Louis Goddard ltrgoddard

	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>