Пример #1
0
def calc_runstats(datablob,sims, seas=None,siglevel=0.05):
    """         
           datablob: 'ctl','pert','diff' should be timeseries of regional means
           sims: a tuple of sim names in the datablob. (prob unnecessary)
           seas: 2 or 3 month seasons

           return allstats: dictionary of 'tpvals', 'fpvals' for sim key and then sea key

    """
    ctldt = datablob['ctl']
    pertdt = datablob['pert']
    
    diffdt = datablob['diff']
    simspdt = con.get_simpairsdict()

    if seas==None:
        seasons=('SON','DJF','MAM','JJA')
    else:
        seasons=seas

    allstats={}; alltpval={}; allfpval={}
    
    for sim in sims:
        ctlsim=ctldt[sim]
        pertsim=pertdt[sim]
        seatpval={}; seafpval={}
        
        for sea in seasons:
            thestats={}
            print sim + ' ' + sea
            ctl=ctlsim[sea]
            pert=pertsim[sea]

            (tstat,tpval,lstat,lpval) = cutl.calc_pvals(pert,ctl)
            
            ## print '    DIFF: ' + str(pert.mean()-ctl.mean()) + ', CTL mean: ' + str(ctl.mean()) + ', PERT mean: ' + str(pert.mean())
            ## tstat, tpval = sp.stats.ttest_ind(pert,ctl) # add autocorr @@
            ## print '    TSTAT: ' + str(tstat) + ' PVAL: ' + str(tpval)
            ## if tpval<=siglevel:
            ##     print '  **The ensemble means are significantly different (' + str(1-siglevel) + ')'
            ## #thestats['tpval'] = pval
            
            ## #fstat, fpval = sp.stats.f_oneway(pert,ctl) # same as t statistic
            ## #print '    FSTAT: ' + str(fstat) + ' PVAL: ' + str(fpval)
            ## #if fpval<=siglevel:
            ## #    print '  **The ensemble means are significantly different (' + str(1-siglevel) + ')'

            ## print '    CTL std: ' + str(ctl.std()) + ', PERT std: ' + str(pert.std())
            ## lstat, lpval = sp.stats.levene(pert,ctl)
            ## print '    LSTAT: ' + str(lstat) + ' PVAL: ' + str(lpval)
            ## if lpval<=siglevel:
            ##     print '  **The ensemble variances are significantly different (' + str(1-siglevel) + ')'

            seatpval[sea]=tpval
            seafpval[sea]=lpval
            
        alltpval[sim]=seatpval
        allfpval[sim]=seafpval

    allstats['tpval']=alltpval
    allstats['fpval']=allfpval
    
    return allstats
Пример #2
0
    ax.plot(tpxx,tppdf_fitted,'r')
    ax.set_xlabel(label)
    ax.set_title('TOT')
    ax.legend(('CTL','PERT'))

    print label
    print 'TOT CTL (mean,std): ' + str(tcnormed)
    print 'TOT PERT (mean,std): ' + str(tpnormed)
    if printtofile:
        fig.savefig('histpdf_' + label + '_ANTTOTsbplt.pdf')


printtofile=False
plot_anttotanom_histpdf(allantr2,alltotr2,
                        label=(field2 + region2 + '_' + str(sea2)),printtofile=printtofile)
cutl.calc_pvals(allantr2,alltotr2)
print '-------------------------'

plot_anttotanom_histpdf(allantr1,alltotr1,
                        label=(field1 + region1 + '_' + str(sea1)),printtofile=printtofile)
cutl.calc_pvals(allantr1,alltotr1)


plot_anttotsbplt_histpdf((allantcr2,allantpr2),(alltotcr2,alltotpr2),
                         label=(field2 + region2 + '_' + str(sea2)),printtofile=printtofile)
print 'ANT: ' + field2, region2
cutl.calc_pvals(allantpr2,allantcr2)
print 'TOT: ' + field2, region2
cutl.calc_pvals(alltotpr2,alltotcr2)

plot_anttotsbplt_histpdf((allantcr1,allantpr1),(alltotcr1,alltotpr1),