def zest_step_5_make_radmat():
# fmt: off
dyemat = npf([
[
[2, 1],
[2, 1],
],
[
[0, 0],
[0, 0],
],
])
sim_params = SimV1Params.from_aa_list_fixture(
["DE", "C"],
error_model=ErrorModel.no_errors(n_channels=2, beta=7500.0,
sigma=0.16),
n_pres=1,
n_mocks=0,
n_edmans=1,
)
radmat = sim_v1_worker._step_5_make_radmat(dyemat, sim_params)
def it_makes_radmat():
assert np.all(radmat[0] > 4000.0)
def it_deals_with_zeros():
assert np.all(radmat[1] == 0.0)
# fmt: on
zest()
def it_computes_p_bright_from_product():
error_model = ErrorModel.no_errors(n_channels=2)
error_model.labels[0].p_failure_to_bind_amino_acid = 0.9
error_model.labels[1].p_failure_to_bind_amino_acid = 0.8
error_model.labels[0].p_failure_to_attach_to_dye = 0.7
error_model.labels[1].p_failure_to_attach_to_dye = 0.6
error_model.dyes[0].p_non_fluorescent = 0.5
error_model.dyes[1].p_non_fluorescent = 0.4
sim_params = SimV1Params.from_aa_list_fixture(("A", "C"),
error_model=error_model,
n_edmans=3)
flu, p_bright = sim_v1_worker._step_1_create_flu_and_p_bright(
pep_seq_df, sim_params)
assert flu.shape == (2, 4)
expected = np.array([
[
((1 - 0.9) * (1 - 0.7) * (1 - 0.5)),
0,
0,
0,
],
[
0,
0,
((1 - 0.8) * (1 - 0.6) * (1 - 0.4)),
0,
],
])
assert np.allclose(p_bright, expected)
def it_labels_in_tail():
sim_params = SimV1Params.from_aa_list_fixture(
("A", "C"),
error_model=ErrorModel.no_errors(n_channels=2),
n_edmans=6,
)
pep_seq_df = pd.DataFrame(
[
(1, "B", 0),
(1, "B", 1),
(1, "B", 2),
(1, "B", 3),
(1, "A", 4),
(1, "C", 5),
],
columns=PrepResult.pep_seqs_columns,
)
flu, p_bright = sim_v1_worker._step_1_create_flu_and_p_bright(
pep_seq_df, sim_params)
assert np.all(flu == [
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
])
assert np.all(p_bright == [
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
])
def _before():
nonlocal sim_params
sim_params = SimV1Params.from_aa_list_fixture(
["DE", "C"],
error_model=ErrorModel.no_errors(n_channels=2, beta=7500.0),
n_pres=0,
n_mocks=1,
n_edmans=1,
)
def start(self):
sim_params = SimV1Params(include_dfs=True, **self.config.parameters)
prep_result = PrepResult.load_from_folder(self.inputs.prep)
sim_result = sim_v1(
sim_params, prep_result, progress=self.progress, pipeline=self
)
sim_result._generate_flu_info(prep_result)
sim_result.save()
def it_extends_short_peptides():
sim_params = SimV1Params.from_aa_list_fixture(
("A", "C"),
error_model=ErrorModel.no_errors(n_channels=2),
n_edmans=6,
)
flu, p_bright = sim_v1_worker._step_1_create_flu_and_p_bright(
pep_seq_df, sim_params)
assert flu.shape == (2, 7)
assert np.all(flu[:, 3:] == 0.0)
def _stub_sim_params(error_model, n_samples):
return SimV1Params.from_aa_list_fixture(
["A", "C"],
error_model=error_model,
n_samples_train=n_samples,
n_samples_test=n_samples,
n_pres=1,
n_mocks=0,
n_edmans=2,
)
def result_from_prep_fixture(prep_result, labels, n_edmans=5, error_model=None):
labels = labels.split(",")
n_labels = len(labels)
if error_model is None:
error_model = ErrorModel.from_defaults(n_labels)
sim_v1_params = SimV1Params.from_aa_list_fixture(
