ExoRM
ExoRM is a model for the mass of an exoplanet given the radius.
Note that the development is tricky; from this package's development, it seems that Chen & Kipping's forecaster is suitable for the limited needs.
- HomePage: https://github.com/kzhu2099/ExoRM
- Issues: https://github.com/kzhu2099/ExoRM/issues
Author: Kevin Zhu
Features
- continuous radius-mass relationship
- smooth
- simple usage, log10 and linear
- method to create your own model, existing model provided
Installation
To install ExoRM, use pip: pip install ExoRM.
However, many prefer to use a virtual environment (or any of their preferred choice).
macOS / Linux:
# make your desired directory
mkdir /path/to/your/directory
cd /path/to/your/directory
# setup the .venv (or whatever you want to name it)
pip install virtualenv
python3 -m venv .venv
# install ExoRM
source .venv/bin/activate
pip install ExoRM
deactivate # when you are completely done
Windows CMD:
# make your desired directory
mkdir C:path\to\your\directory
cd C:path\to\your\directory
# setup the .venv (or whatever you want to name it)
pip install virtualenv
python3 -m venv .venv
# install ExoRM
.venv\Scripts\activate
pip install ExoRM
deactivate # when you are completely done
Usage
To first begin using ExoRM, the data and model must be initialized. This is due to the constant discovery of new exoplanets, adding to the data. You may also call these at any time to update the model.
There is an existing model created in best_inputs.pkl and best_trace.nc, simply provide these paths when you are using to avoid creating your own model.
However, to get your own data and create your own model, simply run get_data() and initialize_model(). Note: import those by using from ExoRM.get_data import get_data() and from ExoRM.initialize_model() import initialize_model(). A plot of the model will be shown for you to see. Both are stored in your OS's Application Data for ExoRM. ExoRM provides built in functions to retrieve from this folder.
Usage of the model requires initializiation of the class and loading of the trace from a .nc file.
Note that all files saved are located in /Users/<username>/Library/Application Support/ExoRM for macOS and C:\Users\<username>\AppData\Local\ExoRM\ExoRM for windows.
The model supports log10 and linear scale in earth radii. When using the model([...]), .__call__([...]), or .predict([...]), the log10 scale is used. Linear predictions are used in .predict_linear([...]).
Uncertainty (upper and lower bounds) can be accessed from predict_full and predict_full_liner.
An example is seen in the example.ipynb. Deep analysis is seen in comparison.ipynb, showing statistical results and a comparison with Forecaster. Those use additional libraries for visualization and statistics (seaborn and SciPy).
Additional notes
ExoRM has an implementation of Forecaster for according to the NASA Exoplanet Archive.
Forecaster: https://github.com/chenjj2/forecaster NASA Exoplanet Archive implementation: https://exoplanetarchive.ipac.caltech.edu/docs/pscp_calc.html
License
The License is an MIT License found in the LICENSE file.
1 maintainer
Metadata
1 maintainer
Release files for ExoRM 4.0.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| exorm-4.0.6.tar.gz | 8.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| exorm-4.0.6-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 16.9 kB
Release files / exorm-4.0.6.tar.gz
| Download URL | exorm-4.0.6.tar.gz |
|---|---|
| Size | 8.3 kB |
| Tags | Source |
|
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Release files / exorm-4.0.6-py3-none-any.whl
| Download URL | exorm-4.0.6-py3-none-any.whl |
|---|---|
| Size | 8.6 kB |
| Tags | Python 3 |
|
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No |
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twine/6.1.0 CPython/3.13.0
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