import warnings
from copy import copy
from typing import Dict, Tuple, Union

import numpy as np
import pandas as pd

from ...utils.colormaps import Colormap, ValidColormapArg
from ...utils.events import Event
from ...utils.events.custom_types import Array
from ...utils.translations import trans
from ..base import Layer
from ..utils._color_manager_constants import ColorMode
from ..utils.color_manager import ColorManager
from ..utils.color_transformations import ColorType
from ..utils.layer_utils import _FeatureTable
from ._vector_utils import fix_data_vectors, generate_vector_meshes


[docs]class Vectors(Layer): """ Vectors layer renders lines onto the canvas. Parameters ---------- data : (N, 2, D) or (N1, N2, ..., ND, D) array An (N, 2, D) array is interpreted as "coordinate-like" data and a list of N vectors with start point and projections of the vector in D dimensions. An (N1, N2, ..., ND, D) array is interpreted as "image-like" data where there is a length D vector of the projections at each pixel. ndim : int Number of dimensions for vectors. When data is not None, ndim must be D. An empty vectors layer can be instantiated with arbitrary ndim. features : dict[str, array-like] or DataFrame Features table where each row corresponds to a vector and each column is a feature. properties : dict {str: array (N,)}, DataFrame Properties for each vector. Each property should be an array of length N, where N is the number of vectors. property_choices : dict {str: array (N,)} possible values for each property. edge_width : float Width for all vectors in pixels. length : float Multiplicative factor on projections for length of all vectors. edge_color : str Color of all of the vectors. 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 vector color if a continuous attribute is used to set edge_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()) 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. Attributes ---------- data : (N, 2, D) array The start point and projections of N vectors in D dimensions. features : Dataframe-like Features table where each row corresponds to a vector 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 vector. Each property should be an array of length N, where N is the number of vectors. edge_width : float Width for all vectors in pixels. length : float Multiplicative factor on projections for length of all vectors. edge_color : str Color of all of the vectors. 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 vector color if a continuous attribute is used to set edge_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()) Notes ----- _view_data : (M, 2, 2) array The start point and projections of N vectors in 2D for vectors whose start point is in the currently viewed slice. _view_face_color : (M, 4) np.ndarray colors for the M in view vectors _view_indices : (1, M) array indices for the M in view vectors _view_vertices : (4M, 2) or (8M, 2) np.ndarray the corner points for the M in view faces. Shape is (4M, 2) for 2D and (8M, 2) for 3D. _view_faces : (2M, 3) or (4M, 3) np.ndarray indices of the _mesh_vertices that form the faces of the M in view vectors. Shape is (2M, 2) for 2D and (4M, 2) for 3D. _property_choices : dict {str: array (N,)} Possible values for the properties in Vectors.properties. _mesh_vertices : (4N, 2) array The four corner points for the mesh representation of each vector as as rectangle in the slice that it starts in. _mesh_triangles : (2N, 3) array The integer indices of the `_mesh_vertices` that form the two triangles for the mesh representation of the vectors. _max_vectors_thumbnail : int The maximum number of vectors that will ever be used to render the thumbnail. If more vectors are present then they are randomly subsampled. """ # The max number of vectors that will ever be used to render the thumbnail # If more vectors are present then they are randomly subsampled _max_vectors_thumbnail = 1024 def __init__( self, data=None, *, ndim=None, features=None, properties=None, property_choices=None, edge_width=1, edge_color='red', edge_color_cycle=None, edge_colormap='viridis', edge_contrast_limits=None, length=1, name=None, metadata=None, scale=None, translate=None, rotate=None, shear=None, affine=None, opacity=0.7, blending='translucent', visible=True, cache=True, experimental_clipping_planes=None, ): if ndim is None and scale is not None: ndim = len(scale) data, ndim = fix_data_vectors(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, ) # events for non-napari calculations self.events.add( length=Event, edge_width=Event, edge_color=Event, edge_color_mode=Event, properties=Event, ) # Save the vector style params self._edge_width = edge_width self._length = float(length) self._data = data vertices, triangles = generate_vector_meshes( self._data[:, :, list(self._dims_displayed)], self.edge_width, self.length, ) self._mesh_vertices = vertices self._mesh_triangles = triangles self._displayed_stored = copy(self._dims_displayed) self._feature_table = _FeatureTable.from_layer( features=features, properties=properties, property_choices=property_choices, num_data=len(self.data), ) self._edge = ColorManager._from_layer_kwargs( n_colors=len(self.data), colors=edge_color, continuous_colormap=edge_colormap, contrast_limits=edge_contrast_limits, categorical_colormap=edge_color_cycle, properties=self.properties