U c6 @sddlZddlZddlZddlZddlZddlZddlZ ddlm Z m Z m ZddlmZddlmZddlmZddlmZddlmZmZddlmZmZdd lmZmZm Z!m"Z#d Z$d Z%d d gZ&d Z'ddddddddddg Z(ddZ)dlddZ*dmd!d"Z+gdfd#d$Z,dnd%d&Z-dod(d)Z.dpd+d,Z/dqd.d/Z0drd0d1Z1dsd3d4Z2dtd7d8Z3dud9d:Z4ifd;d<Z5igd fd=d>Z6igfd?d@Z7gdAdBdd6fdCdDZ8gifdEdFZ9dd5dGdHgfdIdJZ:dHdd dKdHgd5d*dLd d ddMf dNdOZ;e<d fdPdQZ=dRdddKdSdSgdTdgd f dUdVZ>dWdXZ?dvd[d\Z@ddd]ejAejAgd fd^d_ZBde<dfd`daZCddd]ejAejAgd fdbdcZDdwdddeZEdxdfdgZFdydhdiZGdzdjdkZHdS){N)pyplottickerdates)make_axes_locatable)stats) coordinate) ColormapExt)timeseriesKeyNamestimeseriesDatasetNames)draw_lalo_label draw_scalebar)ptimereadfilenetworkutils0g@$@g.@g @z#1f77b4z#ff7f0ez#2ca02cz#d62728z#9467bdz#8c564bz#e377c2z#7f7f7fz#bcbd22z#17becfcCs<|jr>tj|jr>td|jtj|jdtd t |_ |j dk rt |j  dd|_ |j|j dddf|j dddfd d dd|_t |jj dd|_|jdk r8t |j dd|_zR|j|jdddf|jdddfd d dd|_ t |j j dd|_ Wntk r6YnX|S) zRead pts_* optionsz&read points lat/lon from text file: {})r)usecolsdtypeNrrF print_msg)pts_fileospathisfileprintformatnploadtxtbytesastypefloatpts_laloarrayreshape geo2radarpts_yxT radar2geo ValueError)inpsZ coord_objr-H/home/exouser/operations/rsmas_insar/sources/MintPy/mintpy/utils/plot.py read_pts2inps8s2  r/cKsrddlm}ddlm}|dkr0ttjdd}||f||dddd |}|||jj |d d d g|S) Nr) AnchoredText) withStrokezlegend.fontsize)size?F)locproppad borderpadframeonw) foreground linewidth) matplotlib.offsetboxr0matplotlib.patheffectsr1dictpltrcParams add_artisttxt_textset_path_effects)axtitler5r6kwargsr0r1atr-r-r.add_inner_title\s    rK?F?333333?Tc st|ddd}|r*|dd|9<|rB|dd|9<ttt|tt|t|dfdd|D}|rtd|d|d|S)a?Get auto figure size based on input data shape Adjust if display colobar on the right and/or slider on the bottom Parameters: ds_shape - tuple/list of 2 int for the 2D matrix shape in [length, width] scale - floag, scale the final figure size disp_cbar/slider - bool, plot colorbar on the right / slider on the bottom cbar/slider_ratio - float, size ratio of the additional colobar / slider Returns: figsize - list of 2 float for the figure size in [width, lenght] in inches Nrrrcsg|]}|qSr-r-.0iZ fig_scalescaler-r. sz$auto_figure_size..zfigure size : [{:.2f}, {:.2f}])listminmin_figsize_singlemax_figsize_singlemaxmax_figsize_heightrr) ds_shaperS disp_cbarZ disp_sliderZ cbar_ratioZ slider_ratiorZ fig_shapefig_sizer-rRr.auto_figure_sizeis    r^cCs |sg}t|tr|g}tjtj|\}}t|}|d}t|dt|d}|dkrt |dkr|d}d|kr|d7}n(|d d d}| d r|d 7}n|t krXt |dkrXd |kr|d } nd |kr|d } nd} | s |d}nd| |d}z,tj| dd |d} || 7}Wn YnXn|dkrt |dkrz|d}n|d drd}n|}n|dkr t t|} | dkrt |dkrtj|dd} t | dkr| ddnd} | |d}n|}nFtj|}t|dd}|dkrRt |dkrR|d|d7}|ddr|d}|d|d|d|d|kr|d7}|ddr|d7}|d d!tjtjg}|d|d}|dtjkr|t|krt|}|t|7}|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 ifgramStackrrZunwCorZ_unwCor-ionZ_ionref_dateREF_DATENz{}_{} timeseriesgeometrybperp)z.h5z.he5_BANDS1z - {}pix_boxrr;_subwrapF_wrap wrap_range) isinstancestrrrsplitextbasenamerread_attributeintlensplitlower startswithr