def main(ExecutableName):
ArgParser,\
Arguments = ArgumentParser.ParseArguments()
Ext = ''
Log = Logger.Logger(ExecutableName,
Ext)
LogString = '## START TIMESTAMP\n'
LogString += str(Log.GetStartDate())+'\n'
LogString += '## END TIMESTAMP'
print LogString
Log.Write(LogString+'\n')
LogString = Log.GetStartLogString()
print LogString
Log.Write(LogString+'\n')
ArgumentParser.LogArguments(Log,
ArgParser,
Arguments)
BadSNPs = {}
fr = open(Arguments.BadSNPFile,'r')
for Line in fr:
BadSNPs[Line.strip()] = None
fr.close()
TmpPath = os.path.join(os.getcwd(),'Tmp')
if(not os.path.isdir(TmpPath)):
os.mkdir(TmpPath)
SNPInfoDict = {} # will contain a dictionary of two elements: {'chr':int(chr),'pos':int(pos)}
SNPChrArray = scipy.array([])
SNPPosArray = scipy.array([])
SNPXPosArray = scipy.array([])
XLeft = []
XRight = []
XTicks = []
XTickLabels = []
XXMax = None
XXMin = None
if(Arguments.YProperty=='PHE'):
XPath = Arguments.SNPInfoFile
SNPInfoDecompressed = os.path.join(TmpPath,re.sub('\.gz','',Arguments.SNPInfoFile))
os.system('pigz -d -c -k '+XPath+' > '+SNPInfoDecompressed)
FH = open(SNPInfoDecompressed,'r')
HeaderList = FH.readline().strip().split(',')
FH.close()
SNPIDArray = None
ChrArray = None
PosArray = None
SNPIDColumn = None
ChrColumn = None
PosColumn = None
for Entry in HeaderList:
if(Entry=='rsid'):
SNPIDColumn = HeaderList.index(Entry)
if(Entry=='CHR'):
ChrColumn = HeaderList.index(Entry)
if(Entry=='pos'):
PosColumn = HeaderList.index(Entry)
Arrays = scipy.loadtxt(fname=SNPInfoDecompressed,
dtype=str,
skiprows=1,
delimiter=',',
usecols=[SNPIDColumn,
ChrColumn,
PosColumn],
unpack=True)
os.remove(SNPInfoDecompressed)
SNPIDArray = Arrays[0]
ChrArray = Arrays[1].astype(int)
PosArray = Arrays[2].astype(int)
ArgSort = scipy.argsort(ChrArray)
SNPIDArray = SNPIDArray[ArgSort]
ChrArray = ChrArray[ArgSort]
PosArray = PosArray[ArgSort]
TmpSNPIDArray = scipy.array([])
TmpChrArray = scipy.array([])
TmpPosArray = scipy.array([])
TmpXPosArray = scipy.array([])
XMax = 0
for c in range(Arguments.NChr):
Chr = c+1
FilterArray = (ChrArray==Chr)
TmpTmpSNPIDArray = scipy.compress(FilterArray,SNPIDArray)
TmpTmpChrArray = scipy.compress(FilterArray,ChrArray)
TmpTmpPosArray = scipy.compress(FilterArray,PosArray)
ArgSort = scipy.argsort(TmpTmpPosArray)
TmpTmpSNPIDArray = TmpTmpSNPIDArray[ArgSort]
TmpTmpChrArray = TmpTmpChrArray[ArgSort]
TmpTmpPosArray = TmpTmpPosArray[ArgSort]
TmpTmpXPosArray = TmpTmpPosArray+XMax
XLeft.append(float(TmpTmpXPosArray.min()))
XRight.append(float(TmpTmpXPosArray.max()))
XTicks.append(0.5*(XLeft[-1]+XRight[-1]))
XTickLabels.append(r'${\rm '+str(Chr)+r'}$')
TmpSNPIDArray = scipy.append(TmpSNPIDArray,TmpTmpSNPIDArray)
TmpChrArray = scipy.append(TmpChrArray,TmpTmpChrArray)
TmpPosArray = scipy.append(TmpPosArray,TmpTmpPosArray)
TmpXPosArray = scipy.append(TmpXPosArray,TmpTmpXPosArray)
XMax = TmpXPosArray.max()
del TmpTmpSNPIDArray
del TmpTmpChrArray
del TmpTmpPosArray
del TmpTmpXPosArray
SNPIDArray = scipy.array(TmpSNPIDArray)
ChrArray = scipy.array(TmpChrArray)
PosArray = scipy.array(TmpPosArray)
XPosArray = scipy.array(TmpXPosArray)
del TmpSNPIDArray
del TmpChrArray
del TmpPosArray
del TmpXPosArray
for i in range(len(SNPIDArray)):
Entry = SNPIDArray[i]
SNPInfoDict[Entry] = {}
# SNPInfoDict[Entry]['chr'] = ChrArray[i]
# SNPInfoDict[Entry]['pos'] = PosArray[i]
# SNPInfoDict[Entry]['xpos'] = XPosArray[i]
SNPInfoDict[Entry]['index'] = i
SNPChrArray = scipy.array([])
SNPPosArray = scipy.array([])
SNPXPosArray = scipy.array([])
SNPChrArray = scipy.append(SNPChrArray,ChrArray).astype(int)
SNPPosArray = scipy.append(SNPPosArray,PosArray).astype(int)
SNPXPosArray = scipy.append(SNPXPosArray,XPosArray).astype(int)
XXMin = XPosArray.min()
XXMax = XPosArray.max()
LogString = '**** Parsed \"SNPInfo.txt\" ...'
print LogString
Log.Write(LogString+'\n')
# if(Arguments.YProperty=='PHE'):
# XPath = Arguments.GWAOutPath
# XListDir = os.listdir(XPath)
# MinXSpacing = iLARGE
# XMin = None
# XMax = 0
# X = []
# XLeft = []
# XRight = []
# XTicks = []
# XTickLabels = []
# for p in range(1): # if XProperty=='pos', all phenotypes have the same pos file content.
# P = 'PHE'+str(p+1)+'_'
# for c in range(Arguments.NChr):
# C = 'CHR'+str(c+1)+'_'
# for File in XListDir:
# if(re.search(C+P,File)):
# fr = open(os.path.join(XPath,File),'r')
# FMem = []
# for Line in fr.readlines():
# FMem.append(int(Line.strip().split()[0]))
# fr.close()
# for i in range(1,len(FMem)):
# MinXSpacing = min(MinXSpacing,FMem[i]-FMem[i-1])
## XMin = min(FMem)+XMax
# X.extend(list(scipy.array(FMem)+XMax))
# if(p==0):
# XLeft.append(float(min(FMem))+XMax)
# XRight.append(float(max(FMem))+XMax)
# XTicks.append(0.5*(float(min(FMem))+float(max(FMem)))+XMax)
# XTickLabels.append(r'${\rm '+re.sub('_','',C)+'}$')
# XMax = max(X)
# del FMem
# X = scipy.array(X)
# XXMin = X.min()
# XXMax = X.max()
LogString = '**** Parsed x-axis properties ...'
