def setupEnv():
global MODELS_VALUES
global MODELS_DICT
"""
Setup the necessary variables to speed up the genetic algorithm execution
Reading all models
:return:
"""
for index, model_name in enumerate(os.listdir(config.MODELS_DATA_PATH)):
if config.VERBOSE:
print(f'[!] reading model {model_name}')
file_path = os.path.join(config.MODELS_DATA_PATH, model_name)
data_label_base, temp, lon, lat, lev = utils.read_data(file_path)
temp, lon, lat, ilat, ilon = utils.get_zone_obs(temp,
lon,
lat,
size_reduction=CASE,
frlon=frlon,
tolon=tolon,
frlat=frlat,
tolat=tolat)
MODELS_DICT[data_label_base] = index
MODELS_VALUES.append(temp)
MODELS_VALUES = np.array(MODELS_VALUES)
if config.VERBOSE:
print('\n [!] Genetic environment correctly configured.')
# read the trained model file
if CASE.upper() == 'ALL':
obsSOM, true_labels = utils.loadSOM(
save_dir=config.OUTPUT_TRAINED_MODELS_PATH,
file_name=config.ZALL_SOM_3D_MODEL_NAME)
elif CASE.upper() == 'SEL':
obsSOM, true_labels = utils.loadSOM(
save_dir=config.OUTPUT_TRAINED_MODELS_PATH,
file_name=config.ZSEL_SOM_3D_MODEL_NAME)
file_path = os.path.join(config.MODELS_DATA_PATH, MODEL_NAME)
data_label_base, temp, lon, lat, lev = utils.read_data(file_path)
temp, lon, lat, ilat, ilon = utils.get_zone_obs(temp,
lon,
lat,
size_reduction=CASE,
frlon=frlon,
tolon=tolon,
frlat=frlat,
tolat=tolat)
agg_data = utils.aggregate_data(temp, case=CASE)
all_model_data = agg_data.reshape(
12, -1, order='A').T # shape = 9900 (11*25*36), 12
model_data = pd.DataFrame.from_records(all_model_data).dropna(axis=0)
ocean_points_index = model_data.index.values # ocean points index
model_data = model_data.values # train data
model_labels = np.zeros(shape=(len(all_model_data)), dtype=int)