input: Write a function to process 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. Ensure that the function loads the necessary configuration and libraries, defines two custom colormaps, and adjusts the parameters as needed for the data. The function should return True if the plots are generated successfully, and False otherwise. If the data file is empty, the function should return True.
  
  def process_data_and_generate_plots(profileName, frameID, matrixID, min_threshold=25, countour_threshold=50, scaleFactor=10):
      try:
          # 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
          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)
  
          frameManager = framemanager_python.FrameManagerWrapper()
          if not frameManager.load_profile(profileName):
              return False
  
          numTSFrames = frameManager.get_tsframe_count()
          if numTSFrames == 0:
              return True
  
          tsframe = np.copy(frameManager.get_tsframe(frameID, matrixID))
          if tsframe.size == 0:
              return True
  
          cols = tsframe.shape[1]
          rows = tsframe.shape[0]
  
          padding = 2
          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])
          zs = np.vstack([zs, v_padding])
          zs = np.hstack([h_padding, zs])
          zs = np.hstack([zs, h_padding])
  
          cols = zs.shape[1]
          rows = zs.shape[0]
  
          xs = np.arange(0, cols, 1)
          ys = np.arange(0, rows, 1)
  
          xi = np.linspace(xs.min(), xs.max(), cols*scaleFactor)
          yi = np.linspace(ys.min(), ys.max(), rows*scaleFactor)
  
          spline = scipy.interpolate.RectBivariateSpline(ys, xs, zs, kx=3, ky=3, s=0)
          zi = spline(yi, xi)
  
          zi[zi < min_threshold ] = 0
  
          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()
          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')
          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 process_data_and_generate_plots("foam_ball_short.dsa", 230, 1)==True
  assert process_data_and_generate_plots("empty_file.dsa", 230, 1)==True
  assert process_data_and_generate_plots("nonexistent_file.dsa", 230, 1)==False
output: 
  def process_data_and_generate_plots(profileName, frameID, matrixID, min_threshold=25, countour_threshold=50, scaleFactor=10):
      try:
          # 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
          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)
  
          frameManager = framemanager_python.FrameManagerWrapper()
          if not frameManager.load_profile(profileName):
              return False
  
          numTSFrames = frameManager.get_tsframe_count()
          if numTSFrames == 0:
              return True
  
          tsframe = np.copy(frameManager.get_tsframe(frameID, matrixID))
          if tsframe.size == 0:
              return True
  
          cols = tsframe.shape[1]
          rows = tsframe.shape[0]
  
          padding = 2
          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])
          zs = np.vstack([zs, v_padding])
          zs = np.hstack([h_padding, zs])
          zs = np.hstack([zs, h_padding])
  
          cols = zs.shape[1]
          rows = zs.shape[0]
  
          xs = np.arange(0, cols, 1)
          ys = np.arange(0, rows, 1)
  
          xi = np.linspace(xs.min(), xs.max(), cols*scaleFactor)
          yi = np.linspace(ys.min(), ys.max(), rows*scaleFactor)
  
          spline = scipy.interpolate.RectBivariateSpline(ys, xs, zs, kx=3, ky=3, s=0)
          zi = spline(yi, xi)
  
          zi[zi < min_threshold ] = 0
  
          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()
          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')
          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
  