def plot_results(clustered,uncert,expname,lat5,lon5,outfigpath,title=None):
# Set some defaults
ucolors = ('Blues','Purples','Greys','Blues','Reds','Oranges','Greens')
proj = ccrs.PlateCarree(central_longitude=180)
cmap = cm.get_cmap("jet",nclusters)
fig,ax = plt.subplots(1,1,subplot_kw={'projection':proj})
ax = slutil.add_coast_grid(ax)
gl = ax.gridlines(ccrs.PlateCarree(central_longitude=0),draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle="dotted",lw=0.75)
gl.xlabels_top = False
gl.ylabels_right = False
pcm = ax.pcolormesh(lon5,lat5,clustered,cmap=cmap,transform=ccrs.PlateCarree())#,cmap='Accent')#@,cmap='Accent')
plt.colorbar(pcm,ax=ax,orientation='horizontal')
if title is None:
ax.set_title("Clustering Results \n nclusters=%i %s" % (nclusters,expname))
else:
ax.set_title(title)
plt.savefig("%sCluster_results_n%i_%s.png"%(outfigpath,nclusters,expname),dpi=200,transparent=True)
# Plot raw uncertainty
fig,ax = plt.subplots(1,1,subplot_kw={'projection':proj})
ax = slutil.add_coast_grid(ax)
pcm = plt.pcolormesh(lon5,lat5,uncert,cmap='copper',transform=ccrs.PlateCarree())
ax.set_title(r"Uncertainty $(<\sigma^{2}_{out,x}>/<\sigma^{2}_{in,x}>)$")
fig.colorbar(pcm,ax=ax,fraction=0.02)
plt.savefig(outfigpath+"Uncertainty.png",dpi=200)
uncertraw = uncert.copy()
# Apply Thompson and Merrifield thresholds
uncert[uncert>2] = 2
uncert[uncert<0.5]=0
# Plot Cluster Uncertainty
fig1,ax1 = plt.subplots(1,1,subplot_kw={'projection':proj})
ax1 = slutil.add_coast_grid(ax1)
for i in range(nclusters):
cid = i+1
if (i+1) > len(ucolors):
ci=i%len(ucolors)
else:
ci=i
cuncert = uncert.copy()
cuncert[clustered!=cid] *= np.nan
ax1.pcolormesh(lon5,lat5,cuncert,vmin=0,vmax=2,cmap=ucolors[ci],transform=ccrs.PlateCarree())
#fig.colorbar(pcm,ax=ax)
if title is None:
ax1.set_title("Clustering Output (nclusters=%i) %s "% (nclusters,expname))
else:
ax1.set_title(title)
plt.savefig(outfigpath+"Cluster_with_Shaded_uncertainties_%s.png" % expname,dpi=200)
return fig,ax,fig1,ax1
#% Remove NaN points and Examine Low pass filter
slars = sla_lp.reshape(ntimer, nlat5 * nlon5)
# ---
# Locate points where values are all zero
# ---
tsum = slars.sum(0)
zero_pts = np.where(tsum == 0)[0]
ptmap = np.array(tsum == 0)
slars[:, zero_pts] = np.nan
ptmap = ptmap.reshape(nlat5, nlon5)
# Map removed points
fig, ax = plt.subplots(
1, 1, subplot_kw={'projection': ccrs.PlateCarree(central_longitude=0)})
ax = slutil.add_coast_grid(ax)
pcm = ax.pcolormesh(lon5,
lat5,
ptmap,
cmap='bone',
transform=ccrs.PlateCarree(),
alpha=0.88)
fig.colorbar(pcm, ax=ax)
ax.set_title("Removed Zero Points")
# ---
# Visualize Filter Transfer Function
# ---
okdata, knan, okpts = proc.find_nan(slars, 0)
npts5 = okdata.shape[1]
lpdata = okdata.copy()
# Recover clusterout for silhouette plotting
remmask = rempts_in.copy()
