英文:
How can I apply a low pass and high pass filter to a time series dataset using scipy?
问题
我有一个采样率为1/10且样本大小为8390的时间序列数据集。我只想对前1000个和最后1000个样本应用低通和高通滤波器，以便计算傅里叶变换时不受低频干扰的影响。
我尝试使用scipy的巴特沃斯滤波器函数生成一个系数数组'lowpass'，然后将该数组与我的数据集y值'yf_ISLL_11_21_irfft'卷积。
from scipy import signal
lowpass = scipy.signal.butter(2, 0.1, btype='low', analog=False, output='ba', fs=0.1)
yf_ISLL_11_21_irfft = np.convolve(lowpass, yf_ISLL_11_21_irfft)
plt.plot(time_data_ISLL_11_21, yf_ISLL_11_21_irfft)
但是出现错误消息：'数字滤波器临界频率必须满足0 < Wn < 1'，尽管我的Wn == 0.1。
英文:
I have a time series dataset with sample rate 1/10 and sample size 8390. I just want to apply low and high pass filters for the first 1000 and last 1000 samples so I can compute a fourier transform without low end artefacts affecting the result.
I tried using scipy's butterworth filter function to generate an array of coefficients 'lowpass' then convolving that array with my dataset y values 'yf_ISLL_11_21_irfft'.
from scipy import signal
lowpass = scipy.signal.butter(2, 0.1, btype='low', analog=False, output='ba', fs=0.1)
yf_ISLL_11_21_irfft = np.convolve(lowpass, yf_ISLL_11_21_irfft)
plt.plot(time_data_ISLL_11_21, yf_ISLL_11_21_irfft)
But the error message: 'Digital filter critical frequencies must be 0 < Wn < 1' is returned, despite my Wn == 0.1.
答案1
得分: 1
我在运行您的前两行时遇到了一个略有不同的错误信息：
ValueError: 数字滤波器的临界频率必须在 0 < Wn < fs/2 (fs=0.1 -> fs/2=0.05)
但我认为根本原因是相同的。当您为 fs 提供一个值时，Wn 参数需要在 0 和奈奎斯特频率之间。听起来（也许我理解有误），您想将 0.1*fs 作为您的 Wn 值。
英文:
I get a slightly different error message when I run your first two lines:
ValueError: Digital filter critical frequencies must be 0 < Wn < fs/2 (fs=0.1 -> fs/2=0.05)
But I think the underlying reason is the same. When you provide a value for fs, the Wn parameters needs to be between 0 and the Nyquist frequency. It sounds like (and maybe I am misinterpreting), you want to use a value of 0.1*fs as your Wn value.
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