def test_forecast_data_temp_test(self):
sim_config_data = self.sim_config_data
sim_source_data = GagesSource(
sim_config_data, sim_config_data.model_dict["data"]["tRangeTest"])
sim_df = DataModel(sim_source_data)
save_datamodel(sim_df,
"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')
config_data = self.config_data
source_data = GagesSource(config_data,
config_data.model_dict["data"]["tRangeTest"])
df = DataModel(source_data)
save_datamodel(df,
"2",
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')
def test_check_streamflow_data(self):
source_data = GagesSource(
self.config_data,
self.config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False)
t_range_list = hydro_time.t_range_days(["1990-01-01", "2010-01-01"])
# data_temp = source_data.read_usge_gage("01", '01052500', t_range_list)
data_temp = source_data.read_usge_gage("08", '08013000', t_range_list)
print(data_temp)
print(np.argwhere(np.isnan(data_temp)))
def test_screen_some_gauge_and_save(self):
config_dir = definitions.CONFIG_DIR
config_file = os.path.join(config_dir, "transdata/config_exp12.ini")
subdir = r"transdata/exp12"
config_data = GagesConfig.set_subdir(config_file, subdir)
ref_source_data = GagesSource.choose_some_basins(
self.config_data,
self.config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
ref="Ref")
ref_sites_id = ref_source_data.all_configs['flow_screen_gage_id']
ref_sites_id_df = pd.DataFrame({"STAID": ref_sites_id})
dapeng_dir = os.path.join(self.config_data.data_path["DB"], "dapeng")
if not os.path.isdir(dapeng_dir):
os.makedirs(dapeng_dir)
dapeng_v2_gageid_file = os.path.join(dapeng_dir, "v2.csv")
ref_sites_id_df.to_csv(dapeng_v2_gageid_file, index=False)
gages_model = GagesModels(config_data,
screen_basin_area_huc4=False,
major_dam_num=0)
sites_id_df = pd.DataFrame(
{"STAID": gages_model.data_model_train.t_s_dict["sites_id"]})
dapeng_v1_gageid_file = os.path.join(dapeng_dir, "v1.csv")
sites_id_df.to_csv(dapeng_v1_gageid_file, index=False)
print("read and save data screen")
def test_trans_all_forcing_file_to_camels(self):
data_source_dump = os.path.join(self.config_data.data_path["Temp"],
'data_source.txt')
source_data = unserialize_pickle(data_source_dump)
output_dir = os.path.join(self.config_data.data_path["DB"],
"basin_mean_forcing", "daymet")
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
region_names = [
region_temp.split("_")[-1]
for region_temp in source_data.all_configs['regions']
]
# forcing data file generated is named as "allref", so rename the "all"
region_names = ["allref" if r == "all" else r for r in region_names]
year_start = int(source_data.t_range[0].split("-")[0])
year_end = int(source_data.t_range[1].split("-")[0])
years = np.arange(year_start, year_end)
assert (all(x < y for x, y in zip(source_data.gage_dict['STAID'],
source_data.gage_dict['STAID'][1:])))
config_dir = definitions.CONFIG_DIR
for i in range(len(region_names)):
config_file_i = os.path.join(
config_dir, "transdata/config_exp" + str(i + 1) + ".ini")
subdir_i = "transdata/exp" + str(i + 1)
config_data_i = GagesConfig.set_subdir(config_file_i, subdir_i)
source_data_i = GagesSource(
config_data_i,
config_data_i.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False)
for year in years:
trans_daymet_to_camels(source_data.all_configs["forcing_dir"],
output_dir, source_data_i.gage_dict,
region_names[i], year)
def test_explore_damcls_datamodel(self):
config_data = self.config_data
sites_id_dict = unserialize_json(
"/mnt/data/owen411/code/hydro-anthropogenic-lstm/example/data/gages/nid/test/dam_main_purpose_dict.json")
sites_id = list(sites_id_dict.keys())
source_data_dor1 = GagesSource.choose_some_basins(config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
sites_id=sites_id)
norsto = source_data_dor1.read_attr(sites_id, ["STOR_NOR_2009"], is_return_dict=False)
df = pd.DataFrame({"GAGE_ID": sites_id, "STOR_NOR": norsto.flatten()})
# df.to_csv(os.path.join(source_data_dor1.all_configs["out_dir"], '3557basins_NORSTOR.csv'),
# quoting=csv.QUOTE_NONNUMERIC, index=None)
df.to_csv(os.path.join(source_data_dor1.all_configs["out_dir"], '2909basins_NORSTOR.csv'),
quoting=csv.QUOTE_NONNUMERIC, index=None)
def test_explore_dor_dam_num(self):
config_data = self.config_data
dor_2 = 0.1
source_data_dor2 = GagesSource.choose_some_basins(config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
DOR=dor_2)
sites_id_largedam = source_data_dor2.all_configs['flow_screen_gage_id']
sites_id = np.intersect1d(np.array(self.sites_id), np.array(sites_id_largedam)).tolist()
norsto = source_data_dor2.read_attr(sites_id, ["STOR_NOR_2009"], is_return_dict=False)
dam_num = source_data_dor2.read_attr(sites_id, ["NDAMS_2009"], is_return_dict=False)
df = pd.DataFrame({"GAGE_ID": sites_id, "STOR_NOR": norsto.flatten(), "DAM_NUM": dam_num.flatten()})
# df.to_csv(os.path.join(source_data_dor1.all_configs["out_dir"], '3557basins_NORSTOR.csv'),
# quoting=csv.QUOTE_NONNUMERIC, index=None)
sns.distplot(df["DAM_NUM"], bins=50)
plt.show()
df.to_csv(os.path.join(source_data_dor2.all_configs["out_dir"], '1185largedor_basins_NORSTOR_DAMNUM.csv'),
quoting=csv.QUOTE_NONNUMERIC, index=None)
def test_explore_(self):
config_data = self.config_data
sites_id_dict = unserialize_json(
"/mnt/data/owen411/code/hydro-anthropogenic-lstm/example/data/gages/nid/test/dam_main_purpose_dict.json")
sites_id = list(sites_id_dict.keys())
source_data_dor1 = GagesSource.choose_some_basins(config_data,
config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
sites_id=sites_id)
nse_all = pd.read_csv(
"/mnt/data/owen411/code/hydro-anthropogenic-lstm/example/output/gages/basic/exp37/3557basins_ID_NSE_DOR.csv",
dtype={0: str})
sites_ids = nse_all["GAUGE ID"].values
idx = [i for i in range(len(sites_ids)) if sites_ids[i] in sites_id]
df = pd.DataFrame({"GAGE_ID": sites_id, "NSE": nse_all["NSE"].values[idx]})
# df.to_csv(os.path.join(source_data_dor1.all_configs["out_dir"], '3557basins_NORSTOR.csv'),
# quoting=csv.QUOTE_NONNUMERIC, index=None)
df.to_csv(os.path.join(source_data_dor1.all_configs["out_dir"], '2909basins_NSE.csv'),
quoting=csv.QUOTE_NONNUMERIC, index=None)
def test_read_sites_id_see_dor(self):
exp_lst = ["exp18", "exp19", "exp20", "exp21", "exp22", "exp23"]
sub_lst = ["0", "1"]
diff_lst = [
"dictTimeSpace.json", "test_dictTimeSpace.json",
"test_dictTimeSpace_2.json"
]
for exp_str in exp_lst:
for sub_str in sub_lst:
comp_sites = []
for item in diff_lst:
gage_id_file = os.path.join(
