def test_back_proj_pca():
trajs = Trajectories(data.with_gaps_df())
rotated, pca = transformation.do_pca(trajs, suffix='',
append=False, return_pca=True)
back_proj = transformation.back_proj_pca(rotated, pca, coords=['x', 'y', 'z'])
assert_almost_equal(back_proj.iloc[0].x, trajs.iloc[0].x)
def test_brownian_gap_close():
trajs = Trajectories(data.with_gaps_df())
max_speed = 10.
cost_func = BrownianGapCloseCostFunction(parameters={'max_speed': max_speed})
in_idxs = [(3, 0), (3, 0), (5, 1), (13, 2), (13, 2), (16, 3)]
out_idxs = [(5, 3), (7, 4), (7, 4), (15, 5), (18, 6), (18, 6)]
cost_func.context['trajs'] = trajs
cost_func.context['idxs_in'] = in_idxs
cost_func.context['idxs_out'] = out_idxs
cost_func.get_block()
m = cost_func.mat
m_true = [[np.nan, np.nan, np.nan, 2.0553543815820543, 45.74823093541038, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, 1.1530875267623917, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan, 0.8254178191605767, 23.84513283483594],
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 4.916599888362159],
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]]
assert_array_almost_equal(m, m_true)
def test_brownian_gap_close():
trajs = Trajectories(data.with_gaps_df())
max_speed = 10.
cost_func = BrownianGapCloseCostFunction(
parameters={'max_speed': max_speed})
in_idxs = [(3, 0), (3, 0), (5, 1), (13, 2), (13, 2), (16, 3)]
out_idxs = [(5, 3), (7, 4), (7, 4), (15, 5), (18, 6), (18, 6)]
cost_func.context['trajs'] = trajs
cost_func.context['idxs_in'] = in_idxs
cost_func.context['idxs_out'] = out_idxs
cost_func.get_block()
m = cost_func.mat
m_true = [[
np.nan, np.nan, np.nan, 2.0553543815820543, 45.74823093541038, np.nan,
np.nan
], [np.nan, np.nan, np.nan, np.nan, 1.1530875267623917, np.nan, np.nan],
[
np.nan, np.nan, np.nan, np.nan, np.nan, 0.8254178191605767,
23.84513283483594
],
[
np.nan, np.nan, np.nan, np.nan, np.nan, np.nan,
4.916599888362159
], [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]]
assert_array_almost_equal(m, m_true)
def test_back_proj_pca():
trajs = Trajectories(data.with_gaps_df())
rotated, pca = transformation.do_pca(trajs, suffix='',
append=False, return_pca=True)
back_proj = transformation.back_proj_pca(rotated, pca, coords=['x', 'y', 'z'])
assert_almost_equal(back_proj.iloc[0].x, trajs.iloc[0].x)
def test_radial_speed():
trajs = Trajectories(data.with_gaps_df())
trajs.reset_index(level='label', drop=True, inplace=True)
trajs.set_index('true_label', drop=True, append=True, inplace=True)
trajs.index.set_names(['t_stamp', 'label'], inplace=True)
rd_speeds = rotation.radial_speed(trajs,
in_coords=['x', 'y'],
from_polar=False)
def test_by_frame_solver_with_missing_data():
true_trajs = data.with_gaps_df()
solver = ByFrameSolver.for_brownian_motion(true_trajs, max_speed=5, penalty=2.)
trajs = solver.track()
min_chi_square, conserved_trajectories_number, scores = get_scores_on_trajectories(trajs)
assert min_chi_square == 0 and conserved_trajectories_number == 3 / 7.
