mbsfile = outsuf + thre_str + '_' + dset + "-olr-0-0.mbs"
refmbs, refmbt, refch = blb.mbopen(mbsfile)
# Get lots of info about event set
print 'Getting more info about each cloud band...'
dates, cXs, cYs, degs, chs, keys, daynos, tworecdt = sset.evset_info(s,refmbs,refmbt)
# Loop sample domain
for o in range(len(sample_dom)):
smp_dom = sample_dom[o]
print "Running for sample " + smp_dom
# Get sample
dates_d,cXs_d, cYs_d, degs_d, chs_d, keys_d, daynos_d, tworecdt_d = \
sset.sample_arche_cbs(sample,smp_dom, dates,cXs, cYs, degs, chs, keys, daynos, tworecdt)
# Find indices from var file
indices_m1 = []
for e in range(len(dates_d)):
date = dates_d[e]
ix = my.ixdtimes(dtime, [date[0]], [date[1]], [date[2]], [0])
if len(ix) >= 1:
indices_m1.append(ix)
indices_m1 = np.squeeze(np.asarray(indices_m1))
# Select these dates
olrsel = olrdata[indices_m1, :, :] # note this is going to give you in order of dates_d
if show_sample:
nsamp = len(sample_doms)
sampledct = sdict.sample_deets[sample][sample_doms[0]]
ndays = sampledct['ndays']
smp_cXs = np.zeros((ndays, nsamp), dtype=np.float32)
smp_degs = np.zeros((ndays, nsamp), dtype=np.float32)
# Loop sample domain
for o in range(nsamp):
smp_dom = sample_doms[o]
print "Getting data for sample " + smp_dom
# Get sample
dates_s,cXs_s, cYs_s, degs_s, chs_s, keys_s, daynos_s, tworecdt_s = \
sset.sample_arche_cbs(sample,smp_dom,dates_ddm, cXs_ddm, cYs_ddm, degs_ddm, chs_ddm, keys_ddm, daynos_ddm, tworecdt_ddm)
smp_cXs[:, o] = cXs_s
smp_degs[:, o] = degs_s
print smp_dom
print dates_s
# Plotting for this model
print 'Plotting for ' + name
plt.subplot(yplots, xplots, cnt)
plt.scatter(cXs_ddm,
degs_ddm,
c='k',
marker="o",
s=0.3,