Source code for napari.layers.base.base

from __future__ import annotations

import copy
import inspect
import itertools
import logging
import os.path
import uuid
import warnings
from abc import ABC, ABCMeta, abstractmethod
from collections import defaultdict
from collections.abc import Generator, Hashable, Mapping, Sequence
from contextlib import contextmanager
from functools import cached_property
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    ClassVar,
    Optional,
    Union,
)

import magicgui as mgui
import numpy as np
import pint
from npe2 import plugin_manager as pm

from napari.layers.base._base_constants import (
    BaseProjectionMode,
    Blending,
    Mode,
)
from napari.layers.base._base_mouse_bindings import (
    highlight_box_handles,
    transform_with_box,
)
from napari.layers.utils._slice_input import _SliceInput, _ThickNDSlice
from napari.layers.utils.interactivity_utils import (
    drag_data_to_projected_distance,
)
from napari.layers.utils.layer_utils import (
    Extent,
    coerce_affine,
    compute_multiscale_level_and_corners,
    convert_to_uint8,
    dims_displayed_world_to_layer,
    get_extent_world,
)
from napari.layers.utils.plane import ClippingPlane, ClippingPlaneList
from napari.settings import get_settings
from napari.utils._dask_utils import configure_dask
from napari.utils._magicgui import (
    add_layer_to_viewer,
    add_layers_to_viewer,
    get_layers,
)
from napari.utils.events import EmitterGroup, Event, EventedDict
from napari.utils.events.event import WarningEmitter
from napari.utils.geometry import (
    find_front_back_face,
    intersect_line_with_axis_aligned_bounding_box_3d,
)
from napari.utils.key_bindings import KeymapProvider
from napari.utils.migrations import _DeprecatingDict
from napari.utils.misc import StringEnum
from napari.utils.mouse_bindings import MousemapProvider
from napari.utils.naming import magic_name
from napari.utils.status_messages import generate_layer_coords_status
from napari.utils.transforms import Affine, CompositeAffine, TransformChain
from napari.utils.translations import trans

if TYPE_CHECKING:
    import numpy.typing as npt

    from napari.components.dims import Dims
    from napari.components.overlays.base import Overlay
    from napari.layers._source import Source


logger = logging.getLogger('napari.layers.base.base')


def no_op(layer: Layer, event: Event) -> None:
    """
    A convenient no-op event for the layer mouse binding.

    This makes it easier to handle many cases by inserting this as
    as place holder

    Parameters
    ----------
    layer : Layer
        Current layer on which this will be bound as a callback
    event : Event
        event that triggered this mouse callback.

    Returns
    -------
    None

    """
    return


class PostInit(ABCMeta):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        sig = inspect.signature(self.__init__)
        params = tuple(sig.parameters.values())
        self.__signature__ = sig.replace(parameters=params[1:])

    def __call__(self, *args, **kwargs):
        obj = super().__call__(*args, **kwargs)
        obj._post_init()
        return obj


