input: 
  from typing import Tuple
  import os, sys
  import numpy as np
  import matplotlib.pyplot as plt
  import scipy.interpolate
  
  def load_configuration_and_generate_plots(profile_path: str, frame_id: int, padding: int, scale_factor: int, min_threshold: int, contour_threshold: int) -> Tuple[str, str, str]:
      """
      This function loads a configuration file and libraries, imports necessary modules, defines two custom colormaps, 
      loads a data file, performs cubic spline interpolation on the data, applies a threshold to remove small values, 
      and generates three plots: a histogram, an interpolated image, and a contour plot. 
      The function returns the paths to the generated plots.
      
      >>> load_configuration_and_generate_plots("foam_ball_short.dsa", 230, 2, 10, 25, 50)
      ('histogram.pdf', 'interpolation.pdf', 'contour.pdf')
      """
      # 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
  
      # Color map
      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) 
  
      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
      profileName = os.path.abspath(profile_path)
      frameManager = framemanager_python.FrameManagerWrapper()
      frameManager.load_profile(profileName);
      numTSFrames = frameManager.get_tsframe_count();
  
      matrixID = 1
  
      # Load single frame
      tsframe = np.copy( frameManager.get_tsframe(frame_id, 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*scale_factor)
      yi = np.linspace(ys.min(), ys.max(), rows*scale_factor)
  
      # 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')
      histogram_path = "histogram.pdf"
      plt.savefig(histogram_path, format='pdf')
      plt.close() 
  
      # Interpolated image
      fig, ax = plt.subplots()
      cmap = plt.get_cmap('bds_highcontrast')
      cax = ax.imshow(zi, cmap=cmap, 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')
      interpolation_path = "interpolation.pdf"
      plt.savefig(interpolation_path, 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(contour_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')
      contour_path = "contour.pdf"
      plt.savefig(contour_path, format='pdf')
      plt.close() 
  
      return histogram_path, interpolation_path, contour_path
  
output:     
      return histogram_path, interpolation_path, contour_path
  