def stack(signal_list,
axis=None,
new_axis_name='stack_element',
lazy=None,
**kwargs):
"""Concatenate the signals in the list over a given axis or a new axis.
The title is set to that of the first signal in the list.
Parameters
----------
signal_list : list of BaseSignal instances
axis : {None, int, str}
If None, the signals are stacked over a new axis. The data must
have the same dimensions. Otherwise the
signals are stacked over the axis given by its integer index or
its name. The data must have the same shape, except in the dimension
corresponding to `axis`.
new_axis_name : string
The name of the new axis when `axis` is None.
If an axis with this name already
exists it automatically append '-i', where `i` are integers,
until it finds a name that is not yet in use.
lazy: {bool, None}
Returns a LazySignal if True. If None, only returns lazy rezult if at
least one is lazy.
Returns
-------
signal : BaseSignal instance (or subclass, determined by the objects in
signal list)
Examples
--------
>>> data = np.arange(20)
>>> s = hs.stack([hs.signals.Signal1D(data[:10]),
... hs.signals.Signal1D(data[10:])])
>>> s
<Signal1D, title: Stack of , dimensions: (2, 10)>
>>> s.data
array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14, 15, 16, 17, 18, 19]])
"""
from hyperspy.signals import BaseSignal
import dask.array as da
from numbers import Number
# TODO: remove next time
deprecated = ['mmap', 'mmap_dir']
warn_str = "'{}' argument is deprecated, please use 'lazy' instead"
for k in deprecated:
if k in kwargs:
lazy = True
warnings.warn(warn_str.format(k), VisibleDeprecationWarning)
axis_input = copy.deepcopy(axis)
signal_list = list(signal_list)
# Get the real signal with the most axes to get metadata/class/etc
# first = sorted(filter(lambda _s: isinstance(_s, BaseSignal), signal_list),
# key=lambda _s: _s.data.ndim)[-1]
first = next(filter(lambda _s: isinstance(_s, BaseSignal), signal_list))
# Cast numbers as signals. Will broadcast later
for i, _s in enumerate(signal_list):
if isinstance(_s, BaseSignal):
pass
elif isinstance(_s, Number):
sig = BaseSignal(_s)
signal_list[i] = sig
else:
raise ValueError("{} type cannot be stacked.".format(type(_s)))
if lazy is None:
lazy = any(_s._lazy for _s in signal_list)
if not isinstance(lazy, bool):
raise ValueError("'lazy' argument has to be None, True or False")
# Cast all as lazy if required
for i, _s in enumerate(signal_list):
if not _s._lazy:
signal_list[i] = _s.as_lazy()
if len(signal_list) > 1:
newlist = broadcast_signals(*signal_list, ignore_axis=axis_input)
if axis is not None:
step_sizes = [s.axes_manager[axis].size for s in newlist]
axis = newlist[0].axes_manager[axis]
datalist = [s.data for s in newlist]
newdata = da.stack(datalist, axis=0) if axis is None else \
da.concatenate(datalist, axis=axis.index_in_array)
if axis_input is None:
signal = first.__class__(newdata)
signal._lazy = True
signal._assign_subclass()
signal.axes_manager._axes[1:] = copy.deepcopy(
newlist[0].axes_manager._axes)
axis_name = new_axis_name
axis_names = [
axis_.name for axis_ in signal.axes_manager._axes[1:]
]
j = 1
while axis_name in axis_names:
axis_name = new_axis_name + "_%i" % j
j += 1
eaxis = signal.axes_manager._axes[0]
eaxis.name = axis_name
