def make_plots(self):
"""Generate plots of the unit cell clustering."""
uc_params = uc_params_from_experiments(self.data["experiments"])
d = plots.plot_uc_histograms(uc_params)
if "dendrogram" in self.data:
d["uc_clustering"] = plots.scipy_dendrogram_to_plotly_json(
self.data["dendrogram"],
title="Unit cell clustering",
xtitle="Dataset",
ytitle=r"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.
""",
)
graphs = {"unit_cell_graphs": d}
return graphs
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_scipy_dendrogram_to_plotly_json():
# generate some random unit cells
sgi = sgtbx.space_group_info("P1")
crystal_symmetries = [
sgi.any_compatible_crystal_symmetry(volume=random.uniform(990, 1010))
for i in range(10)
]
lattice_ids = flex.int_range(0, len(crystal_symmetries)).as_string()
ucs = UnitCellCluster.from_crystal_symmetries(crystal_symmetries,
lattice_ids=lattice_ids)
_, dendrogram, _ = ucs.ab_cluster(write_file_lists=False, doplot=False)
d = plots.scipy_dendrogram_to_plotly_json(dendrogram,
title="Unit cell clustering")
assert set(d) == {"layout", "data"}
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
def make_plots(self):
"""Generate plots of the unit cell clustering."""
d = OrderedDict()
uc_params = uc_params_from_experiments(self.data["experiments"])
d.update(plots.plot_uc_histograms(uc_params))
if "dendrogram" in self.data:
d["uc_clustering"] = plots.scipy_dendrogram_to_plotly_json(
self.data["dendrogram"],
title="Unit cell clustering",
xtitle="Dataset",
ytitle=r"Distance (Å<sup>2</sup>)",
)
graphs = {"unit_cell_graphs": d}
return graphs
