# kneed Knee-point detection in Python [](https://pepy.tech/project/kneed) [](https://pepy.tech/project/kneed)  [](https://share.streamlit.io/arvkevi/ikneed/main/ikneed.py) [](https://travis-ci.com/arvkevi/kneed) [](https://codecov.io/gh/arvkevi/kneed) This repository is an attempt to implement the kneedle algorithm, published [here](https://www1.icsi.berkeley.edu/~barath/papers/kneedle-simplex11.pdf). Given a set of `x` and `y` values, `kneed` will return the knee point of the function. The knee point is the point of maximum curvature.  ## Table of contents - [Installation](#installation) - [Usage](#usage) - [Input Data](#input-data) - [Find Knee](#find-knee) - [Visualize](#visualize) - [Documentation](#documentation) - [Interactive](#interactive) - [Contributing](#contributing) - [Citation](#citation) ## Installation `kneed` has been tested with Python 3.5, 3.6, 3.7 and 3.8. **anaconda** ```bash $ conda install -c conda-forge kneed **pip** ```bash $ pip install kneed **Clone from GitHub** ```bash $ git clone https://github.com/arvkevi/kneed.git $ python setup.py install ## Usage These steps introduce how to use `kneed` by reproducing Figure 2 from the manuscript. ### Input Data The `DataGenerator` class is only included as a utility to generate sample datasets. > Note: `x` and `y` must be equal length arrays. ```python from kneed import DataGenerator, KneeLocator x, y = DataGenerator.figure2() print([round(i, 3) for i in x]) print([round(i, 3) for i in y]) [0.0, 0.111, 0.222, 0.333, 0.444, 0.556, 0.667, 0.778, 0.889, 1.0] [-5.0, 0.263, 1.897, 2.692, 3.163, 3.475, 3.696, 3.861, 3.989, 4.091] ### Find Knee The knee (or elbow) point is calculated simply by instantiating the `KneeLocator` class with `x`, `y` and the appropriate `curve` and `direction`. Here, `kneedle.knee` and/or `kneedle.elbow` store the point of maximum curvature. ```python kneedle = KneeLocator(x, y, S=1.0, curve="concave", direction="increasing") print(round(kneedle.knee, 3)) 0.222 print(round(kneedle.elbow, 3)) 0.222 The knee point returned is a value along the `x` axis. The `y` value at the knee can be identified: ```python print(round(kneedle.knee_y, 3)) 1.897 ### Visualize The `KneeLocator` class also has two plotting functions for quick visualizations. **Note that all (x, y) are transformed for the normalized plots** ```python # Normalized data, normalized knee, and normalized distance curve. kneedle.plot_knee_normalized()  ```python # Raw data and knee. kneedle.plot_knee()  ## Documentation Documentation of the parameters and a full API reference can be found [here](https://kneed.readthedocs.io/). ## Interactive An interactive streamlit app was developed to help users explore the effect of tuning the parameters. There are two sites where you can test out kneed by copy-pasting your own data: 1. https://share.streamlit.io/arvkevi/ikneed/main/ikneed.py 2. https://ikneed.herokuapp.com/ You can also run your own version -- head over to the [source code for ikneed](https://github.com/arvkevi/ikneed).  ## Contributing Contributions are welcome, please refer to [CONTRIBUTING](https://github.com/arvkevi/kneed/blob/master/CONTRIBUTING.md) to learn more about how to contribute. ## Citation Finding a “Kneedle” in a Haystack: Detecting Knee Points in System Behavior Ville Satopa , Jeannie Albrecht† , David Irwin‡ , and Barath Raghavan§ †Williams College, Williamstown, MA ‡University of Massachusetts Amherst, Amherst, MA International Computer Science Institute, Berkeley, CA