapType='atom_map', log=self.log)
self.stopTimer()
# find number of atoms in structure
numAtms = len(self.PDBarray)
# find atom numbers present in list (repeated atom numbers removed)
seen = set()
seenAdd = seen.add
uniqAtms = [x for x in self.atmmap.vxls_val if not
(x in seen or seenAdd(x))]
# find set of atoms numbers not present
# (i.e atoms not assigned to voxels)
AtmsNotPres = set(range(1, numAtms+1)) - set(uniqAtms)
self.lgwrite(
ln='Number of atoms not assigned to voxels: ' +
'{}'.format(len(AtmsNotPres)))
def readDensityMap(self):
# read in the density map
self.printStepNumber()
self.startTimer()
self.lgwrite(ln='Reading density map file...\n' +
'Density map name: {}'.format(self.densMapIn))
self.densmap = readMap(dirIn=self.filesIn, dirOut=self.filesOut,
mapName=self.densMapIn, mapType='density_map',
atomInds=self.atomIndices, log=self.log)
self.stopTimer()
def readFCMap(self):
# read in the FC (calculated structure factor) density map.
# This method should not be called if no FC density map
# has been provided in the current run.
self.printStepNumber()
self.startTimer()
self.lgwrite(ln='Reading Fcalc density map file...\n' +
'Density map name: {}'.format(self.FCmapIn))
self.FCmap = readMap(dirIn=self.filesIn, dirOut=self.filesOut,
mapName=self.FCmapIn, mapType='density_map',
atomInds=self.atomIndices, log=self.log)
self.stopTimer()
def reportDensMapInfo(self,
numSfs=4, mapType='density'):
# report the density map summary information to a log file
if mapType == 'density':
mp = self.densmap
elif mapType == 'calc':
mp = self.FCmap
totalNumVxls = np.product(list(self.atmmap.nxyz.values()))
structureNumVxls = len(mp.vxls_val)
totalMean = mp.density['mean']
structureMean = np.mean(mp.vxls_val)
solvNumVxls = totalNumVxls - structureNumVxls
solvMean = (totalNumVxls*totalMean -
structureNumVxls*structureMean)/solvNumVxls
self.lgwrite(
ln='\nFor voxels assigned to structure:\n' +
'\tmean structure density : {}\n'.format(
round(structureMean, numSfs)) +
'\tmax structure density : {}\n'.format(
round(max(mp.vxls_val), numSfs)) +
'\tmin structure density : {}\n'.format(
round(min(mp.vxls_val), numSfs)) +
'\tstd structure density : {}\n'.format(
round(np.std(mp.vxls_val), numSfs)) +
'\t# voxels included : {}\n'.format(structureNumVxls) +
'\nFor voxels assigned to solvent:\n' +
'\tmean solvent-region density : {}\n'.format(
round(solvMean), numSfs) +
'\t# voxels included : {}'.format(solvNumVxls))
def checkMapCompatibility(self):
# check that atom-tagged and density map
# can be combined successfully. This
# requirement is met if the maps have the
# the same map header information. Grid
# dimensions are permitted to deviate
# between the two maps, however this is
# flagged at run time
self.printStepNumber()
self.lgwrite(
ln='Checking that maps have same dimensions and sampling...')
self.startTimer()
# Check that the maps have the same dimensions, grid sampling,..
if (self.atmmap.axis != self.densmap.axis or
self.atmmap.gridsamp != self.densmap.gridsamp or
self.atmmap.start != self.densmap.start or
self.atmmap.nxyz != self.densmap.nxyz or
self.atmmap.type != self.densmap.type):
error(text='Incompatible map properties',
log=self.log, type='error')
elif self.atmmap.celldims != self.densmap.celldims:
self.lgwrite(ln='Not exact same map grid dimensions..')
# now check if grid dims same to a
# specific dp and consider continuing
stop = True
for i in list(reversed(list(range(7)))):
count = 0
for key in list(self.atmmap.celldims.keys()):
roundedAtmmapDim = np.round(self.atmmap.celldims[key], i)
roundedDensmapDim = np.round(self.densmap.celldims[key], i)
if roundedAtmmapDim == roundedDensmapDim:
count += 1
if count == 6:
self.lgwrite(
ln='Map grid dimensions same to {}dp\n'.format(i) +
'--> continuing with processing anyway')
stop = False
break
if stop:
error(text='Map grid dimensions still not same to 0dp',
log=self.log, type='error')
else:
self.success()
self.lgwrite(
ln='The atom and density map are of compatible format!')
self.stopTimer()
self.lgwrite(
ln='Total number of voxels assigned to atoms: {}'.format(
len(self.atmmap.vxls_val)))
def createVoxelList(self,
inclOnlyGluAsp=False):
# create dictionary of voxels with atom numbers as keys
self.startTimer()
self.printStepNumber()
self.lgwrite(ln='Combining voxel density and atom values...')
