def align_with_array_1D(self,
shift_array,
axis=-1,
interpolation_method='linear'):
"""Shift each one dimensional object by the amount specify by a
given array
Parameters
----------
shift_map : numpy array
The shift is specify in the units of the selected axis
interpolation_method : str or int
Specifies the kind of interpolation as a string ('linear',
'nearest', 'zero', 'slinear', 'quadratic, 'cubic') or as an
integer specifying the order of the spline interpolator to
use.
"""
axis = self.axes_manager._get_positive_index(axis)
coord = self.axes_manager.axes[axis]
offset = coord.offset
_axis = coord.axis.copy()
maxval = np.cumprod(shift_array.shape)[-1] - 1
pbar = progressbar.progressbar(maxval = maxval)
i = 0
for dat, shift in zip(self.iterate_axis(axis),
utils.iterate_axis(shift_array, axis)):
si = sp.interpolate.interp1d(_axis ,dat,
bounds_error=False,
fill_value=0.,
kind=interpolation_method)
coord.offset = offset - shift[0]
dat[:] = si(coord.axis)
pbar.update(i)
i += 1
coord.offset = offset
# Cropping time
mini, maxi = shift_array.min(), shift_array.max()
if mini < 0:
self.crop_in_units(axis, None, coord.axis[-1] + mini +
coord.scale)
if maxi > 0:
self.crop_in_units(axis, offset + maxi)
def align_with_array_1D(self,
shift_array,
axis=-1,
interpolation_method='linear'):
"""Shift each one dimensional object by the amount specify by a given
array
Parameters
----------
shift_map : numpy array
The shift is specify in the units of the selected axis
interpolation_method : str or int
Specifies the kind of interpolation as a string ('linear',
'nearest', 'zero', 'slinear', 'quadratic, 'cubic') or as an integer
specifying the order of the spline interpolator to use.
"""
axis = self._get_positive_axis_index_index(axis)
coord = self.axes_manager.axes[axis]
offset = coord.offset
_axis = coord.axis.copy()
maxval = np.cumprod(shift_array.shape)[-1] - 1
pbar = progressbar.progressbar(maxval=maxval)
i = 0
for dat, shift in zip(self.iterate_axis(axis),
utils.iterate_axis(shift_array, axis)):
si = sp.interpolate.interp1d(_axis,
dat,
bounds_error=False,
fill_value=0.,
kind=interpolation_method)
coord.offset = offset - shift[0]
dat[:] = si(coord.axis)
pbar.update(i)
i += 1
coord.offset = offset
# Cropping time
mini, maxi = shift_array.min(), shift_array.max()
if mini < 0:
self.crop_in_units(axis, offset - mini)
if maxi > 0:
self.crop_in_units(axis, None, _axis[-1] - maxi)
def estimate_shift_in_index_1D(self,
irange=(None, None),
axis=-1,
reference_indexes=None,
max_shift=None,
interpolate=True,
number_of_interpolation_points=5):
"""Estimate the shifts in a given axis using cross-correlation
This method can only estimate the shift by comparing unidimensional
features that should not change the position in the given axis. To
decrease the memory usage, the time of computation and the accuracy of
the results it is convenient to select the feature of interest setting
the irange keyword.
By default interpolation is used to obtain subpixel precision.
Parameters
----------
axis : int
The axis in which the analysis will be performed.
irange : tuple of ints (i1, i2)
Define the range of the feature of interest. If i1 or i2 are None,
the range will not be limited in that side.
reference_indexes : tuple of ints or None
Defines the coordinates of the spectrum that will be used as a
reference. If None the spectrum of 0 coordinates will be used.
max_shift : int
interpolate : bool
number_of_interpolation_points : int
Number of interpolation points. Warning: making this number too big
can saturate the memory
Return
------
An array with the result of the estimation
"""
ip = number_of_interpolation_points + 1
axis = self.axes_manager.axes[axis]
if reference_indexes is None:
reference_indexes = [
0,
] * (len(self.axes_manager.axes) - 1)
else:
reference_indexes = list(reference_indexes)
reference_indexes.insert(axis.index_in_array, slice(None))
i1, i2 = irange
array_shape = [axis.size for axis in self.axes_manager.axes]
array_shape[axis.index_in_array] = 1
shift_array = np.zeros(array_shape)
ref = self.data[reference_indexes][i1:i2]
if interpolate is True:
ref = utils.interpolate_1D(ip, ref)
for dat, shift in zip(
self.iterate_axis(axis.index_in_array),
utils.iterate_axis(shift_array, axis.index_in_array)):
dat = dat[i1:i2]
if interpolate is True:
dat = utils.interpolate_1D(ip, dat)
shift[:] = np.argmax(np.correlate(ref, dat, 'full')) - len(ref) + 1
if max_shift is not None:
if interpolate is True:
max_shift *= ip
shift_array.clip(a_min=-max_shift, a_max=max_shift)
if interpolate is True:
shift_array /= ip
shift_array *= axis.scale
return shift_array
def estimate_shift_in_index_1D(self,
irange=(None,None),
axis=-1,
reference_indices=None,
max_shift=None,
interpolate=True,
number_of_interpolation_points=5):
"""Estimate the shifts in a given axis using cross-correlation
This method can only estimate the shift by comparing
unidimensional features that should not change the position in
the given axis. To decrease the memory usage, the time of
computation and the accuracy of the results it is convenient to
select the feature of interest setting
the irange keyword.
By default interpolation is used to obtain subpixel precision.
Parameters
----------
axis : int
The axis in which the analysis will be performed.
irange : tuple of ints (i1, i2)
Define the range of the feature of interest. If i1 or i2
are None,
the range will not be limited in that side.
reference_indices : tuple of ints or None
Defines the coordinates of the spectrum that will be used
as a
reference. If None the spectrum of 0 coordinates will be
used.
max_shift : int
interpolate : bool
number_of_interpolation_points : int
Number of interpolation points. Warning: making this number
too big
can saturate the memory
Return
------
An array with the result of the estimation.
"""
ip = number_of_interpolation_points + 1
axis = self.axes_manager.axes[axis]
if reference_indices is None:
reference_indices = [0,] * (len(self.axes_manager.axes) - 1)
else:
reference_indices = list(reference_indices)
reference_indices.insert(axis.index_in_array, slice(None))
i1, i2 = irange
array_shape = [axis.size for axis in self.axes_manager.axes]
array_shape[axis.index_in_array] = 1
shift_array = np.zeros(array_shape)
ref = self.data[reference_indices][i1:i2]
if interpolate is True:
ref = utils.interpolate_1D(ip, ref)
maxval = self.axes_manager.navigation_size
pbar = progressbar.progressbar(maxval=maxval)
i = 0
for dat, shift in zip(self.iterate_axis(axis.index_in_array),
utils.iterate_axis(shift_array,
axis.index_in_array)):
dat = dat[i1:i2]
if interpolate is True:
dat = utils.interpolate_1D(ip, dat)
shift[:] = np.argmax(
np.correlate(ref, dat,'full')) - len(ref) + 1
i+=1
pbar.update(i)
pbar.finish()
if max_shift is not None:
if interpolate is True:
max_shift *= ip
shift_array.clip(a_min = -max_shift, a_max = max_shift)
if interpolate is True:
shift_array /= ip
shift_array *= axis.scale
return shift_array
