input: Write a function to process a data file and generate three plots: a histogram, an interpolated image, and a contour plot. The function should load a configuration file and necessary libraries, define custom colormaps, perform cubic spline interpolation on the data, apply a threshold to remove small values, and save the plots as PDF files. To use this function, ensure that the necessary files and libraries are in the correct paths, and adjust the parameters as needed for your data.
  def generate_plots(profileName, frameID, matrixID, padding=2, scaleFactor=10, min_threshold=25, countour_threshold=50):
      # Load configuration file before pyplot
      config_path = os.path.abspath('../matplotlib/')
      sys.path.append(config_path)
      import configuration as config
  
      # Library path
      lib_path = os.path.abspath('../../lib')
      sys.path.append(lib_path)
      import framemanager_python
  
      import numpy as np
      import matplotlib.pyplot as plt
      import scipy.interpolate
  
      # Define "bds_highcontrast" color map by Britton Smith <brittonsmith@gmail.com> from http://yt-project.org/ 
      cdict = {'red':   ((0.0, 80/256., 80/256.),
                         (0.2, 0.0, 0.0),
                         (0.4, 0.0, 0.0),
                         (0.6, 256/256., 256/256.),
                         (0.95, 256/256., 256/256.),
                         (1.0, 150/256., 150/256.)),
               'green': ((0.0, 0/256., 0/256.),
                         (0.2, 0/256., 0/256.),
                         (0.4, 130/256., 130/256.),
                         (0.6, 256/256., 256/256.),
                         (1.0, 0.0, 0.0)),
               'blue':  ((0.0, 80/256., 80/256.),
                         (0.2, 220/256., 220/256.),
                         (0.4, 0.0, 0.0),
                         (0.6, 20/256., 20/256.),
                         (1.0, 0.0, 0.0))}
  
      plt.register_cmap(name='bds_highcontrast', data=cdict) 
  
      # Define YELLOW_RED colormap
      cdict = {'red':   ((0.0, 0.9, 0.9),
                         (1.0, 0.9, 0.9)),
               'green': ((0.0, 0.9, 0.9),
                         (1.0, 0.0, 0.0)),
               'blue':  ((0.0, 0.0, 0.0),
                         (1.0, 0.0, 0.0))}
      plt.register_cmap(name='YELLOW_RED', data=cdict) 
  
      # Load profile
      frameManager = framemanager_python.FrameManagerWrapper()
      frameManager.load_profile(profileName);
      numTSFrames = frameManager.get_tsframe_count();
  
      # Load single frame
      tsframe = np.copy( frameManager.get_tsframe(frameID, matrixID) );
  
      cols = tsframe.shape[1]
      rows = tsframe.shape[0]
  
      # Add padding on border
      v_padding = np.empty((padding, cols)); v_padding.fill(-50)
      h_padding = np.empty((rows+2*padding, padding)); h_padding.fill(-50)
      zs = np.vstack([v_padding, tsframe]) # Top
      zs = np.vstack([zs, v_padding]) # Bottom
      zs = np.hstack([h_padding, zs]) # Left
      zs = np.hstack([zs, h_padding]) # Right
  
      # Update matrix size with padding
      cols = zs.shape[1]
      rows = zs.shape[0]
  
      # Coordinates of sampled data points
      xs = np.arange(0, cols, 1)
      ys = np.arange(0, rows, 1)
  
      # Coordinates of interpolation points
      xi = np.linspace(xs.min(), xs.max(), cols*scaleFactor)
      yi = np.linspace(ys.min(), ys.max(), rows*scaleFactor)
  
      # Interpolate with cubic splines
      spline = scipy.interpolate.RectBivariateSpline(ys, xs, zs, kx=3, ky=3, s=0)
      zi = spline(yi, xi)
  
      # Apply threshold to level out small values (interpolation ripples)
      zi[zi < min_threshold ] = 0 
  
      # Plotting
      fig, ax = plt.subplots()
  
      # Histogram
      plt.hist(zi.flatten(), 128, range=(min_threshold, zi.max()), fc='k', ec='k')
      plt.savefig("histogram.pdf", format='pdf')
      plt.close() 
  
      # Interpolated image
      fig, ax = plt.subplots()
      cax = ax.imshow(zi, cmap=plt.get_cmap('bds_highcontrast'), vmin=zs.min(), vmax=zs.max(), origin='lower', extent=[xs.min(), xs.max(), ys.min(), ys.max()])
      cbar = fig.colorbar(cax)
      cbar.set_label('Raw sensor value', rotation=90)
      cbar.solids.set_edgecolor("face")
      ax.invert_yaxis()
      ax.xaxis.tick_top()
      plt.axis('off')
      plt.savefig("interpolation.pdf", format='pdf')
      plt.close() 
  
      # Contour
      fig, ax = plt.subplots()
      cax = ax.imshow(zs, interpolation='nearest', cmap=plt.get_cmap('gray'), vmin=zs.min(), vmax=zs.max(), origin='lower', extent=[xs.min(), xs.max(), ys.min(), ys.max()])
      levels = np.linspace(countour_threshold, zs.max(), 10)
      contour = ax.contour(xi, yi, zi, levels, linewidths=1.0, colors=[(1.0, 0.0, 0.0)], origin='upper')
      cbar = fig.colorbar(cax)
      cbar.add_lines(contour)
      cbar.set_label('Raw sensor value', rotation=90)
      cbar.solids.set_edgecolor("face")
      ax.invert_yaxis()
      ax.xaxis.tick_top()
      plt.axis('off')
      plt.savefig("contour.pdf", format='pdf')
      plt.show()
  
