%gbddlmZmZmZddlZddlZddlZddl m Z ejdZ ddl mZddlmZmZmZmZddlmZddlmZe ddlmZdd lmZ dd Z dd Z! ddZ"dZ#ddZ$d dZ%d!dZ&dZ' d"dZ( d#dZ) d$dZ* d%dZ+d&dZ,d'dZ-d(dZ.d)dZ/y#e$re j5d YowxYw#e$re j5d Y~wxYw)*)absolute_importdivisionprint_functionN)performance_metricsz prophet.plot)pyplot) MonthLocatornum2dateAutoDateLocatorAutoDateFormatter) FuncFormatter) deregister_matplotlib_convertersz4Importing matplotlib failed. Plotting will not work.) make_subplotsz9Importing plotly failed. Interactive plots will not work.c `|dnd} |)tjd|} | jd}n|j} |d} |j |j d|j ddd |j | |d d d dd|vr|r|j | |dddd|j rd|vr|r|j | |dddd|r)|jr|j| |d|dd ddtd} t| } |jj| |jj| |jdddd dd|j||j!||r|j#| s| j%| S) aPlot the Prophet forecast. Parameters ---------- m: Prophet model. fcst: pd.DataFrame output of m.predict. ax: Optional matplotlib axes on which to plot. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. plot_cap: Optional boolean indicating if the capacity should be shown in the figure, if available. xlabel: Optional label name on X-axis ylabel: Optional label name on Y-axis figsize: Optional tuple width, height in inches. include_legend: Optional boolean to add legend to the plot. Returns ------- A matplotlib figure. FTw facecolorfigsizeodsyzk.zObserved data points)labelyhat-#0072B2Forecast)lscrcap--kzMaximum capacityfloorzMinimum capacity yhat_lower yhat_upper皙?zUncertainty interval)coloralpharinterval_multiplesmajorgraywhichrrlwr&)pltfigure add_subplot get_figureplothistorylogistic_flooruncertainty_samples fill_betweenr r xaxisset_major_locatorset_major_formattergrid set_xlabel set_ylabellegend tight_layout)mfcstax uncertaintyplot_capxlabelylabelrinclude_legenduser_provided_axfigfcst_tlocator formatters I/var/www/dash_apps/app1/venv/lib/python3.12/site-packages/prophet/plot.pyr3r3*s0!# u zjj38 __S !mmo $ZFGGAIIdOQYYs^T( *GGFDLSIZGH } U ;MNGtO W $#=OPq,, \ 2D4F's:P  R7G!'*IHHw'HH  +GGD6casGCMM&MM&   Jc ~dg}|jd|vr|jdd|jvrd|vr|jdd|jvrd|vr|jd|jt |jDcgc] }||vr|dvr|c}ddd} |j j D] \}} d| | d <dD]<} | | s d j| |vs|jd j| >t|} |r|nd d | zf}tj| d d|\} }| d k(r|g}g}|jdj}|j|jjdj!}t#||D]\}}|dk(rt%||d|||n||jvr|dk(s|j|ddk(r*|t'j(d k(rt+|||||nP|dk(s|j|ddk(rt-|||||n%t/||||n|dvrt%|||||d||j0dvs|j|| j3|D] }t5|}| Scc}w)aPlot the Prophet forecast components. Will plot whichever are available of: trend, holidays, weekly seasonality, yearly seasonality, and additive and multiplicative extra regressors. Parameters ---------- m: Prophet model. fcst: pd.DataFrame output of m.predict. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. plot_cap: Optional boolean indicating if the capacity should be shown in the figure, if available. weekly_start: Optional int specifying the start day of the weekly seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day to Monday, and so on. yearly_start: Optional int specifying the start day of the yearly seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day to Jan 2, and so on. figsize: Optional tuple width, height in inches. Returns ------- A matplotlib figure. trendholidaysweeklyyearly)rRrSFadditivemultiplicativeTmodeextra_regressors_{} r+rrrr)r@rAnamerBrCrDperioddays)r@r[rBrC weekly_startgv@)r@r[rBrC yearly_start)r@r[rBrC)rQextra_regressors_additiveextra_regressors_multiplicativerV)train_holiday_namesappend seasonalitiesextendsortedextra_regressorsitemsformatlenr/subplotsr4diffilocvaluesnonzerominzipplot_forecast_componentpd Timedelta plot_weekly plot_yearlyplot_seasonalitycomponent_modesr?set_y_as_percent)r@rArCrDr`rar componentsr[ regressorspropsrWnpanelrIaxesmultiplicative_axesdtmin_dtrB plot_names rMplot_componentsrbs>J(Z4-?*%1??"x4'7(#1??"x4'7(#0 4 0. plot_cap: Optional boolean indicating if the capacity should be shown in the figure, if available. figsize: Optional tuple width, height in inches. Returns ------- a list of matplotlib artists rrrrrrrrrrr r!_lower_upperr$r%r&Fr'Tr)r*r+r,rV)r/r0r1r3r5r6r7r r r8r9r:r;r<r=rzr{) r@rAr[rBrCrDrartistsrIrJrKrLs rMrtrtsx*G jj38 __S ! $ZF rwwvtDzcYw??G }27764;437??GtO W $#6q,,BOO D)4x+@3$() )7G!'*IHHw'HH  +GGD6casGCMM$MM$ q  !122 b ! NrNc|ddd}|jD]}d||< |jjD]}|d d||d<tj|}|j |}|S)zPrepare dataframe for plotting seasonal components. Parameters ---------- m: Prophet model. ds: List of dates for column ds. Returns ------- A dataframe with seasonal components on ds. ?g)rrr!condition_nameT)rirfrpru DataFramesetup_dataframe)r@rdf_dictr[r~dfs rMseasonality_plot_dfrsR0G"" '')4 ! " ./3GE*+ ,4 g B  2 B IrNcg}|s(tjd|}|jd}tjddtj |z}t ||} |j| } |j}||jtt|| |dd z }|rD|jr8||jtt|| |d z| |d zd d gz }|jdddddd |jtt||j!||j#d|j%||j&|ddk(r t)|}|S)aPlot the weekly component of the forecast. Parameters ---------- m: Prophet model. ax: Optional matplotlib Axes to plot on. One will be created if this is not provided. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. weekly_start: Optional int specifying the start day of the weekly seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day to Monday, and so on. figsize: Optional tuple width, height in inches. name: Name of seasonality component if changed from default 'weekly'. Returns ------- a list of matplotlib artists rrr 2017-01-01r]startperiodsr^rrrrrr$rTr)r*r+r,z Day of weekrWrV)r/r0r1ru date_rangervrpredict_seasonal_componentsday_namer3rangerlr6r7r; set_xticksset_xticklabelsr<r=rfr{) r@rBrCr`rr[rrIr_df_wseass rMrwrwsq(G jj38 __S ! MM a 8 LLl + ,D q$ 'D ( ( .D ==?D rwwuSY'd!!Gq,,BOOE#d)$4$($94x;P*33$@A AGGD6casGCMM%D "#tMM- MM$tV$(88 b ! NrNcg}|s(tjd|}|jd}tjddtj |z}t ||} |j| } ||j| d| |d d z }|r5|jr)||j| d| |d z| |d zd dgz }|jdddd ddttdddd} |jjt!dd|jj#| |j%d|j'||j(|ddk(r t+|}|S)aPlot the yearly component of the forecast. Parameters ---------- m: Prophet model. ax: Optional matplotlib Axes to plot on. One will be created if this is not provided. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. yearly_start: Optional int specifying the start day of the yearly seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day to Jan 2, and so on. figsize: Optional tuple width, height in inches. name: Name of seasonality component if previously changed from default 'yearly'. Returns ------- a list of matplotlib artists rrrrimrr^rrrrrrr$rTr)r*r+r, ) bymonthdayintervalc8djt|SNz{dt:%B} {dt.day}rrkr xposs rMzplot_yearly..gs.55!5ErN Day of yearrWrVN)r/r0r1rurrvrrr3r6r7r;rrr8r:r r9r<r=rfr{) r@rBrCrarr[rrIr_df_yrmonthss rMrxrxAs|(G jj38 __S ! MM c : LLl + ,D q$ 'D ( ( .D rww T DJ3)55Gq,,BOO JTH_-  !#$?@ @GGD6casGC %2,1q AFHH  E"GHHHv&MM- MM$tV$(88 b ! NrNc,g}|s(tjd|}|jd}tjd}|j |d|tj z}d} tjtj|j|j| } t|| } |j| } ||j| d| |d d z }|r5|jr)||j| d| |d z| |d zd dgz }|jdddd dddtjtj|j|jj!} |j#| |dk(rt%d&d}|j'dn|dk(rt%d&d}|j'dni|dk(rt%d&d}|j'dnEdkrt%d&d }|j'd!n!t%d&fd" }|j'd#|j(j+||j-||j |d$d%k(r t/|}|S)'aPlot a custom seasonal component. Parameters ---------- m: Prophet model. name: Seasonality name, like 'daily', 'weekly'. ax: Optional matplotlib Axes to plot on. One will be created if this is not provided. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. figsize: Optional tuple width, height in inches. Returns ------- a list of matplotlib artists rrr2017-01-01 0000r\r^rrrrrrr$rTr)r*r+r,rSc8djt|Srrrs rMrz"plot_seasonality..s 2 9 9Xa[ 9 IrNrrRc8djt|S)Nz{dt:%A}rrrs rMrz"plot_seasonality.. 0 0HQK 0 @rNz Day of Weekdailyc8djt|SNz{dt:%T}rrrs rMrz"plot_seasonality..rrNz Hour of dayrc8djt|Srrrs rMrz"plot_seasonality..rrNHoursc8dj|zdz z S)Nz{:.0f}r+)rk)rrn_ticksr\s rMrz"plot_seasonality..sf ! 0L MrNDaysrWrVr)r/r0r1ru to_datetimerfrvnplinspacevaluerrr3r6r7r; to_pydatetimerr r<r8r:r=r{)r@r[rBrCrrrIrend plot_pointsr_rrxticksfmtrr\s @@rMryryps["G jj38 __S ! NN, -E __T "8 ,F ",,F+ +CK >>"++ekk399kJ KD q$ 'D ( ( .D rwwtDz4:##%%Gq,,BOO JTH_-  !#$?@ @GGD6casGCG ^^BKK SYYH -/ MM& x IK m$   @B m$  @B m$ 1 @B g MO fHH  %MM$tV$(88 b ! NrNcd|jz}|Dcgc]}dj|}}|j|jj|j ||Scc}w)Ndz{0:.4g}%) get_yticksrk set_ytickstolistset_yticklabels)rByticksr yticklabelss rMr{r{sc 2==? "F178A:$$Q'8K8MM"--/((*+{# I9sA2cvg}|r)|j|j|d|d|t|jdkDrG|jt j t j |jdd|k\ng}|D]%} |j|j| ||'|S)aKAdd markers for significant changepoints to prophet forecast plot. Example: fig = m.plot(forecast) add_changepoints_to_plot(fig.gca(), m, forecast) Parameters ---------- ax: axis on which to overlay changepoint markers. m: Prophet model. fcst: Forecast output from m.predict. threshold: Threshold on trend change magnitude for significance. cp_color: Color of changepoint markers. cp_linestyle: Linestyle for changepoint markers. trend: If True, will also overlay the trend. Returns ------- a list of matplotlib artists rrPrrdeltaaxis)rrr) rer3rl changepointsrabsnanmeanparamsaxvline) rBr@rA thresholdcp_color cp_linestylerPrsignif_changepointscps rMadd_changepoints_to_plotrs.G rwwtDz4=HwEF Q^^ q .. rzz!((7+!45B&("FrzzB(|zDEF NrNc|)tjd|}|jd}n|j}t ||gd}t ||g|} t |dj ddz } gd } gd } gd } t| D]4\}}tjd |tj| d ks4n|dj djtjt| z }| dj djtjt| |z }|j|||dd||j|| |d||jd|jdj!| ||j#||S)a$Plot a performance metric vs. forecast horizon from cross validation. Cross validation produces a collection of out-of-sample model predictions that can be compared to actual values, at a range of different horizons (distance from the cutoff). This computes a specified performance metric for each prediction, and aggregated over a rolling window with horizon. This uses prophet.diagnostics.performance_metrics to compute the metrics. Valid values of metric are 'mse', 'rmse', 'mae', 'mape', and 'coverage'. rolling_window is the proportion of data included in the rolling window of aggregation. The default value of 0.1 means 10% of data are included in the aggregation for computing the metric. As a concrete example, if metric='mse', then this plot will show the squared error for each cross validation prediction, along with the MSE averaged over rolling windows of 10% of the data. Parameters ---------- df_cv: The output from prophet.diagnostics.cross_validation. metric: Metric name, one of ['mse', 'rmse', 'mae', 'mape', 'coverage']. rolling_window: Proportion of data to use for rolling average of metric. In [0, 1]. Defaults to 0.1. ax: Optional matplotlib axis on which to plot. If not given, a new figure will be created. figsize: Optional tuple width, height in inches. color: Optional color for plot and error points, useful when plotting multiple model performances on one axis for comparison. Returns ------- a matplotlib figure. rrr)metricsrolling_windowhorizonztimedelta64[ns]g$@)Dhr@smsusns)r_hoursminutesseconds milliseconds microseconds nanoseconds)l"R:l qa lXp7iʚ;i@Birr+r.皙?)r&rrrTz Horizon ({}))r/r0r1r2rmaxastype enumerater timedelta64viewint64floatr3r;r<rkr=)df_cvmetricrrBrr% point_colorrIdf_nonedf_htick_wdtsdt_namesdt_conversionsirx_pltx_plt_hs rMplot_cross_validation_metricrsL zjj38 __S !mmo!%&"MG uvh~ VD#**+<= > DF 0CHN32 >>!R 2>>&$#? ?  I  % %&7 8 = =bhh G%P^_`PaJb bE9o$$%67<?}tj ||@}|S)AaPlot the Prophet forecast with Plotly offline. Plotting in Jupyter Notebook requires initializing plotly.offline.init_notebook_mode(): >>> import plotly.offline as py >>> py.init_notebook_mode() Then the figure can be displayed using plotly.offline.iplot(...): >>> fig = plot_plotly(m, fcst) >>> py.iplot(fig) see https://plot.ly/python/offline/ for details Parameters ---------- m: Prophet model. fcst: pd.DataFrame output of m.predict. uncertainty: Optional boolean to plot uncertainty intervals. plot_cap: Optional boolean indicating if the capacity should be shown in the figure, if available. trend: Optional boolean to plot trend changepoints: Optional boolean to plot changepoints changepoints_threshold: Threshold on trend change magnitude for significance. xlabel: Optional label name on X-axis ylabel: Optional label name on Y-axis Returns ------- A Plotly Figure. rrgba(0, 114, 178, 0.2)blackz#B23B00rActualrr)r%sizemarkers)r[rrmarkerrWr"linesr)widthskip)rrrWline hoverinfo Predictedrr%rtonextynoner[rrrWr fillcolorfillr#)rrrWrrrrrCapdashr%rrr[rrrWrr!FloorTrendrPrr2z line-ns-open)rsymbolr%r)rrrrWrFr+)titledater]1wdaybackward)countrstepstepmode1mmonth6m1yyearall)r+)buttonsT)visible)r%type rangeselector rangeslider) showlegendrheightyaxisr8datalayout)regoScatterr4dictr6r5rlrrrrrlocisinlistFigure)r@rArCrDrPrchangepoints_thresholdrErFrprediction_color error_color actual_color cap_color trend_color line_width marker_sizer=rr>rIs rM plot_plotlyrN.so:!