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Raw.py
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# ___ __ _ ___ _____
# / _ | ___ ___ _/ /_ _____ (_)__ ___ / _/ / _/ /_ _____ _______ ___ _______ ___ _______
# / __ |/ _ \/ _ `/ / // (_-</ (_-< / _ \/ _/ / _/ / // / _ \/ __/ -_|_-</ __/ -_) _ \/ __/ -_)
#/_/ |_/_//_/\_,_/_/\_, /___/_/___/ \___/_/ /_//_/\_,_/\___/_/ \__/___/\__/\__/_//_/\__/\__/
# /___/__ __
# / _ \___ _/ /____ _
# / // / _ `/ __/ _ `/
# /____/\_,_/\__/\_,_/ Author :: Kevin Nelson :: [email protected]
from pylab import *
import sys
import os
import csv
import numpy
def readFile(n,delim):
fileIN=n
Data={}
with open(fileIN) as csvfile:
tableMain=csv.reader(csvfile,delimiter=delim)
k=0
for row in tableMain:
Data[k]=row
k+=1
return(Data)
#data = readFile(sys.argv[1])
def RawParse(data):
starts=[]
ends=[]
names=[]
found=False
for k in range(0,len(data)):
if(len(data[k])==0):
if(found):
ends.append(k)
found=False
pass
else:
if(found==False):
found=True
names.append(data[k][0])
starts.append(k+1)
if(len(ends)<len(starts)):
ends.append(k)
ret={}
# print 'Making Data With'
# print names
# print 'starts'
# print starts
# print 'ends'
# print ends
if(len(ends)==len(starts) and len(starts)==len(names)):
for k in range(0,len(names)):
ret[names[k]]={}
k3=0
for k2 in range(starts[k],ends[k]):
ret[names[k]][k3]=data[k2]
k3+=1
return(ret,names)
def DictPrt2d(sdmB,delim): # This is usful for printing 2 dimenstion dictionaries {{}} -> csv files.
op=''
First=True
for k in sdmB:
if First:
First= False
else:
if(op[len(op)-1]==delim):op=op[0:len(op)-1]+'\n'
for k2 in sdmB[k]: # Patched for {{}} or {[]}
try:
op += str(sdmB[k][k2]) #Dictionary Behavior
except(TypeError):
op+=str(k2) #List Behavior
op += delim
if(op[len(op)-1]==','):op=op[0:len(op)-1]+'\n'
op+='\n'
return(op)
def RoxPlot(Data,name):
t=Data.keys()
t2=Data[t[1]].keys()
#print DictPrt2d(Data[t[1]],'\t')
#print Data[t[1]].keys()
#print t
#print t2
temp=Data[t[1]]
#print(DictPrt2d(temp,'\t'))
t = temp[0][1:-1]
#print t
s = temp[1][1:]
#print s
#print str(len(s))+' : '+str(len(t))
close()
plot(t, s)
xlabel(temp[0][0])
ylabel(temp[1][0])
title('Rox :'+name)
grid(True)
formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
ax = subplot(111)
ax.yaxis.set_major_formatter(formatter)
ax.xaxis.set_major_formatter(formatter)
savefig('Rox :'+name+'.png')
#show()
def ProbePlot(Data,name):
t=Data.keys()
t2=Data[t[1]].keys()
#print DictPrt2d(Data[t[1]],'\t')
#print Data[t[1]].keys()
#print t
#print t2
temp=Data[t[1]]
#print(DictPrt2d(temp,'\t'))
t = temp[0][1:-1]
#print t
s = temp[2][1:]
#print s
#print str(len(s))+' : '+str(len(t))
close()
plot(t, s)
xlabel(temp[0][0])
ylabel(temp[2][0])
title('Probe :'+name)
grid(True)
formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
ax = subplot(111)
ax.yaxis.set_major_formatter(formatter)
ax.xaxis.set_major_formatter(formatter)
savefig('Probe :'+name+'.png')
def qpcrPlot(Data,f,t,s,g):#Data container, file name, sample name, gene name
T = Data[f][t][s][g][1:-1]#Fval
