Zft dZddlZddlZddlZddlZddlZddlZ ddlm Z m Z mZddlmZddlmZddlmZmZddlmZddlmZdd lmZmZmZmZ dd l!m"Z"m#Z#d Z$d Z%d dgZ&dZ'gdZ(de jRd<dZ*dAdZ+ dBdZ,gdfdZ-dCdZ.dDdZ/dEdZ0dFdZ1dGdZ2dHdZ3 dId!Z4dJd"Z5ifd#Z6igdfd$Z7igfd%Z8gd&d'dd fd(Z9gifd)Z:ddd*d+gfd,Z;d+ddd-d+gddd.dddd/f d0Z<e=dfd1Z> dKd2Z?d3Z@dLd4ZAdMd5ZBddd6e jze jgdfd7ZDde=dfd8ZEddd6e jze jgdfd9ZF dNd:ZGdGd;ZHdOd<ZI dPd=ZJ dQd>ZK dRd?ZLdOd@ZMy)SzUtilities for plotting.N)datespyplotticker)make_axes_locatable)stats)TIMESERIES_DSET_NAMESTIMESERIES_KEY_NAMES) ColormapExt) coordinate)networkptimereadfileutils0)draw_lalo_label draw_scalebarg@$@g.@g @) z#1f77b4z#ff7f0ez#2ca02cz#d62728z#9467bdz#8c564bz#e377c2z#7f7f7fz#bcbd22z#17becf)g _g _Bzaxes.formatter.limitsc2|jrtjj|jrVt d|jt j |jdtjt|_ |jt j|jjdd|_ |j|jdddf|jdddfd dd|_t j|jjjdd|_|jt j|jjdd|_ |j!|jdddf|jdddfd dd|_ t j|jjjdd|_ |S|S#t"$rY|SwxYw) zRead pts_* optionsz$read points lat/lon from text file: )r)usecolsdtypeNrrF print_msg)pts_fileospathisfileprintnploadtxtbytesastypefloatpts_laloarrayreshape geo2radarpts_yxT radar2geo ValueError)inps coord_objs J/home/exouser/operations/rsmas_insar/tools/MintPy/src/mintpy/utils/plot.py read_pts2inpsr06s }} 6 4T]]ODE 4==%uMTTUZ[  }} /77A> )) MM!Q$  MM!Q$ *  1  hht{{+--55b!<  {{hht{{+33B:  %// AqD! AqD!0q DM HHT]]355==b!DDM K4K   K s A>LS^+S^+Yq\)I099!I %9H9+34a3!4H4  }Bx{m1=> O :4s 5B< Cc|sg}t|tr|g}tjj tjj |\}}t j|}|d}t|dt|dz}|dk(r]t|dk(r|d}d|vr|dz }n|djd d}|jjd r|d z }n|tvrt|dk(rd |jvr|d } nd |jvr|d } nd} | s|d}n | d|d}d|vrvtjj |jddj|d} | r0|| jdr| nd| z }n|dk(r.t|dk(r|d}n|djdrd}n|}n|dvrtt j|} | dkDr^t|dk(rPtjj |j!dd} t| dkDr| ddznd} | |dz}n[|}nXtjj |}t|j#dd}|dkDrt|dk(r |d|dz }|j#ddr$|d}|d|dz |d|dz z|kr|dz }|j#ddr|dz }|j#dd t$j&zt$j&g}|d|dz }|dt$j&zk7r'|t|k(r t|}|t|z }|S)!amGet auto figure title from meta dict and input options Parameters: fname : str, input file name datasetNames : list of str, optional, dataset to read for multi dataset/group files inps_dict : dict, optional, processing attributes, including: ref_date pix_box wrap Returns: fig_title : str, output figure title Example: 'geo_velocity.h5' = auto_figure_title('geo_velocity.h5', None, vars(inps)) '101020-110220_ERA5_ramp_demErr' = auto_figure_title('timeseries_ERA5_ramp_demErr.h5', '110220') FILE_TYPEWIDTHLENGTH ifgramStackrrunwCor_unwCor-ion_ionref_dateREF_DATEN_ timeseriesgeometrybperp)z.h5z.he5BANDS1 - pix_boxrr=_subwrapF_wrap wrap_range) isinstancestrrrsplitextbasenamerread_attributeintlensplitlower startswithr keysget_dataset_listrsplitgetr pi)fname datasetNames inps_dictfbasefextatrk num_pixel fig_titlern proc_suffixnum_dsetfpartssuffixnum_bandboxr|s r/auto_figure_titlers  ,$$~ ''""277#3#3E#:;KE4  ! !% (C KACL!CH $66IM |  !$QI5 Y& $Q--c215I{{}''.V# " "s<'8A'= ) ) ,H 388: %:HH$QI#*Al1o%67I 5 ''**5177 EaHNNtTUVWKK,B,B3,G[qQ\P]M^^ j |  !$QI !_ ' ' 0II  x0078 a flip up-downz -> flip left-right)rformatrrr invert_xaxis invert_yaxis)metadatarIrflip_lrflip_udmsgs r/auto_flip_directionrsGG ',=,P): ;< % & , , . 9 9# >G % %CG ( (C  #J ~  OO   OO  G rNc&|d|dz |d|dz z|z}|dkDrd}nB|dkDrd}n:|d kDrd }n2|d kDrd }n*|d kDrd}n"|dkDrd}n|dkDrd}n|dkDrd}n |dkDrd}nd}|d|dz |d|dz zdkrd}|dkr|tjd|z z}n!|dkrn|tj|dz z}ttj|}|dkDr+|r)t d|dt dj ||S)a^Calculate the default/auto multilook number based on the input 3D shape. Parameters: box - tuple of 4 int in (x0, y0, x1, y1) for the spatial bounding box num_time - int, the 3rd / time dimension size max_memory - float, max memory in GB Returns: multilook_num - int, multilook number rrr=rgB geAgAg*uA gAg*uAgAg*uAgAi @@ztotal number of pixels: z.1EzL* multilook {0} by {0} with nearest interpolation for display to save memory)r sqrtrceilrr)rnum_time max_memoryrr multilook_nums r/auto_multilook_numr s]Q#a&SVc!f_5@I h "- h "- h "- h "- w !- w !- v !- v !- v !-23- AQCFSVO, 9 Sz!122 s   c!122  ./MqY (389 \ccdqrs rNrcttjt|t|z }t|dt|dz }|d|dz |z }t tj ||zd}t tj ||z d}|dk(r|}n|dk(r|}tj |tj |z|krE|dz|dzkDr|dz }n|dz }tj |tj |z|krEttj |}ttj |}||fS)a"Get optimal row and column number given figure size number of subplots Parameters: subplot_num : int, total number of subplots data_shape : list of 2 float, data size in pixel in row and column direction of each plot fig_size : list of 2 float, figure window size in inches fig_num : int, number of figure windows, optional, default = 1. Returns: row_num : number of subplots in row direction per figure col_num : number of subplots in column direction per figure rr?r6)rr rr$rWrrint) subplot_num data_shaperbfig_numsubplot_num_per_figdata_shape_ratio num_ratiorow_numcol_nums r/auto_row_col_numr:s@bggeK&85>&IJKZ]+eJqM.BB hqk),<z%auto_colormap_name..~s!G!