def test_isolated_continuous_random_walks(self):
# Two 2D random walks
np.random.seed(0)
N = 30
Y = 250
M = 20 # margin, because negative values raise OutOfHash
a = DataFrame({'x': M + random_walk(N), 'y': M + random_walk(N), 'frame': np.arange(N)})
b = DataFrame({'x': M + random_walk(N - 1), 'y': M + Y + random_walk(N - 1), 'frame': np.arange(1, N)})
f = pd.concat([a, b])
expected = f.copy().reset_index(drop=True)
expected['particle'] = np.concatenate([np.zeros(N), np.ones(N - 1)])
pandas_sort(expected, ['particle', 'frame'], inplace=True)
actual = self.link_df(f, 5)
assert_traj_equal(actual, expected)
# Many 2D random walks
np.random.seed(0)
initial_positions = [(100, 100), (200, 100), (100, 200), (200, 200)]
import itertools
c = itertools.count()
def walk(x, y):
i = next(c)
return DataFrame({'x': x + random_walk(N - i),
'y': y + random_walk(N - i),
'frame': np.arange(i, N)})
f = pd.concat([walk(*pos) for pos in initial_positions])
expected = f.copy().reset_index(drop=True)
expected['particle'] = np.concatenate([i*np.ones(N - i) for i in range(len(initial_positions))])
pandas_sort(expected, ['particle', 'frame'], inplace=True)
actual = self.link_df(f, 5)
assert_traj_equal(actual, expected)
def test_isolated_continuous_random_walks(self):
# Two 2D random walks
np.random.seed(0)
N = 30
Y = 250
M = 20 # margin, because negative values raise OutOfHash
a = DataFrame({'x': M + random_walk(N), 'y': M + random_walk(N), 'frame': np.arange(N)})
b = DataFrame({'x': M + random_walk(N - 1), 'y': M + Y + random_walk(N - 1), 'frame': np.arange(1, N)})
f = pd.concat([a, b])
expected = f.copy().reset_index(drop=True)
expected['particle'] = np.concatenate([np.zeros(N), np.ones(N - 1)])
pandas_sort(expected, ['particle', 'frame'], inplace=True)
actual = self.link(f, 5)
assert_traj_equal(actual, expected)
# Many 2D random walks
np.random.seed(0)
initial_positions = [(100, 100), (200, 100), (100, 200), (200, 200)]
import itertools
c = itertools.count()
def walk(x, y):
i = next(c)
return DataFrame({'x': x + random_walk(N - i),
'y': y + random_walk(N - i),
'frame': np.arange(i, N)})
f = pd.concat([walk(*pos) for pos in initial_positions])
expected = f.copy().reset_index(drop=True)
expected['particle'] = np.concatenate([i*np.ones(N - i) for i in range(len(initial_positions))])
pandas_sort(expected, ['particle', 'frame'], inplace=True)
actual = self.link(f, 5)
assert_traj_equal(actual, expected)
def walk(x, y):
i = next(c)
return DataFrame({'x': x + random_walk(N - i),
'y': y + random_walk(N - i),
'frame': np.arange(i, N)})
def walk(x, y):
i = next(c)
return DataFrame({'x': x + random_walk(N - i),
'y': y + random_walk(N - i),
'frame': np.arange(i, N)})