def test_back_proj_interp():
trajs = Trajectories(data.with_gaps_df())
interpolated = transformation.time_interpolate(trajs,
sampling=3, s=0)
back_proj = transformation.back_proj_interp(interpolated, trajs, 3)
assert_almost_equal(back_proj.iloc[0].x, trajs.iloc[0].x)
interpolated = transformation.time_interpolate(trajs,
sampling=3, s=2)
back_proj = transformation.back_proj_interp(interpolated, trajs, 3)
assert_almost_equal(back_proj.iloc[0].x, trajs.iloc[0].x)
def test_back_proj_interp():
trajs = Trajectories(data.with_gaps_df())
interpolated = transformation.time_interpolate(trajs,
sampling=3, s=0)
back_proj = transformation.back_proj_interp(interpolated, trajs, 3)
assert_almost_equal(back_proj.iloc[0].x, trajs.iloc[0].x)
interpolated = transformation.time_interpolate(trajs,
sampling=3, s=2)
back_proj = transformation.back_proj_interp(interpolated, trajs, 3)
assert_almost_equal(back_proj.iloc[0].x, trajs.iloc[0].x)
def test_gap_close():
trajs = data.with_gaps_df()
max_speed = 10.
maximum_gap = 5
gc_solver = GapCloseSolver.for_brownian_motion(trajs,
max_speed=max_speed,
maximum_gap=maximum_gap,
use_t_stamp=True)
gc_solver.track()
seg_shapes = [seg[1].shape for seg in gc_solver.trajs.iter_segments]
assert seg_shapes == [(18, 5), (19, 5), (19, 5)]
def test_gap_close_get_candidates():
trajs = data.with_gaps_df()
max_speed = 10.
maximum_gap = 5
gc_solver = GapCloseSolver.for_brownian_motion(trajs,
max_speed=max_speed,
maximum_gap=maximum_gap,
use_t_stamp=True)
in_idxs, out_idxs = gc_solver._get_candidates()
assert in_idxs == [(3, 0), (3, 0), (5, 1), (13, 2), (13, 2), (16, 3)]
assert out_idxs == [(5, 3), (7, 4), (7, 4), (15, 5), (18, 6), (18, 6)]
def test_by_frame_solver_with_missing_data():
true_trajs = data.with_gaps_df()
solver = ByFrameSolver.for_brownian_motion(true_trajs,
max_speed=5,
penalty=2.)
trajs = solver.track()
min_chi_square, conserved_trajectories_number, scores = get_scores_on_trajectories(
trajs)
assert min_chi_square == 0 and conserved_trajectories_number == 3 / 7.
def test_interpolate():
trajs = Trajectories(data.with_gaps_df())
trajs.set_index('true_label', inplace=True, append=True)
trajs.reset_index(level='label', drop=True, inplace=True)
trajs.index.set_names(['t_stamp', 'label'], inplace=True)
interpolated = trajs.time_interpolate(sampling=3, time_step=0.1, s=1)
# t_stamps_in = interpolated.index.get_level_values('t_stamp')
# indexer = t_stamps_in % 2 == 0
# interpolated.loc[indexer].shape, trajs.shape
# indexer = interpolated.t_stamps % 3 == 0
# assert interpolated.loc[indexer].shape[0] == trajs.shape[0]
dts = interpolated.get_segments()[0].t.diff().dropna()
# All time points should be equaly spaced
assert_almost_equal(dts.min(), dts.max())
def test_interpolate():
trajs = Trajectories(data.with_gaps_df())
trajs.set_index('true_label', inplace=True, append=True)
trajs.reset_index(level='label', drop=True, inplace=True)
trajs.index.set_names(['t_stamp', 'label'], inplace=True)
interpolated = trajs.time_interpolate(sampling=3, time_step=0.1, s=1)
# t_stamps_in = interpolated.index.get_level_values('t_stamp')
# indexer = t_stamps_in % 2 == 0
# interpolated.loc[indexer].shape, trajs.shape
# indexer = interpolated.t_stamps % 3 == 0
# assert interpolated.loc[indexer].shape[0] == trajs.shape[0]
dts = interpolated.get_segments()[0].t.diff().dropna()
# All time points should be equaly spaced
assert_almost_equal(dts.min(), dts.max())
def test_radial_speed():
trajs = Trajectories(data.with_gaps_df())
trajs.reset_index(level='label', drop=True, inplace=True)
trajs.set_index('true_label', drop=True, append=True, inplace=True)
trajs.index.set_names(['t_stamp', 'label'], inplace=True)
rd_speeds = rotation.radial_speed(trajs, in_coords=['x', 'y'], from_polar=False)