From 8be35c2f34a41b653e3966bb6a5391aefc6554f8 Mon Sep 17 00:00:00 2001 From: bl Date: Wed, 5 Jul 2017 01:20:22 -0700 Subject: [PATCH] Custom seasonalities in R --- R/NAMESPACE | 1 + R/R/prophet.R | 169 ++++++++++++++---- R/man/add_seasonality.Rd | 25 +++ R/man/parse_seasonality_args.Rd | 26 +++ R/man/plot_seasonality.Rd | 21 +++ R/man/prophet.Rd | 16 +- R/tests/testthat/test_prophet.R | 35 ++-- docs/_data/nav_docs.yml | 2 +- ... => seasonality_and_holiday_effects.ipynb} | 132 ++++++++++---- 9 files changed, 338 insertions(+), 89 deletions(-) create mode 100644 R/man/add_seasonality.Rd create mode 100644 R/man/parse_seasonality_args.Rd create mode 100644 R/man/plot_seasonality.Rd rename notebooks/{holiday_effects.ipynb => seasonality_and_holiday_effects.ipynb} (58%) diff --git a/R/NAMESPACE b/R/NAMESPACE index b4cf5f0..1ae758b 100644 --- a/R/NAMESPACE +++ b/R/NAMESPACE @@ -2,6 +2,7 @@ S3method(plot,prophet) S3method(predict,prophet) +export(add_seasonality) export(fit.prophet) export(make_future_dataframe) export(predictive_samples) diff --git a/R/R/prophet.R b/R/R/prophet.R index 534cde7..9ae7268 100644 --- a/R/R/prophet.R +++ b/R/R/prophet.R @@ -15,9 +15,11 @@ globalVariables(c( #' Prophet forecaster. #' -#' @param df Dataframe containing the history. Must have columns ds (date type) -#' and y, the time series. If growth is logistic, then df must also have a -#' column cap that specifies the capacity at each ds. +#' @param df (optional) Dataframe containing the history. Must have columns ds +#' (date type) and y, the time series. If growth is logistic, then df must +#' also have a column cap that specifies the capacity at each ds. If not +#' provided, then the model object will be instantiated but not fit; use +#' fit.prophet(m, df) to fit the model. #' @param growth String 'linear' or 'logistic' to specify a linear or logistic #' trend. #' @param changepoints Vector of dates at which to include potential @@ -27,8 +29,10 @@ globalVariables(c( #' if input `changepoints` is supplied. If `changepoints` is not supplied, #' then n.changepoints potential changepoints are selected uniformly from the #' first 80 percent of df$ds. -#' @param yearly.seasonality Fit yearly seasonality; 'auto', TRUE, or FALSE. -#' @param weekly.seasonality Fit weekly seasonality; 'auto', TRUE, or FALSE. +#' @param yearly.seasonality Fit yearly seasonality. Can be 'auto', TRUE, +#' FALSE, or a number of Fourier terms to generate. +#' @param weekly.seasonality Fit weekly seasonality. Can be 'auto', TRUE, +#' FALSE, or a number of Fourier terms to generate. #' @param holidays data frame with columns holiday (character) and ds (date #' type)and optionally columns lower_window and upper_window which specify a #' range of days around the date to be included as holidays. lower_window=-2 @@ -66,7 +70,7 @@ globalVariables(c( #' @export #' @importFrom dplyr "%>%" #' @import Rcpp -prophet <- function(df = df, +prophet <- function(df = NULL, growth = 'linear', changepoints = NULL, n.changepoints = 25, @@ -105,6 +109,7 @@ prophet <- function(df = df, y.scale = NULL, t.scale = NULL, changepoints.t = NULL, + seasonalities = list(), stan.fit = NULL, params = list(), history = NULL, @@ -112,7 +117,7 @@ prophet <- function(df = df, ) validate_inputs(m) class(m) <- append("prophet", class(m)) - if (fit) { + if ((fit) && (!is.null(df))) { m <- fit.prophet(m, df, ...) } @@ -372,6 +377,31 @@ make_holiday_features <- function(m, dates) { return(holiday.mat) } +#' Add a seasonal component with specified period and number of Fourier +#' components. +#' +#' Increasing the number of Fourier components allows the seasonality to change +#' more quickly (at risk of overfitting). +#' +#' @param m Prophet object. +#' @param name String name of the seasonality component. +#' @param period Float number of days in one period. +#' @param fourier.order Int number of Fourier components to use. +#' +#' @return The prophet model with the seasonality added. +#' +#' @importFrom dplyr "%>%" +#' @export +add_seasonality <- function(m, name, period, fourier.order) { + if (!is.null(m$holidays)) { + if (name %in% (unique(m$holidays$holiday) %>% as.character())) { + stop('Name "', name, '" already used for holiday') + } + } + m$seasonalities[[name]] <- c(period, fourier.order) + return(m) +} + #' Dataframe with seasonality features. #' #' @param m Prophet object. @@ -381,19 +411,14 @@ make_holiday_features <- function(m, dates) { #' make_all_seasonality_features <- function(m, df) { seasonal.features <- data.frame(zeros = rep(0, nrow(df))) - if (m$yearly.seasonality) { + for (name in names(m$seasonalities)) { + period <- m$seasonalities[[name]][1] + series.order <- m$seasonalities[[name]][2] seasonal.features <- cbind( seasonal.features, - make_seasonality_features(df$ds, 365.25, 10, 'yearly')) - } - if (m$weekly.seasonality) { - seasonal.features <- cbind( - seasonal.features, - make_seasonality_features(df$ds, 7, 3, 'weekly')) + make_seasonality_features(df$ds, period, series.order, name)) } if(!is.null(m$holidays)) { - # A smaller prior scale will shrink holiday estimates more than seasonality - scale.ratio <- m$holidays.prior.scale / m$seasonality.prior.scale seasonal.features <- cbind( seasonal.features, make_holiday_features(m, df$ds)) @@ -401,6 +426,39 @@ make_all_seasonality_features <- function(m, df) { return(seasonal.features) } +#' Get number of Fourier components for built-in seasonalities. +#' +#' @param m Prophet object. +#' @param name String name of the seasonality component. +#' @param arg 'auto', TRUE, FALSE, or number of Fourier components as +#' provided. +#' @param auto.disable Bool if seasonality should be disabled when 'auto'. +#' @param default.order Int default Fourier order. +#' +#' @return Number of Fourier components, or 0 for disabled. +#' +parse_seasonality_args <- function(m, name, arg, auto.disable, default.order) { + if (arg == 'auto') { + fourier.order <- 0 + if (name %in% names(m$seasonalities)) { + warning('Found custom seasonality named "', name, + '", disabling built-in ', name, ' seasonality.') + } else if (auto.disable) { + warning('Disabling ', name, ' seasonality. Run prophet with ', name, + '.seasonality=TRUE to override this.') + } else { + fourier.order <- default.order + } + } else if (arg == TRUE) { + fourier.order <- default.order + } else if (arg == FALSE) { + fourier.order <- 0 + } else { + fourier.order <- arg + } + return(fourier.order) +} + #' Set seasonalities that were left on auto. #' #' Turns on yearly seasonality if there is >=2 years of history. @@ -414,25 +472,21 @@ make_all_seasonality_features <- function(m, df) { set_auto_seasonalities <- function(m) { first <- min(m$history$ds) last <- max(m$history$ds) - if (m$yearly.seasonality == 'auto') { - if (last - first < 730) { - warning('Disabling yearly seasonality. ', - 'Run prophet with `yearly.seasonality=TRUE` to override this.') - m$yearly.seasonality <- FALSE - } else { - m$yearly.seasonality <- TRUE - } + dt <- diff(m$history$ds) + min.dt <- min(dt[dt > 0]) + + yearly.disable <- last - first < 730 + fourier.order <- parse_seasonality_args( + m, 'yearly', m$yearly.seasonality, yearly.disable, 10) + if (fourier.order > 0) { + m$seasonalities[['yearly']] <- c(365.25, fourier.order) } - if (m$weekly.seasonality == 'auto') { - dt <- diff(m$history$ds) - min.dt <- min(dt[dt > 0]) - if ((last - first < 14) || (min.dt >= 7)) { - warning('Disabling weekly seasonality. ', - 'Run prophet with `weekly.seasonality=TRUE` to override this.') - m$weekly.seasonality <- FALSE - } else { - m$weekly.seasonality <- TRUE - } + + weekly.disable <- ((last - first < 14) || (min.dt >= 7)) + fourier.order <- parse_seasonality_args( + m, 'weekly', m$weekly.seasonality, weekly.disable, 3) + if (fourier.order > 0) { + m$seasonalities[['weekly']] <- c(7, fourier.order) } return(m) } @@ -1053,6 +1107,13 @@ prophet_plot_components <- function( if ("yearly" %in% colnames(df)) { panels[[length(panels) + 1]] <- plot_yearly(m, uncertainty, yearly_start) } + # Plot other seasonalities + for (name in names(m$seasonalities)) { + if (!