def spatiofreq2_season(s,lat,lon,yrs,eventkeys,figno=1,season='coreseason',\
key='noaa-olr-0-all',flagonly=False,file_suffix='test',savefig=False,\
fontdict=False):
'''spatiofreq2_season(s,lat,lon,yrs,figno=1,season='coreseason',\
flagonly=False,file_suffix='test')
Produces subplots of cloud-band gridpoint count by month
'''
if not fontdict: fd = {'fontsize': 14, 'fontweight': 'bold'}
else: fd = fontdict
mbkl = key.split('-')
if mbkl[0] == 'noaa': dclim = (1, 9, 1)
if mbkl[0] == 'hadam3p': dclim = (1, 11, 1)
if isinstance(season, str):
if season == 'coreseason': mns = [10, 11, 12, 1, 2, 3]
elif season == 'fullseason':
mns = [8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7]
elif isinstance(season, list):
mns = season
m, f = blb.SAfrBasemap(lat[4:-7],lon[3:-2],drawstuff=True,prj='cyl',\
fno=figno,rsltn='l',fontdict=fd)
if len(mns) == 12:
plt.close()
plt.figure(figsize=[12, 10])
elif len(mns) == 6:
plt.close()
plt.figure(figsize=[13, 8])
cnt = 1
msklist = []
for mn in mns:
if len(mns) == 12: plt.subplot(4, 3, cnt)
elif len(mns) == 6: plt.subplot(3, 2, cnt)
if flagonly:
allmask=stats.spatiofreq2(m,s,lat,lon,yrs,eventkeys,\
clim=(1.,9.,1.),month=mn,flagonly=True,fontdict=fd)
else:
allmask=stats.spatiofreq2(m,s,lat,lon,yrs,eventkeys,clim=dclim,\
month=mn,flagonly=False,fontdict=fd)
if cnt % 2 == 0:
syp.redrawmap(m,
lns=True,
resol='verylow',
parallel=False,
fontdict=fd)
else:
syp.redrawmap(m, lns=True, resol='verylow', fontdict=fd)
cnt += 1
msklist.append(allmask)
my.xtickfonts()
my.ytickfonts()
plt.subplots_adjust(left=0.05,right=0.92,top=0.97,bottom=0.02,\
wspace=0.02,hspace=0.1)
if savefig:
if flagonly:
plt.savefig(stats.figdir + file_suffix + '_flagonly.png', dpi=150)
else:
plt.savefig(stats.figdir + file_suffix + '.png', dpi=150)
return msklist
def __init__(self, mbskeylist, mbslist, sbst='SA', hrwindow=49, COL=False):
fstr = "hrtime, Label, Angle, cX, cY, minX, maxX, minY, maxY, area, cb.m10, cb.m01, cb.m11, cb.m20, cb.m02, cb.u11, cb.u20, cb.u02, cb.n11, cb.n20, cb.n02, cb.p1, cb.p2, Circularity"
self.fields = [code.strip() for code in fstr.split(',')]
domains = {
'SA': ((-60.0, 0.0), (0.0, 80.0)),
'Fauch08': (
(-40.0, -15.0),
(7.5, 70.0),
),
'SH': ((-90.0, 0.0), (0, 360)),
}
vlt, vln = domains[sbst][0], domains[sbst][1]
self.vaxis = vln[0], vln[1], vlt[0], vlt[1]
self.mbskeys = tuple(mbskeylist)
self.hrwindow = hrwindow
self.blobs = {}
self.tracks = {}
self.trackshr = {}
self.trackscX = {}
self.trackscY = {}
self.events = {}
refm = mbslist[0]
if isinstance(refm, tuple):
for i in xrange(len(mbskeylist)):
self.blobs[mbskeylist[i]] = {
'mbs': mbslist[i][0],
'mbt': mbslist[i][1],
'ch': mbslist[i][2]
}
elif isinstance(refm, str):
#f, extens = refm.split('.')
extens = refm.split('.')[-1]
if extens == 'mbs':
for i in xrange(len(mbskeylist)):
mbs, mbt, ch = blb.mbopen(mbslist[i])
self.blobs[mbskeylist[i]] = {
'mbs': mbs,
'mbt': mbt,
'ch': ch
}
elif extens == 'synop':
tmo = timer()
self.mbskeys, self.hrwindow, self.flagkey, self.blobs,\
self.tracks, self.trackshr, self.trackscX, self.trackscY,\
self.events = self.open(refm)
print "Opening time:", (timer() - tmo), "s"
if len(self.events) > 0:
ks = self.events.keys()
print "Computing trkarrs of", len(ks), "events..."
