method='conservative',
diag=diag,
coordSys='cart')
# Norm
frac = diag['dstAreaFractions']
mask = frac == 0.
frac[mask] = 1.
varo[:] /= frac
varo[:] = MV2.masked_where(mask, varo, copy=0)
# Plot
rc('font', size=9)
kw = dict(vmin=vari.min(), vmax=vari.max())
axis = [xxbi.min(), xxbi.max(), yybo.min(), yybo.max()]
P.figure(figsize=(7, 3))
P.subplot(121, aspect=1)
P.pcolormesh(xxbi, yybi, vari, **kw)
P.colorbar(shrink=0.7)
add_grid(gridi, color='0.5')
add_grid(grido)
P.axis(axis)
P.title('Original: max=%g min=%g' % (vari.max(), vari.min()))
P.subplot(122, aspect=1)
P.pcolormesh(xxbo, yybo, varo, **kw)
P.colorbar(shrink=0.7)
add_grid(gridi)
add_grid(grido, color='0.5')
P.axis(axis)
P.title('Regridded: max=%g min=%g' % (varo.max(), varo.min()))
P.tight_layout()
"""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
"""Test :class:`~vacumm.misc.color.desaturate` """ # Imports from vcmq import P, desaturate, code_file_name, plot_cmap cmap_name = "CMRmap" cmap_old = P.get_cmap(cmap_name) # Max cmap_10 = desaturate(cmap_old, 1) # Medium cmap_05 = desaturate(cmap_old, 0.5) # Min cmap_00 = desaturate(cmap_old, 0) # Plot fig = P.figure(figsize=(6, 2)) kw = dict(fig=fig, figsize=None, aspect=.15, show=False, close=False) P.subplot(221) plot_cmap(cmap_old, title='Original', **kw) P.subplot(222) plot_cmap(cmap_00, title='f = 0.0', **kw) P.subplot(223) plot_cmap(cmap_05, title='f = 0.5', **kw) P.subplot(224) plot_cmap(cmap_10, title='f = 1.0', **kw) P.tight_layout() P.figtext(.5, 1, 'Desaturate '+cmap_name, va='top', ha='center', size=12)
# Input data
nxi = 4
nyi = 3
xxi, yyi = N.meshgrid(N.arange(nxi) + .25, N.arange(nyi) - .25)
for j in xrange(nyi):
xxi[j, :] -= j * 0.5
for i in xrange(nxi):
yyi[:, i] += i * 0.5
gridi = create_grid2d(xxi, yyi) # input cdms grid
vari = MV2.array(N.arange(nyi * nxi).reshape(nyi, nxi)) + 10.
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),
zzov = griddata(xi, yi, zi, (xr, yr), method='nat', ext=True, sub=10)
# -> Tester la methode "carg"
# -> Testez parametre sub=...
# -> Essayer avec GridData
zov2 = xy2xy(xi, yi, zi, xo, yo)
# Krigeage
from vacumm.misc.grid.kriging import krig
zzok = krig(xi, yi, zi, xxr.ravel(), yyr.ravel(), nproc=1).reshape(zzr.shape)
# -> Tester nproc et npmax
# Plots
from vcmq import meshbounds, P
xxrb, yyrb = meshbounds(xr, yr)
P.figure(figsize=(10, 8))
axis = [xxrb.min(), xxrb.max(), yyrb.min(), yyrb.max()]
#
P.subplot(332)
P.pcolormesh(xxrb, yyrb, zzr, **vminmax)
P.scatter(xi, yi, c='k')
P.title('Original')
P.axis(axis)
#
P.subplot(334)
P.pcolormesh(xxrb, yyrb, zzoc, **vminmax)
P.title('CDAT/Natgrid')
P.axis(axis)
#
P.subplot(335)
P.pcolormesh(xxrb, yyrb, zzork, **vminmax)
vari[int(nyi*0.4):int(nyi*0.4)+3, int(nxi*0.4):int(nxi*0.4)+2] = N.ma.masked
xxib, yyib = meshcells(xi, yi)
nxo = 40
nyo = 25
xo = N.linspace(int(nxi*0.2),int(nxi*1.2),nxo)
yo = N.linspace(int(-nyi*0.2),int(nyi*0.8),nyo)
xxob, yyob = meshcells(xo, yo)
vari.shape = (1, )+vari.shape
varo = N.ma.masked_values(dstwgt(vari.filled(), xi, yi, xo, yo, mv, 0), mv)
kw = dict(vmin=vari.min(), vmax=vari.max())
axlims = [min(xi.min(), xo.min()), max(xi.max(), xo.max()),
min(yi.min(), yo.min()), max(yi.max(), yo.max())]
P.figure(figsize=(8, 4))
P.subplot(211)
P.pcolor(xxib, yyib, vari[0], **kw)
P.axis(axlims)
P.title('Original')
P.subplot(212)
P.pcolor(xxob, yyob, varo[0], **kw)
P.axis(axlims)
P.title('Distance weight')
P.tight_layout()
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.show()
P.close()
"""Test the :func:`~vacumm.misc.grid.io.Shapes` class"""
from vcmq import N, P, code_file_name, data_sample
from vacumm.misc.io import Shapes
result = []
# File names
shpfile = data_sample("ne_110m_land/ne_110m_land")
figfile = code_file_name(ext=False) + '_%i.png'
# Basic
S = Shapes(shpfile)
result.append(('assertEqual', [len(S), 127]))
S.plot(title='Basic', show=False)
P.savefig(figfile % 0)
P.close()
# Min area
S = Shapes(shpfile, min_area=1000)
result.append(('assertEqual', [len(S), 3]))
S.plot(title='Min area', show=False)
P.savefig(figfile % 1)
P.close()
# Projection
S = Shapes(shpfile, proj='merc')
result.append(('assertGreater', [S[1].area(), 1e12]))
S.plot(title='Projected', show=False)
P.savefig(figfile % 2)
P.close()
diag = {'dstAreaFractions':None}
varo = vari.regrid(grido, tool='esmf', method='conservative',
diag=diag, coordSys='cart')
# Norm
frac = diag['dstAreaFractions']
mask = frac==0.
frac[mask]=1.
varo[:] /= frac
varo[:] = MV2.masked_where(mask, varo, copy=0)
# Plot
rc('font', size=9)
kw = dict(vmin=vari.min(), vmax=vari.max())
axis = [xxbi.min(), xxbi.max(), yybo.min(), yybo.max()]
P.figure(figsize=(7, 3))
P.subplot(121, aspect=1)
P.pcolormesh(xxbi, yybi, vari, **kw)
P.colorbar(shrink=0.7)
add_grid(gridi, color='0.5')
add_grid(grido)
P.axis(axis)
P.title('Original: max=%g min=%g'%(vari.max(), vari.min()))
P.subplot(122, aspect=1)
P.pcolormesh(xxbo, yybo, varo, **kw)
P.colorbar(shrink=0.7)
add_grid(gridi)
add_grid(grido, color='0.5')
P.axis(axis)
P.title('Regridded: max=%g min=%g'%(varo.max(), varo.min()))
P.tight_layout()
from vacumm.misc.grid.misc import rotate_grid
from vacumm.misc.grid.regridding import CurvedInterpolator
from vcmq import P, N, set_grid, plot2d, MV2, add_grid, code_file_name, os
# Curved grid
nxy = 10
lon = N.arange(nxy*1.)
lat = N.arange(nxy*1.)
gridi = rotate_grid((lon, lat), 30)
xxi = gridi.getLongitude()[:].filled()
yyi = gridi.getLatitude()[:].filled()
vari = MV2.array(yyi)
set_grid(vari, gridi)
kw = dict(vmin=vari.min(), vmax=vari.max())
P.figure(figsize=(10, 3.5))
P.subplot(131, aspect=1)
P.contourf(xxi, yyi, vari.asma(), **kw)
add_grid(gridi, edges=False, centers=-1)
xylims = (xxi.min(), xxi.max(), yyi.min(), yyi.max())
P.axis(xylims)
P.title('Curved grid')
# Interpolate to grid
xg, yg = N.meshgrid(N.arange(-3.5, 14.5), N.arange(-3.5, 14.5))
nxyg = xg.shape
cig = CurvedInterpolator(gridi, (xg, yg), g2g=True)
varog = cig(vari)
P.subplot(132, aspect=1)
P.scatter(xg, yg, c=varog.asma(), s=120, linewidth=0, **kw)
add_grid(gridi, edges=False, centers=-1)
def plot_fields(self,
variables,
surf=None,
bottom=None,
horiz_sections=False,
points=None,
zonal_sections=None,
merid_sections=None,
titlepat="{var_name} - {slice_loc}",
props=None,
subst={},
figpat='sonat.ens.{var_name}_{slice_type}_{slice_loc}.png',
fmtlonlat=u'{:.2f}°{}',
fmtdep='{:04.0f}m',
savefig=True,
obs=None,
close=True,
show=False,
**kwargs):
"""Plot one or several platform like variables
Parameters
----------
depths: string, floats
List of depths for horizontal slices. All by default.
It accepts scalars or list of floats and of the two special values
"bottom" and "surf". In the latter case, these variable must
be explicitely included in the ensemble, like "temp_surf". A depth
of 0 is not equivalent to "surf", despite results may be similar,
or equivalent when the sea level is always 0.
In the case of floats, the
ensemble must contain 3d variables.
props: dict, None
Dictionary of graphic properties passed as keywords to plotting
functions. The keys must be valid diagnostics names such as
"mean", or one of the follow plotting function: map, curve.
"""
# Init
if props is None:
props = {}
kwprops = dict_merge(props, DEFAULT_PLOT_KWARGS_PER_ENS_DIAG)
kwprops.update(fig='new') # TODO: must be more flexible like for obs
figs = OrderedDict()
kwobs = kwfilter(kwargs, 'obs')
kwobs.update(surf=False,
bottom=False,
zonal_sections=None,
merid_sections=None,
horiz_sections=None)
# Loop on variables
for variable in ArgList(variables).get():
var_short_name, var_depth, diag_name = split_varname(variable)
diag_name = getattr(variable, 'sonat_ens_diag', diag_name)
var_name = (var_short_name if var_depth is None else
(var_short_name + '_' + var_depth))
varname = var_name
id = variable.id
self.debug(' Variable: ' + id)
order = variable.getOrder()
if diag_name:
kw = kwprops.get(diag_name, {})
else:
kw = {}
toplot = []
# Maps
if (horiz_sections is not False or surf is not False
or bottom is not False):
# Surf/bottom/3D
if ((var_depth == 'surf' and surf is not False) or
(var_depth == 'bottom' and bottom is not False)): # 2D
slice_loc = var_depth
toplot.append(
dict(slice_loc=slice_loc,
kw=kw,
keys=(var_short_name, 'map', slice_loc),
obs_plot={var_depth: True},
slice_type='map'))
elif (variable.getLevel() is not None
and horiz_sections is not False
and horiz_sections is not None): # 3D
# Surf and bottom
for ok, slice_loc in [(surf, 'surf'), (bottom, 'bottom')]:
if ok is True:
toplot.append(
dict(
slice_loc=slice_loc,
slice_type='map',
keys=(var_short_name, 'map', slice_loc),
kw=dict_merge(dict(depth=slice_loc), kw),
obs_plot={slice_loc: True},
))
# Loop on 3D depths specs
if horiz_sections is True:
depths = variable.getLevel()
else:
depths = horiz_sections
if N.isscalar(depths):
depths = [depths]
depths = list(depths)
for depth in depths:
if isinstance(depth, str):
slice_loc = depth
else:
slice_loc = fmtdep.format(abs(depth))
toplot.append(
dict(
slice_loc=slice_loc,
slice_type='map',
keys=(var_name, 'map', slice_loc),
kw=dict_merge(dict(depth=depth), kw),
obs_plot={'horiz_sections': depth},
))
# Zonal sections
if zonal_sections is not None and zonal_sections is not False:
slice_type = 'zonal'
if N.isscalar(zonal_sections):
zonal_sections = [zonal_sections]
for lat in zonal_sections:
slice_loc = latlab(lat, no_symbol=True)
toplot.append(
dict(
slice_loc=slice_loc,
slice_type=slice_type,
keys=(var_name, slice_type, slice_loc),
kw=dict_merge(dict(lat=lat), kw),
obs_plot={'zonal_sections': lat},
))
# Meridional sections
if merid_sections is not None and merid_sections is not False:
slice_type = 'merid'
if N.isscalar(merid_sections):
merid_sections = [merid_sections]
for lon in merid_sections:
slice_loc = lonlab(lon, no_symbol=True)
toplot.append(
dict(
slice_loc=slice_loc,
slice_type=slice_type,
keys=(var_name, slice_type, slice_loc),
kw=dict_merge(dict(lon=lon), kw),
obs_plot={'merid_sections': lon},
))
# # Points
# if points:
# slice_type = 'point'
# if isinstance(points, tuple):
# points = [points]
# for lon, lat in points:
# slice_loc = (lonlab(lon, no_symbol=True) + '-' +
# latlab(lat, no_symbol=True))
# toplot.append(dict(
# slice_loc=slice_loc,
# slice_type=slice_type,
# keys=(var_name, slice_type, slice_loc),
# kw=dict_merge(dict(lon=lon, lat=lat), kw),
# obs_plot={'points':(lon, lat)},
# ))
# Make all pending plots for this variable
for spec in toplot:
# Get specs
slice_loc = spec['slice_loc']
slice_type = spec['slice_type']
obs_plot = spec['obs_plot']
keys = map(str.title, spec['keys'])
kw = kwargs.copy()
kw.update(spec['kw'])
self.debug(' Location: ' + slice_loc)
# Setup
dfmt = locals()
dfmt.update(subst)
title = titlepat.format(**dfmt)
kw.update(title=title, savefig=False, close=False)
dict_check_defaults(kw,
fig='new',
**DEFAULT_PLOT_KWARGS_PER_ENS_DIAG)
# Plot
p = plot_gridded_var(variable, **kw) # Action !
# Plot obs locations on 2D plots only
if obs and isinstance(p, Plot2D):
#FIXME: should we pass lon/lat/level from variable?
kwo = kwobs.copy()
kwo.update(obs_plot,
full3d=False,
full2d=False,
plotter=p,
savefig=False,
close=False,
title=False,
colorbar=False)
# obs.set_cached_plot('locations', slice_type, slice_loc, p)
obs.plot('locations', **kwo)
# Finalise
if savefig:
figfile = figpat.format(**dfmt)
checkdir(figfile)
p.savefig(figfile)
close = True
self.created(figfile)
kw = {keys[-1]: figfile, '__class__': OrderedDict}
dicttree_set(figs, *keys[:-1], **kw)
if not show and close:
p.close()
if show:
P.show()
elif close:
P.close()
return figs
# 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)
P.axis(axis)
# - interpolated data
P.subplot(122)
P.title('Interpolated points')
P.imshow(zzg, **kwim)
P.scatter(xo, yo, c=zo, marker='s', s=30, **kwsc)
P.axis(axis)
P.tight_layout()
savefigs(code_file_name(), verbose=False)
P.close()
xg, yg, zzg = gridded_gauss3()
xi, yi, zi = random_gauss3(np=npi)
zi = N.vstack((zi, zi)) # simulate several time steps for multifit
# Init kriger
kriger = OrdinaryCloudKriger(xi, yi, zi, npmax=npmax, nproc=nproc)
# Output points
xo, yo = random_points(np=npo)
# Interpolate
t0 = time()
zo = kriger(xo, yo)
# Plot
P.figure(figsize=(6, 8))
axis = [xg.min(), xg.max(), yg.min(), yg.max()]
kw = dict(vmin=zi.min(), vmax=zi.max())
kwim = dict(extent=axis, interpolation='bilinear', origin='lower', alpha=.2, **kw)
kwsc = dict(lw=0.2, **kw)
P.subplot(211)
P.imshow(zzg, **kwim)
P.scatter(xi, yi, c=zi if zi.ndim==1 else zi[0], s=20, **kwsc)
P.title('Input points')
P.subplot(212)
P.imshow(zzg, **kwim)
P.scatter(xo, yo, c=zo if zo.ndim==1 else zo[0], s=40, **kwsc)
P.title('Interpolated points')
P.tight_layout()
savefigs(code_file_name(), verbose=False)
P.close()
"""Test the :func:`~vacumm.misc.grid.io.Shapes` class"""
from vcmq import N, P, code_file_name, data_sample
from vacumm.misc.io import Shapes
result = []
# File names
shpfile = data_sample("ne_110m_land/ne_110m_land")
figfile = code_file_name(ext=False)+'_%i.png'
# Basic
S = Shapes(shpfile)
result.append(('assertEqual', [len(S), 127]))
S.plot(title='Basic', show=False)
P.savefig(figfile%0);P.close()
# Min area
S = Shapes(shpfile, min_area=1000)
result.append(('assertEqual', [len(S), 3]))
S.plot(title='Min area', show=False)
P.savefig(figfile%1);P.close()
# Projection
S = Shapes(shpfile, proj='merc')
result.append(('assertGreater', [S[1].area(), 1e12]))
S.plot(title='Projected', show=False)
P.savefig(figfile%2);P.close()
# Clips
S = Shapes(shpfile, clip=[-10, 42, 10, 51.])
result.append(('assertEqual', [len(S), 3]))
S.plot(title='Clipped', show=False)
yi = N.arange(nxy*1.)
xxi, yyi = N.meshgrid(xi, yi)
zi = N.ma.array(yyi)
zi[int(nxy*0.3):int(nxy*0.8), int(nxy*0.3):int(nxy*0.8)] = N.ma.masked
zi.shape = 1, nxy, nxy
# Output positions
no = 1000
xo = N.random.uniform(-nxy/4., nxy+nxy/4., no)
yo = N.random.uniform(-nxy/4., nxy+nxy/4., no)
# Interpolate
mv = zi.get_fill_value()
zo = nearest2dto1d(xi,yi,zi.filled(mv),xo,yo,mv)
zo = N.ma.masked_values(zo, mv)
# Plot
kw = dict(vmin=zi.min(), vmax=zi.max())
P.figure(figsize=(6, 6))
P.subplot(111, aspect=1)
P.contourf(xxi, yyi, zi[0], **kw)
add_grid((xi, yi), edges=False, centers=True, marker='o')
P.scatter(xo, yo, c=zo[0], s=50, **kw)
P.title('nearest2dto1d')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
nb = 10
xxbi, yybi = meshbounds(xxi, yyi)
# Output grid
grido = rotate_grid((N.linspace(0, 6, 50)-1, N.linspace(0, 4, 35)+1.), -20)
xxo = grido.getLongitude()[:].filled()
yyo = grido.getLatitude()[:].filled()
xxbo, yybo = meshbounds(xxo, yyo)
# Nearest
varo = nearest2d(vari, xxi, yyi, xxo, yyo, nb)
# Plot
vmin = varo.min()
vmax = varo.max()
P.figure(figsize=(8, 4))
P.subplot(121, aspect=1)
P.pcolor(xxbi, yybi, vari[0], vmin=vmin, vmax=vmax)
add_grid(grido)
P.title('original')
P.subplot(122, aspect=1)
P.pcolor(xxbo, yybo, varo[0], vmin=vmin, vmax=vmax)
add_grid(gridi)
P.title('nearest2d')
P.axis('image')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.show()
P.close()
# 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()
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)
# -*- 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)
def plot(xx, yy, target, label, figfiles, figfile, lon=None, lat=None, show=False):
xs, ys, mask = coord2slice(target, lon=lon, lat=lat)
P.figure(figsize=(6, 3.5))
P.title('Target=%(label)s / select: lon=%(lon)s, lat=%(lat)s'%locals())
add_grid((xx, yy))
xx = xx.asma()
yy = yy.asma()
if isinstance(lon, tuple):
P.axvline(lon[0], color='m', ls='--', lw=2)
P.axvline(lon[1], color='m', ls='--', lw=2)
elif isinstance(lon, slice):
i, j, k = lon.indices(xx.shape[1])
P.plot(xx[:, i], yy[:, i], 'c--', lw=2)
P.plot(xx[:, j-1], yy[:, j-1], 'c--', lw=2)
if isinstance(lat, tuple):
P.axhline(lat[0], color='m', ls='--', lw=2)
P.axhline(lat[1], color='m', ls='--', lw=2)
elif isinstance(lat, slice):
i, j, k = lat.indices(yy.shape[0])
P.plot(xx[i], yy[i], 'c--', lw=2)
P.plot(xx[j-1], yy[j-1], 'c--', lw=2)
P.xticks(N.arange(xx.min()-1, xx.max()+1))
P.yticks(N.arange(yy.min()-1, yy.max()+1))
xxi, yyi = xx, yy
xx = xx[ys, xs]
yy = yy[ys, xs]
# mask = mask[ys, xs]
xxb, yyb = meshbounds(xx, yy)
P.pcolor(xxb, yyb, mask, shading='faceted')
P.scatter(xx.ravel(), yy.ravel(), c=(0, 1, 0))
P.grid('on')
P.axis('image')
P.tight_layout()
i = len(figfiles)
savefig = figfile%i
if os.path.exists(savefig): os.remove(savefig)
P.savefig(savefig)
figfiles.append(savefig)
if show: P.show()
else: P.close()
create_time, CurvedInterpolator, rotate_grid)
# Curved grid
nxy = 10
nt = 5
lon = N.arange(nxy * 1.)
lat = N.arange(nxy * 1.)
time = create_time((nt, ), 'years since 2000')
gridi = rotate_grid((lon, lat), 30)
xxi = gridi.getLongitude()[:].filled()
yyi = gridi.getLatitude()[:].filled()
vari = MV2.resize(yyi, (nt, nxy, nxy))
vari.setAxis(0, time)
set_grid(vari, gridi)
kw = dict(vmin=vari.min(), vmax=vari.max())
P.figure(figsize=(10, 3.5))
P.subplot(131, aspect=1)
P.contourf(xxi, yyi, vari[0].asma(), **kw)
add_grid(gridi, edges=False, centers=-1)
xylims = (xxi.min(), xxi.max(), yyi.min(), yyi.max())
P.axis(xylims)
P.title('Curved grid')
# Interpolate to grid
xg, yg = N.meshgrid(N.arange(-3.5, 14.5), N.arange(-3.5, 14.5))
nxyg = xg.shape
cig = CurvedInterpolator(gridi, (xg, yg), g2g=True)
varog = cig(vari)
P.subplot(132, aspect=1)
P.scatter(xg, yg, c=varog[0].asma(), s=120, linewidth=0, **kw)
add_grid(gridi, edges=False, centers=-1)
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()
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)
N.testing.assert_allclose(zzg[-1, -1], farvalue)
# Plot
vmin = min(zi.min(), zo.min())
vmax = max(zi.max(), zo.max(), farvalue)
cmap = 'cmocean_ice'
kw = dict(vmin=vmin, vmax=vmax)
P.pcolormesh(xxg, yyg, zzg, cmap=cmap, **kw)
P.colorbar()
P.scatter(xi, yi, c=zi, s=100, cmap=cmap, **kw)
P.axis('image')
P.title('Simple kriging with fixed far value')
P.savefig(code_file_name(ext='.png'))
P.show()
P.close()
"""Test :class:`~vacumm.misc.color.pastelise` """ # Imports from vcmq import P, pastelise, code_file_name, plot_cmap cmap_name = "jet" cmap_old = P.get_cmap(cmap_name) # Pastelise cmap_new = pastelise(cmap_old, s=.15, v=.9) # Plot fig = P.figure(figsize=(6, 2)) kw = dict(fig=fig, figsize=None, aspect=.15, show=False, close=False) P.subplot(211) plot_cmap(cmap_old, title='Original', **kw) P.subplot(212) plot_cmap(cmap_new, title='Pastelised', **kw) P.tight_layout() P.figtext(.5, 1, 'Pastelise ' + cmap_name, va='top', ha='center', size=12) P.show() P.savefig(code_file_name(ext='png')) P.close()
