fit_waist.py 1.59 KB
from scipy.optimize import curve_fit
import csv, numpy, glob
from scipy.special import erf
import matplotlib.pyplot as plt

'''power function to optimize'''
def P(x, Po, Pmax, xo, w):
    return Po+0.5*Pmax*(1.-erf(2.**0.5*(x-xo)/w))


'''load and fit beam section'''
files = glob.glob('*.dat')
files.sort()
data_waist = []
plt.close()
fig, p = plt.subplots(2, 1)
for f in files:
    with open(f, 'r') as dest_f:
        raw = csv.reader(dest_f, delimiter = '\t', quotechar = '"')
        data = [value for value in raw]
    data = numpy.asarray(data, dtype = float)
    xmes = data[:,0]
    Pmes = data[:,1]

    '''optimization with non-linear least squares method'''
    Ppopt, Pcov = curve_fit(P, xmes, Pmes)
    data_waist.append([int(f[-7:-4]), abs(Ppopt[3])])

    '''plot'''
    p[0].plot(xmes, Pmes, 'o')
    p[0].plot(numpy.linspace(xmes[0], xmes[-1], 100), P(numpy.linspace(xmes[0], xmes[-1], 100), *Ppopt))

p[0].grid()

'''return waist(z) table'''
data_waist = numpy.asarray(data_waist, dtype = float)
print(data_waist)

'''waist function to optimize'''
def W(z, w0, z0):
    return w0*(1.+((z-z0)*1542e-6/(numpy.pi*w0**2))**2)**0.5

popt, cov = curve_fit(W, data_waist[:,0], data_waist[:,1])
print(popt[0], popt[1])

p[1].plot(data_waist[:,0], data_waist[:,1], 'bo')
p[1].plot(data_waist[:,0], -data_waist[:,1], 'bo')
p[1].plot(numpy.linspace(data_waist[0,0], data_waist[-1,0], 100), W(numpy.linspace(data_waist[0,0], data_waist[-1,0], 100), *popt), 'r')
p[1].plot(numpy.linspace(data_waist[0,0], data_waist[-1,0], 100), -W(numpy.linspace(data_waist[0,0], data_waist[-1,0], 100), *popt), 'r')
p[1].grid()

plt.show()