def test_align_array(self):
"""
Test the align_array function.
"""
l1 = LongArray(10)
l1.set_data(numpy.arange(10))
new_indices = LongArray(10)
new_indices.set_data(numpy.asarray([1, 5, 3, 2, 4, 7, 8, 6, 9, 0]))
l1.align_array(new_indices)
self.assertEqual(numpy.allclose([1, 5, 3, 2, 4, 7, 8, 6, 9, 0],
l1.get_npy_array()), True)
def test_constructor(self):
"""
Test the constructor.
"""
l = LongArray(10)
self.assertEqual(l.length, 10)
self.assertEqual(l.alloc, 10)
self.assertEqual(len(l.get_npy_array()), 10)
l = LongArray()
self.assertEqual(l.length, 0)
self.assertEqual(l.alloc, 16)
self.assertEqual(len(l.get_npy_array()), 0)
def test_extend(self):
"""
Tests the extend function.
"""
l1 = LongArray(5)
for i in range(5):
l1[i] = i
l2 = LongArray(5)
for i in range(5):
l2[i] = 5 + i
l1.extend(l2.get_npy_array())
self.assertEqual(l1.length, 10)
self.assertEqual(numpy.allclose(l1.get_npy_array(), numpy.arange(10)), True)
def compute_particle_mass(parray, kernel, density=1000.0, h=0.1, dim=3):
"""
Given a particle array, kernel, target density and interaction radius, find
the mass of each particle.
Note that this method works only when the particle radius is constant. This
may also compute incorrect values when the particle cofiguration has voids
within.
"""
centroid = Point(0, 0, 0)
dist = DoubleArray(0)
indices = LongArray(0)
x = parray.get('x')
centroid.x = numpy.sum(x)/float(len(x))
y = None
z = None
logger.debug('particles to compute_particle_mass %d'%(len(x)))
if dim > 1:
y = parray.get('y')
centroid.y = numpy.sum(y)/float(len(y))
if dim > 2:
z = parray.get('z')
centroid.z = numpy.sum(z)/float(len(z))
else:
z = numpy.zeros(len(x), dtype=numpy.float)
else:
y = numpy.zeros(len(x), dtype=numpy.float)
z = y
logger.debug('Centroid : %s'%(centroid))
radius = kernel.radius()
# find the nearest points in parray of the centroid.
brute_force_nnps(pnt=centroid, search_radius=h*radius,
xa=x, ya=y, za=z,
neighbor_indices=indices,
neighbor_distances=dist)
k = 0.0
logger.info('Number of neighbors : %d'%(indices.length))
pnt = Point()
for i in range(indices.length):
pnt.x = x[indices[i]]
pnt.y = y[indices[i]]
pnt.z = z[indices[i]]
k += kernel.py_function(centroid, pnt, h)
logger.info('Kernel sum : %f'%(k))
logger.info('Requested density : %f'%(density))
m = float(density/k)
logger.info('Computed mass : %f'%(m))
return m
def test_resize(self):
"""
Tests the resize function.
"""
l = LongArray(0)
l.resize(20)
self.assertEqual(l.length, 20)
self.assertEqual(len(l.get_npy_array()), 20)
self.assertEqual(l.alloc >= l.length, True)
def test_reset(self):
"""
Tests the reset function.
"""
l = LongArray(5)
l.reset()
self.assertEqual(l.length, 0)
self.assertEqual(l.alloc, 5)
self.assertEqual(len(l.get_npy_array()), 0)
def test_set_data(self):
"""
Tests the set_data function.
"""
l = LongArray(5)
np = numpy.arange(5)
l.set_data(np)
for i in range(5):
self.assertEqual(l[i], np[i])
self.assertRaises(ValueError, l.set_data, numpy.arange(10))
def test_append(self):
"""
Test the append function.
"""
l = LongArray(0)
l.append(1)
l.append(2)
l.append(3)
self.assertEqual(l.length, 3)
self.assertEqual(l[0], 1)
self.assertEqual(l[1], 2)
self.assertEqual(l[2], 3)
def test_get_set_indexing(self):
"""
Test get/set and [] operator.
"""
l = LongArray(10)
l.set(0, 10)
l.set(9, 1)
self.assertEqual(l.get(0), 10)
self.assertEqual(l.get(9), 1)
l[9] = 2
self.assertEqual(l[9], 2)
def test_pickling(self):
"""
Tests the __reduce__ and __setstate__ functions.
