9eq 0.1.0

A real-time visualizer for the 9 nineeq frequencies

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

Frequency	Color
174 Hz	Deep Red
285 Hz	Orange Red
396 Hz	Orange
417 Hz	Gold
528 Hz	Green
639 Hz	Blue
741 Hz	Purple
852 Hz	Deep Pink
963 Hz	White

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)

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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

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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

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