input: Write a function to load a configuration file, libraries, and 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 all steps complete successfully, otherwise False.
  assert visual_data_processing("foam_ball_short.dsa")==True
  assert visual_data_processing("")==False
output: def visual_data_processing(file_path):
      try:
          import numpy as np
          import matplotlib.pyplot as plt
          from scipy.interpolate import griddata
          from matplotlib.colors import ListedColormap
  
          # Define custom colormaps
          cmap1 = ListedColormap(['blue', 'green', 'red'])
          cmap2 = ListedColormap(['purple', 'yellow', 'cyan'])
  
          # Load data file
          data = np.loadtxt(file_path)
  
          # Perform cubic spline interpolation
          grid_x, grid_y = np.mgrid[0:1:100j, 0:1:100j]
          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(data[:, 2], bins=50, color='blue')
          plt.title('Histogram')
  
          # Interpolated image
          plt.subplot(132)
          plt.imshow(grid_z.T, extent=(0, 1, 0, 1), origin='lower', cmap=cmap1)
          plt.colorbar()
          plt.title('Interpolated Image')
  
          # Contour plot
          plt.subplot(133)
          plt.contourf(grid_x, grid_y, grid_z, levels=20, cmap=cmap2)
          plt.colorbar()
          plt.title('Contour Plot')
  
          plt.show()
  
          return True
      except Exception as e:
          return False