我需要用numpy中的局部正態(tài)分布替換一維數(shù)組的NaN。我選擇一個窗口，計(jì)算該窗口的均值和標(biāo)準(zhǔn)差，然后使用正態(tài)分布替換NaN，而其余信號保持不變。import numpy as npdef replace_nan(signal, window = 5): """ calculate moving average and std of signal window without nan replaces nan values with normal distribution (mean, std) """# add padding in case signal starts/ends with nan signal = np.pad(signal, (window, window), 'mean', stat_length = 2*window) for k in range(window,len(signal)-window): mean = np.nanmean(signal[k-window:k+window]) # window average std = np.nanstd(signal[k-window:k+window]) # window std without nan ind = np.where(np.isnan(signal[k-window:k+window]))[0] print (ind) signal[ind]= np.random.normal(mean, std) signal = signal[window:len(signal)-window] #remove padding return signal#tester signal = np.array([0.71034849, 0.17730998, 0.77577915, 0.38308111, 0.24278947, np.nan, np.nan, 0.68694097, 0.6684736 , 0.47310845, 0.22210945, 0.1189111, np.nan, np.nan, np.nan, 0.5573841 , 0.57531205, 0.74131346, 0.29088101, 0.5573841 , 0.57531205, 0.74131346, np.nan, np.nan, np.nan, np.nan, 0.49534304, 0.18370482, 0.06089498, 0.22210945, 0.1189111]) signal = replace_nan(signal, 5)print(signal)我將 nans 替換為正態(tài)分布 np.random.normal() ，并為大小為 5 的移動窗口計(jì)算均值和標(biāo)準(zhǔn)差。當(dāng)我選擇信號窗口的那些 nans 來替換它們時，出現(xiàn)了問題。這應(yīng)該很容易，我只是python的完整入門者。
1 回答
鴻蒙傳說
TA貢獻(xiàn)1865條經(jīng)驗(yàn) 獲得超7個贊
我還沒有測試過這些數(shù)字是否準(zhǔn)確，但是我認(rèn)為這會起作用：
import numpy as np
def replace_nan(signal, window = 5):
"""
calculate moving average and std of signal window without nan
replaces nan values with normal distribution (mean, std)
"""
# add padding in case signal starts/ends with nan
signal = np.pad(signal, (window, window), 'mean', stat_length = 2*window)
for k in range(window, len(signal) - window + 1):
mean = np.nanmean(signal[k-window:k+window]) # window average
std = np.nanstd(signal[k-window:k+window]) # window std without nan
if np.isnan(signal[k]):
signal[k] = np.random.normal(mean, std)
signal = signal[window:len(signal)-window] #remove padding
return signal
#tester
signal = np.array(
[
0.71034849, 0.17730998, 0.77577915, 0.38308111, 0.24278947, np.nan,
np.nan, 0.68694097, 0.6684736 , 0.47310845, 0.22210945, 0.1189111,
np.nan, np.nan, np.nan, 0.5573841 , 0.57531205, 0.74131346,
0.29088101, 0.5573841 , 0.57531205, 0.74131346, np.nan, np.nan,
np.nan, np.nan, 0.49534304, 0.18370482, 0.06089498, 0.22210945,
0.1189111
]
)
print("Before:")
print(signal)
signal = replace_nan(signal, 5)
print("\nAfter:")
print(signal)
這給出了：
Before:
[ 0.71034849 0.17730998 0.77577915 0.38308111 0.24278947 nan
nan 0.68694097 0.6684736 0.47310845 0.22210945 0.1189111
nan nan nan 0.5573841 0.57531205 0.74131346
0.29088101 0.5573841 0.57531205 0.74131346 nan nan
nan nan 0.49534304 0.18370482 0.06089498 0.22210945
0.1189111 ]
After:
[ 0.71034849 0.17730998 0.77577915 0.38308111 0.24278947 0.35960417
0.508657 0.68694097 0.6684736 0.47310845 0.22210945 0.1189111
0.50282732 0.34906067 0.31206557 0.5573841 0.57531205 0.74131346
0.29088101 0.5573841 0.57531205 0.74131346 0.80133879 0.63122315
0.49236281 0.35630875 0.49534304 0.18370482 0.06089498 0.22210945
0.1189111 ]
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