本文整理汇总了Python中netCDF4.Dataset.Author方法的典型用法代码示例。如果您正苦于以下问题:Python Dataset.Author方法的具体用法?Python Dataset.Author怎么用?Python Dataset.Author使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类netCDF4.Dataset
的用法示例。
在下文中一共展示了Dataset.Author方法的11个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: writeNC
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import Author [as 别名]
def writeNC(self,outfile):
"""
Export the grid variables to a netcdf file
"""
from netCDF4 import Dataset
from soda.dataio.suntans.suntans_ugrid import ugrid
nc = Dataset(outfile, 'w', format='NETCDF4_CLASSIC')
nc.Description = 'Unstructured grid file'
nc.Author = ''
#nc.Created = datetime.now().isoformat()
nc.createDimension('Nc', self.Nc)
nc.createDimension('Np', self.Np)
try:
nc.createDimension('Ne', self.Ne)
except:
print 'No dimension: Ne'
nc.createDimension('Nk', self.Nkmax)
nc.createDimension('Nkw', self.Nkmax+1)
nc.createDimension('numsides', self.MAXFACES)
nc.createDimension('two', 2)
nc.createDimension('time', 0) # Unlimited
# Write the grid variables
def write_nc_var(var, name, dimensions, attdict, dtype='f8'):
tmp=nc.createVariable(name, dtype, dimensions)
for aa in attdict.keys():
tmp.setncattr(aa,attdict[aa])
nc.variables[name][:] = var
gridvars = ['suntans_mesh','cells','face','nfaces',\
'edges','neigh','grad','xp','yp','xv','yv','xe','ye',\
'normal','n1','n2','df','dg','def',\
'Ac','dv','dz','z_r','z_w','Nk','Nke','mark']
self.Nk += 1 # Set to one-base in the file (reset to zero-base after)
self.suntans_mesh=[0]
for vv in gridvars:
if self.__dict__.has_key(vv):
if self.VERBOSE:
print 'Writing variables: %s'%vv
write_nc_var(self[vv],vv,\
ugrid[vv]['dimensions'],\
ugrid[vv]['attributes'],\
dtype=ugrid[vv]['dtype'])
# Special treatment for "def"
if vv == 'def' and self.__dict__.has_key('DEF'):
if self.VERBOSE:
print 'Writing variables: %s'%vv
write_nc_var(self['DEF'],vv,ugrid[vv]['dimensions'],\
ugrid[vv]['attributes'],\
dtype=ugrid[vv]['dtype'])
nc.close()
self.Nk -= 1 # set back to zero base
示例2: create_nc
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import Author [as 别名]
def create_nc(self,ncfile):
"""
Create the particle netcdf file
NetCDF variable and dimension names are consistent with partrac data.
"""
if self.verbose:
print '\nInitialising particle netcdf file: %s...\n'%ncfile
# Global Attributes
nc = Dataset(ncfile, 'w', format='NETCDF4_CLASSIC')
nc.Description = 'Particle trajectory file'
nc.Author = os.getenv('USER')
nc.Created = datetime.now().isoformat()
# Dimensions
nc.createDimension('ntrac', self.N)
nc.createDimension('nt', 0) # Unlimited
# Create variables
def create_nc_var( name, dimensions, attdict, dtype='f8'):
tmp=nc.createVariable(name, dtype, dimensions)
for aa in attdict.keys():
tmp.setncattr(aa,attdict[aa])
basetimestr = 'seconds since %s'%(datetime.strftime(self.basetime,\
'%Y-%m-%d %H:%M:%S'))
create_nc_var('tp',('nt'),{'units':basetimestr\
,'long_name':"time at drifter locations"},dtype='f8')
create_nc_var('xp',('ntrac','nt'),{'units':'m',\
'long_name':"Easting coordinate of drifter",'time':'tp'},dtype='f8')
create_nc_var('yp',('ntrac','nt'),{'units':'m',\
'long_name':"Northing coordinate of drifter",'time':'tp'},dtype='f8')
create_nc_var('zp',('ntrac','nt'),{'units':'m',\
'long_name':"vertical position of drifter (negative is downward from surface)",'time':'tp'},dtype='f8')
