def compare_pre_post_1100_noise(X,L=31,latbounds=None):
time1=('1100-1-1','1399-12-31')
c1=cm.Purples(.8)
time2=('1400-1-1','2005-12-31')
if latbounds is not None:
obs=X.obs(latitude=latbounds)
mma = MV.average(X.model(latitude=latbounds),axis=0)
mma = mask_data(mma,obs[0].mask)
solver = Eof(mma)
obs = mask_data(obs,solver.eofs()[0].mask)
truncnoise=solver.projectField(obs)[:,0]*da.get_orientation(solver)
noisy1=truncnoise(time=time1)
noisy2=truncnoise(time=time2)
else:
noisy1=X.noise(time=time1)
noisy2=X.noise(time=time2)
c2=cm.viridis(.1)
plt.subplot(121)
Plotting.Plotting.time_plot(noisy1,c=c1)
Plotting.Plotting.time_plot(noisy2,c=c2)
plt.ylabel("Projection")
plt.title("(a): Noise time series")
plt.subplot(122)
plt.hist(b.bootstrap_slopes(noisy1,L),color=c1,normed=True,alpha=.5)
da.fit_normals_to_data(b.bootstrap_slopes(noisy1,L),c=c1,label="1100-1400")
plt.hist(b.bootstrap_slopes(noisy2,L),color=c2,normed=True,alpha=.5)
da.fit_normals_to_data(b.bootstrap_slopes(noisy2,L),c=c2,label="1400-2005")
plt.legend()
plt.title("(b): 31-year trend distributions")
return np.std(b.bootstrap_slopes(noisy1,L)),np.std(b.bootstrap_slopes(noisy2,L))
def project_piControl_on_solver(self, solver=None):
direc = "/Volumes/Marvel/PICTRL/PDSI_REGRIDDED_SUMMER/"
files = glob.glob(direc + "*")
npiC = len(files)
fname = files[0]
f = cdms.open(fname)
piC_pdsi_regrid = f("pdsi_summer")
piC_pdsi_regrid = MV.masked_where(np.isnan(piC_pdsi_regrid),
piC_pdsi_regrid)
mask = self.solver.eofs()[0].mask
# grid=self.model.getGrid()
nyears = piC_pdsi_regrid.shape[0]
# tax=cdms.createAxis(np.arange(piC_pdsi.shape[0]))
# tax.designateTime()
# tax.units = 'years since 0000-7-1'
# tax.id="time"
# piC_pdsi.setAxis(0,tax)
# piC_pdsi_regrid = piC_pdsi.regrid(grid,regridTool='regrid2')
piC_mask = mask_data(piC_pdsi_regrid, mask)
newmask = np.prod(~piC_mask.mask, axis=0)
if solver is None:
solver = Eof(mask_data(self.mma, newmask == 0), weights='area')
fac = da.get_orientation(solver)
p = solver.projectField(piC_mask)[:, 0] * fac
for i in range(npiC)[1:]:
fname = files[i]
f = cdms.open(fname)
piC_pdsi_regrid = f("pdsi_summer")
piC_pdsi_regrid = MV.masked_where(np.isnan(piC_pdsi_regrid),
piC_pdsi_regrid)
# piC_pdsi = MV.masked_where(np.isnan(piC_pdsi),piC_pdsi)
nyears += piC_pdsi_regrid.shape[0]
# tax=cdms.createAxis(np.arange(piC_pdsi.shape[0]))
# tax.designateTime()
# tax.units = 'years since 0000-7-1'
# tax.id="time"
# piC_pdsi.setAxis(0,tax)
# piC_pdsi_regrid = piC_pdsi.regrid(grid,regridTool='regrid2')
piC_mask = mask_data(piC_pdsi_regrid, mask)
newmask = np.prod(~piC_mask.mask, axis=0)
solver = Eof(mask_data(self.mma, newmask == 0), weights='area')
fac = da.get_orientation(solver)
f.close()
p = MV.concatenate((p, fac * solver.projectField(piC_mask)[:, 0]))
tax = cdms.createAxis(np.arange(nyears))
tax.designateTime()
tax.units = 'years since 0000-7-1'
tax.id = "time"
p.setAxis(0, tax)
return p
def project_dai_on_solver(self,start='1970-1-1'):
f = cdms.open("../DROUGHT_ATLAS/OBSERVATIONS/DAI_selfcalibrated.nc")
