"""
===============================================
Reconstruct with Generalized Q-Sampling Imaging
===============================================

We show how to apply Generalized Q-Sampling Imaging [Yeh2010]_
to diffusion MRI datasets. You can think of GQI as an analytical version of
DSI orientation distribution function (ODF) (Garyfallidis, PhD thesis, 2012).

First import the necessary modules:

"""

import numpy as np
from dipy.core.gradients import gradient_table
from dipy.data import get_fnames, get_sphere
from dipy.io.gradients import read_bvals_bvecs
from dipy.io.image import load_nifti
from dipy.reconst.gqi import GeneralizedQSamplingModel
from dipy.direction import peaks_from_model

###############################################################################
# Download and get the data filenames for this tutorial.


fraw, fbval, fbvec = get_fnames('taiwan_ntu_dsi')

###############################################################################
# img contains a nibabel Nifti1Image object (data) and gtab contains a
# ``GradientTable`` object (gradient information e.g. b-values). For example to
# read the b-values it is possible to write::
# 
#    print(gtab.bvals)
# 
# Load the raw diffusion data and the affine.


data, affine, voxel_size = load_nifti(fraw, return_voxsize=True)
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
bvecs[1:] = (bvecs[1:] /
                 np.sqrt(np.sum(bvecs[1:] * bvecs[1:], axis=1))[:, None])
gtab = gradient_table(bvals, bvecs)
print('data.shape (%d, %d, %d, %d)' % data.shape)

###############################################################################
# data.shape ``(96, 96, 60, 203)``
# 
# This dataset has anisotropic voxel sizes, therefore reslicing is necessary.
# 
# Instantiate the model and apply it to the data.


gqmodel = GeneralizedQSamplingModel(gtab, sampling_length=3)

###############################################################################
# The parameter ``sampling_length`` is used here to
# 
# Lets just use one slice only from the data.


dataslice = data[:, :, data.shape[2] // 2]

mask = dataslice[..., 0] > 50

gqfit = gqmodel.fit(dataslice, mask=mask)

###############################################################################
# Load an ODF reconstruction sphere


sphere = get_sphere('repulsion724')

###############################################################################
# Calculate the ODFs with this specific sphere


ODF = gqfit.odf(sphere)

print('ODF.shape (%d, %d, %d)' % ODF.shape)

###############################################################################
# ODF.shape ``(96, 96, 724)``
# 
# Using ``peaks_from_model`` we can find the main peaks of the ODFs and other
# properties.


gqpeaks = peaks_from_model(model=gqmodel,
                           data=dataslice,
                           sphere=sphere,
                           relative_peak_threshold=.5,
                           min_separation_angle=25,
                           mask=mask,
                           return_odf=False,
                           normalize_peaks=True)

gqpeak_values = gqpeaks.peak_values

###############################################################################
# ``gqpeak_indices`` show which sphere points have the maximum values.


gqpeak_indices = gqpeaks.peak_indices

###############################################################################
# It is also possible to calculate GFA.


GFA = gqpeaks.gfa

print('GFA.shape (%d, %d)' % GFA.shape)

###############################################################################
# With parameter ``return_odf=True`` we can obtain the ODF using ``gqpeaks.ODF``


gqpeaks = peaks_from_model(model=gqmodel,
                           data=dataslice,
                           sphere=sphere,
                           relative_peak_threshold=.5,
                           min_separation_angle=25,
                           mask=mask,
                           return_odf=True,
                           normalize_peaks=True)

###############################################################################
# This ODF will be of course identical to the ODF calculated above as long as
# the same data and mask are used.


print(np.sum(gqpeaks.odf != ODF) == 0)

###############################################################################
# True
# 
# The advantage of using ``peaks_from_model`` is that it calculates the ODF only
# once and saves it or deletes if it is not necessary to keep.
# 
# .. [Yeh2010] Yeh, F-C et al., Generalized Q-sampling imaging, IEEE Transactions
#    on Medical Imaging, vol 29, no 9, 2010.
# 
# .. include:: ../links_names.inc
#