# python standard library
import random
from  datetime import datetime
import logging

# libraries
import networkx as nx

# this project
# import questions

# setup logger for this module
logger = logging.getLogger(__name__)

# ----------------------------------------------------------------------------
# kowledge state of each student....??
# Contains:
#   state - dict of topics with state of unlocked topics
#   deps  - dependency graph
#   topic_sequence - list with the order of recommended topics
# ----------------------------------------------------------------------------
class StudentKnowledge(object):
    # =======================================================================
    # methods that update state
    # =======================================================================
    def __init__(self, deps, state={}):
        self.deps = deps  # dependency graph shared among students
        self.state = state # {'topic': {'level':0.5, 'date': datetime}, ...}
        self.update_topic_levels() # forgetting factor
        self.topic_sequence = self.recommend_topic_sequence() # ['a', 'b', ...]
        self.unlock_topics()

    # ------------------------------------------------------------------------
    # compute recommended sequence of topics  ['a', 'b', ...]
    # ------------------------------------------------------------------------
    def recommend_topic_sequence(self):
        return list(nx.topological_sort(self.deps))

    # ------------------------------------------------------------------------
    # Updates the proficiency levels of the topics, with forgetting factor
    # FIXME no dependencies are considered yet...
    # ------------------------------------------------------------------------
    def update_topic_levels(self):
        now = datetime.now()
        for s in self.state.values():
            dt = now - s['date']
            s['level'] *= 0.95 ** dt.days   # forgetting factor 0.95


    # ------------------------------------------------------------------------
    # Unlock topics whose dependencies are satisfied (> min_level)
    # ------------------------------------------------------------------------
    def unlock_topics(self):
        # minimum level that the dependencies of a topic must have
        # for the topic to be unlocked.
        min_level = 0.01

        for topic in self.topic_sequence:
            if topic not in self.state:  # if locked
                pred = self.deps.predecessors(topic)
                if all(d in self.state and self.state[d]['level'] > min_level for d in pred): # and all dependencies are done
                    self.state[topic] = {
                        'level': 0.0,           # then unlock
                        'date': datetime.now()
                        }
                    logger.debug(f'Unlocked "{topic}".')


    # ------------------------------------------------------------------------
    # Start a new topic. If not provided, gets a recommendation.
    #    questions: list of generated questions to do in the topic
    #    finished_questions: [] will contain correctly answered questions
    #    current_question: the current question to be presented
    # ------------------------------------------------------------------------
    def init_topic(self, topic=''):
        logger.debug(f'StudentKnowledge.init_topic({topic})')

        if not topic:
            topic = self.get_recommended_topic()

        # check if it's unlocked
        if self.is_locked(topic):
            return False

        self.current_topic = topic

        # generate question instances for current topic
        factory = self.deps.node[topic]['factory']
        questionlist = self.deps.node[topic]['questions']

        self.correct_answers = 0
        self.wrong_answers = 0
        self.finished_questions = []
        self.questions = [factory[qref].generate() for qref in questionlist]
        logger.debug(f'Total: {len(self.questions)} questions')

        try:
            self.current_question = self.questions.pop(0)  # FIXME crash if empty
        except IndexError:
            # self.current_question = None
            self.finish_topic()  # FIXME if no questions, what should be done?
            return False
        else:
            self.current_question['start_time'] = datetime.now()
            return True

    # ------------------------------------------------------------------------
    # The topic has finished and there are no more questions.
    # The topic level is updated in state and unlocks are performed.
    # The current topic is unchanged.
    # ------------------------------------------------------------------------
    def finish_topic(self):
        logger.debug(f'StudentKnowledge.finish_topic({self.current_topic})')

        self.current_question = None
        self.state[self.current_topic] = {
            'date': datetime.now(),
            'level': self.correct_answers / (self.correct_answers + self.wrong_answers)
            }
        self.unlock_topics()


    # ------------------------------------------------------------------------
    # returns the current question with correction, time and comments updated
    # ------------------------------------------------------------------------
    def check_answer(self, answer):
        logger.debug('StudentKnowledge.check_answer()')

        q = self.current_question

        q['answer'] = answer
        q['finish_time'] = datetime.now()
        grade = q.correct()
        logger.debug(f'Grade = {grade:.2} ({q["ref"]})')

        # if answer is correct, get next question
        if grade > 0.999:
            self.correct_answers += 1
            self.finished_questions.append(q)
            try:
                self.current_question = self.questions.pop(0) # FIXME empty?
            except IndexError:
                self.finish_topic()
            else:
                self.current_question['start_time'] = datetime.now()

        # if answer is wrong, keep same question and add a similar one at the end
        else:
            self.wrong_answers += 1
            factory = self.deps.node[self.current_topic]['factory']
            self.questions.append(factory[q['ref']].generate())


        # returns answered and corrected question
        return grade


    # ========================================================================
    # pure functions of the state (no side effects)
    # ========================================================================

    # ------------------------------------------------------------------------
    def get_current_question(self):
        return self.current_question

    def get_finished_questions(self):
        return self.finished_questions

    # ------------------------------------------------------------------------
    def get_current_topic(self):
        return self.current_topic

    # ------------------------------------------------------------------------
    def is_locked(self, topic):
        return topic not in self.state

    # ------------------------------------------------------------------------
    # Return list of {ref: 'xpto', name: 'long name', leve: 0.5}
    # Levels are in the interval [0, 1] if unlocked or None if locked.
    # Topics unlocked but not yet done have level 0.0.
    # ------------------------------------------------------------------------
    def get_knowledge_state(self):
        return [{
            'ref': ref,
            'type': self.deps.nodes[ref]['type'],
            'name': self.deps.nodes[ref]['name'],
            'level': self.state[ref]['level'] if ref in self.state else None
            } for ref in self.topic_sequence ]

    # ------------------------------------------------------------------------
    def get_topic_progress(self):
        return len(self.finished_questions) / (1 + len(self.finished_questions) + len(self.questions))

    # ------------------------------------------------------------------------
    def get_topic_level(self, topic):
        return self.state[topic]['level']

    # ------------------------------------------------------------------------
    def get_topic_date(self, topic):
        return self.state[topic]['date']

    # ------------------------------------------------------------------------
    # Recommends a topic to practice/learn from the state.
    # ------------------------------------------------------------------------
    def get_recommended_topic(self):    # FIXME untested
        return min(self.state.items(), key=lambda x: x[1]['level'])[0]