if not COL: addtrkarrs(self)
elif COL: addCOLs(self)
u = self.uniqueevents()
print "Completion time:", (timer() - tmo), "s"
self.flagkey = self.mbskeys[0] # only here because for synop call..
def datastretched(vrb, lat, lon, v, sub='SA'):
'''This function copies first few steps of Metblobs()
by taking data and then stretching it according to filterdict.py
However it then uses this and the threshold value to calculate grid point frequencies for
values that meet the threshold criteria'''
dct = blb.blobfilters[sub + 'cloudband'][v]
domain = dct['ROI']
dstretch = dct['stretch']
dthresh, highlow = dct['thresh']
data, thrs = blb.Stretch(vrb, dthresh, tval=dstretch)
print "Stretched variable:", v
if highlow == 'low':
#thrs=255-thrs
datamsk = data <= thrs
elif highlow == 'high':
datamsk = data >= thrs
return ~datamsk
def spatiofreq2(m,s,lat,lon,yrs,eventkeys,meanmask=False,figno=1,\
clim=(4,36,4),month=False,flagonly=False,fontdict=False):
'''Get grid-cell frequencies for no. of times a grid-cell falls within a
contour describing a feature from metblobs.
USAGE: If wish to create Basemap within function, m will be "create"
if wish to have only for particular month, month=yourchoice
if wish to only count for flagged days, flagonly=True'''
#plt.close('all')
if not fontdict: fd = {'fontsize': 14, 'fontweight': 'bold'}
else: fd = fontdict
mbskeys = s.mbskeys
refkey = s.events.values()[0].refkey
basekey = refkey
try:
dset, varstr, levsel, deriv, expid = basekey.split('-')
descr = dset + '-' + varstr
except:
dset, varstr, levsel, deriv = basekey.split('-')
descr = dset + '-' + varstr
#vkey='%s-%s-%s-%s' %(dset, varstr, levsel, deriv)
#x1,x2,y1,y2=blb.blobfilters[sub+'cloudband'][vkey]['ROI']
#nx, ny = np.abs(x1-x2)/res, np.abs(y1-y2)/res
#grdsz = (np.int32(nx),np.int32(ny))
if not eventkeys:
eventkeys = []
for ed in s.uniques:
eventkeys.append(ed[0])
x, y = np.array(()), np.array(())
#lon=np.arange(lon[0],lon[-1]+1,0.5)
#lat=np.arange(lat[0],lat[-1]-1,-0.5)
allmask = np.zeros((lat.shape[0], lon.shape[0]), dtype=np.float32)
#Determine which variable we dealing with
countkey = refkey
if flagonly: countkey = s.flagkey
for k in eventkeys:
e = s.events[k]
if month:
mn = month
mst = e.trkdtimes[0, 1]
if mst != mn: continue
if flagonly:
itrk = e.ixflags
else:
trkarr = np.int32(e.trkarrs[countkey])
if trkarr.ndim == 2:
ixt = np.where(trkarr[:, 1] > 0)[0]
uni, iu = np.unique(trkarr[ixt, 0], return_index=True)
itrk = trkarr[ixt, 1]
#print len(itrk),":",itrk
elif trkarr.ndim == 3:
itrk = np.ndarray((0, ), dtype=np.int32)
for d in xrange(trkarr.shape[2]):
ixt = np.where(trkarr[:, 1, d] > 0)[0]
uni, iu = np.unique(trkarr[ixt, 0], return_index=True)
ixt = ixt[iu]
itrk = np.append(itrk, trkarr[ixt, 1, d].squeeze())
# Get masks for each contour feature of a track
for ixtrk in itrk:
mask = my.poly2mask(lon, lat, e.blobs[countkey]['ch'][ixtrk])
allmask = allmask + np.float32(mask)
#cm=plt.cm.PuBu
#cm=plt.cm.gist_earth_r
#cm=plt.cm.YlGnBu
#cm=plt.cm.binary
#cm=plt.cm.OrRd
if isinstance(meanmask, np.ndarray):
cm = plt.cm.RdBu
#cm=plt.cm.bwr
else:
cm = plt.cm.gist_gray_r
#cm=plt.cm.jet
#cm=plt.cm.gray_r
cm.set_under(color='w')
if m == 'create':