self.config_data.config_file["ROOT_DIR"], "temp",
"gages", "ecoregion", exp_str, sub_str, item)
usgs_id = unserialize_json(gage_id_file)["sites_id"]
assert (all(x < y for x, y in zip(usgs_id, usgs_id[1:])))
comp_sites.append(usgs_id)
# mm/year 1-km grid, megaliters total storage per sq km (1 megaliters = 1,000,000 liters = 1,000 cubic meters)
# attr_lst = ["RUNAVE7100", "STOR_NID_2009"]
attr_lst = ["RUNAVE7100", "STOR_NOR_2009"]
source_data = GagesSource.choose_some_basins(
self.config_data,
self.config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
sites_id=usgs_id)
data_attr, var_dict, f_dict = source_data.read_attr(
usgs_id, attr_lst)
run_avg = data_attr[:, 0] * (10**(-3)) * (10**6
) # m^3 per year
nor_storage = data_attr[:, 1] * 1000 # m^3
dors = nor_storage / run_avg
results = [round(i, 3) for i in dors]
hydro_logger.info(
exp_str + "-" + sub_str + "-" + item + " DOR: %s",
results)
hydro_logger.info(
"the intersection of each pair of sites: %s, %s, %s",
np.intersect1d(comp_sites[0], comp_sites[1]),
np.intersect1d(comp_sites[0], comp_sites[2]),
np.intersect1d(comp_sites[1], comp_sites[2]))
def test_gages_dam_stor_hist_basin(self):
nid_dir = os.path.join(
"/".join(self.config_data.data_path["DB"].split("/")[:-1]), "nid",
"test")
dam_storages = unserialize_json(
os.path.join(nid_dir, "dam_storages_dict.json"))
sites = np.array(list(dam_storages.keys()))
dor_2 = 0.02
source_data_dor2 = GagesSource.choose_some_basins(
self.config_data,
self.config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
DOR=dor_2)
sites_id_largedam = source_data_dor2.all_configs['flow_screen_gage_id']
c, ind1, idx_lst_nse_range = np.intersect1d(sites,
sites_id_largedam,
return_indices=True)
num = 4
num_lst = np.sort(np.random.choice(len(c), num, replace=False))
chosen_sites = c[num_lst]
hist_bins = 20
fig = plt.figure(figsize=(8, 9))
gs = gridspec.GridSpec(2, 2)
for i in range(num):
ax_k = plt.subplot(gs[int(i / 2), i % 2])
ax_k.hist(dam_storages[chosen_sites[i]],
hist_bins,
orientation='vertical',
color='red',
alpha=0.5)
plt.show()
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 test_zero_small_dor_basins_locations(self):
conus_exps = self.exp_lst
test_epoch = self.test_epoch
inds_df, pred, obs = load_ensemble_result(self.config_file,
conus_exps,
test_epoch,
return_value=True)
conus_config_data = load_dataconfig_case_exp(self.config_file,
conus_exps[0])
conus_data_model = GagesModel.load_datamodel(
conus_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')
conus_sites = conus_data_model.t_s_dict["sites_id"]
all_lat = conus_data_model.data_source.gage_dict["LAT_GAGE"]
all_lon = conus_data_model.data_source.gage_dict["LNG_GAGE"]
show_ind_key = "NSE"
attr_lst = ["SLOPE_PCT", "ELEV_MEAN_M_BASIN"]
attrs = conus_data_model.data_source.read_attr(conus_sites,
attr_lst,
is_return_dict=False)
western_lon_idx = [i for i in range(all_lon.size) if all_lon[i] < -100]
nse_range = [0, 1]
idx_lst_nse = inds_df[(inds_df[show_ind_key] >= nse_range[0]) & (
inds_df[show_ind_key] < nse_range[1])].index.tolist()
idx_lst_nse = np.intersect1d(western_lon_idx, idx_lst_nse)
# small dor
source_data_dor1 = GagesSource.choose_some_basins(
conus_config_data,
conus_config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