print LogString
Log.Write(LogString+'\n')
YMin = 1
YMax = Arguments.NPhe
MinYSpacing = 1
Y = []
for y in range(YMax):
Y.append(y+1)
LogString = '**** Processed y-axis properties ...'
print LogString
Log.Write(LogString+'\n')
MNumber = None
MName = None
MClass = None
if(Arguments.MetabolitClassesFileName!=''):
MNumber = []
MName = []
MClass = []
fr = open(Arguments.MetabolitClassesFileName,'r')
for Line in fr.readlines():
if(Line[0]=="#"):
continue
LStrp = Line.strip().split()
MNumber.append(LStrp[0])
MName.append(LStrp[1])
MClass.append(LStrp[2])
fr.close()
Classes = list(set(MClass))
ClassDict = {}
ClassRange = {}
for Entry in Classes:
ClassDict[Entry] = []
for i in range(len(MClass)):
if(Entry==MClass[i]):
ClassDict[Entry].append(int(MNumber[i]))
ClassRange[Entry] = [min(ClassDict[Entry]),max(ClassDict[Entry])]
if(Arguments.PhenotypeListFile!=''):
MNumber = []
MName = []
MClass = []
fr = open(Arguments.PhenotypeListFile,'r')
fr.readline()
for Line in fr:
LStrp = Line.strip().split(',')
MNumber.append(LStrp[0])
MName.append(LStrp[-1])
MClass.append('None')
fr.close()
# Classes = list(set(MClass))
# ClassDict = {}
# ClassRange = {}
# for Entry in Classes:
# ClassDict[Entry] = []
# for i in range(len(MClass)):
# if(Entry==MClass[i]):
# ClassDict[Entry].append(int(MNumber[i]))
# ClassRange[Entry] = [min(ClassDict[Entry]),max(ClassDict[Entry])]
ZMin = 0.0
ZMax = -fLARGE
ZPath = Arguments.GWAOutPath
ZListDir = os.listdir(ZPath)
if(Arguments.boReadZExtrFromFile):
LogString = '**** Reading z-axis extrema from file \"'+os.path.join(ZPath,'Extrema.dat')+'\" ...'
print LogString
Log.Write(LogString+'\n')
fr = open(os.path.join(ZPath,'Extrema.dat'),'r')
LSplt = fr.readline().strip().split()
fr.close()
ZMin = float(LSplt[0])
ZMax = float(LSplt[1])
else:
for p in range(Arguments.NPhe): # Scan for the maximum value of Z for the colorbar
P = 'PHE'+str(p+1)+'_'
Z = []
for c in range(Arguments.NChr):
C = 'CHR'+str(c+1)+'_'
File = os.path.join(ZPath,C+P+Arguments.ZProperty+'.dat')
fr = None
if(os.path.basename(File) in ZListDir):
fr = open(File,'r')
for Line in fr:
Value = Line.strip()
if((Value!='-1') or
(not re.search('nan',Value))):
Z.append(float(Value))
else:
Z.append(1.0)
fr.close()
Z = scipy.array(Z)
ZMax = max(ZMax,scipy.real(-scipy.log10(Z)).max())
del Z
LogString = '**** Determining maximal z-axis value, now at '+re.sub('_','',P)+', ZMax = '+str(ZMax)+' ...'
print LogString
Log.Write(LogString+'\n')
LogString = '**** Writing z-axis extrema to file \"'+os.path.join(ZPath,'Extrema.dat')+'\" ...'
print LogString
Log.Write(LogString+'\n')
fw = open(os.path.join(ZPath,'Extrema.dat'),'w')
fw.write(str(ZMin)+' '+str(ZMax)+'\n')
fw.close()
LogString = '**** Parsing in z-axis properties ...'
print LogString
Log.Write(LogString+'\n')
ZSugg = {}
ZSign = {}
YSugg = {}
YSign = {}
XSugg = {}
XSign = {}
NoPlotList = []
ExclMtbList =['C16.OH',\
'C16.1.OH',\
'C16.2',\
'C16.2.OH',\
'C18.1.OH',\
'C3.OH',\
'C3.1',\
'PC.aa.C24.0',\
'PC.aa.C30.2',\
'PC.aa.C38.1',\
'PC.ae.C30.1',\
'lysoPC.a.C6.0',\
'SM.C22.3']
# for p in range(0):
# for p in range(72,73):
# for p in range(2):
for p in range(Arguments.NPhe):
P = 'PHE'+str(p+1)+'_'
PHE = re.sub('_','',P)
Mtb = MName[MNumber.index(str(p+1))]
boExclude = False
for m in Mtb.split('_Over_'):
if(m in ExclMtbList):
boExclude = True
if(boExclude):
continue
FName = os.path.join(Arguments.GWAOutPath,Mtb+'_GWA.out')
os.system('pigz -d -c -k '+FName+'.gz > '+FName)
# if((not os.path.isfile(FName)) or
# (not os.path.islink(FName))):
# NoPlotList.append(p)
# continue
LogString = '** Now at '+PHE+' (\"'+FName+'\") ...'
