[英]Frequency Response Scipy.signal
I'm learning digital signal processing to implement filters and am using python to easily implement a test ideas. 我正在学习数字信号处理以实现过滤器,并使用python轻松实现测试思路。 So I just started using the scipy.signal library to find the impulse response and frequency response of different filters. 所以我刚开始使用scipy.signal库来查找不同滤波器的脉冲响应和频率响应。
Currently I am working through the book "Digital Signals, Processors and Noise by Paul A. Lynn (1992)" (and finding it an amazing resource for learning this stuff). 目前我正在阅读“保罗·林恩(1992)的数字信号,处理器和噪声”一书(并且发现它是学习这些东西的一个惊人的资源)。 In this book they have a filter with the transfer functions shown below: 在本书中,他们有一个过滤器,其传递函数如下所示:
I divided the numerator and denominator by 我将分子和分母除以 in order to get the following equation: 为了得到以下等式:
I then implemented this with Scipy using: 然后我使用Scipy实现了这个:
NumeratorZcoefs = [1, -1, 1, -1]
DenominatorZcoefs = [1, 0.54048, -0.62519, -0.66354, 0.60317, 0.69341]
FreqResponse = scipy.signal.freqz(NumeratorZcoefs, DenominatorZcoefs)
fig = plt.figure(figsize = [8, 6])
ax = fig.add_subplot(111)
ax.plot(FreqResponse[0], abs(np.array(FreqResponse[1])))
ax.set_xlim(0, 2*np.pi)
ax.set_xlabel("$\Omega$")
and produce the plot shown below: 并生成如下图:
However in the book the frequency response is shown to be the following: 然而,在本书中,频率响应显示如下:
They are the same shape but the ratio of the peaks at ~2.3 and 0.5 are very different for the 2 plots, could someone suggest why this is? 它们具有相同的形状,但是对于2个图,峰值在~2.3和0.5处的比例非常不同,有人可能会说明为什么会这样吗?
Edit: 编辑:
To add to this, I've just implemented a function to calculate the frequency response by hand (by calculating the distance from the poles and zeros of the function) and I get a similar ratio to the plot generated by scipy.signal, however the numbers are not the same, does anyone know why this might by? 为了补充一点,我刚刚实现了一个函数来手动计算频率响应(通过计算函数的极点和零点的距离),并得到与scipy.signal生成的图形相似的比率,但是数字不一样,有谁知道为什么会这样?
Implementation is as follows: 实施如下:
def H(omega):
z1 = np.array([0,0]) # zero at 0, 0
z2 = np.array([0,0]) # Another zero at 0, 0
z3 = np.array([0, 1]) # zero at i
z4 = np.array([0, -1]) # zero at -i
z5 = np.array([1, 0]) # zero at 1
z = np.array([z1, z2, z3, z4, z5])
p1 = np.array([-0.8, 0])
p = cmath.rect(0.98, np.pi/4)
p2 = np.array([p.real, p.imag])
p = cmath.rect(0.98, -np.pi/4)
p3 = np.array([p.real, p.imag])
p = cmath.rect(0.95, 5*np.pi/6)
p4 = np.array([p.real, p.imag])
p = cmath.rect(0.95, -5*np.pi/6)
p5 = np.array([p.real, p.imag])
p = np.array([p1, p2, p3, p4, p5])
a = cmath.rect(1,omega)
a_2dvector = np.array([a.real, a.imag])
dz = z-a_2dvector
dp = p-a_2dvector
dzmag = []
for dis in dz:
dzmag.append(np.sqrt(dis.dot(dis)))
dpmag = []
for dis in dp:
dpmag.append(np.sqrt(dis.dot(dis)))
return(np.product(dzmag)/np.product(dpmag))
I then plot the frequency response like so: 然后我像这样绘制频率响应:
omegalist = np.linspace(0,2*np.pi,5000)
Hlist = []
for omega in omegalist:
Hlist.append(H(omega))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(omegalist, Hlist)
ax.set_xlabel("$\Omega$")
ax.set_ylabel("$|H(\Omega)|$")
and get the following plot: 得到以下情节:
The SciPy generated frequency response is correct. SciPy生成的频率响应是正确的。 In any case, I wouldn't trust the book's figure which appears to have been drawn by hand. 无论如何,我不相信这本书似乎是手工绘制的。
If you want to find the frequency response "manually", this can be simply done by defining a function returning the original Z-transform and evaluating it on the unit circle as follows 如果你想“手动”找到频率响应,可以通过定义一个返回原始Z变换并在单位圆上进行评估的函数来完成,如下所示
def H(z):
num = z**5 - z**4 + z**3 - z**2
denom = z**5 + 0.54048*z**4 - 0.62519*z**3 - 0.66354*z**2 + 0.60317*z + 0.69341
return num/denom
import numpy as np
import matplotlib.pyplot as plt
w_range = np.linspace(0, 2*np.pi, 1000)
plt.plot(w_range, np.abs(H(np.exp(1j*w_range))))
The result is exactly the same as SciPy. 结果与SciPy完全相同。
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