DOR=-self.dor)
# basins with dams
source_data_withdams = GagesSource.choose_some_basins(
conus_config_data,
conus_config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
dam_num=[1, 10000])
# basins without dams
source_data_withoutdams = GagesSource.choose_some_basins(
conus_config_data,
conus_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']
sites_id_nodam = source_data_withoutdams.all_configs[
'flow_screen_gage_id']
sites_id_smalldam = np.intersect1d(
np.array(sites_id_dor1), np.array(sites_id_withdams)).tolist()
idx_lst_nodam_in_conus = [
i for i in range(len(conus_sites))
if conus_sites[i] in sites_id_nodam
]
idx_lst_smalldam_in_conus = [
i for i in range(len(conus_sites))
if conus_sites[i] in sites_id_smalldam
]
type_1_index_lst = np.intersect1d(idx_lst_nodam_in_conus,
idx_lst_nse).tolist()
type_2_index_lst = np.intersect1d(idx_lst_smalldam_in_conus,
idx_lst_nse).tolist()
pd.DataFrame({
"GAGE_ID": np.array(conus_sites)[type_1_index_lst]
}).to_csv(
os.path.join(conus_config_data.data_path["Out"],
"western-zero-dor-sites.csv"))
pd.DataFrame({
"GAGE_ID": np.array(conus_sites)[type_2_index_lst]
}).to_csv(
os.path.join(conus_config_data.data_path["Out"],
"western-small-dor-sites.csv"))
frame = []
df_type1 = pd.DataFrame({
"type":
np.full(len(type_1_index_lst), "zero-dor"),
show_ind_key:
inds_df[show_ind_key].values[type_1_index_lst],
"lat":
all_lat[type_1_index_lst],
"lon":
all_lon[type_1_index_lst],
"slope":
attrs[type_1_index_lst, 0],
"elevation":
attrs[type_1_index_lst, 1]
})
frame.append(df_type1)
df_type2 = pd.DataFrame({
"type":
np.full(len(type_2_index_lst), "small-dor"),
show_ind_key:
inds_df[show_ind_key].values[type_2_index_lst],
"lat":
all_lat[type_2_index_lst],
"lon":
all_lon[type_2_index_lst],
"slope":
attrs[type_2_index_lst, 0],
"elevation":
attrs[type_2_index_lst, 1]
})
frame.append(df_type2)
data_df = pd.concat(frame)
idx_lst = [
np.arange(len(type_1_index_lst)),
np.arange(len(type_1_index_lst),
len(type_1_index_lst) + len(type_2_index_lst))
]
plot_gages_map_and_scatter(data_df,
[show_ind_key, "lat", "lon", "slope"],
idx_lst,
cmap_strs=["Reds", "Blues"],
labels=["zero-dor", "small-dor"],
scatter_label=[attr_lst[0], show_ind_key],
wspace=2,
hspace=1.5,
legend_y=.8,
sub_fig_ratio=[6, 4, 1])
plt.tight_layout()
plt.show()
def test_diff_dor(self):
dor_1 = -self.dor
dor_2 = self.dor
test_epoch = self.test_epoch
config_file = self.config_file
conus_exps = ["basic_exp37"]
pair1_exps = ["dam_exp1"]
pair2_exps = ["nodam_exp7"]
pair3_exps = ["dam_exp27"]
nodam_exp_lst = ["nodam_exp1"]
smalldam_exp_lst = [
"dam_exp17"
] # -0.003["dam_exp11"] -0.08["dam_exp17"] -1["dam_exp32"]
largedam_exp_lst = [
"dam_exp4"
] # 0.003["dam_exp12"] 0.08["dam_exp18"] 1["dam_exp33"]
pair1_config_data = load_dataconfig_case_exp(config_file,
pair1_exps[0])
pair2_config_data = load_dataconfig_case_exp(config_file,
pair2_exps[0])
pair3_config_data = load_dataconfig_case_exp(config_file,
pair3_exps[0])
conus_config_data = load_dataconfig_case_exp(config_file,
conus_exps[0])
conus_data_model = GagesModel.load_datamodel(
conus_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')
conus_sites = conus_data_model.t_s_dict["sites_id"]
