def read_dataset(f, nms=None, **kwargs):
""" read several (or all) names from a netCDF file
nms : list of variables to read (default None for all variables)
**kwargs: keyword arguments passed to io.nc.read_variable
"""
kw = _extract_kw(kwargs, ('verbose', ))
f, close = check_file(f, 'r', **kw)
# automatically read all variables to load (except for the dimensions)
if nms is None:
nms, dims = scan(f)
# if nms is str:
# nms = [nms]
data = dict()
for nm in nms:
data[nm] = read_variable(f, nm, **kwargs)
data = Dataset(**data)
# get dataset's metadata
for k in f.ncattrs():
setattr(data, k, f.getncattr(k))
if close: f.close()
return data
def to_dataset(self, axis=0):
""" split a DimArray into a Dataset object (collection of DimArrays)
"""
from dimarray.dataset import Dataset
# iterate over elements of one axis
#data = [val for k, val in self.iter(axis)]
# Dataset(data, keys=self.axes[axis].values)
ds = Dataset()
for k, val in self.iter(axis):
if not isinstance(val, DimArray): # scalar case
val = DimArray(val)
ds[k] = val
return ds
def from_dict(cls, dict_, keys=None, axis=None):
""" Initialize a DimArray for a dictionary of DimArrays
keys, optional: re-order the keys
axis, optional: give a name to the keys axis
"""
assert isinstance(dict_, dict)
from dimarray.dataset import Dataset
if keys is None: keys = dict_.keys()
data = Dataset(dict_)
return data.to_array(axis=axis,
keys=keys,
_constructor=cls._constructor)
