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    \n", - "\n", - "
    \n", - "\n", - "```{important}\n", - "It can be proved, mathematically, that under the null hypothesis, the classical $p$-value will *always* cross $\\alpha$ if the experimenter waits long enough$^{*}$. This means that with increasing data, the probability of falsely rejecting a true $H_0$ approaches to 1!\n", - "```\n", - "\n", - "[*] David Siegmund. 1985. Sequential analysis: tests and confidence intervals.\n", - "Springer." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Summary and key concepts learned \n", - "\n", - "1. A/B testing refers to an experiment, in which users are randomly assigned to one of two variations of a product or service: control (A) and variation (B) to see if variation B should be used for improvement.\n", - "\n", - "\n", - "2. The statistic used to test a hypothesis, the sample size calculation, the type I error rate specification and the desired power are all important and interconnected pieces of the experimental design! \n", - "\n", - "\n", - "3. In classical hypothesis testing theory, the sample size must be fixed in advance when the experiment is designed!!\n", - "\n", - "\n", - "4. Modern platforms allow the users to continuously monitor the p-values and confidence intervals of their tests as data are collected (peeking) in order to re-adjust their experiment dynamically. \n", - "\n", - "\n", - "5. Stopping an experiment and rejecting $H_0$ as soon as the $p$-value is below the specified significance level can drastically inflate the type I error rate\n", - "\n", - "```{attention} \n", - "Controlling the risk of wrongly rejecting the null hypothesis is not an easy task in A/B testing if peeking and early stops are allowed!\n", - "```" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "4.2.3" - }, - "rise": { - "transition": "fade" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/stat301book/_build/html/_sources/w02d1_abTesting.md b/stat301book/_build/html/_sources/w02d1_abTesting.md deleted file mode 100644 index 3f88bfc..0000000 --- a/stat301book/_build/html/_sources/w02d1_abTesting.md +++ /dev/null @@ -1,220 +0,0 @@ ---- -jupytext: - cell_metadata_filter: -all - formats: md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.10.3 -kernelspec: - display_name: R - language: R - name: ir ---- - -# A/B Testing - - - -*Photos/Images by [Optimizely](https://www.optimizely.com/insights/blog/how-obama-raised-60-million-by-running-a-simple-experiment/)* - -## Overview -This chapter provides an introduction to A/B testing and the early peeking problem. The goal is that you understand the main principles of A/B testing that will allow you to properly design, run, and analyze data from A/B tests. While you probably know the main concepts behind A/B testing, the usage of online platforms to collect, store and analyze data from A/B tests open new challenges. For example, people get tempted to peek at results before the A/B test ended and take actions usually result in false discoveries. - -+++ - -A/B Testing is a technique used to compare two variations of a product or service: control (A) and variation (B) - -- A/B testing became very popular in the context of updating and improving websites. - -It's main concepts are founded in the context of hypothesis testing and inference to compare population quantities from 2 distributions - -- for example: comparison of two population means or population proportions - -+++ - -## Case study - -### Obama's 60 million dollar experiment - -In 2008, Obama's campaign was looking to increase the total amount of donations to the campaign. Organizers run an experiment to compare visitors' responses to different versions of the website. - - - -*A full description of this case study can be found [here](https://www.optimizely.com/insights/blog/how-obama-raised-60-million-by-running-a-simple-experiment/).* - -## Experimental Design - -- post the *question(s)* you want to answer using data - -- *design* the experiment to address your question(s) - -- identify appropriate methodologies to analyze the data - -- run the experiment to collect data - -- analyze the data according to the experimental design and make decisions - -### Question: - -> Does visitors of the new website contribute with larger donations for the political campaign? - -### Design: -Different experimental designs will be used depending on the population and the problem we are analyzing. A common choice is a ***randomized controlled experiment***: - -> *Randomly* allocate 1000 visitors to each website. - -### Method: - -A classical hypothesis test can be used to run the analysis. In general, size will be large enough to rely on the CLT results: - - > run a 2-sample t-test, compute $p$-values and confidence intervals - -## A/B testing: what is new about it? - -New platforms have been developed to assist companies to analyze, report and visualize the results of their experiments **in real time** - - - -Figure by D. Meisner in Towards Data Science - -## Early stopping -> **In classical hypothesis testing theory, the sample size must be fixed in advance when the experiment is designed!!** - -These platforms allow the users to *continuously monitor* the $p$-values and confidence intervals in order to re-adjust their experiment dynamically. - -> **Is it ok to peek at results before all the data are collected??** - -**Early stopping** refers to ending the experiment earlier than originally designed. - -### A/A Testing -To examine the problem of early stopping, you will simulate data for which $H_0$ is true (i.e., there is no effect) - - we can think of a scenario where both groups are equal (aka A/A testing) - - - in this scenario, we know that claiming a significant result is a false discovery - - - -### Test experiment - -- run a balanced experiment with a *pre-set* sample size of 1000 visitors per variation (total sample size of 2000) - -- **sequentially collect** the data in batches of 50 visitors per group - -- **sequentially analyze** the data using two-sample $t$-tests - -- **sequentially compute and monitor** (raw) $p$-values - -> ***Can we stop or re-design the experiment earlier if we have supporting evidence to do so??*** - -```{r, code-cell} -library(tidyverse) - -# Two-sample t-test with tracking sequential statistic and p-values by incremental sample sizes until getting to n in each group. - -# @param n (numeric): Initially planned sample size for each group (for simplicity, n needs to be a multiple of sample_increase_step). -# @param d_0 (numeric): effect size. -# @param mean_current (numeric): Population mean for control variation. -# @param sd_current (numeric): Population standard deviation for current variation. -# @param sd_new (numeric): Population standard deviation for new variation. -# @param sample_increase_step (numeric): Sample size increment. - -# @return p.value.df: A tibble that has 3 columns: -# inc_sample_size, statistic, and p_value - -incremental_t_test <- function(n, d_0, mean_current, sd_current, sd_new, sample_increase_step) { - sample_current <- rnorm(n, mean = mean_current, sd = sd_current) - sample_new <- rnorm(n, mean = mean_current + d_0, sd = sd_new) - - p.value.df <- tibble( - inc_sample_size = rep(0, n / sample_increase_step), - statistic = rep(0, n / sample_increase_step), - p_value = rep(0, n / sample_increase_step) - ) - - current_sample_size <- sample_increase_step - - for (i in 1:nrow(p.value.df)) - { - t_test_results <- t.test(sample_new[1:current_sample_size], sample_current[1:current_sample_size], - var.equal = TRUE, - alternative = "greater" - ) - p.value.df[i, "statistic"] <- as_tibble(t_test_results$statistic) - p.value.df[i, "p_value"] <- as_tibble(t_test_results$p.value) - p.value.df[i, "inc_sample_size"] <- current_sample_size - current_sample_size <- current_sample_size + sample_increase_step - } - - return(p.value.df) -} -``` - -```{r, echo=FALSE, message=FALSE, warning=FALSE} -set.seed(301) -answer2.1 <- - incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50) - -sequential_pvalue <- - answer2.1 %>% - ggplot() + - geom_line(aes(x = inc_sample_size, y = p_value)) + - theme( - text = element_text(size = 18), - plot.title = element_text(face = "bold"), - axis.title = element_text(face = "bold") - ) + - geom_point(aes(x = inc_sample_size, y = p_value)) + - ggtitle("Evolution of p-values in Experiment 1") + - ylab("p-value") + - xlab("Sample Size") + - geom_hline( - yintercept = 0.05, - colour = "red", - linetype = "twodash" - ) + - coord_cartesian(ylim = c(0, 1)) + - scale_y_continuous(breaks = seq(0, 1, by = 0.05)) -``` - -```{r, echo=FALSE, message=FALSE, warning=FALSE} -options(repr.plot.width = 15, repr.plot.height = 9) -sequential_pvalue -``` - -### Interpretation - -The organizers would have made a mistake if they stopped the experiment the first time the p-value dropped below 0.05! - -- changing the website is costly and may not really increase the size of the donations as expected - -But, how do we know if this mistake was not due to *randomness* - -- the test was planned so that the probability to falsely rejecting $H_0$ is 5% - -### Simulation - -To know if this mistake occurs only 5% of the times, we need to run *many* of these experiments!! - -The figure below shows the p-value trajectory of 100 experiments. We that the p-values of more than 5% of the experiments is below the significance level. - -+++ - -
    - -
    - -## Conclusion - -- One may be tempted to peek at results of A/B tests as data are being collected - -- Stopping an experiment and rejecting $H_0$ as soon as the $p$-value is below the specified significance level can drastically inflate the type I error rate - -- Controlling the risk of wrongly rejecting the null hypothesis is not an easy task in A/B testing if peeking and early stops are allowed - -**Interesting note**: -> It can be proved, mathematically, that under the null hypothesis, the classical $p$-value will *always* cross $\alpha$ if the experimenter waits long enough$^{*}$. This means that with increasing data, the probability of falsely rejecting a true $H_0$ approaches to 1! - -[*] David Siegmund. 1985. Sequential analysis: tests and confidence intervals. -Springer. diff --git a/stat301book/_build/html/_sources/w02d2_abPeeking.ipynb b/stat301book/_build/html/_sources/w02d2_abPeeking.ipynb deleted file mode 100644 index 403ef86..0000000 --- a/stat301book/_build/html/_sources/w02d2_abPeeking.ipynb +++ /dev/null @@ -1,6447 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a02bfa12010e4d4f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Principled Peeking in A/B Testing\n", - "\n", - "\n", - "\n", - "
    \n", - "\n", - "
    \n", - "\n", - "Image from [Unsplash](https://unsplash.com/photos/F85pIdl7cS8) by Pawel Szvmanski and [Unlocking Peeking in AB-Tests](https://towardsdatascience.com/unlocking-peeking-in-ab-tests-7847b9c2f6bb) by Dennis Meisner" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Overview\n", - "\n", - "In the previous chapter we examined the early peeking problem using a simulation study. We noticed that although classical tests can be used in A/B testing problems, those tests are designed for a fixed sample size. Analyzing a sample smaller than planned and stopping the experiment based on early results seriously inflates the type I error rate of the experiment. The goal of this chapter is to introduce some basic techniques of principled peeking. \n", - "\n", - "```{important}\n", - "Stopping an experiment and rejecting $H_0$ as soon as an observed $p$-value is below the specified significance level can drastically inflate the type I error rate\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d34b54f2891270b6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Sequential testing \n", - "\n", - "**Sequential tests** are decision rules that allows users to test data sequentially as data come in:\n", - "\n", - "- The experiment may be stopped earlier, meaning the sample size is dynamic, rather than fixed\n", - "\n", - "- Many tests (a.k.a., multiple comparisons) are performed (sequentially) \n", - "\n", - "```{caution}\n", - "If you make lots of comparisons, the error rates are inflated!!\n", - "```\n", - "\n", - "There are different classes of sequential approaches: \n", - "\n", - "```{admonition} Definition\n", - ":class: tip\n", - "**Group sequential designs**: the analyst *pre-specifies* when to inspect the data (interim analysis) and performs each analysis as a fixed sample one. \n", - "\n", - "**Full sequential designs**: the analyst performs an analysis after every new observation, sequentially, in a principled way.\n", - "```\n", - "\n", - "New platforms allow users to test data sequentially as data come in: \n", - "\n", - "- Users *are* monitoring results as they collect data and are making decisions accordingly\n", - "\n", - "- Users need to adaptively determine the sample size of the experiments \n", - "\n", - "- There are large opportunity costs associated with longer experiments\n", - "\n", - "- When done correctly, stopping an experiment earlier (or re-designing it) can be beneficial" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Principled Peeking \n", - "\n", - "Can we control the risk of wrongly rejecting the null hypothesis in A/B testing when early peeking and stopping is desired?\n", - "\n", - "Many methods have been proposed to address this problem in the context of **A/B testing experimental designs**: \n", - "\n", - "- A basic way to control the type I error rate inflation in multiple testing problems is to adjust the p-value. For example, p-values (or critical values) can be adjusted using a Bonferroni corection.\n", - "\n", - "- Some new methods propose using a different test statistics and computing $p$-values differently. For example, in the *Optimizely platform*, a mixture sequential probability ratio test (mSPRT) is performed and *always valid* $p$-values are constructed. \n", - " \n", - " \n", - "```{important}\n", - "With a principled peeking approach, the significance level of each interim analysis is set at a level that controls the Type I error rate, even if the experiment is stopped earlier.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-bf767b35df5480d2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Bonferroni\n", - "\n", - "Although originally designed for independent tests, Bonferroni correction can be used to control the type I error rate in A/B testing\n", - "\n", - "**Bonferroni's correction** can be thought as: \n", - "\n", - "- an adjustment of the $p$-values, multiplying them by the number of comparisons, and keeping the significance level at a desired threshold, or \n", - "\n", - "- an adjustment of the significance threshold $\\alpha$, dividing it by the number of comparisons, and using raw p-values, or\n", - "\n", - "- (an adjustment of) the critical value, computed with a sampling distribution, corresponding to the adjusted significance threshold" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "remove_input", - "remove_output" - ] - }, - "outputs": [ - { - "data": { - "application/pdf": 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aEf495Ge8vALLxmlwi7EmBDGmzgQAo6nZrgacz9tFno2bGMcYeTnNJeaVS6IvGS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\nMCBSCj4+CnN0YXJ0eHJlZgoxMzM3OAolJUVPRgo=", 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L/gmr408H694sm8b6hpGifZv7cubYwSzxi4QxhlLv8AcQqmSxJCgnmua/aS/wCC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\nCc37TcPxzbUNZHi2LT/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M9n/OgCCip/ts//AD2f86Pts/8Az2f86AIKKn+2z/8APZ/zo+2z/wDP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\nG0pYLp725stRk8MahMJVE8flSWwt0hIL7cNxhfmNfSXjz4N/Hb9o/wCAfxm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R3dSAAAQLegySgAgMqMHj2a7R3k3bt3EREREgNVnzx5cu7cuWx53LhxZmZmFEKE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fuvVvNTJpkvajRwVVPnPkyP8Ub/b2db6D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\nvLxQEwIAAEFBCFDf4uPjJ0+eXFxczPW4urouXbqUYiQAaGq0t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ggplot(t_dens_data, aes(x = t_x_axis)) + \n", - "geom_line( aes( y = y1, colour = 'H0 is true' ), size = 1.2 ) + \n", - "geom_area( aes( y = y1, x = ifelse(x > crit_unadj, t_x_axis, NA)), fill = 'black',alpha=0.3) + \n", - "geom_area( aes( y = y1, x = ifelse(x > t_stat, t_x_axis, NA)), fill = 'red',alpha=0.3) + \n", - "geom_area( aes( y = y1, x = ifelse(x > crit_bonferroni, t_x_axis, NA)), fill = 'blue',alpha=0.6) + \n", - "theme(text = element_text(size = 18),\n", - " legend.title = element_blank() ) +\n", - "labs(x = '', y = '' ) + \n", - "geom_point(y=0,x=t_stat,size = 3,shape=19,aes(color = \"t-stat\"))+\n", - "geom_point(y=0,x=crit_unadj,size = 3,shape=19,aes(color = \"unadj critical val\"))+\n", - "geom_point(y=0,x=crit_bonferroni,size = 3,shape=1,stroke = 2, aes(color = \"bonferroni critical val\"))+\n", - "scale_colour_manual( breaks = c(\"H0 is true\", \"t-stat\",\"unadj critical val\",\"bonferroni critical val\"), \n", - " values = c(\"black\", \"#f94f21\",\"black\",\"blue\"),\n", - " guide = guide_legend(override.aes = list(\n", - " linetype =c(\"solid\",rep(\"blank\",3)),\n", - " shape = c(NA, rep(16, 2),1))))+\n", - "scale_x_continuous(breaks = c(0))+\n", - "ggtitle(\"Bonferroni: statistical significance adjustment\") " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove_input" - ] - }, - "outputs": [], - "source": [ - "test_plot" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Interpretation\n", - "\n", - "You can compare the **observed t-statistic** with the critical value obtained from the sampling distribuiton to test the null hypothesis:\n", - "\n", - "- **Unadjusted test**: Since the test-statistic (red dot) is greater than the (unadjusted) critical value (black dot), we have evidence to reject the null hypothesis.\n", - "\n", - "- **Bonferroni adjusted test**: Since the test-statistic (red dot) is smaller than the Bonferroni adjusted critical value (blue dot), we do *not* have enough evidence to reject the null hypothesis.\n", - "\n", - "Alternatively, you can compare the **$p$-value** with the pre-set significance level to test the null hypothesis:\n", - "\n", - "- **Unadjusted test**: Since the $p$-value (red area) is smaller than the (unadjusted) significance level (grey area), we have evidence to reject the null hypothesis.\n", - "\n", - "- **Bonferroni adjusted test**: Since the $p$-value (red area) is larger than the Bonferroni adjusted significance level (blue area), we do *not* have enough evidence to reject the null hypothesis.\n", - "\n", - "```{hint}\n", - "Note that you can also adjust the $p$-value of a test using a Bonferroni correction!\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "remove_input" - ] - }, - "outputs": [ - { - "data": { - "application/pdf": 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mVnzcp7arOyVm0PruiUnS9vbYFESKZLWzbFebx5Wnj6U3N2qMKjjcssLE6BgYGz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\n59i4m+wxNqhyUU2mRmqidq4ZCUUESEFsuFdU4DuqztTgT0SBK1C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IChUTMhPH78uBDCx8dHo9Hor+/UqZOyNTMz86UnUQaLmpubT5w4sWTCBAAAAADD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\nPy4ujm4wAIAaDm9AAdTnwYMHvXv3jomJ4XoYhvn++++/+eYbi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"plot without title" - ] - }, - "metadata": { - "application/pdf": { - "height": 300, - "width": 600 - }, - "image/jpeg": { - "height": 300, - "width": 600 - }, - "image/png": { - "height": 300, - "width": 600 - }, - "image/svg+xml": { - "height": 300, - "isolated": true, - "width": 600 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "options(repr.plot.width = 10, repr.plot.height = 5) # Adjust these numbers so the plot looks good in your desktop.\n", - "options(warn=-1)\n", - "\n", - "t_x_axis <- seq(-4, 4, 0.1)\n", - "crit_unadj <- qt(1 - 0.1, 1998)\n", - "crit_bonferroni <- qt(1 - 0.01, 1998)\n", - "t_stat <- 1.8\n", - "adj_t_stat <- qt(1-(1-pt(1.8,1998))*10,1998)\n", - "\n", - "t_dens_data <- data.frame( x = t_x_axis, \n", - " y1 = dt(t_x_axis,1998))\n", - "\n", - "test_plot <- ggplot(t_dens_data, aes(x = t_x_axis)) + \n", - "geom_line( aes( y = y1, colour = 'H0 is true' ), size = 1.2 ) + \n", - "geom_area( aes( y = y1, x = ifelse(x > crit_unadj, t_x_axis, NA)), fill = 'black',alpha=0.3) + \n", - "geom_area( aes( y = y1, x = ifelse(x > adj_t_stat, t_x_axis, NA)), fill = 'red',alpha=0.3) + \n", - "geom_area( aes( y = y1, x = ifelse(x > t_stat, t_x_axis, NA)), fill = 'red',alpha=0.6) + \n", - "theme(text = element_text(size = 18),\n", - " legend.title = element_blank() ) +\n", - "labs(x = '', y = '' ) + \n", - "geom_point(y=0,x=t_stat,size = 3,shape=19,aes(color = \"t-stat\"))+\n", - "geom_point(y=0,x=crit_unadj,size = 3,shape=19,aes(color = \"unadj critical val\"))+\n", - "geom_point(y=0,x=adj_t_stat,size = 3,shape=1,stroke = 2, aes(color = \"bonferroni t-stat\"))+\n", - "scale_colour_manual( breaks = c(\"H0 is true\", \"t-stat\",\"unadj critical val\",\"bonferroni t-stat\"), \n", - " values = c(\"black\", \"#f94f21\",\"black\",\"#f94f21\"),\n", - " guide = guide_legend(override.aes = list(\n", - " linetype =c(\"solid\",rep(\"blank\",3)),\n", - " shape = c(NA, rep(16, 2),1))))+\n", - "scale_x_continuous(breaks = c(0)) +\n", - "ggtitle(\"Bonferroni: p-value adjustment\") \n", - "test_plot" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Since the *adjusted* $p$-value (light red area) is larger than the significance level (grey area), we do *not* have enough evidence to reject the null hypothesis.\n", - "\n", - "\n", - "```{caution}\n", - "The solid red area correspond to the unadjusted $p$-value.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### A/A Testing (again)\n", - "\n", - "To examine if the Bonferroni's correction effectively controls the type I error rate, we will use again the **A/A experimental design** \n", - "\n", - "```{important}\n", - "We know that the (true) effect size is 0 in an A/A experiment!\n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-08411ec27d5e8325", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "remove_input" - ] - }, - "outputs": [], - "source": [ - "options(warn=-1)\n", - "\n", - "# Two-sample t-test with tracking sequential statistic and p-values by incremental sample sizes until getting to n in each group.\n", - "\n", - "# @param n (numeric): Initially planned sample size for each group (for simplicity, n needs to be a multiple of sample_increase_step).\n", - "# @param d_0 (numeric): effect size.\n", - "# @param mean_current (numeric): Population mean for control variation.\n", - "# @param sd_current (numeric): Population standard deviation for current variation.\n", - "# @param sd_new (numeric): Population standard deviation for new variation.\n", - "# @param sample_increase_step (numeric): Sample size increment.\n", - "\n", - "# @return p.value.df: A tibble that has 3 columns:\n", - "# inc_sample_size, statistic, and p_value \n", - "\n", - "incremental_t_test <- function(n, d_0, mean_current, sd_current, sd_new, sample_increase_step) {\n", - " sample_current <- rnorm(n, mean = mean_current, sd = sd_current)\n", - " sample_new <- rnorm(n, mean = mean_current + d_0, sd = sd_new)\n", - "\n", - " p.value.df <- tibble(\n", - " inc_sample_size = rep(0, n / sample_increase_step),\n", - " statistic = rep(0, n / sample_increase_step),\n", - " p_value = rep(0, n / sample_increase_step)\n", - " )\n", - "\n", - " current_sample_size <- sample_increase_step\n", - " \n", - " for (i in 1:nrow(p.value.df))\n", - " {\n", - " t_test_results <- t.test(sample_new[1:current_sample_size], sample_current[1:current_sample_size],\n", - " var.equal = TRUE,\n", - " alternative = \"greater\" \n", - " )\n", - " p.value.df[i, \"statistic\"] <- as_tibble(t_test_results$statistic)\n", - " p.value.df[i, \"p_value\"] <- as_tibble(t_test_results$p.value)\n", - " p.value.df[i, \"inc_sample_size\"] <- current_sample_size\n", - " current_sample_size <- current_sample_size + sample_increase_step\n", - " }\n", - "\n", - " return(p.value.df)\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "remove_input" - ] - }, - "outputs": [ - { - "data": { - "application/pdf": 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vW8ekjE/7RXOhZYvm0oYMquLRXcDUN1ax6qy9OnO5LIH59bWOMoFgdfiPpMN9h9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S6SLzSrC/mh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\n3/Oj+0r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8dv48isG1O+uIxcyvdtb2EKXLsxDuy/bbhiN4Dl/m5AI73/AIKIfsgf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X4pfxJpmiW1s+pQomoXtpbKsk8iFtrTMoyx2tgFvTFJ+z14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9uyZcvevXvPnj17586dgoICmUzm4eERGhoaGRk5ePDgPn368If7ry26d+9+9OjR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\n+LdLGY2kQwefW7fK1ZD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TJxs3btS5cWlp6dGjRysqKgghxcXFJgwR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"height": 300, - "width": 600 - }, - "image/jpeg": { - "height": 300, - "width": 600 - }, - "image/png": { - "height": 300, - "width": 600 - }, - "image/svg+xml": { - "height": 300, - "isolated": true, - "width": 600 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "options(repr.plot.width = 10, repr.plot.height = 5) # Adjust these numbers so the plot looks good in your desktop.\n", - "\n", - "set.seed(301)\n", - "answer2.1 <- \n", - " incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50)\n", - "\n", - "\n", - "crit_unadj <- qt(1 - 0.05, 1998)\n", - "crit_bonferroni <- qt(1 - 0.0025, 1998)\n", - "\n", - "sequential_stat <- \n", - " answer2.1 %>%\n", - " ggplot() +\n", - " geom_line(aes(x = inc_sample_size, y = statistic)) +\n", - " geom_point(aes(x = inc_sample_size, y = statistic,colour =\"#f94f21\"), size = 2) +\n", - "# geom_hline(yintercept = crit_pocock, colour = \"red\", linetype = \"twodash\") +\n", - "# geom_point(aes(x = inc_sample_size, y = crit_pocock), colour = \"red\") +\n", - "# geom_text(x=850, y=crit_pocock + 0.15, size=6, label=\"Pocock\",colour = \"red\") +\n", - " geom_hline(yintercept = crit_bonferroni, colour = \"blue\", linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = rep(crit_bonferroni, 20)), colour = \"blue\") +\n", - " geom_text(x=850, y=crit_bonferroni + 0.15, size=6, label=\"Bonferroni\",colour = \"blue\") +\n", - " geom_hline(yintercept = crit_unadj, linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = rep(crit_unadj, 20))) +\n", - " geom_text(x=850, y=crit_unadj + 0.15, size=6, label=\"Unadjusted\") +\n", - " theme(\n", - " text = element_text(size = 18),\n", - " plot.title = element_text(face = \"bold\"),\n", - " axis.title = element_text(face = \"bold\"),\n", - " legend.position=\"none\") +\n", - " ggtitle(\"Critical values in Sequential Designs\") +\n", - " ylab(\"Statistic\") +\n", - " xlab(\"Sample Size\") +\n", - " coord_cartesian(ylim = c(-1, 3)) +\n", - " scale_y_continuous(breaks = seq(-1, 3, by = 0.5))\n", - "sequential_stat" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Interpretations of Bonferroni correction\n", - "\n", - "In *one* A/A testing simulated experiment, using unadjusted quantities:\n", - "\n", - "- you would reject $H_0$: the observed (unadjusted) t-statistic is bigger than the (unadjusted) critical value\n", - "\n", - "Using Bonferroni's adjusted quantities:\n", - "\n", - "- you would fail to reject $H_0$: the observed (unadjusted) t-statistic is smaller than the (adjusted) critical value\n", - "\n", - "```{important}\n", - "The Bonferroni's adjusted critical value is larger than the unadjusted one: the test is *more conservative*! With the adjustment, we erroneously reject $H_0$ less often!! it controls the type I error rate!! \n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-76077581b4638618", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Pocock's boundaries\n", - "\n", - "Other methods can be used to control the type I error rate in A/B testing experiments with early stops. \n", - "\n", - "The **Pocock method** computes a *common* critical value for all interim analyses. \n", - "\n", - "- the Pocock's boundary is not an adjustment of the quantile of a $t$-distribution \n", - "\n", - "- we can easily get the critical values for this design using `gsDesign::gsDesign()`\n", - "\n", - "```{admonition} Remarks\n", - ":class: attention\n", - "\n", - "**Note 1**: `gsDesign()` outputs a full sequential design. You can read more about this package [here](https://keaven.github.io/gsDesign/reference/gsDesign.html)\n", - "\n", - "**Note 2**: a caveat about this package is that two-sample tests are based on $z$-statistics. However, results are nearly equivalent to a $t$-test. More can be read [here](https://keaven.github.io/gsDesign/articles/nNormal.html)\n", - "\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### A/A Testing (again)\n", - "\n", - "To examine if the Pocock method effectively controls the type I error rate, we can use again the **A/A experimental design** \n", - "\n", - "```{important}\n", - "We know that the (true) effect size is 0 in an A/A experiment!\n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b20f739b98c5f734", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "scrolled": true, - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "hide_input", - "remove_output" - ] - }, - "outputs": [], - "source": [ - "# Run this cell to get a Pocock design!\n", - "library(gsDesign)\n", - "\n", - "design_pocock <- gsDesign(k = 20, #number of interim analysis planned\n", - " test.type = 1, # for one-sided tests\n", - " delta = 0, # default effect size\n", - " alpha = 0.05, #type II error rate\n", - " beta = 0.2, # type II error rate\n", - " sfu = 'Pocock')\n", - " \n", - "crit_pocock <- design_pocock$upper$bound" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "remove_input" - ] - }, - "outputs": [ - { - "data": { - "application/pdf": 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cl9203m3bgvVsfoaB0DRUuC2RqH9AtFi6ctWpv2zyyELZ9sk/i+cBWzQyF2EDDi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S6SLzSrC/mh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\nmXs4pKLUviVtpXtqr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S1t7nSvFd14bFtp9xbzSRqftd550hnR1cGN2LNv2BWCuw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\nUAFczrEMjajMVRiOOQPYV01cxrMjLqUwDEDjofYUAVPs8v8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Y2/J2wxDC8feIyc/Lj2v+y7v/AJ4NQBUoq3/Z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\nE8qxefc6JGsUe4gbnPnHCjOScdBQB7HXL61/yE5v+A/+g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CgmJsa32yyIkydPsj/Wq1fPdTCMSpUq\nrVq16gU2ztlUTk7OC2yEV//+/ZkCIQDEx8ezC4ScN9cNGjSo4FsuupjzxRngPiQkxDUDxwnv\n7NmzTHazfv36Bw8epP9OTk6OjY1t3Lhxhw4dWrRo0bRpU9catiLSoUMHdv0trXr16uyPTMax\n4OrXr//vv//Gx8evXbt27969HrZw48aNt956KzExcfHixcxl682+ZXchA4Bq1apxmhTSWrVq\nVXQFQi/PDY4iOkbe8ON154oz5AkTmzCv0MuXL7M/VqtWjVOgBQCCIOLi4gpeIHyBM0Qg6Q9C\nCCEfwgIhFycLyB5UwCc4b7EvNuyqPwDg7ayVrwMHDmzatOnMmTN3797V6/XFMCBE586dNRoN\ncxR2797NNOWiKOqPP/5glixfvjxv49vijzlfz58/Z3/cvXs373g5bOy84IQJE5gMGQDY7fbE\nxEQ6C0uSZKNGjdq2bduzZ0/O29t9jpNrpHEqUl74eUrXrl27du1qNptPnjyZkJBw4sSJ06dP\nc2rvaUuXLlWr1UyvPG/27aNHj9jTOXVZjCpVqhTwV7wAL88NjiI9Rp4J8LpzxekgwLSJEOYV\nyhlm2d35yeml6dkLnCECSX8QQgj5EPYh5AoLC2N/9HlVwCuvvOLbDRYQZ6jGgICAQq2ekpLS\nrl27jh07rl69+sKFC5mZmcWTw5PJZD169GA+3r59m2mQdvr0afbgfgMHDhRIzPniZEMLgt1U\nrFOnTnPnzuV9EZnNZktMTPz6668bN27cpEmTwo5nWyiBgYGuE11rG7whlUrj4uKmTZu2f//+\ntLS0//77b+7cua7lsYULFzItD73Zt5yaEHd1HUVaB+LlucFRDMfIlWCvO46cnBzOGxqYAqEw\nr9ACnp+F6ubwAmeIQNIfhBBCPoQFQi72EGoAkJ6ezsk0eIn9br3ixHnKW6gsmsVief311znv\nO9ZoNFWrVo2NjY2NjXX3rNonOKNQMI/qPbcX9W/MAEDxvSiMxpuX8oxTUz1p0qSkpKQBAwZ4\neOfB6dOnW7RowR50x7de4Fd4+XU1a9acNGnS5cuXp0+fzp5lMpmYs8Kbfcs5ZO425X3xpkjP\nDS+35iW/X3cFd+rUKYvFwnwkCIJ5r4Ywr9CiOD9f7AwRQvqDEELIh7BAyFW5cuWQkBD2lM2b\nNxdkxZ49e44ePfrUqVNFE5e3OE1hC9VxaMWKFefPn2c+hoaG7t69OzU19erVq2fOnDlz5szs\n2bN9FqiLDh06sIOPj4+n/2C/D7pWrVq1atXyV8y8/aDcvVQdXI7FkCFD8h0O+MSJE5yN1K5d\ne+PGjSkpKVu3bh07dmy9evVcM2dGo3Hw4MHZ2dkv+suEiCTJadOmdenShT2RqTf2Zt9yqs31\nej1vAIVqRu6Xc8OP/JtWFMovv/zC/linTh2meYgwr1DO+ekuDfd5NwdepTb9QQihlxIWCHmw\n34cOAN9//32+t7SEhISdO3euWLGiadOmtWrVEuDbeCMjI9kfC1XtyRmLf/r06W+88Qb7BceF\nev94YdFvXmY+njhxIiMj49atW+whQFyHkynSmEWiPBdOcnKy6zKcEUrYOIPdezPQYlBQUO/e\nvb///vtz587pdLoDBw6MGjWKPdxFSkoKu6elYGVlZSUmJq5aterDDz98/fXXPew9Gud1f8wV\n6s2+5TTndjcyh+dRNIVzbviFf9OKgrt58yZ7tCoAYD9iEOYVyuna5y4N93x++tZLk/4ghFAp\nhwVCHmPHjmV/fPjw4UcffeRh+bS0tJEjRzIfL126VAzP7D20OuPFfl0VAFy4cIH9wgbakydP\nXnnllYhczCrsF0+BS6taADh8+HChgiksdqtRq9W6f/9+dntRgiBcOxAWacycR/XsKhGaXq/n\ntJpja9KkCfvj1atXXzgSNpVK9frrr69YsWLbtm3s6devX/fJ9ovUa6+91qxZs1GjRi1evPjQ\noUP5vk7t8ePH7I9M3Y43+5YzwMalS5dcK1soikpISPCwEWGeGy+ssOmM39OKgsjOzh4yZIjR\naGSmyGQydrIvzCu0atWq7I+XL1927euY7/lZdEp0+oMQQqUcjjLKo3Hjxu3atWNnXFasWGG1\nWhcuXOjaX//KlSv9+/dnjz0jFou/+uqrog6St+bBg5o1a0ZHRzNjjZrN5vXr17/zzjvsZXbs\n2MEepoV5wQOn2RsnF/LPP/9wXpfs4U3ZL6Zt27ahoaHMm8Hi4+MfPHjAzG3WrFn58uU5qxRp\nzJzq1nXr1n322WfsqqHZs2d7qFWuWbPmK6+8wowZ+ODBg4SEhLi4OPYy58+fX7lyZXh4eJky\nZcLDwxs2bBgVFQUASUlJ//zzz9WrV69duxYcHMxp9kZjv5kDfNHnrRj06tXr4sWLzMcff/yx\nXbt2PXv25F34+vXra9euZU9p2LAh/Yc3+7ZmzZqBgYFMe06r1bp27dr333+fve6uXbvY554r\nP54bRaGw6Yzf04p83b9/f/DgwZy2/aNGjWLXDwvzCq1du3ZAQADTktlms61Zs2bChAnsZXbs\n2OH5/PTSy5r+IIRQKYcFQn6rV6+uU6cOu6vP6tWr//jjj379+rVq1YrOOty7d2/v3r2bN2+2\nWq3sdSdNmlSjRg2fhySTydgfL1++fP369YKPgC8Sid59993JkyczUyZPntyyZUumVuTixYvM\ne5ZpTLVnREQEexz2n3/+uUuXLvRoBKdOnerTpw+nGsG3w/AAgFgs7tOnz/Lly+mP8fHx7P4z\nvK8fLNKYOe8OuXLlyrhx4+bPn69QKEwm06JFi7799luVSuVaB0sTiUTvvffetGnTmCmjR49O\nSEhgmoTl5OSMGzfu+PHj9EeSJJlB7Xfu3Pnll1/SfxME0atXL9dS0/r169kfi+Js9LmxY8d+\n9913zB6z2Wy9e/fu27fv0KFD69evHxoaSpKkTqe7du1afHz80qVL2XV3YWFh7du3p//2Zt9K\nJJIuXbps2LCBWXfKlCktW7asU6cO/fHatWtjxozx/EP8eG74hJfpjN/TCkZmZib7OZ1er797\n9+6ePXvWrVvHKbVWr16deW0JTZhXqEwm69Sp05YtW5gp06ZNa9asWbNmzeiPly5dyvf89NLL\nmv4ghFAphwVCflFRURs2bOjbty8765CcnLxs2bJly5Z5WLFTp04zZ84sipAiIiLEYjFT+LTZ\nbC1btuzbt69YLK5bt+7w4cPz3cL48eN//vnna9eu0R/T0tIaNmzYu3fv6OjoGzdubN++nT3m\nXt++fVu1akX/HRcXd+HCBWbW1q1bmzRp0rhx4zt37hw4cMBqtUZHR1erVm3//v30AseOHZs7\nd2716tUbN27M6ffywvr3788UCNnvoyNJsl+/fq7LF2nMPXv2HDduHHt3LVu27JdffomMjHz0\n6BFdWJ0wYYKHM+GDDz743//+x1TYXrt2rXbt2kOGDClfvvyTJ082bdrEziiPGjWKyZGPHDly\n7ty5dHGCoqg+ffp069atefPmQUFBAJCSknL8+PF9+/Yx65YpU6Zr166ef44QhIWFLVmyZMSI\nEcwUiqI2b95ckCGd5s6dK5fLmY8vvG8BYNKkSRs3bmQKLRkZGU2aNOnVq1dMTMydO3e2bt2a\nk5MTGRnpoRLGj+eGT3iZzgghraBt3Lhx48aN+S4WEhKybds219fwCPMKnThxIrtAaDAYWrZs\n2aFDh5iYGPoBpcViiYqKun//fgE3WFgva/qDEEKlXb6Dp5Vmf//9N+e1hJ4NGjTIZDJxNsLJ\n+c2bN8/d1+W7ZJs2bXi/9+OPPy7gFi5cuBAeHp7vD2nQoIFOp2PWun79OqfegE0sFh89enTh\nwoWus44cOVKoPeCBzWaLiIhw/YqOHTvyLl/UMXNaanGMGTOG01dq+vTpnC2cPHmyIG8Ma9q0\nqcFgYK/4v//9L9+1aFKpdO/evQXfyZ5/dUGOI2dAi9dee63g305R1LfffssZlCVfrjuW8mLf\nUhTF6ULM8eqrr86bN489ZebMmZwt+PHc8Mkx8iad8WNaMXr06Hz3GEfNmjVv3brlboN+uULz\n3QOcdv4cFSpU+Pbbb9lTOOen92dI0aU/CCGE/AUHlfGkWbNm169fnzRpErv+gVflypW3bNmy\nfv169gBrPjdnzpx8I/Gsdu3af//9N1P150okEg0fPvyvv/5i54QqV668evVq3p+m0Wh2794d\nFxf39ttvM691Lgoikahv376u03nbi0LRxzxnzhx3PdwGDBiwePFizmujXftKNW3a9MSJE7Vr\n13b3FQRBjBgx4uDBg0qlkj19+PDhv/32W3BwsOcIq1WrduDAgU6dOnleTFA++eSTI0eONG3a\ntCAL161b9/Dhw+x2fYwX3rcAsHDhQncnVYUKFfbt2+dam8Thx3PDJ7xJZ4SQVhSEVqudNWvW\nqVOnPLwXUZhX6A8//OA6hhatfPnyu3fv9vxaee+9xOkPQgiVWthkNB9BQUFz586dPHnygQMH\n9u3bd+HCheTk5JSUFIqitFrtq6++2rx5886dO3fo0KEY3gHduHHjv//+e8aMGceOHdPpdIGB\ngeHh4bGxsd26dSv4RipVqpSQkHDgwIFt27YdP3786dOn9KYqVKjQunXr4cOHc8ZapA0aNKhe\nvXrffffdkSNHHj9+rFQqK1So0LVr1zFjxtANvYKCghISEiZNmvTPP/9kZGRERka2atWqcuXK\nPvvxAP379//hhx/YU+RyOfuNFMUZs0Qi2b59+2+//bZmzZrz58+npaUFBQU1adJk1KhRPXr0\nAADO2yx5X2pXq1atpKSk7du379y5MzEx8enTp9nZ2fSxiIuLGzFiBO+xoHdF165dN23atH//\n/kuXLj169Eiv14tEIrVaHR0d3aBBg549e3bs2JE93H9J0apVq5MnTyYlJe3cufPff/+9cuVK\nSkqKXq8nCEKtVmu12ho1atSvX7979+6c3nocL7xvxWLx+vXr+/Tps2rVqnPnzqWlpYWHh1et\nWnXgwIEDBw5UKpVHjx71/BP8e254z8t0RghphSuSJENCQsqUKRMbG9uhQ4cuXboUpPZPgFeo\nRCLZsGHDm2++uXLlynPnzqWmpoaFhVWpUmXAgAGDBg0KCAg4ePAge/miSARe4vQHIYRKJ4Iq\n5KjiCCFUmv3www/jxo1jPs6cOXPKlCl+jAchtlmzZk2dOpX5uHz5cs+tTBFCCCFsMooQQgi9\nJDg9AMuVK+evSBBCCJUU2KIDIYQQKgESEhJOnTp19+7de/fu3b17d8qUKZz+hCaTiRnBFQBI\nkmzRokWxh4kQQqiEwQIhQgghVAIkJCQwrwEEgKlTp7Zv3549FPZnn32WmprKfOzYsWNgYGCx\nhogQQqgEwgIhQgghVAKMGjVqzpw5zNtxb926VaNGjREjRlSqVOnx48eHDx8+duwYszBJktOn\nT/dTpAghhEoSLBAihBBCJcArr7yybNmy4cOHM6PBpaSkcF48yFi0aFHDhg2LMTqEEEIlFQ4q\ngxBCCJUMb7/99ubNmyMiIjwsExYWtnXr1rFjxxZbVAghhEo0rCFECCGESow+ffp06tRp8+bN\n8fHxSUlJT58+zcnJUavV4eHhDRs27NChQ//+/eVyub/DRAghVGLgewgRQgghhBBCqJTCJqMI\nIYQQQgghVEphgRAhhBBCCCGESiksECKEEEIIIYRQKYUFQoQQQgghhBAqpbBAiBBCCCGEEEKl\nFBYIEUIIIYQQQqiUwgIhQgghhBBCCJVSWCBECCGEEEIIoVIKC4QIIYQQQgghVEqJ/R2AsKSk\npPg7hFItMDBQr9fbbDZ/B1LaKZVKpVKZmZlpNpv9HUtpJ5VKpVKpXq/3dyAIQkNDLRaLTqfz\ndyAItFptRkYGRVH+DqS0U6lUCoVCp9NZLBZ/x1LayWQykiSzs7P9HUhpJxKJgoODzWZzZmam\nv2PJgyRJrVbrbi7WECKEEEIIIYRQKYUFQoQQQgghhBAqpbBAiBBCCCGEEEKlFBYIEUIIIYQQ\nQqiUwgIhQgghhBBCCJVSWCBECCGEEEIIoVIKC4QIIYQQQgghVEphgRAhhBBCCCGESiksECKE\nEEIIIYRQKYUFQoQQQgghhBAqpbBAiBBCCCGEEEKlFBYIEUIIIYQQQqiUEvs7ALHvJIwAACAA\nSURBVLcuXrwYHx9/9erVzMxMmUwWHR3dqlWrTp06kSSZ77rPnz/fsmXLuXPnUlNTFQpF5cqV\nu3fv3qBBg2IIGyGEEEIIIYRKCoEWCDdu3Lhx40YACAoKqlSpUkZGxpUrV65cuXLs2LEZM2ZI\npVIP6968eXPq1KkGgyEwMLB69eo6ne7s2bNnz579v//7v169ehXXL0AIIYQQQgghoRNigfDC\nhQsbN24UiUTjxo1r27YtQRAAkJSUNHv27MuXL//xxx99+vRxt67NZluwYIHBYOjateuIESPo\n6sSkpKSZM2f+8ssvdevWrVixYvH9EoQQQgghVPqUKRNstzt7ZiUnp/gxGB9q1Cjw7l0JABAE\n9fx5qr/DQb4hxD6EBw8eBIDOnTu3a9eOLg0CQN26dbt27QoA//zzj4d1T58+/fDhw3Llyo0a\nNYppXFq3bt1evXrZ7fYdO3YUcewIIYQQQqg4hIWFev4XHh6yapUf8rrt2mnYpUGEBE6IJ2u/\nfv2+/PJL1+adkZGRAGA2mz2sm5iYCABxcXFMSZLWunVreq7dbvdttAghhBBCSIAoipg8OTgq\nSlvM33vxooT+gyCoY8dSXprqQQB4661sgqAIglKpMEf98hBik9GoqKioqCjX6Tdv3gSASpUq\neVj31q1bAFClShXO9HLlyimVyuzs7MePH7/66qu+CxYhhBBCCPlTnTrO2gKDgbh/n7RaCYpy\n1A3k5JApKRAa6ofABg7MqVbND99bdD74wPLBB9hS9GUjxAKhK4PBEB8fHx8fr1ar+/Xr52HJ\np0+fAkBYWJjrrLCwsHv37mGBECGEEELoZXL4cCbv9LAwRymwZs3gZ8/SijEih969s4v/SxEq\nLEEXCFNTU6dMmWIymdLT06VSacuWLd9+++1Q9094bDYb3aBUoVC4zqUn5uTksCfOmTOHaUTa\noEEDumUp8heSJJVKJUVR/g6ktBOLxQAgl8s9j+iLioFIJCJJMiAgwN+BIAAAPBYCIRKJVCqV\nv6NAjpuFQqGQyWT+jcTdhUkQFF1PSFGE6zJKpYypRWSvkp1tYk9p3lx07pyUmeW6llZre/zY\nQv+tUMjZs/r2deRac3KMzESVSuraw1Astmdl5ekVVacOeeOGY/iWMmVsT5+KmU25n+XcbFH8\nOgCoWZO8fVvCuykEAHSfNbFYXLJuFoIuENpstkePHtF/i0SinJycJ0+eeCgQMt0LJRKJ61x6\nosmU59zdsWOH1Wql/yZJslOnTj6JHL0wv99UEANLg8JRkPevomIgEonkcnn+y6GihwdCOIRw\ns8j3fBCJCPYyP/wA48bxL0lRhEIhv3IFmKaeFSrAuXP0nwSnvEdLTydjYsiHDwEACAJ4H2vT\n356SAnyN2AAArFaRUilnj3RRoQLcuOH4XqbIR2/Kw6wi/XX0D8xF4GXojgBvFp5HURF0gTA8\nPHzXrl1ms/np06cnTpzYtm3bmTNnJk6c2LJlS97lmbKExWJxnUtP5JQ3tm3bxtRHqVSq9PR0\nX/4AVEhqtdpgMODAP34nl8sVCoVer+e9lFBxkkgkEokkOxsbHfmfVqu1Wq1ZWVn+DgSBRqPJ\nysrC5iR+p1Ao5HJ5VlYW82y92DlGi+HNv129ChTlWGDz5jyLjBvnHGamb9+cFSuMANC3r/LP\nPx25xBo1qNTUDPrviAgJQACAo6RHENSSJRmDB8PXX5Pz52voZR49csSQmgoAEBISRNezbduW\nTjc+o789PDwIwFGiatvWtGVLNgDUqaN++FBMb18kcn5vWJgMQMl8L/3VAJCenuFhVpH+OgCg\nqAAAut6For8OsREEERQUZLFY9Hq9v2PJQyQSBQYGupsr6AIhTSqV0sPMlC9f/ptvvlmxYkXT\npk156wBFIpFMJjOZTLyZJ4PBAACcRiZly5Zlf0xJeXmGgSqJKIqy2+02m83fgZR2dDYLj4UQ\nkCRJURQeCIHAYyEcNpsNC4R+J5ybxcKFzqaS2dnE1q2Ke/fETNNHkcjesqUzxlq1gpiF6fE/\n6VmbNmUBZNHdDimKWL6cGjnSDgAhIc6NMy/fs9ng009tn36awnRT5N0J7H3Trp2GCYn9vefO\nZaSkQPXqju9lVqhY0flMlv3SP5uNZ5ZMJiNJMjvbVtS/jv3Q3u/HXYBEIhGUwJtFCSgQMpo1\na6ZQKHQ63f37992NNVquXLnbt28/f/6cM04pRVHJyckAgCPKIIQQQgi9TGbOdFv1ERJivXo1\nT0XW8+eORvgiEU+LJKbb4eefa0eO5A6n6c2r2Jl3UWi13ArV0FDn90ZEBD99yh3/5vJlt9/L\nmeWvX4dKNMG9h9But3/33XeTJ09+8OABZxZT2vbQeiomJgYArl27xpl+584do9GoVqvLlCnj\n65ARQgghhJAQpaaKy5ULZk9hqun27+cZd1SpLKp+K8z3Xr/O09IyIoKpguOOBAPg6Z0ZnFn+\n+nWoRBNcgVAkEj169OjSpUtHjx7lzLp27Ro9bAynnSdb8+bNAeDo0aOcitqDBw8CwGuvvcZ5\nYT1CCCGEECrRkpNT2P9ef90ol9vobnUAYDaLwsNDXNeqV49nU/37O4YD5R2i0yfRhoWFuv57\n8kTs7ntfjL9+HSqJBFcgBID27dsDwLZt2w4dOsT0ELh79+6SJUsAoG7duiEhjqt6y5Yty5cv\nP3v2LLNu/fr1Y2Jinj17tmzZMqaL8/Hjx/fu3SuRSHr37l2svwQhhBBCCBWvDRv0Dx6kP3+e\nOnWqjp5CUcS77xboVSUDB+bkv1BRci2YeSiqFbYU5/dfh4RJiH0IO3fufPXq1aNHjy5ZsmTN\nmjURERFZWVnPnj2jKCo8PHwcayTdY8eO3blzJygoqEGDBvQUgiA+/vjjzz///ODBg4mJiZGR\nkWlpaU+fPiUI4v3334+IiPDTb0IIIYQQQsXqgw8ss2ZRuQN+yn/6yZDvKj/9VOSvuKTHevGL\nYvh1qCQSYg0hQRATJkyYPHlyw4YNRSLR7du3dTpdhQoVBg4cuHjx4vDwcM+rlytXbvHixV26\ndFEqldevX8/JyWnWrNm8efPatm1bPPEjhBBCCCEh8FCHlpDAM3H3bmm+K3ppw4Yi2nAe/vp1\nqCQSYg0hrVmzZs2aNfO8zOLFi3mnBwUFjR49evTo0UUQF0IIIYQQKhl4O8vRE998M/jZM+7I\nKyZTUVWWMN/70UfBgwZxvzclBR4+5O/492LfUsy/DpVoeFoghBBCCKGX0JdfypkCYVAQ834/\nxxgTdjs3G3z1qrMAeeCAj9/B0Ly5mfle1/de16gR8vrroWFhoV7WH/rr16ESTbg1hAghhBBC\nCOWrXTsN+2Nysuj5c5HdTrCrB5mXPSQm6pj3rYeFhU6dqvvgAwsAdOyoOXvW2aLS+8o6jh07\nssLDpXRI1auHvvqq9dy5DAAYP165caOCCXXQIK++xV+/DpVoWCBECCGEEEIl2IULUs8LrFmT\np0puy5aUvn0dpaaZMwNnzsyzMEFQRfSK9r/+Sm3Z0vG9Dx+KmZIbo08fH4wC6q9fh0ouLBAi\nhBBCCKGXE0nanz7ldqWLi4Pk5JTw8BDXHoZSqf3RI55XuvtEtWqQnJxSpkywa3tOgqAuX071\n8AL6gvPXr0MlF8G86A8BQIprm25UjAIDA/V6vc1m83cgpZ1SqVQqlZmZmWaz2d+xlHZSqVQq\nler1en8HgiA0NNRiseh0On8HgkCr1WZkZGAGxu9UKpVCodDpdBaLxd+xlHYymYwkyezsbH8H\nUtqJRKLg4GCz2ZyZmenvWPIgSVKr1bqbi4PKIIQQQgghhFAphQVChBBCCCGEECqlsEAoXLt2\nydq3D4qKCmnUSDt/vtJo5DYE96OcHOLrr5UNG2qjokI6dAjasyefztzF7O5dcsQIddWqwVWr\nhgwbprl5k/R3RHkcPizt3DkoOjokNlY7fbpKrxfQkTWbYfFiRd26CqUSWrRQbd4sE1SbrCdP\nRGPHqmvWDI6JCenfX3PxorB6QZ88KenePbBChZD69YM/+ywgPV1AR9Zmg1Wr5K+9po2MDGnR\nQvvLL3K73d8xsaSliSZODKhbN7hChZCePQNPn5b4O6I8zp8X9+sXGBQE0dHiDz4IePZMQLdO\nioLffpPHxQVFRoY0bapdulQhqLZ7mZnE1KmqBg200dEhb7wRePSosI7stWvkkCGaKlVCqlcP\nfucd9YMHwrpZ/PGHMxswd67gsgFz5ihr1pSrVNC2bQDzunOBuHePHDlSXa1acJUqIUOHam7c\nENaR/fNPRzagQQPtV1+psrIEdGTNZliyRNGkiTYyMqR166DffxdWNuDpU9G4cepatYJjYkLe\nfFNz4YKwsgGJiZLu3TUaDVSqJPnkk4C0NAEdWc+wD2EewulDuGGDfPz4APaUHj1Mq1Zl+Sse\nNoqCYcM0e/fmSf1//DGrb1+Tl1v2SR/C5GRRXFxQcrIzxxYURB05kv7qq4LI/+7dKx06NM/o\n2HFxlt9/14mEkcMcPz5gwwY5e8rs2YZ33vHBoGfey8oi2rQJunfPeV9XKKgDBzKqVRNEp9PE\nREm3boHsKfXqWXfvzpB6l03yVR/CGTNU33+vYE+ZMCF78mRB9DYxm4kOHQIvXXLe12Uyavt2\nXaNGVj9Gxbh8WdyxYyA7L16pku3w4QyVShB3z6VLFV99pWJPGTbMOH++IDqd2mzQu3fg33/n\nKQRu3JjZvr23nZN90ofw/n2yTZugzEznkY2IsCckZAQHC+Jm8dtv8nHj8mQDunUzrV4tiGwA\nAAwbpuE8C166NOvNN73NBvhESoooLi7o+XPnbTUwkDpyJD0yUhBHdv9+6ZAhebIBLVtatmzx\nNhvgqz6EH30UsG5dnmzAzJmGd98VRDZAryfatAm6ezdPNmD//ozq1QWRDTh1StK1a55sQJ06\n1r17dVKpIG4WnvsQYoHQyWSCBw8c4/BqNBR9ZVosYDB4Kt8rlRSd4aMo0OkcSyoUIJM5dqxO\nR3jYxxIJMLkKg4Ggn+zabETjxlr2XYq2bl1mkybcZ7+BgRRBAACYzQSTDqjVFEkWMH6gz1R2\n/HI5yOVu4z92TDJ8uCbvZiA4mDp9Oo1ekiRBrXask5NDmDzeIFjxg1gcaDAYbDYbE7/VCp4r\n0Fzj//JLFadIAwA9e5rmzePJIYnFEBDA3f8iEWg0LxJ/drYjVHfxUxTExWmfPOGm+qtXZ3Xr\nZsrI4Nn/mZmEh5oc7+NnTnWzGU6flvTqFchZQCajEhPTmW/hYJ/qXsafnU3QQ9iw4zcaCaPR\nsfDixcofflBwttC2rXnTpkzIe6oHBFBiMUABzh/e+GUyUCgKFD/7VG/VKujKFe6jyq+/NvTr\nZ3RZz4k3qWHit9nAZJJ5KBDyJjXs+LOyCJsNHj4UtWnDvQ2QJBw7ll65suM+yux/goDAQJ79\nz4s3qVGpKInEEb/nJ9/0qbJqlWLyZBVnVr161oMHM+j43eFNatjxm0xEjsdsTEHiHzZMwynS\nAMBHH2WPGZPDm1RKpaBU5tn/7ri7VIOCChq/3Q61a4e4jv109GhGzZpWux1c7yNsvJcqO369\nnrC6L5XnG/+mTfKxYwM4a736qv3PP9PpZJP3UmXuqh7iDwoK0ul0MhnlTfwffqh2rddiF6fZ\nd1VeLxw/jTepoXMFZjPUqBHC3JQZv/6a2ayZIxvg7lJl9n/B42dfqry5Go4TJyRvv83NBgQG\nUomJaQqFM1fjef+7i583V8OLN6lRKuHLL1WrVnGzAd26mRYsyJOQuiaV4CZXlm/87EuVN1fA\noCho3Vr7+DE3G7B4cdYbb5jBTa6MFyepEYtlJEmazdmuuYKCx3/5srhHD55swMmT6Uxiy2D2\nP/tUZ+IH1l013/iZU4U3V8P4/nvFkiVKznbatLGsXZvJ7H92rsZ1/7PxXqq8uYICxt+jR+Dl\ny9xswFdfGQYP5r+P8l6qRRe/REJGRwe53RyFci1dSgE4/p0+nZacnJycnLxunY6ZyPvvhx8y\n6SWvXUthJn7+uSE5l0Jh97D666+bmCXj4sz0xEqVrJ6/lP3v7t0UevUFC7KYiSdOOOLftCmf\n+BctyqKXvH3bGf+kSc74NRpP8bP/NWvmiL9KFSuz+tChOZ7XunHDEf/33zvjT0hIpydu3Zrh\nefV58xzxP3iQ4nlJ3n8tWpiZUDt2NNETK1Rwxj98eD7xX7mSSi+5bFkmM/HQIUf8O3fmEz/9\n74MPsh8/TmY+jhuXzQQQHm7zsGKTJs74u3Z1xB8ZaWMmvvNOtuevvnDBEf/KlZmel+T9N2OG\nnvkuZuK77zrjL1fOU/yxsRZmyZ49jfTEV15xxv/++/nEHxRkp5dcs8Z5qsfHZ9ATDxxI97z6\ntGnO+CUSx8SRI53xR0d7uhjr1nXEzz587H81a+ZzLfMmNdu3O+I/ciSf+HmTmrffzmEmxsR4\nCiAy0nmqDxyYw9mlycnJEycaPAfAm9T8/rsj/r/+SvO8Op3UDBhgdJ1FktTTp8nVqnmKn53U\nvPWWI36Vyhn/p5/mEz+T1GzZ4rxUN2zQ0RNPnswn/o8+cu7/4GDH/u/f38hMrFXL4mF13qRG\nKnXG/8UXes8BLFyYxTt98eKs5OTkM2fyiZ83qend2xl//fqe4mefP0xSQ5IUM7F5c7PnAPbt\nc+z/PXuc+3/1asdd9fz5VM+rv/ceT1LTvbsz/kaNPAUgFvNPf/bMsfrs2fnsf96kZvlyR/yX\nLuUTP29S88YbpuTk5BMn8jl2AFRoqDOp/PBDZ1L56JHjrjpnDv/pwfxjkpo//3TGz8rV5BM/\n7z92rqZVK0/7n53UfPyx81JlcjXffZdP/LxJzaJFWZ6PO/Nv8GBnUskkNW3aOO+qbduaPKzu\nLqlhcjVLluQTP+8/Jldz/34+uZoBA3iSmpYteXI1vP9kskIkNbz/fv3VkVSyk5pvvnHE7+62\nyPzr04cnqWnalCdX4/mfVmv/6itn/BcvFjRXw5vUzJzJk6vh/VfwpMbdPyapmTXLGf/Zs474\n16zJJ34mqfnvP2f87FwNJ4krX97moRAkrKa3/hUZCXFxjmcRzEOjkBA7M5FXmTKO6gOJxLl6\nVJTzmXCLFhaz2W0Rv1Yt59OzOnUcfwcH22/d4mnvXrmyrWxZbm0FmbvgK684Q2UeWmi1+cT/\nyiuODYrFzvijo53xv/aahfOI4uFDEW94NWva6GcV5co5V69SxeY5AEnuk/cyZWzt2lFWqxXA\n+XxOq6U8r87sEJHIEX9Sktj1oY5aTTVowPOgsmZNK/tvulVYRIRzJ8fE5Bs/lRu/c1czD9KC\ngvLEb7XCiRM8XWgUCoognPu/QgXnDmzWzJKR4bYdSbVqzvhr1LDSj0jDwpzxV6qUT/wymeOP\nsDB7tWq2q1d5jmxsrNVdDSG7BQ7zRZUqOeNv0sSSmuo2fqZ6CgBq1LClp1sAgN1eq2JFZ/yX\nL5PslsA05lINC3Puao3GsQW1Op/zJyrK+V0tW5ptNgIAYmLY8VvLl3dbRVixomNJsRhIElzr\ngkJC7HFxnhop8SY1Wq1jYkAA1bq1VSQSuWtHzZvUsPdqw4aWcuXsaWkEb39LdmvbqlUdu5rd\nEjI6Op/zhzepCQ5m4gfPq9NJDbMT2GQyiiQhNtbCpLGueJMapsqoIPHzJjXMGahUUnFxlpMn\nxa5peHi4vXp1W4UKztiaN7fQF2DVqs6rMjbWGhLCf+2Am6RGLGbHn08CrtXy7xx6l8rl+Zz/\nvEkNu/FVgwZWpg7QFW9Sw2725u63t2hhoe9czMY1GucvZTYrl7s9f8RisdVqZS5AYCU1NWo4\nJ9arZ1VyqxOczp8Xu9bAaLWO6h0AKFcun/OHN6kJD2dyBfnsf96kpkYNKwAouI0hnKuUK2fn\nfDsAlC/vDJUgqNz48z1/nEkNsyRzxYnFbuN/9EjE2zm/aVMrJ1dDuK/hYCc15cs7Q+XN1fDi\nTWpeecXOu/cCAqjY2DzZgCpVnPufSWpq12bHb7PZ3AbgLqlhcjURETzx22xw/DhPNiA62l6+\nvA1YuRqSzCf9rFIlT1ITFkZA7vlDY3I1vJjcC7CSmufPRVeuFDQbEBrqmMJOapjzk52r4cXO\nwDBJTfXqPLkahrtsQGSkc1cz9ZNhYfmcP7xJDbt7kefVXZOaY8ckrk2Kypa1s2/KbLxJjTy3\nbjs0NJ/4maRGKnXuf06uxm5nN4mnANx2YsEmo3kIpw+ha/MzuZw6diyDTi/868YNsk2bIJMp\nz1Vat6710KEML7fskz6EP/2kmDqV2/zss8+yP/5YEN2levYMdC0THj6cwTwO8KOnT0VNm2o5\nbYyjo22nTqWTAuiQv3mzbMwYNWfiyJE533xj8Es8HMOHq//4Q8aZuHWrrlUrr4b48Ekfwqws\nomHDYE7v9tBQ+7//pjMlOj/6809p//7c5me9e5uWLxdEd6lJkwJ+/pnb/GzlyqyePf3fXcpq\nhcaNgx88yJNDCgig/vknPTTU/92lzp4Vd+zIbaHUurVl82Zv3+Xokz6E33yjXLCAW16cM0c/\nYoTHdtLFpXXrIHbHWgCQy6m//spgF+P95dYtMi6Omw2oU8d6+LC32QCfWLlS8fnn3GzApEnZ\nn3wiiGxA796Bx45xswEHD2bUq+dVNsAnfQifPxc1bszNBkRF2U6fFkQ2YOtW2bvvcrMBI0YY\n58wRRK/pkSPVO3dyswGbN+tatxbESF/4HsIS6ccf9UzbYtrMmQYhlAYBoHJl27RpeVKc4GBq\n6VJBZN0AYNSonDZt8lx7zZpZxo0TxG0AABYv1jMPdWhffJEthNIgAERE2L/9Vs8eBEWtpn76\nKUsItwEA6NfP1Lt3nix4zZrWKVOEcmS//dYQGZnnCn3vvRwvS4O+olZTS5ZkMf0KAEAup5Yu\n1QuhNAgAbduaR47MkwWvUMEmkHI+AEybZuCMWNC/v0kIpUEAEIvhxx+z2DUtUin13Xd6IZQG\nAaBBA+ukSXmu0IgI+4IFQrlZfPxxDmfgog4dzP/3f4IoDQLAsmXcbMD06QYhlAYBoFIl21df\nCTcbMHx4Trt2eXrWNmli+fBDodwsFi3iZgMmT872sjToK+Hh9nnz8mQDAgKon37SCyQb0KeP\niTN+YY0a1ilThHKzmDPHwG62AwCjR+cIpDSYL6whzEM4NYQAkJYm+uUX+fXrZESEvU8fE7sZ\nhhCcPy/esUP27JmoWjXr0KFGpuWJN3xSQwgAFAU7dsiOH5fY7dCsmaVvX5NAxvCkZWYSa9fK\nL10Sh4bau3UzCWQoRcbVq+T27QHPnkmio02DBxs49y2/27tXeuSI1GiEhg2tAwcaJUIaxD4n\nh1i7Vn7hglijsXfqZPZJadBXo4wCwL175Pr1sgcPyKgo25AhRoEMuMc4elSyf79UrxfVrWsd\nMsTIbo7ld2YzbNggv3w5QCazt2yp79DB20EyfevpU9Gvv8rv3CHLlbP372901zzJX06dksTH\nS1NTRbVqWYcONbprf14oPqkhBACbDTZvlp86JSZJaNXK0q2byUMTx+KXlib69Vf5tWtkRIS9\nd28TuzWjECQliXfvDnj+XFypknHwYAPTgFMIKAp27pTRTfiaNrX07WsSSJGGlpVF/Pqr/NIl\ncUiIvXt3c6NGPrhZ+GqUUQC4do38/Xf548eiihVtQ4caPbTb94t9+6R//unIBgwYYPRyKG/f\nyskh1q9XXLmiDAiwtW2r99zms5jhKKOFIKgCYSnkqwIh8pJSqVQqlZmZmWbX4QtR8fJhgRB5\nKTQ01GKx6HTeNndE3vNVgRB5SaVSKRQKnU5nEdTrL0slHxYIkTdEIlFwcLDZbM7MzPR3LHl4\nLhDioDJ5EIJ6Nlha4VEQCIIg8Fj4HX0I8EAIBF4UwoEHQlDwcPgdkcvfgZR2zCEQ2rHwHA/W\nEOZh9fDGHFT0SJK02+14TvqdSCSiR7bEY+F39A3e7uFliKi4iMViiqKwCYMQkCSJB0II8GYh\nHHizEA5h3iwoipK472aDNYR5ZGQIYoCsUgubjAoE3WTUYDBgk1G/wyajwhEaGmq1WrHJqBBo\ntVqdToeFEL+jm4zq9XpsMup32GRUIOgmoxaLpWQ1GRXSUBsIIYQQQgghhIoRFggRQgghhBBC\nqJTCAiFCCCGEEEIIlVJYIEQIIYQQQgihUgoLhAghhBBCCCFUSmGBECGEEEIIIYRKKSwQIoQQ\nQgghhFAphQVChBBCCCGEECqlsECIEEIIIYQQQqUUFggRQgghhBBCqJQS+zsAhBBCCCGEkO9p\nxmqkN6UAQImo1D2p/g4HCRQWCBFCCCGEUCkS2inU02wCgAI7YU/bm1ZcESHkT9hkFCGEEEII\noVwUAICIEoV2CoV9/g4GoaKHNYQIIYQQQqg0MseY2R8JK0E+IQkzQQBBFwtDF4WmdErxT3AI\nFRcsECKEEEIIodIo84dM3unS9VLNWg39t/ZNbfrv6cUYFELFDQuECCGEEEIIOZkHm6l1FEER\nACDScztYhXQJIeyOKkQHAijC7agtIZ1DmCpHGkVQqT+mQnmXJb3bshKU7rbMs26nEAIIAKCA\nSt2H482UatiHECGEEEIIoTwoUW4xy+6cKNoiCu0cSthYZTaCXhoIOxHaKRSu5t3KXQjtFEpQ\nect4AARFhL4bGjAtoBi2zCvkDUdpEACwNIiEW0N4//79bdu2XbhwIT09XSqVRkZGtmzZsmvX\nriRJeljLYrH06dPH3dzx48e3a9euCIJFCCGEEEIvD7p6ECBP7Unw/4IdBTACcl7PMUwwAIB6\nslp2TkYvEPJRSOpeZ/kq9F3HcKYUQZnrmbO+yVItUMkPyOnCmPy0XA96n2zZvMAsniuW7+XZ\nsquQN0IIO0F/V8pe7CGJhFogPH78+IIFC6xWq1KprFChQmZm5vXr169fv56YmDhjxgyJROJu\nRb1eDwAkSVaoUMF1rkajKcKgEUIIIYTQS+A/cBSZAKxhVvqPoKFBTHUcQxuxMQAAIABJREFU\nuxyV9U1WFmTRr7IgKEK0RWTvaweAwGGBzDJMWc4wwWCYYGDee6HtoU3fme79lmUgoyZTqeNT\nOVt2/WXBnYKZusGUtVgaRADCLBCmpKQsXrzYarV269Zt2LBhUqkUAJKSkmbNmnXp0qXdu3f3\n7NnT3boGgwEAQkJCFixYUHwRI4QQQgihku4qaH7QSG5LnNWDALpfdPQfZLKjkZqd3Yo0FyWi\n6DKkdpU2tW8qAIiTHdlsm9LGWdimsImMIgCgJFSRbplD200roms8CUj5MQU8vo4RlR5CLBAe\nPHjQZDLFxMSMHDmSIBwXZN26dbt167Zly5aTJ096KBBmZ2cDgFKpLKZYEUIIIYRQyZTPG+oB\nUuY769CYUmLaTzwvrLdL7KQpT7cmZvn0bdyauvTt6bxL+nzLbIEjAkmLYzsp41MKMvAMKiWE\nWCCsWrXqgAEDKlWqxJQGaTExMQCQlsZzqTDoJqMqlapII0QIIYQQQi8tAiig2H326IlAARDA\nW5QytjWq9qoAgGmQCTxVdG6/rqi2zLCD5JGjy5U5xgydCr8F9PISYoGwQYMGDRo0cJ2emZkJ\nAMHBwR7WpZuMYg0hQgghhBDyzFTfxP5ISSj9cL3bqjOK9b+LnB45dLHtRRTdlnM5C5MA0ptS\nL7eGXjJCLBDyoijq8OHDANC6dWsPi9FNRlUq1a1btxISEh4/fgwAZcuWbdGiRZUqVYolUoQQ\nQgghVAJkfZPlq02ptuWW2QiPywljyyGdQvBtE4hRYgqEW7ZsuXbtWlRUVIcOHTwsRtcQnj17\n9ujRo+zpO3bs6Nix45gxYzjNUH/99VeKcjyQqVq1au3atX0cNyoMkUgkl8vtdp4e1ag4icVi\nAJBKpZ7f8oKKgVgsJklSoVD4OxAEACASifBYCAFBEAqFgrl9I3+hbxYymYz+oyQq3BVNOCrx\nFNcVUJc7U5rgqHajgOJslnd5325ZLBbzJ1Csd2YYphsgAlSjHa1Pg3sG5+zP8RgWKjS6oCHA\nGzenBMRRMi7gLVu2rFu3TqPRTJkyxXMOle5DmJOT079//7Zt24aFhaWnp+/Zs2fHjh379+/X\narWDBg1iL79s2TKr1TGacL9+/Zo2bVp0vwIVhNCun9JMLpf7OwTkUHIzWy8ZkiSxj7pAYN8Q\n4SjRN4vCXdG5xTbVhyo47TI3t/EpQRCOzYocL7VXfaSCU3kXfgPgee7fZ3y2ZYlEwt0yUyAU\ngaqLCgAgCuA+AIDIKFItUsEXBfnlqHAEeLPwXN0i9EyGzWb78ccfDxw4EBwcPH369IiICM/L\nd+vWLS4uTq1WM10Nw8LChg0bplarf/755+3bt/fu3Zudcn399dfMDnr11VezsnzWcgC9AKVS\naTQasYbQ76RSqUwmy8nJYR6XIH8Ri8VisdhoNPo7EARqtdpms9EdE5B/qVQqukEQ8i+ZTCaV\nSrOzs2027ssPBE4NavqPQmX85NFyyR0JAIDdZcWrrG2uyIIsAAB5WbnkIf/yAc8D6E59Npkt\nOyvb+y3TNYRms5mzZZlNJgUpAFBA6bP0AAC/QEC7AMdbFrdD1geY9fUlgiACAgKsVmtOjrBq\nX+nA3M4VcosLg8EwZ86cpKSk6OjoqVOnhoeHv/CmzGbzm2++abfbZ8+e7aFdaEoKvqDTnwID\nA/V6fYm7r7x8lEqlUqnMzMw0m83+jqW0k0qlUqmUbvuA/Cs0NNRiseh0On8HgkCr1WZkZAg5\nA1NKqFQqhUKh0+ksFou/Yykc5m0TKfsKl/Fjv6ZC967O0tMCAJqPNNIrua06iTxjk4Z2DnUM\nFUOAJdqi+0mn/FapOKpg3hvBBODllo1rjOTXpOIQd8uasRp6CBlKRKXu4VmdMx15SSQSBQcH\nm81meixM4SBJUqvVupsrcjfD7wwGw+eff56UlBQbGzt37lxvSoMAIJVKQ0NDIbdNKUIIIYQQ\nQoWSMj+FGdkl8KfA0E6hoZ1D3ZXZACBlXm6BkwLJXUlop1DlESVTZjM2drb+8HLL6jZq5UH+\nLfP/kB8dqxN2IribpwH8UWkg0AKhyWSaMWPGnTt32rZtO2XKlIL3K6MoKi0tzbWKyW630yV1\njUbj41gRQgghhFBpUAtS9qZQBOUc8DO3otousXPfW0gvvy+FEnFrsymgUr5I0c/Q51myiLbM\nqzxYgxy9QkQWkWIxjuBQqgm0D+GyZcuuXLnSvHnz8ePHex4Vh+O99957/Pjxhx9+2LZtW/b0\no0ePGo1GqVRauXJlXweLEEIIIYRKjMK2FOXgKZ55Xr7AbTJfeMsymYwkSddOzpk/uG24mPFb\nRqG+C73EhFhDeP78+SNHjmi12nxLg1u2bFm+fPnZs2eZKa1atQKAVatWJSUlM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khISGhnL/36+++oqrS48ePcrW2Bs3bnSe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\nlcsGWd26dVu/fv3x48ctFgtNi/S+qMep1eqdO3c+8cQTHTt2F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jxo3bvXs3hjWc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\nmwBwl0JDQxsaGux2u9aB6J3FYvHz82tsbPTCP2d6YzabzWZ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repr.plot.height = 5) # Adjust these numbers so the plot looks good in your desktop.\n", - "\n", - "set.seed(301)\n", - "answer2.1 <- \n", - " incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50)\n", - "\n", - "\n", - "crit_unadj <- qt(1 - 0.05, 1998)\n", - "crit_bonferroni <- qt(1 - 0.0025, 1998)\n", - "\n", - "sequential_stat <- \n", - " answer2.1 %>%\n", - " ggplot() +\n", - " geom_line(aes(x = inc_sample_size, y = statistic)) +\n", - " geom_point(aes(x = inc_sample_size, y = statistic,colour =\"#f94f21\"), size = 2) +\n", - " geom_hline(yintercept = crit_pocock, colour = \"#ff33ff\", linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = crit_pocock), colour = \"#ff33ff\") +\n", - " geom_text(x=850, y=crit_pocock + 0.15, size=6, label=\"Pocock\",colour = \"#ff33ff\") +\n", - " geom_hline(yintercept = crit_bonferroni, colour = \"blue\", linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = rep(crit_bonferroni, 20)), colour = \"blue\") +\n", - " geom_text(x=850, y=crit_bonferroni + 0.15, size=6, label=\"Bonferroni\",colour = \"blue\") +\n", - " geom_hline(yintercept = crit_unadj, linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = rep(crit_unadj, 20))) +\n", - " geom_text(x=850, y=crit_unadj + 0.15, size=6, label=\"Unadjusted\") +\n", - " theme(\n", - " text = element_text(size = 18),\n", - " plot.title = element_text(face = \"bold\"),\n", - " axis.title = element_text(face = \"bold\"),\n", - " legend.position=\"none\") +\n", - " ggtitle(\"Critical values in Sequential Designs\") +\n", - " ylab(\"Statistic\") +\n", - " xlab(\"Sample Size\") +\n", - " coord_cartesian(ylim = c(-1, 3)) +\n", - " scale_y_continuous(breaks = seq(-1, 3, by = 0.5))\n", - "sequential_stat " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Interpretations of Pocock correction\n", - "\n", - "In *one* A/A testing simulated experiment, using unadjusted quantities:\n", - "\n", - "- you would reject $H_0$: the observed (unadjusted) t-statistic is bigger than the (unadjusted) critical value\n", - "\n", - "Using Pocock's critical values:\n", - "\n", - "- you would fail to reject $H_0$: the observed (unadjusted) t-statistic is smaller than the (adjusted) critical value\n", - "\n", - "```{important}\n", - "The Bonferroni's adjusted critical value is larger than Pocock's critical values: the Bonferroni test is the *most conservative* test. Pocock method gives some control of the type I error rate!! Bonferroni gives a more conservative control of the type I error rate!! \n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-76077581b4638618", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## O'Brien-Fleming's boundaries\n", - "\n", - "Another method available in `gsDesign` is the **O’Brien-Fleming method**\n", - "\n", - "Unlike previous methods, the O’Brien-Fleming method uses *non-uniform* boundaries\n", - "\n", - "- the test has conservative critical values for earlier interim analyses and less conservative values (closer to the unadjusted critical values) as more data are collected \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "hide_input" - ] - }, - "outputs": [], - "source": [ - "design_of <- gsDesign(k = 20, #number of interim analysis planned\n", - " test.type = 1, # for one-sided tests\n", - " delta = 0, # default effect size\n", - " alpha = 0.05, #type I error rate\n", - " beta = 0.2, # type II error rate\n", - " sfu = 'OF')\n", - " \n", - "crit_of <- design_of$upper$bound" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "remove_input" - ] - }, - "outputs": [ - { - "data": { - "application/pdf": 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5Pn/9D13y76uguyXE2uIy5O5oIXXK+7/tX1Hy7+iAt2u6DJBTJXnMvv4uQu1KO3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qp0UAXPslr/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APC3/AO/YoAp0Vc/tN/8Anhb/APfsUf2m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\nQBN/Zt1/zwf8qP7Nuv8Ang/5VD50n/PRvzo86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Oez/wDfVAENFTfbJ/8Ans//AH1R9sn/AOez/wDfVAENFTfbJ/8Ans//AH1R9sn/AOez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qW9xqHhLRJUxJGSFE8a723bCiiQ8EKq/wivav\n2Ff2KfFvw88RaR8TPGdvHDqUtpKINLvYJvt1juUxqxbcFUtGSCrKSAQPlORX3mcYrDUcsWf0\nMypwlOl7NQjSpSnKahyuKqyh7TldteZKy0VtEctOMnP2Tg977v8ALYi/Z2UH/gpX8ZyQCRpV\n0R7f6RY0eLFDf8FXvBoIBH9lOef+wddV3dj+yB8VvCfx+8Z/FHwt410Oz1DxC08PkX2jSzLF\nbPKjqhAcZYCKMFu+DxzXQeGv2R/Fkvxan+LPi7xVFqPxDs7OS10d9MsPs+nRlraWENPCwZ3x\n5ufldenOa+Rr5xlUa9XGRxcWpYL2CSU+b2jpqP8ALblT3d9uhuqdSyjy/av8rnl/7SN5o2m/\n8FMfgxceIIVl059ItoIhJCZF+1vNepakDHVZ3hIP8Jw3GK6P/gqTc6dD+zhYRXkLy3UviC2F\nkUk2+VKIpyzsP4l8sSLj1dT2rvfjF+yl4g/aB+EE2g/ELUdK1PxzaXDTaR4i07T5beOyVjFu\nXy95LBlRlYEkH5TgFQR4Mn/BNb4k3niXRta8QfEay8ZTaSES1tvEtldXsCxoSUjKPLzGCc+X\nnaehBBIN5Rj8inWy3HYvHKnUwK5HC02qnK3KMoSSVlLmtLmSelrNasqQq2nGMbqX4HI/Fixu\ndM+Jv7FFne28tpeW9hoEM1vOhSSJ1mtQysp5BBBBB5BFer/8FVr23k+AWg2yXETXMfii2Z4V\ncF1BtLvBI6gV6D8f/wBkvxb8YdT+G3iyx8SW+mePfCIhle6lsmayuJ0ZJd6xD5lBlTOCxG04\nwetfnVon7IHxp+Ivxfk8O6x4V1201S4vGOo65qdpK1vGC2XnacjbIDyRhju6CvqOG5ZVnVTA\n5rWx0KTwKnKcXfXmqTlo3y+7Z7q71SaRhW56alTUb81vyR9P/tm/8n9/Ab/rro//AKdZK/Qm\nvkvVP2M/iL8RPjv4I+Ivj/xlpeov4Zmt5EttH0qS2MiwTNPGuWZh/rDz6rkDB5r6DsfDvxGj\n+Jt7fXes6ZL4FePFtpMemut5G+xRlpt2CNwc429CB2r8l4kxGCxmBy/CYfEwlLD0WpP3rOXP\nfljeKu7a6pLzO+ipRlOTW7OxorxnX/BH7QVxr2pS6P438LWekyXMjWdvc+H5ZJYoSxMaOwkG\n5guATgZIrqPA/hj4q2HhzXofFXiDR9V1qaMjS7rT9Kkght32MAZULkuNxU4BHAI718ZVy2lS\npKqsVTk9PdXPfX1glp119LnSptu3K/6+Z31Mmmjt4nlldYo0G5nc4Cj1JrhNI8MfFWH4b6lZ\naj4g0e58bSSE2eqw6VIlpEmVwHh3kseH53DqPSvkP9tj4Q/tH+Ifhbax32qW3jTR4bwSXel+\nFtImgnxtO2R03MZUU9h0JBI4yPXyXh+hm2Pjg6mOp005ct3za+cbxSd9lzOOpnUqunHmUWz7\n3gniuoUlhkSWJxlXjYMrD1BHWn18C/sN/CT9oHw98JdYj0+7XwVZ3Goxy2Nn4o0qWZigRhMy\nRFlaMMxiwTwdjEDnJ+vPid4Y+Kuqz6cfAniDR9BhSNheLq2lSXRlfI2lCrrtA54560844foZ\nXmc8vhjac4ptc/vWVkn71lKzd7e65ap3sFOq5w53Fo76ivJPBngz47WXiewn8TeMfDep6Ejk\n3VpY6FLBNIu04CuZCFOcHp2rqrLw78Ro/ibeX11rOmS+BXjxbaTHprreRvsUZabdgjcHONvQ\ngdq8StgKdKUoxxNOVo811z2bv8KvBe911srdTRTb6M7GivGfEHgj9oK517UpdH8b+FrPSJLm\nVrO2ufD8sksUBcmNHcSAMwXAJwMkV0/gbwx8VbDw7r0PivxBo+razNGRpd1p+lSQRW77GAMq\nFyXG4qcAjgEd6urltKlSVVYqnJ6e6ue+vrBLTrr6XBTbduVnosd9bQ201tJcRJcSlTHCzgO4\nBOcDqahr8fvj18Af2g9Q/aLv59Q0LXvEXiO5vFksdd0izmW1ZAR5TRSD5YVQY4LDZjk9z+gm\nn/D/APaYjsLZJ/iB4RMyxKHLeHZXO7AzlhIM898D6Cvts54MwmV4bC14ZpSm6sbte9pttyqb\na1teSi9Nt0uaniJTck4PQ99orgPGHhj4q3vhDQLfw34g0fTfEkMaDVb690qSa3uXEYDmKMOC\ngL5IBJwOKX4neGPirqs2mnwJ4g0fQYo42F4uraVJdGV8jaUKuu0DnjnqK+Dp4GnUlCLxEFzO\nWr5tLdXaL0l9m1/Ox1OTXRnfUV5H4N8GfHey8TWE/ibxj4a1PQkcm6tLHQpYZpFwcBXMhCnO\nD07V1dn4d+IyfE68vrnWdMl8CNHi30lNNdbxH2KMtNuwRvDnG3oQO1XWwFOlOUY4mnK0ea65\n7N3+FXgve662VuolNvozudP/AOP63/3x/Ouur5707wj8bU+JcN5J4u8O/wDCHjVPN/s7+xJB\nciy83Ii83zMb9nG/GM84r6ErlxWGhhuXlqxndX93m08neK19LrzKi79LBRRRXCUFFFFABRRR\nQAUV4lrn7Nlp8SviDr2u/Ea9fxNo7+XFomiR3E0FvYRgfOzKjLukY87s+vtjH/ZNkuLLUviZ\noNhqF1qvgjRdc+yaFc3UzTbPlJmhSQklkRtgHJ6k9zQB9C0V4H+3DKh/Z51a0ldY4r2/sLd2\nZsAD7TGxyf8AgNcl4K8Gfsqax4zs7Dw6NCn8QQ3CvapHqFz88qtlfLLPtkORwATn3oA+qa5f\nWj/xMpvw/wDQRXSTTx26hpZEjUnGXYAZrmtWYTX8rxkOhxhlOQeBQBTzRmjafQ0bT6GgAzRm\njafQ0bT6GgAzRmjafQ0bT6GgAzRmjafQ0bT6GgAzRmjafQ0bT6GgAzRmjafQ0bT6GgAzRmja\nfQ0bT6GgAzU0x/c2/wDun/0I1DtPoammU+Tb8H7p/wDQjQBDmjNG0+ho2n0NABmjNG0+ho2n\n0NABmjNG0+ho2n0NABmjNG0+ho2n0NABmjNG0+ho2n0NABmjNG0+ho2n0NABmjNG0+ho2n0N\nABmrmln9/J/1yf8AlVPafQ1c0xSJ5OD/AKpv5UAU80Zo2n0NG0+hoAM0Zo2n0NG0+hoAM0Zo\n2n0NG0+hoAM0Zo2n0NG0+hoAM0Zo2n0NG0+hoAM0Zo2n0NG0+hoAM0Zo2n0NG0+hoAM0Zo2n\n0NG0+hoAmuD8sP8A1zH8zUOamuFO2Hg/6sfzNQ7T6GgAzRmjafQ0bT6GgAzRmjafQ0bT6GgA\nzRmjafQ0bT6GgAzWV4n8OWvivR5tOvNwikwQ6HDIw6EVq7T6GjafQ1E4RqRcJq6e5E4RqRcJ\nq6Zwnhv4P6NoVtexTtJqJu1EbmU7AEDBgBtOeoU5z2FdDd+GlfR7fTrC/u9KS3IMcltJlsAH\n5WLZyOf0FbW0+ho2n0NctPBYejDkpwSVrab2333/ABOWng8PRhyU4JLbTe2++/4nKQ6P4rsJ\nk8vxBbahCGG5L2zCtt7/ADIRk1r3l/qcOsW8EGmC50+QDzLv7QqmI5OfkIyRjHQ961Np9DRt\nPoauNBQVoSa+d/8A0q+5pGgoK0JNfO//AKVc53VvHNlod/LbXttqEMceP9KFo7QtkA8Muc4z\ng8dQauaJ4r0nxHvGm38V2yDLIpIZR6kHmtbafQ1GtpGkrSrCqyMMM4XDEe5pqNdTu5Jx7W1+\n+/6Ao1lO7knHtbX77/oZOpfEjw94VuGt9R1BY5Z4TsWNGkwCeCdoOBxWrbXUV5bxTwSLLDKo\ndHQ5DA9CK8m8XfAi61TV3udANrbWhQNJBKzKIyOCVwDkHrXT6V8PdV0PTbe2sPFF7bmNACkk\nKTRA99qsMgZzxmvLo4rHuvUjWo+6trP/ADav+Fjy6OJx7rzjVo+6trP/ADav+B2+aM1jXcOv\n2ujW6WclnfakjDzpLtWiSRec4C5went1o1jVdT0qC0eHRZtTZ1Pni2kUeUQB0DYLZyenpXqu\nsopuSatbpff0v87HqusopuSatbpff0v87GzmjNc1YeN4ru9htJ9I1fT5pWCKbqzIQn/eGR+N\nay67p76u+li7i/tFBk22758Yz0+hzRDEUpq8ZLe3z7eoQxFKavGS3t8+3qX80ZqPz4isjeYm\nI87zuHy4659KxtG8b6H4gvns9P1GK5uUBJjAYEgdcZA3fhmrlVpwajKSTe2u/oXKrTg1GUkm\n9td/Q3c0Zo2n0NG0+hrU1DNGaNp9DRtPoaADNGaNp9DRtPoaADNGaNp9DRtPoaADNTXR/eL/\nANc0/wDQRUO0+hqa6U+YvB/1af8AoIoAhzRmjafQ0bT6GgAzRmjafQ0bT6GgAzRmjafQ0bT6\nGgAzRmjafQ0bT6GgAzRmjafQ0bT6GgAzRmjafQ0bT6GgAzRmjafQ0bT6GgAzVyz/AOPS9/3B\n/Oqe0+hq5Zqfsl5wfuD+dAFPNGaNp9DRtPoaADNGaNp9DRtPoaADNGaNp9DRtPoaADNeVeJ/\njVqWj+ONR8NaX4SuNduLJEkZ7e5IYqyIxOwRtgAuB1/nXqu0+hr541PxxZ+AP2iPFOoX1reX\ncUllFAEsow7hjHbtkgsOPlPfuK+lyLCU8XVrKdL2jjBtRu1d80V0aezZ9fw3gaWOrV41KPtX\nGm5Rjdq8lKK3i09m+pv337QGtaGI59Z+H2p6bYb1WS5eZsKCe2YlBPXALDPqK9a0PW7TxHpF\npqdhMJ7O6jEkbqex7H0IOQR2IIrw3x58SdT+K3hy50Dw34P1ee3vHija+ni2iNxIrYOAVA4H\nzM4wDk1X8f6VrvgvwH4F8A2V4sdzq0ssFzMjMgZmdf3WR/yzzOQeOQo4GSK9utk9DEwo01BU\nK0pNOPM5e4o8zk7ttNWta6ufRV8hw2LhQoqnHDYic2nHnc17NR5nNpuTTVrJXVz6IzRmvBvi\nR8DNE8IeBZ9Y0BdQh1jSgs6XK3B3yYkXLtxgFV3EFNvTvWX4u8d+IfF/w58Babb3KWt14mkl\ns7qfJUyFJFhGSBwrltzAD26ZB8ujkdPGRp1MJWvCUnFuUeVxtFyvbmldcqbWq7HjYfhynj40\nquCxHNCU3CTlHlcbRc72UpXTim1qtVY+jc0ZrxLxB+zemnW2nXPgq+n03W7WT5rq7umUOhDZ\nbKISG5UcADGcjNZevaZq3xU+MKeE9duRFpmk2cVzeW9hIyRvJ5KFnUMDk+ZMFBIBCZ6HOc6W\nU4XEXnRxN4RjKUrxtJKNl8PM73urarzMqOR4LFXqYfF3pxjKU7wtKKi0vh5nfmure8vOx9A5\nozXzj8UfDEPwN8QaD4k8KR3VnBNI8V1bmYtCyjYRGc5bDgOTknlQRjArW8X6befFf42z+Eb+\n7kg8P6RbrdPb277GcmNDu5BBbdMo56KDjBJzpDI6dSMMRCt+5cZScnHVcjSa5bvW7Vve1uaw\n4cp1YU8VTxH7iUJzcnG0lyNRkuRSd3eUbe9rc99uz+/b6D+Qryv4LfFDVfiNPr6alBZwDT3i\nWL7Ijru3GTO7czf3B0x3qrZfBXUvAnxHttT8IXkdr4ffZ9tsLy4kYsCWDBQF+YKrAruYkN1O\nK8P8D/E+4+Hen+KIdPiB1XUpYkt5pACkAUy7nIPUjcuAeO5yBg+ngMko4zC4iGEkqs2qfK2u\nVxvJ811d20Wur0PXy3h7D4/B4qGBmq02qTi2uVwvN8yau7WS1s3psfZOaM1wXwp+F8fgSwlv\nL1jfeJL/AC97fSEu2SdxRWPOM8k9WPJ6ADvdp9DXxWKp0qNaVOjPniutrX9Fd6dvyR+e4ylQ\noV5U8PU9pFfata/orvTt3XRBmjNG0+ho2n0NcpxBmjNG0+ho2n0NABmjNG0+ho2n0NABmjNG\n0+ho2n0NABmjNG0+ho2n0NABmjNG0+ho2n0NAFy3P/Etu/8AeT+Zqnmrlup/s274P3k/map7\nT6GgAzRmjafQ0bT6GgAzRmjafQ0bT6GgCfTz/p1v/vj+dddXJWCn7dBwfvj+ddbQAUUUUAFF\nFFABRRRQB80/FD4rwfEv4h6n8NrHxlYeCfDelBU8R63NfRW91cu3/LnalyMcZDyDOOns3rng\nTV/h94V8FyWHhPVdGHh7QLUyTJpl2k620QDMXk2Fjk7XYseWIJ5NT6l8Fvh7rOoXF/qHgTwz\nfX1zIZZ7m50e3kllcnJZmZCWJPUmr2ifDPwf4ZtdQttH8KaHpVtqEfk3kNjp0MKXKYI2SBVA\ndcMww2R8x9aAOM+LHxG8BN8JrPxTrukf8Jn4JubiBi8Nml1DEjMQtw6OR8inqQCeRgGvJ/2s\n/GPw+8bfCCz0TwnqGka94uuru0Tw3baFNHLcwy+chLIIzmNdgcc4GSB16fUWn6Fpuk6RHpNj\np1pZ6XHGYUsbeBUgRO6hANoHJ4xisrQfht4R8K6g99ovhbRdHvnBDXNhp8MEjA9csigmgCj4\n5+FXhv4s+FrHRPHGk2/iC1t5I7owysyp9oVGTeNpB6O4/wCBVnaDFoXgeGHwj4ZltbCx0yMR\nw6ZbzBmgTgkYJLYy3f1r0KuI1Lw/pdp4lvNRg020h1CYASXccCrK4IXIZwMnoOp7CtIclnz3\n20t38/I2p+ztL2l720t38/IqyfETTYZHjk8Q2CSISrK11GCCOoIzVm28ZQXtrNc2+r289tCC\nZZopkZEwMncRwOOeaw5vAHhe4meWXw3pEssjFnd7GIsxPJJJXk1cs/DGj6dY3FlaaTY2tncA\nia3htkSOUEYO5QMHjjmuqf1TlXI5X87W8zsqLA8i9m582m9refX7jQXxlA+ntfLq9ubFThrk\nTJ5YOcct06kCqn/CyNK/6GPTv/AuL/Gok8MaPHpT6Wuk2K6Y53NZC2QQscg5KYweQD06iqH/\nAArrwn/0LGjf+C+L/wCJpw+p687lvpa23n5jprAe97Rz30so7eeu5v3fjGGwihlutWt7aKYZ\nieWZFEg6/KT16jp615fYftV6PfeJ10wrdQ2Ty+UmoyFQhOcBivUKfX8wK9Av/DWkarBbQ3ul\nWV5DbDEEc9ujrEMAfKCPl4A6eleV6f8AsxaHZeJ11B7+a401JfNTTZIgQechWfPzL7Y5HevY\nyxZM6dX6+5c1vdt312t12303PdydZA6Vb+03Pmt7tu+u1uu3xe7uev2njyy1C4S3tdcs7md8\n7YobiN2bAycAHJ4BqxF4sSe+kso9ThkvIxl7dJVMijjqvUdR+dYun+C/D2k3cd3Y6DplndR5\n2T29nHG65GDhgMjgkfjVuDQNMtdTl1KHTrSHUJgVku44FWVxxwXAyeg6nsK8Sf1a79nzWtpe\n2/8AkfPVPqd37NytbS9t/Pyt8x8vxE02GR45PENhHIhKsjXUYKkdQRmrNt4ygvbWa5t9Xt57\naEEyzRTIyJgZO4jgcc81hzeAPC9zNJLL4b0iWWRi7u9jEWYnkkkryauWXhnR9OsriztNJsbW\nzuARNbw2yJHKCMHcoGDkcc1U/qnL7jlfztbzLqLA8i9m582m9refX7jQXxlA9g18ur27WSnD\nXImTywc45bp1IFcr40+OukeDtH+2/wBpx6pKziOO2spo3dj6nB4HHWtpPDGjx6U+lppNiumu\ndzWYtkELHIOSmMHkA9O1cl40+CXhnxXo/wBkttPtNDuFcPHd2FrGjA9MMABuXnpn0rswSy32\n6+tuXJfpbbztr62+R3ZcspeJj9ec/Z83S3w+dtfW3TYu+A/j7pfjjT5p2vRpFxC4SS3vZUXr\nnBUnG4HB9+OnTPX6h4zg0mRUvtXtrN3G5VuJkjJHqAa888BfAzw/4N0+aK7gg165mdXae+tk\nYLjOAinO3qe+T+Vdnq3hfRtelSXU9IsdRkRdqPd2ySlR6AsDgVWOWWfW39UcvZf8DpfW1776\n2LzJZOsbL6k5+x/HZbX1te++trFy08eWWoXCW9rrlnczvnbFDcRuzYGTgA5PAq4PFcU10tnD\nqcEl6iEy26SqZF56leo6j8xWDp/grw9pN3HdWOg6ZZ3Uedk9vZxo65GDhgMjgkfjWgPD+l2t\n8upQabZw6jMhEt5HAizSDd/E4GT0HU9hXnz+rXfs+a1tL23/AMjy6n1O79m5Wtpe2/n5W+Y2\nX4h6bBI8cniGwjkQlWRrqMFSOoIzVm28ZQXtrNc2+r289vCCZZopkZIwBk7iOBxzzWHN4A8L\n3M0k03hvSJZZGLvI9jEWYk5JJK8mrll4Y0fTbK4s7TSbG1tLkETW8NsiRygjBDKBg5HHNVP6\npy+45X87W8y6iwPIvZufNpva3n1+44LxL+1NpPh7xE2mxrc6lBEwWa8tmQoD32/3sfUV38Px\nO0i4hjlTxJp+x1DLm6jBwRnoTkV5Z4l/Zn0TXfETaha3smk2cjBpbG3gXb77DkBAfTB/pXfw\n/DTwlBDHEvhnSGVFCgvYxMxwO5K5J9zXu41ZH7Gj9Wc+a3vevnfS/wDh0PpMwXDnsKH1N1Oe\n3v8ArpvfS+/w6HRXfjGGwihludWt7eKcZieWZFWTv8pPXqOnrSah4zg0mRUvtXtrN3G5VuJk\njJHqAaoX/hrSNUgtoL3SrK7hthiCOe3R1iGAPlBGF4A6elJq3hfRtelSTU9JsdRkjG1Hu7ZJ\nSo9AWBwK+fh9WvHn5ut7W+Vv1Pl4fVLx53K2t7W+Vv1/AuWnjyy1C4S3tdcs7md87YobiN2b\nAycAHJ4FTxeLEnvpLKPU4ZLyMZe3WVTIo916jqPzrGsPBXh7SruO6stB0yzuo87J7ezjR1yM\nHDBcjgkfjVuDQNMttTl1KHTrSLUZRtku0gUSuOOC4GT0HfsKc/q137PmtbS9t/8AIdT6nd+z\ncrW0vbfz8rfMqeIfi3pPhrTby7udbt5Dag7reCZHmZgcbQuc5zx/PFcz8Pf2i7Hx7f3VmRNp\nU8MTTg3LqUaNfvHd2IHJz278Vp+I/hZ4Y8Safe28ujWNtPdAlry3to0nVyc7w4Gc559+c9a5\nn4dfAPS/At/d3lxeHWZJ4WtxHNAFjEbfeBXJ3ZHHPGCeK9ygsl+o1fauXtvs/wBLT1v8j6TD\nLh7+zq3tnP2/2fw2tp3vzdNj1BPGUD2DXy6vbtZIcNciZDGDnHLdOpH51U/4WRpX/Qx6d/4F\nxf41EnhjR4tKfTE0mxTTXO57NbZBCxyDkpjB5APTtVD/AIV14T/6FjRv/BfF/wDE140PqevO\n5b6Wtt5+Z8/TWA972jnvpZR289dzfu/GMNhDDLc6tb28U4zE8syKsnf5SevUdKTUPGcGkuiX\n2r21m7jKrcTJGSPUZqhfeGtI1S3toLzSrK7gthiCKe3R1iGAPlBGF4A6elJq3hfRtelSTU9J\nsdRkjG1Hu7ZJSo9AWBwKzh9WvHn5ut7W+Vv1MofVLx53K2t7W+Vv1v8AIuWnjyy1C4S3tdcs\n7md87YobiN2bAycAHJ4Fcb4//aL0/wCHWqpp8sc2rXhTdJFbuqiEHpuP97vj+WRXQWHgrw9p\nV3HdWWg6ZZ3Uedk9vZxo65GDhguRwSPxrj/iB+z9pfxJ1gajFenRr0Rnz3igEizgcgkZX5u2\n7PTHpXrZcsp+tr645eyt+Pny629Ovke5lSyR46P19z9jZ/8AgXny3dvTW/kdZoHxh0bxBpFt\nqEWu21sk67vJup445UOcEMpPBBH+Fbdr4ygvbaa5t9Xt57eEEyyxTIyRgDJ3EcDjnmuQ0D4R\n+FNC0e1sTodhftCuGuby0jklkOcksxX1P4dK37Lwxo+m2VxZ2mk2NraXIKz28FsiRygjBDKB\nhsjjntXHiVgOeX1dytfS9tr/AOR5+MWWc8vqjna+l7bX++9jQTxlBJYNfLq9u1khw1yJkMYO\ncct06kfnVT/hZGlf9DHp3/gXF/jUUfhjR4tKfTE0mxTTZDuezW2QQscg5KYweQD07VQ/4V14\nT/6FjRv/AAXxf/E1jD6nrzuW+lrbefmYU1gPe9o576WUdvPXc37vxjDYQwzXOrW9vFMMxSSz\nIqyDGflJ69R0pmq+N7bQxnUNXt7L5S4WeVUJUdSAeT+FUr7wzo+qW9tb3ulWN3BbDEEU9sjr\nEMAfKCMLwAOPSma14U0XxHg6ppVlqDhCivcwK7oD1CsRkfhUQ+rXjz81tb2t8rfqZ0/qnNH2\njlbW9rfK363OI8LftQab4n8Uw6OkN5arcyeVb3Mu0h2P3QVHK56DryRXpsfixJr57KPU4XvI\nxl7dZVMij3XqOo/OvH/Cn7N+leGPFkGsHU57yG1l863tXjC7WBypZgfmwcHgDkV6jDoGmW2p\ny6lFp1pFqMo2yXaQKJnHHBcDJ6Dv2FevmyylVV/Zzly8v/k3z123t8j3s7WRqsv7KcuXl8/i\nv/e123tpfYfL8Q9NgleOTxBYRyISrI91GCpHUEZ4NWbXxlBe201xb6vb3FvCCZZYpkZIwBk7\niOBxzzWHP4A8MXU0k03hzSJZpGLvI9jEzMxOSSSvJJq5ZeGdH02zuLSz0mxtbS5BWeCC2RI5\nQRghlAw3HHNeVP6py+45X87W8zxKiwPIvZufNpva3n1+40E8ZQSWD3y6vbtZIcNciZDGpzjl\nunUj86qf8LI0r/oY9O/8C4v8aij8MaPFpUmmJpNimmyHc9mtsghY5ByUxg8gHp2qh/wrrwn/\nANCxo3/gvi/+JpwWD153LfS1tvPzHTWA972jnvpZR289dyD4jfGm2+HWk2d5cSS30l6T9mht\nyv7wAAlt3TaARzz1FR+CvjrpPjLR/tv9pR6VKrmOS2vpkRgcA5BJ+Yc9apfEr4Tad8RdHsbN\npjpklhkWskEYKxqQAU2cDbhV4BGMCoPBXwS8N+E9H+yXVla67cM5ke5v7VHOcAYUEHaOOma9\niCyb+zlzuXt7627X0/u2tbzv5HvQWQf2VF1HP6zzO9u13bf3bWt1vfyO5tPHllf3CW9rrtnc\nzv8AdiiuI2ZuM8AHJ4qxH4sSa+eyj1OF7yMZe3WVTIo916jqPzrFsPBXh7SruO6stB0yzuo8\n7J7ezjR1yMHDBcjgkVbh0DTLfVJdSi060i1GUbZLxIFEzjjgvjJ6Dv2FeJP6td8nNa2l7b/5\nHztT6nzP2bla2l7b+flb5li7+IWl28gjfxBYRyqNsiNdRgqwJBBGeDUlr4ygvraa4t9Xt7i3\nhBMssUyMkYAydxHA455rJvvh/wCF7iUTS+G9IlmlG+SR7GIs7EkkkleSfWprLwxo+mWdxaWe\nk2NpaXIKzwQWyIkoIwQygYbjjmqn9U5fccr+dreZdRYHkXs3Pm03tbz6/caCeMoJLB75dXt2\nskOGuRMhjU5xy3TqR+deX6p+1XpGm+Jn0wLdXNnHJ5UmoRFSgOcEqOrKPX8geM+gR+GNHh0q\nTTI9JsU02Q7ns1tkELHIOSmMHkA9O1eV6p+zFoeoeJn1CK/mtNOkk819OiiAAyclVbPyr7Y4\n7V7OVLJnOp/aDlaz5f6XX8O572SrIHUq/wBqOfLZ8tv/ALX7Xb7Pc9jufGcFlbwT3Gr29vBO\nA0UksyKsgxnKk9eCOlLqHjKDSXRL7V7azdxlVuJkjLD1Gaz73wxo+pWtta3mk2N3bWwCwQz2\n6OkQAAAUEEDgAcelGreGNG16SOTU9JsdRkjG1Gu7ZJSo9AWBxXhx+rXjzc1tb7fK36nzsPql\n487lbW9rfK36/gW7Tx7ZX9wkFtrtncTv92KK4jZm4zwAcmrEfixJr57KPU4XvUGXt1lUyKPd\neo6j86xbDwV4d0q7jurLQdMs7qPJSaCzjR1yMHDBcjgkVbh0DTLfVJdSi060j1GUbZLxIFEz\njjgvjJ6Dv2FOf1a75Oa1tL23/wAh1Pqd37NytbS9t/Pyt8ySX4h6dBK8UviCwjkRirI91GCp\nHUEZ4NZviT4t6d4b8N3etNqaX1tblUK2ciSMzt91eDgE+/YGmz+AfDF1PJNN4c0maaRi7ySW\nMTMzE5JJK8knvWf4k+F2ga/4YvNEhsLfSYLhlk32ECRFZF+62AAD6c9ia66Ky72kPaufLdX2\n2vrtrsd2HWVe1p+2c+W65ttr67a7FP4e/tC2Hjz7ajSSaRPaIJXF3Imwx7gu4PwOpAOcfeHW\nut/4WRpX/Qx6d/4Fxf415/8ADv4D6P4HF+13Kuuy3kYhcXVuoiEe4Nt2EsD8yqcn+6OldZ/w\nrrwn/wBCxo3/AIL4v/ia7Meso+sz+qufJpb7tfi137nfmSyL63P6k5+z0tb0V/is977m/deM\nYbGCGe51a3t4ZhmKSWZFWQYz8pPXj0o1DxlBpLol9q9tZO4yq3EyRlh6jNZ994Z0fVLW3tr3\nSrG7t7YYghntkdIhjGFBGF4AHHpRq3hjR9ekjk1PSbHUZIxtRru2SUqPQFgcV48fq148/N1v\nt8rfqeDD6pePO5W1va3yt+v4Fu08e2N/cJBba7Z3E7/diiuI2Zu/AByasR+LElv3sk1OF71B\nlrZZVMij3XqOo/OsWw8E+HdLu47qy0HTLO6jyUmgs40dcjBwwXI4Jq3FoGmW+qSalFp1pHqM\no2veJAomcccF8ZPQd+wpz+rXfJzWtpe2/wDkOp9Tu/ZuVraXtv5+VvmcZ42/aY0vwNrT6WY5\n9XnVMTNbOgWBs9M92GOR2z1rtdA+Idp4l0VNVsdTR7QxiV2Z1BhGMkOP4SOc59K858a/s4aR\n471ybVLa+bRJShkuI4LdWSZs8tjI2sc8nnPXGc57bw74J0bwvoQ0mysIBatEIp98ak3HGCZO\nPnJGc59fSvaxiyf6nR+quXtftfrvp6W+Z9Bj1kP9n0Hg3P2/27/jvp6cvzNpPGUElg98mr27\n2SHDXKzIY1PTlug6j86qf8LI0r/oY9O/8C4v8aii8MaPDpcmmR6TYx6bIdz2a2yCFjkHJTGD\nyB27VQ/4V14T/wChY0b/AMF8X/xNeRD6nrzuW+lrbefmeHTWAvL2jnvpZR289dzfu/GUFhbw\nz3Or29vBMMxSSzIqyDGflJ4PHpUOo+JrHSruOW/1Cys7oqdj3Dxo5HQ4J5xVW+8M6Pqlrb21\n5pNjd21sAIIZ7ZHSIAYAUEYXgAcUat4Y0fXpI5NT0mx1F4xtRru2SUqPQFgcVlFYZuPPzW1v\nt8rfqYxWEbj7Tmtrfb5W/X8DjPF1l4F1iz1W+e/05L54ZpnubW5BkztLM21G+fuSMc14x8Gd\nat9T+IVjAt1Lptxtl+zzKASz7GAHoDgk8+mO9fQ9x8NfCs9rcQr4f0y286J4TLbWkcciqylT\ntYLkHBPIri/Af7POm+CfE8estqc2oNAWNtC8QQISCMsQTuIBPYV1U8q4arU6tbERkqqV4dNd\n/s6PW1+bpsejQyrhOtQr1sTGarJXp9Pe3+zo9bX5umx2q+MZdK0m5s38a6fc6qjkJNevCvl4\nIG1lUjpg+/NVLXx14ve3muIrrw9rFtAMyyWd4U2ADOWJyBxz1rSuPAPhi6nknn8OaTNNIxd5\nJLGJmdickkleST3q3Y+GNH0y0uLWz0mxtLW5BWaCC2RElBGCGUDDcEjmvPq4bBuC9lOopK3V\nW8973+Z5FbDYF017GdRSVuqt573v8ytf/E2Ww0S21NQb+3bAuHsZ4pEt2wMguSAeTio9S+M2\niaVcRxTa3HJvXd5lt++UexKA4NXYvDGjwaXJpkek2MemyHc9mtsghY5ByUxg9B27CqH/AArr\nwn/0LGjf+C+L/wCJqvZ0bTtUkn0vFOy89Y6v1LVPDWn+8knf3fdi9PNXjq/UtWnxZ0W9/wBX\n4htV/wCusgj/APQsVi/Er47Wnw3W3imaXUL6fO22gZQVA6lientxzW7eeFdE1G3hgutHsLmC\nFQkcU1qjqgAwAoIwAAB09K8/+LPwOt/H80N9p00OnakpIlZ1OyYHucdx6+9dOWfVYYin/aTv\nTu+blTWltNE2997dNjryf6jHFUv7VbdO75uVNaW93ROT3+K3TY7PwN8Y9O8eabBcWmoLb3Mj\nFDY3DoJlYckBc8jHOR2/EDoo/FiS372KanC96gy1ssqmRR7r1HUfnXlHg/8AZ90rw5Y2zy31\n3/bETFzfWjiMqSMYXIOBjj35rr9M8CQaOt9cW06ya1cAhdXubaN7hAQBgsACw4HBNZ4qphfr\nNRYaEvZ68rbV/LR2drfPuZY2pgvrdVYSE/Za8t7XvfS97O1u+vc3ZviHp1vK8UviCxjlRiro\n91GGUjggjPBq4njG2vYpZ7TV7a4t4UVpZYZkdY/lySxBwO55rh7j4c2p0mRm0fw7qeuvIXe7\nvNMREkJbJLBQTnHfPXmooNM1rw/bz2kPgvQGtriMCaHTJVgSUFcfMrIAcg9/WueriMHCCtGp\nzO32U15/DfU5q2IwEIK0anNp9lNefw31O5j8ZQS2D3yavbvZIcNcrMhjU+7dB1H51U/4WRpX\n/Qx6d/4Fxf41iX1ppui2FtpUfhVbjTbxv31rZWkTQRtleXTgHnnOP4aytb03wB4du1t7/wAN\nabCWQP5i6OrpgkjqqHnitVXy6HM60pJJ+S0e179TWNfK4c7rymknpstHte/X0Mpf2rNHbxR/\nZm26Fl5vk/2kSvl5zjdt67PfrjnFeoah4yg0l0W+1e3s2cZVbiZIyw9s9a+R4PDnhV/iIswS\n6Ph5rvcICRnbu6Hj7nt1x3zX1Df6P4d8UXO29stM1a4tspi4ijmeLnkcgkc9q9HFY3h7FTp/\n2XUlKKVp73v0tzW87207Hr4/GcMYqpS/sipKUUvf3vfpbmtd73t7vY0bXx7Y31wkFtrtlcTv\nwsUVzGzN9ADk1ZTxYkt+9impwveoMtbLKpkUe69R1H51iWPgnw7pd3HdWWg6XaXMZyk0FnGj\nrxjhguRwauRaBpkGqSanHp1pHqUg2veLAomYYAwXxk9B37CuKf1a75Oa1tL23/yPLqfU+Z+z\ncrW0vb4vPy/Ekm+IWnW8rxS+ILGKVGKuj3UYZSOCCM8GrFp4ygv7eae21e3uIIRmWWKZGWMY\nz8xHA49axLjwF4Yu55J5/DmkzTSsXkkksYmZ2JySSVyST3q3Y+GNH0u0uLWy0mxtLa5BWaGC\n2RElGMYZQMHgkc1U/qnJ7jlzedreZc1geT92582m9refX7ih4x+MVh4P8MtrT339oW5lEES2\nbo/mSEE7cjgYAJPsPpWT4C/aE0rxvbXDSXg0a5gI3Q30yKGB6FWOM9OfSpPGPwp0TxX4VOhx\nW8WjwLMLiFrGFUEcuCN20AA5BINY/gL4C+HvB9tcLfQweIbmYjMt9aoyoBnhFO7HXk554r2q\nSyX6hP2jl7a+np/6Ttv1vsfQ0Vw9/Zs3Vc/b82n+HT/t3a97u99j0m68ZQWNvDcXOr29vBMA\nYpZZkVZBjPyk8HjnijUPGUGksi32r29mzjKi4mSMsPbPWs++8M6Pqdpb2l5pNjd2tsAsME9s\njpEAMAKpGBxxxRq3hjR9eeN9T0mx1F4wVRru2SUqPQbgcV4Ufq1483NbW+3yt+p81D6pePO5\nW1va3yt+v4Fq18e2N9cJBba7ZXE78LFFcxszfQA5NWU8WJLfvYpqcL3qDLWyyqZFHuvUdR+d\nYlj4J8O6Xdx3VnoGl2lzGcpNBZxo6nGOGC5HFXItA0yHVJNTj060j1KQbXvFgUTMMAYL4yeA\nO/anP6td8nNa2l7b/wCQ6n1Pmfs3K1tL23/y/Ekm+IWnW8zxS+ILGKVGKuj3UYZSOCCM8GuA\n8U/tS6P4W1qTTEjuNXChRJd2joUjJ5+X++cfQe9dZceAvDF3cSzz+HNJmnlYvJJJYxMzsTkk\nkrkknvXBeJ/2YtE8UaxPqdletoluAplsra3UxnoPk5ATP0IzXuZYsldX/bnPlt8r/wDbuvp+\nJ9Hk64edb/hSc+Wz9L/9u3fe3TuepWHju11PRk1a31aFtOZQxuDIoVcgHDE/dPI4PNRf8LI0\nr/oY9O/8C4v8aq6d4Q0XTNBXRYNMtf7M2hXtnhVkkwB8zgjDMcAknkkVV/4V14T/AOhY0b/w\nXxf/ABNeQlguaV3K19NtvPzPCSy7mnzOdr6fDt0vrub914ygsbaG4udXt7eCYAxSyzIqyDGR\ntJ4PHPFGoeMoNJZFvtXt7NnGVFxMkZYe2etZ994Y0fU7O3tLzSbG7tbYBYYJ7ZHSIAYAVSML\nxxxRq3hjR9eeN9T0mx1F4wVRru2SUoO4G4HFYx+rXjzc1tb7fK36mEPql487lbW9rfK36/gW\nrXx7Y31wkFtrtlcTucLFFcxszfQA5NV9e+JNj4ds7+e71aLdZRGWa3jkVpgOMDZnOTkY+oqr\nY+CfDumXcd1Z6BpdpcxnKTQWcaOpxjhguRUHiDwBoPiS21FLrTbZLi/iMU17FCizkcYO/GeN\nq9fQVvBYH2y5nLk0vtffX8PxOmmsu9vHnc/Z6X2vvr8rficz4H/aa0vxhqktjO0mjSBS8Ul5\nIgjkA6jd2PfH15rI8IahNN+0T4w1+GXzNMbTEQ6kmDBkJbZHmfdz8jd/4T6Gn+B/2ctE8K6p\nLe6hOuv5UrFb3VsvlJnuVJbcfQ8fT09JsPDGj6Va3FtZaTY2dtcDbNDBbIiSjGMMAMHgkc+t\nfQYvFZVhKlaOW8zjOHLrsrtNtX16dep9PjsbkuCq145SpONSHJrsruLbV/e6devkaEfjKCax\ne9j1e3eyQ4e4WZDGp926DqPzrxv4/wB3/wAJPZ6Jrmh+IbCbVNAme5ihS6iMhzsbcnPLKY1I\nXHOTjnAPqkPhjR7fS5dNi0mxj06U7pLNLZBC545KYweg7dhVD/hXXhP/AKFjRv8AwXxf/E15\neXYvD4DErErmbi3ZWTTTVmnr1TaPGyrHYTLMWsXHmbi3ZWi04tWaevVNrQ8n+J3xm1nxt4Dj\n0WPw1q2mSax5aPcXw8m3Ub1YbJCAGU45ZtoAOelQfEbwJe+FPh94KSw1i3TWPDJkuo4XmRWk\nd3WWR4t2N211GFxkg+vB9uvvDGj6nZ29peaTY3drbALBBPbI6RADACqRheOOKNV8MaPrzxtq\nek2OotECqNd2ySlAeoG4HFerQzyjhvYww9LkhGUpNfFdtcq3e3K2v+Ce3h+JMPg/YQwtHkpw\nnKcl8XM2uVfE9uVuLWm99XqeEeNv2rtevNLtINEt7nQNRLCSeaby5QF+YbFDJyD8p3YBBBGO\n9Qad4t1az+IGmfEDQ5rjxQdSs0tdTs7WNEvNyRLG+6FdxClokkBA25AXIyCe98ffs+aD4vkt\nptP8rw7NENjfY7VPLkXOeUG35vf884GOq8EfDbQ/Amn20NjZwyXkSFW1CSJfPkycnL4zj29A\nK9Wea5Lh8GvqdK05KSlFrdPo5b2uk1Z3R7U874ewmAX1CjaclKMoNbxl0lN62TSceWV12PGf\niH4sl+OfjHRtInhn8P6PpskjXNxq0q2z4JQONpyA42lVGWOTk4GcbnirUNY8JfFm4+IXh0/8\nJPp11AIL+HTZFdowIgu19oYhf3SPuxjK4OOCfVrjwF4ZvLiWefw5pM88rF5JZLGJmdickklc\nkk96t2HhnR9Ktbi2stJsbO3uBtmht7ZESUYxhgBg8Ejn1rzXnuGpwhTo0nyRi48j2ak05a3v\ndtJpq1rI8l8SYSnTp0aFF+zjCUHB6pqbTm+a/NdtJpq1rI8z0v4u+M/HvjeC9017nwx4Isyv\n2ua/hh2yhSScsy/KX+VSFY7c5zXmHws8JaH4l07xlZ+IJ7bS55/JFle3WxXjbdISyb8ZGQm4\nAjIOMjOa+oJPC+jWtlNpkOkWMOmykM9nHbIsLnA5KAYPQdu1Zf8Awrrwn/0LGjf+C+L/AOJq\n6WfYahTqUsPTdNPlScbXXK+a7b3bejvpbQuhxLhMNSq0cLSdFS5FFwtzLklzXk3rKTejvpbT\nucH8GfiHrHhPTT4Z8UtcWlrZll07WLgEWk8S9ESVgAQByhyQVIAxgA3NI/aq0jVfEyaYy3Vr\naSyeVFqExUITnALD+FT6/njnHfX/AIX0bVLGCzvNJsrq0t1CwwTW6MkQAwAgIwvHHFeW6P8A\nsx6HpniZNQmv5b3T4pPMj06aIEHByFds/MvtgZ71FLE5LjZV8RjoyhOSulHa+uqt1ejs9NzO\nli+HswnicTmMJQqSV0o3tza3ats3o7S929z1218e2N9cJBba7ZXE7nCxRXMbM30AOTVlPFiS\nX72K6nC16gy1sJVMijGeV69CPzrEsfBHh3TLuO6s9A0u0uYzlJoLONHU9OGC5FXI9A0yLVZN\nTTTrRNSkG17xYFEzDAGC+MngAde1fKz+q3fJzWt1tv8A5fifF1PqfM/ZuVraXt8X+X4kk3xC\n062mkil8QWMUsbFXR7qMMpHBBBPBqxaeMoNQgmmtdXt7mGEZlkhmR1QYz8xHTgHrWJc+AvDN\n5cSzz+HdJnnlYvJLJYxMzsTkkkrkknvVuw8M6PpVtcW1lpNjZ29wNs0NvbIiSjGMMAMHgkc+\ntVP6pye45c3na3mXNYHk/dufNpva3n1+40I/GUE1jJex6vbvZxnD3CzIY1PHVug6j865Pxv8\neNI8GaUt2NSj1WaR9kdtYzI7E4zkkH5R71uQ+GdHttMl02LSbGLTpTuktEtkELnjkoBg9B27\nCuP8bfA/w34t0pba1srXQrlH3pdWFqiHpghgANw9s9q7cCst+sL625cl/Lbztr62+R35asoe\nKj9ec/Z36W287a+tumxp+BfjxpfjXSZbw3w0mSGQRyw3sqJgkZBDHgg4PvweK62/8ZQaUUF7\nq9vZmQZQXEyJuHtnrXn/AIF+CPh/wfo8tnd28GvSzSCSSa+tkYZAIAVTnbjJ7k8nmuw1Xwxo\n+utE2paTY6i0QKxm7tklKA9QNwOKMast+tP6q5eyu/y0tfXe++th5isoWNl9Sc/Y3fa9rK1r\n67331tbqWrXx9Y31wkFtrtlcTucLHFcxszfQA5NWU8WJJftYrqcLXqDLWwlUyAYzyvXoR+dY\nlj4I8O6Zdx3VnoGl2lzGcpNBZxo6npwwXIq5HoGmRaq+ppp1ompSDa94sCiZhgDBfGTwAOva\nuCf1W75Oa1utt/8AL8TzKn1Pmfs3K1tL2+L/AC/Ekm+IWnW00kM3iCxiljYo8b3UYZSDgggn\ng1YtPGUGoQTTWur29zDCMyyQzI6oMZ+YjpwD1rEufAXhm8uJbi48O6TPPK5eSWSxiZnYnJJJ\nXJJPerdh4Y0fSra4t7LSbGzt7gbZore2RElGCMMAMHgkc+tVP6pye45c3na3mXNYHk/dufNp\nva3n1+44XXP2q9F0LXp9KEVzqEMbhJNQt2Qop/i2j+ID146cZrvY/iZpMsauviTTyrAEZuox\nx+deYa3+y5omua3c6tb6hLpliZFaTToIF25PUI2RsHHTBxniu7j+G/hONFQeGNHIUAAtYxE/\niSuTXu45ZH7Kl9Vc+a3veum99L77aH0mYrhz2ND6m6nNb3vXTe+l9/h0OhufGUFlbQ3Nxq9v\nBbzAGKWWZFSQEZG0ng8c8UX/AIyg0oxi91e3szIMoLiZE3D2z1rPvfDGj6lZW9nd6TY3VpbA\nLBbzWyPHEAMAKpGFwOOKNV8MaPrrRHUtJsdQMQKxm7tkl2A9QNwOK+fj9Wuubmtrfb5W/U+X\nh9UvHncra3tb5W/X8C1a+PrG+uEgttdsridzhY4rmNmY+wByasr4sR79rFdTha9UZa2EqmQD\nGeV69CKxLLwR4c0y6jurPQNLtLmM5SaCzjR1PqCFyKuJoGmRaq+ppp1ompONr3iwKJmGAMF8\nZPAA69qc/qt3yOVrdbb/AOX4jqfU+Z+zcrW0vb4v8vxLtl43sZdXhsW1uza5MwhNv9oTfuzj\nbtznOeMV29ec6f4M8PjX4NQGhaaL/wC0Cb7V9jj83zN2d+7Gd2ec9c16NWVX2Onsr+d7b+Vj\nGt7DT2F/O9t/KwUUUVgcwUUUUAFFFFABRXiWufEr4leLPiDr3hv4d6DotrY6D5cd5rfiv7Qs\nNxM4z5cCRYLBehbOP0ztfA34uan8RZPFGh+JNJg0bxZ4XvVs9Rhs5TJbSBwWjliJ5CsA3B5G\nB64AB6nRXEfGX4o2vwf8A3viKe0k1GdHjt7Owiba91cSMFjjBwcZJyTg4APB6V5nrfxc+Kfw\nng0vxB8RtF8Lt4RurqG1vn0Gaf7TpXmsFWSUyZSRQxAbbjk8ZoA+g65vV5ymoSjYhxjkrk9B\nXSVy+tf8hOb/AID/AOgigCv9pP8Azzi/74FH2k/884v++BUNFAE32k/884v++BR9pP8Azzi/\n74FQ0UATfaT/AM84v++BR9pP/POL/vgVDRQBN9pP/POL/vgUfaT/AM84v++BUNFAE32k/wDP\nOL/vgUfaT/zzi/74FQ0UATfaT/zzi/74FH2k/wDPOL/vgVDRQBN9pP8Azzi/74FH2k/884v+\n+BUNFAE32k/884v++BU01wRFB+7j5U/w/wC0ap1NN/qbf/dP/oRoAPtJ/wCecX/fAo+0n/nn\nF/3wKhooAm+0n/nnF/3wKPtJ/wCecX/fAqGigCb7Sf8AnnF/3wKPtJ/55xf98CoaKAJvtJ/5\n5xf98Cj7Sf8AnnF/3wKhooAm+0n/AJ5xf98Cj7Sf+ecX/fAqGigCb7Sf+ecX/fAo+0n/AJ5x\nf98CoaKAJvtJ/wCecX/fAo+0n/nnF/3wKhooAm+0n/nnF/3wKt6bOWmk+RB+7Y8L7VnVc0v/\nAF8n/XJ/5UAQ/aT/AM84v++BR9pP/POL/vgVDRQBN9pP/POL/vgUfaT/AM84v++BUNFAE32k\n/wDPOL/vgUfaT/zzi/74FQ0UATfaT/zzi/74FH2k/wDPOL/vgVDRQBN9pP8Azzi/74FH2k/8\n84v++BUNFAE32k/884v++BR9pP8Azzi/74FQ0UATfaT/AM84v++BR9pP/POL/vgVDRQBN9pP\n/POL/vgUfaT/AM84v++BUNFAFye4IEX7uPlB/D7moftJ/wCecX/fAouPuw/9cx/M1DQBN9pP\n/POL/vgUfaT/AM84v++BUNFAE32k/wDPOL/vgUfaT/zzi/74FQ0UATfaT/zzi/74FH2k/wDP\nOL/vgVDRQBN9pP8Azzi/74FH2k/884v++BUNFAE32k/884v++BR9pP8Azzi/74FQ0UATfaT/\nAM84v++BR9pP/POL/vgVDRQBN9pP/POL/vgUfaT/AM84v++BUNFAGjYTlhc/JGMQseF9xVT7\nSf8AnnF/3wKm07pd/wDXBv5iqdAE32k/884v++BR9pP/ADzi/wC+BUNFAE32k/8APOL/AL4F\nH2k/884v++BUNFAE32k/884v++BR9pP/ADzi/wC+BUNFAE32k/8APOL/AL4FH2k/884v++BU\nNFAE32k/884v++BR9pP/ADzi/wC+BUNFAE32k/8APOL/AL4FH2k/884v++BUNFAE32k/884v\n++BR9pP/ADzi/wC+BUNFAE32k/8APOL/AL4FTXNwRIv7uM/InVf9kVTqa6/1i/8AXNP/AEEU\nAH2k/wDPOL/vgUfaT/zzi/74FQ0UAcKvwb0BfEX9q4m2+b5ws9w8ndnPpnbntn9OK7S106ws\nbye7t9Os4LmfPmyxQKrSZOSWI6881NRXJSwmHoNulBK7v8zkpYShQu6cEru/zOWf4ZeH1Ja3\ntp7FzzutruZP03YrUttBax0KfTbbUbxWkbct3NJ5s0fTgMw6cdPc1q0URwlCF3CCV+2n5Djh\naELuEEr9tPyMuaz1ePQobS11WMX8bZa9ubVZDIMngqMDuBkelZobxxbHCy+H7xfWWCSNj/3y\nSK6aiiWGjKzUpK3ZsJYeMrWk1bs2cP8AF7xdrXhzQ1fTLaKKKVgkl8oBaInPAUjvj73b6kVz\nHww+J2uy295Hfafea/DGVKzW8KvJGTnIPIzn869cnt4rqF4Zo0micYaORQysPQg1HZafa6bB\n5NnbQ2sOc+XBGEXP0FcFXA4ieLWIhXajbb+tPv1PPq4LETxaxEKzUbbf1p9+pn6J42TW7w23\n9j39hIEL5vbIxrgEDryM81Z0vxjpus3N5b2kkck1ocTq0DJs5I6kAHoelXqCMivSjGrG15J9\n9P8Ag6fienGNVWvJPvp/wdPxMu38faBd/wCq1bS2PoZkB/Imta31KK6iEkBt5oz0eMBgfxFZ\ndx4Y0e7z5+lWM2f+elujfzFNl8LaTLo8mlGxiTTnO5reLKKTkNn5cdwDUR+sK/Nyv71r+JEf\nrCvzcr+9a/ibX2k/884v++BVS58aaNozy2l/qFlZ3MqrsikYKTz39B9ay7vwpZXOj2+mRyXV\nlbW7Boza3DI4wCMbs5I5PWvJPF3wZ8RXevzzacJNUs32n7TdXKmRBgDDliCce2eMVw4/FYvD\nQUqNHmemzb/C1/mcGOxWLw0FKjS5nps7/ha/zPehdZGRHER/uCj7Sf8AnnF/3wK5ix8P6rYx\naNFFrXl29nbxw3Fv9nVxOVGCd55XPSl1eDxOb55NLutMFqQNsN5C5YHHPzKfWu/20lHmlTf4\nP9f67Hf7aSjzSpv8H+v9djpvtJ/55xf98Cj7Sf8AnnF/3wK57Q38Rm6ddZi0wW+zKyWLyFi2\nRwQw6YzUul6jqtx9t+36StmIuYDHcrL5/Xtgbeg6+vtVRrKVtGr+T6d+36lRrKVtGr+T6d+3\n6m59pP8Azzi/74FH2k/884v++BXG/wDCdXkP/Hz4V1pP+uESS/yarWp+NINM8Jza5JZ3cSIC\nBbXERjlLZ2gEHoCe/pzWaxlFpvmtZXd01ovVGaxdFpvm2V3dNaL1R1H2k/8APOL/AL4FH2k/\n884v++BXkHhD45JqWpPb63DBp8DAmO4jLbVI7NnPX1/Su+tfG/h+8dUh1qxZ2OFUzqCT6AE1\njh8xwuKjzU5r56P7mY4fMcLio81Oa+ej+5nQ/aT/AM84v++BR9pP/POL/vgVVa6hSdYWmjWZ\nhkRlgGI+lSV6N09j0bp7E32k/wDPOL/vgUfaT/zzi/74FQ0Uxk32k/8APOL/AL4FH2k/884v\n++BVS5u4LNA9xNHAhOA0jhRn05qUMGAIIIPII70rq9hXV7E32k/884v++BR9pP8Azzi/74FQ\n0Uxlu6uCJ2HlxngdV9qi+0n/AJ5xf98Ci7/17fQfyFQ0ATfaT/zzi/74FH2k/wDPOL/vgVDR\nQBN9pP8Azzi/74FH2k/884v++BUNFAE32k/884v++BR9pP8Azzi/74FQ0UATfaT/AM84v++B\nR9pP/POL/vgVDRQBN9pP/POL/vgUfaT/AM84v++BUNFAE32k/wDPOL/vgUfaT/zzi/74FQ0U\nATfaT/zzi/74FH2k/wDPOL/vgVDRQBowTk6fdHYnBTjbx1NVPtJ/55xf98Cprf8A5Bt3/vJ/\nM1ToAm+0n/nnF/3wKPtJ/wCecX/fAqGigCb7Sf8AnnF/3wKPtJ/55xf98CoaKALtjcFryAeX\nGMuOQvvXU1yOn/8AH9b/AO+P5111ABRRRQAUUUUAFFFFAHm3xb+McfgCaw0LRdNk8TeOdXBG\nmaFbtgsBwZpm6Rwr3Y9cEDoSE+CHwquvhzpWrX+uX0eq+MPEV2dR1m+hXbEZSMLFEOojjHC5\n55J4zgcTcfs7eOrL4m+KvGegfFSHSbzXpFDJceGort4IE/1cCyPNwqjGcBdxAJGa7nwx4G8f\n2OieIrLxD8SF1+6v7UwafeW+hxWLafIVceaAkh8w5ZCASMbOvNAHEfthfJoXwznk4srfx5pU\nt0T0EX70En2yR+lX/wBtaWGL9mTxqJsYeO2RFPUubqHaB75/lXWah8IIfF3weXwJ4y1a48SN\nJbLFcauV8meSVW3LMoy21gwBHJ6c5BIrj4f2dNf1+/0WPx58Sb7xp4f0e5ju7bSG02G0E0sf\n+ra5kUlpsdcHGTyc85AO28XeFvGPiHwJo9h4Y8Y/8IRrcPkvc6g+lxah5iCIq8XlyMAMsVbd\nnPyY7mpdDs77