print LogString
Log.Write(LogString+'\n')
FH = open(FName,'r')
Header = FH.readline().strip().split()
if(len(FH.readline())==0):
FH.close()
continue
FH.close()
SNPIDCol = Header.index('SNP')
PValCol = Header.index('Pvalue')
Arrays = scipy.loadtxt(fname=FName,
dtype=str,
skiprows=1,
usecols=[SNPIDCol,PValCol],
unpack=True)
RsIdArray = Arrays[0]
PValArray = Arrays[1]
if(type(PValArray)==scipy.string_):
PValArray = scipy.array([PValArray])
RsIdArray = scipy.array([RsIdArray])
FilterArray = (PValArray!='NA')
PValArray = scipy.compress(FilterArray,PValArray).astype(float)
RsIdArray = scipy.compress(FilterArray,RsIdArray)
FilterArray = []
for ii in range(len(RsIdArray)):
if(BadSNPs.has_key(RsIdArray[ii])):
FilterArray.append(False)
else:
FilterArray.append(True)
FilterArray = scipy.array(FilterArray)
PValArray = scipy.compress(FilterArray,PValArray)
RsIdArray = scipy.compress(FilterArray,RsIdArray)
del FilterArray
ZZ = scipy.real(-scipy.log10(PValArray))
# TmpArray = scipy.append(SNPIDArray,RSIdArray)
# TmpArray,\
# IndexArray = scipy.unique(ar=TmpArray,
# return_inverse=True)
# TmpArray = scipy.append(RSIdArray,SNPIDArray)
# TmpArray,\
# tTmpArray = scipy.unique(ar=TmpArray,
# return_inverse=True)
# IndexArray = scipy.append(IndexArray,tTmpArray)
# del TmpArray
# del tTmpArray
# IndexArray = scipy.unique(ar=IndexArray)
# for Entry in RSIdArray:
# IndexArray.append(SNPIDList.index(Entry))
# print Entry
# IndexArray = scipy.array(IndexArray)
IndexArray = []
for Entry in RsIdArray:
IndexArray.append(SNPInfoDict[Entry]['index'])
IndexArray = scipy.array(IndexArray)
X = XPosArray[IndexArray]
ChromArray = ChrArray[IndexArray]
# for c in range(Arguments.NChr):
# C = 'CHR'+str(c+1)+'_'
# for File in ZListDir:
# if(re.search(C+P,File)):
# fr = open(os.path.join(ZPath,File),'r')
# FMem = []
# for Line in fr.readlines():
# z = Line.strip().split()[0]
# if(z!='-1'):
# FMem.append(float(z))
# else:
# FMem.append(1.0)
# fr.close()
# FMem = scipy.real(-scipy.log10(scipy.array(FMem)))
# ZZ.extend(list(FMem))
# del FMem
# Sign = (ZZ > (-scipy.log10(5.0e-8)))
# Sugg = (ZZ >= (-scipy.log10(1.0e-6)))
# Sugg *= (ZZ <= (-scipy.log10(5.0e-8)))
# Sign = (ZZ > (-scipy.log10(5.0e-8/150.0)))
# Sugg = (ZZ >= (-scipy.log10(1.0e-6/150.0)))
# Sugg *= (ZZ <= (-scipy.log10(5.0e-8/150.0)))
# Sign = (ZZ > (-scipy.log10(5.0e-8/11325.0)))
# Sugg = (ZZ >= (-scipy.log10(1.0e-6/11325.0)))
# Sugg *= (ZZ <= (-scipy.log10(5.0e-8/11325.0)))
Sign = (ZZ > (-scipy.log10(5.0e-8/130.0)))
Sugg = (ZZ >= (-scipy.log10(1.0e-6/130.0)))
Sugg *= (ZZ <= (-scipy.log10(5.0e-8/130.0)))
# Sign = (ZZ > (-scipy.log10(5.0e-8/float(Arguments.NPhe))))
# Sugg = (ZZ >= (-scipy.log10(1.0e-6/float(Arguments.NPhe))))
# Sugg *= (ZZ <= (-scipy.log10(5.0e-8/float(Arguments.NPhe))))
# ZSugg[PHE] = scipy.compress(Sugg,ZZ)
ZSign[PHE] = scipy.compress(Sign,ZZ)
# YSugg[PHE] = scipy.ones(len(ZSugg[PHE]))*(p+1)
YSign[PHE] = scipy.ones(len(ZSign[PHE]))*(p+1)
# XSugg[PHE] = scipy.compress(Sugg,X)
XSign[PHE] = scipy.compress(Sign,X)
del ZZ
LogString = '** Parsed \"'+FName+'\" ...'
print LogString
Log.Write(LogString+'\n')
os.system('rm '+FName)
# del X
# del Y
# LogString = '**** Parsing in z-axis properties ...'
# print LogString
# Log.Write(LogString+'\n')
# ZSugg = {}
# ZSign = {}
# YSugg = {}
# YSign = {}
# XSugg = {}
# XSign = {}
# for p in range(Arguments.NPhe):
## for p in range(0):
# P = 'PHE'+str(p+1)+'_'
# PHE = re.sub('_','',P)
# LogString = '** Now at '+PHE+' ...'
# print LogString
# Log.Write(LogString+'\n')
# ZZ = []
# for c in range(Arguments.NChr):
# C = 'CHR'+str(c+1)+'_'
# for File in ZListDir:
# if(re.search(C+P,File)):
# fr = open(os.path.join(ZPath,File),'r')
# FMem = []
# for Line in fr.readlines():
# z = Line.strip().split()[0]
# if(z!='-1'):
# FMem.append(float(z))
# else:
# FMem.append(1.0)
# fr.close()
# FMem = scipy.real(-scipy.log10(scipy.array(FMem)))
# ZZ.extend(list(FMem))
# del FMem
# Sign = ZZ > -scipy.log10(5.0e-8)
# Sugg = ZZ >= -scipy.log10(1.0e-6)
# Sugg *= ZZ <= -scipy.log10(5.0e-8)
# ZSugg[PHE] = scipy.compress(Sugg,ZZ)
# ZSign[PHE] = scipy.compress(Sign,ZZ)
# YSugg[PHE] = scipy.ones(len(ZSugg[PHE]))*(p+1)
# YSign[PHE] = scipy.ones(len(ZSign[PHE]))*(p+1)
# XSugg[PHE] = scipy.compress(Sugg,X)
# XSign[PHE] = scipy.compress(Sign,X)
# del ZZ
# del X
# del Y
LogString = '**** Generating 3D-Manhattan plot ...'
print LogString
Log.Write(LogString+'\n')
# Lay-out stuff
figwidth_pt = 1422.0 # pt (from revtex \showthe\columnwidth)
inches_per_pt = 1.0/72.27
figwidth = figwidth_pt*inches_per_pt
golden_mean = (scipy.sqrt(5.0)-1.0)/2.0 # Aesthetic ratio
figheight = figwidth*golden_mean
fig_size = [figwidth,figheight]
params = {'backend': 'pdf',
'patch.antialiased': True,
# 'axes.labelsize': 28,
'axes.labelsize': 18,
'axes.linewidth': 0.5,
'grid.color': '0.75',
'grid.linewidth': 0.25,
'grid.linestyle': ':',
'axes.axisbelow': False,
'text.fontsize': 14,
# 'text.fontsize': 24,
'legend.fontsize': 14,
# 'axes.fontsize': 24,
'xtick.labelsize': 12,
# 'xtick.labelsize': 22,
'ytick.labelsize': 12,
# 'ytick.labelsize': 22,
'ztick.labelsize': 12,
# 'ztick.labelsize': 22,
'text.usetex': True,
'figure.figsize': fig_size}
left = 0.06
bottom = 0.125
width = 0.88-left
height = 0.95-bottom
pylab.rcParams.update(params)
PylabFigure = pylab.figure()
PylabFigure.clf()
Rectangle = [left, bottom, width, height]
PylabAxis = PylabFigure.add_subplot(111,projection='3d')
if(Arguments.XProperty=='pos'):
PylabAxis.set_xlabel(r'${\rm chromosome}$')
if(Arguments.YProperty=='PHE'):
PylabAxis.set_ylabel(r'${\rm metabolite}$')
PylabAxis.set_zlabel(r'$-\log_{10}(p-{\rm value})$')
# for p in range(2):
for p in range(Arguments.NPhe):
P = 'PHE'+str(p+1)
# if(len(ZSugg[P])>0):
# PylabAxis.scatter(x=XSugg[P],
# y=YSugg[P],
# color='black',
# s=ZSugg[P]/ZMax*100.0,
# marker='s',
# alpha=0.15,
# antialiased=True,
# edgecolors='none')
if(ZSign.has_key(P)):
if(len(ZSign[P])>0):
PylabAxis.scatter(xs=XSign[P],
ys=YSign[P],
zs=ZSign[P],
color='black',
marker='o',
alpha=0.25,
edgecolors='none',
antialiased=True)
# PlotFile = 'Manhattan3D.pdf'
PlotFile = 'Manhattan3DPlot.png'
# PlotFile = 'Manhattan3DFastRatios.pdf'
# PlotFile = 'Manhattan3DFastSingleMtbs.pdf'
# PlotFile = 'Manhattan3DFastConservativeSingleMtbs.pdf'
LogString = '**** Writing plot to \"'+PlotFile+'\" ...'
