def __call__(self, signal, axes=None, order=0):
"""Slice the signal according to the ROI, and return it.
Arguments
---------
signal : Signal
The signal to slice with the ROI.
axes : specification of axes to use, default = None
The axes argument specifies which axes the ROI will be applied on.
The items in the collection can be either of the following:
* a tuple of:
- DataAxis. These will not be checked with
signal.axes_manager.
- anything that will index signal.axes_manager
* For any other value, it will check whether the navigation
space can fit the right number of axis, and use that if it
fits. If not, it will try the signal space.
"""
if axes is None and signal in self.signal_map:
axes = self.signal_map[signal][1]
else:
axes = self._parse_axes(axes, signal.axes_manager)
linewidth = self.linewidth / np.min([ax.scale for ax in axes])
profile = Line2DROI.profile_line(signal.data, (self.x1, self.y1),
(self.x2, self.y2),
axes=axes,
linewidth=linewidth,
order=order)
length = np.linalg.norm(np.diff(np.array(
((self.x1, self.y1), (self.x2, self.y2))),
axis=0),
axis=1)[0]
axm = signal.axes_manager.deepcopy()
idx = []
for ax in axes:
idx.append(ax.index_in_axes_manager)
for i in reversed(sorted(idx)): # Remove in reversed order
axm.remove(i)
axis = DataAxis(len(profile),
scale=length / len(profile),
units=axes[0].units,
navigate=axes[0].navigate)
axis.axes_manager = axm
i0 = min(axes[0].index_in_array, axes[1].index_in_array)
axm._axes.insert(i0, axis)
from hyperspy.signals import Signal
roi = Signal(profile,
axes=axm._get_axes_dicts(),
metadata=signal.metadata.deepcopy().as_dictionary(),
original_metadata=signal.original_metadata.deepcopy().
as_dictionary())
return roi
def __call__(self, signal, axes=None, order=0):
"""Slice the signal according to the ROI, and return it.
Arguments
---------
signal : Signal
The signal to slice with the ROI.
axes : specification of axes to use, default = None
The axes argument specifies which axes the ROI will be applied on.
The items in the collection can be either of the following:
* a tuple of:
- DataAxis. These will not be checked with
signal.axes_manager.
- anything that will index signal.axes_manager
* For any other value, it will check whether the navigation
space can fit the right number of axis, and use that if it
fits. If not, it will try the signal space.
"""
if axes is None and signal in self.signal_map:
axes = self.signal_map[signal][1]
else:
axes = self._parse_axes(axes, signal.axes_manager)
linewidth = self.linewidth / np.min([ax.scale for ax in axes])
profile = Line2DROI.profile_line(signal.data,
(self.x1, self.y1),
(self.x2, self.y2),
axes=axes,
linewidth=linewidth,
order=order)
length = np.linalg.norm(np.diff(
np.array(((self.x1, self.y1), (self.x2, self.y2))), axis=0),
axis=1)[0]
axm = signal.axes_manager.deepcopy()
idx = []
for ax in axes:
idx.append(ax.index_in_axes_manager)
for i in reversed(sorted(idx)): # Remove in reversed order
axm.remove(i)
axis = DataAxis(len(profile),
scale=length / len(profile),
units=axes[0].units,
navigate=axes[0].navigate)
axis.axes_manager = axm
i0 = min(axes[0].index_in_array, axes[1].index_in_array)
axm._axes.insert(i0, axis)
from hyperspy.signals import Signal
roi = Signal(profile, axes=axm._get_axes_dicts(),
metadata=signal.metadata.deepcopy().as_dictionary(),
original_metadata=signal.original_metadata.
deepcopy().as_dictionary())
return roi
def __call__(self, signal, out=None, axes=None, order=0):
"""Slice the signal according to the ROI, and return it.
Arguments
---------
signal : Signal
The signal to slice with the ROI.
out : Signal, default = None
If the 'out' argument is supplied, the sliced output will be put
into this instead of returning a Signal. See Signal.__getitem__()
for more details on 'out'.
axes : specification of axes to use, default = None
The axes argument specifies which axes the ROI will be applied on.
The items in the collection can be either of the following:
* a tuple of:
- DataAxis. These will not be checked with
signal.axes_manager.
