import numpy as np
from scipy import sparse
import numba


def _get_mean_var(X, *, axis=0):
    if sparse.issparse(X):
        mean, var = sparse_mean_variance_axis(X, axis=axis)
    else:
        mean = np.mean(X, axis=axis, dtype=np.float64)
        mean_sq = np.multiply(X, X).mean(axis=axis, dtype=np.float64)
        var = mean_sq - mean ** 2
    # enforce R convention (unbiased estimator) for variance
    var *= X.shape[axis] / (X.shape[axis] - 1)
    return mean, var


def sparse_mean_variance_axis(mtx: sparse.spmatrix, axis: int):
    """
    This code and internal functions are based on sklearns
    `sparsefuncs.mean_variance_axis`.
    Modifications:
    * allow deciding on the output type, which can increase accuracy when calculating the mean and variance of 32bit floats.
    * This doesn't currently implement support for null values, but could.
    * Uses numba not cython
    """
    assert axis in (0, 1)
    if isinstance(mtx, sparse.csr_matrix):
        ax_minor = 1
        shape = mtx.shape
    elif isinstance(mtx, sparse.csc_matrix):
        ax_minor = 0
        shape = mtx.shape[::-1]
    else:
        raise ValueError("This function only works on sparse csr and csc matrices")
    if axis == ax_minor:
        return sparse_mean_var_major_axis(
            mtx.data, mtx.indices, mtx.indptr, *shape, np.float64
        )
    else:
        return sparse_mean_var_minor_axis(mtx.data, mtx.indices, *shape, np.float64)


@numba.njit(cache=True)
def sparse_mean_var_minor_axis(data, indices, major_len, minor_len, dtype):
    """
    Computes mean and variance for a sparse matrix for the minor axis.
    Given arrays for a csr matrix, returns the means and variances for each
    column back.
    """
    non_zero = indices.shape[0]

    means = np.zeros(minor_len, dtype=dtype)
    variances = np.zeros_like(means, dtype=dtype)

    counts = np.zeros(minor_len, dtype=np.int64)

    for i in range(non_zero):
        col_ind = indices[i]
        means[col_ind] += data[i]

    for i in range(minor_len):
        means[i] /= major_len

    for i in range(non_zero):
        col_ind = indices[i]
        diff = data[i] - means[col_ind]
        variances[col_ind] += diff * diff
        counts[col_ind] += 1

    for i in range(minor_len):
        variances[i] += (major_len - counts[i]) * means[i] ** 2
        variances[i] /= major_len

    return means, variances


@numba.njit(cache=True)
def sparse_mean_var_major_axis(data, indices, indptr, major_len, minor_len, dtype):
    """
    Computes mean and variance for a sparse array for the major axis.
    Given arrays for a csr matrix, returns the means and variances for each
    row back.
    """
    means = np.zeros(major_len, dtype=dtype)
    variances = np.zeros_like(means, dtype=dtype)

    for i in range(major_len):
        startptr = indptr[i]
        endptr = indptr[i + 1]
        counts = endptr - startptr

        for j in range(startptr, endptr):
            means[i] += data[j]
        means[i] /= minor_len

        for j in range(startptr, endptr):
            diff = data[j] - means[i]
            variances[i] += diff * diff

        variances[i] += (minor_len - counts) * means[i] ** 2
        variances[i] /= minor_len

    return means, variances
