def plot_ts(lon, lat):
experiments = ['US_M36_SMAP_TB_MadKF_DA_it34', 'US_M36_SMOS40_TB_MadKF_DA_it614', 'US_M36_SMOS40_TB_MadKF_DA_it615', 'US_M36_SMOS40_TB_MadKF_DA_it613']
f = plt.figure(figsize=(18,10))
for i, exp in enumerate(experiments):
if 'SMAP' in exp:
ol = 'US_M36_SMAP_TB_OL_noScl'
else:
ol = 'US_M36_SMOS40_TB_OL_noScl'
ds_ol = LDAS_io('ObsFcstAna', ol)
ds_da = LDAS_io('ObsFcstAna', exp)
ts_fcst = ds_ol.read_ts('obs_fcst', lon, lat)
ts_obs = ds_da.read_ts('obs_obs', lon, lat)
ts_ana = ds_da.read_ts('obs_ana', lon, lat)
spc = 1
# if spc == 1:
# spc_tit = 'H pol. / Asc.'
# elif spc == 2:
# spc_tit = 'H pol. / Dsc.'
# elif spc == 3:
# spc_tit = 'V pol. / Asc.'
# else:
# spc_tit = 'V pol. / Dsc.'
df = pd.concat((ts_fcst[spc], ts_obs[spc], ts_ana[spc]), axis='columns').dropna()
df.columns = ['Fcst', 'Obs', 'Ana']
df['time'] = df.index
ax = plt.subplot(4, 1, i+1)
g = sns.lineplot(x='time', y='Tb', hue='Variable', data=df.melt('time', df.columns[0:-1], 'Variable', 'Tb'))
plt.legend(loc='upper right')
if spc != 4:
g.set(xticklabels=[])
ax.set_xlabel('')
ax.set_xlim([date(2010,1,1), date(2020,1,1)])
ax.set_ylim([170,280])
# ax.set_ylabel('')
plt.title(exp)
plt.tight_layout()
plt.show()
def plot_cat_timeseries():
outpath = r'D:\work\LDAS\2018-02_scaling\_new\ismn_eval\timeseries'
fname = r"D:\work\LDAS\2018-02_scaling\_new\ismn_eval\validation.csv"
res = pd.read_csv(fname)
diff_srf = res['corr_DA_cal_pent_ma_sm_surface'] - res[
'corr_DA_uncal_pent_ma_sm_surface']
diff_rz = res['corr_DA_cal_pent_ma_sm_rootzone'] - res[
'corr_DA_uncal_pent_ma_sm_rootzone']
diff_prof = res['corr_DA_cal_pent_ma_sm_profile'] - res[
'corr_DA_uncal_pent_ma_sm_profile']
ind = (diff_srf > 0.2) | (diff_rz > 0.2) | (diff_prof > 0.2)
res = res.loc[ind, ['network', 'station', 'lat', 'lon']]
ismn = ISMN_io()
cal = LDAS_io('xhourly', 'US_M36_SMOS_DA_calibrated_scaled')
uncal = LDAS_io('xhourly', 'US_M36_SMOS_DA_nocal_scaled_pentadal')
variables = ['sm_surface', 'sm_rootzone', 'sm_profile']
for idx, stat in res.iterrows():
fname = os.path.join(outpath,
stat.network + '_' + stat.station + '.png')
ts_ismn = ismn.read(stat.network, stat.station)
lat = stat.lat
lon = stat.lon
plt.figure(figsize=(17, 9))
for i, var in enumerate(variables):
ax = plt.subplot(3, 1, i + 1)
ts_cal = calc_anomaly(cal.read_ts(var, lon, lat), method='ma')
ts_cal.index += pd.to_timedelta('2 hours')
ts_uncal = calc_anomaly(uncal.read_ts(var, lon, lat), method='ma')
ts_uncal.index += pd.to_timedelta('2 hours')
df = pd.DataFrame({
'cal': ts_cal,
'uncal': ts_uncal,
'insitu': calc_anomaly(ts_ismn[var], method='ma')
}).dropna()
if len(df) > 0:
df.plot(ax=ax)
else:
continue
