https://github.com/cscully-allison/hatchet
Tree- or Graph-indexed Pandas DataFrames for analyzing performance data
Science Score: 23.0%
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Found 1 DOI reference(s) in README -
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○Scientific vocabulary similarity
Low similarity (21.0%) to scientific vocabulary
Last synced: 10 months ago
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Repository
Tree- or Graph-indexed Pandas DataFrames for analyzing performance data
Basic Info
- Host: GitHub
- Owner: cscully-allison
- License: mit
- Language: Python
- Default Branch: develop
- Homepage: https://hatchet.readthedocs.io
- Size: 25.6 MB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Fork of hatchet/hatchet
Created about 6 years ago
· Last pushed about 3 years ago
https://github.com/cscully-allison/hatchet/blob/develop/
#Hatchet [](https://github.com/hatchet/hatchet/actions) [](http://hatchet.readthedocs.io) [](https://codecov.io/gh/hatchet/hatchet) [](https://github.com/psf/black) [](https://join.slack.com/t/hatchet-users/shared_invite/zt-twjzzdav-p1s7NUEJzBoejYdOAgeddg) 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: [hatchet-help@listserv.umd.edu](mailto:hatchet-help@listserv.umd.edu). ### Authors Many thanks go to Hatchet's [contributors](https://github.com/hatchet/hatchet/graphs/contributors). Hatchet was created by Abhinav Bhatele, bhatele@cs.umd.edu. ### 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
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Owner
- Name: cscully-allison
- Login: cscully-allison
- Kind: user
- Repositories: 33
- Profile: https://github.com/cscully-allison
Hatchet
[](https://github.com/hatchet/hatchet/actions)
[](http://hatchet.readthedocs.io)
[](https://codecov.io/gh/hatchet/hatchet)
[](https://github.com/psf/black)
[](https://join.slack.com/t/hatchet-users/shared_invite/zt-twjzzdav-p1s7NUEJzBoejYdOAgeddg)
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
```