from __future__ import print_function

import numpy as np
from numpy.linalg import norm
import numpy.testing as npt
from dipy.testing.memory import get_type_refcount

from nose.tools import assert_true, assert_equal, assert_almost_equal
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
                           assert_raises, run_module_suite)

from dipy.tracking.streamline import (set_number_of_points,
                                      length as ds_length,
                                      relist_streamlines,
                                      unlist_streamlines,
                                      center_streamlines,
                                      transform_streamlines,
                                      select_random_set_of_streamlines,
                                      compress_streamlines,
                                      select_by_rois,
                                      orient_by_rois)


streamline = np.array([[82.20181274,  91.36505890,  43.15737152],
                       [82.38442230,  91.79336548,  43.87036514],
                       [82.48710632,  92.27861023,  44.56298065],
                       [82.53310394,  92.78545380,  45.24635315],
                       [82.53793335,  93.26902008,  45.94785309],
                       [82.48797607,  93.75003815,  46.64939880],
                       [82.35533142,  94.25181580,  47.32533264],
                       [82.15484619,  94.76634216,  47.97451019],
                       [81.90982819,  95.28792572,  48.60244370],
                       [81.63336945,  95.78153229,  49.23971176],
                       [81.35479736,  96.24868011,  49.89558792],
                       [81.08713531,  96.69807434,  50.56812668],
                       [80.81504822,  97.14285278,  51.24193192],
                       [80.52591705,  97.56719971,  51.92168427],
                       [80.26599884,  97.98269653,  52.61848068],
                       [80.04635620,  98.38131714,  53.33855820],
                       [79.84691620,  98.77052307,  54.06955338],
                       [79.57667542,  99.13599396,  54.78985596],
                       [79.23351288,  99.43207550,  55.51065063],
                       [78.84815979,  99.64141846,  56.24016571],
                       [78.47383881,  99.77347565,  56.99299240],
                       [78.12837219,  99.81330872,  57.76969528],
                       [77.80438995,  99.85082245,  58.55574799],
                       [77.49439240,  99.88065338,  59.34777069],
                       [77.21414185,  99.85343933,  60.15090561],
                       [76.96416473,  99.82772827,  60.96406937],
                       [76.74712372,  99.80519104,  61.78676605],
                       [76.52263641,  99.79122162,  62.60765076],
                       [76.03757477, 100.08692169,  63.24152374],
                       [75.44867706, 100.35265350,  63.79513168],
                       [74.78033447, 100.57255554,  64.27278900],
                       [74.11605835, 100.77330780,  64.76428986],
                       [73.51222992, 100.98779297,  65.32373047],
                       [72.97387695, 101.23387146,  65.93502045],
                       [72.47355652, 101.49151611,  66.57343292],
                       [71.99834442, 101.72480774,  67.23979950],
                       [71.56909180, 101.98665619,  67.92664337],
                       [71.18083191, 102.29483795,  68.61888123],
                       [70.81879425, 102.63343048,  69.31127167],
                       [70.47422791, 102.98672485,  70.00532532],
                       [70.10092926, 103.28502655,  70.70999908],
                       [69.69512177, 103.51667023,  71.42147064],
                       [69.27423096, 103.71351624,  72.13452911],
                       [68.91260529, 103.81676483,  72.89796448],
                       [68.60788727, 103.81982422,  73.69258118],
                       [68.34162903, 103.76619720,  74.49915314],
                       [68.08542633, 103.70635223,  75.30856323],
                       [67.83590698, 103.60187531,  76.11553955],
                       [67.56822968, 103.44821930,  76.90870667],
                       [67.28399658, 103.25878906,  77.68825531],
                       [67.00117493, 103.03740692,  78.45989227],
                       [66.72718048, 102.80329895,  79.23099518],