}"4!Gsgrayjetz colormap:)rrranyr)r cmap_name datasetNamerds_namesrs @r/auto_colormap_namerqs [)3{+;+A+A#+Fq+IJ K L 0Xk5J5U5UV_5`  H "!Gh!GGFU  k9% rNctj|tj|tj|z}tj|j t }tj|j t }||k(r d}||g}d}ntjtjtj|j t } t||z | z dz} | |kr:| }|| dz z || dz zg}|r2d|jd|d} | dz } t| n t |jdddk\r|dkDrd d lm} | |||} ntj |} d}tj"| tj$| g}d}t|d tj&ztj&z d k\r|d ntj&d z|d <t|dtj&z tj&z d k\r|dntj&|d<|||fS) Nrrz%data has uniform and limited number (z <= )z) of unique values --> discretize colormapdr)multilook_dataMbP?r)r uniqueisnanisfiniterTr#r$rWabsdiffrr4rrmintpy.multilookrr&nanminnanmaxr)data num_multilookmax_discrete_num_stepr unique_valuesmin_valmax_valcmap_lutvlimvstep min_num_steprrdata_mlis r/'auto_adjust_colormap_lut_and_disp_limitrsIIdBHHTN?R[[5F#FGHMff]#**51Gff]#**51G'! rvvbggm456==eDGg-6:;  0 0#HeAg%wq'89D=m>P>P=QQUVkUllmnBBc 4::ab>"c)ma.?;)$ }M88D>HIIh'8)<=D M"%T!Wruu_!5!=!Fd1gBEETVJDG!$T!Wruu_!5!=!Fd1gBEEDG T= ((rNr皙?c t|dtjr:|Dcgc]/}|j|j j dz dz z1}}||d|z }|d|zdz}t |} t || z dz} t |} t || z dz} | dkDr| dz } d} | dkDr| dz } d} | dkr| dz} d} | dkr| dz} d} tj| | ddd} tj| | ddd}n&tj|j\} }|j| ||s|j}|jj|jj}d |j z }t#dt%j&||| z j(zdz d z j+t }|s|d krd}n|d krd }n |dkrd }nd}tj,d |_|j0j3tj4||j0j7tj,d|r9|j0j9tj:t=dd||j?||| |fScc}w)aMAdjust X axis Parameters: ax - matplotlib figure axes object datevector - list of float, date in years i.e. [2007.013698630137, 2007.521917808219, 2007.6463470319634] OR list of datetime.datetime objects every_year - int, number of years per major locator buffer_year - float in years, None for keep the original xlim range. Returns: ax - matplotlib figure axes object xmin/max - datetime.datetime object, X-axis min/max value rrv@Nr皙?(@rg@r=rz%Y-%m-%d %H:%M:%Sz%Y  labelsize) r~dtdatetimeyear timetupletm_ydayrmdatesnum2dateget_xlimset_xlim get_figureget_window_extent transformeddpi_scale_transinvertedwidthrWr rdaysr# DateFormatter fmt_xdataxaxisset_major_locator YearLocatorset_major_formatterset_minor_locator MonthLocatorr tick_params)rI datevectorfontsize every_year buffer_year every_monthrt0t1y0m0y1m1xminxmaxfigbboxrZs r/auto_adjust_xaxis_dater6s*Q--MWXaff 5 5 9VCCX X ]k ) ^k )C / WCbD 01r WCbD 01r 7bAgba 7bAgba 6bAgbb 6bAgbb{{2r1a+{{2r1a+r{{}5 tKKd mmo##%11#2E2E2N2N2PQdjj BGGETD[,>,>$>$G!$KLSSTWXY  1_a 1_a 2 a .2 ''(;HH  !5!5d!;< ""6#6#6uQr+7N#OPNNXN& tT>WYs4Kc tjtj|r tdtj|}tj |}||z }||n|d|zz }||n|d|zz}|j ||g|S)a:Adjust Y axis lower/upper limit. Parameters: ax - matplotlib figure axes object data_list - list(float), Y-axis values fontsize - float, font size ymin/max - float, lower/upper Y-axis limit Returns: ax - matplotlib figure axes object z/All NaN values detected in the input data_list!r)r allrr,rrset_ylim)rI data_listr(yminymaxdmindmaxdranges r/auto_adjust_yaxisr@s vvbhhy!"JKK 99Y D 99Y D D[F#4f )FKKM)RVJ-?FKKM)QVK-@FKKM)XVM-BFKKM)RVL-AFKKM)TVL-AFKKM)QVL-A FKKM)c3ZVF^&&z2J2<=v||C #=G=2<=v||C #=G=ennT#g.?*@%ABCH..x8E: 5 5 78:1= ,sK75K<Lc bd|jvrd|d<d|jvrd|d<d|jvrd|d<d|jvrd|d<d|jvrd |d<d |jvrd |d <d |jvrd |d <d |jvrd|d <d|jvrd|d<d|jvrd|d<d|jvrd|d<d|jvrddg|d<d|jvrd|d<d|jvrd|d<d|jvrd|d<d|jvrd|d<d|jvrd |d<t|dtrt|dj}nJt|dt j jr|d}ntdj|d|d}d} tjt|}tj|\} } tj|d } tj|}t!|} t#j$| }t#j$| }t'| D]W}||j)d!\}}|j+|}|j+|}||||z ||<| || |z ||<Y|rXt-d"t#j.t#j0|d#d$t-d%t#j.|d&tt3t5|t5|z }|sd'|d<|Dcgc]}|j)d!d }}|Dcgc]}|j)d!d}}tjtt3t5||z}tt3t5|t5|z }|Dcgc]}|j+|}}|Dcgc]}|j+|}}|Kt7|}t/|}|dd }|dd}|r8t-d(t-d)||gt-d*j|d|drt9|j;d+|d|d,} t j j=||-}!t j>jA| ||!.}"|"jBjE|d/|"jG|d |d0|Dcgc]}||j+|}#}ttI|#|D$%cgc]\}$}%|% }}$}%|rA|Dcgc]}| | }&}|Dcgc]}|| }'}|jK|&|'d1d|d|d2|r>|Dcgc]}| | }&}|Dcgc]}|| }'}|jK|&|'d1d|dd32|dr|D]}(|(j)d!\})}*|j+|)}+|j+|*},t#jL| |+| |,g}%t#jL||+||,g}-|@||j+|(}.|.z |z z }/|jK|%|-d4|d| ||/5|jK|%|-d4|d| d65|D]}(|(j)d!\})}*|j+|)}+|j+|*},t#jL| |+| |,g}%t#jL||+||,g}-|@||j+|(}.|.z |z z }/|jK|%|-d7|d| ||/5|jK|%|-d7|d| d65|dr|jOd8|d0tQ|| |d|d9d }tS|||d0}|jU|d |d0|jW|d |d0|jEd:d;|ddddd<|d|jY|dd=d6d>|d?|dra|dr\t jZj]ggd6d@dAB}0t jZj]ggd6dCdDB}1|j_|0|1gE|Scc}wcc}wcc}wcc}wcc}wcc}%}$wcc}wcc}wcc}wcc}w)FaPlot Temporal-Perp baseline Network Parameters: ax - matplotlib axes object date12List - list(str) for date12 in YYYYMMDD_YYYYMMDD format dateList - list(str), for date in YYYYMMDD format pbaseList - list(float), perp baseline, len=number of acquisition p_dict - dictionary with the following items: fontsize linewidth markercolor markersize cohList - list(float), coherence value of each interferogram, len = number of ifgrams colormap - str, colormap name disp_title - bool, show figure title or not, default: True disp_drop - bool, show dropped interferograms or not, default: True Returns: ax - matplotlib axes object r(rr?rrDrErFrgNxlabelylabelzPerp Baseline [m]rMzAverage Spatial Coherence cbar_size3%r[TcolormapRdBurrrrG disp_drop disp_legendr)rnumberunrecognized colormap input: {}ffffff?rrpzmax perpendicular baseline: .2fz mzmax temporal baseline: z daysFzshowing coherencez data range: zdisplay range: {}rXr9)vminvmax)cmapnormr rNkoalphamsmfcr--)lwrcrrkzInterferogram NetworkrLrPrQrR)gQ?gq= ףp?z axes fractionxycolorxycoordsr(solidz Ifgram used)rlsrKdashedzIfgram dropped)handles)0rr~rr rvmplcolorsLinearSegmentedColormapr,rr r[rZr]date_list2tbaserYrr rrrindexrrWrrSr\rTr append_axes Normalizecolorbar ColorbarBaserIr& set_labelzipplotr&rcr6r@ set_xlabelrdannotatelinesLine2Dre)2rIrfrl pbaseListrhdate12List_droprrrg transparencyrr' tbaseList ifgram_numpbase12tbase12rm_dates_datem_idxs_idxdate12List_keeprjrk dateList_keep dateList_drop idx_date_keep idx_date_dropdata_mindata_maxdisp_mindisp_maxcaxrcbar cohList_keeprprarny_listridate1date2idx1idx2yvalval_norm solid_line dash_lines2 r/ plot_networkr8s` &FKKM)BF:,>FKKM)AF;,?FKKM)HF=,AFKKM)BF<,@FKKM)DF9,=FKKM)DF8,<FKKM)DWF8,<FKKM)D_F<,@FKKM)DF;,?FKKM)DF;,?FKKM)FF:,> FKKM)S#JF6NFKKM)DF<,@FKKM)DF;,?FKKM)DF=,AFKKM)AF<,@FKKM)DF8,<&$c*6*-.77 F:& (J(J Kj!:AA&BTUVVYGL~~fX./H..x8E:%%h/2I&&z2JZJhhz"Ghhz"G : 9#A,,S1v&v&u% %(88 u% %(88 9  ,RVVBFF7O-DS,ILM ,RVVG_,=UCDT#j/C4H"HIJO #{)881qwws|A8G8(781qwws|A8G8NN6$s7W3D/E*F#GHM4H M0B BCDM0=>1X^^A&>M>0=>1X^^A&>M>w<w<&>!