Python gridded_gauss3 Examples

Example #1

File: test_regrid_kriging_regrid.py Project: acoat/vacumm

"""Test function :func:`~vacumm.misc.grid.kriging.krig` for grid refinement"""

nxi = 15
nyi = 10
r = 3

from vcmq import P, savefigs, code_file_name, N, auto_scale, add_grid
from vacumm.misc.grid.kriging import gridded_gauss3, random_gauss3, random_points, krig

# Generate random gridded field
xi, yi, zzi = gridded_gauss3(nx=nxi, ny=nyi)
xxi, yyi = N.meshgrid(xi, yi)

# 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,

Example #2

File: test_regrid_kriging_variogram.py Project: jfleroux/vacumm

"""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()

Example #3

File: test_regrid_kriging_variogram.py Project: acoat/vacumm

"""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()

Example #4

File: test_regrid_kriging_regrid.py Project: jfleroux/vacumm

"""Test function :func:`~vacumm.misc.grid.kriging.krig` for grid refinement"""

nxi = 15
nyi = 10
r = 3

from vcmq import P, savefigs, code_file_name, N, auto_scale, add_grid
from vacumm.misc.grid.kriging import gridded_gauss3, random_gauss3, random_points, krig

# Generate random gridded field
xi, yi, zzi = gridded_gauss3(nx=nxi, ny=nyi)
xxi, yyi = N.meshgrid(xi, yi)

# 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()

Example #5

File: test_regrid_kriging_krig.py Project: acoat/vacumm

"""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)

Example #6

File: test_regrid_kriging_ock.py Project: jfleroux/vacumm

"""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))