#!/usr/bin/env python
# -*- coding: utf8 -*-
"""Filtres 1D et 2D"""
from vcmq import cdms2, curve2, map2, generic1d, generic2d, shapiro2d, bartlett1d, data_sample, curve2, shapiro1d, gaussian1d, gaussian2d
# Lectures
f = cdms2.open(data_sample('mars3d.t.nc'))
sst1d = f('temp')
f.close()
f = cdms2.open(data_sample('menor.nc'))
sst2d = f('TEMP', time=slice(0, 1), level=slice(-1, None), squeeze=1)
f.close()
# 1D
# - filtrages
sst1d_gen13 = generic1d(sst1d, 13)
sst1d_gen3 = generic1d(sst1d, [1., 1., 1.])
sst1d_sha = shapiro1d(sst1d) # -> SHAPIRO AVEC GENERIC1D
sst1d_bar13 = bartlett1d(sst1d, 13)
# -> TESTEZ GAUSSIEN SUR 13 HEURES
# - plots
curve2(sst1d, 'k', label='Original', show=False, figsize=(12, 5))
curve2(sst1d_gen13, 'r', label='Generic 13 pts', show=False)
curve2(sst1d_gen3, 'g', label='Generic 3 pts', show=False)
curve2(sst1d_sha, 'b', label='shapiro', show=False)
curve2(sst1d_bar13, 'm', label='Bartlett', legend=True)
# -> MASQUEZ UNE PARTIE DES DONNEES ET JOUEZ AVEC LE PARAMETRE MASK=
# -*- coding: utf8 -*-
# Lecture et masquage de Hs
from vcmq import cdms2, MV2, data_sample, code_base_name
f = cdms2.open(data_sample("swan.four.nc"))
lon = f("longitude")
lat = f("latitude")
missing_value = f["HS"]._FillValue
hs = f("HS", squeeze=1)
f.close()
hs[:] = cdms2.MV.masked_object(hs, missing_value, copy=0)
# Trace sur grille irrégulière
from vacumm.misc.plot import map2
map2(
hs,
xaxis=lon,
yaxis=lat,
figsize=(6, 4),
long_name="Significant wave height",
fill="pcolormesh",
contour=False,
savefigs=code_base_name(ext="png"),
left=0.12,
right=1,
show=False,
)
from vcmq import cdms2, P, curve2, savefigs, data_sample
from vacumm.tide.filters import demerliac, godin
from vacumm.tide.filters import extrema, zeros
from vacumm.tide.marigraph import Marigraph
from vacumm.tide.station_info import StationInfo
# Stations
station = StationInfo('Brest')
print station.attributes()
print station.name, station.longitude
print 'Niveau moyen a Brest:', station.nm
# Read sea level at Brest
f = cdms2.open(data_sample("tide.sealevel.BREST.mars.nc"))
sea_level = f('sea_level')
f.close()
# Surcotes/decotes
cotes, tide = demerliac(sea_level, get_tide=True) # -> ESSAYER GODIN
kwp = dict(date_fmt='%b', date_locator='month')
curve2(sea_level, 'b', show=False, figsize=(15, 4), **kwp)
curve2(tide, 'r', **kwp)
curve2(cotes, figsize=(15, 4), **kwp)
# Extremas
slzoom1 = sea_level(('2006-10-01', '2006-10-02'))[::4] # Toutes les heures
bm, pm = extrema(slzoom1, spline=True, ref='mean') # -> SANS SPLINES
"""Test :meth:`~vacumm.misc.core_plot.Plot2D.plot_streamplot`"""
from vcmq import map2, data_sample, cdms2, code_file_name
import warnings
warnings.filterwarnings('ignore', 'Warning: converting a masked element to nan')
f = cdms2.open(data_sample('mars2d.xyt.nc'))
u = f('u', slice(0, 1), squeeze=1)
v = f('v', slice(0, 1), squeeze=1)
f.close()
map2((u, v), fill=False, contour=False, streamplot=True, streamplot_density=3,
streamplot_linewidth='modulus', streamplot_lwmod=3, streamplot_color='modulus',
title='Tidal stream lines', colorbar_shrink=0.7, savefig=code_file_name(),
right=1, show=False)
from vcmq import data_sample, map, cdms2
# Read
import cdms2
f = cdms2.open(data_sample('mars3d.xy.nc'))
sst = f('temp',
time=slice(0, 1),
lat=(47.8, 48.6),
lon=(-5.2, -4.25),
squeeze=1)
f.close()
# Plot
m = map(sst,
title='SST in the Iroise Sea',
linewidth=.7,
fill='pcolormesh',
cmap='cmocean_thermal',
clabel=True,
vmin=9,
top=.9,
figsize=(6, 5),
