CHAP.tomo.models

Tomography Pydantic model classes.

Module Contents

Classes

Detector	Detector class to represent the detector used in the experiment. The image origin is assumed to be in the top-left corner, with rows down (-z in lab frame) and columns sideways (+x in lab frame).
TomoReduceConfig	Class representing the configuration for the tomography image reduction processor.
TomoFindCenterConfig	Class representing the configuration for the tomography center axis finding processor.
TomoReconstructConfig	Class representing the configuration for the tomography image reconstruction processor.
TomoCombineConfig	Class representing the configuration for the combined tomography stacks processor.
TomoSimConfig	Class representing the configuration for the tomography simulator.

API

class CHAP.tomo.models.Detector

Bases: pydantic.BaseModel

Detector class to represent the detector used in the experiment. The image origin is assumed to be in the top-left corner, with rows down (-z in lab frame) and columns sideways (+x in lab frame).

Variables:

• prefix – Prefix of the detector in the SPEC file.

• rows – Number of pixel rows on the detector.

• columns – Number of pixel columns on the detector.

• pixel_size – Pixel size of the detector in mm.

• lens_magnification – Lens magnification for the detector, defaults to 1.0.

prefix: constr(strip_whitespace=True, min_length=1)

None

rows: conint(gt=0)

None

columns: conint(gt=0)

None

pixel_size: conlist(item_type=confloat(gt=0, allow_inf_nan=False), min_length=1, max_length=2)

None

lens_magnification: confloat(gt=0, allow_inf_nan=False)

1.0

class CHAP.tomo.models.TomoReduceConfig

Bases: pydantic.BaseModel

Class representing the configuration for the tomography image reduction processor.

Variables:

• img_row_bounds – Detector image bounds in the row-direction (ignored for id1a3 and id3a).

• delta_theta – Rotation angle increment in image reduction in degrees.

img_row_bounds: Optional[conlist(item_type=conint(ge=-1), min_length=2, max_length=2)]

None

delta_theta: Optional[confloat(gt=0, allow_inf_nan=False)]

None

class CHAP.tomo.models.TomoFindCenterConfig

Bases: pydantic.BaseModel

Class representing the configuration for the tomography center axis finding processor.

Variables:

• center_stack_index – Stack index of the tomography set to find the center axis.

• center_rows – Row indices for the center finding processor.

• center_offsets – Centers at the center finding row indices in pixels.

• center_offset_min – Minimum value of center_offset in center axis finding search in pixels.

• center_offset_max – Maximum value of center_offset in center axis finding search in pixels.

• gaussian_sigma – Standard deviation for the Gaussian filter applied to image reconstruction visualizations, defaults to no filtering performed.

• ring_width – Maximum width of rings to be filtered in the image reconstruction in pixels, defaults to no filtering performed.

center_stack_index: Optional[conint(ge=0)]

None

center_rows: Optional[conlist(item_type=conint(ge=0), min_length=2, max_length=2)]

None

center_offsets: Optional[conlist(item_type=confloat(allow_inf_nan=False), min_length=2, max_length=2)]

None

center_offset_min: Optional[confloat(allow_inf_nan=False)]

None

center_offset_max: Optional[confloat(allow_inf_nan=False)]

None

center_search_range: Optional[conlist(item_type=confloat(allow_inf_nan=False), min_length=1, max_length=3)]

None

gaussian_sigma: Optional[confloat(ge=0, allow_inf_nan=False)]

None

ring_width: Optional[confloat(ge=0, allow_inf_nan=False)]

None

class CHAP.tomo.models.TomoReconstructConfig

Bases: pydantic.BaseModel

Class representing the configuration for the tomography image reconstruction processor.

Variables:

• x_bounds – Reconstructed image bounds in the x-direction.

• y_bounds – Reconstructed image bounds in the y-direction.

• z_bounds – Reconstructed image bounds in the z-direction.

• secondary_iters – Number of secondary iterations in the tomopy image reconstruction algorithm, defaults to 0.

• gaussian_sigma – Standard deviation for the Gaussian filter applied to image reconstruction visualizations, defaults to no filtering performed.

• remove_stripe_sigma – Damping parameter in Fourier space in tomopy’s horizontal stripe removal tool, defaults to no correction performed.

• ring_width – Maximum width of rings to be filtered in the image reconstruction in pixels, defaults to no filtering performed.

x_bounds: Optional[conlist(item_type=conint(ge=-1), min_length=2, max_length=2)]

None

y_bounds: Optional[conlist(item_type=conint(ge=-1), min_length=2, max_length=2)]

None

z_bounds: Optional[conlist(item_type=conint(ge=-1), min_length=2, max_length=2)]

None

secondary_iters: conint(ge=0)

0

gaussian_sigma: Optional[confloat(ge=0, allow_inf_nan=False)]

None

remove_stripe_sigma: Optional[confloat(ge=0, allow_inf_nan=False)]

None

ring_width: Optional[confloat(ge=0, allow_inf_nan=False)]

None

class CHAP.tomo.models.TomoCombineConfig

Bases: pydantic.BaseModel

Class representing the configuration for the combined tomography stacks processor.

Variables:

• x_bounds – Combined image bounds in the x-direction.

• y_bounds – Combined image bounds in the y-direction.

• z_bounds – Combined image bounds in the z-direction.

x_bounds: Optional[conlist(item_type=conint(ge=-1), min_length=2, max_length=2)]

None

y_bounds: Optional[conlist(item_type=conint(ge=-1), min_length=2, max_length=2)]

None

z_bounds: Optional[conlist(item_type=conint(ge=-1), min_length=2, max_length=2)]

None

class CHAP.tomo.models.TomoSimConfig

Bases: pydantic.BaseModel

Class representing the configuration for the tomography simulator.

Variables:

• station – The station name (in ‘idxx’ format).

• detector – Detector used in the tomography experiment.

• sample_type – Sample type for the tomography simulator.

• sample_size – Size of each sample dimension in mm (internally converted to an integer number of pixels). Enter three values for sample_type == 'hollow_pyramid', the height and the side at the respective bottom and the top of the pyramid.

• wall_thickness – Wall thickness for pipe, cube, and brick in mm (internally converted to an integer number of pixels).

• mu – Linear attenuation coefficient in mm^-1, defaults to 0.05.

• theta_step – Rotation angle increment in the tomography simulation in degrees.

• beam_intensity – Initial beam intensity in counts, defaults to 1.e9.

• background_intensity – Background intensity in counts, defaults to 20.

• slit_size – Vertical beam height in mm, defaults to 1.0.

station: Literal[id1a3, id3a, id3b]

None

detector: Detector.model_construct()

None

sample_type: Literal[square_rod, square_pipe, hollow_cube, hollow_brick, hollow_pyramid]

None

sample_size: conlist(item_type=confloat(gt=0, allow_inf_nan=False), min_length=1, max_length=3)

None

wall_thickness: Optional[confloat(ge=0, allow_inf_nan=False)]

None

mu: Optional[confloat(gt=0, allow_inf_nan=False)]

0.05

theta_step: confloat(gt=0, allow_inf_nan=False)

None

beam_intensity: Optional[confloat(gt=0, allow_inf_nan=False)]

1000000000.0

background_intensity: Optional[confloat(gt=0, allow_inf_nan=False)]

20

slit_size: Optional[confloat(gt=0, allow_inf_nan=False)]

1.0