self.success()
vxlDic = {atm: [] for atm in self.atmmap.vxls_val}
xyzDic = {atm: [] for atm in self.atmmap.vxls_val}
self.densmap.reshape1dTo3d()
self.densmap.abs2xyz_params()
for atm, dens in zip(self.atmmap.vxls_val, self.densmap.vxls_val):
vxlDic[atm].append(dens)
self.vxlsPerAtom = vxlDic
# The following is not essential for run and should not be called by default
if self.doXYZanalysis:
# call this extra module that is requried for XYZ analysis
from perAtomClusterAnalysis import perAtomXYZAnalysis
xyz_list = self.densmap.getVoxXYZ(
self.atomIndices, coordType='fractional')
for atm, xyz in zip(self.atmmap.vxls_val, xyz_list):
xyzDic[atm].append(xyz)
# get the mid points for each atom from the set of voxels
# per atom, whilst accounting for symmetry (the asym unit
# may not contain 1 single whole molecule, but split up)
xyzDic2 = {}
for atom in self.PDBarray:
# this is more of testing reasons that any clear use
if inclOnlyGluAsp:
atmTypes = ['GLU-CD', 'GLU-OE1', 'GLU-OE2',
'ASP-OD1', 'ASP-OD2', 'ASP-CG',
'CYS-SG', 'CYS-CB', 'CYS-CA',
'MET-SD', 'MET-CE', 'MET-CG']
tag = '-'.join(atom.getAtomID().split('-')[2:])
if tag not in atmTypes:
continue
xyzAnalysis = perAtomXYZAnalysis(
atomObj=atom, vxlRefPoint=np.mean(xyz_list, 0),
densPerVxl=np.round(np.array(vxlDic[atom.atomnum]), 3),
xyzsPerAtom=xyzDic[atom.atomnum], densMapObj=self.densmap)
xyzAnalysis.getxyzPerAtom()
atom.vxlMidPt = xyzAnalysis.findVoxelMidPt()
xyzDic2[atom.getAtomID()] = xyzAnalysis.keptPts
self.xyzsPerAtom = xyzDic2
if self.calcFCmap:
vxlDic2 = {atm: [] for atm in self.atmmap.vxls_val}
for atm, dens in zip(self.atmmap.vxls_val, self.FCmap.vxls_val):
vxlDic2[atm].append(dens)
self.FCperAtom = vxlDic2
self.deleteMapsAttributes()
self.stopTimer()
def deleteMapsAttributes(self):
# Provide the option to delete atmmap and
# densmap attributes to save memory, if
# they are no longer needed during a run
# del self.atmmap
# if self.calcFCmap:
# del self.FCmap
del self.densmap.vxls_val
def plotDensHistPlots(self,
getVoxelStats=False, perAtmDensHist=False):
# Create histogram or kde plots of number of voxels per atom
self.startTimer()
self.printStepNumber()
self.lgwrite(ln='Plotting histogram plots of voxels per atom...\n' +
'Plots written to "{}plots"'.format(self.filesOut))
stats = plotVxlsPerAtm(pdbName=self.pdbName, where=self.filesOut,
vxlsPerAtom=self.vxlsPerAtom, plotType='both',
returnStats=getVoxelStats)
if stats != '':
print('mean: {}\nstd: {}\nmax: {}\nmin: {}'.format(*stats))
if perAtmDensHist:
plotDensForAtm(pdbName=self.pdbName, where=self.filesOut,
vxlsPerAtom=self.vxlsPerAtom, plotType='both',
PDBarray=self.PDBarray)
self.stopTimer()
def calcDensMetricsForAtom(self,
atom=[], plotDistn=False):
# calculate density metrics for a particular atom.
# This method includes the option to perform
# cluster analysis on the voxel values assigned
# to this atom, howeverm this should not be selected
# for a standard run of the code
try:
atomVxls = self.vxlsPerAtom[atom.atomnum]
except KeyError:
error(
text='No voxels assigned to an atom. Consider ' +
'increasing per-atom search radius parameter in RIDL ' +
'input .txt file.',
log=self.log, type='warning')
atomVxls = [np.nan]
if len(atomVxls) != 0:
atom.meandensity = np.mean(atomVxls)
atom.mediandensity = np.median(atomVxls)
atom.mindensity = min(atomVxls)
atom.maxdensity = max(atomVxls)
atom.stddensity = np.std(atomVxls)
atom.min90tile = np.percentile(atomVxls, 10)
atom.max90tile = np.percentile(atomVxls, 90)
atom.min95tile = np.percentile(atomVxls, 5)
atom.max95tile = np.percentile(atomVxls, 95)
atom.numvoxels = len(atomVxls)
posVals = [w for w in atomVxls if w > 0]
if posVals != []:
atom.meanPosOnly = np.mean(posVals)
else:
atom.meanPosOnly = 0
negVals = [w for w in atomVxls if w < 0]
if negVals != []:
atom.meanNegOnly = np.mean(negVals)
else:
atom.meanNegOnly = 0
if self.calcFCmap:
# if the user has opted to calculate an Fcalc map in addition
# to the difference map, then additional metrics can be
# derived using this map. These metrics typically use the Fcalc
# map density at each voxel to weight the contribution that
# each voxel's difference map value should play when
# calculating damage metrics. Effectively, a voxel far from an
# atom (but still included in the search radius around that
# atom) should not contribute to a damage indicator as much as
# a voxel close to the atomic centre
atomFCvals = self.FCperAtom[atom.atomnum]