title = 'R(ismn - cal) = %.2f , R(ismn - uncal) = %.2f' % (
df.corr().loc['insitu', 'cal'], df.corr().loc['insitu',
'uncal'])
ax.set_title(title, fontsize=12)
ax.set_xlim('2010-01-01', '2016-01-01')
ax.set_ylim(-0.3, 0.3)
ax.set_xlabel('')
plt.tight_layout()
plt.savefig(fname, dpi=150)
plt.close()
def plot_timeseries_cci():
# Colorado
# lat = 39.095962936
# lon = -106.918945312
# Nebraska
# lat = 41.203456192
# lon = -102.249755859
# New Mexico
# lat = 31.522361470
# lon = -108.528442383
# Western Siberia
lats = np.arange(60, 61, 0.1)
lons = np.arange(67, 69, 0.1)
#lats = [60.5]
#lons = [67.1]
#lats = [60.26, 59.45, 52.71]
#lons = [65.80, 42.00, -84.18]
''' get nearest in situ site
fname = '/staging/leuven/stg_00024/l_data/obs_insitu/peatland_data/WTD_peatlands_global_WGS84_DA.csv'
np.genfromtxt(fname, names=True, delimiter=',', dtype=None)
fname = '/staging/leuven/stg_00024/l_data/obs_insitu/peatland_data/000_all_wtd_GLOBAL/CO_Ek_avg.csv'
sitewtd_tmp = np.genfromtxt(fname, names=True, delimiter=',', dtype=None)
sitewtd = pd.DataFrame(sitewtd_tmp)
'''
# Congo
# lats = [1.0]
# lons = [17.5]
exp = 'SMAP_EASEv2_M36_NORTH_DET2'
domain = 'SMAP_EASEv2_M36_NORTH'
outpath = '/data/leuven/317/vsc31786/FIG_tmp'
PEATCLSM = LDAS_io('xhourly', exp=exp, domain=domain)
exp = 'SMAP_EASEv2_M36_NORTH_DET2_CLSM'
domain = 'SMAP_EASEv2_M36_NORTH'
CLSM = LDAS_io('xhourly', exp=exp, domain=domain)
#cci_version = 'v03.3'
cci_version = 'v04.4'
# CCI
fname = '/scratch/leuven/317/vsc31786/CCI/PASSIVE_' + cci_version + '_timeseries.nc'
# fname = '/mnt/vsc_scratch/CCI/PASSIVE_v04.4_timeseries.nc'
CCI_PASSIVE = xr.open_dataset(fname)
fname = '/scratch/leuven/317/vsc31786/CCI/ACTIVE_' + cci_version + '_timeseries.nc'
CCI_ACTIVE = xr.open_dataset(fname)
fname = '/scratch/leuven/317/vsc31786/CCI/COMBINED_' + cci_version + '_timeseries.nc'
CCI_COMBINED = xr.open_dataset(fname)
for lat in lats:
for lon in lons:
col, row = PEATCLSM.grid.lonlat2colrow(lon, lat, domain=True)
title = ''
fontsize = 12
#PEATCLSM.timeseries['sfmc']
ts_obs_PEATCLSM = PEATCLSM.read_ts('sfmc', lon, lat, lonlat=True)
ts_obs_PEATCLSM.name = 'sfmc PEATCLSM'
ts_obs_CLSM = CLSM.read_ts('sfmc', lon, lat, lonlat=True)
ts_obs_CLSM.name = 'sfmc CLSM'
#ts_obs_CCI_PASSIVE = CCI_PASSIVE.read_ts('sm', lon, lat, lonlat=True)
ts_obs_CCI_PASSIVE = CCI_PASSIVE.sel(
lat=lat, lon=lon, method='nearest').sm.to_series()
ts_obs_CCI_PASSIVE.name = 'sm PASSIVE'
#ts_obs_CCI_ACTIVE = CCI_ACTIVE.read_ts('sm', lon, lat, lonlat=True)
ts_obs_CCI_ACTIVE = CCI_ACTIVE.sel(
lat=lat, lon=lon, method='nearest').sm.to_series()
ts_obs_CCI_ACTIVE.name = 'sm ACTIVE'
#ts_obs_CCI_COMBINED = CCI_COMBINED.read_ts('sm', lon, lat, lonlat=True)