keysrget_dataset_listrsplitgetrpi)fname datasetNames inps_dictfbasefextatrk num_pixel fig_titlere processMarkZnum_dsetZfpartssuffixnum_bandboxrrr-r-r.auto_figure_titlesx                 & rcCsd}d}d|krfd|krfd|d}|ddrNd}|d7}n d}|d7}|rft||d k r|rz||r||S||fS) zTCheck flip left-right and up-down based on attribute dict, for radar-coded file onlyFY_FIRSTORBIT_DIRECTIONz{} orbitaTz -> flip up-downz -> flip left-rightN)r~rr|r}r invert_xaxis invert_yaxis)metadatarGrflip_lrflip_udmsgr-r-r.auto_flip_directions$ r@cCsD|d|d|d|d|}|dkr2d}nt|dkr@d}nf|d krNd }nX|d kr\d }nJ|d krjd}n<|dkrxd}n.|dkrd}n |dkrd}n|dkrd}nd}|d|d|d|ddkrd}|dkr|td|9}n|dkrn|t|d}tt|}|dkr@|r@td|td||S)a^Calcualte the default/auto multilook number based on the input 3D shape. 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Returns: row_num : number of subplots in row direction per figure col_num : number of subplots in column direction per figure rrrLr4)ryrrr#rYrrint) Z subplot_num data_shaper]fig_numZsubplot_num_per_figZdata_shape_ratioZ num_ratiorow_numcol_numr-r-r.auto_row_col_num8s   rrrrrc Cs|j\}}tj||dgtd}|d|ddddf<|d|ddddf<|d|ddddf<|d|ddddf<|rt|}g}t|D]$}t|D]}||||qq|S) aiReturn the auto lat/lon label location of subplots Parameters: axs : 2D np.ndarray of matplotlib.axes._subplots.AxesSubplot object loc : tuple of 4 bool, for (left, right, top, bottom) flatten : bool, return variable in 2D np.ndarray or in list of flattened array Returns: locs : 2D np.ndarray of tuple of 4 bool. rrrNrrrr;)shaperzerosryrUtolistrangeappend) axsr5flattennrowsncolslocsZloc_listrQjr-r-r.auto_shared_lalo_locationXs    rc s|sv|dt|ddg}|ddkrB|ddrB|dg7}dd d d d d dddg tfdd|Drrdnd}|rtd||S)z7Get auto/default colormap name based on input metadata.r_rcrrg DATA_TYPEcomplexz.slc coherencetemporalCoherence waterMask shadowMask.corz.mliz.ampz.rampc3s|]}|kVqdSNr-rOZ gray_ds_namesr-r. {sz%auto_colormap_name..grayjetz colormap:)rur{r}anyr)rZ cmap_name datasetNamerds_namesr-rr.auto_colormap_nameos  rrcCst|t|t|}t|t}t|t}||krZd}||g}d}nttt |t} t ||| d} | |kr| }|| d|| dg}|rd |j |} | d7} t | n4ddlm} | |||} d}t| t| g}d}|||fS)Nrrz.data has uniform and limited number ({} <= {})z) of unique values --> discretize colormapr)multilook_data)runiqueisnanisfiniterVr"r#rYabsdiffryrr2rmintpy.multilookrnanminnanmax)data num_multilookZmax_discrete_num_stepr unique_valuesmin_valmax_valcmap_lutvlimZvstepZ min_num_steprrZdata_mlir-r-r.'auto_adjust_colormap_lut_and_disp_limits,    rr皙?cCst|dtjrdd|D}|dk r|d|}|d|d}t|}t||d} t|} t|| d} | dkr|d 7}d } | dkr| d 7} d } | d kr|d 8}d} | d kr| d 8} d} t|| d dd} t| | d dd} nt|\} } || | |s0td t | | j d d  t}|sj|d krFd }n$|d krVd }n|dkrfd }nd}t d|_|jt||jt d|r|jttd d||j|d|| | fS)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 rcSs$g|]}|j|jddqS)rv@)year timetupletm_ydayrOr-r-r.rTsz*auto_adjust_xaxis_date..Nr皙?