mbrenig

fn apply_instruction(program: &mut Vec<u32>, ip: usize) {
    let instruction = program[ip];
    let verb = program[ip+1] as usize;
    let noun = program[ip+2] as usize;
    let target = program[ip+3] as usize;

    program[target] = match instruction {
        1 => program[verb] + program[noun],
        2 => program[verb] * program[noun],
        _ => unimplemented!("Unknown instruction: {}", instruction),

mbrenig

use std::ops::AddAssign;
use std::collections::HashMap;
use std::collections::HashSet;

type Segment = (char, u32);

#[derive(PartialEq, Eq, Hash, Copy, Clone)]
struct Point {
    x: i32,
    y: i32

mbrenig

use std::fs::File;
use std::io::{prelude::*, BufReader};
use std::cmp::max;

fn fuel_for_mass(mass: i32) -> i32 {
    let mut fuel = mass;
    fuel = fuel / 3;  // assume round down
    fuel -= 2;
    //println!("mass: {} requires: {}", mass, max(fuel, 0));
    max(fuel, 0)

mbrenig

# pip install python-constraint
from constraint import Problem, AllDifferentConstraint, ExactSumConstraint

DIGITS = range(1, 10)
COLS = "ABCDEFGHI"
ROWS = "123456789"
COL_GRP = ["ABC", "DEF", "GHI"]
ROW_GRP = ["123", "456", "789"]
CELLS = []
for cg in COL_GRP:

mbrenig

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (C) Metaswitch Networks.
"""Picks two 5 letter words at random from a dictionary,
finds a route between the words by only changing one letter at a time"""


import random
import heapq
import time

mbrenig

"""
profit.py

Pair-wise comparison and adjustment of profit share contribution ratios.
"""

from pprint import pprint
import random

# Seed this with numbers that are approximately sensible...

mbrenig

"""
Map the next 1000 weeks to quarters in a sensible way so that
most quarters are 13 weeks long... and occasionally we introduce
a 14 week quater, so as to maximise the amount of working days that
fall in their correct quarter
"""
from datetime import date, timedelta
from collections import defaultdict

import networkx as nx

mbrenig

"""
csv_entropy.py

Author: MBJ (2017)

Purpose: Command line utility to assess the most "Informative" columns in a CSV file.

Detail:
  * Reads the contents of a CSV file
      - Assumes header row is at the top

mbrenig

import json
from pprint import pprint
from elasticsearch import Elasticsearch  # pip install elasticsearch

host = 'corpus.metaswitch.com'
es = Elasticsearch(hosts=[host])

query = {
  "_source": ["html", "id", "subject", "breadcrumbs.title", "replyCount" ],
  "size" : 100,

mbrenig

# https://pypi.python.org/pypi/python-dateutil/2.5.3
# https://dateutil.readthedocs.io/en/stable/
from dateutil import parser  # pip install python-dateutil

if __name__ == '__main__':
    try:
        a = parser.parse("18 Aug 2001")
        print a
        b = parser.parse("2016-04-05 12:33:55")
        print b