U c @s`ddlZddlZddlZddlZddlZddlmZddlm Z m Z ddl m Z ddlmZddlmZmZmZmZmZdZdd d d d d dgZedZdZdZd?ddZd@ddZddZddZdAddZ dBd d!Z!d"d#Z"dCd$d%Z#dDd'd(Z$dEd*d+Z%dFd-d.Z&dGd/d0Z'dHd2d3Z(d4d5Z)dId7d8Z*dJd9d:Z+dKd;d<Z,e-d=kr\e,ej.d>ddS)LN)linalg) ifgramStackcluster) decorrelation)get_template_content)readfile writefileptimeutils arg_utilszmintpy.networkInversion.obsDatasetName numIfgram weightFunc maskDataset maskThreshold minRedundancyminNormVelocityinvert_networkureferences: Berardino, P., Fornaro, G., Lanari, R., & Sansosti, E. (2002). A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE TGRS, 40(11), 2375-2383. doi:10.1109/TGRS.2002.803792 Pepe, A., and Lanari, R. (2006), On the extension of the minimum cost flow algorithm for phase unwrapping of multitemporal differential SAR interferograms, IEEE-TGRS, 44(9), 2374-2383. Perissin, D., and Wang, T. (2012), Repeat-pass SAR interferometry with partially coherent targets, IEEE TGRS, 50(1), 271-280, doi:10.1109/tgrs.2011.2160644. Samiei-Esfahany, S., Martins, J. E., Van Leijen, F., and Hanssen, R. F. (2016), Phase Estimation for Distributed Scatterers in InSAR Stacks Using Integer Least Squares Estimation, IEEE TGRS, 54(10), 5671-5687. Seymour, M. S., and Cumming, I. G. (1994), Maximum likelihood estimation for SAR interferometry, 1994. IGARSS '94., 8-12 Aug 1994. Yunjun, Z., Fattahi, H., and Amelung, F. (2019), Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction, Computers & Geosciences, 133, 104331, doi:10.1016/j.cageo.2019.104331. Yunjun, Z., Fattahi, H., Brancato, V., Rosen, P., Simons, M. (2021), Oral: Tectonic displacement mapping from SAR offset time series: noise reduction and uncertainty quantification, ID 590, FRINGE 2021, 31 May – 4 Jun, 2021, Virtual. aexample: ifgram_inversion.py inputs/ifgramStack.h5 -t smallbaselineApp.cfg --update ifgram_inversion.py inputs/ifgramStack.h5 -w no # turn off weight for fast processing ifgram_inversion.py inputs/ifgramStack.h5 -c no # turn off parallel processing # offset ifgram_inversion.py inputs/ifgramStack.h5 -i rangeOffset -w no -m waterMask.h5 --md offsetSNR --mt 5 ifgram_inversion.py inputs/ifgramStack.h5 -i azimuthOffset -w no -m waterMask.h5 --md offsetSNR --mt 5 c Csd}tdtdt}tdd}tj|||||d}|jddd|jd d d d d |jddddtdd|jddddd |jddddddd|jdd d!d |jd"d#d$d%d&d'| d(d)}|jd*d+d,d-d-d.d/d0hd1d2|jd3d4d5d6d'|jd7d8d9d:d9gd;d2|jdd'| d?d@}|jdAdBdCdDdEd |jdFdGdHdIdJt dKdLdM|jdNdOdPdQdJt dRdSdMt |}t |}|jdTdUd%dVd'|S)WNz2Invert network of interferograms into time-series. .)synopsis descriptionepilog subparsersifgramStackFilez(interferograms stack file to be inverted)helpz-tz --template templateFileztemplate text file with options)destrz-iz-dz--dsetr zkdataset name of unwrap phase / offset to be used for inversion e.g.: unwrapPhase, unwrapPhase_bridging, ...)rtyperz-mz --water-mask waterMaskFilez4Skip inversion on the masked out