$&>!$  % & L(H!5 67 8 %,,VF^< = + %b)55gvk?RX^_jXk5lC::''XH'ED<<,,St$,GD GG  &*<  = NN6,/&:LN M?NN 0 0 34N N)/L/0R)STA1TT$12q%(22(561)A,66 CF<4HfUbNcd$12q%(22(561)A,66 CF<4HfUk% WF!<<,LE5>>%(D>>%(D%+uT{34A)D/9T?;?H GGAq#&"5\TRZ^G \ GGAq#&"5\SG Q Rl ,vj7I J J 9K+1,+? AAB DB 2y6*3E FBMM&"VJ-?M@MM&"VJ-?M@NN46*;MDt4Ah# F8$S,vj7I  Kkvm4YY%%b"CGM%Z YY%%b"CHL\%]  :y1 2 Iw98?>*OT3636s<c>3d0dd 2d' d< d d"? d' d,c d|jvrd|d<d|jvrd|d<d|jvrd|d<d|jvrd|d<d|jvrd |d<d |jvrd |d <d }tj|}tj|\}}t t t |}g} |D]5} |j| } |j| | j| 7|rE|D cgc]} ||  } } |D cgc]} ||  } } |j| | d ||d|d|d| r>| D cgc]} ||  } } | D cgc]} ||  } } |j| | d||dd|dr|jd|dt|||d|d d}t|||d}|jd|d|Scc} wcc} wcc} wcc} w)a` Plot Perpendicular Spatial Baseline History Parameters: ax - matplotlib axes object dateList - list(str), date in YYYYMMDD format pbaseList - list(float), perp baseline p_dict - dictionary with the following items: fontsize linewidth markercolor markersize disp_title : bool, show figure title or not, default: True every_year : int, number of years for the major tick on xaxis dateList_drop - list(str), date dropped in YYYYMMDD format e.g. ['20080711', '20081011'] Returns: ax - matplotlib axes object r(rr?rrDrErFrGTr)rr|z-ko)rrrrrrrzPerpendicular Baseline HistoryrNrLrzPerpendicular Baseline [m])rr r[r]rSrrrremoverrrcr6r@rd)rIrlrrhrrrr'idx_keepidx_dropridxrnrs r/plot_perp_baseline_histrsG"FKKM)RVJ-?FKKM)QVK-@FKKM)XVM-BFKKM)RVL-AFKKM)TVL-AFKKM)QVL-AL~~h'H..x8E:E#h-()HH nnQ$,-q%(--(011)A,11 \f[>Q,'VM-B  D$,-q%(--(011)A,11 L,'V  5l 5z@R S J 9K+1,+? AAB DB 2y6*3E FBMM. 9KML I-.1 .1s G6+ G; H2 HgFrwct|trt|j}n3t|tj j rntd|tj||}|r|D cgc]} | jdd} } |D cgc]} | jdd} } ttt| | z|D]4} fd| jdD\} }tj||| f<6|j d}tj"tj$|\} }tj&tj(| |z }tj*tj,|}tj||dk(<|j/|ddd }|j1tj2j5j7||j8z|j/||d| }|j1tj2j5j7||j8ztj:|d zd z d z}|j=dtj:|d zd zdz |r|j?d|n|j?d|z||jAjCg|jEjCg|jGd||fScc} wcc} w)zSPlot Rotated Coherence Matrix, suitable for Sentinel-1 data with sequential networkzunrecognized colormap input: rprrc3@K|]}j|ywrr)rr date_lists r/rz4plot_rotate_diag_coherence_matrix..9sH)//!,Hr5gray_rr)rrr)rrrrr?rr|r}off)$r~rr rvrrrr,r`rarrZrSr\r nanrwhererrWrdiagonesimshow set_transform transformsAffine2D rotate_deg transDatarrr9 get_xaxis set_ticks get_yaxisrI)rIcoh_list date12_listdate12_list_droprr disp_halfrrorrjrkrirrnum_imgnum_conndiag_matimr<rs @r/!plot_rotate_diag_coherence_matrixr&s$4 )) D#**<< = 8?@@##K:G,78q1773<?88,78q1773<?884Gg$5 678 & )FHfll36GHJD$"$&&GD$J  ) mmAG288G,,-JD$vvbffTD[)*Hwwrwww'(H!vvHX^ 8(3 ?BS^^,,.99*E TU 7 =BS^^,,.99*E TU 778Q;# $s *DKKBGGGQJN+C/0 At CHd#LLNR LLNR GGEN r6M?98s 7L7L<c  d|jvrd|d<d|jvrd|d<d|jvrd|d<d|jvrd|d<d |jvrd|d <d |jvrd |d <d |jvrd j|d|d <d|jvrd|d<d|jvr|d|d<d|jvrd|d<d|jvrd |d<d|jvrd|d<d|jvrd |d<t|dtrt |dj }nJt|dt jjr|d}ntdj|dtj|}tj||}|r|Dcgc]}|jdd}}|Dcgc]}|jdd} }tt!t#|| z|D]4} fd| jdD\} } t$j&|| | f<6t%j(t%j*|j,d} t$j&| | dk(<|j/| dddd }|j/|||dd|ddd }|j,d}|d!krd"}n|d#krd$}n |d%krd&}nd!}t!t1d||}|j3j5||j7j5||j9d'|d(|j;d'|d(|j=d)d*|dd d d d +|d r|j?|d |drOtA|}|jCd,d-d-.}tEjF||/}|jI|d|d(|rC|dr>|jKggd01|jKggd21|jM|dd3|||fScc}wcc}w)4aPlot Coherence Matrix of input network if date12List_drop is not empty, plot KEPT pairs in the upper triangle and ALL pairs in the lower triangle. Parameters: ax : matplotlib.pyplot.Axes, date12List : list of date12 in YYYYMMDD_YYYYMMDD format cohList : list of float, coherence value date12List_drop : list of date12 for date12 marked as dropped p_dict : dict of plot setting Returns: ax : matplotlib.pyplot.Axes coh_mat : 2D np.array in size of [num_date, num_date] im : mappable rBrCr(rr?rrDrErFrGTrz {} MatrixrvrrMr)rrr[ legend_locrOryr{rprrc3@K|]}j|ywrr)rrrls r/rz(plot_coherence_matrix..sG(..+Grr5rrnearest)rrr interpolationr 2 rrz Image NumberrNrPrQrRrXrur~)rzUpper: Ifgrams usedrJzLower: Ifgrams all)r7 handlelength)'rrr~rr rvrrrr,r rYr`rarrZrSr\r rrrrrrrrrrrdr&rcrrrCrrrre)rIrfrgrrhrrorrjrkrirrrrdate_num tick_step tick_listdividerrrrls @r/plot_coherence_matrixrWslFKKM)[VI->FKKM)RVJ-?FKKM)QVK-@FKKM)XVM-BFKKM)RVL-AFKKM)TVL-AFKKM)[EWEWX^_hXiEjVK-@FKKM)UVJ-?FKKM)VIEVVL-A FKKM)ZVF^FKKM)TVK-@FKKM)VVL-AFKKM)TVM-B&$c*6*-.77 F:& (J(J Kj!:AA&BTUVV&&z2J##J8G,67q1773<?77,67q1773<?77$s7W#4567% )FGV\\#5FGJD$"$&&GD$J  ) wwrwww}}Q/01H!vvHX^ 8(3i XB   F^A  F^A   B}}QH"}Q BR CR%'U1h 23ILLNY'LLNY'MM.6*+=M>MM.6*+=M>NN46*;MDt4Al VK()k%b)!!'4T!:||BC( vl+fZ6HI6-0 B34 B23 f\* ; w?c87s )Q> R r=c z |stjdtj|\} t t j |}tjdd|\}}|d}|j|dd} t fd d Dr.