dtwhaclustering 1.0.7

Codes to perform Dynamic Time Warping Based Hierarchical Agglomerative Clustering of GPS data

Dynamic Time Warping based Hierarchical Agglomerative Clustering

Codes to perform Dynamic Time Warping Based Hierarchical Agglomerative Clustering of GPS data

Details

This package include codes for processing the GPS displacement data including least-square modelling for trend, co-seismic jumps, seasonal and tidal signals. Finally, it can be used to cluster the GPS displacements based on the similarity of the waveforms. The similarity among the waveforms will be obtained using the DTW distance.

Usage

Least-squares modeling

from dtwhaclustering.leastSquareModeling import lsqmodeling
final_dU, final_dN, final_dE = lsqmodeling(dUU, dNN, dEE,stnlocfile="helper_files/stn_loc.txt",  plot_results=True, remove_trend=False, remove_seasonality=True, remove_jumps=False)

Plot station map

from dtwhaclustering import plot_stations
plot_stations.plot_station_map(station_data = 'helper_files/selected_stations_info.txt', outfig=f'{outloc}/station_map.pdf')

Plot linear trend

slopeFile=f'stn_slope_res_U.txt'
df = pd.read_csv(slopeFile, names=['stn','lon','lat','slope'], delimiter='\s+')
plot_linear_trend_on_map(df, outfig=f"Maps/slope-plot_U.pdf")

Note: slopeFile is obtained from lsqmodeling.

License

© 2021 Utpal Kumar

Licensed under the Apache License, Version 2.0