🚀 Python for Robotics: Foundation & Data Analytics

A polished portfolio of Python practice work from a 90-Day Robotics Challenge, built to showcase core algorithms, vectorized sensor processing, and engineering-grade data visualization.

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📚 Table of Contents

• Overview
• Project Architecture
• Core Modules & Progression
• Capstone Highlight
• How to Run
• Why This Repository Matters

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📌 Overview

This repository is a structured Python sandbox created during a 90-Day Robotics Challenge. It captures the progression from fundamental Python and OOP to advanced sensor data processing and engineering visualization for autonomous systems.

Note

The repository is optimized for both robotics learners and engineering reviewers, with a focus on signal processing, visualization, and reproducible telemetry workflows.

Important

The capstone includes a production-style thermal dashboard export and demonstrates end-to-end data pipeline thinking.

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🗂️ Project Architecture

Expand the file tree

python_practice/
│
├── 📁 Root/                            # Core Python Fundamentals
│   ├── Bank_account.py                 # OOP: Classes, Methods, State Management
│   ├── contact_CLI.py                  # File I/O & Persistent Data Storage
│   ├── student_topper_script.py        # Data Sorting & Lambda Functions
│   ├── FIZZBUZZ.py                     # Algorithmic Control Flow
│   └── (Calculater.py, Tip_Calculator.py, number_gussing.py, etc.)
│
├── 📁 numpy/                           # Vectorized Data Processing
│   ├── numpy_tutorial.py               # Arrays, Shapes, and Matrix Math
│   ├── excersice_1.py                  # 1D Autonomous Sensor Noise Filter
│   └── excersice_2.py                  # 2D Grid Occupancy Map (Broadcasting)
│
├── 📁 Matplotlib/                      # Data Visualization
│   ├── matplotlib_tutorial.py          # DataFrames, Bar Charts, and Pie Charts
│   ├── mat_excersice_1.py              # Ultrasonic Sensor Calibration Curve
│   ├── mat_excersice_2.py              # Transit Demand Dashboard (Subplots)
│   └── mat_excersice_3.py              # Autonomous Robot Trajectory Map
│
└── 📁 Num_Mat_excersice/               # 🏆 Capstone Project
    ├── Num_Mat_excersice.py            # 24-Hour Hardware Thermal Simulation
    └── thermal_profile_export.png      # High-Res Output Graphic

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🚀 Core Modules & Progression

Phase	Focus Area	Key Outcome
Phase 1	Python Fundamentals & OOP	Built CLI persistence and object models for banking & contact management
Phase 2	NumPy Vectorization	Developed sensor noise filters and occupancy grid math without loops
Phase 3	Matplotlib Visualization	Produced engineering dashboards and trajectory analytics
Capstone	Thermal Simulation & Export	Simulated 24-hour thermal telemetry, smoothed noisy data, and exported a PNG dashboard

Warning

Advanced reviewers: this repo is intentionally structured for quick vetting, with the full signal processing and visualization workflow isolated in the capstone folder.

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📊 Capstone Highlight: Hardware Thermal Profile

This capstone is the visual signature of the repository. It combines NumPy-driven signal synthesis with Matplotlib dashboard construction to make thermal telemetry instantly interpretable.

What the capstone delivers 1,440 minutes of baseline temperature using a sine wave model Gaussian noise injection with np.random.normal Smoothed trendline via np.convolve Max/min detection using np.argmax and np.argmin Presentation-ready PNG export for technical reporting Note The graph is generated dynamically by Num_Mat_excersice/Num_Mat_excersice.py and exported as thermal_profile_export.png.

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⚙️ How to Run

1. Clone the repository.
2. Install dependencies:

   pip install numpy matplotlib pandas

3. Run the capstone script:

   python "Num_Mat_excersice/Num_Mat_excersice.py"

Important

If you want to reproduce the graph exactly, run the script from the repository root so the image export path resolves correctly.

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💡 Why This Repository Matters

This repository is more than practice code; it is a curated trajectory from basic Python to robotics-aware data analysis. The files show how to move from syntax to sensor fusion, then into polished visual storytelling for engineering teams.

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Designed and built by Rahul Choudhary as part of the journey to mastering autonomous systems.