def test_filter_doubled_cell():
import scitbx.matrix
sgi = sgtbx.space_group_info("P1")
uc1 = sgi.any_compatible_unit_cell(volume=1000)
params = uc1.parameters()
for (m1, m2, m3) in (
(2, 1, 1),
(1, 2, 1),
(1, 1, 2),
(2, 2, 1),
(2, 1, 2),
(1, 2, 2),
(2, 2, 2),
):
uc2 = uctbx.unit_cell((
params[0] * m1,
params[1] * m2,
params[2] * m3,
params[3],
params[4],
params[5],
))
B1 = scitbx.matrix.sqr(uc1.fractionalization_matrix()).transpose()
B2 = scitbx.matrix.sqr(uc2.fractionalization_matrix()).transpose()
solutions = [
model_evaluation.Result(crystal=Crystal(B1, sgi.group()),
n_indexed=100),
model_evaluation.Result(crystal=Crystal(B2, sgi.group()),
n_indexed=100),
]
filtered = model_evaluation.filter_doubled_cell(solutions)
assert len(filtered) == 1
assert filtered[0].crystal == solutions[0].crystal
def test_Result():
rmsds = (0.0269546226916153, 0.03159452239902618, 0.004208711548775884)
fraction_indexed = 0.9683976336148051
model_likelihood = 0.95846970346759
n_indexed = 19152
crystal = Crystal.from_dict(
dict([
("__id__", "crystal"),
(
"real_space_a",
(-45.031874961773504, -14.833784919966813,
-48.766092343826806),
),
(
"real_space_b",
(-32.15263553253188, 3.8725711269478085, 59.82290857456796),
),
(
"real_space_c",
(29.6683003477066, 59.732569113301565, -13.880892871529552),
),
("space_group_hall_symbol", " P 1"),
]))
result = model_evaluation.Result(
rmsds=rmsds,
fraction_indexed=fraction_indexed,
model_likelihood=model_likelihood,
n_indexed=n_indexed,
crystal=crystal,
)
assert result.rmsds == rmsds
assert result.fraction_indexed == fraction_indexed
assert result.model_likelihood == model_likelihood
assert result.n_indexed == n_indexed
assert result.crystal == crystal
def test_ModelRank():
results = [
model_evaluation.Result(
rmsds=(0.0269546226916153, 0.03159452239902618, 0.004208711548775884),
fraction_indexed=0.9683976336148051,
model_likelihood=0.95846970346759,
n_indexed=19152,
crystal=Crystal.from_dict(
dict(
[
("__id__", "crystal"),
(
"real_space_a",
(
-45.031874961773504,
-14.833784919966813,
-48.766092343826806,
),
),
(
"real_space_b",
(-32.15263553253188, 3.8725711269478085, 59.82290857456796),
),
(
"real_space_c",
(29.6683003477066, 59.732569113301565, -13.880892871529552),
),
("space_group_hall_symbol", " P 1"),
]
)
),
hkl_offset=None,
),
model_evaluation.Result(
rmsds=(0.0341397658828684, 0.027401396596305812, 0.00427723439147068),
fraction_indexed=0.9849825554937554,
model_likelihood=0.9562237490188447,
n_indexed=19480,
crystal=Crystal.from_dict(
dict(
[
("__id__", "crystal"),
(
"real_space_a",
(29.66830034770662, 59.73256911330157, -13.880892871529573),
),
(
"real_space_b",
(47.516210146598816, -48.77135532028254, 2.824076640788394),
),
(
"real_space_c",
(
-45.036407933560845,
-14.950807536025826,
-49.06808637024198,
),
),
("space_group_hall_symbol", " P 1"),
]
)
),
hkl_offset=None,
),
model_evaluation.Result(
rmsds=(0.30456791867888355, 0.15679214175133024, 0.009635577811258947),
fraction_indexed=0.33629974212469027,
model_likelihood=0.6574428619874397,
n_indexed=6651,
crystal=Crystal.from_dict(
dict(
[
("__id__", "crystal"),
(
"real_space_a",
(
-11.907050303571122,
34.85499418820148,
30.689745759790572,
),
),
(
"real_space_b",
(-56.943458237132, 19.90418665217566, -18.37834061045143),
),
(
"real_space_c",
(-41.63267941211685, -24.82747139443437, 44.5508337593274),
),
("space_group_hall_symbol", " P 1"),
]
)
),
hkl_offset=None,
),
]
ranker = model_evaluation.ModelRankWeighted()
ranker.extend(results)
assert list(ranker.score_by_fraction_indexed()) == pytest.approx(
[0.02449862002620243, 0.0, 1.550350501381241]
)
assert list(ranker.score_by_rmsd_xy()) == pytest.approx(
[0.0, 0.07598423386955666, 3.044108569529155]
)
assert list(ranker.score_by_volume()) == pytest.approx(
[0.44207271296753703, 0.45026641813391066, 0.0]
)
assert list(ranker.combined_scores()) == pytest.approx(
[0.19602846593366663, 0.20851345109588518, 11.670183660213905]
)
assert (
str(ranker)
== """\
+-------------------------------------+----------+----------------+------------+-------------+-------------------+-----------+-----------------+-----------------+
| unit_cell | volume | volume score | #indexed | % indexed | % indexed score | rmsd_xy | rmsd_xy score | overall score |
|-------------------------------------+----------+----------------+------------+-------------+-------------------+-----------+-----------------+-----------------|
| 68.02 68.03 68.12 109.6 109.5 109.3 | 242890 | 0.44 | 19152 | 97 | 0.02 | 0.04 | 0 | 0.2 |
| 68.12 68.15 68.26 109.5 109.4 109.4 | 244274 | 0.45 | 19480 | 98 | 0 | 0.04 | 0.08 | 0.21 |
| 47.94 63.06 65.84 75.2 71.6 74.5 | 178786 | 0 | 6651 | 34 | 1.55 | 0.34 | 3.04 | 11.67 |
+-------------------------------------+----------+----------------+------------+-------------+-------------------+-----------+-----------------+-----------------+"""
)
best = ranker.best_model()
assert best.n_indexed == 19152
ranker = model_evaluation.ModelRankFilter()
ranker.extend(results)
best = ranker.best_model()
assert best.n_indexed == 19152
assert (
str(ranker)
== """\
+-------------------------------------+----------+-------------+--------------------+--------------+
| unit_cell | volume | n_indexed | fraction_indexed | likelihood |
|-------------------------------------+----------+-------------+--------------------+--------------|
| 68.02 68.03 68.12 109.6 109.5 109.3 | 242890 | 19152 | 97 | 0.96 |
| 68.12 68.15 68.26 109.5 109.4 109.4 | 244274 | 19480 | 98 | 0.96 |
| 47.94 63.06 65.84 75.2 71.6 74.5 | 178786 | 6651 | 34 | 0.66 |
+-------------------------------------+----------+-------------+--------------------+--------------+"""
)