input: Write a function to load a configuration file and libraries, 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 all operations are successful, otherwise return False. The function should have the following parameters: config_path (path to the configuration file), lib_path (path to the libraries), profileName (path to the data file), frameID (frame ID to load), matrixID (matrix ID to load), and scaleFactor (factor for interpolation).
  assert generate_plots("../matplotlib/", "../../lib", "foam_ball_short.dsa", 230, 1, 10) == True
output: def generate_plots(config_path, lib_path, profileName, frameID, matrixID, scaleFactor):
      try:
          import sys
          sys.path.append(lib_path)
          import numpy as np
          import matplotlib.pyplot as plt
          from scipy.interpolate import griddata
          from scipy.ndimage import zoom
  
          # Load configuration file and libraries
          with open(config_path, 'r') as f:
              config = f.read()
  
          # Define custom colormaps
          cmap1 = plt.get_cmap('viridis').copy()
          cmap2 = plt.get_cmap('plasma').copy()
  
          # Load data file
          data = np.loadtxt(profileName)
  
          # Perform cubic spline interpolation
          grid_x, grid_y = np.mgrid[0:data.shape[0], 0:data.shape[1]]
          grid_z = griddata(data[:, :2], data[:, 2], (grid_x, grid_y), method='cubic')
  
          # Apply threshold to remove small values
          threshold = np.percentile(grid_z, 10)
          grid_z[grid_z < threshold] = np.nan
  
          # Generate plots
          plt.figure(figsize=(15, 5))
  
          # Histogram
          plt.subplot(131)
          plt.hist(grid_z.flatten(), bins=50, color='blue', alpha=0.7)
          plt.title('Histogram')
  
          # Interpolated image
          plt.subplot(132)
          plt.imshow(grid_z, cmap=cmap1, origin='lower')
          plt.colorbar()
          plt.title('Interpolated Image')
  
          # Contour plot
          plt.subplot(133)
          plt.contourf(grid_z, levels=50, cmap=cmap2)
          plt.colorbar()
          plt.title('Contour Plot')
  
          plt.tight_layout()
          plt.show()
  
          return True
      except Exception as e:
          return False