import unittest
import os.path
import numpy as np
import numpy.testing as npt
import tempfile

from dipy.core.graph import Graph
from dipy.denoise.enhancement_kernel import EnhancementKernel
from dipy.tracking.fbcmeasures import FBCMeasures
from dipy.core.sphere import Sphere
from dipy.viz import window, actor

class TestDipy(unittest.TestCase):
    def test_scene(self):
        xyz = 10 * np.random.rand(100, 3)
        colors = np.random.rand(100, 4)
        radii = np.random.rand(100) + 0.5

        sphere_actor = actor.sphere(centers=xyz, colors=colors, radii=radii)

        scene = window.Scene()
        scene.add(sphere_actor)
        
        self.assertEqual((0, 0), scene.GetSize())

    def test_graph(self):
        g = Graph()
        g.add_node('a', 5)
        g.add_node('b', 6)
        self.assertEqual(2, len(g.node))

    # From: https://github.com/nipy/dipy/blob/51234a4437638535ff8f6e5819e14ccf7e1d1483/dipy/tracking/tests/test_fbc.py
    def test_fbc(self):
        """Test the FBC measures on a set of fibers"""

        # Generate two fibers of 10 points
        streamlines = []
        for i in range(2):
            fiber = np.zeros((10, 3))
            for j in range(10):
                fiber[j, 0] = j
                fiber[j, 1] = i*0.2
                fiber[j, 2] = 0
                streamlines.append(fiber)

        # Create lookup table.
        # A fixed set of orientations is used to guarantee deterministic results
        D33 = 1.0
        D44 = 0.04
        t = 1
        sphere = Sphere(xyz=np.array([[0.82819078, 0.51050355, 0.23127074],
                                    [-0.10761926, -0.95554309, 0.27450957],
                                    [0.4101745, -0.07154038, 0.90919682],
                                    [-0.75573448, 0.64854889, 0.09082809],
                                    [-0.56874549, 0.01377562, 0.8223982]]))
        k = EnhancementKernel(D33, D44, t, orientations=sphere,
                            force_recompute=True)

        # run FBC
        fbc = FBCMeasures(streamlines, k, verbose=True)

        # get FBC values
        fbc_sl_orig, clrs_orig, rfbc_orig = \
            fbc.get_points_rfbc_thresholded(0, emphasis=0.01)

        # check mean RFBC against tested value
        npt.assert_almost_equal(np.mean(rfbc_orig), 1.0500466494329224)