# Find TTT data
outsuf = botdir + dset + "/" + name + "/" + name + "_"
mbsfile = outsuf + thre_str + '_' + dset + "-olr-0-0.mbs"
syfile = outsuf + thre_str + '_' + dset + '-OLR.synop'
# using events
if wh_count == 'event':
### Open synop file
s = sy.SynopticEvents((), [syfile], COL=False)
### Count number of events
ks = s.events.keys() # all events
kw, ke = stats.spatialsubset(
s, False,
cutlon=40.) # splitting tracks west and east of 40E
### Put n events into array
if doms[do] == 'All':
yvals[cnt] = len(ks)
elif doms[do] == 'Cont':
yvals[cnt] = len(kw)
elif doms[do] == 'Mada':
yvals[cnt] = len(ke)
elif wh_count == 'blob':
refmbs, refmbt, refch = blb.mbopen(mbsfile)
blob_edts = []
thesekeys.append(k)
edts.append(dt)
elif tstep == 'mon':
if int(dt[1] == mon1):
thesekeys.append(k)
edts.append(dt)
edts = np.asarray(edts)
yrs = np.unique(edts[:, 0])
# Split by domain
print 'selecting events for this dom group...'
if len(doms) == 1:
keys = thesekeys
elif len(doms) == 4:
k1, ktmp = stats.spatialsubset(s,
thesekeys,
cutlon=37.5)
k2, k3 = stats.spatialsubset(s,
ktmp,
cutlon=67.5)
if dom == 'All':
keys = thesekeys
elif dom == 'nCont':
keys = k1
elif dom == 'nMada':
keys = k2
elif dom == 'nOcea':
keys = k3
# opening future data
print 'opening future metbot files'
for line in f:
if dset + '\t' + name in line:
thresh = line.split()[2]
print 'thresh=' + str(thresh)
thisthresh = int(thresh)
thre_str = str(thisthresh)
### Open synop file
syfile = outsuf + thre_str + '_' + dset + '-OLR.synop'
s = sy.SynopticEvents((), [syfile], COL=False)
### Count number of events
ks = s.events.keys()
ks.sort() # all events
kw, ke = stats.spatialsubset(
s, False, cutlon=40.) # splitting tracks west and east of 40E
### Calc seasonal cycle
scycle, cyclestats, yrs = stats.seasonalcycle(s, False)
scyclew, cyclestatsw, yrsw = stats.seasonalcycle(s, kw)
scyclee, cyclestatse, yrse = stats.seasonalcycle(s, ke)
### Loop domains
doms = ['All', 'Continental', 'Madagascar']
ndoms = len(doms)
whichc = [scycle, scyclew, scyclee]
for r in range(ndoms):
### Put into data arrays
if meanplot:
edts.append(dt)
elif seas=='JF':
if (int(dt[1]) >= mon1) and (int(dt[1]) <= mon2):
thesekeys.append(k)
edts.append(dt)
elif seas=='all':
thesekeys.append(k)
edts.append(dt)
edts = np.asarray(edts)
yrs = np.unique(edts[:, 0])
count_fulldom=len(thesekeys)
# Split by domain
print 'selecting events for this dom group...'
