#plt.fill_between(xx,y1=minthresh,y2=diff,where=diff<=minthresh,color='b')
ax.set_title(field + ' ' + region + ' ' + skey + ' ' + sea)
ax=axs[1]
nnhi,binshi,patcheshi = ax.hist(high.compressed(),color='r',alpha=0.5,normed=True,histtype='stepfilled')
nnlo,binslo,patcheslo = ax.hist(low.compressed(),color='b',alpha=0.5,normed=True,histtype='stepfilled')
nntot,binstot,patchestot = ax.hist(diff,color='0.5',alpha=0.5,normed=True,histtype='stepfilled')
else:
# Plot pvals for mean anomaly and standard dev
# @@ for some reason if all sims are in dataframe, only some are plotted..??
fig,axs=plt.subplots(1,2)
fig.set_size_inches(12,4)
ax=axs[0]
tpvaldft.plot(linestyle='None',color=ccm.get_colordict().values(),marker='s',markersize=6,ax=ax)
ax.axhline(y=siglevel,color='k')
ax.set_ylabel('PVAL of T statistic for mean')
ax.set_title(fdict['fieldstr'] + ' ' + region)
ax.set_ylim((0,1))
ax=axs[1]
fpvaldft.plot(linestyle='None',colors=ccm.get_colordict().values(),marker='s',markersize=6,ax=ax)#make legend better@@
ax.axhline(y=0.05,color='k')
ax.set_ylabel('PVAL of F statistic for variance')
ax.set_title(fdict['fieldstr'] + ' ' + region)
ax.set_ylim((0,1))
if printtofile:
fig.savefig(fdict['fieldstr']+'_ftpvals' + savestr + '_' + region + '.pdf')
tmp = np.squeeze(flddsea[seaii,lat>latlim,...])
tmpcmp = np.squeeze(flddcsea[seaii,lat>latlim,...])
pcsea[seaii] = cutl.pattcorr(tmp.flatten()*weights.flatten(),
tmpcmp.flatten()*weights.flatten())
pcsea2[seaii],pcsea2pval[seaii] = cutl.pattcorr_pearson(tmp.flatten()*weights.flatten(),
tmpcmp.flatten()*weights.flatten())
seadiffdict[skey] = flddsea
pcseameandict[skey] = pcsea
pcsea2meandict[skey] = pcsea2
pcsea2pvalmeandict[skey] = pcsea2pval
colordict = ccm.get_colordict()
# <codecell>
import pandas as pd
pcmeandf = pd.DataFrame(pcmeandict)
ensmax = pcmeandf.max(axis=1)
ensmin = pcmeandf.min(axis=1)
ensrng = ensmax-ensmin
ensmean = pcmeandf.mean(axis=1)
## # for the purposes of calculating the internal var
## # probably need to take average of the absolute value of corr
## pcmeandfabs = np.abs(pcmeandf)
## ensmeanabs = pcmeandfabs.mean(axis=1)