(@rrrr;rz%Y-%m-%d %H:%M:%Sz%Y  labelsize)rtdtdatetimerymdatesnum2dateget_xlimset_xlimrYrrdaysr" DateFormatter fmt_xdataxaxisset_major_locator YearLocatorset_major_formatterset_minor_locator MonthLocatorr tick_params)rG datevectorfontsize every_yearZ buffer_yearZ every_montht0t1y0m0y1m1xminxmaxr-r-r.auto_adjust_xaxis_datesT   $     rcCsRt|t|}|dkr(t|d|}|dkr@t|d|}|||g|S)aAdjust Y axis Input: ax : matplot figure axes object dataList : list of float, value in y axis fontsize : float, font size ymin : float, lower y axis limit ymax : float, upper y axis limit Output: ax Nr)rYrVset_ylim)rGZdataListryminymaxZ dataRanger-r-r.auto_adjust_yaxiss r c sd|krd|d<d|kr(d|d<d|kr.cSsg|]}|ddqSrjrrrr-r-r.rT sr;rcsg|]}|dqS)rr-rO bar_widthr-r.rTsraxiszMax {}labelzMin {}z({} History: Min/Max of All Related Pairsrrrbest)r5)r~r yyyymmdd_date12sortedyyyymmddrUsetdate_list2vectorut0 most_commonrrrpnetcoherence_matrixbarrrrr set_titlerr set_ylabellegend) rG date12ListcohListp_dictm_datess_datesdateListrrx_listcoh_matr-rr.plot_coherence_historysJ          ((   r3c/ sVd|krd|d<d|kr(d|d<d|krd=q|D]}$|$d!\}%}&|%}'|&}(t&|'|(g})t&|'|(g}*d k r(|$}+|+|||},|j%|)|*d?|d|||,d=n|j%|)|*d?|d|d>d=q|drb|j'd@|dd2t(|| |d|ddAd }t)||dd2}|j*|d |dd2|j+|d |dd2|j"dBdC|ddddddD|dd k r|j,|ddEd>dF|ddG|drR|drRtj-j.ggd>dHdIdJ}-tj-j.ggd>dKdLdJ}.|j/|-|.gdM|S)NaiPlot Temporal-Perp baseline Network Inputs ax : matplotlib axes object date12List : list of string for date12 in YYYYMMDD_YYYYMMDD format dateList : list of string, for date in YYYYMMDD format pbaseList : list of float, perp baseline, len=number of acquisition p_dict : dictionary with the following items: fontsize linewidth markercolor markersize cohList : list of float, coherence value of each interferogram, len = number of ifgrams colormap : string, colormap name disp_title : bool, show figure title or not, default: True disp_drop: bool, show dropped interferograms or not, default: True Output ax : matplotlib axes object rrr=rr r rr,NxlabelylabelzPerp Baseline [m] cbar_labelzAverage Spatial Coherence cbar_size3%r\TcolormapRdBurrrLrZ disp_drop disp_legendrrnumberunrecognized colormap input: {}ffffff?rrjz$max perpendicular baseline: {:.2f} mz#max temporal baseline: {} daysFcSsg|]}|ddqSrrrOr-r-r.rTusz plot_network..cSsg|]}|ddqSrrrOr-r-r.rTvscsg|]}|qSr-indexrOr0r-r.rTyscsg|]}|qSr-r?rOrAr-r.rTzszshowing coherencezdata range: {}zdisplay range: {}rightr7)vminvmax)cmapnormrrcsg|]}|qSr-r?rO)r,r+r-r.rTscSsg|] \}}|qSr-r-)rPrjxr-r-r.rTscsg|] }|qSr-r-rOrr-r.rTscsg|] }|qSr-r-rO pbaseListr-r.rTskoalphamsmfccsg|] }|qSr-r-rOrIr-r.rTscsg|] }|qSr-r-rOrJr-r.rTsr--)lwrNcrrczInterferogram Networkrbothinwhich directionrbottomtopleftrB)gQ?gq= ףp?z axes fractionxycolorxycoordsrsolidz Ifgram used)r^lsrdashedzIfgram dropped)handles)0r~rtrurr9mplcolorsLinearSegmentedColormapr+rr r 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st|\}tt|}tjdd|d\}}|d}|j|ddd} tfdd d Dr|j td td d dddt |dd|j | |d|j d|d|d}|j |t|d|fd|d|jd|d|jd|d|jt|jtjddd|jtjdddd|jt|D]}|jdd |ddddd!qH|d"tj |d} t!d#| |j"| d$dd%d&|rt#n t$|d'S)(zPlot the histogram for the number of triplets with non-zero integer ambiguity. 