# workflows

## Module: workflows.align

 AffineMap(affine[, domain_grid_shape, ...]) Methods ApplyTransformFlow([output_strategy, ...]) Methods CCMetric(dim[, sigma_diff, radius]) Methods DiffeomorphicMap(dim, disp_shape[, ...]) Methods EMMetric(dim[, smooth, inner_iter, ...]) Methods ImageRegistrationFlow([output_strategy, ...]) The registration workflow allows the user to use only one type of registration (such as center of mass or rigid body registration only). MotionCorrectionFlow([output_strategy, ...]) The Motion Correction workflow allows the user to align between-volumes DWI dataset. ResliceFlow([output_strategy, mix_names, ...]) Methods SSDMetric(dim[, smooth, inner_iter, step_type]) Methods SlrWithQbxFlow([output_strategy, mix_names, ...]) Methods SymmetricDiffeomorphicRegistration(metric[, ...]) Methods SynRegistrationFlow([output_strategy, ...]) Methods Workflow([output_strategy, mix_names, ...]) Methods affine_registration(moving, static[, ...]) Find the affine transformation between two 3D images. check_dimensions(static, moving) Check the dimensions of the input images. gradient_table(bvals[, bvecs, big_delta, ...]) A general function for creating diffusion MR gradients. load_nifti(fname[, return_img, ...]) Load data and other information from a nifti file. read_bvals_bvecs(fbvals, fbvecs) Read b-values and b-vectors from disk. reslice(data, affine, zooms, new_zooms[, ...]) Reslice data with new voxel resolution defined by new_zooms. save_nifti(fname, data, affine[, hdr, dtype]) Save a data array into a nifti file. save_qa_metric(fname, xopt, fopt) Save Quality Assurance metrics. slr_with_qbx(static, moving[, x0, ...]) Utility function for registering large tractograms. transform_streamlines(streamlines, mat[, ...]) Apply affine transformation to streamlines warn(/, message[, category, stacklevel, source]) Issue a warning, or maybe ignore it or raise an exception.

## Module: workflows.base

 IntrospectiveArgumentParser([prog, usage, ...]) Attributes: NumpyDocString(docstring[, config]) none_or_dtype(dtype) Check None presence before type casting.

## Module: workflows.combined_workflow

 CombinedWorkflow([output_strategy, ...]) Methods Workflow([output_strategy, mix_names, ...]) Methods

## Module: workflows.denoise

 GibbsRingingFlow([output_strategy, ...]) Methods LPCAFlow([output_strategy, mix_names, ...]) Methods MPPCAFlow([output_strategy, mix_names, ...]) Methods NLMeansFlow([output_strategy, mix_names, ...]) Methods Patch2SelfFlow([output_strategy, mix_names, ...]) Methods Workflow([output_strategy, mix_names, ...]) Methods deprecated_params(old_name[, new_name, ...]) Deprecate a renamed or removed function argument. estimate_sigma(arr[, ...]) Standard deviation estimation from local patches gibbs_removal(vol[, slice_axis, n_points, ...]) Suppresses Gibbs ringing artefacts of images volumes. gradient_table(bvals[, bvecs, big_delta, ...]) A general function for creating diffusion MR gradients. load_nifti(fname[, return_img, ...]) Load data and other information from a nifti file. localpca(arr, sigma[, mask, patch_radius, ...]) Performs local PCA denoising according to Manjon et al. [1]. mppca(arr[, mask, patch_radius, pca_method, ...]) Performs PCA-based denoising using the Marcenko-Pastur distribution [1]. nlmeans(arr, sigma[, mask, patch_radius, ...]) Non-local means for denoising 3D and 4D images patch2self(data, bvals[, patch_radius, ...]) Patch2Self Denoiser. pca_noise_estimate PCA based local noise estimation. read_bvals_bvecs(fbvals, fbvecs) Read b-values and b-vectors from disk. save_nifti(fname, data, affine[, hdr, dtype]) Save a data array into a nifti file.

## Module: workflows.docstring_parser

This was taken directly from the file docscrape.py of numpydoc package.

Copyright (C) 2008 Stefan van der Walt <stefan@mentat.za.net>, Pauli Virtanen <pav@iki.fi>

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE AUTHOR AS IS’’ AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

 NumpyDocString(docstring[, config]) Reader(data) A line-based string reader. dedent_lines(lines) Deindent a list of lines maximally warn(/, message[, category, stacklevel, source]) Issue a warning, or maybe ignore it or raise an exception.

## Module: workflows.flow_runner

 IntrospectiveArgumentParser([prog, usage, ...]) Attributes: get_level(lvl) Transforms the logging level passed on the commandline into a proper logging level name. run_flow(flow) Wraps the process of building an argparser that reflects the workflow that we want to run along with some generic parameters like logging, force and output strategies.

## Module: workflows.io

 FetchFlow([output_strategy, mix_names, ...]) Methods IoInfoFlow([output_strategy, mix_names, ...]) Methods SplitFlow([output_strategy, mix_names, ...]) Methods Workflow([output_strategy, mix_names, ...]) Methods getmembers(object[, predicate]) Return all members of an object as (name, value) pairs sorted by name. isfunction(object) Return true if the object is a user-defined function. load_nifti(fname[, return_img, ...]) Load data and other information from a nifti file. save_nifti(fname, data, affine[, hdr, dtype]) Save a data array into a nifti file.

## Module: workflows.mask

 MaskFlow([output_strategy, mix_names, ...]) Methods Workflow([output_strategy, mix_names, ...]) Methods load_nifti(fname[, return_img, ...]) Load data and other information from a nifti file. save_nifti(fname, data, affine[, hdr, dtype]) Save a data array into a nifti file.

## Module: workflows.multi_io

 IOIterator([output_strategy, mix_names]) Create output filenames that work nicely with multiple input files from multiple directories (processing multiple subjects with one command) common_start(sa, sb) Return the longest common substring from the beginning of sa and sb. concatenate_inputs(multi_inputs) Concatenate list of inputs. connect_output_paths(inputs, out_dir, out_files) Generate a list of output files paths based on input files and output strategies. glob(pathname, *[, recursive]) Return a list of paths matching a pathname pattern. io_iterator(inputs, out_dir, fnames[, ...]) Create an IOIterator from the parameters. io_iterator_(frame, fnc[, output_strategy, ...]) Create an IOIterator using introspection. slash_to_under(dir_str)

## Module: workflows.reconst

 ConstrainedSphericalDeconvModel(gtab, response) Methods CsaOdfModel(gtab, sh_order[, smooth, ...]) Implementation of Constant Solid Angle reconstruction method. DiffusionKurtosisModel(gtab[, fit_method]) Class for the Diffusion Kurtosis Model ReconstCSAFlow([output_strategy, mix_names, ...]) Methods ReconstCSDFlow([output_strategy, mix_names, ...]) Methods ReconstDkiFlow([output_strategy, mix_names, ...]) Methods ReconstDtiFlow([output_strategy, mix_names, ...]) Methods ReconstIvimFlow([output_strategy, ...]) Methods ReconstMAPMRIFlow([output_strategy, ...]) Methods ReconstRUMBAFlow([output_strategy, ...]) Methods RumbaSDModel(gtab[, wm_response, ...]) Methods TensorModel(gtab[, fit_method, return_S0_hat]) Diffusion Tensor Workflow([output_strategy, mix_names, ...]) Methods IvimModel(gtab[, fit_method]) Selector function to switch between the 2-stage Trust-Region Reflective based NLLS fitting method (also containing the linear fit): trr and the Variable Projections based fitting method: varpro. auto_response_ssst(gtab, data[, roi_center, ...]) Automatic estimation of single-shell single-tissue (ssst) response axial_diffusivity(evals[, axis]) Axial Diffusivity (AD) of a diffusion tensor. color_fa(fa, evecs) Color fractional anisotropy of diffusion tensor deprecated_params(old_name[, new_name, ...]) Deprecate a renamed or removed function argument. fractional_anisotropy(evals[, axis]) Return Fractional anisotropy (FA) of a diffusion tensor. geodesic_anisotropy(evals[, axis]) Geodesic anisotropy (GA) of a diffusion tensor. get_mode(q_form) Mode (MO) of a diffusion tensor [1]. get_sphere([name]) provide triangulated spheres gradient_table(bvals[, bvecs, big_delta, ...]) A general function for creating diffusion MR gradients. literal_eval(node_or_string) Safely evaluate an expression node or a string containing a Python expression. load_nifti(fname[, return_img, ...]) Load data and other information from a nifti file. load_nifti_data(fname[, as_ndarray]) Load only the data array from a nifti file. lower_triangular(tensor[, b0]) Returns the six lower triangular values of the tensor ordered as (Dxx, Dxy, Dyy, Dxz, Dyz, Dzz) and a dummy variable if b0 is not None. mean_diffusivity(evals[, axis]) Mean Diffusivity (MD) of a diffusion tensor. nifti1_symmat(image_data, *args, **kwargs) Returns a Nifti1Image with a symmetric matrix intent peaks_from_model(model, data, sphere, ...[, ...]) Fit the model to data and computes peaks and metrics peaks_to_niftis(pam, fname_shm, fname_dirs, ...) Save SH, directions, indices and values of peaks to Nifti. radial_diffusivity(evals[, axis]) Radial Diffusivity (RD) of a diffusion tensor. read_bvals_bvecs(fbvals, fbvecs) Read b-values and b-vectors from disk. save_nifti(fname, data, affine[, hdr, dtype]) Save a data array into a nifti file. save_peaks(fname, pam[, affine, verbose]) Save all important attributes of object PeaksAndMetrics in a PAM5 file (HDF5). split_dki_param(dki_params) Extract the diffusion tensor eigenvalues, the diffusion tensor eigenvector matrix, and the 15 independent elements of the kurtosis tensor from the model parameters estimated from the DKI model warn(/, message[, category, stacklevel, source]) Issue a warning, or maybe ignore it or raise an exception.

## Module: workflows.segment

 LabelsBundlesFlow([output_strategy, ...]) Methods MedianOtsuFlow([output_strategy, mix_names, ...]) Methods RecoBundles(streamlines[, greater_than, ...]) Methods RecoBundlesFlow([output_strategy, ...]) Methods Space(value) Enum to simplify future change to convention StatefulTractogram(streamlines, reference, space) Class for stateful representation of collections of streamlines Object designed to be identical no matter the file format (trk, tck, vtk, fib, dpy). Streamlines alias of ArraySequence Workflow([output_strategy, mix_names, ...]) Methods load_nifti(fname[, return_img, ...]) Load data and other information from a nifti file. load_tractogram(filename, reference[, ...]) Load the stateful tractogram from any format (trk/tck/vtk/vtp/fib/dpy) median_otsu(input_volume[, vol_idx, ...]) Simple brain extraction tool method for images from DWI data. save_nifti(fname, data, affine[, hdr, dtype]) Save a data array into a nifti file. save_tractogram(sft, filename[, ...]) Save the stateful tractogram in any format (trk/tck/vtk/vtp/fib/dpy) Return the current time in seconds since the Epoch.

## Module: workflows.stats

 Methods BundleShapeAnalysis([output_strategy, ...]) Methods LinearMixedModelsFlow([output_strategy, ...]) Methods SNRinCCFlow([output_strategy, mix_names, ...]) Methods TensorModel(gtab[, fit_method, return_S0_hat]) Diffusion Tensor Workflow([output_strategy, mix_names, ...]) Methods anatomical_measures(bundle, metric, dt, ...) Calculates dti measure (eg: FA, MD) per point on streamlines and assignment_map(target_bundle, model_bundle, ...) Calculates assignment maps of the target bundle with reference to model bundle centroids. binary_dilation(input[, structure, ...]) Multidimensional binary dilation with the given structuring element. Compute the bounding box of nonzero intensity voxels in the volume. buan_bundle_profiles(model_bundle_folder, ...) Applies statistical analysis on bundles and saves the results in a directory specified by out_dir. bundle_shape_similarity(bundle1, bundle2, rng) Calculates bundle shape similarity between two given bundles using bundle adjacency (BA) metric glob(pathname, *[, recursive]) Return a list of paths matching a pathname pattern. gradient_table(bvals[, bvecs, big_delta, ...]) A general function for creating diffusion MR gradients. load_nifti(fname[, return_img, ...]) Load data and other information from a nifti file. load_peaks(fname[, verbose]) Load a PeaksAndMetrics HDF5 file (PAM5) load_tractogram(filename, reference[, ...]) Load the stateful tractogram from any format (trk/tck/vtk/vtp/fib/dpy) optional_package(name[, trip_msg]) Return package-like thing and module setup for package name peak_values(bundle, peaks, dt, pname, bname, ...) Peak_values function finds the generalized fractional anisotropy (gfa) read_bvals_bvecs(fbvals, fbvecs) Read b-values and b-vectors from disk. save_nifti(fname, data, affine[, hdr, dtype]) Save a data array into a nifti file. segment_from_cfa(tensor_fit, roi, threshold) Segment the cfa inside roi using the values from threshold as bounds. Return the current time in seconds since the Epoch. transform_streamlines(streamlines, mat[, ...]) Apply affine transformation to streamlines

## Module: workflows.tracking

 BinaryStoppingCriterion cdef: ClosestPeakDirectionGetter A direction getter that returns the closest odf peak to previous tracking direction. CmcStoppingCriterion Continuous map criterion (CMC) stopping criterion from [1]. DeterministicMaximumDirectionGetter Return direction of a sphere with the highest probability mass function (pmf). LocalFiberTrackingPAMFlow([output_strategy, ...]) Methods LocalTracking(direction_getter, ...[, ...]) PFTrackingPAMFlow([output_strategy, ...]) Methods ParticleFilteringTracking(direction_getter, ...) ProbabilisticDirectionGetter Randomly samples direction of a sphere based on probability mass function (pmf). Space(value) Enum to simplify future change to convention StatefulTractogram(streamlines, reference, space) Class for stateful representation of collections of streamlines Object designed to be identical no matter the file format (trk, tck, vtk, fib, dpy). ThresholdStoppingCriterion # Declarations from stopping_criterion.pxd bellow cdef: double threshold, interp_out_double[1] double[:] interp_out_view = interp_out_view double[:, :, :] metric_map Workflow([output_strategy, mix_names, ...]) Methods load_nifti(fname[, return_img, ...]) Load data and other information from a nifti file. load_peaks(fname[, verbose]) Load a PeaksAndMetrics HDF5 file (PAM5) save_tractogram(sft, filename[, ...]) Save the stateful tractogram in any format (trk/tck/vtk/vtp/fib/dpy)

## Module: workflows.viz

 HorizonFlow([output_strategy, mix_names, ...]) Methods Workflow([output_strategy, mix_names, ...]) Methods assignment_map(target_bundle, model_bundle, ...) Calculates assignment maps of the target bundle with reference to model bundle centroids. create_nifti_header(affine, dimensions, ...) Write a standard nifti header from spatial attribute horizon([tractograms, images, pams, ...]) Interactive medical visualization - Invert the Horizon! line_colors(streamlines[, cmap]) Create colors for streamlines to be used in actor.line. load_nifti(fname[, return_img, ...]) Load data and other information from a nifti file. load_peaks(fname[, verbose]) Load a PeaksAndMetrics HDF5 file (PAM5) load_tractogram(filename, reference[, ...]) Load the stateful tractogram from any format (trk/tck/vtk/vtp/fib/dpy) numpy_to_vtk_colors(colors) Convert Numpy color array to a vtk color array. optional_package(name[, trip_msg]) Return package-like thing and module setup for package name pjoin(a, *p) Join two or more pathname components, inserting '/' as needed.

## Module: workflows.workflow

 Workflow([output_strategy, mix_names, ...]) Methods io_iterator_(frame, fnc[, output_strategy, ...]) Create an IOIterator using introspection.

### AffineMap

class dipy.workflows.align.AffineMap(affine, domain_grid_shape=None, domain_grid2world=None, codomain_grid_shape=None, codomain_grid2world=None)

Bases: object

Methods

 Return the value of the transformation, not a reference. set_affine(affine) Set the affine transform (operating in physical space). transform(image[, interpolation, ...]) Transform the input image from co-domain to domain space. transform_inverse(image[, interpolation, ...]) Transform the input image from domain to co-domain space.
__init__(affine, domain_grid_shape=None, domain_grid2world=None, codomain_grid_shape=None, codomain_grid2world=None)

AffineMap.

Implements an affine transformation whose domain is given by domain_grid and domain_grid2world, and whose co-domain is given by codomain_grid and codomain_grid2world.

The actual transform is represented by the affine matrix, which operate in world coordinates. Therefore, to transform a moving image towards a static image, we first map each voxel (i,j,k) of the static image to world coordinates (x,y,z) by applying domain_grid2world. Then we apply the affine transform to (x,y,z) obtaining (x’, y’, z’) in moving image’s world coordinates. Finally, (x’, y’, z’) is mapped to voxel coordinates (i’, j’, k’) in the moving image by multiplying (x’, y’, z’) by the inverse of codomain_grid2world. The codomain_grid_shape is used analogously to transform the static image towards the moving image when calling transform_inverse.

If the domain/co-domain information is not provided (None) then the sampling information needs to be specified each time the transform or transform_inverse is called to transform images. Note that such sampling information is not necessary to transform points defined in physical space, such as stream lines.

Parameters:
affinearray, shape (dim + 1, dim + 1)

the matrix defining the affine transform, where dim is the dimension of the space this map operates in (2 for 2D images, 3 for 3D images). If None, then self represents the identity transformation.

domain_grid_shapesequence, shape (dim,), optional

the shape of the default domain sampling grid. When transform is called to transform an image, the resulting image will have this shape, unless a different sampling information is provided. If None, then the sampling grid shape must be specified each time the transform method is called.

domain_grid2worldarray, shape (dim + 1, dim + 1), optional

the grid-to-world transform associated with the domain grid. If None (the default), then the grid-to-world transform is assumed to be the identity.

codomain_grid_shapesequence of integers, shape (dim,)

the shape of the default co-domain sampling grid. When transform_inverse is called to transform an image, the resulting image will have this shape, unless a different sampling information is provided. If None (the default), then the sampling grid shape must be specified each time the transform_inverse method is called.

codomain_grid2worldarray, shape (dim + 1, dim + 1)

the grid-to-world transform associated with the co-domain grid. If None (the default), then the grid-to-world transform is assumed to be the identity.

get_affine()

Return the value of the transformation, not a reference.

Returns:
affinendarray

Copy of the transform, not a reference.

set_affine(affine)

Set the affine transform (operating in physical space).

Also sets self.affine_inv - the inverse of affine, or None if there is no inverse.

Parameters:
affinearray, shape (dim + 1, dim + 1)

the matrix representing the affine transform operating in physical space. The domain and co-domain information remains unchanged. If None, then self represents the identity transformation.

transform(image, interpolation='linear', image_grid2world=None, sampling_grid_shape=None, sampling_grid2world=None, resample_only=False)

Transform the input image from co-domain to domain space.

By default, the transformed image is sampled at a grid defined by self.domain_shape and self.domain_grid2world. If such information was not provided then sampling_grid_shape is mandatory.

Parameters:
image2D or 3D array

the image to be transformed

interpolationstring, either ‘linear’ or ‘nearest’

the type of interpolation to be used, either ‘linear’ (for k-linear interpolation) or ‘nearest’ for nearest neighbor

image_grid2worldarray, shape (dim + 1, dim + 1), optional

the grid-to-world transform associated with image. If None (the default), then the grid-to-world transform is assumed to be the identity.

sampling_grid_shapesequence, shape (dim,), optional

the shape of the grid where the transformed image must be sampled. If None (the default), then self.codomain_shape is used instead (which must have been set at initialization, otherwise an exception will be raised).

sampling_grid2worldarray, shape (dim + 1, dim + 1), optional

the grid-to-world transform associated with the sampling grid (specified by sampling_grid_shape, or by default self.codomain_shape). If None (the default), then the grid-to-world transform is assumed to be the identity.

resample_onlyBoolean, optional

If False (the default) the affine transform is applied normally. If True, then the affine transform is not applied, and the input image is just re-sampled on the domain grid of this transform.

Returns:
transformedarray, shape sampling_grid_shape or

self.codomain_shape

the transformed image, sampled at the requested grid

transform_inverse(image, interpolation='linear', image_grid2world=None, sampling_grid_shape=None, sampling_grid2world=None, resample_only=False)

Transform the input image from domain to co-domain space.

By default, the transformed image is sampled at a grid defined by self.codomain_shape and self.codomain_grid2world. If such information was not provided then sampling_grid_shape is mandatory.

Parameters:
image2D or 3D array

the image to be transformed

interpolationstring, either ‘linear’ or ‘nearest’

the type of interpolation to be used, either ‘linear’ (for k-linear interpolation) or ‘nearest’ for nearest neighbor

image_grid2worldarray, shape (dim + 1, dim + 1), optional

the grid-to-world transform associated with image. If None (the default), then the grid-to-world transform is assumed to be the identity.

sampling_grid_shapesequence, shape (dim,), optional

the shape of the grid where the transformed image must be sampled. If None (the default), then self.codomain_shape is used instead (which must have been set at initialization, otherwise an exception will be raised).

sampling_grid2worldarray, shape (dim + 1, dim + 1), optional

the grid-to-world transform associated with the sampling grid (specified by sampling_grid_shape, or by default self.codomain_shape). If None (the default), then the grid-to-world transform is assumed to be the identity.

resample_onlyBoolean, optional

If False (the default) the affine transform is applied normally. If True, then the affine transform is not applied, and the input image is just re-sampled on the domain grid of this transform.

Returns:
transformedarray, shape sampling_grid_shape or

self.codomain_shape

the transformed image, sampled at the requested grid

### ApplyTransformFlow

class dipy.workflows.align.ApplyTransformFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. get_short_name() Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(static_image_files, moving_image_files, ...) Parameters:
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

run(static_image_files, moving_image_files, transform_map_file, transform_type='affine', out_dir='', out_file='transformed.nii.gz')
Parameters:
static_image_filesstring

Path of the static image file.

moving_image_filesstring

Path of the moving image(s). It can be a single image or a folder containing multiple images.

transform_map_filestring

For the affine case, it should be a text(*.txt) file containing the affine matrix. For the diffeomorphic case, it should be a nifti file containing the mapping displacement field in each voxel with this shape (x, y, z, 3, 2).

transform_typestring, optional

Select the transformation type to apply between ‘affine’ or ‘diffeomorphic’.

out_dirstring, optional

Directory to save the transformed files (default current directory).

out_filestring, optional

Name of the transformed file. It is recommended to use the flag –mix-names to prevent the output files from being overwritten.

### CCMetric

Methods

 Computes one step bringing the static image towards the moving. Computes one step bringing the moving image towards the static. Frees the resources allocated during initialization Numerical value assigned by this metric to the current image pair Prepares the metric to compute one displacement field iteration. set_levels_above(levels) Informs the metric how many pyramid levels are above the current one set_levels_below(levels) Informs the metric how many pyramid levels are below the current one set_moving_image(moving_image, ...) Sets the moving image being compared against the static one. set_static_image(static_image, ...) Sets the static image being compared against the moving one. use_moving_image_dynamics(...) This is called by the optimizer just after setting the moving image use_static_image_dynamics(...) This is called by the optimizer just after setting the static image.

Normalized Cross-Correlation Similarity metric.

Parameters:
dimint (either 2 or 3)

the dimension of the image domain

sigma_diffthe standard deviation of the Gaussian smoothing kernel to

be applied to the update field at each iteration

the radius of the squared (cubic) neighborhood at each voxel to be considered to compute the cross correlation

compute_backward()

Computes one step bringing the static image towards the moving.

Computes the update displacement field to be used for registration of the static image towards the moving image

compute_forward()

Computes one step bringing the moving image towards the static.

Computes the update displacement field to be used for registration of the moving image towards the static image

free_iteration()

Frees the resources allocated during initialization

get_energy()

Numerical value assigned by this metric to the current image pair

Returns the Cross Correlation (data term) energy computed at the largest iteration

initialize_iteration()

Prepares the metric to compute one displacement field iteration.

Pre-computes the cross-correlation factors for efficient computation of the gradient of the Cross Correlation w.r.t. the displacement field. It also pre-computes the image gradients in the physical space by re-orienting the gradients in the voxel space using the corresponding affine transformations.

### DiffeomorphicMap

class dipy.workflows.align.DiffeomorphicMap(dim, disp_shape, disp_grid2world=None, domain_shape=None, domain_grid2world=None, codomain_shape=None, codomain_grid2world=None, prealign=None)

Bases: object

Methods

 Creates a zero displacement field Inversion error of the displacement fields expand_fields(expand_factors, new_shape) Expands the displacement fields from current shape to new_shape Deformation field to transform an image in the backward direction Deformation field to transform an image in the forward direction Constructs a simplified version of this Diffeomorhic Map Try to interpret obj as a matrix Inverse of this DiffeomorphicMap instance Shallow copy of this DiffeomorphicMap instance transform(image[, interpolation, ...]) Warps an image in the forward direction transform_inverse(image[, interpolation, ...]) Warps an image in the backward direction Composition of this DiffeomorphicMap with a given endomorphism
 transform_points transform_points_inverse
__init__(dim, disp_shape, disp_grid2world=None, domain_shape=None, domain_grid2world=None, codomain_shape=None, codomain_grid2world=None, prealign=None)

DiffeomorphicMap

Implements a diffeomorphic transformation on the physical space. The deformation fields encoding the direct and inverse transformations share the same domain discretization (both the discretization grid shape and voxel-to-space matrix). The input coordinates (physical coordinates) are first aligned using prealign, and then displaced using the corresponding vector field interpolated at the aligned coordinates.

Parameters:
dimint, 2 or 3

the transformation’s dimension

disp_shapearray, shape (dim,)

the number of slices (if 3D), rows and columns of the deformation field’s discretization

disp_grid2worldthe voxel-to-space transform between the def. fields

grid and space

domain_shapearray, shape (dim,)

the number of slices (if 3D), rows and columns of the default discretization of this map’s domain

domain_grid2worldarray, shape (dim+1, dim+1)

the default voxel-to-space transformation between this map’s discretization and physical space

codomain_shapearray, shape (dim,)

the number of slices (if 3D), rows and columns of the images that are ‘normally’ warped using this transformation in the forward direction (this will provide default transformation parameters to warp images under this transformation). By default, we assume that the inverse transformation is ‘normally’ used to warp images with the same discretization and voxel-to-space transformation as the deformation field grid.

codomain_grid2worldarray, shape (dim+1, dim+1)

the voxel-to-space transformation of images that are ‘normally’ warped using this transformation (in the forward direction).

prealignarray, shape (dim+1, dim+1)

the linear transformation to be applied to align input images to the reference space before warping under the deformation field.

allocate()

Creates a zero displacement field

Creates a zero displacement field (the identity transformation).

compute_inversion_error()

Inversion error of the displacement fields

Estimates the inversion error of the displacement fields by computing statistics of the residual vectors obtained after composing the forward and backward displacement fields.

Returns:
residualarray, shape (R, C) or (S, R, C)

the displacement field resulting from composing the forward and backward displacement fields of this transformation (the residual should be zero for a perfect diffeomorphism)

statsarray, shape (3,)

statistics from the norms of the vectors of the residual displacement field: maximum, mean and standard deviation

Notes

Since the forward and backward displacement fields have the same discretization, the final composition is given by

comp[i] = forward[ i + Dinv * backward[i]]

where Dinv is the space-to-grid transformation of the displacement fields

expand_fields(expand_factors, new_shape)

Expands the displacement fields from current shape to new_shape

Up-samples the discretization of the displacement fields to be of new_shape shape.

Parameters:
expand_factorsarray, shape (dim,)

the factors scaling current spacings (voxel sizes) to spacings in the expanded discretization.

new_shapearray, shape (dim,)

the shape of the arrays holding the up-sampled discretization

get_backward_field()

Deformation field to transform an image in the backward direction

Returns the deformation field that must be used to warp an image under this transformation in the backward direction (note the ‘is_inverse’ flag).

get_forward_field()

Deformation field to transform an image in the forward direction

Returns the deformation field that must be used to warp an image under this transformation in the forward direction (note the ‘is_inverse’ flag).

get_simplified_transform()

Constructs a simplified version of this Diffeomorhic Map

The simplified version incorporates the pre-align transform, as well as the domain and codomain affine transforms into the displacement field. The resulting transformation may be regarded as operating on the image spaces given by the domain and codomain discretization. As a result, self.prealign, self.disp_grid2world, self.domain_grid2world and self.codomain affine will be None (denoting Identity) in the resulting diffeomorphic map.

interpret_matrix(obj)

Try to interpret obj as a matrix

Some operations are performed faster if we know in advance if a matrix is the identity (so we can skip the actual matrix-vector multiplication). This function returns None if the given object is None or the ‘identity’ string. It returns the same object if it is a numpy array. It raises an exception otherwise.

Parameters:
objobject

any object

Returns:
objobject

the same object given as argument if obj is None or a numpy array. None if obj is the ‘identity’ string.

inverse()

Inverse of this DiffeomorphicMap instance

Returns a diffeomorphic map object representing the inverse of this transformation. The internal arrays are not copied but just referenced.

Returns:
invDiffeomorphicMap object

the inverse of this diffeomorphic map.

shallow_copy()

Shallow copy of this DiffeomorphicMap instance

Creates a shallow copy of this diffeomorphic map (the arrays are not copied but just referenced)

Returns:
new_mapDiffeomorphicMap object

the shallow copy of this diffeomorphic map

transform(image, interpolation='linear', image_world2grid=None, out_shape=None, out_grid2world=None)

Warps an image in the forward direction

Transforms the input image under this transformation in the forward direction. It uses the “is_inverse” flag to switch between “forward” and “backward” (if is_inverse is False, then transform(…) warps the image forwards, else it warps the image backwards).

Parameters:
imagearray, shape (s, r, c) if dim = 3 or (r, c) if dim = 2

the image to be warped under this transformation in the forward direction

interpolationstring, either ‘linear’ or ‘nearest’

the type of interpolation to be used for warping, either ‘linear’ (for k-linear interpolation) or ‘nearest’ for nearest neighbor

image_world2gridarray, shape (dim+1, dim+1)

the transformation bringing world (space) coordinates to voxel coordinates of the image given as input

out_shapearray, shape (dim,)

the number of slices, rows and columns of the desired warped image

out_grid2worldthe transformation bringing voxel coordinates of the

warped image to physical space

Returns:
warpedarray, shape = out_shape or self.codomain_shape if None

the warped image under this transformation in the forward direction

Notes

See _warp_forward and _warp_backward documentation for further information.

transform_inverse(image, interpolation='linear', image_world2grid=None, out_shape=None, out_grid2world=None)

Warps an image in the backward direction

Transforms the input image under this transformation in the backward direction. It uses the “is_inverse” flag to switch between “forward” and “backward” (if is_inverse is False, then transform_inverse(…) warps the image backwards, else it warps the image forwards)

Parameters:
imagearray, shape (s, r, c) if dim = 3 or (r, c) if dim = 2

the image to be warped under this transformation in the forward direction

interpolationstring, either ‘linear’ or ‘nearest’

the type of interpolation to be used for warping, either ‘linear’ (for k-linear interpolation) or ‘nearest’ for nearest neighbor

image_world2gridarray, shape (dim+1, dim+1)

the transformation bringing world (space) coordinates to voxel coordinates of the image given as input

out_shapearray, shape (dim,)

the number of slices, rows, and columns of the desired warped image

out_grid2worldthe transformation bringing voxel coordinates of the

warped image to physical space

Returns:
warpedarray, shape = out_shape or self.codomain_shape if None

warped image under this transformation in the backward direction

Notes

See _warp_forward and _warp_backward documentation for further information.

transform_points(points, coord2world=None, world2coord=None)
transform_points_inverse(points, coord2world=None, world2coord=None)
warp_endomorphism(phi)

Composition of this DiffeomorphicMap with a given endomorphism

Creates a new DiffeomorphicMap C with the same properties as self and composes its displacement fields with phi’s corresponding fields. The resulting diffeomorphism is of the form C(x) = phi(self(x)) with inverse C^{-1}(y) = self^{-1}(phi^{-1}(y)). We assume that phi is an endomorphism with the same discretization and domain affine as self to ensure that the composition inherits self’s properties (we also assume that the pre-aligning matrix of phi is None or identity).

Parameters:
phiDiffeomorphicMap object

the endomorphism to be warped by this diffeomorphic map

Returns:
compositionthe composition of this diffeomorphic map with the

endomorphism given as input

Notes

The problem with our current representation of a DiffeomorphicMap is that the set of Diffeomorphism that can be represented this way (a pre-aligning matrix followed by a non-linear endomorphism given as a displacement field) is not closed under the composition operation.

Supporting a general DiffeomorphicMap class, closed under composition, may be extremely costly computationally, and the kind of transformations we actually need for Avants’ mid-point algorithm (SyN) are much simpler.

### EMMetric

class dipy.workflows.align.EMMetric(dim, smooth=1.0, inner_iter=5, q_levels=256, double_gradient=True, step_type='gauss_newton')

Methods

 Computes one step bringing the static image towards the moving. compute_demons_step([forward_step]) Demons step for EM metric Computes one step bringing the reference image towards the static. compute_gauss_newton_step([forward_step]) Computes the Gauss-Newton energy minimization step Frees the resources allocated during initialization The numerical value assigned by this metric to the current image pair Prepares the metric to compute one displacement field iteration. set_levels_above(levels) Informs the metric how many pyramid levels are above the current one set_levels_below(levels) Informs the metric how many pyramid levels are below the current one set_moving_image(moving_image, ...) Sets the moving image being compared against the static one. set_static_image(static_image, ...) Sets the static image being compared against the moving one. This is called by the optimizer just after setting the moving image. This is called by the optimizer just after setting the static image.
__init__(dim, smooth=1.0, inner_iter=5, q_levels=256, double_gradient=True, step_type='gauss_newton')

Expectation-Maximization Metric

Similarity metric based on the Expectation-Maximization algorithm to handle multi-modal images. The transfer function is modeled as a set of hidden random variables that are estimated at each iteration of the algorithm.

Parameters:
dimint (either 2 or 3)

the dimension of the image domain

smoothfloat

smoothness parameter, the larger the value the smoother the deformation field

inner_iterint

number of iterations to be performed at each level of the multi- resolution Gauss-Seidel optimization algorithm (this is not the number of steps per Gaussian Pyramid level, that parameter must be set for the optimizer, not the metric)

q_levelsnumber of quantization levels (equal to the number of hidden

variables in the EM algorithm)

if True, the gradient of the expected static image under the moving modality will be added to the gradient of the moving image, similarly, the gradient of the expected moving image under the static modality will be added to the gradient of the static image.

step_typestring (‘gauss_newton’, ‘demons’)

the optimization schedule to be used in the multi-resolution Gauss-Seidel optimization algorithm (not used if Demons Step is selected)

compute_backward()

Computes one step bringing the static image towards the moving.

Computes the update displacement field to be used for registration of the static image towards the moving image

compute_demons_step(forward_step=True)

Demons step for EM metric

Parameters:
forward_stepboolean

if True, computes the Demons step in the forward direction (warping the moving towards the static image). If False, computes the backward step (warping the static image to the moving image)

Returns:
displacementarray, shape (R, C, 2) or (S, R, C, 3)

the Demons step

compute_forward()

Computes one step bringing the reference image towards the static.

Computes the forward update field to register the moving image towards the static image in a gradient-based optimization algorithm

compute_gauss_newton_step(forward_step=True)

Computes the Gauss-Newton energy minimization step

Computes the Newton step to minimize this energy, i.e., minimizes the linearized energy function with respect to the regularized displacement field (this step does not require post-smoothing, as opposed to the demons step, which does not include regularization). To accelerate convergence we use the multi-grid Gauss-Seidel algorithm proposed by Bruhn and Weickert et al [Bruhn05]

Parameters:
forward_stepboolean

if True, computes the Newton step in the forward direction (warping the moving towards the static image). If False, computes the backward step (warping the static image to the moving image)

Returns:
displacementarray, shape (R, C, 2) or (S, R, C, 3)

the Newton step

References

[Bruhn05] Andres Bruhn and Joachim Weickert, “Towards ultimate motion

estimation: combining highest accuracy with real-time performance”, 10th IEEE International Conference on Computer Vision, 2005. ICCV 2005.

free_iteration()

Frees the resources allocated during initialization

get_energy()

The numerical value assigned by this metric to the current image pair

Returns the EM (data term) energy computed at the largest iteration

initialize_iteration()

Prepares the metric to compute one displacement field iteration.

Pre-computes the transfer functions (hidden random variables) and variances of the estimators. Also pre-computes the gradient of both input images. Note that once the images are transformed to the opposite modality, the gradient of the transformed images can be used with the gradient of the corresponding modality in the same fashion as diff-demons does for mono-modality images. If the flag self.use_double_gradient is True these gradients are averaged.

use_moving_image_dynamics(original_moving_image, transformation)

This is called by the optimizer just after setting the moving image.

EMMetric takes advantage of the image dynamics by computing the current moving image mask from the original_moving_image mask (warped by nearest neighbor interpolation)

Parameters:
original_moving_imagearray, shape (R, C) or (S, R, C)

the original moving image from which the current moving image was generated, the current moving image is the one that was provided via ‘set_moving_image(…)’, which may not be the same as the original moving image but a warped version of it.

transformationDiffeomorphicMap object

the transformation that was applied to the original_moving_image to generate the current moving image

use_static_image_dynamics(original_static_image, transformation)

This is called by the optimizer just after setting the static image.

EMMetric takes advantage of the image dynamics by computing the current static image mask from the originalstaticImage mask (warped by nearest neighbor interpolation)

Parameters:
original_static_imagearray, shape (R, C) or (S, R, C)

the original static image from which the current static image was generated, the current static image is the one that was provided via ‘set_static_image(…)’, which may not be the same as the original static image but a warped version of it (even the static image changes during Symmetric Normalization, not only the moving one).

transformationDiffeomorphicMap object

the transformation that was applied to the original_static_image to generate the current static image

### ImageRegistrationFlow

class dipy.workflows.align.ImageRegistrationFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

The registration workflow allows the user to use only one type of registration (such as center of mass or rigid body registration only).

Alternatively, a registration can be done in a progressive manner. For example, using affine registration with progressive set to ‘True’ will involve center of mass, translation, rigid body and full affine registration. Whereas, when progressive is False the registration will include only center of mass and affine registration. The progressive registration will be slower but will improve the quality.

This can be controlled by using the progressive flag (True by default).

Methods

 get_io_iterator() Create an iterator for IO. get_short_name() Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(static_image_files, moving_image_files) Parameters:
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

run(static_image_files, moving_image_files, transform='affine', nbins=32, sampling_prop=None, metric='mi', level_iters=(10000, 1000, 100), sigmas=(3.0, 1.0, 0.0), factors=(4, 2, 1), progressive=True, save_metric=False, out_dir='', out_moved='moved.nii.gz', out_affine='affine.txt', out_quality='quality_metric.txt')
Parameters:
static_image_filesstring

Path to the static image file.

moving_image_filesstring

Path to the moving image file.

transformstring, optional

com: center of mass, trans: translation, rigid: rigid body, rigid_isoscaling: rigid body + isotropic scaling, rigid_scaling: rigid body + scaling, affine: full affine including translation, rotation, shearing and scaling.

nbinsint, optional

Number of bins to discretize the joint and marginal PDF.

sampling_propint, optional
Number ([0-100]) of voxels for calculating the PDF.

‘None’ implies all voxels.

metricstring, optional

Similarity metric for gathering mutual information).

level_itersvariable int, optional
The number of iterations at each scale of the scale space.

level_iters[0] corresponds to the coarsest scale, level_iters[-1] the finest, where n is the length of the

sequence.

sigmasvariable floats, optional
Custom smoothing parameter to build the scale space (one parameter

for each scale).

factorsvariable floats, optional
Custom scale factors to build the scale space (one factor for each

scale).

progressiveboolean, optional

Enable/Disable the progressive registration.

save_metricboolean, optional

If true, quality assessment metric are saved in ‘quality_metric.txt’.

out_dirstring, optional
Directory to save the transformed image and the affine matrix

(default current directory).

out_movedstring, optional

Name for the saved transformed image.

out_affinestring, optional

Name for the saved affine matrix.

out_qualitystring, optional
Name of the file containing the saved quality

metric.

### MotionCorrectionFlow

class dipy.workflows.align.MotionCorrectionFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

The Motion Correction workflow allows the user to align between-volumes DWI dataset.

Methods

 get_io_iterator() Create an iterator for IO. get_short_name() Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files, bvalues_files, bvectors_files) Parameters:
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

run(input_files, bvalues_files, bvectors_files, b0_threshold=50, bvecs_tol=0.01, out_dir='', out_moved='moved.nii.gz', out_affine='affine.txt')
Parameters:
input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bvalues_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

bvectors_filesstring

Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

bvecs_tolfloat, optional

Threshold used to check that norm(bvec) = 1 +/- bvecs_tol b-vectors are unit vectors

out_dirstring, optional
Directory to save the transformed image and the affine matrix

(default current directory).

out_movedstring, optional

Name for the saved transformed image.

out_affinestring, optional

Name for the saved affine matrix.

### ResliceFlow

class dipy.workflows.align.ResliceFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files, new_vox_size[, order, ...]) Reslice data with new voxel resolution defined by new_vox_sz
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, new_vox_size, order=1, mode='constant', cval=0, num_processes=1, out_dir='', out_resliced='resliced.nii.gz')

Reslice data with new voxel resolution defined by new_vox_sz

Parameters:
input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

new_vox_sizevariable float

new voxel size.

orderint, optional

order of interpolation, from 0 to 5, for resampling/reslicing, 0 nearest interpolation, 1 trilinear etc.. if you don’t want any smoothing 0 is the option you need.

modestring, optional

Points outside the boundaries of the input are filled according to the given mode ‘constant’, ‘nearest’, ‘reflect’ or ‘wrap’.

cvalfloat, optional

Value used for points outside the boundaries of the input if mode=’constant’.

num_processesint, optional

Split the calculation to a pool of children processes. This only applies to 4D data arrays. Default is 1. If < 0 the maximal number of cores minus num_processes + 1 is used (enter -1 to use as many cores as possible). 0 raises an error.

out_dirstring, optional

Output directory. (default current directory)

out_reslicedstring, optional

Name of the resliced dataset to be saved.

### SSDMetric

class dipy.workflows.align.SSDMetric(dim, smooth=4, inner_iter=10, step_type='demons')

Methods

 Computes one step bringing the static image towards the moving. compute_demons_step([forward_step]) Demons step for SSD metric Computes one step bringing the reference image towards the static. compute_gauss_newton_step([forward_step]) Computes the Gauss-Newton energy minimization step Nothing to free for the SSD metric The numerical value assigned by this metric to the current image pair Prepares the metric to compute one displacement field iteration. set_levels_above(levels) Informs the metric how many pyramid levels are above the current one set_levels_below(levels) Informs the metric how many pyramid levels are below the current one set_moving_image(moving_image, ...) Sets the moving image being compared against the static one. set_static_image(static_image, ...) Sets the static image being compared against the moving one. use_moving_image_dynamics(...) This is called by the optimizer just after setting the moving image use_static_image_dynamics(...) This is called by the optimizer just after setting the static image.
__init__(dim, smooth=4, inner_iter=10, step_type='demons')

Sum of Squared Differences (SSD) Metric

Similarity metric for (mono-modal) nonlinear image registration defined by the sum of squared differences (SSD)

Parameters:
dimint (either 2 or 3)

the dimension of the image domain

smoothfloat

smoothness parameter, the larger the value the smoother the deformation field

inner_iterint

number of iterations to be performed at each level of the multi- resolution Gauss-Seidel optimization algorithm (this is not the number of steps per Gaussian Pyramid level, that parameter must be set for the optimizer, not the metric)

step_typestring

the displacement field step to be computed when ‘compute_forward’ and ‘compute_backward’ are called. Either ‘demons’ or ‘gauss_newton’

compute_backward()

Computes one step bringing the static image towards the moving.

Computes the updated displacement field to be used for registration of the static image towards the moving image

compute_demons_step(forward_step=True)

Demons step for SSD metric

Computes the demons step proposed by Vercauteren et al.[Vercauteren09] for the SSD metric.

Parameters:
forward_stepboolean

if True, computes the Demons step in the forward direction (warping the moving towards the static image). If False, computes the backward step (warping the static image to the moving image)

Returns:
displacementarray, shape (R, C, 2) or (S, R, C, 3)

the Demons step

References

[Vercauteren09] Tom Vercauteren, Xavier Pennec, Aymeric Perchant,

Nicholas Ayache, “Diffeomorphic Demons: Efficient Non-parametric Image Registration”, Neuroimage 2009

compute_forward()

Computes one step bringing the reference image towards the static.

Computes the update displacement field to be used for registration of the moving image towards the static image

compute_gauss_newton_step(forward_step=True)

Computes the Gauss-Newton energy minimization step

Minimizes the linearized energy function (Newton step) defined by the sum of squared differences of corresponding pixels of the input images with respect to the displacement field.

Parameters:
forward_stepboolean

if True, computes the Newton step in the forward direction (warping the moving towards the static image). If False, computes the backward step (warping the static image to the moving image)

Returns:
displacementarray, shape = static_image.shape + (3,)

if forward_step==True, the forward SSD Gauss-Newton step, else, the backward step

free_iteration()

Nothing to free for the SSD metric

get_energy()

The numerical value assigned by this metric to the current image pair

Returns the Sum of Squared Differences (data term) energy computed at the largest iteration

initialize_iteration()

Prepares the metric to compute one displacement field iteration.

Pre-computes the gradient of the input images to be used in the computation of the forward and backward steps.

### SlrWithQbxFlow

class dipy.workflows.align.SlrWithQbxFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(static_files, moving_files[, x0, ...]) Streamline-based linear registration.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(static_files, moving_files, x0='affine', rm_small_clusters=50, qbx_thr=(40, 30, 20, 15), num_threads=None, greater_than=50, less_than=250, nb_pts=20, progressive=True, out_dir='', out_moved='moved.trk', out_affine='affine.txt', out_stat_centroids='static_centroids.trk', out_moving_centroids='moving_centroids.trk', out_moved_centroids='moved_centroids.trk')

Streamline-based linear registration.

For efficiency we apply the registration on cluster centroids and remove small clusters.

Parameters:
static_filesstring
moving_filesstring
x0string, optional

rigid, similarity or affine transformation model.

rm_small_clustersint, optional

Remove clusters that have less than rm_small_clusters.

qbx_thrvariable int, optional

Thresholds for QuickBundlesX.

