def spatial_mean(self):
""" Moyenne spatiale en chaque pas de temps pour les champs modelises et observes """
import cdutil, cdms2, MV2, numpy
from vacumm.misc.grid import meshweights
#self.modelspatialmean = MV2.average(MV2.average(self.model, axis=-1), axis=-1)
# --> equivalent a nanmean(nanmean(sst')) sous matlab
#self.modelspatialmean = MV2.average(MV2.average(self.model, axis=1), axis=1)
# --> equivalent a nanmean(nanmean(sst)) sous matlab
#print self.modelspatialmean
self.model.spa_mean = list() #Initialise une liste
self.obs.spa_mean = list() #Initialise une liste
# Estimation de la moyenne a chaque pas de temps
tps = self.obs.getTime()
for i, t in enumerate(tps):
if self.obs[i, :, :].count()==0:
#self.obs.spa_mean.append(self.obs.fill_value) # voir pour mettre un masque dans la variable a tracer ... ou pour choisir une autre valeur
self.obs.spa_mean.append(0)
self.model.spa_mean.append(0)
else:
#modif J.Gatti- pour que moyenne spatiale fontionne avec une grille curvilineaire et poids soient en metres
lonmod=self.model.getLongitude()
latmod=self.model.getLatitude()
# lon et lat doivent etre 2D pour que meshweigths calcule des poids en m2
if lonmod.rank()==1:
lonmod,latmod=numpy.meshgrid(lonmod,latmod)
wtsmod=meshweights(lonmod, latmod, proj=True)
# la matrice de poids doit avoir le meme type que la variable moyennee (MV dans notre cas)
wtsmod = MV2.masked_array(wtsmod, MV2.getmask(self.model[i, :, :]), axes=self.model[i, :, :].getAxisList())
self.model.spa_mean.append(cdutil.averager(self.model[i, :, :], axis='yx', weights=wtsmod))
lonobs=self.obs.getLongitude()
latobs=self.obs.getLatitude()
if lonobs.rank()==1:
lonobs,latobs=numpy.meshgrid(lonobs,latobs)
wtsobs=meshweights(lonobs, latobs, proj=True)
wtsobs = MV2.masked_array(wtsobs, MV2.getmask(self.obs[i, :, :]), axes=self.obs[i, :, :].getAxisList())
self.obs.spa_mean.append(cdutil.averager(self.obs[i, :, :], axis='yx', weights=wtsobs))
#self.model.spa_mean.append(cdutil.averager(self.model[i, :, :],axis='yx'))
#self.obs.spa_mean.append(cdutil.averager(self.obs[i, :, :],axis='yx'))
#fin modif J.Gatti
# Transformation en variable cdms2
self.model.spa_mean = cdms2.createVariable(self.model.spa_mean,typecode='f',id='model_spa_mean', attributes=dict(units=self.model.units))
self.obs.spa_mean = cdms2.createVariable(self.obs.spa_mean,typecode='f',id='obs_spa_mean', attributes=dict(units=self.obs.units))
