Utilities

This category contains 19 operator(s).

class AutoCenterOfMassTiltImageAlignment

AutoCenterOfMassTiltImageAlignment

No description available.

Note

This operator is not compatible with external Python execution.

transform(self, dataset)

Results

• alignments (table) - Alignments

Implementation

• Python script: AutoCenterOfMassTiltImageAlignment.py

• JSON descriptor: AutoCenterOfMassTiltImageAlignment.json

class DummyMolecule

DummyMolecule

No description available.

Note

This operator is not compatible with external Python execution.

transform(self, dataset)

Results

• molecule (molecule) - Reconstructed atomic positions

Implementation

• Python script: DummyMolecule.py

• JSON descriptor: DummyMolecule.json

class AutoTiltAxisRotationAlignment

AutoTiltAxisRotationAlignment

No description available.

transform(self, dataset)

Implementation

• Python script: AutoTiltAxisRotationAlignment.py

• JSON descriptor: AutoTiltAxisRotationAlignment.json

class AddConstant

Add Constant

Add a constant value to each voxel in the dataset.

transform(dataset, constant=0.0)

Parameters

• constant (double) - Constant factor added to each voxel.

  • default: 0.0

  • precision: 3 decimal places

Implementation

• Python script: AddConstant.py

• JSON descriptor: AddConstant.json

class CircleMask

Circle Mask

Apply a circle mask to the data (requires tomopy)

transform(dataset, axis, ratio, value)

Parameters

• axis (enumeration) - Axis along which mask will be performed

  • default: 0

  • options:

    • 0 - X

    • 1 - Y

    • 2 - Z

• ratio (double) - Ratio of the mask’s diameter in pixels to the smallest edge size along given axis

  • default: 1.0

  • minimum: 0.0

  • maximum: 1.0

  • precision: 3 decimal places

  • step: 0.05

• value (double) - Value for the masked region

  • default: 0.0

  • precision: 5 decimal places

Implementation

• Python script: CircleMask.py

• JSON descriptor: CircleMask.json

class ClipEdges

Clip Edges

Sets the value of pixels near the edges of the volume to the minimum value leaving only a circular region with data to avoid artifacts near the edge of the reconstruction.

transform(dataset, clipNum=5)

Parameters

• clipNum (int) - The distance from the edge of the volume to the circular region where data will be untouched.

  • default: 5

  • minimum: 0

Implementation

• Python script: ClipEdges.py

• JSON descriptor: ClipEdges.json

class ConnectedComponents

Connected Components

Compute label map of connected components. The components are relabeled in order of increasing volume in the output.

transform(self, dataset, background_value=0)

Parameters

• background_value (int)

  • default: 0

Implementation

• Python script: ConnectedComponents.py

• JSON descriptor: ConnectedComponents.json

class PoreSizeDistribution

Continuous Pore Size Distribution

Continuous Pore Size Distribution calculation

transform(self, dataset, threshold=127, radius_spacing=1, save_to_file=False, output_folder='')

Parameters

• threshold (int)

  • default: 127

  • minimum: 0

• radius_spacing (int)

  • default: 1

  • minimum: 1

• save_to_file (bool)

  • default: False

• output_folder (directory)

  • default: ''

Results

• pore_size_distribution (table) - Pore Size Distribution

Implementation

• Python script: PoreSizeDistribution.py

• JSON descriptor: PoreSizeDistribution.json

class DeleteSlices

Delete Slices

Delete slices, including associated tilt angles, from a dataset

transform(dataset, firstSlice=None, lastSlice=None, axis=2)

Parameters

• firstSlice (int) - The first slice in the slice range (inclusive)

  • default: 0

  • minimum: 0

  • maximum: 1000000

• lastSlice (int) - The last slice in the slice range (inclusive)

  • default: 0

  • minimum: 0

  • maximum: 1000000

• axis (enumeration) - Axis along which to delete slices

  • default: 2

  • options:

    • 0 - X

    • 1 - Y

    • 2 - Z

Implementation

• Python script: DeleteSlices.py

• JSON descriptor: DeleteSlices.json

class ElastixRegistration

Elastix Registration

Perform volume registration between two volumes, one moving, and one fixed, through the use of ITKElastix.

