input: 
  def data_processing_and_plotting():
      """
      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. To use this code, make sure to have the necessary files and libraries in the correct paths, and adjust the parameters as needed for your data.
      
      >>> data_processing_and_plotting()
      """
  
output:     
      import numpy as np
      import matplotlib.pyplot as plt
      from scipy.interpolate import griddata
      import json
      from matplotlib.colors import LinearSegmentedColormap
  
      # Load configuration file
      with open('config.json', 'r') as f:
          config = json.load(f)
  
      # Define custom colormaps
      cmap1 = LinearSegmentedColormap.from_list('custom1', ['blue', 'white', 'red'])
      cmap2 = LinearSegmentedColormap.from_list('custom2', ['green', 'yellow', 'purple'])
  
      # Load data file
      data = np.loadtxt(config['data_file'])
  
      # 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 = config['threshold']
      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 Values')
      plt.xlabel('Value')
      plt.ylabel('Frequency')
      plt.savefig('histogram.png')
  
      # 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')
      plt.savefig('interpolated_image.png')
  
      # Generate contour plot
      plt.figure()
      plt.contourf(grid_x, grid_y, grid_z, levels=20, cmap=cmap2)
      plt.colorbar()
      plt.title('Contour Plot')
      plt.savefig('contour_plot.png')
  
      plt.show()
  