code: def peak_directions(
      odf,
      sphere,
      relative_peak_threshold=0.5,
      min_separation_angle=25,
      is_symmetric=True,
  ):
      """Get the directions of odf peaks.
  
      Peaks are defined as points on the odf that are greater than at least one
      neighbor and greater than or equal to all neighbors. Peaks are sorted in
      descending order by their values then filtered based on their relative size
      and spacing on the sphere. An odf may have 0 peaks, for example if the odf
      is perfectly isotropic.
  
      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 in [0., 1.]
          Only peaks greater than ``min + relative_peak_threshold * scale`` are
          kept, where ``min = max(0, odf.min())`` and
          ``scale = odf.max() - min``.
      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.
      is_symmetric : bool, optional
          If True, v is considered equal to -v.
  
      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
  
      Notes
      -----
      If the odf has any negative values, they will be clipped to zeros.
  
      """
      values, indices = local_maxima(odf, sphere.edges)
  
      # If there is only one peak return
      n = len(values)
      if n == 0 or (values[0] < 0.0):
          return np.zeros((0, 3)), np.zeros(0), np.zeros(0, dtype=int)
      elif n == 1:
          return sphere.vertices[indices], values, indices
  
      odf_min = np.min(odf)
      odf_min = max(odf_min, 0.0)
      # because of the relative threshold this algorithm will give the same peaks
      # as if we divide (values - odf_min) with (odf_max - odf_min) or not so
      # here we skip the division to increase speed
      values_norm = values - odf_min
  
      # Remove small peaks
      n = search_descending(values_norm, relative_peak_threshold)
      indices = indices[:n]
      directions = sphere.vertices[indices]
  
      # Remove peaks too close together
      directions, uniq = remove_similar_vertices(
          directions,
          min_separation_angle,
          return_index=True,
          remove_antipodal=is_symmetric,
      )
      values = values[uniq]
      indices = indices[uniq]
      return directions, values, indices
  
imports: import tempfile
  from multiprocessing import Pool
  from itertools import repeat
  from os import path
  import numpy
  import scipy.optimize
  from dipy.reconst.odf import gfa
  from dipy.reconst.recspeed import local_maxima, remove_similar_vertices, search_descending
  from dipy.core.sphere import Sphere
  from dipy.data import default_sphere
  from dipy.core.ndindex import ndindex
  from dipy.reconst.shm import sh_to_sf_matrix
  from dipy.reconst.eudx_direction_getter import EuDXDirectionGetter
  from dipy.utils.multiproc import determine_num_processes
  from dipy.utils.deprecator import deprecated_params
label_desc: Use image node_id 2 for calling a global function with example usage: peak_directions(odf, sphere, relative_peak_threshold, min_separation_angle, is_symmetric) and returns: directions,  values,  indices, np,  np,  np, sphere,  values,  indices