Source code for napari.layers.points.points

import numbers
import warnings
from copy import copy, deepcopy
from itertools import cycle
from typing import Dict, List, Optional, Sequence, Tuple, Union

import numpy as np
import pandas as pd
from scipy.stats import gmean

from ...utils.colormaps import Colormap, ValidColormapArg
from ...utils.colormaps.standardize_color import hex_to_name, rgb_to_hex
from ...utils.events import Event
from ...utils.events.custom_types import Array
from ...utils.geometry import project_points_onto_plane, rotate_points
from ...utils.status_messages import generate_layer_coords_status
from ...utils.transforms import Affine
from ...utils.translations import trans
from ..base import Layer, no_op
from ..utils._color_manager_constants import ColorMode
from ..utils.color_manager import ColorManager
from ..utils.color_transformations import ColorType
from ..utils.interactivity_utils import displayed_plane_from_nd_line_segment
from ..utils.layer_utils import _features_to_properties, _FeatureTable
from ..utils.text_manager import TextManager
from ._points_constants import SYMBOL_ALIAS, Mode, Shading, Symbol
from ._points_mouse_bindings import add, highlight, select
from ._points_utils import (
    _create_box_from_corners_3d,
    create_box,
    fix_data_points,
    points_to_squares,
)

DEFAULT_COLOR_CYCLE = np.array([[1, 0, 1, 1], [0, 1, 0, 1]])


[docs]class Points(Layer): """Points layer. Parameters ---------- data : array (N, D) Coordinates for N points in D dimensions. ndim : int Number of dimensions for shapes. When data is not None, ndim must be D. An empty points layer can be instantiated with arbitrary ndim. features : dict[str, array-like] or DataFrame Features table where each row corresponds to a point and each column is a feature. properties : dict {str: array (N,)}, DataFrame Properties for each point. Each property should be an array of length N, where N is the number of points. property_choices : dict {str: array (N,)} possible values for each property. text : str, dict Text to be displayed with the points. 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_points_with_text.py. symbol : str Symbol to be used for the point markers. Must be one of the following: arrow, clobber, cross, diamond, disc, hbar, ring, square, star, tailed_arrow, triangle_down, triangle_up, vbar, x. size : float, array Size of the point marker in data pixels. If given as a scalar, all points are made the same size. If given as an array, size must be the same or broadcastable to the same shape as the data. edge_width : float, array Width of the symbol edge in pixels. edge_width_is_relative : bool If enabled, edge_width is interpreted as a fraction of the point size. edge_color : str, array-like, dict Color of the point marker border. Numeric color values should be RGB(A). 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()) face_color : str, array-like, dict Color of the point marker body. Numeric color values should be RGB(A). 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()) out_of_slice_display : bool If True, renders points not just in central plane but also slightly out of slice according to specified point marker size. n_dimensional : bool This property will soon be deprecated in favor of 'out_of_slice_display'. Use that instead. name : str Name of the layer. metadata : dict Layer metadata. scale : tuple of float Scale factors for the layer. translate : tuple of float Translation values for the layer. 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. 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. 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. opacity : float Opacity of the layer visual, between 0.0 and 1.0. 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'}. visible : bool Whether the layer visual is currently being displayed. cache : bool Whether slices of out-of-core datasets should be cached upon retrieval. Currently, this only applies to dask arrays. shading : str, Shading Render lighting and shading on points. Options are: * 'none' No shading is added to the points. * 'spherical' Shading and depth buffer are changed to give a 3D spherical look to the points antialiasing: float Amount of antialiasing in canvas pixels. canvas_size_limits : tuple of float Lower and upper limits for the size of points in canvas pixels. shown : 1-D array of bool Whether to show each point. Attributes ---------- data : array (N, D) Coordinates for N points in D dimensions. features : DataFrame-like Features table where each row corresponds to a point 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,)} or DataFrame Annotations for each point. Each property should be an array of length N, where N is the number of points. text : str Text to be displayed with the points. 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_points_with_text.py. symbol : str Symbol used for all point markers. size : array (N, D) Array of sizes for each point in each dimension. Must have the same shape as the layer `data`. edge_width : array (N,) Width of the marker edges in pixels for all points edge_width : array (N,) Width of the marker edges for all points as a fraction of their size. edge_color : Nx4 numpy array Array of edge color RGBA values, one for each point. 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()) face_color : Nx4 numpy array Array of face color RGBA values, one for each point. 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()) current_size : float Size of the marker for the next point to be added or the currently selected point. current_edge_width : float Edge width of the marker for the next point to be added or the currently selected point. current_edge_color : str Edge color of the marker edge for the next point to be added or the currently selected point. current_face_color : str Face color of the marker edge for the next point to be added or the currently selected point. out_of_slice_display : bool If True, renders points not just in central plane but also slightly out of slice according to specified point marker size. selected_data : set Integer indices of any selected points. mode : str Interactive mode. The normal, default mode is PAN_ZOOM, which allows for normal interactivity with the canvas. In ADD mode clicks of the cursor add points at the clicked location. In SELECT mode the cursor can select points by clicking on them or by dragging a box around them. Once selected points can be moved, have their properties edited, or be deleted. face_color_mode : str Face color setting mode. DIRECT (default mode) allows each point to be set arbitrarily CYCLE allows the color to be set via a color cycle over an attribute COLORMAP allows color to be set via a color map over an attribute edge_color_mode : str Edge color setting mode. DIRECT (default mode) allows each point to be set arbitrarily CYCLE allows the color to be set via a color cycle over an attribute COLORMAP allows color to be set via a color map over an attribute shading : Shading Shading mode. antialiasing: float Amount of antialiasing in canvas pixels. canvas_size_limits : tuple of float Lower and upper limits for the size of points in canvas pixels. shown : 1-D array of bool Whether each point is shown. Notes ----- _view_data : array (M, 2) 2D coordinates of points in the currently viewed slice. _view_size : array (M, ) Size of the point markers in the currently viewed slice. _view_edge_width : array (M, ) Edge width of the point markers in the currently viewed slice. _indices_view : array (M, ) Integer indices of the points in the currently viewed slice and are shown. _selected_view : Integer indices of selected points in the currently viewed slice within the `_view_data` array. _selected_box : array (4, 2) or None Four corners of any box either around currently selected points or being created during a drag action. Starting in the top left and going clockwise. _drag_start : list or None Coordinates of first cursor click during a drag action. Gets reset to None after dragging is done. """ # TODO write better documentation for edge_color and face_color # The max number of points that will ever be used to render the thumbnail # If more points are present then they are randomly subsampled _max_points_thumbnail = 1024 def __init__( self, data=None, *, ndim=None, features=None, properties=None, text=None, symbol='o', size=10, edge_width=0.05, edge_width_is_relative=True, edge_color='dimgray', edge_color_cycle=None, edge_colormap='viridis', edge_contrast_limits=None, face_color='white', face_color_cycle=None, face_colormap='viridis', face_contrast_limits=None, out_of_slice_display=False, n_dimensional=None, name=None, metadata=None, scale=None, translate=None, rotate=None, shear=None, affine=None, opacity=1, blending='translucent', visible=True, cache=True, property_choices=None, experimental_clipping_planes=None, shading='none', canvas_size_limits=(2, 10000), antialiasing=1, shown=True, ): if ndim is None and scale is not None: ndim = len(scale) data, ndim = fix_data_points(data, ndim) super().__init__( data, 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, ) self.events.add( mode=Event, size=Event, edge_width=Event, edge_width_is_relative=Event, face_color=Event, current_face_color=Event, edge_color=Event, current_edge_color=Event, properties=Event, current_properties=Event, symbol=Event, out_of_slice_display=Event, n_dimensional=Event, highlight=Event, shading=Event, antialiasing=Event, canvas_size_limits=Event, features=Event, feature_defaults=Event, ) # Save the point coordinates self._data = np.asarray(data) self._feature_table = _FeatureTable.from_layer( features=features, properties=properties, property_choices=property_choices, num_data=len(self.data), ) self._text = TextManager._from_layer( text=text, features=self.features, ) self._edge_width_is_relative = False self._shown = np.empty(0).astype(bool) # The following point properties are for the new points that will # be added. For any given property, if a list is passed to the # constructor so each point gets its own value then the default # value is used when adding new points self._current_size = np.asarray(size) if np.isscalar(size) else 10 self._current_edge_width = ( np.asarray(edge_width) if np.isscalar(edge_width) else 0.1 ) # Indices of selected points self._selected_data = set() self._selected_data_stored = set() self._selected_data_history = set() # Indices of selected points within the currently viewed slice self._selected_view = [] # Index of hovered point self._value = None self._value_stored = None self._mode = Mode.PAN_ZOOM self._status = self.mode self._highlight_index = [] self._highlight_box = None self._drag_start = None self._drag_normal = None self._drag_up = None # initialize view data self.__indices_view = np.empty(0, int) self._view_size_scale = [] self._drag_box = None self._drag_box_stored = None self._is_selecting = False self._clipboard = {} self._round_index = False color_properties = ( self.properties if self._data.size > 0 else self.property_choices ) self._edge = ColorManager._from_layer_kwargs( n_colors=len(data), colors=edge_color, continuous_colormap=edge_colormap, contrast_limits=edge_contrast_limits, categorical_colormap=edge_color_cycle, properties=color_properties, ) self._face = ColorManager._from_layer_kwargs( n_colors=len(data), colors=face_color, continuous_colormap=face_colormap, contrast_limits=face_contrast_limits, categorical_colormap=face_color_cycle, properties=color_properties, ) if n_dimensional is not None: self._out_of_slice_display = n_dimensional else: self._out_of_slice_display = out_of_slice_display # Save the point style params self.size = size self.shown = shown self.symbol = symbol self.edge_width = edge_width self.edge_width_is_relative = edge_width_is_relative self.canvas_size_limits = canvas_size_limits self.shading = shading self.antialiasing = antialiasing # Trigger generation of view slice and thumbnail self._update_dims() @property def data(self) -> np.ndarray: """(N, D) array: coordinates for N points in D dimensions.""" return self._data @data.setter def data(self, data: Optional[np.ndarray]): data, _ = fix_data_points(data, self.ndim) cur_npoints = len(self._data) self._data = data # Add/remove property and style values based on the number of new points. with self.events.blocker_all(): with self._edge.events.blocker_all(): with self._face.events.blocker_all(): self._feature_table.resize(len(data)) self.text.apply(self.features) if len(data) < cur_npoints: # If there are now fewer points, remove the size and colors of the # extra ones if len(self._edge.colors) > len(data): self._edge._remove( np.arange(len(data), len(self._edge.colors)) ) if len(self._face.colors) > len(data): self._face._remove( np.arange(len(data), len(self._face.colors)) ) self._shown = self._shown[: len(data)] self._size = self._size[: len(data)] self._edge_width = self._edge_width[: len(data)] elif len(data) > cur_npoints: # If there are now more points, add the size and colors of the # new ones adding = len(data) - cur_npoints if len(self._size) > 0: new_size = copy(self._size[-1]) for i in self._dims_displayed: new_size[i] = self.current_size else: # Add the default size, with a value for each dimension new_size = np.repeat( self.current_size, self._size.shape[1] ) size = np.repeat([new_size], adding, axis=0) if len(self._edge_width) > 0: new_edge_width = copy(self._edge_width[-1]) else: new_edge_width = self.current_edge_width edge_width = np.repeat( [new_edge_width], adding, axis=0 ) # add new colors self._edge._add(n_colors=adding) self._face._add(n_colors=adding) shown = np.repeat([True], adding, axis=0) self._shown = np.concatenate( (self._shown, shown), axis=0 ) self.size = np.concatenate((self._size, size), axis=0) self.edge_width = np.concatenate( (self._edge_width, edge_width), axis=0 ) self.selected_data = set( np.arange(cur_npoints, len(data)) ) self._update_dims() self.events.data(value=self.data) self._set_editable() def _on_selection(self, selected): if selected: self._set_highlight() else: self._highlight_box = None self._highlight_index = [] self.events.highlight() @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=len(self.data)) self._update_color_manager( self._face, self._feature_table, "face_color" ) self._update_color_manager( self._edge, self._feature_table, "edge_color" ) 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 @property def property_choices(self) -> Dict[str, np.ndarray]: return self._feature_table.choices() @property def properties(self) -> Dict[str, np.ndarray]: """dict {str: np.ndarray (N,)}, DataFrame: Annotations for each point""" return self._feature_table.properties() @staticmethod def _update_color_manager(color_manager, feature_table, name): if color_manager.color_properties is not None: color_name = color_manager.color_properties.name if color_name not in feature_table.values: color_manager.color_mode = ColorMode.DIRECT color_manager.color_properties = None warnings.warn( trans._( 'property used for {name} dropped', deferred=True, name=name, ), RuntimeWarning, ) else: color_manager.color_properties = { 'name': color_name, 'values': feature_table.values[color_name].to_numpy(), 'current_value': feature_table.defaults[color_name][0], } @properties.setter def properties( self, properties: Union[Dict[str, Array], pd.DataFrame, None] ): self.features = properties @property def current_properties(self) -> Dict[str, np.ndarray]: """dict{str: np.ndarray(1,)}: properties for the next added point.""" 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: update_indices = list(self.selected_data) self._feature_table.set_currents( current_properties, update_indices=update_indices ) current_properties = self.current_properties self._edge._update_current_properties(current_properties) self._face._update_current_properties(current_properties) self.events.current_properties() self.events.feature_defaults() if update_indices is not None: self.events.properties() self.events.features() @property def text(self) -> TextManager: """TextManager: the TextManager object containing containing the text properties""" return self._text @text.setter def text(self, text): self._text._update_from_layer( text=text, features=self.features, )
[docs] def refresh_text(self): """Refresh the text values. This is generally used if the features were updated without changing the data """ self.text.refresh(self.features)
def _get_ndim(self) -> int: """Determine number of dimensions of the layer.""" return self.data.shape[1] @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(self.data, axis=0) mins = np.min(self.data, axis=0) extrema = np.vstack([mins, maxs]) return extrema @property def out_of_slice_display(self) -> bool: """bool: renders points slightly out of slice.""" return self._out_of_slice_display @out_of_slice_display.setter def out_of_slice_display(self, out_of_slice_display: bool) -> None: self._out_of_slice_display = bool(out_of_slice_display) self.events.out_of_slice_display() self.events.n_dimensional() self.refresh() @property def n_dimensional(self) -> bool: """ This property will soon be deprecated in favor of `out_of_slice_display`. Use that instead. """ return self._out_of_slice_display @n_dimensional.setter def n_dimensional(self, value: bool) -> None: self.out_of_slice_display = value @property def symbol(self) -> str: """str: symbol used for all point markers.""" return str(self._symbol) @symbol.setter def symbol(self, symbol: Union[str, Symbol]) -> None: if isinstance(symbol, str): # Convert the alias string to the deduplicated string if symbol in SYMBOL_ALIAS: symbol = SYMBOL_ALIAS[symbol] else: symbol = Symbol(symbol) self._symbol = symbol self.events.symbol() self.events.highlight() @property def size(self) -> np.ndarray: """(N, D) array: size of all N points in D dimensions.""" return self._size @size.setter def size(self, size: Union[int, float, np.ndarray, list]) -> None: try: self._size = np.broadcast_to(size, self.data.shape).copy() except Exception: try: self._size = np.broadcast_to( size, self.data.shape[::-1] ).T.copy() except Exception: raise ValueError( trans._( "Size is not compatible for broadcasting", deferred=True, ) ) self.refresh() @property def current_size(self) -> Union[int, float]: """float: size of marker for the next added point.""" return self._current_size @current_size.setter def current_size(self, size: Union[None, float]) -> None: if (isinstance(size, numbers.Number) and size < 0) or ( isinstance(size, list) and min(size) < 0 ): warnings.warn( message=trans._( 'current_size value must be positive, value will be left at {value}.', deferred=True, value=self.current_size, ), category=RuntimeWarning, ) size = self.current_size self._current_size = size if self._update_properties and len(self.selected_data) > 0: for i in self.selected_data: self.size[i, :] = (self.size[i, :] > 0) * size self.refresh() self.events.size() @property def antialiasing(self) -> float: """Amount of antialiasing in canvas pixels.""" return self._antialiasing @antialiasing.setter def antialiasing(self, value: float): """Set the amount of antialiasing in canvas pixels. Values can only be positive. """ if value < 0: warnings.warn( message=trans._( 'antialiasing value must be positive, value will be set to 0.', deferred=True, ), category=RuntimeWarning, ) self._antialiasing = max(0, value) self.events.antialiasing(value=self._antialiasing) @property def shading(self) -> Shading: """shading mode.""" return self._shading @shading.setter def shading(self, value): self._shading = Shading(value) self.events.shading() @property def canvas_size_limits(self) -> Tuple[float, float]: """Limit the canvas size of points""" return self._canvas_size_limits @canvas_size_limits.setter def canvas_size_limits(self, value): self._canvas_size_limits = float(value[0]), float(value[1]) self.events.canvas_size_limits() @property def shown(self): """ Boolean array determining which points to show """ return self._shown @shown.setter def shown(self, shown): self._shown = np.broadcast_to(shown, self.data.shape[0]).astype(bool) self.refresh() @property def edge_width(self) -> np.ndarray: """(N, D) array: edge_width of all N points.""" return self._edge_width @edge_width.setter def edge_width( self, edge_width: Union[int, float, np.ndarray, list] ) -> None: # broadcast to np.array edge_width = np.broadcast_to(edge_width, self.data.shape[0]).copy() # edge width cannot be negative if np.any(edge_width < 0): raise ValueError( trans._( 'All edge_width must be > 0', deferred=True, ) ) # if relative edge width is enabled, edge_width must be between 0 and 1 if self.edge_width_is_relative and np.any(edge_width > 1): raise ValueError( trans._( 'All edge_width must be between 0 and 1 if edge_width_is_relative is enabled', deferred=True, ) ) self._edge_width = edge_width self.refresh() @property def edge_width_is_relative(self) -> bool: """bool: treat edge_width as a fraction of point size.""" return self._edge_width_is_relative @edge_width_is_relative.setter def edge_width_is_relative(self, edge_width_is_relative: bool) -> None: if edge_width_is_relative and np.any( (self.edge_width > 1) | (self.edge_width < 0) ): raise ValueError( trans._( 'edge_width_is_relative can only be enabled if edge_width is between 0 and 1', deferred=True, ) ) self._edge_width_is_relative = edge_width_is_relative self.events.edge_width_is_relative() @property def current_edge_width(self) -> Union[int, float]: """float: edge_width of marker for the next added point.""" return self._current_edge_width @current_edge_width.setter def current_edge_width(self, edge_width: Union[None, float]) -> None: self._current_edge_width = edge_width if self._update_properties and len(self.selected_data) > 0: for i in self.selected_data: self.edge_width[i] = (self.edge_width[i] > 0) * edge_width self.refresh() self.events.edge_width() @property def edge_color(self) -> np.ndarray: """(N x 4) np.ndarray: Array of RGBA edge colors for each point""" return self._edge.colors @edge_color.setter def edge_color(self, edge_color): self._edge._set_color( color=edge_color, n_colors=len(self.data), properties=self.properties, current_properties=self.current_properties, ) self.events.edge_color() @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.categorical_colormap.fallback_color.values @edge_color_cycle.setter def edge_color_cycle(self, edge_color_cycle: Union[list, np.ndarray]): self._edge.categorical_colormap = edge_color_cycle @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.continuous_colormap @edge_colormap.setter def edge_colormap(self, colormap: ValidColormapArg): self._edge.continuous_colormap = colormap @property def edge_contrast_limits(self) -> Tuple[float, float]: """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 current_edge_color(self) -> str: """str: Edge color of marker for the next added point or the selected point(s).""" hex_ = rgb_to_hex(self._edge.current_color)[0] return hex_to_name.get(hex_, hex_) @current_edge_color.setter def current_edge_color(self, edge_color: ColorType) -> None: if self._update_properties and len(self.selected_data) > 0: update_indices = list(self.selected_data) else: update_indices = [] self._edge._update_current_color( edge_color, update_indices=update_indices ) self.events.current_edge_color() @property def edge_color_mode(self) -> str: """str: Edge color setting mode DIRECT (default mode) allows each point to be set arbitrarily CYCLE allows the color to be set via a color cycle over an attribute COLORMAP allows color to be set via a color map over an attribute """ return self._edge.color_mode @edge_color_mode.setter def edge_color_mode(self, edge_color_mode: Union[str, ColorMode]): self._set_color_mode(edge_color_mode, 'edge') @property def face_color(self) -> np.ndarray: """(N x 4) np.ndarray: Array of RGBA face colors for each point""" return self._face.colors @face_color.setter def face_color(self, face_color): self._face._set_color( color=face_color, n_colors=len(self.data), properties=self.properties, current_properties=self.current_properties, ) self.events.face_color() @property def face_color_cycle(self) -> np.ndarray: """Union[np.ndarray, cycle]: Color cycle for face_color Can be a list of colors defined by name, RGB or RGBA """ return self._face.categorical_colormap.fallback_color.values @face_color_cycle.setter def face_color_cycle(self, face_color_cycle: Union[np.ndarray, cycle]): self._face.categorical_colormap = face_color_cycle @property def face_colormap(self) -> Colormap: """Return the colormap to be applied to a property to get the face color. Returns ------- colormap : napari.utils.Colormap The Colormap object. """ return self._face.continuous_colormap @face_colormap.setter def face_colormap(self, colormap: ValidColormapArg): self._face.continuous_colormap = colormap @property def face_contrast_limits(self) -> Union[None, Tuple[float, float]]: """None, (float, float) : clims for mapping the face_color colormap property to 0 and 1 """ return self._face.contrast_limits @face_contrast_limits.setter def face_contrast_limits( self, contrast_limits: Union[None, Tuple[float, float]] ): self._face.contrast_limits = contrast_limits @property def current_face_color(self) -> str: """Face color of marker for the next added point or the selected point(s).""" hex_ = rgb_to_hex(self._face.current_color)[0] return hex_to_name.get(hex_, hex_) @current_face_color.setter def current_face_color(self, face_color: ColorType) -> None: if self._update_properties and len(self.selected_data) > 0: update_indices = list(self.selected_data) else: update_indices = [] self._face._update_current_color( face_color, update_indices=update_indices ) self.events.current_face_color() @property def face_color_mode(self) -> str: """str: Face color setting mode DIRECT (default mode) allows each point to be set arbitrarily CYCLE allows the color to be set via a color cycle over an attribute COLORMAP allows color to be set via a color map over an attribute """ return self._face.color_mode @face_color_mode.setter def face_color_mode(self, face_color_mode): self._set_color_mode(face_color_mode, 'face') def _set_color_mode( self, color_mode: Union[ColorMode, str], attribute: str ): """Set the face_color_mode or edge_color_mode property Parameters ---------- color_mode : str, ColorMode The value for setting edge or face_color_mode. If color_mode is a string, it should be one of: 'direct', 'cycle', or 'colormap' attribute : str in {'edge', 'face'} The name of the attribute to set the color of. Should be 'edge' for edge_color_mode or 'face' for face_color_mode. """ color_mode = ColorMode(color_mode) color_manager = getattr(self, f'_{attribute}') if color_mode == ColorMode.DIRECT: color_manager.color_mode = color_mode elif color_mode in (ColorMode.CYCLE, ColorMode.COLORMAP): if color_manager.color_properties is not None: color_property = color_manager.color_properties.name else: color_property = '' if color_property == '': if self.features.shape[1] > 0: new_color_property = next(iter(self.features)) color_manager.color_properties = { 'name': new_color_property, 'values': self.features[new_color_property].to_numpy(), 'current_value': np.squeeze( self.current_properties[new_color_property] ), } warnings.warn( trans._( '_{attribute}_color_property was not set, setting to: {new_color_property}', deferred=True, attribute=attribute, new_color_property=new_color_property, ) ) else: raise ValueError( trans._( 'There must be a valid Points.properties to use {color_mode}', deferred=True, color_mode=color_mode, ) ) # ColorMode.COLORMAP can only be applied to numeric properties color_property = color_manager.color_properties.name if (color_mode == ColorMode.COLORMAP) and not issubclass( self.features[color_property].dtype.type, np.number ): raise TypeError( trans._( 'selected property must be numeric to use ColorMode.COLORMAP', deferred=True, ) ) color_manager.color_mode = color_mode
