# hatchet

**Repository Path**: mirrors_LLNL/hatchet

## Basic Information

- **Project Name**: hatchet
- **Description**: Graph-indexed Pandas DataFrames for analyzing hierarchical performance data
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: develop
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-09
- **Last Updated**: 2026-03-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# <img src="https://raw.githubusercontent.com/llnl/hatchet/develop/logo-hex.png" width="64" valign="middle" alt="hatchet"/> Hatchet

[![Build Status](https://github.com/llnl/hatchet/actions/workflows/unit-tests.yaml/badge.svg)](https://github.com/llnl/hatchet/actions)
[![Read the Docs](http://readthedocs.org/projects/llnl-hatchet/badge/?version=latest)](http://llnl-hatchet.readthedocs.io)
[![codecov](https://codecov.io/gh/llnl/hatchet/branch/develop/graph/badge.svg)](https://codecov.io/gh/llnl/hatchet)
[![Code Style: Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

Hatchet is a Python-based library that allows [Pandas](https://pandas.pydata.org) dataframes to be indexed by structured tree and graph data. It is intended for analyzing performance data that has a hierarchy (for example, serial or parallel profiles that represent calling context trees, call graphs, nested regions’ timers, etc.). Hatchet implements various operations to analyze a single hierarchical data set or compare multiple data sets, and its API facilitates analyzing such data programmatically.

To use hatchet, install it with pip:

```
$ pip install llnl-hatchet
```

Or, if you want to develop with this repo directly, run the install script from
the root directory, which will build the cython modules and add the cloned
directory to your `PYTHONPATH`:

```
$ source install.sh
```

<p align="center">
  <img src="https://raw.githubusercontent.com/llnl/hatchet/develop/screenshot.png" width=800>
</p>


### Documentation

See the [Getting Started](https://llnl-hatchet.readthedocs.io/en/latest/getting_started.html) page for basic examples and usage. Full documentation is available in the [User Guide](https://llnl-hatchet.readthedocs.io/en/latest/user_guide.html).

Examples of performance analysis using hatchet are available [here](https://llnl-hatchet.readthedocs.io/en/latest/analysis_examples.html).

The [hatchet tutorial](https://github.com/llnl/hatchet-tutorial) provides a step by step introduction to hatchet features through a live interactive environment in BinderHub.

### Interactive Visualization

Hatchet now provides an interactive visualization which can be run inside of your Jupyter notebooks. It is dependant on different mechanisms for building which we describe here:


The software in the `hatchet/vis` subdirectory requires [Node.js and Node Package Manager](https://nodejs.org/en/download/) for the development and building of javascript code.

#### Installing node packages

One you have Node and NPM installed on your system, you can install all necessary node packages by running the following line in your terminal from the `hatchet/vis/` directory:

```
npm install
```

#### Building out javascript code with webpack

To build out javascript into the static bundles used by the Jupyter visualizations, run the following line from the `hatchet/vis` directory in your terminal:

```
npm run build
```

Alterntinatively if you are developing a visualization, you can run the following line to force bundles to automatically update when you change the javascript source code:

```
npm run watch
```

### Contributing

Hatchet is an open source project. We welcome contributions via pull requests,
and questions, feature requests, or bug reports via issues.

### Authors

Many thanks go to Hatchet's
[contributors](https://github.com/llnl/hatchet/graphs/contributors).

### Citing Hatchet

If you are referencing Hatchet in a publication, please cite the
following [paper](http://www.cs.umd.edu/~bhatele/pubs/pdf/2019/sc2019.pdf):

 * Abhinav Bhatele, Stephanie Brink, and Todd Gamblin. Hatchet: Pruning
   the Overgrowth in Parallel Profiles. In Proceedings of the International
   Conference for High Performance Computing, Networking, Storage and Analysis
   (SC '19). ACM, New York, NY, USA. [DOI](
   http://doi.acm.org/10.1145/3295500.3356219)

### License

Hatchet is distributed under the terms of the MIT license.

All contributions must be made under the MIT license.  Copyrights in the
Hatchet project are retained by contributors.  No copyright assignment is
required to contribute to Hatchet.

See [LICENSE](https://github.com/llnl/hatchet/blob/develop/LICENSE) and
[NOTICE](https://github.com/llnl/hatchet/blob/develop/NOTICE) for details.

SPDX-License-Identifier: MIT

LLNL-CODE-741008