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from __future__ import annotations
import pytest
import iotbx.merging_statistics
from cctbx import uctbx
from scitbx.array_family import flex
from scitbx.math import curve_fitting
from dials.util import resolution_analysis
def test_polynomial_fit():
x = flex.double(range(-50, 50))
p = (2, 3, 5)
yo = flex.double(x.size())
for i in range(len(p)):
yo += p[i] * flex.pow(x, i)
yf = resolution_analysis.polynomial_fit(x, yo, degree=2)
assert yo == pytest.approx(yf)
def test_log_fit():
x = flex.double(range(0, 100)) * 0.01
p = (1, 2)
yo = flex.double(x.size())
for i in range(len(p)):
yo += flex.exp(p[i] * flex.pow(x, i))
yf = resolution_analysis.log_fit(x, yo, degree=2)
assert yo == pytest.approx(yf, abs=1e-2)
def test_log_inv_fit():
x = flex.double(range(0, 100)) * 0.01
p = (1, 2)
yo = flex.double(x.size())
for i in range(len(p)):
yo += 1 / flex.exp(p[i] * flex.pow(x, i))
yf = resolution_analysis.log_inv_fit(x, yo, degree=2)
assert yo == pytest.approx(yf, abs=1e-2)
def test_tanh_fit():
x = flex.double(range(0, 100)) * 0.01
f = curve_fitting.tanh(0.5, 1.5)
n_obs = flex.double(100, 100)
yo = f(x)
yf = resolution_analysis.tanh_fit(x, yo, n_obs=n_obs)
assert yo == pytest.approx(yf, abs=1e-5)
@pytest.fixture
def merging_stats(dials_data):
mtz = str(
dials_data("x4wide_processed", pathlib=True)
/ "AUTOMATIC_DEFAULT_scaled_unmerged.mtz"
)
i_obs, _ = resolution_analysis.miller_array_from_mtz(mtz)
return iotbx.merging_statistics.dataset_statistics(
i_obs=i_obs,
n_bins=20,
binning_method="counting_sorted",
use_internal_variance=False,
eliminate_sys_absent=False,
assert_is_not_unique_set_under_symmetry=False,
cc_one_half_significance_level=0.1,
)
def test_resolution_fit(merging_stats):
d_star_sq = flex.double(uctbx.d_as_d_star_sq(b.d_min) for b in merging_stats.bins)
y_obs = flex.double(b.r_merge for b in merging_stats.bins)
result = resolution_analysis.resolution_fit(
d_star_sq, y_obs, resolution_analysis.log_inv_fit, 0.6, n_obs=None
)
assert result.d_min == pytest.approx(1.278, abs=1e-3)
assert flex.max(flex.abs(result.y_obs - result.y_fit)) < 0.05
def test_resolution_cc_half(merging_stats):
result = resolution_analysis.resolution_cc_half(merging_stats, limit=0.82)
assert result.d_min == pytest.approx(1.242, abs=1e-3)
result = resolution_analysis.resolution_cc_half(
merging_stats,
limit=0.82,
cc_half_method="sigma_tau",
model=resolution_analysis.polynomial_fit,
)
assert result.d_min == pytest.approx(1.233, abs=1e-3)
assert flex.max(flex.abs(result.y_obs - result.y_fit)) < 0.04
assert result.critical_values is not None
assert len(result.critical_values) == len(result.d_star_sq)
def test_resolution_fit_from_merging_stats(merging_stats):
result = resolution_analysis.resolution_fit_from_merging_stats(
merging_stats, "i_over_sigma_mean", resolution_analysis.log_fit, limit=1.5
)
assert result.d_min == pytest.approx(1.295, abs=1e-3)
assert flex.max(flex.abs(result.y_obs - result.y_fit)) < 1
def test_resolution_fit_interpolation_error(merging_stats):
result = resolution_analysis.resolution_fit_from_merging_stats(
merging_stats, "i_over_sigma_mean", resolution_analysis.log_fit, limit=25
)
assert result.d_min is None
def test_plot_result(merging_stats):
result = resolution_analysis.resolution_cc_half(merging_stats, limit=0.82)
d = resolution_analysis.plot_result("cc_half", result)
assert "data" in d
assert "layout" in d
result = resolution_analysis.resolution_fit_from_merging_stats(
merging_stats,
"unmerged_i_over_sigma_mean",
resolution_analysis.log_fit,
limit=0.82,
)
d = resolution_analysis.plot_result("isigma", result)
assert "data" in d
assert "layout" in d
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