title: How to plot large range values with matplotlib?
body: <p>I have to run soak tests for longer duration and capture 3 datasets (before the run, in-between the run, after the run), plot them and manually analyze the plots.</p>
  
  <p>All the datasets span across the very large range (0-10^5). So, when I am plotting this data using matplotlib's bar function, the bar for smaller values is too small to be analyzed.</p>
  
  <pre><code>import matplotlib
  matplotlib.use('Agg')
  
  import sys,os,argparse,json,string,numpy
  from datetime import datetime
  import matplotlib.pyplot as plt
  import matplotlib.dates as mdates
  
  bx = ('smmpg_b1024k', 'smmpg_b10k', 'smmpg_b11k', 'smmpg_b128', 'smmpg_b128k', 'smmpg_b12k', 'smmpg_b13k', 'smmpg_b14k', 'smmpg_b15k', 'smmpg_b160', 'smmpg_b16k', 'smmpg_b17k', 'smmpg_b18k', 'smmpg_b192', 'smmpg_b192k', 'smmpg_b19k', 'smmpg_b1k', 'smmpg_b20k', 'smmpg_b21k', 'smmpg_b224', 'smmpg_b22k', 'smmpg_b23k', 'smmpg_b24k', 'smmpg_b256', 'smmpg_b256k', 'smmpg_b25k', 'smmpg_b26k', 'smmpg_b27k', 'smmpg_b288', 'smmpg_b28k', 'smmpg_b29k', 'smmpg_b2k', 'smmpg_b30k', 'smmpg_b31k', 'smmpg_b32', 'smmpg_b320', 'smmpg_b320k', 'smmpg_b32k', 'smmpg_b33k', 'smmpg_b34k', 'smmpg_b352', 'smmpg_b35k', 'smmpg_b36k', 'smmpg_b37k', 'smmpg_b384', 'smmpg_b384k', 'smmpg_b38k', 'smmpg_b39k', 'smmpg_b3k', 'smmpg_b40k', 'smmpg_b416', 'smmpg_b41k', 'smmpg_b42k', 'smmpg_b43k', 'smmpg_b448', 'smmpg_b448k', 'smmpg_b44k', 'smmpg_b45k', 'smmpg_b46k', 'smmpg_b47k', 'smmpg_b480', 'smmpg_b48k', 'smmpg_b49k', 'smmpg_b4k', 'smmpg_b50k', 'smmpg_b512', 'smmpg_b512k', 'smmpg_b51k', 'smmpg_b52k', 'smmpg_b53k', 'smmpg_b544', 'smmpg_b54k', 'smmpg_b55k', 'smmpg_b56k', 'smmpg_b576', 'smmpg_b576k', 'smmpg_b57k', 'smmpg_b58k', 'smmpg_b59k', 'smmpg_b5k', 'smmpg_b608', 'smmpg_b60k', 'smmpg_b61k', 'smmpg_b62k', 'smmpg_b63k', 'smmpg_b64', 'smmpg_b640', 'smmpg_b640k', 'smmpg_b64k', 'smmpg_b672', 'smmpg_b6k', 'smmpg_b704', 'smmpg_b704k', 'smmpg_b736', 'smmpg_b768', 'smmpg_b768k', 'smmpg_b7k', 'smmpg_b800', 'smmpg_b832', 'smmpg_b832k', 'smmpg_b864', 'smmpg_b896', 'smmpg_b896k', 'smmpg_b8k', 'smmpg_b928', 'smmpg_b96', 'smmpg_b960', 'smmpg_b960k', 'smmpg_b992', 'smmpg_b9k', 'smmpg_ccb', 'smmpg_msb', 'smmpg_twomb', 'total-pages', 'total-size')
  
  before = (0.0, 2.0, 2.0, 4.0, 8.0, 2.0, 2.0, 2.0, 2.0, 6.0, 2.0, 4.0, 44.0, 76.0, 6.0, 2.0, 2.0, 2.0, 18.0, 2.0, 18.0, 30.0, 32.0, 2.0, 12.0, 2.0, 170.0, 0.0, 4.0, 2.0, 0.0, 24.0, 0.0, 2.0, 10.0, 2.0, 12.0, 2.0, 36.0, 0.0, 2.0, 0.0, 0.0, 0.0, 12.0, 22.0, 2.0, 0.0, 272.0, 2.0, 4.0, 2.0, 0.0, 2.0, 4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 4.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0, 8.0, 2.0, 0.0, 2.0, 2.0, 6.0, 0.0, 0.0, 0.0, 34.0, 2.0, 0.0, 2.0, 0.0, 2.0, 92.0, 2.0, 0.0, 2.0, 2.0, 40.0, 2.0, 0.0, 2.0, 2.0, 0.0, 14.0, 2.0, 4.0, 2.0, 2.0, 2.0, 0.0, 18.0, 2.0, 28.0, 4.0, 0.0, 2.0, 2.0, 6.0, 214.0, 26226.0, 13813.0, 27626.0)
  