                       [66.46197510, 102.54130554,  79.99622345],
                       [66.20803833, 102.22305298,  80.74387360],
                       [65.96872711, 101.88980865,  81.48987579],
                       [65.72864532, 101.59316254,  82.25085449],
                       [65.47808075, 101.33383942,  83.02194214],
                       [65.21841431, 101.11295319,  83.80186462],
                       [64.95678711, 100.94080353,  84.59326935],
                       [64.71759033, 100.82022095,  85.40114594],
                       [64.48053741, 100.73490143,  86.21411896],
                       [64.24304199, 100.65074158,  87.02709198],
                       [64.01773834, 100.55318451,  87.84204865],
                       [63.83801651, 100.41996765,  88.66333008],
                       [63.70982361, 100.25119019,  89.48779297],
                       [63.60707855, 100.06730652,  90.31262207],
                       [63.46164322,  99.91001892,  91.13648224],
                       [63.26287842,  99.78648376,  91.95485687],
                       [63.03713226,  99.68377686,  92.76905823],
                       [62.81192398,  99.56619263,  93.58140564],
                       [62.57145309,  99.42708588,  94.38592529],
                       [62.32259369,  99.25592804,  95.18167114],
                       [62.07497787,  99.05770111,  95.97154236],
                       [61.82253647,  98.83877563,  96.75438690],
                       [61.59536743,  98.59293365,  97.53706360],
                       [61.46530151,  98.30503845,  98.32772827],
                       [61.39904785,  97.97928619,  99.11172485],
                       [61.33279419,  97.65353394,  99.89572906],
                       [61.26067352,  97.30914307, 100.67123413],
                       [61.19459534,  96.96743011, 101.44847107],
                       [61.19580460,  96.63417053, 102.23215485],
                       [61.26572037,  96.29887390, 103.01185608],
                       [61.39840698,  95.96297455, 103.78307343],
                       [61.57207870,  95.64262390, 104.55268097],
                       [61.78163528,  95.35540771, 105.32629395],
                       [62.06700134,  95.09746552, 106.08564758],
                       [62.39427185,  94.85724640, 106.83369446],
                       [62.74076462,  94.62278748, 107.57482147],
                       [63.11461639,  94.40107727, 108.30641937],
                       [63.53397751,  94.20418549, 109.02002716],
                       [64.00019836,  94.03809357, 109.71183777],
                       [64.43580627,  93.87523651, 110.42416382],
                       [64.84857941,  93.69993591, 111.14715576],
                       [65.26740265,  93.51858521, 111.86515808],
                       [65.69511414,  93.36718750, 112.58474731],
                       [66.10470581,  93.22719574, 113.31711578],
                       [66.45891571,  93.06028748, 114.07256317],
                       [66.78582001,  92.90560913, 114.84281921],
                       [67.11138916,  92.79004669, 115.62040710],
                       [67.44729614,  92.75711823, 116.40135193],
                       [67.75688171,  92.98265076, 117.16111755],
                       [68.02041626,  93.28012848, 117.91371155],
                       [68.25725555,  93.53466797, 118.69052124],
                       [68.46047974,  93.63263702, 119.51107788],
                       [68.62039948,  93.62007141, 120.34690094],
                       [68.76782227,  93.56475067, 121.18331909],
                       [68.90222168,  93.46326447, 122.01765442],
                       [68.99872589,  93.30039978, 122.84759521],
                       [69.04119873,  93.05428314, 123.66156769],
                       [69.05086517,  92.74394989, 124.45450592],
                       [69.02742004,  92.40427399, 125.23509979],
                       [68.95466614,  92.09059143, 126.02339935],
                       [68.84975433,  91.79674530, 126.81564331],
                       [68.72673798,  91.53726196, 127.61715698],
                       [68.60685730,  91.30300140, 128.42681885],
                       [68.50636292,  91.12481689, 129.25317383],
                       [68.39311218,  91.01572418, 130.08976746],
                       [68.25946808,  90.94654083, 130.92756653]],
                      dtype=np.float32)

