# Lecture d'un fichier xyz et plot
from vacumm.misc.io import XYZ
from vacumm.config import data_sample
xyz = XYZ(data_sample('celtic_sea.xyz'))
# Verifs
print len(xyz)
# -> 23538
print xyz[0:3]
# -> [(-5.93,52.97,25.0),(-5.90,52.97,41.0),(-5.85,52.97,33.0)]
# Exclusion
xyz.exclude([[-6., 52.], [-5.5, 52], [-5.5, 52.5], [-6., 52.5]])
# Modification
print xyz.z.max()
# -> 141.0
xyz *= -1
print xyz.z.max()
# -> -141.0
# Plot
import pylab as P
P.figure(figsize=(5.5, 8))
P.subplots_adjust(left=.12, top=.96, right=1, bottom=.06)
P.subplot(211)
xyz.plot(title='Mer celtique', units='m', m=True,
show=False, size=2, cmap='cmap_bathy')
# Zoom
xyz_zoom = xyz.clip((-6, 51.5, -4, 52.5), long_name='Zoom')
# Lecture des donnnees
import cdms2, MV2, numpy as N
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), 0.0, 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],
# from StringIO import StringIO
import cdms2 as cdms
from vacumm.misc.atime import strftime, ch_units, strptime
import scipy.io as SC
import matplotlib.pyplot as mp
# Inits
drifterfile = "drifter.txt"
adcpfile = "adcp.txt"
# -----------------------------------------------------------------------------------------------------------
# ---- Read an ASCII file ----
print 10*'-'+' ASCII file '+10*'-'
# We load a cdms variable
f = cdms.open(data_sample('mars3d.t.nc'))
temp = f('temp')
u = f('u')
v = f('v')
f.close()
# We get the Time
time = temp.getTime() # axis
ctime = time.asComponentTime() # time cdtime.comptime()
# Create the file with the format : YYYY/MM/DDZHH:MM TEMP U V
f = open('misc.io.ascii.1.dat', 'w')
f.write('# Ligne de commentaire\n')
for it in xrange(len(temp)):
t = strftime('%Y/%m/%dZ%H:%M', ctime[it])
f.write('%s %.4f %f %f\n' % (t, temp[it], u[it], v[it]))
# On recupere une variable cdms
import cdms2 as cdms
from vacumm.config import data_sample
f = cdms.open(data_sample("mars3d.t.nc"))
temp = f("temp")
u = f("u")
v = f("v")
f.close()
# On recupre le temps
time = temp.getTime() # axe
ctime = time.asComponentTime() # temps cdtime.comptime()
# Creation a la main au format : YYYY/MM/DDZHH:MM TEMP U V
from vacumm.misc.atime import strftime, ch_units, strptime
f = open("misc.io.ascii.1.dat", "w")
f.write("# Ligne de commentaire\n")
for it in xrange(len(temp)):
t = strftime("%Y/%m/%dZ%H:%M", ctime[it])
f.write("%s %.4f %f %f\n" % (t, temp[it], u[it], v[it]))
f.close()
# Verification rapide (deux premieres lignes)
f = open("misc.io.ascii.1.dat")
print "".join(f.readlines()[:3])
f.close()
# -> # Ligne de commentaire
# -> 2008/01/07Z00:00 12.0672 0.359631 0.156422
# -> 2008/01/07Z01:00 11.9421 0.493174 0.158244
# -*- coding: utf8 -*-
# Lecture de la temperature
import cdms2, MV2
from vacumm.config import data_sample
from vcmq import code_base_name
f = cdms2.open(data_sample('mars3d.xy.nc'))
temp = f('temp', lon=(-5.5, -4), lat=(47, 49))
f.close()
temp.long_name = 'Original'
# On crée un trou
temp[15:60, 40:80] = MV2.masked
# On rempli des trous
from vacumm.misc.grid.regridding import fill2d
tempf = fill2d(temp, method='carg')
tempf.long_name = 'Rempli'
# On masque la terre
from vacumm.misc.grid.masking import masked_polygon
tempf[:] = masked_polygon(tempf, 'h', copy=0)
# Plots
from vacumm.misc.plot import map2 as map, P
P.rc('font', size=9)
kw = dict(vmin=9, vmax=13)
map(temp,
show=False,
subplot=211,
hspace=.2,
bottom=.05,
import cdms2, MV2
from vacumm.misc.plot import map2
from vacumm.misc.color import cmap_custom
from vacumm.misc.filters import generic2d
from vacumm.misc.phys.units import ms2kt
from vacumm.config import data_sample
from matplotlib import rc
rc('font', size=9)
# Lecture des donnees
f = cdms2.open(data_sample('wrf_2d.nc'))
select = dict(lat=slice(4, -4), lon=slice(4, -4), squeeze=1)
slp = f('pslvl', **select)
rain = f('rain', **select)
u = f('u10m', **select)
v = f('v10m', **select)
f.close()
# Pression de surface
slp[:] = generic2d(slp*0.01, 5)
map2(slp, fill=False, contour=True, show=False, figsize=(6, 5.5),
title='WRF Bretagne',
fillcontinents=True, zorder=5, projection='merc', right=1, bottom=.07,
lowhighs=True, lowhighs_smooth=9, lowhighs_zorder=5, fillcontinents_color='.95',
lowhighs_color=(0, .5, 0), contour_colors=[(0, .5, 0)], drawcoastlines_linewidth=.6)
# Pluie
cmap_rain = cmap_custom([('0.9', 0), ('b', .8), ('r', 1.)])
