Shawn Graham shawngraham

## GPT3.js
// SPDX-License-Identifier: MIT
//
// This code will add a GPT3() function in your Google Sheets
// This code is originally inspired from https://twitter.com/fabianstelzer/status/1572926883179778050
// To use it, insert your API key below, open Google Sheets -> Extensions -> Apps Script -> Copy & Paste this -> Save
//
// Usage: =GPT3(prompt, max_tokens (default=15), model (default=davinci))
// Example usage: =GPT3("Once upon a time,", 1000, "davinci")

var API_KEY = "your-API-key";

## splitAudio.py

#!/usr/bin/python
## Split audio files into chunks
## Daniel Pett 1/5/2020
__author__ = 'portableant'
## Tested on Python 2.7.16 - yes I know I need to upgrade.

import argparse
import os
import speech_recognition as sr

## renderSite.R
# This script builds on Aleszu Bajak's excellent
# [tutorial on building a course website using R Markdown and Github pages](http://www.storybench.org/convert-google-doc-rmarkdown-publish-github-pages/).
# I was excited about the concept but wanted to automate a few of the production steps: namely generating the HTML files
# for the site from the RMD pages (which Aleszu describes doing one-by-one) and generating the site navigation menu,
# which Aleszu handcodes in the `_site.yml` file. This script should automate both processes, though it may have some quirks
# unique to my setup that you'd want to tweak to fit your own. It's likely more loquacious than necessary as well, so feel free
# to condense as you can. Ideally, each time you make updates to your RMD files you can run this script to generate updated HTML
# pages and a new `_site.yml`. Then commit changes to Github and you're up and running!

# Once you've got everything configured for your own site below, you should be able to run `source('rend

## PoetryBot.rmd
---
title: "Programming Literary Bots"
author: "Ryan Cordell"
date: "3/12/2017"
output: html_document
---

## Acknowledgements

This version of my twitterbot assignment was adapted from [an original written in Python](https://www.dropbox.com/s/r1py3zazde2turk/Trendingmore.py?dl=0), which itself adapted code written by Mark Sample. That orginal bot tweeted (I've since stopped it) at [Quoth the Ravbot](https://twitter.com/Quoth__the). The current version owes much to advice and code borrowed from two colleagues at Northeastern University: Jonathan Fitzgerald and Benjamin Schmidt.

## classify_images.py
from __future__ import absolute_import, division, print_function

"""

This is a modification of the classify_images.py
script in Tensorflow. The original script produces
string labels for input images (e.g. you input a picture
of a cat and the script returns the string "cat"); this
modification reads in a directory of images and
generates a vector representation of the image using

## pastec-tutorial.md

      
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                Getting Pastec up and running, 8 August 2016
              
          
    Getting Pastec up and running

Pastec is an open source index and search engine for image recognition. This is how I got it working with lots of help from the hard work of Ryan Baumann, Shawn Graham and Matthew Lincoln.
Installation

Either install Ubuntu 14.04.5 as an operating system, or get a virtual machine from osboxes. Fire up with VirtualBox. Ensure VM is connected to the network (Settings>Network).
Install Pastec by following the documentation. Be sure to download and unzip visualWordsORB.dat into the build subdirectory of Pastec.

  
## min-char-rnn.py
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

## tweet-edits-to-archaeology-articles.R


# get recent changes from wikipedia
library(rvest)
n_changes <- 5000
recent_changes_url <- paste0("https://en.wikipedia.org/w/index.php?title=Special:RecentChanges&limit=", n_changes , "&days=1")

# connect to website
html <- read_html(recent_changes_url)

## asciinator.py
# This line imports the modules we will need. The first is the sys module used
# to read the command line arguments. Second the Python Imaging Library to read
# the image and third numpy, a linear algebra/vector/matrix module.
import sys; from PIL import Image; import numpy as np

# This is a list of characters from low to high "blackness" in order to map the
# intensities of the image to ascii characters
chars = np.asarray(list(' .,:;irsXA253hMHGS#9B&@'))

# Check whether all necessary command line arguments were given, if not exit and show a

## HTML2DTM.r
# get data
setwd("C:/Downloads/html") # this folder has only the HTML files
html <- list.files()

# load packages
library(tm)
library(RCurl)
library(XML)
# get some code from github to convert HTML to text
writeChar(con="htmlToText.R", (getURL(ssl.verifypeer = FALSE, "https://raw.github.com/tonybreyal/Blog-Reference-Functions/master/R/htmlToText/htmlToText.R")))
	// SPDX-License-Identifier: MIT
	//
	// This code will add a GPT3() function in your Google Sheets
	// This code is originally inspired from https://twitter.com/fabianstelzer/status/1572926883179778050
	// To use it, insert your API key below, open Google Sheets -> Extensions -> Apps Script -> Copy & Paste this -> Save
	//
	// Usage: =GPT3(prompt, max_tokens (default=15), model (default=davinci))
	// Example usage: =GPT3("Once upon a time,", 1000, "davinci")

	var API_KEY = "your-API-key";

	#!/usr/bin/python
	## Split audio files into chunks
	## Daniel Pett 1/5/2020
	__author__ = 'portableant'
	## Tested on Python 2.7.16 - yes I know I need to upgrade.

	import argparse
	import os
	import speech_recognition as sr
	# This script builds on Aleszu Bajak's excellent
	# [tutorial on building a course website using R Markdown and Github pages](http://www.storybench.org/convert-google-doc-rmarkdown-publish-github-pages/).
	# I was excited about the concept but wanted to automate a few of the production steps: namely generating the HTML files
	# for the site from the RMD pages (which Aleszu describes doing one-by-one) and generating the site navigation menu,
	# which Aleszu handcodes in the `_site.yml` file. This script should automate both processes, though it may have some quirks
	# unique to my setup that you'd want to tweak to fit your own. It's likely more loquacious than necessary as well, so feel free
	# to condense as you can. Ideally, each time you make updates to your RMD files you can run this script to generate updated HTML
	# pages and a new `_site.yml`. Then commit changes to Github and you're up and running!

	# Once you've got everything configured for your own site below, you should be able to run `source('rend
	---
	title: "Programming Literary Bots"
	author: "Ryan Cordell"
	date: "3/12/2017"
	output: html_document
	---

	## Acknowledgements

	This version of my twitterbot assignment was adapted from [an original written in Python](https://www.dropbox.com/s/r1py3zazde2turk/Trendingmore.py?dl=0), which itself adapted code written by Mark Sample. That orginal bot tweeted (I've since stopped it) at [Quoth the Ravbot](https://twitter.com/Quoth__the). The current version owes much to advice and code borrowed from two colleagues at Northeastern University: Jonathan Fitzgerald and Benjamin Schmidt.
	from __future__ import absolute_import, division, print_function

	"""

	This is a modification of the classify_images.py
	script in Tensorflow. The original script produces
	string labels for input images (e.g. you input a picture
	of a cat and the script returns the string "cat"); this
	modification reads in a directory of images and
	generates a vector representation of the image using
	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)


	# get recent changes from wikipedia
	library(rvest)
	n_changes <- 5000
	recent_changes_url <- paste0("https://en.wikipedia.org/w/index.php?title=Special:RecentChanges&limit=", n_changes , "&days=1")

	# connect to website
	html <- read_html(recent_changes_url)
	# This line imports the modules we will need. The first is the sys module used
	# to read the command line arguments. Second the Python Imaging Library to read
	# the image and third numpy, a linear algebra/vector/matrix module.
	import sys; from PIL import Image; import numpy as np

	# This is a list of characters from low to high "blackness" in order to map the
	# intensities of the image to ascii characters
	chars = np.asarray(list(' .,:;irsXA253hMHGS#9B&@'))

	# Check whether all necessary command line arguments were given, if not exit and show a
	# get data
	setwd("C:/Downloads/html") # this folder has only the HTML files
	html <- list.files()

	# load packages
	library(tm)
	library(RCurl)
	library(XML)
	# get some code from github to convert HTML to text
	writeChar(con="htmlToText.R", (getURL(ssl.verifypeer = FALSE, "https://raw.github.com/tonybreyal/Blog-Reference-Functions/master/R/htmlToText/htmlToText.R")))