[docs] @mgui.register_type(choices=get_layers, return_callback=add_layer_to_viewer) class Layer(KeymapProvider, MousemapProvider, ABC, metaclass=PostInit): """Base layer class. Parameters ---------- data : array or list of array Data that the layer is visualizing. Can be N-dimensional. ndim : int Number of spatial dimensions. affine : n-D array or napari.utils.transforms.Affine (N+1, N+1) affine transformation matrix in homogeneous coordinates. The first (N, N) entries correspond to a linear transform and the final column is a length N translation vector and a 1 or a napari `Affine` transform object. Applied as an extra transform on top of the provided scale, rotate, and shear values. axis_labels : tuple of str, optional Dimension names of the layer data. If not provided, axis_labels will be set to (..., 'axis -2', 'axis -1'). blending : str One of a list of preset blending modes that determines how RGB and alpha values of the layer visual get mixed. Allowed values are {'opaque', 'translucent', 'translucent_no_depth', 'additive', and 'minimum'}. cache : bool Whether slices of out-of-core datasets should be cached upon retrieval. Currently, this only applies to dask arrays. experimental_clipping_planes : list of dicts, list of ClippingPlane, or ClippingPlaneList Each dict defines a clipping plane in 3D in data coordinates. Valid dictionary keys are {'position', 'normal', and 'enabled'}. Values on the negative side of the normal are discarded if the plane is enabled. metadata : dict Layer metadata. mode: str The layer's interactive mode. multiscale : bool Whether the data is multiscale or not. Multiscale data is represented by a list of data objects and should go from largest to smallest. name : str, optional Name of the layer. If not provided then will be guessed using heuristics. opacity : float Opacity of the layer visual, between 0.0 and 1.0. projection_mode : str How data outside the viewed dimensions but inside the thick Dims slice will be projected onto the viewed dimensions. Must fit to cls._projectionclass. rotate : float, 3-tuple of float, or n-D array. If a float convert into a 2D rotation matrix using that value as an angle. If 3-tuple convert into a 3D rotation matrix, using a yaw, pitch, roll convention. Otherwise assume an nD rotation. Angles are assumed to be in degrees. They can be converted from radians with np.degrees if needed. scale : tuple of float Scale factors for the layer. shear : 1-D array or n-D array Either a vector of upper triangular values, or an nD shear matrix with ones along the main diagonal. translate : tuple of float Translation values for the layer. units : tuple of str or pint.Unit, optional Units of the layer data in world coordinates. If not provided, the default units are assumed to be pixels. visible : bool Whether the layer visual is currently being displayed. Attributes ---------- affine : n-D array or napari.utils.transforms.Affine (N+1, N+1) affine transformation matrix in homogeneous coordinates. The first (N, N) entries correspond to a linear transform and the final column is a length N translation vector and a 1 or a napari `Affine` transform object. Applied as an extra transform on top of the provided scale, rotate, and shear values. axis_labels : tuple of str Dimension names of the layer data. blending : Blending Determines how RGB and alpha values get mixed. * ``Blending.OPAQUE`` Allows for only the top layer to be visible and corresponds to ``depth_test=True``, ``cull_face=False``, ``blend=False``. * ``Blending.TRANSLUCENT`` Allows for multiple layers to be blended with different opacity and corresponds to ``depth_test=True``, ``cull_face=False``, ``blend=True``, ``blend_func=('src_alpha', 'one_minus_src_alpha')``, and ``blend_equation=('func_add')``. * ``Blending.TRANSLUCENT_NO_DEPTH`` Allows for multiple layers to be blended with different opacity, but no depth testing is performed. Corresponds to ``depth_test=False``, ``cull_face=False``, ``blend=True``, ``blend_func=('src_alpha', 'one_minus_src_alpha')``, and ``blend_equation=('func_add')``. * ``Blending.ADDITIVE`` Allows for multiple layers to be blended together with different colors and opacity. Useful for creating overlays. It corresponds to ``depth_test=False``, ``cull_face=False``, ``blend=True``, ``blend_func=('src_alpha', 'one')``, and ``blend_equation=('func_add')``. * ``Blending.MINIMUM`` Allows for multiple layers to be blended together such that the minimum of each RGB component and alpha are selected. Useful for creating overlays with inverted colormaps. It corresponds to ``depth_test=False``, ``cull_face=False``, ``blend=True``, ``blend_equation=('min')``. cache : bool Whether slices of out-of-core datasets should be cached upon retrieval. Currently, this only applies to dask arrays. corner_pixels : array Coordinates of the top-left and bottom-right canvas pixels in the data coordinates of each layer. For multiscale data the coordinates are in the space of the currently viewed data level, not the highest resolution level. cursor : str String identifying which cursor displayed over canvas. cursor_size : int | None Size of cursor if custom. None yields default size help : str Displayed in status bar bottom right. interactive : bool Determine if canvas pan/zoom interactivity is enabled. This attribute is deprecated since 0.5.0 and should not be used. Use the mouse_pan and mouse_zoom attributes instead. mouse_pan : bool Determine if canvas interactive panning is enabled with the mouse. mouse_zoom : bool Determine if canvas interactive zooming is enabled with the mouse. multiscale : bool Whether the data is multiscale or not. Multiscale data is represented by a list of data objects and should go from largest to smallest. name : str Unique name of the layer. ndim : int Dimensionality of the layer. opacity : float Opacity of the layer visual, between 0.0 and 1.0. projection_mode : str How data outside the viewed dimensions but inside the thick Dims slice will be projected onto the viewed dimenions. rotate : float, 3-tuple of float, or n-D array. If a float convert into a 2D rotation matrix using that value as an angle. If 3-tuple convert into a 3D rotation matrix, using a yaw, pitch, roll convention. Otherwise assume an nD rotation. Angles are assumed to be in degrees. They can be converted from radians with np.degrees if needed. scale : tuple of float Scale factors for the layer. scale_factor : float Conversion factor from canvas coordinates to image coordinates, which depends on the current zoom level. shear : 1-D array or n-D array Either a vector of upper triangular values, or an nD shear matrix with ones along the main diagonal. source : Source source of the layer (such as a plugin or widget) status : str Displayed in status bar bottom left. translate : tuple of float Translation values for the layer. thumbnail : (N, M, 4) array Array of thumbnail data for the layer. unique_id : Hashable Unique id of the layer. Guaranteed to be unique across the lifetime of a viewer. visible : bool Whether the layer visual is currently being displayed. units: tuple of pint.Unit Units of the layer data in world coordinates. z_index : int Depth of the layer visual relative to other visuals in the scenecanvas. Notes ----- Must define the following: * `_extent_data`: property * `data` property (setter & getter) May define the following: * `_set_view_slice()`: called to set currently viewed slice * `_basename()`: base/default name of the layer """ _modeclass: type[StringEnum] = Mode _projectionclass: type[StringEnum] = BaseProjectionMode ModeCallable = Callable[ ['Layer', Event], Union[None, Generator[None, None, None]] ] _drag_modes: ClassVar[dict[StringEnum, ModeCallable]] = { Mode.PAN_ZOOM: no_op, Mode.TRANSFORM: transform_with_box, } _move_modes: ClassVar[dict[StringEnum, ModeCallable]] = { Mode.PAN_ZOOM: no_op, Mode.TRANSFORM: highlight_box_handles, } _cursor_modes: ClassVar[dict[StringEnum, str]] = { Mode.PAN_ZOOM: 'standard', Mode.TRANSFORM: 'standard', } events: EmitterGroup def __init__( self, data, ndim, *, affine=None, axis_labels=None, blending='translucent', cache=True, # this should move to future "data source" object. experimental_clipping_planes=None, metadata=None, mode='pan_zoom', multiscale=False, name=None, opacity=1.0, projection_mode='none', rotate=None, scale=None, shear=None, translate=None, units=None, visible=True, ): super().