def plot_scatter(self, keys=None, colwrap=3, sharex='none', s=0, mask='RV_mask', aggr=None, title=None): import df_ana df_d = self.df_data.mean(axis=0, level=1) if mask is None: tv = self.df_data.loc[0].iloc[:, 0] df_d = df_d elif mask == 'RV_mask': tv = self.df_data.loc[0].iloc[:, 0][self.RV_mask.loc[s]] df_d = df_d[self.RV_mask.loc[s]] else: tv = self.df_data.loc[0].iloc[:, 0][mask] df_d = df_d[mask] kwrgs = {'tv': tv, 'aggr': aggr, 'title': title} df_ana.loop_df(df_d, df_ana.plot_scatter, keys=keys, colwrap=colwrap, sharex=sharex, kwrgs=kwrgs) return
def plot_scatter(cls, keys=None, colwrap=3, sharex='none', s=0, mask='RV_mask', aggr=None, title=None): df_d = cls.df_data.loc[s] if mask is None: tv = cls.df_data.loc[0].iloc[:, 0] df_d = df_d elif mask == 'RV_mask': tv = cls.df_data.loc[0].iloc[:, 0][cls.RV_mask.loc[s]] df_d = df_d[fcev.RV_mask.loc[s]] else: tv = cls.df_data.loc[0].iloc[:, 0][mask] df_d = df_d[mask] kwrgs = {'tv': tv, 'aggr': aggr, 'title': title} df_ana.loop_df(df_d, df_ana.plot_scatter, keys=keys, colwrap=colwrap, sharex=sharex, kwrgs=kwrgs) return
def apply_df_ana_plot(self, df=None, func=None, hspace=.4, sharex=False, sharey=False, kwrgs_func={}): if df is None: df = self.df_data if func is None: func = df_ana.plot_ac ; kwrgs_func = {'AUC_cutoff':(14,30),'s':60} return df_ana.loop_df(df, function=func, sharex=sharex, sharey=sharey, colwrap=2, hspace=hspace, kwrgs=kwrgs_func)
q=q_sp, tailmean=True, selbox=selbox) q_sp = 90 ds[f'q{q_sp}tail'] = cl.percentile_cluster(var_filename, xrclust, q=q_sp, tailmean=True, selbox=selbox) df_clust = functions_pp.xrts_to_df(ds['ts']) fig = df_ana.loop_df(df_clust, function=df_ana.plot_ac, sharex=False, colwrap=2, kwrgs={ 'AUC_cutoff': (14, 30), 's': 60 }) fig.suptitle('q: {}, n_clusters: {}'.format(q, c), x=.5, y=.97) df_clust = functions_pp.xrts_to_df(ds[f'q{q}tail']) fig = df_ana.loop_df(df_clust, function=df_ana.plot_ac, sharex=False, colwrap=2, kwrgs={ 'AUC_cutoff': (14, 30), 's': 60 })
import df_ana, functions_pp, validation import pandas as pd f_format = '.pdf' fc = list_of_fc[0] # if os.path.isdir(fc.filename) == False : os.makedirs(fc.filename) # filepath = '/'.join(fc.filename.split('/')[:-2]) filepath = '/Users/semvijverberg/surfdrive/MckinRepl/ERA5_mx2t_sst_Northern/c378f_ran_strat10_s30/figures' df_daily = fc.df_data_orig.loc[0][['mx2t' ]].rename(columns={'mx2t': 'ERA5 mx2t'}) df_15 = fc.TV.fullts.rename(columns={'mx2t': 'ERA5 mx2t 15-day mean'}) print(validation.get_bstrap_size(df_daily)) fig = df_ana.loop_df(df_daily, function=df_ana.plot_ac, colwrap=1, kwrgs={'AUC_cutoff': False}) fig.savefig(filepath + '/ac_daily' + f_format, bbox_inches='tight') fig = df_ana.loop_df(df_15, function=df_ana.plot_ac, colwrap=1, kwrgs={'AUC_cutoff': False}) fig.savefig(filepath + '/ac_15-daymean' + f_format, bbox_inches='tight') EC_data = user_dir + '/surfdrive/output_RGCPD/easternUS_EC/EC_tas_tos_Northern/958dd_ran_strat10_s30/data/EC_21-03-20_16hr_lag_10_958dd.h5' fc = fcev(path_data=EC_data, precur_aggr=precur_aggr, use_fold=use_fold, start_end_TVdate=None, stat_model=None,
fc = list_of_fc[-1] df, fig = fc.plot_feature_importances(lag=lag_rel) path_feat = filename + f'ifc{1}_logitregul_l{lag_rel}' + f_format fig.savefig(path_feat, bbox_inches='tight') import df_ana # X_CPPA = list_of_fc[-1]._get_statmodelobject().X_pred[['0..4..sst', '0..6..sst', '0..CPPAsv']] # X_sm = list_of_fc[-2]._get_statmodelobject().X_pred[['0..2..sm2', '0..2..sm3']] # X_merge = X_sm.merge(X_CPPA, left_index=True, right_index=True) # All = fc.TV.RV_ts.merge(X_merge, left_index=True, right_index=True) All = fc.df_data.loc[0][[ '1q65tail', '0..2..sm2', '0..2..sm3', '0..4..sst', '0..6..sst', '0..CPPAsv' ]] df_ana.loop_df(df=All, function=df_ana.plot_ac, kwrgs={'s': 150}) fc.dict_sum[0].loc['Precision'].loc['Precision'] fc.dict_sum[0].loc['Accuracy'].loc['Accuracy'] #%% # im = 0 # il = 1 # ifc = 1 # f_format = '.pdf' # if os.path.isdir(fc.filename) == False : os.makedirs(fc.filename) # import valid_plots as dfplots # if __name__ == "__main__": # for ifc, fc in enumerate(list_of_fc):