print LogString
Log.Write(LogString+'\n')
XXRange = float(XXMax)-float(XXMin)
XXOffset = XXRange*0.005
PylabAxis.set_xlim3d([float(XXMin)-XXOffset,float(XXMax)+XXOffset])
PylabAxis.set_ylim3d([0,YMax+2])
# for Key,Value in ClassRange.iteritems():
# PylabAxis.plot(scipy.array([float(XXMin)-XXOffset,float(XXMax)+XXOffset]),
# scipy.ones(2)*Value[0]-0.5,
# lw=0.25,
# color='black')
# PylabAxis.plot(scipy.array([float(XXMin)-XXOffset,float(XXMax)+XXOffset]),
# scipy.ones(2)*Value[1]+0.5,
# lw=0.25,
# color='black')
# PylabAxis.text(float(XXMax)+XXOffset,
# float(Value[0]+Value[1])*0.5,
# r'${\rm '+Key+'}$',
# verticalalignment='center',
# size=20)
# for Entry in XLeft:
# PylabAxis.plot(scipy.array([Entry,Entry]),
# scipy.array(PylabAxis.get_ylim()),
# lw=0.25,
# color='black')
# for Entry in XRight:
# PylabAxis.plot(scipy.array([Entry,Entry]),
# scipy.array(PylabAxis.get_ylim()),
# lw=0.25,
# color='black')
print PylabAxis.w_xaxis.get_ticklabels()
print len(PylabAxis.w_xaxis.get_ticklabels())
print XTickLabels
print len(XTickLabels)
print XTicks
print len(XTicks)
Ticks = []
for T in XTicks:
Ticks.append(T)
Labels = []
for L in XTickLabels:
Labels.append('')
Labels[0] = XTickLabels[0]
Labels[9] = XTickLabels[9]
Labels[-1] = XTickLabels[-1]
# for t in range(len(XTickLabels)):
# XTickLabels[t] = ''
PylabAxis.w_xaxis.set_ticklabels(Labels)
PylabAxis.w_xaxis.set_major_locator(ticker.FixedLocator(Ticks))
# PylabAxis.set_ylim([0,164])
# PylabAxis.spines['right'].set_visible(False)
# PylabAxis.spines['top'].set_visible(False)
# PylabAxis.xaxis.set_ticks_position('bottom')
# PylabAxis.yaxis.set_ticks_position('left')
# PylabAxis.xaxis.set_ticks(XTicks)
# PylabAxis.xaxis.set_ticklabels(XTickLabels)
# for Label in PylabAxis.xaxis.get_ticklabels():
# Label.set_rotation(90)
# pylab.savefig(PlotFile)
angles = scipy.linspace(315,(315+360),201)[:-1] # A list of 20 angles between 0 and 360
# create a movie with 10 frames per seconds and 'quality' 2000
rotanimate(PylabAxis, angles,'Manhattan3DPlot.mp4',fps=10,bitrate=2000)
# create an animated gif (20ms between frames)
rotanimate(PylabAxis, angles,'Manhattan3DPlot.gif',delay=20)
else:
print '!! NOT IMPLEMENTED YET !!'
LogString = '**** Done :-)'
print LogString
Log.Write(LogString+'\n')
LogString = Log.GetEndLogString()
print LogString
Log.Write(LogString+'\n')
Log.Close()
return
def pathplot(hoc, movie='', ms=15, fs=30, lw=2, res=30, invert=True, pkl=''):
# Initialize figure
fig = plt.figure()
ax = fig.gca(projection='3d')
# Convert given hoc file into a geo object
print('*Building geo object for {}, please wait...'.format(hoc))
geo = demoReadsilent(hoc)
print('*Building heatmap for {}, please wait...'.format(hoc))
# Find the tip segments and their end locations
tips, ends = geo.getTips()
# Calculate the path distance to each tip
pDF = PathDistanceFinder(geo, geo.soma)
pdists = [pDF.distanceTo(seg) for seg in tips]
# Calculate the coordinate position of each tip
coords = [tips[i].coordAt(ends[i]) for i in range(len(tips))]
# Establish color scheme
ic = 'black'
if invert:
ax.set_axis_bgcolor('black')
ax.patch.set_facecolor('black')
fig.patch.set_facecolor('black')
ic = 'white'
for spine in ax.spines: ax.spines[spine].set_color('white')
ax.xaxis.label.set_color('white')
ax.yaxis.label.set_color('white')
ax.tick_params(axis='x', colors='white')
ax.tick_params(axis='y', colors='white')
# Build and plot the neuron skeleton (includes axons)
bpts = []
for b in geo.branches:
bpts.append([[n.x, n.y, n.z] for n in b.nodes])
for b in bpts:
for c in range(len(b)-1):
ax.plot([b[c][0], b[c+1][0]],
[b[c][1], b[c+1][1]],
[b[c][2], b[c+1][2]],
color=ic, alpha=0.5, linewidth=lw)
# Set up the path distance color map with normalized values
vmax = max(pdists)
tfloats = [float(i)/vmax for i in pdists]
cmap = plt.cm.viridis # viridis > inferno >> jet
tcolors = cmap(tfloats)
# Plot points at tips and color by path distance
for t, pts in enumerate(coords):
ax.scatter(pts[0], pts[1], pts[2], c=tcolors[t],
edgecolors='face', alpha=0.9)