region, i.e. water.z-oz--outputoutfile)ZTS_FILEZ TCOH_FILEZ NUM_INV_FILEz)Output file name. (default: %(default)s).)rnargsmetavarrz --ref-dateref_datez&Reference date, first date by default.z--skip-referencez --skip-refskip_ref store_truez:[for offset and testing] do not apply spatial referencing.)ractionrsolverz(solver for the network inversion problemz-wz --weight-funcrvarZfimcohnozfunction used to convert coherence to weight for inversion: var - inverse of phase variance due to temporal decorrelation (default) fim - Fisher Information Matrix as weightcoh - spatial coherence no - no/uniform weight)rdefaultchoicesrz--min-norm-phaser store_falsezoEnable inversion with minimum-norm deformation phase, instead of the default minimum-norm deformation velocity.z--norm residualNormL2L1z;Optimization mehtod, L1 or L2 norm. (default: %(default)s).z --calc-covcalcCovzdCalculate time-series STD via linear propagation from the network of interferograms or offset pairs.maskz&mask observation data before inversionz --mask-dsetz--mask-datasetz--mdrzBdataset used to mask unwrapPhase, e.g. coherence, connectComponentz --mask-thresz--mask-thresholdz--mtrNUM皙?zIthreshold to generate mask when mask is coherence (default: %(default)s).)rr$rr-rz --min-redunz--min-redundancyz--mrr?zWminimum redundancy of interferograms for every SAR acquisition. (default: %(default)s).z--update update_modezEnable update mode, and skip inversion if output timeseries file already exists, readable and newer than input interferograms file) REFERENCETEMPLATEEXAMPLE__name__splitr create_argument_parser add_argumentstradd_argument_groupfloatadd_memory_argumentadd_parallel_argument)rrrnameparserr)r4rGN/home/exouser/operations/rsmas_insar/sources/MintPy/mintpy/ifgram_inversion.py create_parserAsp             rIc Cs8t}|j|d}t|j}|ddkrsz&read_template2inps..)rrr)rr)r0r zmintpy.compute.cs g|]}|kr|qSrGrgrzdask_key_prefixrtrGrHr}s)rconfig)rb) maxMemory)rr,) rxvarsr read_templateutcheck_template_auto_valuelistrgryrBr@) template_filerriDictkeyListruvaluerGrrHr^s<         r^c stdtdd}tddjDs>d}tdjntjdd }|d jd }W5QRXtj jd}||krd}td jdntd jtj d (}t |j j dtjj }W5QRXtddjD}||krd}tdj ntdj |dkrtj tjttj jdd_fdddD}tfddtDrd}tdtnB|rtfdd|Drd}td|ntdttd||S)N2--------------------------------------------------zupdate mode: ONskipcss|]}tj|VqdSN)rdrerfrzrGrGrH szrun_or_skip..runz"1) NOT ALL output files found: {}.rr timeseriesz'1) output file {} is NOT fully written.z"1) output files already exist: {}.MODIFICATION_TIMEcss|]}tj|VqdSr)rdregetmtimerzrGrGrHrsz52) output files are NOT newer than input dataset: {}.z22) output dataset is newer than input dataset: {}.T dropIfgramcsg|]}|kr|qSrGr~rz)atr_tsrGrHr}s zrun_or_skip..)rVrUc3s.