clabel_glow=True,
clabel_glow_alpha=.4,
colorbar_shrink=.8,
show=False,
savefigs=__file__,
close=True)
from vcmq import cdms2, data_sample, N, cdtime, curve2, round_date, create_time, lindates, regrid1d, add_key, P
from vacumm.misc.grid._interp_ import cellerr1d
from scipy.stats import linregress
# Read data
f = cdms2.open(data_sample("radial_speed.nc"))
sp = f("speed")
spe = f("speed_error")
f.close()
# Create hourly time axis
taxi = sp.getTime()
taxi.toRelativeTime("hours since 2000")
ctimesi = taxi.asComponentTime()
ct0 = round_date(ctimesi[0], "hour")
ct1 = round_date(ctimesi[-1], "hour")
taxo = create_time(lindates(ct0, ct1, 1, "hour"), taxi.units)
# Lag error
# - estimation
els = []
lags = N.arange(1, 6)
for lag in lags:
els.append(N.sqrt(((sp[lag:] - sp[:-lag]) ** 2).mean()))
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")
from vcmq import data_sample, map, cdms2
# Read
import cdms2
f = cdms2.open(data_sample('mars3d.xy.nc'))
sst = f('temp', time=slice(0, 1), lat=(47.8, 48.6), lon=(-5.2, -4.25), squeeze=1)
f.close()
# Plot
m = map(sst, title='SST in the Iroise Sea', linewidth=.7,
fill='pcolormesh', cmap='cmocean_thermal', clabel=True,
vmin=9, top=.9, figsize=(6, 5), clabel_glow=True, clabel_glow_alpha=.4,
colorbar_shrink=.8, show=False, savefigs=__file__)
# -*- coding: utf8 -*-
# %% Imports
from vcmq import cdms2, MV2, data_sample, code_base_name, map2
# %% Read Hs
f = cdms2.open(data_sample('swan.four.nc'))
lon = f('longitude')
lat = f('latitude')
hs = f('HS', squeeze=1)
f.close()
# %% Plot it specifying its irregular grid
map2(hs,
xaxis=lon,
yaxis=lat,
figsize=(6, 4),
cmap='cmocean_amp',
long_name='Significant wave height',
fill='pcolormesh',
contour=False,
savefigs=code_base_name(ext='png'),
left=.12,
right=1,
show=False)
from vcmq import cdms2, data_sample, N, cdtime, curve2,round_date, create_time, \
lindates, regrid1d, add_key
from vacumm.misc.grid._interp_ import cellerr1d
from scipy.stats import linregress
# Read data
f = cdms2.open(data_sample('radial_speed.nc'))
sp = f('speed')
spe = f('speed_error')
f.close()
# Create hourly time axis
taxi = sp.getTime()
taxi.toRelativeTime('hours since 2000')
ctimesi = taxi.asComponentTime()
ct0 = round_date(ctimesi[0], 'hour')
ct1 = round_date(ctimesi[-1], 'hour')
taxo = create_time(lindates(ct0, ct1, 1, 'hour'), taxi.units)
# Lag error
# - estimation
els = []
lags = N.arange(1, 6)
for lag in lags:
els.append(N.sqrt(((sp[lag:]-sp[:-lag])**2).mean()))
els = N.array(els)
a, b, _, _, _ = linregress(lags, els)
# - plot
P.figure(figsize=(6, 6))
P.subplot(211)
P.plot(lags, els, 'o')
def generate_pseudo_ensemble(ncpat,
varnames=None,
nrens=50,
enrich=2.,
norms=None,
getmodes=False,
logger=None,
asdicts=False,
anomaly=True,
ncensfile=None,
**kwargs):
"""Generate a static pseudo-ensemble from a single simulation
Parameters
----------
ncpat: string
netcdf file name or pattern
nrens: int
Ensemble size
enrich: float
Enrichment factor
getmodes: bool
Get also EOFs end eigen values
**kwargs:
Extra parameters are passed to :func:`load_model_at_dates`
Return
------
list (or dict) of arrays:
variables with their name as keys
dict: eofs, ev and variance, optional
eofs: list (or dict) of arrays(nmodes, ...), optional
EOFs
ev: array(nmodes), optional
Eigen values
var: array
Variance
"""
# Logger
kwlog = kwfilter(kwargs, 'logger_')
if logger is None:
logger = get_logger(**kwlog)
logger.verbose('Generating pseudo-ensemble')
# Ensembe size
enrich = max(enrich, 1.)