rain[:] = MV2.masked_less(rain, 0.1)
map2(rain, cmap=cmap_rain, vmin=0.,
fill='pcolor', fillcontinents=False, show=False,
# Un axe quelconque
bad_axis = cdms2.createAxis([1], id="pipo")
# Verification des types d'axes avec 'axis_type'
# - affichage pour tous
print " | ".join(["%s:%s" % (axis.id, axis_type(axis)) for axis in time_axis, lon_axis, lat_axis, dep_axis, bad_axis])
# -> time:t | other_time:t | longitude:x | lat:y | depth:z | pipo:-
# - verification ponctuele
print islon(lon_axis), islon(lat_axis), is_geo_axis(lat_axis)
# -> True False False
# Reformattage des axes pour deviner identifier
# ceux geographiques (lon,lat,dep,time)
# Le reformattage peut changer : id, units, long_name.
# - un axe pourri mais avec 'long_name' explitcite
time_axis2 = cdms2.createAxis([6], id="other_time")
time_axis2.long_name = "Time"
check_axis(time_axis2)
print istime(time_axis2) # en fait, 'check_axis' appelle 'istime'
# -> True
# - une variable tiree d'un fichier (voir le tutoriel (*@\ref{lst:misc.io.netcdf}@*))
from vacumm.config import data_sample
f = cdms2.open(data_sample("mars3d.xt.xe.nc"))
var = f("xe", time=slice(0, 1), squeeze=1)
f.close()
check_axes(var)
lon_axis = var.getAxis(0)
print lon_axis.axis, islon(lon_axis)
# -> X True
# -*- coding: utf8 -*-
# Lecture d'une serie 1D de niveau de la mer du modele
import cdms2
from vacumm.config import data_sample
f = cdms2.open(data_sample('tide.sealevel.BREST.mars.nc'))
sea_level = f('sea_level', ('2006-10-01', '2006-10-02'))[1::4] # Toutes les heures
f.close()
# Recuperation des pleines mers, basses mers et zeros
from vacumm.tide.filters import extrema, zeros
bm, pm = extrema(sea_level, spline=True, reference='mean')
zz = zeros(sea_level, ref='mean')
# Plots
from vacumm.misc.plot import curve2 as curve
curve(sea_level, 'ko', markersize=3, figsize=(6, 4), show=False)
curve(zz, 'go', linewidth=0, show=False, xstrict=False)
curve(pm, 'ro', linewidth=0, show=False, xstrict=False)
curve(bm, 'bo', linewidth=0, xstrict=False, title="Niveau de la mer",
savefigs=__file__, savefigs_pdf=True, show=False)
# -*- coding: utf8 -*-
# Lecture des donnees
from matplotlib import rc;rc('font', size=11)
import cdms2, MV2
from vacumm.config import data_sample
zone = dict(yc=slice(0, 40), xc=slice(10, 40))
f = cdms2.open(data_sample('swan.four.nc'))
lon2d = f('longitude', **zone)
lat2d = f('latitude', **zone)
hs = MV2.masked_values(f('HS', squeeze=1, **zone), f['HS']._FillValue)
dlon = float(f.longitude_resolution)
dlat = float(f.latitude_resolution)
f.close()
# Affectation de la grille curvilineaire à la variable
from vacumm.misc.grid import curv_grid, set_grid
cgridi = curv_grid(lon2d, lat2d)
hs = set_grid(hs, cgridi)
# Rotation de la grid
from vacumm.misc.grid import rotate_grid
import numpy as N
cgrido = rotate_grid(hs.getGrid(), -30)
print 'Regrillage'
from vacumm.misc.grid.regridding import regrid2d
print ' - par natgrid'
hs_nat = regrid2d(hs, cgrido, 'nat')
print ' - par SCRIP/conservative'
hs_scripcons = regrid2d(hs, cgrido, 'conservative')
