input: 
  def generate_plots(profile_path: str, frame_id: int, matrix_id: int, threshold: float) -> None:
      """
      Load a configuration file, libraries, and a data file, then 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.
      
      >>> generate_plots("foam_ball_short.dsa", 230, 1, 25.0)
      """
  
output:     
      import numpy as np
      import matplotlib.pyplot as plt
      from scipy.interpolate import griddata
  
      # Load the data file
      data = np.loadtxt(profile_path)
  
      # Extract relevant columns
      x = data[:, 0]
      y = data[:, 1]
      z = data[:, 2]
  
      # Create a grid for interpolation
      grid_x, grid_y = np.mgrid[min(x):max(x):100j, min(y):max(y):100j]
  
      # Perform cubic spline interpolation
      grid_z = griddata((x, y), z, (grid_x, grid_y), method='cubic')
  
      # Apply threshold to remove small values
      grid_z[grid_z < threshold] = np.nan
  
      # Generate histogram
      plt.figure()
      plt.hist(z, bins=50, color='blue', alpha=0.7)
      plt.title('Histogram of Z values')
      plt.xlabel('Z value')
      plt.ylabel('Frequency')
      plt.show()
  
      # Generate interpolated image
      plt.figure()
      plt.imshow(grid_z.T, extent=(min(x), max(x), min(y), max(y)), origin='lower', cmap='viridis')
      plt.colorbar(label='Z value')
      plt.title('Interpolated Image')
      plt.xlabel('X')
      plt.ylabel('Y')
      plt.show()
  
      # Generate contour plot
      plt.figure()
      plt.contourf(grid_x, grid_y, grid_z, levels=20, cmap='viridis')
      plt.colorbar(label='Z value')
      plt.title('Contour Plot')
      plt.xlabel('X')
      plt.ylabel('Y')
      plt.show()
  