This is a series of python scripts for processing GPS time series data, fitting velocities and plotting using plotly
requirements:
python, plotly, numpy, pandas
The original GPS time series data is stored under time_series_data. Each file is a station (.txt).
And below is the format of the data. The columns are:
| date (year+day of year) | longitude (WGS84) | latitude (WGS84) | vertical (ellipsoid height) |
|---|---|---|---|
| 2016306 | 121.59938566 | 25.11217164 | 437.420 |
| 2016307 | 121.59938566 | 25.11217161 | 437.427 |
| 2016308 | 121.59938564 | 25.11217162 | 437.440 |
| 2016309 | 121.59938570 | 25.11217161 | 437.428 |
| 2016310 | 121.59938568 | 25.11217160 | 437.432 |
Below is the intended steps (order) to run the scripts:
This process data in each station and convert lon lat time series to local east west components (vertical is unchanged).
This also create a new time (unit:year, with digits). This is for plotting time series.
The new information are then added to the dataframe and later exported under the folder: processed_timeseries as .csv files
Note that the format for E,N,V is in mm as opposed to the original data being in meters.
This loops through the newly created csv files and plot the time series (.html files) under folder: timeseries_graphs.
This reads the .csv files created in step 1 and fits the velocities. Just before fitting, the user will be prompted to provide a station as reference station (i.e. that station will be completely stationary in the fitted result). If no desired ref station (i.e. use the time series as is), type in 'None'. The referencing step will be skipped. New plotted time series with fitted velocities are then stored under folder: fitted_timeseries_graphs_ref_(ref_site_name). This script also exported the velocity data as a csv file named: fitted_velocity_ref_(ref_site_name).csv
Here is the reference station:
Here is an example of a regular station:
