"""Test functions :func:`~vacumm.misc.grid.kriging.variogram`"""
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()
"""Test function :func:`~vacumm.misc.grid.kriging.krig`"""
npi = 500
npo = 200
from vcmq import P, savefigs, code_file_name
from vacumm.misc.grid.kriging import gridded_gauss3, random_gauss3, random_points, krig
# Generate random field
xg, yg, zzg = gridded_gauss3()
xi, yi, zi = random_gauss3(np=npi)
# Interpolate to random points
xo, yo = random_points(np=npo)
zo = krig(xi, yi, zi, xo, yo)
# Plot
# - source data
axis = [xg.min(), xg.max(), yg.min(), yg.max()]
kw = dict(vmin=zzg.min(), vmax=zzg.max())
kwim = dict(extent=axis,
interpolation='bilinear',
origin='lower',
alpha=.2,
**kw)
kwsc = dict(lw=0.2, **kw)
P.figure(figsize=(6, 3.5))
P.subplot(121)
P.title('Source field')
P.imshow(zzg, **kwim)
P.scatter(xi, yi, c=zi, **kwsc)
"""Test functions :func:`~vacumm.misc.grid.kriging.variogram`"""
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()
"""Test class :func:`~vacumm.misc.grid.kriging.OrdinaryCloudKriger`"""
npi = 50
from vcmq import P, savefigs, code_file_name
from vacumm.misc.grid.kriging import (gridded_gauss3, random_gauss3,
random_points, OrdinaryCloudKriger)
# Generate random field
xg, yg, zzg = gridded_gauss3()
xi, yi, zi = random_gauss3(np=npi)
# Setup kriger
ock = OrdinaryCloudKriger(xi, yi, zi, n=0) # nugget = 0
# Interpolate to grid and get error
xo, yo = P.meshgrid(xg[::4], yg[::4])
xo.shape = yo.shape = xo.size
zo, zoe = ock(xo, yo, geterr=True)
# Relative error
zoemax = ock.variogram_func(P.inf)
zoe /= zoemax
zoe *= 100
# Variogram evolution
rr = P.linspace(0, P.sqrt(xg.ptp()**2+yg.ptp()**2), 100)
vv = ock.variogram_func(rr)
# Plot
P.figure(figsize=(8, 8))