用python实现matlab smooth中的moving average、lowess、loess、sgolay、rlowess、rloess平滑方法具体代码

以下是使用Python实现MATLAB中的平滑方法的代码示例：

1. Moving Average

import numpy as np

def moving_average(arr, k):
    """
    Computes the moving average of an array `arr` with window size `k`.
    """
    weights = np.repeat(1.0, k) / k
    return np.convolve(arr, weights, 'valid')

2. Lowess

from statsmodels.nonparametric.smoothers_lowess import lowess

def lowess_smooth(arr, frac=0.25):
    """
    Applies lowess smoothing to an array `arr` with smoothing parameter `frac`.
    """
    return lowess(arr, np.arange(len(arr)), frac=frac, is_sorted=True)[:, 1]

3. Loess

from statsmodels.nonparametric.smoothers_lowess import lowess

def loess_smooth(arr, frac=0.25, degree=2):
    """
    Applies loess smoothing to an array `arr` with smoothing parameter `frac`
    and polynomial degree `degree`.
    """
    return lowess(arr, np.arange(len(arr)), frac=frac, is_sorted=True, delta=0.01, return_sorted=False, frac_policy='symmetric', it=0, delta_policy='weighted', robust=True, return_std=False, conf_int=False, degree=degree)[:, 1]

4. Savitzky-Golay

from scipy.signal import savgol_filter

def savgol_smooth(arr, window_length=11, polyorder=2):
    """
    Applies Savitzky-Golay filtering to an array `arr` with window length `window_length`
    and polynomial order `polyorder`.
    """
    return savgol_filter(arr, window_length=window_length, polyorder=polyorder, mode='nearest')

5. Robust Lowess

from statsmodels.robust.robust_linear_model import RLM
from statsmodels.robust.robust_linear_model import HuberT

def rlowess_smooth(arr, frac=0.25):
    """
    Applies robust lowess smoothing to an array `arr` with smoothing parameter `frac`.
    """
    x = np.arange(len(arr))
    y = arr
    rlm_model = RLM(y, x, M=HuberT())
    rlm_results = rlm_model.fit()
    res = rlm_results.resid
    loess_model = lowess(res, x, frac=frac, is_sorted=True)
    return arr - loess_model[:, 1]

6. Robust Loess

from statsmodels.robust.robust_linear_model import RLM
from statsmodels.robust.robust_linear_model import HuberT
from statsmodels.nonparametric.smoothers_lowess import lowess

def rloess_smooth(arr, frac=0.25, degree=2):
    """
    Applies robust loess smoothing to an array `arr` with smoothing parameter `frac`
    and polynomial degree `degree`.
    """
    x = np.arange(len(arr))
    y = arr
    rlm_model = RLM(y, x, M=HuberT())
    rlm_results = rlm_model.fit()
    res = rlm_results.resid
    loess_model = lowess(res, x, frac=frac, delta=0.01, return_sorted=False, frac_policy='symmetric', it=0, delta_policy='weighted', robust=True, return_std=False, conf_int=False, degree=degree)
    return arr - loess_model[:, 1]

以上是Python实现MATLAB平滑方法的代码示例，希望对你有所帮助。