old_contents: #------------------------------------------------------------------------------
  # Copyright (c) 2015, 2016, The University of Manchester, UK.
  #
  # Licenced under BSD. See LICENCE for details.
  #
  # Authors: Arturo Wong, Robert Haines
  #------------------------------------------------------------------------------
  
  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  import scipy.stats as stats
  
  def regression(file_name, second_index_quality, order):
  
      COLUMN_SEPARATOR = ','
      metrics_data = pd.DataFrame.from_csv(file_name, sep=COLUMN_SEPARATOR, header=None)
      labels = ['',
                'Test Coverage',
                'Branch Coverage',
                'Cyclomatic Complexity',
                'Lines of Code',
                'Number Of Methods',
                'Improvement Lack of Cohesion in Methods',
                'Lack of Documentation',
                'Depth of Inheritance',
                'Coupling Between Objects'
               ]
  
      x = [0,0]
      ys = [0,0]
      for i in range(0, 2):
          x[i] = metrics_data[i + 1]
          y = metrics_data[second_index_quality]
  
          coeffs = np.polyfit(x[i], y, order)
          polynomial = np.poly1d(coeffs)
          ys[i] = polynomial(x[i])
  
      plt.figure(second_index_quality - 2)
      plt.xlabel("Coverage")
      plt.ylabel(labels[second_index_quality])
      plt.title(labels[second_index_quality])
      plt.plot(x[0], ys[0])
      plt.plot(x[1], ys[1])
      plt.legend([labels[1], labels[2]], loc='upper right')
  
  
  file_path = '../data/project data/combined/Merge/Merge all projects.csv'
  for x in range(3,10):
      regression(file_path, x, 1)
      plt.show()
  
new_contents: #------------------------------------------------------------------------------
  # Copyright (c) 2015, 2016, The University of Manchester, UK.
  #
  # Licenced under BSD. See LICENCE for details.
  #
  # Authors: Arturo Wong, Robert Haines
  #------------------------------------------------------------------------------
  
  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  import scipy.stats as stats
  
  def regression(file_name, second_index_quality, order):
  
      COLUMN_SEPARATOR = ','
      metrics_data = pd.DataFrame.from_csv(file_name, sep=COLUMN_SEPARATOR, header=None)
      labels = ['',
                'Test Coverage',
                'Branch Coverage',
                'Cyclomatic Complexity',
                'Lines of Code',
                'Number Of Methods',
                'Improvement Lack of Cohesion in Methods',
                'Lack of Documentation',
                'Depth of Inheritance',
                'Coupling Between Objects'
               ]
  
      x = [0,0]
      ys = [0,0]
      for i in range(0, 2):
          x[i] = metrics_data[i + 1]
          y = metrics_data[second_index_quality]
  
          coeffs = np.polyfit(x[i], y, order)
          polynomial = np.poly1d(coeffs)
          ys[i] = polynomial(x[i])
  
      plt.figure(second_index_quality - 2)
      plt.xlabel("Coverage")
      plt.ylabel(labels[second_index_quality])
      plt.title(labels[second_index_quality])
      plt.plot(x[0], ys[0])
      plt.plot(x[1], ys[1])
      plt.legend([labels[1], labels[2]], loc='upper right')
  
  
  file_path = '../data/combined/Merge/Merge all projects.csv'
  for x in range(3,10):
      regression(file_path, x, 1)
      plt.show()
  
subject: Change default input file for docker context.