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shapes.py
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shapes.py
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import warnings
from contextlib import contextmanager
from copy import copy, deepcopy
from itertools import cycle
from typing import (
Any,
Callable,
ClassVar,
Optional,
Union,
)
import numpy as np
import numpy.typing as npt
import pandas as pd
from vispy.color import get_color_names
from napari.layers.base import Layer, no_op
from napari.layers.base._base_constants import ActionType
from napari.layers.base._base_mouse_bindings import (
highlight_box_handles,
transform_with_box,
)
from napari.layers.shapes._shape_list import ShapeList
from napari.layers.shapes._shapes_constants import (
Box,
ColorMode,
Mode,
ShapeType,
shape_classes,
)
from napari.layers.shapes._shapes_mouse_bindings import (
add_ellipse,
add_line,
add_path_polygon,
add_path_polygon_lasso,
add_rectangle,
finish_drawing_shape,
highlight,
polygon_creating,
select,
vertex_insert,
vertex_remove,
)
from napari.layers.shapes._shapes_utils import (
create_box,
extract_shape_type,
get_default_shape_type,
get_shape_ndim,
number_of_shapes,
rdp,
validate_num_vertices,
)
from napari.layers.utils.color_manager_utils import (
guess_continuous,
map_property,
)
from napari.layers.utils.color_transformations import (
normalize_and_broadcast_colors,
transform_color_cycle,
transform_color_with_defaults,
)
from napari.layers.utils.interactivity_utils import (
nd_line_segment_to_displayed_data_ray,
)
from napari.layers.utils.layer_utils import _FeatureTable, _unique_element
from napari.layers.utils.text_manager import TextManager
from napari.settings import get_settings
from napari.utils.colormaps import Colormap, ValidColormapArg, ensure_colormap
from napari.utils.colormaps.colormap_utils import ColorType
from napari.utils.colormaps.standardize_color import (
hex_to_name,
rgb_to_hex,
transform_color,
)
from napari.utils.events import Event
from napari.utils.events.custom_types import Array
from napari.utils.misc import ensure_iterable
from napari.utils.translations import trans
DEFAULT_COLOR_CYCLE = np.array([[1, 0, 1, 1], [0, 1, 0, 1]])
class Shapes(Layer):
"""Shapes layer.
Parameters
----------
data : list or array
List of shape data, where each element is an (N, D) array of the
N vertices of a shape in D dimensions. Can be an 3-dimensional
array if each shape has the same number of vertices.
ndim : int
Number of dimensions for shapes. When data is not None, ndim must be D.
An empty shapes layer can be instantiated with arbitrary ndim.
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.
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', and 'additive'}.
cache : bool
Whether slices of out-of-core datasets should be cached upon retrieval.
Currently, this only applies to dask arrays.
edge_color : str, array-like
If string can be any color name recognized by vispy or hex value if
starting with `#`. If array-like must be 1-dimensional array with 3
or 4 elements. If a list is supplied it must be the same length as
the length of `data` and each element will be applied to each shape
otherwise the same value will be used for all shapes.
edge_color_cycle : np.ndarray, list
Cycle of colors (provided as string name, RGB, or RGBA) to map to edge_color if a
categorical attribute is used color the vectors.
edge_colormap : str, napari.utils.Colormap
Colormap to set edge_color if a continuous attribute is used to set face_color.
edge_contrast_limits : None, (float, float)
clims for mapping the property to a color map. These are the min and max value
of the specified property that are mapped to 0 and 1, respectively.
The default value is None. If set the none, the clims will be set to
(property.min(), property.max())
edge_width : float or list
Thickness of lines and edges. If a list is supplied it must be the
same length as the length of `data` and each element will be
applied to each shape otherwise the same value will be used for all
shapes.
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.
face_color : str, array-like
If string can be any color name recognized by vispy or hex value if
starting with `#`. If array-like must be 1-dimensional array with 3
or 4 elements. If a list is supplied it must be the same length as
the length of `data` and each element will be applied to each shape
otherwise the same value will be used for all shapes.
face_color_cycle : np.ndarray, list
Cycle of colors (provided as string name, RGB, or RGBA) to map to face_color if a
categorical attribute is used color the vectors.
face_colormap : str, napari.utils.Colormap
Colormap to set face_color if a continuous attribute is used to set face_color.
face_contrast_limits : None, (float, float)
clims for mapping the property to a color map. These are the min and max value
of the specified property that are mapped to 0 and 1, respectively.
