]h8SSKJrJrJr SSKrSSKrSSKrSSK J r \R"S5r SSK Jr SSKJrJrJrJr SSKJr SSKJr \"5 SSKJr SS KJr SS jr SS jr!SSjr"Sr#SSjr$S Sjr%S!Sjr&Sr'S"Sjr(S#Sjr)S$Sjr*S%Sjr+S&Sjr,S'Sjr-S(Sjr.S)Sjr/g!\a \ R5S 5 N~f=f!\a \ R5S 5 Nf=f)*)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 |UcSOSn Uc'[R"SUS9n U RS5nOUR5n USn UR UR SUR SSS S 9 UR XS S S SS9 SU;aU(aUR XSSSSS9 UR (a"SU;aU(aUR XSSSSS9 U(a*UR(aURXSUSS SSS9 [SS9n [U 5n URRU 5 URRU 5 URSSSS SSS9 URU5 UR!U5 U(aUR#5 U (dU R%5 U $) aXPlot 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 _C:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\prophet/plot.pyr3r3+s0!# u zjj38 __S !mmo $ZFGGAIIdOQYYs^T( *GGFLSIZGH } U ;MNGtO W $#=OPq,, \ 2D4F's:P  R7G!'*IHHw'HH  +GGD6casGCMM&MM&   Jc S/nURbSU;aURS5 SUR;aSU;aURS5 SUR;aSU;aURS5 UR[ UR5Vs/sHnX;dM US;dMUPM sn5 SSS.n UR R 5H upSXS 'M SHEn X(dMS RU 5U;dM%URS RU 55 MG [U5n U(aUOS S U -4n[R"U S SUS9upU S :XaU/n/nURSR5nURURR5SR!5n[#X5HunnUS:Xa[%XSUUUS9 OUUR;awUS:XdURUSS:Xa%U[&R("S S9:Xa [+UUUX$S9 OIUS:XdURUSS:Xa [-UUUX%S9 O [/UUUUS9 OUS;a [%XUUUSS9 UUR0S;dMURU5 M U R35 UHn[5U5nM U $s snf)aPlot the Prophet forecast components. Will plot whichever are available of: trend, holidays, weekly seasonality, yearly seasonality, and additive and multiplicative extra regressors. Parameters ---------- m: Prophet model. fcst: pd.DataFrame output of m.predict. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. plot_cap: Optional boolean indicating if the capacity should be shown in the figure, if available. weekly_start: Optional int specifying the start day of the weekly seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day to Monday, and so on. yearly_start: Optional int specifying the start day of the yearly seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day to Jan 2, and so on. figsize: Optional tuple width, height in inches. Returns ------- A matplotlib figure. trendholidaysweeklyyearly)rRrSFadditivemultiplicativeTmodeextra_regressors_{} r+rrrr)r@rAnamerBrCrDperioddays)r@r[rBrC weekly_startgv@)r@r[rBrC yearly_start)r@r[rBrC)rQextra_regressors_additiveextra_regressors_multiplicativerV)train_holiday_namesappend seasonalitiesextendsortedextra_regressorsitemsformatlenr/subplotsr4diffilocvaluesnonzerominzipplot_forecast_componentpd Timedelta plot_weekly plot_yearlyplot_seasonalitycomponent_modesr?set_y_as_percent)r@rArCrDr`rar componentsr[ regressorspropsrWnpanelrIaxesmultiplicative_axesdtmin_dtrB plot_names rMplot_componentsrcs>J(Z4-?*%1??"x4'7(#1??"x4'7(#00 <  (<< 0$u=J))//1 $( =!2.    