Question

我正在尝试将色条添加到频谱图中。我已经尝试过我在网上找到的每个例子和问题帖子，但没有人解决过这个问题

请注意＆＃39; spl1＆＃39; （数据拼接1）是来自ObsPy的痕迹。

我的代码是：

fig = plt.figure()
ax1 = fig.add_axes([0.1, 0.75, 0.7, 0.2]) #[left bottom width height]
ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.60], sharex=ax1)
ax3 = fig.add_axes([0.83, 0.1, 0.03, 0.6])

t = np.arange(spl1[0].stats.npts) / spl1[0].stats.sampling_rate
ax1.plot(t, spl1[0].data, 'k')

ax,spec = spectrogram(spl1[0].data,spl1[0].stats.sampling_rate, show=False, axes=ax2)
ax2.set_ylim(0.1, 15)
fig.colorbar(spec, cax=ax3)

出现错误：

Traceback (most recent call last):

  File "<ipython-input-18-61226ccd2d85>", line 14, in <module>
    ax,spec = spectrogram(spl1[0].data,spl1[0].stats.sampling_rate, show=False, axes=ax2)

TypeError: 'Axes' object is not iterable

目前为止的最佳结果：

将上面的最后3行替换为：

ax = spectrogram(spl1[0].data,spl1[0].stats.sampling_rate, show=False, axes=ax2)
ax2.set_ylim(0.1, 15)
fig.colorbar(ax,cax=ax3)

产生这个：

和colorbar的错误：

axes object has no attribute 'autoscale_None'

我似乎无法找到一种方法让右边的颜色条工作。

方案吗

我看到的一个解决方案是你需要创建一个＆＃39;图像＆＃39;使用imshow（）你的数据，但是我没有从Spectrogram（）得到一个输出，只有＆＃39; ax＆＃39;。我见过的地方尝试使用＆＃39; ax，spec＆＃39;来自spectrogram（）的输出但是导致了TypeError。

我找到的代码非常相似，但没有工作https://www.nicotrebbin.de/wp-content/uploads/2012/03/bachelorthesis.pdf（ctrl + f＆＃39; colorbar＆＃39;）
查看代码示例from a related question
imshow（）suggestions和example - 无法从频谱图中获取输出以变成图像。第二个链接，我也无法使mlpy模块工作（它不会认为这是一个mlpy.wavelet函数）
an improvement post for obspy解决了这个问题，但他说他发现的解决方案没有给出

我希望有人可以帮忙解决这个问题 - 我现在整天都在努力！

Answer 1

在this link的帮助下解决了这个问题。它还没有显示分贝，但主要问题是获得颜色条：

from obspy.imaging.spectrogram import spectrogram
fig = plt.figure()
ax1 = fig.add_axes([0.1, 0.75, 0.7, 0.2]) #[left bottom width height]
ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.60], sharex=ax1)
ax3 = fig.add_axes([0.83, 0.1, 0.03, 0.6])

#make time vector
t = np.arange(spl1[0].stats.npts) / spl1[0].stats.sampling_rate

#plot waveform (top subfigure)    
ax1.plot(t, spl1[0].data, 'k')

#plot spectrogram (bottom subfigure)
spl2 = spl1[0]
fig = spl2.spectrogram(show=False, axes=ax2)
mappable = ax2.images[0]
plt.colorbar(mappable=mappable, cax=ax3)

Answer 2

我假设您正在使用matplotlib.pyplot。它有明确的颜色要求 matplotlib.pyplot.plot(x-cordinates , y-co-ordinates, color)

示例实现如下。

"""Plots
Time in MS Vs Amplitude in DB of a input wav signal
"""

import numpy
import matplotlib.pyplot as plt
import pylab
from scipy.io import wavfile
from scipy.fftpack import fft


myAudio = "audio.wav"

#Read file and get sampling freq [ usually 44100 Hz ]  and sound object
samplingFreq, mySound = wavfile.read(myAudio)

#Check if wave file is 16bit or 32 bit. 24bit is not supported
mySoundDataType = mySound.dtype

#We can convert our sound array to floating point values ranging from -1 to 1 as follows

mySound = mySound / (2.**15)

#Check sample points and sound channel for duel channel(5060, 2) or  (5060, ) for mono channel

mySoundShape = mySound.shape
samplePoints = float(mySound.shape[0])

#Get duration of sound file
signalDuration =  mySound.shape[0] / samplingFreq

#If two channels, then select only one channel
mySoundOneChannel = mySound[:,0]

#Plotting the tone

# We can represent sound by plotting the pressure values against time axis.
#Create an array of sample point in one dimension
timeArray = numpy.arange(0, samplePoints, 1)

#
timeArray = timeArray / samplingFreq

#Scale to milliSeconds
timeArray = timeArray * 1000

#Plot the tone
plt.plot(timeArray, mySoundOneChannel, color='G')
plt.xlabel('Time (ms)')
plt.ylabel('Amplitude')
plt.show()


#Plot frequency content
#We can get frquency from amplitude and time using FFT , Fast Fourier Transform algorithm

#Get length of mySound object array
mySoundLength = len(mySound)

#Take the Fourier transformation on given sample point 
#fftArray = fft(mySound)
fftArray = fft(mySoundOneChannel)

numUniquePoints = numpy.ceil((mySoundLength + 1) / 2.0)
fftArray = fftArray[0:numUniquePoints]

#FFT contains both magnitude and phase and given in complex numbers in real + imaginary parts (a + ib) format.
#By taking absolute value , we get only real part

fftArray = abs(fftArray)

#Scale the fft array by length of sample points so that magnitude does not depend on
#the length of the signal or on its sampling frequency

fftArray = fftArray / float(mySoundLength)

#FFT has both positive and negative information. Square to get positive only
fftArray = fftArray **2

#Multiply by two (research why?)
#Odd NFFT excludes Nyquist point
if mySoundLength % 2 > 0: #we've got odd number of points in fft
    fftArray[1:len(fftArray)] = fftArray[1:len(fftArray)] * 2

else: #We've got even number of points in fft
    fftArray[1:len(fftArray) -1] = fftArray[1:len(fftArray) -1] * 2  

freqArray = numpy.arange(0, numUniquePoints, 1.0) * (samplingFreq / mySoundLength);

#Plot the frequency
plt.plot(freqArray/1000, 10 * numpy.log10 (fftArray), color='B')
plt.xlabel('Frequency (Khz)')
plt.ylabel('Power (dB)')
plt.show()

#Get List of element in frequency array
#print freqArray.dtype.type
freqArrayLength = len(freqArray)
print "freqArrayLength =", freqArrayLength
numpy.savetxt("freqData.txt", freqArray, fmt='%6.2f')

#Print FFtarray information
print "fftArray length =", len(fftArray)
numpy.savetxt("fftData.txt", fftArray)

将Colorbar添加到频谱图

2 个答案: