dtwhaclustering 1.0.15
Codes to perform Dynamic Time Warping Based Hierarchical Agglomerative Clustering of GPS data
License: Apache 2.0
Requires Python: >=3.5
Keywords: dynamic time warping clustering

# Dynamic Time Warping based Hierarchical Agglomerative Clustering

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

## Documentation
Installation and usage information can be obtained from the documentation: [dtwhaclustering.pdf](docs/build/latex/dtwhaclustering.pdf)

Complete documentation at: [dtwhaclustering-docs](https://dtwhaclustering.readthedocs.io/en/latest/)

## 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

![Load Pickle Data into Pandas DataFrame](https://raw.githubusercontent.com/earthinversion/DTW-based-Hierarchical-Clustering/master/images/load_data.png?token=ADNOWX7G3OSZIVAAFBM2DADAZSOG2)

```
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)
```

![LSQ Model](https://raw.githubusercontent.com/earthinversion/DTW-based-Hierarchical-Clustering/master/images/time_series_SLNP_U.png?token=ADNOWX6QYC7CK3FDFECN4X3AZSOIW)

### 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`.

## Dynamic Time Warping Analysis

```
from dtwhaclustering.dtw_analysis import dtw_signal_pairs, dtw_clustering
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt

np.random.seed(0)
# sampling parameters
fs = 100   # sampling rate, in Hz
T = 1      # duration, in seconds
N = T * fs  # duration, in samples

# time variable
t = np.linspace(0, T, N)

SNR = 0.2 #noise

XX0 = np.sin(2 * np.pi * t * 7+np.pi/2) #+ np.random.randn(1, N) * SNR
XX1 = signal.sawtooth(2 * np.pi * t * 5+np.pi/2) #+ np.random.randn(1, N) * SNR
# XX1 = np.abs(np.cos(2 * np.pi * t * 3)) - 0.5
s1, s2 = XX0, XX1

dtwsig = dtw_signal_pairs(s1, s2, labels=['S1', 'S2'])

dtwsig.plot_signals()
plt.show()
```


```
dtwsig.plot_matrix(windowfrac=0.6, psi=None) #Only allow for shifts up to 60% of the minimum signal length away from the two diagonals.
plt.show()
```

## References
1. Kumar, U., Chao, B.F., Chang, E.T.-Y.Y., 2020. What Causes the Common‐Mode Error in Array GPS Displacement Fields: Case Study for Taiwan in Relation to Atmospheric Mass Loading. Earth Sp. Sci. 0–2. https://doi.org/10.1029/2020ea001159

## License
© 2021 Utpal Kumar

Licensed under the Apache License, Version 2.0