input: Write a function to load a configuration file, libraries, imports necessary modules, 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. Ensure that the necessary files and libraries are in the correct paths, and adjust the parameters as needed for your data. The function should return True if all operations are successful, otherwise False.
  
  assert perform_data_analysis_and_plotting(profileName="foam_ball_short.dsa", frameID=230, matrixID=1, min_threshold=25, countour_threshold=50) == True
  assert perform_data_analysis_and_plotting(profileName="nonexistent.dsa", frameID=230, matrixID=1, min_threshold=25, countour_threshold=50) == False
  assert perform_data_analysis_and_plotting(profileName="foam_ball_short.dsa", frameID=1000, matrixID=1, min_threshold=25, countour_threshold=50) == False
  assert perform_data_analysis_and_plotting(profileName="", frameID=230, matrixID=1, min_threshold=25, countour_threshold=50) == False
  assert perform_data_analysis_and_plotting(profileName="foam_ball_short.dsa", frameID=230, matrixID=1, min_threshold=-10, countour_threshold=50) == False
output: 
  import os
  import numpy as np
  import matplotlib.pyplot as plt
  from scipy.interpolate import griddata
  
  def perform_data_analysis_and_plotting(profileName, frameID, matrixID, min_threshold, countour_threshold):
      if not profileName or min_threshold < 0 or countour_threshold < 0:
          return False
      
      if not os.path.exists(profileName):
          return False
      
      try:
          data = np.load(profileName)
      except Exception as e:
          return False
      
      if frameID >= data.shape[0]:
          return False
      
      frame = data[frameID]
      
      x = np.linspace(0, 1, frame.shape[1])
      y = np.linspace(0, 1, frame.shape[0])
      xx, yy = np.meshgrid(x, y)
      
      points = np.vstack((xx.flatten(), yy.flatten())).T
      values = frame.flatten()
      
      grid_x, grid_y = np.mgrid[0:1:100j, 0:1:100j]
      grid_z = griddata(points, values, (grid_x, grid_y), method='cubic')
      
      grid_z[grid_z < min_threshold] = np.nan
      
      cmap1 = plt.cm.get_cmap('viridis').copy()
      cmap2 = plt.cm.get_cmap('plasma').copy()
      
      plt.figure(figsize=(15, 5))
      
      plt.subplot(131)
      plt.hist(values, bins=50, color='blue', alpha=0.7)
      plt.title('Histogram')
      
      plt.subplot(132)
      plt.imshow(grid_z, extent=(0, 1, 0, 1), origin='lower', cmap=cmap1)
      plt.colorbar()
      plt.title('Interpolated Image')
      
      plt.subplot(133)
      plt.contourf(grid_x, grid_y, grid_z, levels=np.arange(countour_threshold, np.nanmax(grid_z), 10), cmap=cmap2)
      plt.colorbar()
      plt.title('Contour Plot')
      
      plt.show()
      
      return True
  