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 the generated plots as PDF files. Ensure that the function checks for the necessary files and libraries in the correct paths and adjusts the parameters as needed for your data.
  assert generate_plots() == ["histogram.pdf", "interpolation.pdf", "contour.pdf"]
output: 
  def generate_plots():
      import os
      import numpy as np
      import matplotlib.pyplot as plt
      from scipy.interpolate import griddata
      from matplotlib.colors import LinearSegmentedColormap
  
      # Load configuration file and libraries
      config_file = "config.txt"
      if not os.path.exists(config_file):
          raise FileNotFoundError(f"Configuration file {config_file} not found.")
  
      # Define custom colormaps
      cmap1 = LinearSegmentedColormap.from_list('custom1', ['blue', 'green', 'yellow', 'red'])
      cmap2 = LinearSegmentedColormap.from_list('custom2', ['purple', 'cyan', 'lime', 'orange'])
  
      # Load data file
      data_file = "data.csv"
      if not os.path.exists(data_file):
          raise FileNotFoundError(f"Data file {data_file} not found.")
      data = np.loadtxt(data_file, delimiter=',')
  
      # Perform cubic spline interpolation
      grid_x, grid_y = np.mgrid[0:data.shape[0], 0:data.shape[1]]
      points = np.array([(x, y) for x in range(data.shape[0]) for y in range(data.shape[1])])
      values = data.flatten()
      grid_z = griddata(points, values, (grid_x, grid_y), method='cubic')
  
      # Apply threshold to remove small values
      threshold = 0.1 * np.max(grid_z)
      grid_z[grid_z < threshold] = np.nan
  
      # Generate plots
      plt.figure(figsize=(15, 5))
  
      # Histogram
      plt.subplot(131)
      plt.hist(values, bins=50, color='blue', alpha=0.7)
      plt.title('Histogram')
      plt.savefig("histogram.pdf")
  
      # Interpolated image
      plt.subplot(132)
      plt.imshow(grid_z.T, origin='lower', cmap=cmap1)
      plt.title('Interpolated Image')
      plt.colorbar()
      plt.savefig("interpolation.pdf")
  
      # Contour plot
      plt.subplot(133)
      plt.contourf(grid_x, grid_y, grid_z, levels=20, cmap=cmap2)
      plt.title('Contour Plot')
      plt.colorbar()
      plt.savefig("contour.pdf")
  
      return ["histogram.pdf", "interpolation.pdf", "contour.pdf"]
  