def add_array(self,
where,
name,
data_type,
data_shape=(1, ),
data_dimension='0D',
scan_type='',
scan_shape=[],
title='',
array_to_save=None,
array_type=np.float,
enlargeable=False,
metadata=dict([]),
init=False,
add_scan_dim=False):
if data_dimension not in data_dimensions:
raise Exception('Invalid data dimension')
if data_type not in data_types:
raise Exception('Invalid data type')
if scan_type != '':
scan_type = utils.uncapitalize(scan_type)
if scan_type not in scan_types:
raise Exception('Invalid scan type')
if enlargeable:
shape = [0]
if data_shape != (1, ):
shape.extend(data_shape)
shape = tuple(shape)
array = self.h5_file.create_earray(where,
utils.capitalize(name),
tables.Atom.from_dtype(
np.dtype(array_type)),
shape=shape,
title=title,
filters=self.filters)
array._v_attrs['shape'] = shape
else:
if add_scan_dim: #means it is an array initialization to zero
shape = scan_shape[:]
shape.extend(data_shape)
if init or array_to_save is None:
array_to_save = np.zeros(shape)
array = self.h5_file.create_carray(where,
utils.capitalize(name),
obj=array_to_save,
title=title,
filters=self.filters)
array._v_attrs['shape'] = array_to_save.shape
array._v_attrs['type'] = data_type
array._v_attrs['data_dimension'] = data_dimension
array._v_attrs['scan_type'] = scan_type
for metadat in metadata:
array._v_attrs[metadat] = metadata[metadat]
return array
def test_uncapitalize(self):
string = 'ABCDef'
assert utils.uncapitalize(string) == 'aBCDef'
assert utils.uncapitalize(string, 3) == 'abcDef'