def plot_tca_uncertainties(dir_out):
sensors = ['ASCAT', 'SMAP', 'CLSM']
res = pd.read_csv(
'/Users/u0116961/Documents/work/MadKF/CLSM/SM_err_ratio/GEOSldas/sm_validation/Pcorr/result.csv',
index_col=0)
figsize = (15, 5)
fontsize = 12
cb = [0, 0.04]
modes = ['anom_lst', 'anom_lt', 'anom_st']
titles = ['Anomalies', 'LF signal', 'HF signal']
labels = [
'$\widehat{std}(\epsilon_{\Theta,smap})$',
'$\widehat{std}(\epsilon_{\Theta,clsm})$'
]
f = plt.figure(figsize=figsize)
n = 0
pos = []
for s, l in zip(sensors[1::], labels):
for mode, title in zip(modes, titles):
n += 1
plt.subplot(2, 3, n)
tag = 'ubrmse_grid_' + mode + '_m_' + s + '_tc_ASCAT_SMAP_CLSM'
r_asc_smap = res[f'r_grid_{mode}_p_ASCAT_SMAP']
r_asc_clsm = res[f'r_grid_{mode}_p_ASCAT_CLSM']
r_smap_clsm = res[f'r_grid_{mode}_p_SMAP_CLSM']
thres = 0.2
ind_valid = res[(r_asc_smap > thres) & (r_asc_smap > thres) &
(r_asc_smap > thres)].index
im_r = plot_ease_img(res.reindex(ind_valid),
tag,
fontsize=fontsize,
cbrange=cb,
cmap='viridis',
print_mean=True)
# if (n == 6) | (n == 7):
# pos += [im_r.axes.get_position()]
if s == 'SMAP':
plt.title(title, fontsize=fontsize)
if mode == 'anom_lst':
plt.ylabel(l, fontsize=fontsize)
plot_centered_cbar(f, im_r, 3, fontsize=fontsize - 2, bottom=0.07)
fout = dir_out / 'tca_uncertainties.png'
f.savefig(fout, dpi=300, bbox_inches='tight')
plt.close()
def plot_orthogonality_check(dir_out):
sensors = ['ASCAT', 'SMAP', 'CLSM']
res = pd.read_csv(
'/Users/u0116961/Documents/work/MadKF/CLSM/SM_err_ratio/GEOSldas/sm_validation/Pcorr/result.csv',
index_col=0)
figsize = (11, 3)
fontsize = 10
# cb = [-0.02, 0.02]
cb = [-0.02, 0.02]
f = plt.figure(figsize=figsize)
valid = pd.Series(True, index=res.index)
for mode in ['anom_lt', 'anom_st', 'anom_lst']:
r_asc_smap = res[f'r_grid_{mode}_p_ASCAT_SMAP']
r_asc_clsm = res[f'r_grid_{mode}_p_ASCAT_CLSM']
r_smap_clsm = res[f'r_grid_{mode}_p_SMAP_CLSM']
thres = 0.2
valid &= ((r_asc_smap > thres) & (r_asc_smap > thres) &
(r_asc_smap > thres))
ind_valid = res[valid].index
for i, s in enumerate(sensors[1::]):
plt.subplot(1, 2, i + 1)
tag_lt = 'ubrmse_grid_anom_lt_m_' + s + '_tc_ASCAT_SMAP_CLSM'
tag_st = 'ubrmse_grid_anom_st_m_' + s + '_tc_ASCAT_SMAP_CLSM'
tag_lst = 'ubrmse_grid_anom_lst_m_' + s + '_tc_ASCAT_SMAP_CLSM'
res['diff'] = (res[tag_lst] - np.sqrt(res[tag_lt]**2 + res[tag_st]**2))
im_r = plot_ease_img(res.reindex(ind_valid),
'diff',
fontsize=fontsize + 2,
cbrange=cb,
cmap=cc.cm.bjy,
print_meanstd=True)
plt.title(s, fontsize=fontsize)
plot_centered_cbar(f,
im_r,
2,
fontsize=fontsize - 2,
bottom=0.00,
hspace=0.030,
pad=0.02,
wdth=0.04)
fout = dir_out / 'orthogonality_verification.png'
f.savefig(fout, dpi=300, bbox_inches='tight')
plt.close()
def plot_ensvar_ratio(dir_out):
dtype, hdr, length = template_error_Tb40()
f = plt.figure(figsize=(25, 12))
fontsize = 14
cb = [-10, 10]
cmap = cc.cm.bjy
modes = ['4K', 'abs', 'anom_lst', 'anom_lt', 'anom_st']
titles = [
'4K benchmark', 'Total signal', 'Anomalies', 'LF signal', 'HF signal'
]
labels = [
'(V-pol, Asc.)', '(V-pol, Dsc.)', '(H-pol, Asc.)', '(H-pol, Dsc.)'
