dict_rb.update(append_dict)
return df_MCI
if remove_PDO:
lowpass = '******'
keys_ext = [f'PDO{lowpass}bw']
rg.list_import_ts = [('PDO', os.path.join(data_dir, 'df_PDOs.h5'))]
else:
keys_ext = ['0..0..z500_sp']
alpha_level = .05
# freqs = [15, 30]
columns = functions_pp.flatten([[f'{f}-d', f'{f}-d SST->RW', f'{f}-d RW->SST']
for f in freqs])
csvfilenameMCI = os.path.join(rg.path_outmain, name_MCI_csv)
df_MCI = pd.DataFrame(np.concatenate([
np.repeat(west_east, rg.n_spl)[None, :],
np.repeat(period, rg.n_spl)[None, :]
],
axis=0).T,
columns=['target', 'period'])
dict_rb = {
'Target': west_east,
'Period': period,
'Seed': 's{}'.format(rg.kwrgs_traintest['seed'])
}
for f in freqs[:]:
def cond_forecast_table(df_test, df_forcings, score_func_list, n_boot=0):
quantiles = [.15, .25]
metricsused = np.array([m.__name__ for m in score_func_list])
forcings = df_forcings.columns
if forcings.size > 1: # loop over forcings
n_boot = 0
index_level1 = forcings
else:
index_level1 = np.arange(n_boot)
cond_df = np.zeros(
(metricsused.size, index_level1.size, len(quantiles) * 2))
name_fc = 'test'
for i, met in enumerate(metricsused):
# j = 0
for j, col_forc in enumerate(forcings):
df_forcing = df_forcings[col_forc]
for k, l in enumerate(range(0, 4, 2)):
q = quantiles[k]
low = df_forcing < df_forcing.quantile(q)
high = df_forcing > df_forcing.quantile(1 - q)
mask_anomalous = np.logical_or(low, high)
# anomalous Boundary forcing
condfc = df_test[mask_anomalous.values]
condfc = condfc.rename({'causal': name_fc}, axis=1)
cond_verif_tuple_ano = fc_utils.get_scores(
condfc,
score_func_list=score_func_list,
n_boot=n_boot,
score_per_test=False,
blocksize=1,
rng_seed=seed)
df_train_m, df_test_s_m, df_test_m, df_boot = cond_verif_tuple_ano
rg.cond_verif_tuple_ano = cond_verif_tuple_ano
if forcings.size > 1:
cond_df[i, j,
l] = df_test_m[df_test_m.columns[0][0]].loc[0][met]
else:
cond_df[i, :, l] = df_boot[df_boot.columns[0][0]][met]
# mild boundary forcing
larger_low = df_forcing > df_forcing.quantile(.5 - q)
smaller_high = df_forcing < df_forcing.quantile(.5 + q)
mask_mild = np.logical_and(larger_low, smaller_high)
condfc = df_test[mask_mild.values]
condfc = condfc.rename({'causal': name_fc}, axis=1)
cond_verif_tuple_mild = fc_utils.get_scores(
condfc,
score_func_list=score_func_list,
n_boot=n_boot,
score_per_test=False,
blocksize=1,
rng_seed=seed)
df_train_m, df_test_s_m, df_test_m, df_boot = cond_verif_tuple_mild
rg.cond_verif_tuple_mild = cond_verif_tuple_mild
if forcings.size > 1:
cond_df[i, j, l +
1] = df_test_m[df_test_m.columns[0][0]].loc[0][met]
else:
cond_df[i, :, l + 1] = df_boot[df_boot.columns[0][0]][met]
columns = [[f'strong {int(q*200)}%', f'weak {int(q*200)}%']
for q in quantiles]
df_cond_fc = pd.DataFrame(
cond_df.reshape((len(metricsused) * index_level1.size, -1)),
index=pd.MultiIndex.from_product([list(metricsused), index_level1]),
columns=functions_pp.flatten(columns))
return df_cond_fc
def cond_forecast_table(rg_list, score_func_list, n_boot=0):
df_test_m = rg_list[0].verification_tuple[2]
quantiles = [.15, .25]
metrics = df_test_m.columns.levels[1]
if n_boot > 0:
cond_df = np.zeros(
(metrics.size, len(rg_list), len(quantiles) * 2, n_boot))
else:
cond_df = np.zeros((metrics.size, len(rg_list), len(quantiles) * 2))
for i, met in enumerate(metrics):
for j, rg in enumerate(rg_list):
PacAtl_ts = rg.df_forcing
prediction = rg.prediction_tuple[0]
df_test = functions_pp.get_df_test(prediction,
df_splits=rg.df_splits)
# df_test_m = rg.verification_tuple[2]
# cond_df[i, j, 0] = df_test_m[df_test_m.columns[0][0]].loc[0][met]
for k, l in enumerate(range(0, 4, 2)):
q = quantiles[k]
low = PacAtl_ts < PacAtl_ts.quantile(q)
high = PacAtl_ts > PacAtl_ts.quantile(1 - q)
mask_anomalous = np.logical_or(low, high)
# anomalous Boundary forcing
condfc = df_test[mask_anomalous.values]
# condfc = condfc.rename({'causal':periodnames[i]}, axis=1)
cond_verif_tuple = fc_utils.get_scores(
condfc,
score_func_list=score_func_list,
n_boot=n_boot,
score_per_test=False,
blocksize=1,
rng_seed=1)
df_train_m, df_test_s_m, df_test_m, df_boot = cond_verif_tuple
rg.cond_verif_tuple = cond_verif_tuple
if n_boot == 0:
cond_df[i, j,
l] = df_test_m[df_test_m.columns[0][0]].loc[0][met]
else:
cond_df[i, j, l, :] = df_boot[df_boot.columns[0][0]][met]
# mild boundary forcing
higher_low = PacAtl_ts > PacAtl_ts.quantile(.5 - q)
lower_high = PacAtl_ts < PacAtl_ts.quantile(.5 + q)
mask_anomalous = np.logical_and(higher_low,
lower_high) # changed 11-5-21
condfc = df_test[mask_anomalous.values]
# condfc = condfc.rename({'causal':periodnames[i]}, axis=1)
cond_verif_tuple = fc_utils.get_scores(
condfc,
score_func_list=score_func_list,
n_boot=n_boot,
score_per_test=False,
blocksize=1,
rng_seed=1)
df_train_m, df_test_s_m, df_test_m, df_boot = cond_verif_tuple
if n_boot == 0:
cond_df[i, j, l +
1] = df_test_m[df_test_m.columns[0][0]].loc[0][met]
else:
cond_df[i, j,
l + 1, :] = df_boot[df_boot.columns[0][0]][met]
columns = [[f'strong {int(q*200)}%', f'weak {int(q*200)}%']
for q in quantiles]
columns = functions_pp.flatten(columns)
if n_boot > 0:
columns = pd.MultiIndex.from_product([columns, list(range(n_boot))])
df_cond_fc = pd.DataFrame(cond_df.reshape(
(len(metrics) * len(rg_list), -1)),
index=pd.MultiIndex.from_product([
list(metrics),
[rg.fc_month for rg in rg_list]
]),
columns=columns)
return df_cond_fc