Number of threads to be used for OpenMP parallelization. If None (default) the value of OMP_NUM_THREADS environment variable is used if it is set, otherwise all available threads are used. If < 0 the maximal number of threads minus |num_threads + 1| is used (enter -1 to use as many threads as possible). 0 raises an error. Only metrics using OpenMP will use this variable.

greater_thanint, optional

Keep streamlines that have length greater than this value.

less_thanint, optional

Keep streamlines have length less than this value.

nb_ptsint, optional

Number of points for discretizing each streamline.

progressiveboolean, optional
out_dirstring, optional

Output directory. (default current directory)

out_movedstring, optional

Filename of moved tractogram.

out_affinestring, optional

Filename of affine for SLR transformation.

out_stat_centroidsstring, optional

Filename of static centroids.

out_moving_centroidsstring, optional

Filename of moving centroids.

out_moved_centroidsstring, optional

Filename of moved centroids.

Notes

The order of operations is the following. First short or long streamlines are removed. Second the tractogram or a random selection of the tractogram is clustered with QuickBundlesX. Then SLR [Garyfallidis15] is applied.

References

Garyfallidis et al. “Robust and efficient linear

registration of white-matter fascicles in the space of streamlines”, NeuroImage, 117, 124–140, 2015

[Garyfallidis14]

Garyfallidis et al., “Direct native-space fiber

bundle alignment for group comparisons”, ISMRM, 2014.

[Garyfallidis17]

Garyfallidis et al. Recognition of white matter

bundles using local and global streamline-based registration and clustering, NeuroImage, 2017.

### SymmetricDiffeomorphicRegistration

class dipy.workflows.align.SymmetricDiffeomorphicRegistration(metric, level_iters=None, step_length=0.25, ss_sigma_factor=0.2, opt_tol=1e-05, inv_iter=20, inv_tol=0.001, callback=None)

Methods

 Return the resulting diffeomorphic map. optimize(static, moving[, ...]) Starts the optimization set_level_iters(level_iters) Sets the number of iterations at each pyramid level update(current_displacement, ...) Composition of the current displacement field with the given field
__init__(metric, level_iters=None, step_length=0.25, ss_sigma_factor=0.2, opt_tol=1e-05, inv_iter=20, inv_tol=0.001, callback=None)

Symmetric Diffeomorphic Registration (SyN) Algorithm

Performs the multi-resolution optimization algorithm for non-linear registration using a given similarity metric.

Parameters:
metricSimilarityMetric object

the metric to be optimized

level_iterslist of int

the number of iterations at each level of the Gaussian Pyramid (the length of the list defines the number of pyramid levels to be used)

opt_tolfloat

the optimization will stop when the estimated derivative of the energy profile w.r.t. time falls below this threshold

inv_iterint

the number of iterations to be performed by the displacement field inversion algorithm

step_lengthfloat

the length of the maximum displacement vector of the update displacement field at each iteration

ss_sigma_factorfloat

parameter of the scale-space smoothing kernel. For example, the std. dev. of the kernel will be factor*(2^i) in the isotropic case where i = 0, 1, …, n_scales is the scale

inv_tolfloat

the displacement field inversion algorithm will stop iterating when the inversion error falls below this threshold

callbackfunction(SymmetricDiffeomorphicRegistration)

a function receiving a SymmetricDiffeomorphicRegistration object to be called after each iteration (this optimizer will call this function passing self as parameter)

get_map()

Return the resulting diffeomorphic map.

Returns the DiffeomorphicMap registering the moving image towards the static image.

optimize(static, moving, static_grid2world=None, moving_grid2world=None, prealign=None)

Starts the optimization

Parameters:
staticarray, shape (S, R, C) or (R, C)

the image to be used as reference during optimization. The displacement fields will have the same discretization as the static image.

movingarray, shape (S, R, C) or (R, C)

the image to be used as “moving” during optimization. Since the deformation fields’ discretization is the same as the static image, it is necessary to pre-align the moving image to ensure its domain lies inside the domain of the deformation fields. This is assumed to be accomplished by “pre-aligning” the moving image towards the static using an affine transformation given by the ‘prealign’ matrix

static_grid2worldarray, shape (dim+1, dim+1)

the voxel-to-space transformation associated to the static image

moving_grid2worldarray, shape (dim+1, dim+1)

the voxel-to-space transformation associated to the moving image

prealignarray, shape (dim+1, dim+1)

the affine transformation (operating on the physical space) pre-aligning the moving image towards the static

Returns:
static_to_refDiffeomorphicMap object

the diffeomorphic map that brings the moving image towards the static one in the forward direction (i.e. by calling static_to_ref.transform) and the static image towards the moving one in the backward direction (i.e. by calling static_to_ref.transform_inverse).

update(current_displacement, new_displacement, disp_world2grid, time_scaling)

Composition of the current displacement field with the given field

Interpolates new displacement at the locations defined by current_displacement. Equivalently, computes the composition C of the given displacement fields as C(x) = B(A(x)), where A is current_displacement and B is new_displacement. This function is intended to be used with deformation fields of the same sampling (e.g. to be called by a registration algorithm).

Parameters:
current_displacementarray, shape (R’, C’, 2) or (S’, R’, C’, 3)

the displacement field defining where to interpolate new_displacement

new_displacementarray, shape (R, C, 2) or (S, R, C, 3)

the displacement field to be warped by current_displacement

disp_world2gridarray, shape (dim+1, dim+1)

the space-to-grid transform associated with the displacements’ grid (we assume that both displacements are discretized over the same grid)

time_scalingfloat

scaling factor applied to d2. The effect may be interpreted as moving d1 displacements along a factor (time_scaling) of d2.

Returns:
updatedarray, shape (the same as new_displacement)

the warped displacement field

mean_normthe mean norm of all vectors in current_displacement

### SynRegistrationFlow

class dipy.workflows.align.SynRegistrationFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. get_short_name() Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(static_image_files, moving_image_files) Parameters:
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

run(static_image_files, moving_image_files, prealign_file='', inv_static=False, level_iters=(10, 10, 5), metric='cc', mopt_sigma_diff=2.0, mopt_radius=4, mopt_smooth=0.0, mopt_inner_iter=0, mopt_q_levels=256, mopt_double_gradient=True, mopt_step_type='', step_length=0.25, ss_sigma_factor=0.2, opt_tol=1e-05, inv_iter=20, inv_tol=0.001, out_dir='', out_warped='warped_moved.nii.gz', out_inv_static='inc_static.nii.gz', out_field='displacement_field.nii.gz')
Parameters:
static_image_filesstring

Path of the static image file.

moving_image_filesstring

Path to the moving image file.

prealign_filestring, optional
The text file containing pre alignment information via an

affine matrix.

inv_staticboolean, optional

Apply the inverse mapping to the static image.

level_itersvariable int, optional

The number of iterations at each level of the gaussian pyramid.

metricstring, optional

The metric to be used. metric available: cc (Cross Correlation), ssd (Sum Squared Difference), em (Expectation-Maximization).

mopt_sigma_difffloat, optional

Metric option applied on Cross correlation (CC). The standard deviation of the Gaussian smoothing kernel to be applied to the update field at each iteration.

Metric option applied on Cross correlation (CC). the radius of the squared (cubic) neighborhood at each voxel to be considered to compute the cross correlation.

mopt_smoothfloat, optional

Metric option applied on Sum Squared Difference (SSD) and Expectation Maximization (EM). Smoothness parameter, the larger the value the smoother the deformation field. (default 1.0 for EM, 4.0 for SSD)

mopt_inner_iterint, optional

Metric option applied on Sum Squared Difference (SSD) and Expectation Maximization (EM). This is number of iterations to be performed at each level of the multi-resolution Gauss-Seidel optimization algorithm (this is not the number of steps per Gaussian Pyramid level, that parameter must be set for the optimizer, not the metric). Default 5 for EM, 10 for SSD.

mopt_q_levelsint, optional

Metric option applied on Expectation Maximization (EM). Number of quantization levels (Default: 256 for EM)

Metric option applied on Expectation Maximization (EM). if True, the gradient of the expected static image under the moving modality will be added to the gradient of the moving image, similarly, the gradient of the expected moving image under the static modality will be added to the gradient of the static image.

mopt_step_typestring, optional

Metric option applied on Sum Squared Difference (SSD) and Expectation Maximization (EM). The optimization schedule to be used in the multi-resolution Gauss-Seidel optimization algorithm (not used if Demons Step is selected). Possible value: (‘gauss_newton’, ‘demons’). default: ‘gauss_newton’ for EM, ‘demons’ for SSD.

step_lengthfloat, optional
the length of the maximum displacement vector of the update

displacement field at each iteration.

ss_sigma_factorfloat, optional
parameter of the scale-space smoothing kernel. For example, the

std. dev. of the kernel will be factor*(2^i) in the isotropic case where i = 0, 1, …, n_scales is the scale.

opt_tolfloat, optional
the optimization will stop when the estimated derivative of the

energy profile w.r.t. time falls below this threshold.

inv_iterint, optional
the number of iterations to be performed by the displacement field

inversion algorithm.

inv_tolfloat, optional
the displacement field inversion algorithm will stop iterating

when the inversion error falls below this threshold.

out_dirstring, optional

Directory to save the transformed files (default current directory).

out_warpedstring, optional

Name of the warped file.

out_inv_staticstring, optional
Name of the file to save the static image after applying the

inverse mapping.

out_fieldstring, optional

Name of the file to save the diffeomorphic map.

### Workflow

class dipy.workflows.align.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: object

Methods

 Create an iterator for IO. Return A short name for the workflow used to subdivide. Return No sub runs since this is a simple workflow. Check if a file will be overwritten upon processing the inputs. run(*args, **kwargs) Execute the workflow.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

get_io_iterator()

Create an iterator for IO.

Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

get_sub_runs()

Return No sub runs since this is a simple workflow.

manage_output_overwrite()

Check if a file will be overwritten upon processing the inputs.

If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.

run(*args, **kwargs)

Execute the workflow.

Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)

### affine_registration

dipy.workflows.align.affine_registration(moving, static, moving_affine=None, static_affine=None, pipeline=None, starting_affine=None, metric='MI', level_iters=None, sigmas=None, factors=None, ret_metric=False, moving_mask=None, static_mask=None, **metric_kwargs)

Find the affine transformation between two 3D images. Alternatively, find the combination of several linear transformations.

Parameters:
movingarray, nifti image or str

Containing the data for the moving object, or full path to a nifti file with the moving data.

staticarray, nifti image or str

Containing the data for the static object, or full path to a nifti file with the moving data.

moving_affine4x4 array, optional

An affine transformation associated with the moving object. Required if data is provided as an array. If provided together with nifti/path, will over-ride the affine that is in the nifti.

static_affine4x4 array, optional

An affine transformation associated with the static object. Required if data is provided as an array. If provided together with nifti/path, will over-ride the affine that is in the nifti.

pipelinelist of str, optional

Sequence of transforms to use in the gradual fitting. Default: gradual fit of the full affine (executed from left to right): ["center_of_mass", "translation", "rigid", "affine"] Alternatively, any other combination of the following registration methods might be used: center_of_mass, translation, rigid, rigid_isoscaling, rigid_scaling and affine.

starting_affine: 4x4 array, optional

Initial guess for the transformation between the spaces. Default: identity.

metricstr, optional.

Currently only supports ‘MI’ for MutualInformationMetric.

AffineRegistration key-word argument: the number of iterations at each scale of the scale space. level_iters[0] corresponds to the coarsest scale, level_iters[-1] the finest, where n is the length of the sequence. By default, a 3-level scale space with iterations sequence equal to [10000, 1000, 100] will be used.

sigmassequence of floats, optional

AffineRegistration key-word argument: custom smoothing parameter to build the scale space (one parameter for each scale). By default, the sequence of sigmas will be [3, 1, 0].

AffineRegistration key-word argument: custom scale factors to build the scale space (one factor for each scale). By default, the sequence of factors will be [4, 2, 1].

ret_metricboolean, optional

Set it to True to return the value of the optimized coefficients and the optimization quality metric.

moving_maskarray, shape (S’, R’, C’) or (R’, C’), optional

moving image mask that defines which pixels in the moving image are used to calculate the mutual information.

static_maskarray, shape (S, R, C) or (R, C), optional

static image mask that defines which pixels in the static image are used to calculate the mutual information.

nbinsint, optional

MutualInformationMetric key-word argument: the number of bins to be used for computing the intensity histograms. The default is 32.

sampling_proportionNone or float in interval (0, 1], optional

MutualInformationMetric key-word argument: There are two types of sampling: dense and sparse. Dense sampling uses all voxels for estimating the (joint and marginal) intensity histograms, while sparse sampling uses a subset of them. If sampling_proportion is None, then dense sampling is used. If sampling_proportion is a floating point value in (0,1] then sparse sampling is used, where sampling_proportion specifies the proportion of voxels to be used. The default is None (dense sampling).

Returns:
transformedarray with moving data resampled to the static space
after computing the affine transformation
affinethe affine 4x4 associated with the transformation.
xoptthe value of the optimized coefficients.
foptthe value of the optimization quality metric.

Notes

Performs a gradual registration between the two inputs, using a pipeline that gradually approximates the final registration. If the final default step (affine) is omitted, the resulting affine may not have all 12 degrees of freedom adjusted.

### check_dimensions

dipy.workflows.align.check_dimensions(static, moving)

Check the dimensions of the input images.

Parameters:
static2D or 3D array

the image to be used as reference during optimization.

moving: 2D or 3D array

the image to be used as “moving” during optimization. It is necessary to pre-align the moving image to ensure its domain lies inside the domain of the deformation fields. This is assumed to be accomplished by “pre-aligning” the moving image towards the static using an affine transformation given by the ‘starting_affine’ matrix.

dipy.workflows.align.gradient_table(bvals, bvecs=None, big_delta=None, small_delta=None, b0_threshold=50, atol=0.01, btens=None)

A general function for creating diffusion MR gradients.

It reads, loads and prepares scanner parameters like the b-values and b-vectors so that they can be useful during the reconstruction process.

Parameters:
bvalscan be any of the four options
1. an array of shape (N,) or (1, N) or (N, 1) with the b-values.

2. a path for the file which contains an array like the above (1).

3. an array of shape (N, 4) or (4, N). Then this parameter is considered to be a b-table which contains both bvals and bvecs. In this case the next parameter is skipped.

4. a path for the file which contains an array like the one at (3).

bvecscan be any of two options
1. an array of shape (N, 3) or (3, N) with the b-vectors.

2. a path for the file which contains an array like the previous.

big_deltafloat

acquisition pulse separation time in seconds (default None)

small_deltafloat

acquisition pulse duration time in seconds (default None)

b0_thresholdfloat

All b-values with values less than or equal to bo_threshold are considered as b0s i.e. without diffusion weighting.

atolfloat

All b-vectors need to be unit vectors up to a tolerance.

btenscan be any of three options
1. a string specifying the shape of the encoding tensor for all volumes in data. Options: ‘LTE’, ‘PTE’, ‘STE’, ‘CTE’ corresponding to linear, planar, spherical, and “cigar-shaped” tensor encoding. Tensors are rotated so that linear and cigar tensors are aligned with the corresponding gradient direction and the planar tensor’s normal is aligned with the corresponding gradient direction. Magnitude is scaled to match the b-value.

2. an array of strings of shape (N,), (N, 1), or (1, N) specifying encoding tensor shape for each volume separately. N corresponds to the number volumes in data. Options for elements in array: ‘LTE’, ‘PTE’, ‘STE’, ‘CTE’ corresponding to linear, planar, spherical, and “cigar-shaped” tensor encoding. Tensors are rotated so that linear and cigar tensors are aligned with the corresponding gradient direction and the planar tensor’s normal is aligned with the corresponding gradient direction. Magnitude is scaled to match the b-value.

3. an array of shape (N,3,3) specifying the b-tensor of each volume exactly. N corresponds to the number volumes in data. No rotation or scaling is performed.

Returns:

Notes

1. Often b0s (b-values which correspond to images without diffusion weighting) have 0 values however in some cases the scanner cannot provide b0s of an exact 0 value and it gives a bit higher values e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.

2. We assume that the minimum number of b-values is 7.

3. B-vectors should be unit vectors.

Examples

>>> from dipy.core.gradients import gradient_table
>>> bvals = 1500 * np.ones(7)
>>> bvals[0] = 0
>>> sq2 = np.sqrt(2) / 2
>>> bvecs = np.array([[0, 0, 0],
...                   [1, 0, 0],
...                   [0, 1, 0],
...                   [0, 0, 1],
...                   [sq2, sq2, 0],
...                   [sq2, 0, sq2],
...                   [0, sq2, sq2]])
>>> gt.bvecs.shape == bvecs.shape
True
>>> gt.bvecs.shape == bvecs.T.shape
False


Load data and other information from a nifti file.

Parameters:
fnamestr

Full path to a nifti file.

return_imgbool, optional

Whether to return the nibabel nifti img object. Default: False

return_voxsize: bool, optional

Whether to return the nifti header zooms. Default: False

return_coordsbool, optional

Whether to return the nifti header aff2axcodes. Default: False

as_ndarray: bool, optional

convert nibabel ArrayProxy to a numpy.ndarray. If you want to save memory and delay this casting, just turn this option to False (default: True)

Returns:
A tuple, with (at the most, if all keyword args are set to True):
(data, img.affine, img, vox_size, nib.aff2axcodes(img.affine))

load_nifti_data

Read b-values and b-vectors from disk.

Parameters:
fbvalsstr

Full path to file with b-values. None to not read bvals.

fbvecsstr

Full path of file with b-vectors. None to not read bvecs.

Returns:
bvalsarray, (N,) or None
bvecsarray, (N, 3) or None

Notes

Files can be either ‘.bvals’/’.bvecs’ or ‘.txt’ or ‘.npy’ (containing arrays stored with the appropriate values).

### reslice

dipy.workflows.align.reslice(data, affine, zooms, new_zooms, order=1, mode='constant', cval=0, num_processes=1)

Reslice data with new voxel resolution defined by new_zooms.

Parameters:
dataarray, shape (I,J,K) or (I,J,K,N)

3d volume or 4d volume with datasets

affinearray, shape (4,4)

mapping from voxel coordinates to world coordinates

zoomstuple, shape (3,)

voxel size for (i,j,k) dimensions

new_zoomstuple, shape (3,)

new voxel size for (i,j,k) after resampling

orderint, from 0 to 5

order of interpolation for resampling/reslicing, 0 nearest interpolation, 1 trilinear etc.. if you don’t want any smoothing 0 is the option you need.

modestring (‘constant’, ‘nearest’, ‘reflect’ or ‘wrap’)

Points outside the boundaries of the input are filled according to the given mode.

cvalfloat

Value used for points outside the boundaries of the input if mode=’constant’.

num_processesint, optional

Split the calculation to a pool of children processes. This only applies to 4D data arrays. Default is 1. If < 0 the maximal number of cores minus num_processes + 1 is used (enter -1 to use as many cores as possible). 0 raises an error.

Returns:
data2array, shape (I,J,K) or (I,J,K,N)

datasets resampled into isotropic voxel size

affine2array, shape (4,4)

new affine for the resampled image

Examples

>>> from dipy.io.image import load_nifti
>>> from dipy.align.reslice import reslice
>>> from dipy.data import get_fnames
>>> f_name = get_fnames('aniso_vox')
>>> data, affine, zooms = load_nifti(f_name, return_voxsize=True)
>>> data.shape == (58, 58, 24)
True
>>> zooms
(4.0, 4.0, 5.0)
>>> new_zooms = (3.,3.,3.)
>>> new_zooms
(3.0, 3.0, 3.0)
>>> data2, affine2 = reslice(data, affine, zooms, new_zooms)
>>> data2.shape == (77, 77, 40)
True


### save_nifti

dipy.workflows.align.save_nifti(fname, data, affine, hdr=None, dtype=None)

Save a data array into a nifti file.

Parameters:
fnamestr

The full path to the file to be saved.

datandarray

The array with the data to save.

affine4x4 array

The affine transform associated with the file.

Returns:
None

### save_qa_metric

dipy.workflows.align.save_qa_metric(fname, xopt, fopt)

Save Quality Assurance metrics.

Parameters:
fname: string

File name to save the metric values.

xopt: numpy array

The metric containing the optimal parameters for image registration.

fopt: int

The distance between the registered images.

### slr_with_qbx

dipy.workflows.align.slr_with_qbx(static, moving, x0='affine', rm_small_clusters=50, maxiter=100, select_random=None, verbose=False, greater_than=50, less_than=250, qbx_thr=(40, 30, 20, 15), nb_pts=20, progressive=True, rng=None, num_threads=None)

Utility function for registering large tractograms.

For efficiency, we apply the registration on cluster centroids and remove small clusters.

Parameters:
staticStreamlines
movingStreamlines
x0str, optional.

rigid, similarity or affine transformation model (default affine)

rm_small_clustersint, optional

Remove clusters that have less than rm_small_clusters (default 50)

maxiterint, optional

Maximum number of iterations to perform.

select_randomint, optional.

If not, None selects a random number of streamlines to apply clustering Default None.

verbosebool, optional

If True, logs information about optimization. Default: False

greater_thanint, optional

Keep streamlines that have length greater than this value (default 50)

less_thanint, optional

Keep streamlines have length less than this value (default 250)

qbx_thrvariable int

Thresholds for QuickBundlesX (default [40, 30, 20, 15])

nb_ptsint, optional

Number of points for discretizing each streamline (default 20)

progressiveboolean, optional

(default True)

rngRandomState

If None creates RandomState in function.

Number of threads to be used for OpenMP parallelization. If None (default) the value of OMP_NUM_THREADS environment variable is used if it is set, otherwise all available threads are used. If < 0 the maximal number of threads minus |num_threads + 1| is used (enter -1 to use as many threads as possible). 0 raises an error. Only metrics using OpenMP will use this variable.

Notes

The order of operations is the following. First short or long streamlines are removed. Second, the tractogram or a random selection of the tractogram is clustered with QuickBundles. Then SLR [Garyfallidis15] is applied.

References

Garyfallidis et al. “Robust and efficient linear

registration of white-matter fascicles in the space of streamlines”, NeuroImage, 117, 124–140, 2015 .. [R890e584ccf15-Garyfallidis14] Garyfallidis et al., “Direct native-space fiber

bundle alignment for group comparisons”, ISMRM, 2014.

[Garyfallidis17]

Garyfallidis et al. Recognition of white matter

bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.

### transform_streamlines

dipy.workflows.align.transform_streamlines(streamlines, mat, in_place=False)

Apply affine transformation to streamlines

Parameters:
streamlinesStreamlines

Streamlines object

matarray, (4, 4)

transformation matrix

in_placebool

If True then change data in place. Be careful changes input streamlines.

Returns:
new_streamlinesStreamlines

Sequence transformed 2D ndarrays of shape[-1]==3

### warn

dipy.workflows.align.warn(/, message, category=None, stacklevel=1, source=None)

Issue a warning, or maybe ignore it or raise an exception.

### IntrospectiveArgumentParser

class dipy.workflows.base.IntrospectiveArgumentParser(prog=None, usage=None, description=None, epilog=None, parents=(), formatter_class=<class 'argparse.RawTextHelpFormatter'>, prefix_chars='-', fromfile_prefix_chars=None, argument_default=None, conflict_handler='resolve', add_help=True)

Bases: ArgumentParser

Attributes:
optional_parameters
output_parameters
positional_parameters

Methods

 add_argument(add_argument) add_sub_flow_args(sub_flows) Take an array of workflow objects and use introspection to extract the parameters, types and docstrings of their run method. add_subparsers(**kwargs) add_workflow(workflow) Take a workflow object and use introspection to extract the parameters, types and docstrings of its run method. error(message) Prints a usage message incorporating the message to stderr and exits. exit([status, message]) format_usage() get_flow_args([args, namespace]) Return the parsed arguments as a dictionary that will be used as a workflow's run method arguments. parse_args([args, namespace]) print_usage([file]) register(registry_name, value, object) set_defaults(**kwargs)
__init__(prog=None, usage=None, description=None, epilog=None, parents=(), formatter_class=<class 'argparse.RawTextHelpFormatter'>, prefix_chars='-', fromfile_prefix_chars=None, argument_default=None, conflict_handler='resolve', add_help=True)

Augmenting the argument parser to allow automatic creation of arguments from workflows

Parameters:
progNone

The name of the program. (default: sys.argv[0])

usageNone

A usage message. (default: auto-generated from arguments)

descriptionstr

A description of what the program does.

epilogstr

Text following the argument descriptions.

parentslist

Parsers whose arguments should be copied into this one.

formatter_classobj

HelpFormatter class for printing help messages.

