# Path: communication.py
# def send(message, context=None, host='localhost', port=5566):
# 
# Path: utils.py
# DREAM_HOUR = 23
# EVOLVE_HOUR = 4
# SAVE_HOUR = 5
# REBOOT_HOUR = 6
#     A = np.frombuffer(buf, dtype=md['dtype'])
# def save(filename, data):
# def load(filename):
# def filetime(filename):
# def plot_NAP_and_energy(NAP, plt):
# def bytes2human(n, format="%(value)i%(symbol)s"):
# def csv_to_array(filename, delimiter=' '):
# def array_to_csv(filename, data, delimiter=' '):
# def wait_for_wav(filename):
# def filesize(filename):
# def chunks(A, chunk_len):
# def wav_duration(filename):
# def trim(A, threshold=100):
# def trim_right(A, threshold=.2):
# def trim_wav(sound, threshold=100):
# def split_signal(data, threshold=100, length=5000, elbow_grease=100, plot=False, markers=[]):
# def split_wav(filename, threshold=100, length=5000, elbow_grease=100, plot=False):
# def send_zipped_pickle(socket, obj, flags=0, protocol=-1):
# def recv_zipped_pickle(socket, flags=0):
# def send_array(socket, A, flags=0, copy=True, track=False):
# def recv_array(socket, flags=0, copy=True, track=False):
# def diff_to_hex_bad(diff):
# def average_hash(image):
# def a_hash(image, hash_size=8):
# def p_hash(image, hash_size=64):
# def diff_to_hex(difference):
# def d_hash(image, hash_size = 8):
# def hamming_distance(s1, s2):
# def zero_pad(signal, length):
# def exact(signal, length):
# def scale(image):
# def getSoundInfo(filename):
# def get_segments(filename):
# def get_amps(filename):
# def get_most_significant_word(filename):
# def getLatestMemoryWavs(howmany):
# def updateAmbientMemoryWavs(currentFiles):
# def print_exception(msg=''):
# def scheduler(host):
# def true_wait(seconds):
# def reboot():
# def counter(host):
# def delete_loner(counterQ, data, query, protect, deleted_ids):
# def sentinel(host):
#     def __init__(self, me, host='localhost'):
#     def run(self):
#     def __init__(self, host='localhost'):
#     def write(self, txt):
#     def flush(self):
#     def __init__(self, host='localhost'):
#     def write(self, txt):
#     def flush(self):
# def log_sink():
#     def run(self):
#     def run(self):
# def brain_name():
# def find_last_valid_brain():
# def daily_routine(host):
# def load_esn(filename):
# def dump_esn(net, filename):
# class AliveNotifier(threading.Thread):
# class SimpleLogger:
# class Logger:
# class LoggerProcess(mp.Process):
# class MyProcess(mp.Process):


import multiprocessing as mp
import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib.gridspec as gridspec
    import Oger
    import mdp
    import csnd6
from collections import deque
from sklearn import preprocessing as pp
from communication import receive
from utils import net_rmse
class NeverUseMeAgainParser:
    def __init__(self, learn_state, respond_state):
        self.learn_state = learn_state
        self.respond_state = respond_state
        
    def parse(self, message):
        print '[self.] received:', message
        if message == 'learn':
            self.learn_state.value = 1
        if message == 'respond':
            self.respond_state.value = 1
            
if __name__ == '__main__':
    ear_q = mp.Queue()
    memorize_q = mp.Queue()
    learned_q = mp.Queue()
    response_q = mp.Queue()

    manager = mp.Manager()
    brain = manager.list()
    output = manager.list()

    learn_state = mp.Value('i', 0)
    respond_state = mp.Value('i', 0)
    parser = NeverUseMeAgainParser(learn_state, respond_state)
            
    mp.Process(target=learn, args=(memorize_q, brain, learned_q)).start()
    mp.Process(target=respond, args=(ear_q, brain, output, response_q)).start()
    mp.Process(target=plot, args=(learned_q, response_q)).start()
    mp.Process(target=csound, args=(memorize_q, ear_q, output, learn_state, respond_state)).start()

