def __init__(self, size, dtype):
"""Initialize a new instance.
Parameters
----------
size : `int`
The number of dimensions of the space
dtype : `object`
The data type of the storage array. Can be provided in any
way the `numpy.dtype` function understands, most notably
as built-in type, as one of NumPy's internal datatype
objects or as string.
Only scalar data types (numbers) are allowed.
"""
NtuplesBase.__init__(self, size, dtype)
if not is_scalar_dtype(self.dtype):
raise TypeError('{!r} is not a scalar data type.'.format(dtype))
if is_real_dtype(self.dtype):
field = RealNumbers()
self._real_dtype = self.dtype
self._is_real = True
else:
field = ComplexNumbers()
self._real_dtype = _TYPE_MAP_C2R[self.dtype]
self._is_real = False
self._is_floating = is_floating_dtype(self.dtype)
LinearSpace.__init__(self, field)
def test_resizing_op_call(fn_impl):
dtypes = [dt for dt in odl.FN_IMPLS[fn_impl].available_dtypes()
if is_scalar_dtype(dt)]
for dtype in dtypes:
# Minimal test since this operator only wraps resize_array
space = odl.uniform_discr([0, -1], [1, 1], (4, 5), impl=fn_impl)
res_space = odl.uniform_discr([0, -0.6], [2, 0.2], (8, 2),
impl=fn_impl)
res_op = odl.ResizingOperator(space, res_space)
out = res_op(space.one())
true_res = np.zeros((8, 2))
true_res[:4, :] = 1
assert np.array_equal(out, true_res)
out = res_space.element()
res_op(space.one(), out=out)
assert np.array_equal(out, true_res)
# Test also mapping to default impl for other 'fn_impl'
if fn_impl != 'numpy':
space = odl.uniform_discr([0, -1], [1, 1], (4, 5), impl=fn_impl)
res_space = odl.uniform_discr([0, -0.6], [2, 0.2], (8, 2))
res_op = odl.ResizingOperator(space, res_space)
out = res_op(space.one())
true_res = np.zeros((8, 2))
true_res[:4, :] = 1
assert np.array_equal(out, true_res)
out = res_space.element()
res_op(space.one(), out=out)
assert np.array_equal(out, true_res)
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_inverse(padding, fn_impl):
pad_mode, pad_const = padding
dtypes = [dt for dt in odl.FN_IMPLS[fn_impl].available_dtypes()
if is_scalar_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)
# Only left inverse if the operator extentds in all axes
x = noise_element(space)
assert res_op.inverse(res_op(x)) == x
def __init__(self, size, dtype):
"""Initialize a new instance.
Parameters
----------
size : non-negative int
Number of entries in a tuple.
dtype :
Data type for each tuple entry. Can be provided in any
way the `numpy.dtype` function understands, most notably
as built-in type, as one of NumPy's internal datatype
objects or as string.
Only scalar data types (numbers) are allowed.
"""
NtuplesBase.__init__(self, size, dtype)
if not is_scalar_dtype(self.dtype):
raise TypeError('{!r} is not a scalar data type'.format(dtype))
if is_real_dtype(self.dtype):
field = RealNumbers()
self.__is_real = True
self.__real_dtype = self.dtype
self.__real_space = self
try:
self.__complex_dtype = complex_dtype(self.dtype)
except ValueError:
self.__complex_dtype = None
self.__complex_space = None # Set in first call of astype
else:
field = ComplexNumbers()
self.__is_real = False
try:
self.__real_dtype = real_dtype(self.dtype)
except ValueError:
self.__real_dtype = None
self.__real_space = None # Set in first call of astype
self.__complex_dtype = self.dtype
self.__complex_space = self
self.__is_floating = is_floating_dtype(self.dtype)
LinearSpace.__init__(self, field)
def __init__(self, size, dtype):
"""Initialize a new instance.
Parameters
----------
size : `int`
The number of dimensions of the space
dtype : `object`
The data type of the storage array. Can be provided in any
way the `numpy.dtype` function understands, most notably
as built-in type, as one of NumPy's internal datatype
objects or as string.
Only scalar data types (numbers) are allowed.
