def _call(self, f, out=None):
"""Evaluate the gradient in the point ``f``."""
if functional.impl != 'numpy':
raise NotImplementedError(
'gradient not implemented for `impl` {!r}'
''.format(functional.impl))
if isinstance(self.domain, ProductSpace):
tmp = self.domain[0].element()
pwnorm = PointwiseNorm(self.domain)
pwnorm(f, out=tmp)
else:
tmp = out
f.ufunc.absolute(out=tmp)
tmp.ufunc.power(self.exponent - 2, out=tmp)
with writable_array(tmp) as tmp_arr:
tmp_arr[np.isnan(tmp_arr)] = 0 # Handle NaN
if out is None:
out = f.copy()
else:
out.assign(f)
out *= self.exponent
return out
def _call(self, x, out):
"""Implement ``self(x, out)``."""
with writable_array(out) as out_arr:
resize_array(x.asarray(), op.domain.shape,
offset=op.offset, pad_mode=op.pad_mode,
pad_const=0, direction='adjoint',
out=out_arr)
def _call(self, func, out=None, **kwargs):
"""Return ``self(func[, out, **kwargs])``."""
mesh = self.grid.meshgrid
if out is None:
out = func(mesh, **kwargs)
else:
with writable_array(out) as out_arr:
func(mesh, out=out_arr, **kwargs)
return out
def _call(self, x, out):
"""Implement ``self(x, out)``."""
with writable_array(out) as out_arr:
resize_array(x.asarray(),
self.range.shape,
offset=self.offset,
pad_mode=self.pad_mode,
pad_const=self.pad_const,
direction='forward',
out=out_arr)
def _call(self, x, out=None):
"""Raw apply method on input, writing to given output."""
if out is None:
return self.range.element(self.matrix.dot(x))
else:
if self.matrix_issparse:
# Unfortunately, there is no native in-place dot product for
# sparse matrices
out[:] = self.matrix.dot(x)
else:
with writable_array(out) as out_arr:
self.matrix.dot(x, out=out_arr)
def _call(self, x, out=None):
"""Return ``self(x[, out])``."""
if out is None:
return self.range.element(self.matrix.dot(x))
else:
if self.matrix_issparse:
# Unfortunately, there is no native in-place dot product for
# sparse matrices
out[:] = self.matrix.dot(x)
else:
with writable_array(out) as out_arr:
self.matrix.dot(x, out=out_arr)
def _call(self, x, out):
"""Mask ``x`` and store the result in ``out`` if given."""
# Find the indices of the mask min and max. The floating point
# versions are also required for the linear transition.
idx_min_flt = np.array(self.domain.partition.index(self.min_pt,
floating=True),
ndmin=1)
idx_max_flt = np.array(self.domain.partition.index(self.max_pt,
floating=True),
ndmin=1)
# To deal with coinciding boundaries we introduce an epsilon tolerance
epsilon = 1e-6
idx_min = np.floor(idx_min_flt - epsilon).astype(int)
idx_max = np.ceil(idx_max_flt + epsilon).astype(int)
def coeffs(d):
return (1.0 - (idx_min_flt[d] - idx_min[d]),
1.0 - (idx_max[d] - idx_max_flt[d]))
# Need an extra level of indirection in order to capture `d` inside
# the lambda expressions
def fn_pair(d):
return (lambda x: x * coeffs(d)[0], lambda x: x * coeffs(d)[1])
boundary_scale_fns = [fn_pair(d) for d in range(x.ndim)]
slc = tuple(slice(imin, imax) for imin, imax in zip(idx_min, idx_max))
slc_inner = tuple(
slice(imin + 1, imax - 1) for imin, imax in zip(idx_min, idx_max))
# Make a mask that is 1 outside the masking region, 0 inside
# and has a linear transition where the region boundary does not
# coincide with a cell boundary
mask = np.ones_like(x)
mask[slc_inner] = 0
apply_on_boundary(mask[slc],
boundary_scale_fns,
only_once=False,
out=mask[slc])
apply_on_boundary(mask[slc],
lambda x: 1.0 - x,
only_once=True,
out=mask[slc])
# out = masked version of x
out.assign(x)
with writable_array(out) as out_arr:
out_arr[slc] = mask[slc] * out_arr[slc]
def skimage_radon_forward_projector(volume, geometry, proj_space, out=None):
"""Calculate forward projection using skimage.
Parameters
----------
volume : `DiscreteLpElement`
The volume to project.
geometry : `Geometry`
The projection geometry to use.
proj_space : `DiscreteLp`
Space in which the projections (sinograms) live.
out : ``proj_space`` element, optional
Element to which the result should be written.
Returns
-------
sinogram : ``proj_space`` element
Result of the forward projection. If ``out`` was given, the returned
object is a reference to it.
