phlippieb

//
//  LightRibbon2.swift
//  Waveforms
//
//  Created by Phlippie Bosman on 2018/04/12.
//  Copyright © 2018 Phlippie Bosman. All rights reserved.
//

import UIKit

phlippieb

# To get this:
# curl -Lo $HOME/.screenrc 'https://goo.gl/bZv3x2'

# Enable mouse scrolling:
termcapinfo xterm* ti@:te@

# Clear screen when vim/less/etc quits:
altscreen on

# Status line that shows windows:

phlippieb

/*
For use in an XCode Playground
*/

import Foundation
import XCPlayground

XCPlaygroundPage.currentPage.needsIndefiniteExecution = true

// Method 1: Works

phlippieb

//
//  File.swift
//  Kalido
//
//  Created by Phlippie Bosman on 2016/11/17.
//  Copyright © 2016 Kalido. All rights reserved.
//

class MaxHeightTextView: UITextView {
    open var maxHeight: CGFloat?

phlippieb

//
//  RangeTappableTextView.swift
//  Kalido
//
//  Created by Phlippie Bosman on 2016/11/23.
//  Copyright © 2016 Kalido. All rights reserved.
//

protocol RangeTappableTextViewDelegate: class {
    func rangeTappableTextView(_ rangeTappableTextView: RangeTappableTextView,

phlippieb

This naive algorithm compares two strings, needle and haystack. It returns true if haystack "fuzzily" contains needle, which is when (almost) every character in needle is found in (almost) the same order, but not necessarily contiguously, in haystack. Up to allowed_errors characters in needle are allowed to not be found in haystack. Characters may also be swapped.

The following is a python implementation:

def stringFuzzilyContains(needle, haystack):
    # allow this many characters to not be found
    allowed_errors = 0
    
    # the last index where a character was found;

phlippieb

//
//  AutoCompleteTextField.swift
//
//  Created by Phlippie Bosman on 2016/08/23.
//  Copyright © 2016 Kalido. All rights reserved.
//

import Material
import RxSwift

phlippieb

plot.full <- function (datafile, ...)
{
	data <- read.table( datafile, quote="\"" );
	dataframe <- data.frame(x=1:length(rowMeans(data)), y=rowMeans(data));
	plot(dataframe, type='l');

	b <- bsr2 (dataframe$y, dataframe$x, 100);
	slope <- diff( b$fit$model[,2] ) / diff ( predict(b$fit) );

	plot.2pwla(dataframe$x, dataframe$y, cont=FALSE, add=TRUE, type='l', col='red', ...);