"""
super().__init__(size, dtype)
if not is_scalar_dtype(self.dtype):
raise TypeError('{!r} is not a scalar data type.'.format(dtype))
if is_real_dtype(self.dtype):
self._field = RealNumbers()
else:
self._field = ComplexNumbers()
def test_resizing_op_properties(fn_impl, padding):
dtypes = [dt for dt in odl.FN_IMPLS[fn_impl].available_dtypes()
if is_scalar_dtype(dt)]
pad_mode, pad_const = padding
for dtype in dtypes:
# Explicit range
space = odl.uniform_discr([0, -1], [1, 1], (10, 5), dtype=dtype)
res_space = odl.uniform_discr([0, -3], [2, 3], (20, 15), dtype=dtype)
res_op = odl.ResizingOperator(space, res_space, pad_mode=pad_mode,
pad_const=pad_const)
assert res_op.domain == space
assert res_op.range == res_space
assert res_op.offset == (0, 5)
assert res_op.pad_mode == pad_mode
assert res_op.pad_const == pad_const
if pad_mode == 'constant' and pad_const != 0:
assert not res_op.is_linear
else:
assert res_op.is_linear
# Implicit range via ran_shp and offset
res_op = odl.ResizingOperator(space, ran_shp=(20, 15), offset=[0, 5],
pad_mode=pad_mode, pad_const=pad_const)
assert np.allclose(res_op.range.min_pt, res_space.min_pt)
assert np.allclose(res_op.range.max_pt, res_space.max_pt)
assert np.allclose(res_op.range.cell_sides, res_space.cell_sides)
assert res_op.range.dtype == res_space.dtype
assert res_op.offset == (0, 5)
assert res_op.pad_mode == pad_mode
assert res_op.pad_const == pad_const
if pad_mode == 'constant' and pad_const != 0:
assert not res_op.is_linear
else:
assert res_op.is_linear
def __init__(self, size, dtype):
"""Initialize a new instance.
Parameters
----------
size : `int`
The number of dimensions of the space
dtype : `object`
The data type of the storage array. Can be provided in any
way the `numpy.dtype` function understands, most notably
as built-in type, as one of NumPy's internal datatype
objects or as string.
Only scalar data types (numbers) are allowed.
"""
NtuplesBase.__init__(self, size, dtype)
if not is_scalar_dtype(self.dtype):
raise TypeError('{!r} is not a scalar data type'.format(dtype))
if is_real_dtype(self.dtype):
field = RealNumbers()
self._is_real = True
self._real_dtype = self.dtype
self._real_space = self
self._complex_dtype = _TYPE_MAP_R2C.get(self.dtype, None)
self._complex_space = None # Set in first call of astype
else:
field = ComplexNumbers()
self._is_real = False
self._real_dtype = _TYPE_MAP_C2R[self.dtype]
self._real_space = None # Set in first call of astype
self._complex_dtype = self.dtype
self._complex_space = self
self._is_floating = is_floating_dtype(self.dtype)
LinearSpace.__init__(self, field)
def test_is_scalar_dtype():
for dtype in scalar_dtypes:
assert is_scalar_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_scalar_dtype():
for dtype in scalar_dtypes:
assert is_scalar_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 : optional
Set the data type of the vector manually with this option.
By default, the space type is inferred from the input data.
impl : string
The backend to use. See `odl.space.entry_points.NTUPLES_IMPLS` and
`odl.space.entry_points.FN_IMPLS` for available options.
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.
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)
# 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
else:
space_dtype = arr.dtype
# Select implementation
if space_dtype is None or is_scalar_dtype(space_dtype):
space_type = fn
else:
space_type = ntuples
return space_type(len(arr), dtype=space_dtype, impl=impl).element(arr)
def contains_all(self, array):
"""Test if `array` is an array of real or complex numbers."""
dtype = getattr(array, 'dtype', None)
if dtype is None:
dtype = np.result_type(*array)
return is_scalar_dtype(dtype)
def contains_all(self, array):
"""Test if `array` is an array of real or complex numbers."""
dtype = getattr(array, 'dtype', None)
if dtype is None:
dtype = np.result_type(*array)
return is_scalar_dtype(dtype)
def contains_all(self, other):
"""Return ``True`` if ``other`` is a sequence of complex numbers."""
dtype = getattr(other, 'dtype', None)
if dtype is None:
dtype = np.result_type(*other)
return is_scalar_dtype(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 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)
