rberenguel

function $(s){
  z="getElement";n="Name";
	a={"#" : z+"ById",
	   "." : z+"sByClass"+n,
	  }[s[0]];
	a=typeof a==="undefined"?[z+"sByTag"+n,0]:[a,1];
	return document[a[0]](s.slice(a[1]));
}

function $$(s){return S="slice",d=document,t=s[0],g="getElement",n="Name",t=="#"?d[g+"ById"](s[S](1)):t=="."?d[g+"sByClass"+n](s[S](1)):d[g+"sByTag"+n](s)}

kmader

Serialization

As all objects must be Serializable to be used as part of RDD operations in Spark, it can be difficult to work with libraries which do not implement these featuers.

Java Solutions

Simple Classes

For simple classes, it is easiest to make a wrapper interface that extends Serializable. This means that even though UnserializableObject cannot be serialized we can pass in the following object without any issue

public interface UnserializableWrapper extends Serializable {
 public UnserializableObject create(String parm1, String parm2);

lihaoyi

package hello

import java.io.ByteArrayOutputStream

import chisel3._
import chisel3.iotesters.{Driver, PeekPokeTester}

sealed trait Op
object Op{
  case class GotoIfZero(dest: Byte, valueAddr: Byte) extends Op

olafurpg

import sbt._, Keys._
import scala.xml.{Node => XmlNode, NodeSeq => XmlNodeSeq, _}
import scala.xml.transform.{RewriteRule, RuleTransformer}
import sbt.plugins.JvmPlugin

object PPrintPlugin extends AutoPlugin {
  override def requires = JvmPlugin
  override def trigger: PluginTrigger = AllRequirements
  override def projectSettings: Seq[Def.Setting[_]] = List(
    libraryDependencies ++= {

yukoff

Project documentation

The project documentation (stored in the docs directory) is a git subtree of the project wiki. This allows for the documentation to be referenced and edited from within the main project.

Initial local setup

When cloning the main project repository for the first time, the wiki repository must be added as a remote.

git remote add wiki https://github.com//.wiki.git

djspiewak

Quick Tips for Fast Code on the JVM

I was talking to a coworker recently about general techniques that almost always form the core of any effort to write very fast, down-to-the-metal hot path code on the JVM, and they pointed out that there really isn't a particularly good place to go for this information. It occurred to me that, really, I had more or less picked up all of it by word of mouth and experience, and there just aren't any good reference sources on the topic. So… here's my word of mouth.

This is by no means a comprehensive gist. It's also important to understand that the techniques that I outline in here are not 100% absolute either. Performance on the JVM is an incredibly complicated subject, and while there are rules that almost always hold true, the "almost" remains very salient. Also, for many or even most applications, there will be other techniques that I'm not mentioning which will have a greater impact. JMH, Java Flight Recorder, and a good profiler are your very best friend! Mea

benjamineskola

-- run first thing in the morning, e.g., from cron

tell application "Things3"
  set theToken to "your-auth-token"
  
  set theTodos to to dos of list "Today"
  repeat with aTodo in theTodos
    set tagList to tags of aTodo
    repeat with aTag in tagList
      if (name of aTag as text) is "Evening"

HeartSaVioR

/////////////////////////////////////////////////////////////////////////////////////////////
// 1. select with swapping columns, and apply where
/////////////////////////////////////////////////////////////////////////////////////////////
import spark.implicits._

import org.apache.spark.sql.{DataFrame, Dataset}

case class Hello(id: Int, name1: String, name2: String)

val ds = List(Hello(1, "Alice", "Bob"), Hello(2, "Bob", "Alice")).toDS

johnhw

### JHW 2018
import numpy as np
import umap



# This code from the excellent module at:
# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module
    
import random

vegard

# -*- coding: utf-8 -*-
#
# Author: Vegard Nossum <vegard.nossum@gmail.com>

import math
import os
import sys

import cairo