|jt d t d d ddt |d|j| ||jd||d}|j|t j|j!d|fd||j#d||j%d||j&j)t+j,|j.j1t+j2dd|j.j)t+j2ddd|j.j5t+j6|D]}|j9dd |dddd!|j;t<j>jA|dd"} tCd#| |jE| d$dd%&|rtjFy'tjH|y')(zPlot the histogram for the number of triplets with non-zero integer ambiguity. Fig. 3d-e in Yunjun et al. (2019, CAGEO). Parameters: fname - str, path to the numTriNonzeroIntAmbiguity.h5 file. Aggrr)rrfigsizerRdBu_rr)rrc3BK|]}|jvywr)r)rkeyrs r/rzBplot_num_triplet_with_nonzero_integer_ambiguity..s :3#((*  :s)REF_YREF_Xrrswhiter=)rrF)rIr)rIz $T_{int}$rNT)rlogbinsz0# of triplets w non-zero int ambiguity $T_{int}$z # of pixelsr)basenumticks) rrg333333?g?r6333333?r|g?r)rrsubsrPrQrRz.pngzplot and save figure to filetight, bbox_inches transparentdpiN)%rCswitch_backendrreadrr rsubplotsrr8rrrrchistrrrrdr r$rAutoMinorLocatoryaxisr! LogLocatorset_minor_formatter NullFormatterr& tight_layoutrrrrsavefigshowclose) rdisp_fig font_sizerbrrr4rrIrout_figrs @r//plot_num_triplet_with_nonzero_integer_ambiguityrsY  5! e$ID# ryy D||!1h?HC QB 4hi @B :(9 :: CL!3s7|#4cQOe4LLLLL L2 QBGGD"((4. ! ) ) +At9$TGRMMEPYMZMM-)M4HHv6689HHv00dRHIHHv00dR6[ ]^HH  !5!5!78E Vty"4t  EE!!%(+,D1G ('2KKW$CKH    # rNr rOzResidual phase RMS [mm]c  |stjdtj|tj t } | dddfj tjdz} t| dddf}tj|\}}tj|} ttjtj|j!dd zd z }tj| }tj"| \}}|j%|||j&d tj(|}|j%|||||j&ddtj*|d|}||kD}tj,|dk(r&|j%|||||j&ddt/||||\}}}|j1tj||gtj||gdd|t3|tj4|||d}|j7| ||j9dd|dddd| rt;|}|j=dd d!"}|j1tj>|j@tjd#z|d$d%d &|j1tj>|j@tj|d#z||d$d%d&tj,|dk(rK|j1tj>|j@tj|d#z||d$d%d&|j1tjddgtj||gd|jC|jE|jGddg|j9dd|dddd|jIjKg|jMjOg|r|jQ|d'|(| rU|jE\}}||z d)z}||z d#||z zkDr|d*z}|jSd||||z d+zz||zfd,d-|.|r tUd/||jW|d0d1|rtjXytjZy#tjtj|j!d zd z }Y\xYw)2aH Bar plot for the phase residual RMS time series. Parameters: rms_file - str, path to the time series RMS text file Generated by utils1.get_residual_rms(). cutoff - float, cutoff value of MAD outlier detection fig_size - list of 2 float, figure size in inch rrNr@@rr)rr=rrC0)rC1zReference date)rrKr5)centercutoffTdarkgrayz Exclude date)r)z--kzMedian Abs Dev * rJ)r;rNrPrQrRrXz10%z2%r~r6onone)rrF)r7r;r(rr}g?rrrzsave figure to file:r )r r ).rCrr r!r"r#rfloat32rSr r]r&rTr^r_rrrrbrargminmedian_abs_deviation_thresholdrr6rr@rrdr&rrrrr9get_ylimrrrrset_ticklabelsrerrrrr)rms_filer&rrrbr tick_year_numrrydisp_side_plotdisp_thres_textrsfcrms_listrrr'rmrmsr4rIref_idx rms_thresholdex_idxr2r3rax2r;r<yoffs r/plot_timeseries_rmsr;s  5! HE * 1 1# 6B!Q$xrzz*U2HR1XI..y9E: HHUOE8(=(=(?1EFqHJ  ((8 Cll8,GCFF5#y~~TF2iinGFF5>3w<tK[F\66s2fUM = F vvfdl uV}c&k9>>Sab.b*iTabRtGGBHHdD\ "BHHm]-K$Le%fX. 0 2ryymG cW]eij 66&$,  HHRWWSYY 3F;cA3v;PSY_gqH r 1a&!288]M,J#KUS R[[]# aV f #D  F !!"% &&r* j%) D[[] dt s" D C4$;$7 7 CKD 'x0T 4//1EG  D  $g. Gd C     M8FF2775>0023A5a7 s /AT>Uc " ddlm}ddlm}|rtnd}|j \}} t |jtrt|jjn |j} t|} d| d<t| |jd} |jr |jn|jd d } |jr |jn|jd d }d |j!vrFt#j$| |d|d\}}t#j$| |d |d\}}||||f}|j'|| ||j(\}}}|j*dk(r,t-j.d|d| d|dt d|S|j0r|dj3t5j6|j0d|j*t5j6|j0d|j*||d}t5j8|}d}|||d||d||dz }t |d |j!vrt#j:|||\}}|j<r|j>|j>j@dk(r |j>n t5jB|j>d}tE|}|jG||dd\}}|||fdk7}||}||}||}|j*dk(r tId|jJr'|jJ|vrtId|jJd|d }|jLr|d!vrd |_&|d"|d#t|jNdzd$d%d&'}|jLrm|d(d)}||d*k(rd*nd+z }|d,|jLd-z }||jJrd.|jJd/nd/z }|||d0tQ||d1| |d2||d3|jL|d*k(rd*nd+} |jS|| || ||j(|jL|jT||jV4 }!|jJrH|jYj[|jJ}"|!|"}#t5j\|#s|!|#z}!|!| z}!|j^r}t5j`|D$cgc]}$|$|j^vc}$t4jb5}%t5jd|%dkDr&|d6||%||%}||%}||%}|!|%}!t5j\|!}&t5jd|&dkDr-|d7t5jd|&d8|d9||&tg|||!D]C\}'}(})t5j\|)r| |)|z | |z z }*|jh|(|'fd:|*i|En|d;|jh||fd:da\Plot GNSS as scatters on top of the input matplotlib.axes. Parameters: ax - matplotlib.axes object SNWE - tuple of 4 float, for south, north, west and east inps - Namespace object, from view.py metadata - dict, mintpy metadata Returns: ax - matplotlib.axes object r)Geod)gnsscyrrargsrKs r/zplot_gnss..erNmUNITr disp_unitr START_DATENEND_DATEUTM_ZONEr=r) start_dateend_datesourcezNo GNSS found within z during rw!z continue without GNSS plots.WGS84)ellpsz1nearest GNSS site (potential --ref-gnss choice): z at [rQrRrrHzNo GNSS left after --mask-gnss!zinput reference GNSS site "z" not available!re)velocityrq displacementz,WARNING: --gnss-comp is not implemented for z. file yet, set --gnss-comp = None and continuerr6r)r edgecolorsrzorderz------------------------------zplotting GNSS rQrRz in IGS14 reference frame in z directionz with respect to  ...z#number of available GNSS stations: z start date: z end date: zcomponents projection: ) metaobs_type site_namesrKrLrM gnss_comp horz_az_anglerredorz+ignore the following specified stations: zignore the following z5 stations due to limited overlap/observations in time rzshowing GNSS locationsr<)rr()8pyprojr=mintpy.objectsr>rrr~rvrr rBscale_data2disp_unitrGgnss_start_dater gnss_end_daterr^ utm2latlon search_gnss gnss_sourcer4warningswarnref_laloinvr tiler+ latlon2utm mask_gnssmskndimprodr r(r, ref_gnss_sitegnss_componentgnss_marker_sizer get_los_obsrZ gnss_redorrr ex_gnss_sitesr&bool_sumrscatterdisp_gnss_labelrr),rISNWEr-rrr=r>vprintrrrrunit_facrKrLsouthwestnortheastrX site_lats site_lons site_distn_indrrlcoordsite_yssite_xsflagr plot_kwargsrWsite_obsref_indref_valraex_flagnan_flaglatlonobsr site_names, r/ plot_gnssrYs#U%AFJD$2N=OqQQ c X]]_$"~~h 9M 9 ~~$((.