debug=True)
# Calculate autocorrelation
kmonth = hclim[o, a, :].argmax() # kmonth is the INDEX of the mongth
autocorr = {}
for model in range(4):
# Get the data
tsmodel = sst[model]
tsmodel = proc.year2mon(tsmodel) # mon x year
# Deseason (No Seasonal Cycle to Remove)
tsmodel2 = tsmodel - np.mean(tsmodel, 1)[:, None]
# Plot
autocorr[model] = proc.calc_lagcovar(tsmodel2, tsmodel2, lags, kmonth + 1,
0)
#%% Make Test Plot
fig, ax = plt.subplots(1, 1)
#ax.plot(lags,AVG0,color='b',label='CESM1 (YO)')
ax.plot(lags, AVG1, color='c', label='No-Entrain (YO)')
ax.plot(lags, AVG2, color='g', label='Entrain (YO)')
ax.plot(lags, autocorr[2], '-r', label='No-Entrain (GL)')
ax.plot(lags, np.roll(autocorr[3], 0), ':r', label='Entrain (GL)')
ax.legend()
titlestr = 'SST Autocorrelation; Month' + str(
kmonth + 1) + '\n Lon: ' + "-30" + ' Lat: ' + "50"
ax.set(xlabel='Lag (months)',
kmonth = mldcycle[klon,klat,...].argmax()
print(kmonth)
acs =scm.calc_autocorr(sstall,lags,kmonth+1)
xtk2 = np.arange(0,37,2)
# Plot
fig,ax = plt.subplots(1,1)
title = "Autocorrelation, Lag 0 = %s,\n Location: %s" % (mons3[kmonth],loctitle)
ax,ax2 = viz.init_acplot(kmonth,xtk2,lags,ax=ax,title=title)
ax.plot(lags,acs[0],lw=0.85,label="Flux Integration with Q Corretion",color='orange')
ax.plot(lags,acs[1],ls='dashed',lw=0.85,label="Flux Integration",color='b')
ax.plot(lags,acs[2],ls='dashed',lw=0.85,label="Flux Integration, Shifted Forcing",color='g')
ax.plot(lags,slabac,label="CESM-SLAB, Actual Correlation",color='k')
ax.plot(lags,acnoenso,label="CESM-SLAB,ENSO Removed",color='r')
ax.set_ylabel('Correlation')
ax.set_xlabel('Lags (months)')
ax.legend()
plt.savefig(outfigpath + "CESM_SLAB_FluxIntegration_Point%s_detrend_10x.png" % (locfn),dpi=200)
#%% Calculate Autocorrelation but for point without enso
# Get variable
tspoint = tsenso0[klon360,klat,:]
tspoint = tspoint.reshape(int(10776/12),12).T
tspoint = tspoint-tspoint.mean(1)[:,None]
# Calculate Lag correlation
acnoenso = proc.calc_lagcovar(tspoint,tspoint,lags,kmonth+1,0)
kmonth = mld.argmax()
acall = {}
for i,applyfac in enumerate(applyfacs):
ac = {}
for m in range(4):
sst = ssts[i][m]
sst = np.roll(sst,-1) # Roll since first month is feb (use jan, no-entrain 1d)
tsmodel = proc.year2mon(sst) # mon x year
# Deseason
tsmodel2 = tsmodel - np.mean(tsmodel,1)[:,None]
# Compute autocorrelation and save data for region
ac[m] = proc.calc_lagcovar(tsmodel2,tsmodel2,lags,kmonth+1,0)
acall[i] = ac.copy()
#%% Plot autocorrelation (Plot by Model)
applyfaclab = ["Forcing Only","Incl. Seasonal MLD","Incl. Seasonal MLD and Integration Factor"]
modelname = ("MLD Fixed","MLD Max", "MLD Seasonal", "MLD Entrain")
mons3=('Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec')
monname=('January','February','March','April','May','June','July','August','September','October','November','December')
mons3tile = np.tile(np.array(mons3),int(np.floor(len(lags)/12)))
mons3tile = np.roll(mons3tile,-kmonth)
outfigpath = "/Users/gliu/Downloads/02_Research/01_Projects/01_AMV/02_stochmod/02_Figures/20201123_AMVTele/"
lat = pz['lat']
# Find values at point
lonf,latf = point
klon,klat = proc.find_latlon(lonf,latf,lon,lat)
# Get value at point
sst = TS[:,klat,klon]
tsmodel = proc.year2mon(sst) # mon x year
# Deseason
tsmodel2 = tsmodel - np.mean(tsmodel,1)[:,None]
# Compute autocorrelation and save data for region
ac = proc.calc_lagcovar(tsmodel2,tsmodel2,lags,kmonth+1,0)
# Save file
locstring = "pointlon%i_lat%i"%(point[0],point[1])
np.save("%sCESM-SLAB_PIC_autocorrelation_%s.npy"%(outpath,locstring),ac)
# # Flux Calculation options
# monwin = 3 # Window of months
# lags = [1,2,3] # Lags to include
# flux = 'NHFLX'
#%% Calculate regional averages
# Regional Analysis Settings
def scm_synt(hclim,
dampingr,
NAO1,
randts,
lags,
applyfac,
T0=0,
returncomponents=False):