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Parameters: fname - str, path to the numTriNonzeroIntAmbiguity.h5 file. rr)rrfigsizerRdBu_rr)rFrc3s|]}|kVqdSr)r~rPkeyrr-r.rszBplot_num_triplet_with_nonzero_integer_ambiguity..)REF_YREF_Xrrswhiter;)r^rOF)rGr)rGz $T_{int}$rT)rlogbinsz0# of triplets w non-zero int ambiguity $T_{int}$z # of pixelsr)basenumticks) rrg333333?g?r4g333333?r>g?r)rrsubsrTrUrVz{}.pngzplot and save figure to filetight, bbox_inches transparentdpiN)%rreadryrrrAsubplotsrallrnrrjr(histrrror)rrrAutoMinorLocatoryaxisr LogLocatorset_minor_formatter NullFormatterr tight_layoutrrrrvrsavefigshowclose) rdisplay font_sizer]rrEfigrrGrout_figr-rr./plot_num_triplet_with_nonzero_integer_ambiguitysF $&     rrrzResidual phase RMS [mm]c  Cstj|tdt} | dddftjd} t| dddf}t|\}}t |}z(t t j t |dddd }Wn(t t |dd }YnXt | }tj|d \}}|j|||jd d t|}|j|||||jd ddt j|d|d}||k}t|dkrJ|j|||||jdddt||||d\}}}|jt ||gt ||gdd|dt|t|||dd}|j| |d|jdd|ddddd| rt|}|jddd d!}|jt|j tjd"|d#d$d d%|jt|j tj|d"||d#d$d d%t|dkrt|jt|j tj|d"||d#d$dd%|t ddgt ||gd|!|"|#ddg|jdd|ddddd|$%g|&'g|r|j(|d&|d'| rf|"\}}||d(}||d"||kr8|d)9}|j)d||||d*||fd+d,|d-|rt*d.||j+|d/dd0|rt,nt-dS)1aH Bar plot for the phase residual RMS time series. Parameters: rms_file - str, path to the time series RMS text file Generated by utils1.get_residual_rms(). cutoff - float, cutoff value of MAD outlier detection fig_size - list of 2 float, figure size in inch rNr@@rr)rr;rrC0)r^C1zReference date)r^rr3)centercutoffTdarkgrayz Exclude date)rz--kzMedian Abs Dev * {}r)rrrTrUrVrBz10%z2%rCr4onone)rPr^F)r5r9rrrsg?rrr\zsave figure to file:r)rr).rr r!r"rufloat32rUr r"r%rVr#r$rrrArr'rargminmedian_abs_deviation_thresholdrrrnrr rr)rrrhrrrget_ylimrrrrset_ticklabelsr*rprrrr)rms_filerrdisp_figr]rZ tick_year_numrr;Zdisp_side_plotZdisp_thres_textr5fcrms_listrrrrrmsrrGref_idxZ rms_thresholdZex_idxrrrax2rryoffr-r-r.plot_timeseries_rmss  ("   $,,"      rc# s0ddlm}|rtndd}j\}}tjtr>tjjnj} t|} d| d<t | j dd} j rrj n | d d } j rj n | d d } ||| | \}}}|jdkrtd || | td |Sjrfjd k rfjjdkrjntjjdd}t|}|||dd\}}|||fdk}||}||}||}|jdkrftdjrj|krtdj|d}jr|dkrd _|d|jr|dd}||dkrdnd7}|dj7}|jrdjnd7}|||dt||d| |d| |dj|dkrbdnd}|j|||| | jj|j d }jr|!"j}||}t#|s||8}|| 9}j$r4tj%fd!d"|Dtj&d#}t'|dkr4|d$||||}||}||}||}t#|}t'|dkrt|d%t'||d&||t(|||D]J\}}} t#| s| | |||}!|j)|||!j*dd'd(d)d*qn&|d+|j)||j*dd,d'd(d)d-j+r,t(|||D]"\}"}}|j,|"||fj-d.q|S)/a\Plot GNSS as scatters on top of the input matplotlib.axes. Parameters: ax - matplotlib.axes object SNWE - tuple of 4 float, for south, north, west and east inps - Namespace object, from view.py metadata - dict, mintpy metadata Returns: ax - matplotlib.axes object r)gpsc_sdSrr-argsrIr-r-r.Lzplot_gps..mUNITr disp_unitr START_DATENEND_DATEz'No GNSS found within {} during {} - {}!zContinue without GNSS plots.rrzNo GNSS left after --mask-gps!z,input