streamline_64bit = streamline.astype(np.float64)

streamlines = [streamline[[0, 10]], streamline,
               streamline[::2], streamline[::3],
               streamline[::5], streamline[::6]]
streamlines_64bit = [streamline_64bit[[0, 10]], streamline_64bit,
                     streamline_64bit[::2], streamline_64bit[::3],
                     streamline_64bit[::4], streamline_64bit[::5]]

heterogeneous_streamlines = [streamline_64bit,
                             streamline_64bit.reshape((-1, 6)),
                             streamline_64bit.reshape((-1, 2))]


def length_python(xyz, along=False):
    xyz = np.asarray(xyz, dtype=np.float64)
    if xyz.shape[0] < 2:
        if along:
            return np.array([0])
        return 0
    dists = np.sqrt((np.diff(xyz, axis=0)**2).sum(axis=1))
    if along:
        return np.cumsum(dists)
    return np.sum(dists)


def set_number_of_points_python(xyz, n_pols=3):
    def _extrap(xyz, cumlen, distance):
        ''' Helper function for extrapolate '''
        ind = np.where((cumlen-distance) > 0)[0][0]
        len0 = cumlen[ind-1]
        len1 = cumlen[ind]
        Ds = distance-len0
        Lambda = Ds/(len1-len0)
        return Lambda*xyz[ind] + (1-Lambda)*xyz[ind-1]

    cumlen = np.zeros(xyz.shape[0])
    cumlen[1:] = length_python(xyz, along=True)
    step = cumlen[-1] / (n_pols-1)

    ar = np.arange(0, cumlen[-1], step)
    if np.abs(ar[-1] - cumlen[-1]) < np.finfo('f4').eps:
        ar = ar[:-1]

    xyz2 = [_extrap(xyz, cumlen, distance) for distance in ar]
    return np.vstack((np.array(xyz2), xyz[-1]))


def test_set_number_of_points():
    # Test resampling of only one streamline
    nb_points = 12
    modified_streamline_cython = set_number_of_points(
        streamline, nb_points)
    modified_streamline_python = set_number_of_points_python(
        streamline, nb_points)
    assert_equal(len(modified_streamline_cython), nb_points)
    # Using a 5 digits precision because of streamline is in float32.
    assert_array_almost_equal(modified_streamline_cython,
                              modified_streamline_python, 5)

    modified_streamline_cython = set_number_of_points(
        streamline_64bit, nb_points)
    modified_streamline_python = set_number_of_points_python(
        streamline_64bit, nb_points)
    assert_equal(len(modified_streamline_cython), nb_points)
    assert_array_almost_equal(modified_streamline_cython,
                              modified_streamline_python)

    res = []
    simple_streamline = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]], 'f4')
    for nb_points in range(2, 200):
        modified_streamline_cython = set_number_of_points(
            simple_streamline, nb_points)
        res.append(nb_points - len(modified_streamline_cython))
    assert_equal(np.sum(res), 0)

    # Test resampling of multiple streamlines of different nb_points
    nb_points = 12
    modified_streamlines_cython = set_number_of_points(
        streamlines, nb_points)

    for i, s in enumerate(streamlines):
        modified_streamline_python = set_number_of_points_python(s, nb_points)
        # Using a 5 digits precision because of streamline is in float32.
        assert_array_almost_equal(modified_streamlines_cython[i],
                                  modified_streamline_python, 5)

    modified_streamlines_cython = set_number_of_points(
        streamlines_64bit, nb_points)

    for i, s in enumerate(streamlines_64bit):
        modified_streamline_python = set_number_of_points_python(s, nb_points)
        assert_array_almost_equal(modified_streamlines_cython[i],
                                  modified_streamline_python)

    # Test streamlines with mixed dtype
    streamlines_mixed_dtype = [streamline,
                               streamline.astype(np.float64),
                               streamline.astype(np.int32),
                               streamline.astype(np.int64)]
    nb_points_mixed_dtype = [len(s) for s in set_number_of_points(
        streamlines_mixed_dtype, nb_points)]
    assert_array_equal(nb_points_mixed_dtype,
                       [nb_points] * len(streamlines_mixed_dtype))

    # Test streamlines with different shape
    modified_streamlines_cython = set_number_of_points(
        heterogeneous_streamlines, nb_points)

    for i, s in enumerate(heterogeneous_streamlines):
        modified_streamline_python = set_number_of_points_python(s, nb_points)
        assert_array_almost_equal(modified_streamlines_cython[i],
                                  modified_streamline_python)

    # Test streamline with integer dtype
    modified_streamline = set_number_of_points(streamline.astype(np.int32))
    assert_true(modified_streamline.dtype == np.float32)
    modified_streamline = set_number_of_points(streamline.astype(np.int64))
    assert_true(modified_streamline.dtype == np.float64)

