def dspace_type(space, impl, dtype=None):
"""Select the correct corresponding n-tuples space.
Parameters
----------
space : `LinearSpace`
Template space from which to infer an adequate data space. If
it has a `LinearSpace.field` attribute, ``dtype`` must be
consistent with it.
impl : string
Implementation backend for the data space
dtype : `numpy.dtype`, optional
Data type which the space is supposed to use. If ``None`` is
given, the space type is purely determined from ``space`` and
``impl``. Otherwise, it must be compatible with the
field of ``space``.
Returns
-------
stype : type
Space type selected after the space's field, the backend and
the data type
"""
spacetype_map = {RealNumbers: FN_IMPLS,
ComplexNumbers: FN_IMPLS,
type(None): NTUPLES_IMPLS}
field_type = type(getattr(space, 'field', None))
if dtype is None:
pass
elif is_real_floating_dtype(dtype):
if field_type is None or field_type == ComplexNumbers:
raise TypeError('real floating data type {!r} requires space '
'field to be of type RealNumbers, got {}'
''.format(dtype, field_type))
elif is_complex_floating_dtype(dtype):
if field_type is None or field_type == RealNumbers:
raise TypeError('complex floating data type {!r} requires space '
'field to be of type ComplexNumbers, got {!r}'
''.format(dtype, field_type))
elif is_scalar_dtype(dtype):
if field_type == ComplexNumbers:
raise TypeError('non-floating data type {!r} requires space field '
'to be of type RealNumbers, got {!r}'
.format(dtype, field_type))
else:
raise TypeError('non-scalar data type {!r} cannot be combined with '
'a `LinearSpace`'.format(dtype))
stype = spacetype_map[field_type].get(impl, None)
if stype is None:
raise NotImplementedError('no corresponding data space available '
'for space {!r} and implementation {!r}'
''.format(space, impl))
return stype
def test_resizing_op_adjoint(padding, fn_impl):
pad_mode, pad_const = padding
dtypes = [dt for dt in odl.FN_IMPLS[fn_impl].available_dtypes()
if is_real_floating_dtype(dt)]
for dtype in dtypes:
space = odl.uniform_discr([0, -1], [1, 1], (4, 5), dtype=dtype,
impl=fn_impl)
res_space = odl.uniform_discr([0, -1.4], [1.5, 1.4], (6, 7),
dtype=dtype, impl=fn_impl)
res_op = odl.ResizingOperator(space, res_space, pad_mode=pad_mode,
pad_const=pad_const)
if pad_const != 0.0:
with pytest.raises(NotImplementedError):
res_op.adjoint
return
elem = noise_element(space)
res_elem = noise_element(res_space)
inner1 = res_op(elem).inner(res_elem)
inner2 = elem.inner(res_op.adjoint(res_elem))
assert almost_equal(inner1, inner2, places=dtype_places(dtype))
def dspace_type(space, impl, dtype=None):
"""Select the correct corresponding n-tuples space.
Parameters
----------
space : `object`
The template space. If it has a ``field`` attribute,
``dtype`` must be consistent with it
impl : {'numpy', 'cuda'}
The backend for the data space
dtype : `type`, optional
Data type which the space is supposed to use. If `None`, the
space type is purely determined from ``space`` and
``impl``. If given, it must be compatible with the
field of ``space``. Non-floating types result in basic
`Fn`-type spaces.
Returns
-------
stype : `type`
Space type selected after the space's field, the backend and
the data type
"""
impl_ = str(impl).lower()
if impl_ not in ('numpy', 'cuda'):
raise ValueError('implementation type {} not understood.'
''.format(impl))
if impl_ == 'cuda' and not CUDA_AVAILABLE:
raise ValueError('CUDA implementation not available.')
basic_map = {'numpy': Fn, 'cuda': CudaFn}
spacetype_map = {
'numpy': {RealNumbers: Rn, ComplexNumbers: Cn,
type(None): Ntuples},
'cuda': {RealNumbers: CudaRn, ComplexNumbers: None,
type(None): CudaNtuples}
}
field_type = type(getattr(space, 'field', None))
if dtype is None:
stype = spacetype_map[impl_][field_type]
elif is_real_floating_dtype(dtype):
if field_type is None or field_type == ComplexNumbers:
raise TypeError('real floating data type {!r} requires space '
'field to be of type `RealNumbers`, got {}.'
''.format(dtype, field_type))
stype = spacetype_map[impl_][field_type]
elif is_complex_floating_dtype(dtype):
if field_type is None or field_type == RealNumbers:
raise TypeError('complex floating data type {!r} requires space '
'field to be of type `ComplexNumbers`, got {!r}.'
