def read_multinc(fnames,
nms=None,
axis=None,
keys=None,
align=False,
concatenate_only=False,
**kwargs):
""" read multiple netCDF files
parameters:
-----------
fnames: list of file names or file handles to be read
nms: variable names to be read
axis, optional: string, dimension along which the files are concatenated
(created as new dimension if not already existing)
keys, optional: sequence to be passed to stack_nc, if axis is not part of the dataset
align, optional: if True, align axis prior to stacking (default to False)
concatenate_only, optional: if True, only concatenate along existing axis (and raise error if axis not existing)
**kwargs: keyword arguments passed to io.nc.read_variable (cannot
contain 'axis', though, but indices can be passed as a dictionary
if needed, e.g. {'time':2010})
returns:
--------
dimarray's Dataset instance
This function reads several files and call stack_ds or concatenate_ds
depending on whether `axis` is absent from or present in the dataset, respectively
"""
variables = None
dimensions = None
datasets = []
for fn in fnames:
ds = read_dataset(fn, nms, **kwargs)
# check that the same variables are present in the file
if variables is None:
variables = ds.keys()
else:
variables_new = ds.keys()
assert variables == variables_new, \
"netCDF files must contain the same \
subset of variables to be concatenated/stacked"
# check that the same dimensions are present in the file
if dimensions is None:
dimensions = ds.dims
else:
dimensions_new = ds.dims
assert dimensions == dimensions_new, \
"netCDF files must contain the same \
subset of dimensions to be concatenated/stacked"
datasets.append(ds)
# check that dimension in dataset if required
if concatenate_only and axis not in dimensions:
raise Exception('required axis {} not found, only got {}'.format(
axis, dimensions))
# Join dataset
if axis in dimensions:
if keys is None: warnings.warn('keys argument is not used')
ds = concatenate_ds(datasets, axis=axis)
else:
# use file name as keys by default
if keys is None:
keys = [os.path.splitext(fn)[0] for fn in fnames]
ds = stack_ds(datasets, axis=axis, keys=keys, align=align)
return ds
def _read_multinc(fnames, names=None, axis=None, keys=None, align=False, sort=False, join='outer', concatenate_only=False, **kwargs):
""" read multiple netCDF files
Parameters
----------
fnames : list of file names or file handles to be read
names : variable names to be read
axis : str, optional
dimension along which the files are concatenated
(created as new dimension if not already existing)
keys : sequence, optional
to be passed to stack_nc, if axis is not part of the dataset
align : `bool`, optional
if True, reindex axis prior to stacking / concatenating (default to False)
sort : `bool`, optional
if True, sort common axes prior to stacking / concatenating (defaut to False)
join : `str`, optional
join method in align (default to 'outer')
concatenate_only : `bool`, optional
if True, only concatenate along existing axis (and raise error if axis not existing)
**kwargs : keyword arguments passed to DatasetOnDisk.read (cannot
contain 'axis', though, but indices can be passed as a dictionary
if needed, e.g. {'time':2010})
Returns
-------
dimarray's Dataset instance
This function reads several files and call stack_ds or concatenate_ds
depending on whether `axis` is absent from or present in the dataset, respectively
See `dimarray.read_nc` for more complete documentation.
See Also
--------
read_nc
"""
variables = None
dimensions = None
datasets = []
for fn in fnames:
with DatasetOnDisk(fn) as f:
ds = f.read(names, **kwargs)
# check that the same variables are present in the file
if variables is None:
variables = ds.keys()
else:
variables_new = ds.keys()
assert variables == variables_new, \
"netCDF files must contain the same \
subset of variables to be concatenated/stacked"
# check that the same dimensions are present in the file
if dimensions is None:
dimensions = ds.dims
else:
dimensions_new = ds.dims
assert dimensions == dimensions_new, \
"netCDF files must contain the same \
subset of dimensions to be concatenated/stacked"
datasets.append(ds)
# check that dimension in dataset if required
if concatenate_only and axis not in dimensions:
raise Exception('required axis {} not found, only got {}'.format(axis, dimensions))
# Join dataset
if axis in dimensions:
ds = concatenate_ds(datasets, axis=axis, align=align, sort=sort, join=join)
if keys is not None:
ds = ds.reindex_axis(keys, axis=axis)
# elif sort:
# ds = ds.sort_axis(axis=axis)
#warnings.warn('keys argument will be ignored.', RuntimeWarning)
else:
# use file name as keys by default
if keys is None:
keys = [os.path.splitext(fn)[0] for fn in fnames]
ds = stack_ds(datasets, axis=axis, keys=keys, align=align, sort=sort, join=join)
return ds
def read_multinc(fnames, nms=None, axis=None, keys=None, align=False, concatenate_only=False, **kwargs):
""" read multiple netCDF files
parameters:
-----------
fnames: list of file names or file handles to be read
nms: variable names to be read
axis, optional: string, dimension along which the files are concatenated
(created as new dimension if not already existing)
keys, optional: sequence to be passed to stack_nc, if axis is not part of the dataset
align, optional: if True, align axis prior to stacking (default to False)
concatenate_only, optional: if True, only concatenate along existing axis (and raise error if axis not existing)
**kwargs: keyword arguments passed to io.nc.read_variable (cannot
contain 'axis', though, but indices can be passed as a dictionary
if needed, e.g. {'time':2010})
returns:
--------
dimarray's Dataset instance
This function reads several files and call stack_ds or concatenate_ds
depending on whether `axis` is absent from or present in the dataset, respectively
"""
variables = None
dimensions = None
datasets = []
for fn in fnames:
ds = read_dataset(fn, nms, **kwargs)
# check that the same variables are present in the file
if variables is None:
variables = ds.keys()
else:
variables_new = ds.keys()
assert variables == variables_new, \
"netCDF files must contain the same \
subset of variables to be concatenated/stacked"
# check that the same dimensions are present in the file
if dimensions is None:
dimensions = ds.dims
else:
dimensions_new = ds.dims
assert dimensions == dimensions_new, \
"netCDF files must contain the same \
subset of dimensions to be concatenated/stacked"
datasets.append(ds)
# check that dimension in dataset if required
if concatenate_only and axis not in dimensions:
raise Exception('required axis {} not found, only got {}'.format(axis, dimensions))
# Join dataset
if axis in dimensions:
if keys is None: warnings.warn('keys argument is not used')
ds = concatenate_ds(datasets, axis=axis)
else:
# use file name as keys by default
if keys is None:
keys = [os.path.splitext(fn)[0] for fn in fnames]
ds = stack_ds(datasets, axis=axis, keys=keys, align=align)
return ds