__init__() if name is None and data is not None: name = magic_name(data) if scale is not None and not np.all(scale): raise ValueError( trans._( "Layer {name} is invalid because it has scale values of 0. The layer's scale is currently {scale}", deferred=True, name=repr(name), scale=repr(scale), ) ) # Needs to be imported here to avoid circular import in _source from napari.layers._source import current_source self._highlight_visible = True self._unique_id = None self._source = current_source() self.dask_optimized_slicing = configure_dask(data, cache) self._metadata = dict(metadata or {}) self._opacity = opacity self._blending = Blending(blending) self._visible = visible self._freeze = False self._status = 'Ready' self._help = '' self._cursor = 'standard' self._cursor_size = 1 self._mouse_pan = True self._mouse_zoom = True self._value = None self.scale_factor = 1 self.multiscale = multiscale self._experimental_clipping_planes = ClippingPlaneList() self._mode = self._modeclass('pan_zoom') self._projection_mode = self._projectionclass(str(projection_mode)) self._refresh_blocked = False self._ndim = ndim self._slice_input = _SliceInput( ndisplay=2, world_slice=_ThickNDSlice.make_full(ndim=ndim), order=tuple(range(ndim)), ) self._loaded: bool = True self._last_slice_id: int = -1 # Create a transform chain consisting of four transforms: # 1. `tile2data`: An initial transform only needed to display tiles # of an image. It maps pixels of the tile into the coordinate space # of the full resolution data and can usually be represented by a # scale factor and a translation. A common use case is viewing part # of lower resolution level of a multiscale image, another is using a # downsampled version of an image when the full image size is larger # than the maximum allowed texture size of your graphics card. # 2. `data2physical`: The main transform mapping data to a world-like # physical coordinate that may also encode acquisition parameters or # sample spacing. # 3. `physical2world`: An extra transform applied in world-coordinates that # typically aligns this layer with another. # 4. `world2grid`: An additional transform mapping world-coordinates # into a grid for looking at layers side-by-side. if scale is None: scale = [1] * ndim if translate is None: translate = [0] * ndim self._initial_affine = coerce_affine( affine, ndim=ndim, name='physical2world' ) self._transforms: TransformChain[Affine] = TransformChain( [ Affine(np.ones(ndim), np.zeros(ndim), name='tile2data'), CompositeAffine( scale, translate, axis_labels=axis_labels, rotate=rotate, shear=shear, ndim=ndim, name='data2physical', units=units, ), self._initial_affine, Affine(np.ones(ndim), np.zeros(ndim), name='world2grid'), ] ) self.corner_pixels = np.zeros((2, ndim), dtype=int) self._editable = True self._array_like = False self._thumbnail_shape = (32, 32, 4) self._thumbnail = np.zeros(self._thumbnail_shape, dtype=np.uint8) self._update_properties = True self._name = '' self.experimental_clipping_planes = experimental_clipping_planes # circular import from napari.components.overlays.bounding_box import BoundingBoxOverlay from napari.components.overlays.interaction_box import ( SelectionBoxOverlay, TransformBoxOverlay, ) self._overlays: EventedDict[str, Overlay] = EventedDict() self.events = EmitterGroup( source=self, axis_labels=Event, data=Event, metadata=Event, affine=Event, blending=Event, cursor=Event, cursor_size=Event, editable=Event, extent=Event, help=Event, loaded=Event, mode=Event, mouse_pan=Event, mouse_zoom=Event, name=Event, opacity=Event, projection_mode=Event, refresh=Event, reload=Event, rotate=Event, scale=Event, set_data=Event, shear=Event, status=Event, thumbnail=Event, translate=Event, units=Event, visible=Event, interactive=WarningEmitter( trans._( 'layer.events.interactive is deprecated since 0.4.18 and will be removed in 0.6.0. Please use layer.events.mouse_pan and layer.events.mouse_zoom', deferred=True, ), type_name='interactive', ), _extent_augmented=Event, _overlays=Event, ) self.name = name self.mode = mode self._overlays.update( { 'transform_box': TransformBoxOverlay(), 'selection_box': SelectionBoxOverlay(), 'bounding_box': BoundingBoxOverlay(), } ) # TODO: we try to avoid inner event connection, but this might be the only way # until we figure out nested evented objects self._overlays.events.connect(self.events._overlays) def _post_init(self): """Post init hook for subclasses to use.""" def __str__(self) -> str: """Return self.name.""" return self.name def __repr__(self) -> str: cls = type(self) return f'<{cls.__name__} layer {self.name!r} at {hex(id(self))}>' def _mode_setter_helper(self, mode_in: Union[Mode, str]) -> StringEnum: """ Helper to manage callbacks in multiple layers This will return a valid mode for the current layer, to for example refuse to set a mode that is not supported by the layer if it is not editable. This will as well manage the mouse callbacks. Parameters ---------- mode : type(self._modeclass) | str New mode for the current layer. Returns ------- mode : type(self._modeclass) New mode for the current layer. """ mode = self._modeclass(mode_in) # Sub-classes can have their own Mode enum, so need to get members # from the specific mode class set on this layer. PAN_ZOOM = self._modeclass.PAN_ZOOM # type: ignore[attr-defined] TRANSFORM = self._modeclass.TRANSFORM # type: ignore[attr-defined] assert mode is not None if not self.editable: mode = PAN_ZOOM if mode == self._mode: return mode if mode not in self._modeclass: raise ValueError( trans._( 'Mode not recognized: {mode}', deferred=True, mode=mode ) ) for callback_list, mode_dict in [ (self.mouse_drag_callbacks, self._drag_modes), (self.mouse_move_callbacks, self._move_modes), ( self.mouse_double_click_callbacks, getattr( self, '_double_click_modes', defaultdict(lambda: no_op) ), ), ]: if mode_dict[self._mode] in callback_list: callback_list.remove(mode_dict[self._mode]) callback_list.append(mode_dict[mode]) self.cursor = self._cursor_modes[mode] self.mouse_pan = mode == PAN_ZOOM self._overlays['transform_box'].visible = mode == TRANSFORM if mode == TRANSFORM: self.help = trans._( 'hold <space> to pan/zoom, hold <shift> to preserve aspect ratio and rotate in 45° increments' ) elif mode == PAN_ZOOM: self.help = '' return mode def update_transform_box_visibility(self, visible): if 'transform_box' in self._overlays: TRANSFORM = self._modeclass.TRANSFORM # type: ignore[attr-defined] self._overlays['transform_box'].visible = ( self.mode == TRANSFORM and visible ) def update_highlight_visibility(self, visible): self._highlight_visible = visible self._set_highlight(force=True) @property def mode(self) -> str: """str: Interactive mode Interactive mode. The normal, default mode is PAN_ZOOM, which allows for normal interactivity with the canvas. TRANSFORM allows for manipulation of the layer transform. """ return str(self._mode) @mode.setter def mode(self, mode: Union[Mode, str]) -> None: mode_enum = self._mode_setter_helper(mode) if mode_enum == self._mode: return self._mode = mode_enum self.events.mode(mode=str(mode_enum)) @property def projection_mode(self): """Mode of projection of the thick slice onto the viewed dimensions. The sliced data is described by an n-dimensional bounding box ("thick slice"), which needs to be projected onto the visible dimensions to be visible. The projection mode controls the projection logic. """ return self._projection_mode @projection_mode.setter def projection_mode(self, mode): mode = self._projectionclass(str(mode)) if self._projection_mode != mode: self._projection_mode = mode self.events.projection_mode() self.refresh(extent=False) @property def unique_id(self) -> Hashable: """Unique ID of the layer. This is guaranteed to be unique to this specific layer instance over the lifetime of the program. """ if self._unique_id is None: self._unique_id = uuid.uuid4() return self._unique_id @classmethod def _basename(cls) -> str: return f'{cls.__name__}' @property def name(self) -> str: """str: Unique name of the layer.""" return self._name @name.setter def name(self, name: Optional[str]) -> None: if name == self.name: return if not name: name = self._basename() self._name = str(name) self.events.name() @property def metadata(self) -> dict: """Key/value map for user-stored data.""" return self._metadata @metadata.setter def metadata(self, value: dict) -> None: self._metadata.clear() self._metadata.update(value) self.events.metadata() @property def source(self) -> Source: return self._source @property def loaded(self) -> bool: """True if this layer is fully loaded in memory, False otherwise. Layers that only support sync slicing are always fully loaded. Layers that support async slicing can be temporarily not loaded while slicing is occurring. """ return self._loaded def _set_loaded(self, loaded: bool) -> None: """Set the loaded state and notify a change with the loaded event.""" if self._loaded != loaded: self._loaded = loaded self.events.loaded() def _set_unloaded_slice_id(self, slice_id: int) -> None: """Set this layer to be unloaded and associated with a pending slice ID. This is private but accessed externally because it is related to slice state, which is intended to be moved off the layer in the future. """ self._last_slice_id = slice_id self._set_loaded(False) def _update_loaded_slice_id(self, slice_id: int) -> None: """Potentially update the