Can two Gaussian curves be plotted in different colors on the same plot?

Question

I have a data set that I am displaying as a histogram. The data looks like it might be better fit with a double Gaussian rather than a single Gaussian. I am not sure how to get two Gaussians on the same plot. I'd really like if I could fit a Gaussian to each of the peaks and plot them in separate colors. I've tried following the methods on other posts, but I don't want to use randomly generated data. Here is the code that produced this plot and a link to the data. I have also tried using this code which doesn't work.

from scipy.optimize import curve_fit
from scipy import asarray as ar,exp
import matplotlib.pyplot as plt
import numpy as np

data=np.concatenate((np.random.normal(1, .2, 5000), np.random.normal(1.6, .3, 2500)))
y,x,_=plt.hist(data, 100, alpha=.3, label='data')
x=(x[1:]+x[:-1])/2 # for len(x)==len(y)

#x, y inputs can be lists or 1D numpy arrays

def gauss(x, mu, sigma, A):
    return A*np.exp(-(x-mu)**2/2/sigma**2)

def bimodal(x, mu1, sigma1, A1, mu2, sigma2, A2):
    return gauss(x,mu1,sigma1,A1)+gauss(x,mu2,sigma2,A2)

expected = (1, .2, 250, 2, .2, 125)
params, cov = curve_fit(bimodal, x, y, expected)
sigma=np.sqrt(np.diag(cov))
x_fit = np.linspace(x.min(), x.max(), 500)
#plot combined...
plt.plot(x_fit, bimodal(x_fit, *params), color='red', lw=3, label='model')
#...and individual Gauss curves
plt.plot(x_fit, gauss(x_fit, *params[:3]), color='red', lw=1, ls="--", label='distribution 1')
plt.plot(x_fit, gauss(x_fit, *params[3:]), color='red', lw=1, ls=":", label='distribution 2')
#and the original data points if no histogram has been created before
#plt.scatter(x, y, marker="X", color="black", label="original data")
plt.legend()
print(pd.DataFrame(data={'params': params, 'sigma': sigma}, index=bimodal.__code__.co_varnames[1:]))
plt.show() 

enter image description here

Google Sheets link to the data: link