Context, description of the dataset, and analysis are described in the paper [Morgan et al. 2024](LINK HERE)
To use the Random Forest model presented in the paper, Python is necessary as well as the following packages:
pip install numpy pandas scikit-learn xlrd
Once the packages are installed, open a Python instance (it can be a script, a Jupiter Notebook, ipython etc.) and use the following commands to load the Random Forest:
from morgan2024 import *
rf = RF_Morgan2024('BAND')
These lines will work if the dataset file "Morgan2024_dataset.xls" is in the same folder as the python file "morgan2024.py". The 'BAND' parameter in the second line must be substituted with one of the following: 'VIS', 'NUV', or 'NIR'.
Once the Random Forest has been loaded, it will be possible to use it to simulate Earth-like characterization yield by using the following command:
rf.sim(diameter, loc(contrast), throughput, iwa)
Substitute the 'diameter' with the telescope diameter value, the 'log(contrast)' with the loarithm of the contrast ratio, the 'throughput' with the telescope throughput value, and 'iwa' with the inner working angle (IWA) value.
In[1]: from morgan2024 import *
In[2]: rf = RF_Morgan2024('VIS')
In[3]: rf.sim(6, -10, 0.1, 20)
Out[4]: 13.29610730168751
- Rhonda Morgan (Jet Propulsion Laboratory, California Institute of Technology)
- Michael Turmon (Jet Propulsion Laboratory, California Institute of Technology)
- Dmitry Savransky (Cornell University)
- Mario Damiano (Jet Propulsion Laboratory, California Institute of Technology)
- Renyu Hu (Jet Propulsion Laboratory, California Institute of Technology)
- Bertrand Mennesson (Jet Propulsion Laboratory, California Institute of Technology)
- Eric E. Mamajek (Jet Propulsion Laboratory, California Institute of Technology)
- Tyler D. Robinson (University of Arizona)
- Armen Tokadjian (Jet Propulsion Laboratory, California Institute of Technology)
Morgan et al. 2024, [HWO Yield Sensitivities in the NIR and NUV](LINK HERE).
Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).
Copyright © 2024, by the California Institute of Technology. ALL RIGHTS RESERVED. United States Government Sponsorship acknowledged. Any commercial use must be negotiated with the Office of Technology Transfer at the California Institute of Technology.
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Licensed under the Apache License, Version 2.0 (the "Licence"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
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For any issues and bugs please send an e-mail at mario.damiano@jpl.nasa.gov.