The default value is None. If set the none, the clims will be set to
(property.min(), property.max())
feature_defaults : dict[str, Any] or Dataframe-like
The default value of each feature in a table with one row.
features : dict[str, array-like] or Dataframe-like
Features table where each row corresponds to a shape and each column
is a feature.
metadata : dict
Layer metadata.
name : str
Name 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.
properties : dict {str: array (N,)}, DataFrame
Properties for each shape. Each property should be an array of length N,
where N is the number of shapes.
property_choices : dict {str: array (N,)}
possible values for each property.
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.
shape_type : string or list
String of shape shape_type, must be one of "{'line', 'rectangle',
'ellipse', 'path', 'polygon'}". If a list is supplied it must be
the same length as the length of `data` and each element will be
applied to each shape otherwise the same value will be used for all
shapes.
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.
text : str, dict
Text to be displayed with the shapes. If text is set to a key in properties,
the value of that property will be displayed. Multiple properties can be
composed using f-string-like syntax (e.g., '{property_1}, {float_property:.2f}).
A dictionary can be provided with keyword arguments to set the text values
and display properties. See TextManager.__init__() for the valid keyword arguments.
For example usage, see /napari/examples/add_shapes_with_text.py.
translate : tuple of float
Translation values for the layer.
visible : bool
Whether the layer visual is currently being displayed.
z_index : int or list
Specifier of z order priority. Shapes with higher z order are
displayed ontop of others. If a list is supplied it must be the
same length as the length of `data` and each element will be
applied to each shape otherwise the same value will be used for all
shapes.
Attributes
----------
data : (N, ) list of array
List of shape data, where each element is an (N, D) array of the
N vertices of a shape in D dimensions.
features : Dataframe-like
Features table where each row corresponds to a shape and each column
is a feature.
feature_defaults : DataFrame-like
Stores the default value of each feature in a table with one row.
properties : dict {str: array (N,)}, DataFrame
Properties for each shape. Each property should be an array of length N,
where N is the number of shapes.
text : str, dict
Text to be displayed with the shapes. If text is set to a key in properties,
the value of that property will be displayed. Multiple properties can be
composed using f-string-like syntax (e.g., '{property_1}, {float_property:.2f}).
For example usage, see /napari/examples/add_shapes_with_text.py.
shape_type : (N, ) list of str
Name of shape type for each shape.
edge_color : str, array-like
Color of the shape border. Numeric color values should be RGB(A).
face_color : str, array-like
Color of the shape face. Numeric color values should be RGB(A).
edge_width : (N, ) list of float
Edge width for each shape.
z_index : (N, ) list of int
z-index for each shape.
current_edge_width : float
Thickness of lines and edges of the next shape to be added or the
currently selected shape.
current_edge_color : str
Color of the edge of the next shape to be added or the currently
selected shape.
current_face_color : str
Color of the face of the next shape to be added or the currently
selected shape.
selected_data : set
List of currently selected shapes.
nshapes : int
Total number of shapes.
mode : Mode
Interactive mode. The normal, default mode is PAN_ZOOM, which
allows for normal interactivity with the canvas.
The SELECT mode allows for entire shapes to be selected, moved and
resized.
The DIRECT mode allows for shapes to be selected and their individual
vertices to be moved.
The VERTEX_INSERT and VERTEX_REMOVE modes allow for individual
vertices either to be added to or removed from shapes that are already
selected. Note that shapes cannot be selected in this mode.
The ADD_RECTANGLE, ADD_ELLIPSE, ADD_LINE, ADD_PATH, and ADD_POLYGON
modes all allow for their corresponding shape type to be added.
Notes
-----
_data_dict : Dict of ShapeList
Dictionary containing all the shape data indexed by slice tuple
_data_view : ShapeList
Object containing the currently viewed shape data.
_selected_data_history : set
Set of currently selected captured on press of <space>.
_selected_data_stored : set
Set of selected previously displayed. Used to prevent rerendering the
same highlighted shapes when no data has changed.
_selected_box : None | np.ndarray
`None` if no shapes are selected, otherwise a 10x2 array of vertices of
the interaction box. The first 8 points are the corners and midpoints
of the box. The 9th point is the center of the box, and the last point
is the location of the rotation handle that can be used to rotate the
box.