]
ios = [
GEOSldas_io('ObsFcstAna',
exp=f'NLv4_M36_US_DA_SMAP_Pcorr_{mode}').timeseries
for mode in modes
]
io_ol = GEOSldas_io('ObsFcstAna', exp=f'NLv4_M36_US_OL_Pcorr').timeseries
grid = GEOSldas_io().grid
for i, (io_da, tit) in enumerate(zip(ios, titles)):
for spc, label in zip(range(4), labels):
tmp1 = io_da['obs_obsvar'][:, spc, :, :].values
tmp2 = io_ol['obs_fcstvar'][:, spc, :, :].values
avg = np.nanmean(tmp1 / tmp2, axis=0)
# ratio = io_da['obs_obsvar'] / io_ol['obs_fcstvar']
# ratio = io['obs_obsvar']
# avg = ratio.mean(dim='time', skipna=True)
plt.subplot(4, 5, spc * 5 + i + 1)
img = plot_latlon_img(10 * np.log10(avg),
io_da.lon.values,
io_da.lat.values,
fontsize=fontsize,
cbrange=cb,
cmap=cmap,
plot_cb=False)
if spc == 0:
plt.title(tit, fontsize=fontsize)
if i == 0:
plt.ylabel(label, fontsize=fontsize)
plot_centered_cbar(f, img, 5, fontsize=fontsize - 2)
fout = dir_out / 'ensvar_ratio.png'
f.savefig(fout, dpi=300, bbox_inches='tight')
plt.close()
def plot_uncertainty_ratios(dir_out):
sensors = ['ASCAT', 'SMAP', 'CLSM']
res = pd.read_csv(
'/Users/u0116961/Documents/work/MadKF/CLSM/SM_err_ratio/GEOSldas/sm_validation/Pcorr/result.csv',
index_col=0)
res_tc = pd.read_csv(
'/Users/u0116961/Documents/work/MadKF/CLSM/SM_err_ratio/GEOSldas/sm_validation/Pcorr/result.csv',
index_col=0)
tg = GEOSldas_io().grid.tilegrids
res_cols = res.col.values - tg.loc['domain', 'i_offg']
res_rows = res.row.values - tg.loc['domain', 'j_offg']
figsize = (16, 4)
fontsize = 10
cb = [-10, 10]
cmap = cc.cm.bjy
modes = ['4K', 'anom_lst', 'anom_lt', 'anom_st']
titles = ['4K benchmark', 'Anomalies', 'LF signal', 'HF signal']
ios = [
GEOSldas_io('ObsFcstAna',
exp=f'NLv4_M36_US_DA_SMAP_Pcorr_{mode}').timeseries
for mode in modes
]
io_ol = GEOSldas_io('ObsFcstAna', exp=f'NLv4_M36_US_OL_Pcorr').timeseries
grid = GEOSldas_io().grid
f = plt.figure(figsize=figsize)
for n, (mode, title, io_da) in enumerate(zip(modes, titles, ios)):
if n > 0:
plt.subplot(2, 4, n + 1)
tagP = 'ubrmse_grid_' + mode + '_m_CLSM_tc_ASCAT_SMAP_CLSM'
tagR = 'ubrmse_grid_' + mode + '_m_SMAP_tc_ASCAT_SMAP_CLSM'
res['tmp'] = 10 * np.log10(res[tagP]**2 / res[tagR]**2)
r_asc_smap = res_tc[f'r_grid_{mode}_p_ASCAT_SMAP']
r_asc_clsm = res_tc[f'r_grid_{mode}_p_ASCAT_CLSM']
r_smap_clsm = res_tc[f'r_grid_{mode}_p_SMAP_CLSM']
thres = 0.2
ind_valid = res_tc[(r_asc_smap > thres) & (r_asc_smap > thres) &
(r_asc_smap > thres)].index