|]&}tt|t|dkVqdS)NoneN)r@rgetryr{ru)rrrrGrHr sz83) NOT all key configuration parameters are the same: {}c3s|]}||kVqdSrrGr)atr_ifgrrGrHr#sz(3) NOT all the metadata are the same: {}z53) all key configuration parameters are the same: {}.zrun or skip: {}.)rcallr!r]h5pyFilesizerdregetsizerrBr attrsrrminrr[rnlenrget_date12_listr any configKeys)rrflagf fsize_reffsizetito meta_keysrG)rrrrrH run_or_skipsD,     rTh㈵>r7temporalCoherencec  Cs||jdd}|dk r,||jdd}|jdd} |jd} tj| | ftjd} |dkrhtj} nd} d}t||||gd\}\}}}ttj|dkdd |kr| | |fSz8|rb|dk rt j t ||t |||d dd \}}nt j |||d dd \}}|d kr.t ||||||| d } |t |d| f}tj|dd | ddddf<n|dk rt j t ||t |||d dd \}}nt j |||d dd \}}|d krt ||||||| d } || ddddf<Wnt jk rYnX|jd}| | |fS)a' Estimate time-series from a stack/network of interferograms with Least Square minimization on deformation phase / velocity. Problem: A X = y opt 1: X = np.dot(np.dot(numpy.linalg.inv(np.dot(A.T, A)), A.T), y) opt 2: X = np.dot(numpy.linalg.pinv(A), y) opt 3: X = np.dot(scipy.linalg.pinv(A), y) opt 4: X = scipy.linalg.lstsq(A, y)[0] [recommend and used] opt 4 supports weight. scipy.linalg provides more advanced and slighted faster performance than numpy.linalg. This function relies on the LAPACK routine gelsd. It computes the minimum-norm solution to a linear least squares problem using the singular value decomposition of A and a divide and conquer method. opt 4 is faster than opt 1/2/3 because it estimates X directly without calculating the A_inv matrix. opt 2/3 is better than opt 1 because numpy.linalg.inv() can not handle rank defiency of design matrix B Traditional Small BAseline Subsets (SBAS) algorithm (Berardino et al., 2002, IEEE-TGRS) is equivalent to the setting of: min_norm_velocity=True weight_sqrt=None Parameters: A - 2D np.ndarray in size of (num_pair, num_date-1) B - 2D np.ndarray in size of (num_pair, num_date-1), design matrix B, each row represents differential temporal baseline history between reference and secondary date of one interferogram y - 2D np.ndarray in size of (num_pair, num_pixel), phase/offset of all interferograms with no-data value: NaN. tbase_diff - 2D np.ndarray in size of (num_date-1, 1), differential temporal baseline history, in the unit of years weight_sqrt - 2D np.ndarray in size of (num_pair, num_pixel), square root of weight of all interferograms min_norm_velocity - bool, assume minimum-norm deformation velocity, or not rcond - cut-off ratio of small singular values of A or B, to maintain robustness. It's recommend to >= 1e-5 by experience, to generate reasonable result. min_redundancy - float, min redundancy defined as min num_pair for every SAR acquisition inv_quality_name - str, inversion quality type/name temporalCoherence for phase residual for offset no to turn OFF the calcualtion Returns: ts - 2D np.ndarray in size of (num_date, num_pixel), phase time-series inv_quality - 1D np.ndarray in size of (num_pixel), temporal coherence (for phase) or residual (for offset) num_inv_obs - 1D np.ndarray in size of (num_pixel), number of observations (ifgrams / offsets) used during the inversion