nt = int(nrens * enrich)
logger.debug(
' enrich={enrich}, nt={nt}, ncpat={ncpat}, varnames={varnames}'.
format(**locals()))
# Read variables
logger.debug('Reading the model at {} dates'.format(nt))
data = load_model_at_regular_dates(ncpat,
varnames=varnames,
nt=nt,
asdict=False,
**kwargs)
single = not isinstance(data, list)
# Norms
if isinstance(norms, dict):
norms = var_prop_dict2list(data, norms)
# Enrichment
witheofs = nrens != nt
if witheofs:
logger.debug('Computing reduced rank ensemble with EOFs analysis')
# Stack packed variables together
stacker = Stacker(data, norms=norms, logger=logger)
meanstate = N.zeros(stacker.ns)
states = N.asfortranarray(stacker.stacked_data.copy())
# Compute EOFs
stddev, svals, svecs, status = f_eofcovar(dim_fields=stacker.ns,
offsets=1,
remove_mstate=0,
do_mv=0,
states=states,
meanstate=meanstate)
if status != 0:
raise SONATError('Error while calling fortran eofcovar routine')
neof = svals.size # computed
neofr = nrens - 1 # retained
svals = svals[:neofr] * N.sqrt(
(neof - 1.) / neof) # to be consistent with total variance
svecs = svecs[:, :neofr]
# Generate ensemble
sens = f_sampleens(svecs, svals, meanstate, flag=0)
# Unstack
ens = stacker.unstack(sens,
format=2,
rescale='norm' if anomaly else True)
if getmodes:
# Modes
mode_axis = create_axis(N.arange(1, neofr + 1, dtype='i'),
id='mode')
eofs = stacker.unstack(svecs,
firstdims=mode_axis,
id='{id}_eof',
rescale=False,
format=1)
svals = MV2.array(svals,
axes=[mode_axis],
id='ev',
attributes={'long_name': 'Eigen values'})
svals.total_variance = float(stacker.ns)
# Variance
vv = stacker.format_arrays([d.var(axis=0) for d in stacker.datas],
id='{id}_variance',
mode=1)
variance = stacker.unmap(vv)
else: # No enrichment -> take the anomaly if requested
logger.debug('Getting the anomaly to build the ensemble')
ens = data
if anomaly:
if single:
ens[:] = ens.asma() - ens.asma().mean(axis=0)
else:
for i, e in enumerate(ens):
ens[i][:] = e.asma() - e.asma().mean(axis=0)
# Finalize
getmodes = getmodes and witheofs
member_axis = create_axis(N.arange(nrens, dtype='i'),
id='member',
long_name='Member')
if single:
ens.setAxis(0, member_axis)
else:
for var in ens:
var.setAxis(0, member_axis)
# Dump to file
if ncensfile:
logger.debug('Dump the ensemble to netcdf')
checkdir(ncensfile)
f = cdms2.open(ncensfile, 'w')
ensvars = list(ens) if not single else [ens]
if getmodes:
if single:
ensvars.append(eofs)
ensvars.append(variance)
else:
ensvars.extend(eofs)
ensvars.extend(variance)
ensvars.append(svals)
for var in ensvars:
f.write(var)
f.close()
logger.created(ncensfile)
# As dicts
if asdicts:
if single:
ens = OrderedDict([(ens.id, ens)])
if getmodes:
eofs = OrderedDict([(eofs.id, eofs)])
variance = OrderedDict([(variance.id, variance)])
else:
ens = OrderedDict([(var.id, var) for var in ens])
if getmodes:
eofs = OrderedDict([(var.id, var) for var in eofs])
variance = OrderedDict([(var.id, var) for var in variance])
# Return
if not getmodes:
return ens
return ens, dict(eofs=eofs, eigenvalues=svals, variance=variance)
if RE_MATCH_TIME(arg) and not arg.isdigit(): # date
return arg
if re.match('[\w\s]*:[\w\s]*(:[\w\s]*)?$', arg): # args to slice
return slice(*[(None if a=='' else eval(a)) for a in arg.split(':')])
arg = eval(arg)
if isinstance(arg, int): # slice at a single index
return slice(arg, (arg+1) if arg!=-1 else None)
if isinstance(arg, float): # selection of a single position
return (arg, arg, 'cob')
return arg
# Open reader or file
if args.generic:
f = DS(args.ncfile, args.generic, logger_level='info' if args.verbose else 'error')
else:
f = cdms2.open(args.ncfile)
# Find a variable name if not provided
if not args.var:
if args.generic:
allvars = f.dataset[0].listvariables()
else:
