问使用Numba加速以下代码,主要是循环部分很慢？

import laspy
from scipy import spatial
import time
from osgeo import ogr
from osgeo import osr
from osgeo import gdal
from numba import jit
import numpy as np
import open3d as o3d
import math
from osgeo import osr
import pandas as pd

las_file = laspy.file.File('H:\whu\IEEE_GRSS_Titan\plot3and5/plot3_space_feature - Cloud.las', mode='r')
x, y, z = las_file.x, las_file.y, las_file.z



img=gdal.Open('H:\whu\IEEE_GRSS_Titan\IEEE_Contest\plot3_HSI\plot3_warp1.dat')
icols=img.RasterXSize
irows=img.RasterYSize

geotransform = img.GetGeoTransform()
ix_min=geotransform[0]
iy_max=geotransform[3]
xlen=geotransform[1]
ylen=geotransform[5]
ix_max=ix_min+xlen*icols
iy_min=iy_max+ylen*irows
#读取48个波段
#读取成三维数组
bandNumber = img.RasterCount-2
v = [[[0] * bandNumber] * icols] * irows
v=np.zeros((bandNumber,irows,icols))#(维数，行数，列数)
#
for bandIndex in range(bandNumber):
    band = img.GetRasterBand(bandIndex+1)  # 取波段
    datas = band.ReadAsArray(0, 0, icols, irows)
    for row in range(irows):
        for col in range(icols):
            v[bandIndex][row][col] = datas[row][col]
v = v.astype(int)
### 空间分辨率设置
## get extent
x_max, y_max = las_file.header.max[0:2]
x_min, y_min = las_file.header.min[0:2]

pixelWidth, pixelHeight = 1, 1               # 分辨率 1m*1m
### calculate
xOrigin = x_min
yOrigin = y_min

cols = int(math.ceil((x_max - x_min) / pixelWidth))
rows = int(math.ceil((y_max - y_min) / abs(pixelHeight)))

##给每个点云赋予行列号
xOffset = (x - xOrigin) / pixelWidth
xOffset = xOffset.astype(int)
xOffset[np.where(xOffset == 595)]=594
yOffset = (y - yOrigin) / pixelHeight
yOffset = yOffset.astype(int)
yOffset[np.where(yOffset == 600)]=599



#对每个点云进行循环，通过行列索引取光谱值
s=time.time()
print("开始循环时间：",s)
a1 = np.zeros([bandNumber], dtype=np.int32)
#@jit
def traversal_point(a,x,v):

    for i in range(len(x)):
        b=v[:,yOffset[i],xOffset[i]]
        #b.reshape(1,bandNumber)
        a=np.vstack((a,b))

    a=a[1:,:]
    return a

aa=traversal_point(a1,x,v)
ss=time.time()
print("循环用时：",ss-s)
#
#合并xyz
x=x.reshape(-1,1)
y=y.reshape(-1,1)
z=z.reshape(-1,1)
xyz=np.hstack((x,y,z))

final=np.hstack((xyz,aa))
aaa=pd.DataFrame(data=final,columns=['x','y','z','374.4nm','388.7nm','403.1','417.4','431.7','446.1','460.4','474.7','489.0',
                                    '503.4','517.7','532.0','546.3','560.6','574.9','589.2','603.6','617.9','632.2','646.5',
                                    '660.8','675.1','689.4','703.7','718.0','732.3','746.6','760.9','775.2','789.5','803.8',
                                    '818.1','832.4','846.7','861.1','875.4','889.7','904.0','918.3','932.6','946.9','961.2',
                                    '975.5','989.8','1004.2','1018.5','1032.8','1047.1'])
aaa.to_csv("高光谱点云plot3.csv",index=False)