import itertools
import warnings
from collections import namedtuple
from typing import List, Optional
import numpy as np
from ..layers import Layer
from ..utils.events import EventedList
from ..utils.naming import inc_name_count
Extent = namedtuple('Extent', 'data world step')
[docs]class LayerList(EventedList):
"""List-like layer collection with built-in reordering and callback hooks.
Parameters
----------
data : iterable
Iterable of napari.layer.Layer
"""
def __init__(self, data=()):
super().__init__(
data=data,
basetype=Layer,
lookup={str: lambda e: e.name},
)
def __newlike__(self, data):
return LayerList(data)
def _coerce_name(self, name, layer=None):
"""Coerce a name into a unique equivalent.
Parameters
----------
name : str
Original name.
layer : napari.layers.Layer, optional
Layer for which name is generated.
Returns
-------
new_name : str
Coerced, unique name.
"""
if layer is None:
for existing_name in sorted(x.name for x in self):
if name == existing_name:
name = inc_name_count(name)
else:
for _layer in sorted(self, key=lambda x: x.name):
if _layer is layer:
continue
if name == _layer.name:
name = inc_name_count(name)
return name
def _update_name(self, event):
"""Coerce name of the layer in `event.layer`."""
layer = event.source
layer.name = self._coerce_name(layer.name, layer)
[docs] def insert(self, index: int, value: Layer):
"""Insert ``value`` before index."""
new_layer = self._type_check(value)
new_layer.name = self._coerce_name(new_layer.name)
super().insert(index, new_layer)
@property
def selected(self):
"""List of selected layers."""
return [layer for layer in self if layer.selected]
[docs] def move_selected(self, index, insert):
"""Reorder list by moving the item at index and inserting it
at the insert index. If additional items are selected these will
get inserted at the insert index too. This allows for rearranging
the list based on dragging and dropping a selection of items, where
index is the index of the primary item being dragged, and insert is
the index of the drop location, and the selection indicates if
multiple items are being dragged. If the moved layer is not selected
select it.
Parameters
----------
index : int
Index of primary item to be moved
insert : int
Index that item(s) will be inserted at
"""
if not self[index].selected:
self.unselect_all()
self[index].selected = True
moving = (index,)
else:
moving = [i for i, item in enumerate(self) if item.selected]
offset = insert >= index
self.move_multiple(moving, insert + offset)
[docs] def unselect_all(self, ignore=None):
"""Unselects all layers expect any specified in ignore.
Parameters
----------
ignore : Layer | None
Layer that should not be unselected if specified.
"""
for layer in self:
if layer.selected and layer != ignore:
layer.selected = False
[docs] def select_all(self):
"""Selects all layers."""
for layer in self:
if not layer.selected:
layer.selected = True
[docs] def remove_selected(self):
"""Removes selected items from list."""
to_delete = []
for i in range(len(self)):
if self[i].selected:
to_delete.append(i)
to_delete.reverse()
for i in to_delete:
self.pop(i)
if len(to_delete) > 0:
first_to_delete = to_delete[-1]
if first_to_delete == 0 and len(self) > 0:
self[0].selected = True
elif first_to_delete > 0:
self[first_to_delete - 1].selected = True
[docs] def select_next(self, shift=False):
"""Selects next item from list."""
selected = []
for i in range(len(self)):
if self[i].selected:
selected.append(i)
if len(selected) > 0:
if selected[-1] == len(self) - 1:
if shift is False:
self.unselect_all(ignore=self[selected[-1]])
elif selected[-1] < len(self) - 1:
if shift is False:
self.unselect_all(ignore=self[selected[-1] + 1])
self[selected[-1] + 1].selected = True
elif len(self) > 0:
self[-1].selected = True
[docs] def select_previous(self, shift=False):
"""Selects previous item from list."""
selected = []
for i in range(len(self)):
if self[i].selected:
selected.append(i)
if len(selected) > 0:
if selected[0] == 0:
if shift is False:
self.unselect_all(ignore=self[0])
elif selected[0] > 0:
if shift is False:
self.unselect_all(ignore=self[selected[0] - 1])
self[selected[0] - 1].selected = True
elif len(self) > 0:
self[0].selected = True
[docs] def toggle_selected_visibility(self):
"""Toggle visibility of selected layers"""
for layer in self:
if layer.selected:
layer.visible = not layer.visible
@property
def _extent_world(self) -> np.ndarray:
"""Extent of layers in world coordinates.
Default to 2D with (0, 512) min/ max values if no data is present.
Returns
-------
extent_world : array, shape (2, D)
"""
return self._get_extent_world([layer.extent for layer in self])
def _get_extent_world(self, layer_extent_list):
"""Extent of layers in world coordinates.
