Python/C++ library for distribution power system analysis
C-Rank Grade: A (Welcoming) - 67/100
External PR Merge Rate: 75%
Response Time: 14h
First Timer Success: 92%
PowerGridModel/power-grid-model has a recorded first-timer success rate of 91.7%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in PowerGridModel/power-grid-model respond to incoming external pull requests in approximately 14.1 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 66.7 places PowerGridModel/power-grid-model in the A tier.
The external contributor pull request merge rate for PowerGridModel/power-grid-model is 74.7%, based on public PR activity from non-core contributors.
PowerGridModel/power-grid-model currently has 1 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Highly welcoming maintainers in PowerGridModel/power-grid-model. Prompt code reviews with positive guidance for new contributors.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
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Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Maintainers are most responsive around 09:00 UTC (09:00 UTC in your timezone). Recommended submission window is Monday - Wednesday from 09:00 to 13:00 UTC (09:00 - 13:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~9 hours (93% approval). Large PRs (>500 lines) take ~68 hours (34% approval). Keep your diff compact for rapid review.
Describe the bug When running state estimation with a disabled power/current sensor on a disabled branch, the output is empty (energized = 0, p_residual = 0, q_residual = 0). However, when running state estimation with a disabled power/current sensor on a disabled shunt or load/gen, the output is not empty (energized = 1, p_residual = 6924.0000000000009, q_residual = -69280). This behavior is unexpected Input Data Validity I checked the validity of input data using the helper function (examples here). Data Dump Input data { "version": "1.0", "type": "input", "is_batch": false, "attributes": {}, "data": { "node": [ { "id": 1, "u_rated": 10e3 } ], "line": [ { "id": 5, "from_node": 1, "to_node": 1, "from_status": 0, "to_status": 0, "r1": 1000000.0, "x1": 1000000.0, "c1": 2.0e-5, "tan1": 0.1, "r0": 1000000.0, "x0": 1000000.0, "c0": 1.0e-
Problem Extreme loading/generating situations result into voltage being in the non convergence region for newton raphson method. Such cases are common in planning operations. It might be desirable for users to have Newton Raphson results if possible and linear results for cases which do not converge. Current method for this would be to run consecutive calculations: First full batch dataset, then linear powerflow on new dataset constructed from failed scenarios. OpenDSS faced similar issue and chose a different method around it (https://opendss.epri.com/LoadModelsThatNearlyAlwaysConver.html). This signals some demand by the community. Proposal Give out linear power flow results for IterationDiverge cases and continue_on_error=True. We already initialize newton raphson with linear results so its a simple matter of caching that result. These scenarios still get classified as errors. But approximate results are available without reconstructing scenarios. For automatic tap changing we still
Describe the feature request The devcontainer currently provided, provides a nice user experience for the deveopment of the Python wrapper of PGM. In addition it would be nice to extend the devcontainer to have a similar experience for the C++ development: Install necessary build tools like CMake Possibility to debug tests from an IDE I see two possibilities: Extend the current devcontainer such that it can be used for Python and C++ development Create an additional devcontainer for C++ development so that there is one for Python and another one for C++ reference
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When evaluating whether to contribute to PowerGridModel/power-grid-model, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of PowerGridModel/power-grid-model acknowledge new external contributions in approximately 14 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 74.7% were successfully merged into the primary branch.
PowerGridModel/power-grid-model has a recorded first-timer success rate of 91.7%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~14 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 66.7 places PowerGridModel/power-grid-model in the Welcoming tier.
The external pull request merge rate is 74.7%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, PowerGridModel/power-grid-model currently has 1 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for PowerGridModel/power-grid-model are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.