def gaussFit(data):
# Create x-axis
xaxis = np.arange(data.size)
# Estimate parameters
params_estimate = GaussFit.gauss_params_estimate(data)
print "Estimate:", params_estimate
print "Fitting"
fit_params, fit_cov = GaussFit.curve_fit(GaussFit.gauss, xaxis, data,
params_estimate)
fit_params_error = np.sqrt(np.diag(fit_cov))
fit_results = {
'mean': fit_params[0],
'sigma': fit_params[1],
'height': fit_params[2],
'pedestal': fit_params[3],
'mean_error': fit_params_error[0],
'sigma_error': fit_params_error[1],
'height_error': fit_params_error[2],
'pedestal_error': fit_params_error[3]
}
return fit_results
def gaussFit(data):
# Create x-axis
xaxis = np.arange(data.size)
# Estimate parameters
params_estimate = GaussFit.gauss_params_estimate(data)
print "Estimate:", params_estimate
print "Fitting"
fit_params, fit_cov = GaussFit.curve_fit(GaussFit.gauss, xaxis, data,
params_estimate)
fit_params_error = np.sqrt(np.diag(fit_cov))
#print "Final Fit(errors):",zip(fit_params,fit_params_error)
#print "Covariance Matrix:",fit_cov
combinedvals = zip(fit_params, fit_params_error)
print "Fit Values(errors):"
print "mean:", combinedvals[0],
print "sigma:", combinedvals[1],
print "Height:", combinedvals[2],
print "Pedestal:", combinedvals[3]
return fit_params, fit_params_error
def fit_gauss(mirrorpos, sharpness):
"""
Fit mirror position vs sharpness to a Gaussian to find optimal
mirror position
"""
# Estimate parameters
params_estimate = GaussFit.gauss_params_estimate(sharpness)
# Translate mean and sigma from array index units to mirror
# position units
gradient = (mirrorpos[-1] - mirrorpos[0]) / float(mirrorpos.size)
params_estimate[0] = mirrorpos[0] + (params_estimate[0] * gradient)
params_estimate[1] = params_estimate[1] * gradient
# Fit to Gaussian
fit_params, fit_cov = GaussFit.curve_fit(GaussFit.gauss, mirrorpos,
sharpness, params_estimate)
fit_params_error = np.sqrt(np.diag(fit_cov))
# Return the fitted mean position
return fit_params[0], GaussFit.gaus(mirrorpos, *fit_params)