# NOTE: currently set all negative values to zero. This has
# effect of ignoring Fcalc density that is less than the map
# mean. This is implemented such that all per-voxel weights
# (see below) are positive and so therefore sensible
# weighted-means can be calculated. This may need to be
# reconsidered for future use!
atomFCvals = [v if v > 0 else 0 for v in atomFCvals]
atomFCvalsMaxNormed = np.array(atomFCvals)/max(atomFCvals)
minIndex = np.array(atomVxls).argmin()
weightedVxls = np.multiply(atomVxls, atomFCvalsMaxNormed)
atom.densityWeightedMean = np.mean(weightedVxls)
atom.densityWeightedMin = np.min(weightedVxls)
atom.densityWeightedMax = np.max(weightedVxls)
# the following attribute provides an indication of the
# fraction of the local maximum Fcalc map density around
# the current atom at the point where the minimum difference
# map value has been located to be. A higher value (closer to
# 1) indicates that the min density value is found at an
# electron density-rich region of space, whereas a lower
# value (closer to 0) indicates that the min density value is
# located away from where the majority of the electron density
# assigned to the atom is predicted to be.
atom.fracOfMaxAtomDensAtMin = atomFCvalsMaxNormed[minIndex]
posVals = [w for w in weightedVxls if w > 0]
negVals = [w for w in weightedVxls if w < 0]
posValsSum = np.sum(posVals)
negValsSum = np.sum(negVals)
posWeights = [v for v, w in zip(
atomFCvalsMaxNormed, weightedVxls) if w > 0]
negWeights = [v for v, w in zip(
atomFCvalsMaxNormed, weightedVxls) if w < 0]
posWeightsSum = np.sum(posWeights)
negWeightsSum = np.sum(negWeights)
if posVals != []:
atom.densityWeightedMeanPosOnly = posValsSum/posWeightsSum
else:
atom.densityWeightedMeanPosOnly = 0
if negVals != []:
atom.densityWeightedMeanNegOnly = negValsSum/negWeightsSum
else:
atom.densityWeightedMeanNegOnly = 0
if plotDistn:
# typically only to be used for testing purposes
self.plotFCdistnPlot(
atomsToPlot=['GLU-CD', 'CYS-SG'], atomOfInterest=atom,
atomFCvals=atomFCvals, FCatMin=atomFCvals[minIndex],
atomFCvalsMaxNorm=atomFCvalsMaxNormed)
if self.doXYZanalysis:
# provides the user with the option to also run
# per-atom cluster analysis on the spatial
# distribution of voxels assigned to a single atom.
# This would be useful to distinguish 'clumps' of
# positive or negative difference density, in order
# to decide whether an atom may have shifted
# position upon irradiation.
# It should be noted that this option takes a
# significant time to run, and should be deselected
# in a standard run of the code
self.clustDoneOnAtm.append(atom.getAtomID())
clustAnalysis = perAtomXYZAnalysis(
atomObj=atom, vxlMidPt=atom.vxlMidPt,
knownRefPoint=self.knownRefPt1,
knownRefPoint2=self.knownRefPt2)
clustAnalysis.keptPts = self.xyzsPerAtom[atom.getAtomID()]
clustAnalysis.partitionPtsByVec()
# atom.negClusterVal = clustAnalysis.topNegClustMean
# atom.totDensShift = clustAnalysis.netDensShift
self.densByRegion.append(clustAnalysis.densByRegion)
def calcDensMetrics(self,
plotDistn=False, showProgress=True, parallel=False,
makeTrainSet=False, inclOnlyGluAsp=False,
doRandomSubset=False):
# determine density summary metrics per atom. 'includeOnlyGluAsp'
# allows calculations to be performed only for Glu/asp carboxylates
# (this is not typically suitable and will cause later analysis to
# break), however allows quicker generation of per-atom training sets
# for glu/asp groups over a structure. Training sets for supervised
# learning classification can be created by setting the 'makeTrainSet'
# input to True
if makeTrainSet:
self.doXYZanalysis = True
inclOnlyGluAsp = True
self.startTimer()
self.printStepNumber()
self.lgwrite(ln='Calculating electron density statistics per atom...')
total = len(self.PDBarray)
if parallel:
# TODO: this would be great to implement at some point
print('Parallel processing not currently implemented!')
pass
else:
self.densByRegion = []
self.clustDoneOnAtm = []
for i, atom in enumerate(self.PDBarray):
# this is more of testing reasons that any clear use
if inclOnlyGluAsp:
atmTypes = ['GLU-CD', 'GLU-OE1', 'GLU-OE2',
'ASP-OD1', 'ASP-OD2', 'ASP-CG',
'CYS-SG', 'MET-SD']
tag = '-'.join(atom.getAtomID().split('-')[2:])
if tag not in atmTypes:
continue
if self.doXYZanalysis:
num = '-'.join(atom.getAtomID().split('-')[:2])
if tag == 'GLU-CD':
lookFor1 = num+'-GLU-OE1'
lookFor2 = num+'-GLU-OE2'
elif tag == 'GLU-OE1':
lookFor1 = num+'-GLU-CD'
lookFor2 = num+'-GLU-OE2'
elif tag == 'GLU-OE2':
lookFor1 = num+'-GLU-CD'
lookFor2 = num+'-GLU-OE1'
elif tag == 'ASP-CG':
lookFor1 = num+'-ASP-OD1'
lookFor2 = num+'-ASP-OD2'
elif tag == 'ASP-OD1':
lookFor1 = num+'-ASP-CG'
lookFor2 = num+'-ASP-OD2'
elif tag == 'ASP-OD2':
lookFor1 = num+'-ASP-CG'
lookFor2 = num+'-ASP-OD1'
elif tag == 'CYS-SG':
lookFor1 = num+'-CYS-CB'
lookFor2 = num+'-CYS-CA'
elif tag == 'MET-SD':
lookFor1 = num+'-MET-CE'
lookFor2 = num+'-MET-CG'
if i < 10:
srt = 0
else:
srt = i-10
if i < len(self.PDBarray)-10:
stp = i + 10
else:
stp = len(self.PDBarray)
for atm2 in self.PDBarray[srt:stp]:
if atm2.getAtomID() == lookFor1:
self.knownRefPt1 = atm2.vxlMidPt
elif atm2.getAtomID() == lookFor2:
self.knownRefPt2 = atm2.vxlMidPt
# only calculate metrics for a random subset of atoms
# - for testing purposes
if doRandomSubset:
import random
if random.uniform(0, 1) > 0.01:
continue
else:
print('Random atom used: ' + atom.getAtomID())
if showProgress:
sys.stdout.write('\r')
sys.stdout.write(
'{}%'.format(round(100*float(i)/total, 3)))
sys.stdout.flush()
self.calcDensMetricsForAtom(atom=atom, plotDistn=plotDistn)
atom.getAdditionalMetrics()
if makeTrainSet:
self.makeTrainingSet()
self.success()
self.stopTimer()
