from vacumm.config import data_sample
select = dict(lon=(-5.3, -4.72), lat=(47.9, 48.8), time=slice(0, 24))
f = cdms2.open(data_sample('mars2d.xyt.nc'))
v = MV2.masked_values(f('v', **select), 0., copy=False)
f.close()
# Diagonale
lon = v.getLongitude()
lat = v.getLatitude()
nd = N.sqrt(len(lon)**2. + len(lat)**2) / 2.
xo = N.linspace(lon[0], lon[-1], nd)
yo = N.linspace(lat[0], lat[-1], nd)
# Interpolation
from vacumm.misc.grid.regridding import grid2xy
vo = grid2xy(v, xo, yo, method='bilinear')
# Plot
# - variable interpolee
from vacumm.misc.plot import hov2 as hov, savefigs, map2 as map
hov(vo,
cmap='jet',
show=False,
top=.9,
date_fmt='%H',
colorbar_shrink=.5,
left=.13)
# - carte + diagonal
import pylab as P
m = map(v[0],
xhide=True,
select = dict(lon=(-5.3, -4.72), lat=(47.9, 48.8), time=slice(0, 24))
f = cdms2.open(data_sample("mars2d.xyt.nc"))
v = MV2.masked_values(f("v", **select), 0.0, copy=False)
f.close()
# Diagonale
lon = v.getLongitude()
lat = v.getLatitude()
nd = N.sqrt(len(lon) ** 2.0 + len(lat) ** 2) / 2.0
xo = N.linspace(lon[0], lon[-1], nd)
yo = N.linspace(lat[0], lat[-1], nd)
# Interpolation
from vacumm.misc.grid.regridding import grid2xy
vo = grid2xy(v, xo, yo, method="bilinear")
# Plot
# - variable interpolee
from vacumm.misc.plot import hov2 as hov, savefigs, map2 as map
hov(vo, cmap="jet", show=False, top=0.9, date_fmt="%H", colorbar_shrink=0.5, left=0.13)
# - carte + diagonal
import pylab as P
m = map(
v[0],
xhide=True,
yhide=True,
contour=False,
title=False,
def coloc_mod_on_pro(self,
model,
profiles,
varnames,
select=None,
method='nearest'):
'''Colocalize model on profile data.
Load model data corresponding to the selected profiles positions and time.
Returns loaded model longitudes, latitudes, depths and requested variable(s)
:Params:
- **model**: model data :class:`~vacumm.data.misc.dataset.Dataset`
- **profiles**: profile data :class:`~vacumm.data.misc.profile.ProfilesDataset`
- **varnames**: variables to load (ex: ('temp','sal') or (('temp','temperature'),('sal','salinity'))
- **select**: selector
- **method**: coloc method (**nearest** or **interp**)
:Return:
- **lons_mod**: model longitude coordinates, shape: (profile)
- **lats_mod**: model latitude coordinates, shape: (profile)
- **deps_mod**: model depth coordinates, shape: (level,profile)
- **var1**: requested variables, shape: (level,profile)
- ...
- **varN**
.. todo::
- also load and return profile data here
- exclude coords where profile data is masked (no data for specified depth)
- return time coordinates
- return depth and vars with shape (profile,level)
'''
self.verbose(
'Colocalizing %s on %s\nvarnames: %s\nselect: %s\n method: %s',
model.__class__.__name__, profiles.__class__.__name__, varnames,
select, method)
prof_pro = profiles.get_axis('profile', select=select)
if prof_pro is None or not len(prof_pro):
raise Exception('No profiles found, aborting')
lev_pro = profiles.get_axis('level', select=select)
time_pro = profiles.get_variable('time', select=select)
lons_pro = profiles.get_variable('longitude', select=select)
lats_pro = profiles.get_variable('latitude', select=select)
dates = create_time(time_pro).asComponentTime()
self.info('Number of profiles: %s', len(dates))
self.info('Profiles time coverage: %s to %s', dates[0], dates[-1])
# Init model
td = model.get_time_res()
dtmax = (td.days * 86400 + td.seconds, 'seconds')
self.info('Detected model time step: %s', td)
grid_mod = model.get_grid()
xres, yres = resol(grid_mod)
time_mod = model.get_time()
ctime_mod = time_mod.asComponentTime()
self.info('Model time coverage: %s to %s', ctime_mod[0], ctime_mod[-1])
level_mod = model.get_level(select=select)
lons_mod = MV2.zeros((len(prof_pro), )) + MV2.masked
lats_mod = lons_mod.clone()
deps_mod = MV2.zeros((len(level_mod), len(prof_pro))) + MV2.masked
deps_mod.setAxis(1, prof_pro)
lons_mod.id, lats_mod.id, deps_mod.id = 'longitude', 'latitude', 'depth'
# Creation des variables demandees
variables = []
for n in varnames:
v = MV2.zeros((len(level_mod), len(prof_pro))) + MV2.masked
v.setAxis(1, prof_pro)
v.id = is_iterable(n) and n[0] or n
variables.append(v)
cdms2.setAutoBounds(1) # ???
