jameskyle

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import seaborn

import os
import re
import util
import pandas as pd
import numpy as np

jameskyle

from __future__ import division
import pandas as pd
import numpy as np
import scipy.optimize as sopt

def  gradebook():
    grades = np.array([
        [100,  2.5],
        [100,  2.5],
        [100,  2.5],

jameskyle

#!/usr/bin/env python
import json
problem = None
fights = None

def mapit(st):
    st = st.replace("T", "True").replace("F", "False").replace("{", "[")
    st = st.replace("}", "]")
    return st

jameskyle

import unittest
import pprint
from kwik import kwik


def data():
    games = [
        {
            'name':  'instructor_example',
            'numOfPatrons': 2,

jameskyle

from __future__ import division
import timeit
import unittest
import datetime as dt
import marketsim as sim
from marketsim import compute_portfolio_stats

class Mc2P1Test(unittest.TestCase):
    pass

jameskyle

from __future__ import print_function
import sys

def convert(num):
  bit_length = num.bit_length()
  for x in reversed(range(bit_length)):
    bit = (num >> x) & 1
    print("{0}".format(bit), end="")
  print("")

jameskyle

# Create some data
x <- seq(0, 10, by=0.01)
y <- exp(1)^-x
df <- data.frame(x=x, y=y)

# Add some noise.
df$y <- jitter(df$y, amount=.1)

jameskyle

import unittest
import matplotlib
import timeit
import optimization as opt


class TestMCP2(unittest.TestCase):
    def test_runtime(self):
        t = timeit.timeit("opt.optimize_portfolio()",
                          setup="import optimization as opt",

jameskyle

#!/usr/bin/env zsh
# set: ft=sh
set -e
setopt extendedglob
ROOT=/Applications/VMware\ Fusion.app/Contents/Library/

if  ! pwd | grep vmwarevm > /dev/null 2>&1; then
    echo "Doesn't look like we're in a vmware image directory!"
    exit 1
fi

jameskyle

import unittest
import numpy as np

from stdev_code import stdev_p, stdev_s

class TestStdevCode(unittest.TestCase):
    def setUp(self):
        self.data = []
        for x in xrange(100):
            self.data.append(np.random.uniform(0,50000000000,1000))