可以使用函数^{}或^{}创建带通FIR滤波器。也可以使用^{}设计FIR滤波器
下面的代码为创建带通FIR滤波器提供了一些方便的包装。它使用这些来创建与问题中请求的数字对应的带通滤波器。这假设采样是均匀的。如果采样不均匀，则不适合使用FIR滤波器。from scipy.signal import firwin, remez, kaiser_atten, kaiser_beta
# Several flavors of bandpass FIR filters.
def bandpass_firwin(ntaps, lowcut, highcut, fs, window='hamming'):
nyq = 0.5 * fs
taps = firwin(ntaps, [lowcut, highcut], nyq=nyq, pass_zero=False,
window=window, scale=False)
return taps
def bandpass_kaiser(ntaps, lowcut, highcut, fs, width):
nyq = 0.5 * fs
atten = kaiser_atten(ntaps, width / nyq)
beta = kaiser_beta(atten)
taps = firwin(ntaps, [lowcut, highcut], nyq=nyq, pass_zero=False,
window=('kaiser', beta), scale=False)
return taps
def bandpass_remez(ntaps, lowcut, highcut, fs, width):
delta = 0.5 * width
edges = [0, lowcut - delta, lowcut + delta,
highcut - delta, highcut + delta, 0.5*fs]
taps = remez(ntaps, edges, [0, 1, 0], Hz=fs)
return taps
if __name__ == "__main__":
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import freqz
# Sample rate and desired cutoff frequencies (in Hz).
fs = 63.0
lowcut = 0.7
highcut = 4.0
ntaps = 128
taps_hamming = bandpass_firwin(ntaps, lowcut, highcut, fs=fs)
taps_kaiser16 = bandpass_kaiser(ntaps, lowcut, highcut, fs=fs, width=1.6)
taps_kaiser10 = bandpass_kaiser(ntaps, lowcut, highcut, fs=fs, width=1.0)
remez_width = 1.0
taps_remez = bandpass_remez(ntaps, lowcut, highcut, fs=fs,
width=remez_width)
# Plot the frequency responses of the filters.
plt.figure(1, figsize=(12, 9))
plt.clf()
# First plot the desired ideal response as a green(ish) rectangle.
rect = plt.Rectangle((lowcut, 0), highcut - lowcut, 1.0,
facecolor="#60ff60", alpha=0.2)
plt.gca().add_patch(rect)
# Plot the frequency response of each filter.
w, h = freqz(taps_hamming, 1, worN=2000)
plt.plot((fs * 0.5 / np.pi) * w, abs(h), label="Hamming window")
w, h = freqz(taps_kaiser16, 1, worN=2000)
plt.plot((fs * 0.5 / np.pi) * w, abs(h), label="Kaiser window, width=1.6")
w, h = freqz(taps_kaiser10, 1, worN=2000)
plt.plot((fs * 0.5 / np.pi) * w, abs(h), label="Kaiser window, width=1.0")
w, h = freqz(taps_remez, 1, worN=2000)
plt.plot((fs * 0.5 / np.pi) * w, abs(h),
label="Remez algorithm, width=%.1f" % remez_width)
plt.xlim(0, 8.0)
plt.ylim(0, 1.1)
plt.grid(True)
plt.legend()
plt.xlabel('Frequency (Hz)')
plt.ylabel('Gain')
plt.title('Frequency response of several FIR filters, %d taps' % ntaps)
plt.show()
这是脚本生成的情节。当然，在本地运行脚本更有用，因此可以放大细节。
