class TestLMFitModel(unittest.TestCase):
@classmethod
def setUpClass(cls):
file_path = os.path.dirname(os.path.realpath(__file__))
input_path = file_path + '/../../example/data/'
sliced_path = input_path + 'example5_6584.hdf5'
cls.sliced_data = SlicedData.init_from_hdf5(sliced_path)
orbital_paths = [
'PTCDA_C.cube', 'PTCDA_D.cube', 'PTCDA_E.cube', 'PTCDA_F.cube'
]
cls.orbitals = [
OrbitalData.init_from_file(input_path + path, ID)
for ID, path in enumerate(orbital_paths)
]
cls.expected = np.loadtxt(file_path + '/output/weights_PTCDA')
cls.background_expected = np.loadtxt(file_path +
'/output/background_expected')
def setUp(self):
self.lmfit = LMFitModel(TestLMFitModel.sliced_data,
TestLMFitModel.orbitals)
self.crosshair = CrosshairAnnulusModel()
def test_set_crosshair(self):
self.lmfit.set_crosshair(self.crosshair)
self.assertEqual(self.lmfit.crosshair, self.crosshair)
def test_set_axis(self):
step_size = 0.24
range_ = [-3, 3]
axis = np.linspace(*range_,
num=step_size_to_num(range_, step_size),
endpoint=True)
self.lmfit.set_axis_by_step_size(range_, step_size)
npt.assert_almost_equal(self.lmfit.get_sliced_kmap(0).x_axis, axis)
def test_set_slices(self):
self.lmfit.set_slices([1, 2, 3])
self.lmfit.set_slices([0, 1, 2, 3], combined=True)
def test_set_background_equation(self):
self.lmfit.set_background_equation('np.exp(a)')
npt.assert_equal(self.lmfit.background_equation, ['np.exp(a)', ['a']])
self.assertRaises(ValueError, self.lmfit.set_background_equation,
'np.exp(a')
def test_parameters(self):
self.lmfit.set_background_equation('np.exp(a)')
self.assertEqual(self.lmfit.parameters['w_1'].min, 0)
self.assertEqual(self.lmfit.parameters['a'].value, 0)
self.assertEqual(self.lmfit.parameters['E_kin'].max, 150)
def test_edit_parameter(self):
self.lmfit.edit_parameter('w_1', value=1.5)
self.assertEqual(self.lmfit.parameters['w_1'].value, 1.5)
def test_background(self):
range_, dk = [-3.0, 3.0], 0.025
self.lmfit.set_axis_by_step_size(range_, dk)
self.lmfit.set_background_equation(
'(np.exp(-x**2-y**2)-np.exp(-(x-1)**2-(y-1)**2))/2')
background = self.lmfit._get_background(variables={})
npt.assert_almost_equal(background, TestLMFitModel.background_expected)
def test_settings(self):
lmfit_new = LMFitModel(TestLMFitModel.sliced_data,
TestLMFitModel.orbitals)
lmfit_new.set_settings(self.lmfit.get_settings())
def test_PTCDA(self):
if float(config.get_key('orbital', 'dk3D')) != 0.12:
print('WARNING: Test \'test_PTCDA\' from the ' +
'\'test_lmfit\' module has not been run. It requires ' +
'\'dk3D\' setting from the \'cube\' category to be ' +
'to 0.12.')
return
# Set certain parameters not being fitted but desired to be changed
range_, dk = [-3.0, 3.0], 0.04
self.lmfit.set_axis_by_step_size(range_, dk)
self.lmfit.set_polarization('toroid', 'p')
self.lmfit.set_background_equation('c')
# Set certain fit parameter to desired value
self.lmfit.edit_parameter('E_kin', value=27.2)
self.lmfit.edit_parameter('alpha', value=40)
self.lmfit.edit_parameter('c', value=1, vary=True)
# Activate fitting for all weights (i is a dummy ID used in setUpClass)
for i in [0, 1, 2, 3]:
self.lmfit.edit_parameter('w_' + str(i), vary=True)
# Set slices to be used
self.lmfit.set_slices('all', combined=False)
results = self.lmfit.fit()
# Test results
weights = np.array([[
result[1].params['w_0'].value, result[1].params['w_1'].value,
result[1].params['w_2'].value, result[1].params['w_3'].value
] for result in results]).T
npt.assert_almost_equal(weights, TestLMFitModel.expected, decimal=5)
orbitals = [
OrbitalData.init_from_file(data_path / path, ID)
for ID, path in enumerate(orbital_paths)
]
# Initialize fit as LMFitModel object
lmfit = LMFitModel(exp_data, orbitals)
# Set common range and delta-k-grid for exp. and sim. kmaps
range_, dk = [-3.0, 3.0], 0.04
lmfit.set_axis_by_step_size(range_, dk)
lmfit.set_polarization('toroid', 'p')
lmfit.set_background_equation('c')
# Set parameters not intended for fitting to desired value
lmfit.edit_parameter('E_kin', value=27.2, vary=False)
lmfit.edit_parameter('alpha', value=40, vary=False)
# Activate fitting for background ('c') and all orbital weights
lmfit.edit_parameter('c', value=250, vary=True) # constant background
for i in [0, 1, 2, 3]:
lmfit.edit_parameter('w_' + str(i), vary=True)
# Set slices to be used and perform fit
lmfit.set_slices('all', combined=False)
lmfit.set_fit_method(method='matrix_inversion')
results = lmfit.fit()
# Extract fitting results
weights = np.array([[
result[1].params['w_0'].value, result[1].params['w_1'].value,
# Load orbital for fitting as OrbitalData objects
orbital_paths = ['pentacene_HOMO.cube']
orbitals = [OrbitalData.init_from_file(
data_path / path, ID) for ID, path in enumerate(orbital_paths)]
# Initialize fit as LMFitModel object
lmfit = LMFitModel(exp_data, orbitals)
# Set common range and delta-k-grid for exp. and sim. kmaps
range_, dk = [-3.0, 3.0], 0.04
lmfit.set_axis_by_step_size(range_, dk)
lmfit.set_polarization('toroid', 'p')
lmfit.set_symmetrization('2-fold')
# Set parameters not intended for fitting to desired value
lmfit.edit_parameter('E_kin', value=28, vary=False)
lmfit.edit_parameter('alpha', value=40, vary=False)
lmfit.set_background_equation('c')
# Activate fitting for background ('c') and all orbital weights and theta
lmfit.edit_parameter('c', value=1, vary=True) # constant background
for i in [0]:
lmfit.edit_parameter('w_' + str(i), vary=True)
lmfit.edit_parameter('theta_' + str(i), min=0, value=5, vary=True)
lmfit.edit_parameter('phi_' + str(i), value=90, vary=False)
lmfit.edit_parameter('psi_' + str(i), value=90, vary=False)
# Set slices to be used and perform fit
lmfit.set_slices([2], combined=False)
lmfit.set_fit_method(method='leastsq', xtol=1e-12)