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_test_camels(self):
data_model = CamelsModel.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')
with torch.cuda.device(2):
pred, obs = master_test(data_model)
basin_area = data_model.data_source.read_attr(
data_model.t_s_dict["sites_id"], ['area_gages2'],
is_return_dict=False)
mean_prep = data_model.data_source.read_attr(
data_model.t_s_dict["sites_id"], ['p_mean'],
is_return_dict=False)
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
plot_we_need(data_model,
obs,
pred,
id_col="id",
lon_col="lon",
lat_col="lat")
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_test_gages(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')
with torch.cuda.device(1):
data_models = GagesModel.every_model(data_model)
obs_lst = []
pred_lst = []
for i in range(len(data_models)):
print("\n", "Testing model", str(i + 1), ":\n")
pred, obs = master_test(data_models[i])
basin_area = data_models[i].data_source.read_attr(data_models[i].t_s_dict["sites_id"], ['area_gages2'],
is_return_dict=False)
mean_prep = data_models[i].data_source.read_attr(data_models[i].t_s_dict["sites_id"], ['p_mean'],
is_return_dict=False)
pred = _basin_norm(pred, basin_area, mean_prep, to_norm=False)
obs = _basin_norm(obs, basin_area, mean_prep, to_norm=False)
obs_lst.append(obs.flatten())
pred_lst.append(pred.flatten())
preds = np.array(pred_lst)
obss = np.array(obs_lst)
plot_we_need(data_model, obss, preds, id_col="id", lon_col="lon", lat_col="lat")
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 test_explore_test(self):
models_num = 0
dirs = os.listdir(self.config_data.data_path["Temp"])
for dir_temp in dirs:
if os.path.isdir(
os.path.join(self.config_data.data_path["Temp"],
dir_temp)):
models_num += 1
for count in range(models_num):
print("\n", "testing model", str(count + 1), ":\n")
data_model = GagesModel.load_datamodel(
self.config_data.data_path["Temp"],
str(count),
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(data_model)
pred = pred.reshape(pred.shape[0], pred.shape[1])
obs = obs.reshape(obs.shape[0], obs.shape[1])
inds = statError(obs, pred)
show_me_num = 5
t_s_dict = data_model.t_s_dict
sites = np.array(t_s_dict["sites_id"])
t_range = np.array(t_s_dict["t_final_range"])
ts_fig = plot_ts_obs_pred(obs, pred, sites, t_range, show_me_num)
ts_fig.savefig(
os.path.join(
data_model.data_source.data_config.data_path["Out"],
"ts_fig.png"))
# # plot box，使用seaborn库
keys = ["Bias", "RMSE", "NSE"]
inds_test = subset_of_dict(inds, keys)
box_fig = plot_diff_boxes(inds_test)
box_fig.savefig(
os.path.join(
data_model.data_source.data_config.data_path["Out"],
"box_fig.png"))
# plot map
sites_df = pd.DataFrame({
"sites": sites,
keys[2]: inds_test[keys[2]]
})
plot_ind_map(data_model.data_source.all_configs['gage_point_file'],
sites_df)
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_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=300)
flow_pred_file = os.path.join(
data_model.data_source.data_config.data_path['Temp'],
'flow_pred')
flow_obs_file = os.path.join(
data_model.data_source.data_config.data_path['Temp'],
'flow_obs')
serialize_numpy(pred, flow_pred_file)
serialize_numpy(obs, flow_obs_file)
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_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")
