# column_names=['2000-2020']
list_df = [df_1, df_2]
list_df_out = []
for k in range(len(list_df)):
df = list_df[k]
list_dh = []
list_dh_err = []
list_dm = []
list_dm_err = []
df_out = pd.DataFrame()
for period in periods:
df_p = df[df.period == period]
df_global = tt.aggregate_indep_regions_rates(df_p)
df_global['reg'] = 'global'
df_noperiph = tt.aggregate_indep_regions_rates(
df_p[~df_p.reg.isin([5, 19])])
df_noperiph['reg'] = 'global_noperiph'
df_full_p = pd.concat([df_p, df_noperiph, df_global])
column_dh = []
column_err_dh = []
for i in range(len(df_full_p)):
dh = '{:.2f}'.format(df_full_p.dhdt.values[i])
err_dh = '{:.2f}'.format(2 * df_full_p.err_dhdt.values[i])
column_dh.append(dh)
column_err_dh.append(err_dh)
list_df = []
for fn_reg in list_fn_reg:
for period in periods:
df_tmp = tt.aggregate_all_to_period(pd.read_csv(fn_reg),
[tlims[periods.index(period)]],
fn_tarea=fn_tarea,
frac_area=1)
list_df.append(df_tmp)
df = pd.concat(list_df)
list_df_all = []
for period in periods:
df_p = df[df.period == period]
df_global = tt.aggregate_indep_regions_rates(df_p)
df_global['reg'] = 'global'
df_global['period'] = period
df_noperiph = tt.aggregate_indep_regions_rates(
df_p[~df_p.reg.isin([5, 19])])
df_noperiph['reg'] = 'global_noperiph'
df_noperiph['period'] = period
df_full_p = pd.concat([df_p, df_noperiph, df_global])
list_df_all.append(df_full_p)
df_all = pd.concat(list_df_all)
df_g = df_all[df_all.reg == 'global']
df_np = df_all[df_all.reg == 'global_noperiph']
l.get_frame().set_linewidth(0.5)
reg_dir = '/home/atom/ongoing/work_worldwide/vol/final'
list_fn_reg_multann = [os.path.join(reg_dir,'dh_'+str(i).zfill(2)+'_rgi60_int_base_reg_subperiods.csv') for i in np.arange(1,20)]
df_all = pd.DataFrame()
for fn_reg_multann in list_fn_reg_multann:
df_all= df_all.append(pd.read_csv(fn_reg_multann))
tlims = [np.datetime64('20'+str(i).zfill(2)+'-01-01') for i in range(21)]
list_df_glob = []
list_df_per = []
for i in range(len(tlims)-1):
period = str(tlims[i])+'_'+str(tlims[i+1])
df_p = df_all[df_all.period==period]
df_global = tt.aggregate_indep_regions_rates(df_p)
df_global['period']=period
df_noperiph = tt.aggregate_indep_regions_rates(df_p[~df_p.reg.isin([5, 19])])
df_noperiph['period']=period
list_df_glob.append(df_global)
list_df_per.append(df_noperiph)
df_glob = pd.concat(list_df_glob)
df_per = pd.concat(list_df_per)
df_g = df[df.tag=='gard']
df_g_glo = tt.aggregate_indep_regions_rates(df_g)
df_g_per = tt.aggregate_indep_regions_rates(df_g[~df_g.reg.isin([5, 19])])
df_z = df[df.tag=='zemp']
df_reg = pd.read_csv(fn_reg)
df_srocc = aggregate_all_to_period(df_reg, [tlim_srocc],
fn_tarea=fn_tarea,
frac_area=1)
df_srocc['comp'] = 'srocc'
df_full = aggregate_all_to_period(df_reg, [tlim_full],
fn_tarea=fn_tarea,
frac_area=1)
df_full['comp'] = 'full'
df_tmp = pd.concat([df_srocc, df_full])
list_df.append(df_tmp)
df = pd.concat(list_df)
df_s = df[df.comp == 'srocc']
df_global_srocc = aggregate_indep_regions_rates(df_s)
df_global_srocc['reg'] = 22
df_noperiph_srocc = aggregate_indep_regions_rates(
df_s[~df_s.reg.isin([5, 19])])
df_noperiph_srocc['reg'] = 23
df_a_srocc = aggregate_indep_regions_rates(df_s[df_s.reg.isin(
[1, 3, 4, 5, 6, 7, 8, 9])])
df_a_srocc['reg'] = 24
df_m_srocc = aggregate_indep_regions_rates(df_s[df_s.reg.isin(
[1, 2, 6, 8, 10, 11, 12, 21, 16, 17, 18])])
df_m_srocc['reg'] = 25
df_srocc_total = pd.concat(
[df_s, df_global_srocc, df_noperiph_srocc, df_a_srocc, df_m_srocc])
df_srocc_total['comp'] = 'srocc'
df_srocc_total.period = df[df.comp == 'srocc'].period.values[0]