Q9ItLHxDqH/CTa1BGFutWEC2n2luu7yUyqcEDAPau4rl9a/5Cc3/AAH/ANBF\nADftNp/z5H/v6aPtNp/z5H/v6ap0UAXPtNp/z5H/AL+mj7Taf8+R/wC/pqnRQBc+02n/AD5H\n/v6aPtNp/wA+R/7+mqdFAFz7Taf8+R/7+mj7Taf8+R/7+mqdFAFz7Taf8+R/7+mj7Taf8+R/\n7+mqdFAFz7Taf8+R/wC/po+02n/Pkf8Av6ap1z/jHxzpngezin1BpGaYlYoYVBd8dSMkDAyO\nSe9ROcYRcpOyN6FCriaipUYuUnskdZ9ptP8AnyP/AH9NH2m0/wCfI/8Af01xngr4iaV45SYW\nPmw3EIBeCdQGAPcYJBFdPRCcakeaDuh4jD1cLUdGvFxkujLn2m0/58j/AN/TUstxaiKHNpkF\nTgeaeOTWdU03+pt/90/+hGrOcm+02n/Pkf8Av6aPtNp/z5H/AL+mqdFAFz7Taf8APkf+/po+\n02n/AD5H/v6ap0UAXPtNp/z5H/v6aPtNp/z5H/v6ap0UAXPtNp/z5H/v6aPtNp/z5H/v6ap0\nUAXPtNp/z5H/AL+mj7Taf8+R/wC/pqnRQBc+02n/AD5H/v6aPtNp/wA+R/7+mqdFAFz7Taf8\n+R/7+mj7Taf8+R/7+mqdFAFz7Taf8+R/7+mrVhPbNK4S12Hy2JPmE5GOlZNXNL/18n/XJ/5U\nAH2m0/58j/39NH2m0/58j/39NU6KALn2m0/58j/39NH2m0/58j/39NU6KALn2m0/58j/AN/T\nR9ptP+fI/wDf01TooAufabT/AJ8j/wB/TR9ptP8AnyP/AH9NU6KALn2m0/58j/39NH2m0/58\nj/39NU6KALn2m0/58j/39NH2m0/58j/39NU6KALn2m0/58j/AN/TR9ptP+fI/wDf01TooAuf\nabT/AJ8j/wB/TR9ptP8AnyP/AH9NU6KANGa4tQIs2mcpx+9PAyai+02n/Pkf+/pqG4+7D/1z\nH8zUNAFz7Taf8+R/7+mj7Taf8+R/7+mqdFAFz7Taf8+R/wC/po+02n/Pkf8Av6ap0UAXPtNp\n/wA+R/7+mj7Taf8APkf+/pqnRQBc+02n/Pkf+/po+02n/Pkf+/pqnRQBc+02n/Pkf+/po+02\nn/Pkf+/pqnRQBc+02n/Pkf8Av6aPtNp/z5H/AL+mqdFAFz7Taf8APkf+/po+02n/AD5H/v6a\np0UAatlPbMLjba7MREn94TkZHFVvtNp/z5H/AL+mjTul3/1wb+YqnQBc+02n/Pkf+/po+02n\n/Pkf+/pqnRQBc+02n/Pkf+/po+02n/Pkf+/pqnRQBc+02n/Pkf8Av6aPtNp/z5H/AL+mqdFA\nFz7Taf8APkf+/po+02n/AD5H/v6ap0UAXPtNp/z5H/v6aPtNp/z5H/v6ap0UAXPtNp/z5H/v\n6aPtNp/z5H/v6ap0UAXPtNp/z5H/AL+mj7Taf8+R/wC/pqnRQBc+02n/AD5H/v6aluLi1Drm\n03HYvPmkfwis6prr/WL/ANc0/wDQRQBN9ptP+fI/9/TR9ptP+fI/9/TVOigC59ptP+fI/wDf\n00fabT/nyP8A39NU6KALn2m0/wCfI/8Af00fabT/AJ8j/wB/TVOigC59ptP+fI/9/TR9ptP+\nfI/9/TVOigC59ptP+fI/9/TR9ptP+fI/9/TVOigC59ptP+fI/wDf00fabT/nyP8A39NU6KAL\nn2m0/wCfI/8Af00fabT/AJ8j/wB/TVOigC59ptP+fI/9/TVq1ntjbXRW12qFG4eYTu5rJq5Z\n/wDHpe/7g/nQAfabT/nyP/f00fabT/nyP/f01TooAufabT/nyP8A39NH2m0/58j/AN/TVOig\nC59ptP8AnyP/AH9NQX8WmanZzWl1pwmt5lKPG0pwQa+D/wDgjr/ybL4m/wCxvuv/AEisqX/g\nsT/ybL4Z/wCxvtf/AEivaTSkrPYTSkrPY+y/DHw78L+FLuW5tdNlmmkBUNc3G/Yp6hRgfn1r\ndfS9BkdXfQbV3U5DMASD69K+JfFn7X0v7Iv7JP7PuqDwd/wlsGs+GdOtmH9p/Yvs7JYQMv8A\nyxk3bgW9Mbe+eKXi7/go34+8L+BI/iC37OGur8Obub/QdfvtbWBpLdmAhnmhW3cwrIHj2lmK\nMXAR3yCeenhqNKPJCCS9DCnhqNKPJCCS9D7mudK0O81GDUJ9GilvYABHOzneoGSAD+J/Os3V\n/BXhjW7+S9utIkN5JjdNHeyoTgADgEDoB2rxH45/tn+F/gr+z94a+Kb6Vfatb+KIbWXRtJ3x\nwTyme3NwnmnLCNQgwzKHwSAAc14741/4KKfED4SaR4Y8Q/EX9nDWvCXhXW7kQrqDa8ks8QHL\nq0Bt0KS7NzLFMYi+xsEBWIqeHo1E1OCfXVLfv6jnQo1FacE+uy37+p9raD4a0nw7cyS20d7I\nHTZ5VxfPIg5zkA9DWZqF/oPw18OavqviDxI+naUAu/VPEGpRxQ2ZZtinzGCquXdQNx5O0d68\nb/aT/bT8Jfs+/Brw749t7f8A4Sv/AISfyX0HTYbkWj3sMkYlM53qXSJYyuWEbENJEpA35Hyn\n+23+1N8QvEf7M+reHviB8CtY+HGn+LobQ6Tq76tHfIJ47q3ufIuYxFG1uzRRykK+H3JjZgOy\nKNClCyirWva3mKNCnCyirWv+J9J/HTxZo/hmz8N6xrXxA0e40PVrfzdLvr7UbaCO5UhW3Qnc\nFlUq8bb1zww5wRn27wPpmuaN8O7SzEdvHq6uWX7cXZI4y2QhCkHOPfjNfAn7S3xE+HngT9lD\n9mFPiB8LE+JsN34XtWs1Ouz6U1kyWFnvIaFSXD71yCQPkHXt9In9tPH7bC/s+/8ACHdRn/hI\nv7U/6h323/j38n/gH+s/2vavNo5VSo4ieIjKV5K2/wCu/Q86lldKjiJ4iMneStv+u/Q+jNRv\nNXi0uzNlp2nXGo4H2oSzyJETjnZ1PX17VRtfEHiFbiJLrwlaPEzAPLBqhG0Z5OCvOPSvJ/2g\n/wBqD/hRHxN+D/hH/hGv7c/4WDrB0r7Z9v8As/2DE1rF5mzyn83/AI+s7cp9zGecjkfjP+2x\nfeC/jlD8JPhz8MtU+KvjOC3S41WC1u/sMOniQRmLdK0TqRtlRnkbZGnmRjeWLBPQlQk3dVGv\nu/VM75UZOV1Ua+79Uz6g1HXWt9fis18PzXFrIoLX63ICIcHgr17D86zNV8d2mlX8ts3hbWbp\nUxi4tYjJG2QDwQc98dO1fLXhX9u/xXqvxf1H4W+K/ghqvg74iS6Zc3uhaTLrUU8OqywwTSiL\n7SYkREk8hkWZfMTcGBxtNeP/ALGfx9+N/iL9of4w6dfeDNV8V6c/imC21S01DxfG0PgqJru7\nWRIUkyLhVXeMQBQwtV/vLipU6jT5Z217L7v61KlTqNPlnbXsvu/rU/QvSPHGlawbgLpF/amC\nPzHF3G8XHtkc15zpP7Qdvd+IkhutEig0qWTYJBO/mRgnAZj0PuAPzrxXUf2+dW8afF3WvA/w\nW+El98X7fRsQ3+uwaumn2KT/ALzKiV4nj2Hy2CyO6+YyvsDKFZmfsm/HXwv8bviV4k8Na58P\n9Y+G3xF0BRft4Z1aV5k+zHyx5wZoYyjK0sfyOo+WSNkLAts8zF0swk6f1eotH72m/wCfn2PN\nxVLHydP6vUWj97Tf8/PsfV8PxK8Hz4239kP9+72/zxUFj8W/AGqeI7vw/Z+ItCu9etIhcXGl\nwaxE91DGdmHeINuVT5kfJGPnX1FfIFp+3Tq3xT+KPjDwp8I/gOfilp3huRo59dXXra0t5grF\nN6s0TxlXZXMWJC0iKWCjDBfJf2O/Gmm/HL/gol8UfEFz4Um8OJd+EViuvD2qQgSWF1A2mQTx\nMuB92WKQAlVYjBKqSVHoR+sK/M4vtutfxPQj9YV+Zxfbda/ifp8t7YuqstoGVhlSJjgj2p32\nm0/58j/39Nc7qvhPSNasrazvbGOa2tgBDHkqEAGBjBHas61+G2gWF1DcWttNbyROHUJdS7cg\n5GQWwR7U5SxCl7sU1/iaf3cr/MJSrqXuxTX+Jr8OV/mdn9ptP+fI/wDf01BfaxpWmWzXF3FH\nbQL96Sa42qPxNY8+gJPrcWp/bL2N4xt+zx3BEDcEfMnQ9f0FcL8V/AOreILf7Tp15c3pEodt\nPkkURqNuMoOOfYnuaxxVetRoyqU6fM1sr7+f/AMcVXrUaMqlOnzNbK/4/wDAPUbDWtK1S2W4\ns447qBuBJDcblP4irH2m0/58j/39NeW/Bzwfq3hewvX1Im3FywKWhIbbj+M46E9Me3Nddpth\nrVpYXcd3qsWoXbA/Z5jaiJUOONygnPODRhcRVrUYTqU3FtO67fe769NPUeGxFWtRhOpTcW07\nrt97vr009TpPtNp/z5H/AL+mj7Taf8+R/wC/prh/J8cRfdudCnH/AE0imQ/oTWpcTa9DosLx\n21jc6ru/exrKyQ45+6SM+nUetaxxF73hJW8v8rmscRe94SVvL/K50n2m0/58j/39NH2m0/58\nj/39Nc3qmpaxZ2VpJaaMuo3Lj9/Ct0sQjOB0LD5ucjtVC08Ta3Jdww3XhW5tkd1RpVuopFQE\n4LHB6DrTliYRlytP/wABlb77WCWJhGXK0/8AwGVvvtY72Ge2NjckWuEBXK+YeeT3qr9ptP8A\nnyP/AH9NeB+K/jN4h0nxVqFnaNFDYW9wYjbPEG80IcZLHnnrwRwa9qs7g3dnBOUMZljV9jdV\nyM4Nc2EzCjjJzp0r3hvf9DnwuYUcZOdOle8N7/oaX2m0/wCfI/8Af00fabT/AJ8j/wB/TVOi\nvTPSLn2m0/58j/39NH2m0/58j/39NU6KANKzuLVruELabGLjDeaTjmukrkdP/wCP63/3x/Ou\nuoAKKKKACiiigAooooAKK8t+Mv7SPgz4HQga5Pc32o7BKdL0qJZrlIiwXzXBZVRMkDLsMk4G\nTXp0Ey3EEcqghXUMAeuCM0ASUVyPxW+Jml/B/wAC6h4r1mC8urCyaJXhsEV5nMkixqFDMoPL\ngnJHAP0ritO/aNm1HULW1Pwn+JVqJ5Vi8+50SNYo9xA3OfOOFGck46CgD2Oub1cQ/wBoS72c\nNxnAGOg966SvF4fD3xEtPjFrupav4u0y/wDA00ZFhoUGleVc27kR7We43neAA/G0Z3D0oA7j\nFv8A3pf++R/jRi3/AL0v/fI/xr551r4eftF3OsX82mfF7wxZ6dJPI9tbS+FPMeKIsSiM3nDc\nQuATjnFdl4L8L/FXTfA3iKy8TeOdH1nxTcpINJ1W00X7PBZsY8IZIfMPmYf5iMjI4oA9Uxb/\nAN6X/vkf40Yt/wC9L/3yP8a8rtPC3xVj+Et5pVx450eX4gvKGt/EK6LttI081TtNt5nzHYHX\nO7qwPauA/wCFbftMf9Fn8Kf+Eh/9voA+k8W/96X/AL5H+NVE1fSJNSk05L+N9QjUO9oroZVU\n9CUzkD3xXnHxA8MfFLVtB8NweEfG+kaBqttGBq93e6N9qS9bagJjTePK+YOe/wB4DtX5FfDv\n9mD9pK0/ajsZItE1/SvFtvrH2q48X3dtK9ip8zMlw1wQEmjYbiVBO8ErjkigD9x8W/8Ael/7\n5H+NGLf+9L/3yP8AGvD/AIe+CPjnpHjDT7vxh8TvD3iDw5GZPtWm2Phv7JNNmNgm2XzW24cq\nx4OQpHeus0Hw/wDES1+KGralqvi7TL/wLNEy2GhQ6V5Vzbv8m1nuN53gYk42j7w9KAPRMW/9\n6X/vkf40Yt/70v8A3yP8a+eda+Hv7RdzrN/Npnxe8MWWmyXEj2ttL4U8x4oixKIz+cNxC4BO\nOcZrsvBXhf4q6b4H8RWXibxzo+teKbmOQaTqtpov2eCyYx4QyQ+YfMw+GIyMjigD1TFv/el/\n75H+NfPn7TfiLRbPxZ4G8PpdSy+I9aa6W2sVUcQxR+ZJPIxOI412gFm4yw9DW7rEPxD8F/AT\nX7jxD8RNDtvFdjG943ip9F2WVrboyu+628z5sRq653dWB7Yr83vjX8AP2hPj18I1+NWo3Evi\ni815YYV0DTtOaK+TSELtA6xIx+R2KytCu45ZHJO35ca1KNeDpz2Z34DG1cuxEcTRtzR77a6H\n3z+zPrGjal8QfGGlrqAOtaHb26XFpGAwaO4HmJKjg7XTCY3KSAcg4NfR2Lf+9L/3yP8AGvxX\n/Z2+E3xw/Zs8OP8AGg/afAGjQ31tot/HrWms8o06eQCW6e2fa3lRyeSR0JLMRwDn9T/iv4a+\nKvibUNOufhz4+0Twtpgt/wB/FqGijUDO5OQ6v5i4G3Axz60qFGOHgqcNi8xzCtmeIeJr2u+2\n2h6zi3/vS/8AfI/xqaYQeVBlpMbTj5R/ePvXhHw98EfHTSPGGn3fi/4neHvEHh2Mv9r06y8N\n/ZJpgY2CbZfNbbhyrdDkAjvXY6P4e+Itr8TNQ1PVvF+l3/gSaBxYaDDpPlXVs+5cM9xvO8DE\nnG0feHpW55p6Bi3/AL0v/fI/xoxb/wB6X/vkf4188638Pf2irnWb+bTPi74YstNkuJHtbabw\np5jwxFiURn84biFwCcDOM12Xgnwt8VdM8EeI7LxP450fWvFFzHINI1S00X7NDZOYyEMkPmHz\nAHwxGRkcUAek3Or6RZ3tvZ3F/HBd3GfJt5HRZJcddqk5P4Vbxb/3pf8Avkf41+Iv7Un7M37R\nesftI6hcapouueONYvriJrHxHothItm6ADy9jLlbYJ0Ksw2kE5wdx/RnQfhh+1BbaHp0V18Z\nvC4uo7aNJRL4W85g4UBsyeaN5zn5sDPXAoA+ocW/96X/AL5H+NGLf+9L/wB8j/GvLvHvhj4p\naroHhqDwn430jQdWtkA1i7vNF+1R3rbFBMaeYPK+YOcZPDAdqi+LnhX4r+INUsZfh7470bwl\nYRwlbmDU9E+3tLJu4ZW8xdoxxigD1bFv/el/75H+NGLf+9L/AN8j/GvDvh94I+Omk+MNPu/F\n/wATvD3iDw7GX+16dZeG/ss0wKMF2y+a23DlW6HIBHeut0Pw/wDES1+KOq6nqni7TL7wLNEy\n2Ogw6V5dzbv8mGa43neBh+No+8PSgD0TFv8A3pP++R/jVTTdX0jWI5JLC/jvo43MbtbOkgVh\n1UkE4PtXyF+098JP2k/Ffwy8eWmg/EXSNTsLyOX7PoGn6J9lvZrYyZNutz5h+by8qePn5HG7\nj5c/4Jq/Af42+EfiR4t1IaVq/gHTJNCurL7T4g0+WKGS9bAtm8iTaZfLfLkjoAy7hvwQD9a8\nW/8Ael/75H+NGLf+9L/3yP8AGvK7Hwv8VYvhNfaXdeOdHn+IEku628QJou21iTzFO1rbzPmO\nwOud3Vge1cB/wrb9pj/os/hT/wAJD/7fQB9J4t/70v8A3yP8aMW/96X/AL5H+NeW+PfDHxS1\nXw74bt/CfjjSNB1i2RRrF5eaL9qjvW2KCY08weVlg5xk8MB2qP4ueFfiv4g1Swl+HvjvRvCV\njHCVuYNT0T7e0sm7hlbzF2jHGKAPVsW/96X/AL5H+NIdY0nRMzX+oRWMTgxq93IkSsxHCgk8\nn2rw/wCH3gf46aT4v0+78X/E/wAPa/4djL/a9OsvDf2WaYFGC7ZfNbbhyrdDkAjvXwh/wU8+\nBHxs8b/GOw1mx0nV/HHg97YRaVb6Dp8twNOOTvjljjDHexw3mkAMCq5+TFAH6wYtz/FJ/wB8\nj/GjFv8A3pf++R/jXxJ+zj8G/wBqHw58E/CenXPxM0bw00Frtj0fV9A+33VnGXYxxSTeaMkK\nV+XHyjC/w19D+CPC3xV0zwT4ks/FHjnR9b8T3MUi6Rqlpov2aGycxkI0kXmHzQHwxGRkDFAH\nqmLf+9L/AN8j/GjFv/el/wC+R/jXldh4W+KsXwnvtLu/HOjz+P5Jd1t4gTRdtrEm9Tta28z5\njsDrnd1YHtXAf8K2/aY/6LP4U/8ACQ/+30AfSeLf+9L/AN8j/GjFv/el/wC+R/jXlvj3wv8A\nFPVfDnhu38J+ONI0HWbaNRq97eaL9qjvW2KCY4/MHlZYOcZPDAdqo/HLwj8XfEyr/wAK08da\nR4ShFnLHLBqGk/apJZjnayS7x5fYfdODzz0oA9Vt9X0i7vp7KC/jmvLcAzW8bo0keem5Qcj8\nat4t/wC9L/3yP8a/FX9kP9m/48eG/wBrnwxqF34X8R6A+maqJ9b1nUYJY7eS23ZuFM7fLN5q\n7lG0tuLA9ASP1m0Xw/8AES2+KWqanqfi7TL3wJLEy2WgxaV5d1A+EwzXG87wMPxtH3h6UAei\nYt/70v8A3yP8aMW/96X/AL5H+NfPWu/D39om61vUJtK+Lvhix0uS4ke0tZvCnmvDCWJRGfzh\nuIXAJwM4zXY+B/C3xV0zwX4js/FHjnR9b8TXMUi6Rqdpov2aGycxkI0kXmHzQHwxGRkDFAHq\nmLf+9L/3yP8AGjFv/el/75H+NeV6f4W+KsPwnv8AS7zxzo9x4/kl3WviBNF2WsSb0O1rbzPm\nO0OM7urA9q4D/hW37TH/AEWfwp/4SH/2+gD6Nvb3TtNtZLm7uvsttENzzTlURB6kk4FOtrmx\nvbeOe3uDPBIoZJYtrKwPQgg4Ir4D/wCCofwn+K/j/wCD3w9i8PwXviyHSpJD4itNEtnLXNwY\n4hHci3TLFFZZ+Bnb5o7cjA/YT+B37SnhX4MSwWniiD4cadPqEk9ro3ifRGu7jYUQF1RnRoFL\nBvlI5OWwM5IB+j+Lf+9L/wB8j/GjFv8A3pf++R/jXhvgDwP8ddJ8X6dd+Lvif4e1/wAOxs5u\n9OsvDf2WWYFGChZfNbbhip6HIBHeuu0bw/8AES2+KeqanqXi7TLzwHLEVstBi0ry7qB8Jhmu\nN53jIfjaPvD0oA9KnEGIstJ9wYwo9T71Di3/AL0v/fI/xr5/8SfD39oq61u8n0v4veGLLS5Z\npHs7Wbwp5jwQl2KIz+cNxVcAtgZxmus8DeFvirpfgzxHZ+KfHOj654luYnXSNTs9F+zQ2TmM\nhGki8w+aA+GIyMgYoA9Uxb/3pf8Avkf41Uk1fSIdRi0+S/jS/lUvHas6CV1HUhc5I4POK820\n7wt8VYfhRf6XeeOdHuPH8ku618QJouy1hTeh2tbeZ8x2hxnd1YHtX5HfFn9mH9pG/wD2oNRk\nuNE1/XfFV1q32m18W2FtKlk3z5inScfJAijb8pYeWAF7CgD9xcW/96X/AL5H+NGLf+9L/wB8\nj/GvKfG3hb4ran4R8MWvhjx1o+h+ILWFF1nULzRftUV9II1DNHHvXywXDtjnggdqX4ueFvit\n4g1Kwk+HvjrR/CVlHEy3UOp6J9vaaTPDK3mLtAHGOaAPVcW/96X/AL5H+NGLf+9L/wB8j/Gv\nDfAHgf466T4v0678W/E/w9r/AIdjZzd6dZeG/ssswKMFCy+a23DFT0OQCO9ddo/h/wCIlv8A\nFPU9T1Hxdpl54DliK2egxaV5d1A+EwzXG87hkOcbR94elAHomLf+9L/3yP8AGkY2yKWZ5FUD\nJJAAA/OvnzXvh9+0Td67qM+k/FzwxYaVJcyPaWs3hXzXhhLEojP5w3ELgFsDOM15r+1F8J/j\n14j/AGUPGmhS+KrXxt4juLm3lW30DTP7PlmsVP76AKHYyMThsAgsFK4OcEA+xNN1TStZtvtG\nn3yX1vuK+bbOkiZHUZBIzVrFv/el/wC+R/jX5Vf8E2PgX8dvCcnxDu7eO++HNje6bHbWkniT\nTJPLe+FxGwlFrJsLhYVnQtwMyjk4IH2F/wAK2/aY/wCiz+FP/CQ/+30AfSeLf+9L/wB8j/Gj\nFv8A3pf++R/jXlnj3wv8VNV8LeHLbwn450fQteto1Gr395ov2qK9fYoZo4/MHlAuGbGTwQO1\nN+Lnhb4reINR0+T4e+OtH8JWccTLdQ6non29ppM8MreYu0AcY5oA9Vxb/wB6X/vkf40Yt/70\nv/fI/wAa8M8A+B/jtpXi7Trvxb8T/D2veHo2Y3enWXhr7LLMCjBQsvmnbhip6HgEd66/SPD/\nAMRLf4qanqeo+LtMu/AcsRWz0CLSvLuoHwmGa53ncMhzjaPvD0oA9DOsaTpJZbzUIrNrhDHE\nLmRIzI2R8q5PzH2FLi3/AL0v/fI/xr8d/wDgoX+zx8efFnx/n1WfR9Z+IGi3aBdGudA0yWWG\nyt9xxbukYby3XPLHAfO71C/oL+zL4E+L/hL9n2HRvHHiqzuPFh0pItLeWz8x9JfySI0uG3f6\nSyNsycjO0jJ60AfQeLf+9L/3yP8AGjFv/el/75H+NeV6Z4W+KsHwp1DS77xzo9z4+klLWmvx\n6LstYU3IdrW3mfMdocZ3D7wPauA/4Vt+0x/0Wfwp/wCEh/8Ab6APpPFv/el/75H+NGLf+9L/\nAN8j/GvK/Hnhf4qar4U8OWvhPxzo+ha/bRqur6heaL9qivXEahmjj8weUC4ZsZPBA7UfFzwt\n8VfEGoafJ8PPHWj+ErOOJluodT0T7e0z5+VlbzF2gDjHNAHqmLf+9L/3yP8AGqlnq+kajcXM\nFpfx3U9s2yeKF0dom9GAOVPsa+ZvE/ww/aXuvCXiK3/4Wx4e1KafSb2C3tbHw99hned7eRYd\nk/mnyyJCh3Y4xXwr/wAE+f2e/jT4Q/aq0jVr3wt4g8K6RpxuF1u81W1ltoJ4jGw8rLgCYs5Q\ngLu6B+gzQB+x2Lf+9L/3yP8AGjFv/el/75H+NfPniD4fftEXevalPpHxc8MWGky3Mr2dpP4V\n82SCEsTGjP5w3FVwC2BnGa6/wJ4X+Kul+EPEdp4r8c6PrviK5iddI1Gz0X7NDZOYyFaSLzD5\ngDlWxkZAx3oA9Uxb/wB6X/vkf40Yt/70v/fI/wAa8r0vwt8VYPhVqOl33jnR7rx9JKWtNfj0\nXZawpuQhWtvM+Y7Q4zuH3ge1cB/wrb9pj/os/hT/AMJD/wC30AfSeLf+9L/3yP8AGq+oajpm\nkWj3V9eLZWyY3TXDLGi/ViQBXmfjzwv8VNV8JeHLXwp450fQvEFtEq6vqF5ov2qK9cRqGaOL\nzB5QLhmxk4BA7V8c/wDBWb4R/FH4iHwVf+FdL1TxL4SsY5kutL0iB7iSC6LDEzxICzAphQ2D\nt2t038gH6MRSWk8SSxSvJG6hldACGB6EHPIp2Lf+9L/3yP8AGvhj/gnn8Ivjz4A+HPh1fFXi\nCLQfCKXc8w8IatphfUEgZflAlLAwAyZfyypI56bsD6j0rw/8RIPirqWp3/i7TLrwFLEVtNAj\n0rZdQvtQBmud/wAwyHONo+8PSgD0TFv/AHpf++R/jU1yIPMXLSfcToo/uj3r528Q/D79oe71\n/Up9I+LfhnT9JluZXs7Sfwr5skEJcmNGfzhuKrgFsDOM12ngvwt8VtK8KeIbTxb460fXvEVz\nBjSNRstE+yxWTmIhTJF5jeaA5VsZGQCO9AHp+Lf+9L/3yP8AGjFv/el/75H+NeV6V4W+KsHw\nq1HS7/xzo9149llLWmvx6LstYU3IQrW3mHcdocZ3D7w9K4D/AIVt+0x/0Wfwp/4SH/2+gD6I\n/tfSDqR077fH/aATzPsm9PN2/wB7ZnOPfFW8W/8Ael/75H+NfhvbfswftJD9qgSDRPECeMBr\nn2s+MWt5PsOfNz9q+0Y2GPHO3OcfJtz8tfrp8XPC/wAVfEF/pz/Dzxzo/hK0jiZbuLU9F+3t\nM5I2sreYu0AZGOaAPVMW/wDel/75H+NGLf8AvS/98j/GvC/AXgb476V4u0668WfFDw7rvh6N\nmN3p1n4a+yyzLtIAWXzTtwxU9D0x3rsNL8P/ABEg+Kuo6pfeLtMuvAMkRW00CPStl1C+1AGa\n53/MNwc42j7wHagD0TFv/el/75H+NGLf+9L/AN8j/GvnzxD8Pv2h7vX9Tn0f4t+GdP0iW6le\nztJ/CvmyQQlyY0Z/OG4quAWwMkZrr/AXhb4q6V4S8R2vizxzo+veILmJl0nULLRfssVk5jYK\n0kXmHzAHKtjI4BHegD1GaWztoXllmaKJFLO74CqB1JJPAqLT9Q0zVrRLqxvFvLZ/uTW7LIjf\nRgcGviL9t34SfHHxb+yQ+iLrK+OvEcWtx3epxeHtONo93p4jcCJYFZjIVlKOVGScA4+XnxX/\nAIJ6fAb9pDwz4e8T3Gk6w3wv0S7lhKWfirRHna6lAbdJHA7I0YA2gv8Ax/KOdvAB+p+Lf+9L\n/wB8j/GjFv8A3pf++R/jXlfjvwv8VdU8H+HLTwr450fQvEdtEi6vqV5ov2mG9cRgM0cXmDyg\nXDNjJwDjtR8XPC3xV8QX2nP8PfHOj+ErWOJlu4tT0X7e0zkjaynzF2gDIxzQB6pi3/vS/wDf\nI/xoxb/3pf8Avkf414X4D8DfHfSvFunXXiv4oeHdd8PRuxu9Ps/DX2WWZdpACy+aduGKnoem\nO9dhpnh/4iQfFbUNUvvF2mXPgGSIraaBHpWy6hfag3Nc7/mG4OcbR94DtQB6Ji3/AL0v/fI/\nxpDrGk6dutrnUIrae6ASGGeREeU56IpOWPsK+fvEXw//AGh7zxBqc+j/ABb8M6fpEt1K9nZz\n+FfNkggLkxxs/nDcVXALYGSM1+an7Yn7NH7RWu/tA3V5rOka18RLu8EC2OvaBpkv2LYFUKiq\nm5bba27IYjnc+SDmgD9rcW/96X/vkf40Yt/70v8A3yP8a8S+Gngz4v6T+z3a6Hr3jTTpPiSL\nWFYtal0/z4rXCx5SVd489xtkBkyNxbOK5n/hW37TH/RZ/Cn/AISH/wBvoA+k8W/96X/vkf40\nYt/70v8A3yP8a8r8deFvirqng7w5aeFfHOj6H4jtokXV9SvNF+0w3riMBmji8weUC4ZsZOAc\ndqPi34W+Kuv3mmv8PfHOj+E7aKJlvI9T0X7eZ3JG1lPmLtAGeOetAHyL/wAEc/HWix/DXxz8\nPbqaS08V6frj6vJYzBUd7eSGCAlVLbyUkgIf5cL5kWTl8Va/4LI+L9Bh+CXhDwsNTjPiO48Q\nxanHpx/1ptY7a5jeUgdF3yoozjJJxna2Kfij/gldf/FT4pyeLfHXjrTAt/O1xqy+FdG/s6a7\nkK8yLukkiRywDMwj+Yli2WYtXM+Mf+CTtx4Ej8a6/wCBvEL+IbiLTJD4e0zVI8X0N1+7O/z0\nKRmTAmCExgKXQ8MocAHO/wDBRKwGgfsdfs+eHrmaA6zoUFrpmp20FxHOLa5i0+NJIy0bMpIZ\nWHBwccV9df8ABTAQ/wDDEnxH2M5b/iW4yBj/AJCVr71+Y37Pv/BPr4r/ABp8U3ukato2q/D3\nR7VDJcapr+mTRRmVeEjSN9hkbk8g4UZJPIB/U3xT+zz45+MPwM8cfD34o+PbLW5NcWH7DqGi\naStj9kaKRZk3qWfepljj3DgldwBBIIAPm/4/fGXxR4N/ZM/Ze+G/gnRtL1PxD8Q9E0myhuda\ntYLqKB4oLEIiwzo0RaSS4jHmSAhArYXcwdOb/bv8OfFv4cfs0tF8T/2hrbxRPrslpb/8Ifa+\nEbK3S6uEkjmkEd0hWURxFN3meWu7CKyr5mK9z8e/sCXHxb/Zw8L/AA+8ZeLbe98UeEFW28O+\nIrCwMEdtaiKKLyJYd58wFYVJbIYsqEEAEN454j/4JS+PPG1zPeeK/jQni3UntltItQ12yuru\n4tolfeFjd7k4GS3ByPmbjk0Acr+1BqPh3R/BH7AV/wCKRE/hi106yn1dbu38+E2ixaQ04aMB\nvMXYHyuDkcYOcV9T/wDBUbWNF0v9jPxbbXtwsd7qN3p9rpyTJkyXAu4pWVTzg+TFO2eOFYd8\nVynjv/gnZe+JP2a/DXwos/iHeXa6bqCapLqPiON70wSi2WHyrJVdPItxiQrExcDzGyScEcZ8\nU/8AgkxD4oh0a18P/FHXZbHTrH7FDF4vc6l9jQOrKtqIzCsSYDDYQw+YkAHBAB4d/wAFBNn/\nAAyf+yLtLFv+EWTORx/x4afXpc9sYf8AgtdZyyRypbXMRlhkZMean/CPMu5exG5WGR3U+lfO\nv7dvw+8ceDfE/wAM/hzqvjvSviJJomkQ6HpWj6DCEurBlESpFNaq7uJJY2tyrtgyAAAfLz9E\nfAr/AIJveL/iX4e+H/iL4t+LNW8N3Giae1vpun6UfI1jT4lnea2Vrh96psZ5HVQgdd4XcNoC\ngHe/8FBNa0uf9rX9kjR4bxX1K18Tx3dxa8eZHDLf2CxOR6M0EwHvG3pWn8f/ANl/wr8cf2hd\nW8W/B341weC/jXplgzapptjeCd5pI1WCIyeXMJLYAKkMuFkG0x5jBY+ZxEn/AASu8V6d8VLf\nxxpfxajuNXsNcGtWWoa9p8t9eyMkqyQC5kMwWVl2Llgq7izZ4wF73XP+Cc2ofEbxnrXxC8af\nEq/tvibMqNp+veDof7LSOWOERRPKjGRiVCRj900RKg85OaAOf+G37Ufxt+BP7U2gfBf9oO90\nbxVF4mETaf4i0eFUdGn/AHdvtEUUYaMzRvEyyRI4Zi+8ooDan/BPYRf8NN/thbi4H/CXrjAH\n/P7qnWtr4T/8E8tQ+Fer6p49u/iJeeKvjY+8ad4r1lJLi2td8YiZ3gZ98zmJpI8ySMAGXCgq\nDXn9l/wTV+JelfFu++I2m/GSx0zxFf6oNXvBZ6PNHb3M32lLorJH9ow8ZmRXMZ+XIGAMDAB5\nH/wTN8CePfGnhHxzZ+AvjIPhhqdhqMLappv/AAhdnqcs6PGRDI1zMyvgNHOvldEILcGQ19D/\nAAq/ZXsNF/bYt/H3ij9obS/HvxN0yJ5dR8PR6Ja2F7Kj2DW0bNHFcnYFikjbIi5AXONwNT/G\nP/gnLc+M/FGl/EDwP47n+HnxQeAHXNY0dZLe3v7toys9zEkbq8Dyszb9rlSDypYszfCvwJ/Z\nU/aI8OftJaNDpXh7XPCmu6bqIkn8S3tnI1hCgb95I0xwk6Mu75Ax8zJHc4APsL/gjNqPhyX4\nN+PbCIRDxRBr6T3zR22JTZvbotqGlwNy+ZHd7VydpLnA35Nn4Aaxo2u/8FZ/jnc6ROs9inht\nrV3hTAFxC2lxXCkccrMkqk9yCeetZ+mf8EuvEtz8WNU8Yat8W5tKTXJbh9Zj8H2smmz3Cz5M\nyI7SyKqu5yyFSmMgKBgD1L9nP9ge1/Zn/aB1Pxt4X8RK3he60YaQmjXUDSXWNtuXlectgs8s\nBkO1FUbyFCqAAAfX2Lf+9L/3yP8AGjFv/el/75H+NfP3iT4f/tDXniLVLjRfi14Z07R5bqWS\nys5/CvnSQQFyY42fzhvZVwC2BkjOK634f+F/irpPhfxFbeLvHOj6/rtzEV0m+stF+yxWb7GA\naSPzG8wbyrYyOAR3oA9Uxb/3pf8Avkf41FdXVhY20lxc3Bt7eNdzyylVVR6kk4ArzDRfC3xV\ntvhbqmman450e98dyys1lr0Wi+XawJlMK1v5h3kYfncPvD0r4s/bz+Bn7S3ir4S2Ud34pg+J\nGkW98s13ovhjQ2tLj7pCSsiu7TKp/hA4JDYOMqAfpDZ3mn6jax3Npc/araUbkmhKujj1BBwa\nmxb/AN6X/vkf41+a/wDwT/8Agh+0R4V+DmuxWeuD4bW15qkc9jYeJ9Ha5kKCNxM6wMyGIOxi\n5PXyycYIJ+1Pi14X+Kuv3Glt8PvHOj+E4YomW9TUtF+3GdyRtZT5i7ABnjnOaAPVMW/96X/v\nkf40Yt/70v8A3yP8a8K8C+BfjxpfizTrrxV8UfDuueH4nJu9Ps/DX2aWZdpACyeadpzg5wel\ndjYeH/iJF8WL7VLvxdpk/gCSLbbeH00rbdRPsQbmud/zDeHbG3owHagD0TFv/el/75H+NGLf\n+9L/AN8j/Gvn7xL8P/2hb3xHqtxovxa8M6bo0t3LJZWVx4W86S3gLkxxtJ5w3sq4BbAyRnFd\nb8PvC3xV0nwz4itvF/jnR/EGuXMRXSb6y0X7LFZvsYBpI/MbzBuKnGRwpHegDub/AEPwbc6s\nsmoR2La3JtaBJygmkA7hCfnxjrg4xW3i3/vS/wDfI/xr8Qf2h/2Yf2j9V/aX1W5vtD1/xfr1\n5fpLZeKNIspVsmXI8pklXKWwQBRtZhs29ehP6R6b8Mv2notOtUn+M/hXz1iRZN3hPzDuAGct\n5o3c98DPoKzjThBtxSV9/MzjThBtxSV9/M+m8W/96X/vkf40Yt/70v8A3yP8a8r8b+Fvirqf\ngnw5Z+GPHOj6J4ntoo11fVLvRftMN64jAdo4fMHlAvlgMnAOKPiz4W+Kuvz6W3w/8c6P4Uii\niZb1NS0X7cZ3JG1lPmLsAw3HOc+1aGh6pi3/AL0v/fI/xoxb/wB6X/vkf414T4G8C/HnS/Fm\nm3Xin4peHdc8PxSE3en2fhn7NLMu04Cyeadpzg5weldlY+H/AIiRfFm+1S68XaZP8P5Ittt4\nfTStt1E/lqNzXO/5hvDtjb0YDtQB6XYiD7ZBtaTO8Yyo9frXU181aZ4K+OA+KsGoN8S/D7eD\nP7X8/wDsUeHMXH2LzciDz/N+/wCX8u/b15xX0rQAUUUUAFFFFABRRRQB88/tq+HdM0/9nP4i\n6rb2EEOp3408XV2qDzZgl3bqgZupAA4HQc+pr3zTP+Qbaf8AXFP/AEEV88/tTW3xM+JXgzxT\n4B8PfDOW+069NuLfX/7cs41cJLFMT5DsrDlCnJHr0r0Twd4x+IOqeG9c/tX4cN4a1Kwsd2mQ\nz6zbXa384RsRkxn92NyoMsQPn68GgDH/AGsfB/iDx38IJtH8N6U+s6hJqFpK1okyRFo45Q7f\nM5AH3RXP+LvjZ8TvhHbW3iTx54P0L/hC3njivJdB1CWa701ZGCq8gdFWQAkD5OpPUcV2fiOL\n4oeJ/hFp91pEll4Q+IKGK6n0+Xy7i2cqx32zP8+FYY+ZTnIHIBNedfEPR/i5+0F4bTwPq/ge\nz8A6FezQnV9Xk1qG+d4o5FcpBHGMgllBBbHofWgD2b4keNNZ8IeHLXUvDnhG98cXM1wkZsdO\nuYoXWMozebukIUqCqjA5+ce9V9D1C58S6Raanq2ny+GtRuYw82k3brLLbHptZkypOADwe9dn\nbW8dnbRQRLsiiQIi+gAwBXOa1/yE5v8AgP8A6CKAG/Y7f/n9j/75NH2O3/5/Y/8Avk1TooAu\nfY7f/n9j/wC+TR9jt/8An9j/AO+TVOigC59jt/8An9j/AO+TR9jt/wDn9j/75NU6KALn2O3/\nAOf2P/vk0fY7f/n9j/75NU6KALn2O3/5/Y/++TR9jt/+f2P/AL5NU684/aC+Mdr8DPhhqXiR\n7c6jqjMljo+lR5MmoX8p2wQIBySzcnHIVWPagDy/47Iv7Rvxp0r4Gabe7/CGjiHXfH13FuCt\nEG3WmmE/3pmUSOOCEUEHqK+oIdNtLeFIormGKKNQqIkZCqBwAAOgrxv9mX4OXXwg+HhGu3I1\nLxxr9y+s+JdT4JuL+Xl1B/uRjCKBxhcgDJr1ugCr4u8E6L458Lav4d1mSK70nVbWWyuoGU/P\nFIpVh7HB4PavDf2N/EF7ZeHPEPwl8U6kJfFnw2vBpDTyqd19prDdYXY9nhwvrmM55Ne+V8y/\ntKo3wS+K3gr47WgMekQFfDHjEIODpk8g8i5b/rhOVJPUq+OlAH1N9jt/+f2P/vk1LLaQGKEG\n7QAKcHaeeTWajrIiujBlYZDA5BHrU83+pt/90/8AoRoAm+x2/wDz+x/98mj7Hb/8/sf/AHya\np0UAXPsdv/z+x/8AfJo+x2//AD+x/wDfJqnRQBc+x2//AD+x/wDfJo+x2/8Az+x/98mqdFAF\nz7Hb/wDP7H/3yaPsdv8A8/sf/fJqnRQBc+x2/wDz+x/98mj7Hb/8/sf/AHyap0UAXPsdv/z+\nx/8AfJo+x2//AD+x/wDfJqnRQBc+x2//AD+x/wDfJo+x2/8Az+x/98mqdFAFz7Hb/wDP7H/3\nyatWFrCkrlbpHJjYYCnjjrWTVzS/9fJ/1yf+VAB9jt/+f2P/AL5NH2O3/wCf2P8A75NU6KAL\nn2O3/wCf2P8A75NH2O3/AOf2P/vk1TooAufY7f8A5/Y/++TR9jt/+f2P/vk1TooAufY7f/n9\nj/75NH2O3/5/Y/8Avk1TooAufY7f/n9j/wC+TR9jt/8An9j/AO+TVOigC59jt/8An9j/AO+T\nR9jt/wDn9j/75NU6KALn2O3/AOf2P/vk0fY7f/n9j/75NU6KALn2O3/5/Y/++TR9jt/+f2P/\nAL5NU6KANGa0gIizdoMJgfKeeTUX2O3/AOf2P/vk1Dcfdh/65j+ZqGgC59jt/wDn9j/75NH2\nO3/5/Y/++TVOigC59jt/+f2P/vk0fY7f/n9j/wC+TVOigC59jt/+f2P/AL5NH2O3/wCf2P8A\n75NU6KALn2O3/wCf2P8A75NH2O3/AOf2P/vk1TooAufY7f8A5/Y/++TR9jt/+f2P/vk1TooA\nufY7f/n9j/75NH2O3/5/Y/8Avk1TooAufY7f/n9j/wC+TR9jt/8An9j/AO+TVOigDVsrWFBc\nbbpHzEwOFPA45qt9jt/+f2P/AL5NGndLv/rg38xVOgC59jt/+f2P/vk0fY7f/n9j/wC+TVOi\ngC59jt/+f2P/AL5NH2O3/wCf2P8A75NU6KALn2O3/wCf2P8A75NH2O3/AOf2P/vk1TooAufY\n7f8A5/Y/++TR9jt/+f2P/vk1TooAufY7f/n9j/75NH2O3/5/Y/8Avk1TooAufY7f/n9j/wC+\nTR9jt/8An9j/AO+TVOigC59jt/8An9j/AO+TR9jt/wDn9j/75NU6KALn2O3/AOf2P/vk1LcW\nkDOubtF+RRjaf7orOqa6/wBYv/XNP/QRQBN9jt/+f2P/AL5NH2O3/wCf2P8A75NU6KALn2O3\n/wCf2P8A75NH2O3/AOf2P/vk1TooAufY7f8A5/Y/++TR9jt/+f2P/vk1TooAufY7f/n9j/75\nNH2O3/5/Y/8Avk1TooAufY7f/n9j/wC+TR9jt/8An9j/AO+TVOigC59jt/8An9j/AO+TR9jt\n/wDn9j/75NU6KALn2O3/AOf2P/vk0fY7f/n9j/75NU6KALn2O3/5/Y/++TVq1tYVtroC6RgV\nGSFPHNZNXLP/AI9L3/cH86AD7Hb/APP7H/3yaPsdv/z+x/8AfJqnRQBc+x2//P7H/wB8mj7H\nb/8AP7H/AN8mqdFAFz7Hb/8AP7H/AN8mj7Hb/wDP7H/3yap0UAXPsdv/AM/sf/fJo+x2/wDz\n+x/98mqdFAFz7Hb/APP7H/3yaPsdv/z+x/8AfJqnRQBc+x2//P7H/wB8mj7Hb/8AP7H/AN8m\nqdFAHN6j8Dfhxq/i9PFd/wCEvC974pSaK4XW7jRoJL1ZYgoicTlC+5AiBTnI2jGMCuw+x2//\nAD+x/wDfJqnRQBc+x2//AD+x/wDfJo+x2/8Az+x/98mqdFAGjcWkDTEm7RTxxtPpUX2O3/5/\nY/8Avk1Dd/69voP5CoaALn2O3/5/Y/8Avk0fY7f/AJ/Y/wDvk1TooAufY7f/AJ/Y/wDvk0fY\n7f8A5/Y/++TVOigC59jt/wDn9j/75NH2O3/5/Y/++TVOigC59jt/+f2P/vk0fY7f/n9j/wC+\nTVOigC59jt/+f2P/AL5NH2O3/wCf2P8A75NU6KALn2O3/wCf2P8A75NH2O3/AOf2P/vk1Too\nAufY7f8A5/Y/++TR9jt/+f2P/vk1TooA1obaEWNyoukKkrltp45NVfsdv/z+x/8AfJot/wDk\nG3f+8n8zVOgC59jt/wDn9j/75NH2O3/5/Y/++TVOigC59jt/+f2P/vk0fY7f/n9j/wC+TVOi\ngDSs7WFLuErdo5DjChTzzXSVyOn/APH9b/74/nXXUAFFFFABRRRQAUUUUAFFZniHxNo/hLTm\n1DXNWsdFsFYIbrULlIIgx6Dc5AyfTNaSsrqGUhlIyCDkEUALRSMwRSzEKoGST0Fc/onxD8K+\nJtSl0/R/E2j6rfxAmS1sb+KaVMdcorEj8qAOhrm9Xt2fUZWBQA46yKD0HYmukrl9a/5Cc3/A\nf/QRQBX+yv8A3o/+/q/40fZX/vR/9/V/xqGigCb7K/8Aej/7+r/jR9lf+9H/AN/V/wAahooA\nm+yv/ej/AO/q/wCNH2V/70f/AH9X/GoaKAJvsr/3o/8Av6v+NH2V/wC9H/39X/GoaKAJvsr/\nAN6P/v6v+NfLfgGBv2pP2ibvx9KUn+G/w6uJtK8Mozr5Wpat926vxzh0iH7qNuRnLKQQa3v2\nsPH+stZaH8J/BFyYPHvj2R7KK6j5Olaco/0u+bHI2oSqcglm+U5XFeu/DnwBo3wr8C6H4S8P\nWwtNH0i1S1t4+5A6sx7sxyzHuWJ70AdT9lf+9H/39X/Gj7K/96P/AL+r/jUNFAE32V/70f8A\n39X/ABrF8a+BdO+IHhDWfDWtwRXWk6taS2V1EZU+aN1KnBzwRnIPYgGtSigDwL9jnxRqw8J6\n58LvFNys/jP4b3g0S6lkcBryy27rG7GTyskOBnrlCT1r6ImtnMUAzHwp/wCWi/3j718tftFA\n/A34yeC/jjbDytClKeFfGW3hRYzSD7Ndt2HkzkAtydsgUcV9QSkNBbEEEFCQR/vGgA+yv/ej\n/wC/q/40fZX/AL0f/f1f8ahooAm+yv8A3o/+/q/40fZX/vR/9/V/xqGigCb7K/8Aej/7+r/j\nR9lf+9H/AN/V/wAahooAm+yv/ej/AO/q/wCNH2V/70f/AH9X/GoaKAJvsr/3o/8Av6v+NH2V\n/wC9H/39X/GoaKAJvsr/AN6P/v6v+NH2V/70f/f1f8ahooAm+yv/AHo/+/q/40fZX/vR/wDf\n1f8AGoaKAJvsr/3o/wDv6v8AjVvTbdkmkJKf6thxIp7fWs6rml/6+T/rk/8AKgCH7K/96P8A\n7+r/AI0fZX/vR/8Af1f8ahooAm+yv/ej/wC/q/40fZX/AL0f/f1f8ahooAm+yv8A3o/+/q/4\n0fZX/vR/9/V/xqGigCb7K/8Aej/7+r/jR9lf+9H/AN/V/wAahooAm+yv/ej/AO/q/wCNH2V/\n70f/AH9X/GoaKAJvsr/3o/8Av6v+NH2V/wC9H/39X/GoaKAJvsr/AN6P/v6v+NH2V/70f/f1\nf8ahooAm+yv/AHo/+/q/40fZX/vR/wDf1f8AGoaKALc9s5EXMfCD/lovqfeovsr/AN6P/v6v\n+NFx92H/AK5j+ZqGgCb7K/8Aej/7+r/jR9lf+9H/AN/V/wAahooAm+yv/ej/AO/q/wCNH2V/\n70f/AH9X/GoaKAJvsr/3o/8Av6v+NH2V/wC9H/39X/GoaKAJvsr/AN6P/v6v+NH2V/70f/f1\nf8ahooAm+yv/AHo/+/q/40fZX/vR/wDf1f8AGoaKAJvsr/3o/wDv6v8AjR9lf+9H/wB/V/xq\nGigCb7K/96P/AL+r/jR9lf8AvR/9/V/xqGigDQsLdlF1kpzCw4kU9x71V+yv/ej/AO/q/wCN\nTad0u/8Arg38xVOgCb7K/wDej/7+r/jR9lf+9H/39X/GoaKAJvsr/wB6P/v6v+NH2V/70f8A\n39X/ABqGigCb7K/96P8A7+r/AI0fZX/vR/8Af1f8ahooAm+yv/ej/wC/q/40fZX/AL0f/f1f\n8ahooAm+yv8A3o/+/q/40fZX/vR/9/V/xqGigCb7K/8Aej/7+r/jR9lf+9H/AN/V/wAahooA\nm+yv/ej/AO/q/wCNH2V/70f/AH9X/GoaKAJvsr/3o/8Av6v+NS3Ns5kXmP7idZF/uj3qpU11\n/rF/65p/6CKAD7K/96P/AL+r/jR9lf8AvR/9/V/xqGigCb7K/wDej/7+r/jR9lf+9H/39X/G\noaKAJvsr/wB6P/v6v+NH2V/70f8A39X/ABqGigCb7K/96P8A7+r/AI0fZX/vR/8Af1f8ahoo\nAm+yv/ej/wC/q/40fZX/AL0f/f1f8ahooAm+yv8A3o/+/q/40fZX/vR/9/V/xqGigCb7K/8A\nej/7+r/jR9lf+9H/AN/V/wAahooAm+yv/ej/AO/q/wCNW7S3ZbW7GU5QdJFPf61nVcs/+PS9\n/wBwfzoAh+yv/ej/AO/q/wCNH2V/70f/AH9X/GoaKAJvsr/3o/8Av6v+NH2V/wC9H/39X/Go\naKAJvsr/AN6P/v6v+NH2V/70f/f1f8ahooAm+yv/AHo/+/q/40fZX/vR/wDf1f8AGoaKAJvs\nr/3o/wDv6v8AjR9lf+9H/wB/V/xqGigCb7K/96P/AL+r/jR9lf8AvR/9/V/xqGigCb7K/wDe\nj/7+r/jR9lf+9H/39X/GoaKAJvsr/wB6P/v6v+NH2V/70f8A39X/ABqGigC3dWzmdjmPoOsi\n+n1qL7K/96P/AL+r/jRd/wCvb6D+QqGgCb7K/wDej/7+r/jR9lf+9H/39X/GoaKAJvsr/wB6\nP/v6v+NH2V/70f8A39X/ABqGigCb7K/96P8A7+r/AI0fZX/vR/8Af1f8ahooAm+yv/ej/wC/\nq/40fZX/AL0f/f1f8ahooAm+yv8A3o/+/q/40fZX/vR/9/V/xqGigCb7K/8Aej/7+r/jR9lf\n+9H/AN/V/wAahooAm+yv/ej/AO/q/wCNH2V/70f/AH9X/GoaKANGC3YafdLlMkp/y0XHU+9V\nPsr/AN6P/v6v+NTW/wDyDbv/AHk/map0ATfZX/vR/wDf1f8AGj7K/wDej/7+r/jUNFAE32V/\n70f/AH9X/Gj7K/8Aej/7+r/jUNFAF2xt3W8gJMeA46SKe/1rqa5HT/8Aj+t/98fzrrqACiii\ngAooooAKKKKAPlb9uL4WabdfCfxr421C6vNS1K2hsodMtp5T9m05TcwJI0UY43vltznJwcDA\n6/T+mf8AINtP+uKf+givlz9t742+CZPhJ428Arr0R8XA2a/2Z5Mu7IuIJT823Z/qxu6/rXr3\ngn9oPwF4x8L6zqGg66urweHrAXeorBBIrRRhGbgOq5JEb8D0oA5j9sPU7sfDnQfDdrcyWg8W\n+I7Dw/cSwttcQTMxkAPuI9p9QxHeud/af+Ffhj4cfBl/Fng/QdO8O+IPCM9re6de6fbrDIMT\nIjI7KAzqyu2QxOT1rX+ObTfGn4F+HPGvgqzuNUm0rU7PxPYWRj2zXKwswZAvPzbWcgck4wOS\nK5T4z/GXRP2jPAA+HXw/N9qniDxDcW0F5G9hNENJgWVJJZLguoVduzbgE5J4zxkA+g/EfxO8\nMeCvDGn6/wCJtbsvD2mXpjSK41CdYkMjoXVATwTtVjj/AGTVWz1az8ZWkOtaFcxatpN4gkt7\n20bzIpVxjKsOCMgj8K37vwxpOo6Xbadfada6hZW4URw3kKyqpVdoOGBGcEjPuaxrq2h0mY2l\njEllaRALHBbqI40GBwFHAoAP7Ouv+eEn/fNH9nXX/PCT/vmovtEv/PR/++jR9ol/56P/AN9G\ngCX+zrr/AJ4Sf980f2ddf88JP++ai+0S/wDPR/8Avo0faJf+ej/99GgCX+zrr/nhJ/3zR/Z1\n1/zwk/75qL7RL/z0f/vo0faJf+ej/wDfRoAl/s66/wCeEn/fNY/jHXbLwD4V1bxJr0v9naNp\nVrJeXd1KMLHGilmPucDgdScAVp/aJf8Ano//AH0a+XfjPcy/tM/HDT/gzayvN4G8MNb6545l\nViY7l877PTD67yolcf3VHIIxQBtfsoeBdd8Z32u/HXxdps1t4k8bIi6RYzLltJ0NTm1tx6NI\nMTPjqWXgEGvo3+zrr/nhJ/3zUKSvGiojsqqMBQcACl+0S/8APR/++jQBL/Z11/zwk/75o/s6\n6/54Sf8AfNRfaJf+ej/99Gj7RL/z0f8A76NAEv8AZ11/zwk/75o/s66/54Sf981F9ol/56P/\nAN9Gj7RL/wA9H/76NAGN48+HNp8SPBet+FtcsHudJ1i0ksrmPbzsdSCVPZhnIPYgHtXkP7H3\nizW7/wAEan8NvFJkm8cfDa7/AOEf1JmB3XVsAWsrvB52ywbcE8kox717v9ol/wCej/8AfRr5\no/aFkf4D/G7wN8bbZmg8P6mqeE/GZU4VbaWU/Y7x+w8qYhWY5O1wooA+nP7Ouv8AnhJ/3zR/\nZ11/zwk/75qL7RKf+Wr/APfRo+0S/wDPR/8Avo0AS/2ddf8APCT/AL5o/s66/wCeEn/fNRfa\nJf8Ano//AH0aPtEv/PR/++jQBL/Z11/zwk/75o/s66/54Sf981F9ol/56P8A99Gj7RL/AM9H\n/wC+jQBL/Z11/wA8JP8Avmj+zrr/AJ4Sf981F9ol/wCej/8AfRo+0S/89H/76NAEv9nXX/PC\nT/vmj+zrr/nhJ/3zUX2iX/no/wD30aPtEv8Az0f/AL6NAEv9nXX/ADwk/wC+aP7Ouv8AnhJ/\n3zUX2iX/AJ6P/wB9Gj7RL/z0f/vo0AS/2ddf88JP++aP7Ouv+eEn/fNRfaJf+ej/APfRo+0S\n/wDPR/8Avo0AS/2ddf8APCT/AL5q1p1lPHM5aF1BjYcjviqH2iX/AJ6P/wB9GremTSNNJl2P\n7tup9qAIf7Ouv+eEn/fNH9nXX/PCT/vmovtEv/PR/wDvo0faJf8Ano//AH0aAJf7Ouv+eEn/\nAHzR/Z11/wA8JP8AvmovtEv/AD0f/vo0faJf+ej/APfRoAl/s66/54Sf980f2ddf88JP++ai\n+0S/89H/AO+jR9ol/wCej/8AfRoAl/s66/54Sf8AfNH9nXX/ADwk/wC+ai+0S/8APR/++jR9\nol/56P8A99GgCX+zrr/nhJ/3zR/Z11/zwk/75qL7RL/z0f8A76NH2iX/AJ6P/wB9GgCX+zrr\n/nhJ/wB80f2ddf8APCT/AL5qL7RL/wA9H/76NH2iX/no/wD30aAJf7Ouv+eEn/fNH9nXX/PC\nT/vmovtEv/P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+Jnj1ZPGOv+FvCPhbUP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\n81DRQBN9o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gwIAhQ4b4+vq6XjwsLGz58uWbN28+duzY+fPnDQZD9+7dR44c2bp1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ZDMOIoqjAi4UcrL2tkfYQyn9hSUlJYToc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\n2bNn48Lz589bPRm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kOgp+/fXXiYmJhBAvL6/XXnvt5Zdf5v8vhiYZ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\n9bpx48a2bdsOHTqk0+nc3d3nzZv38ssvG4+w0obonu1ep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"image/svg+xml": { - "height": 420, - "isolated": true, - "width": 600 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "options(repr.plot.width = 10, repr.plot.height = 7) # Adjust these numbers so the plot looks good in your desktop.\n", - "\n", - "set.seed(301)\n", - "answer2.1 <- \n", - " incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50)\n", - "\n", - "\n", - "crit_unadj <- qt(1 - 0.05, 1998)\n", - "crit_bonferroni <- qt(1 - 0.0025, 1998)\n", - "\n", - "sequential_stat <- \n", - " answer2.1 %>%\n", - " ggplot() +\n", - " geom_line(aes(x = inc_sample_size, y = statistic)) +\n", - " geom_point(aes(x = inc_sample_size, y = statistic,colour =\"#f94f21\"), size = 2) +\n", - " geom_hline(yintercept = crit_pocock, colour = \"#ff33ff\", linetype = \"twodash\") +\n", - " geom_line(aes(x = inc_sample_size, y = crit_of),colour = 3, linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = crit_of), colour = 3) +\n", - " geom_text(x=160, y= crit_of[1] - 0.15, size=6, label=\"O'Brien-Fleming\",colour = 3) +\n", - " geom_point(aes(x = inc_sample_size, y = crit_pocock), colour = \"#ff33ff\") +\n", - " geom_text(x=850, y=crit_pocock + 0.15, size=6, label=\"Pocock\",colour = \"#ff33ff\") +\n", - " geom_hline(yintercept = crit_bonferroni, colour = \"blue\", linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = rep(crit_bonferroni, 20)), colour = \"blue\") +\n", - " geom_text(x=850, y=crit_bonferroni + 0.15, size=6, label=\"Bonferroni\",colour = \"blue\") +\n", - " geom_hline(yintercept = crit_unadj, linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = rep(crit_unadj, 20))) +\n", - " geom_text(x=850, y=crit_unadj + 0.15, size=6, label=\"Unadjusted\") +\n", - " theme(\n", - " text = element_text(size = 18),\n", - " plot.title = element_text(face = \"bold\"),\n", - " axis.title = element_text(face = \"bold\"),\n", - " legend.position=\"none\") +\n", - " ggtitle(\"Critical values in Sequential Designs\") +\n", - " ylab(\"Statistic\") +\n", - " xlab(\"Sample Size\") +\n", - " coord_cartesian(ylim = c(-1, 8.5)) +\n", - " scale_y_continuous(breaks = seq(-1, 8.5, by = 0.5))\n", - "sequential_stat " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Interpretations of O'Brien-Fleming correction\n", - "\n", - "In *one* A/A testing simulated experiment, using unadjusted quantities:\n", - "\n", - "- you would reject $H_0$: the observed (unadjusted) t-statistic is bigger than the (unadjusted) critical value\n", - "\n", - "Using O'Brien-Fleming's critical values:\n", - "\n", - "- you would fail to reject $H_0$: the observed (unadjusted) t-statistic is smaller than the (adjusted) critical value\n", - "\n", - "```{important}\n", - "The O'Brien-Fleming's critical values are non-constant!! The test is *very* conservative at the beginning when less data is available and becomes less conservative as more data is collected. \n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Type I error rate control\n", - "\n", - "To better understand the type I error rate control of the 3 approaches, you need to repeat the experiments *many* times and calculate the rate at which you erroneously reject $H_0$.\n", - "\n", - "#### Number of erroneous rejections among 100 experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-549d0aab861a20f3", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\n", - "
    A tibble: 1 × 5
    UnadjustedBonferroniPocockOBrienFlemingexpected_n_rejections
    <dbl><dbl><dbl><dbl><dbl>
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    \n" - ], - "text/latex": [ - "A tibble: 1 × 5\n", - "\\begin{tabular}{lllll}\n", - " Unadjusted & Bonferroni & Pocock & OBrienFleming & expected\\_n\\_rejections\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t 24 & 2 & 7 & 4 & 5\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 1 × 5\n", - "\n", - "| Unadjusted <dbl> | Bonferroni <dbl> | Pocock <dbl> | OBrienFleming <dbl> | expected_n_rejections <dbl> |\n", - "|---|---|---|---|---|\n", - "| 24 | 2 | 7 | 4 | 5 |\n", - "\n" - ], - "text/plain": [ - " Unadjusted Bonferroni Pocock OBrienFleming expected_n_rejections\n", - "1 24 2 7 4 5 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "set.seed(120)\n", - "\n", - "### Run this before continuing\n", - "multiple_times_sequential_tests <- tibble(experiment = 1:100) %>% \n", - " mutate(seq_test = map(.x = experiment, \n", - " .f = function(x) incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, \n", - " mean_current = 200, sd_current = 50, sd_new = 50)))\n", - "\n", - "typeI_rate <- multiple_times_sequential_tests %>% \n", - " mutate(reject = map_dbl(.x = seq_test, .f = function(x) sum(x$p_value<0.05) > 0)) %>%\n", - " mutate(n_reject_bonferroni = map_dbl(.x = seq_test, .f = function(x) sum(x$p_value < 0.05/20) > 0)) %>%\n", - " mutate(n_reject_pocock = map_dbl(.x = seq_test, .f = function(x) sum(x$statistic > crit_pocock) > 0)) %>%\n", - " mutate(n_reject_OF = map_dbl(.x = seq_test, .f = function(x) sum(x$statistic > crit_of) > 0)) %>%\n", - " summarise(Unadjusted = sum(reject),\n", - " Bonferroni= sum(n_reject_bonferroni),\n", - " Pocock = sum(n_reject_pocock),\n", - " OBrienFleming = sum(n_reject_OF),\n", - " expected_n_rejections = 5)\n", - "typeI_rate" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-bf09c89e531d91af", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Conclusions\n", - "\n", - "For the simulated experiments in the example above: \n", - "\n", - "- the type I error rate using unadjusted values was 24% (way above the planned 5% value)\n", - "\n", - "- the type I error rate using Bonferroni was 2% (below the planned 5% value)\n", - "\n", - "- the type I error rate using Pocock was 7% (above the planned 5% value)\n", - "\n", - "- the type I error rate using O'Brien-Flemming was 4% (slightly below the planned 5% value)\n", - "\n", - "```{important}\n", - "Using a **principled peeking** procedure, the data can be sequentially analyzed and the experiment can be stopped earlier while controlling the type I error rate.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a3ee7bcb3608e036", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Summary and key concepts learned\n", - "\n", - "\n", - "1. In many A/B testing experiments, users would like to stop their experiments earlier depending on the results of interim analyses\n", - "\n", - "\n", - "2. If you make lots of comparisons, but don’t correct for it, your error rates are inflated!! Naively peeking at the data many times and stopping the experiment earlier than planned increases the probability of *incorrectly* rejecting the null hypothesis. \n", - "\n", - "3. Stops must be part of the experimental design and appropriate testing methods must be used!\n", - "\n", - "\n", - "4. A possible way to control the type I error rated is to use a **Bonferroni adjustment** of the $p$-values (or equivalently the significance level or critical values). As with other multiple comparison problems, the Bonferroni's correction in sequential analysis is very conservative and can affect the power of the test!!\n", - "\n", - "\n", - "9. The **Pocock method** offers a less conservative way of controlling the type I error rate in sequential testing with early stops.\n", - "\n", - "\n", - "10. The **O'Brien-Fleming method** offers a less conservative way of controlling the type I error rate in sequential testing with early stops.\n", - "\n", - "\n", - "```{attention} \n", - "Principled peeking is ok and even beneficial in A/B testing. The experimental design is a very important piece of any statistical analysis! \n", - "```" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "jupytext": { - "formats": "ipynb,Rmd" - }, - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "4.2.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/stat301book/_build/html/_static/__init__.py b/stat301book/_build/html/_static/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/stat301book/_build/html/_static/__pycache__/__init__.cpython-37.pyc b/stat301book/_build/html/_static/__pycache__/__init__.cpython-37.pyc deleted file mode 100644 index c7f995f6bbed30bb6e93cdb5bf1783f6d7830a43..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 151 zcmZ?b<>g`kg3D^9Ng(<$h=2h`Aj1KOi&=m~3PUi1CZpd2KczG$)edCxXCP((0L_CXbpQYW diff --git a/stat301book/_build/html/_static/basic.css b/stat301book/_build/html/_static/basic.css deleted file mode 100644 index 616111c..0000000 --- a/stat301book/_build/html/_static/basic.css +++ /dev/null @@ -1,855 +0,0 @@ -/* - * basic.css - * ~~~~~~~~~ - * - * Sphinx stylesheet -- basic theme. - * - * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/* -- main layout ----------------------------------------------------------- */ - -div.clearer { - clear: both; 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- margin-bottom: 1em; -} - -.line-block .line-block { - margin-top: 0; - margin-bottom: 0; - margin-left: 1.5em; -} - -.guilabel, .menuselection { - font-family: sans-serif; -} - -.accelerator { - text-decoration: underline; -} - -.classifier { - font-style: oblique; -} - -.classifier:before { - font-style: normal; - margin: 0.5em; - content: ":"; -} - -abbr, acronym { - border-bottom: dotted 1px; - cursor: help; -} - -/* -- code displays --------------------------------------------------------- */ - -pre { - overflow: auto; - overflow-y: hidden; /* fixes display issues on Chrome browsers */ -} - -pre, div[class*="highlight-"] { - clear: both; -} - -span.pre { - -moz-hyphens: none; - -ms-hyphens: none; - -webkit-hyphens: none; - hyphens: none; -} - -div[class*="highlight-"] { - margin: 1em 0; -} - -td.linenos pre { - border: 0; - background-color: transparent; - color: #aaa; -} - -table.highlighttable { - display: block; -} - -table.highlighttable tbody { - display: block; -} - -table.highlighttable tr { - display: flex; -} - -table.highlighttable td { - margin: 0; - padding: 0; -} - -table.highlighttable td.linenos { - padding-right: 0.5em; -} - -table.highlighttable td.code { - flex: 1; - overflow: hidden; -} - -.highlight .hll { - display: block; -} - -div.highlight pre, -table.highlighttable pre { - margin: 0; -} - -div.code-block-caption + div { - margin-top: 0; -} - -div.code-block-caption { - margin-top: 1em; - padding: 2px 5px; - font-size: small; -} - -div.code-block-caption code { - background-color: transparent; -} - -table.highlighttable td.linenos, -div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */ - user-select: none; -} - -div.code-block-caption span.caption-number { - padding: 0.1em 0.3em; - font-style: italic; -} - -div.code-block-caption span.caption-text { -} - -div.literal-block-wrapper { - margin: 1em 0; -} - -code.descname { - background-color: transparent; - font-weight: bold; - font-size: 1.2em; -} - -code.descclassname { - background-color: transparent; -} - -code.xref, a code { - background-color: transparent; - font-weight: bold; -} - -h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { - background-color: transparent; -} - -.viewcode-link { - float: right; -} - -.viewcode-back { - float: right; - font-family: sans-serif; -} - -div.viewcode-block:target { - margin: -1px -10px; - padding: 0 10px; -} - -/* -- math display ---------------------------------------------------------- */ - -img.math { - vertical-align: middle; -} - -div.body div.math p { - text-align: center; -} - -span.eqno { - float: right; -} - -span.eqno a.headerlink { - position: absolute; - z-index: 1; -} - -div.math:hover a.headerlink { - visibility: visible; -} - -/* -- printout stylesheet --------------------------------------------------- */ - -@media print { - div.document, - div.documentwrapper, - div.bodywrapper { - margin: 0 !important; - width: 100%; - } - - div.sphinxsidebar, - div.related, - div.footer, - #top-link { - display: none; - } -} \ No newline at end of file diff --git a/stat301book/_build/html/_static/check-solid.svg b/stat301book/_build/html/_static/check-solid.svg deleted file mode 100644 index 92fad4b..0000000 --- a/stat301book/_build/html/_static/check-solid.svg +++ /dev/null @@ -1,4 +0,0 @@ - - - - diff --git a/stat301book/_build/html/_static/clipboard.min.js b/stat301book/_build/html/_static/clipboard.min.js deleted file mode 100644 index 54b3c46..0000000 --- a/stat301book/_build/html/_static/clipboard.min.js +++ /dev/null @@ -1,7 +0,0 @@ -/*! - * clipboard.js v2.0.8 - * https://clipboardjs.com/ - * - * Licensed MIT © Zeno Rocha - */ -!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1 - - - - diff --git a/stat301book/_build/html/_static/copybutton.css b/stat301book/_build/html/_static/copybutton.css deleted file mode 100644 index f1916ec..0000000 --- a/stat301book/_build/html/_static/copybutton.css +++ /dev/null @@ -1,94 +0,0 @@ -/* Copy buttons */ -button.copybtn { - position: absolute; - display: flex; - top: .3em; - right: .3em; - width: 1.7em; - height: 1.7em; - opacity: 0; - transition: opacity 0.3s, border .3s, background-color .3s; - user-select: none; - padding: 0; - border: none; - outline: none; - border-radius: 0.4em; - /* The colors that GitHub uses */ - border: #1b1f2426 1px solid; - background-color: #f6f8fa; - color: #57606a; -} - -button.copybtn.success { - border-color: #22863a; - color: #22863a; -} - -button.copybtn svg { - stroke: currentColor; - width: 1.5em; - height: 1.5em; - padding: 0.1em; -} - -div.highlight { - position: relative; -} - -/* Show the copybutton */ -.highlight:hover button.copybtn, button.copybtn.success { - opacity: 1; -} - -.highlight button.copybtn:hover { - background-color: rgb(235, 235, 235); -} - -.highlight button.copybtn:active { - background-color: rgb(187, 187, 187); -} - -/** - * A minimal CSS-only tooltip copied from: - * https://codepen.io/mildrenben/pen/rVBrpK - * - * To use, write HTML like the following: - * - *

