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ArunScript.py
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ArunScript.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Aug 12 13:31:29 2014
@author: jme2005
"""
import os
import pandas as pd
import numpy as np
from scipy.integrate import odeint
from scipy.optimize import minimize
import matplotlib.pyplot as plt
os.chdir("/Users/johanedvinsson/projects/Arun")
from scipy.stats import cumfreq
def ecdfSyt(df,Group,Conc,threshold):
tmp=df[(df['Conc']==Conc) & (df['Group']==Group)]
tmp['time']=np.round(tmp['time'],1)
tmp=tmp.sort('time')
nbins=np.unique(tmp['time']).size
tmp1=cumfreq(tmp['time'].values,numbins=nbins)[0]
time=np.unique(tmp['time'])
tmparray=np.zeros([nbins,4])
DF=pd.DataFrame(tmparray,columns=["Group","Conc","time","cumfreq"])
DF['Group']=[Group]*nbins; DF['Conc']=[Conc]*nbins; DF['time']=time;DF['cumfreq']=tmp1
DF['cumfreq']=DF['cumfreq']/DF['cumfreq'].max()
DF=DF[DF['cumfreq']>threshold]
DF['cumfreq']=(DF['cumfreq']/(DF['cumfreq'].max()-DF['cumfreq'].min()))-DF['cumfreq'].min()
DF['time']=DF['time']-DF['time'].min()
return DF
def createDF(DF,Group,conc,threshold):
DFtmp=pd.DataFrame
first=True
for i in conc:
tmp=ecdfSyt(DF,Group,i,threshold)
if first:
DFtmp=tmp
first=False
else:
DFtmp=DFtmp.append(tmp)
return DFtmp
def initialConditions(params,Cai):
k_1 = params[1]
k2 = params[2]
k_2 = params[3]
ksr = params[4]
krr = params[5]
Ca = Cai
Kd = params[6]
rmax = params[7]
k1 = (rmax*Ca)/(Ca+Kd)
Caa = params[8]
CaS = params[9]
CaF = params[0]
CaFa = params[10]
CaFS = params[11]
CaFF = params[12]
M=np.matrix([-(k1*CaFa*Ca**Caa),k1*CaFa*Ca**Caa,0,1,k_1,-(k_1+ksr*CaFS*Ca**CaS+k2),k2,1,0,k_2,-(krr*CaFF*Ca**CaF+k_2),1]).reshape(3,4)
dS=np.array([0,0,0,1])
try:
Minv=np.linalg.pinv(M)
except:
return [1.0, 1.5318878112344034e-17, 1.2786737572473748e-16]
yinitial=dS*Minv
yinitial = yinitial.tolist()
return yinitial[0]
def solveequation(params,times,Cai):
timepoints = np.where(times == 0)
yinitial = initialConditions(params,Cai)
yinitial.append(0)
yinitial.append(0)
Ca = 5e-6
k_1 = params[1]
k2 = params[2]
k_2 = params[3]
ksr = params[4]
krr=params[5]
Kd=params[6]
rmax=params[7]
k1=(rmax*Ca)/(Ca+Kd)
Caa=params[8]
CaS=params[9]
CaF=params[0]
CaFa = params[10]
CaFS = params[11]
CaFF = params[12]
def f(yinitial,times):
A = yinitial[0]
B = yinitial[1]
C = yinitial[2]
D = yinitial[3]
E = yinitial[4]
dA = -k1*CaFa*Ca**Caa*A + k_1*B
dB = -(k_1+CaFS*Ca**CaS*ksr+k2)*B+A*k1*CaFa*Ca**Caa+C*k_2
dC = -(krr*CaFa*Ca**CaF+k_2)*C + B*k2
dD = B*ksr*CaFS*Ca**CaS
dE = C*krr*CaFF*Ca**CaF
return [dA,dB,dC,dD,dE]
out = np.array(odeint(f,yinitial,times[timepoints[0][0]:timepoints[0][1]]))
output = out[:,3]+out[:,4]
Ca = 10e-6;
k1 =(rmax*Ca)/(Ca+Kd)
out = np.array(odeint(f,yinitial,times[timepoints[0][1]:timepoints[0][2]]))
output = np.append(output,(out[:,3]+out[:,4]))
Ca = 30e-6;
k1 =(rmax*Ca)/(Ca+Kd)
out = np.array(odeint(f,yinitial,times[timepoints[0][2]:timepoints[0][3]]))
output = np.append(output,(out[:,3]+out[:,4]))
Ca = 100e-6;
k1 =(rmax*Ca)/(Ca+Kd)
out = np.array(odeint(f,yinitial,times[timepoints[0][3]:]))
output = np.append(output,(out[:,3]+out[:,4]))
return output
def CostFunction(params):
if any( x < 0 for x in params):
return 100
target=digitsyt7['target'].values
model = solveequation(params,digitsyt7['time'],1e-9)
RMSE = np.sqrt((target-model)**2)
return RMSE.mean()
def sampleInit(n):
seed=[]
fitvalue=[]
for i in n:
np.random.seed(i)
x = np.random.uniform(-3,3,13)
params = 10**x
fit = minimize(CostFunction,params)
print fit.fun,i
fitvalue.append(fit.fun)
seed.append(i)
output = zip(seed,fitvalue)
return output
# seed 2 work well
def main():
digit = pd.read_csv('latencies.csv')
digit100 = pd.read_csv('100uMLat.csv')
newName = digit.columns.values
newName[2] = 'time'
digit.columns = newName
digit=digit.append(digit100)
digitsyt1 = digit[digit['Group']=='Syt-1']
digitsyt7 = digit[digit['Group']=='Syt-7']
digitsyt1 = createDF(digitsyt1,'Syt-1',[5,10,30,100],0.08)
digitsyt7 = createDF(digitsyt7,'Syt-7',[5,10,30,100],0.08)
DR = pd.read_csv('DigitoninDR.csv')
tmp=DR[['Conc','Syt1']]
tmp.columns=['Conc','Dose']
digitsyt1 = pd.merge(digitsyt1,tmp, how='inner')
tmp=DR[['Conc','Syt7']]
tmp.columns=['Conc','Dose']
digitsyt7 = pd.merge(digitsyt7,tmp,how='inner')
digitsyt1['target']=digitsyt1['cumfreq']*digitsyt1['Dose']
digitsyt7['target']=digitsyt7['cumfreq']*digitsyt7['Dose']
return(digitsyt1,digitsyt7)