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]*120
    lnt=[0]*120
    olnI=[0]*120
    for i in range(0,120):
        lnI[i]=np.log(I[i]+0.0003)
        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.)]*120
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)


def konde(name):
    data = Praktikum.lese_lab_datei(name)
    t = data[:,1]
    I = data[:,2]
    U = data[:,3]
    lnU=[0]*100
    lnt=[0]*100
    olnU=[0]*100
    for i in range(0,100):
        lnU[i]=np.log(U[i]-0.035)
        lnt[i]=t[i]
        olnU[i]=0.005/(sqrt(6.)*U[i])
    return(lnU,lnt,olnU)

U1,t1,oU1=konde('lab/CE1.lab')
U2,t2,oU2=konde('lab/CE2.lab')
U3,t3,oU3=konde('lab/CE3.lab')
U4,t4,oU4=konde('lab/CE4.lab')
U5,t5,oU5=konde('lab/CE5.lab')
ot=[0.00001/sqrt(12.)]*100
b1=Praktikum.lineare_regression_xy(np.array(t1),np.array(U1),np.array(ot),np.array(oU1))
b2=Praktikum.lineare_regression_xy(np.array(t2),np.array(U2),np.array(ot),np.array(oU2))
b3=Praktikum.lineare_regression_xy(np.array(t3),np.array(U3),np.array(ot),np.array(oU3))
b4=Praktikum.lineare_regression_xy(np.array(t4),np.array(U4),np.array(ot),np.array(oU4))
b5=Praktikum.lineare_regression_xy(np.array(t5),np.array(U5),np.array(ot),np.array(oU5))
print(b1)
print(b2)
print(b3)
print(b4)
print(b5)

(-4404.098853488467, 14.065955464325075, -3.259782038528828, 0.005940327701682676, 505.4387482216931, -0.8430663424405965)
(-4390.473689079287, 10.176002174801127, -3.249256204722142, 0.004317266181702983, 270.08771009619403, -0.8429589816350469)
(-4393.9723963644365, 10.72664271443983, -3.300002456496392, 0.004469128293733817, 283.4113818530368, -0.8431204864979276)
(-4384.213201403037, 8.907896598759518, -3.2652162078947997, 0.0037622438515436197, 204.59004714253223, -0.8431112097603243)
(-4346.9782754367625, 13.590635805981947, -3.2848891941091156, 0.0057780633714458055, 495.08910005789846, -0.8419738765667839)
(-4392.971978630669, 4.330087327017931, 1.2106130900360603, 0.002027478365966889, 54.00740512410189, -0.8419353911597972)
(-4270.835447371937, 4.6923076579640135, 1.1654707635312764, 0.0022107551328050534, 68.37143144902036, -0.8410931259211605)
(-4392.658265163242, 3.9318870054830843, 1.1758137727727929, 0.0018326284837392676, 43.983792634721596, -0.8410418803674247)
(-4422.808891550765, 6.923568399887232, 1.204504107472536, 0.0032257264396020804, 134.10705774561762, -0.842119401778825)
(-4116.50795351723, 9.867867746917884, 1.1754104925695255, 0.004739174830668134, 344.61800445106724, -0.8410968935984409)