input: 
  def analyze_data_plots(profile_path: str, frame_id: int) -> str:
      """
      This function loads a configuration file and libraries, imports necessary modules, defines two custom colormaps, loads a data file, performs cubic spline interpolation on the data, applies a threshold to remove small values, and generates three plots: a histogram, an interpolated image, and a contour plot. The function returns the paths of the saved plots.
  
      >>> analyze_data_plots("foam_ball_short.dsa", 230)
      ['histogram.pdf', 'interpolation.pdf', 'contour.pdf']
      """
  
output:     
      import numpy as np
      import matplotlib.pyplot as plt
      from scipy.interpolate import griddata
      import os
  
      # Load configuration file and libraries
      config = np.loadtxt(profile_path, skiprows=1)
  
      # Define custom colormaps
      cmap1 = plt.cm.get_cmap('viridis')
      cmap2 = plt.cm.get_cmap('plasma')
  
      # Load data file
      data = np.loadtxt(profile_path, skiprows=1)
  
      # 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 histogram plot
      plt.figure()
      plt.hist(data[:, 2], bins=50, color='blue', alpha=0.7)
      plt.title('Histogram of Data')
      plt.xlabel('Value')
      plt.ylabel('Frequency')
      histogram_path = 'histogram.pdf'
      plt.savefig(histogram_path)
      plt.close()
  
      # Generate interpolated image plot
      plt.figure()
      plt.imshow(grid_z.T, extent=(0, 1, 0, 1), origin='lower', cmap=cmap1)
      plt.colorbar()
      plt.title('Interpolated Image')
      interpolation_path = 'interpolation.pdf'
      plt.savefig(interpolation_path)
      plt.close()
  
      # Generate contour plot
      plt.figure()
      plt.contourf(grid_x, grid_y, grid_z, levels=15, cmap=cmap2)
      plt.colorbar()
      plt.title('Contour Plot')
      contour_path = 'contour.pdf'
      plt.savefig(contour_path)
      plt.close()
  
      return [histogram_path, interpolation_path, contour_path]
  