# End i loop
#%% Examine sea surface height values during that period
#%% Load in NAISST to see what is happening
# Left off at trying to plot AMV Index, but am having trouble matching the actual index...
ld3 = np.load("/Users/gliu/Downloads/02_Research/01_Projects/01_AMV/02_stochmod/01_Data/CESM-PIC_NASSTI.npz")
nassti = ld3['nassti_full']
# Calculate AMV Index
nassti_ann = proc.ann_avg(nassti,0)
# Apply Low Pass Filter
cutofftime = 10
order = 5
aa_sst = nassti_ann
# Design Butterworth Lowpass Filter
filtfreq = len(aa_sst)/cutofftime
nyquist = len(aa_sst)/2
cutoff = filtfreq/nyquist
b,a = butter(order,cutoff,btype="lowpass")
amv = filtfilt(b,a,aa_sst)
model = 3 # Select Model( 0:hfix || 1:hmax || 2:hvar || 3: entrain)
mon = 6
xl = [0,12000]
#yl = [-1e200,1e200]
sstpt = sst[model]
if model == 3:
Fpt = F[2]
else:
Fpt = F[model]
# Calculate Annual Averages
sstptann = proc.ann_avg(sstpt,0)
Fptann = proc.ann_avg(Fpt,0)
# Set plotting parameters and text
tper = np.arange(0,t_end)
yper = np.arange(0,t_end,12)
fstats = viz.quickstatslabel(Fpt)
tstats = viz.quickstatslabel(sstpt)
# Start plot
fig,ax = plt.subplots(2,1,figsize=(8,6))
plt.style.use('ggplot')
plt.subplot(2,1,1)
plt.plot(tper,Fpt)
plt.plot(yper,Fptann,color='k',label='Ann. Avg')
#%% Plot Timeseries
# -----------------------------------------------------------------
nmean = 10
# -----------------------
# CESM time plot Model Output Time Plot
# -----------------------
fig,ax=plt.subplots(1,1,figsize=(8,3))
csst = [sstfullpt,sstslabpt]
ccol = ['k','gray']
clab = ["CESM-Full","CESM-Slab"]
for i in range(2):
sstann = proc.ann_avg(csst[i],0)
win = np.ones(nmean)/nmean
sstann = np.convolve(sstann,win,mode='valid')
yrs = np.arange(0,sstann.shape[0])
plabel = clab[i] + r", 1$\sigma=%.2f$" % np.std(sstann)
ax.plot(sstann,label=plabel,lw=0.5,color=ccol[i])
print("Std for %s is %.2f"%(labels[i],np.std(sst[i])))
ax.legend(fontsize=8,ncol=3)
ax.set_xlabel("Years")
ax.set_ylabel("degC")
ax.set_title("CESM SST (Annual) at %s (%i-year Running Mean) "% (loctitle,nmean))
ax.grid(True,ls='dotted')
#ax.plot(sstq[klon,klat,:],label="Flux Integration with Q Correction",color='orange',lw=lws,alpha=0.90)
#ax.set_xlim([10276,10776])
ax.set_xlabel("Time (Months)")
ax.set_ylabel("SST (degC)")
ax.grid(True,ls='dotted')
#ax.set_ylim([-2,2])
#ax.set_xlim([0,100])
ax.legend(fontsize=8,ncol=1)
#ax.set_title("SST Integration Comparison \n Location: %s" % (loctitle))
plt.savefig(outfigpath + "CESM_SLAB_FluxIntegrationComp_Point%s_Model_Output_detrend_Quad_all.png" % (locfn),dpi=200)
#%% Do the same as above, but for annual averages
lws = 1
sstqann = proc.ann_avg(sstq[klon,klat,:],0)
sstnann = proc.ann_avg(sstn[klon,klat,:],0)
sstsann = proc.ann_avg(ssts[klon,klat,:],0)
sstslab = proc.ann_avg(tsenso0[klon360,klat,:],0)
fig,ax = plt.subplots(1,1,figsize=(12,3))
ax.plot(sstslab,label="SLAB SST",color='k',lw=lws)
ax.plot(sstnann,label="Flux Integration ",color='b',lw=lws,alpha=0.90)
ax.plot(sstsann,label="Flux Integration, Shifted Forcing",color='g',alpha=0.9)
#ax.plot(sstqann,label="Flux Integration with Q Correction",color='orange',lw=lws,alpha=0.90)
ax.set_xlabel("Time (Years)")
ax.set_xticks(np.arange(0,1000,50))
