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 test_3d_signal(self):
# This code should run smoothly, any exceptions should trigger failure
s = self.m_3d.as_signal()
model = Model1D(s)
p = hs.model.components1D.Polynomial(order=2)
model.append(p)
p.estimate_parameters(s, 0, 100, only_current=False)
np.testing.assert_allclose(p.coefficients.map['values'],
np.tile([0.5, 2, 3], (2, 5, 1)))
def test_3d_signal(self):
# This code should run smoothly, any exceptions should trigger failure
s = self.m_3d.as_signal()
model = Model1D(s)
p = hs.model.components1D.Polynomial(order=2, legacy=False)
model.append(p)
p.estimate_parameters(s, 0, 100, only_current=False)
np.testing.assert_allclose(p.a2.map['values'], 0.5)
np.testing.assert_allclose(p.a1.map['values'], 2)
np.testing.assert_allclose(p.a0.map['values'], 3)
def create_model(self, dictionary=None):
"""Create a model for the current data.
Returns
-------
model : `Model1D` instance.
"""
from hyperspy.models.model1d import Model1D
model = Model1D(self, dictionary=dictionary)
return model
def _remove_background_cli(self,
signal_range,
background_estimator,
fast=True,
show_progressbar=None):
from hyperspy.models.model1d import Model1D
model = Model1D(self)
model.append(background_estimator)
background_estimator.estimate_parameters(self,
signal_range[0],
signal_range[1],
only_current=False)
if not fast:
model.set_signal_range(signal_range[0], signal_range[1])
model.multifit(show_progressbar=show_progressbar)
return self - model.as_signal(show_progressbar=show_progressbar)
