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()
# 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()
rcdefaults()
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()
# Regrilleur de CDAT
regridder = CDATRegridder(gridi, grido, method="cellave") # -> CHANGEZ LA METHODE
ssto2 = regridder.regrid(ssti)
ssto3, cdr = regrid2d(ssti, grido, cdr=regridder, getcdr=True)
# -> VERIFIEZ LE TYPE
# Plots
kwp = dict(
vmin=ssti.min(),
vmax=ssti.max(),
res=None,
show=False,
colorbar=False,
drawmeridians_linewidth=0,
drawparallels_linewidth=0,
grid=False,
yhide="auto",
**select
)
map2(ssti, title="Original", figsize=(15, 5), subplot=131, **kwp)
add_grid(gridi, alpha=1)
map2(ssto, title="Avec regrid2d", subplot=132, **kwp)
add_grid(grido, alpha=1)
add_grid(gridi, edges=False, centers=True, alpha=0.5, markersize=3, marker="o")
map2(ssto2, title="Avec CDATRegridder", subplot=133, **kwp)
add_grid(grido, alpha=1)
add_grid(gridi, edges=False, centers=True, alpha=0.5, markersize=3, marker="o")
P.tight_layout()
P.show()
print method
# Regrillage
ssto = regrid2d(ssti, grido, method=method) # -> CHANGEZ LA METHODE
# -> UTILISEZ USECDR=TRUE
# Regrilleur de CDAT
regridder = CDATRegridder(gridi, grido, method='cellave') # -> CHANGEZ LA METHODE
ssto2 = regridder.regrid(ssti)
ssto3, cdr = regrid2d(ssti, grido, cdr=regridder, getcdr=True)
# -> VERIFIEZ LE TYPE
# Plots
kwp = dict(vmin=ssti.min(), vmax=ssti.max(), res=None, show=False, colorbar=False,
drawmeridians_linewidth=0, drawparallels_linewidth=0, grid=False, yhide='auto', **select)
map2(ssti, title='Original',figsize=(15, 5), subplot=131, **kwp)
add_grid(gridi, alpha=1)
map2(ssto, title='Avec regrid2d', subplot=132, **kwp)
add_grid(grido, alpha=1)
add_grid(gridi, edges=False, centers=True, alpha=.5, markersize=3, marker='o')
map2(ssto2, title='Avec CDATRegridder', subplot=133, **kwp)
add_grid(grido, alpha=1)
add_grid(gridi, edges=False, centers=True, alpha=.5, markersize=3, marker='o')
P.tight_layout()
P.show()
"""Test the fortran function :f:func:`closest2d`"""
from vcmq import N, P, code_file_name, P, os, rotate_grid, add_grid
from vacumm.misc.grid._interp_ import closest2d
# Input grid
grid = rotate_grid((N.arange(5), N.arange(4)), 30)
xxi = grid.getLongitude()[:].filled()
yyi = grid.getLatitude()[:].filled()
# Input random points
N.random.seed(0)
np = 100
xxo = N.random.random(np)*(xxi.max()-xxi.min()) + xxi.min()
yyo = N.random.random(np)*(yyi.max()-yyi.min()) + yyi.min()
# Closest
for xo, yo in zip(xxo, yyo):
i,j = closest2d(xxi, yyi, xo, yo, nogeo=0)
P.plot([xo, xxi[j-1, i-1]], [yo, yyi[j-1, i-1]], 'k')
add_grid(grid, color='k', markersize=10, centers=True, marker='o', alpha=1)
P.scatter(xxo, yyo, c='r', s=40, zorder=11)
P.title('closest2d')
P.axis('image')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
gridco = rotate_grid((xco, yco), -20)
# Interpolate and Plot
# - original
rc('font', size=8)
rc('axes', labelsize=7)
kw = dict(show=False, axes_aspect=1, colorbar=False, grid=False)
kwg = dict(edges=False, centers=True, markersize=2, alpha=1)
ip = 1
