def test_partial_corr():
dataf = msd.random_traj_dataset()
msds = msd.all_msds2(dataf, frames=100)
feat = ft.calculate_features(msds)
pcorr = pca.partial_corr(feat)
npt.assert_equal(24.0, np.round(np.sum(pcorr), 1))
dataf = msd.random_traj_dataset(nparts=10)
msds = msd.all_msds2(dataf, frames=100)
feat = ft.calculate_features(msds)
pcorr = pca.partial_corr(feat)
npt.assert_equal(47.9, np.round(np.sum(pcorr), 1))
dataf = msd.random_traj_dataset(nparts=10, seed=9)
msds = msd.all_msds2(dataf, frames=100)
feat = ft.calculate_features(msds)
pcorr = pca.partial_corr(feat)
npt.assert_equal(33.4, np.round(np.sum(pcorr), 1))
dataf = msd.random_traj_dataset(nparts=10, nframes=40, seed=9)
msds = msd.all_msds2(dataf, frames=40)
feat = ft.calculate_features(msds)
pcorr = pca.partial_corr(feat)
npt.assert_equal(17.4, np.round(np.sum(pcorr), 1))
dataf = msd.random_traj_dataset(nparts=10,
nframes=40,
ndist=(3, 5),
seed=9)
msds = msd.all_msds2(dataf, frames=40)
feat = ft.calculate_features(msds)
pcorr = pca.partial_corr(feat)
npt.assert_equal(35.7, np.round(np.sum(pcorr), 1))
def test_kmo():
dataf = msd.random_traj_dataset(nparts=10, ndist=(1, 1), seed=3)
msds = msd.all_msds2(dataf, frames=100)
feat = ft.calculate_features(msds)
dataset = feat.drop(['frames', 'Track_ID'], axis=1)
corrmatrix = np.corrcoef(dataset.transpose())
npt.assert_equal(np.round(np.sum(corrmatrix), 1), 7.3)
def test_pca_analysis():
dataf = msd.random_traj_dataset(nparts=10, ndist=(2, 6))
msds = msd.all_msds2(dataf, frames=100)
feat = ft.calculate_features(msds)
pcadataset = pca.pca_analysis(feat,
dropcols=['frames', 'Track_ID'],
n_components=5)
npt.assert_equal(np.round(np.sum(pcadataset.components.values), 3), 0.400)
def test_plot_particles_in_frame():
prefix = 'test'
msd_file = 'msd_{}.csv'.format(prefix)
ft_file = 'features_{}.csv'.format(prefix)
dataf = msd.random_traj_dataset(nparts=10, ndist=(1, 1), seed=3)
msds = msd.all_msds2(dataf, frames=100)
msds.to_csv(msd_file)
feat = ft.calculate_features(msds)
feat.to_csv(ft_file)
hm.plot_particles_in_frame(prefix, x_range=100, y_range=20, upload=False)
assert os.path.isfile('in_frame_{}.png'.format(prefix))
def test_plot_trajectories():
prefix = 'test'
msd_file = 'msd_{}.csv'.format(prefix)
ft_file = 'features_{}.csv'.format(prefix)
dataf = msd.random_traj_dataset(nparts=30, ndist=(1, 1), seed=3)
msds = msd.all_msds2(dataf, frames=100)
msds.to_csv(msd_file)
feat = ft.calculate_features(msds)
feat.to_csv(ft_file)
hm.plot_trajectories(prefix, resolution=520, rows=1, cols=1, upload=False)
assert os.path.isfile('traj_{}.png'.format(prefix))
def test_plot_histogram():
prefix = 'test'
msd_file = 'msd_{}.csv'.format(prefix)
ft_file = 'features_{}.csv'.format(prefix)
dataf = msd.random_traj_dataset(nparts=30, ndist=(1, 1), seed=3)
msds = msd.all_msds2(dataf, frames=100)
msds.to_csv(msd_file)
feat = ft.calculate_features(msds)
feat.to_csv(ft_file)
hm.plot_histogram(prefix, fps=1, umppx=1, frames=100, frame_interval=5, frame_range=5, y_range=10, upload=False)
assert os.path.isfile('hist_{}.png'.format(prefix))
def test_plot_individual_msds():
prefix = 'test'
msd_file = 'msd_{}.csv'.format(prefix)
ft_file = 'features_{}.csv'.format(prefix)
dataf = msd.random_traj_dataset(nparts=30, ndist=(1, 1), seed=3)
msds = msd.all_msds2(dataf, frames=100)
msds.to_csv(msd_file)
feat = ft.calculate_features(msds)
feat.to_csv(ft_file)
geomean, gSEM = hm.plot_individual_msds(prefix, umppx=1, fps=1, y_range=400, alpha=0.3, upload=False)
npt.assert_equal(332.8, np.round(np.sum(geomean), 1))
npt.assert_equal(33.1, np.round(np.sum(gSEM), 1))
def test_voronoi_finite_polygons_2d():
prefix = 'test'
msd_file = 'msd_{}.csv'.format(prefix)
ft_file = 'features_{}.csv'.format(prefix)
dataf = msd.random_traj_dataset(nparts=30, ndist=(1, 1), seed=3)
msds = msd.all_msds2(dataf, frames=100)
msds.to_csv(msd_file)
feat = ft.calculate_features(msds)
feat.to_csv(ft_file)
xs = feat['X'].astype(int)
ys = feat['Y'].astype(int)
points = np.zeros((xs.shape[0], 2))
points[:, 0] = xs
points[:, 1] = ys
vor = Voronoi(points)
regions, vertices = hm.voronoi_finite_polygons_2d(vor)
npt.assert_equal(243.8, np.round(np.mean(vertices), 1))
def test_random_traj_dataset():
di = {
'Frame': [float(i) for i in [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]],
'Track_ID': [float(i) for i in [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]],
'X':
np.array([
1., 1.93045975532, 1.0, 1.0, 1.0, 0.0, 0.288183500979,
0.576367001958, 0.864550502937, 0.864550502937
]),
'Y':
np.array([
1., 1., 1., 0.06954024468115816, 1.0, 4.0, 4.0, 4.0, 4.0,
4.288183500978857
]),
'Quality': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10],
'SN_Ratio': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
'Mean_Intensity': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]
}
cols = ['Frame', 'Track_ID', 'X', 'Y']
dfi = pd.DataFrame(data=di)[cols]
pdt.assert_frame_equal(
dfi,
msd.random_traj_dataset(nframes=5, nparts=2, fsize=(0, 5))[cols])
def test_random_traj_dataset():
di = {
'Frame': [float(i) for i in [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]],
'Track_ID': [float(i) for i in [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]],
'X':
np.array([
1., 1., 1., 1.93045976, 1.93045976, 0., -0.2881835, 0., 0.,
0.2881835
]),
'Y':
np.array([
1., 0.06954024, -0.86091951, -0.86091951, 0.06954024, 4., 4., 4.,
4.2881835, 4.2881835
]),
'Quality': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10],
'SN_Ratio': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
'Mean_Intensity': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]
}
cols = ['Frame', 'Track_ID', 'X', 'Y']
dfi = pd.DataFrame(data=di)[cols]
pdt.assert_frame_equal(
dfi,
msd.random_traj_dataset(nframes=5, nparts=2, fsize=(0, 5))[cols])
