title: How to extract data from a wav file using python matplotlib library?
body: <p>I'm trying to extract data from an wav file for audio analysis of each frequency and their amplitude with respect to time, my aim to run this data for a machine learning algorithm for a college project, after a bit of googling I found out that this can be done by Python's matplotlib library, I saw some sample codes that ran a Short Fourier transform and plotted a spectrogram of these wav files but wasn't able to understand how to use this library to extract data (all frequency's amplitude at a given time in the audio file) and store it in an 3D array or a .mat file.
  Here's the code I saw on some <a href="http://www.frank-zalkow.de/en/code-snippets/create-audio-spectrograms-with-python.html?ckattempt=1" rel="nofollow">website</a>:</p>
  
  <pre><code>#!/usr/bin/env python
  
  """ This work is licensed under a Creative Commons Attribution 3.0 Unported License.
  Frank Zalkow, 2012-2013 """
  
  import numpy as np
  from matplotlib import pyplot as plt
  import scipy.io.wavfile as wav
  from numpy.lib import stride_tricks
  
  """ short time fourier transform of audio signal """
  def stft(sig, frameSize, overlapFac=0.5, window=np.hanning):
      win = window(frameSize)
      hopSize = int(frameSize - np.floor(overlapFac * frameSize))
  
      # zeros at beginning (thus center of 1st window should be for sample nr. 0)
      samples = np.append(np.zeros(np.floor(frameSize/2.0)), sig)    
      # cols for windowing
      cols = np.ceil( (len(samples) - frameSize) / float(hopSize)) + 1
      # zeros at end (thus samples can be fully covered by frames)
      samples = np.append(samples, np.zeros(frameSize))
  
      frames = stride_tricks.as_strided(samples, shape=(cols, frameSize), strides=(samples.strides[0]*hopSize, samples.strides[0])).copy()
      frames *= win
  
  
      return np.fft.rfft(frames)    
  
  """ scale frequency axis logarithmically """    
  def logscale_spec(spec, sr=44100, factor=20.):
      timebins, freqbins = np.shape(spec)
  
      scale = np.linspace(0, 1, freqbins) ** factor
      scale *= (freqbins-1)/max(scale)
      scale = np.unique(np.round(scale))
  
      # create spectrogram with new freq bins
      newspec = np.complex128(np.zeros([timebins, len(scale)]))
      for i in range(0, len(scale)):
          if i == len(scale)-1:
              newspec[:,i] = np.sum(spec[:,scale[i]:], axis=1)
          else:        
              newspec[:,i] = np.sum(spec[:,scale[i]:scale[i+1]], axis=1)
  
      # list center freq of bins
      allfreqs = np.abs(np.fft.fftfreq(freqbins*2, 1./sr)[:freqbins+1])
      freqs = []
      for i in range(0, len(scale)):
          if i == len(scale)-1:
              freqs += [np.mean(allfreqs[scale[i]:])]
          else:
              freqs += [np.mean(allfreqs[scale[i]:scale[i+1]])]
  
      return newspec, freqs
  
  """ plot spectrogram"""
  def plotstft(audiopath, binsize=2**10, plotpath=None, colormap="jet"):
      samplerate, samples = wav.read(audiopath)
      s = stft(samples, binsize)
  
      sshow, freq = logscale_spec(s, factor=1.0, sr=samplerate)
      ims = 20.*np.log10(np.abs(sshow)/10e-6) # amplitude to decibel
  
      timebins, freqbins = np.shape(ims)
  
      plt.figure(figsize=(15, 7.5))
      plt.imshow(np.transpose(ims), origin="lower", aspect="auto", cmap=colormap, interpolation="none")
      plt.colorbar()
  
      plt.xlabel("time (s)")
      plt.ylabel("frequency (hz)")
      plt.xlim([0, timebins-1])
      plt.ylim([0, freqbins])
  
      xlocs = np.float32(np.linspace(0, timebins-1, 5))
      plt.xticks(xlocs, ["%.02f" % l for l in ((xlocs*len(samples)/timebins)+(0.5*binsize))/samplerate])
      ylocs = np.int16(np.round(np.linspace(0, freqbins-1, 10)))
      plt.yticks(ylocs, ["%.02f" % freq[i] for i in ylocs])
  
      if plotpath:
          plt.savefig(plotpath, bbox_inches="tight")
      else:
          plt.show()
  
      plt.clf()
  plotstft("abc.wav")
  </code></pre>
  
  <p>Please guide me to understand how to extract the data, if not by matplotlib, recommend me some other library which will help me achieve this.</p>