Closed hardreddata closed 3 years ago
welcome[bot]
commented
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yunjunz
commented
3 years ago It seems like something wrong in the ./inputs/ERA5.h5 file, could you plot it using view.py to see how it looks like?
If there are issues with this file, it's probably caused by the previously unsuccessful run with mintpy.troposphericDelay.method = pyaps, delete the ERA5.h5 file and re-run should solve the issue.
The "unknown SAR platform" msg is fine since it is before running prep_isce.py, which extracts the metadata from ISCE/topsStack.
hardreddata
commented
3 years ago Hi @yunjunz
I was unable to view.py the ERA.h5 file and it failed with the same error. I deleted it and run again and this all works now.
I think the problem was my first smallbaselineApp.py run failed when looking for ECMWF data (no credentials) and after setting mintpy.troposphericDelay.method = height_correlation I should have deleted a bunch of stuff.
How can I prepare plots like the below? I wondered if they should already be in my pic folder?
This works view.py timeseries.h5 but the below does not
tsview.py timeseries.h5
open timeseries file: timeseries.h5
data coverage in y/x: (0, 0, 5033, 2133)
subset coverage in y/x: (0, 0, 5033, 2133)
data coverage in lat/lon: None
subset coverage in lat/lon: None
------------------------------------------------------------------------
ASCENDING orbit -> flip up-down
reading timeseries from file timeseries.h5 ...
reference to pixel: [677, 2775]
Traceback (most recent call last):
File "/home/russell/tools/MintPy/mintpy/tsview.py", line 826, in <module>
main(sys.argv[1:])
File "/home/russell/tools/MintPy/mintpy/tsview.py", line 819, in main
obj.plot()
File "/home/russell/tools/MintPy/mintpy/tsview.py", line 585, in plot
self.ts_data, self.mask = read_timeseries_data(self)[0:2]
File "/home/russell/tools/MintPy/mintpy/tsview.py", line 332, in read_timeseries_data
vprint('reference to date: {}'.format(inps.date_list[inps.ref_idx]))
IndexError: list index out of rangeI tried deleting the timeseries*.hdf and then later starting fresh but am not winning.
Thanks for the help.
yunjunz
commented
3 years ago I prepared the figure using view.py and tsview.py, as you have figured out.
For the error, could you run info.py timeseries.h5 to see the metadata? It seems REF_DATE is missing. I would recommend debugging in tsview.py#L332 if you are familiar with python.
hardreddata
commented
3 years ago Thanks again. The info.py output:
******************** Basic File Info ************************
file name: /media/storage/isce_stack/timeseries.h5
file type: timeseries
coordinates : RADAR
******************** Date Stat Info *************************
Start Date: 20201127
End Date: 20201221
Number of acquisitions : 3
Std. of acquisition times : 0.03 yeras
----------------------
List of dates:
['20201127', '20201209', '20201221']
----------------------
List of dates in years:
[2020.9062286105407, 2020.9390828199862, 2020.971937029432]
******************** HDF5 File Structure ********************
Attributes in / level:
ALOOKS 3
ANTENNA_SIDE -1
AZIMUTH_PIXEL_SIZE 46.77144780487454
CENTER_INCIDENCE_ANGLE 36.16769
CENTER_LINE_UTC 31180.0
DATA_TYPE float32
DATE12 201127-201209
EARTH_RADIUS 6357725.271374925
END_DATE 20201221
FILE_LENGTH 2133
FILE_PATH /media/storage/isce_stack/merged/interferograms/20201127_20201209/filt_fine.unw
FILE_TYPE timeseries
HEADING -13.12479484007516
HEIGHT 709385.3632063618
ISCE_VERSION Release: 2.3, svn-2531, 20190112. Current: svn-Unknown.
LAT_REF1 -34.14015098133027
LAT_REF2 -33.699326972753035
LAT_REF3 -33.89960237436327
LAT_REF4 -33.46126666596746
LENGTH 2133
LON_REF1 149.92407844015443
LON_REF2 151.79302589403324
LON_REF3 149.85289248127688
LON_REF4 151.71116794676286
NCORRLOOKS 16.14177671949939
ORBIT_DIRECTION ASCENDING
PLATFORM sen
POLARIZATION VV
PRF 1717.128973878037
PROCESSOR isce
P_BASELINE_BOTTOM_HDR 49.5275994851483
P_BASELINE_TOP_HDR 49.5275994851483
RANGE_PIXEL_SIZE 20.966059032437908
REF_DATE 20201127
REF_X 2775
REF_Y 677
RLOOKS 9
STARTING_RANGE 803512.7992238925
START_DATE 20201127
UNIT m
WAVELENGTH 0.05546576
WIDTH 5033
access_mode read
altitude 709385.3632063618
azimuthPixelSize 46.77144780487454
azimuthResolution 22.5
beam_mode IW
beam_swath 12
byte_order l
data_type float
earthRadius 6357725.271374925
family unwimage
file_name /media/storage/isce_stack/merged/interferograms/20201127_20201209/filt_fine.unw
firstFrameNumber 1069
first_frame 1069
image_type unw
lastFrameNumber 1070
last_frame 1070
length 2133
mintpy.networkInversion.maskDataset False
mintpy.networkInversion.maskThreshold 0.4
mintpy.networkInversion.minNormVelocity True
mintpy.networkInversion.minRedundancy 1.0
mintpy.networkInversion.numIfgram 2
mintpy.networkInversion.obsDatasetName unwrapPhase
mintpy.networkInversion.weightFunc var
name unwimage_name
number_bands 2
orbitNumber 35431
passDirection ASCENDING
polarization VV
prf 1717.128973878037
radarWavelength 0.05546576
rangePixelSize 20.966059032437908
rangeResolution 2.7
relative_orbit 9
scheme BIL
startUTC 2020-11-27 08:39:40.205553
startingRange 803512.7992238925
stopUTC 2020-11-27 08:39:46.592164
swathNumber 12
trackNumber 9
width 5033
xmax 5033
xmin 0
HDF5 dataset "/bperp ": shape (3,) , dtype <float32>
HDF5 dataset "/date ": shape (3,) , dtype <|S8>
HDF5 dataset "/timeseries ": shape (3, 2133, 5033) , dtype <float32>It seems to know where to begin
******************** Date Stat Info *************************
Start Date: 20201127
End Date: 20201221
Number of acquisitions : 3
Std. of acquisition times : 0.03 yeras
----------------------
List of dates:
['20201127', '20201209', '20201221']but then arrives at things like
startUTC 2020-11-27 08:39:40.205553
startingRange 803512.7992238925
stopUTC 2020-11-27 08:39:46.592164I will take a look in Python but I am very open to any advice based on the above. REF_DATE looks good to me.
yunjunz
commented
3 years ago @RussellGrew The cause is the default ref_idx value of 3, which is larger than the number of acquisitions in the file you have.
Change lin 188-197 from:
if inps.ref_date:
inps.ref_idx = inps.date_list.index(inps.ref_date)
else:
inps.ref_idx = 3
if not inps.idx:
if inps.ref_idx < inps.num_date / 2.:
inps.idx = inps.num_date - 3
else:
inps.idx = 3to
if inps.ref_date:
inps.ref_idx = inps.date_list.index(inps.ref_date)
else:
inps.ref_idx = 1
if not inps.idx:
if inps.ref_idx < inps.num_date / 2.:
inps.idx = inps.num_date - 2
else:
inps.idx = 2should work, could you confirm? If so, please consider issuing a PR for it.
hardreddata
commented
3 years ago I was just reviewing that section of code when I saw your message.
The program no longer fails and displays the cumulative displacement map with slider, and a blank point displacement time series axis. This second plot only shows data after I click a point in the map on the first plot. I am not sure if this is intended?
If this is looking good I can submit the pull request for you.
Cheers.
yunjunz
commented
3 years ago Great. This is working as expected. Do so pls.
Thanks for making this great tool available open source.
Description of the problem
I have prepared my first ISCE Sentinel stack using topsStack.py and I am trying to run MintPy for the first time also. The smallbaselineApp fails during the velocity calculation.
Note that I only have three dates in the stack. I am assuming this is sufficient to test the workflow end to end.
I based the configuration on the example https://mintpy.readthedocs.io/en/stable/dir_structure/
Full script that generated the error
The full output of smallbaselineApp.py is below, including the error
Full error message
See the bottom of the code snippet above.
I tried forcing the
reference.dateand velocity dates but it isn't working. I note thatreferenced_date.txtcontains the middle of my three dates and not the first.System information
########## 1. load_data
a. autoPath - automatic path pattern defined in mintpy.defaults.auto_path.AUTOPATH*
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 = isce #[isce, aria, 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 = auto #[gzip / lzf / no], auto for no.
---------for ISCE only:
mintpy.load.metaFile = /media/storage/isce_stack/reference/IW*.xml #[path of common metadata file for the stack], i.e.: ./reference/IW1.xml, ./referenceShelve/data.dat mintpy.load.baselineDir = /media/storage/isce_stack/baselines #[path of the baseline dir], i.e.: ./baselines
---------interferogram datasets:
mintpy.load.unwFile = /media/storage/iscestack/merged/interferograms/*/filt.unw #[path pattern of unwrapped interferogram files] mintpy.load.corFile = /media/storage/isce_stack/merged/interferograms//filt_.cor #[path pattern of spatial coherence files] mintpy.load.connCompFile = /media/storage/isce_stack/merged/interferograms//filt_*.unw.conncomp #[path pattern of connected components files], optional but recommend mintpy.load.intFile = no #[path pattern of wrapped interferogram files], optional mintpy.load.ionoFile = no #[path pattern of ionospheric delay files], optional mintpy.load.magFile = no #[path pattern of interferogram magnitude files], optional
---------offset datasets (optional):
mintpy.load.azOffFile = auto #[path pattern of azimuth offset file], optional mintpy.load.rgOffFile = auto #[path pattern of range offset file], optional mintpy.load.offSnrFile = auto #[path pattern of offset signal-to-noise ratio file], optional
---------geometry datasets:
mintpy.load.demFile = /media/storage/isce_stack/merged/geom_reference/hgt.rdr #[path of DEM file] mintpy.load.lookupYFile = /media/storage/isce_stack/merged/geom_reference/lat.rdr #[path of latitude /row /y coordinate file], not required for geocoded data mintpy.load.lookupXFile = /media/storage/isce_stack/merged/geom_reference/lon.rdr #[path of longitude/column/x coordinate file], not required for geocoded data mintpy.load.incAngleFile = /media/storage/isce_stack/merged/geom_reference/los.rdr #[path of incidence angle file], optional but recommend mintpy.load.azAngleFile = /media/storage/isce_stack/merged/geom_reference/los.rdr #[path of azimuth angle file], optional mintpy.load.shadowMaskFile = /media/storage/isce_stack/merged/geom_reference/shadowMask.rdr #[path of shadow mask file], optional but recommend mintpy.load.waterMaskFile = auto #[path of water mask file], optional but recommend 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 #[lat0:lat1,lon0:lon1 / no], auto for no
########## 2. modify_network
reference: Yunjun et al. (2019, section 4.2 and 5.3.1); Chaussard et al. (2015, GRL)
1) Coherence-based network modification = (threshold + MST) by default
It calculates a average coherence value for each interferogram using spatial coherence and input mask (with AOI)
Then it finds a minimum spanning tree (MST) network with inverse of average coherence as weight (keepMinSpanTree)
For all interferograms except for MST's, exclude those with average coherence < minCoherence.
