# ProADV - Process Acoustic Doppler Velocimeter
[](https://github.com/farzadasgari/proadv/stargazers)
[](https://github.com/farzadasgari/proadv/network)
[](https://github.com/farzadasgari/proadv/issues)
[](https://github.com/farzadasgari/proadv/blob/main/LICENSE)
[](https://pypi.org/project/proadv/)
[](https://pypi.org/project/proadv/)
[](https://github.com/farzadasgari/proadv/graphs/contributors)
[](https://github.com/farzadasgari/proadv/pulls)
[](https://github.com/farzadasgari/proadv/pulls?q=is%3Apr+is%3Aclosed)
[](https://github.com/farzadasgari/proadv/commits/main)
## 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.
<div>
<img src="https://raw.githubusercontent.com/farzadasgari/proadv/main/examples/plots/trivariate-kernel.png" alt="trivariate-kernel" style="width:300px;"/>
<img src="https://raw.githubusercontent.com/farzadasgari/proadv/main/examples/plots/spectrum.png" alt="trivariate-kernel" style="width:300px;"/>
<img src="https://raw.githubusercontent.com/farzadasgari/proadv/main/examples/plots/phase-space.png" alt="trivariate-kernel" style="width:300px;"/>
</div>
* **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.
<div>
<img src="https://raw.githubusercontent.com/farzadasgari/proadv/main/examples/plots/singular-spectrum.png" alt="singular-spectrum" style="width:300px;"/>
<img src="https://raw.githubusercontent.com/farzadasgari/proadv/main/examples/plots/kalman.png" alt="kalman-filter" style="width:300px;"/>
</div>
### Installation
There are two convenient ways to install ProADV:
1. **Using pip (recommended):**
```bash
pip install proadv
```
2. **From source code:**
a. Clone the repository:
```bash
git clone https://github.com/farzadasgari/proadv.git
```
b. Navigate to the project directory:
```bash
cd proadv
```
c. Install using setup.py:
```bash
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](https://doi.org/10.1016/j.flowmeasinst.2023.102485)
2. [Unleashing the power of three-dimensional kernel density estimation for Doppler Velocimeter data despiking](https://doi.org/10.1016/j.measurement.2023.114053)
### Acknowledgment
- This project was developed under the supervision of **[Dr. Seyed Hossein Mohaeri](https://khu.ac.ir/cv/1139/Seyed-Hossein-Mohajeri)** and **[Dr. Mojtaba Mehraein](https://khu.ac.ir/cv/279/Mojtaba-Mehraein)**.
- We extend our deepest gratitude to **[Dr. Bimlesh Kumar](https://www.researchgate.net/profile/Bimlesh-Kumar)** and **[Dr. Luis Cea](https://www.researchgate.net/profile/Luis-Cea)** for their invaluable guidance and unwavering support throughout our journey.
- Special thanks to [Parvaneh Yaghoubi](https://github.com/parvanehyaghoubi), [Hossein Abazari](https://github.com/HossA12), [Narges Yaghoubi](https://github.com/nargesyaghoubi), [Mojtaba Karimi](https://github.com/mojikarimi), [Hiva Yarandi](https://github.com/Hivayrn), and [Zahra Rezaei](https://github.com/ZahraRezaei672) 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
[](https://farzadasgari.ir/)
[](https://scholar.google.com/citations?user=Rhue_kkAAAAJ&hl=en)
[](https://www.researchgate.net/profile/Farzad-Asgari)
[](https://www.linkedin.com/in/farzad-asgari-5a90942b2/)
##### Seyed Hossein Mohajeri
[](https://khu.ac.ir/cv/1139/Seyed-Hossein-Mohajeri)
[](https://scholar.google.com/citations?user=E8PFUBEAAAAJ&hl=en)
[](https://www.researchgate.net/profile/Seyed-Mohajeri-2)
[](
https://ir.linkedin.com/in/hossein-mohajeri)
##### Mojtaba Mehraein
[](https://khu.ac.ir/cv/279/Mojtaba-Mehraein)
[](https://scholar.google.com/citations?user=GwT49LIAAAAJ&hl=en)
[](https://ir.linkedin.com/in/mojtaba-mehraein-002a03238)
[](
https://ir.linkedin.com/in/mojtaba-mehraein-002a03238)