    # Test empty list
    assert_equal(set_number_of_points([]), [])

    # Test streamline having only one point
    assert_raises(ValueError, set_number_of_points, np.array([[1, 2, 3]]))

    # We do not support list of lists, it should be numpy ndarray.
    streamline_unsupported = [[1, 2, 3], [4, 5, 5], [2, 1, 3], [4, 2, 1]]
    assert_raises(AttributeError, set_number_of_points, streamline_unsupported)

    # Test setting number of points of a numpy with flag WRITABLE=False
    streamline_readonly = streamline.copy()
    streamline_readonly.setflags(write=False)
    assert_equal(len(set_number_of_points(streamline_readonly, nb_points=42)),
                 42)

    # Test setting computing length of a numpy with flag WRITABLE=False
    streamlines_readonly = []
    for s in streamlines:
        streamlines_readonly.append(s.copy())
        streamlines_readonly[-1].setflags(write=False)

    assert_equal(len(set_number_of_points(streamlines_readonly, nb_points=42)),
                 len(streamlines_readonly))

    streamlines_readonly = []
    for s in streamlines_64bit:
        streamlines_readonly.append(s.copy())
        streamlines_readonly[-1].setflags(write=False)

    assert_equal(len(set_number_of_points(streamlines_readonly, nb_points=42)),
                 len(streamlines_readonly))


def test_set_number_of_points_memory_leaks():
    # Test some dtypes
    dtypes = [np.float32, np.float64, np.int32, np.int64]
    for dtype in dtypes:
        rng = np.random.RandomState(1234)
        NB_STREAMLINES = 10000
        streamlines = [rng.randn(rng.randint(10, 100), 3).astype(dtype) for _ in range(NB_STREAMLINES)]

        list_refcount_before = get_type_refcount()["list"]

        rstreamlines = set_number_of_points(streamlines, nb_points=2)
        list_refcount_after = get_type_refcount()["list"]
        del rstreamlines  # Delete `rstreamlines` because it holds a reference to `list`.

        # Calling `set_number_of_points` should increase the refcount of `list` by one
        # since we kept the returned value.
        assert_equal(list_refcount_after, list_refcount_before+1)

    # Test mixed dtypes
    rng = np.random.RandomState(1234)
    NB_STREAMLINES = 10000
    streamlines = []
    for i in range(NB_STREAMLINES):
        dtype = dtypes[i % len(dtypes)]
        streamlines.append(rng.randn(rng.randint(10, 100), 3).astype(dtype))

    list_refcount_before = get_type_refcount()["list"]

    rstreamlines = set_number_of_points(streamlines, nb_points=2)
    list_refcount_after = get_type_refcount()["list"]

    # Calling `set_number_of_points` should increase the refcount of `list` by one
    # since we kept the returned value.
    assert_equal(list_refcount_after, list_refcount_before+1)


def test_length():
    # Test length of only one streamline
    length_streamline_cython = ds_length(streamline)
    length_streamline_python = length_python(streamline)
    assert_almost_equal(length_streamline_cython, length_streamline_python)

    length_streamline_cython = ds_length(streamline_64bit)
    length_streamline_python = length_python(streamline_64bit)
    assert_almost_equal(length_streamline_cython, length_streamline_python)

    # Test computing length of multiple streamlines of different nb_points
    length_streamlines_cython = ds_length(streamlines)

    for i, s in enumerate(streamlines):
        length_streamline_python = length_python(s)
        assert_array_almost_equal(length_streamlines_cython[i],
                                  length_streamline_python)

    length_streamlines_cython = ds_length(streamlines_64bit)

    for i, s in enumerate(streamlines_64bit):
        length_streamline_python = length_python(s)
        assert_array_almost_equal(length_streamlines_cython[i],
                                  length_streamline_python)

    # Test streamlines having mixed dtype
    streamlines_mixed_dtype = [streamline,
                               streamline.astype(np.float64),
                               streamline.astype(np.int32),
                               streamline.astype(np.int64)]
    lengths_mixed_dtype = [ds_length(s)
                           for s in streamlines_mixed_dtype]
    assert_array_equal(ds_length(streamlines_mixed_dtype),
                       lengths_mixed_dtype)

