Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python
C-Rank Grade: A (Welcoming) - 66/100
External PR Merge Rate: 74%
Response Time: 15h
First Timer Success: 100%
helmholtz-analytics/heat has a recorded first-timer success rate of 100.0%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in helmholtz-analytics/heat respond to incoming external pull requests in approximately 14.5 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 65.9 places helmholtz-analytics/heat in the A tier.
The external contributor pull request merge rate for helmholtz-analytics/heat is 73.9%, based on public PR activity from non-core contributors.
helmholtz-analytics/heat currently has 4 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Collaborative maintainer environment in helmholtz-analytics/heat. Reviews community pull requests with focus on project quality.
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.
[](https://getmerged.abhishekco.de/helmholtz-analytics/heat?utm_source=github&utm_medium=badge)
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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Detected: UTC
Maintainers are most responsive around 11:00 UTC (11:00 UTC in your timezone). Recommended submission window is Monday - Wednesday from 11:00 to 15:00 UTC (11:00 - 15:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~9 hours (92% approval). Large PRs (>500 lines) take ~70 hours (33% approval). Keep your diff compact for rapid review.
What happened? PR #2444 has added heat/utils/data to codecov.yaml, but tests for those modules do not exist or are insufficient. achieve test coverage as close to 100% as possible for heat/utils/data/mnist.py, heat/utils/data/_utils.py Code snippet triggering the error Error message or erroneous outcome Version main (development branch) Python version 3.14 PyTorch version 2.14 MPI version
Related Array API Feature functionality Array API compatible version of ht.cumsum Additional context https://data-apis.org/array-api/latest/API_specification/generated/array_api.cumulative_sum.html
Related Array API Feature functionality Array API compatible version of ht.cumprod Additional context https://data-apis.org/array-api/latest/API_specification/generated/array_api.cumulative_prod.html
Related Array API Feature functionality dunder function for ht.abs() Additional context Add any other context or screenshots about the feature request here.
Currently, the heat.io module handles both single files (e.g., a single .h5 or .nc file) and sharded directory-based datasets. The routing logic for sharded datasets is addressed directly in the format-specific function, probably at least some of it is redundant. Moreover, functions are named inconsistently (e.g., load_npy_from_path, load_csv_from_folder, load_multiple_hdf5, load_zarr_groups). We should refactor the io module to cleanly separate the concept of sharded/chunked vs. single-file parallel ingestion. Mid-term, we might want to incorporate at least some of @hmoudaahmed 's hpc-data-loader capabilities into heat.io as well. Task Refactor heat.io so that the MPI directory-routing algorithm (determining which rank gets which files) is separated from the format-reading logic (HDF5, CSV, zarr, and whatever we support next). This will make it easier to plug in new formats like memmap, Parquet, ... without rewriting the sharding logic every time. Open for Discussion What kind of a
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When evaluating whether to contribute to helmholtz-analytics/heat, 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 helmholtz-analytics/heat acknowledge new external contributions in approximately 14 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 73.9% were successfully merged into the primary branch.
helmholtz-analytics/heat has a recorded first-timer success rate of 100.0%. 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 65.9 places helmholtz-analytics/heat in the Welcoming tier.
The external pull request merge rate is 73.9%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, helmholtz-analytics/heat currently has 4 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for helmholtz-analytics/heat 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.