# Plot point at the soma.
ax.scatter(geo.soma.nodes[0].x, geo.soma.nodes[0].y, geo.soma.nodes[0].z,
c = ic, s = 100, edgecolors='face')
# Create a list of indices ordered from shortest path to longest
pord = list(pdists) # Preserve pdists
pind = []
for p in pord:
maxind = pord.index(max(pord))
pind.append(maxind)
pord.insert(maxind, -1)
pord.remove(pord[maxind+1])
pind.reverse()
# Enumerate the paths leading to each tip in a dictionary
tipDict = {}
for d in range(len(tips)):
tipDict[d] = pDF.pathTo(tips[d])
# Overlay a tip path skeleton colored by path distance (discludes axons)
for path in pind:
if path==pind[0]: lw+=0.1
else:lw+=0.001
for seg in tipDict[path]:
ax.plot([seg.nodes[0].x, seg.nodes[1].x],
[seg.nodes[0].y, seg.nodes[1].y],
[seg.nodes[0].z, seg.nodes[1].z],
color=tcolors[path], alpha=1, linewidth=lw)
print('*The maximum linewidth is {}'.format(round(lw, 4)))
print(lw)
# Add colorbar
sc = []
sc.append(plt.scatter([0,0], [0,0], c=[0., 1.], s=0.1,
vmin=0, vmax=vmax, cmap=cmap))
cbar = plt.colorbar(sc[-1])
cbar.ax.yaxis.set_tick_params(color=ic)
plt.setp(plt.getp(cbar.ax.axes, 'yticklabels'), color='white')
# Add labels
ax.set_xlabel('Micrometers', fontsize = fs, color = ic)
ax.set_ylabel('Micrometers', fontsize = fs, color = ic)
ax.set_zlabel('Micrometers', fontsize = fs, color = ic)
ax.set_title('Heat Map of Neuron Tips Colored by Path Length',
fontsize = fs, color = ic)
cbar.set_label("Path Length (um)", fontsize = fs, color = ic)
print('*HEATMAP COMPLETE - Thank you for your patience.')
# Save the figure as a .pickle
if pkl != '':
import pickle
output = open(pkl + '.pickle', 'wb')
pickle.dump(fig, output)
output.close()
# Save the figure as a movie
if movie != '':
# Remove whitespace around skeleton for visual appeal
xpts = []
ypts = []
zpts = []
for b in bpts:
for c in range(len(b)-1):
xpts.append(b[c][0])
ypts.append(b[c][1])
zpts.append(b[c][2])
ax.set_xlim(min(xpts),max(xpts))
ax.set_ylim(min(ypts),max(ypts))
ax.set_zlim(min(zpts),max(zpts))
# Remove axes for visual appeal.
plt.axis('off')
print('*Creating movie for {}, please wait...'.format(hoc))
# Create an animated gif (delay ms between frames)
if os.path.splitext(movie)[1] == '.gif':
rotanimate(fig, ax, res, movie, delay=20,
width = ms, height = ms)
# Create a movie with fps frames per second and bitrate 'quality'
if os.path.splitext(movie)[1] == '.mp4':
rotanimate(fig, ax, res, movie, fps=8, bitrate=2000,
width = ms, height = ms)
# Create an ogv movie with fps frames per second
if os.path.splitext(movie)[1] == '.ogv':
rotanimate(fig, ax, res, movie, fps=8,
width = ms, height = ms)
print('*MOVIE COMPLETE - Thank you for your patience.')
# Display the figure in a new window
fig.show()
def main(ExecutableName):
# Gene2RsIdDict = {}
# RsId2GeneDict = {}
# fr = open('AllAlphaNFHitsSheetUCSCGeneAnot.txt','r')
# fr.readline()
# for Line in fr:
# LSplit = Line.strip().split()
# if(not Gene2RsIdDict.has_key(LSplit[1])):
# Gene2RsIdDict[LSplit[1]] = []
# Gene2RsIdDict[LSplit[1]].append(LSplit[0])
# if(not RsId2GeneDict.has_key(LSplit[0])):
# RsId2GeneDict[LSplit[0]] = []
# RsId2GeneDict[LSplit[0]].append(LSplit[1])
# fr.close()
ArgParser,\
Arguments = ArgumentParser.ParseArguments()
Ext = ''
Log = Logger.Logger(ExecutableName,
Ext)
LogString = '## START TIMESTAMP\n'
LogString += str(Log.GetStartDate())+'\n'
LogString += '## END TIMESTAMP'
print LogString
Log.Write(LogString+'\n')
LogString = Log.GetStartLogString()
print LogString
Log.Write(LogString+'\n')
ArgumentParser.LogArguments(Log,
ArgParser,
Arguments)
TmpPath = os.path.join(os.getcwd(),'Tmp')
if(not os.path.isdir(TmpPath)):
os.mkdir(TmpPath)
SNPInfoDict = {} # will contain a dictionary of two elements: {'chr':int(chr),'pos':int(pos)}
SNPChrArray = scipy.array([])
SNPPosArray = scipy.array([])
SNPXPosArray = scipy.array([])
XLeft = []
XRight = []
XTicks = []
XTickLabels = []
XXMax = None
XXMin = None
if(Arguments.YProperty=='PHE'):
XPath = os.path.join(Arguments.PositionPath,'SNPInfo.txt.gz')
SNPInfoDecompressed = os.path.join(TmpPath,'SNPInfo.txt')
os.system('pigz -d -c -k '+XPath+' > '+SNPInfoDecompressed)
FH = open(SNPInfoDecompressed,'r')
HeaderList = FH.readline().strip().split()
FH.close()
SNPIDArray = None
ChrArray = None
PosArray = None
SNPIDColumn = None
ChrColumn = None
PosColumn = None
for Entry in HeaderList:
if(Entry=='SNPID'):
SNPIDColumn = HeaderList.index(Entry)
if(Entry=='chr'):
ChrColumn = HeaderList.index(Entry)
if(Entry=='position'):
PosColumn = HeaderList.index(Entry)
Arrays = scipy.loadtxt(fname=SNPInfoDecompressed,
dtype=str,
skiprows=1,
usecols=[SNPIDColumn,
ChrColumn,
PosColumn],
unpack=True)
os.remove(SNPInfoDecompressed)
SNPIDArray = Arrays[0]
ChrArray = Arrays[1].astype(int)
PosArray = Arrays[2].astype(int)
ArgSort = scipy.argsort(ChrArray)
SNPIDArray = SNPIDArray[ArgSort]
ChrArray = ChrArray[ArgSort]
PosArray = PosArray[ArgSort]
TmpSNPIDArray = scipy.array([])
TmpChrArray = scipy.array([])
TmpPosArray = scipy.array([])
TmpXPosArray = scipy.array([])
XMax = 0
for c in range(Arguments.NChr):
Chr = c+1
FilterArray = (ChrArray==Chr)
TmpTmpSNPIDArray = scipy.compress(FilterArray,SNPIDArray)
TmpTmpChrArray = scipy.compress(FilterArray,ChrArray)
TmpTmpPosArray = scipy.compress(FilterArray,PosArray)
ArgSort = scipy.argsort(TmpTmpPosArray)
TmpTmpSNPIDArray = TmpTmpSNPIDArray[ArgSort]
TmpTmpChrArray = TmpTmpChrArray[ArgSort]
TmpTmpPosArray = TmpTmpPosArray[ArgSort]
TmpTmpXPosArray = TmpTmpPosArray+XMax
XLeft.append(float(TmpTmpXPosArray.min()))
XRight.append(float(TmpTmpXPosArray.max()))
XTicks.append(0.5*(XLeft[-1]+XRight[-1]))
# XTickLabels.append(r'${\rm CHR'+str(Chr)+r'}$')
XTickLabels.append(str(Chr))
TmpSNPIDArray = scipy.append(TmpSNPIDArray,TmpTmpSNPIDArray)
TmpChrArray = scipy.append(TmpChrArray,TmpTmpChrArray)
TmpPosArray = scipy.append(TmpPosArray,TmpTmpPosArray)
TmpXPosArray = scipy.append(TmpXPosArray,TmpTmpXPosArray)
XMax = TmpXPosArray.max()
del TmpTmpSNPIDArray
del TmpTmpChrArray
del TmpTmpPosArray
del TmpTmpXPosArray
# XTicks.append(XRight[-1])
# XTickLabels.append(r'${\rm CHR'+str(Chr)+r'}$')
# XTickLabels.append(str(23))
SNPIDArray = scipy.array(TmpSNPIDArray)
ChrArray = scipy.array(TmpChrArray)
PosArray = scipy.array(TmpPosArray)
XPosArray = scipy.array(TmpXPosArray)
del TmpSNPIDArray
del TmpChrArray
del TmpPosArray
del TmpXPosArray
for i in range(len(SNPIDArray)):
Entry = SNPIDArray[i]
SNPInfoDict[Entry] = {}
# SNPInfoDict[Entry]['chr'] = ChrArray[i]
# SNPInfoDict[Entry]['pos'] = PosArray[i]
# SNPInfoDict[Entry]['xpos'] = XPosArray[i]
SNPInfoDict[Entry]['index'] = i
SNPChrArray = scipy.array([])
SNPPosArray = scipy.array([])
SNPXPosArray = scipy.array([])
SNPChrArray = scipy.append(SNPChrArray,ChrArray).astype(int)
SNPPosArray = scipy.append(SNPPosArray,PosArray).astype(int)
SNPXPosArray = scipy.append(SNPXPosArray,XPosArray).astype(int)
XXMin = XPosArray.min()
XXMax = XPosArray.max()
LogString = '**** Parsed \"SNPInfo.txt\" ...'
