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 test_plot_uc_histograms():
sgi = sgtbx.space_group_info("P1")
unit_cells = [
sgi.any_compatible_unit_cell(
volume=random.uniform(990, 1010)).parameters() for i in range(10)
]
uc_params = [flex.double(a) for a in zip(*unit_cells)]
d = plots.plot_uc_histograms(uc_params)
assert set(d) == {"uc_scatter", "uc_hist"}
assert len(d["uc_scatter"]["data"]) == 3
assert len(d["uc_hist"]["data"]) == 3
def make_cluster_plots(large_clusters: List[Cluster]) -> dict:
cluster_plots = {}
for n, cluster in enumerate(large_clusters):
uc_params = [flex.double() for i in range(6)]
for c in cluster.members:
ucp = c.crystal_symmetry.unit_cell().parameters()
for i in range(6):
uc_params[i].append(ucp[i])
d_this = cluster_plotter.plot_uc_histograms(uc_params)
d_this["uc_scatter"]["layout"]["title"] += f" cluster {n+1}"
d_this["uc_hist"]["layout"]["title"] += f" cluster {n+1}"
d_this[f"uc_scatter_{n}"] = d_this.pop("uc_scatter")
d_this[f"uc_hist_{n}"] = d_this.pop("uc_hist")
cluster_plots.update(d_this)
return cluster_plots
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
