def test_rebin_errors(self):
with pytest.raises(ValueError):
self.signal.rebin()
with pytest.raises(ValueError):
rebin(self.signal.data, scale=(3, 3, 1), new_shape=(1, 2, 6))
with pytest.raises(ValueError):
rebin(self.signal.data, scale=(2, 2, 2, 2))
def _estimate_gain(ns, cs,
weighted=False,
higher_than=None,
plot_results=False,
binning=0,
pol_order=1):
if binning > 0:
factor = 2 ** binning
remainder = np.mod(ns.shape[1], factor)
if remainder != 0:
ns = ns[:, remainder:]
cs = cs[:, remainder:]
new_shape = (ns.shape[0], ns.shape[1] / factor)
ns = rebin(ns, new_shape)
cs = rebin(cs, new_shape)
noise = ns - cs
variance = np.var(noise, 0)
average = np.mean(cs, 0).squeeze()
# Select only the values higher_than for the calculation
if higher_than is not None:
sorting_index_array = np.argsort(average)
average_sorted = average[sorting_index_array]
average_higher_than = average_sorted > higher_than
variance_sorted = variance.squeeze()[sorting_index_array]
variance2fit = variance_sorted[average_higher_than]
average2fit = average_sorted[average_higher_than]
else:
variance2fit = variance
average2fit = average
fit = np.polyfit(average2fit, variance2fit, pol_order)
if weighted is True:
from hyperspy._signals.signal1D import Signal1D
from hyperspy.models.model1d import Model1D
from hyperspy.components1d import Line
s = Signal1D(variance2fit)
s.axes_manager.signal_axes[0].axis = average2fit
m = Model1D(s)
l = Line()
l.a.value = fit[1]
l.b.value = fit[0]
m.append(l)
m.fit(weights=True)
fit[0] = l.b.value
fit[1] = l.a.value
if plot_results is True:
plt.figure()
plt.scatter(average.squeeze(), variance.squeeze())
plt.xlabel('Counts')
plt.ylabel('Variance')
plt.plot(average2fit, np.polyval(fit, average2fit), color='red')
results = {'fit': fit, 'variance': variance.squeeze(),
'counts': average.squeeze()}
return results
def _estimate_gain(ns, cs,
weighted=False,
higher_than=None,
plot_results=False,
binning=0,
pol_order=1):
if binning > 0:
factor = 2 ** binning
remainder = np.mod(ns.shape[1], factor)
if remainder != 0:
ns = ns[:, remainder:]
cs = cs[:, remainder:]
new_shape = (ns.shape[0], ns.shape[1] / factor)
ns = rebin(ns, new_shape)
cs = rebin(cs, new_shape)
noise = ns - cs
variance = np.var(noise, 0)
average = np.mean(cs, 0).squeeze()
# Select only the values higher_than for the calculation
if higher_than is not None:
sorting_index_array = np.argsort(average)
average_sorted = average[sorting_index_array]
average_higher_than = average_sorted > higher_than
variance_sorted = variance.squeeze()[sorting_index_array]
variance2fit = variance_sorted[average_higher_than]
average2fit = average_sorted[average_higher_than]
else:
variance2fit = variance
average2fit = average
fit = np.polyfit(average2fit, variance2fit, pol_order)
if weighted is True:
from hyperspy._signals.spectrum import Spectrum
from hyperspy.models.model1D import Model1D
from hyperspy.components import Line
s = Spectrum(variance2fit)
s.axes_manager.signal_axes[0].axis = average2fit
m = Model1D(s)
l = Line()
l.a.value = fit[1]
l.b.value = fit[0]
m.append(l)
m.fit(weights=True)
fit[0] = l.b.value
fit[1] = l.a.value
if plot_results is True:
plt.figure()
plt.scatter(average.squeeze(), variance.squeeze())
plt.xlabel('Counts')
plt.ylabel('Variance')
plt.plot(average2fit, np.polyval(fit, average2fit), color='red')
results = {'fit': fit, 'variance': variance.squeeze(),
'counts': average.squeeze()}
return results
def test_rebin(self):
self.signal.estimate_poissonian_noise_variance()
new_s = self.signal.rebin((2, 1, 6))
var = new_s.metadata.Signal.Noise_properties.variance
nt.assert_true(new_s.data.shape == (1, 2, 6))
nt.assert_true(var.data.shape == (1, 2, 6))
from hyperspy.misc.array_tools import rebin
nt.assert_true(np.all(rebin(self.signal.data, (1, 2, 6)) == var.data))
nt.assert_true(
np.all(rebin(self.signal.data, (1, 2, 6)) == new_s.data))
def test_rebin(self):
