"""Test :func:`~vacumm.misc.plot.plot2d` with specified cell bounds"""
# Imports
from vcmq import MV2, N, code_file_name, plot2d, os
# Init
var = MV2.reshape(MV2.arange(4*3), (3, 4))
var.units = 'Count'
var.long_name = 'Navets'
x = var.getAxis(1)
x.units = 'km'
x.long_name = 'Distance'
y = var.getAxis(0)
y.units = 'm'
y.long_name = 'Height'
y.designateLevel()
# Bounds
y[:] = [1.5, 3.5, 4.5]
y2db = N.array([0., 3, 4, 5])
# Plot
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
plot2d(var, savefig=figfile, show=False, close=True, y2db=y2db, fill='pcolor',
xmin=-.5, xmax=3.5, ymin=0, ymax=5, cmap='jet')
# Unittest
result = {"files":figfile}
sill = 9.
range = 40
farvalue = 20
# Input
xi = [20., 26., 50., 70]
yi = [20, 26., 70., 20]
zi = [15., 6., 2., 4.]
# Output positions
nx = ny = 101
xg = N.linspace(1, 100, 101)
yg = N.linspace(1, 100, 101)
# Some inits
xi = N.array(xi)
yi = N.array(yi)
zi = N.array(zi)
xxg, yyg = N.meshgrid(xg, yg)
xo = xxg.ravel()
yo = yyg.ravel()
vgm = variogram_model('linear', n=0, s=sill, r=range)
# Setup the kriger
sck = SimpleCloudKriger(xi, yi, zi, vgf=vgm, farvalue=farvalue)
# Interpolate
zo = sck(xo, yo)
# Check a far value
zzg = zo.reshape(ny, nx)
# Create hourly time axis
taxi = sp.getTime()
taxi.toRelativeTime('hours since 2000')
ctimesi = taxi.asComponentTime()
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,
"""Test the :func:`~vacumm.misc.grid.masking.polygons` function"""
from vcmq import N, P, polygons, create_polygon, plot_polygon, create_grid
from _geoslib import Polygon
# Data
xx = N.array([0., 5., 4., 1.])
yy = N.array([0., 0., 2., 2.])
clip = [-3, -3, 0, 0]
xaxis = yaxis = N.linspace(-2., 2., 20.)
def plot_polygons(polys, **kwargs):
for p in polys:
plot_polygon(p, **kwargs)
## Single known argument
#pp0 = polygons(Polygon(N.array([xx, yy]).T))
#
## Classic data
#pp0 = polygons([N.array([xx, yy]), N.array([xx, yy]).T+6.])
#
## Classic with projection
#proj = lambda x, y: (x*1.5, y*1.5)
#pp1 = polygons([N.array([xx, yy])])
#
## Classic with clipping
#pp2 = polygons([-N.array([xx, yy])], clip=clip)
# From grid
pp3 = polygons([create_grid(xaxis, yaxis)])
for i, np in enumerate(nps):
xc = P.rand(1)*dxy*2.5
yc = P.rand(1)*dxy*2.5
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
# Create hourly time axis
taxi = sp.getTime()
taxi.toRelativeTime("hours since 2000")
ctimesi = taxi.asComponentTime()
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)
"""Test the fortran function :f:func:`cellerr1d`, :f:func:`cellerr1dx` et :f:func:`cellerr1dxx`"""
from vcmq import cdms2, data_sample, N, P, meshcells, minmax, code_file_name, os
from vacumm.misc.grid._interp_ import cellerr1d, cellerr1dx, cellerr1dxx
# Academic example pure 1d
# - data
yi = N.array([-2., 2., 6., 12., 15.])
vari = N.ma.array(yi)
vari[-1] = N.ma.masked
errm = N.ma.array([1, 2, 1., 2., 3.])