_drag_start : None | np.ndarray
If a drag has been started and is in progress then a length 2 array of
the initial coordinates of the drag. `None` otherwise.
_drag_box : None | np.ndarray
If a drag box is being created to select shapes then this is a 2x2
array of the two extreme corners of the drag. `None` otherwise.
_drag_box_stored : None | np.ndarray
If a drag box is being created to select shapes then this is a 2x2
array of the two extreme corners of the drag that have previously been
rendered. `None` otherwise. Used to prevent rerendering the same
drag box when no data has changed.
_is_moving : bool
Bool indicating if any shapes are currently being moved.
_is_selecting : bool
Bool indicating if a drag box is currently being created in order to
select shapes.
_is_creating : bool
Bool indicating if any shapes are currently being created.
_fixed_aspect : bool
Bool indicating if aspect ratio of shapes should be preserved on
resizing.
_aspect_ratio : float
Value of aspect ratio to be preserved if `_fixed_aspect` is `True`.
_fixed_vertex : None | np.ndarray
If a scaling or rotation is in progress then a length 2 array of the
coordinates that are remaining fixed during the move. `None` otherwise.
_fixed_index : int
If a scaling or rotation is in progress then the index of the vertex of
the bounding box that is remaining fixed during the move. `None`
otherwise.
_update_properties : bool
Bool indicating if properties are to allowed to update the selected
shapes when they are changed. Blocking this prevents circular loops
when shapes are selected and the properties are changed based on that
selection
_allow_thumbnail_update : bool
Flag set to true to allow the thumbnail to be updated. Blocking the thumbnail
can be advantageous where responsiveness is critical.
_clipboard : dict
Dict of shape objects that are to be used during a copy and paste.
_colors : list
List of supported vispy color names.
_vertex_size : float
Size of the vertices of the shapes and bounding box in Canvas
coordinates.
_rotation_handle_length : float
Length of the rotation handle of the bounding box in Canvas
coordinates.
_input_ndim : int
Dimensions of shape data.
_thumbnail_update_thresh : int
If there are more than this number of shapes, the thumbnail
won't update during interactive events
"""
_modeclass = Mode
_colors = get_color_names()
_vertex_size = 10
_rotation_handle_length = 20
_highlight_color = (0, 0.6, 1)
_highlight_width = 1.5
_face_color_property: str
_edge_color_property: str
_face_color_cycle: npt.NDArray
_edge_color_cycle: npt.NDArray
_face_color_cycle_values: npt.NDArray
_edge_color_cycle_values: npt.NDArray
_face_color_mode: str
_edge_color_mode: str
# If more shapes are present then they are randomly subsampled
# in the thumbnail
_max_shapes_thumbnail = 100
_drag_modes: ClassVar[dict[Mode, Callable[['Shapes', Event], Any]]] = {
Mode.PAN_ZOOM: no_op,
Mode.TRANSFORM: transform_with_box,
Mode.SELECT: select,
Mode.DIRECT: select,
Mode.VERTEX_INSERT: vertex_insert,
Mode.VERTEX_REMOVE: vertex_remove,
Mode.ADD_RECTANGLE: add_rectangle,
Mode.ADD_ELLIPSE: add_ellipse,
Mode.ADD_LINE: add_line,
Mode.ADD_PATH: add_path_polygon,
Mode.ADD_POLYGON: add_path_polygon,
Mode.ADD_POLYGON_LASSO: add_path_polygon_lasso,
}
_move_modes: ClassVar[dict[Mode, Callable[['Shapes', Event], Any]]] = {
Mode.PAN_ZOOM: no_op,
Mode.TRANSFORM: highlight_box_handles,
Mode.SELECT: highlight,
Mode.DIRECT: highlight,
Mode.VERTEX_INSERT: highlight,
Mode.VERTEX_REMOVE: highlight,
