def test_periodicity():
# First test the simple case were we find the distance between two points
a = [0.1, 0.1, 0.1]
b = [0.9, 0.9, 0.9]
period = 1.
dist = periodic_dist(a, b, period)
yield assert_almost_equal, dist, 0.34641016151377535
dist = periodic_dist(a, b, period, (True, False, False))
yield assert_almost_equal, dist, 1.1489125293076059
dist = periodic_dist(a, b, period, (False, True, False))
yield assert_almost_equal, dist, 1.1489125293076059
dist = periodic_dist(a, b, period, (False, False, True))
yield assert_almost_equal, dist, 1.1489125293076059
dist = periodic_dist(a, b, period, (True, True, False))
yield assert_almost_equal, dist, 0.84852813742385713
dist = periodic_dist(a, b, period, (True, False, True))
yield assert_almost_equal, dist, 0.84852813742385713
dist = periodic_dist(a, b, period, (False, True, True))
yield assert_almost_equal, dist, 0.84852813742385713
dist = euclidean_dist(a, b)
yield assert_almost_equal, dist, 1.3856406460551021
# Now test the more complicated cases where we're calculaing radii based
# on data objects
ds = fake_random_ds(64)
# First we test flattened data
data = ds.all_data()
positions = np.array([data[ax] for ax in 'xyz'])
c = [0.1, 0.1, 0.1]
n_tup = tuple([1 for i in range(positions.ndim - 1)])
center = np.tile(np.reshape(np.array(c), (positions.shape[0], ) + n_tup),
(1, ) + positions.shape[1:])
dist = periodic_dist(positions, center, period, ds.periodicity)
yield assert_almost_equal, dist.min(), 0.00270632938683
yield assert_almost_equal, dist.max(), 0.863319074398
dist = euclidean_dist(positions, center)
yield assert_almost_equal, dist.min(), 0.00270632938683
yield assert_almost_equal, dist.max(), 1.54531407988
# Then grid-like data
data = ds.index.grids[0]
positions = np.array([data[ax] for ax in 'xyz'])
c = [0.1, 0.1, 0.1]
n_tup = tuple([1 for i in range(positions.ndim - 1)])
center = np.tile(np.reshape(np.array(c), (positions.shape[0], ) + n_tup),
(1, ) + positions.shape[1:])
dist = periodic_dist(positions, center, period, ds.periodicity)
yield assert_almost_equal, dist.min(), 0.00270632938683
yield assert_almost_equal, dist.max(), 0.863319074398
dist = euclidean_dist(positions, center)
yield assert_almost_equal, dist.min(), 0.00270632938683
yield assert_almost_equal, dist.max(), 1.54531407988
def test_periodicity():
# First test the simple case were we find the distance between two points
a = [0.1,0.1,0.1]
b = [0.9,0.9,0.9]
period = 1.
dist = periodic_dist(a,b,period)
yield assert_almost_equal, dist, 0.34641016151377535
dist = periodic_dist(a, b, period, (True, False, False))
yield assert_almost_equal, dist, 1.1489125293076059
dist = periodic_dist(a, b, period, (False, True, False))
yield assert_almost_equal, dist, 1.1489125293076059
dist = periodic_dist(a, b, period, (False, False, True))
yield assert_almost_equal, dist, 1.1489125293076059
dist = periodic_dist(a, b, period, (True, True, False))
yield assert_almost_equal, dist, 0.84852813742385713
dist = periodic_dist(a, b, period, (True, False, True))
yield assert_almost_equal, dist, 0.84852813742385713
dist = periodic_dist(a, b, period, (False, True, True))
yield assert_almost_equal, dist, 0.84852813742385713
dist = euclidean_dist(a,b)
yield assert_almost_equal, dist, 1.3856406460551021
# Now test the more complicated cases where we're calculaing radii based
# on data objects
ds = fake_random_ds(64)
# First we test flattened data
data = ds.all_data()
positions = np.array([data[ax] for ax in 'xyz'])
c = [0.1, 0.1, 0.1]
n_tup = tuple([1 for i in range(positions.ndim-1)])
center = np.tile(np.reshape(np.array(c), (positions.shape[0],)+n_tup),(1,)+positions.shape[1:])
dist = periodic_dist(positions, center, period, ds.periodicity)
yield assert_almost_equal, dist.min(), 0.00270632938683
yield assert_almost_equal, dist.max(), 0.863319074398
dist = euclidean_dist(positions, center)
yield assert_almost_equal, dist.min(), 0.00270632938683
yield assert_almost_equal, dist.max(), 1.54531407988
# Then grid-like data
data = ds.index.grids[0]
positions = np.array([data[ax] for ax in 'xyz'])
c = [0.1, 0.1, 0.1]
n_tup = tuple([1 for i in range(positions.ndim-1)])
center = np.tile(np.reshape(np.array(c), (positions.shape[0],)+n_tup),(1,)+positions.shape[1:])
dist = periodic_dist(positions, center, period, ds.periodicity)
yield assert_almost_equal, dist.min(), 0.00270632938683
yield assert_almost_equal, dist.max(), 0.863319074398
dist = euclidean_dist(positions, center)
yield assert_almost_equal, dist.min(), 0.00270632938683
yield assert_almost_equal, dist.max(), 1.54531407988