Casper van Elteren 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))

	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()}

	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)

	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

	import matplotlib.pyplot as plt, cmasher as cmr
	import numpy as np
	layout = np.zeros((10, 10), dtype = object)

	layout[-1, :] = np.arange(1, 11)
	layout[:, -1] = np.arange(20, 30)

	fig = plt.figure(constrained_layout = 1)
	axs = fig.subplot_mosaic(layout)
	ax = axs.get(-1)

	from mpl_toolkits.mplot3d import axes3d
	import matplotlib.pyplot as plt
	from mpl_toolkits.mplot3d.art3d import Line3DCollection

	fig, ax = plt.subplots(subplot_kw = dict(projection = '3d'))

	# generate data
	x = np.linspace(-5, 5, 500)
	y = np.linspace(-5, 5, 500)
	z = np.exp(-(x - 2)**2)

	import sys, time
	class progbar:
	def __init__(self, x, stream = sys.stdout,
	char = "#"):
	self.x = x
	self.stream = stream
	self.char = char

	def __iter__(self):
	self.n = len(self.x)

	# Original by Jake VanderPlas
	# https://gist.github.com/jakevdp/91077b0cae40f8f8244a
	# License: BSD-style
	# cvanelteren: small edit since the original was not compatible with latest mpl
	import matplotlib.pyplot as plt, numpy as np
	def discrete_cmap(N, base_cmap=None):
	"""Create an N-bin discrete colormap from the specified input map"""

	# Note that if base_cmap is a string or None, you can simply do
	# return plt.cm.get_cmap(base_cmap, N)