marcel mpoisot

What forces layout / reflow

All of the below properties or methods, when requested/called in JavaScript, will trigger the browser to synchronously calculate the style and layout*. This is also called reflow or layout thrashing, and is common performance bottleneck.

Generally, all APIs that synchronously provide layout metrics will trigger forced reflow / layout. Read on for additional cases and details.

Element APIs

Getting box metrics

elem.offsetLeft, elem.offsetTop, elem.offsetWidth, elem.offsetHeight, elem.offsetParent

Automatic build versions from git in Xcode (and other goodies)

Installation procedure for pre-build actions to automatically populate Xcode Info.plist with dynamic data.

1. Xcode Scheme pre-action

Edit Xcode Scheme and add a pre-action script. Copy the contents of preaction.sh into the pre-action script box.

	<!doctype html>

	<!-- http://taylor.fausak.me/2015/01/27/ios-8-web-apps/ -->

	<html>
	<head>
	<title>iOS 8 web app</title>

	<!-- CONFIGURATION -->

	#########################
	# .gitignore file for Xcode4 and Xcode5 Source projects
	#
	# Apple bugs, waiting for Apple to fix/respond:
	#
	# 15564624 - what does the xccheckout file in Xcode5 do? Where's the documentation?
	#
	# Version 2.6
	# For latest version, see: http://stackoverflow.com/questions/49478/git-ignore-file-for-xcode-projects
	#

	#!/usr/bin/env node

	var IOS_DEPLOYMENT_TARGET = '7.0';

	var fs = require("fs"),
	path = require("path"),
	shell = require("shelljs"),
	xcode = require('xcode'),
	projectRoot = process.argv[2];

	#!/usr/bin/env node

	var GCC_PREPROCESSOR_DEFINITIONS = '"$(inherited) DISABLE_PUSH_NOTIFICATIONS=1"';

	var fs = require("fs");
	var path = require("path");
	var xcode = require('xcode');
	var projectRoot = process.argv[2];

	function getProjectName(protoPath) {

	class FilterVisualizer():
	def __init__(self, size=56, upscaling_steps=12, upscaling_factor=1.2):
	self.size, self.upscaling_steps, self.upscaling_factor = size, upscaling_steps, upscaling_factor
	self.model = vgg16(pre=True).cuda().eval()
	set_trainable(self.model, False)

	def visualize(self, layer, filter, lr=0.1, opt_steps=20, blur=None):
	sz = self.size
	img = np.uint8(np.random.uniform(150, 180, (sz, sz, 3)))/255 # generate random image
	activations = SaveFeatures(list(self.model.children())[layer]) # register hook