code: def mask_for_response_ssst(
      gtab, data, roi_center=None, roi_radii=10, fa_thr=0.7
  ):
      """Computation of mask for single-shell single-tissue (ssst) response
          function using FA.
  
      Parameters
      ----------
      gtab : GradientTable
      data : ndarray
          diffusion data (4D)
      roi_center : array-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]`.
      roi_radii : int or array-like, (3,)
          radii of cuboid ROI
      fa_thr : float
          FA threshold
  
      Returns
      -------
      mask : ndarray
          Mask of voxels within the ROI and with FA above the FA threshold.
  
      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. This function aims to accomplish that by
      returning a mask of voxels within a ROI, that have a FA value above a
      given threshold. For example we can use a ROI (20x20x20) at
      the center of the volume and store the signal values for the voxels with
      FA values higher than 0.7 (see [1]_).
  
      References
      ----------
      .. [1] Tournier, J.D., et al. NeuroImage 2004. Direct estimation of the
      fiber orientation density function from diffusion-weighted MRI
      data using spherical deconvolution
  
      """
      if len(data.shape) < 4:
          msg = """Data must be 4D (3D image + directions). To use a 2D image,
          please reshape it into a (N, N, 1, ndirs) array."""
          raise ValueError(msg)
  
      if isinstance(roi_radii, numbers.Number):
          roi_radii = (roi_radii, roi_radii, roi_radii)
  
      if roi_center is None:
          roi_center = np.array(data.shape[:3]) // 2
  
      roi_radii = _roi_in_volume(
          data.shape, np.asarray(roi_center), np.asarray(roi_radii)
      )
  
      roi_mask = _mask_from_roi(data.shape[:3], roi_center, roi_radii)
  
      ten = TensorModel(gtab)
      tenfit = ten.fit(data, mask=roi_mask)
      fa = fractional_anisotropy(tenfit.evals)
      fa[np.isnan(fa)] = 0
  
      mask = np.zeros(fa.shape, dtype=np.int64)
      mask[fa > fa_thr] = 1
  
      if np.sum(mask) == 0:
          msg = """No voxel with a FA higher than {} were found.
          Try a larger roi or a lower threshold.""".format(
              str(fa_thr)
          )
          warnings.warn(msg, UserWarning)
  
      return mask
  
imports: import warnings
  import numpy
  import numbers
  from scipy.integrate import quad
  from scipy.special import lpn, gamma
  import scipy.linalg
  import scipy.linalg.lapack
  from dipy.data import small_sphere, get_sphere, default_sphere
  from dipy.core.geometry import cart2sphere
  from dipy.core.ndindex import ndindex
  from dipy.sims.voxel import single_tensor
  from dipy.reconst.multi_voxel import multi_voxel_fit
  from dipy.reconst.dti import TensorModel, fractional_anisotropy
  from dipy.reconst.shm import sph_harm_ind_list, real_sh_descoteaux_from_index, sph_harm_lookup, lazy_index, SphHarmFit, real_sh_descoteaux, sh_to_rh, forward_sdeconv_mat, SphHarmModel
  from dipy.reconst.utils import _roi_in_volume, _mask_from_roi
  from dipy.direction.peaks import peaks_from_model
  from dipy.core.geometry import vec2vec_rotmat
  from dipy.utils.deprecator import deprecate_with_version, deprecated_params
label_desc: Use image node_id 9 for calling a global function with example usage: mask_for_response_ssst(gtab, data, roi_center, roi_radii, fa_thr) and returns: mask