print LogString
Log.Write(LogString+'\n')
# if(Arguments.YProperty=='PHE'):
# XPath = os.path.join(Arguments.SnpTestPath,Arguments.XProperty)
# XListDir = os.listdir(XPath)
# MinXSpacing = iLARGE
# XMin = None
# XMax = 0
# X = []
# XLeft = []
# XTicks = []
# XTickLabels = []
# for p in range(1): # if XProperty=='pos', all phenotypes have the same pos file content.
# P = 'PHE'+str(p+1)+'_'
# for c in range(Arguments.NChr):
# C = 'CHR'+str(c+1)+'_'
# for File in XListDir:
# if(re.search(C+P,File)):
# fr = open(os.path.join(XPath,File),'r')
# FMem = []
# for Line in fr.readlines():
# FMem.append(int(Line.strip().split()[0]))
# fr.close()
# for i in range(1,len(FMem)):
# MinXSpacing = min(MinXSpacing,FMem[i]-FMem[i-1])
## XMin = min(FMem)+XMax
# X.extend(list(scipy.array(FMem)+XMax))
# if(p==0):
# XLeft.append(float(min(FMem))+XMax)
# XRight.append(float(max(FMem))+XMax)
# XTicks.append(0.5*(float(min(FMem))+float(max(FMem)))+XMax)
# XTickLabels.append(r'${\rm '+re.sub('_','',C)+'}$')
# XMax = max(X)
# del FMem
# X = scipy.array(X)
# XXMin = X.min()
# XXMax = X.max()
LogString = '**** Parsed x-axis properties ...'
print LogString
Log.Write(LogString+'\n')
YMin = 1
YMax = Arguments.NPhe
MinYSpacing = 1
Y = []
for y in range(YMax):
Y.append(y+1)
LogString = '**** Processed y-axis properties ...'
print LogString
Log.Write(LogString+'\n')
MNumber = None
MName = None
MClass = None
MInclude = None
if(Arguments.MetabolitClassesFileName!=''):
MNumber = []
MName = []
MClass = []
MInclude = []
fr = open(Arguments.MetabolitClassesFileName,'r')
for Line in fr.readlines():
if(Line[0]=="#"):
continue
LStrp = Line.strip().split()
MNumber.append(LStrp[0])
MName.append(LStrp[1])
MClass.append(LStrp[2])
if(len(LStrp)>3):
MInclude.append(LStrp[3])
fr.close()
Classes = list(set(MClass))
ClassDict = {}
ClassRange = {}
for Entry in Classes:
ClassDict[Entry] = []
for i in range(len(MClass)):
if(Entry==MClass[i]):
ClassDict[Entry].append(int(MNumber[i]))
ClassRange[Entry] = [min(ClassDict[Entry]),max(ClassDict[Entry])]
ZMin = 0.0
ZMax = -fLARGE
ZPath = os.path.join(Arguments.MAOutputPath,Arguments.ZProperty)
ZListDir = os.listdir(ZPath)
if(Arguments.boReadZExtrFromFile):
LogString = '**** Reading z-axis extrema from file \"'+os.path.join(ZPath,'Extrema.dat')+'\" ...'
print LogString
Log.Write(LogString+'\n')
fr = open(os.path.join(ZPath,'Extrema.dat'),'r')
LSplt = fr.readline().strip().split()
fr.close()
ZMin = float(LSplt[0])
ZMax = float(LSplt[1])
# else:
# for p in range(Arguments.NPhe): # Scan for the maximum value of Z for the colorbar
# P = 'PHE'+str(p+1)+'_'
# Z = []
# for c in range(Arguments.NChr):
# C = 'CHR'+str(c+1)+'_'
# File = os.path.join(ZPath,C+P+Arguments.ZProperty+'.dat')
# fr = None
# if(os.path.basename(File) in ZListDir):
# fr = open(File,'r')
# for Line in fr:
# Value = Line.strip()
# if((Value!='-1') or
# (not re.search('nan',Value))):
# Z.append(float(Value))
# else:
# Z.append(1.0)
# fr.close()
# Z = scipy.array(Z)
# ZMax = max(ZMax,scipy.real(-scipy.log10(Z)).max())
# del Z
# LogString = '**** Determining maximal z-axis value, now at '+re.sub('_','',P)+', ZMax = '+str(ZMax)+' ...'
# print LogString
# Log.Write(LogString+'\n')
# LogString = '**** Writing z-axis extrema to file \"'+os.path.join(ZPath,'Extrema.dat')+'\" ...'
# print LogString
# Log.Write(LogString+'\n')
# fw = open(os.path.join(ZPath,'Extrema.dat'),'w')
# fw.write(str(ZMin)+' '+str(ZMax)+'\n')
# fw.close()
LogString = '**** Parsing in z-axis properties ...'