if self._data.size > 0 else self.property_choices, ) # Data containing vectors in the currently viewed slice self._view_data = np.empty((0, 2, 2)) self._displayed_stored = [] self._view_vertices = [] self._view_faces = [] self._view_indices = [] # now that everything is set up, make the layer visible (if set to visible) self._update_dims() self.visible = visible @property def data(self) -> np.ndarray: """(N, 2, D) array: start point and projections of vectors.""" return self._data @data.setter def data(self, vectors: np.ndarray): previous_n_vectors = len(self.data) self._data, _ = fix_data_vectors(vectors, self.ndim) n_vectors = len(self.data) vertices, triangles = generate_vector_meshes( self._data[:, :, list(self._dims_displayed)], self.edge_width, self.length, ) self._mesh_vertices = vertices self._mesh_triangles = triangles self._displayed_stored = copy(self._dims_displayed) # Adjust the props/color arrays when the number of vectors has changed with self.events.blocker_all(): with self._edge.events.blocker_all(): self._feature_table.resize(n_vectors) if n_vectors < previous_n_vectors: # If there are now fewer points, remove the size and colors of the # extra ones if len(self._edge.colors) > n_vectors: self._edge._remove( np.arange(n_vectors, len(self._edge.colors)) ) elif n_vectors > previous_n_vectors: # If there are now more points, add the size and colors of the # new ones adding = n_vectors - previous_n_vectors self._edge._add(n_colors=adding) self._update_dims() self.events.data(value=self.data) self._set_editable() @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)) if self._edge.color_properties is not None: if self._edge.color_properties.name not in self.features: self._edge.color_mode = ColorMode.DIRECT self._edge.color_properties = None warnings.warn( trans._( 'property used for edge_color dropped', deferred=True, ), RuntimeWarning, ) else: edge_color_name = self._edge.color_properties.name property_values = self.features[edge_color_name].to_numpy() self._edge.color_properties = { 'name': edge_color_name, 'values': property_values, 'current_value': self.feature_defaults[edge_color_name][0], } self.events.properties() @property def properties(self) -> Dict[str, np.ndarray]: """dict {str: array (N,)}, DataFrame: Annotations for each point""" return self._feature_table.properties() @properties.setter def properties(self, properties: Dict[str, Array]): self.features = properties @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() def _get_state(self): """Get dictionary of layer state. Returns ------- state : dict Dictionary of layer state. """ state = self._get_base_state() state.update( { 'length': self.length, 'edge_width': self.edge_width, 'edge_color': self.edge_color, 'edge_color_cycle': self.edge_color_cycle, 'edge_colormap': self.edge_colormap.name, 'edge_contrast_limits': self.edge_contrast_limits, 'data': self.data, 'properties': self.properties, 'property_choices': self.property_choices, 'ndim': self.ndim, 'features': self.features, } ) return state def _get_ndim(self) -> int: """Determine number of dimensions of the layer.""" return self.data.shape[2] @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: # Convert from projections to endpoints using the current length data = copy(self.data) data[:, 1, :] = data[:, 0, :] + self.length * data[:, 1, :] maxs = np.max(data, axis=(0, 1)) mins = np.min(data, axis=(0, 1)) extrema = np.vstack([mins, maxs]) return extrema @property def edge_width(self) -> Union[int, float]: """float: Width for all vectors in pixels.""" return self._edge_width @edge_width.setter def edge_width(self, edge_width: Union[int, float]): self._edge_width = edge_width vertices, triangles = generate_vector_meshes( self.data[:, :, list(self._dims_displayed)], self._edge_width, self.length, ) self._mesh_vertices = vertices self._mesh_triangles = triangles self._displayed_stored = copy(self._dims_displayed) self.events.edge_width() self.refresh() @property def length(self) -> Union[int, float]: """float: Multiplicative factor for length of all vectors.""" return self._length @length.setter def length(self, length: Union[int, float]): self._length = float(length) vertices, triangles = generate_vector_meshes( self.data[:, :, list(self._dims_displayed)], self.edge_width, self._length, ) self._mesh_vertices = vertices self._mesh_triangles = triangles self._displayed_stored = copy(self._dims_displayed) self.events.length() self.refresh() @property def edge_color(self) -> np.ndarray: """(1 x 4) np.ndarray: Array of RGBA edge colors (applied to all vectors)""" return self._edge.colors @edge_color.setter def edge_color(self, edge_color: ColorType): self._edge._set_color( color=edge_color, n_colors=len(self.data), properties=self.properties, current_properties=self._feature_table.currents(), ) self.events.edge_color()
[docs] def refresh_colors(self, update_color_mapping: bool = False): """Calculate and update 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 vectors and want them to be colored with the same mapping as the other vectors (i.e., the new vectors 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)