ts_obs_CCI_COMBINED = CCI_COMBINED.sel(
lat=lat, lon=lon, method='nearest').sm.to_series()
ts_obs_CCI_COMBINED.name = 'sm COMBINED'
ts_obs_PEATCLSM_daily = ts_obs_PEATCLSM.groupby(
ts_obs_PEATCLSM.index.date).mean()
ts_obs_CLSM_daily = ts_obs_CLSM.groupby(
ts_obs_CLSM.index.date).mean()
df = pd.concat(
(ts_obs_PEATCLSM_daily, ts_obs_CLSM_daily, ts_obs_CCI_PASSIVE,
ts_obs_CCI_ACTIVE, ts_obs_CCI_COMBINED),
axis=1).dropna()
df_zscore = df.apply(zscore)
if df.empty:
continue
# Fig 1
fname = os.path.join(
outpath, 'PEATCLSM_CCI' + cci_version + '_' + '%0.2f' % lat +
'_' + '%0.2f' % lon + '.png')
print fname
f = plt.figure(figsize=(19, 13),
dpi=90,
facecolor='w',
edgecolor='k')
ax1 = plt.subplot2grid((5, 6), (0, 0), colspan=4)
csel = ts_obs_CLSM.name
ax1.set_title(csel)
df_zscore[csel].plot(ax=ax1,
ylim=[-3, 3],
xlim=['2010-01-01', '2015-01-01'],
fontsize=fontsize,
style=['.'])
plt.xlabel('')
plt.ylabel('zscore(sm)')
ax2 = plt.subplot2grid((5, 6), (1, 0), colspan=4, sharex=ax1)
csel = ts_obs_PEATCLSM.name
ax2.set_title(csel)
df_zscore[csel].plot(ax=ax2,
ylim=[-3, 3],
xlim=['2010-01-01', '2015-01-01'],
fontsize=fontsize,
style=['.'])
#sitewtd.apply(zscore)[wtd](ax=ax2, ylim=[-3, 3], xlim=['2010-01-01', '2015-01-01'], fontsize=fontsize, style=['.'])
plt.xlabel('')
plt.ylabel('zscore(sm)')
ax2 = plt.subplot2grid((5, 6), (2, 0), colspan=4, sharex=ax1)
csel = ts_obs_CCI_PASSIVE.name
ax2.set_title(csel)
df_zscore[csel].plot(ax=ax2,
ylim=[-3, 3],
xlim=['2010-01-01', '2015-01-01'],
fontsize=fontsize,
style=['.'])
plt.xlabel('')
plt.ylabel('zscore(sm)')
ax2 = plt.subplot2grid((5, 6), (3, 0), colspan=4, sharex=ax1)
csel = ts_obs_CCI_ACTIVE.name
ax2.set_title(csel)
df_zscore[csel].plot(ax=ax2,
ylim=[-3, 3],
xlim=['2010-01-01', '2015-01-01'],
fontsize=fontsize,
style=['.'])
plt.xlabel('')
plt.ylabel('zscore(sm)')
ax2 = plt.subplot2grid((5, 6), (4, 0), colspan=4, sharex=ax1)
csel = ts_obs_CCI_COMBINED.name
ax2.set_title(csel)
df_zscore[csel].plot(ax=ax2,
ylim=[-3, 3],
xlim=['2010-01-01', '2015-01-01'],
fontsize=fontsize,
style=['.'])
plt.xlabel('')
plt.ylabel('zscore(sm)')
ax2 = plt.subplot2grid((5, 6), (0, 4), rowspan=2, colspan=2)
plt.plot(df[ts_obs_PEATCLSM.name], df[ts_obs_CLSM.name], '.')
plt.xlabel(ts_obs_PEATCLSM.name)
plt.ylabel(ts_obs_CLSM.name)
#plt.xlim(-3, 3)
#plt.ylim(-3, 3)
ax2 = plt.subplot2grid((5, 6), (2, 4), colspan=1)
plt.plot(df_zscore[ts_obs_CLSM.name],
df_zscore[ts_obs_CCI_PASSIVE.name], '.')
plt.xlabel(ts_obs_CLSM.name)
plt.ylabel(ts_obs_CCI_PASSIVE.name)
plt.xlim(-3, 3)
plt.ylim(-3, 3)
ax2 = plt.subplot2grid((5, 6), (3, 4), colspan=1)
plt.plot(df_zscore[ts_obs_CLSM.name],
df_zscore[ts_obs_CCI_ACTIVE.name], '.')
plt.xlabel(ts_obs_CLSM.name)
plt.ylabel(ts_obs_CCI_ACTIVE.name)
plt.xlim(-3, 3)
plt.ylim(-3, 3)
ax2 = plt.subplot2grid((5, 6), (4, 4), colspan=1)
plt.plot(df_zscore[ts_obs_CLSM.name],
df_zscore[ts_obs_CCI_COMBINED.name], '.')
plt.xlabel(ts_obs_CLSM.name)
plt.ylabel(ts_obs_CCI_COMBINED.name)
plt.xlim(-3, 3)
plt.ylim(-3, 3)
ax2 = plt.subplot2grid((5, 6), (2, 5), colspan=1)
plt.plot(df_zscore[ts_obs_PEATCLSM.name],
df_zscore[ts_obs_CCI_PASSIVE.name], '.')
plt.xlabel(ts_obs_PEATCLSM.name)
plt.ylabel(ts_obs_CCI_PASSIVE.name)
plt.xlim(-3, 3)
plt.ylim(-3, 3)
ax2 = plt.subplot2grid((5, 6), (3, 5), colspan=1)
plt.plot(df_zscore[ts_obs_PEATCLSM.name],
df_zscore[ts_obs_CCI_ACTIVE.name], '.')
plt.xlabel(ts_obs_PEATCLSM.name)
plt.ylabel(ts_obs_CCI_ACTIVE.name)
plt.xlim(-3, 3)
plt.ylim(-3, 3)
ax2 = plt.subplot2grid((5, 6), (4, 5), colspan=1)
plt.plot(df_zscore[ts_obs_PEATCLSM.name],
df_zscore[ts_obs_CCI_COMBINED.name], '.')
plt.xlabel(ts_obs_PEATCLSM.name)
plt.ylabel(ts_obs_CCI_COMBINED.name)
plt.xlim(-3, 3)
plt.ylim(-3, 3)
plt.tight_layout()
plt.savefig(fname, dpi=f.dpi)
plt.close()
#plt.show()
'''
def plot_timeseries():
# Colorado
# lat = 39.095962936
# lon = -106.918945312
# Nebraska
# lat = 41.203456192
# lon = -102.249755859
# New Mexico
# lat = 31.522361470
# lon = -108.528442383
# Oklahoma
lat = 35.205233348
lon = -97.910156250
exp = 'SMAP_EASEv2_M36_NORTH_SCA_SMOSrw_DA'
domain = 'SMAP_EASEv2_M36_NORTH'
cal = LDAS_io('incr', 'US_M36_SMOS_DA_calibrated_scaled')
uncal = LDAS_io('incr', 'US_M36_SMOS_DA_nocal_scaled_pentadal')
incr_var_cal = (cal.timeseries['srfexc'] + cal.timeseries['rzexc'] -
cal.timeseries['catdef']).var(dim='time').values
incr_var_uncal = (uncal.timeseries['srfexc'] + uncal.timeseries['rzexc'] -
uncal.timeseries['catdef']).var(dim='time').values
col, row = LDAS_io().grid.lonlat2colrow(lon, lat, domain=True)
title = 'increment variance (calibrated): %.2f increment variance (uncalibrated): %.2f' % (
incr_var_cal[row, col], incr_var_uncal[row, col])
# title = ''
fontsize = 12
cal = LDAS_io('ObsFcstAna', 'US_M36_SMOS_DA_calibrated_scaled')
uncal = LDAS_io('ObsFcstAna', 'US_M36_SMOS_DA_nocal_scaled_pentadal')
orig = LDAS_io('ObsFcstAna', 'US_M36_SMOS_noDA_unscaled')
ts_obs_cal = cal.read_ts('obs_obs', lon, lat, species=3, lonlat=True)
ts_obs_cal.name = 'Tb obs (calibrated)'
ts_obs_uncal = uncal.read_ts('obs_obs', lon, lat, species=3, lonlat=True)
ts_obs_uncal.name = 'Tb obs (uncalibrated)'
ts_obs_orig = orig.read_ts('obs_obs', lon, lat, species=3, lonlat=True)
ts_obs_orig.name = 'Tb obs (uncalibrated, unscaled)'
ts_fcst_cal = cal.read_ts('obs_fcst', lon, lat, species=3, lonlat=True)
ts_fcst_cal.name = 'Tb fcst (calibrated)'
ts_fcst_uncal = uncal.read_ts('obs_fcst', lon, lat, species=3, lonlat=True)