    # Test streamlines with different shape
    length_streamlines_cython = ds_length(
        heterogeneous_streamlines)

    for i, s in enumerate(heterogeneous_streamlines):
        length_streamline_python = length_python(s)
        assert_array_almost_equal(length_streamlines_cython[i],
                                  length_streamline_python)

    # Test streamline having integer dtype
    length_streamline = ds_length(streamline.astype('int'))
    assert_true(length_streamline.dtype == np.float64)

    # Test empty list
    assert_equal(ds_length([]), 0.0)

    # Test streamline having only one point
    assert_equal(ds_length(np.array([[1, 2, 3]])), 0.0)

    # We do not support list of lists, it should be numpy ndarray.
    streamline_unsupported = [[1, 2, 3], [4, 5, 5], [2, 1, 3], [4, 2, 1]]
    assert_raises(AttributeError, ds_length,
                  streamline_unsupported)

    # Test setting computing length of a numpy with flag WRITABLE=False
    streamlines_readonly = []
    for s in streamlines:
        streamlines_readonly.append(s.copy())
        streamlines_readonly[-1].setflags(write=False)

    assert_array_almost_equal(ds_length(streamlines_readonly),
                              [length_python(s) for s in streamlines_readonly])
    streamlines_readonly = []
    for s in streamlines_64bit:
        streamlines_readonly.append(s.copy())
        streamlines_readonly[-1].setflags(write=False)

    assert_array_almost_equal(ds_length(streamlines_readonly),
                              [length_python(s) for s in streamlines_readonly])


def test_length_memory_leaks():
    # Test some dtypes
    dtypes = [np.float32, np.float64, np.int32, np.int64]
    for dtype in dtypes:
        rng = np.random.RandomState(1234)
        NB_STREAMLINES = 10000
        streamlines = [rng.randn(rng.randint(10, 100), 3).astype(dtype) for _ in range(NB_STREAMLINES)]

        list_refcount_before = get_type_refcount()["list"]

        lengths = ds_length(streamlines)
        list_refcount_after = get_type_refcount()["list"]

        # Calling `ds_length` shouldn't increase the refcount of `list`
        # since the return value is a numpy array.
        assert_equal(list_refcount_after, list_refcount_before)

    # Test mixed dtypes
    rng = np.random.RandomState(1234)
    NB_STREAMLINES = 10000
    streamlines = []
    for i in range(NB_STREAMLINES):
        dtype = dtypes[i % len(dtypes)]
        streamlines.append(rng.randn(rng.randint(10, 100), 3).astype(dtype))

    list_refcount_before = get_type_refcount()["list"]

    lengths = ds_length(streamlines)
    list_refcount_after = get_type_refcount()["list"]

    # Calling `ds_length` shouldn't increase the refcount of `list`
    # since the return value is a numpy array.
    assert_equal(list_refcount_after, list_refcount_before)


def test_unlist_relist_streamlines():
    streamlines = [np.random.rand(10, 3),
                   np.random.rand(20, 3),
                   np.random.rand(5, 3)]
    points, offsets = unlist_streamlines(streamlines)
    assert_equal(offsets.dtype, np.dtype('i8'))
    assert_equal(points.shape, (35, 3))
    assert_equal(len(offsets), len(streamlines))

    streamlines2 = relist_streamlines(points, offsets)
    assert_equal(len(streamlines), len(streamlines2))
    for i in range(len(streamlines)):
        assert_array_equal(streamlines[i], streamlines2[i])


def test_center_and_transform():
    A = np.array([[1, 2, 3], [1, 2, 3.]])
    streamlines = [A for i in range(10)]
    streamlines2, center = center_streamlines(streamlines)
    B = np.zeros((2, 3))
    assert_array_equal(streamlines2[0], B)
    assert_array_equal(center, A[0])

    affine = np.eye(4)
    affine[0, 0] = 2
    affine[:3, -1] = - np.array([2, 1, 1]) * center
    streamlines3 = transform_streamlines(streamlines, affine)
    assert_array_equal(streamlines3[0], B)


def test_select_random_streamlines():
    streamlines = [np.random.rand(10, 3),
                   np.random.rand(20, 3),
                   np.random.rand(5, 3)]
    new_streamlines = select_random_set_of_streamlines(streamlines, 2)
    assert_equal(len(new_streamlines), 2)

    new_streamlines = select_random_set_of_streamlines(streamlines, 4)
    assert_equal(len(new_streamlines), 3)


def compress_streamlines_python(streamline, tol_error=0.01,
                                max_segment_length=10):
    """
    Python version of the FiberCompression found on
    https://github.com/scilus/FiberCompression.
    """
    if streamline.shape[0] <= 2:
        return streamline.copy()

