clausd

sudo stdbuf -oL -eL /usr/bin/tcpdump -i lo -A -s 10240 "tcp port 10000 " | egrep -a --line-buffered ".+(GET |HTTP\/|POST )|^[A-Za-z0-9-]+: " | perl -nle 'BEGIN{$|=1} { s/.*?(GET |HTTP\/[0-9.]* |POST )/\n$1/g; print }'

clausd

pandoc --from=epub+native_divs+native_spans --to=rtf  source.epub   | unrtf | pandoc --from=html-native_divs-native_spans --to=markdown_strict > acceptable.md

clausd

# variables hold values the user enters
# we also use them to guide program flow
# by the way - these are comments. They start with # - after that the rest of the line is ignored
number = 1 # define a variable
string = "Some text" # a string is a variable containing text
list = [1,2,3,4] # we can also define lists of things


number = 2  # changing the value of a variable
number = number + 1 # we can use the variable itself, any expression, to set the new value. The value of number is now 3

clausd

Install the Dokku droplet - smallest possible digitalocean vm works fine
Make sure you add your ssh key so you can git over ssh and ssh into the image later
* On the image be sure to NOT set up IPv6
Boot the image - note the ip
Add a star alias on some domain you control and point it at that ip address - this lets you dokku up a lot of projects at somedomain.yourdomain.com
Go to http://[your IP address] to finalize dokku setup - set up virtual domains to make use of the star alias you just made
* You need to add your ssh public key here as well
* You CANNOT just add your key to authorised_keys - you need to use sshcommand - this enables the whole 'push to deploy' workflow
Follow the Flask-y steps here https://www.linode.com/docs/applications/containers/deploy-a-flask-application-with-dokku/

clausd

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LogisticRegressionCV

from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.utils import np_utils

clausd

import numpy as np
import pandas as pd
import lda
import lda.datasets
from sklearn.feature_extraction.text import CountVectorizer


def load_questions():
    sheet = pd.read_excel('android_watch.xlsx')
    Qs = sheet['Question']

clausd

#! /usr/bin/env python
#
# Mixpanel, Inc. -- http://mixpanel.com/
#
# Python API client library to consume mixpanel.com analytics data.
#
# Copyright 2010-2013 Mixpanel, Inc
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.

clausd

class IntrospectBinding
  
  attr_accessor :binding, :evals
  
  def initialize(binding)
    @binding = binding
    @evals = []
  end
  
  def eval(string, filename = '', lineno = 0)

clausd

#!/usr/bin/ruby
require 'erb'
require 'json'
require 'yaml'
require 'fileutils'

@from = ARGV.shift || 'files'
@to = ARGV.shift || 'dist'

# definitions

clausd

import argparse
import pandas as pd
import mysql.connector
from sqlalchemy import create_engine
from string import Template
import os

import argparse

parser = argparse.ArgumentParser()