  intermediate = (0.0, 2.0, 2.0, 4.0, 8.0, 2.0, 2.0, 2.0, 2.0, 6.0, 2.0, 4.0, 44.0, 76.0, 6.0, 2.0, 2.0, 2.0, 18.0, 2.0, 18.0, 30.0, 32.0, 2.0, 12.0, 2.0, 170.0, 0.0, 4.0, 2.0, 0.0, 24.0, 0.0, 2.0, 10.0, 2.0, 12.0, 2.0, 36.0, 0.0, 2.0, 0.0, 0.0, 0.0, 12.0, 22.0, 2.0, 0.0, 272.0, 2.0, 4.0, 2.0, 0.0, 2.0, 4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 4.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0, 8.0, 2.0, 0.0, 2.0, 2.0, 6.0, 0.0, 0.0, 0.0, 34.0, 2.0, 0.0, 2.0, 0.0, 2.0, 92.0, 2.0, 0.0, 2.0, 2.0, 40.0, 2.0, 0.0, 2.0, 2.0, 0.0, 14.0, 2.0, 4.0, 2.0, 2.0, 2.0, 0.0, 18.0, 2.0, 28.0, 4.0, 0.0, 2.0, 2.0, 6.0, 214.0, 26226.0, 13813.0, 27626.0)
  
  after = (0.0, 2.0, 2.0, 4.0, 8.0, 2.0, 2.0, 2.0, 2.0, 6.0, 2.0, 4.0, 44.0, 76.0, 6.0, 2.0, 2.0, 2.0, 18.0, 2.0, 18.0, 30.0, 32.0, 2.0, 12.0, 2.0, 170.0, 0.0, 4.0, 2.0, 0.0, 24.0, 0.0, 2.0, 10.0, 2.0, 12.0, 2.0, 36.0, 0.0, 2.0, 0.0, 0.0, 0.0, 12.0, 22.0, 2.0, 0.0, 272.0, 2.0, 4.0, 2.0, 0.0, 2.0, 4.0, 2.0, 0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 4.0, 0.0, 2.0, 2.0, 2.0, 0.0, 0.0, 8.0, 2.0, 0.0, 2.0, 2.0, 6.0, 0.0, 0.0, 0.0, 34.0, 2.0, 0.0, 2.0, 0.0, 2.0, 92.0, 2.0, 0.0, 2.0, 2.0, 40.0, 2.0, 0.0, 2.0, 2.0, 0.0, 14.0, 2.0, 4.0, 2.0, 2.0, 2.0, 0.0, 18.0, 2.0, 28.0, 4.0, 0.0, 2.0, 2.0, 6.0, 214.0, 26226.0, 13813.0, 27626.0)
  
  x_locations= numpy.arange(len(bx))
  width=0.27
  fig = plt.figure(figsize=(50, 20))
  ax = fig.add_subplot(111)
  
  before_test_mempools_bar = ax.bar(x_locations, list(before), width, color='r')
  intermediate_test_mempools_bar = ax.bar(x_locations + width, list(intermediate), width, color='g')
  after_test_mempools_bar = ax.bar(x_locations + width *2,list(after), width, color='b')
  ax.set_ylabel('Memory')
  
  ax.set_xticks(x_locations + width)
  ax.set_xticklabels(bx,rotation=90)
  ax.legend((before_test_mempools_bar[0],intermediate_test_mempools_bar[0],after_test_mempools_bar[0]),('BEFORE','INTERMEDIATE','AFTER'))
  
  fig.savefig("plot.png")
  plt.close()
  </code></pre>
  
  <p>The above code produces the following plot:
  <a href="https://i.stack.imgur.com/T08FT.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/T08FT.png" alt="plot"></a></p>
  
  <p><strong>Goal:</strong>
  My goal is to accommodate all the data in the plot that is visually nice and so the plot can be analyzed by any tester in the team.
  Currently, it's hard to see what's happened with a smaller range of values.</p>
  
  <p>One possible approach would be <em>normalization</em> but not sure if the data would be retained original.
  Any possible solutions are appreciated. </p>