# Boucle temporelle
for ip, date in enumerate(dates):
try:
# Limites spatiales
lon = lons_pro[ip]
lat = lats_pro[ip]
lon_min = lon - 2 * xres
lon_max = lon + 2 * xres
lat_min = lat - 2 * yres
lat_max = lat + 2 * yres
date_interval = (add_time(date, -dtmax[0], dtmax[1]),
add_time(date, dtmax[0], dtmax[1]), 'ccb')
self.info('Colocalizing data for date %s, lon: %s, lat: %s',
date, lon, lat)
# Methode 1 : donnees les plus proches
if method == 'nearest':
sel = dict(time=(date, date, 'ccb'),
longitude=(lon, lon, 'ccb'),
latitude=(lat, lat, 'ccb'))
# Verifier la disponibilite des donnees
if time_mod.mapIntervalExt(sel['time']) is None:
self.warning('Time interval %s not found', sel['time'])
continue
if grid_mod.getLatitude().mapInterval(
sel['latitude']) is None:
self.warning('Latitude coordinate %s not found',
sel['latitude'])
continue
if grid_mod.getLongitude().mapInterval(
sel['longitude']) is None:
self.warning('Longitude coordinate %s not found',
sel['longitude'])
continue
# Load tmp depth to get lon & lat coordinates
#tmp = model.get_depth(select=sel, squeeze=False) # tmp squeezed !!! see sigma ?
tmp = model.get_variable(varnames[0],
select=sel,
squeeze=False)
lons_mod[ip] = tmp.getLongitude()[0]
lats_mod[ip] = tmp.getLatitude()[0]
deps_mod[:, ip] = model.get_depth(select=sel, squeeze=True)
for iv, vn in enumerate(varnames):
variables[iv][:, ip] = model.get_variable(vn,
select=sel,
squeeze=True)
# Methode 2 : interpolation
elif method == 'interp':
sel = dict(time=date_interval,
longitude=(lon_min, lon_max),
latitude=(lat_min, lat_max))
if time_mod.mapIntervalExt(sel['time']) is None:
self.warning('Time interval %s not found', sel['time'])
continue
if grid_mod.getLatitude().mapInterval(
sel['latitude']) is None:
self.warning('Latitude coordinate %s not found',
sel['latitude'])
continue
if grid_mod.getLongitude().mapInterval(
sel['longitude']) is None:
self.warning('Longitude coordinate %s not found',
sel['longitude'])
continue
# Lectures
order = 'tzyx'
# lon & lat du profile car interp sur cette position
lons_mod[ip], lats_mod[ip] = lon, lat
deps_mod_tzyx = model.get_depth(select=sel,
order=order,
squeeze=True)
tmp_tzyx = []
for iv, vn in enumerate(varnames):
tmp_tzyx.append(
model.get_variable(vn,
select=sel,
order=order,
squeeze=True))
# Interpolations temporelles
mctime = tmp_tzyx[0].getTime()
mrtime = mctime.asRelativeTime()
d0 = date.torel(mctime.units).value - mrtime[0].value
d1 = mrtime[1].value - date.torel(mctime.units).value
f0 = d0 / (d0 + d1)
f1 = d1 / (d0 + d1)
deps_mod_zyx = f0 * deps_mod_tzyx[0] + f1 * deps_mod_tzyx[1]
tmp_zyx = []
for iv, vn in enumerate(varnames):
tmp_zyx.append(f0 * tmp_tzyx[iv][0] +
f1 * tmp_tzyx[iv][1])
del tmp_tzyx
# Interpolations spatiales
deps_mod[:, ip] = numpy.squeeze(
grid2xy(deps_mod_zyx,
numpy.array([lon]),
numpy.array([lat]),
method='nat'))
for iv, vn in enumerate(varnames):
variables[iv][:, ip] = numpy.squeeze(
grid2xy(tmp_zyx[iv],
numpy.array([lon]),
numpy.array([lat]),
method='nat'))
del tmp_zyx
else:
raise ValueError('Invalid colocation method: %s' %
(method))
except:
self.exception('Failed to colocalize data for date %s', date)
for v in [deps_mod] + variables:
v.getAxis(0).id = 'level'
v.getAxis(0).designateLevel()
data = tuple([lons_mod, lats_mod, deps_mod] + variables)
self.verbose('Colocalized data:\n %s',
'\n '.join(self.describe(o) for o in data))
return data
from vacumm.misc.plot import curve2, minimap
from vacumm.misc.color import cmap_custom
except:
sys.exit("Can't import needed vacumm modules")
# Load bathy
b = NcGriddedBathy(lon=lon, lat=lat, name=options.name,
cfgfile=options.cfgfile, varname=options.varname, lonname=options.lonname,
latname=options.latname, imargin=2, jmargin=2, maxvalue=None,
reverse=options.reverse)
# Interpolate on section
bathy = b.bathy()
lons, lats, xx, yy = transect_specs(bathy.getGrid(),
options.x0, options.y0, options.x1, options.y1, getxy=True)