k1, ktmp = stats.spatialsubset(s, thesekeys, cutlon=clon1)
k2, k3 = stats.spatialsubset(s, ktmp, cutlon=clon2)
count_ttt=len(k2)
print "Total CB events in this domain =" + str(count_ttt)
keys4rain=k2
elif wh_count=='blob':
refmbs, refmbt, refch = blb.mbopen(mbsfile)
count_all=len(refmbt)
blob_edts = []
blob_edts_regsel =[]
for b in range(len(refmbt)):
date = refmbt[b]
mon = int(date[1])
else:
thesekeys.append(k)
edts.append(dt)
edts = np.asarray(edts)
yrs = np.unique(edts[:, 0])
### PLOT GRIDPOINT COUNT FOR WHOLE SEASON
print 'Plotting spatiofrequency for model ' + name
# colour plot
if col_plot:
print 'Plotting spatiofreq plot in colour'
plt.figure(num='col')
plt.subplot(yplots, xplots, cnt)
allmask=stats.spatiofreq4(m,s,name,lat,lon,yrs,thesekeys,per=rate,meanmask=False,clim=nos4cbar,\
month=False,savefig=False,flagonly=True,\
col='col',cens='None',frm_event='all')
m.drawcountries(color='k')
m.drawcoastlines(color='k')
if group:
m.drawmapboundary(color=grcl, linewidth=3)
if bwcen_plot:
print 'Getting sample to plot centroids on b/w spatiofreq'
if sample == 'blon':
best_lon = [33, 58]
ndays = [50, 50]
n_cen = [-22, -22]
s_cen = [-32, -32]
else:
rain, raindates, time = rain[:
365, :, :], raindates[:
365], time[:
365]
# Loop seasons
for ss in range(len(seasons)):
print "Getting seasonal mean for " + seasons[ss]
### Calculate area average
# N.B. the "rain" data should now have the right years and domain
# I am just doing a straight gridbox average
# ...because it's not spanning too many latitudes
rainmean = stats.seasmean(rain,
raindates,
seas=seasons[ss])
rainmean = round(rainmean, 2)
raincollect[r, ss] = rainmean
### Write to textfile
txtfile_pr = txtdir + "precip_mean." + seasons[
ss] + "." + prdom[r] + ".txt"
print "Writing seasonal fldmean to " + txtfile_pr
with open(txtfile_pr, "a") as myfile:
myfile.write(dset + "\t" + name + "\t" +
str(rainmean) + "\n")
### Find location of synop file
print "Preparing to get nTTTs"
outdir = indir + dset + "/" + name + "/"
thisthresh = threshs[t]
print thisthresh
thre_str = str(int(thisthresh))
print thre_str
mbsfile = outsuf + thre_str + '_' + dset + "-olr-0-0.mbs"
syfile = outsuf + thre_str + '_' + dset + '-OLR.synop'
### Open ttt data
s = sy.SynopticEvents((), [syfile], COL=False)
refmbs, refmbt, refch = blb.mbopen(mbsfile)
### Select events
ks = s.events.keys()
ks.sort() # all
kw, ke = stats.spatialsubset(
s, False, cutlon=40.) # events west and east of 40E
### Count number of events
count_all = str(int(len(ks)))
count_cont = str(int(len(kw)))
count_mada = str(int(len(ke)))
### Calc seasonal cycle
scycle, cyclestats, yrs = stats.seasonalcycle(s, False)
scyclew, cyclestatsw, yrsw = stats.seasonalcycle(s, kw)
scyclee, cyclestatse, yrse = stats.seasonalcycle(s, ke)
nNF = scycle[:, 3:7].sum(
1) # summing years for months November to March
### PLOT TIMESERIES OF SEASONAL CYCLE
print 'Plotting timeseries'
print thre_str
mbsfile = outsuf + thre_str + '_' + dset + "-olr-0-0.mbs"
syfile = outsuf + thre_str + '_' + dset + '-OLR.synop'
### Open ttt data
s = sy.SynopticEvents((), [syfile], COL=False)
refmbs, refmbt, refch = blb.mbopen(mbsfile)
### Select events
ks = s.events.keys()
ks.sort() # all
count_all = str(int(len(ks)))
print "Total CB events =" + str(count_all)
if len(doms) == 3:
kw, ke = stats.spatialsubset(
s, False, cutlon=40.) # events west and east of 40E
keys = [ks, kw, ke]
elif len(doms) == 4:
k1, ktmp = stats.spatialsubset(s, False, cutlon=37.5)
k2, k3 = stats.spatialsubset(s, ktmp, cutlon=67.5)
keys = [ks, k1, k2, k3]
### Plot olrmaps
olrbase = olrdir + dset + "/"
my.mkdir_p(olrbase)
mapsuf = seasopt + '_' + sub + '_' + dset + '_' + name + '_' + thre_str + '_key' + refkey + '_4' + monmean
if testfile or testyear:
testq = True
else:
testq = freecol
### Open ttt data - past
s = sy.SynopticEvents((), [syfile], COL=False)
print dset
print name
print "historical"
### Select events - past
ks = s.events.keys()
ks.sort() # all
keything = '0'
refkey = dset + '-olr-0-' + keything
if len(doms) == 4:
k1, ktmp = stats.spatialsubset(s, ks, cutlon=37.5)
k2, k3 = stats.spatialsubset(s, ktmp, cutlon=67.5)
if dom == 'All':
keys = ks
elif dom == 'nCont':
keys = k1
elif dom == 'nMada':
keys = k2
elif dom == 'nOcea':
keys = k3
edts = []
thesekeys = []
for k in keys:
e = s.events[k]
dts = s.blobs[refkey]['mbt'][e.ixflags]