@property def edge_color_mode(self) -> ColorMode: """str: Edge color setting mode DIRECT (default mode) allows each vector 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]): edge_color_mode = ColorMode(edge_color_mode) if edge_color_mode == ColorMode.DIRECT: self._edge_color_mode = edge_color_mode elif edge_color_mode in (ColorMode.CYCLE, ColorMode.COLORMAP): if self._edge.color_properties is not None: color_property = self._edge.color_properties.name else: color_property = '' if color_property == '': if self.properties: color_property = next(iter(self.properties)) self._edge.color_properties = { 'name': color_property, 'values': self.features[color_property].to_numpy(), 'current_value': self.feature_defaults[color_property][ 0 ], } warnings.warn( trans._( 'edge_color property was not set, setting to: {color_property}', deferred=True, color_property=color_property, ), RuntimeWarning, ) else: raise ValueError( trans._( 'There must be a valid Points.properties to use {edge_color_mode}', deferred=True, edge_color_mode=edge_color_mode, ) ) # ColorMode.COLORMAP can only be applied to numeric properties if (edge_color_mode == ColorMode.COLORMAP) and not issubclass( self.properties[color_property].dtype.type, np.number, ): raise TypeError( trans._( 'selected property must be numeric to use ColorMode.COLORMAP', deferred=True, ) ) self._edge.color_mode = edge_color_mode self.events.edge_color_mode() @property def edge_color_cycle(self) -> np.ndarray: """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) -> Tuple[str, 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 _view_face_color(self) -> np.ndarray: """(Mx4) np.ndarray : colors for the M in view vectors""" face_color = np.repeat(self.edge_color[self._view_indices], 2, axis=0) if self._ndisplay == 3 and self.ndim > 2: face_color = np.vstack([face_color, face_color]) return face_color def _set_view_slice(self): """Sets the view given the indices to slice with.""" if not self._dims_displayed == self._displayed_stored: vertices, triangles = generate_vector_meshes( self.data[:, :, list(self._dims_displayed)], self.edge_width, self.length, ) self._mesh_vertices = vertices self._mesh_triangles = triangles self._displayed_stored = copy(self._dims_displayed) vertices = self._mesh_vertices not_disp = list(self._dims_not_displayed) disp = list(self._dims_displayed) indices = np.array(self._slice_indices) if len(self.data) == 0: faces = [] self._view_data = np.empty((0, 2, 2)) self._view_indices = [] elif self.ndim > 2: data = self.data[:, 0, not_disp].astype('int') matches = np.all(data == indices[not_disp], axis=1) matches = np.where(matches)[0] self._view_indices = matches self._view_data = self.data[np.ix_(matches, [0, 1], disp)] if len(matches) == 0: faces = [] else: keep_inds = np.repeat(2 * matches, 2) keep_inds[1::2] = keep_inds[1::2] + 1 if self._ndisplay == 3: keep_inds = np.concatenate( [ keep_inds, len(self._mesh_triangles) // 2 + keep_inds, ], axis=0, ) faces = self._mesh_triangles[keep_inds] else: faces = self._mesh_triangles self._view_data = self.data[:, :, disp] self._view_indices = np.arange(self.data.shape[0]) if len(faces) == 0: self._view_vertices = [] self._view_faces = [] else: self._view_vertices = vertices self._view_faces = faces def _update_thumbnail(self): """Update thumbnail with current vectors and colors.""" # calculate min vals for the vertices and pad with 0.5 # the offset is needed to ensure that the top left corner of the # vectors corresponds to the top left corner of the thumbnail de = self._extent_data offset = (np.array([de[0, d] for d in self._dims_displayed]) + 0.5)[ -2: ] # calculate range of values for the vertices and pad with 1 # padding ensures the entire vector can be represented in the thumbnail # without getting clipped shape = np.ceil( [de[1, d] - de[0, d] + 1 for d in self._dims_displayed] ).astype(int)[-2:] zoom_factor = np.divide(self._thumbnail_shape[:2], shape).min() # vectors = copy(self._data_view[:, :, -2:]) if self._view_data.shape[0] > self._max_vectors_thumbnail: thumbnail_indices = np.random.randint( 0, self._view_data.shape[0], self._max_vectors_thumbnail ) vectors = copy(self._view_data[thumbnail_indices, :, -2:]) thumbnail_color_indices = self._view_indices[thumbnail_indices] else: vectors = copy(self._view_data[:, :, -2:]) thumbnail_color_indices = self._view_indices vectors[:, 1, :] = vectors[:, 0, :] + vectors[:, 1, :] * self.length downsampled = (vectors - offset) * zoom_factor downsampled = np.clip( downsampled, 0, np.subtract(self._thumbnail_shape[:2], 1) ) colormapped = np.zeros(self._thumbnail_shape) colormapped[..., 3] = 1 edge_colors = self._edge.colors[thumbnail_color_indices] for v, ec in zip(downsampled, edge_colors): start = v[0] stop = v[1] step = int(np.ceil(np.max(abs(stop - start)))) x_vals = np.linspace(start[0], stop[0], step) y_vals = np.linspace(start[1], stop[1], step) for x, y in zip(x_vals, y_vals): colormapped[int(x), int(y), :] = ec colormapped[..., 3] *= self.opacity self.thumbnail = colormapped def _get_value(self, position): """Value of the data at a position in data coordinates. Parameters ---------- position : tuple Position in data coordinates. Returns ------- value : None Value of the data at the coord. """ return None