Default to 2D with (0, 512) min/ max values if no data is present.
Returns
-------
extent_world : array, shape (2, D)
"""
if len(self) == 0:
min_v = [np.nan] * self.ndim
max_v = [np.nan] * self.ndim
else:
extrema = [extent.world for extent in layer_extent_list]
mins = [e[0][::-1] for e in extrema]
maxs = [e[1][::-1] for e in extrema]
with warnings.catch_warnings():
# Taking the nanmin and nanmax of an axis of all nan
# raises a warning and returns nan for that axis
# as we have do an explict nan_to_num below this
# behaviour is acceptable and we can filter the
# warning
warnings.filterwarnings(
'ignore', message='All-NaN axis encountered'
)
min_v = np.nanmin(
list(itertools.zip_longest(*mins, fillvalue=np.nan)),
axis=1,
)
max_v = np.nanmax(
list(itertools.zip_longest(*maxs, fillvalue=np.nan)),
axis=1,
)
min_vals = np.nan_to_num(min_v[::-1])
max_vals = np.copy(max_v[::-1])
max_vals[np.isnan(max_vals)] = 511
return np.vstack([min_vals, max_vals])
@property
def _step_size(self) -> np.ndarray:
"""Ideal step size between planes in world coordinates.
Computes the best step size that allows all data planes to be
sampled if moving through the full range of world coordinates.
The current implementation just takes the minimum scale.
Returns
-------
step_size : array, shape (D,)
"""
return self._get_step_size([layer.extent for layer in self])
def _get_step_size(self, layer_extent_list):
if len(self) == 0:
return np.ones(self.ndim)
else:
scales = [extent.step[::-1] for extent in layer_extent_list]
full_scales = list(
np.array(
list(itertools.zip_longest(*scales, fillvalue=np.nan))
).T
)
min_scales = np.nanmin(full_scales, axis=0)
return min_scales[::-1]
@property
def extent(self) -> Extent:
"""Extent of layers in data and world coordinates."""
extent_list = [layer.extent for layer in self]
return Extent(
data=None,
world=self._get_extent_world(extent_list),
step=self._get_step_size(extent_list),
)
@property
def ndim(self) -> int:
"""Maximum dimensionality of layers.
Defaults to 2 if no data is present.
Returns
-------
ndim : int
"""
return max((layer.ndim for layer in self), default=2)
[docs] def save(
self,
path: str,
*,
selected: bool = False,
plugin: Optional[str] = None,
) -> List[str]:
"""Save all or only selected layers to a path using writer plugins.
If ``plugin`` is not provided and only one layer is targeted, then we
directly call the corresponding``napari_write_<layer_type>`` hook (see
:ref:`single layer writer hookspecs <write-single-layer-hookspecs>`)
which will loop through implementations and stop when the first one
returns a non-``None`` result. The order in which implementations are
called can be changed with the Plugin sorter in the GUI or with the
corresponding hook's
:meth:`~napari.plugins._hook_callers._HookCaller.bring_to_front`
method.
If ``plugin`` is not provided and multiple layers are targeted,
then we call
:meth:`~napari.plugins.hook_specifications.napari_get_writer` which
loops through plugins to find the first one that knows how to handle
the combination of layers and is able to write the file. If no plugins
offer :meth:`~napari.plugins.hook_specifications.napari_get_writer` for
that combination of layers then the default
:meth:`~napari.plugins.hook_specifications.napari_get_writer` will
create a folder and call ``napari_write_<layer_type>`` for each layer
using the ``Layer.name`` variable to modify the path such that the
layers are written to unique files in the folder.
If ``plugin`` is provided and a single layer is targeted, then we
call the ``napari_write_<layer_type>`` for that plugin, and if it fails
we error.
If ``plugin`` is provided and multiple layers are targeted, then
we call we call
:meth:`~napari.plugins.hook_specifications.napari_get_writer` for
that plugin, and if it doesn’t return a ``WriterFunction`` we error,
otherwise we call it and if that fails if it we error.
Parameters
----------
path : str
A filepath, directory, or URL to open. Extensions may be used to
specify output format (provided a plugin is available for the
requested format).
selected : bool
Optional flag to only save selected layers. False by default.
plugin : str, optional
Name of the plugin to use for saving. If None then all plugins
corresponding to appropriate hook specification will be looped
through to find the first one that can save the data.
Returns
-------
list of str
File paths of any files that were written.
"""
from ..plugins.io import save_layers
layers = self.selected if selected else list(self)
if not layers:
warnings.warn(f"No layers {'selected' if selected else 'to save'}")
return []
return save_layers(path, layers, plugin=plugin)