"""
clustering.py
-------------

Implements clustering-based classification routines, including SOAP descriptor computation,
dimensionality reduction, and cluster labeling.
"""

import numpy as np

# You will need to import or reference your classes (Partition, ClusteringAnalysis, Compress) accordingly.
# from sage_lib.partition.Partition import Partition
# from sage_lib.miscellaneous.data_mining import Compress, ClusteringAnalysis

def evaluate_Pn(partition, r_cut, n_max, l_max, sigma,
                n_components=10, compress_model='umap',
                eps=0.6, min_samples=2, cluster_model='dbscan', max_clusters=10):
    """
    Evaluate the probability (Pn) for each structure in the given Partition using SOAP descriptors
    and a chosen clustering algorithm.

    Parameters
    ----------
    partition : Partition
        A Partition object containing a list of structures.
    r_cut : float
        The cutoff radius for SOAP descriptors.
    n_max : int
        The maximum number of radial basis functions for SOAP.
    l_max : int
        The maximum degree of spherical harmonics for SOAP.
    sigma : float
        The width of the Gaussian function in SOAP.
    n_components : int, optional
        The number of components for dimensionality reduction, by default 10.
    compress_model : str, optional
        The method used for dimensionality reduction (e.g., 'umap', 'pca'), by default 'umap'.
    eps : float, optional
        The epsilon parameter used in DBSCAN clustering, by default 0.6.
    min_samples : int, optional
        The minimum number of samples for DBSCAN clustering, by default 2.
    cluster_model : str, optional
        The clustering algorithm to use (e.g., 'dbscan', 'kmeans'), by default 'dbscan'.
    max_clusters : int, optional
        The maximum number of clusters to allow, if relevant, by default 10.

    Returns
    -------
    tuple
        (structure_labels, cluster_counts, class_labels) where:
        - structure_labels is a list mapping each structure to cluster labels.
        - cluster_counts is a matrix counting cluster membership per structure.
        - class_labels is a list or array of final cluster labels.
    """
    # Compute SOAP descriptors
    descriptors_by_species, atom_info_by_species = partition.get_SOAP(
        r_cut=r_cut,
        n_max=n_max,
        l_max=l_max,
        sigma=sigma,
        save=False,
        cache=False
    )

    # Dimensionality reduction (example: placeholder, you might refine or remove)
    print(f"Compressing descriptors using {compress_model} with {n_components} components.")
    # Example usage of an external compression class
    # compressor = Compress(unique_labels=partition.uniqueAtomLabels)
    # compressed_data = compressor.verify_and_load_or_compress(
    #     descriptors_by_species,
    #     method=compress_model,
    #     n_components={k: n_components for k in descriptors_by_species.keys()},
    #     load=False,
    #     save=False
    # )

    # For demonstration, we can skip actual compression or just reuse descriptors:
    compressed_data = descriptors_by_species

    # Clustering
    print(f"Clustering using {cluster_model} with eps={eps} and min_samples={min_samples}.")
    cluster_analysis_results = {}
    for species in partition.uniqueAtomLabels:
        # analyzer = ClusteringAnalysis()
        # cluster_analysis_results[species] = analyzer.cluster_analysis(
        #     compressed_data[species],
        #     params={'eps': eps, 'min_samples': min_samples},
        #     output_dir=f'./cluster_results/{species}',
        #     use_cache=False,
        #     methods=[cluster_model],
        #     save=False,
        #     max_clusters=max_clusters,
        # )
        # Placeholder: set everything to cluster "0"
        cluster_analysis_results[species] = {cluster_model: np.zeros(len(compressed_data[species]), dtype=int)}

        print(f"Species: {species}, {cluster_model} clusters: {len(set(cluster_analysis_results[species][cluster_model]))}")

    # Assign cluster labels at the structure level
    structure_labels, cluster_counts, class_labels = partition.generate_atom_labels_and_cluster_counts(
        atom_clusters={key: cluster_analysis_results[key][cluster_model] for key in cluster_analysis_results.keys()},
        atom_structures={key: np.array(atom_info_by_species[key]) for key in atom_info_by_species.keys()},
    )

    return structure_labels, cluster_counts, class_labels
