# Path: tlfp/tools/common.py
# def log_print(file=None, only_log=False):
#         def __init__(self, file):
#         def write(self, message):
#         def __getattr__(self, attr):
# def open_csv(dirpath, filename, delimiter=";"):
# def open_json(dirpath, filename=None):
# def download_daily(url_or_collecter, filename, output_directory):
# def print_json(dico, filename=None):
# def debug_file(data, filename):
# def format_date(d):
# def format_display_date(d):
# def real_lower(text):
# def clean_accents(text):
# def clean_text_for_diff(text):
# def compute_approx_similarity(text1, text2):
# def compute_similarity(text1, text2):
# def compute_similarity_by_articles(text1, text2):
# def identify_room(url_or_institution, legislature):
# def national_assembly_text_legislature(url_text):
# def personalize_link(link, obj, urlapi):
# def slug_groupe(g):
#     def __init__(self, sourcedir, load_parls=False):
#     def get_procedure(self):
#     def get_parlementaires(self):
#     def get_parlementaire(self, urlapi, slug):
#     def get_groupes(self):
#     def get_historique_groupes_senat(self):
#     def get_senateur_groupe(self, slug, object_date, urlapi):
#     def add_groupe(self, groupes, gpe, urlapi):
# def get_text_id(texte_url):
#     class Logger:
# class Context(object):


import os, sys, glob, re
from tlfp.tools.common import strip_text, compute_similarity_by_articles, open_json, print_json, \
    clean_text_for_diff, datize, clean_statuses
    stats["ratio_text_length_growth"] = (stats["output_text_length"] - stats["input_text_length"]) / stats["input_text_length"]

    stats["input_text_word_count"] = step_word_count(first_step)
    stats["output_text_word_count"] = step_word_count(last_step)
    stats["ratio_word_count_growth"] = (stats["output_text_word_count"] - stats["input_text_word_count"]) / stats["input_text_word_count"]

    adopted_step = find_first_and_last_steps(dos, include_CC=False)[1]
    if has_been_censored(dos):
        stats["censored_articles"], stats["fully_censored_articles"] = count_censored_articles(last_step)
        stats["output_text_before_CC_length"] = step_text_length(adopted_step)
        stats["output_text_before_CC_word_count"] = step_word_count(adopted_step)

    stats["ratio_texte_modif"] = 1 - compute_similarity_by_articles(first_arts, last_arts)

    # # # PROCEDURE # # #

    stats["echecs_procedure"] = len([step for step in dos['steps'] if step.get("echec")])

    # TODO: first institution
    stats['last_stage'] = adopted_step.get('stage')
    if stats['last_stage'] == 'CMP':
        stats['last_institution'] = 'CMP'
    else:
        stats['last_institution'] = adopted_step.get('institution')

    maxdate = dos.get('end')
    if not maxdate:
        for step in dos['steps']:
            if step.get('date'):
                maxdate = step.get('enddate') or step.get('date')

# Path: tlfp/tools/common.py
# def log_print(file=None, only_log=False):
#         def __init__(self, file):
#         def write(self, message):
#         def __getattr__(self, attr):
# def open_csv(dirpath, filename, delimiter=";"):
# def open_json(dirpath, filename=None):
# def download_daily(url_or_collecter, filename, output_directory):
# def print_json(dico, filename=None):
# def debug_file(data, filename):
# def format_date(d):
# def format_display_date(d):
# def real_lower(text):
# def clean_accents(text):
# def clean_text_for_diff(text):
# def compute_approx_similarity(text1, text2):
# def compute_similarity(text1, text2):
# def compute_similarity_by_articles(text1, text2):
# def identify_room(url_or_institution, legislature):
# def national_assembly_text_legislature(url_text):
# def personalize_link(link, obj, urlapi):
# def slug_groupe(g):
#     def __init__(self, sourcedir, load_parls=False):
#     def get_procedure(self):
#     def get_parlementaires(self):
#     def get_parlementaire(self, urlapi, slug):
#     def get_groupes(self):
#     def get_historique_groupes_senat(self):
#     def get_senateur_groupe(self, slug, object_date, urlapi):
#     def add_groupe(self, groupes, gpe, urlapi):
# def get_text_id(texte_url):
#     class Logger:
# class Context(object):


# Path: tlfp/tools/compute_stats.py
import os, sys, glob, re
from tlfp.tools.common import strip_text, compute_similarity_by_articles, open_json, print_json, \
    clean_text_for_diff, datize, clean_statuses

    stats["ratio_text_length_growth"] = (stats["output_text_length"] - stats["input_text_length"]) / stats["input_text_length"]

    stats["input_text_word_count"] = step_word_count(first_step)
    stats["output_text_word_count"] = step_word_count(last_step)
    stats["ratio_word_count_growth"] = (stats["output_text_word_count"] - stats["input_text_word_count"]) / stats["input_text_word_count"]

    adopted_step = find_first_and_last_steps(dos, include_CC=False)[1]
    if has_been_censored(dos):
        stats["censored_articles"], stats["fully_censored_articles"] = count_censored_articles(last_step)
        stats["output_text_before_CC_length"] = step_text_length(adopted_step)
        stats["output_text_before_CC_word_count"] = step_word_count(adopted_step)

    stats["ratio_texte_modif"] = 1 - compute_similarity_by_articles(first_arts, last_arts)

    # # # PROCEDURE # # #

    stats["echecs_procedure"] = len([step for step in dos['steps'] if step.get("echec")])

    # TODO: first institution
    stats['last_stage'] = adopted_step.get('stage')
    if stats['last_stage'] == 'CMP':
        stats['last_institution'] = 'CMP'
    else:
        stats['last_institution'] = adopted_step.get('institution')

    maxdate = dos.get('end')
    if not maxdate:
        for step in dos['steps']:
            if step.get('date'):
                maxdate = step.get('enddate') or step.get('date')

    stats["total_days"] = (datize(maxdate) - datize(dos['beginning'])).days + 1