RusselWebber

import logging
import os
import pandas as pd

LOG = logging.getLogger(__name__)

def read_dataframe_from_pickle_or_url(url: str, path: str) -> pd.DataFrame:
    """Read JSON data from the URL and convert to a pandas dataframe.
    
    The dataframe is cached locally at the supplied path.

RusselWebber

def hello_gist(name):
  return f"Hello {name}, this code was imported from a gist."

RusselWebber

class SharedResourceClass:
    """This could be any resource that you would like to share.
    Perhaps because the class is slow to initialise or you want to track usage
    """

    def __init__(self, initial_value: int) -> None:

        self._shared_resource = initial_value

    def access_shared_resource(self) -> int:

RusselWebber

from typing import List, Any


def only_strings(n):
    result = []
    for i in range(int(n)):
        result.append(["col1", "col2"])
    return result

RusselWebber

import asyncio
import numpy as np
from io import BytesIO
from contextlib import closing
from aiokafka import AIOKafkaConsumer

async def kafka_topic_subscription(topic: str, server: str, fps: str) -> np.ndarray:
    """Subscribe to a numpy array from a Kafka topic"""
    consumer = AIOKafkaConsumer(
        topic, bootstrap_servers=server, auto_offset_reset="earliest"

RusselWebber

import asyncio

async def async_counter(stop: int, updates_per_second: int) -> int:
        """Asynchronously generates a sequence of numbers up to stop at a rate of updates_per_second."""
        for i in range(stop):
                await asyncio.sleep(1.0 / updates_per_second)
                yield i

RusselWebber

import time

def counter(stop: int, updates_per_second: int) -> int:
        """Generates a sequence of numbers up to stop at a rate of updates_per_second."""
        for i in range(stop):
                time.sleep(1.0 / updates_per_second)
                yield i