((--1,dhh"''$((2L8$ ??9i@1E7G#$)% dO dO ??a >? ? d00 B6t7I7I6JJZ[\\A q(RR"=aS@nop..1ccRTUK v Q*_z.@ .t/B/B.C:NN t?Q?Q"4#5#5"6d;W]]s 4S_4EFGj\*+hZ()()<)<(=>? "#j:n##!!##)),,$      '')//0B0BCGw'G88G$G# H   hhLAT%7%7 7LTVT\T\]Gvvg"EjQXFYEZ[\'1 %wh/ %wh/ #WH-88H% 66( a  *266(+;*<>K J K I; MsZ c  d} d} gd} t| } dgdg| dz zz}td|dtj||d d }|d }|d }|d}|| d| z}|etj|Dcgc]}||vc}tj }tj |dkDr||}||}||}||}td|dtj|}t|dt|d}}t|j||dd\}}|rtj|dn&tj||ftj }|j}tj|tj tj"z}t%j&|}t)|D]}||||}!} d| cxkr|krEnnBd|!cxkr|kr7nn4||!| fr-| |!| dz|!dzf}"tj|d|"d| z||<|j+|dz|dzd|d|||j-tj.||z }#td|#dd|r=td ||j1j3|}$|||$z}|||$z}td!|tj4tj6|tj6|}%|=td"|d| |%||dk\z}%|%||dkz}%|%||dk\z}%|%||dkz}%||%}||%}||%}td#tj8|dd$tj:|dtd%tj8|dd$tj:|dtj<tj ||z dz|jdz z }&t?j@||d}'td&|&dd| td'|'dtCjDtG||z d(}(td)|(dd*|td+tI|||D]4\})}*}+tG|*|+z |(|zkDst|)dd,|*d-d.|+d-d/| 6| rxtJjLj+d0d1i|HtjN|tjP|g}|d|dz d2z},|d|,z |d|,zg}tKjR|3\}-}.|.jU|d|df|d|dfd4|.jU||d5d67|.jW||.jY||.j[|d8| d9|.j]d:| d9|.j_d;d<|-ja|jcd=}/tKjd|/d>d d?@tdA|/tKjf|||fScc}w)Ba&Scatter plot to compare the velocities between SAR/InSAR and GNSS. Parameters: vel_file - str, path of InSAR LOS velocity HDF5 file. csv_file - str, path of GNSS CSV file, generated after running view.py --gnss-comp msk_file - str, path of InSAR mask file. ref_gnss_site - str, reference GNSS site name cutoff - float, threshold in terms of med abs dev (MAD) for outlier detection xname - str, xaxis label vlim - list of 2 float, display value range in the unit of cm/yr Default is None to grab from data If set, the range will be used to prune the SAR and GNSS observations ex_gnss_sites - list of str, exclude GNSS sites for analysis and plotting. Returns: sites - list of str, GNSS site names used for comparison insar_obs - 1D np.ndarray in float32, InSAR velocity in cm/yr gnss_obs - 1D np.ndarray in float32, GNSS velocity in cm/yr Example: from mintpy.utils import plot as pp csv_file = os.path.join(work_dir, 'geo/gnss_enu2los_UNR.csv') vel_file = os.path.join(work_dir, 'geo/geo_velocity.h5') msk_file = os.path.join(work_dir, 'geo/geo_maskTempCoh.h5') pp.plot_insar_vs_gnss_scatter( vel_file, ref_gnss_site='CACT', csv_file=csv_file, msk_file=msk_file, vlim=[-2.5, 2], ) zcm/yrY@)SiteLonLat DisplacementVelocityU10f8rzread GNSS velocity from file: r,T)r delimiternamesrrrrNrrzread InSAR velocity from file: rgrfr)maxValuerQ)rr/ )rzBmedian offset between InSAR and GNSS [before common referencing]: r}z cm/yearz.referencing both InSAR and GNSS data to site: z*removing sites with NaN values in GNSS or z pruning sites with value range: zGNSS min/max:  / zInSAR min/max: zRMSE = zR^2 = )r%zAPreliminary outliers detection: abs(InSAR - GNSS) > med abs dev (z) * zSite: InSAR GNSSz: z5.1frQr\z font.sizerrr"zk--.r)rz [rRzGNSS [equalrz_vs_gnss_scatter.pdfr r r zsave figure to file)4rrr genfromtxtr&rurvrrrr r(rrr4rr*rr progressBarrupdater nanmedianrrmultiplyrrrrr linregressr^median_abs_deviationrrrCrDrTrWrrrr9rrd set_aspectrrrr)0vel_filecsv_filemsk_fileror&rbxnamerrtdisplayrGr{ col_namesnum_col col_typesr3siteslatslonsgnss_obssiterrlengthrysxsrlnum_site insar_obsprog_barrrarroff_medrrrmser2diff_madr insar_valgnss_valvbufferr4rIrs0 r/plot_insar_vs_gnss_scatterrsw@IHCI)nG4&GaK00I *8A, 78 xyCt LB vJE e9D e9D)B- 8+H ((eDdDM1DBHHU 66'?Q 7(OE>D>D)H +HQ< 89  ! !( +CH &CL(9EF _ & &tT 22A 6FB(0(-- !! $bggvuoUWU]U]6^CzzH4rvv=I  (3H 8_D!ube1 NUNa&c!Q$ia1ac"C#==zsSTUVYaaIaL!qse1XJaaz$BC D  NNll9x/0G NwWZm[c de >}oNO,,.&&}5HW%%Yw''  6ug >? ;;++bhhx.@-@ AD  0a {CD DG## DG## T!W$$ T!W$$~H$I $KE RYYx05S89LS8Q RS OBIIi05S99Mc8R ST 772669x/!34 8IJ KD   )X .q 1B GD:Qyk *+ F2c( ''I,@(A!LH MhWZ^[_`f_g hi *-eY*IT& 9h y8# $x&'8 8 YqMId#32ht_BykR ST  ["-. <FF9%rvvi'89DAwa(C/GGg%tAw'89D,,x0R a$q'"T!Wd1g$6>  8SR0 D D r)A./ yk+, gu% [[]O#78 GdL #W-  )X %%{Es8 Yc |jdvrd}nd}|jd|jdz dz}|jd|z }|jd|z}|js||jdkr||jdk\rd|_n_||jdkDr||jdk\rd|_n3||jdkr||jdkrd |_nd |_t |t j zt j z d krSt |t j z t j z d kr#t j dt j g}nHt|d r:|j.t|jd krt|j}nd}|js'tj||||j|}d} nt||||g||d} d}|jr/|jdkrg| dk(r|j!|jnl|dk(r|j#|jnJ|dk(rD|j%|jn'| dk(r6t'j(|j|_|j-n|dk(r:|j.j1t'j(|jn|dk(r|j2j1t'j(|jnn|j4rb| dk(r|j!|j4nA|dk(r|j#|j4n |dk(r|j%|j4|r_t|dk(rQ|t j dt j gk(r,|dk(r|j7gdn|j9gd|j;d d|j<|j>|j@r |j@} n|jBdk7r |jB} nd} | qtE|j<|j>} | dk(r|jF| fi| ||fS|dk(r|jH| fi| ||fS|dk(r|jJ| fi| ||fS)N)rWrXvertical horizontalrrg-C6?neitherrTrWrPrrr )r orientationextendticksr)rrrimgr)nbinsr=)z-$\pi$0z$\pi$out)rSrTr rrvr(r)&cbar_locrcbar_extdlimrr rhasattrrrrSdisp_dem_blendrCrblend_colorbar cbar_nbinsr set_yticks set_xticksr MaxNLocatorlocator update_ticksrr!r  cbar_ticksset_yticklabelsset_xticklabelsr&r font_colorrMrGrBrrdr) r-rrrepsilonrrrr cbar_typerMrKs r/ plot_colorbarrs }})) " yy|diil*f4G 99Q<' !D 99Q<' !D == TYYq\ !ddiil&:)DM TYYq\ !ddiil&:%DM TYYq\ !ddiil&:%DM#t} 4"%%<255 5(S->-F-N%%BEE"  'D,>,>,JsSWSeSeOfjkOkT''(   ||BC[^cd  sDd|TYZ   ??a E!tyy) *tyy) ,tyy)E!