proadv 2.1.4

Process Acoustic Doppler Velocimeter data with advanced despiking and analysis tools

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:

1. Using pip (recommended):

   pip install proadv
   

2. 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:

1. Fork the repository on GitHub.
2. Clone your forked repository to your local machine.
3. Create a new branch for your changes.
4. Make your changes and commit them with descriptive messages.
5. Push your changes to your forked repository.
6. 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:

1. Exploring the role of signal pollution rate on the performance of despiking velocity time-series algorithms
2. 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:

• [e-mail]
• [e-mail]

Links

Farzad Asgari

Seyed Hossein Mohajeri

Mojtaba Mehraein