CDL Output in Python

cdl.py

def printify(attr):
    '''
    Returns a prettier version of the attribute
    '''
    if isinstance(attr, basestring):
        return '"%s"' % attr
    return attr

def dtype_to_cdl(dtype):
    dtype_map = {
        'S' : 'char',
        'b' : 'byte',
        'h' : 'short',
        'i' : 'int',
        'l' : 'int64',
        'B' : 'ubyte',
        'H' : 'ushort',
        'I' : 'uint',
        'L' : 'uint64',
        'u' : 'uint',
        'f' : 'float',
        'd' : 'double'
    }
    return dtype_map[dtype]



def dump_cdl(nc):
    '''
    Returns a string dump of the CDL for a netCDF File
    '''
    buf = []
    varnames = nc.variables.keys()
    varnames.sort()
    buf.append('dimensions:')
    for dim in nc.dimensions:
        if nc.dimensions[dim].isunlimited():
            buf.append('    %s = UNLIMITED; // (%s currently)' % (dim, len(nc.dimensions[dim])))
        else:
            buf.append('    %s = %s;' % (dim, len(nc.dimensions[dim])))
    buf.append('variables:')
    for v in varnames:
        datatype = nc.variables[v].dtype.char
        if nc.variables[v].dimensions:
            dims = '(%s)' % ', '.join(nc.variables[v].dimensions)
        else:
            dims = ''
        s = '    %s %s%s;' % (dtype_to_cdl(datatype), v, dims)
        buf.append(s)
        for attrname in vars(nc.variables[v]):
            buf.append('        %s:%s = %s;' % (v, attrname, printify(getattr(nc.variables[v], attrname))))
    buf.append('// global attributes:')
    for attr in vars(nc):
        buf.append('    :%s = %s;' % (attr, printify(getattr(nc, attr))))
    return '\n'.join(buf)

def copy_var(nc, nc_out, var, rename=None, new_dims=None, new_dtype=None, copy_data=True):
    '''
    Copies the variable var from nc to nc_out

    Optionally accepts a rename parameter, a new set of dimensions and a new
    dtype, if any of these are set the variable data will not be copied over
    '''
    # Should we copy the data?
    copy_data = copy_data and rename is None and new_dims is None and new_dtype is None

    rename = rename or var
    new_dims = new_dims or nc.variables[var].dimensions
    new_dtype = new_dtype or nc.variables[var].dtype
    fill_value = getattr(nc.variables[var],"_FillValue", None)
    nc_out.createVariable(rename, new_dtype, new_dims, fill_value=fill_value)
    for varattr in vars(nc.variables[var]):
        if varattr == '_FillValue':
            continue
        setattr(nc_out.variables[rename], varattr, getattr(nc.variables[var], varattr))
    if copy_data:
        nc_out.variables[rename][:] = nc.variables[var][:]


def copy_dim(nc, nc_out, dim):
    '''
    Copies the dimension dim from nc to nc_out
    '''
    if nc.dimensions[dim].isunlimited():
        nc_out.createDimension(dim)
    else:
        nc_out.createDimension(dim, len(nc.dimensions[dim]))

def copy_dataset(nc, nc_out, var_skip=None):
    '''
    Copies a dataset from one netCDF Dataset instance to another
    accepts a list of variables to skip
    '''
    var_skip = var_skip or []
    # Dimensions
    for dim in nc.dimensions:
        copy_dim(nc, nc_out, dim)
    # Global Attributes
    for attr in vars(nc):
        setattr(nc_out, attr, getattr(nc, attr))
    # Variables
    for var in nc.variables:
        if var in var_skip:
            continue
        copy_var(nc, nc_out, var)