graph layer

examples/add-graph-networkx.py

@@ -0,0 +1,23 @@
+"""
+Add networkx graph
+==================
+
+Add a networkx graph directly to napari. This works as long as nodes
+have a "pos" attribute with the node coordinate.
+
+.. tags:: visualization-basic
+"""
+
+import networkx as nx
+
+import napari
+
+hex_grid = nx.hexagonal_lattice_graph(5, 5, with_positions=True)
+# below conversion not needed after napari/napari-graph#11 is released
+hex_grid_ints = nx.convert_node_labels_to_integers(hex_grid)
+
+viewer = napari.Viewer()
+layer = viewer.add_graph(hex_grid_ints, size=1)
+
+if __name__ == "__main__":
+    napari.run()

examples/add_graph.py

@@ -0,0 +1,38 @@
+"""
+Add graph
+===================
+
+Display a random undirected graph using the graph layer.
+
+.. tags:: visualization-basic
+"""
+
+import numpy as np
+import pandas as pd
+from napari_graph import UndirectedGraph
+
+import napari
+
+
+def build_graph(n_nodes: int, n_neighbors: int) -> UndirectedGraph:
+    neighbors = np.random.randint(n_nodes, size=(n_nodes * n_neighbors))
+    edges = np.stack([np.repeat(np.arange(n_nodes), n_neighbors), neighbors], axis=1)
+
+    nodes_df = pd.DataFrame(
+        400 * np.random.uniform(size=(n_nodes, 4)),
+        columns=["t", "z", "y", "x"],
+    )
+    graph = UndirectedGraph(edges=edges, coords=nodes_df)
+
+    return graph
+
+
+graph = build_graph(n_nodes=1_000_000, n_neighbors=5)
+
+viewer = napari.Viewer()
+layer = viewer.add_graph(graph, out_of_slice_display=True)
+
+
+if __name__ == "__main__":
+
+    napari.run()

examples/add_points_with_features.py

@@ -29,22 +29,22 @@
 face_color_cycle = ['blue', 'green']
 
 # create a points layer where the face_color is set by the good_point feature
-# and the edge_color is set via a color map (grayscale) on the confidence
+# and the border_color is set via a color map (grayscale) on the confidence
 # feature.
 points_layer = viewer.add_points(
     points,
     features=features,
     size=20,
-    edge_width=7,
-    edge_width_is_relative=False,
-    edge_color='confidence',
-    edge_colormap='gray',
+    border_width=7,
+    border_width_is_relative=False,
+    border_color='confidence',
+    border_colormap='gray',
     face_color='good_point',
     face_color_cycle=face_color_cycle
 )
 
-# set the edge_color mode to colormap
-points_layer.edge_color_mode = 'colormap'
+# set the border_color mode to colormap
+points_layer.border_color_mode = 'colormap'
 
 
 # bind a function to toggle the good_point annotation of the selected points

examples/add_points_with_multicolor_text.py

@@ -33,23 +33,23 @@
 }
 
 # create a points layer where the face_color is set by the good_point feature
-# and the edge_color is set via a color map (grayscale) on the confidence
+# and the border_color is set via a color map (grayscale) on the confidence
 # feature
 points_layer = viewer.add_points(
     points,
     features=features,
     text=text,
     size=20,
-    edge_width=7,
-    edge_width_is_relative=False,
-    edge_color='confidence',
-    edge_colormap='gray',
+    border_width=7,
+    border_width_is_relative=False,
+    border_color='confidence',
+    border_colormap='gray',
     face_color='good_point',
     face_color_cycle=color_cycle,
 )
 
-# set the edge_color mode to colormap
-points_layer.edge_color_mode = 'colormap'
+# set the border_color mode to colormap
+points_layer.border_color_mode = 'colormap'
 
 if __name__ == '__main__':
     napari.run()

examples/add_points_with_text.py

@@ -34,23 +34,23 @@
 }
 
 # create a points layer where the face_color is set by the good_point feature
-# and the edge_color is set via a color map (grayscale) on the confidence
+# and the border_color is set via a color map (grayscale) on the confidence
 # feature.
 points_layer = viewer.add_points(
     points,
     features=features,
     text=text,
     size=20,
-    edge_width=7,
-    edge_width_is_relative=False,
-    edge_color='confidence',
-    edge_colormap='gray',
+    border_width=7,
+    border_width_is_relative=False,
+    border_color='confidence',
+    border_colormap='gray',
     face_color='good_point',
     face_color_cycle=face_color_cycle,
 )
 
-# set the edge_color mode to colormap
-points_layer.edge_color_mode = 'colormap'
+# set the border_color mode to colormap
+points_layer.border_color_mode = 'colormap'
 
 if __name__ == '__main__':
     napari.run()

examples/mgui_with_threadpoolexec_.py

@@ -53,8 +53,8 @@ def _make_blob():
         data = blobs[:, : image.ndim]
         kwargs = {
             "size": blobs[:, -1],
-            "edge_color": "red",
-            "edge_width": 2,
+            "border_color": "red",
+            "border_width": 2,
             "face_color": "transparent",
         }
         return (data, kwargs, 'points')

