Source code for livecellx.core.napari_visualizer

from livecellx.core.single_cell import SingleCellStatic, SingleCellTrajectory, SingleCellTrajectoryCollection
import numpy as np
from napari.viewer import Viewer
from livecellx.plot.visualizer import Visualizer

[docs]class NapariVisualizer:
[docs] def viz_traj(traj: SingleCellTrajectory, viewer: Viewer, viewer_kwargs=None): if viewer_kwargs is None: viewer_kwargs = dict() shapes = traj.get_scs_napari_shapes() shape_layer = viewer.add_shapes(shapes, **viewer_kwargs) return shape_layer
[docs] @staticmethod def map_colors(values, cmap="viridis"): import matplotlib import as cm if values is None or len(values) == 0: return [] minima = min(values) maxima = max(values) norm = matplotlib.colors.Normalize(vmin=minima, vmax=maxima, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap=cmap) res_colors = [mapper.to_rgba(v) for v in values] return res_colors
[docs] def gen_trajectories_shapes( trajectories: SingleCellTrajectoryCollection, viewer: Viewer, bbox=False, contour_sample_num=100, viewer_kwargs=None, text_parameters={ "string": "{track_id:d}\n{status}", "size": 12, "color": "white", "anchor": "center", "translation": [-2, 0], }, ): if viewer_kwargs is None: viewer_kwargs = dict() all_shapes = [] track_ids = [] all_scs = [] all_status = [] for track_id, traj in trajectories: traj_shapes, scs = traj.get_scs_napari_shapes( bbox=bbox, contour_sample_num=contour_sample_num, return_scs=True ) all_shapes.extend(traj_shapes) track_ids.extend([int(track_id)] * len(traj_shapes)) all_scs.extend(scs) all_status.extend([""] * len(traj_shapes)) properties = {"track_id": track_ids, "sc": all_scs, "status": all_status} # Track ID can be UUID, so we need to map it to an integer track_value_indices = [idx for idx, v in enumerate(track_ids)] shape_layer = viewer.add_shapes( all_shapes, properties=properties, face_color=NapariVisualizer.map_colors(track_value_indices), face_colormap="viridis", shape_type="polygon", text=text_parameters, name="trajectories", **viewer_kwargs ) return shape_layer