import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
#get data from txt, usecols=0 is B_x, 1 is B_y, 2 is B_z
B=np.genfromtxt('Data.txt', usecols=1, unpack=True)


#just determining calibrating parameters from several tries
ratio=np.mean([0.49275,0.47778,0.48105,0.51098,0.49541,0.5094,0.4783,0.51914])
dratio=np.std([0.49275,0.47778,0.48105,0.51098,0.49541,0.5094,0.4783,0.51914])
offset=np.mean([7.12939,6.99607,6.8554,6.13588,7.06523,6.78745,3.4305,4.1762])
doffset=np.std([7.12939,6.99607,6.8554,6.13588,7.06523,6.78745,3.4305,4.1762])


print('ratio:',np.round(ratio,3),'+/-',np.round(dratio,3))
print('offset:',np.round(offset,3),'+/-',np.round(doffset,3))

#calibrated new values
B_cal=(-1)*(ratio*B-ratio*offset)
dB_cal=np.sqrt((B*dratio-offset*dratio)**2+(ratio*1.615)**2+(ratio*doffset)**2)

# exemplary plotting and fitting, analyse calibrated values whichever way you want
print(B_cal)
plt.errorbar(x,B_cal,dB-cal)
plt.ylabel('G')
plt.xlabel('mm')
plt.title('Gradient field z-axis 4A AHH, 1A HH')

def linear(x,m,b):
    return m*x+b
popt,pcov=curve_fit(linear,x,B_cal,sigma=dB_cal)
plt.plot(x,linear(x,*popt),label='measurement fit')
print('Gradient:',popt[0]*10,'+/-',np.sqrt(pcov[0][0])*10)
plt.show()