- anything that will index signal.axes_manager
* For any other value, it will check whether the navigation
space can fit the right number of axis, and use that if it
fits. If not, it will try the signal space.
order : The spline interpolation order to use when extracting the line
profile. 0 means nearest-neighbor interpolation, and is both the
default and the fastest.
"""
if axes is None and signal in self.signal_map:
axes = self.signal_map[signal][1]
else:
axes = self._parse_axes(axes, signal.axes_manager)
profile = Line2DROI.profile_line(signal.data, (self.x1, self.y1),
(self.x2, self.y2),
axes=axes,
linewidth=self.linewidth,
order=order)
length = np.linalg.norm(np.diff(np.array(
((self.x1, self.y1), (self.x2, self.y2))),
axis=0),
axis=1)[0]
if out is None:
axm = signal.axes_manager.deepcopy()
i0 = min(axes[0].index_in_array, axes[1].index_in_array)
axm.remove([ax.index_in_array + 3j for ax in axes])
axis = DataAxis(profile.shape[i0],
scale=length / profile.shape[i0],
units=axes[0].units,
navigate=axes[0].navigate)
axis.axes_manager = axm
axm._axes.insert(i0, axis)
from hyperspy.signals import BaseSignal
roi = BaseSignal(
profile,
axes=axm._get_axes_dicts(),
metadata=signal.metadata.deepcopy().as_dictionary(),
original_metadata=signal.original_metadata.deepcopy(
).as_dictionary())
return roi
else:
out.data = profile
i0 = min(axes[0].index_in_array, axes[1].index_in_array)
ax = out.axes_manager._axes[i0]
size = len(profile)
scale = length / len(profile)
axchange = size != ax.size or scale != ax.scale
if axchange:
ax.size = len(profile)
ax.scale = length / len(profile)
out.events.data_changed.trigger(out)
def __call__(self, signal, out=None, axes=None, order=0):
"""Slice the signal according to the ROI, and return it.
Arguments
---------
signal : Signal
The signal to slice with the ROI.
out : Signal, default = None
If the 'out' argument is supplied, the sliced output will be put
into this instead of returning a Signal. See Signal.__getitem__()
for more details on 'out'.
axes : specification of axes to use, default = None
The axes argument specifies which axes the ROI will be applied on.
The items in the collection can be either of the following:
* a tuple of:
- DataAxis. These will not be checked with
signal.axes_manager.
- anything that will index signal.axes_manager
* For any other value, it will check whether the navigation
space can fit the right number of axis, and use that if it
fits. If not, it will try the signal space.
order : The spline interpolation order to use when extracting the line
profile. 0 means nearest-neighbor interpolation, and is both the
default and the fastest.
"""
if axes is None and signal in self.signal_map:
axes = self.signal_map[signal][1]
else:
axes = self._parse_axes(axes, signal.axes_manager)
linewidth = self.linewidth / np.min([ax.scale for ax in axes])
profile = Line2DROI.profile_line(signal.data,
(self.x1, self.y1),
(self.x2, self.y2),
axes=axes,
linewidth=linewidth,
order=order)
length = np.linalg.norm(np.diff(
np.array(((self.x1, self.y1), (self.x2, self.y2))), axis=0),
axis=1)[0]
if out is None:
axm = signal.axes_manager.deepcopy()
i0 = min(axes[0].index_in_array, axes[1].index_in_array)
axm.remove([ax.index_in_array + 3j for ax in axes])
axis = DataAxis(profile.shape[i0],
scale=length / profile.shape[i0],
units=axes[0].units,
navigate=axes[0].navigate)
axis.axes_manager = axm
axm._axes.insert(i0, axis)
from hyperspy.signals import BaseSignal
roi = BaseSignal(profile, axes=axm._get_axes_dicts(),
metadata=signal.metadata.deepcopy(
).as_dictionary(),
original_metadata=signal.original_metadata.
deepcopy().as_dictionary())
return roi
else:
out.data = profile
i0 = min(axes[0].index_in_array, axes[1].index_in_array)
ax = out.axes_manager._axes[i0]
size = len(profile)
scale = length / len(profile)
axchange = size != ax.size or scale != ax.scale
if axchange:
ax.size = len(profile)
ax.scale = length / len(profile)
out.events.data_changed.trigger(out)