'time': ('TIME', ),
'depth': ('DEPH', ), # Force using the DEPH variable as depth
'latitude': ('LATITUDE', ),
'longitude': ('LONGITUDE', ),
'temperature': ('TEMP', ),
},
# The variable argument specify which variables will be used when using the load/save methods.
# Do not specify time, depth, latitude and longitude as they
# are special variables which are always required
variables=('temperature', ),
# We could also add a quality code filter:
# qualities=(1,2)
)
from vacumm.config import data_sample
p.load(data_sample('data.misc.profile.nc'))
##########
# 2) Save profiles into a file suitable for profiles.py, that can be loaded by ProfilesDataset
##########
of = 'data.misc.profile.converted.nc'
p.save(of)
# We could also have specified the variables to write here:
# p.save(of, variables=('temperature',))
##########
# 3) Load the previously converted file using ProfilesDataset, which is more restrictive,
# it is expecting input file to have profile and level axis, time, latitude, longitude variables.
##########
p = P.ProfilesDataset(of)
# -*- coding: utf8 -*-
# Lecture des donnees
from matplotlib import rc;rc('font', size=11)
import cdms2, MV2
from vacumm.config import data_sample
zone = dict(yc=slice(0, 40), xc=slice(10, 40))
f = cdms2.open(data_sample('swan.four.nc'))
lon2d = f('longitude', **zone)
lat2d = f('latitude', **zone)
hs = MV2.masked_values(f('HS', squeeze=1, **zone), f['HS']._FillValue)
dlon = float(f.longitude_resolution)
dlat = float(f.latitude_resolution)
f.close()
# Affectation de la grille curvilineaire à la variable
from vacumm.misc.grid import curv_grid, set_grid
cgridi = curv_grid(lon2d, lat2d)
hs = set_grid(hs, cgridi)
# Rotation de la grid
from vacumm.misc.grid import rotate_grid
import numpy as N
cgrido = rotate_grid(hs.getGrid(), -30)
print 'Regrillage'
from vacumm.misc.grid.regridding import regrid2d
print ' - par SCRIP/conservative'
hs_scripcons = regrid2d(hs, cgrido, 'conservative')
print ' - par SCRIP/bilineaire'
hs_scripbilin = regrid2d(hs, cgrido, 'bilinear')
# *-* coding: utf-8 *-*
# Lecture de la temperature
import cdms2, MV2, numpy as N
from vacumm.config import data_sample
f =cdms2.open(data_sample('mars3d.x.uv.nc'))
v = f('v', squeeze=1, lon=(-5.08, -4.88))
f.close()
v = MV2.masked_values(v, 0.)
