# Copyright 2009-2010 by <NAME>.  All rights reserved.
# Revisions copyright 2010 by <NAME>.  All rights reserved.
#
# Converted by <NAME> from an older unit test copyright 2002
# by <NAME>.
#
# This file is part of the Biopython distribution and governed by your
# choice of the "Biopython License Agreement" or the "BSD 3-Clause License".
# Please see the LICENSE file that should have been included as part of this
# package.

"""Unit tests for those parts of the Bio.PDB module using Bio.KDTree."""

import unittest

try:
    from numpy import array, dot, sqrt, argsort
    from numpy.random import random
except ImportError:
    from Bio import MissingExternalDependencyError
    raise MissingExternalDependencyError(
        "Install NumPy if you want to use Bio.PDB.")

try:
    from Bio.PDB import kdtrees
except ImportError:
    from Bio import MissingExternalDependencyError
    raise MissingExternalDependencyError(
        "C module Bio.PDB.kdtrees not compiled")

from Bio.PDB.NeighborSearch import NeighborSearch


class NeighborTest(unittest.TestCase):

    def test_neighbor_search(self):
        """NeighborSearch: Find nearby randomly generated coordinates.

        Based on the self test in Bio.PDB.NeighborSearch.
        """
        class RandomAtom(object):
            def __init__(self):
                self.coord = 100 * random(3)

            def get_coord(self):
                return self.coord

        for i in range(0, 20):
            atoms = [RandomAtom() for j in range(100)]
            ns = NeighborSearch(atoms)
            hits = ns.search_all(5.0)
            self.assertTrue(isinstance(hits, list), hits)
            self.assertTrue(len(hits) >= 0, hits)
        x = array([250, 250, 250])  # Far away from our random atoms
        self.assertEqual([], ns.search(x, 5.0, "A"))
        self.assertEqual([], ns.search(x, 5.0, "R"))
        self.assertEqual([], ns.search(x, 5.0, "C"))
        self.assertEqual([], ns.search(x, 5.0, "M"))
        self.assertEqual([], ns.search(x, 5.0, "S"))


class KDTreeTest(unittest.TestCase):

    nr_points = 5000     # number of points used in test
    bucket_size = 5      # number of points per tree node
    radius = 0.05        # radius of search (typically 0.05 or so)
    query_radius = 10    # radius of search

    def test_KDTree_exceptions(self):
        bucket_size = self.bucket_size
        nr_points = self.nr_points
        radius = self.radius
        coords = random((nr_points, 3)) * 100000000000000
        with self.assertRaises(Exception) as context:
            kdt = kdtrees.KDTree(coords, bucket_size)
        self.assertTrue("coordinate values should lie between -1e6 and 1e6" in str(context.exception))
        with self.assertRaises(Exception) as context:
            kdt = kdtrees.KDTree(random((nr_points, 3 - 2)), bucket_size)
        self.assertTrue("expected a Nx3 numpy array" in str(context.exception))

    def test_KDTree_point_search(self):
        """Test searching all points within a certain radius of center.

        Using the KDTree C module, search all point pairs that are
        within radius, and compare the results to a manual search.
        """
        bucket_size = self.bucket_size
        nr_points = self.nr_points
        for radius in (self.radius, 100 * self.radius):
            for i in range(0, 10):
                # kd tree search
                coords = random((nr_points, 3))
                center = random(3)
                kdt = kdtrees.KDTree(coords, bucket_size)
                points1 = kdt.search(center, radius)
                points1.sort(key=lambda point: point.index)  # noqa: E731
                # manual search
                points2 = []
                for i in range(0, nr_points):
                    p = coords[i]
                    v = p - center
                    r = sqrt(dot(v, v))
                    if r <= radius:
                        point2 = kdtrees.Point(i, r)
                        points2.append(point2)
                # compare results
                self.assertEqual(len(points1), len(points2))
                for point1, point2 in zip(points1, points2):
                    self.assertEqual(point1.index, point2.index)
                    self.assertAlmostEqual(point1.radius, point2.radius)

    def test_KDTree_neighbor_search_simple(self):
        """Test all fixed radius neighbor search.

        Test all fixed radius neighbor search using the KD tree C
        module, and compare the results to those of a simple but
        slow algorithm.
        """
        bucket_size = self.bucket_size
        nr_points = self.nr_points
        radius = self.radius
        for i in range(0, 10):
            # KD tree search
            coords = random((nr_points, 3))
            kdt = kdtrees.KDTree(coords, bucket_size)
            neighbors1 = kdt.neighbor_search(radius)
            # same search, using a simple but slow algorithm
            neighbors2 = kdt.neighbor_simple_search(radius)
            # compare results
            self.assertEqual(len(neighbors1), len(neighbors2))
            key = lambda neighbor: (neighbor.index1, neighbor.index2)  # noqa: E731
            neighbors1.sort(key=key)
            neighbors2.sort(key=key)
            for neighbor1, neighbor2 in zip(neighbors1, neighbors2):
                self.assertEqual(neighbor1.index1, neighbor2.index1)
                self.assertEqual(neighbor1.index2, neighbor2.index2)
                self.assertAlmostEqual(neighbor1.radius, neighbor2.radius)

    def test_KDTree_neighbor_search_manual(self):
        """Test all fixed radius neighbor search.

        Test all fixed radius neighbor search using the KD tree C
        module, and compare the results to those of a manual search.
        """
        bucket_size = self.bucket_size
        nr_points = self.nr_points // 10  # fewer points to speed up the test
        for radius in (self.radius, 3 * self.radius):
            for i in range(0, 5):
                # KD tree search
                coords = random((nr_points, 3))
                kdt = kdtrees.KDTree(coords, bucket_size)
                neighbors1 = kdt.neighbor_search(radius)
                # manual search
                neighbors2 = []
                indices = argsort(coords[:, 0])
                for j1 in range(nr_points):
                    index1 = indices[j1]
                    p1 = coords[index1]
                    for j2 in range(j1 + 1, nr_points):
                        index2 = indices[j2]
                        p2 = coords[index2]
                        if p2[0] - p1[0] > radius:
                            break
                        v = p1 - p2
                        r = sqrt(dot(v, v))
                        if r <= radius:
                            if index1 < index2:
                                i1, i2 = index1, index2
                            else:
                                i1, i2 = index2, index1
                            neighbor = kdtrees.Neighbor(i1, i2, r)
                            neighbors2.append(neighbor)
                self.assertEqual(len(neighbors1), len(neighbors2))
                key = lambda neighbor: (neighbor.index1, neighbor.index2)  # noqa: E731
                neighbors1.sort(key=key)
                neighbors2.sort(key=key)
                for neighbor1, neighbor2 in zip(neighbors1, neighbors2):
                    self.assertEqual(neighbor1.index1, neighbor2.index1)
                    self.assertEqual(neighbor1.index2, neighbor2.index2)
                    self.assertAlmostEqual(neighbor1.radius, neighbor2.radius)


if __name__ == "__main__":
    runner = unittest.TextTestRunner(verbosity=2)
    unittest.main(testRunner=runner)

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