Source code for biocutils.combine_columns

from functools import singledispatch
from typing import Any
from warnings import warn

import numpy

from ._utils_combine import (
    _check_array_dimensions,
    _coerce_sparse_array,
    _coerce_sparse_matrix,
)
from .convert_to_dense import convert_to_dense
from .is_list_of_type import is_list_of_type
from .package_utils import is_package_installed

__author__ = "jkanche"
__copyright__ = "jkanche"
__license__ = "MIT"




@singledispatch
def combine_columns(*x: Any):
    """Combine n-dimensional objects along the second dimension.

    If all elements are :py:class:`~numpy.ndarray`,
    we combine them using numpy's :py:func:`~numpy.concatenate`.

    If all elements are either :py:class:`~scipy.sparse.spmatrix` or
    :py:class:`~scipy.sparse.sparray`, these objects are combined
    using scipy's :py:class:`~scipy.sparse.hstack`.

    If all elements are :py:class:`~pandas.DataFrame` objects, they are
    combined using :py:func:`~pandas.concat` along the second axis.

    Args:
        x:
            n-dimensional objects to combine. All elements of x are expected
            to be the same class.

    Returns:
        Combined object, typically the same type as the first entry of ``x``
    """
    raise NotImplementedError(
        "no `combine_columns` method implemented for '"
        + type(x[0]).__name__
        + "' objects"
    )



@combine_columns.register
def _combine_columns_dense_arrays(*x: numpy.ndarray):
    _check_array_dimensions(x, active=1)
    x = [convert_to_dense(y) for y in x]
    for y in x:
        if numpy.ma.is_masked(y):
            return numpy.ma.concatenate(x, axis=1)
    return numpy.concatenate(x, axis=1)


if is_package_installed("scipy") is True:
    import scipy.sparse as sp

    def _combine_columns_sparse_matrices(*x):
        _check_array_dimensions(x, 1)
        if is_list_of_type(x, sp.spmatrix):
            combined = sp.hstack(x)
            return _coerce_sparse_matrix(x[0], combined, sp)

        warn("not all elements are scipy sparse matrices")
        x = [convert_to_dense(y) for y in x]
        return numpy.concatenate(x, axis=1)

    try:
        combine_columns.register(sp.spmatrix, _combine_columns_sparse_matrices)
    except Exception:
        pass

    def _combine_columns_sparse_arrays(*x):
        _check_array_dimensions(x, 1)
        if is_list_of_type(x, sp.sparray):
            combined = sp.hstack(x)
            return _coerce_sparse_array(x[0], combined, sp)

        warn("not all elements are scipy sparse arrays")
        x = [convert_to_dense(y) for y in x]
        return numpy.concatenate(x, axis=1)


if is_package_installed("pandas") is True:
    from pandas import DataFrame, concat

    @combine_columns.register(DataFrame)
    def _combine_columns_pandas_dataframe(*x):
        return concat(x, axis=1)