"""
Computes a dendrogram based on a given categorical observation.
"""

from typing import Optional, Sequence, Dict, Any

import pandas as pd
from anndata import AnnData
from pandas.api.types import is_categorical_dtype

from .. import logging as logg
from .._utils import _doc_params
from ..tools._utils import _choose_representation, doc_use_rep, doc_n_pcs


@_doc_params(n_pcs=doc_n_pcs, use_rep=doc_use_rep)
def dendrogram(
    adata: AnnData,
    groupby: str,
    n_pcs: Optional[int] = None,
    use_rep: Optional[str] = None,
    var_names: Optional[Sequence[str]] = None,
    use_raw: Optional[bool] = None,
    cor_method: str = 'pearson',
    linkage_method: str = 'complete',
    optimal_ordering: bool = False,
    key_added: Optional[str] = None,
    inplace: bool = True,
) -> Optional[Dict[str, Any]]:
    """\
    Computes a hierarchical clustering for the given `groupby` categories.

    By default, the PCA representation is used unless `.X`
    has less than 50 variables.

    Alternatively, a list of `var_names` (e.g. features) can be given.

    Average values of either `var_names` or components are used
    to compute a correlation matrix.

    The hierarchical clustering can be visualized using
    :func:`quanp.pl.dendrogram` or multiple other visualizations that can
    include a dendrogram: :func:`~quanp.pl.matrixplot`,
    :func:`~quanp.pl.heatmap`, :func:`~quanp.pl.dotplot`,
    and :func:`~quanp.pl.stacked_violin`.

    .. note::
        The computation of the hierarchical clustering is based on predefined
        groups and not per subject. The correlation matrix is computed using by
        default pearson but other methods are available.

    Parameters
    ----------
    adata
        Annotated data matrix
    {n_pcs}
    {use_rep}
    var_names
        List of var_names to use for computing the hierarchical clustering.
        If `var_names` is given, then `use_rep` and `n_pcs` is ignored.
    use_raw
        Only when `var_names` is not None.
        Use `raw` attribute of `adata` if present.
    cor_method
        correlation method to use.
        Options are 'pearson', 'kendall', and 'spearman'
    linkage_method
        linkage method to use. See :func:`scipy.cluster.hierarchy.linkage`
        for more information.
    optimal_ordering
        Same as the optimal_ordering argument of :func:`scipy.cluster.hierarchy.linkage`
        which reorders the linkage matrix so that the distance between successive
        leaves is minimal.
    key_added
        By default, the dendrogram information is added to
        `.uns[f'dendrogram_{{groupby}}']`.
        Notice that the `groupby` information is added to the dendrogram.
    inplace
        If `True`, adds dendrogram information to `adata.uns[key_added]`,
        else this function returns the information.

    Returns
    -------
    If `inplace=False`, returns dendrogram information,
    else `adata.uns[key_added]` is updated with it.

    Examples
    --------
    >>> import quanp as qp
    >>> adata = qp.datasets.pbmc68k_reduced()
    >>> qp.tl.dendrogram(adata, groupby='bulk_labels')
    >>> qp.pl.dendrogram(adata)
    >>> markers = ['featureA', 'featureB', 'featureC', 'featureD', 'featureE', 'featureF']
    >>> qp.pl.dotplot(adata, markers, groupby='bulk_labels', dendrogram=True)
    """
    if isinstance(groupby, str):
        # if not a list, turn into a list
        groupby = [groupby]
    for group in groupby:
        if group not in adata.obs_keys():
            raise ValueError(
                'groupby has to be a valid observation. '
                f'Given value: {group}, valid observations: {adata.obs_keys()}'
            )
        if not is_categorical_dtype(adata.obs[group]):
            raise ValueError(
                'groupby has to be a categorical observation. '
                f'Given value: {group}, Column type: {adata.obs[group].dtype}'
            )

    if var_names is None:
        rep_df = pd.DataFrame(
            _choose_representation(adata, use_rep=use_rep, n_pcs=n_pcs)
        )
        categorical = adata.obs[groupby[0]]
        if len(groupby) > 1:
            for group in groupby[1:]:
                # create new category by merging the given groupby categories
                categorical = (
                    categorical.astype(str) + "_" + adata.obs[group].astype(str)
                ).astype('category')
        categorical.name = "_".join(groupby)

        rep_df.set_index(categorical, inplace=True)
        categories = rep_df.index.categories
    else:
        if use_raw is None and adata.raw is not None:
            use_raw = True
        feature_names = adata.raw.var_names if use_raw else adata.var_names
        from ..plotting._anndata import _prepare_dataframe

        categories, rep_df = _prepare_dataframe(adata, feature_names, groupby, use_raw)

    # aggregate values within categories using 'mean'
    mean_df = rep_df.groupby(level=0).mean()

    import scipy.cluster.hierarchy as sch

    corr_matrix = mean_df.T.corr(method=cor_method)
    z_var = sch.linkage(
        corr_matrix, method=linkage_method, optimal_ordering=optimal_ordering
    )
    dendro_info = sch.dendrogram(z_var, labels=list(categories), no_plot=True)

    dat = dict(
        linkage=z_var,
        groupby=groupby,
        use_rep=use_rep,
        cor_method=cor_method,
        linkage_method=linkage_method,
        categories_ordered=dendro_info['ivl'],
        categories_idx_ordered=dendro_info['leaves'],
        dendrogram_info=dendro_info,
        correlation_matrix=corr_matrix.values,
    )

    if inplace:
        if key_added is None:
            key_added = f'dendrogram_{groupby}'
        logg.info(f'Storing dendrogram info using `.uns[{key_added!r}]`')
        adata.uns[key_added] = dat
    else:
        return dat