Mode.ADD_RECTANGLE: no_op,
Mode.ADD_ELLIPSE: no_op,
Mode.ADD_LINE: no_op,
Mode.ADD_PATH: polygon_creating,
Mode.ADD_POLYGON: polygon_creating,
Mode.ADD_POLYGON_LASSO: polygon_creating,
}
_double_click_modes: ClassVar[
dict[Mode, Callable[['Shapes', Event], Any]]
] = {
Mode.PAN_ZOOM: no_op,
Mode.TRANSFORM: no_op,
Mode.SELECT: no_op,
Mode.DIRECT: no_op,
Mode.VERTEX_INSERT: no_op,
Mode.VERTEX_REMOVE: no_op,
Mode.ADD_RECTANGLE: no_op,
Mode.ADD_ELLIPSE: no_op,
Mode.ADD_LINE: no_op,
Mode.ADD_PATH: finish_drawing_shape,
Mode.ADD_POLYGON: finish_drawing_shape,
Mode.ADD_POLYGON_LASSO: no_op,
}
_cursor_modes: ClassVar[dict[Mode, str]] = {
Mode.PAN_ZOOM: 'standard',
Mode.TRANSFORM: 'standard',
Mode.SELECT: 'pointing',
Mode.DIRECT: 'pointing',
Mode.VERTEX_INSERT: 'cross',
Mode.VERTEX_REMOVE: 'cross',
Mode.ADD_RECTANGLE: 'cross',
Mode.ADD_ELLIPSE: 'cross',
Mode.ADD_LINE: 'cross',
Mode.ADD_PATH: 'cross',
Mode.ADD_POLYGON: 'cross',
Mode.ADD_POLYGON_LASSO: 'cross',
}
_interactive_modes: ClassVar[set[Mode]] = {
Mode.PAN_ZOOM,
}
def __init__(
self,
data=None,
ndim=None,
*,
affine=None,
blending='translucent',
cache=True,
edge_color='#777777',
edge_color_cycle=None,
edge_colormap='viridis',
edge_contrast_limits=None,
edge_width=1,
experimental_clipping_planes=None,
face_color='white',
face_color_cycle=None,
face_colormap='viridis',
face_contrast_limits=None,
feature_defaults=None,
features=None,
metadata=None,
name=None,
opacity=0.7,
projection_mode='none',
properties=None,
property_choices=None,
rotate=None,
scale=None,
shape_type='rectangle',
shear=None,
text=None,
translate=None,
visible=True,
z_index=0,
) -> None:
if data is None or len(data) == 0:
if ndim is None:
ndim = 2
data = np.empty((0, 0, ndim))
else:
data, shape_type = extract_shape_type(data, shape_type)
data_ndim = get_shape_ndim(data)
if ndim is not None and ndim != data_ndim:
raise ValueError(
trans._(
'Shape dimensions must be equal to ndim',
deferred=True,
)
)
ndim = data_ndim
super().__init__(
data,
ndim=ndim,
name=name,
metadata=metadata,
scale=scale,
translate=translate,
rotate=rotate,
shear=shear,
affine=affine,
opacity=opacity,
blending=blending,
visible=visible,
cache=cache,
experimental_clipping_planes=experimental_clipping_planes,
projection_mode=projection_mode,
)
self.events.add(
edge_width=Event,
edge_color=Event,
face_color=Event,
properties=Event,
current_edge_color=Event,
current_face_color=Event,
current_properties=Event,
highlight=Event,
features=Event,
feature_defaults=Event,
)
# Flag set to false to block thumbnail refresh
self._allow_thumbnail_update = True
self._display_order_stored = []
self._ndisplay_stored = self._slice_input.ndisplay
self._feature_table = _FeatureTable.from_layer(
features=features,
feature_defaults=feature_defaults,
properties=properties,
property_choices=property_choices,
num_data=number_of_shapes(data),
)
# The following shape properties are for the new shapes that will
# be drawn. Each shape has a corresponding property with the
# value for itself
if np.isscalar(edge_width):
self._current_edge_width = edge_width
else:
self._current_edge_width = 1
self._data_view = ShapeList(ndisplay=self._slice_input.ndisplay)
self._data_view.slice_key = np.array(self._data_slice.point)[
self._slice_input.not_displayed
]
self._value = (None, None)
self._value_stored = (None, None)
self._moving_value: tuple[Optional[int], Optional[int]] = (None, None)
self._selected_data = set()
self._selected_data_stored = set()
self._selected_data_history = set()