yo = N.array([5., 15])
mv = 1e20
vari = vari.reshape(1, -1).filled(mv)
errm = errm.reshape(1, -1).filled(mv)
errl = N.ones(1)
# - interp
varo, erro = cellerr1d(vari, yi, yo, mv, errm, errl)
# - truth
errot = N.array([
1 / N.sqrt(1 / (errm[0, 1]**2 + N.abs(yo[0] - yi[1]) * errl[0]) + 1 /
(errm[0, 2]**2 + N.abs(yo[0] - yi[2]) * errl[0])),
N.sqrt(errm[0, 3]**2 + N.abs(yo[1] - yi[3]) * errl[0])
])
varot = N.array([
vari[0, 1] /
(errm[0, 1]**2 + N.abs(yo[0] - yi[1]) * errl[0]) + vari[0, 2] /
(errm[0, 2]**2 + N.abs(yo[0] - yi[2]) * errl[0]), vari[0, 3] /
(errm[0, 3]**2 + N.abs(yo[1] - yi[3]) * errl[0])
]) * errot**2
# - check
result = [(N.testing.assert_allclose, [erro.ravel(), errot]),
x1, y1 = 0., 0.
x2, y2 = 3., 1.
x3, y3 = 2., 4.
x4, y4 = -1., 2.
# 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),
sill = 12.
range = 40
# Input
xi = [20., 26., 50., 70]
yi = [20, 26., 70., 50]
zi = [2., 6., 15., 4.]
ei = [.3, .6, 3., .3]
# Output positions
nx = ny = 101
xg = N.linspace(1, 100, 101)
yg = N.linspace(1, 100, 101)
# Some inits
xi = N.array(xi)
yi = N.array(yi)
zi = N.array(zi)
ei = N.array(ei)
zi -= zi.mean()
xxg, yyg = N.meshgrid(xg, yg)
xo = xxg.ravel()
yo = yyg.ravel()
vgm = variogram_model('linear', n=0, s=sill, r=range)
# Setup the krigers
sck = SimpleCloudKriger(xi, yi, zi, vgf=vgm)#, farvalue=0)
scke = SimpleCloudKriger(xi, yi, zi, vgf=vgm, e=ei)#, farvalue=0)
#sck = OrdinaryCloudKriger(xi, yi, zi, vgf=vgm)
#scke = OrdinaryCloudKriger(xi, yi, zi, vgf=vgm, e=ei)
"""Test the fortran function :f:func:`cellerr1d`, :f:func:`cellerr1dx` et :f:func:`cellerr1dxx`"""
from vcmq import cdms2, data_sample, N, P, meshcells, minmax, code_file_name, os
from vacumm.misc.grid._interp_ import cellerr1d, cellerr1dx, cellerr1dxx
# Academic example pure 1d
# - data
yi = N.array([-2., 2., 6. , 12., 15.])
vari = N.ma.array(yi)
vari[-1] = N.ma.masked
errm = N.ma.array([1, 2, 1., 2., 3.])
yo = N.array([5., 15])
mv = 1e20
vari = vari.reshape(1, -1).filled(mv)
errm = errm.reshape(1, -1).filled(mv)
errl = N.ones(1)
# - interp
varo, erro = cellerr1d(vari, yi, yo, mv, errm, errl)
# - truth
errot = N.array([
1/N.sqrt(
1/(errm[0, 1]**2+N.abs(yo[0]-yi[1])*errl[0]) +
1/(errm[0, 2]**2+N.abs(yo[0]-yi[2])*errl[0])),
N.sqrt(errm[0, 3]**2+N.abs(yo[1]-yi[3])*errl[0])
])
varot = N.array([
vari[0, 1]/(errm[0, 1]**2+N.abs(yo[0]-yi[1])*errl[0]) +
vari[0, 2]/(errm[0, 2]**2+N.abs(yo[0]-yi[2])*errl[0]),
vari[0, 3]/(errm[0, 3]**2+N.abs(yo[1]-yi[3])*errl[0])
])*errot**2
# - check
result = [(N.testing.assert_allclose, [erro.ravel(), errot]),
"""Test the :func:`~vacumm.misc.grid.masking.polygons` function"""
from vcmq import N, P, polygons, create_polygon, plot_polygon, create_grid
from _geoslib import Polygon
# Data
xx = N.array([0., 5., 4., 1.])
yy = N.array([0., 0., 2., 2.])
clip = [-3, -3, 0, 0]
xaxis = yaxis = N.linspace(-2., 2., 20.)
def plot_polygons(polys, **kwargs):
for p in polys:
plot_polygon(p, **kwargs)
## Single known argument
#pp0 = polygons(Polygon(N.array([xx, yy]).T))
#
## Classic data
#pp0 = polygons([N.array([xx, yy]), N.array([xx, yy]).T+6.])
#
## Classic with projection
#proj = lambda x, y: (x*1.5, y*1.5)
#pp1 = polygons([N.array([xx, yy])])
#
## Classic with clipping
#pp2 = polygons([-N.array([xx, yy])], clip=clip)
# From grid
pp3 = polygons([create_grid(xaxis, yaxis)])
x1, y1 = 0., 0.
x2, y2 = 3., 1.
x3, y3 = 2., 4.
x4, y4 = -1., 2.
# 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')
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)
# Lecture de la température
f =cdms2.open(data_sample('mars3d.tsigyx.nc'))
data_in = f('TEMP', time=slice(0,2)) # T-YX (- = level)
# Détermination des profondeurs d'après les sigma
sigma_converter = NcSigma.factory(f) # détection auto et initialisation du convertisseur
# -> VERIFIER QUE sigma_class EST BIEN SigmaGeneralized
depths_in = sigma_converter.sigma_to_depths(selector=dict(time=slice(0,2))).filled() # lecture eta, etc + conversion
# (Equivalent à depths_in = sigma_converter(selector=dict(time=slice(0,2))).filled())
f.close()
# Creation de l'axe des profondeurs cibles
depths = N.array([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,
22,24,26,28,30,32,34,36,38,40,45,50,55,60,65,70,75,80,85,90,95,100,120,140,160])
depths = -depths[::-1] # croissantes et négatives
depth_out = create_depth(depths)
# Interpolation
data_out = regrid1d(data_in, depth_out, axi=depths_in, axis=1, method='linear', extrap=1)
# Plot
kw = dict(vmin=10, vmax=14, xhide='auto', add_grid=True, ymax=0, fill='contourf') # FILL=PCOLOR ?
section2(data_in[0, :, 10], yaxis=depths_in[0, :, 10], subplot=211, title='Sigma', show=False, **kw)
s = section2(data_out[0, :, 10], subplot=212, title='Z', show=False, savefig=__file__, **kw)
"""Test the :func:`~vacumm.misc.grid.masking.create_polygon` function""" from vcmq import N, P, create_polygon, plot_polygon, code_file_name from _geoslib import Polygon # Data xx = N.array([0., 5., 4., 1.]) 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
"""Test the :func:`~vacumm.misc.grid.masking.create_polygon` function""" from vcmq import N, P, create_polygon, plot_polygon, code_file_name from _geoslib import Polygon # Data xx = N.array([0., 5., 4., 1.]) 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
f = cdms2.open(data_sample('mars3d.tsigyx.nc'))
data_in = f('TEMP', time=slice(0, 2)) # T-YX (- = level)
# Détermination des profondeurs d'après les sigma
sigma_converter = NcSigma.factory(
f) # détection auto et initialisation du convertisseur
# -> VERIFIER QUE sigma_class EST BIEN SigmaGeneralized
depths_in = sigma_converter.sigma_to_depths(selector=dict(
time=slice(0, 2))).filled() # lecture eta, etc + conversion
# (Equivalent à depths_in = sigma_converter(selector=dict(time=slice(0,2))).filled())
f.close()
# Creation de l'axe des profondeurs cibles
depths = N.array([
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,
90, 95, 100, 120, 140, 160
])
depths = -depths[::-1] # croissantes et négatives
depth_out = create_depth(depths)
# Interpolation
data_out = regrid1d(data_in,
depth_out,
axi=depths_in,
axis=1,
method='linear',
extrap=1)
# Plot
kw = dict(vmin=10,
