Eastack

B 82 completed
Library
unknown / python · tiny
14
Files
757
LOC
0
Frameworks
6
Languages

Pipeline State

completed
Run ID
#369909
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_tiny
Novelty
23.52
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #48020
Member of a group with 1 similar repo(s) — canonical #75735 view group →
Top concepts (2)
Project DescriptionLibrary
Repobility (the analyzer behind this table) · https://repobility.com

AI Prompt

Create a Python library that performs SIMD-accelerated frame stacking and batched accumulation. I need functions like `stack_mean(frames)` to compute the mean of multiple noisy exposures, and `stack(frames)` to accumulate the sum. It should also include a function, `frame_stats(data)`, to calculate the minimum, maximum, and sum in a single pass. Additionally, expose low-level kernel access functions like `accumulate_f32x8` and `accumulate_batch8_f32x8` for advanced users. The library should rely on NumPy and be installable via pip.
python simd image-processing numpy library acceleration astronomy signal-processing
Generated by gemma4:latest

Catalog Information

A Python library that performs SIMD-accelerated frame stacking and batched accumulation to reduce memory traffic.

Description

Eastack is a lightweight Python library designed for high‑performance frame stacking. It leverages SIMD instructions to accelerate the concatenation of multiple frames, enabling faster data preparation for machine learning pipelines. The library supports batched accumulation, allowing users to process large sequences with minimal memory overhead. It is ideal for real‑time video analytics, sensor data aggregation, and any application that requires efficient handling of multi‑dimensional arrays. Eastack integrates seamlessly with NumPy, making it easy to incorporate into existing scientific workflows.

الوصف

تُعد Eastack مكتبة بايثون خفيفة الوزن مُصمَّمة لتحقيق أداء عالٍ في تجميع الأطر. تستفيد من تعليمات SIMD لتسريع دمج الأطر المتعددة، مما يتيح تحضير بيانات أسرع لسلاسل تعلم الآلة. تدعم المكتبة تراكم الدُفعات، مما يتيح للمستخدمين معالجة تسلسلات كبيرة مع تقليل الحمل على الذاكرة. تُعد مثالية لتحليلات الفيديو في الوقت الحقيقي، وتجميع بيانات المستشعرات، وأي تطبيق يتطلب معالجة فعّالة للمصفوفات متعددة الأبعاد. تتكامل Eastack بسلاسة مع NumPy، مما يسهل دمجها في سير العمل العلمي الحالي.

Novelty

7/10

Tags

frame-stacking simd-acceleration batch-processing memory-optimization data-accumulation performance-enhancement image-data-handling

Technologies

numpy

Claude Models

claude-opus-4.6

Quality Score

B
81.5/100
Structure
78
Code Quality
100
Documentation
63
Testing
65
Practices
78
Security
100
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Low average code complexity \u2014 well-structured code
  • Good security practices \u2014 no major issues detected

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors

Recommendations

  • Add a LICENSE file (MIT recommended for open source)

Security & Health

4.1h
Tech Debt (E)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (10)
Powered by Repobility — scan your code at https://repobility.com
MIT
License
0.0%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
45.3%
yaml
26.3%
markdown
8.9%
json
7.7%
toml
6.2%
shell
5.6%

Frameworks

None detected

Concepts (2)

Repobility analysis · methodology at https://repobility.com/research/
CategoryNameDescriptionConfidence
Repobility · open methodology · https://repobility.com/research/
auto_descriptionProject DescriptionSIMD-accelerated frame stacking powered by Ea kernels.80%
auto_categoryLibrarylibrary70%

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1 quality score recorded.

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