eaxis.navigate = True # This triggers _update_parameters
signal.metadata = copy.deepcopy(first.metadata)
# Get the title from 1st object
signal.metadata.General.title = ("Stack of " +
first.metadata.General.title)
signal.original_metadata = DictionaryTreeBrowser({})
else:
signal = newlist[0]._deepcopy_with_new_data(newdata)
signal._lazy = True
signal._assign_subclass()
signal.get_dimensions_from_data()
signal.original_metadata.add_node('stack_elements')
for i, obj in enumerate(signal_list):
signal.original_metadata.stack_elements.add_node('element%i' % i)
node = signal.original_metadata.stack_elements['element%i' % i]
node.original_metadata = \
obj.original_metadata.as_dictionary()
node.metadata = \
obj.metadata.as_dictionary()
if axis_input is None:
axis_input = signal.axes_manager[-1 + 1j].index_in_axes_manager
step_sizes = 1
signal.metadata._HyperSpy.set_item('Stacking_history.axis', axis_input)
signal.metadata._HyperSpy.set_item('Stacking_history.step_sizes',
step_sizes)
if np.all([
s.metadata.has_item('Signal.Noise_properties.variance')
for s in signal_list
]):
variance = stack([
s.metadata.Signal.Noise_properties.variance
for s in signal_list
], axis)
signal.metadata.set_item('Signal.Noise_properties.variance',
variance)
else:
signal = signal_list[0]
# Leave as lazy or compute
if lazy:
signal = signal.as_lazy()
else:
signal.compute(False)
return signal
def stack(signal_list,
axis=None,
new_axis_name="stack_element",
lazy=None,
show_progressbar=None,
**kwargs):
"""Concatenate the signals in the list over a given axis or a new axis.
The title is set to that of the first signal in the list.
Parameters
----------
signal_list : list of BaseSignal instances
List of signals to stack.
axis : {None, int, str}
If None, the signals are stacked over a new axis. The data must
have the same dimensions. Otherwise the
signals are stacked over the axis given by its integer index or
its name. The data must have the same shape, except in the dimension
corresponding to `axis`.
new_axis_name : string
The name of the new axis when `axis` is None.
If an axis with this name already
exists it automatically append '-i', where `i` are integers,
until it finds a name that is not yet in use.
lazy : {bool, None}
Returns a LazySignal if True. If None, only returns lazy result if at
least one is lazy.
%s
Returns
-------
signal : BaseSignal instance
Examples
--------
>>> data = np.arange(20)
>>> s = hs.stack([hs.signals.Signal1D(data[:10]),
... hs.signals.Signal1D(data[10:])])
>>> s
<Signal1D, title: Stack of , dimensions: (2, 10)>
>>> s.data
array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14, 15, 16, 17, 18, 19]])
"""
from hyperspy.signals import BaseSignal
import dask.array as da
from numbers import Number
for k in [k for k in ["mmap", "mmap_dir"] if k in kwargs]:
lazy = True
warnings.warn(
f"'{k}' argument is deprecated and will be removed in "
"HyperSpy v2.0. Please use 'lazy=True' instead.",
VisibleDeprecationWarning,
)
axis_input = copy.deepcopy(axis)
signal_list = list(signal_list)
# Get the real signal with the most axes to get metadata/class/etc
# first = sorted(filter(lambda _s: isinstance(_s, BaseSignal), signal_list),
# key=lambda _s: _s.data.ndim)[-1]
first = next(filter(lambda _s: isinstance(_s, BaseSignal), signal_list))
