# -*- coding: utf-8 -*- # Creation d'un jeu de precipitations horaires import MV2, cdms2, numpy as N from vacumm.misc.axes import create_time hours = create_time((12*60, 25.*60, 60), 'minutes since 2000') precip = MV2.sin(N.arange(len(hours))*.2)*10 precip.setAxis(0, hours) precip.units = 'mm' precip.long_name = 'Original' # Nouvel echantillonnage / 2h hours2 = create_time((10, 30., 2), 'hours since 2000') # Regrillage 1D conservatif from vacumm.misc.grid.regridding import regrid1d precip2 = regrid1d(precip, hours2, 'conservative') precip2.long_name = 'Regridded' # Verifications print 'Total precip.:' print '- original =', precip.sum() print '- remapped =', precip2.sum() # > Total precip.: # > - original = 89.957242832779755 # > - remapped = 89.957237 # Plots from vacumm.misc.plot import savefigs from vacumm.misc.plot import bar2 kwplot = dict(color='#00ffff',edgecolor='#55aaaa',
# Détermination des profondeurs d'après les sigma # - détection auto de la classe de sigma d'après fichier sigma_class = NcSigma.factory(f) # - initialisation du convertisseur sigma_converter = sigma_class(copyaxes=True) # - lecture de eta (sans sélection de domaine) et calcul des profondeurs depths_in = sigma_converter().filled() f.close() # Creation de l'axe des profondeurs cibles depth_out = create_depth(depths) # Interpolation xmap = (0, 2, 3) # la profondeur varie en T/Y/X xmapper = np.rollaxis(depths_in, 1, 4) # profondeur = dernier axe data_out = regrid1d(data_in, depth_out, axi=depths_in, axis=1, method='linear', extrap=1) # Plot kw = dict(show=False, vmin=10, vmax=14, xhide='auto', add_grid=True, ymax=0) section2(data_in[0, :, 10], yaxis=depths_in[0, :, 10], subplot=211, title='Sigma', **kw) s = section2(data_out[0, :, 10], subplot=212, title='Z', savefigs=__file__, **kw) # Sauvegarde outfile = __file__[:-2]+'nc' if os.path.isfile(outfile): os.remove(outfile) netcdf3() f2 = cdms2.open(outfile,'w') f2.write(data_out) f2.close() print 'Saved to', outfile
# Détermination des profondeurs d'après les sigma
# - détection auto de la classe de sigma d'après fichier
sigma_class = NcSigma.factory(f)
# - initialisation du convertisseur
sigma_converter = sigma_class(copyaxes=True)
# - lecture de eta (sans sélection de domaine) et calcul des profondeurs
depths_in = sigma_converter().filled()
f.close()
# Creation de l'axe des profondeurs cibles
depth_out = create_depth(depths)
# Interpolation
xmap = (0, 2, 3) # la profondeur varie en T/Y/X
xmapper = np.rollaxis(depths_in, 1, 4) # profondeur = dernier axe
data_out = regrid1d(data_in, depth_out, axi=depths_in, axis=1, method='linear', extrap=1)
# Plot
kw = dict(show=False, vmin=10, vmax=14, xhide='auto', add_grid=True, ymax=0)
section2(data_in[0, :, 10], yaxis=depths_in[0, :, 10], subplot=211, title='Sigma', **kw)
s = section2(data_out[0, :, 10], subplot=212, title='Z', savefigs=__file__,
close=True, **kw)
# Sauvegarde
outfile = __file__[:-2]+'nc'
if os.path.isfile(outfile):
os.remove(outfile)
netcdf3()
f2 = cdms2.open(outfile,'w')
f2.write(data_out)
f2.close()
# - basse résolution
lon_lr = create_lon((lon.min()+dlon*.7, lon.max()+dlon, dlon*3.3))
# - haute résolution
lon_hr = create_lon((lon.min()+dlon, lon.max()+dlon, dlon/3.3))
# - dict
lons = dict(haute=lon_hr, basse=lon_lr)
# Regrillages et plots
from matplotlib import rcParams ; rcParams['font.size'] = 9
from vacumm.misc.grid.regridding import regrid1d
from vacumm.misc.plot import curve2,savefigs, yscale ; import pylab as P
P.figure(figsize=(5.5, 6))
kwplot = dict(show=False,vmin=v.min(),vmax=v.max(),alpha=.7)
for ilh,resdst in enumerate(['basse', 'haute']):
# Regrillage
vlinear = regrid1d(v, lons[resdst], 'linear')
vremap = regrid1d(v, lons[resdst], 'cellave')
# Plots
P.subplot(2, 1, ilh+1)
curve2(v, 'o', markersize=4, color='k', label=u'Original', hspace=.3, **kwplot)
curve2(vremap, 'o', markersize=2, label=u'Cellave', linewidth=1.2, color='b', **kwplot)
curve2(vlinear, 'o', markersize=2, label=u'Linear', linewidth=1.2, color='r', **kwplot)
yscale(1.1)
P.title(u'Vers la %s resolution'%resdst)
if not ilh: P.legend(loc='lower left').legendPatch.set_alpha(.6)
savefigs(__file__, pdf=True)
h0 = f('h0', lon=(-4.9, -4.43)).filled(0.)
f.close()
t.long_name = 'Original'
# Creation d'un axe de profondeur
from vacumm.misc.axes import create_dep
ddep = 5.
dep = create_dep((-h0.max(), 0. + ddep, ddep))
# Creation de l'axe etendu (taille (ndep,nx))
depths = -N.outer(t.getAxis(0)[::-1], h0)
dep[-1] = depths[-1].max()
# Regrillage lineaire
from vacumm.misc.grid.regridding import regrid1d
tr = regrid1d(t, dep, 'linear', axi=depths, axis=0)
tr.long_name = 'Interpolated'
t.getAxis(0).designateLevel()
# Plot
from vacumm.misc.plot import savefigs, yscale, add_grid, section2
import pylab as P
P.figure(figsize=(5.5, 6))
kwplot = dict(show=False, bgcolor='.5', ylim=(-80, 0))
# - original
section2(t, yaxis=depths, subplot=211, hspace=.3, **kwplot)
add_grid((t.getLongitude(), depths[:]), linewidth=.3)
# - regridded
section2(tr, subplot=212, **kwplot)
add_grid((tr.getLongitude(), dep[:]), linewidth=.3)
# -*- coding: utf-8 -*- # Creation d'un jeu de precipitations horaires import MV2, cdms2, numpy as N from vacumm.misc.axes import create_time hours = create_time((12*60, 25.*60, 60), 'minutes since 2000') precip = MV2.sin(N.arange(len(hours))*.2)*10 precip.setAxis(0, hours) precip.units = 'mm' precip.long_name = 'Original' # Nouvel echantillonnage / 2h hours2 = create_time((10, 30., 2), 'hours since 2000') # Regrillage 1D conservatif from vacumm.misc.grid.regridding import regrid1d precip2 = regrid1d(precip, hours2, 'conservative') precip2.long_name = 'Regridded' # Verifications print 'Total precip.:' print '- original =', precip.sum() print '- remapped =', precip2.sum() # > Total precip.: # > - original = 89.957242832779755 # > - remapped = 89.957237 # Plots from vacumm.misc.plot import savefigs from vacumm.misc.plot import bar2 kwplot = dict(color='#00ffff',edgecolor='#55aaaa',
P.subplot(133, sharex=ax, sharey=ax)
P.pcolormesh(xbo, ybo, voc, **kw)
P.ylim(ymin=min(ybi.min(), ybo.min()), ymax=max(ybi.max(), ybo.max()))
P.title('Cellave')
P.tight_layout()
P.savefig(figfile)
# 1d->1d
depi1d = create_dep(N.arange(-4500., 1, 500))
depo1d = create_dep(N.arange(-4000., 1, 333.33))
nzi = depi1d.shape[0]
vari = MV2.asarray(
N.ma.resize(depi1d[:], (nt, ny, nx, nzi)).transpose([0, 3, 1, 2]))
vari.setAxis(1, depi1d)
varol1 = regrid1d(vari, depo1d, method='linear')
varol2 = regrid1d(vari, depo1d, method='linear', iaxi=0, iaxo=0, axi=depi1d)
result.append(('assertEqual', [(varol1 - varol2).std(), 0]))
varoc = regrid1d(vari, depo1d, method='cellave')
myplot(vari, depi1d, varol1, varoc, depo1d, code_file_name(ext='_0.png'))
# 4d->1d
depi1d = N.arange(-4500., 1, 500)
nzi = depi1d.shape[0]
depi4d = N.resize(N.resize(depi1d, (nx, ny, nzi)).T, (nt, nzi, ny, nx))
depi4d += 500 * (N.random.random(depi4d.shape) - 0.5)
depo1d = create_dep(N.arange(-4000., 1, 333.33))
vari = MV2.array(depi4d, fill_value=1e20)
vari.getAxis(1).designateLevel()
depi4d = MV2.asarray(depi4d)
depi4d.getAxis(1).designateLevel()
lon_lr = create_lon((lon.min()+dlon*.7, lon.max()+dlon, dlon*3.3))
# - haute résolution
lon_hr = create_lon((lon.min()+dlon, lon.max()+dlon, dlon/3.3))
# - dict
lons = dict(haute=lon_hr, basse=lon_lr)
# Regrillages et plots
from matplotlib import rcParams ; rcParams['font.size'] = 9
from vacumm.misc.grid.regridding import regrid1d
from vacumm.misc.plot import curve2,savefigs, yscale ; import pylab as P
P.figure(figsize=(5.5, 6))
kwplot = dict(show=False,vmin=v.min(),vmax=v.max(),alpha=.7)
for ilh,resdst in enumerate(['basse', 'haute']):
# Regrillage
vlinear = regrid1d(v, lons[resdst], 'linear')
vremap = regrid1d(v, lons[resdst], 'cellave')
# Plots
P.subplot(2, 1, ilh+1)
curve2(v, 'o', markersize=4, color='k', label=u'Original', hspace=.3, **kwplot)
curve2(vremap, 'o', markersize=2, label=u'Cellave', linewidth=1.2, color='b', **kwplot)
curve2(vlinear, 'o', markersize=2, label=u'Linear', linewidth=1.2, color='r', **kwplot)
yscale(1.1)
P.title(u'Vers la %s resolution'%resdst)
if not ilh: P.legend(loc='lower left').legendPatch.set_alpha(.6)
savefigs(__file__, pdf=True)
P.close()
h0 = f('h0', lon=(-4.9, -4.43)).filled(0.)
f.close()
t.long_name = 'Original'
# Creation d'un axe de profondeur
from vacumm.misc.axes import create_dep
ddep = 5.
dep = create_dep((-h0.max(),0.+ddep , ddep))
# Creation de l'axe etendu (taille (ndep,nx))
depths = -N.outer(t.getAxis(0)[::-1], h0)
dep[-1] = depths[-1].max()
# Regrillage lineaire
from vacumm.misc.grid.regridding import regrid1d
tr = regrid1d(t, dep, 'linear', axi=depths, axis=0)
tr.long_name = 'Interpolated'
t.getAxis(0).designateLevel()
# Plot
from vacumm.misc.plot import savefigs,yscale, add_grid, section2
import pylab as P
P.figure(figsize=(5.5, 6))
kwplot = dict(show=False, bgcolor='.5', ylim=(-80, 0))
# - original
section2(t, yaxis=depths, subplot=211, hspace=.3, **kwplot)
add_grid((t.getLongitude(), depths[:]), linewidth=.3)
# - regridded
section2(tr, subplot=212, **kwplot)
add_grid((tr.getLongitude(), dep[:]), linewidth=.3)
P.title('Linear')
P.subplot(133, sharex=ax, sharey=ax)
P.pcolormesh(xbo, ybo, voc, **kw)
P.ylim(ymin=min(ybi.min(), ybo.min()), ymax=max(ybi.max(), ybo.max()))
P.title('Cellave')
P.tight_layout()
P.savefig(figfile)
# 1d->1d
depi1d = create_dep(N.arange(-4500., 1, 500))
depo1d = create_dep(N.arange(-4000., 1, 333.33))
nzi = depi1d.shape[0]
vari = MV2.asarray(N.ma.resize(depi1d[:], (nt, ny, nx, nzi)).transpose([0, 3, 1, 2]))
vari.setAxis(1, depi1d)
varol1 = regrid1d(vari, depo1d, method='linear')
varol2 = regrid1d(vari, depo1d, method='linear', iaxi=0, iaxo=0, axi=depi1d)
result.append(('assertEqual', [(varol1-varol2).std(), 0]))
varoc = regrid1d(vari, depo1d, method='cellave')
myplot(vari, depi1d, varol1, varoc, depo1d, code_file_name(ext='_0.png'))
# 4d->1d
depi1d = N.arange(-4500., 1, 500)
nzi = depi1d.shape[0]
depi4d = N.resize(N.resize(depi1d, (nx, ny, nzi)).T, (nt, nzi, ny, nx))
depi4d += 500*(N.random.random(depi4d.shape)-0.5)
depo1d = create_dep(N.arange(-4000., 1, 333.33))
vari = MV2.array(depi4d, fill_value=1e20)
vari.getAxis(1).designateLevel()
depi4d = MV2.asarray(depi4d)