"""
# Lazy import due to significant import time
from skimage.transform import radon
# Check basic requirements. Fully checking should be in wrapper
assert volume.shape[0] == volume.shape[1]
theta = np.degrees(geometry.angles)
skimage_range = skimage_proj_space(geometry, volume.space, proj_space)
# Rotate volume from (x, y) to (rows, cols), then project
sino_arr = radon(
np.rot90(volume.asarray(), 1), theta=theta, circle=False
)
sinogram = skimage_range.element(sino_arr.T)
if out is None:
out = proj_space.element()
with writable_array(out) as out_arr:
point_collocation(
clamped_interpolation(skimage_range, sinogram),
proj_space.grid.meshgrid,
out=out_arr,
)
scale = volume.space.cell_sides[0]
out *= scale
return out
def _call(self, x, out=None):
"""Apply resampling operator.
The element ``x`` is resampled using the sampling and interpolation
operators of the underlying spaces.
"""
interpolator = per_axis_interpolator(
x, self.domain.grid.coord_vectors, self.interp
)
out_ctx = nullcontext() if out is None else writable_array(out)
with out_ctx as out_arr:
return point_collocation(
interpolator, self.range.meshgrid, out=out_arr
)
def _call(self, x, out=None):
"""Calculate partial derivative of ``x``."""
if out is None:
out = self.range.element()
# TODO: this pipes CUDA arrays through NumPy. Write native operator.
with writable_array(out) as out_arr:
finite_diff(x.asarray(),
axis=self.axis,
dx=self.dx,
method=self.method,
pad_mode=self.pad_mode,
pad_const=self.pad_const,
out=out_arr)
return out
def _call(self, x, out=None):
"""Calculate the spatial gradient of ``x``."""
if out is None:
out = self.range.element()
x_arr = x.asarray()
ndim = self.domain.ndim
dx = self.domain.cell_sides
for axis in range(ndim):
with writable_array(out[axis]) as out_arr:
finite_diff(x_arr,
axis=axis,
dx=dx[axis],
method=self.method,
pad_mode=self.pad_mode,
pad_const=self.pad_const,
out=out_arr)
return out
def _call(self, x, out=None):
"""Calculate the spatial Laplacian of ``x``."""
if out is None:
out = self.range.zero()
else:
out.set_zero()
x_arr = x.asarray()
out_arr = out.asarray()
tmp = np.empty(out.shape, out.dtype, order=out.space.default_order)
ndim = self.domain.ndim
dx = self.domain.cell_sides
with writable_array(out) as out_arr:
for axis in range(ndim):
# TODO: this can be optimized
finite_diff(x_arr,
axis=axis,
dx=dx[axis]**2,
method='forward',
pad_mode=self.pad_mode,
pad_const=self.pad_const,
out=tmp)
out_arr += tmp
finite_diff(x_arr,
axis=axis,
dx=dx[axis]**2,
method='backward',
pad_mode=self.pad_mode,
pad_const=self.pad_const,
out=tmp)
out_arr -= tmp
return out
def _call(self, x, out=None):
"""Calculate the divergence of ``x``."""
if out is None:
out = self.range.element()
ndim = self.range.ndim
dx = self.range.cell_sides
tmp = np.empty(out.shape, out.dtype, order=out.space.default_order)
with writable_array(out) as out_arr:
for axis in range(ndim):
finite_diff(x[axis],
axis=axis,
dx=dx[axis],
method=self.method,
pad_mode=self.pad_mode,
pad_const=self.pad_const,
out=tmp)
if axis == 0:
out_arr[:] = tmp
else:
out_arr += tmp
return out
def astra_cpu_forward_projector(vol_data, geometry, proj_space, out=None):
"""Run an ASTRA forward projection on the given data using the CPU.
Parameters
----------
vol_data : `DiscreteLpElement`
Volume data to which the forward projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
proj_space : `DiscreteLp`
Space to which the calling operator maps
out : ``proj_space`` element, optional
Element of the projection space to which the result is written. If
``None``, an element in ``proj_space`` is created.
Returns
-------
out : ``proj_space`` element
Projection data resulting from the application of the projector.
If ``out`` was provided, the returned object is a reference to it.