self._selected_box = None
self._last_cursor_position = None
self._drag_start = None
self._fixed_vertex = None
self._fixed_aspect = False
self._aspect_ratio = 1
self._is_moving = False
# _moving_coordinates are needed for fixing aspect ratio during
# a resize, it stores the last pointer coordinate value that happened
# during a mouse move to that pressing/releasing shift
# can trigger a redraw of the shape with a fixed aspect ratio.
self._moving_coordinates = None
self._fixed_index = 0
self._is_selecting = False
self._drag_box = None
self._drag_box_stored = None
self._is_creating = False
self._clipboard: dict[str, Shapes] = {}
self._status = self.mode
self._init_shapes(
data,
shape_type=shape_type,
edge_width=edge_width,
edge_color=edge_color,
edge_color_cycle=edge_color_cycle,
edge_colormap=edge_colormap,
edge_contrast_limits=edge_contrast_limits,
face_color=face_color,
face_color_cycle=face_color_cycle,
face_colormap=face_colormap,
face_contrast_limits=face_contrast_limits,
z_index=z_index,
)
# set the current_* properties
if len(data) > 0:
self._current_edge_color = self.edge_color[-1]
self._current_face_color = self.face_color[-1]
elif len(data) == 0 and len(self.properties) > 0:
self._initialize_current_color_for_empty_layer(edge_color, 'edge')
self._initialize_current_color_for_empty_layer(face_color, 'face')
elif len(data) == 0 and len(self.properties) == 0:
self._current_edge_color = transform_color_with_defaults(
num_entries=1,
colors=edge_color,
elem_name='edge_color',
default='black',
)
self._current_face_color = transform_color_with_defaults(
num_entries=1,
colors=face_color,
elem_name='face_color',
default='black',
)
self._text = TextManager._from_layer(
text=text,
features=self.features,
)
# Trigger generation of view slice and thumbnail
self.refresh()
def _initialize_current_color_for_empty_layer(
self, color: ColorType, attribute: str
):
"""Initialize current_{edge,face}_color when starting with empty layer.
Parameters
----------
color : (N, 4) array or str
The value for setting edge or face_color
attribute : str in {'edge', 'face'}
The name of the attribute to set the color of.
Should be 'edge' for edge_color or 'face' for face_color.
"""
color_mode = getattr(self, f'_{attribute}_color_mode')
if color_mode == ColorMode.DIRECT:
curr_color = transform_color_with_defaults(
num_entries=1,
colors=color,
elem_name=f'{attribute}_color',
default='white',
)
elif color_mode == ColorMode.CYCLE:
color_cycle = getattr(self, f'_{attribute}_color_cycle')
curr_color = transform_color(next(color_cycle))
# add the new color cycle mapping
color_property = getattr(self, f'_{attribute}_color_property')
prop_value = self.feature_defaults[color_property][0]
color_cycle_map = getattr(self, f'{attribute}_color_cycle_map')
color_cycle_map[prop_value] = np.squeeze(curr_color)
setattr(self, f'{attribute}_color_cycle_map', color_cycle_map)
elif color_mode == ColorMode.COLORMAP:
color_property = getattr(self, f'_{attribute}_color_property')
prop_value = self.feature_defaults[color_property][0]
colormap = getattr(self, f'{attribute}_colormap')
contrast_limits = getattr(self, f'_{attribute}_contrast_limits')
curr_color, _ = map_property(
prop=prop_value,
colormap=colormap,
contrast_limits=contrast_limits,
)
setattr(self, f'_current_{attribute}_color', curr_color)
@property
def data(self):
"""list: Each element is an (N, D) array of the vertices of a shape."""
