# Source code for crystal_toolkit.components.localenv

    import itertools
    from multiprocessing import cpu_count
    from warnings import warn

    import dash_mp_components as mpc
    from dash import callback_context, dcc, html
    from dash.dependencies import Input, Output
    from dash.exceptions import PreventUpdate
    from pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies import (
        SimplestChemenvStrategy,
    )
    from pymatgen.analysis.chemenv.coordination_environments.coordination_geometries import (
        AllCoordinationGeometries,
    )
    from pymatgen.analysis.chemenv.coordination_environments.coordination_geometry_finder import (
        LocalGeometryFinder,
    )
    from pymatgen.analysis.chemenv.coordination_environments.structure_environments import (
        LightStructureEnvironments,
    )
    from pymatgen.analysis.graphs import MoleculeGraph, StructureGraph
    from pymatgen.analysis.local_env import LocalStructOrderParams, cn_opt_params
    from pymatgen.core.structure import Molecule, Structure
    from pymatgen.ext.matproj import MPRester
    from pymatgen.io.ase import AseAtomsAdaptor
    from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
    from pymatgen.util.string import unicodeify, unicodeify_species
    from sklearn.preprocessing import normalize

    from crystal_toolkit.components.structure import StructureMoleculeComponent
    from crystal_toolkit.core.legend import Legend
    from crystal_toolkit.core.panelcomponent import PanelComponent
    from crystal_toolkit.helpers.layouts import (
        H5,
        Column,
        Columns,
        Label,
        Loading,
        cite_me,
        get_data_list,
        get_tooltip,
    )

    try:
        from dscribe.descriptors import SOAP
        from dscribe.kernels import REMatchKernel
    except ImportError:
        warn(
            "Using dscribe SOAP and REMatchKernel requires the dscribe package "
            "which was made optional since it in turn requires numba and numba "
            "was a common source of installation issues."
        )
        SOAP = None

    def _get_local_order_parameters(structure_graph, n):
        """
        A copy of the method in pymatgen.analysis.local_env which
        can operate on StructureGraph directly.

        Calculate those local structure order parameters for
        the given site whose ideal CN corresponds to the
        underlying motif (e.g., CN=4, then calculate the
        square planar, tetrahedral, see-saw-like,
        rectangular see-saw-like order parameters).
        Args:
            structure_graph: StructureGraph object
            n (int): site index.
        Returns (Dict[str, float]):
            A dict of order parameters (values) and the
            underlying motif type (keys; for example, tetrahedral).
        """
        # TODO: move me to pymatgen once stable

        # code from @nisse3000, moved here from graphs to avoid circular
        # import, also makes sense to have this as a general NN method
        cn = structure_graph.get_coordination_of_site(n)
        if cn in [int(k_cn) for k_cn in cn_opt_params]:
            names = [k for k in cn_opt_params[cn]]
            types = []
            params = []
            for name in names:
                types.append(cn_opt_params[cn][name][0])
                tmp = (
                    cn_opt_params[cn][name][1] if len(cn_opt_params[cn][name]) > 1 else None
                )
                params.append(tmp)
            lostops = LocalStructOrderParams(types, parameters=params)
            sites = [structure_graph.structure[n]] + [
                connected_site.site
                for connected_site in structure_graph.get_connected_sites(n)
            ]
            lostop_vals = lostops.get_order_parameters(
                sites, 0, indices_neighs=[i for i in range(1, cn + 1)]
            )
            d = {}
            for i, lostop in enumerate(lostop_vals):
                d[names[i]] = lostop
            return d
        else:
            return None

    class LocalEnvironmentPanel(PanelComponent):
        def __init__(self, *args, **kwargs):
            super().__init__(*args, **kwargs)
            self.create_store("graph")
            self.create_store(
                "display_options",
                initial_data={"color_scheme": "Jmol", "color_scale": None},
            )

        @property
        def title(self):
            return "Local Environments"

        @property
        def description(self):
            return "Analyze the local chemical environments in your crystal."

        @property
        def loading_text(self):
            return "Analyzing environments"

        def contents_layout(self) -> html.Div:

            algorithm_choices = self.get_choice_input(
                label="Analysis method",
                kwarg_label="algorithm",
                state={"algorithm": "chemenv"},
                options=[
                    {"label": "ChemEnv", "value": "chemenv"},
                    {"label": "LocalEnv", "value": "localenv"},
                    {"label": "Bonding Graph", "value": "bondinggraph"},
                    {"label": "SOAP", "value": "soap"},
                ],
                help_str="Choose an analysis method to examine the local chemical environment. "
                "Several methods exist and there is no guaranteed correct answer, so try multiple!",
            )

            analysis = html.Div(id=self.id("analysis"))

            return html.Div([algorithm_choices, html.Br(), analysis, html.Br()])

        @staticmethod
        def get_graph_data(graph, display_options):

            color_scheme = display_options.get("color_scheme", "Jmol")

            nodes = []
            edges = []

            struct_or_mol = StructureMoleculeComponent._get_struct_or_mol(graph)
            legend = Legend(struct_or_mol, color_scheme=color_scheme)

            for idx, node in enumerate(graph.graph.nodes()):

                # TODO: fix for disordered
                node_color = legend.get_color(
                    struct_or_mol[node].species.elements[0], site=struct_or_mol[node]
                )

                nodes.append(
                    {
                        "id": node,
                        "title": f"{struct_or_mol[node].species_string} site "
                        f"({graph.get_coordination_of_site(idx)} neighbors)",
                        "color": node_color,
                    }
                )

            for u, v, d in graph.graph.edges(data=True):

                edge = {"from": u, "to": v, "arrows": ""}

                to_jimage = d.get("to_jimage", (0, 0, 0))

                # TODO: check these edge weights
                if isinstance(struct_or_mol, Structure):
                    dist = struct_or_mol.get_distance(u, v, jimage=to_jimage)
                else:
                    dist = struct_or_mol.get_distance(u, v)
                edge["length"] = 50 * dist

                if to_jimage != (0, 0, 0):
                    edge["arrows"] = "to"
                    label = f"{dist:.2f} Å to site at image vector {to_jimage}"
                else:
                    label = f"{dist:.2f} Å between sites"

                if label:
                    edge["title"] = label

                # if 'weight' in d:
                #   label += f" {d['weight']}"

                edges.append(edge)

            return {"nodes": nodes, "edges": edges}

        def generate_callbacks(self, app, cache):

            super().generate_callbacks(app, cache)

            @app.callback(
                Output(self.id("analysis"), "children"),
                [Input(self.get_kwarg_id("algorithm"), "value")],
            )
            def run_algorithm(algorithm):

                algorithm = self.reconstruct_kwarg_from_state(
                    callback_context.inputs, "algorithm"
                )

                if algorithm == "chemenv":

                    state = {"distance_cutoff": 1.4, "angle_cutoff": 0.3}

                    description = (
                        "The ChemEnv algorithm is developed by David Waroquiers et al. to analyze "
                        'local chemical environments. In this interactive app, the "SimplestChemenvStrategy" '
                        'and "LightStructureEnvironments" are used. For more powerful analysis, please use '
                        "the *pymatgen* code directly. Note that this analysis determines its own bonds independent "
                        "of those shown in the main crystal visualizer."
                    )

                    distance_cutoff = self.get_numerical_input(
                        label="Distance cut-off",
                        kwarg_label="distance_cutoff",
                        state=state,
                        help_str="Defines search radius by considering any atom within a radius "
                        "of the minimum nearest neighbor distance multiplied by the distance "
                        "cut-off.",
                        shape=(),
                    )
                    angle_cutoff = self.get_numerical_input(
                        label="Angle cut-off",
                        kwarg_label="angle_cutoff",
                        state=state,
                        help_str="Defines a tolerance whereby a neighbor atom is excluded if the solid angle "
                        "circumscribed by its Voronoi face is smaller than the angle tolerance "
                        "multiplied by the largest solid angle present in the crystal.",
                        shape=(),
                    )

