def __init__(self, shape=None, dtype=None, buffer=None, copy=False):
# check what buffer is and convert if necessary
if buffer is not None:
self.__dataset = __cvt_jobj(_jinput(buffer), dtype=dtype, copy=copy, force=True)
if shape is not None:
self.__dataset.setShape(asIterable(shape))
else:
dtype = _translatenativetype(dtype)
self.__dataset = _df.zeros(dtype.elements, asIterable(shape), dtype.value)
def zeros(shape, dtype=float64, elements=None):
'''Create a dataset filled with 0'''
dtype = _translatenativetype(dtype)
if elements is not None:
if type(dtype) is _types.FunctionType:
dtype = dtype(elements)
else:
dtype.elements = elements
elif type(dtype) is _types.FunctionType:
raise ValueError, "Given data-type is a function and needs elements defining"
return _df.zeros(dtype.elements, asIterable(shape), dtype.value)
def zeros(shape, dtype=float64, elements=None):
'''Create a dataset filled with 0'''
dtype = _translatenativetype(dtype)
if elements is not None:
if type(dtype) is _types.FunctionType:
dtype = dtype(elements)
else:
dtype.elements = elements
elif type(dtype) is _types.FunctionType:
raise ValueError, "Given data-type is a function and needs elements defining"
return _df.zeros(dtype.elements, asIterable(shape), dtype.value)
def __init__(self, shape=None, dtype=None, buffer=None, copy=False):
# check what buffer is and convert if necessary
if buffer is not None:
self.__dataset = __cvt_jobj(_jinput(buffer),
dtype=dtype,
copy=copy,
force=True)
if shape is not None:
self.__dataset.setShape(asIterable(shape))
else:
dtype = _translatenativetype(dtype)
self.__dataset = _df.zeros(dtype.elements, asIterable(shape),
dtype.value)
def zeros_like(a):
return _df.zeros(a)
def ones_like(a):
return _df.zeros(a).fill(1)
def zeros_like(a):
return _df.zeros(a)
def ones_like(a):
return _df.zeros(a).fill(1)
