[Python] Curve fitting of the laser beam radius W(z) versus propagation distance z

I am taking 5 measurements of the laser beam radius at various distances between 1-200 cm. Without knowing where the beam waist Wo is, I am trying to use the curve_fit() function to find the opt value of Wo and Zo. The output I am getting seems wrong. The focus it finds is 0.75mm at z=0.1cm. However my measurements show a point where the radius reached 0.595mm (<0.75mm) and that point is at z=160cm. so zo is 160 cm from the laser source. I am uploading the code I've written and the output plot.graph-->

import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt


def BeamRadius(z, Wo, Zo):
return Wo * np.sqrt(1 + (((4.03225 * 10**-13) / (np.pi**2)) * ((z-Zo) / Wo)**2))


def difdiv(l, Wo):
return 2*(l/(np.pi*Wo))


def main():
# z : distance from laser, zo: where the waist/ laser focus is
z1 = 10*10
z2= 20*10 # *10 to get mm
z3= 30*10
z4= 50*10
z5= 70*10
z6= 100*10
z7 = 110*10
z8= 140*10
z9= 150*10
z10= 155*10
z11= 160*10
z12= 165*10
z13= 170*10
z14= 180*10
# Beam Radius in mm at z cm from the laser
wz1 = 1.02
wz2= 1.0035 # mm
wz3= 0.9695
wz4= 0.9385
wz5= 0.885
wz6= 0.7465
wz7 = 0.715
wz8= 0.625
wz9= 0.613775
wz10= 0.60936
wz11= 0.595
wz12= 0.605
wz13= 0.61815
wz14= 0.6465
z_array = np.array([z1, z2, z3, z4, z5, z6, z7, z8, z9, z10, z11, z12, z13, z14])
wz_array = np.array([wz1, wz2, wz3, wz4, wz5, wz6, wz7, wz8, wz9,wz10,wz11,wz12,wz13,wz14])
print(z_array, "and ", wz_array)
p_opt, p_cov = curve_fit(BeamRadius, z_array, wz_array)
BeamFit = BeamRadius(z_array, p_opt[0], p_opt[1])


I am not sure if curve_fit is correct or if I should use fitpredict. I haven't found much on fitpredict and I don't know how to use it. I have thought of trying supervised learning but haven't made any progress. I appreciate any ideas/feedback you can give.

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