                    return html.Div(
                        [
                            dcc.Markdown(description),
                            html.Br(),
                            cite_me(
                                cite_text="How to cite ChemEnv",
                                doi="10.1107/S2052520620007994",
                            ),
                            html.Br(),
                            distance_cutoff,
                            angle_cutoff,
                            html.Br(),
                            Loading(id=self.id("chemenv_analysis")),
                        ]
                    )

                elif algorithm == "localenv":

                    description = (
                        "The LocalEnv algorithm is developed by Nils Zimmerman et al. whereby "
                        "an 'order parameter' is calculated that measures how well that "
                        "environment matches an ideal polyhedra. The order parameter "
                        "is a number from zero to one, with one being a perfect match."
                    )

                    return html.Div(
                        [
                            dcc.Markdown(description),
                            html.Br(),
                            cite_me(
                                cite_text="How to cite LocalEnv",
                                doi="10.3389/fmats.2017.00034",
                            ),
                            html.Br(),
                            Loading(id=self.id("localenv_analysis")),
                        ]
                    )

                elif algorithm == "bondinggraph":

                    description = (
                        "This is an alternative way to display the same bonds present in the "
                        "visualizer. Here, the bonding is displayed as a crystal graph, with "
                        "nodes as atoms and edges as bonds. The graph visualization is shown in an "
                        "abstract two-dimensional space."
                    )

                    return html.Div(
                        [
                            dcc.Markdown(description),
                            html.Br(),
                            Loading(id=self.id("bondinggraph_analysis")),
                        ]
                    )

                elif algorithm == "soap":

                    state = {
                        "rcut": 5.0,
                        "nmax": 2,
                        "lmax": 2,
                        "sigma": 0.2,
                        "crossover": True,
                        "average": False,
                        "rbf": "gto",
                        "alpha": 0.1,
                        "threshold": 1e-4,
                        "metric": "linear",
                        "normalize_kernel": True,
                    }

                    description = (
                        'The "Smooth Overlap of Atomic Positions" (SOAP) descriptor provides information on the local '
                        "atomic environment by encoding that environment as a power spectrum derived from the "
                        "spherical harmonics of atom-centered gaussian densities. The SOAP formalism is complex but is "
                        "described well in [Bartók et al.](https://doi.org/10.1103/PhysRevB.87.184115) "
                        "and the REMatch similarity kernel in [De et al.](https://doi.org/10.1039/c6cp00415f) "
                        "The implementation of SOAP in this "
                        "web app is provided by [DScribe](https://doi.org/10.1016/j.cpc.2019.106949).  "
                        ""
                        "SOAP kernels are commonly used in machine learning applications. This interface is provided to "
                        "help gain intuition and exploration of the behavior of SOAP kernels."
                    )

                    rcut = self.get_numerical_input(
                        label="Radial cut-off /Å",
                        kwarg_label="rcut",
                        state=state,
                        help_str="The radial cut-off that defines the local region being considered",
                        shape=(),
                        min=1.0001,
                    )

                    nmax = self.get_numerical_input(
                        label="N max.",
                        kwarg_label="nmax",
                        state=state,
                        help_str="Number of radial basis functions",
                        shape=(),
                        is_int=True,
                        min=1,
                        max=9,
                    )

                    lmax = self.get_numerical_input(
                        label="L max.",
                        kwarg_label="lmax",
                        state=state,
                        help_str="Maximum degree of spherical harmonics",
                        shape=(),
                        is_int=True,
                        min=1,
                        max=9,
                    )

                    sigma = self.get_numerical_input(
                        label="Sigma",
                        kwarg_label="sigma",
                        state=state,
                        help_str="The standard deviation of gaussians used to build atomic density",
                        shape=(),
                        min=0.00001,
                    )