"""
if not isinstance(vol_data, DiscreteLpElement):
raise TypeError('volume data {!r} is not a `DiscreteLpElement` '
'instance.'.format(vol_data))
if vol_data.space.impl != 'numpy':
raise TypeError('dspace {!r} of the volume is not an '
'instance of `NumpyNtuples`'
''.format(vol_data.space.dspace))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(proj_space, DiscreteLp):
raise TypeError('projection space {!r} is not a DiscreteLp '
'instance.'.format(proj_space))
if proj_space.impl != 'numpy':
raise TypeError('data type {!r} of the reconstruction space is not an '
'instance of NumpyNtuples'.format(proj_space.dspace))
if vol_data.ndim != geometry.ndim:
raise ValueError('dimensions {} of volume data and {} of geometry '
'do not match'
''.format(vol_data.ndim, geometry.ndim))
if out is None:
out = proj_space.element()
else:
if out not in proj_space:
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpElement instance'.format(out))
ndim = vol_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(vol_data.space)
proj_geom = astra_projection_geometry(geometry)
# Create projector
if not all(s == vol_data.space.interp_byaxis[0]
for s in vol_data.space.interp_byaxis):
raise ValueError('volume interpolation must be the same in each '
'dimension, got {}'.format(vol_data.space.interp))
vol_interp = vol_data.space.interp
proj_id = astra_projector(vol_interp,
vol_geom,
proj_geom,
ndim,
impl='cpu')
# Create ASTRA data structures
vol_id = astra_data(vol_geom,
datatype='volume',
data=vol_data,
allow_copy=True)
with writable_array(out, dtype='float32', order='C') as arr:
sino_id = astra_data(proj_geom,
datatype='projection',
data=arr,
ndim=proj_space.ndim)
# Create algorithm
algo_id = astra_algorithm('forward',
ndim,
vol_id,
sino_id,
proj_id,
impl='cpu')
# Run algorithm
astra.algorithm.run(algo_id)
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out
def astra_cpu_back_projector(proj_data, geometry, reco_space, out=None):
"""Run an ASTRA back-projection on the given data using the CPU.
Parameters
----------
proj_data : `DiscreteLpElement`
Projection data to which the back-projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
reco_space : `DiscreteLp`
Space to which the calling operator maps
out : ``reco_space`` element, optional
Element of the reconstruction space to which the result is written.
If ``None``, an element in ``reco_space`` is created.
Returns
-------
out : ``reco_space`` element
Reconstruction data resulting from the application of the backward
projector. If ``out`` was provided, the returned object is a
reference to it.
"""
if not isinstance(proj_data, DiscreteLpElement):
raise TypeError('projection data {!r} is not a DiscreteLpElement '
'instance'.format(proj_data))
if proj_data.space.impl != 'numpy':
raise TypeError('data type {!r} of the projection space is not an '
'instance of NumpyNtuples'
''.format(proj_data.shape.dspace))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(reco_space, DiscreteLp):
raise TypeError('reconstruction space {!r} is not a DiscreteLp '
'instance'.format(reco_space))
if reco_space.impl != 'numpy':
raise TypeError('data type {!r} of the reconstruction space is not an '
'instance of NumpyNtuples'.format(reco_space.dspace))
if reco_space.ndim != geometry.ndim:
raise ValueError('dimensions {} of reconstruction space and {} of '
'geometry do not match'.format(
reco_space.ndim, geometry.ndim))
if out is None:
out = reco_space.element()
else:
if out not in reco_space:
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpElement instance'.format(out))
ndim = proj_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(reco_space)
proj_geom = astra_projection_geometry(geometry)
# Create ASTRA data structure
sino_id = astra_data(proj_geom,
datatype='projection',
data=proj_data,
allow_copy=True)
# Create projector
# TODO: implement with different schemes for angles and detector
if not all(s == proj_data.space.interp_byaxis[0]
for s in proj_data.space.interp_byaxis):
raise ValueError('data interpolation must be the same in each '
'dimension, got {}'
''.format(proj_data.space.interp_byaxis))
proj_interp = proj_data.space.interp
proj_id = astra_projector(proj_interp,
vol_geom,
proj_geom,
ndim,
impl='cpu')
# convert out to correct dtype and order if needed
with writable_array(out, dtype='float32', order='C') as arr:
vol_id = astra_data(vol_geom,
datatype='volume',
data=arr,
ndim=reco_space.ndim)
# Create algorithm
algo_id = astra_algorithm('backward',
ndim,
vol_id,
sino_id,
proj_id,
impl='cpu')
# Run algorithm and delete it
astra.algorithm.run(algo_id)
# Weight the adjoint by appropriate weights
scaling_factor = float(proj_data.space.weighting.const)
scaling_factor /= float(reco_space.weighting.const)
out *= scaling_factor
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out
def astra_cpu_back_projector(proj_data,
geometry,
vol_space,
out=None,
astra_proj_type=None):
"""Run an ASTRA back-projection on the given data using the CPU.
Parameters
----------
proj_data : `DiscreteLpElement`
Projection data to which the back-projector is applied.
geometry : `Geometry`
Geometry defining the tomographic setup.
vol_space : `DiscreteLp`
Space to which the calling operator maps.
out : ``vol_space`` element, optional
Element of the reconstruction space to which the result is written.
If ``None``, an element in ``vol_space`` is created.
astra_proj_type : str, optional
Type of projector that should be used. See `the ASTRA documentation
<http://www.astra-toolbox.com/docs/proj2d.html>`_ for details.
By default, a suitable projector type for the given geometry is
selected, see `default_astra_proj_type`.
Returns
-------
out : ``vol_space`` element
Reconstruction data resulting from the application of the backward
projector. If ``out`` was provided, the returned object is a
reference to it.
