<gh_stars>0
import numpy as np

def weighted_percentile(a, q, weights=None, sorter=None):
    """
    Returns the weighted percentile of a at q given weights.

    Parameters
    ----------
    a: array-like, shape=(n_samples,)
        samples at which the quantile.

    q: int
        quantile.

    weights: array-like, shape=(n_samples,)
        weights[i] is the weight given to point a[i] while computing the
        quantile. If weights[i] is zero, a[i] is simply ignored during the
        percentile computation.

    sorter: array-like, shape=(n_samples,)
        If provided, assume that a[sorter] is sorted.

    Returns
    -------
    percentile: float
        Weighted percentile of a at q.

    References
    ----------
    1. https://en.wikipedia.org/wiki/Percentile#The_Weighted_Percentile_method

    Notes
    -----
    Note that weighted_percentile(a, q) is not equivalent to
    np.percentile(a, q). This is because in np.percentile
    sorted(a)[i] is assumed to be at quantile 0.0, while here we assume
    sorted(a)[i] is given a weight of 1.0 / len(a), hence it is at the
    1.0 / len(a)th quantile.
    """
    if weights is None:
        weights = np.ones_like(a)
    if q > 100 or q < 0:
        raise ValueError("q should be in-between 0 and 100, "
                         "got %d" % q)

    a = np.asarray(a, dtype=np.float32)
    weights = np.asarray(weights, dtype=np.float32)
    if len(a) != len(weights):
        raise ValueError("a and weights should have the same length.")

    if sorter is not None:
        a = a[sorter]
        weights = weights[sorter]

    nz = weights != 0
    a = a[nz]
    weights = weights[nz]

    if sorter is None:
        sorted_indices = np.argsort(a)
        sorted_a = a[sorted_indices]
        sorted_weights = weights[sorted_indices]
    else:
        sorted_a = a
        sorted_weights = weights

    # Step 1
    sorted_cum_weights = np.cumsum(sorted_weights)
    total = sorted_cum_weights[-1]

    # Step 2
    partial_sum = 100.0 / total * (sorted_cum_weights - sorted_weights / 2.0)
    start = np.searchsorted(partial_sum, q) - 1
    if start == len(sorted_cum_weights) - 1:
        return sorted_a[-1]
    if start == -1:
        return sorted_a[0]

    # Step 3.
    fraction = (q - partial_sum[start]) / (partial_sum[start + 1] - partial_sum[start])
    return sorted_a[start] + fraction * (sorted_a[start + 1] - sorted_a[start])

import numpy as np

def weighted_percentile(a, q, weights=None, sorter=None):
    """
    Returns the weighted percentile of a at q given weights.

    Parameters
    ----------
    a: array-like, shape=(n_samples,)
        samples at which the quantile.

    q: int
        quantile.

    weights: array-like, shape=(n_samples,)
        weights[i] is the weight given to point a[i] while computing the
        quantile. If weights[i] is zero, a[i] is simply ignored during the
        percentile computation.

    sorter: array-like, shape=(n_samples,)
        If provided, assume that a[sorter] is sorted.

    Returns
    -------
    percentile: float
        Weighted percentile of a at q.

    References
    ----------
    1. https://en.wikipedia.org/wiki/Percentile#The_Weighted_Percentile_method

    Notes
    -----
    Note that weighted_percentile(a, q) is not equivalent to
    np.percentile(a, q). This is because in np.percentile
    sorted(a)[i] is assumed to be at quantile 0.0, while here we assume
    sorted(a)[i] is given a weight of 1.0 / len(a), hence it is at the
    1.0 / len(a)th quantile.
    """
    if weights is None:
        weights = np.ones_like(a)
    if q > 100 or q < 0:
        raise ValueError("q should be in-between 0 and 100, "
                         "got %d" % q)

    a = np.asarray(a, dtype=np.float32)
    weights = np.asarray(weights, dtype=np.float32)
    if len(a) != len(weights):
        raise ValueError("a and weights should have the same length.")

    if sorter is not None:
        a = a[sorter]
        weights = weights[sorter]

    nz = weights != 0
    a = a[nz]
    weights = weights[nz]

    if sorter is None:
        sorted_indices = np.argsort(a)
        sorted_a = a[sorted_indices]
        sorted_weights = weights[sorted_indices]
    else:
        sorted_a = a
        sorted_weights = weights

    # Step 1
    sorted_cum_weights = np.cumsum(sorted_weights)
    total = sorted_cum_weights[-1]

    # Step 2
    partial_sum = 100.0 / total * (sorted_cum_weights - sorted_weights / 2.0)
    start = np.searchsorted(partial_sum, q) - 1
    if start == len(sorted_cum_weights) - 1:
        return sorted_a[-1]
    if start == -1:
        return sorted_a[0]

    # Step 3.
    fraction = (q - partial_sum[start]) / (partial_sum[start + 1] - partial_sum[start])
    return sorted_a[start] + fraction * (sorted_a[start + 1] - sorted_a[start])
Returns the weighted percentile of a at q given weights.      Parameters     ----------     a: array-like, shape=(n_samples,)         samples at which the quantile.      q: int         quantile.      weights: array-like, shape=(n_samples,)         weights[i] is the weight given to point a[i] while computing the         quantile. If weights[i] is zero, a[i] is simply ignored during the         percentile computation.      sorter: array-like, shape=(n_samples,)         If provided, assume that a[sorter] is sorted.      Returns     -------     percentile: float         Weighted percentile of a at q.      References     ----------     1. https://en.wikipedia.org/wiki/Percentile#The_Weighted_Percentile_method      Notes     -----     Note that weighted_percentile(a, q) is not equivalent to     np.percentile(a, q). This is because in np.percentile     sorted(a)[i] is assumed to be at quantile 0.0, while here we assume     sorted(a)[i] is given a weight of 1.0 / len(a), hence it is at the     1.0 / len(a)th quantile. # Step 1 # Step 2 # Step 3.