m, f = blb.SAfrBasemap(lat[3:-6],lon[3:-3],drawstuff=True,\
prj='cyl',fno=figno,rsltn='l')
#df=m.transform_scalar(allmask[::-1,:],lon,lat[::-1],len(lon),len(lat))
#m.imshow(df,cm,interpolation='nearest')
#m.pcolor(lon,lat,allmask,cmap=cm)
#plt.clim(300,1200)
lon, lat = np.meshgrid(lon, lat)
#m.contourf(lon,lat,(allmask/len(yrs)),cmap=cm)
#
### Next is dirty trick to drop out permanent mid-latitude cloudiness
### allowing colormap to enhance cloud bands better
allmask[-9:, :] = 0
std_mask = allmask / len(yrs)
if isinstance(m, bool):
return std_mask
# IF M WAS NOT A BOOLEAN (False) THIS WILL CONTINUE TO PLOTTING
if isinstance(meanmask, np.ndarray):
std_mask = std_mask - meanmask
std_mask = np.where(np.abs(std_mask) < .5, np.nan, std_mask)
lnmin,lnmx,latmn,latmx =\
blb.filters.blobfilters['SAcloudband'][countkey]['ROI']
latmask = (lat < latmn) & (lat > latmx) # this is for S. Hemisphere
meanmasked = np.ma.MaskedArray(meanmask, mask=~latmask)
cs = m.contour(lon, lat, meanmasked, [1, 2, 3, 4], colors='k')
labels=plt.clabel(cs, [1,2,3,4],inline_spacing=2, fmt='%d',\
fontsize=14)
#m.contourf(lon,lat,meanmasked,[2,4,14],hatches=['.','..'],\
# colors='none',linestyles='-',linewidths='1',alpha=.1)
## NEED TO DO THIS SINCE PCOLOR IS NOT SHADING VALUES OUTSIDE OF THE CLIMS
cstd_mask = np.where(std_mask > clim[1], clim[1], std_mask)
cstd_mask = np.where(cstd_mask < clim[0], clim[0], cstd_mask)
# Plot pcolor
pcolmap = m.pcolormesh(lon, lat, cstd_mask, cmap=cm, zorder=1)
img = plt.gci()
for k in eventkeys:
e = s.events[k]
if month:
mn = month
mst = e.trkdtimes[0, 1]
if mst != mn: continue
m.plot(e.trkcX[0], e.trkcY[0], color='w', marker='o', markersize=4)
if 'COL' in e.mbskeys:
if len(e.assoctrks['COL']) > 0:
trar = e.trkarrs['COL']
for ntrk in xrange(trar.shape[2]):
ixx = np.where(trar[:, 1, ntrk] > 0)[0]
xx, yy = trar[ixx, 2, ntrk], trar[ixx, 3, ntrk]
off = np.random.rand() * .5
m.scatter(xx[-1]+off,yy[-1]+off,50,color='none',\
edgecolor='k',marker='o',linewidth='2')
plt.sci(img)
#m.plot(e.trkcX,e.trkcY,'0.5')
plt.clim(clim[0], clim[1])
bounds = np.arange(clim[0], clim[1] + clim[2], clim[2])
#vals=np.arange(0,35,2)
if not month:
f, ax = plt.gcf(), plt.gca()
axcol = f.add_axes([0.93, 0.2, 0.02, 0.6])
plt.colorbar(mappable=img, cax=axcol, boundaries=bounds)
my.ytickfonts()
if isinstance(meanmask, np.ndarray):
plt.ylabel('anomaly grid-point count / year', fontdict=fd)
else:
plt.ylabel('grid-point count / year', fontdict=fd)
plt.axes(ax)
plt.title('Cloudband Annual Grid-Point Count Climatology: '\
+descr.upper(),fontsize='14',fontdict=fd)
fname = figdir + '/FootprintFreqencygray-' + descr + '.png'
if flagonly:
fname = figdir + '/FootprintFreqencygray-' + descr + '_flagonly.png'
plt.savefig(fname, dpi=150)
elif month:
f, ax = plt.gcf(), plt.gca()
axcol = f.add_axes([0.93, 0.2, 0.02, 0.6])
plt.colorbar(cax=axcol, boundaries=bounds)
my.ytickfonts()
if isinstance(meanmask, np.ndarray):
plt.ylabel('anomaly grid-point count / year', fontdict=fd)
else:
plt.ylabel('grid-point count / year', fontdict=fd)
plt.axes(ax)
plt.title(mndict[month], fontweight='demibold')
return std_mask
def rawdatamasks(varlist):
noaaolr = False
ncep2 = False
time6, time24 = [], []
