fn_fig = '/Users/dbyrne/Projects/CO9_AMM15/data/figs/ha_variance'
ha = xr.open_dataset(fn_out)
ana = xr.open_dataset(fn_ana)
const = 'M2'
file_type = '.png'
#%% Anal map plots
ind0 = ana.ana_length <= 30
ind1 = np.logical_and(ana.ana_length <= 90, ana.ana_length > 30)
ind2 = np.logical_and(ana.ana_length <= 180, ana.ana_length > 90)
ind3 = np.logical_and(ana.ana_length <= 365, ana.ana_length > 180)
ind4 = ana.ana_length > 365
f, a = pu.create_geo_axes([-15, 15], [45, 65])
sca = a.scatter(ana.longitude[ind0], ana.latitude[ind0], zorder=100, s=2)
sca = a.scatter(ana.longitude[ind1], ana.latitude[ind1], zorder=70, s=4)
sca = a.scatter(ana.longitude[ind2], ana.latitude[ind2], zorder=60, s=6)
sca = a.scatter(ana.longitude[ind3], ana.latitude[ind3], zorder=50, s=10)
sca = a.scatter(ana.longitude[ind4],
ana.latitude[ind4],
zorder=40,
s=10,
marker='s',
color='k')
f.legend(
['l <= 30', '30 < l <= 90', '90 < l <= 180', '180 < l <= 365', 'l > 365'],
loc='lower right')
a.set_title('tideanal.edited locations and analysis length')
fn = "ana_lengths{0}".format(file_type)
def __init__(self,
fn_ssh_hourly_stats1,
fn_ssh_hourly_stats2,
dn_out,
run_name1,
run_name2,
file_type='.png'):
stats1 = xr.open_dataset(fn_ssh_hourly_stats1)
stats2 = xr.open_dataset(fn_ssh_hourly_stats2)
lonmax = np.max(stats1.longitude)
lonmin = np.min(stats1.longitude)
latmax = np.max(stats1.latitude)
latmin = np.min(stats1.latitude)
lonbounds = [lonmin - 4, lonmax + 4]
latbounds = [latmin - 4, latmax + 4]
### GEOGRAPHICAL SCATTER PLOTS
# Plot correlations
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.ntr_corr,
vmin=.85,
vmax=1,
edgecolors='k',
linewidths=.5,
zorder=100)
f.colorbar(sca)
a.set_title(
'Hourly NTR Correlations with Tide Gauge | {0}'.format(run_name),
fontsize=9)
fn = "ssh_hourly_ntr_correlations_" + run_name + file_type
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# Plot std_err
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.std_err,
vmin=-.15,
vmax=.15,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='seismic')
f.colorbar(sca)
a.set_title('Hourly SSH St. Dev Error (m) | {0}'.format(run_name),
fontsize=9)
fn = "ssh_hourly_stderr_" + run_name + file_type
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# Plot mae
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.ntr_mae,
vmin=-.05,
vmax=.05,
edgecolors='k',
linewidths=.5,
zorder=100)
f.colorbar(sca)
a.set_title('Hourly NTR MAE (m) | {0}'.format(run_name), fontsize=9)
fn = "ssh_hourly_ntr_mae_" + run_name + file_type
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
### HARMONIC ERRORS
constit = stats.constituent.values
n_constit = len(constit)
for cc in range(0, n_constit):
# AMPLITUDE MAP
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.amp_err[:, cc],
vmin=-.2,
vmax=.2,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='seismic')
f.colorbar(sca)
a.set_title('Amplitude Error (m) | {1} - Obs | {0}'.format(
constit[cc], run_name),
fontsize=9)
fn = "ssh_hourly_amp_err_{0}_{1}{2}".format(
constit[cc], run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# PHASE MAP
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.pha_err[:, cc],
vmin=-15,
vmax=15,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='seismic')
f.colorbar(sca)
a.set_title('Phase Error (deg) | {1} - Obs | {0}'.format(
constit[cc], run_name),
fontsize=9)
fn = "ssh_hourly_pha_err_{0}_{1}{2}".format(
constit[cc], run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# AMPLITUDE SCATTER
f, a = pu.scatter_with_fit(stats.amp_mod[:, cc], stats.amp_obs[:,
cc])
a.set_title('Amplitude Comparison | {0} | {1}'.format(
constit[cc], run_name),
fontsize=9)
a.set_xlabel("Model Amplitude (m)")
a.set_ylabel("Observed Amplitude (m)")
fn = "ssh_hourly_scatter_amp_{0}_{1}{2}".format(
constit[cc], run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# PHASE SCATTER
f, a = pu.scatter_with_fit(stats.pha_mod[:, cc], stats.pha_obs[:,
cc])
a.set_title('Phase Comparison | {0} | {1}'.format(
constit[cc], run_name),
fontsize=9)
a.set_xlabel("Model Phase(deg)")
a.set_ylabel("Observed Phase (deg)")
fn = "ssh_hourly_scatter_pha_{0}_{1}{2}".format(
constit[cc], run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
### CLIM VAR ERROR
# Jan
m_ind = [0, 3, 6, 9]
m_str = ['Jan', 'Mar', 'Jul', 'Oct']
n_m = len(m_ind)
for mm in range(0, n_m):
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.ntr_mod_clim_var[:, m_ind[mm]] -
stats.ntr_obs_clim_var[:, m_ind[mm]],
vmin=-.05,
vmax=.05,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='seismic')
f.colorbar(sca)
a.set_title(
'Climatological Variance Error | {1} | var({0}) - var(Obs)'.
