diff options
Diffstat (limited to 'examples/example_fit_multi_datasets.py')
| -rw-r--r-- | examples/example_fit_multi_datasets.py | 25 |
1 files changed, 10 insertions, 15 deletions
diff --git a/examples/example_fit_multi_datasets.py b/examples/example_fit_multi_datasets.py index 2920f0d..587b9da 100644 --- a/examples/example_fit_multi_datasets.py +++ b/examples/example_fit_multi_datasets.py @@ -5,9 +5,9 @@ Fit Multiple Data Sets Fitting multiple (simulated) Gaussian data sets simultaneously. All minimizers require the residual array to be one-dimensional. Therefore, in -the ``objective`` we need to ```flatten``` the array before returning it. +the ``objective`` function we need to ``flatten`` the array before returning it. -TODO: this should be using the Model interface / built-in models! +TODO: this could/should be using the Model interface / built-in models! """ import matplotlib.pyplot as plt @@ -23,9 +23,9 @@ def gauss(x, amp, cen, sigma): def gauss_dataset(params, i, x): """Calculate Gaussian lineshape from parameters for data set.""" - amp = params['amp_%i' % (i+1)] - cen = params['cen_%i' % (i+1)] - sig = params['sig_%i' % (i+1)] + amp = params[f'amp_{i+1}'] + cen = params[f'cen_{i+1}'] + sig = params[f'sig_{i+1}'] return gauss(x, amp, cen, sig) @@ -44,7 +44,7 @@ def objective(params, x, data): ############################################################################### # Create five simulated Gaussian data sets - +np.random.seed(2021) x = np.linspace(-1, 2, 151) data = [] for _ in np.arange(5): @@ -58,31 +58,26 @@ data = np.array(data) ############################################################################### # Create five sets of fitting parameters, one per data set - fit_params = Parameters() for iy, y in enumerate(data): - fit_params.add('amp_%i' % (iy+1), value=0.5, min=0.0, max=200) - fit_params.add('cen_%i' % (iy+1), value=0.4, min=-2.0, max=2.0) - fit_params.add('sig_%i' % (iy+1), value=0.3, min=0.01, max=3.0) + fit_params.add(f'amp_{iy+1}', value=0.5, min=0.0, max=200) + fit_params.add(f'cen_{iy+1}', value=0.4, min=-2.0, max=2.0) + fit_params.add(f'sig_{iy+1}', value=0.3, min=0.01, max=3.0) ############################################################################### # Constrain the values of sigma to be the same for all peaks by assigning # sig_2, ..., sig_5 to be equal to sig_1. - for iy in (2, 3, 4, 5): - fit_params['sig_%i' % iy].expr = 'sig_1' + fit_params[f'sig_{iy}'].expr = 'sig_1' ############################################################################### # Run the global fit and show the fitting result - out = minimize(objective, fit_params, args=(x, data)) report_fit(out.params) ############################################################################### # Plot the data sets and fits - plt.figure() for i in range(5): y_fit = gauss_dataset(out.params, i, x) plt.plot(x, data[i, :], 'o', x, y_fit, '-') -plt.show() |