%11H !!# * ++F,>,>T__,UV , ++F,>,>T__,UV    NN4?? + J & NN4?? + L ( NN4?? + UqUvq"%%.@%@ * $    : ;    : ;OO&ET^^TXTcTcOd __ 3 ^^  t~~T__E   DNN: 0 0 : J & CNN: 0 0 :L ( CNN: 0 0 :rNc@|rtd|tj||||}t|dk(r+t j d|d|dtd||fSt|dkrd n|}t jt|| }t|D]>\}} |j| d d df| d d d fd ||j|d zd@|j|\} } } } |j| | |j| | ||fS)aPlot fault lines. Parameters: ax - matplotlib.axes object faultline_file - str, path to the fault line file in GMT lonlat format SNWE - tuple of 4 float, for south, north, west and east Returns: ax - matplotlib.axes object faults - list of 2D np.ndarray in size of [num_point, 2] in float32 with each row for one point in [lon, lat] in degrees z'plot fault lines from GMT lonlat file: )rymin_distrrzNo fault lines found within z with length >= z km!z continue without fault lines.F)rrNrzk-)rr)every)rrread_gmt_lonlat_filerrerfr r enumeraterrrrr9)rIfaultline_fileryr?rrfaultsrrfaultSNWEs r/plot_faultliners0 77GHI * *  F 6{a 4TF:J8*TXYZ /06zVt+I  #f+KHf%'5 ac E!A#J;!2&' NNJAq!QKK1KK1 v:rNc ||j}|j}|j}||j}t j t j ||z }|dk(r)|dz}||jk\r&|jdz }|dz }n|dz }|dkrd}|dz}|jjd||||f||||ftd|d|} | S) aAdd an arrow to a line. Link: https://stackoverflow.com/questions/34017866 Parameters: line - Line2D object position - x-position of the arrow. If None, mean of xdata is taken direction - 'left' or 'right' size - size of the arrow in fontsize points color - if None, line color is taken. Returns: ann - matplotlib.text.Annotation object rXrrrtz->r) arrowstyler linestyle)xytextr arrowpropsr4) get_color get_xdata get_ydatameanr r+absoluter4axesrrB) linepositionrTr4rxdataydata start_indend_indanns r/ add_arrowrs }  NN E NN E::< "++eh&678IGa- ejj jj1nG! Ia- a<G! I ))  Ri % "23 'NE'N +4uE   C JrNr}c|s{|djdd}|jdd}|dj}|dvr7|jdd j d r|j d sd }n|d vrd}|ro|jdd dvrd}t d|||fS|jdd dk7r.|d|d z dtjzk(rd}|r t d||fS)zGet auto disp_unit for input dataset Example: if not inps.disp_unit: inps.disp_unit = pp.auto_disp_unit(atr) rerrtrr*rE)rqgiantTimeseriesrQHDFEOSrrrDrcm)mlislcampdB)radiandegreerDr mmrvpixelFz-WARNING: re-wrap is disabled for disp_unit = rrrz+change disp_unit = radian due to rewrapping) replacerrrendswithrrr r)rrGrzr|rftypers r/check_disp_unit_and_wraprGs %--c"5 [)4V$**, L LOOC(+44S9((3I + +I ??3  "*] ]D A)M N d? __S !! $ 0jmjQRm6SXZ[][`[`X`5` ICD d?rNcD|sd|d<d}|djjd}|jjd}||k(r||d|fS|djdr|ddk(r|dz}n|ddk(r|d z}n|dd k(r|d z}n|ddk(r|dz}n|dd k(r|d z}nr|ddvr|dz}ne|ddvr|dz}nX|ddvr|dz}nK|ddvr|dz}n>|ddvr(dtjzt |dz }||z}nt d|d|ddk(r|d z}n|ddk(r|dz}n|dd k(r|dz}n|dd k(r|dz}n|ddk(rt |d dtjzz }|ddk(r ||dzz}nC|ddk(r ||d zz}n1|dd k(r ||d zz}n|ddk(r ||dzz}n |dd k(r ||dzdz z}n|ddvr ||dzz}n|ddvr ||dzz}n|ddvr ||dzz}n|ddvr ||dzz}n|ddvrnt d|dn|dd k(r|dd!k(rl|jd#|d<|d$d%vr/d&tjtj|dd"'z}nTd(tjtj|dd"'z}n% |t |dz}nt d*||dg}t|d+k(rP |dd,vrd-|d<n3|dd.vr d/|d<|d0z}n!|dd1vr d2|d<|d3z}nt d4|d|ddz|dz}n|d}|)|dk7r$|tj||j5z}|||fS#t d)|dYxYw#|jd-YoxYw)6aScale data based on data unit and display unit Parameters: data - 2D np.ndarray metadata - dictionary, meta data disp_unit - str, display unit Returns: data - 2D np.ndarray, data after scaling disp_unit - str, display unit Default data file units in MintPy are: m, m/yr, radian, 1 rDrErrrrr!r rdmrkmr)rQinchgo_C@)ftfootgD )? @)ydyardgA 3m?)mimilegp\D?)radiansrradrg WAVELENGTHz'Unrecognized display phase/length unit:g{Gz?rrrrzUnrecognized phase/length unit:rvdbNrre)rz.intz.unwr)a_mina_maxrzUn-scalable display unit:zUn-scalable data unit:r)ryrrr)rDmonmonthr*r )ddayr-rz#Unrecognized time unit for display:r) rrrr rr$rlog10cliprrr&r)rrrGrZ data_unit range2phase phase2ranges r/r_r_hs  E &&(..s3I!'',IIXf%u,,|S! q\T !EVOE q\T !EUNE q\T !ETME q\S ESLE q\T !EUNE q\] *Ug-=U q\] *Ug-=U q\] *Ug-=U q\] *Uk-AU q\; ;+h|.D(EEK [ E ;Yq\ Jq\T !EUNE q\T !ETME q\T !ESLE q\T !EUNE 1 !Xl344"%%@ q\T !E[6-A$AE q\T !E[5-@$@E q\T !E[4-?$?E q\S E[3->$>E q\T !E[1_V-C$CE q\] *UkG6K-KU q\] *UkG6K-KU q\] *UkG6K-KU q\] *UkK6O-OU q\; ;  3Yq\ B  1  Q<4 D$4IaL $(@@BHHRWWTT%JKKBHHRWWTT%JKK Ay|,, & 2f%&  9~ %1!44Vil1!44UilEUYME1!44UilEU[OE=y|LaL$Yq\1 aL  ESL djj11 E !!/ A19Q<@ %   V $s=M40AN 4N Nc|st|d|||\}}d}||dk(st|||\}}}|r;|dtj||dz |d|dz z}|rt d|||||fS) aScale 2D matrix value according to display unit and re-wrapping flag Parameters: data - 2D np.ndarray metadata - dict, including the following attributes: UNIT FILE_TYPE WAVELENGTH disp_unit - str, optional wrap - bool, optional Returns: data - 2D np.ndarray, scaled data matrix disp_unit - str wrap - bool N)rGrzr|rrrErFrrzre-wrapping data to )rr_r modr)rrrGrzr|r disp_scales r/scale_data4disp_unit_and_rewrapr6s 2 !   4J ( (&: ' #i !}rvvdZ]&:JqMJWXM.rCrNre)rQrqrl)zmaskTempCoh.h5z maskResInv.h5 PhaseVelocityNgeo_c3XK|]!