input: Write a function to load a configuration file and libraries, import necessary modules, define two custom colormaps, load a data file, perform cubic spline interpolation on the data, apply a threshold to remove small values, and generate three plots: a histogram, an interpolated image, and a contour plot. The function should return True if the plots are successfully generated and saved as PDF files, otherwise return False. If the data file is not found, the function should return False.
  def generate_plots(config_path, lib_path, profile_name, frame_id, matrix_id, min_threshold, countour_threshold, scale_factor):
      try:
          import os, sys
          import numpy as np
          import matplotlib.pyplot as plt
          import scipy.interpolate
  
          sys.path.append(config_path)
          sys.path.append(lib_path)
          import configuration as config
          import framemanager_python
  
          # Define custom colormaps
          cdict1 = {'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=cdict1)
  
          cdict2 = {'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=cdict2)
  
          # Load data
          frameManager = framemanager_python.FrameManagerWrapper()
          frameManager.load_profile(profile_name)
          tsframe = np.copy(frameManager.get_tsframe(frame_id, matrix_id))
  
          # Add padding and interpolate
          padding = 2
          v_padding = np.empty((padding, tsframe.shape[1])); v_padding.fill(-50)
          h_padding = np.empty((tsframe.shape[0]+2*padding, padding)); h_padding.fill(-50)
          zs = np.vstack([v_padding, tsframe, v_padding])
          zs = np.hstack([h_padding, zs, h_padding])
  
          xs = np.arange(0, zs.shape[1], 1)
          ys = np.arange(0, zs.shape[0], 1)
          xi = np.linspace(xs.min(), xs.max(), zs.shape[1]*scale_factor)
          yi = np.linspace(ys.min(), ys.max(), zs.shape[0]*scale_factor)
  
          spline = scipy.interpolate.RectBivariateSpline(ys, xs, zs, kx=3, ky=3, s=0)
          zi = spline(yi, xi)
  
          zi[zi < min_threshold ] = 0 
  
          # Generate plots
          fig, ax = plt.subplots()
          plt.hist(zi.flatten(), 128, range=(min_threshold, zi.max()), fc='k', ec='k')
          plt.savefig("histogram.pdf", format='pdf')
          plt.close()
  
          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()
  
          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()
  
          return True
      except Exception as e:
          print(f"Error: {e}")
          return False
  
  assert generate_plots("../matplotlib/", "../../lib", "foam_ball_short.dsa", 230, 1, 25, 50, 10) == True
  assert generate_plots("../matplotlib/", "../../lib", "nonexistent_file.dsa", 230, 1, 25, 50, 10) == False
output: 
  def generate_plots(config_path, lib_path, profile_name, frame_id, matrix_id, min_threshold, countour_threshold, scale_factor):
      try:
          import os, sys
          import numpy as np
          import matplotlib.pyplot as plt
          import scipy.interpolate
  
          sys.path.append(config_path)
          sys.path.append(lib_path)
          import configuration as config
          import framemanager_python
  
          # Define custom colormaps
          cdict1 = {'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=cdict1)
  
          cdict2 = {'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=cdict2)
  
          # Load data
          frameManager = framemanager_python.FrameManagerWrapper()
          frameManager.load_profile(profile_name)
          tsframe = np.copy(frameManager.get_tsframe(frame_id, matrix_id))
  
          # Add padding and interpolate
          padding = 2
          v_padding = np.empty((padding, tsframe.shape[1])); v_padding.fill(-50)
          h_padding = np.empty((tsframe.shape[0]+2*padding, padding)); h_padding.fill(-50)
          zs = np.vstack([v_padding, tsframe, v_padding])
          zs = np.hstack([h_padding, zs, h_padding])
  
          xs = np.arange(0, zs.shape[1], 1)
          ys = np.arange(0, zs.shape[0], 1)
          xi = np.linspace(xs.min(), xs.max(), zs.shape[1]*scale_factor)
          yi = np.linspace(ys.min(), ys.max(), zs.shape[0]*scale_factor)
  
          spline = scipy.interpolate.RectBivariateSpline(ys, xs, zs, kx=3, ky=3, s=0)
          zi = spline(yi, xi)
  
          zi[zi < min_threshold ] = 0 
  
          # Generate plots
          fig, ax = plt.subplots()
          plt.hist(zi.flatten(), 128, range=(min_threshold, zi.max()), fc='k', ec='k')
          plt.savefig("histogram.pdf", format='pdf')
          plt.close()
  
          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()
  
          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()
  
          return True
      except Exception as e:
          print(f"Error: {e}")
          return False
  