def setUp(self):
# Create three signals with dimensions:
# s1 : <Signal, title: , dimensions: (4, 3, 2|2, 3)>
# s2 : <Signal, title: , dimensions: (2, 3|4, 3, 2)>
# s12 : <Signal, title: , dimensions: (2, 3|4, 3, 2)>
# Where s12 data is transposed in respect to s2
dc1 = np.random.random((2, 3, 4, 3, 2))
dc2 = np.rollaxis(np.rollaxis(dc1, -1), -1)
s1 = signals.Signal(dc1.copy())
s2 = signals.Signal(dc2)
s12 = signals.Signal(dc1.copy())
for i, axis in enumerate(s1.axes_manager._axes):
if i < 3:
axis.navigate = True
else:
axis.navigate = False
for i, axis in enumerate(s2.axes_manager._axes):
if i < 2:
axis.navigate = True
else:
axis.navigate = False
for i, axis in enumerate(s12.axes_manager._axes):
if i < 3:
axis.navigate = False
else:
axis.navigate = True
self.s1 = s1
self.s2 = s2
self.s12 = s12
def setUp(self):
gaussian = components.Gaussian()
gaussian.A.value = 10
gaussian.centre.value = 10
gaussian.sigma.value = 1
self.signal = signals.Signal(gaussian.function(np.arange(0, 20, 0.01)))
self.signal.axes_manager[0].scale = 0.01
def setUp(self):
s = signals.Signal(np.zeros(1))
self.factors = np.ones([2, 3])
self.loadings = np.ones([2, 3])
s.learning_results.factors = self.factors.copy()
s.learning_results.loadings = self.loadings.copy()
self.s = s
def setUp(self):
s = signals.Signal(np.empty(1))
s.learning_results.explained_variance_ratio = np.empty(10)
self.s = s
def setUp(self):
s = signals.Signal(np.empty(1))
self.s = s
def denoised_data_to_signal(self):
signal = signals.Signal(self.Y)
if self.signal_type == "spectrum":
return signal.as_spectrum(2)
if self.signal_type == "image":
return signal.as_image((0, 1))