# delete vxlsPerAtom since no longer needed
del self.vxlsPerAtom
# ############################################################################
# # TEST: cluster the density values per atom based off xyz.
# # KEEP THIS COMMENTED WHEN USING THE CODE
# from sklearn.cluster import KMeans
# from sklearn.decomposition import PCA
# d = self.densByRegion
# numClusts = 5
# reduced_data = PCA(n_components=2).fit_transform(d)
# kmeans = KMeans(init='k-means++', n_clusters=numClusts)
# kmeans.fit(reduced_data)
# # Step size of the mesh. Decrease to increase the quality of the VQ.
# h = .02 # point in the mesh [x_min, x_max]x[y_min, y_max].
# # Plot decision boundary. For that, we will assign a color to each
# x_min = reduced_data[:, 0].min()-0.5*np.abs(reduced_data[:, 0].min())
# x_max = reduced_data[:, 0].max()+0.5*np.abs(reduced_data[:, 0].max())
# y_min = reduced_data[:, 1].min()-0.5*np.abs(reduced_data[:, 1].min())
# y_max = reduced_data[:, 1].max()+0.5*np.abs(reduced_data[:, 1].max())
# xx, yy = np.meshgrid(
# np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# # Obtain labels for each point in mesh. Use last trained model.
# Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])
# Zatoms = kmeans.predict(reduced_data)
# atmNames = [x for _, x in sorted(zip(Zatoms, self.clustDoneOnAtm))]
# Zatoms.sort()
# for Za, atom in zip(Zatoms, atmNames):
# print('{} --> {}'.format(atom, Za))
# # Put the result into a color plot
# Z = Z.reshape(xx.shape)
# plt.figure(1)
# plt.clf()
# plt.imshow(Z, interpolation='nearest',
# extent=(xx.min(), xx.max(), yy.min(), yy.max()),
# cmap=plt.cm.Paired,
# aspect='auto', origin='lower')
# plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)
# # Plot the centroids as a white X
# centroids = kmeans.cluster_centers_
# plt.scatter(centroids[:, 0], centroids[:, 1],
# marker='x', s=169, linewidths=3,
# color='w', zorder=10)
# import pylab as pl
# for i in range(numClusts):
# pl.text(centroids[i, 0], centroids[i, 1],
# str(i), color="white", fontsize=20)
# plt.title('K-means clustering on per-atom density (PCA-reduced data)\n'
# 'Centroids are marked with white cross')
# plt.xlim(x_min, x_max)
# plt.ylim(y_min, y_max)
# plt.xticks(())
# plt.yticks(())
# plt.show()
# import sys
# sys.exit()
# ############################################################################
def makeTrainingSet(self,
killNow=True, standardise=False):
# make a training set of per-atom density values on
# which a supervised-learning classifier could be trained.
# NOTE: This should NOT be included in a standard run
print('Preparing classifier training dataset')
if standardise:
from sklearn.preprocessing import StandardScaler
X = StandardScaler().fit_transform(self.densByRegion)
else:
X = self.densByRegion
# get bfactors for atoms on which densByRegion is known
bfactors = []
for atmID in self.clustDoneOnAtm:
for atm in self.PDBarray:
if atm.getAtomID() == atmID:
bfactors.append(atm.Bfactor)
break
# Write classification features to output file here
f = lambda x: '{}clusterTrainingSet-{}.trset'.format(self.filesOut, x)
i = 1
while os.path.isfile(f(i)):
i += 1
print('Writing calculated features to file: "{}"'.format(f(i)))
csvIn = open(f(i), 'w')
for i, (atmID, dens) in enumerate(zip(self.clustDoneOnAtm, X)):
csvIn.write(atmID+',' +
','.join([str(np.round(d, 3)) for d in dens]) +
',{}\n'.format(bfactors[i]))
csvIn.close()
if killNow:
import sys
sys.exit()
def plotFCdistnPlot(self,
plot=True, atomOfInterest='',
atomsToPlot=['GLU-CD', 'CYS-SG'], atomFCvals=[],
atomFCvalsMaxNorm=[], FCatMin=[],
plotType='.png', axesFont=18):
