为进行矢量化处理，我将掩盖边缘情况，并在最后担心它们。无论如何，您只需要考虑position == size条件，因为在各个列中的低条件仅为零，out数组已经满足了这一条件。

def frac(line,points):
    pos = np.searchsorted(line,points,side='right')
    low = pos == 0
    high = pos == line.shape[0]
    m = ~(low | high)
    ii = points[m]
    jj = pos[m]
    frac = (ii - line[jj-1]) / (line[jj] - line[jj-1])
    out = np.zeros((points.shape[0],3))
    out[:,0] = points
    out[m,1] = jj - 1
    out[m,2] = frac
    out[high,1] = line.shape[0] - 1
    return out

基准

n = 10**5
line = np.linspace(-500,500,101)
points = np.random.random(n) * np.random.choice([1,-1],n) * 500

In [5]: %timeit run_multiple(line,points)
1.23 s ± 53.1 ms per loop (mean ± std. dev. of 7 runs,1 loop each)

In [7]: %timeit frac(line,points)
13.4 ms ± 290 µs per loop (mean ± std. dev. of 7 runs,100 loops each)

In [8]: np.allclose(frac(line,points),run_multiple(line,points))
Out[8]: True

如果Numba（或您使用的版本）不支持某些功能，最好查看Numba source code并查看已经存在的功能。 通常至少已经解决了一部分问题。

代码

import numpy as np
import numba as nb

#almost copied from Numba source
#https://github.com/numba/numba/blob/master/numba/targets/arraymath.py
"""Copyright (c) 2012,Anaconda,Inc.
All rights reserved.

Redistribution and use in source and binary forms,with or without
modification,are permitted provided that the following conditions are
met:

Redistributions of source code must retain the above copyright notice,this list of conditions and the following disclaimer.

Redistributions in binary form must reproduce the above copyright
notice,this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,INCLUDING,BUT NOT
LIMITED TO,THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,INDIRECT,INCIDENTAL,SPECIAL,EXEMPLARY,OR CONSEQUENTIAL DAMAGES (INCLUDING,PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,DATA,OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY,WHETHER IN CONTRACT,STRICT LIABILITY,OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE,EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
@nb.njit()
def searchsorted_right(a,v):
    n = len(a)
    if np.isnan(v):
        # Find the first nan (i.e. the last from the end of a,# since there shouldn't be many of them in practice)
        for i in range(n,-1):
            if not np.isnan(a[i - 1]):
                return i
        return 0
    lo = 0
    hi = n
    while hi > lo:
        mid = (lo + hi) >> 1
        if a[mid]<= v:
            # mid is too low => go up
            lo = mid + 1
        else:
            # mid is too high,or is a NaN => go down
            hi = mid
    return lo

@nb.njit()
def get_position(line,point):
    position = searchsorted_right(line,point)
    size = line.shape[0]
    if position == 0:
        main = 0
        fraction = 0
    elif position == size:
        main = size-1
        fraction = 0
    else:
        main = position - 1
        fraction = (point - line[position-1]) / (line[position] - line[position-1])
    return main,fraction

@nb.njit(parallel=True)
def run_multiple(line,data):
    out = np.empty((data.shape[0],3))
    for i in nb.prange(data.shape[0]):
        idx,frac = get_position(line,data[i])
        out[i,0] = data[i]
        out[i,1] = idx
        out[i,2] = frac
    return out

时间

n = 10**5
line = np.linspace(-500,n) * 500

%timeit run_multiple(line,points)
#1.08 ms ± 14 µs per loop (mean ± std. dev. of 7 runs,1000 loops each)

#@user3483203
%timeit frac(line,points)
#8.65 ms ± 266 µs per loop (mean ± std. dev. of 7 runs,100 loops each)