def systematic_bias(self):
""" Biais systematique (+1: sous-estimation du modele, -1: sur-estimation du modele) """
import numpy as np
import cdms2
from vacumm.misc.grid import get_grid, set_grid
map_bias = list() #Initialise une liste
# Estimation de la moyenne a chaque pas de temps
tps = self.obs.getTime()
for i, t in enumerate(tps):
map_bias.append(np.sign(self.obs[i, :, :] - self.model[i, :, :]))
map_bias = cdms2.createVariable(map_bias,
typecode='f',
id='computation')
res = np.sum(map_bias, axis=0)
res
# Trouve valeurs egale a la longueur de la serie temporelle (<=> uniquement valeurs positives)
one = (res == len(map_bias)).nonzero()
# Trouve valeurs egales a moins la longueur de la serie temporelle (<=> uniquement valeurs negatives)
mone = (res == -len(map_bias)).nonzero()
self.biassyst = np.zeros(res.shape)
self.biassyst[one] = 1.
self.biassyst[mone] = -1.
self.biassyst = cdms2.createVariable(self.biassyst,
typecode='f',
id='syst_bias')
ggm = get_grid(self.model)
set_grid(self.biassyst, ggm, axes=True)
def temporal_std(self):
""" Ecart-type en chaque point geographique """
from genutil import statistics
from vacumm.misc.grid import get_grid, set_grid
# centered and biased std (cf. http://www2-pcmdi.llnl.gov/cdat/manuals/cdutil/cdat_utilities-2.html)
self.model.temp_std = () #Initialise un tuple
self.obs.temp_std = () #Initialise un tuple
self.model.temp_std = statistics.std(self.model, axis=0)
self.obs.temp_std = statistics.std(self.obs, axis=0)
ggm = get_grid(self.model)
set_grid(self.model.temp_std, ggm, axes=True)
ggo = get_grid(self.obs)
set_grid(self.obs.temp_std, ggm, axes=True)
def temporal_std(self):
""" Ecart-type en chaque point geographique """
from genutil import statistics
from vacumm.misc.grid import get_grid, set_grid
# centered and biased std (cf. http://www2-pcmdi.llnl.gov/cdat/manuals/cdutil/cdat_utilities-2.html)
self.model.temp_std = () #Initialise un tuple
self.obs.temp_std = () #Initialise un tuple
self.model.temp_std = statistics.std(self.model, axis = 0)
self.obs.temp_std = statistics.std(self.obs, axis = 0)
ggm = get_grid(self.model)
set_grid(self.model.temp_std, ggm, axes=True)
ggo = get_grid(self.obs)
set_grid(self.obs.temp_std, ggm, axes=True)
def systematic_bias(self):
""" Biais systematique (+1: sous-estimation du modele, -1: sur-estimation du modele) """
import numpy as np
import cdms2
from vacumm.misc.grid import get_grid, set_grid
map_bias = list() #Initialise une liste
# Estimation de la moyenne a chaque pas de temps
tps = self.obs.getTime()
for i, t in enumerate(tps):
map_bias.append(np.sign(self.obs[i, :, :]-self.model[i, :, :] ))
map_bias = cdms2.createVariable(map_bias,typecode='f',id='computation')
res = np.sum(map_bias, axis=0)
res
# Trouve valeurs egale a la longueur de la serie temporelle (<=> uniquement valeurs positives)
one = (res==len(map_bias)).nonzero()
# Trouve valeurs egales a moins la longueur de la serie temporelle (<=> uniquement valeurs negatives)
mone = (res==-len(map_bias)).nonzero()
self.biassyst = np.zeros(res.shape)
self.biassyst[one]=1.
self.biassyst[mone]=-1.
self.biassyst = cdms2.createVariable(self.biassyst, typecode='f',id='syst_bias')
ggm = get_grid(self.model)
set_grid(self.biassyst, ggm, axes=True)
def density(temp,
sal,
depth=None,
lat=None,
potential=False,
getdepth=False,
getlat=False,
format_axes=False):
"""Compute density from temperature, salinity and depth (and latitude)
:Params:
- **temp**: Insitu or potential temperature.
- **sal**: Salinity.
- **depth**, optional: Depth at temperature and salinty points.
Assumed to be 0 if not found.
- **lat**, optional: Latitude. Error when not found.
- **potential**, optional: True to get the potential density (at atmospheric
pressure).
