9eq

Real-time audio visualizer.

[!NOTE] The CLI command is nineeq (nine-e-q) and the Python package is 9eq

Features

• Real-time Visualization - Live frequency spectrum analysis with colorized bars
• Precise Detection - FFT-based frequency detection with configurable tolerance
• Audio File Analysis - Analyze pre-recorded audio files
• Tone Generation - Generate pure nineeq frequency tones
• Color Mapping - Each frequency has a unique color based on chakra theory
• Multiple Visualization Modes - Bar chart and waveform displays
• CLI Interface - Easy command-line usage
• Modular Design - Use as a library in your own projects

Installation

Quick Install

# Clone the repository
git clone https://github.com/clxrityy/9eq.git
cd 9eq

# Install with pip
pip install -e .

From PyPI

pip install 9eq

Manual Installation

# Install dependencies
pip install -r requirements.txt

# Install in development mode
pip install -e .

Usage

Command Line Interface

List all frequencies

nineeq list

Real-time visualization

# Basic visualization
nineeq visualize

# Choose visualization mode
nineeq visualize --mode wave

# Specify audio device
nineeq visualize --device 1

Analyze an audio file

nineeq analyze path/to/audio.wav

# Analyze only first 30 seconds
nineeq analyze path/to/audio.wav --duration 30

Generate a tone

# Generate 528 Hz tone (5 seconds)
nineeq generate 528

# Custom duration and output
nineeq generate 528 --duration 10 --output miracle_tone.wav

Python Library Usage

import importlib

# Import the package (note: package name is 'nineeq')
_nineeq = importlib.import_module('nineeq')
FrequencyVisualizer = _nineeq.FrequencyVisualizer
FrequencyDetector = _nineeq.FrequencyDetector
ToneGenerator = _nineeq.ToneGenerator

# Real-time visualization
visualizer = FrequencyVisualizer(mode='bar')
visualizer.start()

# Analyze audio file
visualizer.analyze_file('meditation.wav')

# Detect frequencies in audio data
import numpy as np
detector = FrequencyDetector()
audio_data = np.random.randn(4096)  # Your audio data
detected = detector.detect_frequencies(audio_data)

# Generate a tone
generator = ToneGenerator()
generator.save_tone('528hz.wav', frequency=528, duration=5.0)

Frequency Detection

The visualizer uses Fast Fourier Transform (FFT) to convert time-domain audio signals into frequency-domain data. For each target nineeq frequency:

1. Windowing: Applies a Hann window to reduce spectral leakage
2. FFT Analysis: Computes the frequency spectrum
3. Peak Detection: Identifies magnitude peaks within ±5 Hz of target frequencies
4. Normalization: Scales magnitudes for visualization

Running Tests

pytest tests/

Code Formatting

black nineeq/

Requirements

• Python 3.8+
• NumPy >= 1.21.0
• SciPy >= 1.7.0
• Matplotlib >= 3.5.0
• sounddevice >= 0.4.5
• soundfile >= 0.11.0

pip install -r requirements.txt