joegaudet

# == Schema Information
#
# Table name: group_order_members
#
#  id                          :integer          not null, primary key
#  name                        :string
#  email                       :string

class GroupOrderMember < ApplicationRecord
  include Accounting::Interfaces::Invoiceable

joegaudet

module Accounting
  module Interfaces
    module Invoiceable
      extend ActiveSupport::Concern

      included do

        # Quack like a duck
        attribute :can_invoice?, :boolean, default: true

joegaudet

class Accounting::LedgerItem < ApplicationRecord
  acts_as_ledger_item

  belongs_to :recipient, polymorphic: true
  belongs_to :sender, polymorphic: true

  has_many :line_items

  # # specific line items

joegaudet

class Accounting::LedgerItem < ApplicationRecord
  # super class
  has_many :line_items, class_name: 'Accounting::LineItem', as: :ledger_item

  # specific line items
  has_many :ordered_items, class_name: 'Accounting::LineItems::OrderedItem', as: :ledger_item
  has_many :fees, class_name: 'Accounting::LineItems::Fee', as: :ledger_item
  has_many :payments, class_name: 'Accounting::LineItems::Payment', as: :ledger_item
  has_many :discounts, class_name: 'Accounting::LineItems::Discount', as: :ledger_item
end

joegaudet

with menu_item_order_counts as (
    select o.id             as order_id,
           o.menu_id,
           oi.menu_item_id,
           sum(oi.quantity) as quantity
    from orders o
             join order_items oi on o.id = oi.order_id
             join menu_items mi on oi.menu_item_id = mi.id
             join menu_groups mg on mi.menu_group_id = mg.id
    where o.menu_id is not null

joegaudet

# == Schema Information
#
# Table name: restaurants_menu_item_rankings
#
#  id             :integer
#  menu_id        :integer
#  menu_item_name :string
#  count          :decimal(, )
#

joegaudet

m = Menu.includes(:menu_groups).find(1)
m.menu_groups.all

joegaudet

from pprint import pprint

import numpy as np
from pandas import DataFrame, read_csv
from sklearn.cluster import KMeans

data = read_csv('features.csv').to_numpy()
matrix = data.transpose()
matrix = np.delete(matrix, 0, 0)

joegaudet

import Ember from 'ember';

const {computed} = Ember;

export default Ember.Controller.extend({
  appName: 'Ember Twiddle',
  
  bar: '',

  foo: computed('bar', function() {

joegaudet

i = 0

1000.times do |i|
  Thread.new {
    puts "#{i} #{i.even?}"
    i += 1
    puts "#{i} #{i.even?}"
  }
end