loaded state based on the given completed slice ID. This is private but accessed externally because it is related to slice state, which is intended to be moved off the layer in the future. """ if self._last_slice_id == slice_id: self._set_loaded(True) @property def opacity(self) -> float: """float: Opacity value between 0.0 and 1.0.""" return self._opacity @opacity.setter def opacity(self, opacity: float) -> None: if not 0.0 <= opacity <= 1.0: raise ValueError( trans._( 'opacity must be between 0.0 and 1.0; got {opacity}', deferred=True, opacity=opacity, ) ) self._opacity = float(opacity) self._update_thumbnail() self.events.opacity() @property def blending(self) -> str: """Blending mode: Determines how RGB and alpha values get mixed. Blending.OPAQUE Allows for only the top layer to be visible and corresponds to depth_test=True, cull_face=False, blend=False. Blending.TRANSLUCENT Allows for multiple layers to be blended with different opacity and corresponds to depth_test=True, cull_face=False, blend=True, blend_func=('src_alpha', 'one_minus_src_alpha'), and blend_equation=('func_add'). Blending.TRANSLUCENT_NO_DEPTH Allows for multiple layers to be blended with different opacity, but no depth testing is performed. Corresponds to ``depth_test=False``, cull_face=False, blend=True, blend_func=('src_alpha', 'one_minus_src_alpha'), and blend_equation=('func_add'). Blending.ADDITIVE Allows for multiple layers to be blended together with different colors and opacity. Useful for creating overlays. It corresponds to depth_test=False, cull_face=False, blend=True, blend_func=('src_alpha', 'one'), and blend_equation=('func_add'). Blending.MINIMUM Allows for multiple layers to be blended together such that the minimum of each RGB component and alpha are selected. Useful for creating overlays with inverted colormaps. It corresponds to depth_test=False, cull_face=False, blend=True, blend_equation=('min'). """ return str(self._blending) @blending.setter def blending(self, blending): self._blending = Blending(blending) self.events.blending() @property def visible(self) -> bool: """bool: Whether the visual is currently being displayed.""" return self._visible @visible.setter def visible(self, visible: bool) -> None: self._visible = visible if visible: # needed because things might have changed while invisible # and refresh is noop while invisible self.refresh(extent=False) self.events.visible() @property def editable(self) -> bool: """bool: Whether the current layer data is editable from the viewer.""" return self._editable @editable.setter def editable(self, editable: bool) -> None: if self._editable == editable: return self._editable = editable self._on_editable_changed() self.events.editable() def _reset_editable(self) -> None: """Reset this layer's editable state based on layer properties.""" self.editable = True def _on_editable_changed(self) -> None: """Executes side-effects on this layer related to changes of the editable state.""" @property def axis_labels(self) -> tuple[str, ...]: """tuple of axis labels for the layer.""" return self._transforms['data2physical'].axis_labels @axis_labels.setter def axis_labels(self, axis_labels: Optional[Sequence[str]]) -> None: prev = self._transforms['data2physical'].axis_labels # mypy bug https://github.com/python/mypy/issues/3004 self._transforms['data2physical'].axis_labels = axis_labels # type: ignore[assignment] if self._transforms['data2physical'].axis_labels != prev: self.events.axis_labels() @property def units(self) -> tuple[pint.Unit, ...]: """List of units for the layer.""" return self._transforms['data2physical'].units @units.setter def units(self, units: Optional[Sequence[pint.Unit]]) -> None: prev = self.units # mypy bug https://github.com/python/mypy/issues/3004 self._transforms['data2physical'].units = units # type: ignore[assignment] if self.units != prev: self.events.units() @property def scale(self) -> npt.NDArray: """array: Anisotropy factors to scale data into world coordinates.""" return self._transforms['data2physical'].scale @scale.setter def scale(self, scale: Optional[npt.NDArray]) -> None: if scale is None: scale = np.array([1] * self.ndim) self._transforms['data2physical'].scale = np.array(scale) self.refresh() self.events.scale() @property def translate(self) -> npt.NDArray: """array: Factors to shift the layer by in units of world coordinates.""" return self._transforms['data2physical'].translate @translate.setter def translate(self, translate: npt.ArrayLike) -> None: self._transforms['data2physical'].translate = np.array(translate) self.refresh() self.events.translate() @property def rotate(self) -> npt.NDArray: """array: Rotation matrix in world coordinates.""" return self._transforms['data2physical'].rotate @rotate.setter def rotate(self, rotate: npt.NDArray) -> None: self._transforms['data2physical'].rotate = rotate self.refresh() self.events.rotate() @property def shear(self) -> npt.NDArray: """array: Shear matrix in world coordinates.""" return self._transforms['data2physical'].shear @shear.setter def shear(self, shear: npt.NDArray) -> None: self._transforms['data2physical'].shear = shear self.refresh() self.events.shear() @property def affine(self) -> Affine: """napari.utils.transforms.Affine: Extra affine transform to go from physical to world coordinates.""" return self._transforms['physical2world'] @affine.setter def affine(self, affine: Union[npt.ArrayLike, Affine]) -> None: # Assignment by transform name is not supported by TransformChain and # EventedList, so use the integer index instead. For more details, see: # https://github.com/napari/napari/issues/3058 self._transforms[2] = coerce_affine( affine, ndim=self.ndim, name='physical2world' ) self.refresh() self.events.affine() def _reset_affine(self) -> None: self.affine = self._initial_affine @property def _translate_grid(self) -> npt.NDArray: """array: Factors to shift the layer by.""" return self._transforms['world2grid'].translate @_translate_grid.setter def _translate_grid(self, translate_grid: npt.NDArray) -> None: if np.array_equal(self._translate_grid, translate_grid): return self._transforms['world2grid'].translate = np.array(translate_grid) self.events.translate() def _update_dims(self) -> None: """Update the dimensionality of transforms and slices when data changes.""" ndim = self._get_ndim() old_ndim = self._ndim if old_ndim > ndim: keep_axes = range(old_ndim - ndim, old_ndim) self._transforms = self._transforms.set_slice(keep_axes) elif old_ndim < ndim: new_axes = range(ndim - old_ndim) self._transforms = self._transforms.expand_dims(new_axes) self._slice_input = self._slice_input.with_ndim(ndim) self._ndim = ndim self.refresh() @property @abstractmethod def data(self): # user writes own docstring raise NotImplementedError @data.setter @abstractmethod def data(self, data): raise NotImplementedError @property @abstractmethod def _extent_data(self) -> np.ndarray: """Extent of layer in data coordinates. Returns ------- extent_data : array, shape (2, D) """ raise NotImplementedError @property def _extent_data_augmented(self) -> np.ndarray: """Extent of layer in data coordinates. Differently from Layer._extent_data, this also includes the "size" of data points; for example, Point sizes and Image pixel width are included. Returns ------- extent_data : array, shape (2, D) """ return self._extent_data @property def _extent_world(self) -> np.ndarray: """Range of layer in world coordinates. Returns ------- extent_world : array, shape (2, D) """ # Get full nD bounding box return get_extent_world(self._extent_data, self._data_to_world) @property def _extent_world_augmented(self) -> np.ndarray: """Range of layer in world coordinates. Differently from Layer._extent_world, this also includes the "size" of data points; for example, Point sizes and Image pixel width are included. Returns ------- extent_world : array, shape (2, D) """ # Get full nD bounding box return get_extent_world( self._extent_data_augmented, self._data_to_world ) @cached_property def extent(self) -> Extent: """Extent of layer in data and world coordinates. For image-like layers, these coordinates are the locations of the pixels in `Layer.data` which are treated like sample points that are centered in the rendered version of those pixels. For other layers, these coordinates are the points or vertices stored in `Layer.data`. Lower and upper bounds are inclusive. """ extent_data = self._extent_data data_to_world = self._data_to_world extent_world = get_extent_world(extent_data, data_to_world) return Extent( data=extent_data, world=extent_world, step=abs(data_to_world.scale), ) @cached_property def _extent_augmented(self) -> Extent: """Augmented extent of layer in data and world coordinates. Differently from Layer.extent, this also includes the "size" of data points; for example, Point sizes and Image pixel width are included. For image-like layers, these coordinates are the locations of the pixels in `Layer.data` which are treated like sample points that are centered in the rendered version of those pixels. For other layers, these coordinates are the points or vertices stored in `Layer.data`. """ extent_data = self._extent_data_augmented data_to_world = self._data_to_world extent_world = get_extent_world(extent_data, data_to_world) return Extent( data=extent_data, world=extent_world, step=abs(data_to_world.scale), ) def _clear_extent(self) -> None: """Clear extent cache and emit extent event.""" if 'extent' in self.