    # Euclidean distance
    def segment_length(prev, next):
        return np.sqrt(((prev-next)**2).sum())

    # Projection of a 3D point on a 3D line, minimal distance
    def dist_to_line(prev, next, curr):
        return norm(np.cross(next-prev, curr-next)) / norm(next-prev)

    nb_points = 0
    compressed_streamline = np.zeros_like(streamline)

    # Copy first point since it is always kept.
    compressed_streamline[0, :] = streamline[0, :]
    nb_points += 1
    prev = streamline[0]
    prev_id = 0

    for next_id, next in enumerate(streamline[2:], start=2):
        # Euclidean distance between last added point and current point.
        if segment_length(prev, next) > max_segment_length:
            compressed_streamline[nb_points, :] = streamline[next_id-1, :]
            nb_points += 1
            prev = streamline[next_id-1]
            prev_id = next_id-1
            continue

        # Check that each point is not offset by more than `tol_error` mm.
        for o, curr in enumerate(streamline[prev_id+1:next_id],
                                 start=prev_id+1):
            dist = dist_to_line(prev, next, curr)

            if np.isnan(dist) or dist > tol_error:
                compressed_streamline[nb_points, :] = streamline[next_id-1, :]
                nb_points += 1
                prev = streamline[next_id-1]
                prev_id = next_id-1
                break

    # Copy last point since it is always kept.
    compressed_streamline[nb_points, :] = streamline[-1, :]
    nb_points += 1

    # Make sure the array have the correct size
    return compressed_streamline[:nb_points]


def test_compress_streamlines():
    for compress_func in [compress_streamlines_python, compress_streamlines]:
        # Small streamlines (less than two points) are uncompressable.
        for small_streamline in [np.array([[]]),
                                 np.array([[1, 1, 1]]),
                                 np.array([[1, 1, 1], [2, 2, 2]])]:
            c_streamline = compress_func(small_streamline)
            assert_equal(len(c_streamline), len(small_streamline))
            assert_array_equal(c_streamline, small_streamline)

        # Compressing a straight streamline that is less than 10mm long
        # should output a two points streamline.
        linear_streamline = np.linspace(0, 5, 100*3).reshape((100, 3))
        c_streamline = compress_func(linear_streamline)
        assert_equal(len(c_streamline), 2)
        assert_array_equal(c_streamline, [linear_streamline[0],
                                          linear_streamline[-1]])

        # The distance of consecutive points must be less or equal than some
        # value.
        max_segment_length = 10
        linear_streamline = np.linspace(0, 100, 100*3).reshape((100, 3))
        linear_streamline[:, 1:] = 0.
        c_streamline = compress_func(linear_streamline,
                                     max_segment_length=max_segment_length)
        segments_length = np.sqrt((np.diff(c_streamline,
                                           axis=0)**2).sum(axis=1))
        assert_true(np.all(segments_length <= max_segment_length))
        assert_equal(len(c_streamline), 12)
        assert_array_equal(c_streamline, linear_streamline[::9])

        # A small `max_segment_length` should keep all points.
        c_streamline = compress_func(linear_streamline,
                                     max_segment_length=0.01)
        assert_array_equal(c_streamline, linear_streamline)

        # Test we can set `max_segment_length` to infinity
        # (like the C++ version)
        compress_func(streamline, max_segment_length=np.inf)

        # Uncompressable streamline when `tol_error` == 1.
        simple_streamline = np.array([[0, 0, 0],
                                      [1, 1, 0],
                                      [1.5, np.inf, 0],
                                      [2, 2, 0],
                                      [2.5, 20, 0],
                                      [3, 3, 0]])

