U xb3@sddlZddlZddlZddlZddlZddlZddlm Z m Z ej ej ej ej ej ej ej ejejejejejejejejejejejejejejdZdddgZdddd d d gZd d ddddddddddg Zddddddddd d!d"d#d$d%gZd&d'd'd&d'd(d(d(d(d'd)d*d*d'd'd*d*d)d'd'd'd)d)d)d)d)d)d)d'd+d,d'd-d-d-d-d-d-d-d'd'd&d&d&d'd)d)d)d'd'd.2ZGd/ddZGd0d1d1ZGd2d3d3ZGd4d5d5ZGd6ddZdS)7N)ptime time_func)boolbyteflagintint16shortint32int64longfloatfloat32float_float64complex complex64Z cpx_float32cfloatZcfloat32 complex128complex_Z cpx_float64 timeseriesHDFEOSgiantTimeseriesrawtroposphericDelaytopographicResidualramp displacementheightlatitude longitude rangeCoord azimuthCoordincidenceAngle azimuthAngleslantRangeDistance shadowMask waterMask commonMaskbperp unwrapPhaseZ!unwrapPhase_bridging_phaseClosureZunwrapPhase_bridgingZunwrapPhase_phaseClosure coherenceconnectComponent wrapPhase magnitude azimuthOffsetazimuthOffsetStd rangeOffsetrangeOffsetStd offsetSNRZrefPhaseZradian1Zpixelmdegreezm/yearzm/year^2mm)2r*r+r,r-r.r/r0r1r2r3rrr r!r"r#r$r%r&r'r(r)rrrrrrtemporalCoherenceZvelocityZ accelerationmaskrreconsrawtssar_aps igram_apsfigramigramcmaskifgcntZunwrflatZcorZdemZhgtZhgt_simr.Z intensityc@seZdZdZd ddZd!ddZd"dd Zd d Zd d ZddZ d#ddZ d$ddZ d%ddZ d&ddZ d'ddZddZd(ddZdS))ra  Time-series object for displacement of a set of SAR images from the same platform and track. It contains three datasets in root level: date, bperp and timeseries. File structure: https://mintpy.readthedocs.io/en/latest/api/data_structure/#timeseries NcCs||_d|_dS)NrfilenameselfrDrHK/home/exouser/operations/rsmas_insar/sources/MintPy/mintpy/objects/stack.py__init__sztimeseries.__init__TcCs<z*|j|r(tdtj|jWn YnXdSNzclose timeseries file: {}fcloseprintformatospathbasenamerDrG print_msgrHrHrIrNs ztimeseries.closec s~|r tdjtjj j j _ t jdF}z,|ddd_jjj8_Wnd_YnXW5QRXtjd_tfddjD_djkstdd jDr(d jkr(tjd tfd djD_tjd djjjDtjd _ddjD_fddjD_dS)Nopen {} file: {}rr)rcsg|]}tj|jqSrHdtdatetimestrptime dateFormat.0irGrHrI sz#timeseries.open..Tcss"|]}|jdko|jdkVqdS)rN)hourminuter]rHrHrI sz"timeseries.open..CENTER_LINE_UTCcsg|]}|tjdqS))seconds)rY timedeltar])utc_secrHrIrascSsg|]}|j|jdqSiQdaysrgr]rHrHrIrasdtypecSs$g|]}|j|jddqS)v@)year timetupletm_ydayr]rHrHrIrascsg|]}dj|qSz{}-{})rPrEr]r`rHrIras) rOrPrErQrRrSrD get_metadataget_size get_date_listlengthwidthnumPixelh5pyFilepbaserefIndexrget_date_str_formatdateListr\nparraytimesallmetadatakeysr rtbaseyearList 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timeseries('timeseries_ERA5_demErr.h5') data = tsobj.read(datasetName='20161020') data = tsobj.read(datasetName='20161020', box=(100,300,500,800)) data = tsobj.read(datasetName=['20161020','20161026','20161101']) data = tsobj.read(box=(100,300,500,800)) !reading {} data from file: {} ...FrUrcSs g|]}|ddddqS)r-replacer]rHrHrIrasz#timeseries.read..rWrmTNrrocss|]}|dkVqdSroNrHr]rHrHrIresz"timeseries.read..)rOrPrErDr