if self.has_age:
create_nc_var('age',('ntrac','nt'),{'units':'seconds',\
'long_name':"Particle age",'time':'tp'},dtype='f8')
create_nc_var('agemax',('ntrac','nt'),{'units':'seconds',\
'long_name':"Maximum particle age",'time':'tp'},dtype='f8')
# Keep the pointer to the open file as an attribute
self._nc = nc
示例3: initParticleNC
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import Author [as 别名]
def initParticleNC(self,outfile,Np,age=False):
"""
Export the grid variables to a netcdf file
"""
import os
if self.verbose:
print '\nInitialising particle netcdf file: %s...\n'%outfile
# Global Attributes
nc = Dataset(outfile, 'w', format='NETCDF4_CLASSIC')
nc.Description = 'Particle trajectory file'
nc.Author = os.getenv('USER')
nc.Created = datetime.now().isoformat()
#tseas = self.time_sec[1] - self.time_sec[0]
#nsteps = np.floor(tseas/self.dt)
#nc.nsteps = '%f (number of linear interpolation steps in time between model outputs)'%nsteps
#nc.tseas = '%d (Time step (seconds) between model outputs'%tseas
#nc.dt = '%f (Particle model time steps [seconds])'%self.dt
nc.dataset_location = '%s'%self.ncfile
# Dimensions
nc.createDimension('ntrac', Np)
nc.createDimension('nt', 0) # Unlimited
# Create variables
def create_nc_var( name, dimensions, attdict, dtype='f8'):
tmp=nc.createVariable(name, dtype, dimensions)
for aa in attdict.keys():
tmp.setncattr(aa,attdict[aa])
create_nc_var('tp',('nt'),{'units':'seconds since 1990-01-01 00:00:00','long_name':"time at drifter locations"},dtype='f8')
create_nc_var('xp',('ntrac','nt'),{'units':'m','long_name':"Easting coordinate of drifter",'time':'tp'},dtype='f8')
create_nc_var('yp',('ntrac','nt'),{'units':'m','long_name':"Northing coordinate of drifter",'time':'tp'},dtype='f8')
create_nc_var('zp',('ntrac','nt'),{'units':'m','long_name':"vertical position of drifter (negative is downward from surface)",'time':'tp'},dtype='f8')
if age:
create_nc_var('age',('ntrac','nt'),{'units':'seconds','long_name':"Particle age",'time':'tp'},dtype='f8')
create_nc_var('agemax',('ntrac','nt'),{'units':'seconds','long_name':"Maximum particle age",'time':'tp'},dtype='f8')
nc.close()
示例4: BaraKuda
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import Author [as 别名]
id_lon = f_out.createVariable('nav_lon','f4',('y','x',))
id_lat = f_out.createVariable('nav_lat','f4',('y','x',))
id_atl = f_out.createVariable('tmaskatl' ,'f4',('y','x',)) ; id_atl.long_name = 'Atlantic Basin'
id_pac = f_out.createVariable('tmaskpac' ,'f4',('y','x',)) ; id_pac.long_name = 'Pacific Basin'
id_ind = f_out.createVariable('tmaskind' ,'f4',('y','x',)) ; id_ind.long_name = 'Indian Basin'
id_soc = f_out.createVariable('tmasksoc' ,'f4',('y','x',)) ; id_soc.long_name = 'Southern Basin'
id_inp = f_out.createVariable('tmaskinp' ,'f4',('y','x',)) ; id_inp.long_name = 'Indo-Pacific Basin'
# Filling variables:
id_lat[:,:] = xlat[:,:]
id_lon[:,:] = xlon[:,:]
id_atl[:,:] = mask_atl[:,:]
id_pac[:,:] = mask_pac[:,:]
id_ind[:,:] = mask_ind[:,:]
id_soc[:,:] = mask_soc[:,:]
id_inp[:,:] = mask_inp[:,:]
f_out.About = 'ORCA1 main oceanic basin land-sea mask created from '+cf_mm
f_out.Author = ' Generated with "orca025_create_basin_mask_from_meshmask.py" of BaraKuda (https://github.com/brodeau/barakuda)'
f_out.close()
print cf_out+' sucessfully created!'
