input: def plot_data_and_calculations():
      """This script performs a series of calculations and plots based on input data. Before running the script, run eval_spectrum.py and calibration.py with appropriate measurements. The script imports necessary modules and functions from external sources. The main 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 script also calculates thermal resistance and displays it on the plot.
      >>> plot_data_and_calculations()
      Max emission at (15.0) degC at (5.0) W absorbed power: (3.0) W
      Max emission at (22.5) degC at (7.0) W absorbed power: (5.0) W
      Thermal impedance: (1.5) K/W
      """
output:     import numpy as np
      import matplotlib.pyplot as plt
      import pandas as pd
  
      # Load input data
      data = pd.read_csv('input_data.csv')
  
      # Perform calibration
      data['calibrated_power'] = data['measured_power'] * 0.95  # Example calibration factor
  
      # Calculate various values
      data['emitted_power'] = data['calibrated_power'] - data['absorbed_power']
      data['reflectivity'] = 1 - (data['absorbed_power'] / data['incident_power'])
      data['dissipated_power'] = data['absorbed_power'] * (1 - data['reflectivity'])
  
      # Load instructions from text file
      with open('plot_instructions.txt', 'r') as file:
          instructions = file.readlines()
  
      # Generate plots based on instructions
      for instruction in instructions:
          if 'absorbed_power vs emitted_power' in instruction:
              plt.figure()
              plt.plot(data['absorbed_power'], data['emitted_power'], label='Emitted Power')
              plt.xlabel('Absorbed Power (W)')
              plt.ylabel('Emitted Power (W)')
              plt.title('Absorbed Power vs Emitted Power')
              plt.legend()
              plt.show()
  
          elif 'absorbed_power vs reflectivity' in instruction:
              plt.figure()
              plt.plot(data['absorbed_power'], data['reflectivity'], label='Reflectivity')
              plt.xlabel('Absorbed Power (W)')
              plt.ylabel('Reflectivity')
              plt.title('Absorbed Power vs Reflectivity')
              plt.legend()
              plt.show()
  
          elif 'dissipated_power vs wavelength' in instruction:
              plt.figure()
              plt.plot(data['wavelength'], data['dissipated_power'], label='Dissipated Power')
              plt.xlabel('Wavelength (nm)')
              plt.ylabel('Dissipated Power (W)')
              plt.title('Dissipated Power vs Wavelength')
              plt.legend()
              plt.show()
  
      # Calculate thermal resistance
      max_emission_points = data.groupby('temperature').apply(lambda x: x.loc[x['emitted_power'].idxmax()])
      thermal_impedance = (max_emission_points['temperature'].diff().mean()) / (max_emission_points['absorbed_power'].diff().mean())
  
      # Display thermal resistance on the plot
      for i, row in max_emission_points.iterrows():
          print(f"Max emission at ({row['temperature']}) degC at ({row['absorbed_power']}) W absorbed power: ({row['emitted_power']}) W")
      print(f"Thermal impedance: ({thermal_impedance}) K/W")