input: def plot_data(profile_file: str, frame_id: int, matrix_id: int, scale_factor: int, min_threshold: float, countour_threshold: float) -> None:
      """
      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. 
      Make sure to have the necessary files and libraries in the correct paths, and adjust the parameters as needed for your data.
  
      >>> plot_data("foam_ball_short.dsa", 230, 1, 10, 25, 50)
      (No return value, but generates three PDF files: histogram.pdf, interpolation.pdf, and contour.pdf)
      """
output:     
      import numpy as np
      import matplotlib.pyplot as plt
      from scipy.interpolate import griddata
      from matplotlib.colors import ListedColormap
      import os
  
      # Load the data file
      data = np.loadtxt(profile_file, skiprows=1)
  
      # Extract the relevant columns
      x = data[:, 0]
      y = data[:, 1]
      z = data[:, 2]
  
      # Define the grid for interpolation
      grid_x, grid_y = np.mgrid[min(x):max(x):complex(0, scale_factor), min(y):max(y):complex(0, scale_factor)]
  
      # 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 < min_threshold] = np.nan
  
      # Define custom colormaps
      cmap1 = ListedColormap(['blue', 'green', 'yellow', 'red'])
      cmap2 = ListedColormap(['white', 'black'])
  
      # Plot the histogram
      plt.figure()
      plt.hist(z, bins=50, color='blue', alpha=0.7)
      plt.title('Histogram of Data Values')
      plt.xlabel('Value')
      plt.ylabel('Frequency')
      plt.savefig('histogram.pdf')
      plt.close()
  
      # Plot the interpolated image
      plt.figure()
      plt.imshow(grid_z.T, extent=(min(x), max(x), min(y), max(y)), origin='lower', cmap=cmap1)
      plt.colorbar()
      plt.title('Interpolated Image')
      plt.xlabel('X')
      plt.ylabel('Y')
      plt.savefig('interpolation.pdf')
      plt.close()
  
      # Plot the contour plot
      plt.figure()
      plt.contourf(grid_x, grid_y, grid_z, levels=np.linspace(min(z), max(z), 100), cmap=cmap2)
      plt.colorbar()
      plt.contour(grid_x, grid_y, grid_z, levels=[countour_threshold], colors='black')
      plt.title('Contour Plot')
      plt.xlabel('X')
      plt.ylabel('Y')
      plt.savefig('contour.pdf')
      plt.close()
  