""" Testing DKI microstructure """

import numpy as np
import random
import dipy.reconst.dki_micro as dki_micro
from numpy.testing import (assert_array_almost_equal, assert_almost_equal,
                           assert_, assert_raises)
from dipy.sims.voxel import (multi_tensor_dki, _check_directions, multi_tensor)
from dipy.io.gradients import read_bvals_bvecs
from dipy.core.gradients import gradient_table
from dipy.data import get_fnames
from dipy.reconst.dti import (eig_from_lo_tri)

from dipy.data import default_sphere, get_sphere

fimg, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
gtab = gradient_table(bvals, bvecs)

# 2 shells for techniques that requires multishell data
bvals_2s = np.concatenate((bvals, bvals * 2), axis=0)
bvecs_2s = np.concatenate((bvecs, bvecs), axis=0)
gtab_2s = gradient_table(bvals_2s, bvecs_2s)

# single fiber simulate (which is the assumption of our model)
FIE = np.array([[[0.30, 0.32], [0.74, 0.51]],
                [[0.47, 0.21], [0.80, 0.63]]])
RDI = np.zeros((2, 2, 2))
ADI = np.array([[[1e-3, 1.3e-3], [0.8e-3, 1e-3]],
                [[0.9e-3, 0.99e-3], [0.89e-3, 1.1e-3]]])
ADE = np.array([[[2.2e-3, 2.3e-3], [2.8e-3, 2.1e-3]],
                [[1.9e-3, 2.5e-3], [1.89e-3, 2.1e-3]]])
Tor = np.array([[[2.6, 2.4], [2.8, 2.1]],
                [[2.9, 2.5], [2.7, 2.3]]])
RDE = ADE / Tor

# prepare simulation:
DWIsim = np.zeros((2, 2, 2, gtab_2s.bvals.size))

# Diffusion microstructural model assumes that signal does not have Taylor
# approximation components larger than the fourth order. Thus parameter
# estimates are only equal to the ground truth values of the simulation
# if signals taylor components larger than the fourth order are removed.
# Signal without this taylor components can be generated using the
# multi_tensor_dki simulations. Therefore we used this function to test the
# expected estimates of the model.

DWIsim_all_taylor = np.zeros((2, 2, 2, gtab_2s.bvals.size))

# Signal with all taylor components can be simulated using the function
# multi_tensor. Generating this signals will be useful to test the prediction
# procedures of DKI-based microstructural model.


for i in range(2):
    for j in range(2):
        for k in range(2):
            ADi = ADI[i, j, k]
            RDi = RDI[i, j, k]
            ADe = ADE[i, j, k]
            RDe = RDE[i, j, k]
            fie = FIE[i, j, k]
            mevals = np.array([[ADi, RDi, RDi], [ADe, RDe, RDe]])
            frac = [fie*100, (1 - fie)*100]
            theta = random.uniform(0, 180)
            phi = random.uniform(0, 320)
            angles = [(theta, phi), (theta, phi)]
            signal, dt, kt = multi_tensor_dki(gtab_2s, mevals,
                                              angles=angles,
                                              fractions=frac, snr=None)
            DWIsim[i, j, k, :] = signal
            signal, sticks = multi_tensor(gtab_2s, mevals, angles=angles,
                                          fractions=frac, snr=None)
            DWIsim_all_taylor[i, j, k, :] = signal


def test_single_fiber_model():
    # single fiber simulate (which is the assumption of our model)
    fie = 0.49
    ADi = 0.00099
    ADe = 0.00226
    RDi = 0
    RDe = 0.00087

    # prepare simulation:
    theta = random.uniform(0, 180)
    phi = random.uniform(0, 320)
    angles = [(theta, phi), (theta, phi)]
    mevals = np.array([[ADi, RDi, RDi], [ADe, RDe, RDe]])
    frac = [fie*100, (1 - fie)*100]
    signal, dt, kt = multi_tensor_dki(gtab_2s, mevals, angles=angles,
                                      fractions=frac, snr=None)
    # DKI fit
    dkiM = dki_micro.DiffusionKurtosisModel(gtab_2s, fit_method="WLS")
    dkiF = dkiM.fit(signal)

    # Axonal Water Fraction
    AWF = dki_micro.axonal_water_fraction(dkiF.model_params, default_sphere,
                                          mask=None, gtol=1e-5)
    assert_almost_equal(AWF, fie)