[docs] def refresh_colors(self, update_color_mapping: bool = False): """Calculate and update face and edge colors if using a cycle or color map Parameters ---------- update_color_mapping : bool If set to True, the function will recalculate the color cycle map or colormap (whichever is being used). If set to False, the function will use the current color cycle map or color map. For example, if you are adding/modifying points and want them to be colored with the same mapping as the other points (i.e., the new points shouldn't affect the color cycle map or colormap), set ``update_color_mapping=False``. Default value is False. """ self._edge._refresh_colors(self.properties, update_color_mapping) self._face._refresh_colors(self.properties, update_color_mapping)
def _get_state(self): """Get dictionary of layer state. Returns ------- state : dict Dictionary of layer state. """ state = self._get_base_state() state.update( { 'symbol': self.symbol, 'edge_width': self.edge_width, 'edge_width_is_relative': self.edge_width_is_relative, 'face_color': self.face_color if self.data.size else [self.current_face_color], 'face_color_cycle': self.face_color_cycle, 'face_colormap': self.face_colormap.name, 'face_contrast_limits': self.face_contrast_limits, 'edge_color': self.edge_color if self.data.size else [self.current_edge_color], 'edge_color_cycle': self.edge_color_cycle, 'edge_colormap': self.edge_colormap.name, 'edge_contrast_limits': self.edge_contrast_limits, 'properties': self.properties, 'property_choices': self.property_choices, 'text': self.text.dict(), 'out_of_slice_display': self.out_of_slice_display, 'n_dimensional': self.out_of_slice_display, 'size': self.size, 'ndim': self.ndim, 'data': self.data, 'features': self.features, 'shading': self.shading, 'antialiasing': self.antialiasing, 'canvas_size_limits': self.canvas_size_limits, 'shown': self.shown, } ) return state @property def selected_data(self) -> set: """set: set of currently selected points.""" return self._selected_data @selected_data.setter def selected_data(self, selected_data): self._selected_data = set(selected_data) self._selected_view = list( np.intersect1d( np.array(list(self._selected_data)), self._indices_view, return_indices=True, )[2] ) # Update properties based on selected points if not len(self._selected_data): self._set_highlight() return index = list(self._selected_data) edge_colors = np.unique(self.edge_color[index], axis=0) if len(edge_colors) == 1: edge_color = edge_colors[0] with self.block_update_properties(): self.current_edge_color = edge_color face_colors = np.unique(self.face_color[index], axis=0) if len(face_colors) == 1: face_color = face_colors[0] with self.block_update_properties(): self.current_face_color = face_color # Calculate the mean size across the displayed dimensions for # each point to be consistent with `_view_size`. mean_size = np.mean( self.size[np.ix_(index, self._dims_displayed)], axis=1 ) size = np.unique(mean_size) if len(size) == 1: size = size[0] with self.block_update_properties(): self.current_size = size edge_width = np.unique(self.edge_width[index]) if len(edge_width) == 1: edge_width = edge_width[0] with self.block_update_properties(): self.current_edge_width = edge_width properties = {} for k, v in self.properties.items(): # pandas uses `object` as dtype for strings by default, which # combined with the axis argument breaks np.unique axis = 0 if v.ndim > 1 else None properties[k] = np.unique(v[index], axis=axis) n_unique_properties = np.array([len(v) for v in properties.values()]) if np.all(n_unique_properties == 1): with self.block_update_properties(): self.current_properties = properties self._set_highlight()
[docs] def interaction_box(self, index) -> Optional[np.ndarray]: """Create the interaction box around a list of points in view. Parameters ---------- index : list List of points around which to construct the interaction box. Returns ------- box : np.ndarray or None 4x2 array of corners of the interaction box in clockwise order starting in the upper-left corner. """ if len(index) > 0: data = self._view_data[index] size = self._view_size[index] data = points_to_squares(data, size) return create_box(data) return None
@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. In ADD mode clicks of the cursor add points at the clicked location. In SELECT mode the cursor can select points by clicking on them or by dragging a box around them. Once selected points can be moved, have their properties edited, or be deleted. """ return str(self._mode) _drag_modes = { Mode.ADD: add, Mode.SELECT: select, Mode.PAN_ZOOM: no_op, Mode.TRANSFORM: no_op, } _move_modes = { Mode.ADD: no_op, Mode.SELECT: highlight, Mode.PAN_ZOOM: no_op, Mode.TRANSFORM: no_op, } _cursor_modes = { Mode.ADD: 'crosshair', Mode.SELECT: 'standard', Mode.PAN_ZOOM: 'standard', Mode.TRANSFORM: 'standard', } @mode.setter def mode(self, mode): old_mode = self._mode mode, changed = self._mode_setter_helper(mode, Mode) if not changed: return assert mode is not None, mode if mode == Mode.ADD: self.selected_data = set() self.interactive = True elif mode == Mode.PAN_ZOOM: self.interactive = True if mode != Mode.SELECT or old_mode != Mode.SELECT: self._selected_data_stored = set() self._set_highlight() self.events.mode(mode=mode) @property def _indices_view(self): return self.__indices_view @_indices_view.setter def _indices_view(self, value): if len(self._shown) == 0: self.__indices_view = np.empty(0, int) else: self.__indices_view = value[self.shown[value]] @property def _view_data(self) -> np.ndarray: """Get the coords of the points in view Returns ------- view_data : (N x D) np.ndarray Array of coordinates for the N points in view """ if len(self._indices_view) > 0: data = self.data[np.ix_(self._indices_view, self._dims_displayed)] else: # if no points in this slice send dummy data data = np.zeros((0, self._ndisplay)) return data @property def _view_text(self) -> np.ndarray: """Get the values of the text elements in view Returns ------- text : (N x 1) np.ndarray Array of text strings for the N text elements in view """ # This may be triggered when the string encoding instance changed, # in which case it has no cached values, so generate them here. self.text.string._apply(self.features) return self.text.view_text(self._indices_view) @property def _view_text_coords(self) -> Tuple[np.ndarray, str, str]: """Get the coordinates of the text elements in view Returns ------- text_coords : (N x D) np.ndarray Array of coordinates for the N text elements in view anchor_x : str The vispy text anchor for the x axis anchor_y : str The vispy text anchor for the y axis """ return self.text.compute_text_coords(self._view_data, self._ndisplay) @property def _view_text_color(self) -> np.ndarray: """Get the colors of the text elements at the given indices.""" self.text.color._apply(self.features) return self.text._view_color(self._indices_view) @property def _view_size(self) -> np.ndarray: """Get the sizes of the points in view Returns ------- view_size : (N x D) np.ndarray Array of sizes for