                    rbf = self.get_choice_input(
                        label="Radial basis function",
                        kwarg_label="rbf",
                        state=state,
                        help_str="Polynomial basis is faster, spherical gaussian based was used in original formulation",
                        options=[
                            {"label": "Spherical gaussian basis", "value": "gto"},
                            {"label": "Polynomial basis", "value": "polynomial"},
                        ],
                        style={"width": "16rem"},  # TODO: remove in-line style
                    )

                    crossover = self.get_bool_input(
                        label="Crossover",
                        kwarg_label="crossover",
                        state=state,
                        help_str="If enabled, the power spectrum will include all combinations of elements present.",
                    )

                    average = self.get_bool_input(
                        label="Average",
                        kwarg_label="average",
                        state=state,
                        help_str="If enabled, the SOAP vector will be averaged across all sites.",
                    )

                    alpha = self.get_numerical_input(
                        label="Alpha",
                        kwarg_label="alpha",
                        state=state,
                        help_str="Determines the entropic penalty in the REMatch kernel. As alpha goes to infinity, the "
                        "behavior of the REMatch kernel matches the behavior of the kernel where SOAP vectors "
                        "are averaged across all sites. As alpha goes to zero, the kernel matches the best match "
                        "kernel.",
                        shape=(),
                        min=0.00001,
                    )

                    threshold = self.get_numerical_input(
                        label="Sinkhorn threshold",
                        kwarg_label="threshold",
                        state=state,
                        help_str="Convergence threshold for the Sinkhorn algorithm. If values are too small, convergence "
                        "may not be possible, and calculation time will increase.",
                        shape=(),
                    )

                    metric = self.get_choice_input(
                        label="Metric",
                        kwarg_label="metric",
                        state=state,
                        help_str='See scikit-learn\'s documentation on "Pairwise metrics, Affinities and Kernels" '
                        "for an explanation of available metrics.",
                        options=[
                            # {"label": "Additive χ2", "value": "additive_chi2"},  # these seem to be unstable
                            # {"label": "Exponential χ2", "value": "chi2"},
                            {"label": "Linear", "value": "linear"},
                            {"label": "Polynomial", "value": "polynomial"},
                            {"label": "Radial basis function", "value": "rbf"},
                            {"label": "Laplacian", "value": "laplacian"},
                            {"label": "Sigmoid", "value": "sigmoid"},
                            {"label": "Cosine", "value": "cosine"},
                        ],
                        style={"width": "16rem"},  # TODO: remove in-line style
                    )

                    normalize_kernel = self.get_bool_input(
                        label="Normalize",
                        kwarg_label="normalize_kernel",
                        state=state,
                        help_str="Whether or not to normalize the resulting similarity kernel.",
                    )

                    # metric_kwargs = self.get_dict_input()

                    return html.Div(
                        [
                            dcc.Markdown(description),
                            html.Br(),
                            H5("SOAP parameters"),
                            rcut,
                            nmax,
                            lmax,
                            sigma,
                            rbf,
                            crossover,
                            average,
                            html.Br(),  # TODO: remove all html.Br(), add appropriate styles instead
                            html.Br(),
                            html.Div(id=self.id("soap_analysis")),
                            html.Br(),
                            html.Br(),
                            H5("Similarity metric parameters"),
                            html.Div(
                                "This will calculate structural similarity scores from materials in the "
                                "Materials Project in the same chemical system. Note that for large chemical "
                                "systems this step can take several minutes."
                            ),
                            html.Br(),
                            alpha,
                            threshold,
                            metric,
                            # normalize_kernel,
                            html.Br(),
                            html.Br(),
                            Loading(id=self.id("soap_similarities")),
                        ]
                    )

            def _get_soap_graph(feature, label):

                spectrum = {
                    "data": [
                        {
                            "coloraxis": "coloraxis",
                            # 'hovertemplate': 'x: %{x}<br>y: %{y}<br>color: %{z}<extra></extra>',
                            "type": "heatmap",
                            "z": feature.tolist(),
                        }
                    ]
                }