remmask[~np.isnan(remmask
)] = np.nan # Convert all removed points to NaN
remmask[np.isnan(rempts_in)] = 1
clusterout, knan, okpts = proc.find_nan(
(clusternew * remmask).flatten(), 0)
# Ugly Fix, but not sure why sometimes s_in size doesnt match clusterin (non-nan)
if len(clusterout) != len(s_in):
clusterout, knan, okpts = proc.find_nan((clusternew).flatten(), 0)
# Upper Row, the clustering result
ax = axs[coll, 0]
#fig,ax = plt.subplots(1,1,subplot_kw={'projection':proj})
ax = slutil.add_coast_grid(ax, leftlab=False, botlab=False)
pcm = ax.pcolormesh(lon5,
lat5,
clusternew,
cmap=cmapn,
transform=ccrs.PlateCarree())
fig.colorbar(pcm, ax=ax, fraction=0.025)
ax.set_title("Clusters (Year %s)" % (yearstr))
#plt.savefig("%sCESM1PIC_%s_ClustersMap_k%s%i_%s.png"%(outfigpath,expname,kmode,ii,yearstr),dpi=200,bbox_inches='tight')
# Plot 1: the silhoutte value map
ax = axs[coll, 1]
cmap = "RdBu_r"
silmap = np.zeros(nlat5 * nlon5) * np.nan
silmap[okpts] = s_in
silmap = silmap.reshape(nlat5, nlon5)
tw = 18
datname = "CESM_PIC_remGMSL%i" % (rem_gmsl)
outname = "%sSSHA_LP_%s_order%i_cutoff%i.npz" % (datpath,datname,order,tw)
ld = np.load(outname,allow_pickle=True)
cesm_sla = ld['sla_lp']
ctimes = ld['times']
#%% Make a plot comparing their differences
proj = ccrs.PlateCarree(central_longitude=180)
fig,axs = plt.subplots(1,2,figsize=(10,3.5),subplot_kw={'projection':proj})
vm = .1
cmap = 'copper'
ax = axs[0]
ax = slutil.add_coast_grid(ax=ax,proj=proj)
pcm = ax.pcolormesh(lon5,lat5,aviso_sla.std(0),vmin=0,vmax=vm,transform=ccrs.PlateCarree(),cmap=cmap)
#fig.colorbar(pcm,ax=ax)
ax.set_title("AVISO (1993-2013)")
ax = axs[1]
ax = slutil.add_coast_grid(ax=ax,proj=proj)
pcm = ax.pcolormesh(lon5,lat5,cesm_sla.std(0),vmin=0,vmax=vm,transform=ccrs.PlateCarree(),cmap=cmap)
ax.set_title("CESM1 Preindustrial Control (400-2200)")
fig.colorbar(pcm,ax=axs.ravel().tolist(),orientation='horizontal',shrink=0.45,pad=0.08)
plt.suptitle("$1\sigma$ SSH Anomalies (m)",y=.95)
plt.savefig("%sAVISO-CESMPIC-Compare.png"%(outfigpath),dpi=200,transparent=True)
stat = sscore
labs = True
fig, axs = plt.subplots(2, 2, subplot_kw={'projection': proj}, figsize=(10, 7))
for m in range(2):
name = mnl[m]
instat = sscore[m]
inclust = clusters[m]
kmin = np.argmin(instat)
kmax = np.argmax(instat)
ax = axs[m, 0]
ax = slutil.add_coast_grid(ax, proj=proj, leftlab=labs, botlab=labs)
pcm = ax.pcolormesh(lon,
lat,
inclust[kmin, :, :],
cmap=cmapn,
transform=ccrs.PlateCarree())
ax.set_title("%s \n Min Silhouette Score (%.2e)" % (name, instat[kmin]))
ax = axs[m, 1]
ax = slutil.add_coast_grid(ax, proj=proj, leftlab=labs, botlab=labs)
pcm = ax.pcolormesh(lon,
lat,
inclust[kmax, :, :],
cmap=cmapn,
transform=ccrs.PlateCarree())
ax.set_title("%s \n Max Silhouette Score (%.2e)" % (name, instat[kmax]))