# plot a kde & histrogram distribution plot for the FCalc values for an
# atom, both raw, and after being divided by the maximum FCalc value
# attained for that atom (normalised-FCalc). The plot will also include
# vertical lines indicating the FCalc and normalised-FCalc values
# attained for the voxel where the most negative density map (not FC
# map) voxel within the local region around the atom (this is the
# voxel corresponding to the DLoss metric value).
for tag in atomsToPlot:
if tag in atomOfInterest.getAtomID():
sns.set_style("dark")
sns.set_context(rc={"figure.figsize": (10, 6)})
fig = plt.figure()
ax = plt.subplot(111)
sns.distplot(np.array(atomFCvals), label='Fcalc')
sns.distplot(np.array(atomFCvalsMaxNorm),
label='Fcalc/max(Fcalc)')
ylims = ax.get_ylim()
plt.plot((FCatMin, FCatMin),
(ylims[0], ylims[1]),
label='Fcalc, at position of min diff density')
leg = plt.legend(frameon=1)
frame = leg.get_frame()
frame.set_color('white')
plt.xlabel('Per-voxel density map values', fontsize=axesFont)
plt.ylabel('Normed-frequency', fontsize=axesFont)
plt.title('Distribution of Fcalc density values: {}'.format(
atomOfInterest.getAtomID()))
fig.savefig('{}testDistnPlot-{}{}'.format(
self.filesOut, atomOfInterest.getAtomID(), plotType))
def plotDensScatterPlots(self,
printText=False, clustAnalys=False):
# plot scatter plots for density metrics for
# quick assessment of whether per-atom metrics
# are behaving as expecting
self.startTimer()
self.fillerLine(style='line')
self.lgwrite(
ln='Plotting scatter plots for electron density statistics...',
forcePrint=printText)
plotVars = [['meandensity', 'maxdensity'],
['meandensity', 'mediandensity'],
['meandensity', 'mindensity'],
['mindensity', 'maxdensity'],
['meandensity', 'stddensity'],
['mindensity', 'min90tile'],
['maxdensity', 'max90tile'],
['min90tile', 'min95tile'],
['max90tile', 'max95tile'],
['meandensity', 'meanPosOnly'],
['meandensity', 'meanNegOnly'],
['mindensity', 'meanNegOnly'],
['maxdensity', 'meanPosOnly']]
# # only include below if per-atom clusters are
# # calculated - currently very slow
if clustAnalys:
plotVars += [['negClusterVal', 'meandensity'],
['negClusterVal', 'mindensity'],
['totDensShift', 'meandensity'],
['totDensShift', 'mindensity']]
if self.calcFCmap:
plotVars.append(['meandensity', 'densityWeightedMean'])
plotVars.append(['mindensity', 'densityWeightedMin'])
plotVars.append(['maxdensity', 'densityWeightedMax'])
plotVars.append(['maxdensity', 'densityWeightedMeanPosOnly'])
plotVars.append(['mindensity', 'densityWeightedMeanNegOnly'])
plotVars.append(['meanNegOnly', 'densityWeightedMeanNegOnly'])
plotVars.append(['meanPosOnly', 'densityWeightedMeanPosOnly'])
plotVars.append(
['densityWeightedMean', 'densityWeightedMeanPosOnly'])
plotVars.append(
['densityWeightedMean', 'densityWeightedMeanNegOnly'])
for pVars in plotVars:
logStr = edens_scatter(outputDir=self.filesOut, metrics=pVars,
PDBarray=self.PDBarray,
pdbName=self.pdbName)
self.lgwrite(ln=logStr)
def startTimer(self):
# start a timer
self.timeStart = time.time()
def stopTimer(self,
includeInLog=False):
# stop a timer (must run startTimer before)
elapsedTime = time.time() - self.timeStart
if includeInLog:
self.lgwrite(
ln='section time: {}s\n'.format(round(elapsedTime, 3)))
sys.stdout.flush()
def success(self):
# report success to log file
self.lgwrite(ln='---> success')
def fillerLine(self,
style='blank'):
# print a filler line (several styles)
# to command line
if style == 'stars':
ln = '\n***'
elif style == 'line':
ln = '\n'+'-'*30
elif style == 'blank':
ln = '\n'
self.lgwrite(ln=ln)
def lgwrite(self,
ln='', strip=True, forcePrint=False):
# write line to log file
self.log.writeToLog(str=ln, strip=strip, forcePrint=forcePrint)
def printStepNumber(self):
# print a string indicating the current pipeline
# step number directory to the command line
try:
self.stepNumber
except AttributeError:
self.stepNumber = 1
self.lgwrite(ln='\n_______' +
'\nSTEP {})'.format(self.stepNumber))
self.stepNumber += 1
class maps2DensMetrics():
# assign values within a density map to specific atoms, using
# the an atom-tagged map to determine which regions of space
# are to be assigned to each atom
def __init__(self,
filesIn = '',
filesOut = '',
pdbName = '',
atomTagMap = '',
densityMap = '',
FCmap = '',
plotScatter = False,
plotHist = False,
logFile = logFile,
calcFCmap = True):
self.filesIn = filesIn # the input directory
self.filesOut = filesOut # the output directory
self.pdbName = pdbName # the pdb file name
self.map1 = atomTagMap # atom-tagged map
self.map2 = densityMap # density map (typically Fo-Fo)
self.map3 = FCmap # FC map
self.plotScatter = plotScatter # (bool) plot scatter plots or not
self.plotHist = plotHist # (bool) plot histogram plots or not
self.log = logFile
self.calcFCmap = calcFCmap # whether FC map should be generated
def maps2atmdensity(self):
# the map run method for this class. Will read in an atom-tagged map
# and density map and assign density values for each individual atom
# (as specified within the atom-tagged map). From these summary metrics
# describing the density map behaviour in the vicinity of each refined
# atom can be calculated
self.readPDBfile()
self.readAtomMap()
self.readDensityMap()
self.reportDensMapInfo()
self.checkMapCompatibility()
if self.calcFCmap:
self.readFCMap()
self.createVoxelList()
if self.plotHist:
self.plotDensHistPlots()
self.calcDensMetrics()
if self.plotScatter:
self.plotDensScatterPlots()
self.pickleAtomList()
def readPDBfile(self):
# read in pdb file info here
self.printStepNumber()
self.startTimer()
self.lgwrite(ln = 'Reading pdb file: {}'.format(self.pdbName))
# read in the pdb file to fill list of atom objects
self.PDBarray = PDBtoList('{}{}.pdb'.format(self.filesIn,self.pdbName))
self.stopTimer()
# make sure array of atoms ordered by atom number
self.PDBarray.sort(key = lambda x: x.atomnum)
# need to get VDW radius for each atom:
for atom in self.PDBarray:
atom.VDW_get()
def readAtomMap(self):
# read in the atom map
self.printStepNumber()
self.startTimer()
self.lgwrite(ln = 'Reading atom-tagged map file...')