ax.set_ylabel("SST (degC)")
#ax.set_xlim([800,900])
ax.grid(True,ls='dotted')
#ax.legend(fontsize=8,ncol=3)
#%% Calculate AMV Index
amv = {}
aa = {}
annsst = {}
# Load in dat for each mode and compute amv index
for mode in hvarmode:
print(mode)
#sst[mode] = np.load(datpath+"stoch_output_1000yr_funiform%i_entrain0_hvar%i.npy" % (funiform,mode))
annsst[mode] = proc.ann_avg(sst[mode],2)
# Calculate AMV Index
amv[mode],aa[mode] = proc.calc_AMV(lon,lat,annsst[mode],bbox,order,cutofftime,1)
# # Repeat, but for forcing
# if funiform == 2:
# lpforcing = {}
# aaforcing = {}
# for mode in hvarmode:
# annforcing = ann_avg(F[mode],2)
# lpforcing[mode],aaforcing[mode] = calc_AMV(lon,lat,annforcing,bbox,order,cutofftime,1)
# else:
# annforcing = ann_avg(F,2)
plot_AMV_spatial(dtr.T, hlon, hlatnew, bbox, cmap)
plt.title("Detrended Monthly SST")
#%% AMV <Raw>
dtamv, dtaa = calc_AMV(hlon, hlatnew, hsstnew, bbox, order, cutofftime, 1)
dtr = proc.regress2ts(hsstnew, dtamv, 0, 1)
plot_AMV_spatial(dtr.T, hlon, hlatnew, bbox, cmap)
plt.title("Raw Monthly SST")
#%% Trying annually averaged data
aaraw = proc.ann_avg(hsstnew, 2)
#dtraw = ann_avg(dt_hsst,2)
#adtraw = ann_avg(ahsst,2)
# Detrend annual data
invar = ahsst
cutofftime = 120
dtamv, dtaa = calc_AMV(hlon, hlatnew, invar, bbox, order, cutofftime, 1)
dtr = proc.regress2ts(invar, dtamv / np.std(dtamv), 0, 1)
xtk = np.arange(1, 101, 10)
xlbs = np.arange(1920, 2020, 10)
#plt.subplots(211)
plot_AMV_spatial(dtr.T, hlon, hlatnew, bbox, cmap, cint=cint, clab=clab)
#plot_AMV_spatial(dtr.T,hlon,hlatnew,bbox,cmap)
#plt.title("Detrended, Deanomalized, Monthly SST")
h_amv,h_regr = proc.calc_AMVquick(hsst,hlon,hlat,bbox)
#%% Try Another Calculation Method
#Methods
# 0) Regress anomaly onto global mean
# 1...N) Remove N-degree polynomial
# Get timedim
nyr = int(hsst.shape[2]/12)
x = np.arange(0,nyr,1)
# First get annual averaged data
hsstann = proc.ann_avg(hsstnew,2)
hsstann = hsstann - hsstann.mean(2)[:,:,None]
# Get nan points
hsstann = hsstann.reshape(360*180,nyr)
okdata,knan,okpts = proc.find_nan(hsstann,1)
if method == 0:
# Calculate global mean SST
glomean = okdata.mean(0)
# 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]
ax.grid(True, which='both', ls='dotted')
#ax.set_xlabel("Frequency (cycles/sec)",fontsize=13)
ax.set_ylabel(r"Frequency x Power $(^{\circ}C)^{2}$", fontsize=13)
ax.set_title("Power Spectrum at %s \n" % (locstringtitle) +
"nsmooth=%i, taper=%.2f" % (nsmooth, pct))
plt.tight_layout()
plt.savefig("%sPowerSpectra_%s_nsmooth%i_pct%03d_axopt%i_%s.png" %
(outpath, 'COMPARISON', nsmooth, pct * 100, axopt, locstring),
dpi=200)
# --------------------------------------------------
#%% Spectral Analysis, but using annual averaged data
# --------------------------------------------------
slabann = proc.ann_avg(cesmslab, 0)
fullann = proc.ann_avg(cesmfull, 0)
sstann = []
for i in range(4):
sstann.append(proc.ann_avg(sst[i], 0))
nyr = sstann[0].shape[0]
simtime = np.arange(0, sst[0].shape[0])
years = np.arange(0, nyr)
# Check Annual Averaging
i = 3
fig, ax = plt.subplots(1, 1)
ax.plot(simtime, sst[i], color='g', lw=0.5)
ax.plot(simtime[::12], sstann[i], color='k')
ax.set_xlim([0, 120])