direction

Module: direction.peaks

PeaksAndMetrics	Attributes:
peak_directions_nl(sphere_eval, *[, ...])	Non Linear Direction Finder.
peaks_from_model(model, data, sphere, ...[, ...])	Fit the model to data and computes peaks and metrics
reshape_peaks_for_visualization(peaks)	Reshape peaks for visualization.
peaks_from_positions(positions, odfs, ...[, ...])	Extract the peaks at each positions.

PeaksAndMetrics

class dipy.direction.peaks.PeaksAndMetrics

Bases: EuDXDirectionGetter

Attributes:

ang_thr

qa_thr

total_weight

Methods

initial_direction(point)

The best starting directions for fiber tracking from point

generate_streamline
get_direction

peak_directions_nl

dipy.direction.peaks.peak_directions_nl(sphere_eval, *, relative_peak_threshold=0.25, min_separation_angle=25, sphere=<dipy.core.sphere.HemiSphere object>, xtol=1e-07)

Non Linear Direction Finder.

Parameters:

sphere_evalcallable

A function which can be evaluated on a sphere.

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.

sphereSphere

A discrete Sphere. The points on the sphere will be used for initial estimate of maximums.

xtolfloat

Relative tolerance for optimization.

Returns:

directionsarray (N, 3)

Points on the sphere corresponding to N local maxima on the sphere.

valuesarray (N,)

Value of sphere_eval at each point on directions.

peaks_from_model

dipy.direction.peaks.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_max=8, sh_basis_type=None, legacy=True, 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.

maskarray, optional

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_order_maxint, optional

Maximum SH order (l) in the SH fit. For sh_order_max, there will be (sh_order_max + 1) * (sh_order_max + 2) / 2 SH coefficients (default 8).

sh_basis_type{None, ‘tournier07’, ‘descoteaux07’}

None for the default DIPY basis, tournier07 for the Tournier 2007 :footcite:p:Tournier2007` basis, and descoteaux07 for the Descoteaux 2007 :footcite:p:Descoteaux2007` basis (None defaults to descoteaux07).

legacy: bool, optional

True to use a legacy basis definition for backward compatibility with previous tournier07 and descoteaux07 implementations.

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

reshape_peaks_for_visualization

dipy.direction.peaks.reshape_peaks_for_visualization(peaks)

Reshape peaks for visualization.

Reshape and convert to float32 a set of peaks for visualisation with mrtrix or the fibernavigator.

Parameters:

peaks: nd array (…, N, 3) or PeaksAndMetrics object

The peaks to be reshaped and converted to float32.

Returns:

peaksnd array (…, 3*N)

peaks_from_positions

dipy.direction.peaks.peaks_from_positions(positions, odfs, sphere, affine, *, pmf_gen=None, relative_peak_threshold=0.5, min_separation_angle=25, is_symmetric=True, npeaks=5)

Extract the peaks at each positions.

Parameters:

positionarray, (N, 3)

World coordinates of the N positions.

odfsarray, (X, Y, Z, M)

Orientation distribution function (spherical function) represented on a sphere of M points.

sphereSphere

A discrete Sphere. The M points on the sphere correspond to the points of the odfs.

affinearray (4, 4)

The mapping between voxel indices and the point space for positions.

pmf_genPmfGen

Probability mass function generator from voxel orientation information. Replaces odfs and sphere when used.

relative_peak_thresholdfloat, optional

Only peaks greater than min + relative_peak_threshold * scale are kept, where min = max(0, odf.min()) and scale = odf.max() - min. The relative_peak_threshold should be in the range [0, 1].

min_separation_anglefloat, optional

The minimum distance between directions. If two peaks are too close only the larger of the two is returned. The min_separation_angle should be in the range [0, 90].

is_symmetricbool, optional

If True, v is considered equal to -v.

npeaksint, optional

The maximum number of peaks to extract at from each position.

Returns:

peaks_arrarray (N, npeaks, 3)