tbathy = grid2xy(bathy, lons, lats)
if not isinstance(options.along, basestring) or options.along not in ['lon', 'lat', 'm', 'km', 'dist', 'auto']:
options.along = 'auto'
if options.along=='auto':
options.along = 'lon' if xx.max()-xx.min() > yy.max()-yy.min() else 'lat'
if options.along=='lon':
taxis = create_lon(lons)
elif options.along=='lat':
taxis = create_lat(lats)
else:
xy = N.sqrt((xx-xx[0])**2+(yy-yy[0])**2)
if xy.max()-xy.min()>2500 or options.along=='km':
units = 'km'
xy *= 1e-3
else:
units = 'm'
lonname=options.lonname,
latname=options.latname,
imargin=2,
jmargin=2,
maxvalue=None,
reverse=options.reverse)
# Interpolate on section
bathy = b.bathy()
lons, lats, xx, yy = transect_specs(bathy.getGrid(),
options.x0,
options.y0,
options.x1,
options.y1,
getxy=True)
tbathy = grid2xy(bathy, lons, lats)
if not isinstance(options.along, basestring) or options.along not in [
'lon', 'lat', 'm', 'km', 'dist', 'auto'
]:
options.along = 'auto'
if options.along == 'auto':
options.along = 'lon' if xx.max() - xx.min() > yy.max() - yy.min(
) else 'lat'
if options.along == 'lon':
taxis = create_lon(lons)
elif options.along == 'lat':
taxis = create_lat(lats)
else:
xy = N.sqrt((xx - xx[0])**2 + (yy - yy[0])**2)
if xy.max() - xy.min() > 2500 or options.along == 'km':
units = 'km'
def coloc_mod_on_pro(self, model, profiles, varnames, select=None, method='nearest'):
'''Colocalize model on profile data.
Load model data corresponding to the selected profiles positions and time.
Returns loaded model longitudes, latitudes, depths and requested variable(s)
:Params:
- **model**: model data :class:`~vacumm.data.misc.dataset.Dataset`
- **profiles**: profile data :class:`~vacumm.data.misc.profile.ProfilesDataset`
- **varnames**: variables to load (ex: ('temp','sal') or (('temp','temperature'),('sal','salinity'))
- **select**: selector
- **method**: coloc method (**nearest** or **interp**)
:Return:
- **lons_mod**: model longitude coordinates, shape: (profile)
- **lats_mod**: model latitude coordinates, shape: (profile)
- **deps_mod**: model depth coordinates, shape: (level,profile)
- **var1**: requested variables, shape: (level,profile)
- ...
- **varN**
.. todo::
- also load and return profile data here
- exclude coords where profile data is masked (no data for specified depth)
- return time coordinates
- return depth and vars with shape (profile,level)
'''
self.verbose('Colocalizing %s on %s\nvarnames: %s\nselect: %s\n method: %s', model.__class__.__name__, profiles.__class__.__name__, varnames, select, method)
prof_pro = profiles.get_axis('profile', select=select)
if prof_pro is None or not len(prof_pro):
raise Exception('No profiles found, aborting')
lev_pro = profiles.get_axis('level', select=select)
time_pro = profiles.get_variable('time', select=select)
lons_pro = profiles.get_variable('longitude', select=select)
lats_pro = profiles.get_variable('latitude', select=select)
dates = create_time(time_pro).asComponentTime()
self.info('Number of profiles: %s', len(dates))
self.info('Profiles time coverage: %s to %s', dates[0], dates[-1])
# Init model
td = model.get_time_res()
dtmax = (td.days*86400+td.seconds, 'seconds')
self.info('Detected model time step: %s', td)
grid_mod = model.get_grid()
xres, yres = resol(grid_mod)
time_mod = model.get_time()
ctime_mod = time_mod.asComponentTime()
self.info('Model time coverage: %s to %s', ctime_mod[0], ctime_mod[-1])
level_mod = model.get_level(select=select)
lons_mod = MV2.zeros((len(prof_pro),))+MV2.masked
lats_mod = lons_mod.clone()
deps_mod = MV2.zeros((len(level_mod), len(prof_pro)))+MV2.masked
deps_mod.setAxis(1, prof_pro)
lons_mod.id, lats_mod.id, deps_mod.id = 'longitude', 'latitude', 'depth'
# Creation des variables demandees
variables = []
for n in varnames:
v = MV2.zeros((len(level_mod), len(prof_pro)))+MV2.masked
v.setAxis(1, prof_pro)
v.id = is_iterable(n) and n[0] or n
variables.append(v)
cdms2.setAutoBounds(1) # ???