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"description": "# ProADV - Process Acoustic Doppler Velocimeter\n\n[](https://github.com/farzadasgari/proadv/stargazers)\n[](https://github.com/farzadasgari/proadv/network)\n[](https://github.com/farzadasgari/proadv/issues)\n[](https://github.com/farzadasgari/proadv/blob/main/LICENSE)\n[](https://pypi.org/project/proadv/)\n[](https://pypi.org/project/proadv/)\n[](https://github.com/farzadasgari/proadv/graphs/contributors)\n[](https://github.com/farzadasgari/proadv/pulls)\n[](https://github.com/farzadasgari/proadv/pulls?q=is%3Apr+is%3Aclosed)\n[](https://github.com/farzadasgari/proadv/commits/main)\n\n\n## Streamline Your ADV Data Analysis\n\n**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.\n\n### Key Features\n\n* **Despiking and Denoising:** ProADV tackles the challenge of spikes and noise in ADV data, providing a variety of robust algorithms for effective data cleaning. \n * **Spike Detection:** \n * **ACC (Acceleration Thresholding):** Identifies spikes based on exceeding a user-defined acceleration threshold.\n * **PST (Phase-Space Thresholding):** Utilizes a combination of velocity and its temporal derivative to detect spikes.\n * **mPST (Modified Phase-Space Thresholding):** An enhanced version of PST with improved sensitivity.\n * **VC (Velocity Correlation):** Detects spikes based on deviations from the correlation between neighboring data points.\n * **KDE (Kernel Density Estimation):** Employs a statistical approach to identify outliers based on the probability density function. \n * **3d-KDE (Three-dimensional Kernel Density Estimation):** Extends KDE to three dimensions for more robust spike detection in complex data.\n * **m3d-KDE (Modified Three-dimensional Kernel Density Estimation):** Further refines 3d-KDE for enhanced performance.\n * **Replacement Methods:** ProADV offers several options to replace detected spikes with more reliable values:\n * **LVD (Last Valid Data):** Replaces spikes with the last valid data point before the spike.\n * **MV (Mean Value):** Replaces spikes with the mean value of velocity component. \n * **LI (Linear Interpolation):** Uses linear interpolation between surrounding points to estimate the missing value.\n * **12PP (12 Points Cubic Polynomial):** Employs a 12-point cubic polynomial to fit a smoother curve and replace spikes.\n\n\n<div>\n <img src=\"https://raw.githubusercontent.com/farzadasgari/proadv/main/examples/plots/trivariate-kernel.png\" alt=\"trivariate-kernel\" style=\"width:300px;\"/>\n <img src=\"https://raw.githubusercontent.com/farzadasgari/proadv/main/examples/plots/spectrum.png\" alt=\"trivariate-kernel\" style=\"width:300px;\"/>\n <img src=\"https://raw.githubusercontent.com/farzadasgari/proadv/main/examples/plots/phase-space.png\" alt=\"trivariate-kernel\" style=\"width:300px;\"/>\n</div>\n\n\n* **Statistical Analysis:** ProADV equips you with essential statistical tools to characterize your ADV data:\n * **Minimum, Maximum:** Provides the range of measured velocities.\n * **Mean, Median, Mode:** Calculates central tendency measures.\n * **Skewness, Kurtosis:** Analyzes the distribution characteristics of your data.\n\n* **Advanced Analysis:** In addition to cleaning and basic statistics, ProADV offers advanced functionalities for deeper insights:\n * **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. \n * **SSA (Singular Spectrum Analysis):** Extracts underlying patterns and trends from time series data.\n * **Kalman Filter:** Implements the Kalman filter algorithm for state estimation and prediction in time series data. \n * **PR (Pollution Rate) Calculation:** Estimates the level of noise or pollution within the data.\n * **Spectral Analysis:**\n * **PSD (Power Spectral Density):** Analyzes the distribution of energy across different frequencies within the data.