def main(args):
#environmental constants
if platform.system() == 'Windows':
in_dir='../examples/'
out_dir='../regressors/'
reg_dir='../regressors/'#${in_dir}'regresory_2013/'
nc_gen=True
pdf_gen=False
plus = ''
else:
n_samples = int(os.environ['n_samples'])
in_dir = os.environ['in_dir']
#out_dir = os.environ['out_dir']
reg_dir = os.environ['reg_dir']
pdf_gen = os.environ['pdf_gen']
nc_gen = os.environ['nc_gen']
what_re = args.what_re
vari = args.vari
i_year = args.i_year
s_year = args.s_year
e_year = args.e_year
in_file_name = args.in_file_name
if args.verbose:
print('dataset: ', what_re)
print('variable: ', vari)
print('initial year of dataset: ', i_year)
print('initial year of analysis: ', s_year)
print('end year of analysis: ', e_year)
print('input filename: ', in_file_name)
print('data opening')
in_netcdf = in_dir + in_file_name
print(in_netcdf)
ds = xr.open_dataset(in_netcdf)
#print(ds)
lat_name = fce.get_coords_name(ds, 'latitude')
lat = ds.coords[lat_name]
nlat = lat.shape[0]
lev_name = fce.get_coords_name(ds, 'pressure')
if ds.coords[lev_name].attrs['units'] == 'Pa':
lev = ds.coords[lev_name]/100.
ds[lev_name] = lev
else:
lev = ds.coords[lev_name]
n = ds.coords['time'].shape[0]
#it may happen that the field is 3D (longitude is missing)
try:
lon_name = fce.get_coords_name(ds, 'longitude')
lon = ds.coords[lon_name]
nlon = lon.shape[0]
except:
nlon = 1
#print nlat, nlev, n, nlon
#zonal mean
if nlon != 1:
uwnd = ds[vari].mean(lon_name)
else:
uwnd = ds[vari]
#equatorial average and level selection
sel_dict = {lev_name: fce.coord_Between(lev,10,50), lat_name: fce.coord_Between(lat,-10,10)}
zm_u = uwnd.sel(**sel_dict).mean(lat_name)
#period selection
times = pd.date_range(str(s_year)+'-01-01', str(e_year)+'-12-31', name='time', freq = 'M')
zm_u_sel = zm_u.sel(time = times, method='ffill') #nearest
#remove seasonality
climatology = zm_u_sel.groupby('time.month').mean('time')
anomalies = zm_u_sel.groupby('time.month') - climatology
#print anomalies
#sys.exit()
#additional constants
npca = 30
norm=2 #5
norms=3 #5
what_sp = '' # what solar proxy?
print("regressors' openning")
global reg#, reg_names, nr
reg, reg_names, history = fce.configuration_ccmi(what_re, what_sp, norm, 'no_qbo' , i_year, s_year, e_year, reg_dir)
nr = reg.shape[1]
#print(anomalies)
#extracting of other variability by MLR
stacked = anomalies.stack(allpoints = [lev_name])
stacked = stacked.reset_coords(drop=True)
resids = stacked.groupby('allpoints').apply(xr_regression)
resids = resids.rename({'dim_0': 'time'})
resids['time'] = times
#EOF analysis
solver = Eof(resids.T, weights=None)
#sys.exit()
#coslat = np.cos(np.deg2rad(lat)).clip(0.,1.)
#wgts = np.sqrt(coslat)[np.newaxis,...]
for i in range(npca):
var_eofs = solver.varianceFraction(neigs=i)
#print var_eofs
if np.sum(var_eofs) > 0.95:
npca=i
total_variance = np.sum(var_eofs)
print(total_variance, ' % based on ', i, ' components')
break
var_eofs = solver.varianceFraction(neigs=npca)
pcs = solver.pcs(npcs=npca, pcscaling=1)
nte = solver.northTest(neigs=npca, vfscaled=True)
subdir = './'
if pdf_gen:
fig = plt.figure(figsize=(11,8))
ax1 = fig.add_subplot(111)
ax1.set_title(str(npca)+' PCAs cover '+str(np.round(total_variance*100, 2))+'% of total variance')
for i in xrange(npca):
#plotting
pcs[:,i].plot(linewidth = 2, ax = ax1, label = 'pca '+str(i+1))
ax1.set_xlabel('time [years]')
ax1.set_ylabel('QBO index')
ax1.set_title('')
ax1.legend(loc = 'best')
plt.savefig(reg_dir+'qbo_'+what_re+'_pcas.pdf', bbox_inches='tight')
plt.close(fig)
if nc_gen:
#save to netcdf
#print(pcs[:,0])
for i in range(npca):
pcs_ds = pcs[:,i].to_dataset(name = 'index')
pcs_ds.to_netcdf(reg_dir+r'qbo_'+what_re+'_pc'+str(i+1)+pripona_nc)
'''
If *True*, the mean along the first axis of *dataset* (the
time-mean) will be removed prior to analysis. If *False*,
the mean along the first axis will not be removed. Defaults
to *True* (mean is removed).
The covariance interpretation relies on the input data being
anomaly data with a time-mean of 0. Therefore this option
should usually be set to *True*. Setting this option to
*True* has the useful side effect of propagating missing
values along the time dimension, ensuring that a solution
can be found even if missing values occur in different
locations at different times.
'''
lambdas = solver.eigenvalues()
vf = solver.varianceFraction()
Nerror = solver.northTest(vfscaled=True)
pcs = solver.pcs() #(time, mode)
eofs = solver.eofsAsCovariance()
'''
plt.figure()
plt.subplot(3,2,1)
pcs[:, 0].plot()#color='b', linewidth=2)
ax = plt.gca()
ax.axhline(0, color='k')
ax.set_xlabel('Year')
ax.set_ylabel('PC1 amplitude')
plt.grid()
plt.subplot(3,2,2)
pcs[:, 1].plot()
ax = plt.gca()
ax.axhline(0, color='k')
for lo in range(0,len(lon_obs)):
valid = ~np.isnan(sst_obs[:,la,lo])
if (valid.any()==True):
sst_obs[:,la,lo] = signal.detrend(sst_obs[:,la,lo], axis=0, \
type='linear')
elif (valid.all()==False):
sst_obs[:,la,lo] = np.nan
'''
# EOF for model
coslat_mdl = np.cos(np.deg2rad(sst_mdl.coords['lat'].values))
wgts_mdl = np.sqrt(coslat_mdl)[..., np.newaxis]
solver_mdl = Eof(sst_mdl, weights=wgts_mdl, center=True)
lambdas_mdl=solver_mdl.eigenvalues()
vf_mdl = solver_mdl.varianceFraction()
Nerror_mdl = solver_mdl.northTest(vfscaled=True)
pcs_mdl = solver_mdl.pcs() #(time, mode)
eofs_mdl = solver_mdl.eofs()
# EOF for obs
coslat_obs = np.cos(np.deg2rad(sst_obs.coords['lat'].values))
wgts_obs = np.sqrt(coslat_obs)[..., np.newaxis]
solver_obs = Eof(sst_obs, weights=wgts_obs, center=True)
lambdas_obs=solver_obs.eigenvalues()
vf_obs = solver_obs.varianceFraction()
Nerror_obs = solver_obs.northTest(vfscaled=True)
pcs_obs = solver_obs.pcs() #(time, mode)
eofs_obs = solver_obs.eofs()
def main(args):
#environmental constants
if platform.system() == 'Windows':
in_dir = '../examples/'
out_dir = '../regressors/'
reg_dir = '../regressors/' #${in_dir}'regresory_2013/'
nc_gen = True
pdf_gen = False
plus = ''
else:
n_samples = int(os.environ['n_samples'])
in_dir = os.environ['in_dir']
#out_dir = os.environ['out_dir']
reg_dir = os.environ['reg_dir']
pdf_gen = os.environ['pdf_gen']
nc_gen = os.environ['nc_gen']
what_re = args.what_re
vari = args.vari
i_year = args.i_year
s_year = args.s_year
e_year = args.e_year
in_file_name = args.in_file_name
if args.verbose:
print('dataset: ', what_re)
print('variable: ', vari)
print('initial year of dataset: ', i_year)
print('initial year of analysis: ', s_year)
print('end year of analysis: ', e_year)
print('input filename: ', in_file_name)
print('data opening')
in_netcdf = in_dir + in_file_name
print(in_netcdf)
ds = xr.open_dataset(in_netcdf)
#print(ds)
lat_name = fce.get_coords_name(ds, 'latitude')
lat = ds.coords[lat_name]
nlat = lat.shape[0]
lev_name = fce.get_coords_name(ds, 'pressure')
if ds.coords[lev_name].attrs['units'] == 'Pa':
lev = ds.coords[lev_name] / 100.
ds[lev_name] = lev
else:
lev = ds.coords[lev_name]
n = ds.coords['time'].shape[0]
#it may happen that the field is 3D (longitude is missing)
try:
lon_name = fce.get_coords_name(ds, 'longitude')
lon = ds.coords[lon_name]
nlon = lon.shape[0]
except:
nlon = 1
#print nlat, nlev, n, nlon
#zonal mean
if nlon != 1:
uwnd = ds[vari].mean(lon_name)
else:
uwnd = ds[vari]
#equatorial average and level selection
sel_dict = {
lev_name: fce.coord_Between(lev, 10, 50),
lat_name: fce.coord_Between(lat, -10, 10)
}
zm_u = uwnd.sel(**sel_dict).mean(lat_name)
#period selection
times = pd.date_range(str(s_year) + '-01-01',
str(e_year) + '-12-31',
name='time',
freq='M')
zm_u_sel = zm_u.sel(time=times, method='ffill') #nearest
#remove seasonality
climatology = zm_u_sel.groupby('time.month').mean('time')
anomalies = zm_u_sel.groupby('time.month') - climatology
#print anomalies
#sys.exit()
#additional constants
npca = 30
norm = 2 #5
norms = 3 #5
what_sp = '' # what solar proxy?
print("regressors' openning")
global reg #, reg_names, nr
reg, reg_names, history = fce.configuration_ccmi(what_re, what_sp, norm,
'no_qbo', i_year, s_year,
e_year, reg_dir)
nr = reg.shape[1]
#print(anomalies)
#extracting of other variability by MLR
stacked = anomalies.stack(allpoints=[lev_name])
stacked = stacked.reset_coords(drop=True)
resids = stacked.groupby('allpoints').apply(xr_regression)
resids = resids.rename({'dim_0': 'time'})
resids['time'] = times
#EOF analysis
solver = Eof(resids.T, weights=None)
#sys.exit()
#coslat = np.cos(np.deg2rad(lat)).clip(0.,1.)
#wgts = np.sqrt(coslat)[np.newaxis,...]
for i in range(npca):
var_eofs = solver.varianceFraction(neigs=i)
#print var_eofs
if np.sum(var_eofs) > 0.95:
npca = i
total_variance = np.sum(var_eofs)
print(total_variance, ' % based on ', i, ' components')
break
var_eofs = solver.varianceFraction(neigs=npca)
pcs = solver.pcs(npcs=npca, pcscaling=1)
nte = solver.northTest(neigs=npca, vfscaled=True)
subdir = './'
if pdf_gen:
fig = plt.figure(figsize=(11, 8))
ax1 = fig.add_subplot(111)
ax1.set_title(
str(npca) + ' PCAs cover ' +
str(np.round(total_variance * 100, 2)) + '% of total variance')
for i in xrange(npca):
#plotting
pcs[:, i].plot(linewidth=2, ax=ax1, label='pca ' + str(i + 1))
ax1.set_xlabel('time [years]')
ax1.set_ylabel('QBO index')
ax1.set_title('')
ax1.legend(loc='best')
plt.savefig(reg_dir + 'qbo_' + what_re + '_pcas.pdf',
bbox_inches='tight')
plt.close(fig)
if nc_gen:
#save to netcdf
#print(pcs[:,0])
for i in range(npca):
pcs_ds = pcs[:, i].to_dataset(name='index')
pcs_ds.to_netcdf(reg_dir + r'qbo_' + what_re + '_pc' + str(i + 1) +
pripona_nc)
# --- read netcdf file
dset = xr.open_dataset('asstdt_pacific.nc')
# --- select djf months
sst = dset['sst'].sel(time=np.in1d(dset['time.month'], [1, 2, 12]))
# --- square-root of cosine of latitude weights
coslat = np.cos(np.deg2rad(sst.coords['lat'].values))
wgts = np.sqrt(coslat)[..., np.newaxis]
# --- eof solver
solver = Eof(sst, weights=wgts)
# --- eof results
eofs = solver.eofsAsCorrelation(neofs=2)
pcs = solver.pcs(npcs=2, pcscaling=1)
variance_fractions = solver.varianceFraction()
north_test = solver.northTest(vfscaled=True)
# --- spatial patterns
fig, ax = plot.subplots(axwidth=5, nrows=2, tight=True, proj='pcarree',
proj_kw={'lon_0': 180})
# --- format options
ax.format(land=False, coast=True, innerborders=True, borders=True,
large='15px', labels=False,
latlim=(31, -31), lonlim=(119, 291),
geogridlinewidth=0,
abcloc='ul')
# a) first EOF mode
map1 = ax[0].contourf(dset['lon'], dset['lat'], eofs[0, :, :],
levels=np.arange(-0.5, 0.6, 0.1), cmap='Div', extend='both')
