lat = np.flip(lat)
vanom = np.flip(vanom, axis=1)
# Detrend the variable (taken from calc_amv_hadisst.py)
# ----------------------------------------------------
start = time.time()
indata = vanom.reshape(nmon, nlat * nlon).T # Transpose to [Space x Time]
okdata, knan, okpts = proc.find_nan(indata, 1)
x = np.arange(0, nmon, 1)
if detrend == 0:
# Compute global weighted average
glomean = proc.area_avg(vanom.transpose(2, 1, 0), [0, 360, -90, 90],
lon, lat, 1)
# Regress back to the original data to get the global component
beta, b = proc.regress_2d(glomean, okdata)
# Subtract this from the original data
okdt = okdata - beta[:, None]
else:
# Polynomial Detrend
okdt, model = proc.detrend_poly(x, okdata, detrend)
if debug:
fig, ax = plt.subplots(1, 1)
ax.scatter(x, okdata[44, :], label='raw')
ax.plot(x, model[44, :], label='fit')
ax.scatter(x, okdt[:, 44], label='dt')
ax.set_title("Visualize Detrending Method %i" % detrend)
#okdt = okdt.T
data_dt = np.zeros((nmon, nlat * nlon)) * np.nan
usedtfunction = 1 # Set to 1 to use the new detrending function
if usedtfunction == 1:
start = time.time()
dt_hsst, ymodall, _, _ = proc.detrend_dim(hsstnew, 2)
print("Detrended in %.2fs" % (time.time() - start))
else:
# Reshape to [Time x Space] and remove NaN Points
start = time.time()
hsstnew = np.reshape(hsstnew, (360 * 180, 1176)).T
hsstok, knan, okpts = proc.find_nan(hsstnew, 0)
tper = np.arange(0, hsstok.shape[0])
beta, b = proc.regress_2d(tper, hsstok) # Perform regression
# Detrend
dt_hsst = hsstnew[:, okpts] - (beta[:, None] * tper + b[:, None]).T
# Replace NaN vaues back into the system
hsstall = np.zeros(hsstnew.shape) * np.nan
hsstall[:, okpts] = dt_hsst
# Also save the linear model
ymodall = np.zeros(hsstnew.shape) * np.nan
ymodall[:, okpts] = (beta[:, None] * tper + b[:, None]).T
# Reshape again
dt_hsst = np.reshape(hsstall.T, (360, 180, 1176))
hsstnew = np.reshape(hsstnew.T, (360, 180, 1176))
# Get dimension sizes
nens, nyr, nlat, nlon = var.shape
npc = pcall.shape[2]
# Combine lat and lon dimensions
var = np.reshape(var, (nens, nyr, nlat * nlon))
#%% Regress for each mode and ensemble membe
varpattern = np.zeros((npc, nens, nlat * nlon))
for n in range(npc):
for e in range(nens):
pcin = pcall[e, :, n]
datain = var[e, ...]
varpattern[n, e, :], _ = proc.regress_2d(pcin, datain)
msg = '\rCompleted Regression for PC %02i/%02i, ENS %02i/%02i' % (
n + 1, npc, e + 1, nens)
print(msg, end="\r", flush=True)
# Reshape variable [pc, ens, lat, lon]
varout = np.reshape(varpattern, (npc, nens, nlat, nlon))
F = np.copy(varout)
outname = datpath + "NAO_EAP_%s_ForcingDJFM.npy" % flux
np.save(outname, F)
flx = flx[:, [-1, 0, 1, 2], :].mean(1) # Select DJFM
# Restrict PC (drop first 2 years due to ensolag + 3monwin, and end year due to 3monwin)
pcin = pcall[2:-1, :]
# Standardize PC
pcstd = pcin / np.std(pcin, 0)
# Preallocate
pattern = np.zeros((nlat * nlon, N_mode)) # [space x pc]
for p in range(N_mode):
pc = pcstd[:, p]
pattern[:, p], _ = proc.regress_2d(pc, flx, nanwarn=1)
print("Regressed nhflx to PC %i" % (p + 1))
pattern = pattern.reshape(nlat, nlon, N_mode)
if debug:
plt.pcolormesh(lon, lat, pattern[:, :, 0],
cmap=cmap), plt.colorbar(), plt.title("EOF 1"), plt.show()
#%% Do the same for PSL
# Read out psl values
psl_all = dsall.PSL.values
# Calculate DJFM Mean
ntime, nlat, nlon = psl_all.shape
okdata, knan, okpts = proc.find_nan(varens, 1)
okdatap, knanp, okptsp = proc.find_nan(varglo, 1)
#% Perform EOF -------------
_, pcs, varexpall[e, :] = proc.eof_simple(okdata, N_mode, 1)
# Standardize pc before regression along the time dimension
pcstd = np.squeeze(pcs / np.std(pcs, 0))
#Loop for each mode... (NOte, can vectorize this to speed it up,.)
psleofs = np.ones((192, 288, 3))
for pcn in range(N_mode):
# Regress back to SLP
eofpatokp, _ = proc.regress_2d(pcstd[:, pcn], okdatap, nanwarn=0)
# Reshape regression pattern and put back (for psl)
eofpatp = np.ones((192 * 288)) * np.nan
eofpatp[okptsp] = eofpatokp
eofpatp = np.reshape(eofpatp, (192, 288))
# ----- Check Sign start -----
if pcn == 0:
# Check the signs and flip if necessary
# EOF 1 only (need to figure out the other EOFs..)
# First check using reykjavik
rboxlon = np.where((lon >= lonReykjavik - tol)
& (lon <= lonReykjavik + tol))[0]
rboxlat = np.where((lat >= latReykjavik - tol)
& (lat <= latReykjavik + tol))[0]