# Path: communication.py
# def send(message, context=None, host='localhost', port=5566):
# 
# Path: utils.py
# DREAM_HOUR = 23
# EVOLVE_HOUR = 4
# SAVE_HOUR = 5
# REBOOT_HOUR = 6
#     A = np.frombuffer(buf, dtype=md['dtype'])
# def save(filename, data):
# def load(filename):
# def filetime(filename):
# def plot_NAP_and_energy(NAP, plt):
# def bytes2human(n, format="%(value)i%(symbol)s"):
# def csv_to_array(filename, delimiter=' '):
# def array_to_csv(filename, data, delimiter=' '):
# def wait_for_wav(filename):
# def filesize(filename):
# def chunks(A, chunk_len):
# def wav_duration(filename):
# def trim(A, threshold=100):
# def trim_right(A, threshold=.2):
# def trim_wav(sound, threshold=100):
# def split_signal(data, threshold=100, length=5000, elbow_grease=100, plot=False, markers=[]):
# def split_wav(filename, threshold=100, length=5000, elbow_grease=100, plot=False):
# def send_zipped_pickle(socket, obj, flags=0, protocol=-1):
# def recv_zipped_pickle(socket, flags=0):
# def send_array(socket, A, flags=0, copy=True, track=False):
# def recv_array(socket, flags=0, copy=True, track=False):
# def diff_to_hex_bad(diff):
# def average_hash(image):
# def a_hash(image, hash_size=8):
# def p_hash(image, hash_size=64):
# def diff_to_hex(difference):
# def d_hash(image, hash_size = 8):
# def hamming_distance(s1, s2):
# def zero_pad(signal, length):
# def exact(signal, length):
# def scale(image):
# def getSoundInfo(filename):
# def get_segments(filename):
# def get_amps(filename):
# def get_most_significant_word(filename):
# def getLatestMemoryWavs(howmany):
# def updateAmbientMemoryWavs(currentFiles):
# def print_exception(msg=''):
# def scheduler(host):
# def true_wait(seconds):
# def reboot():
# def counter(host):
# def delete_loner(counterQ, data, query, protect, deleted_ids):
# def sentinel(host):
#     def __init__(self, me, host='localhost'):
#     def run(self):
#     def __init__(self, host='localhost'):
#     def write(self, txt):
#     def flush(self):
#     def __init__(self, host='localhost'):
#     def write(self, txt):
#     def flush(self):
# def log_sink():
#     def run(self):
#     def run(self):
# def brain_name():
# def find_last_valid_brain():
# def daily_routine(host):
# def load_esn(filename):
# def dump_esn(net, filename):
# class AliveNotifier(threading.Thread):
# class SimpleLogger:
# class Logger:
# class LoggerProcess(mp.Process):
# class MyProcess(mp.Process):


# Path: bkup/imitate_microphone.py
import multiprocessing as mp
import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib.gridspec as gridspec
    import Oger
    import mdp
    import csnd6
from collections import deque
from sklearn import preprocessing as pp
from communication import receive
from utils import net_rmse

class NeverUseMeAgainParser:
    def __init__(self, learn_state, respond_state):
        self.learn_state = learn_state
        self.respond_state = respond_state
        
    def parse(self, message):
        print '[self.] received:', message
        if message == 'learn':
            self.learn_state.value = 1
        if message == 'respond':
            self.respond_state.value = 1
            
if __name__ == '__main__':
    ear_q = mp.Queue()
    memorize_q = mp.Queue()
    learned_q = mp.Queue()
    response_q = mp.Queue()

    manager = mp.Manager()
    brain = manager.list()
    output = manager.list()

    learn_state = mp.Value('i', 0)
    respond_state = mp.Value('i', 0)
    parser = NeverUseMeAgainParser(learn_state, respond_state)
            
    mp.Process(target=learn, args=(memorize_q, brain, learned_q)).start()
    mp.Process(target=respond, args=(ear_q, brain, output, response_q)).start()
    mp.Process(target=plot, args=(learned_q, response_q)).start()
    mp.Process(target=csound, args=(memorize_q, ear_q, output, learn_state, respond_state)).start()

    mp.Process(target=receive, args=(parser.parse,)).start()