def plot(xx,
yy,
target,
label,
figfiles,
figfile,
lon=None,
lat=None,
show=False):
xs, ys, mask = coord2slice(target, lon=lon, lat=lat)
P.figure(figsize=(6, 3.5))
P.title('Target=%(label)s / select: lon=%(lon)s, lat=%(lat)s' % locals())
add_grid((xx, yy))
xx = xx.asma()
yy = yy.asma()
if isinstance(lon, tuple):
P.axvline(lon[0], color='m', ls='--', lw=2)
P.axvline(lon[1], color='m', ls='--', lw=2)
elif isinstance(lon, slice):
i, j, k = lon.indices(xx.shape[1])
P.plot(xx[:, i], yy[:, i], 'c--', lw=2)
P.plot(xx[:, j - 1], yy[:, j - 1], 'c--', lw=2)
if isinstance(lat, tuple):
P.axhline(lat[0], color='m', ls='--', lw=2)
P.axhline(lat[1], color='m', ls='--', lw=2)
elif isinstance(lat, slice):
i, j, k = lat.indices(yy.shape[0])
P.plot(xx[i], yy[i], 'c--', lw=2)
P.plot(xx[j - 1], yy[j - 1], 'c--', lw=2)
P.xticks(N.arange(xx.min() - 1, xx.max() + 1))
P.yticks(N.arange(yy.min() - 1, yy.max() + 1))
xxi, yyi = xx, yy
xx = xx[ys, xs]
yy = yy[ys, xs]
# mask = mask[ys, xs]
xxb, yyb = meshbounds(xx, yy)
P.pcolormesh(xxb, yyb, mask, shading='faceted')
P.scatter(xx.ravel(), yy.ravel(), c=(0, 1, 0))
P.grid(True)
P.axis('image')
P.tight_layout()
i = len(figfiles)
savefig = figfile % i
if os.path.exists(savefig): os.remove(savefig)
P.savefig(savefig)
figfiles.append(savefig)
if show: P.show()
else: P.close()
def myplot(vari, depi, varol, varoc, depo, figfile):
def getdv(var, dep):
v = var[1, :, 1, :]
if dep[:].ndim == var.ndim:
dep = dep[1, :, 1, :]
elif dep[:].ndim == 3:
dep = dep[:, 1, :]
xb, yb = meshcells(v.getAxis(-1), dep)
return xb, yb, v.asma()
xbi, ybi, vi = getdv(vari, depi)
xbo, ybo, vol = getdv(varol, depo)
_, _, voc = getdv(varoc, depo)
vmin, vmax = minmax(vi, vol, voc)
kw = dict(vmin=vmin, vmax=vmax)
P.figure(figsize=(10, 4))
ax = P.subplot(131)
P.pcolormesh(xbi, ybi, vi, **kw)
P.title('Original')
P.subplot(132, sharex=ax, sharey=ax)
P.pcolormesh(xbo, ybo, vol, **kw)
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)
def myplot(vari, depi, varol, varoc, depo, figfile):
def getdv(var, dep):
v = var[1, :, 1, :]
if dep[:].ndim==var.ndim:
dep = dep[1, :, 1, :]
elif dep[:].ndim==3:
dep = dep[:, 1, :]
xb, yb = meshcells(v.getAxis(-1), dep)
return xb, yb, v.asma()
xbi, ybi, vi = getdv(vari, depi)
xbo, ybo, vol = getdv(varol, depo)
_, _, voc = getdv(varoc, depo)
vmin, vmax = minmax(vi, vol, voc)
kw = dict(vmin=vmin, vmax=vmax)
P.figure(figsize=(10, 4))
ax = P.subplot(131)
P.pcolormesh(xbi, ybi, vi, **kw)
P.title('Original')
P.subplot(132, sharex=ax, sharey=ax)
P.pcolormesh(xbo, ybo, vol, **kw)
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)
yi = N.array(yi)
zi = N.array(zi)
xxg, yyg = N.meshgrid(xg, yg)
xo = xxg.ravel()
yo = yyg.ravel()
vgm = variogram_model('linear', n=0, s=sill, r=range)
# Setup the kriger
sck = SimpleCloudKriger(xi, yi, zi, vgf=vgm, farvalue=farvalue)
# Interpolate
zo = sck(xo, yo)
# Check a far value
zzg = zo.reshape(ny, nx)
N.testing.assert_allclose(zzg[-1, -1],farvalue)
