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 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