<commit_before>"""
This is an implementation of the sparse fascicle model described in
[Rokem2014]_.


_[Rokem2014]

"""
import warnings

import numpy as np

from dipy.utils.optpkg import optional_package
import dipy.core.geometry as geo
import dipy.core.gradients as grad
import dipy.sims.voxel as sims
import dipy.reconst.dti as dti
import dipy.data as dpd
from dipy.reconst.base import ReconstModel, ReconstFit
from dipy.reconst.cache import Cache
from dipy.core.onetime import auto_attr
lm, has_sklearn, _ = optional_package('sklearn.linear_model')

# If sklearn is unavailable, we can fall back on nnls (but we also warn the
# user that we are about to do that):
if not has_sklearn:<commit_msg>DOC: Reference<commit_after>"""
This is an implementation of the sparse fascicle model described in
[Rokem2014]_.


.. [Rokem2014] <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>
   (2014). Evaluating the accuracy of diffusion MRI models in white
   matter. http://arxiv.org/abs/1411.0721

"""
import warnings

import numpy as np

from dipy.utils.optpkg import optional_package
import dipy.core.geometry as geo
import dipy.core.gradients as grad
import dipy.sims.voxel as sims
import dipy.reconst.dti as dti
import dipy.data as dpd
from dipy.reconst.base import ReconstModel, ReconstFit
from dipy.reconst.cache import Cache
from dipy.core.onetime import auto_attr
lm, has_sklearn, _ = optional_package('sklearn.linear_model')

# If sklearn is unavailable, we can fall back on nnls (but we also warn the
# user that we are about to do that):
if not has_sklearn:
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