    # Extra-cellular and intra-cellular components
    edt, idt = dki_micro.diffusion_components(dkiF.model_params,
                                              default_sphere)
    EDT = eig_from_lo_tri(edt)
    IDT = eig_from_lo_tri(idt)

    # check eigenvalues
    assert_array_almost_equal(EDT[0:3], np.array([ADe, RDe, RDe]))
    assert_array_almost_equal(IDT[0:3], np.array([ADi, RDi, RDi]))
    # first eigenvalue should be the direction of the fibers
    fiber_direction = _check_directions([(theta, phi)])
    f_norm = abs(np.dot(fiber_direction, np.array((EDT[3], EDT[6], EDT[9]))))
    assert_almost_equal(f_norm, 1.)
    f_norm = abs(np.dot(fiber_direction, np.array((IDT[3], IDT[6], IDT[9]))))
    assert_almost_equal(f_norm, 1.)

    # Test model and fit objects
    wmtiM = dki_micro.KurtosisMicrostructureModel(gtab_2s, fit_method="WLS")
    wmtiF = wmtiM.fit(signal)
    assert_almost_equal(wmtiF.awf, AWF)
    assert_array_almost_equal(wmtiF.hindered_evals,
                              np.array([ADe, RDe, RDe]))
    assert_array_almost_equal(wmtiF.restricted_evals,
                              np.array([ADi, RDi, RDi]))
    assert_almost_equal(wmtiF.hindered_ad, ADe)
    assert_almost_equal(wmtiF.hindered_rd, RDe)
    assert_almost_equal(wmtiF.axonal_diffusivity, ADi)
    assert_almost_equal(wmtiF.tortuosity, ADe/RDe, decimal=4)

    # Test diffusion_components when a kurtosis tensors is associated with
    # negative kurtosis values. E.g of this cases is given below:
    dkiparams = np.array([1.67135726e-03, 5.03651205e-04, 9.35365328e-05,
                          -7.11167583e-01, 6.23186820e-01, -3.25390313e-01,
                          -1.75247376e-02, -4.78415563e-01, -8.77958674e-01,
                          7.02804064e-01, 6.18673368e-01, -3.51154825e-01,
                          2.18384153, -2.76378153e-02, 2.22893297,
                          -2.68306546e-01, -1.28411610, -1.56557645e-01,
                          -1.80850619e-01, -8.33152110e-01, -3.62410766e-01,
                          1.57775442e-01, 8.73775381e-01, 2.77188975e-01,
                          -3.67415502e-02, -1.56330984e-01, -1.62295407e-02])
    edt, idt = dki_micro.diffusion_components(dkiparams)
    assert_(np.all(np.isfinite(edt)))


def test_wmti_model_multi_voxel():
    # DKI fit
    dkiM = dki_micro.DiffusionKurtosisModel(gtab_2s, fit_method="WLS")
    dkiF = dkiM.fit(DWIsim)

    # Axonal Water Fraction
    sphere = get_sphere()
    AWF = dki_micro.axonal_water_fraction(dkiF.model_params, sphere, mask=None,
                                          gtol=1e-5)
    assert_almost_equal(AWF, FIE)

    # Extra-cellular and intra-cellular components
    edt, idt = dki_micro.diffusion_components(dkiF.model_params, sphere)
    EDT = eig_from_lo_tri(edt)
    IDT = eig_from_lo_tri(idt)

    # check eigenvalues
    assert_array_almost_equal(EDT[..., 0], ADE, decimal=3)
    assert_array_almost_equal(EDT[..., 1], RDE, decimal=3)
    assert_array_almost_equal(EDT[..., 2], RDE, decimal=3)
    assert_array_almost_equal(IDT[..., 0], ADI, decimal=3)
    assert_array_almost_equal(IDT[..., 1], RDI, decimal=3)
    assert_array_almost_equal(IDT[..., 2], RDI, decimal=3)

    # Test methods performance when a signal with all zeros is present
    FIEc = FIE.copy()
    RDIc = RDI.copy()
    ADIc = ADI.copy()
    ADEc = ADE.copy()
    Torc = Tor.copy()
    RDEc = RDE.copy()
    DWIsimc = DWIsim.copy()

    FIEc[0, 0, 0] = 0