[Python] How to fix gaussian fit not behaving like expected?

I have a set of data showing radition not being absorbed as a function of velocity. The data shows a very clear dip or if we plot the inverse of the data the absorbtion, we get a clear peak instead. I have no reason not to belive this peak to be a gaussian and would like to make a fit to get the variance of this peak. So I've tried to use scipy.optimize.curve_fit, to achieve this. Both using scipy.stats.norm.pdf and a self written version of the function. No matter initial guesses. The resulting fit is way of. I attached the code and a picture of the resulting graph. What am I doing wrong? Are there any general tricks for these kind of tasks?

import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import numpy as np
cts = []
vel = []
file = open("Heisenberg/Mössbauer/Final.lst", "r")
linesArr = file.readlines()
for i in range(210, 260):
lineList1 = linesArr.split()
cts.append(int(lineList1[1]))

chn = (int(lineList1[0]))
tempVel = -0.04 * chn + 9.3
vel.append(tempVel)

def func (x, mu,sigma):
return (1 / (np.sqrt(np.pi * 2) *sigma)) * np.exp(-0.5*((x-mu)/sigma)**2)

data = np.array(cts)
cts_norm = (data - data.min())/ (data.max() - data.min())
cts_inv = 1 - cts_norm
fit, error = curve_fit(func, vel, cts_inv, p0=[0.2, 0.2])
print(fit)


plt.plot(vel, cts_inv, 'bo')
plt.plot(vel, func(vel, fit[0],fit[1]),"r")




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