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137 lines
4.7 KiB
Python

from parse_trees import load_trees_from_json
from medialab import crear_base_datos, paso
from random import shuffle, random
# creating Markov Chain in text & trees
def path(word, words_tree, words_path, trees):
# Collects a list of trees to visit
tree_index = {}
itinerary = []
current_step = word.capitalize() + ' '
previous_steps = ''
markov_decision_traces = [ ( word, 0, [word]) ]
posibilities, dice, next_word = paso(word, words_tree, words_path)
while len(itinerary) < 50 and next_word not in '.!?':
if next_word in ',:;\)':
current_step = current_step[:-1]
current_step += ' '
breath = random()
if breath < 0.1:
separator = '\n'
else:
separator = ' '
current_step += (next_word + separator)
markov_decision_traces.append(( next_word, dice, posibilities ))
if next_word in words_tree:
# Current word is a tree word, this step in the itinerary is 'complete'
# Word is not yet in the index, add a tree for this word
if next_word not in tree_index:
# Add tree to index and remove from list of available trees
tree_index[next_word] = trees.pop(0)
# Retreive tree linked to this word from the index
tree = tree_index[next_word]
# Get a next word from the database
word = next_word
posibilities, dice, next_word = paso(word, words_tree, words_path)
# Try to look ahead to the next word, if the next word
# is interpunction, add it to the current step
# but first remove trailing space
if next_word in '.,:;!?\)':
current_step = current_step[:-1] + next_word + ' '
# Request a new next word to continue generation
markov_decision_traces.append(( next_word, dice, posibilities ))
# Test whether the next word marks the end of a sentence,
# thus the end of the itinerary. Then don't touch it so the
# while will break.
if next_word not in '.!?':
word = next_word
posibilities, dice, next_word = paso(word, words_tree, words_path)
# Add the current step, and the tree to the itinerary
itinerary.append((
current_step,
previous_steps,
tree,
markov_decision_traces
))
previous_steps += current_step
# Clear the current step
current_step = ''
markov_decision_traces = []
else:
word = next_word
posibilities, dice, next_word = paso(word, words_tree, words_path)
return itinerary
# Genera un camino a partir de un texto y una palabra del texto
def crear_camino(nombre_archivo, palabra_inicial):
trees = load_trees_from_json()
shuffle(trees)
#print("Starting to read text")
(palabras_arboles, palabras_camino) = crear_base_datos(nombre_archivo)
#print("Amount of tree words: ", len(palabras_arboles))
return path(palabra_inicial, palabras_arboles, palabras_camino, trees)
if __name__ == '__main__':
import os.path
basepath = os.path.dirname(__file__)
#EJECUCIÓN__________________________________________________________________
print('Puedes elegir una novela para crear tu Paseo por árboles de Madrid.')
print('Opción 1: La novela "La madre naturaleza" de la escritora feminista Emilia Pardo Bazán \
fue publicada en 1887. Usa en esta obra una prosa poética y descriptiva, y en sus páginas se \
siente el amor que profesa al paisaje gallego, con un conocimiento de la botánica y de \
las costumbres rurales muy superior al de sus contemporáneos.')
print('Opción 2: La novela "Miau" del escritor Benito Pérez Galdós fue publicada en 1888. \
Enmarcada en el género realista, satiriza el Madrid burocrático de finales del siglo XIX \
a partir de las vicisitudes vitales de su protagonista, Ramón Villaamil, \
un competente exempleado del Ministerio de Hacienda, al que una serie de intrigas \
han dejado cesante.')
novel = input('Por favor, marca 1 o 2: ')
first_word = 'un'
if novel == '1':
novel = os.path.join(basepath, '../data/emilia_prueba.txt')
author = 'Emilia Pardo Bazán'
title = 'La Madre Naturaleza'
else:
novel = os.path.join(basepath, '../data/prueba.txt')
author = 'Benito Pérez Gáldos'
title = 'Miau'
# Create title/subtitle
print('\nPaseo por los árboles de Madrid con', author, 'y', title, '\n')
print('-------------------------------------------------------------------------------------------\n')
# Create chapters
path = crear_camino(novel, first_word)
sentences = []
for sentence, concatenated_steps, tree, traces in path:
for word, dice, options in traces:
print('Dice rolled - {} -'.format(dice))
print('New word - {} - chosen from {}'.format(word, options))
print('')
sentences.append(sentence)
print('Itinerary:\n{} \n'.format(''.join(sentences)))
print('Tree linked to last word :', tree['properties']['NOMBRE_COMUN'], ' en ', tree['properties']['MINTDIRECCIONAUX'], '\n')
print('\n')