isinstancestrr{r|Grouprzerosrbool_rrryrxanyrsqueeze) rG datasetNameboxrrUrMdsdateFlagedatarHrHrIreads>       ztimeseries.readc Cs|s |j}|rt|}|jdd|dkr0|j}|dkr>|j}|dkrL|j}|dkrzt|d} | tdj }W5QRX|j ddt j |t j d}t j |t jd}t j |t j d}t|}|j|d<tjtj|} tj| st| td| td |t|d } td jt|j|j|d | jd |d|dtdt|j|j| jd|d|jdkrtdt|j|j| jd|d|D]\} } t| | j| <qW5QRXtd||S)a7 Parameters: data : 3D array of float32 dates : 1D array/list of string in YYYYMMDD format bperp : 1D array/list of float32 (optional) metadata : dict outFile : string refFile : string compression : string or None Returns: outFile : string Examples: from mintpy.objects import timeseries ##Generate a new timeseries file tsobj = timeseries('timeseries.h5') timeseries.write(data, dates=dateList, bperp=bperp, metadata=atr) ##Generate a timeseries with same attributes and same date/bperp info tsobj = timeseries('timeseries_demErr.h5') timeseries.write(data, refFile='timeseries.h5') FrNrWrrm FILE_TYPEzcreate directory: {}z+create timeseries HDF5 file: {} with w modewzIcreate dataset /timeseries of {t:<10} in size of {s} with compression={c})tscrT)rchunks compressionz2create dataset /dates of {:<10} in size of {}r)rrHz2create dataset /bperp of {:<10} in size of {}r)zfinished writing to {})rDrrrrr}r{r|timeseriesDatasetNamesrrNrrrstring_rrErQrRdirnameabspathisdirmakedirsrOrPrrnrcreate_datasetrr)rGroutFilerr)rZrefFilerZrefobjrfZoutDirrMrrrHrHrI write2hdf5sZ     ztimeseries.write2hdf5c Cs|}|r:t|}td|tj|dd|dkf<tj|dd|_d}|d|j7}|d|7}|d 7}|st j t j t j |jd t j t j |jd}tj|tt|jd d |jd d fd d|dtd||S)zxCalculate the standard deviation 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date_listnum_dater9prog_barr_rrrHrHrItimeseries_rms{s<      ,ztimeseries.timeseries_rmsFcCs|jdd|j|d}|rVtj|rVtd|t|j|d}tj||t |k<|dk rvd|||k<d|||k<tj |dd}||j fS) NFrrrrorrr) rrrQrRrrOrrrrrr)rGrr reverseMask thresholdrr9dmeanrHrHrIspatial_averages     ztimeseries.spatial_averagecCs:td|j|jdd|jdd}tj|dd}|S)Nz7calculating the temporal average of timeseries file: {}Fr)rrr)rOrPrDrrrr)rGrrrHrHrItemporal_averages   ztimeseries.temporal_average bl_list.txtc Cst|jdddd|jD}|j}td|t|d,}t||D]\}}|d||qJW5QRX|S)z2Generate bl_list.txt file from timeseries h5 file.FrcSsg|]}|ddqS)rrHr]rHrHrIrasz0timeseries.save2bl_list_file..z$write baseline list info to file: {}rz{} {} )rrr}tolistrOrPzipwrite)rGout_fileZ date6_listZ pbase_listrMdr}rHrHrIsave2bl_list_files   ztimeseries.save2bl_list_file)N)T)T)NNTT)NNNNNN)NN)NN)NNFN)r)__name__ __module__ __qualname____doc__rJrNrrurvrwrrrrrrrrHrHrHrIrs   2 K  & c@sReZdZdZdddZdddZddd Zd d Zd d Ze dddfddZ dS)geometryzp Geometry object. File structure: https://mintpy.readthedocs.io/en/latest/api/data_structure/#geometry NcCs||_d|_dS)NrrCrFrHrHrIrJszgeometry.