:Algo:
>>> pressure = seawater.csiro.pres(depth, lat)
>>> density = seawater.csiro.dens(sal, temp, depth)
"""
# Compute
if not potential and depth is not False: # In-situ
# Get depth and latitude
lat = grow_lat(temp, lat, mode='raise', getvar=False)
if lat is None: raise VACUMMError('No latitude found for density')
depth = grow_depth(temp, depth, mode='raise', getvar=False)
if N.abs(depth.max()) < N.abs(depth.min()): # positive
depth = -depth
if (depth.asma() < 0).any():
depth = depth - depth.min() # top=0
# Get density
pres = sw_pres(depth, lat)
dens = sw_dens(sal, temp, pres)
del pres
else: # Potential
dens = sw_dens0(sal, temp)
getdepth = getlat = False
# Format
dens.setAxisList(temp.getAxisList())
set_grid(dens, get_grid(temp))
format_var(dens, 'dens', format_axes=format_axes)
# Out
if not getdepth and not getlat: return dens
dens = dens,
if getdepth: dens += depth,
if getlat: dens += lat,
return dens
def density(temp, sal, depth=None, lat=None, potential=False,
getdepth=False, getlat=False, format_axes=False):
"""Compute density from temperature, salinity and depth (and latitude)
:Params:
- **temp**: Insitu or potential temperature.
- **sal**: Salinity.
- **depth**, optional: Depth at temperature and salinty points.
Assumed to be 0 if not found.
- **lat**, optional: Latitude. Error when not found.
- **potential**, optional: True to get the potential density (at atmospheric
pressure).
:Algo:
>>> pressure = seawater.csiro.pres(depth, lat)
>>> density = seawater.csiro.dens(sal, temp, depth)
"""
# Compute
if not potential and depth is not False: # In-situ
# Get depth and latitude
lat = grow_lat(temp, lat, mode='raise', getvar=False)
if lat is None: raise VACUMMError('No latitude found for density')
depth = grow_depth(temp, depth, mode='raise', getvar=False)
if N.abs(depth.max())<N.abs(depth.min()): # positive
depth = -depth
if (depth.asma()<0).any():
depth = depth-depth.min() # top=0
# Get density
pres = sw_pres(depth, lat)
dens = sw_dens(sal, temp, pres) ; del pres
else: # Potential
dens = sw_dens0(sal, temp)
getdepth = getlat = False
# Format
dens.setAxisList(temp.getAxisList())
set_grid(dens, get_grid(temp))
format_var(dens, 'dens', format_axes=format_axes)
# Out
if not getdepth and not getlat: return dens
dens = dens,
if getdepth: dens += depth,
if getlat: dens += lat,
return dens
def temporal_corr(self):
""" Correlation entre modele et observations - calculee en chaque point sur la dimension
temporelle / Resultat => Carte de Correlation uncentered and biased """
from genutil import statistics
from vacumm.misc.grid import get_grid, set_grid
self.temp_corr = () #Initialise un tuple
self.temp_corr = statistics.correlation(self.model, self.obs, axis=0)
gg = get_grid(self.model)
set_grid(self.temp_corr, gg, axes=True)
def obs_coverage(self):
""" Calcul du taux de couverture des observations en chaque point sur la periode """
import numpy as np
from vacumm.misc.grid import get_grid, set_grid
import cdms2
self.obs_cov = () #Initialise un tuple
self.obs_cov = cdms2.createVariable(self.obs.count(axis=0),typecode='f',id='obs_cov', attributes=dict(units='%'))
self.obs_cov = self.obs_cov / len(self.obs) * 100.
gg = get_grid(self.obs)
set_grid(self.obs_cov, gg, axes=True)
def temporal_corr(self):
""" Correlation entre modele et observations - calculee en chaque point sur la dimension
temporelle / Resultat => Carte de Correlation uncentered and biased """
from genutil import statistics
from vacumm.misc.grid import get_grid, set_grid
self.temp_corr = () #Initialise un tuple
self.temp_corr = statistics.correlation(self.model, self.obs, axis = 0)
gg = get_grid(self.model)
set_grid(self.temp_corr, gg, axes=True)
def temporal_rmsc(self):
""" RMS entre modele et observations - calculee en chaque point sur la dimension
temporelle / Resultat => Carte de RMS centered and biased """
from genutil import statistics
from vacumm.misc.grid import get_grid, set_grid
self.temp_rmsc = () #Initialise un tuple
self.temp_rmsc = statistics.rms(self.model, self.obs, axis = 0, centered = 1)
gg = get_grid(self.model)
set_grid(self.temp_rmsc, gg, axes=True)
def temporal_rmsc(self):
""" RMS entre modele et observations - calculee en chaque point sur la dimension
temporelle / Resultat => Carte de RMS centered and biased """
from genutil import statistics
from vacumm.misc.grid import get_grid, set_grid
self.temp_rmsc = () #Initialise un tuple
self.temp_rmsc = statistics.rms(self.model,
self.obs,
axis=0,
centered=1)
gg = get_grid(self.model)
set_grid(self.temp_rmsc, gg, axes=True)
def obs_coverage(self):
""" Calcul du taux de couverture des observations en chaque point sur la periode """
import numpy as np
from vacumm.misc.grid import get_grid, set_grid
import cdms2
self.obs_cov = () #Initialise un tuple
self.obs_cov = cdms2.createVariable(self.obs.count(axis=0),
typecode='f',
id='obs_cov',
attributes=dict(units='%'))
self.obs_cov = self.obs_cov / len(self.obs) * 100.