Note

This operator is not compatible with external Python execution.

transform(moving_dataset, fixed_dataset, max_num_iterations, num_resolutions, disable_rotation, interpolator, bspline_interpolation_order, resample_interpolator, resample_bspline_interpolation_order, lower_threshold, upper_threshold, show_elastix_console_output)

Parameters

• fixed_dataset (dataset) - The dataset to which the moving dataset will be aligned

• max_num_iterations (int) - Maps to: MaximumNumberOfIterations

  • default: 256

• num_resolutions (int) - Maps to: NumberOfResolutions

  • default: 4

• disable_rotation (bool) - Only perform translation

  • default: False

• interpolator (enumeration) - The interpolator that is used during optimization

  • default: 'LinearInterpolator'

  • options:

    • 'NearestNeighborInterpolator' - NearestNeighborInterpolator

    • 'LinearInterpolator' - LinearInterpolator

    • 'BSplineInterpolator' - BSplineInterpolator

    • 'BSplineInterpolatorFloat' - BSplineInterpolatorFloat

• bspline_interpolation_order (int) - For BSpline interpolation only

  • default: 3

  • minimum: 0

• resample_interpolator (enumeration) - The interpolator that is used to generate the final result

  • default: 'FinalBSplineInterpolator'

  • options:

    • 'FinalNearestNeighborInterpolator' - FinalNearestNeighborInterpolator

    • 'FinalLinearInterpolator' - FinalLinearInterpolator

    • 'FinalBSplineInterpolator' - FinalBSplineInterpolator

    • 'FinalBSplineInterpolatorFloat' - FinalBSplineInterpolatorFloat

• resample_bspline_interpolation_order (int) - For Resample BSpline interpolation only

  • default: 3

  • minimum: 0

• lower_threshold (double) - Minimum histogram value to use for registration

  • default: -1000.0

  • precision: 5 decimal places

• upper_threshold (double) - Maximum histogram value to use for registration

  • default: 1000.0

  • precision: 5 decimal places

• show_elastix_console_output (bool) - Show Elastix console output

  • default: False

Implementation

• Python script: ElastixRegistration.py

• JSON descriptor: ElastixRegistration.json

class FourierShellCorrelation

Fourier Shell Correlation

Note

This operator is not compatible with external Python execution.

transform(self, dataset, selected_scalars=None)

Parameters

• selected_scalars (select_scalars)

Results

• plot (table) - FSC

Implementation

• Python script: FourierShellCorrelation.py

• JSON descriptor: FourierShellCorrelation.json

class GenerateTiltSeries

Generate Tilt Series

Generate electron tomography tilt series from volume dataset. Object is rotated about the x-axis and projected along the z-axis.

transform(self, dataset, start_angle=-90.0, angle_increment=3.0, num_tilts=60)

Parameters

• start_angle (double)

  • default: -90.0

  • minimum: -180.0

  • maximum: 180.0

• angle_increment (double)

  • default: 3.0

  • minimum: -180.0

  • maximum: 180.0

• num_tilts (int)

  • default: 60

  • minimum: 1

Implementation

• Python script: GenerateTiltSeries.py

• JSON descriptor: GenerateTiltSeries.json

class ManualManipulation

Manual Manipulation

Manually manipulate a dataset with scaling, translations, and rotations

Note

This operator is not compatible with external Python execution.

transform(dataset, scaling=None, rotation=None, shift=None, align_with_reference=False, reference_spacing=None, reference_shape=None)

Parameters

• unknown (xyz_header)

• scaling (double) - The scaling to apply

  • default: [0.0, 0.0, 0.0]

  • minimum: negative_size

  • maximum: size

• rotation (double) - The rotation to apply

  • default: [0.0, 0.0, 0.0]

  • minimum: negative_size

  • maximum: size

• shift (int) - The shift to apply

  • default: [0, 0, 0]

