Michael Dorner michaeldorner

## count_ipynb_loc.py
def count_ipynb_loc(ipynb_file):
    with open(ipynb_file) as file:
        content = json.load(file)
    cells = content['cells']
    loc = sum(len(c['source']) for c in cells if c['cell_type'] == 'code')
    return loc

## contacts.csv

          
            person_1000205
            person_1000322
            person_1000414
            person_1000701
            person_1000835
            person_1000866
            person_1001146
            person_1001525
            person_1001934
            person_1002609
            person_1002682
            person_1002751
            person_1002975
            person_1003224
            person_1003247
            person_1003259
            person_1003338
            person_1003473
            person_1003618
            person_1003801
            person_1003821
            person_1003840
            person_1003913
            person_1003966
            person_1004017
            person_1004395
            person_1004424
            person_1004689
            person_1004882
            person_1005592
            person_1005610
            person_1005920
            person_1005967
            person_1005970
            person_1006073
            person_1006166
            person_1006322
            person_1006497
            person_1006514
            person_1006692
            person_1006773
            person_1006805
            person_1010049
            person_1010050
            person_1010055
            person_1010056
            person_1010077
            person_1010103
            person_1010110
            person_1010111
            person_1010186
            person_1010211
            person_1010334
            person_1010465
            person_1010674
            person_1011530
            person_1012974
            person_1013003
            person_1013030
            person_1013478
            person_1013508
            person_1013984
            person_1014288
            person_1014443
            person_1014453
            person_1014468
            person_1014491
            person_1014545
            per

## get_github_pulls.py
import argparse
import json
from tqdm.auto import tqdm
from agithub.GitHub import GitHub


def get_pulls(client, owner, repository):
    pulls = []
    status, pulls = client.repos[owner][repository].pulls.get(state='all')
    if status != 200:

## code_review.tex
\documentclass{standalone}

\usepackage{amsmath,amssymb}

\usepackage{charter}
\usepackage[charter]{mathdesign}

%\font\nullfont=cmr10


## hypergraph_example.tex
\documentclass{minimal}

\usepackage{tikz}
\usepackage{tikz-network}

\usetikzlibrary{calc, backgrounds, positioning}
\input{tikz-hypergraph}

\usepackage{xcolor}

## zenodo_upload.py
import argparse
import os

import requests
from urllib3.util.retry import Retry
from requests.adapters import HTTPAdapter
from tqdm import tqdm
from tqdm.utils import CallbackIOWrapper

HEADERS = {'Content-Type': 'application/json'}

## temporal_graph.tikz
\documentclass{minimal}

\usepackage{tikz}
\usetikzlibrary{backgrounds, positioning}

\usepackage{xcolor}

\definecolor{ACMYellow}{RGB}{255, 214, 0}
\definecolor{ACMOrange}{RGB}{252, 146, 0}
\definecolor{ACMRed}{RGB}{253, 27, 20}

## benchmark_bfs.py
import timeit
import random
import networkx as nx

# from https://networkx.org/documentation/stable/_modules/networkx/algorithms/components/connected.html#connected_components
def _plain_bfs(G, source):
    """A fast BFS node generator"""
    G_adj = G.adj
    seen = set()
    nextlevel = {source}

## simulation_cycle.tex
\begin{figure}[!htp] \centering
\begin{tikzpicture}

\def \radius {3cm}
\def \margin {10} % margin in angles, depends on the radius
\def \n {6}

\draw[color=white, name path=c] (0:\radius) arc (0:360:\radius);

\node[draw, rounded corners, fill=white, name path=realitynode] at ({360/\n * 2}:\radius) (reality) {Reality of interest};

## information_diffusion.py
import networkx as nx

def diffuse_information(G: nx.MultiGraph, seed: list, case='best_case'):
    informed_nodes = {n: None for n in seed}
    needs_update = True

    while needs_update:
        needs_update = False
        for u, v, dt in G.edges(nbunch=informed_nodes, data='diffusion_time'):
            if u in informed_nodes:
	def count_ipynb_loc(ipynb_file):
	with open(ipynb_file) as file:
	content = json.load(file)
	cells = content['cells']
	loc = sum(len(c['source']) for c in cells if c['cell_type'] == 'code')
	return loc
	import argparse
	import json
	from tqdm.auto import tqdm
	from agithub.GitHub import GitHub


	def get_pulls(client, owner, repository):
	pulls = []
	status, pulls = client.repos[owner][repository].pulls.get(state='all')
	if status != 200:
	\documentclass{standalone}

	\usepackage{amsmath,amssymb}

	\usepackage{charter}
	\usepackage[charter]{mathdesign}

	%\font\nullfont=cmr10
	\documentclass{minimal}

	\usepackage{tikz}
	\usepackage{tikz-network}

	\usetikzlibrary{calc, backgrounds, positioning}
	\input{tikz-hypergraph}

	\usepackage{xcolor}
	import argparse
	import os

	import requests
	from urllib3.util.retry import Retry
	from requests.adapters import HTTPAdapter
	from tqdm import tqdm
	from tqdm.utils import CallbackIOWrapper

	HEADERS = {'Content-Type': 'application/json'}
	\documentclass{minimal}

	\usepackage{tikz}
	\usetikzlibrary{backgrounds, positioning}

	\usepackage{xcolor}

	\definecolor{ACMYellow}{RGB}{255, 214, 0}
	\definecolor{ACMOrange}{RGB}{252, 146, 0}
	\definecolor{ACMRed}{RGB}{253, 27, 20}
	import timeit
	import random
	import networkx as nx

	# from https://networkx.org/documentation/stable/_modules/networkx/algorithms/components/connected.html#connected_components
	def _plain_bfs(G, source):
	"""A fast BFS node generator"""
	G_adj = G.adj
	seen = set()
	nextlevel = {source}
	\begin{figure}[!htp] \centering
	\begin{tikzpicture}

	\def \radius {3cm}
	\def \margin {10} % margin in angles, depends on the radius
	\def \n {6}

	\draw[color=white, name path=c] (0:\radius) arc (0:360:\radius);

	\node[draw, rounded corners, fill=white, name path=realitynode] at ({360/\n * 2}:\radius) (reality) {Reality of interest};
	import networkx as nx

	def diffuse_information(G: nx.MultiGraph, seed: list, case='best_case'):
	informed_nodes = {n: None for n in seed}
	needs_update = True

	while needs_update:
	needs_update = False
	for u, v, dt in G.edges(nbunch=informed_nodes, data='diffusion_time'):
	if u in informed_nodes: