def compute_ipo(sst_anoms, years_pass=11, N=2.0):
high = np.int(years_pass * 12.)
B, A = signal.butter(N, N / high, btype='lowpass', output='ba')
def filter_SST(x):
if any(np.isnan(x)):
z = x
else:
z = signal.filtfilt(B, A, x)
return z
sst_anoms['sst_filtered'] = (('time', 'lat', 'lon'),
np.apply_along_axis(
filter_SST, 0,
sst_anoms['sst_masked'].data))
lat = sst_anoms['lat'].values
lon = sst_anoms['lon'].values
lons, lats = np.meshgrid(lon, lat)
coslat = np.cos(np.deg2rad(lat))
wgts = np.sqrt(coslat)[..., np.newaxis]
sst_anoms.load()
X = sst_anoms['sst_filtered'].data
solver = Eof(X, weights=wgts)
eofs = solver.eofsAsCorrelation(neofs=5)
pcs = solver.pcs(npcs=5, pcscaling=1)
PCs = pd.DataFrame(pcs, index=sst_anoms['time'].to_index())
PCs_monthly = solver.projectField(sst_anoms['sst_masked'].data, 5)
PCs_monthly = pd.DataFrame(PCs_monthly,
index=sst_anoms['time'].to_index())
return eofs, PCs, lons, lats, PCs_monthly