}t|t|k(#ywr)r)rrratr_msks r/rzread_mask..s'T#3ws|$CH 5Ts'*)rgrfc3,K|] }|ddk(yw)rerhNr)rrVs r/rzread_mask.."sUdtK(M9UsrkrzconnectComponent-)rrrrzread mask from file:z7WARNING: input file has different size from mask file: z data file z row/column number: rgrrfz mask file z Continue without mask.zCan not open mask file:)r mask)rrzread z contained mask dataset.z PARAMS.h5r$cmaskrz contained cmask datasetrzhide pixels with mask value < zhide pixels with mask value > numTriNonzeroIntAmbiguityzEkeep pixels with numTriNonzeroIntAmbiguity == 0 and mask out the restzP--mask-vmin/vmax is specified, skip translating numTriNonzeroIntAmbiguity values)rrrrrrrjoindirnamerrr8rrrrh5pyFilerrr r&r)r mask_filerrxstepysteprrrrzr mask_datadsNamerirfxsizeysizer>rr<s @@r/ read_maskrMs; U%AF  ! !% (C KA //U"< !I%' ww&11&9"9+.  RWW__U%;S^LIww~~i(  ! I ww~~c)n% 9--i8GTASTTUsGnUU(..s3A6F09F%MM &'    -rww/?/? /JK! OPY{[)%0DS]OSVWZ[bWcVdee))4HQYIZH[[^_fgn_o^pqq55s j   S !! $(= =I e9UVWXI U1#56 7  $ YYuc " ,a' 1 +I ,rww''.//GHI!2IOOA&./IOOA&./c%'l1E1!%'l1E123 fuffufn- 4 ?1xx "  $D 3D6: ;  $D 3D6: ; 77  I & 1 12M N| ^_ A~ij%&RXXi !qy ?o 9I ,i 8 , ,sCN' N:' N7:Oc|r,tdtjj|dt j |}|ddk(rd}nd}t |}|ddt|dt|df}|r|j|}n|}|j|d }t j|||| \} }|||k7r|r td tj|d |dz |d|dz f| jtjz} | | |d|dz |d |dz |d|dz |d|dz f<tj| } | ||fS)Nz reading DEM: rUrerrheightrrfrgFr)rrrz align DEM to the input data filer=rrr)rrrrrrr r box_geo2pixelcheck_box_within_data_coveragerr rrrr&) dem_filerxgeo_boxrdem_metarIr dem_pix_boxbox2readdemdem_tmps r/read_demrYts bgg..x89>?&&x0H  * x EaXg./Xh5G1HI))'2  33K53QHMM  MCx;6  4 5((KN[^;'N[^;=DGIIOQSQWQWXJM  KN*8A;{1~+EE KN*8A;{1~+EEF Ghhw + %%rNcd}d}d}|jdk(rtj|dz|_|jr,ddlm}||j |j}tj5tjdt|j||jtd j|j |j }dddtj"|tj$|dddddf<|rWd |j d d |jd d}|d|jd|j d |jdz }t'||j(rtj|tj*|z |j,dzkrYdtj*|ddtj|dd}|d|j,dzddz }|dz }t'|nddlm} | j3||j4d}tj6|j,d|j,}|r-d|j,d}|d|j4z }t'||r|j8r||||fD cgc]} | | j:ddc} d} |j<j:| k(rR|r t'd | tj"||j<dk(<|,tj"||j<dk(<n t'd!|||fS#1swYexYwcc} w)"aPrepare to plot DEM on background Parameters: dem - 2D np.int16 matrix, dem data inps - Namespace with the following 4 items: 'disp_dem_shade' : bool, True/False 'disp_dem_contour' : bool, True/False 'dem_contour_step' : float, 200.0 'dem_contour_smooth': float, 3.0 Returns: dem_shade - 3D np.ndarray in size of (length, width, 4) dem_contour - 2D np.ndarray in size of (length, width) dem_contour_seq - 1D np.ndarray Examples: from mintpy.cli import view from mintpy.utils import plot as pp inps = view.cmd_line_parse() dem = readfile.read('inputs/geometryRadar.h5')[0] dem_shade, dem_contour, dem_contour_seq = pp.prep_dem_background( dem=dem, inps=inps, ) Ng8@ir LightSourceazdegaltdegignore)categoryr) vert_exagrrrz show shaded relief DEM (min/max=z.0frz m; zexag=z ; az/alt=z deg)rzWARNING: elevation range (rPrkz m)z < 2 contour levels (z+ --> skip plotting DEM contour and continue)ndimage)sigmaorderi(#)stepzshow contour in step of z m zwith a smoothing factor of z2mask DEM to be consistent with valid data coveragezJWARNING: DEM has different size than mask, ignore --mask-dem and continue.) shade_maxr rdisp_dem_shadematplotlib.colorsr\ shade_azdeg shade_altdegrecatch_warnings simplefilterRuntimeWarningshade shade_exagr rv shade_minrrrdisp_dem_contourrdem_contour_stepscipyrcgaussian_filterdem_contour_smootharangemask_demrrl) rWr-r dem_shade dem_contourdem_contour_seqr\rrrcra dem_shapes r/prep_dem_backgroundr}s.IKO ~~3$. 1 t//8I8I J  $ $ &   ! !(^ D// (11^^^^ !I 35&& "((9Q1W-./ 4T^^C4H$..Y\I]]abC U4??+9T5E5E4FaHYHYGZZ_` `C #J  IIcNRYYs^ +t/D/Dq/H H.ryy~c.B!BIIcNSVCWWZ[C *4+@+@+B3*GsK KC @ @C #J &!11#T=T=T\]1^K ii(=(=t$J_J_`O01F1F0GsK4T5L5L4MNNc   9#8K>Y &JK$+-66 $((a-(&-/VV DHHM* ^ _ k? 22[  DUs8A#L>$M ,M >Mctd|||fDr|t|||\}}}||d|d|d|df}nyt|drt|j} n9|||fD cgc]} | |  c} d} dd| j d| j df} | ddz | ddz | ddz | ddz f} |Ut d dd } ||j|fd i| n/t|tjr|j|fd  i| ||t d d |jdd} |d| d<|dtj|d|d|j ddz|d|d|j ddzf\}}|j||||fd i| |St|tjr|j||fd  i| |Scc} w)aPlot DEM as background. Parameters: ax - matplotlib.pyplot.Axes or BasemapExt object geo_box - tuple of 4 float in order of (E, N, W, S), geo bounding box dem_shade - 3D np.ndarray in size of (length, width, 4) dem_contour - 2D np.ndarray in size of (length, width) dem_contour_seq - 1D np.ndarray dem - 2D np.array of DEM data inps - Namespace with the following 4 items: 'disp_dem_shade' : bool, True/False 'disp_dem_contour' : bool, True/False 'dem_contour_step' : float, 200.0 'dem_contour_smooth': float, 3.0 'pix_box' : 4-tuple of int, (x0, y0, x1, y1) Returns: ax - matplotlib.pyplot.Axes or BasemapExt object Examples: ax = pp.plot_dem_background(ax, geo_box=inps.geo_box, dem=dem, inps=inps) ax = pp.plot_dem_background(ax, geo_box=None, inps=inps, dem_shade=dem_shade, dem_contour=dem_contour, dem_contour_seq=dem_contour_seq) c3$K|]}|du ywrr)rrs r/rz&plot_dem_background.. s H19 Hs)r-rrrr=rrxr6spline16upper)rrToriginextentblack)rr linewidthsrrTrTy?)r8r}rtuplerxrrBrr~rCAxesdem_contour_linewidthr mgridcontour)rIrSryrzr{rWr-r geo_extentrxrr rdr_extentrKyyxxs r/plot_dem_backgroundrs@0 Hy+G HHS_2E 3 / ;aj'!*aj'!*.  4 #DLL)G #Y <N! ANqQD!TZZ]DJJqM:Gajngajnajngajn6 JqI   BIIi = =f = CHH % BIIi = =f =?#>WW!%!;!;+   F8   XXgajK4E4Ea4H4KK%ajK4E4Ea4H4KKLMFB BJJr2{O YJ YRX Y ICHH % BJJ{O QJ Q& Q IIOs (G 0G cddlm}d} d} tjtjdd| j ddd| f} tjtj dz tj dz | } tj| tjd| zz} |d k(r d| |d|dg}n$|d|dd| g}| j} | j} ||j|j }tjjtj j#dd|j$ }|j'| d d d d d d f}|j)|| |||}|j+||dt-|j.