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"""This script removes solutions from a given notebook and saves it as a new notebook.

Input: a Jupyter notebook with solutions 

The solutions are marked according to the nbgrader's format:
### BEGIN SOLUTION 
...
### END SOLUTION

Output: a path to a new notebook without solutions."""

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#!/usr/bin/env python
"""Print the dates of certain day of week given date range."""
import argparse
from datetime import date

import pandas as pd


def parse_dayofweek(dow):
    """Return pandas frequency string from day of the week name."""

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#!/usr/bin/env python3
import re
import sys

if __name__ == "__main__":
    template_path = sys.argv[1]
    content = open(template_path).read()

    var_pattern = re.compile('{.+?}')
    variables = set(re.findall(var_pattern, content))

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while true; do lynx --dump "https://shop.lego.com/en-US/LEGO-NASA-Apollo-Saturn-V-21309" | grep "Available now" && osascript -e 'display notification "Lego set available!" with title "Lego alert!!"'; sleep 60; done

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def logodds(corpora_dic, bg_counter):
    """ It calculates the log odds ratio of term i's frequency between 
    a target corpus and another corpus, with the prior information from
    a background corpus. Inputs are:
    
    - a dictionary of Counter objects (corpora of our interest)
    - a Counter objects (background corpus)
    
    Output is a dictionary of dictionaries. Each dictionary contains the log 
    odds ratio of each word. 

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#!/usr/bin/env python
import sys
import json
import httplib

class FuncAssociate(object):
    """Query funcassociate to find out enriched GO terms."""
    
    host = 'llama.mshri.on.ca'
    query_url = '/cgi/funcassociate/serv'

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TEX = pdflatex -interaction nonstopmode
BIB = bibtex
GS = gs -q -dNOPAUSE -dBATCH -sDEVICE=pdfwrite 

COVER = cover
PAPER = paper_main
SUPP = paper_supp
BIBFILE = temp.bib
BUNDLE = paper_bundle.pdf