input: 
  def analyze_and_plot_data(logfile: str, calib_folder: str) -> None:
      """ This function performs a series of calculations and plots based on input data. 
      Before running this script, run eval_spectrum.py and calibration.py with appropriate measurements. 
      The function imports necessary modules and functions from external sources. 
      It 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.
      
      >>> analyze_and_plot_data('../24_LL_ev/20150211_sample21-1-d6/spot333um.csv', '../24_LL_ev/20150204_calib_333um_s21-1-d6')
      None
      """
  
output:     
      import pandas as pd
      import numpy as np
      import matplotlib.pyplot as plt
      from scipy.constants import Boltzmann, Planck, speed_of_light
      from scipy.optimize import curve_fit
  
      # Load the logfile data
      data = pd.read_csv(logfile)
  
      # Load calibration data
      calib_files = [f for f in os.listdir(calib_folder) if f.endswith('.csv')]
      calib_data = pd.concat([pd.read_csv(os.path.join(calib_folder, f)) for f in calib_files])
  
      # Perform calibration
      data['calibrated_power'] = data['power'] * calib_data['calibration_factor'].mean()
  
      # Calculate absorbed power
      data['absorbed_power'] = data['input_power'] - data['reflected_power']
  
      # Calculate emitted power
      data['emitted_power'] = data['calibrated_power'] * (1 - data['reflectivity'])
  
      # Calculate dissipated power
      data['dissipated_power'] = data['absorbed_power'] - data['emitted_power']
  
      # Calculate thermal resistance
      data['thermal_resistance'] = data['temperature_difference'] / data['dissipated_power']
  
      # Plot absorbed power vs emitted power
      plt.figure(figsize=(10, 6))
      plt.plot(data['absorbed_power'], data['emitted_power'], 'bo')
      plt.xlabel('Absorbed Power (W)')
      plt.ylabel('Emitted 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(data['absorbed_power'], data['reflectivity'], 'ro')
      plt.xlabel('Absorbed Power (W)')
      plt.ylabel('Reflectivity')
      plt.title('Absorbed Power vs Reflectivity')
      plt.grid(True)
      plt.show()
  
      # Plot dissipated power vs wavelength
      plt.figure(figsize=(10, 6))
      plt.plot(data['wavelength'], 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(data['wavelength'], data['thermal_resistance'], 'mo')
      plt.xlabel('Wavelength (nm)')
      plt.ylabel('Thermal Resistance (K/W)')
      plt.title('Thermal Resistance vs Wavelength')
      plt.grid(True)
      plt.show()
  