Loading...
rlabbe/Kalman-and-Bayesian-Filters-in-Python is a Jupyter Notebook open source project on GitHub: Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
This page provides real-time analytics for rlabbe/Kalman-and-Bayesian-Filters-in-Python, including star history, commit activity, pull request and issue trends, contributor statistics, geographic distribution, and code change frequency. Data is sourced from GitHub events via GH Archive and updated in near real-time.
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
| rlabbe/Kalman-and-Bayesian-Filters-in-Python | |
|---|---|
| Stars | |
| Commits | |
| Issues | |
| Forks | 4,172 |
| PR Creators | |
| Language | Jupyter Notebook |
| License | other |
The growth trend and the specific number of stars since the repository was established.
Stargazers, Issue creators, and Pull Request creators' geographical distribution around the world (analyzed with the public GitHub information).
| No. | Location | Share |
|---|---|---|
| 1 | -- | |
| 2 | -- | |
| 3 | -- | |
| 4 | -- | |
| 5 | -- | |
| 6 | -- | |
| 7 | -- | |
| 8 | -- | |
| 9 | -- | |
| 10 | -- |
Company information about Stargazers, Issue creators, and Pull Request creators (analyzed with the public GitHub information).
| No. | Company | Share |
|---|---|---|
| 1 | -- | |
| 2 | -- | |
| 3 | -- | |
| 4 | -- | |
| 5 | -- | |
| 6 | -- | |
| 7 | -- | |
| 8 | -- | |
| 9 | -- | |
| 10 | -- |
The trend of the total number of commits/pushes per month in a repository since it was created.
* Note: A push action can include multiple commit actions.
The bars show the additions or deletions of code in Pull Requests monthly.
The line chart demonstrates the total lines of code in Pull Requests (additions + deletions).
The Heat Maps below describe the number of commit events that occur at a particular point of time.
| rlabbe/Kalman-and-Bayesian-Filters-in-Python | |
|---|---|
| Total PRs | |
| Creators | |
| Reviews | |
| Reviewers |
We divide the size of Pull Request into six intervals, from xs to xxl (based on the changes of code lines). Learn more about PR size.
The time of a Pull Request from submitting to merging.
p25/p75: 25%/75% Pull Requests are merged within X minute/hour/day.
e.g. p25: 1h means 25% Pull Requests are merged within 1 hour.
| rlabbe/Kalman-and-Bayesian-Filters-in-Python | |
|---|---|
| Total Issues | |
| Creators | |
| Comments | |
| Commenters |
The time of an issue from open to first-responded (exclude bots).
p25/p75: 25%/75% issues are responded within X minute/hour/day.
e.g. p25: 1h means 25% issues are responded within 1 hour.
Monthly opened/closed issues and the historical totals.
Check the activity of contributors in the repository last month, including push and commit events, issue open/close/comment events, code review comments/PRs/submits.