print LogString
Log.Write(LogString+'\n')
ZSugg = {}
ZSign = {}
YSugg = {}
YSign = {}
XSugg = {}
XSign = {}
Color = {}
NoPlotList = []
# for p in range(0):
# for p in range(72,73):
# for Gene in ['FADS2']:
# for Gene in Gene2RsIdDict.iterkeys():
XS = []
YS = []
ZS = []
for p in range(Arguments.NPhe):
P = 'PHE'+str(p+1)+'_'
PHE = re.sub('_','',P)
Mtb = MName[MNumber.index(str(p+1))]
FName = os.path.join(Arguments.MAOutputPath,'FilteredAlpha5e-08_MetaAnalysis_'+Mtb+'_1.csv')
if(not (os.path.isfile(FName) or
os.path.islink(FName))):
NoPlotList.append(p)
continue
LogString = '** Now at '+PHE+' (\"'+FName+'\") ...'
print LogString
Log.Write(LogString+'\n')
FH = open(FName,'r')
Header = FH.readline().strip().split(',')
CountLines = 0
for Line in FH:
CountLines += 1
FH.close()
if(CountLines==0):
continue
SNPIDCol = Header.index('MarkerName')
PValCol = Header.index('P-value')
Arrays = scipy.loadtxt(fname=FName,
dtype=str,
skiprows=1,
delimiter=',',
usecols=[SNPIDCol,PValCol],
unpack=True)
RsIdArray = Arrays[0]
PValArray = Arrays[1].astype(float)
ZZ = scipy.real(-scipy.log10(PValArray))
if(type(RsIdArray)==scipy.string_):
RsIdArray = scipy.array([RsIdArray])
PValArray = scipy.array([PValArray])
ZZ = scipy.real(-scipy.log10(PValArray))
ColorList = []
# if(RsId2GeneDict.has_key[Rsid])
for RsId in RsIdArray:
ColorList.append('black')
# if(RsId in Gene2RsIdDict[Gene]):
# if(RsId2GeneDict.has_key(RsId)):
# ColorList.append('red')
# ColorList.append('black')
# else:
# TmpArray = scipy.append(SNPIDArray,RSIdArray)
# TmpArray,\
# IndexArray = scipy.unique(ar=TmpArray,
# return_inverse=True)
# TmpArray = scipy.append(RSIdArray,SNPIDArray)
# TmpArray,\
# tTmpArray = scipy.unique(ar=TmpArray,
# return_inverse=True)
# IndexArray = scipy.append(IndexArray,tTmpArray)
# del TmpArray
# del tTmpArray
# IndexArray = scipy.unique(ar=IndexArray)
# for Entry in RSIdArray:
# IndexArray.append(SNPIDList.index(Entry))
# print Entry
# IndexArray = scipy.array(IndexArray)
IndexArray = []
for Entry in RsIdArray:
IndexArray.append(SNPInfoDict[Entry]['index'])
IndexArray = scipy.array(IndexArray)
X = XPosArray[IndexArray]
ChromArray = ChrArray[IndexArray]
# for c in range(Arguments.NChr):
# C = 'CHR'+str(c+1)+'_'
# for File in ZListDir:
# if(re.search(C+P,File)):
# fr = open(os.path.join(ZPath,File),'r')
# FMem = []
# for Line in fr.readlines():
# z = Line.strip().split()[0]
# if(z!='-1'):
# FMem.append(float(z))
# else:
# FMem.append(1.0)
# fr.close()
# FMem = scipy.real(-scipy.log10(scipy.array(FMem)))
# ZZ.extend(list(FMem))
# del FMem
Sign = (ZZ > (-scipy.log10(5.0e-8/float(46))))
Sugg = (ZZ >= (-scipy.log10(1.0e-6/float(46))))
Sugg *= (ZZ <= (-scipy.log10(5.0e-8/float(46))))
# Sign = (ZZ > (-scipy.log10(5.0e-8/float(14))))
# Sugg = (ZZ >= (-scipy.log10(1.0e-6/float(14))))
# Sugg *= (ZZ <= (-scipy.log10(5.0e-8/float(14))))
# Sign = (ZZ > (-scipy.log10(5.0e-8/float(Arguments.NPhe))))
# Sugg = (ZZ >= (-scipy.log10(1.0e-6/float(Arguments.NPhe))))
# Sugg *= (ZZ <= (-scipy.log10(5.0e-8/float(Arguments.NPhe))))
# ZSugg[PHE] = scipy.compress(Sugg,ZZ)
ZSign[PHE] = scipy.compress(Sign,ZZ)
# YSugg[PHE] = scipy.ones(len(ZSugg[PHE]))*(p+1)
YSign[PHE] = scipy.ones(len(ZSign[PHE]))*(p+1)
# XSugg[PHE] = scipy.compress(Sugg,X)
XSign[PHE] = scipy.compress(Sign,X)
Color[PHE] = scipy.compress(Sign,scipy.array(ColorList)).tolist()
del ZZ
LogString = '** Parsed \"'+FName+'\" ...'
print LogString
Log.Write(LogString+'\n')
# del X
# del Y
LogString = '**** Generating 3D-Manhattan plot ...'
print LogString
Log.Write(LogString+'\n')
## Lay-out stuff
# figwidth_pt = 1422.0 # pt (from revtex \showthe\columnwidth)
# inches_per_pt = 1.0/72.27
# figwidth = figwidth_pt*inches_per_pt
# golden_mean = (scipy.sqrt(5.0)-1.0)/2.0 # Aesthetic ratio
# figheight = figwidth*golden_mean
# fig_size = [figwidth,figheight]
# params = {'backend': 'pdf',
# 'patch.antialiased': True,
# 'axes.labelsize': 28,
# 'axes.linewidth': 0.5,
# 'grid.color': '0.75',
# 'grid.linewidth': 0.25,
# 'grid.linestyle': ':',
# 'axes.axisbelow': False,
# 'text.fontsize': 24,
# 'legend.fontsize': 20,
# 'xtick.labelsize': 22,
# 'ytick.labelsize': 22,
# 'text.usetex': True,
# 'figure.figsize': fig_size}
# left = 0.06
# bottom = 0.125
# width = 0.88-left
# height = 0.95-bottom
#
# pylab.rcParams.update(params)
PylabFigure = pylab.figure()
PylabFigure.clf()
# Rectangle = [left, bottom, width, height]
# PylabAxis = PylabFigure.add_axes(Rectangle)
PylabAxis = PylabFigure.add_subplot(111,projection='3d')
if(Arguments.XProperty=='pos'):
PylabAxis.set_xlabel('chromosome')
if(Arguments.YProperty=='PHE'):
PylabAxis.set_ylabel('metabolite')
PylabAxis.set_zlabel('-log(p-value)')
# for p in range(0,2):
# for p in range(72,73):
for p in range(Arguments.NPhe):
if(p in NoPlotList):
continue
P = 'PHE'+str(p+1)
# if(len(ZSugg[P])>0):
# PylabAxis.scatter(x=XSugg[P],
# y=YSugg[P],
# color='black',
# s=ZSugg[P]/ZMax*100.0,
# marker='s',
# alpha=0.15,
# antialiased=True,
# edgecolors='none')
if((ZSign.has_key(P)) and
(len(ZSign[P])>0)):
# XS.extend(XSign[P].tolist())
# YS.extend(YSign[P].tolist())
# ZS.extend(ZSign[P].tolist())
PylabAxis.scatter(xs=XSign[P],
ys=YSign[P],
zs=ZSign[P],
# zs=ZSign[P]/ZMax*100.0,
color=Color[P],
marker='o',
alpha=0.5,
antialiased=True,
edgecolors='none')#,
# label=r'$\rm '+Gene+r'$')
# PylabAxis.scatter(xs=XSign[P],
# ys=YSign[P],
# zs=0.0,
# color='black',
# s=0.5,
# marker='o',
# alpha=0.25,
# antialiased=True,
# edgecolors='none')
# XI = scipy.linspace(min(XS),max(XS),endpoint=True,num=1000)
# print len(XI)
# YI = scipy.linspace(min(YS),max(YS),num=163,endpoint=True)
# print len(YI)
## X, Y = scipy.meshgrid(XI,YI)
## print len(X),
## print len(Y)
# Z = griddata(scipy.array(XS), scipy.array(YS), scipy.array(ZS), XI, YI)
#
# PylabAxis.plot_surface(XI,
# YI,
# Z,
# rstride=8,
# cstride=8,
# alpha=0.3)
# PylabAxis.plot_wireframe(XS,
# YS,
# ZS)
PlotFile = 'Manhattan3D.pdf'
LogString = '**** Writing plot to \"'+PlotFile+'\" ...'
