Dan Gayle dangayle

running:

bash create-vod-hls.sh beach.mkv

will produce:

    beach/
      |- playlist.m3u8
 |- 360p.m3u8

Create estimate of population affected by active fires

The goal is to estimate the amount of people affected by active fires in a given area, using VIIRS satellite data and US Census data

Cluster fire data points from VIIRS data into groups, representing active fire areas
- NASA VIIRS Active Fire Data
- scikit-learn KMeans Clustering
For each group, create a polygon that contains all points (a convex hull), this represents the boundaries of a given fire area

	from app import db
	from sqlalchemy import func, types
	from sqlalchemy.dialects import postgresql


	class JSONCache(db.Model):
	id = db.Column(db.Integer, primary_key=True)
	key = db.Column(db.String, nullable=False, index=True)
	data = db.Column(postgresql.JSONB, nullable=False)
	timestamp = db.Column(types.TIMESTAMP, server_default=func.now(), nullable=False)

	mkdir dash && \
	ffmpeg -hide_banner -i original.mkv -c:v libvpx-vp9 -row-mt 1 -keyint_min 150 -g 150 -tile-columns 4 -frame-parallel 1 \
	-movflags faststart -f webm -dash 1 -speed 3 -threads 4 -an -vf scale=426:240 -b:v 400k -r 30 -dash 1 dash/426x240-30-400k.webm && \
	ffmpeg -hide_banner -i original.mkv -c:v libvpx-vp9 -row-mt 1 -keyint_min 150 -g 150 -tile-columns 4 -frame-parallel 1 \
	-movflags faststart -f webm -dash 1 -speed 3 -threads 4 -an -vf scale=426:240 -b:v 600k -r 30 -dash 1 dash/426x240-30-600k.webm && \
	ffmpeg -hide_banner -i original.mkv -c:v libvpx-vp9 -row-mt 1 -keyint_min 150 -g 150 -tile-columns 4 -frame-parallel 1 \
	-movflags faststart -f webm -dash 1 -speed 3 -threads 4 -an -vf scale=640:360 -b:v 700k -r 30 -dash 1 dash/640x360-30-700k.webm && \
	ffmpeg -hide_banner -i original.mkv -c:v libvpx-vp9 -row-mt 1 -keyint_min 150 -g 150 -tile-columns 4 -frame-parallel 1 \
	-movflags faststart -f webm -dash 1 -speed 3 -threads 4 -an -vf scale=640:360 -b:v 900k -r 30 -dash 1 dash/640x360-30-900k.we

	/*
	No jQuery necessary.
	Thanks to Dan's StackOverflow answer for this:
	http://stackoverflow.com/questions/123999/how-to-tell-if-a-dom-element-is-visible-in-the-current-viewport
	*/

	function isElementInViewport(el) {
	var rect = el.getBoundingClientRect();
	return (
	rect.top >= 0 &&

	from textblob.classifiers import NaiveBayesClassifier
	from textblob import TextBlob

	train = [
	('Take me off', 'stop'),
	('Stop texting','stop'),
	('stop messaging','stop'),
	('Don\'t talk', 'stop'),
	('Stop messaging','stop'),
	('dont want to talk anymore','stop'),

	<html>
	<head>
	<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.2.1/jquery.min.js"></script>
	<script>
	function submitToAPI() {
	var URL = ‘/contact’;
	var data = {
	name: $(‘#name-input’).val(),
	email: $(‘#email-input’).val(),
	description: $(‘#description-input’).val()

	import React from 'react';
	import Tachyons from 'tachyons/css/tachyons.min.css'

	const App = () => (
	<div className="mw9 center">
	<h2 className="red sans-serif tc">Hello, world</h2>
	</div>
	);

	export default App;

	{% load static mogrify %}
	<img
	class="lazyload {{ class }}"
	src="{% static 'sv3/img/spokesman-logo.png' %}"
	data-sizes="auto"
	data-srcset="
	{% mogrify img_url resize '300x169' %} 300w,
	{% mogrify img_url resize '530x298' %} 530w,
	{% mogrify img_url resize '720x408' %} 720w,
	{% mogrify img_url resize '1170x658' %} 1170w,"

	function hasClass(elem, className) {
	return new RegExp(' ' + className + ' ').test(' ' + elem.className + ' ');
	}

	function addClass(elem, className) {
	if (!hasClass(elem, className)) {
	elem.className += ' ' + className;
	}
	}