gg = get_grid(self.obs)
set_grid(self.obs_cov, gg, axes=True)
def mixed_layer_depth(data, depth=None, lat=None, zaxis=None,
mode=None, deltatemp=.2, deltadens=.01, kzmax=0.0005,
potential=True, format_axes=False):
"""Get mixed layer depth from temperature and salinity
:Params:
- **temp**: Insitu or potential temperature.
- **sal**: Salinity.
- **depth**, optional: Depth at temperature and salinty points.
- **lat**, optional: Latitude.
- **mode**, optional: ``"deltatemp"``, ``"deltadens"``, ``"kz"``
or ``"twolayers"``
:Raise: :class:`~vacumm.VACUMMError` if can't get depth (and latitude for density).
"""
# TODO: positive up
# Inspection
if isinstance(data, tuple): # data = temp,sal
temp, sal=data
# Get density
if mode!='deltatemp':
res = density(temp, sal, depth=depth, lat=lat,
format_axes=False, potential=potential, getdepth=True)
if isinstance(res, tuple):
dens, depth = res
else:
dens = res
dens = dens.asma()
if mode is None:
mode = 'deltadens'
else:
temp = data[0]
# Check mode
if mode == 'kz':
warn("Switching MLD computation mode to 'deltadens'")
mode = "deltadens"
elif match_var(data, 'temp', mode='nslu'):
if mode is not None and mode!='deltatemp':
warn("Switching MLD computation mode to 'deltatemp'")
mode = 'deltatemp'
temp = data
elif match_var(data, 'dens', mode='nslu'):
if mode in ['kz', 'deltatemp']:
warn("Switching MLD computation mode to 'deltadens'")
mode = None
if mode is None:
mode = "deltadens"
dens = data
elif match_var(data, 'kz', mode='nslu'):
if mode is None:
mode = "kz"
if mode != "kz":
warn("Switching MLD computation mode to 'kz'")
kz = data
else:
if mode in ['deltadens', 'twolayers']:
dens = data
elif mode == "deltatemp":
temp = data
elif mode == "kz":
kz = data
elif mode is not None:
raise VACUMMError("Invalid MLD computation mode : '%s'"%mode)
else:
raise VACUMMError("Can't guess MLD computation mode")
temp = delta
# Find Z dim
data0 = data[0] if isinstance(data, tuple) else data
depth = grow_depth(data0, depth, mode='raise', getvar=False)
zaxis = get_zdim(data0, axis=zaxis)
if zaxis is None:
raise VACUMMError("Can't guess zaxis")
slices = get_axis_slices(data0, zaxis)
# Init MLD
axes = data0.getAxisList()
del axes[zaxis]
mld = MV2.array(data0.asma()[slices['first']], copy=1, axes=axes, copyaxes=False)
set_grid(mld, get_grid(data0))
format_var(mld, 'mld', format_axes=format_axes)
mld[:] = MV2.masked
# Two-layers
if mode=='twolayers':
densbot = dens[slices['first']]
denstop = dens[slices['last']]
del dens
H = 1.5*depth[slices['first']] - 0.5*depth[slices['firstp1']]
H = -1.5*depth[slices['last']] + 0.5*depth[slices['lastm1']]
mld[:] = -H*(densbot-denstop)/(densbot-denstop)
del H
elif mode=='deltadens':
denscrit = dens[slices['last']]+deltadens
mld[:] = -_val2z_(dens, depth, denscrit, zaxis, -1)
del dens
elif mode=='deltatemp':
tempcrit = temp[slices['last']]-deltatemp
mld[:] = -_val2z_(temp, depth, tempcrit, zaxis, 1)
elif mode=='kz':
mld[:] = -_valmin2z_(kz, depth, kzmax, zaxis, 1)
else:
raise VACUMMError("Invalid mode for computing MLD (%s)."%mode +
"Please choose one of: deltadens, twolayers")
# Mask zeros
mld[:] = MV2.masked_values(mld, 0., copy=0)
return mld
def _template_t2ht_(self, tpl):
htpl = MV2.resize(tpl.astype('l'), (self.nbins,)+tpl.shape)
htpl.setAxisList([self._baxis]+tpl.getAxisList())
set_grid(htpl, get_grid(tpl))
cp_atts(tpl, htpl)
return htpl
def _template_t2ht_(self, tpl):
htpl = MV2.resize(tpl.astype('l'), (self.nbins,)+tpl.shape)
htpl.setAxisList([self._baxis]+tpl.getAxisList())
set_grid(htpl, get_grid(tpl))
return htpl
def mixed_layer_depth(data,
depth=None,
lat=None,
zaxis=None,
mode=None,
deltatemp=.2,
deltadens=.03,
kzmax=0.0005,
potential=True,
format_axes=False):
"""Get mixed layer depth from temperature and salinity
:Params:
- **temp**: Insitu or potential temperature.
