def test_validate_dtype_valid(self):
struct_dtype = np.dtype({
'names': ['r', 'g', 'b'],
'formats': [np.float32, np.uint16, np.float64]
})
for dtype in [
np.float32, np.float16, np.complex, np.complex64, np.uint8,
np.int16, struct_dtype
]:
self.assertTrue(dtype_utils.validate_dtype(dtype))
def create_empty_dataset(source_dset,
dtype,
dset_name,
h5_group=None,
new_attrs=None,
skip_refs=False):
"""
Creates an empty dataset in the h5 file based on the provided dataset in the same or specified group
Parameters
----------
source_dset : h5py.Dataset object
Source object that provides information on the group and shape of the dataset
dtype : dtype
Data type of the fit / guess datasets
dset_name : String / Unicode
Name of the dataset
h5_group : h5py.Group object, optional. Default = None
Group within which this dataset will be created
new_attrs : dictionary (Optional)
Any new attributes that need to be written to the dataset
skip_refs : boolean, optional
Should ObjectReferences and RegionReferences be skipped when copying attributes from the
`source_dset`
Returns
-------
h5_new_dset : h5py.Dataset object
Newly created dataset
"""
import h5py
from pyUSID.io.dtype_utils import validate_dtype
from pyUSID.io.hdf_utils import copy_attributes, check_if_main, write_book_keeping_attrs
from pyUSID import USIDataset
import sys
if sys.version_info.major == 3:
unicode = str
if not isinstance(source_dset, h5py.Dataset):
raise TypeError('source_deset should be a h5py.Dataset object')
_ = validate_dtype(dtype)
if new_attrs is not None:
if not isinstance(new_attrs, dict):
raise TypeError('new_attrs should be a dictionary')
else:
new_attrs = dict()
if h5_group is None:
h5_group = source_dset.parent
else:
if not isinstance(h5_group, (h5py.Group, h5py.File)):
raise TypeError(
'h5_group should be a h5py.Group or h5py.File object')
if not isinstance(dset_name, (str, unicode)):
raise TypeError('dset_name should be a string')
dset_name = dset_name.strip()
if len(dset_name) == 0:
raise ValueError('dset_name cannot be empty!')
if '-' in dset_name:
warn(
'dset_name should not contain the "-" character. Reformatted name from:{} to '
'{}'.format(dset_name, dset_name.replace('-', '_')))
dset_name = dset_name.replace('-', '_')
if dset_name in h5_group.keys():
if isinstance(h5_group[dset_name], h5py.Dataset):
warn('A dataset named: {} already exists in group: {}'.format(
dset_name, h5_group.name))
h5_new_dset = h5_group[dset_name]
# Make sure it has the correct shape and dtype
if any((source_dset.shape != h5_new_dset.shape,
dtype != h5_new_dset.dtype)):
warn(
'Either the shape (existing: {} desired: {}) or dtype (existing: {} desired: {}) of the dataset '
'did not match with expectations. Deleting and creating a new one.'
.format(h5_new_dset.shape, source_dset.shape,
h5_new_dset.dtype, dtype))
del h5_new_dset, h5_group[dset_name]
h5_new_dset = h5_group.create_dataset(
dset_name,
shape=source_dset.shape,
dtype=dtype,
chunks=source_dset.chunks)
else:
raise KeyError('{} is already a {} in group: {}'.format(
dset_name, type(h5_group[dset_name]), h5_group.name))
else:
h5_new_dset = h5_group.create_dataset(dset_name,
shape=source_dset.shape,
dtype=dtype,
chunks=source_dset.chunks)
# This should link the ancillary datasets correctly
h5_new_dset = copy_attributes(source_dset,
h5_new_dset,
skip_refs=skip_refs)
h5_new_dset.attrs.update(new_attrs)
if check_if_main(h5_new_dset):
h5_new_dset = USIDataset(h5_new_dset)
# update book keeping attributes
write_book_keeping_attrs(h5_new_dset)
return h5_new_dset
def test_invalid(self):
for dtype in [6, 'dssds', np.arange(5), True]:
with self.assertRaises(TypeError):
dtype_utils.validate_dtype(dtype)