input: Write a function to generate plots including a histogram, an interpolated image, and a contour plot from a given data file using cubic spline interpolation, applying a threshold to remove small values, and using custom colormaps. Ensure the function loads necessary libraries and configuration files, and adjusts parameters as needed.
  assert generate_plots("foam_ball_short.dsa", 230, 1, "histogram.pdf", "interpolation.pdf", "contour.pdf")==True
  assert generate_plots("nonexistent_file.dsa", 230, 1, "histogram.pdf", "interpolation.pdf", "contour.pdf")==False
  assert generate_plots("foam_ball_short.dsa", 999, 1, "histogram.pdf", "interpolation.pdf", "contour.pdf")==False
output: 
  def generate_plots(data_file, threshold, spline_order, hist_file, interp_file, contour_file):
      try:
          import numpy as np
          import matplotlib.pyplot as plt
          from scipy.interpolate import griddata
          from scipy.ndimage import gaussian_filter
          from matplotlib.colors import ListedColormap
  
          data = np.loadtxt(data_file)
          if np.max(data) < threshold:
              return False
  
          x, y, z = data[:, 0], data[:, 1], data[:, 2]
          xi, yi = np.linspace(x.min(), x.max(), 100), 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()
          plt.hist(z, bins=50, color='blue', alpha=0.7)
          plt.savefig(hist_file)
          plt.close()
  
          plt.figure()
          plt.imshow(zi, extent=(x.min(), x.max(), y.min(), y.max()), origin='lower', cmap='viridis')
          plt.colorbar()
          plt.savefig(interp_file)
          plt.close()
  
          plt.figure()
          plt.contourf(xi, yi, zi, levels=20, cmap='plasma')
          plt.colorbar()
          plt.savefig(contour_file)
          plt.close()
  
          return True
      except Exception as e:
          return False
  