from typing import Union, Optional, Tuple, Collection, Sequence, Iterable
from anndata import AnnData
import numpy as np
from scipy.sparse import issparse, isspmatrix_csr, csr_matrix, spmatrix
import scanpy


def filter_genes(
    adata: AnnData,
    min_counts: Optional[int] = None,
    min_cells: Optional[int] = None,
    max_counts: Optional[int] = None,
    max_cells: Optional[int] = None,
    inplace: bool = True,
) -> Union[AnnData, None, Tuple[np.ndarray, np.ndarray]]:
    """\
    Wrap function scanpy.pp.filter_genes

    Filter genes based on number of cells or counts.
    Keep genes that have at least `min_counts` counts or are expressed in at
    least `min_cells` cells or have at most `max_counts` counts or are expressed
    in at most `max_cells` cells.
    Only provide one of the optional parameters `min_counts`, `min_cells`,
    `max_counts`, `max_cells` per call.
    Parameters
    ----------
    data
        An annotated data matrix of shape `n_obs` × `n_vars`. Rows correspond
        to cells and columns to genes.
    min_counts
        Minimum number of counts required for a gene to pass filtering.
    min_cells
        Minimum number of cells expressed required for a gene to pass filtering.
    max_counts
        Maximum number of counts required for a gene to pass filtering.
    max_cells
        Maximum number of cells expressed required for a gene to pass filtering.
    inplace
        Perform computation inplace or return result.
    Returns
    -------
    Depending on `inplace`, returns the following arrays or directly subsets
    and annotates the data matrix
    gene_subset
        Boolean index mask that does filtering. `True` means that the
        gene is kept. `False` means the gene is removed.
    number_per_gene
        Depending on what was tresholded (`counts` or `cells`), the array stores
        `n_counts` or `n_cells` per gene.
    """

    scanpy.pp.filter_genes(
        adata,
        min_counts=min_counts,
        min_cells=min_cells,
        max_counts=max_counts,
        max_cells=max_cells,
        inplace=inplace,
    )

92ff3ad5447537f09fe7808f212683127b1cebea
<mask token>

<mask token>


def filter_genes(adata: AnnData, min_counts: Optional[int]=None, min_cells:
    Optional[int]=None, max_counts: Optional[int]=None, max_cells: Optional
    [int]=None, inplace: bool=True) ->Union[AnnData, None, Tuple[np.ndarray,
    np.ndarray]]:
    """    Wrap function scanpy.pp.filter_genes

    Filter genes based on number of cells or counts.
    Keep genes that have at least `min_counts` counts or are expressed in at
    least `min_cells` cells or have at most `max_counts` counts or are expressed
    in at most `max_cells` cells.
    Only provide one of the optional parameters `min_counts`, `min_cells`,
    `max_counts`, `max_cells` per call.
    Parameters
    ----------
    data
        An annotated data matrix of shape `n_obs` × `n_vars`. Rows correspond
        to cells and columns to genes.
    min_counts
        Minimum number of counts required for a gene to pass filtering.
    min_cells
        Minimum number of cells expressed required for a gene to pass filtering.
    max_counts
        Maximum number of counts required for a gene to pass filtering.
    max_cells
        Maximum number of cells expressed required for a gene to pass filtering.
    inplace
        Perform computation inplace or return result.
    Returns
    -------
    Depending on `inplace`, returns the following arrays or directly subsets
    and annotates the data matrix
    gene_subset
        Boolean index mask that does filtering. `True` means that the
        gene is kept. `False` means the gene is removed.
    number_per_gene
        Depending on what was tresholded (`counts` or `cells`), the array stores
        `n_counts` or `n_cells` per gene.
    """
    scanpy.pp.filter_genes(adata, min_counts=min_counts, min_cells=
        min_cells, max_counts=max_counts, max_cells=max_cells, inplace=inplace)

from typing import Union, Optional, Tuple, Collection, Sequence, Iterable
from anndata import AnnData
import numpy as np
from scipy.sparse import issparse, isspmatrix_csr, csr_matrix, spmatrix
import scanpy


def filter_genes(adata: AnnData, min_counts: Optional[int]=None, min_cells:
    Optional[int]=None, max_counts: Optional[int]=None, max_cells: Optional
    [int]=None, inplace: bool=True) ->Union[AnnData, None, Tuple[np.ndarray,
    np.ndarray]]:
    """    Wrap function scanpy.pp.filter_genes

