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.pcolor(xxb, yyb, mask, shading='faceted')
P.scatter(xx.ravel(), yy.ravel(), c=(0, 1, 0))
P.grid('on')
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()
dyi = (yi[1]-yi[0])*0.49
dyo = (yo[1]-yo[0])*0.49
yyi = N.resize(yi, vari.shape)+N.random.uniform(-dyi, dyi, vari.shape)
yyo = N.resize(yo, (nx, len(yo)))+N.random.uniform(-dyo, dyo, (nx, len(yo)))
yyib, xxib = meshcells(yyi, x)
yyob, xxob = meshcells(yyo, x)
varon = N.ma.masked_values(interp1dxx(vari.filled(), yyi, yyo, mv, 0, extrap=0), mv)
varol = N.ma.masked_values(interp1dxx(vari.filled(), yyi, yyo, mv, 1, extrap=0), mv)
varoh = N.ma.masked_values(interp1dxx(vari.filled(), yyi, yyo, mv, 3, extrap=0), mv)
kw = dict(vmin=vari.min(), vmax=vari.max())
axlims = [x[0], x[-1], yo[0], yo[-1]]
P.figure(figsize=(8, 8))
P.subplot(221)
P.pcolor(xxib, yyib, vari)
P.axis(axlims)
P.title('Original')
P.subplot(222)
P.pcolor(xxob, yyob, varon, **kw)
P.axis(axlims)
P.title('Nearest1dxx')
P.subplot(223)
P.pcolor(xxob, yyob, varol, **kw)
P.axis(axlims)
P.title('Linear1dxx')
P.subplot(224)
P.pcolor(xxob, yyob, varoh, **kw)
P.axis(axlims)
P.title('Hermit1dxx')
P.tight_layout()
vari[int(nyi*0.4):int(nyi*0.4)+3, int(nxi*0.4):int(nxi*0.4)+2] = N.ma.masked
xxib, yyib = meshcells(xi, yi)
nxo = 40
nyo = 25
xo = N.linspace(int(nxi*0.2),int(nxi*1.2),nxo)
yo = N.linspace(int(-nyi*0.2),int(nyi*0.8),nyo)
xxob, yyob = meshcells(xo, yo)
vari.shape = (1, )+vari.shape
varo = N.ma.masked_values(dstwgt(vari.filled(), xi, yi, xo, yo, mv, 0), mv)
kw = dict(vmin=vari.min(), vmax=vari.max())
axlims = [min(xi.min(), xo.min()), max(xi.max(), xo.max()),
min(yi.min(), yo.min()), max(yi.max(), yo.max())]
P.figure(figsize=(8, 4))
P.subplot(211)
P.pcolor(xxib, yyib, vari[0], **kw)
P.axis(axlims)
P.title('Original')
P.subplot(212)
P.pcolor(xxob, yyob, varo[0], **kw)
P.axis(axlims)
P.title('Distance weight')
P.tight_layout()
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.show()
P.close()
yyo = N.resize(yo, (nx, len(yo))) + N.random.uniform(-dyo, dyo, (nx, len(yo)))
yyib, xxib = meshcells(yyi, x)
yyob, xxob = meshcells(yyo, x)
varon = N.ma.masked_values(
interp1dxx(vari.filled(), yyi, yyo, mv, 0, extrap=0), mv)
varol = N.ma.masked_values(
interp1dxx(vari.filled(), yyi, yyo, mv, 1, extrap=0), mv)
varoh = N.ma.masked_values(
interp1dxx(vari.filled(), yyi, yyo, mv, 3, extrap=0), mv)
kw = dict(vmin=vari.min(), vmax=vari.max())
axlims = [x[0], x[-1], yo[0], yo[-1]]
P.figure(figsize=(8, 8))
P.subplot(221)
P.pcolor(xxib, yyib, vari)
P.axis(axlims)
P.title('Original')
P.subplot(222)
P.pcolor(xxob, yyob, varon, **kw)
P.axis(axlims)
P.title('Nearest1dxx')
P.subplot(223)
P.pcolor(xxob, yyob, varol, **kw)
P.axis(axlims)
P.title('Linear1dxx')
P.subplot(224)
P.pcolor(xxob, yyob, varoh, **kw)
P.axis(axlims)
P.title('Hermit1dxx')
P.tight_layout()
nb = 10
xxbi, yybi = meshbounds(xxi, yyi)
# Output grid
grido = rotate_grid((N.linspace(0, 6, 50) - 1, N.linspace(0, 4, 35) + 1.), -20)
xxo = grido.getLongitude()[:].filled()
yyo = grido.getLatitude()[:].filled()
xxbo, yybo = meshbounds(xxo, yyo)
# Nearest
varo = nearest2d(vari, xxi, yyi, xxo, yyo, nb)
# Plot
vmin = varo.min()
vmax = varo.max()
P.figure(figsize=(8, 4))
P.subplot(121, aspect=1)
P.pcolor(xxbi, yybi, vari[0], vmin=vmin, vmax=vmax)
add_grid(grido)
P.title('original')
P.subplot(122, aspect=1)
P.pcolor(xxbo, yybo, varo[0], vmin=vmin, vmax=vmax)
add_grid(gridi)
P.title('nearest2d')
P.axis('image')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.show()
P.close()
xo.setBounds(bounds1d(xo))
yo.setBounds(bounds1d(yo))
xxob, yyob = meshbounds(xo, yo)
# Regridding
varo, wo = vari.regrid(grido, tool='regrid2', returnTuple=1)
# Plot
kw = dict(fill='pcolor', contour=False, xhide=True, yhide=True,
xticks=[], yticks=[], cmap='jet',
colorbar=False, show=False)
P.figure(figsize=(6, 3))
p = plot2d(vari, subplot=131, title='Original', **kw)
add_grid(gridi)
add_grid(grido)
P.axis('image')
p = plot2d(varo, subplot=132, title='Regridded', **kw)
add_grid(gridi)
add_grid(grido)
P.axis('image')
P.subplot(133)
P.pcolor(xxob, yyob, wo)
add_grid(grido)
P.title("Output weights")
P.tight_layout()
savefigs(code_file_name(),pdf=True, verbose=False)
P.close()
zzbi = relpos2index(iib, jjb, nyi)
# Input random points
N.random.seed(0)
np = 100
xxo = N.random.random(np)*(xxbi.max()-xxbi.min()) + xxbi.min()
yyo = N.random.random(np)*(yybi.max()-yybi.min()) + yybi.min()
xxo = N.concatenate((xxo, xxi.ravel()))
yyo = N.concatenate((yyo, yyi.ravel()))
np = xxo.size
# Convert to relative indices
zzo = N.zeros(np)-1
for i, (xo, yo) in enumerate(zip(xxo, yyo)):
p, q = curv2rel_single(xxbi, yybi, xo, yo)
# print xo, yo, '|', p, q, (q-1) * nxi + p - 1
if q>=0:
zzo[i] = relpos2index(p-1, q-1, nyi)
# Plot
vmin = zzbi.min()
vmax = zzbi.max()
P.pcolor(xxbi, yybi, zzi, vmin=vmin, vmax=vmax)
P.scatter(xxo, yyo, c=zzo, vmin=vmin, vmax=vmax, s=80)
P.grid()
P.title('curv2rel_single')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
iib, jjb = meshbounds(ii, jj)
zzi = jj * nyi + ii #relpos2index(ii, jj)
zzbi = jjb * nyi + iib # ny+1? relpos2index(iib, jjb)
# Input random points
N.random.seed(0)
np = 100
xxo = N.random.random(np) * (xxbi.max() - xxbi.min()) + xxbi.min()
yyo = N.random.random(np) * (yybi.max() - yybi.min()) + yybi.min()
xxo = N.concatenate((xxo, xxi.ravel()))
yyo = N.concatenate((yyo, yyi.ravel()))
np = xxo.size
# Convert to relative indices
pp, qq = curv2rel(xxbi, yybi, xxo, yyo)
bad = pp < 0
zzo = (qq - 1) * nyi + pp - 1 #relpos2index(pp-1, qq-1)
zzo[bad] = -1
# Plot
vmin = zzbi.min()
vmax = zzbi.max()
P.pcolor(xxbi, yybi, zzi, vmin=vmin, vmax=vmax)
P.scatter(xxo, yyo, c=zzo, vmin=vmin, vmax=vmax, s=80)
P.grid()
P.title('curv2rel')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
nb = 10
xxbi, yybi = meshbounds(xxi, yyi)
# Output grid
grido = rotate_grid((N.linspace(0, 6, 50)-1, N.linspace(0, 4, 35)+1.), -20)
xxo = grido.getLongitude()[:].filled()
yyo = grido.getLatitude()[:].filled()
xxbo, yybo = meshbounds(xxo, yyo)
# Nearest
varo = nearest2d(vari, xxi, yyi, xxo, yyo, nb)
# Plot
vmin = varo.min()
vmax = varo.max()
P.figure(figsize=(8, 4))
P.subplot(121, aspect=1)
P.pcolor(xxbi, yybi, vari[0], vmin=vmin, vmax=vmax)
add_grid(grido)
P.title('original')
P.subplot(122, aspect=1)
P.pcolor(xxbo, yybo, varo[0], vmin=vmin, vmax=vmax)
add_grid(gridi)
P.title('nearest2d')
P.axis('image')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.show()
P.close()
# Regridding
varo, wo = vari.regrid(grido, tool='regrid2', returnTuple=1)
# Plot
kw = dict(fill='pcolor',
contour=False,
xhide=True,
yhide=True,
xticks=[],
yticks=[],
cmap='jet',
colorbar=False,
show=False)
P.figure(figsize=(6, 3))
p = plot2d(vari, subplot=131, title='Original', **kw)
add_grid(gridi)
add_grid(grido)
P.axis('image')
p = plot2d(varo, subplot=132, title='Regridded', **kw)
add_grid(gridi)
add_grid(grido)
P.axis('image')
P.subplot(133)
P.pcolor(xxob, yyob, wo)
add_grid(grido)
P.title("Output weights")
P.tight_layout()
savefigs(code_file_name(), pdf=True, verbose=False)
P.close()
