#!/usr/bin/python3

import collections
import functools
import time

from lib.ArgParser import get_config, get_model_from_config
from lib.Controller import simulate, ReplayController
from lib.Cost import DeviationCost, NegativeCost
from lib.Env import CentralHeatingHistoryEnv

# ARGS
config = get_config()

name_past_future = (
	('past', ('temp_in', 'temp_out', 'mode'), tuple()),
	('fore', ('temp_in', 'temp_out', 'mode'), ('temperature', 'wind_speed', 'radiation', 'humid')),
)

for name, past, future in name_past_future:
	config['model_past_fields'] = past
	config['model_future_fields'] = future

	# MODEL
	start_time = time.clock()
	model = get_model_from_config(config)
	end_time = time.clock()

	# PLOT
	config['plot_fields'] = ['time', 'temp_in', 'temp_in_calc']
	config['past_fields'] = ['mode']
	config['past_values'] = 1
	config['future_values'] = 0

	# ENV
	env = CentralHeatingHistoryEnv()
	env.cost_class = DeviationCost
	env.model = model
	env.config = config
	env.reset()

	# MAIN
	controller = ReplayController()
	simulate(env, controller, render=False)

	print('RESULT {}_{}_{}_{}{} {} {} {}'.format(name, config['model_past_values'], config['aggregate'] or 1, config['model_type'], ('_' + str(config['model_epochs'])) if config['model_type']=='neural' else '', env.cost.costs['diff'], env.cost.costs['sq_err'], end_time - start_time))