cvanelteren

import proplot as plt, networkx as nx, pandas as pd
from datashader.bundling import hammer_bundle

def bundle(g: nx.Graph, pos: dict):
    edges = []
    for u, v in g.edges():
        row = dict(source=u, target=v)
        edges.append(row)
    edges = pd.DataFrame(edges)

cvanelteren

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):

    """

cvanelteren

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

cvanelteren

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

cvanelteren

{"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}]}

cvanelteren

here is a gif

cvanelteren

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()}

cvanelteren

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)

cvanelteren

cp ~/library.bib library.bib
# flatex is a python package
# flatex paper.tex arxiv_paper.tex
# now called pydflatex?
pydflatex paper.tex arxiv_paper.tex
arxiv-collector arxiv_paper.tex

cvanelteren

Uploading to arxiv

• Use pydflatex to flatten books
• Use arxiv-collector to generate a compatible zip for arxiv
• Minted needs to be compiled with finalizedcache=true and then with frozencache=true (see gpoore/minted#113 (comment))

The first steps can be combined with https://gist.github.com/cvanelteren/ba4a82350e35f3b48262c2aa1089c64f