def load_LE(fdict,casename='historical',conv=1,season=None,timesel1='1979-01-01,1989-12-31',timesel2='2002-01-01,2012-12-31'):
ledatc = le.load_LEdata(fdict,casename,timesel=timesel1,
rettype='ndarray',conv=conv,ftype='fullts',local=True,verb=False)
ledatp = le.load_LEdata(fdict,casename,timesel=timesel2,
rettype='ndarray',conv=conv,ftype='fullts',local=True,verb=False)
lediff = ledatp-ledatc
print lediff.shape
leclimo,_ = cutl.climatologize(lediff.T)
print leclimo.shape
return leclimo
def load_field(fdict,casename,timesel,seas,ftype='fullts',conv=1,local=False,verb=False):
""" TAKEN FROM load_field() in canesm_le_composite.py
returns [numens x space.flat] or [numens]
"""
ledat = le.load_LEdata(fdict,casename,timesel=timesel,
rettype='ndarray',conv=conv,ftype=ftype,local=local,verb=verb)
# time needs to be first dimension
try:
if ledat.ndim==2:
ledat = ledat.T
elif ledat.ndim==3:
ledat = np.transpose(ledat,(1,0,2))
else:
print 'Loaded data is not 2 or 3 dimensions. Do not understand.'
raise Exception
except:
raise
lesea = cutl.seasonalize_monthlyts(ledat,season=seas).mean(axis=0) # numens x space.flat
return lesea
def load_field(fdict,casename,timesel,seas,ftype='fullts',conv=1,local=False,verb=False):
"""
returns [numens x space.flat] or [numens]
"""
ledat = le.load_LEdata(fdict,casename,timesel=timesel,
rettype='ndarray',conv=conv,ftype=ftype,local=local,verb=verb)
print '@@@ ledat.shape ' + str(ledat.shape) # why does the 3d data have space flattened already...
# time needs to be first dimension
try:
if ledat.ndim==2:
ledat = ledat.T
elif ledat.ndim==3:
ledat = np.transpose(ledat,(1,0,2))
else:
print 'Loaded data is not 2 or 3 dimensions. Do not understand.'
raise Exception
except:
raise
lesea = cutl.seasonalize_monthlyts(ledat,season=seas).mean(axis=0) # numens x space.flat
return lesea
# These time selections start with month of eruption ## timselagung ='1963-02-01,1965-12-31' ## timselchichon = '1982-04-01,1984-12-31' ## timselpinatubo = '1991-06-01,1993-12-31' # These time selections start with beginning of year of eruption # (so can easily remove seasonal cycle) # index into time series for eruption month, and index of eruption month (in a year) timselagung ='1962-01-01,1968-12-31'; agstidx=13; ageridx=1 timselchichon = '1981-01-01,1987-12-31'; chstidx=15; cheridx=3 timselpinatubo = '1990-01-01,1996-12-31'; pistidx=17; pieridx=5 # Need to remove each run's annual cycle: get seasonal cycle climo natclimo = le.load_LEdata(fdict,'historicalNat',ftype='1950-2020_climo',rettype='ndarray',conv=conv) #@@@histclimo = le.load_LEdata(fdict,'historical',ftype='1950-2020_climo',rettype='ndarray',conv=conv) # Get months surrounding each volcano agungdat=le.load_LEdata(fdict,'historicalNat',timesel=timselagung, rettype='ndarray',conv=conv) chichondat=le.load_LEdata(fdict,'historicalNat',timesel=timselchichon, rettype='ndarray',conv=conv) pinatubodat=le.load_LEdata(fdict,'historicalNat',timesel=timselpinatubo, rettype='ndarray',conv=conv) flist=le.build_filenames(fdict,'historicalNat',ftype='fullts') numens=len(flist) fname = flist[0] lev = cnc.getNCvar(fname,'plev') lat = cnc.getNCvar(fname,'lat') nlev=len(lev)
timeselc='1979-01-01,1989-12-31'
timeselp='2002-01-01,2012-12-31'
lefield1='zg50000.00'; lencfield1='zg'; comp1='Amon'; leconv1=1 #region1='bksmori'
lefield2='tas'; lencfield2='tas'; comp2='Amon'; #region2='eurasiamori'
fdict1 = {'field': lefield1+region1, 'ncfield': lencfield1, 'comp': comp1}
fdict2 = {'field': lefield2+region2, 'ncfield': lencfield2, 'comp': comp2}
ftype='fullts' # 'fullclimo' or 'climo' or 'fullts'
lefdict1 = {'field': lefield1+region1, 'ncfield': lencfield1, 'comp': comp1}
lefdict2 = {'field': lefield2+region2, 'ncfield': lencfield2, 'comp': comp2}
# historical
lecdat1 = le.load_LEdata(fdict1,'historical',timesel=timeselc, rettype='ndarray',conv=leconv1,ftype=ftype)
(numens1,ntime1) = lecdat1.shape
lepdat1=le.load_LEdata(fdict1,'historical',timesel=timeselp, rettype='ndarray',conv=leconv1,ftype=ftype)
lecsea1 = cutl.seasonalize_monthlyts(lecdat1.T,season=sea1).T
lepsea1 = cutl.seasonalize_monthlyts(lepdat1.T,season=sea1).T
lefld1=lepsea1.mean(axis=1)-lecsea1.mean(axis=1)
lecdat2 = le.load_LEdata(fdict2,'historical',timesel=timeselc, rettype='ndarray',conv=conv2,ftype=ftype)
(numens2,ntime2) = lecdat1.shape
lepdat2=le.load_LEdata(fdict2,'historical',timesel=timeselp, rettype='ndarray',conv=conv2,ftype=ftype)
lecsea2 = cutl.seasonalize_monthlyts(lecdat2.T,season=sea2).T
lepsea2 = cutl.seasonalize_monthlyts(lepdat2.T,season=sea2).T
lefld2=lepsea2.mean(axis=1)-lecsea2.mean(axis=1)
obs=plt.scatter(erareg.mean(),gisreg.mean(),color='blue',marker='s',s=8**2)