"""
l1 = LongArray(10)
l1.set_data(numpy.arange(10))
import pickle
l1_dump = pickle.dumps(l1)
l1_load = pickle.loads(l1_dump)
self.assertEqual((l1_load.get_npy_array() == l1.get_npy_array()).all(), True)
def test_get_npy_array(self):
"""
Tests the get_npy_array array.
"""
l = LongArray(3)
l[0] = 1
l[1] = 2
l[2] = 3
nparray = l.get_npy_array()
self.assertEqual(len(nparray), 3)
for i in range(3):
self.assertEqual(nparray[0], l[0])
def test_squeeze(self):
"""
Tests the squeeze function.
"""
l = LongArray(5)
l.append(4)
self.assertEqual(l.alloc > l.length, True)
l.squeeze()
self.assertEqual(l.length, 6)
self.assertEqual(l.alloc == l.length, True)
self.assertEqual(len(l.get_npy_array()), 6)
def test_copy_subset(self):
"""
Tests the copy_subset function.
"""
l1 = LongArray(10)
l1.set_data(numpy.arange(10))
l2 = LongArray(4)
l2[0] = 4
l2[1] = 3
l2[2] = 2
l2[3] = 1
# a valid copy.
l1.copy_subset(l2, 5, 9)
self.assertEqual(numpy.allclose([0, 1, 2, 3, 4, 4, 3, 2, 1, 9],
l1.get_npy_array()), True)
# try to copy different sized arrays without any index specification.
l1.set_data(numpy.arange(10))
# copy to the last k values of source array.
l1.copy_subset(l2, start_index=6)
self.assertEqual(numpy.allclose([0, 1, 2, 3, 4, 5, 4, 3, 2, 1],
l1.get_npy_array()), True)
l1.set_data(numpy.arange(10))
l1.copy_subset(l2, start_index=7)
self.assertEqual(numpy.allclose([0, 1, 2, 3, 4, 5, 6, 4, 3, 2],
l1.get_npy_array()), True)
# some invalid operations.
l1.set_data(numpy.arange(10))
self.assertRaises(ValueError, l1.copy_subset, l2, -1, 1)
self.assertRaises(ValueError, l1.copy_subset, l2, 3, 2)
self.assertRaises(ValueError, l1.copy_subset, l2, 0, 11)
self.assertRaises(ValueError, l1.copy_subset, l2, 10, 20)
self.assertRaises(ValueError, l1.copy_subset, l2, -1, -1)
def test_reserve(self):
"""
Tests the reserve function.
"""
l = LongArray(0)
l.reserve(10)
self.assertEqual(l.alloc, 16)
self.assertEqual(l.length, 0)
self.assertEqual(len(l.get_npy_array()), 0)
l.reserve(20)
self.assertEqual(l.alloc, 20)
self.assertEqual(l.length, 0)
self.assertEqual(len(l.get_npy_array()), 0)
def test_update_min_max(self):
"""
Tests the update_min_max function.
"""
l1 = LongArray(10)
l1.set_data(numpy.arange(10))
l1.update_min_max()
self.assertEqual(l1.minimum, 0)
self.assertEqual(l1.maximum, 9)
l1[9] = -1
l1[0] = -20
l1[4] = 200
l1.update_min_max()
self.assertEqual(l1.minimum, -20)
self.assertEqual(l1.maximum, 200)
def test_remove(self):
"""
Tests the remove function.
"""
l1 = LongArray(10)
l1.set_data(numpy.arange(10))
rem = [0, 4, 3]
l1.remove(numpy.array(rem, dtype=numpy.int))
self.assertEqual(l1.length, 7)
self.assertEqual(numpy.allclose([7, 1, 2, 8, 9, 5, 6],
l1.get_npy_array()), True)
l1.remove(numpy.array(rem, dtype=numpy.int))
self.assertEqual(l1.length, 4)
self.assertEqual(numpy.allclose([6, 1, 2, 5], l1.get_npy_array()), True)
rem = [0, 1, 3]
l1.remove(numpy.array(rem, dtype=numpy.int))
self.assertEqual(l1.length, 1)
self.assertEqual(numpy.allclose([2], l1.get_npy_array()), True)
l1.remove(numpy.array([0], dtype=numpy.int))
self.assertEqual(l1.length, 0)
self.assertEqual(len(l1.get_npy_array()), 0)
