I have some measured data which can be either a well established gaussian or something that seems to be a gamma distribution, I currently have the following code (snippet) which performs quite well for data that is nicely gaussian:
def gaussFunction(x, A, mu, sigma):
return A*numpy.exp(-(x-mu)**2/(2.*sigma**2))
# Snippet of the code that does the fitting
p0 = [numpy.max(y_points), x_points[numpy.argmax(y_points)],0.1]
# Attempt to fit a gaussian function to the calibrant space
try:
coeff, var_matrix = curve_fit(self.gaussFunction, x_points, y_points, p0)
newX = numpy.linspace(x_points[0],x_points[-1],1000)
newY = self.gaussFunction(newX, *coeff)
fig = plt.figure()
ax = fig.add_subplot(111)
plt.plot(x_points, y_points, 'b*')
plt.plot(newX,newY, '--')
plt.show()
Demonstration that it works well for datapoints which are nicely gaussian:
The problem however arises that some of my datapoints are not matching with a good gaussian and I get this:
I would be tempted to try a cubic spline but conceptually I would like to stick to a Gaussian curve fit since that is the data structure that should be within the data (which can occur with a knee or a tail in some data as shown in the second figure). I would highly appreciate if someone has any tip or suggestion on how to deal with this 'issue'.