the N points in view """ if len(self._indices_view) > 0: # Get the point sizes and scale for ndim display sizes = ( self.size[ np.ix_(self._indices_view, self._dims_displayed) ].mean(axis=1) * self._view_size_scale ) else: # if no points, return an empty list sizes = np.array([]) return sizes @property def _view_edge_width(self) -> np.ndarray: """Get the edge_width of the points in view Returns ------- view_edge_width : (N,) np.ndarray Array of edge_widths for the N points in view """ return self.edge_width[self._indices_view] @property def _view_face_color(self) -> np.ndarray: """Get the face colors of the points in view Returns ------- view_face_color : (N x 4) np.ndarray RGBA color array for the face colors of the N points in view. If there are no points in view, returns array of length 0. """ return self.face_color[self._indices_view] @property def _view_edge_color(self) -> np.ndarray: """Get the edge colors of the points in view Returns ------- view_edge_color : (N x 4) np.ndarray RGBA color array for the edge colors of the N points in view. If there are no points in view, returns array of length 0. """ return self.edge_color[self._indices_view] def _set_editable(self, editable=None): """Set editable mode based on layer properties.""" if editable is None: self.editable = True if not self.editable: self.mode = Mode.PAN_ZOOM if self.ndim < 3 and self._ndisplay == 3: # interaction currently does not work for 2D # layers being rendered in 3D. self.editable = False def _slice_data( self, dims_indices ) -> Tuple[List[int], Union[float, np.ndarray]]: """Determines the slice of points given the indices. Parameters ---------- dims_indices : sequence of int or slice Indices to slice with. Returns ------- slice_indices : list Indices of points in the currently viewed slice. scale : float, (N, ) array If in `out_of_slice_display` mode then the scale factor of points, where values of 1 corresponds to points located in the slice, and values less than 1 correspond to points located in neighboring slices. """ # Get a list of the data for the points in this slice not_disp = list(self._dims_not_displayed) # We want a numpy array so we can use fancy indexing with the non-displayed # indices, but as dims_indices can (and often/always does) contain slice # objects, the array has dtype=object which is then very slow for the # arithmetic below. As Points._round_index is always False, we can safely # convert to float to get a major performance improvement. not_disp_indices = np.array(dims_indices)[not_disp].astype(float) if len(self.data) > 0: if self.out_of_slice_display is True and self.ndim > 2: distances = abs(self.data[:, not_disp] - not_disp_indices) sizes = self.size[:, not_disp] / 2 matches = np.all(distances <= sizes, axis=1) size_match = sizes[matches] size_match[size_match == 0] = 1 scale_per_dim = (size_match - distances[matches]) / size_match scale_per_dim[size_match == 0] = 1 scale = np.prod(scale_per_dim, axis=1) slice_indices = np.where(matches)[0].astype(int) return slice_indices, scale else: data = self.data[:, not_disp] distances = np.abs(data - not_disp_indices) matches = np.all(distances <= 0.5, axis=1) slice_indices = np.where(matches)[0].astype(int) return slice_indices, 1 else: return [], np.empty(0) def _get_value(self, position) -> Union[None, int]: """Index of the point at a given 2D position in data coordinates. Parameters ---------- position : tuple Position in data coordinates. Returns ------- value : int or None Index of point that is at the current coordinate if any. """ # Display points if there are any in this slice view_data = self._view_data selection = None if len(view_data) > 0: displayed_position = [position[i] for i in self._dims_displayed] # Get the point sizes # TODO: calculate distance in canvas space to account for canvas_size_limits. # Without this implementation, point hover and selection (and anything depending # on self.get_value()) won't be aware of the real extent of points, causing # unexpected behaviour. See #3734 for details. distances = abs(view_data - displayed_position) in_slice_matches = np.all( distances <= np.expand_dims(self._view_size, axis=1) / 2, axis=1, ) indices = np.where(in_slice_matches)[0] if len(indices) > 0: selection = self._indices_view[indices[-1]] return selection def _get_value_3d( self, start_point: np.ndarray, end_point: np.ndarray, dims_displayed: List[int], ) -> Union[int, None]: """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 : Union[int, None] The data value along the supplied ray. """ if (start_point is None) or (end_point is None): # if the ray doesn't intersect the data volume, no points could have been intersected return None plane_point, plane_normal = displayed_plane_from_nd_line_segment( start_point, end_point, dims_displayed ) # project the in view points onto the plane projected_points, projection_distances = project_points_onto_plane( points=self._view_data, plane_point=plane_point, plane_normal=plane_normal, ) # rotate points and plane to be axis aligned with normal [0, 0, 1] rotated_points, rotation_matrix = rotate_points( points=projected_points, current_plane_normal=plane_normal, new_plane_normal=[0, 0, 1], ) rotated_click_point = np.dot(rotation_matrix, plane_point) # find the points the click intersects distances = abs(rotated_points[:, :2] - rotated_click_point[:2]) in_slice_matches = np.all( distances <= np.expand_dims(self._view_size, axis=1) / 2, axis=1, ) indices = np.where(in_slice_matches)[0] if len(indices) > 0: # find the point that is most in the foreground candidate_point_distances = projection_distances[indices] closest_index = indices[np.argmin(candidate_point_distances)] selection = self._indices_view[closest_index] else: selection = None return selection def _display_bounding_box_augmented(self, dims_displayed: np.ndarray): """An augmented, axis-aligned (self._ndisplay, 2) bounding box. This bounding box for includes the full size of displayed points and enables calculation of intersections in `Layer._get_value_3d()`. """ if len(self._view_size) == 0: return None max_point_size = np.max(self._view_size) bounding_box = np.copy( self._display_bounding_box(dims_displayed) ).astype(float) bounding_box[:, 0] -= max_point_size / 2 bounding_box[:, 1] += max_point_size / 2 return bounding_box
