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# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
import unittest
try:
import numpy
del numpy
except ImportError:
from Bio import MissingExternalDependencyError
raise MissingExternalDependencyError(
"Install NumPy if you want to use Bio.KDTree.")
try:
from Bio.KDTree import _CKDTree
del _CKDTree
except ImportError:
from Bio import MissingExternalDependencyError
raise MissingExternalDependencyError(
"C module in Bio.KDTree not compiled")
from Bio.KDTree.KDTree import KDTree
from numpy import sum, sqrt, array
from numpy import random
nr_points = 5000
dim = 3
bucket_size = 5
radius = 0.01
query_radius = 10
def _dist(p, q):
diff = p - q
return sqrt(sum(diff * diff))
def neighbor_test(nr_points, dim, bucket_size, radius):
"""Test all fixed radius neighbor search.
Test all fixed radius neighbor search using the
KD tree C module.
Arguments:
- nr_points: number of points used in test
- dim: dimension of coords
- bucket_size: nr of points per tree node
- radius: radius of search (typically 0.05 or so)
Returns true if the test passes.
"""
# KD tree search
kdt = KDTree(dim, bucket_size)
coords = random.random((nr_points, dim))
kdt.kdt.set_data(coords)
neighbors = kdt.kdt.neighbor_search(radius)
r = [neighbor.radius for neighbor in neighbors]
if r is None:
l1 = 0
else:
l1 = len(r)
# now do a slow search to compare results
neighbors = kdt.kdt.neighbor_simple_search(radius)
r = [neighbor.radius for neighbor in neighbors]
if r is None:
l2 = 0
else:
l2 = len(r)
if l1 == l2:
# print("Passed.")
return True
else:
print("Not passed: %i != %i." % (l1, l2))
return False
def test(nr_points, dim, bucket_size, radius):
"""Test neighbor search.
Test neighbor search using the KD tree C module.
Arguments:
- nr_points: number of points used in test
- dim: dimension of coords
- bucket_size: nr of points per tree node
- radius: radius of search (typically 0.05 or so)
Returns true if the test passes.
"""
# kd tree search
kdt = KDTree(dim, bucket_size)
coords = random.random((nr_points, dim))
center = coords[0]
kdt.kdt.set_data(coords)
kdt.kdt.search_center_radius(center, radius)
r = kdt.get_indices()
if r is None:
l1 = 0
else:
l1 = len(r)
l2 = 0
# now do a manual search to compare results
for i in range(0, nr_points):
p = coords[i]
if _dist(p, center) <= radius:
l2 = l2 + 1
if l1 == l2:
# print("Passed.")
return True
else:
print("Not passed: %i != %i." % (l1, l2))
return False
def test_search(nr_points, dim, bucket_size, radius):
"""Test search all points within radius of center.
Search all point pairs that are within radius.
Arguments:
- nr_points: number of points used in test
- dim: dimension of coords
- bucket_size: nr of points per tree node
- radius: radius of search
Returns true if the test passes.
"""
kdt = KDTree(dim, bucket_size)
coords = random.random((nr_points, dim))
kdt.set_coords(coords)
kdt.search(coords[0], radius * 100)
radii = kdt.get_radii()
l1 = 0
for i in range(0, nr_points):
p = coords[i]
if _dist(p, coords[0]) <= radius * 100:
l1 = l1 + 1
if l1 == len(radii):
return True
else:
return False
def test_all_search(nr_points, dim, bucket_size, query_radius):
"""Test fixed neighbor search.
Search all point pairs that are within radius.
Arguments:
- nr_points: number of points used in test
- dim: dimension of coords
- bucket_size: nr of points per tree node
- query_radius: radius of search
Returns true if the test passes.
"""
kdt = KDTree(dim, bucket_size)
coords = random.random((nr_points, dim))
kdt.set_coords(coords)
kdt.all_search(query_radius)
indices = kdt.all_get_indices()
if indices is None:
l1 = 0
else:
l1 = len(indices)
radii = kdt.all_get_radii()
if radii is None:
l2 = 0
else:
l2 = len(radii)
if l1 == l2:
return True
else:
return False
class KDTreeTest(unittest.TestCase):
def test_KDTree_exceptions(self):
kdt = KDTree(dim, bucket_size)
with self.assertRaises(Exception) as context:
kdt.set_coords(random.random((nr_points, dim)) * 100000000000000)
self.assertTrue("Points should lie between -1e6 and 1e6" in str(context.exception))
with self.assertRaises(Exception) as context:
kdt.set_coords(random.random((nr_points, dim - 2)))
self.assertTrue("Expected a Nx%i NumPy array" % dim in str(context.exception))
with self.assertRaises(Exception) as context:
kdt.search(array([0, 0, 0]), radius)
self.assertTrue("No point set specified" in str(context.exception))
def test_KDTree_neighbour(self):
for i in range(0, 10):
self.assertTrue(neighbor_test(nr_points, dim, bucket_size, radius))
def test_KDTree(self):
for i in range(0, 10):
self.assertTrue(test(nr_points, dim, bucket_size, radius))
def test_all_search(self):
for i in range(0, 5):
self.assertTrue(test_all_search((nr_points // 10), dim, bucket_size, query_radius))
def test_search(self):
for i in range(0, 5):
self.assertTrue(test_search(nr_points, dim, bucket_size, radius))
if __name__ == "__main__":
runner = unittest.TextTestRunner(verbosity=2)
unittest.main(testRunner=runner)
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