def setUp(self):
s = signals.Spectrum(np.ones((5, 4, 3, 6)))
for axis, name in zip(
s.axes_manager._get_axes_in_natural_order(),
['x', 'y', 'z', 'E']):
axis.name = name
self.s = s
def setUp(self):
pl = components.PowerLaw()
pl.A.value = 1e10
pl.r.value = 3
self.signal = signals.Spectrum(
pl.function(np.arange(100, 200)))
self.signal.axes_manager[0].offset = 100
self.signal.metadata.Signal.binned = False
def setup(self):
offset = 3
scale = 0.1
x = np.arange(-offset, offset, scale)
s = signals.Spectrum(np.sin(x))
s.axes_manager[0].offset = x[0]
s.axes_manager[0].scale = scale
self.s = s
def setUp(self):
gaussian = components.Gaussian()
gaussian.A.value = 10
gaussian.centre.value = 10
gaussian.sigma.value = 1
self.signal = signals.Spectrum(
gaussian.function(np.arange(0, 20, 0.01)))
self.signal.axes_manager[0].scale = 0.01
self.signal.metadata.Signal.binned = False
def setup(self):
# Some test require consistent random data for reference to be correct
np.random.seed(0)
s = signals.Spectrum(np.random.rand(5, 4, 3, 6))
for axis, name in zip(
s.axes_manager._get_axes_in_natural_order(),
['x', 'y', 'z', 'E']):
axis.name = name
self.s = s
def setUp(self):
s = signals.Spectrum(np.random.random((2, 3, 4, 5)))
sa = s.axes_manager[-1]
na = s.axes_manager[0]
sa.offset = 100
sa.scale = 0.1
s.learning_results.factors = np.arange(5 * 5).reshape((5, 5))
s.learning_results.loadings = np.arange(24 * 5).reshape((24, 5))
s.learning_results.bss_factors = np.arange(5 * 2).reshape((5, 2))
s.learning_results.bss_loadings = np.arange(24 * 2).reshape((24, 2))
self.s = s
self.na = na
self.sa = sa