__init__TcCs<z*|j|r(tdtj|jWn YnXdS)Nzclose geometry file: {}rLrTrHrHrIrNs  zgeometry.closec s|r td|jtj|j|||j |j |_ d|_ d|j krXd|_ t|jdfddD|_t|j|_dkrd dd ddD|_t|j|_z|jdWn YnX|jd d|jD7_nd|_W5QRXdS) NrVFY_FIRSTTrWcs g|]}t|tjr|qSrH)rr{Datasetr]rMrHrIrasz!geometry.open..r)cSsg|]}|dqSrrr]rHrHrIrasrcSsg|] }d|qS)zbperp-rHr^rrHrHrIras)rOrPrErQrRrSrDrurvrxryrzZgeocodedrrr{r| datasetNameslistrrrrremoverTrHrrIrs(   z geometry.openc Cstt|jdT}dd|Dd}||j}t|dkrR|dd\|_|_n |\|_|_W5QRX|j|jfS)NrWcSsg|]}|tkr|qSrH)geometryDatasetNamesr]rHrHrIrasz%geometry.get_size..rrro)r{r|rDrrrrxry)rGrMdsNameZdsShaperHrHrIrvs  zgeometry.get_sizec Cslt|jd}t|j|_W5QRX|jD]4\}}z|d|j|<Wq0||j|<Yq0Xq0|jS)NrWr)r{r|rDrrrrr)rGrMrrrHrHrIruszgeometry.get_metadatarc s|jdd|dkr$dd|j|jf}|dkr6td}nt|trF|g}t|jd:}|d dd|}|rt d |jt |j dkr|dd}nt |j d kr||d|d |d|d f}ntj|j dtjd }fd d |D}tdd|Dr"d|dd<n|D]}d||j|<q&|dd|d|d |d|d f|}tdd|j Drt|}W5QRX|S)axRead 2D / 3D dataset with bounding box in space Parameters: datasetName : (list of) string, to point to specific 2D dataset, e.g.: height incidenceAngle bperp ... bperp-20161020 bperp-20161026 bperp-... box : tuple of 4 int, for (x0,y0,x1,y1) print_msg : bool Returns: data : 2D or 3D array Example: obj = geometry('./inputs/geometryRadar.h5') obj.read(datasetName='height') obj.read(datasetName='incidenceAngle') obj.read(datasetName='bperp') obj.read(datasetName='bperp-20161020') obj.read(datasetName=['bperp-20161020', 'bperp-20161026']) FrNrrWrz%reading {:<15} data from file: {} ...rorrrmcs g|]}|dddqSrrrr]Z familyNamerHrIra5sz!geometry.read..css|] }| VqdSNrHr]rHrHrIre6sz geometry.read..Tcss|]}|dkVqdSrrHr]rHrHrIreAs)rryrxr rrr{r|rDsplitrOrPrrrrrrrrr) rGrrrUrMrrrrrHr rIr sD   &z geometry.read)N)T)T) rrrrrJrNrrvrur rrHrHrHrIrs     rc@seZdZdZd4ddZd5ddZd6dd Zd d Zd7d dZddZ d8ddZ d9ddZ d:ddZ ddZ d;ddZdd"d#Zd$d%Zd?d&d'Zd@d(d)ZdAd*d+Zed,d-ZedBd.d/ZdCd0d1Zd2d3ZdS)D ifgramStackz Interferograms Stack object. File structure: https://mintpy.readthedocs.io/en/latest/api/data_structure/#ifgramstack NcCs||_d|_dS)NrrCrFrHrHrIrJOszifgramStack.__init__TcCs@z.|j|r,td|jtj|jWn YnXdS)Nzclose {} file: {}) rMrNrOrPrErQrRrSrDrTrHrHrIrNSs   zifgramStack.closec s|r tdjtjj j j _ ddt jjD_tjddjjDtjd_tjdnddd_d dd_fd dDfd dtD_jd dD7_W5QRXg_jD]$jfd djD7_qjdd_ t!j _"z$t#j$d_%t#j$d_&Wnd_%d_&YnXz$t'j$d_(t'j$d_)Wnd_(d_)YnXdS)z Time format/rules: All datetime.datetime objects named with time All string in YYYYMMDD named with date (following roipac) rVcSsg|]\}}d||qS{}_{}rPr^r_jrHrHrIraisz$ifgramStack.open..cSsg|]}|j|jdqSrjrkr]rHrHrIrajsrmrW dropIfgramNr)cs>g|]6}t|tjr|jddjjfkr|qS)N)rr{rrrxryr])rMrGrHrIrasscsg|]}|kr|qSrHrHr])dsNamesrHrIravscSsg|]}|tkr|qSrH)ifgramDatasetNamesr]rHrHrIrawscsg|]}d|qSrtrr])r rHrIra|sFrREF_YZREF_XREF_LATREF_LON)*rOrPrErQrRrSrDrurvread_datetimesrxryrzrmDatessDates date12ListrrsTimesmTimesr tbaseIfgramr{r|r pbaseIfgramrrrrrwrrrrrrefYrefXr ZrefLatZrefLonrTrH)r rrMrGrIr[sF  " "  zifgramStack.openc Cst|jd&}t|j|_|ddd}W5QRX|jD]4\}}z|d|j|<WqD||j|<YqDXqDt