datavudeja

#!/usr/bin/env -S uv run
# /// script
# requires-python = ">=3.10"
# ///
"""Convert a Pipfile to requirements.txt format.

Usage:
    pipfile-to-reqs.py [-d|--dev] [Pipfile]

This will output the dependencies in a format suitable for a requirements.txt file.

datavudeja

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2010 Radim Rehurek <[email protected]>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html

"""Various general utility functions."""

from __future__ import with_statement
from contextlib import contextmanager

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import logging
from datetime import datetime, timedelta
from contextlib import closing, contextmanager

import sqlite3


logger = logging.getLogger('util.cache')
logger.setLevel(logging.WARNING)

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import argparse
import os
import sys
import time
from collections import defaultdict
from datetime import datetime
from pathlib import Path
from types import SimpleNamespace

import cv2

datavudeja

# ./tests/conftest.py
import pytest
from neomodel import config, db

from converter import Converter


@pytest.fixture(scope="session")
def db_connection():
    """Setup Neo4j database connection for all tests"""

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"""Inference pipelines and utilities.

This module contains the classes and high level APIs for predicting instances on new
data using trained models.

The inference logic is implemented at two levels:

- Low-level `InferenceModel`s which subclass `tf.keras.Model` and implement the core
  TensorFlow operations surrounding inference. These should only be used when
  implementing custom inference routines, such as real-time or performance-critical

datavudeja

Suppose you want to want to track all calls (args, kwargs and result) made to a given method while running a command, you can do it using the simple decorator defined in the spy.py module of this gist.

Usage

You can use it to spy either on a single instance or to all instances of given class.

The calls will be stored in a list that must be passed by instantiated beforehand and passed to the decorator.

Spy on single instance

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"""
A mechanism to keep track of no. of class instances created.
"""

from __future__ import print_function

class CountInstances(type):
	def __new__(meta, classname, supers, classdict):
		classdict['numInstances'] = 0
		return type.__new__(meta, classname, supers, classdict)

datavudeja

#!/usr/bin/env python

import subprocess
import tempfile
import os
import signal


class ShellCmd:
    """Helpful class to spawn commands and keep track of them"""

datavudeja

import json
import os
import sys
import tempfile
from typing import cast

import requests
from requests.models import Response

CLOUDFLARE_API_TOKEN = "YOUR_API_TOKEN"