用DFA法计算的Hurst指数全部正确的python代码，需要单维EXCEL数据的读取代码

以下是用DFA法计算Hurst指数的Python代码，并包含单维EXCEL数据的读取代码：

import numpy as np
import pandas as pd

def dfa(x, nvals=None, overlap=True):
    """
    Detrended Fluctuation Analysis (DFA).
    
    Parameters
    ----------
    x : array_like, shape (n_samples,)
        One-dimensional time series.
    nvals : array_like, optional (default=None)
        Values of n to use. If None, nvals will be logarithmically spaced between
        10 and len(x)//4 (inclusive).
    overlap : bool, optional (default=True)
        If True, windows are allowed to overlap.
        
    Returns
    -------
    alpha : float
        The slope of the line fit to the log of the fluctuation function.
    """
    x = np.asarray(x, dtype=np.float64)
    if x.ndim != 1:
        raise ValueError("x must be one-dimensional.")
    if nvals is None:
        nvals = np.logspace(1, int(np.log2(len(x) // 4)), base=2, dtype=int)
    else:
        nvals = np.asarray(nvals, dtype=int)
    if np.any(nvals > len(x) // 2):
        raise ValueError("Maximum n is len(x) // 2.")
    if overlap:
        step = 1
    else:
        step = nvals[0]
    F = np.zeros_like(nvals, dtype=np.float64)
    for i, n in enumerate(nvals):
        w = np.ones(n, dtype=np.float64)
        w /= np.sqrt(np.sum(w))
        if overlap:
            y = np.convolve(x, w, mode='valid')
        else:
            y = [np.mean(x[j:j+n]) for j in range(0, len(x)-n+1, n)]
        yi = np.cumsum(y - np.mean(y))
        fi = np.sqrt(np.sum(yi**2)) / np.sqrt(n)
        F[i] = fi
    p = np.polyfit(np.log(nvals), np.log(F), deg=1)
    return p[0]

# 读取单维EXCEL数据
data = pd.read_excel('data.xlsx', sheet_name='Sheet1')
x = data.iloc[:, 0].values

# 计算Hurst指数
hurst = dfa(x)
print("Hurst exponent: {:.3f}".format(hurst))

其中，`data.xlsx`为包含单维数据的EXCEL文件，`Sheet1`为数据所在的工作表。你可以将数据按照一列的方式存储在EXCEL文件中，然后通过`pandas`库的`read_excel`函数读取数据。读取后，将数据存储在`x`变量中，再调用`dfa`函数计算Hurst指数即可。