ts_fcst_uncal.name = 'Tb fcst (uncalibrated)'
df = pd.concat(
(ts_obs_cal, ts_obs_uncal, ts_obs_orig, ts_fcst_cal, ts_fcst_uncal),
axis=1).dropna()
plt.figure(figsize=(19, 8))
ax1 = plt.subplot(211)
df.plot(ax=ax1,
ylim=[140, 300],
xlim=['2010-01-01', '2017-01-01'],
fontsize=fontsize,
style=['-', '--', ':', '-', '--'],
linewidth=2)
plt.xlabel('')
plt.title(title, fontsize=fontsize + 2)
cols = df.columns.values
for i, col in enumerate(df):
df[col] = calc_anomaly(df[col], method='ma', longterm=True).values
if i < 3:
cols[i] = col[0:7] + 'anomaly ' + col[7::]
else:
cols[i] = col[0:7] + ' anomaly' + col[7::]
df.columns = cols
df.dropna(inplace=True)
ax2 = plt.subplot(212, sharex=ax1)
df.plot(ax=ax2,
ylim=[-60, 60],
xlim=['2010-01-01', '2017-01-01'],
fontsize=fontsize,
style=['-', '--', ':', '-', '--'],
linewidth=2)
plt.xlabel('')
plt.tight_layout()
plt.show()
species = ObsFcstAna.timeseries['species'].values
ds = ncfile_init(result_file, lats[:, 0], lons[0, :], species, tags)
# [col, row] = get_M09_ObsFcstAna(ObsFcstAna,lon,lat)
# [col, row] = get_M09_ObsFcstAna(ObsFcstAna,lons.min()+2,lats.min()+2)
# col = col%4
for row in range(poros.shape[0]):
print("row: " + str(row))
for col in range(poros.shape[1]):
if poros[row, col] > 0.6:
for i_spc, spc in enumerate(species):
[col_obs, row_obs] = get_M09_ObsFcstAna(ObsFcstAna, col, row, lonlat=False)
ts_sfmc = lsm.read_ts('sfmc', col, row, lonlat=False)
ts_tp1 = lsm.read_ts('tp1', col, row, lonlat=False)
ts_obsobs = ObsFcstAna.read_ts('obs_obs', col_obs, row_obs, species=i_spc + 1, lonlat=False)
ts_obsobs.name = 'obsobs'
df = pd.concat((ts_sfmc, ts_tp1, ts_obsobs), axis=1)
ts_emissivity = df['obsobs'] / df['tp1']
df = pd.concat((ts_sfmc, ts_emissivity), axis=1)
if not np.isnan(df.corr().values[0, 1]):
ds.variables['pearsonR'][row, col, i_spc] = df.corr().values[0, 1]
# pearsonr([0]['obs_obs'][i_spc] - [0]['obs_fcst'][i_spc]).mean(dim='time').values
# tmp = [0]['obs_obs'][i_spc].values
# np.place(tmp, ~np.isnan(tmp), 1.)
# np.place(tmp, np.isnan(tmp), 0.)
# natural
lon = 138.6
lat = -5.1
lon = 144.7
lat = -6.8
#sebangau forest
lon = 114.2
lat = -3
# Drained Indonesia
lon = 103
lat = -0.75
[col, row] = lsm.grid.lonlat2colrow(lon, lat, domain=True)
sfmc = lsm.read_ts('sfmc', col, row, lonlat=False)
zbar = lsm.read_ts('zbar', col, row, lonlat=False)
df = pd.concat((sfmc, zbar), axis=1)
fig = plt.figure(figsize=(20, 10))
fontsize = 18
ax1 = plt.subplot2grid((2, 1), (0, 0), rowspan=1, colspan=1, fig=None)
df['sfmc'].plot(ax=ax1,
style=style,
markersize=5,
fontsize=fontsize + 8,
linewidth=2)
plt.ylabel('Surface moisture content (vol/vol)', fontsize=18)
plt.legend(fontsize=16)
plt.xticks(fontsize=18)