S = range(1,len(T)+1,1) #Cycle Number
Fmax=max(T)
k2=0
for k in T:
T[k2] = int(k)
k2+=1
print T
print ' len: ' + str(len(T))
print S
print ' len: ' + str(len(S))
#print s
#print str(len(s))+' : '+str(len(t))
close()
plot(S,T)
xlabel('cycle')
ylabel('fluorescence')
print type(f)
print type(t)
print type(s)
print type(g)
title(t+':'+s+':'+g)
grid(True)
formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
ax = subplot(111)
ax.yaxis.set_major_formatter(formatter)
ax.xaxis.set_major_formatter(formatter)
savefig(t+'_'+s+'_'+g+'.png')
def askYesNo():
print 'please anwser "Y" or "N"'
ans = raw_input()
if (ans == 'Y' or ans == 'y' or ans == 'Yes' or ans == 'yes'):
return(True)
elif (ans == 'N' or ans == 'n' or ans == 'No' or ans == 'no'):
return(False)
elif (True):
return(askYesNo())
def QuickPlot(x,y):
#close()
plot(x,y)
xlabel('x')
ylabel('y')
grid(True)
formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
ax = subplot(111)
ax.yaxis.set_major_formatter(formatter)
ax.xaxis.set_major_formatter(formatter)
show()
# print 'save image (Y/N):'
# ans = askYesNo()
# if(ans):
# print 'please enter file name:'
# ans = raw_input()
# savefig(ans+'.png')
def QuickerPlot(x):
close()
y=[]
for k in range(1,len(x)+1):
y.append(k)
plot(x,y)
xlabel('x')
ylabel('y')
grid(True)
formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
ax = subplot(111)
ax.yaxis.set_major_formatter(formatter)
ax.xaxis.set_major_formatter(formatter)
show()
def ClearPlot():
close()
def RoxPlots():
files = [f for f in os.listdir('.') if os.path.isfile(f)]
for f in files:
if(f[-3:]=='csv'):
Data , names = RawParse(readFile(f,','))
if not os.path.exists('OutPut'):# Check if the folder 'OutPut' exists
os.makedirs('OutPut') # If not make it
os.chdir('OutPut')
RoxPlot(Data,f[:-4])
os.chdir('..')
def FamMgbPlots():
files = [f for f in os.listdir('.') if os.path.isfile(f)]
for f in files:
if(f[-3:]=='csv'):
Data , names = RawParse(readFile(f,','))
if not os.path.exists('OutPut'):
os.makedirs('OutPut')
os.chdir('OutPut')
FamMgbPlot(Data,f[:-4])
os.chdir('..')
def GetAssayResults():
keyErrorCount=0
ret={}
ret2={}
files = [f for f in os.listdir('.') if os.path.isfile(f)]
for f in files:
if(f[-3:]=='csv'):
print 'Reading file : ' + f
ret[f]={}
Data , names = RawParse(readFile(f,','))
ret2[f]=Data
keyNames={}
for k in Data['Experiment Information']:
#print k
keyNames[Data['Experiment Information'][k][0]]=Data['Experiment Information'][k][1:]
if not os.path.exists('OutPut'):# Check if the folder 'OutPut' exists
os.makedirs('OutPut') # If not make it
os.chdir('OutPut')
for k in Data:
if(k[-7:]=='FAM-MGB'):
ret[f][k]={}
for k2 in Data[k]:
try:
works=True
temp =[keyNames[Data[k][k2][0]][0],keyNames[Data[k][k2][0]][3],Data[k][k2]]
except(KeyError):
works=False
keyErrorCount+=1
#print '!--#KeyError#--!'
if works:
if((keyNames[Data[k][k2][0]][0] in ret[f][k].keys())==False):
ret[f][k][keyNames[Data[k][k2][0]][0]]={}
ret[f][k][keyNames[Data[k][k2][0]][0]][keyNames[Data[k][k2][0]][3]]=Data[k][k2]
os.chdir('..')
