Gridfia
C+ 79 completed
Data Tool
unknown / python · small
167
Files
38,070
LOC
1
Frameworks
7
Languages
Pipeline State
completedRun ID
#360712Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
51.67Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #47949
Member of a group with 1 similar repo(s) — canonical #14833 view group →
Top concepts (2)
Project DescriptionTesting
Repobility · code-quality intelligence · https://repobility.com
AI Prompt
Create a Python tool, similar to the gridfia library, for performing spatial raster analysis on USDA Forest Service BIGMAP data. I need it to handle converting BIGMAP GeoTIFF data into cloud-optimized Zarr arrays. The tool should allow users to specify a US state and county to enable localized analysis. Key functionalities must include calculating forest diversity metrics like Shannon, Simpson, and richness, and finally, visualizing the results to generate publication-ready maps with automatic boundary detection. The project should be structured using pytest for testing.
python raster-analysis gis zarr spatial usda bigmap pytest
Generated by gemma4:latest
Catalog Information
The gridfia project provides spatial raster analysis capabilities for the USDA Forest Service's BIGMAP data.
Description
Gridfia is a Python-based tool designed to perform spatial raster analysis on the USDA Forest Service's BIGMAP data. It leverages popular libraries such as matplotlib, numpy, and pandas to process and visualize geospatial data. The project aims to facilitate efficient and accurate analysis of large-scale forest datasets.
الوصف
هذا المشروع يهدف إلى تحليل الرسومات الجغرافية الموزعة على USDA Forest Service BIGMAP بيانات. يستخدم هذا الأداة لغة بايثون و مكتبات متعددة مثل matplotlib, numpy, pandas لتحليل وتحليل البيانات الجغرافية.
Novelty
5/10Tags
spatial-raster-analysis geospatial-data forest-datasets usda-forest-service bigmap-data
Technologies
matplotlib numpy pandas pydantic rich scikit-learn
Claude Models
claude-opus-4.6
Quality Score
C+
79.3/100
Structure
88
Code Quality
75
Documentation
90
Testing
85
Practices
51
Security
92
Dependencies
60
Strengths
- CI/CD pipeline configured (github_actions)
- Good test coverage (63% 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
Weaknesses
- 8 bare except/catch blocks swallowing errors
- 1662 duplicate lines detected \u2014 consider DRY refactoring
- 6 'god files' with >500 LOC need decomposition
Recommendations
- Replace bare except/catch blocks with specific exception types
Security & Health
6.3h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (0)
Source: Repobility analyzer · https://repobility.com
MIT
License
3.8%
Duplication
Languages
Frameworks
pytest
Concepts (2)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Open data scored by Repobility · https://repobility.com | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_description | Project Description | GridFIA provides efficient Zarr-based storage and processing for localized forest biomass analysis using USDA Forest Service BIGMAP data. | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Testing | testing | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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