This Jupyter Notebook provides a step-by-step guide on how to clean and smooth noisy data to improve the visualization of density plots using Python. It demonstrates the use of various filtering techniques to enhance the quality of your data visualizations.
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Data Preprocessing: Learn how to read and preprocess data from CSV files, making it suitable for visualization.
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Data Visualization: Visualize your data using Matplotlib, allowing you to explore raw data and identify noise or irregularities.
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Data Filtering: Apply different filtering techniques such as Gaussian, Median, Spline, Savgol, and FFT filters to reduce noise and enhance the quality of your density plots.
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Data Cropping: Crop the frequency and field range of your dataset to focus on specific regions of interest.
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Data Integration: Perform data integration to create a smoother representation of your data, which can help reveal underlying trends or patterns.
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Experimentation: Experiment with various filtering methods and parameters to find the best approach for your specific dataset.
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Adaptation: The code and techniques presented in this tutorial can be adapted to your unique data and requirements.
By following the tutorial, you can effectively enhance your density plots, making them more informative and visually appealing, which is crucial for data analysis and presentation.
This code has been developed for PhysLab @ LUMS.
Developer: Mahad Naveed
Supervisior: Dr. Sabieh Anwar
Mentor: Dr. Adnan Raza.
Explore PhysLab here: http://www.physlab.org