# Plot
vmin = min(zi.min(), zo.min())
vmax = max(zi.max(), zo.max(), farvalue)
cmap = 'cmocean_ice'
kw = dict(vmin=vmin, vmax=vmax)
P.pcolormesh(xxg, yyg, zzg, cmap=cmap, **kw)
P.colorbar()
P.scatter(xi, yi, c=zi, s=100, cmap=cmap, **kw)
P.axis('image')
P.title('Simple kriging with fixed far value')
P.savefig(code_file_name(ext='.png'))
P.show()
P.close()
zov2 = xy2xy(xi, yi, zi, xo, yo)
# Krigeage
from vacumm.misc.grid.kriging import krig
zzok = krig(xi, yi, zi, xxr.ravel(), yyr.ravel(), nproc=1).reshape(zzr.shape)
# -> Tester nproc et npmax
# Plots
from vcmq import meshbounds, P
xxrb, yyrb = meshbounds(xr, yr)
P.figure(figsize=(10, 8))
axis = [xxrb.min(), xxrb.max(), yyrb.min(), yyrb.max()]
#
P.subplot(332)
P.pcolormesh(xxrb, yyrb, zzr, **vminmax)
P.scatter(xi, yi, c='k')
P.title('Original')
P.axis(axis)
#
P.subplot(334)
P.pcolormesh(xxrb, yyrb, zzoc, **vminmax)
P.title('CDAT/Natgrid')
P.axis(axis)
#
P.subplot(335)
P.pcolormesh(xxrb, yyrb, zzork, **vminmax)
P.title('MPL/R.K. grid')
P.axis(axis)
#
P.subplot(336)
coordSys='cart')
# Norm
frac = diag['dstAreaFractions']
mask = frac == 0.
frac[mask] = 1.
varo[:] /= frac
varo[:] = MV2.masked_where(mask, varo, copy=0)
# Plot
rc('font', size=9)
kw = dict(vmin=vari.min(), vmax=vari.max())
axis = [xxbi.min(), xxbi.max(), yybo.min(), yybo.max()]
P.figure(figsize=(7, 3))
P.subplot(121, aspect=1)
P.pcolormesh(xxbi, yybi, vari, **kw)
P.colorbar(shrink=0.7)
add_grid(gridi, color='0.5')
add_grid(grido)
P.axis(axis)
P.title('Original: max=%g min=%g' % (vari.max(), vari.min()))
P.subplot(122, aspect=1)
P.pcolormesh(xxbo, yybo, varo, **kw)
P.colorbar(shrink=0.7)
add_grid(gridi)
add_grid(grido, color='0.5')
P.axis(axis)
P.title('Regridded: max=%g min=%g' % (varo.max(), varo.min()))
P.tight_layout()
savefigs(code_file_name(), verbose=False)
P.close()
frac = diag['dstAreaFractions']
if frac is not None:
mask = frac<=1.e-3
frac[mask] = 1.
frac = N.resize(frac, varo.shape)
mask = N.resize(mask, varo.shape)
varo[:] /= frac
varo[:] = MV2.masked_where(mask, varo, copy=0)
# del r
gc.collect()
if rank==0:
print >>f, ' plot'
P.figure(figsize=(12, 6))
P.subplots_adjust(right=0.9)
P.subplot(121)
P.pcolormesh(xi, yi, vari[0,0].asma(),vmin=vmin,vmax=vmax)
P.axis([xmin, xmax, ymin, ymax])
P.colorbar()
P.title('Original')
P.subplot(122)
P.pcolormesh(xo, yo, varo[0,0].asma(),vmin=vmin,vmax=vmax)
P.axis([xmin, xmax, ymin, ymax])
P.title(tool.upper()+' / '+method.upper())
P.colorbar(extend='min')#cax=P.axes([0.92, 0.3, 0.02, 0.6]))
figfile = basefile+'_%(tool)s_%(method)s.png'%vars()
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
del varo
if rank==0:print >>f, 'Done'
f.close()
zzge = zoe.reshape(ny, nx)
# Plot
vmin = min(zi.min(), zo.min(), zoe.min())
vmax = max(zi.max(), zo.max(), zoe.max())
vmax = max(abs(vmin), abs(vmax))
vmin = -vmax
cmap = 'cmocean_tempo'
cmap = 'cmocean_delta'
kw = dict(vmin=vmin, vmax=vmax)
P.figure(figsize=(6, 5.5))
ax = P.subplot(221)
P.pcolormesh(xxg, yyg, zzg, cmap=cmap, **kw)
P.colorbar()
P.scatter(xi, yi, c=zi, s=100, cmap=cmap, **kw)
P.title('Without obs error')
P.subplot(222, sharex=ax, sharey=ax)
P.pcolormesh(xxg, yyg, zzge, cmap=cmap, **kw)
P.colorbar()
P.scatter(xi, yi, c=zi, s=100, cmap=cmap, **kw)
#P.axis('image')
P.title('With obs error')
P.subplot(223, sharex=ax, sharey=ax)
vmax = abs(zzge-zzg).max()
vmin = -vmax
P.pcolormesh(xxg, yyg, zzge-zzg, cmap=cmap, vmin=vmin, vmax=vmax)
yi = N.array(yi)
zi = N.array(zi)
xxg, yyg = N.meshgrid(xg, yg)
xo = xxg.ravel()
yo = yyg.ravel()
vgm = variogram_model('linear', n=0, s=sill, r=range)
# Setup the kriger
sck = SimpleCloudKriger(xi, yi, zi, vgf=vgm, farvalue=farvalue)
# Interpolate
zo = sck(xo, yo)
# Check a far value
zzg = zo.reshape(ny, nx)
N.testing.assert_allclose(zzg[-1, -1], farvalue)
# Plot
vmin = min(zi.min(), zo.min())
vmax = max(zi.max(), zo.max(), farvalue)
cmap = 'cmocean_ice'
kw = dict(vmin=vmin, vmax=vmax)
P.pcolormesh(xxg, yyg, zzg, cmap=cmap, **kw)
P.colorbar()
P.scatter(xi, yi, c=zi, s=100, cmap=cmap, **kw)
P.axis('image')
P.title('Simple kriging with fixed far value')
P.savefig(code_file_name(ext='.png'))
P.show()
P.close()
diag=diag, coordSys='cart')
# Norm
frac = diag['dstAreaFractions']
mask = frac==0.
frac[mask]=1.
varo[:] /= frac
varo[:] = MV2.masked_where(mask, varo, copy=0)
# Plot
rc('font', size=9)
kw = dict(vmin=vari.min(), vmax=vari.max())
axis = [xxbi.min(), xxbi.max(), yybo.min(), yybo.max()]
P.figure(figsize=(7, 3))
P.subplot(121, aspect=1)
P.pcolormesh(xxbi, yybi, vari, **kw)
P.colorbar(shrink=0.7)
add_grid(gridi, color='0.5')
add_grid(grido)
P.axis(axis)
P.title('Original: max=%g min=%g'%(vari.max(), vari.min()))
P.subplot(122, aspect=1)
P.pcolormesh(xxbo, yybo, varo, **kw)
P.colorbar(shrink=0.7)
add_grid(gridi)
add_grid(grido, color='0.5')
P.axis(axis)
P.title('Regridded: max=%g min=%g'%(varo.max(), varo.min()))
P.tight_layout()
savefigs(code_file_name(), verbose=False)
P.close()
# Ajustment for conservative methode
frac = diag['dstAreaFractions']
if method=='conservative': # divide by dstAreaFractions for conservative
log(f, ' frac: %s'%frac)
mask = frac==0.
frac[mask] = 1.
varo[:] /= frac
varo[:] = MV2.masked_where(mask, varo, copy=0)
log(f, ' dstareas: %s'%diag['dstAreas'])
log(f, ' varo: %s'%varo)
# Plot
P.figure(figsize=(6,3))
P.subplot(121).set_aspect(1)
P.pcolormesh(xxib,yyib,vari.asma(),**kw)
#P.colorbar()
P.title('Original')
kwg = dict(alpha=1, linewidth=1., centers=True)
add_grid(gridi, color=(0,0,.2), marker='o', **kwg)
add_grid(grido, color=(.2,0,0), marker='+', markersize=10,**kwg)
P.axis('image')
axis = P.axis()
P.subplot(122).set_aspect(1)
P.pcolormesh(xxob,yyob,varo.asma(),**kw)
#P.colorbar()
P.title('%(tool)s / %(method)s'%locals())
add_grid(gridi, color=(0,0,.2), marker='o', **kwg)
add_grid(grido, color=(.2,0,0), marker='+',markerlinewidth=1,markersize=8,**kwg)
P.axis(axis)
ifig = len(figfiles)