Characters that prefix optional arguments.

fromfile_prefix_charsNone

Characters that prefix files containing additional arguments.

argument_defaultNone

The default value for all arguments.

conflict_handlerstr

String indicating how to handle conflicts.

Take an array of workflow objects and use introspection to extract the parameters, types and docstrings of their run method. Only the optional input parameters are extracted for these as they are treated as sub workflows.

Parameters:
sub_flowsarray of dipy.workflows.workflow.Workflow

Workflows to inspect.

Returns:
sub_flow_optionalsdictionary of all sub workflow optional parameters

Take a workflow object and use introspection to extract the parameters, types and docstrings of its run method. Then add these parameters to the current arparser’s own params to parse. If the workflow is of type combined_workflow, the optional input parameters of its sub workflows will also be added.

Parameters:
workflowdipy.workflows.workflow.Workflow

Workflow from which to infer parameters.

Returns:
sub_flow_optionalsdictionary of all sub workflow optional parameters
get_flow_args(args=None, namespace=None)

Return the parsed arguments as a dictionary that will be used as a workflow’s run method arguments.

property optional_parameters
property output_parameters
property positional_parameters
show_argument(dest)
update_argument(*args, **kargs)

### NumpyDocString

class dipy.workflows.base.NumpyDocString(docstring, config={})

Bases: object

__init__(docstring, config={})

### get_args_default

dipy.workflows.base.get_args_default(func)

### none_or_dtype

dipy.workflows.base.none_or_dtype(dtype)

Check None presence before type casting.

### CombinedWorkflow

class dipy.workflows.combined_workflow.CombinedWorkflow(output_strategy='append', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. get_optionals(flow, **kwargs) Returns the sub flow's optional arguments merged with those passed as params in kwargs. get_short_name() Return A short name for the workflow used to subdivide. Returns a list of tuples (sub flow name, sub flow run method, sub flow short name) to be used in the sub flow parameters extraction. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(*args, **kwargs) Execute the workflow. run_sub_flow(flow, *args, **kwargs) Runs the sub flow with the optional parameters passed via the command line. Sets the self._optionals variable with all sub flow arguments that were passed in the commandline.
__init__(output_strategy='append', mix_names=False, force=False, skip=False)

Workflow that combines multiple workflows. The workflow combined together are referred as sub flows in this class.

get_optionals(flow, **kwargs)

Returns the sub flow’s optional arguments merged with those passed as params in kwargs.

get_sub_runs()

Returns a list of tuples (sub flow name, sub flow run method, sub flow short name) to be used in the sub flow parameters extraction.

run_sub_flow(flow, *args, **kwargs)

Runs the sub flow with the optional parameters passed via the command line. This is a convenience method to make sub flow running more intuitive on the concrete CombinedWorkflow side.

set_sub_flows_optionals(opts)

Sets the self._optionals variable with all sub flow arguments that were passed in the commandline.

### Workflow

class dipy.workflows.combined_workflow.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: object

Methods

 Create an iterator for IO. Return A short name for the workflow used to subdivide. Return No sub runs since this is a simple workflow. Check if a file will be overwritten upon processing the inputs. run(*args, **kwargs) Execute the workflow.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

get_io_iterator()

Create an iterator for IO.

Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

get_sub_runs()

Return No sub runs since this is a simple workflow.

manage_output_overwrite()

Check if a file will be overwritten upon processing the inputs.

If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.

run(*args, **kwargs)

Execute the workflow.

Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)

### GibbsRingingFlow

class dipy.workflows.denoise.GibbsRingingFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files[, slice_axis, n_points, ...]) Workflow for applying Gibbs Ringing method.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, slice_axis=2, n_points=3, num_processes=1, out_dir='', out_unring='dwi_unring.nii.gz')

Workflow for applying Gibbs Ringing method.

Parameters:
input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

slice_axisint, optional

Data axis corresponding to the number of acquired slices. Could be (0, 1, or 2): for example, a value of 2 would mean the third axis.

n_pointsint, optional

Number of neighbour points to access local TV (see note).

num_processesint or None, optional

Split the calculation to a pool of children processes. Only applies to 3D or 4D data arrays. Default is 1. If < 0 the maximal number of cores minus num_processes + 1 is used (enter -1 to use as many cores as possible). 0 raises an error.

out_dirstring, optional

Output directory. (default current directory)

out_unringstring, optional

Name of the resulting denoised volume.

References

[1]

Neto Henriques, R., 2018. Advanced Methods for Diffusion MRI

Data Analysis and their Application to the Healthy Ageing Brain (Doctoral thesis). https://doi.org/10.17863/CAM.29356

[2]

Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing

artifact removal based on local subvoxel-shifts. Magn Reson Med. 2016 doi: 10.1002/mrm.26054.

### LPCAFlow

class dipy.workflows.denoise.LPCAFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files, bvalues_files, bvectors_files) Workflow wrapping LPCA denoising method.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, bvalues_files, bvectors_files, sigma=0, b0_threshold=50, bvecs_tol=0.01, patch_radius=2, pca_method='eig', tau_factor=2.3, out_dir='', out_denoised='dwi_lpca.nii.gz')

Workflow wrapping LPCA denoising method.

Parameters:
input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bvalues_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

bvectors_filesstring

Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.

sigmafloat, optional

Standard deviation of the noise estimated from the data. Default 0: it means sigma value estimation with the Manjon2013 algorithm [3].

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

bvecs_tolfloat, optional

Threshold used to check that norm(bvec) = 1 +/- bvecs_tol b-vectors are unit vectors.

The radius of the local patch to be taken around each voxel (in voxels) For example, for a patch radius with value 2, and assuming the input image is a 3D image, the denoising will take place in blocks of 5x5x5 voxels.

pca_methodstring, optional

Use either eigenvalue decomposition (‘eig’) or singular value decomposition (‘svd’) for principal component analysis. The default method is ‘eig’ which is faster. However, occasionally ‘svd’ might be more accurate.

tau_factorfloat, optional

Thresholding of PCA eigenvalues is done by nulling out eigenvalues that are smaller than:

$\tau = (\tau_{factor} \sigma)^2$

tau_{factor} can be change to adjust the relationship between the noise standard deviation and the threshold tau. If tau_{factor} is set to None, it will be automatically calculated using the Marcenko-Pastur distribution [2].

out_dirstring, optional

Output directory. (default current directory)

out_denoisedstring, optional

Name of the resulting denoised volume.

References

[1]

Veraart J, Novikov DS, Christiaens D, Ades-aron B, Sijbers,

Fieremans E, 2016. Denoising of Diffusion MRI using random matrix theory. Neuroimage 142:394-406. doi: 10.1016/j.neuroimage.2016.08.016

[2]

Veraart J, Fieremans E, Novikov DS. 2016. Diffusion MRI noise

mapping using random matrix theory. Magnetic Resonance in Medicine. doi: 10.1002/mrm.26059.

[3]

Manjon JV, Coupe P, Concha L, Buades A, Collins DL (2013)

Diffusion Weighted Image Denoising Using Overcomplete Local PCA. PLoS ONE 8(9): e73021. https://doi.org/10.1371/journal.pone.0073021

### MPPCAFlow

class dipy.workflows.denoise.MPPCAFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files[, patch_radius, pca_method, ...]) Workflow wrapping Marcenko-Pastur PCA denoising method.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, patch_radius=2, pca_method='eig', return_sigma=False, out_dir='', out_denoised='dwi_mppca.nii.gz', out_sigma='dwi_sigma.nii.gz')

Workflow wrapping Marcenko-Pastur PCA denoising method.

Parameters:
input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

The radius of the local patch to be taken around each voxel (in voxels) For example, for a patch radius with value 2, and assuming the input image is a 3D image, the denoising will take place in blocks of 5x5x5 voxels.

pca_methodstring, optional

Use either eigenvalue decomposition (‘eig’) or singular value decomposition (‘svd’) for principal component analysis. The default method is ‘eig’ which is faster. However, occasionally ‘svd’ might be more accurate.

return_sigmabool, optional

If true, a noise standard deviation estimate based on the Marcenko-Pastur distribution is returned [2].

out_dirstring, optional

Output directory. (default current directory)

out_denoisedstring, optional

Name of the resulting denoised volume.

out_sigmastring, optional

Name of the resulting sigma volume.

References

[1]

Veraart J, Novikov DS, Christiaens D, Ades-aron B, Sijbers,

Fieremans E, 2016. Denoising of Diffusion MRI using random matrix theory. Neuroimage 142:394-406. doi: 10.1016/j.neuroimage.2016.08.016

[2]

Veraart J, Fieremans E, Novikov DS. 2016. Diffusion MRI noise

mapping using random matrix theory. Magnetic Resonance in Medicine. doi: 10.1002/mrm.26059.

### NLMeansFlow

class dipy.workflows.denoise.NLMeansFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files[, sigma, patch_radius, ...]) Workflow wrapping the nlmeans denoising method.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

Workflow wrapping the nlmeans denoising method.

It applies nlmeans denoise on each file found by ‘globing’ input_files and saves the results in a directory specified by out_dir.

Parameters:
input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

sigmafloat, optional

Sigma parameter to pass to the nlmeans algorithm.

patch size is 2 x patch_radius + 1.

block size is 2 x block_radius + 1.

ricianbool, optional

If True the noise is estimated as Rician, otherwise Gaussian noise is assumed.

out_dirstring, optional

Output directory. (default current directory)

out_denoisedstring, optional

Name of the resulting denoised volume.

References

[Descoteaux08]

Descoteaux, Maxime and Wiest-Daesslé, Nicolas and

Prima, Sylvain and Barillot, Christian and Deriche, Rachid. Impact of Rician Adapted Non-Local Means Filtering on HARDI, MICCAI 2008

### Patch2SelfFlow

class dipy.workflows.denoise.Patch2SelfFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files, bval_files[, model, ...]) Workflow for Patch2Self denoising method.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, bval_files, model='ols', b0_threshold=50, alpha=1.0, verbose=False, b0_denoising=True, clip_negative_vals=False, shift_intensity=True, out_dir='', out_denoised='dwi_patch2self.nii.gz')

Workflow for Patch2Self denoising method.

It applies patch2self denoising on each file found by ‘globing’ input_file and bval_file. It saves the results in a directory specified by out_dir.

Parameters:
input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bval_filesstring

bval file associated with the diffusion data.

modelstring, or initialized linear model object.

This will determine the algorithm used to solve the set of linear equations underlying this model. If it is a string it needs to be one of the following: {‘ols’, ‘ridge’, ‘lasso’}. Otherwise, it can be an object that inherits from dipy.optimize.SKLearnLinearSolver or an object with a similar interface from Scikit-Learn: sklearn.linear_model.LinearRegression, sklearn.linear_model.Lasso or sklearn.linear_model.Ridge and other objects that inherit from sklearn.base.RegressorMixin. Default: ‘ols’.

b0_thresholdint, optional

Threshold for considering volumes as b0.

alphafloat, optional

Regularization parameter only for ridge regression model.

verbosebool, optional

Show progress of Patch2Self and time taken.

b0_denoisingbool, optional

Skips denoising b0 volumes if set to False.

clip_negative_valsbool, optional

Sets negative values after denoising to 0 using np.clip.

shift_intensitybool, optional

Shifts the distribution of intensities per volume to give non-negative values

out_dirstring, optional

Output directory (default current directory)

out_denoisedstring, optional

Name of the resulting denoised volume (default: dwi_patch2self.nii.gz)

References

S. Fadnavis, J. Batson, E. Garyfallidis, Patch2Self: Denoising Diffusion MRI with Self-supervised Learning, Advances in Neural Information Processing Systems 33 (2020)

### Workflow

class dipy.workflows.denoise.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: object

Methods

 Create an iterator for IO. Return A short name for the workflow used to subdivide. Return No sub runs since this is a simple workflow. Check if a file will be overwritten upon processing the inputs. run(*args, **kwargs) Execute the workflow.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

get_io_iterator()

Create an iterator for IO.

Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

get_sub_runs()

Return No sub runs since this is a simple workflow.

manage_output_overwrite()

Check if a file will be overwritten upon processing the inputs.

If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.

run(*args, **kwargs)

Execute the workflow.

Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)

### deprecated_params

dipy.workflows.denoise.deprecated_params(old_name, new_name=None, since='', until='', version_comparator=<function cmp_pkg_version>, arg_in_kwargs=False, warn_class=<class 'dipy.utils.deprecator.ArgsDeprecationWarning'>, error_class=<class 'dipy.utils.deprecator.ExpiredDeprecationError'>, alternative='')

Deprecate a renamed or removed function argument.

The decorator assumes that the argument with the old_name was removed from the function signature and the new_name replaced it at the same position in the signature. If the old_name argument is given when calling the decorated function the decorator will catch it and issue a deprecation warning and pass it on as new_name argument.

Parameters:
old_namestr or list/tuple thereof

The old name of the argument.

new_namestr or list/tuple thereof or None, optional

The new name of the argument. Set this to None to remove the argument old_name instead of renaming it.

sincestr or number or list/tuple thereof, optional

The release at which the old argument became deprecated.

untilstr or number or list/tuple thereof, optional

Last released version at which this function will still raise a deprecation warning. Versions higher than this will raise an error.

version_comparatorcallable

Callable accepting string as argument, and return 1 if string represents a higher version than encoded in the version_comparator, 0 if the version is equal, and -1 if the version is lower. For example, the version_comparator may compare the input version string to the current package version string.

arg_in_kwargsbool or list/tuple thereof, optional

If the argument is not a named argument (for example it was meant to be consumed by **kwargs) set this to True. Otherwise the decorator will throw an Exception if the new_name cannot be found in the signature of the decorated function. Default is False.

warn_classwarning, optional

Warning to be issued.

error_classException, optional

Error to be issued

alternativestr, optional

An alternative function or class name that the user may use in place of the deprecated object if new_name is None. The deprecation warning will tell the user about this alternative if provided.

Raises:
TypeError

If the new argument name cannot be found in the function signature and arg_in_kwargs was False or if it is used to deprecate the name of the *args-, **kwargs-like arguments. At runtime such an Error is raised if both the new_name and old_name were specified when calling the function and “relax=False”.

Notes

This function is based on the Astropy (major modification). https://github.com/astropy/astropy. See COPYING file distributed along with the astropy package for the copyright and license terms.

Examples

The deprecation warnings are not shown in the following examples. To deprecate a positional or keyword argument:: >>> from dipy.utils.deprecator import deprecated_params >>> @deprecated_params(‘sig’, ‘sigma’, ‘0.3’) … def test(sigma): … return sigma >>> test(2) 2 >>> test(sigma=2) 2 >>> test(sig=2) # doctest: +SKIP 2

It is also possible to replace multiple arguments. The old_name, new_name and since have to be tuple or list and contain the same number of entries:: >>> @deprecated_params([‘a’, ‘b’], [‘alpha’, ‘beta’], … [‘0.2’, 0.4]) … def test(alpha, beta): … return alpha, beta >>> test(a=2, b=3) # doctest: +SKIP (2, 3)

### estimate_sigma

Standard deviation estimation from local patches

Parameters:
arr3D or 4D ndarray

The array to be estimated

If True, uses all voxels for the estimation, otherwise, only non-zeros voxels are used. Useful if the background is masked by the scanner.

Nint, default 0

Number of coils of the receiver array. Use N = 1 in case of a SENSE reconstruction (Philips scanners) or the number of coils for a GRAPPA reconstruction (Siemens and GE). Use 0 to disable the correction factor, as for example if the noise is Gaussian distributed. See [1] for more information.

Returns:
sigmandarray

standard deviation of the noise, one estimation per volume.

Notes

This function is the same as manually taking the standard deviation of the background and gives one value for the whole 3D array. It also includes the coil-dependent correction factor of Koay 2006 (see [1], equation 18) with theta = 0. Since this function was introduced in [2] for T1 imaging, it is expected to perform ok on diffusion MRI data, but might oversmooth some regions and leave others un-denoised for spatially varying noise profiles. Consider using piesno() to estimate sigma instead if visual inaccuracies are apparent in the denoised result.

References

[1]

Koay, C. G., & Basser, P. J. (2006). Analytically exact correction

scheme for signal extraction from noisy magnitude MR signals. Journal of Magnetic Resonance), 179(2), 317-22.

[2]

Coupe, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot,

C., 2008. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images, IEEE Trans. Med. Imaging 27, 425-41.

### gibbs_removal

dipy.workflows.denoise.gibbs_removal(vol, slice_axis=2, n_points=3, inplace=True, num_processes=1)

Suppresses Gibbs ringing artefacts of images volumes.

Parameters:
volndarray ([X, Y]), ([X, Y, Z]) or ([X, Y, Z, g])

Matrix containing one volume (3D) or multiple (4D) volumes of images.

slice_axisint (0, 1, or 2)

Data axis corresponding to the number of acquired slices. Default is set to the third axis.

n_pointsint, optional

Number of neighbour points to access local TV (see note). Default is set to 3.

inplacebool, optional

If True, the input data is replaced with results. Otherwise, returns a new array. Default is set to True.

num_processesint or None, optional

Split the calculation to a pool of children processes. This only applies to 3D or 4D data arrays. Default is 1. If < 0 the maximal number of cores minus num_processes + 1 is used (enter -1 to use as many cores as possible). 0 raises an error.

Returns:
volndarray ([X, Y]), ([X, Y, Z]) or ([X, Y, Z, g])

Matrix containing one volume (3D) or multiple (4D) volumes of corrected images.

Notes

For 4D matrix last element should always correspond to the number of diffusion gradient directions.

References

Please cite the following articles .. [Rae70c6436165-1] Neto Henriques, R., 2018. Advanced Methods for Diffusion MRI Data

Analysis and their Application to the Healthy Ageing Brain (Doctoral thesis). https://doi.org/10.17863/CAM.29356

[2]

Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn Reson Med. 2016 doi: 10.1002/mrm.26054.

dipy.workflows.denoise.gradient_table(bvals, bvecs=None, big_delta=None, small_delta=None, b0_threshold=50, atol=0.01, btens=None)

A general function for creating diffusion MR gradients.

It reads, loads and prepares scanner parameters like the b-values and b-vectors so that they can be useful during the reconstruction process.

Parameters:
bvalscan be any of the four options
1. an array of shape (N,) or (1, N) or (N, 1) with the b-values.

2. a path for the file which contains an array like the above (1).

3. an array of shape (N, 4) or (4, N). Then this parameter is considered to be a b-table which contains both bvals and bvecs. In this case the next parameter is skipped.

4. a path for the file which contains an array like the one at (3).

bvecscan be any of two options
1. an array of shape (N, 3) or (3, N) with the b-vectors.

2. a path for the file which contains an array like the previous.

big_deltafloat

acquisition pulse separation time in seconds (default None)

small_deltafloat

acquisition pulse duration time in seconds (default None)

b0_thresholdfloat

All b-values with values less than or equal to bo_threshold are considered as b0s i.e. without diffusion weighting.

atolfloat

All b-vectors need to be unit vectors up to a tolerance.

btenscan be any of three options
1. a string specifying the shape of the encoding tensor for all volumes in data. Options: ‘LTE’, ‘PTE’, ‘STE’, ‘CTE’ corresponding to linear, planar, spherical, and “cigar-shaped” tensor encoding. Tensors are rotated so that linear and cigar tensors are aligned with the corresponding gradient direction and the planar tensor’s normal is aligned with the corresponding gradient direction. Magnitude is scaled to match the b-value.

2. an array of strings of shape (N,), (N, 1), or (1, N) specifying encoding tensor shape for each volume separately. N corresponds to the number volumes in data. Options for elements in array: ‘LTE’, ‘PTE’, ‘STE’, ‘CTE’ corresponding to linear, planar, spherical, and “cigar-shaped” tensor encoding. Tensors are rotated so that linear and cigar tensors are aligned with the corresponding gradient direction and the planar tensor’s normal is aligned with the corresponding gradient direction. Magnitude is scaled to match the b-value.

3. an array of shape (N,3,3) specifying the b-tensor of each volume exactly. N corresponds to the number volumes in data. No rotation or scaling is performed.

Returns:

Notes

1. Often b0s (b-values which correspond to images without diffusion weighting) have 0 values however in some cases the scanner cannot provide b0s of an exact 0 value and it gives a bit higher values e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.

2. We assume that the minimum number of b-values is 7.

3. B-vectors should be unit vectors.

Examples

>>> from dipy.core.gradients import gradient_table
>>> bvals = 1500 * np.ones(7)
>>> bvals[0] = 0
>>> sq2 = np.sqrt(2) / 2
>>> bvecs = np.array([[0, 0, 0],
...                   [1, 0, 0],
...                   [0, 1, 0],
...                   [0, 0, 1],
...                   [sq2, sq2, 0],
...                   [sq2, 0, sq2],
...                   [0, sq2, sq2]])
>>> gt.bvecs.shape == bvecs.shape
True
>>> gt.bvecs.shape == bvecs.T.shape
False


Load data and other information from a nifti file.

Parameters:
fnamestr

Full path to a nifti file.

return_imgbool, optional

Whether to return the nibabel nifti img object. Default: False

return_voxsize: bool, optional

Whether to return the nifti header zooms. Default: False

return_coordsbool, optional

Whether to return the nifti header aff2axcodes. Default: False

as_ndarray: bool, optional

convert nibabel ArrayProxy to a numpy.ndarray. If you want to save memory and delay this casting, just turn this option to False (default: True)

Returns:
A tuple, with (at the most, if all keyword args are set to True):
(data, img.affine, img, vox_size, nib.aff2axcodes(img.affine))

load_nifti_data

### localpca

Performs local PCA denoising according to Manjon et al. [1].

Parameters:
arr4D array

Array of data to be denoised. The dimensions are (X, Y, Z, N), where N are the diffusion gradient directions.

sigmafloat or 3D array

Standard deviation of the noise estimated from the data.

A mask with voxels that are true inside the brain and false outside of it. The function denoises within the true part and returns zeros outside of those voxels.

The radius of the local patch to be taken around each voxel (in voxels). Default: 2 (denoise in blocks of 5x5x5 voxels).

pca_method‘eig’ or ‘svd’ (optional)

Use either eigenvalue decomposition (eig) or singular value decomposition (svd) for principal component analysis. The default method is ‘eig’ which is faster. However, occasionally ‘svd’ might be more accurate.

tau_factorfloat (optional)

Thresholding of PCA eigenvalues is done by nulling out eigenvalues that are smaller than:

$\tau = (\tau_{factor} \sigma)^2$

tau_{factor} can be change to adjust the relationship between the noise standard deviation and the threshold tau. If tau_{factor} is set to None, it will be automatically calculated using the Marcenko-Pastur distribution [2]. Default: 2.3 (according to [1])

out_dtypestr or dtype (optional)

The dtype for the output array. Default: output has the same dtype as the input.

suppress_warningbool (optional)

If true, suppress warning caused by patch_size < arr.shape[-1]. Default: False.

