Fast Azimuthal Integration in Python
C-Rank Grade: A (Welcoming) - 67/100
External PR Merge Rate: 73%
Response Time: 6h
First Timer Success: 100%
silx-kit/pyFAI 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 silx-kit/pyFAI respond to incoming external pull requests in approximately 5.7 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 67.4 places silx-kit/pyFAI in the A tier.
The external contributor pull request merge rate for silx-kit/pyFAI is 73.3%, based on public PR activity from non-core contributors.
silx-kit/pyFAI currently has 4 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Active maintainer team in silx-kit/pyFAI welcoming community pull requests and bug fixes.
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 15:00 UTC (15:00 UTC in your timezone). Recommended submission window is Wednesday - Friday from 12:00 to 16:00 UTC (12:00 - 16:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~4 hours (92% approval). Large PRs (>500 lines) take ~27 hours (33% approval). Keep your diff compact for rapid review.
Claude thinks pyFAI should be improved this way, Since parallax correction has been implemented earlier this year, we have now all information needed. Summary The normalization chain supports a per-pixel absorption term all the way down (integrate1d_ng -> preproc -> Cython/OpenCL kernels), but nothing in pyFAI ever computes it. A grep over src/pyFAI/integrator/ finds no assignment to absorption: it stays None unless the user builds the array by hand, so the sensor efficiency silently defaults to 1 for every integration. Meanwhile src/pyFAI/detectors/sensors.py already knows how to compute it (SensorMaterial.mu(energy) / absorbance() for Si, Ge, CdTe, GaAs, CZT, Se, Gd2O2S, BaFBrI, plus a SensorConfig(material, thickness) dataclass). The two halves are simply not connected. Detector.sensor is also None for every built-in detector, including Pilatus1M and Eiger2_4M. Why this matters Sensor efficiency depends on the incidence angle: eta(alpha) = 1 - exp(-mu.t/cos alpha). Unlike solid angl
./tutorial/Detector/Pilatus_Calibration/Pilatus_ID15.ipynb: "/tmp/ipykernel_508620/754752315.py:8: DeprecationWarning: Setting the shape on a NumPy array has been deprecated in NumPy 2.5.\n", ./tutorial/Detector/Pilatus_Calibration/Pilatus900kw-ID06.ipynb: "/tmp/ipykernel_508293/2801870092.py:11: DeprecationWarning: Setting the shape on a NumPy array has been deprecated in NumPy 2.5.\n", ./tutorial/Detector/Pilatus_Calibration/.ipynb_checkpoints/Pilatus_ID15-checkpoint.ipynb: "/tmp/ipykernel_266265/754752315.py:8: DeprecationWarning: Setting the shape on a NumPy array has been deprecated in NumPy 2.5.\n", ./tutorial/Detector/Pilatus_Calibration/.ipynb_checkpoints/Pilatus900kw-ID06-checkpoint.ipynb: "/tmp/ipykernel_266959/2801870092.py:11: DeprecationWarning: Setting the shape on a NumPy array has been deprecated in NumPy 2.5.\n", ./tutorial/Detector/CCD_Calibration/CCD_calibration.ipynb: "/tmp/ipykernel_506524/999835849.py:3: DeprecationWarning: `distance_matri
In this section: cnt = 0 while not os.path.isdir(home): cnt += 1 home = os.path.split(home)[0] for _ in range(cnt): n = os.listdir(home)[0] home = os.path.join(home, n) with error message: Could not rebuild C:\projects\pyfai\build Traceback (most recent call last): File "C:\projects\pyfai\run_tests.py", line 369, in <module> build_dir = build_project(PROJECT_NAME, PROJECT_DIR) File "C:\projects\pyfai\bootstrap.py", line 84, in build_project n = os.listdir(home)[0] ~~~~~~~~~~^^^^^^ NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\projects\\pyfai\\build\\.gitignore' Probably not very complicated ...
New WAXS detector for BM29: Eiger 4M with a different type of mask.
When there is some binning, one should have access both to pixel size and binned pixel size. shape and max_shape. Maybe with some context bubble ?
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When evaluating whether to contribute to silx-kit/pyFAI, 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 silx-kit/pyFAI acknowledge new external contributions in approximately 5 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 73.3% were successfully merged into the primary branch.
silx-kit/pyFAI 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 ~5 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 67.4 places silx-kit/pyFAI in the Welcoming tier.
The external pull request merge rate is 73.3%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, silx-kit/pyFAI 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 silx-kit/pyFAI 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.