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Description:

proadv 2.1.4

ProADV - Process Acoustic Doppler Velocimeter

Streamline Your ADV Data Analysis
ProADV is a comprehensive Python package designed to empower researchers and engineers working with acoustic Doppler velocimeter (ADV) data. It offers a comprehensive suite of tools for efficient cleaning, analysis, and visualization of ADV data, streamlining your workflow and extracting valuable insights from your measurements.
Key Features

Despiking and Denoising: ProADV tackles the challenge of spikes and noise in ADV data, providing a variety of robust algorithms for effective data cleaning.

Spike Detection:

ACC (Acceleration Thresholding): Identifies spikes based on exceeding a user-defined acceleration threshold.
PST (Phase-Space Thresholding): Utilizes a combination of velocity and its temporal derivative to detect spikes.
mPST (Modified Phase-Space Thresholding): An enhanced version of PST with improved sensitivity.
VC (Velocity Correlation): Detects spikes based on deviations from the correlation between neighboring data points.
KDE (Kernel Density Estimation): Employs a statistical approach to identify outliers based on the probability density function.
3d-KDE (Three-dimensional Kernel Density Estimation): Extends KDE to three dimensions for more robust spike detection in complex data.
m3d-KDE (Modified Three-dimensional Kernel Density Estimation): Further refines 3d-KDE for enhanced performance.

Replacement Methods: ProADV offers several options to replace detected spikes with more reliable values:

LVD (Last Valid Data): Replaces spikes with the last valid data point before the spike.
MV (Mean Value): Replaces spikes with the mean value of velocity component.
LI (Linear Interpolation): Uses linear interpolation between surrounding points to estimate the missing value.
12PP (12 Points Cubic Polynomial): Employs a 12-point cubic polynomial to fit a smoother curve and replace spikes.

Statistical Analysis: ProADV equips you with essential statistical tools to characterize your ADV data:

Minimum, Maximum: Provides the range of measured velocities.
Mean, Median, Mode: Calculates central tendency measures.
Skewness, Kurtosis: Analyzes the distribution characteristics of your data.

Advanced Analysis: In addition to cleaning and basic statistics, ProADV offers advanced functionalities for deeper insights:

Moving Average: Smooths out data fluctuations for better visualization and trend analysis. Provided in simple moving average, exponential moving average, and weighted moving average methods.
SSA (Singular Spectrum Analysis): Extracts underlying patterns and trends from time series data.
Kalman Filter: Implements the Kalman filter algorithm for state estimation and prediction in time series data.
PR (Pollution Rate) Calculation: Estimates the level of noise or pollution within the data.
Spectral Analysis:

PSD (Power Spectral Density): Analyzes the distribution of energy across different frequencies within the data.
PDF (Probability Density Function): Provides the probability of encountering specific velocity values.

Normality Test: Evaluates whether your data follows a normal distribution.
Normalization: Scales data to a common range for further analysis or visualization.

Installation
There are two convenient ways to install ProADV:

Using pip (recommended):
pip install proadv

From source code:
a. Clone the repository:
git clone https://github.com/farzadasgari/proadv.git

b. Navigate to the project directory:
cd proadv

c. Install using setup.py:
python setup.py install

Collaboration
We encourage collaboration and contributions from the community to improve ProADV. Here's how to contribute:

Fork the repository on GitHub.
Clone your forked repository to your local machine.
Create a new branch for your changes.
Make your changes and commit them with descriptive messages.
Push your changes to your forked repository.
Submit a pull request for review and merging.

References
For further information and in-depth understanding of the algorithms employed in ProADV, refer to the following resources:

Exploring the role of signal pollution rate on the performance of despiking velocity time-series algorithms
Unleashing the power of three-dimensional kernel density estimation for Doppler Velocimeter data despiking

Acknowledgment

This project was developed under the supervision of Dr. Seyed Hossein Mohaeri and Dr. Mojtaba Mehraein.
We extend our deepest gratitude to Dr. Bimlesh Kumar and Dr. Luis Cea for their invaluable guidance and unwavering support throughout our journey.
Special thanks to Narges Yaghoubi, Hiva Yarandi, Mojtaba Karimi, Parvaneh Yaghoubi, Hossein Abazari, and Zahra Rezaei for their valuable contributions to this project.

Contact
For any inquiries, please contact:

std_farzad.asgari@alumni.khu.ac.ir
khufarzadasgari@gmail.com

Links
Farzad Asgari

Seyed Hossein Mohajeri

Mojtaba Mehraein