allvars = f.listvariables()
for var in allvars:
if 'bounds' not in var:
if args.generic:
var = '+'+var
args.var = [var]
break
else:
args.var = args.var[:3]
return slice(*[(None if a == '' else eval(a)) for a in arg.split(':')])
arg = eval(arg)
if isinstance(arg, int): # slice at a single index
return slice(arg, (arg + 1) if arg != -1 else None)
if isinstance(arg, float): # selection of a single position
return (arg, arg, 'cob')
return arg
# Open reader or file
if args.generic:
f = DS(args.ncfile,
args.generic,
logger_level='info' if args.verbose else 'error')
else:
f = cdms2.open(args.ncfile)
# Find a variable name if not provided
if not args.var:
if args.generic:
allvars = f.dataset[0].listvariables()
else:
allvars = f.listvariables()
for var in allvars:
if 'bounds' not in var:
if args.generic:
var = '+' + var
args.var = [var]
break
else:
args.var = args.var[:3]
"""Testing to read/modidy/(re)write NEtCDF files without loosing attributes"""
# Inits
ncfile = "nobounds.nc"
outfile = "result.nc"
# Imports
from vcmq import cdms2, warnings, data_sample, code_dir_name, os
from vacumm.misc.misc import get_atts
# Warnings off
warnings.simplefilter('ignore')
# Open input file
f = cdms2.open(data_sample(ncfile))
# Open output file
outfile = os.path.join(code_dir_name(), outfile)
fw = cdms2.open(outfile, 'w')
# Cancel the default behaviour (<=> generating bounds variables)
cdms2.setAutoBounds('off')
# -- Read and write in another file variables
for var in f.listvariable():
print '>> ' + var + ' <<'
data = f(var)
try: # - Variable is an n-dimension array
data.getValue()
# - Example of moficiation - mask an area
#!/usr/bin/env python
# -*- coding: utf8 -*-
"""Manipulation de bathymetrie sur grille (:mod:`vacumm.bathy.bathy`)"""
from vcmq import cdms2, data_sample, create_grid
from vacumm.bathy.bathy import GriddedBathy, NcGriddedBathy, bathy_list, GriddedBathyMerger, plot_bathy
# Lecture d'une variable dans un fichier
cdms2.axis.latitude_aliases.append('y')
cdms2.axis.longitude_aliases.append('x')
f = cdms2.open(data_sample('ETOPO2v2g_flt.grd'))
var = f('z', lon=(-6.1, -4), lat=(47.8, 48.6))
f.close()
# Creation de l'objet de bathy grillee
bathy = GriddedBathy(var)
# Versions masquees
bathy_masked1 = bathy.masked_bathy() # -> nb de points masques ?
bathy.set_shoreline('f') # -> essayer 'i'
bathy_masked2 = bathy.masked_bathy() # -> nb de points masques ?
# Plot
bathy.plot(title='Direct')
# Bathy standard
from vacumm.bathy.bathy import NcGriddedBathy, bathy_list
print bathy_list().keys()
sbathy = NcGriddedBathy(lon=(-5.2, -3), lat=(48., 48.9), name='etopo2')
sbathy.plot(title='Bathy par defaut')
"""Test :meth:`~vacumm.misc.core_plot.Plot2D.plot_streamplot`"""
from vcmq import map2, data_sample, cdms2, code_file_name
import warnings
warnings.filterwarnings('ignore',
'Warning: converting a masked element to nan')
f = cdms2.open(data_sample('mars2d.xyt.nc'))
u = f('u', slice(0, 1), squeeze=1)
v = f('v', slice(0, 1), squeeze=1)
f.close()
map2((u, v),
fill=False,
contour=False,
streamplot=True,
streamplot_density=3,
streamplot_linewidth='modulus',
streamplot_lwmod=3,
streamplot_color='modulus',
title='Tidal stream lines',
colorbar_shrink=0.7,
savefig=code_file_name(),
right=1,
show=False)
#!/usr/bin/env python
# -*- coding: utf8 -*-
"""Filtres 1D et 2D"""
from vcmq import cdms2, curve2, map2, generic1d, generic2d, shapiro2d, bartlett1d, data_sample, curve2, shapiro1d, gaussian1d, gaussian2d
# Lectures
f = cdms2.open(data_sample('mars3d.t.nc'))
sst1d = f('temp')
f.close()
f = cdms2.open(data_sample('menor.nc'))
sst2d = f('TEMP', time=slice(0, 1), level=slice(-1, None), squeeze=1)
f.close()
# 1D
# - filtrages
sst1d_gen13 = generic1d(sst1d, 13)
sst1d_gen3 = generic1d(sst1d, [1., 1., 1.])
