cmap,
pcolor=0,
ax=axs[mode])
axs[mode].set_title("MLD %s" % modelname[mode], fontsize=12)
#plt.suptitle("AMV Pattern | Forcing: %s; fscale: %ix" % (forcingname[funiform],fscale),ha='center')
#fig.tight_layout(rect=[0, 0.03, .75, .95])
outname = outpath + 'AMVpattern_%s_allmodels.png' % (expid)
plt.savefig(outname, bbox_inches="tight", dpi=200)
#%Make AMV Time Plots
xlm = [24, 240]
ylm = [-0.5, 0.5]
#xtk = np.arange(xlm[0],xlm[1]+20,20)
fig, axs = plt.subplots(1, 4, figsize=(12, 1.5))
for mode in range(4):
viz.plot_AMV(amvidx[mode], ax=axs[mode])
axs[mode].set_title("MLD %s" % modelname[mode], fontsize=12)
axs[mode].set_xlim(xlm)
#axs[mode].set_xticks(xtk)
axs[mode].set_xlabel('Year')
#axs[mode].set_ylim(ylm)
axs[0].set_ylabel('AMV Index')
#plt.suptitle("AMV Index | Forcing: %s; fscale: %ix" % (forcingname[funiform],fscale))
#fig.tight_layout(rect=[0, 0.03, 1, 0.95])
outname = outpath + 'AMVIndex_%s_allmodels.png' % (expid)
plt.savefig(outname, bbox_inches="tight", dpi=200)
#% Compute Autocorrelatioin for each region
start = time.time()
kmonths = {}
autocorr_region = {}
axs[mode].set_title("%s" % modelname[mode], fontsize=12)
#plt.suptitle("AMV Pattern | Forcing: %s; fscale: %ix" % (forcingname[funiform],fscale),ha='center')
#fig.tight_layout(rect=[0, 0.03, .75, .95])
outname = outpathfig + 'AMVpattern_%s_allmodels_region%s.png' % (
expid, regions[region])
plt.savefig(outname, bbox_inches="tight", dpi=200)
#%Make AMV Time Plots
xlm = [24, 240]
ylm = [-0.5, 0.5]
#xtk = np.arange(xlm[0],xlm[1]+20,20)
fig, axs = plt.subplots(1, 4, figsize=(12, 1.5))
for mode in range(4):
viz.plot_AMV(invar2[f, region, mode, :], ax=axs[mode])
axs[mode].set_title("MLD %s" % modelname[mode], fontsize=12)
axs[mode].set_xlim(xlm)
#axs[mode].set_xticks(xtk)
axs[mode].set_xlabel('Year')
#axs[mode].set_ylim(ylm)
axs[0].set_ylabel('AMV Index')
#plt.suptitle("AMV Index | Forcing: %s; fscale: %ix" % (forcingname[funiform],fscale))
#fig.tight_layout(rect=[0, 0.03, 1, 0.95])
outname = outpathfig + 'AMVIndex_%s_allmodels_region%s.png' % (
expid, regions[region])
plt.savefig(outname, bbox_inches="tight", dpi=200)
#%% Create individual AMV Plots
invar = amvsp.mean(0) #[5 x 4 x 4 x 97 x 117]
invar2 = amvid.mean(0)
#axs[mode].set_title("MLD %s" % modelname[mode],fontsize=12)
axs[mode].set_title("%s" % modelname[mode],fontsize=12)
outname = outpathfig+'%s_AMVpattern_%s_allmodels_vscale%i.png' % (regions[region],expid,vscale)
plt.savefig(outname, bbox_inches="tight",dpi=200)
#%Make AMV Time Plots
xlm = [24,240]
ylm = [-0.5,0.5]
#xtk = np.arange(xlm[0],xlm[1]+20,20)
fig,axs = plt.subplots(1,4,figsize=(12,1.5))
for mode in range(4):
viz.plot_AMV(amvidx[region][mode]*vscale,ax=axs[mode])
axs[mode].set_title("%s" % modelname[mode],fontsize=12)
axs[mode].set_xlim(xlm)
#axs[mode].set_xticks(xtk)
axs[mode].set_xlabel('Year')
axs[mode].set_ylim(ylm)
axs[0].set_ylabel('AMV Index')
#plt.suptitle("AMV Index | Forcing: %s; fscale: %ix" % (forcingname[funiform],fscale))
#fig.tight_layout(rect=[0, 0.03, 1, 0.95])
# <<<<<<< Updated upstream
# outname = outpathfig+'%s_AMVIndex_%s_allmodels_region%s.png' % (regions[region],expid)
# =======
#plt.tight_layout()
outname = outpathfig+'%s_AMVIndex_%s_allmodels_vscale%i.png' % (regions[region],expid,vscale)
# >>>>>>> Stashed changes
plt.savefig(outname, bbox_inches="tight",dpi=200)
#%%
# -------------------------------
# Plot AMV from the 4 experiments
# -------------------------------
xrange = [100,300]
htimeall = np.arange(0,len(amv[1]))
hticks = np.arange(0,1100,100)
fig,ax = plt.subplots(4,1,sharex=True,sharey=True,figsize=(4,8))
for mode in range(4):
plt.style.use("ggplot")
plt.subplot(4,1,mode+1)
#plt.plot(htimeall,aa[mode],color=[0.75,0.75,0.75])
viz.plot_AMV(amv[mode])
#plt.ylim([-1.5,1.5])
#plt.xticks(np.arange(0,len(amv[1])+1200,1200),hticks,fontsize=16)
#plt.yticks(np.arange(-1.5,2.0,0.5),fontsize=16)
plt.title("AMV Index for %s" % hvarnames[mode],fontsize=12)
plt.xlim(xrange)
maxval = np.max(np.abs(amv[mode]))
plt.ylim([maxval*-1,maxval])
#ax[mode+1].tick_params(labelsize=16)
plt.legend(fancybox=True,shadow=True,loc='upper center')
#leg = ax.legend([l1,l2,l3,l4],labels=regions,ncol=4,bbox_to_anchor=(0, -0.1),loc='upper left')
#leg(fancybox=True,shadow=True)
#ax.legend([l1,l2,l3,l4],labels=regions,ncol=4,bbox_to_anchor=(-0.1, 1.1),loc='upper left')
outname = '%sHadISST_AMVpattern_%s-%s_dtmethod%i_TROOnly.png' % (outpath,startyr,hyr[0,-1],method)
plt.savefig(outname, bbox_inches="tight",dpi=200)
#%% Plot AMV INdex
xtks = np.arange(0,140,20)
xtkl = np.arange(startyr,startyr+140,20)
fig,ax = plt.subplots(1,1,figsize=(5,1))
ax = viz.plot_AMV(h_amv,ax=ax)
ytks = np.arange(-0.5,0.75,0.25)
ax.set_xticks(xtks)
ax.set_xticklabels(xtkl)
ax.set_ylim(-.5,.5)
ax.set_yticks(ytks)
ax.set_title("HadISST AMV Index (%s to %s)" % (startyr,hyr[0,-1]),fontsize=10)
outname = '%sHadISST_AMVIDX_%s-%s_dtmethod%i.png' % (outpath,startyr,hyr[0,-1],method)
plt.savefig(outname, bbox_inches="tight",dpi=200)
#%% UPDATE (MAY 24th 2021, Plots for Generals Presentation)
def plot_AMV_generals(lat,lon,amvpattern,vscale=1):
"""