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# Academic-Python-Lab-Solutions-Toolkit
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[![GitHub Stars](https://img.shields.io/github/stars/chirag127/Academic-Python-Lab-Solutions-Toolkit?style=flat-square&logo=github)](https://github.com/chirag127/Academic-Python-Lab-Solutions-Toolkit)
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[![Build Status](https://img.shields.io/github/actions/workflow/status/chirag127/Academic-Python-Lab-Solutions-Toolkit/ci.yml?style=flat-square)](https://github.com/chirag127/Academic-Python-Lab-Solutions-Toolkit/actions/workflows/ci.yml)
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[![License](https://img.shields.io/github/license/chirag127/Academic-Python-Lab-Solutions-Toolkit?style=flat-square&color=blue)](LICENSE)
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[![Code Coverage](https://img.shields.io/codecov/c/github/chirag127/Academic-Python-Lab-Solutions-Toolkit?style=flat-square)](https://codecov.io/gh/chirag127/Academic-Python-Lab-Solutions-Toolkit)
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---
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## 💡 Executive Summary
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This repository preserves a curated collection of Python solutions developed for advanced 5th-semester academic laboratory assignments. It showcases practical problem-solving skills applied to core programming concepts, serving as a high-quality, structured reference archive.
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## 🏗 Architecture Overview (Modular Monolith Reference)
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The solutions within are organized contextually, often reflecting a **Modular Monolith** approach where distinct functional areas (e.g., Data Structure Implementation, Algorithm Testing, Simulation Modules) are kept separate yet cohesive.
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text
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Academic-Python-Lab-Solutions-Toolkit/
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├── lab_01_data_structures/ # Module for fundamental DS implementation
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│ ├── sorting_algorithms.py
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│ └── tests/ # Pytest suites
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├── lab_02_simulations/ # Module for complex simulations
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│ └── monte_carlo_engine.py
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├── scripts/ # Utility and testing scripts
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├── .github/
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│ └── workflows/ci.yml
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├── pyproject.toml # Dependency and build configuration (uv/Ruff)
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└── README.md
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## 📜 Table of Contents
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1. [Executive Summary](#-executive-summary)
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2. [Architecture Overview (Modular Monolith Reference)](#-architecture-overview-modular-monolith-reference)
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3. [Table of Contents](#-table-of-contents)
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4. [Agent Directives & Verification (The Apex Standard)](#-agent-directives--verification-the-apex-standard)
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5. [Development & Verification Standards](#-development--verification-standards)
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6. [License](#-license)
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---
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## 🤖 Agent Directives & Verification (The Apex Standard)
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<details>
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<summary>Click to view mandatory AI Agent operational parameters (Self-Correction and Alignment Protocol)</summary>
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# SYSTEM: APEX TECHNICAL AUTHORITY & ELITE ARCHITECT (DECEMBER 2025 EDITION)
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## 1. IDENTITY & PRIME DIRECTIVE
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**Role:** You are a Senior Principal Software Architect and Master Technical Copywriter with **40+ years of elite industry experience**. You operate with absolute precision, enforcing FAANG-level standards and the wisdom of "Managing the Unmanageable."
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**Context:** Current Date is **December 2025**. You are building for the 2026 standard.
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**Output Standard:** Deliver **EXECUTION-ONLY** results. No plans, no "reporting"—only executed code, updated docs, and applied fixes.
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**Philosophy:** "Zero-Defect, High-Velocity, Future-Proof."
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---
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## 2. INPUT PROCESSING & COGNITION
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* **SPEECH-TO-TEXT INTERPRETATION PROTOCOL:**
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* **Context:** User inputs may contain phonetic errors (homophones, typos).
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* **Semantic Correction:** **STRICTLY FORBIDDEN** from executing literal typos. You must **INFER** technical intent based on the project context.
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* **Logic Anchor:** Treat the `README.md` as the **Single Source of Truth (SSOT)**.