|j0d}t-|j.|j0}|d k(r||j2ddddd||j4|j6fddi||j8j;d|j8j=|j?gny|j2ddddd||j@|j6fi||jBj;d|jBjE|jGg|(|d k(r|jG|y |j?|y )aCreate a shade-illuminated colorbar. Parameters: cax - colorbar axis inps - inps : Namespace with the following items: 'shade_azdeg' : float, True/False 'shade_alt' : float, 200.0 'colormap' : string or matplotlib.colors.colormap class 'cbar_label' : string vlim - list of 2 float, colorbar range orientation - str, vertical or horizontal ticks - list of float or None, colorbar ticks fraction - float, increases or decreases the contrast of the hillshade blend_mode - {'hsv', 'overlay', 'soft'} or callable vert_exag - float, the amount to exaggerate the elevation values by when calculating illumination Examples: blend_colorbar(cax, inps, vlim=[-3, 6], orientation='vertical') rr[rrrrrrr]rrNr=fraction blend_moderbauto)raspectrP)r rrSrF)rUrV labelbottomrotationZrX)rWrXlabeltoprUr)$rir\r rilinspacer'r ones_likecosr*rjrkrCr ScalarMappablerrrrvto_rgba shade_rgbrrBrrr&rdrMrset_label_position tick_rightrrr  tick_bottomr)rr-rrrrrrbr\nxnyarrraelevrrmappableimg_rgb illum_rgb tick_kwargs label_kwargss r/rrAs&. B B ''"++aB'//A6B @C RUUF1HbeeAgr*A << rvvc!e} ,Dj Ra$q'*q'47Ar*eevv 4++D4E4E FBvv$$#***>*>q*C$--$XHs#Aq"1"H-G  IJJyJ7vVKtGLj Ru%URkRtDD|D $$W-  rOU%%O;Ot7,7 $$X.  r  * $ NN5 !  NN5 ! rNc ddlm}|j|jk7rddlm}t d|jd|jdd}t dd d d }|j|jkr>t d |jd |jd|d|||jfi|}n=t d|jd |jd|d|||jfi|}tjj|}tjj|}|r|ntj|}|r|ntj|}||z ||z z }tjjt j"j%dd|}|j'|ddddddf}|||j(ddf<||| }|j+||||| }tj,|tj.|dddddfg}| r| ||j(df<| r| ||j(df<|S)aPrepare the illuminated RGB array for the data, using shaded relief DEM from a light source, like the `gmt grdimage -I` feature, i.e. hillshade + DEM-blended data. Parameters: data - 2D np.ndarray in size of (m, n), data to be blended dem - 2D np.ndarray in size of (m, n), dem data vmin/max - float, lower/upper display limit of the data cmap - str or matplotlib.colors.colormap class base_color - float or color hex codes shade_frac - float, increases or decreases the contrast of the hillshade blend_mode - {'hsv', 'overlay', 'soft'} or callable az/altdeg - float, azimuth/altitude angle of the light source vert_exag - float, the amount to exaggerate the elevation values by when calculating illumination mask_nan_dem - bool, whether to mask blended image based on nan dem pixels mask_nan_data - bool, whether to mask blended image based on nan data pixels fill_value - float, set the masked pixels as alpha = fill_value (transparent) Returns: illum_rgb - 3D np.ndarray of float32 in size of (m, n, 4), ranging between 0-1. 1st to 3rd layers are the RGB values; 4th layer is the transparency Examples: illum_rgb = pp.prep_blend_image(data, dem, vmin, vmax) rr[)resizez*different dimension detected between data z and DEM rNz%via skimage.transform.resize(order=1)redgeT)remode anti_aliasingpreserve_rangezresampling data from z to rz...zresampling DEM from rNr=r]rr)rir\rskimage.transformrrrBr4r mamasked_invalidrrrCr rrrrrr>rdstackr)rrWrrr base_color shade_fracrr^r_rb mask_nan_dem mask_nan_data fill_valuer\rrrK data_normrrrrs r/prep_blend_imagers&0. zzSYY, :4::,iPSPYPY{Z[\]5AF$tT 99sxx  )$**T#))AcU#N O$ 4V4D ( 4 |1SEM Ndjj3F3C 55   %D 55   $C4RYYt_D4RYYt_D-Ivv$$#***>*>q*C$$OHy)!Q(3G'GDIIqL 5 0B  I 9bll9Q1W3E&FGHI ", #((B,#- $))R- rNc|rbd}|d|jddz }|d|jdd|jdz }|d|jd|jd z }t |t |||jd |jd |j|j|j|j|j|j|j|j }|j||jd d dtjjt j"j%|jd |jd |j}|S)aPlot DEM-blended image. Parameters: ax - matplotlib.pyplot.Axes or BasemapExt object data - 2D np.ndarray, image to be blended dem - 2D np.ndarray, topography used for blending inps - Namespace object with the following items: 'base_color' : float 'blend_mode' : str 'colormap' : str or matplotlib.colors.colormap class 'extent' : tuple of 4 float 'shade_altdeg' : float 'shade_azdeg' : float 'shade_exag' : float 'shade_frac' : float 'mask_dem' : bool 'vlim' : list of 2 float print_msg - bool, print verbose message or not Returns: im - matplotlob.pyplot.AxesImage zplotting data z'blended by DEM shaded relief (contrast=rPrQz base_color=z, exag=zaz/alt=rz deg) ...rr) rrrrrrr^r_rbrrr)rrrTrr)rrrprjrkrrrrvrrxrrrCr rrrr)rIrrWr-rr blend_imgrs r/plot_blend_imagersA( 88MRPP T__S18ILL ))*!D,=,=+>iHH c ! c YYq\ YYq\ ]]??????  //mm IIIi :aX_I`   CJJ$8$81tyyQR|$T[_[h[h  iB IrNr)rFFg?g333333?T)NT)rT)r))rrrrF)NNT)rrT)rNrN)rNN) zgnss_enu2los_UNR.csvNNr )rrInSARNNT)r6rT)NrXrN)NNNrrNNT)T)NNNNNNT)rNg?softip) NNviridisr|r6overlayi;-r6TFr)N__doc__rrrrerC matplotlibrnumpyr rrrrCrmpl_toolkits.axes_grid1rrtrr^rr mintpy.objects.colorsr mintpy.objects.coordr mintpy.utilsr r`r rrr^mintpy.utils.maprrrUrVdefault_figsize_multirX MPL_COLORSrDr0rMrcrrrrrrrr6r@rprrrrrr;rBrrrrrrrr_r6rMrYr}rrrrrrNr/rsZ  ==7F-+HH;s   )6 $%$P HMCG@+-`F8-`@.*+)\VY'+9x<<>'T>@QS_cgT=?bAHSU15Fe^a.bDFbZzEJUWcdefbg= @*+D4"#Q2QSY$(u8_ J(,v]@hlbf'+M&`_D(V,b26EsSUSXSXyZ\Z_Z_N`lpB#TVte"P?C\_`b`e`e\egiglgl[m.2%-PQR.2vx(&VP3f]a7;K^CG?CJ Z