input: Write a function to perform the following tasks: 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 all operations are successful, otherwise it should return False. Ensure the function checks for the existence of required files and libraries before proceeding.
  
  assert process_data("foam_ball_short.dsa") == True
  assert process_data("nonexistent_file.dsa") == False
  assert process_data("foam_ball_short.dsa", threshold=50) == True
  assert process_data("foam_ball_short.dsa", threshold=200) == True
  assert process_data("foam_ball_short.dsa", plot_only=True) == True
  
output: def process_data(file_path, threshold=100, plot_only=False):
      try:
          import numpy as np
          import matplotlib.pyplot as plt
          from scipy.interpolate import griddata
          from scipy.interpolate import interp2d
          from scipy.ndimage import gaussian_filter
          import os
  
          if not os.path.exists(file_path):
              return False
  
          data = np.loadtxt(file_path)
          x, y, z = data[:, 0], data[:, 1], data[:, 2]
  
          if not plot_only:
              xi = np.linspace(x.min(), x.max(), 100)
              yi = np.linspace(y.min(), y.max(), 100)
              xi, yi = np.meshgrid(xi, yi)
              zi = griddata((x, y), z, (xi, yi), method='cubic')
              zi[zi < threshold] = np.nan
  
          plt.figure(figsize=(15, 5))
  
          plt.subplot(131)
          plt.hist(z, bins=50, color='blue', alpha=0.7)
          plt.title('Histogram of Data')
  
          if not plot_only:
              plt.subplot(132)
              plt.imshow(zi, extent=(x.min(), x.max(), y.min(), y.max()), origin='lower', cmap='viridis')
              plt.colorbar()
              plt.title('Interpolated Image')
  
              plt.subplot(133)
              plt.contourf(xi, yi, zi, levels=20, cmap='plasma')
              plt.colorbar()
              plt.title('Contour Plot')
  
          plt.show()
          return True
      except Exception as e:
          return False