img = plot_ease_img(res.reindex(ind_valid),
'tmp',
fontsize=fontsize,
cbrange=cb,
cmap=cmap,
plot_cb=False)
plt.title(title, fontsize=fontsize)
if n == 1:
plt.ylabel('TCA unc. ratio', fontsize=fontsize)
for n, (mode, title, io_da) in enumerate(zip(modes, titles, ios)):
if mode != '4K':
r_asc_smap = res_tc[f'r_grid_{mode}_p_ASCAT_SMAP']
r_asc_clsm = res_tc[f'r_grid_{mode}_p_ASCAT_CLSM']
r_smap_clsm = res_tc[f'r_grid_{mode}_p_SMAP_CLSM']
thres = 0.2
ind_valid = res_tc[(r_asc_smap > thres) & (r_asc_smap > thres) &
(r_asc_smap > thres)].index
else:
ind_valid = res.index
avg = np.full(io_da['obs_obsvar'].shape[1::], np.nan)
for spc in range(4):
tmp1 = io_da['obs_obsvar'][:, spc, :, :].values
tmp2 = io_ol['obs_fcstvar'][:, spc, :, :].values
avg[spc, :, :] = np.nanmean(tmp2 / tmp1, axis=0)
avg = np.nanmean(avg, axis=0)
res['avg'] = 10 * np.log10(avg[res_rows, res_cols])
plt.subplot(2, 4, n + 5)
img = plot_ease_img(res.reindex(ind_valid),
'avg',
fontsize=fontsize,
cbrange=cb,
cmap=cmap,
plot_cb=False)
if n == 0:
plt.title(title, fontsize=fontsize)
plt.ylabel('Ens. var. ratio', fontsize=fontsize)
plot_centered_cbar(f, img, 4, fontsize=fontsize, bottom=0.07)
fout = dir_out / 'uncertainty_ratio.png'
f.savefig(fout, dpi=300, bbox_inches='tight')
plt.close()
def plot_predicted_skillgain(dir_out):
res = pd.read_csv(
'/Users/u0116961/Documents/work/MadKF/CLSM/SM_err_ratio/GEOSldas/sm_validation/Pcorr/result.csv',
index_col=0)
R_anom_lst = res[f'ubrmse_grid_anom_lst_m_SMAP_tc_ASCAT_SMAP_CLSM']**2
P_anom_lst = res[f'ubrmse_grid_anom_lst_m_CLSM_tc_ASCAT_SMAP_CLSM']**2
R_anom_lt = res[f'ubrmse_grid_anom_lt_m_SMAP_tc_ASCAT_SMAP_CLSM']**2
P_anom_lt = res[f'ubrmse_grid_anom_lt_m_CLSM_tc_ASCAT_SMAP_CLSM']**2
R_anom_st = res[f'ubrmse_grid_anom_st_m_SMAP_tc_ASCAT_SMAP_CLSM']**2
P_anom_st = res[f'ubrmse_grid_anom_st_m_CLSM_tc_ASCAT_SMAP_CLSM']**2
R_lt_st_arr = (R_anom_lst - (R_anom_lt + R_anom_st)) / 2
P_lt_st_arr = (P_anom_lst - (P_anom_lt + P_anom_st)) / 2
P_lt_st_arr_rho = P_lt_st_arr / np.sqrt(P_anom_lt * P_anom_st)
# Baseline estimates
# R2 = res[f'r2_grid_abs_m_CLSM_tc_ASCAT_SMAP_CLSM']
R2 = res[f'r2_grid_anom_lst_m_CLSM_tc_ASCAT_SMAP_CLSM']
SNR = R2 / (1 - R2)
SIG = SNR * P_anom_lst
modes = ['anom_lt', 'anom_st', 'anom_lst', 'anom_lst']
titles = [
'LF signal', 'HF signal', 'Anomalies (lumped)', 'Anomalies (joint)'
]
result = pd.DataFrame(index=res.index, columns=modes)
result['row'] = res.row
