def sha(ens):
""" returns a dictionary linking the name of all
the file in the ensemble and the SHA1# of
those files
Parameters
----------
ens : Datanode
Return
------
dictionary of strings
"""
def hashfile(array):
sha1 = hashlib.sha1(array)
return sha1.hexdigest()
data = {}
for f in ens.objects('ncfile'):
data[f.name] = hashfile(cdo.readMaArray(f.name, varname=f.parent.name))
return data
def sha(ens):
""" returns a dictionary linking the name of all
the file in the ensemble and the SHA1# of
those files
Parameters
----------
ens : Datanode
Return
------
dictionary of strings
"""
def hashfile(array):
sha1 = hashlib.sha1(array)
return sha1.hexdigest()
data = {}
for f in ens.objects('ncfile'):
data[f.name] = hashfile(cdo.readMaArray(f.name, varname=f.parent.name))
return data
def loadvar(ifile, varname, cdostr=None, **kwargs):
"""
Load variables from a NetCDF file with optional pre-processing.
Load a CMIP5 netcdf variable "varname" from "ifile" and an optional
cdo string for preprocessing the data from the netCDF files.
Requires netCDF4, CDO and CDO python bindings.
Returns a masked array, var.
"""
# Open the variable using NetCDF4 to get scale and offset attributes.
nc = Dataset(ifile, 'r')
ncvar = nc.variables[varname]
# apply cdo string if it exists
if (cdostr):
opslist = cdostr.split()
base_op = opslist[0].replace('-', '')
if len(opslist) > 1:
ops_str = ' '.join(opslist[1::]) + ' ' + ifile
var = getattr(cdo, base_op)(input=ops_str, returnMaArray=varname)
else:
var = getattr(cdo, base_op)(input=ifile, returnMaArray=varname)
else:
var = cdo.readMaArray(ifile, varname=varname)
# Apply any scaling and offsetting needed:
try:
var_offset = ncvar.add_offset
except:
var_offset = 0
try:
var_scale = ncvar.scale_factor
except:
var_scale = 1
# var = var*var_scale + var_offset
# return var
return np.squeeze(var)
def loadvar(ifile, varname, cdostr=None, **kwargs):
"""
Load variables from a NetCDF file with optional pre-processing.
Load a CMIP5 netcdf variable "varname" from "ifile" and an optional
cdo string for preprocessing the data from the netCDF files.
Requires netCDF4, CDO and CDO python bindings.
Returns a masked array, var.
"""
# Open the variable using NetCDF4 to get scale and offset attributes.
nc = Dataset(ifile, 'r')
ncvar = nc.variables[varname]
# apply cdo string if it exists
if(cdostr):
opslist = cdostr.split()
base_op = opslist[0].replace('-', '')
if len(opslist) > 1:
ops_str = ' '.join(opslist[1::]) + ' ' + ifile
var = getattr(cdo, base_op)(input=ops_str, returnMaArray=varname)
else:
var = getattr(cdo, base_op)(input=ifile, returnMaArray=varname)
else:
var = cdo.readMaArray(ifile, varname=varname)
# Apply any scaling and offsetting needed:
try:
var_offset = ncvar.add_offset
except:
var_offset = 0
try:
var_scale = ncvar.scale_factor
except:
var_scale = 1
# var = var*var_scale + var_offset
# return var
return np.squeeze(var)