- **sal**: Salinity.
- **depth**, optional: Depth at temperature and salinty points.
- **lat**, optional: Latitude.
- **mode**, optional: ``"deltatemp"``, ``"deltadens"``, ``"kz"``
or ``"twolayers"``
:Raise: :class:`~vacumm.VACUMMError` if can't get depth (and latitude for density).
"""
# TODO: positive up
# Inspection
if isinstance(data, tuple): # data = temp,sal
temp, sal = data
# Get density
if mode != 'deltatemp':
res = density(temp,
sal,
depth=depth,
lat=lat,
format_axes=False,
potential=potential,
getdepth=True)
if isinstance(res, tuple):
dens, depth = res
else:
dens = res
dens = dens.asma()
if mode is None:
mode = 'deltadens'
else:
temp = data[0]
# Check mode
if mode == 'kz':
warn("Switching MLD computation mode to 'deltadens'")
mode = "deltadens"
elif match_var(data, 'temp', mode='nslu'):
if mode is not None and mode != 'deltatemp':
warn("Switching MLD computation mode to 'deltatemp'")
mode = 'deltatemp'
temp = data
elif match_var(data, 'dens', mode='nslu'):
if mode in ['kz', 'deltatemp']:
warn("Switching MLD computation mode to 'deltadens'")
mode = None
if mode is None:
mode = "deltadens"
dens = data
elif match_var(data, 'kz', mode='nslu'):
if mode is None:
mode = "kz"
if mode != "kz":
warn("Switching MLD computation mode to 'kz'")
kz = data
else:
if mode in ['deltadens', 'twolayers']:
dens = data
elif mode == "deltatemp":
temp = data
elif mode == "kz":
kz = data
elif mode is not None:
raise VACUMMError("Invalid MLD computation mode : '%s'" % mode)
else:
raise VACUMMError("Can't guess MLD computation mode")
temp = delta
# Find Z dim
data0 = data[0] if isinstance(data, tuple) else data
depth = grow_depth(data0, depth, mode='raise', getvar=False)
zaxis = get_zdim(data0, axis=zaxis)
if zaxis is None:
raise VACUMMError("Can't guess zaxis")
slices = get_axis_slices(data0, zaxis)
# Init MLD
axes = data0.getAxisList()
del axes[zaxis]
mld = MV2.array(data0.asma()[slices['first']],
copy=1,
axes=axes,
copyaxes=False)
set_grid(mld, get_grid(data0))
format_var(mld, 'mld', format_axes=format_axes)
mld[:] = MV2.masked
# Two-layers
if mode == 'twolayers':
densbot = dens[slices['first']]
denstop = dens[slices['last']]
del dens
H = 1.5 * depth[slices['first']] - 0.5 * depth[slices['firstp1']]
H = -1.5 * depth[slices['last']] + 0.5 * depth[slices['lastm1']]
mld[:] = -H * (densbot - denstop) / (densbot - denstop)
del H
elif mode == 'deltadens':
denscrit = dens[slices['last']] + deltadens
mld[:] = -_val2z_(dens, depth, denscrit, zaxis, -1)
del dens
elif mode == 'deltatemp':
tempcrit = temp[slices['last']] - deltatemp
mld[:] = -_val2z_(temp, depth, tempcrit, zaxis, 1)
elif mode == 'kz':
mld[:] = -_valmin2z_(kz, depth, kzmax, zaxis, 1)
else:
raise VACUMMError("Invalid mode for computing MLD (%s)." % mode +
"Please choose one of: deltadens, twolayers")
# Mask zeros
mld[:] = MV2.masked_values(mld, 0., copy=0)
return mld
