"""
=====================
Gradients and Spheres
=====================

This example shows how you can create gradient tables and sphere objects using
DIPY_.

Usually, as we saw in :ref:`sphx_glr_examples_built_quick_start_quick_start.py`,
you load your b-values and b-vectors from disk and then you can create your own
gradient table. But this time let's say that you are an MR physicist and you
want to design a new gradient scheme or you are a scientist who wants to
simulate many different gradient schemes.

Now let's assume that you are interested in creating a multi-shell
acquisition with 2-shells, one at b=1000 $s/mm^2$ and one at b=2500 $s/mm^2$.
For both shells let's say that we want a specific number of gradients (64) and
we want to have the points on the sphere evenly distributed.

This is possible using the ``disperse_charges`` which is an implementation of
electrostatic repulsion [Jones1999]_.
"""

import numpy as np
from dipy.core.sphere import disperse_charges, Sphere, HemiSphere

"""
We can first create some random points on a ``HemiSphere`` using spherical polar
coordinates.
"""

n_pts = 64
theta = np.pi * np.random.rand(n_pts)
phi = 2 * np.pi * np.random.rand(n_pts)
hsph_initial = HemiSphere(theta=theta, phi=phi)

"""
Next, we call ``disperse_charges`` which will iteratively move the points so that
the electrostatic potential energy is minimized.
"""

hsph_updated, potential = disperse_charges(hsph_initial, 5000)

"""
In ``hsph_updated`` we have the updated ``HemiSphere`` with the points nicely
distributed on the hemisphere. Let's visualize them.
"""

from dipy.viz import window, actor

# Enables/disables interactive visualization
interactive = False

scene = window.Scene()
scene.SetBackground(1, 1, 1)

scene.add(actor.point(hsph_initial.vertices, window.colors.red,
                      point_radius=0.05))
scene.add(actor.point(hsph_updated.vertices, window.colors.green,
                      point_radius=0.05))

print('Saving illustration as initial_vs_updated.png')
window.record(scene, out_path='initial_vs_updated.png', size=(300, 300))
if interactive:
    window.show(scene)

"""
.. figure:: initial_vs_updated.png
   :align: center

   Illustration of electrostatic repulsion of red points which become
   green points.

We can also create a sphere from the hemisphere and show it in the
following way.

"""

sph = Sphere(xyz=np.vstack((hsph_updated.vertices, -hsph_updated.vertices)))

scene.clear()
scene.add(actor.point(sph.vertices, window.colors.green, point_radius=0.05))

print('Saving illustration as full_sphere.png')
window.record(scene, out_path='full_sphere.png', size=(300, 300))
if interactive:
    window.show(scene)

"""
.. figure:: full_sphere.png
   :align: center

   Full sphere.

It is time to create the Gradients. For this purpose we will use the
function ``gradient_table`` and fill it with the ``hsph_updated`` vectors that
we created above.
"""

from dipy.core.gradients import gradient_table

vertices = hsph_updated.vertices
values = np.ones(vertices.shape[0])

"""
We need two stacks of ``vertices``, one for every shell, and we need two sets
of b-values, one at 1000 $s/mm^2$, and one at 2500 $s/mm^2$, as we discussed
previously.
"""

bvecs = np.vstack((vertices, vertices))
bvals = np.hstack((1000 * values, 2500 * values))

"""
We can also add some b0s. Let's add one at the beginning and one at the end.
"""

bvecs = np.insert(bvecs, (0, bvecs.shape[0]), np.array([0, 0, 0]), axis=0)
bvals = np.insert(bvals, (0, bvals.shape[0]), 0)

print(bvals)

"""

::

    [    0.  1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.
      1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.
      1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.
      1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.
      1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.
      1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.
      1000.  1000.  1000.  1000.  1000.  2500.  2500.  2500.  2500.  2500.
      2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.
      2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.
      2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.
      2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.
      2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.
      2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.  2500.     0.]

"""

print(bvecs)

"""

::

    [[ 0.          0.          0.        ]
     [-0.80451777 -0.16877559  0.56944355]
     [ 0.32822557 -0.94355999  0.04430036]
     [-0.23584135 -0.96241331  0.13468285]
     [-0.39207424 -0.73505312  0.55314981]
     [-0.32539386 -0.16751384  0.93062235]
     [-0.82043195 -0.39411534  0.41420347]
     [ 0.65741493  0.74947875  0.07802061]
     [ 0.88853765  0.45303621  0.07251925]
     [ 0.39638642 -0.15185138  0.90543855]
                     ...
     [ 0.10175269  0.08197111  0.99142681]
     [ 0.50577702 -0.37862345  0.77513476]
     [ 0.42845026  0.40155296  0.80943535]
     [ 0.26939707  0.81103868  0.51927014]
     [-0.48938584 -0.43780086  0.75420946]
     [ 0.          0.          0.        ]]

Both b-values and b-vectors look correct. Let's now create the
``GradientTable``.
"""

gtab = gradient_table(bvals, bvecs)

scene.clear()

"""
We can also visualize the gradients. Let's color the first shell blue and
the second shell cyan.
"""

colors_b1000 = window.colors.blue * np.ones(vertices.shape)
colors_b2500 = window.colors.cyan * np.ones(vertices.shape)
colors = np.vstack((colors_b1000, colors_b2500))
colors = np.insert(colors, (0, colors.shape[0]), np.array([0, 0, 0]), axis=0)
colors = np.ascontiguousarray(colors)

scene.add(actor.point(gtab.gradients, colors, point_radius=100))

print('Saving illustration as gradients.png')
window.record(scene, out_path='gradients.png', size=(300, 300))
if interactive:
    window.show(scene)

"""
.. figure:: gradients.png
   :align: center

   Diffusion gradients.

References
----------

.. [Jones1999] Jones, DK. et al. Optimal strategies for measuring diffusion in
   anisotropic systems by magnetic resonance imaging, Magnetic Resonance in
   Medicine, vol 42, no 3, 515-525, 1999.

.. include:: ../links_names.inc

"""
