Conner Cowling connerxyz

## d3-scaling-tick-number-with-window-inner-width.js
var xScale = d3.scaleTime()
  .domain(d3.extent(data, function(d) { return d.key; })) // Input
  .range([0, width]); // output

// Quantize 400px - 1600px window range
var tickScale = d3.scaleQuantize()
  .domain([400, 1600])
  .range([3, 7, 10]);

// Set number of ticks based on window height

## d3-dynamic-discrete-values-to-continuous-color.js
// An array of discrete values (possibly dynamically generated)
var discreteValues = ['John', 'Jack', 'Jill', 'Jane', 'Jim', 'Joan'];
// 1st scale establishes relationship between continuous [0,1] and
// the array index of the discrete values
var preColor = d3.scaleLinear()
  .domain([0, discreteValues.length - 1])
  .range([0, 1]);
console.log(preColor(discreteValues.indexOf('John'))) // 0
console.log(preColor(discreteValues.indexOf('Jack'))) // 0.2
console.log(preColor(discreteValues.indexOf('Joan'))) // 1.0

## env.save.sh
#!/bin/bash

# Usage string
usage() {
  cat << USAGE

    This script exists to serialize a local conda environment.
    The output files produced by this script are version-controllable, allowing
    for the local conda env definition to included with the repository, distributed,
    and recreated on other hosts using either conda *or* pip.

## store-magic-for-meta-pattern.md

      
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                connerxyz
                / store-magic-for-meta-pattern.md
            
            
              Last active
              September 16, 2021 15:04
            
              
                Using %store magic to capture meta within/across Jupyter notebooks
              
          
    Overview

The %store magic allows data to be stored in the ipython database, outside of a notebook's particular kernel,
so it can be accessed from other notebooks.
Instructions

Initialization

If you're executing only one notebook, or this is the

  
## dict-to-json.hdfs.py
# You've got a Python dict in the driver program or shell
x = {"a":1,"b":2}

# You want to write it to a single HDFS file in JSON format
sc.parallelize([x]).toDF().coalesce(1).write.mode('append').json(/hdfs/path/...)

## semantic-ui-sidebar-example.html
<!DOCTYPE HTML>
<html>
	<head>
	    <meta charset="utf-8" />
	    <title>Semantic UI Sidebar Example</title>
	    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/semantic-ui/2.3.2/semantic.min.css"/>
	    <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.1.1/jquery.min.js"></script>
	    <script src="https://cdnjs.cloudflare.com/ajax/libs/semantic-ui/2.3.2/semantic.min.js"></script>
	</head>
	<body>

## README.md

      
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                connerxyz
                / README.md
            
            
              Last active
              June 28, 2018 16:20
            
              
                Porter stemmer: Python's NLTK vs. Java's OpenNLP: How to get *almost* identical Porter stemmers
              
          
    Porter stemmer: Python's NLTK vs. Java's OpenNLP:

How to get almost equivalent Porter stemmers between Java and Python.
One remaining difference is how they handle non-dictionary words ending in "s":
Assuming you tokenize the following string after lowercase normalization using [a-z]\w+...

"EMI"s request for an appeal should be denied until a final judgment is entered. Document filed by Michael Robertson. (js) (Entered: 02/01/2012)"


## pandas-dataframe.remove-all-styling.py
"""
Remove *all* default, hard-coded, CSS classes and inline styling attached to tables when calling df.to_html(),
and add your own classes to each tag ad hoc.

This will probably be extremely simple in the future when the styling API is more mature. For now, we bleach it.

May need to `conda install bleach` or `pip install bleach`
"""

import pandas as pd

## app.py
from flask import Flask

app = Flask(__name__)

# Register core.utils with all template contexts
import utils
@app.context_processor
def utils_context():
    return utils.registry

## mkdocs-video-renders.md

      
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                connerxyz
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              October 31, 2017 02:35
            
              
                Experimenting with rendering video via Markdown/MkDocs (and IPython.display)
              
          
    Rendering Video w/ MkDocs

Render video via HTML5 <video> tags in markdown cell?

Method 1

Using the <source> element:
	var xScale = d3.scaleTime()
	.domain(d3.extent(data, function(d) { return d.key; })) // Input
	.range([0, width]); // output

	// Quantize 400px - 1600px window range
	var tickScale = d3.scaleQuantize()
	.domain([400, 1600])
	.range([3, 7, 10]);

	// Set number of ticks based on window height
	// An array of discrete values (possibly dynamically generated)
	var discreteValues = ['John', 'Jack', 'Jill', 'Jane', 'Jim', 'Joan'];
	// 1st scale establishes relationship between continuous [0,1] and
	// the array index of the discrete values
	var preColor = d3.scaleLinear()
	.domain([0, discreteValues.length - 1])
	.range([0, 1]);
	console.log(preColor(discreteValues.indexOf('John'))) // 0
	console.log(preColor(discreteValues.indexOf('Jack'))) // 0.2
	console.log(preColor(discreteValues.indexOf('Joan'))) // 1.0
	#!/bin/bash

	# Usage string
	usage() {
	cat << USAGE

	This script exists to serialize a local conda environment.
	The output files produced by this script are version-controllable, allowing
	for the local conda env definition to included with the repository, distributed,
	and recreated on other hosts using either conda or pip.
	# You've got a Python dict in the driver program or shell
	x = {"a":1,"b":2}

	# You want to write it to a single HDFS file in JSON format
	sc.parallelize([x]).toDF().coalesce(1).write.mode('append').json(/hdfs/path/...)
	from flask import Flask

	app = Flask(__name__)

	# Register core.utils with all template contexts
	import utils
	@app.context_processor
	def utils_context():
	return utils.registry