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#!/usr/bin/env python3
"""
project euler # 17

If the numbers 1 to 5 are written out in words: one, two, three, four, five,
then there are 3 + 3 + 5 + 4 + 4 = 19 letters used in total.

If all the numbers from 1 to 1000 (one thousand) inclusive were written out in
words, how many letters would be used?

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ls | grep -E '*-(0[6-9]|1[1-5])$' > plates_06_to_15.txt

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import pandas as pd
import numpy as np

def find_correlation(data, threshold=0.9, remove_negative=False):
    """
    Given a numeric pd.DataFrame, this will find highly correlated features,
    and return a list of features to remove.
    Parameters
    -----------
    data : pandas DataFrame

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#!/bin/bash

# start a jupyter server running on a remote host
# and port-forward to local machine
remote_host=$1
terminal=x-terminal-emulator

# start remote jupyter session on port:8889
$terminal ssh $remote_host "ipython notebook --no-browser --port=8889"

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#!/usr/bin/env python3.6
# sensible solution
import re
import sys
import itertools

def get_seq(path):
    """return fasta sequence from file"""
    lines = open(path).readlines()
    seq = ""

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rsync -a -f"+ */" -f"- *" source/ destination/

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#!/usr/bin/env python

"""re-rerun failed jobs
    >>> ./find_failed.py $results_dir $path_to_batchlist
"""

import os
from sys import argv

def has_failed(directory, expected):

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import random

def train_test_split(data, labels, test_prop=0.3):
    """roll your own train test split"""
    assert len(data) == len(labels)
    n_test = round(test_prop * len(data))
    n_train = len(data) - n_test
    combined = list(zip(data, labels))
    random.shuffle(combined)
    x_train, y_train = zip(*combined[:n_train])

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using StatsBase

function otsu_threshold(img, bit_depth = 256)
    counts = fit(Histogram, img[:], nbins = bit_depth).weights
    const total = prod(size(img))
    current_max, threshold = 0, 0
    weightB, sumB = 0, 0
    sumT = sum([i * counts[i] for i in 1:bit_depth])
    for (i, count) in enumerate(counts)
        weightB += count