问题'模块'matplotlib.pyplot'没有属性'plot_filterz'

Question

我多次尝试修复这段代码，但它仍然存在同样的问题。 这是我收到的 Python 语言的错误代码：

#----------------------------------------------------
#FIR filter

n=100 #Design filter
b=signal.firwin(n, cutoff= 0.2, window = "hamming")
#Plot properties
plt .plot_filterz(b)

x = np.sin(np . linspace(0,500,1024))+0.5*np. cos(np. linspace(0,750,1024))+ np.random.randn(1024)*0.2 +0.2*np. cos(np . linspace(0, 10000, 1024))
xfiltered = signal.lfilter(b, 1, x) 

plt.plot(x[:200], label = "Original signal")
plt.plot(xfiltered[:200], "--k", label = "Filtered signal")
plt.legend()
plt.show()  

我想不通为什么我一直收到“模块‘matplotlib.pyplot’没有属性‘plot_filterz’object 没有属性‘firwin’？ 这是我的完整代码（已编辑）： https://docs.google.com/document/d/1VPjlRgssIMfPp4xEP5wV3vG0ROLxXoos4oMMTjMoyVs/edit

Answer 1

我用 sample.wav 文件运行你的代码，发现你的代码中有很多错误。 我已经重构了您的代码，这些是现在从重构代码生成的图。

错误亮点：

• plot_filterz 是自定义的 function，但被错误地称为 plt.plot_filterz
• np.fromstring 需要一个字符串，但提供了字节
• numpy 没有 freqz function, scipy.signal 有 freqz function
• numpy 没有 function 称为脉冲 np.impulse
• numpy.arctan2 function 被调用了一个参数，它需要两个 arguments

重构代码：

import numpy as np
import matplotlib.pyplot as plt
import wave
from scipy import signal

def plot_freqz(c, a=1):
    w, h = signal.freqz(c, a)
    h_dB = 20 * np.log10(abs(h))
    plt.plot(w / np.pi, h_dB)
    plt.ylim([max(min(h_dB), -100), 5])
    plt.ylabel('Magnitude (db)')
    plt.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
    plt.title(r'Amplitude response')


def plot_phasez(c, a=1):
    w, h = signal.freqz(c, a)
    h_Phase = np.unwrap(np.arctan(np.imag(h)))
    plt.plot(w / np.pi, h_Phase)
    plt.ylabel('Phase(radians)')
    plt.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
    plt.title(r'Phase response')


def plot_impz(c, a=1):
    if type(a) == int:  # FIR
        factor = 10000  # using this factor to speed up the plotting. change as per your need
        l = int(len(c) / factor)

    else:  # IIR
        l = 100
    impulse = np.repeat(0., l)
    impulse[0] = 1
    x = np.linspace(0, 1, l)
    response = signal.lfilter(c, a, impulse)

    plt.stem(x, response, linefmt='b-', basefmt='b-', markerfmt='bo')
    plt.ylabel('Amplitude')
    plt.xlabel(r'n(samples)')
    plt.title(r'Impulse response')


def plot_stepz(c, a=1):
    if type(a) == int:  # FIR
        factor = 10000 # using this factor to speed up the plotting. change as per your need
        l = int(len(c)/factor)

    else:  # IIR
        l = 100
    impulse = np.repeat(0., l)
    impulse[0] = 1
    x = np.arange(0, l)
    response = signal.lfilter(c, a, impulse)
    plt.plot(x, np.cumsum(response))
    plt.ylabel(' Amplitude')
    plt.xlabel(r'n(samples)')
    plt.title(r'Step response')


def plot_filterz(c, a=1):
    print('plotting freqz plot')
    plt.subplot(221)
    plot_freqz(c, a)
    print('plotting phasez plot')
    plt.subplot(222)
    plot_phasez(c, a)
    print('plotting impz plot')
    plt.subplot(223)
    plot_impz(c, a)
    print('plotting stepz plot')
    plt.subplot(224)
    plot_stepz(c, a)
    plt.subplots_adjust(hspace=0.5, wspace=0.3)
    plt.savefig('filter_properties.png')
    plt.show()
    print('done plotting properties')


def ma(x, n=5):
    b = np.ones(n) / n
    xf = signal.lfilter(b, 1, x)
    return xf


# ----------------------------------------------------
# FIR filter
if __name__ == '__main__':
    spf = wave.open('BabyElephantWalk60.wav', 'r') #sample .wav file, replace with your .wav file

    # Extract Original Audio from Wav File
    c = spf.readframes(-1)
    # c = np.fromstring(c, np.int16)

    c= np.frombuffer(c, dtype=np.int16)
    n = 100  # Design filter
    b = signal.firwin(n, cutoff=0.2, window="hamming")
    # Plot properties
    plot_filterz(c)

    x = np.sin(np.linspace(0, 500, 1024)) + 0.5 * np.cos(np.linspace(0, 750, 1024)) + np.random.randn(
        1024) * 0.2 + 0.2 * np.cos(np.linspace(0, 10000, 1024))
    print('calculating lfilter of x')
    xfiltered = signal.lfilter(b, 1, x)

    print('plotting original signal and filtered signal')
    plt.title('Original signal v/s filtered signal')
    plt.plot(x[:200], label="Original signal")
    plt.plot(xfiltered[:200], "--k", label="Filtered signal")
    plt.legend()
    plt.savefig('orignal_vs_filtered_signal.png')
    plt.show()