subtitles = np.array([[r'$corr(SST_{t}' + rf', {t}_t)$']])
else:
subtitles = np.array([[r'$parcorr(SST_{t-lag}, mx2t_t\ |\ SST_{t-1},mx2t_{t-1})$']])
kwrgs_plot = {'row_dim':'split', 'col_dim':'lag',
'aspect':2, 'hspace':-.57, 'wspace':-.22, 'size':4, 'cbar_vert':-.02,
'subtitles':subtitles, 'units':'Corr. Coeff. [-]',
'zoomregion':(130,260,-10,60),
'map_proj':ccrs.PlateCarree(central_longitude=220),
'x_ticks':np.array([]), 'y_ticks':np.array([]),
'drawbox':[(0,0), sst_green_bb],
'clim':(-.6,.6)}
rg.plot_maps_corr(var='SST', save=save, min_detect_gc=min_detect_gc,
kwrgs_plot=kwrgs_plot)
rg.list_for_MI[1].selbox = sst_green_bb
#%% Get PDO
df_PDO, PDO_patterns = climate_indices.PDO(rg.list_precur_pp[1][1],
None) #rg.df_splits)
from func_models import standardize_on_train
# summerdates = core_pp.get_subdates(dates, start_end_TVdate)
df_PDOsplit = df_PDO.loc[0]#.loc[summerdates]
# standardize = preprocessing.StandardScaler()
# standardize.fit(df_PDOsplit[df_PDOsplit['TrainIsTrue'].values].values.reshape(-1,1))
# df_PDOsplit = pd.DataFrame(standardize.transform(df_PDOsplit['PDO'].values.reshape(-1,1)),
# index=df_PDOsplit.index, columns=['PDO'])
df_PDOsplit = df_PDOsplit[['PDO']].apply(standardize_on_train,
args=[df_PDO.loc[0]['TrainIsTrue']],
result_type='broadcast')
# Butter Lowpass
yr = 2
dates = df_PDOsplit.index
#%%
# df_States_lin = (df_States_lin - df_States_lin.mean(0)) / df_States_lin.std(0)
FG = sns.FacetGrid(df_States_loess.stack().reset_index(), col='variable', col_wrap=4)
for ia, ax in enumerate(FG.fig.axes):
ax.plot(df_States[cols[ia]], label=cols[ia])
ax.set_title(cols[ia])
# ax.axhline(y=0) ; ax.set_ylim(-3,3)
#%%
rg_mistake.pp_TV(name_ds='Soy_Yield', ext_annual_to_mon=False,
detrend=True)
rg_mistake.pp_precursors()
df_PDO, patterns = climate_indices.PDO(rg_mistake.list_precur_pp[0][1])
df_PDO = df_PDO.groupby(df_PDO.loc[0].index.year).mean()
df_PDO.index = rg_mistake.df_fullts.index
df_Pac = functions_pp.load_hdf5('/Users/semvijverberg/surfdrive/output_paper3/hindcast/USDA_Soy_hindcast/bimonthly_random_20_s1/pandas_dfs_04-06-21_10hr.h5')
df_Pac = df_Pac['df_data']
df_Pac = df_Pac[['Soy_Yield'] + [k for k in df_Pac.columns if '..1..sst' in k]]
df_Pac = df_Pac.mean(axis=0, level=1).mean(axis=1) # mean over folds, mean over months March-October
df_Pac = pd.DataFrame(df_Pac, columns=['eastern Pacific'])
df_merge = rg_mistake.df_fullts.merge(df_PDO[['PDO']], left_index=True, right_index=True)
df_merge = df_merge.merge(df_Pac, left_index=True, right_index=True)
df_merge = df_merge.merge(rg_always.df_fullts.rename({rg_always.df_fullts.columns[0]:'new'}, axis=1),
left_index=True, right_index=True)
df_merge = (df_merge - df_merge.mean(axis=0)) / df_merge.std(0)
name_or_cluster_label = 'z500'
name_ds = f'0..0..{name_or_cluster_label}_sp'
start_end_date = ('1-1', start_end_TVdate[-1])
filepath_df_PDOs = os.path.join(path_data, 'df_PDOs_monthly.h5')
filepath_df_ENSO = os.path.join(path_data, 'df_ENSOs_monthly.h5')
#%% Get PDO and apply low-pass filter
if 'parcorr' == exper:
try:
df_PDOs = functions_pp.load_hdf5(filepath_df_PDOs)['df_data']
except:
SST_pp_filepath = user_dir + '/surfdrive/ERA5/input_raw/preprocessed/sst_1979-2020_jan_dec_monthly_1.0deg.nc'
if 'df_ENSO' not in globals():
df_PDO, PDO_patterns = climate_indices.PDO(SST_pp_filepath,
None)
PDO_plot_kwrgs = {'units':'[-]', 'cbar_vert':-.1,
# 'zoomregion':(130,260,20,60),
'map_proj':ccrs.PlateCarree(central_longitude=220),
'y_ticks':np.array([25,40,50,60]),
'x_ticks':np.arange(130, 280, 25),
'clevels':np.arange(-.6,.61,.075),
'clabels':np.arange(-.6,.61,.3),
'subtitles':np.array([['PDO loading pattern']])}
fig = plot_maps.plot_corr_maps(PDO_patterns[0], **PDO_plot_kwrgs)
filepath = os.path.join(path_out_main, 'PDO_pattern')
fig.savefig(filepath + '.pdf', bbox_inches='tight')
fig.savefig(filepath + '.png', bbox_inches='tight')
# summerdates = core_pp.get_subdates(dates, start_end_TVdate)
df_PDOsplit = df_PDO.loc[0]#.loc[summerdates]
lags_store = lags_i[:2]
for l, lag in enumerate(lags_i):
print(f'lag {l}')
df_data_ts = ts_lag(actor, lag)
df_data_ts = df_data_ts.merge(df_splits, left_index=True, right_index=True)
df_data_lag = add_spactov(dict_ds, lag, df_data_ts, actor)
if l == 0:
# PDO and ENSO do not depend on lag
filepath = os.path.join(ex['path_pp'], ex['filename_precur'])
# if ex['datafolder'] == 'EC':
# df_PDO, PDO_patterns = climate_indices.PDO(actor.precur_arr, df_splits)
# else:
print('calculating PDO')
df_PDO, PDO_patterns = climate_indices.PDO(filepath, df_splits)
df_data_lag = df_PDO.merge(df_data_lag,
left_index=True,
right_index=True)
print('calculating ENSO')
df_ENSO_34 = climate_indices.ENSO_34(filepath, df_splits)
df_data_lag = df_ENSO_34.merge(df_data_lag,
left_index=True,
right_index=True)
df_data_lag = add_RV(df_data_lag, RV)
dict_of_dfs = {'df_data': df_data_lag}
fname = '{}_{}_lag_{}_{}.h5'.format(ex['datafolder'], today, lag,
ex['hash'])
file_path = os.path.join(ex['path_data_out'], fname)
functions_pp.store_hdf_df(dict_of_dfs, file_path)
#%% Get PDO and apply low-pass filter
import climate_indices
# filepath_df_PDOs = os.path.join(data_dir, 'df_PDOs_daily.h5')
filepath_df_PDOs = os.path.join(data_dir, 'df_PDOs.h5')
try:
df_PDOs = functions_pp.load_hdf5(filepath_df_PDOs)['df_data']
except:
SST_pp_filepath = user_dir + '/surfdrive/ERA5/input_raw/preprocessed/sst_1979-2020_1jan_31dec_daily_1.0deg.nc'
if 'df_PDOsplit' not in globals():
# get PDO timeseries, using the same training sample as for RGDR
df_PDO, PDO_patterns = climate_indices.PDO(SST_pp_filepath,
rg.df_splits)
# summerdates = core_pp.get_subdates(dates, start_end_TVdate)
# df_PDOsplit = df_PDO.loc[0]#.loc[summerdates]
# # standardize = preprocessing.StandardScaler()
# # standardize.fit(df_PDOsplit[df_PDOsplit['TrainIsTrue'].values].values.reshape(-1,1))
# # df_PDOsplit = pd.DataFrame(standardize.transform(df_PDOsplit['PDO'].values.reshape(-1,1)),
# # index=df_PDOsplit.index, columns=['PDO'])
# df_PDOsplit = df_PDOsplit[['PDO']].apply(standardize_on_train,
# args=[df_PDO.loc[0]['TrainIsTrue']],
# result_type='broadcast')
# # Butter Lowpass
# dates = df_PDOsplit.index
# freqraw = (dates[1] - dates[0]).days
# ls = ['solid', 'dotted', 'dashdot', 'dashed']
# fig, ax = plt.subplots(1,1, figsize=(10,5))
if 'cmap' in kwrgs_plot_c.keys(): kwrgs_plot_c.pop('cmap')
kwrgs_plot_c.update({'cbar_vert':-.14, 'units':'[-] Corr. Coef.',
'size':2,
# 'subtitles':[['$t=$'+'July-Aug'+' mean']],
'subtitles':[[r'$corr(SST_{MJ}, T^{E}_{JA})$']],
'clevels':np.arange(-.6,.61,.075),
'clabels':np.arange(-.6,.61,.3)})
g = plot_maps.plot_corr_maps(corr.sel(months='July-Aug').where(~np.isnan(labels.sel(months='July-Aug'))),
**kwrgs_plot_c)
fig_path = os.path.join(rg.path_outsub1, 'Corr_map_julyaugust')+rg.figext
g.fig.savefig(fig_path, bbox_inches='tight')
#%% Plot PDO
import climate_indices
df_PDO, PDO_patterns = climate_indices.PDO(rg.list_precur_pp[0][1],None)
PDO_patterns *= -1
PDO_patterns.name = 'sst'
#%%
kwrgs_plot_p = kwrgs_plot_c.copy()
# if 'cmap' in kwrgs_plot_p.keys(): kwrgs_plot_p.pop('cmap')
kwrgs_plot_p.update({'units':'[-] EOF loading pattern',
'zoomregion':(110,250,20,70),
'subtitles':[['PDO pattern (negative phase)']]})
g = plot_maps.plot_corr_maps(PDO_patterns[0], **kwrgs_plot_p)
fig_path = os.path.join(rg.path_outsub1, 'PDO_pattern')+rg.figext
g.fig.savefig(fig_path, bbox_inches='tight')
#%%
# df = df_test_b.stack().reset_index(level=1)
