"""Preprocessing recipes from the literature"""
from typing import Optional

from anndata import AnnData


from .. import preprocessing as pp
from ._deprecated.highly_variable_genes import (
    filter_genes_dispersion,
    filter_genes_cv_deprecated,
)
from ._normalization import normalize_total
from .. import logging as logg
from .._utils import AnyRandom


def recipe_weinreb17(
    adata: AnnData,
    log: bool = True,
    mean_threshold: float = 0.01,
    cv_threshold: int = 2,
    n_pcs: int = 50,
    svd_solver='randomized',
    random_state: AnyRandom = 0,
    copy: bool = False,
) -> Optional[AnnData]:
    """\
    Normalization and filtering as of [Weinreb17]_.

    Expects non-logarithmized data.
    If using logarithmized data, pass `log=False`.

    Parameters
    ----------
    adata
        Annotated data matrix.
    log
        Logarithmize data?
    copy
        Return a copy if true.
    """
    from ._deprecated import normalize_per_cell_weinreb16_deprecated, zscore_deprecated
    from scipy.sparse import issparse

    if issparse(adata.X):
        raise ValueError('`recipe_weinreb16 does not support sparse matrices.')
    if copy:
        adata = adata.copy()
    if log:
        pp.log1p(adata)
    adata.X = normalize_per_cell_weinreb16_deprecated(
        adata.X, max_fraction=0.05, mult_with_mean=True
    )
    gene_subset = filter_genes_cv_deprecated(adata.X, mean_threshold, cv_threshold)
    adata._inplace_subset_var(gene_subset)  # this modifies the object itself
    X_pca = pp.pca(
        zscore_deprecated(adata.X),
        n_comps=n_pcs,
        svd_solver=svd_solver,
        random_state=random_state,
    )
    # update adata
    adata.obsm['X_pca'] = X_pca
    return adata if copy else None


def recipe_seurat(
    adata: AnnData, log: bool = True, plot: bool = False, copy: bool = False
) -> Optional[AnnData]:
    """\
    Normalization and filtering as of Seurat [Satija15]_.

    This uses a particular preprocessing.

    Expects non-logarithmized data.
    If using logarithmized data, pass `log=False`.
    """
    if copy:
        adata = adata.copy()
    pp.filter_cells(adata, min_genes=200)
    pp.filter_genes(adata, min_cells=3)
    normalize_total(adata, target_sum=1e4)
    filter_result = filter_genes_dispersion(
        adata.X, min_mean=0.0125, max_mean=3, min_disp=0.5, log=not log
    )
    if plot:
        from ..plotting import (
            _preprocessing as ppp,
        )  # should not import at the top of the file

        ppp.filter_genes_dispersion(filter_result, log=not log)
    adata._inplace_subset_var(filter_result.gene_subset)  # filter genes
    if log:
        pp.log1p(adata)
    pp.scale(adata, max_value=10)
    return adata if copy else None


def recipe_zheng17(
    adata: AnnData,
    n_top_genes: int = 1000,
    log: bool = True,
    plot: bool = False,
    copy: bool = False,
) -> Optional[AnnData]:
    """\
    Normalization and filtering as of [Zheng17]_.

    Reproduces the preprocessing of [Zheng17]_ – the Cell Ranger R Kit of 10x
    Genomics.

    Expects non-logarithmized data.
    If using logarithmized data, pass `log=False`.

    The recipe runs the following steps

    .. code:: python

        sc.pp.filter_genes(adata, min_counts=1)         # only consider genes with more than 1 count
        sc.pp.normalize_per_cell(                       # normalize with total UMI count per cell
             adata, key_n_counts='n_counts_all'
        )
        filter_result = sc.pp.filter_genes_dispersion(  # select highly-variable genes
            adata.X, flavor='cell_ranger', n_top_genes=n_top_genes, log=False
        )
        adata = adata[:, filter_result.gene_subset]     # subset the genes
        sc.pp.normalize_per_cell(adata)                 # renormalize after filtering
        if log: sc.pp.log1p(adata)                      # log transform: adata.X = log(adata.X + 1)
        sc.pp.scale(adata)                              # scale to unit variance and shift to zero mean


    Parameters
    ----------
    adata
        Annotated data matrix.
    n_top_genes
        Number of genes to keep.
    log
        Take logarithm.
    plot
        Show a plot of the gene dispersion vs. mean relation.
    copy
        Return a copy of `adata` instead of updating it.

    Returns
    -------
    Returns or updates `adata` depending on `copy`.
    """
    start = logg.info('running recipe zheng17')
    if copy:
        adata = adata.copy()
    # only consider genes with more than 1 count
    pp.filter_genes(adata, min_counts=1)
    # normalize with total UMI count per cell
    normalize_total(adata, key_added='n_counts_all')
    filter_result = filter_genes_dispersion(
        adata.X, flavor='cell_ranger', n_top_genes=n_top_genes, log=False
    )
    if plot:  # should not import at the top of the file
        from ..plotting import _preprocessing as ppp

        ppp.filter_genes_dispersion(filter_result, log=True)
    # actually filter the genes, the following is the inplace version of
    #     adata = adata[:, filter_result.gene_subset]
    adata._inplace_subset_var(filter_result.gene_subset)  # filter genes
    normalize_total(adata)  # renormalize after filtering
    if log:
        pp.log1p(adata)  # log transform: X = log(X + 1)
    pp.scale(adata)
    logg.info('    finished', time=start)
    return adata if copy else None