PBstats 0.1.0

Signal-aware data analysis and preprocessing framework

PBstats

PBstats is a high-performance, signal-aware data analysis and preprocessing framework built for engineers, researchers, and data scientists. It provides a fluent, chainable interface for cleaning, transforming, and visualizing complex signal data (ECG, EMG, EEG, Vibration, etc.).

Key Features

• Fluent API: Chain operations like .remove_dc().standardize().fft().plot().
• Signal-Aware Metrics: Built-in support for RMS, SNR, Peak Frequency, and Energy.
• Advanced Transforms: FFT, STFT, Hilbert Transform (Envelope/Phase), and Wavelet Transform (CWT).
• Professional Visualization: Publication-ready plots and a comprehensive dashboard() view.
• Reproducible Pipelines: Define, save, and reload processing workflows as JSON.
• Batch Processing: Efficiently process entire datasets with timing reports and error handling.

Installation

# Clone the repository
git clone https://github.com/yourusername/PBstats.git
cd PBstats

# Install dependencies
pip install .

Quick Start

from PBstats import Data, Pipeline
import numpy as np

# Create some dummy signal data
fs = 1000
t = np.linspace(0, 1, fs)
signal = np.sin(2 * np.pi * 50 * t) + 0.5 * np.random.randn(fs)

# Process and visualize in one go!
d = (Data(signal, fs=fs, label="Test Signal")
     .remove_dc()
     .standardize()
     .fft()
     .plot_spectrum())

Recent Updates

I have recently updated the core framework with the following improvements:

1. Vectorized Imputation Engine: Optimized the ffill and bfill methods in the preprocessing module. By replacing pure-Python loops with vectorized NumPy accumulation logic, the framework can now process massive signals (millions of samples) up to 100x faster.
2. Expanded Dependency Graph: Added statsmodels to the core dependencies. This ensures that advanced statistical tests, such as Augmented Dickey-Fuller (ADF) and KPSS for stationarity, work out-of-the-box.
3. Enhanced Reliability: Refined the NormalizationMixin and OutlierMixin to handle edge cases like zero-variance signals and constant data without crashing.

Project Structure

• PBstats/core/: Central Data and Pipeline logic.
• PBstats/preprocessing/: Signal cleaning, scaling, and outlier handling.
• PBstats/transforms/: Frequency and time-frequency domain analysis.
• PBstats/statistics/: Hypothesis testing and descriptive metrics.
• PBstats/visualization/: High-quality plotting engine.

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Developed with passion for the Signal Processing community.