"""
if not isinstance(proj_data, DiscreteLpElement):
raise TypeError('projection data {!r} is not a DiscreteLpElement '
'instance'.format(proj_data))
if proj_data.space.impl != 'numpy':
raise TypeError('`proj_data` must be a `numpy.ndarray` based, '
"container got `impl` {!r}"
"".format(proj_data.space.impl))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(vol_space, DiscreteLp):
raise TypeError('volume space {!r} is not a DiscreteLp '
'instance'.format(vol_space))
if vol_space.impl != 'numpy':
raise TypeError("`vol_space.impl` must be 'numpy', got {!r}"
"".format(vol_space.impl))
if vol_space.ndim != geometry.ndim:
raise ValueError('dimensions {} of reconstruction space and {} of '
'geometry do not match'.format(
vol_space.ndim, geometry.ndim))
if out is None:
out = vol_space.element()
else:
if out not in vol_space:
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpElement instance'.format(out))
ndim = proj_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(vol_space)
proj_geom = astra_projection_geometry(geometry)
# Create ASTRA data structure
sino_id = astra_data(proj_geom,
datatype='projection',
data=proj_data,
allow_copy=True)
# Create projector
if astra_proj_type is None:
astra_proj_type = default_astra_proj_type(geometry)
proj_id = astra_projector(astra_proj_type, vol_geom, proj_geom, ndim)
# Convert out to correct dtype and order if needed.
with writable_array(out, dtype='float32', order='C') as out_arr:
vol_id = astra_data(vol_geom,
datatype='volume',
data=out_arr,
ndim=vol_space.ndim)
# Create algorithm
algo_id = astra_algorithm('backward',
ndim,
vol_id,
sino_id,
proj_id,
impl='cpu')
# Run algorithm
astra.algorithm.run(algo_id)
# Weight the adjoint by appropriate weights
scaling_factor = float(proj_data.space.weighting.const)
scaling_factor /= float(vol_space.weighting.const)
out *= scaling_factor
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out
def astra_cpu_forward_projector(vol_data, geometry, proj_space, out=None,
astra_proj_type=None):
"""Run an ASTRA forward projection on the given data using the CPU.
Parameters
----------
vol_data : `DiscretizedSpaceElement`
Volume data to which the forward projector is applied.
geometry : `Geometry`
Geometry defining the tomographic setup.
proj_space : `DiscretizedSpace`
Space to which the calling operator maps.
out : ``proj_space`` element, optional
Element of the projection space to which the result is written. If
``None``, an element in ``proj_space`` is created.
astra_proj_type : str, optional
Type of projector that should be used. See `the ASTRA documentation
<http://www.astra-toolbox.com/docs/proj2d.html>`_ for details.
By default, a suitable projector type for the given geometry is
selected, see `default_astra_proj_type`.
Returns
-------
out : ``proj_space`` element
Projection data resulting from the application of the projector.
If ``out`` was provided, the returned object is a reference to it.
"""
if not isinstance(vol_data, DiscretizedSpaceElement):
raise TypeError(
'volume data {!r} is not a `DiscretizedSpaceElement` instance'
''.format(vol_data)
)
if vol_data.space.impl != 'numpy':
raise TypeError(
"`vol_data.space.impl` must be 'numpy', got {!r}"
"".format(vol_data.space.impl)
)
if not isinstance(geometry, Geometry):
raise TypeError(
'geometry {!r} is not a Geometry instance'.format(geometry)
)
if not isinstance(proj_space, DiscretizedSpace):
raise TypeError(
'`proj_space` {!r} is not a DiscretizedSpace instance.'
''.format(proj_space)
)
if proj_space.impl != 'numpy':
raise TypeError(
"`proj_space.impl` must be 'numpy', got {!r}"
"".format(proj_space.impl)
)
if vol_data.ndim != geometry.ndim:
raise ValueError(
'dimensions {} of volume data and {} of geometry do not match'
''.format(vol_data.ndim, geometry.ndim)
)
if out is None:
out = proj_space.element()
else:
if out not in proj_space:
raise TypeError(
'`out` {} is neither None nor a `DiscretizedSpaceElement` '
'instance'.format(out)
)
ndim = vol_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(vol_data.space)
proj_geom = astra_projection_geometry(geometry)
# Create projector
if astra_proj_type is None:
astra_proj_type = default_astra_proj_type(geometry)
proj_id = astra_projector(astra_proj_type, vol_geom, proj_geom, ndim)
# Create ASTRA data structures
vol_data_arr = np.asarray(vol_data)
vol_id = astra_data(vol_geom, datatype='volume', data=vol_data_arr,
allow_copy=True)
with writable_array(out, dtype='float32', order='C') as out_arr:
sino_id = astra_data(proj_geom, datatype='projection', data=out_arr,
ndim=proj_space.ndim)
# Create algorithm
algo_id = astra_algorithm('forward', ndim, vol_id, sino_id, proj_id,
impl='cpu')
# Run algorithm
astra.algorithm.run(algo_id)
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out
def _varlp_prox_call(f, out, exponent, sigma, impl, num_newton_iter, conj):
"""Implementation of the proximals of the modular and its conjugate.