result['col'] = res.col
for i, mode in enumerate(modes):
P = res[f'ubrmse_grid_{mode}_m_CLSM_tc_ASCAT_SMAP_CLSM']**2
R = res[f'ubrmse_grid_{mode}_m_SMAP_tc_ASCAT_SMAP_CLSM']**2
K = P / (R + P)
if i < 3:
K_4K = P / (4**2 + P)
P_upd = K * R + (1 - K) * P
NSR_upd = P_upd / SIG
R2upd = 1 / (1 + NSR_upd)
result[f'{i}_4K'] = np.sqrt(R2upd) - np.sqrt(R2)
if i < 2:
# Single signal assimilation
# R2 = res[f'r2_grid_{mode}_m_CLSM_tc_ASCAT_SMAP_CLSM']
# NSR = (1 - R2) / R2
# R2upd = 1 / (1 + (1 - K) * NSR)
P_upd = (1 - K) * P
NSR_upd = P_upd / SIG
R2upd = 1 / (1 + NSR_upd)
result[i] = np.sqrt(R2upd) - np.sqrt(R2)
result[f'P_upd_{i}'] = P_upd
elif i == 2:
# Joint signal assimilation
result[i] = np.nan
for cnt, idx in enumerate(res.index):
print(f'{cnt} / {len(res)}')
R11 = R_anom_lt.loc[idx]
R22 = R_anom_st.loc[idx]
R12 = R_lt_st_arr.loc[idx]
P11 = P_anom_lt.loc[idx]
P22 = P_anom_st.loc[idx]
P12 = P_lt_st_arr.loc[idx]
S = np.matrix([[R11, R12, 0, 0], [R12, R22, 0, 0],
[0, 0, P11, P12], [0, 0, P12, P22]])
A = np.matrix(
[K.loc[idx], K.loc[idx], 1 - K.loc[idx], 1 - K.loc[idx]])
P_upd = (A * S * A.T)[0, 0]
NSR_upd = P_upd / SIG.loc[idx]
R2upd = 1 / (1 + NSR_upd)
result.loc[idx, i] = np.sqrt(R2upd) - np.sqrt(R2.loc[idx])
else:
P_upd = result[f'P_upd_0'] + result[
f'P_upd_1'] + 2 * P_lt_st_arr_rho * np.sqrt(
result[f'P_upd_0'] * result[f'P_upd_1'])
NSR_upd = P_upd / SIG
R2upd = 1 / (1 + NSR_upd)
result[i] = np.sqrt(R2upd) - np.sqrt(R2)
f = plt.figure(figsize=(23, 7))
for i, title in enumerate(titles):
plt.subplot(2, 4, i + 1)
im = plot_ease_img(result,
i,
fontsize=12,
cbrange=[-0.2, 0.2],
cmap=cc.cm.bjy,
log_scale=False,
title=title,
plot_cb=False)
if i < 3:
plt.subplot(2, 4, i + 5)
im = plot_ease_img(result,
f'{i}_4K',
fontsize=12,
cbrange=[-0.2, 0.2],
cmap=cc.cm.bjy,
log_scale=False,
title=title + ' (4K)',
plot_cb=False)
plot_centered_cbar(f, im, 3, fontsize=10)
plt.tight_layout()
plt.show()
def plot_perturbations(dir_out):
root = Path(
'/Users/u0116961/Documents/work/MadKF/CLSM/SM_err_ratio/GEOSldas/observation_perturbations/Pcorr'
)
res_tc = pd.read_csv(
'/Users/u0116961/Documents/work/MadKF/CLSM/SM_err_ratio/GEOSldas/sm_validation/Pcorr/result.csv',
index_col=0)
pc = 'Pcorr'
io = GEOSldas_io('ObsFcstAna')
io2 = LDASsa_io('ObsFcstAna')
lut = pd.read_csv(Paths().lut, index_col=0)
ind = np.vectorize(io.grid.colrow2tilenum)(lut.ease2_col,
lut.ease2_row,
local=False)