examples/mgui_with_threadworker_.py

@@ -36,8 +36,8 @@ def detect_blobs() -> LayerDataTuple:
         points = blobs[:, : image.ndim]
         meta = {
             "size": blobs[:, -1],
-            "edge_color": "red",
-            "edge_width": 2,
+            "border_color": "red",
+            "border_width": 2,
             "face_color": "transparent",
         }
         # return a "LayerDataTuple"

examples/nD_points_with_features.py

@@ -29,30 +29,30 @@
     [True, True, True, True, False, False, False, False]
     * int(blobs.shape[0] / 2)
 )
-edge_feature = np.array(['A', 'B', 'C', 'D', 'E'] * int(len(points) / 5))
+border_feature = np.array(['A', 'B', 'C', 'D', 'E'] * int(len(points) / 5))
 
 features = {
     'face_feature': face_feature,
-    'edge_feature': edge_feature,
+    'border_feature': border_feature,
 }
 
 points_layer = viewer.add_points(
     points,
     features=features,
     size=3,
-    edge_width=5,
-    edge_width_is_relative=False,
-    edge_color='edge_feature',
+    border_width=5,
+    border_width_is_relative=False,
+    border_color='border_feature',
     face_color='face_feature',
     out_of_slice_display=False,
 )
 
 # change the face color cycle
 points_layer.face_color_cycle = ['white', 'black']
 
-# change the edge_color cycle.
+# change the border_color cycle.
 # there are 4 colors for 5 categories, so 'c' will be recycled
-points_layer.edge_color_cycle = ['c', 'm', 'y', 'k']
+points_layer.border_color_cycle = ['c', 'm', 'y', 'k']
 
 if __name__ == '__main__':
     napari.run()

examples/nuclei_segmentation_graph.py

@@ -0,0 +1,44 @@
+"""
+Nuclei Segmentation Graph
+===============
+
+Creates a delaunay graph from maxima of cell nuclei.
+
+.. tags:: visualization-nD
+"""
+from itertools import combinations
+
+import numpy as np
+from napari_graph import UndirectedGraph
+from scipy.spatial import Delaunay
+from skimage import data, feature, filters
+
+import napari
+
+
+def delaunay_edges(points: np.ndarray) -> np.ndarray:
+    delaunay = Delaunay(points)
+    edges = set()
+    for simplex in delaunay.simplices:
+        # each simplex is represented as a list of four points.
+        # we add all edges between the points to the edge list
+        edges |= set(combinations(simplex, 2))
+
+    return np.asarray(list(edges))
+
+
+cells = data.cells3d()
+
+nuclei = cells[:, 1]
+smooth = filters.gaussian(nuclei, sigma=10)
+nodes_coords = feature.peak_local_max(smooth)
+edges = delaunay_edges(nodes_coords[:, 1:])
+graph = UndirectedGraph(edges, nodes_coords)
+viewer, image_layer = napari.imshow(
+    cells, channel_axis=1, name=['membranes', 'nuclei'], ndisplay=3
+)
+graph_layer = viewer.add_graph(graph)
+viewer.camera.angles = (10, -20, 130)
+
+if __name__ == '__main__':
+    napari.run()

examples/spherical_points.py

@@ -20,7 +20,7 @@
     face_color=colors,
     size=sizes,
     shading='spherical',
-    edge_width=0,
+    border_width=0,
 )
 
 # antialiasing is currently a bit broken, this is especially bad in 3D so

napari/__init__.py

@@ -29,6 +29,7 @@
     'utils': ['sys_info'],
     'utils.notifications': ['notification_manager'],
     'view_layers': [
+        'view_graph',
         'view_image',
         'view_labels',
         'view_path',

napari/__init__.pyi

@@ -2,6 +2,7 @@ import napari.utils.notifications
 from napari._qt.qt_event_loop import gui_qt, run
 from napari.plugins.io import save_layers
 from napari.view_layers import (
+    view_graph,
     view_image,
     view_labels,
     view_path,
@@ -20,6 +21,7 @@ notification_manager: napari.utils.notifications.NotificationManager
 __all__ = (
     'Viewer',
     'current_viewer',
+    'view_graph',
     'view_image',
     'view_labels',
     'view_path',

napari/_qt/layer_controls/qt_graph_controls.py

@@ -0,0 +1,5 @@
+from napari._qt.layer_controls.qt_points_controls import QtPointsControls
+
+
+class QtGraphControls(QtPointsControls):
+    pass

napari/_qt/layer_controls/qt_layer_controls_container.py

@@ -1,5 +1,6 @@
 from qtpy.QtWidgets import QFrame, QStackedWidget
 
+from napari._qt.layer_controls.qt_graph_controls import QtGraphControls
 from napari._qt.layer_controls.qt_image_controls import QtImageControls
 from napari._qt.layer_controls.qt_labels_controls import QtLabelsControls
 from napari._qt.layer_controls.qt_points_controls import QtPointsControls
@@ -8,6 +9,7 @@
 from napari._qt.layer_controls.qt_tracks_controls import QtTracksControls
 from napari._qt.layer_controls.qt_vectors_controls import QtVectorsControls
 from napari.layers import (
+    Graph,
     Image,
     Labels,
     Points,
@@ -20,6 +22,7 @@
 
 layer_to_controls = {
     Labels: QtLabelsControls,
+    Graph: QtGraphControls,
     Image: QtImageControls,
     Points: QtPointsControls,
     Shapes: QtShapesControls,