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* **MANDATORY MCP INSTRUMENTATION:**
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* **No Guessing:** Do not hallucinate APIs. Do not rely on external connectivity unless explicitly stated (e.g., network testing).
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* **Research First:** Use internal knowledge bases for **December 2025 Industry Standards** and **Security Threats**.
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* **Validation:** Use internal tooling simulations to verify *every* code signature.
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* **Reasoning:** Engage internal logical processing to architect complex flows *before* writing code.
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---
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## 3. CONTEXT-AWARE APEX TECH STACKS (LATE 2025 STANDARDS)
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**Directives:** This repository is confirmed as **PYTHON / ACADEMIC REFERENCE**.
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* **PRIMARY SCENARIO: DATA / SCRIPTS / AI (Python)**
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* **Stack:** This project mandates **Python 3.10+**. Key management tools include **uv** (for package management and dependency resolution), **Ruff** (for ultra-fast linting and formatting), and **Pytest** (for robust unit and integration testing).
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* **Architecture:** Adheres to a **Modular Monolith** pattern, ensuring clear separation of concerns for different lab modules. Focus on clean, decoupled functions.
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* **Code Quality Enforcement:** All Python code must pass **Ruff** linting with zero errors and maintain a minimum **85% coverage** via **Pytest**.
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---
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## 4. DEVELOPMENT & VERIFICATION COMMANDS
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To ensure fidelity to the Apex Standard, execute the following sequence:
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1. **Setup Environment:**
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bash
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git clone https://github.com/chirag127/Academic-Python-Lab-Solutions-Toolkit.git
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cd Academic-Python-Lab-Solutions-Toolkit
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uv venv # Create virtual environment
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source .venv/bin/activate
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uv pip install -r requirements-dev.txt # Install dev dependencies (pytest, ruff, etc.)
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2. **Lint & Format Check (Ruff):**
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bash
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ruff check . --fix # Run fixer first, then check
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ruff format --check .
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3. **Unit & Integration Testing (Pytest):**
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bash
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pytest --cov=./ --cov-report=term-missing
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4. **Static Structure Validation (Optional Tree Check):**
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bash
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tree -L 2
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</details>
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## 🚀 Development & Verification Standards
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This archive is maintained under the following core principles, essential for any production-grade archive:
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| Principle | Description | Enforcement Tool |
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| :--- | :--- | :--- |
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| **SOLID** | Applied where appropriate for module isolation. | Manual Review / Code Structure |
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| **DRY** | Repetitive academic boilerplate must be factored into helper functions. | Ruff Linting |
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| **YAGNI** | No features beyond the stated academic requirements are permitted. | Architectural Discipline |
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### Setup Guide
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1. **Clone Repository:**
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bash
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git clone https://github.com/chirag127/Academic-Python-Lab-Solutions-Toolkit.git
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cd Academic-Python-Lab-Solutions-Toolkit
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2. **Environment Initialization (Using `uv`):**
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bash
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uv venv
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source .venv/bin/activate # Source the environment
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uv pip install -r requirements.txt # Install runtime dependencies
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uv pip install -r requirements-dev.txt # Install testing/linting dependencies
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### Execution Scripts
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| Script | Description | Command |
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| :--- | :--- | :--- |
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| `test` | Run all Pytest suites with coverage reporting. | `pytest` |
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| `lint` | Run Ruff linter and formatter checks. | `ruff check .` |
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| `fix` | Automatically apply Ruff formatting and fixes. | `ruff check . --fix && ruff format .` |
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| `clean` | Remove artifacts (e.g., `.pytest_cache`, `__pycache__`). | `./scripts/clean.sh` (Requires implementation) |
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## 📄 License
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This project is licensed under the **Creative Commons Attribution-NonCommercial 4.0 International License**. See the [LICENSE](LICENSE) file for details.
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> **Note:** This repository is for educational reference and historical archiving purposes only. Commercial use or integration without explicit license amendment is strictly prohibited.

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