# Cast numbers as signals. Will broadcast later.
for i, _s in enumerate(signal_list):
if isinstance(_s, BaseSignal):
pass
elif isinstance(_s, Number):
sig = BaseSignal(_s)
signal_list[i] = sig
else:
raise ValueError(f"Objects of type {type(_s)} cannot be stacked")
if lazy is None:
lazy = any(_s._lazy for _s in signal_list)
if not isinstance(lazy, bool):
raise ValueError("'lazy' argument has to be None, True or False")
for i, _s in enumerate(signal_list):
# Cast all as lazy if required
if not _s._lazy:
signal_list[i] = _s.as_lazy()
if len(signal_list) > 1:
# Matching axis calibration is checked here
newlist = broadcast_signals(*signal_list, ignore_axis=axis_input)
if axis is not None:
step_sizes = [s.axes_manager[axis].size for s in newlist]
axis = newlist[0].axes_manager[axis]
datalist = [s.data for s in newlist]
newdata = (da.stack(datalist, axis=0) if axis is None else
da.concatenate(datalist, axis=axis.index_in_array))
if axis_input is None:
signal = first.__class__(newdata)
signal._lazy = True
signal._assign_subclass()
signal.axes_manager._axes[1:] = copy.deepcopy(
newlist[0].axes_manager._axes)
axis_name = new_axis_name
axis_names = [
axis_.name for axis_ in signal.axes_manager._axes[1:]
]
j = 1
while axis_name in axis_names:
axis_name = f"{new_axis_name}_{j}"
j += 1
eaxis = signal.axes_manager._axes[0]
eaxis.name = axis_name
eaxis.navigate = True # This triggers _update_parameters
signal.metadata = copy.deepcopy(first.metadata)
# Get the title from 1st object
signal.metadata.General.title = f"Stack of {first.metadata.General.title}"
signal.original_metadata = DictionaryTreeBrowser({})
else:
signal = newlist[0]._deepcopy_with_new_data(newdata)
signal._lazy = True
signal._assign_subclass()
signal.get_dimensions_from_data()
signal.original_metadata.add_node("stack_elements")
for i, obj in enumerate(signal_list):
signal.original_metadata.stack_elements.add_node(f"element{i}")
node = signal.original_metadata.stack_elements[f"element{i}"]
node.original_metadata = obj.original_metadata.as_dictionary()
node.metadata = obj.metadata.as_dictionary()
if axis_input is None:
axis_input = signal.axes_manager[-1 + 1j].index_in_axes_manager
step_sizes = 1
signal.metadata._HyperSpy.set_item("Stacking_history.axis", axis_input)
signal.metadata._HyperSpy.set_item("Stacking_history.step_sizes",
step_sizes)
if np.all([
s.metadata.has_item("Signal.Noise_properties.variance")
for s in signal_list
]):
variance = stack([
s.metadata.Signal.Noise_properties.variance
for s in signal_list
], axis)
signal.metadata.set_item("Signal.Noise_properties.variance",
variance)
else:
signal = signal_list[0]
# Leave as lazy or compute
if lazy:
signal = signal.as_lazy()
else:
signal.compute(False, show_progressbar=show_progressbar)
return signal
def test_boardcast_signals_error():
with pytest.raises(ValueError):
broadcast_signals([0, 1], [2, 3])
with pytest.raises(ValueError):
broadcast_signals(Signal1D([0, 1]))
def stack(signal_list, axis=None, new_axis_name='stack_element',
lazy=None, **kwargs):
"""Concatenate the signals in the list over a given axis or a new axis.
The title is set to that of the first signal in the list.
Parameters
----------
signal_list : list of BaseSignal instances
axis : {None, int, str}
If None, the signals are stacked over a new axis. The data must
have the same dimensions. Otherwise the
signals are stacked over the axis given by its integer index or
its name. The data must have the same shape, except in the dimension
corresponding to `axis`.
new_axis_name : string
The name of the new axis when `axis` is None.
If an axis with this name already
exists it automatically append '-i', where `i` are integers,
until it finds a name that is not yet in use.
lazy: {bool, None}
Returns a LazySignal if True. If None, only returns lazy rezult if at
least one is lazy.