v.long_name = 'Original'
# On ajoute un peu de bruit
v[:] += N.random.random(len(v))
# Creation des deux axes de longitudes
from vacumm.misc.axes import create_lon
lon = v.getLongitude().getValue()
dlon = N.diff(lon).mean()
# - basse résolution
lon_lr = create_lon((lon.min()+dlon*.7, lon.max()+dlon, dlon*3.3))
# - haute résolution
lon_hr = create_lon((lon.min()+dlon, lon.max()+dlon, dlon/3.3))
# - dict
lons = dict(haute=lon_hr, basse=lon_lr)
# Regrillages et plots
from matplotlib import rcParams ; rcParams['font.size'] = 9
from vacumm.misc.grid.regridding import regrid1d
from vacumm.misc.plot import curve2,savefigs, yscale ; import pylab as P
P.figure(figsize=(5.5, 6))
kwplot = dict(show=False,vmin=v.min(),vmax=v.max(),alpha=.7)
for ilh,resdst in enumerate(['basse', 'haute']):
# -*- coding: utf8 -*-
# Lecture du niveau de la mer sur 9 pas de temps à une latitude
import cdms2, MV2
from vacumm.config import data_sample
f =cdms2.open(data_sample('mars3d.xt.xe.nc'))
xe = f('xe', squeeze=1, time=slice(0, 9), lon=(-5, -4.83))
f.close()
xe.long_name = 'Original'
# On crée un trou
xe[3:4, 20:30] = MV2.masked
# Nouvel axe temporel plus précis
from vacumm.misc.axes import create_time
#old_time = xe.getTime()
old_time=create_time((xe.shape[0], ), 'hours since 2000')
xe.setAxis(0, old_time)
dt = (old_time[1]-old_time[0])/10.
new_time = create_time((old_time[0], old_time[-1]+dt, dt), old_time.units)
# Interpolation
from vacumm.misc.grid.regridding import interp1d
# - nearest
xe_nea = interp1d(xe, new_time, method='nearest')
xe_nea.long_name = 'Nearest'
# - linear
xe_lin = interp1d(xe, new_time, method='linear')
xe_lin.long_name = 'Linear'
# - cubic
xe_cub = interp1d(xe, new_time, method='cubic')
xe_cub.long_name = 'Cubic'
from vacumm.misc.plot import section2
# Initialisation pour un compteur de temps de calcul
print_time_format = "%a, %d %b %Y %H:%M:%S"
t0 = time()
time_format = "%Y%m%d"
date = strftime(time_format)
print "Begin : " + strftime(print_time_format)
# Profondeurs sur lesquelles nous souhaitons interpoler (en m)
depths = np.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
# Lecture de la température
f = cdms2.open(data_sample('mars3d.tsigyx.nc'))
data_in = f('TEMP') # T-YX (- = level)
# Détermination des profondeurs d'après les sigma
# - détection auto de la classe de sigma d'après fichier
sigma_class = NcSigma.factory(f)
# - initialisation du convertisseur
sigma_converter = sigma_class(copyaxes=True)
# - lecture de eta (sans sélection de domaine) et calcul des profondeurs
depths_in = sigma_converter().filled()
f.close()
# Creation de l'axe des profondeurs cibles
depth_out = create_depth(depths)
# Interpolation
# On recupere une variable cdms
import cdms2 as cdms
from vacumm.config import data_sample
f = cdms.open(data_sample('mars3d.t.nc'))
temp = f('temp')
u = f('u')
v = f('v')
f.close()
# On recupre le temps
time = temp.getTime() # axe
ctime = time.asComponentTime() # temps cdtime.comptime()
# Creation a la main au format : YYYY/MM/DDZHH:MM TEMP U V
from vacumm.misc.atime import strftime, ch_units, strptime
f = open('misc.io.ascii.1.dat', 'w')
f.write('# Ligne de commentaire\n')
for it in xrange(len(temp)):
t = strftime('%Y/%m/%dZ%H:%M', ctime[it])
f.write('%s %.4f %f %f\n' % (t, temp[it], u[it], v[it]))
f.close()
# Verification rapide (deux premieres lignes)
f = open('misc.io.ascii.1.dat')
print ''.join(f.readlines()[:3])
f.close()
# -> # Ligne de commentaire
# -> 2008/01/07Z00:00 12.0672 0.359631 0.156422
# -> 2008/01/07Z01:00 11.9421 0.493174 0.158244
# Ecriture rapide via numpy
# -*- coding: utf8 -*-
import pylab as P
