def add_att(self, attname, attvalue, atttype=False):
'''
Creates file or variable attribute
atttype can be a netcdf type name, numpy type name, dtype or
numeric typecode
'''
if self._interface == 'pycdf':
if atttype: Type = nctypes.type_2pycdf(atttype)
else: Type = nctypes.type_var2pycdf(attvalue)
a = self._nc.attr(attname)
a.put(Type, attvalue)
elif self._interface in ('scientific', 'netcdf4'):
setattr(self._nc, attname, attvalue)
newatt = Pyncatt(self._nc,
attname,
attvalue,
atttype=atttype,
ncversion=self.ncversion,
interface=self._interface)
# update self.atts and self.attnames:
self.atts[attname] = newatt
# update ncdump_info:
if not isinstance(self, Pyncvar):
if not self._ncdump_info is False:
self._ncdump_info['attributes'][attname] = {}
return newatt
def add_att(self,attname,attvalue,atttype=False):
'''
Creates file or variable attribute
atttype can be a netcdf type name, numpy type name, dtype or
numeric typecode
'''
if self._interface=='pycdf':
if atttype: Type=nctypes.type_2pycdf(atttype)
else: Type=nctypes.type_var2pycdf(attvalue)
a=self._nc.attr(attname)
a.put(Type,attvalue)
elif self._interface in ('scientific','netcdf4'):
setattr(self._nc,attname,attvalue)
newatt=Pyncatt(self._nc,attname,attvalue,
atttype=atttype,ncversion=self.ncversion,
interface=self._interface)
# update self.atts and self.attnames:
self.atts[attname]=newatt
# update ncdump_info:
if not isinstance(self,Pyncvar):
if not self._ncdump_info is False:
self._ncdump_info['attributes'][attname]={}
return newatt
def update(self, value, atttype=False):
self.value = value
self.atttype = atttype
self.set_types()
if self._interface == 'pycdf':
if self.atttype: Type = nctypes.type_2pycdf(self.atttype)
else: Type = nctypes.type_var2pycdf(self.value)
a = self._nc.attr(self.name)
a.put(Type, self.value)
elif self._interface in ('scientific', 'netcdf4'):
setattr(self._nc, self.name, value)
def update(self,value,atttype=False):
self.value=value
self.atttype=atttype
self.set_types()
if self._interface=='pycdf':
if self.atttype: Type=nctypes.type_2pycdf(self.atttype)
else: Type=nctypes.type_var2pycdf(self.value)
a=self._nc.attr(self.name)
a.put(Type,self.value)
elif self._interface in ('scientific','netcdf4'):
setattr(self._nc,self.name,value)
def add_var(self, varname, vartype, dimnames, **kargs):
'''
Creates netcdf variable
Ex: add_var('temp','float32',('lon','lat','z'))
About compression:
Compression kargs options (available for netcdf4 interface)
zlib, default True, turn on compression
lsd (least_significant_digit), default is None: lossy
compression with lsd precision
complevel, 1..9, copression level, default 4
About vartype:
i) netcdf type names:
for netcdf3:
byte,char,short,int,float,double
for netcdf4:
+ int64, ushort, uint,uint64, string
ii) Numeric type code: fd1silbwuc
iii) Numpy dtype or type name
kargs are required when vartype is a netcdf type name
and the interface is scientific, see type_2numeric
and when interface is netcdf4, see type_2dtype
No need for kargs when:
1. interface = pycdf
2. interface is scientific and vartype is numpy type name or
dtype, or vartipe is numeric typecode
3. interface is netcdf4 and vartype is numpy type name or dtype
'''
if self._interface == 'pycdf':
vartype = nctypes.type_2pycdf(vartype)
self._nc.def_var(varname, vartype, dimnames)
elif self._interface == 'scientific':
vartype = type_2numeric(vartype, **kargs)
self._nc.createVariable(varname, vartype, dimnames)
elif self._interface == 'netcdf4':
vartype = nctypes.type_2numpy(vartype, **kargs)
# NOTA: ocorre erro ao criar ex int64 em ncver 3. Pode fazer sentido
# converter para int? ie, qd o tipo nao e suportado pela ncver!?
zlib = kargs.get('zlib', True)
lsd = kargs.get('lsd', None)
complevel = kargs.get('complevel', 4)
fill_value = kargs.get('fill_value', None)
self._nc.createVariable(varname,
vartype,
dimnames,
zlib=zlib,
complevel=complevel,
least_significant_digit=lsd,
fill_value=fill_value)
newvar = Pyncvar(self, varname)
# update self.vars and self.varnames:
self.vars[varname] = newvar
# update ncdump_info:
if not self._ncdump_info is False:
self._ncdump_info['variables'][varname] = cbt.odict()
return newvar
def add_var(self,varname,vartype,dimnames,**kargs):
'''
Creates netcdf variable
Ex: add_var('temp','float32',('lon','lat','z'))
About compression:
Compression kargs options (available for netcdf4 interface)
gzip, default True: gzip compression
lsd (least_significant_digit), default is None: lossy
compression with lsd precision
About vartype:
i) netcdf type names:
for netcdf3:
byte,char,short,int,float,double
for netcdf4:
+ int64, ushort, uint,uint64, string
ii) Numeric type code: fd1silbwuc
iii) Numpy dtype or type name
kargs are required when vartype is a netcdf type name
and the interface is scientific, see type_2numeric
and when interface is netcdf4, see type_2dtype
No need for kargs when:
1. interface = pycdf
2. interface is scientific and vartype is numpy type name or
dtype, or vartipe is numeric typecode
3. interface is netcdf4 and vartype is numpy type name or dtype
'''
if self._interface=='pycdf':
vartype=nctypes.type_2pycdf(vartype)
self._nc.def_var(varname,vartype,dimnames)
elif self._interface=='scientific':
vartype=type_2numeric(vartype,**kargs)
self._nc.createVariable(varname,vartype,dimnames)
elif self._interface=='netcdf4':
vartype=nctypes.type_2numpy(vartype,**kargs)
# NOTA: ocorre erro ao criar ex int64 em ncver 3. Pode fazer sentido
# converter para int? ie, qd o tipo nao e suportado pela ncver!?
zlib=True
lsd=None
fill_value=None
if 'gzip' in kargs: zlib = kargs['gzip']
if 'lsd' in kargs: lsd = kargs['lsd']
if 'fill_value' in kargs: fill_value = kargs['fill_value']
self._nc.createVariable(varname,vartype,dimnames,zlib=zlib,
least_significant_digit=lsd,fill_value=fill_value)
newvar=Pyncvar(self,varname)
# update self.vars and self.varnames:
self.vars[varname]=newvar
# update ncdump_info:
if not self._ncdump_info is False:
self._ncdump_info['variables'][varname]=cbt.odict()
return newvar