askilondz

Adaptive Streaming has become the neccessity for streaming video and audio. Unfortantely, as of this post, there isn't a whole lot of tutorials that accumulate all of the steps to get this working. Hopefully this post achieves that. This post focuses on using Amazon Web Services (AWS) to transcode for HLS and DASH and be the Content Delivery Network (CDN) that delivers the stream to your web page. We'll be using Video.js for the HTML5 player as well as javascript support libaries to make Video.js work with HLS and DASH.

So Here's what you need:

• An AWS Account
• Video.js
• videojs-contrib-hls.min.js
• dash.all.min.js
• videojs-dash.min.js

Set up three S3 buckets

alexellis

FROM golang:1.7.3
RUN mkdir -p /go/src/github.com/alexellis2/jaas
WORKDIR /go/src/github.com/alexellis2/jaas
COPY ./app.go ./

RUN go get -v -d
RUN go build

hugke729

# Example animations using matplotlib's FuncAnimation
# Ken Hughes. 18 June 2016.

# For more detail, see
# https://brushingupscience.wordpress.com/2016/06/21/matplotlib-animations-the-easy-way/

# Examples include
#    - line plot
#    - pcolor plot
#    - scatter plot

marians

This is an unofficial manual for the couchdb Python module I wish I had had.

Installation

pip install couchdb

Importing the module

hfreire

# Install QEMU OSX port with ARM support
sudo port install qemu +target_arm
export QEMU=$(which qemu-system-arm)

# Dowload kernel and export location
curl -OL \
https://github.com/dhruvvyas90/qemu-rpi-kernel/blob/master/kernel-qemu-4.1.7-jessie
export RPI_KERNEL=./kernel-qemu-4.1.7-jessie

# Download filesystem and export location

GOFAI

class Deepcl < Formula
  desc "OpenCL library to train deep convolutional neural networks"
  homepage "https://github.com/hughperkins/DeepCL"
  url "https://github.com/hughperkins/DeepCL/archive/v8.1.3.tar.gz"
  sha256 "e0301ba5ae4464ae27a3ec2c777b77faa90b9ee6d1059b200f06a9100d182de1"

  resource "EasyCL" do
    url "https://github.com/hughperkins/EasyCL/archive/b9023cdf25c10524ad06227275d3756bbe0c0ed2.tar.gz"
    sha256 "96c6061385e850081cfce17673353b41dbf470fc6b6f3a15e047b1d6506ba80e"
  end

imneme

/*
 * Random-Number Utilities (randutil)
 *     Addresses common issues with C++11 random number generation.
 *     Makes good seeding easier, and makes using RNGs easy while retaining
 *     all the power.
 *
 * The MIT License (MIT)
 *
 * Copyright (c) 2015-2022 Melissa E. O'Neill
 *

johnhany97

Create a file with this:

#!/bin/bash

oldtext=$1
newtext=$2
pdffile=$3

cp $pdffile $pdffile.bak
pdftk $pdffile output $pdffile.tmp uncompress

joferkington

import numpy as np
import matplotlib.pyplot as plt

class DrawDragPoints(object):
    """
    Demonstrates a basic example of the "scaffolding" you need to efficiently
    blit drawable/draggable/deleteable artists on top of a background.
    """
    def __init__(self):
        self.fig, self.ax = self.setup_axes()

jedi4ever

Some notes on remote debugging mac builds on Travisci.  It's hard to tell when something hangs what the cause it. Trial and error via commits is tedious. And on Mac , sometimes it's the gui asking for input. So I worked my around to get the access I needed for faster debugging a build.

#################################################
# Enable remote ssh access to travisci build for debugging
#################################################
# Add a key so we can login to travisci vm
- cat ssh/travisci.pub >> ~/.ssh/authorized_keys
- chmod 600 ssh/travisci
  
# Install netcat