# vim: set filetype=cfg:
##------------------------------- ISCE/topsStack ----------------------##
##-------------------------------- MintPy -----------------------------##
########## 1. Load Data (--load to exit after this step)
## load_data.py -H to check more details and example inputs.
mintpy.load.processor        = isce
##---------for ISCE only:
mintpy.load.metaFile         =  isce_work_space/reference/IW*.xml
mintpy.load.baselineDir      =  isce_work_space/baselines
##---------interferogram datasets:
mintpy.load.unwFile          =  isce_work_space/merged/interferograms/*/filt_*.unw
mintpy.load.corFile          =  isce_work_space/merged/interferograms/*/filt_*.cor
mintpy.load.connCompFile     =  isce_work_space/merged/interferograms/*/filt_*.unw.conncomp
##---------geometry datasets:
mintpy.load.demFile          =  isce_work_space/merged/geom_reference/hgt.rdr
mintpy.load.lookupYFile      =  isce_work_space/merged/geom_reference/lat.rdr
mintpy.load.lookupXFile      =  isce_work_space/merged/geom_reference/lon.rdr
mintpy.load.incAngleFile     =  isce_work_space/merged/geom_reference/los.rdr
mintpy.load.azAngleFile      =  isce_work_space/merged/geom_reference/los.rdr
mintpy.load.shadowMaskFile   =  isce_work_space/merged/geom_reference/shadowMask.rdr
##---------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
########## 2. modify_network
########## 3. reference_point
mintpy.reference.maskFile      = no   #[filename / no], auto for maskConnComp.h5
mintpy.reference.minCoherence  = auto   #[0.0-1.0], auto for 0.85, minimum coherence for auto method
########## 4. correct_unwrap_error (optional)
## a. phase_closure          - suitable for highly redundant network
## b. bridging               - suitable for regions separated by narrow decorrelated features, e.g. rivers, narrow water bodies
## c. bridging+phase_closure - recommended when there is a small percentage of errors left after bridging
mintpy.unwrapError.method          = no #[bridging / phase_closure / bridging+phase_closure / no], auto for no
mintpy.unwrapError.waterMaskFile   = no  #[waterMask.h5 / no], auto for waterMask.h5 or no [if not found]
########## 5. invert_network
mintpy.networkInversion.waterMaskFile   = no #[filename / no], auto for waterMask.h5 or no [if not found]
mintpy.networkInversion.maskDataset   = no #[coherence / connectComponent / offsetSNR / no], auto for no
## Temporal coherence is calculated and used to generate the mask as the reliability measure
## reference: Pepe & Lanari (2006, IEEE-TGRS)
mintpy.networkInversion.shadowMask  = auto #[yes / no], auto for yes [if shadowMask is in geometry file] or no.
########## 6. correct_troposphere (optional but recommended)
mintpy.troposphericDelay.method = height_correlation #[pyaps / height_correlation / gacos / no], auto for pyaps
## Notes for height_correlation:
## Extra multilooking is applied to estimate the empirical phase/elevation ratio ONLY.
## For an dataset with 5 by 15 looks, looks=8 will generate phase with (5*8) by (15*8) looks
## to estimate the empirical parameter; then apply the correction to original phase (with 5 by 15 looks),
## if the phase/elevation correlation is larger than minCorrelation.
mintpy.troposphericDelay.polyOrder      = auto  #[1 / 2 / 3], auto for 1
mintpy.troposphericDelay.looks          = auto  #[1-inf], auto for 8, extra multilooking num
mintpy.troposphericDelay.minCorrelation = auto  #[0.0-1.0], auto for 0
########## 7. deramp (optional)
########## 8. correct_topography (optional but recommended)
########## 9.1 residual_RMS (root mean squares for noise evaluation)
########## 9.2 reference_date
mintpy.reference.date = auto   #[reference_date.txt / 20090214 / no], auto for reference_date.txt
########## 10. velocity
## Estimate linear velocity and its standard deviation from time-series
## and from tropospheric delay file if exists.
## reference: Fattahi and Amelung (2015, JGR)
mintpy.velocity.excludeDate    = auto   #[exclude_date.txt / 20080520,20090817 / no], auto for exclude_date.txt
mintpy.velocity.startDate      = auto   #[20070101 / no], auto for no
mintpy.velocity.endDate        = auto   #[20101230 / no], auto for no
## Bootstrapping
## refernce: Efron and Tibshirani (1986, Stat. Sci.)
mintpy.velocity.bootstrap      = auto   #[yes / no], auto for no, use bootstrap
mintpy.velocity.bootstrapCount = auto   #[int>1], auto for 400, number of iterations for bootstrapping