Fiplsim

F 47 completed
Web App
unknown / python · tiny
26
Files
7,160
LOC
0
Frameworks
4
Languages

Pipeline State

completed
Run ID
#368911
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
31.74
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47544
Member of a group with 2 similar repo(s) — canonical #14176 view group →
Top concepts (1)
Web Backend
Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/

AI Prompt

I want to build a financial planning and analysis simulation tool using Python. The project should handle pipe network simulations, incorporating modules for hydraulics, pipe network calculations, and potentially Darcy verification. It needs to read data from CSV files like `FiPLSim_Bead_K_Values.csv` and `FiPLSim_Darcy_Verification.csv`. Please structure the core logic in files like `simulation.py` and `app.py`, and include setup scripts for running tests and generating documentation.
python simulation financial-analysis hydraulics piping
Generated by gemma4:latest

Catalog Information

The FiPLSim project is a Python-based simulation tool for financial planning and analysis.

Description

FiPLSim is a web application that uses machine learning algorithms to simulate financial scenarios, providing users with insights into their investment decisions. It leverages popular data science libraries such as NumPy, Pandas, SciPy, and Plotly to process and visualize complex financial data. Streamlit is used for building the user interface.

الوصف

هذا المشروع هو أداة تقدير مالية تستخدم في التخطيط المالي والتحليل، وتستند إلى برامج تعلم الآلة لتقديم المستخدمين بمعلومات حول قرارات الاستثمار. يستفيد من مكتبات العلوم البيانية الشائعة مثل NumPy وPandas وSciPy وPlotly لprocessing وتصوير البيانات المالية المعقدة، ويستخدم Streamlit في بناء الواجهة المستخدم.

Novelty

5/10

Tags

financial-planning investment-analysis machine-learning data-visualization simulation-tool

Technologies

numpy pandas plotly scipy streamlit

Claude Models

claude-opus-4.6

Quality Score

F
46.7/100
Structure
38
Code Quality
40
Documentation
34
Testing
30
Practices
63
Security
100
Dependencies
60

Strengths

  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected

Weaknesses

  • Missing README file \u2014 critical for project understanding
  • No LICENSE file \u2014 legal ambiguity for contributors
  • No CI/CD configuration \u2014 manual testing and deployment
  • 913 duplicate lines detected \u2014 consider DRY refactoring
  • 5 'god files' with >500 LOC need decomposition

Recommendations

  • Add a comprehensive README.md explaining purpose, setup, usage, and architecture
  • Add a test suite \u2014 start with critical path integration tests
  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a linter configuration to enforce code style consistency
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

5.6h
Tech Debt (B)
A
OWASP (100%)
FAIL
Quality Gate
A
Risk (14)
All rows scored by the Repobility analyzer (https://repobility.com)
Unknown
License
8.1%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
95.9%
markdown
3.6%
text
0.3%
toml
0.2%

Frameworks

None detected

Concepts (1)

Data scored by Repobility · https://repobility.com
CategoryNameDescriptionConfidence
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auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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