<div align="center">
<h1>ProADV - Process Acoustic Doppler Velocimeter</h1>
</div>
[](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 align="center">
<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 align="center">
<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 [Narges Yaghoubi](https://github.com/nargesyaghoubi), [Hiva Yarandi](https://github.com/Hivayrn), [Mojtaba Karimi](https://github.com/mojikarimi), [Parvaneh Yaghoubi](https://github.com/parvanehyaghoubi), [Hossein Abazari](https://github.com/HossA12), 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)
Raw data
{
"_id": null,
"home_page": "https://github.com/farzadasgari/proadv",
"name": "proadv",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "ProADV, python, signal processing, data processing, acoustic Doppler velocimeter, ADV, Denoising, Despiking",
"author": "Farzad Asgari",
"author_email": "std_farzad.asgari@alumni.khu.ac.ir",
"download_url": "https://files.pythonhosted.org/packages/fc/22/2aedf21249229bcfb3b081e18a2f33ec218b457750721aeec098a98f704a/proadv-2.1.4.tar.gz",
"platform": null,
"description": "<div align=\"center\">\n <h1>ProADV - Process Acoustic Doppler Velocimeter</h1>\n</div>\n\n\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 align=\"center\">\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 align=\"center\">\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 [Narges Yaghoubi](https://github.com/nargesyaghoubi), [Hiva Yarandi](https://github.com/Hivayrn), [Mojtaba Karimi](https://github.com/mojikarimi), [Parvaneh Yaghoubi](https://github.com/parvanehyaghoubi), [Hossein Abazari](https://github.com/HossA12), 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",
"bugtrack_url": null,
"license": null,
"summary": "Process Acoustic Doppler Velocimeter data with advanced despiking and analysis tools",
"version": "2.1.4",
"project_urls": {
"Documentation": "https://proadv.readthedocs.io/en/stable/",
"Download URL": "https://pypi.org/project/proadv/",
"Homepage": "https://github.com/farzadasgari/proadv",
"Source Code": "https://github.com/farzadasgari/proadv"
},
"split_keywords": [
"proadv",
" python",
" signal processing",
" data processing",
" acoustic doppler velocimeter",
" adv",
" denoising",
" despiking"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "ec1a3800a6a61b4c5c44688846d95b16582d71193bc911d4b2997c02dd8b5f27",
"md5": "aba1e92dc2d85abdcc28d1d099fb959e",
"sha256": "4344c2df1c3dbd76f36858ada5b0b189a0f5ff6a030d11d996c2711c625e6eda"
},
"downloads": -1,
"filename": "proadv-2.1.4-py3-none-any.whl",
"has_sig": false,
"md5_digest": "aba1e92dc2d85abdcc28d1d099fb959e",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 54821,
"upload_time": "2024-08-02T22:22:23",
"upload_time_iso_8601": "2024-08-02T22:22:23.391422Z",
"url": "https://files.pythonhosted.org/packages/ec/1a/3800a6a61b4c5c44688846d95b16582d71193bc911d4b2997c02dd8b5f27/proadv-2.1.4-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "fc222aedf21249229bcfb3b081e18a2f33ec218b457750721aeec098a98f704a",
"md5": "66172d6732e24f2acce4aacd8428a6a4",
"sha256": "c6cd12e3aa398050058b8fecad9be22144694438e946c5d334bd4975a168fae1"
},
"downloads": -1,
"filename": "proadv-2.1.4.tar.gz",
"has_sig": false,
"md5_digest": "66172d6732e24f2acce4aacd8428a6a4",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 46713,
"upload_time": "2024-08-02T22:22:24",
"upload_time_iso_8601": "2024-08-02T22:22:24.481904Z",
"url": "https://files.pythonhosted.org/packages/fc/22/2aedf21249229bcfb3b081e18a2f33ec218b457750721aeec098a98f704a/proadv-2.1.4.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-08-02 22:22:24",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "farzadasgari",
"github_project": "proadv",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "proadv"
}
JSON
