df_dwd2 = transform_to_bools(
df_dwd2, percentile_level)
df_rea2 = transform_to_bools(
df_rea2, percentile_level)
# except Exception as msg:
# print(msg)
cmn_vals1 = df_dwd1.loc[cmn_idx].values.ravel()
cmn_vals2 = df_dwd2.loc[cmn_idx].values.ravel()
cmn_rea1 = df_rea1.loc[cmn_idx].values.ravel()
cmn_rea2 = df_rea2.loc[cmn_idx].values.ravel()
# np.nansum(df_dwd1)
# df_dwd1.max()
try:
spr_corr = spr(cmn_vals1, cmn_vals2)[0]
prs_corr = prs(cmn_vals1, cmn_vals2)[0]
sep_dist = distance_sorted[ix2]
spr_corr_rea = spr(cmn_rea1, cmn_rea2)[0]
prs_corr_rea = prs(cmn_rea1, cmn_rea2)[0]
# sep_dist_rea = distance_sorted[ix2]
except Exception as msg:
print(msg)
if np.isnan(spr_corr):
print('corr_is_nan')
df_distance_corr.loc[stn_id,
'sep_dist_%s' % _id2] = sep_dist
df_distance_corr.loc[stn_id,
'pears_corr_%s' % _id2] = spr_corr
df_distance_corr.loc[stn_id,
dwd_pcp = dwd_hdf5_de.get_pandas_dataframe(stn_id).dropna()
in_df_rea6_stn = in_df_rea6.loc[:, stn_id].dropna()
cmn_idx = dwd_pcp.index.intersection(in_df_rea6_stn.index)
#break
df_dwd1 = resampleDf(dwd_pcp.loc[cmn_idx,:], temp_agg)
df_rea1 = resampleDf(in_df_rea6_stn.loc[cmn_idx], temp_agg)
if df_dwd1.size > 0:
if test_for_extremes:
df_dwd1 = transform_to_bools(df_dwd1, percentile_level)
df_rea1 = transform_to_bools(df_rea1, percentile_level)
spr_corr_dwd_rea = spr(df_dwd1.values.ravel(), df_rea1.values.ravel())[0]
prs_corr_dwd_rea = prs(df_dwd1.values.ravel(), df_rea1.values.ravel())[0]
else:
spr_corr_dwd_rea = spr(df_dwd1.values.ravel(), df_rea1.values.ravel())[0]
prs_corr_dwd_rea = prs(df_dwd1.values.ravel(), df_rea1.values.ravel())[0]
df_distance_corr.loc[stn_id,
'prs_corr_dwd_rea'] = prs_corr_dwd_rea
df_distance_corr.loc[stn_id,
'spr_corr_dwd_rea'] = spr_corr_dwd_rea
# all stns
#======================================================================