"""Testing cross-validation analysis."""

import warnings

import numpy as np
import numpy.testing as npt
import dipy.reconst.cross_validation as xval
import dipy.data as dpd
import dipy.reconst.dti as dti
import dipy.core.gradients as gt
import dipy.sims.voxel as sims
import dipy.reconst.csdeconv as csd
import dipy.reconst.base as base
from dipy.io.image import load_nifti_data
from dipy.reconst.shm import descoteaux07_legacy_msg


# We'll set these globally:
fdata, fbval, fbvec = dpd.get_fnames('small_64D')


def test_coeff_of_determination():
    model = np.random.randn(10, 10, 10, 150)
    data = np.copy(model)
    # If the model predicts the data perfectly, the COD is all 100s:
    cod = xval.coeff_of_determination(data, model)
    npt.assert_array_equal(100, cod)


def test_dti_xval():
    data = load_nifti_data(fdata)
    gtab = gt.gradient_table(fbval, fbvec)
    dm = dti.TensorModel(gtab, 'LS')
    # The data has 102 directions, so will not divide neatly into 10 bits
    npt.assert_raises(ValueError, xval.kfold_xval, dm, data, 10)

    # In simulation with no noise, COD should be perfect:
    psphere = dpd.get_sphere('symmetric362')
    bvecs = np.concatenate(([[0, 0, 0]], psphere.vertices))
    bvals = np.zeros(len(bvecs)) + 1000
    bvals[0] = 0
    gtab = gt.gradient_table(bvals, bvecs)
    mevals = np.array(([0.0015, 0.0003, 0.0001], [0.0015, 0.0003, 0.0003]))
    mevecs = [np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
              np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])]
    S = sims.single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)

    dm = dti.TensorModel(gtab, 'LS')
    kf_xval = xval.kfold_xval(dm, S, 2)
    cod = xval.coeff_of_determination(S, kf_xval)
    npt.assert_array_almost_equal(cod, np.ones(kf_xval.shape[:-1]) * 100)

    # Test with 2D data for use of a mask
    S = np.array([[S, S], [S, S]])
    mask = np.ones(S.shape[:-1], dtype=bool)
    mask[1, 1] = 0
    kf_xval = xval.kfold_xval(dm, S, 2, mask=mask)
    cod2d = xval.coeff_of_determination(S, kf_xval)
    npt.assert_array_almost_equal(np.round(cod2d[0, 0]), cod)


def test_csd_xval():
    # First, let's see that it works with some data:
    data = load_nifti_data(fdata)[1:3, 1:3, 1:3]  # Make it *small*
    gtab = gt.gradient_table(fbval, fbvec)
    S0 = np.mean(data[..., gtab.b0s_mask])
    response = ([0.0015, 0.0003, 0.0001], S0)

    # In simulation, it should work rather well (high COD):
    psphere = dpd.get_sphere('symmetric362')
    bvecs = np.concatenate(([[0, 0, 0]], psphere.vertices))
    bvals = np.zeros(len(bvecs)) + 1000
    bvals[0] = 0
    gtab = gt.gradient_table(bvals, bvecs)
    mevals = np.array(([0.0015, 0.0003, 0.0001], [0.0015, 0.0003, 0.0003]))
    mevecs = [np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
              np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])]
    S0 = 100
    S = sims.single_tensor(gtab, S0, mevals[0], mevecs[0], snr=None)
    with warnings.catch_warnings():
        warnings.filterwarnings(
            "ignore", message=descoteaux07_legacy_msg,
            category=PendingDeprecationWarning)
        sm = csd.ConstrainedSphericalDeconvModel(gtab, response)
    np.random.seed(12345)
    response = ([0.0015, 0.0003, 0.0001], S0)
    with warnings.catch_warnings():
        warnings.filterwarnings(
            "ignore", message=descoteaux07_legacy_msg,
            category=PendingDeprecationWarning)
        kf_xval = xval.kfold_xval(sm, S, 2, response, sh_order=2)
    # Because of the regularization, COD is not going to be perfect here:
    cod = xval.coeff_of_determination(S, kf_xval)
    # We'll just test for regressions:
    csd_cod = 97  # pre-computed by hand for this random seed

    # We're going to be really lenient here:
    npt.assert_array_almost_equal(np.round(cod), csd_cod)
    # Test for sD data with more than one voxel for use of a mask:
    S = np.array([[S, S], [S, S]])
    mask = np.ones(S.shape[:-1], dtype=bool)
    mask[1, 1] = 0
    with warnings.catch_warnings():
        warnings.filterwarnings(
            "ignore", message=descoteaux07_legacy_msg,
            category=PendingDeprecationWarning)
        kf_xval = xval.kfold_xval(sm, S, 2, response, sh_order=2,
                                  mask=mask)

    cod = xval.coeff_of_determination(S, kf_xval)
    npt.assert_array_almost_equal(np.round(cod[0]), csd_cod)


def test_no_predict():
    # Test that if you try to do this with a model that doesn't have a `predict`
    # method, you get something reasonable.
    class NoPredictModel(base.ReconstModel):
        def __init__(self, gtab):
            base.ReconstModel.__init__(self, gtab)

        def fit(self, data, mask=None):
            return NoPredictFit(self, data, mask=mask)

    class NoPredictFit(base.ReconstFit):
        def __init__(self, model, data, mask=None):
            base.ReconstFit.__init__(self, model, data)

    gtab = gt.gradient_table(fbval, fbvec)
    my_model = NoPredictModel(gtab)
    data = load_nifti_data(fdata)[1:3, 1:3, 1:3]  # Whatever

    npt.assert_raises(ValueError,  xval.kfold_xval, my_model, data, 2)
