コード例 #1
0
  CHUNK_TYPE = 'Fixed Time' # 'Fixed beats number' 

  if CHUNK_TYPE == 'Fixed Time':
    CHUNK_TIME_INTERVAL = 5.0 # minutes
    MIN_NUMBER_OF_BEATS_IN_CHUNK = 10 # beats

  elif CHUNK_TYPE == 'Fixed beats number':
    NUMBER_OF_BEATS_IN_CHUNK = 1000

  TEST_PARTITION = 'random' # method for train / test separation
  TEST_PORTION = 0.3
  
  ###############################################################
  # Initial configuration
  np.random.seed(SEED)
  logg.configure_logging() # For more details use logg.configure_logging(console_level=logging.DEBUG)

  stat_info = { 'mortality':   dl.read_dta('mortality_SAHR_ver101214', data_folder=conf.path_to_dta),
                'selected_pp': dl.read_dta('selected_pp', data_folder=conf.path_to_dta),
                'sleep':       dl.get_sleep_time(conf.path_to_dta),
                'selected':    pd.read_csv(conf.path_to_dta+'selected.csv')
              }
  #print stat_info['selected'] #!!!
  #zxc

  #################################################################
  
  # Specify patients GIDNS for study 
  if FIXED_GIDNS_LIST is not None:
    logging.warning('Only %s patients are used in this study: %s'%(len(FIXED_GIDNS_LIST), FIXED_GIDNS_LIST))
    asked_GIDNS = FIXED_GIDNS_LIST
コード例 #2
0
  trainX = h5f['trainX'][:]
  trainY = h5f['trainY'][:]
  testX = h5f['testX'][:]
  testY = h5f['testY'][:]
  h5f.close()

  picke_filename = conf.path_to_sample+sample_name+'.pkl'
  with open(picke_filename, 'rb') as f:
    sample_info = pickle.load(f)
  sample_info['path'] = hdf5_filename

  return trainX, trainY, testX, testY, sample_info


if __name__ == '__main__':
  logg.configure_logging(console_level=logging.DEBUG)

  mortality = read_dta('mortality_SAHR_ver101214', data_folder=conf.path_to_dta)
  selected_pp = read_dta('selected_pp', data_folder=conf.path_to_dta)
  GIDNS = get_GIDNS(path_to_dta=conf.path_to_dta)

  print GIDNS
  print len(GIDNS), 'patients'

  GIDN = GIDNS[0]
  data_RR = load_RR_data(GIDN, path=conf.path_to_RR)
  print '\ndata_RR:\n', data_RR, '\n'

  data_RR = load_RR_data(GIDN=1234567890, path=conf.path_to_RR)
  print 'data_RR:', data_RR
コード例 #3
0
def load_hdf5_sample(sample_name):
  hdf5_filename = conf.path_to_sample+sample_name+'.h5'
  
  h5f = h5py.File(hdf5_filename,'r')
  trainX = h5f['trainX'][:]
  trainY = h5f['trainY'][:]
  testX = h5f['testX'][:]
  testY = h5f['testY'][:]

  sample_info = {'path': hdf5_filename}
  return trainX, trainY, testX, testY, sample_info


if __name__ == '__main__':
  logg.configure_logging(console_level=logging.DEBUG)

  mortality = read_dta('mortality_SAHR_ver101214', data_folder=conf.path_to_dta)
  selected_pp = read_dta('selected_pp', data_folder=conf.path_to_dta)
  GIDNS = get_GIDNS(path_to_dta=conf.path_to_dta)

  print GIDNS
  print len(GIDNS), 'patients'
  
  GIDN = GIDNS[0]
  data_RR = load_RR_data(GIDN, path=conf.path_to_RR)
  print '\ndata_RR:\n', data_RR, '\n'

  data_RR = load_RR_data(GIDN=1234567890, path=conf.path_to_RR)
  print 'data_RR:', data_RR
  
コード例 #4
0
    CHUNK_TYPE = 'Fixed Time'  # 'Fixed beats number'

    if CHUNK_TYPE == 'Fixed Time':
        CHUNK_TIME_INTERVAL = 5.0  # minutes
        MIN_NUMBER_OF_BEATS_IN_CHUNK = 10  # beats

    elif CHUNK_TYPE == 'Fixed beats number':
        NUMBER_OF_BEATS_IN_CHUNK = 1000

    TEST_PARTITION = 'random'  # method for train / test separation
    TEST_PORTION = 0.3

    ###############################################################
    # Initial configuration
    np.random.seed(SEED)
    logg.configure_logging(
    )  # For more details use logg.configure_logging(console_level=logging.DEBUG)

    stat_info = {
        'mortality':
        dl.read_dta('mortality_SAHR_ver101214', data_folder=conf.path_to_dta),
        'selected_pp':
        dl.read_dta('selected_pp', data_folder=conf.path_to_dta),
        'sleep':
        dl.read_dta('sleep', data_folder=conf.path_to_dta)
    }

    #################################################################

    # Specify patients GIDNS for study
    if FIXED_GIDNS_LIST is not None:
        logging.warning('Only %s patients are used in this study: %s' %