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PS2_Mintpy_template.cfg
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PS2_Mintpy_template.cfg
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# vim: set filetype=cfg:
##------------------------ smallbaselineApp.cfg ------------------------##
########## computing resource configuration
mintpy.compute.maxMemory = 16 #[float > 0.0], auto for 4, max memory to allocate in GB
## parallel processing with dask
## currently apply to steps: invert_network, correct_topography
## cluster = none to turn off the parallel computing
## numWorker = all to use all locally available cores (for cluster = local only)
## config = none to rollback to the default name (same as the cluster type; for cluster != local)
mintpy.compute.cluster = local #[local / slurm / pbs / lsf / none], auto for none, cluster type
mintpy.compute.numWorker = 20 #[int > 1 / all], auto for 4 (local) or 40 (non-local), num of workers
mintpy.compute.config = auto #[none / slurm / pbs / lsf ], auto for none (same as cluster), config name
########## 1. load_data
##---------add attributes manually
## MintPy requires attributes listed at: https://mintpy.readthedocs.io/en/latest/api/attributes/
## Missing attributes can be added below manually (uncomment #), e.g.
# ORBIT_DIRECTION = ascending
# PLATFORM = CSK
# ...
## a. autoPath - automatic path pattern defined in mintpy.defaults.auto_path.AUTO_PATH_*
## b. load_data.py -H to check more details and example inputs.
## c. compression to save disk usage for ifgramStack.h5 file:
## no - save 0% disk usage, fast [default]
## lzf - save ~57% disk usage, relative slow
## gzip - save ~62% disk usage, very slow [not recommend]
mintpy.load.processor = cosicorr #cosicorr #[isce, aria, hyp3, gmtsar, snap, gamma, roipac], auto for isce
mintpy.load.autoPath = auto #[yes / no], auto for no, use pre-defined auto path
mintpy.load.updateMode = auto #[yes / no], auto for yes, skip re-loading if HDF5 files are complete
mintpy.load.compression = lzf #[gzip / lzf / no], auto for no.
##---------for ISCE only:
mintpy.load.metaFile = METAFILE_FN #[path of common metadata file for the stack], i.e.: ./reference/IW1.xml, ./referenceShelve/data.dat
mintpy.load.baselineDir = auto #[path of the baseline dir], i.e.: ./baselines
##---------interferogram datasets:
mintpy.load.unwFile = auto #[path pattern of unwrapped interferogram files]
mintpy.load.corFile = auto #[path pattern of spatial coherence files]
mintpy.load.connCompFile = auto #[path pattern of connected components files], optional but recommended
mintpy.load.intFile = auto #[path pattern of wrapped interferogram files], optional
mintpy.load.ionoFile = auto #[path pattern of ionospheric delay files], optional
mintpy.load.magFile = auto #[path pattern of interferogram magnitude files], optional
##---------offset datasets (optional):
mintpy.load.azOffFile = azOffFile_FN #[path pattern of azimuth offset file], optional
mintpy.load.rgOffFile = rgOffFile_FN #[path pattern of range offset file], optional
mintpy.load.offSnrFile = mask_FN #[path pattern of offset signal-to-noise ratio file], optional
mintpy.load.azOffStdFile = azOffStdFile_FN
mintpy.load.rgOffStdFile = rgOffStdFile_FN
##---------geometry datasets:
mintpy.load.demFile = demFile_FN #[path of DEM file]
mintpy.load.lookupYFile = auto #[path of latitude /row /y coordinate file], not required for geocoded data
mintpy.load.lookupXFile = auto #[path of longitude/column/x coordinate file], not required for geocoded data
mintpy.load.incAngleFile = auto #[path of incidence angle file], optional but recommended
mintpy.load.azAngleFile = auto #[path of azimuth angle file], optional
mintpy.load.shadowMaskFile = auto #[path of shadow mask file], optional but recommended
mintpy.load.waterMaskFile = auto #[path of water mask file], optional but recommended
mintpy.load.bperpFile = auto #[path pattern of 2D perpendicular baseline file], optional
##---------multilook (optional):
## multilook while loading data with nearest interpolation, to reduce dataset size
mintpy.load.ystep = auto #[int >= 1], auto for 1 - no multilooking
mintpy.load.xstep = auto #[int >= 1], auto for 1 - no multilooking
##---------subset (optional):
## if both yx and lalo are specified, use lalo option unless a) no lookup file AND b) dataset is in radar coord
mintpy.subset.yx = auto #[y0:y1,x0:x1 / no], auto for no
mintpy.subset.lalo = auto #[S:N,W:E / no], auto for no