def arange(start, stop=None, step=1, dtype=None):
'''Create a 1D dataset of given type where values range from specified start up to
but not including stop in given steps
Arguments:
start -- optional starting value, defaults to 0
stop -- exclusive stop value
step -- difference between neighbouring values, defaults to 1
dtype -- defaults to None which means the type is inferred from given start, stop, step values
'''
if stop is None:
stop = start
start = 0
if dtype is None:
if type(start) is _types.ComplexType or type(stop) is _types.ComplexType or type(step) is _types.ComplexType:
dtype = complex128
elif type(start) is _types.FloatType or type(stop) is _types.FloatType or type(step) is _types.FloatType:
dtype = float64
elif type(start) is _types.IntType or type(stop) is _types.IntType or type(step) is _types.IntType:
dtype = int32
else:
raise ValueError, "Unknown or invalid type of input value"
else:
dtype = _translatenativetype(dtype)
if dtype == bool:
return None
return _df.createRange(start, stop, step, dtype.value)
def arange(start, stop=None, step=1, dtype=None):
'''Create a 1D dataset of given type where values range from specified start up to
but not including stop in given steps
Arguments:
start -- optional starting value, defaults to 0
stop -- exclusive stop value
step -- difference between neighbouring values, defaults to 1
dtype -- defaults to None which means the type is inferred from given start, stop, step values
'''
if stop is None:
stop = start
start = 0
if dtype is None:
if type(start) is _types.ComplexType or type(
stop) is _types.ComplexType or type(
step) is _types.ComplexType:
dtype = complex128
elif type(start) is _types.FloatType or type(
stop) is _types.FloatType or type(step) is _types.FloatType:
dtype = float64
elif type(start) is _types.IntType or type(
stop) is _types.IntType or type(step) is _types.IntType:
dtype = int32
else:
raise ValueError, "Unknown or invalid type of input value"
else:
dtype = _translatenativetype(dtype)
if dtype == bool:
return None
return _df.createRange(start, stop, step, 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(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 __cvt_jobj(obj, dtype=None, copy=True, force=False):
'''Convert object to java object'''
if isinstance(obj, ndarray):
obj = obj._jdataset()
if isinstance(obj, _ds):
if copy:
if dtype is None or _translatenativetype(dtype).value == obj.dtype:
return obj.clone()
else:
return obj.cast(_translatenativetype(dtype).value)
else:
if dtype is None:
return obj
return obj.cast(_translatenativetype(dtype).value)
if not isinstance(obj, list):
if isinstance(obj, _matrix): # cope with JAMA matrices
if dtype is None:
dtype = float64
obj = obj.getArray()
elif len(obj) == 0 and dtype is None:
dtype = float64
obj = _cvt2j(obj)
try:
iter(obj)
except:
if not force:
if isinstance(obj, complex):
return _jcomplex(obj.real, obj.imag)
return obj
if dtype is None:
dtype = _getdtypefromobj(obj)
else:
dtype = _translatenativetype(dtype)
return _df.createFromObject(obj, dtype.value)
def __cvt_jobj(obj, dtype=None, copy=True, force=False):
'''Convert object to java object'''
if isinstance(obj, ndarray):
obj = obj._jdataset()
if isinstance(obj, _ds):
if copy:
if dtype is None or _translatenativetype(dtype).value == obj.dtype:
return obj.clone()
else:
return obj.cast(_translatenativetype(dtype).value)
else:
if dtype is None:
return obj
return obj.cast(_translatenativetype(dtype).value)
if not isinstance(obj, list):
if isinstance(obj, _matrix): # cope with JAMA matrices
if dtype is None:
dtype = float64
obj = obj.getArray()
obj = _cvt2j(obj)
try:
iter(obj)
except:
if not force:
if isinstance(obj, complex):
return _jcomplex(obj.real, obj.imag)
return obj
if dtype is None:
dtype = _getdtypefromobj(obj)
else:
dtype = _translatenativetype(dtype)
return _df.createFromObject(obj, dtype.value)
def zeros_like(a):
return _df.zeros(a)
def ones_like(a):
return _df.zeros(a).fill(1)
def ones(shape, dtype=float64):
'''Create a dataset filled with 1'''
dtype = _translatenativetype(dtype)
return _df.ones(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 ones(shape, dtype=float64):
'''Create a dataset filled with 1'''
dtype = _translatenativetype(dtype)
return _df.ones(dtype.elements, asIterable(shape), dtype.value)
