[text] abcd

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  1. # steady dataset
  2. steady_score=pd.read_csv("/Users/dhanushka/Desktop/mydata/fypit/anaconda/data/steadyscore.csv")
  3. score_calculated = 1/steady_score['sdX']/steady_score['sdY']
  4. # check for steady value
  5. # positive normal
  6. print((4.85-score_calculated.mean())/score_calculated.std())
  7. if abs((4.85-score_calculated.mean())/score_calculated.std())<3.5:
  8.     print "normal"
  9. plt.scatter(steady_score['distance'], score_calculated,color='green')
  10. plt.scatter([[16]], [[4.85]],color='red')
  11.  
  12.  
  13.  
  14.  
  15. # check for unsteady value
  16. # positive abnormal
  17. print((0.28-score_calculated.mean())/score_calculated.std())
  18.  
  19. if abs((0.28-score_calculated.mean())/score_calculated.std())>3.5:
  20.     print "abnormal"
  21. plt.scatter(steady_score['distance'], score_calculated,color='green')
  22. plt.scatter([[16]], [[0.28]],color='red')
  23.  
  24.  
  25.  
  26. # unsteady dataset
  27. steady_score=pd.read_csv("/Users/dhanushka/Desktop/mydata/fypit/anaconda/data/unsteadyscore.csv")
  28. score_calculated = 1/steady_score['sdX']/steady_score['sdY']
  29.  
  30. # check for steady value
  31. # negative abnormal
  32. print(abs((4.85-score_calculated.mean())/score_calculated.std()))
  33.  
  34. if abs((4.85-score_calculated.mean())/score_calculated.std())>3.5:
  35.     print "abnormal"
  36. plt.scatter(steady_score['distance'], score_calculated,color='green')
  37. plt.scatter([[28]], [[4.85]],color='red')
  38.  
  39.  
  40.  
  41. # check for unsteady value
  42. # negative normal
  43. print((0.28-score_calculated.mean())/score_calculated.std())
  44.  
  45. if abs((0.28-score_calculated.mean())/score_calculated.std())<3.5:
  46.     print "normal"
  47. plt.scatter(steady_score['distance'], score_calculated,color='green')
  48. plt.scatter([[28]], [[0.28]],color='red')
  49.  

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  • 23 May-2020
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