drvinceknight

from __future__ import division
import random
from csv import reader

class Player:
    """
    A class for a player
    """
    def __init__(self, name):
        self.name = name

drvinceknight

import random

def utility(s1, s2):
    if s1 == s2 == 'B':
        return 4,4
    if s1 == s2 == 'C':
        return 2,2
    if s1 == 'C' and s2 == 'B':
        return 5,0
    return 0,5

drvinceknight

cost_of_items = {'apple': 4,
                 'cake': 6,
                 'doughnut': 12}

class ShoppingBasket:
    """
    A class for a shopping basket with attributes for the cost and the contents of a shopping basket
    """
    def __init__(self):
        self.cost = 0  # Initialising a cost of 0 (basically the value of the basket when someone picks it up

drvinceknight

import random
deck = ['1S','2S','3S','4S', '1H', '2H', '3H', '4H']
p1 = []
p2 = []

random.shuffle(deck)
while len(deck) > 0:
    p1.append(deck.pop())
    p2.append(deck.pop())

drvinceknight

#!/bin/sh
tmux new-session -d  # Create a new session
tmux split-window -v -p 25  # Create a horizontal split (25 % of the screen)
tmux split-window -h  # Create a vertical split
tmux selectp -t 1  # Go to top pane
tmux -2 attach-session -d  # Attach

drvinceknight

"""
Going to simulate 100 '1st two days of xmas'
"""
from __future__ import division
import random
partidges = []
turtle_doves= []

for xmas in range(100):
    partidges.append(random.choice([0,1,1,1,2]))

drvinceknight

# Shortest possible simulation of an MM1 Q (with warmup). Contributors: Geraint, Jason, Vince, Harald and Chris.
from random import expovariate
lmbda, mu, T, warmup, waits, sample, arrival_time, start_time, end_time = 1, 2, 5000, 1000, [], lambda x: expovariate(x), 0, 0, 0
while end_time < T:
    arrival_time += sample(lmbda)
    start_time = max(end_time, arrival_time)
    end_time = start_time + sample(mu)
    if arrival_time > warmup: waits.append(start_time - arrival_time)
print 'Mean wait: ' + str(sum(waits) / len(waits))

drvinceknight

for i in range(10):
    print i

drvinceknight

@interact
def _(A=matrix(RDF,[[1,3,1],[5,-2,2],[3,3,4]]),n=("$n$",slider(0,100,1)),Precision=slider(0,10,1,4)):
    exp_A=exp(A)
    approx_exp=sum([A^i/factorial(i) for i in range(n+1)]).n(digits=Precision)
    p=list_plot([(sum([A^i/factorial(i) for i in range(n+1)])-exp(A)).norm(2) for n in range(0,21)])
    p.axes_labels(['$n$','$|| e^{A}-\sum_{i=0}^{n}A^{i}/{i!}||_2$'])
    print "\n"
    print "Value of approximation for n=%s: \n%s" % (n, approx_exp)
    print "\n"
    print "Exact value: \n%s" % exp(A)

drvinceknight

@interact
def _(A=matrix(RDF,3,3,[8,2,0,1,7,2,1,3,6])/10,x_init=("$\pi^{(0)}$",vector([.2,.5,.3]))):
    x_init = vector(x_init)
    data = [x_init * (A^i) for i in range(20)]
    pi = zip(*data)
    p = line(enumerate(pi[0]),color='red',legend_label='$\pi_1$',thickness=3)
    p += line(enumerate(pi[1]),color='blue',legend_label='$\pi_2$',thickness=3)
    p += line(enumerate(pi[2]),color='green',legend_label='$\pi_3$',thickness=3)
    p.ymax(1)
    show(p)