                spectrum["layout"] = {
                    "xaxis": {"visible": False},
                    "yaxis": {"visible": False},
                    "paper_bgcolor": "rgba(0,0,0,0)",
                    "plot_bgcolor": "rgba(0,0,0,0)",
                    "coloraxis": {
                        "colorscale": [
                            [0.0, "#0d0887"],
                            [0.1111111111111111, "#46039f"],
                            [0.2222222222222222, "#7201a8"],
                            [0.3333333333333333, "#9c179e"],
                            [0.4444444444444444, "#bd3786"],
                            [0.5555555555555556, "#d8576b"],
                            [0.6666666666666666, "#ed7953"],
                            [0.7777777777777778, "#fb9f3a"],
                            [0.8888888888888888, "#fdca26"],
                            [1.0, "#f0f921"],
                        ],
                        "showscale": False,
                    },
                    "margin": {"l": 0, "b": 0, "t": 0, "r": 0, "pad": 0},
                    # "height": 20*feature.shape[0],  # for fixed size plots
                    # "width": 20*feature.shape[1]
                }

                return Columns(
                    [
                        Column(Label(label), size="1"),
                        Column(
                            dcc.Graph(
                                figure=spectrum,
                                config={"displayModeBar": False},
                                responsive=True,
                                style={"height": "60px"},
                            )
                        ),
                    ]
                )

            @app.callback(
                Output(self.id("soap_analysis"), "children"),
                [Input(self.id(), "data"), Input(self.get_all_kwargs_id(), "value")],
            )
            def update_soap_analysis(struct, all_kwargs):

                if not struct:
                    raise PreventUpdate

                if not SOAP:
                    return mpc.Markdown(
                        "This feature will not work unless `dscribe` is installed on the server."
                    )

                struct = self.from_data(struct)
                kwargs = self.reconstruct_kwargs_from_state(callback_context.inputs)

                # TODO: make sure is_int kwarg information is enforced so that int() conversion is unnecessary
                desc = SOAP(
                    species=[e.number for e in struct.composition.elements],
                    sigma=kwargs["sigma"],
                    rcut=kwargs["rcut"],
                    nmax=int(kwargs["nmax"]),
                    lmax=int(kwargs["lmax"]),
                    periodic=True,
                    crossover=kwargs["crossover"],
                    sparse=False,
                    average=kwargs["average"],
                )

                adaptor = AseAtomsAdaptor()
                atoms = adaptor.get_atoms(struct)
                feature = normalize(desc.create(atoms, n_jobs=cpu_count()))

                return _get_soap_graph(feature, "SOAP vector for this material")

            @cache.memoize(timeout=360)
            def _get_all_structs_from_elements(elements):
                structs = {}
                all_chemsyses = []
                for i in range(len(elements)):
                    for els in itertools.combinations(elements, i + 1):
                        all_chemsyses.append("-".join(sorted(els)))

                with MPRester() as mpr:
                    docs = mpr.query(
                        {"chemsys": {"$in": all_chemsyses}}, ["task_id", "structure"]
                    )
                structs.update({d["task_id"]: d["structure"] for d in docs})
                return structs

            @app.callback(
                Output(self.id("soap_similarities"), "children"),
                [Input(self.id(), "data"), Input(self.get_all_kwargs_id(), "value")],
            )
            def update_soap_similarities(struct, all_kwargs):

                if not struct:
                    raise PreventUpdate

                if not SOAP:
                    return mpc.Markdown(
                        "This feature will not work unless `dscribe` is installed on the server."
                    )

                structs = {"input": self.from_data(struct)}
                kwargs = self.reconstruct_kwargs_from_state(callback_context.inputs)

                elements = [str(el) for el in structs["input"].composition.elements]
                structs.update(_get_all_structs_from_elements(elements))

                if not structs:
                    raise PreventUpdate

                elements = {
                    elem for s in structs.values() for elem in s.composition.elements
                }
                # TODO: make sure is_int kwarg information is enforced so that int() conversion is unnecessary
                desc = SOAP(
                    species=[e.number for e in elements],
                    sigma=kwargs["sigma"],
                    rcut=kwargs["rcut"],
                    nmax=int(kwargs["nmax"]),
                    lmax=int(kwargs["lmax"]),
                    periodic=True,
                    crossover=kwargs["crossover"],
                    sparse=False,
                    average=kwargs["average"],
                )