source_data_dor1 = GagesSource.choose_some_basins(
conus_config_data,
conus_config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
DOR=dor_1)
source_data_dor2 = GagesSource.choose_some_basins(
conus_config_data,
conus_config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
DOR=dor_2)
# basins with dams
source_data_withdams = GagesSource.choose_some_basins(
conus_config_data,
conus_config_data.model_dict["data"]["tRangeTrain"],
screen_basin_area_huc4=False,
dam_num=[1, 10000])
# basins without dams
source_data_withoutdams = GagesSource.choose_some_basins(
conus_config_data,
conus_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']
sites_id_nodam = source_data_withoutdams.all_configs[
'flow_screen_gage_id']
sites_id_smalldam = np.intersect1d(
np.array(sites_id_dor1), np.array(sites_id_withdams)).tolist()
sites_id_largedam = source_data_dor2.all_configs['flow_screen_gage_id']
# sites_id_nolargedam = np.sort(np.union1d(np.array(sites_id_nodam), np.array(sites_id_largedam))).tolist()
# pair1_sites = np.sort(np.intersect1d(np.array(sites_id_dor1), np.array(conus_sites))).tolist()
# pair2_sites = np.sort(np.intersect1d(np.array(sites_id_nolargedam), np.array(conus_sites))).tolist()
# pair3_sites = np.sort(np.intersect1d(np.array(sites_id_withdams), np.array(conus_sites))).tolist()
pair1_data_model = GagesModel.load_datamodel(
pair1_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')
pair1_sites = pair1_data_model.t_s_dict["sites_id"]
pair2_data_model = GagesModel.load_datamodel(
pair2_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')
pair2_sites = pair2_data_model.t_s_dict["sites_id"]
pair3_data_model = GagesModel.load_datamodel(
pair3_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')
pair3_sites = pair3_data_model.t_s_dict["sites_id"]
idx_lst_nodam_in_pair1 = [
i for i in range(len(pair1_sites))
if pair1_sites[i] in sites_id_nodam
]
idx_lst_nodam_in_pair2 = [
i for i in range(len(pair2_sites))
if pair2_sites[i] in sites_id_nodam
]
idx_lst_nodam_in_pair3 = [
i for i in range(len(pair3_sites))
if pair3_sites[i] in sites_id_nodam
]
idx_lst_nodam_in_conus = [
i for i in range(len(conus_sites))
if conus_sites[i] in sites_id_nodam
]
idx_lst_smalldam_in_pair1 = [
i for i in range(len(pair1_sites))
if pair1_sites[i] in sites_id_smalldam
]
idx_lst_smalldam_in_pair2 = [
i for i in range(len(pair2_sites))
if pair2_sites[i] in sites_id_smalldam
]
idx_lst_smalldam_in_pair3 = [
i for i in range(len(pair3_sites))
if pair3_sites[i] in sites_id_smalldam
]
idx_lst_smalldam_in_conus = [
i for i in range(len(conus_sites))
if conus_sites[i] in sites_id_smalldam
]
idx_lst_largedam_in_pair1 = [
i for i in range(len(pair1_sites))
if pair1_sites[i] in sites_id_largedam
]
idx_lst_largedam_in_pair2 = [
i for i in range(len(pair2_sites))
if pair2_sites[i] in sites_id_largedam
]
idx_lst_largedam_in_pair3 = [
i for i in range(len(pair3_sites))
if pair3_sites[i] in sites_id_largedam
]
idx_lst_largedam_in_conus = [
i for i in range(len(conus_sites))
if conus_sites[i] in sites_id_largedam
]
print("multi box")
inds_df_pair1 = load_ensemble_result(config_file, pair1_exps,
test_epoch)
inds_df_pair2 = load_ensemble_result(config_file, pair2_exps,
test_epoch)
inds_df_pair3 = load_ensemble_result(config_file, pair3_exps,
