content: # Import Modules as needed
  import numpy as np
  #import seaborn as sn
  import pandas as pd
  from pylab import *
  from mylocal_functions import *
  
  # ======== T2 MSME============= #
  # Make list of all T2.txt files
  T2_list = get_ipython().getoutput('ls ../Study_03_CBA/*T2.txt')
  
  # Allocate variables needed for analysis
  T2DF=pd.DataFrame()
  TR=np.linspace(.012,.012*12,12)
  # Fit T2 and construct dataframe
  for names in T2_list:
      #Convert txt file to array
      YDataMatrix=txt_2_array(names)
      #Estimate T2
      T2time=fitT2(TR,YDataMatrix)
      #convert to data frame
      df_T2=pd.DataFrame(T2time.T,columns=["Infected","Healthy_R","St_Inf","Healthy_L"])
      #df_T2=pd.DataFrame(T2time.T,columns=["ROI-1","ROI-2","ROI-3","ROI-4"])
      df_info=name_2_df(names)
      df_final=pd.concat([df_T2,df_info], axis=1)
      T2DF=T2DF.append(df_final,ignore_index=True)
  
  # Plot T2 Density ROIs 1 and 2
  #T2DF[T2DF.Slice==1].iloc[:,:4].plot.density(); title("Slice 01"); xlim((0.025,.15))
  #T2DF[T2DF.Slice==2].iloc[:,:4].plot.density(); title("Slice 02"); xlim((0.025,.15))
  #T2DF[T2DF.Slice==3].iloc[:,:4].plot.density(); title("Slice 03"); xlim((0.025,.15))
  #T2DF[T2DF.Slice==4].iloc[:,:4].plot.density(); title("Slice 04"); xlim((0.025,.15))
  #T2DF[T2DF.Slice==5].iloc[:,:4].plot.density(); title("Slice 05"); xlim((0.025,.15))
  
  
  # ======== CEST============= #
  # Make list of all T2.txt files
  CEST_list=get_ipython().getoutput('ls ../Study_03_CBA/*CEST.txt')
  CEST_DF=pd.DataFrame()
  Z=np.zeros((4,110))
  
  def normalize_data(DataMatrix):
      rows,cols = DataMatrix.shape
      newData = np.zeros_like(DataMatrix)
      for row in range(rows):
          newData[row,:]=DataMatrix[row,:]/DataMatrix[row,8]
      return newData
  
  for names in CEST_list:
      #Convert txt file to array
      D=txt_2_array(names);
      Zn=normalize_data(D.T)
      Z=np.concatenate((Z,Zn))
  
  Z=Z[4::,9::]
  
  # define offsets in ppm
  a1=np.linspace(-55,-50,9)
  ppm=np.linspace(-8,8,101)
  full_ppm = np.concatenate((a1, ppm))
  
  
  # fit CEST data.
  y=Z[12,:]
  p=fit_L2_scale(ppm,y)
  Yhat=Lscale(ppm,p[0],p[1],p[2],p[3],p[4],p[5],p[6]);
  
  plt.figure(figsize=(10,6))
  plt.plot(ppm,y,'o',label='Signal'); 
  plt.plot(ppm,1-Yhat,'-',label='Fit'); 
  plt.legend()
  
  ## ======   BUILD CEST Predictors ======== #####
  CEST_predictors=np.zeros_like(Z)
  rows,cols = CEST_predictors.shape
  Tissue_Class=np.zeros((4,rows))
  
  for i in range(rows):
      p=fit_L2_scale(ppm,Z[i,:])
      CEST_predictors[i,:]=Lscale(ppm,p[0],p[1],p[2],p[3],p[4],p[5],p[6]);
  
  Tissue_Class=np.zeros((64,1))
  
  for i in range(4):
      Tissue_Class[i::4]=i
  
  
  
  CEST_Dataframe=pd.DataFrame(CEST_predictors)   
  CEST_Dataframe["Tissue_Class"]=Tissue_Class
  
  pd.DataFrame.to_csv(CEST_Dataframe,"CEST_infections.csv",header=True,index=False)
  
  