Louis Goddard ltrgoddard

## github-email.txt
Date: Tue, 14 Apr 2026 09:23:34 -0700
From: GitHub Security <no-reply@github.com>
To: Louis Goddard <louisgoddard@gmail.com>
Message-ID: <69de6a0681681_1717fa110878942@github-lowworker-4c41d26.va3-iad.github.net.mail>
Subject: Action needed: Rotate webhook secrets in your GitHub account
Mime-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: quoted-printable
X-Auto-Response-Suppress: All

## observations.csv

          
            timestamp_utc
            timestamp_local
            flare_detected
            flare_score
            warm_bright_pixels
            max_brightness
            avg_warm_intensity
            snapshot_file

            
              2026-03-12T12:43:16Z
              2026-03-12T22:13:16+09:30
              1
              176.59
              5430
              255.0
              194.6

## sparkbar.R
# Takes an ordered vector of numeric values and returns a small bar chart made
# out of Unicode block elements. Works well inside dplyr mutate() or summarise()
# calls on grouped data frames.

sparkbar <- function(values) {
  span <- max(values) - min(values)
  if(span > 0 & !is.na(span)) {
    steps <- round(values / (span /  7))
    blocks <- c('▁', '▂', '▃', '▄', '▅', '▆', '▇', '█')
    paste(sapply(steps - (min(steps) - 1), function(i) blocks[i]), collapse = '')

## tweet.py
#!/usr/bin/env python

# This is a simple script to set up a Twitter 'bot' based on a character-level recurrent neural network. Clone sherjilozair's
# char-rnn-tensorflow (https://github.com/sherjilozair/char-rnn-tensorflow) and train it on the material of your choice.
# Then drop this script into the main directory, create a Twitter account and Twitter app for the bot and enter the
# relevant authentication information at the commented points below. Run this script and whenever somebody
# @mentions the bot it will reply with a sample from your neural network.

# Louis Goddard <louisgoddard@gmail.com>
	Date: Tue, 14 Apr 2026 09:23:34 -0700
	From: GitHub Security <no-reply@github.com>
	To: Louis Goddard <louisgoddard@gmail.com>
	Message-ID: <69de6a0681681_1717fa110878942@github-lowworker-4c41d26.va3-iad.github.net.mail>
	Subject: Action needed: Rotate webhook secrets in your GitHub account
	Mime-Version: 1.0
	Content-Type: text/plain; charset=UTF-8
	Content-Transfer-Encoding: quoted-printable
	X-Auto-Response-Suppress: All
	timestamp_utc	timestamp_local	flare_detected	flare_score	warm_bright_pixels	max_brightness	avg_warm_intensity	snapshot_file
	2026-03-12T12:43:16Z	2026-03-12T22:13:16+09:30	1	176.59	5430	255.0	194.6
	# Takes an ordered vector of numeric values and returns a small bar chart made
	# out of Unicode block elements. Works well inside dplyr mutate() or summarise()
	# calls on grouped data frames.

	sparkbar <- function(values) {
	span <- max(values) - min(values)
	if(span > 0 & !is.na(span)) {
	steps <- round(values / (span / 7))
	blocks <- c('▁', '▂', '▃', '▄', '▅', '▆', '▇', '█')
	paste(sapply(steps - (min(steps) - 1), function(i) blocks[i]), collapse = '')
	#!/usr/bin/env python

	# This is a simple script to set up a Twitter 'bot' based on a character-level recurrent neural network. Clone sherjilozair's
	# char-rnn-tensorflow (https://github.com/sherjilozair/char-rnn-tensorflow) and train it on the material of your choice.
	# Then drop this script into the main directory, create a Twitter account and Twitter app for the bot and enter the
	# relevant authentication information at the commented points below. Run this script and whenever somebody
	# @mentions the bot it will reply with a sample from your neural network.

	# Louis Goddard <louisgoddard@gmail.com>