labels, error_model=error_model, n_pres=1, n_mocks=0, n_edmans=n_edmans,
)
return sim_v1_worker.sim_v1(sim_v1_params, prep_result)
def it_copies():
error_model = ErrorModel.from_defaults(n_channels=2)
src_params = SimV1Params.from_aa_list_fixture(
["DE", "Y"], error_model=error_model
)
src_params.dfs()
src_bleach = src_params.error_model.dyes[0].p_bleach
dst_params = src_params.copy()
dst_params.error_model.set_dye_param("p_bleach", 1.0)
_src_bleach = src_params.error_model.dyes[0].p_bleach
_dst_bleach = dst_params.error_model.dyes[0].p_bleach
assert _src_bleach == src_bleach
assert _dst_bleach == 1.0
def _make_sim_params(aa_list,
n_edmans,
n_train_samples=4,
n_test_samples=4):
params = SimV1Params.from_aa_list_fixture(
aa_list,
error_model=ErrorModel.no_errors(n_channels=len(aa_list)),
n_samples_train=n_train_samples,
n_samples_test=n_test_samples,
n_pres=1,
n_mocks=0,
n_edmans=n_edmans,
)
return params
def zest_step_1_create_flu():
sim_params = SimV1Params.from_aa_list_fixture(
("A", "C"),
error_model=ErrorModel.no_errors(n_channels=2),
)
pep_seq_df = pd.DataFrame([
(1, "A", 0),
(1, "B", 1),
(1, "C", 2),
],
columns=PrepResult.pep_seqs_columns)
def it_return_flu_and_p_bright():
flu, p_bright = sim_v1_worker._step_1_create_flu_and_p_bright(
pep_seq_df, sim_params)
assert np.all(flu == [
[
1,
0,
0,
],
[
0,
0,
1,
],
])
assert np.all(p_bright == [
[
1,
0,
0,
],
[
0,
0,
1,
],
])
def it_extends_short_peptides():
sim_params = SimV1Params.from_aa_list_fixture(
("A", "C"),
error_model=ErrorModel.no_errors(n_channels=2),
n_edmans=6,
)
flu, p_bright = sim_v1_worker._step_1_create_flu_and_p_bright(
pep_seq_df, sim_params)
assert flu.shape == (2, 7)
assert np.all(flu[:, 3:] == 0.0)
def it_computes_p_bright_from_product():
error_model = ErrorModel.no_errors(n_channels=2)
error_model.labels[0].p_failure_to_bind_amino_acid = 0.9
error_model.labels[1].p_failure_to_bind_amino_acid = 0.8
error_model.labels[0].p_failure_to_attach_to_dye = 0.7
error_model.labels[1].p_failure_to_attach_to_dye = 0.6
error_model.dyes[0].p_non_fluorescent = 0.5
error_model.dyes[1].p_non_fluorescent = 0.4
sim_params = SimV1Params.from_aa_list_fixture(("A", "C"),
error_model=error_model,
n_edmans=3)
flu, p_bright = sim_v1_worker._step_1_create_flu_and_p_bright(
pep_seq_df, sim_params)
assert flu.shape == (2, 4)
expected = np.array([
[
((1 - 0.9) * (1 - 0.7) * (1 - 0.5)),
0,
0,
0,
],
[
0,
0,
((1 - 0.8) * (1 - 0.6) * (1 - 0.4)),
0,
],
])
assert np.allclose(p_bright, expected)
def it_labels_in_tail():
sim_params = SimV1Params.from_aa_list_fixture(
("A", "C"),
error_model=ErrorModel.no_errors(n_channels=2),
n_edmans=6,
)
pep_seq_df = pd.DataFrame(
[
(1, "B", 0),
(1, "B", 1),
(1, "B", 2),
(1, "B", 3),
(1, "A", 4),
(1, "C", 5),
],
columns=PrepResult.pep_seqs_columns,
)
flu, p_bright = sim_v1_worker._step_1_create_flu_and_p_bright(
pep_seq_df, sim_params)
assert np.all(flu == [
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
])
assert np.all(p_bright == [
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
])
zest()