"""
========================================================
Extracting AFQ tract profiles from segmented bundles.
========================================================

In this example, we will we will extract the values of a statistic from a
volume, using the coordinates along the length of a bundle. These are called
`tract profiles`

One of the challenges of extracting tract profiles is that some of the
streamlines in a bundle may diverge significantly from the bundle in some
locations. To overcome this challenge, we will use a strategy similar to that
described in [Yeatman2012]_: We will weight the contribution of each streamline
to the bundle profile based on how far this streamline is from the mean
trajectory of the bundle at that location.

"""

import matplotlib.pyplot as plt
import numpy as np
import os.path as op

"""

To get started, we will grab the bundles that were extracted in the bundle
extraction example. If the example hasn't been run yet, these files don't
yet exist, and we'll need to run that example:

"""

if not (op.exists("CST_L.trk") and
        op.exists("AF_L.trk") and
        op.exists("slr_transform.npy")):
    import bundle_extraction

"""

Either way, we can use the `dipy.io` API to read in the bundles from file.
`load_trk` returns both the streamlines, as well as header information.

"""

from dipy.io.streamline import load_trk
cst_l, hdr = load_trk("CST_L.trk")
af_l, hdr = load_trk("AF_L.trk")

transform = np.load("slr_transform.npy")

"""

In the next step, we need to make sure that all the streamlines in each bundle
are oriented the same way. For example, for the CST, we want to make sure that
all the bundles have their cortical termination at one end of the streamline.
This is that when we later extract values from a volume, we won't have different
streamlines going in opposite directions.

To orient all the streamlines in each bundles, we will create standard
streamlines, by finding the centroids of the left AF and CST bundle models.

The advantage of using the model bundles is that we can use the same standard
for different subjects, which means that we'll get roughly the same orientation
"""

import dipy.data as dpd
from dipy.data.fetcher import get_two_hcp842_bundles
model_af_l_file, model_cst_l_file = get_two_hcp842_bundles()

model_af_l, hdr = load_trk(model_af_l_file)
model_cst_l, hdr = load_trk(model_cst_l_file)


from dipy.segment.metric import (AveragePointwiseEuclideanMetric,
                                 ResampleFeature)
from dipy.segment.clustering import QuickBundles

feature = ResampleFeature(nb_points=100)
metric = AveragePointwiseEuclideanMetric(feature)

"""
Since we are going to include all of the streamlines in the single cluster
from the streamlines, we set the threshold to `np.inf`. We pull out the
centroid as the standard.
"""

qb = QuickBundles(np.inf, metric=metric)

cluster_cst_l = qb.cluster(model_cst_l)
standard_cst_l = cluster_cst_l.centroids[0]

cluster_af_l = qb.cluster(model_af_l)
standard_af_l = cluster_af_l.centroids[0]

"""
We use the centroid streamline for each atlas bundle as the standard to orient
all of the streamlines in each bundle from the individual subject. Here, the
affine used is the one from the transform between the atlas and individual
tractogram. This is so that the orienting is done relative to the space of the
individual, and not relative to the atlas space.
"""

import dipy.tracking.streamline as dts

oriented_cst_l = dts.orient_by_streamline(cst_l, standard_cst_l,
                                          affine=transform)
oriented_af_l = dts.orient_by_streamline(af_l, standard_af_l,
                                         affine=transform)


"""
Read volumetric data from an image corresponding to this subject.

For the purpose of this, we've extracted only the FA within the bundles in question,
but in real use, this is where you would add the FA map of your subject.
"""

files, folder = dpd.fetch_bundle_fa_hcp()

import nibabel as nib
img = nib.load(op.join(folder, "hcp_bundle_fa.nii.gz"))
fa = img.get_fdata()


"""
Calculate weights for each bundle:
"""

import dipy.segment.bundles as dsb

w_cst_l = dsb.gaussian_weights(oriented_cst_l)
w_af_l = dsb.gaussian_weights(oriented_af_l)

"""
And then use the weights to calculate the tract profiles for each bundle
"""

profile_cst_l = dsb.afq_profile(fa, oriented_cst_l, affine=img.affine,
                                weights=w_cst_l)

profile_af_l = dsb.afq_profile(fa, oriented_af_l, affine=img.affine,
                               weights=w_af_l)

fig, (ax1, ax2) = plt.subplots(1, 2)

ax1.plot(profile_cst_l)
ax1.set_ylabel("Fractional anisotropy")
ax1.set_xlabel("Node along CST")
ax2.plot(profile_af_l)
ax2.set_xlabel("Node along AF")
fig.savefig("tract_profiles")

"""
.. figure:: tract_profiles.png
   :align: center

   Bundle profiles for the fractional anisotropy in left CST (left) and left
   AF (right).
"""

"""
References
----------

.. [Yeatman2012] Yeatman, Jason D., Robert F. Dougherty, Nathaniel J. Myall,
    Brian A. Wandell, and Heidi M. Feldman. 2012. "Tract Profiles of White
    Matter Properties: Automating Fiber-Tract Quantification" PloS One 7 (11):
    e49790.

.. [Garyfallidis17] Garyfallidis et al. Recognition of white matter bundles
   using local and global streamline-based registration and clustering,
   Neuroimage, 2017.

.. [Garyfallidis12] Garyfallidis E. et al., QuickBundles a method for
   tractography simplification, Frontiers in Neuroscience, vol 6, no 175, 2012.
"""