def test_plot_create_map():
# Read bathy
f = cdms2.open(NCFILE_MANGA0)
bathy = -f('H0')
f.close()
alon = bathy.getLongitude()
alat = bathy.getLatitude()
lon = (alon[:].min(), alon[:].max())
lat = (alat[:].min(), alat[:].max())
# 2D maps
# - simple
m = create_map(lon, lat)
P.savefig(func_name() + '.2d.simple.png', title="2D / simple")
P.close()
# - bathy
m = create_map(lon, lat, bathy=bathy, title="2D / bathy")
P.savefig(func_name() + '.2d.bathy.png')
P.close()
# 3D maps
# - simple
m = create_map(lon, lat, level=-200, title="3D / simple")
P.savefig(func_name() + '.3d.simple.png')
P.close()
# - bathy
m = create_map(lon,
lat,
axes="3d",
bathy=bathy,
level=-200,
title="3D / bathy")
P.savefig(func_name() + '.3d.bathy.png')
P.close()
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.pcolormesh(xxb, yyb, mask, shading='faceted')
P.scatter(xx.ravel(), yy.ravel(), c=(0, 1, 0))
P.grid(True)
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()
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()
# -*- 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()
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)
P.title('Hermit1dxx')
P.tight_layout()
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
P.savefig(figfile)
P.close()
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
"""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)