format(run_name, m_str[mm]),
fontsize=9)
fn = "ssh_hourly_clim_var_error_{0}_{1}{2}".format(
m_str[mm], run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
### FITTED SCATTER PLOTS
# Plot st.dev comparisons
f, a = pu.scatter_with_fit(stats.skew_mod, stats.skew_obs)
a.set_title('Skew Surge Comparison | {0}'.format(run_name), fontsize=9)
a.set_xlabel("{0} SSH st. dev (m)".format(run_name))
a.set_ylabel("PSMSL SSH st. dev (m)")
fn = "ssh_hourly_skew_scatter_{0}{1}".format(run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
return
def __init__(self,
fn_monthly_ssh_stats0,
fn_monthly_ssh_stats1,
dn_out,
run_names,
file_type='.png'):
stats0 = xr.open_dataset(fn_monthly_ssh_stats0)
stats1 = xr.open_dataset(fn_monthly_ssh_stats1)
lonmax = np.max(stats0.longitude)
lonmin = np.min(stats0.longitude)
latmax = np.max(stats0.latitude)
latmin = np.min(stats0.latitude)
lonbounds = [lonmin - 4, lonmax + 4]
latbounds = [latmin - 4, latmax + 4]
# Plot correlations
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats0.longitude,
stats0.latitude,
c=stats0.corr - stats1.corr,
vmin=-.03,
vmax=.03,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='PiYG')
f.colorbar(sca)
a.set_title(
"SSH correlation difference {1} - {0} (m)\n Correlations between monthly mean model SSH and PSMSL"
.format(run_names[1], run_names[0]),
fontsize=9)
fn = "ssh_diff_corr_{0}_{1}{2}".format(run_names[0], run_names[1],
file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# Plot std_err
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats0.longitude,
stats0.latitude,
c=np.abs(stats1.std_err) - np.abs(stats0.std_err),
vmin=-.01,
vmax=.01,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='PiYG')
f.colorbar(sca)
a.set_title(
'Absolute St. Dev Error Difference {0} - {1} (m) \n Error between monthly mean model SSH and PSMSL'
.format(run_names[1], run_names[0]),
fontsize=9)
fn = "ssh_diff_stderr_{0}_{1}{2}".format(run_names[0], run_names[1],
file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# Plot mae
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats0.longitude,
stats0.latitude,
c=stats1.mae - stats0.mae,
vmin=-.01,
vmax=.01,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='PiYG')
f.colorbar(sca)
a.set_title(
'MAE Difference {0} - {1} (m) \n Error between monthly mean model SSH and PSMSL'
.format(run_names[1], run_names[0]),
fontsize=9)
fn = "ssh_diff_mae_{0}_{1}{2}".format(run_names[0], run_names[1],
file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
def __init__(self,
fn_ssh_hourly_stats,
dn_out,
run_name,
file_type='.png'):
stats = xr.open_dataset(fn_ssh_hourly_stats)
lonmax = np.max(stats.longitude)
lonmin = np.min(stats.longitude)
latmax = np.max(stats.latitude)
latmin = np.min(stats.latitude)
lonbounds = [lonmin - 4, lonmax + 4]
latbounds = [latmin - 4, latmax + 4]
### GEOGRAPHICAL SCATTER PLOTS
# Plot correlations
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.ntr_corr,
vmin=.85,
vmax=1,
edgecolors='k',
linewidths=.5,
zorder=100)
f.colorbar(sca)
a.set_title(
'Hourly NTR Correlations with Tide Gauge | {0}'.format(run_name),
fontsize=9)
fn = "ssh_hourly_ntr_correlations_" + run_name + file_type
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# Plot std_err
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.std_err,
vmin=-.15,
vmax=.15,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='seismic')
f.colorbar(sca)
a.set_title('Hourly SSH St. Dev Error (m) | {0}'.format(run_name),
fontsize=9)
fn = "ssh_hourly_stderr_" + run_name + file_type
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# Plot mae
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.ntr_mae,
vmin=-.05,
vmax=.05,
edgecolors='k',
linewidths=.5,
zorder=100)
f.colorbar(sca)
a.set_title('Hourly NTR MAE (m) | {0}'.format(run_name), fontsize=9)
fn = "ssh_hourly_ntr_mae_" + run_name + file_type
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
### HARMONIC ERRORS
constit = stats.constituent.values
n_constit = len(constit)
for cc in range(0, n_constit):
# AMPLITUDE MAP
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.amp_err[:, cc],
vmin=-.2,
vmax=.2,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='seismic')
f.colorbar(sca)
a.set_title('Amplitude Error (m) | {1} - Obs | {0}'.format(
constit[cc], run_name),
fontsize=9)
fn = "ssh_hourly_amp_err_{0}_{1}{2}".format(
constit[cc], run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# PHASE MAP
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.pha_err[:, cc],
vmin=-15,
vmax=15,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='seismic')
f.colorbar(sca)
a.set_title('Phase Error (deg) | {1} - Obs | {0}'.format(
constit[cc], run_name),
fontsize=9)
fn = "ssh_hourly_pha_err_{0}_{1}{2}".format(
constit[cc], run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# AMPLITUDE SCATTER
f, a = pu.scatter_with_fit(stats.amp_mod[:, cc], stats.amp_obs[:,
cc])
a.set_title('Amplitude Comparison | {0} | {1}'.format(
constit[cc], run_name),
fontsize=9)
a.set_xlabel("Model Amplitude (m)")
a.set_ylabel("Observed Amplitude (m)")
fn = "ssh_hourly_scatter_amp_{0}_{1}{2}".format(
constit[cc], run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# PHASE SCATTER
f, a = pu.scatter_with_fit(stats.pha_mod[:, cc], stats.pha_obs[:,
cc])
a.set_title('Phase Comparison | {0} | {1}'.format(
constit[cc], run_name),
fontsize=9)
a.set_xlabel("Model Phase(deg)")
a.set_ylabel("Observed Phase (deg)")
fn = "ssh_hourly_scatter_pha_{0}_{1}{2}".format(
constit[cc], run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
### CLIM VAR ERROR
# Jan
m_ind = [0, 3, 6, 9]
m_str = ['Jan', 'Mar', 'Jul', 'Oct']
n_m = len(m_ind)
for mm in range(0, n_m):
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.ntr_mod_clim_var[:, m_ind[mm]] -
stats.ntr_obs_clim_var[:, m_ind[mm]],
vmin=-.05,
vmax=.05,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='seismic')
f.colorbar(sca)
a.set_title(
'Climatological Variance Error | {1} | var({0}) - var(Obs)'.
format(run_name, m_str[mm]),
fontsize=9)
fn = "ssh_hourly_clim_var_error_{0}_{1}{2}".format(
m_str[mm], run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
### FITTED SCATTER PLOTS
# Plot st.dev comparisons
f, a = pu.scatter_with_fit(stats.skew_mod.values.flatten(),
stats.skew_obs.values.flatten())
a.set_title('Skew Surge Comparison | {0}'.format(run_name), fontsize=9)
a.set_xlabel("Model Skew Surge (m)".format(run_name))
a.set_ylabel("Observed Skew Surge (m)")
a.set_xlim(-3, 3)
a.set_ylim(-3, 3)
a.set_title(
"Comparison of modelled and observed skew surges | {0}".format(
run_name))
fn = "ssh_hourly_skew_scatter_{0}{1}".format(run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# THRESHOLD PLOTS
prop = stats.thresh_freq_ntr_mod / stats.thresh_freq_ntr_obs
plot_thresh = [5, 10, 15]
for pp in range(0, len(plot_thresh)):
prop_tmp = prop.isel(threshold=plot_thresh[pp])
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=prop_tmp,
vmin=0,
vmax=2,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='seismic')
f.colorbar(sca)
a.set_title(
'Number of model NTR peaks as a proportion of observed NTR peaks \n Threshold = {0}m | {1}'
.format(prop_tmp.threshold.values, run_name),
fontsize=9)
fn = "thresh_freq_{0}_{1}{2}".format(prop_tmp.threshold.values,
run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
prop = stats.thresh_int_ntr_mod / stats.thresh_int_ntr_obs
plot_thresh = [5, 10, 15]
for pp in range(0, len(plot_thresh)):
prop_tmp = prop.isel(threshold=plot_thresh[pp])
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=prop_tmp,
vmin=0,
vmax=2,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='seismic')
f.colorbar(sca)
a.set_title(
'Model hours over threshold as a proportion of observed time \n Threshold = {0}m | {1}'
.format(prop_tmp.threshold.values, run_name),
fontsize=9)
fn = "thresh_int_{0}_{1}{2}".format(prop_tmp.threshold.values,
run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
def __init__(self,
fn_monthly_ssh_stats,
dn_out,
run_name,
file_type='.png'):
stats = xr.open_dataset(fn_monthly_ssh_stats)
lonmax = np.max(stats.longitude)
lonmin = np.min(stats.longitude)
latmax = np.max(stats.latitude)
latmin = np.min(stats.latitude)
lonbounds = [lonmin - 4, lonmax + 4]
latbounds = [latmin - 4, latmax + 4]
# Plot correlations
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.corr,
vmin=.75,
vmax=1,
edgecolors='k',
linewidths=.5,
zorder=100)
f.colorbar(sca)
a.set_title(
'SSH correlations \n Monthly mean PSMSL tide gauge vs {0} \n Mean Corr = {1}'
.format(run_name, np.nanmean(stats.corr)),
fontsize=9)
fn = "ssh_monthly_correlations_{0}{1}".format(run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# Plot std_err
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.std_err,
vmin=-.075,
vmax=.075,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='seismic')
f.colorbar(sca)
a.set_title(
'Standard Deviation Difference (m) \n Monthly mean {0} - PSMSL Tide Gauge \n Mean err = {1}'
.format(run_name, np.nanmean(stats.std_err)),
fontsize=9)
fn = "ssh_monthly_stderr_{0}{1}".format(run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# Plot me
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.me,
vmin=-.05,
vmax=.05,
edgecolors='k',
linewidths=.5,
zorder=100,
cmap='seismic')
f.colorbar(sca)
a.set_title(
'Mean Error (m) \n Monthly mean {0} - PSMSL Tide Gauge \n Mean ME = {1}'
.format(run_name, np.nanmean(stats.me)),
fontsize=9)
fn = "ssh_monthly_me_{0}{1}".format(run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# Plot mae
f, a = pu.create_geo_axes(lonbounds, latbounds)
sca = a.scatter(stats.longitude,
stats.latitude,
c=stats.mae,
vmin=0,
vmax=.05,
edgecolors='k',
linewidths=.5,
zorder=100)
f.colorbar(sca)
a.set_title(
'Mean Absolute Error (m) \n Monthly mean {0} - PSMSL Tide Gauge \n Mean MAE = {1}'
.format(run_name, np.nanmean(stats.mae)),
fontsize=9)
fn = "ssh_monthly_mae_{0}{1}".format(run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')
# Plot st.dev comparisons
f, a = pu.scatter_with_fit(stats.std_mod, stats.std_obs)
a.set_title(
'Monthly SSH standard deviation comparison (m) \n {0} vs {1}'.
format(run_name, 'PSMSL'),
fontsize=9)
a.set_xlabel("{0} SSH st. dev (m)".format(run_name))
a.set_ylabel("PSMSL SSH st. dev (m)")
fn = "ssh_monthly_stdev_scatter_{0}{1}".format(run_name, file_type)
f.savefig(os.path.join(dn_out, fn))
plt.close('all')