reference GPS site "{}" not available!r_)velocityrg displacementzZWARNING: --gps-comp is not implemented for {} file yet, set --gps-comp = None and continuez------------------------------z plotting GPS rrz in {} directionz with respect to {} ...z ...z$number of available GPS stations: {}zstart date: {}zend date: {}zcomponents projection: {}) metaobs_type site_names start_dateend_dategps_comp horz_az_anglerredocsg|]}|jkqSr- ex_gps_sitesrPrHr,r-r.rTszplot_gps..rz-ignore the following specified stations: {}zLignore the following {} stations due to limited overlap/observations in timez {}rr4r)r^r edgecolorsrRzorderzshowing GPS locationsr:)rr^rrRr)r]r).mintpy.objectsrrrrtr9rurr@scale_data2disp_unitrgps_start_dater gps_end_date search_gpsr2warningswarnrmask_gpsmskndimrprodrr'r+ ref_gps_site gps_componentrzget_gps_los_obsrgps_redorr@rrr%bool_sumrmscattergps_marker_sizedisp_gps_labelrpr)#rGSNWEr,rrrvprintrDrErFrunit_facrrr site_lats site_lonsr coordZsite_ysZsite_xsflagrrrsite_obsref_indref_valex_flagZnan_flaglatlonobsr^ site_namer-rr.plot_gpsAs   "         $r$zgps_enu2los.csvrZInSARc / snd} d} dddddg} t| } dgd g| d }td |tj||d d d}|d}|d}|d}|| d| }rtjfdd|Dtjd}t|dkr||}||}||}||}td|t |}t |dt |d}}t | ||dd\}}|r4t |dntj||ftjd}|j}tj|tjdtj}tj|d}t|D]}||||}} d|kr|krnnZd| kr|krnn>|| |fr|| |d | d f}!tj |d|!dd| ||<|j|d d|d |||dqx|t||}"td|"dd |rtd!|||}#|||#8}|||#8}td"|tt|t|}$|dk rtd#|| |$||dk9}$|$||d k9}$|$||dk9}$|$||d k9}$||$}||$}||$}td$t|t|td%t|t|tt||d|jd }%t !||d}&td&|%| td'|&t"j#t$||dd(}'td)|'dd*|td+t%|||D]F\}(})}*t$|)|*|'|krt|(d,d-|)d.d/|*d.d0| q| rdt&j'd1d2i|dkrt(|t)|g}|d |dd3}+|d|+|d |+g}t&j*|d4\},}-|-+|d|d f|d|d fd5|-j+||d6d7d8|-,||--||-.|d9| d:|-/d;| d:|-0d|2}.t&j3|.d?d d@dAtdB|.t&4|||fS)CaScatter plot to compare the velocities between SAR/InSAR and GPS. Parameters: vel_file - str, path of InSAR LOS velocity HDF5 file. csv_file - str, path of GNSS CSV file, generated after running view.py --gps-comp msk_file - str, path of InSAR mask file. ref_gps_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 GPS observations ex_gps_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 gps_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/gps_enu2los.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_gps_scatter(vel_file, ref_gps_site='CACT', csv_file=csv_file, msk_file=msk_file, vlim=[-2.5, 2]) zcm/yrY@SiteLonLat DisplacementVelocityU10f8rzread GPS velocity from file: {},T)r delimiternamesrcsg|] }|kqSr-r-rrr-r.rTsz-plot_insar_vs_gps_scatter..rrz!read InSAR velocity from file: {}rar`Nr)maxValuer)rrz{}/{} {})rzAmedian offset between InSAR and GPS [before common referencing]: z.2fz cm/yearz-referencing both InSAR and GPS data to site: z+removing sites with NaN values in GPS or {}z%pruning sites with value range: {} {}zGPS min/max: {:.2f} / {:.2f}zInSAR min/max: {:.2f} / {:.2f}zRMSE = {:.2f} {}z R^2 = {:.2f})rzAPreliminary outliers detection: abs(InSAR - GNSS) > med abs dev (z) * zSite: InSAR GNSSrz: z5.1fz, z z font.sizerrrzk--.r)rOz []zGNSS [equalrz{}_vs_gps_scatter.pdfrrrzsave figure to file)5rzrrr genfromtxtr%rrrrxryrr'rrr2rrrr progressBarrupdater nanmedianrr@multiplyrrrrrZ linregressr#median_abs_deviationrrmrArBrVrYrrnrrror) set_aspectrr|rr)/Zvel_filecsv_fileZmsk_filer rr]xnamerrrrr col_namesnum_col col_typesrZsiteslatslonsZgps_obsrrlengthwidthysxsr num_siteZ insar_obsprog_barrQrHrrZoff_medrrrmser2Zdiff_madr#Z insar_valZgps_valZvbufferrrGrr-rr.plot_insar_vs_gps_scatters     (  F(   "( &    rJc Cs|js|jd|jdd}|jd|}|jd|}||jdkr^||jdkr^d|_nN||jdkr||jdkrd|_n*||jdkr||jdkrd|_nd|_|jdkrd }nd }t|d d}|jr&|jd|jdd tjkr&t j |||tj dtjgd }|j dddgnD|dk rVt |jdkrVt j ||||jd }nt j ||||jd}|jr|jdkr||jntj|jd|_||j jdd|j|jd|jr|j|j|j|jdn"|jdkr|j|j|j|jd||fS)NrrMbP?neitherrVrYrT)r[rBvertical horizontalrr)ry orientationticksz-$\pi$0z$\pi$r)ryrOextendr)nbinsout)rWrXrre)rr^rm)cbar_extrdlimcbar_locgetattrrprrrrrArjrGset_yticklabelsrzr cbar_nbinsrr MaxNLocatorlocator update_ticksrr font_colorr6rlr) r,rryepsilonrDrErOrrzr-r-r. plot_colorbarIs@  (  r`rBrc Cs|dkr|}|}|}|dkr0|}tt||}|dkrr|d}||jkr|jd}|d}n|d}|dkrd}|d}|jj d||||f||||ft d|dd|d } | S) aAdd an arrow to a line. Link: https://stackoverflow.com/questions/34017866 Parameters: line - Line2D object position - x-position of the arrow. If None, mean of xdata is taken direction - 'left' or 'right' size - size of the arrow in fontsize points color - if None, line color is taken. Returns: ann - matplotlib.text.Annotation object NrBrrrkz->rQ) arrowstyler^ linestyle)xytextr] arrowpropsr2) get_color get_xdata get_ydatameanrrabsoluter2axesrpr@) linepositionrXr2r^xdataydataZ start_indZend_indannr-r-r. add_arrow}s.     rprscCs|sd|ddd}|dd}|d}|dkrX|dd d rX|d sXd }n |d krdd}|r|dd dkrd}td|n<|dd dkr|d|d dtj krd}|rtd||fS)zGet auto disp_unit for input dataset Example: if not inps.disp_unit: inps.disp_unit = pp.auto_disp_unit(atr) r_r1rkrrr)rggiantTimeseriesrHDFEOS/rrrcm)mlislcampdB)radianrrtmmrmpixelFz/WARNING: re-wrap is disabled for disp_unit = {}ryrrz+change disp_unit = radian due to rewrapping) replacerr|r{endswithr}rrrr)rrrprrrftyperr-r-r.check_disp_unit_and_wraps*  ,rcCs|s d|d<d}|dd}|d}||krF||d|fS|ddr|ddkrl|d9}n|ddkr|d 9}n|dd kr|d 9}n|ddkr|d9}n|dd kr|d 9}n|ddkr|d9}n|ddkr|d9}nl|ddkr|d9}nT|ddkr |d9}n<|ddkrNdtjt|d}||9}ntd|d|ddkrt|d 9}nF|ddkr|d9}n.|dd kr|d9}n|dd kr|d9}n|ddkrt|d dtj}|ddkr||d9}q|ddkr||d 9}n|dd kr:||d 9}n|ddkrV||d9}n|dd krv||dd9}n|ddkr||d9}nr|ddkr||d9}nV|ddkr||d9}n:|ddkr||d9}n|ddkrntd|dn|dd kr|dd!krz|d"k rzd#|d<|d$d%kr^d&ttj|dd"d'}nd(ttj|dd"d'}n0z|t|d}Wntd)|dYnXntd*||dg}t |d+krfzj|dd,krd-|d<nN|dd.krd/|d<|d09}n.