Parameters
----------
f : domain element
Element at which to evaluate the operator.
out : range element
Element to which the result is written.
exponent : base space element
Variable exponent used in the modular.
sigma : positive float
Scaling parameter in the proximal operator.
impl : string
Implementation back-end for the proximal operator.
num_newton_iter : int, optional
Number of Newton iterations.
conj : bool
Apply the proximal of the convex conjugate if ``True``, otherwise
the proximal of the variable Lp modular.
"""
num_newton_iter = int(num_newton_iter)
sigma = float(sigma)
# Compute the magnitude of the input function
if isinstance(f.space, ProductSpace):
pwnorm = PointwiseNorm(f.space)
prox_fac = pwnorm(f)
else:
prox_fac = out
f.ufuncs.absolute(out=prox_fac)
if conj:
prox_npy = _numpy_impl.varlp_cc_prox_factor_npy
prox_numba = _numba_impl.varlp_cc_prox_factor_numba
prox_cython = _cython_impl.varlp_cc_prox_factor_cython
prox_gpuary = _gpuarray_impl.varlp_cc_prox_factor_gpuary
else:
prox_npy = _numpy_impl.varlp_prox_factor_npy
prox_numba = _numba_impl.varlp_prox_factor_numba
prox_cython = _cython_impl.varlp_prox_factor_cython
prox_gpuary = _gpuarray_impl.varlp_prox_factor_gpuary
# Compute the multiplicative factor
if impl == 'numpy':
with writable_array(prox_fac) as out_arr:
prox_npy(prox_fac.asarray(),
exponent.asarray(),
sigma,
num_newton_iter,
out=out_arr)
elif impl.startswith('numba'):
_, target = impl.split('_')
prox_fac[:] = prox_numba(prox_fac.asarray(),
exponent.asarray(),
sigma,
num_newton_iter,
target=target)
elif impl == 'cython':
if not _cython_impl.CYTHON_EXTENSION_BUILT:
raise RuntimeError(
'Cython extension has not been built. '
'Run `python setup.py build_ext` to build the extension.'
'For development installations of this package '
'(`pip install -e`), add the `--inplace` option to the '
'build command.')
with writable_array(prox_fac) as out_arr:
prox_cython(prox_fac.asarray(),
exponent.asarray(),
sigma,
num_newton_iter,
out=out_arr)
elif impl == 'gpuarray':
if not _gpuarray_impl.PYGPU_AVAILABLE:
raise RuntimeError(
'`pygpu` package not installed. You need Anaconda '
'(or Miniconda) to install it via `conda install pygpu`. '
'Alternatively, you can build it from source. See '
'https://github.com/Theano/libgpuarray for more information.')
# TODO: context manager for GPU arrays
if isinstance(f.space, ProductSpace):
dom_impl = f.space[0].impl
else:
dom_impl = f.space.impl
if dom_impl == 'gpuarray':
out_arr = prox_fac.tensor.data
prox_fac_arr = prox_fac.tensor.data
else:
out_arr = None
prox_fac_arr = prox_fac.asarray()
result = prox_gpuary(prox_fac_arr,
exponent.asarray(),
sigma,
num_newton_iter,
out=out_arr)
if out_arr is None:
prox_fac[:] = result
else:
raise RuntimeError('bad impl {!r}'.format(impl))
# Perform the multiplication
if isinstance(f.space, ProductSpace):
# out is empty
out.assign(f)
out *= prox_fac
else:
# out already stores the factor
out *= f
return f
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
"""Interface to Numpy's ufunc machinery.
This method is called by Numpy version 1.13 and higher as a single
point for the ufunc dispatch logic. An object implementing
``__array_ufunc__`` takes over control when a `numpy.ufunc` is
called on it, allowing it to use custom implementations and
output types.
This includes handling of in-place arithmetic like
``npy_array += custom_obj``. In this case, the custom object's
``__array_ufunc__`` takes precedence over the baseline
`numpy.ndarray` implementation. It will be called with
``npy_array`` as ``out`` argument, which ensures that the
returned object is a Numpy array. For this to work properly,
``__array_ufunc__`` has to accept Numpy arrays as ``out`` arguments.
See the `corresponding NEP`_ and the `interface documentation`_
for further details. See also the `general documentation on
Numpy ufuncs`_.
.. note::
This basic implementation casts inputs and
outputs to Numpy arrays and evaluates ``ufunc`` on those.
For `numpy.ndarray` based data storage, this incurs no
significant overhead compared to direct usage of Numpy arrays.
For other (in particular non-local) implementations, e.g.,
GPU arrays or distributed memory, overhead is significant due
to copies to CPU main memory. In those classes, the
``__array_ufunc__`` mechanism should be overridden in favor of
a native implementations if possible.