    Short

    - */ - .o-tooltip--left { - position: relative; - } - - .o-tooltip--left:after { - opacity: 0; - visibility: hidden; - position: absolute; - content: attr(data-tooltip); - padding: .2em; - font-size: .8em; - left: -.2em; - background: grey; - color: white; - white-space: nowrap; - z-index: 2; - border-radius: 2px; - transform: translateX(-102%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); -} - -.o-tooltip--left:hover:after { - display: block; - opacity: 1; - visibility: visible; - transform: translateX(-100%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); - transition-delay: .5s; -} - -/* By default the copy button shouldn't show up when printing a page */ -@media print { - button.copybtn { - display: none; - } -} diff --git a/stat301book/_build/html/_static/copybutton.js b/stat301book/_build/html/_static/copybutton.js deleted file mode 100644 index 2ea7ff3..0000000 --- a/stat301book/_build/html/_static/copybutton.js +++ /dev/null @@ -1,248 +0,0 @@ -// Localization support -const messages = { - 'en': { - 'copy': 'Copy', - 'copy_to_clipboard': 'Copy to clipboard', - 'copy_success': 'Copied!', - 'copy_failure': 'Failed to copy', - }, - 'es' : { - 'copy': 'Copiar', - 'copy_to_clipboard': 'Copiar al portapapeles', - 'copy_success': '¡Copiado!', - 'copy_failure': 'Error al copiar', - }, - 'de' : { - 'copy': 'Kopieren', - 'copy_to_clipboard': 'In die Zwischenablage kopieren', - 'copy_success': 'Kopiert!', - 'copy_failure': 'Fehler beim Kopieren', - }, - 'fr' : { - 'copy': 'Copier', - 'copy_to_clipboard': 'Copier dans le presse-papier', - 'copy_success': 'Copié !', - 'copy_failure': 'Échec de la copie', - }, - 'ru': { - 'copy': 'Скопировать', - 'copy_to_clipboard': 'Скопировать в буфер', - 'copy_success': 'Скопировано!', - 'copy_failure': 'Не удалось скопировать', - }, - 'zh-CN': { - 'copy': '复制', - 'copy_to_clipboard': '复制到剪贴板', - 'copy_success': '复制成功!', - 'copy_failure': '复制失败', - }, - 'it' : { - 'copy': 'Copiare', - 'copy_to_clipboard': 'Copiato negli appunti', - 'copy_success': 'Copiato!', - 'copy_failure': 'Errore durante la copia', - } -} - -let locale = 'en' -if( document.documentElement.lang !== undefined - && messages[document.documentElement.lang] !== undefined ) { - locale = document.documentElement.lang -} - -let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; -if (doc_url_root == '#') { - doc_url_root = ''; -} - -/** - * SVG files for our copy buttons - */ -let iconCheck = ` - ${messages[locale]['copy_success']} - - -` - -// If the user specified their own SVG use that, otherwise use the default -let iconCopy = ``; -if (!iconCopy) { - iconCopy = ` - ${messages[locale]['copy_to_clipboard']} - - - -` -} - -/** - * Set up copy/paste for code blocks - */ - -const runWhenDOMLoaded = cb => { - if (document.readyState != 'loading') { - cb() - } else if (document.addEventListener) { - document.addEventListener('DOMContentLoaded', cb) - } else { - document.attachEvent('onreadystatechange', function() { - if (document.readyState == 'complete') cb() - }) - } -} - -const codeCellId = index => `codecell${index}` - -// Clears selected text since ClipboardJS will select the text when copying -const clearSelection = () => { - if (window.getSelection) { - window.getSelection().removeAllRanges() - } else if (document.selection) { - document.selection.empty() - } -} - -// Changes tooltip text for a moment, then changes it back -// We want the timeout of our `success` class to be a bit shorter than the -// tooltip and icon change, so that we can hide the icon before changing back. -var timeoutIcon = 2000; -var timeoutSuccessClass = 1500; - -const temporarilyChangeTooltip = (el, oldText, newText) => { - el.setAttribute('data-tooltip', newText) - el.classList.add('success') - // Remove success a little bit sooner than we change the tooltip - // So that we can use CSS to hide the copybutton first - setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) - setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) -} - -// Changes the copy button icon for two seconds, then changes it back -const temporarilyChangeIcon = (el) => { - el.innerHTML = iconCheck; - setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) -} - -const addCopyButtonToCodeCells = () => { - // If ClipboardJS hasn't loaded, wait a bit and try again. This - // happens because we load ClipboardJS asynchronously. - if (window.ClipboardJS === undefined) { - setTimeout(addCopyButtonToCodeCells, 250) - return - } - - // Add copybuttons to all of our code cells - const COPYBUTTON_SELECTOR = 'div.highlight pre'; - const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) - codeCells.forEach((codeCell, index) => { - const id = codeCellId(index) - codeCell.setAttribute('id', id) - - const clipboardButton = id => - `` - codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) - }) - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} - - -var copyTargetText = (trigger) => { - var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - - // get filtered text - let exclude = '.linenos'; - - let text = filterText(target, exclude); - return formatCopyText(text, '', false, true, true, true, '', '') -} - - // Initialize with a callback so we can modify the text before copy - const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) - - // Update UI with error/success messages - clipboard.on('success', event => { - clearSelection() - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) - temporarilyChangeIcon(event.trigger) - }) - - clipboard.on('error', event => { - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) - }) -} - -runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/stat301book/_build/html/_static/copybutton_funcs.js b/stat301book/_build/html/_static/copybutton_funcs.js deleted file mode 100644 index dbe1aaa..0000000 --- a/stat301book/_build/html/_static/copybutton_funcs.js +++ /dev/null @@ -1,73 +0,0 @@ -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -export function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} diff --git a/stat301book/_build/html/_static/css/blank.css b/stat301book/_build/html/_static/css/blank.css deleted file mode 100644 index 8a686ec..0000000 --- a/stat301book/_build/html/_static/css/blank.css +++ /dev/null @@ -1,2 +0,0 @@ -/* This file is intentionally left blank to override the stylesheet of the -parent theme via theme.conf. The parent style we import directly in theme.css */ \ No newline at end of file diff --git a/stat301book/_build/html/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css b/stat301book/_build/html/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css deleted file mode 100644 index 9b1c5d7..0000000 --- a/stat301book/_build/html/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css +++ /dev/null @@ -1,6 +0,0 @@ -/*! - * Bootstrap v4.5.0 (https://getbootstrap.com/) - * Copyright 2011-2020 The Bootstrap Authors - * Copyright 2011-2020 Twitter, Inc. - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) - */:root{--blue:#007bff;--indigo:#6610f2;--purple:#6f42c1;--pink:#e83e8c;--red:#dc3545;--orange:#fd7e14;--yellow:#ffc107;--green:#28a745;--teal:#20c997;--cyan:#17a2b8;--white:#fff;--gray:#6c757d;--gray-dark:#343a40;--primary:#007bff;--secondary:#6c757d;--success:#28a745;--info:#17a2b8;--warning:#ffc107;--danger:#dc3545;--light:#f8f9fa;--dark:#343a40;--breakpoint-xs:0;--breakpoint-sm:540px;--breakpoint-md:720px;--breakpoint-lg:960px;--breakpoint-xl:1200px;--font-family-sans-serif:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";--font-family-monospace:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier 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i.fas{vertical-align:middle;font-style:normal;font-size:1.5rem;line-height:1.25}#navbar-icon-links i.fa-github-square:before{color:#333}#navbar-icon-links i.fa-twitter-square:before{color:#55acee}#navbar-icon-links i.fa-gitlab:before{color:#548}#navbar-icon-links i.fa-bitbucket:before{color:#0052cc}.tocsection{border-left:1px solid #eee;padding:.3rem 1.5rem}.tocsection i{padding-right:.5rem}.editthispage{padding-top:2rem}.editthispage a{color:var(--pst-color-sidebar-link-active)}.xr-wrap[hidden]{display:block!important}.toctree-checkbox{position:absolute;display:none}.toctree-checkbox~ul{display:none}.toctree-checkbox~label i{transform:rotate(0deg)}.toctree-checkbox:checked~ul{display:block}.toctree-checkbox:checked~label i{transform:rotate(180deg)}.bd-sidebar li{position:relative}.bd-sidebar label{position:absolute;top:0;right:0;height:30px;width:30px;cursor:pointer;display:flex;justify-content:center;align-items:center}.bd-sidebar label:hover{background:rgba(var(--pst-color-sidebar-expander-background-hover),1)}.bd-sidebar label i{display:inline-block;font-size:.75rem;text-align:center}.bd-sidebar label i:hover{color:rgba(var(--pst-color-sidebar-link-hover),1)}.bd-sidebar li.has-children>.reference{padding-right:30px}div.doctest>div.highlight span.gp,span.linenos,table.highlighttable td.linenos{user-select:none;-webkit-user-select:text;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none}.docutils.container{padding-left:unset;padding-right:unset} \ No newline at end of file diff --git a/stat301book/_build/html/_static/css/theme.css b/stat301book/_build/html/_static/css/theme.css deleted file mode 100644 index 2e03fe3..0000000 --- a/stat301book/_build/html/_static/css/theme.css +++ /dev/null @@ -1,120 +0,0 @@ -/* Provided by the Sphinx base theme template at build time */ -@import "../basic.css"; - -:root { - /***************************************************************************** - * Theme config - **/ - --pst-header-height: 60px; - - /***************************************************************************** - * Font size - **/ - --pst-font-size-base: 15px; /* base font size - applied at body / html level */ - - /* heading font sizes */ - --pst-font-size-h1: 36px; - --pst-font-size-h2: 32px; - --pst-font-size-h3: 26px; - --pst-font-size-h4: 21px; - --pst-font-size-h5: 18px; - --pst-font-size-h6: 16px; - - /* smaller then heading font sizes*/ - --pst-font-size-milli: 12px; - - --pst-sidebar-font-size: .9em; - --pst-sidebar-caption-font-size: .9em; - - /***************************************************************************** - * Font family - **/ - /* These are adapted from https://systemfontstack.com/ */ - --pst-font-family-base-system: -apple-system, BlinkMacSystemFont, Segoe UI, "Helvetica Neue", - Arial, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol; - --pst-font-family-monospace-system: "SFMono-Regular", Menlo, Consolas, Monaco, - Liberation Mono, Lucida Console, monospace; - - --pst-font-family-base: var(--pst-font-family-base-system); - --pst-font-family-heading: var(--pst-font-family-base); - --pst-font-family-monospace: var(--pst-font-family-monospace-system); - - /***************************************************************************** - * Color - * - * Colors are defined in rgb string way, "red, green, blue" - **/ - --pst-color-primary: 19, 6, 84; - --pst-color-success: 40, 167, 69; - --pst-color-info: 0, 123, 255; /*23, 162, 184;*/ - --pst-color-warning: 255, 193, 7; - --pst-color-danger: 220, 53, 69; - --pst-color-text-base: 51, 51, 51; - - --pst-color-h1: var(--pst-color-primary); - --pst-color-h2: var(--pst-color-primary); - --pst-color-h3: var(--pst-color-text-base); - --pst-color-h4: var(--pst-color-text-base); - --pst-color-h5: var(--pst-color-text-base); - --pst-color-h6: var(--pst-color-text-base); - --pst-color-paragraph: var(--pst-color-text-base); - --pst-color-link: 0, 91, 129; - --pst-color-link-hover: 227, 46, 0; - --pst-color-headerlink: 198, 15, 15; - --pst-color-headerlink-hover: 255, 255, 255; - --pst-color-preformatted-text: 34, 34, 34; - --pst-color-preformatted-background: 250, 250, 250; - --pst-color-inline-code: 232, 62, 140; - - --pst-color-active-navigation: 19, 6, 84; - --pst-color-navbar-link: 77, 77, 77; - --pst-color-navbar-link-hover: var(--pst-color-active-navigation); - --pst-color-navbar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-link: 77, 77, 77; - --pst-color-sidebar-link-hover: var(--pst-color-active-navigation); - --pst-color-sidebar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-expander-background-hover: 244, 244, 244; - --pst-color-sidebar-caption: 77, 77, 77; - --pst-color-toc-link: 119, 117, 122; - --pst-color-toc-link-hover: var(--pst-color-active-navigation); - --pst-color-toc-link-active: var(--pst-color-active-navigation); - - /***************************************************************************** - * Icon - **/ - - /* font awesome icons*/ - --pst-icon-check-circle: '\f058'; - --pst-icon-info-circle: '\f05a'; - --pst-icon-exclamation-triangle: '\f071'; - --pst-icon-exclamation-circle: '\f06a'; - --pst-icon-times-circle: '\f057'; - --pst-icon-lightbulb: '\f0eb'; - - /***************************************************************************** - * Admonitions - **/ - - --pst-color-admonition-default: var(--pst-color-info); - --pst-color-admonition-note: var(--pst-color-info); - --pst-color-admonition-attention: var(--pst-color-warning); - --pst-color-admonition-caution: var(--pst-color-warning); - --pst-color-admonition-warning: var(--pst-color-warning); - --pst-color-admonition-danger: var(--pst-color-danger); - --pst-color-admonition-error: var(--pst-color-danger); - --pst-color-admonition-hint: var(--pst-color-success); - --pst-color-admonition-tip: var(--pst-color-success); - --pst-color-admonition-important: var(--pst-color-success); - - --pst-icon-admonition-default: var(--pst-icon-info-circle); - --pst-icon-admonition-note: var(--pst-icon-info-circle); - --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle); - --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-error: var(--pst-icon-times-circle); - --pst-icon-admonition-hint: var(--pst-icon-lightbulb); - --pst-icon-admonition-tip: var(--pst-icon-lightbulb); - --pst-icon-admonition-important: var(--pst-icon-exclamation-circle); - -} diff --git a/stat301book/_build/html/_static/doctools.js b/stat301book/_build/html/_static/doctools.js deleted file mode 100644 index daccd20..0000000 --- a/stat301book/_build/html/_static/doctools.js +++ /dev/null @@ -1,315 +0,0 @@ -/* - * doctools.js - * ~~~~~~~~~~~ - * - * Sphinx JavaScript utilities for all documentation. - * - * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/** - * select a different prefix for underscore - */ -$u = _.noConflict(); - -/** - * make the code below compatible with browsers without - * an installed firebug like debugger -if (!window.console || !console.firebug) { - var names = ["log", "debug", "info", "warn", "error", "assert", "dir", - "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", - "profile", "profileEnd"]; - window.console = {}; - for (var i = 0; i < names.length; ++i) - window.console[names[i]] = function() {}; -} - */ - -/** - * small helper function to urldecode strings - */ -jQuery.urldecode = function(x) { - return decodeURIComponent(x).replace(/\+/g, ' '); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. 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environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var flat = arr.flat ? function( array ) { - return arr.flat.call( array ); -} : function( array ) { - return arr.concat.apply( [], array ); -}; - - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - -var isFunction = function isFunction( obj ) { - - // Support: Chrome <=57, Firefox <=52 - // In some browsers, typeof returns "function" for HTML elements - // (i.e., `typeof document.createElement( "object" ) === "function"`). - // We don't want to classify *any* DOM node as a function. - return typeof obj === "function" && typeof obj.nodeType !== "number"; - }; - - -var isWindow = function isWindow( obj ) { - return obj != null && obj === obj.window; - }; - - -var document = window.document; - - - - var preservedScriptAttributes = { - type: true, - src: true, - nonce: true, - noModule: true - }; - - function DOMEval( code, node, doc ) { - doc = doc || document; - - var i, val, - script = doc.createElement( "script" ); - - script.text = code; - if ( node ) { - for ( i in preservedScriptAttributes ) { - - // Support: Firefox 64+, Edge 18+ - // Some browsers don't support the "nonce" property on scripts. - // On the other hand, just using `getAttribute` is not enough as - // the `nonce` attribute is reset to an empty string whenever it - // becomes browsing-context connected. - // See https://github.com/whatwg/html/issues/2369 - // See https://html.spec.whatwg.org/#nonce-attributes - // The `node.getAttribute` check was added for the sake of - // `jQuery.globalEval` so that it can fake a nonce-containing node - // via an object. - val = node[ i ] || node.getAttribute && node.getAttribute( i ); - if ( val ) { - script.setAttribute( i, val ); - } - } - } - doc.head.appendChild( script ).parentNode.removeChild( script ); - } - - -function toType( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; -} -/* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.5.1", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } - - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - even: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return ( i + 1 ) % 2; - } ) ); - }, - - odd: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return i % 2; - } ) ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - copy = options[ name ]; - - // Prevent Object.prototype pollution - // Prevent never-ending loop - if ( name === "__proto__" || target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { - src = target[ name ]; - - // Ensure proper type for the source value - if ( copyIsArray && !Array.isArray( src ) ) { - clone = []; - } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { - clone = {}; - } else { - clone = src; - } - copyIsArray = false; - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - // Evaluates a script in a provided context; falls back to the global one - // if not specified. - globalEval: function( code, options, doc ) { - DOMEval( code, { nonce: options && options.nonce }, doc ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return flat( ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( _i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = toType( obj ); - - if ( isFunction( obj ) || isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.5 - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://js.foundation/ - * - * Date: 2020-03-14 - */ -( function( window ) { -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - nonnativeSelectorCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ( {} ).hasOwnProperty, - arr = [], - pop = arr.pop, - pushNative = arr.push, - push = arr.push, - slice = arr.slice, - - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[ i ] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + - "ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram - identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + - "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - - // "Attribute values must be CSS identifiers [capture 5] - // or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + - whitespace + "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + - whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + - "*" ), - rdescend = new RegExp( whitespace + "|>" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + - whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + - whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + - "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + - "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rhtml = /HTML$/i, - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), - funescape = function( escape, nonHex ) { - var high = "0x" + escape.slice( 1 ) - 0x10000; - - return nonHex ? - - // Strip the backslash prefix from a non-hex escape sequence - nonHex : - - // Replace a hexadecimal escape sequence with the encoded Unicode code point - // Support: IE <=11+ - // For values outside the Basic Multilingual Plane (BMP), manually construct a - // surrogate pair - high < 0 ? - String.fromCharCode( high + 0x10000 ) : - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + - ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - inDisabledFieldset = addCombinator( - function( elem ) { - return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - ( arr = slice.call( preferredDoc.childNodes ) ), - preferredDoc.childNodes - ); - - // Support: Android<4.0 - // Detect silently failing push.apply - // eslint-disable-next-line no-unused-expressions - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - pushNative.apply( target, slice.call( els ) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - - // Can't trust NodeList.length - while ( ( target[ j++ ] = els[ i++ ] ) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - setDocument( context ); - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { - - // ID selector - if ( ( m = match[ 1 ] ) ) { - - // Document context - if ( nodeType === 9 ) { - if ( ( elem = context.getElementById( m ) ) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && ( elem = newContext.getElementById( m ) ) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[ 2 ] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !nonnativeSelectorCache[ selector + " " ] && - ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && - - // Support: IE 8 only - // Exclude object elements - ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { - - newSelector = selector; - newContext = context; - - // qSA considers elements outside a scoping root when evaluating child or - // descendant combinators, which is not what we want. - // In such cases, we work around the behavior by prefixing every selector in the - // list with an ID selector referencing the scope context. - // The technique has to be used as well when a leading combinator is used - // as such selectors are not recognized by querySelectorAll. - // Thanks to Andrew Dupont for this technique. - if ( nodeType === 1 && - ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - - // We can use :scope instead of the ID hack if the browser - // supports it & if we're not changing the context. - if ( newContext !== context || !support.scope ) { - - // Capture the context ID, setting it first if necessary - if ( ( nid = context.getAttribute( "id" ) ) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", ( nid = expando ) ); - } - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + - toSelector( groups[ i ] ); - } - newSelector = groups.join( "," ); - } - - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - nonnativeSelectorCache( selector, true ); - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return ( cache[ key + " " ] = value ); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement( "fieldset" ); - - try { - return !!fn( el ); - } catch ( e ) { - return false; - } finally { - - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split( "|" ), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[ i ] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( ( cur = cur.nextSibling ) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return ( name === "input" || name === "button" ) && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - inDisabledFieldset( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction( function( argument ) { - argument = +argument; - return markFunction( function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ ( j = matchIndexes[ i ] ) ] ) { - seed[ j ] = !( matches[ j ] = seed[ j ] ); - } - } - } ); - } ); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - var namespace = elem.namespaceURI, - docElem = ( elem.ownerDocument || elem ).documentElement; - - // Support: IE <=8 - // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes - // https://bugs.jquery.com/ticket/4833 - return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9 - 11+, Edge 12 - 18+ - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( preferredDoc != document && - ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, - // Safari 4 - 5 only, Opera <=11.6 - 12.x only - // IE/Edge & older browsers don't support the :scope pseudo-class. - // Support: Safari 6.0 only - // Safari 6.0 supports :scope but it's an alias of :root there. - support.scope = assert( function( el ) { - docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); - return typeof el.querySelectorAll !== "undefined" && - !el.querySelectorAll( ":scope fieldset div" ).length; - } ); - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert( function( el ) { - el.className = "i"; - return !el.getAttribute( "className" ); - } ); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert( function( el ) { - el.appendChild( document.createComment( "" ) ); - return !el.getElementsByTagName( "*" ).length; - } ); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert( function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - } ); - - // ID filter and find - if ( support.getById ) { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute( "id" ) === attrId; - }; - }; - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode( "id" ); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( ( elem = elems[ i++ ] ) ) { - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find[ "TAG" ] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { - - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert( function( el ) { - - var input; - - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll( "[selected]" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push( "~=" ); - } - - // Support: IE 11+, Edge 15 - 18+ - // IE 11/Edge don't find elements on a `[name='']` query in some cases. - // Adding a temporary attribute to the document before the selection works - // around the issue. - // Interestingly, IE 10 & older don't seem to have the issue. - input = document.createElement( "input" ); - input.setAttribute( "name", "" ); - el.appendChild( input ); - if ( !el.querySelectorAll( "[name='']" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + - whitespace + "*(?:''|\"\")" ); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll( ":checked" ).length ) { - rbuggyQSA.push( ":checked" ); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push( ".#.+[+~]" ); - } - - // Support: Firefox <=3.6 - 5 only - // Old Firefox doesn't throw on a badly-escaped identifier. - el.querySelectorAll( "\\\f" ); - rbuggyQSA.push( "[\\r\\n\\f]" ); - } ); - - assert( function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement( "input" ); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll( "[name=d]" ).length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: Opera 10 - 11 only - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll( "*,:x" ); - rbuggyQSA.push( ",.*:" ); - } ); - } - - if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector ) ) ) ) { - - assert( function( el ) { - - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - } ); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - ) ); - } : - function( a, b ) { - if ( b ) { - while ( ( b = b.parentNode ) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { - - // Choose the first element that is related to our preferred document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( a == document || a.ownerDocument == preferredDoc && - contains( preferredDoc, a ) ) { - return -1; - } - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( b == document || b.ownerDocument == preferredDoc && - contains( preferredDoc, b ) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - return a == document ? -1 : - b == document ? 1 : - /* eslint-enable eqeqeq */ - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( ( cur = cur.parentNode ) ) { - ap.unshift( cur ); - } - cur = b; - while ( ( cur = cur.parentNode ) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[ i ] === bp[ i ] ) { - i++; - } - - return i ? - - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[ i ], bp[ i ] ) : - - // Otherwise nodes in our document sort first - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - ap[ i ] == preferredDoc ? -1 : - bp[ i ] == preferredDoc ? 1 : - /* eslint-enable eqeqeq */ - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - setDocument( elem ); - - if ( support.matchesSelector && documentIsHTML && - !nonnativeSelectorCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch ( e ) { - nonnativeSelectorCache( expr, true ); - } - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( context.ownerDocument || context ) != document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( elem.ownerDocument || elem ) != document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return ( sel + "" ).replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - - // If no nodeType, this is expected to be an array - while ( ( node = elem[ i++ ] ) ) { - - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[ 1 ] = match[ 1 ].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[ 3 ] = ( match[ 3 ] || match[ 4 ] || - match[ 5 ] || "" ).replace( runescape, funescape ); - - if ( match[ 2 ] === "~=" ) { - match[ 3 ] = " " + match[ 3 ] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[ 1 ] = match[ 1 ].toLowerCase(); - - if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { - - // nth-* requires argument - if ( !match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[ 4 ] = +( match[ 4 ] ? - match[ 5 ] + ( match[ 6 ] || 1 ) : - 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); - match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); - - // other types prohibit arguments - } else if ( match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[ 6 ] && match[ 2 ]; - - if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[ 3 ] ) { - match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - - // Get excess from tokenize (recursively) - ( excess = tokenize( unquoted, true ) ) && - - // advance to the next closing parenthesis - ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { - - // excess is a negative index - match[ 0 ] = match[ 0 ].slice( 0, excess ); - match[ 2 ] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { - return true; - } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - ( pattern = new RegExp( "(^|" + whitespace + - ")" + className + "(" + whitespace + "|$)" ) ) && classCache( - className, function( elem ) { - return pattern.test( - typeof elem.className === "string" && elem.className || - typeof elem.getAttribute !== "undefined" && - elem.getAttribute( "class" ) || - "" - ); - } ); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - /* eslint-disable max-len */ - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - /* eslint-enable max-len */ - - }; - }, - - "CHILD": function( type, what, _argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, _context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( ( node = node[ dir ] ) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( ( node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - - // Use previously-cached element index if available - if ( useCache ) { - - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - - // Use the same loop as above to seek `elem` from the start - while ( ( node = ++nodeIndex && node && node[ dir ] || - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || - ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction( function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[ i ] ); - seed[ idx ] = !( matches[ idx ] = matched[ i ] ); - } - } ) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - - // Potentially complex pseudos - "not": markFunction( function( selector ) { - - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction( function( seed, matches, _context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( ( elem = unmatched[ i ] ) ) { - seed[ i ] = !( matches[ i ] = elem ); - } - } - } ) : - function( elem, _context, xml ) { - input[ 0 ] = elem; - matcher( input, null, xml, results ); - - // Don't keep the element (issue #299) - input[ 0 ] = null; - return !results.pop(); - }; - } ), - - "has": markFunction( function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - } ), - - "contains": markFunction( function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; - }; - } ), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - - // lang value must be a valid identifier - if ( !ridentifier.test( lang || "" ) ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( ( elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); - return false; - }; - } ), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && - ( !document.hasFocus || document.hasFocus() ) && - !!( elem.type || elem.href || ~elem.tabIndex ); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return ( nodeName === "input" && !!elem.checked ) || - ( nodeName === "option" && !!elem.selected ); - }, - - "selected": function( elem ) { - - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - // eslint-disable-next-line no-unused-expressions - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos[ "empty" ]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( ( attr = elem.getAttribute( "type" ) ) == null || - attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo( function() { - return [ 0 ]; - } ), - - "last": createPositionalPseudo( function( _matchIndexes, length ) { - return [ length - 1 ]; - } ), - - "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - } ), - - "even": createPositionalPseudo( function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "odd": createPositionalPseudo( function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? - argument + length : - argument > length ? - length : - argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ) - } -}; - -Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || ( match = rcomma.exec( soFar ) ) ) { - if ( match ) { - - // Don't consume trailing commas as valid - soFar = soFar.slice( match[ 0 ].length ) || soFar; - } - groups.push( ( tokens = [] ) ); - } - - matched = false; - - // Combinators - if ( ( match = rcombinators.exec( soFar ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - - // Cast descendant combinators to space - type: match[ 0 ].replace( rtrim, " " ) - } ); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || - ( match = preFilters[ type ]( match ) ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - type: type, - matches: match - } ); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[ i ].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || ( elem[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || - ( outerCache[ elem.uniqueID ] = {} ); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( ( oldCache = uniqueCache[ key ] ) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return ( newCache[ 2 ] = oldCache[ 2 ] ); - } else { - - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[ i ]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[ 0 ]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[ i ], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( ( elem = unmatched[ i ] ) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction( function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( - selector || "*", - context.nodeType ? [ context ] : context, - [] - ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( ( elem = temp[ i ] ) ) { - matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) ) { - - // Restore matcherIn since elem is not yet a final match - temp.push( ( matcherIn[ i ] = elem ) ); - } - } - postFinder( null, ( matcherOut = [] ), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) && - ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { - - seed[ temp ] = !( results[ temp ] = elem ); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - } ); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[ 0 ].type ], - implicitRelative = leadingRelative || Expr.relative[ " " ], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - ( checkContext = context ).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { - matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; - } else { - matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[ j ].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens - .slice( 0, i - 1 ) - .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), - - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), - len = elems.length; - - if ( outermost ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - outermostContext = context == document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( !context && elem.ownerDocument != document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( ( matcher = elementMatchers[ j++ ] ) ) { - if ( matcher( elem, context || document, xml ) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - - // They will have gone through all possible matchers - if ( ( elem = !matcher && elem ) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( ( matcher = setMatchers[ j++ ] ) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !( unmatched[ i ] || setMatched[ i ] ) ) { - setMatched[ i ] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[ i ] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( - selector, - matcherFromGroupMatchers( elementMatchers, setMatchers ) - ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( ( selector = compiled.selector || selector ) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[ 0 ] = match[ 0 ].slice( 0 ); - if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && - context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { - - context = ( Expr.find[ "ID" ]( token.matches[ 0 ] - .replace( runescape, funescape ), context ) || [] )[ 0 ]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[ i ]; - - // Abort if we hit a combinator - if ( Expr.relative[ ( type = token.type ) ] ) { - break; - } - if ( ( find = Expr.find[ type ] ) ) { - - // Search, expanding context for leading sibling combinators - if ( ( seed = find( - token.matches[ 0 ].replace( runescape, funescape ), - rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || - context - ) ) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert( function( el ) { - - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; -} ); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert( function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute( "href" ) === "#"; -} ) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - } ); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert( function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -} ) ) { - addHandle( "value", function( elem, _name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - } ); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert( function( el ) { - return el.getAttribute( "disabled" ) == null; -} ) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; - } - } ); -} - -return Sizzle; - -} )( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - - - -function nodeName( elem, name ) { - - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - -}; -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - } - - // Single element - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - } - - // Arraylike of elements (jQuery, arguments, Array) - if ( typeof qualifier !== "string" ) { - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not; - } ); - } - - // Filtered directly for both simple and complex selectors - return jQuery.filter( qualifier, elements, not ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - if ( elems.length === 1 && elem.nodeType === 1 ) { - return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; - } - - return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, _i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, _i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, _i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - if ( elem.contentDocument != null && - - // Support: IE 11+ - // elements with no `data` attribute has an object - // `contentDocument` with a `null` prototype. - getProto( elem.contentDocument ) ) { - - return elem.contentDocument; - } - - // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only - // Treat the template element as a regular one in browsers that - // don't support it. - if ( nodeName( elem, "template" ) ) { - elem = elem.content || elem; - } - - return jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = locked || options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && toType( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject, noValue ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: - // * false: [ value ].slice( 0 ) => resolve( value ) - // * true: [ value ].slice( 1 ) => resolve() - resolve.apply( undefined, [ value ].slice( noValue ) ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.apply( undefined, [ value ] ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( _i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // rejected_handlers.disable - // fulfilled_handlers.disable - tuples[ 3 - i ][ 3 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock, - - // progress_handlers.lock - tuples[ 0 ][ 3 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the master Deferred - master = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - master.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, - !remaining ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( master.state() === "pending" || - isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return master.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); - } - - return master.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( toType( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, _key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - if ( chainable ) { - return elems; - } - - // Gets - if ( bulk ) { - return fn.call( elems ); - } - - return len ? fn( elems[ 0 ], key ) : emptyGet; -}; - - -// Matches dashed string for camelizing -var rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g; - -// Used by camelCase as callback to replace() -function fcamelCase( _all, letter ) { - return letter.toUpperCase(); -} - -// Convert dashed to camelCase; used by the css and data modules -// Support: IE <=9 - 11, Edge 12 - 15 -// Microsoft forgot to hump their vendor prefix (#9572) -function camelCase( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); -} -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( Array.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( camelCase ); - } else { - key = camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnothtmlwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function getData( data ) { - if ( data === "true" ) { - return true; - } - - if ( data === "false" ) { - return false; - } - - if ( data === "null" ) { - return null; - } - - // Only convert to a number if it doesn't change the string - if ( data === +data + "" ) { - return +data; - } - - if ( rbrace.test( data ) ) { - return JSON.parse( data ); - } - - return data; -} - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = getData( data ); - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || Array.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var documentElement = document.documentElement; - - - - var isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ); - }, - composed = { composed: true }; - - // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only - // Check attachment across shadow DOM boundaries when possible (gh-3504) - // Support: iOS 10.0-10.2 only - // Early iOS 10 versions support `attachShadow` but not `getRootNode`, - // leading to errors. We need to check for `getRootNode`. - if ( documentElement.getRootNode ) { - isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ) || - elem.getRootNode( composed ) === elem.ownerDocument; - }; - } -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - isAttached( elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, scale, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = elem.nodeType && - ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Support: Firefox <=54 - // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) - initial = initial / 2; - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - while ( maxIterations-- ) { - - // Evaluate and update our best guess (doubling guesses that zero out). - // Finish if the scale equals or crosses 1 (making the old*new product non-positive). - jQuery.style( elem, prop, initialInUnit + unit ); - if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { - maxIterations = 0; - } - initialInUnit = initialInUnit / scale; - - } - - initialInUnit = initialInUnit * 2; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ); - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); - -var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); - - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; - - // Support: IE <=9 only - // IE <=9 replaces "; - support.option = !!div.lastChild; -} )(); - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
    " ], - col: [ 2, "", "
    " ], - tr: [ 2, "", "
    " ], - td: [ 3, "", "
    " ], - - _default: [ 0, "", "" ] -}; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - -// Support: IE <=9 only -if ( !support.option ) { - wrapMap.optgroup = wrapMap.option = [ 1, "" ]; -} - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret; - - if ( typeof context.getElementsByTagName !== "undefined" ) { - ret = context.getElementsByTagName( tag || "*" ); - - } else if ( typeof context.querySelectorAll !== "undefined" ) { - ret = context.querySelectorAll( tag || "*" ); - - } else { - ret = []; - } - - if ( tag === undefined || tag && nodeName( context, tag ) ) { - return jQuery.merge( [ context ], ret ); - } - - return ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, attached, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( toType( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - attached = isAttached( elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( attached ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -var - rkeyEvent = /^key/, - rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, - rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 - 11+ -// focus() and blur() are asynchronous, except when they are no-op. -// So expect focus to be synchronous when the element is already active, -// and blur to be synchronous when the element is not already active. -// (focus and blur are always synchronous in other supported browsers, -// this just defines when we can count on it). -function expectSync( elem, type ) { - return ( elem === safeActiveElement() ) === ( type === "focus" ); -} - -// Support: IE <=9 only -// Accessing document.activeElement can throw unexpectedly -// https://bugs.jquery.com/ticket/13393 -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Only attach events to objects that accept data - if ( !acceptData( elem ) ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = Object.create( null ); - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - - // Make a writable jQuery.Event from the native event object - event = jQuery.event.fix( nativeEvent ), - - handlers = ( - dataPriv.get( this, "events" ) || Object.create( null ) - )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // If the event is namespaced, then each handler is only invoked if it is - // specially universal or its namespaces are a superset of the event's. - if ( !event.rnamespace || handleObj.namespace === false || - event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, handleObj, sel, matchedHandlers, matchedSelectors, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Find delegate handlers - if ( delegateCount && - - // Support: IE <=9 - // Black-hole SVG instance trees (trac-13180) - cur.nodeType && - - // Support: Firefox <=42 - // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) - // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click - // Support: IE 11 only - // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) - !( event.type === "click" && event.button >= 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { - matchedHandlers = []; - matchedSelectors = {}; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matchedSelectors[ sel ] === undefined ) { - matchedSelectors[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matchedSelectors[ sel ] ) { - matchedHandlers.push( handleObj ); - } - } - if ( matchedHandlers.length ) { - handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - cur = this; - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - click: { - - // Utilize native event to ensure correct state for checkable inputs - setup: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Claim the first handler - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - // dataPriv.set( el, "click", ... ) - leverageNative( el, "click", returnTrue ); - } - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Force setup before triggering a click - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - leverageNative( el, "click" ); - } - - // Return non-false to allow normal event-path propagation - return true; - }, - - // For cross-browser consistency, suppress native .click() on links - // Also prevent it if we're currently inside a leveraged native-event stack - _default: function( event ) { - var target = event.target; - return rcheckableType.test( target.type ) && - target.click && nodeName( target, "input" ) && - dataPriv.get( target, "click" ) || - nodeName( target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -// Ensure the presence of an event listener that handles manually-triggered -// synthetic events by interrupting progress until reinvoked in response to -// *native* events that it fires directly, ensuring that state changes have -// already occurred before other listeners are invoked. -function leverageNative( el, type, expectSync ) { - - // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add - if ( !expectSync ) { - if ( dataPriv.get( el, type ) === undefined ) { - jQuery.event.add( el, type, returnTrue ); - } - return; - } - - // Register the controller as a special universal handler for all event namespaces - dataPriv.set( el, type, false ); - jQuery.event.add( el, type, { - namespace: false, - handler: function( event ) { - var notAsync, result, - saved = dataPriv.get( this, type ); - - if ( ( event.isTrigger & 1 ) && this[ type ] ) { - - // Interrupt processing of the outer synthetic .trigger()ed event - // Saved data should be false in such cases, but might be a leftover capture object - // from an async native handler (gh-4350) - if ( !saved.length ) { - - // Store arguments for use when handling the inner native event - // There will always be at least one argument (an event object), so this array - // will not be confused with a leftover capture object. - saved = slice.call( arguments ); - dataPriv.set( this, type, saved ); - - // Trigger the native event and capture its result - // Support: IE <=9 - 11+ - // focus() and blur() are asynchronous - notAsync = expectSync( this, type ); - this[ type ](); - result = dataPriv.get( this, type ); - if ( saved !== result || notAsync ) { - dataPriv.set( this, type, false ); - } else { - result = {}; - } - if ( saved !== result ) { - - // Cancel the outer synthetic event - event.stopImmediatePropagation(); - event.preventDefault(); - return result.value; - } - - // If this is an inner synthetic event for an event with a bubbling surrogate - // (focus or blur), assume that the surrogate already propagated from triggering the - // native event and prevent that from happening again here. - // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the - // bubbling surrogate propagates *after* the non-bubbling base), but that seems - // less bad than duplication. - } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { - event.stopPropagation(); - } - - // If this is a native event triggered above, everything is now in order - // Fire an inner synthetic event with the original arguments - } else if ( saved.length ) { - - // ...and capture the result - dataPriv.set( this, type, { - value: jQuery.event.trigger( - - // Support: IE <=9 - 11+ - // Extend with the prototype to reset the above stopImmediatePropagation() - jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), - saved.slice( 1 ), - this - ) - } ); - - // Abort handling of the native event - event.stopImmediatePropagation(); - } - } - } ); -} - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || Date.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - code: true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - - which: function( event ) { - var button = event.button; - - // Add which for key events - if ( event.which == null && rkeyEvent.test( event.type ) ) { - return event.charCode != null ? event.charCode : event.keyCode; - } - - // Add which for click: 1 === left; 2 === middle; 3 === right - if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { - if ( button & 1 ) { - return 1; - } - - if ( button & 2 ) { - return 3; - } - - if ( button & 4 ) { - return 2; - } - - return 0; - } - - return event.which; - } -}, jQuery.event.addProp ); - -jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { - jQuery.event.special[ type ] = { - - // Utilize native event if possible so blur/focus sequence is correct - setup: function() { - - // Claim the first handler - // dataPriv.set( this, "focus", ... ) - // dataPriv.set( this, "blur", ... ) - leverageNative( this, type, expectSync ); - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function() { - - // Force setup before trigger - leverageNative( this, type ); - - // Return non-false to allow normal event-path propagation - return true; - }, - - delegateType: delegateType - }; -} ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - // Support: IE <=10 - 11, Edge 12 - 13 only - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -// Prefer a tbody over its parent table for containing new rows -function manipulationTarget( elem, content ) { - if ( nodeName( elem, "table" ) && - nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return jQuery( elem ).children( "tbody" )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { - elem.type = elem.type.slice( 5 ); - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.get( src ); - events = pdataOld.events; - - if ( events ) { - dataPriv.remove( dest, "handle events" ); - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = flat( args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - valueIsFunction = isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( valueIsFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( valueIsFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl && !node.noModule ) { - jQuery._evalUrl( node.src, { - nonce: node.nonce || node.getAttribute( "nonce" ) - }, doc ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && isAttached( node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html; - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = isAttached( elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - -var swap = function( elem, options, callback ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.call( elem ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - -var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - container.style.cssText = "position:absolute;left:-11111px;width:60px;" + - "margin-top:1px;padding:0;border:0"; - div.style.cssText = - "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + - "margin:auto;border:1px;padding:1px;" + - "width:60%;top:1%"; - documentElement.appendChild( container ).appendChild( div ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; - - // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 - // Some styles come back with percentage values, even though they shouldn't - div.style.right = "60%"; - pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; - - // Support: IE 9 - 11 only - // Detect misreporting of content dimensions for box-sizing:border-box elements - boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; - - // Support: IE 9 only - // Detect overflow:scroll screwiness (gh-3699) - // Support: Chrome <=64 - // Don't get tricked when zoom affects offsetWidth (gh-4029) - div.style.position = "absolute"; - scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - function roundPixelMeasures( measure ) { - return Math.round( parseFloat( measure ) ); - } - - var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, - reliableTrDimensionsVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - jQuery.extend( support, { - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelBoxStyles: function() { - computeStyleTests(); - return pixelBoxStylesVal; - }, - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - }, - scrollboxSize: function() { - computeStyleTests(); - return scrollboxSizeVal; - }, - - // Support: IE 9 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Behavior in IE 9 is more subtle than in newer versions & it passes - // some versions of this test; make sure not to make it pass there! - reliableTrDimensions: function() { - var table, tr, trChild, trStyle; - if ( reliableTrDimensionsVal == null ) { - table = document.createElement( "table" ); - tr = document.createElement( "tr" ); - trChild = document.createElement( "div" ); - - table.style.cssText = "position:absolute;left:-11111px"; - tr.style.height = "1px"; - trChild.style.height = "9px"; - - documentElement - .appendChild( table ) - .appendChild( tr ) - .appendChild( trChild ); - - trStyle = window.getComputedStyle( tr ); - reliableTrDimensionsVal = parseInt( trStyle.height ) > 3; - - documentElement.removeChild( table ); - } - return reliableTrDimensionsVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - - // Support: Firefox 51+ - // Retrieving style before computed somehow - // fixes an issue with getting wrong values - // on detached elements - style = elem.style; - - computed = computed || getStyles( elem ); - - // getPropertyValue is needed for: - // .css('filter') (IE 9 only, #12537) - // .css('--customProperty) (#3144) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !isAttached( elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style, - vendorProps = {}; - -// Return a vendor-prefixed property or undefined -function vendorPropName( name ) { - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -// Return a potentially-mapped jQuery.cssProps or vendor prefixed property -function finalPropName( name ) { - var final = jQuery.cssProps[ name ] || vendorProps[ name ]; - - if ( final ) { - return final; - } - if ( name in emptyStyle ) { - return name; - } - return vendorProps[ name ] = vendorPropName( name ) || name; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - rcustomProp = /^--/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }; - -function setPositiveNumber( _elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { - var i = dimension === "width" ? 1 : 0, - extra = 0, - delta = 0; - - // Adjustment may not be necessary - if ( box === ( isBorderBox ? "border" : "content" ) ) { - return 0; - } - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin - if ( box === "margin" ) { - delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); - } - - // If we get here with a content-box, we're seeking "padding" or "border" or "margin" - if ( !isBorderBox ) { - - // Add padding - delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // For "border" or "margin", add border - if ( box !== "padding" ) { - delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - - // But still keep track of it otherwise - } else { - extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - - // If we get here with a border-box (content + padding + border), we're seeking "content" or - // "padding" or "margin" - } else { - - // For "content", subtract padding - if ( box === "content" ) { - delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // For "content" or "padding", subtract border - if ( box !== "margin" ) { - delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - // Account for positive content-box scroll gutter when requested by providing computedVal - if ( !isBorderBox && computedVal >= 0 ) { - - // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border - // Assuming integer scroll gutter, subtract the rest and round down - delta += Math.max( 0, Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - computedVal - - delta - - extra - - 0.5 - - // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter - // Use an explicit zero to avoid NaN (gh-3964) - ) ) || 0; - } - - return delta; -} - -function getWidthOrHeight( elem, dimension, extra ) { - - // Start with computed style - var styles = getStyles( elem ), - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). - // Fake content-box until we know it's needed to know the true value. - boxSizingNeeded = !support.boxSizingReliable() || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - valueIsBorderBox = isBorderBox, - - val = curCSS( elem, dimension, styles ), - offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); - - // Support: Firefox <=54 - // Return a confounding non-pixel value or feign ignorance, as appropriate. - if ( rnumnonpx.test( val ) ) { - if ( !extra ) { - return val; - } - val = "auto"; - } - - - // Support: IE 9 - 11 only - // Use offsetWidth/offsetHeight for when box sizing is unreliable. - // In those cases, the computed value can be trusted to be border-box. - if ( ( !support.boxSizingReliable() && isBorderBox || - - // Support: IE 10 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Interestingly, in some cases IE 9 doesn't suffer from this issue. - !support.reliableTrDimensions() && nodeName( elem, "tr" ) || - - // Fall back to offsetWidth/offsetHeight when value is "auto" - // This happens for inline elements with no explicit setting (gh-3571) - val === "auto" || - - // Support: Android <=4.1 - 4.3 only - // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) - !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && - - // Make sure the element is visible & connected - elem.getClientRects().length ) { - - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Where available, offsetWidth/offsetHeight approximate border box dimensions. - // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the - // retrieved value as a content box dimension. - valueIsBorderBox = offsetProp in elem; - if ( valueIsBorderBox ) { - val = elem[ offsetProp ]; - } - } - - // Normalize "" and auto - val = parseFloat( val ) || 0; - - // Adjust for the element's box model - return ( val + - boxModelAdjustment( - elem, - dimension, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles, - - // Provide the current computed size to request scroll gutter calculation (gh-3589) - val - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "gridArea": true, - "gridColumn": true, - "gridColumnEnd": true, - "gridColumnStart": true, - "gridRow": true, - "gridRowEnd": true, - "gridRowStart": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: {}, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ), - style = elem.style; - - // Make sure that we're working with the right name. We don't - // want to query the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append - // "px" to a few hardcoded values. - if ( type === "number" && !isCustomProp ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - if ( isCustomProp ) { - style.setProperty( name, value ); - } else { - style[ name ] = value; - } - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ); - - // Make sure that we're working with the right name. We don't - // want to modify the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( _i, dimension ) { - jQuery.cssHooks[ dimension ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, dimension, extra ); - } ) : - getWidthOrHeight( elem, dimension, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = getStyles( elem ), - - // Only read styles.position if the test has a chance to fail - // to avoid forcing a reflow. - scrollboxSizeBuggy = !support.scrollboxSize() && - styles.position === "absolute", - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) - boxSizingNeeded = scrollboxSizeBuggy || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - subtract = extra ? - boxModelAdjustment( - elem, - dimension, - extra, - isBorderBox, - styles - ) : - 0; - - // Account for unreliable border-box dimensions by comparing offset* to computed and - // faking a content-box to get border and padding (gh-3699) - if ( isBorderBox && scrollboxSizeBuggy ) { - subtract -= Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - parseFloat( styles[ dimension ] ) - - boxModelAdjustment( elem, dimension, "border", false, styles ) - - 0.5 - ); - } - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ dimension ] = value; - value = jQuery.css( elem, dimension ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( prefix !== "margin" ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( Array.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && ( - jQuery.cssHooks[ tween.prop ] || - tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, inProgress, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function schedule() { - if ( inProgress ) { - if ( document.hidden === false && window.requestAnimationFrame ) { - window.requestAnimationFrame( schedule ); - } else { - window.setTimeout( schedule, jQuery.fx.interval ); - } - - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = Date.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 15 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY and Edge just mirrors - // the overflowX value there. - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( Array.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - // If there's more to do, yield - if ( percent < 1 && length ) { - return remaining; - } - - // If this was an empty animation, synthesize a final progress notification - if ( !length ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - } - - // Resolve the animation and report its conclusion - deferred.resolveWith( elem, [ animation ] ); - return false; - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - result.stop.bind( result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - // Attach callbacks from options - animation - .progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - return animation; -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnothtmlwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !isFunction( easing ) && easing - }; - - // Go to the end state if fx are off - if ( jQuery.fx.off ) { - opt.duration = 0; - - } else { - if ( typeof opt.duration !== "number" ) { - if ( opt.duration in jQuery.fx.speeds ) { - opt.duration = jQuery.fx.speeds[ opt.duration ]; - - } else { - opt.duration = jQuery.fx.speeds._default; - } - } - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = Date.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Run the timer and safely remove it when done (allowing for external removal) - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - jQuery.fx.start(); -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( inProgress ) { - return; - } - - inProgress = true; - schedule(); -}; - -jQuery.fx.stop = function() { - inProgress = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - - // Attribute names can contain non-HTML whitespace characters - // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 - attrNames = value && value.match( rnothtmlwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - if ( tabindex ) { - return parseInt( tabindex, 10 ); - } - - if ( - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && - elem.href - ) { - return 0; - } - - return -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -// eslint rule "no-unused-expressions" is disabled for this code -// since it considers such accessions noop -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - - // Strip and collapse whitespace according to HTML spec - // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace - function stripAndCollapse( value ) { - var tokens = value.match( rnothtmlwhite ) || []; - return tokens.join( " " ); - } - - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -function classesToArray( value ) { - if ( Array.isArray( value ) ) { - return value; - } - if ( typeof value === "string" ) { - return value.match( rnothtmlwhite ) || []; - } - return []; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value, - isValidValue = type === "string" || Array.isArray( value ); - - if ( typeof stateVal === "boolean" && isValidValue ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( isValidValue ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = classesToArray( value ); - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, valueIsFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - // Handle most common string cases - if ( typeof ret === "string" ) { - return ret.replace( rreturn, "" ); - } - - // Handle cases where value is null/undef or number - return ret == null ? "" : ret; - } - - return; - } - - valueIsFunction = isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( valueIsFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( Array.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - stripAndCollapse( jQuery.text( elem ) ); - } - }, - select: { - get: function( elem ) { - var value, option, i, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length; - - if ( index < 0 ) { - i = max; - - } else { - i = one ? index : 0; - } - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( Array.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -support.focusin = "onfocusin" in window; - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, - stopPropagationCallback = function( e ) { - e.stopPropagation(); - }; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = lastElement = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - lastElement = cur; - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( - dataPriv.get( cur, "events" ) || Object.create( null ) - )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - - if ( event.isPropagationStopped() ) { - lastElement.addEventListener( type, stopPropagationCallback ); - } - - elem[ type ](); - - if ( event.isPropagationStopped() ) { - lastElement.removeEventListener( type, stopPropagationCallback ); - } - - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - - // Handle: regular nodes (via `this.ownerDocument`), window - // (via `this.document`) & document (via `this`). - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = { guid: Date.now() }; - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) { - xml = undefined; - } - - if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { - jQuery.error( "Invalid XML: " + data ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( Array.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && toType( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - if ( a == null ) { - return ""; - } - - // If an array was passed in, assume that it is an array of form elements. - if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ) - .filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ) - .map( function( _i, elem ) { - var val = jQuery( this ).val(); - - if ( val == null ) { - return null; - } - - if ( Array.isArray( val ) ) { - return jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ); - } - - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rantiCache = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; - - if ( isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() + " " ] = - ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) - .concat( match[ 2 ] ); - } - } - match = responseHeaders[ key.toLowerCase() + " " ]; - } - return match == null ? null : match.join( ", " ); - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 15 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available and should be processed, append data to url - if ( s.data && ( s.processData || typeof s.data === "string" ) ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add or update anti-cache param if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rantiCache, "$1" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + - uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : - s.accepts[ "*" ] - ); - - // Check for headers option - for ( i in s.headers ) { - jqXHR.setRequestHeader( i, s.headers[ i ] ); - } - - // Allow custom headers/mimetypes and early abort - if ( s.beforeSend && - ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { - - // Abort if not done already and return - return jqXHR.abort(); - } - - // Aborting is no longer a cancellation - strAbort = "abort"; - - // Install callbacks on deferreds - completeDeferred.add( s.complete ); - jqXHR.done( s.success ); - jqXHR.fail( s.error ); - - // Get transport - transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); - - // If no transport, we auto-abort - if ( !transport ) { - done( -1, "No Transport" ); - } else { - jqXHR.readyState = 1; - - // Send global event - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); - } - - // If request was aborted inside ajaxSend, stop there - if ( completed ) { - return jqXHR; - } - - // Timeout - if ( s.async && s.timeout > 0 ) { - timeoutTimer = window.setTimeout( function() { - jqXHR.abort( "timeout" ); - }, s.timeout ); - } - - try { - completed = false; - transport.send( requestHeaders, done ); - } catch ( e ) { - - // Rethrow post-completion exceptions - if ( completed ) { - throw e; - } - - // Propagate others as results - done( -1, e ); - } - } - - // Callback for when everything is done - function done( status, nativeStatusText, responses, headers ) { - var isSuccess, success, error, response, modified, - statusText = nativeStatusText; - - // Ignore repeat invocations - if ( completed ) { - return; - } - - completed = true; - - // Clear timeout if it exists - if ( timeoutTimer ) { - window.clearTimeout( timeoutTimer ); - } - - // Dereference transport for early garbage collection - // (no matter how long the jqXHR object will be used) - transport = undefined; - - // Cache response headers - responseHeadersString = headers || ""; - - // Set readyState - jqXHR.readyState = status > 0 ? 4 : 0; - - // Determine if successful - isSuccess = status >= 200 && status < 300 || status === 304; - - // Get response data - if ( responses ) { - response = ajaxHandleResponses( s, jqXHR, responses ); - } - - // Use a noop converter for missing script - if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) { - s.converters[ "text script" ] = function() {}; - } - - // Convert no matter what (that way responseXXX fields are always set) - response = ajaxConvert( s, response, jqXHR, isSuccess ); - - // If successful, handle type chaining - if ( isSuccess ) { - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - modified = jqXHR.getResponseHeader( "Last-Modified" ); - if ( modified ) { - jQuery.lastModified[ cacheURL ] = modified; - } - modified = jqXHR.getResponseHeader( "etag" ); - if ( modified ) { - jQuery.etag[ cacheURL ] = modified; - } - } - - // if no content - if ( status === 204 || s.type === "HEAD" ) { - statusText = "nocontent"; - - // if not modified - } else if ( status === 304 ) { - statusText = "notmodified"; - - // If we have data, let's convert it - } else { - statusText = response.state; - success = response.data; - error = response.error; - isSuccess = !error; - } - } else { - - // Extract error from statusText and normalize for non-aborts - error = statusText; - if ( status || !statusText ) { - statusText = "error"; - if ( status < 0 ) { - status = 0; - } - } - } - - // Set data for the fake xhr object - jqXHR.status = status; - jqXHR.statusText = ( nativeStatusText || statusText ) + ""; - - // Success/Error - if ( isSuccess ) { - deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); - } else { - deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); - } - - // Status-dependent callbacks - jqXHR.statusCode( statusCode ); - statusCode = undefined; - - if ( fireGlobals ) { - globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", - [ jqXHR, s, isSuccess ? success : error ] ); - } - - // Complete - completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); - - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); - - // Handle the global AJAX counter - if ( !( --jQuery.active ) ) { - jQuery.event.trigger( "ajaxStop" ); - } - } - } - - return jqXHR; - }, - - getJSON: function( url, data, callback ) { - return jQuery.get( url, data, callback, "json" ); - }, - - getScript: function( url, callback ) { - return jQuery.get( url, undefined, callback, "script" ); - } -} ); - -jQuery.each( [ "get", "post" ], function( _i, method ) { - jQuery[ method ] = function( url, data, callback, type ) { - - // Shift arguments if data argument was omitted - if ( isFunction( data ) ) { - type = type || callback; - callback = data; - data = undefined; - } - - // The url can be an options object (which then must have .url) - return jQuery.ajax( jQuery.extend( { - url: url, - type: method, - dataType: type, - data: data, - success: callback - }, jQuery.isPlainObject( url ) && url ) ); - }; -} ); - -jQuery.ajaxPrefilter( function( s ) { - var i; - for ( i in s.headers ) { - if ( i.toLowerCase() === "content-type" ) { - s.contentType = s.headers[ i ] || ""; - } - } -} ); - - -jQuery._evalUrl = function( url, options, doc ) { - return jQuery.ajax( { - url: url, - - // Make this explicit, since user can override this through ajaxSetup (#11264) - type: "GET", - dataType: "script", - cache: true, - async: false, - global: false, - - // Only evaluate the response if it is successful (gh-4126) - // dataFilter is not invoked for failure responses, so using it instead - // of the default converter is kludgy but it works. - converters: { - "text script": function() {} - }, - dataFilter: function( response ) { - jQuery.globalEval( response, options, doc ); - } - } ); -}; - - -jQuery.fn.extend( { - wrapAll: function( html ) { - var wrap; - - if ( this[ 0 ] ) { - if ( isFunction( html ) ) { - html = html.call( this[ 0 ] ); - } - - // The elements to wrap the target around - wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); - - if ( this[ 0 ].parentNode ) { - wrap.insertBefore( this[ 0 ] ); - } - - wrap.map( function() { - var elem = this; - - while ( elem.firstElementChild ) { - elem = elem.firstElementChild; - } - - return elem; - } ).append( this ); - } - - return this; - }, - - wrapInner: function( html ) { - if ( isFunction( html ) ) { - return this.each( function( i ) { - jQuery( this ).wrapInner( html.call( this, i ) ); - } ); - } - - return this.each( function() { - var self = jQuery( this ), - contents = self.contents(); - - if ( contents.length ) { - contents.wrapAll( html ); - - } else { - self.append( html ); - } - } ); - }, - - wrap: function( html ) { - var htmlIsFunction = isFunction( html ); - - return this.each( function( i ) { - jQuery( this ).wrapAll( htmlIsFunction ? 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- { binary: xhr.response } : - { text: xhr.responseText }, - xhr.getAllResponseHeaders() - ); - } - } - }; - }; - - // Listen to events - xhr.onload = callback(); - errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); - - // Support: IE 9 only - // Use onreadystatechange to replace onabort - // to handle uncaught aborts - if ( xhr.onabort !== undefined ) { - xhr.onabort = errorCallback; - } else { - xhr.onreadystatechange = function() { - - // Check readyState before timeout as it changes - if ( xhr.readyState === 4 ) { - - // Allow onerror to be called first, - // but that will not handle a native abort - // Also, save errorCallback to a variable - // as xhr.onerror cannot be accessed - window.setTimeout( function() { - if ( callback ) { - errorCallback(); - } - } ); - } - }; - } - - // Create the abort callback - callback = callback( "abort" ); - - try { - - // Do send the request (this may raise an exception) - xhr.send( options.hasContent && options.data || null ); - } catch ( e ) { - - // #14683: Only rethrow if this hasn't been notified as an error yet - if ( callback ) { - throw e; - } - } - }, - - abort: function() { - if ( callback ) { - callback(); - } - } - }; - } -} ); - - - - -// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) -jQuery.ajaxPrefilter( function( s ) { - if ( s.crossDomain ) { - s.contents.script = false; - } -} ); - -// Install script dataType -jQuery.ajaxSetup( { - accepts: { - script: "text/javascript, application/javascript, " + - "application/ecmascript, application/x-ecmascript" - }, - contents: { - script: /\b(?:java|ecma)script\b/ - }, - converters: { - "text script": function( text ) { - jQuery.globalEval( text ); - return text; - } - } -} ); - -// Handle cache's special case and crossDomain -jQuery.ajaxPrefilter( "script", function( s ) { - if ( s.cache === undefined ) { - s.cache = false; - } - if ( s.crossDomain ) { - s.type = "GET"; - } -} ); - -// Bind script tag hack transport -jQuery.ajaxTransport( "script", function( s ) { - - // This transport only deals with cross domain or forced-by-attrs requests - if ( s.crossDomain || s.scriptAttrs ) { - var script, callback; - return { - send: function( _, complete ) { - script = jQuery( " -{% endmacro %} \ No newline at end of file diff --git a/stat301book/_build/html/genindex.html b/stat301book/_build/html/genindex.html deleted file mode 100644 index 8a67b02..0000000 --- a/stat301book/_build/html/genindex.html +++ /dev/null @@ -1,228 +0,0 @@ - - - - - - - - Index — Statistical Modelling for Data Science - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - By Gabriela Cohen Freue
    - - © Copyright 2021.
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    - - - - - \ No newline at end of file diff --git a/stat301book/_build/html/index.html b/stat301book/_build/html/index.html deleted file mode 100644 index 3157386..0000000 --- a/stat301book/_build/html/index.html +++ /dev/null @@ -1 +0,0 @@ - diff --git a/stat301book/_build/html/intro.html b/stat301book/_build/html/intro.html deleted file mode 100644 index 6864859..0000000 --- a/stat301book/_build/html/intro.html +++ /dev/null @@ -1,324 +0,0 @@ - - - - - - - - Statistical modelling for Data Science — Statistical Modelling for Data Science - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Statistical modelling for Data Science

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    Contents

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    Statistical modelling for Data Science

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    This book is a compilation of STAT301 class notes written by Professor Gabriela Cohen Freue with contributions from Professors Rodolfo Lourenzutti and Alexi Rodriguez-Arelis.