self.lgwrite(ln = 'Atom map name: {}'.format(self.map1))
self.atmmap,self.atomIndices = readMap(dirIn = self.filesIn,
dirOut = self.filesOut,
mapName = self.map1,
mapType = 'atom_map',
log = self.log)
self.stopTimer()
# find number of atoms in structure
num_atoms = len(self.PDBarray)
# find atom numbers present in list (repeated atom numbers removed)
seen = set()
seen_add = seen.add
uniq_atms = [x for x in self.atmmap.vxls_val if not (x in seen or seen_add(x))]
# find set of atoms numbers not present (i.e atoms not assigned to voxels)
Atms_notpres = set(range(1,num_atoms+1)) - set(uniq_atms)
self.lgwrite(ln = 'Number of atoms not assigned to voxels: {}'.format(len(Atms_notpres)))
def readDensityMap(self):
# read in the density map
self.printStepNumber()
self.startTimer()
self.lgwrite(ln = 'Reading density map file...')
self.lgwrite(ln = 'Density map name: {}'.format(self.map2))
self.densmap = readMap(dirIn = self.filesIn,
dirOut = self.filesOut,
mapName = self.map2,
mapType = 'density_map',
atomInds = self.atomIndices,
log = self.log)
self.stopTimer()
def readFCMap(self):
# read in the FC (calculated structure factor) density map
self.printStepNumber()
self.startTimer()
self.lgwrite(ln = 'Reading Fcalc density map file...')
self.lgwrite(ln = 'Density map name: {}'.format(self.map3))
self.FCmap = readMap(dirIn = self.filesIn,
dirOut = self.filesOut,
mapName = self.map3,
mapType = 'density_map',
atomInds = self.atomIndices,
log = self.log)
self.stopTimer()
def reportDensMapInfo(self):
# print density map summary information to command line
totalNumVxls = np.product(self.atmmap.nxyz.values())
structureNumVxls = len(self.densmap.vxls_val)
totalMean = self.densmap.density['mean']
structureMean = np.mean(self.densmap.vxls_val)
solvNumVxls = totalNumVxls - structureNumVxls
solvMean = (totalNumVxls*totalMean - structureNumVxls*structureMean)/solvNumVxls
txt = '\nFor voxels assigned to structure:\n'+\
'\tmean structure density : {}\n'.format(round(structureMean,4))+\
'\tmax structure density : {}\n'.format(round(max(self.densmap.vxls_val),4))+\
'\tmin structure density : {}\n'.format(round(min(self.densmap.vxls_val),4))+\
'\tstd structure density : {}\n'.format(round(np.std(self.densmap.vxls_val),4))+\
'\t# voxels included : {}\n'.format(structureNumVxls)+\
'\nFor voxels assigned to solvent:\n'+\
'\tmean solvent-region density : {}\n'.format(round(solvMean),4)+\
'\t# voxels included : {}'.format(solvNumVxls)
self.lgwrite(ln = txt)
def checkMapCompatibility(self):
# check that atom-tagged and density map
# can be combined successfully
self.printStepNumber()
self.lgwrite(ln = 'Checking that maps have same dimensions and sampling properties...' )
self.startTimer()
# Check that the maps have the same dimensions, grid sampling,..
if (self.atmmap.axis != self.densmap.axis or
self.atmmap.gridsamp != self.densmap.gridsamp or
self.atmmap.start != self.densmap.start or
self.atmmap.nxyz != self.densmap.nxyz or
self.atmmap.type != self.densmap.type):
self.lgwrite(ln = 'Incompatible map properties --> terminating script')
sys.exit()
elif self.atmmap.celldims != self.densmap.celldims:
self.lgwrite(ln = 'Not exact same map grid dimensions..')