rrNdtyperesidualmat_listaxis)condr,)inv_quality_name weight_sqrtrS)reshapeshapenpzerosfloat32nanskip_invalid_obsrsumrlstsqmultiplycalc_inv_qualitytilecumsum LinAlgError)ABy tbase_diffrmin_norm_velocityrcondmin_redundancyrrSnum_date num_pixelts inv_quality num_inv_obsXe2Zts_diffrGrGrHestimate_timeseries/sd5             rcCs||jdd}||jdd}tj|jd|jdftjd}t|||gd\}\}}ttj|dkdd|kr||St |}t t | }tj |||jg}|S)a!Estimate the time-series covariance from network of STD via linear propagation. Pixel by pixel only. For a system of linear equations: A X = y, propagate the STD from y to X. Parameters: G - 2D np.ndarray in size of (num_pair, num_date-1), design matrix y - 2D np.ndarray in size of (num_pair, 1), stack of obs y_std - 2D np.ndarray in size of (num_pair, 1), stack of obs std. dev. Returns: ts_cov - 2D np.ndarray in size of (num_date-1, num_date-1), time-series obs std. dev. rrrrrrr)rrrrrrrrrpinvdiagsquareflatten multi_dotT)Gry_stdrrts_covZGplusZ stack_covrGrGrHestimate_timeseries_covs   rcCsrtt|rjt|dddf}||ddf}t|D]$\}}|dk rD||ddf||<qD||fS)aSkip invalid observations in the stack of phase/offset and update corresponding matrices. This applies to the pixel-wised inversion only, because the region-wised inversion has valid obs in all pairs. Parameters: obs - 2D np.ndarray in size of (num_pair, num_pixel), observations (phase / offset) of all interferograms with no-data value: NaN. mat_list - list of 2D np.ndarray in size of (num_pair, *) or None Returns: obs / mat_list Nr)rrisnanr enumerate)obsrrr|matrGrGrHrsrc Cs~|j\}}tj|tjd} ttd|} || krtt|| } |r^td || | t | D]b} | | } t | d| |}|dkr|dd| |ft ||dd| |f}t tjtd|dd || | |<n|d kr|dk rV|dd| |ft ||dd| |f}ttjt |d dd | | |<n4|| |jdkr|t|| |ntj| | |<tdtt| d }|rf| d|dkrftd | d| qfn|dkr |t ||}t tjtd|dd |} nn|d krl|dk rN|| |}ttjt |d dd } n|jdkrdt|ntj} ntd|| S)aCalculate the inversion quality of the time series estimation. Parameters: G - 2D np.ndarray in size of (num_pair, num_date-1), design matrix A or B X - 2D np.ndarray in size of (num_date-1, num_pixel), solution y - 2D np.ndarray in size of (num_pair, num_pixel), phase or offset e2 - 1D np.ndarray in size of (num_pixel,), square of the sum of the 2-norm residual inv_quality_name - str, name of the inversion quality parameter temporalCoherence for phase residual for offset weight_sqrt - 2D np.ndarray in size of (num_pair, num_pixel), weight square root, None for un-weighted estimation. Returns: inv_quality - 1D np.ndarray in size of (num_pixel), temporalCoherence / residual rgjAz=calculating {} in chunks of {} pixels: {} chunks in total ...rrNy?rrrr chunk {} / {}z&un-recognized inversion quality name: )rrrrintr round_to_1ceilrcr]rangerdotabsrexpsqrtrrmaxr\)rrrrrrrSnum_pairrr chunk_size num_chunkr|c0c1eZ chunk_steprGrGrHrsF  0,  0(4 $   rr*cCst|jdd}t|d}|dkrNtj||jdkrtdtdnTtj||jdkrtdtd td td td td tdt|S)z< Check Rank of Design matrix for weighted inversion Trrr,rz@WARNING: singular design matrix! Inversion result can be biased!z-continue using its SVD solution on all pixelszERROR: singular design matrix!z; Input network of interferograms is not fully connected!z6 Can not invert the weighted least square solution.zYou could try:zC 1) Add more interferograms to make the network fully connected:z6 a.k.a., no multiple subsets nor network islandsz8 2) Use '-w no' option for non-weighted SVD solution.) rrget_design_matrix4timeseriesrr matrix_rankrrc Exception) ifgram_file weight_func date12_listrrGrGrHcheck_design_matrix@s  rrMc Cs|j|dd}|r&td||||j|||dd|d}|dk rV|rtdnJd |jkr|rltd t|jd }|d dd}W5QRXnt d t |D]8} || ddfd k} || ddf| || 8<q|S)a&Read unwrapPhase / azimuthOffset / rangeOffset from ifgramStack file Parameters: stack_obj - ifgramStack object box - tuple of 4 int ref_phase - 1D array or None Returns: stack_obs - 2D array of unwrapPhase in size of (num_pair, num_pixel) rrreading {} in {} * {} ...F datasetNameboxrrSrNzuse input reference valuerefPhasezread reference phase from filerzJNo reference value input/found on file! unwrapped phase is not referenced!r) get_sizercr]readrrkrrfilerr) rvr ref_phase obs_ds_namerrSr stack_obsrr|r4rGrGrHread_stack_obsXs0    "r r6c Cs|j|dd}|r||jkr|r8td||||j|||dd|d} d| t| <tj| j tj d} |dkr| | dk9} |rtd |n|d kr| | |k9} |rtd ||n| d r| | |k9} |rtd ||d} tj |j ddftj d} t|j dD] } t|| | | | | <qt|t| d|j df| d}t| |dk|| k}d| |<|rtd||d| ntd|tj|| dk<|dk rtj|| dk<~ ~ ||fS)z5Mask input unwrapped phase by setting them to np.nan.rrrFrrrconnectComponentz3mask out pixels with {} == 0 by setting them to NaN coherence offsetSNRz3mask out pixels with {} < {} by setting them to NaN OffsetStdz3mask out pixels with {} > {} by setting them to NaN rrrz'keep pixels with {} <= {} and SNR >= {}#Un-recognized mask dataset name: {}rN)rrkrcr]rrrronesrbool_endswithrrr nanmedianrrrr\r)r rvr mask_ds_namemask_threshold stack_stdrrSrZmsk_datamskZmin_snrZobs_medr|Zobs_snrZmsk_snrrGrGrHmask_stack_obssR    ( rcCsP|j|dd}|r$td|||jd||dd|d}d|t|<|S) z Read spatial coherence rrz reading coherence in {} * {} ...rFrrr)rrcr]rrrr)rvrrrSrZcoh_datarGrGrHread_coherencesr順c Cs\tdt|||d}|jd}d|jkr>t|jd}n$t|jdt|jd}|d}tt | td}tt ||}tdj ||d t |D]} | |} t| d||} | d krd } nd } tj|d d | | f||d| d|d d | | f<t|d d | | f|d d | | f<| ddd krtd | d|q|S)zTRead coherence and calculate weight_sqrt from it, chunk by chunk to save memory z-calculating weight from spatial coherence ...)rrr NCORRLOOKSALOOKSRLOOKSg ףp= ?zLconvert coherence to weight in chunks of {c} pixels: {n} chunks in total ...)cnrTFNg?)LepsilonrSr)rcrrmetadatargrBrrrrintastyperr]rrdecorZcoherence2weightr) rvrrrrweightrr&rr|rrrSrGrGrHcalc_weight_sqrts:  *r-c Cstjtj|j}tj|d}t|d}|dkrXtj|d}tj|sXd}|rtj|rtt j |t d t}|sd}|d7}t ||jd d }|jd d }|j||d d }t ||k}||} || ||fS) aGet the design matrix for time-series STD calculation. Parameters: stack_obj - ifgramStack object Returns: A_std - 2D