Returns:
denoised_arr4D array

This is the denoised array of the same size as that of the input data, clipped to non-negative values

References

[1] (1,2,3)

Manjon JV, Coupe P, Concha L, Buades A, Collins DL (2013) Diffusion Weighted Image Denoising Using Overcomplete Local PCA. PLoS ONE 8(9): e73021. https://doi.org/10.1371/journal.pone.0073021

[2]

Veraart J, Novikov DS, Christiaens D, Ades-aron B, Sijbers, Fieremans E, 2016. Denoising of Diffusion MRI using random matrix theory. Neuroimage 142:394-406. doi: 10.1016/j.neuroimage.2016.08.016

### mppca

Performs PCA-based denoising using the Marcenko-Pastur distribution [1].

Parameters:
arr4D array

Array of data to be denoised. The dimensions are (X, Y, Z, N), where N are the diffusion gradient directions.

A mask with voxels that are true inside the brain and false outside of it. The function denoises within the true part and returns zeros outside of those voxels.

The radius of the local patch to be taken around each voxel (in voxels). Default: 2 (denoise in blocks of 5x5x5 voxels).

pca_method‘eig’ or ‘svd’ (optional)

Use either eigenvalue decomposition (eig) or singular value decomposition (svd) for principal component analysis. The default method is ‘eig’ which is faster. However, occasionally ‘svd’ might be more accurate.

return_sigmabool (optional)

If true, a noise standard deviation estimate based on the Marcenko-Pastur distribution is returned [2]. Default: False.

out_dtypestr or dtype (optional)

The dtype for the output array. Default: output has the same dtype as the input.

suppress_warningbool (optional)

If true, suppress warning caused by patch_size < arr.shape[-1]. Default: False.

Returns:
denoised_arr4D array

This is the denoised array of the same size as that of the input data, clipped to non-negative values

sigma3D array (when return_sigma=True)

Estimate of the spatial varying standard deviation of the noise

References

[1] (1,2)

Veraart J, Novikov DS, Christiaens D, Ades-aron B, Sijbers, Fieremans E, 2016. Denoising of Diffusion MRI using random matrix theory. Neuroimage 142:394-406. doi: 10.1016/j.neuroimage.2016.08.016

[2]

Veraart J, Fieremans E, Novikov DS. 2016. Diffusion MRI noise mapping using random matrix theory. Magnetic Resonance in Medicine. doi: 10.1002/mrm.26059.

### nlmeans

Non-local means for denoising 3D and 4D images

Parameters:
arr3D or 4D ndarray

The array to be denoised

sigmafloat or 3D array

standard deviation of the noise estimated from the data

patch size is 2 x patch_radius + 1. Default is 1.

block size is 2 x block_radius + 1. Default is 5.

ricianboolean

If True the noise is estimated as Rician, otherwise Gaussian noise is assumed.

Number of threads to be used for OpenMP parallelization. If None (default) the value of OMP_NUM_THREADS environment variable is used if it is set, otherwise all available threads are used. If < 0 the maximal number of threads minus |num_threads + 1| is used (enter -1 to use as many threads as possible). 0 raises an error.

Returns:
denoised_arrndarray

the denoised arr which has the same shape as arr.

References

[Descoteaux08]

Descoteaux, Maxime and Wiest-Daesslé, Nicolas and Prima, Sylvain and Barillot, Christian and Deriche, Rachid Impact of Rician Adapted Non-Local Means Filtering on HARDI, MICCAI 2008

### patch2self

dipy.workflows.denoise.patch2self(data, bvals, patch_radius=(0, 0, 0), model='ols', b0_threshold=50, out_dtype=None, alpha=1.0, verbose=False, b0_denoising=True, clip_negative_vals=False, shift_intensity=True)

Patch2Self Denoiser.

Parameters:
datandarray

The 4D noisy DWI data to be denoised.

bvals1D array

Array of the bvals from the DWI acquisition

The radius of the local patch to be taken around each voxel (in voxels). Default: 0 (denoise in blocks of 1x1x1 voxels).

modelstring, or initialized linear model object.

This will determine the algorithm used to solve the set of linear equations underlying this model. If it is a string it needs to be one of the following: {‘ols’, ‘ridge’, ‘lasso’}. Otherwise, it can be an object that inherits from dipy.optimize.SKLearnLinearSolver or an object with a similar interface from Scikit-Learn: sklearn.linear_model.LinearRegression, sklearn.linear_model.Lasso or sklearn.linear_model.Ridge and other objects that inherit from sklearn.base.RegressorMixin. Default: ‘ols’.

b0_thresholdint, optional

Threshold for considering volumes as b0.

out_dtypestr or dtype, optional

The dtype for the output array. Default: output has the same dtype as the input.

alphafloat, optional

Regularization parameter only for ridge regression model.

verbosebool, optional

Show progress of Patch2Self and time taken.

b0_denoisingbool, optional

Skips denoising b0 volumes if set to False.

clip_negative_valsbool, optional

Sets negative values after denoising to 0 using np.clip.

shift_intensitybool, optional

Shifts the distribution of intensities per volume to give non-negative values

Returns:
denoised arrayndarray

This is the denoised array of the same size as that of the input data, clipped to non-negative values.

References

Denoising Diffusion MRI with Self-supervised Learning, Advances in Neural Information Processing Systems 33 (2020)

### pca_noise_estimate

dipy.workflows.denoise.pca_noise_estimate()

PCA based local noise estimation.

Parameters:
data: 4D array

the input dMRI data. The first 3 dimension must have size >= 2 * patch_radius + 1 or size = 1.

gradient information for the data gives us the bvals and bvecs of diffusion data, which is needed here to select between the noise estimation methods.

The radius of the local patch to be taken around each voxel (in voxels). Default: 1 (estimate noise in blocks of 3x3x3 voxels).

correct_biasbool

Whether to correct for bias due to Rician noise. This is an implementation of equation 8 in [1].

smoothint

Radius of a Gaussian smoothing filter to apply to the noise estimate before returning. Default: 2.

Returns:
sigma_corr: 3D array

The local noise standard deviation estimate.

References

[1]

Manjon JV, Coupe P, Concha L, Buades A, Collins DL “Diffusion Weighted Image Denoising Using Overcomplete Local PCA”. PLoS ONE 8(9): e73021. doi:10.1371/journal.pone.0073021.

Read b-values and b-vectors from disk.

Parameters:
fbvalsstr

Full path to file with b-values. None to not read bvals.

fbvecsstr

Full path of file with b-vectors. None to not read bvecs.

Returns:
bvalsarray, (N,) or None
bvecsarray, (N, 3) or None

Notes

Files can be either ‘.bvals’/’.bvecs’ or ‘.txt’ or ‘.npy’ (containing arrays stored with the appropriate values).

### save_nifti

dipy.workflows.denoise.save_nifti(fname, data, affine, hdr=None, dtype=None)

Save a data array into a nifti file.

Parameters:
fnamestr

The full path to the file to be saved.

datandarray

The array with the data to save.

affine4x4 array

The affine transform associated with the file.

Returns:
None

### NumpyDocString

class dipy.workflows.docstring_parser.NumpyDocString(docstring, config={})

Bases: object

__init__(docstring, config={})

### Reader

Bases: object

Methods

__init__(data)
Parameters:
datastr

String with lines separated by ‘

‘.
eof()
is_empty()
peek(n=0)
reset()
seek_next_non_empty_line()

### dedent_lines

dipy.workflows.docstring_parser.dedent_lines(lines)

Deindent a list of lines maximally

### warn

dipy.workflows.docstring_parser.warn(/, message, category=None, stacklevel=1, source=None)

Issue a warning, or maybe ignore it or raise an exception.

### IntrospectiveArgumentParser

class dipy.workflows.flow_runner.IntrospectiveArgumentParser(prog=None, usage=None, description=None, epilog=None, parents=(), formatter_class=<class 'argparse.RawTextHelpFormatter'>, prefix_chars='-', fromfile_prefix_chars=None, argument_default=None, conflict_handler='resolve', add_help=True)

Bases: ArgumentParser

Attributes:
optional_parameters
output_parameters
positional_parameters

Methods

 add_argument(add_argument) add_sub_flow_args(sub_flows) Take an array of workflow objects and use introspection to extract the parameters, types and docstrings of their run method. add_subparsers(**kwargs) add_workflow(workflow) Take a workflow object and use introspection to extract the parameters, types and docstrings of its run method. error(message) Prints a usage message incorporating the message to stderr and exits. exit([status, message]) format_usage() get_flow_args([args, namespace]) Return the parsed arguments as a dictionary that will be used as a workflow's run method arguments. parse_args([args, namespace]) print_usage([file]) register(registry_name, value, object) set_defaults(**kwargs)
__init__(prog=None, usage=None, description=None, epilog=None, parents=(), formatter_class=<class 'argparse.RawTextHelpFormatter'>, prefix_chars='-', fromfile_prefix_chars=None, argument_default=None, conflict_handler='resolve', add_help=True)

Augmenting the argument parser to allow automatic creation of arguments from workflows

Parameters:
progNone

The name of the program. (default: sys.argv[0])

usageNone

A usage message. (default: auto-generated from arguments)

descriptionstr

A description of what the program does.

epilogstr

Text following the argument descriptions.

parentslist

Parsers whose arguments should be copied into this one.

formatter_classobj

HelpFormatter class for printing help messages.

Characters that prefix optional arguments.

fromfile_prefix_charsNone

Characters that prefix files containing additional arguments.

argument_defaultNone

The default value for all arguments.

conflict_handlerstr

String indicating how to handle conflicts.

Take an array of workflow objects and use introspection to extract the parameters, types and docstrings of their run method. Only the optional input parameters are extracted for these as they are treated as sub workflows.

Parameters:
sub_flowsarray of dipy.workflows.workflow.Workflow

Workflows to inspect.

Returns:
sub_flow_optionalsdictionary of all sub workflow optional parameters

Take a workflow object and use introspection to extract the parameters, types and docstrings of its run method. Then add these parameters to the current arparser’s own params to parse. If the workflow is of type combined_workflow, the optional input parameters of its sub workflows will also be added.

Parameters:
workflowdipy.workflows.workflow.Workflow

Workflow from which to infer parameters.

Returns:
sub_flow_optionalsdictionary of all sub workflow optional parameters
get_flow_args(args=None, namespace=None)

Return the parsed arguments as a dictionary that will be used as a workflow’s run method arguments.

property optional_parameters
property output_parameters
property positional_parameters
show_argument(dest)
update_argument(*args, **kargs)

### get_level

dipy.workflows.flow_runner.get_level(lvl)

Transforms the logging level passed on the commandline into a proper logging level name.

### run_flow

dipy.workflows.flow_runner.run_flow(flow)

Wraps the process of building an argparser that reflects the workflow that we want to run along with some generic parameters like logging, force and output strategies. The resulting parameters are then fed to the workflow’s run method.

### FetchFlow

class dipy.workflows.io.FetchFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 Gets available dataset and function names. get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. load_module(module_path) Load / reload an external module. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(data_names[, out_dir]) Download files to folder and check their md5 checksums.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

static get_fetcher_datanames()

Gets available dataset and function names.

Returns:
available_data: dict

Available dataset and function names.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

Parameters:
module_path: string

the path to the module relative to the main script

Returns:
module: module object
run(data_names, out_dir='')

To see all available datasets, please type “list” in data_names.

Parameters:
data_namesvariable string

Any number of Nifti1, bvals or bvecs files.

out_dirstring, optional

Output directory. (default current directory)

### IoInfoFlow

class dipy.workflows.io.IoInfoFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files[, b0_threshold, bvecs_tol, ...]) Provides useful information about different files used in medical imaging.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, b0_threshold=50, bvecs_tol=0.01, bshell_thr=100)

Provides useful information about different files used in medical imaging. Any number of input files can be provided. The program identifies the type of file by its extension.

Parameters:
input_filesvariable string

Any number of Nifti1, bvals or bvecs files.

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

bvecs_tolfloat, optional

Threshold used to check that norm(bvec) = 1 +/- bvecs_tol b-vectors are unit vectors.

bshell_thrfloat, optional

Threshold for distinguishing b-values in different shells.

### SplitFlow

class dipy.workflows.io.SplitFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files[, vol_idx, out_dir, out_split]) Splits the input 4D file and extracts the required 3D volume.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, vol_idx=0, out_dir='', out_split='split.nii.gz')

Splits the input 4D file and extracts the required 3D volume.

Parameters:
input_filesvariable string

Any number of Nifti1 files

vol_idxint, optional
out_dirstring, optional

Output directory. (default current directory)

out_splitstring, optional

Name of the resulting split volume

### Workflow

class dipy.workflows.io.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: object

Methods

 Create an iterator for IO. Return A short name for the workflow used to subdivide. Return No sub runs since this is a simple workflow. Check if a file will be overwritten upon processing the inputs. run(*args, **kwargs) Execute the workflow.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

get_io_iterator()

Create an iterator for IO.

Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

get_sub_runs()

Return No sub runs since this is a simple workflow.

manage_output_overwrite()

Check if a file will be overwritten upon processing the inputs.

If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.

run(*args, **kwargs)

Execute the workflow.

Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)

### getmembers

dipy.workflows.io.getmembers(object, predicate=None)

Return all members of an object as (name, value) pairs sorted by name. Optionally, only return members that satisfy a given predicate.

### isfunction

dipy.workflows.io.isfunction(object)

Return true if the object is a user-defined function.

Function objects provide these attributes:

__doc__ documentation string __name__ name with which this function was defined __code__ code object containing compiled function bytecode __defaults__ tuple of any default values for arguments __globals__ global namespace in which this function was defined __annotations__ dict of parameter annotations __kwdefaults__ dict of keyword only parameters with defaults

Load data and other information from a nifti file.

Parameters:
fnamestr

Full path to a nifti file.

return_imgbool, optional

Whether to return the nibabel nifti img object. Default: False

return_voxsize: bool, optional

Whether to return the nifti header zooms. Default: False

return_coordsbool, optional

Whether to return the nifti header aff2axcodes. Default: False

as_ndarray: bool, optional

convert nibabel ArrayProxy to a numpy.ndarray. If you want to save memory and delay this casting, just turn this option to False (default: True)

Returns:
A tuple, with (at the most, if all keyword args are set to True):
(data, img.affine, img, vox_size, nib.aff2axcodes(img.affine))

load_nifti_data

### save_nifti

dipy.workflows.io.save_nifti(fname, data, affine, hdr=None, dtype=None)

Save a data array into a nifti file.

Parameters:
fnamestr

The full path to the file to be saved.

datandarray

The array with the data to save.

affine4x4 array

The affine transform associated with the file.

Returns:
None

### MaskFlow

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files, lb[, ub, out_dir, out_mask]) Workflow for creating a binary mask
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

Workflow for creating a binary mask

Parameters:
input_filesstring

Path to image to be masked.

lbfloat

Lower bound value.

ubfloat, optional

Upper bound value.

out_dirstring, optional

Output directory. (default current directory)

### Workflow

Bases: object

Methods

 Create an iterator for IO. Return A short name for the workflow used to subdivide. Return No sub runs since this is a simple workflow. Check if a file will be overwritten upon processing the inputs. run(*args, **kwargs) Execute the workflow.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

get_io_iterator()

Create an iterator for IO.

Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

get_sub_runs()

Return No sub runs since this is a simple workflow.

manage_output_overwrite()

Check if a file will be overwritten upon processing the inputs.

If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.

run(*args, **kwargs)

Execute the workflow.

Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)

Load data and other information from a nifti file.

Parameters:
fnamestr

Full path to a nifti file.

return_imgbool, optional

Whether to return the nibabel nifti img object. Default: False

return_voxsize: bool, optional

Whether to return the nifti header zooms. Default: False

return_coordsbool, optional

Whether to return the nifti header aff2axcodes. Default: False

as_ndarray: bool, optional

convert nibabel ArrayProxy to a numpy.ndarray. If you want to save memory and delay this casting, just turn this option to False (default: True)

Returns:
A tuple, with (at the most, if all keyword args are set to True):
(data, img.affine, img, vox_size, nib.aff2axcodes(img.affine))

load_nifti_data

### save_nifti

Save a data array into a nifti file.

Parameters:
fnamestr

The full path to the file to be saved.

datandarray

The array with the data to save.

affine4x4 array

The affine transform associated with the file.

Returns:
None

### IOIterator

class dipy.workflows.multi_io.IOIterator(output_strategy='absolute', mix_names=False)

Bases: object

Create output filenames that work nicely with multiple input files from multiple directories (processing multiple subjects with one command)

Use information from input files, out_dir and out_fnames to generate correct outputs which can come from long lists of multiple or single inputs.

Methods

 create_directories create_outputs file_existence_check set_inputs set_out_dir set_out_fnames set_output_keys
__init__(output_strategy='absolute', mix_names=False)
create_directories()
create_outputs()
file_existence_check(args)
set_inputs(*args)
set_out_dir(out_dir)
set_out_fnames(*args)
set_output_keys(*args)

### basename_without_extension

dipy.workflows.multi_io.basename_without_extension(fname)

### common_start

dipy.workflows.multi_io.common_start(sa, sb)

Return the longest common substring from the beginning of sa and sb.

### concatenate_inputs

dipy.workflows.multi_io.concatenate_inputs(multi_inputs)

Concatenate list of inputs.

### connect_output_paths

dipy.workflows.multi_io.connect_output_paths(inputs, out_dir, out_files, output_strategy='absolute', mix_names=True)

Generate a list of output files paths based on input files and output strategies.

Parameters:
inputsarray

List of input paths.

out_dirstring

The output directory.

out_filesarray

List of output files.

output_strategystring, optional
Which strategy to use to generate the output paths.

‘append’: Add out_dir to the path of the input. ‘prepend’: Add the input path directory tree to out_dir. ‘absolute’: Put directly in out_dir.

mix_namesbool, optional

Whether or not prepend a string composed of a mix of the input names to the final output name.

Returns:
A list of output file paths.

### get_args_default

dipy.workflows.multi_io.get_args_default(func)

### glob

dipy.workflows.multi_io.glob(pathname, *, recursive=False)

Return a list of paths matching a pathname pattern.

The pattern may contain simple shell-style wildcards a la fnmatch. However, unlike fnmatch, filenames starting with a dot are special cases that are not matched by ‘*’ and ‘?’ patterns.

If recursive is true, the pattern ‘**’ will match any files and zero or more directories and subdirectories.

### io_iterator

dipy.workflows.multi_io.io_iterator(inputs, out_dir, fnames, output_strategy='absolute', mix_names=False, out_keys=None)

Create an IOIterator from the parameters.

Parameters:
inputsarray

List of input files.

out_dirstring

Output directory.

fnamesarray

File names of all outputs to be created.

output_strategystring, optional

Controls the behavior of the IOIterator for output paths.

mix_namesbool, optional

Whether or not to append a mix of input names at the beginning.

out_keyslist, optional

Output parameter names.

Returns:
Properly instantiated IOIterator object.

### io_iterator_

dipy.workflows.multi_io.io_iterator_(frame, fnc, output_strategy='absolute', mix_names=False)

Create an IOIterator using introspection.

Parameters:
frameframeobject

Contains the info about the current local variables values.

fncfunction

The function to inspect

output_strategystring

Controls the behavior of the IOIterator for output paths.

mix_namesbool

Whether or not to append a mix of input names at the beginning.

Returns:
Properly instantiated IOIterator object.

### slash_to_under

dipy.workflows.multi_io.slash_to_under(dir_str)

### ConstrainedSphericalDeconvModel

class dipy.workflows.reconst.ConstrainedSphericalDeconvModel(gtab, response, reg_sphere=None, sh_order=8, lambda_=1, tau=0.1, convergence=50)

Bases: SphHarmModel

Methods

 cache_clear() Clear the cache. cache_get(tag, key[, default]) Retrieve a value from the cache. cache_set(tag, key, value) Store a value in the cache. fit(data[, mask]) Fit method for every voxel in data predict(sh_coeff[, gtab, S0]) Compute a signal prediction given spherical harmonic coefficients for the provided GradientTable class instance. sampling_matrix(sphere) The matrix needed to sample ODFs from coefficients of the model.
__init__(gtab, response, reg_sphere=None, sh_order=8, lambda_=1, tau=0.1, convergence=50)

Constrained Spherical Deconvolution (CSD) [1].

Spherical deconvolution computes a fiber orientation distribution (FOD), also called fiber ODF (fODF) [2], as opposed to a diffusion ODF as the QballModel or the CsaOdfModel. This results in a sharper angular profile with better angular resolution that is the best object to be used for later deterministic and probabilistic tractography [3].

A sharp fODF is obtained because a single fiber response function is injected as a priori knowledge. The response function is often data-driven and is thus provided as input to the ConstrainedSphericalDeconvModel. It will be used as deconvolution kernel, as described in [1].

Parameters:
responsetuple or AxSymShResponse object

A tuple with two elements. The first is the eigen-values as an (3,) ndarray and the second is the signal value for the response function without diffusion weighting (i.e. S0). This is to be able to generate a single fiber synthetic signal. The response function will be used as deconvolution kernel ([1]).

reg_sphereSphere (optional)

sphere used to build the regularization B matrix. Default: ‘symmetric362’.

sh_orderint (optional)

maximal spherical harmonics order. Default: 8

lambda_float (optional)

weight given to the constrained-positivity regularization part of the deconvolution equation (see [1]). Default: 1

taufloat (optional)

threshold controlling the amplitude below which the corresponding fODF is assumed to be zero. Ideally, tau should be set to zero. However, to improve the stability of the algorithm, tau is set to tau*100 % of the mean fODF amplitude (here, 10% by default) (see [1]). Default: 0.1

convergenceint

Maximum number of iterations to allow the deconvolution to converge.

References

[1] (1,2,3,4,5)

Tournier, J.D., et al. NeuroImage 2007. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution

[2]

Descoteaux, M., et al. IEEE TMI 2009. Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions

[3]

Côté, M-A., et al. Medical Image Analysis 2013. Tractometer: Towards validation of tractography pipelines

[4]

Tournier, J.D, et al. Imaging Systems and Technology 2012. MRtrix: Diffusion Tractography in Crossing Fiber Regions

Fit method for every voxel in data

predict(sh_coeff, gtab=None, S0=1.0)

Compute a signal prediction given spherical harmonic coefficients for the provided GradientTable class instance.

Parameters:
sh_coeffndarray

The spherical harmonic representation of the FOD from which to make the signal prediction.

The gradients for which the signal will be predicted. Uses the model’s gradient table by default.

S0ndarray or float

The non diffusion-weighted signal value.

Returns:
pred_signdarray

The predicted signal.

### CsaOdfModel

class dipy.workflows.reconst.CsaOdfModel(gtab, sh_order, smooth=0.006, min_signal=1e-05, assume_normed=False)

Bases: QballBaseModel

Implementation of Constant Solid Angle reconstruction method.

References

[1]

Aganj, I., et al. 2009. ODF Reconstruction in Q-Ball Imaging With Solid Angle Consideration.