5 < 0. plot_cap: Optional boolean indicating if the capacity should be shown in the figure, if available. figsize: Optional tuple width, height in inches. Returns ------- a list of matplotlib artists rrrrrrrrrrr r!_lower_upperr$r%r&Fr'Tr)r*r+r,rV)r/r0r1r3r5r6r7r r r8r9r:r;r<r=rzr{) r@rAr[rBrCrDrartistsrIrJrKrLs rMrtrtsf*G jj38 __S ! $ZF wwvDzcYw??G }776;437??GtO W $#6q,,OO )4x+@3$() )7G!'*IHHw'HH  +GGD6casGCMM$MM$   !122 b ! NrNcUSSS.nURHnSX#'M URR5HnUScM SX$S'M [R"U5nUR U5nU$)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"" #'') ! " ./3G*+ ,* g B  2 B IrNc  /nU(d&[R"SUS9nURS5n[R"SSS9[R "US9-n[ X5n URU 5n UR5nXaR[[U55XSS S 9- nU(aCUR(a2XaR[[U55XS -XS -S S S9/- nURSSSSSS S9 UR[[U555 UR!U5 UR#S5 UR%U5 UR&USS:Xa [)U5nU$)auPlot the weekly component of the forecast. Parameters ---------- m: Prophet model. ax: Optional matplotlib Axes to plot on. One will be created if this is not provided. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. weekly_start: Optional int specifying the start day of the weekly seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day to Monday, and so on. figsize: Optional tuple width, height in inches. name: Name of seasonality component if changed from default 'weekly'. Returns ------- a list of matplotlib artists rrr 2017-01-01r]startperiodsr^rrrrrr$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 rMrwrwse(G jj38 __S ! MM a 8 LLl + ,D q 'D ( ( .D ==?D wwuSY'!!Gq,,OOE#d)$4$($94x;P*33$@A AGGD6casGCMM%D "#tMM- MM$tV$(88 b ! NrNc /nU(d&[R"SUS9nURS5n[R"SSS9[R "US9-n[ X5n URU 5n XaRU SXS S S 9- nU(a4UR(a#XaRU SXS -XS -S SS9/- nURSSSS SSS9 [[SS5SSS9n URR[!SSj55 URR#U 5 UR%S5 UR'U5 UR(USS:Xa [+U5nU$)a~Plot the yearly component of the forecast. Parameters ---------- m: Prophet model. ax: Optional matplotlib Axes to plot on. One will be created if this is not provided. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. yearly_start: Optional int specifying the start day of the yearly seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day to Jan 2, and so on. figsize: Optional tuple width, height in inches. name: Name of seasonality component if previously changed from default 'yearly'. Returns ------- a list of matplotlib artists rrrrimrr^rrrrrrr$rTr)r*r+r, ) bymonthdayintervalc2SR[U5S9$Nz{dt:%B} {dt.day}rrkr xposs rMplot_yearly..hs.55!5ErN Day of yearrWrVN)r/r0r1rurrvrrr3r6r7r;rrr8r:r r9r<r=rfr{) r@rBrCrarr[rrIr_df_yrmonthss rMrxrxBsp(G jj38 __S ! MM c : LLl + ,D q 'D ( ( .D ww T DJ3)55Gq,,OO JH_-  !#$?@ @GGD6casGC %2,1q AFHH  E"GHHHv&MM- MM$tV$(88 b ! NrNc D^^/nU(d&[R"SUS9nURS5n[R"S5nUR USmU[R "TS9-nSn [R"[R"URURU 55n [X 5n URU 5n XRRU SXS S S 9- nU(a4UR(a#XRRU SXS -XS -S SS9/- nURSSSS SSS9 Sm[R"[R"URURT55R!5n UR#U 5 US:Xa [%S&Sj5nUR'S5 OUS:Xa [%S&Sj5nUR'S5 OoUS:Xa [%S&Sj5nUR'S5 OITS::a [%S&S j5nUR'S!5 O#[%S&UU4S"jj5nUR'S#5 UR(R+U5 UR-U5 UR US$S%:Xa [/U5nU$)'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$rTr)r*r+r,rSc2SR[U5S9$rrrs rMr"plot_seasonality..s 2 9 9Xa[ 9 IrNrrRc2SR[U5S9$)Nz{dt:%A}rrrs rMrr 0 0HQK 0 @rNz Day of Weekdailyc2SR[U5S9$Nz{dt:%T}rrrs rMrrrrNz Hour of dayrc2SR[U5S9$rrrs rMrrrrNHoursc8>SRUT-TS- - 5$)Nz{:.0f}r+)rk)rrn_ticksr\s rMrrsf ! 0L MrNDaysrWrVr)r/r0r1ru to_datetimerfrvnplinspacevaluerrr3r6r7r; to_pydatetimerr r<r8r:r=r{)r@r[rBrCrrrIrend plot_pointsr_rrxticksfmtrr\s @@rMryryqsO"G jj38 __S ! NN, -E __T "8 ,F ",,F+ +CK >>"++ekk399kJ KD q 'D ( ( .D wwtDz4:##%%Gq,,OO JH_-  !#$?@ @GGD6casGCG ^^BKK SYYH -/ MM& x IK m$   @B m$  @B m$ 1 @B g MO fHH  %MM$tV$(88 b ! NrNcSUR5-nUVs/sHnSRU5PM nnURUR5R55 UR U5 U$s snf)Ndz{0:.4g}%) get_yticksrk set_ytickstolistset_yticklabels)rByticksr yticklabelss rMr{r{sd 2==? "F178A:$$Q'K8MM"--/((*+{# I9sA5c v/nU(a&URURUSUSUS95 [UR5S:aFUR[R "[R "URSSS95U:O/nUH"n URURXUS95 M$ U$)a Add 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&("rzzB|zDE" NrNcbUc'[R"SUS9nURS5nOUR5n[ X/SS9n[ X/US9n [ USR S55S- n /S Qn /S Qn /S Qn [U 5H7up[R"S U5[R"U S 5:dM7 O USR S5R[R5[U W5- nU SR S5R[R5[X5- nURUXSSUS9 URUXSUS9 URS5 URSR!X55 UR#U5 U$)aPlot 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', 'mdape', 'smape', 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', 'mdape', 'smape', '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_metricr sL zjj38 __S !mmo!%"MG uh~ VD#**+<= > DF 0CHN3 >>!R 2>>&$#? ?   I  % %&7 8 = =bhh G%P^_`PaJb bE9o$$%67<9S?9n[R "UUS@9nU$)AaPlot the Prophet forecast with Plotly offline. Plotting in Jupyter Notebook requires initializing plotly.offline.init_notebook_mode(): >>> import plotly.offline as py >>> py.init_notebook_mode() Then the figure can be displayed using plotly.offline.iplot(...): >>> fig = plot_plotly(m, fcst) >>> py.iplot(fig) see https://plot.ly/python/offline/ for details Parameters ---------- m: Prophet model. fcst: pd.DataFrame output of m.predict. uncertainty: Optional boolean to plot uncertainty intervals. plot_cap: Optional boolean indicating if the capacity should be shown in the figure, if available. trend: Optional boolean to plot trend changepoints: Optional boolean to plot changepoints changepoints_threshold: Threshold on trend change magnitude for significance. xlabel: Optional label name on X-axis ylabel: Optional label name on Y-axis figsize: The plot's size (in px). Returns ------- A Plotly Figure. rrgba(0, 114, 178, 