''.format(dtype, field_type))
stype = spacetype_map[impl_][field_type]
elif is_scalar_dtype(dtype):
if field_type == ComplexNumbers:
raise TypeError('non-floating data type {!r} requires space field '
'to be of type `RealNumbers`, got {!r}.'
.format(dtype, field_type))
elif field_type == RealNumbers:
stype = basic_map[impl_]
else:
stype = spacetype_map[impl_][field_type]
elif field_type is None: # Only in this case are arbitrary types allowed
stype = spacetype_map[impl_][field_type]
else:
raise TypeError('non-scalar data type {!r} cannot be combined with '
'a `LinearSpace`.'.format(dtype))
if stype is None:
raise NotImplementedError('no corresponding data space available '
'for space {!r} and implementation {!r}.'
''.format(space, impl))
return stype
def test_is_real_floating_dtype():
for dtype in real_float_dtypes:
assert is_real_floating_dtype(dtype)
def dspace_type(space, impl, dtype=None):
"""Select the correct corresponding n-tuples space.
Parameters
----------
space :
Template space from which to infer an adequate data space. If
it has a `LinearSpace.field` attribute, ``dtype`` must be
consistent with it.
impl : {'numpy', 'cuda'}
Implementation backend for the data space
dtype : `type`, optional
Data type which the space is supposed to use. If `None`, the
space type is purely determined from ``space`` and
``impl``. If given, it must be compatible with the
field of ``space``.
Returns
-------
stype : `type`
Space type selected after the space's field, the backend and
the data type
"""
impl, impl_in = str(impl).lower(), impl
if impl not in ('numpy', 'cuda'):
raise ValueError("`impl` '{}' not understood"
''.format(impl_in))
if impl == 'cuda' and not CUDA_AVAILABLE:
raise ValueError("'cuda' implementation not available")
basic_map = {'numpy': Fn, 'cuda': CudaFn}
spacetype_map = {
'numpy': {RealNumbers: Fn, ComplexNumbers: Fn,
type(None): Ntuples},
'cuda': {RealNumbers: CudaFn, ComplexNumbers: None,
type(None): CudaNtuples}
}
field_type = type(getattr(space, 'field', None))
if dtype is None:
stype = spacetype_map[impl][field_type]
elif is_real_floating_dtype(dtype):
if field_type is None or field_type == ComplexNumbers:
raise TypeError('real floating data type {!r} requires space '
'field to be of type RealNumbers, got {}'
''.format(dtype, field_type))
stype = spacetype_map[impl][field_type]
elif is_complex_floating_dtype(dtype):
if field_type is None or field_type == RealNumbers:
raise TypeError('complex floating data type {!r} requires space '
'field to be of type ComplexNumbers, got {!r}'
''.format(dtype, field_type))
stype = spacetype_map[impl][field_type]
elif is_scalar_dtype(dtype):
if field_type == ComplexNumbers:
raise TypeError('non-floating data type {!r} requires space field '
'to be of type RealNumbers, got {!r}'
.format(dtype, field_type))
elif field_type == RealNumbers:
stype = basic_map[impl]
else:
stype = spacetype_map[impl][field_type]
elif field_type is None: # Only in this case are arbitrary types allowed
stype = spacetype_map[impl][field_type]
else:
raise TypeError('non-scalar data type {!r} cannot be combined with '
'a `LinearSpace`'.format(dtype))
if stype is None:
raise NotImplementedError('no corresponding data space available '
'for space {!r} and implementation {!r}'
''.format(space, impl))
return stype
def test_is_real_floating_dtype():
for dtype in real_float_dtypes:
assert is_real_floating_dtype(dtype)
def vector(array, dtype=None, impl="numpy"):
"""Create an n-tuples type vector from an array.
Parameters
----------
array : array-like
Array from which to create the vector. Scalars become
one-dimensional vectors.
dtype : `object`, optional
Set the data type of the vector manually with this option.
By default, the space type is inferred from the input data.
impl : {'numpy', 'cuda'}
Implementation backend for the vector
Returns
-------
vec : `NtuplesBaseVector`
Vector created from the input array. Its concrete type depends
on the provided arguments.
Notes
-----
This is a convenience function and not intended for use in
speed-critical algorithms. It creates a NumPy array first, hence
especially CUDA vectors as input result in a large speed penalty.