self.signal.estimate_poissonian_noise_variance()
new_s = self.signal.rebin((2, 1, 6))
var = new_s.metadata.Signal.Noise_properties.variance
nt.assert_equal(new_s.data.shape, (1, 2, 6))
nt.assert_equal(var.data.shape, (1, 2, 6))
from hyperspy.misc.array_tools import rebin
np.testing.assert_array_equal(rebin(self.signal.data, (1, 2, 6)),
var.data)
np.testing.assert_array_equal(rebin(self.signal.data, (1, 2, 6)),
new_s.data)
def test_rebin(self):
self.signal.estimate_poissonian_noise_variance()
new_s = self.signal.rebin((2, 1, 6))
var = new_s.metadata.Signal.Noise_properties.variance
nt.assert_equal(new_s.data.shape, (1, 2, 6))
nt.assert_equal(var.data.shape, (1, 2, 6))
from hyperspy.misc.array_tools import rebin
np.testing.assert_array_equal(rebin(self.signal.data, (1, 2, 6)),
var.data)
np.testing.assert_array_equal(rebin(self.signal.data, (1, 2, 6)),
new_s.data)
def test_rebin(self):
self.signal.estimate_poissonian_noise_variance()
new_s = self.signal.rebin((2, 1, 6))
var = new_s.metadata.Signal.Noise_properties.variance
nt.assert_true(new_s.data.shape == (1, 2, 6))
nt.assert_true(var.data.shape == (1, 2, 6))
from hyperspy.misc.array_tools import rebin
nt.assert_true(np.all(rebin(self.signal.data, (1, 2, 6)) == var.data))
nt.assert_true(
np.all(
rebin(
self.signal.data, (1, 2, 6)) == new_s.data))
def test_rebin_new_shape(self):
self.signal.estimate_poissonian_noise_variance()
new_s = self.signal.rebin(new_shape=(1, 2, 6))
var = new_s.metadata.Signal.Noise_properties.variance
assert new_s.data.shape == (2, 1, 6)
assert var.data.shape == (2, 1, 6)
np.testing.assert_array_equal(
rebin(self.signal.data, new_shape=(2, 1, 6)), new_s.data
)
np.testing.assert_array_equal(
rebin(self.signal.data, new_shape=(2, 1, 6)), var.data
)
new_s = self.signal.rebin(new_shape=(4, 2, 6))
assert new_s.data.shape == self.data.shape
def test_rebin(self):
self.signal.estimate_poissonian_noise_variance()
new_s = self.signal.rebin(scale=(2, 2, 1))
var = new_s.metadata.Signal.Noise_properties.variance
assert new_s.data.shape == (1, 2, 6)
assert var.data.shape == (1, 2, 6)
from hyperspy.misc.array_tools import rebin
np.testing.assert_array_equal(rebin(self.signal.data, scale=(2, 2, 1)),
var.data)
np.testing.assert_array_equal(rebin(self.signal.data, scale=(2, 2, 1)),
new_s.data)
if self.signal._lazy:
new_s = self.signal.rebin(scale=(2, 2, 1), rechunk=False)
np.testing.assert_array_equal(
rebin(self.signal.data, scale=(2, 2, 1)), var.data)
np.testing.assert_array_equal(
rebin(self.signal.data, scale=(2, 2, 1)), new_s.data)
def test_rebin(self):
self.signal.estimate_poissonian_noise_variance()
new_s = self.signal.rebin(scale=(2, 2, 1))
var = new_s.metadata.Signal.Noise_properties.variance
assert new_s.data.shape == (1, 2, 6)
assert var.data.shape == (1, 2, 6)
from hyperspy.misc.array_tools import rebin
np.testing.assert_array_equal(rebin(self.signal.data, scale=(2, 2, 1)),
var.data)
np.testing.assert_array_equal(rebin(self.signal.data, scale=(2, 2, 1)),
new_s.data)
if self.signal._lazy:
new_s = self.signal.rebin(scale=(2, 2, 1), rechunk=False)
np.testing.assert_array_equal(rebin(self.signal.data, scale=(2, 2, 1)),
var.data)
np.testing.assert_array_equal(rebin(self.signal.data, scale=(2, 2, 1)),
new_s.data)
def test_rebin(self):
self.signal.estimate_poissonian_noise_variance()
new_s = self.signal.rebin(scale=(2, 2, 1))
var = new_s.metadata.Signal.Noise_properties.variance
assert new_s.data.shape == (1, 2, 6)
assert var.data.shape == (1, 2, 6)
from hyperspy.misc.array_tools import rebin
if self.signal._lazy:
from distutils.version import LooseVersion
import dask
if LooseVersion(np.__version__) >= "1.12.0" and \
LooseVersion(dask.__version__) <= "0.13.0":
pytest.skip("Dask not up to date with new numpy")
np.testing.assert_array_equal(rebin(self.signal.data, scale=(2, 2, 1)),
var.data)
np.testing.assert_array_equal(rebin(self.signal.data, scale=(2, 2, 1)),
new_s.data)