0.2)blackz#B23B00rActualrr)r%sizemarkers)r[rrmarkerrWr"linesr)widthskip)rrrWline hoverinfo Predictedrr%rtonextynoner[rrrWr fillcolorfillr#)rrrWrrrrrCapdashr%r rr[rrrWrr!FloorTrendrPrr2z line-ns-open)rsymbolr%r)rrrrWrFr+)titledater]1wdaybackward)countrstepstepmode1mmonth6m1yyearall)r-)buttonsT)visible)r'type rangeselector rangeslider) showlegendrheightyaxisr8datalayout)regoScatterr4dictr6r5rlrrrrrlocisinlistFigure)r@rArCrDrPrchangepoints_thresholdrErFrprediction_color error_color actual_color cap_color trend_color line_width marker_sizer?rr@rIs rM plot_plotlyrP/sk<!*KLIKJK DKK  ))D/ ))C.,[9  q,, BJJ4j< A    KK  t* v,  ( ;%!*?*?YVq,, BJJ4j< A!   } BJJ4j5kIF*E    GtO BJJ4j7mIF*E     BJJ4j7mK:   ANN+a/nn FF2::ahhw/a8 9=S S  BJJ!hhtDz':;WDERk! 35   ajqz q##",.q#%",.q#%",.q#$",.e$#*1 #FH ))f -C JrNc 0n[XSX#5US'URbSU;a[XSU5US'SSS.nURR5H upxSXhS'M SHRn Xi(dMSR U 5U;dM%[XSR U 55USR U 5'MT UR Hn [ X 5XZ'M [[U5SSS 9n U S R[R"SUS US[U5-S 95 [U5HupU S :XaU S S n U S SnO4U S SR U S-5n U S SR U S-5nU RXWS 5 URXWS5 XWSHnU RXS-S5 M M U $)avPlot the Prophet forecast components using Plotly. See plot_plotly() for Plotly setup instructions Will plot whichever are available of: trend, holidays, weekly seasonality, yearly seasonality, and additive and multiplicative extra regressors. Parameters ---------- m: Prophet model. fcst: pd.DataFrame output of m.predict. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. plot_cap: Optional boolean indicating if the capacity should be shown in the figure, if available. figsize: Set the size for the subplots (in px). Returns ------- A Plotly Figure. rPrQFrTTrWrXr+)rowscols print_gridr@r)r;rr<r8r=zxaxis{}zyaxis{}traces)#get_forecast_component_plotly_propsrdrirjrkrfget_seasonality_plotly_propsrrlupdaterALayoutr append_trace)r@rArCrDrr|r}r[r~rW seasonalityrIrr8r=traces rMplot_components_plotlyr]s2J= +1Jw(Z4-?!D Z". :$u=J))//1 $( =!2.    5 < =J,33D9 :/ ">q"N ' S_1 GCMajqzC O+ Z( 6M'*EM'*EM)"2"21q5"9:EM)"2"21q5"9:E Z%g./ Z%g./%h/E   UE1 -0) JrNc[XX#U5n[R"USUSSUSUSS9n[R"USUS9nU$) aPlot a 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<r;r8r=rUr>)rVrArYrG) r@rAr[rCrDrr~r@rIs rMplot_forecast_component_plotlyr` sZ& 0H UE YYajqzGnGn F ))x 8C JrNc[XU5n[R"USUSSUSUSS9n[R"USUS9nU$) awPlot a custom seasonal component using Plotly. See plot_plotly() for Plotly setup instructions Parameters ---------- m: Prophet model. name: Seasonality name, like 'daily', 'weekly'. uncertainty: Optional boolean to plot uncertainty intervals, which will only be done if m.uncertainty_samples > 0. figsize: Set the plot's size (in px). Returns ------- A Plotly Figure. rr+Fr8r=r_rUr>)rWrArYrG)r@r[rCrr~r@rIs