import Praktikum
import numpy as np
from pylab import *
import matplotlib.pyplot as plt


def konda(name):
    data = Praktikum.lese_lab_datei(name)
    t = data[:,1]
    I = data[:,2]
    U = data[:,3]
    lnI=[0]*100
    lnt=[0]*100
    olnI=[0]*100
    for i in range(0,100):
        lnI[i]=np.log(I[i]+0.00035)
        lnt[i]=t[i]
        olnI[i]=0.00005/(sqrt(6.)*I[i])
    return(lnI,lnt,olnI)

I1,t1,oI1=konda('lab/CA1.lab')
I2,t2,oI2=konda('lab/CA2.lab')
I3,t3,oI3=konda('lab/CA3.lab')
I4,t4,oI4=konda('lab/CA4.lab')
I5,t5,oI5=konda('lab/CA5.lab')
ot=[0.00001/sqrt(12.)]*100
a1=Praktikum.lineare_regression_xy(np.array(t1),np.array(I1),np.array(ot),np.array(oI1))
a2=Praktikum.lineare_regression_xy(np.array(t2),np.array(I2),np.array(ot),np.array(oI2))
a3=Praktikum.lineare_regression_xy(np.array(t3),np.array(I3),np.array(ot),np.array(oI3))
a4=Praktikum.lineare_regression_xy(np.array(t4),np.array(I4),np.array(ot),np.array(oI4))
a5=Praktikum.lineare_regression_xy(np.array(t5),np.array(I5),np.array(ot),np.array(oI5))
print(a1)
print(a2)
print(a3)
print(a4)
print(a5)