dabrahams

// See commentary below this gist.
import Foundation
import QuartzCore

// Implementation from https://talk.objc.io/episodes/S01E90-concurrent-map
public final class ThreadSafe<A> {
    var _value: A
    let queue = DispatchQueue(label: "ThreadSafe")
    init(_ value: A) { self._value = value }

dwijnand

+-----------------------------------------------------------------------------------------------------+
|  name   |  bits  | bytes  |  min  | actual min value           |  max   | actual max value          |
|---------+--------+--------+-------+----------------------------+--------+---------------------------|
| boolean |  1-bit |   --   | false |                            | true   |                           |
| byte    |  8-bit | 1-byte | -2^7  | -128                       | 2^7-1  | 127                       |
| short   | 16-bit | 2-byte | -2^15 | -32'768                    | 2^15-1 | 32'767                    |
| char    | 16-bit | 2-byte | 0     | '\u0000'                   | 2^16-1 | '\uffff'  (65535)         |
| int     | 32-bit | 4-byte | -2^31 | -2'147'483'648             | 2^31-1 | 2'147'483'647             |
| long    | 64-bit | 8-byte | -2^63 | -9'223'372'036'854'775'808 | 2^63-1 | 9'223'372'036'854'775'807 |
| float   | 32-bit | 4-byte | +/- -3.4028235 * 10^38             | +/- 3.4028235 * 10^38

scrooloose

"Chuck this in [a vim runtime]/nerdtree_plugin/gitignore_filter.vim


if exists("loaded_nerdtree_gitignore_filter")
    finish
endif
let loaded_nerdtree_gitignore_filter = 1

call NERDTreeAddPathFilter('NERDTreeGitIgnoreFilter')

djspiewak

Introduction to scalaz-stream

Every application ever written can be viewed as some sort of transformation on data. Data can come from different sources, such as a network or a file or user input or the Large Hadron Collider. It can come from many sources all at once to be merged and aggregated in interesting ways, and it can be produced into many different output sinks, such as a network or files or graphical user interfaces. You might produce your output all at once, as a big data dump at the end of the world (right before your program shuts down), or you might produce it more incrementally. Every application fits into this model.

The scalaz-stream project is an attempt to make it easy to construct, test and scale programs that fit within this model (which is to say, everything). It does this by providing an abstraction around a "stream" of data, which is really just this notion of some number of data being sequentially pulled out of some unspecified data source. On top of this abstraction, sca

rdlester

import os
import ycm_core

flags = [
'-resource-dir',
'/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../lib/clang/6.0',
'-isysroot',
'/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.10.sdk',
'-I/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.10.sdk/usr/include',
'-F/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.10.sdk/System/Library/Frameworks',

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

pchiusano

When creating streams from an asynchronous process, the idiomatic thing is to create a stream from that process at the earliest possible stage, rather than using a queue to invert control after the fact. See the creating streams examples - generally, you just use the Process.eval and Process.repeatEval functions to build a stream by running some asynchronous task repeatedly.

That said, if you have some existing logic that you need to bind to that's already based on callbacks and side effects, you can use the functions in scalaz.stream.async. Here's an example, using a queue to invert control:

import scalaz.stream.async

val (q, src) = async.queue[Int]

// Thread 1

dan-blanchard

For ETS's SKLL project, we found out the hard way that Travis-CI's support for numpy and scipy is pretty abysmal. There are pre-installed versions of numpy for some versions of Python, but those are seriously out of date, and scipy is not there are at all. The two most popular approaches for working around this are to (1) build everything from scratch, or (2) use apt-get to install more recent (but still out of date) versions of numpy and scipy. Both of these approaches lead to longer build times, and with the second approach, you still don't have the most recent versions of anything. To circumvent these issues, we've switched to using Miniconda (Anaconda's lightweight cousin) to install everything.

A template for installing a simple Python package that relies on numpy and scipy using Miniconda is provided below. Since it's a common s

steipete

// Taken from the commercial iOS PDF framework http://pspdfkit.com.
// Copyright (c) 2014 Peter Steinberger, PSPDFKit GmbH. All rights reserved.
// Licensed under MIT (http://opensource.org/licenses/MIT)
//
// You should only use this in debug builds. It doesn't use private API, but I wouldn't ship it.

// PLEASE DUPE rdar://27192338 (https://openradar.appspot.com/27192338) if you would like to see this in UIKit.

#import <objc/runtime.h>
#import <objc/message.h>

gavrix

source ~/.bash_profile


hash oclint &> /dev/null
if [ $? -eq 1 ]; then
echo >&2 "oclint not found, analyzing stopped"
exit 1
fi