#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)