        # Because of np.inf, compressing that streamline causes a warning.
        with np.errstate(invalid='ignore'):
            c_streamline = compress_func(simple_streamline, tol_error=1)
            assert_array_equal(c_streamline, simple_streamline)

    # Create a special streamline where every other point is increasingly
    # farther from a straigth line formed by the streamline endpoints.
    tol_errors = np.linspace(0, 10, 21)
    orthogonal_line = np.array([[-np.sqrt(2)/2, np.sqrt(2)/2, 0]],
                               dtype=np.float32)
    special_streamline = np.array([range(len(tol_errors)*2+1)] * 3,
                                  dtype=np.float32).T
    special_streamline[1::2] += orthogonal_line * tol_errors[:, None]

    # # Uncomment to see the streamline.
    # import pylab as plt
    # plt.plot(special_streamline[:, 0], special_streamline[:, 1], '.-')
    # plt.axis('equal'); plt.show()

    # Test different values for `tol_error`.
    for i, tol_error in enumerate(tol_errors):
        cspecial_streamline = compress_streamlines(special_streamline,
                                                   tol_error=tol_error+1e-4,
                                                   max_segment_length=np.inf)

        # First and last points should always be the same as the original ones.
        assert_array_equal(cspecial_streamline[0], special_streamline[0])
        assert_array_equal(cspecial_streamline[-1], special_streamline[-1])

        assert_equal(len(cspecial_streamline),
                     len(special_streamline)-((i*2)+1))

        # Make sure Cython and Python versions are the same.
        cstreamline_python = compress_streamlines_python(
                                            special_streamline,
                                            tol_error=tol_error+1e-4,
                                            max_segment_length=np.inf)
        assert_equal(len(cspecial_streamline), len(cstreamline_python))
        assert_array_almost_equal(cspecial_streamline, cstreamline_python)


def test_compress_streamlines_memory_leaks():
    # Test some dtypes
    dtypes = [np.float32, np.float64, np.int32, np.int64]
    for dtype in dtypes:
        rng = np.random.RandomState(1234)
        NB_STREAMLINES = 10000
        streamlines = [rng.randn(rng.randint(10, 100), 3).astype(dtype) for _ in range(NB_STREAMLINES)]

        list_refcount_before = get_type_refcount()["list"]

        cstreamlines = compress_streamlines(streamlines)
        list_refcount_after = get_type_refcount()["list"]
        del cstreamlines  # Delete `cstreamlines` because it holds a reference to `list`.

        # Calling `compress_streamlines` should increase the refcount of `list` by one
        # since we kept the returned value.
        assert_equal(list_refcount_after, list_refcount_before+1)

    # Test mixed dtypes
    rng = np.random.RandomState(1234)
    NB_STREAMLINES = 10000
    streamlines = []
    for i in range(NB_STREAMLINES):
        dtype = dtypes[i % len(dtypes)]
        streamlines.append(rng.randn(rng.randint(10, 100), 3).astype(dtype))

    list_refcount_before = get_type_refcount()["list"]

    cstreamlines = compress_streamlines(streamlines)
    list_refcount_after = get_type_refcount()["list"]

    # Calling `compress_streamlines` should increase the refcount of `list` by one
    # since we kept the returned value.
    assert_equal(list_refcount_after, list_refcount_before+1)

def test_select_by_rois():
    streamlines = [np.array([[0, 0., 0.9],
                             [1.9, 0., 0.]]),
                   np.array([[0.1, 0., 0],
                             [0, 1., 1.],
                             [0, 2., 2.]]),
                   np.array([[2, 2, 2],
                             [3, 3, 3]])]

    # Make two ROIs:
    mask1 = np.zeros((4, 4, 4), dtype=bool)
    mask2 = np.zeros_like(mask1)
    mask1[0, 0, 0] = True
    mask2[1, 0, 0] = True

    selection = select_by_rois(streamlines, [mask1], [True],
                               tol=1)

    npt.assert_array_equal(list(selection), [streamlines[0],
                           streamlines[1]])

    selection = select_by_rois(streamlines, [mask1, mask2], [True, True],
                               tol=1)

    npt.assert_array_equal(list(selection), [streamlines[0],
                           streamlines[1]])

    selection = select_by_rois(streamlines, [mask1, mask2], [True, False])

    npt.assert_array_equal(list(selection), [streamlines[1]])

