Metadata-Version: 2.4
Name: ExoRM
Version: 1.0.0
Summary: a model for the mass of an exoplanet given the radius
Home-page: https://github.com/kzhu2099/ExoRM
Author: Kevin Zhu
Author-email: kzhu2099@gmail.com
Maintainer: Kevin Zhu
Maintainer-email: kzhu2099@gmail.com
License: MIT License
        
        Copyright (c) 2025 Kevin Zhu
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
Project-URL: Homepage, https://github.com/kzhu2099/ExoRM
Project-URL: Issues, https://github.com/kzhu2099/ExoRM/issues
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: astropy
Requires-Dist: astroquery
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: platformdirs
Requires-Dist: scipy

# ExoRM

- HomePage: https://github.com/kzhu2099/ExoRM
- Issues: https://github.com/kzhu2099/ExoRM/issues

[![PyPI Downloads](https://static.pepy.tech/badge/ExoRM)](https://pepy.tech/projects/ExoRM)

Author: Kevin Zhu

## Features

- continuous radius-mass relationship
- smooth with lower residuals
- simple usage, log10 and linear
- best-fit for Terran, Neptunian, and Jovian

## Installation

To install ExoRM, use pip: ```pip install ExoRM```.

However, many prefer to use a virtual environment.

macOS / Linux:

```sh
# 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:

```sh
# 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.

Furthermore, this requires periodic updating to include the most recent information.

Simply run `process_data.py` and `initialize_model.py`. Both are stored in your OS's Application Data for ExoRM. ExoRM provides built in functions to retrieve from this folder.

To use the model, call `ExoRM.load_model()` which returns the model from the filepath. If you wish, you may use `model.save(...)` to save it to your own directory.

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()`.

The high amount of uncertainty can be accessed from ExoRM. There is only log10 uncertainty due to the linear scale's differences, which may be accessed through `.calculate_error()` for the most recent values or `.error` for the value calculated at initialization.

ExoRM's data limitations required overrides for certain areas. By default, `override_min()` and `override_max()` are set to the inverse power law relationship found by Chen and kipping (2017). The transition points to those are smooth and are calculated to be the closest intersection between the model and the relationship.

An example is seen in the `example.ipynb`.
## License

The License is an MIT License found in the LICENSE file.