__dict__: del self.extent self.events.extent() def _clear_extent_augmented(self) -> None: """Clear extent_augmented cache and emit extent_augmented event.""" if '_extent_augmented' in self.__dict__: del self._extent_augmented self.events._extent_augmented() @property def _data_slice(self) -> _ThickNDSlice: """Slice in data coordinates.""" if len(self._slice_input.not_displayed) == 0: # all dims are displayed dimensions # early return to avoid evaluating data_to_world.inverse return _ThickNDSlice.make_full(point=(np.nan,) * self.ndim) return self._slice_input.data_slice( self._data_to_world.inverse, ) @abstractmethod def _get_ndim(self) -> int: raise NotImplementedError def _get_base_state(self) -> dict[str, Any]: """Get dictionary of attributes on base layer. This is useful for serialization and deserialization of the layer. And similarly for plugins to pass state without direct dependencies on napari types. Returns ------- dict of str to Any Dictionary of attributes on base layer. """ base_dict = { 'affine': self.affine.affine_matrix, 'axis_labels': self.axis_labels, 'blending': self.blending, 'experimental_clipping_planes': [ plane.dict() for plane in self.experimental_clipping_planes ], 'metadata': self.metadata, 'name': self.name, 'opacity': self.opacity, 'projection_mode': self.projection_mode, 'rotate': [list(r) for r in self.rotate], 'scale': list(self.scale), 'shear': list(self.shear), 'translate': list(self.translate), 'units': self.units, 'visible': self.visible, } return base_dict @abstractmethod def _get_state(self) -> dict[str, Any]: raise NotImplementedError @property def _type_string(self) -> str: return self.__class__.__name__.lower() def as_layer_data_tuple(self): state = self._get_state() state.pop('data', None) if hasattr(self.__init__, '_rename_argument'): state = _DeprecatingDict(state) for element in self.__init__._rename_argument: state.set_deprecated_from_rename(**element._asdict()) return self.data, state, self._type_string @property def thumbnail(self) -> npt.NDArray[np.uint8]: """array: Integer array of thumbnail for the layer""" return self._thumbnail @thumbnail.setter def thumbnail(self, thumbnail: npt.NDArray) -> None: if 0 in thumbnail.shape: thumbnail = np.zeros(self._thumbnail_shape, dtype=np.uint8) if thumbnail.dtype != np.uint8: thumbnail = convert_to_uint8(thumbnail) padding_needed = np.subtract(self._thumbnail_shape, thumbnail.shape) pad_amounts = [(p // 2, (p + 1) // 2) for p in padding_needed] thumbnail = np.pad(thumbnail, pad_amounts, mode='constant') # blend thumbnail with opaque black background background = np.zeros(self._thumbnail_shape, dtype=np.uint8) background[..., 3] = 255 f_dest = thumbnail[..., 3][..., None] / 255 f_source = 1 - f_dest thumbnail = thumbnail * f_dest + background * f_source self._thumbnail = thumbnail.astype(np.uint8) self.events.thumbnail() @property def ndim(self) -> int: """int: Number of dimensions in the data.""" return self._ndim @property def help(self) -> str: """str: displayed in status bar bottom right.""" return self._help @help.setter def help(self, help_text: str) -> None: if help_text == self.help: return self._help = help_text self.events.help(help=help_text) @property def interactive(self) -> bool: warnings.warn( trans._( 'Layer.interactive is deprecated since napari 0.4.18 and will be removed in 0.6.0. Please use Layer.mouse_pan and Layer.mouse_zoom instead' ), FutureWarning, stacklevel=2, ) return self.mouse_pan or self.mouse_zoom @interactive.setter def interactive(self, interactive: bool) -> None: warnings.warn( trans._( 'Layer.interactive is deprecated since napari 0.4.18 and will be removed in 0.6.0. Please use Layer.mouse_pan and Layer.mouse_zoom instead' ), FutureWarning, stacklevel=2, ) with self.events.interactive.blocker(): self.mouse_pan = interactive self.mouse_zoom = interactive @property def mouse_pan(self) -> bool: """bool: Determine if canvas interactive panning is enabled with the mouse.""" return self._mouse_pan @mouse_pan.setter def mouse_pan(self, mouse_pan: bool) -> None: if mouse_pan == self._mouse_pan: return self._mouse_pan = mouse_pan self.events.mouse_pan(mouse_pan=mouse_pan) self.events.interactive( interactive=self.mouse_pan or self.mouse_zoom ) # Deprecated since 0.5.0 @property def mouse_zoom(self) -> bool: """bool: Determine if canvas interactive zooming is enabled with the mouse.""" return self._mouse_zoom @mouse_zoom.setter def mouse_zoom(self, mouse_zoom: bool) -> None: if mouse_zoom == self._mouse_zoom: return self._mouse_zoom = mouse_zoom self.events.mouse_zoom(mouse_zoom=mouse_zoom) self.events.interactive( interactive=self.mouse_pan or self.mouse_zoom ) # Deprecated since 0.5.0 @property def cursor(self) -> str: """str: String identifying cursor displayed over canvas.""" return self._cursor @cursor.setter def cursor(self, cursor: str) -> None: if cursor == self.cursor: return self._cursor = cursor self.events.cursor(cursor=cursor) @property def cursor_size(self) -> int: """int: Size of cursor if custom. None yields default size.""" return self._cursor_size @cursor_size.setter def cursor_size(self, cursor_size: int) -> None: if cursor_size == self.cursor_size: return self._cursor_size = cursor_size self.events.cursor_size(cursor_size=cursor_size) @property def experimental_clipping_planes(self) -> ClippingPlaneList: return self._experimental_clipping_planes @experimental_clipping_planes.setter def experimental_clipping_planes( self, value: Union[ dict, ClippingPlane, list[Union[ClippingPlane, dict]], ClippingPlaneList, ], ) -> None: self._experimental_clipping_planes.clear() if value is None: return if isinstance(value, (ClippingPlane, dict)): value = [value] for new_plane in value: plane = ClippingPlane() plane.update(new_plane) self._experimental_clipping_planes.append(plane) @property def bounding_box(self) -> Overlay: return self._overlays['bounding_box'] def set_view_slice(self) -> None: with self.dask_optimized_slicing(): self._set_view_slice() @abstractmethod def _set_view_slice(self): raise NotImplementedError def _slice_dims( self, dims: Dims, force: bool = False, ) -> None: """Slice data with values from a global dims model. Note this will likely be moved off the base layer soon. Parameters ---------- dims : Dims The dims model to use to slice this layer. force : bool True if slicing should be forced to occur, even when some cache thinks it already has a valid slice ready. False otherwise. """ logger.debug( 'Layer._slice_dims: %s, dims=%s, force=%s', self, dims, force, ) slice_input = self._make_slice_input(dims) if force or (self._slice_input != slice_input): self._slice_input = slice_input self._refresh_sync( data_displayed=True, thumbnail=True, highlight=True, extent=True, ) def _make_slice_input( self, dims: Dims, ) -> _SliceInput: world_ndim: int = self.ndim if dims is None else dims.ndim if dims is None: # if no dims is given, "world" has same dimensionality of self # this happens for example if a layer is not in a viewer # in this case, we assume all dims are displayed dimensions world_slice = _ThickNDSlice.make_full((np.nan,) * self.ndim) else: world_slice = _ThickNDSlice.from_dims(dims) order_array = ( np.arange(world_ndim) if dims.order is None else np.asarray(dims.order) ) order = tuple( self._world_to_layer_dims( world_dims=order_array, ndim_world=world_ndim, ) ) return _SliceInput( ndisplay=dims.ndisplay, world_slice=world_slice[-self.ndim :], order=order[-self.ndim :], ) @abstractmethod def _update_thumbnail(self): raise NotImplementedError @abstractmethod def _get_value(self, position): """Value of the data at a position in data coordinates. Parameters ---------- position : tuple Position in data coordinates. Returns ------- value : tuple Value of the data. """ raise NotImplementedError