dai_jja=f("pdsi")
f.close()
dai_jja_mask = mask_data(dai_jja,self.obs[0].mask)(time=(start,'2018-12-31'))
newmask = np.prod(~dai_jja_mask.mask,axis=0)
dai_jja_mask = mask_data(dai_jja_mask,newmask==0)
solver = Eof(mask_data(self.mma,newmask==0))
dai_jja_mask = mask_data(dai_jja_mask,solver.eofs()[0].mask)
fac = da.get_orientation(solver)
return solver.projectField(dai_jja_mask)[:,0]*fac
def model_projections(self):
to_proj = mask_data(self.model,self.solver.eofs()[0].mask)
P=MV.zeros(to_proj.shape[:2])
for i in range(to_proj.shape[0]):
tp = to_proj[i]
mma_mask = mask_data(self.mma,tp[0].mask)
solver = Eof(mma_mask)
fac=da.get_orientation(solver)
P[i] = solver.projectField(tp)[:,0]*fac
P.setAxisList(to_proj.getAxisList()[:2])
self.P=P
def project_cru_on_solver(self, start='1970-1-1', solver=None):
f = cdms.open("../DROUGHT_ATLAS/OBSERVATIONS/CRU_selfcalibrated.nc")
cru_jja = f("pdsi")
f.close()
cru_jja_mask = mask_data(cru_jja,
self.obs[0].mask)(time=(start, '2018-12-31'))
newmask = np.prod(~cru_jja_mask.mask, axis=0)
cru_jja_mask = mask_data(cru_jja_mask, newmask == 0)
if solver is None:
solver = Eof(mask_data(self.mma, newmask == 0), weights='area')
cru_jja_mask = mask_data(cru_jja_mask, solver.eofs()[0].mask)
fac = da.get_orientation(solver)
return solver.projectField(cru_jja_mask)[:, 0] * fac
def project_piControl_on_solver(self, depth):
if depth in self.noise.keys():
pass
else:
direc = "/Volumes/Marvel/PICTRL/SM" + depth + "_REGRIDDED_SUMMER/"
files = glob.glob(direc + "*")
npiC = len(files)
fname = files[0]
f = cdms.open(fname)
piC_pdsi_regrid = f("sm" + depth + "_summer")
piC_pdsi_regrid = MV.masked_where(np.isnan(piC_pdsi_regrid),
piC_pdsi_regrid)
mask = self.solvers[depth].eofs()[0].mask
grid = self.soilmoisture[depth].getGrid()
nyears = piC_pdsi_regrid.shape[0]
piC_mask = mask_data(piC_pdsi_regrid, mask)
newmask = np.prod(~piC_mask.mask, axis=0)
solver = Eof(mask_data(self.mma[depth], newmask == 0),
weights='area')
fac = da.get_orientation(solver)
p = solver.projectField(piC_mask)[:, 0] * fac
for i in range(npiC)[1:]:
fname = files[i]
f = cdms.open(fname)
piC_pdsi_regrid = f("sm" + depth + "_summer")
piC_pdsi_regrid = MV.masked_where(np.isnan(piC_pdsi_regrid),
piC_pdsi_regrid)
nyears += piC_pdsi_regrid.shape[0]
piC_mask = mask_data(piC_pdsi_regrid, mask)
newmask = np.prod(~piC_mask.mask, axis=0)
solver = Eof(mask_data(self.mma[depth], newmask == 0),
weights='area')
fac = da.get_orientation(solver)
f.close()
p = MV.concatenate(
(p, fac * solver.projectField(piC_mask)[:, 0]))
tax = cdms.createAxis(np.arange(nyears))
tax.designateTime()
tax.units = 'years since 0000-7-1'
tax.id = "time"
p.setAxis(0, tax)
self.noise[depth] = p
def standardize_soilmoisture(dataset):
f = cdms.open("../DROUGHT_ATLAS/PROCESSED/ALL_ANZDA.nc")
obs = f("pdsi")
obs = MV.masked_where(np.isnan(obs), obs)
obs = MV.masked_where(np.abs(obs) > 90, obs)
obs = obs(time=('1921-1-1', '2000-12-31'))
pdsi = mask_data(
obs, obs.mask[0]
) #Make all the obs have the same mask as the first datapoint
