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https://github.com/hatchet/hatchet
Analyze graph/hierarchical performance data using pandas dataframes
https://github.com/hatchet/hatchet
comparative-analysis data-analytics graphs hierarchical-data hpc pandas performance performance-analysis python trees
Last synced: 3 months ago
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Analyze graph/hierarchical performance data using pandas dataframes
- Host: GitHub
- URL: https://github.com/hatchet/hatchet
- Owner: hatchet
- License: mit
- Created: 2017-10-06T18:47:34.000Z (about 7 years ago)
- Default Branch: develop
- Last Pushed: 2024-05-24T17:34:53.000Z (6 months ago)
- Last Synced: 2024-07-05T14:00:44.477Z (4 months ago)
- Topics: comparative-analysis, data-analytics, graphs, hierarchical-data, hpc, pandas, performance, performance-analysis, python, trees
- Language: Python
- Homepage: https://hatchet.readthedocs.io
- Size: 24.8 MB
- Stars: 103
- Watchers: 10
- Forks: 37
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# 
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[](http://hatchet.readthedocs.io)
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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 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
```
### Documentation
See the [Getting Started](https://hatchet.readthedocs.io/en/latest/getting_started.html) page for basic examples and usage. Full documentation is available in the [User Guide](https://hatchet.readthedocs.io/en/latest/user_guide.html).
Examples of performance analysis using hatchet are available [here](https://hatchet.readthedocs.io/en/latest/analysis_examples.html).
### Contributing
Hatchet is an open source project. We welcome contributions via pull requests,
and questions, feature requests, or bug reports via issues.
You can connect with the hatchet community on
[slack](https://join.slack.com/t/hatchet-users/shared_invite/zt-twjzzdav-p1s7NUEJzBoejYdOAgeddg).
You can also reach the hatchet developers by email at:
[[email protected]](mailto:[email protected]).
### Authors
Many thanks go to Hatchet's
[contributors](https://github.com/hatchet/hatchet/graphs/contributors).
Hatchet was created by Abhinav Bhatele, [email protected].
### 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/hatchet/hatchet/blob/develop/LICENSE) and
[NOTICE](https://github.com/hatchet/hatchet/blob/develop/NOTICE) for details.
SPDX-License-Identifier: MIT
LLNL-CODE-741008