|dd1kr(d2|d<|d39}ntd4|dWn| d-YnX|dd|d}n|d}|d"k r|dkr|tj ||j d59}|||fS)6aQScale data based on data unit and display unit Inputs: data : 2D np.array metadata : dictionary, meta data disp_unit : str, display unit Outputs: data : 2D np.array, data after scaling disp_unit : str, display unit Default data file units in MintPy are: m, m/yr, radian, 1 rrrLrsrrzrrtr%dmrkmrK)rUinchgo_C@)ftfootgD )? @)ydyardgA 3m?)mimilegp\D?)radiansryradrg WAVELENGTHz'Unrecognized display phase/length unit:g{Gz?rryrrzUnrecognized phase/length unit:rmdbNrxr_)rz.intz.unwr)a_mina_maxrzUn-scalable display unit:zUn-scalable data unit:r)ryrrr)rmonmonthrr)ddayrrz#Unrecognized time unit for display:r) r|r{r}rrr#rlog10cliprzrr%r)rrrrS data_unitZ range2phase phase2ranger-r-r.rs                    rcCs|st|d|||d\}}d}||dks>t|||d\}}}|r||dt||d|d|d}|r|td|||||fS) arScale 2D matrix value according to display unit and re-wrapping flag Inputs: data - 2D np.array metadata - dict, including the following attributes: UNIT FILE_TYPE WAVELENGTH disp_unit - string, optional wrap - bool, optional Outputs: data disp_unit wrap N)rrprrrrLrrrrzre-wrapping data to {})rrrmodrr)rrrrprrr disp_scaler-r-r.scale_data4disp_unit_and_rewrap9s$   (rc  s$|rtndd} t|d} |s| dkrd|krdD]\}d|krJd}tj|d rfd |}tjtj |t |}tj |rqq:d}q:d} tj t |rzt|t fd d d Dr2d} t dd fDr| dd} d| } tj||| |dd} | dtj|nRd}d|}|d|dd7}|d|dd7}|d7}| |Wnd}| d|YnXn| dkr| ddtkr<|}tj|d|dd} | d| nR|d r<|}t|d!}|d"ddd#k} W5QRX| d$tj|| dk r||dkrt| jd|}t| jd|}| t|d%d|t|d%d|f} | d|d|f} | dk rd| t| <|dk rd| | |k<| d&||dk rd| | |k<| d'|| dk} | |fS)(aFind and read mask for input data file fname Parameters: fname : string, data file name/path mask_file : string, optional, mask file name datasetName : string, optional, dataset name for HDFEOS file type box : tuple of 4 int, for reading part of data Returns: mask : 2D np.ndarray in bool, mask data mask_file : string, file name of mask data c_sdSrr-rr-r-r.rirzread_mask..r_)rrgr )zmaskTempCoh.h5z maskResInv.h5Z PhaseVelocityNZgeo_zgeo_{}c3s&|]}t|t|kVqdSr)ryrrZatr_mskr-r.rszread_mask..)rar`css|]}|ddkVqdS)r_rbNr-)rPrr-r-r.rsrcrzconnectComponent-{})rrrrzread mask from file:z9WARNING: input file has different size from mask file: {}z, data file {} row/column number: {} / {}rar`z, mask file {} row/column number: {} / {}z Continue without mask.zCan not open mask file:)rrmask)rrzread {} contained mask dataset.z PARAMS.h5rcmaskrLzread {} contained cmask datasetrzhide pixels with mask value < zhide pixels with mask value > )rrrxrrrwr}rjoindirnamerurrr{rr r}h5pyFileryrrr)r mask_filerrxstepysteprDrErrrrdsNamerrfxsizeysizer-rr. read_mask_s                rc CsH|rtdtj|t|}|ddkr6d}nd}t|}|dkrfddt|dt|df}|rv| |}n|}|j |dd }tj ||||d \} }|dk r>||kr>|rtd t j |d |d |d|df| jdt j} | | |d |d |d |d |d|d|d|df<t | } | ||fS)Nzreading DEM: {} ...r_rhheightrr`raFr)rrrz align DEM to the input data filer;rrr)rrrrrwrrxrry box_geo2pixelcheck_box_within_data_coveragerrrrrr%) dem_filerngeo_boxr dem_metadatarr dem_pix_boxZbox2readdemZdem_tmpr-r-r.read_demsF      rc Cstd}d}d}|dkrt}|jdkr4t|d|_|jrddlm}||j|jd}t 4t j dt d|j ||jtd j|j|jd }W5QRXtj|t|dddddf<|rd |jd d |jd d}|d|jd|jd |jd7}t||jrt|t||jdkrzdt|ddt|dd}|d|jddd7}|d7}t|n^ddlm} | j||jdd}tj|jd|jd}|rd|jd}|d|j7}t||rj|jrj|dk s|dk rjd d!