def from_value(cls, val):
"""
Creates a :py:class:`Type` object corresponding to the given value.
"""
if isinstance(val, Type):
# Creating a new object, because ``val`` may be some derivative of Type,
# used as a syntactic sugar, and we do not want it to confuse us later.
return cls(val.dtype,
shape=val.shape,
strides=val.strides,
offset=val.offset,
nbytes=val.nbytes)
elif numpy.issctype(val):
return cls(val)
elif hasattr(val, 'dtype') and hasattr(val, 'shape'):
strides = val.strides if hasattr(val, 'strides') else None
offset = val.offset if hasattr(val, 'offset') else 0
nbytes = val.nbytes if hasattr(val, 'nbytes') else None
return cls(val.dtype,
shape=val.shape,
strides=strides,
offset=offset,
nbytes=nbytes)
else:
return cls(dtypes.detect_type(val))
def normalize_value(thr, gpu_array_type, val):
"""
Transforms a given value (a scalar or an array)
to a value that can be passed to a kernel.
"""
if isinstance(val, gpu_array_type):
return val
elif isinstance(val, numpy.ndarray):
return thr.to_device(val)
else:
dtype = dtypes.detect_type(val)
return numpy.cast[dtype](val)
def add_const(arr_t, param):
"""
Returns an addition transformation with a fixed parameter (1 output, 1 input):
``output = input + param``.
"""
param_dtype = dtypes.detect_type(param)
return Transformation(
[Parameter('output', Annotation(arr_t, 'o')),
Parameter('input', Annotation(arr_t, 'i'))],
"${output.store_same}(${add}(${input.load_same}, ${param}));",
render_kwds=dict(
add=functions.add(arr_t.dtype, param_dtype, out_dtype=arr_t.dtype),
param=dtypes.c_constant(param, dtype=param_dtype)))
def div_const(arr_t, param):
"""
Returns a scaling transformation with a fixed parameter (1 output, 1 input):
``output = input / param``.
"""
param_dtype = dtypes.detect_type(param)
return Transformation(
[Parameter('output', Annotation(arr_t, 'o')),
Parameter('input', Annotation(arr_t, 'i'))],
"${output.store_same}(${div}(${input.load_same}, ${param}));",
render_kwds=dict(
div=functions.div(arr_t.dtype, param_dtype, out_dtype=arr_t.dtype),
param=dtypes.c_constant(param, dtype=param_dtype)))
def normalize_value(thr, gpu_array_type, val):
"""
Transforms a given value (a scalar or an array)
to a value that can be passed to a kernel.
Returns a pair (result, is_array), where ``is_array`` is a boolean
that helps distinguishing between zero-dimensional arrays and scalars.
"""
if isinstance(val, gpu_array_type):
return val, True
elif isinstance(val, numpy.ndarray):
return thr.to_device(val), True
else:
dtype = dtypes.detect_type(val)
return numpy.cast[dtype](val), False
def from_value(cls, val):
"""
Creates a :py:class:`Type` object corresponding to the given value.
"""
if isinstance(val, Type):
# Creating a new object, because ``val`` may be some derivative of Type,
# used as a syntactic sugar, and we do not want it to confuse us later.
return cls(val.dtype, shape=val.shape, strides=val.strides)
elif numpy.issctype(val):
return cls(val)
elif hasattr(val, 'dtype') and hasattr(val, 'shape'):
strides = val.strides if hasattr(val, 'strides') else None
return cls(val.dtype, shape=val.shape, strides=strides)
else:
return cls(dtypes.detect_type(val))