def align_zero_loss_peak(
self,
calibrate=True,
also_align=[],
print_stats=True,
subpixel=True,
mask=None,
**kwargs):
"""Align the zero-loss peak.
This function first aligns the spectra using the result of
`estimate_zero_loss_peak_centre` and afterward, if subpixel is True,
proceeds to align with subpixel accuracy using `align1D`. The offset
is automatically correct if `calibrate` is True.
Parameters
----------
calibrate : bool
If True, set the offset of the spectral axis so that the
zero-loss peak is at position zero.
also_align : list of signals
A list containing other spectra of identical dimensions to
align using the shifts applied to the current spectrum.
If `calibrate` is True, the calibration is also applied to
the spectra in the list.
print_stats : bool
If True, print summary statistics the ZLP maximum before
the aligment.
subpixel : bool
If True, perform the alignment with subpixel accuracy
using cross-correlation.
mask : Signal of bool data type.
It must have signal_dimension = 0 and navigation_shape equal to the
current signal. Where mask is True the shift is not computed
and set to nan.
See Also
--------
estimate_zero_loss_peak_centre, align1D, estimate_shift1D.
Notes
-----
Any extra keyword arguments are passed to `align1D`. For
more information read its docstring.
"""
def substract_from_offset(value, signals):
for signal in signals:
signal.axes_manager[-1].offset -= value
zlpc = self.estimate_zero_loss_peak_centre(mask=mask)
mean_ = without_nans(zlpc.data).mean()
if print_stats is True:
print
print(underline("Initial ZLP position statistics"))
zlpc.print_summary_statistics()
for signal in also_align + [self]:
signal.shift1D(-zlpc.data + mean_)
if calibrate is True:
zlpc = self.estimate_zero_loss_peak_centre(mask=mask)
substract_from_offset(without_nans(zlpc.data).mean(),
also_align + [self])
if subpixel is False:
return
left, right = -3., 3.
if calibrate is False:
mean_ = without_nans(self.estimate_zero_loss_peak_centre(
mask=mask).data).mean()
left += mean_
right += mean_
left = (left if left > self.axes_manager[-1].axis[0]
else self.axes_manager[-1].axis[0])
right = (right if right < self.axes_manager[-1].axis[-1]
else self.axes_manager[-1].axis[-1])
self.align1D(left, right, also_align=also_align, **kwargs)
zlpc = self.estimate_zero_loss_peak_centre(mask=mask)
if calibrate is True:
substract_from_offset(without_nans(zlpc.data).mean(),
also_align + [self])
def align_zero_loss_peak(self,
calibrate=True,
also_align=[],
print_stats=True,
subpixel=True,
mask=None,
**kwargs):
"""Align the zero-loss peak.
This function first aligns the spectra using the result of
`estimate_zero_loss_peak_centre` and afterward, if subpixel is True,
proceeds to align with subpixel accuracy using `align1D`. The offset
is automatically correct if `calibrate` is True.
Parameters
----------
calibrate : bool
If True, set the offset of the spectral axis so that the
zero-loss peak is at position zero.
also_align : list of signals
A list containing other spectra of identical dimensions to
align using the shifts applied to the current spectrum.
If `calibrate` is True, the calibration is also applied to
the spectra in the list.
print_stats : bool
If True, print summary statistics the ZLP maximum before
the aligment.
subpixel : bool
If True, perform the alignment with subpixel accuracy
using cross-correlation.
mask : Signal of bool data type.
It must have signal_dimension = 0 and navigation_shape equal to the
current signal. Where mask is True the shift is not computed
and set to nan.
See Also
--------
estimate_zero_loss_peak_centre, align1D, estimate_shift1D.
Notes
-----
Any extra keyword arguments are passed to `align1D`. For
more information read its docstring.
"""
def substract_from_offset(value, signals):
for signal in signals:
signal.axes_manager[-1].offset -= value
zlpc = self.estimate_zero_loss_peak_centre(mask=mask)
mean_ = without_nans(zlpc.data).mean()
if print_stats is True:
print
print(underline("Initial ZLP position statistics"))
zlpc.print_summary_statistics()
for signal in also_align + [self]:
signal.shift1D(-zlpc.data + mean_)
if calibrate is True:
zlpc = self.estimate_zero_loss_peak_centre(mask=mask)
substract_from_offset(
without_nans(zlpc.data).mean(), also_align + [self])
if subpixel is False:
return
left, right = -3., 3.
if calibrate is False:
mean_ = without_nans(
self.estimate_zero_loss_peak_centre(mask=mask).data).mean()
left += mean_
right += mean_
left = (left if left > self.axes_manager[-1].axis[0] else
self.axes_manager[-1].axis[0])
right = (right if right < self.axes_manager[-1].axis[-1] else
self.axes_manager[-1].axis[-1])
self.align1D(left, right, also_align=also_align, **kwargs)
zlpc = self.estimate_zero_loss_peak_centre(mask=mask)
if calibrate is True:
substract_from_offset(
without_nans(zlpc.data).mean(), also_align + [self])
