"""
==============================================================
Crossing invariant fiber response function with FORECAST model
==============================================================

We show how to obtain a voxel specific response function in the form of
axially symmetric tensor and the fODF using the FORECAST model from
[Anderson2005]_ , [Kaden2016]_ and [Zucchelli2017]_.

First import the necessary modules:
"""

import matplotlib.pyplot as plt
from dipy.reconst.forecast import ForecastModel
from dipy.viz import actor, window
from dipy.data import fetch_cenir_multib, read_cenir_multib, get_sphere

"""
Download and read the data for this tutorial.
Our implementation of FORECAST requires multi-shell data.
fetch_cenir_multib() provides data acquired using the shells at b-values 1000,
2000, and 3000 (see MAPMRI example for more information on this dataset).
"""

fetch_cenir_multib(with_raw=False)

bvals = [1000, 2000, 3000]
img, gtab = read_cenir_multib(bvals)
data = img.get_data()

"""
Let us consider only a single slice for the FORECAST fitting
"""

data_small = data[18:87, 51:52, 10:70]
mask_small = data_small[..., 0] > 1000

"""
Instantiate the FORECAST Model.

"sh_order" is the spherical harmonics order used for the fODF.

dec_alg is the spherical deconvolution algorithm used for the FORECAST basis fitting, in this case
we used the Constrained Spherical Deconvolution (CSD) algorithm.
"""

fm = ForecastModel(gtab, sh_order=6, dec_alg='CSD')

"""
Fit the FORECAST to the data
"""

f_fit = fm.fit(data_small, mask_small)

"""
Calculate the crossing invariant tensor indices [Kaden2016]_ : the parallel diffusivity,
the perpendicular diffusivity, the fractional anisotropy and the mean
diffusivity.
"""

d_par = f_fit.dpar
d_perp = f_fit.dperp
fa = f_fit.fractional_anisotropy()
md = f_fit.mean_diffusivity()

"""
Show the indices and save them in FORECAST_indices.png.
"""

fig = plt.figure(figsize=(6, 6))
ax1 = fig.add_subplot(2, 2, 1, title='parallel diffusivity')
ax1.set_axis_off()
ind = ax1.imshow(d_par[:, 0, :].T, interpolation='nearest',
                 origin='lower', cmap=plt.cm.gray)
plt.colorbar(ind, shrink=0.6)
ax2 = fig.add_subplot(2, 2, 2, title='perpendicular diffusivity')
ax2.set_axis_off()
ind = ax2.imshow(d_perp[:, 0, :].T, interpolation='nearest',
                 origin='lower', cmap=plt.cm.gray)
plt.colorbar(ind, shrink=0.6)
ax3 = fig.add_subplot(2, 2, 3, title='fractional anisotropy')
ax3.set_axis_off()
ind = ax3.imshow(fa[:, 0, :].T, interpolation='nearest',
                 origin='lower', cmap=plt.cm.gray)
plt.colorbar(ind, shrink=0.6)
ax4 = fig.add_subplot(2, 2, 4, title='mean diffusivity')
ax4.set_axis_off()
ind = ax4.imshow(md[:, 0, :].T, interpolation='nearest',
                 origin='lower', cmap=plt.cm.gray)
plt.colorbar(ind, shrink=0.6)
plt.savefig('FORECAST_indices.png', dpi=300, bbox_inches='tight')

"""
.. figure:: FORECAST_indices.png
   :align: center

   **FORECAST scalar indices**.

"""

"""
Load an ODF reconstruction sphere
"""

sphere = get_sphere('symmetric724')

"""
Compute the fODFs.
"""

odf = f_fit.odf(sphere)
print('fODF.shape (%d, %d, %d, %d)' % odf.shape)

"""
Display a part of the fODFs
"""

odf_actor = actor.odf_slicer(odf[16:36, :, 30:45], sphere=sphere,
                             colormap='plasma', scale=0.6)
odf_actor.display(y=0)
odf_actor.RotateX(-90)
ren = window.Renderer()
ren.add(odf_actor)
window.record(ren, out_path='fODFs.png', size=(600, 600), magnification=4)

"""
.. figure:: fODFs.png
   :align: center

   **Fiber Orientation Distribution Functions, in a small ROI of the brain**.

References
----------

.. [Anderson2005] Anderson A. W., "Measurement of Fiber Orientation Distributions
       Using High Angular Resolution Diffusion Imaging", Magnetic
       Resonance in Medicine, 2005.

.. [Kaden2016] Kaden E. et al., "Quantitative Mapping of the Per-Axon Diffusion
       Coefficients in Brain White Matter", Magnetic Resonance in
       Medicine, 2016.

.. [Zucchelli2017] Zucchelli E. et al., "A generalized SMT-based framework for
       Diffusion MRI microstructural model estimation", MICCAI Workshop
       on Computational DIFFUSION MRI (CDMRI), 2017.

.. include:: ../links_names.inc

"""