# now check if grid dims same to a specific dp and consider continuing
stop = True
for i in list(reversed(range(7))):
count = 0
for key in self.atmmap.celldims.keys():
if np.round(self.atmmap.celldims[key],i) == np.round(self.densmap.celldims[key],i):
count += 1
if count == 6:
str = 'Map grid dimensions same to {}dp\n'.format(i)+\
'--> continuing with processing anyway'
self.lgwrite(ln = str)
stop = False
break
if stop:
err = 'Map grid dimensions still not same to 0dp\n'+\
' --> terminating script'
self.lgwrite(ln = err)
sys.exit()
else:
self.success()
self.lgwrite(ln = 'The atom and density map are of compatible format!')
self.stopTimer()
str = 'Total number of voxels assigned to atoms: {}'.format(len(self.atmmap.vxls_val))
self.lgwrite(ln = str)
def createVoxelList(self):
# create dictionary of voxels with atom numbers as keys
self.startTimer()
self.printStepNumber()
self.lgwrite(ln = 'Combining voxel density and atom values...')
self.success()
vxlDic = {atm:[] for atm in self.atmmap.vxls_val}
xyzDic = {atm:[] for atm in self.atmmap.vxls_val}
self.densmap.reshape1dTo3d()
self.densmap.abs2xyz_params()
for atm,dens in zip(self.atmmap.vxls_val,self.densmap.vxls_val):
vxlDic[atm].append(dens)
xyz_list = self.densmap.getVoxXYZ(self.atomIndices,coordType = 'fractional')
for atm,xyz in zip(self.atmmap.vxls_val,xyz_list):
xyzDic[atm].append(xyz)
self.vxlsPerAtom = vxlDic
self.xyzsPerAtom = xyzDic # not essential for run
if self.calcFCmap:
vxlDic2 = {atm:[] for atm in self.atmmap.vxls_val}
for atm,dens in zip(self.atmmap.vxls_val,self.FCmap.vxls_val):
vxlDic2[atm].append(dens)
self.FCperAtom = vxlDic2
self.deleteMapsAttributes()
self.stopTimer()
def deleteMapsAttributes(self):
# delete atmmap and densmap attributes to save memory
del self.atmmap
del self.FCmap
del self.densmap.vxls_val
def plotDensHistPlots(self,
getVoxelStats = False,
perAtmDensHist = False):
# histogram & kde plots of number of voxels per atom
self.startTimer()
self.printStepNumber()
self.lgwrite(ln = 'Plotting histogram plots of voxels per atom...')
self.lgwrite(ln = 'Plots written to "{}plots"'.format(self.filesOut))
stats = plotVxlsPerAtm(pdbName = self.pdbName,
where = self.filesOut,
vxlsPerAtom = self.vxlsPerAtom,
plotType = 'both',
returnStats = getVoxelStats)
if stats != '':
print 'mean: {}\nstd: {}\nmax: {}\nmin: {}'.format(*stats)
if perAtmDensHist:
plotDensForAtm(pdbName = self.pdbName,
where = self.filesOut,
vxlsPerAtom = self.vxlsPerAtom,
plotType = 'both',
PDBarray = self.PDBarray)
self.stopTimer()
def calcDensMetricsForAtom(self,
atom = [],
plotDistn = False,
clustAnalys = False):
# calculate density metrics for a particular atom
try:
atomVxls = self.vxlsPerAtom[atom.atomnum]
except KeyError:
err = 'Warning!: No voxels assigned to an atom. Consider increasing '+\
'per-atom search radius parameter in RIDL input .txt file.'
self.lgwrite(ln = err,forcePrint = True)
atomVxls = [np.nan]
if self.calcFCmap:
# calculate reliability measures based on electron
# density probability at position of min density
atomFCvals = self.FCperAtom[atom.atomnum]
atomFCvalsMaxNormalised = np.array(atomFCvals)/max(atomFCvals)
minIndex = np.array(atomVxls).argmin()
reliability = atomFCvalsMaxNormalised[minIndex]
FCatMin = atomFCvals[minIndex]
weightedVxls = np.multiply(atomVxls,atomFCvalsMaxNormalised)
if len(atomVxls) != 0:
atom.meandensity = np.mean(atomVxls)
atom.mediandensity = np.median(atomVxls)
atom.mindensity = min(atomVxls)
atom.maxdensity = max(atomVxls)
atom.stddensity = np.std(atomVxls)
atom.min90tile = np.percentile(atomVxls,10)
atom.max90tile = np.percentile(atomVxls,90)
atom.min95tile = np.percentile(atomVxls,5)
atom.max95tile = np.percentile(atomVxls,95)
atom.numvoxels = len(atomVxls)
if self.calcFCmap:
atom.reliability = reliability
atom.wMean = np.mean(weightedVxls)
if plotDistn:
self.plotFCdistnPlot(atomOfInterest = atom,
atomFCvals = atomFCvals,
atomFCvalsMaxNorm = atomFCvalsMaxNormalised,
FCatMin = FCatMin,
reliability = reliability)
if clustAnalys:
# if 'MET-SD' in atom.getAtomID():
rnd = np.random.random()
if rnd < 0.05:
# if atom.side_or_main() == 'sidechain':
print atom.getAtomID()
clustAnalysis = perAtomClusterAnalysis(atmNum = atom.atomnum,
atmId = atom.getAtomID(),
densMapObj = self.densmap,
xyzsPerAtom = self.xyzsPerAtom,
vxlsPerAtom = self.vxlsPerAtom)
atom.negClusterVal = clustAnalysis.output[0]
atom.totDensShift = clustAnalysis.output[-1]
def calcDensMetrics(self,
plotDistn = False,
clustAnalys = False,
showProgress = True,
parallel = False):
# determine density summary metrics per atom
self.startTimer()
self.printStepNumber()
self.lgwrite(ln = 'Calculating electron density statistics per atom...')
