Aef Loader

B+ 86 completed
Library
unknown / python · tiny
36
Files
5,102
LOC
1
Frameworks
4
Languages

Pipeline State

completed
Run ID
#348017
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
35.48
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47778
Member of a group with 1 similar repo(s) — canonical #22814 view group →
Top concepts (2)
Project DescriptionTesting
Repobility's GitHub App fixes findings like these · https://github.com/apps/repobility-bot

AI Prompt

Create a Python library called `aef-loader` that provides efficient virtualized access to Alpha Earth Foundations (AEF) embeddings for data analysis. The tool should allow users to rapidly download and query indexes from both Google Cloud Storage (GCS) and the Source Cooperative. Key functionalities include lazily loading COGs as a VirtualiZarr datatree organized by UTM zone, caching COG headers for cheap repeated reads, and providing utilities to dequantize/requantize embeddings or split the dataset into 64 individual datasets. The project should be testable using pytest.
python geospatial data-analysis virtualization pytorch pytest
Generated by gemma4:latest

Catalog Information

Provides efficient virtualized access to AEF embeddings for data analysis.

Description

This library offers a streamlined way to load and access AEF embeddings using virtual array techniques, enabling users to work with large datasets without loading everything into memory. It leverages a virtualizarr interface to map embedding files directly into memory‑mapped arrays, providing fast random access and lazy loading. The API is lightweight, with simple functions to open, slice, and iterate over embeddings, making it easy to integrate into existing data pipelines. Target users include data scientists and machine learning engineers who need to explore or analyze high‑dimensional embeddings at scale. The tool solves the common problem of memory bottlenecks when handling millions of vectors, and it supports efficient streaming for real‑time analytics.

الوصف

توفر هذه المكتبة وسيلة مبسطة لتحميل والوصول إلى تضمينات AEF باستخدام تقنيات المصفوفات الافتراضية، مما يمكّن المستخدمين من التعامل مع مجموعات بيانات كبيرة دون تحميلها بالكامل في الذاكرة. تعتمد على واجهة virtualizarr لربط ملفات التضمين مباشرةً بمصفوفات مخرطة في الذاكرة، وتوفر وصولاً عشوائياً سريعاً وتحميلاً كسولاً. واجهة برمجة التطبيقات بسيطة، مع وظائف أساسية لفتح، تقطيع، وتكرار التضمينات، ما يجعل دمجها في خطوط الأنابيب الحالية سهلاً. تستهدف المستخدمين علماء البيانات ومهندسي التعلم الآلي الذين يحتاجون إلى استكشاف أو تحليل متجهات عالية الأبعاد على نطاق واسع. تحل هذه الأداة مشكلة الاختناق في الذاكرة عند التعامل مع ملايين المتجهات، وتدعم أيضاً تدفق البيانات بكفاءة للمعالجة اللحظية. يميزها التفاعل السلس مع بيئات تحليل البيانات مثل Jupyter، مع توفير واجهة برمجية واضحة ومتوائمة مع معايير العمل العلمي.

Novelty

6/10

Tags

embeddings virtual-array data-loading memory-efficiency large-scale-data scientific-computing streaming-access analysis

Technologies

numpy pandas

Claude Models

claude-opus-4.6 claude (unknown version)

Quality Score

B+
86.5/100
Structure
86
Code Quality
90
Documentation
79
Testing
85
Practices
82
Security
100
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Good test coverage (125% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected
  • Properly licensed project

Security & Health

4.3h
Tech Debt (C)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (2)
Want this analysis on your repo? https://repobility.com/scan/
Apache-2.0
License
0.0%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
79.4%
markdown
10.9%
yaml
6.6%
toml
3.1%

Frameworks

pytest

Concepts (2)

Generated by the Repobility scanner · https://repobility.com
CategoryNameDescriptionConfidence
Repobility · severity-and-effort ranking · https://repobility.com
auto_descriptionProject DescriptionVirtualizarr access for AEF embeddings as an analysis ready data cube, alongside rapid querying of the GCS and Source Coop index. 2x quicker than rioxarray for single tile downloads.80%
auto_categoryTestingtesting70%

Quality Timeline

1 quality score recorded.

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