def test_main_result(self):
worker = create_worker('worker')
worker.create_model(self.model_dictionary, self.model_letter)
worker.setup_test(self.gt_dump)
worker.set_optional_names(
{self.model[comp].name
for comp in self.optional_comps})
self.vals.update({
'signal.data':
self.model.signal(),
'fitting_kwargs': {},
'variance.data':
self.model.signal.metadata.Signal.Noise_properties.variance()
})
keyword, (_id, _ind, result, found_solution) = \
worker.run_pixel(self.ind, self.vals)
nt.assert_equal(_id, 'worker')
nt.assert_equal(_ind, self.ind)
nt.assert_true(found_solution)
nt.assert_equal(result['dof.data'][()], 9)
lor_components = [
key for key in result['components'].keys() if key.find('l') == 0
]
nt.assert_equal(len(result['components']), 3)
nt.assert_equal(len(lor_components), 2)
gauss_name = list(
set(result['components'].keys()) - set(lor_components))[0]
gauss = result['components'][gauss_name]
np.testing.assert_allclose(gauss['A'][0]['values'],
self.areas[0],
rtol=0.05)
np.testing.assert_allclose(gauss['sigma'][0]['values'],
self.widths[0],
rtol=0.05)
np.testing.assert_allclose(gauss['centre'][0]['values'],
self.centres[0],
rtol=0.05)
lor1 = result['components'][lor_components[0]]
lor1_values = tuple(lor1[par][0]['values']
for par in ['A', 'gamma', 'centre'])
lor2 = result['components'][lor_components[1]]
lor2_values = tuple(lor2[par][0]['values']
for par in ['A', 'gamma', 'centre'])
possible_values1 = (self.areas[1], self.widths[1], self.centres[1])
possible_values2 = (self.areas[2], self.widths[2], self.centres[2])
nt.assert_true(
(np.allclose(lor1_values, possible_values1, rtol=0.05)
or np.allclose(lor1_values, possible_values2, rtol=0.05)))
nt.assert_true(
(np.allclose(lor2_values, possible_values1, rtol=0.05)
or np.allclose(lor2_values, possible_values2, rtol=0.05)))
def test_add_model(self):
worker = create_worker('worker')
worker.create_model(self.model_dictionary, self.model_letter)
from hyperspy.model import BaseModel
nt.assert_is_instance(worker.model, BaseModel)
for component in worker.model:
nt.assert_false(component.active_is_multidimensional)
nt.assert_true(component.active)
def test_add_model(self):
worker = create_worker('worker')
worker.create_model(self.model_dictionary, self.model_letter)
from hyperspy.model import BaseModel
nt.assert_is_instance(worker.model, BaseModel)
for component in worker.model:
nt.assert_false(component.active_is_multidimensional)
nt.assert_true(component.active)
def test_add_model(self):
worker = create_worker('worker')
worker.create_model(self.model_dictionary, self.model_letter)
from hyperspy.model import BaseModel
assert isinstance(worker.model, BaseModel)
for component in worker.model:
assert not component.active_is_multidimensional
assert component.active
def test_add_model(self):
worker = create_worker('worker')
worker.create_model(self.model_dictionary, self.model_letter)
from hyperspy.model import BaseModel
assert isinstance(worker.model, BaseModel)
for component in worker.model:
assert not component.active_is_multidimensional
assert component.active
def test_create_worker_defaults():
worker = create_worker('worker')
nt.assert_equal(worker.identity, 'worker')
nt.assert_is_none(worker.shared_queue)
nt.assert_is_none(worker.result_queue)
nt.assert_is_none(worker.individual_queue)
np.testing.assert_equal(worker.best_AICc, np.inf)
np.testing.assert_equal(worker.best_values, [])
np.testing.assert_equal(worker.best_dof, np.inf)
np.testing.assert_equal(worker.last_time, 1)
def test_create_worker_defaults():
worker = create_worker('worker')
nt.assert_equal(worker.identity, 'worker')
nt.assert_is_none(worker.shared_queue)
nt.assert_is_none(worker.result_queue)
nt.assert_is_none(worker.individual_queue)
np.testing.assert_equal(worker.best_AICc, np.inf)
np.testing.assert_equal(worker.best_values, [])
np.testing.assert_equal(worker.best_dof, np.inf)
np.testing.assert_equal(worker.last_time, 1)
def test_main_result(self):
worker = create_worker('worker')
worker.create_model(self.model_dictionary, self.model_letter)
worker.setup_test(self.gt_dump)
worker.set_optional_names({self.model[comp].name for comp in
self.optional_comps})
self.vals.update({
'signal.data': self.model.signal(),
'fitting_kwargs': {},
'variance.data':
self.model.signal.metadata.Signal.Noise_properties.variance()
})
keyword, (_id, _ind, result, found_solution) = \
worker.run_pixel(self.ind, self.vals)
nt.assert_equal(_id, 'worker')
nt.assert_equal(_ind, self.ind)
nt.assert_true(found_solution)
nt.assert_equal(result['dof.data'][()], 9)
lor_components = [key for key in result['components'].keys() if
key.find('l') == 0]
nt.assert_equal(len(result['components']), 3)
nt.assert_equal(len(lor_components), 2)
gauss_name = list(set(result['components'].keys()) -
set(lor_components))[0]
gauss = result['components'][gauss_name]
np.testing.assert_allclose(gauss['A'][0]['values'], self.areas[0],
rtol=0.05)
np.testing.assert_allclose(gauss['sigma'][0]['values'], self.widths[0],
rtol=0.05)
np.testing.assert_allclose(gauss['centre'][0]['values'],
self.centres[0], rtol=0.05)
lor1 = result['components'][lor_components[0]]
lor1_values = tuple(lor1[par][0]['values'] for par in ['A', 'gamma',
'centre'])
lor2 = result['components'][lor_components[1]]
lor2_values = tuple(lor2[par][0]['values'] for par in ['A', 'gamma',
'centre'])
possible_values1 = (self.areas[1], self.widths[1], self.centres[1])
possible_values2 = (self.areas[2], self.widths[2], self.centres[2])
nt.assert_true((np.allclose(lor1_values, possible_values1, rtol=0.05)
or
np.allclose(lor1_values, possible_values2, rtol=0.05)))
nt.assert_true((np.allclose(lor2_values, possible_values1, rtol=0.05)
or
np.allclose(lor2_values, possible_values2, rtol=0.05)))