dtype, hdr, length = template_error_Tb40()
f = plt.figure(figsize=(22, 8))
fontsize = 14
cbrange = [0, 8]
cmap = cc.cm.bjy_r
# cmap='viridis'
modes = ['anom_lst', 'anom_lt', 'anom_st']
titles = ['Anomalies', 'LF signal', 'HF signal']
for i, (mode, title) in enumerate(zip(modes, titles)):
fA = root / f'{mode}' / 'SMOS_fit_Tb_A.bin'
fD = root / f'{mode}' / 'SMOS_fit_Tb_D.bin'
imgA = io2.read_fortran_binary(fA, dtype, hdr=hdr, length=length)
imgD = io2.read_fortran_binary(fD, dtype, hdr=hdr, length=length)
imgA.index += 1
imgD.index += 1
r_asc_smap = res_tc[f'r_grid_{mode}_p_ASCAT_SMAP']
r_asc_clsm = res_tc[f'r_grid_{mode}_p_ASCAT_CLSM']
r_smap_clsm = res_tc[f'r_grid_{mode}_p_SMAP_CLSM']
thres = 0.2
ind_valid = res_tc[(r_asc_smap > thres) & (r_asc_smap > thres) &
(r_asc_smap > thres)].index
ind_valid = np.vectorize(io.grid.colrow2tilenum)(
res_tc.loc[ind_valid, 'col'].values,
res_tc.loc[ind_valid, 'row'].values,
local=False)
plt.subplot(3, 4, i * 4 + 1)
im = plot_ease_img2(imgA.reindex(ind).reindex(ind_valid),
'err_Tbv',
cbrange=cbrange,
cmap=cmap,
io=io,
plot_cmap=False)
if i == 0:
plt.title('$\hat{R}$ (V-pol, Asc.)', fontsize=fontsize)
plt.ylabel(title, fontsize=fontsize)
plt.subplot(3, 4, i * 4 + 2)
plot_ease_img2(imgD.reindex(ind).reindex(ind_valid),
'err_Tbv',
cbrange=cbrange,
cmap=cmap,
io=io,
plot_cmap=False)
if i == 0:
plt.title('$\hat{R}$ (V-pol, Dsc.)', fontsize=fontsize)
plt.subplot(3, 4, i * 4 + 3)
plot_ease_img2(imgA.reindex(ind).reindex(ind_valid),
'err_Tbh',
cbrange=cbrange,
cmap=cmap,
io=io,
plot_cmap=False)
if i == 0:
plt.title('$\hat{R}$ (H-pol, Asc.)', fontsize=fontsize)
plt.subplot(3, 4, i * 4 + 4)
plot_ease_img2(imgD.reindex(ind).reindex(ind_valid),
'err_Tbh',
cbrange=cbrange,
cmap=cmap,
io=io,
plot_cmap=False)
if i == 0:
plt.title('$\hat{R}$ (H-pol, Dsc.)', fontsize=fontsize)
plot_centered_cbar(f, im, 4, fontsize=fontsize - 4, bottom=0.07)
plt.savefig(dir_out / f'perturbations.png', dpi=300, bbox_inches='tight')
plt.close()
def plot_ascat_eval_relative(res_path, dir_out):
refs = ['Pcorr_OL', 'Pcorr_4K']
runs = [['Pcorr_anom_lst', 'Pcorr_anom_lt_ScDY', 'Pcorr_anom_st_ScYH'],
'Pcorr_LTST']
titles = [
'R$_{TC}$ - R$_{OL}$ (Individual)', 'R$_{TC}$ - R$_{OL}$ (Joint)',
'R$_{TC}$ - R$_{4K}$ (Individual)', 'R$_{TC}$ - R$_{4K}$ (Joint)'
]
res = pd.read_csv(res_path / 'ascat_eval.csv', index_col=0)
res_tc = pd.read_csv(
'/Users/u0116961/Documents/work/MadKF/CLSM/SM_err_ratio/GEOSldas/sm_validation/Pcorr/result.csv',