print LogString
Log.Write(LogString+'\n')
XXRange = float(XXMax)-float(XXMin)
XXOffset = XXRange*0.005
PylabAxis.set_xlim3d([float(XXMin)-XXOffset,float(XXMax)+XXOffset])
# PylabAxis.set_ylim([0,YMax+2])
# for Key,Value in ClassRange.iteritems():
# PylabAxis.plot(scipy.array([float(XXMin)-XXOffset,float(XXMax)+XXOffset]),
# scipy.ones(2)*Value[0]-0.5,
# 0.0,
# lw=0.25,
# color='black')
# PylabAxis.plot(scipy.array([float(XXMin)-XXOffset,float(XXMax)+XXOffset]),
# scipy.ones(2)*Value[1]+0.5,
# 0.0,
# lw=0.25,
# color='black')
# PylabAxis.text(float(XXMax)+XXOffset,
# float(Value[0]+Value[1])*0.5,
# 0.0,
# Key,
# verticalalignment='center')
# for Entry in XLeft:
# PylabAxis.plot(scipy.array([Entry,Entry]),
# scipy.array(PylabAxis.get_ylim()),
# lw=0.25,
# color='black')
# for Entry in XRight:
# PylabAxis.plot(scipy.array([Entry,Entry]),
# scipy.array(PylabAxis.get_ylim()),
# lw=0.25,
# color='black')
#
# for p in range(Arguments.NPhe):
# if(len(MInclude)>0):
# if(MInclude[p]=='False'):
# PylabAxis.plot(scipy.array(PylabAxis.get_xlim()),
# scipy.array([float(p+1),float(p+1)]),
# color='blue',
# ls='-',
# lw=4.25,
# alpha=0.125,
# zorder=0)
# PylabAxis.set_ylim([0,164])
# PylabAxis.spines['right'].set_visible(False)
# PylabAxis.spines['top'].set_visible(False)
# PylabAxis.xaxis.set_ticks_position('bottom')
# PylabAxis.yaxis.set_ticks_position('left')
# PylabAxis.w_xaxis.set_ticks(XTicks)
# YTicks = PylabAxis.get_yticks()
# YTickLabels = PylabAxis.get_yticklabels()
# YTickLabels = list(YTickLabels)
# for t in range(len(YTickLabels)):
# YTickLabels[t] = ''
# PylabAxis.w_yaxis.set_ticklabels(YTickLabels)
# PylabAxis.w_yaxis.set_major_locator(ticker.FixedLocator(YTicks))
print PylabAxis.w_xaxis.get_ticklabels()
print len(PylabAxis.w_xaxis.get_ticklabels())
print XTickLabels
print len(XTickLabels)
print XTicks
print len(XTicks)
Ticks = []
for T in XTicks:
Ticks.append(T)
Labels = []
for L in XTickLabels:
Labels.append('')
Labels[0] = XTickLabels[0]
Labels[9] = XTickLabels[9]
Labels[-1] = XTickLabels[-1]
# for t in range(len(XTickLabels)):
# XTickLabels[t] = ''
PylabAxis.w_xaxis.set_ticklabels(Labels)
PylabAxis.w_xaxis.set_major_locator(ticker.FixedLocator(Ticks))
PylabAxis.set_zlim3d([0.0,ZMax+5.0])
# PylabAxis.set_zlim3d([0.0,PylabAxis.get_zlim3d()[-1]])
# ZTicks = PylabAxis.zaxis.get_ticks()
# ZTickLabels = PylabAxis.zaxis.get_ticklabels()
# ZTickLabels = list(ZTickLabels)
# for t in range(len(ZTickLabels)):
# ZTickLabels[t] = r'${'+str(ZTickLabels[t])+r'}$'
# PylabAxis.w_zaxis.set_ticklabels(ZTickLabels)
# PylabAxis.w_zaxis.set_major_locator(ticker.FixedLocator(ZTicks))
# for Label in PylabAxis.xaxis.get_ticklabels():
# Label.set_rotation(90)
# Handles,Labels = PylabAxis.get_legend_handles_labels()
# PylabAxis.legend([Handles[0]],
# [Labels[0]],
# fancybox=True,
# shadow=True,
# loc='lower left',
# numpoints=1,
# scatterpoints=1)
# pylab.savefig(re.sub('.pdf','_'+Gene+'.pdf',PlotFile))
# pylab.savefig(PlotFile)
# pylab.show()
# pylab.close()
angles = scipy.linspace(315,(315+360),201)[:-1] # A list of 20 angles between 0 and 360
# create an animated gif (20ms between frames)
rotanimate(PylabAxis, angles,'movie.gif',delay=20)
# create a movie with 10 frames per seconds and 'quality' 2000
rotanimate(PylabAxis, angles,'movie.mp4',fps=10,bitrate=2000)
# # create an ogv movie
# rotanimate(ax, angles, 'movie.ogv',fps=10)
# create still of first snapshot
rotanimate(PylabAxis, angles,'movie.pdf')
else:
print '!! NOT IMPLEMENTED YET !!'
LogString = '**** Done :-)'
print LogString
Log.Write(LogString+'\n')
LogString = Log.GetEndLogString()
print LogString
Log.Write(LogString+'\n')
Log.Close()
return
