Dmitry Messerman mdmitry1

General Performance Comparison

VF2 advantages

VF2 tends to perform better on graphs that are sparse (fewer connections), small, or have a regular structure (e.g., 2D meshes or bounded valence graphs with low valence). In some cases, Nauty cannot find a solution for these regular graphs if they exceed a certain size (a few dozen nodes).

Nauty advantages

Nauty (via pynauty) is generally the better algorithm for dense or large randomly connected graphs.

	#!/usr/bin/python3.14
	import arxiv,sh
	from json import load, loads, dump, dumps, JSONDecodeError
	from sys import argv
	from os import makedirs, listdir, getcwd
	from os.path import join, isdir, isfile, realpath, dirname
	from typing import List, Tuple
	from pydantic import RootModel
	from operator import itemgetter
	from rich import print as rprint

	#!/usr/bin/python3.12
	import json
	import pandas as pd

	# Read the JSON file
	with open('funds.json', 'r', encoding='utf-8') as f:
	data = json.load(f)

	# Extract the result array
	funds_list = data['funds']['result']

	#!/usr/bin/python3.12
	# coding: utf-8
	# author: github.com/vuiseng9
	# dependency ubt1804: graphviz libgraphviz-dev libgl1-mesa-glx

	import os, sys
	import pygraphviz
	from glob import glob
	from natsort import natsorted
	from rich import print as rprint

	#!/usr/bin/python3.14
	"""Query GitHub API"""
	from datetime import datetime
	from urllib.request import urlopen
	from pprint import pformat
	from sys import argv
	import json
	from os import popen

	def user_time(login):

	#!/usr/bin/python3.12
	import questionary
	from questionary import Style
	from rich.console import Console
	console_256=Console(color_system="256")
	fancy = Style([
	('qmark', 'fg:#673ab7 bold'),
	('question', 'bold'),
	('answer', 'fg:#ff00ff bg:#ffffff nobold'),
	('pointer', 'fg:#673ab7 bold'),

	MODULE=disjoint_set_c
	VER=12
	EXT=cpython-3$(VER)-$(HOSTTYPE)-gnu.so
	SO=$(MODULE).$(EXT)
	%.$(EXT): %_setup.py %.pyx
	env PYTHONPATH=/usr/local/lib/python3.12/dist-packages python3.$(VER) $< build_ext -i
	strip $@
	-rm -rf build
	all: $(SO)
	clean:

	#!/usr/bin/python3.12
	'''
	https://www.pythonguis.com/tutorials/qtableview-modelviews-numpy-pandas/
	'''
	from sys import argv, exit
	from rich import print as rprint
	from pandas import read_csv
	from os import name as osname, popen
	from os.path import realpath, basename, splitext, split
	from re import sub

	MODULE=primes_cython
	VER=14
	EXT=cpython-3$(VER)-$(HOSTTYPE)-gnu.so
	SO=$(MODULE).$(EXT)
	%: %.cpp
	g++ -O2 -o $@ $<
	strip $@
	%.$(EXT): %_setup.py %.pyx
	env PYTHONPATH=/usr/local/lib/python3.14/site-packages python3.$(VER) $< build_ext -i
	strip $@

	#!/usr/bin/tcsh -f
	"/usr/bin/true" '''\'
	setenv PYTHONPATH /home/mdmitry/python3.12/dist-packages:/home/mdmitry/python3.12/dist-packages6
	exec /usr/bin/python3.12 $0
	'''
	'''
	https://octave.sourceforge.io/octave/function/sombrero.html
	'''
	import numpy as np
	import matplotlib