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dipy.reconst.odf

class dipy.reconst.odf.OdfModel

An abstract class to be sub-classed by specific odf models

All odf models should provide a fit method which may take data as it’s first and only argument.

Methods

fit
fit(data)

To be implemented by specific odf models

dipy.reconst.odf.gfa(samples)

The general fractional anisotropy of a function evaluated on the unit sphere

dipy.reconst.odf.peak_directions(odf, sphere, relative_peak_threshold=0.25, min_separation_angle=45)

Get the directions of odf peaks

Parameters :

odf : 1d ndarray

The odf function evaluated on the vertices of sphere

sphere : Sphere

The Sphere providing discrete directions for evaluation.

relative_peak_threshold : float

Only return peaks greater than relative_peak_threshold * m where m is the largest peak.

min_separation_angle : float in [0, 90] The minimum distance between

directions. If two peaks are too close only the larger of the two is returned.

Returns :

directions : (N, 3) ndarray

N vertices for sphere, one for each peak

values : (N,) ndarray

peak values

indices : (N,) ndarray

peak indices of the directions on the sphere

dipy.reconst.odf.peak_directions_nl(sphere_eval, relative_peak_threshold=0.25, min_separation_angle=45, sphere=<dipy.core.sphere.HemiSphere object at 0x4228bd0>, xtol=1e-07)

Non Linear Direction Finder

Parameters :

sphere_eval : callable

A function which can be evaluated on a sphere.

relative_peak_threshold : float

Only return peaks greater than relative_peak_threshold * m where m is the largest peak.

min_separation_angle : float in [0, 90]

The minimum distance between directions. If two peaks are too close only the larger of the two is returned.

sphere : Sphere

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

xtol : float

Relative tolerance for optimization.

Returns :

directions : array (N, 3)

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

values : array (N,)

Value of sphere_eval at each point on directions.

dipy.reconst.odf.peaks_from_model(model, data, sphere, relative_peak_threshold, min_separation_angle, mask=None, return_odf=False, gfa_thr=0.02, normalize_peaks=False)

Fits the model to data and computes peaks and metrics

Parameters :

model : a model instance

model will be used to fit the data.

sphere : Sphere

The Sphere providing discrete directions for evaluation.

relative_peak_threshold : float

Only return peaks greater than relative_peak_threshold * m where m is the largest peak.

min_separation_angle : float in [0, 90] The minimum distance between

directions. If two peaks are too close only the larger of the two is returned.

mask : array, optional

If mask is provided, voxels that are False in mask are skipped and no peaks are returned.

return_odf : bool

If True, the odfs are returned.

gfa_thr : float

Voxels with gfa less than gfa_thr are skipped, no peaks are returned.

normalize_peaks : bool

If true, all peak values are calculated relative to max(odf).