"""
=========================================
Tractography Clustering with QuickBundles
=========================================

This example explains how we can use QuickBundles [Garyfallidis12]_ to
simplify/cluster streamlines.

First import the necessary modules.
"""

import numpy as np
from dipy.io.streamline import load_tractogram
from dipy.segment.clustering import QuickBundles
from dipy.io.pickles import save_pickle
from dipy.data import get_fnames
from dipy.viz import window, actor, colormap

###############################################################################
# For educational purposes we will try to cluster a small streamline bundle known
# from neuroanatomy as the fornix.


fname = get_fnames('fornix')

###############################################################################
# Load fornix streamlines.


fornix = load_tractogram(fname, 'same', bbox_valid_check=False)
streamlines = fornix.streamlines

###############################################################################
# Perform QuickBundles clustering using the MDF metric and a 10mm distance
# threshold. Keep in mind that since the MDF metric requires streamlines to have
# the same number of points, the clustering algorithm will internally use a
# representation of streamlines that have been automatically downsampled/upsampled
# so they have only 12 points (To set manually the number of points,
# see :ref:`clustering-examples-ResampleFeature`).


qb = QuickBundles(threshold=10.)
clusters = qb.cluster(streamlines)

###############################################################################
# `clusters` is a `ClusterMap` object which contains attributes that
# provide information about the clustering result.


print("Nb. clusters:", len(clusters))
print("Cluster sizes:", map(len, clusters))
print("Small clusters:", clusters < 10)
print("Streamlines indices of the first cluster:\n", clusters[0].indices)
print("Centroid of the last cluster:\n", clusters[-1].centroid)

###############################################################################
# 
# ::
# 
#     Nb. clusters: 4
# 
#     Cluster sizes: [64, 191, 47, 1]
# 
#     Small clusters: array([False, False, False, True], dtype=bool)
# 
#     Streamlines indices of the first cluster:
#     [0, 7, 8, 10, 11, 12, 13, 14, 15, 18, 26, 30, 33, 35, 41, 65, 66, 85, 100,
#      101, 105, 115, 116, 119, 122, 123, 124, 125, 126, 128, 129, 135, 139, 142,
#      143, 144, 148, 151, 159, 167, 175, 180, 181, 185, 200, 208, 210, 224, 237,
#      246, 249, 251, 256, 267, 270, 280, 284, 293, 296, 297, 299]
# 
#     Centroid of the last cluster:
#     array([[  84.83773804,  117.92590332,   77.32278442],
#            [  86.10850525,  115.84362793,   81.91885376],
#            [  86.40357208,  112.25676727,   85.72930145],
#            [  86.48336792,  107.60327911,   88.13782501],
#            [  86.23897552,  102.5100708 ,   89.29447174],
#            [  85.04563904,   97.46020508,   88.54240417],
#            [  82.60240173,   93.14851379,   86.84208679],
#            [  78.98937225,   89.57682037,   85.63652039],
#            [  74.72344208,   86.60827637,   84.9391861 ],
#            [  70.40846252,   85.15874481,   82.4484024 ],
#            [  66.74534607,   86.00262451,   78.82582092],
#            [  64.02451324,   88.43942261,   75.0697403 ]], dtype=float32)
# 
# 
# `clusters` also has attributes such as `centroids` (cluster representatives), and
# methods like `add`, `remove`, and `clear` to modify the clustering result.
# 
# Let's first show the initial dataset.


# Enables/disables interactive visualization
interactive = False

scene = window.Scene()
scene.SetBackground(1, 1, 1)
scene.add(actor.streamtube(streamlines, window.colors.white))
window.record(scene, out_path='fornix_initial.png', size=(600, 600))
if interactive:
    window.show(scene)

###############################################################################
# .. figure:: fornix_initial.png
#    :align: center
# 
#    Initial Fornix dataset.
# 
# Show the centroids of the fornix after clustering (with random colors):


colormap = colormap.create_colormap(np.arange(len(clusters)))

scene.clear()
scene.SetBackground(1, 1, 1)
scene.add(actor.streamtube(streamlines, window.colors.white, opacity=0.05))
scene.add(actor.streamtube(clusters.centroids, colormap, linewidth=0.4))
window.record(scene, out_path='fornix_centroids.png', size=(600, 600))
if interactive:
    window.show(scene)

###############################################################################
# .. figure:: fornix_centroids.png
#    :align: center
# 
#    Showing the different QuickBundles centroids with random colors.
# 
# Show the labeled fornix (colors from centroids).


colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
    colormap_full[cluster.indices] = color

scene.clear()
scene.SetBackground(1, 1, 1)
scene.add(actor.streamtube(streamlines, colormap_full))
window.record(scene, out_path='fornix_clusters.png', size=(600, 600))
if interactive:
    window.show(scene)

###############################################################################
# .. figure:: fornix_clusters.png
#    :align: center
# 
#    Showing the different clusters.
# 
# It is also possible to save the complete `ClusterMap` object with pickling.


save_pickle('QB.pkl', clusters)

###############################################################################
# Finally, here is a video of QuickBundles applied on a larger dataset.
# 
# .. raw:: html
# 
#     <iframe width="420" height="315" src="http://www.youtube.com/embed/kstL7KKqu94" frameborder="0" allowfullscreen></iframe>
# 
# .. include:: ../links_names.inc
# 
# References
# ----------
# 
# .. [Garyfallidis12] Garyfallidis E. et al., QuickBundles a method for
#                     tractography simplification, Frontiers in Neuroscience, vol
#                     6, no 175, 2012.
#