Rodolfo Ferro RodolfoFerro

To forward X11 from inside a docker container to a host running macOS

Install XQuartz: https://www.xquartz.org/
Launch XQuartz. Under the XQuartz menu, select Preferences
Go to the security tab and ensure "Allow connections from network clients" is checked.
Run xhost + ${hostname} to allow connections to the macOS host *
Setup a HOSTNAME env var export HOSTNAME=`hostname`*
Add the following to your docker-compose:

 environment:

It's not immediately obvious how to pull down the code for a PR and test it locally. But it's pretty easy. (This assumes you have a remote for the main repo named upstream.)

Getting the PR code

Make note of the PR number. For example, Rod's latest is PR #37: Psiphon-Labs/psiphon-tunnel-core#37
Fetch the PR's pseudo-branch (or bookmark or rev pointer whatever the word is), and give it a local branch name. Here we'll name it pr37:

$ git fetch upstream pull/37/head:pr37

	#!/usr/bin/env python3
	#ps3 wireless controller interface by Robin Newman July 2017
	#converts controller inputs to OSC messages
	#which can be output to Sonic Pi 3, running on the local computer, or on an external machine
	#rtested with ps3 "afterglow" wireless controller, dongle in Pi usb socket
	#needs sudo apt-get install joystick after sudo apt-get update
	#also needs sudo pip3 install python-osc
	#Version 2. Modified to ensure clean exit on Ubuntu

	import subprocess,pygame,sys

	# Objective
	# Use non-linear curve fitting to estimate the relaxation rate of an exponential
	# decaying signal.

	# Steps
	# 1. Simulate data (instead of collecting data)
	# 2. Define the objective function for the least squares algorithm
	# 3. Perform curve fitting
	# 4. Compare results

	ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
	alias brew=/usr/local/bin/brew
	brew install python3
	alias python=python3
	brew install mercurial
	brew install git
	brew install sdl sdl_image sdl_mixer sdl_ttf
	brew tap homebrew/headonly
	brew install --HEAD smpeg
	brew install portmidi

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	use_debug false
	use_bpm 130

	# Our mixer!
	master = (ramp *range(0, 1, 0.01))
	kick_volume = 1
	bass_volume = 1
	revbass_volume = 1
	snare_volume = 0.5
	hats_volume = 0.5

	#!/bin/bash

	airflow run add_gcp_connection add_gcp_connection_python 2001-01-01

	source 'https://rubygems.org'
	gem 'osc-ruby', '1.1.1'
	gem 'serialport'

	# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
	# Requires: numpy, sklearn, theano

	# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
	# where x' = [x \| 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
	# Similarly, for h * W_2 + b_2
	import theano
	from theano import tensor as T
	import numpy as np
	from sklearn import datasets