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    Overview

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    This course introduces main concepts of explanatory and predictive data analysis with multiple explanatory variables. By the end of the course, students are expected to be able to choose the right methods to apply based on the statistical question and data at hand. Special emphasis is placed on case studies and real data sets, as well as reproducible and transparent workflows when writing computer scripts for analysis and reports.

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    Learning Outcomes

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    By the end of the course, students are expected to be able to:

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    • Describe real-world examples of explanatory modelling (e.g. A/B testing optimization & regression with variable selection) and predictive modelling problems.

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    • Explain the trade-offs between model-based and non-model based approaches, and describe situations where each might be the preferred approach.

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    • Explain the difference between creating models for explanation vs prediction, in the context of both how you choose and evaluate models as well as how you interpret the results.

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    • Choose & apply a suitable method (e.g., regression, GLM’s, sample size estimation, controlling for multiple testing, peeking, bandit algorithms, variable selection, model diagnostics) based on the statistical question and data at hand. Discuss the advantages and disadvantages of different methods that may be suitable for a given problem.

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    • Correctly interpret computer output when performing the statistical analyses presented in this course, in the context of the statistical question being asked and the audience being reported to.

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    • Identify the assumptions / conditions required for each method to produce reliable results. Choose techniques to check (or at least be able to falsify) those assumptions. Discuss the consequence(s) of mapping the wrong methods to the question and/or data type.

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    - - By Gabriela Cohen Freue
    - - © Copyright 2021.
    -

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    - - - - - \ No newline at end of file diff --git a/stat301book/_build/html/objects.inv b/stat301book/_build/html/objects.inv deleted file mode 100644 index 0e898f20a9a460792aa25d498d2375dfeafaa376..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 304 zcmV-00nh#;AX9K?X>NERX>N99Zgg*Qc_4OWa&u{KZXhxWBOp+6Z)#;@bUGkVd30!R zZVDqHR%LQ?X>V>iAPOTORA^-&a%F8{X>Md?av*PJAarPHb0B7EY-J#6b0A}HZE$jB zb8}^6Aa!$TZf78RY-wUH3V7OukHHRtFc3uVe8v6%DjdCOym&An!Mj=7wQQ^{DK*O9 z3$!F)NW5)k=IzcZGHfJ$p!IUEu_6s=1}dtX+CMd%hcRK}ogHJ+iXk)#AyInt+#*Yax C4}=l` diff --git a/stat301book/_build/html/reports/w02d1_abTesting.log b/stat301book/_build/html/reports/w02d1_abTesting.log deleted file mode 100644 index 6a8e6ee..0000000 --- a/stat301book/_build/html/reports/w02d1_abTesting.log +++ /dev/null @@ -1,102 +0,0 @@ -Traceback (most recent call last): - File "/Users/gcohen/opt/anaconda3/lib/python3.7/site-packages/jupyter_cache/executors/utils.py", line 56, in single_nb_execution - record_timing=False, - File "/Users/gcohen/opt/anaconda3/lib/python3.7/site-packages/nbclient/client.py", line 1087, in execute - return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute() - File "/Users/gcohen/opt/anaconda3/lib/python3.7/site-packages/nbclient/util.py", line 74, in wrapped - return just_run(coro(*args, **kwargs)) - File "/Users/gcohen/opt/anaconda3/lib/python3.7/site-packages/nbclient/util.py", line 53, in just_run - return loop.run_until_complete(coro) - File "/Users/gcohen/opt/anaconda3/lib/python3.7/asyncio/base_events.py", line 587, in run_until_complete - return future.result() - File "/Users/gcohen/opt/anaconda3/lib/python3.7/site-packages/nbclient/client.py", line 541, in async_execute - cell, index, execution_count=self.code_cells_executed + 1 - File "/Users/gcohen/opt/anaconda3/lib/python3.7/site-packages/nbclient/client.py", line 832, in async_execute_cell - self._check_raise_for_error(cell, exec_reply) - File "/Users/gcohen/opt/anaconda3/lib/python3.7/site-packages/nbclient/client.py", line 740, in _check_raise_for_error - raise CellExecutionError.from_cell_and_msg(cell, exec_reply['content']) -nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: ------------------- -# FUNCTION -# Two-sample t-test with tracking sequential statistic and p-values by incremental sample sizes until getting to n in each group. - -# @param n (numeric): Initially planned sample size for each group (for simplicity, n needs to be a multiple of sample_increase_step). -# @param d_0 (numeric): effect size. -# @param mean_current (numeric): Population mean for control variation. -# @param sd_current (numeric): Population standard deviation for current variation. -# @param sd_new (numeric): Population standard deviation for new variation. -# @param sample_increase_step (numeric): Sample size increment. - -# @return p.value.df: A tibble that has 3 columns: -# inc_sample_size, statistic, and p_value - -incremental_t_test <- function(n, d_0, mean_current, sd_current, sd_new, sample_increase_step) { - sample_current <- rnorm(n, mean = mean_current, sd = sd_current) - sample_new <- rnorm(n, mean = mean_current + d_0, sd = sd_new) - - p.value.df <- tibble( - inc_sample_size = rep(0, n / sample_increase_step), - statistic = rep(0, n / sample_increase_step), - p_value = rep(0, n / sample_increase_step) - ) - - current_sample_size <- sample_increase_step - - for (i in 1:nrow(p.value.df)) - { - t_test_results <- t.test(sample_new[1:current_sample_size], sample_current[1:current_sample_size], - var.equal = TRUE, - alternative = "greater" - ) - p.value.df[i, "statistic"] <- as_tibble(t_test_results$statistic) - p.value.df[i, "p_value"] <- as_tibble(t_test_results$p.value) - p.value.df[i, "inc_sample_size"] <- current_sample_size - current_sample_size <- current_sample_size + sample_increase_step - } - - return(p.value.df) -} - -#--------------------------------------------------------------- - -#TEST and PLOT - -options(repr.plot.width = 10, repr.plot.height = 5) -options(warn=-1) - -set.seed(301) -answer2.1 <- - incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50) - -sequential_pvalue <- - answer2.1 %>% - ggplot() + - geom_line(aes(x = inc_sample_size, y = p_value)) + - theme( - text = element_text(size = 18), - plot.title = element_text(face = "bold"), - axis.title = element_text(face = "bold") - ) + - geom_point(aes(x = inc_sample_size, y = p_value)) + - ggtitle("Evolution of p-values in Experiment 1") + - ylab("p-value") + - xlab("Sample Size") + - geom_hline( - yintercept = 0.05, - colour = "red", - linetype = "twodash" - ) + - coord_cartesian(ylim = c(0, 1)) + - scale_y_continuous(breaks = seq(0, 1, by = 0.05)) - -sequential_pvalue ------------------- - -Error in tibble(inc_sample_size = rep(0, n/sample_increase_step), statistic = rep(0, : could not find function "tibble" -Traceback: - -1. incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, - . mean_current = 200, sd_current = 50, sd_new = 50) -ERROR: Error in tibble(inc_sample_size = rep(0, n/sample_increase_step), statistic = rep(0, : could not find function "tibble" - - diff --git a/stat301book/_build/html/reports/w02d2_abPeeking.log b/stat301book/_build/html/reports/w02d2_abPeeking.log deleted file mode 100644 index 7b23908..0000000 --- a/stat301book/_build/html/reports/w02d2_abPeeking.log +++ /dev/null @@ -1,39 +0,0 @@ -Traceback (most recent call last): - File "/Users/gcohen/opt/anaconda3/lib/python3.7/site-packages/jupyter_cache/executors/utils.py", line 56, in single_nb_execution - record_timing=False, - File "/Users/gcohen/opt/anaconda3/lib/python3.7/site-packages/nbclient/client.py", line 1087, in execute - return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute() - File "/Users/gcohen/opt/anaconda3/lib/python3.7/site-packages/nbclient/util.py", line 74, in wrapped - return just_run(coro(*args, **kwargs)) - File "/Users/gcohen/opt/anaconda3/lib/python3.7/site-packages/nbclient/util.py", line 53, in just_run - return loop.run_until_complete(coro) - File "/Users/gcohen/opt/anaconda3/lib/python3.7/asyncio/base_events.py", line 587, in run_until_complete - return future.result() - File "/Users/gcohen/opt/anaconda3/lib/python3.7/site-packages/nbclient/client.py", line 541, in async_execute - cell, index, execution_count=self.code_cells_executed + 1 - File "/Users/gcohen/opt/anaconda3/lib/python3.7/site-packages/nbclient/client.py", line 832, in async_execute_cell - self._check_raise_for_error(cell, exec_reply) - File "/Users/gcohen/opt/anaconda3/lib/python3.7/site-packages/nbclient/client.py", line 740, in _check_raise_for_error - raise CellExecutionError.from_cell_and_msg(cell, exec_reply['content']) -nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: ------------------- -# Run this cell to get a Pocock design! -library(gsDesign) - -design_pocock <- gsDesign(k = 20, #number of interim analysis planned - test.type = 1, # for one-sided tests - delta = 0, # default effect size - alpha = 0.05, #type II error rate - beta = 0.2, # type II error rate - sfu = 'Pocock') - -crit_pocock <- design_pocock$upper$bound ------------------- - -Error in library(gsDesign): there is no package called ‘gsDesign’ -Traceback: - -1. library(gsDesign) -ERROR: Error in library(gsDesign): there is no package called ‘gsDesign’ - - diff --git a/stat301book/_build/html/search.html b/stat301book/_build/html/search.html deleted file mode 100644 index 32324d9..0000000 --- a/stat301book/_build/html/search.html +++ /dev/null @@ -1,247 +0,0 @@ - - - - - - - - Search — Statistical Modelling for Data Science - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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\ No newline at end of file diff --git a/stat301book/_build/html/w02d1_abTesting.html b/stat301book/_build/html/w02d1_abTesting.html deleted file mode 100644 index 30a8383..0000000 --- a/stat301book/_build/html/w02d1_abTesting.html +++ /dev/null @@ -1,611 +0,0 @@ - - - - - - - - A/B Testing — Statistical Modelling for Data Science - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    A/B Testing

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    Photos/Images by Optimizely

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    Overview

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    This chapter provides an introduction to A/B testing and the early peeking problem. The goal is that you understand the main principles of A/B testing that will allow you to properly design, run, and analyze data from A/B tests. While you probably know the main concepts behind A/B testing, the usage of online platforms to collect, store and analyze data from A/B tests open new challenges. For example, people get tempted to peek at results before the A/B test ended and take actions usually result in false discoveries.

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    A/B Testing is a technique used to compare two variations of a product or service: control (A) and variation (B)

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    • A/B testing became very popular in the context of updating and improving websites.

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    It’s main concepts are founded in the context of hypothesis testing and inference to compare population quantities from 2 distributions

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    • for example: comparison of two population means or population proportions

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    Analysis Design

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    As in any other statistical anlaysis, important steps of an A/B testing experiment include,

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    • post the question(s) you want to answer using data

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    • design the experiment to address your question(s)

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    • identify appropriate methodologies to analyze the data

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    • run the experiment to collect data

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    • analyze the data according to the experimental design and make decisions

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    Case study

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    Obama’s 60 million dollar experiment

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    In 2008, Obama’s campaign was looking to increase the total amount of donations to the campaign. Organizers run an experiment to compare visitors’ responses to different versions of the website.

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    A full description of this case study can be found here.

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    While there is not a unique way to design an A/B testing experiment, randomized controlled experiments are commonly used in practice. When participants are randomly assigned to the experimental group or the control group, you only expect to observe differences driven by the controlled effect (e.g., a new website) and not by other confounding factors.

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    Other decisions need to be made when designing an A/B testing experiment, including sample size, number of tests, methodology. Since the choices made would influence the results, companies usually run some calibration tests before running the actual experiment.

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    Example

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    Question: Does visitors of the new website contribute with larger donations for the political campaign?

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    Design: Randomly allocate 1000 visitors to each website (control and new)

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    Method: Analyze the data sequentially in batches of 50 visitors per website. Use a classical \(t\)-test to run the analysis, compute \(p\)-values and confidence intervals

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    Decision: Stop the experiment if the \(p\)-value of a test drops below a significance level of \(0.05\)

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    So, what is new about A/B testing?

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    Early stopping

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    New platforms have been developed to assist companies to analyze, report and visualize the results of their experiments in real time

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    Figure by D. Meisner in Towards Data Science

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    These platforms allow the users to continuously monitor the \(p\)-values and confidence intervals in order to re-adjust their experiment dynamically.

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    Caution

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    In classical hypothesis testing theory, the sample size must be fixed in advance when the experiment is designed!! Is it ok to peek at results before all the data are collected??

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    In general, there are large opportunity costs associated with longer experiments. Thus, users may prefer to adaptively determine the sample size of the experiments and make decisions before the planned experiment ends.

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    Definition

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    Early stopping refers to ending the experiment earlier than originally designed.

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    Can we stop or re-design the experiment earlier if we have supporting evidence to do so?? Let’s answer this question using data!

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    A/A Testing

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    To examine the problem of early stopping, let’s simulate data for which \(H_0\) is true (i.e., there is no effect)

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    Example

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    All users are allocated to the same version of a website.

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    In such a scenario, data from both groups are generated from the same distribution. Thus, we know that rejecting \(H_0\) is a mistake!

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    Simulation Example

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    • generate 1000 data points per group (control and variation) from the same distribution (total sample size of 2000)

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    • sequentially analyze the data in batches of 50 observations per group using a two-sample \(t\)-tests

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    • sequentially compute and monitor the (raw) \(p\)-values

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    • reject the null hypothesis if a \(p\)-value drops below \(0.05\) and stop the experiment.

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    Simulating this kind of data and running the same analysis many times allows you examine the properties of the procedure used. For example, you can estimate the type I error rate by counting in how many experiments the \(p\)-value has dropped below the significance level and you erroneously reject the null hypothesis

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    Wrong Rejection

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    The data were generated from the same distribution (e.g., all users are allocated to the same version of a website). Thus, in principle, the \(p\)-value should not drop below 0.05. However, due to randomness, you may still find significant differences between the two groups. But how often does that occur??

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    Let’s start by simulating one such experiments and visualizing the results.

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    # FUNCTION
    -# Two-sample t-test with tracking sequential statistic and p-values by incremental sample sizes until getting to n in each group.
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    -# @param n (numeric): Initially planned sample size for each group (for simplicity, n needs to be a multiple of sample_increase_step).
    -# @param d_0 (numeric): effect size.
    -# @param mean_current (numeric): Population mean for control variation.
    -# @param sd_current (numeric): Population standard deviation for current variation.
    -# @param sd_new (numeric): Population standard deviation for new variation.
    -# @param sample_increase_step (numeric): Sample size increment.
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    -# @return p.value.df: A tibble that has 3 columns:
    -# inc_sample_size, statistic, and p_value 
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    -incremental_t_test <- function(n, d_0, mean_current, sd_current, sd_new, sample_increase_step) {
    -  sample_current <- rnorm(n, mean = mean_current, sd = sd_current)
    -  sample_new <- rnorm(n, mean = mean_current + d_0, sd = sd_new)
    -
    -  p.value.df <- tibble(
    -    inc_sample_size = rep(0, n / sample_increase_step),
    -    statistic = rep(0, n / sample_increase_step),
    -    p_value = rep(0, n / sample_increase_step)
    -  )
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    -  current_sample_size <- sample_increase_step
    -  
    -  for (i in 1:nrow(p.value.df))
    -  {
    -    t_test_results <- t.test(sample_new[1:current_sample_size], sample_current[1:current_sample_size],
    -      var.equal = TRUE,
    -      alternative = "greater"                      
    -    )
    -    p.value.df[i, "statistic"] <- as_tibble(t_test_results$statistic)
    -    p.value.df[i, "p_value"] <- as_tibble(t_test_results$p.value)
    -    p.value.df[i, "inc_sample_size"] <- current_sample_size
    -    current_sample_size <- current_sample_size + sample_increase_step
    -  }
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    -  return(p.value.df)
    -}
    -
    -#---------------------------------------------------------------
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    -#TEST and PLOT
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    -options(repr.plot.width = 10, repr.plot.height = 5) 
    -options(warn=-1)
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    -set.seed(301)
    -answer2.1 <- 
    -    incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50)
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    -sequential_pvalue <- 
    -  answer2.1 %>%
    -  ggplot() +
    -  geom_line(aes(x = inc_sample_size, y = p_value)) +
    -  theme(
    -    text = element_text(size = 18),
    -    plot.title = element_text(face = "bold"),
    -    axis.title = element_text(face = "bold")
    -  ) + 
    -  geom_point(aes(x = inc_sample_size, y = p_value)) +
    -  ggtitle("Evolution of p-values in Experiment 1") +
    -  ylab("p-value") +
    -  xlab("Sample Size") +
    -  geom_hline(
    -    yintercept = 0.05,
    -    colour = "red",
    -    linetype = "twodash"
    -  ) +
    -  coord_cartesian(ylim = c(0, 1)) +
    -  scale_y_continuous(breaks = seq(0, 1, by = 0.05))
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    -sequential_pvalue
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    -_images/w02d1_abTesting_10_0.png -
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    Interpretation

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    After collecting data from 100 participants the \(p\)-value drops below \(0.05\) so the experiment is stopped. But we know that claiming a significance difference in this experiment is a mistake.

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    Example

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    Changing the website is costly and may not really increase the size of the donations as expected

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    But, isn’t this mistake one potential result of the test?

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    Definition

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    In statistical hypothesis testing, rejecting \(H_0\) when it is true is known as the type I error. The probability of a type I error is equal to the significance level of the test.

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    In our example, the test was planned so that the probability to falsely rejecting \(H_0\) is 5%. So, why is this a problem??

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    Type I error rate inflation

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    The problem is that the probability of falsely rejecting \(H_0\) may be larger than expected!

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    To know if the probability of falsely rejecting \(H_0\) is larger than 5%, we need to run many of these experiments!!

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    The figure below shows the p-value trajectory of 100 experiments. We see that the p-values of more than 5% of the experiments are below the significance level.

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    Important

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    It can be proved, mathematically, that under the null hypothesis, the classical \(p\)-value will always cross \(\alpha\) if the experimenter waits long enough\(^{*}\). This means that with increasing data, the probability of falsely rejecting a true \(H_0\) approaches to 1!

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    [*] David Siegmund. 1985. Sequential analysis: tests and confidence intervals. -Springer.

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    Summary and key concepts learned

    -
      -
    1. A/B testing refers to an experiment, in which users are randomly assigned to one of two variations of a product or service: control (A) and variation (B) to see if variation B should be used for improvement.

    2. -
    3. The statistic used to test a hypothesis, the sample size calculation, the type I error rate specification and the desired power are all important and interconnected pieces of the experimental design!

    4. -
    5. In classical hypothesis testing theory, the sample size must be fixed in advance when the experiment is designed!!

    6. -
    7. Modern platforms allow the users to continuously monitor the p-values and confidence intervals of their tests as data are collected (peeking) in order to re-adjust their experiment dynamically.

    8. -
    9. Stopping an experiment and rejecting \(H_0\) as soon as the \(p\)-value is below the specified significance level can drastically inflate the type I error rate

    10. -
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    Attention

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    Controlling the risk of wrongly rejecting the null hypothesis is not an easy task in A/B testing if peeking and early stops are allowed!

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    - - By Gabriela Cohen Freue
    - - © Copyright 2021.
    -

    -
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    - - -
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    - - - - - \ No newline at end of file diff --git a/stat301book/_build/html/w02d2_abPeeking.html b/stat301book/_build/html/w02d2_abPeeking.html deleted file mode 100644 index 51f293c..0000000 --- a/stat301book/_build/html/w02d2_abPeeking.html +++ /dev/null @@ -1,775 +0,0 @@ - - - - - - - - Principled Peeking in A/B Testing — Statistical Modelling for Data Science - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Principled Peeking in A/B Testing

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    Image from Unsplash by Pawel Szvmanski and Unlocking Peeking in AB-Tests by Dennis Meisner

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    Overview

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    In the previous chapter we examined the early peeking problem using a simulation study. We noticed that although classical tests can be used in A/B testing problems, those tests are designed for a fixed sample size. Analyzing a sample smaller than planned and stopping the experiment based on early results seriously inflates the type I error rate of the experiment. The goal of this chapter is to introduce some basic techniques of principled peeking.

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    Important

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    Stopping an experiment and rejecting \(H_0\) as soon as an observed \(p\)-value is below the specified significance level can drastically inflate the type I error rate

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    Sequential testing

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    Sequential tests are decision rules that allows users to test data sequentially as data come in:

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    • The experiment may be stopped earlier, meaning the sample size is dynamic, rather than fixed

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    • Many tests (a.k.a., multiple comparisons) are performed (sequentially)

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    Caution

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    If you make lots of comparisons, the error rates are inflated!!

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    There are different classes of sequential approaches:

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    Definition

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    Group sequential designs: the analyst pre-specifies when to inspect the data (interim analysis) and performs each analysis as a fixed sample one.

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    Full sequential designs: the analyst performs an analysis after every new observation, sequentially, in a principled way.

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    New platforms allow users to test data sequentially as data come in:

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    • Users are monitoring results as they collect data and are making decisions accordingly

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    • Users need to adaptively determine the sample size of the experiments

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    • There are large opportunity costs associated with longer experiments

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    • When done correctly, stopping an experiment earlier (or re-designing it) can be beneficial

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    Principled Peeking

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    Can we control the risk of wrongly rejecting the null hypothesis in A/B testing when early peeking and stopping is desired?

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    Many methods have been proposed to address this problem in the context of A/B testing experimental designs:

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    • A basic way to control the type I error rate inflation in multiple testing problems is to adjust the p-value. For example, p-values (or critical values) can be adjusted using a Bonferroni corection.

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    • Some new methods propose using a different test statistics and computing \(p\)-values differently. For example, in the Optimizely platform, a mixture sequential probability ratio test (mSPRT) is performed and always valid \(p\)-values are constructed.

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    Important

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    With a principled peeking approach, the significance level of each interim analysis is set at a level that controls the Type I error rate, even if the experiment is stopped earlier.

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    Bonferroni

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    Although originally designed for independent tests, Bonferroni correction can be used to control the type I error rate in A/B testing

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    Bonferroni’s correction can be thought as:

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    • an adjustment of the \(p\)-values, multiplying them by the number of comparisons, and keeping the significance level at a desired threshold, or

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    • an adjustment of the significance threshold \(\alpha\), dividing it by the number of comparisons, and using raw p-values, or

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    • (an adjustment of) the critical value, computed with a sampling distribution, corresponding to the adjusted significance threshold

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    Interpretation

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    You can compare the observed t-statistic with the critical value obtained from the sampling distribuiton to test the null hypothesis:

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    • Unadjusted test: Since the test-statistic (red dot) is greater than the (unadjusted) critical value (black dot), we have evidence to reject the null hypothesis.

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    • Bonferroni adjusted test: Since the test-statistic (red dot) is smaller than the Bonferroni adjusted critical value (blue dot), we do not have enough evidence to reject the null hypothesis.

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    Alternatively, you can compare the \(p\)-value with the pre-set significance level to test the null hypothesis:

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    • Unadjusted test: Since the \(p\)-value (red area) is smaller than the (unadjusted) significance level (grey area), we have evidence to reject the null hypothesis.

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    • Bonferroni adjusted test: Since the \(p\)-value (red area) is larger than the Bonferroni adjusted significance level (blue area), we do not have enough evidence to reject the null hypothesis.

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    Hint

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    Note that you can also adjust the \(p\)-value of a test using a Bonferroni correction!

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    Since the adjusted \(p\)-value (light red area) is larger than the significance level (grey area), we do not have enough evidence to reject the null hypothesis.

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    Caution

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    The solid red area correspond to the unadjusted \(p\)-value.

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    A/A Testing (again)

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    To examine if the Bonferroni’s correction effectively controls the type I error rate, we will use again the A/A experimental design

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    Important

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    We know that the (true) effect size is 0 in an A/A experiment!

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    Interpretations of Bonferroni correction

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    In one A/A testing simulated experiment, using unadjusted quantities:

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    • you would reject \(H_0\): the observed (unadjusted) t-statistic is bigger than the (unadjusted) critical value

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    Using Bonferroni’s adjusted quantities:

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    • you would fail to reject \(H_0\): the observed (unadjusted) t-statistic is smaller than the (adjusted) critical value

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    Important

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    The Bonferroni’s adjusted critical value is larger than the unadjusted one: the test is more conservative! With the adjustment, we erroneously reject \(H_0\) less often!! it controls the type I error rate!!

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    Pocock’s boundaries

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    Other methods can be used to control the type I error rate in A/B testing experiments with early stops.

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    The Pocock method computes a common critical value for all interim analyses.

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    • the Pocock’s boundary is not an adjustment of the quantile of a \(t\)-distribution

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    • we can easily get the critical values for this design using gsDesign::gsDesign()

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    Remarks

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    Note 1: gsDesign() outputs a full sequential design. You can read more about this package here

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    Note 2: a caveat about this package is that two-sample tests are based on \(z\)-statistics. However, results are nearly equivalent to a \(t\)-test. More can be read here

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    A/A Testing (again)

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    To examine if the Pocock method effectively controls the type I error rate, we can use again the A/A experimental design

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    Important

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    We know that the (true) effect size is 0 in an A/A experiment!

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    # Run this cell to get a Pocock design!
    -library(gsDesign)
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    -design_pocock <- gsDesign(k = 20, #number of interim analysis planned
    -                          test.type = 1, # for one-sided tests
    -                          delta = 0, # default effect size
    -                          alpha = 0.05, #type II error rate
    -                          beta = 0.2, # type II error rate
    -                          sfu = 'Pocock')
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    -crit_pocock <- design_pocock$upper$bound
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    -_images/w02d2_abPeeking_17_0.svg
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    Interpretations of Pocock correction

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    In one A/A testing simulated experiment, using unadjusted quantities:

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    • you would reject \(H_0\): the observed (unadjusted) t-statistic is bigger than the (unadjusted) critical value

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    Using Pocock’s critical values:

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    • you would fail to reject \(H_0\): the observed (unadjusted) t-statistic is smaller than the (adjusted) critical value

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    Important

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    The Bonferroni’s adjusted critical value is larger than Pocock’s critical values: the Bonferroni test is the most conservative test. Pocock method gives some control of the type I error rate!! Bonferroni gives a more conservative control of the type I error rate!!

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    O’Brien-Fleming’s boundaries

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    Another method available in gsDesign is the O’Brien-Fleming method

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    Unlike previous methods, the O’Brien-Fleming method uses non-uniform boundaries

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    • the test has conservative critical values for earlier interim analyses and less conservative values (closer to the unadjusted critical values) as more data are collected

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    design_of <- gsDesign(k = 20, #number of interim analysis planned
    -                          test.type = 1, # for one-sided tests
    -                          delta = 0, # default effect size
    -                          alpha = 0.05, #type I error rate
    -                          beta = 0.2, # type II error rate
    -                          sfu = 'OF')
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    -crit_of <- design_of$upper$bound
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    Interpretations of O’Brien-Fleming correction

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    In one A/A testing simulated experiment, using unadjusted quantities:

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    Using O’Brien-Fleming’s critical values:

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    Important

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    The O’Brien-Fleming’s critical values are non-constant!! The test is very conservative at the beginning when less data is available and becomes less conservative as more data is collected.

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    Type I error rate control

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    To better understand the type I error rate control of the 3 approaches, you need to repeat the experiments many times and calculate the rate at which you erroneously reject \(H_0\).

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    Number of erroneous rejections among 100 experiments

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    set.seed(120)
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    -### Run this before continuing
    -multiple_times_sequential_tests <- tibble(experiment = 1:100) %>% 
    -    mutate(seq_test = map(.x = experiment, 
    -                          .f = function(x) incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, 
    -                              mean_current = 200, sd_current = 50, sd_new = 50)))
    -
    -typeI_rate  <- multiple_times_sequential_tests %>% 
    -    mutate(reject = map_dbl(.x = seq_test, .f = function(x) sum(x$p_value<0.05) > 0)) %>%
    -    mutate(n_reject_bonferroni = map_dbl(.x = seq_test, .f = function(x) sum(x$p_value < 0.05/20) > 0)) %>%
    -    mutate(n_reject_pocock = map_dbl(.x = seq_test, .f = function(x) sum(x$statistic > crit_pocock) > 0)) %>%
    -    mutate(n_reject_OF = map_dbl(.x = seq_test, .f = function(x) sum(x$statistic > crit_of) > 0)) %>%
    -    summarise(Unadjusted = sum(reject),
    -              Bonferroni= sum(n_reject_bonferroni),
    -              Pocock = sum(n_reject_pocock),
    -              OBrienFleming = sum(n_reject_OF),
    -              expected_n_rejections = 5)
    -typeI_rate
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    A tibble: 1 × 5
    UnadjustedBonferroniPocockOBrienFlemingexpected_n_rejections
    <dbl><dbl><dbl><dbl><dbl>
    242745
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    Conclusions

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    For the simulated experiments in the example above:

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    • the type I error rate using unadjusted values was 24% (way above the planned 5% value)

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    • the type I error rate using Bonferroni was 2% (below the planned 5% value)

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    • the type I error rate using Pocock was 7% (above the planned 5% value)

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    • the type I error rate using O’Brien-Flemming was 4% (slightly below the planned 5% value)

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    Important

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    Using a principled peeking procedure, the data can be sequentially analyzed and the experiment can be stopped earlier while controlling the type I error rate.

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    Summary and key concepts learned

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    1. In many A/B testing experiments, users would like to stop their experiments earlier depending on the results of interim analyses

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    3. If you make lots of comparisons, but don’t correct for it, your error rates are inflated!! Naively peeking at the data many times and stopping the experiment earlier than planned increases the probability of incorrectly rejecting the null hypothesis.

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    5. Stops must be part of the experimental design and appropriate testing methods must be used!

    6. -
    7. A possible way to control the type I error rated is to use a Bonferroni adjustment of the \(p\)-values (or equivalently the significance level or critical values). As with other multiple comparison problems, the Bonferroni’s correction in sequential analysis is very conservative and can affect the power of the test!!

    8. -
    9. The Pocock method offers a less conservative way of controlling the type I error rate in sequential testing with early stops.

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    11. The O’Brien-Fleming method offers a less conservative way of controlling the type I error rate in sequential testing with early stops.

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    Attention

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    Principled peeking is ok and even beneficial in A/B testing. The experimental design is a very important piece of any statistical analysis!