[docs] def get_ray_intersections( self, position: List[float], view_direction: np.ndarray, dims_displayed: List[int], world: bool = True, ) -> Union[Tuple[np.ndarray, np.ndarray], Tuple[None, None]]: """Get the start and end point for the ray extending from a point through the displayed bounding box. This method overrides the base layer, replacing the bounding box used to calculate intersections with a larger one which includes the size of points in view. 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 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. """ if len(dims_displayed) != 3: return None, None # create the bounding box in data coordinates bounding_box = self._display_bounding_box_augmented(dims_displayed) if bounding_box is None: return None, None 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 _set_view_slice(self): """Sets the view given the indices to slice with.""" # get the indices of points in view indices, scale = self._slice_data(self._slice_indices) # Update the _view_size_scale in accordance to the self._indices_view setter. # If out_of_slice_display is False, scale is a number and not an array. # Therefore we have an additional if statement checking for # self._view_size_scale being an integer. if not isinstance(scale, np.ndarray): self._view_size_scale = scale elif len(self._shown) == 0: self._view_size_scale = np.empty(0, int) else: self._view_size_scale = scale[self.shown[indices]] self._indices_view = np.array(indices, dtype=int) # get the selected points that are in view self._selected_view = list( np.intersect1d( np.array(list(self._selected_data)), self._indices_view, return_indices=True, )[2] ) with self.events.highlight.blocker(): self._set_highlight(force=True) def _set_highlight(self, force=False): """Render highlights of shapes including boundaries, vertices, interaction boxes, and the drag selection box when appropriate. Highlighting only occurs in Mode.SELECT. Parameters ---------- force : bool Bool that forces a redraw to occur when `True` """ # Check if any point ids have changed since last call if ( self.selected_data == self._selected_data_stored and self._value == self._value_stored and np.all(self._drag_box == self._drag_box_stored) ) and not force: return self._selected_data_stored = copy(self.selected_data) self._value_stored = copy(self._value) self._drag_box_stored = copy(self._drag_box) if self._value is not None or len(self._selected_view) > 0: if len(self._selected_view) > 0: index = copy(self._selected_view) # highlight the hovered point if not in adding mode if ( self._value in self._indices_view and self._mode == Mode.SELECT and not self._is_selecting ): hover_point = list(self._indices_view).index(self._value) if hover_point not in index: index.append(hover_point) index.sort() else: # only highlight hovered points in select mode if ( self._value in self._indices_view and self._mode == Mode.SELECT and not self._is_selecting ): hover_point = list(self._indices_view).index(self._value) index = [hover_point] else: index = [] self._highlight_index = index else: self._highlight_index = [] # only display dragging selection box in 2D if self._is_selecting: if self._drag_normal is None: pos = create_box(self._drag_box) else: pos = _create_box_from_corners_3d( self._drag_box, self._drag_normal, self._drag_up ) pos = pos[list(range(4)) + [0]] else: pos = None self._highlight_box = pos self.events.highlight() def _update_thumbnail(self): """Update thumbnail with current points and colors.""" colormapped = np.zeros(self._thumbnail_shape) colormapped[..., 3] = 1 view_data = self._view_data if len(view_data) > 0: # Get the zoom factor required to fit all data in the thumbnail. de = self._extent_data min_vals = [de[0, i] for i in self._dims_displayed] shape = np.ceil( [de[1, i] - de[0, i] + 1 for i in self._dims_displayed] ).astype(int) zoom_factor = np.divide( self._thumbnail_shape[:2], shape[-2:] ).min() # Maybe subsample the points. if len(view_data) > self._max_points_thumbnail: thumbnail_indices = np.random.randint( 0, len(view_data), self._max_points_thumbnail ) points = view_data[thumbnail_indices] else: points = view_data thumbnail_indices = self._indices_view # Calculate the point coordinates in the thumbnail data space. thumbnail_shape = np.clip( np.ceil(zoom_factor * np.array(shape[:2])).astype(int), 1, # smallest side should be 1 pixel wide self._thumbnail_shape[:2], ) coords = np.floor( (points[:, -2:] - min_vals[-2:] + 0.5) * zoom_factor ).astype(int) coords = np.clip(coords, 0, thumbnail_shape - 1) # Draw single pixel points in the colormapped thumbnail. colormapped = np.zeros(tuple(thumbnail_shape) + (4,)) colormapped[..., 3] = 1 colors = self._face.colors[thumbnail_indices] colormapped[coords[:, 0], coords[:, 1]] = colors colormapped[..., 3] *= self.opacity self.thumbnail = colormapped
[docs] def add(self, coord): """Adds point at coordinate. Parameters ---------- coord : sequence of indices to add point at """ self.data = np.append(self.data, np.atleast_2d(coord), axis=0)
[docs] def remove_selected(self): """Removes selected points if any.""" index = list(self.selected_data) index.sort() if len(index): self._shown = np.delete(self._shown, index, axis=0) self._size = np.delete(self._size, index, axis=0) self._edge_width = np.delete(self._edge_width, index, axis=0) with self._edge.events.blocker_all(): self._edge._remove(indices_to_remove=index) with self._face.events.blocker_all(): self._face._remove(indices_to_remove=index) self._feature_table.remove(index) self.text.remove(index) if self._value in self.selected_data: self._value = None else: if self._value is not None: # update the index of self._value to account for the # data being removed indices_removed = np.array(index) < self._value offset = np.sum(indices_removed) self._value -= offset self._value_stored -= offset self.data = np.delete(self.data, index, axis=0) self.selected_data = set()
def _move( self, selection_indices: Sequence[int], position: Sequence[Union[int, float]], ) -> None: """Move points relative to drag start location. Parameters ---------- selection_indices : Sequence[int] Integer indices of points to move in self.data position : tuple Position to move points to in data coordinates. """ if len(selection_indices) > 0: selection_indices = list(selection_indices) disp = list(self._dims_displayed) self._set_drag_start(selection_indices, position) center = self.data[np.ix_(selection_indices, disp)].mean(axis=0) shift = np.array(position)[disp] - center - self._drag_start self.data[np.ix_(selection_indices, disp)] = ( self.data[np.ix_(selection_indices, disp)] + shift ) self.refresh() self.events.data(value=self.data) def _set_drag_start( self, selection_indices: Sequence[int], position: Sequence[Union[int, float]], center_by_data: bool = True, ) -> None: """Store the initial position at the start of a drag event. Parameters ---------- selection_indices : set of int integer indices of selected data used to index into self.data position : Sequence of numbers position of the drag start in data coordinates. center_by_data: bool Center the drag start based on the selected data. Used for modifier drag_box selection. """ selection_indices = list(selection_indices) dims_displayed = list(self._dims_displayed) if self._drag_start is None: self._drag_start = np.array(position, dtype=float)[dims_displayed] if len(selection_indices) > 0 and center_by_data: center = self.data[ np.ix_(selection_indices, dims_displayed) ].mean(axis=0) self._drag_start -= center def _paste_data(self): """Paste any point from clipboard and select them.""" npoints = len(self._view_data) totpoints = len(self.data) if len(self._clipboard.keys()) > 0: not_disp = self._dims_not_displayed