Implement Mobility Model and Extend RADP Library with Essential Helper Functions #13
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Tanzim patch 1
* changed location for handler and added unit tests * fixed hanlder position * fixed import locations * used black on test & helper files * fixed name and removed redundant code in test * added docstring * added HOW TO RUN comment * blacked * fixed typo * made pr comment changes * added eu-tracks-params test code * fixed test_ue_params * added docstring * changed venv issues
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paulvarkey
requested changes
Oct 1, 2024
| """ | ||
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| # Initialize the UE data | ||
| data = UETracksGenerationParams(params) |
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call this ue_tracks_params
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| if ue_tracks_generation.empty: | ||
| ue_tracks_generation = ue_tracks_generation_batch | ||
| else: | ||
| ue_tracks_generation = pd.concat( | ||
| [ue_tracks_generation, ue_tracks_generation_batch], ignore_index=True | ||
| ) |
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no need for if/else -- concat works on empty
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Overview
This pull request introduces a comprehensive update to the RADP library, integrating a new mobility model for user equipment (UE) and adding five essential helper functions. These enhancements are designed to improve our ability to analyze, visualize, and predict UE behavior based on dynamic data inputs.
Functions
get_ue_data: Fetches UE data from the database.plot_ue_tracks: Visualizes UE tracks on a map.plot_ue_tracks_side_by_side: Allows comparative visualization of actual vs predicted UE tracks.calculate_distances_and_velocities: Computes distances and velocities between consecutive UE points to analyze movement speed and trajectory.preprocess_ue_data(data): Prepares and cleans UE data for analysis.get_predicted_alpha: Estimates the alpha parameter using regression techniques within the mobility model.