Semnodime

## kmeansExample.py
"""
This is a pure Python3 implementation of the K-means Clustering algorithm.
It is based on a GitHub Gist which uses Python2:
https://gist.github.com/iandanforth/5862470
I have refactored the code and to assure the code obeys Python Enhancement Proposals (PEPs) rules.
After reading through this code you should understand clearly how K-means works.
This script specifically avoids using numpy or other more obscure libraries.
It is meant to be *clear* not fast.
I have also added integration with the plot.ly plotting library.
So you can see the clusters found by this algorithm. To install plotly run:

## aiohttp_demo.py
"""Small demo of a single async request utilizing the aiohttp framework. The point is to understand what is going on."""
import aiohttp
import asyncio


async def main():
    async with aiohttp.ClientSession() as session:
        # session.get() returns a context manager that can be used by `await` and `async with`
        _req_context_manager = session.get('http://localhost:10000')
        # print(_req_context_manager)

## How to solve coding errors.txt
A good way to find issues in your program is to talk to a duck.
Explain it in a way a duck can understand!
If that is too silly, file a well documented stack overflow issue but before actually sending the question,
think a short period of time about something totally different… and then try to answer the question yourself in a well documented way.
If you thereby solve an issue which turns out to not be a silly coding error, then post the documented problem and your own solution if you found one.

## imreconstruct.py
import cv2
import numpy as np


def imreconstruct(marker: np.ndarray, mask: np.ndarray, radius: int = 1):
    """Iteratively expand the markers white keeping them limited by the mask during each iteration.

    :param marker: Grayscale image where initial seed is white on black background.
    :param mask: Grayscale mask where the valid area is white on black background.
    :param radius Can be increased to improve expansion speed while causing decreased isolation from nearby areas.

## space2exchange.css
@-moz-document regexp("https?://(\\w*\\.)?stack(exchange|overflow)\\.com/posts/\\d+/edit") {
#sidebar,
#left-sidebar,
#footer {
    display: none;
}

#content,
#mainbar,
.container {

## main.py
"""Small snippet that demonstrates how to calculate the crc for packets transmitted via the Nordic NRF24L01."""


def bin2hex(x):
    def bin2hex_helper(r):
        while r:
            yield r[0:2].upper()
            r = r[2:]

    hex_data = hex(int(x, 2))[2:]

## filter_signal.py
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np

description = """Script to preprocess IQ-signals in-place: Remove noise and combine nearby signal parts into one."""


def list_strong_signal_indices(signal, threshold: int):
    """
    Return a list of all indices where the magnitude of the I-part is not smaller than the threshold.

## weird.py
(... == ...,) != ([...,0],[0,...])  # Python can be weird, if you want it to be….
True

## pdf_flatedecode.py
#!/usr/bin/env python3

import re
import sys
import zlib

def main(filename:str):
    """This script will find each FlateDecode stream in the given PDF document using a regular expression, unzip it, and print out the unzipped data."""

    with open(filename, 'rb') as pdf_file:

## nansi
sed -r "s/\x1B\[[0-9;]*[mK]//g"
	"""
	This is a pure Python3 implementation of the K-means Clustering algorithm.
	It is based on a GitHub Gist which uses Python2:
	https://gist.github.com/iandanforth/5862470
	I have refactored the code and to assure the code obeys Python Enhancement Proposals (PEPs) rules.
	After reading through this code you should understand clearly how K-means works.
	This script specifically avoids using numpy or other more obscure libraries.
	It is meant to be clear not fast.
	I have also added integration with the plot.ly plotting library.
	So you can see the clusters found by this algorithm. To install plotly run:
	"""Small demo of a single async request utilizing the aiohttp framework. The point is to understand what is going on."""
	import aiohttp
	import asyncio


	async def main():
	async with aiohttp.ClientSession() as session:
	# session.get() returns a context manager that can be used by `await` and `async with`
	_req_context_manager = session.get('http://localhost:10000')
	# print(_req_context_manager)
	A good way to find issues in your program is to talk to a duck.
	Explain it in a way a duck can understand!
	If that is too silly, file a well documented stack overflow issue but before actually sending the question,
	think a short period of time about something totally different… and then try to answer the question yourself in a well documented way.
	If you thereby solve an issue which turns out to not be a silly coding error, then post the documented problem and your own solution if you found one.
	import cv2
	import numpy as np


	def imreconstruct(marker: np.ndarray, mask: np.ndarray, radius: int = 1):
	"""Iteratively expand the markers white keeping them limited by the mask during each iteration.

	:param marker: Grayscale image where initial seed is white on black background.
	:param mask: Grayscale mask where the valid area is white on black background.
	:param radius Can be increased to improve expansion speed while causing decreased isolation from nearby areas.
	@-moz-document regexp("https?://(\\w*\\.)?stack(exchange\|overflow)\\.com/posts/\\d+/edit") {
	#sidebar,
	#left-sidebar,
	#footer {
	display: none;
	}

	#content,
	#mainbar,
	.container {
	"""Small snippet that demonstrates how to calculate the crc for packets transmitted via the Nordic NRF24L01."""


	def bin2hex(x):
	def bin2hex_helper(r):
	while r:
	yield r[0:2].upper()
	r = r[2:]

	hex_data = hex(int(x, 2))[2:]
	#! /usr/bin/env python3
	# -- coding: utf-8 --
	import numpy as np

	description = """Script to preprocess IQ-signals in-place: Remove noise and combine nearby signal parts into one."""


	def list_strong_signal_indices(signal, threshold: int):
	"""
	Return a list of all indices where the magnitude of the I-part is not smaller than the threshold.
	(... == ...,) != ([...,0],[0,...]) # Python can be weird, if you want it to be….
	True
	#!/usr/bin/env python3

	import re
	import sys
	import zlib

	def main(filename:str):
	"""This script will find each FlateDecode stream in the given PDF document using a regular expression, unzip it, and print out the unzipped data."""

	with open(filename, 'rb') as pdf_file: