fit_stack.py¶
Fit time series of elevation data to a stack of DEMs.
usage: fit_stack.py [-h] [-te EXTENT EXTENT EXTENT EXTENT] [-ref_dem REF_DEM]
[-ref_date REF_DATE] [-f FILT_REF]
[-filt_thresh FILT_THRESH] [-inc_mask INC_MASK]
[-exc_mask EXC_MASK] [-n NPROC] [-m METHOD] [-opt_gpr]
[-filt_ls] [-ci CI] [-t TLIM TLIM] [-ts TSTEP]
[-o OUTFILE] [-wf] [-c] [--merge_dates] [-d]
stack
Positional Arguments¶
stack | NetCDF file of stacked DEMs to fit. |
Named Arguments¶
-te, --extent | Extent over which to limit fit, given as [xmin xmax ymin ymax] |
-ref_dem | Filename for input reference DEM. |
-ref_date | Date of reference DEM. |
-f, --filt_ref | Type of filtering to do. One of min_max, time, or both Default: “min_max” |
-filt_thresh | Maximum dh/dt from reference DEM for time filtering. |
-inc_mask | Filename of optional inclusion mask (i.e., land). |
-exc_mask | Filename of optional exclusion mask (i.e., glaciers). |
-n, --nproc | number of processors to use [1]. Default: 1 |
-m, --method | Fitting method. One of Gaussian Process Regression (gpr, default),Ordinary Least Squares (ols), or Weighted Least Squares (wls) Default: “gpr” |
-opt_gpr | Run learning optimization in the GPR Fitting [False] Default: False |
-filt_ls | Filter least squares with a first fit [False] Default: False |
-ci | Confidence Interval to filter least squares fit [0.99] Default: 0.99 |
-t, --tlim | Start and end years to fit time series to (default is read from input file). |
-ts, --tstep | Temporal step (in years) for fitted stack [0.25] Default: 0.25 |
-o, --outfile | File to save results to. [fit.nc] Default: “fit.nc” |
-wf, --write_filt | |
Write filtered stack to file [False] Default: False | |
-c, --clobber | Clobber existing outfile [False]. Default: False |
--merge_dates | Merge any DEMs with same acquisition date [False] Default: False |
-d, --dask_parallel | |
Run with dask parallel tools [False] Default: False |