# Ajout de l'axe des temps correspondant a self...
self.model.spa_mean.setAxis(0, tps)
self.obs.spa_mean.setAxis(0, tps)
def spatial_mean(self):
""" Moyenne spatiale en chaque pas de temps pour les champs modelises et observes """
import cdutil, cdms2, MV2, numpy
from vacumm.misc.grid import meshweights
#self.modelspatialmean = MV2.average(MV2.average(self.model, axis=-1), axis=-1)
# --> equivalent a nanmean(nanmean(sst')) sous matlab
#self.modelspatialmean = MV2.average(MV2.average(self.model, axis=1), axis=1)
# --> equivalent a nanmean(nanmean(sst)) sous matlab
#print self.modelspatialmean
self.model.spa_mean = list() #Initialise une liste
self.obs.spa_mean = list() #Initialise une liste
# Estimation de la moyenne a chaque pas de temps
tps = self.obs.getTime()
for i, t in enumerate(tps):
if self.obs[i, :, :].count() == 0:
#self.obs.spa_mean.append(self.obs.fill_value) # voir pour mettre un masque dans la variable a tracer ... ou pour choisir une autre valeur
self.obs.spa_mean.append(0)
self.model.spa_mean.append(0)
else:
#modif J.Gatti- pour que moyenne spatiale fontionne avec une grille curvilineaire et poids soient en metres
lonmod = self.model.getLongitude()
latmod = self.model.getLatitude()
# lon et lat doivent etre 2D pour que meshweigths calcule des poids en m2
if lonmod.rank() == 1:
lonmod, latmod = numpy.meshgrid(lonmod, latmod)
wtsmod = meshweights(lonmod, latmod, proj=True)
# la matrice de poids doit avoir le meme type que la variable moyennee (MV dans notre cas)
wtsmod = MV2.masked_array(
wtsmod,
MV2.getmask(self.model[i, :, :]),
axes=self.model[i, :, :].getAxisList())
self.model.spa_mean.append(
cdutil.averager(self.model[i, :, :],
axis='yx',
weights=wtsmod))
lonobs = self.obs.getLongitude()
latobs = self.obs.getLatitude()
if lonobs.rank() == 1:
lonobs, latobs = numpy.meshgrid(lonobs, latobs)
wtsobs = meshweights(lonobs, latobs, proj=True)
wtsobs = MV2.masked_array(wtsobs,
MV2.getmask(self.obs[i, :, :]),
axes=self.obs[i, :, :].getAxisList())
self.obs.spa_mean.append(
cdutil.averager(self.obs[i, :, :],
axis='yx',
weights=wtsobs))
#self.model.spa_mean.append(cdutil.averager(self.model[i, :, :],axis='yx'))
#self.obs.spa_mean.append(cdutil.averager(self.obs[i, :, :],axis='yx'))
#fin modif J.Gatti
# Transformation en variable cdms2
self.model.spa_mean = cdms2.createVariable(
self.model.spa_mean,
typecode='f',
id='model_spa_mean',
attributes=dict(units=self.model.units))
self.obs.spa_mean = cdms2.createVariable(
self.obs.spa_mean,
typecode='f',
id='obs_spa_mean',
attributes=dict(units=self.obs.units))
# Ajout de l'axe des temps correspondant a self...
self.model.spa_mean.setAxis(0, tps)
self.obs.spa_mean.setAxis(0, tps)
id='maxvegetfrac')
print '--------------------------------------------------->>>>>> 4d variable', pft_13.shape
pft_13.long_name = 'Vegetation types'
pft_13.units = '-'
pft13_axislist_pft = pft_13.getAxisList(('veget', 'lat', 'lon'))
timax_slide = cdms2.createAxis(np.asarray([2010], np.float32))
timax_slide.designateTime(calendar=cdtime.MixedCalendar)
timax_slide.id = 'time_counter'
timax_slide.units = 'years since 0-1-1'
#********************************************************************************************************
#********************************************************************************************************
#Get the LUH masking
#mask_file = cdms2.open('esa_mask.nc')
#esa_mask = mask_file('Mask_only',latitude=latrange, longitude=lonrange)
luh_mask = MV2.getmask(primf[0, :, :])
print 'luh_mask', luh_mask.shape
icwtr = MV2.masked_array(icwtr, mask=luh_mask)
#Total land frac in ORCmap (Sum of all land units in ORCmap is 1)
orc_pft = MV2.masked_array(pft)
for n1 in range(0, 15):
orc_pft[n1, :, :] = MV2.masked_array(orc_pft[n1, :, :], mask=luh_mask)
#print n1
lndf_esa = MV2.minimum(MV2.sum(orc_pft, axis=0), 1)
lndf_esa.id = 'lndfrac_esa'
lndf_esa.long_name = 'Sum of all ORCmap PFTs'
#Total land frac in Hurtt data set (sum of all land units in LUH is one after including icwtr)
lndf_hurt = primf[nyears - 1, :, :] + secdf[nyears - 1, :, :] + primn[
nyears -
1, :, :] + secdn[nyears - 1, :, :] + c3ann[nyears - 1, :, :] + c4ann[
