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import matplotlib.pyplot as plt | |
from matplotlib.offsetbox import OffsetImage, AnnotationBbox | |
g = nx.krackhardt_kite_graph() | |
pos = nx.kamada_kawai_layout(g) | |
example = "unknown.jpg" | |
fig, ax = plt.subplots() | |
output_size = 25 |
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import proplot as plt, cmasher as cmr, pandas as pd,\ | |
numpy as np, os, sys, networkx as nx, warnings,\ | |
re | |
from pathlib import Path | |
warnings.simplefilter("ignore") | |
g = nx.krackhardt_kite_graph() | |
pos = nx.kamada_kawai_layout(g) |
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import proplot as plt, networkx as nx, pandas as pd | |
def bundle(g: nx.Graph, pos: dict): | |
from datashader.bundling import hammer_bundle | |
edges = [] | |
for u, v in g.edges(): | |
row = dict(source=u, target=v) | |
edges.append(row) | |
edges = pd.DataFrame(edges) |
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import numpy as np | |
from mpl_toolkits.mplot3d import Axes3D | |
from mpl_toolkits.mplot3d.art3d import Poly3DCollection | |
def fill_between_3d(ax,x1,y1,z1,x2,y2,z2,mode=1,c='steelblue',alpha=0.6): | |
""" |
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def arc_layout( | |
G: nx.Graph, subset_key="subset", radius=1, rotation=0, offset=0 | |
) -> dict: | |
"""Arc layout for networkx | |
Provides a layout where a multipartite graph is | |
displayed on a unit circle. This could provide clear | |
visuals for data that is highly clustered. | |
Parameters |
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import proplot as plt, cmasher as cmr, pandas as pd, numpy as np, os, sys, networkx as nx, warnings | |
def multilayer_layout( | |
G: nx.Graph, | |
subset_key="layer", | |
layout=nx.spring_layout, | |
separation: float = 10.0, | |
) -> dict: | |
# set positions |
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{"directed": false, "multigraph": false, "graph": {"name": "Krackhardt Kite Social Network"}, "nodes": [{"id": 0}, {"id": 1}, {"id": 2}, {"id": 3}, {"id": 4}, {"id": 5}, {"id": 6}, {"id": 7}, {"id": 8}, {"id": 9}], "links": [{"source": 0, "target": 1}, {"source": 0, "target": 2}, {"source": 0, "target": 3}, {"source": 0, "target": 5}, {"source": 1, "target": 3}, {"source": 1, "target": 4}, {"source": 1, "target": 6}, {"source": 2, "target": 3}, {"source": 2, "target": 5}, {"source": 3, "target": 4}, {"source": 3, "target": 5}, {"source": 3, "target": 6}, {"source": 4, "target": 6}, {"source": 5, "target": 6}, {"source": 5, "target": 7}, {"source": 6, "target": 7}, {"source": 7, "target": 8}, {"source": 8, "target": 9}]} |
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here is a gif |
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import networkx as nx, numpy as np, matplotlib.pyplot as plt | |
np.random.seed(0) | |
g = nx.florentine_families_graph() | |
# g = nx.krackhardt_kite_graph() | |
pos = nx.random_layout(g) | |
x = {node: p[0] for node, p in pos.items()} | |
y = {node: p[1] for node, p in pos.items()} | |
l = {node: node for node in g.nodes()} |
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import matplotlib.pyplot as plt | |
import numpy as np, networkx as nx | |
from matplotlib.collections import LineCollection | |
def setup( | |
g: nx.Graph, layout: dict or callable, node_kwargs={}, edge_kwargs={}, **kwargs | |
) -> list: | |
""" | |
Simple setup: creates scatter points and line segments (graph) |
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