def setUp(self):
np.random.seed(17)
ax = np.arange(250)
self.widths = [5, 10, 15]
self.centres = [50, 105, 180]
self.areas = [5000, 10000, 20000]
g = hs.model.components1D.Gaussian()
g.sigma.value = self.widths[0]
g.A.value = self.areas[0]
l = hs.model.components1D.Lorentzian()
l.gamma.value = self.widths[1]
l.A.value = self.areas[1]
l1 = hs.model.components1D.Lorentzian()
l1.gamma.value = self.widths[2]
l1.A.value = self.areas[2]
d = g.function(ax - self.centres[0]) + \
l.function(ax - self.centres[1]) + \
l1.function(ax - self.centres[2])
s = hs.signals.Signal1D(np.array([d, d]))
s.add_poissonian_noise()
s.metadata.Signal.set_item("Noise_properties.variance",
s.deepcopy() + 1.)
m = s.create_model()
m.append(hs.model.components1D.Gaussian())
m[-1].name = 'g1'
m.append(hs.model.components1D.Lorentzian())
m[-1].name = 'l1'
m.append(hs.model.components1D.Lorentzian())
m[-1].name = 'l2'
m.append(hs.model.components1D.Gaussian())
m[-1].name = 'g2'
m.append(hs.model.components1D.Gaussian())
m[-1].name = 'g3'
m.append(hs.model.components1D.Lorentzian())
m[-1].name = 'l3'
for c in m:
c.active_is_multidimensional = True
vals = {
'g1': {},
'g2': {},
'g3': {},
'l1': {},
'l2': {},
'l3': {},
}
vals['g1']['centre'] = [50, 150]
vals['g1']['sigma'] = [5]
vals['g1']['A'] = [10000]
vals['l1']['centre'] = [43]
vals['l1']['gamma'] = [25]
vals['l1']['A'] = [10000]
vals['l2']['centre'] = [125]
vals['l2']['gamma'] = [8]
vals['l2']['A'] = [10000]
vals['g2']['centre'] = [105]
vals['g2']['sigma'] = [20]
vals['g2']['A'] = [10000]
vals['l3']['centre'] = [185]
vals['l3']['gamma'] = [11]
vals['l3']['A'] = [10000]
vals['g3']['centre'] = [175]
vals['g3']['sigma'] = [12]
vals['g3']['A'] = [10000]
self.vals = vals
self.model = m
self.q = Mock_queue()
self.ind = (1, )
self.args = {}
self.model_letter = 'sldkfjg'
from hyperspy.samfire_utils.fit_tests import red_chisq_test as rct
self.gt_dump = dill.dumps(rct(tolerance=1.0))
m_slice = m.inav[self.ind[::-1]]
m_slice.store(self.model_letter)
m_dict = m_slice.signal._to_dictionary(False)
m_dict['models'] = m_slice.signal.models._models.as_dictionary()
self.model_dictionary = m_dict
self.optional_comps = [1, 2, 3, 4, 5]
def setUp(self):
np.random.seed(17)
ax = np.arange(250)
self.widths = [5, 10, 15]
self.centres = [50, 105, 180]
self.areas = [5000, 10000, 20000]
g = hs.model.components1D.Gaussian()
g.sigma.value = self.widths[0]
g.A.value = self.areas[0]
l = hs.model.components1D.Lorentzian()
l.gamma.value = self.widths[1]
l.A.value = self.areas[1]
l1 = hs.model.components1D.Lorentzian()
l1.gamma.value = self.widths[2]
l1.A.value = self.areas[2]
d = g.function(ax - self.centres[0]) + \
l.function(ax - self.centres[1]) + \
l1.function(ax - self.centres[2])
s = hs.signals.Signal1D(np.array([d, d]))
s.add_poissonian_noise()
s.metadata.Signal.set_item("Noise_properties.variance",
s.deepcopy() + 1.)
m = s.create_model()
m.append(hs.model.components1D.Gaussian())
m[-1].name = 'g1'
m.append(hs.model.components1D.Lorentzian())
m[-1].name = 'l1'
m.append(hs.model.components1D.Lorentzian())
m[-1].name = 'l2'
m.append(hs.model.components1D.Gaussian())
m[-1].name = 'g2'
m.append(hs.model.components1D.Gaussian())
m[-1].name = 'g3'
m.append(hs.model.components1D.Lorentzian())
m[-1].name = 'l3'
for c in m:
c.active_is_multidimensional = True
vals = {'g1': {},
'g2': {},
'g3': {},
'l1': {},
'l2': {},
'l3': {},
}
vals['g1']['centre'] = [50, 150]
vals['g1']['sigma'] = [5]
vals['g1']['A'] = [10000]
vals['l1']['centre'] = [43]
vals['l1']['gamma'] = [25]
vals['l1']['A'] = [10000]
vals['l2']['centre'] = [125]
vals['l2']['gamma'] = [8]
vals['l2']['A'] = [10000]
vals['g2']['centre'] = [105]
vals['g2']['sigma'] = [20]
vals['g2']['A'] = [10000]
vals['l3']['centre'] = [185]
vals['l3']['gamma'] = [11]
vals['l3']['A'] = [10000]
vals['g3']['centre'] = [175]
vals['g3']['sigma'] = [12]
vals['g3']['A'] = [10000]
self.vals = vals
self.model = m
self.q = Mock_queue()
self.ind = (1,)
self.args = {}
self.model_letter = 'sldkfjg'
from hyperspy.samfire_utils.fit_tests import red_chisq_test as rct
self.gt_dump = dill.dumps(rct(tolerance=1.0))
m_slice = m.inav[self.ind[::-1]]
m_slice.store(self.model_letter)
m_dict = m_slice.signal._to_dictionary(False)
m_dict['models'] = m_slice.signal.models._models.as_dictionary()
self.model_dictionary = m_dict
self.optional_comps = [1, 2, 3, 4, 5]