dd|D}|d|jd<|d|jd <|jS) NrWrrcSsg|]}|dqSrrr]rHrHrIrasz,ifgramStack.get_metadata..rrrr) r{r|rDrrrflattenrrsortedrrHrHrIrus zifgramStack.get_metadataFc szt|jdV|dkr.fdddDd}|j\|_|_|_|r`tddd|_W5QRX|j|j|jfS)NrWcsg|]}|kr|qSrHrr]rrHrIras z(ifgramStack.get_size..)r*r1r/rr) r{r|rDr numIfgramrxryrsum)rGrrrHrrIrvs"zifgramStack.get_sizec stjd}|ddd}W5QRXt|d_tdd|dddfD_tdd|ddd fD_ tfd djD_ tfd dj D_ dS) z4Read date1/2 into array of datetime.datetime objectsrWrN)rrcSsg|]}|dqSrrr]rHrHrIrasz.ifgramStack.read_datetimes..rcSsg|]}|dqSrrr]rHrHrIrasrocsg|]}tj|jqSrHrXr]r`rHrIrascsg|]}tj|jqSrHrXr]r`rHrIras) r{r|rDrrr\rrrr r#r")rGrMrrHr`rIrs""zifgramStack.read_datetimesr*c sb|jdd|jdd}|dkr(dg}nt|tr8|g}t|jd}|ddd|}|rztd |jt j |j t j d }fd d |D}td d |Dr|r|ddd}qd|dd<n|D]} d||| <q|dkrdd|j|jf}|dd|d|d|d|df|} tdd | jDrTt | } W5QRX| S)aHRead 3D dataset with bounding box in space Parameters: datasetName : string, to point to specific 2D dataset, e.g.: unwrapPhase coherence connectComponent ... unwrapPhase-20161020_20161026 unwrapPhase-... coherence-20161020_20161026 ... ['unwrapPhase-20161020_20161026', 'unwrapPhase-20161020_20161101', ...] box : tuple of 4 int, for (x0,y0,x1,y1) print_msg : bool Returns: data : 2D or 3D array Example: obj = ifgramStack('./inputs/ifgramStack.h5') obj.read(datasetName='unwrapPhase') obj.read(datasetName='coherence') obj.read(datasetName='unwrapPhase-20161020_20161026') obj.read(datasetName=['unwrapPhase-20161020_20161026', 'unwrapPhase-20161020_20161101']) FrNr*rWrrrrmcs g|]}|dddqSr rr]r rHrIrasz$ifgramStack.read..css|] }| VqdSrrHr]rHrHrIresz#ifgramStack.read..rTrorrcss|]}|dkVqdSrrHr]rHrHrIres)rvget_date12_listrrr{r|rDrrOrPrrr+rrrryrxrr) rGrrrUrr!rMrrrrrHr rIrsB    zifgramStack.readr+c Cs|dkr d}|r$td||jntd||j|rdtj|rdtd|t|j|d}nd}t |jd}||} | j d} t j | t j d } tj| d } t| D]} | j| d d | d | d | | |d |d|d|df}|rt j||t|k<|dkr(t j||dk<|dk rJd |||k<d|||k<|r`t || | <qt || | <q| W5QRX| |jfS)z Calculate the spatial average.Nr+z/calculating spatial median of {} in file {} ...z-calculating spatial mean of {} in file {} ...rrrWrrmrrorrrr)rOrPrDrQrRrrrr{r|rrrrrrrrrr nanmedianrrNr!)rGrrrZ useMedianrrr9rMdsetr+rrr_rrHrHrIrs:    &    zifgramStack.spatial_averagec Cst|jd6}|ddd}|r@||dddddf}W5QRXtdd|dddfD}tdd|dddfD}d dt||D}|S) NrWrrcSsg|]}|dqSrrr]rHrHrIra8sz/ifgramStack.get_date12_list..rcSsg|]}|dqSrrr]rHrHrIra9srocSsg|]\}}d||qSrrrrHrHrIra:sr{r|rDrrr)rGrrMrrr r!rHrHrIr-3s&  zifgramStack.get_date12_listc Cst|jd4}|ddd}||dddddf}W5QRXtdd|dddfD}tdd|dddfD}d dt||D}|S) NrWrrcSsg|]}|dqSrrr]rHrHrIraAsz4ifgramStack.get_drop_date12_list..rcSsg|]}|dqSrrr]rHrHrIraBsrocSsg|]\}}d||qSrrrrHrHrIraCsr0)rGrMrrr r!rHrHrIget_drop_date12_list=s(  z ifgramStack.get_drop_date12_listc Cst|jd6}|ddd}|r@||dddddf}W5QRXdd|dddfD}dd|dddfD}ttt||}|S) NrWrrcSsg|]}|dqSrrr]rHrHrIraKsz-ifgramStack.get_date_list..rcSsg|]}|dqSrrr]rHrHrIraLsro)r{r|rDr)rset)rGrrMrrr rrHrHrIrwFs&zifgramStack.get_date_listcCs|jdd|r4t|j|ddtj}tdnXd|jkrLtdn@td |j |j ||j ||j |j |j d |j d f|dd }|S) a_Get reference value Parameters: unwDatasetName : string, unwrapPhase, or unwrapPhase_unwCor skip_reference : bool, skip reference value (for simulation only) dropIfgram : bool, skip ifgrams marked as dropped or not Returns: ref_phase : 1D np.array in size of (num_ifgram,) in float32 FrrrzIskip checking reference pixel info - This is for offset and testing ONLY.rzCNo REF_X/Y found! run reference_point.py to select reference pixel.z1reference pixel in y/x: ({}, {}) from dataset: {}ro)rrrrU) rrrrvrrOrr ValueErrorrPr&r'r)rGZunwDatasetNameZskip_referencer ref_phaserHrHrIget_reference_phasePs   zifgramStack.get_reference_phasec s&|jddt|jd|dkr<fdddDd}td ||}tj|jd d tj d }tj|jdtj d }|r|j }t |}t |d}t j|d } t|D]V} | j| d d| d |d||| ddddf} d|| dk<d|t| <q| W5QRX|S)z|Return the common mask of pixels with non-zero value in dataset of all ifgrams. Ignoring dropped ifgrams FrrWNcsg|]}|kr|qSrHr*r]rrHrIrals z,ifgramStack.nonzero_mask..)r,r*rz:calculate the common mask of pixels with non-zero {} valuerorrmrrr)rr{r|rDrOrPronesrrrr,whererrrrisnanrN) rGrrUrr/r9ZdropIfgramFlagZnum2readZidx2readrr_rrHrrI nonzero_maskes,     zifgramStack.nonzero_maskc Cs|jdd|dkrd}td||jtj|jtjd}|r\|j}t |dkr\t dd|krd t |j d d tj }tj|jtjdd }||}t|jd  }||}d} d|kr6|jdk r6d|jkr|jkr6nnF|jdk r6d|jkr|jkr6nn|dd|j|jf|} tj|tjd|j|jd} tt| d|d} tt|j| dd} tt|j| } tj|jddtjd} tj | d}t!| D]}|| }t"|| |j}|j#|dd||jd|dd||ddf|}d|kr| dk rd|t$| %d ddd|jd|jdf8}t!|jdD],}||ddddf|||9<qrtj&|dd| ||ddf<q|'W5QRX| S)NFrr+z3calculate the temporal average of {} in file {} ...rmr6zMALL interferograms are marked as dropped, can not calculate temporal average.r*rZ WAVELENGTHg@rprWrr;r@ rorz lines {}/{}rrr)(rrOrPrDrr7r+rrr Exceptionr rpirr$rr{r|r&ryr'rxr,r rceilrintrrrrrrminrtilerrrN)rGrr max_memoryZ ifgram_flagZ phase2rangerrMr/Zref_valds_sizeZnum_stepZrow_steprrr_Zr0Zr1rrrHrHrIrsf          ,*"zifgramStack.temporal_averagecCs|}|j|ddd}tj|jdtjd}t|jdD]<}||ddf}t|dkdt|dkd||<q>t|S)Nr)refDatermror) r-get_design_matrix4timeseriesrrrrrr8max)rG date12_listAZnum_connr_ZAirHrHrIget_max_connection_numbers*z%ifgramStack.get_max_connection_numbercCs|jdd|j}|j}|s,|jd|j}|||d}tt|d|d}tt||dd}tt||}|r|dkrtd |td ||ftd |g} t |D]2} | |} t || |g} d | || f} | | q| |fS) aSplit into chunks in rows to reduce memory usage. Parameters: max_memory - float, max memory to use in GB dim0_size - the 1st dimension size of all used datasets e.g., dim0_size = num_pair * 2 + num_date print_msg - bool Returns: box_list - list of tuple of 4 int num_box - int, number of boxes Frrr;g?r<r=rozmaximum memory size: %.1E GBz-split %d lines into %d patches for processingz" with each patch up to %d linesr) rrxryr+rrrr@rOrrBappend)rGrDZ dim0_sizerUrxryrEZnum_boxy_stepZbox_listr_Zy0y1rrHrHrI split2boxess(       zifgramStack.split2boxesc s|jdd|j|d}t|}g}t||D]}g}t|D]*}|d||||||dq@|d|||||tfdd|Dr0|fdd|Dq0tj|tj d }tj |d d }|S) aGet the indices of interferograms that forms the given connection level closure loop. Parameters: conn - int, connection level dropIfgram - bool, exclude the dropped interferograms. Returns: cp_idx - 2D np.ndarray in int16 in size of (num_cp, conn + 1) Each row for the indices of interferograms for one closure loop. num_cp <= num_date - conn Frrroc3s|]}|kVqdSrrHr^xrIrHrIre sz6ifgramStack.get_closure_phase_index..csg|]}|qSrHrrPrRrHrIra sz7ifgramStack.get_closure_phase_index..rmrr) r-rwrrrLrPrrrrunique) rGconnrrrcp_idxr_Zcp_date12_listrrHrRrIget_closure_phase_indexs   (z#ifgramStack.get_closure_phase_indexcCst|jdd}|d|d}|d|d}|j|dd}|jd}td||td ||s|||krd |d ||d } | d 7} t| n|dkrtd|d|j|dd} |jdd} t| jdD](} | | dk} | | | | | 8<qt j |||ft j d}t|D]P} || ddf|| df}}t j | |dd| |}t d||| <q(|sd}nD|dkrt j |dd}n*|dkrt j|dd}ntd|dt |||fS)a Computes wrapped sequential closure phases for a given conneciton level. Reference: Equation (21) in Zheng et al. (2022, TGRS) For conn = 5, seq_closure_phase = p12 + p23 + p34 + p45 + p56 - p16. Parameters: box - tuple of 4 int, bounding box in (x0, y0, x1, y1) conn - int, connection level of the closure phase post_proc - str, post processing of the closure phase: None - 3D array in float32, seq closure phase sum - 2D array in complex64, sum in time of the complex seq closure phase mean - 2D array in complex64, mean in time of the complex seq closure phase Returns: cp_w - 3D np.ndarray in float32 in size of (num_cp, box_len, box_wid) wrapped sequential closure phase for the given connection level. sum_cp - None or 2D np.ndarray in complex64 in size of (box_len, box_width) wrapped average seq closure phase for the given connection level, controlled by post_proc. num_cp - int, number of seq closure phase for the given connection level. Trrrrro)rUrz)number of closure measurements expected: z)number of closure measurements found : znum_cp (z) < num_date - conn ()z% --> some interferograms are missing!z'No triplets found at connection level: !F)rrUr6rmNrry?r,meanzun-recognized post_proc=z! Available choices: sum, mean.)rrwrWrrOr>rr5rrrrr,exprZr3angle)rGrrUZ post_procrZbox_widZbox_lenrVZnum_cpmsgZphaser4r_r9Zcp_wZidx_plusZ idx_minorZcp0_wZsum_cprHrHrIget_sequential_closure_phases@        z(ifgramStack.get_sequential_closure_phasec Cst|}dd|D}ddt|D}tttj|}t|d}t|}g}|D]\}}} ||f} || f} || f} z|| } || }|| }Wnt k rYqXYnXt j |t j d}d|| <d||<d||<| |qXt|d krtd |d St |t jS) aGenerate the design matrix of ifgram triangle for unwrap error correction using phase closure Parameters: date12_list : list of string in YYYYMMDD_YYYYMMDD format Returns: C : 2D np.array in size of (num_tri, num_ifgram) consisting 