input: 
  from typing import Tuple
  
  def calculate_and_plot_power_data(logfile: str, calib_folder: str) -> Tuple[float, float, float]:
      """ Perform a series of calculations and generate plots based on the input data from the logfile and calibration folder.
      Before running this function, ensure to run eval_spectrum.py and calibration.py with appropriate measurements.
      The function loads the input data, performs calibration, calculates various values such as absorbed power, emitted power,
      reflected power, and dissipated power. It also generates plots showing absorbed power vs emitted power, absorbed power vs reflectivity,
      and dissipated power vs wavelength. Additionally, it calculates the thermal resistance and displays it on the plot.
      
      >>> calculate_and_plot_power_data('../24_LL_ev/20150211_sample21-1-d6/spot333um.csv', '../24_LL_ev/20150204_calib_333um_s21-1-d6')
      (0.05, 0.45, 2.15)
      """
  
output:     
      import pandas as pd
      import numpy as np
      import matplotlib.pyplot as plt
      from scipy.constants import c, h, k
  
      # Load the logfile data
      data = pd.read_csv(logfile)
  
      # Load calibration data
      calib_data = pd.read_csv(f"{calib_folder}/calibration_data.csv")
  
      # Perform calibration
      data['calibrated_power'] = data['power'] * calib_data['calibration_factor']
  
      # Calculate absorbed power
      absorbed_power = data['calibrated_power'].sum()
  
      # Calculate emitted power
      emitted_power = data['emitted_power'].sum()
  
      # Calculate reflected power
      reflected_power = data['reflected_power'].sum()
  
      # Calculate dissipated power
      dissipated_power = absorbed_power - emitted_power - reflected_power
  
      # Generate plots
      plt.figure(figsize=(12, 8))
  
      # Plot absorbed power vs emitted power
      plt.subplot(2, 2, 1)
      plt.plot(data['emitted_power'], data['calibrated_power'], 'bo')
      plt.xlabel('Emitted Power')
      plt.ylabel('Absorbed Power')
      plt.title('Absorbed Power vs Emitted Power')
  
      # Plot absorbed power vs reflectivity
      plt.subplot(2, 2, 2)
      plt.plot(data['reflected_power'] / data['calibrated_power'], data['calibrated_power'], 'ro')
      plt.xlabel('Reflectivity')
      plt.ylabel('Absorbed Power')
      plt.title('Absorbed Power vs Reflectivity')
  
      # Plot dissipated power vs wavelength
      plt.subplot(2, 2, 3)
      plt.plot(data['wavelength'], dissipated_power, 'go')
      plt.xlabel('Wavelength')
      plt.ylabel('Dissipated Power')
      plt.title('Dissipated Power vs Wavelength')
  
      # Calculate thermal resistance
      thermal_resistance = (data['temperature'].max() - data['temperature'].min()) / dissipated_power
      plt.text(0.5, 0.5, f'Thermal Resistance: {thermal_resistance:.2f}', transform=plt.gca().transAxes)
  
      plt.tight_layout()
      plt.show()
  
      return (absorbed_power, emitted_power, dissipated_power)
  