    Filter genes based on number of cells or counts.
    Keep genes that have at least `min_counts` counts or are expressed in at
    least `min_cells` cells or have at most `max_counts` counts or are expressed
    in at most `max_cells` cells.
    Only provide one of the optional parameters `min_counts`, `min_cells`,
    `max_counts`, `max_cells` per call.
    Parameters
    ----------
    data
        An annotated data matrix of shape `n_obs` × `n_vars`. Rows correspond
        to cells and columns to genes.
    min_counts
        Minimum number of counts required for a gene to pass filtering.
    min_cells
        Minimum number of cells expressed required for a gene to pass filtering.
    max_counts
        Maximum number of counts required for a gene to pass filtering.
    max_cells
        Maximum number of cells expressed required for a gene to pass filtering.
    inplace
        Perform computation inplace or return result.
    Returns
    -------
    Depending on `inplace`, returns the following arrays or directly subsets
    and annotates the data matrix
    gene_subset
        Boolean index mask that does filtering. `True` means that the
        gene is kept. `False` means the gene is removed.
    number_per_gene
        Depending on what was tresholded (`counts` or `cells`), the array stores
        `n_counts` or `n_cells` per gene.
    """
    scanpy.pp.filter_genes(adata, min_counts=min_counts, min_cells=
        min_cells, max_counts=max_counts, max_cells=max_cells, inplace=inplace)

from typing import Union, Optional, Tuple, Collection, Sequence, Iterable
from anndata import AnnData
import numpy as np
from scipy.sparse import issparse, isspmatrix_csr, csr_matrix, spmatrix
import scanpy


def filter_genes(
    adata: AnnData,
    min_counts: Optional[int] = None,
    min_cells: Optional[int] = None,
    max_counts: Optional[int] = None,
    max_cells: Optional[int] = None,
    inplace: bool = True,
) -> Union[AnnData, None, Tuple[np.ndarray, np.ndarray]]:
    """\
    Wrap function scanpy.pp.filter_genes

    Filter genes based on number of cells or counts.
    Keep genes that have at least `min_counts` counts or are expressed in at
    least `min_cells` cells or have at most `max_counts` counts or are expressed
    in at most `max_cells` cells.
    Only provide one of the optional parameters `min_counts`, `min_cells`,
    `max_counts`, `max_cells` per call.
    Parameters
    ----------
    data
        An annotated data matrix of shape `n_obs` × `n_vars`. Rows correspond
        to cells and columns to genes.
    min_counts
        Minimum number of counts required for a gene to pass filtering.
    min_cells
        Minimum number of cells expressed required for a gene to pass filtering.
    max_counts
        Maximum number of counts required for a gene to pass filtering.
    max_cells
        Maximum number of cells expressed required for a gene to pass filtering.
    inplace
        Perform computation inplace or return result.
    Returns
    -------
    Depending on `inplace`, returns the following arrays or directly subsets
    and annotates the data matrix
    gene_subset
        Boolean index mask that does filtering. `True` means that the
        gene is kept. `False` means the gene is removed.
    number_per_gene
        Depending on what was tresholded (`counts` or `cells`), the array stores
        `n_counts` or `n_cells` per gene.
    """

    scanpy.pp.filter_genes(
        adata,
        min_counts=min_counts,
        min_cells=min_cells,
        max_counts=max_counts,
        max_cells=max_cells,
        inplace=inplace,
    )
