xLaszlo

import gzip
import json
from dataclasses import asdict
from pydantic import BaseModel


class DataFile:

    def __init__(self, data_type, filename, mode, loader=None):
        if mode not in ['r', 'w', 'rt', 'wt']:

xLaszlo

# !pip install GitPython

from git import Repo
from git.exc import NoSuchPathError
from git.exc import InvalidGitRepositoryError

class RepoData:
    def __init__(self, repo_directory):
        self.repo_directory = repo_directory
        self.repo_exists = False

xLaszlo

import re
import os
import json
import typer
import pandas as pd


def nb_query(query, fnames=None):
    if isinstance(query, str):
        query_fun = lambda line: re.match(f'.*{query}.*', line)

xLaszlo

import json
from datetime import datetime
from datetime import date
from pydantic.dataclasses import dataclass
from dataclasses import asdict

@dataclass(frozen=True, order=True)
class Foo:                                      
    name: str 
    int_value: int

xLaszlo

# from http://www.keithschwarz.com/darts-dice-coins/ by Keith Schwarz (htiek@cs.stanford.edu)
# and https://github.com/asmith26/Vose-Alias-Method/blob/master/vose_sampler/vose_sampler.py

class VoseAlias:

    def __init__(self, probs, seed=42, rng=None):
        self.N = len(probs)
        self.rng = rng or np.random.default_rng(seed)
        self.aliases = np.zeros(self.N, dtype=np.int64)
        self.probs = np.zeros(self.N)

xLaszlo

MIT License

Copyright (c) 2024 Laszlo Sragner

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

xLaszlo

import numpy as np
import scipy.sparse as sps
from collections import Counter
from sklearn.feature_extraction.text import CountVectorizer

# Use sklearn's vectorizers with a custom tokenizer to turn a string into a one-hot vector of 2 and 3 long substrings.
# Store a normalised version in a sparse matrix.

class StringSearcher: