def test_storage_test(self):
with torch.cuda.device(0):
df1 = GagesModel.load_datamodel(self.config_data_natflow.data_path["Temp"], "1",
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json', flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy', attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
df1.update_model_param('train', nEpoch=300)
df2 = GagesModel.load_datamodel(self.config_data_storage.data_path["Temp"], "2",
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json', flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy', attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
data_model = GagesStorageDataModel(df1, df2)
test_epoch = self.test_epoch
pred, obs = test_lstm_storage(data_model, epoch=test_epoch)
basin_area = df2.data_source.read_attr(df2.t_s_dict["sites_id"], ['DRAIN_SQKM'], is_return_dict=False)
mean_prep = df2.data_source.read_attr(df2.t_s_dict["sites_id"], ['PPTAVG_BASIN'], is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(df2.data_source.data_config.data_path['Temp'], test_epoch, pred, obs)
def test_test_gages_wo_attr(self):
config_dir = definitions.CONFIG_DIR
config_file = os.path.join(config_dir, "susquehanna/config_exp2.ini")
subdir = r"susquehanna/exp2"
config_data = GagesConfig.set_subdir(config_file, subdir)
data_model = GagesModelWoBasinNorm.load_datamodel(
config_data.data_path["Temp"],
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
with torch.cuda.device(2):
pred, obs = master_test(data_model, epoch=self.test_epoch)
save_result(data_model.data_source.data_config.data_path['Temp'],
self.test_epoch, pred, obs)
plot_we_need(data_model,
obs,
pred,
id_col="STAID",
lon_col="LNG_GAGE",
lat_col="LAT_GAGE")
def test_pretrained_model_test(self):
data_model = GagesModel.load_datamodel(
self.majordam_config_data.data_path["Temp"],
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
with torch.cuda.device(1):
pretrained_model_file = os.path.join(
self.nomajordam_config_data.data_path["Out"],
"model_Ep" + str(self.test_epoch) + ".pt")
pretrained_model_name = "nodam" + self.nomajordam_subdir.split(
"/")[1] + "_pretrained_model"
pred, obs = master_test_with_pretrained_model(
data_model, pretrained_model_file, pretrained_model_name)
basin_area = data_model.data_source.read_attr(
data_model.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = data_model.data_source.read_attr(
data_model.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_dir = os.path.join(
data_model.data_source.data_config.data_path['Out'],
pretrained_model_name)
save_result(save_dir, self.test_epoch, pred, obs)
def test_test_gages_sim(self):
with torch.cuda.device(1):
data_model1 = GagesModel.load_datamodel(self.config_data_natflow.data_path["Temp"], "1",
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
data_model1.update_model_param('train', nEpoch=300)
data_model2 = GagesModel.load_datamodel(self.config_data_lstm.data_path["Temp"], "2",
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
model_input = GagesSimDataModel(data_model1, data_model2)
pred, obs = master_test_natural_flow(model_input, epoch=self.test_epoch)
basin_area = model_input.data_model2.data_source.read_attr(model_input.data_model2.t_s_dict["sites_id"],
['DRAIN_SQKM'], is_return_dict=False)
mean_prep = model_input.data_model2.data_source.read_attr(model_input.data_model2.t_s_dict["sites_id"],
['PPTAVG_BASIN'], is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(model_input.data_model2.data_source.data_config.data_path['Temp'], str(self.test_epoch), pred,
obs)
def test_dam_test(self):
with torch.cuda.device(0):
gages_input = GagesModel.load_datamodel(
self.config_data.data_path["Temp"],
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
pred, obs = master_test(gages_input, epoch=cfg.TEST_EPOCH)
basin_area = gages_input.data_source.read_attr(
gages_input.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = gages_input.data_source.read_attr(
gages_input.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(gages_input.data_source.data_config.data_path['Temp'],
cfg.TEST_EPOCH, pred, obs)
plot_we_need(gages_input,
obs,
pred,
id_col="STAID",
lon_col="LNG_GAGE",
lat_col="LAT_GAGE")
def test_pretrained_model_test(self):
data_model = GagesModel.load_datamodel(
self.config_data.data_path["Temp"],
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
test_epoch = self.config_data.config_file.TEST_EPOCH
with torch.cuda.device(0):
pretrained_model_file = self.pretrianed_file_name
pretrained_model_name = self.pretrained_model_dir
pred, obs = master_test_with_pretrained_model(
data_model, pretrained_model_file, pretrained_model_name)
basin_area = data_model.data_source.read_attr(
data_model.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = data_model.data_source.read_attr(
data_model.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_dir = data_model.data_source.data_config.data_path['Temp']
save_result(save_dir, test_epoch, pred, obs)
def test_data_temp_test_damcls(self):
with torch.cuda.device(0):
nid_dir = os.path.join("/".join(self.config_data.data_path["DB"].split("/")[:-1]), "nid", "quickdata")
gage_main_dam_purpose = unserialize_json(os.path.join(nid_dir, "dam_main_purpose_dict.json"))
gage_main_dam_purpose_lst = list(gage_main_dam_purpose.values())
gage_main_dam_purpose_unique = np.unique(gage_main_dam_purpose_lst)
for i in range(0, gage_main_dam_purpose_unique.size):
df = GagesModel.load_datamodel(self.config_data.data_path["Temp"], gage_main_dam_purpose_unique[i],
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json', flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy', attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
new_temp_dir = os.path.join(df.data_source.data_config.model_dict["dir"]["Temp"],
gage_main_dam_purpose_unique[i])
new_out_dir = os.path.join(df.data_source.data_config.model_dict["dir"]["Out"],
gage_main_dam_purpose_unique[i])
df.update_datamodel_dir(new_temp_dir, new_out_dir)
pred, obs = master_test(df, epoch=self.test_epoch)
basin_area = df.data_source.read_attr(df.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = df.data_source.read_attr(df.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(new_temp_dir, self.test_epoch, pred, obs)
def test_dam_test(self):
quick_data_dir = os.path.join(self.config_data.data_path["DB"],
"quickdata")
data_dir = os.path.join(quick_data_dir,
"conus-all_90-10_nan-0.0_00-1.0")
data_model_train = GagesModel.load_datamodel(
data_dir,
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json',
flow_file_name='flow.npy',
forcing_file_name='forcing.npy',
attr_file_name='attr.npy',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
data_model_test = GagesModel.load_datamodel(
data_dir,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
gages_model_train = GagesModel.update_data_model(
self.config_data, data_model_train)
gages_model_test = GagesModel.update_data_model(
self.config_data,
data_model_test,
train_stat_dict=gages_model_train.stat_dict)
nid_dir = os.path.join(
"/".join(self.config_data.data_path["DB"].split("/")[:-1]), "nid",
"quickdata")
nid_input = NidModel.load_nidmodel(
nid_dir,
nid_file=self.nid_file,
nid_source_file_name='nid_source.txt',
nid_data_file_name='nid_data.shp')
gage_main_dam_purpose = unserialize_json(
os.path.join(nid_dir, "dam_main_purpose_dict.json"))
data_input = GagesDamDataModel(gages_model_test, nid_input, True,
gage_main_dam_purpose)
gages_input = choose_which_purpose(data_input)
pred, obs = master_test(gages_input)
basin_area = gages_input.data_source.read_attr(
gages_input.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = gages_input.data_source.read_attr(
gages_input.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(gages_input.data_source.data_config.data_path['Temp'],
self.test_epoch, pred, obs)
def camels_lstm(args):
update_cfg(cfg, args)
random_seed = cfg.RANDOM_SEED
test_epoch = cfg.TEST_EPOCH
gpu_num = cfg.CTX
train_mode = cfg.TRAIN_MODE
cache = cfg.CACHE.STATE
print("train and test in CAMELS: \n")
config_data = GagesConfig(cfg)
camels531_gageid_file = os.path.join(config_data.data_path["DB"],
"camels531", "camels531.txt")
gauge_df = pd.read_csv(camels531_gageid_file, dtype={"GaugeID": str})
gauge_list = gauge_df["GaugeID"].values
all_sites_camels_531 = np.sort(
[str(gauge).zfill(8) for gauge in gauge_list])
gages_model = GagesModels(config_data,
screen_basin_area_huc4=False,
sites_id=all_sites_camels_531.tolist())
gages_model_train = gages_model.data_model_train
gages_model_test = gages_model.data_model_test
if cache:
save_datamodel(gages_model_train,
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json',
flow_file_name='flow',
forcing_file_name='forcing',
attr_file_name='attr',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
save_datamodel(gages_model_test,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow',
forcing_file_name='test_forcing',
attr_file_name='test_attr',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
with torch.cuda.device(gpu_num):
if train_mode:
master_train(gages_model_train, random_seed=random_seed)
pred, obs = master_test(gages_model_test, epoch=test_epoch)
basin_area = gages_model_test.data_source.read_attr(
gages_model_test.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = gages_model_test.data_source.read_attr(
gages_model_test.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(gages_model_test.data_source.data_config.data_path['Temp'],
test_epoch, pred, obs)
def test_test_pub_ecoregion(self):
for i in range(self.split_num):
data_model = GagesModel.load_datamodel(self.config_data.data_path["Temp"], str(i),
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy', attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
data_model_majordam = GagesModel.load_datamodel(self.config_data.data_path["Temp"], str(i),
data_source_file_name='test_data_source_majordam.txt',
stat_file_name='test_Statistics_majordam.json',
flow_file_name='test_flow_majordam.npy',
forcing_file_name='test_forcing_majordam.npy',
attr_file_name='test_attr_majordam.npy',
f_dict_file_name='test_dictFactorize_majordam.json',
var_dict_file_name='test_dictAttribute_majordam.json',
t_s_dict_file_name='test_dictTimeSpace_majordam.json')
with torch.cuda.device(1):
pred, obs = master_test(data_model, epoch=self.test_epoch)
basin_area = data_model.data_source.read_attr(data_model.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = data_model.data_source.read_attr(data_model.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(data_model.data_source.data_config.data_path['Temp'], self.test_epoch, pred, obs)
pred_majordam, obs_majordam = master_test(data_model_majordam, epoch=self.test_epoch,
save_file_suffix="majordam")
basin_area_majordam = data_model_majordam.data_source.read_attr(
data_model_majordam.t_s_dict["sites_id"], ['DRAIN_SQKM'], is_return_dict=False)
mean_prep_majordam = data_model_majordam.data_source.read_attr(data_model_majordam.t_s_dict["sites_id"],
['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep_majordam = mean_prep_majordam / 365 * 10
pred_majordam = _basin_norm(pred_majordam, basin_area_majordam, mean_prep_majordam, to_norm=False)
obs_majordam = _basin_norm(obs_majordam, basin_area_majordam, mean_prep_majordam, to_norm=False)
save_result(data_model_majordam.data_source.data_config.data_path['Temp'], self.test_epoch,
pred_majordam, obs_majordam, pred_name='flow_pred_majordam', obs_name='flow_obs_majordam')
def test_test_susquehanna(self):
t_test = self.config_data.model_dict["data"]["tRangeTest"]
source_data = SusquehannaSource(self.config_data, t_test)
# 构建输入数据类对象
data_model = SusquehannaModel(source_data)
with torch.cuda.device(1):
# pred, obs = master_test(data_model)
pred, obs = master_test(data_model, epoch=self.test_epoch)
basin_area = data_model.data_attr[:, 0:1]
mean_prep = data_model.ppt_avg_basin
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(data_model.data_source.data_config.data_path['Temp'],
self.test_epoch, pred, obs)
plot_we_need(data_model,
obs,
pred,
id_col="id",
lon_col="lon",
lat_col="lat")
def test_test_gages4susquehanna(self):
config_dir = definitions.CONFIG_DIR
config_file = os.path.join(config_dir, "susquehanna/config_exp4.ini")
subdir = r"susquehanna/exp4"
config_data = GagesConfig.set_subdir(config_file, subdir)
dor = -0.02
gages_model = GagesModels(config_data,
screen_basin_area_huc4=False,
DOR=dor)
save_datamodel(gages_model.data_model_test,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow',
forcing_file_name='test_forcing',
attr_file_name='test_attr',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
data_model = gages_model.data_model_test
with torch.cuda.device(2):
pred, obs = master_test(data_model, epoch=self.test_epoch)
basin_area = data_model.data_source.read_attr(
data_model.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = data_model.data_source.read_attr(
data_model.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(data_model.data_source.data_config.data_path['Temp'],
self.test_epoch, pred, obs)
plot_we_need(data_model,
obs,
pred,
id_col="STAID",
lon_col="LNG_GAGE",
lat_col="LAT_GAGE")
def test_test_gages(self):
data_model_origin = GagesModel.load_datamodel(
self.config_data.data_path["Temp"],
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
warmup_len = 120
t_range_all = data_model_origin.t_s_dict["t_final_range"]
t_range_lst = hydro_time.t_range_days(t_range_all)
t_range_warmup = hydro_time.t_days_lst2range(t_range_lst[:warmup_len])
t_range_test = hydro_time.t_days_lst2range(t_range_lst[warmup_len:])
data_model_warmup, data_model = GagesModel.data_models_of_train_test(
data_model_origin, t_range_warmup, t_range_test)
data_model.stat_dict = data_model_origin.stat_dict
with torch.cuda.device(0):
pred, obs = master_test(data_model, epoch=self.test_epoch)
basin_area = data_model.data_source.read_attr(
data_model.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = data_model.data_source.read_attr(
data_model.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(data_model.data_source.data_config.data_path['Temp'],
self.test_epoch, pred, obs)
plot_we_need(data_model,
obs,
pred,
id_col="STAID",
lon_col="LNG_GAGE",
lat_col="LAT_GAGE")
def test_dam_test(self):
with torch.cuda.device(1):
quick_data_dir = os.path.join(self.config_data.data_path["DB"],
"quickdata")
data_dir = os.path.join(quick_data_dir,
"allnonref-dam_95-05_nan-0.1_00-1.0")
data_model_test = GagesModel.load_datamodel(
data_dir,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
gages_input = GagesModel.update_data_model(self.config_data,
data_model_test)
pred, obs = master_test(gages_input, epoch=self.test_epoch)
basin_area = gages_input.data_source.read_attr(
gages_input.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = gages_input.data_source.read_attr(
gages_input.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(gages_input.data_source.data_config.data_path['Temp'],
self.test_epoch, pred, obs)
plot_we_need(gages_input,
obs,
pred,
id_col="STAID",
lon_col="LNG_GAGE",
lat_col="LAT_GAGE")
def dor_lstm(args):
update_cfg(cfg, args)
random_seed = cfg.RANDOM_SEED
test_epoch = cfg.TEST_EPOCH
gpu_num = cfg.CTX
train_mode = cfg.TRAIN_MODE
dor = cfg.GAGES.attrScreenParams.DOR
cache = cfg.CACHE.STATE
print("train and test in some dor basins: \n")
config_data = GagesConfig(cfg)
gages_model = GagesModels(config_data, screen_basin_area_huc4=False, DOR=dor)
gages_model_train = gages_model.data_model_train
gages_model_test = gages_model.data_model_test
if cache:
save_datamodel(gages_model_train, data_source_file_name='data_source.txt',
stat_file_name='Statistics.json', flow_file_name='flow', forcing_file_name='forcing',
attr_file_name='attr', f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json', t_s_dict_file_name='dictTimeSpace.json')
save_datamodel(gages_model_test, data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json', flow_file_name='test_flow',
forcing_file_name='test_forcing', attr_file_name='test_attr',
f_dict_file_name='test_dictFactorize.json', var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
with torch.cuda.device(gpu_num):
if train_mode:
master_train(gages_model_train, random_seed=random_seed)
pred, obs = master_test(gages_model_test, epoch=test_epoch)
basin_area = gages_model_test.data_source.read_attr(gages_model_test.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = gages_model_test.data_source.read_attr(gages_model_test.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(gages_model_test.data_source.data_config.data_path['Temp'], test_epoch, pred, obs)
def dam_lstm(args):
update_cfg(cfg, args)
random_seed = cfg.RANDOM_SEED
test_epoch = cfg.TEST_EPOCH
gpu_num = cfg.CTX
train_mode = cfg.TRAIN_MODE
dor = cfg.GAGES.attrScreenParams.DOR
cache = cfg.CACHE.STATE
print("train and test in basins with dams: \n")
config_data = GagesConfig(cfg)
source_data_dor1 = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
DOR=dor)
# basins with dams
source_data_withdams = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
dam_num=[1, 100000])
sites_id_dor1 = source_data_dor1.all_configs['flow_screen_gage_id']
sites_id_withdams = source_data_withdams.all_configs['flow_screen_gage_id']
sites_id_chosen = np.intersect1d(np.array(sites_id_dor1),
np.array(sites_id_withdams)).tolist()
gages_model = GagesModels(config_data,
screen_basin_area_huc4=False,
sites_id=sites_id_chosen)
gages_model_train = gages_model.data_model_train
gages_model_test = gages_model.data_model_test
if cache:
save_datamodel(gages_model_train,
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json',
flow_file_name='flow',
forcing_file_name='forcing',
attr_file_name='attr',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
save_datamodel(gages_model_test,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow',
forcing_file_name='test_forcing',
attr_file_name='test_attr',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
with torch.cuda.device(gpu_num):
if train_mode:
master_train(gages_model_train, random_seed=random_seed)
pred, obs = master_test(gages_model_test, epoch=test_epoch)
basin_area = gages_model_test.data_source.read_attr(
gages_model_test.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = gages_model_test.data_source.read_attr(
gages_model_test.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(gages_model_test.data_source.data_config.data_path['Temp'],
test_epoch, pred, obs)
def synergy_ecoregion(args):
update_cfg(cfg, args)
cache = cfg.CACHE.STATE
train_mode = cfg.TRAIN_MODE
test_epoch = cfg.TEST_EPOCH
config_data = GagesConfig(cfg)
eco_names = [("ECO2_CODE", 5.2), ("ECO2_CODE", 5.3), ("ECO2_CODE", 6.2),
("ECO2_CODE", 7.1), ("ECO2_CODE", 8.1), ("ECO2_CODE", 8.2),
("ECO2_CODE", 8.3), ("ECO2_CODE", 8.4), ("ECO2_CODE", 8.5),
("ECO2_CODE", 9.2), ("ECO2_CODE", 9.3), ("ECO2_CODE", 9.4),
("ECO2_CODE", 9.5), ("ECO2_CODE", 9.6), ("ECO2_CODE", 10.1),
("ECO2_CODE", 10.2), ("ECO2_CODE", 10.4), ("ECO2_CODE", 11.1),
("ECO2_CODE", 12.1), ("ECO2_CODE", 13.1)]
quick_data_dir = os.path.join(config_data.data_path["DB"], "quickdata")
data_dir = os.path.join(quick_data_dir, "conus-all_90-10_nan-0.0_00-1.0")
data_model_train = GagesModel.load_datamodel(
data_dir,
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json',
flow_file_name='flow.npy',
forcing_file_name='forcing.npy',
attr_file_name='attr.npy',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
data_model_test = GagesModel.load_datamodel(
data_dir,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
for eco_name in eco_names:
source_data = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
ecoregion=eco_name)
sites_id = source_data.all_configs['flow_screen_gage_id']
sites_id_inter = np.intersect1d(data_model_train.t_s_dict["sites_id"],
sites_id)
if sites_id_inter.size < 1:
continue
config_data = GagesConfig.set_subdir(cfg, str(eco_name[1]))
gages_model_train = GagesModel.update_data_model(
config_data,
data_model_train,
sites_id_update=sites_id,
data_attr_update=True,
screen_basin_area_huc4=False)
gages_model_test = GagesModel.update_data_model(
config_data,
data_model_test,
sites_id_update=sites_id,
data_attr_update=True,
train_stat_dict=gages_model_train.stat_dict,
screen_basin_area_huc4=False)
if cache:
save_datamodel(gages_model_train,
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json',
flow_file_name='flow',
forcing_file_name='forcing',
attr_file_name='attr',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
save_datamodel(gages_model_test,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow',
forcing_file_name='test_forcing',
attr_file_name='test_attr',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
print("save ecoregion " + str(eco_name[1]) + " data model")
with torch.cuda.device(0):
if train_mode:
master_train(gages_model_train)
pred, obs = master_test(gages_model_test, epoch=test_epoch)
basin_area = gages_model_test.data_source.read_attr(
gages_model_test.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = gages_model_test.data_source.read_attr(
gages_model_test.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(
gages_model_test.data_source.data_config.data_path['Temp'],
test_epoch, pred, obs)
def test_dam_test(self):
quick_data_dir = os.path.join(self.config_data_1.data_path["DB"],
"quickdata")
data_dir = os.path.join(quick_data_dir,
"allnonref_85-05_nan-0.1_00-1.0")
# for inv model, datamodel of train and test are same
data_model_8595 = GagesModel.load_datamodel(
data_dir,
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json',
flow_file_name='flow.npy',
forcing_file_name='forcing.npy',
attr_file_name='attr.npy',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
# for 2nd model, datamodel of train and test belong to parts of the test time
data_model_9505 = GagesModel.load_datamodel(
data_dir,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
t_range1_test = self.config_data_1.model_dict["data"]["tRangeTest"]
# Because we know data of period "90-95", so that we can get its statistics according to this period
gages_model1_test = GagesModel.update_data_model(
self.config_data_1,
data_model_8595,
t_range_update=t_range1_test,
data_attr_update=True)
t_range2_train = self.config_data_2.model_dict["data"]["tRangeTrain"]
t_range2_test = self.config_data_2.model_dict["data"]["tRangeTest"]
gages_model2_train = GagesModel.update_data_model(
self.config_data_2,
data_model_8595,
t_range_update=t_range2_train,
data_attr_update=True)
gages_model2_test = GagesModel.update_data_model(
self.config_data_2,
data_model_9505,
t_range_update=t_range2_test,
data_attr_update=True,
train_stat_dict=gages_model2_train.stat_dict)
nid_dir = os.path.join(
"/".join(self.config_data_2.data_path["DB"].split("/")[:-1]),
"nid", "quickdata")
nid_input = NidModel.load_nidmodel(
nid_dir,
nid_file=self.nid_file,
nid_source_file_name='nid_source.txt',
nid_data_file_name='nid_data.shp')
gage_main_dam_purpose = unserialize_json(
os.path.join(nid_dir, "dam_main_purpose_dict.json"))
data_input1 = GagesDamDataModel(gages_model1_test, nid_input, True,
gage_main_dam_purpose)
df1 = choose_which_purpose(data_input1)
data_input2 = GagesDamDataModel(gages_model2_test, nid_input, True,
gage_main_dam_purpose)
df2 = choose_which_purpose(data_input2)
with torch.cuda.device(2):
data_model = GagesInvDataModel(df1, df2)
pred, obs = test_lstm_inv(data_model, epoch=self.test_epoch)
basin_area = df2.data_source.read_attr(df2.t_s_dict["sites_id"],
['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = df2.data_source.read_attr(df2.t_s_dict["sites_id"],
['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(df2.data_source.data_config.data_path['Temp'],
self.test_epoch, pred, obs)
pred, obs = master_test_with_pretrained_model(
gages_model_test, pretrained_model_file,
pretrained_model_name)
basin_area = gages_model_test.data_source.read_attr(
gages_model_test.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = gages_model_test.data_source.read_attr(
gages_model_test.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_dir = os.path.join(
gages_model_test.data_source.data_config.data_path['Out'],
pretrained_model_name)
save_result(save_dir, test_epoch, pred, obs)
# for i in range(len(camels_exp_lst)):
# ref_config_data = load_dataconfig_case_exp(camels_exp_lst[i])
# with torch.cuda.device(1):
# pretrained_model_file = os.path.join(ref_config_data.data_path["Out"],
# "model_Ep" + str(test_epoch) + ".pt")
# pretrained_model_name = camels_exp_lst[i] + "_pretrained_model"
# pred, obs = master_test_with_pretrained_model(gages_model_test, pretrained_model_file,
# pretrained_model_name)
# basin_area = gages_model_test.data_source.read_attr(gages_model_test.t_s_dict["sites_id"],
# ['DRAIN_SQKM'],
# is_return_dict=False)
# mean_prep = gages_model_test.data_source.read_attr(gages_model_test.t_s_dict["sites_id"],
# ['PPTAVG_BASIN'],
# is_return_dict=False)
data_attr_update=True,
train_stat_dict=gages_model_train.stat_dict,
screen_basin_area_huc4=False)
with torch.cuda.device(gpu_lst[i]):
pred, obs = master_test(gages_model_test, epoch=test_epoch)
basin_area = gages_model_test.data_source.read_attr(
gages_model_test.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = gages_model_test.data_source.read_attr(
gages_model_test.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(
gages_model_test.data_source.data_config.data_path['Temp'],
test_epoch, pred, obs)
plot_we_need(gages_model_test,
obs,
pred,
id_col="STAID",
lon_col="LNG_GAGE",
lat_col="LAT_GAGE")
# plot box - latency
if 'post' in doLst:
config_data = load_dataconfig_case_exp(cfg, exp_lst[0])
quick_data_dir = os.path.join(config_data.data_path["DB"], "quickdata")
data_dir = os.path.join(quick_data_dir, "conus-all_90-10_nan-0.0_00-1.0")
data_model_train = GagesModel.load_datamodel(
data_dir,
def pub_lstm(args):
update_cfg(cfg, args)
random_seed = cfg.RANDOM_SEED
test_epoch = cfg.TEST_EPOCH
gpu_num = cfg.CTX
train_mode = cfg.TRAIN_MODE
cache = cfg.CACHE.STATE
pub_plan = cfg.PUB_PLAN
plus = cfg.PLUS
dor = cfg.GAGES.attrScreenParams.DOR
split_num = cfg.SPLIT_NUM
print("train and test for PUB: \n")
config_data = GagesConfig(cfg)
if cache:
eco_names = [
("ECO2_CODE", 5.2), ("ECO2_CODE", 5.3), ("ECO2_CODE", 6.2),
("ECO2_CODE", 7.1), ("ECO2_CODE", 8.1), ("ECO2_CODE", 8.2),
("ECO2_CODE", 8.3), ("ECO2_CODE", 8.4), ("ECO2_CODE", 8.5),
("ECO2_CODE", 9.2), ("ECO2_CODE", 9.3), ("ECO2_CODE", 9.4),
("ECO2_CODE", 9.5), ("ECO2_CODE", 9.6), ("ECO2_CODE", 10.1),
("ECO2_CODE", 10.2), ("ECO2_CODE", 10.4), ("ECO2_CODE", 11.1),
("ECO2_CODE", 12.1), ("ECO2_CODE", 13.1)
]
quick_data_dir = os.path.join(config_data.data_path["DB"], "quickdata")
data_dir = os.path.join(quick_data_dir,
"conus-all_90-10_nan-0.0_00-1.0")
data_model_train = GagesModel.load_datamodel(
data_dir,
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json',
flow_file_name='flow.npy',
forcing_file_name='forcing.npy',
attr_file_name='attr.npy',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
data_model_test = GagesModel.load_datamodel(
data_dir,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
conus_sites_id = data_model_train.t_s_dict["sites_id"]
if pub_plan == 0:
"""do a pub test like freddy's"""
camels531_gageid_file = os.path.join(config_data.data_path["DB"],
"camels531", "camels531.txt")
gauge_df = pd.read_csv(camels531_gageid_file,
dtype={"GaugeID": str})
gauge_list = gauge_df["GaugeID"].values
all_sites_camels_531 = np.sort(
[str(gauge).zfill(8) for gauge in gauge_list])
sites_id_train = np.intersect1d(conus_sites_id,
all_sites_camels_531)
# basins not in CAMELS
sites_id_test = [
a_temp_site for a_temp_site in conus_sites_id
if a_temp_site not in all_sites_camels_531
]
assert (all(x < y
for x, y in zip(sites_id_test, sites_id_test[1:])))
elif pub_plan == 1 or pub_plan == 4:
source_data_dor1 = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
DOR=-dor)
# basins with dams
source_data_withdams = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
dam_num=[1, 100000])
# basins without dams
source_data_withoutdams = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
dam_num=0)
sites_id_dor1 = source_data_dor1.all_configs['flow_screen_gage_id']
sites_id_withdams = source_data_withdams.all_configs[
'flow_screen_gage_id']
if pub_plan == 1:
sites_id_train = source_data_withoutdams.all_configs[
'flow_screen_gage_id']
sites_id_test = np.intersect1d(
np.array(sites_id_dor1),
np.array(sites_id_withdams)).tolist()
else:
sites_id_train = np.intersect1d(
np.array(sites_id_dor1),
np.array(sites_id_withdams)).tolist()
sites_id_test = source_data_withoutdams.all_configs[
'flow_screen_gage_id']
elif pub_plan == 2 or pub_plan == 5:
source_data_dor1 = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
DOR=dor)
# basins without dams
source_data_withoutdams = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
dam_num=0)
if pub_plan == 2:
sites_id_train = source_data_withoutdams.all_configs[
'flow_screen_gage_id']
sites_id_test = source_data_dor1.all_configs[
'flow_screen_gage_id']
else:
sites_id_train = source_data_dor1.all_configs[
'flow_screen_gage_id']
sites_id_test = source_data_withoutdams.all_configs[
'flow_screen_gage_id']
elif pub_plan == 3 or pub_plan == 6:
dor_1 = -dor
dor_2 = dor
source_data_dor1 = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
DOR=dor_1)
# basins with dams
source_data_withdams = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
dam_num=[1, 100000])
sites_id_dor1 = source_data_dor1.all_configs['flow_screen_gage_id']
sites_id_withdams = source_data_withdams.all_configs[
'flow_screen_gage_id']
source_data_dor2 = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
DOR=dor_2)
if pub_plan == 3:
sites_id_train = np.intersect1d(
np.array(sites_id_dor1),
np.array(sites_id_withdams)).tolist()
sites_id_test = source_data_dor2.all_configs[
'flow_screen_gage_id']
else:
sites_id_train = source_data_dor2.all_configs[
'flow_screen_gage_id']
sites_id_test = np.intersect1d(
np.array(sites_id_dor1),
np.array(sites_id_withdams)).tolist()
else:
print("wrong plan")
sites_id_train = None
sites_id_test = None
train_sites_in_conus = np.intersect1d(conus_sites_id, sites_id_train)
test_sites_in_conus = np.intersect1d(conus_sites_id, sites_id_test)
if plus == 0:
all_index_lst_train_1 = []
# all sites come from train1 dataset
sites_lst_train = []
all_index_lst_test_1 = []
sites_lst_test_1 = []
all_index_lst_test_2 = []
sites_lst_test_2 = []
np.random.seed(random_seed)
kf = KFold(n_splits=split_num,
shuffle=True,
random_state=random_seed)
eco_name_chosen = []
for eco_name in eco_names:
eco_source_data = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
ecoregion=eco_name)
eco_sites_id = eco_source_data.all_configs[
'flow_screen_gage_id']
train_sites_id_inter = np.intersect1d(train_sites_in_conus,
eco_sites_id)
test_sites_id_inter = np.intersect1d(test_sites_in_conus,
eco_sites_id)
if train_sites_id_inter.size < split_num or test_sites_id_inter.size < 1:
continue
for train, test in kf.split(train_sites_id_inter):
all_index_lst_train_1.append(train)
sites_lst_train.append(train_sites_id_inter[train])
all_index_lst_test_1.append(test)
sites_lst_test_1.append(train_sites_id_inter[test])
if test_sites_id_inter.size < test.size:
all_index_lst_test_2.append(
np.arange(test_sites_id_inter.size))
sites_lst_test_2.append(test_sites_id_inter)
else:
test2_chosen_idx = np.random.choice(
test_sites_id_inter.size, test.size, replace=False)
all_index_lst_test_2.append(test2_chosen_idx)
sites_lst_test_2.append(
test_sites_id_inter[test2_chosen_idx])
eco_name_chosen.append(eco_name)
elif plus == -1:
print("camels pub, only do pub on the camels basins")
all_index_lst_train_1 = []
# all sites come from train1 dataset
sites_lst_train = []
all_index_lst_test_1 = []
sites_lst_test_1 = []
np.random.seed(random_seed)
kf = KFold(n_splits=split_num,
shuffle=True,
random_state=random_seed)
eco_name_chosen = []
for eco_name in eco_names:
eco_source_data = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
ecoregion=eco_name)
eco_sites_id = eco_source_data.all_configs[
'flow_screen_gage_id']
train_sites_id_inter = np.intersect1d(train_sites_in_conus,
eco_sites_id)
if train_sites_id_inter.size < split_num:
continue
for train, test in kf.split(train_sites_id_inter):
all_index_lst_train_1.append(train)
sites_lst_train.append(train_sites_id_inter[train])
all_index_lst_test_1.append(test)
sites_lst_test_1.append(train_sites_id_inter[test])
eco_name_chosen.append(eco_name)
elif plus == -2:
print(
"camels pub, only do pub on the camels basins, same with freddy's split method"
)
all_index_lst_train_1 = []
# all sites come from train1 dataset
sites_lst_train = []
all_index_lst_test_1 = []
sites_lst_test_1 = []
np.random.seed(random_seed)
kf = KFold(n_splits=split_num,
shuffle=True,
random_state=random_seed)
for train, test in kf.split(train_sites_in_conus):
all_index_lst_train_1.append(train)
sites_lst_train.append(train_sites_in_conus[train])
all_index_lst_test_1.append(test)
sites_lst_test_1.append(train_sites_in_conus[test])
else:
sites_lst_train = []
sites_lst_test_1 = []
sites_lst_test_2 = []
np.random.seed(random_seed)
kf = KFold(n_splits=split_num,
shuffle=True,
random_state=random_seed)
eco_name_chosen = []
for eco_name in eco_names:
eco_source_data = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
ecoregion=eco_name)
eco_sites_id = eco_source_data.all_configs[
'flow_screen_gage_id']
sites_id_inter_1 = np.intersect1d(train_sites_in_conus,
eco_sites_id)
sites_id_inter_2 = np.intersect1d(test_sites_in_conus,
eco_sites_id)
if sites_id_inter_1.size < sites_id_inter_2.size:
if sites_id_inter_1.size < split_num:
continue
for train, test in kf.split(sites_id_inter_1):
sites_lst_train_1 = sites_id_inter_1[train]
sites_lst_test_1.append(sites_id_inter_1[test])
chosen_lst_2 = random_choice_no_return(
sites_id_inter_2, [train.size, test.size])
sites_lst_train_2 = chosen_lst_2[0]
sites_lst_test_2.append(chosen_lst_2[1])
sites_lst_train.append(
np.sort(
np.append(sites_lst_train_1,
sites_lst_train_2)))
else:
if sites_id_inter_2.size < split_num:
continue
for train, test in kf.split(sites_id_inter_2):
sites_lst_train_2 = sites_id_inter_2[train]
sites_lst_test_2.append(sites_id_inter_2[test])
chosen_lst_1 = random_choice_no_return(
sites_id_inter_1, [train.size, test.size])
sites_lst_train_1 = chosen_lst_1[0]
sites_lst_test_1.append(chosen_lst_1[1])
sites_lst_train.append(
np.sort(
np.append(sites_lst_train_1,
sites_lst_train_2)))
eco_name_chosen.append(eco_name)
for i in range(split_num):
sites_ids_train_ilst = [
sites_lst_train[j] for j in range(len(sites_lst_train))
if j % split_num == i
]
sites_ids_train_i = np.sort(
reduce(lambda x, y: np.hstack((x, y)), sites_ids_train_ilst))
sites_ids_test_ilst_1 = [
sites_lst_test_1[j] for j in range(len(sites_lst_test_1))
if j % split_num == i
]
sites_ids_test_i_1 = np.sort(
reduce(lambda x, y: np.hstack((x, y)), sites_ids_test_ilst_1))
if plus >= 0:
sites_ids_test_ilst_2 = [
sites_lst_test_2[j] for j in range(len(sites_lst_test_2))
if j % split_num == i
]
sites_ids_test_i_2 = np.sort(
reduce(lambda x, y: np.hstack((x, y)),
sites_ids_test_ilst_2))
config_data_i = GagesConfig.set_subdir(cfg, str(i))
gages_model_train_i = GagesModel.update_data_model(
config_data_i,
data_model_train,
sites_id_update=sites_ids_train_i,
data_attr_update=True,
screen_basin_area_huc4=False)
gages_model_test_baseline_i = GagesModel.update_data_model(
config_data_i,
data_model_test,
sites_id_update=sites_ids_train_i,
data_attr_update=True,
train_stat_dict=gages_model_train_i.stat_dict,
screen_basin_area_huc4=False)
gages_model_test_i_1 = GagesModel.update_data_model(
config_data_i,
data_model_test,
sites_id_update=sites_ids_test_i_1,
data_attr_update=True,
train_stat_dict=gages_model_train_i.stat_dict,
screen_basin_area_huc4=False)
if plus >= 0:
gages_model_test_i_2 = GagesModel.update_data_model(
config_data_i,
data_model_test,
sites_id_update=sites_ids_test_i_2,
data_attr_update=True,
train_stat_dict=gages_model_train_i.stat_dict,
screen_basin_area_huc4=False)
save_datamodel(gages_model_train_i,
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json',
flow_file_name='flow',
forcing_file_name='forcing',
attr_file_name='attr',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
save_datamodel(gages_model_test_baseline_i,
data_source_file_name='test_data_source_base.txt',
stat_file_name='test_Statistics_base.json',
flow_file_name='test_flow_base',
forcing_file_name='test_forcing_base',
attr_file_name='test_attr_base',
f_dict_file_name='test_dictFactorize_base.json',
var_dict_file_name='test_dictAttribute_base.json',
t_s_dict_file_name='test_dictTimeSpace_base.json')
save_datamodel(gages_model_test_i_1,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow',
forcing_file_name='test_forcing',
attr_file_name='test_attr',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
if plus >= 0:
save_datamodel(gages_model_test_i_2,
data_source_file_name='test_data_source_2.txt',
stat_file_name='test_Statistics_2.json',
flow_file_name='test_flow_2',
forcing_file_name='test_forcing_2',
attr_file_name='test_attr_2',
f_dict_file_name='test_dictFactorize_2.json',
var_dict_file_name='test_dictAttribute_2.json',
t_s_dict_file_name='test_dictTimeSpace_2.json')
print("save ecoregion " + str(i) + " data model")
with torch.cuda.device(gpu_num):
if train_mode:
for i in range(split_num):
data_model = GagesModel.load_datamodel(
config_data.data_path["Temp"],
str(i),
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json',
flow_file_name='flow.npy',
forcing_file_name='forcing.npy',
attr_file_name='attr.npy',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
master_train(data_model, random_seed=random_seed)
for i in range(split_num):
data_model_baseline = GagesModel.load_datamodel(
config_data.data_path["Temp"],
str(i),
data_source_file_name='test_data_source_base.txt',
stat_file_name='test_Statistics_base.json',
flow_file_name='test_flow_base.npy',
forcing_file_name='test_forcing_base.npy',
attr_file_name='test_attr_base.npy',
f_dict_file_name='test_dictFactorize_base.json',
var_dict_file_name='test_dictAttribute_base.json',
t_s_dict_file_name='test_dictTimeSpace_base.json')
data_model = GagesModel.load_datamodel(
config_data.data_path["Temp"],
str(i),
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
if plus >= 0:
data_model_2 = GagesModel.load_datamodel(
config_data.data_path["Temp"],
str(i),
data_source_file_name='test_data_source_2.txt',
stat_file_name='test_Statistics_2.json',
flow_file_name='test_flow_2.npy',
forcing_file_name='test_forcing_2.npy',
attr_file_name='test_attr_2.npy',
f_dict_file_name='test_dictFactorize_2.json',
var_dict_file_name='test_dictAttribute_2.json',
t_s_dict_file_name='test_dictTimeSpace_2.json')
pred_baseline, obs_baseline = master_test(data_model_baseline,
epoch=test_epoch,
save_file_suffix="base")
basin_area_baseline = data_model_baseline.data_source.read_attr(
data_model_baseline.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep_baseline = data_model_baseline.data_source.read_attr(
data_model_baseline.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep_baseline = mean_prep_baseline / 365 * 10
pred_baseline = _basin_norm(pred_baseline,
basin_area_baseline,
mean_prep_baseline,
to_norm=False)
obs_baseline = _basin_norm(obs_baseline,
basin_area_baseline,
mean_prep_baseline,
to_norm=False)
save_result(
data_model_baseline.data_source.data_config.data_path['Temp'],
test_epoch,
pred_baseline,
obs_baseline,
pred_name='flow_pred_base',
obs_name='flow_obs_base')
pred, obs = master_test(data_model, epoch=test_epoch)
basin_area = data_model.data_source.read_attr(
data_model.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = data_model.data_source.read_attr(
data_model.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(data_model.data_source.data_config.data_path['Temp'],
test_epoch, pred, obs)
if plus >= 0:
pred_2, obs_2 = master_test(data_model_2,
epoch=test_epoch,
save_file_suffix="2")
basin_area_2 = data_model_2.data_source.read_attr(
data_model_2.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep_2 = data_model_2.data_source.read_attr(
data_model_2.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep_2 = mean_prep_2 / 365 * 10
pred_2 = _basin_norm(pred_2,
basin_area_2,
mean_prep_2,
to_norm=False)
obs_2 = _basin_norm(obs_2,
basin_area_2,
mean_prep_2,
to_norm=False)
save_result(
data_model_2.data_source.data_config.data_path['Temp'],
test_epoch,
pred_2,
obs_2,
pred_name='flow_pred_2',
obs_name='flow_obs_2')
def test_some_reservoirs(self):
print("train and test in basins with different combination: \n")
dam_plan = self.dam_plan
config_data = self.config_data
test_epoch = self.test_epoch
if dam_plan == 2:
dam_num = 0
dor = self.config_file.GAGES.attrScreenParams.DOR
source_data_dor1 = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
DOR=dor)
# basins with dams
source_data_withoutdams = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
dam_num=dam_num)
sites_id_dor1 = source_data_dor1.all_configs['flow_screen_gage_id']
sites_id_withoutdams = source_data_withoutdams.all_configs[
'flow_screen_gage_id']
sites_id_chosen = np.sort(
np.union1d(np.array(sites_id_dor1),
np.array(sites_id_withoutdams))).tolist()
elif dam_plan == 3:
dam_num = [1, 100000]
# basins with dams
source_data_withdams = GagesSource.choose_some_basins(
config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
dam_num=dam_num)
sites_id_chosen = source_data_withdams.all_configs[
'flow_screen_gage_id']
else:
print("wrong choice")
sites_id_chosen = None
gages_model = GagesModels(config_data,
screen_basin_area_huc4=False,
sites_id=sites_id_chosen)
gages_model_train = gages_model.data_model_train
gages_model_test = gages_model.data_model_test
if self.cache:
save_datamodel(gages_model_train,
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json',
flow_file_name='flow',
forcing_file_name='forcing',
attr_file_name='attr',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
save_datamodel(gages_model_test,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow',
forcing_file_name='test_forcing',
attr_file_name='test_attr',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
with torch.cuda.device(self.gpu_num):
if self.train_mode:
master_train(gages_model_train, random_seed=self.random_seed)
pred, obs = master_test(gages_model_test, epoch=test_epoch)
basin_area = gages_model_test.data_source.read_attr(
gages_model_test.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = gages_model_test.data_source.read_attr(
gages_model_test.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(
gages_model_test.data_source.data_config.data_path['Temp'],
test_epoch, pred, obs)
def test_dam_test(self):
quick_data_dir = os.path.join(self.config_data.data_path["DB"],
"quickdata")
sim_data_dir = os.path.join(quick_data_dir,
"allref_85-05_nan-0.1_00-1.0")
data_dir = os.path.join(quick_data_dir,
"allnonref_85-05_nan-0.1_00-1.0")
data_model_sim8595 = GagesModel.load_datamodel(
sim_data_dir,
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json',
flow_file_name='flow.npy',
forcing_file_name='forcing.npy',
attr_file_name='attr.npy',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
data_model_8595 = GagesModel.load_datamodel(
data_dir,
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json',
flow_file_name='flow.npy',
forcing_file_name='forcing.npy',
attr_file_name='attr.npy',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
data_model_sim9505 = GagesModel.load_datamodel(
sim_data_dir,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
data_model_9505 = GagesModel.load_datamodel(
data_dir,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
sim_gages_model_train = GagesModel.update_data_model(
self.sim_config_data, data_model_sim8595, data_attr_update=True)
gages_model_train = GagesModel.update_data_model(self.config_data,
data_model_8595,
data_attr_update=True)
sim_gages_model_test = GagesModel.update_data_model(
self.sim_config_data,
data_model_sim9505,
data_attr_update=True,
train_stat_dict=sim_gages_model_train.stat_dict)
gages_model_test = GagesModel.update_data_model(
self.config_data,
data_model_9505,
data_attr_update=True,
train_stat_dict=gages_model_train.stat_dict)
nid_dir = os.path.join(
"/".join(self.config_data.data_path["DB"].split("/")[:-1]), "nid",
"quickdata")
nid_input = NidModel.load_nidmodel(
nid_dir,
nid_file=self.nid_file,
nid_source_file_name='nid_source.txt',
nid_data_file_name='nid_data.shp')
gage_main_dam_purpose = unserialize_json(
os.path.join(nid_dir, "dam_main_purpose_dict.json"))
gage_main_dam_purpose_lst = list(gage_main_dam_purpose.values())
gage_main_dam_purpose_unique = np.unique(gage_main_dam_purpose_lst)
data_input = GagesDamDataModel(gages_model_test, nid_input, True,
gage_main_dam_purpose)
for i in range(0, gage_main_dam_purpose_unique.size):
sim_gages_model_test.update_model_param('train', nEpoch=300)
gages_input = choose_which_purpose(
data_input, purpose=gage_main_dam_purpose_unique[i])
new_temp_dir = os.path.join(
gages_input.data_source.data_config.model_dict["dir"]["Temp"],
gage_main_dam_purpose_unique[i])
new_out_dir = os.path.join(
gages_input.data_source.data_config.model_dict["dir"]["Out"],
gage_main_dam_purpose_unique[i])
gages_input.update_datamodel_dir(new_temp_dir, new_out_dir)
model_input = GagesSimDataModel(sim_gages_model_test, gages_input)
pred, obs = master_test_natural_flow(model_input,
epoch=self.test_epoch)
basin_area = model_input.data_model2.data_source.read_attr(
model_input.data_model2.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = model_input.data_model2.data_source.read_attr(
model_input.data_model2.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(
model_input.data_model2.data_source.data_config.
