def it_compares_to_all_dyetracks_with_row_fit():
_priors = PriorsMLEFixtures.illumination(row_k_sigma=0.2)
radmat, true_dyt_iz, true_ks = synthetic_radmat_from_dyemat(
dyemat, _priors, n_channels=1, n_samples=500)
mask = true_dyt_iz > 0
radmat = radmat[mask]
true_ks = true_ks[mask]
nn_v2_context = _test(
radmat,
n_neighbors=0,
run_against_all_dyetracks=True,
run_row_k_fit=True,
)
# Uncomment this to compare to random
# true_ks = np.random.normal(1.0, 0.5, true_ks.shape[0])
# In this mode I expect to get back outputs for every radrow vs every dytrow
assert nn_v2_context.against_all_dyetrack_pvals.shape == (
radmat.shape[0],
dyemat.shape[0],
)
assert nn_v2_context.against_all_dyetrack_pred_ks.shape == (
radmat.shape[0],
dyemat.shape[0],
)
pks = nn_v2_context.pred_ks
mask = ~np.isnan(pks)
pear_r, _ = stats.pearsonr(true_ks[mask], pks[mask])
assert pear_r > 0.5
def it_enforces_reverse_sort_on_count_per_dyt():
_dyepeps = np.array(
[
[1, 1, 10],
[1, 2, 30],
],
dtype=np.uint64,
)
radmat, true_dyt_iz, true_ks = synthetic_radmat_from_dyemat(
dyemat, priors, n_channels=1, n_samples=5)
with zest.raises(CException,
in_args="must be reverse sorted by count per dyt"):
_test(radmat, _dyepeps=_dyepeps)
def it_catches_non_sequential_dyt_iz_in_dyepeps():
_dyepeps = np.array(
[
[1, 1, 10],
[2, 1, 10],
[1, 2, 30],
],
dtype=np.uint64,
)
radmat, true_dyt_iz, true_ks = synthetic_radmat_from_dyemat(
dyemat, priors, n_channels=1, n_samples=5)
with zest.raises(CException, in_args="Non sequential dyt_i"):
_test(radmat, _dyepeps=_dyepeps)
def it_generates_multichannel_radmat_different_betas():
_priors = PriorsMLEFixtures.illumination_multi_channel(
gain_mus_ch=[5000, 15000],
gain_sigmas_ch=[0, 0],
bg_sigmas_ch=[0, 0],
row_k_sigma=0.0,
)
radmat, true_dyt_iz, true_ks = synthetic_radmat_from_dyemat(
dyemat_2ch, _priors, n_channels=2, n_samples=1)
# fmt off
expected = np.array([
[
0,
0,
0,
0,
0,
0,
],
[
5000,
0,
0,
15000,
0,
0,
],
[
10000,
5000,
5000,
30000,
15000,
15000,
],
[
15000,
10000,
5000,
45000,
30000,
15000,
],
], )
# fmt on
assert np.allclose(radmat, expected)
def it_classifies_multichannel():
_priors = PriorsMLEFixtures.illumination_multi_channel(
gain_mus_ch=[5000, 15000],
gain_sigmas_ch=[0, 0],
bg_sigmas_ch=[0, 0],
)
# gain_model = GainModel.multi_channel(
# beta=[5000, 15000], sigma=[0, 0], zero_sigma=[0, 0]
# )
radmat, true_dyt_iz, true_ks = synthetic_radmat_from_dyemat(
dyemat_2ch, _priors, n_channels=2, n_samples=5)
nn_v2_context = _test(radmat, _dyemat=dyemat_2ch)
n_same = (true_dyt_iz == nn_v2_context.pred_dyt_iz).sum()
# This is almost always higher than 0.5, but how to prevent the
# occasional hiccup? Classify n times? You don't want to just
# retry and allow a terrible result to slip through.
assert n_same >= int(0.5 * true_dyt_iz.shape[0])
def it_fits_k():
# Does it make a copy of
_priors = PriorsMLEFixtures.illumination(row_k_sigma=0.2)
radmat, true_dyt_iz, true_ks = synthetic_radmat_from_dyemat(
dyemat, _priors, n_channels=1, n_samples=500)
mask = true_dyt_iz > 0
radmat = radmat[mask]
true_ks = true_ks[mask]
nn_v2_context = _test(radmat)
# Uncomment this to compare to random
# true_ks = np.random.normal(1.0, 0.5, true_ks.shape[0])
# Check that there's a reasonable correlation between true and pred k
# I ran this several times and found with random true_ks
# ie: true_ks = np.random.normal(1.0, 0.5, true_ks.shape[0])
# was like 0.02 where real was > 0.4
pks = nn_v2_context.pred_ks
mask = ~np.isnan(pks)
pear_r, _ = stats.pearsonr(true_ks[mask], pks[mask])
assert pear_r > 0.4
def it_compares_to_all_dyetracks_without_row_fit():
radmat, true_dyt_iz, true_ks = synthetic_radmat_from_dyemat(
dyemat, priors, n_channels=1, n_samples=5)
nn_v2_context = _test(radmat,
n_neighbors=0,
run_against_all_dyetracks=True,
run_row_k_fit=False)
# In this mode I expect to get back outputs for every radrow vs every dytrow
# But the radrows generated from empties can be ignore
mask = true_dyt_iz > 0
n_good_calls = (
true_dyt_iz[mask] == nn_v2_context.pred_dyt_iz[mask]).sum()
assert n_good_calls >= int(0.75 * mask.sum())
assert np.all(nn_v2_context.against_all_dyetrack_pred_ks[:, 1:] == 1.0)
assert nn_v2_context.against_all_dyetrack_pvals.shape == (
radmat.shape[0],
dyemat.shape[0],
)
assert nn_v2_context.against_all_dyetrack_pred_ks.shape == (
radmat.shape[0],
dyemat.shape[0],
)
def it_classifies():
radmat, true_dyt_iz, true_ks = synthetic_radmat_from_dyemat(
dyemat, priors, n_channels=1, n_samples=5)
nn_v2_context = _test(radmat)
n_same = (true_dyt_iz == nn_v2_context.pred_dyt_iz).sum()
assert n_same >= int(0.65 * true_dyt_iz.shape[0])