*KLIKJK DKK  ))D/ ))C.,[9  q,, BJJ4j< A    KK  t* v,  ( ;%!*?*?YVq,, BJJ4j< A!   } BJJ4j5kIF*E    GtO BJJ4j7mIF*E     BJJ4j7mKz:   ANN+a/nn FF2::ahhw/a8 9=S S  BJJ!hhtDz':;WDERk! 35   ajqz q##",.q#%",.q#%",.q#$",.e$#*1 #FH ))f -C JrNc i}t||d|||d<|jd|vrt||d||d<ddd}|jjD] \}}d||d<dD]J} || s dj | |vst||dj | |dj | <L|j D]} t || || <tt|dd } | d jtjd|d |dt|z t|D]\} }| d k(r| d d } | d d}n4| d dj | dz} | d dj | dz}| j||d |j||d||dD]}| j|| dzd| S)aPlot the Prophet forecast components using Plotly. See plot_plotly() for Plotly setup instructions Will plot whichever are available of: trend, holidays, weekly seasonality, yearly seasonality, and additive and multiplicative extra regressors. Parameters ---------- m: Prophet model. fcst: pd.DataFrame output of m.predict. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. plot_cap: Optional boolean indicating if the capacity should be shown in the figure, if available. figsize: Set the size for the subplots (in px). Returns ------- A Plotly Figure. rPrQFrTTrWrXr+)rowscols print_gridr>r)r9rr:r8r;zxaxis{}zyaxis{}traces)#get_forecast_component_plotly_propsrdrirjrkrfget_seasonality_plotly_propsrrlupdater?Layoutr append_trace)r@rArCrDrr|r}r[r~rW seasonalityrIrr8r;traces rMplot_components_plotlyr[s.2J= 4+x1Jw(Z4-?!D tZ". :$u=J))//1) e$( 5=!).= d  5 < =J,33D9 :=O ">q+"N ;O S_1 GCMajqzC O+ Z( .4 6M'*EM'*EM)"2"21q5"9:EM)"2"21q5"9:E Z%g./ Z%g./%h/ .E   UAE1 - . . JrNct|||||}tj|d|dd|d|d}tj|d|}|S) a?Plot an particular component of the forecast using Plotly. See plot_plotly() for Plotly setup instructions Parameters ---------- m: Prophet model. fcst: pd.DataFrame output of m.predict. name: Name of the component to plot. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. plot_cap: Optional boolean indicating if the capacity should be shown in the figure, if available. figsize: The plot's size (in px). Returns ------- A Plotly Figure. rr+Fr8r;rr:r9r8r;rSr<)rTr?rWrE) r@rAr[rCrDrr~r>rIs rMplot_forecast_component_plotlyr^ s^& 04{H UE YYajqzGnGn F ))x 8C JrNct|||}tj|d|dd|d|d}tj|d|}|S) aPlot a custom seasonal component using Plotly. See plot_plotly() for Plotly setup instructions Parameters ---------- m: Prophet model. name: Seasonality name, like 'daily', 'weekly'. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. figsize: Set the plot's size (in px). Returns ------- A Plotly Figure. rr+Fr8r;r]rSr<)rUr?rWrE)r@r[rCrr~r>rIs rMplot_seasonality_plotlyr`(sZ )D+ >E YYajqzGnGn F ))x 8C JrNc Dd}d}d}d}d} |dj|djz dz} |dj| z |dj| zg} d} d } |d k(r|j|d}|j|d|\}}}|jj j d d d |_|jj j dd d |_tjd |j} |jD]K\}}| |jt| d k7zxxdz cc<| |jtxx|z cc<Mg}|jtj||d||| tj j#|| | |r |j$r||dz||dzk7j'r| dk(r1|dj)t+dd ||dz||dzd|n|jtj|dz|d||dz| tj j#d||jtj|dz|d||dz| tj j#d||dd|vrQ|rO|jtjd |d|dd tj j#|d!