# Boucle temporelle
for ip, date in enumerate(dates):
try:
# Limites spatiales
lon = lons_pro[ip]
lat = lats_pro[ip]
lon_min = lon-2*xres
lon_max = lon+2*xres
lat_min = lat-2*yres
lat_max = lat+2*yres
date_interval = (add_time(date, - dtmax[0], dtmax[1]), add_time(date, dtmax[0], dtmax[1]), 'ccb')
self.info('Colocalizing data for date %s, lon: %s, lat: %s', date, lon, lat)
# Methode 1 : donnees les plus proches
if method == 'nearest':
sel = dict(time=(date, date, 'ccb'), longitude=(lon, lon, 'ccb'), latitude=(lat, lat, 'ccb'))
# Verifier la disponibilite des donnees
if time_mod.mapIntervalExt(sel['time']) is None:
self.warning('Time interval %s not found', sel['time'])
continue
if grid_mod.getLatitude().mapInterval(sel['latitude']) is None:
self.warning('Latitude coordinate %s not found', sel['latitude'])
continue
if grid_mod.getLongitude().mapInterval(sel['longitude']) is None:
self.warning('Longitude coordinate %s not found', sel['longitude'])
continue
# Load tmp depth to get lon & lat coordinates
#tmp = model.get_depth(select=sel, squeeze=False) # tmp squeezed !!! see sigma ?
tmp = model.get_variable(varnames[0], select=sel, squeeze=False)
lons_mod[ip] = tmp.getLongitude()[0]
lats_mod[ip] = tmp.getLatitude()[0]
deps_mod[:, ip] = model.get_depth(select=sel, squeeze=True)
for iv,vn in enumerate(varnames):
variables[iv][:,ip] = model.get_variable(vn, select=sel, squeeze=True)
# Methode 2 : interpolation
elif method == 'interp':
sel = dict(time=date_interval, longitude=(lon_min, lon_max), latitude=(lat_min, lat_max))
if time_mod.mapIntervalExt(sel['time']) is None:
self.warning('Time interval %s not found', sel['time'])
continue
if grid_mod.getLatitude().mapInterval(sel['latitude']) is None:
self.warning('Latitude coordinate %s not found', sel['latitude'])
continue
if grid_mod.getLongitude().mapInterval(sel['longitude']) is None:
self.warning('Longitude coordinate %s not found', sel['longitude'])
continue
# Lectures
order = 'tzyx'
# lon & lat du profile car interp sur cette position
lons_mod[ip], lats_mod[ip] = lon, lat
deps_mod_tzyx = model.get_depth(select=sel, order=order, squeeze=True)
tmp_tzyx = []
for iv,vn in enumerate(varnames):
tmp_tzyx.append(model.get_variable(vn, select=sel, order=order, squeeze=True))
# Interpolations temporelles
mctime = tmp_tzyx[0].getTime()
mrtime = mctime.asRelativeTime()
d0 = date.torel(mctime.units).value - mrtime[0].value
d1 = mrtime[1].value - date.torel(mctime.units).value
f0 = d0 / (d0 + d1)
f1 = d1 / (d0 + d1)
deps_mod_zyx = f0 * deps_mod_tzyx[0] + f1 * deps_mod_tzyx[1]
tmp_zyx = []
for iv,vn in enumerate(varnames):
tmp_zyx.append(f0 * tmp_tzyx[iv][0] + f1 * tmp_tzyx[iv][1])
del tmp_tzyx
# Interpolations spatiales
deps_mod[:,ip] = numpy.squeeze(grid2xy(deps_mod_zyx, numpy.array([lon]), numpy.array([lat]), method='nat'))
for iv,vn in enumerate(varnames):
variables[iv][:,ip] = numpy.squeeze(grid2xy(tmp_zyx[iv], numpy.array([lon]), numpy.array([lat]), method='nat'))
del tmp_zyx
else:
raise ValueError('Invalid colocation method: %s'%(method))
except:
self.exception('Failed to colocalize data for date %s', date)
for v in [deps_mod] + variables:
v.getAxis(0).id = 'level'
v.getAxis(0).designateLevel()
data = tuple([lons_mod, lats_mod, deps_mod] + variables)
self.verbose('Colocalized data:\n %s', '\n '.join(self.describe(o) for o in data))
return data
