blainerothrock

{
    "id": "flexible-sample",
    "schema_version": "1.0",
    "created_at": "2022-04-13T22:11:39.000Z",
    "updated_at": "2022-04-13T22:11:39.000Z",
    "tags": ["development"],
    "ble_registered_devices": [
        {
            "name": "Polar H10 821A5D29",
            "services": ["heart-rate", "battery"],

blainerothrock

{
    "name": "allegations",
    "children": [
        {
            "name": "Bribery / Official Corruption",
            "children": [
                {
                    "name": "An Act To Circumvent Criminal Prosecution",
                    "value": 1
                },

blainerothrock

{
    "name": "allegations",
    "children": [
        {
            "name": "Bribery / Official Corruption",
            "children": [
                {
                    "name": "An Act To Circumvent Criminal Prosecution",
                    "value": 25
                },

blainerothrock

blainerothrock

blainerothrock

blainerothrock

blainerothrock

import Foundation

extension Array {
    subscript (lookup index: Index) -> Element? {
        return indices.contains(index) ? self[index] : nil
    }
}

let array = [1,2,3,4,5]
array[lookup: 7] // nil

blainerothrock

import Foundation

extension String {
    func isUppercased(at: Index) -> Bool {
        let range = at..<self.index(after: at)
        return self.rangeOfCharacter(from: .uppercaseLetters, options: [], range: range) != nil
    }
}

extension Character {

blainerothrock

#!/usr/bin/env xcrun swift -O

/*
gen.swift is a direct port of cfdrake's helloevolve.py from Python 2.7 to Swift 3
-------------------- https://gist.github.com/cfdrake/973505 ---------------------
gen.swift implements a genetic algorithm that starts with a base
population of randomly generated strings, iterates over a certain number of
generations while implementing 'natural selection', and prints out the most fit
string.
The parameters of the simulation can be changed by modifying one of the many