                adaptor = AseAtomsAdaptor()
                atomss = {
                    mpid: adaptor.get_atoms(struct) for mpid, struct in structs.items()
                }

                print(f"Calculating {len(atomss)} SOAP vectors")
                features = {
                    mpid: normalize(desc.create(atoms, n_jobs=cpu_count()))
                    for mpid, atoms in atomss.items()
                }

                re = REMatchKernel(
                    metric=kwargs["metric"],
                    alpha=kwargs["alpha"],
                    threshold=kwargs["threshold"],
                    # normalize_kernel=kwargs["normalize_kernel"],
                )

                print("Calculating similarity kernel")
                similarities = {
                    mpid: re.get_global_similarity(
                        re.get_pairwise_matrix(features["input"], feature)
                    )
                    for mpid, feature in features.items()
                    if mpid != "input"
                }

                sorted_mpids = sorted(similarities, key=lambda x: -similarities[x])

                print("Generating similarity graphs")
                # TODO: was much slower using px.imshow (see prev commit)
                all_graphs = [
                    _get_soap_graph(
                        features[mpid],
                        [
                            html.Span(
                                f"{unicodeify(structs[mpid].composition.reduced_formula)}"
                            ),
                            dcc.Markdown(f"[{mpid}](https://materialsproject.org/{mpid})"),
                            html.Span(f"{similarities[mpid]:.5f}"),
                        ],
                    )
                    for mpid in sorted_mpids
                ]

                print("Returning similarity graphs")
                return html.Div(all_graphs)

            @app.callback(
                Output(self.id("localenv_analysis"), "children"),
                [Input(self.id("graph"), "data")],
            )
            def update_localenv_analysis(graph):

                if not graph:
                    raise PreventUpdate

                graph = self.from_data(graph)

                return html.Div(
                    [
                        str(_get_local_order_parameters(graph, 0)),
                        html.Br(),
                        html.Small("This functionality is still under development."),
                    ]
                )

            @app.callback(
                Output(self.id("bondinggraph_analysis"), "children"),
                [
                    Input(self.id("graph"), "data"),
                    Input(self.id("display_options"), "data"),
                ],
            )
            def update_bondinggraph_analysis(graph, display_options):

                if not graph:
                    raise PreventUpdate

                graph = self.from_data(graph)
                display_options = self.from_data(display_options)

                graph_data = self.get_graph_data(graph, display_options)

                options = {
                    "interaction": {
                        "hover": True,
                        "tooltipDelay": 0,
                        "zoomView": False,
                        "dragView": False,
                    },
                    "edges": {
                        "smooth": {"type": "dynamic"},
                        "length": 250,
                        "color": {"inherit": "both"},
                    },
                    "physics": {
                        "solver": "forceAtlas2Based",
                        "forceAtlas2Based": {"avoidOverlap": 1.0},
                        "stabilization": {"fit": True},
                    },
                }

                return html.Div(
                    [mpc.GraphComponent(graph=graph_data, options=options)],
                    style={"width": "65vmin", "height": "65vmin"},
                )

            @app.callback(
                Output(self.id("chemenv_analysis"), "children"),
                [
                    Input(self.id(), "data"),
                    Input(self.get_kwarg_id("distance_cutoff"), "value"),
                    Input(self.get_kwarg_id("angle_cutoff"), "value"),
                ],
            )
            def get_chemenv_analysis(struct, distance_cutoff, angle_cutoff):

                if not struct:
                    raise PreventUpdate

                struct = self.from_data(struct)
                kwargs = self.reconstruct_kwargs_from_state(callback_context.inputs)
                distance_cutoff = kwargs["distance_cutoff"]
                angle_cutoff = kwargs["angle_cutoff"]

