sglyon

#!/usr/bin/env python
import os
import urllib
from threading import Thread
from Queue import Queue
import pandas as pd


def hist_data_threaded(syms, max_threads=100, source='yahoo',
                       start='1/1/2010', end='12/31/2013'):

sglyon

# file:  QuantMacro.jl

module QuantMacro

export Models #, other tools

include("models.jl")

end  # module

sglyon

#Set up data partition
sudo mkdir /data
sudo chmod 777 /data
sudo "echo /dev/xvdb   /data	ext4	rw,user,exec,comment=cloudconfig	0	2 >> /etc/fstab"
sudo mount /data

#Install build environment
sudo sed -i "s/enabled=0/enabled=1" /etc/yum.repos.d/epel.epo
sudo yum -y update
sudo yum -y upgrade

sglyon

##### Install a lot of stuff first #####

$sudo apt-get update

##install python 
$ wget http://09c8d0b2229f813c1b93-c95ac804525aac4b6dba79b00b39d1d3.r79.cf1.rackcdn.com/Anaconda-2.0.1-Linux-x86_64.sh
$ sudo bash anaconda........sh
 
##install necessary libs
$ sudo apt-get install -y python-matplotlib python-tornado ipython ipython-notebook python-setuptools python-pip

sglyon

using Base.LinAlg: BlasChar, BlasInt, blas_int, chkstride1, chksquare
using Base: blasfunc
using Base.LinAlg.LAPACK: liblapack, @lapackerror

A = [1. 2 3
      4 5 6
      7 8 9]
B = diagm(randn(3))

args = ('V', 'V', copy(A), copy(B))

sglyon

Code/data for cs2014

To actually run this code you need to get my gibbs branch of MCMC.jl:

Pkg.clone("git@github.com:spencerlyon2/MCMC.jl.git")
Pkg.checkout("MCMC", "gibbs")

You will also need a special version of the StateSpace.jl package:

sglyon

# load in the model and associated tools
require("../nk_model.jl")
require("../nk_model_tools.jl")

# load packages we use
import Gadfly
import PyPlot
using Distributions
using DataFrames

sglyon

The template is called example.tmpl and it contains the lecture source as well as defines various blocks.

Each of the blocks has a default argument that is simply the content that is found in the current python only version of the site.

The cool thing is how we can use this system to easily generate lecture files for both languages, by only having to define the blocks for julia. This happens in example_jl.tmpl.

Notice I don't do anything beyond filling in the blocks here. i.e. there is no actual exposition here -- just replacing the default block parameters with their julia equivalent.

The python script run_example.py shows how we could load the template and evaluate it. If you have python run the file it will print out the python and julia versions of the file for you.

sglyon

#!/usr/bin/env python
"""
simple example script for running notebooks and reporting exceptions.

Usage: `checkipnb.py foo.ipynb [bar.ipynb [...]]`

Each cell is submitted to the kernel, and checked for errors.
"""

import os,sys,time

sglyon

class AssetPrices(object):
    """
    docstring
    """
        def __init__(self, beta, P, s, gamma):
        self.beta, self.gamma = beta, gamma
        self.P, self.s = P, s
        self.n = self.P.shape[0]

    @property