(name %in% c('weekly', 'yearly')) && (name %in% colnames(df))) { + panels[[length(panels) + 1]] <- plot_seasonality(m, name, uncertainty) + } + } + # Make the plot. grid::grid.newpage() grid::pushViewport(grid::viewport(layout = grid::grid.layout(length(panels), @@ -1190,4 +1251,44 @@ plot_yearly <- function(m, uncertainty = TRUE, yearly_start = 0) { return(gg.yearly) } +#' Plot a custom seasonal component. +#' +#' @param m Prophet model object. +#' @param name String name of the seasonality. +#' @param uncertainty Boolean to plot uncertainty intervals. +#' +#' @return A ggplot2 plot. +plot_seasonality <- function(m, name, uncertainty = TRUE) { + # Compute seasonality from Jan 1 through a single period. + start <- zoo::as.Date('2017-01-01') + period <- m$seasonalities[[name]][1] + end <- start + period + plot.points <- as.numeric(end - start) + df.y <- data.frame( + ds=seq.Date(from=start, to=end, length.out=plot.points), cap=1.) + df.y <- setup_dataframe(m, df.y)$df + seas <- predict_seasonal_components(m, df.y) + seas$ds <- df.y$ds + gg.s <- ggplot2::ggplot( + seas, ggplot2::aes_string(x = 'ds', y = name, group = 1)) + + ggplot2::geom_line(color = "#0072B2", na.rm = TRUE) + if (period < 14) { + fmt.str <- '%m/%d %R' + } else { + fmt.str <- '%m/%d' + } + gg.s <- gg.s + ggplot2::scale_x_date(labels = scales::date_format(fmt.str)) + if (uncertainty) { + gg.s <- gg.s + + ggplot2::geom_ribbon( + ggplot2::aes_string( + ymin = paste0(name, '_lower'), ymax = paste0(name, '_upper') + ), + alpha = 0.2, + fill = "#0072B2", + na.rm = TRUE) + } + return(gg.s) +} + # fb-block 3 diff --git a/R/man/add_seasonality.Rd b/R/man/add_seasonality.Rd new file mode 100644 index 0000000..0446b9b --- /dev/null +++ b/R/man/add_seasonality.Rd @@ -0,0 +1,25 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prophet.R +\name{add_seasonality} +\alias{add_seasonality} +\title{Add a seasonal component with specified period and number of Fourier +components.} +\usage{ +add_seasonality(m, name, period, fourier.order) +} +\arguments{ +\item{m}{Prophet object.} + +\item{name}{String name of the seasonality component.} + +\item{period}{Float number of days in one period.} + +\item{fourier.order}{Int number of Fourier components to use.} +} +\value{ +The prophet model with the seasonality added. +} +\description{ +Increasing the number of Fourier components allows the seasonality to change +more quickly (at risk of overfitting). +} diff --git a/R/man/parse_seasonality_args.Rd b/R/man/parse_seasonality_args.Rd new file mode 100644 index 0000000..9a5f0a0 --- /dev/null +++ b/R/man/parse_seasonality_args.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prophet.R +\name{parse_seasonality_args} +\alias{parse_seasonality_args} +\title{Get number of Fourier components for built-in seasonalities.} +\usage{ +parse_seasonality_args(m, name, arg, auto.disable, default.order) +} +\arguments{ +\item{m}{Prophet object.} + +\item{name}{String name of the seasonality component.} + +\item{arg}{'auto', TRUE, FALSE, or number of Fourier components as +provided.} + +\item{auto.disable}{Bool if seasonality should be disabled when 'auto'.} + +\item{default.order}{Int default Fourier order.} +} +\value{ +Number of Fourier components, or 0 for disabled. +} +\description{ +Get number of Fourier components for built-in seasonalities. +} diff --git a/R/man/plot_seasonality.Rd b/R/man/plot_seasonality.Rd new file mode 100644 index 0000000..0e4a22d --- /dev/null +++ b/R/man/plot_seasonality.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prophet.R +\name{plot_seasonality} +\alias{plot_seasonality} +\title{Plot a custom seasonal component.} +\usage{ +plot_seasonality(m, name, uncertainty = TRUE) +} +\arguments{ +\item{m}{Prophet model object.} + +\item{name}{String name of the seasonality.} + +\item{uncertainty}{Boolean to plot uncertainty intervals.} +} +\value{ +A ggplot2 plot. +} +\description{ +Plot a custom seasonal component. +} diff --git a/R/man/prophet.Rd b/R/man/prophet.Rd index 3c67466..a5cd71e 100644 --- a/R/man/prophet.Rd +++ b/R/man/prophet.Rd @@ -4,7 +4,7 @@ \alias{prophet} \title{Prophet forecaster.} \usage{ -prophet(df = df, growth = "linear", changepoints = NULL, +prophet(df = NULL, growth = "linear", changepoints = NULL, n.changepoints = 25, yearly.seasonality = "auto", weekly.seasonality = "auto", holidays = NULL, seasonality.prior.scale = 10, holidays.prior.scale = 10, @@ -12,9 +12,11 @@ prophet(df = df, growth = "linear", changepoints = NULL, uncertainty.samples = 1000, fit = TRUE, ...) } \arguments{ -\item{df}{Dataframe containing the history. Must have columns ds (date type) -and y, the time series. If growth is logistic, then df must also have a -column cap that specifies the capacity at each ds.} +\item{df}{(optional) Dataframe containing the history. Must have columns ds +(date type) and y, the time series. If growth is logistic, then df must +also have a column cap that specifies the capacity at each ds. If not +provided, then the model object will be instantiated but not fit; use +fit.prophet(m, df) to fit the model.} \item{growth}{String 'linear' or 'logistic' to specify a linear or logistic trend.} @@ -28,9 +30,11 @@ if input `changepoints` is supplied. If `changepoints` is not supplied, then n.changepoints potential changepoints are selected uniformly from the first 80 percent of df$ds.} -\item{yearly.seasonality}{Fit yearly seasonality; 'auto', TRUE, or FALSE.} +\item{yearly.seasonality}{Fit yearly seasonality. Can be 'auto', TRUE, +FALSE, or a number of Fourier terms to generate.} -\item{weekly.seasonality}{Fit weekly seasonality; 'auto', TRUE, or FALSE.} +\item{weekly.seasonality}{Fit weekly seasonality. Can be 'auto', TRUE, +FALSE, or a number of Fourier terms to generate.