"""Functions for reading data from the sentiment dictionary and tweet files."""

import os
import re
import string
from datetime import datetime
from ucb import main, interact

DATA_PATH = 'data' + os.sep

def load_sentiments(file_name="data"+os.sep+"sentiments.csv"):
    """Read the sentiment file and return a dictionary containing the sentiment
    score of each word, a value from -1 to +1.
    """
    sentiments = {}
    for line in open(file_name, encoding='utf8'):
        word, score = line.split(',')
        sentiments[word] = float(score.strip())
    return sentiments

word_sentiments = load_sentiments()

def file_name_for_term(term):
    """Return a valid filename that corresponds to an arbitrary term string."""
    valid_characters = '-_' + string.ascii_letters + string.digits
    no_space = term.replace(' ', '_')
    return ''.join(c for c in no_space if c in valid_characters) + '.txt'

def generate_filtered_file(unfiltered_name, term):
    """Return the path to a file containing tweets that match term, generating
    that file if necessary.
    """
    filtered_path = DATA_PATH + file_name_for_term(term)
    if not os.path.exists(filtered_path):
        print('Generating filtered tweets file for "{0}".'.format(term))
        r = re.compile('\W' + term + '\W', flags=re.IGNORECASE)
        with open(filtered_path, mode='w', encoding='utf8') as out:
            unfiltered = open(DATA_PATH + unfiltered_name, encoding='utf8')
            matches = [l for l in unfiltered if term in l.lower()]
            for line in matches:
                if r.search(line):
                    out.write(line)
    return filtered_path

def load_tweets(make_tweet, term='my job', file_name='all_tweets.txt'):
    """Return the list of tweets in file_name that contain term.
    
    make_tweet -- a constructor that takes four arguments:
      - a string containing the words in the tweet
      - a datetime.datetime object representing the time of the tweet
      - a longitude coordinate
      - a latitude coordinate
    """
    term = term.lower()
    filtered_path = generate_filtered_file(file_name, term)
    tweets = []
    for line in open(filtered_path, encoding='utf8'):
        if len(line.strip().split("\t")) >=4:
            loc, _, time_text, text = line.strip().split("\t")
            time = datetime.strptime(time_text, '%Y-%m-%d %H:%M:%S')
            lat, lon = eval(loc)
            tweet = make_tweet(text.lower(), time, lat, lon)
            tweets.append(tweet)
    return tweets