sst1d_sha = shapiro1d(sst1d) # -> SHAPIRO AVEC GENERIC1D
sst1d_bar13 = bartlett1d(sst1d, 13)
# -> TESTEZ GAUSSIEN SUR 13 HEURES
# - plots
curve2(sst1d, 'k', label='Original', show=False, figsize=(12, 5))
curve2(sst1d_gen13, 'r', label='Generic 13 pts', show=False)
curve2(sst1d_gen3, 'g', label='Generic 3 pts', show=False)
curve2(sst1d_sha, 'b', label='shapiro', show=False)
curve2(sst1d_bar13, 'm', label='Bartlett', legend=True)
#!/usr/bin/env python
# -*- coding: utf8 -*-
"""Calculs sur les gros fichiers"""
from vcmq import cdms2, Intervals, data_sample, StatAccum, cdtime, curve2, N
# Initialisations
# - ouverture du fichier et infos temporelles
f = cdms2.open(data_sample("mars3d.xt.xe.nc"))
ctimes = f["xe"].getTime().asComponentTime()
# - passage par un accumulateur de stats
sa = StatAccum(tmean=True)
# Boucle sur des intervals journaliers
for itv in Intervals((ctimes[0], ctimes[-1], "cc"), "day"): # -> ESSAYER 2 JOURS
# Lecture
print itv
tmp = f("xe", time=itv)
# Accumulation
sa += tmp
del tmp
# Finalisation
# - restitution
daxis = create_dep((-100., 1, 5.))
nz = len(daxis)
np = len(lons)
temp_error = N.resize([.2, .5, .3, .8], (nz, np))
sal_error = N.resize([.3, .1, .15, .4], (nz, np))
temp_error = MV2.array(temp_error, id='temp_error')
sal_error = MV2.array(sal_error, id='sal_error')
temp_error[:nz / 2, 2] = MV2.masked
sal_error[:nz / 2, 2] = MV2.masked
temp_error[:3 * nz / 4, 3] = MV2.masked
sal_error[:3 * nz / 4, 3] = MV2.masked
mob = MV2.array([0, 1, 1, 0], id='mobility', fill_value=-1)
paxis = lons.getAxis(0)
paxis.id = 'station'
axes = [daxis, paxis]
f = cdms2.open(ncfile, 'w')
for var in lons, lats, mob, temp_error, sal_error:
var.setAxis(-1, paxis)
if var.ndim == 2:
var.setAxis(0, daxis)
f.write(var)
f.close()
print os.path.abspath(ncfile)
# HF radars
ncfile = '../data/obs.hfradars.nc'
lon = create_lon((-5.8, -4.7, .1))
lat = create_lat((47.8, 48.3, .1))
nx = len(lon)
ny = len(lat)
u_error = MV2.ones((ny, nx), id='u_error', axes=[lat, lon])
"""Test :func:`~vacumm.misc.grid.regridding.transect` on a curvilinear grid"""
# Inits
ncfile = "swan.four.nc"
lon0 = -5.1
lon1 = -4.9
lat0 = 48.35
lat1 = 48.55
# Imports
from vcmq import cdms2, data_sample, N, transect, curve2, code_file_name, os, \
add_map_lines, P, add_shadow
# Read data
f = cdms2.open(data_sample(ncfile))
hs = f('HS', squeeze=1)
f.close()
# Compute transect
hst, lons, lats = transect(hs, (lon0,lon1), (lat0,lat1), getcoords=True)
# Plot along time
s = curve2(hst, figsize=(8,3), title='Spatial transect on curved grid',
show=False, top=0.85)
# Add a small map to show the transect positions
o = add_map_lines(hs, lons, lats, map_bgcolor='w', map_bbox= [.6, .2, .3, .5], map_anchor='W')
# Save
figfile = code_file_name(ext='png')
#!/usr/bin/env python
# -*- coding: utf8 -*-
"""Le regrillage"""
from vcmq import N, cdms2, curve2, map2, regrid1d, regrid2d, data_sample, \
create_grid, regrid_method, CDATRegridder, resol, add_grid, P
# Lecture
f = cdms2.open(data_sample('mfs.nc'))
select = dict(lat=(42.5, 43.5), lon=(3, 4))
ssti = f('votemper', level=slice(0, 1), squeeze=1, **select)
f.close()
gridi = ssti.getGrid()
dxi, dyi = resol(gridi)
# Nouvelle grille
factor = 0.634 # -> CHANGEZ LE FACTEUR
dxo = dxi*factor
dyo = dyi*factor
grido = create_grid((3.13, 3.8, dxo), (42.62, 43.4, dyo))