t_end = randts.shape[0] # Get simulation length
# ----------------------------
# % Set-up damping parameters and kprev
# ----------------------------
# Converts damping parameters from raw form (Watts/m2) to (deg/sec)
# Also calculates beta and FAC
# Note: Consider combining with NAO Forcing conversion?
# Find Kprev
kpreva, _ = scm.find_kprev(hclim)
viz.viz_kprev(hclim, kpreva)
lbd, lbd_entr, FAC, beta = scm.set_stochparams(hclim,
dampingr,
dt,
ND=False,
rho=rho,
cp0=cp0,
hfix=hfix)
"""
Out Format:
lbd -> Dict (keys 0-3) representing each mode, damping parameter
lbd_entr -> array of entrainment damping
FAC -> Dict (keys 0-3) representing each model, integration factor
beta ->array [Mon]
kprev -> array [Mon]
"""
# ----------------------------
# % Set Up Forcing ------------------------------------------------
# ----------------------------
# Convert NAO from W/m2 to degC/sec. Returns dict with keys 0-2 (same as scm.convert_NAO)
NAOF = {}
conversionfac = dt / cp0 / rho
if applyfac == 0: # Dont apply MLD to NAOF
for hi in range(3):
NAOF[hi] = NAO1 * conversionfac
else: # Apply each MLD case to NAOF
NAOF[0] = NAO1 * conversionfac / hfix
NAOF[1] = NAO1 * conversionfac / hclim.max()
NAOF[2] = NAO1 * conversionfac / hclim
# Use random time series to scale the forcing pattern
F = {}
tilecount = int(12 / NAOF[0].shape[0] * nyr)
for hi in range(3):
F[hi] = np.tile(NAOF[hi].squeeze(), tilecount) * randts * fscale
# Save Forcing if option is set
if saveforcing == 1:
np.save(output_path + "stoch_output_%s_Forcing.npy" % (runid), F)
"""
Output:
F - dict (keys = 0-2, representing each MLD treatment) [time (simulation length)]
Fseas - dict (keys = 0-2, representing each MLD treatment) [ month]
"""
# ----------
# %RUN MODELS -----------------------------------------------------------------
# ----------
# Set mulFAC condition based on applyfac
if applyfac == 2:
multFAC = 1 # Don't apply integrationreduction factor if applyfac is set to 0 or 1
else:
multFAC = 0
#% Run Models <SET>
"""
Inputs:
FAC - Dict of Integration Factors (0-3)
lbd - Dict of Damping Parameters (0-3)
F - Dict of Forcings (0-2)
kprev - Array/Vector of Entraining Months
beta - Array/Vector of Entrainment velocities
hclima - Array/Vector of MLD cycle
Fixed Params: T0 (Initial SST), t_end (end timestep), multFAC (0 or 1)
Output
sst - Dict of model output (0-3)
"""
# Preallocate dictionary to store results (Use tuple instead?)
sst = {}
# Run Model Without Entrainment
# Loop for each Mixed Layer Depth Treatment
for hi in range(3):
start = time.time()
# Select damping and FAC based on MLD
FACh = FAC[hi]
lbdh = lbd[hi]
# Select Forcing
Fh = F[hi]
# Run Point Model
start = time.time()
sst[hi], _, _ = scm.noentrain(t_end,
lbdh,
T0,
Fh,
FACh,
multFAC=multFAC)
elapsed = time.time() - start
tprint = "\nNo Entrain Model, hvarmode %i, ran in %.2fs" % (hi,
elapsed)
print(tprint)
# Run Model With Entrainment
start = time.time()
Fh = F[2]
FACh = FAC[3]
sst[3] = scm.entrain(t_end,
lbd[3],
T0,
Fh,
beta,
hclim,
kpreva,
FACh,
multFAC=multFAC)
elapsed = time.time() - start
tprint = "\nEntrain Model ran in %.2fs" % (elapsed)
print(tprint)
# Calculate Autocorrelation
kmonth = hclim.argmax() # kmonth is the INDEX of the mongth
autocorr = {}
for model in range(4):
# Get the data
tsmodel = sst[model]
tsmodel = proc.year2mon(tsmodel) # mon x year
# Deseason (No Seasonal Cycle to Remove)
tsmodel2 = tsmodel - np.mean(tsmodel, 1)[:, None]
# Plot
autocorr[model] = proc.calc_lagcovar(tsmodel2, tsmodel2, lags,
kmonth + 1, 0)
if returncomponents:
return sst, autocorr, lbd, FAC, beta, kpreva, F
return sst, autocorr