input: Write a function to load a configuration file, import necessary modules, define 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. Ensure the function loads the necessary files and libraries from correct paths and adjusts parameters as needed for the data.
  assert plot_data([], 'foam_ball_short.dsa', 230, 1) == True
  assert plot_data([], 'nonexistent_file.dsa', 230, 1) == False
  assert plot_data([], 'foam_ball_short.dsa', 2000, 1) == False
output: def plot_data(config_file, data_file, threshold, scale):
      try:
          import numpy as np
          import matplotlib.pyplot as plt
          from scipy.interpolate import griddata
          from scipy.ndimage import gaussian_filter
          from configparser import ConfigParser
          import os
  
          if not os.path.exists(data_file):
              return False
  
          config = ConfigParser()
          config.read(config_file)
  
          data = np.loadtxt(data_file)
          x, y, z = data[:, 0], data[:, 1], data[:, 2]
  
          grid_x, grid_y = np.mgrid[min(x):max(x):100j, min(y):max(y):100j]
          grid_z = griddata((x, y), z, (grid_x, grid_y), method='cubic')
  
          grid_z[grid_z < threshold] = np.nan
  
          plt.figure(figsize=(15, 5))
  
          plt.subplot(131)
          plt.hist(z, bins=50, color='blue')
          plt.title('Histogram')
  
          plt.subplot(132)
          plt.imshow(grid_z.T, extent=(min(x), max(x), min(y), max(y)), origin='lower', cmap='viridis')
          plt.colorbar()
          plt.title('Interpolated Image')
  
          plt.subplot(133)
          plt.contourf(grid_x, grid_y, grid_z, levels=20, cmap='viridis')
          plt.colorbar()
          plt.title('Contour Plot')
  
          plt.show()
          return True
      except Exception as e:
          return False