"""
=================================================
Using Various Stopping Criterion for Tractography
=================================================
The stopping criterion determines if the tracking stops or continues at each
tracking position. The tracking stops when it reaches an ending region
(e.g. low FA, gray matter or corticospinal fluid regions) or exits the image
boundaries. The tracking also stops if the direction getter has no direction
to follow.

Each stopping criterion determines if the stopping is 'valid' or
'invalid'. A streamline is 'valid' when the stopping criterion determines if
the streamline stops in a position classified as 'ENDPOINT' or 'OUTSIDEIMAGE'.
A streamline is 'invalid' when it stops in a position classified as
'TRACKPOINT' or 'INVALIDPOINT'. These conditions are described below. The
'LocalTracking' generator can be set to output all generated streamlines
or only the 'valid' ones. See Girard et al. (2004) [Girard2014]_ and Smith et
al.(2012) [Smith2012]_ for more details on these methods.

This example is an extension of the
:ref:`sphx_glr_examples_built_fiber_tracking_tracking_deterministic.py` example.
We begin by loading the data, creating a seeding mask from white matter voxels
of the corpus callosum, fitting a Constrained Spherical Deconvolution (CSD)
reconstruction model and creating the maximum deterministic direction getter.
"""

import matplotlib.pyplot as plt
import numpy as np

from dipy.core.gradients import gradient_table
from dipy.data import get_fnames, default_sphere
from dipy.direction import DeterministicMaximumDirectionGetter
from dipy.io.gradients import read_bvals_bvecs
from dipy.io.image import load_nifti, load_nifti_data
from dipy.io.streamline import save_trk
from dipy.io.stateful_tractogram import Space, StatefulTractogram
from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel,
                                   auto_response_ssst)
from dipy.reconst.dti import fractional_anisotropy, TensorModel
from dipy.tracking import utils
from dipy.tracking.local_tracking import LocalTracking
from dipy.tracking.streamline import Streamlines
from dipy.tracking.stopping_criterion import (ActStoppingCriterion,
                                              BinaryStoppingCriterion,
                                              ThresholdStoppingCriterion)
from dipy.viz import window, actor, colormap, has_fury


# Enables/disables interactive visualization
interactive = False

hardi_fname, hardi_bval_fname, hardi_bvec_fname = get_fnames('stanford_hardi')
label_fname = get_fnames('stanford_labels')
_, _, f_pve_wm = get_fnames('stanford_pve_maps')

data, affine, hardi_img = load_nifti(hardi_fname, return_img=True)
labels = load_nifti_data(label_fname)
bvals, bvecs = read_bvals_bvecs(hardi_bval_fname, hardi_bvec_fname)
gtab = gradient_table(bvals, bvecs)

white_matter = load_nifti_data(f_pve_wm)

seed_mask = (labels == 2)
seed_mask[white_matter < 0.5] = 0
seeds = utils.seeds_from_mask(seed_mask, affine, density=2)

response, ratio = auto_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd_model.fit(data, mask=white_matter)

dg = DeterministicMaximumDirectionGetter.from_shcoeff(csd_fit.shm_coeff,
                                                      max_angle=30.,
                                                      sphere=default_sphere)

"""
Threshold Stopping Criterion
============================
A scalar map can be used to define where the tracking stops. The threshold
stopping criterion uses a scalar map to stop the tracking whenever the
interpolated scalar value is lower than a fixed threshold. Here, we show
an example using the fractional anisotropy (FA) map of the DTI model.
The threshold stopping criterion uses a trilinear interpolation at the
tracking position.

**Parameters**

- metric_map: numpy array [:, :, :]
- threshold: float

**Stopping States**

- 'ENDPOINT': stops at a position where metric_map < threshold; the streamline
reached the target stopping area.
- 'OUTSIDEIMAGE': stops at a position outside of metric_map; the streamline
reached an area outside the image where no direction data is available.
- 'TRACKPOINT': stops at a position because no direction is available; the
streamline is stopping where metric_map >= threshold, but there is no valid
direction to follow.
- 'INVALIDPOINT': N/A.
"""


tensor_model = TensorModel(gtab)
tenfit = tensor_model.fit(data, mask=labels > 0)
FA = fractional_anisotropy(tenfit.evals)

threshold_criterion = ThresholdStoppingCriterion(FA, .2)

fig = plt.figure()
mask_fa = FA.copy()
mask_fa[mask_fa < 0.2] = 0
plt.xticks([])
plt.yticks([])
plt.imshow(mask_fa[:, :, data.shape[2] // 2].T, cmap='gray', origin='lower',
           interpolation='nearest')
fig.tight_layout()
fig.savefig('threshold_fa.png')