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    - - By Gabriela Cohen Freue
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    - - - - - \ No newline at end of file diff --git a/stat301book/_build/jupyter_execute/w02d1_abTesting.ipynb b/stat301book/_build/jupyter_execute/w02d1_abTesting.ipynb deleted file mode 100644 index e5bc04b..0000000 --- a/stat301book/_build/jupyter_execute/w02d1_abTesting.ipynb +++ /dev/null @@ -1,467 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# A/B Testing\n", - "\n", - "\n", - "\n", - "*Photos/Images by [Optimizely](https://www.optimizely.com/insights/blog/how-obama-raised-60-million-by-running-a-simple-experiment/)*" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - }, - "tags": [] - }, - "source": [ - "## Overview\n", - "\n", - "This chapter provides an introduction to A/B testing and the early peeking problem. The goal is that you understand the main principles of A/B testing that will allow you to properly design, run, and analyze data from A/B tests. While you probably know the main concepts behind A/B testing, the usage of online platforms to collect, store and analyze data from A/B tests open new challenges. For example, people get tempted to peek at results before the A/B test ended and take actions usually result in false discoveries. \n", - "\n", - "\n", - "A/B Testing is a technique used to compare two variations of a product or service: control (A) and variation (B) \n", - "\n", - "- A/B testing became very popular in the context of updating and improving websites. \n", - " \n", - "It's main concepts are founded in the context of hypothesis testing and inference to compare population quantities from 2 distributions\n", - "\n", - "- for example: comparison of two population means or population proportions\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - }, - "tags": [] - }, - "source": [ - "## Analysis Design\n", - "\n", - "As in any other statistical anlaysis, important steps of an A/B testing experiment include, \n", - "\n", - "- post the *question(s)* you want to answer using data\n", - "\n", - "- *design* the experiment to address your question(s)\n", - "\n", - "- identify appropriate methodologies to analyze the data\n", - "\n", - "- run the experiment to collect data \n", - "\n", - "- analyze the data according to the experimental design and make decisions\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - }, - "tags": [] - }, - "source": [ - "## Case study\n", - "\n", - "### Obama's 60 million dollar experiment\n", - "\n", - "In 2008, Obama's campaign was looking to increase the total amount of donations to the campaign. Organizers run an experiment to compare visitors' responses to different versions of the website.\n", - "\n", - "\n", - "\n", - "*A full description of this case study can be found [here](https://www.optimizely.com/insights/blog/how-obama-raised-60-million-by-running-a-simple-experiment/).*\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - }, - "tags": [] - }, - "source": [ - "While there is not a unique way to design an A/B testing experiment, ***randomized controlled experiments*** are commonly used in practice. When participants are randomly assigned to the experimental group or the control group, you only expect to observe differences driven by the controlled effect (e.g., a new website) and not by other confounding factors. \n", - "\n", - "Other decisions need to be made when designing an A/B testing experiment, including sample size, number of tests, methodology. Since the choices made would influence the results, companies usually run some calibration tests before running the actual experiment. \n", - "\n", - "```{admonition} Example\n", - "\n", - "**Question**: Does visitors of the new website contribute with larger donations for the political campaign? \n", - "\n", - "**Design**: Randomly allocate 1000 visitors to each website (control and new)\n", - "\n", - "**Method**: Analyze the data sequentially in batches of 50 visitors per website. Use a classical $t$-test to run the analysis, compute $p$-values and confidence intervals\n", - "\n", - "**Decision**: Stop the experiment if the $p$-value of a test drops below a significance level of $0.05$\n", - "```\n", - "\n", - "So, what is new about A/B testing?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - }, - "tags": [] - }, - "source": [ - "## Early stopping\n", - "\n", - "New platforms have been developed to assist companies to analyze, report and visualize the results of their experiments **in real time**\n", - "\n", - "\n", - "\n", - "Figure by D. Meisner in Towards Data Science " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "These platforms allow the users to *continuously monitor* the $p$-values and confidence intervals in order to re-adjust their experiment dynamically. \n", - "\n", - "```{caution}\n", - "In classical hypothesis testing theory, the sample size must be fixed in advance when the experiment is designed!! Is it ok to peek at results before all the data are collected??\n", - "```\n", - "\n", - "In general, there are large opportunity costs associated with longer experiments. Thus, users may prefer to adaptively determine the sample size of the experiments and make decisions before the planned experiment ends. \n", - "\n", - "```{admonition} Definition\n", - ":class: tip\n", - "**Early stopping** refers to ending the experiment earlier than originally designed.\n", - "```\n", - "\n", - "Can we stop or re-design the experiment earlier if we have supporting evidence to do so?? Let's answer this question using data!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### A/A Testing\n", - "To examine the problem of early stopping, let's simulate data for which $H_0$ is true (i.e., there is no effect)\n", - "\n", - "```{admonition} Example\n", - "All users are allocated to the same version of a website.\n", - "```\n", - " \n", - "\n", - "\n", - "In such a scenario, data from both groups are generated from the same distribution. Thus, we know that rejecting $H_0$ is a mistake! " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - }, - "tags": [] - }, - "source": [ - "```{admonition} Simulation Example\n", - "\n", - "- generate 1000 data points per group (control and variation) from the same distribution (total sample size of 2000) \n", - "\n", - "- sequentially analyze the data in batches of 50 observations per group using a two-sample $t$-tests\n", - "\n", - "- sequentially compute and monitor the (raw) $p$-values \n", - "\n", - "- reject the null hypothesis if a $p$-value drops below $0.05$ and stop the experiment. \n", - "\n", - "```\n", - "\n", - "Simulating this kind of data and running the same analysis many times allows you examine the properties of the procedure used. For example, you can estimate the type I error rate by counting in how many experiments the $p$-value has dropped below the significance level and you erroneously reject the null hypothesis\n", - "\n", - "```{admonition} Wrong Rejection\n", - ":class: caution\n", - "The data were generated from the same distribution (e.g., all users are allocated to the same version of a website). Thus, in principle, the $p$-value should not drop below 0.05. However, due to randomness, you may still find significant differences between the two groups. But how often does that occur?? \n", - "```\n", - "\n", - "Let's start by simulating one such experiments and visualizing the results." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "remove_output", - "remove_input" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.0 ──\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.2 \u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 0.3.4\n", - "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.0.4 \u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 1.0.2\n", - "\u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 1.1.2 \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n", - "\u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 1.4.0 \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 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bRIC24xQTBDGV5gEfAWL8RQOmAWuWiksHlF09ZoyqZLvwYOHChQxnTu3NnsPhSi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\nkvrjmzVrVmQdMwggkFuBunXruvuB7gmx/6ZMmeKCV+3bt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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/gcohen/Documents/GitRepositories/stat-301-instructor/stat301book/_build/jupyter_execute/w02d1_abTesting_10_0.png" - }, - "image/png": { - "height": 300, - "width": 600 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# FUNCTION\n", - "# Two-sample t-test with tracking sequential statistic and p-values by incremental sample sizes until getting to n in each group.\n", - "\n", - "# @param n (numeric): Initially planned sample size for each group (for simplicity, n needs to be a multiple of sample_increase_step).\n", - "# @param d_0 (numeric): effect size.\n", - "# @param mean_current (numeric): Population mean for control variation.\n", - "# @param sd_current (numeric): Population standard deviation for current variation.\n", - "# @param sd_new (numeric): Population standard deviation for new variation.\n", - "# @param sample_increase_step (numeric): Sample size increment.\n", - "\n", - "# @return p.value.df: A tibble that has 3 columns:\n", - "# inc_sample_size, statistic, and p_value \n", - "\n", - "incremental_t_test <- function(n, d_0, mean_current, sd_current, sd_new, sample_increase_step) {\n", - " sample_current <- rnorm(n, mean = mean_current, sd = sd_current)\n", - " sample_new <- rnorm(n, mean = mean_current + d_0, sd = sd_new)\n", - "\n", - " p.value.df <- tibble(\n", - " inc_sample_size = rep(0, n / sample_increase_step),\n", - " statistic = rep(0, n / sample_increase_step),\n", - " p_value = rep(0, n / sample_increase_step)\n", - " )\n", - "\n", - " current_sample_size <- sample_increase_step\n", - " \n", - " for (i in 1:nrow(p.value.df))\n", - " {\n", - " t_test_results <- t.test(sample_new[1:current_sample_size], sample_current[1:current_sample_size],\n", - " var.equal = TRUE,\n", - " alternative = \"greater\" \n", - " )\n", - " p.value.df[i, \"statistic\"] <- as_tibble(t_test_results$statistic)\n", - " p.value.df[i, \"p_value\"] <- as_tibble(t_test_results$p.value)\n", - " p.value.df[i, \"inc_sample_size\"] <- current_sample_size\n", - " current_sample_size <- current_sample_size + sample_increase_step\n", - " }\n", - "\n", - " return(p.value.df)\n", - "}\n", - "\n", - "#---------------------------------------------------------------\n", - "\n", - "#TEST and PLOT\n", - "\n", - "options(repr.plot.width = 10, repr.plot.height = 5) \n", - "options(warn=-1)\n", - "\n", - "set.seed(301)\n", - "answer2.1 <- \n", - " incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50)\n", - "\n", - "sequential_pvalue <- \n", - " answer2.1 %>%\n", - " ggplot() +\n", - " geom_line(aes(x = inc_sample_size, y = p_value)) +\n", - " theme(\n", - " text = element_text(size = 18),\n", - " plot.title = element_text(face = \"bold\"),\n", - " axis.title = element_text(face = \"bold\")\n", - " ) + \n", - " geom_point(aes(x = inc_sample_size, y = p_value)) +\n", - " ggtitle(\"Evolution of p-values in Experiment 1\") +\n", - " ylab(\"p-value\") +\n", - " xlab(\"Sample Size\") +\n", - " geom_hline(\n", - " yintercept = 0.05,\n", - " colour = \"red\",\n", - " linetype = \"twodash\"\n", - " ) +\n", - " coord_cartesian(ylim = c(0, 1)) +\n", - " scale_y_continuous(breaks = seq(0, 1, by = 0.05))\n", - "\n", - "sequential_pvalue" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Interpretation\n", - "\n", - "After collecting data from 100 participants the $p$-value drops below $0.05$ so the experiment is stopped. But we know that claiming a significance difference in this experiment is a mistake.\n", - "\n", - "```{admonition} Example\n", - "Changing the website is costly and may not really increase the size of the donations as expected\n", - "```\n", - "\n", - "But, isn't this mistake one potential result of the test? \n", - "\n", - "```{admonition} Definition\n", - ":class: tip\n", - "In statistical hypothesis testing, rejecting $H_0$ when it is true is known as the **type I error**. The probability of a type I error is equal to the significance level of the test.\n", - "```\n", - "\n", - "In our example, the test was planned so that the probability to falsely rejecting $H_0$ is 5%. So, why is this a problem??\n", - "\n", - "```{admonition} Type I error rate inflation\n", - ":class: caution\n", - "The problem is that the probability of falsely rejecting $H_0$ may be larger than expected! \n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "To know if the probability of falsely rejecting $H_0$ is larger than 5%, we need to run *many* of these experiments!!\n", - "\n", - "The figure below shows the p-value trajectory of 100 experiments. We see that the p-values of more than 5% of the experiments are below the significance level. \n", - "\n", - "\n", - "
    \n", - "\n", - "
    \n", - "\n", - "```{important}\n", - "It can be proved, mathematically, that under the null hypothesis, the classical $p$-value will *always* cross $\\alpha$ if the experimenter waits long enough$^{*}$. This means that with increasing data, the probability of falsely rejecting a true $H_0$ approaches to 1!\n", - "```\n", - "\n", - "[*] David Siegmund. 1985. Sequential analysis: tests and confidence intervals.\n", - "Springer." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Summary and key concepts learned \n", - "\n", - "1. A/B testing refers to an experiment, in which users are randomly assigned to one of two variations of a product or service: control (A) and variation (B) to see if variation B should be used for improvement.\n", - "\n", - "\n", - "2. The statistic used to test a hypothesis, the sample size calculation, the type I error rate specification and the desired power are all important and interconnected pieces of the experimental design! \n", - "\n", - "\n", - "3. In classical hypothesis testing theory, the sample size must be fixed in advance when the experiment is designed!!\n", - "\n", - "\n", - "4. Modern platforms allow the users to continuously monitor the p-values and confidence intervals of their tests as data are collected (peeking) in order to re-adjust their experiment dynamically. \n", - "\n", - "\n", - "5. Stopping an experiment and rejecting $H_0$ as soon as the $p$-value is below the specified significance level can drastically inflate the type I error rate\n", - "\n", - "```{attention} \n", - "Controlling the risk of wrongly rejecting the null hypothesis is not an easy task in A/B testing if peeking and early stops are allowed!\n", - "```" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "4.0.3" - }, - "rise": { - "transition": "fade" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/stat301book/_build/jupyter_execute/w02d1_abTesting.r b/stat301book/_build/jupyter_execute/w02d1_abTesting.r deleted file mode 100644 index f8ebf58..0000000 --- a/stat301book/_build/jupyter_execute/w02d1_abTesting.r +++ /dev/null @@ -1,78 +0,0 @@ -options(warn=-1) -options(messages=-1) -library(tidyverse) -library(ggplot2) - -# FUNCTION -# Two-sample t-test with tracking sequential statistic and p-values by incremental sample sizes until getting to n in each group. - -# @param n (numeric): Initially planned sample size for each group (for simplicity, n needs to be a multiple of sample_increase_step). -# @param d_0 (numeric): effect size. -# @param mean_current (numeric): Population mean for control variation. -# @param sd_current (numeric): Population standard deviation for current variation. -# @param sd_new (numeric): Population standard deviation for new variation. -# @param sample_increase_step (numeric): Sample size increment. - -# @return p.value.df: A tibble that has 3 columns: -# inc_sample_size, statistic, and p_value - -incremental_t_test <- function(n, d_0, mean_current, sd_current, sd_new, sample_increase_step) { - sample_current <- rnorm(n, mean = mean_current, sd = sd_current) - sample_new <- rnorm(n, mean = mean_current + d_0, sd = sd_new) - - p.value.df <- tibble( - inc_sample_size = rep(0, n / sample_increase_step), - statistic = rep(0, n / sample_increase_step), - p_value = rep(0, n / sample_increase_step) - ) - - current_sample_size <- sample_increase_step - - for (i in 1:nrow(p.value.df)) - { - t_test_results <- t.test(sample_new[1:current_sample_size], sample_current[1:current_sample_size], - var.equal = TRUE, - alternative = "greater" - ) - p.value.df[i, "statistic"] <- as_tibble(t_test_results$statistic) - p.value.df[i, "p_value"] <- as_tibble(t_test_results$p.value) - p.value.df[i, "inc_sample_size"] <- current_sample_size - current_sample_size <- current_sample_size + sample_increase_step - } - - return(p.value.df) -} - -#--------------------------------------------------------------- - -#TEST and PLOT - -options(repr.plot.width = 10, repr.plot.height = 5) -options(warn=-1) - -set.seed(301) -answer2.1 <- - incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50) - -sequential_pvalue <- - answer2.1 %>% - ggplot() + - geom_line(aes(x = inc_sample_size, y = p_value)) + - 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    y3)iSjs8`9I;r+czqea9kAaU|rV);LUf_#Kf` zsUZXIdqnpqJ|DyLTq{nLMd^dgz3k@UbVI$xP#?-d`efiDiZ+VDp~~c%=8r_R=`F6t zVHKFx!g;|c$IZaPGRT^m&t#^B@B3!9frTi<1g;CQ&W80_4me64_HHRh*$2~8xr;q` zJ2*`}bmN_xI73>t@Acb@xP$4R%B4&hPGQ$lsO)IZ-B5oyvyaZh&BX|iCmp$VqQ1!J zAmeJxld`*00|j?4K&>|x#dP&v{Wz&`3xK&0U-Ww0N)va!O!tRr?~B60a#7rTHUNy| z0a1SuY$<8iK%yy2ee-p#vlg_RSPia=i}fhFB;*?!6UrdxT3%}*qvr;5k#SKpY$FRx zoA@9VR*i=@hckua<-V-PuAQ4xW;7TRYE*gF`#X z0+s_;G)?)RLtG7o1UYaD>IFQGo(^N%O6UCb(%m*$8Ur}8uKtF61JArVsW{S^XDlub z`H!z`A$^|O3;udI?%A|D`0(pGCA&eDPCwQoQ{kZ?E6nxoMAu2JFd3m&oxgku%ZwfB z@k7XkSVjP8g5_%`hmnk5ha>)~u*<`IvMudMABn84bhE&?RW%hJop(z0@13a*80fVfzkCiIK3d>DSKeTsUnS7J^^W9`tv61! z*Gdgym(eT&bW8Dx7uQ)V1^zVOqM=#aG!Vr5=c@;x2zgZE#k#uo2S@vacF~0UaD$C* zHIR2$JhWZrzhBhUQrKb2tWBX%yU=C=T_kxyF|Q%S?*DZ2hinwP7q%mD20@RYtrjD8 zh2-?=Cm|?_7G8#Tz}&~gowd^1K)iX7%}=643AH(;MhQx{<8AO7LsvG#XKzk2UU}K$ zAZg{vfdAUu*Kk*?da61JC$b%h*Io85KIpIW*drxd z|D4<5Cf@9RX(ARR2ixR1D+XA6WlXwAReMH*wK-Gx8A@x!8T!bvYc#61kr`6{Lj>Vs z&LsDK8W`~b5)3%wn|e)D$w~w);ljRNoa%Z zM@*^vQ1`UoVWBzL+~E|s__wqVO+dA%Ch8P-OeduY>F`DJ7??w0#(j4M&bd+q%?P}3 zyi)VcbHoW~ieq=CxHTvTMw2^PNSF%3vwxL2*$0!xZJ@ddqI-y~YA?^UxyG)5$wp8H zp)m2KM%JgZrdxN*80buLPR232oq2!CFcYIHlz8hN=I`gE?Gfm22*2T#6_P#?p0$}C^I)p82Eh5pMa2kWgXFP_oKUHS zd)oyaoJ;7j%S;A1+%)4_hyNT*&(&~aNGG%siw@TPpRch0`8)^o z-uw%gfqzDd;w`Gyn@-}-_5RNXskh;y&2;|nU&P6-<6{c=Qve@q0OZ%TodI>B+9AB- z`M>`0HyYqAg@G`@wLsK3@|W*LWemuQZ0*}P#=l(6FjBg2qntMW@}{yhfPA*)^Z)!0 jesb_}a5a==b$hrb&(\n", - "\n", - "\n", - "\n", - "Image from [Unsplash](https://unsplash.com/photos/F85pIdl7cS8) by Pawel Szvmanski and [Unlocking Peeking in AB-Tests](https://towardsdatascience.com/unlocking-peeking-in-ab-tests-7847b9c2f6bb) by Dennis Meisner" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Overview\n", - "\n", - "In the previous chapter we examined the early peeking problem using a simulation study. We noticed that although classical tests can be used in A/B testing problems, those tests are designed for a fixed sample size. Analyzing a sample smaller than planned and stopping the experiment based on early results seriously inflates the type I error rate of the experiment. The goal of this chapter is to introduce some basic techniques of principled peeking. \n", - "\n", - "```{important}\n", - "Stopping an experiment and rejecting $H_0$ as soon as an observed $p$-value is below the specified significance level can drastically inflate the type I error rate\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d34b54f2891270b6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Sequential testing \n", - "\n", - "**Sequential tests** are decision rules that allows users to test data sequentially as data come in:\n", - "\n", - "- The experiment may be stopped earlier, meaning the sample size is dynamic, rather than fixed\n", - "\n", - "- Many tests (a.k.a., multiple comparisons) are performed (sequentially) \n", - "\n", - "```{caution}\n", - "If you make lots of comparisons, the error rates are inflated!!\n", - "```\n", - "\n", - "There are different classes of sequential approaches: \n", - "\n", - "```{admonition} Definition\n", - ":class: tip\n", - "**Group sequential designs**: the analyst *pre-specifies* when to inspect the data (interim analysis) and performs each analysis as a fixed sample one. \n", - "\n", - "**Full sequential designs**: the analyst performs an analysis after every new observation, sequentially, in a principled way.\n", - "```\n", - "\n", - "New platforms allow users to test data sequentially as data come in: \n", - "\n", - "- Users *are* monitoring results as they collect data and are making decisions accordingly\n", - "\n", - "- Users need to adaptively determine the sample size of the experiments \n", - "\n", - "- There are large opportunity costs associated with longer experiments\n", - "\n", - "- When done correctly, stopping an experiment earlier (or re-designing it) can be beneficial" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Principled Peeking \n", - "\n", - "Can we control the risk of wrongly rejecting the null hypothesis in A/B testing when early peeking and stopping is desired?\n", - "\n", - "Many methods have been proposed to address this problem in the context of **A/B testing experimental designs**: \n", - "\n", - "- A basic way to control the type I error rate inflation in multiple testing problems is to adjust the p-value. For example, p-values (or critical values) can be adjusted using a Bonferroni corection.\n", - "\n", - "- Some new methods propose using a different test statistics and computing $p$-values differently. For example, in the *Optimizely platform*, a mixture sequential probability ratio test (mSPRT) is performed and *always valid* $p$-values are constructed. \n", - " \n", - " \n", - "```{important}\n", - "With a principled peeking approach, the significance level of each interim analysis is set at a level that controls the Type I error rate, even if the experiment is stopped earlier.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-bf767b35df5480d2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Bonferroni\n", - "\n", - "Although originally designed for independent tests, Bonferroni correction can be used to control the type I error rate in A/B testing\n", - "\n", - "**Bonferroni's correction** can be thought as: \n", - "\n", - "- an adjustment of the $p$-values, multiplying them by the number of comparisons, and keeping the significance level at a desired threshold, or \n", - "\n", - "- an adjustment of the significance threshold $\\alpha$, dividing it by the number of comparisons, and using raw p-values, or\n", - "\n", - "- (an adjustment of) the critical value, computed with a sampling distribution, corresponding to the adjusted significance threshold" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "remove_input", - "remove_output" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message:\n", - "“replacing previous import ‘ellipsis::check_dots_unnamed’ by ‘rlang::check_dots_unnamed’ when loading ‘tibble’”\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message:\n", - "“replacing previous import ‘ellipsis::check_dots_used’ by ‘rlang::check_dots_used’ when loading ‘tibble’”\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message:\n", - "“replacing previous import ‘ellipsis::check_dots_empty’ by ‘rlang::check_dots_empty’ when loading ‘tibble’”\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "── \u001b[1mAttaching packages\u001b[22m ───────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.2 \u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 0.3.4\n", - "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.0.4 \u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 1.0.2\n", - "\u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 1.1.2 \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n", - "\u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 1.4.0 \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.0\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "── \u001b[1mConflicts\u001b[22m ──────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──\n", - "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n", - "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n", - "\n" - ] - } - ], - "source": [ - "library(tidyverse)\n", - "library(ggplot2)\n", - "\n", - "options(repr.plot.width = 10, repr.plot.height = 5) # Adjust these numbers so the plot looks good in your desktop.\n", - "options(warn=-1)\n", - "\n", - "t_x_axis <- seq(-4, 4, 0.1)\n", - "crit_unadj <- qt(1 - 0.1, 1998)\n", - "crit_bonferroni <- qt(1 - 0.01, 1998)\n", - "t_stat <- 1.8\n", - "\n", - "t_dens_data <- data.frame( x = t_x_axis, \n", - " y1 = dt(t_x_axis,1998))\n", - "\n", - "test_plot <- ggplot(t_dens_data, aes(x = t_x_axis)) + \n", - "geom_line( aes( y = y1, colour = 'H0 is true' ), size = 1.2 ) + \n", - "geom_area( aes( y = y1, x = ifelse(x > crit_unadj, t_x_axis, NA)), fill = 'black',alpha=0.3) + \n", - "geom_area( aes( y = y1, x = ifelse(x > t_stat, t_x_axis, NA)), fill = 'red',alpha=0.3) + \n", - "geom_area( aes( y = y1, x = ifelse(x > crit_bonferroni, t_x_axis, NA)), fill = 'blue',alpha=0.6) + \n", - "theme(text = element_text(size = 18),\n", - " legend.title = element_blank() ) +\n", - "labs(x = '', y = '' ) + \n", - "geom_point(y=0,x=t_stat,size = 3,shape=19,aes(color = \"t-stat\"))+\n", - "geom_point(y=0,x=crit_unadj,size = 3,shape=19,aes(color = \"unadj critical val\"))+\n", - "geom_point(y=0,x=crit_bonferroni,size = 3,shape=1,stroke = 2, aes(color = \"bonferroni critical val\"))+\n", - "scale_colour_manual( breaks = c(\"H0 is true\", \"t-stat\",\"unadj critical val\",\"bonferroni critical val\"), \n", - " values = c(\"black\", \"#f94f21\",\"black\",\"blue\"),\n", - " guide = guide_legend(override.aes = list(\n", - " linetype =c(\"solid\",rep(\"blank\",3)),\n", - " shape = c(NA, rep(16, 2),1))))+\n", - "scale_x_continuous(breaks = c(0))+\n", - "ggtitle(\"Bonferroni: statistical significance adjustment\") " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "tags": [ - "remove_input" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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XLq1Kkj3bp1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\nAkEX8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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/gcohen/Documents/GitRepositories/stat-301-instructor/stat301book/_build/jupyter_execute/w02d2_abPeeking_6_0.png" - }, - "image/png": { - "height": 300, - "width": 600 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "test_plot" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Interpretation\n", - "\n", - "You can compare the **observed t-statistic** with the critical value obtained from the sampling distribuiton to test the null hypothesis:\n", - "\n", - "- **Unadjusted test**: Since the test-statistic (red dot) is greater than the (unadjusted) critical value (black dot), we have evidence to reject the null hypothesis.\n", - "\n", - "- **Bonferroni adjusted test**: Since the test-statistic (red dot) is smaller than the Bonferroni adjusted critical value (blue dot), we do *not* have enough evidence to reject the null hypothesis.\n", - "\n", - "Alternatively, you can compare the **$p$-value** with the pre-set significance level to test the null hypothesis:\n", - "\n", - "- **Unadjusted test**: Since the $p$-value (red area) is smaller than the (unadjusted) significance level (grey area), we have evidence to reject the null hypothesis.\n", - "\n", - "- **Bonferroni adjusted test**: Since the $p$-value (red area) is larger than the Bonferroni adjusted significance level (blue area), we do *not* have enough evidence to reject the null hypothesis.\n", - "\n", - "```{hint}\n", - "Note that you can also adjust the $p$-value of a test using a Bonferroni correction!\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "remove_input" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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Lli0Fz5QzEUAAAQQQQAABBHwnQADLd7eMAiOAAAIIeEFg2bJlMmnS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DMKxVzdKmTRvzIfdJgASCgECxYsWkfPnyyv7KISWTrbBgrU4hARIgARIgARIggVAi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\nAKO3m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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/gcohen/Documents/GitRepositories/stat-301-instructor/stat301book/_build/jupyter_execute/w02d2_abPeeking_8_0.png" - }, - "image/png": { - "height": 300, - "width": 600 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "options(repr.plot.width = 10, repr.plot.height = 5) # Adjust these numbers so the plot looks good in your desktop.\n", - "options(warn=-1)\n", - "\n", - "t_x_axis <- seq(-4, 4, 0.1)\n", - "crit_unadj <- qt(1 - 0.1, 1998)\n", - "crit_bonferroni <- qt(1 - 0.01, 1998)\n", - "t_stat <- 1.8\n", - "adj_t_stat <- qt(1-(1-pt(1.8,1998))*10,1998)\n", - "\n", - "t_dens_data <- data.frame( x = t_x_axis, \n", - " y1 = dt(t_x_axis,1998))\n", - "\n", - "test_plot <- ggplot(t_dens_data, aes(x = t_x_axis)) + \n", - "geom_line( aes( y = y1, colour = 'H0 is true' ), size = 1.2 ) + \n", - "geom_area( aes( y = y1, x = ifelse(x > crit_unadj, t_x_axis, NA)), fill = 'black',alpha=0.3) + \n", - "geom_area( aes( y = y1, x = ifelse(x > adj_t_stat, t_x_axis, NA)), fill = 'red',alpha=0.3) + \n", - "geom_area( aes( y = y1, x = ifelse(x > t_stat, t_x_axis, NA)), fill = 'red',alpha=0.6) + \n", - "theme(text = element_text(size = 18),\n", - " legend.title = element_blank() ) +\n", - "labs(x = '', y = '' ) + \n", - "geom_point(y=0,x=t_stat,size = 3,shape=19,aes(color = \"t-stat\"))+\n", - "geom_point(y=0,x=crit_unadj,size = 3,shape=19,aes(color = \"unadj critical val\"))+\n", - "geom_point(y=0,x=adj_t_stat,size = 3,shape=1,stroke = 2, aes(color = \"bonferroni t-stat\"))+\n", - "scale_colour_manual( breaks = c(\"H0 is true\", \"t-stat\",\"unadj critical val\",\"bonferroni t-stat\"), \n", - " values = c(\"black\", \"#f94f21\",\"black\",\"#f94f21\"),\n", - " guide = guide_legend(override.aes = list(\n", - " linetype =c(\"solid\",rep(\"blank\",3)),\n", - " shape = c(NA, rep(16, 2),1))))+\n", - "scale_x_continuous(breaks = c(0)) +\n", - "ggtitle(\"Bonferroni: p-value adjustment\") \n", - "test_plot" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Since the *adjusted* $p$-value (light red area) is larger than the significance level (grey area), we do *not* have enough evidence to reject the null hypothesis.\n", - "\n", - "\n", - "```{caution}\n", - "The solid red area correspond to the unadjusted $p$-value.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### A/A Testing (again)\n", - "\n", - "To examine if the Bonferroni's correction effectively controls the type I error rate, we will use again the **A/A experimental design** \n", - "\n", - "```{important}\n", - "We know that the (true) effect size is 0 in an A/A experiment!\n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-08411ec27d5e8325", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "remove_input" - ] - }, - "outputs": [], - "source": [ - "options(warn=-1)\n", - "\n", - "# Two-sample t-test with tracking sequential statistic and p-values by incremental sample sizes until getting to n in each group.\n", - "\n", - "# @param n (numeric): Initially planned sample size for each group (for simplicity, n needs to be a multiple of sample_increase_step).\n", - "# @param d_0 (numeric): effect size.\n", - "# @param mean_current (numeric): Population mean for control variation.\n", - "# @param sd_current (numeric): Population standard deviation for current variation.\n", - "# @param sd_new (numeric): Population standard deviation for new variation.\n", - "# @param sample_increase_step (numeric): Sample size increment.\n", - "\n", - "# @return p.value.df: A tibble that has 3 columns:\n", - "# inc_sample_size, statistic, and p_value \n", - "\n", - "incremental_t_test <- function(n, d_0, mean_current, sd_current, sd_new, sample_increase_step) {\n", - " sample_current <- rnorm(n, mean = mean_current, sd = sd_current)\n", - " sample_new <- rnorm(n, mean = mean_current + d_0, sd = sd_new)\n", - "\n", - " p.value.df <- tibble(\n", - " inc_sample_size = rep(0, n / sample_increase_step),\n", - " statistic = rep(0, n / sample_increase_step),\n", - " p_value = rep(0, n / sample_increase_step)\n", - " )\n", - "\n", - " current_sample_size <- sample_increase_step\n", - " \n", - " for (i in 1:nrow(p.value.df))\n", - " {\n", - " t_test_results <- t.test(sample_new[1:current_sample_size], sample_current[1:current_sample_size],\n", - " var.equal = TRUE,\n", - " alternative = \"greater\" \n", - " )\n", - " p.value.df[i, \"statistic\"] <- as_tibble(t_test_results$statistic)\n", - " p.value.df[i, \"p_value\"] <- as_tibble(t_test_results$p.value)\n", - " p.value.df[i, \"inc_sample_size\"] <- current_sample_size\n", - " current_sample_size <- current_sample_size + sample_increase_step\n", - " }\n", - "\n", - " return(p.value.df)\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "remove_input" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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HwEOIUwPm7NOgvKh+OPPz4sDzjqvuyyy8L8Y5mEsEQ66aSTxG1lPDi29hIS+QUT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\nL/Pnzzd2tcNyLJnctm2bXiKJ8+CBNrRq1Up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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/gcohen/Documents/GitRepositories/stat-301-instructor/stat301book/_build/jupyter_execute/w02d2_abPeeking_12_0.png" - }, - "image/png": { - "height": 300, - "width": 600 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "options(repr.plot.width = 10, repr.plot.height = 5) # Adjust these numbers so the plot looks good in your desktop.\n", - "\n", - "set.seed(301)\n", - "answer2.1 <- \n", - " incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50)\n", - "\n", - "\n", - "crit_unadj <- qt(1 - 0.05, 1998)\n", - "crit_bonferroni <- qt(1 - 0.0025, 1998)\n", - "\n", - "sequential_stat <- \n", - " answer2.1 %>%\n", - " ggplot() +\n", - " geom_line(aes(x = inc_sample_size, y = statistic)) +\n", - " geom_point(aes(x = inc_sample_size, y = statistic,colour =\"#f94f21\"), size = 2) +\n", - "# geom_hline(yintercept = crit_pocock, colour = \"red\", linetype = \"twodash\") +\n", - "# geom_point(aes(x = inc_sample_size, y = crit_pocock), colour = \"red\") +\n", - "# geom_text(x=850, y=crit_pocock + 0.15, size=6, label=\"Pocock\",colour = \"red\") +\n", - " geom_hline(yintercept = crit_bonferroni, colour = \"blue\", linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = rep(crit_bonferroni, 20)), colour = \"blue\") +\n", - " geom_text(x=850, y=crit_bonferroni + 0.15, size=6, label=\"Bonferroni\",colour = \"blue\") +\n", - " geom_hline(yintercept = crit_unadj, linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = rep(crit_unadj, 20))) +\n", - " geom_text(x=850, y=crit_unadj + 0.15, size=6, label=\"Unadjusted\") +\n", - " theme(\n", - " text = element_text(size = 18),\n", - " plot.title = element_text(face = \"bold\"),\n", - " axis.title = element_text(face = \"bold\"),\n", - " legend.position=\"none\") +\n", - " ggtitle(\"Critical values in Sequential Designs\") +\n", - " ylab(\"Statistic\") +\n", - " xlab(\"Sample Size\") +\n", - " coord_cartesian(ylim = c(-1, 3)) +\n", - " scale_y_continuous(breaks = seq(-1, 3, by = 0.5))\n", - "sequential_stat" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Interpretations of Bonferroni correction\n", - "\n", - "In *one* A/A testing simulated experiment, using unadjusted quantities:\n", - "\n", - "- you would reject $H_0$: the observed (unadjusted) t-statistic is bigger than the (unadjusted) critical value\n", - "\n", - "Using Bonferroni's adjusted quantities:\n", - "\n", - "- you would fail to reject $H_0$: the observed (unadjusted) t-statistic is smaller than the (adjusted) critical value\n", - "\n", - "```{important}\n", - "The Bonferroni's adjusted critical value is larger than the unadjusted one: the test is *more conservative*! With the adjustment, we erroneously reject $H_0$ less often!! it controls the type I error rate!! \n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-76077581b4638618", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Pocock's boundaries\n", - "\n", - "Other methods can be used to control the type I error rate in A/B testing experiments with early stops. \n", - "\n", - "The **Pocock method** computes a *common* critical value for all interim analyses. \n", - "\n", - "- the Pocock's boundary is not an adjustment of the quantile of a $t$-distribution \n", - "\n", - "- we can easily get the critical values for this design using `gsDesign::gsDesign()`\n", - "\n", - "```{admonition} Remarks\n", - ":class: attention\n", - "\n", - "**Note 1**: `gsDesign()` outputs a full sequential design. You can read more about this package [here](https://keaven.github.io/gsDesign/reference/gsDesign.html)\n", - "\n", - "**Note 2**: a caveat about this package is that two-sample tests are based on $z$-statistics. However, results are nearly equivalent to a $t$-test. More can be read [here](https://keaven.github.io/gsDesign/articles/nNormal.html)\n", - "\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### A/A Testing (again)\n", - "\n", - "To examine if the Pocock method effectively controls the type I error rate, we can use again the **A/A experimental design** \n", - "\n", - "```{important}\n", - "We know that the (true) effect size is 0 in an A/A experiment!\n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b20f739b98c5f734", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "scrolled": true, - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "hide_input", - "remove_output" - ] - }, - "outputs": [ - { - "ename": "ERROR", - "evalue": "Error in library(gsDesign): there is no package called ‘gsDesign’\n", - "output_type": "error", - "traceback": [ - "Error in library(gsDesign): there is no package called ‘gsDesign’\nTraceback:\n", - "1. library(gsDesign)" - ] - } - ], - "source": [ - "# Run this cell to get a Pocock design!\n", - "library(gsDesign)\n", - "\n", - "design_pocock <- gsDesign(k = 20, #number of interim analysis planned\n", - " test.type = 1, # for one-sided tests\n", - " delta = 0, # default effect size\n", - " alpha = 0.05, #type II error rate\n", - " beta = 0.2, # type II error rate\n", - " sfu = 'Pocock')\n", - " \n", - "crit_pocock <- design_pocock$upper$bound" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "remove_input" - ] - }, - "outputs": [ - { - "data": { - "application/pdf": 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cl9203m3bgvVsfoaB0DRUuC2RqH9AtFi6ctWpv2zyyELZ9sk/i+cBWzQyF2EDDi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S6SLzSrC/mh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\nmXs4pKLUviVtpXtqr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S1t7nSvFd14bFtp9xbzSRqftd550hnR1cGN2LNv2BWCuw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\nUAFczrEMjajMVRiOOQPYV01cxrMjLqUwDEDjofYUAVPs8v8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Y2/J2wxDC8feIyc/Lj2v+y7v/AJ4NQBUoq3/Z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\nE8qxefc6JGsUe4gbnPnHCjOScdBQB7HXL61/yE5v+A/+g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CgmJsa32yyIkydPsj/Wq1fPdTCMSpUq\nrVq16gU2ztlUTk7OC2yEV//+/ZkCIQDEx8ezC4ScN9cNGjSo4FsuupjzxRngPiQkxDUDxwnv\n7NmzTHazfv36Bw8epP9OTk6OjY1t3Lhxhw4dWrRo0bRpU9catiLSoUMHdv0trXr16uyPTMax\n4OrXr//vv//Gx8evXbt27969HrZw48aNt956KzExcfHixcxl682+ZXchA4Bq1apxmhTSWrVq\nVXQFQi/PDY4iOkbe8ON154oz5AkTmzCv0MuXL7M/VqtWjVOgBQCCIOLi4gpeIHyBM0Qg6Q9C\nCCEfwgIhFycLyB5UwCc4b7EvNuyqPwDg7ayVrwMHDmzatOnMmTN3797V6/XFMCBE586dNRoN\ncxR2797NNOWiKOqPP/5glixfvjxv49vijzlfz58/Z3/cvXs373g5bOy84IQJE5gMGQDY7fbE\nxEQ6C0uSZKNGjdq2bduzZ0/O29t9jpNrpHEqUl74eUrXrl27du1qNptPnjyZkJBw4sSJ06dP\nc2rvaUuXLlWr1UyvPG/27aNHj9jTOXVZjCpVqhTwV7wAL88NjiI9Rp4J8LpzxekgwLSJEOYV\nyhlm2d35yeml6dkLnCECSX8QQgj5EPYh5AoLC2N/9HlVwCuvvOLbDRYQZ6jGgICAQq2ekpLS\nrl27jh07rl69+sKFC5mZmcWTw5PJZD169GA+3r59m2mQdvr0afbgfgMHDhRIzPniZEMLgt1U\nrFOnTnPnzuV9EZnNZktMTPz6668bN27cpEmTwo5nWyiBgYGuE11rG7whlUrj4uKmTZu2f//+\ntLS0//77b+7cua7lsYULFzItD73Zt5yaEHd1HUVaB+LlucFRDMfIlWCvO46cnBzOGxqYAqEw\nr9ACnp+F6ubwAmeIQNIfhBBCPoQFQi72EGoAkJ6ezsk0eIn9br3ixHnKW6gsmsVief311znv\nO9ZoNFWrVo2NjY2NjXX3rNonOKNQMI/qPbcX9W/MAEDxvSiMxpuX8oxTUz1p0qSkpKQBAwZ4\neOfB6dOnW7RowR50x7de4Fd4+XU1a9acNGnS5cuXp0+fzp5lMpmYs8Kbfcs5ZO425X3xpkjP\nDS+35iW/X3cFd+rUKYvFwnwkCIJ5r4Ywr9CiOD9f7AwRQvqDEELIh7BAyFW5cuWQkBD2lM2b\nNxdkxZ49e44ePfrUqVNFE5e3OE1hC9VxaMWKFefPn2c+hoaG7t69OzU19erVq2fOnDlz5szs\n2bN9FqiLDh06sIOPj4+n/2C/D7pWrVq1atXyV8y8/aDcvVQdXI7FkCFD8h0O+MSJE5yN1K5d\ne+PGjSkpKVu3bh07dmy9evVcM2dGo3Hw4MHZ2dkv+suEiCTJadOmdenShT2RqTf2Zt9yqs31\nej1vAIVqRu6Xc8OP/JtWFMovv/zC/linTh2meYgwr1DO+ekuDfd5NwdepTb9QQihlxIWCHmw\n34cOAN9//32+t7SEhISdO3euWLGiadOmtWrVEuDbeCMjI9kfC1XtyRmLf/r06W+88Qb7BceF\nev94YdFvXmY+njhxIiMj49atW+whQFyHkynSmEWiPBdOcnKy6zKcEUrYOIPdezPQYlBQUO/e\nvb///vtz587pdLoDBw6MGjWKPdxFSkoKu6elYGVlZSUmJq5aterDDz98/fXXPew9Gud1f8wV\n6s2+5TTndjcyh+dRNIVzbviFf9OKgrt58yZ7tCoAYD9iEOYVyuna5y4N93x++tZLk/4ghFAp\nhwVCHmPHjmV/fPjw4UcffeRh+bS0tJEjRzIfL126VAzP7D20OuPFfl0VAFy4cIH9wgbakydP\nXnnllYhczCrsF0+BS6taADh8+HChgiksdqtRq9W6f/9+dntRgiBcOxAWacycR/XsKhGaXq/n\ntJpja9KkCfvj1atXXzgSNpVK9frrr69YsWLbtm3s6devX/fJ9ovUa6+91qxZs1GjRi1evPjQ\noUP5vk7t8ePH7I9M3Y43+5YzwMalS5dcK1soikpISPCwEWGeGy+ssOmM39OKgsjOzh4yZIjR\naGSmyGQydrIvzCu0atWq7I+XL1927euY7/lZdEp0+oMQQqUcjjLKo3Hjxu3atWNnXFasWGG1\nWhcuXOjaX//KlSv9+/dnjz0jFou/+uqrog6St+bBg5o1a0ZHRzNjjZrN5vXr17/zzjvsZXbs\n2MEepoV5wQOn2RsnF/LPP/9wXpfs4U3ZL6Zt27ahoaHMm8Hi4+MfPHjAzG3WrFn58uU5qxRp\nzJzq1nXr1n322WfsqqHZs2d7qFWuWbPmK6+8wowZ+ODBg4SEhLi4OPYy58+fX7lyZXh4eJky\nZcLDwxs2bBgVFQUASUlJ//zzz9WrV69duxYcHMxp9kZjv5kDfNHnrRj06tXr4sWLzMcff/yx\nXbt2PXv25F34+vXra9euZU9p2LAh/Yc3+7ZmzZqBgYFMe06r1bp27dr333+fve6uXbvY554r\nP54bRaGw6Yzf04p83b9/f/DgwZy2/aNGjWLXDwvzCq1du3ZAQADTktlms61Zs2bChAnsZXbs\n2OH5/PTSy5r+IIRQKYcFQn6rV6+uU6cOu6vP6tWr//jjj379+rVq1YrOOty7d2/v3r2bN2+2\nWq3sdSdNmlSjRg2fhySTydgfL1++fP369YKPgC8Sid59993JkyczUyZPntyyZUumVuTixYvM\ne5ZpTLVnREQEexz2n3/+uUuXLvRoBKdOnerTpw+nGsG3w/AAgFgs7tOnz/Lly+mP8fHx7P4z\nvK8fLNKYOe8OuXLlyrhx4+bPn69QKEwm06JFi7799luVSuVaB0sTiUTvvffetGnTmCmjR49O\nSEhgmoTl5OSMGzfu+PHj9EeSJJlB7Xfu3Pnll1/SfxME0atXL9dS0/r169kfi+Js9LmxY8d+\n9913zB6z2Wy9e/fu27fv0KFD69evHxoaSpKkTqe7du1afHz80qVL2XV3YWFh7du3p//2Zt9K\nJJIuXbps2LCBWXfKlCktW7asU6cO/fHatWtjxozx/EP8eG74hJfpjN/TCkZmZib7OZ1er797\n9+6ePXvWrVvHKbVWr16deW0JTZhXqEwm69Sp05YtW5gp06ZNa9asWbNmzeiPly5dyvf89NLL\nmv4ghFAphwVCflFRURs2bOjbty8765CcnLxs2bJly5Z5WLFTp04zZ84sipAiIiLEYjFT+LTZ\nbC1btuzbt69YLK5bt+7w4cPz3cL48eN//vnna9eu0R/T0tIaNmzYu3fv6OjoGzdubN++nT3m\nXt++fVu1akX/HRcXd+HCBWbW1q1bmzRp0rhx4zt37hw4cMBqtUZHR1erVm3//v30AseOHZs7\nd2716tUbN27M6ffywvr3788UCNnvoyNJsl+/fq7LF2nMPXv2HDduHHt3LVu27JdffomMjHz0\n6BFdWJ0wYYKHM+GDDz743//+x1TYXrt2rXbt2kOGDClfvvyTJ082bdrEziiPGjWKyZGPHDly\n7ty5dHGCoqg+ffp069atefPmQUFBAJCSknL8+PF9+/Yx65YpU6Zr166ef44QhIWFLVmyZMSI\nEcwUiqI2b95ckCGd5s6dK5fLmY8vvG8BYNKkSRs3bmQKLRkZGU2aNOnVq1dMTMydO3e2bt2a\nk5MTGRnpoRLGj+eGT3iZzgghraBt3Lhx48aN+S4WEhKybds219fwCPMKnThxIrtAaDAYWrZs\n2aFDh5iYGPoBpcViiYqKun//fgE3WFgva/qDEEKlXb6Dp5Vmf//9N+e1hJ4NGjTIZDJxNsLJ\n+c2bN8/d1+W7ZJs2bXi/9+OPPy7gFi5cuBAeHp7vD2nQoIFOp2PWun79OqfegE0sFh89enTh\nwoWus44cOVKoPeCBzWaLiIhw/YqOHTvyLl/UMXNaanGMGTOG01dq+vTpnC2cPHmyIG8Ma9q0\nqcFgYK/4v//9L9+1aFKpdO/evQXfyZ5/dUGOI2dAi9dee63g305R1LfffssZlCVfrjuW8mLf\nUhTF6ULM8eqrr86bN489ZebMmZwt+PHc8Mkx8iad8WNaMXr06Hz3GEfNmjVv3brlboN+uULz\n3QOcdv4cFSpU+Pbbb9lTOOen92dI0aU/CCGE/AUHlfGkWbNm169fnzRpErv+gVflypW3bNmy\nfv169gBrPjdnzpx8I/Gsdu3af//9N1P150okEg0fPvyvv/5i54QqV668evVq3p+m0Wh2794d\nFxf39ttvM691Lgoikahv376u03nbi0LRxzxnzhx3PdwGDBiwePFizmujXftKNW3a9MSJE7Vr\n13b3FQRBjBgx4uDBg0qlkj19+PDhv/32W3BwsOcIq1WrduDAgU6dOnleTFA++eSTI0eONG3a\ntCAL161b9/Dhw+x2fYwX3rcAsHDhQncnVYUKFfbt2+dam8Thx3PDJ7xJZ4SQVhSEVqudNWvW\nqVOnPLwXUZhX6A8//OA6hhatfPnyu3fv9vxaee+9xOkPQgiVWthkNB9BQUFz586dPHnygQMH\n9u3bd+HCheTk5JSUFIqitFrtq6++2rx5886dO3fo0KEY3gHduHHjv//+e8aMGceOHdPpdIGB\ngeHh4bGxsd26dSv4RipVqpSQkHDgwIFt27YdP3786dOn9KYqVKjQunXr4cOHc8ZapA0aNKhe\nvXrffffdkSNHHj9+rFQqK1So0LVr1zFjxtANvYKCghISEiZNmvTPP/9kZGRERka2atWqcuXK\nPvvxAP379//hhx/YU+RyOfuNFMUZs0Qi2b59+2+//bZmzZrz58+npaUFBQU1adJk1KhRPXr0\nAADO2yx5X2pXq1atpKSk7du379y5MzEx8enTp9nZ2fSxiIuLGzFiBO+xoHdF165dN23atH//\n/kuXLj169Eiv14tEIrVaHR0d3aBBg549e3bs2JE93H9J0apVq5MnTyYlJe3cufPff/+9cuVK\nSkqKXq8nCEKtVmu12ho1atSvX7979+6c3nocL7xvxWLx+vXr+/Tps2rVqnPnzqWlpYWHh1et\nWnXgwIEDBw5UKpVHjx71/BP8e254z8t0RghphSuSJENCQsqUKRMbG9uhQ4cuXboUpPZPgFeo\nRCLZsGHDm2++uXLlynPnzqWmpoaFhVWpUmXAgAGDBg0KCAg4ePAge/miSARe4vQHIYRKJ4Iq\n5KjiCCFUmv3www/jxo1jPs6cOXPKlCl+jAchtlmzZk2dOpX5uHz5cs+tTBFCCCFsMooQQgi9\nJDg9AMuVK+evSBBCCJUU2KIDIYQQKgESEhJOnTp19+7de/fu3b17d8qUKZz+hCaTiRnBFQBI\nkmzRokWxh4kQQqiEwQIhQgghVAIkJCQwrwEEgKlTp7Zv3549FPZnn32WmprKfOzYsWNgYGCx\nhogQQqgEwgIhQgghVAKMGjVqzpw5zNtxb926VaNGjREjRlSqVOnx48eHDx8+duwYszBJktOn\nT/dTpAghhEoSLBAihBBCJcArr7yybNmy4cOHM6PBpaSkcF48yFi0aFHDhg2LMTqEEEIlFQ4q\ngxBCCJUMb7/99ubNmyMiIjwsExYWtnXr1rFjxxZbVAghhEo0rCFECCGESow+ffp06tRp8+bN\n8fHxSUlJT58+zcnJUavV4eHhDRs27NChQ//+/eVyub/DRAghVGLgewgRQgghhBBCqJTCJqMI\nIYQQQgghVEphgRAhhBBCCCGESiksECKEEEIIIYRQKYUFQoQQQgghhBAqpbBAiBBCCCGEEEKl\nFBYIEUIIIYQQQqiUwgIhQgghhBBCCJVSWCBECCGEEEIIoVIKC4QIIYQQQgghVEqJ/R2AsKSk\npPg7hFItMDBQr9fbbDZ/B1LaKZVKpVKZmZlpNpv9HUtpJ5VKpVKpXq/3dyAIQkNDLRaLTqfz\ndyAItFptRkYGRVH+DqS0U6lUCoVCp9NZLBZ/x1LayWQykiSzs7P9HUhpJxKJgoODzWZzZmam\nv2PJgyRJrVbrbi7WECKEEEIIIYRQKYUFQoQQQgghhBAqpbBAiBBCCCGEEEKlFBYIEUIIIYQQ\nQqiUwgIhQgghhBBCCJVSWCBECCGEEEIIoVIKC4QIIYQQQgghVEphgRAhhBBCCCGESiksECKE\nEEIIIYRQKYUFQoQQQgghhBAqpbBAiBBCCCGEEEKlFBYIEUIIIYQQQqiUEvs7ALHvJIwAACAA\nSURBVLcuXrwYHx9/9erVzMxMmUwWHR3dqlWrTp06kSSZ77rPnz/fsmXLuXPnUlNTFQpF5cqV\nu3fv3qBBg2IIGyGEEEIIIYRKCoEWCDdu3Lhx40YACAoKqlSpUkZGxpUrV65cuXLs2LEZM2ZI\npVIP6968eXPq1KkGgyEwMLB69eo6ne7s2bNnz579v//7v169ehXXL0AIIYQQQgghoRNigfDC\nhQsbN24UiUTjxo1r27YtQRAAkJSUNHv27MuXL//xxx99+vRxt67NZluwYIHBYOjateuIESPo\n6sSkpKSZM2f+8ssvdevWrVixYvH9EoQQQgghVPqUKRNstzt7ZiUnp/gxGB9q1Cjw7l0JABAE\n9fx5qr/DQb4hxD6EBw8eBIDOnTu3a9eOLg0CQN26dbt27QoA//zzj4d1T58+/fDhw3Llyo0a\nNYppXFq3bt1evXrZ7fYdO3YUcewIIYQQQqg4hIWFev4XHh6yapUf8rrt2mnYpUGEBE6IJ2u/\nfv2+/PJL1+adkZGRAGA2mz2sm5iYCABxcXFMSZLWunVreq7dbvdttAghhBBCSIAoipg8OTgq\nSlvM33vxooT+gyCoY8dSXprqQQB4661sgqAIglKpMEf98hBik9GoqKioqCjX6Tdv3gSASpUq\neVj31q1bAFClShXO9HLlyimVyuzs7MePH7/66qu+CxYhhBBCCPlTnTrO2gKDgbh/n7RaCYpy\n1A3k5JApKRAa6ofABg7MqVbND99bdD74wPLBB9hS9GUjxAKhK4PBEB8fHx8fr1ar+/Xr52HJ\np0+fAkBYWJjrrLCwsHv37mGBECGEEELoZXL4cCbv9LAwRymwZs3gZ8/SijEih969s4v/SxEq\nLEEXCFNTU6dMmWIymdLT06VSacuWLd9+++1Q9094bDYb3aBUoVC4zqUn5uTksCfOmTOHaUTa\noEEDumUp8heSJJVKJUVR/g6ktBOLxQAgl8s9j+iLioFIJCJJMiAgwN+BIAAAPBYCIRKJVCqV\nv6NAjpuFQqGQyWT+jcTdhUkQFF1PSFGE6zJKpYypRWSvkp1tYk9p3lx07pyUmeW6llZre/zY\nQv+tUMjZs/r2deRac3KMzESVSuraw1Astmdl5ekVVacOeeOGY/iWMmVsT5+KmU25n+XcbFH8\nOgCoWZO8fVvCuykEAHSfNbFYXLJuFoIuENpstkePHtF/i0SinJycJ0+eeCgQMt0LJRKJ61x6\nosmU59zdsWOH1Wql/yZJslOnTj6JHL0wv99UEANLg8JRkPevomIgEonkcnn+y6GihwdCOIRw\ns8j3fBCJCPYyP/wA48bxL0lRhEIhv3IFmKaeFSrAuXP0nwSnvEdLTydjYsiHDwEACAJ4H2vT\n356SAnyN2AAArFaRUilnj3RRoQLcuOH4XqbIR2/Kw6wi/XX0D8xF4GXojgBvFp5HURF0gTA8\nPHzXrl1ms/np06cnTpzYtm3bmTNnJk6c2LJlS97lmbKExWJxnUtP5JQ3tm3bxtRHqVSq9PR0\nX/4AVEhqtdpgMODAP34nl8sVCoVer+e9lFBxkkgkEokkOxsbHfmfVqu1Wq1ZWVn+DgSBRqPJ\nysrC5iR+p1Ao5HJ5VlYW82y92DlGi+HNv129ChTlWGDz5jyLjBvnHGamb9+cFSuMANC3r/LP\nPx25xBo1qNTUDPrviAgJQACAo6RHENSSJRmDB8PXX5Pz52voZR49csSQmgoAEBISRNezbduW\nTjc+o789PDwIwFGiatvWtGVLNgDUqaN++FBMb18kcn5vWJgMQMl8L/3VAJCenuFhVpH+OgCg\nqAAAut6For8OsREEERQUZLFY9Hq9v2PJQyQSBQYGupsr6AIhTSqV0sPMlC9f/ptvvlmxYkXT\npk156wBFIpFMJjOZTLyZJ4PBAACcRiZly5Zlf0xJeXmGgSqJKIqy2+02m83fgZR2dDYLj4UQ\nkCRJURQeCIHAYyEcNpsNC4R+J5ybxcKFzqaS2dnE1q2Ke/fETNNHkcjesqUzxlq1gpiF6fE/\n6VmbNmUBZNHdDimKWL6cGjnSDgAhIc6NMy/fs9ng009tn36awnRT5N0J7H3Trp2GCYn9vefO\nZaSkQPXqju9lVqhY0flMlv3SP5uNZ5ZMJiNJMjvbVtS/jv3Q3u/HXYBEIhGUwJtFCSgQMpo1\na6ZQKHQ63f37992NNVquXLnbt28/f/6cM04pRVHJyckAgCPKIIQQQgi9TGbOdFv1ERJivXo1\nT0XW8+eORvgiEU+LJKbb4eefa0eO5A6n6c2r2Jl3UWi13ArV0FDn90ZEBD99yh3/5vJlt9/L\nmeWvX4dKNMG9h9But3/33XeTJ09+8OABZxZT2vbQeiomJgYArl27xpl+584do9GoVqvLlCnj\n65ARQgghhJAQpaaKy5ULZk9hqun27+cZd1SpLKp+K8z3Xr/O09IyIoKpguOOBAPg6Z0ZnFn+\n+nWoRBNcgVAkEj169OjSpUtHjx7lzLp27Ro9bAynnSdb8+bNAeDo0aOcitqDBw8CwGuvvcZ5\nYT1CCCGEECrRkpNT2P9ef90ol9vobnUAYDaLwsNDXNeqV49nU/37O4YD5R2i0yfRhoWFuv57\n8kTs7ntfjL9+HSqJBFcgBID27dsDwLZt2w4dOsT0ELh79+6SJUsAoG7duiEhjqt6y5Yty5cv\nP3v2LLNu/fr1Y2Jinj17tmzZMqaL8/Hjx/fu3SuRSHr37l2svwQhhBBCCBWvDRv0Dx6kP3+e\nOnWqjp5CUcS77xboVSUDB+bkv1BRci2YeSiqFbYU5/dfh4RJiH0IO3fufPXq1aNHjy5ZsmTN\nmjURERFZWVnPnj2jKCo8PHwcayTdY8eO3blzJygoqEGDBvQUgiA+/vjjzz///ODBg4mJiZGR\nkWlpaU+fPiUI4v3334+IiPDTb0IIIYQQQsXqgw8ss2ZRuQN+yn/6yZDvKj/9VOSvuKTHevGL\nYvh1qCQSYg0hQRATJkyYPHlyw4YNRSLR7du3dTpdhQoVBg4cuHjx4vDwcM+rlytXbvHixV26\ndFEqldevX8/JyWnWrNm8efPatm1bPPEjhBBCCCEh8FCHlpDAM3H3bmm+K3ppw4Yi2nAe/vp1\nqCQSYg0hrVmzZs2aNfO8zOLFi3mnBwUFjR49evTo0UUQF0IIIYQQKhl4O8vRE998M/jZM+7I\nKyZTUVWWMN/70UfBgwZxvzclBR4+5O/492LfUsy/DpVoeFoghBBCCKGX0JdfypkCYVAQ834/\nxxgTdjs3G3z1qrMAeeCAj9/B0Ly5mfle1/de16gR8vrroWFhoV7WH/rr16ESTbg1hAghhBBC\nCOWrXTsN+2Nysuj5c5HdTrCrB5mXPSQm6pj3rYeFhU6dqvvgAwsAdOyoOXvW2aLS+8o6jh07\nssLDpXRI1auHvvqq9dy5DAAYP165caOCCXXQIK++xV+/DpVoWCBECCGEEEIl2IULUs8LrFmT\np0puy5aUvn0dpaaZMwNnzsyzMEFQRfSK9r/+Sm3Z0vG9Dx+KmZIbo08fH4wC6q9fh0ouLBAi\nhBBCCKGXE0nanz7ldqWLi4Pk5JTw8BDXHoZSqf3RI55XuvtEtWqQnJxSpkywa3tOgqAuX071\n8AL6gvPXr0MlF8G86A8BQIprm25UjAIDA/V6vc1m83cgpZ1SqVQqlZmZmWaz2d+xlHZSqVQq\nler1en8HgiA0NNRiseh0On8HgkCr1WZkZGAGxu9UKpVCodDpdBaLxd+xlHYymYwkyezsbH8H\nUtqJRKLg4GCz2ZyZmenvWPIgSVKr1bqbi4PKIIQQQgghhFAphQVChBBCCCGEECqlsEAoXLt2\nydq3D4qKCmnUSDt/vtJo5DYE96OcHOLrr5UNG2qjokI6dAjasyefztzF7O5dcsQIddWqwVWr\nhgwbprl5k/R3RHkcPizt3DkoOjokNlY7fbpKrxfQkTWbYfFiRd26CqUSWrRQbd4sE1SbrCdP\nRGPHqmvWDI6JCenfX3PxorB6QZ88KenePbBChZD69YM/+ywgPV1AR9Zmg1Wr5K+9po2MDGnR\nQvvLL3K73d8xsaSliSZODKhbN7hChZCePQNPn5b4O6I8zp8X9+sXGBQE0dHiDz4IePZMQLdO\nioLffpPHxQVFRoY0bapdulQhqLZ7mZnE1KmqBg200dEhb7wRePSosI7stWvkkCGaKlVCqlcP\nfucd9YMHwrpZ/PGHMxswd67gsgFz5ihr1pSrVNC2bQDzunOBuHePHDlSXa1acJUqIUOHam7c\nENaR/fNPRzagQQPtV1+psrIEdGTNZliyRNGkiTYyMqR166DffxdWNuDpU9G4cepatYJjYkLe\nfFNz4YKwsgGJiZLu3TUaDVSqJPnkk4C0NAEdWc+wD2EewulDuGGDfPz4APaUHj1Mq1Zl+Sse\nNoqCYcM0e/fmSf1//DGrb1+Tl1v2SR/C5GRRXFxQcrIzxxYURB05kv7qq4LI/+7dKx06NM/o\n2HFxlt9/14mEkcMcPz5gwwY5e8rs2YZ33vHBoGfey8oi2rQJunfPeV9XKKgDBzKqVRNEp9PE\nREm3boHsKfXqWXfvzpB6l03yVR/CGTNU33+vYE+ZMCF78mRB9DYxm4kOHQIvXXLe12Uyavt2\nXaNGVj9Gxbh8WdyxYyA7L16pku3w4QyVShB3z6VLFV99