Semnodime

## ANSI.md

      
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                Semnodime
                / ANSI.md
            
            
              Created
              May 6, 2024 10:54
                — forked from fnky/ANSI.md
            
              
                ANSI Escape Codes
              
          
    ANSI Escape Sequences

Standard escape codes are prefixed with Escape:

Ctrl-Key: ^[
Octal: \033
Unicode: \u001b
Hexadecimal: \x1B
Decimal: 27


## 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:

## 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:
	#!/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:
	"""
	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: