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()
# On construit un grille reguliere
import numpy as N
from vacumm.misc.grid import meshbounds
xr = N.arange(20.)
yr = N.arange(10.)
xxr, yyr = N.meshgrid(xr, yr)
xrb, yrb = meshbounds(xr, yr)
zzr = (N.sin(xxr*N.pi/6)*N.sin(yyr*N.pi/6) + \
N.exp(-((xxr-7.)**2+(yyr-7.)**2)/4.**2))*100.
zzr -= zzr.min()
vminmax=dict(vmin=zzr.min(), vmax=zzr.max())
# On construit un echantillon irregulier a partir du regulier
ij = N.unique((N.random.rand(50)*zzr.size).astype('i'))
xi, yi, zi = xxr.flat[ij], yyr.flat[ij], zzr.flat[ij]
# Interpolation sur la grille reguliere
# - natgrid
from vacumm.misc.grid.regridding import griddata
zirn = griddata(xi, yi, zi, (xr, yr), method='nat', ext=True, sub=6)
# - krigeage
zirk = griddata(xi, yi, zi, (xr, yr), method='carg')
# Plot de verif
import pylab as P
from vacumm.misc.plot import savefigs
P.figure(1, figsize=(6, 8))
P.subplots_adjust(hspace=.3, bottom=.05, top=.95, left=.06)
# - regulier
P.subplot(311)
P.pcolor(xrb, yrb, zzr, **vminmax)
def plot_bathy(bathy, shadow=True, contour=True, shadow_stretch=1., shadow_shapiro=False,
show=True, shadow_alpha=1., shadow_black=.3, white_deep=False, nmax=30,m=None, alpha=1.,
zmin=None, zmax=None, **kwargs):
"""Plot a bathymetry
- *lon*: Longitude range.
- *lat*: Latitude range.
- *show*:Display the figure [default: True]
- *pcolor*: Use pcolor instead of contour [default: False]
- *contour*: Add line contours [default: True]
- *shadow*:Plot south-west shadows instead of filled contours.
- *nmax*: Max number of levels for contours [default: 30]
- *white_deep*: Deep contours are white [default: False]
- All other keyword are passed to :func:`~vacumm.misc.plot.map2`
"""
# Input
bb = bathy
if isinstance(bathy, GriddedBathy):
bathy = bathy.bathy()
if shadow:
xxs = getattr(bb, '_xxs', None)
yys = getattr(bb, '_yys', None)
if xxs is None:
lon2d = bb._lon2d
lat2d = bb._lat2d
elif shadow:
xxs = yys = None
lon2d,lat2d = meshgrid(get_axis(bathy, -1).getValue(),get_axis(bathy, -2).getValue())
# Masking
if 'maxdep' in kwargs:
zmin = -maxdep
if 'maxalt' in kwargs:
zmax = maxalt
if zmin is not None:
bathy[:] = MV2.masked_less(bathy, zmin)
if zmax is not None:
bathy[:] = MV2.masked_greater(bathy, zmax)
# Default arguments for map
if hasattr(bathy, 'long_name'):
kwargs.setdefault('title',bathy.long_name)
if 'cmap' not in kwargs:
vmin, vmax = minmax(bathy)
# print 'cmap topo', vmin, vmax
kwargs['cmap'] = auto_cmap_topo((kwargs.get('vmin', vmin), kwargs.get('vmax', vmax)))
# kwargs.setdefault('ticklabel_size','smaller')
kwargs.setdefault('clabel_fontsize', 8)
kwargs.setdefault('clabel_alpha',.7*alpha)
kwargs.setdefault('clabel_glow_alpha', kwargs['clabel_alpha'])
kwargs.setdefault('fill', 'contourf')
kwargs['nmax'] = nmax
kwargs['show'] = False
kwargs['contour'] = contour
if shadow: kwargs.setdefault('alpha',.5*alpha)
kwargs.setdefault('projection', 'merc')
kwargs.setdefault('fmt', BathyFormatter())
kwargs.setdefault('colorbar_format', BathyFormatter())
kwargs.setdefault('units', False)
kwargs.setdefault('levels_mode','normal')
kwargs.setdefault('bgcolor', '0.8')
kwargs.setdefault('contour_linestyle', '-')
savefig = kwargs.pop('savefig', None)
kwsavefig = kwfilter(kwargs, 'savefig_')
# White contour when dark
if contour and white_deep:
levels = auto_scale(bathy,nmax=nmax)
colors = []
nlevel = len(levels)
for i in range(nlevel):
if i < old_div(nlevel,2):
colors.append('w')
else:
colors.append('k')
kwargs.setdefault('contour_colors',tuple(colors))
# Call to map
m = map2(bathy, m=m, **kwargs)
# Add shadow
if shadow:
# Filter
data = MV.array(bathy,'f',fill_value=0.)
if shadow_shapiro:
data = shapiro2d(data,fast=True).shape
# Gradient
grd = deriv2d(data,direction=45.,fast=True,fill_value=0.).filled(0.)
grdn = refine(grd, 3)
grdn = norm_atan(grdn,stretch=shadow_stretch).clip(0,1.) ; del grd
# Grid
# im = m.map.imshow(grdn,cmap=P.get_cmap('gist_yarg'),alpha=1) # gist_yarg , YlGnBu
if xxs is None or yys is None:
xx, yy = m(lon2d,lat2d)
xxr = refine(xx, 3)
yyr = refine(yy, 3)
xxs, yys = meshbounds(xxr, yyr)
if isinstance(bb, GriddedBathy):
bb._xxs = xxs
bb._yys = yys
del xx, yy, xxr, yyr
# Cmap
cmap = cmap_custom(( ((1, )*3, 0), ((shadow_black, )*3, 1) ))
# Plot
pp = m.map.pcolormesh(xxs, yys, grdn,cmap=cmap)#P.get_cmap('gist_yarg'))
pp.set_zorder(.9)
pp.set_linewidth(0)
pp.set_alpha(shadow_alpha*N.clip(alpha*2, 0, 1))
del grdn
# Show it?
if savefig:
m.savefig(savefig, **kwsavefig)
if show:
P.show()
return m
# -*- coding: utf8 -*-
# Input variables
import numpy as N, cdms2 as cdms, MV2 as MV
from vacumm.misc.grid import meshbounds
xi = N.arange(20.)
yi = N.arange(10.)
xxi, yyi = N.meshgrid(xi, yi)
xib, yib = meshbounds(xi, yi)
vari = (N.sin(xxi*N.pi/6)*N.sin(yyi*N.pi/6) +
N.exp(-((xxi-7.)**2+(yyi-7.)**2)/4.**2))*100.
vminmax=dict(vmin=vari.min(), vmax=vari.max())
vari = cdms.createVariable(vari)
vari.setAxis(-2, cdms.createAxis(yi))
vari.setAxis(-1, cdms.createAxis(xi))
vari[3:4, 3:7] = MV.masked
# Output grid
xo = cdms.createAxis(N.linspace(-3., 23., 70))
yo = cdms.createAxis(N.linspace(-3., 13., 40))
# Interpolation
from vacumm.misc.grid.regridding import interp2d
# - bilinear
varob = interp2d(vari, (xo, yo), method='bilinear')
# - nearest
varon = interp2d(vari, (xo, yo), method='nearest')
# Plot
import pylab as P
from vacumm.misc.plot import savefigs, add_grid
xob, yob = meshbounds(xo[:], yo[:])
# On construit un grille reguliere
import numpy as N
from vacumm.misc.grid import meshbounds
xr = N.arange(20.)
yr = N.arange(10.)
xxr, yyr = N.meshgrid(xr, yr)
xrb, yrb = meshbounds(xr, yr)
zzr = (N.sin(xxr*N.pi/6)*N.sin(yyr*N.pi/6) + \
N.exp(-((xxr-7.)**2+(yyr-7.)**2)/4.**2))*100.
zzr -= zzr.min()
vminmax = dict(vmin=zzr.min(), vmax=zzr.max())
# On construit un echantillon irregulier a partir du regulier
ij = N.unique((N.random.rand(50) * zzr.size).astype('i'))
xi, yi, zi = xxr.flat[ij], yyr.flat[ij], zzr.flat[ij]
# Interpolation sur la grille reguliere
# - natgrid
from vacumm.misc.grid.regridding import griddata
zirn = griddata(xi, yi, zi, (xr, yr), method='nat', ext=True, sub=6)
# - krigeage
zirk = griddata(xi, yi, zi, (xr, yr), method='carg')
# Plot de verif
import pylab as P
from vacumm.misc.plot import savefigs
P.figure(1, figsize=(6, 8))
P.subplots_adjust(hspace=.3, bottom=.05, top=.95, left=.06)
# - regulier
P.subplot(311)
P.pcolor(xrb, yrb, zzr, **vminmax)