[docs] def get_value( self, position: npt.ArrayLike, *, view_direction: Optional[npt.ArrayLike] = None, dims_displayed: Optional[list[int]] = None, world: bool = False, ) -> Optional[tuple]: """Value of the data at a position. If the layer is not visible, return None. Parameters ---------- position : tuple of float Position in either data or world coordinates. view_direction : Optional[np.ndarray] A unit vector giving the direction of the ray in nD world coordinates. The default value is None. dims_displayed : Optional[List[int]] A list of the dimensions currently being displayed in the viewer. The default value is None. world : bool If True the position is taken to be in world coordinates and converted into data coordinates. False by default. Returns ------- value : tuple, None Value of the data. If the layer is not visible return None. """ position = np.asarray(position) if self.visible: if world: ndim_world = len(position) if dims_displayed is not None: # convert the dims_displayed to the layer dims.This accounts # for differences in the number of dimensions in the world # dims versus the layer and for transpose and rolls. dims_displayed = dims_displayed_world_to_layer( dims_displayed, ndim_world=ndim_world, ndim_layer=self.ndim, ) position = self.world_to_data(position) if (dims_displayed is not None) and (view_direction is not None): if len(dims_displayed) == 2 or self.ndim == 2: value = self._get_value(position=tuple(position)) elif len(dims_displayed) == 3: view_direction = self._world_to_data_ray(view_direction) start_point, end_point = self.get_ray_intersections( position=position, view_direction=view_direction, dims_displayed=dims_displayed, world=False, ) value = self._get_value_3d( start_point=start_point, end_point=end_point, dims_displayed=dims_displayed, ) else: value = self._get_value(position) else: value = None # This should be removed as soon as possible, it is still # used in Points and Shapes. self._value = value return value
def _get_value_3d( self, start_point: Optional[np.ndarray], end_point: Optional[np.ndarray], dims_displayed: list[int], ) -> Union[ float, int, None, tuple[Union[float, int, None], Optional[int]] ]: """Get the layer data value along a ray Parameters ---------- start_point : np.ndarray The start position of the ray used to interrogate the data. end_point : np.ndarray The end position of the ray used to interrogate the data. dims_displayed : List[int] The indices of the dimensions currently displayed in the Viewer. Returns ------- value The data value along the supplied ray. """
[docs] def projected_distance_from_mouse_drag( self, start_position: npt.ArrayLike, end_position: npt.ArrayLike, view_direction: npt.ArrayLike, vector: np.ndarray, dims_displayed: list[int], ) -> npt.NDArray: """Calculate the length of the projection of a line between two mouse clicks onto a vector (or array of vectors) in data coordinates. Parameters ---------- start_position : np.ndarray Starting point of the drag vector in data coordinates end_position : np.ndarray End point of the drag vector in data coordinates view_direction : np.ndarray Vector defining the plane normal of the plane onto which the drag vector is projected. vector : np.ndarray (3,) unit vector or (n, 3) array thereof on which to project the drag vector from start_event to end_event. This argument is defined in data coordinates. dims_displayed : List[int] (3,) list of currently displayed dimensions Returns ------- projected_distance : (1, ) or (n, ) np.ndarray of float """ start_position = np.asarray(start_position) end_position = np.asarray(end_position) view_direction = np.asarray(view_direction) start_position = self._world_to_displayed_data( start_position, dims_displayed ) end_position = self._world_to_displayed_data( end_position, dims_displayed ) view_direction = self._world_to_displayed_data_ray( view_direction, dims_displayed ) return drag_data_to_projected_distance( start_position, end_position, view_direction, vector )
@contextmanager def block_update_properties(self) -> Generator[None, None, None]: previous = self._update_properties self._update_properties = False try: yield finally: self._update_properties = previous def _set_highlight(self, force: bool = False) -> None: """Render layer highlights when appropriate. Parameters ---------- force : bool Bool that forces a redraw to occur when `True`. """ @contextmanager def _block_refresh(self): """Prevent refresh calls from updating view.""" previous = self._refresh_blocked self._refresh_blocked = True try: yield finally: self._refresh_blocked = previous
[docs] def refresh( self, event: Optional[Event] = None, *, thumbnail: bool = True, data_displayed: bool = True, highlight: bool = True, extent: bool = True, force: bool = False, ) -> None: """Refresh all layer data based on current view slice.""" if self._refresh_blocked: logger.debug('Layer.refresh blocked: %s', self) return logger.debug('Layer.refresh: %s', self) # If async is enabled then emit an event that the viewer should handle. if get_settings().experimental.async_: self.events.reload(layer=self) # Otherwise, slice immediately on the calling thread. else: self._refresh_sync( thumbnail=thumbnail, data_displayed=data_displayed, highlight=highlight, extent=extent, force=force, )
def _refresh_sync( self, *, thumbnail: bool = False, data_displayed: bool = False, highlight: bool = False, extent: bool = False, force: bool = False, ) -> None: logger.debug('Layer._refresh_sync: %s', self) if not (self.visible or force): return if extent: self._clear_extent() self._clear_extent_augmented() if data_displayed: self.set_view_slice() self.events.set_data() if thumbnail: self._update_thumbnail() if highlight: self._set_highlight(force=True)
[docs] def world_to_data(self, position: npt.ArrayLike) -> npt.NDArray: """Convert from world coordinates to data coordinates. Parameters ---------- position : tuple, list, 1D array Position in world coordinates. If longer then the number of dimensions of the layer, the later dimensions will be used. Returns ------- tuple Position in data coordinates. """ position = np.asarray(position) if len(position) >= self.ndim: coords = list(position[-self.ndim :]) else: coords = [0] * (self.ndim - len(position)) + list(position) simplified = self._transforms[1:].simplified return simplified.inverse(coords)
[docs] def data_to_world(self, position): """Convert from data coordinates to world coordinates. Parameters ---------- position : tuple, list, 1D array Position in data coordinates. If longer then the number of dimensions of the layer, the later dimensions will be used. Returns ------- tuple Position in world coordinates. """ if len(position) >= self.ndim: coords = list(position[-self.ndim :]) else: coords = [0] * (self.ndim - len(position)) + list(position) return tuple(self._transforms[1:].simplified(coords))
def _world_to_displayed_data( self, position: np.ndarray, dims_displayed: list[int] ) -> npt.NDArray: """Convert world to data coordinates for displayed dimensions only. Parameters ---------- position : tuple, list, 1D array Position in world coordinates. If longer then the number of dimensions of the layer, the later dimensions will be used. dims_displayed : list[int] Indices of displayed dimensions of the data. Returns ------- tuple Position in data coordinates for the displayed dimensions only """ position_nd = self.world_to_data(position) position_ndisplay = position_nd[dims_displayed] return position_ndisplay @property def _data_to_world(self) -> Affine: """The transform from data to world coordinates. This affine transform is composed from the affine property and the other transform properties in the following order: affine * (rotate * shear * scale + translate) """ return self._transforms[1:3].simplified def _world_to_data_ray(self, vector: npt.ArrayLike) -> npt.NDArray: """Convert a vector defining an orientation from world coordinates to data coordinates. For example, this would be used to convert the view ray. Parameters ---------- vector : tuple, list, 1D array A vector in world coordinates. Returns ------- tuple Vector in data coordinates. """ p1 = np.asarray(self.world_to_data(vector)) p0 = np.asarray(self.world_to_data(np.zeros_like(vector))) normalized_vector = (p1 - p0) / np.linalg.norm(p1 - p0) return normalized_vector def _world_to_displayed_data_ray( self, vector_world: npt.ArrayLike, dims_displayed: list[int] ) -> np.ndarray: """Convert an orientation from world to displayed data coordinates. For example, this would be used to convert the view ray. Parameters ---------- vector_world : 1D array A vector in world coordinates. Returns ------- tuple Vector in data coordinates. """ vector_data_nd = self._world_to_data_ray(vector_world) vector_data_ndisplay = vector_data_nd[dims_displayed] vector_data_ndisplay /= np.linalg.norm(vector_data_ndisplay) return vector_data_ndisplay def _world_to_layer_dims( self, *, world_dims: npt.NDArray, ndim_world: int ) -> np.ndarray: """Map world dimensions to layer dimensions while maintaining order. This is used to map dimensions from the full world space defined by ``Dims`` to the subspace that a layer inhabits, so that those can be used to index the layer's data and associated coordinates. For example a world ``Dims.order`` of [2, 1, 0, 3] would map to [0, 1] for a layer with two dimensions and [1, 0, 2] for a layer with three dimensions as those correspond to the relative order of the last two