Returns
-------
signal : BaseSignal instance (or subclass, determined by the objects in
signal list)
Examples
--------
>>> data = np.arange(20)
>>> s = hs.stack([hs.signals.Signal1D(data[:10]),
... hs.signals.Signal1D(data[10:])])
>>> s
<Signal1D, title: Stack of , dimensions: (2, 10)>
>>> s.data
array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14, 15, 16, 17, 18, 19]])
"""
from itertools import zip_longest
from hyperspy.signals import BaseSignal
import dask.array as da
from numbers import Number
# TODO: remove next time
deprecated = ['mmap', 'mmap_dir']
warn_str = "'{}' argument is deprecated, please use 'lazy' instead"
for k in deprecated:
if k in kwargs:
lazy = True
warnings.warn(warn_str.format(k), VisibleDeprecationWarning)
axis_input = copy.deepcopy(axis)
signal_list = list(signal_list)
# Get the real signal with the most axes to get metadata/class/etc
# first = sorted(filter(lambda _s: isinstance(_s, BaseSignal), signal_list),
# key=lambda _s: _s.data.ndim)[-1]
first = next(filter(lambda _s: isinstance(_s, BaseSignal), signal_list))
# Cast numbers as signals. Will broadcast later
for i, _s in enumerate(signal_list):
if isinstance(_s, BaseSignal):
pass
elif isinstance(_s, Number):
sig = BaseSignal(_s)
signal_list[i] = sig
else:
raise ValueError("{} type cannot be stacked.".format(type(_s)))
if lazy is None:
lazy = any(_s._lazy for _s in signal_list)
if not isinstance(lazy, bool):
raise ValueError("'lazy' argument has to be None, True or False")
# Cast all as lazy if required
for i, _s in enumerate(signal_list):
if not _s._lazy:
signal_list[i] = _s.as_lazy()
if len(signal_list) > 1:
newlist = broadcast_signals(*signal_list, ignore_axis=axis_input)
if axis is not None:
step_sizes = [s.axes_manager[axis].size for s in newlist]
axis = newlist[0].axes_manager[axis]
datalist = [s.data for s in newlist]
newdata = da.stack(datalist, axis=0) if axis is None else \
da.concatenate(datalist, axis=axis.index_in_array)
if axis_input is None:
signal = first.__class__(newdata)
signal._lazy = True
signal._assign_subclass()
signal.axes_manager._axes[1:] = copy.deepcopy(
newlist[0].axes_manager._axes)
axis_name = new_axis_name
axis_names = [
axis_.name for axis_ in signal.axes_manager._axes[
1:]]
j = 1
while axis_name in axis_names:
axis_name = new_axis_name + "_%i" % j
j += 1
eaxis = signal.axes_manager._axes[0]
eaxis.name = axis_name
eaxis.navigate = True # This triggers _update_parameters
signal.metadata = copy.deepcopy(first.metadata)
# Get the title from 1st object
signal.metadata.General.title = (
"Stack of " + first.metadata.General.title)
signal.original_metadata = DictionaryTreeBrowser({})
else:
signal = newlist[0]._deepcopy_with_new_data(newdata)
signal._lazy = True
signal._assign_subclass()
signal.get_dimensions_from_data()
signal.original_metadata.add_node('stack_elements')
for i, obj in enumerate(signal_list):
signal.original_metadata.stack_elements.add_node('element%i' % i)
node = signal.original_metadata.stack_elements['element%i' % i]
node.original_metadata = \
obj.original_metadata.as_dictionary()
node.metadata = \
obj.metadata.as_dictionary()
if axis_input is None:
axis_input = signal.axes_manager[-1 + 1j].index_in_axes_manager
step_sizes = 1
signal.metadata._HyperSpy.set_item('Stacking_history.axis', axis_input)
signal.metadata._HyperSpy.set_item('Stacking_history.step_sizes',
step_sizes)
if np.all([
s.metadata.has_item('Signal.Noise_properties.variance')
for s in signal_list
]):
variance = stack([
s.metadata.Signal.Noise_properties.variance for s in signal_list
], axis)
signal.metadata.set_item(
'Signal.Noise_properties.variance', variance)
else:
signal = signal_list[0]
# Leave as lazy or compute
if lazy:
signal = signal.as_lazy()
else:
signal.compute(False)
return signal
