import json
import random
import pandas as pd
#load moves from json file
#eclude moves with power equal to null,
# do not consider the effect,effects or changes fields
available_moves = dict()

keys_to_skip = ['effect','effects','changes']
with open('moves.json', 'r') as file:
    for line in file:
        m = json.loads(line)
        
        if m['power'] == None or m['accuracy'] == None:
            continue
        else:
            
            available_moves[m['name']] = {}
            m_keys = list(m.keys())
            for k in m_keys:
                if k in keys_to_skip:
                    continue
                else:
                    available_moves[m['name']][k] = m[k]
                    continue
    available_moves_df = pd.DataFrame.from_dict(available_moves,orient='index')

#loading pokemons from json file
#assume levell = 1 for all
pokemons_dict = dict()
with open('pokemons.json', 'r') as file:
    for line in file:
        p = json.loads(line)
        p['level'] = 1
        pokemons_dict[p['name']] = p
        #pokemons_dict[p['name']]['level'] = 1

        current_pokemon_types = p['types'] + ['normal']
        compatible_moves = []
        for m in available_moves.keys():
            if available_moves[m]['type'] in current_pokemon_types:
                compatible_moves.append(m)
        #select two random moves
        selected_moves = [random.choice(compatible_moves) for _ in range(2)]
        pokemons_dict[p['name']]['moves'] = selected_moves
pokemons_df = pd.DataFrame.from_dict(pokemons_dict,orient='index')


available_pokemon_names = list(pokemons_dict.keys())
available_starter_names = ['bulbasaur', 'charmander', 'squirtle']

#type effectiveness
type_effectiveness = []
available_types = dict()
with open('type_effectiveness.json', 'r') as file:
    for line in file:
        loaded_line = json.loads(line)
        type_effectiveness.append(loaded_line)
        available_types[loaded_line['attack']] = loaded_line['attack']
type_effectiveness_df = pd.DataFrame.from_dict(type_effectiveness)
#NOTE: from a practical point of view, since type_effectiveness is accessed many times
#in the main code, using pandas DataFrame is not efficient. I decided to keep using dictionaries
#for the type effectiveness. The justification arises especially if we consider 
#how long data generation will take.