def test_fit_2_sigmas(sigmas=[0.01, 0.03],
n_tracks=5000,
D_free=0.1,
plot=False):
"""
Simulate confined state
"""
sim1 = simulate.tracks(n_tracks=n_tracks,
D_free=D_free,
loc_error=sigmas[0],
use_tqdm=False)
sim2 = simulate.tracks(n_tracks=n_tracks,
D_free=D_free,
loc_error=sigmas[1],
use_tqdm=False)
sim = sim1 + sim2
fit_2_sigmas = fit2.fit_spoton_2_0(
sim,
D=[0, 0.0, 0.1],
F=[0.3, 0.3, 0.4],
fit_D=[False, False, True],
fit_F=[1, 1, 1],
sigma=[0.01, 0.04],
fit_sigma=[True, True],
n_bins=100,
dt=0.06,
n_lags=3,
plot=plot,
)
fit_1_sigmas = fit2.fit_spoton_2_0(
sim,
D=[0, 0.1],
F=[0.3, 0.7],
fit_D=[False, True],
fit_F=[1, 1],
sigma=[0.01],
fit_sigma=[True, True],
n_bins=100,
dt=0.06,
n_lags=3,
plot=plot,
)
for s, S in zip(sigmas, fit_2_sigmas["sigma"]):
np.testing.assert_almost_equal(S, s, 2)
assert fit_1_sigmas["chi2_norm"] > fit_2_sigmas["chi2_norm"]
return fit_2_sigmas
def test_fit_tracks():
sim_params = dict(
num_tracks=4e3,
dt=0.06,
D_bound=0.0,
D_free=0.05,
loc_error=0.03,
p_binding=0.0001,
p_unbinding=0.001,
p_bleaching=1e-1,
p_out_of_focus=1e-5,
min_len=5,
use_tqdm=False,
)
fit_params = dict(
n_lags=7,
plot=False,
dt=0.06,
D=(0.0, 0.1),
fit_D=(True, True),
F=(0.5, 0.5),
fit_F=(True, True),
sigma=(0.2, ),
fit_sigma=(True, ),
n_bins=100,
max_um=2,
verbose=False,
)
F_bound = sim_params["p_binding"] / (sim_params["p_binding"] +
sim_params["p_unbinding"])
tracks = simulate.tracks(**sim_params)
assert len(tracks) == sim_params["num_tracks"]
fit = fit2.fit_spoton_2_0(tracks, **fit_params)
np.testing.assert_almost_equal(
fit["D"][1],
(sim_params["D_free"]),
decimal=0,
err_msg=f"D_free input: {sim_params['D_free']}, fit: {fit['D'][1]}",
)
np.testing.assert_almost_equal(fit["sigma"][0],
sim_params["loc_error"],
decimal=2)
np.testing.assert_almost_equal(
fit["F"][0],
F_bound,
decimal=1,
err_msg=f"Fitted F_bound {fit['F'][0]:.2f}, \
simulated {F_bound:.2f}",
)
def test_fit_return_fit():
tracks = simulate.tracks(num_tracks=100, min_len=3)
fit = fit2.fit_spoton_2_0(tracks,
n_lags=5,
plot=False,
return_hists=False,
return_fit_result=True)
fit_result = fit["fit_result"]
assert isinstance(fit_result, fit2.lmfit.minimizer.MinimizerResult)
def test_result_2_table():
data = [simulate.tracks(100) for _ in range(2)]
fits = [fit2.fit_spoton_2_0(d, plot=False) for d in data]
table = fit2.result_2_table(*fits)
assert isinstance(table, pd.DataFrame)
assert len(table) == 2
def test_fit_return_hists():
tracks = simulate.tracks(num_tracks=100, min_len=3)
fit = fit2.fit_spoton_2_0(tracks, n_lags=5, plot=False, return_hists=True)
hists = fit["hists"]
assert len(hists) == 5
assert [isinstance(h, fit2.JumpLengthHistogram) for h in hists]
def test_fit_high_lags():
tracks = simulate.tracks(num_tracks=100, min_len=3)
fit = fit2.fit_spoton_2_0(tracks, n_lags=5, plot=False)
assert isinstance(fit, dict)