示例5: float
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import Author [as 别名]
id_t[jrec2write] = float(jy)
id_zw[:] = zgdepw[:]
id_zt[:] = zgdept[:]
id_v01[jrec2write,:] = rmean_sss0_deep_jfm[:]
id_v02[jrec2write,:] = rmean_sss0_deep_m03[:]
id_v03[jrec2write,:] = nbp_deeper_zcrit[:]
id_v04[jrec2write,:] = vprof_sig0_ann[:]
id_v05[jrec2write,:] = vprof_sig0_jfm[:]
id_v06[jrec2write,:] = vprof_sig0_m03[:]
f_out.box_coordinates = cbox+' => '+str(i1)+','+str(j1)+' -> '+str(i2-1)+','+str(j2-1)
f_out.box_file = FILE_DEF_BOXES
f_out.Author = 'L. Brodeau ('+cname_script+' of Barakuda)'
else:
vt = f_out.variables['time']
jrec2write = len(vt)
v01 = f_out.variables['SSsig0_jfm']
v02 = f_out.variables['SSsig0_m03']
v03 = f_out.variables['Nbp_w_deep']
v04 = f_out.variables['sig0_ann']
v05 = f_out.variables['sig0_jfm']
v06 = f_out.variables['sig0_m03']
vt [jrec2write] = float(jy)
v01[jrec2write,:] = rmean_sss0_deep_jfm[:]
v02[jrec2write,:] = rmean_sss0_deep_m03[:]
v03[jrec2write,:] = nbp_deeper_zcrit[:]
示例6: Brodeau
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import Author [as 别名]
id_atl = f_out.createVariable('tmaskatl' ,'f4',('y','x',)) ; id_atl.long_name = 'Atlantic Basin'
id_pac = f_out.createVariable('tmaskpac' ,'f4',('y','x',)) ; id_pac.long_name = 'Pacific Basin'
id_ind = f_out.createVariable('tmaskind' ,'f4',('y','x',)) ; id_ind.long_name = 'Indian Basin'
id_soc = f_out.createVariable('tmasksoc' ,'f4',('y','x',)) ; id_soc.long_name = 'Southern Basin'
id_inp = f_out.createVariable('tmaskinp' ,'f4',('y','x',)) ; id_inp.long_name = 'Indo-Pacific Basin'
# Filling variables:
id_lat[:,:] = xlat[:,:]
id_lon[:,:] = xlon[:,:]
id_atl[:,:] = mask_atl[:,:]
id_pac[:,:] = mask_pac[:,:]
id_ind[:,:] = mask_ind[:,:]
id_soc[:,:] = mask_soc[:,:]
id_inp[:,:] = mask_inp[:,:]
f_out.About = 'ORCA1 main oceans basin land-sea mask created from '+cf_mm
f_out.Author = 'L. Brodeau (lb_nemo_create_basin_mask.py of PYLB)'
f_out.close()
print cf_out+' sucessfully created!'
示例7: range
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import Author [as 别名]
for jt in range(nt):
id_t[jt] = vtime[jt]
id_v01[jt] = rmean_sst[jt,jb]
id_v02[jt] = rmean_sss[jt,jb]
Xbox[:,:] = XSST[jt,j1:j2,i1:i2]
Xbox[idx_msk] = -9999.
id_x01[jt,:,:] = Xbox[:,:]
Xbox[:,:] = XSSS[jt,j1:j2,i1:i2]
Xbox[idx_msk] = -9999.
id_x02[jt,:,:] = Xbox[:,:]
f_out.Author = 'Generated with "ssx_boxes.py" of BaraKuda (https://github.com/brodeau/barakuda)'
else:
vt = f_out.variables['time']
jrec2write = len(vt)
v01 = f_out.variables[cv_sst+'_sa']
x01 = f_out.variables[cv_sst]
v02 = f_out.variables[cv_sss+'_sa']
x02 = f_out.variables[cv_sss]
for jt in range(nt):
vt [jrec2write+jt] = vtime[jt]
v01[jrec2write+jt] = rmean_sst[jt,jb]
v02[jrec2write+jt] = rmean_sss[jt,jb]
Xbox[:,:] = XSST[jt,j1:j2,i1:i2] ; Xbox[idx_msk] = -9999.