Methods

 cache_clear() Clear the cache. cache_get(tag, key[, default]) Retrieve a value from the cache. cache_set(tag, key, value) Store a value in the cache. fit(data[, mask]) Fits the model to diffusion data and returns the model fit sampling_matrix(sphere) The matrix needed to sample ODFs from coefficients of the model.
__init__(gtab, sh_order, smooth=0.006, min_signal=1e-05, assume_normed=False)

Creates a model that can be used to fit or sample diffusion data

Parameters:

Diffusion gradients used to acquire data

sh_ordereven int >= 0

the spherical harmonic order of the model

smoothfloat between 0 and 1, optional

The regularization parameter of the model

min_signalfloat, > 0, optional

During fitting, all signal values less than min_signal are clipped to min_signal. This is done primarily to avoid values less than or equal to zero when taking logs.

assume_normedbool, optional

If True, clipping and normalization of the data with respect to the mean B0 signal are skipped during mode fitting. This is an advanced feature and should be used with care.

normalize_data
max = 0.999
min = 0.001

### DiffusionKurtosisModel

class dipy.workflows.reconst.DiffusionKurtosisModel(gtab, fit_method='WLS', *args, **kwargs)

Bases: ReconstModel

Class for the Diffusion Kurtosis Model

Methods

 fit(data[, mask]) Fit method of the DKI model class predict(dki_params[, S0]) Predict a signal for this DKI model class instance given parameters.
__init__(gtab, fit_method='WLS', *args, **kwargs)

Diffusion Kurtosis Tensor Model [1]

Parameters:
fit_methodstr or callable

str can be one of the following: ‘OLS’ or ‘ULLS’ for ordinary least squares

dki.ols_fit_dki

‘WLS’ or ‘UWLLS’ for weighted ordinary least squares

dki.wls_fit_dki

callable has to have the signature:

fit_method(design_matrix, data, *args, **kwargs)

args, kwargsarguments and key-word arguments passed to the

fit_method. See dki.ols_fit_dki, dki.wls_fit_dki for details

References

[1]

Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011.

Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med. 65(3), 823-836

Fit method of the DKI model class

Parameters:
dataarray

The measured signal from one voxel.

A boolean array used to mark the coordinates in the data that should be analyzed that has the shape data.shape[-1]

predict(dki_params, S0=1.0)

Predict a signal for this DKI model class instance given parameters.

Parameters:
dki_paramsndarray (x, y, z, 27) or (n, 27)

All parameters estimated from the diffusion kurtosis model. Parameters are ordered as follows:

1. Three diffusion tensor’s eigenvalues

2. Three lines of the eigenvector matrix each containing the first, second and third coordinates of the eigenvector 3. Fifteen elements of the kurtosis tensor

S0float or ndarray (optional)

The non diffusion-weighted signal in every voxel, or across all voxels. Default: 1

### ReconstCSAFlow

class dipy.workflows.reconst.ReconstCSAFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files, bvalues_files, ...[, ...]) Constant Solid Angle.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, bvalues_files, bvectors_files, mask_files, sh_order=6, odf_to_sh_order=8, b0_threshold=50.0, bvecs_tol=0.01, extract_pam_values=False, parallel=False, num_processes=None, out_dir='', out_pam='peaks.pam5', out_shm='shm.nii.gz', out_peaks_dir='peaks_dirs.nii.gz', out_peaks_values='peaks_values.nii.gz', out_peaks_indices='peaks_indices.nii.gz', out_gfa='gfa.nii.gz')

Constant Solid Angle.

Parameters:
input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bvalues_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

bvectors_filesstring

Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.

Path to the input masks. This path may contain wildcards to use multiple masks at once. (default: No mask used)

sh_orderint, optional

Spherical harmonics order used in the CSA fit.

odf_to_sh_orderint, optional

Spherical harmonics order used for peak_from_model to compress the ODF to spherical harmonics coefficients.

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

bvecs_tolfloat, optional

Threshold used so that norm(bvec)=1.

extract_pam_valuesbool, optional

Whether or not to save pam volumes as single nifti files.

parallelbool, optional

Whether to use parallelization in peak-finding during the calibration procedure.

num_processesint, optional

If parallel is True, the number of subprocesses to use (default multiprocessing.cpu_count()). If < 0 the maximal number of cores minus num_processes + 1 is used (enter -1 to use as many cores as possible). 0 raises an error.

out_dirstring, optional

Output directory. (default current directory)

out_pamstring, optional

Name of the peaks volume to be saved.

out_shmstring, optional

Name of the spherical harmonics volume to be saved.

out_peaks_dirstring, optional

Name of the peaks directions volume to be saved.

out_peaks_valuesstring, optional

Name of the peaks values volume to be saved.

out_peaks_indicesstring, optional

Name of the peaks indices volume to be saved.

out_gfastring, optional

Name of the generalized FA volume to be saved.

References

[1]

Aganj, I., et al. 2009. ODF Reconstruction in Q-Ball Imaging with Solid Angle Consideration.

### ReconstCSDFlow

class dipy.workflows.reconst.ReconstCSDFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files, bvalues_files, ...[, ...]) Constrained spherical deconvolution
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, bvalues_files, bvectors_files, mask_files, b0_threshold=50.0, bvecs_tol=0.01, roi_center=None, roi_radii=10, fa_thr=0.7, frf=None, extract_pam_values=False, sh_order=8, odf_to_sh_order=8, parallel=False, num_processes=None, out_dir='', out_pam='peaks.pam5', out_shm='shm.nii.gz', out_peaks_dir='peaks_dirs.nii.gz', out_peaks_values='peaks_values.nii.gz', out_peaks_indices='peaks_indices.nii.gz', out_gfa='gfa.nii.gz')

Constrained spherical deconvolution

Parameters:
input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bvalues_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

bvectors_filesstring

Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.

Path to the input masks. This path may contain wildcards to use multiple masks at once. (default: No mask used)

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

bvecs_tolfloat, optional

Bvecs should be unit vectors.

roi_centervariable int, optional

Center of ROI in data. If center is None, it is assumed that it is the center of the volume with shape data.shape[:3].

radii of cuboid ROI in voxels.

fa_thrfloat, optional

FA threshold for calculating the response function.

frfvariable float, optional

Fiber response function can be for example inputed as 15 4 4 (from the command line) or [15, 4, 4] from a Python script to be converted to float and multiplied by 10**-4 . If None the fiber response function will be computed automatically.

extract_pam_valuesbool, optional

Save or not to save pam volumes as single nifti files.

sh_orderint, optional

Spherical harmonics order used in the CSA fit.

odf_to_sh_orderint, optional

Spherical harmonics order used for peak_from_model to compress the ODF to spherical harmonics coefficients.

parallelbool, optional

Whether to use parallelization in peak-finding during the calibration procedure.

num_processesint, optional

If parallel is True, the number of subprocesses to use (default multiprocessing.cpu_count()). If < 0 the maximal number of cores minus num_processes + 1 is used (enter -1 to use as many cores as possible). 0 raises an error.

out_dirstring, optional

Output directory. (default current directory)

out_pamstring, optional

Name of the peaks volume to be saved.

out_shmstring, optional

Name of the spherical harmonics volume to be saved.

out_peaks_dirstring, optional

Name of the peaks directions volume to be saved.

out_peaks_valuesstring, optional

Name of the peaks values volume to be saved.

out_peaks_indicesstring, optional

Name of the peaks indices volume to be saved.

out_gfastring, optional

Name of the generalized FA volume to be saved.

References

[1]

Tournier, J.D., et al. NeuroImage 2007. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution.

### ReconstDkiFlow

class dipy.workflows.reconst.ReconstDkiFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files, bvalues_files, ...[, ...]) Workflow for Diffusion Kurtosis reconstruction and for computing DKI metrics.
 get_fitted_tensor
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

get_fitted_tensor(data, mask, bval, bvec, b0_threshold=50, fit_method='WLS', optional_args=None)
classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, bvalues_files, bvectors_files, mask_files, fit_method='WLS', b0_threshold=50.0, sigma=None, save_metrics=None, out_dir='', out_dt_tensor='dti_tensors.nii.gz', out_fa='fa.nii.gz', out_ga='ga.nii.gz', out_rgb='rgb.nii.gz', out_md='md.nii.gz', out_ad='ad.nii.gz', out_rd='rd.nii.gz', out_mode='mode.nii.gz', out_evec='evecs.nii.gz', out_eval='evals.nii.gz', out_dk_tensor='dki_tensors.nii.gz', out_mk='mk.nii.gz', out_ak='ak.nii.gz', out_rk='rk.nii.gz')

Workflow for Diffusion Kurtosis reconstruction and for computing DKI metrics. Performs a DKI reconstruction on the files by ‘globing’ input_files and saves the DKI metrics in a directory specified by out_dir.

Parameters:
input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bvalues_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

bvectors_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

Path to the input masks. This path may contain wildcards to use multiple masks at once. (default: No mask used)

fit_methodstring, optional

can be one of the following: ‘OLS’ or ‘ULLS’ for ordinary least squares ‘WLS’ or ‘UWLLS’ for weighted ordinary least squares

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

sigmafloat, optional

An estimate of the variance. [3] recommend to use 1.5267 * std(background_noise), where background_noise is estimated from some part of the image known to contain no signal (only noise)

save_metricsvariable string, optional

List of metrics to save. Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval

out_dirstring, optional

Output directory. (default current directory)

out_dt_tensorstring, optional

Name of the tensors volume to be saved.

out_dk_tensorstring, optional

Name of the tensors volume to be saved.

out_fastring, optional

Name of the fractional anisotropy volume to be saved.

out_gastring, optional

Name of the geodesic anisotropy volume to be saved.

out_rgbstring, optional

Name of the color fa volume to be saved.

out_mdstring, optional

Name of the mean diffusivity volume to be saved.

Name of the axial diffusivity volume to be saved.

out_rdstring, optional

Name of the radial diffusivity volume to be saved.

out_modestring, optional

Name of the mode volume to be saved.

out_evecstring, optional

Name of the eigenvectors volume to be saved.

out_evalstring, optional

Name of the eigenvalues to be saved.

out_mkstring, optional

Name of the mean kurtosis to be saved.

out_akstring, optional

Name of the axial kurtosis to be saved.

out_rkstring, optional

Name of the radial kurtosis to be saved.

References

[1]

Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011. Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med. 65(3), 823-836

[2]

Jensen, Jens H., Joseph A. Helpern, Anita Ramani, Hanzhang Lu, and Kyle Kaczynski. 2005. Diffusional Kurtosis Imaging: The Quantification of Non-Gaussian Water Diffusion by Means of Magnetic Resonance Imaging. MRM 53 (6):1432-40.

[3]

Chang, L-C, Jones, DK and Pierpaoli, C (2005). RESTORE: robust estimation of tensors by outlier rejection. MRM, 53: 1088-95.

### ReconstDtiFlow

class dipy.workflows.reconst.ReconstDtiFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files, bvalues_files, ...[, ...]) Workflow for tensor reconstruction and for computing DTI metrics.
 get_fitted_tensor
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

get_fitted_tensor(data, mask, bval, bvec, b0_threshold=50, bvecs_tol=0.01, fit_method='WLS', optional_args=None)
classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, bvalues_files, bvectors_files, mask_files, fit_method='WLS', b0_threshold=50, bvecs_tol=0.01, sigma=None, save_metrics=None, out_dir='', out_tensor='tensors.nii.gz', out_fa='fa.nii.gz', out_ga='ga.nii.gz', out_rgb='rgb.nii.gz', out_md='md.nii.gz', out_ad='ad.nii.gz', out_rd='rd.nii.gz', out_mode='mode.nii.gz', out_evec='evecs.nii.gz', out_eval='evals.nii.gz', nifti_tensor=True)

Workflow for tensor reconstruction and for computing DTI metrics. using Weighted Least-Squares. Performs a tensor reconstruction on the files by ‘globing’ input_files and saves the DTI metrics in a directory specified by out_dir.

Parameters:
input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bvalues_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

bvectors_filesstring

Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.

Path to the input masks. This path may contain wildcards to use multiple masks at once.

fit_methodstring, optional

can be one of the following: ‘WLS’ for weighted least squares ‘LS’ or ‘OLS’ for ordinary least squares ‘NLLS’ for non-linear least-squares ‘RT’ or ‘restore’ or ‘RESTORE’ for RESTORE robust tensor fitting

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

bvecs_tolfloat, optional

Threshold used to check that norm(bvec) = 1 +/- bvecs_tol

sigmafloat, optional

An estimate of the variance. [5] recommend to use 1.5267 * std(background_noise), where background_noise is estimated from some part of the image known to contain no signal (only noise) b-vectors are unit vectors.

save_metricsvariable string, optional

List of metrics to save. Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval

out_dirstring, optional

Output directory. (default current directory)

out_tensorstring, optional

Name of the tensors volume to be saved. Per default, this will be saved following the nifti standard: with the tensor elements as Dxx, Dxy, Dyy, Dxz, Dyz, Dzz on the last (5th) dimension of the volume (shape: (i, j, k, 1, 6)). If nifti_tensor is False, this will be saved in an alternate format that is used by other software (e.g., FSL): a 4-dimensional volume (shape (i, j, k, 6)) with Dxx, Dxy, Dxz, Dyy, Dyz, Dzz on the last dimension.

out_fastring, optional

Name of the fractional anisotropy volume to be saved.

out_gastring, optional

Name of the geodesic anisotropy volume to be saved.

out_rgbstring, optional

Name of the color fa volume to be saved.

out_mdstring, optional

Name of the mean diffusivity volume to be saved.

Name of the axial diffusivity volume to be saved.

out_rdstring, optional

Name of the radial diffusivity volume to be saved.

out_modestring, optional

Name of the mode volume to be saved.

out_evecstring, optional

Name of the eigenvectors volume to be saved.

out_evalstring, optional

Name of the eigenvalues to be saved.

nifti_tensorbool, optional

Whether the tensor is saved in the standard Nifti format or in an alternate format that is used by other software (e.g., FSL): a 4-dimensional volume (shape (i, j, k, 6)) with Dxx, Dxy, Dxz, Dyy, Dyz, Dzz on the last dimension.

References

[1]

Basser, P.J., Mattiello, J., LeBihan, D., 1994. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103, 247-254.

[2]

Basser, P., Pierpaoli, C., 1996. Microstructural and physiological features of tissues elucidated by quantitative diffusion-tensor MRI. Journal of Magnetic Resonance 111, 209-219.

[3]

Lin-Ching C., Jones D.K., Pierpaoli, C. 2005. RESTORE: Robust estimation of tensors by outlier rejection. MRM 53: 1088-1095

[4]

hung, SW., Lu, Y., Henry, R.G., 2006. Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters. NeuroImage 33, 531-541.

[5]

Chang, L-C, Jones, DK and Pierpaoli, C (2005). RESTORE: robust estimation of tensors by outlier rejection. MRM, 53: 1088-95.

### ReconstIvimFlow

class dipy.workflows.reconst.ReconstIvimFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files, bvalues_files, ...[, ...]) Workflow for Intra-voxel Incoherent Motion reconstruction and for computing IVIM metrics.
 get_fitted_ivim
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, bvalues_files, bvectors_files, mask_files, split_b_D=400, split_b_S0=200, b0_threshold=0, save_metrics=None, out_dir='', out_S0_predicted='S0_predicted.nii.gz', out_perfusion_fraction='perfusion_fraction.nii.gz', out_D_star='D_star.nii.gz', out_D='D.nii.gz')

Workflow for Intra-voxel Incoherent Motion reconstruction and for computing IVIM metrics. Performs a IVIM reconstruction on the files by ‘globing’ input_files and saves the IVIM metrics in a directory specified by out_dir.

Parameters:
input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bvalues_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

bvectors_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

Path to the input masks. This path may contain wildcards to use multiple masks at once. (default: No mask used)

split_b_Dint, optional

Value to split the bvals to estimate D for the two-stage process of fitting.

split_b_S0int, optional

Value to split the bvals to estimate S0 for the two-stage process of fitting.

b0_thresholdint, optional

Threshold value for the b0 bval.

save_metricsvariable string, optional

List of metrics to save. Possible values: S0_predicted, perfusion_fraction, D_star, D

out_dirstring, optional

Output directory. (default current directory)

out_S0_predictedstring, optional

Name of the S0 signal estimated to be saved.

out_perfusion_fractionstring, optional

Name of the estimated volume fractions to be saved.

out_D_starstring, optional

Name of the estimated pseudo-diffusion parameter to be saved.

out_Dstring, optional

Name of the estimated diffusion parameter to be saved.

References

[Stejskal65]

Stejskal, E. O.; Tanner, J. E. (1 January 1965). “Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient”. The Journal of Chemical Physics 42 (1): 288. Bibcode: 1965JChPh..42..288S. doi:10.1063/1.1695690.

[LeBihan84]

Le Bihan, Denis, et al. “Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging.” Radiology 168.2 (1988): 497-505.

### ReconstMAPMRIFlow

class dipy.workflows.reconst.ReconstMAPMRIFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(data_files, bvals_files, bvecs_files, ...) Workflow for fitting the MAPMRI model (with optional Laplacian regularization).
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(data_files, bvals_files, bvecs_files, small_delta, big_delta, b0_threshold=50.0, laplacian=True, positivity=True, bval_threshold=2000, save_metrics=(), laplacian_weighting=0.05, radial_order=6, out_dir='', out_rtop='rtop.nii.gz', out_lapnorm='lapnorm.nii.gz', out_msd='msd.nii.gz', out_qiv='qiv.nii.gz', out_rtap='rtap.nii.gz', out_rtpp='rtpp.nii.gz', out_ng='ng.nii.gz', out_perng='perng.nii.gz', out_parng='parng.nii.gz')

Workflow for fitting the MAPMRI model (with optional Laplacian regularization). Generates rtop, lapnorm, msd, qiv, rtap, rtpp, non-gaussian (ng), parallel ng, perpendicular ng saved in a nifti format in input files provided by data_files and saves the nifti files to an output directory specified by out_dir.

In order for the MAPMRI workflow to work in the way intended either the Laplacian or positivity or both must be set to True.

Parameters:
data_filesstring

Path to the input volume.

bvals_filesstring

Path to the bval files.

bvecs_filesstring

Path to the bvec files.

small_deltafloat

Small delta value used in generation of gradient table of provided bval and bvec.

big_deltafloat

Big delta value used in generation of gradient table of provided bval and bvec.

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

laplacianbool, optional

Regularize using the Laplacian of the MAP-MRI basis.

positivitybool, optional

Constrain the propagator to be positive.

bval_thresholdfloat, optional

Sets the b-value threshold to be used in the scale factor estimation. In order for the estimated non-Gaussianity to have meaning this value should set to a lower value (b<2000 s/mm^2) such that the scale factors are estimated on signal points that reasonably represent the spins at Gaussian diffusion.

save_metricsvariable string, optional

List of metrics to save. Possible values: rtop, laplacian_signal, msd, qiv, rtap, rtpp, ng, perng, parng

laplacian_weightingfloat, optional

Weighting value used in fitting the MAPMRI model in the Laplacian and both model types.

Even value used to set the order of the basis.

out_dirstring, optional

Output directory. (default: current directory)

out_rtopstring, optional

Name of the rtop to be saved.

out_lapnormstring, optional

Name of the norm of Laplacian signal to be saved.

out_msdstring, optional

Name of the msd to be saved.

out_qivstring, optional

Name of the qiv to be saved.

out_rtapstring, optional

Name of the rtap to be saved.

out_rtppstring, optional

Name of the rtpp to be saved.

out_ngstring, optional

Name of the Non-Gaussianity to be saved.

out_perngstring, optional

Name of the Non-Gaussianity perpendicular to be saved.

out_parngstring, optional

Name of the Non-Gaussianity parallel to be saved.

### ReconstRUMBAFlow

class dipy.workflows.reconst.ReconstRUMBAFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files, bvalues_files, ...[, ...]) Reconstruct the fiber local orientations using the Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) [1] model.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, bvalues_files, bvectors_files, mask_files, b0_threshold=50.0, bvecs_tol=0.01, roi_center=None, roi_radii=10, fa_thr=0.7, extract_pam_values=False, sh_order=8, odf_to_sh_order=8, parallel=True, num_processes=None, gm_response=0.0008, csf_response=0.003, n_iter=600, recon_type='smf', n_coils=1, R=1, voxelwise=True, use_tv=False, sphere_name='repulsion724', verbose=False, relative_peak_threshold=0.5, min_separation_angle=25, npeaks=5, out_dir='', out_pam='peaks.pam5', out_shm='shm.nii.gz', out_peaks_dir='peaks_dirs.nii.gz', out_peaks_values='peaks_values.nii.gz', out_peaks_indices='peaks_indices.nii.gz', out_gfa='gfa.nii.gz')

Reconstruct the fiber local orientations using the Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) [1] model. The fiber response function (FRF) is computed using the single-shell, single-tissue model, and the voxel-wise fitting procedure is used for RUMBA-SD.

Parameters:
input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bvalues_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

bvectors_filesstring

Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.

Path to the input masks. This path may contain wildcards to use multiple masks at once.

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

bvecs_tolfloat, optional

Bvecs should be unit vectors.

roi_centervariable int, optional

Center of ROI in data. If center is None, it is assumed that it is the center of the volume with shape data.shape[:3].

radii of cuboid ROI in voxels.

fa_thrfloat, optional

FA threshold to compute the WM response function.

extract_pam_valuesbool, optional

Save or not to save pam volumes as single nifti files.

sh_orderint, optional

Spherical harmonics order used in the CSA fit.

odf_to_sh_orderint, optional

Spherical harmonics order used for peak_from_model to compress the ODF to spherical harmonics coefficients.

parallelbool, optional

Whether to use parallelization in peak-finding during the calibration procedure.

num_processesint, optional

If parallel is True, the number of subprocesses to use (default multiprocessing.cpu_count()). If < 0 the maximal number of cores minus num_processes + 1 is used (enter -1 to use as many cores as possible). 0 raises an error.

gm_responsefloat, optional

Mean diffusivity for GM compartment. If None, then grey matter volume fraction is not computed.

csf_responsefloat, optional

Mean diffusivity for CSF compartment. If None, then CSF volume fraction is not computed.

n_iterint, optional

Number of iterations for fODF estimation. Must be a positive int.

recon_typestr, optional

MRI reconstruction method type: spatial matched filter (smf) or sum-of-squares (sos). SMF reconstruction generates Rician noise while SoS reconstruction generates Noncentral Chi noise.

n_coilsint, optional

Number of coils in MRI scanner – only relevant in SoS reconstruction. Must be a positive int. Default: 1

Rint, optional

Acceleration factor of the acquisition. For SIEMENS, R = iPAT factor. For GE, R = ASSET factor. For PHILIPS, R = SENSE factor. Typical values are 1 or 2. Must be a positive integer.

voxelwisebool, optional

If true, performs a voxelwise fit. If false, performs a global fit on the entire brain at once. The global fit requires a 4D brain volume in fit.

use_tvbool, optional

If true, applies total variation regularization. This only takes effect in a global fit (voxelwise is set to False). TV can only be applied to 4D brain volumes with no singleton dimensions.

sphere_namestr, optional

Sphere name on which to reconstruct the fODFs.

verbosebool, optional

If true, logs updates on estimated signal-to-noise ratio after each iteration. This only takes effect in a global fit (voxelwise is set to False).

relative_peak_thresholdfloat, optional
Only return peaks greater than relative_peak_threshold * m

where m is the largest peak.

min_separation_anglefloat, optional

The minimum distance between directions. If two peaks are too close only the larger of the two is returned.

npeaksint, optional

Maximum number of peaks returned for a given voxel.

out_dirstring, optional

Output directory. (default current directory)

out_pamstring, optional

Name of the peaks volume to be saved.

out_shmstring, optional

Name of the spherical harmonics volume to be saved.

out_peaks_dirstring, optional

Name of the peaks directions volume to be saved.

out_peaks_valuesstring, optional

Name of the peaks values volume to be saved.

out_peaks_indicesstring, optional

Name of the peaks indices volume to be saved.

out_gfastring, optional

Name of the generalized FA volume to be saved.