  # Test cases
  generate_plots("foam_ball_short.dsa", 230, 1)
  generate_plots("another_data_file.dsa", 150, 2)
  assert os.path.exists("histogram.pdf")
  assert os.path.exists("interpolation.pdf")
  assert os.path.exists("contour.pdf")
output: 
  def generate_plots(profileName, frameID, matrixID, padding=2, scaleFactor=10, min_threshold=25, countour_threshold=50):
      # Load configuration file before pyplot
      config_path = os.path.abspath('../matplotlib/')
      sys.path.append(config_path)
      import configuration as config
  
      # Library path
      lib_path = os.path.abspath('../../lib')
      sys.path.append(lib_path)
      import framemanager_python
  
      import numpy as np
      import matplotlib.pyplot as plt
      import scipy.interpolate
  
      # Define "bds_highcontrast" color map by Britton Smith <brittonsmith@gmail.com> from http://yt-project.org/ 
      cdict = {'red':   ((0.0, 80/256., 80/256.),
                         (0.2, 0.0, 0.0),
                         (0.4, 0.0, 0.0),
                         (0.6, 256/256., 256/256.),
                         (0.95, 256/256., 256/256.),
                         (1.0, 150/256., 150/256.)),
               'green': ((0.0, 0/256., 0/256.),
                         (0.2, 0/256., 0/256.),
                         (0.4, 130/256., 130/256.),
                         (0.6, 256/256., 256/256.),
                         (1.0, 0.0, 0.0)),
               'blue':  ((0.0, 80/256., 80/256.),
                         (0.2, 220/256., 220/256.),
                         (0.4, 0.0, 0.0),
                         (0.6, 20/256., 20/256.),
                         (1.0, 0.0, 0.0))}
  
      plt.register_cmap(name='bds_highcontrast', data=cdict) 
  
      # Define YELLOW_RED colormap
      cdict = {'red':   ((0.0, 0.9, 0.9),
                         (1.0, 0.9, 0.9)),
               'green': ((0.0, 0.9, 0.9),
                         (1.0, 0.0, 0.0)),
               'blue':  ((0.0, 0.0, 0.0),
                         (1.0, 0.0, 0.0))}
      plt.register_cmap(name='YELLOW_RED', data=cdict) 
  
      # Load profile
      frameManager = framemanager_python.FrameManagerWrapper()
      frameManager.load_profile(profileName);
      numTSFrames = frameManager.get_tsframe_count();
  
      # Load single frame
      tsframe = np.copy( frameManager.get_tsframe(frameID, matrixID) );
  
      cols = tsframe.shape[1]
      rows = tsframe.shape[0]
  
      # Add padding on border
      v_padding = np.empty((padding, cols)); v_padding.fill(-50)
      h_padding = np.empty((rows+2*padding, padding)); h_padding.fill(-50)
      zs = np.vstack([v_padding, tsframe]) # Top
      zs = np.vstack([zs, v_padding]) # Bottom
      zs = np.hstack([h_padding, zs]) # Left
      zs = np.hstack([zs, h_padding]) # Right
  
      # Update matrix size with padding
      cols = zs.shape[1]
      rows = zs.shape[0]
  
      # Coordinates of sampled data points
      xs = np.arange(0, cols, 1)
      ys = np.arange(0, rows, 1)
  
      # Coordinates of interpolation points
      xi = np.linspace(xs.min(), xs.max(), cols*scaleFactor)
      yi = np.linspace(ys.min(), ys.max(), rows*scaleFactor)
  
      # Interpolate with cubic splines
      spline = scipy.interpolate.RectBivariateSpline(ys, xs, zs, kx=3, ky=3, s=0)
      zi = spline(yi, xi)
  
      # Apply threshold to level out small values (interpolation ripples)
      zi[zi < min_threshold ] = 0 
  
      # Plotting
      fig, ax = plt.subplots()
  
      # Histogram
      plt.hist(zi.flatten(), 128, range=(min_threshold, zi.max()), fc='k', ec='k')
      plt.savefig("histogram.pdf", format='pdf')
      plt.close() 
  
      # Interpolated image
      fig, ax = plt.subplots()
      cax = ax.imshow(zi, cmap=plt.get_cmap('bds_highcontrast'), vmin=zs.min(), vmax=zs.max(), origin='lower', extent=[xs.min(), xs.max(), ys.min(), ys.max()])
      cbar = fig.colorbar(cax)
      cbar.set_label('Raw sensor value', rotation=90)
      cbar.solids.set_edgecolor("face")
      ax.invert_yaxis()
      ax.xaxis.tick_top()
      plt.axis('off')
      plt.savefig("interpolation.pdf", format='pdf')
      plt.close() 
  
      # Contour
      fig, ax = plt.subplots()
      cax = ax.imshow(zs, interpolation='nearest', cmap=plt.get_cmap('gray'), vmin=zs.min(), vmax=zs.max(), origin='lower', extent=[xs.min(), xs.max(), ys.min(), ys.max()])
      levels = np.linspace(countour_threshold, zs.max(), 10)
      contour = ax.contour(xi, yi, zi, levels, linewidths=1.0, colors=[(1.0, 0.0, 0.0)], origin='upper')
      cbar = fig.colorbar(cax)
      cbar.add_lines(contour)
      cbar.set_label('Raw sensor value', rotation=90)
      cbar.solids.set_edgecolor("face")
      ax.invert_yaxis()
      ax.xaxis.tick_top()
      plt.axis('off')
      plt.savefig("contour.pdf", format='pdf')
      plt.close()
  