.. note::
If no ``out`` parameter is provided, this implementation
just returns the raw array and does not attempt to wrap the
result in any kind of space.
Parameters
----------
ufunc : `numpy.ufunc`
Ufunc that should be called on ``self``.
method : str
Method on ``ufunc`` that should be called on ``self``.
Possible values:
``'__call__'``, ``'accumulate'``, ``'at'``, ``'outer'``,
``'reduce'``, ``'reduceat'``
input1, ..., inputN:
Positional arguments to ``ufunc.method``.
kwargs:
Keyword arguments to ``ufunc.method``.
Returns
-------
ufunc_result : `Tensor`, `numpy.ndarray` or tuple
Result of the ufunc evaluation. If no ``out`` keyword argument
was given, the result is a `Tensor` or a tuple
of such, depending on the number of outputs of ``ufunc``.
If ``out`` was provided, the returned object or tuple entries
refer(s) to ``out``.
References
----------
.. _corresponding NEP:
https://docs.scipy.org/doc/numpy/neps/ufunc-overrides.html
.. _interface documentation:
https://docs.scipy.org/doc/numpy/reference/arrays.classes.html\
#numpy.class.__array_ufunc__
.. _general documentation on Numpy ufuncs:
https://docs.scipy.org/doc/numpy/reference/ufuncs.html
.. _reduceat documentation:
https://docs.scipy.org/doc/numpy/reference/generated/\
numpy.ufunc.reduceat.html
"""
# --- Process `out` --- #
# Unwrap out if provided. The output parameters are all wrapped
# in one tuple, even if there is only one.
out_tuple = kwargs.pop('out', ())
# Check number of `out` args, depending on `method`
if method == '__call__' and len(out_tuple) not in (0, ufunc.nout):
raise ValueError("ufunc {}: need 0 or {} `out` arguments for "
"`method='__call__'`, got {}"
''.format(ufunc.__name__, ufunc.nout,
len(out_tuple)))
elif method != '__call__' and len(out_tuple) not in (0, 1):
raise ValueError(
'ufunc {}: need 0 or 1 `out` arguments for `method={!r}`, '
'got {}'.format(ufunc.__name__, method, len(out_tuple)))
# We allow our own tensors, the data container type and
# `numpy.ndarray` objects as `out` (see docs for reason for the
# latter)
valid_types = (type(self), type(self.data), np.ndarray)
if not all(isinstance(o, valid_types) or o is None for o in out_tuple):
return NotImplemented
# Assign to `out` or `out1` and `out2`, respectively
out = out1 = out2 = None
if len(out_tuple) == 1:
out = out_tuple[0]
elif len(out_tuple) == 2:
out1 = out_tuple[0]
out2 = out_tuple[1]
# --- Process `inputs` --- #
# Convert inputs that are ODL tensors or their data containers to
# Numpy arrays so that the native Numpy ufunc is called later
inputs = tuple(
np.asarray(inp) if isinstance(inp, (type(self),
type(self.data))) else inp
for inp in inputs)
# --- Get some parameters for later --- #
# Arguments for `writable_array` and/or space constructors
out_dtype = kwargs.get('dtype', None)
if out_dtype is None:
array_kwargs = {}
else:
array_kwargs = {'dtype': out_dtype}
# --- Evaluate ufunc --- #
if method == '__call__':
if ufunc.nout == 1:
# Make context for output (trivial one returns `None`)
if out is None:
out_ctx = nullcontext()
else:
out_ctx = writable_array(out, **array_kwargs)
# Evaluate ufunc
with out_ctx as out_arr:
kwargs['out'] = out_arr
res = ufunc(*inputs, **kwargs)
# Return result (may be a raw array or a space element)
return res
elif ufunc.nout == 2:
# Make contexts for outputs (trivial ones return `None`)
if out1 is not None:
out1_ctx = writable_array(out1, **array_kwargs)
else:
out1_ctx = nullcontext()
if out2 is not None:
out2_ctx = writable_array(out2, **array_kwargs)
else:
out2_ctx = nullcontext()
# Evaluate ufunc
with out1_ctx as out1_arr, out2_ctx as out2_arr:
kwargs['out'] = (out1_arr, out2_arr)
res1, res2 = ufunc(*inputs, **kwargs)
# Return results (may be raw arrays or space elements)
return res1, res2
else:
raise NotImplementedError('nout = {} not supported'
''.format(ufunc.nout))
else: # method != '__call__'
# Make context for output (trivial one returns `None`)
if out is None:
out_ctx = nullcontext()
else:
out_ctx = writable_array(out, **array_kwargs)
# Evaluate ufunc method
if method == 'at':
with writable_array(inputs[0]) as inp_arr:
res = ufunc.at(inp_arr, *inputs[1:], **kwargs)
else:
with out_ctx as out_arr:
kwargs['out'] = out_arr
res = getattr(ufunc, method)(*inputs, **kwargs)
# Return result (may be scalar, raw array or space element)
return res
def astra_cpu_back_projector(proj_data, geometry, reco_space, out=None):
"""Run an ASTRA back-projection on the given data using the CPU.