\n * **PDF (Probability Density Function):** Provides the probability of encountering specific velocity values.\n * **Normality Test:** Evaluates whether your data follows a normal distribution.\n * **Normalization:** Scales data to a common range for further analysis or visualization.\n\n<div>\n <img src=\"https://raw.githubusercontent.com/farzadasgari/proadv/main/examples/plots/singular-spectrum.png\" alt=\"singular-spectrum\" style=\"width:300px;\"/>\n <img src=\"https://raw.githubusercontent.com/farzadasgari/proadv/main/examples/plots/kalman.png\" alt=\"kalman-filter\" style=\"width:300px;\"/>\n</div>\n\n### Installation\n\nThere are two convenient ways to install ProADV:\n\n1. **Using pip (recommended):**\n ```bash\n pip install proadv\n ```\n\n2. **From source code:**\n\n a. Clone the repository:\n ```bash\n git clone https://github.com/farzadasgari/proadv.git\n ```\n b. Navigate to the project directory:\n ```bash\n cd proadv\n ```\n c. Install using setup.py:\n ```bash\n python setup.py install\n ```\n\n### Collaboration\n\nWe encourage collaboration and contributions from the community to improve ProADV. Here's how to contribute:\n\n1. Fork the repository on GitHub.\n2. Clone your forked repository to your local machine.\n3. Create a new branch for your changes.\n4. Make your changes and commit them with descriptive messages.\n5. Push your changes to your forked repository.\n6. Submit a pull request for review and merging.\n\n### References\n\nFor further information and in-depth understanding of the algorithms employed in ProADV, refer to the following resources:\n\n1. [Exploring the role of signal pollution rate on the performance of despiking velocity time-series algorithms](https://doi.org/10.1016/j.flowmeasinst.2023.102485)\n2. [Unleashing the power of three-dimensional kernel density estimation for Doppler Velocimeter data despiking](https://doi.org/10.1016/j.measurement.2023.114053)\n\n\n### Acknowledgment\n- This project was developed under the supervision of **[Dr. Seyed Hossein Mohaeri](https://khu.ac.ir/cv/1139/Seyed-Hossein-Mohajeri)** and **[Dr. Mojtaba Mehraein](https://khu.ac.ir/cv/279/Mojtaba-Mehraein)**.\n- We extend our deepest gratitude to **[Dr. Bimlesh Kumar](https://www.researchgate.net/profile/Bimlesh-Kumar)** and **[Dr. Luis Cea](https://www.researchgate.net/profile/Luis-Cea)** for their invaluable guidance and unwavering support throughout our journey.\n- Special thanks to [Parvaneh Yaghoubi](https://github.com/parvanehyaghoubi), [Hossein Abazari](https://github.com/HossA12), [Narges Yaghoubi](https://github.com/nargesyaghoubi), [Mojtaba Karimi](https://github.com/mojikarimi), [Hiva Yarandi](https://github.com/Hivayrn), and [Zahra Rezaei](https://github.com/ZahraRezaei672) for their valuable contributions to this project.\n\n\n### Contact\nFor any inquiries, please contact:\n- std_farzad.asgari@alumni.khu.ac.ir\n- khufarzadasgari@gmail.com\n\n\n### Links\n\n##### Farzad Asgari\n[](https://farzadasgari.ir/)\n\n[](https://scholar.google.com/citations?user=Rhue_kkAAAAJ&hl=en)\n\n[](https://www.researchgate.net/profile/Farzad-Asgari)\n\n[](https://www.linkedin.com/in/farzad-asgari-5a90942b2/)\n\n\n##### Seyed Hossein Mohajeri\n[](https://khu.ac.ir/cv/1139/Seyed-Hossein-Mohajeri)\n\n[](https://scholar.google.com/citations?user=E8PFUBEAAAAJ&hl=en)\n\n[](https://www.researchgate.net/profile/Seyed-Mohajeri-2)\n\n[](\nhttps://ir.linkedin.com/in/hossein-mohajeri)\n\n\n##### Mojtaba Mehraein\n[](https://khu.ac.ir/cv/279/Mojtaba-Mehraein)\n\n[](https://scholar.google.com/citations?user=GwT49LIAAAAJ&hl=en)\n\n[](https://ir.linkedin.com/in/mojtaba-mehraein-002a03238)\n\n[](\nhttps://ir.linkedin.com/in/mojtaba-mehraein-002a03238)\n",
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