scturtle

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from transforms3d import euler

fig = plt.figure()
ax = fig.gca(projection='3d')

p1 = np.zeros((3, 100))
p1[0, :] = np.linspace(1, 3, 100)

CodaFi

//
//  Channel.swift
//  Sessions
//
//  Created by Robert Widmann on 2/27/17.
//  Copyright © 2017 TypeLift. All rights reserved.
//

enum FakeError : Error {
  case Error

notheotherben

Command Line Parsing Grammar

This grammar allows you to parse command lines for program execution into their various components - specifically: environment variables, the executable itself and any arguments passed to the executable.

It will take an input like the following:

ENV_X=true ENV_Y="yes please" ./test/my_exec arg1 -f1 "arg with spaces" 'another arg' --flag2 yet\ another\ arg --flag=10

zwaldowski

import Foundation

/// A type representing an concrete part of an application, such that it can be
/// identified in logs.
///
/// A typical use is as a nested type:
///
///     extension MyViewController {
///         enum Log: Error {
///             case user

bzamecnik

Anaconda Python script running as systemd service

This way a Python daemon can be installed on Rasbian, Ubuntu or similar systems using systemd.

Installing:

sudo cp hello.service /lib/systemd/system/hello.service
sudo systemctl daemon-reload
sudo systemctl enable hello.service

zwaldowski

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<!-- iOS 10, macOS Sierra, and friends bring a new logging subsystem that's
supposed to scale from the kernel, up to frameworks, and up to apps. It defaults
to a more regimented, privacy-focused approach that large apps and complex
systems need.

It, along with Activity Tracing introduced in iOS 8 and macOS Yosemite and the
Console app in macOS Sierra, hope to help you graduate from caveman debugging to

zwaldowski

//
//  Activity.swift
//
//  Created by Zachary Waldowski on 8/21/16.
//  Copyright © 2016 Zachary Waldowski. Licensed under MIT.
//

import os.activity

private final class LegacyActivityContext {

andymatuschak

A composable pattern for pure state machines with effects

State machines are everywhere in interactive systems, but they're rarely defined clearly and explicitly. Given some big blob of code including implicit state machines, which transitions are possible and under what conditions? What effects take place on what transitions?

There are existing design patterns for state machines, but all the patterns I've seen complect side effects with the structure of the state machine itself. Instances of these patterns are difficult to test without mocking, and they end up with more dependencies. Worse, the classic patterns compose poorly: hierarchical state machines are typically not straightforward extensions. The functional programming world has solutions, but they don't transpose neatly enough to be broadly usable in mainstream languages.

Here I present a composable pattern for pure state machiness with effects,

JoshCheek

Why I chose Fish over Bash for students

I'm currently the lead instructor at Code Platoon and an instructor/developer at the Turing School of Software and Design.

I've been advocating the Fish shell and when the choice is up to me, I choose that for my students. Enough people ask about the decision, particularly in relation to the preinstalled Bash shell, that I figured it's worth laying out my reasoning.

TL;DR

eerwitt

# Typical setup to include TensorFlow.
import tensorflow as tf

# Make a queue of file names including all the JPEG images files in the relative
# image directory.
filename_queue = tf.train.string_input_producer(
    tf.train.match_filenames_once("./images/*.jpg"))

# Read an entire image file which is required since they're JPEGs, if the images
# are too large they could be split in advance to smaller files or use the Fixed