data_path['Temp'], str(self.test_epoch), pred, obs)
plot_we_need(gages_input,
obs,
pred,
id_col="STAID",
lon_col="LNG_GAGE",
lat_col="LAT_GAGE")
def test_test_gages(self):
with torch.cuda.device(self.gpu_num):
for i in range(self.split_num):
data_model_baseline = GagesModel.load_datamodel(self.config_data.data_path["Temp"], str(i),
data_source_file_name='test_data_source_base.txt',
stat_file_name='test_Statistics_base.json',
flow_file_name='test_flow_base.npy',
forcing_file_name='test_forcing_base.npy',
attr_file_name='test_attr_base.npy',
f_dict_file_name='test_dictFactorize_base.json',
var_dict_file_name='test_dictAttribute_base.json',
t_s_dict_file_name='test_dictTimeSpace_base.json')
data_model = GagesModel.load_datamodel(self.config_data.data_path["Temp"], str(i),
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow.npy',
forcing_file_name='test_forcing.npy',
attr_file_name='test_attr.npy',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
if self.plus >= 0:
data_model_2 = GagesModel.load_datamodel(self.config_data.data_path["Temp"], str(i),
data_source_file_name='test_data_source_2.txt',
stat_file_name='test_Statistics_2.json',
flow_file_name='test_flow_2.npy',
forcing_file_name='test_forcing_2.npy',
attr_file_name='test_attr_2.npy',
f_dict_file_name='test_dictFactorize_2.json',
var_dict_file_name='test_dictAttribute_2.json',
t_s_dict_file_name='test_dictTimeSpace_2.json')
pred_baseline, obs_baseline = master_test(data_model_baseline, epoch=self.test_epoch,
save_file_suffix="base")
basin_area_baseline = data_model_baseline.data_source.read_attr(
data_model_baseline.t_s_dict["sites_id"], ['DRAIN_SQKM'], is_return_dict=False)
mean_prep_baseline = data_model_baseline.data_source.read_attr(data_model_baseline.t_s_dict["sites_id"],
['PPTAVG_BASIN'], is_return_dict=False)
mean_prep_baseline = mean_prep_baseline / 365 * 10
pred_baseline = _basin_norm(pred_baseline, basin_area_baseline, mean_prep_baseline, to_norm=False)
obs_baseline = _basin_norm(obs_baseline, basin_area_baseline, mean_prep_baseline, to_norm=False)
save_result(data_model_baseline.data_source.data_config.data_path['Temp'], self.test_epoch,
pred_baseline, obs_baseline, pred_name='flow_pred_base', obs_name='flow_obs_base')
pred, obs = master_test(data_model, epoch=self.test_epoch)
basin_area = data_model.data_source.read_attr(data_model.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = data_model.data_source.read_attr(data_model.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(data_model.data_source.data_config.data_path['Temp'], self.test_epoch, pred, obs)
if self.plus >= 0:
pred_2, obs_2 = master_test(data_model_2, epoch=self.test_epoch, save_file_suffix="2")
basin_area_2 = data_model_2.data_source.read_attr(data_model_2.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep_2 = data_model_2.data_source.read_attr(data_model_2.t_s_dict["sites_id"],
['PPTAVG_BASIN'], is_return_dict=False)
mean_prep_2 = mean_prep_2 / 365 * 10
pred_2 = _basin_norm(pred_2, basin_area_2, mean_prep_2, to_norm=False)
obs_2 = _basin_norm(obs_2, basin_area_2, mean_prep_2, to_norm=False)
save_result(data_model_2.data_source.data_config.data_path['Temp'], self.test_epoch, pred_2, obs_2,
pred_name='flow_pred_2', obs_name='flow_obs_2')
def conus_other_lstm(args):
update_cfg(cfg, args)
random_seed = cfg.RANDOM_SEED
test_epoch = cfg.TEST_EPOCH
gpu_num = cfg.CTX
train_mode = cfg.TRAIN_MODE
print("train and test in CONUS: \n")
print(cfg)
config_data = GagesConfig(cfg)
gages_model = GagesModels(config_data, screen_basin_area_huc4=False)
gages_model_train = gages_model.data_model_train
gages_model_test = gages_model.data_model_test
if cfg.CACHE.GEN_QUICK_DATA:
if not os.path.isdir(cfg.CACHE.DATA_DIR):
os.makedirs(cfg.CACHE.DATA_DIR)
save_quick_data(gages_model_train,
cfg.CACHE.DATA_DIR,
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json',
flow_file_name='flow',
forcing_file_name='forcing',
attr_file_name='attr',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
save_quick_data(gages_model_test,
cfg.CACHE.DATA_DIR,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow',
forcing_file_name='test_forcing',
attr_file_name='test_attr',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
if cfg.CACHE.STATE:
save_datamodel(gages_model_train,
data_source_file_name='data_source.txt',
stat_file_name='Statistics.json',
flow_file_name='flow',
forcing_file_name='forcing',
attr_file_name='attr',
f_dict_file_name='dictFactorize.json',
var_dict_file_name='dictAttribute.json',
t_s_dict_file_name='dictTimeSpace.json')
save_datamodel(gages_model_test,
data_source_file_name='test_data_source.txt',
stat_file_name='test_Statistics.json',
flow_file_name='test_flow',
forcing_file_name='test_forcing',
attr_file_name='test_attr',
f_dict_file_name='test_dictFactorize.json',
var_dict_file_name='test_dictAttribute.json',
t_s_dict_file_name='test_dictTimeSpace.json')
with torch.cuda.device(gpu_num):
if train_mode:
data_set = StreamflowInputDataset(gages_model_train)
master_train_easier_lstm(data_set)
test_data_set = StreamflowInputDataset(gages_model_test,
train_mode=False)
pred, obs = master_test_easier_lstm(test_data_set,
load_epoch=test_epoch)
basin_area = gages_model_test.data_source.read_attr(
gages_model_test.t_s_dict["sites_id"], ['DRAIN_SQKM'],
is_return_dict=False)
mean_prep = gages_model_test.data_source.read_attr(
gages_model_test.t_s_dict["sites_id"], ['PPTAVG_BASIN'],
is_return_dict=False)
mean_prep = mean_prep / 365 * 10
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
save_result(gages_model_test.data_source.data_config.data_path['Temp'],
test_epoch, pred, obs)