plot2d(varri, title='Original rectangular', figure=10,
figsize=(7, 7), subplot=(4,3,ip), **kw);ip+=1
plot2d(varci, title='Original curvilinear', subplot=(4,3,ip), **kw);ip+=1
# - nearest
varo = regrid2d(varci, gridco, method='nearest', tool='vacumm')
plot2d(varo, title='VACUMM / Nearest', subplot=(4,3,ip), **kw);ip+=1
add_grid(gridci, **kwg)
# - bilin r2r / vacumm
varo = regrid2d(varri, gridro, method='bilinear', tool='vacumm')
plot2d(varo, title='VACUMM / Bilinear', subplot=(4,3,ip), **kw);ip+=1
add_grid(gridri, **kwg)
# - bilin c2c / vacumm
varo = regrid2d(varci, gridco, method='bilinear', tool='vacumm')
plot2d(varo, title='VACUMM / Bilinear', subplot=(4,3,ip), **kw);ip+=1
add_grid(gridci, **kwg)
# - bilin c2c / emsf
varo = regrid2d(varci, gridco, method='bilinear', tool='esmf')
plot2d(varo, title='ESMF / Bilinear', subplot=(4,3,ip), **kw);ip+=1
add_grid(gridci, **kwg)
# - bilin c2c / libcf
varo = regrid2d(varci, gridco, method='bilinear', tool='libcf')
plot2d(varo, title='LibCF / Bilinear', subplot=(4,3,ip), **kw);ip+=1
for iyi in iyis:
label = iyi==iyis[0]
P.plot(xi, zzi[iyi], 'ob-', markersize=8, label='Original' if label else None)
P.plot(xo, zzo[iyi*r], 'or-', markersize=5, lw=.8, label='Interpolated' if label else None)
P.legend(loc='best', framealpha=0.5)
P.grid()
P.title('Section')
P.tight_layout()
savefigs(code_file_name(ext='_0.png'), verbose=False, pdf=True)
# Maps
P.figure(figsize=(8, 4))
levels = auto_scale(vmin=zzi.min(), vmax=zzi.max(), nmax=30)
P.subplot(121)
P.contourf(xxi, yyi, zzi, levels=levels)
P.contour(xxi, yyi, zzi, linewidths=0.1, levels=levels, colors='k')
add_grid((xxi, yyi), alpha=.3, centers=True)
for iyi in iyis:
P.axhline(yi[iyi], linestyle='--', color='k')
P.title('Original')
P.subplot(122)
P.contourf(xxo, yyo, zzo, levels=levels)
P.contour(xxo, yyo, zzo, linewidths=0.1, levels=levels, colors='k')
add_grid((xxo, yyo), alpha=.2, centers=True)
for iyi in iyis:
P.axhline(yi[iyi], linestyle='--', color='k')
P.title('Interpolated')
P.tight_layout()
savefigs(code_file_name(ext='_1.png'), verbose=False, pdf=True)
P.close()
yo = N.random.uniform(lat0, lat1, np)
zo = N.random.uniform(dep0, dep1, np)
to = comptime(N.random.uniform(reltime(time0, time.units).value,
reltime(time1, time.units).value, np),
time.units)
# Rectangular xyzt with 1d z
vo = grid2xy(vi, xo=xo, yo=yo, zo=zo, to=to, method='linear')
von = grid2xy(vi, xo=xo, yo=yo, zo=zo, to=to, method='nearest')
assert vo.shape==(ne, np)
N.testing.assert_allclose(vo[0], yo)
kwp = dict(vmin=vi.min(), vmax=vi.max())
P.figure(figsize=(6, 3))
P.subplot(121)
P.scatter(xo, yo, c=vo[0], cmap='jet', **kwp)
add_grid(vi.getGrid())
P.title('linear4d')
P.subplot(122)
P.scatter(xo, yo, c=von[0], cmap='jet', **kwp)
add_grid(vi.getGrid())
P.title('nearest4d')
P.figtext(.5, .98, 'grid2xy in 4D', va='top', ha='center', weight='bold')
P.tight_layout()
P.savefig(code_file_name(ext='.png'))
P.close()
# Reversed z and y
vi_revz = vi[:, :, ::-1, ::-1, :]
vo = grid2xy(vi_revz, xo=xo, yo=yo, zo=zo, to=to, method='linear')
N.testing.assert_allclose(vo[0], yo)
var['t'].setAxis(0, dep)
# Arakawa manager
ag = CGrid()
# Interpolations
for p in 'u', 'v', 'f', 'w':
var[p] = ag.interp(var['t'], 't', p, mode='extrap')
# Surface plots
vmin, vmax = minmax(*[var[p][-1] for p in ['u', 'v', 'f']])
kw = dict(show=False, res=None, vmin=vmin, vmax=vmax, colorbar=False, grid=False, cmap='jet')
m = map2(var['t'][-1], fill='pcolor',
title='Interpolations on an Arakawa C grid: T->U/V/F', **kw)
add_grid(var['t'], linestyle='-')
kw.update(fill='scatter', contour=False, fill_s=60)
markers = dict(u='>', v='^', f='D', t='o')
for p in 't', 'u', 'v', 'f':
m = map2(var[p][-1], fill_marker=markers[p], shadow=True, zorder=100, **kw)
m.savefig(code_file_name(ext='_1.png'))
m.close()
# Vertical plot
curve2(var['t'][:, 0, 0], 'o-b', ymax=0, show=False,
title='Interpolations on an Arakawa C grid: T->W')
curve2(var['w'][:, 0, 0], '^r', show=False, savefig=code_file_name(ext='_2.png'))
result = [
('assertEqual', [var['t'][0, 0, :2].mean(), var['u'][0, 0, 0]]),
nxy = 10
nt = 5
lon = N.arange(nxy * 1.)
lat = N.arange(nxy * 1.)
time = create_time((nt, ), 'years since 2000')
gridi = rotate_grid((lon, lat), 30)
xxi = gridi.getLongitude()[:].filled()
yyi = gridi.getLatitude()[:].filled()
vari = MV2.resize(yyi, (nt, nxy, nxy))
vari.setAxis(0, time)
set_grid(vari, gridi)
kw = dict(vmin=vari.min(), vmax=vari.max())
P.figure(figsize=(10, 3.5))
P.subplot(131, aspect=1)
P.contourf(xxi, yyi, vari[0].asma(), **kw)
add_grid(gridi, edges=False, centers=-1)
xylims = (xxi.min(), xxi.max(), yyi.min(), yyi.max())
P.axis(xylims)
P.title('Curved grid')
# Interpolate to grid
xg, yg = N.meshgrid(N.arange(-3.5, 14.5), N.arange(-3.5, 14.5))
nxyg = xg.shape
cig = CurvedInterpolator(gridi, (xg, yg), g2g=True)
varog = cig(vari)
P.subplot(132, aspect=1)
P.scatter(xg, yg, c=varog[0].asma(), s=120, linewidth=0, **kw)
add_grid(gridi, edges=False, centers=-1)
xylims = (xxi.min(), xxi.max(), yyi.min(), yyi.max())
P.axis(xylims)
P.title('Interp to grid')
# 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()
rcdefaults()
# Curved grid
nxy = 10
lon = N.arange(nxy*1.)
lat = N.arange(nxy*1.)
gridi = rotate_grid((lon, lat), 30)
xxi = gridi.getLongitude()[:].filled()
yyi = gridi.getLatitude()[:].filled()
vari = MV2.array(yyi)
set_grid(vari, gridi)
kw = dict(vmin=vari.min(), vmax=vari.max())
P.figure(figsize=(10, 3.5))
P.subplot(131, aspect=1)
P.contourf(xxi, yyi, vari.asma(), **kw)
add_grid(gridi, edges=False, centers=-1)
xylims = (xxi.min(), xxi.max(), yyi.min(), yyi.max())
P.axis(xylims)
P.title('Curved grid')
# Interpolate to grid
xg, yg = N.meshgrid(N.arange(-3.5, 14.5), N.arange(-3.5, 14.5))
nxyg = xg.shape
cig = CurvedInterpolator(gridi, (xg, yg), g2g=True)
varog = cig(vari)
P.subplot(132, aspect=1)
P.scatter(xg, yg, c=varog.asma(), s=120, linewidth=0, **kw)
add_grid(gridi, edges=False, centers=-1)
xylims = (xxi.min(), xxi.max(), yyi.min(), yyi.max())
P.axis(xylims)
P.title('Interp to grid')
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)
ff = figfile%vars()
P.tight_layout()
P.savefig(ff)
figfiles.append(ff)
yi = N.arange(nxy*1.)
xxi, yyi = N.meshgrid(xi, yi)
zi = N.ma.array(yyi)
zi[int(nxy*0.3):int(nxy*0.8), int(nxy*0.3):int(nxy*0.8)] = N.ma.masked
zi.shape = 1, nxy, nxy
# Output positions
no = 1000
xo = N.random.uniform(-nxy/4., nxy+nxy/4., no)
yo = N.random.uniform(-nxy/4., nxy+nxy/4., no)
# Interpolate
mv = zi.get_fill_value()
zo = nearest2dto1d(xi,yi,zi.filled(mv),xo,yo,mv)
zo = N.ma.masked_values(zo, mv)
# Plot
kw = dict(vmin=zi.min(), vmax=zi.max())
P.figure(figsize=(6, 6))
P.subplot(111, aspect=1)
P.contourf(xxi, yyi, zi[0], **kw)
add_grid((xi, yi), edges=False, centers=True, marker='o')
P.scatter(xo, yo, c=zo[0], s=50, **kw)
P.title('nearest2dto1d')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
# Interpolate and Plot
# - original
rc('font', size=6)
rc('axes', labelsize=7)
kw = dict(show=False, axes_aspect=1, colorbar=False, grid=False, linewidth=.3,
cmap='viridis')
kwg = dict(edges=False, centers=True, markersize=2, alpha=1)
ip = 1
plot2d(varri[0], title='Original rectangular', figure=10,
figsize=(7, 7), subplot=(4,3,ip), **kw);ip+=1
plot2d(varci[0], title='Original curvilinear', subplot=(4,3,ip), **kw);ip+=1
# - nearest
varo = regrid2d(varci, gridco, method='nearest', tool='vacumm')
plot2d(varo[0], title='VACUMM / Nearest', subplot=(4,3,ip), **kw);ip+=1
add_grid(gridci, **kwg)
# - bilin r2r / vacumm
varo = regrid2d(varri, gridro, method='bilinear', tool='vacumm')
plot2d(varo[0], title='VACUMM / Bilinear', subplot=(4,3,ip), **kw);ip+=1
add_grid(gridri, **kwg)
# - bilin c2c / vacumm
varo = regrid2d(varci, gridco, method='bilinear', tool='vacumm')
plot2d(varo[0], title='VACUMM / Bilinear', subplot=(4,3,ip), **kw);ip+=1
add_grid(gridci, **kwg)
# - bilin c2c / emsf
varo = regrid2d(varci, gridco, method='bilinear', tool='esmf')
plot2d(varo[0], title='ESMF / Bilinear', subplot=(4,3,ip), **kw);ip+=1
add_grid(gridci, **kwg)
# - bilin c2c / libcf
varo = regrid2d(varci, gridco, method='bilinear', tool='libcf')
plot2d(varo[0], title='LibCF / Bilinear', subplot=(4,3,ip), **kw);ip+=1
var[p] = ag.interp(var['t'], 't', p, mode='extrap')
# Surface plots
vmin, vmax = minmax(*[var[p][-1] for p in ['u', 'v', 'f']])
kw = dict(show=False,
res=None,
vmin=vmin,
vmax=vmax,
colorbar=False,
grid=False,
cmap='jet')
m = map2(var['t'][-1],
fill='pcolor',
title='Interpolations on an Arakawa C grid: T->U/V/F',
**kw)
add_grid(var['t'], linestyle='-')
kw.update(fill='scatter', contour=False, fill_s=60)
markers = dict(u='>', v='^', f='D', t='o')
for p in 't', 'u', 'v', 'f':
m = map2(var[p][-1], fill_marker=markers[p], shadow=True, zorder=100, **kw)
m.savefig(code_file_name(ext='_1.png'))
m.close()
# Vertical plot
curve2(var['t'][:, 0, 0],
'o-b',
ymax=0,
show=False,
title='Interpolations on an Arakawa C grid: T->W')
curve2(var['w'][:, 0, 0],
'^r',
nxy = 15
xi = N.arange(nxy * 1.)
yi = N.arange(nxy * 1.)
xxi, yyi = N.meshgrid(xi, yi)
zi = N.ma.array(yyi)
zi[int(nxy * 0.3):int(nxy * 0.8), int(nxy * 0.3):int(nxy * 0.8)] = N.ma.masked
zi.shape = 1, nxy, nxy
# Output positions
no = 1000
xo = N.random.uniform(-nxy / 4., nxy + nxy / 4., no)
yo = N.random.uniform(-nxy / 4., nxy + nxy / 4., no)
# Interpolate
mv = zi.get_fill_value()
zo = dstwgt2dto1d(xi, yi, zi.filled(mv), xo, yo, mv)
zo = N.ma.masked_values(zo, mv)
# Plot
kw = dict(vmin=zi.min(), vmax=zi.max())
P.figure(figsize=(6, 6))
P.subplot(111, aspect=1)
P.contourf(xxi, yyi, zi[0], **kw)
add_grid((xi, yi), edges=False, centers=True, marker='o')
P.scatter(xo, yo, c=zo[0], s=50, **kw)
P.title('dstwgt2dto1d')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
xo = grido.getLongitude()
yo = grido.getLatitude()
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()
'or-',
markersize=5,
lw=.8,
label='Interpolated' if label else None)
P.legend(loc='best', framealpha=0.5)
P.grid()
P.title('Section')
P.tight_layout()
savefigs(code_file_name(ext='_0.png'), verbose=False, pdf=True)
# Maps
P.figure(figsize=(8, 4))
levels = auto_scale(vmin=zzi.min(), vmax=zzi.max(), nmax=30)
P.subplot(121)
P.contourf(xxi, yyi, zzi, levels=levels)
P.contour(xxi, yyi, zzi, linewidths=0.1, levels=levels, colors='k')
add_grid((xxi, yyi), alpha=.3, centers=True)
for iyi in iyis:
P.axhline(yi[iyi], linestyle='--', color='k')
P.title('Original')
P.subplot(122)
P.contourf(xxo, yyo, zzo, levels=levels)
P.contour(xxo, yyo, zzo, linewidths=0.1, levels=levels, colors='k')
add_grid((xxo, yyo), alpha=.2, centers=True)
for iyi in iyis:
P.axhline(yi[iyi], linestyle='--', color='k')
P.title('Interpolated')
P.tight_layout()
savefigs(code_file_name(ext='_1.png'), verbose=False, pdf=True)
P.close()
"""Test the fortran function :f:func:`closest2d`"""
from vcmq import N, P, code_file_name, P, os, rotate_grid, add_grid
from vacumm.misc.grid._interp_ import closest2d
# Input grid
grid = rotate_grid((N.arange(5), N.arange(4)), 30)
xxi = grid.getLongitude()[:].filled()
yyi = grid.getLatitude()[:].filled()
# Input random points
N.random.seed(0)
np = 100
xxo = N.random.random(np) * (xxi.max() - xxi.min()) + xxi.min()
yyo = N.random.random(np) * (yyi.max() - yyi.min()) + yyi.min()
# Closest
for xo, yo in zip(xxo, yyo):
i, j = closest2d(xxi, yyi, xo, yo, nogeo=0)
P.plot([xo, xxi[j - 1, i - 1]], [yo, yyi[j - 1, i - 1]], 'k')
add_grid(grid, color='k', markersize=10, centers=True, marker='o', alpha=1)
P.scatter(xxo, yyo, c='r', s=40, zorder=11)
P.title('closest2d')
P.axis('image')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