#print 'keyErrorCount='+str(keyErrorCount)
return(ret,ret2)
def getNormalization(Data,f,s,g):
a = Data[f]['Raw Data for Probe FAM-MGB'][s][g][1:-1]
#print a
b = Data[f]['Bkgd Data for Probe FAM-MGB'][s][g][1:-1]
#print b
#print len(a)==len(b)
c=[]
n=[]
for k in range(0,len(a)):
c.append(int(a[k])-int(b[k]))
n.append(int(k))
return(c)
def getNormalizationMinusNegitive(Data,f,s,g):
a = Data[f]['Raw Data for Probe FAM-MGB'][s][g][1:-1]
#print a
b = Data[f]['Bkgd Data for Probe FAM-MGB'][s][g][1:-1]
neg = Data[f]['Raw Data for Probe FAM-MGB']['Negative'][g][1:-1]
negbk= Data[f]['Bkgd Data for Probe FAM-MGB']['Negative'][g][1:-1]
#print b
#print len(a)==len(b)
c=[]
n=[]
for k in range(0,len(a)):
c.append(int(a[k])-int(b[k])-(int(neg[k])-int(negbk[k])))
n.append(int(k))
return(c)
def getPolynomialFunc(xs,ys,i):
z = np.polyfit(xs, ys, i)
f = np.poly1d(z)
return(f)
def logFit(xs,ys,i):
print 'before'
print xs
logxs=[]
for k in xs:
logxs.append(numpy.log(k))
print 'after'
print logxs
getPolynomialFunc(logxs,ys,i)
def sigmoid(p,x):
x0,y0,c,k=p
y = c / (1 + numpy.exp(-k*(x-x0))) + y0
return y
def residuals(p,x,y):
return y - sigmoid(p,x)
def sigfit(x,y):
p_guess=(numpy.median(x),numpy.median(y),1.0,1.0)
p, cov, infodict, mesg, ier = scipy.optimize.leastsq(residuals,p_guess,args=(x,y),full_output=1)
x0,y0,c,k=p
return(x0,y0,c,k)
def getFits(xs):
ret={}
logFits=[]
#polyFits=[]
for k in range(0, len(xs)):
ys.append(k)
for k in range(0,4):
logFits.append(logFit(xs,ys,k))
polyFits.append(getPolynomialFunc(xs,ys,k))
ret['PolyFit']=polyFits
ret['logFit']=logFits
ret['sigFit']=sigfit(xs,ys)
return(ret)
#http://www.biomedcentral.com/1471-2105/9/326
def scf(Data,f,s,g):#Data container, file name, sample name, gene name
d = Data[f]['Raw Data for Probe FAM-MGB'][s][g][1:-1]#Fval
fits=getFits(xs)
k2=0
for k in d:
d[k2]=float(k)
k2+=1
#print Data[f]['Bkgd Data for Probe FAM-MGB'][s].keys()
Bkgd = Data[f]['Bkgd Data for Probe FAM-MGB'][s][g][1:-1]
k2=0
for k in Bkgd:
Bkgd[k2]=float(k)
k2+=1
fMax= float(max(d))
k2=1
for k in d:
if(k>=(fMax/2)):
break
k2+=1
if(d[k2]-d[k2-1])!=0.:
c = (k2 - 1)+((fMax/2)-d[k2-1])/(d[k2]-d[k2-1])
else:
return(0)
#b = d[k2]-d[k2-1]
b = scipy.misc.derivative(numpy.exp(fits['LogFit'][2]),c,dx=1.0,n=1,args=(),order=2)
return(fMax/(1+numpy.e**(c/b)))
def runscf(Data):
ret={}
print Data
for k in Data:
print '\n'
print Data[k].keys()
print '\n'
ret[k]={}
#print Data[k]['Bkgd Data for Probe FAM-MGB']
for k3 in Data[k]['Raw Data for Probe EvaGreen']:
ret[k][k3]={}
for k4 in Data[k]['Raw Data for Probe EvaGreen'][k3]:
ret[k][k3][k4] = scf(Data,k,k3,k4)
return (ret)