input: 
  def generate_plots(logfile: str, calib_folder: str, instrfile: str) -> None:
      """
      This function performs a series of calculations and plots based on input data. Before running this function, ensure that eval_spectrum.py and calibration.py have been executed with appropriate measurements. The function loads the input data, performs calibration, calculates various values, and generates plots based on instructions provided in a separate text file. The generated plots show absorbed power vs emitted power, absorbed power vs reflectivity, and dissipated power vs wavelength. The function also calculates thermal resistance and displays it on the plot.
      
      >>> generate_plots('../24_LL_ev/20150211_sample21-1-d6/spot333um.csv', '../24_LL_ev/20150204_calib_333um_s21-1-d6', '../24_LL_ev/20150211_sample21-1-d6/spot333um_instr.csv')
      # Outputs plots showing absorbed vs emitted power, absorbed vs reflectivity, and dissipated vs wavelength
      """
  
output:     
      import pandas as pd
      import numpy as np
      import matplotlib.pyplot as plt
      from scipy.constants import Stefan_Boltzmann
  
      # Load the logfile data
      log_data = pd.read_csv(logfile)
  
      # Load the calibration data
      calib_data = pd.read_csv(f"{calib_folder}/calibration_data.csv")
  
      # Load the instrument file data
      instr_data = pd.read_csv(instrfile)
  
      # Perform calibration
      log_data['calibrated_power'] = log_data['power'] * calib_data['calibration_factor']
  
      # Calculate absorbed power
      log_data['absorbed_power'] = log_data['calibrated_power'] - log_data['reflected_power']
  
      # Calculate emitted power using Stefan-Boltzmann law
      log_data['emitted_power'] = Stefan_Boltzmann * log_data['temperature']**4
  
      # Calculate dissipated power
      log_data['dissipated_power'] = log_data['absorbed_power'] - log_data['emitted_power']
  
      # Calculate thermal resistance
      log_data['thermal_resistance'] = log_data['dissipated_power'] / log_data['temperature']
  
      # Plot absorbed power vs emitted power
      plt.figure(figsize=(10, 6))
      plt.plot(log_data['emitted_power'], log_data['absorbed_power'], 'bo-')
      plt.xlabel('Emitted Power (W)')
      plt.ylabel('Absorbed Power (W)')
      plt.title('Absorbed Power vs Emitted Power')
      plt.grid(True)
      plt.show()
  
      # Plot absorbed power vs reflectivity
      plt.figure(figsize=(10, 6))
      plt.plot(log_data['reflected_power'], log_data['absorbed_power'], 'ro-')
      plt.xlabel('Reflectivity (W)')
      plt.ylabel('Absorbed Power (W)')
      plt.title('Absorbed Power vs Reflectivity')
      plt.grid(True)
      plt.show()
  
      # Plot dissipated power vs wavelength
      plt.figure(figsize=(10, 6))
      plt.plot(log_data['wavelength'], log_data['dissipated_power'], 'go-')
      plt.xlabel('Wavelength (nm)')
      plt.ylabel('Dissipated Power (W)')
      plt.title('Dissipated Power vs Wavelength')
      plt.grid(True)
      plt.show()
  
      # Display thermal resistance on the plot
      plt.figure(figsize=(10, 6))
      plt.plot(log_data['temperature'], log_data['thermal_resistance'], 'mo-')
      plt.xlabel('Temperature (K)')
      plt.ylabel('Thermal Resistance (K/W)')
      plt.title('Thermal Resistance vs Temperature')
      plt.grid(True)
      plt.show()
  