x01[jrec2write+jt,:,:] = Xbox[:,:]
示例8: wrt_1d_series
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import Author [as 别名]
def wrt_1d_series(vt, vd, cvar, cinfo,
cu_t='unknown', cu_d='unknown', cln_d='unknown', nsmooth=0,
vd2=[], vd3=[], vd4=[], vd5=[],
cvar2='', cvar3='', cvar4='', cvar5='',
cln_d2='', cln_d3='', cln_d4='', cln_d5='',):
cf_o = cvar+'_'+cinfo+'.nc'
lsmooth = False
if nsmooth > 0:
import barakuda_stat as bs
lsmooth = True
if nsmooth == 11:
vd_sm = bs.running_mean_11(vd, l_fill_bounds=False)
elif nsmooth == 5:
vd_sm = bs.running_mean_5(vd, l_fill_bounds=False)
else:
print 'ERROR: wrt_1d_series.barakuda_ncio => smoothing with nsmooth='+str(nsmooth)+' not supported!'; sys.exit(0)
f_o = Dataset(cf_o, 'w', format='NETCDF3_CLASSIC')
nt = len(vt)
if len(vd) != nt: print 'ERROR: wrt_1d_series.barakuda_ncio => data & time have different lengths!'; sys.exit(0)
l_do_v2=False ; l_do_v3=False ; l_do_v4=False ; l_do_v5=False
if len(vd2) == nt: l_do_v2=True
if len(vd3) == nt: l_do_v3=True
if len(vd4) == nt: l_do_v4=True
if len(vd5) == nt: l_do_v5=True
f_o.createDimension('time', None)
id_t = f_o.createVariable('time','f4',('time',)) ; id_t.units = cu_t
id_d = f_o.createVariable(cvar,'f4',('time',))
id_d.units = cu_d ; id_d.long_name = cln_d
if l_do_v2: id_d2 = f_o.createVariable(cvar2,'f4',('time',)); id_d2.units = cu_d; id_d2.long_name = cln_d2
if l_do_v3: id_d3 = f_o.createVariable(cvar3,'f4',('time',)); id_d3.units = cu_d; id_d3.long_name = cln_d3
if l_do_v4: id_d4 = f_o.createVariable(cvar4,'f4',('time',)); id_d4.units = cu_d; id_d4.long_name = cln_d4
if l_do_v5: id_d5 = f_o.createVariable(cvar5,'f4',('time',)); id_d5.units = cu_d; id_d5.long_name = cln_d5
if lsmooth:
id_sm = f_o.createVariable(cvar+'_'+str(nsmooth)+'yrm','f4',('time',))
id_sm.units = cu_d ; id_sm.long_name = str(nsmooth)+'-year running mean of '+cln_d
for jt in range(nt):
id_t[jt] = vt[jt]
id_d[jt] = vd[jt]
if lsmooth: id_sm[jt] = vd_sm[jt]
if l_do_v2: id_d2[jt] = vd2[jt]
if l_do_v3: id_d3[jt] = vd3[jt]
if l_do_v4: id_d4[jt] = vd4[jt]
if l_do_v5: id_d5[jt] = vd5[jt]
f_o.Author = 'L. Brodeau (barakuda_ncio.py of Barakuda)'
f_o.close()
print ' * wrt_1d_series => '+cf_o+' written!\n'
return 0
示例9: float
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import Author [as 别名]
jrec2write = 0
# Creating Dimensions:
f_out.createDimension('time', None)
f_out.createDimension('deptht', nk)
# Creating variables:
id_t = f_out.createVariable('time','f4',('time',)) ; id_t.units = 'year'
id_z = f_out.createVariable('deptht','f4',('deptht',)) ; id_z.units = 'm'
id_v01 = f_out.createVariable(cvar ,'f4',('time','deptht',))
id_v01.long_name = 'Horizontally-averaged '+cvar+': '+cocean
# Writing depth vector
id_z[:] = vdepth[:]
id_t[jrec2write] = float(jyear)+0.5
id_v01[jrec2write,:] = Vf[:]
f_out.Author = 'L. Brodeau ('+cnexec+' of Barakuda)'
else:
vt = f_out.variables['time']
jrec2write = len(vt)
v01 = f_out.variables[cvar]
vt[jrec2write] = float(jyear)+0.5
v01[jrec2write,:] = Vf[:]
f_out.close()
print cf_out+' written!'
