Flat2dDetector¶

class odl.tomo.geometry.detector.Flat2dDetector(partition, axes, check_bounds=True)[source]¶

Bases: Detector

A 2D flat panel detector aligned two given axes in 3D space.

Attributes:

axes: Fixed array of unit vectors with which the detector is aligned.
check_bounds: If True, methods computing vectors check input arguments.
grid: Sampling grid of the parameters.
ndim: Number of dimensions of the parameters (= surface dimension).
params: Surface parameter set of this detector.
partition: Partition of the detector parameter set into subsets.
shape: Number of subsets (pixels) of the detector per axis.
size: Total number of pixels.
space_ndim: Number of dimensions of the embedding space.

Methods

`surface`(param)	Return the detector surface point corresponding to `param`.
`surface_deriv`(param)	Return the surface derivative at `param`.
`surface_measure`(param)	Density function of the surface measure.
`surface_normal`(param)	Unit vector perpendicular to the detector surface at `param`.

__init__(partition, axes, check_bounds=True)[source]¶

Initialize a new instance.

Parameters:

partition2-dim. RectPartition: Partition of the parameter rectangle, corresponding to the pixels.
axessequence of array-like's: Fixed pair of of unit vectors with which the detector is aligned. The vectors must have shape (3,) and be linearly independent.
check_boundsbool, optional: If True, methods computing vectors check input arguments. Checks are vectorized and add only a small overhead.

Examples

>>> part = odl.uniform_partition([0, 0], [1, 1], (10, 10))
>>> det = Flat2dDetector(part, axes=[(1, 0, 0), (0, 0, 1)])
>>> det.axes
array([[ 1.,  0.,  0.],
       [ 0.,  0.,  1.]])
>>> det.surface_normal([0, 0])
array([ 0., -1.,  0.])