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 ensure that the necessary files and libraries are in the correct paths and adjust the parameters as needed for the data.
  
  def generate_plots(profileName, frameID, matrixID, countour_threshold=50, min_threshold=25, scaleFactor=10):
      import os, sys
      import numpy as np
      import matplotlib.pyplot as plt
      import scipy.interpolate
  
      # 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
  
      # 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)
  
      # 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
      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])  # 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)
  
      # Evaluate splines
      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()
      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()
  
      # 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()
  
  assert generate_plots("foam_ball_short.dsa", 230, 1) == None
output: 
  import os, sys
  import numpy as np
  import matplotlib.pyplot as plt
  import scipy.interpolate
  
  # 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
  
  # 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)
  
  # 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
  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])  # 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)
  
  # Evaluate splines
  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()
  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()
  
  # 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()
  