def main(): data = get_processed_data() first_id = get_id(filename) models = get_models() for i in range(len(models)): m = models.pop(0) test = ModelTest(m, *data, id_=first_id + i, logfile=filename) test.log() del m del test
# test for Rabi flop fits from model_test import ModelTest from fit_rabi import Rabi import numpy as np test = ModelTest(Rabi, 'Rabi') true_params = [2 * np.pi / (10), 10, 0.05, 0., 0, 0.6] test.generate_data(0, 30, 300, 0.02, true_params) test.fit() test.print_results() test.plot(fit=True)
# test for Gaussian fits from model_test import ModelTest from fit_gaussian import Gaussian test = ModelTest(Gaussian, 'Gaussian') true_params = [130., 4., 5., 0.1] test.generate_data(100, 200, 200, 0.02, true_params) test.fit() test.print_results() test.plot()
# test script for Lorentzian fits from model_test import ModelTest from fit_bessel import Bessel test = ModelTest(Bessel, 'Bessel') true_params = [130., 1., 5., 0.1, 1] test.generate_data(100, 200, 200, 0.02, 0.5, true_params) test.fit() test.print_results() test.plot()
# test for Rabi flop fits from model_test import ModelTest from fit_rabi import Rabi import numpy as np test = ModelTest(Rabi, 'Rabi') true_params = [2*np.pi/(10), 10, 0.05, 0., 0, 0.6] test.generate_data(0, 30, 300, 0.02, true_params) test.fit() test.print_results() test.plot(fit=True)
# test for linear fits from model_test import ModelTest from fit_linear import Linear test = ModelTest(Linear, 'Linear') true_params = [0.3, 4] test.generate_data(10, 20, 40, 1, true_params) test.fit() test.print_results() test.plot()