index_col=0)
modes = ['anom_lst', 'anom_lt', 'anom_st']
labels = [f'Anomaly skill', f'LF skill', f'HF skill']
cb_r = [-0.2, 0.2]
fontsize = 14
f = plt.figure(figsize=(22, 8))
for i, (m, label) in enumerate(zip(modes, labels)):
for j, ref in enumerate(refs):
ref_col = f'ana_r_corr_{ref}_{m}'
res[ref_col][res[ref_col] < 0] = 0
res[ref_col][res[ref_col] > 1] = 1
for k, run in enumerate(runs):
r = run if k == 1 else run[i]
ax = plt.subplot(3, 4, i * 4 + j * 2 + k + 1)
col = f'ana_r_corr_{r}_{m}'
res[col][res[col] < 0] = 0
res[col][res[col] > 1] = 1
res['diff'] = res[col] - res[ref_col]
if i == 0:
title = titles[j * 2 + k]
else:
title = ''
if (j == 0) & (k == 0):
ylabel = label
else:
ylabel = ''
r_asc_smap = res_tc[f'r_grid_{m}_p_ASCAT_SMAP']
r_asc_clsm = res_tc[f'r_grid_{m}_p_ASCAT_CLSM']
r_smap_clsm = res_tc[f'r_grid_{m}_p_SMAP_CLSM']
thres = 0.2
ind_valid = res_tc[(r_asc_smap > thres) & (r_asc_smap > thres)
& (r_asc_smap > thres)].index
im = plot_ease_img(res.reindex(ind_valid),
'diff',
title=title,
cmap=cc.cm.bjy,
cbrange=cb_r,
fontsize=fontsize,
print_mean=True,
plot_cb=False)
ax.set_ylabel(ylabel)
plot_centered_cbar(f, im, 4, fontsize=fontsize - 2, pad=0.02)
f.savefig(dir_out / f'ascat_eval_rel.png', dpi=300, bbox_inches='tight')
plt.close()
def plot_ascat_eval_absolute(res_path, dir_out):
runs = [
'Pcorr_OL', 'Pcorr_4K',
['Pcorr_anom_lst', 'Pcorr_anom_lt_ScDY', 'Pcorr_anom_st_ScYH'],
'Pcorr_LTST'
]
titles = [
'Open-loop', '4K Benchmark', 'Individual assimilation',
'Joint assimilation'
]
res = pd.read_csv(res_path / 'ascat_eval.csv', index_col=0)
res_tc = pd.read_csv(
'/Users/u0116961/Documents/work/MadKF/CLSM/SM_err_ratio/GEOSldas/sm_validation/Pcorr/result.csv',
index_col=0)
modes = ['anom_lst', 'anom_lt', 'anom_st']
cb_r = [0.6, 1]
fontsize = 14
f = plt.figure(figsize=(22, 8))
for i, m in enumerate(modes):
for cnt, (run, tit) in enumerate(zip(runs, titles)):
r = run if cnt != 2 else run[i]
ax = plt.subplot(3, 4, i * 4 + cnt + 1)
col = f'ana_r_corr_{r}_{m}'
res[col][res[col] < 0] = 0
res[col][res[col] > 1] = 1
if i == 0:
title = tit
else:
title = ''
if cnt == 0:
if i == 0:
ylabel = 'Anomaly skill'
elif i == 1:
ylabel = 'LF skill'
else:
ylabel = 'HF skill'
else:
ylabel = ''
r_asc_smap = res_tc[f'r_grid_{m}_p_ASCAT_SMAP']
r_asc_clsm = res_tc[f'r_grid_{m}_p_ASCAT_CLSM']
r_smap_clsm = res_tc[f'r_grid_{m}_p_SMAP_CLSM']