0, 1, -1 for 3 SAR acquisition in t1, t2 and t3 in time order, ifg1 for (t1, t2) with 1 ifg2 for (t1, t3) with -1 ifg3 for (t2, t3) with 1 Examples: obj = ifgramStack('./inputs/ifgramStack.h5') date12_list = obj.get_date12_list(dropIfgram=True) C = ifgramStack.get_design_matrix4triplet(date12_list) cSsg|]}t|dqS)_)tuplerrrHrHrIransz9ifgramStack.get_design_matrix4triplet..cSsi|]\}}||qSrHrH)r^idxZifgrHrHrI qsz9ifgramStack.get_design_matrix4triplet..rrmrorrz9 WARNING: No triangles found from input date12_list: {}! N)r enumerater)r2 itertoolschain from_iterable combinationsrKeyErrorrrint8rLrOrPstackastyper)rIZ date12_tuplesZ ifg_to_idxrZ closure_listMZC_listZdate1Zdate2Zdate3Zifg12Zifg23Zifg13Zidx12Zidx23Zidx13rowrHrHrIget_design_matrix4triplet]s4    z%ifgramStack.get_design_matrix4tripletcst|}dd|D}dd|D}ttt||t|}t}tdtfddD}dd||dD}tj|tjdd}t ||ftj}t ||ftj} t |D]} fd d||  d D\} } d || | f<d || | f<| | kr8|| d | d || | | | | | f<q|| | || d | d | | | | f<q|d kr|dkrtt|d kr|d}nd}|r |} t |ddd| f|dd| d dff}| dddd f} || fS)aReturn design matrix of the input ifgramStack for timeseries estimation Parameters: date12_list - list of string in YYYYMMDD_YYYYMMDD format refDate - str, date in YYYYMMDD format set to None for the 1st date set to 'no' to disable reference date Returns: A - 2D array of float32 in size of (num_ifgram, num_date-1) B - 2D array of float32 in size of (num_ifgram, num_date-1) Examples: obj = ifgramStack('./inputs/ifgramStack.h5') A, B = obj.get_design_matrix4timeseries(obj.get_date12_list(dropIfgram=True)) A = ifgramStack.get_design_matrix4timeseries(date12_list, refDate='20101022')[0] A = ifgramStack.get_design_matrix4timeseries(date12_list, refDate=0)[0] #do not omit the 1st column cSsg|]}|ddqS)r_rrr]rHrHrIrasz.cSsg|]}|ddqS)r_roror]rHrHrIrasrcsg|]}tj|qSrH)rYrZr[r]) date_formatrHrIrascSsg|]}|j|jdqSrjrkr]rHrHrIrasrmrpcsg|]}|qSrHrSr)rrHrIrasr_rronoN)rr)r2rrrrrrrrrrr)rIrFZdate1sZdate2sZ num_ifgramrrrrJBr_ind1ind2Zind_rrH)rprrIrGs:     ..    2z(ifgramStack.get_design_matrix4timeseriesc Cst|jd.}|ddd}|r8||ddd}W5QRX|j|d}||d}tj|jddtjd}tj j ||dd d|dd<|S) z[Get spatial perpendicular baseline in timeseries from ifgramStack, ignoring dropped ifgramsrWr)Nrrrrorm)rcond) r{r|rDr-rGrrrrlinalglstsq)rGrrMr%r!rJZpbaseTimeseriesrHrHrIget_perp_baseline_timeseriess z(ifgramStack.get_perp_baseline_timeseriesc sdkr dS|jdd}|jdd}ttt|t|}|krPtddSt|jd}td|jtdt j fd d |Dt j d |d dd<t D]>}|| krtd |tt||jd<tdqW5QRXdS)z8Update dropIfgram dataset based on input date12List2DropNFrTz^The same date12List2Drop / dropIfgram is already marked in the file, skip updating dropIfgram.zr+zopen file {} with r+ modez"update HDF5 dataset "/dropIfgram".csg|] }|kqSrHrHr]date12List2DroprHrIrasz2ifgramStack.update_drop_ifgram..rmrz.update MODIFICATION_TIME in HDF5 dataset "/{}"ZMODIFICATION_TIMEro)r-r)rr2rOr{r|rDrPrrrrrrtimersleep)rGrzZ date12ListAllZdate12ListKeptOldZdate12List2DropOldrMr rHryrIupdate_drop_ifgrams"  * zifgramStack.update_drop_ifgram)N)T)T)FN)r*NTF)r+NNFFN)T)F)r*FF)NTT)r+Tr;)r;NT)T)N)N)T)rrrrrJrNrrurvrrrr-r1rwr5r:rrKrOrWr^ staticmethodrnrGrxr}rHrHrHrIrIs8   5  A 3    > ( ! J 8 ? rc@s eZdZdddZdddZdS)rNcCs ||_dSr)rDrFrHrHrIrJszsingleDataset.