mauicv

import asyncio
from threading import Thread, Timer

import logging
import sys


logging.basicConfig(
    level=logging.INFO,
    format='%(threadName)10s %(name)18s: %(message)s',

mauicv

from functools import reduce
import operator
from itertools import tee


def list_get(dictionary, list):
    return reduce(operator.getitem, list, dictionary)


def pairwise(iterable):

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var state = {
  prop: undefined
  doSomething: (envName) => {},
  doSomethingElse: (_) => {},
  setProp: (v) => {
  	this.prop = v
  },
}

process.stdin.setEncoding('utf-8')

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from subprocess import Popen, PIPE
import json


class Manager:
    def __init__(self, name):
        self.ps = Popen(
                ['nodejs', 'src/js/index.js'],
                stdin=PIPE, stdout=PIPE)
        self.name = name

mauicv

"""
Simple wrapper to compute a parrallelize a function.

given a function f that takes a single argument, `arg`, the batch_job decorator
turns f into a function that takes a list of args and computes each result in
a seperate process. It then returns the results as a list.

___

use:

mauicv

"""Functions that can be used as decorators to check the state of class datastructures are sound.

The class_validator function adds a validation step to each method in the decorated class if the environment is
in TESTING mode. This is used primarily for testing in order to catch cases where class instances enter states
they shouldn't.

Example:

```
def simple_validator(self, *args, **kwargs):

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# taken from https://github.com/bulletphysics/bullet3/issues/3131

import ctypes
import sys
import os


class RedirectStream(object):
    @staticmethod
    def _flush_c_stream(stream):

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mauicv

mauicv