def test_multiple_clusters_3D(self):
numbers = np.random.randint(1, 10, self.N)
for number in numbers:
pos = dummy_clusters(number, 10, 1, 3)
df = pos_to_df(pos)
df = cluster(df, 1)
assert_equal(df['cluster'].nunique(), number)
def test_single_cluster_3D(self):
separation = np.random.random(self.N) * 10
for sep in separation:
pos = dummy_clusters(1, 10, sep, 3)
df = pos_to_df(pos)
df = cluster(df, sep)
assert_equal(df['cluster_size'].values, len(pos[0]))
def test_multiple_clusters_3D(self):
numbers = np.random.randint(1, 10, self.N)
for number in numbers:
pos = dummy_clusters(number, 10, 1, 3)
df = pos_to_df(pos)
df = cluster(df, 1)
assert_equal(df['cluster'].nunique(), number)
def test_single_cluster_3D(self):
separation = np.random.random(self.N) * 10
for sep in separation:
pos = dummy_clusters(1, 10, sep, 3)
df = pos_to_df(pos)
df = cluster(df, sep)
assert_equal(df['cluster_size'].values, len(pos[0]))
def test_line_cluster_3D(self):
separation = np.random.random(self.N) * 10
phi = np.random.random(self.N) * 2 * np.pi
theta = np.random.random(self.N) * np.pi
ds = np.array([
np.cos(theta),
np.cos(phi) * np.sin(theta),
np.sin(phi) * np.sin(theta)
]).T
for vec, sep in zip(ds, separation):
pos = np.arange(10)[:, np.newaxis] * vec[np.newaxis, :] * sep
df = pos_to_df([pos])
df = cluster(df, sep * 1.1)
assert_equal(df['cluster_size'].values, 10)
df = cluster(df, sep * 0.9)
assert_equal(df['cluster_size'].values, 1)
df = pos_to_df([pos[::-1]])
df = cluster(df, sep * 1.1)
assert_equal(df['cluster_size'].values, 10)
df = cluster(df, sep * 0.9)
assert_equal(df['cluster_size'].values, 1)
ind = np.arange(10)
np.random.shuffle(ind)
pos = ind[:, np.newaxis] * vec[np.newaxis, :] * sep
df = pos_to_df([pos])
df = cluster(df, sep * 1.1)
assert_equal(df['cluster_size'].values, 10)
df = cluster(df, sep * 0.9)
assert_equal(df['cluster_size'].values, 1)
def test_line_cluster_3D(self):
separation = np.random.random(self.N) * 10
phi = np.random.random(self.N) * 2 * np.pi
theta = np.random.random(self.N) * np.pi
ds = np.array([np.cos(theta),
np.cos(phi)*np.sin(theta),
np.sin(phi)*np.sin(theta)]).T
for vec, sep in zip(ds, separation):
pos = np.arange(10)[:, np.newaxis] * vec[np.newaxis, :] * sep
df = pos_to_df([pos])
df = cluster(df, sep*1.1)
assert_equal(df['cluster_size'].values, 10)
df = cluster(df, sep*0.9)
assert_equal(df['cluster_size'].values, 1)
df = pos_to_df([pos[::-1]])
df = cluster(df, sep*1.1)
assert_equal(df['cluster_size'].values, 10)
df = cluster(df, sep*0.9)
assert_equal(df['cluster_size'].values, 1)
ind = np.arange(10)
np.random.shuffle(ind)
pos = ind[:, np.newaxis] * vec[np.newaxis, :] * sep
df = pos_to_df([pos])
df = cluster(df, sep*1.1)
assert_equal(df['cluster_size'].values, 10)
df = cluster(df, sep*0.9)
assert_equal(df['cluster_size'].values, 1)
