This repository contains Python scripts designed for post-processing ODT (One Dimensional Turbulence) data. The scripts offer user-friendly and efficient ways to perform data analysis, extraction, and transformation on ODT files. Whether you're working with text documents, reports, or any ODT data, these scripts will assist in automating tasks and streamlining your workflow.
This Python script processes and visualizes velocity field data from a collection of .dat files. It offers the following features:
- Data extraction and manipulation.
- Customizable color mapping for visualizing velocity.
- Export the visualized velocity field as a PDF file.
- Detailed comments for easy understanding.
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Clone this repository to your local machine:
git clone https://github.com/Sparsh-Sharma/ODT_scripts.git
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Install the required Python packages:
pip install -r requirements.txt
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Run the script, providing the directory containing the
.datfiles and the file prefix.python VelField_OneRlz.py --input_dir path/to/input/odt/files --file_prefix data_00700
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The script will process the ODT data, visualize the velocity field, and save it as a PDF file.
This Python script calculates and visualizes the ensemble average velocity field based on data from multiple simulation directories. Key features include:
- Data extraction, ensemble averaging, and visualization.
- Customizable color mapping for visualizing velocity.
- Export the ensemble average velocity field as a PDF file.
- User-friendly with detailed comments.
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Clone this repository to your local machine:
git clone https://github.com/Sparsh-Sharma/ODT_scripts.git
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Install the required Python packages:
pip install -r requirements.txt
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Run the script, providing the base directory containing simulation data and the number of ensembles.
python avgVelField_MultRlz.py --base_dir path/to/simulation/data --num_ensembles 5
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The script will calculate the ensemble average, visualize the velocity field, and save it as a PDF file.
This Python script calculates the ensemble average velocity field and visualizes the mean A field based on data from 'data_' directories within a specified base directory. Key features include:
- Data extraction, ensemble averaging, and visualization.
- Customizable color mapping for visualizing the mean A field.
- Export the ensemble average velocity field and mean A field as high-resolution PDF files.
- User-friendly with detailed comments.
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Clone this repository to your local machine.
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Install the required Python packages:
pip install -r requirements.txt
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Run the script, providing the base directory containing simulation data.
python TKE_MultRlz.py
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The script will calculate the ensemble average velocity field and TKE field, visualize them, and save as PDF files.
This Python script visualizes eddy sequences based on data from '.dat' files. Key features include:
- Data extraction and visualization of eddy sequences.
- Customizable color mapping for visualizing eddies.
- Export the visualized eddy sequences as a PDF file.
- User-friendly with detailed comments.
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Clone this repository to your local machine.
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Install the required Python packages:
pip install -r requirements.txt
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Run the script, providing the path to the eddy sequence data.
python plot2d.py
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The script will visualize the eddy sequences and save them as a PDF file.
This Python script generates histograms and cumulative distribution functions (CDF) for eddy sizes. Key features include:
- Data extraction and visualization of eddy size histograms and CDFs.
- Customizable bin count and starting point for data analysis.
- User-friendly with detailed comments.
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Clone this repository to your local machine.
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Install the required Python packages:
pip install -r requirements.txt
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Run the script, providing the case name and shift as arguments. You can also specify the number of bins and starting point (optional).
python plot2dEddySequence.py CASE_NAME SHIFT [NBINS] [NSTART]
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The script will generate histograms and CDFs for eddy sizes.
This Python script performs an analysis of interior vs. near-wall eddy sizes. Key features include:
- Data extraction and visualization of interior and near-wall eddy sizes.
- User-friendly with detailed comments.
- Customizable bin count and starting point for data analysis.
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Clone this repository to your local machine.
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Install the required Python packages:
pip install -r requirements.txt
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Run the script, providing the case name and shift as arguments. You can also specify the number of bins and starting point (optional).
python plotEddyStats.py CASE_NAME SHIFT [NBINS] [NSTART]
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The script will perform the analysis and display the results.
This Python script calculates sound pressure levels (Lp) based on provided data. Key features include:
- Data processing and sound pressure level calculation.
- Customizable input parameters for calculations.
- Visualization of the results with Strouhal number on a logarithmic scale.
- User-friendly with detailed comments.
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Clone this repository to your local machine.
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Install the required Python packages:
pip install -r requirements.txt
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Run the script and follow the prompts to provide input data, including the value of 'r' and 'theta', the number of .dat files to process, and the number of points to skip between data points.
python ODT_Tam_Aur.py
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The script will calculate sound pressure levels and generate visualizations.
This project is licensed under the MIT License. See the LICENSE file for details.
- Sparsh Sharma
- sparsh.sharma@dlr.de
- DLR Braunschweig
If you find these scripts helpful in your research related to one-dimensional turbulence (ODT), please consider citing our paper:
- Features of far-downstream asymptotic velocity fluctuations in a round jet: A one-dimensional turbulence study
- Author(s): Sparsh Sharma; Marten Klein; Heiko Schmidt
- Published in: Physics of Fluids 34, 085134 (2022)
- DOI: https://doi.org/10.1063/5.0101270
Feel free to customize the content, add more sections, or provide further details about each script as needed.