"""Calculate scores based on the expression of gene lists.
"""
from typing import Sequence, Optional

import numpy as np
import pandas as pd
from anndata import AnnData
from scipy.sparse import issparse

from .. import logging as logg
from .._utils import AnyRandom
from scanpy._utils import _check_use_raw


def _sparse_nanmean(X, axis):
    """
    np.nanmean equivalent for sparse matrices
    """
    if not issparse(X):
        raise TypeError("X must be a sparse matrix")

    # count the number of nan elements per row/column (dep. on axis)
    Z = X.copy()
    Z.data = np.isnan(Z.data)
    Z.eliminate_zeros()
    n_elements = Z.shape[axis] - Z.sum(axis)

    # set the nans to 0, so that a normal .sum() works
    Y = X.copy()
    Y.data[np.isnan(Y.data)] = 0
    Y.eliminate_zeros()

    # the average
    s = Y.sum(axis, dtype='float64')  # float64 for score_genes function compatibility)
    m = s / n_elements

    return m


def score_genes(
    adata: AnnData,
    gene_list: Sequence[str],
    ctrl_size: int = 50,
    gene_pool: Optional[Sequence[str]] = None,
    n_bins: int = 25,
    score_name: str = 'score',
    random_state: AnyRandom = 0,
    copy: bool = False,
    use_raw: bool = None,
) -> Optional[AnnData]:
    """\
    Score a set of genes [Satija15]_.

    The score is the average expression of a set of genes subtracted with the
    average expression of a reference set of genes. The reference set is
    randomly sampled from the `gene_pool` for each binned expression value.

    This reproduces the approach in Seurat [Satija15]_ and has been implemented
    for Scanpy by Davide Cittaro.

    Parameters
    ----------
    adata
        The annotated data matrix.
    gene_list
        The list of gene names used for score calculation.
    ctrl_size
        Number of reference genes to be sampled from each bin. If `len(gene_list)` is not too
        low, you can set `ctrl_size=len(gene_list)`.
    gene_pool
        Genes for sampling the reference set. Default is all genes.
    n_bins
        Number of expression level bins for sampling.
    score_name
        Name of the field to be added in `.obs`.
    random_state
        The random seed for sampling.
    copy
        Copy `adata` or modify it inplace.
    use_raw
        Whether to use `raw` attribute of `adata`. Defaults to `True` if `.raw` is present.

        .. versionchanged:: 1.4.5
           Default value changed from `False` to `None`.

    Returns
    -------
    Depending on `copy`, returns or updates `adata` with an additional field
    `score_name`.

    Examples
    --------
    See this `notebook <https://github.com/theislab/scanpy_usage/tree/master/180209_cell_cycle>`__.
    """
    start = logg.info(f'computing score {score_name!r}')
    adata = adata.copy() if copy else adata

    if random_state is not None:
        np.random.seed(random_state)

    gene_list_in_var = []
    var_names = adata.raw.var_names if use_raw else adata.var_names
    genes_to_ignore = []
    for gene in gene_list:
        if gene in var_names:
            gene_list_in_var.append(gene)
        else:
            genes_to_ignore.append(gene)
    if len(genes_to_ignore) > 0:
        logg.warning(f'genes are not in var_names and ignored: {genes_to_ignore}')
    gene_list = set(gene_list_in_var[:])

    if len(gene_list) == 0:
        raise ValueError("No valid genes were passed for scoring.")

    if gene_pool is None:
        gene_pool = list(var_names)
    else:
        gene_pool = [x for x in gene_pool if x in var_names]

    # Trying here to match the Seurat approach in scoring cells.
    # Basically we need to compare genes against random genes in a matched
    # interval of expression.

    use_raw = _check_use_raw(adata, use_raw)
    _adata = adata.raw if use_raw else adata

    _adata_subset = (
        _adata[:, gene_pool] if len(gene_pool) < len(_adata.var_names) else _adata
    )
    if issparse(_adata_subset.X):
        obs_avg = pd.Series(
            np.array(_sparse_nanmean(_adata_subset.X, axis=0)).flatten(),
            index=gene_pool,
        )  # average expression of genes
    else:
        obs_avg = pd.Series(
            np.nanmean(_adata_subset.X, axis=0), index=gene_pool
        )  # average expression of genes

    obs_avg = obs_avg[
        np.isfinite(obs_avg)
    ]  # Sometimes (and I don't know how) missing data may be there, with nansfor

    n_items = int(np.round(len(obs_avg) / (n_bins - 1)))
    obs_cut = obs_avg.rank(method='min') // n_items
    control_genes = set()

