def setUp(self):
s = Spectrum(range(100))
m = s.create_model()
m.append(Gaussian())
m.components.Gaussian.A.value = 13
m.components.Gaussian.name = 'something'
self.m = m
def setUp(self):
s = Spectrum(range(100))
m = s.create_model()
m.append(Gaussian())
m.components.Gaussian.A.value = 13
m.components.Gaussian.name = 'something'
self.m = m
def setUp(self):
s = Spectrum(np.array([1.0, 2, 4, 7, 12, 7, 4, 2, 1]))
m = s.create_model()
self.model = m
self.A = 38.022476979172588
self.sigma = 1.4764966133859543
self.centre = 4.0000000002462945
def setUp(self):
s = Spectrum(np.array([1.0, 2, 4, 7, 12, 7, 4, 2, 1]))
m = s.create_model()
self.model = m
self.A = 38.022476979172588
self.sigma = 1.4764966133859543
self.centre = 4.0000000002462945
def setUp(self):
g = Gaussian()
g.A.value = 10000.0
g.centre.value = 5000.0
g.sigma.value = 500.0
axis = np.arange(10000)
s = Spectrum(g.function(axis))
m = s.create_model()
self.model = m
self.g = g
self.axis = axis
self.rtol = 0.00
def setUp(self):
s = Spectrum(np.array([1.0, 2, 4, 7, 12, 7, 4, 2, 1]))
m = s.create_model()
m.low_loss = (s + 3.0).deepcopy()
self.model = m
self.s = s
m.append(Gaussian())
m.append(Gaussian())
m.append(ScalableFixedPattern(s * 0.3))
m[0].A.twin = m[1].A
m.fit()
def setUp(self):
s = Spectrum(np.array([1.0, 2, 4, 7, 12, 7, 4, 2, 1]))
m = s.create_model()
m._low_loss = (s + 3.0).deepcopy()
self.model = m
self.s = s
m.append(Gaussian())
m.append(Gaussian())
m.append(ScalableFixedPattern(s * 0.3))
m[0].A.twin = m[1].A
m.fit()
def setUp(self):
g = Gaussian()
g.A.value = 10000.0
g.centre.value = 5000.0
g.sigma.value = 500.0
axis = np.arange(10000)
s = Spectrum(g.function(axis))
m = s.create_model()
self.model = m
self.g = g
self.axis = axis
self.rtol = 0.00
def setUp(self):
g1 = Gaussian()
g2 = Gaussian()
g3 = Gaussian()
s = Spectrum(np.arange(1000).reshape(10, 10, 10))
m = s.create_model()
m.append(g1)
m.append(g2)
m.append(g3)
self.g1 = g1
self.g2 = g2
self.g3 = g3
self.model = m
def setUp(self):
g1 = Gaussian()
g2 = Gaussian()
g3 = Gaussian()
s = Spectrum(np.arange(10))
m = s.create_model()
m.append(g1)
m.append(g2)
m.append(g3)
self.g1 = g1
self.g2 = g2
self.g3 = g3
self.model = m
def setUp(self):
gs1 = Gaussian()
gs1.A.value = 10000.0
gs1.centre.value = 5000.0
gs1.sigma.value = 500.0
gs2 = Gaussian()
gs2.A.value = 60000.0
gs2.centre.value = 2000.0
gs2.sigma.value = 300.0
gs3 = Gaussian()
gs3.A.value = 20000.0
gs3.centre.value = 6000.0
gs3.sigma.value = 100.0
axis = np.arange(10000)
total_signal = (gs1.function(axis) +
gs2.function(axis) +
gs3.function(axis))
s = Spectrum(total_signal)
m = s.create_model()
g1 = Gaussian()
g2 = Gaussian()
g3 = Gaussian()
m.append(g1)
m.append(g2)
m.append(g3)
self.model = m
self.gs1 = gs1
self.gs2 = gs2
self.gs3 = gs3
self.g1 = g1
self.g2 = g2
self.g3 = g3
self.axis = axis
self.rtol = 0.01
def setUp(self):
gs1 = Gaussian()
gs1.A.value = 10000.0
gs1.centre.value = 5000.0
gs1.sigma.value = 500.0
gs2 = Gaussian()
gs2.A.value = 60000.0
gs2.centre.value = 2000.0
gs2.sigma.value = 300.0
gs3 = Gaussian()
gs3.A.value = 20000.0
gs3.centre.value = 6000.0
gs3.sigma.value = 100.0
axis = np.arange(10000)
total_signal = (gs1.function(axis) + gs2.function(axis) +
gs3.function(axis))
s = Spectrum(total_signal)
m = s.create_model()
g1 = Gaussian()
g2 = Gaussian()
g3 = Gaussian()
m.append(g1)
m.append(g2)
m.append(g3)
self.model = m
self.gs1 = gs1
self.gs2 = gs2
self.gs3 = gs3
self.g1 = g1
self.g2 = g2
self.g3 = g3
self.axis = axis
self.rtol = 0.01
def setUp(self):
s = Spectrum(range(100))
m = s.create_model()
m.append(Gaussian())
m.fit()
self.m = m
def setUp(self):
s = Spectrum(range(100))
m = s.create_model()
m.append(Gaussian())
m.fit()
self.m = m