and three world dimensions respectively. Let's keep in mind a few facts: - each dimension index is present exactly once. - the lowest represented dimension index will be 0 That is to say both the `world_dims` input and return results are _some_ permutation of 0...N Examples -------- `[2, 1, 0, 3]` sliced in N=2 dimensions. - we want to keep the N=2 dimensions with the biggest index - `[2, None, None, 3]` - we filter the None - `[2, 3]` - reindex so that the lowest dimension is 0 by subtracting 2 from all indices - `[0, 1]` `[2, 1, 0, 3]` sliced in N=3 dimensions. - we want to keep the N=3 dimensions with the biggest index - `[2, 1, None, 3]` - we filter the None - `[2, 1, 3]` - reindex so that the lowest dimension is 0 by subtracting 1 from all indices - `[1, 0, 2]` Conveniently if the world (layer) dimension is bigger than our displayed dims, we can return everything Parameters ---------- world_dims : ndarray The world dimensions. ndim_world : int The number of dimensions in the world coordinate system. Returns ------- ndarray The corresponding layer dimensions with the same ordering as the given world dimensions. """ return self._world_to_layer_dims_impl( world_dims, ndim_world, self.ndim ) @staticmethod def _world_to_layer_dims_impl( world_dims: npt.NDArray, ndim_world: int, ndim: int ) -> npt.NDArray: """ Static for ease of testing """ world_dims = np.asarray(world_dims) assert world_dims.min() == 0 assert world_dims.max() == len(world_dims) - 1 assert world_dims.ndim == 1 offset = ndim_world - ndim order = world_dims - offset order = order[order >= 0] return order - order.min() def _display_bounding_box(self, dims_displayed: list[int]) -> npt.NDArray: """An axis aligned (ndisplay, 2) bounding box around the data""" return self._extent_data[:, dims_displayed].T def _display_bounding_box_augmented( self, dims_displayed: list[int] ) -> npt.NDArray: """An augmented, axis-aligned (ndisplay, 2) bounding box. This bounding box includes the size of the layer in best resolution, including required padding """ return self._extent_data_augmented[:, dims_displayed].T def _display_bounding_box_augmented_data_level( self, dims_displayed: list[int] ) -> npt.NDArray: """An augmented, axis-aligned (ndisplay, 2) bounding box. If the layer is multiscale layer, then returns the bounding box of the data at the current level """ return self._display_bounding_box_augmented(dims_displayed)
[docs] def click_plane_from_click_data( self, click_position: npt.ArrayLike, view_direction: npt.ArrayLike, dims_displayed: list[int], ) -> tuple[np.ndarray, np.ndarray]: """Calculate a (point, normal) plane parallel to the canvas in data coordinates, centered on the centre of rotation of the camera. Parameters ---------- click_position : np.ndarray click position in world coordinates from mouse event. view_direction : np.ndarray view direction in world coordinates from mouse event. dims_displayed : List[int] dimensions of the data array currently in view. Returns ------- click_plane : Tuple[np.ndarray, np.ndarray] tuple of (plane_position, plane_normal) in data coordinates. """ click_position = np.asarray(click_position) view_direction = np.asarray(view_direction) plane_position = self.world_to_data(click_position)[dims_displayed] plane_normal = self._world_to_data_ray(view_direction)[dims_displayed] return plane_position, plane_normal
[docs] def get_ray_intersections( self, position: npt.ArrayLike, view_direction: npt.ArrayLike, dims_displayed: list[int], world: bool = True, ) -> tuple[Optional[np.ndarray], Optional[np.ndarray]]: """Get the start and end point for the ray extending from a point through the data bounding box. Parameters ---------- position the position of the point in nD coordinates. World vs. data is set by the world keyword argument. view_direction : np.ndarray a unit vector giving the direction of the ray in nD coordinates. World vs. data is set by the world keyword argument. dims_displayed : List[int] a list of the dimensions currently being displayed in the viewer. world : bool True if the provided coordinates are in world coordinates. Default value is True. Returns ------- start_point : np.ndarray The point on the axis-aligned data bounding box that the cursor click intersects with. This is the point closest to the camera. The point is the full nD coordinates of the layer data. If the click does not intersect the axis-aligned data bounding box, None is returned. end_point : np.ndarray The point on the axis-aligned data bounding box that the cursor click intersects with. This is the point farthest from the camera. The point is the full nD coordinates of the layer data. If the click does not intersect the axis-aligned data bounding box, None is returned. """ position = np.asarray(position) view_direction = np.asarray(view_direction) if len(dims_displayed) != 3: return None, None # create the bounding box in data coordinates bounding_box = self._display_bounding_box(dims_displayed) # bounding box is with upper limit excluded in the uses below bounding_box[:, 1] += 1 start_point, end_point = self._get_ray_intersections( position=position, view_direction=view_direction, dims_displayed=dims_displayed, world=world, bounding_box=bounding_box, ) return start_point, end_point
def _get_offset_data_position(self, position: npt.NDArray) -> npt.NDArray: """Adjust position for offset between viewer and data coordinates.""" return np.asarray(position) def _get_ray_intersections( self, position: npt.NDArray, view_direction: np.ndarray, dims_displayed: list[int], bounding_box: npt.NDArray, world: bool = True, ) -> tuple[Optional[np.ndarray], Optional[np.ndarray]]: """Get the start and end point for the ray extending from a point through the data bounding box. Parameters ---------- position the position of the point in nD coordinates. World vs. data is set by the world keyword argument. view_direction : np.ndarray a unit vector giving the direction of the ray in nD coordinates. World vs. data is set by the world keyword argument. dims_displayed : List[int] a list of the dimensions currently being displayed in the viewer. world : bool True if the provided coordinates are in world coordinates. Default value is True. bounding_box : np.ndarray A (2, 3) bounding box around the data currently in view Returns ------- start_point : np.ndarray The point on the axis-aligned data bounding box that the cursor click intersects with. This is the point closest to the camera. The point is the full nD coordinates of the layer data. If the click does not intersect the axis-aligned data bounding box, None is returned. end_point : np.ndarray The point on the axis-aligned data bounding box that the cursor click intersects with. This is the point farthest from the camera. The point is the full nD coordinates of the layer data. If the click does not intersect the axis-aligned data bounding box, None is returned.""" # get the view direction and click position in data coords # for the displayed dimensions only if world is True: view_dir = self._world_to_displayed_data_ray( view_direction, dims_displayed ) click_pos_data = self._world_to_displayed_data( position, dims_displayed ) else: # adjust for any offset between viewer and data coordinates position = self._get_offset_data_position(position) view_dir = view_direction[dims_displayed] click_pos_data = position[dims_displayed] # Determine the front and back faces front_face_normal, back_face_normal = find_front_back_face( click_pos_data, bounding_box, view_dir ) if front_face_normal is None and back_face_normal is None: # click does not intersect the data bounding box return None, None # Calculate ray-bounding box face intersections start_point_displayed_dimensions = ( intersect_line_with_axis_aligned_bounding_box_3d( click_pos_data, view_dir, bounding_box, front_face_normal ) ) end_point_displayed_dimensions = ( intersect_line_with_axis_aligned_bounding_box_3d( click_pos_data, view_dir, bounding_box, back_face_normal ) ) # add the coordinates for the axes not displayed start_point = position.copy() start_point[dims_displayed] = start_point_displayed_dimensions end_point = position.copy() end_point[dims_displayed] = end_point_displayed_dimensions return start_point, end_point def _update_draw( self, scale_factor, corner_pixels_displayed, shape_threshold ): """Update canvas scale and corner values on draw. For layer multiscale determining if a new resolution level or tile is required. Parameters ---------- scale_factor : float Scale factor going from canvas to world coordinates. corner_pixels_displayed : array, shape (2, 2) Coordinates of the top-left and bottom-right canvas pixels in world coordinates. shape_threshold : tuple Requested shape of field of view in data coordinates. """ self.scale_factor = scale_factor displayed_axes = self._slice_input.displayed # we need to compute all four corners to compute a complete, # data-aligned