# Lecture de la température
f =cdms2.open(data_sample('mars3d.tsigyx.nc'))
data_in = f('TEMP', time=slice(0,2)) # T-YX (- = level)
# Détermination des profondeurs d'après les sigma
sigma_converter = NcSigma.factory(f) # détection auto et initialisation du convertisseur
# -> VERIFIER QUE sigma_class EST BIEN SigmaGeneralized
depths_in = sigma_converter.sigma_to_depths(selector=dict(time=slice(0,2))).filled() # lecture eta, etc + conversion
# (Equivalent à depths_in = sigma_converter(selector=dict(time=slice(0,2))).filled())
f.close()
# Creation de l'axe des profondeurs cibles
depths = N.array([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,
22,24,26,28,30,32,34,36,38,40,45,50,55,60,65,70,75,80,85,90,95,100,120,140,160])
depths = -depths[::-1] # croissantes et négatives
depth_out = create_depth(depths)
# Interpolation
data_out = regrid1d(data_in, depth_out, axi=depths_in, axis=1, method='linear', extrap=1)
# Plot
kw = dict(vmin=10, vmax=14, xhide='auto', add_grid=True, ymax=0, fill='contourf') # FILL=PCOLOR ?
section2(data_in[0, :, 10], yaxis=depths_in[0, :, 10], subplot=211, title='Sigma', show=False, **kw)
s = section2(data_out[0, :, 10], subplot=212, title='Z', show=False, savefig=__file__, **kw)
# - plot
P.figure(figsize=(6, 6))
P.subplot(211)
P.plot(lags, els, 'o')
P.plot([0, lags[-1]], [b, a * lags[-1] + b], 'g')
P.axhline(b, color='0.8', ls='--')
P.ylim(ymin=0)
P.xlabel('Lag [hour]')
P.ylabel('Error [m s-1]')
add_key(1)
P.title('Linear lag error model')
# Interpolation
sph, speh = regrid1d(sp,
taxo,
method='cellerr',
erri=spe,
errl=-a,
geterr=True)
# Time zoom for plot clarity
tzoom = (ct1.sub(7, cdtime.Hour), ctimesi[-1])
sp = sp(tzoom)
spe = spe(tzoom)
sph = sph(tzoom)
speh = speh(tzoom)
# Main plot
curve2(sp,
'o',
err=spe.asma() / 2.,
markersize=2,
def slice_gridded_var(var, member=None, time=None, depth=None, lat=None, lon=None):
"""Make slices of a variable and squeeze out singletons to reduce it
The "member" axis is considered here as a generic name for the first
axis of unkown type.
.. warning:: All axes must be 1D
"""
# Check order
var = var(squeeze=1)
order = var.getOrder()
# Unkown axis
if '-' in order and member is not None:
i = order.find('-')
id = var.getAxisIds()[i]
if isinstance(member, slice):
kw = {id:member}
var = var(**kw)
else:
axo = create_axis(member)
cp_atts(var.getAxis(i), axo)
var = regrid1d(var, axo, iaxi=i)(squeeze=N.isscalar(member))
# Time interpolation
if 't' in order and time is not None:
axi = var.getTime()
if isinstance(time, slice):
var = var(time=time)
else:
axo = create_time(time, axi.units)
var = regrid1d(var, axo)(squeeze=N.isscalar(time))
# Depth interpolation
if 'z' in order and depth is not None:
if depth=='bottom':
var = slice_bottom(var)
else:
if depth=='surf':
depth = slice(-1, None)
if isinstance(depth, slice):
var = var(level=depth, squeeze=1) # z squeeze only?
elif (N.isscalar(depth) and var.getLevel()[:].ndim==1 and
depth in var.getLevel()):
var = var(level=depth)
else:
axo = create_dep(depth)
if axo[:].max()>10:
sonat_warn('Interpolation depth is positive. Taking this opposite')
axo[:] *=-1
var = regrid1d(var, axo)(squeeze=N.isscalar(depth))
# Point
if (order.endswith('yx') and lon is not None and lat is not None and
not isinstance(lat, slice) and not isinstance(lon, slice)):
var = grid2xy(var, lon, lat)(squeeze=N.isscalar(lon))
else:
# Latitude interpolation
if 'y' in order and lat:
if isinstance(lat, slice):
var = var(lat=lat)
else:
axo = create_lat(lat)
var = regrid1d(var, axo)(squeeze=N.isscalar(lat))
# Longitude interpolation
if 'x' in order and lon:
if isinstance(lon, slice):
var = var(lon=lon)
else:
axo = create_lon(lon)
var = regrid1d(var, axo)(squeeze=N.isscalar(lon))
return var
els = N.array(els)
a, b, _, _, _ = linregress(lags, els)
# - plot
P.figure(figsize=(6, 6))
P.subplot(211)
P.plot(lags, els, "o")
P.plot([0, lags[-1]], [b, a * lags[-1] + b], "g")
P.axhline(b, color="0.8", ls="--")
P.ylim(ymin=0)
P.xlabel("Lag [hour]")
P.ylabel("Error [m s-1]")
add_key(1)
P.title("Linear lag error model")
# Interpolation
sph, speh = regrid1d(sp, taxo, method="cellerr", erri=spe, errl=-a, geterr=True)
# Time zoom for plot clarity
tzoom = (ct1.sub(7, cdtime.Hour), ctimesi[-1])
sp = sp(tzoom)
spe = spe(tzoom)
sph = sph(tzoom)
speh = speh(tzoom)
# Main plot
curve2(sp, "o", err=spe.asma() / 2.0, markersize=2, ymin=-0.4, ymax=0.1, show=False, subplot=212, label="Original")
curve2(
sph,
"-r",
err=speh.asma() / 2.0,
linewidth=1.5,
# (Equivalent à depths_in = sigma_converter(selector=dict(time=slice(0,2))).filled())
f.close()
# Creation de l'axe des profondeurs cibles
depths = N.array([
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,
90, 95, 100, 120, 140, 160
])
depths = -depths[::-1] # croissantes et négatives
depth_out = create_depth(depths)
# Interpolation
data_out = regrid1d(data_in,
depth_out,
axi=depths_in,
axis=1,
method='linear',
extrap=1)
# Plot
kw = dict(vmin=10,
vmax=14,
xhide='auto',
add_grid=True,
ymax=0,
fill='contourf') # FILL=PCOLOR ?
section2(data_in[0, :, 10],
yaxis=depths_in[0, :, 10],
subplot=211,
title='Sigma',
show=False,
