def test_scaling_exponent_trends_nonlinear_function(self):
'''Ensure that alpha is > 1 when fed superdiffusive data and alpha < 1 when fed subdiffusive data.'''
for degrees_of_freedom in [1,2,3]:
calculated_subdiffusive_alpha = diffusion_analysis.fit_anomalous_diffusion_data(self.linearly_increasing_time_array,self.MSD_array_subdiffusive,degrees_of_freedom=degrees_of_freedom)[2]
self.assertLess(calculated_subdiffusive_alpha,1.0)
calculated_superdiffusive_alpha = diffusion_analysis.fit_anomalous_diffusion_data(self.linearly_increasing_time_array,self.MSD_array_superdiffusive,degrees_of_freedom=degrees_of_freedom)[2]
self.assertGreater(calculated_superdiffusive_alpha,1.0)
def test_scaling_exponent_trends_nonlinear_function(self):
'''Ensure that alpha is > 1 when fed superdiffusive data and alpha < 1 when fed subdiffusive data.'''
for degrees_of_freedom in [1, 2, 3]:
calculated_subdiffusive_alpha = diffusion_analysis.fit_anomalous_diffusion_data(
self.linearly_increasing_time_array,
self.MSD_array_subdiffusive,
degrees_of_freedom=degrees_of_freedom)[2]
self.assertLess(calculated_subdiffusive_alpha, 1.0)
calculated_superdiffusive_alpha = diffusion_analysis.fit_anomalous_diffusion_data(
self.linearly_increasing_time_array,
self.MSD_array_superdiffusive,
degrees_of_freedom=degrees_of_freedom)[2]
self.assertGreater(calculated_superdiffusive_alpha, 1.0)
def test_fractional_diffusion_constant_calculation_linear_data_nonlinear_function(
self):
'''Since linear diffusion is merely a special case of the anomalous diffusion equation, the anomalous diffusion code should return the correct values given linear input data.'''
for degrees_of_freedom in [1, 2,
3]: #test for all normal degrees of freedom
alpha_expected = 1. #input data is perfectly linear
coefficient = degrees_of_freedom * 2. #2,4,6 for 1D, 2D, 3D, respectively
# 2 = slope test
fractional_diffusion_coefficient, standard_deviation_fractional_diffusion_coefficient, alpha, standard_deviation_alpha, sample_fitting_data_X_values_nanoseconds, sample_fitting_data_Y_values_Angstroms = diffusion_analysis.fit_anomalous_diffusion_data(
self.linearly_increasing_time_array,
self.linear_MSD_array_for_slope_of_two,
degrees_of_freedom=degrees_of_freedom)
D_expected = 2. / coefficient #simple linear D
self.assertEqual(fractional_diffusion_coefficient, D_expected)
self.assertEqual(alpha, alpha_expected)
# 10 = slope test
fractional_diffusion_coefficient, standard_deviation_fractional_diffusion_coefficient, alpha, standard_deviation_alpha, sample_fitting_data_X_values_nanoseconds, sample_fitting_data_Y_values_Angstroms = diffusion_analysis.fit_anomalous_diffusion_data(
self.linearly_increasing_time_array,
self.linear_MSD_array_for_slope_of_ten,
degrees_of_freedom=degrees_of_freedom)
D_expected = 10. / coefficient #simple linear D
self.assertEqual(fractional_diffusion_coefficient, D_expected)
self.assertEqual(alpha, alpha_expected)
def test_fractional_diffusion_constant_calculation_linear_data_nonlinear_function(self):
"""Since linear diffusion is merely a special case of the anomalous diffusion equation, the anomalous diffusion code should return the correct values given linear input data."""
for degrees_of_freedom in [1, 2, 3]: # test for all normal degrees of freedom
alpha_expected = 1.0 # input data is perfectly linear
coefficient = degrees_of_freedom * 2.0 # 2,4,6 for 1D, 2D, 3D, respectively
# 2 = slope test
fractional_diffusion_coefficient, standard_deviation_fractional_diffusion_coefficient, alpha, standard_deviation_alpha, sample_fitting_data_X_values_nanoseconds, sample_fitting_data_Y_values_Angstroms = diffusion_analysis.fit_anomalous_diffusion_data(
self.linearly_increasing_time_array,
self.linear_MSD_array_for_slope_of_two,
degrees_of_freedom=degrees_of_freedom,
)
D_expected = 2.0 / coefficient # simple linear D
self.assertEqual(fractional_diffusion_coefficient, D_expected)
self.assertEqual(alpha, alpha_expected)
# 10 = slope test
fractional_diffusion_coefficient, standard_deviation_fractional_diffusion_coefficient, alpha, standard_deviation_alpha, sample_fitting_data_X_values_nanoseconds, sample_fitting_data_Y_values_Angstroms = diffusion_analysis.fit_anomalous_diffusion_data(
self.linearly_increasing_time_array,
self.linear_MSD_array_for_slope_of_ten,
degrees_of_freedom=degrees_of_freedom,
)
D_expected = 10.0 / coefficient # simple linear D
self.assertEqual(fractional_diffusion_coefficient, D_expected)
self.assertEqual(alpha, alpha_expected)