bounding box, because top-left/bottom-right may not # remain top-left and bottom-right after transformations. all_corners = list(itertools.product(*corner_pixels_displayed.T)) # Note that we ignore the first transform which is tile2data data_corners = ( self._transforms[1:] .simplified.set_slice(displayed_axes) .inverse(all_corners) ) # find the maximal data-axis-aligned bounding box containing all four # canvas corners and round them to ints data_bbox = np.stack( [np.min(data_corners, axis=0), np.max(data_corners, axis=0)] ) data_bbox_int = np.stack( [np.floor(data_bbox[0]), np.ceil(data_bbox[1])] ).astype(int) if self._slice_input.ndisplay == 2 and self.multiscale: level, scaled_corners = compute_multiscale_level_and_corners( data_bbox_int, shape_threshold, self.downsample_factors[:, displayed_axes], ) corners = np.zeros((2, self.ndim), dtype=int) # The corner_pixels attribute stores corners in the data # space of the selected level. Using the level's data # shape only works for images, but that's the only case we # handle now and downsample_factors is also only on image layers. max_coords = np.take(self.data[level].shape, displayed_axes) - 1 corners[:, displayed_axes] = np.clip(scaled_corners, 0, max_coords) display_shape = tuple( corners[1, displayed_axes] - corners[0, displayed_axes] ) if any(s == 0 for s in display_shape): return if self.data_level != level or not np.array_equal( self.corner_pixels, corners ): self._data_level = level self.corner_pixels = corners self.refresh(extent=False, thumbnail=False) else: # set the data_level so that it is the lowest resolution in 3d view if self.multiscale is True: self._data_level = len(self.level_shapes) - 1 # The stored corner_pixels attribute must contain valid indices. corners = np.zeros((2, self.ndim), dtype=int) # Some empty layers (e.g. Points) may have a data extent that only # contains nans, in which case the integer valued corner pixels # cannot be meaningfully set. displayed_extent = self.extent.data[:, displayed_axes] if not np.all(np.isnan(displayed_extent)): data_bbox_clipped = np.clip( data_bbox_int, displayed_extent[0], displayed_extent[1] ) corners[:, displayed_axes] = data_bbox_clipped self.corner_pixels = corners def _get_source_info(self) -> dict: components = {} if self.source.reader_plugin: components['layer_name'] = self.name components['layer_base'] = os.path.basename(self.source.path or '') components['source_type'] = 'plugin' try: components['plugin'] = pm.get_manifest( self.source.reader_plugin ).display_name except KeyError: components['plugin'] = self.source.reader_plugin return components if self.source.sample: components['layer_name'] = self.name components['layer_base'] = self.name components['source_type'] = 'sample' try: components['plugin'] = pm.get_manifest( self.source.sample[0] ).display_name except KeyError: components['plugin'] = self.source.sample[0] return components if self.source.widget: components['layer_name'] = self.name components['layer_base'] = self.name components['source_type'] = 'widget' components['plugin'] = self.source.widget._function.__name__ return components components['layer_name'] = self.name components['layer_base'] = self.name components['source_type'] = '' components['plugin'] = '' return components def get_source_str(self) -> str: source_info = self._get_source_info() source_str = source_info['layer_name'] if source_info['layer_base'] != source_info['layer_name']: source_str += '\n' + source_info['layer_base'] if source_info['source_type']: source_str += ( '\n' + source_info['source_type'] + ' : ' + source_info['plugin'] ) return source_str
[docs] def get_status( self, position: Optional[npt.ArrayLike] = None, *, view_direction: Optional[npt.ArrayLike] = None, dims_displayed: Optional[list[int]] = None, world: bool = False, ) -> dict: """ Status message information of the data at a coordinate position. Parameters ---------- position : tuple of float Position in either data or world coordinates. view_direction : Optional[np.ndarray] A unit vector giving the direction of the ray in nD world coordinates. The default value is None. dims_displayed : Optional[List[int]] A list of the dimensions currently being displayed in the viewer. The default value is None. world : bool If True the position is taken to be in world coordinates and converted into data coordinates. False by default. Returns ------- source_info : dict Dictionary containing a information that can be used as a status update. """ if position is not None: position = np.asarray(position) value = self.get_value( position, view_direction=view_direction, dims_displayed=dims_displayed, world=world, ) else: value = None source_info = self._get_source_info() if position is not None: source_info['coordinates'] = generate_layer_coords_status( position[-self.ndim :], value ) else: source_info['coordinates'] = generate_layer_coords_status( position, value ) return source_info
def _get_tooltip_text( self, position: npt.NDArray, *, view_direction: Optional[np.ndarray] = None, dims_displayed: Optional[list[int]] = None, world: bool = False, ) -> str: """ tooltip message of the data at a coordinate position. Parameters ---------- position : ndarray Position in either data or world coordinates. view_direction : Optional[ndarray] A unit vector giving the direction of the ray in nD world coordinates. The default value is None. dims_displayed : Optional[List[int]] A list of the dimensions currently being displayed in the viewer. The default value is None. world : bool If True the position is taken to be in world coordinates and converted into data coordinates. False by default. Returns ------- msg : string String containing a message that can be used as a tooltip. """ return ''
[docs] def save(self, path: str, plugin: Optional[str] = None) -> list[str]: """Save this layer to ``path`` with default (or specified) plugin. 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). 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 napari.plugins.io import save_layers return save_layers(path, [self], plugin=plugin)
def __copy__(self): """Create a copy of this layer. Returns ------- layer : napari.layers.Layer Copy of this layer. Notes ----- This method is defined for purpose of asv memory benchmarks. The copy of data is intentional for properly estimating memory usage for layer. If you want a to copy a layer without coping the data please use `layer.create(*layer.as_layer_data_tuple())` If you change this method, validate if memory benchmarks are still working properly. """ data, meta, layer_type = self.as_layer_data_tuple() return self.create(copy.copy(data), meta=meta, layer_type=layer_type)
[docs] @classmethod def create( cls, data: Any, meta: Optional[Mapping] = None, layer_type: Optional[str] = None, ) -> Layer: """Create layer from `data` of type `layer_type`. Primarily intended for usage by reader plugin hooks and creating a layer from an unwrapped layer data tuple. Parameters ---------- data : Any Data in a format that is valid for the corresponding `layer_type`. meta : dict, optional Dict of keyword arguments that will be passed to the corresponding layer constructor. If any keys in `meta` are not valid for the corresponding layer type, an exception will be raised. layer_type : str Type of layer to add. Must be the (case insensitive) name of a Layer subclass. If not provided, the layer is assumed to be "image", unless data.dtype is one of (np.int32, np.uint32, np.int64, np.uint64), in which case it is assumed to be "labels". Raises ------ ValueError If ``layer_type`` is not one of the recognized layer types. TypeError If any keyword arguments in ``meta`` are unexpected for the corresponding `add_*` method for this layer_type. Examples -------- A typical use case might be to upack a tuple of layer data with a specified layer_type. >>> data = ( ... np.random.random((10, 2)) * 20, ... {'face_color': 'blue'}, ... 'points', ... ) >>> Layer.create(*data) """ from napari import layers from napari.layers.image._image_utils import guess_labels layer_type = (layer_type or '').lower() # assumes that big integer type arrays are likely labels. if not layer_type: layer_type = guess_labels(data) if layer_type is None or layer_type not in layers.NAMES: raise ValueError( trans._( "Unrecognized layer_type: '{layer_type}'. Must be one of: {layer_names}.", deferred=True, layer_type=layer_type, layer_names=layers.NAMES, ) ) Cls = getattr(layers, layer_type.title()) try: return Cls(data, **(meta or {})) except Exception as exc: if 'unexpected keyword argument' not in str(exc): raise bad_key = str(exc).split('keyword argument ')[-1] raise TypeError( trans._( '_add_layer_from_data received an unexpected keyword argument ({bad_key}) for layer type {layer_type}', deferred=True, bad_key=bad_key, layer_type=layer_type, ) ) from exc
mgui.register_type(type_=list[Layer], return_callback=add_layers_to_viewer)