pWJPGTbMOH++IDqd2mzQu3fg33/n\nKQRu3JjZvr23nZN90ofw/n2yTZugzEznkY2IsCckZAQHC+Jm8dtv8nHj8mQDunUzrV4tiGwA\nAAwbpuE8C166NOvNN73NBvhESoooLi7o+XPnbTUwkDpyJD0yUhBHdv9+6ZAhebIBLVtatmzx\nNhvgqz6EH30UsG5dnmzAzJmGd98VRDZAryfatAm6ezdPNmD//ozq1QWRDTh1StK1a55sQJ06\n1r17dVKpIG4WnvsQYoHQyWSCBw8c4/BqNBR9ZVosYDB4Kt8rlRSd4aMo0OkcSyoUIJM5dqxO\nR3jYxxIJMLkKg4Ggn+zabETjxlr2XYq2bl1mkybcZ7+BgRRBAACYzQSTDqjVFEkWMH6gz1R2\n/HI5yOVu4z92TDJ8uCbvZiA4mDp9Oo1ekiRBrXask5NDmDzeIFjxg1gcaDAYbDYbE7/VCp4r\n0Fzj//JLFadIAwA9e5rmzePJIYnFEBDA3f8iEWg0LxJ/drYjVHfxUxTExWmfPOGm+qtXZ3Xr\nZsrI4Nn/mZmEh5oc7+NnTnWzGU6flvTqFchZQCajEhPTmW/hYJ/qXsafnU3QQ9iw4zcaCaPR\nsfDixcofflBwttC2rXnTpkzIe6oHBFBiMUABzh/e+GUyUCgKFD/7VG/VKujKFe6jyq+/NvTr\nZ3RZz4k3qWHit9nAZJJ5KBDyJjXs+LOyCJsNHj4UtWnDvQ2QJBw7ll65suM+yux/goDAQJ79\nz4s3qVGpKInEEb/nJ9/0qbJqlWLyZBVnVr161oMHM+j43eFNatjxm0xEjsdsTEHiHzZMwynS\nAMBHH2WPGZPDm1RKpaBU5tn/7ri7VIOCChq/3Q61a4e4jv109GhGzZpWux1c7yNsvJcqO369\nnrC6L5XnG/+mTfKxYwM4a736qv3PP9PpZJP3UmXuqh7iDwoK0ul0MhnlTfwffqh2rddiF6fZ\nd1VeLxw/jTepoXMFZjPUqBHC3JQZv/6a2ayZIxvg7lJl9n/B42dfqry5Go4TJyRvv83NBgQG\nUomJaQqFM1fjef+7i583V8OLN6lRKuHLL1WrVnGzAd26mRYsyJOQuiaV4CZXlm/87EuVN1fA\noCho3Vr7+DE3G7B4cdYbb5jBTa6MFyepEYtlJEmazdmuuYKCx3/5srhHD55swMmT6Uxiy2D2\nP/tUZ+IH1l013/iZU4U3V8P4/nvFkiVKznbatLGsXZvJ7H92rsZ1/7PxXqq8uYICxt+jR+Dl\ny9xswFdfGQYP5r+P8l6qRRe/REJGRwe53RyFci1dSgE4/p0+nZacnJycnLxunY6ZyPvvhx8y\n6SWvXUthJn7+uSE5l0Jh97D666+bmCXj4sz0xEqVrJ6/lP3v7t0UevUFC7KYiSdOOOLftCmf\n+BctyqKXvH3bGf+kSc74NRpP8bP/NWvmiL9KFSuz+tChOZ7XunHDEf/33zvjT0hIpydu3Zrh\nefV58xzxP3iQ4nlJ3n8tWpiZUDt2NNETK1Rwxj98eD7xX7mSSi+5bFkmM/HQIUf8O3fmEz/9\n74MPsh8/TmY+jhuXzQQQHm7zsGKTJs74u3Z1xB8ZaWMmvvNOtuevvnDBEf/KlZmel+T9N2OG\nnvkuZuK77zrjL1fOU/yxsRZmyZ49jfTEV15xxv/++/nEHxRkp5dcs8Z5qsfHZ9ATDxxI97z6\ntGnO+CUSx8SRI53xR0d7uhjr1nXEzz587H81a+ZzLfMmNdu3O+I/ciSf+HmTmrffzmEmxsR4\nCiAy0nmqDxyYw9mlycnJEycaPAfAm9T8/rsj/r/+SvO8Op3UDBhgdJ1FktTTp8nVqnmKn53U\nvPWWI36Vyhn/p5/mEz+T1GzZ4rxUN2zQ0RNPnswn/o8+cu7/4GDH/u/f38hMrFXL4mF13qRG\nKnXG/8UXes8BLFyYxTt98eKs5OTkM2fyiZ83qend2xl//fqe4mefP0xSQ5IUM7F5c7PnAPbt\nc+z/PXuc+3/1asdd9fz5VM+rv/ceT1LTvbsz/kaNPAUgFvNPf/bMsfrs2fnsf96kZvlyR/yX\nLuUTP29S88YbpuTk5BMn8jl2AFRoqDOp/PBDZ1L56JHjrjpnDv/pwfxjkpo//3TGz8rV5BM/\n7z92rqZVK0/7n53UfPyx81JlcjXffZdP/LxJzaJFWZ6PO/Nv8GBnUskkNW3aOO+qbduaPKzu\nLqlhcjVLluQTP+8/Jldz/34+uZoBA3iSmpYteXI1vP9kskIkNbz/fv3VkVSyk5pvvnHE7+62\nyPzr04cnqWnalCdX4/mfVmv/6itn/BcvFjRXw5vUzJzJk6vh/VfwpMbdPyapmTXLGf/Zs474\n16zJJ34mqfnvP2f87FwNJ4krX97moRAkrKa3/hUZCXFxjmcRzEOjkBA7M5FXmTKO6gOJxLl6\nVJTzmXCLFhaz2W0Rv1Yt59OzOnUcfwcH22/d4mnvXrmyrWxZbm0FmbvgK684Q2UeWmi1+cT/\nyiuODYrFzvijo53xv/aahfOI4uFDEW94NWva6GcV5co5V69SxeY5AEnuk/cyZWzt2lFWqxXA\n+XxOq6U8r87sEJHIEX9Sktj1oY5aTTVowPOgsmZNK/tvulVYRIRzJ8fE5Bs/lRu/c1czD9KC\ngvLEb7XCiRM8XWgUCoognPu/QgXnDmzWzJKR4bYdSbVqzvhr1LDSj0jDwpzxV6qUT/wymeOP\nsDB7tWq2q1d5jmxsrNVdDSG7BQ7zRZUqOeNv0sSSmuo2fqZ6CgBq1LClp1sAgN1eq2JFZ/yX\nL5PslsA05lINC3Puao3GsQW1Op/zJyrK+V0tW5ptNgIAYmLY8VvLl3dbRVixomNJsRhIElzr\ngkJC7HFxnhop8SY1Wq1jYkAA1bq1VSQSuWtHzZvUsPdqw4aWcuXsaWkEb39LdmvbqlUdu5rd\nEjI6Op/zhzepCQ5m4gfPq9NJDbMT2GQyiiQhNtbCpLGueJMapsqoIPHzJjXMGahUUnFxlpMn\nxa5peHi4vXp1W4UKztiaN7fQF2DVqs6rMjbWGhLCf+2Am6RGLGbHn08CrtXy7xx6l8rl+Zz/\nvEkNu/FVgwZWpg7QFW9Sw2725u63t2hhoe9czMY1GucvZTYrl7s9f8RisdVqZS5AYCU1NWo4\nJ9arZ1VyqxOczp8Xu9bAaLWO6h0AKFcun/OHN6kJD2dyBfnsf96kpkYNKwAouI0hnKuUK2fn\nfDsAlC/vDJUgqNz48z1/nEkNsyRzxYnFbuN/9EjE2zm/aVMrJ1dDuK/hYCc15cs7Q+XN1fDi\nTWpeecXOu/cCAqjY2DzZgCpVnPufSWpq12bHb7PZ3AbgLqlhcjURETzx22xw/DhPNiA62l6+\nvA1YuRqSzCf9rFIlT1ITFkZA7vlDY3I1vJjcC7CSmufPRVeuFDQbEBrqmMJOapjzk52r4cXO\nwDBJTfXqPLkahrtsQGSkc1cz9ZNhYfmcP7xJDbt7kefVXZOaY8ckrk2Kypa1s2/KbLxJjTy3\nbjs0NJ/4maRGKnXuf06uxm5nN4mnANx2YsEmo3kIpw+ha/MzuZw6diyDTi/868YNsk2bIJMp\nz1Vat6710KEML7fskz6EP/2kmDqV2/zss8+yP/5YEN2levYMdC0THj6cwTwO8KOnT0VNm2o5\nbYyjo22nTqWTAuiQv3mzbMwYNWfiyJE533xj8Es8HMOHq//4Q8aZuHWrrlUrr4b48Ekfwqws\nomHDYE7v9tBQ+7//pjMlOj/6809p//7c5me9e5uWLxdEd6lJkwJ+/pnb/GzlyqyePf3fXcpq\nhcaNgx88yJNDCgig/vknPTTU/92lzp4Vd+zIbaHUurVl82Zv3+Xokz6E33yjXLCAW16cM0c/\nYoTHdtLFpXXrIHbHWgCQy6m//spgF+P95dYtMi6Omw2oU8d6+LC32QCfWLlS8fnn3GzApEnZ\nn3wiiGxA796Bx45xswEHD2bUq+dVNsAnfQifPxc1bszNBkRF2U6fFkQ2YOtW2bvvcrMBI0YY\n58wRRK/pkSPVO3dyswGbN+tatxbESF/4HsIS6ccf9UzbYtrMmQYhlAYBoHJl27RpeVKc4GBq\n6VJBZN0AYNSonDZt8lx7zZpZxo0TxG0AABYv1jMPdWhffJEthNIgAERE2L/9Vs8eBEWtpn76\nKUsItwEA6NfP1Lt3nix4zZrWKVOEcmS//dYQGZnnCn3vvRwvS4O+olZTS5ZkMf0KAEAup5Yu\n1QuhNAgAbduaR47MkwWvUMEmkHI+AEybZuCMWNC/v0kIpUEAEIvhxx+z2DUtUin13Xd6IZQG\nAaBBA+ukSXmu0IgI+4IFQrlZfPxxDmfgog4dzP/3f4IoDQLAsmXcbMD06QYhlAYBoFIl21df\nCTcbMHx4Trt2eXrWNmli+fBDodwsFi3iZgMmT872sjToK+Hh9nnz8mQDAgKon37SCyQb0KeP\niTN+YY0a1ilThHKzmDPHwG62AwCjR+cIpDSYL6whzEM4NYQAkJYm+uUX+fXrZESEvU8fE7sZ\nhhCcPy/esUP27JmoWjXr0KFGpuWJN3xSQwgAFAU7dsiOH5fY7dCsmaVvX5NAxvCkZWYSa9fK\nL10Sh4bau3UzCWQoRcbVq+T27QHPnkmio02DBxs49y2/27tXeuSI1GiEhg2tAwcaJUIaxD4n\nh1i7Vn7hglijsXfqZPZJadBXo4wCwL175Pr1sgcPyKgo25AhRoEMuMc4elSyf79UrxfVrWsd\nMsTIbo7ld2YzbNggv3w5QCazt2yp79DB20EyfevpU9Gvv8rv3CHLlbP372901zzJX06dksTH\nS1NTRbVqWYcONbprf14oPqkhBACbDTZvlp86JSZJaNXK0q2byUMTx+KXlib69Vf5tWtkRIS9\nd28TuzWjECQliXfvDnj+XFypknHwYAPTgFMIKAp27pTRTfiaNrX07WsSSJGGlpVF/Pqr/NIl\ncUiIvXt3c6NGPrhZ+GqUUQC4do38/Xf548eiihVtQ4caPbTb94t9+6R//unIBgwYYPRyKG/f\nyskh1q9XXLmiDAiwtW2r99zms5jhKKOFIKgCYSnkqwIh8pJSqVQqlZmZmWbX4QtR8fJhgRB5\nKTQ01GKx6HTeNndE3vNVgRB5SaVSKRQKnU5nEdTrL0slHxYIkTdEIlFwcLDZbM7MzPR3LHl4\nLhDioDJ5EIJ6Nlha4VEQCIIg8Fj4HX0I8EAIBF4UwoEHQlDwcPgdkcvfgZR2zCEQ2rHwHA/W\nEOZh9fDGHFT0SJK02+14TvqdSCSiR7bEY+F39A3e7uFliKi4iMViiqKwCYMQkCSJB0II8GYh\nHHizEA5h3iwoipK472aDNYR5ZGQIYoCsUgubjAoE3WTUYDBgk1G/wyajwhEaGmq1WrHJqBBo\ntVqdToeFEL+jm4zq9XpsMup32GRUIOgmoxaLpWQ1GRXSUBsIIYQQQgghhIoRFggRQgghhBBC\nqJTCAiFCCCGEEEIIlVJYIEQIIYQQQgihUgoLhAghhBBCCCFUSmGBECGEEEIIIYRKKSwQIoQQ\nQgghhFAphQVChBBCCCGEECqlsECIEEIIIYQQQqUUFggRQgghhBBCqJQS+zsAhBBCCCGEkO9p\nxmqkN6UAQImo1D2p/g4HCRQWCBFCCCGEUCkS2inU02wCgAI7YU/bm1ZcESHkT9hkFCGEEEII\noVwUAICIEoV2CoV9/g4GoaKHNYQIIYQQQqg0MseY2R8JK0E+IQkzQQBBFwtDF4WmdErxT3AI\nFRcsECKEEEIIodIo84dM3unS9VLNWg39t/ZNbfrv6cUYFELFDQuECCGEEEIIOZkHm6l1FEER\nACDScztYhXQJIeyOKkQHAijC7agtIZ1DmCpHGkVQqT+mQnmXJb3bshKU7rbMs26nEAIIAKCA\nSt2H482UatiHECGEEEIIoTwoUW4xy+6cKNoiCu0cSthYZTaCXhoIOxHaKRSu5t3KXQjtFEpQ\nect4AARFhL4bGjAtoBi2zCvkDUdpEACwNIiEW0N4//79bdu2XbhwIT09XSqVRkZGtmzZsmvX\nriRJeljLYrH06dPH3dzx48e3a9euCIJFCCGEEEIvD7p6ECBP7Unw/4IdBTACcl7PMUwwAIB6\nslp2TkYvEPJRSOpeZ/kq9F3HcKYUQZnrmbO+yVItUMkPyOnCmPy0XA96n2zZvMAsniuW7+XZ\nsquQN0IIO0F/V8pe7CGJhFogPH78+IIFC6xWq1KprFChQmZm5vXr169fv56YmDhjxgyJROJu\nRb1eDwAkSVaoUMF1rkajKcKgEUIIIYTQS+A/cBSZAKxhVvqPoKFBTHUcQxuxMQAAIABJREFU\nuxyV9U1WFmTRr7IgKEK0RWTvaweAwGGBzDJMWc4wwWCYYGDee6HtoU3fme79lmUgoyZTqeNT\nOVt2/WXBnYKZusGUtVgaRADCLBCmpKQsXrzYarV269Zt2LBhUqkUAJKSkmbNmnXp0qXdu3f3\n7NnT3boGgwEAQkJCFixYUHwRI4QQQgihku4qaH7QSG5LnNWDALpfdPQfZLKjkZqd3Yo0FyWi\n6DKkdpU2tW8qAIiTHdlsm9LGWdimsImMIgCgJFSRbplD200roms8CUj5MQU8vo4RlR5CLBAe\nPHjQZDLFxMSMHDmSIBwXZN26dbt167Zly5aTJ096KBBmZ2cDgFKpLKZYEUIIIYRQyZTPG+oB\nUuY769CYUmLaTzwvrLdL7KQpT7cmZvn0bdyauvTt6bxL+nzLbIEjAkmLYzsp41MKMvAMKiWE\nWCCsWrXqgAEDKlWqxJQGaTExMQCQlsZzqTDoJqMqlapII0QIIYQQQi8tAiig2H326IlAARDA\nW5QytjWq9qoAgGmQCTxVdG6/rqi2zLCD5JGjy5U5xgydCr8F9PISYoGwQYMGDRo0cJ2emZkJ\nAMHBwR7WpZuMYg0hQgghhBDyzFTfxP5ISSj9cL3bqjOK9b+LnB45dLHtRRTdlnM5C5MA0ptS\nL7eGXjJCLBDyoijq8OHDANC6dWsPi9FNRlUq1a1btxISEh4/fgwAZcuWbdGiRZUqVYolUoQQ\nQgghVAJkfZPlq02ptuWW2QiPywljyyGdQvBtE4hRYgqEW7ZsuXbtWlRUVIcOHTwsRtcQnj17\n9ujRo+zpO3bs6Nix45gxYzjNUH/99VeKcjyQqVq1au3atX0cNyoMkUgkl8vtdp4e1ag4icVi\nAJBKpZ7f8oKKgVgsJklSoVD4OxAEACASifBYCAFBEAqFgrl9I3+hbxYymYz+oyQq3BVNOCrx\nFNcVUJc7U5rgqHajgOJslnd5325ZLBbzJ1Csd2YYphsgAlSjHa1Pg3sG5+zP8RgWKjS6oCHA\nGzenBMRRMi7gLVu2rFu3TqPRTJkyxXMOle5DmJOT079//7Zt24aFhaWnp+/Zs2fHjh379+/X\narWDBg1iL79s2TKr1TGacL9+/Zo2bVp0vwIVhNCun9JMLpf7OwTkUHIzWy8ZkiSxj7pAYN8Q\n4SjRN4vCXdG5xTbVhyo47TI3t/EpQRCOzYocL7VXfaSCU3kXfgPgee7fZ3y2ZYlEwt0yUyAU\ngaqLCgAgCuA+AIDIKFItUsEXBfnlqHAEeLPwXN0i9EyGzWb78ccfDxw4EBwcPH369IiICM/L\nd+vWLS4uTq1WM10Nw8LChg0bplarf/755+3bt/fu3Zudcn399dfMDnr11VezsnzWcgC9AKVS\naTQasYbQ76RSqUwmy8nJYR6XIH8Ri8VisdhoNPo7EARqtdpms9EdE5B/qVQqukEQ8i+ZTCaV\nSrOzs2027ssPBE4NavqPQmX85NFyyR0JAIDdZcWrrG2uyIIsAAB5WbnkIf/yAc8D6E59Npkt\nOyvb+y3TNYRms5mzZZlNJgUpAFBA6bP0AAC/QEC7AMdbFrdD1geY9fUlgiACAgKsVmtOjrBq\nX+nA3M4VcosLg8EwZ86cpKSk6OjoqVOnhoeHv/CmzGbzm2++abfbZ8+e7aFdaEoKvqDTnwID\nA/V6fYm7r7x8lEqlUqnMzMw0m83+jqW0k0qlUqmUbvuA/Cs0NNRiseh0On8HgkCr1WZkZAg5\nA1NKqFQqhUKh0+ksFou/Yykc5m0TKfsKl/Fjv6ZC967O0tMCAJqPNNIrua06iTxjk4Z2DnUM\nFUOAJdqi+0mn/FapOKpg3hvBBODllo1rjOTXpOIQd8uasRp6CBlKRKXu4VmdMx15SSQSBQcH\nm81meixM4SBJUqvVupsrcjfD7wwGw+eff56UlBQbGzt37lxvSoMAIJVKQ0NDIbdNKUIIIYQQ\nQoWSMj+FGdkl8KfA0E6hoZ1D3ZXZACBlXm6BkwLJXUlop1DlESVTZjM2drb+8HLL6jZq5UH+\nLfP/kB8dqxN2IribpwH8UWkg0AKhyWSaMWPGnTt32rZtO2XKlIL3K6MoKi0tzbWKyW630yV1\njUbj41gRQgghhFBpUAtS9qZQBOUc8DO3otousXPfW0gvvy+FEnFrsymgUr5I0c/Q51myiLbM\nqzxYgxy9QkQWkWIxjuBQqgm0D+GyZcuuXLnSvHnz8ePHex4Vh+O99957/Pjxhx9+2LZtW/b0\no0ePGo1GqVRauXJlXweLEEIIIYRKjMK2FOXgKZ55Xr7AbTJfeMsymYwkSddOzpk/uG24mPFb\nRqG+C73EhFhDeP78+SNHjmi12nxLg1u2bFm+fPnZs2eZKa1atQKAVatWJSUlM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khISGhnL/36+++oqrS48ePcrW2Bs3bnSe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\nlcsGWd26dVu/fv3x48ctFgtNi/S+qMep1eqdO3c+8cQTHTt2F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jxo3bvXs3hjWc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\nmwBwl0JDQxsaGux2u9aB6J3FYvHz82tsbPTCP2d6YzabzWZ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"/Users/gcohen/Documents/GitRepositories/stat-301-instructor/stat301book/_build/jupyter_execute/w02d2_abPeeking_17_0.jpg", - "image/png": "/Users/gcohen/Documents/GitRepositories/stat-301-instructor/stat301book/_build/jupyter_execute/w02d2_abPeeking_17_0.png", - "image/svg+xml": "/Users/gcohen/Documents/GitRepositories/stat-301-instructor/stat301book/_build/jupyter_execute/w02d2_abPeeking_17_0.svg" - }, - "image/jpeg": { - "height": 300, - "width": 600 - }, - "image/png": { - "height": 300, - "width": 600 - }, - "image/svg+xml": { - "height": 300, - "isolated": true, - "width": 600 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "options(repr.plot.width = 10, repr.plot.height = 5) # Adjust these numbers so the plot looks good in your desktop.\n", - "\n", - "set.seed(301)\n", - "answer2.1 <- \n", - " incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50)\n", - "\n", - "\n", - "crit_unadj <- qt(1 - 0.05, 1998)\n", - "crit_bonferroni <- qt(1 - 0.0025, 1998)\n", - "\n", - "sequential_stat <- \n", - " answer2.1 %>%\n", - " ggplot() +\n", - " geom_line(aes(x = inc_sample_size, y = statistic)) +\n", - " geom_point(aes(x = inc_sample_size, y = statistic,colour =\"#f94f21\"), size = 2) +\n", - " geom_hline(yintercept = crit_pocock, colour = \"#ff33ff\", linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = crit_pocock), colour = \"#ff33ff\") +\n", - " geom_text(x=850, y=crit_pocock + 0.15, size=6, label=\"Pocock\",colour = \"#ff33ff\") +\n", - " geom_hline(yintercept = crit_bonferroni, colour = \"blue\", linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = rep(crit_bonferroni, 20)), colour = \"blue\") +\n", - " geom_text(x=850, y=crit_bonferroni + 0.15, size=6, label=\"Bonferroni\",colour = \"blue\") +\n", - " geom_hline(yintercept = crit_unadj, linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = rep(crit_unadj, 20))) +\n", - " geom_text(x=850, y=crit_unadj + 0.15, size=6, label=\"Unadjusted\") +\n", - " theme(\n", - " text = element_text(size = 18),\n", - " plot.title = element_text(face = \"bold\"),\n", - " axis.title = element_text(face = \"bold\"),\n", - " legend.position=\"none\") +\n", - " ggtitle(\"Critical values in Sequential Designs\") +\n", - " ylab(\"Statistic\") +\n", - " xlab(\"Sample Size\") +\n", - " coord_cartesian(ylim = c(-1, 3)) +\n", - " scale_y_continuous(breaks = seq(-1, 3, by = 0.5))\n", - "sequential_stat " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Interpretations of Pocock correction\n", - "\n", - "In *one* A/A testing simulated experiment, using unadjusted quantities:\n", - "\n", - "- you would reject $H_0$: the observed (unadjusted) t-statistic is bigger than the (unadjusted) critical value\n", - "\n", - "Using Pocock's critical values:\n", - "\n", - "- you would fail to reject $H_0$: the observed (unadjusted) t-statistic is smaller than the (adjusted) critical value\n", - "\n", - "```{important}\n", - "The Bonferroni's adjusted critical value is larger than Pocock's critical values: the Bonferroni test is the *most conservative* test. Pocock method gives some control of the type I error rate!! Bonferroni gives a more conservative control of the type I error rate!! \n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-76077581b4638618", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## O'Brien-Fleming's boundaries\n", - "\n", - "Another method available in `gsDesign` is the **O’Brien-Fleming method**\n", - "\n", - "Unlike previous methods, the O’Brien-Fleming method uses *non-uniform* boundaries\n", - "\n", - "- the test has conservative critical values for earlier interim analyses and less conservative values (closer to the unadjusted critical values) as more data are collected \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "hide_input" - ] - }, - "outputs": [], - "source": [ - "design_of <- gsDesign(k = 20, #number of interim analysis planned\n", - " test.type = 1, # for one-sided tests\n", - " delta = 0, # default effect size\n", - " alpha = 0.05, #type I error rate\n", - " beta = 0.2, # type II error rate\n", - " sfu = 'OF')\n", - " \n", - "crit_of <- design_of$upper$bound" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "remove_input" - ] - }, - "outputs": [ - { - "data": { - "application/pdf": 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5Pn/9D13y76uguyXE2uIy5O5oIXXK+7/tX1Hy7+iAt2u6DJBTJXnMvv4uQu1KO3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qp0UAXPslr/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APC3/AO/YoAp0Vc/tN/8Anhb/APfsUf2m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\nQBN/Zt1/zwf8qP7Nuv8Ang/5VD50n/PRvzo86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Oez/wDfVAENFTfbJ/8Ans//AH1R9sn/AOez/wDfVAENFTfbJ/8Ans//AH1R9sn/AOez\n/wDfVAENFTfbJ/8Ans//AH1R9sn/AOez/wDfVAENFTfbJ/8Ans//AH1R9sn/AOez/wDfVAEN\nTXX+sX/rmn/oIo+2T/8APZ/++qmubuZZFxK4+RD1/wBkUAU6Km+2T/8APZ/++qPtk/8Az2f/\nAL6oAhoqb7ZP/wA9n/76o+2T/wDPZ/8AvqgCGipvtk//AD2f/vqj7ZP/AM9n/wC+qAIaKm+2\nT/8APZ/++qPtk/8Az2f/AL6oAhoqb7ZP/wA9n/76o+2T/wDPZ/8AvqgCGipvtk//AD2f/vqj\n7ZP/AM9n/wC+qAIaKm+2T/8APZ/++qPtk/8Az2f/AL6oAhq5Z/8AHpe/7g/nUP2yf/ns/wD3\n1Vu0uZWtbsmRiQowc9OaAM6ipvtk/wDz2f8A76o+2T/89n/76oAhoqb7ZP8A89n/AO+qPtk/\n/PZ/++qAIaKm+2T/APPZ/wDvqj7ZP/z2f/vqgCGipvtk/wDz2f8A76o+2T/89n/76oAhoqb7\nZP8A89n/AO+qPtk//PZ/++qAIaKm+2T/APPZ/wDvqj7ZP/z2f/vqgCGipvtk/wDz2f8A76o+\n2T/89n/76oAhoqb7ZP8A89n/AO+qPtk//PZ/++qAC7/17fQfyFQ1buruZZ2AlcDA7+1RfbJ/\n+ez/APfVAENFTfbJ/wDns/8A31R9sn/57P8A99UAQ0VN9sn/AOez/wDfVH2yf/ns/wD31QBD\nRU32yf8A57P/AN9UfbJ/+ez/APfVAENFTfbJ/wDns/8A31R9sn/57P8A99UAQ0VN9sn/AOez\n/wDfVH2yf/ns/wD31QBDRU32yf8A57P/AN9UfbJ/+ez/APfVAENFTfbJ/wDns/8A31R9sn/5\n7P8A99UATW//ACDbv/eT+ZqnWjBcynT7pjIxYFMHPTk1U+2T/wDPZ/8AvqgCGipvtk//AD2f\n/vqj7ZP/AM9n/wC+qAIaKm+2T/8APZ/++qPtk/8Az2f/AL6oAdp//H9b/wC+P5111ctY3Uz3\nkAaVyC4yCfeupoAK+X/22P2SPhn8d/BkWt+LL7TPA0miXaanf+KxYw+fJaxxurQSykqfLJdT\nyTyi4HNfUFfD/wDwV/1G6tP2VNOtI55bfTNR8U2FrqbxdrbZPJz6gSRxH6gUAV/HXwh/Yh+H\nPwi0n4ma34S0ODwhrCo2mXSJeNLelwWURw7t5JAJOVGAMnFev/sYax+z74j8G6rqXwE07StO\nsjOkWqRWto8F4kgBMaziQeYRjcVJJX72053V8TftC+GfAvxr/bC/Z0/Z/wDBM9trHw18M2KS\nz22n3xuYfKYtPPGZQxJJt7eP5t2f3vBBr1n9lbwn4e+Fv/BTX42+DfAdrDpng5PC9tczadZs\nTb292GszsHJxgzTnH8JZgMYwAD9CaKKKAPL/AI5/ELUfDem6d4Z8LbZfHPieVrLSkPItlxma\n7f8A2IkO73O0YPNYH7HOr6trXwRtJta1e91y/j1G9ga91Cd5pnCTsq5ZyT0HTPFch4ePxV0P\n4p+LPGWq/CW68Q6neyGx0yeLX7CKOy01GykUatISC5+dycZJHAxTv2H/ABJq7fDC7ttS8PNp\nWh215fXK61NfQtG7m4YvGYwdy7OcsflO3igD0X9pb4iav8O/hzE3h144vEet6lbaJpk0qhli\nnnYjeQeDhVcjPGcZBHFeb/FHwv4n/Zx8IQ/ELTPiD4p8UHSbm3OtaZrt4Li2vbeSRY5PKjIH\nksC4K7TwB377v7XlxDL8O/Bfiy2lS80nw/4s0zWbme3YSJ9nR3RmBGQRmReasftla7a3X7PW\npaZZTRXt/wCJZbOw0mGFwxu5JJ42Hl4+98oJyPb1oA92gnjuoI5omDxSKHRh0IIyDXN61/yE\n5v8AgP8A6CKz/GXwuTxv4M0vw/L4j8Q+HRZNE/23w1qT2NxIUjZNjSLyUO7JX1VT2pdF0keC\n9KtdES7u9aWzjEYv9amN1dzd8ySty55xk9gKAFoq5/aR/wCfa2/79Cj+0j/z7W3/AH6FAFOi\nrn9pH/n2tv8Av0KP7SP/AD7W3/foUAU6Kuf2kf8An2tv+/Qo/tI/8+1t/wB+hQBToq5/aR/5\n9rb/AL9Cj+0j/wA+1t/36FAFOirn9pH/AJ9rb/v0KP7SP/Ptbf8AfoUAU6Kuf2kf+fa2/wC/\nQo/tI/8APtbf9+hQBToq5/aR/wCfa2/79Cj+0j/z7W3/AH6FAFOppv8AU2/+6f8A0I1N/aR/\n59rb/v0Kll1AiKE/Z7c5UnBjHHJoAzqKuf2kf+fa2/79Cj+0j/z7W3/foUAU6Kuf2kf+fa2/\n79Cj+0j/AM+1t/36FAFOirn9pH/n2tv+/Qo/tI/8+1t/36FAFOirn9pH/n2tv+/Qo/tI/wDP\ntbf9+hQBzPjTw83irwzfaYkogknUbHPQMrBhn2yK89+Hfws13wt4gi1K4ns0RMxvEGZyyEck\nYAwemM17R/aR/wCfa2/79Cj+0j/z7W3/AH6FebXy+hiK8MTNPmjtr8zza+X0MRXhiZ35o7a/\nMwbHxFY6hql5p0UjC9tf9ZDIhU4/vDI5HuK0qsS3glVx9nt0dlK+YkQDAexrjYr3xJ4JTb5K\n+KdIXu8a/boh9ekv863nVlRV6iutdUtu11dv5r8DedWVFXqK611S27XV2/mvwOqoqay1xb60\nguEtIUSZA6rJBtYAjPIPQ1N/aR/59rb/AL9CupNNXR1Jpq6KdXNL/wBfJ/1yf+VH9pH/AJ9r\nb/v0Ks6ffGSVx5EC4jY5WMDtTGZVFXP7SP8Az7W3/foUf2kf+fa2/wC/QoAp0Vc/tI/8+1t/\n36FH9pH/AJ9rb/v0KAKdFXP7SP8Az7W3/foUf2kf+fa2/wC/QoAp0Vc/tI/8+1t/36FH9pH/\nAJ9rb/v0KAKdFXP7SP8Az7W3/foUf2kf+fa2/wC/QoAp0Vc/tI/8+1t/36FH9pH/AJ9rb/v0\nKAKdFXP7SP8Az7W3/foUf2kf+fa2/wC/QoAp0Vc/tI/8+1t/36FH9pH/AJ9rb/v0KAIbj7sP\n/XMfzNQ1ozagVEX+j25ymeYxxyai/tI/8+1t/wB+hQBToq5/aR/59rb/AL9Cj+0j/wA+1t/3\n6FAFOirn9pH/AJ9rb/v0KP7SP/Ptbf8AfoUAU6Kuf2kf+fa2/wC/Qo/tI/8APtbf9+hQBToq\n5/aR/wCfa2/79Cj+0j/z7W3/AH6FAFOirn9pH/n2tv8Av0KP7SP/AD7W3/foUAU6Kuf2kf8A\nn2tv+/Qo/tI/8+1t/wB+hQBToq5/aR/59rb/AL9Cj+0j/wA+1t/36FABp3S7/wCuDfzFU61b\nK+MguP3EC7Ymb5YwM8jg+1Vv7SP/AD7W3/foUAU6Kuf2kf8An2tv+/Qo/tI/8+1t/wB+hQBT\noq5/aR/59rb/AL9Cj+0j/wA+1t/36FAFOirn9pH/AJ9rb/v0KP7SP/Ptbf8AfoUAU6Kuf2kf\n+fa2/wC/Qo/tI/8APtbf9+hQBToq5/aR/wCfa2/79Cj+0j/z7W3/AH6FAFOirn9pH/n2tv8A\nv0KP7SP/AD7W3/foUAU6Kuf2kf8An2tv+/Qo/tI/8+1t/wB+hQBTqa6/1i/9c0/9BFTf2kf+\nfa2/79CpbjUCrqPs9ufkU8xj+6KAM6irn9pH/n2tv+/Qo/tI/wDPtbf9+hQBToq5/aR/59rb\n/v0KP7SP/Ptbf9+hQBToq5/aR/59rb/v0KP7SP8Az7W3/foUAU6Kuf2kf+fa2/79Cj+0j/z7\nW3/foUAU6Kuf2kf+fa2/79Cj+0j/AM+1t/36FAFOirn9pH/n2tv+/Qo/tI/8+1t/36FAFOir\nn9pH/n2tv+/Qo/tI/wDPtbf9+hQBTq5Z/wDHpe/7g/nR/aR/59rb/v0Ks2t8XtrpvIgG1QcC\nMAHnvQBlUVc/tI/8+1t/36FH9pH/AJ9rb/v0KAKdFXP7SP8Az7W3/foUf2kf+fa2/wC/QoAp\n0Vc/tI/8+1t/36FH9pH/AJ9rb/v0KAKdFXP7SP8Az7W3/foUf2kf+fa2/wC/QoAp0Vc/tI/8\n+1t/36FH9pH/AJ9rb/v0KAKdFXP7SP8Az7W3/foUf2kf+fa2/wC/QoAp0Vc/tI/8+1t/36FH\n9pH/AJ9rb/v0KAKdFXP7SP8Az7W3/foUf2kf+fa2/wC/QoAhu/8AXt9B/IVDWhcagVmI+z25\n6cmMelR/2kf+fa2/79CgCnRVz+0j/wA+1t/36FH9pH/n2tv+/QoAp0Vc/tI/8+1t/wB+hR/a\nR/59rb/v0KAKdFXP7SP/AD7W3/foUf2kf+fa2/79CgCnRVz+0j/z7W3/AH6FH9pH/n2tv+/Q\noAp0Vc/tI/8APtbf9+hR/aR/59rb/v0KAKdFXP7SP/Ptbf8AfoUf2kf+fa2/79CgCnRVz+0j\n/wA+1t/36FH9pH/n2tv+/QoALf8A5Bt3/vJ/M1TrVhvi1jcv5EA2lflEYweT1qt/aR/59rb/\nAL9CgCnRVz+0j/z7W3/foUf2kf8An2tv+/QoAp0Vc/tI/wDPtbf9+hR/aR/59rb/AL9CgCPT\n/wDj+t/98fzrrq5uzvzJdwr5EC5cDKxgEc10lAFLW9asPDejX+r6reQ6fpdhbyXd3eXLhIoI\nUUs8jseAqqCST0Arxf45+JPgb8Y/2fJj468X6E/w08SyfY7fW/7SjihkuEdypgnzjzEeCQjG\neY2BBGRXq3xA8F2PxI8BeJPCOqSTxaZr+mXOlXUlqwWVYp4micoSCAwVzgkEZxwa+O/2m/2e\n/gR8G/2RvAHww8f634us/AGmeJlXS7vTnilv5L2YXsoWVvJK7MTT9EGNqc9cgHM/sr+Bf2Pv\n2T/FWo+JtA+NWi69r91A1pFeazrto32WFiCyxrGFAZtoBY5OBgYBIPp/7EngD4DfDvxb46Pw\n1+Iuk+PvGPiO4l1XUHtb6KaW3tBMSsaRoSVjRplBYklmYE/wgcD/AMOXfgZ/0H/HX/gytP8A\n5FrR/ZC/Z/8AgR+zx+1l448I+AtZ8X3vxD0nw+I9Rt9clgks/sk72s26No4kJcEwA56bjweD\nQB9z0UUUAFcrofwv8MeHPBl74T07TPs/h+9Fws9n9olbeJ93m/OzFhu3N0IxnjFdVRQBgaL4\nD0Dw/wCDYPCdlpkQ8Ow25tF0+ctPGYTnKN5hYsDk/eJrkvB/7Nnw18BeIItb0PwpbWmpw5MM\n8kss3kE9TEsjssfU/dA616ZRQAVy+tf8hOb/AID/AOgiuorm9XkjXUJQ0W48c7iOwoAzaKm8\n2H/nh/4+aPNh/wCeH/j5oAhoqbzYf+eH/j5o82H/AJ4f+PmgCGipvNh/54f+PmjzYf8Anh/4\n+aAIaKm82H/nh/4+aPNh/wCeH/j5oAhoqbzYf+eH/j5o82H/AJ4f+PmgCGipvNh/54f+Pmjz\nYf8Anh/4+aAIaKm82H/nh/4+aPNh/wCeH/j5oAhqab/U2/8Aun/0I0ebD/zw/wDHzU00kXlQ\nfucjacfMePmNAFOipvNh/wCeH/j5o82H/nh/4+aAIaKm82H/AJ4f+PmjzYf+eH/j5oAhoqbz\nYf8Anh/4+aPNh/54f+PmgCGipvNh/wCeH/j5o82H/nh/4+aAIaKm82H/AJ4f+PmjzYf+eH/j\n5oAhoqbzYf8Anh/4+aPNh/54f+PmgDG1fw7a61c2VxK88NxaPvilglKEcjcpxwQcAGs7WvFd\nx4c1Bzf6bKdGIG3ULY+Z5Zxz5iAZUZ7jNdV5sP8Azw/8fNBkhIwYMj/eNc06Td3SfLJ9bb+v\n9J+ZzzpN3dN8sn1tv6/0n5lGw1C21S1S5s547m3cZWSJgwNaml/6+T/rk/8AKsH+w7XS7TUT\nodnbadfXXzbyCY94HBKgj9P1rkfEnxkn8Cva2d1o8Fzq7wlrkQ3DLCoJIG0kEnIGfbPesKuM\nhhIc+K081dr/ADv5fnY56uLjhIc2K081dr/O/l/kegUVQ8LeJrPxVoVrqdvbNGkwOY2c5VgS\nCPfkda1fNh/54f8Aj5rthONSKnB3T1R2wnGpFTg7p6ohoqbzYf8Anh/4+aPNh/54f+PmrLIa\nKm82H/nh/wCPmjzYf+eH/j5oAhoqbzYf+eH/AI+aPNh/54f+PmgCGipvNh/54f8Aj5o82H/n\nh/4+aAIaKm82H/nh/wCPmjzYf+eH/j5oAhoqbzYf+eH/AI+aPNh/54f+PmgCGipvNh/54f8A\nj5o82H/nh/4+aAC4+7D/ANcx/M1DVyeSILFmHPyD+I+pqHzYf+eH/j5oAhoqbzYf+eH/AI+a\nPNh/54f+PmgCGipvNh/54f8Aj5o82H/nh/4+aAIaKm82H/nh/wCPmjzYf+eH/j5oAhoqbzYf\n+eH/AI+aPNh/54f+PmgCGipvNh/54f8Aj5o82H/nh/4+aAIaKm82H/nh/wCPmjzYf+eH/j5o\nAhoqbzYf+eH/AI+aPNh/54f+PmgCbTul3/1wb+YqnWjYSRkXOIsfuWz8x55FVPNh/wCeH/j5\noAhoqbzYf+eH/j5o82H/AJ4f+PmgCGipvNh/54f+PmjzYf8Anh/4+aAIaKm82H/nh/4+aPNh\n/wCeH/j5oAhoqbzYf+eH/j5o82H/AJ4f+PmgCGipvNh/54f+PmjzYf8Anh/4+aAIaKm82H/n\nh/4+aPNh/wCeH/j5oAhoqbzYf+eH/j5o82H/AJ4f+PmgCGprr/WL/wBc0/8AQRR5sP8Azw/8\nfNTXMkQkXMOfkT+I/wB0UAU6Km82H/nh/wCPmjzYf+eH/j5oAhoqbzYf+eH/AI+aPNh/54f+\nPmgCGipvNh/54f8Aj5o82H/nh/4+aAIaKm82H/nh/wCPmjzYf+eH/j5oAhoqbzYf+eH/AI+a\nPNh/54f+PmgCGipvNh/54f8Aj5o82H/nh/4+aAIaKm82H/nh/wCPmjzYf+eH/j5oAhq5Z/8A\nHpe/7g/nUPmw/wDPD/x81btJIza3eIsAIMjceeaAM6ipvNh/54f+PmjzYf8Anh/4+aAIaKm8\n2H/nh/4+aPNh/wCeH/j5oAhoqbzYf+eH/j5o82H/AJ4f+PmgCGipvNh/54f+PmjzYf8Anh/4\n+aAIaKm82H/nh/4+aPNh/wCeH/j5oAhoqbzYf+eH/j5o82H/AJ4f+PmgCGipvNh/54f+Pmjz\nYf8Anh/4+aAIaKm82H/nh/4+aPNh/wCeH/j5oALv/Xt9B/IVDVu6kiE7ZhzwP4j6VF5sP/PD\n/wAfNAENFTebD/zw/wDHzR5sP/PD/wAfNAENFTebD/zw/wDHzR5sP/PD/wAfNAENFTebD/zw\n/wDHzR5sP/PD/wAfNAENFTebD/zw/wDHzR5sP/PD/wAfNAENFTebD/zw/wDHzR5sP/PD/wAf\nNAENFTebD/zw/wDHzR5sP/PD/wAfNAENFTebD/zw/wDHzR5sP/PD/wAfNAE1v/yDbv8A3k/m\nap1owSR/2fdERYGUyNx55NVPNh/54f8Aj5oAhoqbzYf+eH/j5o82H/nh/wCPmgCGipvNh/54\nf+PmjzYf+eH/AI+aAHaf/wAf1v8A74/nXXVy1jJEbyACHB3jncfWupoAK/Ln/goz+wX4/wDF\nXiJ/HXgfxB4k8Zrrmuxef4RWJ5Y9MLRSf6VGQ+BGpTbjYCPN+8c1+o1fNv8AwUA/aI139mz9\nn2bXvCsSP4q1fUoND0qSSISrDPKsj+ZsPDEJE+0HI3FcgjIIB8bf8Op/2gv+i8xf+DDUK91/\nYM/YrtP2Yviv4w1bxF8StL8Z/Ee801ba40uymzNaWskkcjSyh2MrF2SLDFVAA6tu4+RrrRP2\n5Z/jNb/De4+I2qweNb3QD4mWwXXkSNbbzmiK7k/dq+9SNoOPevfP+CY3wy8U23xz+KXjH4n+\nML2++K9laJoOreHNVaSS9toneOSOeSViVkRlgQRmMsu0nkZGQD9I6KKKAPGdf+Pus3fjbWvD\nHgDwHdeObvQyseqXbajFp9rbysMiJZJAd7gdQBxXR/B34x2vxZs9YifSrvw9r+iXf2LVdHvS\nrSW0uMjDrw6nBwwxnB46E6HxHg8a2uiB/h1b+HP7akn3XH/CQecsLR7WyR5IyX3bOvGM89K8\nv/ZVkS21v4i2Oux3UXxMGoxXPiQXBTyn3xk27W+zjydmcA/MM89RQB6N8cfil/wpn4aan4rG\nl/2zJaSQRpY/aPI81pJkj+/tbGN+funOMcZzXM6d8Svi/c6haxXfwXt7O0klVJrkeLreQxIS\nAz7RFlsDJx3xV79pX4b678Vvhi+geHXsU1Br62uf+JjIyQlI5A5BKqx7DtXBfEnxX8YPgRoS\n+Odb8Q6H4x8O208Q1fR7bSjZvbxSSKm63k8xi5DMB8/1I64APpKuX1r/AJCc3/Af/QRVT4h3\n3jafwxZ3Pw4h8P3eqyzI7jxHLPHb/ZijElTEpbfu8vAIxgt3xUWhtqcmkWjeLktYfEZjH21N\nGZntA/8A0zMgDEYx1HXNADqKuYsP71z+S0YsP71z+S0AU6KuYsP71z+S0YsP71z+S0AU6KuY\nsP71z+S0YsP71z+S0AU6KuYsP71z+S0YsP71z+S0AU6KuYsP71z+S0YsP71z+S0AU6KuYsP7\n1z+S0YsP71z+S0AU6KuYsP71z+S0YsP71z+S0AU6mm/1Nv8A7p/9CNTYsP71z+S1LKLHyoct\ncY2nGAvqaAM6irmLD+9c/ktGLD+9c/ktAFOirmLD+9c/ktGLD+9c/ktAFOirmLD+9c/ktGLD\n+9c/ktAFOirmLD+9c/ktGLD+9c/ktAFOirmLD+9c/ktGLD+9c/ktAFOirmLD+9c/ktGLD+9c\n/ktAFOirmLD+9c/ktGLD+9c/ktAFOuZ8T/CvSfHt8lxdyXFrcRREeZbkDeoyQCCD6nmuyxYf\n3rn8lq1YCz81/LM+fLbO4DpjmsK1CliIezqxujCtQp4iHs6sbo8/0n4eWfh65tpdJvb6xjjI\n823E2+Kf13K2cE+oxWvb3+pNrk9rNpoSwC74b5JgQ3TKsvUHJP4Ct/Fh/eufyWjFh/eufyWo\nhh4Uko0vdV9lt/wPlYmGHhSSjS91X2W39eljF0zXNP1pXNjeQ3WwlXETglT7jqKvVma14F8M\n61P9paK6s74cre2REUwPruHX8c0Olx4c8PhYDeeJb2N/+WrRwO0efXkEgfmaUalWF/ax0XVa\n/hvf0uTGpVhf2sdF1Wv4b39LmnRUZ1vRbeOxF7PLp9xdpuS3uGRWzgEr1wSMjpWhjT/71x+S\n10xlGWzOmMoy2ZToq5iw/vXP5LRiw/vXP5LVFFOirmLD+9c/ktGLD+9c/ktAFOirmLD+9c/k\ntGLD+9c/ktAFOirmLD+9c/ktGLD+9c/ktAFOirmLD+9c/ktGLD+9c/ktAENx92H/AK5j+ZqG\ntGYWOItzXH3OMBemTUWLD+9c/ktAFOirmLD+9c/ktGLD+9c/ktAFOirmLD+9c/ktGLD+9c/k\ntAFOirmLD+9c/ktGLD+9c/ktAFOirmLD+9c/ktGLD+9c/ktAFOirmLD+9c/ktGLD+9c/ktAF\nOirmLD+9c/ktGLD+9c/ktAFOirmLD+9c/ktGLD+9c/ktABp3S7/64N/MVTrVshZ4uNhn/wBU\n27cB046e9VsWH965/JaAKdFXMWH965/JaMWH965/JaAKdFXMWH965/JaMWH965/JaAKdFXMW\nH965/JaMWH965/JaAKdFXMWH965/JaMWH965/JaAKdFXMWH965/JaMWH965/JaAKdFXMWH96\n5/JaMWH965/JaAKdFXMWH965/JaMWH965/JaAKdTXX+sX/rmn/oIqbFh/eufyWpbgWO9dzXG\ndi9AvTaMUAZ1FXMWH965/JaMWH965/JaAKdFXMWH965/JaMWH965/JaAKdFXMWH965/JaMWH\n965/JaAKdFXMWH965/JaMWH965/JaAKdFXMWH965/JaMWH965/JaAKdFXMWH965/JaMWH965\n/JaAKdFXMWH965/JaMWH965/JaAKdXLP/j0vf9wfzoxYf3rn8lq1aiz+zXW0z7do3ZAzjPag\nDJoq5iw/vXP5LRiw/vXP5LQBToq5iw/vXP5LRiw/vXP5LQBToq5iw/vXP5LRiw/vXP5LQBTo\nq5iw/vXP5LRiw/vXP5LQBToq5iw/vXP5LRiw/vXP5LQBToq5iw/vXP5LRiw/vXP5LQBToq5i\nw/vXP5LRiw/vXP5LQBToq5iw/vXP5LRiw/vXP5LQBDd/69voP5Coa0bgWPnHc1xnjoF9KixY\nf3rn8loAp0VcxYf3rn8loxYf3rn8loAp0VcxYf3rn8loxYf3rn8loAp0VcxYf3rn8loxYf3r\nn8loAp0VcxYf3rn8loxYf3rn8loAp0VcxYf3rn8loxYf3rn8loAp0VcxYf3rn8loxYf3rn8l\noAp0VcxYf3rn8loxYf3rn8loALf/AJBt3/vJ/M1TrWhFn9hudpn8vK7sgZ74xVXFh/eufyWg\nCnRVzFh/eufyWjFh/eufyWgCnRVzFh/eufyWjFh/eufyWgCPT/8Aj+t/98fzrrq5uzFl9rh2\nNPv3jG4DGc10lABXzv8At4/s6ar+01+z9e+GvD1zHbeJ9OvYdZ0gzSeWj3EQZdhf+EskkgDd\nAxXOBk19EV+UP/BUD9pT4w6Pqsfg6fwPqPhLwnp3iOG90nxfYzzxrqqpDKBAZE+T5vMZimd3\n7sHFAHQfHTwX+1B8P5vgz8etA0W78SfEvTPC7eHPFWmx2SXkuRNLIC8EH+sRvNOWi6FFOec1\n2f8AwT28HfG3x1+0P8QPjp8XtBufC8msaNHo0Fjd2LWLTESQspSB/nVI0gA3P94ycE4OPL/+\nHvnxc/6IZF+d3/8AG67/AP4J5eNPiz8dv2qfid8WvGHh2+8NeGNT0WKyS0aKaKzFyrW6xCES\n/fYRQyFmHd+cbgKAP0cooooA8KuvhZ8S/APjnxLrXw51zQLzSfEVz9uutH8VLcbbW4I+d4ZI\ncnDddpAA4HbNXvhr8HfFHg2Lx74o1PXLDU/iR4qjDG5ihaOxtXiiZLeNQQWKKSMkjJAHBIyf\nZ6KAPLvEfw/8a+NvhFp+k33i06B47tzFcvrGhs627XCMTtZPkLxMDgqRjocHGK4jxR8I/i18\nZNOtfDXxC1zwrp/hDz4ptQTw1Hcm61FY2DCNjLhY1JUE7c4I79K+iKKAGxxrDGsaKERQFVVG\nAAOgrmda/wCQnN/wH/0EV1Fc3q8SNqEpMyqeOCD6D2oAzaKm8mP/AJ+E/wC+W/wo8mP/AJ+E\n/wC+W/woAhoqbyY/+fhP++W/wo8mP/n4T/vlv8KAIaKm8mP/AJ+E/wC+W/wo8mP/AJ+E/wC+\nW/woAhoqbyY/+fhP++W/wo8mP/n4T/vlv8KAIaKm8mP/AJ+E/wC+W/wo8mP/AJ+E/wC+W/wo\nAhoqbyY/+fhP++W/wo8mP/n4T/vlv8KAIaKm8mP/AJ+E/wC+W/wo8mP/AJ+E/wC+W/woAhqa\nb/U2/wDun/0I0eTH/wA/Cf8AfLf4VNNDGYoP36D5T2bn5j7UAU6Km8mP/n4T/vlv8KPJj/5+\nE/75b/CgCGipvJj/AOfhP++W/wAKPJj/AOfhP++W/wAKAIaKm8mP/n4T/vlv8KPJj/5+E/75\nb/CgCGipvJj/AOfhP++W/wAKPJj/AOfhP++W/wAKAIaKm8mP/n4T/vlv8KPJj/5+E/75b/Cg\nCGipvJj/AOfhP++W/wAKPJj/AOfhP++W/wAKAIaKm8mP/n4T/vlv8KPJj/5+E/75b/CgCGrm\nl/6+T/rk/wDKofJj/wCfhP8Avlv8Kt6bEizSYmVv3bdAfT6UAZ1FTeTH/wA/Cf8AfLf4UeTH\n/wA/Cf8AfLf4UAQ0VN5Mf/Pwn/fLf4UeTH/z8J/3y3+FAGbqmjWOt25gv7SG7i7LKgOPceh9\nxWBb+FZvCrSXWmaleNYQxO50qZvNRiFO1UY/MnOPWux8mP8A5+E/75b/AAo8mP8A5+E/75b/\nAArmqYenUlzte8uvX/hvLY56mHp1Jc7XvLr1/wCG8tjwDwZ8VfEWo+MbKG6uBc2t1MI3txGo\nVFJ6ggZG3r16DmvadJ8Qabrse/T76C7A6iKQEj6jqPxqW08LaNYXz3ttbWUF2+d00cGGOevO\nO9UtX+H3h/WpPNnggjuc5FxbhopAfXcoBP415mDw2NwlNqdRVHfrf8Hr91jy8HhsZhKbUqiq\nO/W/4PX8jYorKk0a/wBJ0JLXSdSju7uNsiXVmkfeuSSCyjPTgH2pNV8QJ4es7GXUIZ5GlX9+\n9jA8scDADO44ztyTg47V6jrKCvUVrK77feeo6ygr1Fayu+33mtRVHR/EWia+oOn6xa3TYzsU\nkOPqpGR+IrU8mP8A5+E/75b/AArWE41FzQd15GsJxqLmg7ryIaKm8mP/AJ+E/wC+W/wo8mP/\nAJ+E/wC+W/wqyyGipvJj/wCfhP8Avlv8KPJj/wCfhP8Avlv8KAC4+7D/ANcx/M1DVyeGMiL9\n+g+Qdm9T7VD5Mf8Az8J/3y3+FAENFTeTH/z8J/3y3+FHkx/8/Cf98t/hQBDRU3kx/wDPwn/f\nLf4UeTH/AM/Cf98t/hQBDRU3kx/8/Cf98t/hR5Mf/Pwn/fLf4UAQ0VN5Mf8Az8J/3y3+FHkx\n/wDPwn/fLf4UAQ0VN5Mf/Pwn/fLf4UeTH/z8J/3y3+FAENFTeTH/AM/Cf98t/hR5Mf8Az8J/\n3y3+FAENFTeTH/z8J/3y3+FHkx/8/Cf98t/hQBNp3S7/AOuDfzFU60bCJALnEytmFhwDxyOe\nlVPJj/5+E/75b/CgCGipvJj/AOfhP++W/wAKPJj/AOfhP++W/wAKAIaKm8mP/n4T/vlv8KPJ\nj/5+E/75b/CgCGipvJj/AOfhP++W/wAKPJj/AOfhP++W/wAKAIaKm8mP/n4T/vlv8KPJj/5+\nE/75b/CgCGipvJj/AOfhP++W/wAKPJj/AOfhP++W/wAKAIaKm8mP/n4T/vlv8KPJj/5+E/75\nb/CgCGipvJj/AOfhP++W/wAKPJj/AOfhP++W/wAKAIamuv8AWL/1zT/0EUeTH/z8J/3y3+FT\nXMMZkXM6D5E7N/dHtQBToqbyY/8An4T/AL5b/CjyY/8An4T/AL5b/CgCGipvJj/5+E/75b/C\njyY/+fhP++W/woAhoqbyY/8An4T/AL5b/CjyY/8An4T/AL5b/CgCGipvJj/5+E/75b/CjyY/\n+fhP++W/woAhoqbyY/8An4T/AL5b/CjyY/8An4T/AL5b/CgCGipvJj/5+E/75b/CjyY/+fhP\n++W/woAhoqbyY/8An4T/AL5b/CjyY/8An4T/AL5b/CgCGrln/wAel7/uD+dQ+TH/AM/Cf98t\n/hVu0iQWt2BMpBUZODxz9KAM6ipvJj/5+E/75b/CjyY/+fhP++W/woAhoqbyY/8An4T/AL5b\n/CjyY/8An4T/AL5b/CgCGipvJj/5+E/75b/CjyY/+fhP++W/woAhoqbyY/8An4T/AL5b/Cjy\nY/8An4T/AL5b/CgCGipvJj/5+E/75b/CjyY/+fhP++W/woAhoqbyY/8An4T/AL5b/CjyY/8A\nn4T/AL5b/CgCGipvJj/5+E/75b/CjyY/+fhP++W/woAhoqbyY/8An4T/AL5b/CjyY/8An4T/\nAL5b/CgAu/8AXt9B/IVDVu6hjM7ZnQcDjDen0qLyY/8An4T/AL5b/CgCGipvJj/5+E/75b/C\njyY/+fhP++W/woAhoqbyY/8An4T/AL5b/CjyY/8An4T/AL5b/CgCGipvJj/5+E/75b/CjyY/\n+fhP++W/woAhoqbyY/8An4T/AL5b/CjyY/8An4T/AL5b/CgCGipvJj/5+E/75b/CjyY/+fhP\n++W/woAhoqbyY/8An4T/AL5b/CjyY/8An4T/AL5b/CgCGipvJj/5+E/75b/CjyY/+fhP++W/\nwoAmt/8AkG3f+8n8zVOtGCJBp90POUglOcHjk+1VPJj/AOfhP++W/wAKAIaKm8mP/n4T/vlv\n8KPJj/5+E/75b/CgCGipvJj/AOfhP++W/wAKPJj/AOfhP++W/wAKAHaf/wAf1v8A74/nXXVy\n1jEgvICJ0Y7xwA3PP0rqaACvjD46ftJfCb4pftR+D/2bfEfg1fGhXUlurzULm5aK3029W0le\nEIq8yyYfa3KqvmH7xBA+z6/Nv9hs+HLH9vL9qy+8Sy2Nv4nttYuZNMmvmVZI7I3dyZ2Qt0UI\nLXJHYjtQB3P7a3/BRzVfgD8UbX4ZfDnwhD4y8ZLDHcX32kSypBvXekKwxYd3MeHJyAqsvByd\nvo/7Cv7blp+194b1yHUNGXw14y8PyIuo6dHIXikjfcElj3AMBlGVlOSpA5O4V8//APBOcW/x\nu/a+/aI+NRQXdmb06bo924zmCWVypU9iIbaAE+j46E1L+yNNZXH/AAVT/aKk0Extoh0ycTm3\nx5Zuxc2Yk6cZ8wXGffdQB+j9FFFAHnvxA/aA+H/wu1OPTfEviWCw1F08z7HFDLcTKvZmSJWK\nj3IFdJ4K8d+H/iNoUes+GtWttY02RigntmyFYdVYHlWGRwQDyPWuG8TQ+HPgLca540sfCHiH\nxFq/iK8X7e2hWjX90SEYqSCw8uIBccHAJXiuO/ZDhttXf4h+MbWSzs4/Eetef/YNrJufTCik\nFZ1wNkrlizLjHTk9gD37U9Ts9F065v8AULqGysbaNpZrm4cJHGgGSzMeAAO9eeeEf2lfhp46\n8QQ6Joviu2utTnJEEMkM0AnI6iNpEVZD7KTXH/tkyNe+B/B3h1mYWPiTxdpmkXqqcb4HZ3ZS\nfrGtSftl6Hawfs76rqFnDHZ33h2W0v8ASpoUCm0kjnjUGPH3flJGB60Ae81y+tf8hOb/AID/\nAOgis3xp8WtO+HvgrSvEerafqt5BfNFGIdGsJLyVXeNnyUQEhQFI3dMkDvTtD1mHx5pFpr9j\nFPa2l9GJI4dRiNvOo6YeNuVPHQ0AOoq5/Zcv9+H/AL+Cj+y5f78P/fwUAU6Kuf2XL/fh/wC/\ngo/suX+/D/38FAFOirn9ly/34f8Av4KP7Ll/vw/9/BQBToq5/Zcv9+H/AL+Cj+y5f78P/fwU\nAU6Kuf2XL/fh/wC/go/suX+/D/38FAFOirn9ly/34f8Av4KP7Ll/vw/9/BQBToq5/Zcv9+H/\nAL+Cj+y5f78P/fwUAU6mm/1Nv/un/wBCNTf2XL/fh/7+CpZdNlaKEb4uFI/1g9TQBnUVc/su\nX+/D/wB/BR/Zcv8Afh/7+CgCnRVz+y5f78P/AH8FH9ly/wB+H/v4KAKdFXP7Ll/vw/8AfwUf\n2XL/AH4f+/goAp0Vc/suX+/D/wB/BR/Zcv8Afh/7+CgCnRVz+y5f78P/AH8FH9ly/wB+H/v4\nKAKdFXP7Ll/vw/8AfwUf2XL/AH4f+/goAp0Vc/suX+/D/wB/BR/Zcv8Afh/7+CgCnVzS/wDX\nyf8AXJ/5Uf2XL/fh/wC/gq1YafJFK5LxnMbDhwe1AGTRVz+y5f78P/fwUf2XL/fh/wC/goAp\n0Vc/suX+/D/38FH9ly/34f8Av4KAKdFXP7Ll/vw/9/BR/Zcv9+H/AL+CgCnRVz+y5f78P/fw\nUf2XL/fh/wC/goAp0Vc/suX+/D/38FH9ly/34f8Av4KAMKbw5pdxqMN+9hbm9ibclwIwHB+o\n6/jTbfSryDW57w6rNLZyr/x4yIpVG4AKtjIHB4963/7Ll/vw/wDfwUf2XL/fh/7+CsfZQvdK\n2t9NNfO2/wAzH2UL3StrfTTXztv8zybxz8Tdc8IRrDNosEE8zt5Fw0/mxOg6naMEHkcH1ra+\nG3j1/GWjXFxexR2txbSCORkOI2yOCMnj0xn+ddV4j8BWXiuyW11KKGaNW3Iwl2sh9QRUNh8N\n9L03Q5NIhtbY2EpzJE8u7zDxySTkngflXlRw+Ohi3U9rzU7aJ73+S79d7HlxoY6GLdT2nNTt\nonvf5Lv13sX6Ko3XgaRNGt9O0rUDoyW7AxNbyK2Bz8pDZyOc49hWfBovjbTJ41e70jWrXcA7\nufs8wGeTgZWvRlWnBpSg/Var/P8AA9CVacGlKD9Vqv8AP8DpLj7sP/XMfzNQ1DqGp/ZtestJ\na0ui00QK3aRbrfPzHaXHQ4Hf1qWKa0mvpbJNRsWvIjh7cXKeYpxnlc56GtVVg3a/W3zNVVg3\na/W3zFoq5/Zcv9+H/v4KP7Ll/vw/9/BWpqU6Kuf2XL/fh/7+Cj+y5f78P/fwUAU6Kuf2XL/f\nh/7+Cj+y5f78P/fwUAU6Kuf2XL/fh/7+Cj+y5f78P/fwUAU6Kuf2XL/fh/7+Cj+y5f78P/fw\nUAU6Kuf2XL/fh/7+Cj+y5f78P/fwUAU6Kuf2XL/fh/7+Cj+y5f78P/fwUAGndLv/AK4N/MVT\nrVstPkjFxl4zuiKjDg9xVb+y5f78P/fwUAU6Kuf2XL/fh/7+Cj+y5f78P/fwUAU6Kuf2XL/f\nh/7+Cj+y5f78P/fwUAU6Kuf2XL/fh/7+Cj+y5f78P/fwUAU6Kuf2XL/fh/7+Cj+y5f78P/fw\nUAU6Kuf2XL/fh/7+Cj+y5f78P/fwUAU6Kuf2XL/fh/7+Cj+y5f78P/fwUAU6Kuf2XL/fh/7+\nCj+y5f78P/fwUAU6muv9Yv8A1zT/ANBFTf2XL/fh/wC/gqW402R3Uh4vuKOZB/dFAGdRVz+y\n5f78P/fwUf2XL/fh/wC/goAp0Vc/suX+/D/38FH9ly/34f8Av4KAKdFXP7Ll/vw/9/BR/Zcv\n9+H/AL+CgCnRVz+y5f78P/fwUf2XL/fh/wC/goAp0Vc/suX+/D/38FH9ly/34f8Av4KAKdFX\nP7Ll/vw/9/BR/Zcv9+H/AL+CgCnRVz+y5f78P/fwUf2XL/fh/wC/goAp1cs/+PS9/wBwfzo/\nsuX+/D/38FWrXT5EtrpS8eWUAYcetAGTRVz+y5f78P8A38FH9ly/34f+/goAp0Vc/suX+/D/\nAN/BR/Zcv9+H/v4KAKdFcL+0lpskf7OvxTYvEQPCuqnhxn/j0lr4K/Zq/Yz+CvxS+APh/wAY\neMfGU+h61epdteRjXLS1WAR3M0atskQlfkRT8x756Gvu8m4ewmPy2pmWNxMqUY1I00o0vaNu\nUXK/xxstH3OWpVlGahFX0vvY/S+ivzj/AGRtOPw4/bn134ffC3xHJ4t+GP2B5r26fUo7qDZ9\nlhf7QrRFImkW5KQBlUkK7L6sPof4k/tkz6R8Z3+F3w28A3nxT8U2eRqf2DUEtrazbKDBm2SL\n8rPtkZ9ixthSxYkL05lwVjMNj44LAS9spUo1uZ2p8sJf8/OeVoNdVKXVdxQxMXDmlprbv93c\n+laK+cPhV+19d+JfjQ3wt+Ifw/vvhf4suUL6ZHe3oure+2+ZuCyiNFwfLbY670cq4DBgobjf\nFX7fmqaH8bvEnwu0z4T33iTxHY3j2Omx6bqoY38iup3OPI/cp5PmSFsvt2AHClnXgp8GZ5Vx\nEsNCgnKMFUfv0+XkbtzqfNyuN92m0tb2syniKSXNfy2e59g1yviv4s+B/Aeox2Hibxl4f8O3\n8kQnS11bVILWV4ySocLI4JUlWGemVPpXiPwy/a+1/wASftBWXwj8c/Cu++H3iC/t5prSWbVE\nvI5TGjyZDCJFaMrDMBIjOCybcdSvyXd2/jD4hft7+JbvXvgna+J/E1vYxTzeA7/xFZvbwAWd\nvGsj3DqYZRtdX2AcNIO6GveyfgbEV8TXp5pJUo06PtlapSfOm7RtJz5eVu9535Y2Sk05RvlU\nxSSThrd22f8AkfqZRXzb8UP2xJtB+Mq/DD4b+BLv4reKrZT/AGoun34trewbcihWmMbr8pcC\nRmKpGWVSxbcqTfB79rm98afGmb4V+PPh7qHwz8XyxSS6bBd3Ru4b4RiQy7ZREi7dsTski7o3\nCOA4IUP80+E85WEeNdH3FD2luaHPyfz+z5vacvW/La2u2pt7enzct/L5+ux9GUV8X+Pf+Che\nueFPjR4g+G2k/B+98Va1pt9NZ28emas8k14IwW3rClqzD92C5ALYAPOBmj4mf8FC9c+G3jfR\nfCk3wfvb3W9U0zTr2LT11Z0uhPdQI/2XyfspYyJI5ix1JX7oJ2j06fAPEdV04ww6vUjzx/eU\ntY6e98ei1W9vwZDxVFX1202Z9rXf+vb6D+QqGvmHx5+3EfAfw48G6lrPw81a2+JXix5Esfh5\n5zC9iCXRgVpi0QkQSBcx/uSXY7VBAZhk+IP23fFvwt1fw4/xW+CWreA/C2sMinXo9VXUEtt6\nsUDpHCAH+UloiwlCqxCMRtPJS4Lz2srwoLVySXPTvJwbUuSLneaTTV4KSfRsp4mkt3+D69+x\n9aUV81ftQfto2v7MnjbwppV54WPiPRdbtFvW1Ww1NVeOLzNr+XF5ZWQhcMMyKGyBkda4zxf+\n3j44+Flr4d1X4gfALWfCvhzV5I1GotrCzPErEkqY/IXbNtV2EMjRsQp6DJDwnBee46hSxFCg\nnGrdwvUppyte6jFzUm1Z3SV121QSxNKLab28mfU+tfEXwp4b1+w0LVvE+jaXreoFBZ6be6hF\nDc3Jd9ieXGzBn3MCowDkjA5roa+Q/wBp/wAY+BvD/wC138H9A8Q/DiHxN4mvJtOOm+JF1ya1\nOnGS/ZIyYUUrMEdfMw5wckcAkn7I/suX+/D/AN/BXl5nlX1DCYPEcsl7aDl73LZ2dvd5ZN2/\nxKLv0tqXCfPKS7FOirn9ly/34f8Av4KP7Ll/vw/9/BXzhsU6Kuf2XL/fh/7+Cj+y5f78P/fw\nUAU6Kuf2XL/fh/7+Cj+y5f78P/fwUAU6Kuf2XL/fh/7+Cj+y5f78P/fwUAU6Kuf2XL/fh/7+\nCj+y5f78P/fwUAFv/wAg27/3k/map1rQ6fItjcoXjyxXBDjHBNVf7Ll/vw/9/BQBToq5/Zcv\n9+H/AL+Cj+y5f78P/fwUAU6Kuf2XL/fh/wC/go/suX+/D/38FAEen/8AH9b/AO+P5111c3Z6\ndJHdwsXiIDg8OCetdJQAV8L/ALXf/BLLw/8AtL/E2fx3ovi+TwTrV+qLqkTacLyC6dFCCVVE\nkZR9qqDyQ20HAJJP3RRQB89/Cr9kLSPgR+zD4j+FfgvU7mPUtXsL1ZvEErmCeW+ngMYuMx8x\nhcIFC5KhByTknn/2EP2JbX9kDwjq76hqsXiDxpr7xvqWoQIVhjRN2yGLd8xUF2YsQCxI4G0V\n9SUUAFFFFAHhV18VPHvwy8c+JbLxV4P1/wAYeG7m5+0aFqXhXTlumhhI/wCPeaNSrAqeN5zn\nntWb8G9N8T2HiT4ufFTVPC19o0WvJBLp3hllH22ZbWBgGdFztkkOAF65LdeCfoeigDw34t+H\nde+OnwM0fVtJ0a40TxZY3dt4g0/SNVHlSx3ELkiKTcBtYqWAzjkjOBzXJfE3xL4p/aQ8J2/w\n9074feKPCq6pc2/9uanr1oLe2s7eORZJBDJu/fMSgC7RyOuMnH0/RQBHBCltDHDEoSONQiqO\ngAGAK5vWv+QnN/wH/wBBFdRXkkWgfEWP4ra1fanrel3vgmWMix0y2050uon+TaXm3EMAA+QA\nOo9K6KVGNVTbmo8qvrfXyVk9fWy8xN26HS0V4pq3gb9oebVb2TTvHXhS2095na2hm8PSu8cR\nY7FZvNGSBgE9663wj4Y+Ktn4N1628R+INH1HxNMjjS76y0qSG3tmMeEMsZclwH5OCMjivUrZ\nbSpU1UWLpybtoue+vrBLTrr6XM1Nt25X+H+Z31FcDa+GPiqnwwurGfxBo8njxpMwaumlSCyR\nPMU4aDfknYHGd3Ug9q4j/hAv2lP+igeEP/Cbm/8AjtVRyylV574unHlbWvPr5q0Ho+l7PyBz\na+y/w/zPdahW9t3u3tVnia5RQ7QhxvC+pXriuI8ceGPinf6LoEXhXX9I0rVIUA1We/0p547l\ntq5MShhsG4Oec8Eelflp4C/Z/wD2h7b9pW0kh0PxBYeMIdV8+58R3drK1oPny8zzY2yRMM8A\nneDtAOcV9Xw3wlhc+oYitWzCnRdKLaTvd7735bR03XM9VdX0MK2IlSaSg3c/YiivKPAvg744\n2HiqxuPFni/w5q3h9N/2qz0/Q5beeTKME2uZCBhypPHIBHeum0Xw78RoPiNql5qms6ZdeCpI\n2FlpcGmul3C/yYLzFiGHD8bR1HpXxtfAU6MpRjiYStG91zWbvblV4L3uutlbrfQ6FJvozsaK\n8V1fwN+0PNq17Jp3jnwpbae87tbQzeHpXeOIsdis3mjJAwCe9dX4R8MfFWz8Ha9beI/EGj6l\n4lmRxpV9ZaVJDb2zGPCGWMuS4D4JwRkcVrWy2lSpqosXTk3bRc99fWCWnXX0uJTbduV/18zv\n6bLKkMbSSOscajczMcAD1Jrg7Twx8VU+GN3Y3HiDR5PHbSZg1ZNKkFmieYpw0G/JOwOM7upB\n7V8nftnfCL9pHxD8IxDd6vaeMdKjvEkvNK8L6PLb3LoFbDsu5jKinGVA64bB25Hq5NkFDNcb\nHCVMdTppz5bvm1X80bxSaey5pRbe9iKlVwjzKLZ9129zFdwJNBKk0LjKyRsGVh6gjrUlfnv+\nwf8ACH9oPw94E1ttPuR4K0W5vYZLey8UaVLK0uFfzXiiJUxg5jG48Nt46V9j/FDwv8WNV1Kz\nfwF4i0bQbFYSLiLVtJkunkk3cMrK64GMDFaZ1w7h8qzSeX08dTnFN+/71lZJ+9ZSs9baOWqd\n7Cp1nUhzuLR6DU03+pt/90/+hGvIvAvg745WHiqxn8WeL/DmreH0L/arPT9DlgnkyjBdrmQg\nYcqTxyAR3rqtK8PfEaH4h315qetaXd+CXhYWOlW+mul5C+VwXm3EMOH42j7w9K8GvgKdGUox\nxMJWje65rN3tyq8F73XWyt1voaqTfRnW0V4rrHgb9oebVr2TTfHPhS20553a2hn8PSvJHEWO\nxWbzRkhcAnvXV+EPDHxVsvB2vW3iTxBo+peJZkcaVfWWlSQ29sxjIQyxlyXAfBIBGRxWtbLa\nVKmqixdOTdtFz319YJaddfS4lNt25X/XzO5lvbeC4igknijnlz5cbOAz464HU1NX5B/tJ/AT\n9oHU/wBoO8n1bQ9c8U63dTxtp+taJYzC0ZQBs8phkQhO4LDaQSTzuP31ovw9/aag0awjufiB\n4U+0pbxrL5vh+WV94UZ3OJAGOc5IAz1wK+2zjgzCZZhMLiYZpSk60bte9pttyqTa1teSi7rb\ndLnp4iU5STg9D6Corg/G3hn4p32ieH4vC2v6RpeqwoBq1xf6VJPHcttUExKHGwbg55J4I9Ki\n+KHhf4sarqVk/gLxFo2g2SREXMWraTJdPJJnhlKuuBjjFfBUsDTqOCeIhHm5t+b3bd7RfxfZ\ntfzsdTk1fRnoNFeT+BvB3xysPFVjP4r8X+HNW8PoX+1Wen6HLBPICjBdrmQgYYqTx0BHeun0\nbw78RoPiPqd7qes6Zc+CZI2FlpcGmul3E/yYLzbiGHD8bR1HpVV8BToylGOJhK0b3XPZu9uV\nXgve662VuvQSk30Z2NQ2t7b3yM1tcRXCqxRmicMAw6g4718w/tF/DT9o/X/h341ttD8U6Re2\nVxHJ5Gk6ZpMkF/Jb78mFJi5G8x5U8fNyBjNfNf8AwT0+Dfxl8N+PvFN7Bo+reEdMfRri2M2u\n6fNHbvekD7OTE4UyFH+Y46KGGRu5+1wXB+FxWTYnNZZjSUqVrQV9dt2+Vpu9laMk2tzmliJR\nqKHI9T9OKK4Cz8MfFVPhjeWNz4g0eXx20mbfVk0qRbNE3qcNDvyTsDjO7qQe1cT/AMIF+0p/\n0UDwh/4Tc3/x2vkaOWUqrmni6ceV2159fNWg9H52fkdDm19l/h/me60VwXjXwz8U77QvD8Xh\nfX9H0vVoUA1a4v8ASpJ4rltqgmJQ42DcGPJPBHpTPih4X+K+q6jZP4C8RaNoFkkRFzFq2kyX\nTySZ4ZSrrgY4xXNSwNOpKCeIhHm5t+b3bd7RfxfZtfzsU5NdGegVNbX1tp7s91cRWyMjIGmc\nICxHABPevIfA/g745WHimxn8V+L/AA5qugIX+1Wen6HLBPINjBdrmQhcNtJ46AjvXxR/wUd+\nDvxj8UfFWx1KPR9W8U+EWgEWlRaNYSzLZNk70lRAf3jHB3kfMNoH3cD6rIOF8NnOaxy2pj6c\nIuLlzq7V19lKSheXXe1urehhVrunT51Fn6X0V8lfAL4W/tN6L8IPDNlceLdF0VorbEena5o0\nt1eW0e4lI5JBIMkLt4xlRhT0r3fwf4Y+Ktl4Q1+38S+INH1LxJNG40q9stKkhgtnMZCGWMuS\n4D4JAIyBivIzLJaOAq1KcMbSqcsuX3ee71tf4bW6u0nptc0hUckm4tHfUVwFl4Y+KsfwyvbG\n68QaPL46eTNtq0elSLZxpvU4aHfknaHGd3Ug9q4n/hAv2lP+igeEP/Cbm/8AjtctHLKVVzTx\ndOPK7a8+vmrQej87PyG5tfZf4f5nutFcF418MfFS+0Dw/D4X8QaPperwoBq1zf6VJPFctsUE\nxIHGwbgx5J4I9KpfGTwn8XdbVf8AhXXiLSNAi+ySJLHqemvPI8xztZHDAJ26g88+1YUcBTqz\npxeIhHmvq+a0bfzWi/i6Wv52G5NX0Z6JHe281xLbx3ETzxYMkSuCyZ6ZHUVNX5I/sr/An436\nL+1P4fvJ/DPiTR57LUvN1vU7+2lSJ7fdmcPMw2yeYu4DBO4kEdMj9PdI8O/EaH4kale6jrOm\nXPgmSMiz0qHTXS7ifCYLzbiGHD8bR1HpX1nFHCuG4exMaFHHQrJw57rTW9uVcvMrvdXauu2l\n8KFd1o3cWtTsaK8W1nwP+0NPrF9Jpnjnwra6a88jWsE/h6V5I4ix2KzCUbiFwCccmuq8HeGP\nirZeEdft/EviDR9T8RzRuNKvbHSpIYLZzGQhljLkuA+CQCMgYr5irltKlSVRYunJu2i576+s\nEtOuvpc2U23blf4f5nf0VwNj4Y+KsfwyvbG78QaPL46eTNtq0elSLZxpvU4aHfknaHGd3Ug9\nq4j/AIQL9pT/AKKB4Q/8Jub/AOO1VHLKVVzTxdOPK7a8+vmrQej87PyBza+y/wAP8z3K4uIr\nWF5p5EhiQZaSRgqqPUk9KWKVJ4kkidZI3G5XQ5BHqDXxT/wUf+GfxU8X/CbwNHo1lf8AiC2s\nHc+IbbRrWRvPn8uMRzCFcsYwwn9du9c+oxf2LPhH+0Z4e+EUkNjqdt4M02W+kltdM8U6RLPO\nEKrl0TcpiQsG+U9TlgBnJ+rpcI4WrkEc5eYU4yc3Hkd9LNrdXld25kuT4WncweIkqvs+R+p9\n5UV5N4H8G/HOw8U2M/irxf4c1XQELfarOw0KWCeQbGC7XMhC4baenQEV1Gk+HfiND8SNRvdQ\n1nTLjwQ8ZFnpUOmul5E+FwXm3YYZD8bR1HpXxtbAU6MpRjiYStHmuuezd7cqvBe911srdToU\nm+jO3uPuw/8AXMfzNYOr+DtE15i99plvPIesuzbJ/wB9DB/WvMvEHgj9oa41e7k0zx14UttM\neV2tYJvD0ryRQlyUR2Eo3MFwCcDNdP4N8MfFWy8Ja/b+JvEGj6n4imjcaVe2OlSQwWzmMhTL\nGXJcB8EgEZAxTxOU4f2KlLE0p3t7vvt6+sEtOuvpczko1VyVIXXnax0mjeF4/D8V0lleXZWV\nQI47qYzJCRnBUHnvyM84FeB674n8U2fjxrOXUrj+2Fm2w20bFRIAcArHnBU/TnPNesWHhj4q\nx/DO9sbvxBo83jp5M22rR6VItnGm9Thod+SdocZ3dSD2r8tvih8AP2hrv9pK+kudC8Qar4tn\n1Pz7TxDYWsy2rDf+7ljmxtijUY4JGwDB6V6+S+H+E4pnVw7zOGGVG7W75rdUm4JR13vdfy2P\nBzPCOpTpwpOUEn0/4ffsfqyvxCFkoGr6JqmmMB88ptzLCD/vJn+VdGNYsfItZWuookulDQ+a\nwQuCAeAcHoRxXFeL/C/xXvfC/hyDw34h0fTNegiQavd32lPPDdPsUMYkDLsBcMec8ECqfxj+\nH3xI8XXunHwhqvh/TLKGNvOj1zRnvXMmeGRgy7RgV83Ry6bcYvF0/ev8XMuW38zUX8X2bJ+d\nj2F7enF6821rq34p/oj0yivDdD+E/wAb7vxfp194o8faVNpEIZLiDRNLmtJpF2ttAYyMvDFS\ncqcgEV1/hfwZ8UtK+Il1c6r4rstX8EGNhb6adL8u9RsLtLTDhsHd27itq2DVGrKn7aEko8yl\nFys9bcusU+brta32r6GkatRys6bt3uvx1/zPQ6CQASTgCvCdR8JftJXnifVhY+KvCun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qW9xqHhLRJUxJGSFE8a723bCiiQ8EKq/wivav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\nT/vmovtEv/P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+Jnj1ZPGOv+FvCPhbUP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\n81DRQBN9o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gwIAhQ4b4+vq6XjwsLGz58uWbN28+duzY+fPnDQZD9+7dR44c2bp1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ZDMOIoqjAi4UcrL2tkfYQyn9hSUlJYToc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\n2bNn48Lz589bPRm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kOgp+/fXXiYmJhBAvL6/XXnvt5Zdf5v8vhiYZ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\n9bpx48a2bdsOHTqk0+nc3d3nzZv38ssvG4+w0obonu1ep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"/Users/gcohen/Documents/GitRepositories/stat-301-instructor/stat301book/_build/jupyter_execute/w02d2_abPeeking_21_0.pdf", - "image/jpeg": "/Users/gcohen/Documents/GitRepositories/stat-301-instructor/stat301book/_build/jupyter_execute/w02d2_abPeeking_21_0.jpg", - "image/png": "/Users/gcohen/Documents/GitRepositories/stat-301-instructor/stat301book/_build/jupyter_execute/w02d2_abPeeking_21_0.png", - "image/svg+xml": "/Users/gcohen/Documents/GitRepositories/stat-301-instructor/stat301book/_build/jupyter_execute/w02d2_abPeeking_21_0.svg" - }, - "image/jpeg": { - "height": 420, - "width": 600 - }, - "image/png": { - "height": 420, - "width": 600 - }, - "image/svg+xml": { - "height": 420, - "isolated": true, - "width": 600 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "options(repr.plot.width = 10, repr.plot.height = 7) # Adjust these numbers so the plot looks good in your desktop.\n", - "\n", - "set.seed(301)\n", - "answer2.1 <- \n", - " incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50)\n", - "\n", - "\n", - "crit_unadj <- qt(1 - 0.05, 1998)\n", - "crit_bonferroni <- qt(1 - 0.0025, 1998)\n", - "\n", - "sequential_stat <- \n", - " answer2.1 %>%\n", - " ggplot() +\n", - " geom_line(aes(x = inc_sample_size, y = statistic)) +\n", - " geom_point(aes(x = inc_sample_size, y = statistic,colour =\"#f94f21\"), size = 2) +\n", - " geom_hline(yintercept = crit_pocock, colour = \"#ff33ff\", linetype = \"twodash\") +\n", - " geom_line(aes(x = inc_sample_size, y = crit_of),colour = 3, linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = crit_of), colour = 3) +\n", - " geom_text(x=160, y= crit_of[1] - 0.15, size=6, label=\"O'Brien-Fleming\",colour = 3) +\n", - " geom_point(aes(x = inc_sample_size, y = crit_pocock), colour = \"#ff33ff\") +\n", - " geom_text(x=850, y=crit_pocock + 0.15, size=6, label=\"Pocock\",colour = \"#ff33ff\") +\n", - " geom_hline(yintercept = crit_bonferroni, colour = \"blue\", linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = rep(crit_bonferroni, 20)), colour = \"blue\") +\n", - " geom_text(x=850, y=crit_bonferroni + 0.15, size=6, label=\"Bonferroni\",colour = \"blue\") +\n", - " geom_hline(yintercept = crit_unadj, linetype = \"twodash\") +\n", - " geom_point(aes(x = inc_sample_size, y = rep(crit_unadj, 20))) +\n", - " geom_text(x=850, y=crit_unadj + 0.15, size=6, label=\"Unadjusted\") +\n", - " theme(\n", - " text = element_text(size = 18),\n", - " plot.title = element_text(face = \"bold\"),\n", - " axis.title = element_text(face = \"bold\"),\n", - " legend.position=\"none\") +\n", - " ggtitle(\"Critical values in Sequential Designs\") +\n", - " ylab(\"Statistic\") +\n", - " xlab(\"Sample Size\") +\n", - " coord_cartesian(ylim = c(-1, 8.5)) +\n", - " scale_y_continuous(breaks = seq(-1, 8.5, by = 0.5))\n", - "sequential_stat " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Interpretations of O'Brien-Fleming correction\n", - "\n", - "In *one* A/A testing simulated experiment, using unadjusted quantities:\n", - "\n", - "- you would reject $H_0$: the observed (unadjusted) t-statistic is bigger than the (unadjusted) critical value\n", - "\n", - "Using O'Brien-Fleming's critical values:\n", - "\n", - "- you would fail to reject $H_0$: the observed (unadjusted) t-statistic is smaller than the (adjusted) critical value\n", - "\n", - "```{important}\n", - "The O'Brien-Fleming's critical values are non-constant!! The test is *very* conservative at the beginning when less data is available and becomes less conservative as more data is collected. \n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Type I error rate control\n", - "\n", - "To better understand the type I error rate control of the 3 approaches, you need to repeat the experiments *many* times and calculate the rate at which you erroneously reject $H_0$.\n", - "\n", - "#### Number of erroneous rejections among 100 experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-549d0aab861a20f3", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\n", - "
    A tibble: 1 × 5
    UnadjustedBonferroniPocockOBrienFlemingexpected_n_rejections
    <dbl><dbl><dbl><dbl><dbl>
    242745