    # now pick `ctrl_size` genes from every cut
    for cut in np.unique(obs_cut.loc[gene_list]):
        r_genes = np.array(obs_cut[obs_cut == cut].index)
        np.random.shuffle(r_genes)
        # uses full r_genes if ctrl_size > len(r_genes)
        control_genes.update(set(r_genes[:ctrl_size]))

    # To index, we need a list – indexing implies an order.
    control_genes = list(control_genes - gene_list)
    gene_list = list(gene_list)

    X_list = _adata[:, gene_list].X
    if issparse(X_list):
        X_list = np.array(_sparse_nanmean(X_list, axis=1)).flatten()
    else:
        X_list = np.nanmean(X_list, axis=1, dtype='float64')

    X_control = _adata[:, control_genes].X
    if issparse(X_control):
        X_control = np.array(_sparse_nanmean(X_control, axis=1)).flatten()
    else:
        X_control = np.nanmean(X_control, axis=1, dtype='float64')

    score = X_list - X_control

    adata.obs[score_name] = pd.Series(
        np.array(score).ravel(), index=adata.obs_names, dtype='float64'
    )

    logg.info(
        '    finished',
        time=start,
        deep=(
            'added\n'
            f'    {score_name!r}, score of gene set (adata.obs).\n'
            f'    {len(control_genes)} total control genes are used.'
        ),
    )
    return adata if copy else None


def score_genes_cell_cycle(
    adata: AnnData,
    s_genes: Sequence[str],
    g2m_genes: Sequence[str],
    copy: bool = False,
    **kwargs,
) -> Optional[AnnData]:
    """\
    Score cell cycle genes [Satija15]_.

    Given two lists of genes associated to S phase and G2M phase, calculates
    scores and assigns a cell cycle phase (G1, S or G2M). See
    :func:`~scanpy.tl.score_genes` for more explanation.

    Parameters
    ----------
    adata
        The annotated data matrix.
    s_genes
        List of genes associated with S phase.
    g2m_genes
        List of genes associated with G2M phase.
    copy
        Copy `adata` or modify it inplace.
    **kwargs
        Are passed to :func:`~scanpy.tl.score_genes`. `ctrl_size` is not
        possible, as it's set as `min(len(s_genes), len(g2m_genes))`.

    Returns
    -------
    Depending on `copy`, returns or updates `adata` with the following fields.

    **S_score** : `adata.obs`, dtype `object`
        The score for S phase for each cell.
    **G2M_score** : `adata.obs`, dtype `object`
        The score for G2M phase for each cell.
    **phase** : `adata.obs`, dtype `object`
        The cell cycle phase (`S`, `G2M` or `G1`) for each cell.

    See also
    --------
    score_genes

    Examples
    --------
    See this `notebook <https://github.com/theislab/scanpy_usage/tree/master/180209_cell_cycle>`__.
    """
    logg.info('calculating cell cycle phase')

    adata = adata.copy() if copy else adata
    ctrl_size = min(len(s_genes), len(g2m_genes))
    # add s-score
    score_genes(
        adata, gene_list=s_genes, score_name='S_score', ctrl_size=ctrl_size, **kwargs
    )
    # add g2m-score
    score_genes(
        adata,
        gene_list=g2m_genes,
        score_name='G2M_score',
        ctrl_size=ctrl_size,
        **kwargs,
    )
    scores = adata.obs[['S_score', 'G2M_score']]

    # default phase is S
    phase = pd.Series('S', index=scores.index)

    # if G2M is higher than S, it's G2M
    phase[scores.G2M_score > scores.S_score] = 'G2M'

    # if all scores are negative, it's G1...
    phase[np.all(scores < 0, axis=1)] = 'G1'

    adata.obs['phase'] = phase
    logg.hint('    \'phase\', cell cycle phase (adata.obs)')
    return adata if copy else None