                # TODO: remove these brittle guard statements, figure out more robust way to handle multiple input types
                if isinstance(struct, StructureGraph):
                    struct = struct.structure

                def get_valences(struct):
                    valences = [getattr(site.specie, "oxi_state", None) for site in struct]
                    valences = [v for v in valences if v is not None]
                    if len(valences) == len(struct):
                        return valences
                    else:
                        return "undefined"

                # decide which indices to present to user
                sga = SpacegroupAnalyzer(struct)
                symm_struct = sga.get_symmetrized_structure()
                inequivalent_indices = [
                    indices[0] for indices in symm_struct.equivalent_indices
                ]
                wyckoffs = symm_struct.wyckoff_symbols

                lgf = LocalGeometryFinder()
                lgf.setup_structure(structure=struct)

                se = lgf.compute_structure_environments(
                    maximum_distance_factor=distance_cutoff + 0.01,
                    only_indices=inequivalent_indices,
                    valences=get_valences(struct),
                )
                strategy = SimplestChemenvStrategy(
                    distance_cutoff=distance_cutoff, angle_cutoff=angle_cutoff
                )
                lse = LightStructureEnvironments.from_structure_environments(
                    strategy=strategy, structure_environments=se
                )
                all_ce = AllCoordinationGeometries()

                envs = []
                unknown_sites = []

                for index, wyckoff in zip(inequivalent_indices, wyckoffs):

                    datalist = {
                        "Site": unicodeify_species(struct[index].species_string),
                        "Wyckoff Label": wyckoff,
                    }

                    if not lse.neighbors_sets[index]:
                        unknown_sites.append(f"{struct[index].species_string} ({wyckoff})")
                        continue

                    # represent the local environment as a molecule
                    mol = Molecule.from_sites(
                        [struct[index]] + lse.neighbors_sets[index][0].neighb_sites
                    )
                    mol = mol.get_centered_molecule()
                    mg = MoleculeGraph.with_empty_graph(molecule=mol)
                    for i in range(1, len(mol)):
                        mg.add_edge(0, i)

                    view = html.Div(
                        [
                            StructureMoleculeComponent(
                                struct_or_mol=mg,
                                disable_callbacks=True,
                                id=f"{struct.composition.reduced_formula}_site_{index}",
                                scene_settings={"enableZoom": False, "defaultZoom": 0.6},
                            )._sub_layouts["struct"]
                        ],
                        style={"width": "300px", "height": "300px"},
                    )

                    env = lse.coordination_environments[index]
                    co = all_ce.get_geometry_from_mp_symbol(env[0]["ce_symbol"])
                    name = co.name
                    if co.alternative_names:
                        name += f" (also known as {', '.join(co.alternative_names)})"

                    datalist.update(
                        {
                            "Environment": name,
                            "IUPAC Symbol": co.IUPAC_symbol_str,
                            get_tooltip(
                                "CSM",
                                "The continuous symmetry measure (CSM) describes the similarity to an "
                                "ideal coordination environment. It can be understood as a 'distance' to "
                                "a shape and ranges from 0 to 100 in which 0 corresponds to a "
                                "coordination environment that is exactly identical to the ideal one. A "
                                "CSM larger than 5.0 already indicates a relatively strong distortion of "
                                "the investigated coordination environment.",
                            ): f"{env[0]['csm']:.2f}",
                            "Interactive View": view,
                        }
                    )

                    envs.append(get_data_list(datalist))

                # TODO: switch to tiles?
                envs_grouped = [envs[i : i + 2] for i in range(0, len(envs), 2)]
                analysis_contents = []
                for env_group in envs_grouped:
                    analysis_contents.append(
                        Columns([Column(e, size=6) for e in env_group])
                    )

                if unknown_sites:
                    unknown_sites = html.Strong(
                        f"The following sites were not identified: {', '.join(unknown_sites)}. "
                        f"Please try changing the distance or angle cut-offs to identify these sites, "
                        f"or try an alternative algorithm such as LocalEnv."
                    )
                else:
                    unknown_sites = html.Span()

                return html.Div([html.Div(analysis_contents), html.Br(), unknown_sites])