Parameters
----------
proj_data : `DiscreteLpElement`
Projection data to which the back-projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
reco_space : `DiscreteLp`
Space to which the calling operator maps
out : ``reco_space`` element, optional
Element of the reconstruction space to which the result is written.
If ``None``, an element in ``reco_space`` is created.
Returns
-------
out : ``reco_space`` element
Reconstruction data resulting from the application of the backward
projector. If ``out`` was provided, the returned object is a
reference to it.
"""
if not isinstance(proj_data, DiscreteLpElement):
raise TypeError('projection data {!r} is not a DiscreteLpElement '
'instance'.format(proj_data))
if proj_data.space.impl != 'numpy':
raise TypeError('data type {!r} of the projection space is not an '
'instance of NumpyNtuples'
''.format(proj_data.shape.dspace))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(reco_space, DiscreteLp):
raise TypeError('reconstruction space {!r} is not a DiscreteLp '
'instance'.format(reco_space))
if reco_space.impl != 'numpy':
raise TypeError('data type {!r} of the reconstruction space is not an '
'instance of NumpyNtuples'.format(reco_space.dspace))
if reco_space.ndim != geometry.ndim:
raise ValueError('dimensions {} of reconstruction space and {} of '
'geometry do not match'.format(
reco_space.ndim, geometry.ndim))
if out is None:
out = reco_space.element()
else:
if out not in reco_space:
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpElement instance'.format(out))
ndim = proj_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(reco_space)
proj_geom = astra_projection_geometry(geometry)
# Create ASTRA data structure
sino_id = astra_data(proj_geom, datatype='projection', data=proj_data,
allow_copy=True)
# Create projector
# TODO: implement with different schemes for angles and detector
if not all(s == proj_data.space.interp_by_axis[0]
for s in proj_data.space.interp_by_axis):
raise ValueError('data interpolation must be the same in each '
'dimension, got {}'
''.format(proj_data.space.interp_by_axis))
proj_interp = proj_data.space.interp
proj_id = astra_projector(proj_interp, vol_geom, proj_geom, ndim,
impl='cpu')
# convert out to correct dtype and order if needed
with writable_array(out, dtype='float32', order='C') as arr:
vol_id = astra_data(vol_geom, datatype='volume', data=arr,
ndim=reco_space.ndim)
# Create algorithm
algo_id = astra_algorithm('backward', ndim, vol_id, sino_id, proj_id,
impl='cpu')
# Run algorithm and delete it
astra.algorithm.run(algo_id)
# Angular integration weighting factor
# angle interval weight by approximate cell volume
extent = float(geometry.motion_partition.extent)
size = float(geometry.motion_partition.size)
scaling_factor = extent / size
# Fix inconsistent scaling: parallel2d & fanflat scale with (voxel
# stride)**2 / (pixel stride), currently only square voxels are supported
scaling_factor *= float(geometry.det_partition.cell_sides[0])
scaling_factor /= float(reco_space.partition.cell_sides[0]) ** 2
out *= scaling_factor
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out
def skimage_radon_back_projector(sinogram, geometry, vol_space, out=None):
"""Calculate forward projection using skimage.
Parameters
----------
sinogram : `DiscreteLpElement`
Sinogram (projections) to backproject.
geometry : `Geometry`
The projection geometry to use.
vol_space : `DiscreteLp`
Space in which reconstructed volumes live.
out : ``vol_space`` element, optional
An element to which the result should be written.
Returns
-------
volume : ``vol_space`` element
Result of the back-projection. If ``out`` was given, the returned
object is a reference to it.
"""
# Lazy import due to significant import time
from skimage.transform import iradon
theta = np.degrees(geometry.angles)
skimage_range = skimage_proj_space(geometry, vol_space, sinogram.space)
skimage_sinogram = skimage_range.element()
with writable_array(skimage_sinogram) as sino_arr:
point_collocation(
clamped_interpolation(sinogram.space, sinogram),
skimage_range.grid.meshgrid,
out=sino_arr,
)
if out is None:
out = vol_space.element()
else:
# Only do asserts here since these are backend functions
assert out in vol_space
# Rotate back from (rows, cols) to (x, y), then back-project (no filter)
backproj = iradon(
skimage_sinogram.asarray().T,
theta,
output_size=vol_space.shape[0],
filter=None,
circle=False,
)
out[:] = np.rot90(backproj, -1)
# Empirically determined value, gives correct scaling
scaling_factor = 4 * geometry.motion_params.length / (2 * np.pi)
# Correct in case of non-weighted spaces
proj_volume = np.prod(sinogram.space.partition.extent)
proj_size = sinogram.space.partition.size
proj_weighting = proj_volume / proj_size
scaling_factor *= sinogram.space.weighting.const / proj_weighting
scaling_factor /= vol_space.weighting.const / vol_space.cell_volume
# Correctly scale the output
out *= scaling_factor
return out
def astra_cpu_forward_projector(vol_data, geometry, proj_space, out=None):
"""Run an ASTRA forward projection on the given data using the CPU.
