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)
# -*- coding: utf8 -*-
# Read sea level at Brest
from vcmq import cdms2, P, curve2, savefigs, data_sample
f = cdms2.open(data_sample("tide.sealevel.BREST.mars.nc"))
sea_level = f('sea_level')
f.close()
# Filtering
from vacumm.tide.filters import demerliac
cotes, tide = demerliac(sea_level, get_tide=True)
# Plots
kwplot = dict(date_fmt='%d/%m', show=False, date_rotation=0)
# - tidal signal
curve2(sea_level,
'k',
subplot=211,
label='Original',
title='Sea level at Brest',
**kwplot)
curve2(tide, 'b', label='Tidal signal', **kwplot)
P.legend().legendPatch.set_alpha(.7)
# - surcotes/decotes
curve2(cotes, 'r', subplot=212, hspace=.3, label='Demerliac', **kwplot)
P.legend().legendPatch.set_alpha(.7)
savefigs(__file__, savefigs_pdf=True)
P.close()
zirn = griddata(xi, yi, zi, (xr, yr), method='linear', ext=True, sub=6)
# - ifremer cargen
zirk = griddata(xi, yi, zi, (xr, yr), method='carg')
# %% Plots
P.figure(1, figsize=(6, 8))
P.subplots_adjust(hspace=.3, bottom=.05, top=.95, left=.06)
# - original
P.subplot(311)
P.pcolor(xrb, yrb, zzr, **vminmax)
P.xlim(xrb.min(), xrb.max())
P.ylim(yrb.min(), yrb.max())
P.title('Original')
stdref = zzr.std()
# - linear interp
P.subplot(312)
P.pcolor(xrb, yrb, zirn, **vminmax)
P.plot(xi, yi, 'ko')
P.xlim(xrb.min(), xrb.max())
P.ylim(yrb.min(), yrb.max())
P.title('Scipy/linear (err = %02i%%)' % ((zzr - zirn).std() * 100 / stdref))
# - ifremer cargen interp
P.subplot(313)
P.pcolor(xrb, yrb, zirk, **vminmax)
P.plot(xi, yi, 'ko')
P.xlim(xrb.min(), xrb.max())
P.ylim(yrb.min(), yrb.max())
P.title('Cargen (err = %02i%%)' % ((zzr - zirk).std() * 100 / stdref))
savefigs(__file__)
P.close()
set_grid(vari, gridi) # set grid and axes
# Extend and plot
rc('font', size=9)
P.figure(figsize=(6, 6))
kw = dict(xmin=xxi.min() - 3,
xmax=xxi.max() + 3,
ymin=yyi.min() - 3,
ymax=yyi.max() + 3,
show=False,
xhide='auto',
yhide='auto')
# - original
plot2d(vari, title='Original', subplot=(2, 2, 1), **kw)
# - extend1d
for i, (axis, ext, mode) in enumerate([(-1, (2, 2), 'same'),
(-2, 2, 'linear')]):
varo = extend1d(vari, ext=ext, axis=axis, mode=mode)
plot2d(varo,
subplot=(2, 2, i + 3),
title='interp1d: axis=%s\next=%s, mode=%s' % (axis, ext, mode),
**kw)
varo = extend2d(vari, iext=2, jext=2, mode='linear')
plot2d(varo,
subplot=(2, 2, 2),
title='interp2d: mode=linear\niext=2, jext=2',
**kw)
P.tight_layout()
savefigs(code_file_name(), verbose=False)
P.close()
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()
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()
# 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:
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)
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()
# -*- coding: utf8 -*-
# Read sea level at Brest
from vcmq import cdms2, P, curve2, savefigs, data_sample
f = cdms2.open(data_sample("tide.sealevel.BREST.mars.nc"))
sea_level = f('sea_level')
f.close()
# Filtering
from vacumm.tide.filters import demerliac
cotes, tide = demerliac(sea_level, get_tide=True)
# Plots
kwplot = dict(date_fmt='%d/%m', show=False, date_rotation=0)
# - tidal signal
curve2(sea_level, 'k', subplot=211, label='Original', title='Sea level at Brest',**kwplot)
curve2(tide, 'b', label='Tidal signal', **kwplot)
P.legend().legendPatch.set_alpha(.7)
# - surcotes/decotes
curve2(cotes, 'r', subplot=212, hspace=.3, label='Demerliac', **kwplot)
P.legend().legendPatch.set_alpha(.7)
savefigs(__file__, savefigs_pdf=True)