__init__c Csjt|jd&}t|d}||dd}W5QRX|dk rf||d|d|d|df}|S)NrWrrorr)r{r|rDrr)rGrrMr rrHrHrIrszsingleDataset.read)N)N)rrrrJrrHrHrHrIrs rc@sHeZdZdZdddZdddZddd Zd d Zd d ZdddZ dS)ra3 Time-series object in HDF-EOS5 format for Univ of Miami's InSAR Time-series Web Viewer Link: http://insarmaps.miami.edu It contains a "timeseries" group and three datasets: date, bperp and timeseries. File structure: https://mintpy.readthedocs.io/en/latest/hdfeos5/#file_structure NcCs<||_d|_ddddddddddddddddddd|_dS)NrZ observationqualityr)rrrrrrr8r9r+varianceZ uncertaintyrr#r%r$r&r'r))rDrEdatasetGroupNameDictrFrHrHrIrJs*zHDFEOS.__init__TcCs<z*|j|r(tdtj|jWn YnXdSrKrLrTrHrHrIrN3s  z HDFEOS.closec s&|r td|jtj|j|t|j d|_ t|j d|_ g|_ t |jd}d|}dd|dddD|_|d dd|_t|j|_|j fd d|jD7_ d D]P|}|D]:}t||t jrt||jd kr|j d |qqW5QRXdS)NrVLENGTHWIDTHrW#HDFEOS/GRIDS/timeseries/observationcSsg|]}|dqSrrr]rHrHrIraFszHDFEOS.open..rr)csg|]}d|qS)z{}/displacement-{}rr]gnamerHrIraKs)zHDFEOS/GRIDS/timeseries/qualityz HDFEOS/GRIDS/timeseries/geometryrr)rOrPrErQrRrSrDrurrrxryrr{r|rr}rrrrrrrL)rGrUrMgrrHrrIr;s$  "z HDFEOS.openc Cst|jd"}t|j|_|ddd}W5QRX|jD]4\}}z|d|j|<Wq@||j|<Yq@Xq@|j|jd<dd|D}d|j kr|d|jd<| |jd|_ |jS) NrWz(HDFEOS/GRIDS/timeseries/observation/daterrcSsg|]}|dqSrrr]rHrHrIra_sz'HDFEOS.get_metadata..rr) r{r|rDrrrrrrErrr~rrHrHrIruSs  zHDFEOS.get_metadatac CsDt|jd*}|d}dd|dddD|_W5QRX|jS)NrWrcSsg|]}|dqSrrr]rHrHrIrahsz(HDFEOS.get_date_list..rr)rGrMrrHrHrIrwes&zHDFEOS.get_date_listc s|jdd|dkr$dd|j|jg}|dkr8tdg}nt|trH|g}t|jdL}|d dd|j }|d |}|rt d |jt |jd kr|dd}nt |jd kr||d |d |d|d f}ntj|jdtjd }fdd|D}tdd|Dr6d|dd<n|D]} d||j| <q:|dd|d |d |d|d f|}tdd|jDrt|}W5QRX|S)aRead dataset from HDF-EOS5 file Parameters: self : HDFEOS object datasetName : (list of) str box : tuple of 4 int, for (x0, y0, x1, y1) print_msg : bool Returns: data: 2D or 3D array Example: obj = HDFEOS('S1_IW1_128_0593_0597_20141213_20171221.he5') obj.read(datasetName='displacement') obj.read(datasetName='displacement-20150915') obj.read(datasetName=['displacement-20150915', 'displacement-20150921']) obj.read(datasetName='incidenceAngle') FrNrrrWrzHDFEOS/GRIDS/timeseries/{}/{}rrorrrmcs g|]}|dddqSr rr]r rHrIraszHDFEOS.read..css|] }| VqdSrrHr]rHrHrIreszHDFEOS.read..Tcss|]}|dkVqdSrrHr]rHrHrIres)rryrxrrrr{r|rDrrrPrOrrrrrrrrr) rGrrrUrMZ groupNamerrrrrHr rIrksF    &z HDFEOS.read)N)T)T)NNT) rrrrrJrNrrurwrrHrHrHrIrs   )rQr{rdrZrYr{numpyr mintpy.utilsrrrrr r rrrrZ dataTypeDictZtimeseriesKeyNamesrr rZdatasetUnitDictrrrrrrHrHrHrI s  E9?