newvarlist = varlist[:]
retlist = []
for v in varlist:
dset, varstr, lev, drv = v.split('-')
retlist.append('datam' + varstr + '_' + lev)
if dset == 'noaa' and varstr == 'olr':
noaaolr = True
newvarlist.remove(v)
if dset == 'ncep2': ncep2 = True
if ncep2:
if varstr == 'uvmag':
newvarlist.remove(v)
newvarlist.extend([
'ncep2-uwnd-' + lev + '-del2',
'ncep2-vwnd-' + lev + '-del2'
])
if varstr == 'quvmag':
newvarlist.remove(v)
newvarlist.extend(
['ncep2-qu-' + lev + '-del2', 'ncep2-qv-' + lev + '-del2'])
sub = "SA"
picdir = "/home/neil/work/computervision/metblobs/" + sub + "/pickles/"
hgtthresh = 1.3
jetthresh = 1.0
moistthresh = 0.5
# CONSTRUCT A REFTIME FROM CENTRES OF THE SEASON 01-JAN
refsubset = True
hrwindow = 24 * 30 * 3
refdates = []
for yr in np.arange(1979, 2012):
refdates.append((yr, 01, 01, 0))
refdates = np.asarray(refdates)
reftime = my.dates2hrnum(refdates)
mycm = plt.cm.PuBu
# OPENING FILES
# Liebmann & Smith Interpolated OLR
if noaaolr:
v = "noaa-olr-0-0"
print '\n', v
dset, varstr, lev, drv = v.split('-')
dsrc = "/home/neil/data/olr/"
#olr, time, lat, lon, dtime = mync.openolr(dsrc+'olr.NDJF.Fauch08.nc','olr')
olr, time, lat, lon, dtime = mync.openolr(dsrc + 'olr.day.mean.nc',
'olr',
subs=sub)
time = time - 15769752.0
olr = olr[1675:, :, :]
time = time[1675:]
dtime = dtime[1675:] # gets all the data since 1979-01-01 00:00
if refsubset:
exec("ixt, [time, " + varstr +
", dtime] = my.ixtwindow(reftime,time,hrwindow,time," +
varstr + ",dtime)")
time24 = time
exec('datamsk' + varstr + '_' + lev + ' = datastretched(' + varstr +
',lat,lon,v,sub=sub)')
exec('datam' + varstr + '_' + lev + ' = np.ma.MaskedArray(' + varstr +
',mask=datamsk' + varstr + '_' + lev + ',dtype=np.float32)')
#m, f = blb.SAfrBasemap(lat[3:-6],lon[3:-3],drawstuff=True,prj='cyl',fno=1,rsltn='l')
#exec('plt.figure();syp.redrawmap(m);m.pcolor(lon,lat,nfreq'+varstr+'_'+lev+',cmap=mycm);plt.clim(0.01,0.5);\
#plt.title("'+varstr+'_'+lev+'")')
# NCEP2 subset file of your choice (created in by ncep2subset in subsetfiles.py)
if ncep2:
print "Analysing ncep2 data set..."
#hgtlist=['ncep2-hgt-850-del2']#,'ncep2-hgt-500-del2','ncep2-hgt-250-del2']
#uvlist=['ncep2-uwnd-250-del2','ncep2-vwnd-250-del2']#,'ncep2-uwnd-500-del2','ncep2-vwnd-500-del2','ncep2-uwnd-250-del2','ncep2-vwnd-250-del2']
#quvlist=['ncep2-qu-700-del2','ncep2-qv-700-del2','ncep2-qu-500-del2','ncep2-qv-500-del2','ncep2-qu-850-del2','ncep2-qv-850-del2']
#quvlist=['ncep2-qu-850-del2','ncep2-qv-850-del2']
#varlist=[]
#varlist.extend(hgtlist)
#varlist.extend(uvlist)
#varlist.extend(quvlist)
for v in newvarlist:
print '\n', v
# OPEN FILE
sbst = sub
dset, varstr, lev, deriv = v.split('-')
dsrc = "/home/neil/data/ncep2/6hourly/" + varstr + "/"
ncfileout = dsrc + "%s.%s_%s.nc" % (varstr, sbst, lev)
exec(varstr + ", time, lat, lon, dtime = mync.open" + dset +
"(ncfileout,\'" + varstr + "\')")
# SUBSET IN TIME BY REFTIME
if refsubset:
exec("ixt, [time, " + varstr +
", dtime] = my.ixtwindow(reftime,time,hrwindow,time," +
varstr + ",dtime)")
time6 = time
# CALCULATION OF DERIVED VARIABLES
# Laplacian of pressure surface
if deriv == 'del2' and varstr == 'hgt':
print "Get laplacian of hgt..."
del2 = my.del2(hgt, time, lat, lon)
del2 = my.coriolosscale(del2, lat, lon)
slims, thrs = blb.calclims(del2,
scalestd=hgtthresh,
disttype='normal')
blb.blobfilters[sub + 'cloudband'][v]['stretch'] = slims
blb.blobfilters[sub + 'cloudband'][v]['thresh'] = (thrs[1],
'high')
exec('datamsk' + varstr + '_' + lev +
' = datastretched(del2,lat,lon,v,sub=sub)')
exec('datam' + varstr + '_' + lev + ' = np.ma.MaskedArray(' +
varstr + ',mask=datamsk' + varstr + '_' + lev +
',dtype=np.float32)')
del del2, hgt
#m, f = blb.SAfrBasemap(lat[3:-6],lon[3:-3],drawstuff=True,prj='cyl',fno=1,rsltn='l')
#exec('plt.figure();syp.redrawmap(m);m.pcolor(lon,lat,nfreq'+varstr+'_'+lev+',cmap=mycm);plt.clim(0.01,0.9);\
#plt.title("'+varstr+'_'+lev+'")')
#plt.colorbar()
# Laplacian of u,v magnitude
elif 'uwnd' in locals() and 'vwnd' in locals() and deriv == 'del2':
print "Get laplacian of vector wind magnitude..."
vmag = dset + "-uvmag-" + lev + "-" + deriv
dset, varstr, lev, deriv = vmag.split('-')
mag, degdir = my.magdir(uwnd, vwnd)
del uwnd, vwnd
del2 = my.del2(mag, time, lat, lon)
del degdir
del2 = my.coriolosscale(del2, lat, lon)
slims, thrs = blb.calclims(del2,
scalestd=jetthresh,
disttype='normal')
blb.blobfilters[sub + 'cloudband'][vmag]['stretch'] = slims
blb.blobfilters[sub + 'cloudband'][vmag]['thresh'] = (thrs[0],
'low')
exec('datamsk' + varstr + '_' + lev +
' = datastretched(del2,lat,lon,vmag,sub=sub)')
exec('datam' + varstr + '_' + lev +
' = np.ma.MaskedArray(mag,mask=datamsk' + varstr + '_' +
lev + ',dtype=np.float32)')
#exec('datam'+varstr+'_'+lev+' = np.ma.MaskedArray(v,mask=datamsk'+varstr+'_'+lev+',dtype=np.float32)')
del del2, mag
#m, f = blb.SAfrBasemap(lat[3:-6],lon[3:-3],drawstuff=True,prj='cyl',fno=1,rsltn='l')
#exec('plt.figure();syp.redrawmap(m);m.pcolor(lon,lat,nfreq'+varstr+'_'+lev+',cmap=mycm);plt.clim(0.01,0.5);\
#plt.title("'+varstr+'_'+lev+'")')
# Laplacian of qu,qv magnitude
elif 'qu' in locals() and 'qv' in locals() and deriv == 'del2':
print "Get laplacian of vector moisture flux magnitude..."
vmag = dset + "-quvmag-" + lev + "-" + deriv
dset, varstr, lev, deriv = vmag.split('-')
mag, degdir = my.magdir(qu, qv)
del qu, qv
del2 = my.del2(mag, time, lat, lon)
del degdir
#del2 = my.coriolosscale(del2,lat,lon)
slims, thrs = blb.calclims(del2,
scalestd=moistthresh,
disttype='normal')
blb.blobfilters[sub + 'cloudband'][vmag]['stretch'] = slims
blb.blobfilters[sub + 'cloudband'][vmag]['thresh'] = (thrs[0],
'low')
exec('datamsk' + varstr + '_' + lev +
' = datastretched(del2,lat,lon,vmag,sub=sub)')
exec('datam' + varstr + '_' + lev +
' = np.ma.MaskedArray(mag,mask=datamsk' + varstr + '_' +
lev + ',dtype=np.float32)')
#exec('datam'+varstr+'_'+lev+' = np.ma.MaskedArray(v,mask=datamsk'+varstr+'_'+lev+',dtype=np.float32)')
del del2, mag
#m, f = blb.SAfrBasemap(lat[3:-6],lon[3:-3],drawstuff=True,prj='cyl',fno=1,rsltn='l')
#exec('plt.figure();syp.redrawmap(m);m.pcolor(lon,lat,nfreq'+varstr+'_'+lev+',cmap=mycm);plt.clim(0.01,0.5);\
#plt.title("'+varstr+'_'+lev+'")')
exec('out=(' + ", ".join(retlist) + ')')
return out, time24, time6