# Quelle méthode ?
method = regrid_method(gridi, grido)
print method
# Regrillage
from vcmq import (cdms2, data_sample, N, cdtime, curve2, round_date,
create_time, lindates, regrid1d, add_key, P)
from vacumm.misc.grid._interp_ import cellerr1d
from scipy.stats import linregress
# Read data
f = cdms2.open(data_sample('radial_speed.nc'))
sp = f('speed')
spe = f('speed_error')
f.close()
# Create hourly time axis
taxi = sp.getTime()
taxi.toRelativeTime('hours since 2000')
ctimesi = taxi.asComponentTime()
ct0 = round_date(ctimesi[0], 'hour')
ct1 = round_date(ctimesi[-1], 'hour')
taxo = create_time(lindates(ct0, ct1, 1, 'hour'), taxi.units)
# Lag error
# - estimation
els = []
lags = N.arange(1, 6)
for lag in lags:
els.append(N.sqrt(((sp[lag:] - sp[:-lag])**2).mean()))
els = N.array(els)
a, b, _, _, _ = linregress(lags, els)
# - plot
P.figure(figsize=(6, 6))
P.subplot(211)
P.plot(lags, els, 'o')
# Imports
#from matplotlib import use ; use('Agg')
from vcmq import cdms2, bounds1d, bounds2d, data_sample, MV2, cp_props, N, minmax, code_file_name, P, os, psinfo, gc
#from cdms2.mvCdmsRegrid import CdmsRegrid
from time import time
from traceback import format_exc
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
#if rank==0: print 'read'
f = cdms2.open(data_sample(ncfile))
vari2d = f(ncvar)[0,yslice,xslice]
f.close()
gridi = vari2d.getGrid()
vari2d[:] += 3*vari2d.mean()
vari2d[:, -1] = MV2.masked
nyi,nxi = vari2d.shape
#if rank==0: print 'expand in time and depth'
vari = MV2.resize(vari2d, (nt, nz)+vari2d.shape)
cp_props(vari2d, vari)
#if rank==0: print 'grido'
loni = gridi.getLongitude()
lati = gridi.getLatitude()
#!/usr/bin/env python
# -*- coding: utf8 -*-
"""Gestion des coordonnées sigma et interpolation 1D"""
from vcmq import NcSigma, SigmaGeneralized, sigma2depths, cdms2, N, section2, create_depth, regrid1d, data_sample
# 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)
curve2,
map2,
regrid1d,
regrid2d,
data_sample,
create_grid,
regrid_method,
CDATRegridder,
resol,
add_grid,
P,
)
# Lecture
f = cdms2.open(data_sample("mfs.nc"))
select = dict(lat=(42.5, 43.5), lon=(3, 4))
ssti = f("votemper", level=slice(0, 1), squeeze=1, **select)
f.close()
gridi = ssti.getGrid()
dxi, dyi = resol(gridi)
# Nouvelle grille
factor = 0.634 # -> CHANGEZ LE FACTEUR
dxo = dxi * factor
dyo = dyi * factor
grido = create_grid((3.13, 3.8, dxo), (42.62, 43.4, dyo))
# Quelle méthode ?
# -*- 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()
#!/usr/bin/env python
# -*- coding: utf8 -*-
"""Gestion des coordonnées sigma et interpolation 1D"""
from vcmq import NcSigma, SigmaGeneralized, sigma2depths, cdms2, N, section2, create_depth, regrid1d, data_sample
# 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,