# Création de bathymétries fictives à partir de Smith and Sandwell
import cdms2
from vacumm.config import data_sample
cdms2.axis.longitude_aliases.append('x')
cdms2.axis.latitude_aliases.append('y')
f = cdms2.open(data_sample('ETOPO2v2g_flt.grd'))
# - large
var_large = f('z', lon=(-7, -1), lat=(46, 49))
# - petite
var_small = f('z', lon=(-4.5, -.5), lat=(44.5, 46.5))
f.close()
# - regrillage de la large vers une grille moins fine
from vacumm.misc.grid import regridding, resol, create_grid
xr, yr = resol(var_large.getGrid())
grid_large = create_grid((-7., -1, xr * 4.5), (46., 49, yr * 4.5))
var_large = regridding.regrid2d(var_large, grid_large)
# On ajoute un traît de côte pour le masquage
from vacumm.bathy.bathy import GriddedBathy, GriddedBathyMerger
bathy_large = GriddedBathy(var_large, shoreline='i')
bathy_small = var_small
# Création de la grille finale de résolution intermédiaire
final_grid = create_grid((-6., .5, xr * 2.5), (45, 47., yr * 2.5))
# On crée maintenant le merger
merger = GriddedBathyMerger(final_grid)
# - ajout de la bathy basse resolution en premier (en dessous)
# Lecture d'un fichier xyz et plot
from vacumm.misc.io import XYZ
from vacumm.config import data_sample
xyz = XYZ(data_sample('celtic_sea.xyz'))
# Verifs
print len(xyz)
# -> 23538
print xyz[0:3]
# -> [(-5.93,52.97,25.0),(-5.90,52.97,41.0),(-5.85,52.97,33.0)]
# Exclusion
xyz.exclude([[-6., 52.], [-5.5, 52], [-5.5, 52.5], [-6., 52.5]])
# Modification
print xyz.z.max()
# -> 141.0
xyz *= -1
print xyz.z.max()
# -> -141.0
# Plot
import pylab as P
P.figure(figsize=(5.5, 8))
P.subplots_adjust(left=.12, top=.96, right=1, bottom=.06)
P.subplot(211)
xyz.plot(title='Mer celtique', units='m', m=True,
show=False, size=2, cmap='cmap_bathy')
# Zoom
xyz_zoom = xyz.clip((-6, 51.5, -4, 52.5), long_name='Zoom')
"""Basic stick plot"""
from vacumm.config import data_sample
from vacumm.misc.plot import stick2
# Read speed
import cdms2
f = cdms2.open(data_sample('mars3d.t.nc'))
u = f('u')
v = f('v')
f.close()
# Plot
stick2(u, v, title='Current in the Iroise Sea', units='m/s',
bottom=.2, top=.85, quiver_headwidth=2,
quiverkey_value=.5, quiver_width=0.002, quiver_scale=5.,
savefigs=__file__, figsize=(5.5, 3), show=False)
# Parameters
ncfile = "menor.nc"
lon=(3.,4.5)
lat=(42, 42.8)
# Imports
import cdms2
from vacumm.config import data_sample
from vacumm.diag.thermdyn import mixed_layer_depth
from vacumm.misc.plot import map2
from vacumm.data.misc.sigma import NcSigma
from vacumm.misc.grid import curv2rect
# Read temperature
print 'Read'
ncfile = data_sample(ncfile)
f = cdms2.open(ncfile)
temp = curv2rect(f('TEMP'))(lon=lon, lat=lat, squeeze=1)
# Compute depth
print 'depth'
s = NcSigma.factory(f)
depth = curv2rect(s())(lon=lon, lat=lat, squeeze=1)
f.close()
# Compute MLD
print 'mld'
print temp.shape, depth.shape
mld = mixed_layer_depth(temp, depth, mode='deltatemp', deltatemp=0.1)
# Plot
# -*- coding: utf8 -*-
# Lecture d'une serie 1D de niveau de la mer du modele
import cdms2
from vacumm.config import data_sample
f = cdms2.open(data_sample('tide.sealevel.BREST.mars.nc'))
sea_level = f('sea_level', ('2006-10-01', '2006-10-02'))[1::4] # Toutes les heures
f.close()
# Recuperation des pleines mers, basses mers et zeros
from vacumm.tide.filters import extrema, zeros
bm, pm = extrema(sea_level, spline=True, reference='mean')
zz = zeros(sea_level, ref='mean')
# Plots
from vacumm.misc.plot import curve2 as curve
curve(sea_level, 'ko', markersize=3, figsize=(6, 4), show=False)
curve(zz, 'go', linewidth=0, show=False, xstrict=False)
curve(pm, 'ro', linewidth=0, show=False, xstrict=False)
curve(bm, 'bo', linewidth=0, xstrict=False, title="Niveau de la mer",
