Signal Processing Package - ICT Dhriti (92510133033)

A comprehensive digital signal processing package for generating and manipulating discrete-time signals. This package provides essential tools for signal analysis, generation of common signals, and basic signal operations with automatic visualization capabilities.

🚀 Features

• Unitary Signals: Unit step, unit impulse, and ramp signal generation
• Trigonometric Signals: Sine waves, cosine waves, and exponential signals
• Signal Operations: Time shifting, time scaling, signal addition, and multiplication
• Enhanced Visualization: Automatic plot generation with comparison views
• Educational Focus: Perfect for learning and teaching digital signal processing concepts

📦 Installation

From TestPyPI (Recommended for testing)

pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ signal-ict-dhriti-92510133033

From Local Wheel

pip install dist/signal_ict_dhriti_92510133033-0.1.0-py3-none-any.whl

Dependencies

• numpy >= 1.20.0
• matplotlib >= 3.3.0

🎯 Quick Start

import signal_ICT_Dhriti_92510133033 as sig
import numpy as np

# Create time vector
n = np.arange(0, 20)

# Generate basic signals
sine_wave = sig.sine_wave(frequency=1, amplitude=2, n=n, save_fig=True)
unit_step = sig.unit_step(n, save_fig=True)
unit_impulse = sig.unit_impulse(n, save_fig=True)

# Apply signal operations
shifted_signal = sig.time_shift(sine_wave, k=5, save_fig=True)
scaled_signal = sig.time_scale(sine_wave, k=3, save_fig=True)

# Combine signals
combined = sig.signal_addition(sine_wave, unit_step, save_fig=True)

📚 Module Structure

signal_ICT_Dhriti_92510133033/
├── __init__.py              # Package initialization and exports
├── unitary_signals.py       # Step, impulse, ramp functions
├── trigonometric_signals.py # Sine, cosine, exponential functions
└── operations.py           # Time shift, scale, arithmetic operations

🛠 Available Functions

Unitary Signals (unitary_signals.py)

Function	Description	Parameters
unit_step(n, save_fig=True)	Generates unit step function u[n]	n: sample indices, save_fig: save plot
unit_impulse(n, save_fig=True)	Generates unit impulse δ[n]	n: sample indices, save_fig: save plot
ramp_signal(n, save_fig=True)	Generates ramp function r[n] = n·u[n]	n: sample indices, save_fig: save plot

Trigonometric Signals (trigonometric_signals.py)

Function	Description	Parameters
sine_wave(freq, amp, n, phase=0, save_fig=True)	Generates sine wave	freq: frequency, amp: amplitude, n: samples, phase: phase shift
cosine_wave(freq, amp, n, phase=0, save_fig=True)	Generates cosine wave	freq: frequency, amp: amplitude, n: samples, phase: phase shift
exponential_signal(a, n, save_fig=True)	Generates exponential signal aⁿ	a: base, n: sample indices, save_fig: save plot

Signal Operations (operations.py)

Function	Description	Parameters
time_shift(signal, k, save_fig=True)	Shifts signal by k samples	signal: input signal, k: shift amount, save_fig: save comparison plot
time_scale(signal, k, save_fig=True)	Scales signal in time by factor k	signal: input signal, k: scale factor, save_fig: save comparison plot
signal_addition(s1, s2, save_fig=True)	Adds two signals	s1, s2: input signals, save_fig: save result plot
signal_multiplication(s1, s2, save_fig=True)	Multiplies two signals	s1, s2: input signals, save_fig: save result plot

📊 Enhanced Visualization

The package provides comprehensive plotting capabilities:

• Individual signal plots: Each function generates its own visualization
• Comparison plots: Operations show before/after comparisons
• Multiple formats: Both stem plots (discrete) and line plots where appropriate
• Automatic saving: All plots saved as high-resolution PNG files

Plot Types Generated

1. Basic signal plots: sine_wave.png, unit_step.png, etc.
2. Operation comparison plots: time_shift_comparison_k5.png
3. Individual operation plots: time_shift_k5.png (backward compatibility)

💡 Usage Examples

Example 1: Basic Signal Generation

import signal_ICT_Dhriti_92510133033 as sig
import numpy as np

# Create time samples
n = np.linspace(0, 2*np.pi, 50)

# Generate and visualize different signals
sine = sig.sine_wave(frequency=2, amplitude=1.5, n=n)
cosine = sig.cosine_wave(frequency=2, amplitude=1.5, n=n)
step = sig.unit_step(np.arange(-10, 11))

Example 2: Signal Operations

# Create a test signal
n = np.arange(0, 20)
test_signal = sig.sine_wave(1, 1, n)

# Apply time operations
delayed = sig.time_shift(test_signal, 3)    # Delay by 3 samples
advanced = sig.time_shift(test_signal, -2)  # Advance by 2 samples
stretched = sig.time_scale(test_signal, 2)  # Stretch by factor of 2

Example 3: Signal Arithmetic

# Create two signals
n = np.arange(0, 10)
signal1 = sig.sine_wave(1, 1, n)
signal2 = sig.cosine_wave(1, 0.5, n)

# Combine signals
sum_signal = sig.signal_addition(signal1, signal2)
product_signal = sig.signal_multiplication(signal1, signal2)

🔧 Development

Local Development Setup

# Clone the repository
git clone https://github.com/yourusername/signal-processing-package.git
cd signal-processing-package

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e .

# Run tests
python main.py

Building Distribution

# Install build tools
pip install --upgrade build twine

# Build package
python -m build

# Upload to TestPyPI
twine upload --repository testpypi dist/*

📝 Version History

• v0.1.0: Initial release
  • Basic signal generation functions
  • Time domain operations
  • Enhanced visualization with comparison plots
  • Complete package structure with proper metadata

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👤 Author

Dhriti (Student ID: 92510133033)

• 📧 Email: [dhritiambasana@gmail.com]
• 🔗 GitHub: [https://github.com/dhritiambasana]

🙏 Acknowledgments

• Created for ICT coursework in Digital Signal Processing
• Thanks to the Python scientific computing community
• Built using NumPy and Matplotlib

---

Note: This package is designed for educational purposes and provides fundamental digital signal processing operations. For advanced signal processing applications, consider using specialized libraries like SciPy.