# 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')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
"""Test :func:`~vacumm.misc.plot.add_logo`"""
# Imports
from vcmq import N, P, add_logo, os, code_file_name, data_sample
import matplotlib.image as mpimg
# Inits
logofile = data_sample('logo_ifremer.png')
P.plot([2, 6])
# Default
add_logo(logofile, scale=1)
# Upper right / no rescale
add_logo(logofile, loc='upper right')
# Rescale
add_logo(logofile, loc='upper left', scale=2)
# Alpha
add_logo(logofile, loc='lower right', alpha=0.2)
# Save
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
iib, jjb = meshbounds(ii, jj)
zzi = jj * nyi + ii #relpos2index(ii, jj)
zzbi = jjb * nyi + iib # ny+1? relpos2index(iib, jjb)
# Input random points
N.random.seed(0)
np = 100
xxo = N.random.random(np) * (xxbi.max() - xxbi.min()) + xxbi.min()
yyo = N.random.random(np) * (yybi.max() - yybi.min()) + yybi.min()
xxo = N.concatenate((xxo, xxi.ravel()))
yyo = N.concatenate((yyo, yyi.ravel()))
np = xxo.size
# Convert to relative indices
pp, qq = curv2rel(xxbi, yybi, xxo, yyo)
bad = pp < 0
zzo = (qq - 1) * nyi + pp - 1 #relpos2index(pp-1, qq-1)
zzo[bad] = -1
# Plot
vmin = zzbi.min()
vmax = zzbi.max()
P.pcolor(xxbi, yybi, zzi, vmin=vmin, vmax=vmax)
P.scatter(xxo, yyo, c=zzo, vmin=vmin, vmax=vmax, s=80)
P.grid()
P.title('curv2rel')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
"""Test :class:`~vacumm.misc.color.pastelise` """ # Imports from vcmq import P, pastelise, code_file_name, plot_cmap cmap_name = "jet" cmap_old = P.get_cmap(cmap_name) # Pastelise cmap_new = pastelise(cmap_old, s=.15, v=.9) # Plot fig = P.figure(figsize=(6, 2)) kw = dict(fig=fig, figsize=None, aspect=.15, show=False, close=False) P.subplot(211) plot_cmap(cmap_old, title='Original', **kw) P.subplot(212) plot_cmap(cmap_new, title='Pastelised', **kw) P.tight_layout() P.figtext(.5, 1, 'Pastelise '+cmap_name, va='top', ha='center', size=12) P.show() P.savefig(code_file_name(ext='png')) P.close()
"""Test :class:`~vacumm.misc.color.darken` """ # Imports from vcmq import P, darken, code_file_name, plot_cmap cmap_name = "CMRmap" cmap_old = P.get_cmap(cmap_name) # Max cmap_10 = darken(cmap_old, 1) # Medium cmap_05 = darken(cmap_old, 0.5) # Min cmap_00 = darken(cmap_old, 0) # Plot fig = P.figure(figsize=(6, 2)) kw = dict(fig=fig, figsize=None, aspect=.15, show=False, close=False) P.subplot(221) plot_cmap(cmap_old, title='Original', **kw) P.subplot(222) plot_cmap(cmap_00, title='f = 0.0', **kw) P.subplot(223) plot_cmap(cmap_05, title='f = 0.5', **kw) P.subplot(224) plot_cmap(cmap_10, title='f = 1.0', **kw) P.figtext(.5, 1, 'Darken ' + cmap_name, va='top', ha='center', size=12) P.tight_layout() P.savefig(code_file_name(ext='png'))
nb = 10
xxbi, yybi = meshbounds(xxi, yyi)
# Output grid
grido = rotate_grid((N.linspace(0, 6, 50) - 1, N.linspace(0, 4, 35) + 1.), -20)
xxo = grido.getLongitude()[:].filled()
yyo = grido.getLatitude()[:].filled()
xxbo, yybo = meshbounds(xxo, yyo)
# Nearest
varo = nearest2d(vari, xxi, yyi, xxo, yyo, nb)
# Plot
vmin = varo.min()
vmax = varo.max()
P.figure(figsize=(8, 4))
P.subplot(121, aspect=1)
P.pcolor(xxbi, yybi, vari[0], vmin=vmin, vmax=vmax)
add_grid(grido)
P.title('original')
P.subplot(122, aspect=1)
P.pcolor(xxbo, yybo, varo[0], vmin=vmin, vmax=vmax)
add_grid(gridi)
P.title('nearest2d')
P.axis('image')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.show()
P.close()
m.add_key('B', pos='top right', xmargin=20)
# Point
m.add_point(-5.3, 48.3, color='b', shadow=True, edgecolor='r', size=120)
# Place = point + text
m.add_place(-4.62138, 48.38197, 'Plouzane', size=100)
# Add lines
m.add_line([-5.3, 48, -5, 49], color='r')
m.add_lines([-5.3, -4.7, -5], [48, 48.5, 49], color='g')
# Add box
m.add_box([-6.1, 48.1, -5.5, 48.9], color='k', shadow=True, linewidth=3)
# Add arrow
m.add_arrow(-6, 48.5, 5, 5)
# Add map scale (maps only)
m.add_mapscale(barstyle='fancy')
# Add compass(maps only)
m.add_compass(pos=(0.97, .5))
# Add both!
m.add_mscp(pos='upper left')
P.tight_layout()
P.subplots_adjust(bottom=0.08)
m.savefigs(__file__)
"""Test function :func:`~vacumm.misc.grid.kriging.cloud_split`"""
nps = [100, 60, 40]
npmax = 90
dxy = 5
from vcmq import P, simple_colors, savefigs, code_file_name, N
from vacumm.misc.grid.kriging import cloud_split
# Input clouds
xx = []
yy = []
P.figure(figsize=(7, 3.5))
P.subplot(121, aspect=1)
colors = simple_colors[1:]
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)
zzbi = relpos2index(iib, jjb, nyi)
# Input random points
N.random.seed(0)
np = 100
xxo = N.random.random(np)*(xxbi.max()-xxbi.min()) + xxbi.min()
yyo = N.random.random(np)*(yybi.max()-yybi.min()) + yybi.min()
xxo = N.concatenate((xxo, xxi.ravel()))
yyo = N.concatenate((yyo, yyi.ravel()))
np = xxo.size
# Convert to relative indices
zzo = N.zeros(np)-1
for i, (xo, yo) in enumerate(zip(xxo, yyo)):
p, q = curv2rel_single(xxbi, yybi, xo, yo)
# print xo, yo, '|', p, q, (q-1) * nxi + p - 1
if q>=0:
zzo[i] = relpos2index(p-1, q-1, nyi)
# Plot
vmin = zzbi.min()
vmax = zzbi.max()
P.pcolor(xxbi, yybi, zzi, vmin=vmin, vmax=vmax)
P.scatter(xxo, yyo, c=zzo, vmin=vmin, vmax=vmax, s=80)
P.grid()
P.title('curv2rel_single')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
plot2d(varo[0], title='ESMF / Bilinear', subplot=(4,3,ip), **kw);ip+=1 add_grid(gridci, **kwg) # - bilin c2c / libcf varo = regrid2d(varci, gridco, method='bilinear', tool='libcf') plot2d(varo[0], title='LibCF / Bilinear', subplot=(4,3,ip), **kw);ip+=1 add_grid(gridci, **kwg) # - dstwgt c2c / vacumm varo = regrid2d(varci, gridco, method='dstwgt', tool='vacumm') plot2d(varo[0], title='VACUMM / Dstwgt', subplot=(4,3,ip), **kw);ip+=1 add_grid(gridci, **kwg) # - patch c2c / emsf varo = regrid2d(varci, gridco, method='patch', tool='esmf') plot2d(varo[0], title='ESMF / Patch', subplot=(4,3,ip), **kw);ip+=1 add_grid(gridci, **kwg) # - cellave r2r / regrid2 varo = regrid2d(varri, gridro, method='cellave', tool='regrid2') plot2d(varo[0], title='Regrid2 / Cellave', subplot=(4,3,ip), **kw);ip+=1 add_grid(gridri, **kwg) # - cellave c2c / esmf varo = regrid2d(varci, gridco, method='cellave', tool='esmf') plot2d(varo[0], title='ESMF / Cellave', subplot=(4,3,ip), **kw);ip+=1 add_grid(gridci, **kwg) # - conserv c2c / esmf varo = regrid2d(varci, gridco, method='conserv', tool='esmf') plot2d(varo[0], title='ESMF / Conserv', subplot=(4,3,ip), **kw);ip+=1 add_grid(gridci, **kwg) P.tight_layout() P.savefig(code_file_name(ext='png')) rcdefaults() P.close()
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))
axis = [xg.min(), xg.max(), yg.min(), yg.max()]
"""Test :class:`~vacumm.misc.color.to_grey` """ # Imports from vcmq import P, to_grey, code_file_name, plot_cmap cmap_name = "CMRmap" cmap_old = P.get_cmap(cmap_name) # Max cmap_10 = to_grey(cmap_old, 1) # Medium cmap_05 = to_grey(cmap_old, 0.5) # Min cmap_00 = to_grey(cmap_old, 0) # Plot fig = P.figure(figsize=(6, 2)) kw = dict(fig=fig, figsize=None, aspect=.15, show=False, close=False) P.subplot(221) plot_cmap(cmap_old, title='Original', **kw) P.subplot(222) plot_cmap(cmap_00, title='f = 0.0', **kw) P.subplot(223) plot_cmap(cmap_05, title='f = 0.5', **kw) P.subplot(224) plot_cmap(cmap_10, title='f = 1.0', **kw) P.tight_layout()
fill_value=1e20)
N.random.seed(0)
xo = N.random.uniform(lon0, lon1, np)
yo = N.random.uniform(lat0, lat1, np)
zo = N.random.uniform(dep0, dep1, np)
to = comptime(N.random.uniform(reltime(time0, time.units).value,
reltime(time1, time.units).value, np),
time.units)
# Rectangular xyzt with 1d z
vo = grid2xy(vi, xo=xo, yo=yo, zo=zo, to=to, method='linear')
von = grid2xy(vi, xo=xo, yo=yo, zo=zo, to=to, method='nearest')
assert vo.shape==(ne, np)
N.testing.assert_allclose(vo[0], yo)
kwp = dict(vmin=vi.min(), vmax=vi.max())
P.figure(figsize=(6, 3))
P.subplot(121)
P.scatter(xo, yo, c=vo[0], cmap='jet', **kwp)
add_grid(vi.getGrid())
P.title('linear4d')
P.subplot(122)
P.scatter(xo, yo, c=von[0], cmap='jet', **kwp)
add_grid(vi.getGrid())
P.title('nearest4d')
P.figtext(.5, .98, 'grid2xy in 4D', va='top', ha='center', weight='bold')
P.tight_layout()
P.savefig(code_file_name(ext='.png'))
P.close()
# Reversed z and y
vi_revz = vi[:, :, ::-1, ::-1, :]
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 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)
P.axis(axis)
# - interpolated data
P.subplot(122)
P.title('Interpolated points')
P.imshow(zzg, **kwim)
P.scatter(xo, yo, c=zo, marker='s', s=30, **kwsc)
P.axis(axis)
P.tight_layout()
savefigs(code_file_name(), verbose=False)
P.close()
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)
# Time zoom for plot clarity
tzoom = (ct1.sub(7, cdtime.Hour), ctimesi[-1])
zo = sck(xo, yo)
zoe = scke(xo, yo)
zzg = zo.reshape(ny, nx)
zzge = zoe.reshape(ny, nx)
# Plot
vmin = min(zi.min(), zo.min(), zoe.min())
vmax = max(zi.max(), zo.max(), zoe.max())
vmax = max(abs(vmin), abs(vmax))
vmin = -vmax
cmap = 'cmocean_tempo'
cmap = 'cmocean_delta'
kw = dict(vmin=vmin, vmax=vmax)
P.figure(figsize=(6, 5.5))
ax = P.subplot(221)
P.pcolormesh(xxg, yyg, zzg, cmap=cmap, **kw)
P.colorbar()
P.scatter(xi, yi, c=zi, s=100, cmap=cmap, **kw)
P.title('Without obs error')
P.subplot(222, sharex=ax, sharey=ax)
P.pcolormesh(xxg, yyg, zzge, cmap=cmap, **kw)
P.colorbar()
P.scatter(xi, yi, c=zi, s=100, cmap=cmap, **kw)
#P.axis('image')
P.title('With obs error')
P.subplot(223, sharex=ax, sharey=ax)
nxy = 15
xi = N.arange(nxy * 1.)
yi = N.arange(nxy * 1.)
xxi, yyi = N.meshgrid(xi, yi)
zi = N.ma.array(yyi)
zi[int(nxy * 0.3):int(nxy * 0.8), int(nxy * 0.3):int(nxy * 0.8)] = N.ma.masked
zi.shape = 1, nxy, nxy
# Output positions
no = 1000
xo = N.random.uniform(-nxy / 4., nxy + nxy / 4., no)
yo = N.random.uniform(-nxy / 4., nxy + nxy / 4., no)
# Interpolate
mv = zi.get_fill_value()
zo = dstwgt2dto1d(xi, yi, zi.filled(mv), xo, yo, mv)
zo = N.ma.masked_values(zo, mv)
# Plot
kw = dict(vmin=zi.min(), vmax=zi.max())
P.figure(figsize=(6, 6))
P.subplot(111, aspect=1)
P.contourf(xxi, yyi, zi[0], **kw)
add_grid((xi, yi), edges=False, centers=True, marker='o')
P.scatter(xo, yo, c=zo[0], s=50, **kw)
P.title('dstwgt2dto1d')
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
# Output
grido = create_grid(xi[:]+2*2.5, yi[:]+3*1.5)
xo = grido.getLongitude()
yo = grido.getLatitude()
xo.setBounds(bounds1d(xo))
yo.setBounds(bounds1d(yo))
xxob, yyob = meshbounds(xo, yo)
# Regridding
varo, wo = vari.regrid(grido, tool='regrid2', returnTuple=1)
# Plot
kw = dict(fill='pcolor', contour=False, xhide=True, yhide=True,
xticks=[], yticks=[], cmap='jet',
colorbar=False, show=False)
P.figure(figsize=(6, 3))
p = plot2d(vari, subplot=131, title='Original', **kw)
add_grid(gridi)
add_grid(grido)
P.axis('image')
p = plot2d(varo, subplot=132, title='Regridded', **kw)
add_grid(gridi)
add_grid(grido)
P.axis('image')
P.subplot(133)
P.pcolor(xxob, yyob, wo)
add_grid(grido)
P.title("Output weights")
P.tight_layout()
savefigs(code_file_name(),pdf=True, verbose=False)
P.close()
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,
print method
# Regrillage
ssto = regrid2d(ssti, grido, method=method) # -> CHANGEZ LA METHODE
# -> UTILISEZ USECDR=TRUE
# Regrilleur de CDAT
regridder = CDATRegridder(gridi, grido, method='cellave') # -> CHANGEZ LA METHODE
ssto2 = regridder.regrid(ssti)
ssto3, cdr = regrid2d(ssti, grido, cdr=regridder, getcdr=True)
# -> VERIFIEZ LE TYPE
# Plots
kwp = dict(vmin=ssti.min(), vmax=ssti.max(), res=None, show=False, colorbar=False,
drawmeridians_linewidth=0, drawparallels_linewidth=0, grid=False, yhide='auto', **select)
map2(ssti, title='Original',figsize=(15, 5), subplot=131, **kwp)
add_grid(gridi, alpha=1)
map2(ssto, title='Avec regrid2d', subplot=132, **kwp)
add_grid(grido, alpha=1)
add_grid(gridi, edges=False, centers=True, alpha=.5, markersize=3, marker='o')
map2(ssto2, title='Avec CDATRegridder', subplot=133, **kwp)
add_grid(grido, alpha=1)
add_grid(gridi, edges=False, centers=True, alpha=.5, markersize=3, marker='o')
P.tight_layout()
P.show()
plot2d(varo, title='ESMF / Bilinear', subplot=(4,3,ip), **kw);ip+=1 add_grid(gridci, **kwg) # - bilin c2c / libcf varo = regrid2d(varci, gridco, method='bilinear', tool='libcf') plot2d(varo, title='LibCF / Bilinear', subplot=(4,3,ip), **kw);ip+=1 add_grid(gridci, **kwg) # - dstwgt c2c / vacumm varo = regrid2d(varci, gridco, method='dstwgt', tool='vacumm') plot2d(varo, title='VACUMM / Dstwgt', subplot=(4,3,ip), **kw);ip+=1 add_grid(gridci, **kwg) # - patch c2c / emsf varo = regrid2d(varci, gridco, method='patch', tool='esmf') plot2d(varo, title='ESMF / Patch', subplot=(4,3,ip), **kw);ip+=1 add_grid(gridci, **kwg) # - cellave r2r / regrid2 varo = regrid2d(varri, gridro, method='cellave', tool='regrid2') plot2d(varo, title='Regrid2 / Cellave', subplot=(4,3,ip), **kw);ip+=1 add_grid(gridri, **kwg) # - cellave c2c / esmf varo = regrid2d(varci, gridco, method='cellave', tool='esmf') plot2d(varo, title='ESMF / Cellave', subplot=(4,3,ip), **kw);ip+=1 add_grid(gridci, **kwg) # - conserv c2c / esmf varo = regrid2d(varci, gridco, method='conserv', tool='esmf') plot2d(varo, title='ESMF / Conserv', subplot=(4,3,ip), **kw);ip+=1 add_grid(gridci, **kwg) P.tight_layout() P.savefig(code_file_name(ext='png')) rcdefaults() P.close()
continue
if rank==0:
print >>f, tool.upper(), method, ':', '%5.1f'%(time()-t0), 'seconds', psinfo()
frac = diag['dstAreaFractions']
if frac is not None:
mask = frac<=1.e-3
frac[mask] = 1.
frac = N.resize(frac, varo.shape)
mask = N.resize(mask, varo.shape)
varo[:] /= frac
varo[:] = MV2.masked_where(mask, varo, copy=0)
# del r
gc.collect()
if rank==0:
print >>f, ' plot'
P.figure(figsize=(12, 6))
P.subplots_adjust(right=0.9)
P.subplot(121)
P.pcolormesh(xi, yi, vari[0,0].asma(),vmin=vmin,vmax=vmax)
P.axis([xmin, xmax, ymin, ymax])
P.colorbar()
P.title('Original')
P.subplot(122)
P.pcolormesh(xo, yo, varo[0,0].asma(),vmin=vmin,vmax=vmax)
P.axis([xmin, xmax, ymin, ymax])
P.title(tool.upper()+' / '+method.upper())
P.colorbar(extend='min')#cax=P.axes([0.92, 0.3, 0.02, 0.6]))
figfile = basefile+'_%(tool)s_%(method)s.png'%vars()
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
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)
N.testing.assert_allclose(zzg[-1, -1],farvalue)
# Plot
vmin = min(zi.min(), zo.min())
vmax = max(zi.max(), zo.max(), farvalue)
cmap = 'cmocean_ice'
kw = dict(vmin=vmin, vmax=vmax)
P.pcolormesh(xxg, yyg, zzg, cmap=cmap, **kw)
P.colorbar()
P.scatter(xi, yi, c=zi, s=100, cmap=cmap, **kw)
P.axis('image')
P.title('Simple kriging with fixed far value')
P.savefig(code_file_name(ext='.png'))
P.show()
P.close()
yi = N.random.uniform(ymin, ymax, 500)
zi = N.ma.masked_array(get_values(xi, yi))
mask = (xi > (xmin + xmax) * .5) & (yi > (ymin + ymax) * .5)
zi[mask] = 1e20
zi[mask] = N.ma.masked
# - output
xo = N.random.uniform(xmin, xmax, 500)
yo = N.random.uniform(ymin, ymax, 500)
zot = N.ma.masked_array(get_values(xo, yo))
mask = (xo > (xmin + xmax) * .5) & (yo > (ymin + ymax) * .5)
zot[mask] = 1e20
zot[mask] = N.ma.masked
# Call and plot
kw = dict(vmin=zi.min(), vmax=zi.max())
P.figure(figsize=(8, 8))
P.subplot(221)
trii = get_tri((xi, yi), ttype='mpl', mask=zi)
P.tripcolor(trii, zi, **kw)
cc = N.zeros(len(xi), '|S3')
cc[:] = 'k'
cc[zi.mask] = '.5'
P.scatter(xi, yi, c=cc, s=7, marker='o')
P.title('Input')
for i, method in enumerate(['nearest', 'linear', 'cubic']):
# Interp
zo = xy2xy(xi, yi, zi, xo, yo, method=method) #[0]
# Triangles
trio = get_tri((xo, yo), ttype='mpl', mask=zo)
# 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)
# Regrilleur de CDAT
regridder = CDATRegridder(gridi, grido, method="cellave") # -> CHANGEZ LA METHODE
ssto2 = regridder.regrid(ssti)
ssto3, cdr = regrid2d(ssti, grido, cdr=regridder, getcdr=True)
# -> VERIFIEZ LE TYPE
# Plots
kwp = dict(
vmin=ssti.min(),
vmax=ssti.max(),
res=None,
show=False,
colorbar=False,
drawmeridians_linewidth=0,
drawparallels_linewidth=0,
grid=False,
yhide="auto",
**select
)
map2(ssti, title="Original", figsize=(15, 5), subplot=131, **kwp)
add_grid(gridi, alpha=1)
map2(ssto, title="Avec regrid2d", subplot=132, **kwp)
add_grid(grido, alpha=1)
add_grid(gridi, edges=False, centers=True, alpha=0.5, markersize=3, marker="o")
map2(ssto2, title="Avec CDATRegridder", subplot=133, **kwp)
add_grid(grido, alpha=1)
add_grid(gridi, edges=False, centers=True, alpha=0.5, markersize=3, marker="o")
P.tight_layout()
P.show()
vari[nx/3:2*nx/3, nyi/3:2*nyi/3] = N.ma.masked
x = N.arange(nx)
dyi = (yi[1]-yi[0])*0.49
dyo = (yo[1]-yo[0])*0.49
yyi = N.resize(yi, vari.shape)+N.random.uniform(-dyi, dyi, vari.shape)
yyo = N.resize(yo, (nx, len(yo)))+N.random.uniform(-dyo, dyo, (nx, len(yo)))
yyib, xxib = meshcells(yyi, x)
yyob, xxob = meshcells(yyo, x)
varon = N.ma.masked_values(interp1dxx(vari.filled(), yyi, yyo, mv, 0, extrap=0), mv)
varol = N.ma.masked_values(interp1dxx(vari.filled(), yyi, yyo, mv, 1, extrap=0), mv)
varoh = N.ma.masked_values(interp1dxx(vari.filled(), yyi, yyo, mv, 3, extrap=0), mv)
kw = dict(vmin=vari.min(), vmax=vari.max())
axlims = [x[0], x[-1], yo[0], yo[-1]]
P.figure(figsize=(8, 8))
P.subplot(221)
P.pcolor(xxib, yyib, vari)
P.axis(axlims)
P.title('Original')
P.subplot(222)
P.pcolor(xxob, yyob, varon, **kw)
P.axis(axlims)
P.title('Nearest1dxx')
P.subplot(223)
P.pcolor(xxob, yyob, varol, **kw)
P.axis(axlims)
P.title('Linear1dxx')
P.subplot(224)
P.pcolor(xxob, yyob, varoh, **kw)
P.axis(axlims)
dyo = (yo[1] - yo[0]) * 0.49
yyi = N.resize(yi, vari.shape) + N.random.uniform(-dyi, dyi, vari.shape)
yyo = N.resize(yo, (nx, len(yo))) + N.random.uniform(-dyo, dyo, (nx, len(yo)))
yyib, xxib = meshcells(yyi, x)
yyob, xxob = meshcells(yyo, x)
varon = N.ma.masked_values(
interp1dxx(vari.filled(), yyi, yyo, mv, 0, extrap=0), mv)
varol = N.ma.masked_values(
interp1dxx(vari.filled(), yyi, yyo, mv, 1, extrap=0), mv)
varoh = N.ma.masked_values(
interp1dxx(vari.filled(), yyi, yyo, mv, 3, extrap=0), mv)
kw = dict(vmin=vari.min(), vmax=vari.max())
axlims = [x[0], x[-1], yo[0], yo[-1]]
P.figure(figsize=(8, 8))
P.subplot(221)
P.pcolor(xxib, yyib, vari)
P.axis(axlims)
P.title('Original')
P.subplot(222)
P.pcolor(xxob, yyob, varon, **kw)
P.axis(axlims)
P.title('Nearest1dxx')
P.subplot(223)
P.pcolor(xxob, yyob, varol, **kw)
P.axis(axlims)
P.title('Linear1dxx')
P.subplot(224)
P.pcolor(xxob, yyob, varoh, **kw)
P.axis(axlims)
# -*- 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()
def myplot(vari, depi, varol, varoc, depo, figfile):
def getdv(var, dep):
v = var[1, :, 1, :]
if dep[:].ndim==var.ndim:
dep = dep[1, :, 1, :]
elif dep[:].ndim==3:
dep = dep[:, 1, :]
xb, yb = meshcells(v.getAxis(-1), dep)
return xb, yb, v.asma()
xbi, ybi, vi = getdv(vari, depi)
xbo, ybo, vol = getdv(varol, depo)
_, _, voc = getdv(varoc, depo)
vmin, vmax = minmax(vi, vol, voc)
kw = dict(vmin=vmin, vmax=vmax)
P.figure(figsize=(10, 4))
ax = P.subplot(131)
P.pcolormesh(xbi, ybi, vi, **kw)
P.title('Original')
P.subplot(132, sharex=ax, sharey=ax)
P.pcolormesh(xbo, ybo, vol, **kw)
P.title('Linear')
P.subplot(133, sharex=ax, sharey=ax)
P.pcolormesh(xbo, ybo, voc, **kw)
P.ylim(ymin=min(ybi.min(), ybo.min()), ymax=max(ybi.max(), ybo.max()))
P.title('Cellave')
P.tight_layout()
P.savefig(figfile)