} \item{holidays}{data frame with columns holiday (character) and ds (date type)and optionally columns lower_window and upper_window which specify a diff --git a/R/tests/testthat/test_prophet.R b/R/tests/testthat/test_prophet.R index 0bc63fa..258ad73 100644 --- a/R/tests/testthat/test_prophet.R +++ b/R/tests/testthat/test_prophet.R @@ -219,38 +219,51 @@ test_that("make_future_dataframe", { test_that("auto_weekly_seasonality", { skip_if_not(Sys.getenv('R_ARCH') != '/i386') - # Should be True + # Should be enabled N.w <- 15 train.w <- DATA[1:N.w, ] m <- prophet(train.w, fit = FALSE) expect_equal(m$weekly.seasonality, 'auto') m <- prophet:::fit.prophet(m, train.w) - expect_equal(m$weekly.seasonality, TRUE) - # Should be False due to too short history + expect_true('weekly' %in% names(m$seasonalities)) + expect_equal(m$seasonalities[['weekly']], c(7, 3)) + # Should be disabled due to too short history N.w <- 9 train.w <- DATA[1:N.w, ] m <- prophet(train.w) - expect_equal(m$weekly.seasonality, FALSE) + expect_false('weekly' %in% names(m$seasonalities)) m <- prophet(train.w, weekly.seasonality = TRUE) - expect_equal(m$weekly.seasonality, TRUE) + expect_true('weekly' %in% names(m$seasonalities)) # Should be False due to weekly spacing train.w <- DATA[seq(1, nrow(DATA), 7), ] m <- prophet(train.w) - expect_equal(m$weekly.seasonality, FALSE) + expect_false('weekly' %in% names(m$seasonalities)) + m <- prophet(DATA, weekly.seasonality=2) + expect_equal(m$seasonalities[['weekly']], c(7, 2)) }) test_that("auto_yearly_seasonality", { skip_if_not(Sys.getenv('R_ARCH') != '/i386') - # Should be True + # Should be enabled m <- prophet(DATA, fit = FALSE) expect_equal(m$yearly.seasonality, 'auto') m <- prophet:::fit.prophet(m, DATA) - expect_equal(m$yearly.seasonality, TRUE) - # Should be False due to too short history + expect_true('yearly' %in% names(m$seasonalities)) + expect_equal(m$seasonalities[['yearly']], c(365.25, 10)) + # Should be disabled due to too short history N.w <- 240 train.y <- DATA[1:N.w, ] m <- prophet(train.y) - expect_equal(m$yearly.seasonality, FALSE) + expect_false('yearly' %in% names(m$seasonalities)) m <- prophet(train.y, yearly.seasonality = TRUE) - expect_equal(m$yearly.seasonality, TRUE) + expect_true('yearly' %in% names(m$seasonalities)) + m <- prophet(DATA, yearly.seasonality=7) + expect_equal(m$seasonalities[['yearly']], c(365.25, 7)) +}) + +test_that("custom_seasonality", { + skip_if_not(Sys.getenv('R_ARCH') != '/i386') + m <- prophet() + m <- add_seasonality(m, name='monthly', period=30, fourier.order=5) + expect_equal(m$seasonalities[['monthly']], c(30, 5)) }) diff --git a/docs/_data/nav_docs.yml b/docs/_data/nav_docs.yml index 9152f38..7824290 100644 --- a/docs/_data/nav_docs.yml +++ b/docs/_data/nav_docs.yml @@ -4,7 +4,7 @@ - id: quick_start - id: forecasting_growth - id: trend_changepoints - - id: holiday_effects + - id: seasonality_and_holiday_effects - id: uncertainty_intervals - id: outliers - id: non-daily_data diff --git a/notebooks/holiday_effects.ipynb b/notebooks/seasonality_and_holiday_effects.ipynb similarity index 58% rename from notebooks/holiday_effects.ipynb rename to notebooks/seasonality_and_holiday_effects.ipynb index 3fce9a9..7a803a5 100644 --- a/notebooks/holiday_effects.ipynb +++ b/notebooks/seasonality_and_holiday_effects.ipynb @@ -4,10 +4,17 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "block_hidden": true, - "collapsed": false + "block_hidden": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:fbprophet.forecaster:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n" + ] + } + ], "source": [ "%load_ext rpy2.ipython\n", "%matplotlib inline\n", @@ -24,17 +31,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "metadata": { - "block_hidden": true, - "collapsed": false + "block_hidden": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/usr/lib/python2.7/dist-packages/rpy2/rinterface/__init__.py:186: RRuntimeWarning: Loading required package: Rcpp\n", + "/usr/local/lib/python2.7/dist-packages/rpy2/rinterface/__init__.py:186: RRuntimeWarning: Loading required package: Rcpp\n", "\n", " warnings.warn(x, RRuntimeWarning)\n" ] @@ -42,18 +48,7 @@ { "data": { "text/plain": [ - "STAN OPTIMIZATION COMMAND (LBFGS)\n", - "init = user\n", - "save_iterations = 1\n", - "init_alpha = 0.001\n", - "tol_obj = 1e-12\n", - "tol_grad = 1e-08\n", - "tol_param = 1e-08\n", - "tol_rel_obj = 10000\n", - "tol_rel_grad = 1e+07\n", - "history_size = 5\n", - "seed = 1733845324\n", - "initial log joint probability = -19.4685\n", + "Initial log joint probability = -19.4685\n", "Optimization terminated normally: \n", " Convergence detected: relative gradient magnitude is below tolerance\n" ] @@ -71,6 +66,82 @@ "future <- make_future_dataframe(m, periods=366)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specifying Seasonalities\n", + "\n", + "Prophet will by default fit weekly and yearly seasonalities, if the time series is more than two cycles long. It will also fit daily seasonality for a sub-daily time series. You can add other seasonalities (monthly, quarterly, hourly) using the `add_seasonality` method (Python) or function (R).\n", + "\n", + "The inputs to this function are a name, the period of the seasonality in days, and the number of Fourier terms for the seasonality. Increasing the number of Fourier terms allows the seasonality to fit faster changing cycles, but can also lead to overfitting: $N$ Fourier terms corresponds to $2N$ variables used for modeling the cycle. For reference, by default Prophet uses 3 terms for weekly seasonality and 10 for yearly seasonality.\n", + "\n", + "As an example, here we fit the Peyton Manning data from the Quickstart, but replace the weekly seasonality with monthly seasonality:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:fbprophet.forecaster:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n" + ] + }, + { + "data": { + "image/png": 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V+o7KL5GlYzkkIr0TBAFphwrx0s/HUVzViEdifDC1awh6tHaHlYwXTRCTRCLBG/0i0CPU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\nYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "m = Prophet(weekly_seasonality=False)\n", + "m.add_seasonality(name='monthly', period=30.5, fourier_order=5)\n", + "forecast = m.fit(df).predict(future)\n", + "m.plot_components(forecast);" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Initial log joint probability = -19.4685\n", + "Optimization terminated normally: \n", + " Convergence detected: relative gradient magnitude is below tolerance\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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bRody6JQFh598cTY/S0LAUluQoAEAoHGubNr8eM9tSWF+TsyPTuR+dDL3fqlN\nRzRCggYAgKYGLzsyKNM6uW1LCovz5H59oWDRsexrBTW2mUACCRoAABBqboIJm4aPjXTdlhQ2MNBp\n442S2QezzuRIdVYeOm3TmYTV1dXm/LqxJIqZBzEf4aVhoD4OgsbwTAAIGoNFG0OzRZEQQnq94Vxu\n1fZ7T5Va/fgot0GhznQKXl8sqYXVlxo7rN7J6bli9DAOGgAA/mVMr02naRzH+gc69wtwvlZYveP+\n09/ulSZFCsfEeFg8n9o0QZtZ5AkKmNUHAAXMoDHUBwCNwRqNIcKVaZzG0rR4T068Z0BaqXxHWtn2\ne6XDw5xHR7hqNExLhQF90AAA0IiW1zyKducsH+j//fCwwmr1L7dLLBgDdHEAAEDj6qeDt0SIgP1Z\n3w56iz7VgztoAAB4qdbWDsUtWl4aEjQAADTFnPrOZoIEDQAAzSAqR0OCBgAAkoIEDQAAzSPkJhoS\nNAAAtIjtczQkaAAAaCkb52jTx0Hv2bPn+vXrCCG5XN6lS5eZM2cihDQazbp162pra319fadPn26p\nKAEAgCRaNTjaTKbfQSclJa1atWrVqlWRkZGvv/66cWNqaqqnp+eyZctKSkoKCwstFCQAAJCIze6j\nzZ1JmJ+fz2Kx3N3djd+KRKLIyEiEkL+/v0gk8vHxQQilpaUplUoajRYQEGDOuYwz7mk0mpkxm4lC\noRAbA4VCQURfB8IvAjSG+gAQ0deB8ItASGPo3MGpQUElHMepVKo5kej1+gZbzE3Qe/funT17dv23\nCoXCxcUFISQQCOTyv5ce2LZtW0lJiZOT05o1a8w8HUKIw+GYfxBz4DhufFcQGAD6551JYAzEBmAE\njQEaQz3bN4YewZw7Rf/WOMUwzHgdTI7kxaqtZiXo2trauro6B4d/i5+y2WyJRBIYGCiRSFxdXY0b\nV65cafyioqLCnNNhGObi4iKVSs05iPnYbLZCoSA2AAzD6j//iIqB2IsAjaE+AGgMBDaGAC6q74+u\nr4stlZpejoPLfa7zxKxRHLdv346Ojn52S3BwcF5eHkIoPz8/ODjYnIMDAAD5WbU/2qwEffPmzdjY\nWOPXWVlZycnJCQkJYrF45cqVQqHQ2AENAAD2zXo52qwujqVLl9Z/HRISEhISghBavHixuUEBAACA\niSoAAGA+K91EQ4IGAAAL6OTlaPFjQoIGAEKIe6gAACAASURBVADLsPh9NCRoAACwGMvmaEjQAABA\nUpCgAQCApCBBAwAASUGCBgAAkoIEDQAAJIUZDKbX9bAxmUw2efLkgwcPEh0IwVJSUpRKpXGFhHYL\nGoNRSkpKXV3drFmziA6ESHbcGMwtN2pLBoNBLBYTHQXxampqiC0eRgbQGIxkMhmxpezIwI4bQ1tK\n0BiGeXl5ER0F8RwdHel0OtFREAwag5GDg4OxSHx7ZseNoS11cQAAQLsCDwkBAICkSPfPkUKhWL58\nuVarZbPZH3zwAY7jzy4T3mDVcLlcvmLFCp1OJxQK33nnHePSZPah6etg3Oerr756//33mUymvS6m\n3qqL0GBnBoNBaOyW1KrrYPz2/v37Z86cee+99wgL2tJadRH0ev2mTZvKy8sdHR0XLlzYdjMD5fPP\nPyc6huecPHnSxcVl0aJFYrG4oqKitLTUYDAsWLDg+PHjfn5+6enpz35769YtgUCwYMGCq1evCgQC\n43KI9qHp64Dj+CeffPLgwYOkpCQqlXr16tVnf8rj8YgO3zJadREa7BwYGEh0+BbTquuAEFIoFOvX\nr+dyud26dSM6dotp1UW4efOmTCZbsGCBWq2m0+nPLsvXtpCuiyMoKKhPnz4IIQcHBxqNJhKJjO80\n4zLhDb51dXUtKCiorKyUSCR8Pp/YyC2r6evA5XK/+eabqKgo484Nfkpc1BbWqovQYGfCgraCVl0H\nhNDWrVvHjBlDVLRW0qqL8OjRI51Ot27dOoVC4e7uTmDYZiJdgg4NDRUIBDdu3Lhy5Up8fHyDZcIb\nfBsUFJSdnf3tt99SKBQ7S9BNXwfj+sHGFZ3RSxZTtwOtuggNdiY0cAtr1XW4ceOGm5ubr68voSFb\nXqsuQm1tbWlp6fjx469fv3737l1CAzcL6RK0wWDYvXv35cuXP/30UzabbVwmHCEkkUg4HE6Db3fv\n3j1t2rTly5fHxMScP3+e4NAtqunr0GDnpn/adrXqIjTYmYh4raVV1+HPP/98+PDh+vXrHzx4cOLE\nCSLitYpWXQQOhzNgwAChUPjqq69mZ2cTEa9lkC5BX7t2rba2dvHixcblxxssE97gW61Wq9frEUJ6\nvV6r1RIZt6U1fR0a7Gyvi6m36iI02NmetOo6fP3115999tmCBQuioqJee+0120drJa26CEFBQca+\nvpycHDc3N5sHazGke0h45MiR9PT0CxcunD59msFgdOvW7cyZM5cuXRIKhb179/b09Hz2W19f361b\nt54/f766unrSpEn2NGK/6etg3OfcuXOvvvoqlUptcFmIjdyCWnURGuxsT//jt+o6GL+Vy+VpaWn2\n9JCwte+I48ePHz161GAwjBs3rr7ro82BiSoAAEBSbfWDBQAA7B4kaAAAIClI0AAAQFKQoAEAgKQg\nQQMAAElBggbt0YoVK9auXUt0FAA0AxI0AACQFCRo0F6o1eq5c+f6+vrGx8enpaUhhKqrq4cOHert\n7R0UFHTmzBmiAwSgIUjQoL3YvHlzXl5eVlbW4cOHL1++jBDavn27k5NTYWHhxo0bDx06RHSAADQE\nCRq0FxcuXJg/fz6DwXBzcxs7dixCqHv37pcuXfrPf/7D5XKhSxqQECRo0F7gOF6/sgaFQkEIxcbG\n3r1718vL6/PPPx81ahSh0QHQCKjFAdqLn3766fDhwwcOHKipqYmPj1+4cGF1dbVOp/vyyy+Li4tD\nQkJqamrablUdYJfsp/wbAE2bMWPG3bt3Q0NDXV1dp0yZ4uTkNHz48PHjx//22280Gi05ORmyMyAb\nuIMGAACSglsGAAAgKUjQAABAUpCgAQCApCBBAwAASUGCBgAAkoIEDQAAJAUJGgAASAoSNAAAkBQk\naAAAICmCp3rL5XLTfpFOp2s0GvJMg8QwDMdxnU5HdCD/olKper1er9cTHci/cBw3GAzkedUQQjiO\n4ziu1WqJDuQ5FApFr9eT6kIhhOh0ulqtJjqKhigUCqned+ifUlymRcXhcJ79luAEXVdXZ9ovcjgc\nmUxGnuyD4ziDwTD5z7EGHo+nVqtVKhXRgfyLTqcbDAaNRkN0IP9iMpk4jpPqhUMIsVgslUpFnuZt\nxOFwampqSPWxgWEYi8Ui28vH5XJ1Op1pUTVI0NDFAQAAJAUJGgAASAoSNAAAkFR7rwedJ1WeyZYW\nVqtYNDze2/FVX0cKjhEdFAAAINSeE/TjcsWX5/KvFcr6+vMDnZlSpe6jEzlUCvbd4MB4bweiowMA\ngHaZoPUGw7pr4u+vFc+L9/j5jRAHBuXv7Ym+KWnlE3ZnfPOa/5gIAbFBAgBAu0vQCo1+zkFRgVR5\ncnpUsAvr2R/hGDY5RhgmYE3YneFAp7wW7ERUkAAAgKzxkPCrr75SKpX132o0mtWrV3/xxRdbt261\n+Llaq6pOOzLlIULo+LSG2bleZy+Hn94IXnQsu6CaRCOIAQDtkCUTtEwmW7p06c2bN5/dmJqa6unp\nuWzZspKSksLCQguerrUqFZqRKQ9DXFhbR4WwaU394f0C+JNjhO8ezSbTkHwAQLtjyS4OLpf7zTff\nfPbZZ89uFIlEkZGRCCF/f3+RSOTj44MQysnJUalUNBpNIDC9q5dCobR8GeYalXbs7ow4L8e1Q4Jw\nrPlxGh/38evx050DGZVjo4QtOb5xxjCVSqIuI+Psc1KFRKFQyDbV29iKSHWVEELGkMg2kxAhRKVS\nSfXykbCRI4SMecmEqF58xS35h2EY9mLSVCgULi4uCCGBQFBfeWPVqlVisdjFxWXTpk0mn47L5bZw\nT6VWP2nb9Y5ujpvHx7QkOyOEHBD67o2IhX8+nBzvz6A2/zGAYRiGYXQ6vYUh2QCO4ywWi8lkEh3I\nvzAMQwiR7R2OYZiDA7nG7RgrB5DqQhm1/E1nMziO02g0oqN4jrHmjAlRPds5bGT1Tx42my2RSAID\nAyUSiaurq3HjDz/8YPyioqLCtMMKBILq6uqW3GLoDYZZB0Q0pF8z0KdaKm35Kbq709w51LXnMmZ3\n9mh2Z3LW4lAqlVCLo2lMJpNGo8lkMqIDeQ45a3EIBAKpVEqqjw1jLQ6FQkF0IM9pS7U4goOD8/Ly\nEEL5+fnBwcHWPt2LPjuTny9VbhkVSqO0+o/96FWfDanFGh253icAgHbCigk6KysrOTk5ISFBLBav\nXLlSKBQaO6Btaeudp0cyK1PGhnHoFBN+vZcvz5VDO/BYYvHAAACgWRix/7CY08VRWVnZ9P+AF/Oq\nZx7I+nNiRKQb27SzIIT2Paz44UbxmRnRTe8GXRwtAV0cLUTaLg6JRAJdHM0yp4ujwbgJuy2WlCdV\nvnVQtPb1AHOyM0JoWJiLuEZ1t6TWUoEBAEAL2WeClqt1U/dmvvmK+9BQFzMPRadgE6KFv919apHA\nAACg5ewwQRsMaNHRbD8n5tIe3hY54OQYt0MZkjoNuf7fBADYPTtM0BuuFz8qV2wYGtSyEc/NC3Rm\nhgrYx0WVljkcAAC0jL0l6CsFNWuvibeO+rdGnUWMiRDsSS+34AEBAKBZdpWgS2vVs//MWj0oIFRg\n1oPBF43oKLiUV12pINHwAwCA3bOfBK3VG976M2tER8EbHc19MPgiFza1q4/jcVGVxY8MAAAvYz8J\nevnFQo0OLevXwUrHHxbqcvCxiaO2AQDABARXgWow8bxV2Gx2/Zj5E1mS7ffLrs7v6uRordpASZ28\n/3MmT0th8JiNXLRGC0URi0KhMBgMUhX6MlazI1VJKSqViuO4Oe3QGqhUqvFaER1IQxwOh2xR0Wg0\nzFLjASzE+PKZkA1enMNF8Lu3vr5daxmnDxmnWhXL1G/uffD960HONJ3JB2wWB0PR7pyDaeLRja2G\nRcKZhFQqVaVSwUzCphlnElqv2ZiGnDMJWSyWXC4nVYK2v5mEDZDo9so0Wr1hzsGssZGuNlihanCw\n81+iykYTNABtyN2S2h9vlKQW1ii1+iAX1uhwwaRYNwaFXPehANlBH/S3lwtVWsOnfazV9fysQcFO\nZ3Oq1VDcDrRZOr3hy3MFSTsfB7uwtid1PDolctYr7vsfVfT8+d5NMbkKkgDU1u+gL+ZV/3r76ekZ\n0fTWlxI1QZALy5VDvVEk6+nLs8HpALAsvcHw9pEnIkndxTdjPB3+fhIQ5MIa2VGw40HZ+F0Z/030\nmxDtSmyQ4FltOEFXKDTzDz9ZNSjAl8+w2Un7BzidzpZCggZt0RfnCjIrFH9OjGjwoBvD0MRoYbgr\ne8LujFq17q3O7kRFCBpoq10ceoNh3sGs14KcrDHquQmJQU5nclqxLAsAJHE0s3L3g/LtSR0bHYaE\nEIr14B6YGL7mStG2+2U2jg28TFtN0N+eyy6t1fxvgJ+Nz9u9g2O+VFlUTaKhEQA0q7JOu/SvnPXD\ngup7NhoV5sreNa7jF2fzT8CcLHJoqwk6SMDZPDKU2YLlXC2LQcG6+ziey4WbaNCW/Od0XmIgv38A\nv9k9o905P78R/PaRJ2ml5Bp62D611QQ9OtojRMAi5NR9A/jnoJcDtB03i2Snsqu+6Ofbwv37BvA/\n69th8t6M0lq1VQOzGY1Of7ek9nBm5V+iSpHEupMV1DpLjhNvww8JidLHn7f6SpFOb6DgMG4UkJ3B\ngD47m7+0p7czm9by35oa6/ZEUjd5T8bhyZEsWlu9jUMIFcvUyanFe9PLuQyKH5+p0ekzK+ocGJSp\nscJZr3hYtuYlQihPqhyV8ujagm58hmUumiUTtEajWbduXW1tra+v7/Tp040bKyoq3nvvPaFQiBBa\nvHixl5eXBc9IiFABm0XF75XWvuLpQHQsADTjxJPKslr19E5urf3FZX19p+3PnHdY9OvIENy8udR5\nUuVtca24RkWj4D48ejcfngvb6reGBgPafKd0+YXCYWHOhyZHdHT9u8KlTm+4Wliz8XrJTzfvfpXo\nN8Zy887qNPoZ+zMnRAtd2DSdTmeRY1ryMqWmpnp6ek6YMGH58uWFhYXGNbzLysqGDBkybtw4C56I\ncK/68S7l10CCBiRnMKAVFwvf7+ltwkQBCo79NDz4je0PPzmdv9ykp/EqnWHH/bItd0oLqlWdvRx8\neHSNznA4QzL3oKh/oNOSHt7R7tYqgaLQ6N8+LMqoqNs/MTzm+bNQcKyXL6+XL+98rvS94zl/iSq/\nGxxokVvp945nu3PpS3pY8h7UkglaJBJFRkYihPz9/UUiUX2CFovFycnJkZGRffv2Ne556tQpmUzG\nYrF69epl8ukYDAZRZQH6Bwu23S39qO+/hZkwDKNSqUymtUo1mQDHcbLVkaFSqQaDgUKx8P+V5qDR\naBQKhVQvHELIWOLK/OZ9LLNCoTFM7uxDNak7jslE+6bEDtx8Z9XVkk/7+SOEmExmS6IyGFDK/dKv\nzuR485gf9wt4PdTl2U+Icrn611vFo3c8Sop2++/AILYZXSiNvu8kCs3oHQ/duPTLc7tw6C9tbIM6\nuncPELz9Z8ag39N3TYwOcjHrmdZ3lwvulcrPze7MZv5dKcmEl0+r1TbYYskErVAoXFxcEEICgaC+\n+gyLxYqMjIyLi1u7dq2zs3NMTAxCKC0trby8nMfj9evXz+TT0Wg0ohJ0Yqhw4aFMLcJZtL9ffgzD\nSFXKDv1TYI9UCdp4iUh1oYzvJRqtFf2zNmB84cxv3qsuFXzQN4DFML18oLcT7fiszgM23cRwfPnQ\niJYUR7xXXLPo4OMalXbDyIhBoY10IHjyaZ8mBr/ZtcP8A4/6/Hx716SYEFcTb6WNjfzZl69crn59\n672uPrz1I8KbfUrkQqPtmNRp1cXc/r/cTpkY0yfA2bQw9qc/XXul4PyceFcHFkLImJ1NaFQvvuKW\nTNBsNlsikQQGBkokElfXvyeMdu3a1fhFv379srKyjAl6yZIlxo0VFSZWWGYwGLW1tUSV++Ii5Mtn\nnnlc3Nv/73FLJKxmx+PxlEolVLNrmrGanUxGrjIUFqlmd7WgpkhaNzyIa+Zf50xFf04MT9r5qKRW\n87++Xk3UVKqq0668XLTrQdni7l5zu3jQKHgTp+YgtHVE0PfXivr8dGPLyJDuHRxNiK1BNbtKhWZE\nyqMEH8dvEjso5LUtPMjcOIEPBx/7xz3TZrpfzq+euy/rjzGhHky98e81p5qdg8NzHaeWvJcJDg7O\ny8tDCOXn5wcHBxs3pqSk3Lt3DyFUUFDg4eFhwdMRq5cf73IBud7VADxrw/XiN19xt0iZGh8e4/jU\nqOJq5YAtadeLGmn2MpVu3TVxws/3yuXqy2/FLkzworXgvBiG3u3uvWpQwJS9mcezzF2UWabSjdud\n0cmD+81A/9b+3zgk1HnvhI7/PZ//3/MF+tb843K9SDZjf9a6IYGmfcA0y5J30AkJCevXr1+5cqVQ\nKPTx8cnKyjpx4sS4cePWrFmzd+9egUDQrVs3C56OWD07OK6/XoyQD9GBANAIkaTuWmHND8OCLHVA\nJxb1xOyEb089nLovs6OANTTMJUzAouBYfpXySqHsaKaksyd32+jQLt6tfnI+LNSZz6TM2J/1rVY/\nMtzEMRVKrX7K3owOPMaawQGm9ep18uCemBY1ZV9GRoViw9Cgl02If9aZHOmcg6LVgwKGhJrYN9Is\nC/RzmcPkLg6BQFBZWUlgRfOqOm1k8u2Mdzobn/9CF0dLQBdHC5nfxfH+iRwqjps2+uJlBAKBRCKp\nVWkPZkjOZEufSOoQQh4O9K4+jsNCnc18yHajSDZpT8b/BviNjWxFJ4Oxi6NaVjt9f5ZWb/hjTKiZ\n/zEoNPr3jmffEst+GBYc//IPG4MBbbxRvOaq+MfhwYmBDednmtPFIRA89xEFE1VM5MSihrmyUotq\nBgRafaEAAFqlqk67J73i7MxoaxycQ6dMjBZOjBZa9rDx3g67xnWcsCdDpdVPiW3FqG2t3jDnoKhG\npd09Ltz8/hw2Df9xeHBKWtnEPRlvhLks6en9YvWSx+WKj0/mlis0h58ZXm0lkKBN19OXdzkfEjQg\nne33y7r7OAY4kWvsYLPiPLl7x3ccu/NxtVK3IMGzJb+i0enf2p1eWqvZPb6jBWc8TowW9g/gf32h\nsNtPd/sFOPX24xlrGmdXKU+Iqm4Xy+Z28Xinu7cN1qBpPEGXlZUZ5/6BJvT0dfzmYiHRUQDwHJ3e\n8Oud0tWDA4kOxBRRbpzDkyPG7noslqm/6u/b9PDtWrVu1oEstR7bPb4j9+XjnU3jxqV/PyTw494+\nBx9LzudJxTVqg8Hg58QcFua8aUQwvwU91BbR+Gk6d+78yiuvTJ8+ffDgwaRag5lUErwdH5fXVSu1\nLXmeAIBtnMquYlDxPn5tdU2JIBfWX9Oipu/PHL3j0Q/DgrwcG1+OI7NCMeuAKFTA2jIuWq9WWikY\ndy59ThePOV0IG37W+D8FOTk5c+bM2b17d0hIyOLFi43j5EADDgxKhJB9rbCG6EAA+Nfm209nxrmT\naX5Sqwk5tD8nRsS4c3pvTlufWlyrfq6uRbVSu+Ji4eDf08dFCQipOWxLjd/6UanUQYMGJSQkbN++\n/aOPPtqyZUtAQEBycnKPHj1sHB/J9fTlXcmvGRRsrUE2ALTKE0ndLbFs88hgogMxF52Cfdnfb1S4\n4L8XCtdcLertxw9zZWn1KLNCcSFX+qof//jUyFABu01/DrVE4wl6+/btu3btunv37tChQ48cOdKz\nZ8/79+8nJSVlZ2fbOD6S6+Hr+L/zBURHAcDfNt8uTYp0dWTYSZ9brAd37/iOuVXK87nSJxIlnYol\nBvL/l+jnw7PdMqTEavyFPHv27IIFC/r161c/9T4uLu7rr7+2YWBtQ1dvh8yKukqFRsBtLy0GkFat\nWrc7veLIlAiiA7Ewfyemv1M7Xce24USV8ePHN7rfzp07rXH6+ppKrcXhcBQKBbGzbIz6/nx7Sa8O\nw8KFVCqVbFMwtFrti/WxCEShUAwGA4HTi15Eo9FwHCfVdB6EEI1G02q1rW3eP10vOvy4/Mj0TlaK\nijxvumfR6XS1mlwrvzAYDL1eb0I20Gq1PN5zT3cb3kG/+eabZoXWSiZPveNwOEqlkgxv9W4+3HNP\nKgYEOJJtJqGx4ZIq9ZBwJqGx6hipXjij1s4kNBjQxtTCZX07WO9v4XA4dXV1pErQGIZhGEa2l49C\noZg8k7CBhgk6MTFRIpH8/PPPH3/8sflHbw96dHD88lw+0VGA9u5srlSj0w8MgmlTdqWRESo8Hm/b\ntm05OTm2j6Yt6urtIJIoJQoS3RWCdujHGyVvdfYwc20qQDaNPCSkUqkRERGdOnXq3r07h/N3Ie29\ne/faNrA2g0OnxHhwrhXWjHbiEh0LaKcelyvuFNfaweg60EDjozgWLVq0aNEiG4fSdvXwcbyUVz06\nukXVAwCwuA3Xi6fECu1mdB2o1/gknO7duwsEAi6Xy+VyGQzGihUrbBxW29LT1/FyfjXRUYB2Slyj\nOpwheatzOx2IZt8a/8hdvHjxsWPHSktLO3Xq9OjRowULFtg4rLalq49jbmXd01q1I4lWQwXtxYbr\nxSPDBS+rWQHatMbvoE+ePPnw4cPFixevXr369u3b6enpNg6rbWFS8c5eDhdzq4gOBLQ7ZXJNSlr5\nwgQvogMBVtF4gpZKpQih+Pj4ixcv+vr65ubm2jaqtqeXH/9CDiRoYGvrromHhjoHOrex0s+ghRrv\n4hg1atSwYcP++OOPDz74oKysrMEqLC+j0WjWrVtXW1vr6+s7ffr0Jjban1f9ePMOP0HIl+hAQDtS\nVK3afr/s3CyrrJwCyKDxO+jk5ORVq1YJhcJNmzZxOJwNGza05Fipqamenp7Lli0rKSkpLCxsYqP9\nifPkVirUuVXWqksLwIu+uVw0LsrVjw+3z3ar8QSNYdj9+/ffeeed+Pj4iIiIwMAWrc4gEomMe/r7\n+4tEoiY22h8aBe/l73QhF8ZyABu5U1x7PKtySQ9vogMBVtR4F8eyZctu3ryZl5eHYdiGDRuuX7++\ncuXKZo+lUChcXFwQQgKBoL4KUqMbV69eXV5ezuPx3nvvPZND53K55CkLgGHYgBDhxRzJwt6tXnbe\nSigUCpPJJNWCODiOI4TIUEGlHoVCwXHcwYEsr5oRhUKh0WhNNG+d3vDJmUf/1y8wwN2mtci5XHLN\nxsIwjEKhUCjkGj5F/Udrf/HFqk+NH2Lnzp23b98eOXIklUr966+/goODW5Kg2Wy2RCIJDAyUSCSu\nrq5NbIyOjpbJZCwWy+S6OQwGQ6PRkCpB9wt0+vxkVp1K3fRCajZDpVJ1Oh2pqtlRqVSDwaDT6Zrf\n1bZIVb/JqOlqdt9fKdDq9bO7eNgycgaDYUKNPavCMAyR7+XDcdy0omAvvjUaT9Bqtbr+6EqlksVi\nteTowcHBeXl58fHx+fn53bt3b2LjgAEDjF9UVFS09m8w4nK5rS33ZVU4jgc6MRwYlKs5FfHepLgd\nM36GQTW7llAqyfXwAMOwJpr3wzLFNxfyDk+O1KpVtvz45XK5SqWSbAkawzCyvXzGeyOLRNV4H/Tb\nb789cODA3NzclStX9urVa968eS05VkJCglgsXrlypVAo9PHxycrKSk5ObrDR/IjJrK8/72yOlOgo\ngD2rVmpnHcj86FWfCCGb6FiA1TUs2F/vzJkz58+fZ7PZiYmJXbp0sdLpTb6DFggElZWVpLqDZjAY\ne+4VJacWn5gWRXQ4CCHE4/GUSiXcQTeNyWTSaDSZTEZ0IM9hsViN3kGrdIYJux67O9A3DA2yfd06\ngUAgkUjIdgfNYrEUCgXRgTyHy+WaXA+6wZjmxrs4ysrK+vfv379/f1Oia8d6+/HnHhRVKDQCNo3o\nWIC9kat1Mw5kUSnY2tcDoapoO9F4F0fnzp1Hjhx58OBBsq0lQ3IODEpXH8fT2dDLASzsYZli8O/p\nbBr+x5gwOgXSc3vReILOycmZM2fO7t27Q0JCFi9efO/ePRuH1XYNCHI6KaokOgpgP9KfKhYfyx62\nLX1spOuWkaEMyM7tSeNdHFQqddCgQQkJCdu3b//oo4+2bNkSEBCQnJzco0cPG8fX5gwMclp5qVCt\nM8BtDmgJvcGQVVH3pFJZKlMpNHqN3qDQ6KlUqlShLqpWppXK1Tr9mAjXi7NivHlQr67daTxBb9++\nfdeuXXfv3h06dOiRI0d69ux5//79pKSk7OxsG8fX5gQ4Mb0cGRfzqhMD+eYfTac3HHgs2ZtenidV\nCti014KdZsS5c+nkGpYPTHNLLPvtXtkJUSWOoVAB28uRwaLiCCE+i4oQ8nSkd/LgfNjLJ0LIppBj\nZD2wvcYT9NmzZxcsWNCvX7/6yTBxcXFff/21DQNrw4aEOB/JlJifoPOlqjkHRQqNbtYr7uFCdolM\nnXK/bPPtp5tGBHfxIsVQa2CaJ5K6/zuVm/ZUPq2T28FJER1dGw6Ye9koDtDeNJ6gN2/e/OLGcePG\nWTkYOzE01GXMzodafYA5UwofPJWP2/V4cozwg14+9ccZHuayO718wu6Mn94I7h9ggTt0YHtb7pR+\ndb7gzVfct44OY9MafwgEgBEsYmZ5kW5sHoN6Kb+mrz/PtCOIJHVjdz7+6FWfaZ3cGvxobKSrC5s2\n56Bo7/iOsR7kKowAmqbVGz48kXsuV3pgYkSMO4focEAbAB/gVjE6QrD/kYlzcKrqtJP2ZMzv6vFi\ndjbqH8Bf1rfDzANZVXUkqrMBmqbW6WcdyHpYpjg9IxqyM2ihl84ktA3TJtsghFgsFqnKAhiratVX\nJhJVKHr9eCP3w14sWuse6OkNhpG/33NzYPw8KrzpPd/c+1Ch0aVMeGmxdmNpG1JVJqJQKAaDgVRd\nq1QqFcdxa4/31+kNk3amVdVp90+J5bTgGa+xmAN5mrcR2d50RjQajVRzUxFCxkqEJtQp02g0jo6O\nz24huIujvgBpaxnnd5LnrW6c6l3/eePJQiEurJ13CsdGurbqOGuvFomldb+OCGr2ynzVz7vXpvsp\ntwre6OjS6A5UKlWlUllqqndRtaqoPLytiwAAIABJREFURs1jUoJdWCb3rZN2qrfJ7bCFFh/LFkuV\n+yeGI41S3oK/npwPCVksllwuJ1WCtr+p3g1AH7S1TI4R/nHvaasS9O1iWXJqyfGpkUxq811Pjgzq\nqsEBi4/l9A3gOTKs+Doey6pcfaXoiaTOl8+UKDRavWFqJ/eFCR5WPak9WXu16GpBzfFpUfBIELQW\ntBhrGdHR5WGZIrOipZ/tcrVu3qEny/p1CBG0qLgrQmhAoFNnL+43F4tMjbEZSq1+3iHR/53Km9PF\nI2tx/MU3Yx4u6rxnfHhGuaL35rSbReQqMEROf4kqN94sTRnb0ZkFn2eg1SBBWwuHTpkYLdx4o6SF\n+39yOi/MlT01tvEHgy/zVX+/lLSyrAoL/DPVgEylG7PjUWWd9vys6LGRrvUzjKPdOX+MCf2wl8/4\n3RnHsmBSe1OeSOoWHsn+cVgQrLoNTAMJ2ormdvE48KjiaW3zD6AOZ1aezpZ+N7hFaz8+y4fHeKuz\n+7KzeabE93IqnWHqvkwhh7ZtTCif2cit3/go162jQxYdzT4hqrLsqe2Gsfjc2109+sKIdWAqSNBW\n5M1jDA9zWX1F3PRuBdWqJcez1w8NdGGb8l/wom5e90vlF/MsuV7tkuPZOIZ+fCOYRnlpC+nly/tl\nRPD8w0/uFNda8NR2Y+lfOX585jvdYFFXYDpI0Nb10as+e9LLn0he2gWh1OpnHciaEefWx9/E+ywu\nnfJ+T+8vz+Vb6un6L7dKbhTJfhkRQn95djbq48//X6Lv1H2ZpS34L6Fd+f3e0+tFsuShULgZmAUS\ntHV5OTLmxnu8cyxbp28kfRoMaNHRbGcW9YOeZi0GNiXWTa7RH3hs4tSYZ90vlS+/WPTryFCnlj3U\nGh8tHNHRZeaBLI2OXGPCCPSwTPHF2YLNI0Ma7R0CoOUgQVvd4u5edRr9iouFDbYbDOiDkzmZ5YpN\nI4LNLFdGxbFPevt8faFAbV6WVGj0cw+JPn7VO9KtFevdfd7PF0foq/MF5pzabtSqdbMOZH7c26cT\nTMQHZrNkgtZoNKtXr/7iiy+2bt1av7GiomLq1KlLly5dunSpWNxMb6xdolPw30aH7nxQvupykf6f\nbojKOu2MA5k3i2T7JoZbZEDxkBAXIZe+5c5Tcw7yxbl8Xx5j1iserfotKo79PCJkd3r5qWx4YIje\nPZYd7sp+8xV3ogMB9sCS/4KlpqZ6enpOmDBh+fLlhYWFxjW8y8rKhgwZ0s4r4fnwGAcnR8w6kHko\nQ9IvgF9Vpz2WVTkwyOnolMiWTPxtCQxDy/r6Tt2bMT7KlWfSf9YXcqV/PpZcnBVtQreppwN93ZCg\nRUeyz82KdufSTTi7fdh0q/RBqfz0jJfOvwegVSx5By0SiQIDAxFC/v7+IpHIuLGsrEwsFicnJ587\nd86C52pzApyYJ6ZFfdDLm4pj/s7MPydFbBgWZKnsbNTV26F7B8c1V035N6VGpX33eM7yAX5upqbX\ngUFOIzq6vH34iZ5MU4FtKbVQ9s2lwq2jQx0YsKICsAxL3kErFAoXFxeEkEAgqC9uwGKxIiMj4+Li\n1q5d6+zsHBMTgxCaP3++WCx2cXHZtGmTyafj8Uws5mkNGIZhGMZkNjMfYYrAZYo1w1g9Iqrz2isL\nXg0OceXgOM7hcNjsFvUmL92d1rWD06weweacfe1oXrd1Vzbfr/qgb+MDujEMQwiRrZgDhmFOTk5m\nHqegqu7Ng3c2jIrsHuJpflQ4jrNYLFJdKCM+n3Rjuo1lcIiO4jk4jhsMhmazwYuUSmWDLRZI0KdP\nn05PT09ISGCz2RKJJDAwUCKRuLr+XYOia9euxi/69euXlZVlTNBLly5VqVQ0Gk0mM3G6MJ/Pr62t\nJU8LxnGcRqNZqjKRyYR09FZnj4X70/ZOjORyuSqVqiWViY5mSo4/fnp17ismvxz1No0MGfjr/S4e\nzM5eji/+1OQqX9bDYDCoVKqZxZLkat0bv92fEOU6JNDB/GtojEqj0ZCtWBLZ3nQIIQzDGAzGi3mN\nWGw2W6/XmxCVXq/ncJ4rRWuBBJ2YmJiYmIgQ0mg0eXl58fHx+fn53bt3N/40JSUlPDw8Nja2oKAg\nKCjIuDEgIMD4RUWF6SPDdDodeVowjuPPlhsl0LvdPHtsurf3wdNpCRy9Xt9sSKW16neOZK0bEsSj\nY+bHH8inf9Hfd+a+jLMzo18cZGa8syDDVapnLDdqTkhavWH63owOPMbHr3pb6k+j0WharZY8zbue\nVqslW4I2XiuiA3mOXq/X6XQWicqSfdAJCQlisXjlypVCodDHxycrKys5OTkxMXHnzp2ffvqpVCrt\n1q2bBU8HGsWi4d8OCvj4ZG6FvPnJIzq9Yf6hJyPDXQcGmfs/fr3JMcIEH8f5h9pFZ7TeYHj3WHa1\nUrdxWDAOk1KApRFcsN/kO2iBQFBZWUmeW4wG9aAJ987R7GqNIWVshFrdVK/LV+cLLuZVH5kSWV8L\nySLqNPrXfnswJNT5w17PTcAhbT1o0/olDAb0/omcW2LZgYkRLZzX00LkrActEAgkEgnZ7qDtrB60\nQCB49luYqGKflg/0f1KhWHM5v4l9dqSV70wr+3VkiGWzM0KIRcP/GBO25U6pyet+kZ9Wb1h0LPtm\nkWzfBAtnZwDqQYK2T2wavn/aK+uvFe16UN7oDvsfVXx2Jm9bUpgPzypPwH35jN9Gh73/V+6FXKk1\njk8smUo3eU9GtqTuz0kRppW4AqAlIEHbrWABe++k6M/O5q+7Jn62O1irN6y+UvTRydztSWFWnY7c\n1dvhh2FBs/4UXc63ZKU9wmWUKwb9/oDLoOyfGA73zsCqoHnZs1e8HA5NCp/9p+jA44pJ0cIOfGZ2\npXL7/TImFftrWlSAk9WryL8W7JQ8JHDavqzvXg8YHtb42oltiN5g2Hz76YqLhe/18Jof7wkPBYG1\nQYK2c6EC9ukZUQceS/7Kqtz/WOLtyHi/p/ewMGebDTkYHOK8LYk6Y3/m7eLaZYmBFu/vtpn7pfKP\nT+XWqnQHJoZHu3Oa/wUAzAYJ2v7RKPjYSNfWri9uQd18HM/MiF5wJLvnT7c/7+8/wN+xbd175kmV\n31wsPJUtfbeb15wu7k0sYgCAZUGCBrbg5cjYPyH8YJb