return self._data_view.data
@data.setter
def data(self, data):
self._finish_drawing()
prior_data = len(self.data) > 0
data, shape_type = extract_shape_type(data)
n_new_shapes = number_of_shapes(data)
# not given a shape_type through data
if shape_type is None:
shape_type = self.shape_type
edge_widths = self._data_view.edge_widths
edge_color = self._data_view.edge_color
face_color = self._data_view.face_color
z_indices = self._data_view.z_indices
# fewer shapes, trim attributes
if self.nshapes > n_new_shapes:
shape_type = shape_type[:n_new_shapes]
edge_widths = edge_widths[:n_new_shapes]
z_indices = z_indices[:n_new_shapes]
edge_color = edge_color[:n_new_shapes]
face_color = face_color[:n_new_shapes]
# more shapes, add attributes
elif self.nshapes < n_new_shapes:
n_shapes_difference = n_new_shapes - self.nshapes
shape_type = (
shape_type
+ [get_default_shape_type(shape_type)] * n_shapes_difference
)
edge_widths = edge_widths + [1] * n_shapes_difference
z_indices = z_indices + [0] * n_shapes_difference
edge_color = np.concatenate(
(
edge_color,
self._get_new_shape_color(n_shapes_difference, 'edge'),
)
)
face_color = np.concatenate(
(
face_color,
self._get_new_shape_color(n_shapes_difference, 'face'),
)
)
data_not_empty = (
data is not None
and (isinstance(data, np.ndarray) and data.size > 0)
or (isinstance(data, list) and len(data) > 0)
)
kwargs = {
'value': self.data,
'vertex_indices': ((),),
'data_indices': tuple(i for i in range(len(self.data))),
}
if prior_data and data_not_empty:
kwargs['action'] = ActionType.CHANGING
elif data_not_empty:
kwargs['action'] = ActionType.ADDING
kwargs['data_indices'] = tuple(i for i in range(len(data)))
else:
kwargs['action'] = ActionType.REMOVING
self.events.data(**kwargs)
self._data_view = ShapeList(ndisplay=self._slice_input.ndisplay)
self._data_view.slice_key = np.array(self._data_slice.point)[
self._slice_input.not_displayed
]
self._add_shapes(
data,
shape_type=shape_type,
edge_width=edge_widths,
edge_color=edge_color,
face_color=face_color,
z_index=z_indices,
n_new_shapes=n_new_shapes,
)
self._update_dims()
kwargs['data_indices'] = tuple(i for i in range(len(data)))
kwargs['value'] = self.data
if prior_data and data_not_empty:
kwargs['action'] = ActionType.CHANGED
elif data_not_empty:
kwargs['action'] = ActionType.ADDED
else:
kwargs['action'] = ActionType.REMOVED
self.events.data(**kwargs)
self._reset_editable()
def _on_selection(self, selected: bool):
# this method is slated for removal. don't add anything new.
if not selected:
self._finish_drawing()
@property
def features(self):
"""Dataframe-like features table.
It is an implementation detail that this is a `pandas.DataFrame`. In the future,
we will target the currently-in-development Data API dataframe protocol [1].
This will enable us to use alternate libraries such as xarray or cuDF for
additional features without breaking existing usage of this.
If you need to specifically rely on the pandas API, please coerce this to a
`pandas.DataFrame` using `features_to_pandas_dataframe`.
References
----------
.. [1]: https://data-apis.org/dataframe-protocol/latest/API.html
"""
return self._feature_table.values
@features.setter
def features(
self,
features: Union[dict[str, np.ndarray], pd.DataFrame],
) -> None:
self._feature_table.set_values(features, num_data=self.nshapes)
if self._face_color_property and (
self._face_color_property not in self.features
):
self._face_color_property = ''
warnings.warn(
trans._(
'property used for face_color dropped',
deferred=True,
),
RuntimeWarning,
)
if self._edge_color_property and (
self._edge_color_property not in self.features
):
self._edge_color_property = ''
warnings.warn(
trans._(
'property used for edge_color dropped',
deferred=True,
),
RuntimeWarning,
)
self.text.refresh(self.features)
self.events.properties()
self.events.features()
@property
def feature_defaults(self):
"""Dataframe-like with one row of feature default values.
See `features` for more details on the type of this property.
"""
return self._feature_table.defaults
@feature_defaults.setter
def feature_defaults(
self, defaults: Union[dict[str, Any], pd.DataFrame]
) -> None:
self._feature_table.set_defaults(defaults)
self.events.current_properties()
self.events.feature_defaults()
@property
def properties(self) -> dict[str, np.ndarray]:
"""dict {str: np.ndarray (N,)}, DataFrame: Annotations for each shape"""
return self._feature_table.properties()
@properties.setter
def properties(self, properties: dict[str, Array]):
self.features = properties
@property
def property_choices(self) -> dict[str, np.ndarray]:
return self._feature_table.choices()
def _get_ndim(self):
"""Determine number of dimensions of the layer."""
ndim = self.ndim if self.nshapes == 0 else self.data[0].shape[1]
return ndim
@property
def _extent_data(self) -> np.ndarray:
"""Extent of layer in data coordinates.
