thoolihan

import numpy as np
from math import factorial

def sin(x):
    val = np.float64(0)
    for n in range(0, 25):
        term = (((-1) ** n) * (x ** (2*n+1)) / factorial(2*n + 1))
        val += term
        print("for x_{0}, term is {1}, sin({2}) approximation is {3}".format(
            n, term, x, val))

thoolihan

brew install python3
brew install apple-gcc42
brew unlink gcc
ln -sf /usr/local/bin/gfortran-4.2 /usr/local/bin/gfortran

#optional: create virtualenv
python3 -mvenv ~/venvs/my_venv
source ~/venvs/my_venv/bin/activate
#end optional

thoolihan

import re

p = re.compile('\"([^\"]+)\"')

str = 'I said "Hello There." She replied "hi"'

matches = p.findall(str)

for m in matches:
    print('found: {0}'.format(m))

thoolihan

#include <iostream>
#include <iomanip>
#include "math.h"

using namespace std;

float pmf(int k, double lambda) {
    // https://en.wikipedia.org/wiki/Poisson_distribution#Definition
    return pow(M_E, k * log(lambda) - lambda - lgamma(k + 1.0));
}

thoolihan

import boto
import boto.s3
from boto.s3.key import Key
from glob import glob
import sys
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("-p","--pattern", help="pattern to upload")
args = parser.parse_args()

thoolihan

import numpy as np
import matplotlib.pyplot as plt
from math import pi, cos, sin

arc_size = pi / 2
points = 20
inner_ratio = .4

theta = np.arange(0, arc_size, arc_size / points)

thoolihan

# start cmd.exe as admin
# start taskmanager and close explorer.exe
cd %userprofile%\appdata\local
del iconcache.db /a
# start explorer.exe

thoolihan

sudo yum -y install epel-release
sudo yum -y install gcc gcc-c++ python-pip python-devel atlas atlas-devel gcc-gfortran openssl-devel libffi-devel
# use pip or pip3 as you prefer for python or python3
pip install --upgrade virtualenv
virtualenv --system-site-packages ~/venvs/tensorflow
source ~/venvs/tensorflow/bin/activate
pip install --upgrade numpy scipy wheel cryptography #optional
pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0rc0-cp35-cp35m-linux_x86_64.whl
# or below if you want gpu, support, but cuda and cudnn are required, see docs for more install instructions
pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.0rc0-cp35-cp35m-linux_x86_64.whl

thoolihan

library(ggplot2)

g <- qplot(c(0, 2 * pi), stat="function", fun=sin, 
           geom="line", color="sin", ylim=c(-2, 2),
           ylab="ratio", xlab="radians") +
  stat_function(fun=cos, aes(color="cos")) + 
  stat_function(fun=tan, aes(color="tan")) 

print(g)

thoolihan

skus = Dir.glob("*.jpg").collect do |file|
  file.sub(".jpg","")
end

output = "final"

task :medium_images do
  puts "creating medium images 250px wide..."
  skus.each do |sku|
    sh "convert #{sku}.jpg -strip -resize 250 -quality 100 #{output}/#{sku}_table.jpg"