content: """
  MOST OF THIS CODE IS NOT USED
  ITS COPY/PASTED AND LEFT HERE FOR CONVENIENCE
  """
  
  import os
  import sys
  
  # in case our module isn't installed (running from this folder)
  if not os.path.abspath('../../../') in sys.path:
      sys.path.append('../../../') # helps spyder get docs
  
  import swhlab
  import swhlab.common as cm
  import matplotlib.pyplot as plt
  import numpy as np
  
  import warnings # suppress VisibleDeprecationWarning warning
  warnings.filterwarnings("ignore", category=np.VisibleDeprecationWarning)
  
  def analyzeSweep(abf,plotToo=True,color=None,label=None):
      Y=abf.sweepYsmartbase()[abf.pointsPerSec*.5:]
      AV,SD=np.average(Y),np.std(Y)
      dev=5 # number of stdevs from the avg to set the range
      R1,R2=[(AV-SD)*dev,(AV+SD)*dev]
      nBins=1000
      hist,bins=np.histogram(Y,bins=nBins,range=[R1,R2],density=True)
      histSmooth=abf.convolve(hist,cm.kernel_gaussian(nBins/5))
  
      if plotToo:
          plt.plot(bins[1:],hist,'.',color=color,alpha=.2,ms=10)
          plt.plot(bins[1:],histSmooth,'-',color=color,lw=5,alpha=.5,label=label)
  
      return
  
  if __name__=="__main__":
      #abfFile=R"C:\Users\scott\Documents\important\demodata\abfs\16d07022.abf"
      abfFile=R"X:\Data\2P01\2016\2016-09-01 PIR TGOT\16d07022.abf"
      abf=swhlab.ABF(abfFile)
  
      # prepare figure
      plt.figure(figsize=(10,10))
      plt.grid()
      plt.title("smart baseline value distribution")
      plt.xlabel(abf.units2)
      plt.ylabel("normalized density")
  
      # do the analysis
      abf.kernel=abf.kernel_gaussian(sizeMS=500)
      abf.setsweep(175)
      analyzeSweep(abf,color='b',label="baseline")
      abf.setsweep(200)
      analyzeSweep(abf,color='g',label="TGOT")
      abf.setsweep(375)
      analyzeSweep(abf,color='y',label="washout")
  
      # show figure
      plt.legend()
      plt.margins(0,.1)
      plt.show()
  
      print("DONE")
  