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 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()
"""Test :func:`~vacumm.misc.plot.add_logo`"""
# Imports
from vcmq import N, P, add_logo, os, code_file_name, data_sample
import matplotlib.image as mpimg
# Inits
logofile = data_sample('logo_ifremer.png')
P.plot([2, 6])
# Default
add_logo(logofile, scale=1)
# Upper right / no rescale
add_logo(logofile, loc='upper right')
# Rescale
add_logo(logofile, loc='upper left', scale=2)
# Alpha
add_logo(logofile, loc='lower right', alpha=0.2)
# Save
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
xo = N.linspace(xi[0], xi[-1], (nxi - 1) * r + 1)
yo = N.linspace(yi[0], yi[-1], (nyi - 1) * r + 1)
xxo, yyo = N.meshgrid(xo, yo)
# Interpolate
zzo = krig(xxi.ravel(), yyi.ravel(), zzi.ravel(), xxo.ravel(), yyo.ravel())
zzo.shape = xxo.shape
# Section
P.figure(figsize=(8, 4))
iyis = [3, 4]
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
ct0 = round_date(ctimesi[0], 'hour')
ct1 = round_date(ctimesi[-1], 'hour')
taxo = create_time(lindates(ct0, ct1, 1, 'hour'), taxi.units)
# Lag error
# - estimation
els = []
lags = N.arange(1, 6)
for lag in lags:
els.append(N.sqrt(((sp[lag:] - sp[:-lag])**2).mean()))
els = N.array(els)
a, b, _, _, _ = linregress(lags, els)
# - plot
P.figure(figsize=(6, 6))
P.subplot(211)
P.plot(lags, els, 'o')
P.plot([0, lags[-1]], [b, a * lags[-1] + b], 'g')
P.axhline(b, color='0.8', ls='--')
P.ylim(ymin=0)
P.xlabel('Lag [hour]')
P.ylabel('Error [m s-1]')
add_key(1)
P.title('Linear lag error model')
# Interpolation
sph, speh = regrid1d(sp,
taxo,
method='cellerr',
erri=spe,
errl=-a,
geterr=True)
nx = ny = 50
np = 500
mtype = 'gauss'
dmax = 2.5
from vcmq import N, P, code_file_name, savefigs
from vacumm.misc.grid.kriging import gridded_gauss3, random_gauss3, variogram, variogram_fit
# Generate random field
xxg, yyg, zzg = gridded_gauss3(nx=nx, ny=ny)
x, y, z = random_gauss3(np=np)
# Variogram from data
d, v = variogram(x, y, z, dmax=dmax)
# Variogram fit with null nugget
vm = variogram_fit(x, y, z, mtype, n=0, dmax=dmax)
D = N.linspace(0, d.max())
V = vm(D)
# Compare
P.figure(figsize=(6, 4))
P.title('Variogram')
P.plot(d, v, 'ob', label='From data')
P.plot(D, V, '-r', label='Fitted model (%s)'%mtype)
P.legend(loc='best')
P.ylim(ymin=0)
savefigs(code_file_name(ext=False), pdf=True, verbose=False)
P.close()
ct0 = round_date(ctimesi[0], "hour")
ct1 = round_date(ctimesi[-1], "hour")
taxo = create_time(lindates(ct0, ct1, 1, "hour"), taxi.units)
# Lag error
# - estimation
els = []
lags = N.arange(1, 6)
for lag in lags:
els.append(N.sqrt(((sp[lag:] - sp[:-lag]) ** 2).mean()))
els = N.array(els)
a, b, _, _, _ = linregress(lags, els)
# - plot
P.figure(figsize=(6, 6))
P.subplot(211)
P.plot(lags, els, "o")
P.plot([0, lags[-1]], [b, a * lags[-1] + b], "g")
P.axhline(b, color="0.8", ls="--")
P.ylim(ymin=0)
P.xlabel("Lag [hour]")
P.ylabel("Error [m s-1]")
add_key(1)
P.title("Linear lag error model")
# Interpolation
sph, speh = regrid1d(sp, taxo, method="cellerr", erri=spe, errl=-a, geterr=True)
# Time zoom for plot clarity
tzoom = (ct1.sub(7, cdtime.Hour), ctimesi[-1])
sp = sp(tzoom)
spe = spe(tzoom)
nx = ny = 50
np = 500
mtype = 'gauss'
distmax = 2.5
from vcmq import N, P, code_file_name, savefigs
from vacumm.misc.grid.kriging import gridded_gauss3, random_gauss3, variogram, variogram_fit
# Generate random field
xxg, yyg, zzg = gridded_gauss3(nx=nx, ny=ny)
x, y, z = random_gauss3(np=np)
# Variogram from data
d, v = variogram(x, y, z, distmax=distmax)
# Variogram fit with null nugget
vm = variogram_fit(x, y, z, mtype, n=0, distmax=distmax)
D = N.linspace(0, d.max())
V = vm(D)
# Compare
P.figure(figsize=(6, 4))
P.title('Variogram')
P.plot(d, v, 'ob', label='From data')
P.plot(D, V, '-r', label='Fitted model (%s)' % mtype)
P.legend(loc='best')
P.ylim(ymin=0)
savefigs(code_file_name(ext=False), pdf=True, verbose=False)
P.close()
# Refined grid
xo = N.linspace(xi[0], xi[-1], (nxi-1)*r+1)
yo = N.linspace(yi[0], yi[-1], (nyi-1)*r+1)
xxo, yyo = N.meshgrid(xo, yo)
# Interpolate
zzo = krig(xxi.ravel(), yyi.ravel(), zzi.ravel(), xxo.ravel(), yyo.ravel())
zzo.shape = xxo.shape
# Section
P.figure(figsize=(8, 4))
iyis = [3, 4]
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:
rr = P.linspace(0, P.sqrt(xg.ptp()**2+yg.ptp()**2), 100)
vv = ock.variogram_func(rr)
# Plot
P.figure(figsize=(8, 8))
axis = [xg.min(), xg.max(), yg.min(), yg.max()]
kwmm = dict(vmin=zi.min(), vmax=zi.max())
kwsc = dict(lw=0.2, **kwmm)
ax = P.subplot(221, aspect=1)
P.imshow(zzg, extent=axis, interpolation='bilinear', origin='lower', alpha=.2, **kwmm)
P.scatter(xi, yi, c=zi, s=20, lw=0.2, **kwmm)
P.axis(axis)
P.title('Input points')
P.subplot(222, aspect=1)
P.scatter(xo, yo, c=zo, s=40, lw=0.2, **kwmm)
P.axis(axis)
P.title('Interpolated points')
P.subplot(223, aspect=1)
P.scatter(xo, yo, c=zoe, s=40, lw=0.2, vmin=0, vmax=100)
P.axis(axis)
P.colorbar(shrink=.8)
P.title('Relative interpolation error')
P.subplot(224)
P.plot(rr, vv)
P.xlabel('Distance')
P.ylabel('Error')
P.title('Fitted variogram')
P.tight_layout()
savefigs(code_file_name(), verbose=False)
P.close()
# Format and convert
xx, yy = N.meshgrid(N.arange(-2, 4, 0.25), N.arange(-1, 5, 0.25))
nxy = xx.shape
xx.shape = -1
yy.shape = -1
pp, qq = [], []
for x, y in zip(xx, yy):
p, q = curv2rect(x1,x2,x3,x4,y1,y2,y3,y4,x,y)
pp.append(p)
qq.append(q)
pp = N.array(pp)
qq = N.array(qq)
# Plot
xp = [x1, x2, x3, x4, x1]
yp = [y1, y2, y3, y4, y1]
P.subplot(211)
levels = N.array([-10, 0, 1, 10.])
o = P.contourf(xx.reshape(nxy), yy.reshape(nxy), pp.reshape(nxy), levels=levels)
P.colorbar(o)
P.plot(xp, yp, 'k')
P.subplot(212)
o = P.contourf(xx.reshape(nxy), yy.reshape(nxy), qq.reshape(nxy), levels=levels)
P.colorbar(o)
P.plot(xp, yp, 'k')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
pp, qq = [], []
for x, y in zip(xx, yy):
p, q = curv2rect(x1, x2, x3, x4, y1, y2, y3, y4, x, y)
pp.append(p)
qq.append(q)
pp = N.array(pp)
qq = N.array(qq)
# Plot
xp = [x1, x2, x3, x4, x1]
yp = [y1, y2, y3, y4, y1]
P.subplot(211)
levels = N.array([-10, 0, 1, 10.])
o = P.contourf(xx.reshape(nxy),
yy.reshape(nxy),
pp.reshape(nxy),
levels=levels)
P.colorbar(o)
P.plot(xp, yp, 'k')
P.subplot(212)
o = P.contourf(xx.reshape(nxy),
yy.reshape(nxy),
qq.reshape(nxy),
levels=levels)
P.colorbar(o)
P.plot(xp, yp, 'k')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
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()
"""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()
