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#!/usr/bin/python3
'''
Sample from the twitter API and post results to a file or to Kafka.
To use, set credientials as enviornment variables, e.g.
export TWITTER_ACCESS_TOKEN=... 
or source myfile
where myfile exports the authorization variables 
'''
import twython, json, re, argparse, subprocess, os, sys, time
from socket import timeout

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# image segmentation demo
# author: rwalker (ryan@ryanwalker.us)
# license: MIT

library("jpeg")
library("png")
library("graphics")
library("ggplot2")
library("gridExtra")
#######################################################################################

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import org.json4s.{DefaultFormats, Formats}
import scala.language.postfixOps
import scalate.ScalateSupport
import org.scalatra._
import java.io.PrintWriter

class Chunks extends ScalatraServlet with ScalateSupport {
  /*
    Simple example of chunked transfer encoding in Scalatra (scala web framework)
  */

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library(foreign)
library(car)
library(ggplot2)
library(scales)

# load the CHIS data
file <- "~/projects/CHIS/chis15_adult_stata/Data/ADULT.dta"  # your file
CHIS   <- read.dta(file, convert.factors = TRUE)

# Recode insurance type

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library(quadprog)
library(osqp)

# This gist compares the quadprog solver against OSPQ on a very simple problem.

##
##  The following example comes directly from `?? quadprog` documentation`
##
## Assume we want to minimize: -(0 5 0) %*% b + 1/2 b^T b
## under the constraints:      A^T b >= b0

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import json
import csv
from collections import defaultdict

# load the mapping (supplied by the dataset) from id into book_id
with open("goodreads/book_id_map.csv") as f:
    reader = csv.reader(f)
    _ = next(reader)
    book_id_map = {int(_id): int(book_id) for _id, book_id in reader}

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import json
from scipy.sparse import coo_matrix, save_npz
import numpy as np


print("reading data...")
with open("goodreads/interactions.json") as f:
    data = json.load(f)
    books = data["books"]
    interactions = data["interactions"]

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{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:ListBucket"
            ],
            "Resource": [
                "arn:aws:s3:::fancy-bucket"

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import argparse
import json
import sys
import warnings
import numpy as np

from scipy.sparse import load_npz, coo_matrix

class CooccurenceRecommender:
    

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