thres = 0.2
ind_valid = res_tc[(r_asc_smap > thres) & (r_asc_smap > thres) &
(r_asc_smap > thres)].index
im = plot_ease_img(res.reindex(ind_valid),
col,
title=title,
cmap='viridis',
cbrange=cb_r,
fontsize=fontsize,
print_mean=True,
plot_cb=False)
ax.set_ylabel(ylabel)
plot_centered_cbar(f, im, 4, fontsize=fontsize - 2, pad=0.02)
f.savefig(dir_out / f'ascat_eval_abs.png', dpi=300, bbox_inches='tight')
plt.close()
def plot_filter_diagnostics(res_path, dir_out):
fname = res_path / 'filter_diagnostics.nc'
res = pd.read_csv(
'/Users/u0116961/Documents/work/MadKF/CLSM/SM_err_ratio/GEOSldas/sm_validation/Pcorr/result.csv',
index_col=0)
res_tc = pd.read_csv(
'/Users/u0116961/Documents/work/MadKF/CLSM/SM_err_ratio/GEOSldas/sm_validation/Pcorr/result.csv',
index_col=0)
tg = GEOSldas_io().grid.tilegrids
res_cols = res.col.values - tg.loc['domain', 'i_offg']
res_rows = res.row.values - tg.loc['domain', 'j_offg']
r_asc_smap = res_tc[f'r_grid_anom_lst_p_ASCAT_SMAP']
r_asc_clsm = res_tc[f'r_grid_anom_lst_p_ASCAT_CLSM']
r_smap_clsm = res_tc[f'r_grid_anom_lst_p_SMAP_CLSM']
thres = 0.2
ind_valid = res_tc[(r_asc_smap > thres) & (r_asc_smap > thres) &
(r_asc_smap > thres)].index
fontsize = 14
root = Path('/Users/u0116961/data_sets/GEOSldas_runs')
runs = [run.name for run in root.glob('*_DA_SMAP_*')]
runs += ['NLv4_M36_US_OL_Pcorr', 'NLv4_M36_US_OL_noPcorr']
tags = ['OL_Pcorr', 'Pcorr_4K', f'Pcorr_anom_lst', 'Pcorr_LTST']
iters = [np.where([tag in run for run in runs])[0][0] for tag in tags]
titles = ['Open-loop', '4K constant', 'Anomalies']
labels = ['H pol. / Asc.', 'V pol. / Asc.']
with Dataset(fname) as ds:
lons = ds.variables['lon'][:]
lats = ds.variables['lat'][:]
lons, lats = np.meshgrid(lons, lats)
var = 'innov_autocorr'
cbrange = [0, 0.7]
step = 0.2
cmap = 'viridis'
f = plt.figure(figsize=(19, 6))
for j, (spc, label) in enumerate(zip([0, 2], labels)):
for i, (it_tit, it) in enumerate(zip(titles, iters)):
title = it_tit if j == 0 else ''
plt.subplot(2, 3, j * 3 + i + 1)
data = ds.variables[var][:, :, it, spc]
res['tmp'] = data[res_rows, res_cols]
im = plot_ease_img(res.reindex(ind_valid),
'tmp',
fontsize=fontsize,
cbrange=cbrange,
cmap=cmap,
title=title,
plot_cb=False,
print_meanstd=True)
if i == 0:
plt.ylabel(label, fontsize=fontsize)
plot_centered_cbar(f, im, 3, fontsize=fontsize - 2, bottom=0.07)
fout = dir_out / f'{var}.png'
f.savefig(fout, dpi=300, bbox_inches='tight')
plt.close()