||fDd} |jj| krb|r,td"|dk rFtj||jdk<|dk rjtj||jdk<ntd#|||fS)$aPrepare to plot DEM on background Parameters: dem : 2D np.int16 matrix, dem data inps : Namespace with the following 4 items: 'disp_dem_shade' : bool, True/False 'disp_dem_contour' : bool, True/False 'dem_contour_step' : float, 200.0 'dem_contour_smooth': float, 3.0 Returns: dem_shade : 3D np.array in size of (length, width, 4) dem_contour : 2D np.array in size of (length, width) dem_contour_sequence : 1D np.array Examples: dem = readfile.read('inputs/geometryRadar.h5')[0] dem_shade, dem_contour, dem_contour_seq = pp.prepare_dem_background(dem=dem) Ng8@ir) LightSource)azdegaltdegignore)categoryr) vert_exagrFrDrEz show shaded relief DEM (min/max=z.0frsz m; zexag=z ; az/alt=z deg)rzWARNING: elevation range (z.1frcz m)z < 2 contour levels (z+ --> skip plotting DEM contour and continue)ndimage)sigmaorderi(#)stepzshow contour in step of z m zwith a smoothing factor of cSs"g|]}|dk r|jddqS)Nr)rrr-r-r.rT/sz*prepare_dem_background..z2mask DEM to be consistent with valid data coveragezJWARNING: DEM has different size than mask, ignore --mask-dem and continue.) cmd_line_parse shade_maxrrdisp_dem_shadematplotlib.colorsr shade_azdeg shade_altdegrcatch_warnings simplefilterRuntimeWarningshade shade_exagrr9 shade_minrrrdisp_dem_contourrdem_contour_stepscipyrgaussian_filterdem_contour_smootharangemask_demr r) rr,r dem_shade dem_contourZdem_contour_sequencerrarrZ dem_shaper-r-r.prepare_dem_backgrounds\    "" "  "  rcCs|dkrt}tdd|||fDrB|dk rBt|||d\}}}|dk rh|d|d|d|df}nrt|d r~t|j} n0d d |||fDd} dd| jd| jdf} | dd | dd | dd | dd f} |dk r8td ddd} |dk r|j|fd|i| n$t |t j r8|j|fd| i| |dk r|dk rtdd|j d dd} |dkrrd| d<|dk rt j|d|d|jdd|d|d|jddf\} }|j|| ||fd|i| n&t |t j r|j||fd| i| |S)a`Plot 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.array in size of (length, width, 4) dem_contour : 2D np.array in size of (length, width) dem_contour_sequence : 1D np.array 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: m = pp.plot_dem_background(m, geo_box=inps.geo_box, dem=dem, inps=inps) ax = pp.plot_dem_background(ax=ax, geo_box=None, dem_shade=dem_shade, dem_contour=dem_contour, dem_contour_seq=dem_contour_seq) Ncss|]}|dkVqdSrr-rOr-r-r.rUsz&plot_dem_background..)r,rrrr;rrncSsg|]}|dk r|qSrr-rOr-r-r.rTbsz'plot_dem_background..r4spline16upper)rroriginextentblack)rre linewidthsrNrry?)rrrhasattrtuplernrr@rrtrAAxesdem_contour_linewidthrmgridcontour)rGrrrdem_contour_seqrr,rZ geo_extentrnrZ rdr_extentrIyyxxr-r-r.plot_dem_background=sX           r)N)rLFFrMrNT)NT)rT)r)rF)NNT)rrT)rNrN)rNN)NrBrN)NNNrrNNT)NNT)NT)NNNNNNT)Irargparserrrrnumpyr matplotlibrdrrArrrmpl_toolkits.axes_grid1rrrmintpy.objects.coordrZmintpy.objects.colorsrrr r Zmintpy.utils.mapr r mintpy.utilsr rrr%rr#rWrXdefault_figsize_multirZ mplColorsr/rKr^rrrrrrrrr r3rrrrrrr@r$rJr`rprrrrrrrrr-r-r-r. s     $  d  0   & 8  ).F 1Z< a  4 1!j & l ( J