f.close()
mu_p = MV.average(pdsi, axis=0)
sig_p = genutil.statistics.std(pdsi, axis=0)
f = cdms.open("../DROUGHT_ATLAS/OBSERVATIONS/" + dataset +
"_soilmoisture_summerseason.nc")
gleam30cm = f("smsurf")
mask = pdsi[0].mask
gleam30cmmask = b.mask_data(gleam30cm, mask)
mu_s = MV.average(gleam30cmmask, axis=0)
sig_s = genutil.statistics.std(gleam30cmmask, axis=0)
surf = (gleam30cmmask - mu_s + mu_p) * (sig_p / sig_s)
gleam2m = f("smroot")
mask = pdsi[0].mask
gleam2mmask = b.mask_data(gleam2m, mask)
mu_s2 = MV.average(gleam2mmask, axis=0)
sig_s2 = genutil.statistics.std(gleam2mmask, axis=0)
root = (gleam2mmask - mu_s2 + mu_p) * (sig_p / sig_s)
return surf, root
def get_tree_ring_projection(self, solver=None):
if solver is None:
return self.projection
else:
fac = da.get_orientation(solver)
projection = solver.projectField(mask_data(
self.obs, self.eofmask))[:, 0] * fac
return projection
def get_noise(self, solver=None):
if solver is None:
return self.noise
else:
proj = solver.projectField(mask_data(
self.obs, self.eofmask))[:, 0] * da.get_orientation(solver)
noise = proj(time=('1-1-1', '1850-1-1'))
return noise
def regional_DA(OWDA,
region,
start_time=None,
typ='fingerprint',
return_noise=False):
if start_time is None:
start_time = cdtime.comptime(2000, 1, 1)
times = np.arange(10, 76)
modeldata = mask_data(OWDA.model(region), OWDA.obs(region)[0].mask)
if typ == 'fingerprint':
mma = MV.average(modeldata, axis=0)
solver = Eof(mma, weights='area')
to_proj = mask_data(modeldata, solver.eofs()[0].mask)
P = MV.zeros(to_proj.shape[:2])
for i in range(to_proj.shape[0]):
tp = to_proj[i]
mma_mask = mask_data(mma, tp[0].mask)
solver = Eof(mma_mask, weights='area')
fac = da.get_orientation(solver)
P[i] = solver.projectField(tp)[:, 0] * fac
P.setAxisList(to_proj.getAxisList()[:2])
noise = solver.projectField(OWDA.obs(region))[:, 0]
else:
P = cdutil.averager(modeldata, axis='xy')
noise = cdutil.averager(OWDA.obs(region), axis='xy')
if return_noise:
return P, noise
else:
nmod, nyears = P.shape
TOE = MV.zeros((nmod, len(times)))
for i in range(len(times)):
L = times[i]
stop_time = start_time.add(L, cdtime.Years)
modslopes = cmip5.get_linear_trends(P(time=(start_time,
stop_time)))
noiseterm = np.std(bootstrap_slopes(noise, L))
TOE[:, i] = modslopes / noiseterm
TOE.setAxis(0, P.getAxis(0))
return TOE
def model_projections(self, depth):
if depth in self.P.keys():
pass
else:
to_proj = mask_data(self.soilmoisture[depth],
self.solvers[depth].eofs()[0].mask)
P = MV.zeros(to_proj.shape[:2])
for i in range(to_proj.shape[0]):
tp = to_proj[i]
mma_mask = mask_data(self.mma[depth], tp[0].mask)
solver = Eof(mma_mask, weights='area')
tp = mask_data(tp, solver.eofs()[0].mask)
fac = da.get_orientation(solver)
P[i] = solver.projectField(tp)[:, 0] * fac
P.setAxisList(to_proj.getAxisList()[:2])
self.P[depth] = P
self.P[depth].getTime().id = "time"
def mask_models(CO, writename=None):
"""Create mask based on availability of observations at first time step and apply to CMIP5 hist+85 PDSI."""
f = cdms.open("../DROUGHT_ATLAS/CMIP5/pdsi.ensemble.hist.rcp85.nc")
models = f("pdsi")
models = MV.masked_where(np.isnan(models), models)
masked_models = mask_data(models, CO[0].mask)
masked_models.id = 'pdsi'
if writename is not None:
fw = cdms.open(writename, "w")
fw.write(masked_models)
fw.close()
return masked_models
def truncated_solver(D,the_start=None,the_stop=None,include_cru=False,include_dai=False):
thedata = D.ALL.model(time=(the_start,the_stop))
the_mma=MV.average(cmip5.ensemble2multimodel(thedata),axis=0)
if include_cru:
f = cdms.open("../DROUGHT_ATLAS/OBSERVATIONS/CRU_selfcalibrated.nc")
cru_jja=f("pdsi")
f.close()
cru_jja_mask = mask_data(cru_jja,D.ALL.obs[0].mask)(time=(the_start,'2018-12-31'))
newmask = np.prod(~cru_jja_mask.mask,axis=0)
cru_jja_mask = mask_data(cru_jja_mask,newmask==0)
thesolver = Eof(mask_data(D.ALL.mma(time=(the_start,the_stop)),newmask==0),weights='area')
elif include_dai:
f = cdms.open("../DROUGHT_ATLAS/OBSERVATIONS/DAI_selfcalibrated.nc")
dai_jja=f("pdsi")
f.close()
dai_jja_mask = mask_data(dai_jja,D.ALL.obs[0].mask)(time=(the_start,'2018-12-31'))
newmask = np.prod(~dai_jja_mask.mask,axis=0)
dai_jja_mask = mask_data(dai_jja_mask,newmask==0)
thesolver = Eof(mask_data(D.ALL.mma(time=(the_start,the_stop)),newmask==0),weights='area')
else:
thesolver = Eof(the_mma,weights="area")
return thesolver
def soilmoisture(depth,mask=None):
if depth == "pdsi":
f = cdms.open("../DROUGHT_ATLAS/CMIP5/pdsi.ensemble.hist.rcp85.nc")
variable="pdsi"
else:
f = cdms.open("../DROUGHT_ATLAS/CMIP5/sm"+depth+".ensemble.hist.rcp85.nc")
variable = "sm"+depth
sm = get_rid_of_bad(f(variable))
sm = MV.masked_where(np.isnan(sm),sm)
f.close()
if mask is not None:
sm = mask_data(sm,mask)
return sm
def model_projections(self, solver=None):
if solver is None:
make_own_solver = True
else:
make_own_solver = False
if solver is None:
to_proj = mask_data(self.model, self.solver.eofs()[0].mask)
else:
to_proj = cmip5.cdms_clone(MV.filled(self.model, fill_value=0),
self.model)
to_proj = mask_data(to_proj, solver.eofs()[0].mask)
P = MV.zeros(to_proj.shape[:2])
for i in range(to_proj.shape[0]):
tp = to_proj[i]
if make_own_solver:
mma_mask = mask_data(self.mma, tp[0].mask)
solver = Eof(mma_mask, weights='area')
fac = da.get_orientation(solver)
P[i] = solver.projectField(tp)[:, 0] * fac
P.setAxisList(to_proj.getAxisList()[:2])
return P
def write_combinations():
"""
Make t combined NH drought atlases: ALL_OBS, which starts in 1100 and includes MADA, OWDA, and NADA, and ALL_OBS_plus_MEX, which includes the former + MXDA
"""
list_of_obs = []
for atlas_name in ["OWDA2.5", "MADA2.5", "NADA2.5"]:
f = cdms.open("../DROUGHT_ATLAS/PROCESSED/" + atlas_name + ".nc")
obs = f("pdsi")
obs = MV.masked_where(np.isnan(obs), obs)
obs = MV.masked_where(np.abs(obs) > 90, obs)
obs = obs(time=('1100-7-1', '2020-12-31'))
obs = mask_data(
obs, obs.mask[0]
) #Make all the obs have the same mask as the first datapoint
f.close()
list_of_obs += [obs]
writename = "../DROUGHT_ATLAS/PROCESSED/ALL_OBS.nc"
CO = combine_observations(list_of_obs, writename=writename)
modelwritename = "../DROUGHT_ATLAS/CMIP5/pdsi.ALL_OBS.hist.rcp85.nc"
mm = mask_models(CO, writename=modelwritename)
#now add in MXDA:
atlas_name = "MXDA2.5"
f = cdms.open("../DROUGHT_ATLAS/PROCESSED/" + atlas_name + ".nc")
obs = f("pdsi")
obs = MV.masked_where(np.isnan(obs), obs)
obs = MV.masked_where(np.abs(obs) > 90, obs)
obs = obs(time=('1100-7-1', '2020-12-31'))
obs = mask_data(
obs, obs.mask[0]
) #Make all the obs have the same mask as the first datapoint
f.close()
list_of_obs += [obs]
writename = "../DROUGHT_ATLAS/PROCESSED/ALL_OBS_plus_MEX.nc"
CO = combine_observations(list_of_obs, writename=writename)
modelwritename = "../DROUGHT_ATLAS/CMIP5/pdsi.ALL_OBS_plus_MEX.hist.rcp85.nc"
mm = mask_models(CO, writename=modelwritename)
def write_big_drought_atlas():
list_of_obs = []
for atlas_name in ["OWDA2.5", "MADA2.5", "NADA2.5", "MXDA2.5", "ANZDA2.5"]:
f = cdms.open("../DROUGHT_ATLAS/PROCESSED/" + atlas_name + ".nc")
obs = f("pdsi")
obs = MV.masked_where(np.isnan(obs), obs)
obs = MV.masked_where(np.abs(obs) > 90, obs)
obs = obs(time=('1100-7-1', '2020-12-31'))
obs = mask_data(
obs, obs.mask[0]
) #Make all the obs have the same mask as the first datapoint
f.close()
list_of_obs += [obs]
writename = "../DROUGHT_ATLAS/PROCESSED/ALL_ANZDA.nc"
CO = combine_observations(list_of_obs, writename=writename)
modelwritename = "../DROUGHT_ATLAS/CMIP5/pdsi.ALL_ANZDA.hist.rcp85.nc"
mm = mask_models(CO, writename=modelwritename)
def write_regridded():
fnames = glob.glob("../DROUGHT_ATLAS/PROCESSED/*")
for fil in fnames:
atlas_name = fil.split("/")[-1].split(".")[0]
if atlas_name != "ANZDA":
f = cdms.open("../DROUGHT_ATLAS/PROCESSED/" + atlas_name + ".nc")
obs = f("pdsi")
obs = MV.masked_where(np.isnan(obs), obs)
obs = MV.masked_where(np.abs(obs) > 90, obs)
obs = obs(time=('1100-7-1', '2020-12-31'))
obs = mask_data(
obs, obs.mask[0]
) #Make all the obs have the same mask as the first datapoint
f.close()
writefname = "../DROUGHT_ATLAS/PROCESSED/" + atlas_name + "2.5.nc"
CO = combine_observations([obs], grid='2.5', writename=writefname)
modelwritename = "../DROUGHT_ATLAS/CMIP5/pdsi." + atlas_name + "2.5.hist.rcp85.nc"
mm = mask_models(CO, writename=modelwritename)
def exclude_NADA():
"""
Make a combined NH drought atlas ALL_NONADA, which starts in 1400 and includes MADA, OWDA, ANZDA but NOT NADA
"""
list_of_obs = []
for atlas_name in ["OWDA2.5", "MADA2.5", "MXDA2.5", "ANZDA2.5"]:
f = cdms.open("../DROUGHT_ATLAS/PROCESSED/" + atlas_name + ".nc")
obs = f("pdsi")
obs = MV.masked_where(np.isnan(obs), obs)
obs = MV.masked_where(np.abs(obs) > 90, obs)
obs = obs(time=('1400-7-1', '2020-12-31'))
obs = mask_data(
obs, obs.mask[0]
) #Make all the obs have the same mask as the first datapoint
f.close()
list_of_obs += [obs]
writename = "../DROUGHT_ATLAS/PROCESSED/ALL_NONADA.nc"
CO = combine_observations(list_of_obs, writename=writename)
modelwritename = "../DROUGHT_ATLAS/CMIP5/pdsi.ALL_NONADA.hist.rcp85.nc"
mm = mask_models(CO, writename=modelwritename)
def individual_plots():
fnames = glob.glob("../DROUGHT_ATLAS/PROCESSED/ORIGINAL_GRID/*")
for fil in fnames:
atlas_name = fil.split("/")[-1].split(".")[0]
f = cdms.open("../DROUGHT_ATLAS/PROCESSED/ORIGINAL_GRID/" +
atlas_name + ".nc")
obs = f("pdsi")
obs = MV.masked_where(np.isnan(obs), obs)
obs = MV.masked_where(np.abs(obs) > 90, obs)
obs = obs(time=('1400-7-1', '2005-12-31'))
obs = mask_data(
obs, obs.mask[0]
) #Make all the obs have the same mask as the first datapoint
f.close()
plt.figure()
m = plot_regional(obs[-1], atlas_name, vmin=-6, vmax=6)
plt.title(atlas_name + ": 2005 PDSI")
m.drawcoastlines(color="gray")
plt.tight_layout()
plt.savefig("../DroughtAtlasPaper/FIGS/ForTalk/" + atlas_name +
"_2005.png")
plt.close()
def project_soilmoisture(self, dataset):
mask = self.mask
self.OBS_PROJECTIONS[dataset] = {}
surf, root = standardize_soilmoisture(dataset)
surfsolver = Eof(mask_data(self.mma["30cm"], mask), weights='area')
surfmask = mask_data(surf, surfsolver.eofs()[0].mask)
surfsolver = Eof(mask_data(self.mma["30cm"], surfmask[-1].mask),
weights='area')
surfanom = surfmask - MV.average(surfmask, axis=0)
self.OBS_PROJECTIONS[dataset]["30cm"] = surfsolver.projectField(
surfanom)[:, 0] * da.get_orientation(surfsolver)
rootsolver = Eof(mask_data(self.mma["2m"], mask), weights='area')
rootmask = mask_data(root, rootsolver.eofs()[0].mask)
rootsolver = Eof(mask_data(self.mma["2m"], rootmask[-1].mask),
weights='area')
rootanom = rootmask - MV.average(rootmask, axis=0)
self.OBS_PROJECTIONS[dataset]["2m"] = rootsolver.projectField(
rootanom)[:, 0] * da.get_orientation(rootsolver)
def observations():
# Read in PET and PR files
#regridded PET
f = cdms.open("OBS/cru_ts4.01.1901.2016.pet.dat.REGRID.nc")
pet = f("pet")
f.close()
landmask = pet[0].mask
# Regridded GPCC
fpr = cdms.open("OBS/precip.mon.total.2.5x2.5.v7.nc")
pr = fpr("precip")
fpr.close()
pr = pr / 30. #Approximate mm-> mm/day by assuming 30 days/month
#Put them both on the same time axis
startpet = cmip5.start_time(pet)
stoppet = cmip5.stop_time(pet)
startpr = cmip5.start_time(pr)
stoppr = cmip5.stop_time(pr)
if cdtime.compare(startpet, startpr) > 0:
start = startpet
else:
start = startpr
if cdtime.compare(stoppet, stoppr) > 0:
stop = stoppr
else:
stop = stoppet
start = cdtime.comptime(start.year, start.month, 1)
stop = cdtime.comptime(stop.year, stop.month, 31)
pr = pr(time=(start, stop))
pet = pet(time=(start, stop))
#Calculate R and P
Rpr, Ppr = sc.fast_annual_cycle(pr)
Rpet, Ppet = sc.fast_annual_cycle(pet)
#Convert phase to month of maximum (VECTORIZE THIS?)
#How to handle "month of maximum" if it's fluctuating between 1 and 12? (physically, what does this mean when our timesteps are every year?)
#Should we modify the code in phase detection to start at phase 0? Ie start in a month such that the maximum is 6 months away?
#Calculate variance maps for p and pet
test_period = ('1979-1-1', '2004-12-31'
) #for overlap with CMIP5 historical
pet_vmap = sc.variance_map(pet(time=test_period))
pr_vmap = sc.variance_map(pr(time=test_period))
#make phase maps
variance_threshold = 0.25 #Can we come up with a physically meaningful threshold here? Null hypothesis of no correlation ruled out at 99% confidence?
Ppet_clim = sc.phase_climatology(Ppet)
Ppet_clim_month = sc.mask_data(sc.phase_to_month(Ppet_clim), landmask)
Ppr_clim = sc.phase_climatology(Ppr)
Ppr_clim_month = sc.mask_data(sc.phase_to_month(Ppr_clim), landmask)
months = [
"JAN", "FEB", "MAR", "APR", "MAY", "JUN", "JUL", "AUG", "SEP", "OCT",
"NOV", "DEC"
]
from land_seasonal_cycle import landplot
plt.subplot(211)
m = landplot(MV.masked_where(pet_vmap < variance_threshold,
Ppet_clim_month),
cmap=cm.hsv,
vmin=0,
vmax=12)
m.drawcoastlines(color='gray')
cbar = plt.colorbar(orientation="horizontal")
cbar.set_ticks(np.arange(12))
cbar.set_ticklabels(months)
plt.title("PET phase")
plt.subplot(212)
m = landplot(MV.masked_where(pr_vmap < variance_threshold, Ppr_clim_month),
cmap=cm.hsv,
vmin=0,
vmax=12)
m.drawcoastlines(color='gray')
cbar = plt.colorbar(orientation="horizontal")
cbar.set_ticks(np.arange(12))
cbar.set_ticklabels(months)
plt.title("PR phase")
#phase trends
Pa_pet = sc.get_phase_anomalies(Ppet)
Pa_pet_trends = cmip5.get_linear_trends(Pa_pet)
Pa_pr = sc.get_phase_anomalies(Ppr)
Pa_pr_trends = cmip5.get_linear_trends(Pa_pr)
def average_over_biome(K,X,biome):
basicmask = K!=biome
biome_mask = sc.mask_data(X,basicmask)
return cdutil.averager(biome_mask,'xy')
def __init__(self, name, cutoff='0001-1-1'):
if name.find("2.5") >= 0:
self.name = name.split("2.5")[0]
else:
self.name = name
#if name.find("+")<0:
f = cdms.open("../DROUGHT_ATLAS/PROCESSED/" + name + ".nc")
obs = f("pdsi")
self.obs = MV.masked_where(np.isnan(obs), obs)
self.obs = MV.masked_where(np.abs(self.obs) > 90, self.obs)
self.obs = self.obs(time=(cutoff, '2020-12-31'))
self.obs = mask_data(
self.obs, self.obs.mask[0]
) #Make all the obs have the same mask as the first datapoint
f.close()
fm = cdms.open("../DROUGHT_ATLAS/CMIP5/pdsi." + name +
".hist.rcp85.nc")
self.model = get_rid_of_bad(fm("pdsi"))
self.model = MV.masked_where(np.isnan(self.model), self.model)
fm.close()
# else:
#DEPRECATED: MERGE observations onto common grid using old code
# name1,name2=name.split("+")
# f1 = cdms.open("../DROUGHT_ATLAS/PROCESSED/"+name1+".nc")
# obs1 = f1("pdsi")
# obs1 = MV.masked_where(np.isnan(obs1),obs1)
# obs1 = MV.masked_where(np.abs(obs1)>90,obs1)
# obs1 = obs1(time=(cutoff,'2017-12-31'))
# obs1=mask_data(obs1,obs1.mask[0])
# f1.close()
# fm1 = cdms.open("../DROUGHT_ATLAS/CMIP5/pdsi."+name1+".hist.rcp85.nc")
# model1=get_rid_of_bad(fm1("pdsi"))
# model1=MV.masked_where(np.isnan(model1),model1)
# fm1.close()
# f2 = cdms.open("../DROUGHT_ATLAS/PROCESSED/"+name2+".nc")
# obs2 = f2("pdsi")
# obs2 = MV.masked_where(np.isnan(obs2),obs2)
# obs2 = MV.masked_where(np.abs(obs2)>90,obs2)
# obs2 = obs2(time=(cutoff,'2017-12-12'))
# obs2=mask_data(obs2,obs2.mask[0])
# f2.close()
# fm2 = cdms.open("../DROUGHT_ATLAS/CMIP5/pdsi."+name2+".hist.rcp85.nc")
# model2=get_rid_of_bad(fm2("pdsi"))
# model2=MV.masked_where(np.isnan(model2),model2)
# fm2.close()
# self.obs=merge.merge(obs1,obs2)
# self.model=merge.merge(model1,model2)
mma = MV.average(self.model, axis=0)
self.mma = mask_data(
mma, self.obs[0].mask) #make all the models have the same mask
self.solver = Eof(self.mma, weights='area')
self.eofmask = self.solver.eofs()[0].mask
self.fac = da.get_orientation(self.solver)
self.projection = self.solver.projectField(
mask_data(self.obs, self.eofmask))[:, 0] * self.fac
self.noise = self.projection(time=('1-1-1', '1850-1-1'))
self.P = self.model_projections()