0P6dyPkOG0RGuffx4MR5cOrlvqEWSunWp\nxYczJJOihalzYgVsGtERgfYFEjSwEQxDY6OEoyMEJzIrDmdI5hwSlcjUng50Dwe6gENzZFD4TKqA\nTfN0ZAQ6Mzu6splUIm9U75XUfn+t+HyudHKMMHVObHsu0QcIRHCCplJND4BCoeA4Wf7ZxDAMx3Fz\n/hyLI2FIxpdsUKhgUKgAISRVanMq657WqiUKTbVSV1WnKahRXy2UPZHUFcvUcZ7cQSEuYyJcva0z\nEPDZkJ69SrfEshUX8u8Uy2Z38fx+aLAzEXfNxpDINlEFIUSlUkk1UQX9c62IjuI5xrxkQlQvvuIE\n/2EUiumFGSkUCnnaijEbmvPnWBwJQzLW4qgPyYVDceE0nnwlCs3F3KrDGZKVF2/1DXBe0qtDF+9G\nqi+Zz1jNzhjSw6e1X5zJvVlUs7C7zx/jIrkWLQbbKjiO4ziOka+rnlRvOiOyNXL0fKNqlRevLUz1\ntgyyTfVGCPF4PKVSSXiBvWeZMNW7qk679e7TH28Ux3s7ft7PN9DZwkMDjVO9c59W/u98wcEMyZwu\nHvPjPQkv6AxTvVsIpnoDQCQnFnVxd69b8+JCBazELWkrLxWqdZZMEDq94cfUwm4/3dPqDVdnx37Y\ny4fw7AxAPXL13QDQKAcG5dM+HcZFub53PPtwZuW6IYEWmQP54Kl86YmHOoMhJSmsi7eD+QcEwLLg\nDhq0GcEurEOTIqfFCsfseGzmEuZ1Gv0X5/JHbH80OsL1yvwEyM6AnCBBg7YEw9CbnT3OzIxKLZQl\nbnlwr6TWhIOczpb23HTvUZni3KzoRT06UHHSPYsDwAi6OEDb48dn/jkpfPPtp6N3PB4f5fpBL+8W\nLmSeXan8/Gz+3ZLaL/v7jgoXNP8LABAK7qBBm4Rj2Fud3S/Mii6WqeN/vPvD9WK5WtfE/jlVysXH\nshO3pIUIWKlzYiE7gzYB7qBBG+bNY2wZFXK9SLbyUuGaq+KR4S6Dgp3jPLjGKqB6gyGnUnmloOZw\nZuUtsWxCtPDK7FhPB5gTCNoMSNCgzevq7bBvQvjjcsXehxVfncvPrKhj0XAKhik0ehYN7+rtMDzM\n+deRwY4MaO2gjbF8k/3qq6/ef/99JvPvCQUajWbdunW1tbW+vr7Tp0+3+OkAMOroyv5Pnw7/6dNB\nrdM/rdXoDYhDx6G8EWjTLNkHLZPJli5devPmzWc3pqamenp6Llu2rKSkpLCw0IKnA6BRdAruw2P4\n8hmQnUFbZ8k7aC6X+80333z22WfPbhSJRJGRkQghf39/kUjk4+ODEJLL5TqdzsxqA8YJ72bGbCnY\nP4gOpCFShWQMhmwhkfCFI2dUiGSvHSL3hbJIVJZM0Mb6IA0qzCkUChcXF4SQQCCQy+XGjVOnTs3P\nzxcKhceOHTP5dE5OTuZEaw1sNpvoEJ5Do8EtZIsYmyipkK0tGTk7OxMdQiNYLBbRITTChFfwxaIi\nFkjQp0+fTk9PT0hISEhIePGnbDZbIpEEBgZKJBJX178X9di3b5/xCyiWZD32USzJ2ozFkmQyGdGB\nPAeKJbWQ/RVLapDWLZCgExMTExMTX/bT4ODgvLy8+Pj4/Pz87t27N/hpg9JNLTdq1Khff/2VbJ/n\nHA6Jlqr77LPPXnvttR49ehAdCKkdP348PT39/fffJzqQhkjVlhBCBoPhjTfe2L59O5drxZXgTUO2\n/zZ++OEHNze30aNHm38oK05UycrKSk5OTkhIEIvFK1euFAqFxg5oiyguLibb/QXZVFRUkO3OgoTk\ncnlVVRXRUbQNYrEY3nQtIZVKa2pqLHIoqwyzM34REhISEhKCEFq8eLHFz+Lp6Ume5VTISSAQkO3O\ngoQ4HA4JH2aQk5eXF7zpWoLP5zs6WmZ9CYIL9gMAAHgZ+DwEAACSonz++ecEnr6mpuaLL77o37+/\nNQ7+/+3dd1hT5xoA8O9kEDKAACFA2JAAIshQQREXbkXraN0DR1uv1VZbbW3rqPbWqvW2XvHW1lUX\nWq3auquCE/dAQEXZKzLDDCGQkNw/cstNQ0RGknOSvL+nTx89nvHm45yXk+985/1kMtnWrVsvX76c\nl5cXEhKiWvj1119HREQQbZbJjlq9enVycnLrh67q/vjjj9zc3KysrLKyMnd3d2TSDdIGrefYhQsX\nWppFnck3UWpq6rZt265cuXLz5k1PT082m/3GTfbv389isdr5QN6UGpAI2clE7qC1Pg3TeIlR64uO\nxqiurq6hoSErK6uN8WoSiWT8+PGjRo1SX2iqDaJDpt1EJSUlBw4cWLly5TfffLNw4cIffvhBYyhY\nSkrK8ePHO7HnlgvQtBuwc7qSnQjxO00kEv30008IIRqNtmzZssTExJycHCqVWlZW9sknn/z555/O\nzs69e/c+cuRIcHCwo6OjxsrJyck0Gk0qlS5YsIDD4axaterzzz9nMpkaLzEOHjy49YuOxuju3bsR\nERElJSWpqak9e/bcv3+/VColkUiVlZUffvjhzZs3VQ0SEBCgMYDfVBukPU6fPq1+FqkWbty40UzO\nGZXExMSJEydaWVkhhJycnCIjI+/duxccHPyf//wHIeTj4yMUCl+9etW9e/fffvsNwzAWi/Xhhx8i\nhH7//fempia5XL5ixQqFQrF169ampiZ7e/vFixe3XIBLly5FpniO4ZudCHEHXVlZOWnSpC+//FKh\nUJSXlyOEaDTa/PnzPTw8nj9//saV2Wz20qVLIyIiHjx4UFVVRafTVWNINV5i1PqiozG6detWnz59\nevfuffv2bdUSBweHd999NyAgICEhAf3VIK03NNUG6TTzOWdUSktLXVxcWv7q4uJSUlLy+++/Dx8+\nfNWqVQ0NDf3794+MjHz06NGAAQNWr17NYrEePnyIEPLw8Pjyyy+7d++emJh47ty5gQMHrl+/3sXF\n5ebNm+jv55vpNSC+2Qm3hktJSUEIqcaQsNnsS5cu/fjjj4WFhaol3t7eCCE6na4+7rK5uVnrygKB\nACHUq1evx48f3759u+XVDNVLjAghkUhEtGH/nSYWi58+fRofH3/x4sV79+6p2kTVXN7e3iUlJeiv\nBmnNJBukDernWAtVi6mYyTnTgsPhqLKGSllZmb29fVFRkeqEmTdvnqoIZXFxsWqArK+vb3FxseoP\nCCF/f//S0tLi4uKkpKS4uLji4mLVSyvq55tpNCBxshNuCXr//v1VVVVCoZDJZJ46dWrQoEGLFi1i\ns9mqT6X+m4RMJovFYqVS+fLlS4RQ65VVFSesrKyam5tv3boVERGh2lD1EiNCKD8//3U5y+jcv39/\n/PjxK1euXLt2bVBQkOp3eEZGBkIoPT2dx+Oh15fgMMkGaYP6OaZxFqmYyTnTYuDAgSdOnFB1iVZU\nVNy4cSMiIsLR0TE7OxshtG/fPtXrFY6OjllZWQihzMxMR0dH1R8QQi9evHBxceHxeD179lyyZElY\nWJizszP6+/lmGg1InOyE2ygOBoOxc+fOJ0+ezJgxg8fjnTp16uHDh2w2u6ysjMFgUKlUDw+PnJwc\nR0dHgUAQHx9/7949Npvt7+/v5OSkdWWEUG1tbU1NzeDBg1WH4PF4iYmJN2/e5HK5AwcOVC28evXq\ngAEDjO6BcotDhw7FxMSo3q1QKpWPHz+mUCj5+flJSUnl5eUzZswoKChQNUh2djaVSm1qaqLRaKrh\nCibZIG1QP8c0zqLKykpVs5jDOdNC9QLFrl27bty4cf/+/QULFvB4PE9Pz8OHD1+9etXa2rpXr16/\n/fbbmDFjLly4cOvWLYVCMXHixJycnOLi4itXrlRWVk6fPt3Hx+fo0aNJSUn19fWDBg1SdciqLkBk\nKg1InOxkUi+qnDx5ksvlRkVF4R2IQe3fv79fv358Ph/vQIySeZ4zwPA6d6YZcee9hitXrqiK6uEd\nCDAacM4Aw+j0mWZSd9AAAGBKTOcOGgAATAwkaAAAIChI0AAAQFCQoIGRkUqlGIY5OTk5OjryeLz5\n8+frasKq2NhYncyCAYCuwENCYGSkUimdTledtxKJZPny5aWlpS2zXHZafX29r6+vUCjURYxILpfX\n1NQQcC5aYFzgDhoYMQaD8f3339+8ebOoqEihUCxevJjH4wUEBHz00UcKhWL+/Pnx8fEIIblc7u7u\nXlZW1rKhUqlcu3atj48Pn89ft26dUqlcvHixSCSaO3duyzpaN9+yZYuXl5efn9/atWuVSmXrgyYl\nJU2ePDk4OHjnzp0Gbw9gcpQAGBVVhUz1JQMHDkxISEhNTR0+fHhjY2NjYyOfz09PT7948eLYsWOV\nSuWFCxfGjx+vvsnZs2cjIiLEYrFYLO7du/eFCxfq6uo8PDzU12m9eUJCQs+ePSsqKmpqakaOHHnw\n4MHWB71586a1tXVWVpa+2wGYA6N5+RKANmAYFhQUdPDgwcuXL9+/f7+kpEQqlUZHR8+fP7+6uvrg\nwYOxsbHq61+7dm3OnDmqIjUzZ868du1a61e8Wm9+7dq1qqqqKVOmIISEQuGDBw9mzpypcVCEUGRk\npI+Pj0E+NzBx0MUBjFtjY+Pz5899fX1v3749aNCg9PT0MWPGhIaGIoQoFEpMTEx8fPytW7c05i5Q\nKpUYhqn+jGGYeom7Fq03ZzAY77//fkJCQkJCQlpa2pYtW1ofFCFkvFXcANFAggZGrLGxccWKFVFR\nUa6urgkJCWPHjl2+fLmjo2N6erpqupmpU6euXLlywoQJFhYW6hsOHDjw0KFDDQ0NEonk0KFDgwYN\n0rp/jc2HDBly+PDh2trapqam4cOHnzp1SutBAdAVSNDAKLm6urq4uHh7e9fV1e3btw8hNH369OTk\n5LCwsI8//viDDz5QlWmMiooik8lz5szR2DwmJmbQoEHBwcHBwcEjR44cPXq01qNobB4eHj5nzpze\nvXvz+fywsLBJkyZpPSgAugLD7IApe/z48YIFCx4/fozL5gB0EdxBA5N19OjRd955Jy4uDpfNAeg6\nuIMGAACCgjtoAAAgKEjQAABAUJCgAQCAoCBBAwAAQUGCBgAAgoIEDQAABAUJGgAACAoSNAAAEBQk\naAAAIChI0AAAQFDEKthfX1+vv52TyWQMw+Ryuf4O0c4wtFYfNiQMwygUCu61MUkkkmraCHzDsLCw\naGpqwjcGDMMwDFMoFPiGQaVSm5ubcQ+DCNcImUxGCBk+DI1i4sRK0KrZjPSETqeTyWS9HqI9mEwm\n7jFQqVQajVZbW4tvGDQaTS6X434pMpnM2tpafH9PkMlkKpWqmpAFRzQaTSqV4v7rigjXCJPJVP41\nv5qBj6v+V+jiAAAAgoIEDQAABAUJGgAACAoSNHiDtNL6S1lVEhnOD44AMEPEekgIiKayQT7t2As6\nBSuua+rjbhPtZRPtzfZ3YOAdFwBmARI0aMuKP3OivW22jeGLJPJbBTXXc2v+c++5EqHBXuwRAtuB\nnjY2lnAKAaAvcHWB1zqcWvakWHxtfjBCyJ5BGedvP87f/ruRXmmlkuu51QeelC06nenvwBjOtx0h\nsOvhyMQwvCMGwLToMkHLZLJt27aJxWIPD4/Y2Fj1f/r6669XrFhhaWnZxjqAUPKrG9ck5h9+x9+K\nRlZfTsKwYCdmsBPzw74ulQ3ya7nVidnVU4+mk0nYxuFeMX52eAUMgOnR5UPCu3fv8ni8tWvXFhcX\nFxYWqhbW1dUtX778wYMHbawDiKZZoVx0JnNuqGO4q1Ubq9nRKRMDOP8Zy3/2Yc/vRnh9fCG7RIzz\nOw4AmBJd3kFnZmYGBgYihLy8vDIzM93c3BBCLBZr06ZNa9asaWOd+vr65uZmEomE6fNLMvYX/R2i\n/WHgHkPL/7X69x2hVK74dIBbO0MlY9hoP/vreTWfXMg5PLlb+8MgQmugNpvCYAEQpCkQYVoD9xgQ\nAZpClwlaIpHY29sjhDgcTktVDQzDyGQyiURqY53Zs2fn5+dzudzz58/rMB6taDSavg/xRpaWlniH\ngBBCqh9Ea4+Lav5z/9XtJVHO3LZun1v799vs4C3XL+ZLp4e56CJAw7GzI0TPjMZrvriwtrbGOwSE\nCHON0Ol0Qx5OIpFoLNFlgmYwGCKRyMfHRyQSOTg4tH+dEydOqP5QUVGhw3g0qGpxiMVi/R2iPZhM\npl5rQrUHlUplsVhVVVWt/6lBpph6IPXLAW6OlMaKisaO7vm7ER4LTqaG2pMcmNQ3rkyQWhwcDkck\nEkEtDoQQm82WSCREqMWB+zWiqsXROmPqG4PxtzGsuuyDFggEeXl5CKH8/HyBQNDpdQCOvrqa72pD\nmxvm1LnN+3vYjPa1W3kpV7dRAWCedJmg+/TpIxQKN2/ezOVy3dzcMjIy4uLi2l5Hh0cHXXc1p/pU\nekXcGJ+u9LytH+L56JX4zMtK3cUFgJnCcK/Gqw66OAxDaxdHZYN8wO6UDcM8x/lr75tuv4Ts6sVn\nM2+9G2rPaKsPDbo4WkAXhzoiXCN4dXFwOBz1v0ItDvA/K/7MGeLN7np2RggN9WEP9bFdlQAdHQB0\nCSRogBBC8SllT4rF/xzqqasdfjPU81ZB7Tno6ACgCyBBA1RQ07j2Sv72sXyNlwa7wsaS8t0I788u\n5VZLcZ5jDADjBQna3CmUysVnsub3dOzrpuMBsCMEtn3drNYk5ul2twCYD0jQ5u6H28J6WfMn/Vz1\nsfNNI7wTsquv5FTrY+cAmDxI0GYttaR+x/3in8cJLMh6ORPs6JQNw7yWns+ugY4OADoOErT5apAp\n3j+d+eVAN769Ht9nHd/NvifP6p/XC/R3CABMFSRo8/XD7SIPG1psaCdfGmy/TSO8Tr+oTMqv0feB\nADAxkKDNlFyhjE8p+7S/mwHKdXGZ1PXRHh+ey65vwvmdFACMCyRoM3X6Wak9gxrGYxnmcFOCHAId\nmd9ARwcAHQEJ2kztvlcwO9TRkEfcPMLr+LOKu4V1hjwoAEYNErQ5EtY23sqrmhTAefOquuPEslgz\n2GPp+SypXGHI4wJgvIhVLKmhoUF/O6dQKBiGyWQy/R2iPahUKu4xbLiam1PZsHtSgIGPq1SisfuT\nw92s1wzxQQiRyWSlUqlQ4Jyv6XS6Xk+89lDNayGX4zwYkSDlq4hwjVCpVISQgcOQyWQaEyYQa1Zv\nvZawUlWzI0KVLHxjUCiV+x4WHZgWiksY/4x2G7E/bXI3WzcbGkHSAZ1Ol0gkUM0OIaSKAarZIfyq\n2WmALg6zcyWnxpJC6u+FzyRPAnv6tCDu+qv5uBwdAOMCCdrsHHxSOivUCcfJMFf0d72RV3OnsBa3\nCAAwEpCgzUt5vexKTvWUIC6OMbAtKZ/2d/vycp6CSM8/ACAgSNDm5XBq2VAftpMVzlObx4Y6yhXK\nw09K8Q0DAIKDBG1GlEp0OKVsVohBhz9rRSZh3wzz/Coxp64R3i0E4LUgQZuRpIKaxmblQE8bvANB\nCKH+HjZhPKutt4vwDgQA4oIEbUYOPSmbGcwlk/B7Pvh3m0bxdz14lVuF89gyAAgLErS5qJTILmRW\nTg1ywDuQ//Oypc8OdfwahtwB8BqQoM3Fb88q+rlbu9rg/HhQw2cDPO4V1d0ugCF3AGgBCdpcHEkt\nmxmM/+NBDVY08oooty8TcpsVMOQOAE2QoM3Cg6K6snrZcD4b70C0mBXCVSjQkbRyvAMBgHAgQZuF\nQyll03twqfqZeLCLVEPuvrlWUNsI8xYC8Dfar9iysjIDxwH0p76p+cxL0fRgPN8ebFuUh01vV6ut\nt1/hHQgAxKK9ml2vXr169uwZGxs7atQoCwuLdu5LJpNt27ZNLBZ7eHjExsZqXVhRUfHxxx9zuVyE\n0LJly1xcXHTxKUBbjj+rCHY9GDpOAAAgAElEQVRiedta4h1IW74e4jloT8rMEC7B4wTAkLTfQefk\n5Lz//vvHjh3z9fVdtmzZkydP2rOvu3fv8ni8tWvXFhcXFxYWal1YVlY2ZsyYLVu2bNmyBbKzYRxK\nKZsVQtzbZxUPNi02zHHdFRhyB8D/ab+DplAoI0eO7NOnT3x8/MqVK3/55Rdvb++4uLh+/fq1sa/M\nzMzAwECEkJeXV2ZmppubW+uFJBJJKBTGxcUFBgYOHjxYteGhQ4eqqqpYLNbUqVN1/Pn+/qEwDGMy\nmfo7RHtQqVRDxvC0VJxfLX0n1M2S8v9fxiQSiQhNoSqC3FKIefUw3x4/3L5bLB3CtzdwJAwGw8BH\n1IBhGIlEIpPJ+IZBIpEsLS1VtepxZOBr5HUxIIQww1Z9bD0/gPY76Pj4+HHjxgUFBT19+vTs2bMi\nkWj37t2zZ89ue+8SicTe3h4hxOFwWuptayyk0+mBgYHTpk1LTExMSUnp6gcCb7LnftH0EJ56diYs\nKxplzVCfT89nyGHIHQAIodfdQV+5cmXx4sXR0dEUyv9WCAsL27BhQ9v7YjAYIpHIx8dHJBI5ODho\nXRgREaFaHh0dnZGRERwcjBCaOXOmamFFRUXXP9LrmOGMKlK54mhKyR8zumsckUqlUigU3Jui9Ywq\nb/vb7LyLdt7OnWPACW1hRpUWMKOKegxEmFFFM0G3dDLs3bt37969Lct//fXXKVOmtL0vgUCQl5cX\nHh6en58fGRmpdeHhw4cDAgJCQkIKCgr4fL7uPgjQ4lS6SMChd+fi/P29/UgYtmGY15wTL8d3s7ex\nJNZ8bAAYnuY1sGDBgk7vq0+fPtu3b9+8eTOXy3Vzc8vIyLh48eLChQvVFw4dOvT7778/fvw4h8Pp\n27dv14IHb3AopXQmgUfXaRXuatXHzer728J10R54xwIAzrTP6l1XV3flyhX1qY71+viuhQG6OMRi\nsf4O0R4G+/qWJWoYvj8tbXFPpoXmoycqlcpisaqqqgwQRhteN2lsfnXjoD0pl2OD+PZ0A4TB4XBE\nIhF0cSCE2Gy2RCKBLg6EXxcHh8NR/6v2b5Fz5swpLS0NDQ1tWWKYBA106FBK2YRunNbZmfg82LTF\nfXjvnsq4MDvIKB5vAqAn2hP0s2fP0tPTSSS4NoxVU7PiaFr5kcn+eAfSScsiXR4U1S07n71jnADv\nWADAjfYUHBISkp8PrwwYsT8zq7hMaogzC+9AOomEYTvGCe4X1R2EeQuBGdO8g3777bcRQtXV1Xw+\nv2/fvk5OTqrlx48fN3RooAsOpZTNNuBINX2wpVN2T/CddOR5kCPTeH/TANAVmgl66dKlqj989dVX\nho4F6EhRTeO9wtqdbxl950CoM2v1IPe5v2ckxgbZMXB+vQ0Aw9NM0FFRUQihRYsW/fjjjy0L58yZ\no1oOjMKBJ6UxfvZskxhHPDfM6YFQvPhs9qF3/EiGfe8WANxpXsP+/v4IoYKCgitXrqiWyOVyNpuI\nhd6BVk3NikMpZQcm+eEdiM58P8p79IGnW2+/+rgfVNcC5kUzQSclJaFWd9CQoI3IH+kiJ5ZFLxcr\nvAPRGUsKafcE3xH70kJ5rMFeNniHA4DhaCZo1TDpPXv2JCYmtgybp1AoqoeHgPj2Pip9r7cz3lHo\nmLetZVyMz8LTmQmxQW4Em/cWAP3R3k05b968xsZGb29v1V9JJBIkaKOQWlKfW9UwIYDz5lWNzUiB\n3bSgugV/ZJyZGWhBhs5oYBa0J2i5XH769GkDhwK6btejklkhjjQTzV+rBrlPOvJ87ZX8b4d54h0L\nAIag/UUVPp+Pe6EG0FGVDfLT6RXGPvy5DRQStnu879kXoqMwBTgwD9rvoIVCIY/H69u3r52dnWqJ\nYV5UodP1WByHSqWSSCS9HqI9KBSKnmI48iBvCN/ez+nNT3TJZDKGYURoCgqFolAo2r+JO50ePzVo\n0qGUCE/7blydTbpBp9PxLZZEIpFU09zgGIMqDAsLC9wndtHfNdKhGJCeM1JrcrnmxPbaE/SiRYsW\nLVqk/3g0qdfP0wcymazvQ7wRiUTSRwzNCuWeB8IfRnm3Z+dUKpVKpeLeFK+rZte2Hg4WH/V1mXo4\n5XJsDyuaDlIJk8lsaGiAanYIIRqN1tTUhHs1Oz1dIx2NQalU4h+G1qX9+vUTi8Vnz549depUTU1N\n21MRAiL4M7OKSsIGeJrFgMgPIngBDowPzmbhmlQB0DvtCXrdunVr1qzh8Xju7u7r169ft26dgcMC\nHbX3ccm7vZzw/opsIBiGtsXwsysbfn5YjHcsAOiR9i6OgwcPpqamqibWnTdvXmhoKJTmILKMiobk\nYvG+iabz9uAbsSzIu8f7xhx8xrejD/Uxi+8NwAy9tuJzy/MKqApNfL8kl04OdNBJh6wR6ebA2DVe\nsPB0JpQkBaZK+x30jBkzhgwZMnv2bAzDDh48+MbpYgGOxE3Nx9LKz87qjncgOIj2Zp+e2X3a0fT0\ncsk/h3pCNSVgYl7bB71q1aqcnJzs7OyVK1euX7/ewGGB9juaVh7izOzmYDRTd+tWgAPj3KzAm/m1\n/zid1dRs1g8Nq6Xynx8UF9fhPAYD6JD2BN3Y2IgQCg4ODg4OrqmpOXr0qGGjAu2lVKK9j0vmhTnh\nHQieXG1o52Z1r5DI3j7yvKpBcySpmbiYWRW1K+VwSlnfnU++uJxbVi/DOyKgA9q7OCZMmMBgMNzc\n3FR/JZFIMGksMV3Pq5bIFCMFtngHgjNrGuXolG6fXcwdsT/tyORuPnaWeEdkODVS+fqrBRcyKzeP\n8I7xsyusadx6W9hrx+N3ujt82t/VkWWBd4Cg87QnaAsLixMnThg4FNAJex+XxoY6kknQ94ooJOxf\no7x3PiwefSDtwNv+Ea6mU3C1DQnZ1R9fyO7tYpX0bogdnYIQcrOh/WuU90eRLv++Ley788nUIIdl\nka4OTJiPxihp7+IQCATnz59vbGyU/8XAYYH2KKxpvJ5bPTPYZItvdMJ7vZzXRXtMP/bi3MtKvGPR\nr6oG+aIzWR+dy9o43GvPBF9Vdm7hbkP71yjv87MCS+qa+vz8ZOONwhqpeV3FEpni80u5Q39JvVVQ\ni3csnffaWZEmTpyoqg2tUlRUZJB4QAfsTy4d629vzzCFqa10aGoPLs+atuCPjKLaxvdNrjS2yoWM\nyhUXc6M8rG8uCG5jtkZ/B8beiX7PyiSbbhb22pH8fm/n93s7m8NwzNsFtR+eyxLY06f24M47+XKQ\nF3vdEA8nI+zt0X5t37lzRyQSqV5UAcTU1KyMTyk7PNkf70CIaICnzekZ3af/9iKjomHTCC+KCXUB\nqfU4e8X42bdnk+5cxoFJfunlki1JRb1+Sp4f5rgw3NmaZpq/16VyxXdJRQeSS1cPdp8V7Ihh6O3u\nnM03CyN3PlnY23lppKtxFRPX/kPy9fXtREUrmUy2bds2sVjs4eERGxurdaHWdUAnnHxW7sG2DHVm\n4R0IQfk7MC7MDpz+24v5v2f8NE5Ap5rC+1YXMiqX/5kzwNPm1rshtvSOZdhuDow9E3wfCus2JxX1\n25mSMDfI9J4fttw431gQ7Gz1v0/HtqRsGOY1NYj72aXc0y8qvx3u2d/DaCZO037WVlZWurq6jh07\ndvxf2rOvu3fv8ni8tWvXFhcXFxYWal2odR3QCXsel87rCb3PbXFkWZye0V3WrBx/+JmxDzurlMgW\nns5c/mfOlpHeO8YJOpqdW/RysTo2pdtIX9vlf+bqNkJ8SWSKzy/nzTnxcnmU25HJ3Vqyc4seTszz\nswIXRTi/90fmgj8yXhnJaHHtP+YVK1asWLGio/vKzMwMDAxECHl5eWVmZqpG6WkszMvLa70O6KhH\nr+qKahrHdzPBqa10i2lBPvi236qEvCF7U3dP8DXSoR05VdK3Dj3r72mT1PEbZ63WDvYYsDvl2NPy\nyYEOXd8b7m4V1H7U6sa5NQxD03twR/vabbxR2H9XytJI3vu9nS3IhP5qpf2HHRUV1Yl9SSQSe3t7\nhBCHw6mvr9e6UOs6s2fPLioq4nA4+n4jBsMwGg3/KUctLbs6SvfQpcL3+no6czufoDEMU/0gzMHO\naZxDj4pm/PZs3QjfD/t7tV6hZWIKfGl96vOqVjrlWMqS/l6fDxHo6kD2CP0yLXTKwcdvhXryrP9/\nNmIYZmVFiN9h7bxG6puaPz//Iv5R0b/GBcT2btfdnj1Cu6Y5LhLWLPn92bFnz7aN7z7UV8tvKQzD\nlEqlgQv2ty4/rcsHBQwGQyQS+fj4iEQiBwcHrQu1rvPdd9/J5XIymVxdXa3DeDRYWlqSSCSJRKK/\nQ7QHnU7vYhXw8vqmk6mv7v2jZ6ebi0KhMBiM2lqchx9ZWFjI5fIOzajSaTE+LM9ZgXOOv0jKLts6\nRsBQ65K2tbWtqanBfUYVKpWqeoNXXbVUHnMgbRSf/Y+eDrq9OsI4lLf87ecfeXxkSkDLQisrK6lU\nKpPh3B3UzmskKb/mw7OZvvaMpPdCna0sOtQ+Xkx0ZkbAkdTSGfGP+7pZbxjhw/v7rTeDwTB8wX6F\nQsFg/K1mgy4TtEAgyMvLCw8Pz8/Pj4yM1LqQSqW2XsfR8X99qRUVFTqMR4NCocAwrKPzd+icUqns\nYgz7H5dEe9s6MSmd3o+qQiHuTaFQKBQKhcHC6MaxvBwbuOhM1rC9T/ZN8vO2/f89WnNzM74JGiHU\nuikaZIqpvz4P5DLWRXvoo5XWDHIbsDvl8JOSKUH/v4U05E/kdd54jTQ2K9cm5p18XrE+2mNqDy7q\n7Mk8JZAzks/+9kbhiF9SLsUGcdVe51EoFF2/VLtOl/0vffr0EQqFmzdv5nK5bm5uGRkZcXFxGgs1\n/qrDo5sJuUK5P7l0Pjwe7BQbS8qht/3f6mY/Yl/an5mEfpNFrlAu+CPDhkbZOtpbT0X6mBbkf4/x\nWZ2YZ3T1lb5KzEsrrb8+P1iVnbvCxpKycbjXSIFt7MmXBCy2heF+46BOr3fQdDqdTCaLxWL9HaI9\nmExmS+d7J5x5WbnpRuHNBcFduWipVCqLxcJ94vbOzUmoE0n5Ne+dypzWg/vFADdHroNIJCLUnIRK\nJfrofFZGhfTk9ACGngcIrryUm10pPTalG4YhNpstkUhwn5Ow7WvkgbBu+rEX1+b3cLHW2fMkuUI5\n+dd0nrXF9hh+SwxKpdLwPaLqrwci3d5BAwPY87B4fk9HqHvcRVEeNpdjg5LyayYdeV5ap9nzi7uv\nruYnF9cfmeyv7+yMEFo72CO/Wnr0abm+D6QTDTLF4jNZ64Z46DA7I4QoJGzPBN/7RXU77hNrEjVI\n0MbkRbkkrVRiGkOjcOdiTTs9M9CdTYv4980nxTh/r1K37Y7wzAvRsSnddDKi7o3oVNLW0T6rE/KM\nYmjwN9cLPNi0aUFd7dlozZZOOTDJ71+3iq7m1uh8550GCdpoNDUrVlzMmRvmyLQw/VoKhkEjY9vG\n8L8YKph4+PmhlDK8w0EIofiUsh33i3+bGtDGeF6di3S3frs75+Pz2QY7YufcK6r7Na38h9E+evoG\n6e/A2B7j8/6pjCyRQQdvtAEStNH49GIuGcM+6++KdyCm5r0+HsenBWxJKlp6PrsR18dE5zMqv7qS\n/+sUHOpZrx7skV0p3feQuDXRpHLFR+ey1g52123nhoaRArtFEbxZJ14SpPgfJGjjsPNh8c28mr0T\n/ajEfvHJSIXxWIlzewhrm4b9kppejs9I+aS86iVns/dO9A12wqFIGYNK2hbj8+nZF8JawvXIq2y4\nXuBmY2mA4rof9XEJcmTOPprWrMB/AAVc7UbgWm715ptFh97xtzNIp6R5smdQjk3pNjOEO+bg0213\nhArDDup4XlY/4+jTH0Z741jHp6+b9cwwl0WnXhJpYNf/PCiqO5Ja/sMofY04VIdhKC6GX9UgW3Up\nS+8HexNI0ESXX934j9NZ22N8zHZaWIPBMPReL+dzswKPP6uYcvRFqdhAD83yqqUT459+PshznD/O\nL9//c6RvYbU0PpUQ3fEtmpqVyy7krB7s7mpjoDoNNDJ2bEbw8bRS3J9MQIImtLrG5pnHXywMdx4p\nIESxCHPQzYFxKTZIYG85aE/q5Wy9DxWvkMimHk1/tzdvYQT+TxcYFuSfxvuvu5JfVEOgjo5vrhdw\nmdRZhp05yMmKdmxG8NrE/DuFeFZEgARNXM0K5YI/MrpzGR/2ccE7FvNiSSFtGOb13UivxWezVyXk\n6e8Fs9SS+gmHn0d7sz+JIspbteFu1lODHD7+M4cgHR0PhXWHnpT9e4y+Rm60IZRn9c1QjwW/41mb\nFBI0ca27ml/VIN+qt0FFoG0xfvZX5/V4ViYZvi/1ZYWOnxxWSmQrLuZMOPx8WpDDP4d66nbnXfTF\nQPeCaunBlFK8A0GNzcqPzmevGuTmZqjODQ1Te3DfDnSYffxFg8wQJb1agwRNUEfTyk8+F+2b5GdJ\ngZ8RbnhWFienBUwP5o468HTnw2Kd3FQqlMqjaeWRu568qm26Pr/HoggeiWC/gelUUtwY/ldX8vOr\nce7o2HSjkMu0iA11wjGGtYPduUyLRWcycflKARc/ET0Q1n1xOe/AJD+eAd9WAFqpnhyemBaw52HJ\n7BMvKxu6NDz2obBu+L60H++9+mWiX/w7/gZ76tVRvV2tZgZzP8G1o+PxK/H+5FJcOjfUkTBsxzj+\ni4qGrXeEhj86sYol6bWSkYWFBYZhrUvuGpiFhUXbxWgKq6WDdj3aMII/pYe+noqQyWQajYZ7aWwK\nhaKqOIpvGCwWq76+/o0XQn1T8/LzGQlZVT9P8I/26fAz2/L6pjWXs8+mV3wx2OvdcBeNeWxJJBKZ\nTMa9EDODwWhsbFSVr2qQNQ/4+dHsMOclkYbuH7ewsKiTSKN+evhuuMt74fg8gKHRaEqlsuVSzRI1\nRO96uP0t/3Hd9FhoQS6Xs9ls9SXEGler1xOUTCaTSCTcrwEKhdJGDBKZYuqRtOk9uBO72ekvVKVS\naWFhgXtTqMpz415yFyEkk8nemKAtMLRtDP9UesWcY88cmNRwV+s+btYRbtZ8+zdMuiFXKPc8LN50\no2CMv/29f4Q5MC2UzXLZ3z+0qkI37j8RVTFoVRgUhHZN8B29PzXSjdXDyaBzE1MolG+u5NgzKHNC\nHPBqEyqVqlQqW47uYU35ZZL/nOMv8ivrF+rtd0brmxVi3UGbeblRpRLN//1lk0J5YJKfXvslodyo\nOg6H06Fyo1K54vEr8d3CugfC2gdCMYWEertYR7hahbtahTizLMh/+8HdKaxdeTGXSsY2Dvfq5fLa\n2aQ0yo3ipXW50V0PS355XJIwt4cB6uq1eFHdPGbv48R5QZ5sQ7/y3kJrudH0csms4y8i3a23jPTR\n+EHrika5UUjQHVMtledUSnOrpDlVUqlcEe5iFeFmxbbswBeRNhL0d0lFZ16Izs8OZOm5HBIkaHUd\nTdDqFErly4qG+0V1qv9e1TWGOLPCXa3CXay8bC233hFezan+YqD7rBBu279xCZuglUo0/bd0ZyuL\n70f5GCaGpmblsH1pM4O57/bC89ng6+pBV0pkC/7IlDYr9k30U5+BRVcgQbc3Qdc2/j8X51Sq/t9Q\n09jsam3hbWvpbUenkLB7RXXPyiR8O8tId+sIV6u+7tZOrDc81ntdgj7zsnLFn9kX5/TwYOv9wREk\naHVdSdAayupl94tq7xXWPRCKn5aKpwVzP+/vZsd482VM2ASNEKqQyAbuTtk0wjvGzxBvS224XnD/\nVf3Jqf74Dm5po2C/XKFcnZB3PqNy/yS/EGcdd/5oJGhi9UHjrkYqP5RSdiGjMrtSWtkgd7G28LGj\ne9laBjkx3+pm721r6c6maczTLm5qfiAU3y2s+eVx6ZKzWU5WFn3dbfq6WUW4Wr+uJplSiSob5JUN\nssoGuUgiq2yQl4llP95/tX+SnwGyM9AfLpMa42cf42dS06VzGNTtYwULT2eGOjP1WkkOIXS/qG73\no5LbiyJIGIFuHDVQSNi3w726OzInHUnfPMJrUnfOm7fp9LH0t2vj8rJCsvtRyfGnFX3drN8P5/nZ\n0z1tLdvTzcSyIA/2shnsZYMQampWJheL7xTUnHkhWpWQR6eQIlytvOzoVWq5uLJBXimRYxiyo1Ps\n6FQ7OsWeQeUwKdtjfPq5W+v/gwLQYYO9bKYEchadyTo5LYBM0teNbXaldNbxF9+N8PaxZ3RlWjjD\nmBnM9bWnzz358lmZ5MuBbnpqFnPv4lAolQnZ1bseljx+JZ4SxFnQy1l9sueuaFYo08sb7hTWvqpr\ntLWkcJhUVUZ2sbOiI1l7vvbqD3RxqNNhF0enEbmLQ6WpWTlyf9pYf/tlkXoZw1BeLxt1IG1WiONH\nfV26OG+nTrRzTkJhbePsEy+5TOrPbwmsaTq434Uujv8RNzWffF7x84PiJrlyVgh313hBh571vRGZ\nhAU6MgIdNUvQEeHkA6CjLMjYngm+w/alRblb93Z97XCUzpHKFbNPvBzoyf6or5GVnXGxpp2fHfTJ\nhexhv6QdfNvfl/OGMZcdZY4JOrdKeiilbH9yqR+H/ll/tzG+dvr71gaAyfCytVw72H3h6cxr84Ot\naDobaNSsUL5/KtPWkrJphJeu9mlINDK2PYa/82Hx6INP/xPDHyGw1eHOTTxByxVKcVMzQkgqV6AG\nlF0ljbuZe6+obnKQw4XZgYI3vWIAAFA3K8TxSk71pxdzdowT6GqfKy/nloib/pjenWLM90nv9XL2\nZFt+cCZraaTLBxE8Xe3WRBL0qoS8PzMrEUINMoWqOGRNo1y9U9GCTGJQSY5WtBk9HHaO101vEQBm\n6IfRPoP2pP72tPwdXcwuv/V20bWcmgtzAukGfBFGT4bzbc/NCow9+TLam62r6TVMJE/NDOaqThca\nGaNTyQghlgWZTEIYQjZ/9SwT5E1CAIwa25Ly41j+7BMve7tadfFNv+PPKn56UHxuVhAH12fmOuTL\nod9YEKzDrwImkqD9YTooAAwl0t16Xpjje39knpvVvdOzGN8uqF15KffIZH/DT2GuV7rtqDH6rxUA\nAMP7tL8bhYx9l1TUuc1fVkjm/p6xdbR379fXJwFIt3fQMpls27ZtYrHYw8MjNjZW68KKioqPP/6Y\ny+UihJYtW+biYmSjagAACCEKCftpnCB6b2p/T5uOzkReIm6aeuzFJ/1cTOyVS33Q5R303bt3eTze\n2rVri4uLCwsLtS4sKysbM2bMli1btmzZAtkZAOPlbkP710jvD85kVUo6UBFU3NQ87Vj6OH/793o5\n6y82k6HLO+jMzMzAwECEkJeXV2ZmppubW+uFJBJJKBTGxcUFBgYOHjxYteHly5fr6urodHr//v11\nGI8GKpWKYZilJc4dXhQKBfcYyGQyQZqCTCbjXrAfIWRpaYnvm4Sqgv04BtASBpVKVRWnbo8poS4J\nubXvn8me14sX7GzlZUtvu8CRrFkx/1iagMP6dpRvG7WQiHCNUCgUpVJp4DDkcs35enSZoCUSib29\nPUKIw+G0vCynsZDL5QYGBoaFhW3dutXOzi44OBghlJqaWl5ebmNjEx0drcN4NJBIJAzDqFScnxer\nrgHcYyBCU6iyMxGKDVAoOD8txzCs/WlRr2FQKJQORRI3PmDDlZxdD16lFouVSNnDySqEZx3Cswrm\nWXXjstSfmCmV6B+nnjY2K36Z0oPW5kybBLlGEEIGDqP1taCD8zIhIeHp06d9+vRhMBgikcjHx0ck\nEjk4/G+MpMbCiIgI1fLo6OiMjAxVgv7kk09UCwlVblRPiPCqt6oWR11dHb5hEKQWB41GE4vFUIsD\nIcRmsxsaGtqekq21L6KcEXJWKlF+jTStVJJaLD78uOjzC/W1UnkAlxnkyAxyZAY5Mf7MqLqXX3Vu\nVqCsob7tPhEiXCPtrMWhc1ZWf3tqqoMEPXTo0KFDhyKEZDJZXl5eeHh4fn5+ZGSk6l8FAoH6wsOH\nDwcEBISEhBQUFPD5/K4fHQBABBiGPNmWnmzLsX+VjS4RN6WV1qeVSK7n1cTdFTY1K8/NDrSlm8jQ\nXsPQZTU7mUy2fft2mUzG5XJjY2MzMjIuXry4cOFC9YVlZWXff/89hULhcDhLlizR6HeDO2jDgGp2\n6qCaXYs2qtkZEhGuEbzuoGFGFUjQkKD/BhJ0C0jQ6jEQIUHj/1wCAACAVpCgAQCAoCBBAwAAQRGr\nD1qvTp48+erVq8WLF+MdCP6eP3++Y8eOuLg4vAPBn0KhmDBhwpEjRxgMqLeFli9fPmPGjNDQULwD\nwd+uXbtYLNa0adPwDcOMhryIxeLq6mq8oyCExsbG0tJSvKMgBKVSKRQKzec2pW1lZWW4P6gkiJqa\nGiKcFWaUoFksFpvNxjsKQqDRaI6OjnhHQQgYhrm4uGBtv6FsNrhcLu7vWBOEjY0Ni8XCOwpz6uIA\nAADjAg8JAQCAoEyqi0NrQeqvv/56xYoVqi9uJSUlz549e/LkScs6Eonk22+/lcvlDAbj008/pdFo\neH4A3elEU9TV1X333XdSqdTf33/evHl4Rq9r7WmNjIyMAQMGtCxUKBS7du0qLy+3trZesmSJyfSB\ntKcpkpOTb9++3XJRSCSSjRs3kkgkLpe7dOlSs2qKu3fv3rlzp+Wzy+Xy1pvolUndQWvUnq6rq1u+\nfPmDBw9aVnj06FFTU5P6OlevXg0ODv722299fHxu3LiBY/C61YmmOHPmTFRU1KZNm8rLy/Pz83EM\nXufa0xoCgUB94cOHD5lM5qpVq0JDQ0tKSnAKXPfa0xQ1NTXqF8WlS5eGDRv27bffNjY2Zmdn4xi8\nbrWnKUQikfpn11ryXq9MKkFnZmb6+Pigv2pPs1isTZs2BQUFtawgFouLi4vV1+Hz+YMGDUIIWVlZ\n4V7hUIc60RQlJSXe3t4YhgkEgszMTNxC14P2tIaTk5P6wufPnzc3N2/btk0ikTg5OeETtx60pylC\nQ0PVL4qBAwdGRkZWVBaxaqoAAANDSURBVFTU1taa0mP29jTFmDFj1D+7xiYGCNKkErRG7WkMw8hk\ncktxW4lEwmAwNNbx8/PjcDj379+/detWeHg4ntHrVCeawtvb++rVqyUlJbdu3cK9Yolutac1NBaK\nxeKSkpKpU6feu3cvOTkZt9B1rT1NoXFRODk5kUikTZs2KZVKJpOJa/i61J6m0PjsWkve65VJJWhV\n7WmEkEgkan0mJScnh4WFaayjVCqPHTuWlJS0atUqU3pVoRNNMWbMGCsrq19//dXNzU2jKK2xa09r\naCxkMpnDhg3jcrkDBgwwpe/17WkKjYtCoVDQaLTNmze7urreunULj6j1oj1NofHZ295EH0wqQatq\nTyOE8vPzBQKBxr8WFRXxeDyNde7cuSMWi5ctW0aEMY861ImmyMjI6NGjx9KlS6VSabdu3Qwesh61\npzU0FvL5fNV32JycHFMaM96eptC4KLZv3/7y5UsMw9hsNhGmfdGV9jSFxmdvexN9MJ3mRgj16dNH\nKBRu3ryZy+WqZkRsoVAoVPM8aayTkpLy5MmTlStXfvbZZ6b0kLATTeHp6Xnq1KkNGzbw+fzWCcuo\ntac1Wm+Sl5f3+eefV1RU9OvXz4DB6ld7mkLjopgwYcLevXvXrFkjFAqjoqLwilzn2tMUGp+9jU30\nBF5UAQAAgjKpO2gAADAlkKABAICgIEEDAABBQYIGAACCggQNAAAEBQkaAIQQ2rhx49atW/GOAoC/\ngQQNAAAEBQkamK+mpqaFCxd6eHiEh4enpqYihGpqamJiYlxdXfl8fmJiIt4BAnMHCRqYrz179uTl\n5WVkZJw5cyYpKQkhFB8fb2trW1hYuGPHjtOnT+MdIDB3kKCB+bp+/fqiRYtUMzROnjwZIRQZGXnz\n5s3Vq1ezWCzokga4gwQNzJd6FQ4ymYwQCgkJSU5OdnFx+eqrryZOnIhrdABALQ5gxn7++eczZ878\n/vvvtbW14eHhS5YsqampaW5uXr9+/atXr3x9fWtra02pfhswOiY1JyEAHTJ37tzk5GQ/Pz8HB4dZ\ns2bZ2tqOGzdu6tSp+/fvp1KpcXFxkJ0BvuAOGgAACApuEAAAgKAgQQMAAEFBggYAAIKCBA0AAAT1\nX6W4l0rPO5nGAAAAAElFTkSuQmCC\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "m <- prophet(weekly.seasonality=FALSE)\n", + "m <- add_seasonality(m, name='monthly', period=30.5, fourier.order=5)\n", + "m <- fit.prophet(m, df)\n", + "forecast <- predict(m, future)\n", + "prophet_plot_components(m, forecast)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -114,7 +185,6 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false, "output_hidden": true }, "outputs": [ @@ -176,9 +246,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "m = Prophet(holidays=holidays)\n", @@ -189,7 +257,6 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": false, "output_hidden": true }, "outputs": [ @@ -235,7 +302,6 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": false, "output_hidden": true }, "outputs": [ @@ -270,9 +336,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -387,7 +451,6 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false, "output_hidden": true }, "outputs": [ @@ -409,9 +472,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -438,7 +499,6 @@ "cell_type": "code", "execution_count": 11, "metadata": { - "collapsed": false, "output_hidden": true }, "outputs": [ @@ -490,9 +550,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -626,5 +684,5 @@ } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 }