"""
.. figure:: threshold_fa.png
 :align: center

 **Thresholded fractional anisotropy map.**
"""

streamline_generator = LocalTracking(dg,
                                     threshold_criterion,
                                     seeds,
                                     affine,
                                     step_size=.5,
                                     return_all=True)
streamlines = Streamlines(streamline_generator)
sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM)
save_trk(sft, "tractogram_probabilistic_thresh_all.trk")

if has_fury:
    scene = window.Scene()
    scene.add(actor.line(streamlines, colormap.line_colors(streamlines)))
    window.record(scene, out_path='tractogram_deterministic_thresh_all.png',
                  size=(800, 800))
    if interactive:
        window.show(scene)

"""
.. figure:: tractogram_deterministic_thresh_all.png
 :align: center

 **Corpus Callosum using deterministic tractography with a thresholded
 fractional anisotropy mask.**
"""
"""
Binary Stopping Criterion
=========================
A binary mask can be used to define where the tracking stops. The binary
stopping criterion stops the tracking whenever the tracking position is outside
the mask. Here, we show how to obtain the binary stopping criterion from
the white matter mask defined above. The binary stopping criterion uses a
nearest-neighborhood interpolation at the tracking position.

**Parameters**

- mask: numpy array [:, :, :]

**Stopping States**

- 'ENDPOINT': stops at a position where mask = 0; the streamline
reached the target stopping area.
- 'OUTSIDEIMAGE': stops at a position outside of metric_map; the streamline
reached an area outside the image where no direction data is available.
- 'TRACKPOINT': stops at a position because no direction is available; the
streamline is stopping where mask > 0, but there is no valid direction to
follow.
- 'INVALIDPOINT': N/A.
"""


binary_criterion = BinaryStoppingCriterion(white_matter == 1)

fig = plt.figure()
plt.xticks([])
plt.yticks([])
fig.tight_layout()
plt.imshow(white_matter[:, :, data.shape[2] // 2].T, cmap='gray',
           origin='lower', interpolation='nearest')

fig.savefig('white_matter_mask.png')

"""
.. figure:: white_matter_mask.png
 :align: center

 **White matter binary mask.**
"""

streamline_generator = LocalTracking(dg,
                                     binary_criterion,
                                     seeds,
                                     affine,
                                     step_size=.5,
                                     return_all=True)
streamlines = Streamlines(streamline_generator)
sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM)
save_trk(sft, "tractogram_deterministic_binary_all.trk")

if has_fury:
    scene = window.Scene()
    scene.add(actor.line(streamlines, colormap.line_colors(streamlines)))
    window.record(scene, out_path='tractogram_deterministic_binary_all.png',
                  size=(800, 800))
    if interactive:
        window.show(scene)

"""
.. figure:: tractogram_deterministic_binary_all.png
 :align: center

 **Corpus Callosum using deterministic tractography with a binary white
 matter mask.**
"""

"""
ACT Stopping Criterion
======================
Anatomically-constrained tractography (ACT) [Smith2012]_ uses information from
anatomical images to determine when the tractography stops. The ``include_map``
defines when the streamline reached a 'valid' stopping region (e.g. gray
matter partial volume estimation (PVE) map) and the ``exclude_map`` defines
when the streamline reached an 'invalid' stopping region (e.g. corticospinal
fluid PVE map). The background of the anatomical image should be added to the
``include_map`` to keep streamlines exiting the brain (e.g. through the
brain stem). The ACT stopping criterion uses a trilinear interpolation
at the tracking position.

**Parameters**

- ``include_map``: numpy array ``[:, :, :]``,
- ``exclude_map``: numpy array ``[:, :, :]``,

**Stopping States**

- 'ENDPOINT': stops at a position where ``include_map`` > 0.5; the streamline
reached the target stopping area.
- 'OUTSIDEIMAGE': stops at a position outside of ``include_map`` or
``exclude_map``; the streamline reached an area outside the image where no
direction data is available.
- 'TRACKPOINT': stops at a position because no direction is available; the
streamline is stopping where ``include_map`` < 0.5 and ``exclude_map`` < 0.5,
but there is no valid direction to follow.
- 'INVALIDPOINT': ``exclude_map`` > 0.5; the streamline reach a position which
is anatomically not plausible.
"""


f_pve_csf, f_pve_gm, f_pve_wm = get_fnames('stanford_pve_maps')
pve_csf_data = load_nifti_data(f_pve_csf)
pve_gm_data = load_nifti_data(f_pve_gm)
pve_wm_data = load_nifti_data(f_pve_wm)

background = np.ones(pve_gm_data.shape)
background[(pve_gm_data + pve_wm_data + pve_csf_data) > 0] = 0

include_map = pve_gm_data
include_map[background > 0] = 1
exclude_map = pve_csf_data

act_criterion = ActStoppingCriterion(include_map, exclude_map)

fig = plt.figure()
plt.subplot(121)
plt.xticks([])
plt.yticks([])
plt.imshow(include_map[:, :, data.shape[2] // 2].T, cmap='gray',
           origin='lower', interpolation='nearest')

plt.subplot(122)
plt.xticks([])
plt.yticks([])
plt.imshow(exclude_map[:, :, data.shape[2] // 2].T, cmap='gray',
           origin='lower', interpolation='nearest')

fig.tight_layout()
fig.savefig('act_maps.png')

"""
.. figure:: act_maps.png
 :align: center

 **Include (left) and exclude (right) maps for ACT.**
"""

streamline_generator = LocalTracking(dg,
                                     act_criterion,
                                     seeds,
                                     affine,
                                     step_size=.5,
                                     return_all=True)
streamlines = Streamlines(streamline_generator)
sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM)
save_trk(sft, "tractogram_deterministic_act_all.trk")

if has_fury:
    scene = window.Scene()
    scene.add(actor.line(streamlines, colormap.line_colors(streamlines)))
    window.record(scene, out_path='tractogram_deterministic_act_all.png',
                  size=(800, 800))
    if interactive:
        window.show(scene)

"""
.. figure:: tractogram_deterministic_act_all.png
 :align: center

 **Corpus Callosum using deterministic tractography with ACT stopping
 criterion.**
"""

streamline_generator = LocalTracking(dg,
                                     act_criterion,
                                     seeds,
                                     affine,
                                     step_size=.5,
                                     return_all=False)
streamlines = Streamlines(streamline_generator)
sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM)
save_trk(sft, "tractogram_deterministic_act_valid.trk")

if has_fury:
    scene = window.Scene()
    scene.add(actor.line(streamlines, colormap.line_colors(streamlines)))
    window.record(scene, out_path='tractogram_deterministic_act_valid.png',
                  size=(800, 800))
    if interactive:
        window.show(scene)

"""
.. figure:: tractogram_deterministic_act_valid.png
 :align: center

 **Corpus Callosum using deterministic tractography with ACT stopping
 criterion. Streamlines ending in gray matter region only.**
"""

"""
The threshold and binary stopping criterion use respectively a scalar map and a
binary mask to stop the tracking. The ACT stopping criterion use partial volume
fraction (PVE) maps from an anatomical image to stop the tracking.
Additionally, the ACT stopping criterion determines if the tracking stopped in
expected regions (e.g. gray matter) and allows the user to get only
streamlines stopping in those regions.

Notes
-----
Currently,the proposed method that cuts streamlines going through
subcortical gray matter regions is not implemented. The
backtracking technique for streamlines reaching INVALIDPOINT is not
implemented either [Smith2012]_.


References
----------

.. [Smith2012] Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A.
    Anatomically-constrained tractography: Improved diffusion MRI
    streamlines tractography through effective use of anatomical
    information. NeuroImage, 63(3), 1924-1938, 2012.

.. [Girard2014] Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M.
    Towards quantitative connectivity analysis: reducing tractography biases.
    NeuroImage, 98, 266-278, 2014.

.. include:: ../links_names.inc
"""