test_epoch)
inds_df_conus = load_ensemble_result(config_file, conus_exps,
test_epoch)
fig = plt.figure(figsize=(15, 8))
gs = gridspec.GridSpec(1, 3)
keys_nse = "NSE"
color_chosen = ["Greens", "Blues", "Reds"]
median_loc = 0.015
decimal_places = 2
sns.despine()
sns.set(font_scale=1.5)
attr_nodam = "zero_dor"
cases_exps_legends_nodam = [
"LSTM-Z", "LSTM-ZS", "LSTM-ZL", "LSTM-CONUS"
]
frames_nodam = []
inds_df_nodam = load_ensemble_result(config_file, nodam_exp_lst,
test_epoch)
df_nodam_alone = pd.DataFrame({
attr_nodam:
np.full([inds_df_nodam.shape[0]], cases_exps_legends_nodam[0]),
keys_nse:
inds_df_nodam[keys_nse]
})
frames_nodam.append(df_nodam_alone)
df_nodam_in_pair1 = pd.DataFrame({
attr_nodam:
np.full([
inds_df_pair1[keys_nse].iloc[idx_lst_nodam_in_pair1].shape[0]
], cases_exps_legends_nodam[1]),
keys_nse:
inds_df_pair1[keys_nse].iloc[idx_lst_nodam_in_pair1]
})
frames_nodam.append(df_nodam_in_pair1)
df_nodam_in_pair2 = pd.DataFrame({
attr_nodam:
np.full([
inds_df_pair2[keys_nse].iloc[idx_lst_nodam_in_pair2].shape[0]
], cases_exps_legends_nodam[2]),
keys_nse:
inds_df_pair2[keys_nse].iloc[idx_lst_nodam_in_pair2]
})
frames_nodam.append(df_nodam_in_pair2)
df_nodam_in_conus = pd.DataFrame({
attr_nodam:
np.full([
inds_df_conus[keys_nse].iloc[idx_lst_nodam_in_conus].shape[0]
], cases_exps_legends_nodam[3]),
keys_nse:
inds_df_conus[keys_nse].iloc[idx_lst_nodam_in_conus]
})
frames_nodam.append(df_nodam_in_conus)
result_nodam = pd.concat(frames_nodam)
ax1 = plt.subplot(gs[0])
# ax1.set_title("(a)")
ax1.set_xticklabels(ax1.get_xticklabels(), rotation=30)
ax1.set_ylim([0, 1])
sns.boxplot(ax=ax1,
x=attr_nodam,
y=keys_nse,
data=result_nodam,
showfliers=False,
palette=color_chosen[0])
medians_nodam = result_nodam.groupby(
[attr_nodam], sort=False)[keys_nse].median().values
median_labels_nodam = [
str(np.round(s, decimal_places)) for s in medians_nodam
]
pos1 = range(len(medians_nodam))
for tick, label in zip(pos1, ax1.get_xticklabels()):
ax1.text(pos1[tick],
medians_nodam[tick] + median_loc,
median_labels_nodam[tick],
horizontalalignment='center',
size='x-small',
weight='semibold')
attr_smalldam = "small_dor"
cases_exps_legends_smalldam = [
"LSTM-S", "LSTM-ZS", "LSTM-SL", "LSTM-CONUS"
]
frames_smalldam = []
inds_df_smalldam = load_ensemble_result(config_file, smalldam_exp_lst,
test_epoch)
df_smalldam_alone = pd.DataFrame({
attr_smalldam:
np.full([inds_df_smalldam.shape[0]],
cases_exps_legends_smalldam[0]),
keys_nse:
inds_df_smalldam[keys_nse]
})
frames_smalldam.append(df_smalldam_alone)
df_smalldam_in_pair1 = pd.DataFrame({
attr_smalldam:
np.full([
inds_df_pair1[keys_nse].iloc[idx_lst_smalldam_in_pair1].
shape[0]
], cases_exps_legends_smalldam[1]),
keys_nse:
inds_df_pair1[keys_nse].iloc[idx_lst_smalldam_in_pair1]
})
frames_smalldam.append(df_smalldam_in_pair1)
df_smalldam_in_pair3 = pd.DataFrame({
attr_smalldam:
np.full([
inds_df_pair3[keys_nse].iloc[idx_lst_smalldam_in_pair3].
shape[0]
], cases_exps_legends_smalldam[2]),
keys_nse:
inds_df_pair3[keys_nse].iloc[idx_lst_smalldam_in_pair3]
})
frames_smalldam.append(df_smalldam_in_pair3)
df_smalldam_in_conus = pd.DataFrame({
attr_smalldam:
np.full([
inds_df_conus[keys_nse].iloc[idx_lst_smalldam_in_conus].
