Created
September 17, 2020 05:39
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boilerplate gaussian fit
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| #!/usr/bin/env python3 | |
| import numpy as np | |
| from scipy import optimize | |
| from astropy import modelling | |
| import matplotlib.pyplot as plt | |
| def gaussian(x, amplitude, mean, stddev): | |
| return amplitude * np.exp(-((x - mean) / 4 / stddev)**2) | |
| m = modeling.models.Gaussian1D(amplitude=10, mean=30, stddev=5) ### Varies | |
| x = np.linspace(0, 100, 2000) ### Varies | |
| data = m(x) | |
| data = data + np.sqrt(data) * np.random.random(x.size) - 0.5 | |
| data -= data.min() | |
| plt.plot(x, data) | |
| popt, _ = optimize.curve_fit(gaussian, x, data) | |
| fitter = modeling.fitting.LevMarLSQFitter() | |
| model = modeling.models.Gaussian1D() # initial values may be necessary; data dependent | |
| fitted_model = fitter(model, x, data) | |
| plt.plot(x, data) | |
| plt.plot(x, gaussian(x, *popt)) | |
| ### to predict | |
| fitted_model(100) # fitted_model(<val>) |
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