savefigs=__file__, savefigs_pdf=True, show=False, close=True)
# -*- coding: utf8 -*-
# Récupération d'une bathy sous la forme d'une variable cdat
import cdms2
cdms2.axis.latitude_aliases.append('y')
cdms2.axis.longitude_aliases.append('x')
from vacumm.config import data_sample
f = cdms2.open(data_sample('ETOPO2v2g_flt.grd'))
var = f('z', lon=(-6.1, -3), lat=(47.8, 48.8))
f.close()
# Création de l'objet de bathy grillee
from vacumm.bathy.bathy import GriddedBathy
bathy = GriddedBathy(var)
# On définit le traît de côte pour le masquage
bathy.set_shoreline('f')
# On récupère une version masquée
bathy_orig = bathy.bathy(mask=False)
bathy_masked = bathy.bathy(mask=True)
# Definition d'une grille zoom
from vacumm.misc.grid import create_grid
new_grid = create_grid((-5.5, -4.6, 0.009), (48.25, 48.6, .006))
# On interpole la bathy masquée sur une autre grille
interp_bathy_masked = bathy.regrid(new_grid)
# On interpole et masque la bathy originale sur une autre grille
# - interpolation
# Lecture des donnnees
import cdms2, MV2, numpy as N
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
# Ouverture
import cdms2
from vacumm.config import data_sample
f = cdms2.open(data_sample('mars2d.xyt.nc'))
# Lister les variables
print f.variables.keys()
# -> ['bounds_lon', 'h0', 'v', 'xe', 'u', 'bounds_lat']
# Avoir des informations sur une variables sans la lire, via []
nt = f['xe'].shape[0]
print f['xe'].getTime().asComponentTime()[0:nt:nt-1]
# -> [2008-8-15 0:0:0.0, 2008-8-15 23:0:0.0]
# Lire une selection de la variable
import cdtime
xe = f('xe', ('2008-8-15',cdtime.comptime(2008,8,15,12), 'cc'),
lon=slice(5,6), lat=(48.1, 48.5), squeeze=1)
print xe.shape
# -> (13, 29)
# squeeze a supprime l'axes des longitudes de dim 1
# Fermer le fichier lu
f.close()
# Definir la compression netcdf4
cdms2.setNetcdfShuffleFlag(1)
cdms2.setNetcdfDeflateFlag(1)
cdms2.setNetcdfDeflateLevelFlag(3)
# Creer un nouveau fichier
# Parameters
ncfile = "menor.nc"
lon=(3.,4.5)
lat=(42, 42.8)
# Imports
import cdms2
from vacumm.config import data_sample
from vacumm.diag.thermdyn import mixed_layer_depth
from vacumm.misc.plot import map2
from vacumm.data.misc.sigma import NcSigma
from vacumm.misc.grid import curv2rect
# Read temperature
print 'Read'
ncfile = data_sample(ncfile)
f = cdms2.open(ncfile)
temp = curv2rect(f('TEMP'))(lon=lon, lat=lat, squeeze=1)
# Compute depth
print 'depth'
s = NcSigma.factory(f)
depth = curv2rect(s())(lon=lon, lat=lat, squeeze=1)
f.close()
# Compute MLD
print 'mld'
print temp.shape, depth.shape
mld = mixed_layer_depth(temp, depth, mode='deltatemp', deltatemp=0.1)
# Plot
'time':('TIME',),
'depth':('DEPH',), # Force using the DEPH variable as depth
'latitude':('LATITUDE',),
'longitude':('LONGITUDE',),
'temperature':('TEMP',),
},
# The variable argument specify which variables will be used when using the load/save methods.
# Do not specify time, depth, latitude and longitude as they
# are special variables which are always required
variables=('temperature',),
# We could also add a quality code filter:
# qualities=(1,2)
)
from vacumm.config import data_sample
p.load(data_sample('data.misc.profile.nc'))
##########
# 2) Save profiles into a file suitable for profiles.py, that can be loaded by ProfilesDataset
##########
of = 'data.misc.profile.converted.nc'
p.save(of)
# We could also have specified the variables to write here:
# p.save(of, variables=('temperature',))
##########
# 3) Load the previously converted file using ProfilesDataset, which is more restrictive,
# it is expecting input file to have profile and level axis, time, latitude, longitude variables.
##########
p = P.ProfilesDataset(of)