Returns
-------
extent_data : array, shape (2, D)
"""
if len(self.data) == 0:
extrema = np.full((2, self.ndim), np.nan)
else:
maxs = np.max([np.max(d, axis=0) for d in self.data], axis=0)
mins = np.min([np.min(d, axis=0) for d in self.data], axis=0)
extrema = np.vstack([mins, maxs])
return extrema
@property
def nshapes(self):
"""int: Total number of shapes."""
return len(self._data_view.shapes)
@property
def current_edge_width(self):
"""float: Width of shape edges including lines and paths."""
return self._current_edge_width
@current_edge_width.setter
def current_edge_width(self, edge_width):
self._current_edge_width = edge_width
if self._update_properties:
for i in self.selected_data:
self._data_view.update_edge_width(i, edge_width)
self.events.edge_width()
@property
def current_edge_color(self):
"""str: color of shape edges including lines and paths."""
hex_ = rgb_to_hex(self._current_edge_color)[0]
return hex_to_name.get(hex_, hex_)
@current_edge_color.setter
def current_edge_color(self, edge_color):
self._current_edge_color = transform_color(edge_color)
if self._update_properties:
for i in self.selected_data:
self._data_view.update_edge_color(i, self._current_edge_color)
self.events.edge_color()
self._update_thumbnail()
self.events.current_edge_color()
@property
def current_face_color(self):
"""str: color of shape faces."""
hex_ = rgb_to_hex(self._current_face_color)[0]
return hex_to_name.get(hex_, hex_)
@current_face_color.setter
def current_face_color(self, face_color):
self._current_face_color = transform_color(face_color)
if self._update_properties:
for i in self.selected_data:
self._data_view.update_face_color(i, self._current_face_color)
self.events.face_color()
self._update_thumbnail()
self.events.current_face_color()
@property
def current_properties(self) -> dict[str, np.ndarray]:
"""dict{str: np.ndarray(1,)}: properties for the next added shape."""
return self._feature_table.currents()
@current_properties.setter
def current_properties(self, current_properties):
update_indices = None
if (
self._update_properties
and len(self.selected_data) > 0
and self._mode in [Mode.SELECT, Mode.PAN_ZOOM]
):
update_indices = list(self.selected_data)
self._feature_table.set_currents(
current_properties, update_indices=update_indices
)
if update_indices is not None:
self.refresh_colors()
self.events.properties()
self.events.features()
self.events.current_properties()
self.events.feature_defaults()
@property
def shape_type(self):
"""list of str: name of shape type for each shape."""
return self._data_view.shape_types
@shape_type.setter
def shape_type(self, shape_type):
self._finish_drawing()
new_data_view = ShapeList()
shape_inputs = zip(
self._data_view.data,
ensure_iterable(shape_type),
self._data_view.edge_widths,
self._data_view.edge_color,
self._data_view.face_color,
self._data_view.z_indices,
)
self._add_shapes_to_view(shape_inputs, new_data_view)
self._data_view = new_data_view
self._update_dims()
@property
def edge_color(self):
"""(N x 4) np.ndarray: Array of RGBA face colors for each shape"""
return self._data_view.edge_color
@edge_color.setter
def edge_color(self, edge_color):
self._set_color(edge_color, 'edge')
self.events.edge_color()
self._update_thumbnail()
@property
def edge_color_cycle(self) -> np.ndarray:
"""Union[list, np.ndarray] : Color cycle for edge_color.
Can be a list of colors defined by name, RGB or RGBA
"""
return self._edge_color_cycle_values
@edge_color_cycle.setter
def edge_color_cycle(self, edge_color_cycle: Union[list, np.ndarray]):
self._set_color_cycle(np.asarray(edge_color_cycle), 'edge')
@property
def edge_colormap(self) -> Colormap:
"""Return the colormap to be applied to a property to get the edge color.
Returns
-------
colormap : napari.utils.Colormap
The Colormap object.
"""
return self._edge_colormap
@edge_colormap.setter
def edge_colormap(self, colormap: ValidColormapArg):
self._edge_colormap = ensure_colormap(colormap)
@property
def edge_contrast_limits(self) -> Union[tuple[float, float], None]:
"""None, (float, float): contrast limits for mapping
the edge_color colormap property to 0 and 1
"""
return self._edge_contrast_limits
@edge_contrast_limits.setter
def edge_contrast_limits(
self, contrast_limits: Union[None, tuple[float, float]]
):
self._edge_contrast_limits = contrast_limits
@property
def edge_color_mode(self) -> str: