# -*- 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()
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')
yy = N.array([0., 0., 2., 2.])
# From Polygon
P.figure(figsize=(5, 5))
p0 = create_polygon(Polygon(N.array([xx, yy]).T))
plot_polygon(p0, color='b', label='From poly')
# From data
p1 = create_polygon(N.array([xx, yy]).T - .25)
plot_polygon(p1, color='r', label='From data')
# From transposed data
p2 = create_polygon(N.array([xx, yy]) - 0.5)
plot_polygon(p2, color='g', label='From transposed data')
# From min/max
p3 = create_polygon([xx.min(), yy.min(), xx.max(), yy.max()])
plot_polygon(p3, color='m', label='From min/max')
# With projection
proj = lambda x, y: (x * 1.5, y * 1.5)
p4 = create_polygon(p0, proj=proj)
plot_polygon(p4, color='cyan', label='With projection')
# Save
P.title('create_polygon')
P.legend(loc='best')
P.savefig(code_file_name(ext='png'))
P.show()
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()
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:
P.axhline(yi[iyi], linestyle='--', color='k')
P.title('Original')
x = P.rand(np)*dxy+xc
y = P.rand(np)*dxy+yc
xx.extend(x)
yy.extend(y)
P.scatter(x, y, c=colors[i:i+1], s=40)
P.title('Original (mixed clouds)')
# Spliting
indices, centroids = cloud_split(xx, yy, getdist=False, getcent=True, npmax=npmax)
# Plot
P.subplot(122, aspect=1)
xx = N.array(xx)
yy = N.array(yy)
msize = 0
for i, select in enumerate(indices):
x = xx[select]
y = yy[select]
P.scatter(x, y, c=colors[i:i+1], s=40, label='size=%i'%len(x))
P.scatter(centroids[i:i+1, 0], centroids[i:i+1, 1], c=colors[i:i+1], s=100, lw=2)
msize = max(len(x), msize)
P.legend(loc='upper left').get_frame().set_alpha(.5)
P.title('Split clouds: max size=%i'%npmax)
savefigs(code_file_name(ext='png'), pdf=True, verbose=False)
#P.tight_layout()
P.close()
# For unitest
result = {'assertLess':[msize, npmax]}
# -*- 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)
# Spliting
indices, centroids = cloud_split(xx,
yy,
getdist=False,
getcent=True,
npmax=npmax)
# Plot
P.subplot(122, aspect=1)
xx = N.array(xx)
yy = N.array(yy)
msize = 0
for i, select in enumerate(indices):
x = xx[select]
y = yy[select]
P.scatter(x, y, c=colors[i:i + 1], s=40, label='size=%i' % len(x))
P.scatter(centroids[i:i + 1, 0],
centroids[i:i + 1, 1],
c=colors[i:i + 1],
s=100,
lw=2)
msize = max(len(x), msize)
P.legend(loc='upper left').get_frame().set_alpha(.5)
P.title('Split clouds: max size=%i' % npmax)
savefigs(code_file_name(ext='png'), pdf=True, verbose=False)
#P.tight_layout()
P.close()
# For unitest
result = {'assertLess': [msize, npmax]}