  • minimum: negative_size

  • maximum: size

• align_with_reference (bool) - Make spacing and voxels of output match reference data

  • default: False

• reference_spacing (double) - The reference spacing

  • default: [0.0, 0.0, 0.0]

  • minimum: negative_size

  • maximum: size

• reference_shape (int) - The reference shape

  • default: [0, 0, 0]

  • minimum: negative_size

  • maximum: size

Implementation

• Python script: ManualManipulation.py

• JSON descriptor: ManualManipulation.json

class PeronaMalikAnisotropicDiffusion

Perona-Malik Anistropic Diffusion

This filter performs anisotropic diffusion on an image using the classic Perona-Malik, gradient magnitude-based equation.

Note

This operator is not compatible with external Python execution.

transform(self, dataset, conductance=1.0, iterations=100, timestep=0.0625)

Parameters

• conductance (double)

  • default: 1.0

  • minimum: 0.0

• iterations (int)

  • default: 100

  • minimum: 0

• timestep (double)

  • default: 0.0625

  • minimum: 0.0

  • precision: 4 decimal places

Implementation

• Python script: PeronaMalikAnisotropicDiffusion.py

• JSON descriptor: PeronaMalikAnisotropicDiffusion.json

class PowerSpectrumDensity

Power Spectrum Density

Note

This operator is not compatible with external Python execution.

transform(self, dataset, selected_scalars=None)

Parameters

• selected_scalars (select_scalars)

Results

• plot (table) - PSD

Implementation

• Python script: PowerSpectrumDensity.py

• JSON descriptor: PowerSpectrumDensity.json

class RemoveArrays

Remove Arrays

Select which scalar arrays to keep. All unselected arrays will be removed from the dataset.

transform(dataset, selected_scalars=None)

Parameters

• selected_scalars (select_scalars)

Implementation

• Python script: RemoveArrays.py

• JSON descriptor: RemoveArrays.json

class SimilarityMetrics

Similarity Metrics

Compute similarity metrics between the current dataset and a reference dataset.

transform(self, dataset, reference_dataset=None, selected_scalars=None, axis=2)

Parameters

• selected_scalars (select_scalars)

• axis (enumeration) - The axis along which to process slices

  • default: 2

  • options:

    • 0 - X

    • 1 - Y

    • 2 - Z

• reference_dataset (dataset) - The reference dataset to compare against

Results

• similarity (table) - Similarity

Implementation

• Python script: SimilarityMetrics.py

• JSON descriptor: SimilarityMetrics.json

class Tortuosity

Tortuosity

Tortuosity calculation

transform(self, dataset, phase=1, distance_method=DistanceMethod.Eucledian, propagation_direction=PropagationDirection.Xpos, save_to_file=False, output_folder='')

Parameters

• phase (int)

  • default: 1

  • minimum: 0

• distance_method (enumeration)

  • default: 0

  • options:

    • 0 - Eucledian

    • 1 - CityBlock

    • 2 - ChessBoard

• propagation_direction (enumeration)

  • default: 0

  • options:

    • 0 - X +

    • 1 - X -

    • 2 - Y +

    • 3 - Y -

    • 4 - Z +

    • 5 - Z -

• save_to_file (bool)

  • default: False

• output_folder (directory)

  • default: ''

Results

• tortuosity (table) - Tortuosity

• path_length (table) - Path Length

• tortuosity_distribution (table) - Tortuosity Distribution

Implementation

• Python script: Tortuosity.py

• JSON descriptor: Tortuosity.json

class UnsharpMask

Unsharp Mask

Sharpen the image with the unsharp mask technique. sharpened=original+[abs(original-blurred)-threshold]*amount

Note

This operator is not compatible with external Python execution.

transform(self, dataset, amount=0.5, threshold=0.0, sigma=1.0)

Parameters

• amount (double)

  • default: 0.5

  • minimum: 0.0

• threshold (double)

  • default: 0.0

  • minimum: 0.0

• sigma (double)

  • default: 1.0

  • minimum: 0.0

Implementation

• Python script: UnsharpMask.py

• JSON descriptor: UnsharpMask.json