knee 0.1.1
(Multi)Knee/Elbow point detection library
Requires Python: >=3.8

# (Multi)Knee/Elbow point detection library

Estimating the knee/elbow point in performance curves is a challenging task.
However, most of the time these points represent ideal compromises between cost and performance.

This library implements several well-known knee detection algorithms:
1. Discrete Curvature 
2. DFDT
3. Kneedle
4. L-method
5. Menger curvature

Furthermore, the code in this library expands the ideas on these algorithms to 
detect multi-knee/elbow points in complex curves.
We implemented a recursive method that allows each of the previously mentioned methods
to detect multi-knee and elbow points.
Some methods natively support multi-knee detection, such as:
1. Kneedle
2. Fusion
3. Z-method

Finally, we also implemented additional methods that help with knee detection tasks.
As a preprocessing step, we develop a custom RDP algorithm that reduced a discrete 
set of points while keeping the reconstruction error to a minimum.
As a post-processing step we implemented several algorithms:
1. 1D dimensional clustering, is used to merge close knee points
2. Several filters out non-relevant knees
3. Knee ranking algorithms that used several criteria to assess the quality of a knee point

## Running unit tests

Several unit tests were written to validate some corner cases.
The unit tests were written in [unittest](https://docs.python.org/3/library/unittest.html).
Run the following commands to execute the unit tests.

```bash
python3 -m venv venv
source venv/bin/activate
python -m pip install --upgrade pip
pip install .
python -m unittest
```

## Documentation

This library was documented using the google style docstring, it can be accessed [here](https://mariolpantunes.github.io/knee/).
Run the following commands to produce the documentation for this library.

```bash
python3 -m venv venv
source venv/bin/activate
pip install pdoc
pdoc --math -d google -o docs src/knee \
--logo https://raw.githubusercontent.com/mariolpantunes/knee/main/media/knee.png \
--favicon https://raw.githubusercontent.com/mariolpantunes/knee/main/media/knee.png
```

## Instalation

To install the library locally, simple execute the following commands:

```bash
python3 -m venv venv
source venv/bin/activate
python -m pip install --upgrade pip
pip install .
```
You can also use the PyPI repository for easy access to the library:

```txt
knee>=0.1
```

## Runing the demos

The demos can be execute as python modules using the following code:

```bash
python -m demos.curvature -i [trace]
python -m demos.dfdt -i [trace]
python -m demos.fusion -i [trace]
python -m demos.kneedle_classic -i [trace]
python -m demos.kneedle_rec -i [trace]
python -m demos.kneedle -i [trace]
python -m demos.lmethod -i [trace]
python -m demos.menger -i [trace]
python -m demos.zmethod -i [trace]
```
Most demos have the same parameters (with the exception of zmethod and kneedle_classic):

```bash
python -m demos.curvature -husage: curvature.py [-h] -i I [-a] [-r R] [-t T] [-c C] [-o] [-g] [-k {left,linear,right,hull}]

Multi Knee evaluation app

optional arguments:
  -h, --help            show this help message and exit
  -i I                  input file
  -a                    add even spaced points
  -r R                  RDP reconstruction threshold
  -t T                  clustering threshold
  -c C                  corner threshold
  -o                    store output (debug)
  -g                    display output (debug)
  -k {left,linear,right,hull}
                        Knee ranking method
```

## Authors

* [**Mário Antunes**](https://github.com/mariolpantunes)

* [**Tyler Estro**](https://www.fsl.cs.stonybrook.edu/~tyler/)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Copyright

This project is under the following [COPYRIGHT](COPYRIGHT).

![Python CI](https://github.com/mariolpantunes/knee/workflows/Python%20CI/badge.svg)

MIT License

Copyright (c) 2020 Mário Antunes

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.

Copyright (c) 2021-2023 Stony Brook University
Copyright (c) 2021-2023 The Research Foundation of SUNY