def unit_cell_analysis(self):
from dials.command_line.unit_cell_histogram import uc_params_from_experiments
# from dials.command_line.unit_cell_histogram import panel_distances_from_experiments
experiments = self._data_manager.experiments
uc_params = uc_params_from_experiments(experiments)
# panel_distances = panel_distances_from_experiments(experiments)
d = OrderedDict()
from xia2.Modules.MultiCrystal.plots import plot_uc_histograms
d.update(plot_uc_histograms(uc_params))
# self._plot_uc_vs_detector_distance(uc_params, panel_distances, outliers, params.steps_per_angstrom)
# self._plot_number_of_crystals(experiments)
clustering, dendrogram = self.unit_cell_clustering(
experiments,
threshold=self.params.unit_cell_clustering.threshold,
log=self.params.unit_cell_clustering.log,
)
from dials.algorithms.clustering.plots import scipy_dendrogram_to_plotly_json
d["uc_clustering"] = scipy_dendrogram_to_plotly_json(
dendrogram,
title="Unit cell clustering",
xtitle="Dataset",
ytitle="Distance (Å^2)",
)
return d
def test_plot_uc_histograms():
params = (50, 60, 70, 90, 90, 90)
rand_norm = scitbx.random.normal_distribution(mean=0, sigma=0.5)
g = scitbx.random.variate(rand_norm)
n = 20
uc_params = [p + g(n) for p in params]
d = plots.plot_uc_histograms(uc_params)
assert set(d.keys()) == {"uc_scatter", "uc_hist"}
for v in d.values():
assert set(v.keys()) == {"layout", "data"}
def test_plot_uc_histograms():
params = (50, 60, 70, 90, 90, 90)
rand_norm = scitbx.random.normal_distribution(mean=0, sigma=0.5)
g = scitbx.random.variate(rand_norm)
n = 20
uc_params = [p + g(n) for p in params]
d = plots.plot_uc_histograms(uc_params)
# ensure the following dictionary keys are present
assert set(d) >= {"uc_scatter", "uc_hist"}
for v in d.values():
assert set(v) >= {"layout", "data", "help"}
def unit_cell_analysis(self):
from dials.command_line.unit_cell_histogram import uc_params_from_experiments
# from dials.command_line.unit_cell_histogram import panel_distances_from_experiments
experiments = self._data_manager.experiments
lattice_ids = [
self._data_manager.identifiers_to_ids_map[i]
for i in experiments.identifiers()
]
uc_params = uc_params_from_experiments(experiments)
# panel_distances = panel_distances_from_experiments(experiments)
d = OrderedDict()
from xia2.Modules.MultiCrystal.plots import plot_uc_histograms
d.update(plot_uc_histograms(uc_params))
# self._plot_uc_vs_detector_distance(uc_params, panel_distances, outliers, params.steps_per_angstrom)
# self._plot_number_of_crystals(experiments)
clustering, dendrogram = self.unit_cell_clustering(
experiments,
lattice_ids,
threshold=self.params.unit_cell_clustering.threshold,
log=self.params.unit_cell_clustering.log,
)
from dials.algorithms.clustering.plots import scipy_dendrogram_to_plotly_json
d["uc_clustering"] = scipy_dendrogram_to_plotly_json(
dendrogram,
title="Unit cell clustering",
xtitle="Dataset",
ytitle="Distance (Å<sup>2</sup>)",
help="""\
The results of single-linkage hierarchical clustering on the unit cell parameters using
the Andrews–Bernstein NCDist distance metric (Andrews & Bernstein, 2014). The height at
which two clusters are merged in the dendrogram is a measure of the similarity between
the unit cells in each cluster. A larger separation between two clusters may be
indicative of a higher degree of non-isomorphism between the clusters. Conversely, a
small separation between two clusters suggests that their unit cell parameters are
relatively isomorphous.
""",
)
return d