shape[0]
], cases_exps_legends_smalldam[3]),
keys_nse:
inds_df_conus[keys_nse].iloc[idx_lst_smalldam_in_conus]
})
frames_smalldam.append(df_smalldam_in_conus)
result_smalldam = pd.concat(frames_smalldam)
ax2 = plt.subplot(gs[1])
# ax2.set_title("(b)")
ax2.set_xticklabels(ax2.get_xticklabels(), rotation=30)
ax2.set_ylim([0, 1])
ax2.set(ylabel=None)
sns.boxplot(ax=ax2,
x=attr_smalldam,
y=keys_nse,
data=result_smalldam,
showfliers=False,
palette=color_chosen[1])
medians_smalldam = result_smalldam.groupby(
[attr_smalldam], sort=False)[keys_nse].median().values
median_labels_smalldam = [
str(np.round(s, decimal_places)) for s in medians_smalldam
]
pos2 = range(len(medians_smalldam))
for tick, label in zip(pos2, ax2.get_xticklabels()):
ax2.text(pos2[tick],
medians_smalldam[tick] + median_loc,
median_labels_smalldam[tick],
horizontalalignment='center',
size='x-small',
weight='semibold')
attr_largedam = "large_dor"
cases_exps_legends_largedam = [
"LSTM-L", "LSTM-ZL", "LSTM-SL", "LSTM-CONUS"
]
frames_largedam = []
inds_df_largedam = load_ensemble_result(config_file, largedam_exp_lst,
test_epoch)
df_largedam_alone = pd.DataFrame({
attr_largedam:
np.full([inds_df_largedam.shape[0]],
cases_exps_legends_largedam[0]),
keys_nse:
inds_df_largedam[keys_nse]
})
frames_largedam.append(df_largedam_alone)
df_largedam_in_pair2 = pd.DataFrame({
attr_largedam:
np.full([
inds_df_pair2[keys_nse].iloc[idx_lst_largedam_in_pair2].
shape[0]
], cases_exps_legends_largedam[1]),
keys_nse:
inds_df_pair2[keys_nse].iloc[idx_lst_largedam_in_pair2]
})
frames_largedam.append(df_largedam_in_pair2)
df_largedam_in_pair3 = pd.DataFrame({
attr_largedam:
np.full([
inds_df_pair3[keys_nse].iloc[idx_lst_largedam_in_pair3].
shape[0]
], cases_exps_legends_largedam[2]),
keys_nse:
inds_df_pair3[keys_nse].iloc[idx_lst_largedam_in_pair3]
})
frames_largedam.append(df_largedam_in_pair3)
df_largedam_in_conus = pd.DataFrame({
attr_largedam:
np.full([
inds_df_conus[keys_nse].iloc[idx_lst_largedam_in_conus].
shape[0]
], cases_exps_legends_largedam[3]),
keys_nse:
inds_df_conus[keys_nse].iloc[idx_lst_largedam_in_conus]
})
frames_largedam.append(df_largedam_in_conus)
result_largedam = pd.concat(frames_largedam)
ax3 = plt.subplot(gs[2])
# ax3.set_title("(c)")
ax3.set_xticklabels(ax3.get_xticklabels(), rotation=30)
ax3.set_ylim([0, 1])
ax3.set(ylabel=None)
sns.boxplot(ax=ax3,
x=attr_largedam,
y=keys_nse,
data=result_largedam,
showfliers=False,
palette=color_chosen[2])
medians_largedam = result_largedam.groupby(
[attr_largedam], sort=False)[keys_nse].median().values
median_labels_largedam = [
str(np.round(s, decimal_places)) for s in medians_largedam
]
pos3 = range(len(medians_largedam))
for tick, label in zip(pos3, ax3.get_xticklabels()):
ax3.text(pos3[tick],
medians_largedam[tick] + median_loc,
median_labels_largedam[tick],
horizontalalignment='center',
size='x-small',
weight='semibold')
# sns.despine()
plt.tight_layout()
plt.show()