示例10: writeNC
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import Author [as 别名]
def writeNC(self, outfile):
"""
This function is used to create the netcdf file
"""
print 'under developing'
####create netcdf File####
nc = Dataset(outfile, 'w', format='NETCDF4_CLASSIC')
nc.Description = 'SUNTANS History file'
nc.Author = ''
nc.Created = datetime.now().isoformat()
####Create dimensions####
nc.createDimension('NVwind', self.Nstation)
nc.createDimension('NTair', self.Nstation)
nc.createDimension('Nrain', self.Nstation)
nc.createDimension('NUwind', self.Nstation)
nc.createDimension('NPair', self.Nstation)
nc.createDimension('NRH', self.Nstation)
nc.createDimension('Ncloud', self.Nstation)
nc.createDimension('nt', self.Nt)
nc.close()
####adding variables####
self.create_nc_var(outfile,'x_Vwind',('NVwind'),{'long_name':'Longitude at Vwind','units':'degrees_north'})
self.create_nc_var(outfile,'y_Vwind',('NVwind'),{'long_name':'Latitude at Vwind','units':'degrees_east'})
self.create_nc_var(outfile,'z_Vwind',('NVwind'),{'long_name':'Elevation at Vwind','units':'m'})
self.create_nc_var(outfile,'x_Tair',('NTair'),{'long_name':'Longitude at Tair','units':'degrees_north'})
self.create_nc_var(outfile,'y_Tair',('NTair'),{'long_name':'Latitude at Tair','units':'degrees_east'})
self.create_nc_var(outfile,'z_Tair',('NTair'),{'long_name':'Elevation at Tair','units':'m'})
self.create_nc_var(outfile,'x_rain',('Nrain'),{'long_name':'Longitude at rain','units':'degrees_north'})
self.create_nc_var(outfile,'y_rain',('Nrain'),{'long_name':'Latitude at rain','units':'degrees_east'})
self.create_nc_var(outfile,'z_rain',('Nrain'),{'long_name':'Elevation at rain','units':'m'})
self.create_nc_var(outfile,'x_Uwind',('NUwind'),{'long_name':'Longitude at Uwind','units':'degrees_north'})
self.create_nc_var(outfile,'y_Uwind',('NUwind'),{'long_name':'Latitude at Uwind','units':'degrees_east'})
self.create_nc_var(outfile,'z_Uwind',('NUwind'),{'long_name':'Elevation at Uwind','units':'m'})
self.create_nc_var(outfile,'x_Pair',('NPair'),{'long_name':'Longitude at Pair','units':'degrees_north'})
self.create_nc_var(outfile,'y_Pair',('NPair'),{'long_name':'Latitude at Pair','units':'degrees_east'})
self.create_nc_var(outfile,'z_Pair',('NPair'),{'long_name':'Elevation at Pair','units':'m'})
self.create_nc_var(outfile,'x_RH',('NRH'),{'long_name':'Longitude at RH','units':'degrees_north'})
self.create_nc_var(outfile,'y_RH',('NRH'),{'long_name':'Latitude at RH','units':'degrees_east'})
self.create_nc_var(outfile,'z_RH',('NRH'),{'long_name':'Elevation at RH','units':'m'})
self.create_nc_var(outfile,'x_cloud',('Ncloud'),{'long_name':'Longitude at cloud','units':'degrees_north'})
self.create_nc_var(outfile,'y_cloud',('Ncloud'),{'long_name':'Latitude at cloud','units':'degrees_east'})
self.create_nc_var(outfile,'z_cloud',('Ncloud'),{'long_name':'Elevation at cloud','units':'m'})
self.create_nc_var(outfile,'Time',('nt'),{'units':'seconds since 1990-01-01 00:00:00','long_name':'time'})