mintpy.network.coherenceBased = auto #[yes / no], auto for no, exclude interferograms with coherence < minCoherence mintpy.network.keepMinSpanTree = auto #[yes / no], auto for yes, keep interferograms in Min Span Tree network mintpy.network.minCoherence = auto #[0.0-1.0], auto for 0.7 mintpy.network.maskFile = auto #[file name, no], auto for waterMask.h5 or no [if no waterMask.h5 found] mintpy.network.aoiYX = auto #[y0:y1,x0:x1 / no], auto for no, area of interest for coherence calculation mintpy.network.aoiLALO = auto #[lat0:lat1,lon0:lon1 / no], auto for no - use the whole area
2) Network modification based on temporal/perpendicular baselines, date etc.
mintpy.network.tempBaseMax = auto #[1-inf, no], auto for no, max temporal baseline in days mintpy.network.perpBaseMax = auto #[1-inf, no], auto for no, max perpendicular spatial baseline in meter mintpy.network.connNumMax = auto #[1-inf, no], auto for no, max number of neighbors for each acquisition mintpy.network.startDate = auto #[20090101 / no], auto for no mintpy.network.endDate = auto #[20110101 / no], auto for no mintpy.network.excludeDate = auto #[20080520,20090817 / no], auto for no mintpy.network.excludeIfgIndex = auto #[1:5,25 / no], auto for no, list of ifg index (start from 0) mintpy.network.referenceFile = auto #[date12_list.txt / ifgramStack.h5 / no], auto for no
########## 3. reference_point
Reference all interferograms to one common point in space
auto - randomly select a pixel with coherence > minCoherence
however, manually specify using prior knowledge of the study area is highly recommended
with the following guideline (section 4.3 in Yunjun et al., 2019):
1) located in a coherence area, to minimize the decorrelation effect.
2) not affected by strong atmospheric turbulence, i.e. ionospheric streaks
3) close to and with similar elevation as the AOI, to minimize the impact of spatially correlated atmospheric delay
mintpy.reference.yx = auto #[257,151 / auto] mintpy.reference.lalo = auto #[31.8,130.8 / auto] mintpy.reference.maskFile = auto #[filename / no], auto for maskConnComp.h5 mintpy.reference.coherenceFile = auto #[filename], auto for avgSpatialCoh.h5 mintpy.reference.minCoherence = auto #[0.0-1.0], auto for 0.85, minimum coherence for auto method
########## quick_overview
A quick assessment of:
1) possible groud deformation
using the velocity from the traditional interferogram stacking
reference: Zebker et al. (1997, JGR)
2) distribution of phase unwrapping error
from the number of interferogram triplets with non-zero integer ambiguity of closue phase
reference: T_int in Yunjun et al. (2019, CAGEO). Related to section 3.2, equation (8-9) and Fig. 3d-e.
########## 4. correct_unwrap_error (optional)
connected components (mintpy.load.connCompFile) are required for this step.
reference: Yunjun et al. (2019, section 3)
supported methods:
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
mintpy.unwrapError.method = auto #[bridging / phase_closure / bridging+phase_closure / no], auto for no mintpy.unwrapError.waterMaskFile = auto #[waterMask.h5 / no], auto for waterMask.h5 or no [if not found]
briding options:
ramp - a phase ramp could be estimated based on the largest reliable region, removed from the entire interferogram
before estimating the phase difference between reliable regions and added back after the correction.
bridgePtsRadius - half size of the window used to calculate the median value of phase difference
mintpy.unwrapError.ramp = auto #[linear / quadratic], auto for no; recommend linear for L-band data mintpy.unwrapError.bridgePtsRadius = auto #[1-inf], auto for 50, half size of the window around end points
########## 5. invert_network
Invert network of interferograms into time-series using weighted least sqaure (WLS) estimator.
weighting options for least square inversion [fast option available but not best]:
a. var - use inverse of covariance as weight (Tough et al., 1995; Guarnieri & Tebaldini, 2008) [recommended]
b. fim - use Fisher Information Matrix as weight (Seymour & Cumming, 1994; Samiei-Esfahany et al., 2016).
c. coh - use coherence as weight (Perissin & Wang, 2012)
d. no - uniform weight (Berardino et al., 2002) [fast]
SBAS (Berardino et al., 2002) = minNormVelocity (yes) + weightFunc (no)
mintpy.networkInversion.weightFunc = auto #[var / fim / coh / no], auto for var mintpy.networkInversion.waterMaskFile = auto #[filename / no], auto for waterMask.h5 or no [if not found] mintpy.networkInversion.minNormVelocity = auto #[yes / no], auto for yes, min-norm deformation velocity / phase mintpy.networkInversion.residualNorm = auto #[L2 ], auto for L2, norm minimization solution
mask options for unwrapPhase of each interferogram before inversion (recommed if weightFunct=no):
a. coherence - mask out pixels with spatial coherence < maskThreshold
b. connectComponent - mask out pixels with False/0 value
c. no - no masking [recommended].
d. offsetSNR - mask out pixels with offset SNR < maskThreshold [for offset]
mintpy.networkInversion.maskDataset = auto #[coherence / connectComponent / offsetSNR / no], auto for no mintpy.networkInversion.maskThreshold = auto #[0-inf], auto for 0.4 mintpy.networkInversion.minRedundancy = auto #[1-inf], auto for 1.0, min num_ifgram for every SAR acquisition
Temporal coherence is calculated and used to generate the mask as the reliability measure
reference: Pepe & Lanari (2006, IEEE-TGRS)
mintpy.networkInversion.minTempCoh = auto #[0.0-1.0], auto for 0.7, min temporal coherence for mask mintpy.networkInversion.minNumPixel = auto #[int > 1], auto for 100, min number of pixels in mask above mintpy.networkInversion.shadowMask = auto #[yes / no], auto for yes [if shadowMask is in geometry file] or no.
########## correct_LOD
Local Oscillator Drift (LOD) correction (for Envisat only)
reference: Marinkovic and Larsen (2013, Proc. LPS)
automatically applied to Envisat data (identified via PLATFORM attribute)
and skipped for all the other satellites.
########## 6. correct_troposphere (optional and recommended)
correct tropospheric delay using the following methods:
a. height_correlation - correct stratified tropospheric delay (Doin et al., 2009, J Applied Geop)
b. pyaps - use Global Atmospheric Models (GAMs) data (Jolivet et al., 2011; 2014)
ERA5 - ERA-5 from ECMWF [need to install pyaps3 on GitHub; recommended and turn ON by default]
MERRA - MERRA-2 from NASA [need to install pyaps on Caltech/EarthDef]
NARR - NARR from NOAA [need to install pyaps on Caltech/EarthDef; recommended for N America]
c. gacos - use GACOS with the iterative tropospheric decomposition model (Yu et al., 2017, JGR; 2018, JGR; 2018, RSE)
Manually download GACOS products at http://www.gacos.net/index.html for all acquisitions before running this step.
mintpy.troposphericDelay.method = height_correlation #[pyaps / height_correlation / gacos / no], auto for pyaps
Notes for pyaps:
a. GAM data latency: with the most recent SAR data, there will be GAM data missing, the correction
will be applied to dates with GAM data available and skipped for the others.
b. WEATHER_DIR: if you define an environmental variable named WEATHER_DIR to contain the path to a
directory, then MintPy applications will download the GAM files into the indicated directory.
MintPy application will look for the GAM files in the directory before downloading a new one to
prevent downloading multiple copies if you work with different dataset that cover the same date/time.
mintpy.troposphericDelay.weatherModel = auto #[ERA5 / MERRA / NARR], auto for ERA5 mintpy.troposphericDelay.weatherDir = auto #[path2directory], auto for WEATHER_DIR or "./"
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 (58) by (158) 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
Notes for gacos:
Set the path below to directory that contains the downloaded .ztd and .ztd.rsc files
mintpy.troposphericDelay.gacosDir = auto # [path2directory], auto for "./GACOS"
########## 7. deramp (optional)
Estimate and remove a phase ramp for each acquisition based on the reliable pixels.
Recommended for localized deformation signals, i.e. volcanic deformation, landslide and land subsidence, etc.
NOT recommended for long spatial wavelength deformation signals, i.e. co-, post- and inter-seimic deformation.
mintpy.deramp = auto #[no / linear / quadratic], auto for no - no ramp will be removed mintpy.deramp.maskFile = auto #[filename / no], auto for maskTempCoh.h5, mask file for ramp estimation
########## 8. correct_topography (optional and recommended)
Topographic residual (DEM error) correction
reference: Fattahi and Amelung (2013, IEEE-TGRS)
stepFuncDate - Specify stepFuncDate option if you know there are sudden displacement jump in your area,
i.e. volcanic eruption, or earthquake, and check timeseriesStepModel.h5 afterward for their estimation.
excludeDate - Dates excluded for error estimation only
pixelwiseGeometry - Use pixel-wise geometry info, i.e. incidence angle and slant range distance
yes - use pixel-wise geometry when they are available [slow; used by default]
no - use mean geometry [fast]
mintpy.topographicResidual = auto #[yes / no], auto for yes mintpy.topographicResidual.polyOrder = auto #[1-inf], auto for 2, poly order of temporal deformation model mintpy.topographicResidual.phaseVelocity = auto #[yes / no], auto for no - phase, use phase velocity for minimization mintpy.topographicResidual.stepFuncDate = auto #[20080529,20100611 / no], auto for no, date of step jump mintpy.topographicResidual.excludeDate = auto #[20070321 / txtFile / no], auto for exclude_date.txt mintpy.topographicResidual.pixelwiseGeometry = auto #[yes / no], auto for yes, use pixel-wise geometry info
########## 9.1 residual_RMS (root mean squares for noise evaluation)
Calculate the Root Mean Square (RMS) of residual phase time-series for each acquisition
reference: Yunjun et al. (2019, section 4.9 and 5.4)
To get rid of long wavelength component in space, a ramp is removed for each acquisition
Set optimal reference date to date with min RMS
Set exclude dates (outliers) to dates with RMS > cutoff * median RMS (Median Absolute Deviation)
mintpy.residualRMS.maskFile = auto #[file name / no], auto for maskTempCoh.h5, mask for ramp estimation mintpy.residualRMS.deramp = auto #[quadratic / linear / no], auto for quadratic mintpy.residualRMS.cutoff = auto #[0.0-inf], auto for 3
########## 9.2 reference_date
Reference all time-series to one date in time
reference: Yunjun et al. (2019, section 4.9)
no - do not change the default reference date (1st date)
mintpy.reference.date = 20201127 #[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
########## 11.1 geocode (post-processing)
for input dataset in radar coordinates only
commonly used resolution in meters and in degrees (on equator)
100, 60, 50, 30, 20, 10
0.000925926, 0.000555556, 0.000462963, 0.000277778, 0.000185185, 0.000092593
mintpy.geocode = auto #[yes / no], auto for yes mintpy.geocode.SNWE = auto #[-1.2,0.5,-92,-91 / none ], auto for none, output extent in degree mintpy.geocode.laloStep = auto #[-0.000555556,0.000555556 / None], auto for None, output resolution in degree mintpy.geocode.interpMethod = auto #[nearest], auto for nearest, interpolation method mintpy.geocode.fillValue = auto #[np.nan, 0, ...], auto for np.nan, fill value for outliers.
########## 11.2 google_earth (post-processing) mintpy.save.kmz = auto #[yes / no], auto for yes, save geocoded velocity to Google Earth KMZ file
########## 11.3 hdfeos5 (post-processing) mintpy.save.hdfEos5 = auto #[yes / no], auto for no, save time-series to HDF-EOS5 format mintpy.save.hdfEos5.update = auto #[yes / no], auto for no, put XXXXXXXX as endDate in output filename mintpy.save.hdfEos5.subset = auto #[yes / no], auto for no, put subset range info in output filename
########## 11.4 plot mintpy.plot = auto #[yes / no], auto for yes, plot files generated by default processing to pic folder