https://github.com/zgana/histlite
A somewhat "lite" histogram library
Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
○DOI references
-
○Academic publication links
-
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (9.6%) to scientific vocabulary
Keywords
Repository
A somewhat "lite" histogram library
Basic Info
- Host: GitHub
- Owner: zgana
- Language: Python
- Default Branch: main
- Homepage: https://histlite.readthedocs.io
- Size: 3.15 MB
Statistics
- Stars: 6
- Watchers: 0
- Forks: 4
- Open Issues: 0
- Releases: 3
Topics
Metadata Files
README.md
histlite
See documentation on ReadTheDocs.
histlite is a histogram calculation and plotting library that tries to be "lite" on data structures but rich in statistics and visualization features. So far, development has taken place during my (Mike Richman) time as a graduate student and post-doctoral researcher in the field of particle astrophysics — specifically, working with the IceCube Neutrino Observatory. Histlite is intended both to facilitate high-paced exploratory data analysis as well as to serve as a building block for potentially very complex maximum likelihood data analysis implementations.
The core design considerations are:
- It must be trivial to work with and interchange between 1D, 2D, or ND histograms.
- It should be as simple as possible to perform bin-wise arithmetic operations on one or more histograms; to perform sums, integrals, etc. and thus normalizations along one or more axes simultaneously; and to perform spline or user-defined functional fits
- It should be as simple as possible to achieve publication-quality plots.
The primary histogramming functionality consists of a thin wrapper around
numpy.histogramdd. Statistical tools leverage scipy but include
custom solutions for some use cases. (Importantly, error propagation is
currently handled manually but may be migrated to the uncertainties
package in the future.) Plotting is done using matplotlib.
Owner
- Name: Michael Richman
- Login: zgana
- Kind: user
- Website: zgana.github.io
- Repositories: 5
- Profile: https://github.com/zgana
Data Scientist at Cadent. Former neutrino astrophysicist with IceCube at Drexel & Maryland.
GitHub Events
Total
- Create event: 5
- Release event: 1
- Issues event: 1
- Watch event: 1
- Delete event: 1
- Issue comment event: 1
- Push event: 6
- Pull request event: 2
- Pull request review event: 1
Last Year
- Create event: 5
- Release event: 1
- Issues event: 1
- Watch event: 1
- Delete event: 1
- Issue comment event: 1
- Push event: 6
- Pull request event: 2
- Pull request review event: 1
Committers
Last synced: 10 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Michael Richman | m****n@g****m | 38 |
Issues and Pull Requests
Last synced: 8 months ago
All Time
- Total issues: 1
- Total pull requests: 8
- Average time to close issues: almost 2 years
- Average time to close pull requests: 2 months
- Total issue authors: 1
- Total pull request authors: 2
- Average comments per issue: 2.0
- Average comments per pull request: 0.13
- Merged pull requests: 8
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 3
- Average time to close issues: N/A
- Average time to close pull requests: 13 minutes
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.33
- Merged pull requests: 3
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- MACampana (1)
Pull Request Authors
- zgana (8)
- GerritRo (2)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- ipykernel *
- nbsphinx *
- numpy *
- scipy *