| "|j,rUd#|vrQ|rO|jtjd$|d|d#d tj j#|d!| "tj.j1d%| &}tj.j3|d'k(rd(nd)tj.j4j7|*|+}||j8d,vr|j)d-d./|||d0S)1aPrepares a dictionary for plotting the selected forecast component with Plotly Parameters ---------- m: Prophet model. fcst: pd.DataFrame output of m.predict. name: Name of the component to plot. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. plot_cap: Optional boolean indicating if the capacity should be shown in the figure, if available. Returns ------- A dictionary with Plotly traces, xaxis and yaxis rr r #AAArr皙?NrrQ_delim_F)regexz+0)r=indexz
r)r[rrrWrtextrrrrr=)r6 symmetricarray arrayminusrr%)error_yrr%r rrrrrrr!r!r&)r6rrPnormaltozerorh) rangemoder% zerolinecolorrV%.2% tickformat hoverformatrSr8r;)rrrconstruct_holiday_dataframemake_holiday_featurescolumnsstrreplaceruSeriesrgrjrboolrer?r@scatterLiner6anyrVrAr5r>XAxisYAxisr;Titlerz)r@rAr[rCrDrGrHrJzeroline_colorrL range_marginrange_xrhrWrQholiday_features_holiday_featureidxsrSr8r;s rMrTrTDs"!*KINJJNN$tDz~~'774?LDz~~,.T 0@<0OPG D D z00d<!"!8!8dX!N!Q#3#;#;#?#?#G#G SU]b#G#c #3#;#;#?#?#G#GbX]#G#^ yyb(8(>(>?%5%;%;%= 7 !OT T"dbj1 2f < 2 T" # 6 # 7F MM"**  t* t*  ZZ__#3:_ F  q,,$th2G4PTW_P_K`2`1e1e1g 9  1I  #th/#D8O4%    MM"**H_t*th'ZZ__1K_@   MM"**H_t*th'ZZ__1K_@%  } bjj4j5kyvZP    GtO bjj4j7myvZP    IIOO   E IIOO$'/hx"$))//"7"7T"7"B*8  :E q  !122  7uu ==rNc ~d}d}d}d}tjd}|j|d}|tj|z} |jdj j d k(jr)tj|jt} n|jdj jd k(jr,tj|d zjt} n.tj|d zd zjt} tjtj|j| j| d } t|| } |j!| } g}|j#t%j&|| d| |dt$j(j+|||r|j,r| |dz| |dzk7j/r|j#t%j&|dz| d| |dzdt$j(j+d ||j#t%j&|dz| d| |dzdt$j(j+d ||d|dkrd}n|dkrd}n |dkrd}nd}| dj1| dj3z dz}t$j4j7|d| dj3|z | dj1|zg}t$j4j9t$j4j:j=|| }|j|d!d"k(r|j?d#d$%|||d&S)'awPrepares a dictionary for plotting the selected seasonality with Plotly Parameters ---------- m: Prophet model. name: Name of the component to plot. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. Returns ------- A dictionary with Plotly traces, xaxis and yaxis rr rrbrr\r^rr<F)endpointrrr rrrmrrz%H:%Mr]z%A %H:%Mz%Az%B %ercr&)rvr6rrp)r%rrrWrVrsrtrurx) rurrfrvr4rhourr3rr!rintminuterrrrrer?r@rrr6rrrrr>rrr;rrV)r@r[rCrGrHrLrrr\rrr_rrrSrvrr8r;s rMrUrUs@!*KJN NN, -E __T "8 ,F ",,F+ +C $1$))+hhv&--c2 ))D/   # #q ( - - /hhv{+2237 hhv{R/077< >>"++ekk399kTYZ [D q$ 'D ( ( .D F MM"**  t* t*  ZZ__#3:_ F q,,$th2G4PTW_P_K`2`1e1e1g bjj4j4(?#q <     bjj4j4(?#q <!  { ! "  JNN$tDz~~'774?L IIOO Dz~~,.T 0@<0OP  E IIOO"))//"7"7T"7"B*8  :EtV$(88  7uu ==rN)NTTrr r/F)TTrrN)NTFr)NTrrrR)NTrrrS)NTr){Gz?rrT)rNrbr*)TTFFrrr)iX)TT)rr)TFri,)Tr)TF)T)0 __future__rrrloggingnumpyrpandasruprophet.diagnosticsr getLoggerlogger matplotlibrr/matplotlib.datesrr r r matplotlib.tickerr pandas.plottingr ImportErrorerrorplotly.graph_objs graph_objsr?plotly.subplotsrr3rrtrrwrxryr{rrrNr[r^r`rTrUrNrMrs6A@3   > * I( 0@$&N"- LO$)5rLM fTGN-`0+\,^@FIMFHKNbUZNXYz;E<~>8g>TQ>]I LLGHI N LLLMNs#%B, C,CCC! C!