Python NumPy带位移符合计数代码加速优化求助

我此前曾发布过提升代码运行效率的相关问题，对应代码的核心功能是计算两个存储双通道时间值的NumPy数组之间的平均「符合计数（coincidences）」：当给定时间区间内两个通道均存在至少一个时间值时，即判定为一次符合事件。
我会先将长数组拆分为长度约10k-100k的分块（chunks），对分块做分箱（binned）处理后，配对计算对应分箱间的符合数：即数组1第1个分块的第n号分箱，与数组2第1个分块的第n号分箱配对统计。参考此前社区给出的优化建议修改代码后，运行速度已有显著提升。
目前我需要新增位移符合计数的计算逻辑：分别计算两个分块中第n号分箱与第n+shift号分箱间的符合数，遍历多个不同的shift取值完成全部计算。修改后的代码虽然比初始版本更快，但面对待分析的庞大数据量仍然运行过慢。

当前实现代码如下：

from numba import njit
import numpy as np
import matplotlib.pyplot as plt

@njit
def numba_histogram(a, b, n):
    hista, histb = np.zeros(n, dtype=np.intp), np.zeros(n, dtype=np.intp)
    a_min, a_max = min(a[0], b[0]), max(a[-1], b[-1])
    for x, y in zip(a, b):
        bin = n * (x - a_min) / (a_max - a_min)
        if x == a_max:
            hista[n - 1] += 1
        elif bin >= 0 and bin < n:
            hista[int(bin)] += 1        
        bin = n * (y - a_min) / (a_max - a_min)
        if y == a_max:
            histb[n - 1] += 1
        elif bin >= 0 and bin < n:
            histb[int(bin)] += 1
    return hista, histb

@njit 
def calc_coincidence(t1,t2, NumChunks, NumofBins, td):
    CoincAllChunks = 0
    last = 0.0
    avg = len(t1) / NumChunks
    
    while last < len(t1):
        t1chunks = t1[int(last):int(last + avg)]
        t2chunks = t2[int(last):int(last + avg)]
        if (len(t1chunks)<=1) or (len(t2chunks)<=1):
            last += avg
            
        else:
            Hist1, Hist2 = numba_histogram(t1chunks,t2chunks,NumofBins)
            Hist2 = Hist2[td:]
            appendthis = np.zeros(td)
            Hist2 = np.append(Hist2, appendthis)

            CoincOneChunk = np.sum( (Hist1 > 0) * (Hist2 > 0) )
            CoincAllChunks += CoincOneChunk  
            last += avg 
        
    return CoincAllChunks

##### 参数配置：分块数、分箱大小、位移数量 ########   
NumChunks = 10
HowManyUnitsofns = 100
BinSize = HowManyUnitsofns*10**(-9)
NumberofDelays= 10
###################################################

NumberofFiles = 10
CoinAllDelaysAllFiles = np.zeros(NumberofDelays, dtype=np.int64)
ListofCoinforDiffDelays = []
for n in range(NumberofFiles):
    specificfile = 'file{}.h5'.format(n)
    t1, t2, DataTime = ReadFile(specificfile)
    
    #################### 归一化计算 ################
    ChunkSize = (DataTime)/(NumChunks)
    NumofBins = int(ChunkSize/BinSize)
    Singles1 = len(t1)
    Singles2 = len(t2)
    Normalization = (Singles1*Singles2*BinSize)/DataTime
    ####################################################
    
    CoinAllDelaysOneFile = []
    for i in range(NumberofDelays):  
        CoinOneDelay = 0
        NormCoinOneDelay = (calc_coincidence(t1,t2, NumChunks, NumofBins,i))/Normalization
        CoinAllDelaysOneFile = np.append(CoinAllDelaysOneFile, NormCoinOneDelay)
        
    CoinAllDelaysAllFiles = np.vstack((CoinAllDelaysAllFiles, CoinAllDelaysOneFile))

for k in range(NumberofDelays):
    coineachdelay = np.sum(CoinAllDelaysAllFiles[:,k])
    ListofCoinforDiffDelays = np.append(ListofCoinforDiffDelays, coineachdelay)
    NormListofCoinforDiffDelays = ListofCoinforDiffDelays/(NumberofFiles)
    
print(NormListofCoinforDiffDelays)
tau = HowManyUnitsofns*np.arange(NumberofDelays)
plt.scatter(tau,NormListofCoinforDiffDelays)
plt.ylim(0,2)

• 核心目标：观测符合计数随位移值的变化规律，计算每个位移对应的符合数，最终绘制符合数-位移值关系曲线
• 已准备对应测试数据可用于性能验证

请提供可行的代码优化方向，进一步提升这段代码的运行速度，所有优化建议都欢迎。

内容的提问来源于stack exchange，提问作者Sonali Gera