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