def __init__(self, bind):
""" Create a new Datatype object by binding to a low-level TypeID.
"""
with phil:
if not isinstance(bind, TypeID):
# todo: distinguish type from other hl objects
raise ValueError("%s is not a TypeID" % bind)
HLObject.__init__(self, bind)
self._dtype = h5json.createDataType(self.id.type_json)
def __init__(self, bind):
""" Create a new Datatype object by binding to a low-level TypeID.
"""
with phil:
if not isinstance(bind, TypeID):
# todo: distinguish type from other hl objects
raise ValueError("%s is not a TypeID" % bind)
HLObject.__init__(self, bind)
self._dtype = h5json.createDataType(self.id.type_json)
def __getitem__(self, name):
""" Read the value of an attribute.
"""
#attr = h5a.open(self._id, self._e(name))
req = self._req_prefix + name
attr_json = self._parent.GET(req)
shape_json = attr_json['shape']
type_json = attr_json['type']
#if attr.get_space().get_simple_extent_type() == h5s.NULL:
if shape_json['class'] == 'H5S_NULL':
raise IOError("Empty attributes cannot be read")
value_json = attr_json['value']
#dtype = readtime_dtype(attr.dtype, [])
dtype = h5json.createDataType(type_json)
#shape = attr.shape
if 'dims' in shape_json:
shape = shape_json['dims']
else:
shape = ()
# Do this first, as we'll be fiddling with the dtype for top-level
# array types
#htype = h5t.py_create(dtype)
htype = dtype
# NumPy doesn't support top-level array types, so we have to "fake"
# the correct type and shape for the array. For example, consider
# attr.shape == (5,) and attr.dtype == '(3,)f'. Then:
if dtype.subdtype is not None:
subdtype, subshape = dtype.subdtype
shape = attr.shape + subshape # (5, 3)
dtype = subdtype # 'f'
#arr = numpy.ndarray(shape, dtype=dtype, order='C')
#attr.read(arr, mtype=htype)
#print "value:", rsp['value']
#print "new_dtype:", new_dtype
arr = numpy.array(value_json, dtype=htype)
if len(arr.shape) == 0:
return arr[()]
return arr
def __init__(self, bind):
""" Create a new Dataset object by binding to a low-level DatasetID.
"""
from threading import local
if not isinstance(bind, DatasetID):
raise ValueError("%s is not a DatasetID" % bind)
HLObject.__init__(self, bind)
self._dcpl = self.id.dcpl_json
self._filters = [] # filters.get_filters(self._dcpl) # todo
self._local = None #local()
# make a numpy dtype out of the type json
self._dtype = h5json.createDataType(self.id.type_json)
if self.id.shape_json['class'] == 'H5S_SCALAR':
self._shape = []
else:
self._shape = self.id.shape_json['dims']