Parameters
----------
vol_data : `DiscreteLpElement`
Volume data to which the forward projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
proj_space : `DiscreteLp`
Space to which the calling operator maps
out : ``proj_space`` element, optional
Element of the projection space to which the result is written. If
``None``, an element in ``proj_space`` is created.
Returns
-------
out : ``proj_space`` element
Projection data resulting from the application of the projector.
If ``out`` was provided, the returned object is a reference to it.
"""
if not isinstance(vol_data, DiscreteLpElement):
raise TypeError('volume data {!r} is not a `DiscreteLpElement` '
'instance.'.format(vol_data))
if vol_data.space.impl != 'numpy':
raise TypeError('dspace {!r} of the volume is not an '
'instance of `NumpyNtuples`'
''.format(vol_data.space.dspace))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(proj_space, DiscreteLp):
raise TypeError('projection space {!r} is not a DiscreteLp '
'instance.'.format(proj_space))
if proj_space.impl != 'numpy':
raise TypeError('data type {!r} of the reconstruction space is not an '
'instance of NumpyNtuples'.format(proj_space.dspace))
if vol_data.ndim != geometry.ndim:
raise ValueError('dimensions {} of volume data and {} of geometry '
'do not match'
''.format(vol_data.ndim, geometry.ndim))
if out is None:
out = proj_space.element()
else:
if out not in proj_space:
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpElement instance'.format(out))
ndim = vol_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(vol_data.space)
proj_geom = astra_projection_geometry(geometry)
# Create projector
if not all(s == vol_data.space.interp_by_axis[0]
for s in vol_data.space.interp_by_axis):
raise ValueError('volume interpolation must be the same in each '
'dimension, got {}'.format(vol_data.space.interp))
vol_interp = vol_data.space.interp
proj_id = astra_projector(vol_interp, vol_geom, proj_geom, ndim,
impl='cpu')
# Create ASTRA data structures
vol_id = astra_data(vol_geom, datatype='volume', data=vol_data,
allow_copy=True)
with writable_array(out, dtype='float32', order='C') as arr:
sino_id = astra_data(proj_geom, datatype='projection', data=arr,
ndim=proj_space.ndim)
# Create algorithm
algo_id = astra_algorithm('forward', ndim, vol_id, sino_id, proj_id,
impl='cpu')
# Run algorithm
astra.algorithm.run(algo_id)
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out
def _call(self, f):
"""Return ``self(f)``."""
# Compute the magnitude of the input function
if isinstance(self.domain, ProductSpace):
pwnorm = PointwiseNorm(self.domain)
integrand = pwnorm(f)
else:
integrand = f.ufuncs.absolute()
# Compute the integrand
if self.impl == 'numpy':
with writable_array(integrand) as out_arr:
_numpy_impl.varlp_cc_integrand_npy(integrand.asarray(),
self.exponent.asarray(),
out=out_arr)
elif self.impl.startswith('numba'):
_, target = self.impl.split('_')
integrand[:] = _numba_impl.varlp_cc_integrand_numba(
integrand.asarray(), self.exponent.asarray(), target=target)
elif self.impl == 'cython':
if not _cython_impl.CYTHON_EXTENSION_BUILT:
raise RuntimeError(
'Cython extension has not been built. '
'Run `python setup.py build_ext` to build the extension.'
'For development installations of this package '
'(`pip install -e`), add the `--inplace` option to the '
'build command.')
with writable_array(integrand) as out_arr:
_cython_impl.varlp_cc_integrand_cython(integrand.asarray(),
self.exponent.asarray(),
out=out_arr)
elif self.impl == 'gpuarray':
if not _gpuarray_impl.PYGPU_AVAILABLE:
raise RuntimeError(
'`pygpu` package not installed. You need Anaconda '
'(or Miniconda) to install it via `conda install pygpu`. '
'Alternatively, you can build it from source. See '
'https://github.com/Theano/libgpuarray for more '
'information.')
# TODO: context manager for GPU arrays
if isinstance(self.domain, ProductSpace):
dom_impl = self.domain[0].impl
else:
dom_impl = self.domain.impl
if dom_impl == 'gpuarray':
out_arr = integrand.data
integrand_arr = integrand.data
else:
out_arr = None
integrand_arr = integrand.asarray()
result = _gpuarray_impl.varlp_cc_integrand_gpuary(
integrand_arr, self.exponent.asarray(), out=out_arr)
if out_arr is None:
integrand[:] = result
else:
raise RuntimeError('bad impl {!r}'.format(self.impl))
# Integrate
if isinstance(self.domain, ProductSpace):
return integrand.inner(self.domain[0].one())
else:
return integrand.inner(self.domain.one())
