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
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
def plot_layer_mod_on_pro(self,
model,
profiles,
varname,
depth,
select=None,
**kwargs):
'''Get a layer of variable for a specified depth.
:Params:
- **varname**: variable to process
- **depth**: output depth(s)
Other params, see: :func:`coloc_mod_on_pro`
'''
self.verbose(
'Plotting layer of colocalized %s on %s\nvarname: %s\ndepth: %s\nselect: %s\nkwargs: %s',
model.__class__.__name__, profiles.__class__.__name__, varname,
depth, select, kwargs)
lons_mod, lats_mod, deps_mod, var_mod = \
self.coloc_mod_on_pro(
model, profiles,
# If varname is a list of possible names, wrap it into a
# tuple of one element as we are requiring only one variable
len(numpy.shape(varname)) and (varname,) or varname,
select, **kwargs)
odep = create_dep(is_iterable(depth) and depth or [depth])
var_mod = var_mod.reorder('-z')
var_mod = interp1d(var_mod, axo=odep, xmap=0,
xmapper=deps_mod) # Required order: ...z
var_mod = var_mod.reorder('z-')
model.verbose('layer: %s', var_mod)
lons_pro, lats_pro, var_pro = profiles.get_layer(varname,
depth,
select=select)
profiles.verbose('layer: %s', var_pro)
# =====
# Tracés
vmin = var_pro.min()
vmax = var_pro.max()
if var_mod.count():
vmin = min(var_mod.min(), vmin)
vmax = max(var_mod.max(), vmax)
else:
self.warning('No model data')
if 'latitude' in select:
lat_min, lat_max = select['latitude'][:2]
else:
la = model.get_latitude()
lat_min, lat_max = min(la), max(la)
if 'longitude' in select:
lon_min, lon_max = select['longitude'][:2]
else:
lo = model.get_longitude()
lon_min, lon_max = min(lo), max(lo)
levels = auto_scale((vmin, vmax))
vmin = levels[0]
vmax = levels[-1]
# Création de la palette
cmap = cmap_magic(levels)
# On trace de champ du modèle moyené sur la période choisie
m = map2(lon=(lon_min, lon_max), lat=(lat_min, lat_max), show=False)
# On trace le modèle en fond
# =====
msca = None
try:
msca = m.map.scatter(lons_mod,
lats_mod,
s=100,
c=var_mod,
vmin=vmin,
vmax=vmax,
cmap=cmap,
label='model')
except:
self.exception('Failed plotting model data')
# =====
# Triangulation du modèle
# import matplotlib.tri as tri
# triang = tri.Triangulation(lons_mod, lats_mod)
# # On trace le modèle en fond
# mod = m.axes.tripcolor(triang, var_mod, vmin=vmin, vmax=vmax, cmap=cmap)
# =====
# On trace les observations avec des points plus petits
psca = None
try:
psca = m.map.scatter(lons_pro,
lats_pro,
s=25,
c=var_pro,
vmin=m.vmin,
vmax=m.vmax,
cmap=m.cmap,
label='profiles')
except:
self.exception('Failed plotting profile data')
# Colorbar
if msca is not None:
colorbar(msca)
elif psca is not None:
colorbar(psca)
def plot_layer_mod_on_pro(self, model, profiles, varname, depth, select=None, **kwargs):
'''Get a layer of variable for a specified depth.
:Params:
- **varname**: variable to process
- **depth**: output depth(s)
Other params, see: :func:`coloc_mod_on_pro`
'''
self.verbose('Plotting layer of colocalized %s on %s\nvarname: %s\ndepth: %s\nselect: %s\nkwargs: %s',
model.__class__.__name__, profiles.__class__.__name__, varname, depth, select, kwargs)
lons_mod, lats_mod, deps_mod, var_mod = \
self.coloc_mod_on_pro(
model, profiles,
# If varname is a list of possible names, wrap it into a
# tuple of one element as we are requiring only one variable
len(numpy.shape(varname)) and (varname,) or varname,
select, **kwargs)
odep = create_dep(is_iterable(depth) and depth or [depth])
var_mod = var_mod.reorder('-z')
var_mod = interp1d(var_mod, axo=odep, xmap=0, xmapper=deps_mod) # Required order: ...z
var_mod = var_mod.reorder('z-')
model.verbose('layer: %s', var_mod)
lons_pro, lats_pro, var_pro = profiles.get_layer(varname, depth, select=select)
profiles.verbose('layer: %s', var_pro)
# =====
# Tracés
vmin = var_pro.min()
vmax = var_pro.max()
if var_mod.count():
vmin = min(var_mod.min(), vmin)
vmax = max(var_mod.max(), vmax)
else:
self.warning('No model data')
if 'latitude' in select:
lat_min, lat_max = select['latitude'][:2]
else:
la = model.get_latitude()
lat_min, lat_max = min(la), max(la)
if 'longitude' in select:
lon_min, lon_max = select['longitude'][:2]
else:
lo = model.get_longitude()
lon_min, lon_max = min(lo), max(lo)
levels = auto_scale((vmin, vmax))
vmin = levels[0]
vmax = levels[-1]
# Création de la palette
cmap = cmap_magic(levels)
# On trace de champ du modèle moyené sur la période choisie
m = map2(lon=(lon_min, lon_max), lat=(lat_min, lat_max), show=False)
# On trace le modèle en fond
# =====
msca = None
try: msca = m.map.scatter(lons_mod, lats_mod, s=100, c=var_mod, vmin=vmin, vmax=vmax, cmap=cmap, label='model')
except: self.exception('Failed plotting model data')
# =====
# Triangulation du modèle
# import matplotlib.tri as tri
# triang = tri.Triangulation(lons_mod, lats_mod)
# # On trace le modèle en fond
# mod = m.axes.tripcolor(triang, var_mod, vmin=vmin, vmax=vmax, cmap=cmap)
# =====
# On trace les observations avec des points plus petits
psca = None
try: psca = m.map.scatter(lons_pro, lats_pro, s=25, c=var_pro, vmin=m.vmin, vmax=m.vmax, cmap=m.cmap, label='profiles')
except: self.exception('Failed plotting profile data')
# Colorbar
if msca is not None:
colorbar(msca)
elif psca is not None:
colorbar(psca)