total = len(self.PDBarray)
if parallel:
from test import testRun
testRun()
else:
# tRun=time.time()
for i,atom in enumerate(self.PDBarray):
if showProgress:
sys.stdout.write('\r')
sys.stdout.write('{}%'.format(round(100*float(i)/total,3)))
sys.stdout.flush()
self.calcDensMetricsForAtom(atom = atom,
plotDistn = plotDistn,
clustAnalys = clustAnalys)
# atomIDs = [atom.getAtomID() for atom in self.PDBarray if not np.isnan(atom.totDensShift)]
# shifts = [atom.totDensShift for atom in self.PDBarray if not np.isnan(atom.totDensShift)]
# shifts, atomIDs = (list(t) for t in zip(*sorted(zip(shifts, atomIDs))))
# for s,a in zip(shifts,atomIDs):
# print s,a
# print 'Run time: {}s'.format(round(time.time()-tRun,3))
self.success()
self.stopTimer()
# delete the vxlsPerAtom list now to save memory
del self.vxlsPerAtom
# get additional metrics per atom
for atom in self.PDBarray:
atom.getAdditionalMetrics()
def plotFCdistnPlot(self,
plot = True,
atomOfInterest = '',
atomsToPlot = ['GLU-CD','CYS-SG'],
atomFCvals = [],
atomFCvalsMaxNorm = [],
FCatMin = [],
reliability = [],
plotType = '.png',
axesFont = 18):
# plot a kde & histrogram distribution plot for the FCalc values for an
# atom, both raw, and after being divided by the maximum FCalc value
# attained for that atom (normalised-FCalc). The plot will also include
# vertical lines indicating the FCalc and normalised-FCalc values attained
# for the voxel where the most negative density map (not FC map) voxel
# within the local region around the atom (this is the voxel corresponding
# to the DLoss metric value).
for tag in atomsToPlot:
if tag in atomOfInterest.getAtomID():
sns.set_style("dark")
sns.set_context(rc = {"figure.figsize": (10, 6)})
fig = plt.figure()
ax = plt.subplot(111)
sns.distplot(np.array(atomFCvals), label = 'Fcalc')
sns.distplot(np.array(atomFCvalsMaxNorm), label = 'Fcalc/max(Fcalc)')
ylims = ax.get_ylim()
plt.plot((FCatMin,FCatMin),
(ylims[0],ylims[1]),
label='Fcalc, at position of min diff density')
plt.plot((reliability,reliability),
(ylims[0],ylims[1]),
label = 'Fcalc/max(Fcalc), at position of min diff density')
leg = plt.legend(frameon = 1)
frame = leg.get_frame()
frame.set_color('white')
plt.xlabel('Per-voxel density map values', fontsize = axesFont)
plt.ylabel('Normed-frequency', fontsize = axesFont)
plt.title('Distribution of Fcalc density values: {}'.format(atomOfInterest.getAtomID()))
fig.savefig('{}testDistnPlot-{}{}'.format(self.filesOut,atomOfInterest.getAtomID(),plotType))
def plotDensScatterPlots(self,
printText = False,
clustAnalys = False):
# plot scatter plots for density metrics
self.startTimer()
self.fillerLine(style = 'line')
str = 'Plotting scatter plots for electron density statistics...'
self.lgwrite(ln = str,forcePrint = printText)
plotVars = [['meandensity','maxdensity'],
['meandensity','mediandensity'],
['meandensity','mindensity'],
['mindensity','maxdensity'],
['meandensity','stddensity'],
['mindensity','min90tile'],
['maxdensity','max90tile'],
['min90tile','min95tile'],
['max90tile','max95tile']]
# # only include below if per-atom clusters are
# # calculated - currently very slow
if clustAnalys:
plotVars += [['negClusterVal','meandensity'],
['negClusterVal','mindensity'],
['totDensShift','meandensity'],
['totDensShift','mindensity']]
if self.calcFCmap:
plotVars.append(['meandensity','wMean'])
plotVars.append(['mindensity','wMean'])
for pVars in plotVars:
logStr = edens_scatter(outputDir = self.filesOut,
metrics = pVars,
PDBarray = self.PDBarray,
pdbName = self.pdbName)
self.lgwrite(ln = logStr)
def pickleAtomList(self):
# save list of atom objects to a .pkl file
self.pklFileName = save_objectlist(self.PDBarray,self.pdbName)
def startTimer(self):
# start a timer
self.timeStart = time.time()
def stopTimer(self,
includeInLog = False):
# stop a timer (must run startTimer before)
elapsedTime = time.time() - self.timeStart
if includeInLog:
ln = 'section time: {}s\n'.format(round(elapsedTime,3))
self.lgwrite(ln = ln)
sys.stdout.flush()
def success(self):
# report success to log file
self.lgwrite(ln = '---> success')
def fillerLine(self,
style = 'blank'):
# print a filler line (several styles)
# to command line
if style == 'stars':
ln = '\n***'
elif style == 'line':
ln = '\n'+'-'*30
elif style == 'blank':
ln = '\n'
self.lgwrite(ln = ln)
def lgwrite(self,
ln = '',
strip = True,
forcePrint = False):
# write line to log file
self.log.writeToLog(str = ln,
strip = strip,
forcePrint = forcePrint)
def printStepNumber(self):
# print a string indicating the current pipeline
# step number directory to the command line
try:
self.stepNumber
except AttributeError:
self.stepNumber = 1
ln = '\n_______'+\
'\nSTEP {})'.format(self.stepNumber)
self.lgwrite(ln = ln)
self.stepNumber += 1