rMplot_seasonality_plotlyrb*sX )+ >E YYajqzGnGn F ))x 8C JrNcSnSnSnSnSn USR5USR5- S-n USR5U - USR5U -/n Sn S n US :XGaURUS5nURUSU5un nURR R S S S S9UlURR R SS S S9Ul[R"S URS9n UR5HLunnU UR[5U S :g-==S- ss'U UR[5==U- ss'MN /nUR[R"UUSXU [R R#XYS9U S95 U(GaUR$(aXS-XS-:gR'5(aU S:Xa)USR)[+SS XS-XS-SUS9S9 OUR[R"US-USXS-U [R R#SUS9S95 UR[R"US-USXS-U [R R#SUS9USS95 SU;aQU(aJUR[R"S USUSS [R R#US!U S"9S95 UR,(aWS#U;aQU(aJUR[R"S$USUS#S [R R#US!U S"9S95 [R.R1S%U S&9n[R.R3US':XaS(OS)[R.R4R7US*9US+9nX R8S,;aUR)S-S.S/9 UUUS0.$)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?)r8 symmetricarray arrayminusrr%)error_yrr%r"rrrrr r!r!r#r()r8rrPnormaltozerorj) rangemoder' zerolinecolorrV%.2% tickformat hoverformatrUr8r=)rrrconstruct_holiday_dataframemake_holiday_featurescolumnsstrreplaceruSeriesrirjrboolrerArBscatterLiner6anyrXrCr5r@XAxisYAxisr=Titlerz)r@rAr[rCrDrIrJrLzeroline_colorrN range_marginrange_xrjrWrQholiday_features_holiday_featureidxsrUr8r=s rMrVrVFs"!*KINJJNN$tDz~~'774?LDz~~,.T 0@<0OPG D D z00d<!"!8!8dX!N!Q#3#;#;#?#?#G#G SU]b#G#c #3#;#;#?#?#G#GbX]#G#^ yyb(8(>(>?%5%;%;%= !OT T"dbj1 2f < 2 T" # 6 #&>F MM"**  t* *  ZZ__#3_ F  q,,$h2G4W_P_K`2`1e1e1g1g 9  1I  #h/#8O4%    MM"**H_t*h'ZZ__1K_@   MM"**H_t*h'ZZ__1K_@%  } bjj4j5kyvZP    GtO bjj4j7myvZP    IIOO   E IIOO$'/hx"$))//"7"7T"7"B*8  :E   !122  7uu ==rNc vSnSnSnSn[R"S5nURUSnU[R"US9-n URSR R S :HR5(a*[R"U5R[5n OURSR RS :HR5(a-[R"US -5R[5n O/[R"US -S -5R[5n [R"[R"URU RU S S 95n [X 5n UR!U 5n /nUR#[$R&"UU SXS[$R(R+X5S9S95 U(aUR,(aXS-XS-:gR/5(aUR#[$R&"US-U SXS-S[$R(R+S US9S95 UR#[$R&"US-U SXS-S[$R(R+S US9USS95 US::aSnOUS:aSnO US:aSnOSnU SR15U SR35- S-n[$R4R7USU SR35U- U SR15U-/S9n[$R4R9[$R4R:R=US9US 9nURUS!S":XaUR?S#S$S%9 UUUS&.$)'aOPrepares 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 rrdrr\r^rr<F)endpointrrr"rrrorrz%H:%Mr]z%A %H:%Mz%Az%B %erer()rxr8rrr)r'rtrWrVrurvrwrz) rurrfrvr4rhourr5rr!rintminuterrrrrerArBrrr6rrrrr@rrr=rrX)r@r[rCrIrJrNrrr\rrr_rrrUrxrr8r=s rMrWrWs:!*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* *  ZZ__#3_ F q,,$h2G4W_P_K`2`1e1e1g1g bjj4j(?#q <     bjj4j(?#q <!  { ! "  JNN$tDz~~'774?L IIOO Dz~~,.T 0@<0OP  E IIOO"))//"7"7T"7"B*8  :EtV$(88  7uu ==rN)NTTrr r1F)TTrrN)NTFr)NTrrrR)NTrrrS)NTr){Gz?rrT)rNrbr*)TTFFrrr)iX)TT)rr)TFri,)Tr)TF)T)0 __future__rrrloggingnumpyrpandasruprophet.diagnosticsr getLoggerlogger matplotlibrr/matplotlib.datesrr r r matplotlib.tickerr pandas.plottingr ImportErrorerrorplotly.graph_objs graph_objsrAplotly.subplotsrr3rrtrrwrxryr{rr rPr]r`rbrVrWrNrMrs6A@4   > * I( 0@$&N"- LO$)5rLM fTGN-`0+\,^@FIMFHKNbUZNXZ|;E<~>8g>TQ>_I LLGHI N LLLMNs#%B: C:CCC10C1