np.ndarray of float32 in size of (num_ifg, num_date-1) ref_ind - int, index of the reference date in date_list ref_date - str, reference date flag_std - 1D np.ndarray of bool in size of (num_date) zsmallbaselineApp.cfgzmintpy.reference.dateautozreference_date.txtNrz;reference date is required for time-series STD calculation,z'but NOT found in mintpy.reference.date!Tr)refDater)rdredirnamer joinrrrfr@rloadtxtbytesr*r\r get_date_listrarrayindex) rvZ mintpy_dirZcfg_filer%rwr date_listA_stdZflag_stdref_indrGrGrHget_design_matrix4stds&     r:Fc B Cs t|} | jddtj|\} } |rJ|d|d}|d|d}n | j}| j}||}| jdd}t|}t t |dt j d }t |d d}| jdd}| j|d dd\}}d }d }|d r@|dkrt| ||ddd}| r0t| d d\}}|d}|dkr(d|}ndt| |dddd}nd|kr"| s^|dkr0td|d|t|| j|d|dddt|d }d|t |<d||dk<tdd|}| rt| d d\}}|d}d|}|dkrd }n"|dkr ntd|d|dntd|t| |||dd}d|krlt j||d k<td!|t|| ||||dd"\}}t |t j}|r|1\}6}7}8|6|-d d |3f<|7|/|3<|8|0|3<t )|4dkrt#t )|4}9t *|4d}:t|2d?|9d;|9|+d6d<d=t j.|9d@};t/|9D]x}<|:|<}=t-f|d d |=fd d>|1\}6}7}8|6%|-d d |=f<|7|/|=<|8|0|=<|;j0||1\}6}7}8|6%|-d d |=f<|7|/|=<|8|0|=<|;j0||>d |d |f|.d |d ||=f<|>d ||d f|.d ||d |=f<|>|d d |f|.|d d ||=f<|>|d |d f|.|d |d |=f<|;j0|.)rrrz/skip pixels with {} = NaN in all interferogramsrrz../avgSpatialSNR.h5rWrz../avgSpatialCohIon.h5z../avgSpatialCoh.h5z'skip pixels with zero value in file: {})rz2number of pixels to invert: {} out of {} ({:.1f}%)d)rrrrrrz-estimating time-series for pixels with valid z inz all ifgrams (z pixels; z.1fz%) ...)rrz some ifgrams ()maxValuez {}/{} pixels)everysuffixz1estimating time-series via WLS pixel-by-pixel ...zepropagating std. dev. from network of interferograms to time-series (Yunjun et al., 2021, FRINGE) ...)rrr WAVELENGTHg@z.converting LOS phase unit from radian to meterrX PROCESSORcosicorrr"z=converting azimuth offset unit from pixel ({:.2f} m) to meterrYRANGE_PIXEL_SIZEr#z;converting range offset unit from pixel ({:.2f} m) to meter)9rrjrdrer=lengthwidthr4rrr5r date_list2tbaserdiffrrrrlr-r:rhrcr]rrr\r rrrrrmrr[rget_dataset_listrrr1rfrwhererint16r progressBarrupdatecloserrBr(pirrazimuth_ground_resolution)Brrr r rwater_mask_filerrrrcalc_covrvZ stack_dirZ stack_basenum_rownum_colrr7rtbaserrrrrrr8r0r1r r4Zatr_mskZlen_mskZwid_mskdsNamesdsNamer@rZstack_quality_fileZ atr_stackZ len_stackZ wid_stackquality num_pixel2inv idx_pixel2invrrrrkwargsrwZ mask_all_netZ mask_part_netZnum_pixel2inv_allZtsiZ inv_qualiZnum_obsiZnum_pixel2inv_partZidx_pixel2inv_partprog_barr|idxZts_covi phase2range az_pixel_sizeZ rg_pixel_sizerGrGrHifgram_inversion_patch$s                        $ $   $    $  &&&&$      rgc* Cs6|s t}t}td}t|j}|jdd|jdd}|jdd}|j |j }}|j |j |j dd|_||d}|jd|jdd} } | |_|jrt|d } d | } nd } d } |jrd }nd}| d|7} | d|j7} | d|j7} | d|j| 7} |jr|jdkr4d|j}nN|jdkrRd|j|j}n0|jdrrd|j|j}ntd|j| d|7} n| d7} tj||jdkr| d7} | d7} | d7} | d7} t| td | td!| td"|td#|t |j!}t"D]}t#t$|||t%|<q d$|d%<d&|d'<|d|d(<tj&|tj'd)}|j(dd}|j)| f|gtj*| f|gtj*| ||fd*gd+}t+j,|j-||d,|jr0t.j/0|j-d}||j 1d-rd.nd 7}|d/}| |d(<|j)| f|gtj*| | ||fd*gd0}t+,|||d1t.j/2|j34krVd1}d2|d'<n d3}d|d'<||d%<|5d(|d%tj*||fgi}t+j,|j3||d,d4|d%<d|d'<d4tj*||fgi}t+j,|j6||d,|j7|j8d5\}}|j|j|j |j|j|j9|j|j|j|jd6 }t:|D]&\}}|d |d}|d7|d}|dkr|td8|d|td9|td:|||d;<|jst;f|d*d<\}}} }!ntd=t<| ||ftj*}|jrt<| | ||ftj*nd*}t<||ftj*} t<||ftj*}!tj=|j|j>|j?d>}"|"|"j@t;|||| |!gd?\}}} }!|"Atd@d| |d|d7|d|d g}#t+jB|j-|d$|#dA|jrd| d| |d|d7|d|d g}#t+jB||d$|#dA|d|d7|d|d g}#t+jB|j3| ||#dAt+jB|j6|!d4|#dA|dkrtCt|dB\}$}%tdC|$|%q|j stD|j!dD}&tD|j!dE}'tdFtdG|&|'|d1krdnd}(tdH||(tEF|j3dI})|(|)||&|'f<W5QRXtdJ| tEF|j6dI})| |)d4|&|'f<W5QRXtG|tCt|dB\}$}%tdC|$|%d*S)KzPhase triangulatino of small baseline interferograms Parameters: inps - namespace Example: inps = cmd_line_parse() ifgram_inversion(inps) rLFrRTr)unwDatasetNameskip_referencerrrrz with REF_DATE = rQzP------------------------------------------------------------------------------- zdeformation velocityzdeformation phasez/least-squares solution with L2 min-norm on: {} zminimum redundancy: {} zweight function: {} zcalculate covariance: {} {} rz{} == 0rz{} < {}rz{} > {}rzmask out pixels with: {} z mask: no z0***WARNING: the network is NOT fully connected. z! Inversion result can be biased! zF Continue to use SVD to resolve the offset between different subsets. zO-------------------------------------------------------------------------------znumber of interferograms: {}znumber of acquisitions : {}znumber of lines : {}znumber of columns : {}rrKmUNITREF_DATErN)datebperpr)r(rMZDecorzCov.h5)rmrrpixelrr4) max_memory) rr r rrrVrrrrWr"z5 ------- processing patch {} out of {} --------------zbox width: {}zbox length: {}rrz6 ------- start parallel processing using Dask -------) config_name)func func_dataresultsz.------- finished parallel processing ------- )datarblock<z(time used: {:02.0f} mins {:02.1f} secs. rVrUrz.update values on the reference pixel: ({}, {})z$set {} on the reference pixel to {}.zr+z3set # of observations on the reference pixel as {})Hrxtimerset_num_threadsrrrjrr4rJrKget_reference_phaser r&rrrr r3r:rr]rrrrrr\rrrrcr_r(rr@rryr5string_get_perp_baseline_timeseriesrrr layout_hdf5rnrdresplitextrlrmrorhpoprp split2boxesrr rrgrr`rbrrrSwrite_hdf5_blockdivmodrrrroll_back_num_threads)*rr start_timenum_threads_dictrvrr7rJrKrrrZ ref_date4stdZref_msgrwrEmetarudatespbase ds_name_dictfbaseZ tsStdFilerbox_listnum_box data_kwargsr|rbox_widbox_lenrrrr cluster_objrvrjsref_yref_xref_valrrGrGrHifgram_inversiontsD                   "  $  rcCs@t|}|jr t|dkr |jS|jdkr4t|ntddS)Nrr1z)L1 norm minimization is not fully tested.)rxr8rr!r0rNotImplementedError)rqrrrGrGrHmainws  r__main__r)N)N)NTrr7rT)rr7)rNT)r*)rMTT)Nr6NTT)TT)r*Tr ) NNrMr*NTNr6r7F)N)N)/rdsysrxrnumpyrscipyrmintpy.objectsrrZmintpy.simulationrr+mintpy.defaults.templater mintpy.utilsrrr r rr ryrr:r9r;rIrxr^rrrrrrr rrr-r:rgrrr<argvrGrGrGrH s     G J(5  & M  * <  /' R