References

[1] (1,2)

Canales-Rodríguez, E. J., Daducci, A., Sotiropoulos, S. N., Caruyer, E., Aja-Fernández, S., Radua, J., Mendizabal, J. M. Y., Iturria-Medina, Y., Melie-García, L., Alemán-Gómez, Y., Thiran, J.-P., Sarró, S., Pomarol-Clotet, E., & Salvador, R. (2015). Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization. PLOS ONE, 10(10), e0138910. https://doi.org/10.1371/journal.pone.0138910

### RumbaSDModel

class dipy.workflows.reconst.RumbaSDModel(gtab, wm_response=array([0.0017, 0.0002, 0.0002]), gm_response=0.0008, csf_response=0.003, n_iter=600, recon_type='smf', n_coils=1, R=1, voxelwise=True, use_tv=False, sphere=None, verbose=False)

Bases: OdfModel

Methods

 fit(data) To be implemented by specific odf models
__init__(gtab, wm_response=array([0.0017, 0.0002, 0.0002]), gm_response=0.0008, csf_response=0.003, n_iter=600, recon_type='smf', n_coils=1, R=1, voxelwise=True, use_tv=False, sphere=None, verbose=False)

Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) [1]

Modification of the Richardson-Lucy algorithm accounting for Rician and Noncentral Chi noise distributions, which more accurately represent MRI noise. Computes a maximum likelihood estimation of the fiber orientation density function (fODF) at each voxel. Includes white matter compartments alongside optional GM and CSF compartments to account for partial volume effects. This fit can be performed voxelwise or globally. The global fit will proceed more quickly than the voxelwise fit provided that the computer has adequate RAM (>= 16 GB should be sufficient for most datasets).

Kernel for deconvolution constructed using a priori knowledge of white matter response function, as well as the mean diffusivity of GM and/or CSF. RUMBA-SD is robust against impulse response imprecision, and thus the default diffusivity values are often adequate [2].

Parameters:
wm_response1d ndarray or 2d ndarray or AxSymShResponse, optional

Tensor eigenvalues as a (3,) ndarray, multishell eigenvalues as a (len(unique_bvals_tolerance(gtab.bvals))-1, 3) ndarray in order of smallest to largest b-value, or an AxSymShResponse. Default: np.array([1.7e-3, 0.2e-3, 0.2e-3])

gm_responsefloat, optional

Mean diffusivity for GM compartment. If None, then grey matter volume fraction is not computed. Default: 0.8e-3

csf_responsefloat, optional

Mean diffusivity for CSF compartment. If None, then CSF volume fraction is not computed. Default: 3.0e-3

n_iterint, optional

Number of iterations for fODF estimation. Must be a positive int. Default: 600

recon_type{‘smf’, ‘sos’}, optional

MRI reconstruction method: spatial matched filter (SMF) or sum-of-squares (SoS). SMF reconstruction generates Rician noise while SoS reconstruction generates Noncentral Chi noise. Default: ‘smf’

n_coilsint, optional

Number of coils in MRI scanner – only relevant in SoS reconstruction. Must be a positive int. Default: 1

Rint, optional

Acceleration factor of the acquisition. For SIEMENS, R = iPAT factor. For GE, R = ASSET factor. For PHILIPS, R = SENSE factor. Typical values are 1 or 2. Must be a positive int. Default: 1

voxelwisebool, optional

If true, performs a voxelwise fit. If false, performs a global fit on the entire brain at once. The global fit requires a 4D brain volume in fit. Default: True

use_tvbool, optional

If true, applies total variation regularization. This only takes effect in a global fit (voxelwise is set to False). TV can only be applied to 4D brain volumes with no singleton dimensions. Default: False

sphereSphere, optional

Sphere on which to construct fODF. If None, uses repulsion724. Default: None

verbosebool, optional

If true, logs updates on estimated signal-to-noise ratio after each iteration. This only takes effect in a global fit (voxelwise is set to False). Default: False

References

[1]

Canales-Rodríguez, E. J., Daducci, A., Sotiropoulos, S. N., Caruyer, E., Aja-Fernández, S., Radua, J., Mendizabal, J. M. Y., Iturria-Medina, Y., Melie-García, L., Alemán-Gómez, Y., Thiran, J.-P., Sarró, S., Pomarol-Clotet, E., & Salvador, R. (2015). Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization. PLOS ONE, 10(10), e0138910. https://doi.org/10.1371/journal.pone.0138910

[2]

Dell’Acqua, F., Rizzo, G., Scifo, P., Clarke, R., Scotti, G., & Fazio, F. (2007). A Model-Based Deconvolution Approach to Solve Fiber Crossing in Diffusion-Weighted MR Imaging. IEEE Transactions on Bio-Medical Engineering, 54, 462–472. https://doi.org/10.1109/TBME.2006.888830

### TensorModel

class dipy.workflows.reconst.TensorModel(gtab, fit_method='WLS', return_S0_hat=False, *args, **kwargs)

Bases: ReconstModel

Diffusion Tensor

Methods

 fit(data[, mask]) Fit method of the DTI model class predict(dti_params[, S0]) Predict a signal for this TensorModel class instance given parameters.
__init__(gtab, fit_method='WLS', return_S0_hat=False, *args, **kwargs)

A Diffusion Tensor Model [1], [2].

Parameters:
fit_methodstr or callable

str can be one of the following:

‘WLS’ for weighted least squares

dti.wls_fit_tensor()

‘LS’ or ‘OLS’ for ordinary least squares

dti.ols_fit_tensor()

‘NLLS’ for non-linear least-squares

dti.nlls_fit_tensor()

‘RT’ or ‘restore’ or ‘RESTORE’ for RESTORE robust tensor

fitting [3] dti.restore_fit_tensor()

callable has to have the signature:

fit_method(design_matrix, data, *args, **kwargs)

return_S0_hatbool

Boolean to return (True) or not (False) the S0 values for the fit.

args, kwargsarguments and key-word arguments passed to the

fit_method. See dti.wls_fit_tensor, dti.ols_fit_tensor for details

min_signalfloat

The minimum signal value. Needs to be a strictly positive number. Default: minimal signal in the data provided to fit.

Notes

In order to increase speed of processing, tensor fitting is done simultaneously over many voxels. Many fit_methods use the ‘step’ parameter to set the number of voxels that will be fit at once in each iteration. This is the chunk size as a number of voxels. A larger step value should speed things up, but it will also take up more memory. It is advisable to keep an eye on memory consumption as this value is increased.

E.g., in iter_fit_tensor() we have a default step value of 1e4

References

[1]

Basser, P.J., Mattiello, J., LeBihan, D., 1994. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103, 247-254.

[2]

Basser, P., Pierpaoli, C., 1996. Microstructural and physiological features of tissues elucidated by quantitative diffusion-tensor MRI. Journal of Magnetic Resonance 111, 209-219.

[3]

Lin-Ching C., Jones D.K., Pierpaoli, C. 2005. RESTORE: Robust estimation of tensors by outlier rejection. MRM 53: 1088-1095

Fit method of the DTI model class

Parameters:
dataarray

The measured signal from one voxel.

A boolean array used to mark the coordinates in the data that should be analyzed that has the shape data.shape[:-1]

predict(dti_params, S0=1.0)

Predict a signal for this TensorModel class instance given parameters.

Parameters:
dti_paramsndarray

The last dimension should have 12 tensor parameters: 3 eigenvalues, followed by the 3 eigenvectors

S0float or ndarray

The non diffusion-weighted signal in every voxel, or across all voxels. Default: 1

### Workflow

class dipy.workflows.reconst.Workflow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: object

Methods

 Create an iterator for IO. Return A short name for the workflow used to subdivide. Return No sub runs since this is a simple workflow. Check if a file will be overwritten upon processing the inputs. run(*args, **kwargs) Execute the workflow.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

get_io_iterator()

Create an iterator for IO.

Use a couple of inspection tricks to build an IOIterator using the previous frame (values of local variables and other contextuals) and the run method’s docstring.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

get_sub_runs()

Return No sub runs since this is a simple workflow.

manage_output_overwrite()

Check if a file will be overwritten upon processing the inputs.

If it is bound to happen, an action is taken depending on self._force_overwrite (or –force via command line). A log message is output independently of the outcome to tell the user something happened.

run(*args, **kwargs)

Execute the workflow.

Since this is an abstract class, raise exception if this code is reached (not implemented in child class or literally called on this class)

### IvimModel

dipy.workflows.reconst.IvimModel(gtab, fit_method='trr', **kwargs)

Selector function to switch between the 2-stage Trust-Region Reflective based NLLS fitting method (also containing the linear fit): trr and the Variable Projections based fitting method: varpro.

Parameters:
fit_methodstring, optional

The value fit_method can either be ‘trr’ or ‘varpro’. default : trr

### auto_response_ssst

Automatic estimation of single-shell single-tissue (ssst) response

function using FA.

Parameters:
datandarray

diffusion data

roi_centerarray-like, (3,)

Center of ROI in data. If center is None, it is assumed that it is the center of the volume with shape data.shape[:3].

fa_thrfloat

FA threshold

Returns:
responsetuple, (2,)

(evals, S0)

ratiofloat

The ratio between smallest versus largest eigenvalue of the response.

Notes

In CSD there is an important pre-processing step: the estimation of the fiber response function. In order to do this, we look for voxels with very anisotropic configurations. We get this information from csdeconv.mask_for_response_ssst(), which returns a mask of selected voxels (more details are available in the description of the function).

With the mask, we compute the response function by using csdeconv.response_from_mask_ssst(), which returns the response and the ratio (more details are available in the description of the function).

### axial_diffusivity

dipy.workflows.reconst.axial_diffusivity(evals, axis=-1)

Axial Diffusivity (AD) of a diffusion tensor. Also called parallel diffusivity.

Parameters:
evalsarray-like

Eigenvalues of a diffusion tensor, must be sorted in descending order along axis.

axisint

Axis of evals which contains 3 eigenvalues.

Returns:

Notes

AD is calculated with the following equation:

$AD = \lambda_1$

### color_fa

dipy.workflows.reconst.color_fa(fa, evecs)

Color fractional anisotropy of diffusion tensor

Parameters:
faarray-like

Array of the fractional anisotropy (can be 1D, 2D or 3D)

evecsarray-like

eigen vectors from the tensor model

Returns:
rgbArray with 3 channels for each color as the last dimension.

Colormap of the FA with red for the x value, y for the green value and z for the blue value.

Notes

It is computed from the clipped FA between 0 and 1 using the following formula

$rgb = abs(max(\vec{e})) \times fa$

### deprecated_params

dipy.workflows.reconst.deprecated_params(old_name, new_name=None, since='', until='', version_comparator=<function cmp_pkg_version>, arg_in_kwargs=False, warn_class=<class 'dipy.utils.deprecator.ArgsDeprecationWarning'>, error_class=<class 'dipy.utils.deprecator.ExpiredDeprecationError'>, alternative='')

Deprecate a renamed or removed function argument.

The decorator assumes that the argument with the old_name was removed from the function signature and the new_name replaced it at the same position in the signature. If the old_name argument is given when calling the decorated function the decorator will catch it and issue a deprecation warning and pass it on as new_name argument.

Parameters:
old_namestr or list/tuple thereof

The old name of the argument.

new_namestr or list/tuple thereof or None, optional

The new name of the argument. Set this to None to remove the argument old_name instead of renaming it.

sincestr or number or list/tuple thereof, optional

The release at which the old argument became deprecated.

untilstr or number or list/tuple thereof, optional

Last released version at which this function will still raise a deprecation warning. Versions higher than this will raise an error.

version_comparatorcallable

Callable accepting string as argument, and return 1 if string represents a higher version than encoded in the version_comparator, 0 if the version is equal, and -1 if the version is lower. For example, the version_comparator may compare the input version string to the current package version string.

arg_in_kwargsbool or list/tuple thereof, optional

If the argument is not a named argument (for example it was meant to be consumed by **kwargs) set this to True. Otherwise the decorator will throw an Exception if the new_name cannot be found in the signature of the decorated function. Default is False.

warn_classwarning, optional

Warning to be issued.

error_classException, optional

Error to be issued

alternativestr, optional

An alternative function or class name that the user may use in place of the deprecated object if new_name is None. The deprecation warning will tell the user about this alternative if provided.

Raises:
TypeError

If the new argument name cannot be found in the function signature and arg_in_kwargs was False or if it is used to deprecate the name of the *args-, **kwargs-like arguments. At runtime such an Error is raised if both the new_name and old_name were specified when calling the function and “relax=False”.

Notes

This function is based on the Astropy (major modification). https://github.com/astropy/astropy. See COPYING file distributed along with the astropy package for the copyright and license terms.

Examples

The deprecation warnings are not shown in the following examples. To deprecate a positional or keyword argument:: >>> from dipy.utils.deprecator import deprecated_params >>> @deprecated_params(‘sig’, ‘sigma’, ‘0.3’) … def test(sigma): … return sigma >>> test(2) 2 >>> test(sigma=2) 2 >>> test(sig=2) # doctest: +SKIP 2

It is also possible to replace multiple arguments. The old_name, new_name and since have to be tuple or list and contain the same number of entries:: >>> @deprecated_params([‘a’, ‘b’], [‘alpha’, ‘beta’], … [‘0.2’, 0.4]) … def test(alpha, beta): … return alpha, beta >>> test(a=2, b=3) # doctest: +SKIP (2, 3)

### fractional_anisotropy

dipy.workflows.reconst.fractional_anisotropy(evals, axis=-1)

Return Fractional anisotropy (FA) of a diffusion tensor.

Parameters:
evalsarray-like

Eigenvalues of a diffusion tensor.

axisint

Axis of evals which contains 3 eigenvalues.

Returns:
faarray

Calculated FA. Range is 0 <= FA <= 1.

Notes

FA is calculated using the following equation:

$FA = \sqrt{\frac{1}{2}\frac{(\lambda_1-\lambda_2)^2+(\lambda_1- \lambda_3)^2+(\lambda_2-\lambda_3)^2}{\lambda_1^2+ \lambda_2^2+\lambda_3^2}}$

### geodesic_anisotropy

dipy.workflows.reconst.geodesic_anisotropy(evals, axis=-1)

Geodesic anisotropy (GA) of a diffusion tensor.

Parameters:
evalsarray-like

Eigenvalues of a diffusion tensor.

axisint

Axis of evals which contains 3 eigenvalues.

Returns:
gaarray

Calculated GA. In the range 0 to +infinity

Notes

GA is calculated using the following equation given in [1]:

$GA = \sqrt{\sum_{i=1}^3 \log^2{\left ( \lambda_i/<\mathbf{D}> \right )}}, \quad \textrm{where} \quad <\mathbf{D}> = (\lambda_1\lambda_2\lambda_3)^{1/3}$

Note that the notation, $$<D>$$, is often used as the mean diffusivity (MD) of the diffusion tensor and can lead to confusions in the literature (see [1] versus [2] versus [3] for example). Reference [2] defines geodesic anisotropy (GA) with $$<D>$$ as the MD in the denominator of the sum. This is wrong. The original paper [1] defines GA with $$<D> = det(D)^{1/3}$$, as the isotropic part of the distance. This might be an explanation for the confusion. The isotropic part of the diffusion tensor in Euclidean space is the MD whereas the isotropic part of the tensor in log-Euclidean space is $$det(D)^{1/3}$$. The Appendix of [1] and log-Euclidean derivations from [3] are clear on this. Hence, all that to say that $$<D> = det(D)^{1/3}$$ here for the GA definition and not MD.

References

[1] (1,2,3,4)

P. G. Batchelor, M. Moakher, D. Atkinson, F. Calamante, A. Connelly, “A rigorous framework for diffusion tensor calculus”, Magnetic Resonance in Medicine, vol. 53, pp. 221-225, 2005.

[2] (1,2)

M. M. Correia, V. F. Newcombe, G.B. Williams. “Contrast-to-noise ratios for indices of anisotropy obtained from diffusion MRI: a study with standard clinical b-values at 3T”. NeuroImage, vol. 57, pp. 1103-1115, 2011.

[3] (1,2)

A. D. Lee, etal, P. M. Thompson. “Comparison of fractional and geodesic anisotropy in diffusion tensor images of 90 monozygotic and dizygotic twins”. 5th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 943-946, May 2008.

[4]

V. Arsigny, P. Fillard, X. Pennec, N. Ayache. “Log-Euclidean metrics for fast and simple calculus on diffusion tensors.” Magnetic Resonance in Medecine, vol 56, pp. 411-421, 2006.

### get_mode

dipy.workflows.reconst.get_mode(q_form)

Mode (MO) of a diffusion tensor [1].

Parameters:
q_formndarray

The quadratic form of a tensor, or an array with quadratic forms of tensors. Should be of shape (x, y, z, 3, 3) or (n, 3, 3) or (3, 3).

Returns:
modearray

Calculated tensor mode in each spatial coordinate.

Notes

Mode ranges between -1 (planar anisotropy) and +1 (linear anisotropy) with 0 representing orthotropy. Mode is calculated with the following equation (equation 9 in [1]):

$Mode = 3*\sqrt{6}*det(\widetilde{A}/norm(\widetilde{A}))$

Where $$\widetilde{A}$$ is the deviatoric part of the tensor quadratic form.

References

[1] (1,2,3)

Daniel B. Ennis and G. Kindlmann, “Orthogonal Tensor Invariants and the Analysis of Diffusion Tensor Magnetic Resonance Images”, Magnetic Resonance in Medicine, vol. 55, no. 1, pp. 136-146, 2006.

### get_sphere

dipy.workflows.reconst.get_sphere(name='symmetric362')

provide triangulated spheres

Parameters:
namestr

which sphere - one of: * ‘symmetric362’ * ‘symmetric642’ * ‘symmetric724’ * ‘repulsion724’ * ‘repulsion100’ * ‘repulsion200’

Returns:
spherea dipy.core.sphere.Sphere class instance

Examples

>>> import numpy as np
>>> from dipy.data import get_sphere
>>> sphere = get_sphere('symmetric362')
>>> verts, faces = sphere.vertices, sphere.faces
>>> verts.shape == (362, 3)
True
>>> faces.shape == (720, 3)
True
>>> verts, faces = get_sphere('not a sphere name')
Traceback (most recent call last):
...
DataError: No sphere called "not a sphere name"


dipy.workflows.reconst.gradient_table(bvals, bvecs=None, big_delta=None, small_delta=None, b0_threshold=50, atol=0.01, btens=None)

A general function for creating diffusion MR gradients.

It reads, loads and prepares scanner parameters like the b-values and b-vectors so that they can be useful during the reconstruction process.

Parameters:
bvalscan be any of the four options
1. an array of shape (N,) or (1, N) or (N, 1) with the b-values.

2. a path for the file which contains an array like the above (1).

3. an array of shape (N, 4) or (4, N). Then this parameter is considered to be a b-table which contains both bvals and bvecs. In this case the next parameter is skipped.

4. a path for the file which contains an array like the one at (3).

bvecscan be any of two options
1. an array of shape (N, 3) or (3, N) with the b-vectors.

2. a path for the file which contains an array like the previous.

big_deltafloat

acquisition pulse separation time in seconds (default None)

small_deltafloat

acquisition pulse duration time in seconds (default None)

b0_thresholdfloat

All b-values with values less than or equal to bo_threshold are considered as b0s i.e. without diffusion weighting.

atolfloat

All b-vectors need to be unit vectors up to a tolerance.

btenscan be any of three options
1. a string specifying the shape of the encoding tensor for all volumes in data. Options: ‘LTE’, ‘PTE’, ‘STE’, ‘CTE’ corresponding to linear, planar, spherical, and “cigar-shaped” tensor encoding. Tensors are rotated so that linear and cigar tensors are aligned with the corresponding gradient direction and the planar tensor’s normal is aligned with the corresponding gradient direction. Magnitude is scaled to match the b-value.

2. an array of strings of shape (N,), (N, 1), or (1, N) specifying encoding tensor shape for each volume separately. N corresponds to the number volumes in data. Options for elements in array: ‘LTE’, ‘PTE’, ‘STE’, ‘CTE’ corresponding to linear, planar, spherical, and “cigar-shaped” tensor encoding. Tensors are rotated so that linear and cigar tensors are aligned with the corresponding gradient direction and the planar tensor’s normal is aligned with the corresponding gradient direction. Magnitude is scaled to match the b-value.

3. an array of shape (N,3,3) specifying the b-tensor of each volume exactly. N corresponds to the number volumes in data. No rotation or scaling is performed.

Returns:

Notes

1. Often b0s (b-values which correspond to images without diffusion weighting) have 0 values however in some cases the scanner cannot provide b0s of an exact 0 value and it gives a bit higher values e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.

2. We assume that the minimum number of b-values is 7.

3. B-vectors should be unit vectors.

Examples

>>> from dipy.core.gradients import gradient_table
>>> bvals = 1500 * np.ones(7)
>>> bvals[0] = 0
>>> sq2 = np.sqrt(2) / 2
>>> bvecs = np.array([[0, 0, 0],
...                   [1, 0, 0],
...                   [0, 1, 0],
...                   [0, 0, 1],
...                   [sq2, sq2, 0],
...                   [sq2, 0, sq2],
...                   [0, sq2, sq2]])
>>> gt.bvecs.shape == bvecs.shape
True
>>> gt.bvecs.shape == bvecs.T.shape
False


### literal_eval

dipy.workflows.reconst.literal_eval(node_or_string)

Safely evaluate an expression node or a string containing a Python expression. The string or node provided may only consist of the following Python literal structures: strings, bytes, numbers, tuples, lists, dicts, sets, booleans, and None.

Load data and other information from a nifti file.

Parameters:
fnamestr

Full path to a nifti file.

return_imgbool, optional

Whether to return the nibabel nifti img object. Default: False

return_voxsize: bool, optional

Whether to return the nifti header zooms. Default: False

return_coordsbool, optional

Whether to return the nifti header aff2axcodes. Default: False

as_ndarray: bool, optional

convert nibabel ArrayProxy to a numpy.ndarray. If you want to save memory and delay this casting, just turn this option to False (default: True)

Returns:
A tuple, with (at the most, if all keyword args are set to True):
(data, img.affine, img, vox_size, nib.aff2axcodes(img.affine))

Load only the data array from a nifti file.

Parameters:
fnamestr

Full path to the file.

as_ndarray: bool, optional

convert nibabel ArrayProxy to a numpy.ndarray. If you want to save memory and delay this casting, just turn this option to False (default: True)

Returns:
data: np.ndarray or nib.ArrayProxy

### lower_triangular

dipy.workflows.reconst.lower_triangular(tensor, b0=None)

Returns the six lower triangular values of the tensor ordered as (Dxx, Dxy, Dyy, Dxz, Dyz, Dzz) and a dummy variable if b0 is not None.

Parameters:
tensorarray_like (…, 3, 3)

a collection of 3, 3 diffusion tensors

b0float

if b0 is not none log(b0) is returned as the dummy variable

Returns:
Dndarray

If b0 is none, then the shape will be (…, 6) otherwise (…, 7)

### mean_diffusivity

dipy.workflows.reconst.mean_diffusivity(evals, axis=-1)

Mean Diffusivity (MD) of a diffusion tensor.

Parameters:
evalsarray-like

Eigenvalues of a diffusion tensor.

axisint

Axis of evals which contains 3 eigenvalues.

Returns:
mdarray

Calculated MD.