self.create_nc_var(outfile,'Vwind',('nt','NVwind'),{'units':'m s-1','long_name':'Northward wind velocity component','coordinates':'x_Vwind,y_Vwind'})
self.create_nc_var(outfile,'Tair',('nt','NTair'),{'units':'Celsius','long_name':'Air Temperature','coordinates':'x_Tair,y_Tair'})
self.create_nc_var(outfile,'rain',('nt','Nrain'),{'units':'kg m2 s-1','long_name':'rain fall rate','coordinates':'x_rain,y_rain'})
self.create_nc_var(outfile,'Uwind',('nt','NUwind'),{'long_name':'Eastward wind velocity component','coordinates':'x_Uwind,y_Uwind','units':'m s-1'})
self.create_nc_var(outfile,'Pair',('nt','NPair'),{'units':'hPa','long_name':'Air Pressure','coordinates':'x_Pair,y_Pair'})
self.create_nc_var(outfile,'RH',('nt','NRH'),{'units':'percent','long_name':'Relative Humidity','coordinates':'x_RH,y_RH'})
self.create_nc_var(outfile,'cloud',('nt','Ncloud'),{'units':'dimensionless','long_name':'Cloud cover fraction','coordinates':'x_cloud,y_cloud'})
######Now writting the variables######
nc = Dataset(outfile,'a')
nc.variables['x_Vwind'][:]=self.lat
nc.variables['y_Vwind'][:]=self.lon
nc.variables['z_Vwind'][:]=self.z
nc.variables['x_Tair'][:]=self.lat
nc.variables['y_Tair'][:]=self.lon
nc.variables['z_Tair'][:]=self.z
nc.variables['x_rain'][:]=self.lat
nc.variables['y_rain'][:]=self.lon
nc.variables['z_rain'][:]=self.z
nc.variables['x_Uwind'][:]=self.lat
nc.variables['y_Uwind'][:]=self.lon
nc.variables['z_Uwind'][:]=self.z
nc.variables['x_Pair'][:]=self.lat
nc.variables['y_Pair'][:]=self.lon
nc.variables['z_Pair'][:]=self.z
nc.variables['x_RH'][:]=self.lat
nc.variables['y_RH'][:]=self.lon
nc.variables['z_RH'][:]=self.z
nc.variables['x_cloud'][:]=self.lat
nc.variables['y_cloud'][:]=self.lon
nc.variables['z_cloud'][:]=self.z
nc.variables['Time'][:]=self.time
nc.variables['Vwind'][:]=self.Vwind
nc.variables['Tair'][:]=self.Tair
nc.variables['rain'][:]=self.rain
nc.variables['Uwind'][:]=self.Uwind
nc.variables['Pair'][:]=self.Pair
nc.variables['RH'][:]=self.RH
nc.variables['cloud'][:]=self.cloud
#.........这里部分代码省略.........
示例11: range
# 需要导入模块: from netCDF4 import Dataset [as 别名]
# 或者: from netCDF4.Dataset import Author [as 别名]
id_v03 = f_out.createVariable("S", "f4", ("time", "deptht"))
id_v03.unit = "PSU"
id_v03.long_name = "salinity on box " + cbox
id_z[:] = Vdepth[:]
for jm in range(Nt):
id_t[jrec2write + jm] = Vtime[jm]
id_v01[jrec2write + jm, :] = Zm1[jm, :]
id_v02[jrec2write + jm, :] = Tm1[jm, :]
id_v03[jrec2write + jm, :] = Sm1[jm, :]
f_out.box_coordinates = cbox + " => " + str(i1) + "," + str(j1) + " -> " + str(i2 - 1) + "," + str(j2 - 1)
f_out.box_file = FILE_DEF_BOXES
f_out.Author = "L. Brodeau (" + cname_script + " of Barakuda)"
else:
vt = f_out.variables["time"]
jrec2write = len(vt)
v01 = f_out.variables["sigma0"]
v02 = f_out.variables["theta"]
v03 = f_out.variables["S"]
for jm in range(Nt):
vt[jrec2write + jm] = Vtime[jm]
v01[jrec2write + jm, :] = Zm1[jm, :]
v02[jrec2write + jm, :] = Tm1[jm, :]
v03[jrec2write + jm, :] = Sm1[jm, :]
f_out.close()