    # Setting tolerance too low gets overridden:
    selection = select_by_rois(streamlines, [mask1, mask2], [True, False],
                               tol=0.1)
    npt.assert_array_equal(list(selection), [streamlines[1]])

    selection = select_by_rois(streamlines, [mask1, mask2], [True, True],
                               tol=0.87)

    npt.assert_array_equal(list(selection), [streamlines[1]])

    mask3 = np.zeros_like(mask1)
    mask3[0, 2, 2] = 1
    selection = select_by_rois(streamlines, [mask1, mask2, mask3],
                               [True, True, False], tol=1.0)

    npt.assert_array_equal(list(selection), [streamlines[0]])

    # Select using only one ROI
    selection = select_by_rois(streamlines, [mask1], [True], tol=0.87)
    npt.assert_array_equal(list(selection), [streamlines[1]])

    selection = select_by_rois(streamlines, [mask1], [True], tol=1.0)
    npt.assert_array_equal(list(selection), [streamlines[0],
                           streamlines[1]])

    # Use different modes:
    selection = select_by_rois(streamlines, [mask1, mask2, mask3],
                               [True, True, False],
                               mode="all",
                               tol=1.0)
    npt.assert_array_equal(list(selection), [streamlines[0]])

    selection = select_by_rois(streamlines, [mask1, mask2, mask3],
                               [True, True, False],
                               mode="either_end",
                               tol=1.0)
    npt.assert_array_equal(list(selection), [streamlines[0]])

    selection = select_by_rois(streamlines, [mask1, mask2, mask3],
                               [True, True, False],
                               mode="both_end",
                               tol=1.0)
    npt.assert_array_equal(list(selection), [streamlines[0]])

    mask2[0, 2, 2] = True
    selection = select_by_rois(streamlines, [mask1, mask2, mask3],
                               [True, True, False],
                               mode="both_end",
                               tol=1.0)

    npt.assert_array_equal(list(selection), [streamlines[0],
                                             streamlines[1]])

    # Test with generator input:
    def generate_sl(streamlines):
        for sl in streamlines:
            yield sl

    selection = select_by_rois(generate_sl(streamlines), [mask1], [True],
                               tol=1.0)
    npt.assert_array_equal(list(selection), [streamlines[0],
                           streamlines[1]])


def test_orient_by_rois():
    streamlines = [np.array([[0, 0., 0],
                             [1, 0., 0.],
                             [2, 0., 0.]]),
                   np.array([[2, 0., 0.],
                             [1, 0., 0],
                             [0, 0,  0.]])]

    # Make two ROIs:
    mask1_vol = np.zeros((4, 4, 4), dtype=bool)
    mask2_vol = np.zeros_like(mask1_vol)
    mask1_vol[0, 0, 0] = True
    mask2_vol[1, 0, 0] = True
    mask1_coords = np.array(np.where(mask1_vol)).T
    mask2_coords = np.array(np.where(mask2_vol)).T

    # If there is an affine, we'll use it:
    affine = np.eye(4)
    affine[:, 3] = [-1, 100, -20, 1]
    # Transform the streamlines:
    x_streamlines = [sl + affine[:3, 3] for sl in streamlines]

    for copy in [True, False]:
        for sl, affine in zip([streamlines, x_streamlines], [None, affine]):
            for mask1, mask2 in \
              zip([mask1_vol, mask1_coords], [mask2_vol, mask2_coords]):
                new_streamlines = orient_by_rois(sl, mask1, mask2,
                                                 affine=affine, copy=copy)
                if copy:
                    flipped_sl = [sl[0], sl[1][::-1]]
                else:
                    flipped_sl = [np.array([[0, 0., 0],
                                            [1, 0., 0.],
                                            [2, 0., 0.]]),
                                  np.array([[0, 0., 0.],
                                            [1, 0., 0],
                                            [2, 0,  0.]])]
                    if affine is not None:
                        flipped_sl = [s + affine[:3, 3] for s in flipped_sl]

                npt.assert_equal(new_streamlines, flipped_sl)


if __name__ == '__main__':
    run_module_suite()