    \n" - ], - "text/latex": [ - "A tibble: 1 × 5\n", - "\\begin{tabular}{lllll}\n", - " Unadjusted & Bonferroni & Pocock & OBrienFleming & expected\\_n\\_rejections\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t 24 & 2 & 7 & 4 & 5\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 1 × 5\n", - "\n", - "| Unadjusted <dbl> | Bonferroni <dbl> | Pocock <dbl> | OBrienFleming <dbl> | expected_n_rejections <dbl> |\n", - "|---|---|---|---|---|\n", - "| 24 | 2 | 7 | 4 | 5 |\n", - "\n" - ], - "text/plain": [ - " Unadjusted Bonferroni Pocock OBrienFleming expected_n_rejections\n", - "1 24 2 7 4 5 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "set.seed(120)\n", - "\n", - "### Run this before continuing\n", - "multiple_times_sequential_tests <- tibble(experiment = 1:100) %>% \n", - " mutate(seq_test = map(.x = experiment, \n", - " .f = function(x) incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, \n", - " mean_current = 200, sd_current = 50, sd_new = 50)))\n", - "\n", - "typeI_rate <- multiple_times_sequential_tests %>% \n", - " mutate(reject = map_dbl(.x = seq_test, .f = function(x) sum(x$p_value<0.05) > 0)) %>%\n", - " mutate(n_reject_bonferroni = map_dbl(.x = seq_test, .f = function(x) sum(x$p_value < 0.05/20) > 0)) %>%\n", - " mutate(n_reject_pocock = map_dbl(.x = seq_test, .f = function(x) sum(x$statistic > crit_pocock) > 0)) %>%\n", - " mutate(n_reject_OF = map_dbl(.x = seq_test, .f = function(x) sum(x$statistic > crit_of) > 0)) %>%\n", - " summarise(Unadjusted = sum(reject),\n", - " Bonferroni= sum(n_reject_bonferroni),\n", - " Pocock = sum(n_reject_pocock),\n", - " OBrienFleming = sum(n_reject_OF),\n", - " expected_n_rejections = 5)\n", - "typeI_rate" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-bf09c89e531d91af", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Conclusions\n", - "\n", - "For the simulated experiments in the example above: \n", - "\n", - "- the type I error rate using unadjusted values was 24% (way above the planned 5% value)\n", - "\n", - "- the type I error rate using Bonferroni was 2% (below the planned 5% value)\n", - "\n", - "- the type I error rate using Pocock was 7% (above the planned 5% value)\n", - "\n", - "- the type I error rate using O'Brien-Flemming was 4% (slightly below the planned 5% value)\n", - "\n", - "```{important}\n", - "Using a **principled peeking** procedure, the data can be sequentially analyzed and the experiment can be stopped earlier while controlling the type I error rate.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a3ee7bcb3608e036", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Summary and key concepts learned\n", - "\n", - "\n", - "1. In many A/B testing experiments, users would like to stop their experiments earlier depending on the results of interim analyses\n", - "\n", - "\n", - "2. If you make lots of comparisons, but don’t correct for it, your error rates are inflated!! Naively peeking at the data many times and stopping the experiment earlier than planned increases the probability of *incorrectly* rejecting the null hypothesis. \n", - "\n", - "3. Stops must be part of the experimental design and appropriate testing methods must be used!\n", - "\n", - "\n", - "4. A possible way to control the type I error rated is to use a **Bonferroni adjustment** of the $p$-values (or equivalently the significance level or critical values). As with other multiple comparison problems, the Bonferroni's correction in sequential analysis is very conservative and can affect the power of the test!!\n", - "\n", - "\n", - "9. The **Pocock method** offers a less conservative way of controlling the type I error rate in sequential testing with early stops.\n", - "\n", - "\n", - "10. The **O'Brien-Fleming method** offers a less conservative way of controlling the type I error rate in sequential testing with early stops.\n", - "\n", - "\n", - "```{attention} \n", - "Principled peeking is ok and even beneficial in A/B testing. The experimental design is a very important piece of any statistical analysis! \n", - "```" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "jupytext": { - "formats": "ipynb,Rmd" - }, - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "4.0.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/stat301book/_build/jupyter_execute/w02d2_abPeeking.r b/stat301book/_build/jupyter_execute/w02d2_abPeeking.r deleted file mode 100644 index ee3903b..0000000 --- a/stat301book/_build/jupyter_execute/w02d2_abPeeking.r +++ /dev/null @@ -1,259 +0,0 @@ -library(tidyverse) -library(ggplot2) - -options(repr.plot.width = 10, repr.plot.height = 5) # Adjust these numbers so the plot looks good in your desktop. -options(warn=-1) - -t_x_axis <- seq(-4, 4, 0.1) -crit_unadj <- qt(1 - 0.1, 1998) -crit_bonferroni <- qt(1 - 0.01, 1998) -t_stat <- 1.8 - -t_dens_data <- data.frame( x = t_x_axis, - y1 = dt(t_x_axis,1998)) - -test_plot <- ggplot(t_dens_data, aes(x = t_x_axis)) + -geom_line( aes( y = y1, colour = 'H0 is true' ), size = 1.2 ) + -geom_area( aes( y = y1, x = ifelse(x > crit_unadj, t_x_axis, NA)), fill = 'black',alpha=0.3) + -geom_area( aes( y = y1, x = ifelse(x > t_stat, t_x_axis, NA)), fill = 'red',alpha=0.3) + -geom_area( aes( y = y1, x = ifelse(x > crit_bonferroni, t_x_axis, NA)), fill = 'blue',alpha=0.6) + -theme(text = element_text(size = 18), - legend.title = element_blank() ) + -labs(x = '', y = '' ) + -geom_point(y=0,x=t_stat,size = 3,shape=19,aes(color = "t-stat"))+ -geom_point(y=0,x=crit_unadj,size = 3,shape=19,aes(color = "unadj critical val"))+ -geom_point(y=0,x=crit_bonferroni,size = 3,shape=1,stroke = 2, aes(color = "bonferroni critical val"))+ -scale_colour_manual( breaks = c("H0 is true", "t-stat","unadj critical val","bonferroni critical val"), - values = c("black", "#f94f21","black","blue"), - guide = guide_legend(override.aes = list( - linetype =c("solid",rep("blank",3)), - shape = c(NA, rep(16, 2),1))))+ -scale_x_continuous(breaks = c(0))+ -ggtitle("Bonferroni: statistical significance adjustment") - -test_plot - -options(repr.plot.width = 10, repr.plot.height = 5) # Adjust these numbers so the plot looks good in your desktop. -options(warn=-1) - -t_x_axis <- seq(-4, 4, 0.1) -crit_unadj <- qt(1 - 0.1, 1998) -crit_bonferroni <- qt(1 - 0.01, 1998) -t_stat <- 1.8 -adj_t_stat <- qt(1-(1-pt(1.8,1998))*10,1998) - -t_dens_data <- data.frame( x = t_x_axis, - y1 = dt(t_x_axis,1998)) - -test_plot <- ggplot(t_dens_data, aes(x = t_x_axis)) + -geom_line( aes( y = y1, colour = 'H0 is true' ), size = 1.2 ) + -geom_area( aes( y = y1, x = ifelse(x > crit_unadj, t_x_axis, NA)), fill = 'black',alpha=0.3) + -geom_area( aes( y = y1, x = ifelse(x > adj_t_stat, t_x_axis, NA)), fill = 'red',alpha=0.3) + -geom_area( aes( y = y1, x = ifelse(x > t_stat, t_x_axis, NA)), fill = 'red',alpha=0.6) + -theme(text = element_text(size = 18), - legend.title = element_blank() ) + -labs(x = '', y = '' ) + -geom_point(y=0,x=t_stat,size = 3,shape=19,aes(color = "t-stat"))+ -geom_point(y=0,x=crit_unadj,size = 3,shape=19,aes(color = "unadj critical val"))+ -geom_point(y=0,x=adj_t_stat,size = 3,shape=1,stroke = 2, aes(color = "bonferroni t-stat"))+ -scale_colour_manual( breaks = c("H0 is true", "t-stat","unadj critical val","bonferroni t-stat"), - values = c("black", "#f94f21","black","#f94f21"), - guide = guide_legend(override.aes = list( - linetype =c("solid",rep("blank",3)), - shape = c(NA, rep(16, 2),1))))+ -scale_x_continuous(breaks = c(0)) + -ggtitle("Bonferroni: p-value adjustment") -test_plot - -options(warn=-1) - -# Two-sample t-test with tracking sequential statistic and p-values by incremental sample sizes until getting to n in each group. - -# @param n (numeric): Initially planned sample size for each group (for simplicity, n needs to be a multiple of sample_increase_step). -# @param d_0 (numeric): effect size. -# @param mean_current (numeric): Population mean for control variation. -# @param sd_current (numeric): Population standard deviation for current variation. -# @param sd_new (numeric): Population standard deviation for new variation. -# @param sample_increase_step (numeric): Sample size increment. - -# @return p.value.df: A tibble that has 3 columns: -# inc_sample_size, statistic, and p_value - -incremental_t_test <- function(n, d_0, mean_current, sd_current, sd_new, sample_increase_step) { - sample_current <- rnorm(n, mean = mean_current, sd = sd_current) - sample_new <- rnorm(n, mean = mean_current + d_0, sd = sd_new) - - p.value.df <- tibble( - inc_sample_size = rep(0, n / sample_increase_step), - statistic = rep(0, n / sample_increase_step), - p_value = rep(0, n / sample_increase_step) - ) - - current_sample_size <- sample_increase_step - - for (i in 1:nrow(p.value.df)) - { - t_test_results <- t.test(sample_new[1:current_sample_size], sample_current[1:current_sample_size], - var.equal = TRUE, - alternative = "greater" - ) - p.value.df[i, "statistic"] <- as_tibble(t_test_results$statistic) - p.value.df[i, "p_value"] <- as_tibble(t_test_results$p.value) - p.value.df[i, "inc_sample_size"] <- current_sample_size - current_sample_size <- current_sample_size + sample_increase_step - } - - return(p.value.df) -} - -options(repr.plot.width = 10, repr.plot.height = 5) # Adjust these numbers so the plot looks good in your desktop. - -set.seed(301) -answer2.1 <- - incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50) - - -crit_unadj <- qt(1 - 0.05, 1998) -crit_bonferroni <- qt(1 - 0.0025, 1998) - -sequential_stat <- - answer2.1 %>% - ggplot() + - geom_line(aes(x = inc_sample_size, y = statistic)) + - geom_point(aes(x = inc_sample_size, y = statistic,colour ="#f94f21"), size = 2) + -# geom_hline(yintercept = crit_pocock, colour = "red", linetype = "twodash") + -# geom_point(aes(x = inc_sample_size, y = crit_pocock), colour = "red") + -# geom_text(x=850, y=crit_pocock + 0.15, size=6, label="Pocock",colour = "red") + - geom_hline(yintercept = crit_bonferroni, colour = "blue", linetype = "twodash") + - geom_point(aes(x = inc_sample_size, y = rep(crit_bonferroni, 20)), colour = "blue") + - geom_text(x=850, y=crit_bonferroni + 0.15, size=6, label="Bonferroni",colour = "blue") + - geom_hline(yintercept = crit_unadj, linetype = "twodash") + - geom_point(aes(x = inc_sample_size, y = rep(crit_unadj, 20))) + - geom_text(x=850, y=crit_unadj + 0.15, size=6, label="Unadjusted") + - theme( - text = element_text(size = 18), - plot.title = element_text(face = "bold"), - axis.title = element_text(face = "bold"), - legend.position="none") + - ggtitle("Critical values in Sequential Designs") + - ylab("Statistic") + - xlab("Sample Size") + - coord_cartesian(ylim = c(-1, 3)) + - scale_y_continuous(breaks = seq(-1, 3, by = 0.5)) -sequential_stat - -# Run this cell to get a Pocock design! -library(gsDesign) - -design_pocock <- gsDesign(k = 20, #number of interim analysis planned - test.type = 1, # for one-sided tests - delta = 0, # default effect size - alpha = 0.05, #type II error rate - beta = 0.2, # type II error rate - sfu = 'Pocock') - -crit_pocock <- design_pocock$upper$bound - -options(repr.plot.width = 10, repr.plot.height = 5) # Adjust these numbers so the plot looks good in your desktop. - -set.seed(301) -answer2.1 <- - incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50) - - -crit_unadj <- qt(1 - 0.05, 1998) -crit_bonferroni <- qt(1 - 0.0025, 1998) - -sequential_stat <- - answer2.1 %>% - ggplot() + - geom_line(aes(x = inc_sample_size, y = statistic)) + - geom_point(aes(x = inc_sample_size, y = statistic,colour ="#f94f21"), size = 2) + - geom_hline(yintercept = crit_pocock, colour = "#ff33ff", linetype = "twodash") + - geom_point(aes(x = inc_sample_size, y = crit_pocock), colour = "#ff33ff") + - geom_text(x=850, y=crit_pocock + 0.15, size=6, label="Pocock",colour = "#ff33ff") + - geom_hline(yintercept = crit_bonferroni, colour = "blue", linetype = "twodash") + - geom_point(aes(x = inc_sample_size, y = rep(crit_bonferroni, 20)), colour = "blue") + - geom_text(x=850, y=crit_bonferroni + 0.15, size=6, label="Bonferroni",colour = "blue") + - geom_hline(yintercept = crit_unadj, linetype = "twodash") + - geom_point(aes(x = inc_sample_size, y = rep(crit_unadj, 20))) + - geom_text(x=850, y=crit_unadj + 0.15, size=6, label="Unadjusted") + - theme( - text = element_text(size = 18), - plot.title = element_text(face = "bold"), - axis.title = element_text(face = "bold"), - legend.position="none") + - ggtitle("Critical values in Sequential Designs") + - ylab("Statistic") + - xlab("Sample Size") + - coord_cartesian(ylim = c(-1, 3)) + - scale_y_continuous(breaks = seq(-1, 3, by = 0.5)) -sequential_stat - -design_of <- gsDesign(k = 20, #number of interim analysis planned - test.type = 1, # for one-sided tests - delta = 0, # default effect size - alpha = 0.05, #type I error rate - beta = 0.2, # type II error rate - sfu = 'OF') - -crit_of <- design_of$upper$bound - -options(repr.plot.width = 10, repr.plot.height = 7) # Adjust these numbers so the plot looks good in your desktop. - -set.seed(301) -answer2.1 <- - incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50) - - -crit_unadj <- qt(1 - 0.05, 1998) -crit_bonferroni <- qt(1 - 0.0025, 1998) - -sequential_stat <- - answer2.1 %>% - ggplot() + - geom_line(aes(x = inc_sample_size, y = statistic)) + - geom_point(aes(x = inc_sample_size, y = statistic,colour ="#f94f21"), size = 2) + - geom_hline(yintercept = crit_pocock, colour = "#ff33ff", linetype = "twodash") + - geom_line(aes(x = inc_sample_size, y = crit_of),colour = 3, linetype = "twodash") + - geom_point(aes(x = inc_sample_size, y = crit_of), colour = 3) + - geom_text(x=160, y= crit_of[1] - 0.15, size=6, label="O'Brien-Fleming",colour = 3) + - geom_point(aes(x = inc_sample_size, y = crit_pocock), colour = "#ff33ff") + - geom_text(x=850, y=crit_pocock + 0.15, size=6, label="Pocock",colour = "#ff33ff") + - geom_hline(yintercept = crit_bonferroni, colour = "blue", linetype = "twodash") + - geom_point(aes(x = inc_sample_size, y = rep(crit_bonferroni, 20)), colour = "blue") + - geom_text(x=850, y=crit_bonferroni + 0.15, size=6, label="Bonferroni",colour = "blue") + - geom_hline(yintercept = crit_unadj, linetype = "twodash") + - geom_point(aes(x = inc_sample_size, y = rep(crit_unadj, 20))) + - geom_text(x=850, y=crit_unadj + 0.15, size=6, label="Unadjusted") + - theme( - text = element_text(size = 18), - plot.title = element_text(face = "bold"), - axis.title = element_text(face = "bold"), - legend.position="none") + - ggtitle("Critical values in Sequential Designs") + - ylab("Statistic") + - xlab("Sample Size") + - coord_cartesian(ylim = c(-1, 8.5)) + - scale_y_continuous(breaks = seq(-1, 8.5, by = 0.5)) -sequential_stat - -set.seed(120) - -### Run this before continuing -multiple_times_sequential_tests <- tibble(experiment = 1:100) %>% - mutate(seq_test = map(.x = experiment, - .f = function(x) incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, - mean_current = 200, sd_current = 50, sd_new = 50))) - -typeI_rate <- multiple_times_sequential_tests %>% - mutate(reject = map_dbl(.x = seq_test, .f = function(x) sum(x$p_value<0.05) > 0)) %>% - mutate(n_reject_bonferroni = map_dbl(.x = seq_test, .f = function(x) sum(x$p_value < 0.05/20) > 0)) %>% - mutate(n_reject_pocock = map_dbl(.x = seq_test, .f = function(x) sum(x$statistic > crit_pocock) > 0)) %>% - mutate(n_reject_OF = map_dbl(.x = seq_test, .f = function(x) sum(x$statistic > crit_of) > 0)) %>% - summarise(Unadjusted = sum(reject), - Bonferroni= sum(n_reject_bonferroni), - Pocock = sum(n_reject_pocock), - OBrienFleming = 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a/stat301book/_build/html/_panels_static/panels-variables.06eb56fa6e07937060861dad626602ad.css +++ /dev/null @@ -1,7 +0,0 @@ -:root { ---tabs-color-label-active: hsla(231, 99%, 66%, 1); ---tabs-color-label-inactive: rgba(178, 206, 245, 0.62); ---tabs-color-overline: rgb(207, 236, 238); ---tabs-color-underline: rgb(207, 236, 238); ---tabs-size-label: 1rem; -} \ No newline at end of file diff --git a/stat301book/_build/html/_sources/intro.md b/stat301book/_build/html/_sources/intro.md deleted file mode 100644 index 9072fa7..0000000 --- a/stat301book/_build/html/_sources/intro.md +++ /dev/null @@ -1,22 +0,0 @@ -# Statistical modelling for Data Science - -This book is a compilation of STAT301 class notes written by Professor Gabriela Cohen Freue with contributions from Professors Rodolfo Lourenzutti and Alexi Rodriguez-Arelis. - -## Overview - -This course introduces main concepts of explanatory and predictive data analysis with multiple explanatory variables. By the end of the course, students are expected to be able to choose the right methods to apply based on the statistical question and data at hand. Special emphasis is placed on case studies and real data sets, as well as reproducible and transparent workflows when writing computer scripts for analysis and reports. - -## Learning Outcomes -By the end of the course, students are expected to be able to: - -- Describe real-world examples of explanatory modelling (e.g. A/B testing optimization & regression with variable selection) and predictive modelling problems. - -- Explain the trade-offs between model-based and non-model based approaches, and describe situations where each might be the preferred approach. - - - Explain the difference between creating models for explanation vs prediction, in the context of both how you choose and evaluate models as well as how you interpret the results. - -- Choose & apply a suitable method (e.g., regression, GLM's, sample size estimation, controlling for multiple testing, peeking, bandit algorithms, variable selection, model diagnostics) based on the statistical question and data at hand. Discuss the advantages and disadvantages of different methods that may be suitable for a given problem. - -- Correctly interpret computer output when performing the statistical analyses presented in this course, in the context of the statistical question being asked and the audience being reported to. - -- Identify the assumptions / conditions required for each method to produce reliable results. Choose techniques to check (or at least be able to falsify) those assumptions. Discuss the consequence(s) of mapping the wrong methods to the question and/or data type. diff --git a/stat301book/_build/html/_sources/w02d1_abTesting.ipynb b/stat301book/_build/html/_sources/w02d1_abTesting.ipynb deleted file mode 100644 index 1ed83f9..0000000 --- a/stat301book/_build/html/_sources/w02d1_abTesting.ipynb +++ /dev/null @@ -1,1213 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# A/B Testing\n", - "\n", - "\n", - "\n", - "*Photos/Images by [Optimizely](https://www.optimizely.com/insights/blog/how-obama-raised-60-million-by-running-a-simple-experiment/)*" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - }, - "tags": [] - }, - "source": [ - "## Overview\n", - "\n", - "This chapter provides an introduction to A/B testing and the early peeking problem. The goal is that you understand the main principles of A/B testing that will allow you to properly design, run, and analyze data from A/B tests. While you probably know the main concepts behind A/B testing, the usage of online platforms to collect, store and analyze data from A/B tests open new challenges. For example, people get tempted to peek at results before the A/B test ended and take actions usually result in false discoveries. \n", - "\n", - "\n", - "A/B Testing is a technique used to compare two variations of a product or service: control (A) and variation (B) \n", - "\n", - "- A/B testing became very popular in the context of updating and improving websites. \n", - " \n", - "It's main concepts are founded in the context of hypothesis testing and inference to compare population quantities from 2 distributions\n", - "\n", - "- for example: comparison of two population means or population proportions\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - }, - "tags": [] - }, - "source": [ - "## Analysis Design\n", - "\n", - "As in any other statistical anlaysis, important steps of an A/B testing experiment include, \n", - "\n", - "- post the *question(s)* you want to answer using data\n", - "\n", - "- *design* the experiment to address your question(s)\n", - "\n", - "- identify appropriate methodologies to analyze the data\n", - "\n", - "- run the experiment to collect data \n", - "\n", - "- analyze the data according to the experimental design and make decisions\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - }, - "tags": [] - }, - "source": [ - "## Case study\n", - "\n", - "### Obama's 60 million dollar experiment\n", - "\n", - "In 2008, Obama's campaign was looking to increase the total amount of donations to the campaign. Organizers run an experiment to compare visitors' responses to different versions of the website.\n", - "\n", - "\n", - "\n", - "*A full description of this case study can be found [here](https://www.optimizely.com/insights/blog/how-obama-raised-60-million-by-running-a-simple-experiment/).*\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - }, - "tags": [] - }, - "source": [ - "While there is not a unique way to design an A/B testing experiment, ***randomized controlled experiments*** are commonly used in practice. When participants are randomly assigned to the experimental group or the control group, you only expect to observe differences driven by the controlled effect (e.g., a new website) and not by other confounding factors. \n", - "\n", - "Other decisions need to be made when designing an A/B testing experiment, including sample size, number of tests, methodology. Since the choices made would influence the results, companies usually run some calibration tests before running the actual experiment. \n", - "\n", - "```{admonition} Example\n", - "\n", - "**Question**: Does visitors of the new website contribute with larger donations for the political campaign? \n", - "\n", - "**Design**: Randomly allocate 1000 visitors to each website (control and new)\n", - "\n", - "**Method**: Analyze the data sequentially in batches of 50 visitors per website. Use a classical $t$-test to run the analysis, compute $p$-values and confidence intervals\n", - "\n", - "**Decision**: Stop the experiment if the $p$-value of a test drops below a significance level of $0.05$\n", - "```\n", - "\n", - "So, what is new about A/B testing?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - }, - "tags": [] - }, - "source": [ - "## Early stopping\n", - "\n", - "New platforms have been developed to assist companies to analyze, report and visualize the results of their experiments **in real time**\n", - "\n", - "\n", - "\n", - "Figure by D. Meisner in Towards Data Science " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "These platforms allow the users to *continuously monitor* the $p$-values and confidence intervals in order to re-adjust their experiment dynamically. \n", - "\n", - "```{caution}\n", - "In classical hypothesis testing theory, the sample size must be fixed in advance when the experiment is designed!! Is it ok to peek at results before all the data are collected??\n", - "```\n", - "\n", - "In general, there are large opportunity costs associated with longer experiments. Thus, users may prefer to adaptively determine the sample size of the experiments and make decisions before the planned experiment ends. \n", - "\n", - "```{admonition} Definition\n", - ":class: tip\n", - "**Early stopping** refers to ending the experiment earlier than originally designed.\n", - "```\n", - "\n", - "Can we stop or re-design the experiment earlier if we have supporting evidence to do so?? Let's answer this question using data!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### A/A Testing\n", - "To examine the problem of early stopping, let's simulate data for which $H_0$ is true (i.e., there is no effect)\n", - "\n", - "```{admonition} Example\n", - "All users are allocated to the same version of a website.\n", - "```\n", - " \n", - "\n", - "\n", - "In such a scenario, data from both groups are generated from the same distribution. Thus, we know that rejecting $H_0$ is a mistake! " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - }, - "tags": [] - }, - "source": [ - "```{admonition} Simulation Example\n", - "\n", - "- generate 1000 data points per group (control and variation) from the same distribution (total sample size of 2000) \n", - "\n", - "- sequentially analyze the data in batches of 50 observations per group using a two-sample $t$-tests\n", - "\n", - "- sequentially compute and monitor the (raw) $p$-values \n", - "\n", - "- reject the null hypothesis if a $p$-value drops below $0.05$ and stop the experiment. \n", - "\n", - "```\n", - "\n", - "Simulating this kind of data and running the same analysis many times allows you examine the properties of the procedure used. For example, you can estimate the type I error rate by counting in how many experiments the $p$-value has dropped below the significance level and you erroneously reject the null hypothesis\n", - "\n", - "```{admonition} Wrong Rejection\n", - ":class: caution\n", - "The data were generated from the same distribution (e.g., all users are allocated to the same version of a website). Thus, in principle, the $p$-value should not drop below 0.05. However, due to randomness, you may still find significant differences between the two groups. But how often does that occur?? \n", - "```\n", - "\n", - "Let's start by simulating one such experiments and visualizing the results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove_output", - "remove_input" - ] - }, - "outputs": [], - "source": [ - "options(warn=-1)\n", - "options(messages=-1)\n", - "library(tidyverse)\n", - "library(ggplot2)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "slideshow": { - "slide_type": "skip" - }, - "tags": [ - "hide_input" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────── tidyverse 2.0.0 ──\n", - "\u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 1.1.3 \u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 2.1.4\n", - "\u001b[32m✔\u001b[39m \u001b[34mforcats \u001b[39m 1.0.0 \u001b[32m✔\u001b[39m \u001b[34mstringr \u001b[39m 1.5.0\n", - "\u001b[32m✔\u001b[39m \u001b[34mggplot2 \u001b[39m 3.4.3 \u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.2.1\n", - "\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.2 \u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 1.3.0\n", - "\u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 1.0.1 \n", - "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n", - "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n", - "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n", - "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m\u001b[39m\u001b[23m) to force all conflicts to become errors\n" - ] - }, - { - "data": { - "application/pdf": 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5isWreCEuCtSMEtZ3VmvZkSjUAkedzd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\ngtt3xhOlUhcwn8dvnvA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o+3p/z6Qf8A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F/wB9ij7Kf+ekX/fYqCigCf7Kf+ekX/fYqVrY/ZUHmR/fb+MegqnUzf8AHnH/ANdG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rF59zokaxR7iBuc+ccKM5Jx0FXfiB+0Np3g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\no3WP/POf/vof4UAQ/bJv+ehqa1upWuEBckZo3WP/ADz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A8Iy8rK0tLS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jo0ePnj179syZM/PmzbO1tZWTk2tubu5s\ncSkgdg8ePLh06ZK+vv6qVauIjgUAIEFUVFTOnz9/5MgRHo8XGBi4dOnSqqoqooMCAPQN4pxo\nrqqqin8RFRUlyv7Z2dkIIXNz81afMqdOnYq/KqR0GD4XlMFgCL+EiLsBMDC9fv06KCjIyMho\n9+7dUlJS69aty83N3blzp2C60ezZsyMiIm7evLlt2zYOh7N//35iAx7g2Gz22rVrMQzbu3cv\nNB4EALTl5uZ2+/btcePGJSUlWVhYiN5EGgAwkIkzIYyLi8O/KCwsFGX/oqIihJCOjk6r7Wpq\najIyMo2NjWVlZR0di88F/erQn4i7ATDQPH/+3NfXd+LEiadOnRo8ePCOHTuePn26fv36jnoc\ne3h4aGlpXbx4UcR3N+gJv/32W0FBwaJFi6AbJACgI9ra2jdv3ly3bl15ebmTk1NQUBCHwyE6\nKACARBPDGsL6+vrS0tKYmJgdO3Z06sDy8nLUYlyxJVVV1dLS0rKyso4WRuNzQRkMRlFRUWZm\nJp46Dhs2bPLkyS0zTBF3A2DgePTo0aFDh1JTUxFCY8aM8fHxmTt3rpBepTgymRwYGOjj47Nn\nzx4R54QD8SoqKtqzZ8+gQYOCgoKIjgUAINGkpKTWr19vaWm5atWqU6dOZWVlHTt27NtvvyU6\nLgCAhBI1IRTyeZHH47Xaoqen99UT8ng8/JFVu61U8Y0NDQ0dHY4P/eXm5mZkZLTcnpiYaGNj\ns2rVKnwaqoi7CYSFhQm+HQMDg++///6r3wjoIXiHA5juKy5cLvfatWt79+59+vQpQmjSpEmB\ngYEzZsz46rJA/L0vIyPj6ekZFhaWkJCwfv16AwOD3gga/Befz1+/fn1jY+PJkyc7agIEegf+\njqDRaIKl74AoJBJJRkaGz+cTHYiEMjc3z87O3rRp05kzZ2bMmPHzzz//9NNPYm9Kgf8RkZKS\ngr/XhKNQKGQy+atPeEFPw99lfesdIeovTdusTwgvL6+v7iOYwNDuShh8I5vN7uhwfHFgQ0OD\nm5ubpaWlqqpqVVXVjRs3EhMTU1JSFBUV3d3dRd9NICoqqqmpCf/axcUFX80ICAT3te6rq6s7\nffr0vn373rx5QyKR7OzsNm/e3NmHHXjd4G3btjk5Oe3atSshIaFnggXtO3PmTEZGho2Njaen\nJ9GxAIQQolAosIxTEohS0nwgk5aWDg8PnzVr1vLly4ODgzMyMiIiInqiKQWZTG73+T4AA5ak\nvSOEVGZBCGEiPloTvbrg7NmzExISvvrotLm52cHBASF05swZFRWVVq+uX78+Pz8/MDDQ3Ny8\n3cOrqqo+f/7MZDJbLXmKj4+PiIig0Wjnzp2j0+ki7ibYnp+fL/gfoqioCH9pCESlUslkspBR\nYvBVnz59On369KlTp1gsFo1Gc3BwCAwM1NbW7tRJGAwGhUL5/PkzfiuZPn16Tk5OWlrahAkT\neiZq0BqLxfr+++/r6uqePXvW7hx70JuoVKqMjEx9fT2syyIck8msq6sT/ikH4CoqKn788cfU\n1FQ5Obk9e/a4uLiI68wkEklOTo7L5eJzsgCBpKWlBfPvAIHk5OQwDKupqSE6kP+Bv1U7elVs\nwy9kMtnIyGjZsmXLli0TZSINiUSi0WhsNhtf5tcKflsRMtiqqKioqKjYdrudnV1UVBSbzS4o\nKDAwMBBxN8H2VpNdP3369NVvBPQQMpmMYZhgwBZ0ytu3b48dO3b+/PmGhgYmk+nt7e3v7z9k\nyBCEUGf/l+IftpqamvAv1q9f7+rqunPnzkuXLvVE5KCtoKCgT58+/fLLL6NGjaquriY6nIEO\nn7bQ3NwMdyfC8fl8wa0JCKekpHT+/Plz585t3rx5+fLl169f37t3r4KCQvfPjM+Og3eEJGhu\nbubxePCDIByfz5fAT7DCszNRE8KCggIhr9LpdFVVVRqN1om4EFJTUysuLq6oqFBXV2+5nc/n\nf/z4ESHUhVkNVCpVRUWloqICnyzazd0A6HPy8vLCwsLi4+ObmpoGDRrk5+e3YsUKeXl5cZ3f\nwsLC1NQ0PT39/v37pqam4jot6Ag0HgQAiAWGYQsXLpw4caKPj09SUlJubu7Ro0d/+OEHouMC\nABBP1IRQS0tL7NfW0tIqLi5++fJlq7lnJSUljY2NTCZz8ODBHR3L5/Orqqrk5eVb5bvNzc2f\nP39GCOGjoiLuBkA/gJcPTUtL4/P5o0aNWrZs2aJFizr7mEYUmzZtsrOz27lzZ3JysthPDlri\ncDjQeBAAIEY6Ojq3bt3av3//vn37HBwcli1btnXrViqVSnRcAAAiibnYVKfgz6UyMjJaVaxJ\nS0tDCJmamgpZuOjj4+Pl5ZWZmdlqe0ZGRmNjI5VKxRdKibgbAH1Xc3NzSkqKra2tnZ1damqq\nsbFxeHh4dnb28uXLeyIbRAh9//33FhYWjx8/vnv3bk+cHwjs378fGg8CAMSLQqGsX7/+2rVr\nGhoap06dmjZt2j///EN0UAAAIrU/Qpidnd3N806cOPGr+xgZGWlpaRUWFoaFhfn4+OALM+7d\nu3fz5k0KheLk5CTYMy4urrKy0tjYePz48fgWMzOzmJiY06dPKysrGxoa4htzcnLCw8MRQnZ2\ndvjjLhF3A6Av4nA4CQkJBw4cKCwsxDDM2to6ICDAxMSkFy79888/Z2Rk7Ny508LCQvSKU6BT\nioqKDh8+DI0HAQA9wdjY+O7du1u3bo2KirK2tl6/fr2fn5/Ym1IAAPqE9quMdv8TnojFS9+/\nfx8UFFRVVcVkMkeMGMFiscrLyzEMCwgIsLS0FOwWEBBQUlLi4eHh5uaGb+FwONu2bXvx4gVC\nSFVVVVlZuaKigsViIYRMTEw2btyIp5ci7tYRKCpDIBqNJiUlBVXL2vXly5eLFy8ePny4vLyc\nQqE4Ojr6+/vr6ur2xLWYTCaNRmOxWK0qN3h5eSUnJ589e9bOzq4nrjvA8fn8uXPnZmVlnTp1\nCi/IjNcHg6IyhKPT6bKysrW1tY2NjUTHMtApKCgICiCDLrt27VpgYGBVVdXUqVMPHTo0dOjQ\nTh1OIpGUlJTYbPaXL196KEIgIgaD0dTUJKRnG+gdioqKJBKpsrKS6ED+B5lMbrfQJo7ghBAh\nVF1dHRsbm5OTU1lZyWAwRo8ePXfuXB0dnZb7tE0IEULNzc1paWkZGRmvX79ubGyUkZHR1NSc\nNm2amZlZy/hF3K1dkBASCBLCdlVUVERERJw8ebKmpobBYLi7u/v6+vZop/KOEsL8/Hxzc3NN\nTc179+5Bh26xu3DhQkBAgIWFhaCaKySEEgISQskBCaG4VFRU+Pv737lzR15ePjQ0tOUUra+C\nhFByQEIoISAh/P9ETwglGSSEBIKEsJWSkpJTp07h7VJUVFQWL17s7e0t5L0tLh0lhAghHx+f\nuLi4sLAwMba0AgghFov1ww8/1NXV3bt3T0NDA98ICaGEgIRQckBCKEZ8Pv/UqVPbtm3jcDiu\nrq6///67rKysKAdCQig5ICGUEP0nIcT7lXVHeXl5N88gCSStp+SAQqFQyGQyfORCCGVnZx8/\nfvzatWs8Hm/kyJErV6708vKi0+m9c3UZGRkKhfLly5e2n7pKS0uNjY2HDBmSk5MDy3HFaOXK\nlTExMVu3bl29erVgI4ZhDAYDOuUQjkqlSktLNzQ0QPdnwsnKytbV1fWPB9ASIj8/39vb+8WL\nF+rq6idOnJg0adJXDyGRSEwmk8vltttWGvQmOp3O4/G4XC7RgQx0TCYTwzC8nYHkwN+qHb3a\nfkIIcJCNEAga0/P5/Bs3buzdu/fhw4cIobFjx/r7+7u5uQlf+Cp2eGbOZrPbvVf4+vqeOXPm\nyJEjS5cu7c2o+rGsrCwbG5vRo0c/fPiwZasJDMMoFAokIYQjk8kUCoXL5baqjw16H5VK5XK5\n8DFGvNhsdkhIyP79+zEMW7169S+//CL8eR+GYTQaDfIQSSAlJcXn8+HWRDi8xrsEDtUKGUuA\nhFAYmDJKoAEyZbSpqenq1at///23goKCtbW1vr4+QojL5cbHxx85ciQ/Px8hZGJi4u/vb2Nj\nQ0iEQqaMIoTKy8uNjY0VFBSePHnSa4OW/RiHw5k6dWpRUdH169dbtZqAKaMSAqaMSg6YMtpz\nsrKy/Pz8ysrKjIyMwsLChDSjhimjkgOmjEqIvjhlVMz1hVetWjVy5MiW05wAAB2pqqqysrJa\nuXLlkSNHtm/fPm3atIMHD548edLY2NjPz+/Vq1fW1tapqanJyclEZYNfNWTIkIULF5aXl0dG\nRhIdS3+wb98+aDwIACDclClTsrKyXFxcnj59amFhcfLkSRg/AKAfE+cIIZ/PnzJlyv3790eO\nHFlSUiLiURUVFXFxcU+fPq2srJSWltbW1ra3txf0G+wIl8udO3duR68GBARYWVl18xIIRggJ\nNRBGCPGiLG23U6lUBweHNWvWfPPNN70fVSvCRwgRQpWVlRMmTKBSqX/++aeIRQhAu4qKiszM\nzBQUFO7fv6+goNDqVRghlBAwQig5YISwFyQlJQUGBlZXV1tYWBw6dKhtjQkYIZQcMEIoIfri\nCGHnFiNxOJzExMT79+9/+vSp1Rzlpqamf/75599//0UIlZWViXjCwsLC4ODguro6eXl5fX39\nmpqa3Nzc3NzcxYsXOzo6CjkQr6xAJpNHjRrV9lU5ObnuXwKAHsXj8ZKSktpuNzExiYqKUlZW\n7v2QukZZWXnZsmUHDhw4derUTz/9RHQ4fRWfz1+zZg2Hw9mxY0fbbBAAAAhhb29vZGTk6+ub\nnp5uYWFx4MABiZ2xAgDosk4khP/++6+9vX1hYeFX91RVVRXlhDweb9++fXV1dXZ2dkuXLsVb\nmT179iwkJCQyMtLQ0FBTU7OjY/GBI2Vl5X379vXQJQDoUbAIHvkAACAASURBVBwOp90CIaNH\nj+5D2SDO19f37NmzR48eXbx4MSQzXXPx4sUHDx5YWVnhbegBAEBCjBgxIjEx8ciRI7///vuC\nBQsWLlwYEhIiIyNDdFwAALERdQ3h58+fbW1tRckGEUIt28cL8fjx43fv3qmpqXl7ewsaWxsa\nGjo6OjY3NycmJgo5Fq9u/NX7UXcuAUCPkpaWHjp0aNvteF2ZvkVBQcHX17empubo0aNEx9In\nVVZWbtu2TVpaOjQ0lOhYAACgNRKJ5O/vf+vWLV1d3aioKAsLi9zcXKKDAgCIjagJ4bFjx968\nefOVc5FIRkZGO3bs2LVrlyjnzM7ORgiZm5tjGNZy+9SpU/FXhSwMwKeMMhiMnrsEAD0qKyur\nbaNLfX19d3d3QuLppuXLl6uoqJw8efLjx49Ex9L3/PLLLywWa926derq6kTHAgAA7TMwMLhz\n546/v//r169nzZoVEhKSnZ3t6uo6dOjQcePG7dixo38v+wegHxM1IRQsdlJXV1+zZo3gMbav\nr+/mzZvnzJmD90aztbVdt26diC2qi4qKEEI6OjqttqupqcnIyDQ2NgpZi4jfdL46QtidSwDQ\nc2JiYubNm8fhcDw8PPDKMXQ63cHBISYmpo82b2AwGP7+/vX19YcOHSI6lj7mwYMHly9f1tfX\nX7lyJdGxAACAMDQaLTg4+NKlS6qqqocOHZo9e/adO3fKy8vz8/MPHDjg4eEBTfAA6ItEXUOI\nV4sZNGjQ8+fP5eXlEULr169HCM2bN2/y5MkIoaKiImdn5127dr158+b8+fOinLO8vBx1sOBQ\nVVW1tLS0rKxs+PDh7R6LTxllMBhFRUWZmZl4Xjds2LDJkye3TP+6cwkAegKfz9+9e/eePXsY\nDMapU6emTZuGEKqvr6fRaIJZzX3UkiVLjh8/fvbs2ZUrV6qpqREdTt/A4XDWrl2LYdjevXtb\ntqEHAACJZW5unpWVNX78+M+fP7fcfv/+/fj4eBcXF6ICAwB0jagJIf6eNzU1xbNBAUHXim++\n+SY5OVlfXz86OtrBwcHZ2Vn4CXk8Hl5RQ1pauu2r+MaGhoaODsdHCHNzczMyMlpuT0xMtLGx\nWbVqFYZhXbiEk5OT4OHWzJkzly1bJvy7AD0HwzAMw0Qcbe4r2Gz2smXLLl68qKGhkZSUNGbM\nGHy7kELAhCORSAihVm/8jmzevHnVqlVHjx49duxYD8fVT2zdurWgoGDlypXW1tbC98TfEZL8\nqzJA4AsQZGRk2v3LAnoTiURSUFCA/niEoNForbJBXF5e3vLly3s/HoAQIpFIfD4f6v0QDn++\nL2l/r4XfKkWdMopPY8PH5VpqeTsYNmwY3v1PlM+CgvqK7T4UxzcKaaWCryFsaGhwc3M7ceJE\nfHx8eHj43LlzyWRySkrKxYsXu38JAMSrsrLSxsbm4sWL33333f379wXZYH+yePFiTU3NiIgI\nfLY2EO7ff/8NDQ0dOnTo9u3biY4FAAA6QUpKCn9c2AqNRuv9YAAA3STqCOHw4cNfvnyZnp5+\n9+5dS0tLhBCNRmOz2WlpabNmzRLshndyz8vL++oJBbcMLpfb9lV8o5DbyuzZs83NzZlMppKS\nEr5FVVV10aJFTCYzIiIiISHBycmpC5eIj49v+U9oTE+gftaYvrS0dP78+QUFBTNnzjx+/DiN\nRquqqiI6KJHgjelrampErMC0Zs0aPz+/4OBgqDgqHJ/PX7VqFYfDCQkJ4fP5X/19gMb0EgJv\nTF9fXw+N6QkHjemJZW5unp6e3mrjlClT+spft/5HjI3pb9++fffu3YaGhvHjx8+bNw9WNHQK\n3phe0t4IwhvTizpCaGJighDicDhWVlZ4RZlhw4YhhA4fPrxly5YnT57cu3dv9erVWVlZCCFR\n/heQSCQ8GWs76oj+OyNUSBFRRUVFDQ0NQTYoYGdnRyKR2Gx2QUFBNy8BgLjk5OTY2toWFBR4\ne3ufPXu2f880c3Z21tXVjYuL++eff4iORaJduHAhKyvL0tJyzpw5RMcCAACdtnfvXhUVlVYb\n3759S0gwQIz8/f3nz59/6tSp8+fPr1mzZvr06fi8PNCPiZoQenp6Cr7Gp2LiKWJzc/Ovv/5q\nYmIyZcqUgwcP4juIWKYFLztRUVHRajufz8cr13eh3AuVSsVvT/jvbk9cAoBOuXbtmqOjY1VV\n1a5du3bu3NnuHJv+hEwmb9iwobm5effu3UTHIrlYLNavv/4KjQcBAH3XiBEjHj58uGnTJkdH\nxyVLloSGhiooKAQGBt6+fZvo0EDXJSUl4QuvBPLy8kJCQoiKB/QOUT+bTp8+vdVjbC8vr452\nnj17tijn1NLSQgi9fPmy1faSkpLGxkYmkzl48OCOjuXz+SwWq2114+bmZnxZo5ycXDcvAUD3\nHThwYOnSpWQy+dy5cwOnRpGdnd3YsWOTk5OfPXtGdCwSKjg4GG88qKGhQXQsAADQRQoKCmvX\nro2Pjw8LC1u8eHFERASJRFqyZMmTJ0+IDg100Y0bN0TcCPqTTgxWREdHe3p6Cjq829raenh4\ntN1t+PDhmzZtEuWEP/zwA0IoIyOjVV6XlpaGEDI1NW3VTb4lHx8fLy+vzMzMVtszMjIaGxup\nVKq2tnY3LwFAd3C53ICAgB07dgwZMuTatWvTp08nOqLeg2HYpk2b+Hz+zp07iY5FEv3xxx+X\nL18ePXo0NB4EAPQnpqamJ06cwFvs5ufnEx0O6DQWi4X3mWul3bVXoD/pRELIYDCioqIKCwud\nnJzwLRERETt37hQ0HGMwGO7u7tnZ2SIOuxkZGWlpaX348CEsLKypqQnfeO/evZs3b1IoFMFV\nEEJxcXEnTpzIzc0VbDEzM0MInT59uuUQRE5OTnh4OELIzs4Ob1cg+iUAEKOampp58+ZduHBh\nzJgxt27dMjAwIDqi3jZt2jQTE5O7d+8+evSI6FgkC5vNXrduHTQeBAD0S7Nmzdq9e3d1dbWb\nm9u7d++IDgeIhMPh3LhxY9GiRQYGBu2u/zc0NOz9qEBvwsTSwKeqqorNZquoqEhJiVq2FPf+\n/fugoKCqqiomkzlixAgWi1VeXo5hWEBAAF7LFBcQEFBSUuLh4eHm5oZv4XA427Zte/HiBUJI\nVVVVWVm5oqKCxWIhhExMTDZu3CiIRMRLtAuqjBKo71YZLSsrc3d3z8vLs7CwCA8PZzKZREfU\nLXiVURaL1dlSftnZ2bNnz/7++++vX7/eQ7H1Rb/99tvevXuXLFny+++/d+pAqDIqIfAqo7W1\ntVBllHBQZVQSkEgkJSUlNpv95csXwcb9+/fv3Llz1KhRycnJqqqqBIY3oHShymh+fv7ly5cv\nXLiAf+LV1taeOXNmfHx8q+JABw4caHdWIGgXXmW0srKS6ED+h/Aqo+StW7eKcpZz585paWl1\n1CVcWlpaVla2C9Uy5OTkLCwsOBzOx48f3717h2HY+PHjAwICJkyY0HK3W7duVVdXjx079ttv\nv/2/uMlkCwsLZWXl+vr6jx8/VlRUkMlkfX39BQsWeHp64h0hO3WJdsEQOYHwHkfttgyRZLm5\nuQ4ODq9fv16wYMHx48f7QUFRPDNvaGjo7MOj4cOHP378+P79+yYmJiNHjuyZ6PqYoqIiHx8f\nZWXliIgIvLmr6DAMo9FokIQQTkpKikqlcjgcwawTQBQ6nc5ms6ExPbEwDJOWlubxeILmzwih\nSZMmffny5fbt2w8ePHBycuro0yMQLyqV2tzc3La+RlsfPny4cOHCunXrfvvtt0ePHlEoFDc3\ntx07dvzyyy/m5ub29vb48AmJRNLT0/v06VNOTs6cOXPw8hzgq6SlpTEMa2hoIDqQ/0EikYR8\nKBV1hBDDMFlZWScnJ09PT0tLy35fKREHI4QE6osjhMnJyT4+Po2NjWvXrl2/fj3R4YhHl0cI\nEUJ//fWXtbW1oaFhamoqrNfl8/lz587Nyso6ffp0F1pNwAihhIARQskBI4SSoN0RQoQQn8/3\n9/ePiYmZMmVKTEwM5IS94KsjhGw2OyUlJTY29u7du01NTWQyefLkyS4uLnPmzBHyjPLo0aNb\nt2799ttvk5OTZWRkeib2fqUvjhB2IiEUfK2mpubh4bFw4cIxY8aIIUAJBn/vCUQmkzEM60PP\n4I8cObJhwwYqlXry5EkXFxeiwxEbCoVCJpO7/BjexcXl+vXrly5dErH4cD8WERHh4+NjbW19\n9erVLhyOYRiFQmn5DB4QgkwmUygULpcrymN40KOoVCqXy4URQmLhkxd4PF7bGT1cLtfFxSUl\nJcXFxQUvQEpIhAOHlJQUn89v99b09OnT6Ojoixcv4qur9PX1PTw8PD09Bw0aJMqZV61adfbs\nWXt7+4sXL8LP8avwLuidmrvbO4Sk/V1JCAWMjIwWLlw4f/78/tq8oaamhugQBi48D+kTOTmP\nx9u4ceOpU6eUlZWjo6MnTpxIdETiJCMjQ6FQvnz50rXH8P/++6+pqamuru79+/cH8l8RFotl\nbGxcX1//8OHDrk2gxTCMwWBAd2DCUalUaWnphoYGSM4JJysrW1dXBwkhsUgkEpPJ5HK57a6y\naWhocHBwePTo0dKlS/fu3dv74Q0odDq9VWb+/v37y5cvnzt3rqioCCE0ZMgQBwcHd3f3sWPH\ndurMXC7XwcHh/v37Gzdu3Lhxo5jj7neYTCaGYXgbPMmBv1U7elXUhHDo0KHl5eXtviQlJWVt\nbe3p6Tlnzpx+sGKqJZgySqC+MmW0rq5u+fLlqampurq6Fy9eHDFiBNERiVl3pozili9fnpCQ\ncOLEiYFc19fX1/fSpUu//PLLjz/+2LUzwJRRCQFTRiUHTBmVBB1NGRVgsVizZ89+9erVxo0b\nAwMDezm8AUUwZbSxsTE1NTUqKuqPP/7g8/lUKnXq1Klubm4zZszocnVrFos1ffr0t2/fnjhx\nwtHRUbyR9zP9ecoon89/8OBBfHx8QkJCSUlJu/vIyck5OzsvXLjQzMxM9PVCFRUVcXFxT58+\nrayslJaW1tbWtre3Hz9+vIiHC/z7778bN27k8/kbNmwwNTXFN3K53Llz53Z0SEBAgJWVlZBz\nQkJIoD6REJaXl3t4eDx//tzMzOzMmTPy8vJERyR+3U8Ii4uLTU1N1dXV79+/39lCxP3D/fv3\nHR0d9fT07ty50+U/xpAQSghICCUHJISS4KsJIULoP//5z6xZs96+fbtr165ly5b1ZngDirS0\n9P3796Ojo69cuYJ/fDI0NHR1dXV2dlZSUur++f/+++9Zs2Y1NzcnJSUZGRl1/4T9VX9OCFv6\n66+/4uPj4+Pj8/Ly2t1BQ0Pj9evXopyqsLAwODi4rq5OXl5eXV29pqbmzZs3CKHFixd36vED\nh8Px9/cvKytDCLVMCKuqqhYtWkQmk0eNGtX2qPnz5xsbGws5LSSEBJL8hPDff/+dP3/++/fv\n582bt2/fvv7aU677CSFCKCAg4MKFCwOzbjWHwzE3Ny8uLr5+/brwG45wkBBKCEgIJQckhJJA\nlIQQIVRcXDxr1iwWi3Xq1Cl7e/teC2+AKCgoSEhIuHz5Mv7xe9iwYc7Ozh4eHpqamuK9UHJy\n8pIlSwYNGpSamjp06FDxnrzf6IsJYVee1o8bN27cuHG//vprQUHBlStXEhISnjx50jKxLC0t\nFeU8PB5v3759dXV1dnZ2S5cuxXtFPHv2LCQkJDIy0tDQUPTf43PnzpWVlcnJybWasIunE8rK\nyvv27RP12wNABOnp6UuXLq2trV23bl2/KSjac9atWxcXF7dnzx4XF5cBUmuuuLg4KyuroaEh\nLy+vsLBwyZIl3ckGAQCgT9PU1IyOjnZycvLx8WEymRYWFkRH1B/U1NRcvXo1NjYW/xwuLS3t\n6Ojo4eHRqZl6nTJr1qy1a9eGhoZ6enpev369s/2TgMTqVo0HbW3tjRs3Pnr06M2bN7///ntn\n+5M8fvz43bt3ampq3t7egs6BhoaGjo6Ozc3NiYmJIp4nPz8/KSlpzJgxgi6FAvgSZyiSC8Tr\n3Llz7u7ubDY7LCwMskFRDB8+fOHChe/evYuKiiI6lt5w9OjRKVOmrF27Njg4OCYmhkajbdiw\ngeigAACASOPHj4+MjEQILVmy5Pnz50SH04fxeLzMzExfX99vv/02MDAwJyfH2Nh47969paWl\nkZGR5ubmPdrnae3atY6Ojs+ePYMVof1Jd4v+VVRUnDlzxs/Pb9u2bZ0tp5OdnY0QavuLO3Xq\nVPxVUeaBcDicgwcPUigUPz+/tq/iFfkYDEanAgOgI3w+PzQ0dM2aNUwmMy4uztnZmeiI+oyf\nfvpJWlp6z549kjwNWCwePny4devWliUo2Wz26dOnCQwJAAAkgbm5+eHDh+vr611dXQsLC4kO\np+/Jz88PCQkxMDBwdna+dOnS8OHD161b9+TJk+Tk5IULF8rKyvZCDBiGHTp0yMjI6NKlS0eO\nHOmFK4Je0MWE8OXLl6GhoZMnTx46dOjSpUuvXr3abrlh4fAauDo6Oq22q6mpycjINDY24msC\nhTt37tz79+/d3d3V1NTavop/9IQRQiAWDQ0NXl5eu3fv1tTUTElJmTRpEtER9SWDBg3y9vau\nrKzs96nRpUuX2m6MiYnp/UgAAEDSODk5/frrr5WVlfPmzfvw4QPR4fQNHz58CAsLMzMzmzJl\nyqFDh5qampYsWXLr1q2HDx+uX79eXV29l+Oh0+kRERGDBg0KCQlJTU3t5auDntCJNYTNzc0P\nHjxISkq6evXqq1ev2t0HwzAzMzN3d3dRToj3sVBVVW37kqqqamlpaVlZ2fDhw4WcAZ8sqqOj\n4+Dg0O4OeJrKYDCKiooyMzPxDHPYsGGTJ09um4gCIERFRcWCBQuePn06ceLEyMhIsRTsGmh8\nfX0jIiKOHj26ePHizs4w70Pwtr+tSNricgAAIMqKFSs+ffp04MABNze3pKSkfvznoJvYbPbN\nmzdjY2PT09N5PB6FQrGxsZk3b561tTXhq/GHDRsWGRnp4OCwcuXKGzdu6OnpERsP6CZRE8LF\nixcnJyd//Pixox0MDQ09PDzmz58vPIUT4PF4+JSqdlsX4hsbGhqEnAGfLEomkwMCAjpqeI2P\nEObm5mZkZLTcnpiYaGNjs2rVqlazVfPz8wXVcRQVFWGxLIHIZDKJRJKQLgX5+fmurq5v3751\ndnY+cuQIjUYjOqLeg7+5pKSkul/Kb9CgQb6+vrt27Tp+/HhQUJA4opNEOjo6N27caLuxm7/M\nJBIJwzAJeUcMZPg7QnLuTgMZ/o6AKqPE6to74pdffqmsrDx37pynp+eVK1cG5set+vr6kpIS\nVVXVQYMGtXrp0aNHMTExCQkJNTU1CCEDA4P58+c7Ozu3O4giQCKRyGRyr92aJk6cePDgwZUr\nV3p6et65cwcelAvgyYWk/Y3oKFfCiRprREREu9tHjRo1f/58Dw+P0aNHdyoswQKbdov14xvZ\nbLaQM5w/f/79+/eenp5CWoHjawgbGhrc3NwsLS1VVVWrqqpu3LiRmJiYkpKiqKjYajDTy8ur\nqakJ/9rFxQXqQBBOElKvO3fuODs7V1dX+/v779+/X/g7qr8S1xPcoKCg8PDwY8eOrV27tu2f\nwP6h3QelO3bsUFBQ6P7JxXIS0H0yMjKwGEESwOCShKBQKJ29O509e7ahoSEuLm758uXx8fGS\n9um5RzU1NQUFBR08eBD/MGxpaXnq1ClNTc13795FR0eHh4cXFBQghJSUlJYvX75ixYpOdefu\nzVvTihUriouLQ0NDvby8bt++Tfi4pUSRtL/Xwp+ddfHtp6qq6urq6u7u/sMPP3TtDIIP+lwu\nt+2r+EYhyUB+fv7Vq1e1tLScnJyEXGX27Nnm5uZMJlPw3EJVVXXRokVMJjMiIiIhIcHJyanl\ncykHBwfB/y9DQ0NoM0UgMpmMYZggPydKVFSUn58fn88/fPjwsmXLWlYKGSAoFAqZTGaz2V3o\nWdqWlJTU6tWrg4KCdu3atWvXru6fUNJcvnzZz89PWlp6xIgR+NT6IUOG7Nq1y9LSspv3EwzD\nKBTKAPwNlDRkMplCoXC5XB6PR3QsAx2VSuVyuWK5NYEuwzCMRqPxeLx2P84JFx4ezmKxrl27\ntmjRotOnT/dobUyJsnXr1t27dwv+effuXXNz8zFjxty+fZvH49FotJkzZ3p4eMyePRsfIBHx\nz4eUlBSfz+/lW9OWLVvy8vKSk5OXL19+/Pjx3ry0xMLzF+HDWoQQMhTfuYRQVlZ2zpw5Hh4e\n06dP7/70JxqNxmaz261Gg0/17Kg6qGCyqL+/v6BfRbsUFRXbbcJoZ2cXFRXFZrMLCgoMDAwE\n2zdu3NhyN2hMTyDCG9Pz+fydO3ceOHBAXl7+7NmzU6ZMwQecBxomk0kmk+vq6sQ1L8vT0/PQ\noUPHjx9funTpkCFDxHJOCRETE7N69WoZGZnY2FhjY2MWi1VXV4dPYej+Lw/emH5g/hJKFDqd\nTqFQ2Gw2PDEknIKCghhvTaBr8I9zTU1NXbs74X3qL1y4oKqqunnzZrGHJ4EaGhoOHDjQauO7\nd+/evXtnbGzs5uY2Z84cfHCJzWZ3KqlgMBhNTU29n4ccPny4qKgoMjJSX19/6dKlvXx1CUSh\nUEgkkqT9vSaTyWJICO3s7Nzd3efMmSPGkWg1NbXi4uKKiopW9ZH4fD6+WLGj5Yh37tx5//69\niopKq8J9L1++RAglJiZmZWWNGTNm9uzZHV2aSqWqqKhUVFRI2k8LSAg2m+3n55eYmKiurn7x\n4kUoQSRGdDr9p59+2rBhw759+0JDQ4kOR2zOnz8fGBjIZDJjY2O/++47hJCSkhKsqQAAACHk\n5OQuXbo0c+bMgwcPqqiorFy5kuiIelxZWVm7OduyZcv66MQZJpN5/vx5a2vrzZs3a2trm5mZ\nER0R6DRRV0Ndu3Zt/vz54p2XrKWlhf6bxbVUUlLS2NjIZDIHDx7c7oH4c9lPnz49+F94Hb+X\nL18+ePAAn37N5/NZLFbb0fPm5ma8ayIsPwBtsVgsR0fHxMTE77777tatW5ANit2CBQtGjBgR\nHR395s0bomMRj4iIiDVr1sjJycXFxeHZIAAAAFEMGjQoLi5OVVX1l19+uXz5MtHh9DgFBYV2\nJ8f26UKdGhoaZ86cwTBs2bJlJSUlRIcDOq3r5TEOHjw44b+6dgZ8/WFGRkarhC0tLQ0hZGpq\n2tFsckdHx6T24CfcsGFDUlLSmjVrEEI+Pj5eXl6ZmZmtzpCRkdHY2EilUrW1tbsWPOivSkpK\nZs6c+eTJk1mzZiUkJAiv6AW6hkqlrlu3jsPhtFxE0XdFRkauX79eXl7+0qVL48aNIzocAADo\nY0aOHHnlyhU5ObmAgIDbt28THU4PevHixfz589sufFVSUpo5cyYhIYmLqanpnj17qqqqPDw8\n8OKooA/pekL49u3bP/+ra2cwMjLS0tLCu20Kaofcu3fv5s2bFAqlZbWYuLi4EydO5ObmdvYS\n+LD16dOnnz17JtiYk5MTHh6OELKzs4OCSKClJ0+ezJgxo6ioyNvb+8yZM+32RAFi4erqqqOj\nc/ny5Y6amvYVZ8+eXbdunZKS0tWrV42MjIgOBwAA+iR9ff3IyEgSibT4/7F333FNXf3jwM+9\nWSQhCCiIjTiBSrWNq7aAgoO6GApItWhRi7QqKk+lLiqPtjjqosVaHEVUtKKVIjJcaEXRSimF\norXgAKyrzLAhIev3x309+fFlpCiQc5N83n/hubn3fOLNvcnnnrV48a+//oo7nO7X2NgYHh7+\n3nvv5ebmTp8+veXEoRYWFocOHdKDB9B+fn4BAQEPHz4MDAyEabd0C85JfgmCCAkJCQ0NTUtL\ny8zMtLa2FovFJSUlBEEEBQW1nG0iIyOjuLjY1NT0pSbeRQjNmTPn3r17d+/eDQsLs7Cw6N27\nd1lZGbVs9Lhx4xYsWNDNbwnosnPnzgUFBcnl8q+++gpGRfc0BoOxZs2awMDAnTt3RkdH4w7n\nFUVFRW3atKlPnz4JCQn29va4wwEAAB3m5OQUHR29ePFiPz+/pKSk4cOH446o21y6dGndunXP\nnz8fOHDgrl27Jk2apFKpbt++XVBQYGlpSc2HjzvG7rFly5aHDx9eu3YtPDx88+bNuMMBnYV5\nRTWhUBgZGenm5sbj8R48eNDU1OTg4LBr167Jkyd3y/HZbHZ4eHhQUNDw4cMbGxsfPnwok8lE\nIlFISMjnn39uUIveAM0OHToUGBjIZDKPHz8O2aB2zJo1a8SIEUlJSXfv3sUdy6vYt2/fpk2b\nLCwsIBsEAIBuMX369G+++aauru6DDz54+vQp7nC6wT///LN48eIFCxaUlZUFBgbeuHFj0qRJ\nCCGCIBwdHT/66CN3d3e9yQYRQkwmMzo6esiQId99992JEydwhwM6i3jlBXw+++yzPXv2UH/r\n6ypAsOwERlpbdqK5uXn16tWnT5+2srI6efJky2VIAEJIIBBwOByxWNwTc7tfunRpwYIFU6dO\n/eGHH7r94D1q79694eHhlpaWCQkJr7/+uhZqpJadqK6u1kJdQAMjIyNjY+P6+npYdgI7U1PT\n2tpaWHYCL5Ikzc3NpVJpXV1ddx0zIiJi+/btgwcPTk1N1d2OlHK5PCYmZtu2bQ0NDe+8887u\n3bt7etoYXMtOtPXo0aPp06c3NjbGx8e/8orlusvMzIwkSWqqS/pgMBjtLsVHwdxCCABe1dXV\nc+fOPX369BtvvHHx4kXIBrVs2rRpb7/99uXLl3/77TfcsbwEKhsUCoUpKSnayQYBAMBwrF69\neunSpcXFxfPnz8e4HHFX3LlzZ8aMGVRntK1btyYlJen0JKIvy8bG5vvvv1cqlYsXL9ab6cT1\nG7QQagIjYjEiCIIgiB599FtcXOzp6Zmfnz916tRTp07BGiTtIkmSIIieuxZ+/vnnqVOnTpgw\n4dq1az1URffavHnzli1bBgwYkJaWNnToUG1WTZIk4C3YfAAAIABJREFUNIZgRxAEdSL09YtP\nh8AVQRMMBkOlUnXvuVCpVAEBAbGxsZMmTUpJSeFwON148B5VU1OzefPmqKgohUIxZ86cvXv3\nWlpaaqdqkiRVKhV9bk0RERFr164ViUQ3btzg8/m4w9EeBoOB6JdEqFQqDWPlXn0QXb9+/fRp\nvG+7qqqqcIdguHq6y+jvv/++YMGCiooKf3//HTt2KBQKON3torqM1tTU9NAPr1GjRk2YMCEj\nIyMpKWnChAk9UUU32rZt29dff21tbZ2QkGBubq7Nzwx0GaUJqstoY2MjdBnFDrqM0gHVZbS5\nubkbu4xSvvrqq+fPn1+9enXBggUHDhwgSR3o1Hbp0qW1a9e+ePFi0KBBO3fupIYLau2bgj5d\nRimLFy++e/fu8ePH/fz8YmJidOIMdguqyyjdflX2VJfRkJCQP//nlQ8CABYpKSmzZ8+uqqoK\nCwvbs2cPTC+E18aNGwmCCA8Pp89zzbZUKtXGjRupbDAxMXHgwIG4IwIAAH3GYrGOHDny9ttv\nnz17dv369bjD+RdPnjyZN2/eggULysvLV61adfPmTSobNHA7duxwdHRMTU3dvXs37liAJq/+\nO1gul9fV1REEIRAIqLbRV1NWVhYfH5+bm1tZWcnlcm1tbT09PV92eQmEUH5+/vr161Uq1bp1\n65ycnHqiCqCjmpqasrKyysvLhw0bNmLEiEOHDoWFhRkZGR05cmTGjBm4owNo9OjRrq6uaWlp\naWlpU6dOxR1OO6hs8NChQ0OGDDl79uxrr72GOyIAANB/XC73xIkTHh4eR44csbKyWr16Ne6I\n2tFy8ph333139+7dMLZcjcrqp06dunv3bltbWy8vL9wRgfa93BhCpVKZkJDw448/ZmVlPXny\nhNqXJEkbGxtHR0c/P7/33nvvpap/9OhRWFhYQ0NDr169BgwYUFNTQ409Xbx48Ut9aJqbm1et\nWvXixQuEUKuEsCtVwCyjGHVXl9Fff/116dKlz549o/5pbW399OnTvn37njhxYuTIkV0OU//1\n6Cyjavn5+RMnTnz99dfT09Pp1qtEpVKFhoZGR0fb2NgkJCT069cPSxjQZZQmYJZR+oAuo3TQ\nE7OMtvLPP/+4ubk9ffp0+/btS5Ys6aFaXk1eXl5ISEheXp6pqWlYWNiHH35IEASuYOjWZVTt\n3r17M2fOVCqVSUlJo0aNwh1Oj9PFWUZfooWwsLDQx8cnLy+vVblSqXzw4MGDBw+OHj3q6uoa\nFxfXp0+fzhxQoVBEREQ0NDS4u7sHBARQzYx5eXnh4eHHjh0TiURDhgzpZGzHjx9/8eKFiYlJ\nbW1tD1UBdJFYLP7oo4/KysrUJU+fPu3Vq9eFCxesra0xBgZasbe3d3d3T0pKSk5OnjVrFu5w\n/j+VSrV+/fqYmBhbW9uEhAQrKyvcEQEAgGHp169ffHy8m5vb559/bmlp6enpiTsihBCqqanZ\nsWPH4cOHlUqlp6fnzp07e/fujTsomho+fHhUVNRHH33k7+9/+fJlXM9VgQadfRIvFotdXFza\nZoOtXLlyZdKkSZ18bpqVlfXs2TOhUBgYGKjudCoSiby8vJRKZWJiYidjKygoSEpKGj58+IgR\nI3qoCqCjUlJSWmaDlPr6+l69emGJB2iwfv16BoPx1Vdf0WdiLqVSGRwcHBMTY29vn5SUBNkg\nAABgMWTIkOPHjxsZGS1fvvzmzZu4w0EJCQmOjo7ff//9kCFDEhMTDx8+DNmgZm5ubmvWrCkp\nKfnoo49o2IYJOpsQbt++/fnz55155Z9//rl3797OvDIzMxMh5OLi0qp5feLEidTWzvQDaW5u\njoyMZLFYK1as6KEqgO4qLS1tW6hQKKAzMA3Z2tr6+vo+evToxx9/xB0LQggpFIqVK1fGxcWN\nGDEiMTGxkx0fAAAA9ISxY8fGxMQolUp/f/+7d+/iCuPx48fvv//+J598Ul1dvXbt2uvXr7ea\ntwJ0JCQkZNasWdnZ2fQcC2rgOpsQtmxMGzFixNatW5OTk2/duvXLL7+kpKRs37695RwtcXFx\nnTlmYWEhQsjOzq5VuVAo5PF4EomEGhOo2fHjx58/f+7n5ycUCnuoCqC72u0XymazoamHntas\nWcNms3ft2tXc3Iw3Erlcvnz58h9//PGtt96iVpjAGw8AAIApU6ZERkbW19fPnTu3uLhYy7XL\nZLJvvvmGWjV3/PjxN27coL6ztByG7iII4ttvvxWJRD/++OO3336LOxzwf3Q2IXz69Cn1x/Ll\ny+/cuRMaGuru7u7o6Ojg4ODm5rZ+/frff/9906ZN1GuoNOxflZSUIIQsLCzabqIK/zVbozqL\n2tnZzZ49u4eqADrNw8Nj8ODBrQqXLFnC4/GwxAM0GzBgwPz5858+fXrixAmMYVBtgwkJCW+9\n9VZ8fLyGQdgAAAC0ydfXd/v27eXl5XPmzGm3E1APycrKmjJlytatW7lc7p49exISEoYOHaq1\n2vUGNW1sv379tmzZcunSJdzhgP+vs5PKCAQCqVTK5/N3797d0QRKmzZtOnz4sHo6R80UCgXV\nCMDlcttupQqbmpo0HIHqLMpgMIKDg9udlvAVqoiKilKPX3rzzTffeeedzrwX0BMYDAZJknw+\nvysH4fP58fHx06ZNU/cRDQwM3LZtGzzS6zxqkUYej6edRQLDwsJOnz799ddfBwQEYMnbZTKZ\nv7//uXPnRo8enZycTJ9skCCIrl8RoOuoK4LD4XRlvSXQLUiS1NqtCXSE+k3IZDK1dndatWpV\nZWXlrl275s6dm5aWZmpq2qPV1dTUhIeHHzhwQKVS+fn5ffXVV7QdQcBisRgMBs2XVh46dGh8\nfLyrq+uyZct+/vnn4cOH446o+1FZiW59X3f2Q2Nvb5+RkdG3b992kysKQRCDBw9+9uxZ2y6a\nbam7hLFYrLZbqULNo05PnDjx/PnzDz/8sKPpIl+hitjYWLlcTv3t6+tLDTUEGHX9viYSiUiS\nNDMz++mnn0aMGNFuczH4V0ZGRtqpaPDgwcuWLduzZ8+RI0c+++wz7VSq1tzc7Ofnd+7cOScn\np/Pnz5uYmGg5gH+l4Q4MtInFYrX7zQK0TGu3JqAZg8HQ5t1px44dVVVV0dHRPj4+V65c6bmn\nh2fOnFmxYkVZWZmtrW1UVJSrq2sPVWRQHB0dDx486O/v7+vrm5WVpU8/zCQSybfffpuenq5Q\nKJydnYODg+mTFmqeNqWzv7bnz5+fkZHx+PHjqqqqjh6ZKxSKv/76CyH0wQcf/OsBORwO9YdM\nJmu7lSpUv6atgoKCc+fO2djYeHt7d2MVLafDsbS0rKmp0fAWQI+iHnR1faWvGzdulJWV+fv7\nU8Nc4Zy+LB6Px2Kx6urqtDYD0/Llyw8dOrR9+/Z58+YJBALtVIoQam5uXrhw4YULF959993T\np0+rVCpafVqoxpD6+nrcgRg6NpvN5XKbmpqwj3QFxsbGjY2NMDkcXiRJCgQCmUzW2NiozXp3\n7NhRVlaWlJTk4+Nz4sSJbm8WKy4u/uyzz65evWpkZLR+/fpPP/2Uw+HQ6kuhLSMjI4VC0e6P\nXrrx9PQMDg6OjIycNWvWuXPn9KPflkQimTZtmnpFhkuXLh07duzatWs0yQmpS7WjrZ29fpYs\nWXLmzJmrV68GBQUdPXq07ZlTqVQbN26srKwcPXr0ypUrOxMWh8ORSqXt3kGo5cg7+h9UdxZd\ntWqVhk47r1DFuHHjWv4T5qLEiCRJgiC6fl+j5kNyc3PTiVskDVE/tmQymdZ+dZmYmHz88cd7\n9uzZt2+f1hoJm5ubP/roo0uXLjk4OMTFxXE4HLp9YEiSVKlUdIvKAFFfOrryq0u/UVcEJIR4\nUb3jlEql9q+I/fv319TUXLx4cfny5fv27euuFeFlMtmRI0e2bt3a2Njo6Oi4e/duW1tb1EHr\nAq2w2WwdujWFhobev3//4sWL//nPfyIjI3GH0w2++eabVuvzPXjwYMuWLV9++SWukFrSPMyh\nswmhTCY7dOjQ2rVr4+LisrKyFi5c+O6771paWpIkWVVV9ccff8TGxv7++++Ojo5fffVVXl5e\n25XE3n333VYlQqGwqKiorKxswIABLctVKlV5eTlCqH///u0Gc/Xq1efPn/fp0+fUqVMty+/f\nv48QSkxMzMjIGD58uIeHxytXAfSDUqk8f/58r169xo8fjzsW8BKWL18eExOzf//+jz76SAsz\nfDY1NX344YfXr1+fOHFibGwsdMsEAAD6Y7PZMTExnp6eP/74Y79+/TZu3Nj1Y96+fXvNmjX3\n79/v06fP7t27fX19u35M0C6SJKOiombOnHny5Mm33norICAAd0Rddf369baF6enpWg/kVXQ2\nIWz5C6mwsPC///1vuy/75ZdfnJ2d293Udti3jY1NUVHR/fv3x44d27K8uLhYIpEIBIK+ffu2\neyiqG2FFRUW7LXhUWki1Yb5yFUA/ZGdnl5SUvP/++/rRG8FwmJiYrFixIjw8fN++fR3dbbpL\nY2Pj/Pnzb968OXny5NjYWA091QEAANCKiYnJ6dOn3dzcIiMjLSwsPvnkk1c+VFVV1ebNm6mF\n0xYsWPDf//6XPpOK6SuBQHDixImpU6du3LjR1ta2owxCJ4jF4nYXbNeVLgydXXaiJzg6OiKE\nqJGXLcvT0tIQQk5OTh21/nt5eSW1hzrgunXrkpKSqFUvX7kKoB+SkpIQQp6enrgDAS9tyZIl\nlpaW33///T///NNztaizwSlTpkA2CAAAOqdv377x8fGWlpZhYWGtOo51HvUb8uTJk4MHD/7p\np5++/vpryAa1Y+DAgbGxsSRJLlmypKioCHc4ryIvLy8kJGTkyJFPnjxpu7VtB0l6wpkQjho1\nysbGprS0NCoqSj23582bNy9cuMBisVrOFhMfH3/w4MGcnJyeqwLoH5VKlZqayufzYbZYXcTj\n8f7zn/9IJJKWUz11r8bGRj8/v5s3b7733nuQDQIAgI4aNGhQfHy8iYnJ6tWrr1y58lL7Pn78\n+P333w8ICKivr1+zZk1GRsaECRN6KE7QrnfeeWfXrl1VVVXz58+n+bQ9LUml0qSkpJkzZ7q6\nusbGxpqamgYHB7caodavX7/Q0FBcEb6UznYZJUmSy+WyWCw2m91drWoEQYSEhISGhqalpWVm\nZlpbW4vF4pKSEoIggoKCrKys1K/MyMgoLi42NTWlJorsiSqA/snNzX327Jm3tzf80NdRCxcu\nPHDgQGxs7LJly1rdZLuutrZ27ty52dnZU6dOPXLkCHQqBgAA3WVvb3/s2LG5c+cuXrw4Pj6+\nM+tIy2Sy/fv379y5UyqVOjk57d6928bGRguhgrb8/Pzu3Llz+PDhwMDAuLg4mq/yWlhYePLk\nyePHj1dVVZEk6eLi4u/vP3PmTCaTGRQU9M0339y+fVulUo0dOzYkJEQL8yB0i84mhG0niekW\nQqEwMjLy9OnT2dnZDx484PP5Dg4OPj4+nVnJkD5VAHpKSUlBCHl4eOAOBLwiNpv96aeffvrp\npxEREd988003Hrmmpmbu3Lm///67p6fngQMHYEE5AADQdU5OTtHR0YsXL/bz80tKStK84nlm\nZuZnn312//59CwuLTZs2zZ07V2txgnZt2bLl4cOH165d+/LLL7/44gvc4bRDoVBcuXLl+++/\nv3HjhkqlMjU1DQwMXLp0acsH1mZmZl988YWZmRlJkpWVlRijfVlE27legBosO4ERh8NhMpnU\n6iCv5u233y4tLS0oKOi5JWsNgUAg4HA4YrEYy8BohUIxfvz44uLijIwMauLvrqupqfH19c3N\nzZ09e/b+/fu7ffWqHkKSpImJSXV1Ne5ADJ2RkZGxsXF9fX3XV0kFXWRqalpbW6srczboK5Ik\nzc3NpVJpXV0d7lgQQig2NjYkJKRfv36pqanW1tZtX1BdXR0eHn78+HGEkK+vb3h4uK604fwr\nPp8vl8ulUinuQF5RdXX1tGnTioqKIiIiPvzwQ9zh/H+lpaWnT5+OiYmhpo0RiUT+/v7vv/++\nkZFRu6+nZ0LIYDA0jIyFhFATHerKrH+6uDD93bt3J0yY4OnpGRsb272BGRrtL0zfypkzZwID\nA+fMmRMdHd31o1VXV3t5eeXm5vr4+Bw8eFBXskGEEEEQfD4fFqbHDhampw9jY+OGhgb4GYMX\nroXpNdi1a9fWrVuHDh165MiRP/74o7a29q233qJmsExMTPzss88qKireeOONr7/+ujM9S3WI\nDi1M35GHDx+6uro2NjYmJiY6OTnhDgdlZmYeOHAgNTVVJpMZGxvPmTNnyZIlI0aM0LyXQCAg\nCKK2tlY7QXaS5oXpISHURHefsugBkiRJklRPBfSyvvjii+3bt1MjCro3MEPDYrFIkmxubsZ1\nr1Aqle++++6ff/6ZmZn51ltvdeVQ5eXlM2bM+PPPPxcuXLh//35qPWVdQRAEi8WCJAQ7BoPB\nZDLlcnkPjaQAncdms2UyGfyMwYsgCDabjWVheg3Wrl27d+9ekiTVjzLfeecdIyOj69evc7nc\nkJCQtWvX6t/QcSaTqVQqdb3N/MqVK7NmzTI1Nb158+bgwYOxxFBTUxMfH//dd9/99ddfCCE7\nOzt/f/+AgIBOzj1LfbRo+H2tYU4NSAg1gS6jGHWxy6iTk9Pjx48LCgo0PA4BnYG3yyjl/Pnz\nCxcunDFjRlfae8vLy318fPLz8xcsWLBnzx7dygYRdBmlDegySh/QZZQO6NZllPLXX39Nnjy5\n7VOb8ePH79q1S18nj9H1LqNqUVFRmzZtGj58+Pnz57U86icvLy82NjY+Pr6xsZHNZk+fPt3f\n39/Z2fmlJtTUxS6jOtNdCoDOu3///oMHD6ZOnQrZoH6YMWPGqFGjLl68mJOT87JTDVNKSkq8\nvb0fPny4ePHiHTt2wAKkAACgx86ePds2G+RwOD/99JPOPQ00QMuXL8/Pzz916lRQUFBMTIwW\nvrKbmpoSEhKOHDmSl5eHELK2tv70008XLFjQp0+fnq6aJvAnhGVlZfHx8bm5uZWVlVwu19bW\n1tPTs5O/+e7evZuSklJQUFBbW8vhcAYOHOjs7Dx9+nT1fLUymczHx6ej3YODg6dMmdI9bwPQ\nCcwvqmcIgtiwYcP777+/ffv2M2fOvOzuL1688PLyKioqWrJkybZt2yAbBAAA/SYWi9sWSqVS\niUQC88zphD179hQVFaWkpOzcuXPdunU9V9GjR4+OHj166tSpmpoakiRdXV0XL17s6upqaA8O\nMCeEjx49CgsLa2ho6NWrl729fU1NTU5OTk5OzuLFi728vDTvGxcXFxcXhxAyNTUdOnRodXV1\nfn5+fn5+RkbGl19+SfXfpSZgYDAY7fZCNjEx6YH3BPBLTk5msVjTpk3DHQjoNpMmTXJ0dExP\nT79169ZLDTR/9uyZl5fX48ePly1b9sUXX0A2CAAAeq/dTqFCoRCyQV3BZrOPHj06derUPXv2\nDBs2bNasWd17fLlcfvHixSNHjmRkZKhUqt69e69atWrhwoXdvuixrsCZECoUioiIiIaGBnd3\n94CAAKpZLy8vLzw8/NixYyKRaMiQIR3te+fOnbi4OJIkV65cOXnyZOpHXl5e3tatW//666/k\n5GSqYZAagda7d++IiAhtvS2AWVFR0b179yZNmtTJsb9AV3z++edubm7btm1LTU3t5C7qbDAo\nKGjz5s09GR0AAAC68PPz+/77758+fdqycP369bjiAa/AwsLixIkTM2fOXLFixYABA0aNGtUt\nhy0rKzt16tSRI0eePXuGOrGGhIHA2R6alZX17NkzoVAYGBio7uQpEom8vLyUSmViYqKGfdPS\n0hBCM2bMmDJlivqRv0gkcnd3Rwj99ttvVAk1CTI8EDIoycnJCPqL6qNx48ZNnjw5Kyvr6tWr\nnXn906dPZ82a9fjx45UrV0I2CAAAhqNXr16nTp1SdycxNzffsWPHvHnz8EYFXtbw4cOjoqKa\nm5v9/f3/+eefLh7t119/DQgIGDlyZHh4eHl5+fvvv3/t2rUrV674+/sbeDaI8LYQZmZmIoRc\nXFxadeKaOHHi6dOnMzMzlUplR114fX19J06c2HbJUapEPdMr1WWUz+d3e/CAtlJSUhgMxowZ\nM3AHArpfaGjotWvXtm3bpu4X0JGioiIvL68XL16sWrUqLCxMaxECAACgAzs7u8TExJqampqa\nmv79+xvakDC94ebmtmbNmh07dnz44YcpKSmvkLnV1tYmJiZGR0fn5+cjhGxsbD744IMPP/wQ\n+pG1hDMhLCwsRAjZ2dm1Kqc6eTc2Nr548aJ///7t7jtgwIB2u/k+evQIITR06FDqn1SXUWgh\nNBxPnz7Ny8tzcnIynImhDIpIJHJzc0tJSUlNTaW6A7SrsLDQy8vrn3/+Wbt27Zo1a7QZIQAA\nAPro1atXr169cEcBuiQkJOThw4cJCQkhISHfffdd53dstYaEp6fnK6whYSBwJoQlJSUIIQsL\ni7abLCws/v77bw0JYVsNDQ0pKSk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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "application/pdf": { - "height": 300, - "width": 600 - }, - "image/jpeg": { - "height": 300, - "width": 600 - }, - "image/png": { - "height": 300, - "width": 600 - }, - "image/svg+xml": { - "height": 300, - "isolated": true, - "width": 600 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# FUNCTION\n", - "# Two-sample t-test with tracking sequential statistic and p-values by incremental sample sizes until getting to n in each group.\n", - "\n", - "# @param n (numeric): Initially planned sample size for each group (for simplicity, n needs to be a multiple of sample_increase_step).\n", - "# @param d_0 (numeric): effect size.\n", - "# @param mean_current (numeric): Population mean for control variation.\n", - "# @param sd_current (numeric): Population standard deviation for current variation.\n", - "# @param sd_new (numeric): Population standard deviation for new variation.\n", - "# @param sample_increase_step (numeric): Sample size increment.\n", - "\n", - "# @return p.value.df: A tibble that has 3 columns:\n", - "# inc_sample_size, statistic, and p_value \n", - "\n", - "incremental_t_test <- function(n, d_0, mean_current, sd_current, sd_new, sample_increase_step) {\n", - " sample_current <- rnorm(n, mean = mean_current, sd = sd_current)\n", - " sample_new <- rnorm(n, mean = mean_current + d_0, sd = sd_new)\n", - "\n", - " p.value.df <- tibble(\n", - " inc_sample_size = rep(0, n / sample_increase_step),\n", - " statistic = rep(0, n / sample_increase_step),\n", - " p_value = rep(0, n / sample_increase_step)\n", - " )\n", - "\n", - " current_sample_size <- sample_increase_step\n", - " \n", - " for (i in 1:nrow(p.value.df))\n", - " {\n", - " t_test_results <- t.test(sample_new[1:current_sample_size], sample_current[1:current_sample_size],\n", - " var.equal = TRUE,\n", - " alternative = \"greater\" \n", - " )\n", - " p.value.df[i, \"statistic\"] <- as_tibble(t_test_results$statistic)\n", - " p.value.df[i, \"p_value\"] <- as_tibble(t_test_results$p.value)\n", - " p.value.df[i, \"inc_sample_size\"] <- current_sample_size\n", - " current_sample_size <- current_sample_size + sample_increase_step\n", - " }\n", - "\n", - " return(p.value.df)\n", - "}\n", - "\n", - "#---------------------------------------------------------------\n", - "\n", - "#TEST and PLOT\n", - "\n", - "options(repr.plot.width = 10, repr.plot.height = 5) \n", - "options(warn=-1)\n", - "\n", - "set.seed(301)\n", - "answer2.1 <- \n", - " incremental_t_test(n = 1000, d_0 = 0, sample_increase_step = 50, mean_current = 200, sd_current = 50, sd_new = 50)\n", - "\n", - "sequential_pvalue <- \n", - " answer2.1 %>%\n", - " ggplot() +\n", - " geom_line(aes(x = inc_sample_size, y = p_value)) +\n", - " theme(\n", - " text = element_text(size = 18),\n", - " plot.title = element_text(face = \"bold\"),\n", - " axis.title = element_text(face = \"bold\")\n", - " ) + \n", - " geom_point(aes(x = inc_sample_size, y = p_value)) +\n", - " ggtitle(\"Evolution of p-values in Experiment 1\") +\n", - " ylab(\"p-value\") +\n", - " xlab(\"Sample Size\") +\n", - " geom_hline(\n", - " yintercept = 0.05,\n", - " colour = \"red\",\n", - " linetype = \"twodash\"\n", - " ) +\n", - " coord_cartesian(ylim = c(0, 1)) +\n", - " scale_y_continuous(breaks = seq(0, 1, by = 0.05))\n", - "\n", - "sequential_pvalue" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Interpretation\n", - "\n", - "After collecting data from 100 participants the $p$-value drops below $0.05$ so the experiment is stopped. But we know that claiming a significance difference in this experiment is a mistake.\n", - "\n", - "```{admonition} Example\n", - "Changing the website is costly and may not really increase the size of the donations as expected\n", - "```\n", - "\n", - "But, isn't this mistake one potential result of the test? \n", - "\n", - "```{admonition} Definition\n", - ":class: tip\n", - "In statistical hypothesis testing, rejecting $H_0$ when it is true is known as the **type I error**. The probability of a type I error is equal to the significance level of the test.\n", - "```\n", - "\n", - "In our example, the test was planned so that the probability to falsely rejecting $H_0$ is 5%. So, why is this a problem??\n", - "\n", - "```{admonition} Type I error rate inflation\n", - ":class: caution\n", - "The problem is that the probability of falsely rejecting $H_0$ may be larger than expected! \n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "To know if the probability of falsely rejecting $H_0$ is larger than 5%, we need to run *many* of these experiments!!\n", - "\n", - "The figure below shows the p-value trajectory of 100 experiments. We see that the p-values of more than 5% of the experiments are below the significance level. \n", - "\n", - "\n", - "

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