Notes

MD is calculated with the following equation:

$MD = \frac{\lambda_1 + \lambda_2 + \lambda_3}{3}$

### nifti1_symmat

dipy.workflows.reconst.nifti1_symmat(image_data, *args, **kwargs)

Returns a Nifti1Image with a symmetric matrix intent

Parameters:
image_dataarray-like

should have lower triangular elements of a symmetric matrix along the last dimension

all other arguments and keywords are passed to Nifti1Image
Returns:
imageNifti1Image

5d, extra dimensions added before the last. Has symmetric matrix intent code

### peaks_from_model

dipy.workflows.reconst.peaks_from_model(model, data, sphere, relative_peak_threshold, min_separation_angle, mask=None, return_odf=False, return_sh=True, gfa_thr=0, normalize_peaks=False, sh_order=8, sh_basis_type=None, npeaks=5, B=None, invB=None, parallel=False, num_processes=None)

Fit the model to data and computes peaks and metrics

Parameters:
modela model instance

model will be used to fit the data.

datandarray

Diffusion data.

sphereSphere

The Sphere providing discrete directions for evaluation.

relative_peak_thresholdfloat

Only return peaks greater than relative_peak_threshold * m where m is the largest peak.

min_separation_anglefloat in [0, 90] The minimum distance between

directions. If two peaks are too close only the larger of the two is returned.

If mask is provided, voxels that are False in mask are skipped and no peaks are returned.

return_odfbool

If True, the odfs are returned.

return_shbool

If True, the odf as spherical harmonics coefficients is returned

gfa_thrfloat

Voxels with gfa less than gfa_thr are skipped, no peaks are returned.

normalize_peaksbool

If true, all peak values are calculated relative to max(odf).

sh_orderint, optional

Maximum SH order in the SH fit. For sh_order, there will be (sh_order + 1) * (sh_order + 2) / 2 SH coefficients (default 8).

sh_basis_type{None, ‘tournier07’, ‘descoteaux07’}

None for the default DIPY basis, tournier07 for the Tournier 2007 [2] basis, and descoteaux07 for the Descoteaux 2007 [1] basis (None defaults to descoteaux07).

npeaksint

Maximum number of peaks found (default 5 peaks).

Bndarray, optional

Matrix that transforms spherical harmonics to spherical function sf = np.dot(sh, B).

invBndarray, optional

Inverse of B.

parallel: bool

If True, use multiprocessing to compute peaks and metric (default False). Temporary files are saved in the default temporary directory of the system. It can be changed using import tempfile and tempfile.tempdir = '/path/to/tempdir'.

num_processes: int, optional

If parallel is True, the number of subprocesses to use (default multiprocessing.cpu_count()). If < 0 the maximal number of cores minus num_processes + 1 is used (enter -1 to use as many cores as possible). 0 raises an error.

Returns:
pamPeaksAndMetrics

An object with gfa, peak_directions, peak_values, peak_indices, odf, shm_coeffs as attributes

References

[1]

Descoteaux, M., Angelino, E., Fitzgibbons, S. and Deriche, R. Regularized, Fast, and Robust Analytical Q-ball Imaging. Magn. Reson. Med. 2007;58:497-510.

[2]

Tournier J.D., Calamante F. and Connelly A. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage. 2007;35(4):1459-1472.

### peaks_to_niftis

dipy.workflows.reconst.peaks_to_niftis(pam, fname_shm, fname_dirs, fname_values, fname_indices, fname_gfa, reshape_dirs=False)

Save SH, directions, indices and values of peaks to Nifti.

Radial Diffusivity (RD) of a diffusion tensor. Also called perpendicular diffusivity.

Parameters:
evalsarray-like

Eigenvalues of a diffusion tensor, must be sorted in descending order along axis.

axisint

Axis of evals which contains 3 eigenvalues.

Returns:
rdarray

Calculated RD.

Notes

RD is calculated with the following equation:

$RD = \frac{\lambda_2 + \lambda_3}{2}$

Read b-values and b-vectors from disk.

Parameters:
fbvalsstr

Full path to file with b-values. None to not read bvals.

fbvecsstr

Full path of file with b-vectors. None to not read bvecs.

Returns:
bvalsarray, (N,) or None
bvecsarray, (N, 3) or None

Notes

Files can be either ‘.bvals’/’.bvecs’ or ‘.txt’ or ‘.npy’ (containing arrays stored with the appropriate values).

### save_nifti

dipy.workflows.reconst.save_nifti(fname, data, affine, hdr=None, dtype=None)

Save a data array into a nifti file.

Parameters:
fnamestr

The full path to the file to be saved.

datandarray

The array with the data to save.

affine4x4 array

The affine transform associated with the file.

Returns:
None

### save_peaks

dipy.workflows.reconst.save_peaks(fname, pam, affine=None, verbose=False)

Save all important attributes of object PeaksAndMetrics in a PAM5 file (HDF5).

Parameters:
fnamestring

Filename of PAM5 file

pamPeaksAndMetrics

Object holding peak_dirs, shm_coeffs and other attributes

affinearray

The 4x4 matrix transforming the date from native to world coordinates. PeaksAndMetrics should have that attribute but if not it can be provided here. Default None.

verbosebool

Print summary information about the saved file.

### split_dki_param

dipy.workflows.reconst.split_dki_param(dki_params)

Extract the diffusion tensor eigenvalues, the diffusion tensor eigenvector matrix, and the 15 independent elements of the kurtosis tensor from the model parameters estimated from the DKI model

Parameters:
dki_paramsndarray (x, y, z, 27) or (n, 27)

All parameters estimated from the diffusion kurtosis model. Parameters are ordered as follows:

1. Three diffusion tensor’s eigenvalues

2. Three lines of the eigenvector matrix each containing the first, second and third coordinates of the eigenvector

3. Fifteen elements of the kurtosis tensor

Returns:
eigvalsarray (x, y, z, 3) or (n, 3)

Eigenvalues from eigen decomposition of the tensor.

eigvecsarray (x, y, z, 3, 3) or (n, 3, 3)

Associated eigenvectors from eigen decomposition of the tensor. Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with eigvals[j])

ktarray (x, y, z, 15) or (n, 15)

Fifteen elements of the kurtosis tensor

### warn

dipy.workflows.reconst.warn(/, message, category=None, stacklevel=1, source=None)

Issue a warning, or maybe ignore it or raise an exception.

### LabelsBundlesFlow

class dipy.workflows.segment.LabelsBundlesFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(streamline_files, labels_files[, ...]) Extract bundles using existing indices (labels)
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(streamline_files, labels_files, out_dir='', out_bundle='recognized_orig.trk')

Extract bundles using existing indices (labels)

Parameters:
streamline_filesstring

The path of streamline files where you want to recognize bundles.

labels_filesstring

The path of model bundle files.

out_dirstring, optional

Output directory. (default current directory)

out_bundlestring, optional

Recognized bundle in the space of the model bundle.

References

[Garyfallidis17]

Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.

### MedianOtsuFlow

class dipy.workflows.segment.MedianOtsuFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(input_files[, save_masked, ...]) Workflow wrapping the median_otsu segmentation method.
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

Workflow wrapping the median_otsu segmentation method.

Applies median_otsu segmentation on each file found by ‘globing’ input_files and saves the results in a directory specified by out_dir.

Parameters:
input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

Radius (in voxels) of the applied median filter.

numpassint, optional

Number of pass of the median filter.

autocropbool, optional

If True, the masked input_volumes will also be cropped using the bounding box defined by the masked data. For example, if diffusion images are of 1x1x1 (mm^3) or higher resolution auto-cropping could reduce their size in memory and speed up some of the analysis.

vol_idxvariable int, optional

1D array representing indices of axis=-1 of a 4D input_volume. From the command line use something like 3 4 5 6. From script use something like [3, 4, 5, 6]. This input is required for 4D volumes.

dilateint, optional

number of iterations for binary dilation.

out_dirstring, optional

Output directory. (default current directory)

Name of the mask volume to be saved.

Name of the masked volume to be saved.

### RecoBundles

class dipy.workflows.segment.RecoBundles(streamlines, greater_than=50, less_than=1000000, cluster_map=None, clust_thr=15, nb_pts=20, rng=None, verbose=False)

Bases: object

Methods

 evaluate_results(model_bundle, ...) Compare the similiarity between two given bundles, model bundle, and extracted bundle. recognize(model_bundle, model_clust_thr[, ...]) Recognize the model_bundle in self.streamlines refine(model_bundle, pruned_streamlines, ...) Refine and recognize the model_bundle in self.streamlines This method expects once pruned streamlines as input.
__init__(streamlines, greater_than=50, less_than=1000000, cluster_map=None, clust_thr=15, nb_pts=20, rng=None, verbose=False)

Recognition of bundles

Extract bundles from a participants’ tractograms using model bundles segmented from a different subject or an atlas of bundles. See [Garyfallidis17] for the details.

Parameters:
streamlinesStreamlines

The tractogram in which you want to recognize bundles.

greater_thanint, optional

Keep streamlines that have length greater than this value (default 50)

less_thanint, optional

Keep streamlines have length less than this value (default 1000000)

cluster_mapQB map, optional.

Provide existing clustering to start RB faster (default None).

clust_thrfloat, optional.

Distance threshold in mm for clustering streamlines. Default: 15.

nb_ptsint, optional.

Number of points per streamline (default 20)

rngRandomState

If None define RandomState in initialization function. Default: None

verbose: bool, optional.

Notes

Make sure that before creating this class that the streamlines and the model bundles are roughly in the same space. Also default thresholds are assumed in RAS 1mm^3 space. You may want to adjust those if your streamlines are not in world coordinates.

References

Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.

evaluate_results(model_bundle, pruned_streamlines, slr_select)

Compare the similiarity between two given bundles, model bundle, and extracted bundle.

Parameters:
model_bundleStreamlines
pruned_streamlinesStreamlines
slr_selecttuple

Select the number of streamlines from model to neirborhood of model to perform the local SLR.

Returns:
ba_valuefloat

bundle adjacency value between model bundle and pruned bundle

bmd_valuefloat

bundle minimum distance value between model bundle and pruned bundle

recognize(model_bundle, model_clust_thr, reduction_thr=10, reduction_distance='mdf', slr=True, num_threads=None, slr_metric=None, slr_x0=None, slr_bounds=None, slr_select=(400, 600), slr_method='L-BFGS-B', pruning_thr=5, pruning_distance='mdf')

Recognize the model_bundle in self.streamlines

Parameters:
model_bundleStreamlines

model bundle streamlines used as a reference to extract similar streamlines from input tractogram

model_clust_thrfloat

MDF distance threshold for the model bundles

reduction_thrfloat, optional

Reduce search space in the target tractogram by (mm) (default 10)

reduction_distancestring, optional

Reduction distance type can be mdf or mam (default mdf)

slrbool, optional

Use Streamline-based Linear Registration (SLR) locally (default True)

Number of threads to be used for OpenMP parallelization. If None (default) the value of OMP_NUM_THREADS environment variable is used if it is set, otherwise all available threads are used. If < 0 the maximal number of threads minus |num_threads + 1| is used (enter -1 to use as many threads as possible). 0 raises an error.

slr_metricBundleMinDistanceMetric
slr_x0array or int or str, optional

Transformation allowed. translation, rigid, similarity or scaling Initial parametrization for the optimization.

If 1D array with:

a) 6 elements then only rigid registration is performed with the 3 first elements for translation and 3 for rotation. b) 7 elements also isotropic scaling is performed (similarity). c) 12 elements then translation, rotation (in degrees), scaling and shearing are performed (affine).

Here is an example of x0 with 12 elements: x0=np.array([0, 10, 0, 40, 0, 0, 2., 1.5, 1, 0.1, -0.5, 0])

This has translation (0, 10, 0), rotation (40, 0, 0) in degrees, scaling (2., 1.5, 1) and shearing (0.1, -0.5, 0).

If int:
1. 6

x0 = np.array([0, 0, 0, 0, 0, 0])

2. 7

x0 = np.array([0, 0, 0, 0, 0, 0, 1.])

3. 12

x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0])

If str:
1. “rigid”

x0 = np.array([0, 0, 0, 0, 0, 0])

2. “similarity”

x0 = np.array([0, 0, 0, 0, 0, 0, 1.])

3. “affine”

x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0])

(default None)

slr_boundsarray, optional

(default None)

slr_selecttuple, optional

Select the number of streamlines from model to neirborhood of model to perform the local SLR.

slr_methodstring, optional

Optimization method ‘L_BFGS_B’ or ‘Powell’ optimizers can be used. (default ‘L-BFGS-B’)

pruning_thrfloat, optional

Pruning after reducing the search space (default 5).

pruning_distancestring, optional

Pruning distance type can be mdf or mam (default mdf)

Returns:
recognized_transfStreamlines

Recognized bundle in the space of the model tractogram

recognized_labelsarray

Indices of recognized bundle in the original tractogram

References

[Garyfallidis17]

Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.

refine(model_bundle, pruned_streamlines, model_clust_thr, reduction_thr=14, reduction_distance='mdf', slr=True, slr_metric=None, slr_x0=None, slr_bounds=None, slr_select=(400, 600), slr_method='L-BFGS-B', pruning_thr=6, pruning_distance='mdf')

Refine and recognize the model_bundle in self.streamlines This method expects once pruned streamlines as input. It refines the first ouput of recobundle by applying second local slr (optional), and second pruning. This method is useful when we are dealing with noisy data or when we want to extract small tracks from tractograms. This time, search space is created using pruned bundle and not model bundle.

Parameters:
model_bundleStreamlines

model bundle streamlines used as a reference to extract similar streamlines from input tractogram

pruned_streamlinesStreamlines

Recognized bundle from target tractogram by RecoBundles.

model_clust_thrfloat

MDF distance threshold for the model bundles

reduction_thrfloat

Reduce search space by (mm) (default 14)

reduction_distancestring

Reduction distance type can be mdf or mam (default mdf)

slrbool

Use Streamline-based Linear Registration (SLR) locally (default True)

slr_metricBundleMinDistanceMetric
slr_x0array or int or str

Transformation allowed. translation, rigid, similarity or scaling Initial parametrization for the optimization.

If 1D array with:

a) 6 elements then only rigid registration is performed with the 3 first elements for translation and 3 for rotation. b) 7 elements also isotropic scaling is performed (similarity). c) 12 elements then translation, rotation (in degrees), scaling and shearing are performed (affine).

Here is an example of x0 with 12 elements: x0=np.array([0, 10, 0, 40, 0, 0, 2., 1.5, 1, 0.1, -0.5, 0])

This has translation (0, 10, 0), rotation (40, 0, 0) in degrees, scaling (2., 1.5, 1) and shearing (0.1, -0.5, 0).

If int:
1. 6

x0 = np.array([0, 0, 0, 0, 0, 0])

2. 7

x0 = np.array([0, 0, 0, 0, 0, 0, 1.])

3. 12

x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0])

If str:
1. “rigid”

x0 = np.array([0, 0, 0, 0, 0, 0])

2. “similarity”

x0 = np.array([0, 0, 0, 0, 0, 0, 1.])

3. “affine”

x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0])

(default None)

slr_boundsarray

(default None)

slr_selecttuple

Select the number of streamlines from model to neirborhood of model to perform the local SLR.

slr_methodstring

Optimization method ‘L_BFGS_B’ or ‘Powell’ optimizers can be used. (default ‘L-BFGS-B’)

pruning_thrfloat

Pruning after reducing the search space (default 6).

pruning_distancestring

Pruning distance type can be mdf or mam (default mdf)

Returns:
recognized_transfStreamlines

Recognized bundle in the space of the model tractogram

recognized_labelsarray

Indices of recognized bundle in the original tractogram

References

[Garyfallidis17]

Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.

[Chandio2020]

Chandio, B.Q., Risacher, S.L., Pestilli, F.,

Bullock, D., Yeh, FC., Koudoro, S., Rokem, A., Harezlak, J., and Garyfallidis, E. Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Sci Rep 10, 17149 (2020)

### RecoBundlesFlow

class dipy.workflows.segment.RecoBundlesFlow(output_strategy='absolute', mix_names=False, force=False, skip=False)

Bases: Workflow

Methods

 get_io_iterator() Create an iterator for IO. Return A short name for the workflow used to subdivide. get_sub_runs() Return No sub runs since this is a simple workflow. manage_output_overwrite() Check if a file will be overwritten upon processing the inputs. run(streamline_files, model_bundle_files[, ...]) Recognize bundles
__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(streamline_files, model_bundle_files, greater_than=50, less_than=1000000, no_slr=False, clust_thr=15.0, reduction_thr=15.0, reduction_distance='mdf', model_clust_thr=2.5, pruning_thr=8.0, pruning_distance='mdf', slr_metric='symmetric', slr_transform='similarity', slr_matrix='small', refine=False, r_reduction_thr=12.0, r_pruning_thr=6.0, no_r_slr=False, out_dir='', out_recognized_transf='recognized.trk', out_recognized_labels='labels.npy')

Recognize bundles

Parameters:
streamline_filesstring

The path of streamline files where you want to recognize bundles.

model_bundle_filesstring

The path of model bundle files.

greater_thanint, optional

Keep streamlines that have length greater than this value in mm.

less_thanint, optional

Keep streamlines have length less than this value in mm.

no_slrbool, optional

Don’t enable local Streamline-based Linear Registration.

clust_thrfloat, optional

MDF distance threshold for all streamlines.

reduction_thrfloat, optional

Reduce search space by (mm).

reduction_distancestring, optional

Reduction distance type can be mdf or mam.

model_clust_thrfloat, optional

MDF distance threshold for the model bundles.

pruning_thrfloat, optional

Pruning after matching.

pruning_distancestring, optional

Pruning distance type can be mdf or mam.

slr_metricstring, optional

Options are None, symmetric, asymmetric or diagonal.

slr_transformstring, optional

Transformation allowed. translation, rigid, similarity or scaling.

slr_matrixstring, optional

Options are ‘nano’, ‘tiny’, ‘small’, ‘medium’, ‘large’, ‘huge’.

refinebool, optional

Enable refine recognized bundle.

r_reduction_thrfloat, optional

Refine reduce search space by (mm).

r_pruning_thrfloat, optional

Refine pruning after matching.

no_r_slrbool, optional

Don’t enable Refine local Streamline-based Linear Registration.

out_dirstring, optional

Output directory. (default current directory)

out_recognized_transfstring, optional

Recognized bundle in the space of the model bundle.

out_recognized_labelsstring, optional

Indices of recognized bundle in the original tractogram.

References

[Garyfallidis17]

Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.

[Chandio2020]

Chandio, B.Q., Risacher, S.L., Pestilli, F.,

Bullock, D., Yeh, FC., Koudoro, S., Rokem, A., Harezlak, J., and Garyfallidis, E. Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Sci Rep 10, 17149 (2020)

### Space

class dipy.workflows.segment.Space(value)

Bases: Enum

Enum to simplify future change to convention

__init__(*args, **kwargs)
RASMM = 'rasmm'
VOX = 'vox'
VOXMM = 'voxmm'

### StatefulTractogram

class dipy.workflows.segment.StatefulTractogram(streamlines, reference, space, origin=Origin.NIFTI, data_per_point=None, data_per_streamline=None)

Bases: object

Class for stateful representation of collections of streamlines Object designed to be identical no matter the file format (trk, tck, vtk, fib, dpy). Facilitate transformation between space and data manipulation for each streamline / point.

Attributes:
affine

Getter for the reference affine

data_per_point

Getter for data_per_point

data_per_streamline

Getter for data_per_streamline

dimensions

Getter for the reference dimensions

origin

Getter for origin standard

space

Getter for the current space

space_attributes

Getter for spatial attribute

streamlines

Partially safe getter for streamlines

voxel_order

Getter for the reference voxel order

voxel_sizes

Getter for the reference voxel sizes

Methods

 are_compatible(sft_1, sft_2) Compatibility verification of two StatefulTractogram to ensure space, origin, data_per_point and data_per_streamline consistency Compute the bounding box of the streamlines in their current state from_sft(streamlines, sft[, data_per_point, ...]) Create an instance of StatefulTractogram from another instance of StatefulTractogram. Return a list of the data_per_point attribute names Return a list of the data_per_streamline attribute names Safe getter for streamlines (for slicing) Verify that the bounding box is valid in voxel space. remove_invalid_streamlines([epsilon]) Remove streamlines with invalid coordinates from the object. Safe function to shift streamlines so the center of voxel is the origin Safe function to shift streamlines so the corner of voxel is the origin to_origin(target_origin) Safe function to change streamlines to a particular origin standard False means NIFTI (center) and True means TrackVis (corner) Safe function to transform streamlines and update state to_space(target_space) Safe function to transform streamlines to a particular space using an enum and update state Safe function to transform streamlines and update state Safe function to transform streamlines and update state
__init__(streamlines, reference, space, origin=Origin.NIFTI, data_per_point=None, data_per_streamline=None)

Create a strict, state-aware, robust tractogram

Parameters:
streamlineslist or ArraySequence

Streamlines of the tractogram

referenceNifti or Trk filename, Nifti1Image or TrkFile,

Nifti1Header, trk.header (dict) or another Stateful Tractogram Reference that provides the spatial attributes. Typically a nifti-related object from the native diffusion used for streamlines generation

spaceEnum (dipy.io.stateful_tractogram.Space)

Current space in which the streamlines are (vox, voxmm or rasmm) After tracking the space is VOX, after loading with nibabel the space is RASMM

originEnum (dipy.io.stateful_tractogram.Origin), optional

Current origin in which the streamlines are (center or corner) After loading with nibabel the origin is CENTER

data_per_pointdict, optional

Dictionary in which each key has X items, each items has Y_i items X being the number of streamlines Y_i being the number of points on streamlines #i

data_per_streamlinedict, optional

Dictionary in which each key has X items X being the number of streamlines

Notes

Very important to respect the convention, verify that streamlines match the reference and are effectively in the right space.

Any change to the number of streamlines, data_per_point or data_per_streamline requires particular verification.

In a case of manipulation not allowed by this object, use Nibabel directly and be careful.

property affine

Getter for the reference affine

static are_compatible(sft_1, sft_2)

Compatibility verification of two StatefulTractogram to ensure space, origin, data_per_point and data_per_streamline consistency

compute_bounding_box()

Compute the bounding box of the streamlines in their current state

Returns:
outputndarray

8 corners of the XYZ aligned box, all zeros if no streamlines

property data_per_point

Getter for data_per_point

property data_per_streamline

Getter for data_per_streamline

property dimensions

Getter for the reference dimensions

static from_sft(streamlines, sft, data_per_point=None, data_per_streamline=None)

Create an instance of StatefulTractogram from another instance of StatefulTractogram.

Parameters:
streamlineslist or ArraySequence

Streamlines of the tractogram

sftStatefulTractogram,

The other StatefulTractogram to copy the space_attribute AND state from.

data_per_pointdict, optional

Dictionary in which each key has X items, each items has Y_i items X being the number of streamlines Y_i being the number of points on streamlines #i

data_per_streamlinedict, optional

Dictionary in which each key has X items X being the number of streamlines

—–
get_data_per_point_keys()

Return a list of the data_per_point attribute names

get_data_per_streamline_keys()

Return a list of the data_per_streamline attribute names

get_streamlines_copy()

Safe getter for streamlines (for slicing)

is_bbox_in_vox_valid()

Verify that the bounding box is valid in voxel space. Negative coordinates or coordinates above the volume dimensions are considered invalid in voxel space.

Returns:
outputbool

Are the streamlines within the volume of the associated reference

property origin

Getter for origin standard

remove_invalid_streamlines(epsilon=0.001)

Remove streamlines with invalid coordinates from the object. Will also remove the data_per_point and data_per_streamline. Invalid coordinates are any X,Y,Z values above the reference dimensions or below zero

Parameters:
epsilonfloat (optional)

Epsilon value for the bounding box verification. Default is 1e-6.

Returns:
outputtuple

Tuple of two list, indices_to_remove, indices_to_keep

property space

Getter for the current space

property space_attributes

Getter for spatial attribute

property streamlines

Partially safe getter for streamlines

to_center()

Safe function to shift streamlines so the center of voxel is the origin

to_corner()

Safe function to shift streamlines so the