Examples
--------
>>> vector([1, 2, 3]) # No automatic cast to float
Fn(3, 'int').element([1, 2, 3])
>>> vector([1, 2, 3], dtype=float)
Rn(3).element([1.0, 2.0, 3.0])
>>> vector([1 + 1j, 2, 3 - 2j])
Cn(3).element([(1+1j), (2+0j), (3-2j)])
Non-scalar types are also supported:
>>> vector([u'Hello,', u' world!'])
Ntuples(2, '<U7').element([u'Hello,', u' world!'])
Scalars become a one-element vector:
>>> vector(0.0)
Rn(1).element([0.0])
"""
# Sanitize input
arr = np.array(array, copy=False, ndmin=1)
impl = str(impl).lower()
# Validate input
if arr.ndim > 1:
raise ValueError("array has {} dimensions, expected 1." "".format(arr.ndim))
# Set dtype
if dtype is not None:
space_dtype = dtype
elif arr.dtype == float and impl == "cuda":
# Special case, default float is float32 on cuda
space_dtype = "float32"
else:
space_dtype = arr.dtype
# Select implementation
if impl == "numpy":
if is_real_floating_dtype(space_dtype):
space_type = Rn
elif is_complex_floating_dtype(space_dtype):
space_type = Cn
elif is_scalar_dtype(space_dtype):
space_type = Fn
else:
space_type = Ntuples
elif impl == "cuda":
if not CUDA_AVAILABLE:
raise ValueError("CUDA implementation not available.")
if is_real_floating_dtype(space_dtype):
space_type = CudaRn
elif is_complex_floating_dtype(space_dtype):
raise NotImplementedError("complex spaces in CUDA not supported.")
elif is_scalar_dtype(space_dtype):
space_type = CudaFn
else:
space_type = CudaNtuples
else:
raise ValueError("implementation '{}' not understood.".format(impl))
return space_type(len(arr), dtype=space_dtype).element(arr)
def vector(array, dtype=None, impl='numpy'):
"""Create an n-tuples type vector from an array.
Parameters
----------
array : `array-like`
Array from which to create the vector. Scalars become
one-dimensional vectors.
dtype : `object`, optional
Set the data type of the vector manually with this option.
By default, the space type is inferred from the input data.
impl : {'numpy', 'cuda'}
Implementation backend for the vector
Returns
-------
vec : `NtuplesBaseVector`
Vector created from the input array. Its concrete type depends
on the provided arguments.
Notes
-----
This is a convenience function and not intended for use in
speed-critical algorithms. It creates a NumPy array first, hence
especially CUDA vectors as input result in a large speed penalty.
Examples
--------
>>> vector([1, 2, 3]) # No automatic cast to float
Fn(3, 'int').element([1, 2, 3])
>>> vector([1, 2, 3], dtype=float)
Rn(3).element([1.0, 2.0, 3.0])
>>> vector([1 + 1j, 2, 3 - 2j])
Cn(3).element([(1+1j), (2+0j), (3-2j)])
Non-scalar types are also supported:
>>> vector([True, False])
Ntuples(2, 'bool').element([True, False])
Scalars become a one-element vector:
>>> vector(0.0)
Rn(1).element([0.0])
"""
# Sanitize input
arr = np.array(array, copy=False, ndmin=1)
impl, impl_in = str(impl).lower(), impl
# Validate input
if arr.ndim > 1:
raise ValueError('array has {} dimensions, expected 1'
''.format(arr.ndim))
# Set dtype
if dtype is not None:
space_dtype = dtype
elif arr.dtype == np.dtype('float64') and impl == 'cuda':
# Special case, default float is float32 on cuda
space_dtype = 'float32'
else:
space_dtype = arr.dtype
# Select implementation
if impl == 'numpy':
if is_real_floating_dtype(space_dtype):
space_type = Rn
elif is_complex_floating_dtype(space_dtype):
space_type = Cn
elif is_scalar_dtype(space_dtype):
space_type = Fn
else:
space_type = Ntuples
elif impl == 'cuda':
if not CUDA_AVAILABLE:
raise ValueError("'cuda' implementation not available")
if is_real_floating_dtype(space_dtype):
space_type = CudaRn
elif is_complex_floating_dtype(space_dtype):
raise NotImplementedError('complex spaces in CUDA not supported')
elif is_scalar_dtype(space_dtype):
space_type = CudaFn
else:
space_type = CudaNtuples
else:
raise ValueError("`impl` '{}' not understood".format(impl_in))
return space_type(len(arr), dtype=space_dtype).element(arr)
