fluffywaffles

import pprint

import math

pp = pprint.PrettyPrinter(indent=2, width=120)

pp.pprint("my god it's full of stars")

def turbine_layout(rows, cols, startx=25000, starty=2000, rowgap=500, colgap=750, skewx=lambda _: 0):
  turbines = []

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"my god it's full of stars"
{ (25000, 2000, 0, 0): { 'east': [(25500, 2000, 0, 1)],
                         'northeast': [(25250, 2750, 1, 0)],
                         'northwest': [],
                         'southeast': [],
                         'southwest': [],
                         'west': []},
  (25000, 3500, 2, 0): { 'east': [(25500, 3500, 2, 1)],
                         'northeast': [(25250, 4250, 3, 0)],
                         'northwest': [],

fluffywaffles

function helloWorldWithOptions (toWhom, { postfix1: p1, postfix2: p2='' }, greeting='Hello', additionalOptions={}) {
  let { pre: preFn=String } = additionalOptions

  return preFn(`${greeting}, ${toWhom}${p1}${p2}`)
}

helloWorldWithOptions('world', { postfix1: '!' }) // => 'Hello, world!'
helloWorldWithOptions('world', { postfix1: '!', postfix2: '1' }) // => 'Hello, world!1'
helloWorldWithOptions('Mom', { postfix1: '?' }, undefined, additionalOptions={ pre: (str) => str.toUpperCase() })
// => 'HELLO, MOM?'

fluffywaffles

Botloader Spec

An example

// app.js
var botloader = require('botloader')

botloader.with({
  slack: new Slack(<Bot Access Key>),
  octopus: require('./botconfig')

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<link2>

  <a each={ items } class="btn btn-success" role="button" onclick={ goto( itemname ) }>
    Get Buyer Link
  </a>

  <script>
    this.items = [{
  		itemname:"LEI",
  		itemtag: "lei", 

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def recursive_size(s, nodes):
    '''
    tail recursive size
    '''
    num = len(nodes)
    if (num == 0): return s
    children = n.children.values()
    return sum([ recursive_size(s + num, children) for n in nodes ])

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import math
from node import Node
from collections import Counter
#import sys
import timeit

iterations = 0

notNone = lambda x: x is not None

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    def classify(self, instance):
        '''
        given a single observation, will return the output of the tree
        '''
        attr = self.decision_attribute

        if self.label is not None:
            return self.label
        elif self.splitting_value:

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def mean(lst):
    '''
    Calculate the mean of the input list.
    '''
    l = len(lst)
    return float(sum(lst)) / l if l > 0 else None


def list_mode(lst):
    return max(lst, key=lst.count)

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def bias_replace_missing_with_avg(data_set):
    '''
    For some reason, this is my longest function.
    It's all the partitioning and partition undoing.

    Replace 'None' values (missing values) with the average of all existing
    values for that attribute.
    '''
    notNone = lambda x: x is not None