data = deepcopy(self._clipboard['data']) offset = [ self._slice_indices[i] - self._clipboard['indices'][i] for i in not_disp ] data[:, not_disp] = data[:, not_disp] + np.array(offset) self._data = np.append(self.data, data, axis=0) self._shown = np.append( self.shown, deepcopy(self._clipboard['shown']), axis=0 ) self._size = np.append( self.size, deepcopy(self._clipboard['size']), axis=0 ) self._feature_table.append(self._clipboard['features']) self.text._paste(**self._clipboard['text']) self._edge_width = np.append( self.edge_width, deepcopy(self._clipboard['edge_width']), axis=0, ) self._edge._paste( colors=self._clipboard['edge_color'], properties=_features_to_properties( self._clipboard['features'] ), ) self._face._paste( colors=self._clipboard['face_color'], properties=_features_to_properties( self._clipboard['features'] ), ) self._selected_view = list( range(npoints, npoints + len(self._clipboard['data'])) ) self._selected_data = set( range(totpoints, totpoints + len(self._clipboard['data'])) ) self.refresh() def _copy_data(self): """Copy selected points to clipboard.""" if len(self.selected_data) > 0: index = list(self.selected_data) self._clipboard = { 'data': deepcopy(self.data[index]), 'edge_color': deepcopy(self.edge_color[index]), 'face_color': deepcopy(self.face_color[index]), 'shown': deepcopy(self.shown[index]), 'size': deepcopy(self.size[index]), 'edge_width': deepcopy(self.edge_width[index]), 'features': deepcopy(self.features.iloc[index]), 'indices': self._slice_indices, 'text': self.text._copy(index), } else: self._clipboard = {}
[docs] def to_mask( self, *, shape: tuple, data_to_world: Optional[Affine] = None, isotropic_output: bool = True, ): """Return a binary mask array of all the points as balls. Parameters ---------- shape : tuple The shape of the mask to be generated. data_to_world : Optional[Affine] The data-to-world transform of the output mask image. This likely comes from a reference image. If None, then this is the same as this layer's data-to-world transform. isotropic_output : bool If True, then force the output mask to always contain isotropic balls in data/pixel coordinates. Otherwise, allow the anisotropy in the data-to-world transform to squash the balls in certain dimensions. By default this is True, but you should set it to False if you are going to create a napari image layer from the result with the same data-to-world transform and want the visualized balls to be roughly isotropic. Returns ------- np.ndarray The output binary mask array of the given shape containing this layer's points as balls. """ if data_to_world is None: data_to_world = self._data_to_world mask = np.zeros(shape, dtype=bool) mask_world_to_data = data_to_world.inverse points_data_to_mask_data = self._data_to_world.compose( mask_world_to_data ) points_in_mask_data_coords = np.atleast_2d( points_data_to_mask_data(self.data) ) # Calculating the radii of the output points in the mask is complex. # Points.size tells the size of the points in pixels in each dimension, # so we take the arithmetic mean across dimensions to define a scalar size # per point, which is consistent with visualization. mean_radii = np.mean(self.size, axis=1, keepdims=True) / 2 # Scale each radius by the geometric mean scale of the Points layer to # keep the balls isotropic when visualized in world coordinates. # Then scale each radius by the scale of the output image mask # using the geometric mean if isotropic output is desired. # The geometric means are used instead of the arithmetic mean # to maintain the volume scaling factor of the transforms. point_data_to_world_scale = gmean(np.abs(self._data_to_world.scale)) mask_world_to_data_scale = ( gmean(np.abs(mask_world_to_data.scale)) if isotropic_output else np.abs(mask_world_to_data.scale) ) radii_scale = point_data_to_world_scale * mask_world_to_data_scale output_data_radii = mean_radii * np.atleast_2d(radii_scale) for coords, radii in zip( points_in_mask_data_coords, output_data_radii ): # Define a minimal set of coordinates where the mask could be present # by defining an inclusive lower and exclusive upper bound for each dimension. lower_coords = np.maximum(np.floor(coords - radii), 0).astype(int) upper_coords = np.minimum( np.ceil(coords + radii) + 1, shape ).astype(int) # Generate every possible coordinate within the bounds defined above # in a grid of size D1 x D2 x ... x Dd x D (e.g. for D=2, this might be 4x5x2). submask_coords = [ range(lower_coords[i], upper_coords[i]) for i in range(self.ndim) ] submask_grids = np.stack( np.meshgrid(*submask_coords, copy=False, indexing='ij'), axis=-1, ) # Update the mask coordinates based on the normalized square distance # using a logical or to maintain any existing positive mask locations. normalized_square_distances = np.sum( ((submask_grids - coords) / radii) ** 2, axis=-1 ) mask[np.ix_(*submask_coords)] |= normalized_square_distances <= 1 return mask
[docs] def get_status( self, position: Optional[Tuple] = None, *, view_direction: Optional[np.ndarray] = None, dims_displayed: Optional[List[int]] = None, world: bool = False, ) -> dict: """Status message information of the data at a coordinate position. # Parameters # ---------- # position : tuple # 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 # Dict containing information that can be used in a status update. #""" if position is not None: value = self.get_value( position, view_direction=view_direction, dims_displayed=dims_displayed, world=world, ) else: value = None source_info = self._get_source_info() source_info['coordinates'] = generate_layer_coords_status( position, value ) # if this points layer has properties properties = self._get_properties( position, view_direction=view_direction, dims_displayed=dims_displayed, world=world, ) if properties: source_info['coordinates'] += "; " + ", ".join(properties) return source_info
def _get_tooltip_text( self, position, *, view_direction: Optional[np.ndarray] = None, dims_displayed: Optional[List[int]] = None, world: bool = False, ): """ tooltip message of the data at a coordinate position. Parameters ---------- position : tuple 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 ------- msg : string String containing a message that can be used as a tooltip. """ return "\n".join( self._get_properties( position, view_direction=view_direction, dims_displayed=dims_displayed, world=world, ) ) def _get_properties( self, position, *, view_direction: Optional[np.ndarray] = None, dims_displayed: Optional[List[int]] = None, world: bool = False, ) -> list: if self.features.shape[1] == 0: return [] value = self.get_value( position, view_direction=view_direction, dims_displayed=dims_displayed, world=world, ) # if the cursor is not outside the image or on the background if value is None or value > self.data.shape[0]: return [] return [ f'{k}: {v[value]}' for k, v in self.features.items() if k != 'index' and len(v) > value and v[value] is not None and not (isinstance(v[value], float) and np.isnan(v[value])) ]